Radiomics analysis for prediction and classification of submucosal tumors based on gastrointestinal endoscopic ultrasonography.

Objectives: To identify and classify submucosal tumors by building and validating a radiomics model with gastrointestinal endoscopic ultrasonography (EUS) images. Methods: A total of 144 patients diagnosed with submucosal tumors through gastrointestinal EUS were collected between January 2019 and October 2020. There are 1952 radiomic features extracted from each patient's EUS images. The statistical test and the customized least absolute shrinkage and selection operator regression were used for feature selection. Subsequently, an extremely randomized trees algorithm was utilized to construct a robust radiomics classification model specifically tailored for gastrointestinal EUS images. The performance of the model was measured by evaluating the area under the receiver operating characteristic curve. Results: The radiomics model comprised 30 selected features that showed good discrimination performance in the validation cohorts. During validation, the area under the receiver operating characteristic curve was calculated as 0.9203 and the mean value after 10-fold cross-validation was 0.9260, indicating excellent stability and calibration. These results confirm the clinical utility of the model. Conclusions: Utilizing the dataset provided curated from gastrointestinal EUS examinations at our collaborating hospital, we have developed a well-performing radiomics model. It can be used for personalized and non-invasive prediction of the type of submucosal tumors, providing physicians with aid for early treatment and management of tumor progression.

Using seasonal physiological and biochemical responses to select forest components adapted to soybean and corn intercropping.

Given the increasing utilization of forest components in integration systems worldwide, coupled with the growing demand for food in regions facing water restrictions, this study aims to evaluate how physiological and biochemical parameters contribute to the diversification of adaptive mechanisms among native species and eucalyptus genotypes intercropped with soybean or corn. The native tree species Anadenanthera macrocarpa and Dipteryx alata, and the eucalyptus genotypes Urograndis I-144 and Urocam VM01, were grown in soybean and corn intercropping areas and evaluated in fall, winter, spring, and summer. The study evaluated morning water potential, chloroplast pigment concentration, gas exchange, cell damage, and antioxidant enzyme activity. Intercropped with soybean, development the of A. macrocarpa improved through instantaneous water use efficiency, energy use by the electron transport chain, chloroplast pigments, and catalase enzyme activity. On the other hand, A. macrocarpa when, intercropped with corn, despite increasing energy absorption by the reaction center, there is a need for non-photochemical dissipation and in the activity of the enzymes superoxide dismutase and ascorbate peroxidase in response to water and oxidative deficits. In D. alata, the physiological and biochemical responses were not influenced by intercropping but by seasons, with increased chloroplast pigments in fall and electron transport in summer. However, in corn intercropping, the dissipation of excess energy allowed leaf acclimatization. The I-144 and VM01 genotypes also showed no significant differences between intercrops. The results describe photosynthetic and biochemical challenges in the native species A. macrocarpa intercropped with corn, such as a greater need for enzymatic and non-enzymatic defense mechanisms in response to more negative water potential. In D. alata, the challenges are present in both intercrops due to improved mechanisms to protect the photosynthetic apparatus. The survival of the I-144 genotype may be inefficient in both intercrops under prolonged drought conditions, as it modifies the photosystem; in contrast, genotype VM01 was the most adapted to the system for using captured energy, reducing water loss and being resilient.

iDHS-RGME: Identification of DNase I hypersensitive sites by integrating information on nucleotide composition and physicochemical properties.

As pivotal markers of chromatin accessibility, DNase I hypersensitive sites (DHSs) intimately link to fundamental biological processes encompassing gene expression regulation and disease pathogenesis. Developing efficient and precise algorithms for DHSs identification holds paramount importance for unraveling genome functionality and elucidating disease mechanisms. This study innovatively presents iDHS-RGME, an Extremely Randomized Trees (Extra-Trees)-based algorithm that integrates unique feature extraction techniques for enhanced DHSs prediction. Specifically, iDHS-RGME utilizes two feature extraction approaches: Reverse Complementary Kmer (RCKmer) and Geary Spatial Autocorrelation (GSA), which comprehensively capture sequence attributes from diverse angles, bolstering information richness and accuracy. To address data imbalance, Borderline-SMOTE is employed, followed by Maximum Information Coefficient (MIC) for meticulous feature selection. Comparative evaluations underscored the superiority of the Extra-Trees classifier, which was subsequently adopted for model prediction. Through rigorous five-fold cross-validation, iDHS-RGME achieved remarkable accuracies of 94.71 % and 95.07 % on two independent datasets, outperforming previous models in terms of both precision and effectiveness.

Leaf stomatal configuration and photosynthetic traits jointly affect leaf water use efficiency in forests along climate gradients.

Water use efficiency (WUE) represents the trade-off between carbon assimilation and water loss in plants. It remains unclear how leaf stomatal and photosynthetic traits regulate the spatial variation of leaf WUE in different natural forest ecosystems. We investigated 43 broad-leaf tree species spanning from cold-temperate to tropical forests in China. We quantified leaf WUE using leaf δ13C and measured stomatal traits, photosynthetic traits as well as maximum stomatal conductance ( G w max $$ {G}_{{\mathrm{w}}_{\mathrm{max}}} $$ ) and maximum carboxylation capacity ( V c max $$ {V}_{{\mathrm{c}}_{\mathrm{max}}} $$ ). We found that leaves in cold-temperate forests displayed 'fast' carbon economics, characterized by higher leaf nitrogen, Chl, specific leaf area, and V c max $$ {V}_{{\mathrm{c}}_{\mathrm{max}}} $$ , as an adaptation to the shorter growing season. However, these leaves exhibited 'slow' hydraulic traits, with larger but fewer stomata and similar G w max $$ {G}_{{\mathrm{w}}_{\mathrm{max}}} $$ , resulting in higher leaf WUE. By contrast, leaves in tropical forests had smaller and denser stomata, enabling swift response to heterogeneous light conditions. However, this stomatal configuration increased potential water loss, and coupled with their low photosynthetic capacity, led to lower WUE. Our findings contribute to understanding how plant photosynthetic and stomatal traits regulate carbon-water trade-offs across climatic gradients, advancing our ability to predict the impacts of climate changes on forest carbon and water cycles.

Teaching old docks new tricks with machine learning enhanced ensemble docking.

We here introduce Ensemble Optimizer (EnOpt), a machine-learning tool to improve the accuracy and interpretability of ensemble virtual screening (VS). Ensemble VS is an established method for predicting protein/small-molecule (ligand) binding. Unlike traditional VS, which focuses on a single protein conformation, ensemble VS better accounts for protein flexibility by predicting binding to multiple protein conformations. Each compound is thus associated with a spectrum of scores (one score per protein conformation) rather than a single score. To effectively rank and prioritize the molecules for further evaluation (including experimental testing), researchers must select which protein conformations to consider and how best to map each compound's spectrum of scores to a single value, decisions that are system-specific. EnOpt uses machine learning to address these challenges. We perform benchmark VS to show that for many systems, EnOpt ranking distinguishes active compounds from inactive or decoy molecules more effectively than traditional ensemble VS methods. To encourage broad adoption, we release EnOpt free of charge under the terms of the MIT license.

Tracking climate vulnerability across spatial distribution and functional traits in Magnolia gentryi in the Peruvian tropical montane cloud forest.

PREMISE: Understanding the responses of functional traits in tree species to climate variability is essential for predicting the future of tropical montane cloud forest (TMCF) tree species, especially in Andean montane environments where fog pockets act as moisture traps. METHODS: We studied the distribution of Magnolia gentryi, measured its spatial arrangement, identified local hotspots, and evaluated the extent to which climate-related factors are associated with its distribution. We then analyzed the variation in 13 functional traits of M. gentryi and the relationship with climate. RESULTS: Andean TMCF climatic factors constrain M. gentryi spatial distribution with significant patches or gaps that are associated with high precipitation and mean minimum temperature. The functional traits of M. gentryi are limited by the Andean TMCF climatic factors, resulting in reduced within-species variation in traits associated with water deficit. CONCLUSIONS: The association between functional traits and climate oscillation is crucial for understanding the growth conditions of relict-endemic species and is essential for conservation efforts. Forest trait diversity and species composition change because of fluctuations in hydraulic safety-efficiency gradients.

Predicting Basketball Shot Outcome From Visuomotor Control Data Using Explainable Machine Learning.

Quiet eye (QE), the visual fixation on a target before initiation of a critical action, is associated with improved performance. While QE is trainable, it is unclear whether QE can directly predict performance, which has implications for training interventions. This study predicted basketball shot outcome (make or miss) from visuomotor control variables using a decision tree classification approach. Twelve basketball athletes completed 200 shots from six on-court locations while wearing mobile eye-tracking glasses. Training and testing data sets were used for modeling eight predictors (shot location, arm extension time, and absolute and relative QE onset, offset, and duration) via standard and conditional inference decision trees and random forests. On average, the trees predicted over 66% of makes and over 50% of misses. The main predictor, relative QE duration, indicated success for durations over 18.4% (range: 14.5%-22.0%). Training to prolong QE duration beyond 18% may enhance shot success.

Development and Implementation of Clinical Decision Algorithms for Oncology.

Decision-making in healthcare often relies on narrative guidelines; however, these instruments are poorly accessible for supporting clinical decision-making. This study explores the application of rule-based decision logic in algorithmic modeling, emphasizing its great potential in clinical decision support and research. Integrating rule-based algorithms with existing information systems and real-world data poses a serious challenge. Integrating decision algorithms with information standards increases their effectiveness across various applications. This study outlines a method for constructing clinical decision trees (CDTs), highlighting their transparency and interpretability, using information standards as a design principle. We use the digitization of the Dutch breast cancer guideline through CDTs as a case study to exemplify their versatility and practical significance. The process step 'primary treatment' has been successfully translated from the narrative guidelines format to the anticipated ted computational format.

Genome sequence of Heterobasidion occidentale, a fungus that causes annosus root and butt rot among conifer trees in North America.

We report an annotated draft genome of Heterobasidion occidentale, a fungus (Basidiomycota, Agaricomycetes) that has pathogenic and saprophytic lifestyles. This fungus belongs to the H. annosum (Fr.) Bref. sensu lato species complex that comprises several root rot pathogens. Heterobasidion occidentale causes annosus root and butt rot primarily in true fir (Abies spp.) and spruce (Picea spp.) species throughout western North America.

Mathematical models for traffic-source PM2.5 dispersion in an urban street canyon considering the capture capability of roadside trees.

Rapid urbanization increases the densely built-up blocks, the population and vehicles. Large amounts of particulate matter (PM), especially PM2.5 (PM with an aerodynamic diameter of 2.5µm or less), from vehicle exhaust are critical to human health. In typical street canyons in hot and humid regions, traffic-source PM usually diffuses to the densely built-up blocks through roadside trees. Roadside trees are a double-edged sword, serving as "guards" to absorb PM2.5 while may lead to PM2.5 gathering in street levels, thereby influencing the PM2.5 dispersion in the densely built-up blocks. To quantify the dispersion process, this study proposed traffic-source PM2.5 dynamic dispersion models considering the capture capability of roadside trees and built-up blocks based on the OSPM model. Due to the difficulty in obtaining the adsorption and deposition rate of the proposed models, the numerical simulations by ENVI-met software were used to solve and obtain the relationship between capture capability and characteristic index of roadside trees. Subsequently, The accuracy and effectiveness of the proposed traffic-source PM2.5 dynamic dispersion models were verified through field experimental data. Results show that the calculated PM2.5 concentration significantly linearly increased with the measured values with the determined coefficient (R2) of 0.98, and the first-order coefficient close to 1. It indicates that the proposed traffic-source PM2.5 dispersion model accurately quantified the impact of roadside trees on PM2.5 and its concentration dispersion process to the built-up blocks. This study provides suggestions for designing characteristic indexes of roadside trees and built-up blocks to improve the air quality of urban street canyons.

ß-Aminobutyric acid promotes stress tolerance, physiological adjustments, as well as broad epigenetic changes at DNA and RNA nucleobases in field elms (Ulmus minor).

BACKGROUND: ß-Aminobutyric acid (BABA) has been successfully used to prime stress resistance in numerous plant species; however, its effectiveness in forest trees has been poorly explored thus far. This study aimed to investigate the influence of BABA on morphological, physiological, and epigenetic parameters in field elms under various growth conditions. Epigenetic changes were assessed in both DNA and RNA through the use of reversed-phase ultra-performance liquid chromatography (UPLC) coupled with sensitive mass spectrometry. RESULTS: The presented results confirm the influence of BABA on the development, physiology, and stress tolerance in field elms. However, the most important findings are related to the broad epigenetic changes promoted by this amino acid, which involve both DNA and RNA. Our findings confirm, for the first time, that BABA influences not only well-known epigenetic markers in plants, such as 5-methylcytosine, but also several other non-canonical nucleobases, such as 5-hydroxymethyluracil, 5-formylcytosine, 5-hydroxymethylcytosine, N6-methyladenine, uracil (in DNA) and thymine (in RNA). The significant effect on the levels of N6-methyladenine, the main bacterial epigenetic marker, is particularly noteworthy. In this case, the question arises as to whether this effect is due to epigenetic changes in the microbiome, the plant genome, or both. CONCLUSIONS: The plant phenotype is the result of complex interactions between the plant's DNA, the microbiome, and the environment. We propose that different types of epigenetic changes in the plant and microbiome may play important roles in the largely unknown memory process that enables plants to adapt faster to changing environmental conditions.

Phylogeny structures species' interactions in experimental ecological communities.

Species' traits and interactions are products of evolutionary history. Despite the long-standing hypothesis that closely related species possess similar traits, and thus experience stronger competition, measuring the effect of evolutionary history on the ecology of natural communities remains challenging. We propose a novel framework to test whether phylogeny influences patterns of coexistence and abundance of species assemblages. In our approach, phylogenetic trees are used to parameterize species' interactions, which in turn determine the abundance of species in a given assemblage. We use likelihoods to score models parameterized with a given phylogeny, and contrast them with models built using random trees, allowing us to test whether phylogenetic information helps to predict species' abundances. Our statistical framework reveals that interactions are indeed structured by phylogeny in a large set of experimental plant communities. Our results confirm that evolutionary history can help predict, and potentially manage or conserve, the structure and function of complex ecological communities.

Addressing bias in bagging and boosting regression models.

As artificial intelligence (AI) becomes widespread, there is increasing attention on investigating bias in machine learning (ML) models. Previous research concentrated on classification problems, with little emphasis on regression models. This paper presents an easy-to-apply and effective methodology for mitigating bias in bagging and boosting regression models, that is also applicable to any model trained through minimizing a differentiable loss function. Our methodology measures bias rigorously and extends the ML model's loss function with a regularization term to penalize high correlations between model errors and protected attributes. We applied our approach to three popular tree-based ensemble models: a random forest model (RF), a gradient-boosted model (GBT), and an extreme gradient boosting model (XGBoost). We implemented our methodology on a case study for predicting road-level traffic volume, where RF, GBT, and XGBoost models were shown to have high accuracy. Despite high accuracy, the ML models were shown to perform poorly on roads in minority-populated areas. Our bias mitigation approach reduced minority-related bias by over 50%.

Understanding healthy ageing in India: insights from multivariate regression trees.

BACKGROUND: Population ageing represents a significant global challenge, particularly pronounced in countries like India. AIMS: This study aims to explore how factors such as socio-economic status, behaviour, and health influence healthy ageing across the Indian older population. METHODS: In this study, we utilized the Longitudinal Ageing Study in India - wave 1 dataset for analysis purposes. Scores were generated for five dimensions of healthy aging, including physical, functional, mental, cognitive, and social aspects and these scores were treated as the target variables. Multivariate Regression Trees analysis was employed to identify the behavioural and socio-demographic factors associated with each dimension of healthy ageing. RESULTS: Years of education emerge as crucial across all dimensions, positively impacting cognitive health and mitigating age-related decline in healthy ageing. Marital status, engagement in household activities, spiritual practices, and living arrangements impacts the scores of different aspects of healthy ageing. Gender disparities in healthy aging are noticeable in the 60-74 age group, with women generally having lower scores. Safety of the living environment is a crucial determinant of the mental health of the elderly across all age groups.These findings highlight the complex interplay of factors in healthy ageing outcomes. CONCLUSION: Our study emphasizes the pivotal role of education in fostering healthy ageing in India. Factors such as environmental safety and social participation also influence well-being. Targeted interventions addressing education, gender equality, safety, and healthcare access are vital for enhancing the ageing experience and overall well-being of older adults.

Designing and validating a clinical decision support algorithm for diabetic nephroprotection in older patients.

BACKGROUND: Older patients with diabetic kidney disease (DKD) often do not receive optimal pharmacological treatment. Current clinical practice guidelines (CPGs) do not incorporate the concept of personalised care. Clinical decision support (CDS) algorithms that consider both evidence and personalised care to improve patient outcomes can improve the care of older adults. The aim of this research is to design and validate a CDS algorithm for prescribing renin-angiotensin-aldosterone system inhibitors (RAASi) for older patients with diabetes. METHODS: The design of the CDS tool included the following phases: (1) gathering evidence from systematic reviews and meta-analyses of randomised clinical trials to determine the number needed to treat (NNT) and time-to-benefit (TTB) values applicable to our target population for use in the algorithm. (2) Building a list of potential cases that addressed different prescribing scenarios (starting, adding or switching to RAASi). (3) Reviewing relevant guidelines and extracting all recommendations related to prescribing RAASi for DKD. (4) Matching NNT and TTB with specific clinical cases. (5) Validating the CDS algorithm using Delphi technique. RESULTS: We created a CDS algorithm that covered 15 possible scenarios and we generated 36 personalised and nine general recommendations based on the calculated and matched NNT and TTB values and considering the patient's life expectancy and functional capacity. The algorithm was validated by experts in three rounds of Delphi study. CONCLUSION: We designed an evidence-informed CDS algorithm that integrates considerations often overlooked in CPGs. The next steps include testing the CDS algorithm in a clinical trial.

One Health as a potential platform to rescue the neglected fruit trees in Yucatan, Mexico.

Neglected and underutilized species of plants (NUS) have been identified by the Food and Agriculture Organization as valuable resources for fighting poverty, hunger and malnutrition as they can help make agricultural production systems more sustainable and resilient. Adaptation of NUS to changing environments over several millennia has rendered most of these plants resistant to pests and climate change. In this paper, we explore the potential values of some of the Mayan fruit trees justifying conservation efforts in their native habitats. Our research was primarily based on a scoping review using Google Scholar. We considered articles published in English, Spanish and Portuguese. Our review rendered two sets of articles including those focusing on the nutritional and medicinal properties of NUS and their products, and those focusing on their uses in traditional medicine. Both sets of papers strongly support arguments for conservation of NUS. Additionally, our scoping review expands and includes a case study on the conservation of NUS, highlighting the critical role of civil society on how it can spearhead rescue efforts of botanical resources through the creation of what is possibly the first arboretum of its kind in the Americas. Among the project's key selling points was not only the rescue of an important component of Yucatan's cultural heritage but its nutritional value as well as its potential medicinal properties. Our paper is not prescriptive on how to preserve or even commercially exploit NUS. It is intended as a thought-provoking piece on the potential of a One Health approach as a multisectoral platform to support conservation efforts, while stimulating greater interest in the subject and encouraging more action from the academic and pharmaceutical sectors as well as civil society.

Variation in insect herbivory across an urbanization gradient: The role of abiotic factors and leaf secondary metabolites.

Urbanization impacts plant-herbivore interactions, which are crucial for ecosystem functions such as carbon sequestration and nutrient cycling. While some studies have reported reductions in insect herbivory in urban areas (relative to rural or natural forests), this trend is not consistent and the underlying causes for such variation remain unclear. We conducted a continental-scale study on insect herbivory along urbanization gradients for three European tree species: Quercus robur, Tilia cordata, and Fraxinus excelsior, and further investigated their biotic and abiotic correlates to get at mechanisms. To this end, we quantified insect leaf herbivory and foliar secondary metabolites (phenolics, terpenoids, alkaloids) for 176 trees across eight European cities. Additionally, we collected data on microclimate (air temperature) and soil characteristics (pH, carbon, nutrients) to test for abiotic correlates of urbanization effects directly or indirectly (through changes in plant secondary chemistry) linked to herbivory. Our results showed that urbanization was negatively associated with herbivory for Q. robur and F. excelsior, but not for T. cordata. In addition, urbanization was positively associated with secondary metabolite concentrations, but only for Q. robur. Urbanization was positively associated with air temperature for Q. robur and F. excelsior, and negatively with soil nutrients (magnesium) in the case of F. excelsior, but these abiotic variables were not associated with herbivory. Contrary to expectations, we found no evidence for indirect effects of abiotic factors via plant defences on herbivory for either Q. robur or F. excelsior. Additional biotic or abiotic drivers must therefore be accounted for to explain observed urbanization gradients in herbivory and their interspecific variation.

Estimating optimal decision trees for treatment assignment: The case of K > 2 treatment alternatives.

For many problems in clinical practice, multiple treatment alternatives are available. Given data from a randomized controlled trial or an observational study, an important challenge is to estimate an optimal decision rule that specifies for each client the most effective treatment alternative, given his or her pattern of pretreatment characteristics. In the present paper we will look for such a rule within the insightful family of classification trees. Unfortunately, however, there is dearth of readily accessible software tools for optimal decision tree estimation in the case of more than two treatment alternatives. Moreover, this primary tree estimation problem is also cursed with two secondary problems: a structural missingness in typical studies on treatment evaluation (because every individual is assigned to a single treatment alternative only), and a major issue of replicability. In this paper we propose solutions for both the primary and the secondary problems at stake. We evaluate the proposed solution in a simulation study, and illustrate with an application on the search for an optimal tree-based treatment regime in a randomized controlled trial on K = 3 different types of aftercare for younger women with early-stage breast cancer. We conclude by arguing that the proposed solutions may have relevance for several other classification problems inside and outside the domain of optimal treatment assignment.

The Role of Vitamin D Metabolism Genes and Their Genomic Background in Shaping Cyclosporine A Dosage Parameters after Kidney Transplantation.

Background: Kidney transplantation is followed by immunosuppressive therapy involving calcineurin inhibitors (CNIs) such as cyclosporin A. However, long-term high CNIs doses can lead to vitamin D deficiency, and genetic variations influencing vitamin D levels can indirectly impact the necessary CNIs dosage. This study investigates the impact of genetic variations of vitamin D binding protein (DBP) rs2282679 and CYP2R1 hydroxylase rs10741657 polymorphisms on the cyclosporin A dosage in kidney transplant recipients. Additional polymorphisims of genes that are predicted to influence the pharmacogenetic profile were included. Methods: Gene polymorphisms in 177 kidney transplant recipients were analyzed using data mining techniques, including the Random Forest algorithm and Classification and Regression Trees (C&RT). The relationship between the concentration/dose (C/D) ratio of cyclosporin A and genetic profiles was assessed to determine the predictive value of DBP rs2282679 and CYP2R1 rs10741657 polymorphisms. Results: Polymorphic variants of the DBP (rs2282679) demonstrated a strong predictive value for the cyclosporin A C/D ratio in post-kidney transplantation patients. By contrast, the CYP2R1 polymorphism (rs10741657) did not show predictive significance. Additionally, the immune response genes rs231775 CTLA4 and rs1800896 IL10 were identified as predictors of cyclosporin A response, though these did not result in statistically significant differences. Conclusions:DBP rs2282679 polymorphisms can significantly predict the cyclosporin A C/D ratio, potentially enhancing the accuracy of CNI dosing. This can help identify patient groups at risk of vitamin D deficiency, ultimately improving the management of kidney transplant recipients. Understanding these genetic influences allows for more personalized and effective treatment strategies, contributing to better long-term outcomes for patients.