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Connectomic analysis of taste circuits in Drosophila
Abstract Our sense of taste is critical for regulating food consumption. The fruit fly Drosophila represents a highly tractable model to investigate mechanisms of taste processing, but taste circuits beyond sensory neurons are largely unidentified. Here, we use a whole-brain connectome to investigate the organization of Drosophila taste circuits. We trace pathways from four populations of sensory neurons that detect different taste modalities and project to the subesophageal zone (SEZ), the primary taste region of the fly brain. We find that second-order taste neurons are primarily located within the SEZ and largely segregated by taste modality, whereas third-order neurons have more projections outside the SEZ and more overlap between modalities. Taste projections out of the SEZ innervate regions implicated in feeding, olfactory processing, and learning. We analyze interconnections within and between taste pathways, characterize modality-dependent differences in taste neuron properties, identify other types of inputs onto taste pathways, and use computational simulations to relate neuronal connectivity to predicted activity. These studies provide insight into the architecture of Drosophila taste circuits.
N-Protonated Acridinium Catalyst Enables Anti-Markovnikov Hydration of Unconjugated Tri- and Disubstituted Olefins
Improving landslide susceptibility prediction through ensemble recursive feature elimination and meta-learning framework
Abstract Landslides pose significant threats to ecosystems, lives, and economies, particularly in the geologically fragile Sub-Himalayan region of West Bengal, India. This study enhances landslide susceptibility prediction by developing an ensemble framework integrating Recursive Feature Elimination (RFE) with meta-learning techniques. Seven advanced machine learning models- Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Extremely Randomized Trees (ET), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and a Meta Classifier (MC) were applied using Remote Sensing and GIS tools to identify key landslide-conditioning factors and classify susceptibility zones. Model performance was assessed through metrics such as accuracy, precision, recall, F1 score, and AUC of the ROC curve. Among the models, the Meta Classifier (MC) achieved the highest accuracy (0.956) and AUC (0.987), demonstrating superior predictive ability. Gradient Boosting (GB), XGBoost, and RF also performed well, with accuracies of 0.943 and AUC values of 0.987 (GB and XGBoost) and 0.983 (RF). Extremely Randomized Trees (ET) exhibited the highest accuracy (0.946) among individual models and an AUC of 0.985. SVM and LR, while slightly less accurate (0.941 and 0.860, respectively), provided valuable insights, with SVM achieving an AUC of 0.972 and LR achieving 0.935. The models effectively delineated landslide susceptibility into five zones (very low, low, moderate, high, and very high), with high and very high susceptibility zones concentrated in Darjeeling and Kalimpong subdivisions. These zones are influenced by intense rainfall, unstable geological structures, and anthropogenic activities like deforestation and urbanization. Notably, ET, RF, GB, and XGBoost demonstrated efficiency in feature selection, requiring fewer input variables while maintaining high performance. This study establishes a benchmark for landslide susceptibility mapping, providing a scalable and adaptable framework for geospatial hazard prediction. The findings hold significant implications for land-use planning, disaster management, and environmental conservation in vulnerable regions worldwide.
Ionic Remote α-C–H Allenylation of Silyl Ethers Involving a [1,5]-Hydride Shift Promoted by Silylium-Ion Regeneration
BRISC inactivation alleviates alcohol-induced liver injury in mice
Sulfur Lone Pairs Open Avenues for π* → n Orange-to-Red TADF and OLEDs
De novo Transcriptomic analysis to unveil the deltamethrin induced resistance mechanisms in Callosobruchus chinensis (L.)
Photolytic and Thermal Reactions of [C<sub>6</sub>H<sub>4</sub>(PPh<sub>2</sub>)<sub>2</sub>(μ-N<sub>2</sub>)] and Its Lewis Acid Adducts: N–N Bond Cleavage and Liberation of N<sub>2</sub>
Oxidative stress index-based scoring for prediction of long-term prognosis in patients with colorectal cancer with liver metastases
Dimerization of Hexaphyrin with an Appendant Pyrrole Possessing a Reactive Site to Alleviate the Steric Hindrance
Evaluation of the effects on the tensile properties of medical gloves after repeated disinfection
Pd<sub>1</sub>Ni<sub>2</sub> Trimer Sites Drive Efficient and Durable Hydrogen Oxidation in Alkaline Media
Author Correction: Public attitudes towards social media field experiments
Polarization Switching from Valence Trapping in an Oxo-Bridged Trinuclear Iron Complex
Development and validation of a stability-indicating HPLC method for the simultaneous determination of anticoccidial drugs in veterinary formulations: greenness and whiteness assessment
Abstract Intestinal coccidiosis is a significant parasitic disease affecting poultry, resulting in substantial economic losses for the industry. It compromises the nutrition absorption, leading to weight loss and elevated mortality rates. Furthermore, the stress caused by the infection can compromise the immune system, making poultry more susceptible to secondary infections and reducing overall productivity. As a result, simple analytical techniques are critical for determining anticoccidial drugs. A new, sensitive, and environmentally friendly HPLC method was developed for determining amprolium (AMP), sulfaquinoxaline (SUL), diaveridine (DIV), and vitamin K3 (VIT K3) in their formulations for the first time. Stability tests were performed under diverse stress conditions to verify the safety and efficiency of the formulation throughout its designated shelf time. These investigations ascertain the influence of various environmental conditions on a drug’s chemical stability and physical characteristics. A Supelcosil C18 column was used as the stationary phase, and 0.05 M KH2PO4 and acetonitrile were mixed in a ratio of 80:20 (v/v) as the developing system with a flow rate of 2.0 mL min−1. The proposed drugs were quantified at 260 nm. It was tested and found that the novel analytical method was linear for AMP and SUL between 20.0 and 60.0 µg mL−1, 2.0–6.0 µg mL−1 for VIT K3, and 2.1–6.3 µg mL−1 for DIV. The anticipated method was validated according to ICH guidelines. Advanced evaluation tools, such as GAPI, Red Green Blue (RGB 12, whiteness), Blue Applicability Grade Index (BAGI), the Analytical Eco-Scale, and (AGREE) assessed the sustainability profile of the proposed method, illustrating its enhanced environmental friendliness and sustainability.