Browse Articles

Assessing the effectiveness of mangrove rehabilitation using above-ground biomass and structural diversity

Author Correction: Seasonal advance of intense tropical cyclones in a warming climate

Fine-tuning gibberellin improves rice alkali–thermal tolerance and yield

Correction: National income accounting attributes and economic welfare. Evidence from Pakistan

Climate change and variability drive increasing exposure of marine heatwaves across US estuaries

Eosinopenia as a predictor of clinical outcomes in hospitalized patients with community-acquired pneumonia: A retrospective cohort study

Eosinopenia has been reported as a predictor of unfavorable outcomes and a marker of severity in bacterial infections. We describe the association between eosinopenia and clinical outcomes in hospitalized patients with CAP. We conducted a retrospective study of hospitalized adult patients with community-acquired pneumonia at a large US academic medical center from January 2009 to December 2019. We collected data on patient demographics, disease severity, comorbidities, smoking history, inflammatory markers, blood eosinophil levels, mortality, length of hospital stay, and need for intensive care unit (ICU) or mechanical ventilation. According to blood eosinophil count, patients were grouped as eosinopenic (<50/μL) and non-eosinopenic (≥50/μL) based on prior studies. Analysis was performed using nonparametric Wilcoxon rank-sum test for continuous variables and the chi-square test for categorical variables. A logistic regression analysis with robust standard errors was used to assess the associations between eosinopenia and patient centered outcomes (in-hospital mortality, 30-day mortality, length of hospital stay, need for mechanical ventilation support, vasopressor support and ICU admission). Of the 3285 patients with CAP infection included in our analysis, 1304 (39.70%) were eosinopenic. Age, gender, race, and smoking status were similar between the two groups. The eosinopenic group had significantly higher inflammatory markers as measured by C-reactive protein (CRP), and higher disease severity scores., After adjusting for disease severity, chronic obstructive pulmonary (COPD), and CRP there was no significant difference in hospital mortality (odds ratio [OR] 2.16, 95% confidence interval [CI] 0.68-6.8), ICU admission (OR: 1.21, 95% CI: 0.83-1.79), invasive and non-invasive ventilatory support (OR: 1.21, 95% CI: 0.52-2.81). Contrary to previously published data, our analysis did not demonstrate an association between eosinopenia and increased mortality risk in hospitalized patients with CAP highlighting the complexity of CAP prognosis.

Genome-wide association study for resistance to Macrophomina phaseolina in maize (Zea mays L.)

Abstract Maize (Zea mays L.) is a frequently used food source in human and animal nutrition. Macrophomina phaseolina is a fungal pathogen causing charcoal rot disease in many plants, especially maize. This pathogen causes high yield losses in maize. The development of resistant maize genotypes is of great importance in controlling this disease. In this study, the population structure of 120 different maize genotypes with varying levels of disease resistance was determined and genome-wide association studies were performed. Each genotype was subjected to the pathogen under controlled conditions and their phenotypic responses to the disease were analyzed. Afterwards, single nucleotide polymorphisms were determined by DArT-seq sequencing. After filtering the SNP data, 37,470 clean SNPs were obtained. The population structure was analyzed with STRUCTURE software, and it was determined that the population was divided into two subgroups. The relationship between phenotypic and genotypic data was analyzed using the MLM (Q + K) model in TASSEL software. As a result, seven SNPs markers located on four different chromosomes were associated with disease resistance. The related markers can be used in the future for the development of maize varieties resistant to M. phaseolina by marker-assisted selection.

An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior

The accurate prediction and interpretation of corporate Environmental, Social, and Governance (ESG) greenwashing behavior is crucial for enhancing information transparency and improving regulatory effectiveness. This paper addresses the limitations in hyperparameter optimization and interpretability of existing prediction models by introducing an optimized machine learning framework. The framework integrates an Improved Hunter-Prey Optimization (IHPO) algorithm, an eXtreme Gradient Boosting (XGBoost) model, and SHapley Additive exPlanations (SHAP) theory to predict and interpret corporate ESG greenwashing behavior. Initially, a comprehensive ESG greenwashing prediction dataset was developed through an extensive literature review and expert interviews. The IHPO algorithm was then employed to optimize the hyperparameters of the XGBoost model, forming an IHPO-XGBoost ensemble learning model for predicting corporate ESG greenwashing behavior. Finally, SHAP was used to interpret the model’s prediction outcomes. The results demonstrate that the IHPO-XGBoost model achieves outstanding performance in predicting corporate ESG greenwashing, with R², RMSE, MAE, and adjusted R² values of 0.9790, 0.1376, 0.1000, and 0.9785, respectively. Compared to traditional HPO-XGBoost models and XGBoost models combined with other optimization algorithms, the IHPO-XGBoost model exhibits superior overall performance. The interpretability analysis using SHAP theory highlights the key features influencing the prediction outcomes, revealing the specific contributions of feature interactions and the impacts of individual sample features. The findings provide valuable insights for regulators and investors to more effectively identify and assess potential corporate ESG greenwashing behavior, thereby enhancing regulatory efficiency and investment decision-making.

Trehalose-6-phosphate synthase gene expression analysis under abiotic and biotic stresses in bottle gourd (Lagenaria siceraria)

Centrality nearest-neighbor projected-distance regression (C-NPDR) feature selection for correlation-based predictors with application to resting-state fMRI study of major depressive disorder

Background Nearest-neighbor projected-distance regression (NPDR) is a metric-based machine learning feature selection algorithm that uses distances between samples and projected differences between variables to identify variables or features that may interact to affect the prediction of complex outcomes. Typical tabular bioinformatics data consist of separate variables of interest, such as genes or proteins. In contrast, resting-state functional MRI (rs-fMRI) data are composed of time-series for brain regions of interest (ROIs) for each subject, and these within-brain time-series are typically transformed into correlations between pairs of ROIs. These pairs of variables of interest can then be used as inputs for feature selection or other machine learning methods. Straightforward feature selection would return the most significant pairs of ROIs; however, it would also be beneficial to know the importance of individual ROIs. Results We extend NPDR to compute the importance of individual ROIs from correlation-based features. We introduce correlation-difference and centrality-based versions of NPDR. Centrality-based NPDR can be coupled with any centrality method and can be coupled with importance scores other than NPDR, such as random forest importance scores. We develop a new simulation method using random network theory to generate artificial correlation data predictors with variations in correlations that affect class prediction. Conclusions We compared feature selection methods based on detection of functional simulated ROIs, and we applied the new centrality NPDR approach to a resting-state fMRI study of major depressive disorder (MDD) participants and healthy controls. We determined that the areas of the brain that have the strongest network effect on MDD include the middle temporal gyrus, the inferior temporal gyrus, and the dorsal entorhinal cortex. The resulting feature selection and simulation approaches can be applied to other domains that use correlation-based features.

Loneliness and susceptibility to social pain mediate the association between autistic traits and psychotic experiences in young non-clinical adults

Foreign researchers in China face tightening restrictions

Correction: Efficacy of probiotic supplements in the treatment of sarcopenia: A systematic review and meta-analysis

Spatial mode conversion of single photons at the C-band using in fiber long-period gratings

Abstract The ability to convert the spatial mode of single photons opens up a promising path to enhancing quantum communication protocols by enabling high-dimensional encoding and efficient multiplexing. In this work, we demonstrate spatial mode conversion of single photons at 1550.6 nm using a fiber long-period grating (LPG). The fundamental $$\hbox {LP}_{01}$$ mode was converted to higher-order modes $$\hbox {LP}_{11}$$ and $$\hbox {LP}_{02}$$ , with quantum mode conversion efficiencies of 87.5 ± 1.4% and 96.1 ± 1.6%, respectively. The characterization of the converted single photons was carried out using a time-of-flight technique and coincidence measurements, by taking advantage of the differences in group velocity between the modes. We also performed loss measurements at the single-photon level and demonstrated mode re-conversion by using a second LPG to restore the photons back to the fundamental mode. These results highlight the potential of LPGs as a versatile tool for spatial mode manipulation at the single-photon level, with applications in high-dimensional quantum communication and nonlinear optical interactions.

Trans researchers under attack: LGBTQ+ biologists face hostile work environment

Limnological data derived from high frequency monitoring buoys are asynchronous in a large lake

Autonomous data collection is rapidly becoming an integral part of water quality monitoring, particularly for agencies looking to manage and protect aquatic ecosystems. While beneficial, it is unclear how the collection of these data can be applied in spatially complex large lakes (e.g., Laurentian Great Lakes) given the spatial heterogeneity of the ecosystem. To address this potential shortcoming in large lakes, we assessed the synchrony of sensor variables between 10 pairs of static buoys in the western basin of Lake Erie (western basin surface area =  3,282 km2). Within western Lake Erie, water temperature was highly synchronous whereas dissolved oxygen, turbidity, chlorophyll and phycocyanin were asynchronous. The extent of this asynchrony was higher with increasing spatial distance between buoys. We found that between pairs of static buoys, temperature, dissolved oxygen, and turbidity all experienced decreasing correlations with increasing distance. Our results show that if researchers intend to leverage these data to answer important questions and provide real-time applications related to environmental issues like harmful algal/cyanobacterial blooms, monitoring networks need to be designed carefully with spatial complexity in mind. While autonomous data collection has many benefits, the reliance on a single or limited network of anchored monitoring buoys in large lake ecosystems has a high probability of missing important spatial features of these systems.

Modified protocol comparing sporicidal activity of different non-thermal plasma generating devices

The financial shackling of historically Black universities in the United States

Methanol transfer supports metabolic syntrophy between bacteria and archaea

Promoting language and literacy through shared book reading in the NICU: A scoping review

Background Infants in the neonatal intensive care unit (NICU) are at a heightened risk for language and literacy delays and disorders. Despite the well-established empirical support for early shared reading, the available evidence to date has been scant, revealing mixed results. This study sought to characterize current research on shared reading in the NICU using a scoping review methodology. Methods Studies were eligible for inclusion if they were peer-reviewed, written in the English language, focused on human infants in the NICU, and published between January 1, 2003, and December 31, 2023. No population age range was applied, and quantitative, qualitative, or mixed methods designs were considered. Database searches yielded 338 articles with only eight articles meeting eligibility criteria for inclusion. Conclusion In spite of a modest number of studies on this topic, utilizing limited methodologies, the evidence from this scoping review shows the benefits of shared reading for infants and their caregivers during their NICU stay. Expanding such efforts by embedding shared reading as part of standard practice is recommended.