Forest

In developing countries, one-quarter of young women have suffered from anemia. However, the available studies in Ethiopia have been usually used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of anemia among youth girls in Ethiopia. A total of 5642 weighted samples of young girls from the 2016 Ethiopian Demographic and Health Survey dataset were utilized. The data underwent preprocessing, with 80% of the observations used for training the model and 20% for testing...

RNA modifications are pivotal in the development of newly synthesized structures, showcasing a vast array of alterations across various RNA classes. Among these, 5-hydroxymethylcytosine (5HMC) stands out, playing a crucial role in gene regulation and epigenetic changes, yet its detection through conventional methods proves cumbersome and costly. To address this, we propose Deep5HMC, a robust learning model leveraging machine learning algorithms and discriminative feature extraction techniques for accurate 5HMC sample identification...

BACKGROUND: The objective of this research was to create and validate an interpretable prediction model for drug-induced liver injury (DILI) during tuberculosis (TB) treatment. METHODS: A dataset of TB patients from Ningbo City was used to develop models employing the eXtreme Gradient Boosting (XGBoost), random forest (RF), and the least absolute shrinkage and selection operator (LASSO) logistic algorithms. The model's performance was evaluated through various metrics, including the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPR) alongside the decision curve...

BACKGROUND: Oral microbiota imbalance is associated with the progression of various lung diseases, including lung cancer. Pulmonary nodules (PNs) are often considered a critical stage for the early detection of lung cancer; however, the relationship between oral microbiota and PNs remains unknown. METHODS: We conducted a 'Microbiome with pulmonary nodule series study 1' (MCEPN-1) where we compared PN patients and healthy controls (HCs), aiming to identify differences in oral microbiota characteristics and discover potential microbiota biomarkers for non-invasive, radiation-free PNs diagnosis and warning in the future...

BACKGROUND: Paclitaxel is commonly used as a second-line therapy for advanced gastric cancer (AGC). The decision to proceed with second-line chemotherapy and select an appropriate regimen is critical for vulnerable patients with AGC progressing after first-line chemotherapy. However, no predictive biomarkers exist to identify patients with AGC who would benefit from paclitaxel-based chemotherapy. METHODS: This study included 288 patients with AGC receiving second-line paclitaxel-based chemotherapy between 2017 and 2022 as part of the K-MASTER project, a nationwide government-funded precision medicine initiative...

OBJECTIVES: Colistin-induced nephrotoxicity prolongs hospitalization and increases mortality. The study aimed to construct machine learning models to predict colistin-induced nephrotoxicity in patients with multidrug-resistant gram-negative infection. METHODS: Patients receiving colistin from three hospitals in the Clinical Research Database were included. Data were divided into a derivation cohort (2011∼2017) and a temporal validation cohort (2018∼2020)...

High reactive nitrogen (N) emissions due to anthropogenic activities in China have led to an increase in N deposition and ecosystem degradation. The Chinese government has strictly regulated reactive N emissions since 2010, however, determining whether N deposition has reduced requires long-term monitoring. Here, we report the patterns of N deposition at a rural forest site (Qingyuan) in northeastern China over the last decade. We collected 456 daily precipitation samples from 2014 to 2022 and analysed the temporal dynamics of N deposition...

Resource patchiness caused by external events breaks the continuity and homogeneity of resource distribution in the original ecosystem. For local organisms, this leads to drastic changes in the availability of resources, breaks down the co-existence of species, and reshuffles the local ecosystem. West Lake is a freshwater lake with resource patchiness caused by multiple exogenous disturbances that has strong environmental heterogeneity that prevents clear observation of seasonal changes in the microbial communities...

PURPOSES: This study aimed to develop and validate algorithms for automating intensity modulated radiation therapy (IMRT) planning in breast cancer patients, with a focus on patient anatomical characteristics. MATERIAL AND METHODS: We retrospectively selected 400 breast cancer patients without lymph node involvement for automated treatment planning. Automation was achieved using the Eclipse Scripting Application Programming Interface (ESAPI) integrated into the Eclipse Treatment Planning System...

BACKGROUND: Preoperative microvascular invasion (MVI) of liver cancer is an effective method to reduce the recurrence rate of liver cancer. Hepatectomy with extended resection and additional adjuvant or targeted therapy can significantly improve the survival rate of MVI+ patients by eradicating micrometastasis. Preoperative prediction of MVI status is of great clinical significance for surgical decision-making and the selection of other adjuvant therapy strategies to improve the prognosis of patients...

Epistemic uncertainty in data-driven landslide susceptibility assessment often tends to be increased by the limited accuracy of an individual model, as well as uncertainties associated with the selection of non-landslide samples. To address these issues, this paper centers on the landslide disaster in Ji'an City, China, and proposes a heterogeneous ensemble learning method incorporating frequency ratio (FR) and semi-supervised sample expansion. Based on the superimposed results of 12 environmental factor frequency ratios (FFR), non-landslide samples were selected and input into light gradient boosting machine (LightGBM), random forest (RF), and convolutional neural network (CNN) models for prediction along with historical landslide samples...

OBJECTIVES: The aim of our study is to explore the transcriptional and microbial characteristics of head and neck cancer's immune phenotypes using a multi-omics approach. MATERIALS AND METHODS: Employing TCGA data, we analyzed head and neck squamous cell carcinoma (HNSCC) immune cells with CIBERSORT and identified differentially expressed genes using DESeq2. Microbial profiles, obtained from the TCMA database, were analyzed using LEfSe algorithm to identify differential microbes in immune cell infiltration (ICI) subgroups...

BACKGROUND: Lichen is a symbiotic association of algae and fungi, recognized as a self-sustaining ecosystem that constitutes an indeterminant number of bacteria, actinomycetes, fungi, and protozoa. We evaluated the endolichenic fungal assemblage given the dearth of knowledge on endolichenic fungi (ELFs), particularly from part of the Central Western Ghats, Karnataka, and conducted a phylogenetic analysis of xylariaceous fungi, the most diversified group of fungi using ITS and ITS+Tub2 gene set...

In this study, the levels of sulfur dioxide (SO2 ) and nitrogen dioxide (NO2 ) were measured indoors and outdoors using passive samplers in Tymar village (20 homes), an industrial area, and Haji Wsu (15 homes), a non-industrial region, in the summer and the winter seasons. In comparison to Haji Wsu village, the results showed that Tymar village had higher and more significant mean SO2 and NO2 concentrations indoors and outdoors throughout both the summer and winter seasons. The mean outdoor concentration of SO2 was the highest in summer, while the mean indoor NO2 concentration was the highest in winter in both areas...

Gas sensors play a crucial role in various industries and applications. In recent years, there has been an increasing demand for gas sensors in society. However, the current method for screening gas-sensitive materials is time-, energy-, and cost-consuming. Consequently, an imperative exists to enhance the screening efficiency. In this study, we proposed a collaborative screening strategy through integration of density functional theory and machine learning. Taking zinc oxide (ZnO) as an example, the responsiveness of ZnO to the target gas was determined quickly on the basis of the changes in the electronic state and structure before and after gas adsorption...

PREMISE: Endophytic and mycorrhizal fungi are crucial in facilitating plant nutrition acquisition and stress tolerance. In epiphytic habitats, plants face nutrition and water stress, but their roots are mostly nonmycorrhizal and especially lacking in arbuscular mycorrhizal associations. Ophioderma pendulum is an epiphytic fern with a partially mycoheterotrophic lifestyle, likely heavily reliant on symbiotic fungi. To characterize fungal associations in the sporophyte of O. pendulum, we focused on leaves and roots of O...

PREMISE: Functional traits reflect species' responses to environmental variation and the breadth of their ecological niches. Fagus grandifolia and Oreomunnea mexicana have restricted distribution in upper montane cloud forests (1700-2000 m a.s.l.) in Mexico. These species were introduced into plantings at lower elevations (1200-1600 m a.s.l.) that have climates predicted for montane forests in 2050 and 2070. The aim was to relate morphological leaf traits to the ecological niche structure of each species...

OBJECTIVE: Prediction of lymph node metastasis (LNM) for intrahepatic cholangiocarcinoma (ICC) is critical for the treatment regimen and prognosis. We aim to develop and validate machine learning (ML)-based predictive models for LNM in patients with ICC. METHODS: A total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled. The predictors of LNM were identified by the least absolute shrinkage and selection operator (LASSO) and logistic analysis...

Extreme droughts can have long-lasting effects on forest community dynamics and species interactions. Yet, our understanding of how drought legacy modulates ecological relationships is just unfolding. We tested the hypothesis that leaf chemistry and herbivory show long-term responses to premature defoliation caused by an extreme drought event in European beech (Fagus sylvatica L.). For two consecutive years after the extreme European summer drought in 2018, we collected leaves from the upper and lower canopy of adjacently growing drought-stressed and unstressed trees...

In real-world classification problems, it is important to build accurate prediction models and provide information that can improve decision-making. Decision-support tools are often based on network models, and this article uses information encoded by social networks to solve the problem of employer turnover. However, understanding the factors behind black-box prediction models can be challenging. Our question was about the predictability of employee turnover, given information from the multilayer network that describes collaborations and perceptions that assess the performance of organizations that indicate the success of cooperation...