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Results for "KMAP"

Hacking the SARS-CoV-2 genome

KAUST ·

KAUST researchers are analyzing the SARS-CoV-2 genome to identify potential targets for treatment and vaccine development. They are using the KAUST Metagenome Analysis Platform (KMAP) and the university's supercomputer to compare and analyze genomic data. The research focuses on identifying key genes for detection and treatment of COVID-19. Why it matters: This research contributes to the global effort to combat the pandemic and highlights KAUST's capabilities in genomic data analysis and computational bioscience.

KAUST releases largest catalog of ocean DNA

KAUST ·

KAUST researchers, in collaboration with Spanish scientists, have released the Global Ocean Gene Catalog 1.0, the world's largest open-source catalog of marine microbes. The catalog, created using the KAUST Metagenomic Analysis Platform (KMAP), matches microbial class with gene function, geographic location, and habitat type, including 317 million unique gene clusters. The catalog analyzes 2102 ocean samples taken from different depths and locations around the world. Why it matters: This resource will enable researchers to investigate ocean ecosystems, track pollution impact, and explore biotechnology applications, potentially driving significant advances in fields like antibiotic discovery and plastic degradation.

MBZUAI and BioMap establish first biocomputing innovation research lab in Middle East

MBZUAI ·

MBZUAI and BioMap have signed an MoU to establish the first biocomputing innovation research lab in the Middle East, located on MBZUAI's campus. The collaboration will focus on applying AI protein generation to life science models, addressing needs in drug design, energy, and environmental protection. The lab will research de novo design of oil degradation enzymes and identify drug targets for aging-associated and rare diseases. Why it matters: This partnership signals a growing focus on applying AI to critical life science challenges in the region, potentially leading to breakthroughs in drug discovery and sustainable energy solutions.

New climate maps predict major changes in vegetation by end of century

KAUST ·

A KAUST-led study published in Scientific Data provides updated global climate classification maps from 1901-2020 and projects future conditions up to 2099. Researchers used a refined selection of climate models, excluding those with unrealistic CO2-induced warming rates, to ensure accuracy. Projections indicate significant shifts in land surface climate, with large areas transitioning to warmer climate zones by the end of the century under moderate emission scenarios. Why it matters: The updated maps provide a crucial tool for understanding climate change impacts, ecological studies, and informing policy decisions in the face of global warming, especially for a region like the Middle East that is highly vulnerable to climate change.

KVL releases new open source to visualize supercomputer simulations

KAUST ·

KAUST's Visualization Core Lab (KVL) has released inshimtu, a pseudo in situ visualization system for scientists working with large datasets and supercomputer simulations. Inshimtu simplifies the implementation of in situ visualization by using existing simulation output files without requiring changes to the simulation code. It helps scientists determine if implementing a full in situ visualization into their code is worthwhile. Why it matters: This open-source tool can improve the efficiency of supercomputing research in the region by allowing researchers to assess the value of in situ visualization before fully committing to it.

Designing for KAUST

KAUST ·

The Maker Space self-directed group at KAUST promotes DIY culture and provides training on using machines, tools, and materials. In March 2017, Maker Space launched the "Design for KAUST" workshop in collaboration with the University’s Residential Maintenance Department. The winning teams in the workshop received sponsorship, including a total of SAR 10,000 in prizes, a Local Impact Award and an opportunity to test the prototypes in the field. Why it matters: This initiative fosters innovation and problem-solving within the KAUST community, addressing practical challenges in daily life through technology and promoting local impact.

Inferring and Improving Street Maps with Data-Driven Automation

arXiv ·

Researchers at MIT and QCRI developed Mapster, a human-in-the-loop street map editing system. Mapster incorporates high-precision automatic map inference, data refinement, and machine-assisted map editing. Evaluation across forty cities using satellite imagery, GPS trajectories, and ground-truth data demonstrates Mapster's ability to make automation practical for map editing. Why it matters: This system could significantly improve the accuracy and completeness of street maps in rapidly developing urban areas across the Middle East.