Forest

Chemical points of departure (PODs) for critical health effects are crucial for evaluating and managing human health risks and impacts from exposure. However, PODs are unavailable for most chemicals in commerce due to a lack of in vivo toxicity data. We therefore developed a two-stage machine learning (ML) framework to predict human-equivalent PODs for oral exposure to organic chemicals based on chemical structure. Utilizing ML-based predictions for structural/physical/chemical/toxicological properties from OPERA 2...

OBJECTIVE: The high mortality rate of gastric cancer, traditionally managed through surgery, underscores the urgent need for advanced therapeutic strategies. Despite advancements in treatment modalities, outcomes remain suboptimal, necessitating the identification of novel biomarkers to predict sensitivity to immunotherapy. This study focuses on utilizing single-cell sequencing for gene identification and developing a random forest model to predict immunotherapy sensitivity in gastric cancer patients...

Brazil's Atlantic Forest (BAF) is a highly fragmented, strategic environmental and socio-economic region that represents the fourth biodiversity hotspot while also producing many commodities that are exported globally. Human disturbance plays a pivotal role as a driver of BAF's soil dynamics and behaviors. The soils under Late Primary and Secondary Semideciduous Seasonal Forests (LPSF and LSSF) were characterized by high to moderate resilience, with improved chemical properties as human disturbance decreased...

OBJECTIVES: Using a random forest algorithm, we previously found that teicoplanin-associated gene A (tcaA) might play a role in resistance of methicillin-resistant Staphylococcus aureus (MRSA) to β-lactams, which we have investigated further here. METHODS: Representative MRSA strains of prevalent clones were selected to identify the role of tcaA in the MRSA response to β-lactams. tcaA genes were deleted by homologous recombination in the selected MRSA strains, and antibiotic susceptibility tests were applied to evaluate the effect of tcaA on the minimum inhibitory concentrations (MICs) of glycopeptides and β-lactams...

Systematic reviews are the apex of the evidence-based pyramid, representing the strongest form of evidence synthesizing results from multiple primary studies. In particular, a quantitative systematic review, or meta-analysis, pools results from multiple studies to help answer a respective research question. The aim of this review is to serve as a guide on how to: 1) design; 2) execute, and 3) publish an orthopaedic arthroplasty systematic review. In Part II, we focus on methods to assess data quality through the Cochrane Risk of Bias, Methodological Index for Non-Randomized Studies (MINORS) criteria, or Newcastle-Ottawa scale; enumerate various methods for appropriate data interpretation and analysis; and summarize how to convert respective findings to a publishable manuscript (providing a previously published example)...

The misuse of antitussives preparations is a continuing problem in the world, and imply that they might have potential new psychoactive substances (NPS) activity. However, few study focus on their ecological toxicity towards fish. In the present study, the machine learning (ML) methods gcForest and random forest (RF) were employed to predict NPS activity in 30 antitussives. The potential toxic target, mode of action (MOA), acute toxicity and chronic toxicity to fish were further investigated. The results showed that both gcForest and RF achieved optimal performance when utilizing combined features of molecular fingerprint (MF) and molecular descriptor (MD), with area under the curve (AUC) = 0...

Epidermal growth factor receptor (EGFR) plays a pivotal role in the initiation and progression of gliomas. In particular, in glioblastoma, EGFR amplification emerges as a catalyst for invasion, proliferation, and resistance to radiotherapy and chemotherapy. Current approaches are not capable of providing rapid diagnostic results of molecular pathology. In this study, we propose a terahertz spectroscopic approach for predicting the EGFR amplification status of gliomas for the first time. A machine learning model was constructed using the terahertz response of the measured glioma tissues, including the absorption coefficient, refractive index, and dielectric loss tangent...

Woody invasive alien species can have profound impacts on ecosystem processes and functions, including fire regulation, which can significantly affect landscape resilience. Acacia dealbata, a widespread invasive alien plant in the Iberian Peninsula, holds well-known fire-adaptation traits (e.g., massive soil seed banks and heat-stimulated seed germination). In this study, we assess to what extent fire suppression and land-use strategies could affect the potential distribution of A. dealbata in a fire-prone transboundary protected mountain area of Portugal and Spain, using Habitat Suitability Models...

Microplastics (MPs) are an emerging global concern due to severe toxicological risks for ecosystems and public health. Therefore, this is the first study in Bangladesh to assess MP pollution and its associated risks for ecosystems and human health in the outdoor urban environment using machine learning and multivariate approaches. The occurrences of MPs in the urban road dust were 52.76 ± 20.24 particles/g with high diversity, where fiber shape (77%), 0.1-0.5 mm size MPs (75%), blue color (26%), and low-density polyethylene (24%) polymer was the dominating MPs category...

BACKGROUND & AIMS: Evidence on the impact of beverage consumption on depression is limited in the Asian population. Specifically, there is little information available on vegetable and fruit juices, while whole vegetables and fruits are reportedly protective against depression. Furthermore, evidence is scarce in differentiating the impacts of sweetened and black coffee. We aimed to examine the association of the consumption of total sugary drinks, carbonated beverages, vegetable and fruit juices, sweetened and black coffee, and green tea with subsequent depression in a general population sample...

OBJECTIVE: The aim of this study was to provide a quantitative synthesis of the survival outcomes for patients with skull base chordomas, focusing on the role of 1) the extent of resection (gross-total [GTR] vs non-GTR), 2) the type of surgery (primary vs revision), 3) tumor histology, and 4) the different use of adjuvant therapies (proton beam radiotherapy [PBRT], photon radiotherapy [RT], or none). METHODS: A systematic review with a meta-analysis was conducted following the 2020 PRISMA guidelines...

BACKGROUND: Air pollution has several negative health effects. Particulate matter (PM) is a pollutant that is often linked to health adversities. PM2.5 (PM with an aerodynamic diameter of ≤2.5μm) exposure has been associated with negative cardiovascular (CV) outcomes. However, the impact of PM10 (PM with an aerodynamic diameter of ≤10μm) exposure is often overlooked due to its limited ability to pass the alveolar barrier. This study aims to assess the association between PM10 exposure and risk of myocardial infarction (MI) amongst adults (≥18 years of age) as this has been poorly studied...

Forest-savanna transition zones of West Africa are uniquely characterized by forest groves, forest patches, or forest islands, the importance of which for sustainable resource management and their potential for carbon sequestration and nutrient cycling is often underrated. Our study conducted a comparative analysis of the soil organic carbon and nutrient characteristics of the Anogeissus groves in the old Opara forest reserve and their adjoining arable lands. We established 30 sampling frames of 100 m × 100 m plots with 15 frames per land use type...

The expansion of cocoa farms is a major driver of deforestation and emissions in Ghana's high forest zone. The Ghana Cocoa Forest Reducing Emissions from Deforestation and Forest Degradation Program (REDD+) was launched as the world's first commodity-based initiative to address emissions from deforestation caused by cocoa production and generate non-carbon benefits. Hotspot Intervention Areas were established to implement the Ghana Cocoa REDD+ program. This study combines Q-methodology with focus group discussions and interviews to assess stakeholder perceptions in the Juabuso-Bia cocoa landscape regarding the capacity of the Hotspot Intervention Area to facilitate the generation of governance and economic non-carbon benefits to sustain emission reductions...

Infection post-hematopoietic stem cell transplantation (HSCT) is one of the main causes of patient mortality. Fever is the most crucial clinical symptom indicating infection. However, current microbial detection methods are limited. Therefore, timely diagnosis of infectious fever and administration of antimicrobial drugs can effectively reduce patient mortality. In this study, serum samples were collected from 181 patients with HSCT with or without infection, as well as the clinical information. And more than 80 infectious-related microRNAs in the serum were selected according to the bulk RNA-seq result and detected in the 345 time-pointed serum samples by Q-PCR...

Small headwater streams can mobilize large amounts of terrestrially derived dissolved organic matter (DOM). While the molecular composition of DOM has important controls on biogeochemical cycles and carbon cycling, how stationary landscape metrics affect DOM composition is poorly understood, particularly in relation to non-stationary effects from hydrological changes across seasons. Here, we apply a combination of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and absorbance spectroscopy to characterize stream DOM from 13 diverse watersheds across the central Canadian boreal forests and statistically relate DOM compositional characteristics to landscape topography and hydrological metrics...

BACKGROUND: We aimed to develop the first machine learning models to predict citation counts and the translational impact, defined as inclusion in guidelines or policy documents, of headache research, and assess which factors are most predictive. METHODS: Bibliometric data and the titles, abstracts, and keywords from 8600 publications in three headache-oriented journals from their inception to 31 December 2017 were used. A series of machine learning models were implemented to predict three classes of 5-year citation count intervals (0-5, 6-14 and, >14 citations); and the translational impact of a publication...

Land cover and land use have established effects on hazard and exposure to vector-borne diseases. While our understanding of the proximate and distant causes and consequences of land use decisions has evolved, the focus on the proximate effects of landscape on disease ecology remains dominant. We argue that land use governance, viewed through a land system lens, affects tick-borne disease risk. Governance affects land use trajectories and potentially shapes landscapes favourable to ticks or increases contact with ticks by structuring human-land interactions...

INTRODUCTION: The COVID-19 pandemic has exacerbated mental health challenges, particularly depression among college students. Detecting at-risk students early is crucial but remains challenging, particularly in developing countries. Utilizing data-driven predictive models presents a viable solution to address this pressing need. AIMS: 1) To develop and compare machine learning (ML) models for predicting depression in Argentinean students during the pandemic. 2) To assess the performance of classification and regression models using appropriate metrics...

PURPOSE: The aim of this study is to construct a combined model that integrates radiomics, clinical risk factors and machine learning algorithms to predict para-laryngeal lymph node metastasis in esophageal squamous cell carcinoma. METHODS: A retrospective study included 361 patients with esophageal squamous cell carcinoma from 2 centers. Radiomics features were extracted from the computed tomography scans. Logistic regression, k nearest neighbor, multilayer perceptron, light Gradient Boosting Machine, support vector machine, random forest algorithms were used to construct radiomics models...