Machine Learning Analysis Research Articles

Smart aquaculture analytics: Enhancing shrimp farming in Bangladesh through real-time IoT monitoring and predictive machine learning analysis

Water quality is a critical factor in shrimp farming, and the success of shrimp production is closely tied to the overall condition of the water. Challenges such as rapid population growth, environmental pollution, and global warming have led to a decline in fisheries production, particularly in the freshwater shrimp sector. This study addresses these challenges by monitoring multiple water parameters in shrimp farms, including pH, temperature, TDS, EC, and salinity. Traditional manual monitoring systems are known to be cumbersome, time-consuming, and lacking real-time capabilities. Consequently, a continuous and automated monitoring system becomes imperative for efficient and real-time metrics handling. This study introduces a real-time freshwater shrimp (locally named Galda, i.e., Macrobrachium Rosenbergii) farm monitoring system. The proposed system incorporates technologies such as microcontroller-based physical devices, IoT, cloud storage with service, machine learning models, and web applications. This integrated system enables users to remotely monitor shrimp farms and receive alerts when water parameters fall outside the optimal range. The physical implementation involves a set of sensors for collecting data on water metrics in shrimp farms. Regression analysis is employed for predicting next-day values, and a newly developed decision-based algorithm classifies shrimp production levels into low, medium, and maximum categories using six well-known classification algorithms. The system demonstrates a high success rate for next-day predictions (r2 of 0.94) by multiple linear regression, and the accuracy in classifying shrimp production is 97.84 % by Random Forest. Additionally, a ‘Smart Aquaculture Analytics’ web application has been developed, offering features such as real-time dashboards, historical data visualization, prediction and classification tools, and automated notifications to farmers in Bangladesh.

Bowel preparation before elective right colectomy: Multitreatment machine-learning analysis on 2,617 patients

BackgroundIn the worldwide, real-life setting, some candidates for right colectomy still receive no bowel preparation, some receive oral antibiotics alone, some receive mechanical bowel preparation alone, and some receive mechanical bowel preparation with oral antibiotics, with varying degrees of compliance to preoperative intravenous antibiotic prophylaxis. Previous studies mainly focused on left-sided colorectal anastomoses while less attention has been devoted to right-sided ileocolic anastomoses. When high-level evidence from randomized clinical trials is lacking, multiple-treatment propensity score weighting analysis of prospective data on the basis of generalized boosted model is superior to a simple propensity score-matching analysis and to an inverse probability weighting in terms of external validity and bias reduction. MethodsThis is an analysis on the basis of machine-learning procedures of 2,617 patients who underwent elective right colectomies. ResultsThe risk of surgical-site infections (5.0% after no bowel preparation) was significantly lower after mechanical bowel preparation with oral antibiotics (4.0%, P = .017), significantly greater after mechanical bowel preparation alone (8.6%, P = .019), and comparable after oral antibiotics alone (3.9%). The risk of anastomotic leakage (3.2% after no bowel preparation) was significantly greater after oral antibiotics alone (4.8%, P = .013). Concerning secondary outcomes, no significant differences were recorded for the risk of overall morbidity and reoperation. The risk of readmission (3.0% after no bowel preparation) was significantly reduced after mechanical bowel preparation with oral antibiotics (1.5%, P = .046), and the risk of major morbidity (5.1% after no bowel preparation) was significantly greater after oral antibiotics alone (6.7%, P = .007). ConclusionThis multitreatment machine-learning analysis, despite some limitations, showed that mechanical bowel preparation with oral antibiotics is associated with a decrease in surgical-site infections after elective right colectomy compared with no bowel preparation.

Machine Learning for the Identification of Key Predictors to Bayley Outcomes: A Preterm Cohort Study.

The aim of this study was to understand how neurological development of preterm infants can be predicted at earlier stages and explore the possibility of applying personalized approaches. Our study included a cohort of 64 preterm infants, between 24 and 34 weeks of gestation. Linear and nonlinear models were used to evaluate feature predictability to Bayley outcomes at the corrected age of 2 years. The outcomes were classified into motor, language, cognitive, and socio-emotional categories. Pediatricians' opinions about the predictability of the same features were compared with machine learning. According to our linear analysis sepsis, brain MRI findings and Apgar score at 5th minute were predictive for cognitive, Amiel-Tison neurological assessment at 12 months of corrected age for motor, while sepsis was predictive for socio-emotional outcome. None of the features were predictive for language outcome. Based on the machine learning analysis, sepsis was the key predictor for cognitive and motor outcome. For language outcome, gestational age, duration of hospitalization, and Apgar score at 5th minute were predictive, while for socio-emotional, gestational age, sepsis, and duration of hospitalization were predictive. Pediatricians' opinions were that cardiopulmonary resuscitation is the key predictor for cognitive, motor, and socio-emotional, but gestational age for language outcome. The application of machine learning in predicting neurodevelopmental outcomes of preterm infants represents a significant advancement in neonatal care. The integration of machine learning models with clinical workflows requires ongoing education and collaboration between data scientists and healthcare professionals to ensure the models' practical applicability and interpretability.

Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data

Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. Machine learning (ML) models were developed using selected features from blood-secretory proteins collected from the curated databases. The logistic regression (LR) model demonstrated the optimal performance. Transcriptome analysis across 7 LC cohorts revealed 231 common differentially expressed genes (DEGs). The encoded proteins of these DEGs were compared with the ML dataset, revealing 29 proteins overlapping with the blood-secretory dataset. The LR model also predicted 29 additional proteins as blood-secretory with the remaining protein-coding genes. As a result, 58 potential blood-secretory proteins were obtained. Among the top 20 genes, 13 common hub genes were identified. Further, area under the receiver operating characteristic curve (ROC AUC) analysis was performed to assess the genes as potential diagnostic blood biomarkers. Six genes, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6, exhibited an AUC value higher than 0.85 and were predicted as blood-secretory. This study highlights the potential of an integrative computational approach for discovering non-invasive blood-based biomarkers in LC, facilitating for further validation and clinical translation. SignificanceLiver cancer is one of the leading causes of premature death worldwide, with its prevalence and mortality rates projected to increase. Although current diagnostic methods are highly sensitive, they are invasive and unsuitable for repeated testing. Blood biomarkers offer a promising non-invasive alternative, but their wide dynamic range of protein concentration poses experimental challenges. Therefore, utilizing available omics data to develop a diagnostic model could provide a potential solution for accurate diagnosis. This study developed a computational method integrating machine learning and bioinformatics analysis to identify potential blood biomarkers. As a result, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6 biomarkers were identified, holding significant promise for improving diagnosis and understanding of liver cancer. The integrated method can be applied to other cancers, offering a possible solution for early detection and improved patient outcomes.

Predicting business bankruptcy: A comparative analysis with machine learning models

Business failure prediction has become crucially important for today's firms, enabling them to reduce financial risks and make informed decisions. This study uses a dataset of 6819 companies and 96 financial and macroeconomic variables to present a comparative analysis of machine learning (ML) models for predicting corporate bankruptcies. Behind this research is to improve the accuracy of bankruptcy prediction, which can help companies make more informed decisions and reduce financial risks. This study aims to evaluate the effectiveness of 16 ML algorithms in terms of accuracy, sensitivity, and other relevant metrics. The work uses methodologies that include data collection and cleaning, exploratory data analysis, model preprocessing and training, and model performance evaluation. Data preprocessing and hyperparameter optimization techniques were used to improve model performance. The evaluated algorithms include Classifiers such as Stacking Classifier (SCC), Randomized Search Classifier (RCV), Historical Gradient Boosting Classifier (HGBC), MLP Classifier (MLPC), K-Neighbors Classifier (KNC), Decision Tree Classifier (DTC), XGBRF Classifier (XGBRFC), Support Vector Classifier (SVC), Logistic Regression Classifier (LR), Linear SVC Classifier (LSVC). With an accuracy of 97.63 %, recall of 97.63 %, and F1-score of 97.63 %, the results show that the SCC algorithm was the best. Other models, such as RCV and DTC, also showed good results, with accuracies above 97 %. However, models such as PAC and BNB had lower performance and accuracy below 90 %. Finally, this study compares the results of ML models in predicting business failures and highlights their effectiveness. The SCC algorithm is considered the most suitable model for this task, as it suggests that it can help economic actors make more informed decisions and reduce financial risks in the context of firms.

Identification of Beef Odors under Different Storage Day and Processing Temperature Conditions Using an Odor Sensing System.

This study used an odor sensing system with a 16-channel electrochemical sensor array to measure beef odors, aiming to distinguish odors under different storage days and processing temperatures for quality monitoring. Six storage days ranged from purchase (D0) to eight days (D8), with three temperature conditions: no heat (RT), boiling (100 °C), and frying (180 °C). Gas chromatography-mass spectrometry (GC-MS) analysis showed that odorants in the beef varied under different conditions. Compounds like acetoin and 1-hexanol changed significantly with the storage days, while pyrazines and furans were more detectable at higher temperatures. The odor sensing system data were visualized using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). PCA and unsupervised UMAP clustered beef odors by storage days but struggled with the processing temperatures. Supervised UMAP accurately clustered different temperatures and dates. Machine learning analysis using six classifiers, including support vector machine, achieved 57% accuracy for PCA-reduced data, while unsupervised UMAP reached 49.1% accuracy. Supervised UMAP significantly enhanced the classification accuracy, achieving over 99.5% with the dimensionality reduced to three or above. Results suggest that the odor sensing system can sufficiently enhance non-destructive beef quality and safety monitoring. This research advances electronic nose applications and explores data downscaling techniques, providing valuable insights for future studies.

Wood discrimination of six commonly traded Phoebe and Machilus species using high-resolution plastid and nuclear DNA barcodes

Abstract Phoebe and Machilus trees, whose wood is known as Nanmu in China, face endangerment owing to long-term manmade destruction and climate change. Therefore, effective and accurate forensic tools to identify Nanmu species are required. This study developed a method for DNA identification of woods from six commonly traded Phoebe and Machilus species. DNA was efficiently extracted from oil cell-rich wood tissues, DNA barcodes were screened, species were compared, and a Nanmu DNA database was constructed. Comparison and analysis involved 26 sets of plastid genomes of Phoebe and Machilus. The five highest variable regions (Pi>0.009; psbA-trnH, psbJ, psbJ-petA, rpl32-trnL, and ycf 1) were identified, and three nuclear regions (ITS, LEAFY, and RPB2) were compared. Among single DNA fragments, the nuclear DNA region LEAFY proved optimal for identifying Nanmu species, while psbA-trnH+psbJ+ycf1 was the optimal plastid combination locus. Compared with distance-based (TaxonDNA) and machine-learning analyses, the tree-based method had a lower success rate for species identification. Four labelled Nanmu samples were selected to validate the method and clustered with P. zhennan and P. sheareri data based on the LEAFY region and plastid combination locus. These findings will contribute to the conservation, classification, architecture maintenance, and trade monitoring of Nanmu natural resources.

Comparative Analysis of Machine Learning and Autoregressive Models for Forecasting Economic Growth: A Case Study

Comparative Analysis of Machine Learning and Autoregressive Models for Forecasting Economic Growth: A Case Study

Using matrix assisted laser desorption ionisation mass spectrometry combined with machine learning for vaccine authenticity screening

The global population is increasingly reliant on vaccines to maintain population health with billions of doses used annually in immunisation programmes. Substandard and falsified vaccines are becoming more prevalent, caused by both the degradation of authentic vaccines but also deliberately falsified vaccine products. These threaten public health, and the increase in vaccine falsification is now a major concern. There is currently no coordinated global infrastructure or screening methods to monitor vaccine supply chains. In this study, we developed and validated a matrix-assisted laser desorption/ionisation-mass spectrometry (MALDI-MS) workflow that used open-source machine learning and statistical analysis to distinguish authentic and falsified vaccines. We validated the method on two different MALDI-MS instruments used worldwide for clinical applications. Our results show that multivariate data modelling and diagnostic mass spectra can be used to distinguish authentic and falsified vaccines providing proof-of-concept that MALDI-MS can be used as a screening tool to monitor vaccine supply chains.

Organomics: A Concept Reflecting the Importance of PET/CT Healthy Organ Radiomics in Non-Small Cell Lung Cancer Prognosis Prediction Using Machine Learning.

Non-small cell lung cancer is the most common subtype of lung cancer. Patient survival prediction using machine learning (ML) and radiomics analysis proved to provide promising outcomes. However, most studies reported in the literature focused on information extracted from malignant lesions. This study aims to explore the relevance and additional value of information extracted from healthy organs in addition to tumoral tissue using ML algorithms. This study included PET/CT images of 154 patients collected from available online databases. The gross tumor volume and 33 volumes of interest defined on healthy organs were segmented using nnU-Net deep learning-based segmentation. Subsequently, 107 radiomic features were extracted from PET and CT images (Organomics). Clinical information was combined with PET and CT radiomics from organs and gross tumor volumes considering 19 different combinations of inputs. Finally, different feature selection (FS; 5 methods) and ML (6 algorithms) algorithms were tested in a 3-fold data split cross-validation scheme. The performance of the models was quantified in terms of the concordance index (C-index) metric. For an input combination of all radiomics information, most of the selected features belonged to PET Organomics and CT Organomics. The highest C-index (0.68) was achieved using univariate C-index FS method and random survival forest ML model using CT Organomics + PET Organomics as input as well as minimum depth FS method and CoxPH ML model using PET Organomics as input. Considering all 17 combinations with C-index higher than 0.65, Organomics from PET or CT images were used as input in 16 of them. The selected features and C-indices demonstrated that the additional information extracted from healthy organs of both PET and CT imaging modalities improved the ML performance. Organomics could be a step toward exploiting the whole information available from multimodality medical images, contributing to the emerging field of digital twins in health care.

Auto-Scaling Techniques in Cloud Computing: Issues and Research Directions.

In the dynamic world of cloud computing, auto-scaling stands as a beacon of efficiency, dynamically aligning resources with fluctuating demands. This paper presents a comprehensive review of auto-scaling techniques, highlighting significant advancements and persisting challenges in the field. First, we overview the fundamental principles and mechanisms of auto-scaling, including its role in improving cost efficiency, performance, and energy consumption in cloud services. We then discuss various strategies employed in auto-scaling, ranging from threshold-based rules and queuing theory to sophisticated machine learning and time series analysis approaches. After that, we explore the critical issues in auto-scaling practices and review several studies that demonstrate how these challenges can be addressed. We then conclude by offering insights into several promising research directions, emphasizing the development of predictive scaling mechanisms and the integration of advanced machine learning techniques to achieve more effective and efficient auto-scaling solutions.

A machine learning analysis of the value-added intellectual coefficient’s effect on firm performance

Purpose Recently, machine learning (ML) methods gained popularity in finance and accounting research as alternatives to econometric analysis. Their success in high-dimensional settings is promising as a cure for the shortcomings of econometric analysis. The purpose of this study is to prove further the relationship between intellectual capital (IC) efficiency and firm performance using ML methods. Design/methodology/approach This study used the double selection, partialing-out and cross-fit partialing-out LASSO estimators to analyze the IC efficiency’s linear and nonlinear effects on firm performance using a sample of 2,581 North American firms from 1999 to 2021. The value-added intellectual capital (VAIC) and its components are used as indicators of IC efficiency. Firm performance is measured by return on equity, return on assets and market-to-book ratio. Findings The findings revealed significant connections between IC measures and firm performance. First, the VAIC, as an aggregate measure, significantly impacts both firm profitability and value. When the VAIC is decomposed into its breakdowns, it is revealed that structural capital efficiency substantially affects firm value, and capital employed efficiency has the same function for firm profitability. In contrast to the prevalent belief in the area, human capital efficiency’s impact is found to be less important than the others. Nonlinearities are also detected in the relationships. Originality/value As ML tools are most recently introduced to the IC literature, only a few studies have used them to expand the current knowledge. However, none of these studies investigated the role of IC as a determinant of firm performance. The present study fills this gap in the literature by investigating the effect of IC efficiency on firm performance using supervised ML methods. It also provides a novel approach by comparing the estimation results of three LASSO estimators. To the best of the author’s knowledge, this is the first study that has used LASSO in IC research.

Development of a biomarker prediction model for post-trauma multiple organ failure/dysfunction syndrome based on the blood transcriptome

BackgroundMultiple organ failure/dysfunction syndrome (MOF/MODS) is a major cause of mortality and morbidity among severe trauma patients. Current clinical practices entail monitoring physiological measurements and applying clinical score systems to diagnose its onset. Instead, we aimed to develop an early prediction model for MOF outcome evaluated soon after traumatic injury by performing machine learning analysis of genome-wide transcriptome data from blood samples drawn within 24 h of traumatic injury. We then compared its performance to baseline injury severity scores and detection of infections.MethodsBuffy coat transcriptome and linked clinical datasets from blunt trauma patients from the Inflammation and the Host Response to Injury Study (“Glue Grant”) multi-center cohort were used. According to the inclusion/exclusion criteria, 141 adult (age ≥ 16 years old) blunt trauma patients (excluding penetrating) with early buffy coat (≤ 24 h since trauma injury) samples were analyzed, with 58 MOF-cases and 83 non-cases. We applied the Least Absolute Shrinkage and Selection Operator (LASSO) and eXtreme Gradient Boosting (XGBoost) algorithms to select features and develop models for MOF early outcome prediction.ResultsThe LASSO model included 18 transcripts (AUROC [95% CI]: 0.938 [0.890–0.987] (training) and 0.833 [0.699–0.967] (test)), and the XGBoost model included 41 transcripts (0.999 [0.997–1.000] (training) and 0.907 [0.816–0.998] (test)). There were 16 overlapping transcripts comparing the two panels (0.935 [0.884–0.985] (training) and 0.836 [0.703–0.968] (test)). The biomarker models notably outperformed models based on injury severity scores and sex, which we found to be significantly associated with MOF (APACHEII + sex—0.649 [0.537–0.762] (training) and 0.493 [0.301–0.685] (test); ISS + sex—0.630 [0.516–0.744] (training) and 0.482 [0.293–0.670] (test); NISS + sex—0.651 [0.540–0.763] (training) and 0.525 [0.335–0.714] (test)).ConclusionsThe accurate assessment of MOF from blood samples immediately after trauma is expected to aid in improving clinical decision-making and may contribute to reduced morbidity, mortality and healthcare costs. Moreover, understanding the molecular mechanisms involving the transcripts identified as important for MOF prediction may eventually aid in developing novel interventions.

KRT18 as a Novel Biomarker of Urothelial Papilloma while Evaluating Low-Grade Papillary Urothelial Neoplasms: Bi-Center Analysis.

Although urothelial papilloma (UP) is an indolent papillary neoplasm that can mimic the morphology of low-grade papillary urothelial carcinoma (PUC), there is no immunomarker to differentiate reliably these two entities. In addition, the molecular characteristics of UP are not fully understood. We conducted an in-depth proteomic analysis of papillary urothelial lesions (n = 31), including UP and PUC along with normal urothelium. Protein markers distinguishing UP and PUC were selected with machine learning analysis, followed by internal and external validation using immunohistochemistry. In the proteomic analysis, UP and PUC showed overlapping proteomic profiles. We identified EHD4 and KRT18 as candidate diagnostic biomarkers of UP. Through immunohistochemical validation in two independent cohorts (n = 120), KRT18 was suggested as a novel UP diagnostic marker, able to differentiate UP from low-grade PUC. We also found that 3.5% of patients with UP developed urothelial carcinoma in subsequent resections, supporting the malignant potential of UP. KRT18 downregulation was significantly associated with UPs subsequently progressing to urothelial carcinoma, following their initial diagnosis. This is the first study that successfully revealed UPs comprehensive proteomic landscape, while it also identified KRT18 as a potential diagnostic biomarker of UP.

Mental issues, internet addiction and quality of life predict burnout among Hungarian teachers: a machine learning analysis

BackgroundBurnout is usually defined as a state of emotional, physical, and mental exhaustion that affects people in various professions (e.g. physicians, nurses, teachers). The consequences of burnout involve decreased motivation, productivity, and overall diminished well-being. The machine learning-based prediction of burnout has therefore become the focus of recent research. In this study, the aim was to detect burnout using machine learning and to identify its most important predictors in a sample of Hungarian high-school teachers.MethodsThe final sample consisted of 1,576 high-school teachers (522 male), who completed a survey including various sociodemographic and health-related questions and psychological questionnaires. Specifically, depression, insomnia, internet habits (e.g. when and why one uses the internet) and problematic internet usage were among the most important predictors tested in this study. Supervised classification algorithms were trained to detect burnout assessed by two well-known burnout questionnaires. Feature selection was conducted using recursive feature elimination. Hyperparameters were tuned via grid search with 10-fold cross-validation. Due to class imbalance, class weights (i.e. cost-sensitive learning), downsampling and a hybrid method (SMOTE-ENN) were applied in separate analyses. The final model evaluation was carried out on a previously unseen holdout test sample.ResultsBurnout was detected in 19.7% of the teachers included in the final dataset. The best predictive performance on the holdout test sample was achieved by random forest with class weigths (AUC = 0.811; balanced accuracy = 0.745, sensitivity = 0.765; specificity = 0.726). The best predictors of burnout were Beck’s Depression Inventory scores, Athen’s Insomnia Scale scores, subscales of the Problematic Internet Use Questionnaire and self-reported current health status.ConclusionsThe performances of the algorithms were comparable with previous studies; however, it is important to note that we tested our models on previously unseen holdout samples suggesting higher levels of generalizability. Another remarkable finding is that besides depression and insomnia, other variables such as problematic internet use and time spent online also turned out to be important predictors of burnout.

Factors controlling the water quality of rock glacier springs in European and American mountain ranges

Rock glaciers (RGs) provide significant water resources in mountain areas under climate change. Recent research has highlighted high concentrations of solutes including trace elements in RG-fed waters, with negative implications on water quality. Yet, sparse studies from a few locations hinder conclusions about the main drivers of solute export from RGs. Here, in an unprecedented effort, we collected published and unpublished data on rock glacier hydrochemistry around the globe. We considered 201 RG springs from mountain ranges across Europe, North and South America, using a combination of machine learning, multivariate and univariate analyses, and geochemical modeling. We found that 35 % of springs issuing from intact RGs (containing internal ice) have water quality below drinking water standards, compared to 5 % of springs connected to relict RGs (without internal ice). The interaction of ice and bedrock lithology is responsible for solute concentrations in RG springs. Indeed, we found higher concentrations of sulfate and trace elements in springs sourcing from intact RGs compared to water originating from relict RGs, mostly in specific lithological settings. Enhanced sulfide oxidation in intact RGs is responsible for the elevated trace element concentrations. Challenges for water management may arise in mountain catchments rich in intact RGs, and where the predisposing geology would make these areas geochemical RG hotspots. Our work represents a first comprehensive attempt to identify the main drivers of solute concentrations in RG waters.

Brain lncRNA-mRNA co-expression regulatory networks and alcohol use disorder

Prolonged alcohol consumption can disturb the expression of both coding and noncoding genes in the brain. These dysregulated genes may co-express in modules and interact within networks, consequently influencing the susceptibility to developing alcohol use disorder (AUD). In the present study, we performed an RNA-seq analysis of the expression of both long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) in 192 postmortem tissue samples collected from eight brain regions (amygdala, caudate nucleus, cerebellum, hippocampus, nucleus accumbens, prefrontal cortex, putamen, and ventral tegmental area) of 12 AUD and 12 control subjects of European ancestry. Applying the limma-voom method, we detected a total of 57 lncRNAs and 51 mRNAs exhibiting significant differential expression (Padj < 0.05 and fold-change ≥2) across at least one of the eight brain regions investigated. Machine learning analysis further confirmed the potential of these top genes in predicting AUD. Through Weighted Gene Co-expression Network Analysis (WGCNA), we identified distinct lncRNA-mRNA co-expression modules associated with AUD in each of the eight brain regions. Additionally, lncRNA-mRNA co-expression networks were constructed for each brain region using Cytoscape to reveal gene regulatory interactions implicated in AUD. Hub genes within these networks were found to be enriched in several key KEGG pathways, including Axon Guidance, MAPK Signaling, p53 Signaling, Adherens Junction, and Neurodegeneration. Our results underscore the significance of networks involving AUD-associated lncRNAs and mRNAs in modulating neuroplasticity in response to alcohol exposure. Further elucidating these molecular mechanisms holds promise for the development of targeted therapeutic interventions for AUD.

Predictive modeling of colorectal cancer using exhaustive analysis of microbiome information layers available from public metagenomic data.

This study aimed to compare the microbiome profiles of patients with colorectal cancer (CRC, n = 380) and colorectal adenomas (CRA, n = 110) against generally healthy participants (n = 2,461) from various studies. The overarching objective was to conduct a real-life experiment and develop a robust machine learning model applicable to the general population. A total of 2,951 stool samples underwent a comprehensive analysis using the in-house MetaBakery pipeline. This included various data matrices such as microbial taxonomy, functional genes, enzymatic reactions, metabolic pathways, and predicted metabolites. The study found no statistically significant difference in microbial diversity among individuals. However, distinct clusters were identified for healthy, CRC, and CRA groups through linear discriminant analysis (LDA). Machine learning analysis demonstrated consistent model performance, indicating the potential of microbiome layers (microbial taxa, functional genes, enzymatic reactions, and metabolic pathways) as prediagnostic indicators for CRC and CRA. Notable biomarkers on the taxonomy level and microbial functionality (gene families, enzymatic reactions, and metabolic pathways) associated with CRC were identified. The research presents promising avenues for practical clinical applications, with potential validation on external clinical datasets in future studies.

Observational evidence for groundwater influence on crop yields in the United States

As climate change shifts crop exposure to dry and wet extremes, a better understanding of factors governing crop response is needed. Recent studies identified shallow groundwater-groundwater within or near the crop rooting zone-as influential, yet existing evidence is largely based on theoretical crop model simulations, indirect or static groundwater data, or small-scale field studies. Here, we use observational satellite yield data and dynamic water table simulations from 1999 to 2018 to provide field-scale evidence for shallow groundwater effects on maize yields across the United States Corn Belt. We identify three lines of evidence supporting groundwater influence: 1) crop model simulations better match observed yields after improvements in groundwater representation; 2) machine learning analysis of observed yields and modeled groundwater levels reveals a subsidy zone between 1.1 and 2.5 m depths, with yield penalties at shallower depths and no effect at deeper depths; and 3) locations with groundwater typically in the subsidy zone display higher yield stability across time. We estimate an average 3.4% yield increase when groundwater levels are at optimum depth, and this effect roughly doubles in dry conditions. Groundwater yield subsidies occur ~35% of years on average across locations, with 75% of the region benefitting in at least 10% of years. Overall, we estimate that groundwater-yield interactions had a net monetary contribution of approximately $10 billion from 1999 to 2018. This study provides empirical evidence for region-wide groundwater yield impacts and further underlines the need for better quantification of groundwater levels and their dynamic responses to short- and long-term weather conditions.

Prediction of secondary metabolites in maize under different nitrogen inputs by hyperspectral sensing and machine learning

Flavonoids are compounds resulting from secondary plant metabolism that provide benefits to human health by food. This study aimed to accuracy of predicting flavonoids in maize plants subjected to different nitrogen rates using hyperspectral reflectance and machine learning (ML) algorithms. The experiment was carried out in randomized blocks in a 4 × 5 factorial design (four N inputs: 0; 30; 60 and 120 % of the recommended N input; and five readings of the reflectance spectra in maize leaves from different vegetative stages: V6, V8, V10, V12 and V14, in four replications, totaling 80 treatments. N rates were applied in the V4 and V8 phenological stages, using urea as the N source. For hyperspectral analysis, four leaves from each treatment were collected and analyzed using a spectroradiometer (FieldSpec 4 HRes, Analytical Spectral Devices), capturing the spectrum in the 350 to 2500 nm range. Subsequently, the leaf samples used in the reflectance readings were dried, ground and subjected to isoflavone quantification, analyzed by ultra-performance liquid chromatography in three repetitions, quantifying daidzein 1 (D1), daidzein 2 (D2), genistein 1 (G1), genistein 2 (G2), and total isoflavones. Data obtained was subjected to machine learning analysis, testing two data set input configurations: wavelengths (WL) and calculated spectral bands (B), and D1, D2, G1, G2 and total isoflavones as output variables. The ML algorithms tested were artificial neural networks (ANN), REPTree (DT), M5P decision tree (M5P), ZeroR (R), Random Forest (RF) and support vector machine (SVM), evaluated according to their performance by the correlation coefficient (r) and mean absolute error (MAE). The results show that the SVM algorithm had the highest accuracy in predicting the variables D1, D2, G1, G2 and total isoflavones, outperforming the other algorithms when WL was used as input in dataset.