Forty years of tracking trees reveals how global change is impacting Amazon and Andean Forest diversity
Peer-Reviewed Publication
Updates every hour. Last Updated: 24-Jan-2026 01:11 ET (24-Jan-2026 06:11 GMT/UTC)
New research published in Nature Ecology and Evolution reveals significant recent shifts in tree diversity among the tropical forests of the Andes and Amazon, driven by global change.
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Extracellular vesicles (EVs) play pivotal roles in cancer from inception through all phases of development.
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EV-mediated signaling profoundly influences the tumor microenvironment (TME), fostering immune evasion.
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EV-based liquid biopsies are promising when biopsy sampling is challenging.
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Engineered EVs show potential for targeting tumors more precisely.
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The lack of an acceptable and reliable guide for selecting in vitro or in vivo brain metastasis models hinders the development of brain metastasis therapies.
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There is an urgent need to employ accurate in vitro and in vivo models to recapitulate the complexities of brain tumor metastasis and to unravel the intricate cellular and physiological processes involved.
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Precise in vitro and in vivo brain metastasis models are crucial for investigating cellular and molecular mechanisms and serve as preclinical platforms to assess novel treatments.
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An array of emerging techniques, such as bio-three-dimensional (3D) printing, novel real-time imaging, artificial intelligence, and precise gene editing, holds promise for redefining the landscape of cancer brain metastasis model development.
Researchers have conducted the comprehensive review of recent advances in multimodal natural interaction techniques for Extended Reality (XR) headsets, revealing significant trends in spatial computing technologies. This timely review analyzes how recent breakthroughs in artificial intelligence (AI) and large language models (LLMs) are transforming how users interact with virtual environments, offering valuable insights for the future development of more natural, efficient, and immersive XR experiences.
Grammatical error correction (GEC) is a key task in natural language processing (NLP), widely applied in education, news, and publishing. Traditional methods mainly rely on sequence-to-sequence (Seq2Seq) and sequence-to-edit (Seq2Edit) models, while large language models (LLMs) have recently shown strong performance in this area.
In machine learning, it is often necessary to statistically compare the overall performance of two algorithms (e.g., our proposed algorithm and each compared baseline) based on multiple benchmark datasets. In this case, our proposed algorithm is typically referred to as the control algorithm. However, in some cases, it is also essential to conduct pairwise statistical comparisons of multiple algorithms without a control algorithm.