NAB 2019: Cloud Solutions on Hand From MESA Members

This is the first of two stories previewing some of the cloud solutions being showcased at this year’s NAB Show in Las Vegas. Members of the Media & Entertainment Services Alliance (MESA) representing the workflow solutions space at the 2019 NAB Show discussed here include Akamai, AWS, Caringo, Google Cloud, Microsoft Azure, Prime Focus Technologies and Qumulo.

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Codiak BioSciences

Codiak is a clinical-stage biopharmaceutical company focused on pioneering the development of exosome-based therapeutics

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MedTech

AI and Biotechnology: The Future of Healthcare Industry

Article | July 11, 2022

Artificial intelligence has grasped the foundation in biotech. It can have the most innovative impact on biotechnology. AI has already established its presence in our day-to-day life. AI has made the existence of self-driving cars possible. Likewise, the benefits and quality that it can contribute to biotech can also be felt. With AI, bio technicians will be able to enhance virtual screening, overlook preliminary datasets from clinics, and decipher an enormous amount of information. It can also help in improving the medication process by gathering and analyzing every bit of information. The Significance of AI in Biotechnology In the past few years, the application of artificial intelligence in the biotechnology industry has shifted from being sci-fi to sci-fact. A vast number of biotech companies like Deep Genomics are adopting AI for making data-driven decisions and use analytics tools to work efficiently. Unlike the AI robots in sci-fi that are ready to take over the world. AI designed for biotech has been designed to solve certain problems or complete a bunch of tasks by using automated algorithms. The aim of AI technology for biotech is to collect insights along with hidden patterns from large amounts of data. All the different industries of biotech including agriculture, animal, medical, industrial, and bioinformatics are gradually being affected by artificial intelligence. Moreover, the biotech industry is realizing that AI enables them some of the important strength to their business, including: Expanding accessibility Cost-effectiveness Critical predictions Efficient decision-making Research centers like PwC have also estimated output of $15.7 trillion by 2030 solely with AI contribution in industries. A survey revealed that about 44% of life science experts are using AI for R&D activities, as well. Use of AI in Biotechnology Altering Biomedical and Clinical Data So far the most developed use of AI is its ability to read voluminous data records and interpret them. It can prove to be a life-save for bio technicians who would have to examine that much data from research publications by themselves for the validation of their hypothesis. With the help of AI, clinical studies of patients will also become easier as all the examination reports and prescriptions will be stored in one place for cross-reference. Furthermore, it will also help in blending and fetching data into usable formats for analysis. Test Result Prediction Through trial and error, AI along with machine learning can help in predicting the response of the patient to certain drugs to provide more effective outcomes. Drug Design & Discovery AI plays a vital role whether it’s designing a new molecule or identifying new biological targets. It helps in identifying and validating drugs. It reduces the cost and time spent on the entire drug trial process and reaches the market. Personalized Medications for Rare Diseases With the combination of body scan results, patients’ body and analytics, AI can also help in detecting dangerous diseases at an early stage. Improving Process of Manufacturing To improve the process of manufacturing in biotechnology, AI offers a wide range of opportunities. It controls quality, reduces wastage, improves useability, and minimizes the designing time. Moving Towards AI-Enhanced Biotech Future Ever since the concept of artificial intelligence has arrived, being curious by nature, humans have started working towards achieving this goal. It has been growing at a fast pace while showing unbelievable growth and achievements at times. In comparison to the traditional methods used in the biotechnology industry, AI-based methods seem more reliable and accurate. In the upcoming years, it will show its success by improving the quality of health people have. You can also develop your AI-based application or know more about it by taking IT consultations.

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MedTech

5 Biotech Stocks Winning the Coronavirus Race

Article | July 12, 2022

There are quite a few companies that have found ways to grow their business during the ongoing COVID-19 pandemic. This is especially true for a number of biotechs now working on developing a potential treatment for, or vaccine against, the virus; shares of such companies have largely surged over the past couple of months. Although many of these treatments and vaccines are still have quite a way to go before they're widely available, it's still worth taking some time to look through what's going on in the COVID-19 space right now. Here are five biotech stocks that are leading the way when it comes to addressing COVID-19. Regeneron Pharmaceuticals (NASDAQ:REGN) wasn't among the initial wave of companies to announce a potential COVID-19 drug. However, investor excitement quickly sent shares surging when the company announced that its rheumatoid arthritis drug, Kevzara, could help treat COVID-19 patients.

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MedTech

Top 3 Biotech Clinical Data Management Trends to Watch in 2022

Article | October 7, 2022

Introduction The administration of medical records and data has advanced significantly during the past few decades. Clinical data management, which was once only a small subset of biotech research organizations, has now developed into a mission-critical, specialized unit. In the late 1990s, electronic data capture (EDC) began to alter the traditional function of clinical data management. After that, the data configuration and management of data queries for the EDC system fell under the purview of clinical data management services. Today, clinical data management is not only responsible for managing the clinical data configuration and data queries but also developing and implementing data administration plans, ensuring data accuracy and completeness, and maintaining optimum data security. In recent years, as digital technologies have gained acceptance around the globe, data has become a vital aspect in decision-making across numerous industries, and the life sciences and biotechnology sectors are no exception. Using data has provided granular insights to biotech organizations, assisting them in creating breakthroughs in drug development and medical research and signifying the importance of clinical trial management systems in these medical verticals. The Biggest Biotech Clinical Data Management Trends to Know About Today The future of clinical data management is contingent upon the implementation of systems and regulations. It is imperative for all organizations participating in a medical or life science trial to have transparent rules in place for sharing and retaining patient data. Also, there is a need to have a standardized format for maintaining these records and documents related to trials. This assists biotech organizations in reducing the chances of ambiguity regarding who owns what kind of data or paperwork at any given time. Over the past couple of years, the focus of the life science and biotechnology industries has shifted towards developing more effective medications and therapies, implementing personalized treatment, and finding cures for diseases such as cancer and AIDS. In response to this, a substantial rise in the number of clinical trials is being witnessed globally. As the number of clinical trials continues to accelerate, the spending on these trials rises as well. In response to this, the worldwide cost of conducting clinical trials is anticipated to reach US$ 49.80 billion in 2022. With the transition of the world from traditional to digital, medical professionals and biotech businesses are increasingly shifting towards adopting high-tech and reliable clinical trial management systems for various applications, starting from diagnosis and clinical trials to patient data documentation. But, what are the future trends in biotechnology clinical data management? Let’s discuss. Cloud-Based Clinical Metadata Repositories Automation is emerging as a new frontier in the biotech clinical data management domain, along with other innovative technologies such as artificial intelligence and machine learning. Because of this, life science establishments are witnessing a huge shift from paper-based documentation toward data-based documentation, which is creating mountains of research, compliance, and clinical data. The growing demand for new and more effective medications and drugs is augmenting the need to expedite clinical trials. This is resulting in an increased number of initiatives aimed at optimizing clinical trial processes to prepare and launch successful trials. However, pharmaceutical and biotechnology laboratories are encountering several challenges in collecting, managing, and analyzing metadata due to its complexities. So, what is the best solution to this problem? The answer to this is cloud-based clinical metadata repositories. Clinical research facilities are leveraging advanced, all-in-one, cloud-based clinical metadata repositories to assist them in centralizing and managing metadata; increasing metadata quality, consistency, and accuracy; and speeding up clinical trial management, documentation, and compliance processes. Shift Towards Digital Solutions Electronic Case Report Form Adequate research and accurate data are crucial for a clinical trial to succeed. Whether developing new drugs, medication, or therapies; conducting life science research; or studying the latest clinical trial systems, it is best to use electronic solutions as it reduces the room for mistakes during the transition of clinical data from paper-based format. Realizing this, biotech organizations are shifting towards using electronic case report forms to speed up record retrieval, improve record security, and cut down on operational costs associated with running clinical trials. The electronic case report form assists in lowering the failure rate of the clinical trial, enhancing efficiency, and optimizing security along with improving clinical trial documentation and productivity, further driving its adoption in the medical space. Electronic Clinical Outcome Assessment Electronic clinical outcome assessment is surfacing as one of the fast-growing future trends in biotechnology. It allows clinical trial facilities to automate data entry and improve the reliability of the collected information. The technology enables clinical trial institutions to automatically record patient-provided information about side effects, symptoms, drug timing, and other aspects during the clinical trial for increased precision. It also helps these institutions analyze the results of medication or therapy in clinical trials and lets clinical researchers use medical technologies like biosensor-enabled devices, self-service applications, and medical wearables for evaluation. Hence, biotech clinical facilities are increasingly deploying advanced electronic clinical outcome assessment systems to ensure adherence to protocols and regulations. Clinical Trial Customization The success of a new drug is determined by numerous factors other than its effectiveness, safety, and creativity of its developers, such as a successful clinical trial. Each clinical trial involves a number of decision-making points, and one wrong choice in any of these aspects can jeopardize the success of the entire endeavor. A crucial component of making well-informed decisions is data management, which is a part of clinical study as a whole. Clinical trial customization is emerging as one of the most prominent biotech clinical trial management trends. Every clinical trial is unique and needs a tailored approach to be successful. With the emergence of the trend of personalized treatment around the globe, biotech and pharmaceutical organizations are adopting innovative customized clinical trial management solutions to accelerate the pace of clinical trials and approvals. This is giving clinical researchers innovative ways to come up with new medicines for patients and streamline the clinical data as per the requirements for faster approvals. What Are the Key Clinical Data Management Challenges Faced by Biotech Companies? Groundbreaking medical interventions are of no use without reliable, accurate, and extensive clinical trial data. Without the data, biotech and pharmaceutical companies will not be able to provide the assurance of safety and efficacy required to bring the medication to market. Regulatory bodies such as the Food and Drug Administration (FDA), the Medicines and Healthcare Products Regulatory Agency (MHRA), and others are putting stricter rules in place to ensure the quality of clinical data. In addition, the fast-changing clinical development environment is creating more obstacles for biotech and medical spaces to ensure the accuracy, standard, and completeness of the clinical trial data. Hence, clinical teams are spending valuable time cleaning up data instead of analyzing it. Time spent trying to figure out issues with clinical trial data is detrimental and expensive but also mission-critical. This is because a small issue in the data can lead to numerous consequences, from small delays to calamitous setbacks, making it necessary to rerun clinical trials. This problem will only get more challenging to address as the volume of data and the types of data sources continue to grow. Here are some of the major clinical data management challenges that biotech firms encounter Standardization of Clinical Metadata Stringent Regulatory Compliance Increased Clinical Trial Complexity Mid-Study Changes Why Are Clinical Data Management Systems Garnering Popularity in the Biotech Industry? With the changing regulatory and clinical landscape, biotech and pharmaceutical companies are facing several obstacles in the management of clinical data and clinical trials. In addition, regulatory agencies are moving toward integrated electronic systems, which is making it more and more important for clinical laboratories to change the format of their submissions. Because of this, several biotech clinical labs are focusing on adopting innovative laboratory solutions, such as biotech clinical data management systems, to meet the need for standardized data inputs and replace all manual ways of working with electronic systems. A clinical data management system establishes the framework for error-free data collection and high-quality data submission, resulting in speedier drug discovery and shorter time-to-market. These solutions are gaining huge traction among biotech and pharmaceutical companies, owing to their ability to effectively manage clinical data, accelerate clinical trials, and ensure compliance. Let’s see some of the features of biotech clinical data management software that are most sought after by life-science companies Controlled, standardized data repository. Centralized data analysis and administration. Reduced operational expenditures for clinical data processes. Enhanced process effectiveness. Superior submission quality Compliance with predefined standards. Clinical Data Management Systems: The Future The role of clinical data management systems is evolving at a rapid pace as the life science and medical industries continue to incorporate digital solutions for diverse operations. These systems are being used in a variety of biotech clinical settings, ranging from clinical data compliance to data science and analytics, to help them analyze large and growing volumes of clinical data. Hence, a number of high-tech medical companies are aiming at integrating innovative technologies, such as artificial intelligence and machine learning, into clinical data management software to automate clinical data management tasks, improve clinical data submission, and enhance data quality. These new biotech clinical management technologies are anticipated to help life science laboratories gain a better understanding of diseases and speed up clinical trials in the coming years. FAQ What is a clinical data management system? A clinical data management system (CDMS) is a tool used in clinical research to track, record, and manage clinical trial data across medical establishments such as biotech laboratories. What are the key functions of the biotech clinical data management system? Some of the key functions of biotech clinical data management system are Documentation of Protocols and Regulations Patient Recruitment Real-time Clinical Study Analytics Reporting Investigator Relationship Management Electronic Visit Report Why is a clinical data management system needed for clinical trials today? A clinical data management system helps shorten the time from drug development to marketing by assisting in the collection of high-quality, statistically sound, and accurate data from clinical trials.

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MedTech

Immunology: A New Frontier in Medical Science

Article | July 16, 2022

Introduction Recent developments in the bioengineering of monoclonal antibodies (mAbs) have revolutionized the treatment of numerous rheumatic and immunological disorders. Currently, several immunological disorders are successfully being targeted and treated using innovative medical techniques such as immunotherapy. Leading companies are increasingly investing in research activities to expand the usage and application of immunology for the treatment of various infectious diseases, including multiple sclerosis, inflammatory bowel disorders, lupus, and psoriasis, leading companies are increasingly investing in research activities. Today, the efforts of researchers in immunology, with a long history of study and research, have borne fruit, as bioengineered mAbs are now being employed in clinical practices. Accelerating Investments: Paving the Way for Immunology The increasing prevalence of infectious diseases, cancer, and immune-mediated inflammatory disorders (IMIDs) is raising the need for more precise classification and an in-depth understanding of the pathology underlying these ailments. Numerous leaders in the biotechnology domain are thus focusing on undertaking numerous strategies, such as new facility launches and collaborations, to address the need by finding deeper inroads into immunology and its use in disease treatments. For instance, in 2022, the University of Texas MD Anderson Cancer Center announced the launch of a visionary research and innovation hub, the James P. Allison Institute, to find new roads in immunotherapy, develop new treatments, and foster groundbreaking science. These developments will result in better diagnosis through the use of selective biomarkers, and early detection of fatal diseases and their treatment, which will prevent complications from happening. Also, the identification of high-risk populations through a deeper understanding of genetic and environmental factors can assist in the prevention of disease through immunotherapy. The Way Forward Immunology has led to the development of biotechnology, making it possible to develop novel drugs and vaccines, as well as diagnostic tests, that can be used to prevent, diagnose, and treat a wide range of autoimmune, infectious, and cancerous diseases. With the rapid advancement in technology and the integration of artificial intelligence, immunology is finding its way into an array of domains and industries, encompassing several research areas including medicine, pharmaceuticals, agriculture, and space. Today, not only researchers but also leading biotech and pharmaceutical companies have recognized that conventional therapies with pharmaceutical and chemical products are being replaced by products derived from immunology. This is because they work well for health problems, are environmentally friendly, and are also emerging as a wealth-generating business in the medical field.

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Codiak BioSciences

Codiak is a clinical-stage biopharmaceutical company focused on pioneering the development of exosome-based therapeutics

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Synthace Unveils First Life Sciences R&D Cloud Addressing Complexity, Speed & Reproducibility for Scientists

Synthace | August 03, 2021

Synthace, a leading life sciences software company, today announced the first life sciences R&D cloud that includes a no-code software platform addressing the largest barriers to innovation that R&D life scientists currently face. With the release of this new platform, Synthace is also the first vendor to enable scientists to automate experimentation and insight sharing in a unified, global R&D environment. Scientists can now focus on asking the most impactful questions and unlock the true potential of biology. To solve humanity’s hardest problems, such as delivering breakthrough therapies or alternative food sources, the life sciences industry is under tremendous pressure to simultaneously overcome biology’s complexity, accelerate speed to scientific insight, and ensure the reproducibility of experiments. Synthace alleviates these challenges by empowering scientists to improve and accelerate decision-making with more statistically powerful, automated experiments that can minimize human error. Furthermore, Synthace’s cloud platform leverages intelligent, dynamic automation to produce the highest quality data sets that are primed for machine learning (ML) and other advanced analyses to lead to better insights. With Synthace, the life sciences can now benefit from a quantum leap in experimentation capabilities, accelerating development timelines that would have previously been impossible. Customers Accelerate R&D with Synthace Ipsen has been using Synthace to automate the design and construction of therapeutic candidates. With Synthace, Ipsen produced approximately 90 constructs five times faster than previous methods, substantially increasing the number of molecules entering the screening cascade. The platform also achieved a 10-fold reduction in costs associated with DNA synthesis. Karen Bunting, Director of Protein Sciences at Ipsen commented, “Synthace sits very well at the beginning of our drug discovery process. It allows us to explore larger drug design space by simplifying planning and production of multiple molecule variants with combinatorial construct assembly. These throughput improvements help us deliver well-tolerated and effective therapeutic solutions more rapidly to our patients.” Microsoft Research also uses Synthace to automatically generate biological data at a volume that allows its ML algorithms to rapidly improve. As part of reporting on advancements in programming biological systems, a member of its Biocomputation Group noted: “Synthace really comes into its own when we’re performing experiments with complex layouts like combinatorial construct assembly and design of experiments. When we’re building 12 constructs at a time, Synthace automates all the planning that would go into setting up such an experiment and allows it to become routine.” Synthace Life Sciences R&D Cloud The platform provides end-to-end management of the experimental lifecycle, from design through execution to data visualization and knowledge transfer. Synthace adheres to FAIR principles to support interoperability with other major lab informatics platforms to ensure streamlined data management for all of its customers. Only the Synthace Life Sciences R&D Cloud delivers: Complete experimental design, planning and automation, requiring no coding expertise. Scientists can define more informative and impactful experiments that would otherwise be impossible to run and easily implement Quality by Design (QbD) and Design of Experiments (DOE). Seamless, cloud-based data capture, processing, and visualization. R&D teams can deliver deeper and faster insights from fully contextualized, machine learning-ready data sets that are automatically generated from the laboratory. Minimal deployment and onboarding. Customers experience rapid time-to-insight through Synthace’s out-of-the-box platform features and pre-validated protocols for common applications such as ELISA and high-throughput purification, helping them shorten R&D cycles and study more candidates per program. About Synthace Synthace is a life sciences software company enabling life science the way it should be done. Delivering a life sciences R&D cloud to scientists who want to innovate faster, the Synthace platform seamlessly automates experimentation and insight sharing so that scientists can focus on asking the most impactful questions to unlock the true potential of biology. Top global pharmaceuticals, high-growth biotech companies, leading CDMOs, and innovators in artificial intelligence all turn to Synthace to discover solutions to humanity’s hardest problems.

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VMware Advances Hybrid Cloud Operations and Automation with Refreshed vRealize Cloud Management Platform

albawaba | April 03, 2019

VMware vRealize Operations delivers self-driving operations management from applications to infrastructure to optimize, plan and scale hybrid clouds including on-prem environments. It delivers continuous performance optimization based on operational and business intent, efficient capacity management, proactive planning, intelligent remediation and integrated compliance. VMware vRealize Operations 7.5 will extend self-driving capabilities to help customers achieve:

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VMware advances hybrid cloud operations and automation

datacenternews | April 03, 2019

The new product releases vRealize Operations 7.5, vRealize Network Insight 4.1, vRealize Automation 7.6, and vRealize Suite Lifecycle Manager 2.1 will supposedly combine to provide expanded self-driving operations and enhanced programmable provisioning capabilities across private and hybrid clouds.

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Research

Synthace Unveils First Life Sciences R&D Cloud Addressing Complexity, Speed & Reproducibility for Scientists

Synthace | August 03, 2021

Synthace, a leading life sciences software company, today announced the first life sciences R&D cloud that includes a no-code software platform addressing the largest barriers to innovation that R&D life scientists currently face. With the release of this new platform, Synthace is also the first vendor to enable scientists to automate experimentation and insight sharing in a unified, global R&D environment. Scientists can now focus on asking the most impactful questions and unlock the true potential of biology. To solve humanity’s hardest problems, such as delivering breakthrough therapies or alternative food sources, the life sciences industry is under tremendous pressure to simultaneously overcome biology’s complexity, accelerate speed to scientific insight, and ensure the reproducibility of experiments. Synthace alleviates these challenges by empowering scientists to improve and accelerate decision-making with more statistically powerful, automated experiments that can minimize human error. Furthermore, Synthace’s cloud platform leverages intelligent, dynamic automation to produce the highest quality data sets that are primed for machine learning (ML) and other advanced analyses to lead to better insights. With Synthace, the life sciences can now benefit from a quantum leap in experimentation capabilities, accelerating development timelines that would have previously been impossible. Customers Accelerate R&D with Synthace Ipsen has been using Synthace to automate the design and construction of therapeutic candidates. With Synthace, Ipsen produced approximately 90 constructs five times faster than previous methods, substantially increasing the number of molecules entering the screening cascade. The platform also achieved a 10-fold reduction in costs associated with DNA synthesis. Karen Bunting, Director of Protein Sciences at Ipsen commented, “Synthace sits very well at the beginning of our drug discovery process. It allows us to explore larger drug design space by simplifying planning and production of multiple molecule variants with combinatorial construct assembly. These throughput improvements help us deliver well-tolerated and effective therapeutic solutions more rapidly to our patients.” Microsoft Research also uses Synthace to automatically generate biological data at a volume that allows its ML algorithms to rapidly improve. As part of reporting on advancements in programming biological systems, a member of its Biocomputation Group noted: “Synthace really comes into its own when we’re performing experiments with complex layouts like combinatorial construct assembly and design of experiments. When we’re building 12 constructs at a time, Synthace automates all the planning that would go into setting up such an experiment and allows it to become routine.” Synthace Life Sciences R&D Cloud The platform provides end-to-end management of the experimental lifecycle, from design through execution to data visualization and knowledge transfer. Synthace adheres to FAIR principles to support interoperability with other major lab informatics platforms to ensure streamlined data management for all of its customers. Only the Synthace Life Sciences R&D Cloud delivers: Complete experimental design, planning and automation, requiring no coding expertise. Scientists can define more informative and impactful experiments that would otherwise be impossible to run and easily implement Quality by Design (QbD) and Design of Experiments (DOE). Seamless, cloud-based data capture, processing, and visualization. R&D teams can deliver deeper and faster insights from fully contextualized, machine learning-ready data sets that are automatically generated from the laboratory. Minimal deployment and onboarding. Customers experience rapid time-to-insight through Synthace’s out-of-the-box platform features and pre-validated protocols for common applications such as ELISA and high-throughput purification, helping them shorten R&D cycles and study more candidates per program. About Synthace Synthace is a life sciences software company enabling life science the way it should be done. Delivering a life sciences R&D cloud to scientists who want to innovate faster, the Synthace platform seamlessly automates experimentation and insight sharing so that scientists can focus on asking the most impactful questions to unlock the true potential of biology. Top global pharmaceuticals, high-growth biotech companies, leading CDMOs, and innovators in artificial intelligence all turn to Synthace to discover solutions to humanity’s hardest problems.

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VMware Advances Hybrid Cloud Operations and Automation with Refreshed vRealize Cloud Management Platform

albawaba | April 03, 2019

VMware vRealize Operations delivers self-driving operations management from applications to infrastructure to optimize, plan and scale hybrid clouds including on-prem environments. It delivers continuous performance optimization based on operational and business intent, efficient capacity management, proactive planning, intelligent remediation and integrated compliance. VMware vRealize Operations 7.5 will extend self-driving capabilities to help customers achieve:

Read More

VMware advances hybrid cloud operations and automation

datacenternews | April 03, 2019

The new product releases vRealize Operations 7.5, vRealize Network Insight 4.1, vRealize Automation 7.6, and vRealize Suite Lifecycle Manager 2.1 will supposedly combine to provide expanded self-driving operations and enhanced programmable provisioning capabilities across private and hybrid clouds.

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