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Dotmatics Introduces Luma Antibody & Protein Engineering Solution for End-to-End Antibody Discovery

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The first in a series of Dotmatics Luma Multimodal R&D Solutions

Geneious has been seamlessly composed into Luma showcasing cross-therapeutic capabilities within a unified platform

BOSTON, Oct. 22, 2024 /PRNewswire/ — Dotmatics®, a leader in R&D scientific software connecting science, data, and decision-making, today announced the launch of Dotmatics Luma™ Multimodal R&D Solutions in support of the discovery and development of therapeutics across multiple modalities of biopharmaceutical science. The first solution is the Luma Antibody & Protein Engineering solution, a comprehensive end-to-end solution for streamlining the antibody discovery and development process, with an emphasis on monoclonal and multispecific antibodies.

The Luma Antibody & Protein Engineering Solution and other future multimodal solutions combine the Dotmatics Luma Scientific Intelligence Platform and key Dotmatics scientific tools, thoughtfully composed within Luma. Together, these solutions will offer researchers a multimodal approach to therapeutic discovery supporting CAR-T cell discovery, siRNA, antibody-drug conjugates (ADCs), CRISPR therapeutics, and vaccine discovery.

“Increasingly our customers are asking for tools that support multiple modalities of therapeutic discovery,” said Michael Swartz, Chief Strategy Officer at Dotmatics. “Scientists want to pick the best therapy or combination of therapies to address a particular target by researching and testing across different domains of science – not just one. Dotmatics gives them a scientific intelligence platform that is natively built to support this.”

These multimodal Luma solutions will leverage capabilities from Dotmatics’ best-in-class scientific software products including Geneious®, GraphPad® Prism, OMIQ®, Protein Metrics Byos®, and FCS Express®, composed to work together harmoniously within Luma, enhancing overall functionality without friction. This approach allows researchers to use familiar tools while eliminating data and process silos, creating a more cohesive and collaborative environment.

Introducing Geneious Luma

The first of these scientific products seamlessly composed within Luma and being introduced today is Geneious Luma. With Geneious Luma, researchers will have access to analyze and visualize DNA, RNA, and protein sequences from Geneious Prime, and the antibody discovery and screening capabilities of Geneious Biologics.

Geneious Luma, and the other future Dotmatics scientific products composed within Luma, will each come pre-configured to work seamlessly within Luma, while allowing IT and scientists to compose their own sequence of sophisticated low-code actions, eliminating the need for lengthy customization or complex integrations. As a result, researchers can benefit from the end-to-end solution immediately. In addition, these products will be available to license together with the Luma platform and will include out-of-the-box Luma applications to fill critical gaps in the end-to-end workflow, including:

Multimodal Registration: Versioned, entity-specific, and extensible registration models driven by linked ontologies allow for the highest levels of scientific depth and precision. Luma links these models to how scientists naturally work, so registration happens automatically and asynchronously, along with the tracking and storage of experimental and product-related information throughout the research and development process. When test results indicate a synthesized molecule does not match the design, Luma can modify the linked registered entity without losing lineage or context of the original.Unified Results: All relevant project data—such as molecule designs, characterization data, assay data, endpoint data, and registered materials—can be correlated and unified into normalized data structures and customizable dashboards to quickly make decisions about which antibodies are best to advance. This happens automatically as people work, eliminating the need for hours of data wrangling currently implicit in activities from registration to analysis.Customer Apps: Customers can safely extend Dotmatics Luma apps or create their own low-code apps to contextualize, flow, and correlate data from disparate sources in ways that suit their needs – with scientifically smart dashboards and visualizations to match. These apps follow industry standard SDLC processes and FAIR data principles to make getting data into and out of Luma in a governed way simple and straightforward. Data integration options include REST, GraphQL, and JDBC.Artificial Intelligence: Luma will include significant generative and predictive ML capabilities for dry lab and wet lab tasks that customers can define and extend. Assistive GenAI will help customers accelerate the introspection, summarization, and visualization of their data. Domain-specific AI will allow Luma to leverage protein prediction, flow cytometry autogating, and other scientifically-focused models within their workflows. Composite AI will allow the use of multiple models or data sets generated from independent models to be brought together for higher order model training, selection, and execution.

The Luma Antibody & Protein Engineering Solution

By composing Geneious Luma and other scientific products within Luma, Dotmatics can offer researchers with a new solution for addressing the complexities of antibody and protein engineering.

“The Luma Antibody & Protein Engineering solution addresses long-standing gaps in the antibody discovery workflow by providing an end-to-end process that seamlessly composes many of scientists’ favorite tools directly into Luma,” said Kalim Sailba, Chief Product Officer at Dotmatics. “Researchers can now streamline workflows from planning and in-silico design to wet lab production, validation, and data analysis, all within a unified AI-native platform.”

The following products, composed within Luma, will support the Luma Antibody & Protein Engineering solution:

Geneious Luma: Enables researchers to use the advanced bioinformatics, molecular biology, and antibody discovery capabilities of Geneious Prime® and Geneious Biologics® to annotate and filter sequences and to design constructs. Then, seamlessly working together with Luma, researchers can execute their cloning, expression, and purification tasks using Luma’s Adaptive Workflow capabilities.

Customers will also have access to BioGlyph®, following Dotmatics’ recent investment, enabling the design and generation of multispecific antibody formats and the execution of predictive machine learning models to score potential multispecific antibodies, allowing researchers to explore all combinations of multispecific formats and proceed with only those that are most promising.

Prism Luma: Leverage GraphPad Prism, the industry-leading data analysis software, for antibody assay development and screening. This product includes native normalization and pre-processing of results prior to analysis in Prism (mitigating the need to use Excel) and contextual linking between assay results, registered materials, and experimental context.Byos & Byosphere Luma: Use Protein Metrics Byos, a comprehensive software for streamlining protein characterization, to characterize protein structures and behaviors for higher fidelity, then feed these characterizations back into predictive machine learning models to drive the next round of synthesis. Also leverage Protein Metrics Byosphere enterprise platform to combine biotherapeutic mass spec data, analysis, and reporting and to curate, interrogate, and share information gained in the analytical lab from Byos.Luma Lab Connect: Automate data ingestion from lab instruments including flow cytometry, liquid chromatography, mass spectrometry and other data sources. Lab Connect auto-associates instrument results with the correct workflow steps, materials, and endpoint data, mitigating a common manual pain point of SDMS and LIMS systems today.Flow Luma: Use OMIQ, the modern flow cytometry solution, to evaluate the molecular properties of antibody candidates, the cells that produce them, and phenotype their therapeutic outcomes.

Expanding Beyond Biopharma

The future use of Dotmatics Luma extends beyond biopharma and drug discovery into broader scientific domains, like material science and chemistry, industrial biotech, and agritech. Every solution built on the Luma platform ensures a unified user experience and seamless work and data flows, helping organizations achieve scientific breakthroughs faster and with greater accuracy. By delivering a cohesive and scalable platform, Dotmatics aims to support researchers across multiple industries, fostering innovation and collaboration.

Forward Looking Statements  

This press release contains forward-looking statements that are intended to outline our general product strategy. It is intended for informational purposes only and speaks only as of the date the statements are first published. It is not a commitment to deliver any functionality and should not be relied upon for making purchase decisions. You should not put any contractual reliance on these statements. The development, release, and timing of any products or capabilities remains at the sole discretion of Dotmatics.

No representation or warranty, express or implied, is provided in relation to the fairness, accuracy, correctness, completeness or reliability of the information, opinions or conclusions expressed herein. Only those representations or warranties that are made in or pursuant to one or more definitive agreements involving the parties will have any legal effect.

About Dotmatics
Dotmatics is a leader in R&D scientific software connecting science, data, and decision-making. Its enterprise R&D platform and scientists’ favorite applications drive efficiency and accelerate innovation. More than 2 million scientists and 10,000 customers trust Dotmatics to help them create a healthier, cleaner, safer world. Dotmatics is a global team of more than 800 people dedicated to supporting its customers in over 180 countries. Dotmatics has made 14 acquisitions since 2017. The company’s principal office is in Boston, with 14 offices and R&D teams located around the world.

Dotmatics is backed by Insight Partners, a leading global venture capital and private equity firm investing in high-growth technology and software scaleup companies. Learn more about Dotmatics, its platform, and applications including GraphPad Prism, Geneious, SnapGene, Protein Metrics, and LabArchives at https://www.dotmatics.com.

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SOURCE Dotmatics Inc

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BinBase Expands 2026 BIN Dataset with Instant Payout Intelligence for iGaming, Gambling, and Cross-Border Transfers

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BinBase updates its 2026 dataset with specialized Fast Funds, Visa Direct, and Mastercard MoneySend indicators to help iGaming operators and payout platforms execute seamless, instant card disbursements.

MIAMI, July 23, 2026 /PRNewswire-PRWeb/ — BinBase, a global provider of payment routing intelligence and card issuing data, has introduced specialized instant payout indicators as part of its upgraded 2026 BIN Database. Tailored for iGaming operators, online gambling platforms, crypto-to-fiat ramps, and payout aggregators, the updated dataset helps platform engineers streamline real-time card disbursements and Push-to-Card (P2C) transactions.

In high-velocity sectors such as online betting and gaming, instantaneous player payouts are a primary driver of customer retention. However, executing Push-to-Card transactions through protocols like Visa Direct and Mastercard MoneySend requires knowing whether the receiving card issuer supports Fast Funds for specific merchant category codes (MCCs). Attempting instant payouts on non-eligible cards leads to declined transactions, elevated processing fees, and poor user experiences.

The 2026 BinBase release solves this operational bottleneck by delivering dedicated attributes for real-time fund disbursements:

Fast Funds Eligibility: Granular indicators identifying domestic and cross-border Fast Funds support across global Visa and Mastercard ranges.Online Gambling Fast Funds (OG FF): Dedicated flags specifically identifying card ranges authorized to receive real-time gambling and betting payouts.Mastercard MoneySend & Visa Direct Indicators: Precise protocol compatibility markers (MS Ind & MT Ind) ensuring push transactions are routed only to eligible recipient cards.Direct Debit & Pull-Funds Support: Indicators for recurring collections and account-funding transactions.

“Player payouts in iGaming cannot wait for standard 2-to-3-day ACH settlements,” said a spokesperson for Damiko Inc. “By embedding our Fast Funds and Gambling FF flags into their payment engines, operators can instantly validate recipient cards before initiating a transfer, guaranteeing high success rates and instant liquidity for their users.”

Fintech engineers and payout architects can examine the full 29-field database schema and access a free 2026 sample dataset on GitHub.

To explore commercial licensing, bulk database downloads, or custom data feeds, visit BinBase at https://binbase.com.

About Damiko Inc

Damiko Inc is a US-based fintech data provider specializing in card issuer analytics, payment routing data, and global BIN database solutions. Operating through its flagship product, BinBase.com, the company supplies high-precision transaction intelligence to help merchants and payment facilitators worldwide optimize approval rates and mitigate processing fees.

Media Contact
Fedor Lavrikoff, BinBase, 1 7866133334, sales@binbase.com, www.binbase.com 

View original content:https://www.prweb.com/releases/binbase-expands-2026-bin-dataset-with-instant-payout-intelligence-for-igaming-gambling-and-cross-border-transfers-302829344.html

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Walnut Coding’s Young Coders Serve as ‘Instructors’ at Huawei Cloud Developer Training Camp

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Ages 8 and 15, students showcase AI-era project-building skills from concept to working application

BEIJING, July 23, 2026 /PRNewswire/ — Walnut Coding (the “Company”), a leading online platform for youth coding education, said two of its students – ages 8 and 15 – have joined the “instructor” lineup at Huawei Cloud Developer Training Camp, making them among the youngest “instructors” in the program’s history. The Company cites the pair as a prime example of how young learners can combine coding fundamentals with AI tools to turn ideas into working applications.

The two students, Bolin Du, 8, and Peiqi Gao, 15, built working applications using Huawei Cloud CodeArts, an AI coding assistant, then presented the projects to the training camp themselves – walking the audience through their design choices, technical builds, and debugging process.

The move lands at a moment when AI coding tools are forcing a rethink across the education sector. Tools that can generate functioning code from a plain-language prompt have undercut the traditional argument for teaching children to program – that they need the skill to build things themselves. Walnut Coding’s answer is that the more valuable skill now is judgment – knowing what problem to solve, breaking it into parts, and determining whether an AI’s output actually works.

Gao built a travel-planning application that generates routes, itineraries, and recommendations based on user input, handling the project end to end, from requirements and design through coding and debugging. Du, the younger of the two, built an interactive calendar application, using HTML for page structure, CSS for visual design, and JavaScript for interactive features. Both students then took on an instructor’s role at the camp, presenting their project goals and technical implementation to the audience – a step Walnut Coding says separated the work from a typical classroom assignment.

These were not classroom exercises but working projects, built and presented inside a professional developer-training environment. The experience demanded more from both students than simply producing something functional – they needed to articulate their reasoning, defend technical choices, and refine the final result under scrutiny. Their participation signals a broader shift underway in what youth coding education can deliver.

AI is making code generation easier, but it is also redrawing which skills actually matter. A student who relies only on one-click generation may get a rough prototype quickly, but still struggle to spot logical flaws, judge whether the output is reliable, or turn an abstract idea into a product that actually works. Students with programming foundations, by contrast, are better positioned to define requirements, evaluate what the AI produces, correct its errors, and treat the technology as a tool rather than a shortcut to lean on.

“AI can help children generate code faster, but it cannot decide for them what problem they should solve, nor can it make the final judgment about whether the result is truly effective,” said Pengxuan Zeng, founder and CEO of Walnut Coding. “What these two students demonstrated is not just coding technique, but the ability to define needs, break down tasks, verify outcomes, and turn an idea into a working product. That is why we believe young people still need to learn programming in the AI era.”

Walnut Coding structures its courses around that thesis, pairing student-led project work with teaching-assistant guidance and AI-assisted support. According to the Company, this data is continuously fed back into its systems to refine the personalization of AI-assisted feedback — a closed-loop process linking teaching, practice, feedback, and curriculum development.

The Company frames the payoffs less around producing professional software engineers than around a broader form of literacy. As AI continues to reshape how tasks get done, the ability to understand the technology, structure problems clearly and collaborate effectively with intelligent tools may prove one of the most durable skills a young learner can develop.

Walnut Coding says it plans to keep expanding opportunities for students to build practical projects, partner with industry technology platforms such as Huawei Cloud, and develop the core capabilities needed to build with technology in the AI era.

View original content:https://www.prnewswire.com/news-releases/walnut-codings-young-coders-serve-as-instructors-at-huawei-cloud-developer-training-camp-302833884.html

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MOREH Showcases High-Performance LLM Inference on AMD GPUs at AMD Advancing AI 2026

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SAN FRANCISCO, July 23, 2026 /PRNewswire/ — Moreh, an AI infrastructure software company, led by CEO Gangwon Jo, participated in AMD Advancing AI 2026, AMD’s flagship annual AI event held in San Francisco on July 22–23 (local time), where it demonstrated its distributed inference solution, the MoAI Inference Framework, running on AMD GPUs.

At the event, Moreh presented a live demonstration of the GLM-5.1 large language model (LLM) powered by the MoAI Inference Framework on a system equipped with 32 AMD Instinct™ MI300X GPUs across four nodes. Visitors experienced the chatbot firsthand, evaluating its response speed and service quality while observing performance across a range of real-world use cases.

Unlike conventional demonstrations that simply run an AI model, Moreh’s showcase displayed key inference service metrics in real time, including GPU utilization, Tokens Per Second (TPS), Time To First Token (TTFT), and Time Per Output Token (TPOT). This enabled attendees to directly verify both inference performance and GPU resource efficiency in a production-like service environment.

Global AI industry leaders and enterprise customers attending the event expressed strong interest in the system’s fast response times and stable performance. In particular, the live deployment of the computationally demanding GLM-5.1 model on AMD GPUs at production-grade service levels received positive feedback from visitors.

Moreh’s MoAI Inference Framework is widely recognized as the world’s first commercially deployed distributed inference solution built for the AMD ecosystem. Its distributed inference and heterogeneous computing technologies are designed to dramatically reduce AI service costs, enabling broader adoption of AI worldwide. The technology addresses one of the industry’s biggest challenges-the rapidly rising infrastructure and service costs caused by increasingly larger AI models-by delivering a more efficient inference infrastructure.

Moreh CEO Gangwon Jo stated, “This event provided an opportunity for global customers to verify firsthand that top-tier inference performance can be achieved on AMD GPU environments,” and added “We will continue advancing our AI infrastructure software so enterprises can operate AI services as efficiently as possible, regardless of the underlying GPU platform.”

Moreh develops its own AI infrastructure engine and has expanded its end-to-end AI capabilities through its foundation LLM subsidiary, Motif Technologies, covering both AI infrastructure and foundation models. The company is also strengthening its presence in the global AI market through strategic partnerships with leading technology companies, including AMD and Tenstorrent.

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