Phasio

phasio

Scientific American Supplement, No. 392, July 7, 1883 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . THE DEVELOPMENTAL SIGNIFICANCE OF HUMAN PHYSIOGNOMY. here THE DEVELOPMENTAL SIGNIFICANCE OF HUMAN PHYSIOGNOMY. [Footnote: Abstract of a lecture delivered before the Franklin Institute of Philadelphia, Jan. 20.1881, in exposition of principles laid down in The Hypothesis of Evolution, New Haven, 1870, p. 31.] By E. D. COPE. The ability to read character in the form of the human face and figure is a gift possessed by comparatively few persons, although most people interpret, more or less correctly, the salient points of human expression. The transient appearances of the face reveal temporary phases of feeling which are common to all men; but the constant qualities of the mind should be expressed, if at all, in the permanent forms of the executive instrument of the mind, the body. To detect the peculiarities of the mind by external marks has been the aim of the physiognomist of all times; but it is only in the light of modern evolutionary science that much progress in this direction can be made. The mind, as a function of part of the body, partakes of its perfections and its defects, and exhibits parallel types of development. Every peculiarity of the body has probably some corresponding significance in the mind; and the causes of the former are the remoter causes of the latter. Hence, before a true physiognomy can be attempted, the origin of the features of the face and general form must be known. Not that a perfect physiognomy will ever be possible. A mental constitution so complex as that of man cannot be expected to exhibit more than its leading features in the body; but these include, after all, most of what it is important for us to be able to read, from a practical point of view. The present essay will consider the probable origin of the structural points which constitute the permanent expression. These may be divided into three heads, viz.: Those of the general form or figure. Those of the surface or integument of the body, with its appendages. Those of the forms of the head and face. The principal points to be considered under each of these heads are the following: I. THE GENERAL FORM. The size of the head. The squareness or slope of the shoulders. The length of the arms. The constriction of the waist. The width of the hips. The length of the leg, principally of the thigh. The sizes of the hands and feet. The relative sizes of the muscles. II. THE SURFACES. 9. The structure of the hair (whether curled or not). The length and position of the hair. The size and shape of the nails. The smoothness of the skin. The color of the skin, hair, and irides. III. THE HEAD AND FACE. 14. The relative size of the cerebral to the facial regions. The prominence of the forehead. The prominence of the superciliary (eyebrow) ridges. The prominence of the alveolar borders (jaws). The prominence and width of the chin. The relation of length to width of skull. The prominence of the malar (cheek) bones. The form of the nose. The relative size of the orbits and eyes. The size of the mouth and lips. The significance of these, as of the more important structural characters of man and the lower animals, must be considered from two standpoints, the paleontological and the embryological. The immediate paleontological history of man is unknown, but may be easily inferred from the characteristics displayed by his nearest relatives of the order Quadrumana. If we compare these animals with man, we find the following general differences. The numbers correspond to those of the list above given: I. As to General Form.--(3) In the apes the arms are longer; (8) the extensor muscles of the leg are smaller. II. As to Surface.--(9) The body is covered with hair which is not crisp or woolly; (10) the hair of the head is short; (18) the color of the skin, etc., is dark. III. As to Head and Face.--(14) The facial region of the skull is large as compared with the cerebral; (15) the forehead is not prominent, and is generally retreating; (16) the superciliary ridges are more prominent; (17) the edges of the jaws are more prominent; (18) the chin is less prominent; (20) the cheek bones are more prominent; (21) the nose is without bridge, and with short and flat cartilages; (22) the orbits and eyes are smaller (except in Nyctipithecus); (24) the mouth is small and the lips are thin. It is evident that the possession of any one of the above characteristics by a man approximates him more to the monkeys, so far as it goes. He retains features which have been obliterated in other persons in the process of evolution. In considering the physiognomy of man from an embryological standpoint, we must consider the peculiarities of the infant at birth. The numbers of the following list correspond with those already used (Fig. 3). I. As to the General Form.--(1) The head of the infant is relatively much larger than in the adult; (3) the arms are relatively longer; (4) there is no waist; (6) the leg, and especially the thigh, is much shorter. II. As to the Surfaces.--(10) The body is covered with fine hair, and that of the head is short. III. The Head and Face.--(14) The cerebral part of the skull greatly predominates over the facial; (16) the superciliary ridges are not developed; (17) the alveolar borders are not prominent; (20) the malar bones are not prominent; (21) the nose is without bridge and the cartilages are flat and generally short; (22) the eyes are larger. It is evident that persons who present any of the characters cited in the above list are more infantile or embryonic in those respects than are others; and that those who lack them have left them behind in reaching maturity. We have now two sets of characters in which men may differ from each other. In the one set the characters are those of monkeys, in the other they are those of infants. Let us see whether there be any identities in the two lists, i. e., whether there be any of the monkey-like characters which are also infantile. We find the following to be such: I. As to General Form.--(3) The arms are longer. II. Surface.--(10) The hair of the head is short, and the hair on the body is more distributed. III. As to Head and Face.--(21) The nose is without bridge and the cartilages are short and flat. Three characters only out of twenty-three. On the other hand, the following characters of monkey-like significance are the opposites of those included in the embryonic list: (14) The facial region of the skull is large as compared with the cerebral; (15) the forehead is not prominent; (16) the superciliary ridges are more prominent; (17) the edges of the jaws are more prominent. Four characters, all of the face and head. It is thus evident that in attaining maturity man resembles more and more the apes in some important parts of his facial expression. It must be noted here that the difference between the young and embryonic monkeys and the adults is quite the same as those just mentioned as distinguishing the young from the adult of man (Figs. 1 and 2). The change, however, in the case of the monkeys is greater than in the case of man. That is, in the monkeys the jaws and superciliary ridges become still more prominent than in man. As these characters result from a fuller course of growth from the infant, it is evident that in these respects the apes are more fully developed than man. Man stops short in the development of the face, and is in so far more embryonic.[1] The prominent forehead and reduced jaws of man are characters of "retardation." The characters of the prominent nose with its elevated bridge, is a result of "acceleration," since it is a superaddition to the quadrumanous type from both the standpoints of paleontology and embryology.[2] The development of the bridge of the nose is no doubt directly connected with the development of the front of the cerebral part of the skull and ethmoid bone, which sooner or later carries the nasal bones with it. [Footnote 1: This fact has been well stated by C. S. Minot in the Naturalist for 1882, p. 511.] [Footnote 2: See Cope, The Hypothesis of Evolution, New Haven, 1870, p. 31.] If we now examine the leading characters of the physiognomy of three of the principal human sub-species, the Negro, the Mongolian, and the Indo-European, we can readily observe that it is in the two first named that there is a predominance of the quadrumanous features which are retarded in man; and that the embryonic characters which predominate are those in which man is accelerated. In race description the prominence of the edges of the jaws is called prognathism, and its absence orthognathism. The significance of the two lower race characters as compared with those of the Indo-European is as follows: Negro.--Hair crisp (a special character), short (quadrum. accel.); prognathous (quadrum. accel.); nose flat, without bridge (quadrum. retard)[1]; malar bones prominent (quadrum. accel.); beard short (quadrum. retard.); arms longer (quadrum. accel.); extensor muscles of legs small (quadrum. retard.). [Footnote 1: In the Bochimans, the flat nasal bones are co-ossified with the adjacent elements as in the apes (Thulié).] Mongolian.--Hair straight, long (accel.); jaws prognathous (quadrum. accel.); nose flat or prominent with or without bridge; malar bones prominent (quadrum. accel.); beard none (embryonic); arms shorter (retard.); extensor muscles of leg smaller (quad. retard.). Indo-European.--Hair long (accel.); jaws orthognathous (embryonic retard.); nose (generally) prominent with bridge (accel.); malar bones reduced (retard.); beard long (accel.); arms shorter (retard.); extensor muscles of the leg large (accel.). The Indo-European race is then the highest by virtue of the acceleration of growth in the development of the muscles by which the body is maintained in the erect position (extensors of the leg), and in those important elements of beauty, a well-developed nose and beard. It is also superior in these points in which it is more embryonic than the other races, viz., the want of prominence of the jaws and cheekbones, since these are associated with a greater predominance of the cerebral part of the skull, increased size of cerebral hemispheres, and greater intellectual power. A comparison between the two sexes of the Indo-Europeans expresses their physical and mental relations in a definite way. I select the sexes of the most civilized races, since it is in these, according to Broca and Topinard, that the sex characters are most pronounced. They may be contrasted as follows. The numbers are those of the list already used. I first consider those which are used in the tables of embryonic, quadrumanous, and race characters: MALE.                       FEMALE.
               I. _The General Form_.
   2. Shoulders square.          Shoulders slope.
   4. Waist less constricted.    Waist more constricted.
   5. Hips narrower.             Hips wider.
   6. Legs longer.               Legs shorter (very frequently).
   8. Muscles larger.            Muscles smaller.


               II. _The Integuments, etc_.
  10. More hair on body, that    Less hair on body, that of head
      of head shorter; beard.    longer; no beard.
  12. Skin rougher (generally).  Skin smoother.


               III. _The Head and Face_.
  16. Superciliary ridges more   Superciliary ridges low.
      prominent.
  22. Eyes often smaller.        Eyes often larger. The characters in which the male is the most like the infant are two, viz., the narrow hips and short hair. Those in which the female is most embryonic are five, viz., the shorter legs, smaller muscles, absence of beard, low superciliary ridges, and frequently larger eyes. To these may be added two others not mentioned in the above lists; these are 1, the high pitched voice, which never falls an octave, as does that of the male; and 2, the structure of the generative organs, which in all mammalia more nearly resemble the embryo and the lower vertebrata in the female than in the male. Nevertheless, as Bischoff has pointed out, one of the most important distinctions between man and the apes is to be found in the external reproductive organs of the female. From the preceding rapid sketch the reader will be able to explain the meaning of most of the peculiarities of face and form which he will meet with. Many persons possess at least one quadrumanous or embryonic character. The strongly convex upper lip frequently seen among the lower classes of the Irish is a modified quadrumanous character. Many people, especially those of the Sclavic races, have more or less embryonic noses. A retreating chin is a marked monkey character. Shortness of stature is mostly due to shortness of the femur, or thigh; the inequalities of people sitting are much less than those of people standing. A short femur is embryonic; so is a very large head. The faces of some people are always partially embryonic, in having a short face and light lower jaw. Such faces are still more embryonic when the forehead and eyes are protuberant. Retardation of this kind is frequently seen in children, and less frequently in women. The length of the arms would appear to have grown less in comparatively recent times. Thus the humerus in most of the Greek statues, including the Apollo Belvidere, is longer than those of modern Europeans, according to a writer in the Bulletin de la Société d'Anthropologie of Paris, and resembles more nearly that of the modern Nubians than any other people. This is a quadrumanous approximation. The miserably developed calves of many of the savages of Australia, Africa, and America are well known. The fine, swelling gastroenemius and soleus muscles characterize the highest races, and are most remote from the slender shanks of the monkeys. The gluteus muscles developed in the lower races as well as in the higher distinguish them well from the monkeys with their flat posterior outline. It must be borne in mind that the quadrumanous indications are found in the lower classes of the most developed races. The status of a race or family is determined by the percentage of its individuals who do and do not present the features in question. Some embryonic characters may also appear in individuals of any race, as a consequence of special circumstances. Such are, however, as important to the physiognomist as the more normal variations. Some of these features have a purely physical significance, but the majority of them are, as already remarked, intimately connected with the development of the mind, either as a cause or as a necessary coincidence. I will examine these relations in a future article. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2005). Scientific American Supplement, No. 392, July 7, 1883. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/8742/pg8742-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

THE DEVELOPMENTAL SIGNIFICANCE OF HUMAN PHYSIOGNOMY

Anatomy, Zoology and Physiology

Studies in the Psychology of Sex, Volume 4 by Havelock Ellis is part of the HackerNoon Books Series. You can jump to any chapter in this book here . SMELL II SMELL II Rise of the Study of Olfaction—Cloquet—Zwaardemaker—The Theory of Smell—The Classification of Odors—The Special Characteristics of Olfactory Sensation in Man—Smell as the Sense of Imagination—Odors as Nervous Stimulants—Vasomotor and Muscular Effects—Odorous Substances as Drugs. During the eighteenth century a great impetus was given to the physiological and psychological study of the senses by the philosophical doctrines of Locke and the English school generally which then prevailed in Europe. These thinkers had emphasized the immense importance of the information derived through the senses in building up the intellect, so that the study of all the sensory channels assumed a significance which it had never possessed before. The olfactory sense fully shared in the impetus thus given to sensory investigation. At the beginning of the nineteenth century a distinguished French physician, Hippolyte Cloquet, a disciple of Cabanis, devoted himself more especially to this subject. After publishing in 1815 a preliminary work, he issued in 1821 his Osphrésiologie, ou Traité des odeurs, du sens et des organes de l'Olfaction, a complete monograph on the anatomy, physiology, psychology, and pathology of the olfactory organ and its functions, and a work that may still be consulted with profit, if indeed it can even yet be said to be at every point superseded. After Cloquet's time the study of the sense of smell seems to have fallen into some degree of discredit. For more than half a century no important progress was made in this field. Serious investigators seemed to have become shy of the primitive senses generally, and the subject of smell was mainly left to those interested in "curious" subjects. Many interesting observations were, however, incidentally made; thus Laycock, who was a pioneer in so many by-paths of psychology and anthropology, showed a special interest in the olfactory sense, and frequently touched on it in his Nervous Diseases of Women and elsewhere. The writer who more than any other has in recent years restored the study of the sense of smell from a by-path to its proper position as a highway for investigation is without doubt Professor Zwaardemaker, of Utrecht. The invention of his first olfactometer in 1888 and the appearance in 1895 of his great work Die Physiologie des Geruchs have served to give the physiology of the sense of smell an assured status and to open the way anew for much fruitful investigation, while a number of inquirers in many countries have had their attention directed to the elucidation of this sense. Notwithstanding, however, the amount of work which has been done in this field during recent years, it cannot be said that the body of assured conclusions so far reached is large. The most fundamental principles of olfactory physiology and psychology are still somewhat vague and uncertain. Although sensations of smell are numerous and varied, in this respect approaching the sensations of vision and hearing, smell still remains close to touch in the vagueness of its messages (while the most sensitive of the senses, remarks Passy, it is the least precise), the difficulty of classifying them, the impossibility of so controlling them as to found upon them any art. It seems better, therefore, not to attempt to force the present study of a special aspect of olfaction into any general scheme which may possibly not be really valid. The earliest and most general tendency in regard to the theory of smell was to regard it as a kind of chemical sense directly stimulated by minute particles of solid substance. A vibratory theory of smell, however, making it somewhat analogous to hearing, easily presents itself. When I first began the study of physiology in 1881, a speculation of this kind presented itself to my mind. Long before Philipp von Walther, a professor at Landshut, had put forward a dynamic theory of olfaction (Physiologie des Menschen, 1807-8, vol. ii, p. 278). "It is a purely dynamic operation of the odorous substance in the olfactory organ," he stated. Odor is conveyed by the air, he believed, in the same way as heat. It must be added that his reasons for this theory will not always bear examination. More recently a similar theory has been seriously put forward in various quarters. Sir William Ramsay tentatively suggested such a theory (Nature, vol. xxv, p. 187) in analogy with light and sound. Haycraft (Proceedings of the Royal Society of Edinburgh, 1883-87, and Brain, 1887-88), largely starting from Mendelieff's law of periodicity, similarly sought to bring smell into line with the higher senses, arguing that molecules with the same vibration have the same smell. Rutherford (Nature, August 11, 1892, p. 343), attaching importance to the evidence brought forward by von Brunn showing that the olfactory cells terminate in very delicate short hairs, also stated his belief that the different qualities of smell result from differences in the frequency and form of the vibrations initiated by the action of the chemical molecules on these olfactory cells, though he admitted that such a conception involved a very subtle conception of molecular vibration. Vaschide and Van Melle (Paris Academy of Sciences, December 26, 1899) have, again, argued that smell is produced by rays of short wave-lengths, analogous to light-rays, Röntgen rays, etc. Chemical action is however, a very important factor in the production of odors; this has been well shown by Ayrton (Nature, September 8, 1898). We seem to be forced in the direction of a chemico-vibratory theory, as pointed out by Southerden (Nature, March 26, 1903), the olfactory cells being directly stimulated, not by the ordinary vibrations of the molecules, but by the agitations accompanying chemical changes. The vibratory hypothesis of the action of odors has had some influence on the recent physiologists who have chiefly occupied themselves with olfaction. "It is probable," Zwaardemaker writes (L'Année Psychologique, 1898), "that aroma is a physico-chemical attribute of the molecules"; he points out that there is an intimate analogy between color and odor, and remarks that this analogy leads us to suppose in an aroma ether vibrations of which the period is determined by the structure of the molecule. Since the physiology of olfaction is yet so obscure it is not surprising that we have no thoroughly scientific classification of smells, notwithstanding various ambitious attempts to reach a classification. The classification adopted by Zwaardemaker is founded on the ancient scheme of Linnæus, and may here be reproduced:— I. Ethereal odors (chiefly esters; Rimmel's fruity series). II. Aromatic odors (terpenes, camphors, and the spicy, herbaceous, rosaceous, and almond series; the chemical types are well determined: cineol, eugenol, anethol, geraniol, benzaldehyde). III. The balsamic odors (chiefly aldehydes, Rimmel's jasmin, violet, and balsamic series, with the chemical types: terpineol, ionone, vanillin). IV. The ambrosiacal odors (ambergris and musk). V. The alliaceous odors, with the cacodylic group (asafœtida, ichthyol, etc.).VI. Empyreumatic odors. VII. Valerianaceous odors (Linnæus's Odores hircini, the capryl group, largely composed of sexual odors). VIII. Narcotic odors (Linnæus's Odores tetri). IX. Stenches. A valuable and interesting memoir, "Revue Générale sur les Sensations Olfactives," by J. Passy, the chief French authority on this subject, will be found in the second volume of L'Année Psychologique, 1895. In the fifth issue of the same year-book (for 1898) Zwaardemaker presents a full summary of his work and views, "Les Sensations Olfactives, leurs Combinaisons et leurs Compensations." A convenient, but less authoritative, summary of the facts of normal and pathological olfaction will be found in a little volume of the "Actualités Médicales" series by Dr. Collet, L'Odorat et ses Troubles, 1904. In a little book entitled Wegweiser zu einer Psychologie des Geruches (1894) Giessler has sought to outline a psychology of smell, but his sketch can only be regarded as tentative and provisional. At the outset, nevertheless, it seems desirable that we should at least have some conception of the special characteristics which mark the great and varied mass of sensations reaching the brain through the channel of the olfactory organ. The main special character of olfactory images seems to be conditioned by the fact that they are intermediate in character between those of touch or taste and those of sight or sound, that they have much of the vagueness of the first and something of the richness and variety of the second. Æsthetically, also, they occupy an intermediate position between the higher and the lower senses. They are, at the same time, less practically useful than either the lower or the higher senses. They furnish us with a great mass of what we may call by-sensations, which are of little practical use, but inevitably become intimately mixed with the experiences of life by association and thus acquire an emotional significance which is often very considerable. Their emotional force, it may well be, is connected with the fact that their anatomical seat is the most ancient part of the brain. They lie in a remote almost disused storehouse of our minds and show the fascination or the repulsiveness of all vague and remote things. It is for this reason that they are—to an extent that is remarkable when we consider that they are much more precise than touch sensations—subject to the influence of emotional associations. The very same odor may be at one moment highly pleasant, at the next moment highly unpleasant, in accordance with the emotional attitude resulting from its associations. Visual images have no such extreme flexibility; they are too definite to be so easily influenced. Our feelings about the beauty of a flower cannot oscillate so easily or so far as may our feelings about the agreeableness of its odor. Our olfactory experiences thus institute a more or less continuous series of by-sensations accompanying us through life, of no great practical significance, but of considerable emotional significance from their variety, their intimacy, their associational facility, their remote ancestral reverberations through our brains. [26] It is the existence of these characteristics—at once so vague and so specific, so useless and so intimate—which led various writers to describe the sense of smell as, above all others, the sense of imagination. No sense has so strong a power of suggestion, the power of calling up ancient memories with a wider and deeper emotional reverberation, while at the same time no sense furnishes impressions which so easily change emotional color and tone, in harmony with the recipient's general attitude. Odors are thus specially apt both to control the emotional life and to become its slaves. With the use of incense religions have utilized the imaginative and symbolical virtues of fragrance. All the legends of the saints have insisted on the odor of sanctity that exhales from the bodies of holy persons, especially at the moment of death. Under the conditions of civilization these primitive emotional associations of odor tend to be dispersed, but, on the other hand, the imaginative side of the olfactory sense becomes accentuated, and personal idiosyncrasies of all kinds tend to manifest themselves in the sphere of smell. Rousseau (in Emile, Bk. II) regarded smell as the sense of the imagination. So, also, at an earlier period, it was termed (according to Cloquet) by Cardano. Cloquet frequently insisted on the qualities of odors which cause them to appeal to the imagination; on their irregular and inconstant character; on their power of intoxicating the mind on some occasions; on the curious individual and racial preferences in the matter of odors. He remarked on the fact that the Persians employed asafœtida as a seasoning, while valerian was accounted a perfume in antiquity. (Cloquet, Osphrésiologie, pp. 28, 45, 71, 112.) It may be added, as a curious example familiar to most people of the dependence of the emotional tone of a smell on its associations, that, while the exhalations of other people's bodies are ordinarily disagreeable to us, such is not the case with our own; this is expressed in the crude and vigorous dictum of the Elizabethan poet, Marston, "Every man's dung smell sweet i' his own nose." There are doubtless many implications, moral as well as psychological, in that statement. The modern authorities on olfaction, Passy and Zwaardemaker, both alike insist on the same characteristics of the sense of smell: its extreme acuity and yet its vagueness. "We live in a world of odor," Zwaardemaker remarks (L'Année Psychologique, 1898, p. 203), "as we live in a world of light and of sound. But smell yields us no distinct ideas grouped in regular order, still less that are fixed in the memory as a grammatical discipline. Olfactory sensations awake vague and half-understood perceptions, which are accompanied by very strong emotion. The emotion dominates us, but the sensation which was the cause of it remains unperceived." Even in the same individual there are wide variations in the sensitiveness to odors at different times, more especially as regards faint odors; Passy (L'Année Psychologique, 1895, p. 387) brings forward some observations on this point. Maudsley noted the peculiarly suggestive power of odors; "there are certain smells," he remarked, "which never fail to bring back to me instantly and visibly scenes of my boyhood"; many of us could probably say the same. Another writer (E. Dillon, "A Neglected Sense," Nineteenth Century, April, 1894) remarks that "no sense has a stronger power of suggestion." Ribot has made an interesting investigation as to the prevalence and nature of the emotional memory of odors (Psychology of the Emotions, Chapter XI). By "emotional memory" is meant the spontaneous or voluntary revivability of the image, olfactory or other. (For the general question, see an article by F. Pillon, "La Mémoire Affective, son Importance Théorique et Pratique," Revue Philosophique, February, 1901; also Paulhan, "Sur la Mémoire Affective," Revue Philosophique, December, 1902 and January, 1903.) Ribot found that 40 per cent. of persons are unable to revive any such images of taste or smell; 48 per cent, could revive some; 12 per cent, declared themselves capable of reviving all, or nearly all, at pleasure. In some persons there is no necessary accompanying revival of visual or tactile representations, but in the majority the revived odor ultimately excites a corresponding visual image. The odors most frequently recalled were pinks, musk, violets, heliotrope, carbolic acid, the smell of the country, of grass, etc. Piéron (Revue Philosophique, December, 1902) has described the special power possessed by vague odors, in his own case, of evoking ancient impressions. Dr. J. N. Mackenzie (American Journal of the Medical Sciences, January, 1886) considers that civilization exerts an influence in heightening or encouraging the influence of olfaction as it affects our emotions and judgment, and that, in the same way, as we ascend the social scale the more readily our minds are influenced and perhaps perverted by impressions received through the sense of smell. Odors are powerful stimulants to the whole nervous system, causing, like other stimulants, an increase of energy which, if excessive or prolonged, leads to nervous exhaustion. Thus, it is well recognized in medicine that the aromatics containing volatile oils (such as anise, cinnamon, cardamoms, cloves, coriander, and peppermint) are antispasmodics and anæsthetics, and that they stimulate digestion, circulation, and the nervous system, in large doses producing depression. The carefully arranged plethysmographic experiments of Shields, at the Johns Hopkins University, have shown that olfactory sensations, by their action on the vasomotor system, cause an increase of blood in the brain and sometimes in addition stimulation of the heart; musk, wintergreen, wood violet, and especially heliotrope were found to act strongly in these ways. [27] Féré's experiments with the dynamometer and the ergograph have greatly contributed to illustrate the stimulating effects of odors. Thus, he found that smelling musk suffices to double muscular effort. With a number of odorous substances he has found that muscular work is temporarily heightened; when taste stimulation was added the increase of energy, notably when using lemon was "colossal." A kind of "sensorial intoxication" could be produced by the inhalation of odors and the whole system stimulated to greater activity; the visual acuity was increased, and electric and general excitability heightened. Such effects may be obtained in perfectly healthy persons, though both Shields and Féré have found that in highly nervous persons the effects are liable to be much greater. It is doubtless on this account that it is among civilized peoples that attention is chiefly directed to perfumes, and that under the conditions of modern life the interest in olfaction and its study has been revived. [28] It is the genuinely stimulant qualities of odorous substances which led to the widespread use of the more potent among them by ancient physicians, and has led a few modern physicians to employ them still. Thus, vanilla, according to Eloy, deserves to be much more frequently used therapeutically than it is, on account of its excitomotor properties; he states that its qualities as an excitant of sexual desire have long been recognized and that Fonssagrives used to prescribe it for sexual frigidity. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Havelock Ellis (2004). Studies in the Psychology of Sex, Volume 4. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/13613/pg13613-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org, located at https://www.gutenberg.org/policy/license.html.

Physiological and Psychological study of the senses

The Evolution of Modern Medicine, by William Osler is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book . here CHAPTER IV — THE RENAISSANCE AND THE RISE OF ANATOMY AND PHYSIOLOGY THE "reconquest of the classic world of thought was by far the most important achievement of the fifteenth and sixteenth centuries. It absorbed nearly the whole mental energy of the Italians.... The revelation of what men were and what they wrought under the influence of other faiths and other impulses, in distant ages with a different ideal for their aim, not only widened the narrow horizon of the Middle Ages, but it also restored self-confidence to the reason of humanity."(1) Everywhere throughout the Middle Ages learning was the handmaid of theology. Even Roger Bacon with his strong appeal for a new method accepted the dominant mediaeval conviction—that all the sciences did but minister to their queen, Theology. A new spirit entered man's heart as he came to look upon learning as a guide to the conduct of life. A revolution was slowly effected in the intellectual world. It is a mistake to think of the Renaissance as a brief period of sudden fruitfulness in the North Italian cities. So far as science is concerned, the thirteenth century was an aurora followed by a long period of darkness, but the fifteenth was a true dawn that brightened more and more unto the perfect day. Always a reflex of its period, medicine joined heartily though slowly in the revolt against mediaevalism. How slowly I did not appreciate until recently. Studying the earliest printed medical works to catch the point of view of the men who were in the thick of the movement up to 1480—which may be taken to include the first quarter of a century of printing—one gets a startling record. The mediaeval mind still dominates: of the sixty-seven authors of one hundred and eighty-two editions of early medical books, twenty-three were men of the thirteenth and fourteenth centuries, thirty men of the fifteenth century, eight wrote in Arabic, several were of the School of Salernum, and only six were of classical antiquity, viz., Pliny (first 1469), Hippocrates (1473) (Hain (*)7247), Galen (1475) (Hain 7237), Aristotle (1476), Celsus (1478), and Dioscorides (1478).(**) The medical profession gradually caught the new spirit. It has been well said that Greece arose from the dead with the New Testament in the one hand and Aristotle in the other. There was awakened a perfect passion for the old Greek writers, and with it a study of the original sources, which had now become available in many manuscripts. Gradually Hippocrates and Galen came to their own again. Almost every professor of medicine became a student of the MSS. of Aristotle and of the Greek physicians, and before 1530 the presses had poured out a stream of editions. A wave of enthusiasm swept over the profession, and the best energies of its best minds were devoted to a study of the Fathers. Galen became the idol of the schools. A strong revulsion of feeling arose against the Arabians, and Avicenna, the Prince, who had been clothed with an authority only a little less than divine, became anathema. Under the leadership of the Montpellier School, the Arabians made a strong fight, but it was a losing battle all along the line. This group of medical humanists—men who were devoted to the study of the old humanities, as Latin and Greek were called—has had a great and beneficial influence upon the profession. They were for the most part cultivated gentlemen with a triple interest—literature, medicine and natural history. How important is the part they played may be gathered from a glance at the "Lives" given by Bayle in his "Biographic Medicale" (Paris, 1855) between the years 1500 and 1575. More than one half of them had translated or edited works of Hippocrates or Galen; many of them had made important contributions to general literature, and a large proportion of them were naturalists: Leonicenus, Linacre, Champier, Fernel, Fracastorius, Gonthier, Caius, J. Sylvius, Brasavola, Fuchsius, Matthiolus, Conrad Gesner, to mention only those I know best, form a great group. Linacre edited Greek works for Aldus, translated works of Galen, taught Greek at Oxford, wrote Latin grammars and founded the Royal College of Physicians.(*) Caius was a keen Greek scholar, an ardent student of natural history, and his name is enshrined as co-founder of one of the most important of the Cambridge colleges. Gonthier, Fernel, Fuchs and Mattioli were great scholars and greater physicians. Champier, one of the most remarkable of the group, was the founder of the Hotel Dieu at Lyons, and author of books of a characteristic Renaissance type and of singular bibliographical interest. In many ways greatest of all was Conrad Gesner, whose mors inopinata at forty-nine, bravely fighting the plague, is so touchingly and tenderly mourned by his friend Caius.(2) Physician, botanist, mineralogist, geologist, chemist, the first great modern bibliographer, he is the very embodiment of the spirit of the age.(2a) On the flyleaf of my copy of the "Bibliotheca Universalis" (1545), is written a fine tribute to his memory. I do not know by whom it is, but I do know from my reading that it is true: "Conrad Gesner, who kept open house there for all learned men who came into his neighborhood. Gesner was not only the best naturalist among the scholars of his day, but of all men of that century he was the pattern man of letters. He was faultless in private life, assiduous in study, diligent in maintaining correspondence and good-will with learned men in all countries, hospitable—though his means were small—to every scholar that came into Zurich. Prompt to serve all, he was an editor of other men's volumes, a writer of prefaces for friends, a suggestor to young writers of books on which they might engage themselves, and a great helper to them in the progress of their work. But still, while finding time for services to other men, he could produce as much out of his own study as though he had no part in the life beyond its walls." A large majority of these early naturalists and botanists were physicians.(3) The Greek art of observation was revived in a study of the scientific writings of Aristotle, Theophrastus and Dioscorides and in medicine, of Hippocrates and of Galen, all in the Greek originals. That progress was at first slow was due in part to the fact that the leaders were too busy scraping the Arabian tarnish from the pure gold of Greek medicine and correcting the anatomical mistakes of Galen to bother much about his physiology or pathology. Here and there among the great anatomists of the period we read of an experiment, but it was the art of observation, the art of Hippocrates, not the science of Galen, not the carefully devised experiment to determine function, that characterized their work. There was indeed every reason why men should have been content with the physiology and pathology of that day, as, from a theoretical standpoint, it was excellent. The doctrine of the four humors and of the natural, animal and vital spirits afforded a ready explanation for the symptoms of all diseases, and the practice of the day was admirably adapted to the theories. There was no thought of, no desire for, change. But the revival of learning awakened in men at first a suspicion and at last a conviction that the ancients had left something which could be reached by independent research, and gradually the paralytic-like torpor passed away. The sixteenth and seventeenth centuries did three things in medicine—shattered authority, laid the foundation of an accurate knowledge of the structure of the human body and demonstrated how its functions should be studied intelligently—with which advances, as illustrating this period, may be associated the names of Paracelsus, Vesalius and Harvey. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Urbana, Illinois: Project Gutenberg. Retrieved April 2022 from Osler, William. 2006. The Evolution of Modern Medicine. https://www.gutenberg.org/files/1566/1566-h/1566-h.htm#link2HCH0004 This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html Photo by on National Cancer Institute Unsplash

The Evolution of Modern Medicine: CHAPTER IV — THE RENAISSANCE AND THE RISE OF ANATOMY AND PHYSIOLOG

Dropping support of a Python runtime can be a tricky task for any package maintainer. Python 2.6 is out of maintenance, 3.3 is now out of maintenance and both have syntax gaps with the common subset found in 2.7 and 3.4. When you might want to later only offer Python 3 support on a package, you don’t want to completely leave your Python 2 users in the cold. The Python Packaging Authority have been working on solving that issue, with a combination of updates to PyPi (the packaging service), the Packaging API, Pip (the command line installer), setuptools (the library used to create the metadata) and the metadata format. All of these changes have come together to allow package maintainers to publish “maintenance releases” to old Python runtimes and new releases to supported Python runtimes. How does it work today To publish a package to PyPi, you need to follow 3 steps: Define a setup.py file that includes distutils or setuptools and calls with some information about the package setup( Call or if you want to include a wheel file python setup.py sdist python setup.py sdist bdist_wheel Upload the package to PyPi using . twine upload dist/* Defining the PKG-INFO Within a Python source package (the zip or the tar-gz file you download) is a text file called PKG-INFO. This file is generated by distutils or setuptools when it generates the source package. The file contains a set of keys and values, the list of keys is part of the PyPa standard metadata format. You can see the contents of the generated file like this: tar xvfz dist/my-package-1.0.0.tar.gz -O | cat */PKG-INFO At the top of the file should be the header , which upon the version of setuptools or distutils you have installed. So the first thing you should do is Metadata-Version depends upgrade setuptools and stop using distutils. pip install — upgrade setuptools Setuptools is the package being now, so make sure at the top of setup.py you are importing the setup method from the setuptools module. maintained by PyPa If you have upgraded correctly, the Metadata-Version value should be 1.2 or higher. Metadata 1.2 of Python required for this package. introduced a new field for the version You can specify version ranges and exclusion rules, such as at least Python 3. Or, Python 2.7 and 3.4 beyond. Requires-Python: >=3Requires-Python: >2.7,!=3.0.*,!=3.1.*, !=3.2.*,!=3.3.* The way to set those values is within the call to within your setup.py script. setup Now, calling and then will publish version 1 of this package specifying that only Python 2.7+ is supported. python setup.py sdist twine upload dist/* Verifying before you publish Since it’s almost impossible to edit a published package on PyPi, you really want to check first, so run this command to validate the metadata has been generated correctly. tar xvfz dist/my-package-1.0.0.tar.gz -O | grep “Requires-Python” Using Twine to publish Anyone used to using to publish the package to PyPi, you can also stop doing that now. Twine has a number of advantages, apart from being faster is now the supported method for publishing packages. setup.py Make sure you are using the newest version of twine (1.9 at the time of writing). Dropping a Python release Once you have published a package with the metadata, you can then make a further update removing that Python runtime from support. It must be done in this order for the automated fail-back to work. This is a pretty typical timeline You publish 1.0 of your package before reading this article You publish 1.1.0 after the reading the PEP on semantic versioning You read this article and added in setup.py for all versions 2.7+ python_requires Many seasons pass, versions go by You publish 1.6.0 supporting Python 2.7+ You announce to your users that you are dropping Python 2 support in a month You modify the field to >3.3 and publish version 2.0.0 of your package python_requires Now, if the user has pip >9.0 and they install your package from Python 2 without specifying a version number, they get the last version of the package to support their Python distribution! If a user has an in-support Python distribution as specified in your then they get the latest version. setup.py Try it out on a package I published to PyPi called . friendly-deprecation-test But wait… Someone raises a critical bug against v1.6.0 and you don’t want to leave your Python 2 users at risk. What do you do? Assuming you have 1.6 in a branch, apply the fix. Bump the version on the branch to 1.6.1 and publish it with the attribute. Release it to PyPi. requires_python >= 2.7 Apply the fix to the master branch and release 2.0.1 with the attribute. requires_python >= 3.4 Now the same process applies, all Python 2 users will get 1.6.1 and Python 3.4+ will get 2.0.1.

Phasing out Python runtimes gracefully

Digital Health: LLMs for Prompt and Quality Sleep?

AI: Sleep Computational Neuroscience, Dreams, Loneliness, and Predictive Coding

As stress increasingly contributes to heart diseases and, consequently, mortality, well-being practices become more important. Massage is one of the most effective ones. However, not everyone across the world has access to it. How to address this gap and lower stress levels? The answer is obvious: create and use technologies. Human Death Factor According to the latest available data from the World Health Organization (WHO), the top two causes of mortality worldwide are stroke and ischemic heart disease. Moreover, the National Centre for Health Statistics of CDS (Centers for Disease and Prevention) which indicates the mortality index in the US confirms states that heart disease is the number one cause of death with about 700,000 cases per year. One of the general reasons for cardiovascular diseases and strokes is stress. The work of Andrew Steptoe and Mika Kivimäki, "Stress and Cardiovascular Disease," among others, confirms that, indicating that chronic stress predicts the occurrence of coronary heart disease (CHD). Moreover, short-term emotional stress can act as a trigger for cardiac events among those with advanced atherosclerosis. Among those at greater risk are people who experience work-related stress. This correlation is shown in the latest research by the Health and Safety Executive. According to this data, 875,000 workers suffered from work-related stress, depression, or anxiety (new or long-standing cases) in 2022 and 2023. Human Life Factor Fortunately, there are techniques to decrease stress levels. Breathing exercises, laughter, helpful thinking, mindfulness, and social activities, as mentioned in the Human Resources Datasheet of the University of Michigan, are among them. Additionally, the WHO recommends maintaining a daily routine, connecting with others, ensuring quality sleep, paying attention to nutrition, and engaging in regular physical exercise. Montana State University provides a more precise explanation of the biological mechanisms behind stress reduction through exercise and other wellness practices. They reduce adrenaline and cortisol, two stress hormones. To maintain cortisol within the norm, scholars advise monitoring caffeine and alcohol as both can increase cortisol. Time management and maintaining work-life balance can also contribute to mental well-being. Body treatments and massages also play a role as anti-stress techniques. This is confirmed in the case study "Physiological Adjustments to Stress Measures Following Massage Therapy: A Review of the Literature" by Albert Moraska and his partners from various health institutions across the US. A study by the Touch Research Institutes at the University of Miami School of Medicine further reveals that massages can reduce cortisol by an average of 31% in a single session. It also improves sleep quality and lowers heart rate and blood pressure, thereby mitigating risks associated with cardiovascular disease. Massage to Masses However, not everyone can access body therapy and massage. Common barriers include a lack of available or efficient massage therapists and their limited working hours. This means that when you need a session after long hours at work, you may have to wait until a suitable hour. Costs also play a role, with one session averaging up to $100. It's not surprising that human performance comes at a high price in today's world. Imagine going to a theater with only one visitor - you. It would be expensive. However, when you're among many, economic dynamics allow services to be more accessible and affordable. Technologies offer a liberating solution. They have already changed numerous industries. Consider the field of education: digital platforms enable students from around the globe to participate in the same programs for much less money than traditional institutions offer. You can now access information from Cambridge in your hometown while having a bath. In the realm of body therapy and massage, we see a similar shift. While tech solutions may not entirely replace massage specialists they can help bridge existing gaps in labor and lower therapy costs in the future. Some solutions transform Tui Na, a form of traditional Chinese medicine bodywork similar to deep tissue massage. They use sensors to pinpoint pressure points and deliver customized massages. Other devices available on the market are designed for home use with various attachments for different massage experiences. The success of robotic solutions is foreseeable, given the widespread use of robotic and AI-driven technologies in healthcare. In 2022, the global artificial intelligence in the healthcare market reached around $15.1 billion, with projections expecting a surge to approximately $187.95 billion by 2030, boasting an impressive growth rate of 37% from 2022 to 2030. North America’s AI in the healthcare market alone reached $6.8 billion in 2022. The market for AI in robotics is expected to grow even faster, with a projected compound annual growth rate (CAGR) of 27.2% over the next five years. As stress increasingly contributes to heart diseases and, consequently, mortality, well-being practices become more important. Massage is one of the most effective ones. However, not everyone across the world has access to it. How to address this gap and lower stress levels? The answer is obvious: create and use technologies. Human Death Factor Human Death Factor According to the latest available data from the World Health Organization (WHO), the top two causes of mortality worldwide are stroke and ischemic heart disease. Moreover, the National Centre for Health Statistics of CDS (Centers for Disease and Prevention) which indicates the mortality index in the US confirms states that heart disease is the number one cause of death with about 700,000 cases per year. data data confirms confirms One of the general reasons for cardiovascular diseases and strokes is stress. The work of Andrew Steptoe and Mika Kivimäki, "Stress and Cardiovascular Disease," among others, confirms that, indicating that chronic stress predicts the occurrence of coronary heart disease (CHD). Moreover, short-term emotional stress can act as a trigger for cardiac events among those with advanced atherosclerosis. confirms confirms Among those at greater risk are people who experience work-related stress. This correlation is shown in the latest research by the Health and Safety Executive. According to this data, 875,000 workers suffered from work-related stress, depression, or anxiety (new or long-standing cases) in 2022 and 2023. shown shown Human Life Factor Human Life Factor Fortunately, there are techniques to decrease stress levels. Breathing exercises, laughter, helpful thinking, mindfulness, and social activities, as mentioned in the Human Resources Datasheet of the University of Michigan, are among them. Additionally, the WHO recommends maintaining a daily routine, connecting with others, ensuring quality sleep, paying attention to nutrition, and engaging in regular physical exercise. mentioned mentioned Montana State University provides a more precise explanation of the biological mechanisms behind stress reduction through exercise and other wellness practices. They reduce adrenaline and cortisol, two stress hormones. To maintain cortisol within the norm, scholars advise monitoring caffeine and alcohol as both can increase cortisol. Time management and maintaining work-life balance can also contribute to mental well-being. provides provides caffeine caffeine alcohol alcohol Body treatments and massages also play a role as anti-stress techniques. This is confirmed in the case study "Physiological Adjustments to Stress Measures Following Massage Therapy: A Review of the Literature" by Albert Moraska and his partners from various health institutions across the US. A study by the Touch Research Institutes at the University of Miami School of Medicine further reveals that massages can reduce cortisol by an average of 31% in a single session. It also improves sleep quality and lowers heart rate and blood pressure, thereby mitigating risks associated with cardiovascular disease. confirmed confirmed study study improves improves lowers lowers Massage to Masses Massage to Masses However, not everyone can access body therapy and massage. Common barriers include a lack of available or efficient massage therapists and their limited working hours. This means that when you need a session after long hours at work, you may have to wait until a suitable hour. Costs also play a role, with one session averaging up to $100. It's not surprising that human performance comes at a high price in today's world. Imagine going to a theater with only one visitor - you. It would be expensive. However, when you're among many, economic dynamics allow services to be more accessible and affordable. averaging averaging Technologies offer a liberating solution. They have already changed numerous industries. Consider the field of education: digital platforms enable students from around the globe to participate in the same programs for much less money than traditional institutions offer. You can now access information from Cambridge in your hometown while having a bath. In the realm of body therapy and massage, we see a similar shift. While tech solutions may not entirely replace massage specialists they can help bridge existing gaps in labor and lower therapy costs in the future. Some solutions transform Tui Na, a form of traditional Chinese medicine bodywork similar to deep tissue massage. They use sensors to pinpoint pressure points and deliver customized massages. Other devices available on the market are designed for home use with various attachments for different massage experiences. The success of robotic solutions is foreseeable, given the widespread use of robotic and AI-driven technologies in healthcare. In 2022, the global artificial intelligence in the healthcare market reached around $15.1 billion, with projections expecting a surge to approximately $187.95 billion by 2030, boasting an impressive growth rate of 37% from 2022 to 2030. North America’s AI in the healthcare market alone reached $6.8 billion in 2022. The market for AI in robotics is expected to grow even faster, with a projected compound annual growth rate (CAGR) of 27.2% over the next five years. surge surge projected projected

How Technology Can Make Stress-Relief More Accessible in the Near Future

Authors:
(1) J. Quetzalcóatl Toledo-Marín, University of British Columbia, BC Children’s Hospital Research Institute Vancouver BC, Canada (Email: j.toledo.mx@gmail.com);
(2) James A. Glazier, Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47408, USA (Email: jaglazier@gmail.com);
(3) Geoffrey Fox, University of Virginia, Computer Science and Biocomplexity Institute, 994 Research Park Blvd, Charlottesville, Virginia, 22911, USA (Email: gcfexchange@gmail.com). Table of Links Abstract and Introduction Methods Results Discussion Conclusions and References 5 Conclusions When selecting a NN for a specific task, it is important to consider the function and requirements of the task at hand. There is currently no consensus on which NN is optimal for a given task, primarily due to the large number of NN options available, the rapidly evolving nature of the field, and the lack of a comprehensive deep learning theory. This leads to a reliance on empirical results. Our paper is an important step at establishing best practices for this type of problems. we focused on randomly placed sources with random fluxes which yield large variations in the field. Our method can be generalized for different diffusion equations. As part of future steps, we will increase the complexity of the problem being solved by considering conditions closer to real-problems, i.e., by considering less symmetrical sources, different diffusivities and different boundary conditions. In addition, further design is required for these models to be used in a production environment in a reliable way, i.e., how to deal with error performance edge cases on-the-fly? Ultimately, to be able to deploy the model for production, one requires a method to keep the performance error below a predefined bound. For instance, one can train an additional NN that takes the predicted stationary solution and predicts the initial condition, which is then compared with the ground truth initial condition. This framework allows a comparison between ground truth input and predicted input without requiring the ground truth steady-state solution. However, this approach would only be reliable if the input error is always proportional to the steady-state error and hence requires further investigation. A perhaps simpler approach consists in sampling the NN’s output for the same input using a drop-out feature, such that if the fluctuations from the samples are small enough, then the NN’s prediction is robust enough. Both cases require benchmark design. We have developed a process in this paper that can easily be replicated for more complicated problems of this type, and provided a variety of benchmarks. 6 Acknowledgements JQTM acknowledges a Mitacs Postdoctoral Fellowship. GCF acknowledges partial support from DOE DE-SC0023452 and NSF OAC-2204115. References [1] William E Schiesser. The numerical method of lines: integration of partial differential equations. Elsevier, 2012. [2] Amir Barati Farimani, Joseph Gomes, and Vijay S Pande. Deep learning the physics of transport phenomena. arXiv preprint arXiv:1709.02432, 2017. [3] Rishi Sharma, Amir Barati Farimani, Joe Gomes, Peter Eastman, and Vijay Pande. Weakly-supervised deep learning of heat transport via physics informed loss. arXiv preprint arXiv:1807.11374, 2018. [4] Haiyang He and Jay Pathak. An unsupervised learning approach to solving heat equations on chip based on auto encoder and image gradient. arXiv preprint arXiv:2007.09684, 2020. [5] Mohammad Edalatifar, Mohammad Bagher Tavakoli, Mohammad Ghalambaz, and Farbod Setoudeh. Using deep learning to learn physics of conduction heat transfer. Journal of Thermal Analysis and Calorimetry, pages 1–18, 2020. [6] Angran Li, Ruijia Chen, Amir Barati Farimani, and Yongjie Jessica Zhang. Reaction diffusion system prediction based on convolutional neural network. Scientific reports, 10(1):1–9, 2020. [7] Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar. Fourier neural operator for parametric partial differential equations. arXiv preprint arXiv:2010.08895, 2020. [8] Shengze Cai, Zhicheng Wang, Sifan Wang, Paris Perdikaris, and George Karniadakis. Physics-informed neural networks (pinns) for heat transfer problems. Journal of Heat Transfer, 2021. [9] Geoffrey Fox and Shantenu Jha. Learning everywhere: A taxonomy for the integration of machine learning and simulations. In 2019 15th International Conference on eScience (eScience), pages 439–448. IEEE, 2019. [10] Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud. Neural ordinary differential equations. arXiv preprint arXiv:1806.07366, 2018. [11] Christopher Rackauckas, Mike Innes, Yingbo Ma, Jesse Bettencourt, Lyndon White, and Vaibhav Dixit. Diffeqflux.jl - A julia library for neural differential equations. CoRR, abs/1902.02376, 2019. [12] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Scorebased generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020. [13] Maziar Raissi, Paris Perdikaris, and George E Karniadakis. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378:686–707, 2019. [14] Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, and Cecilia Clementi. Machine learning for molecular simulation. Annual review of physical chemistry, 71:361–390, 2020. [15] Maciej Majewski, Adrià Pérez, Philipp Thölke, Stefan Doerr, Nicholas E Charron, Toni Giorgino, Brooke E Husic, Cecilia Clementi, Frank Noé, and Gianni De Fabritiis. Machine learning coarse-grained potentials of protein thermodynamics. arXiv preprint arXiv:2212.07492, 2022. [16] Mike Entwistle, Zeno Schätzle, Paolo A Erdman, Jan Hermann, and Frank Noé. Electronic excited states in deep variational monte carlo. arXiv preprint arXiv:2203.09472, 2022. [17] J Quetzalcóatl Toledo-Marín, Geoffrey Fox, James P Sluka, and James A Glazier. Deep learning approaches to surrogates for solving the diffusion equation for mechanistic real-world simulations. Frontiers in Physiology, 12, 2021. [18] Christoph Baur, Stefan Denner, Benedikt Wiestler, Nassir Navab, and Shadi Albarqouni. Autoencoders for unsupervised anomaly segmentation in brain mr images: A comparative study. Medical Image Analysis, page 101952, 2020. [19] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. [20] Kishan KC, Rui Li, and MohammadMahdi Gilany. Joint inference for neural network depth and dropout regularization. Advances in Neural Information Processing Systems, 34, 2021. [21] Michael Innes, Elliot Saba, Keno Fischer, Dhairya Gandhi, Marco Concetto Rudilosso, Neethu Mariya Joy, Tejan Karmali, Avik Pal, and Viral Shah. Fashionable modelling with flux. CoRR, abs/1811.01457, 2018. [22] Christopher Rackauckas and Qing Nie. Differentialequations.jl–a performant and feature-rich ecosystem for solving differential equations in julia. Journal of Open Research Software, 5(1), 2017. [23] Andre˘ı Nikolaevich Tikhonov and Aleksandr Andreevich Samarskii. Equations of mathematical physics. Courier Corporation, 2013. [24] J. Quetzalcoatl Toledo-Marin. Steady state diffusion surrogate. https://github.com/jquetzalcoatl/ DiffSolver, 2023. [25] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. [26] Peter J Huber. Robust estimation of a location parameter. In Breakthroughs in statistics, pages 492–518. Springer, 1992. [27] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234–241. Springer, 2015. [28] Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari S Morcos. Beyond neural scaling laws: beating power law scaling via data pruning. arXiv preprint arXiv:2206.14486, 2022. [29] Jeyan Thiyagalingam, Mallikarjun Shankar, Geoffrey Fox, and Tony Hey. Scientific machine learning benchmarks. Nature Reviews Physics, 4(6):413–420, 2022. This paper is available on arxiv under CC 4.0 license. Authors: (1) J. Quetzalcóatl Toledo-Marín, University of British Columbia, BC Children’s Hospital Research Institute Vancouver BC, Canada (Email: j.toledo.mx@gmail.com); (2) James A. Glazier, Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47408, USA (Email: jaglazier@gmail.com); (3) Geoffrey Fox, University of Virginia, Computer Science and Biocomplexity Institute, 994 Research Park Blvd, Charlottesville, Virginia, 22911, USA (Email: gcfexchange@gmail.com). Authors: Authors: (1) J. Quetzalcóatl Toledo-Marín, University of British Columbia, BC Children’s Hospital Research Institute Vancouver BC, Canada (Email: j.toledo.mx@gmail.com); (2) James A. Glazier, Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47408, USA (Email: jaglazier@gmail.com); (3) Geoffrey Fox, University of Virginia, Computer Science and Biocomplexity Institute, 994 Research Park Blvd, Charlottesville, Virginia, 22911, USA (Email: gcfexchange@gmail.com). Table of Links Abstract and Introduction Abstract and Introduction Methods Methods Results Results Discussion Discussion Conclusions and References Conclusions and References 5 Conclusions When selecting a NN for a specific task, it is important to consider the function and requirements of the task at hand. There is currently no consensus on which NN is optimal for a given task, primarily due to the large number of NN options available, the rapidly evolving nature of the field, and the lack of a comprehensive deep learning theory. This leads to a reliance on empirical results. Our paper is an important step at establishing best practices for this type of problems. we focused on randomly placed sources with random fluxes which yield large variations in the field. Our method can be generalized for different diffusion equations. As part of future steps, we will increase the complexity of the problem being solved by considering conditions closer to real-problems, i.e., by considering less symmetrical sources, different diffusivities and different boundary conditions. In addition, further design is required for these models to be used in a production environment in a reliable way, i.e., how to deal with error performance edge cases on-the-fly? Ultimately, to be able to deploy the model for production, one requires a method to keep the performance error below a predefined bound. For instance, one can train an additional NN that takes the predicted stationary solution and predicts the initial condition, which is then compared with the ground truth initial condition. This framework allows a comparison between ground truth input and predicted input without requiring the ground truth steady-state solution. However, this approach would only be reliable if the input error is always proportional to the steady-state error and hence requires further investigation. A perhaps simpler approach consists in sampling the NN’s output for the same input using a drop-out feature, such that if the fluctuations from the samples are small enough, then the NN’s prediction is robust enough. Both cases require benchmark design. We have developed a process in this paper that can easily be replicated for more complicated problems of this type, and provided a variety of benchmarks. 6 Acknowledgements JQTM acknowledges a Mitacs Postdoctoral Fellowship. GCF acknowledges partial support from DOE DE-SC0023452 and NSF OAC-2204115. References [1] William E Schiesser. The numerical method of lines: integration of partial differential equations. Elsevier, 2012. [2] Amir Barati Farimani, Joseph Gomes, and Vijay S Pande. Deep learning the physics of transport phenomena. arXiv preprint arXiv:1709.02432, 2017. [3] Rishi Sharma, Amir Barati Farimani, Joe Gomes, Peter Eastman, and Vijay Pande. Weakly-supervised deep learning of heat transport via physics informed loss. arXiv preprint arXiv:1807.11374, 2018. [4] Haiyang He and Jay Pathak. An unsupervised learning approach to solving heat equations on chip based on auto encoder and image gradient. arXiv preprint arXiv:2007.09684, 2020. [5] Mohammad Edalatifar, Mohammad Bagher Tavakoli, Mohammad Ghalambaz, and Farbod Setoudeh. Using deep learning to learn physics of conduction heat transfer. Journal of Thermal Analysis and Calorimetry, pages 1–18, 2020. [6] Angran Li, Ruijia Chen, Amir Barati Farimani, and Yongjie Jessica Zhang. Reaction diffusion system prediction based on convolutional neural network. Scientific reports, 10(1):1–9, 2020. [7] Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar. Fourier neural operator for parametric partial differential equations. arXiv preprint arXiv:2010.08895, 2020. [8] Shengze Cai, Zhicheng Wang, Sifan Wang, Paris Perdikaris, and George Karniadakis. Physics-informed neural networks (pinns) for heat transfer problems. Journal of Heat Transfer, 2021. [9] Geoffrey Fox and Shantenu Jha. Learning everywhere: A taxonomy for the integration of machine learning and simulations. In 2019 15th International Conference on eScience (eScience), pages 439–448. IEEE, 2019. [10] Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud. Neural ordinary differential equations. arXiv preprint arXiv:1806.07366, 2018. [11] Christopher Rackauckas, Mike Innes, Yingbo Ma, Jesse Bettencourt, Lyndon White, and Vaibhav Dixit. Diffeqflux.jl - A julia library for neural differential equations. CoRR, abs/1902.02376, 2019. [12] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Scorebased generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020. [13] Maziar Raissi, Paris Perdikaris, and George E Karniadakis. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378:686–707, 2019. [14] Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, and Cecilia Clementi. Machine learning for molecular simulation. Annual review of physical chemistry, 71:361–390, 2020. [15] Maciej Majewski, Adrià Pérez, Philipp Thölke, Stefan Doerr, Nicholas E Charron, Toni Giorgino, Brooke E Husic, Cecilia Clementi, Frank Noé, and Gianni De Fabritiis. Machine learning coarse-grained potentials of protein thermodynamics. arXiv preprint arXiv:2212.07492, 2022. [16] Mike Entwistle, Zeno Schätzle, Paolo A Erdman, Jan Hermann, and Frank Noé. Electronic excited states in deep variational monte carlo. arXiv preprint arXiv:2203.09472, 2022. [17] J Quetzalcóatl Toledo-Marín, Geoffrey Fox, James P Sluka, and James A Glazier. Deep learning approaches to surrogates for solving the diffusion equation for mechanistic real-world simulations. Frontiers in Physiology, 12, 2021. [18] Christoph Baur, Stefan Denner, Benedikt Wiestler, Nassir Navab, and Shadi Albarqouni. Autoencoders for unsupervised anomaly segmentation in brain mr images: A comparative study. Medical Image Analysis, page 101952, 2020. [19] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. [20] Kishan KC, Rui Li, and MohammadMahdi Gilany. Joint inference for neural network depth and dropout regularization. Advances in Neural Information Processing Systems, 34, 2021. [21] Michael Innes, Elliot Saba, Keno Fischer, Dhairya Gandhi, Marco Concetto Rudilosso, Neethu Mariya Joy, Tejan Karmali, Avik Pal, and Viral Shah. Fashionable modelling with flux. CoRR, abs/1811.01457, 2018. [22] Christopher Rackauckas and Qing Nie. Differentialequations.jl–a performant and feature-rich ecosystem for solving differential equations in julia. Journal of Open Research Software, 5(1), 2017. [23] Andre˘ı Nikolaevich Tikhonov and Aleksandr Andreevich Samarskii. Equations of mathematical physics. Courier Corporation, 2013. [24] J. Quetzalcoatl Toledo-Marin. Steady state diffusion surrogate. https://github.com/jquetzalcoatl/ DiffSolver, 2023. [25] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. [26] Peter J Huber. Robust estimation of a location parameter. In Breakthroughs in statistics, pages 492–518. Springer, 1992. [27] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234–241. Springer, 2015. [28] Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari S Morcos. Beyond neural scaling laws: beating power law scaling via data pruning. arXiv preprint arXiv:2206.14486, 2022. [29] Jeyan Thiyagalingam, Mallikarjun Shankar, Geoffrey Fox, and Tony Hey. Scientific machine learning benchmarks. Nature Reviews Physics, 4(6):413–420, 2022. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Generalizing Deep Learning Models for Varied Diffusion Equations

Features of CBDC Applications That Can Shape the Future of Finance

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Author:
(1) Shadab Ahamed, University of British Columbia, Vancouver, BC, Canada, BC Cancer Research Institute, Vancouver, BC, Canada. He was also a Mitacs Accelerate Fellow (May 2022 - April 2023) with Microsoft AI for Good Lab, Redmond, WA, USA (e-mail: shadabahamed1996@gmail.com);
(2) Yixi Xu, Microsoft AI for Good Lab, Redmond, WA, USA;
(3) Claire Gowdy, BC Children’s Hospital, Vancouver, BC, Canada;
(4) Joo H. O, St. Mary’s Hospital, Seoul, Republic of Korea;
(5) Ingrid Bloise, BC Cancer, Vancouver, BC, Canada;
(6) Don Wilson, BC Cancer, Vancouver, BC, Canada;
(7) Patrick Martineau, BC Cancer, Vancouver, BC, Canada;
(8) Franc¸ois Benard, BC Cancer, Vancouver, BC, Canada;
(9) Fereshteh Yousefirizi, BC Cancer Research Institute, Vancouver, BC, Canada;
(10) Rahul Dodhia, Microsoft AI for Good Lab, Redmond, WA, USA;
(11) Juan M. Lavista, Microsoft AI for Good Lab, Redmond, WA, USA;
(12) William B. Weeks, Microsoft AI for Good Lab, Redmond, WA, USA;
(13) Carlos F. Uribe, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada;
(14) Arman Rahmim, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada. Table of Links Abstract and Intro
Related Work
Materials and Methods
Results
Discussion
Conclusion and References Abstract This study performs comprehensive evaluation of four neural network architectures (UNet, SegResNet, DynUNet, and SwinUNETR) for lymphoma lesion segmentation from PET/CT images. These networks were trained, validated and tested on a diverse, multi-institutional dataset of 611 cases. Internal testing (88 cases; total metabolic tumor volume (TMTV) range [0.52, 2300] ml) showed SegResNet as the top performer with a median Dice similarity coefficient (DSC) of 0.76 and median false positive volume (FPV) of 4.55 ml; all networks had a median false negative volume (FNV) of 0 ml. On the unseen external test set (145 cases with TMTV range: [0.10, 2480] ml), SegResNet achieved the best median DSC of 0.68 and FPV of 21.46 ml, while UNet had the best FNV of 0.41 ml. We assessed reproducibility of six lesion measures, calculated their prediction errors, and examined DSC performance in relation to these lesion measures, offering insights into segmentation accuracy and clinical relevance. Additionally, we introduced three lesion detection criteria, addressing the clinical need for identifying lesions, counting them, and segmenting based on metabolic characteristics. We also performed expert intra-observer variability analysis revealing the challenges in segmenting “easy” vs. “hard” cases, to assist in the development of more resilient segmentation algorithms. Finally, we performed inter-observer agreement assessment underscoring the importance of a standardized ground truth segmentation protocol involving multiple expert annotators. Code is available at: https://github.com/microsoft/lymphoma-segmentationdnn. Index Terms— Positron emission tomography, computed tomography, deep learning, segmentation, detection, lesion measures, intra-observer variability, inter-observer variability I. INTRODUCTION F LUORODEOXYGLUCOSE (18F-FDG) PET/CT imaging is the standard of care for lymphoma patients, providing accurate diagnoses, staging, and therapy response evaluation. However, traditional qualitative assessments, like Deauville scores [1], can introduce variability due to observer’s subjectivity in image interpretation. Using quantitative PET analysis that incorporates lesion measures such as mean lesion standardized uptake value (SUVmean), total metabolic tumor volume (TMTV), and total lesion glycolysis (TLG) offers a promising path to more reliable prognostic decisions, enhancing our ability to predict patient outcomes in lymphoma with greater precision and confidence [2]. Quantitative assessment in PET/CT imaging often relies on manual lesion segmentation, which is time-consuming and prone to intra- and inter-observer variabilities. Traditional thresholding-based automated techniques can miss low-uptake disease and produce false positives in regions of physiological high uptake of radiotracers. Therefore, deep learning offers promise for automating lesion segmentation, reducing variability, increasing patient throughput, and potentially aiding in the detection of challenging lesions [3]. Although promising, deep learning methods face challenges of their own. Convolutional neural networks (CNNs) require large, well-annotated datasets that can be difficult to obtain. Models trained on small datasets may not be generalizable. Moreover, lymphoma lesions vary significantly in size, shape, and metabolic activity, making training deep networks accurately challenging in the absence of well-defined priors. Deep learning aims to reduce observer variability, but inconsistent manual annotations used for training can lead to error perpetuation. Understanding these challenges is crucial towards harnessing the full potential of these methods in PET/CT quantitative analysis. This paper is available on arxiv under CC 4.0 license. Author: (1) Shadab Ahamed, University of British Columbia, Vancouver, BC, Canada, BC Cancer Research Institute, Vancouver, BC, Canada. He was also a Mitacs Accelerate Fellow (May 2022 - April 2023) with Microsoft AI for Good Lab, Redmond, WA, USA (e-mail: shadabahamed1996@gmail.com); (2) Yixi Xu, Microsoft AI for Good Lab, Redmond, WA, USA; (3) Claire Gowdy, BC Children’s Hospital, Vancouver, BC, Canada; (4) Joo H. O, St. Mary’s Hospital, Seoul, Republic of Korea; (5) Ingrid Bloise, BC Cancer, Vancouver, BC, Canada; (6) Don Wilson, BC Cancer, Vancouver, BC, Canada; (7) Patrick Martineau, BC Cancer, Vancouver, BC, Canada; (8) Franc¸ois Benard, BC Cancer, Vancouver, BC, Canada; (9) Fereshteh Yousefirizi, BC Cancer Research Institute, Vancouver, BC, Canada; (10) Rahul Dodhia, Microsoft AI for Good Lab, Redmond, WA, USA; (11) Juan M. Lavista, Microsoft AI for Good Lab, Redmond, WA, USA; (12) William B. Weeks, Microsoft AI for Good Lab, Redmond, WA, USA; (13) Carlos F. Uribe, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada; (14) Arman Rahmim, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada. Author: Author: (1) Shadab Ahamed, University of British Columbia, Vancouver, BC, Canada, BC Cancer Research Institute, Vancouver, BC, Canada. He was also a Mitacs Accelerate Fellow (May 2022 - April 2023) with Microsoft AI for Good Lab, Redmond, WA, USA (e-mail: shadabahamed1996@gmail.com); (2) Yixi Xu, Microsoft AI for Good Lab, Redmond, WA, USA; (3) Claire Gowdy, BC Children’s Hospital, Vancouver, BC, Canada; (4) Joo H. O, St. Mary’s Hospital, Seoul, Republic of Korea; (5) Ingrid Bloise, BC Cancer, Vancouver, BC, Canada; (6) Don Wilson, BC Cancer, Vancouver, BC, Canada; (7) Patrick Martineau, BC Cancer, Vancouver, BC, Canada; (8) Franc¸ois Benard, BC Cancer, Vancouver, BC, Canada; (9) Fereshteh Yousefirizi, BC Cancer Research Institute, Vancouver, BC, Canada; (10) Rahul Dodhia, Microsoft AI for Good Lab, Redmond, WA, USA; (11) Juan M. Lavista, Microsoft AI for Good Lab, Redmond, WA, USA; (12) William B. Weeks, Microsoft AI for Good Lab, Redmond, WA, USA; (13) Carlos F. Uribe, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada; (14) Arman Rahmim, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada. Table of Links Abstract and Intro Related Work Materials and Methods Results Discussion Conclusion and References Abstract and Intro Abstract and Intro Related Work Related Work Materials and Methods Materials and Methods Results Results Discussion Discussion Conclusion and References Conclusion and References Abstract Abstract This study performs comprehensive evaluation of four neural network architectures (UNet, SegResNet, DynUNet, and SwinUNETR) for lymphoma lesion segmentation from PET/CT images. These networks were trained, validated and tested on a diverse, multi-institutional dataset of 611 cases. Internal testing (88 cases; total metabolic tumor volume (TMTV) range [0.52, 2300] ml) showed SegResNet as the top performer with a median Dice similarity coefficient (DSC) of 0.76 and median false positive volume (FPV) of 4.55 ml; all networks had a median false negative volume (FNV) of 0 ml. On the unseen external test set (145 cases with TMTV range: [0.10, 2480] ml), SegResNet achieved the best median DSC of 0.68 and FPV of 21.46 ml, while UNet had the best FNV of 0.41 ml. We assessed reproducibility of six lesion measures, calculated their prediction errors, and examined DSC performance in relation to these lesion measures, offering insights into segmentation accuracy and clinical relevance. Additionally, we introduced three lesion detection criteria, addressing the clinical need for identifying lesions, counting them, and segmenting based on metabolic characteristics. We also performed expert intra-observer variability analysis revealing the challenges in segmenting “easy” vs. “hard” cases, to assist in the development of more resilient segmentation algorithms. Finally, we performed inter-observer agreement assessment underscoring the importance of a standardized ground truth segmentation protocol involving multiple expert annotators. Code is available at: https://github.com/microsoft/lymphoma-segmentationdnn . https://github.com/microsoft/lymphoma-segmentationdnn Index Terms — Positron emission tomography, computed tomography, deep learning, segmentation, detection, lesion measures, intra-observer variability, inter-observer variability Index Terms I. INTRODUCTION F LUORODEOXYGLUCOSE (18F-FDG) PET/CT imaging is the standard of care for lymphoma patients, providing accurate diagnoses, staging, and therapy response evaluation. However, traditional qualitative assessments, like Deauville scores [1], can introduce variability due to observer’s subjectivity in image interpretation. Using quantitative PET analysis that incorporates lesion measures such as mean lesion standardized uptake value (SUVmean), total metabolic tumor volume (TMTV), and total lesion glycolysis (TLG) offers a promising path to more reliable prognostic decisions, enhancing our ability to predict patient outcomes in lymphoma with greater precision and confidence [2]. Quantitative assessment in PET/CT imaging often relies on manual lesion segmentation, which is time-consuming and prone to intra- and inter-observer variabilities. Traditional thresholding-based automated techniques can miss low-uptake disease and produce false positives in regions of physiological high uptake of radiotracers. Therefore, deep learning offers promise for automating lesion segmentation, reducing variability, increasing patient throughput, and potentially aiding in the detection of challenging lesions [3]. Although promising, deep learning methods face challenges of their own. Convolutional neural networks (CNNs) require large, well-annotated datasets that can be difficult to obtain. Models trained on small datasets may not be generalizable. Moreover, lymphoma lesions vary significantly in size, shape, and metabolic activity, making training deep networks accurately challenging in the absence of well-defined priors. Deep learning aims to reduce observer variability, but inconsistent manual annotations used for training can lead to error perpetuation. Understanding these challenges is crucial towards harnessing the full potential of these methods in PET/CT quantitative analysis. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Deep Neural Networks to Detect and Quantify Lymphoma Lesions: Abstract and Intro

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Tiefe neuronale Netzwerke zur Erkennung und Quantifizierung von Lymphomläsionen: Zusammenfassung und Einführung

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The Food and Drug Administration’s (FDA’s) recent approval of the loss drug Wegovy means it’ll soon sweep the nation. However, as its popularity and price have surged, it’s become less accessible to the average American. Can wearables provide a solution to this problem? The Problem With Wegovy and How Wearables Can Fix It The weight loss market is at an all-time high — and indicators suggest it’ll continue expanding. According to one estimate, its value will surpass $399.3 billion by 2032, achieving a CAGR of 10.3%. This growth has driven demand for drugs like Wegovy. Wegovy is a treatment for chronic weight management. Its recent FDA approval means it will likely become one of the leading weight loss drugs in the United States. That said, despite the country’s high obesity rate most — 41.9% of adults over 20 are obese — Americans won’t benefit from it. Most people don’t have an extra $1,300 to spend each month — that’s Wegovy’s list price. Although one report found its average net price was $700 monthly between 2022 to 2023, it’s still more than most can afford. What’s worse is that Wegovy isn’t just a weight loss drug — new evidence suggests it can also lower the risk of cardiovascular issues, prolonging people’s lives. A 17,000-patient study discovered those taking it had a 20% lower risk of experiencing a cardiac-related health event than those taking a placebo. Skyrocketing demand, high research and development costs, the limited availability of clinical trials, and technology gaps might make it almost impossible for Novo Nordisk — the drugmaker behind Wegovy — to improve the accessibility of its lifesaving, fat-burning drug. That’s where wearable tech comes in. Wearables could improve Wegovy’s treatment efficacy, increasing its ability to accomplish desired outcomes. Their unique data collection capabilities could also accelerate research, potentially lowering costs and increasing accessibility. How Wearables Can Track and Enhance Treatment Efficacy Wearable tech has emerged as the next horizon in health care. It has proven exceptionally valuable in clinical trials because it can gather patient data 24/7 regardless of participants’ location — specialized facilities and invasive observation technology can move aside. Medical professionals can track treatment efficacy by monitoring the real-time patient data internet-connected wearables constantly send. Being able to view data-driven results immediately instead of waiting for testing or in-person visits streamlines their processes. Eventually, clinical trial teams can gather enough information to segment participants by demographics, physical states, or preexisting conditions. This way, they can tailor factors like dosage or medication frequency to each group, increasing the drug’s effectiveness. Unlike other sensor technologies, wearable tech often pairs with a supplementary app. Drugmakers and clinical trial leaders could use this unique functionality to send helpful push notifications or help people track their progress. Wearable Tech Tracks Patients’ Treatment Responses Wearable tech can monitor physiological responses like glucose levels and heart rate without invasive procedures like blood drawing. It can track metrics like blood pressure, blood oxygen level, or daily calories burned the same way. Non-invasive sensor tech — meaning no needles, swallowable capsules, or electrodes — could help people feel better about participating in clinical trials or taking new drugs. There’s no longer any need for repetitive, painful, or time-consuming testing. How Wearable Tech Can Promote Self-Efficacy One overlooked opportunity for wearables involves self-efficacy — a person’s belief in their ability to reach a specific goal. When people wear something that tracks their activity and health, they feel more inclined to improve their habits. While a medication can help people slim down, weight loss usually requires various efforts. In a 68-week clinical trial, Wegovy lowered participants’ mean body weight by 14.9% by targeting hormones responsible for appetite and food intake — a noticeable difference from the placebo’s 2.4% reduction. Notably, the loss is within the 5%–15% range recommended to improve obesity-related conditions. Although research shows Wegovy outperforms similar drugs, its long-term outcomes remain unproven — the 68-week clinical trial saw participants adopt healthy habits and take the drug to achieve clinically relevant body weight reduction. In other words, the self-efficacy wearables could prompt lifestyle changes, increasing the drug’s effectiveness. Research suggests wearables increase self-efficacy in weight loss trial participants by encouraging physical activity, motivating and enhancing awareness. While there’s no clear link between diet, exercise, and this technology, researchers have made some connections. Wearables could go a long way in making Wegovy more accessible to the average American seeking to shed a few pounds. Typically, people gain weight quickly after stopping these kinds of drugs, but most won’t be able to afford the $700 monthly bill. Pairing the medication with lifestyle changes could enhance its effectiveness, increasing long-term success. Integration Obstacles That Need Overcoming One of the largest obstacles drugmakers and clinical trial leaders must overcome is data accuracy. Although 24/7 at-home monitoring makes processes easier, it can also mess them up. Countless variables could affect results since participants aren’t inside a controlled environment. Wegovy isn’t supposed to be used with other semaglutide-containing products for safety reasons. This factor is simple enough to control in a lab. However, it’s easy for someone at home to pick up an over-the-counter prescription or use an herbal remedy without reading the label to check for semaglutide. Privacy concerns are another obstacle since wearable tech tracks excess information. While the abundance of measurements is excellent for researchers, patients might hesitate to share so much. There’s also the issue of data breaches and leaks. If the weight loss market plans to integrate wearables into health care — a sector notorious for its late adoption of technology due to several legal, moral, and regulatory hurdles — they’ll need to consider the upfront cost. Do clinical trial participants pay for the tech? Who pays when someone breaks theirs? Decision-makers should also consider long-term effectiveness. While this tech can inspire lifestyle changes, people might be unwilling to stick to their new fitness routines. Unless they ensure participants stay dedicated, it’ll look like their decreasing use correlates to diminishing efficacy. The Power of Wearable Tech for Treatment Efficacy If clinical trial leaders and medical professionals give people wearables when they hand out Wegovy, there’s a good chance they’ll enhance treatment success, leading to better long-term patient outcomes. Eventually, these improvements could lead to a more affordable drug and higher demand — a win-win for both the drugmaker and those looking to lose weight. The Food and Drug Administration’s (FDA’s) recent approval of the loss drug Wegovy means it’ll soon sweep the nation. However, as its popularity and price have surged, it’s become less accessible to the average American. Can wearables provide a solution to this problem? The Problem With Wegovy and How Wearables Can Fix It The Problem With Wegovy and How Wearables Can Fix It The weight loss market is at an all-time high — and indicators suggest it’ll continue expanding. According to one estimate, its value will surpass $399.3 billion by 2032, achieving a CAGR of 10.3%. This growth has driven demand for drugs like Wegovy. will surpass $399.3 billion will surpass $399.3 billion Wegovy is a treatment for chronic weight management. Its recent FDA approval means it will likely become one of the leading weight loss drugs in the United States. That said, despite the country’s high obesity rate most — 41.9% of adults over 20 are obese — Americans won’t benefit from it. 41.9% of adults over 20 41.9% of adults over 20 Most people don’t have an extra $1,300 to spend each month — that’s Wegovy’s list price. Although one report found its average net price was $700 monthly between 2022 to 2023, it’s still more than most can afford. net price was $700 monthly net price was $700 monthly What’s worse is that Wegovy isn’t just a weight loss drug — new evidence suggests it can also lower the risk of cardiovascular issues, prolonging people’s lives. A 17,000-patient study discovered those taking it had a 20% lower risk of experiencing a cardiac-related health event than those taking a placebo. had a 20% lower risk had a 20% lower risk Skyrocketing demand, high research and development costs, the limited availability of clinical trials, and technology gaps might make it almost impossible for Novo Nordisk — the drugmaker behind Wegovy — to improve the accessibility of its lifesaving, fat-burning drug. That’s where wearable tech comes in. Wearables could improve Wegovy’s treatment efficacy, increasing its ability to accomplish desired outcomes. Their unique data collection capabilities could also accelerate research, potentially lowering costs and increasing accessibility. How Wearables Can Track and Enhance Treatment Efficacy Wearable tech has emerged as the next horizon in health care. It has proven exceptionally valuable in clinical trials because it can gather patient data 24/7 regardless of participants’ location — specialized facilities and invasive observation technology can move aside. has proven exceptionally valuable has proven exceptionally valuable Medical professionals can track treatment efficacy by monitoring the real-time patient data internet-connected wearables constantly send. Being able to view data-driven results immediately instead of waiting for testing or in-person visits streamlines their processes. Eventually, clinical trial teams can gather enough information to segment participants by demographics, physical states, or preexisting conditions. This way, they can tailor factors like dosage or medication frequency to each group, increasing the drug’s effectiveness. Unlike other sensor technologies, wearable tech often pairs with a supplementary app. Drugmakers and clinical trial leaders could use this unique functionality to send helpful push notifications or help people track their progress. Wearable Tech Tracks Patients’ Treatment Responses Wearable tech can monitor physiological responses like glucose levels and heart rate without invasive procedures like blood drawing. It can track metrics like blood pressure, blood oxygen level, or daily calories burned the same way. Non-invasive sensor tech — meaning no needles, swallowable capsules, or electrodes — could help people feel better about participating in clinical trials or taking new drugs. There’s no longer any need for repetitive, painful, or time-consuming testing. How Wearable Tech Can Promote Self-Efficacy One overlooked opportunity for wearables involves self-efficacy — a person’s belief in their ability to reach a specific goal. When people wear something that tracks their activity and health, they feel more inclined to improve their habits. While a medication can help people slim down, weight loss usually requires various efforts. In a 68-week clinical trial, Wegovy lowered participants’ mean body weight by 14.9% by targeting hormones responsible for appetite and food intake — a noticeable difference from the placebo’s 2.4% reduction. Notably, the loss is within the 5%–15% range recommended to improve obesity-related conditions. mean body weight by 14.9% mean body weight by 14.9% Although research shows Wegovy outperforms similar drugs, its long-term outcomes remain unproven — the 68-week clinical trial saw participants adopt healthy habits and take the drug to achieve clinically relevant body weight reduction. In other words, the self-efficacy wearables could prompt lifestyle changes, increasing the drug’s effectiveness. Research suggests wearables increase self-efficacy in weight loss trial participants by encouraging physical activity, motivating and enhancing awareness. While there’s no clear link between diet, exercise, and this technology, researchers have made some connections. wearables increase self-efficacy in weight loss wearables increase self-efficacy in weight loss Wearables could go a long way in making Wegovy more accessible to the average American seeking to shed a few pounds. Typically, people gain weight quickly after stopping these kinds of drugs, but most won’t be able to afford the $700 monthly bill. Pairing the medication with lifestyle changes could enhance its effectiveness, increasing long-term success. Integration Obstacles That Need Overcoming One of the largest obstacles drugmakers and clinical trial leaders must overcome is data accuracy. Although 24/7 at-home monitoring makes processes easier, it can also mess them up. Countless variables could affect results since participants aren’t inside a controlled environment. Wegovy isn’t supposed to be used with other semaglutide-containing products for safety reasons. This factor is simple enough to control in a lab. However, it’s easy for someone at home to pick up an over-the-counter prescription or use an herbal remedy without reading the label to check for semaglutide. with other semaglutide-containing products with other semaglutide-containing products Privacy concerns are another obstacle since wearable tech tracks excess information. While the abundance of measurements is excellent for researchers, patients might hesitate to share so much. There’s also the issue of data breaches and leaks. If the weight loss market plans to integrate wearables into health care — a sector notorious for its late adoption of technology due to several legal, moral, and regulatory hurdles — they’ll need to consider the upfront cost. Do clinical trial participants pay for the tech? Who pays when someone breaks theirs? Decision-makers should also consider long-term effectiveness. While this tech can inspire lifestyle changes, people might be unwilling to stick to their new fitness routines. Unless they ensure participants stay dedicated, it’ll look like their decreasing use correlates to diminishing efficacy. The Power of Wearable Tech for Treatment Efficacy If clinical trial leaders and medical professionals give people wearables when they hand out Wegovy, there’s a good chance they’ll enhance treatment success, leading to better long-term patient outcomes. Eventually, these improvements could lead to a more affordable drug and higher demand — a win-win for both the drugmaker and those looking to lose weight.

Wearable Tech and Its Potential to Monitor Wegovy Treatment Efficacy

Supernormal Stimuli: Are We Amusing Ourselves to Death?

অতিপ্রাকৃতিক উদ্দীপনা: আমরা কি মৃত্যুর জন্য মজা করছি?

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Estímulos sobrenaturales: ¿nos estamos divirtiendo hasta la muerte?

Stimuli supranormaux : sommes-nous en train de nous amuser à mourir ?

अलौकिक उत्तेजनाएँ: क्या हम अपने आप को मौत तक मनोरंजन कर रहे हैं?

超常刺激：私たちは死ぬほど楽しんでいるのか？

초자연적 자극: 우리는 죽을 때까지 즐겁게 지내고 있나요?

Estímulos supranormais: estamos nos divertindo até a morte?

Сверхъестественные стимулы: развлекаемся ли мы до смерти?

Olağanüstü Uyarıcılar: Kendimizi Ölesiye Eğlendiriyor muyuz?

Kích thích siêu thường: Chúng ta có đang vui vẻ đến chết không?

超常刺激：我们是不是正在自娱自乐？

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