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1. Mechanistic approaches to investigate soil microbe‐mediated plant competition

   https://doi.org/10.1111/1365-2745.14156  

   Chung, Y Anny; Ke, Po‐Ju; Adler, Peter B (August 2023, Journal of Ecology)

   Abstract Interactions between plants and soil microbes can influence plant population dynamics and diversity in plant communities. Traditional theoretical paradigms view the microbial community as a black box with net effects described by phenomenological models.This approach struggles to quantify the importance of plant–microbe interactions relative to other competition and coexistence mechanisms and to explain context dependence in microbe effects.We argue that a mechanistic framework focused on microbial functional groups will lead to conceptual and empirical advances, as demonstrated by extending resource ratio theory to plant–microbe interactions. We review the diverse pathways by which different microbial functional groups can influence plant resource competition. Finally, we suggest approaches to link theory with observations to measure the key parameters of our framework.Synthesis: Our review highlights recent experimental advancements for uncovering microbial mechanisms that alter plant host resource competition and coexistence. We synthesize these mechanisms into a conceptual model that provides a framework for future experiments to investigate the importance of plant–microbe interactions in structuring plant populations and communities. 

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2. Review on functional data classification

   https://doi.org/10.1002/wics.1638  

   Wang, Shuoyang; Huang, Yuan; Cao, Guanqun (January 2024, WIREs Computational Statistics)

   Abstract A fundamental problem in functional data analysis is to classify a functional observation based on training data. The application of functional data classification has gained immense popularity and utility across a wide array of disciplines, encompassing biology, engineering, environmental science, medical science, neurology, social science, and beyond. The phenomenal growth of the application of functional data classification indicates the urgent need for a systematic approach to develop efficient classification methods and scalable algorithmic implementations. Therefore, we here conduct a comprehensive review of classification methods for functional data. The review aims to bridge the gap between the functional data analysis community and the machine learning community, and to intrigue new principles for functional data classification. This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and ClassificationStatistical Models > Classification ModelsData: Types and Structure > Time Series, Stochastic Processes, and Functional Data 

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3. Sample‐to‐answer salivary miRNA testing: New frontiers in point‐of‐care diagnostic technologies

   https://doi.org/10.1002/wnan.1969  

   Zhang, Zhikun; Liu, Tianyi; Dong, Ming; Ahamed, Md Ahasan; Guan, Weihua (May 2024, WIREs Nanomedicine and Nanobiotechnology)

   Abstract MicroRNA (miRNA), crucial non‐coding RNAs, have emerged as key biomarkers in molecular diagnostics, prognosis, and personalized medicine due to their significant role in gene expression regulation. Salivary miRNA, in particular, stands out for its non‐invasive collection method and ease of accessibility, offering promising avenues for the development of point‐of‐care diagnostics for a spectrum of diseases, including cancer, neurodegenerative disorders, and infectious diseases. Such development promises rapid and precise diagnosis, enabling timely treatment. Despite significant advancements in salivary miRNA‐based testing, challenges persist in the quantification, multiplexing, sensitivity, and specificity, particularly for miRNA at low concentrations in complex biological mixtures. This work delves into these challenges, focusing on the development and application of salivary miRNA tests for point‐of‐care use. We explore the biogenesis of salivary miRNA and analyze their quantitative expression and their disease relevance in cancer, infection, and neurodegenerative disorders. We also examined recent progress in miRNA extraction, amplification, and multiplexed detection methods. This study offers a comprehensive view of the development of salivary miRNA‐based point‐of‐care testing (POCT). Its successful advancement could revolutionize the early detection, monitoring, and management of various conditions, enhancing healthcare outcomes. This article is categorized under:Diagnostic Tools > BiosensingDiagnostic Tools > Diagnostic Nanodevices 

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4. Sequential change‐point detection: Computation versus statistical performance

   https://doi.org/10.1002/wics.1628  

   Wang, Haoyun; Xie, Yao (January 2024, WIREs Computational Statistics)

   Abstract Change‐point detection studies the problem of detecting the changes in the underlying distribution of the data stream as soon as possible after the change happens. Modern large‐scale, high‐dimensional, and complex streaming data call for computationally (memory) efficient sequential change‐point detection algorithms that are also statistically powerful. This gives rise to a computation versus statistical power trade‐off, an aspect less emphasized in the past in classic literature. This tutorial takes this new perspective and reviews several sequential change‐point detection procedures, ranging from classic sequential change‐point detection algorithms to more recent non‐parametric procedures that consider computation, memory efficiency, and model robustness in the algorithm design. Our survey also contains classic performance analysis, which provides useful techniques for analyzing new procedures. This article is categorized under:Statistical Models > Time Series ModelsAlgorithms and Computational Methods > AlgorithmsData: Types and Structure > Time Series, Stochastic Processes, and Functional Data 

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5. pnetr : An R package for the PnET family of forest ecosystem models

   https://doi.org/10.1101/2024.11.26.625449  

   Gao, Xiaojie; Zhou, Zaixing; Ollinger, Scott V; Matthes, Jaclyn Hatala; Jiao, Wenzhe; Thompson, Jonathan R (November 2024, bioRxiv)

   Abstract Ecosystem models offer a rigorous way to formalize scientific theories and are critical to evaluating complex interactions among ecological and biogeochemical processes. In addition to simulation and prediction, ecosystem models are a valuable tool for testing hypotheses about mechanisms and empirical findings because they reveal critical internal processes that are difficult to observe directly.However, many ecosystem models are difficult to manage and apply by scientists who lack advanced computing skills due to complex model structures, lack of consistent documentation, and low-level programming implementation, which facilitates computing but reduces accessibility.Here, we present the ‘pnetr’ R package, which is designed to provide an easy-to-manage ecosystem modeling framework and detailed documentation in both model structure and programming. The framework implements a family of widely used PnET (net photosynthesis, evapotranspiration) ecosystem models, which are relatively parsimonious but capture essential biogeochemical cycles of water, carbon, and nutrients. We chose the R programming language since it is familiar to many ecologists and has abundant statistical modeling resources. We showcase examples of model simulations and test the effects of phenology on carbon assimilation and wood production using data measured by the Environmental Measurement Station (EMS) eddy-covariance flux tower at Harvard Forest, MA.We hope ‘pnetr’ can facilitate further development of ecological theory and increase the accessibility of ecosystem modeling and ecological forecasting. 

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