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Styrax paralleloneurus is a resin-producing tree native to Sumatra, Indonesia. This study investigated the effects of tapping, bark wounding and forest type on bacterial biota in the stem of styrax in natural and community forests. Amplicon metagenomic sequencing of the 16S rRNA region was deployed to identify the bacterial communities associated with tapped and untapped trees across various environmental and experimental conditions. The results of the study showed that tapped trees had lower abundance and diversity of Pseudomonas compared to untapped trees, largely due to their increased exposure to external microbe communities and environmental elements. Serratia and Pantoea were more abundant in natural forest than community forest, while Bradyrhizobium lablabi was found abundantly in untapped trees. Additionally, the taxonomic analysis revealed distinct responses of bacterial genera to tapping and forest type, indicating that community forests could play a significant role in promoting biodiversity in forest ecosystems. This finding underscores the importance of community forests in biodiversity conservation. These insights can inform future conservation and management strategies to enhance biodiversity and underscore the need for sustainable forest management practices to maintain forest health and productivity.
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http://dx.doi.org/10.1111/1758-2229.70184 | DOI Listing |
Front Immunol
September 2025
Guangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical University, Nanning, Guangxi, China.
Background: People living with HIV(PLWH) are a high-risk population for cancer. We conducted a pioneering study on the gut microbiota of PLWH with various types of cancer, revealing key microbiota.
Methods: We collected stool samples from 54 PLWH who have cancer (PLWH-C), including Kaposi's sarcoma (KS, n=7), lymphoma (L, n=22), lung cancer (LC, n=12), and colorectal cancer (CRC, n=13), 55 PLWH who do not have cancer (PLWH-NC), and 49 people living without HIV (Ctrl).
Glob Chang Biol
September 2025
State Key Laboratory of Vegetation Structure, Function and Construction (VegLab), Institute of Ecology, and College of Urban and Environmental Sciences, Peking University, Beijing, China.
Increasingly frequent extreme droughts pose a serious threat to global vegetation. However, previous studies have not characterized the whole response process of vegetation to drought, and there are uncertainties in their methods and indicators. In this study, we developed a new indicator system and derived the response modes of global vegetation to extreme drought.
View Article and Find Full Text PDFJ Anim Ecol
September 2025
Terrestrial Ecology Research Group, Department of Ecology and Ecosystem Management, School of Life Sciences, Technische Universität München, Freising, Germany.
Land-use change and intensification are major drivers of biodiversity loss, yet their effects on diversity have usually been studied within a single habitat type or land-use category, limiting our understanding of cross-habitat patterns. Moths, a species-rich taxon worldwide, represent a significant portion of the biodiversity in both temperate forests and grasslands, functioning as pollinators and herbivores. While increasing land-use intensity (LUI) in both habitats is expected to negatively impact moth assemblages, the strength of this effect remains uncertain.
View Article and Find Full Text PDFJTCVS Open
August 2025
Department of Cardiothoracic Surgery, Wake Forest University School of Medicine, Winston-Salem, North Carolina.
Background: Social media use among cardiothoracic surgeons has yet to be analyzed. This study aimed to explore how online media utilization by cardiothoracic surgeons differs by subspecialty, sex, geographic region, practice type, level of experience, and training pathway.
Methods: A list of 223 cardiothoracic surgeons was generated by querying the 1066 members of the American Association for Thoracic Surgery and randomly selecting 223 actively practicing surgeons.
Multivariate Behav Res
September 2025
Department of Statistics, TU Dortmund University, Dortmund, Germany.
Predicting ordinal responses such as school grades or rating scale data is a common task in the social and life sciences. Currently, two major streams of methodology exist for ordinal prediction: traditional statistical models such as the proportional odds model and machine learning (ML) methods such as random forest (RF) adapted to ordinal prediction. While methods from the latter stream have displayed high predictive performance, particularly for data characterized by non-linear effects, most of these methods do not support hierarchical data.
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