10x Genomics (NASDAQ: TXG) is one of the fastest growing biotech companies, growing from $3M in revenue in 2015 to $240M in 2019

10x Genomics, Inc., a life science technology company, develops and sells instruments, consumables, and software for analyzing biological systems in North America, Europe, the Middle East, Africa, China, and the Asia Pacific. The company provides chromium and chromium connect instruments, microfluidic chips, slides, reagents, and other consumables products. Its single cell solutions runs on its chromium instruments, which include single cell gene expression for measuring gene activity on a cell-by-cell basis; single cell immune profiling for measuring the activity of immune cells and their targets; single cell Assay for Transposase Accessible Chromati (ATAC) for measuring epigenetics comprising the physical organization of DNA; and single cell multiome ATAC + gene expression for measuring the genetic activity and epigenetic programming in the same cells across tens of thousands of cells in a single experiment. The company also provides visium spatial gene expression solution for measuring spatial gene expression patterns across a single tissue sample or gene expression and protein co-detection when combined with immunofluorescence. It serves various academic, government, biopharmaceutical, biotechnology, and other institutions. The company was formerly known as 10X Technologies, Inc. and changed its name to 10x Genomics, Inc. in November 2014. 10x Genomics, Inc. was incorporated in 2012 and is headquartered in Pleasanton, California.

10x Genomics

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There is nothing new under the Sun, the saying goes, and that also holds true for . FPGAs though are back with a rage, with major cloud vendors offering FPGA-as-a-service and chip manufacturers renewing their interest in the space. Among other facts, Intel acquired Altera for $16B and is now shipping the new Intel Xeon CPUs with an integrated FPGA. Field Programmable Gate Arrays At the same time, applications like data analytics, machine learning, and genomics are extremely computationally intensive and require a large amount of processing power -let alone manpower- to manage the clusters. To face this challenge, warehouse data centers and cloud providers have recently started to adopt the use of hardware accelerators. Hardware accelerators (e.g. FPGAs) can speed up the application processing and at the same time reduce the energy consumption in data centers. Tech titans like Amazon Web Services (AWS), IBM, Intel, Baidu, and Alibaba have recently announced that they will start hosting in their public clouds hardware accelerators in the form of IP (Intellectual Property) cores. FPGAs is not a novel technology however it has been costly to procure and program for. FPGAs are used to implement a single algorithm in the pursuit of improved performance. Typical computers like a laptop or a server are using general purpose CPUs. You can think of a CPU as a calculator and an FPGA as a special-purpose calculator that can be programmed to solve only one equation. By sacrificing flexibility for performance, the equation can be solved extremely fast using the special-purpose calculator (FPGA). As the industry is challenged by the slowing of Moore’s law and big data analytics are becoming mission critical but also costly, companies are increasingly looking for alternative computation platforms to accelerate workloads. We have seen the rise of GPUs and Nvidia, as these chips are ideal for deep learning applications (large sparse array algebra). FPGAs have also been in the mix of large companies, resulting in what we call vertical computing or vertical integration, where we have problem specific hardware. is riding the wave of the proliferation of FPGAs via their availability in public clouds and the increased adoption of several standard algorithms provided by open source computation frameworks like Apache Spark. provides hardware accelerators that are a drop-in replacement for the Apache Spark MLLib, resulting in 3–10x speedups. All it takes is to run a node of Apache Spark on an F1 AWS instance, run the IP core that you can find on the and replace one line on your code running on Spark! Your CIO will thank you. InAccel InAccel InAccel AWS Marketplace We are excited to partner with , a researcher who has spent his entire career in the field of FPGAs, along with his co-founders and . Vertically integrated computation is the future for intense and critical workloads and we look forward to InAccel becoming the hub of hardware accelerators. Christophoros Kachris Elias Koromilas Ioannis Stamelos 👉 If you are interested in the future of vertical computation InAccel is . hiring

Using FPGA superpowers to speed up cloud workloads with InAccel 🚀

I originally wrote this piece as an internal R&D direction document for my colleagues at . We’ve decided to share it publicly because we feel that it’s important that as many people as possible are thinking about these issues. doc.ai has throughout history been a artisanal vocation — that is, it has focused on the skill and experience of the individual doctor, rather than looking to build a standardized process for diagnosing and treating patients. In recent years this has started to change, as initiatives like Evidence Based Medicine and Precision Medicine have tried to inject additional rigor and data-driven practices into the field. However, the vast majority of medical care is provided through the traditional Hippocratic philosophy. Medicine This needs to change. The largest population centers on the planet have less than 1/10th of the doctors they need, and it will take hundreds of years to fill the gap. Misdiagnoses, late diagnoses, and over-diagnoses kill millions and cost tens of billions. The technology is now being developed to fix this problem — to give medical workers and patients a clear summary of the exact information they need, when they need it. Such technology can give a remote area community health worker access to a distillation of the world’s medical knowledge. It can make doctors in the developed world dramatically more productive and accurate , while giving patients and families more control over and insight into their medical care. AI, specifically , has already shown that it can be a powerful diagnostic tool, for instance, showing super-human performance in medical imaging work such as: deep learning Google’s diagnostic retinopathy system Stanford’s dermatology algorithm detection and malignancy classification Enlitic’s work in lung cancer The challenges Labeled historical data It is widely believed that deep learning algorithms require vast amounts of data to be effective. This is not necessarily true. For instance, Enlitic’s lung cancer algorithm had access to scans of just a thousand patients with cancer. It’s important to understand that although the dataset (from the National Lung Screening Trial) was relatively small, it had the key characteristics to allow effective modeling: It contained annual scans over 3 years for each patient; seeing the development of a disease over time is critical to creating diagnostic algorithms Radiologists had provided annotations showing roughly where nodules were located, allowing the algorithm to focus on the important information The dataset included information about each patient’s medical outcomes after the 3 years of the trial — labels showing things such as patient survival are necessary for creating diagnostic systems. It’s also useful to see what couldn’t be provided by this project: treatment recommendations. Because the dataset did not contain longitudinal data for the patients showing what interventions were provided and how they responded, the algorithm developed was only useful for diagnosis, not treatment planning. However, even this is extraordinarily powerful: currently the death rate for people diagnosed with lung cancer is nearly 90%, with nodules not found until they’re on average 40 mm in size. The system developed by Enlitic reliably found nodules of 5 mm or smaller. When found early, probability of survival is 10x higher! Now think about how unusual this kind of dataset is. How often can we access a unified medical record containing all information about tests, diagnoses, and interventions for a patient over a multi-year period? The information is spread over multiple institutions, and within an institution over multiple departments. Legal conservatism Even when the data is available in a central location, or can be cobbled together from multiple sources, it is frequently the case that the institutions that hold it are wary of sharing it with the data scientists that can build these powerful algorithms. Legal staff recognize that one failure of privacy could end their career and cost their institution millions, whereas the theoretical cost of the missed opportunity (from saying ‘no’ to a data request) is hard to pin down. Yet when patients are asked if they’re willing to share their medical data if it could help others in the future, most are very happy to grant permission to use their data — especially when that sharing could lead to better options for their own treatment in the future. The opportunity for patients Patient-controlled data This leads to a clear opportunity: let patients be in control of their own medical data, across all their visits to different institutions and departments, as well as their own collected information (such as data from wearables, and self-reported data). Give those patients the opportunity to opt-in to sharing that data with particular data scientists for particular projects, give them a secure data environment , and in return give them: Early access to the resulting medical breakthroughs Financial remuneration Information about the work that was completed thanks to their data, and how it’s helping other patients. This may be the only way we’re going to see the true potential of deep learning in medicine — at least in the US (some centrally managed countries may be able to create the needed datasets through government decree). There’s a very related opportunity for families caring for loved ones with rare or untreatable diseases: get together with other patients in the same situation and agree to pool patient data. The more other patients that can be brought into the pool, the more chance there is that the critical information will be available. The Blockchain Not all data is created equal. Data from people with rare diseases is critical to diagnosing and treating those diseases. Data over many years is more valuable than a short time period. At the other end, bad actors may even fake data in an attempt to fraudulently gain remuneration. By using a blockchain we can create a clear auditable record of medical data sources. Based on this record, data providers can be rewarded based on how useful their data is in practice. Therefore, the more complete and accurate and relevant the data they provide, the more the reward. It also provides very interesting opportunities for institutions, who (with their patient’s permission) can provide whole datasets to researchers, and in return get both financial returns and access to the technology that results from the data. I n the longer term, the patient can authorize their institutions to pass on their data to data scientists through the same blockchain-based approach. The opportunity for data scientists Most data scientists say they’d like to do something meaningful with their skills, but few get the opportunity. Much work for data scientists is in areas like ad-tech, hedge fund trading, and product recommendations. The major things stopping them from being able to do more meaningful work are access to data, knowing what problems need to be solved, and having a way for their solution to get noticed and implemented. In order to turn data into useful results, data scientists need to be able to complete the following steps (which in practice are repeated multiple times in various orders): Data cleaning Exploratory data analysis Creating a validation set Building a model Analyzing and validating the model To do these steps, data scientists need a rich analytical environment where they can use their choice of tools, libraries, and visualization solutions. Most data scientists doing this kind of work today use R (generally R Studio) or Python (generally Jupyter Notebook). By providing such an environment with the data pre-installed and the problem to solve clearly defined, the data scientist can quickly get to work on a meaningful problem. It may even be possible for multiple data scientists to independently work on the same problem, with rewards being shared based on the utility of their work. What we need to provide Data gathering We need to give each patient the ability to gather and maintain their personal medical data, including: Lab tests and imaging studies Diagnoses Medications prescribed Non prescription medications and supplements taken Other medical interventions Exercise and eating records Family history (ideally, automatically maintained by linking across family members) Self reported progress such as energy levels, happiness level, and so forth Genomics and other tests This means being able to download data from each patient’s medical providers, both a one-time download at setup, and on a regular basis after that, as well as using APIs for individual health tracking and wearable apps to import their data. Data sharing Each patient needs to be able to opt in or out of each request for their data. If the system is successful, there could be many requests and dealing with each individually could be burdensome — in this case we can give them the ability to set rules on which requests to automatically accept or reject, and which require manual intervention. Each piece of data needs to be tagged with its source in an auditable way. It need not necessarily be stored on the patient’s device; indeed some types of medical data can be too large for on-device storage. Once a patient gives a project access to some of their data, that data needs to be made available to the researcher. Each data scientist will need a rich analytical environment to be provided for their work. This will show them information about the problem they’ve been asked to solve, and show how to access the data for the project. The big opportunity Providing patients with the ability to have control over their medical data, and data scientists the ability to solve pressing medical problems, is a powerful idea. But it’s only the tip of the iceberg. The bigger opportunity is what happens when the models can be continually improved, and then all these models can be combined. Each data scientist’s feature engineering steps can be saved and made available to future researchers (and they would be compensated when their approaches are re-used), and their pre-trained model activations can be automatically brought into new models to see if they add predictive power. Allowing new data to continuously improve existing models requires that the meaning and format of all data sources is consistent. This is a complex topic, but experienced data product project managers should have past experience. Changes to data source formats or semantics need to be identified up front, and constant model testing is critical. Through re-using pre-trained models, we get all the benefits of combining data across all datasets, with none of the logistical or privacy challenges. This also means that rare diseases and pediatric diseases, where there is only a small amount of data, can be effectively tackled. Pre-trained models will be used to analyze the data in those situations, and very simple models with few parameters can be used to combine them. As we move forward, this approach to gathering and analyzing data will lead to new insights and will provide medical workers and patients a clear summary of the exact information they need, when they need it.

Medicine and the need for AI

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Let's learn about via these 52 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Image Processing /Learn Repo 1. How to Print images with TSPL and JavaScript How to Print images with TSPL and JavaScript 2. How Computer Vision Turns Images Into Arrays How Images are turned into arrays in Computer Vision 3. How To Convert WebP Images Into Other Image Formats If you are having trouble loading an image on the web, do not worry because you always have the option of converting it into another format that is more widely 4. What Is Digital Image Processing? With digital image processing, users can enhance digital pictures, extract useful information, and save them in different formats. 5. 5 Best Online Tools to Compress and Optimize Your Images Here are the 5 Best Online Tools to Compress and Optimize Your Images. 6. This Is How Easy it Is to Read and Display Images Working with images is more and more popularized; we are going to take dive into this field, first with the basics and starting with an open CV library. 7. JPEG Sucks but Deep Learning Can Help It Improve In this post, I’ll show how you can reduce image sizes by an additional 20–50% with a single line of code. 8. How Image to Text Technology Can Ease Your Writing Process To collect the information from the images, you do not have to write it down manually.  Instead of that, you can use image-to-text technology. 9. How to Create a High-Performing QR Code Scanner in Flutter In this article, I will take you on a journey through the process of creating a scanner that is not only functional but also visually stunning. 10. Implementing Handwritten Text Recognition to Automate Clinical Trials Manual processing of data (overviews, summaries,study reports) is significantly slowed down and inefficient.AI-driven handwritten text recognition is a solution 11. How To Optimize Images in Django Using ImageEngine Learn how to optimize images in Django using an easy-to-use ImageCDN. ImageEngine automatically serves your images in next-gen formats on the global CDN. 12. 3 Simple Ways to Optimize Your Images and Improve Core Web Vitals Image performance plays a huge role in #CoreWebVitals, which in turn play a huge role in SEO. We'll show 3 quick and simple ways to optimize images. #webperf 13. AVIF Is the Next-Gen Image Format You Will Hear a Lot More About The AVIF Format is the next-gen image format based on the AV1 video encoding format, that reduces file size, while improving #CoreWebVitals. Learn how. 14. New Gatsby Plugin Image: One Of The Coolest Innovations At GatsbyConf 2021 March is off to a great start with the coming & going of GatsbyConf 2021 and the big announcement of Gatsby v3.0! Let's talk about the new Gatsby Image Plugin! 15. 5 Ways to Load Less Imagery to Increase Ranking and Client Happiness Improve your ranking by both search engines and humans by not forcing the client to download unnecessary imagery. 16. Building a FastAPI OCR Microservice In this article, I will try to explain basic ideas on how to create your own OCR service for free, using python, fastAPI, tesseract, redis, celery and docker. 17. Speed up AMP pages with an Image CDN It takes a lot of time and effort to build a profitable website that offers great user experience on any given device type. When you start from a clean slate, you have to take care of everything on your own: browser compatibility, performance, responsive design, discoverability and a whole slew of other factors. 18. Will Generative Models Be The Next Machine Learning Boom? Machine Learning is a rapidly growing and very complex field of study. Generative Models might prove to be a new breakthrough that will make a new boom. 19. How to Integrate an Image CDN in Your React Application One of the most crucial maxims that I internalized during my career in web engineering is that every technical decision embodies tradeoffs. No matter the benefits, you’ll always face certain costs and restrictions: be it creating an abstraction in your code, scaling a distributed data store or choosing between web and mobile app format for the initial product launch. 20. Introducing CropScore: An Image Cropper for Content Creators ✂✨ If you are a content creator, the chances are you have come across stretched, shrank, non-centered or pixelated images after the posting of your material. 21. Improve Core Web Vitals Using Lazy Loading and BlurHash Lazy loading is an important way to improve your PageSpeed Insights. Combined with #Blurhash, you can improve load times. #lazyloading #corewebvitals #SEO 22. Automatic Image Optimization Using the Nuxt Image Component with imgix See how you can use imgix and the Nuxt Image Component to accelerate page speed, simplify image workflow, and transform images at scale. 23. 5 Common Mistakes to Avoid When Adding Images to Your Website We spend a lot of time crafting the perfect pitch in our blog posts and web copies. However, when it comes to visuals, many skimp out and add just about any relevant image available – without getting into the details. 24. How to Implement Gaussian Blurs A Gaussian blur is applied by convolving the image with a Gaussian function. We’ll take the Gaussian function and we’ll generate an n x m matrix. 25. Feature/Permission Policies Overview It's already challenging enough to create a website that is blazing fast, adheres to all the latest best practices and offers optimal experience for various network conditions and device types. As the website and its codebase evolves over time, it can become even harder to maintain the desired user experience over time. To prevent UX deterioration, it’s often a good idea to place guardrails which ensure that crucial website elements, such as images, hold to a certain criteria. In this article, we are going to look at a robust mechanism which helps to steer your website in the right direction - an HTTP header called Feature Policies. 26. Canva Business Model Breakdown: Product, Revenue, Competition, and The Big Question What is Canva? Canva is a software product that helps designers of any skill level to create media 10x faster. 27. Improve the User Experience in Your Mobile App by Using Low-light Enhancement Tech This article introduces how to use AI technology to solve the image quality of Low-Light Images and improve user experience. 28. A Python Library for Face Detection and Extraction with OpenCV Using HOG/Neural Network Many people, including me, use a combination of libraries to work on the images, such as: OpenCV itself, Dlib, Pillow etc. But this is a very confusing and problematic process. Dlib installation, for example, can be extremely complex and frustrating. 29. How Online Classifieds Can Innovate With Immersive Technologies What solutions allow app developers to embed immersive content displaying into a mobile app. 30. Making a Music Visualization with P5JS I had the immense pleasure of attending several creative coding workshops on April 4th. They were streamed live on the SpacyCloud Twitch channel. There were additional sessions involving Hydra, Raspberry Pi, Haskell, and more. However for this post I want to focus on the first session which was a P5JS tutorial. In this post I hope to translate the P5JS tutorial into a written format, for posterity and to share what I learned. I'm going to review what was taught in the live session. Hopefully SpacyCloud will have another live stream in the future so I can catch up on what I missed. Here is the landing page for the event schedule. 31. What it Means to be a Creative Coder Programmers rarely agree on whether or not coding is a creative profession. My interest in coding always stemmed from what I could create with the code. Seeing an interesting visual result from my efforts is usually the most satisfying part. Most programmers are less concerned with how their app looks and more concerned with the functionality. Usually, as long as the app works the way it is supposed to, most programmers are satisfied. 32. PULSE: Photo Upsampling Makes Blurry Faces 60 Times Sharper The new PULSE: Photo Upsampling algorithm transforms a blurry image into a high-resolution image. 33. Dall-E May Be Awesome, but It Still Can't Count. OpenAI's "Dall-E" artificial intelligence can be very frustrating for some professional uses. Here are a few things that Dall-E just can't seem to do. 34. Obtaining Data From Images Using Exif: How To Automate The Process A how-to guide for automating the EXIF data from batch image files. 35. I Found The One Tool To Sort All Your Pictures Like A Boss For years I have been frustrated and I wanted to find a proper way of sorting my pictures. I had photos coming from several devices, and made manual copies on my laptop and on my external hard drive. I ended up with tons of folders and a lot of duplicates. 36. How is Artificial Intelligence Transforming Life Sciences? 4 Main Ways AI has been revolutionizing the life sciences by using its ability to process data quickly to assist with diagnoses and other important medical procedures. 37. Essential Guide to Image Processing with WebAssembly Simple image manipulations with WebAssembly and AssemblyScript in the web browser. 38. Filestack And Other OCR APIs OCR software enables the digitization of essential data in paper documents and photographs. 39. Image Optimization and Transformation with Cloudinary The web is now dominated with more visual content than ever hence the need to consider ways of delivering these images with the smallest possible file size while maintaining optimal visual quality. 40. How to Keep Your e-Store Core Web Vitals Healthy with an Image CDN As we now know, how users interact with your page is just as influential as what is on your pages when it comes to user satisfaction. Search engines, like Google, have wised up to this dynamic by introducing user experience as a significant component in their ranking factors. 41. The Best Ways to Improve Your NFT Marketplace Performance In NFT marketplaces,  page experience, SEO, and credibility can all determine success. Here are 7 ways to improve performance through visual media optimization. 42. Understanding Basic Image Processing Algorithms [A Hands-on JavaScript Tutorial] We've had a lot of digital image processing tools for a long time: Photoshop, Lightroom, GIMP, PhotoScape, and many more. However, in the past few years, one became popular among non-expert users due to its easiness of use and social features: Instagram. Have you ever wondered how Instagram filters work? It is actually pretty simple matrix operations! So simple we can build our own without any external library, just pure and simple HTML + JS. Let's build one now. 43. Use Cases and Roll-Out Tips for Image Recognition in Retail Heavily shattered by the pandemic, the retail sector is on the lookout for innovation. 44. An In-Depth Look at Image Scaling Concepts Considerations upon image sizing for web display 45. Image Processing Algorithms: Adjusting Contrast And Image Brightness Let's take a look at the common approaches for implementing image contrast adjustments. We'll go over histogram stretching and histogram equalization. 46. How to Build an Image Search Engine to Find Similar Images After reading this article, you will be able to create a search engine for similar images for your objective from scratch 47. Intro to Image Processing in Python with Pillow Pillow is Python Imaging Library that is free and open-source an additional library for the Python programming language that adds support for opening, manipulating, and saving in a variety of extension. 48. ColorDetection Module: Python  Color Detection Algorithms Images. That's it. Images. As a point of practicality, take a fashion designer (as a forum member vividly described to me at one point). You are given an image or have an image at your disposal that simply tickles your curiosity and want to incorporate it in one of your new lines. Let's swerve a little into the genetics section. Given a petri dish image for instance, with pigmented bacteria or similar organisms, and you would like to find the abundance of that organism or organisms in this specific image. Get the gist? 49. A Quick Guide to Image Processing in Computer Vision Using OpenCV The image processing library which stands for Open-Source Computer Vision Library was invented by intel in 1999 and written in C/C++ 50. High Quality 8x Upscaling with AI! Have you ever had an image you really liked and could only manage to find a small version of it that looked like this image above on the left? How cool would it be if you could take this image and make it twice look as good? It’s great, but what if you could make it even four or eight times more high definition? Now we’re talking, just look at that. 51. How To Identify Trees with Deep Learning Idea / inspiration 52. How To Resize an Image Correctly in JavaScript JavaScript implementation of so-called Seam Carving algorithm for the content-aware image resizing and objects removal. Dynamic programming approach is applied Thank you for checking out the 52 most read stories about Image Processing on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

52 Stories To Learn About Image Processing

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Bodybuilders obsess over obscure muscles that nobody has even heard of. Most gym bros are happy with 6-pack abs. But Bodybuilders? No. They’ve got to have an 8-pack (genetics permitting) with some razor-sharp serratus anterior on the side. They’ll spend two hours a week working out a muscle that most people think is a bone! Every muscle counts. Every muscle is an important part of the final masterpiece. So why isn’t anybody talking about the masseter? That’s the meaty muscle on the side of your jaw. The one Brad Pitt is famous for. There are more articles, videos, and information about getting deep cuts in your hamstrings than there are about building your jawline! How many people see your hamstrings compared to your face? One for every hundred? One for every thousand? I think I know why. It’s because people don’t think there’s anything you can do about your jawline: “Sure, you can lose weight- but that’s about it. You can’t change your genetics.” I get so annoyed when I hear that. You are a Bodybuilder. Your job is to build muscle and deny your genetics. Having a strong jawline is mainly due to having developed masseter muscles. It’s a muscle, and it can be grown just like any other. There’s just not a lot of information on how. Do Those Rubber “Jawline Exercisers” Work? You’ve probably seen the rise of jawline exercisers over the years. You may have even tried them. In any case, these rubber squash ball-type things are popping up all over everybody’s Instagram feeds. Do they work? Not in my own personal experience. But there are before and after photos floating that show mixed results. I don’t doubt that they give your face a workout… just not the type of workout that you actually want. The problem is: it’s not a natural movement. Our jaw structures weren’t designed to bite down with our incisor teeth repeatedly with our mouth wide open- that’s essentially the range of motion these things simulate. It places a lot of stress on the jaw hinge and will ultimately lead to TMJ (Temporomandibular Joint). Not only that- it doesn’t even train the right muscle! Chewing with your mouth wide open like that puts more stress on the temporalis muscle than it does on the masseter. The temporalis is higher up on the side of your head, and not particularly “aesthetically pleasing”. You don’t exactly want to build it, because it contributes to a “round face” look. You want to solely emphasize the masseter. It’s stimulated when your teeth are closer together and you’re chewing and grinding something like tough meat. That’s the motion you want to repeatedly simulate to stress the masseter into growing. The temporalis muscle on the side of your head is NOT a muscle you want to grow! The SAFE Jawline Workout That Actually Works Believe it or not, chewing gum is a great way to improve your jawline. It’s a natural movement (just like chewing food) and you don’t have to dedicate part of your day to do it. You can chew gum while you’re working, driving, or training. Apart from actually being effective, it’s a far more convenient way to work out your jaw muscles than any rubber tool. But obviously chewing gum doesn’t provide much of a workout. You could chew for hours and hardly stress your muscles. It’s like trying to build muscle by running a marathon. You’ve got to add weight and increase the resistance. You’ve got to chew a harder type of gum. Natural tree resins and saps are the best you’ll find. , for example, is 10x harder than regular gum. It’s sugar and calorie-free. And it’s 100% natural. It’s literally a sap picked from a tree on the Greek island Chios. It’s had wars fought over it and it was even once more valuable gold- but that’s another story. Mastic gum 10x harder means 10x more resistance for your masseter. It turns the chewing movement from cardio to a power lift. I’ve seen firsthand how effective it can be. And I’ve yet to hear of anyone complaining of TMJ or a clicking jaw. It seems to be safe and there hasn’t been a backlash against it from the Orthodontics community like there was with the other jawline exercisers. If you want to work out your “sexiest” muscle, then chewing mastic gum is a good bet. It’s one I’m taking, and I’ve seen newbie gains even though I’ve been lifting for 10 years. That seems to be common because it’s a muscle most people haven’t trained before. Get Lean & Stay Lean Just because you’ve built the muscle, doesn’t mean you can see the muscle- you know that. You’ve got to cut after a bulk in order to see the definition. Same for your jawline. Chewing mastic gum (or whatever hard gum you like) will build mass in your masseter. The next step is to get lean so that all your hard work is visible. It’s worthless having a strong, muscular jawline if it’s covered in fat and invisible. How you do that is up to you. I don’t have any new innovative tips or concoctions to help. What works for me might not work for you. All I know is every time I get lean I get compliments on how defined my face is. And every time, I make a mental note that I should stay lean forever… but then I get keen to build muscle and bulk again. It’s a crazy, endless cycle. But even if you’re not too concerned with improving your jawline- don’t forget that losing weight is the best thing you can do to improve your entire appearance. Period. Conclusion Building and sculpting the jawline is still in the experimental phase. It’s not like building barn-door lats where everyone knows to Deadlift, Row and Pull-up. I think many of the current jaw exercisers will be revealed to be dangerous for the jaw joint in the long run. Perhaps chewing hard gum will be too. But if you want to build your jaw muscles, I’m betting that it’s at least a lot safer than anything else. It’s already proven to be more effective.

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Since 2012

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