Huntingtin disease (HD) is a heritable neurodegenerative disorder affecting up to 1 in 10,000 people, with an average survival duration of 17-20 years post symptom onset. HD patients typically suffer from severe motor/coordination decline and weight loss. There is no cure for HD, and traditional small molecule drugs only address symptom management. Prior approaches to treatment have failed for several reasons. Protein-targeting approaches such as ubiquitin ligase lack specificity, degrading both mutant and wild type huntingtin protein indiscriminately. Other approaches such as antisense oligonucleotides (ASOs) can target mutant RNA but require many doses over the patient’s lifetime. The disorder affects the huntingtin gene (HTT), which is essential in transcription, reactive oxygen species detection, DNA damage repair, and axonal transport. HD is caused by a heterozygous polyglutamine repeat expansion in exon 1 of HTT. Genome editing is an attractive alternative therapy for HD, as it would require a single dose and is permanent. UC Berkeley researchers have developed a system for CRISPR-based genome editing for genetic diseases like HD. Allele specific excision is possible through two different mechanisms: heterozygous SNPs that create/remove a PAM site, and heterozygous SNPs that create a mismatch within the seed region. For patients with these genotypes, the invention allows selective excision of the pathogenic repeats from only that allele. Over 20% of HD patients can be treated with just one of our novel candidate pairs, and about half of all patients could benefit from one of our novel candidate pairs.

WiFi-based indoor positioning has been a widely researched area for the past five years, with systems traditionally relying on signal telemetry data including Received Signal Strength Indicator (RSSI), Channel State Information (CSI), and Fine Timing Measurement (FTM). However, adoption in practice has remained limited due to environmental challenges including signal fading, multipath effects, and interference that significantly impact positioning accuracy. Existing machine learning approaches typically require extensive manual feature engineering, preprocessing steps like filtering and data scaling, and struggle with missing or incomplete telemetry data while lacking flexibility across heterogeneous environments. Furthermore, there is currently no unified model capable of handling variations in telemetry data formats from different WiFi device vendors, use-case requirements, and environmental conditions, forcing practitioners to develop separate models for each specific deployment scenario.

Researchers at the University of California, Davis have developed an approach to enhance autonomous vehicle path prediction through efficient information sharing and distributed learning among AI agents.

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POEM is a groundbreaking 3D bioprinting technology enabling high-resolution, multi-material, and cell-laden structure fabrication with enhanced cell viability.

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This technology represents a groundbreaking approach to generating and using biomolecule-loaded extracellular vesicles (EVs) for targeted cellular reprogramming.

This technology offers a novel approach to treating epilepsy by preventing the spread of epileptic networks and improving memory deficits through targeted electrical stimulation.