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Somatotopy-independent reduction of audio-tactile intersensory facilitation for looming sounds within the peripersonal space during arm movements execution
No full textAbstract Auditory events occurring in the peripersonal space (PPS) or near specific body parts such as the peritrunk (PTS) and perihand (PHS) space, have been shown to facilitate tactile processing in a somatotopic manner. Furthermore, previous research has demonstrated that audio-tactile intersensory effects are influenced by the execution of full body transport movements (i.e., walking or cycling), so that far-off auditory stimuli that approach the body in a direction coherent with the movement facilitate tactile processing on the body. However, whether these motor-related intersensory effects are modulated by non-transport movements is not known. Here, in two experiments, we sought to determine whether audio-tactile intersensory effects for looming sounds in the PHS and PTS are somatotopically modulated by arm movement compared to when being still. By controlling the role of temporal expectation, we confirm that looming sounds enhance tactile reactivity as they approach the participants’ hand and trunk. We further show that this facilitation is eliminated by limb movements, regardless of somatotopy, and that executing movements blur the distinction between intersensory effects in extrapersonal and peripersonal space. These results contribute to understanding the complex relation between motor execution and intersensory processing
PARP-1 couples β-catenin/TCF4 signaling to epithelial–mesenchymal transition in endometriosis
No full textAbstract Endometriosis exhibits epithelial–mesenchymal transition (EMT)-aligned traits that promote invasion and lesion persistence. We investigated whether poly(ADP-ribose) polymerase-1 (PARP-1) coordinates β-catenin/TCF4 activity with EMT programs and whether these features are amenable to pharmacologic inhibition. PARP-1 and EMT markers were profiled in normal endometrium and ovarian endometriotic lesions. In endometriotic epithelial cells, PARP-1 levels were modulated by overexpression or siRNA and effects on EMT markers and motility were quantified. Association of PARP-1 with β-catenin/TCF4 complexes was evaluated, and a lesion model was used to test the impact of PARP inhibition on EMT features and lesion burden. PARP-1 was elevated in ectopic lesions and aligned with EMT-associated marker shifts. PARP-1 gain increased β-catenin/TCF4 activity, remodeled EMT markers, and enhanced motility, whereas PARP-1 loss produced the opposite pattern. Analyses supported PARP-1 association with β-catenin/TCF4 complexes. Pharmacologic PARP inhibition attenuated β-catenin/TCF4-aligned EMT features in vitro and reduced lesion growth with concurrent marker normalization in vivo. These findings indicate that PARP-1 couples Wnt/β-catenin signaling to EMT programs in endometriosis and identify PARP inhibition as a tractable approach to modulate these phenotypes
Predicting energy prices and renewable energy adoption through an optimized tree-based learning framework with explainable artificial intelligence
No full textAbstract This research offers a comprehensive analysis of global energy consumption, focusing on predicting two key metrics: the Energy Price Index and the Renewable Energy Share. The study employs advanced Machine Learning (ML) regression techniques, all further optimized using metaheuristic algorithms. In addition, a primary objective of this study is to determine which variables most significantly affect model performance and predictive accuracy. Through SHAP (SHapley Additive exPlanations) and CAM (Cosine Amplitude Method) sensitivity analyses, the study systematically interprets model outputs and quantifies the influence of each input feature. Findings demonstrate that, according to the SHAP-based model interpretation, the prediction of Renewable Energy Share is most strongly influenced by fossil fuel dependency and carbon emissions. These results underscore the pivotal role of consumption intensity and environmental indicators in shaping both global energy price trajectories and renewable energy adoption rates. Integrating optimization algorithms with advanced models improved both predictive accuracy and model robustness. The resulting analytical framework provides a technically rigorous and interpretable approach to global energy forecasting. Such a framework is valuable for informing energy policy, supporting sustainability strategies, and enabling stakeholders to monitor environmental impacts and optimize energy system performance. By leveraging data-driven insights, this study advances practical tools and methodologies for strategic planning in the context of a sustainable global energy future
Machine learning prediction of food addiction in university students using demographic, anthropometric and personality traits
No full textAbstract People’s eating habits are influenced by psychological, social, cultural, and behavioral factors. Research shows that certain personality types expose people to risky eating behaviors. Given the complexity of nutrition-related factors and the limitations of traditional statistical methods, the use of new approaches such as artificial intelligence and machine learning can play an effective role in analyzing multidimensional data and identifying complex patterns. This cross-sectional pilot study aimed to predict food addiction among university students by integrating demographic, anthropometric and personality data with machine learning methods. The data consisted of 210 samples, which were first preprocessed to ensure data quality and integrity. Tomek Links and SMOTE techniques were used to remove class imbalance. Feature selection was performed using the twelve different algorithms to identify the most important features related to food addiction prediction. Then, ten different machine learning models were implemented, including Logistic Regression (LR), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Support Vector Classifier (SVC) with probability estimation, Decision Tree (DT), Random Forest (RF), AdaBoost, Gradient Boosting Classifier (GBC), CatBoost and LightGBM. The models were trained on the training dataset and their performance was evaluated using the accuracy, precision, recall, F1-Score and AUC metrics on the test dataset. In addition, the SHAP (SHapley Additive exPlanations) method was used to analyze the importance of features and interpret the advanced models to determine the impact of each psychological and behavioral feature on the prediction of food addiction. The results showed that more advanced models, especially ensemble methods such as Random Forest and CatBoost, have high power in identifying complex patterns and accurately predicting food addiction behaviors. SHAP analysis also showed that psychological characteristics such as feelings of worthlessness, impulsivity, anger, psychological distress, rigid cognitive styles, weight and height, body mass index (BMI) were related the most important factors affecting prediction. Although limitations such as small sample size, focusing on a specific student population, and the use of self-report instruments reduce the generalizability of the results, the innovation of this study in combining psychological and artificial intelligence approaches for early identification of high-risk individuals is remarkable. Overall, the integration of personality profiles with advanced computational models can form the basis for the development of artificial intelligence-based screening tools and targeted interventions to improve nutritional behaviors in young populations
Arabica coffee SBP transcription factor family members respond to brown leaf spot stress by regulating their expression
No full textAbstract Coffee brown spot is a prevalent foliar fungal disease that seriously threatens the survival and yield of Coffea arabica L. To explore the molecular basis of disease resistance, we conducted a genome-wide identification and analysis of the SBP gene family in C. arabica L. A total of 22 CaSBP genes were identified and classified into three phylogenetic subfamilies, distributed across 12 chromosomes and including nine segmentally duplicated gene pairs. Cis-element analysis revealed a high proportion (55.37%) of hormone-related regulatory elements in CaSBP promoters. Integrated transcriptomic and metabolomic analyses showed that brown spot infection significantly altered the expression of seven CaSBP genes, which was further confirmed by qRT-PCR, accompanied by marked changes in jasmonic acid (JA) and salicylic acid (SA) levels. Functional enrichment analysis indicated that five differentially expressed CaSBP genes were associated with the brassinosteroid (BR) signaling pathway. Together, these results suggest that CaSBP genes may participate in hormone-mediated defense responses during brown spot infection, providing molecular insights for breeding disease-resistant coffee cultivars
Genome-wide transcriptional and metabolic responses of Eschscholzia californica to salt stress
No full textAbstract Eschscholzia californica, as an abiotic stress-tolerant ornamental plant, has long been studied for phylogenetic analysis and biosynthesis of benzylisoquinoline alkaloids. However, little is known about its environmental adaptation mechanisms. In this study, we performed comprehensive investigations on responses of E. californica to salt stress, one of serious threats to modern agriculture. The effects of 250 mM NaCl solution treatment on plant growth and several stress response related indices were investigated at 0d, 7d, and 10d, representing non-stressed (CK), mildly-stressed (St0), and severely-stressed (St1), respectively. RNA-seq analysis revealed salt-stress responsive and differentially expressed genes (DEGs) enriched in photosynthesis-related pathways, phenylpropanoid biosynthesis, ɑ-Linolenic acid metabolism, isoquinoline alkaloid biosynthesis, benzoxazinoid biosynthesis, etc. With the aggravation of salt stress, pathways of the tryptophan metabolism, plant hormone signal transduction, ɑ-Linolenic acid metabolism, and isoquinoline alkaloid biosynthesis, etc., were more significantly enriched. We also identified DEGs involved in intracellular ion homeostasis, osmotic regulation, oxidative stress and detoxification, plant hormone biosynthesis and signaling, as well as those encoding transcription factors. Furthermore, Comparison of metabolites in CK and St1 showed that the differentially accumulated metabolites under salt stress were mainly alkaloids (23.6%), phenolic acids (16.5%), lipids (15.2%), organic acids (8.1%), amino acids and their derivatives (7.6%), flavonoids (6.5%), etc. Further joint analysis of transcriptome and metabolome data revealed key pathways in responding to salt stress. The jasmonic acid biosynthesis and signaling and isoquinoline alkaloid biosynthesis were particularly noteworthy due to their extremely significant up-regulation by salt stress and unclear or debatable roles in regulating salt stress tolerance. The DEGs in the two pathways, such as AOX, LOX, and JAZs, as well as NCS1, 6OMT, TNMT, and BBEs, etc., are therefore valuable for uncovering the functional roles of two pathways in salt stress tolerance of E. californica, and future breeding salt-tolerant crops
Correction: Refined chloroplast annotations, repeat profiles, and phylogenomic evidence reveal maternal lineage shifts and independent evolution in the Triticum-Aegilops complex
No full textPublisher Correction: Silencing of S100A11 attenuates murine metabolic dysfunction-associated steatohepatitis
No full textTrend analysis of meteorological data and community perception of climate change in Southern Ethiopia
No full textAbstract Understanding community perceptions of climate change at the local level is essential for designing adaptation strategies in local context. Smallholder farmers in agropastoral and pastoral communities face significant threats from climate change due to heavy dependence on rain-fed agriculture. Yet local-scale trends in agropastoral and pastoral district like Adola Rede remain untapped. This study analyzes 35-year (1983–2018) rainfall and temperature trends in four villages of Adola Rede district using Ethiopian Meteorological Institute data and NASA POWER data. To analyze community perception of climate change trend, 328 households were randomly selected. Moreover, four focused group discussions (FGDs) and ten key informant interviews (KIIs) were conducted. The study employed the standardized precipitation index and Mann–Kendall test to assess rainfall and temperature trend across four villages. Results revel that there is an increasing night-time temperatures around the highland areas. Results reveal that there are more intense late-season precipitation during the month of August, which increased from 1.05 to 1.19 mm/yr. Drought was detected in 1991and 1999 with standardized precipitation index (SPI) value of less than 2.0. The highlands areas have been warming significantly faster than the lowlands. Climatic trends directly impacted agro-pastoral livelihoods, with 90% of the community reporting droughts and rainfall irregularities. Drought and rainfall irregularities can significantly affect sustainable development which requires climate change adaptation strategies such as drought resistant crop and improved livestock varieties. As climate change is evident in the study area, water harvesting, drought-resistant crops and early warning systems are essential to minimize potential impacts of climate change on community livelihoods
Evaluation of trace elements in Iraqi soils using multivariate statistical analysis and pollution indices
No full textAbstract The soil samples, gathered from twenty-four sites in Iraq, were analysed using X-Ray Fluorescence device (XRF) for the concentrations of the thirty trace elements (TEs), including Ti, Cu, Mn, Ni, Co, Cd, Mo, Pb, Cr, V, Zn, Ga, Ge, As, Se, Br, Rb, Y, Ag, Sr, Sb, Sn, I, Ba, W, Hg, Th, Bi, TI, and U, to evaluate their spatial distribution, sources, and pollution levels. Statistical methods of principal component analysis (PCA) and agglomerative hierarchical cluster analysis (AHCA) were used to identify pollution sources and clustering of stations, indicating that seven PCs account for > 90% of the variability in the TEs results. The PC1, accounting for 58.69% of the total variants (TV), showed a strong factor loading for Ti, Cr, Mn, Ni, Cu, Rb, Y, and Tl, indicating sources from rock weathering. It also had moderate loading of V, W, Pb, Th, and U, which are derived from natural and anthropogenic sources. The PC2 explains 12.84% of the TV and had strong loadings of As, Se, Br and moderate loadings for I, which indicates the natural factors and the effect of anthropogenic release of these elements into the soils. The PC3 explains 3.152% of TV and had a strong factor loading of Ba, which was due to the high concentration of Ba in sample 24 because of contamination with the drilling fluids that are used in the petroleum production processes in Basra oil fields located in the south of Iraq. The soil samples from the middle of Iraq stations 7, 8, and 9 and the soil samples from south of Iraq 17, 18, and 22 generally recorded higher levels of pollution by TEs. These areas are close to many contamination sources, such as Al-Dora crude oil refinery, Al-Dora and South Baghdad electrical power plants in the middle of Iraq and petroleum production and Al-Basra oil refineries in the south of Iraq. Agricultural practices and poultry farms also release TEs such as Cr, Co, Cd, Sb, Ba, Hg, and Bi into the soils of these areas. The result of the Enrichment Factor (EF) showed that most TEs had moderate EF values less than 5. On the other hand, the TEs showed high to extremely high EF were Cr, Co, As, Se, Br, Mo, Cd, Sb, I and Hg. The results of the Contamination Factor (CF) ranged from 0.01 to 176.19, and the TEs of Cr, Ni, Sn, W, and Bi showed considerable contamination levels in most samples. TI showed considerable contamination levels in all the samples, Co in 50% of the samples, and As, Br, Se and Sr in a few samples. While TEs showed very high contamination levels of Cr, Co, Ge, Se, Sr, Sb, I, Ba, W and Hg in a few samples. The Mo, Ag and Cd showed very high contamination levels in all samples and Bi in most samples. The elevated TE concentration has severe environmental consequences and affects plant and human life in the long run, for which remedial and preventive strategies need to be devised to prevent at-risk areas and populations
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