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Weekly phone-call tutoring restored learning during school closures in five-country trial

A study spanning randomized trials in India, Kenya, Nepal, the Philippines and Uganda found that 20-minute weekly tutoring calls to primary-school children produced substantial learning gains during COVID-19-related school closures. The approach paired widely available mobile phones with tutoring tailored to each child's current skill level, costing about $11 per child, and worked whether delivered by government teachers or NGO staff.

Weekly phone tutoring sessions boost learning during school closures, five-country trial finds

A study led by Oxford researcher Noam Angrist tested a low-cost intervention combining text-message nudges and a weekly 20-minute phone call from a teacher to support children's learning when schools shut down. The randomized trials, involving more than 16,000 households across India, Kenya, Nepal, the Philippines and Uganda during COVID-19 closures, followed an earlier pilot in Botswana and were published in Nature.

Study finds phone-based tutoring curbs learning loss during school closures

A new Nature study by Angrist and colleagues examined how remote phone-based tutoring affected children's learning during periods when schools were shut, such as during the COVID-19 pandemic. The research found strong evidence that this low-tech approach helped students keep learning despite the disruption, offering a rare data-backed solution to an otherwise poorly understood problem.

Nature study shows satellite imagery can complement news-based conflict data

A new Nature paper by Sticher et al. argues that combining satellite imagery with traditional news-coded reports could reveal aspects of armed conflict that text-based monitoring misses. For over 20 years, conflict researchers have relied on coders converting news reports into structured event data, a method with inherent blind spots where reporting is scarce or biased.

Oxford researcher argues AI should be used to challenge ideas, not deliver answers

An Oxford University researcher who studies AI's effect on decision-making argues that generative AI tools should be treated as intellectual sparring partners rather than sources of ready-made answers. The piece cites data showing 94% of UK undergraduates use AI for assessed coursework and that over half of 2025 scientific papers show signs of LLM-generated text, up from roughly one in ten in 2023. The author contends fears about AI eroding critical thinking are misplaced, and that the real risk lies in passive use rather than active engagement with the technology.

Study maps 'proximity antigens' on tumor cell surfaces using AI-guided graph learning

Researchers cultured dozens of cancer cell lines alongside normal primary epithelial cells to profile surface proteins near known tumor markers. They applied a graph-learning computational method to identify 'proximity antigens'—proteins located near established cancer targets on the cell membrane—that could serve as new markers for tumor-specific therapies. The work combined wet-lab cell culture across breast, lung, colon, pancreatic, gastric and other cancer types with a novel AI-driven proximity mapping approach.

Researchers use AlphaFold database to identify TM184C as a GPCR-like regulator

A research team mined all 214.5 million structure predictions in Google DeepMind's AlphaFold database to search for undiscovered 7TM (seven-transmembrane) folds resembling G-protein-coupled receptors. Using the rhodopsin structure as a reference and the TM-align algorithm, they identified TM184C as a previously unrecognized GPCR-like protein involved in regulating cell-to-cell exchange and autophagy. The analysis required processing 24 tebibytes of compressed structural data on university supercomputing clusters.

Study maps mutational signatures behind prostate cancer using largest PPCG genome dataset

Researchers analyzed whole-genome sequencing data from 1,001 prostate cancer patients (1,172 tumour samples) collected through the PPCG consortium, applying computational tools to classify structural variants, including simple, complex, and chromothripsis-related genomic rearrangements. The team combined breakpoint detection, copy number segmentation, and statistical tests to build a detailed picture of mutational processes driving the disease.

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