MUSCLE: multi-view and multi-scale attentional feature fusion for microRNA-disease associations prediction.

MicroRNAs (miRNAs) synergize with various biomolecules in human cells resulting in diverse functions in regulating a wide range of biological processes. Predicting potential disease-associated miRNAs as valuable biomarkers contributes to the treatment of human diseases. However, few previous methods take a holistic perspective and only concentrate on isolated miRNA and disease objects, thereby ignoring that human cells are responsible for multiple relationships. In this work, we first constructed a multi-view graph based on the relationships between miRNAs and various biomolecules, and then utilized graph attention neural network to learn the graph topology features of miRNAs and diseases for each view. Next, we added an attention mechanism again, and developed a multi-scale feature fusion module, aiming to determine the optimal fusion results for the multi-view topology features of miRNAs and diseases. In addition, the prior attribute knowledge of miRNAs and diseases was simultaneously added to achieve better prediction results and solve the cold start problem. Finally, the learned miRNA and disease representations were then concatenated and fed into a multi-layer perceptron for end-to-end training and predicting potential miRNA-disease associations. To assess the efficacy of our model (called MUSCLE), we performed 5- and 10-fold cross-validation (CV), which got average the Area under ROC curves of 0.966${\pm }$0.0102 and 0.973${\pm }$0.0135, respectively, outperforming most current state-of-the-art models. We then examined the impact of crucial parameters on prediction performance and performed ablation experiments on the feature combination and model architecture. Furthermore, the case studies about colon cancer, lung cancer and breast cancer also fully demonstrate the good inductive capability of MUSCLE. Our data and code are free available at a public GitHub repository: https://github.com/zht-code/MUSCLE.git.

OAS Deep Q-Learning-Based Fast and Smooth Control Method for Traffic Signal Transition in Urban Arterial Tidal Lanes.

To address traffic flow fluctuations caused by changes in traffic signal control schemes on tidal lanes and maintain smooth traffic operations, this paper proposes a method for controlling traffic signal transitions on tidal lanes. Firstly, the proposed method includes designing an intersection overlap phase scheme based on the traffic flow conflict matrix in the tidal lane scenario and a fast and smooth transition method for key intersections based on the flow ratio. The aim of the control is to equalize average queue lengths and minimize average vehicle delays for different flow directions at the intersection. This study also analyses various tidal lane scenarios based on the different opening states of the tidal lanes at related intersections. The transitions of phase offsets are emphasized after a comprehensive analysis of transition time and smoothing characteristics. In addition, this paper proposes a coordinated method for tidal lanes to optimize the phase offset at arterial intersections for smooth and rapid transitions. The method uses Deep Q-Learning, a reinforcement learning algorithm for optimal action selection (OSA), to develop an adaptive traffic signal transition control and enhance its efficiency. Finally, a simulation experiment using a traffic control interface is presented to validate the proposed approach. This study shows that this method leads to smoother and faster traffic signal transitions across different tidal lane scenarios compared to the conventional method. Implementing this solution can benefit intersection groups by reducing traffic delays, improving traffic efficiency, and decreasing air pollution caused by congestion.

A multi-source molecular network representation model for protein-protein interactions prediction.

The prediction of potential protein-protein interactions (PPIs) is a critical step in decoding diseases and understanding cellular mechanisms. Traditional biological experiments have identified plenty of potential PPIs in recent years, but this problem is still far from being solved. Hence, there is urgent to develop computational models with good performance and high efficiency to predict potential PPIs. In this study, we propose a multi-source molecular network representation learning model (called MultiPPIs) to predict potential protein-protein interactions. Specifically, we first extract the protein sequence features according to the physicochemical properties of amino acids by utilizing the auto covariance method. Second, a multi-source association network is constructed by integrating the known associations among miRNAs, proteins, lncRNAs, drugs, and diseases. The graph representation learning method, DeepWalk, is adopted to extract the multisource association information of proteins with other biomolecules. In this way, the known protein-protein interaction pairs can be represented as a concatenation of the protein sequence and the multi-source association features of proteins. Finally, the Random Forest classifier and corresponding optimal parameters are used for training and prediction. In the results, MultiPPIs obtains an average 86.03% prediction accuracy with 82.69% sensitivity at the AUC of 93.03% under five-fold cross-validation. The experimental results indicate that MultiPPIs has a good prediction performance and provides valuable insights into the field of potential protein-protein interactions prediction. MultiPPIs is free available at https://github.com/jiboyalab/multiPPIs .

MHGTMDA: Molecular heterogeneous graph transformer based on biological entity graph for miRNA-disease associations prediction.

MicroRNAs (miRNAs) play a crucial role in the prevention, prognosis, diagnosis, and treatment of complex diseases. Existing computational methods primarily focus on biologically relevant molecules directly associated with miRNA or disease, overlooking the fact that the human body is a highly complex system where miRNA or disease may indirectly correlate with various types of biomolecules. To address this, we propose a novel prediction model named MHGTMDA (miRNA and disease association prediction using heterogeneous graph transformer based on molecular heterogeneous graph). MHGTMDA integrates biological entity relationships of eight biomolecules, constructing a relatively comprehensive heterogeneous biological entity graph. MHGTMDA serves as a powerful molecular heterogeneity map transformer, capturing structural elements and properties of miRNAs and diseases, revealing potential associations. In a 5-fold cross-validation study, MHGTMDA achieved an area under the receiver operating characteristic curve of 0.9569, surpassing state-of-the-art methods by at least 3%. Feature ablation experiments suggest that considering features among multiple biomolecules is more effective in uncovering miRNA-disease correlations. Furthermore, we conducted differential expression analyses on breast cancer and lung cancer, using MHGTMDA to further validate differentially expressed miRNAs. The results demonstrate MHGTMDA's capability to identify novel MDAs.

Bleomycin promotes rAAV2 transduction via DNA-PKcs/Artemis-mediated DNA break repair pathways.

Because it is safe and has a simple genome, recombinant adeno-associated virus (rAAV) is an extremely appealing vector for delivery in in vivo gene therapy. However, its low transduction efficiency for some cells, limits its further application in the field of gene therapy. Bleomycin is a chemotherapeutic agent approved by the FDA whose effect on rAAV transduction has not been studied. In this study, we systematically investigated the effect of Bleomycin on the second-strand synthesis and used CRISPR/CAS9 and RNAi methods to understand the effects of Bleomycin on rAAV vector transduction, particularly the effect of DNA repair enzymes. The results showed that Bleomycin could promote rAAV2 transduction both in vivo and in vitro. Increased transduction was discovered to be a direct result of decreased cytoplasmic rAAV particle degradation and increased second-strand synthesis. TDP1, PNKP, and SETMAR are required to repair the DNA damage gap caused by Bleomycin, TDP1, PNKP, and SETMAR promote rAAV second-strand synthesis. Bleomycin induced DNA-PKcs phosphorylation and phosphorylated DNA-PKcs and Artemis promoted second-strand synthesis. The current study identifies an effective method for increasing the capability and scope of in-vivo and in-vitro rAAV applications, which can amplify cell transduction at Bleomycin concentrations. It also supplies information on combining tumor gene therapy with chemotherapy.

DYNet: A Printed Book Detection Model Using Dual Kernel Neural Networks.

Target detection has always been a hotspot in image processing/computer vision research, and small-target detection is a frequently encountered problem in the field of target detection. With the continuous innovation of target detection technology, people always hope that the detection of small targets can reach the real-time accuracy of large-target detection. In this paper, a small-target detection model based on dual-core convolutional neural networks (CNN) is proposed, which is mainly used for the intelligent detection of books in the production line of printed books. The model is mainly composed of two modules, including a region prediction module and suspicious target search module. The region prediction module uses a CNN to predict suspicious region blocks in a large context. The suspicious target search module uses a different CNN from the above to find tiny targets in the predicted region blocks. Comparative testing of four small book target samples using this model shows that this model has better book small-target detection accuracy compared to other models.

Modeling analysis of armed conflict risk in sub-Saharan Africa, 2000-2019.

Sub-Saharan Africa has suffered frequent outbreaks of armed conflict since the end of the Cold War. Although several efforts have been made to understand the underlying causes of armed conflict and establish an early warning mechanism, there is still a lack of a comprehensive assessment approach to model the incidence risk of armed conflict well. Based on a large database of armed conflict events and related spatial datasets covering the period 2000-2019, this study uses a boosted regression tree (BRT) approach to model the spatiotemporal distribution of armed conflict risk in sub-Saharan Africa. Evaluation of accuracy indicates that the simulated models obtain high performance with an area under the receiver operator characteristic curve (ROC-AUC) mean value of 0.937 and an area under the precision recall curves (PR-AUC) mean value of 0.891. The result of the relative contribution indicates that the background context factors (i.e., social welfare and the political system) are the main driving factors of armed conflict risk, with a mean relative contribution of 92.599%. By comparison, the climate change-related variables have relatively little effect on armed conflict risk, accounting for only 7.401% of the total. These results provide novel insight into modelling the incidence risk of armed conflict, which may help implement interventions to prevent and minimize the harm of armed conflict.

An integrated deep-learning and multi-level framework for understanding the behavior of terrorist groups.

Human security is threatened by terrorism in the 21st century. A rapidly growing field of study aims to understand terrorist attack patterns for counter-terrorism policies. Existing research aimed at predicting terrorism from a single perspective, typically employing only background contextual information or past attacks of terrorist groups, has reached its limits. Here, we propose an integrated deep-learning framework that incorporates the background context of past attacked locations, social networks, and past actions of individual terrorist groups to discover the behavior patterns of terrorist groups. The results show that our framework outperforms the conventional base model at different spatio-temporal resolutions. Further, our model can project future targets of active terrorist groups to identify high-risk areas and offer other attack-related information in sequence for a specific terrorist group. Our findings highlight that the combination of a deep-learning approach and multi-scalar data can provide groundbreaking insights into terrorism and other organized violent crimes.

HF-DDI: Predicting Drug-Drug Interaction Events Based on Multimodal Hybrid Fusion.

Drug-drug interactions (DDIs) can have a significant impact on patient safety and health. Predicting potential DDIs before administering drugs to patients is a critical step in drug development and can help prevent adverse drug events. In this study, we propose a novel method called HF-DDI for predicting DDI events based on various drug features, including molecular structure, target, and enzyme information. Specifically, we design our model with both early fusion and late fusion strategies and utilize a score calculation module to predict the likelihood of interactions between drugs. Our model was trained and tested on a large data set of known DDIs, achieving an overall accuracy of 0.948. The results suggest that incorporating multiple drug features can improve the accuracy of DDI event prediction and may be useful for improving drug safety and patient outcomes.

Adjuvant therapeutic strategy decision support for an elderly population with localized breast cancer: A monocentric cohort retrospective study.

Guidelines for the management of elderly patients with early breast cancer are scarce. Additional adjuvant systemic treatment to surgery for early breast cancer in elderly populations is challenged by increasing comorbidities with age. In non-metastatic settings, treatment decisions are often made under considerable uncertainty; this commonly leads to undertreatment and, consequently, poorer outcomes. This study aimed to develop a decision support tool that can help to identify candidate adjuvant post-surgery treatment schemes for elderly breast cancer patients based on tumor and patient characteristics. Our approach was to generate predictions of patient outcomes for different courses of action; these predictions can, in turn, be used to inform clinical decisions for new patients. We used a cohort of elderly patients (≥ 70 years) who underwent surgery with curative intent for early breast cancer to train the models. We tested seven classification algorithms using 5-fold cross-validation, with 80% of the data being randomly selected for training and the remaining 20% for testing. We assessed model performance using accuracy, precision, recall, F1-score, and AUC score. We used an autoencoder to perform dimensionality reduction prior to classification. We observed consistently better performance using logistic regression and linear discriminant analysis models when compared to the other models we tested. Classification performance generally improved when an autoencoder was used, except for when we predicted the need for adjuvant treatment. We obtained overall best results using a logistic regression model without autoencoding to predict the need for adjuvant treatment (F1-score = 0.869).

MiR133b-mediated inhibition of EGFR-PTK pathway promotes rAAV2 transduction by facilitating intracellular trafficking and augmenting second-strand synthesis.

Recombinant adeno-associated virus (rAAV) is an extremely attractive vector in the in vivo delivery of gene therapy as it is safe and its genome is simple. However, challenges including low permissiveness to specific cells and restricted tissue specificity have hindered its clinical application. Based on the previous studies, epidermal growth factor receptor-protein tyrosine kinase (EGFR-PTK) negatively regulated rAAV transduction, and EGFR-positive cells were hardly permissive to rAAV transduction. We constructed a novel rAAV-miRNA133b vector, which co-expressed miRNA133b and transgene, and investigated its in vivo and in vitro transduction efficiency. Confocal microscopy, live-cell imaging, pharmacological reagents and labelled virion tracking were used to analyse the effect of miRNA133b on rAAV2 transduction and the underlying mechanisms. The results demonstrated that miRNA133b could promote rAAV2 transduction and the effects were limited to EGFR-positive cells. The increased transduction was found to be a direct result of decreased rAAV particles degradation in the cytoplasm and enhanced second-strand synthesis. ss-rAAV2-miRNA133b vector specifically increased rAAV2 transduction in EGFR-positive cells or tissues, while ss-rAAV2-Fluc-miRNA133b exerted an antitumor effect. rAAV-miRNA133b vector might emerge as a promising platform for delivering various transgene to treat EGFR-positive cell-related diseases, such as non-small-cell lung cancer.

Exploring the worldwide impact of COVID-19 on conflict risk under climate change.

Objectives: Understand whether and how the COVID-19 pandemic affects the risk of different types of conflict worldwide in the context of climate change. Methodology: Based on the database of armed conflict, COVID-19, detailed climate, and non-climate data covering the period 2020-2021, we applied Structural Equation Modeling specifically to reorganize the links between climate, COVID-19, and conflict risk. Moreover, we used the Boosted Regression Tree method to simulate conflict risk under the influence of multiple factors. Findings: The transmission risk of COVID-19 seems to decrease as the temperature rises. Additionally, COVID-19 has a substantial worldwide impact on conflict risk, albeit regional and conflict risk variations exist. Moreover, when testing a one-month lagged effect, we find consistency across regions, indicating a positive influence of COVID-19 on demonstrations (protests and riots) and a negative relationship with non-state and violent conflict risk. Conclusion: COVID-19 has a complex effect on conflict risk worldwide under climate change. Implications: Laying the theoretical foundation of how COVID-19 affects conflict risk and providing some inspiration for the implementation of relevant policies.

Copper-Catalyzed C(sp)-H Bond Hydrazidation.

A catalytic, direct synthetic strategy for preparing ynehydrazides with terminal alkynes and dialkyl azodicarboxylates is described. The protocol utilizes a cheap copper catalyst in combination with a catalytic amount of a weak base. The high sustainability, good practicality, broad substrate scope, and wide functional group tolerance comprised the advantages of this reaction. Synthetic applications and preliminary mechanistic studies have been conducted.

Reactive oxygen species enhance rAAV transduction by promoting its escape from late endosomes.

BACKGROUND: Recent seminal studies have revealed that endosomal reactive oxygen species (ROS) promote rather than inhibit viral infection. Some ROS generators, including shikonin and H2O2, have the potential to enhance recombinant adeno-associated virus (rAAV) transduction. However, the impact of ROS on rAAV intracellular trafficking remains unclear. METHODS: To understand the effects of ROS on the transduction of rAAV vectors, especially the rAAV subcellular distribution profiles, this study systematically explored the effect of ROS on each step of rAAV intracellular trafficking pathway using fluorescently-labeled rAAV and qPCR quantification determination. RESULTS: The results showed promoted in-vivo and in-vitro rAAV transduction by ROS exposure, regardless of vector serotype or cell type. ROS treatment directed rAAV intracellular trafficking towards a more productive pathway by upregulating the expression of cathepsins B and L, accelerating the rAAV transit in late endosomes, and increasing the rAAV nucleus entry. CONCLUSIONS: These data support that ROS generative drugs, such as shikonin, have the potential to promote rAAV vector transduction by promoting rAAV's escape from late endosomes, and enhancing its productive trafficking to the nucleus.

Prediction of hospital readmission of multimorbid patients using machine learning models.

OBJECTIVE: The objective of this study is twofold. First, we seek to understand the characteristics of the multimorbid population that needs hospital care by using all diagnoses information (ICD-10 codes) and two aggregated multimorbidity and frailty scores. Second, we use machine learning prediction models on these multimorbid patients characteristics to predict rehospitalization within 30 and 365 days and their length of stay. METHODS: This study was conducted on 8 882 anonymized patients hospitalized at the University Hospital of Saint-Étienne. A descriptive statistical analysis was performed to better understand the characteristics of the patient population. Multimorbidity was measured using raw diagnoses information and two specific scores based on clusters of diagnoses: the Hospital Frailty Risk Score and the Calderon-Larrañaga index. Based on these variables different machine learning models (Decision Tree, Random forest and k-nearest Neighbors) were used to predict near future rehospitalization and length of stay (LoS). RESULTS: The use of random forest algorithms yielded better performance to predict both 365 and 30 days rehospitalization and using the diagnoses ICD-10 codes directly was significantly more efficient. However, using the Calderon-Larrañaga's clusters of diagnoses can be used as an efficient substitute for diagnoses information for predicting readmission. The predictive power of the algorithms is quite low on length of stay indicator. CONCLUSION: Using machine learning techniques using patients' diagnoses information and Calderon-Larrañaga's score yielded efficient results to predict hospital readmission of multimorbid patients. These methods could help improve the management of care of multimorbid patients in hospitals.

Prevalence of antibiotic resistance of Helicobacter pylori isolates in Shanghai, China.

OBJECTIVE: This study aimed to evaluate the resistance of Helicobacter pylori (H. pylori) to common antibiotics in Shanghai. METHODS: A total of 1171 eligible subjects participated in the study. Antibiotic susceptibility to six common antibiotics was examined with the disk diffusion method. Mutations in resistant-related genes were identified via Sanger sequencing analysis. RESULTS: Overall, the resistance rates of strains to amoxicillin, clarithromycin, levofloxacin, metronidazole, tetracycline, and furazolidone were 0.1%, 27.8%, 31.1%, 79.9%, 0.1%, and 0.5%, respectively. Compared with untreated patients, resistance rates of clarithromycin (P < 0.01), levofloxacin (P < 0.01), and metronidazole were significantly higher in re-treated patients (P < 0.05). The total multiple resistance rate was 40.5%. Age (levofloxacin), gender (clarithromycin, levofloxacin, and metronidazole) and endoscopic findings (clarithromycin and levofloxacin) were independent factors influencing antibiotic resistance. High correlation was observed between the drug susceptibility test and molecular test for the resistance to clarithromycin and levofloxacin. CONCLUSIONS: The resistance rates of H. pylori to amoxicillin, tetracycline, and furazolidone were low, whereas the resistance rates of H. pylori to clarithromycin, levofloxacin, and metronidazole were high, especially in re-treated patients. Our results indicate that the clinical resistance patterns of clarithromycin and levofloxacin could be guided by relevant gene mutations.

Exploring the direct and indirect impacts of climate variability on armed conflict in South Asia.

Although numerous studies have examined the effects of climate variability on armed conflict, the complexity of these linkages requires deeper understanding to assess the causes and effects. Here, we assembled an extensive database of armed conflict, climate, and non-climate data for South Asia. We used structural equation modeling to quantify both the direct and indirect impacts of climate variability on armed conflict. We found that precipitation impacts armed conflict via direct and indirect effects which are contradictory in sign. Temperature affects armed conflict only through a direct path, while indirect effects were insignificant. Yet, an in-depth analysis of indirect effects showed that the net impact is weak due to two strong contradictory effects offsetting each other. Our findings illustrate the complex link between climate variability and armed conflict, highlighting the importance of a detailed analysis of South Asia's underlying mechanisms at the regional scale.

PIFA-mediated selenylative spirocyclization of indolyl ynones: facile access to selenated spiro[cyclopentenone-1,3'-indoles].

A fast selenylative spirocyclization of indolyl ynones mediated by PIFA has been developed. This transformation was enabled by the reactive RSeOCOCF3 species generated in situ from diselenides with PIFA, involving an electrophilic dearomative cascade cyclization. This protocol provides a facile and efficient method for the synthesis of selenated spiro[cyclopentenone-1,3'-indoles] and tolerates broad functional groups.

Dexamethasone and potassium canrenoate alleviate hyperalgesia by competitively regulating IL-6/JAK2/STAT3 signaling pathway during inflammatory pain in vivo and in vitro.

BACKGROUND: Dexamethasone (Dexa) and potassium canrenoate (Cane) modulate nociceptive behavior via glucocorticoid receptor (GR) and mineralocorticoid receptor (MR) by two mechanisms (genomic and nongenomic pathways). This study was designed to investigate the Dexa- or Cane-mediated nongenomic and genomic effects on mechanical nociception and inflammation-induced changes in interleukin-6 (IL-6) mediated signaling pathway in rats. METHODS: Freund's complete adjuvant (FCA) was used to trigger an inflammation of the right hind paw in male Sprague-Dawley rats. First, the mechanical nociceptive behavioral changes were examined following intraplantar administration of GR agonist Dexa and/or MR antagonist Cane in vivo. Subsequently, the protein levels of IL-6, IL-6Rα, JAK2, pJAK2, STAT3, pSTAT3Ser727 , migration inhibitory factor, and cyclooxygenase-2 were assessed by Western blot following intraplantar injection of Dexa or Cane or the combination. Moreover, the molecular docking studies determined the interaction between Dexa, Cane, and IL-6. The competition binding assay was carried out using enzyme-linked immunosorbent assays (ELISA). RESULTS: Administration of Dexa and Cane dose-dependently attenuated FCA-induced inflammatory pain. The sub-additive effect of Dexa/Cane combination was elucidated by isobologram analysis, accompanied by decrease in the spinal levels of IL-6, pJAK2, and pSTAT3Ser727 . The molecular docking study demonstrated that both Dexa and Cane displayed a firm interaction with THR138 binding site of IL-6 via a strong hydrogen bond. ELISA revealed that Dexa has a higher affinity to IL-6 than Cane. CONCLUSIONS: There was no additive or negative effect of Dexa and Cane, and they modulate the IL-6/JAK2/STAT3 signaling pathway through competitive binding with IL-6 and relieves hypersensitivity during inflammatory pain.