Leaf Functional Traits and Their Influencing Factors in Six Typical Vegetation Communities.

Leaf functional traits (LFTs) have become a popular topic in ecological research in recent years. Here, we measured eight LFTs, namely leaf area (LA), specific leaf area (SLA), leaf thickness (LT), leaf dry matter content (LDMC), leaf carbon content (LCC), leaf nitrogen content (LNC), leaf phosphorus content (LPC), and leaf potassium content (LKC), in six typical vegetation communities (sclerophyllous evergreen broad-leaved forests, temperate evergreen coniferous forests, cold-temperate evergreen coniferous forests, alpine deciduous broad-leaved shrubs, alpine meadows, and alpine scree sparse vegetation) in the Chayu River Basin, southeastern Qinghai-Tibet Plateau. Our aim was to explore their relationships with evolutionary history and environmental factors by combining the RLQ and the fourth-corner method, and the method of testing phylogenetic signal.

A Point Cloud Graph Neural Network for Protein-Ligand Binding Site Prediction.

Predicting protein-ligand binding sites is an integral part of structural biology and drug design. A comprehensive understanding of these binding sites is essential for advancing drug innovation, elucidating mechanisms of biological function, and exploring the nature of disease. However, accurately identifying protein-ligand binding sites remains a challenging task.

SLAM medical imaging enabled by pre-chirp and gain jointly managed Yb-fiber laser.

We demonstrate a pre-chirp and gain jointly managed Yb-fiber laser that drives simultaneous label-free autofluorescence-multiharmonic (SLAM) medical imaging. We show that a gain managed Yb-fiber amplifier produces high-quality compressed pulses when the seeding pulses exhibit proper negative pre-chirp. The resulting laser source can generate 43-MHz, 34-fs pulses centered at 1110 nm with more than 90-nJ energy.

Caregivers' understanding of childhood influenza vaccination during the epidemic in China. A mixed-methods study.

Introduction: Influenza vaccination uptake among young children has been poor in China, but it is unclear how it changed during the COVID-19. This study aimed to investigate the uptake status and reasons of childhood influenza vaccination during the pandemic in China.

Methods: A mixed-methods study combining a questionnaire survey and semi-structured interviews was conducted in Anhui, Shaanxi, and Guangdong provinces between September and November 2021.

Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review.

Background: Social media and digital technologies have played essential roles in disseminating information and promoting vaccination during the COVID-19 pandemic. There is a need to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines.

Objective: We aimed to synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination.

Childhood Influenza Vaccination and Its Determinants during 2020-2021 Flu Seasons in China: A Cross-Sectional Survey.

Young children aged 6−59 months are recommended as one of the priority groups for seasonal influenza vaccination in China. This study assessed influenza vaccination coverage and the factors associated with vaccination uptake among children in three Chinese provinces. In September 2021, 2081 caregivers with children <5 years completed self-administered questionnaires as part of a cross-sectional survey.

Application of Multiphoton Microscopic Imaging in Study of Gastric Cancer.

Multiphoton microscopy (MPM) imaging relies on the nonlinear interaction between ultrashort optical pulses and the samples to achieve image contrast. Featuring larger penetration depth, less phototoxicity, 3-dimensional sectioning capability, no need for labeling, MPM become a powerful medical imaging technique that can identify structural characteristics of tissues at the cellular and subcellular levels. In this review paper, we introduce the working principle of MPM imaging, present the current results of MPM imaging applied to the study of gastric tumors, and discuss the future prospects of this interdisciplinary research field.

Erratum: Author Correction: Mapping global acceptance and uptake of COVID-19 vaccination: a systematic review and meta-analysis.

[This corrects the article DOI: 10.1038/s43856-022-00177-6.].

Mapping global acceptance and uptake of COVID-19 vaccination: A systematic review and meta-analysis.

Background: The COVID-19 pandemic exit strategies depend on widespread acceptance of COVID-19 vaccines. We aim to estimate the global acceptance and uptake of COVID-19 vaccination, and their variations across populations, countries, time, and sociodemographic subgroups.

Methods: We searched four peer-reviewed databases (PubMed, EMBASE, Web of Science, and EBSCO) for papers published in English from December 1, 2019 to February 27, 2022.

Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19.

Estimating the changes of epidemiological parameters, such as instantaneous reproduction number, Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation.

OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data.

High-dimensional omics data contain intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data, due to the large number of molecular features and small number of available samples, which is also called "the curse of dimensionality" in machine learning. To tackle this problem and pave the way for machine learning-aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules.

ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers.

A prevailing way of extracting valuable information from biomedical literature is to apply text mining methods on unstructured texts. However, the massive amount of literature that needs to be analyzed poses a big data challenge to the processing efficiency of text mining. In this paper, we address this challenge by introducing parallel processing on a supercomputer.

GTZ: a fast compression and cloud transmission tool optimized for FASTQ files.

Background: The dramatic development of DNA sequencing technology is generating real big data, craving for more storage and bandwidth. To speed up data sharing and bring data to computing resource faster and cheaper, it is necessary to develop a compression tool than can support efficient compression and transmission of sequencing data onto the cloud storage.

Results: This paper presents GTZ, a compression and transmission tool, optimized for FASTQ files.

[Determination of bis (p-fluorobenzyl) trisulfide and bis (p-fluoro-benzyl) disulfide in lungs of rat by HPLC].

Objective: To establish an HPLC method for analysis of bis(p-fluorobenzyl) trisulfide(BFTS) and bis(p-fluorobenzyl)disulfide(BFDS) in the lungs of rat.

Methods: 5.0 ml extract solvent (n-hexane: isopropyl alcohol=95:5, v/v) and 20 microl of 11.