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Abstract Predicting rainfall is a challenging and critical task due to its significant impact on society. Timely and accurate predictions are essential for minimizing human and financial losses. The dependence of approximately 60% of agricultural land in India on monsoon rainfall implies the crucial nature of accurate rainfall prediction. Precise rainfall forecasts can facilitate early preparedness for disasters associated with heavy rains, enabling the public and government to take necessary precautions. In the North-Western Himalayas, where meteorological data are limited, the need for improved accuracy in traditional modeling methods for rainfall forecasting is pressing. To address this, our study proposes the application of advanced machine learning (ML) algorithms, including random forest (RF), support vector regression (SVR), artificial neural network (ANN), and k-nearest neighbour (KNN) along with various deep learning (DL) algorithms such as long short-term memory (LSTM), bi-directional LSTM, deep LSTM, gated recurrent unit (GRU), and simple recurrent neural network (RNN). These advanced techniques hold the potential to significantly improve the accuracy of rainfall prediction, offering hope for more reliable forecasts. Additionally, time series techniques, including autoregressive integrated moving average (ARIMA) and trigonometric, Box-Cox transform, arma errors, trend, and seasonal components (TBATS), are proposed for predicting rainfall across the altitudinal gradients of India’s North-Western Himalayas. This approach can potentially revolutionise how we approach rainfall forecasting, ushering in a new era of accuracy and reliability. The effectiveness and accuracy of the proposed algorithms were assessed using meteorological data obtained from six weather stations at different elevations spanning from 1980 to 2021. The results indicate that DL methods exhibit the highest accuracy in predicting rainfall, as measured by the root mean squared error (RMSE) and mean absolute error (MAE), followed by ML algorithms and time series techniques. Among the DL algorithms, the accuracy order was bi-directional LSTM, LSTM, RNN, deep LSTM, and GRU. For the ML algorithms, the accuracy order was ANN, KNN, SVR, and RF. These findings suggest that altitude significantly affects the accuracy of the models, highlighting the need for additional weather stations in this mountainous region to enhance the precision of rainfall prediction.

Abstract Neuroendocrine tumor (NET) is a rare malignant tumor, notably small cell lung cancer (SCLC), a type of lung neuroendocrine tumor, which has a survival rate of less than 7%. Although various biomarkers including CHGA (Chromogranin A), INSM1 (Insulinoma-associated protein 1), and SYP (Synaptophysin) are extensively used for the diagnostic testing of NET, their diverse specificities and sensitivities are acknowledged as limitations. Here, we demonstrate that RepID (Replication initiation determinant protein), a component of CRL4 (Cullin-RING ubiquitin E3 ligase 4), holds promise as a biomarker for identifying NET and SCLC. Analysis of the Cancer Cell Line Encyclopedia (CCLE) via the CellMinerCDB portal reveals a high correlation between RepID transcript levels and mRNA expression of NE signature genes. Additionally, RepID protein is highly expressed in SCLC patient tissues and a subset of SCLC cell lines. Viability analysis following treatment with pevonedistat and SZL-P1-41 in SCLC cell lines and human SCLC-organoid models indicates that RepID expression determines the sensitivity to CRL-targeting anti-cancer drugs. These findings suggest that RepID represents a novel biomarker for NET and SCLC, and insights from RepID research in these cancers could lead to innovative therapeutic strategies.

Abstract In the race to combat ever-evolving diseases, the drug discovery process often faces the hurdles of high-cost and time-consuming procedures. To tackle these challenges and enhance the efficiency of identifying new therapeutic agents, we introduce VirtuDockDL, which is a streamlined Python-based web platform utilizing deep learning for drug discovery. This pipeline employs a Graph Neural Network to analyze and predict the effectiveness of various compounds as potential drug candidates. During the validation phase, VirtuDockDL was instrumental in identifying non-covalent inhibitors against the VP35 protein of the Marburg virus, a critical target given the virus’s high fatality rate and limited treatment options. Further, in benchmarking, VirtuDockDL achieved 99% accuracy, an F1 score of 0.992, and an AUC of 0.99 on the HER2 dataset, surpassing DeepChem (89% accuracy) and AutoDock Vina (82% accuracy). Compared to RosettaVS, MzDOCK, and PyRMD, VirtuDockDL outperformed them by combining both ligand- and structure-based screening with deep learning. While RosettaVS excels in accurate docking but lacks high-throughput screening, and PyRMD focuses on ligand-based methods without AI integration, VirtuDockDL offers superior predictive accuracy and full automation for large-scale datasets, making it ideal for comprehensive drug discovery workflows. These results underscore the tool’s capability to identify high-affinity inhibitors accurately across various targets, including the HER2 protein for cancer therapy, TEM-1 beta-lactamase for bacterial infections, and the CYP51 enzyme for fungal infections like Candidiasis. To sum up, VirtuDockDL combines user-friendly interface design with powerful computational capabilities to facilitate rapid, cost-effective drug discovery and development. The integration of AI in drug discovery could potentially transform the landscape of pharmaceutical research, providing faster responses to global health challenges. The VirtuDockDL is available at https://github.com/FatimaNoor74/VirtuDockDL .

Abstract Melanoma is the most aggressive type of skin cancer and can metastasize during primary tumor formation. This research aimed to determine the relationship between the prevention of melanoma development in a mouse model treated with liposomes loaded with soybean lunasin and amaranth unsaponifiable matter (UM + LunLip) and cell cycle arrest. Tumors excised from C57BL/6 mice treated topically or subcutaneously with UM + LunLip were subjected to immunohistochemistry. Markers related to cell cycle inhibition (p16, p21, p27, and p53) and markers involved in cell cycle progression (cyclin-dependent kinase, CDK6, and cyclin D1) were assessed. The results showed that UM + LunLip had antitumor activity in C57BL/6 mice treated either topically or subcutaneously by p16, p21, p27, and p53 overexpression (up to 572-, 134-, 30-, and 57-fold change, FC, respectively) in the tumors of mice treated with 30 mg UM + LunLip/kg body weight compared with the tumor-bearing untreated control. However, CDK6 and cyclin D1 expression was not inhibited (up to 1.37 FC and 2.09 FC, respectively), which is a typical behavior of cyclin D in melanoma. Therefore, melanoma tumor development was prevented by the overexpression of cell cycle inhibitors p16, p21, p27, and p53 due to UM + LunLip treatments. Since the topical application was effective, less invasive, and more practical for the user, this application will be recommended for future steps in in vivo studies.

Abstract Compare Zcalc with other formulas on spherical equivalent prediction errors (PE) and explore the relationship between PE and ocular parameters. Optimize 709 IOL power calculation. 114 eyes with age-related cataract and preoperative regular corneal astigmatism of more than 1.5D were included. They underwent cataract surgery followed by implantation of a toric intraocular lens (Zeiss Torbi 709 M) calculated by Zcalc formula. The prediction errors of spherical equivalent (SE) were analysed 3 months post-surgery. The refractive prediction errors of Zcalc, Barrett Universal II, Kane, and K6 formulas were 0.07 ± 0.63D, − 0.36 ± 0.65D, − 0.41 ± 0.67D, and − 0.31 ± 0.66D, respectively. Statistically significant difference were observed between Zcalc and other formulas. The Zcalc was prone to positive deviation, Barrett Universal II, Kane and K6 were prone to negative deviation. When the axial length is greater than 29.0 mm, the prediction errors of Kane is higher, and prone to negative deviation. Prediction errors of Zcalc are small in all eyes and prone to positive deviation. For more accurate prediction, we recommend Barrett Universal II formula for 709 IOL power calculation.

Abstract To explore the relationship between morphological and hemodynamic parameters, baseline characteristics, and long-term outcomes in patients with moyamoya disease (MMD) using a computational fluid dynamics model. We retrospectively reviewed 129 patients at Beijing Tiantan hospital between July 2020 and December 2021. Perioperative clinical variables and Suzuki stage were recorded. Logistic regression analysis was employed to identify the risk factors for unfavorable long-term outcomes. The association between morphological, CT perfusion parameters, hemodynamic parameters and the Suzuki stage, clinical variables of MMD was also analyzed. Patients with high relative Wall Shear Stress (rWSS) were older and had more cases with higher Suzuki stage and worse follow-up mRS scores (p

Abstract Among the various classes of fast-adapting (FA) tactile afferents found in hairy and glabrous skin, FA2 afferents, associated with Pacinian corpuscles (PC), preferentially signal high-frequency sinusoidal events corresponding with vibration percepts, in contrast to other classes associated with lower frequency flutter percepts. The FA2-PC complex is also uniquely sensitive to distant sources of vibration mechanically transmitted through anatomical structures. In the present study, we used a pulsatile waveform to assess the contribution of FA2 afferents to the perception of flutter-range frequency stimuli (~ 20 Hz) in combination with two methods to abolish local FA inputs and force a dependence on FA2 via transmission from adjacent structures. Firstly, we examined frequency discrimination and perception of vibration applied to the hairy skin overlying the ulnar styloid before and during the blockade of intradermal receptors by local anaesthesia. Secondly, we tested frequency discrimination on the digital glabrous skin before and during the blockade of myelinated fibres by ulnar nerve compression. Despite reliance on vibration transmission to activate remote PCs, we found that flutter-range frequency discrimination was unimpeded across both skin types. Comparisons with stimuli applied to the contralateral side also indicated that perceived frequency was unaffected. This confirms that flutter-range frequency perception can be encoded by the FA2-PC system. Our results demonstrate that input from receptors specialised for low-frequency signalling is not mandatory for flutter-range frequency perception. This explains how the constancy of frequency perception might be achieved across different skin regions, irrespective of the afferent type activated for transmitting these signals.

Abstract Drosophila suzukii (D. suzukii), a pervasive pest originating from Southeast Asia, presents a substantial risk to global agriculture. The ability of the female flies to lay eggs within fruits of varying maturity stages, combined with the accelerated offspring development within warmer climates, results in rapid population growth. This poses significant challenges for fruit production and viticulture, exacerbated by the increasing prevalence of pesticide resistance. We propose a solution to this growing issue using an attract-and-kill approach by making use of upcycled organic waste materials made from orange peels. Specifically, we have tested an innovative salty orange peel product (OPP) in a choice experiment, in which OPP and hydrogel (control) were made available to fruit flies in Petri dishes situated beneath red wine grapes. The number of dead flies in both Petri dishes were counted each day and fly maggots inside berries were extracted after four days. Since Petri dishes were covered with a red lid, flies only selected on the basis of olfactory cues. Our results showed a higher number of captured flies in Petri dishes containing OPP compared to those with the hydrogel control. Furthermore, a notable reduction in the number of maggots was observed inside grapes located above OPP compared to the grapes closer to the hydrogel control. Dilution of OPP was followed by a lower count of dead flies. In additional choice experiments, the concentration of NaCl was found to be positively correlated with the number of dead flies. This suggests an important lethal effect caused by high salt concentrations. In a final experiment, OPP was also compared to the commercially-available attractant called Drosalure™, which resulted in a slightly higher attractiveness of OPP to D. suzukii. These findings suggest that OPP holds potential as a cost-efficient and eco-friendly biocide made from organic waste material. OPP offered in attract-and-kill traps equipped with small entry holes is safe for bees and may replace other less eco-friendly control measures for D. suzukii in organic vineyards.

Abstract Cells can compensate a disruptive change in one ion channel by compensatory changes in other channels. We have simulated the adaptation of a multicellular aggregate of non-excitable cells to the electrophysiological perturbation produced by the external blocking of a cation channel. In the biophysical model employed, we consider that this blocking provokes a cell depolarization that opens a voltage-gated calcium channel, thus allowing toxic Ca2+ levels. The cell adaptation to this externally-induced perturbation is ascribed to the multiplicity of channels available to keep the cell membrane potential within a physiological window. We propose that the cell depolarization provokes the upregulated expression of a compensatory channel protein that resets the cell potential to the correct polarized value, which prevents the calcium entry. To this end, we use two different simulation algorithms based on deterministic and stochastic methods. The simulations suggest that because of the local correlations coupling the cell potential to transcription, short-term bioelectrical perturbations can trigger long-term biochemical adaptations to novel stressors in multicellular aggregates. Previous experimental data on planarian flatworms’ adaptation to a barium-containing environment is also discussed.

Abstract Male germ cell tumors (GCT) have excellent survival. Long-term sequelae in cancer survivors are an evolving field. We evaluated the risk of patients with GCT to develop primary hypogonadism and adherence to guideline-recommended therapy in a real-world cohort. Monocentric study at a tertiary cancer centre to evaluate treated GCT-patients (2001–2019). Post therapeutic male endocrine function, International Index of Erectile Function (IIEF)-5 and The aging males’ symptoms rating scale (AMS) questionnaires were assessed. The overall response rates were low, with 44 of 402 contacted patients participating in the study. From these, 32(73%) underwent blood analysis, 42(95%) answered the IIEF-5 and 43(98%) the AMS. Latent hypogonadism (serum testosterone 8–12 nmol/l) was found in n = 9 (28%) and manifest hypogonadism (testosterone 50%, which underscored the clinical need to evaluate endocrine function in cancer survivors. We further indicated the difficulties of today’s research and provided starting points to assess barriers for study participations.

Abstract Trachinotus blochii is a high-value tropical mariculture species. The present study evaluated the gut microbial impact of therapeutic exposure (80 mg/day/kg biomass for 10 days) to oxytetracycline, the most common aquaculture antibiotic in T. blochii. The cultivable counts, α-diversity measures of taxonomic and functional metagenomics, microbial dysbiosis (MD) index, and microbial taxon abundances showed the resilience of gut microbiota at 16–26 days of treatment. A significant reduction in bacterial abundance, diversity measures, Firmicutes and Actinobacteria and an increase in γ-Proteobacteria was recorded on the 6th and 11th day of treatment. The increased metagenomic stress signatures, decreased beneficial bacterial abundances, decreased abundance of microbial pathways on energy metabolism, and MD index indicated short-term transient stress during the initial days of therapeutic withdrawal, warranting health management measures. Therapeutic exposure reduced the abundance of fish pathogens, including Vibrio spp., kanamycin and ampicillin-resistant bacteria. Strikingly, oxytetracycline treatment did not increase tetracycline-resistant bacterial counts and the predicted abundance of tetracycline resistance encoding genes in the gut, illustrating that therapeutic application would not pose a risk in the context of antimicrobial resistance in short term. Altogether, the present study provides a foundation for oxytetracycline treatment to develop suitable risk minimization tactics in sustainable aquaculture.

Abstract This paper proposes a new optimization technique to make an integration between the Optimal Reactive Power Dispatch (ORPD) problem and Electric Vehicles (EV). Here, a modified metaheuristic algorithm, called the Promoted Osprey Optimizer (POO) is used for this purpose. Inspired by the hunting behavior of ospreys, a predatory bird species, the POO algorithm employs various strategies like diving, soaring, and gliding to effectively explore the search space and avoid local optima. To evaluate its performance, the POO-based model has been applied to the IEEE 118-bus and IEEE 57-bus systems, considering different scenarios of EV penetration. The experimental findings demonstrate that the POO algorithm can effectively optimize the reactive power dispatch problem with EV integration, achieving significant reductions in active power losses and voltage deviations toward several existing metaheuristic optimization techniques in different terms. The POO algorithm demonstrates a significant reduction in power loss, achieving up to 22.2% and 16.2% in the 57-bus and 118-bus systems, respectively. This improvement is accompanied by reductions in voltage deviation of up to 20.6% and 15.7%. In the 57-bus system, power loss is reduced from 2.35 MW to 1.93 MW, while voltage deviation decreases from 0.034 p.u. to 0.027 p.u. For the 118-bus system, power loss is lowered from 4.21 MW to 3.53 MW, and voltage deviation is reduced from 0.051 p.u. to 0.043 p.u. Furthermore, the POO algorithm surpasses other optimization methods in minimizing voltage deviation, achieving reductions of up to 0.056 p.u. in the 57-bus system and up to 0.163 p.u. in the 118-bus system. Consequently, the POO algorithm holds great potential as a valuable tool for power system operators and planners to optimize reactive power dispatch and enhance power system performance with EV integration.

Abstract Unlike classic audio guides, intelligent audio guides can detect users’ level of attention and help them regain focus. In this paper, we investigate the detection of mind wandering (MW) from eye movements in a use case with a long focus distance. We present a novel MW annotation method for combined audio-visual stimuli and collect annotated MW data for the use case of audio-guided city panorama viewing. In two studies, MW classifiers are trained and validated, which are able to successfully detect MW in a 1-s time window. In study 1 (n = 27), MW classifiers from gaze features with and without eye vergence are trained (area under the curve of at least 0.80). We then re-validate the classifier with unseen data (study 2, n = 31) that are annotated using a memory task and find a positive correlation (repeated measure correlation = 0.49, p < 0.001) between incorrect quiz answering and the percentage of time users spent mind wandering. Overall, this paper contributes significant new knowledge on the detection of MW from gaze for use cases with audio-visual stimuli.

Abstract Low fluid shear stress (FSS, ≤ 2 dyn/cm2) has been shown to exert protective effects on chondrocytes, but the underlying molecular mechanisms remain unclear. This study aimed to elucidate the mechanisms by which FSS promotes chondrocyte proliferation and extracellular matrix (ECM) stability. We exposed SW1353 chondrocytes to low FSS (1.8 dyn/cm2, 60 min) and found that it led to a significant downregulation of microRNA-143-3p (miR-143-3p), which was associated with increased chondrocyte proliferation and ECM secretion, including type II collagen (COL2A1) and aggrecan. Further investigation revealed that miR-143-3p directly targeted ERK5, a key component of the ERK5/KLF4 signaling pathway. Overexpression of miR-143-3p suppressed ERK5/KLF4 pathway activation, resulting in reduced chondrocyte proliferation and ECM production. Our findings demonstrate that low FSS promotes chondrocyte proliferation and ECM secretion by downregulating miR-143-3p, leading to the activation of the ERK5/KLF4 signaling pathway. This study reveals a novel mechanism by which FSS regulates chondrocyte behavior and ECM secretion, highlighting the potential of FSS as a therapeutic target for cartilage-related diseases.

Abstract In conservation strategies, getting precise and repeatable information on the species’ diet and health without relying on invasive or laborious methods is challenging. Here, we developed an efficient and non-invasive workflow for the sequencing and analysis of four taxonomic markers from fecal DNA to characterize the gut microbiota, parasites, and plants and lichens composing the winter diet of caribou (Rangifer tarandus), Canada’s most iconic endangered species. Sequencing of the 18S rRNA gene of eukaryotes from seven locations in Manitoba and Saskatchewan, Canada, allowed for the detection of five genera of parasites in caribou feces (including Nematodirella and Parelaphostrongylus) with variable frequency of occurrence depending on sampling location and sex. Our workflow also revealed a rich winter plant and lichen diet in caribou, with respectively 29 and 18 genera identified across all samples through plant and fungal ITS2 sequencing. Relationships between the gut microbiota and both the diet and parasite richness were also identified. Of note, the Central Saskatchewan sampling location was characterized by a clearly distinct gut microbiota which could be linked to an epiphytic lichen-rich diet. Overall, our results showed the potential of this multi-marker DNA metabarcoding workflow as an efficient tool to provide insights into the species biology and ecology.

Abstract DNA damage occurs in all living cells. γ-H2AX imaging by fluorescent microscopy is widely used across disciplines in the analysis of double-strand break (DSB) DNA damage. Here we demonstrate a method for the quantitative analysis of such DBSs. Ionising radiation, well known to induce DSBs, is used in this demonstration, and additional DBSs are induced if high-Z nanoparticles are present during irradiation. As a deliberate test of the methodology, cells are exposed to a spatially fractionated ionising radiation field, characterised by regions of high and low absorbed radiation dose that are only ever qualitatively verified biologically via γ-H2AX imaging. Here we validate our bio-dosimetric quantification method using γ-H2AX assays in the assessment of DSB enhancement. Our method reliably quantifies DSB enhancement in cells when exposed to either a spatially contiguous or fractionated irradiation fields. Using the γ-H2AX assay, we deduce the biological dose response, and for the first time, demonstrate equivalence to the independently measured physical absorbed dose. Using our novel method, we are also able quantify the nanoparticle DSB enhancement at the cellular level, which is not possible using physical dose measurement techniques. Our method therefore provides a new paradigm in γ-H2AX image quantification of DSBs, as well as an independently validated bio-dosimetry technique.

Abstract Previous studies identified individual-level socioeconomic factors as key determinants of cognitive health. This study investigated the effect of area-based socioeconomic deprivation on cognitive outcomes in midlife to early late-life New Zealanders without cognitive impairment at baseline. Data stemmed from a subsample of the New Zealand Health, Work and Retirement Study, a cohort study on ageing, who completed face-to-face interviews and were reassessed two years later. Cognitive functioning was measured using Addenbrooke’s Cognitive Examination–Revised, adapted for culturally acceptable use in Aotearoa New Zealand. Area-based socioeconomic deprivation was assessed using the New Zealand Deprivation Index (NZDep2006). Linear mixed-effects models analysed the association between area-based socioeconomic deprivation and cognitive outcomes. The analysis included 783 participants without cognitive impairment at baseline (54.7% female, mean age 62.7 years, 25.0% Māori, the Indigenous people of Aotearoa New Zealand). There was an association between higher area-based socioeconomic deprivation and lower cognitive functioning (B = -0.08, 95%CI: -0.15;-0.01; p = .050) and cognitive decline (B = -0.12, 95%CI: -0.20;-0.04, p = .013) over two years, while controlling for covariates. The findings emphasise the importance of considering neighbourhood characteristics and broader socioeconomic factors in strategies aimed at mitigating cognitive health disparities and reducing the impact of dementia in disadvantaged communities.

Abstract The expression of tsRNA in white adipose tissue (WAT) of VD deficiency male mice with obesity has not been reported. The healthy male C57BL/6J mice aged 4-6 weeks were divided into 4 groups according to the VD3 and fat energy supplement in daily diets. The qPCR verification further demonstrated that tRF5-20-HisGTG-3 were significantly up-regulated and mt-tRF3a-ProTGG was significantly down-regulated not only in HFVDD vs HFVDS, but aslo in HFVDD vs ConVDS. tRF5-22-CysGCA-27 were significantly up-regulated and mt-5'tiRNA-32-SerTGA, mt-5'tiRNA-33-SerTGA and mt-5'tiRNA-33-AlaTGC was significantly down-regulated only in HFVDD vs ConVDS. Enrichment analysis of the qPCR verified DE tsRNAs showed that the 3 up-regulated tsRNAs seemed to be associated with FoxO signaling pathway, GnRH secretion, 2−Oxocarboxylic acid metabolism, Autophagy-animal, Glucagon and insulin signaling pathway, while 4 down-regulated tsRNA seemed to be associated with cell communication, primary metabolic process, metabolic process, response to stimulus, multicellular organismal process, cellular metabolic process, cellular process and biological regulation. The tsRNAs were differentially expressed in VD deficiency with obesity, especially tRF5-20-HisGTG-3, tRF5-22-CysGCA-27, tRF3a-GlyGCC-1, mt-5'tiRNA-33-AlaTGC, mt-5'tiRNA-33-SerTGA, mt-5'tiRNA-32-SerTGA and mt-tRF3a-ProTGG. These tsRNAs seemed to be associated with FoxO signaling pathway, GnRH secretion, 2−oxocarboxylic acid metabolism, autophagy, glucagon and insulin signaling pathway, metabolic process and biological regulation.

Abstract To clarify the relationship between GNSS (Global Navigation Satellite System) crustal deformation and the preparation of strong earthquakes in the western Chinese Mainland, a series of methods including velocity field, strain rate field, and strain parameter time series are adopted based on the GNSS observation data from the CMONOC (Crustal Movement Observation Network of China). The abnormal characteristics of crustal deformation movement before the M ≥ 5 earthquakes in the area since 1999 are studied. The corresponding variety of the crustal movement before earthquakes is discussed in combination with the seismogenic structure of earthquakes. The abnormalities of the GNSS velocity field, strain field, and strain parameter time series before earthquakes with different magnitudes are proposed. Finally, the anomaly criterion before medium-strong-large earthquakes in the western Chinese Mainland is proposed according to the GNSS and its combination with the characteristics of geological structure. The results can provide a basis for the forecasting of the trend and location of medium-strong-large earthquakes in the western Chinese Mainland.

Abstract In this research, we present PerceptHashing, a technique designed to categorize million-scale agricultural scenic images by incorporating human gaze shifting paths (GSPs) into a hashing framework. For each agricultural image, we identify visually and semantically significant object patches, such as fields, crops, and water bodies. These patches are linked to form a graphlet, establishing a network of spatially adjacent patches, and a GSP is then extracted using an active learning algorithm. The GSP reflects the distribution of human gaze across different regions of each agricultural scene, typically involving fewer than 12 regions, as validated by cross-validation. We then design a binary hashing framework that effectively leverages the semantics encoded in these GSPs. This framework integrates three key elements: (i) refinement of noisy labels, (ii) incorporation of deep image-level agricultural semantics, and (iii) updates to the adaptive data graph. The resulting hash codes from each GSP are converted into a kernelized visual descriptor for classification. To evaluate the influence of GSPs on agricultural image classification both qualitatively and quantitatively, we conducted an extensive user study comparing GSPs from typical observers with those from Alzheimer’s patients. The results demonstrate that: (1) the classification accuracy of 1.22 million agricultural aerial images using our approach is significantly higher than those processed by other methods, and (2) GSPs from 33 Alzheimer’s patients differ markedly from those of 37 normal observers, leading to notable differences in classification accuracy ( $$acc_{normal}=0.694$$ a c c normal = 0.694 versus $$acc_{patient}=0.322$$ a c c patient = 0.322 ).

Abstract CNS Drug discovery has been challenging due to the lack of clarity on CNS diseases’ basic biological and pathological mechanisms. Despite the difficulty, some CNS drugs have been developed based on phenotypic effects. Herein, we propose a phenotype-structure relationship model, which predicts an anti-neuroinflammatory potency based on 3D molecular structures of the phenotype-active or inactive compounds without specifying targets. For this chemo-centric study, a predictive model of the nitric oxide (NO) inhibitory potency in hyper-activated microglia is built from the 548 agents, which were collected from 95 research articles (28 substructures consisting of natural products and synthetic scaffolds) and doubly externally validated by the agents of 9 research articles as third set. 3D Structures (multi-conformer ensemble) of every agent were encoded into the E3FP molecular fingerprint of the Keiser group as a 3D molecular representation. The location information of the molecular fingerprints could be learned and validated to classify the inhibitory potency of compounds (IC50 cut-off between the active and inactive: 37.1 µM): (1) multi-layer perceptron (MLP) (AUC-CV: 0.997, AUC-Test: 0.992), (2) recurrent neural network (RNN) (AUC-CV: 0.999, AUC-Test: 0.995), and (3) convolutional neural network (CNN) (AUC-CV: 0.998, AUC-Test: 0.994). The high performance of these models was compared with that of four classical machine classification models (Logistic, Ridge, Lasso, and Naïve Bayes). We named the binary classification models NO-Classifier. Independent test set validation and decision region analysis of the independent test set doubly demonstrated NO-Classifier effectively discerned the anti-inflammatory potency of testing compounds in inflammatory cell phenotype with the webserver in https://no-classifier.onrender.com.

Abstract Abdominal aortic aneurysm consists of a permanent dilation in the abdominal portion of the aorta and, along with its associated pathologies like calcifications and intraluminal thrombi, is one of the most important pathologies of the circulatory system. The shape of the aorta is among the primary drivers for these health issues, with particular reference to all the characteristics which affect the hemodynamics. Starting from the computed tomography angiography of a patient, we propose to summarize such information using tools derived from Topological Data Analysis, obtaining persistence diagrams which describe the irregularities of the lumen of the aorta. We showcase the effectiveness of such shape-related descriptors with a series of supervised and unsupervised case studies.

Abstract Traumatic brain injury (TBI) represents a common and severe medical condition necessitating prompt risk stratification to enhance patient outcomes. Although substantial research has been conducted on the prognostic utility of various biomarkers for TBI, no single biomarker has been definitively recognized as the most precise predictor of disease outcomes. In comparison to other markers, the neutrophil-albumin ratio (NAR) has emerged as a cost-effective and reproducible inflammatory biomarker, demonstrating potential in evaluating the severity of inflammation and prognosticating outcomes in infections and cerebrovascular diseases. This study evaluated the prognostic significance of the NAR in comparison to two other readily accessible and cost-effective composite indices: the Neutrophil-Lymphocyte Ratio (NLR) and the Platelet-Lymphocyte Ratio (PLR) in individuals with TBI. We conducted a retrospective cohort analysis involving 297 hospitalized TBI patients, gathering comprehensive demographic, anthropometric, medical, clinical, laboratory, and imaging data to assess the expression changes of these biomarkers. Our findings suggest that both the NAR and the NLR possess predictive value regarding prognosis following TBI. However, receiver operating characteristic (ROC) curve analysis revealed that NAR outperformed NLR as a prognostic predictor. In conclusion, our examination of blood biochemistry composite indicators indicates that, while both NAR and NLR serve as significant prognostic markers, NAR is a more effective predictor of outcomes in patients with TBI.

Abstract Annual outbreaks of Lassa fever have resulted in a public health threat in Nigeria and other endemic countries in Sub-Saharan Africa. While the Lassa Virus (LASV) is endemic in rodent populations, zoonotic spillover to humans causes annual outbreaks. This study reviewed the burden of Lassa fever (LF) in Nigeria between 2020 and 2023 and conducted a cross-sectional survey of Nigerians to evaluate their risk perceptions of LF. During the period under review, 28,780 suspected and 4,036 confirmed cases of LF were reported from 34 of the 37 states of Nigeria. These cases resulted in 762 deaths (a CFR of 18.9%). The overall case positivity rate was 14% (4,036/28,780), with more positive cases in 2020 (17.5%, n = 1,189/6,791). A total of 2,150 study participants were enrolled in the prospective cross-sectional study, with most of them (87.5%, n = 1,881/2,150) having previously heard of Lassa fever (LF). The numerical scoring system revealed that 35.43% (n = 762/1,881) of those aware of LF have poor knowledge of its preventive measures, route of transmission, and control measures. Approximately 6.84% (n = 147/2,150) of them were at a high risk of contracting LF, with 27.6% (n = 584/2,150) of study participants feeling concerned about contracting LF because of the presence of rodents in their immediate vicinity, occupational exposure to healthcare workers, and the probability of contamination of food by infected rodents without necessary food safety measures. Multivariable logistic regression analysis revealed that tertiary education was associated with an increased likelihood of better LF knowledge (OR: 17.32; 95% CI: 10.62, 28.26; p

Abstract The rapid advancement of artificial intelligence and robot technology has spurred the proposal and innovation of a coal gangue sorting robot system (CGSRS) paradigm. The time-varying raw coal flow (TVRCF) with multi-scene and full working conditions affects the gangue queue. Configuring the CGSRS scheme correctly is combative. The field environment puts forward higher requirements for the time complexity of the CGSRS multi-task allocation strategy. Therefore, this paper proposes a scheme evaluation method of the CGSRS with time-varying multi-scenario based on deep learning. Firstly, the gangue queue data set of multi-scene and full-condition TVRCF was obtained according to the belt speed, the maximum coal flow, and the uncorrelated nonlinear changes of coal flow and gangue content. The CGSRS scheme is established based on robot number and rule combination, and the multi-task allocation strategy is adjusted to generate the labels of the gangue queue. Then, the RGB sample set is established based on the labels of the gangue queue. The CGSRS scheme evaluation model is trained based on DenseNet. Finally, the CGSRS scheme evaluation method was designed to realize the prediction of a random gangue queue. In this paper, the CGSRS scheme evaluation model, the stability of the solution, and the comparison of methods are carried out. Experimental results show that the solution of the CGSRS scheme evaluation model is accurate and stable. The time complexity is significantly reduced and very stable. The CGSRS scheme evaluation method is applied to the CGSRS multi-task allocation problem, and the stability of the solution is not affected by the data. It is significantly better than the multi-task allocation strategy. The proposed method is the first attempt to apply deep learning to a multi-task allocation problem in CGSRS.

Abstract Controlling the absorption and diffusion of sound in the audible range constitutes an exciting field of research. Acoustic absorbers and diffusers perform extraordinarily well at high frequencies with sizes comparable to the wavelength of the working frequency. Conversely, efficient low-frequency attenuators demand large volumes leading to unpractical sizes, and there is now interest in determining whether the size of the resonator can be reduced while not compromising – or perhaps even decreasing – the working frequency. One viable approach is through the use of metamaterials to enable the control of device dynamics such that heavy sub-wavelength attenuation can be efficiently realised. To achieve this goal, the theoretical (including a mathematical model and the use of finite element analysis) and experimental characterisation of 3D-printed membrane-coupled Helmholtz resonator (HR) acoustic metamaterials (AMMs) is explored. The results reveal good agreement between theory and experiments, and show that membrane-coupled HR AMMs feature heavy sub-wavelength acoustic attenuation (λ/55) while also showcasing directional responses under open air conditions. These features are explained by the interplay between resonator size, membrane characteristics, and the presence of two acoustic ports. It is anticipated that, together with recent advances on smart AMMs, these systems will foster new progress in the development of dynamic AMMs for wideband attenuation.

Abstract Atrial fibrillation (AF) is a heart disease affecting millions of Americans. Clinicians evaluate AF-related risk by assessing the temporal pattern, variation, and severity of AF episodes through AF burden (AFB). However, existing prognostic tools based on these metrics are suboptimal, as they do not account for electrical complexity of AF signals. This study introduced Electrical Burden (EB) as a new marker to assess electrical instability and complexity of AF. We also developed a Complexity AF score that incorporates AFB, EB, and Poincaré analysis to assess the severity of AF. Electrocardiogram (ECG) from 50 AF patients in the Long-term AF database were analyzed. EB was calculated using four metrics and combined with AFB and Poincaré metrics to derive the Complexity AF score for each patient. Our results show that AFB, EB, and Poincaré metrics are independent markers, each describing different aspects of AF complexity. The Complexity AF score effectively distinguished between terminated (2.82 ± 1.29, 17 patients) and non-terminated AF groups (4 ± 1.46, 33 patients) (p-value

Abstract This study aimed to evaluate the growth performance and health status of commercial pigs in different body weight (BW) groups and develop methods for identifying slow-growing pigs. The research observed 79 commercial pigs grouped from 104 to 202 days of age, collecting data on BW, feed intake, and body condition score (BCS) from caliper. Results showed that BCS were highly correlated with BW (r > 0.85, P

Abstract This study aimed to develop and validate a risk prediction model based on real-world data to assess the risk of delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent video-assisted thoracoscopic (VATS) radical resection. This study is a retrospective study of real-world data. A total of 257 elderly lung adenocarcinoma patients who underwent VATS radical resection from January 2022 to December 2023 in a tertiary hospital in Wuhan were selected. Patients were divided into delayed recovery (n = 42) and non- delayed recovery group (n = 215) according to whether delayed recovery occurred after anesthesia. Lasso regression was used to screen the independent variables. Logistic regression was used to analyze the risk factors of delayed recovery from anesthesia, and a nomogram model was established. Bootstrap method was used to internally verify the nomogram model. Delayed recovery from anesthesia occurred in 42 of 257 elderly lung adenocarcinoma patients underwent VATS radical resection (16.34%). Logistic regression analysis showed that anesthesia duration, intraoperative infusion volume, inhaled desflurane, preoperative respiratory tract infection, intraoperative hypothermia and diagnosed with hypertension were risk factors for delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent VATS radical resection (P

Abstract Purpose The relationship between exposure doses of 2.5-micrometer Particulate Matter (PM2.5), Inhalable particles (PM10), Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2) and Ozone (O3) in the first trimester and spontaneous abortion of pregnant women was evaluated by global average method and nearest monitoring station method, respectively. Method Retrospective analysis of the clinical data of pregnant women with spontaneous abortion and full-term pregnant women in the Department of Obstetrics and Gynecology of two third-class hospitals in a valley city in Northwest China. According to the age factor, the eligible pregnant women were matched at a ratio of 1 : 4. The global average method and the nearest monitoring station method were used to evaluate the exposure of pollutants. The rank-sum test and conditional logistic regression were used to analyze the correlation between air pollutants and spontaneous abortion. Results Although the global average method and the nearest monitoring station method are slightly different in the assessment of exposure dose, they do not affect the correlation evaluation with spontaneous abortion. The exposure of pregnant women to PM2.5(OR1 = 1.156, OR2 = 1.036), SO2 (OR1 = 1.432, OR2 = 1.429) and NO2 (OR1 = 1.121, OR2 = 1.159) in the first trimester is related to the occurrence of spontaneous abortion.(OR1: the global average method, OR2: the nearest monitoring station method) Conclusion The exposure of PM2.5, SO2 and NO2 in the first trimester in valley cities is associated with the occurrence of spontaneous abortion in pregnant women.

Abstract Privacy and security concerns restrict access to original training datasets, posing significant challenges for model compression. Data-Free Knowledge Distillation (DFKD) emerges as a solution, aiming to transfer knowledge from teacher to student networks without accessing original data. Existing DFKD methods struggle to generate high-quality synthetic samples that capture the complexities of real-world data, leading to suboptimal knowledge transfer. Moreover, these approaches often fail to preserve the spatial attributes of the teacher network, resulting in shortcut learning and limited generalization.To address these issues, a novel DFKD strategy is proposed with three innovations: (1) an enhanced DCGAN generator with an attention module for synthesizing samples with improved micro-discriminative features; (2) a Multi-Scale Spatial Activation Region Consistency (MSARC) mechanism to accurately replicate the teacher’s spatial attributes; and (3) an adversarial learning framework that creates a dynamic competitive environment between the generative and distillation phases. Rigorous evaluation of the method on several benchmark datasets, including CIFAR-10, CIFAR-100, Tiny-ImageNet, and medical imaging datasets such as PathMNIST, BloodMNIST, and PneumoniaMNIST, demonstrates superior performance compared to existing DFKD methods. Specifically, on CIFAR-100, the student network attains an accuracy of 77.85%, surpassing previous methods like CMI and SpaceshipNet. On BloodMNIST, the method achieves an accuracy of 80.50%, outperforming the next best method by over 5%.

Abstract We study the prevalence of SARS-CoV-2 antibodies in a diverse population in Milwaukee County, Wisconsin from May 2021 to June 2022. We find that 99.4% (523/526) of the participants had positive results for antibodies to the SARS CoV2 spike protein over April-June 2022, soon after the early-2022 Omicron surge. Positive tests for spike protein antibodies were very high (86%; 19/22) even among unvaccinated persons who reported no knowledge of prior infection. Thus, by mid-2022, almost all persons were no longer COVID-naïve, defined as vaccination, infection (often without symptoms), or both. Nucleocapsid antibody tests, especially the Abbott test, were far less sensitive than spike protein tests, and Abbott test sensitivity faded with time since infection. Thus, studies which rely on nucleocapsid tests will understate prior infection rates. We also report large sample evidence on the performance of the Abbott and Roche spike and nucleocapsid protein tests in capturing prior vaccination, infection, or both. The Roche spike protein test outperforms the Abbott spike test, and the Roche nucleocapsid test greatly outperforms the Abbott nucleocapsid test.

Abstract Today, artificial intelligence (AI) is used in the design and production of artworks as a powerful tool that helps artists and designers to create more creative and attractive artworks. AI as a new tool has created new opportunities to create and improve artworks. Therefore, in this study, a new model for recommending and simulating the style of handmade artworks using generative adversarial networks (GANs) is presented. This model operates in two distinct phases: style recommendation and style simulation. The reason for using these two separate phases is to increase the accuracy and efficiency of the model. In the first phase, a pre-trained GAN is used to suggest different styles to the artist. In the second phase, a new GAN specifically designed to produce realistic images of handmade artwork is used to simulate the artist’s selected style. Also, in order to improve the accuracy, the genetic algorithm (GA) was used to adjust the activity configuration of Self-Attention (SA) modules. The results of a case study on two types of data, including samples without and with the initial background pattern, GA-SAGAN model has been able to significantly reduce the validation error. As a result, adjusting the activity configuration of SA modules using GA can be effective in producing realistic and accurate artworks. The outputs produced by the GA-SAGAN model have a loss rate close to zero. Also, the Entropy values and Precision and Recall indices are higher than GAN and SAGAN models, it shows the higher diversity of production output patterns and the superiority of the proposed model.

Abstract Acute kidney injury (AKI) is a common clinical problem, and patients who survive AKI have a high risk of chronic kidney disease (CKD). The acute protective effects of Cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors in AKI have been examined, there is still relatively little known regarding the impact of acute CDK4/6 inhibition on the chronic sequence of AKI. Therefore, we utilized the CDK4/6 inhibitor Palbociclib to examine the long-term effects of CDK4/6 inhibition in a rodent model of ischemic AKI. Palbociclib (Palb) was administered during the acute stage or post the acute stage of AKI and mice were sacrificed 21 days post injury. We found that Palb could cause renal senescence and renal fibrosis. Furthermore, dasatinib (D) plus quercetin (Q) were used to eliminate senescent cells in ischemic AKI murine model and Palb was administered post the treatment of D + Q. We found that Palb could reverse the senolytic and antifibrotic effects induced by D + Q, which indicates that the profibrotic effect of Palb could be ascribed to its pro-senescent effects. Our results demonstrate that CDK4/6 inhibitors treatment might be deleterious on the chronic sequence of AKI.

Abstract Cadmium (Cd) can harm the yield and quality of vegetables, threatening food safety. Essential microelements such as iron are crucial for plant growth and can help alleviate heavy metal stress. Recently, nanoparticles have been studied as eco-friendly solutions for mitigating heavy metal stress in plants. In the present study, iron nanoparticles (FeNPs) at 0, 100, and 300 mg/L were applied as soil drenches and foliar sprays to tomato plants under cadmium stress. A comparison was made between the application methods of FeNPs by evaluating the growth parameters of tomato plants, including shoot length (SL), root length (RL), number of branches (NB), number of leaves per plant (NL), and leaf area (LA), as well as by assessing biochemical and antioxidant enzyme parameters. In the Cd stress treatment, the protein content decreased by 24.71%, and the phenolic and flavonoid content of the tomato plants also decreased due to cadmium stress, with levels decreasing from 16.07 to 6.9 µg and from 0.36 to 0.16 µg, respectively. Compared with the soil drench, 100 mg/L FeNPs significantly improved the parameters of Cd-stressed plants when used as a foliar spray, leading to increases in shoot length, root length, fruit weight, number of fruits, number of leaves, and number of branches by 42%, 66%, 24%, 66%, 173%, and 45%, respectively. Tomato plants treated with this spray presented increased carotenoid and lycopene contents. FeNP foliar spray also reduced Cd accumulation in plant tissues. This technique shows promise in alleviating Cd stress in vulnerable vegetable plants such as tomatoes.

Abstract The application of polymer flooding is challenging in harsh temperature and salinity conditions in Middle-Eastern carbonate reservoirs, as they can deteriorate the commonly used polymers such as Hydrolyzed Polyacrylamide (HPAM). One solution to this issue is the use of newly developed Acrylamido-Tertiary-Butyl Sulfonate (ATBS) based polymers, which can endure adverse temperature and salinity conditions. However, they also tend to adsorb onto carbonate rocks with positive surface charge. This study aims to tackle the problem of high polymer retention by employing low-salinity polymer flooding. For that coreflooding experiments were conducted on an ATBS-based polymer in salinities ranging from 400 to 167,000 ppm using fully water-saturated cores and cores at residual oil saturation (Sor). The single-phase retention experiments determined polymer retention values of around 25 µg/g-rock when using diluted brines, which is about half of the retention values (47–56 µg/g-rock) observed with high salinity seawater (43,000 ppm) and formation water (167,000 ppm). Furthermore, the retention of the ATBS-based polymer was further reduced by 50% in the presence of oil compared to the experiments conducted in the absence of oil. The results demonstrated that an optimal salinity threshold of 10,000 ppm and lower yields significant improvements in the efficiency of polymer flooding.

Abstract Adults with cancer may perceive cannabis as beneficial for managing their cancer-related symptoms, but the evidence supporting its medical use is varied and inconclusive. This study characterized associations of cannabis use with cancer-related symptom trajectories. Participants were adults undergoing cancer treatment at the Stephenson Cancer Center (SCC; n = 218) in Oklahoma; they were 71% female, 10% minoritized race, and 45% had stage III or IV cancer. Participants completed surveys at baseline and 2-, 4-, and 6-months post-baseline. Assessments queried about cannabis use behavior, physical and psychological distress via the Rotterdam Symptoms Checklist (RSCL), respiratory symptoms via the American Thoracic Society Questionnaire (ATSQ), and quality of life indices (physical and social functioning, pain interference, sleep quality, fatigue) via the Patient Reported Outcomes Measurement Information System (PROMIS-29). Cannabis use status was categorized based on self-reported past 30-day cannabis use at each assessment as non-use [no use at any assessment], occasional-use [use at 1–2 assessments], or consistent-use [use at 3–4 assessments]. Longitudinal hierarchical linear modeling evaluated associations of cannabis use status with average symptoms and symptom trajectories across all four assessments. One-third (33%) of participants reported past 30-day cannabis use at ≥ 1 assessment. Participants who reported cannabis use (occasional-use and consistent-use) had more severe symptoms overall across assessments. While most cancer symptom trajectories did not differ by cannabis use status, participants who reported consistent cannabis use uniquely showed worsening physical function over time. Cannabis use was associated with greater cancer-related symptom severity over time.

Abstract The growing need for energy and the depletion of oil wells necessitate advanced Enhanced Oil Recovery (EOR) techniques, particularly water and polymer flooding, which play a crucial role in augmenting hydrocarbon recovery rates. However, water flooding in high-permeability layers often leads to water breakthroughs, reduced sweep efficiency, and the formation of preferential channels, posing significant challenges to oil recovery and reservoir management. Conformance control treatments, including the use of polymer microspheres, offer a promising solution by sealing high-permeability zones and enhancing sweep efficiency. This study focuses on the application of fluorescent polymer microspheres based on polyacrylamide, which is extensively employed in the oil sector as an oil displacement agent. Fluorescent polymers called Poly 400, Poly 200, and Poly 600, incorporating cationic methacrylamide monomers, were synthesized through copolymerization to create amphiphilic polymers with enhanced stability and functionality. These fluorescent polymers were evaluated through flooding tests using a quarter-five-spot model of transparent quartz glass under UV light, allowing for instantaneous measurement and observation of fluorescence intensity. At reservoir conditions, the oil displacement experiments confirm that the incremental oil after water flooding by Poly 400, Poly 200, and Poly 600, is 13.1%, 9.1%, and 6.1% of OOIP respectively. The findings showed that fluorescent polymer microspheres could efficiently target high-permeability layers, adapt to varying pore throat sizes, and improve the plugging rate of high-permeability channels, thereby optimizing oil recovery. A subsequent simulation study using the CMG simulator provided further insights into the efficacy of these fluorescent polymers as EOR agents, revealing their potential to enhance sweep efficiency and enhance oil recovery. Simulation results showed that oil saturation decreased from 68% (initial) to 13.5%, 16.1%, and 18.3% after Poly 400, Poly 200, and Poly 600 flooding respectively. This work highlights the potential of fluorescent polymer microspheres as a valuable tool for EOR applications, offering significant advancements in reservoir management and oil recovery optimization.

Abstract A non-Newtonian liquid motion across a stretchable surface is relevant to various industrial applications, including extruding plastic sheets and stretching plastic films. In connection with this, the impact of endothermic and exothermic chemical reactions on the flow of rate-type liquid via an oscillatory stretching sheet in the presence of permeable media with the Maxwell liquid model is elucidated in the present study. Scientists and engineers may enhance the efficiency of chemical reactions or heat transmission by optimising system flow and investigating the effect of reactions on flow dynamics. The present study’s governing partial differential equations (PDEs) are transformed into their non-dimensional form using similarity variables. The resulting equations are numerically solved using the finite difference method (FDM). Outcomes disclose that the temperature profile declines as the activation energy and unsteadiness parameters increase. The influence of the Maxwell and unsteadiness parameters on the fluid’s velocity with respect to time is represented. The rise in the values of chemical reaction parameter upsurges the thermal profile. As the activation energy and unsteadiness parameters upsurge, the thermal profile declines. As the chemical reaction parameter and the ratio of oscillation frequency to stretching rate values rise, the concentration profile falls.

Abstract Tumor immune modulation can be achieved using intratumoral injection of different immunomodulators during different phases of the cancer-immunity cycle. Intratumoral infusion catheters have been used in brain tumors, but these are not suitable outside the brain, where breathing motion results in catheter migration. Here, we use microstereolithography to manufacture a barbed sidehole catheter, modeled after the barbs in a bee stinger, where the barbs maintain the catheter position in the tumor, and sideholes within the barbs infuse drug into tumor tissue. In pig liver, we demonstrated 183-fold higher local drug concentration using the barbed sidehole catheter, compared to intravenous injection of water-soluble drug. High resistance sideholes and pulsatile injection both generate higher pressure in the catheter, which overcomes the tissue pressure, resulting in more drug delivery into tumor. A physical model of intratumoral infusion catheters accurately predicts the observed drug delivery results. Our catheter design is retained in the liver (and does not migrate out with breathing motion), and it preferentially infuses the drug into tumor tissue (not intratumoral vessels).

Abstract Cyanobacterial phycobiliproteins, such as phycoerythrin (PE) and phycocyanin (PC), are colored potential bioactive proteins that have antioxidant and antimicrobial properties. In this study, we formulated a new food prototype based on PE and PC-fortified low-fat yogurt and cream cheese. Four distinct low-fat yogurt and cream cheese products were manufactured, including a control group (No PE and PC), samples produced with phycoerythrin (+ PE), samples produced with phycocyanin (+ PC), and samples produced with both phycoerythrin and phycocyanin (PC + PE). Afterwards statistically compared the physicochemical composition, colorimetric properties, antioxidant and antimicrobial activities, and sensory profile of the fortified foods at 4 °C and 8 °C for 28 and 42 days. Additionally, we confirmed that PE and PC are not toxic to Caenorhabditis elegans at concentrations up to 1 mg/mL. The results showed that the MIC of PE and PC against E. coli was significantly higher than against S. aureus (3.12 ± 0.05 µg/mL vs. 1.56 ± 0.01 µg/mL, respectively; p ≤ 0.05). Additionally, the maximum diameter of the inhibition zone of PE and PC against S. aureus was significantly higher than against E. coli (6.6 ± 0.011 mm vs. 11.66 ± 0.02 mm, respectively; p ≤ 0.05). Results of color parameters showed that the control group had significantly higher L* values than the samples enriched with PE and PC. Moreover PE and PC significantly increased the a* and b* values respectively. The amount of ΔE in the control yogurts and cream cheese was higher than in the samples with PE and PC. Overall, the results showed that adding PE and PC had a significant effect on all measured factors (p

Abstract β-Ionone is the end-ring counterpart of β-carotenoids, which are widely found in fruits and vegetables. Recent studies have illustrated the antimetastatic, anti-proliferative, and apoptosis-inducing activities of β-ionone both in vitro and in vivo. We aimed to explore the anti-cancer potency of β-Ionone-derived ester, (E)-4-(2,6,6-trimethylcyclohex-1-enyl) but-3-en-2-ylpyrazine-2-carboxylate (4-TM.P). The cytotoxic effects of the compound on K562 cells were evaluated by MTT assay. The mechanisms of apoptosis induction were investigated by acridine orange/ethidium bromide (AO/EtBr) double staining, cell cycle analysis, and Annexin V/PI staining. Furthermore, the 4-TM.P-DNA interactions have been thoroughly elucidated by various methods, such as ultraviolet–visible spectroscopy, fluorescence assays, viscosity measurements, molecular docking, and dynamic simulation. The MTT cytotoxicity assay revealed that the growth of K562 cells was inhibited by treatment with β-ionone-derived ester, with an IC50 of 25 ± 5.0 µM at 72 h. Morphological studies revealed the occurrence of apoptosis in treated cells, and G0/G1 cell cycle arrest was observed after treatment of the cells with the IC50 value of the compound. Analyses of multi-spectroscopy and viscosity assays revealed that 4-TM.P binds to DNA in the minor groove mode, which was supported by molecular docking studies. The dynamic stability of the complex was also confirmed using molecular dynamic simulation analyses.

Abstract In the life cycle of the oriental fruit fly where larvae reside within fruits and adults exhibit high activity, the pupal stage occurs in the soil, closely tied to agricultural soil management. This study investigates the impact of four variables (body orientation, burial depth, soil particle size, and pH) on Bactrocera dorsalis’ physiological preferences, eclosion rates, and pupal stage duration. Notably, body orientation, burial depth, soil texture (particle size), and pH affect eclosion rates. The fruit fly demonstrates a preference for a supine position during eclosion, with average eclosion rates of 97.33%, contrasting with decreases to 78.00% in a head-down position. Soil with mixed particle sizes is detrimental to pupal eclosion, reducing the eclosion rate to 56.67%. And at a pH of 9.98, eclosion rates decrease to 16.67%. These factors significantly influence eclosion rates However, these factors do not significantly alter the duration of the pupal stage, with mean changes not exceeding one day when experimental conditions are modified. These findings suggests that soil manipulations affecting these variables could reduce eclosion rates without compromising the uniformity of adult emergence. This study provides a foundation for environmentally friendly pest control practices and addresses gaps in the pupal stage research of the oriental fruit fly.

Abstract This study aimed to test the load to fracture of flowable and high viscosity bulk-fill composites compared to a convetional (packable) composite used to build up pulpless teeth. 45 endodontically treated premolars were restored with fiber posts then divided into three groups (N = 15) according to the core build-up material. SDR group: (Smart Dentine Replacement), (BF): Filtek One Bulk Fill and (CC) Filtek Z350 packable composite. After core build up the teeth received full coverage crowns. Load to fracture (Newton) was recorded for each specimen. One-way ANOVA and Tukey multiple comparisons test were used. The highest load at fracture was shown by (CC) group (1067 N), followed by (BF) group (879 N), and the SDR group (838 N). One way ANOVA and Tukey’s multiple comparisons test demonstrated no significant differences between CC, SDR, and BF groups (p > 0.05). The three materials showed comparable load to fracture. Further studies with simulation to occlusal forces and oral temperature are required.

Abstract Embroidery images carry rich historical information and are an important form of embroidery art. In the field of combination query image retrieval, how to efficiently retrieve the embroidery image information required by users has become a current research challenge. In recent years, convolutional neural networks (CNNs) have achieved significant success in image feature extraction, but they tend to focus on local information, making it easy to ignore global context information when processing such textured embroidery images. Therefore, we propose a combination query retrieval method for embroidery images. First, we propose Blend-Transformer, which introduces Group External Attention (GEA). GEA can integrate feature information from three different dimensions, effectively capturing the local and global context information of embroidery images. Second, we propose Enhanced CNN, which introduces Shuffle Attention (SA), regrouping the reference image features extracted by CNN and reaggregating them by channel to enhance the richness of embroidery image feature information. Through experiments on the TCE-S and ICR2020 standard datasets, we verify the excellent performance of the proposed algorithm in embroidery image retrieval. Our method fills the gap in embroidery image retrieval research and provides a new perspective for the protection of embroidery art.

Abstract To mitigate the challenges posed by the non-linear multi-peak power-voltage output characteristics of photovoltaic (PV) systems operating under partial shading conditions, which often lead to suboptimal performance of conventional Maximum Power Point Tracking (MPPT) algorithms, a novel approach was introduced. We introduce the LGWGCA-P&O method, which synergistically combines an modified Great Wall Construction Algorithm (LGWGCA) with the Perturbation and Observation (P&O) technique. The LGWGCA is refined with a positional update mechanism inspired by the Grey Wolf Optimization (GWO) algorithm, optimizing the distribution of solution agents, while a Levy flight strategy is employed to reduce excessive randomness during agent replacement and recombination, thereby accelerating the tracking process. As the algorithm approaches the maximum power point, it seamlessly transitions to the P&O method, leveraging its rapid convergence to ensure precise and efficient power point identification. Experimental results demonstrate that the LGWGCA-P&O method achieves a PV conversion efficiency exceeding 99.96%, significantly minimizing power losses during MPPT. Furthermore, the method enhances convergence speed by approximately 40% compared to traditional GWO-P&O approaches, and it consistently outperforms Particle Swarm Optimization and Cuckoo Search Optimization algorithms under extreme conditions, demonstrating superior computational efficiency and robustness.

Abstract We sought to explore the risk factors for anxiety and depression in patients with coronary heart disease and subclinical hypothyroidism through logistic regression analysis. A retrospective analysis was conducted on 168 patients with coronary heart disease and subclinical hypothyroidism admitted to the Department of Cardiology of our hospital from February 2020 to November 2022. Patients were categorized into the control group, anxiety group, and depression group based on the Hamilton Anxiety Scale (HAMA) and Hamilton Depression Rating Scale (HAMD) scores. All participants were informed about the protocol and provided signed informed consent upon inclusion. The study examined influencing factors for anxiety and depression in patients with coronary heart disease and subclinical hypothyroidism. Collect patients’ gender, age, presence or absence of chronic diseases (including Diabetes, hypertension and hyperthyroidism), sleep quality, dietary habits, psychosocial stress, living environment, social support, education level, and blood TSH levels. The linear relationship between anxiety, depression, and each influencing factor was quantified using the Pearson correlation coefficient. Blood level of TSH and free T4 were detected by chemiluminescence immunoassay. Multiple logistic regression was applied to analyze the factors influencing anxiety and depression in these patients. Various factors were identified as significant influencers of anxiety and depression in patients with coronary heart disease and subclinical hypothyroidism. For anxiety, presence or absence of chronic diseases, sleep quality, dietary habits, psychosocial pressure, living environment, and blood TSH levels were found to be influential (P 0.05). Presence or absence of chronic diseases and TSH levels were positively correlated with depressive symptoms in patients with coronary heart diseaseand subclinical hypothyroidism (P 0.05). Notably, TSH levels in both the anxiety and depression groups were higher than those in the control group (P 0.05). The combination of chronic illness types, living habits (sleep quality, dietary habits), psychosocial pressure, living environment, and TSH levels emerged as risk factors for anxiety in patients with coronary heart disease and subclinical hypothyroidism (P

Abstract Depression is characterized by a loss of positive and pronounced negative memory bias, which persists after remission. While theoretical accounts of depressive realism, emotional inertia, and mood-congruency substantiate the compelling evidence of weak positive memory in depression, they cannot fully explain negative memory bias in depression. We used Ecologically Momentary Assessments (EMA) of memory bias to provide insight into the accuracy and depression status-dependency of recall of previous positive and negative mood states. Currently- (n = 46), remitted- (n = 90), and never-depressed individuals (n = 55) provided positive mood and negative mood ratings (7x/day for six days), while also recalling their recent (i.e., previous prompt; 3x/day) or distal (i.e., one day lag; 1x/day) mood states. Currently depressed individuals displayed most accuracy and hence least bias in recall of both positive and negative mood; with accuracy in currently and remitted depressed individuals being independent of their current mood state. Conversely, mood at the time of recall significantly related to memory accuracy among never-depressed individuals with more negative mood, resulting in a depressotypic memory bias. Results are consistent with depressive realism and mood-congruency accounts, as well as with evidence for loss of positive memory bias (but not for negative memory bias) in depression.

Abstract One of the Sustainable Development Goals is to enable cities to thrive, improve resource efficiency, and reduce pollution. However, balancing economic development with pollution control and fostering green growth remains a major challenge for developing countries. This study, using panel data from 284 prefecture-level and above cities in China from 2006 to 2020, evaluates urban green economy efficiency (GEE). Then, adopting implementation of “12th Five-Year Plan for Key Regional Air Pollution Prevention and Control” in China as a quasi-natural experiment, this study investigates the impact of air pollution prevention and control (APPC) on urban GEE. Findings reveal an overall upward trajectory in urban GEE across China, marked by discernible regional differentiations following implementation of national Plan. Considering the “hysteresis effect” and spatially “pollution halo effect”, APPC notably improves urban GEE, particularly within cities of eastern zone, high-population density cities, large-sized cities, non resource-based cities and major-APPC cities. Importantly, APPC acts as a catalyst for both enhancing green innovation quality and optimizing industrial structures, ultimately fostering urban GEE in China. These findings are significant and also provide a valuable supplement to the existing study.

Abstract Fuzzy logic presents a promising approach for Species Distribution Modelling by generating a value that can be used for comparative purposes termed ‘environmental favourability’. In contrast to ‘presence probability’, ‘environmental favourability’ remains robust regardless of species prevalence. This characteristic facilitates effective comparisons across species with varying levels of prevalence. In this study, presence probability was predicted using three commonly used Species Distribution Models: Generalised Linear Model, Generalised Additive Modelling, and Boosted Regression Trees for two beetle species, Euwallacea fornicatus and Euwallacea perbrevis in Australia. Fuzzy logic was then employed to derive environmental favourability values based on these models. Additionally, Maxent modelling was included to compare prediction outputs and facilitate a comprehensive analysis. Model performance was evaluated using standard metrics (Area under the receiver operating characteristic curve, True statistical skill, Correct classification rate), as well as Hosmer-Lemeshow test. The research explored fuzzy similarity, fuzzy intersection and potential biotic interaction of these closely related borers, and revealed a favourable distribution pattern for Euwallacea fornicatus across Australia. This study supports the efficacy of fuzzy logic in Species Distribution Modelling and highlights the value of environmental favourability function in enhancing the comparative analysis of the geographical relationship across species. This approach offers a more nuanced perspective on Species Distribution Modelling.