MiRAGE: mining relationships for advanced generative evaluation in drug repositioning.

MOTIVATION: Drug repositioning, the identification of new therapeutic uses for existing drugs, is crucial for accelerating drug discovery and reducing development costs. Some methods rely on heterogeneous networks, which may not fully capture the complex relationships between drugs and diseases. However, integrating diverse biological data sources offers promise for discovering new drug-disease associations (DDAs). Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. However, the challenge lies in effectively integrating different biological data sources to identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms. RESULTS: In response to this challenge, we present MiRAGE, a novel computational method for drug repositioning. MiRAGE leverages a three-step framework, comprising negative sampling using hard negative mining, classification employing random forest models, and feature selection based on feature importance. We evaluate MiRAGE on multiple benchmark datasets, demonstrating its superiority over state-of-the-art algorithms across various metrics. Notably, MiRAGE consistently outperforms other methods in uncovering novel DDAs. Case studies focusing on Parkinson's disease and schizophrenia showcase MiRAGE's ability to identify top candidate drugs supported by previous studies. Overall, our study underscores MiRAGE's efficacy and versatility as a computational tool for drug repositioning, offering valuable insights for therapeutic discoveries and addressing unmet medical needs.

Auxin synthesis promotes N metabolism and optimizes root structure enhancing N acquirement in maize (Zea mays L.).

MAIN CONCLUSION: Foliar NAA increases photosynthate supplied by enhancing photosynthesis, to strengthen root activity and provide a large sink for root carbohydrate accumulation, which is beneficial to acquire more nitrogen. The improvement of grain yield is an effective component in the food security. Auxin acts as a well-known plant hormone, plays an important role in maize growth and nutrient uptake. In this study, with maize variety Zhengdan 958 (ZD958) as material, the effects of auxin on nitrogen (N) uptake and assimilation of seedling maize were studied by hydroponic experiments. With water as the control, naphthalene acetic acid (NAA, 0.1 mmol/L) and aminoethoxyvinylglycine (AVG, 0.1 mmol/L, an auxin synthesis inhibitor) were used for foliar spraying. The results showed that NAA significantly improved photosynthetic rate and plant biomass by 58.6% and 91.7%, respectively, while the effect of AVG was opposite to that of NAA. At the same time, key enzymes activities related N assimilation in NAA leaves were significantly increased, and the activities of nitrate reductase (NR), glutamine synthetase (GS) and glutamate synthase (GOGAT) were increased by 32.3%, 22.9%, and 16.2% in new leaves. Furthermore, NAA treatment promoted underground growth. When compared with control, total root length, root surface area, root tip number, branch number and root activity were significantly increased by 37.8%, 22.2%, 35.1%, 28.8% and 21.2%. Root growth is beneficial to N capture in maize. Ultimately, the total N accumulation of NAA treatment was significantly increased by 74.5%, as compared to the control. In conclusion, NAA foliar spraying increased endogenous IAA content, and enhanced the activity of N assimilation-related enzymes and photosynthesis rate, in order to build a large sink for carbohydrate accumulation. In addition, NAA strengthened root activity and regulated root morphology and architecture, which facilitated further N uptake and plant growth.

Centennial Records of Microplastics in Lake Cores in Huguangyan Maar Lake, China.

Microplastic records from lake cores can reconstruct the plastic pollution history. However, the associations between anthropogenic activities and microplastic accumulation are not well understood. Huguangyan Maar Lake (HML) is a deep-enclosed lake without inlets and outlets, where the sedimentary environment is ideal for preserving a stable and historical microplastic record. Microplastic (size: 10-500 µm) characteristics in the HML core were identified using the Laser Direct Infrared Imaging system. The earliest detectable microplastics appeared unit in 1955 (1.1 items g-1). The microplastic abundance ranged from n.d. to 615.2 items g-1 in 1955-2019 with an average of 134.9 items g-1. The abundance declined slightly during the 1970s and then increased rapidly after China's Reform and Opening Up in 1978. Sixteen polymer types were detectable, with polyethylene and polypropylene dominating, accounting for 23.5 and 23.3% of the total abundance, and the size at 10-100 µm accounted for 80%. Socioeconomic factors dominated the microplastic accumulation based on the random forest modeling, and the contributions of GDP per capita, plastic-related industry yield, and total crop yield were, respectively, 13.9, 35.1, and 9.3% between 1955-2019. The total crop yield contribution further increased by 1.7% after 1978. Coarse sediment particles increased with soil erosion exacerbated microplastics discharging into the sediment.

Microbiome-mediated effects of habitat fragmentation on native plant performance.

Habitat fragmentation is a leading cause of biodiversity and ecosystem function loss in the Anthropocene. Despite the importance of plant-microbiome interactions to ecosystem productivity, we have limited knowledge of how fragmentation affects microbiomes and even less knowledge of its consequences for microbial interactions with plants. Combining field surveys, microbiome sequencing, manipulative experiments, and random forest models, we investigated fragmentation legacy effects on soil microbiomes in imperiled pine rocklands, tested how compositional shifts across 14 fragmentation-altered soil microbiomes affected performance and resource allocation of three native plant species, and identified fragmentation-responding microbial families underpinning plant performance. Legacies of habitat fragmentation were associated with significant changes in microbial diversity and composition (across three of four community axes). Experiments showed plants often strongly benefited from the microbiome's presence, but fragmentation-associated changes in microbiome composition also significantly affected plant performance and resource allocation across all seven metrics examined. Finally, random forest models identified ten fungal and six bacterial families important for plant performance that changed significantly with fragmentation. Our findings not only support the existence of significant fragmentation effects on natural microbiomes, but also demonstrate for the first time that fragmentation-associated changes in microbiomes can have meaningful consequences for native plant performance and investment.

[Exposure assessment of air pollution in Italy 2016-2019 for future studies on air pollution and COVID-19]. / La valutazione dell'esposizione della popolazione italiana all'inquinamento atmosferico nel periodo 2016-2019 per lo studio della relazione tra inquinamento atmosferico e COVID-19.

Air pollution is one of the leading causes of death worldwide, with adverse effects related both to short-term and long-term exposure. It has also recently been linked to COVID-19 pandemic. To analyze this possible association in Italy, studies on the entire area of the peninsula are necessary, both urban and non-urban areas. Therefore, there is a need for a homogeneous and applicable exposure assessment tool throughout the country.Experiences of high spatio-temporal resolution models for Italian territory already exist for PM estimation, using space-time predictors, satellite data, air quality monitoring data.This work completes the availability of these estimations for the most recent years (2016-2019) and is also applied to nitrogen oxides and ozone. The spatial resolution is 1x1 km.The model confirms its capability of capturing most of PM variability (R2=0.78 and 0.74 for PM10 e PM2.5, respectively), and provides reliable estimates also for ozone (R2=0.76); for NO2 the model performance is lower (R2=0.57). The model estimations were used to calculate the PWE (population-weighted exposure) as the annual mean, weighted on the resident population in each individual cell, which represents the estimation of the Italian population's chronic exposure to air pollution.These estimates are ready to be used in studies on the association between chronic exposure to air pollution and COVID-19 pathology, as well as for investigations on the role of air pollution on the health of the Italian population.

Free-water imaging reveals unique brain microstructural deficits in hispanic individuals with Dementia.

Prior evidence suggests that Hispanic and non-Hispanic individuals differ in potential risk factors for the development of dementia. Here we determine whether specific brain regions are associated with cognitive performance for either ethnicity along various stages of Alzheimer's disease. For this cross-sectional study, we examined 108 participants (61 Hispanic vs. 47 Non-Hispanic individuals) from the 1Florida Alzheimer's Disease Research Center (1Florida ADRC), who were evaluated at baseline with diffusion-weighted and T1-weighted imaging, and positron emission tomography (PET) amyloid imaging. We used FreeSurfer to segment 34 cortical regions of interest. Baseline Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used as measures of cognitive performance. Group analyses assessed free-water measures (FW) and volume. Statistically significant FW regions based on ethnicity x group interactions were used in a stepwise regression function to predict total MMSE and MoCA scores. Random forest models were used to identify the most predictive brain-based measures of a dementia diagnosis separately for Hispanic and non-Hispanic groups. Results indicated elevated FW values for the left inferior temporal gyrus, left middle temporal gyrus, left banks of the superior temporal sulcus, left supramarginal gyrus, right amygdala, and right entorhinal cortex in Hispanic AD subjects compared to non-Hispanic AD subjects. These alterations occurred in the absence of different volumes of these regions in the two AD groups. FW may be useful in detecting individual differences potentially reflective of varying etiology that can influence cognitive decline and identify MRI predictors of cognitive performance, particularly among Hispanics.

Unveiling the spatiotemporal dynamics and influencing factors of carbon stocks in the yangtze river basin over the past two decades.

Terrestrial ecosystems are critical to the global carbon cycle and climate change mitigation. Over the past two decades, the Yangtze River Basin (YRB) has implemented various ecological restoration projects and active management measures, significantly impacting carbon stock patterns. This study employed random forest models to analyze the spatial and temporal patterns of carbon stocks in the YRB from 2001 to 2021. In 2021, carbon density in the YRB ranged from 8.5 to 177.4 MgC/ha, with a total carbon stock of 18.05 PgC. Over 20 years, the YRB sequestered 1.26 billion tons of carbon, accounting for 11.28 % of the region's fossil fuel carbon emissions. Notably, forests exhibited the highest carbon density, averaging 98.01 ± 25.01 MgC/ha (2021) with a carbon stock growth rate of 51.6 TgC/yr. Piecewise structural equation model was used to assess the effects of climate and human activities on carbon density, revealing regional variability, with unique patterns observed in the source region. Human activities primarily influence carbon density indirectly through vegetation alterations., while climate change directly impacts ecosystem biophysical processes. These findings offer critical insights for climate mitigation and adaptation strategies, enhancing the understanding of carbon dynamics for sustainable development and global carbon management.

Spatial patterns of rural opioid-related hospital emergency department visits: A machine learning analysis.

As opioid-related overdose emergency department visits continue to rise in the United States, there is a need to understand the location and magnitude of the crisis, especially in at-risk rural areas. We analyzed sets of ZIP code level electronic health records for emergency department visits from 6 hospitals for two rural regions of Maryland with higher opioid-related overdose rates. Analysis of the demographics of visits found Black or African American emergency department visits in both rural regions were higher than the proportion of their population per region. We trained random forest models with socio-demographic factors and health risk factors on the visits data to understand drivers and risk factors for opioid misuse. The models ranked factors relating to opioid prescribing rates, race, housing, and poor mental health as highly important. Factors associated with opioid-related overdose emergency department visits were found to vary by race, gender, and location and may provide useful insights for designing mitigation initiatives.

Quantifying allo-grooming in wild chacma baboons (Papio ursinus) using tri-axial acceleration data and machine learning.

Quantification of activity budgets is pivotal for understanding how animals respond to changes in their environment. Social grooming is a key activity that underpins various social processes with consequences for health and fitness. Traditional methods use direct (focal) observations to calculate grooming rates, providing systematic but sparse data. Accelerometers, in contrast, can quantify activity budgets continuously but have not been used to quantify social grooming. We test whether grooming can be accurately identified using machine learning (random forest model) trained on labelled acceleration data from wild chacma baboons (Papio ursinus). We successfully identified giving and receiving grooming with high precision (81% and 91%) and recall (87% and 79%). Giving grooming was associated with a distinct rhythmical signal along the surge axis. Receiving grooming had similar acceleration signals to resting, and thus was more difficult to assign. We applied our machine learning model to n = 680 collar data days from n = 12 baboons and found that grooming rates obtained from accelerometers were significantly and positively correlated with direct observation rates for giving but not receiving grooming. The ability to collect continuous grooming data in wild populations will allow researchers to re-examine and expand upon long-standing questions regarding the formation and function of grooming bonds.

Revising the index of watershed integrity national maps.

Watersheds provide a range of services valued by society, incorporating biotic and abiotic functions within their boundaries. Recently, an operational definition of watershed integrity was applied and indices of watershed integrity (IWI) and catchment integrity (ICI) were developed and mapped for the conterminous United States. However, these indices were originally derived using equally-weighted first-order approximations of relationships between anthropogenic stressors (obtained from the U.S. EPA's StreamCat dataset) and six watershed functions. In addition, the original calculations of the IWI and ICI did not standardize metrics across these differing scales, resulting in IWI and ICI values that are not directly comparable. We provide an example of how to iteratively update the stressor-watershed function relationships using random forest models and a nationwide response metric representative of one of the six watershed functions. Specifically, we focused on the chemical regulation function (CHEM) of IWI and ICI by relating a composite metric of chemical water quality from 1914 samples to land use metrics explicit to CHEM to refine the nature of these relationships (e.g., non-linear versus linear). The rate of nitrogen fertilizer, agricultural land use, and urban land use were found to be the three most important stressors predicting the national water quality response metric. Revision of CHEM values improved the prediction of several regional- to national-scale water quality indicators. In all cases, exponential decay curves replaced the original negative linear relationship for CHEM. Therefore, the original IWI and ICI values are probably over-estimates of the actual integrity of the Nation's watersheds and catchments. With these revisions, we provide updated national maps of IWI and ICI. The methods outlined here can be implemented iteratively as more and better data become available for all six of the watershed functions to elevate the accuracy and applicability of these indices to various land management issues.

Spatial distribution modelling of Culicoides (Diptera: Ceratopogonidae) biting midges, potential vectors of African horse sickness and bluetongue viruses in Senegal.

BACKGROUND: In Senegal, the last epidemic of African horse sickness (AHS) occurred in 2007. The western part of the country (the Niayes area) concentrates modern farms with exotic horses of high value and was highly affected during the 2007 outbreak that has started in the area. Several studies were initiated in the Niayes area in order to better characterize Culicoides diversity, ecology and the impact of environmental and climatic data on dynamics of proven and suspected vectors. The aims of this study are to better understand the spatial distribution and diversity of Culicoides in Senegal and to map their abundance throughout the country. METHODS: Culicoides data were obtained through a nationwide trapping campaign organized in 2012. Two successive collection nights were carried out in 96 sites in 12 (of 14) regions of Senegal at the end of the rainy season (between September and October) using OVI (Onderstepoort Veterinary Institute) light traps. Three different modeling approaches were compared: the first consists in a spatial interpolation by ordinary kriging of Culicoides abundance data. The two others consist in analyzing the relation between Culicoides abundance and environmental and climatic data to model abundance and investigate the environmental suitability; and were carried out by implementing generalized linear models and random forest models. RESULTS: A total of 1,373,929 specimens of the genus Culicoides belonging to at least 32 different species were collected in 96 sites during the survey. According to the RF (random forest) models which provided better estimates of abundances than Generalized Linear Models (GLM) models, environmental and climatic variables that influence species abundance were identified. Culicoides imicola, C. enderleini and C. miombo were mostly driven by average rainfall and minimum and maximum normalized difference vegetation index. Abundance of C. oxystoma was mostly determined by average rainfall and day temperature. Culicoides bolitinos had a particular trend; the environmental and climatic variables above had a lesser impact on its abundance. RF model prediction maps for the first four species showed high abundance in southern Senegal and in the groundnut basin area, whereas C. bolitinos was present in southern Senegal, but in much lower abundance. CONCLUSIONS: Environmental and climatic variables of importance that influence the spatial distribution of species abundance were identified. It is now crucial to evaluate the vector competence of major species and then combine the vector densities with densities of horses to quantify the risk of transmission of AHS virus across the country.

Canada lynx use of burned areas: Conservation implications of changing fire regimes.

A fundamental problem in ecology is forecasting how species will react to major disturbances. As the climate warms, large, frequent, and severe fires are restructuring forested landscapes at large spatial scales, with unknown impacts on imperilled predators. We use the United States federally Threatened Canada lynx as a case study to examine how predators navigate recent large burns, with particular focus on habitat features and the spatial configuration (e.g., distance to edge) that enabled lynx use of these transformed landscapes. We coupled GPS location data of lynx in Washington in an area with several recent large fires and a number of GIS layers of habitat data to develop models of lynx habitat selection in recent burns. Random Forest habitat models showed lynx-selected islands of forest skipped by large fires, residual vegetation, and areas where some trees survived to use newly burned areas. Lynx used burned areas as early as 1 year postfire, which is much earlier than the 2-4 decades postfire previously thought for this predator. These findings are encouraging for predator persistence in the face of fires, but increasingly severe fires or management that reduces postfire residual trees or slow regeneration will likely jeopardize lynx and other predators. Fire management should change to ensure heterogeneity is retained within the footprint of large fires to enable viable predator populations as fire regimes worsen with climate change.

Distribution, diversity and drivers of blood-borne parasite co-infections in Alaskan bird populations.

Avian species are commonly infected by multiple parasites, however few studies have investigated the environmental determinants of the prevalence of co-infection over a large scale. Here we believe that we report the first, detailed ecological study of the prevalence, diversity and co-infections of four avian blood-borne parasite genera: Plasmodium spp., Haemoproteus spp., Leucocytozoon spp. and Trypanosoma spp. We collected blood samples from 47 resident and migratory bird species across a latitudinal gradient in Alaska. From the patterns observed at collection sites, random forest models were used to provide evidence of associations between bioclimatic conditions and the prevalence of parasite co-infection distribution. Molecular screening revealed a higher prevalence of haematozoa (53%) in Alaska than previously reported. Leucocytozoons had the highest diversity, prevalence and prevalence of co-infection. Leucocytozoon prevalence (35%) positively correlated with Trypanosoma prevalence (11%), negatively correlated with Haemoproteus prevalence (14%) and had no correlation with Plasmodium prevalence (7%). We found temperature, precipitation and tree cover to be the primary environmental drivers that show a relationship with the prevalence of co-infection. The results provide insight into the impacts of bioclimatic drivers on parasite ecology and intra-host interactions, and have implications for the study of infectious diseases in rapidly changing environments.