Introduction – Company Background

GuangXin Industrial Co., Ltd. is a specialized manufacturer dedicated to the development and production of high-quality insoles.

With a strong foundation in material science and footwear ergonomics, we serve as a trusted partner for global brands seeking reliable insole solutions that combine comfort, functionality, and design.

With years of experience in insole production and OEM/ODM services, GuangXin has successfully supported a wide range of clients across various industries—including sportswear, health & wellness, orthopedic care, and daily footwear.

From initial prototyping to mass production, we provide comprehensive support tailored to each client’s market and application needs.

At GuangXin, we are committed to quality, innovation, and sustainable development. Every insole we produce reflects our dedication to precision craftsmanship, forward-thinking design, and ESG-driven practices.

By integrating eco-friendly materials, clean production processes, and responsible sourcing, we help our partners meet both market demand and environmental goals.

Core Strengths in Insole Manufacturing

At GuangXin Industrial, our core strength lies in our deep expertise and versatility in insole and pillow manufacturing. We specialize in working with a wide range of materials, including PU (polyurethane), natural latex, and advanced graphene composites, to develop insoles and pillows that meet diverse performance, comfort, and health-support needs.

Whether it's cushioning, support, breathability, or antibacterial function, we tailor material selection to the exact requirements of each project-whether for foot wellness or ergonomic sleep products.

We provide end-to-end manufacturing capabilities under one roof—covering every stage from material sourcing and foaming, to precision molding, lamination, cutting, sewing, and strict quality control. This full-process control not only ensures product consistency and durability, but also allows for faster lead times and better customization flexibility.

With our flexible production capacity, we accommodate both small batch custom orders and high-volume mass production with equal efficiency. Whether you're a startup launching your first insole or pillow line, or a global brand scaling up to meet market demand, GuangXin is equipped to deliver reliable OEM/ODM solutions that grow with your business.

Customization & OEM/ODM Flexibility

GuangXin offers exceptional flexibility in customization and OEM/ODM services, empowering our partners to create insole products that truly align with their brand identity and target market. We develop insoles tailored to specific foot shapes, end-user needs, and regional market preferences, ensuring optimal fit and functionality.

Our team supports comprehensive branding solutions, including logo printing, custom packaging, and product integration support for marketing campaigns. Whether you're launching a new product line or upgrading an existing one, we help your vision come to life with attention to detail and consistent brand presentation.

With fast prototyping services and efficient lead times, GuangXin helps reduce your time-to-market and respond quickly to evolving trends or seasonal demands. From concept to final production, we offer agile support that keeps you ahead of the competition.

Quality Assurance & Certifications

Quality is at the heart of everything we do. GuangXin implements a rigorous quality control system at every stage of production—ensuring that each insole meets the highest standards of consistency, comfort, and durability.

We provide a variety of in-house and third-party testing options, including antibacterial performance, odor control, durability testing, and eco-safety verification, to meet the specific needs of our clients and markets.

Our products are fully compliant with international safety and environmental standards, such as REACH, RoHS, and other applicable export regulations. This ensures seamless entry into global markets while supporting your ESG and product safety commitments.

ESG-Oriented Sustainable Production

At GuangXin Industrial, we are committed to integrating ESG (Environmental, Social, and Governance) values into every step of our manufacturing process. We actively pursue eco-conscious practices by utilizing eco-friendly materials and adopting low-carbon production methods to reduce environmental impact.

To support circular economy goals, we offer recycled and upcycled material options, including innovative applications such as recycled glass and repurposed LCD panel glass. These materials are processed using advanced techniques to retain performance while reducing waste—contributing to a more sustainable supply chain.

We also work closely with our partners to support their ESG compliance and sustainability reporting needs, providing documentation, traceability, and material data upon request. Whether you're aiming to meet corporate sustainability targets or align with global green regulations, GuangXin is your trusted manufacturing ally in building a better, greener future.

Let’s Build Your Next Insole Success Together

Looking for a reliable insole manufacturing partner that understands customization, quality, and flexibility? GuangXin Industrial Co., Ltd. specializes in high-performance insole production, offering tailored solutions for brands across the globe. Whether you're launching a new insole collection or expanding your existing product line, we provide OEM/ODM services built around your unique design and performance goals.

From small-batch custom orders to full-scale mass production, our flexible insole manufacturing capabilities adapt to your business needs. With expertise in PU, latex, and graphene insole materials, we turn ideas into functional, comfortable, and market-ready insoles that deliver value.

Contact us today to discuss your next insole project. Let GuangXin help you create custom insoles that stand out, perform better, and reflect your brand’s commitment to comfort, quality, and sustainability.

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Eco-friendly pillow OEM factory Taiwan

Are you looking for a trusted and experienced manufacturing partner that can bring your comfort-focused product ideas to life? GuangXin Industrial Co., Ltd. is your ideal OEM/ODM supplier, specializing in insole production, pillow manufacturing, and advanced graphene product design.

With decades of experience in insole OEM/ODM, we provide full-service manufacturing—from PU and latex to cutting-edge graphene-infused insoles—customized to meet your performance, support, and breathability requirements. Our production process is vertically integrated, covering everything from material sourcing and foaming to molding, cutting, and strict quality control.Taiwan OEM factory for footwear and bedding

Beyond insoles, GuangXin also offers pillow OEM/ODM services with a focus on ergonomic comfort and functional innovation. Whether you need memory foam, latex, or smart material integration for neck and sleep support, we deliver tailor-made solutions that reflect your brand’s values.

We are especially proud to lead the way in ESG-driven insole development. Through the use of recycled materials—such as repurposed LCD glass—and low-carbon production processes, we help our partners meet sustainability goals without compromising product quality. Our ESG insole solutions are designed not only for comfort but also for compliance with global environmental standards.Taiwan OEM/ODM hybrid insole development factory

At GuangXin, we don’t just manufacture products—we create long-term value for your brand. Whether you're developing your first product line or scaling up globally, our flexible production capabilities and collaborative approach will help you go further, faster.Taiwan ergonomic pillow OEM factory supplier

📩 Contact us today to learn how our insole OEM, pillow ODM, and graphene product design services can elevate your product offering—while aligning with the sustainability expectations of modern consumers.Indonesia insole ODM design and production

It was originally believed that these selfish genes would not remain in populations for long periods of time. The Finding Could Alter Our Understanding of How Parasitic DNA Affects Genome Evolution Meiotic drivers, a kind of selfish gene, are indeed selfish. They are found in virtually all species’ genomes, including humans, and unjustly transfer their genetic material to more than half of their offspring, resulting in infertility and impaired organism health. Their longevity over evolutionary time was thought to be brief due to their parasitic potential, until recently. The Stowers Institute for Medical Research, in collaboration with the National Institute for Biological Sciences in Beijing, China, has discovered a selfish gene family that has survived for over 100 million years—ten times longer than any other meiotic driver ever identified—calling into question established beliefs about how natural selection and evolution deal with these threatening sequences. The wtf meiotic driver gene family has unexpectedly persisted for over 100 million years. Credit: Stowers Institute for Medical Research, Mark Miller “The thinking has always been that because these genes are so nasty, they won’t stick around in populations for very long,” said Associate Investigator Sarah Zanders, Ph.D. “We just found out, that isn’t true, that the genomes simply can’t always get rid of them.” How Meiotic Drivers Sabotage Genomes Meiotic drivers are thus named because they can literally “drive” the transmission of their genes throughout a genome, often with negative consequences. Natural selection is therefore the primary force opposing selfish genes, favoring genetic variations that eliminate drive for a species’ recovery of fertility and overall health. “Natural selection has a limited ability to remove meiotic drivers from a population,” said Zanders. “Imagine holding soccer team tryouts (natural selection) to recruit the best players (genes that promote fitness). Drivers are players that sabotage the other players trying out. Drivers make the team, but not because they are good at soccer.” Stowers Investigator Sarah Zanders provides insight into the discovery. Credit: Stowers Institute for Medical Research In a recent study published in the journal eLife, led by researcher Mickael De Carvalho, Ph.D., from the Zanders Lab, and Guo-Song Jia, a predoctoral researcher in the lab of Li-Lin Du, Ph.D., identified for the first time that a family of selfish genes called wtf have not only flourished in the fission yeast, Schizosaccharomyces pombe, but have been passed on to three unique yeast species that diverged from S. pombe around 119 million years ago. “This finding is particularly novel as a family of drive genes has thrived at least ten times longer than what geneticists ever believed possible,” said Zanders. During meiosis, the specialized cell division that gives rise to reproductive cells like sperm and eggs, the inheritance of genetic material from a set of chromosomes from each parent is 50/50, or equally probable for each reproductive cell. Meiotic drivers in yeast are in fact a more potent genetic parasite. The wtf gene family are killer meiotic drivers; they not only transmit the selfish gene to over 50 percent of offspring but then destroy the reproductive cells—or spores in yeast—that do not inherit the drive gene. Overcoming Natural Selection Through Mutation Natural selection in a genome typically rescues a species from selfish genes by favoring genes that suppress, or silence drive, rendering it useless. How the wtf gene family evaded suppression is largely due to their rapid rates of mutation. This persistence alters our perception of how a species can overcome the expected increase in infertility that typically leads to extinction. It also changes the way scientists may look for and identify families of selfish genes in different species, including humans. “Until now, when looking for candidate drivers within a genome, I wouldn’t have considered “old” genes as a possibility,” said Zanders. “Since selfish genes are major drivers of evolution, this new finding opens the door for thinking about how drivers can have persistent, long-term effects on genome evolution.” Reference: “The wtf meiotic driver gene family has unexpectedly persisted for over 100 million years” by Mickaël De Carvalho, Guo-Song Jia, Ananya Nidamangala Srinivasa, R. Blake Billmyre, Yan-Hui Xu, Jeffrey J. Lange, Ibrahim M. Sabbarini, Li-Lin Du and Sarah E. Zanders, 13 October 2022, eLife. DOI: 10.7554/eLife.81149 The study was funded by the National Institutes for Health, the Stowers Institute for Medical Research, the Chinese Ministry of Science and Technology, and the Beijing Municipal Government. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

The Svalbard reindeer, despite significant inbreeding and low genetic diversity, boasts a robust population of over 20,000, having adapted to Arctic conditions with unique traits like smaller size and the ability to digest mosses. Although they have evolved rapidly to past environmental changes, scientists fear the pace of current global warming may outstrip their capacity to adapt, posing a serious threat to their survival. Reindeer have endured for over 7,000 years on the Arctic archipelago of Svalbard. Will they be able to withstand climate change? Despite the challenges of inbreeding and limited genetic diversity, the Svalbard reindeer have remarkably adapted to harsh living conditions in an extraordinarily short period, a situation researchers term a genetic paradox. However, the question remains: can they withstand the impacts of climate change? “Of all the subspecies of reindeer found in the high north, the Svalbard reindeer has the most inbreeding and the lowest genetic diversity,” says Nicolas Dussex, a postdoc at the Norwegian University of Science and Technology’s (NTNU) Department of Natural History. It was only 7000-8000 years ago that the first reindeer migrated to Svalbard, most likely from Russia via Novaya Zemlya and the islands of Franz Josef Land. Perhaps there were no more than a few animals that established themselves on the Arctic archipelago. Evolutionary theory suggests this is a poor starting point since inbreeding can quickly lead to an accumulation of harmful mutations and genetic variants followed by disease and death. Among their many adaptations to life on the Svalbard, reindeer have developed the ability to digest moss instead of lichen. Credit: Bart Peeters Rapid adaptation to an extreme environment But this has not prevented the Svalbard reindeer from evolving into what is today a viable population of more than 20,000 animals. “Despite the low genetic diversity, they have managed to develop a number of adaptations to life in the High Arctic. They are, for example, smaller in size and have shorter legs than other northern reindeer and caribou subspecies,” says Dussex. The ability to digest mosses in the absence of lichens, and to adjust their circadian rhythm to the extreme seasonal variations on Svalbard, are also traits the Svalbard reindeer have developed over the relatively short time they have lived isolated on the archipelago. Now, researchers at NTNU and collaborating institutions have analyzed genetic samples from 91 reindeer to see how they differ from their relatives on the mainland. Svalbard reindeer. Credit: Bart Peeters “Populations living on isolated islands are often small and are well-suited to studying genetic problems. The Svalbard reindeer has been isolated for at least 7000 years and has a very high degree of inbreeding. In addition, they were nearly extinct in the early 1900s due to excessive hunting,” says Michael D. Martin, a professor at NTNU’s Department of Natural History. Getting Rid of Harmful Mutations This near-extinction, where only a few individuals with their unique genetic variants survive, is called a bottleneck in population biology. “In this case, we are dealing with a population that suffers from a high degree of inbreeding, which is usually bad news for a small population. But inbreeding can also help a population to get rid of harmful mutations, a phenomenon technically called ‘purging’,” says Martin. Mathilde Le Moullec, a postdoc at NTNU, has collected “sub-fossil” bone samples from reindeer on Svalbard. The bones can be used to study how the genetics of the reindeer have changed over the centuries. Credit: Brage Bremset Hansen, NTNU In a population with a high degree of inbreeding, offspring are more likely to inherit harmful mutations from both mother and father. Therefore, these “dangerous” mutations more quickly manifest in the form of genetic diseases and poorer health. Offspring carrying these mutations become less “fit”, and they will either die before they have the chance to reproduce or they will have fewer offspring. Consequently, these dangerous mutations are less likely to be passed on to subsequent generations. “Paradoxically, in the long run, inbreeding can be beneficial,” says Dussex. Punctuated Evolution or Steady and Continuous? Similar phenomena have been observed elsewhere in nature. In New Zealand, Kakapo parrots (Strigops habroptilus), which had lived isolated on the islands for at least 10,000 years, became endangered after the arrival of non-native species brought to the islands by humans. In 1995, there were only 60 individuals left, but today the population has grown to around 200. Here too, Dussex and his colleagues found that harmful genetic variants had disappeared from the population thanks to a long period of inbreeding. “This is important knowledge when it comes to population management. The fact that the Svalbard reindeer is in relatively good genetic condition considering harmful mutations, is good news,” says Brage Bremset Hansen, professor of conservation biology at NTNU’s Department of Biology and Center for Biodiversity Dynamics. Hansen is also a senior researcher at the Norwegian Institute for Nature Research (NINA). This knowledge about the Svalbard reindeer can also change the way researchers study the effects of genetic bottlenecks, Dussex said. “What we still do not know enough about is how quickly such harmful mutations are selected against. We will continue to work on this, using DNA samples collected from bone remains and antlers of animals that lived several thousand years ago. This way, we can see whether these mutations have disappeared quickly over a few centuries or if it has happened gradually over several thousand years,” he said. The researchers are also very interested in examining the development of beneficial mutations, which have allowed the Svalbard reindeer to adapt to the unique ecosystem. “This is a ‘work in progress’,” says Martin, who also worked closely with researcher Mathilde Le Moullec, who over past years did the fieldwork to collect most of the bone samples from various locations on Svalbard. Climate Change May Be Too Fast It is far from certain that the Svalbard reindeer will be able to adapt as well to the rapid changes that result from global warming. The adaptations the reindeer have developed for the extreme arctic climate may fall short as the archipelago is now rapidly warming, which is changing both snow cover and vegetation. “Global warming is causing Svalbard’s climate to change faster than anywhere else in the world. Even though our results show that the Svalbard reindeer managed to adapt relatively quickly to a completely new environment after they colonized the islands, they might have trouble adapting to today’s rapid warming. They may have simply lost too much genetic variation,” says Hansen. This also applies to other terrestrial animals that have limited opportunities to move as climate change makes life difficult for them. “But this work now provides us with a better basis for understanding how quickly species can adapt to new environments,” says Martin. Reference: “Adaptation to the High-Arctic island environment despite long-term reduced genetic variation in Svalbard reindeer” by Nicolas Dussex, Ole K. Tørresen, Tom van der Valk, Mathilde Le Moullec, Vebjørn Veiberg, Ave Tooming-Klunderud, Morten Skage, Benedicte Garmann-Aarhus, Jonathan Wood, Jacob A. Rasmussen, Åshild Ø. Pedersen, Sarah L.F. Martin, Knut H. Røed, Kjetill S. Jakobsen, Love Dalén, Brage B. Hansen and Michael D. Martin, 1 September 2023, iScience. DOI: 10.1016/j.isci.2023.107811

A new artificial intelligence algorithm can pick out an RNA molecule’s 3D shape from incorrect shapes. Computational prediction of the structures into which RNAs fold is particularly important – and particularly difficult – because so few structures are known. Credit: Camille L.L. Townshend Stanford machine learning algorithm predicts biological structures more accurately than ever before. Stanford researchers develop machine learning methods that accurately predict the 3D shapes of drug targets and other important biological molecules, even when only limited data is available. Determining the 3D shapes of biological molecules is one of the hardest problems in modern biology and medical discovery. Companies and research institutions often spend millions of dollars to determine a molecular structure – and even such massive efforts are frequently unsuccessful. Using clever, new machine learning techniques, Stanford University PhD students Stephan Eismann and Raphael Townshend, under the guidance of Ron Dror, associate professor of computer science, have developed an approach that overcomes this problem by predicting accurate structures computationally. Most notably, their approach succeeds even when learning from only a few known structures, making it applicable to the types of molecules whose structures are most difficult to determine experimentally. Their work is demonstrated in two papers detailing applications for RNA molecules and multi-protein complexes, published in Science on August 27, 2021, and in Proteins in December 2020, respectively. The paper in Science is a collaboration with the Stanford laboratory of Rhiju Das, associate professor of biochemistry. “Structural biology, which is the study of the shapes of molecules, has this mantra that structure determines function,” said Townshend. The algorithm designed by the researchers predicts accurate molecular structures and, in doing so, can allow scientists to explain how different molecules work, with applications ranging from fundamental biological research to informed drug design practices. “Proteins are molecular machines that perform all sorts of functions. To execute their functions, proteins often bind to other proteins,” said Eismann. “If you know that a pair of proteins is implicated in a disease and you know how they interact in 3D, you can try to target this interaction very specifically with a drug.” Eismann and Townshend are co-lead authors of the Science paper with Stanford postdoctoral scholar Andrew Watkins of the Das lab, and also co-lead authors of the Proteins paper with former Stanford PhD student Nathaniel Thomas. Designing the algorithm Instead of specifying what makes a structural prediction more or less accurate, the researchers let the algorithm discover these molecular features for itself. They did this because they found that the conventional technique of providing such knowledge can sway an algorithm in favor of certain features, thus preventing it from finding other informative features. “The problem with these hand-crafted features in an algorithm is that the algorithm becomes biased towards what the person who picks these features thinks is important, and you might miss some information that you would need to do better,” said Eismann. “The network learned to find fundamental concepts that are key to molecular structure formation, but without explicitly being told to,” said Townshend. “The exciting aspect is that the algorithm has clearly recovered things that we knew were important, but it has also recovered characteristics that we didn’t know about before.” Having shown success with proteins, the researchers next applied their algorithm to another class of important biological molecules, RNAs. They tested their algorithm in a series of “RNA Puzzles” from a long-standing competition in their field, and in every case, the tool outperformed all the other puzzle participants and did so without being designed specifically for RNA structures. Broader applications The researchers are excited to see where else their approach can be applied, having already had success with protein complexes and RNA molecules. “Most of the dramatic recent advances in machine learning have required a tremendous amount of data for training. The fact that this method succeeds given very little training data suggests that related methods could address unsolved problems in many fields where data is scarce,” said Dror, who is senior author of the Proteins paper and, with Das, co-senior author of the Science paper. Specifically for structural biology, the team says that they’re only just scratching the surface in terms of scientific progress to be made. “Once you have this fundamental technology, then you’re increasing your level of understanding another step and can start asking the next set of questions,” said Townshend. “For example, you can start designing new molecules and medicines with this kind of information, which is an area that people are very excited about.” References: “Geometric deep learning of RNA structure” by Raphael J. L. Townshend, Stephan Eismann, Andrew M. Watkins, Ramya Rangan, Maria Karelina, Rhiju Das and Ron O. Dror, 27 August 2021, Science. DOI: 10.1126/science.abe5650 “Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes” by Stephan Eismann, Raphael J.L. Townshend, Nathaniel Thomas, Milind Jagota, Bowen Jing and Ron O. Dror, 2 December 2020, Proteins. DOI: 10.1002/prot.26033 Other co-authors of the Science paper include Stanford PhD students Ramya Rangan and Maria Karelina. Other co-authors of the Proteins paper include former Stanford students Milind Jagota and Bowen Jing. Das is also a member of Stanford Bio-X and the Wu Tsai Neurosciences Institute. Dror is also a member of Stanford Bio-X, the Institute for Computational and Mathematical Engineering (ICME), the Wu Tsai Neurosciences Institute, and the Stanford Artificial Intelligence Laboratory, a faculty affiliate of the Institute for Human-Centered Artificial Intelligence (HAI), and faculty fellow of Stanford ChEM-H . The research was funded by the National Science Foundation, the U.S. Department of Energy, a Stanford Bio-X Bowes Fellowship, the Army Research Office, the Air Force Office of Scientific Research, Intel Corporation, a Stanford Bio-X seed grant and the National Institutes of Health.

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