Using the Pearson's correlation coefficient as the sole metric to measure the accuracy of quantitative trait prediction: is it sufficient?

How to evaluate the accuracy of quantitative trait prediction is crucial to choose the best model among several possible choices in plant breeding. Pearson's correlation coefficient (PCC), serving as a metric for quantifying the strength of the linear association between two variables, is widely used to evaluate the accuracy of the quantitative trait prediction models, and generally performs well in most circumstances. However, PCC may not always offer a comprehensive view of predictive accuracy, especially in cases involving nonlinear relationships or complex dependencies in machine learning-based methods.

Misinformation exploits outrage to spread online.

We tested a hypothesis that misinformation exploits outrage to spread online, examining generalizability across multiple platforms, time periods, and classifications of misinformation. Outrage is highly engaging and need not be accurate to achieve its communicative goals, making it an attractive signal to embed in misinformation. In eight studies that used US data from Facebook (1,063,298 links) and Twitter (44,529 tweets, 24,007 users) and two behavioral experiments (1475 participants), we show that (i) misinformation sources evoke more outrage than do trustworthy sources; (ii) outrage facilitates the sharing of misinformation at least as strongly as sharing of trustworthy news; and (iii) users are more willing to share outrage-evoking misinformation without reading it first.

Development and Validation of a Mechanistic, Weather-Based Model for Walnut Blight Caused by pv. .

Walnut blight, caused by Xanthomonas arboricola pv. juglandis (Xaj), occurs worldwide in almost all areas where the Persian walnut (Juglans regia) is grown, causing significant reductions in nut yield via defoliation and fruit drop. The disease control relies on the calendar-based, repeated use of chemical bactericides, negatively impacting economic and environmental sustainability and potentially inducing Xaj resistance to chemicals.

Combined Analysis of Grade Differences in Lapsang Souchong Black Tea Using Sensory Evaluation, Electronic Nose, and HS-SPME-GC-MS, Based on Chinese National Standards.

Tea standard samples are the benchmark for tea product quality control. Understanding the inherent differences in Chinese national standards for Lapsang Souchong black tea of different grades is crucial for the scientific development of tea standardization work. In this study, Lapsang Souchong black tea of different grades that meet Chinese national standards was selected as the research object.

Time course of visual attention in rats by atomic magnetometer.

Atomic magnetometer (AM) is utilized to non-invasively detect event-related magnetic fields (ERMFs) evoked by visual stimuli in rats. The aim of this study was to investigate the relationship between N2-like amplitude and visual attention. To achieve this, we combined the AM with a visual stimulation system and employed the passive single-stimulus paradigm.

Ethnopharmacology, genetic diversity, phytochemistry and pharmacological effects of Panax vietnamensis Ha et Grushv.: A review.

Ginseng is considered as a beneficial herbal remedy and over recent years its efficacy and safety have been verified in clinical therapy. There are two typical species of ginseng including Asian and American ginseng. The varieties of both species have been applied for commercialized materials in different stages of processing from raw to processed products.

Application of Machine Learning in the Diagnosis of Early Gastric Cancer Using the Kyoto Classification Score and Clinical Features Collected from Medical Consultations.

The early detection accuracy of early gastric cancer (EGC) determines the choice of the optimal treatment strategy and the related medical expenses. We aimed to develop a simple, affordable, and time-saving diagnostic model using six machine learning (ML) algorithms for the diagnosis of EGC. It is based on the endoscopy-based Kyoto classification score obtained after the completion of endoscopy and other clinical features obtained after medical consultation.

HoLLiECares - Development of a multi-functional robot for professional care.

Germany's healthcare sector suffers from a shortage of nursing staff, and robotic solutions are being explored as a means to provide quality care. While many robotic systems have already been established in various medical fields (e.g.

NIGMS Sandbox: a learning platform toward democratizing cloud computing for biomedical research.

Biomedical data are growing exponentially in both volume and levels of complexity, due to the rapid advancement of technologies and research methodologies. Analyzing these large datasets, referred to collectively as "big data," has become an integral component of research that guides experimentation-driven discovery and a new engine of discovery itself as it uncovers previously unknown connections through mining of existing data. To fully realize the potential of big data, biomedical researchers need access to high-performance-computing (HPC) resources.

The Automatic Context Measurement Tool (ACMT) to Compile Participant-Specific Built and Social Environment Measures for Health Research: Development and Usability Study.

Background: The environment shapes health behaviors and outcomes. Studies exploring this influence have been limited to research groups with the geographic information systems expertise required to develop built and social environment measures (eg, groups that include a researcher with geographic information system expertise).

Objective: The goal of this study was to develop an open-source, user-friendly, and privacy-preserving tool for conveniently linking built, social, and natural environmental variables to study participant addresses.

Serum-deprivation response of ARPE-19 cells; expression patterns relevant to age-related macular degeneration.

ARPE-19 cells are derived from adult human retinal pigment epithelium (RPE). The response of these cells to the stress of serum deprivation mimics some important processes relevant to age-related macular degeneration (AMD). Here we extend the characterization of this response using RNASeq and EGSEA gene set analysis of ARPE-19 cells over nine days of serum deprivation.

How large language models can reshape collective intelligence.

Collective intelligence underpins the success of groups, organizations, markets and societies. Through distributed cognition and coordination, collectives can achieve outcomes that exceed the capabilities of individuals-even experts-resulting in improved accuracy and novel capabilities. Often, collective intelligence is supported by information technology, such as online prediction markets that elicit the 'wisdom of crowds', online forums that structure collective deliberation or digital platforms that crowdsource knowledge from the public.

The effectiveness of nirmatrelvir/ritonavir regimen in hospitalized renal transplant patients with prolonged COVID-19 infection: a multicenter clinical experience.

Objectives: Effectiveness of nirmatrelvir/ritonavir (NR) in kidney transplant recipients (KTRs) infected COVID-19 for more than 5 days has not been evaluated.

Methods: In this multicenter retrospective study, 85 KTRs with COVID-19 were enrolled, including 50 moderate, 21 severe, and 14 critical patients.

Results: The median time from onset to starting NR treatment was 14 (IQR, 11-19) days.

Introducing ART: A new method for testing auditory memory with circular reproduction tasks.

Theories of visual working memory have seen significant progress through the use of continuous reproduction tasks. However, these tasks have mainly focused on studying visual features, with limited examples existing in the auditory domain. Therefore, it is unknown to what extent newly developed memory models reflect domain-general limitations or are specific to the visual domain.

Using machine learning to predict bacteremia in urgent care patients on the basis of triage data and laboratory results.

Background: Despite advancements in antimicrobial therapies, bacteremia remains a life-threatening condition. Appropriate antimicrobials must be promptly administered to ensure patient survival. However, diagnosing bacteremia based on blood cultures is time-consuming and not something emergency department (ED) personnel are routinely trained to do.

NFDI4Health Workflow and Service for Synthetic Data Generation, Assessment and Risk Management.

Individual health data is crucial for scientific advancements, particularly in developing Artificial Intelligence (AI); however, sharing real patient information is often restricted due to privacy concerns. A promising solution to this challenge is synthetic data generation. This technique creates entirely new datasets that mimic the statistical properties of real data, while preserving confidential patient information.

Blockchain-based proxy re-encryption access control method for biological risk privacy protection of agricultural products.

In today's globalized agricultural system, information leakage of agricultural biological risk factors can lead to business risks and public panic, jeopardizing corporate reputation. To solve the above problems, this study constructs a blockchain network for agricultural product biological risk traceability based on agricultural product biological risk factor data to achieve traceability of biological risk traceability data of agricultural product supply chain to meet the sustainability challenges. To guarantee the secure and flexible sharing of agricultural product biological risk privacy information and limit the scope of privacy information dissemination, the blockchain-based proxy re-encryption access control method (BBPR-AC) is designed.

Predicting the oil content of individual corn kernels combining NIR-HSI and multi-stage parameter optimization techniques.

Predicting the oil content of individual corn kernels using hyperspectral imaging and ML offers the advantages of being rapid and non-destructive. However, traditional methods rely on expert experience for setting parameters. In response to these limitations, this study has designed an innovative multi-stage grid search technique, tailored to the characteristics of spectral data.