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Integrated TWAS, GWAS, and RNAseq results identify candidate genes associated with reproductive traits in cows
Retraction Note: Conceptualising a channel-based overlapping CNN tower architecture for COVID-19 identification from CT-scan images
Beyond despair: Leveraging ecosystem restoration for psychosocial resilience
Ecosystem restoration has historically been viewed as an ecological endeavor, but restoration possesses significant, yet largely untapped, potential as a catalyst for personal and social transformation. We highlight the opportunity for restoration to enhance community resilience by increasing agency and collective action and countering the pervasive perception that we are powerless witnesses to environmental decline. In this perspective, we take a “bright spots” approach and highlight successful examples of ecosystem restoration that have helped to nurture a sense of place, foster optimism, and cultivate stronger and more diverse social networks. These three individual- and community-level capacities have the potential to lead to increased psychosocial resilience, which is a key component of community resilience. Our aim is to spark discussion and research to better understand how we can transform restoration from a largely technical endeavor to a practice and process through which human–nature relationships are infused with deliberate meaning and human well-being is improved. With current calls to upscale and technologize restoration to meet sustainable development goals, we cannot lose sight of the value of community-engaged ecosystem restoration as a strategy with great potential for psychosocial benefits.
Assessment of salicylic acid and potassium nitrate to mitigate frost stress in autumn-sown potato crop cv. Sutlej
Abstract Potato is cultivated all the year round in Pakistan. However, the major crop is the autumn crop which is planted in mid-October and contributes 80–85% of the total production. The abrupt climate change has affected the weather patterns all over the world, resulting in the reduction of the mean air temperature in autumn by almost 1.6 °C in Pakistan, which in turn, has adversely affected the crop performance and tuber yield. This trial, therefore, was conducted to optimize and evaluate the concentration of salicylic acid (SA) and potassium nitrate (KNO3) for inducing frost stress tolerance in an autumn-sown potato crop cv. Sutlej. The findings revealed that the foliar application of 0.5 mM SA significantly enhanced the growth, yield, fluorescent, and biochemical indices of potato plants outperforming 100 mM KNO₃ application in comparison with the control. This included increased plant height by 14% and 17.6%, leaf area index by 6.3% and 26.3%, shoot biomass by 15.4% and 46.2%, crop growth rate by 16.7% and 43.3%, average tuber weight by 8.2% and 23%, tuber diameter by 6.8% and 12.2%, tuber yield by 26.1% and 46.3%, leaf angle by 16.2% and 21.6%, quantum yield of photosystem II by 20.6% and 28.2%, photosynthetically active radiations by 20.5% and 32.4%, chlorophyll content by 6.3% and 14.6%, leaf thickness by 14% and 29%, linear electron flow by 20% and 32.7%, O–2 by 6% and 14.4%, H2O2 by 11.7% and 27.6%, enzyme activities of catalase by 20.7% and 28.5%, superoxide dismutase by 28.6% and 28.5%, peroxidase by 8.3% and 13.5%, ascorbate peroxidase by 17.2% and 37.8%, total protein by 21% and 37%, proline by 36.2% and 114%, and phenolic content by 33% and 63.3% with a reduction in non-photochemical quenching by 12.7% and 29.6%, non-regulatory energy dissipation by 169.5% and 268.5%, and leaf electrolyte leakage by 57.5% and 180%, compared to KNO3 and the control, respectively. Based on the above findings, it can be concluded and recommended that 0.5 mM foliar spray of SA can be utilized on potato crop cv. Sutlej in frost-sensitive regions. However, the application rate of KNO3 needs to be optimized in order to use its maximal frost stress tolerance potential.
Decreased risk of cardiovascular disease mortality associated with occasional positive screens following cancer screenings
Tomato ripeness and stem recognition based on improved YOLOX
Abstract To address the challenges of unbalanced class labels with varying maturity levels of tomato fruits and low recognition accuracy for both fruits and stems in intelligent harvesting, we propose the YOLOX-SE-GIoU model for identifying tomato fruit maturity and stems. The SE focus module was incorporated into YOLOX to improve the identification accuracy, addressing the imbalance in the number of tomato fruits and stems. Additionally, we optimized the loss function to GIoU loss to minimize discrepancies across different scales of fruits and stems. The mean average precision (mAP) of the improved YOLOX-SE-GIoU model reaches 92.17%. Compared to YOLOv4, YOLOv5, YOLOv7, and YOLOX models, the improved model shows an improvement of 1.17–22.21%. The average precision (AP) for unbalanced semi-ripe tomatoes increased by 1.68–26.66%, while the AP for stems increased by 3.78–45.03%. Experimental results demonstrate that the YOLOX-SE-GIoU model exhibits superior overall recognition performance for unbalanced and scale-variant samples compared to the original model and other models in the same series. It effectively reduces false and missed detections during tomato harvesting, improving the identification accuracy of tomato fruits and stems. The findings of this work provide a technical foundation for developing advanced fruit harvesting techniques.
Reply to Lockhart et al.: Advancing the understanding of sex differences in functional brain organization with innovative AI tools
Association of homocysteine and uric acid with type 2 diabetes mellitus: a case-control study
Not so binary or generalizable: Brain sex differences with artificial neural networks
Investigating slope stability of multiple stopes prone to instability in the Ziluoyi iron ore mining site
Author Correction: Mechanical properties and damage characterization of cracked granite after cyclic temperature action
Improved YOLOv8n based helmet wearing inspection method
DNA barcoding of invasive Gambusia holbrooki Girard, 1859 and Atherina boyeri Risso, 1810 inhabiting Upper Euphrates River Basin, Türkiye
Phylogeographic analyses reveal recent dispersal and multiple Wolbachia infections of the bright-eyed ringlet Erebia oeme within the European mountain systems
Abstract The genus Erebia comprises numerous species in Europe. Due to preference of cold environments, most species have disjunct distributions in the European mountain systems. However, their biogeographical patterns may differ significantly. The Bright-eyed ringlet Erebia oeme is widespread in high-altitude grasslands of the European high mountains, hence showing a disjunct distribution pattern. Over its distribution, E. oeme shows high morphological variability indicating pronounced intraspecific differentiation. We analysed two mitochondrial (COI, Cytb) and eight nuclear markers as well as the Wolbachia surface protein-coding gene (WSP). A total of four lineages were identified: two Balkan lineages, one Slovenian lineage as well as one lineage containing all other individuals (Alps, Massif Central, Pyrenees). COI data only indicate a fifth lineage in the southern Carpathians. The region of origin of E. oeme is most likely the western Balkans. From here, E. oeme spread to the eastern Balkan area and further to the southern Carpathians as well as to Slovenia, from where it rapidly crossed the Alps to Massif Central and Pyrenees. Wolbachia was found to be highly prevalent with multiple strains, partly within one population. However, recent Wolbachia impact on the mitochondrial DNA and consequently influence on the intraspecific genetic structure was not detected.
Workflow-driven catalytic modulation from single-atom catalysts to Au–alloy clusters on graphene
Abstract Gold-based (Au) nanostructures are efficient catalysts for CO oxidation, hydrogen evolution (HER), and oxygen evolution (OER) reactions, but stabilizing them on graphene (Gr) is challenging due to weak affinity from delocalized $$p_{z}$$ carbon orbitals. This study investigates forming metal alloys to enhance stability and catalytic performance of Au-based nanocatalysts. Using ab initio density functional theory, we characterize $${\text {M}_{(n-x)}\text {Au}_{x}}$$ sub-nanoclusters (M = Ni, Pd, Pt, Cu, and Ag) with atomicities $$n=1-4$$ , both in gas-phase and supported on Gr. We find that M atoms act as “anchors,” enhancing binding to Gr and modulating catalytic efficiency. Notably, $${\text {Pt}_{(n-x)}\text {Au}_{x}}$$ /Gr shows improved stability, with segregation tendencies mitigated upon adsorption on Gr. The d-band center ( $$\varepsilon _{\text {d}}$$ ) model indicates catalytic potential, correlating an optimal $$\varepsilon _{\text {d}}$$ range of $$-1 \text { to }-2$$ eV for HER and OER catalysts. Incorporating Au into $${\text{M}_n}$$ adjusts $$\varepsilon _{\text {d}}$$ closer to the Fermi level, especially for Group-10 alloys, offering designs with improved stability and efficiency comparable to pure Au nanocatalysts. Our methodology leveraged SimStack, a workflow framework enabling modeling and analysis, enhancing reproducibility, and accelerating discovery. This work demonstrates SimStack’s pivotal role in advancing the understanding of composition-dependent stability and catalytic properties of Au-alloy clusters, providing a systematic approach to optimize metal-support interactions in catalytic applications.