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EDA Complex‐Driven Desaturation of Heterocyclic Carbonyl Compounds Enabled by HFIP
Abstract Recently, electron donor–acceptor (EDA) complex‐mediated organic synthetic strategies have emerged as powerful tools for diverse bond‐forming transformations; however, their efficiency often diminishes when ionic reactants are involved. This limitation arises from the requirement of polar solvents such as DMSO or DMF to solubilize ionic species for the formation of effective EDA complex. Consequently, these solvents engage in competing EDA complex formation or disrupt ionization equilibria. In parallel, there is a pressing necessity of modern and efficient strategy to achieve dehydrogenation reactions, which are in general limited by the drawbacks of traditional approaches. To address both, herein, we disclose an innovative desaturation strategy based on the formation of an EDA complex between a dihydrogenated organic substrate and an N‐methoxy pyridinium salt. In our study, solubility issues, which are associated with the pyridinium salt, are effectively addressed by using hexafluoroisopropanol (HFIP). Beyond enhancing solubility, HFIP also functions as a transient H‐shuttle, significantly reducing the activation energy for this transformation. This cooperative interplay between HFIP and the pyridinium salt enables the efficient and selective desaturation of a broad range of heterocyclic carbonyl compounds—including quinolinones, coumarins, and flavones—which are valuable scaffolds in pharmaceutical and agrochemical research. At the end, detailed mechanistic studies with the aid of experiments as well as DFT studies clearly disclose the mechanism as well as the important role of HFIP in this reaction.
RALGEF inhibitors suppress RAS-driven pancreatic cancer and metastasis
Engineered Cu3P–ZnWO4 heterojunction integrated with porous polymer monolithic template for enhanced photocatalytic degradation of organic pollutants
Abstract The strategic fabrication of efficient, renewable, and sustainable visible photon-responsive advanced heterogeneous photocatalysts is currently relevant for decontaminating pharmaceutical pollutants. Here, we report the fabrication of a unique Cu 3 P–ZnWO 4 (CZ) heterojunction nanocomposite (NC) uniformly decorated onto a porous poly(EGDMA) monolith (PEM) template, which features a remarkable surface area, excellent structural integrity, and high porosity. Varying ratios of Cu 3 P to that of ZnWO 4 reveal a sequence of Z-scheme heterostructured NCs, i.e., CZ-5, CZ-10, CZ-15, CZ-20, and CZ-25. The structurally engineered translucent PEM template and the CZ NCs decorated PEM are characterized by p-XRD, FT-IR, FE-SEM-EDAX, HR-TEM-SAED, VB-XPS, BET/BJH, UV–Vis-DRS, and PL analysis to confirm the formation of the desired photocatalyst with impressive structural and surface morphological features. The photocatalytic degradation efficiency shows that the CZ-20 NC-dispersed PEM (CZ-20@PEM) photocatalyst proffers robust photocatalytic performance for decontaminating moxifloxacin residues. Moreover, to determine the optimal conditions for fast and efficient photocatalysis, the influence of various analytical parameters, including solution pH (2–9), photocatalyst dosage (10–150 mg), pollutant concentration (10–50 ppm), oxidizers (KBrO 3 & H 2 O 2 ), and light intensities (150–300 W/m 2 ) has been comprehensively studied. The CZ-20@PEM photocatalyst exhibits ≥ 99.4% moxifloxacin dissipation in ≤ 20 min, using 240 W/m 2 visible light intensity. Based on VB-XPS analysis and trapping experiments, a feasible photocatalytic mechanism was proposed to clarify the reactive species predominantly participating in the photocatalytic process. This work demonstrates an efficient and sustainable approach for removing moxifloxacin drug residues, underscoring the potential of nanocomposite-encapsulated polymer monoliths as a next-generation photocatalytic platform for future water treatment applications.
Network vulnerability of cattle movement in Minas Gerais, Brazil, from 2013 to 2022
The analysis of networks from cattle movements is an important approach to investigate areas and premises where disease outbreaks can occur and be contained. Vulnerability analysis allows a more profound understanding of the network by combining network measures with the strategic removal of nodes, helping to identify more vulnerable areas and the best metrics to support disease control planning. Therewith, the aim of this study was to analyze the network vulnerability of cattle movements from 2013 to 2022, in Minas Gerais, Brazil and to identify the spatial spreaders into the network to improve infectious disease control programs by targeted risk-based surveillance and intervention. The vulnerability was calculated considering the graphs diameter and the spatial spreaders with a threshold distance of 300 km, for incoming (IN) and outgoing (OUT) movements. Additionally, a risk-based analysis was performed in the more vulnerable region. The results showed Triângulo Mineiro/ Alto Paranaíba with higher vulnerability and many IN spatial spreaders, as well as Vale do Mucurí region with many OUT spatial spreaders. The risk-based analysis revealed betweenness and out degree as the most effective measures to be considered for intervention. Therefore, the vulnerability analysis and the spatial spreader were observed as great tools for risk-based interventions and surveillance. Furthermore, Triângulo Mineiro/ Alto Paranaíba and Vale do Mucuri regions were important regions, considering restriction of animal infectious disease spread in Minas Gerais, Brazil.
Structural interactions of ankyrin B with NrCAM and β2 spectrin
Automated classification of lung cancer subtypes cells using microscopic images and ensembled deep learning architectures
Abstract In the intricate domain of lung cancer diagnostics, this research presents a groundbreaking for the early detection of lung cancer at the cellular level which remains a critical challenge due to the subtle morphological differences among its subtypes. This study introduces a hybridized deep‐learning framework that combines the global feature expertise of ResNet-50 with the spatial-attention capabilities of Attention U-Net to analyse microscopic images of individual lung cells. A comprehensive image‐processing pipeline featuring Contrast Limited Adaptive Histogram Equalization (CLAHE), median‐filter for denoising, Otsu’s adaptive thresholding for feature extraction and targeted grey- midtone lightening to amplify diagnostically irrelevant textures and suppresses artifacts, which boosts the signal-to-noise ratio by 23%. Using a balanced dataset of 4,650 grayscale images (1,500 per subtype) and enriched extensive image augmentations the model learned robust representations across adenocarcinoma, neuroendocrine carcinoma, and squamous cell carcinoma. On a 25% hold-out test set, it achieved 99.85% overall accuracy, with precision, recall, and F1-scores all exceeding 0.99 (for neuroendocrine carcinoma). Five-fold stratified cross-validation confirmed this performance (mean accuracy 99.69% ± 0.16%), demonstrating exceptional consistency and minimal variance. By detecting cancer at its very inception in single‐cell images, this approach paves the way for ultra early diagnostics and personalized treatment planning in clinical practice at the very initially cellular level.
Photocatalytic Partial Water Dissociation by Protonated Carbon Nitride for Hydrogenation Reactions
Abstract Catalytic hydrogenation using pressurized hydrogen at elevated temperature is of utmost importance in refinery and fine chemical industries, but heavily relies on the hydrocarbon reforming industry. Photocatalytic water dissociation provides a sustainable solution for hydrogenation; however, the strong O─H bond and instant recombination of generated hydrogen atoms and hydroxyl radicals need to be solved. Here, we demonstrate efficient hydrogenation of unsaturated aromatics using water by a protonated graphitic carbon nitride photocatalyst with dioxane as a hydroxyl radical trap under mild conditions. The dissociation energy of the H─O bond is reduced from 5.15 to only 1.48 eV, enabling the formation of H atoms and surface adsorbed hydroxyl radicals ( • OH ads ) under visible light. The • OH ads reacts with dioxane, yielding value‐added dioxane‐2‐ol and prolonging the lifetime of hydrogen atoms for hydrogenation. A high quantum efficiency of ∼6% can be realized under visible light irradiation for the selective hydrogenation of aromatic bromides and aldehydes with high conversion. This approach is scalable in a flow system, revealing financial and environmental potential for hydrogenation and deuteration at a practical level.
High-Resolution Melting assays development for discrimination of fungal pathogens causing Grapevine Trunk Diseases
Grapevine Trunk Diseases are a set of fungal diseases that mainly affect wood tissues of grapevine, reducing plant fitness and yield. These diseases limit grape production, so producers need to employ several management strategies to avoid great losses. Nevertheless, due to complex etiology and irregular symptom onset that can be influenced by environmental conditions, producers find it difficult to predict outbreaks and thus to implement management practices in a timely manner. Additionally, fungal infections can remain quiescent for several years, colonizing the plant tissues without symptoms, with symptoms emergence usually occurring several years later. Therefore, the identification of infected grapevines is essential for the definition of effective management strategies. The objective of this work was to design a set of assays based on High Resolution Melting that can detect and identify the most common fungal species responsible for four Grapevine Trunk Diseases. The beta-tubulin gene ( Tub2 ) was selected to design an HRM assay, considering a 130 bp amplicon. The assays distinguish ten of the most common fungal species responsible for Botryosphaeria dieback, Esca, Phomopsis dieback and Eutypa dieback, based on difference curve profiles against a known reference, Neofusicoccum parvum . When applied to mixtures of two fungal species, this strategy allowed both the identification and relative quantification of the pathogens. The application of such an analysis to grapevines will allow the assessment of the health status of the vines so informed management practices can be applied. This will also contribute to the reduction of the potentially pathogenic fungal load by the removal of part or the whole infected plant, ultimately reducing viticulture production costs.
Conserved +1 translational frameshifting in the Saccharomyces cerevisiae gene encoding YPL034W
A portable dry-electrode ECG device for rapid and accurate neonatal heart rate monitoring during resuscitation
Trend analysis and prediction of fabric tear performance testing processes based on the BLTT-FT model
Fabric tearing performance testing experiment is an important part of evaluating fabric durability. The aim of this paper is to solve the problem of real-time prediction of fabric tearing performance testing by effectively extracting key features from experimental data and constructing a prediction model applicable to the process of fabric tearing performance testing. In this study, the trend prediction model for the experimental process of fabric tear performance testing (BLTT-FT) based on the “bidirectional long- and short-term attention mechanism” is adopted. A prediction model combining the improved Bi-directional Long Short-Term Memory (BiLSTM) structure, Transformer encoding layer, and Temporal Convolutional Network (TCN) layer is proposed. While considering sequence information globally, the model captures the bidirectional dependence of time series, reduces model complexity through the TCN layer, and finally optimizes prediction accuracy via the fully connected layer and activation function, thus achieving multi-step prediction. Analysis of variance (ANOVA) indicates that, across multiple datasets constructed from fabrics with different elasticity grades, the model shows extremely significant differences (p < 0.001) in the metrics of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) at each prediction step. Furthermore, it maintains a low error level even in the long-range prediction scope: the average RMSE of multi-step prediction is 0.0881, the average MAE of multi-step prediction is 0.0609, the average MAPE of multi-step prediction is as low as 3.06%, and the average coefficient of determination (R 2 ) of multi-step prediction is as high as 0.9572. The ablation experiments confirm that multi-modular hierarchical modeling effectively solves the problem of detail accuracy of single-step prediction and long-range dependence of multi-step prediction. The results show that the proposed model performs well in real-time trend prediction results for different data sets constructed from fabrics with different elasticity grades. By predicting the dynamics of the experimental process of fabric tearing performance testing in real time, this study has exploratory value in improving the experimental efficiency and optimizing the experimental process.
J-domain proteins cooperate with Hsp70 to drive multiphase separation of RNA-binding-deficient TDP-43
Reinforcement learning with graph neural network (RL-GNN) fusion for real-time financial fraud detection: a context-aware community mining approach
Synergistic Mechanism of Nanochannels and Chain Migration in Bioinspired Solid‐State Gel Electrolytes for Ultrahigh‐Rate, Long‐Life Flexible Tin‐Air Batteries
Abstract Flexible high‐performance tin‐air batteries (FTABs) exhibit transformative potential for wearable electronics. Nonetheless, the rate performance and cycle life are still limited by the sluggish hydroxide ion transport in gel polymer electrolytes (GPEs), along with the inactive tin and dendrites. Herein, drawing inspiration from the structure of neurons, we show a calcium‐coordinated multidimensional nanochannel GPE, incorporating one‐ and two‐dimensional of polyacrylamide, cellulose nanofibers‐graphene oxide, and calcium ion‐adsorbed single‐layer montmorillonite (PAM/CNF‐GO/CaSMMT). The bionic GPEs demonstrate remarkable tensile property (1350%), and ultra‐high ionic conductivity (294 mS·cm −1 ). The integration of kinetic isotope effect (KIE) technology and molecular dynamics simulations has, for the first time, elucidated the continuous multidimensional nanochannels and chain migration mechanism. The FTABs exhibit an ultralong cycle life up to 195 h, outperforming the previous tin‐air batteries. In addition, the device achieves an exceptional rate capability of 50 mA·cm −2 , while almost maintaining structural integrity under various deformations. This work has driven the development of flexible tin‐air batteries and set a new benchmark for the next generation of wearable energy storage.
Gender differences in well-being among people living with non-communicable disease: The influence of social capital and grants
Background This study explores how non-communicable diseases (NCDs), social capital, and government grants (social grants) influence subjective well-being (SWB) among individuals aged 40 and older in rural South Africa. Understanding gender differences in these relationships provides insights for improving public health interventions in resource-constrained settings. Methods Data from 2,432 participants in the HAALSI Wave 3 study were analyzed to examine the predictors of SWB using regression models. Key covariates included age, education, marital status, employment, wealth, religion, social capital, and social grants. Interaction effects between NCDs, social capital, and social grants were evaluated, with gender-stratified analyses to explore disparities. SWB scores were computed, and statistical significance was assessed at various thresholds. Results About a third of the sample had hypertension (58%), one-fifth had diabetes (20%), and nearly two-fifths had depression (36%). Having an NCD) was significantly associated with lower subjective wellbeing (β = −0.855, p < 0.001), with a slightly stronger negative effect observed among women than men. Older age (particularly 80+), and lower education were also associated with reduced wellbeing. Social capital did not moderate the negative impact of NCDs, as individuals with NCDs reported similarly low wellbeing regardless of high or low social capital. However, access to social grants showed some buffering effect: individuals with NCDs and high social grants reported better wellbeing outcomes compared to those with NCDs and low grants, particularly among males. Health insurance coverage was positively associated with wellbeing across all groups. Conclusions These findings suggest that while NCDs significantly reduce wellbeing, social capital alone may not mitigate this burden, whereas targeted material support through grants may offer partial protection, particularly for men. We recommend the development of NCD financing strategies within the public healthcare funding schemes.