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Abstract Pretrained Graph Neural Networks have been widely adopted for various molecular property prediction tasks. Despite their ability to encode structural and relational features of molecules, traditional fine-tuning of such pretrained GNNs on the target task can lead to poor generalization. To address this, we explore the adaptation of pretrained GNNs to the target task by jointly training them with multiple auxiliary tasks. This could enable the GNNs to learn both general and task-specific features, which may benefit the target task. However, a major challenge is to determine the relatedness of auxiliary tasks with the target task. To address this, we investigate multiple strategies to measure the relevance of auxiliary tasks and integrate such tasks by adaptively combining task gradients or by learning task weights via bi-level optimization. Additionally, we propose a novel gradient surgery-based approach, Rotation of Conflicting Gradients ( $$\mathop {\texttt{RCGrad}}\limits$$ RCGrad ), that learns to align conflicting auxiliary task gradients through rotation. Our experiments with state-of-the-art pretrained GNNs demonstrate the efficacy of our proposed methods, with improvements of up to 7.7% over fine-tuning. This suggests that incorporating auxiliary tasks along with target task fine-tuning can be an effective way to improve the generalizability of pretrained GNNs for molecular property prediction. Scientific contribution We introduce a novel framework for adapting pretrained GNNs to molecular tasks using auxiliary learning to address the critical issue of negative transfer. Leveraging novel gradient surgery techniques such as $$\mathop {\texttt{RCGrad}}\limits$$ RCGrad , the proposed adaptation framework represents a significant departure from the dominant pretraining fine-tuning approach for molecular GNNs. Our contributions are significant for drug discovery research, especially for tasks with limited data, filling a notable gap in the efficient adaptation of pretrained models for molecular GNNs.

Objective Riveting quality issues cannot be visually judged. By employing the far-field eddy current testing method, internal quality defects in riveted structural components can be detected, providing theoretical and technical support for product quality inspection. Method Riveting quality inspection and classification of clearance types are conducted without damaging the workpiece. Far-field eddy current testing experiments are carried out for the intermediate plate clearance and rivet hole clearance of rail vehicle riveted structural components respectively. Testing research on metro vehicle anti-roll torsion bar seats riveted to the body frame side beams is also conducted. Result & Conclusion From the results of the three sets of experiments, it is evident that the range of phase angles can distinguish between rivet and hole clearances and plate clearances. The phase amplitude range for hole clearances is 60° to 90°, while for plate clearances, it is 0 to 10°.

Abstract Due to cancer’s complex nature and variable response to therapy, precision oncology informed by omics sequence analysis has become the current standard of care. However, the amount of data produced for each patient makes it difficult to quickly identify the best treatment regimen. Moreover, limited data availability has hindered computational methods’ abilities to learn patterns associated with effective drug-cell line pairs. In this work, we propose the use of contrastive learning to improve learned drug and cell line representations by preserving relationship structures associated with drug mechanisms of action and cell line cancer types. In addition to achieving enhanced performance relative to a state-of-the-art method, we find that classifiers using our learned representations exhibit a more balanced reliance on drug- and cell line-derived features when making predictions. This facilitates more personalized drug prioritizations that are informed by signals related to drug resistance.

The developmental toxicity and human health risks of triazole fungicides (TFs) have attracted worldwide attention due to the ability to enter the human body in a variety of ways. Nevertheless, the specific mechanism by which TFs exert remains incompletely understood. Given that retinoic acid (RA) signaling pathway are closely related to development, this study aimed to screen and identify developmentally disabled chemicals in commonly used TFs and to reveal the potential effects of TFs on developmental retardation through the RA signaling pathway in mouse embryonic stem cells (mESCs). Specifically, six typical TFs (myclobutanil, tebuconazole, hexaconazole, propiconazole, difenoconazole, and flusilazole) were exposed through the construction of an embryoid bodies (EBs)-based in vitro global differentiation models. Our results clarified that various TFs disturbed lineage commitment during early embryonic development. Crucially, the activation of RA signaling pathway, which alters the expression of key genes and interferes the transport and metabolism of retinol, may be responsible for this effect. Furthermore, molecular docking, molecular dynamics simulations, and experiments using a retinoic acid receptor α inhibitor provide evidence supporting the potential modulatory role of the retinoic acid signaling pathway in developmental injury. The current study offers new insights into the TFs involved in the RA signaling pathway that interfere with the differentiation process of mESCs, which is crucial for understanding the impact of TFs on pregnancy and early development.

ObjectiveExisting literature has not clearly elucidated whether SARS-CoV-2 infection increases the incidence of Parkinson’s disease or if Parkinson’s disease patients are more susceptible to the effects of SARS-CoV-2 infection. To clarify the issue, this study employs a genetic epidemiological approach to investigate the association.MethodsThis study utilizes a two-sample Mendelian randomization analysis. The primary analysis employs the inverse variance-weighted (IVW) method, supplemented by secondary analyses including MR-Egger regression, weighted median, IVW radial method, and weighted mode, to evaluate the bidirectional causal relationship between Parkinson’s disease and SARS-CoV-2 infection.ResultsIVW results showed no genetic causality between SARS-CoV-2 susceptibility, hospitalization rate and severity and Parkinson’s disease. (IVW method: p = 0.408 OR = 1.10 95% CI: 0.87 ~ 1.39; p = 0.744 OR = 1.11 95% CI: 0.94 ~ 1.09; p = 0.436 OR = 1.05 95% CI: 0.93 ~ 1.17). Parkinson’s disease was not genetically associated with susceptibility to new crown infections, hospitalization rates, and severity (IVW method: p = 0.173 OR = 1.01 95% CI: 0.99 ~ 1.03; p = 0.109 OR = 1.05 95% CI: 0.99 ~ 1.12; p = 0.209 OR = 1.03 95% CI: 0.99 ~ 1.07). MR-Egger regression, weighted median, IVW radial method, and weighted mode results are consistent with the results of the IVW method.ConclusionThis study does not support a genetic link between Parkinson’s disease and SARS-CoV-2 infection, and the association observed in previous cohort studies and observational studies may be due to other confounding factors.

Odor compounds produced in salted egg yolk during thermal processing were identify by sensory evaluation and gas chromatography-mass spectrometry (GC-MS) combined with multivariate statistical analysis such as hierarchical cluster analysis (HCA), orthogonal partial least squares-discriminant analysis (OPLS-DA) and principal component analysis (PCA). By using quantitative descriptive sensory analysis, it was found that there was a significant difference in the flavor profile between egg yolk samples with and without unpleasant odor, and it was determined that some egg yolks had obvious off-flavor during hot processing. Cluster analysis showed that the six groups of salted egg yolk samples were clearly divided into two categories, which was consistent with the sensory evaluation results. Furthermore, OPLS-DA identified 33 major differential components with variable importance in the projection (VIP) scores greater than one between samples with and without unpleasant odor, and aldehydes and alcohols were the most significant differential components. Finally, the volatile compounds showing a positive correlation with sour odor were screened by PCA, and n-hexanal, n-valeraldehyde, 1-octen-3-ol, trans,trans-2,4-decadienal, styrene, dimethyl disulfide and 2-nonone were identified due to their odor activity values (OAV) greater than one as the major odor compounds of salted egg yolk during thermal processing. The results provide a reference for analysis of the flavor characteristics and possible off-flavor components of salted egg yolk after thermal processing and provide a scientific basis for accessing the quality of salted egg yolk.

To characterize the quality of widely consumed pork in China, the chemical compositions and other indexes of five breeds of pork were compared. The results indicated that the moisture content, sensory flavor, and overall acceptability of Pipa pork (PPP) were superior to other breeds. The fat content and essential amino acids (EAA) of Yihao native pork (YNP) were significantly (p < 0.05) higher than in other breeds. The protein content, the total amount of amino acids, and perceptible flavor of Tibetan black pork (TBP) were higher than in other breeds. The protein nutrition profiles of manor black pork (MBP) and TBP were better than in others. The equivalent umami concentration (EUC) value of white pork (WP) was significantly (p < 0.05) higher than in others; however, the health risk of its fatty acid content was higher. There were unique protein and flavor chemicals in YNP, TBP, and PPP, which may be useful for distinguishing their authenticity.

Abstract Background Computational drug repurposing is crucial for identifying candidate therapeutic medications to address the urgent need for developing treatments for newly emerging infectious diseases. The recent COVID-19 pandemic has taught us the importance of rapidly discovering candidate drugs and providing them to medical and pharmaceutical experts for further investigation. Network-based approaches can provide repurposable drugs quickly by leveraging comprehensive relationships among biological components. However, in a case of newly emerging disease, applying a repurposing methods with only pre-existing knowledge networks may prove inadequate due to the insufficiency of information flow caused by the novel nature of the disease. Methods We proposed a network-based complementary linkage method for drug repurposing to solve the lack of incoming new disease-specific information in knowledge networks. We simulate our method under the controlled repurposing scenario that we faced in the early stage of the COVID-19 pandemic. First, the disease-gene-drug multi-layered network was constructed as the backbone network by fusing comprehensive knowledge database. Then, complementary information for COVID-19, containing data on 18 comorbid diseases and 17 relevant proteins, was collected from publications or preprint servers as of May 2020. We estimated connections between the novel COVID-19 node and the backbone network to construct a complemented network. Network-based drug scoring for COVID-19 was performed by applying graph-based semi-supervised learning, and the resulting scores were used to validate prioritized drugs for population-scale electronic health records-based medication analyses. Results The backbone networks consisted of 591 diseases, 26,681 proteins, and 2,173 drug nodes based on pre-pandemic knowledge. After incorporating the 35 entities comprised of complemented information into the backbone network, drug scoring screened top 30 potential repurposable drugs for COVID-19. The prioritized drugs were subsequently analyzed in electronic health records obtained from patients in the Penn Medicine COVID-19 Registry as of October 2021 and 8 of these were found to be statistically associated with a COVID-19 phenotype. Conclusion We found that 8 of the 30 drugs identified by graph-based scoring on complemented networks as potential candidates for COVID-19 repurposing were additionally supported by real-world patient data in follow-up analyses. These results show that our network-based complementary linkage method and drug scoring algorithm are promising strategies for identifying candidate repurposable drugs when new emerging disease outbreaks.

Abstract Retrosynthesis is a procedure where a target molecule is transformed into potential reactants and thus the synthesis routes can be identified. Recently, computational approaches have been developed to accelerate the design of synthesis routes. In this paper,we develop a generative framework G 2 Retro for one-step retrosynthesis prediction. G 2 Retro imitates the reversed logic of synthetic reactions. It first predicts the reaction centers in the target molecules (products), identifies the synthons needed to assemble the products, and transforms these synthons into reactants. G 2 Retro defines a comprehensive set of reaction center types, and learns from the molecular graphs of the products to predict potential reaction centers. To complete synthons into reactants, G 2 Retro considers all the involved synthon structures and the product structures to identify the optimal completion paths, and accordingly attaches small substructures sequentially to the synthons. Here we show that G 2 Retro is able to better predict the reactants for given products in the benchmark dataset than the state-of-the-art methods.

Objective: To investigate the performance of machine learning (ML) methods for predicting outcomes from inpatient rehabilitation for subjects with TBI using a dataset with a large number of predictor variables. Our second objective was to identify top predictive features selected by the ML models for each outcome and to validate the interpretability of the models. Design: Secondary analysis using computational modeling of relationships between patients, injury and treatment activities and 6 outcomes, applied to the large multi-site, prospective, longitudinal observational dataset collected during the traumatic brain injury inpatient rehabilitation study. Setting: Acute inpatient rehabilitation. Participants: 1946 patients aged 14 years or older, who sustained a severe, moderate, or complicated mild TBI, and were admitted to 1 of 9 US inpatient rehabilitation sites between 2008 and 2011 (N=1946). Main Outcome Measures: Rehabilitation length of stay, discharge to home, FIM cognitive and FIM motor at discharge and at 9-months post discharge. Results: Advanced ML models, specifically gradient boosting tree model, performed consistently better than all other models, including classical linear regression models. Top ranked predictive features were identified for each of the 6 outcome variables. Level of effort, days to rehabilitation admission, age at rehabilitation admission, and advanced mobility activities were the most frequently top ranked predictive features. The highest-ranking predictive feature differed across the specific outcome variable. Conclusions: Identifying patient, injury, and rehabilitation treatment variables that are predictive of better outcomes will contribute to cost-effective care delivery and guide evidence-based clinical practice. ML methods can contribute to these efforts.

Consumption and emission reduction has become an inevitable trend in the development the food processing industry. While liquid-state osmotic pretreatment can save energy effectively in freeze-dried fruit processing, there is still a need to explore the effect of solid-state osmotic pretreatment. The effects of solid- and liquid-state osmotic pretreatment on waste liquid, resource consumption and product quality in the processing of freeze-dried mango were compared in this study. Results showed that compared with liquid-state infiltration (sucrose concentration of 60%, and solid-to-liquid ratio of 1:1; designated as L60), solid-state infiltration with sucrose concentration of 30% and 40% (designated as S30 and S40, respectively) significantly reduced the waste liquid discharge by 61.00% and 51.71%, the consumption of sucrose by 50.01% and 33.24%, and the total chemical oxygen demand of waste liquid by 60.13% and 41.83%, respectively. Compared with non-osmosed samples, the carbon emission of S30, S40 and L60 during processing was reduced by 10.59%, 12.21% and 8.80%, respectively. Compared with the liquid-state osmosis group, the solid-state osmosis group had lower nutrient retention (total phenolics and VC) during osmosis, but higher nutrient retention during lyophilization. After freeze-drying, the structure of the solid-state osmosis group was shrunk without collapse. In the trend of sustainable development, solid-state osmosis is superior to liquid-state osmosis in energy conservation and emission reduction, which is worthy of further research and exploration.

Particulate matter of size ≤ 2.5 μm (PM2.5) is a critical environmental threat that considerably contributes to the global disease burden. However, accompanied by the rapid research progress in this field, the existing research on developmental toxicity is still constrained by limited data sources, varying quality, and insufficient in-depth mechanistic analysis. This review includes the currently available epidemiological and laboratory evidence and comprehensively characterizes the adverse effects of PM2.5 on developing individuals in different regions and various pollution sources. In addition, this review explores the effect of PM2.5 exposure to individuals of different ethnicities, genders, and socioeconomic levels on adverse birth outcomes and cardiopulmonary and neurological development. Furthermore, the molecular mechanisms involved in the adverse health effects of PM2.5 primarily encompass transcriptional and translational regulation, oxidative stress, inflammatory response, and epigenetic modulation. The primary findings and novel perspectives regarding the association between public health and PM2.5 were examined, highlighting the need for future studies to explore its sources, composition, and sex-specific effects. Additionally, further research is required to delve deeper into the more intricate underlying mechanisms to effectively prevent or mitigate the harmful effects of air pollution on human health.

The unique imaging modality of synthetic aperture radar (SAR) has posed significant challenges for object detection, making it more complex to acquire and interpret than optical images. Recently, numerous studies have proposed cross-domain adaptive methods based on convolutional neural networks (CNNs) to promote SAR object detection using optical data. However, existing cross-domain methods focus on image features, lack improvement on input data, and ignore the valuable supervision provided by few labeled SAR images. Therefore, we propose a semi-supervised cross-domain object detection framework that uses optical data and few SAR data to achieve knowledge transfer for SAR object detection. Our method focuses on the data processing aspects to gradually reduce the domain shift at the image, instance, and feature levels. First, we propose a data augmentation method of image mixing and instance swapping to generate a mixed domain that is more similar to the SAR domain. This method fully utilizes few SAR annotation information to reduce domain shift at image and instance levels. Second, at the feature level, we propose an adaptive optimization strategy to filter out mixed domain samples that significantly deviate from the SAR feature distribution to train feature extractor. In addition, we employ Vision Transformer (ViT) as feature extractor to handle the global feature extraction of mixed images. We propose a detection head based on normalized Wasserstein distance (NWD) to enhance objects with smaller effective regions in SAR images. The effectiveness of our proposed method is evaluated on public SAR ship and oil tank datasets.

Tebuconazole, one of the most widely used triazole fungicides, is reported to potentially pose a risk of inducing neurological disorders in human beings. Considering the increasing exposure, whether it could influence cognitive function remains to be elucidated. Herein, we used a mouse model to evaluate the potential cognitive risks and possible mechanisms from the continuous edible application of tebuconazole at low concentrations. Our study revealed that tebuconazole deteriorated spatial learning and memory and downregulated the expression of glutamate receptor subunits. Importantly, metagenomic analysis indicated that tebuconazole not only led to significant shifts in the composition and diversity of the gut microbiota but also changed intestinal homeostasis. Specifically, after exposure, tebuconazole circulated in the bloodstream and largely entered the gut–brain axis for disruption, including disturbing the Firmicutes/Bacteroidetes ratio, interrelated neurotransmitters and systemic immune factors. Moreover, pretreatment with probiotics improved immune factor expression and restored the deterioration of synaptic function and spatial learning and memory. The current study provides novel insights concerning perturbations of the gut microbiome and its functions as a potential new mechanism by which tebuconazole exposes cognitive function-related human health.

Background and purpose: Serine/arginine-rich splicing factor 1 (SRSF1) is closely related to the development of tumor. Esophageal carcinoma is the common malignant tumor of digestive system. However, the role of SRSF1 in esophageal carcinoma is rarely reported. This study aimed to detect the expression of SRSF1 in esophageal squamous cell carcinoma (ESCC) and its effects on the proliferation, invasion and migration of ESCC cells, and to explore its mechanism. Methods: Forty pairs of ESCC tissues and paracancerous tissues resected at the Fourth Hospital of Hebei Medical University from January 2020 to December 2021 were collected for this study. The expression of SRSF1 in ESCC was detected by immunohistochemistry. The mRNA expression and protein level of SRSF1 in ESCC cells were detected by real-time fluorescence quantitative polymerase chain reaction (RTFQ-PCR) and Western blot, respectively. Eca9706 cell with high expression of SRSF1 was selected. The mRNA expression of SRSF1 was reduced by small interfering RNA (siRNA). The proliferation, migration and invasion of Eca9706 cells were detected by cell counting kit-8 (CCK-8), transwell and Matrigel matrix gel assays, respectively. Database was used to analyze the expression of vascular endothelial growth factor A (VEGFA) in ESCC tissues, and the correlation between expressions of SRSF1 and VEGFA was also analyzed. RTFQ-PCR was used to detect the expression levels of VEGFA Iso8a and Iso8b isoform in Eca9706 cells, and the change in VEGFA Iso8a and Iso8b isoform expressions was also detected after knocking down SRSF1. Results: The expression of SRSF1 was significantly higher in ESCC tissues than in normal esophageal tissues (P

Abstract Effective and successful clinical trials are essential in developing new drugs and advancing new treatments. However, clinical trials are very expensive and easy to fail. The high cost and low success rate of clinical trials motivate research on inferring knowledge from existing clinical trials in innovative ways for designing future clinical trials. In this manuscript, we present our efforts on constructing the first publicly available Clinical Trials Knowledge Graph, denoted as $$\mathop {\mathtt {CTKG}}\limits$$ CTKG . $$\mathop {\mathtt {CTKG}}\limits$$ CTKG includes nodes representing medical entities in clinical trials (e.g., studies, drugs and conditions), and edges representing the relations among these entities (e.g., drugs used in studies). Our embedding analysis demonstrates the potential utilities of $$\mathop {\mathtt {CTKG}}\limits$$ CTKG in various applications such as drug repurposing and similarity search, among others.

Immunoassays have been widely used for the determination of various analytes in the fields of disease diagnosis, food safety, and environmental monitoring. Dual-signal immunoassays are now advanced and integrated detection technologies with excellent self-correction and self-validation capabilities. In this work, we summarize the recent advances in the development of optical and electrochemical dual-signal immunoassays, including colorimetric, fluorescence, surface-enhanced Raman spectroscopy (SERS), electrochemical, electrochemiluminescence, and photoelectrochemical methods. This review particularly emphasizes the working principle of diverse dual-signal immunoassays and the utilization of dual-functional molecules and nanomaterials. It also outlines the challenges and prospects of future research on dual-signal immunoassays. [ABSTRACT FROM AUTHOR]

In this work, we report on an electrochemical method for the signal-on detection of caspase-3 and the evaluation of apoptosis based on the biotinylation reaction and the signal amplification of methylene blue (MB)-loaded metal–organic frameworks (MOFs). Zr-based UiO-66-NH2 MOFs were used as the nanocarriers to load electroactive MB molecules. Recombinant hexahistidine (His6)-tagged streptavidin (rSA) was attached to the MOFs through the coordination interaction between the His6 tag in rSA and the metal ions on the surface of the MOFs. The acetylated peptide substrate Ac-GDEVDGGGPPPPC was immobilized on the gold electrode. In the presence of caspase-3, the peptide was specifically cleaved, leading to the release of the Ac-GDEVD sequence. A N-terminal amine group was generated and then biotinylated in the presence of biotin-NHS. Based on the strong interaction between rSA and biotin, rSA@MOF@MB was captured by the biotinylated peptide-modified electrode, producing a significantly amplified electrochemical signal. Caspase-3 was sensitively determined with a linear range from 0.1 to 25 pg/mL and a limit of detection down to 0.04 pg/mL. Further, the active caspase-3 in apoptosis inducer-treated HeLa cells was further quantified by this method. The proposed signal-on biosensor is compatible with the complex biological samples and shows great potential for apoptosis-related diagnosis and the screening of caspase-targeting drugs. [ABSTRACT FROM AUTHOR]

As one of the important traditional medicinal plants listed in the Chinese pharmacopoeia, Vernonia anthelmintica (L.) Willd has been shown to possess various biological activities. In this study, we characterized culturable endophytic bacteria associated with the medicinal plant V. anthelmintica collected from Hotan within the Xinjiang autonomous region of China. Bacterial endophytes were identified via 16S rRNA gene sequence analysis and compared to similar sequences from the GenBank. Isolated strains exhibited 99.08–100% similarity to Bacillus haynesii XJB-5, Bacillus proteolyticus XJB-16, Bacillus halotolerans XJB-35, Bacillus safensis XJB-71, Pseudomonas punonensis XJB-7, Lysinibacillus fusiformis XJB-17, Streptococcus lutetiensis XJB-66, Leclercia adecarboxylata XJB-12, Paenibacillus alvei XJB-14, and Pantoea agglomerans XJB-62. The ethyl acetate extracts of the bacterial endophytes demonstrated various pharmacological properties, such as antimicrobial, cytotoxic, antidiabetic, and antioxidant activity, according to a melanin content assay and have shown tyrosinase activity in murine B16 cells. A crude extract of B. halotolerans XJB-35 displayed more powerful biological activities than other bacterial endophytes; therefore, this strain was studied further in order to select the optimized parameters for enhancing the synthesis of bioactive compounds. The optimal culture medium was found to be nutrient broth (NB) medium, using peptone as its carbon source and yeast extract as its nitrogen source. A 24 h incubation time produced the optimal conditions for the maximum growth of B. halotolerans XJB-35 and the production of bioactive compounds. Moreover, we investigated the volatile chemical component of the dichloromethane fraction using GC-MS analysis. Our findings provide valuable information regarding the synthesize of bioactive natural products by B. halotolerans XJB-35 for use by the medicinal and pharmaceutical industries.