Enhancers

Existing studies have shown that the abnormal expression of microRNAs (miRNAs) usually leads to the occurrence and development of human diseases. Identifying disease-related miRNAs contributes to studying the pathogenesis of diseases at the molecular level. As traditional biological experiments are time-consuming and expensive, computational methods have been used as an effective complement to infer the potential associations between miRNAs and diseases. However, most of the existing computational methods still face three main challenges: (i) learning of high-order relations; (ii) insufficient representation learning ability; (iii) importance learning and integration of multi-view embedding representation...

An amino acid-conjugated naphthalene diimide (NDI)-based highly red fluorescent radical anion has been found in a water medium under the photoradiated condition. This molecule has failed to form the radical anion in the monomeric state; however, the J aggregation in the aqueous medium has ensured the formation of radical anion in the ambient condition after the irradiation of both sunlight and UV light exposure. Electron paramagnetic resonance (EPR) studies clearly suggest the formation of radical anions. Herein, the stability of the radical anion in the aqueous medium is only a few minutes as a small amount of shaking is enough to quench the radical anion in the solution state...

BACKGROUND: Youth who experience homelessness engage in behaviors that place them at high risk for disease and injury. Despite their health risk behaviors, these youth display psychological capital, positive attributes of hope, efficacy, resilience, and optimism that motivate them to engage in health-promoting behaviors such as safer sex. However, this array of positive psychological attributes has not been studied in this vulnerable population. OBJECTIVES: The specific aim of this analysis was to determine whether factors of psychological capital mediated the relationship between background risk factors (e...

Actinomycetes are prolific producers of natural products, particularly antibiotics. However, a significant proportion of its biosynthetic gene clusters (BGCs) remain silent under typical laboratory conditions. This limits the effectiveness of conventional isolation methods for the discovery of novel natural products. Genetic interventions targeting the activation of silent gene clusters are necessary to address this challenge. Streptomyces antibiotic regulatory proteins (SARPs) act as cluster-specific activators and can be used to target silent BGCs for the discovery of new antibiotics...

Endoscopic submucosal dissection (ESD) is an effective method for resecting early-stage tumors in the digestive system. To achieve a low injection pressure of the injected fluid and continuous elevation of the mucosa following injection during the ESD technique, we introduced an innovative injectable sodium-alginate-based drug-loaded microsphere (Cipro-ThSA) for ESD surgery, which was generated through an emulsion reaction involving cysteine-modified sodium alginate (ThSA) and ciprofloxacin. Cipro-ThSA microspheres exhibited notable adhesiveness, antioxidant activity, and antimicrobial properties, providing a certain level of postoperative wound protection...

Gliomas, the most prevalent primary brain tumors, pose considerable challenges due to their heterogeneity, intricate tumor microenvironment (TME), and blood-brain barrier (BBB), which restrict the effectiveness of traditional treatments like surgery and chemotherapy. This review provides an overview of engineered cell membrane technologies in glioma therapy, with a specific emphasis on targeted drug delivery and modulation of the immune microenvironment. This study investigates the progress in engineered cell membranes, encompassing physical, chemical, and genetic alterations, to improve drug delivery across the BBB and effectively target gliomas...

Utilising Machine Learning (ML) models to predict dosimetric parameters in pencil beam scanning proton therapy presents a promising and practical approach. The study developed Artificial Neural Network (ANN) models to predict proton beam spot size and relative positional errors using 9000 proton spot data. The irradiation log files as input variables and corresponding scintillation detector measurements as the label values. The ANN models were developed to predict six variables: spot size in the x -axis, y -axis, major axis, minor axis, and relative positional errors in the x -axis and y -axis...

PURPOSE: Adjuvant, pre-operative intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections have been used to reduce peri-operative bleeding in eyes undergoing pars-plana vitrectomy for complications of proliferative diabetic retinopathy (PDR). To address the concern over their potential off-target effects of progressive fibrous contraction, we sought to dissect the transcriptional changes in the surgically extracted fibrovascular membranes (FVMs). METHODS: We analyzed surgically extracted FVMs from 10 eyes: 4 eyes pretreated with intravitreal bevacizumab (IVB) and 6 untreated eyes...

We endeavor on a rarely explored task named thermal infrared video denoising. Perception in the thermal infrared significantly enhances the capabilities of machine vision. Nonetheless, noise in imaging systems is one of the factors that hampers the large-scale application of equipment. Existing thermal infrared denoising methods, primarily focusing on the image level, inadequately utilize time-domain information and insufficiently conduct investigation of system-level mixed noise, presenting the inferior ability in the video-recorded era; while video denoising methods, commonly applied to RGB cameras, exhibit uncertain effectiveness owing to substantial dissimilarities in the noise models and modalities between RGB and thermal infrared images...

In industrial production processes, the mechanical properties of materials will directly determine the stability and consistency of product quality. However, detecting the current mechanical property is time-consuming and labor-intensive, and the material quality cannot be controlled in time. To achieve high-quality steel materials, developing a novel intelligent manufacturing technology that can satisfy multitask predictions for material properties has become a new research trend. This article proposes a multiobjective evolutionary learning method based on a two-stage model with topological sparse autoencoder (TSAE) and ensemble learning...

Contrastive learning (CL) has emerged as a powerful approach for self-supervised learning. However, it suffers from sampling bias, which hinders its performance. While the mainstream solutions, hard negative mining (HNM) and supervised CL (SCL), have been proposed to mitigate this critical issue, they do not effectively address graph CL (GCL). To address it, we propose graph positive sampling (GPS) and three contrastive objectives. The former is a novel learning paradigm designed to leverage the inherent properties of graphs for improved GCL models, which utilizes four complementary similarity measurements, including node centrality, topological distance, neighborhood overlapping, and semantic distance, to select positive counterparts for each node...

Knowledge tracing (KT) refers to predicting learners' performance in the future according to their historical responses, which has become an essential task in intelligent tutoring systems. Most deep learning-based methods usually model the learners' knowledge states via recurrent neural networks (RNNs) or attention mechanisms. Recently emerging graph neural networks (GNNs) assist the KT model to capture the relationships such as question-skill and question-learner. However, non-pairwise and complex higher-order information among responses is ignored...

Deep Neural Networks (DNNs) are known to be vulnerable to both backdoor and adversarial attacks. In the literature, these two types of attacks are commonly treated as distinct robustness problems and solved separately, since they belong to training-time and inference-time attacks respectively. However, this paper revealed that there is an intriguing connection between them: (1) planting a backdoor into a model will significantly affect the model's adversarial examples; (2) for an infected model, its adversarial examples have similar features as the triggered images...

In virtual reality applications, in addition to visual feedback, real objects can be used as props for virtual objects to provide passive haptic feedback, which greatly enhances user immersion. Usually, real object props are not one-to-one correspondence with virtual objects. Haptic retargeting technique is proposed to establish the virtual-real correspondence by introducing an offset between the virtual hand and the real hand. Sometimes, the offset is too large to cause user discomfort, and it is necessary to introduce a reset between two haptic retargeting operations to force the virtual hand and the real hand to coincide in order to eliminate the offset...

To enhance the appeal and informativeness of data news, there is an increasing reliance on data analysis techniques and visualizations, which poses a high demand for journalists' abilities. While numerous visual analytics systems have been developed for deriving insights, few tools specifically support and disseminate viewpoints for journalism. Thus, this work aims to facilitate the automatic creation of sports news from natural language insights. To achieve this, we conducted an extensive preliminary study on the published sports articles...

The issue of traffic congestion poses a significant obstacle to the development of global cities. One promising solution to tackle this problem is intelligent traffic signal control (TSC). Recently, TSC strategies leveraging reinforcement learning (RL) have garnered attention among researchers. However, the evaluation of these models has primarily relied on fixed metrics like reward and queue length. This limited evaluation approach provides only a narrow view of the model's decision-making process, impeding its practical implementation...

The paper presents a new method for constructing self-supporting surfaces using arch beams that are designed to convert their thrust into supporting force, thereby eliminating shear stress and bending moments. Our method allows for the placement of the arch beams on the boundary or within a surface and partitions the surface into multiple self-supporting parts. The use of arch beams enhances stability and durability, adds aesthetic appeal, and allows for greater flexibility in the design process. We develop an iterative algorithm for designing selfsupporting surfaces with arch beams that enables the user to control the shape of the beams and surface through intuitive parameters and specify the desired location of the arch beams...

Emotion recognition from electroencephalogram (EEG) signals is a critical domain in biomedical research with applications ranging from mental disorder regulation to human-computer interaction. In this paper, we address two fundamental aspects of EEG emotion recognition: continuous regression of emotional states and discrete classification of emotions. While classification methods have garnered significant attention, regression methods remain relatively under-explored. To bridge this gap, we introduce MASA-TCN, a novel unified model that leverages the spatial learning capabilities of Temporal Convolutional Networks (TCNs) for EEG emotion regression and classification tasks...

Proximal femoral fracture segmentation in computed tomography (CT) is essential in the preoperative planning of orthopedic surgeons. Recently, numerous deep learning-based approaches have been proposed for segmenting various structures within CT scans. Nevertheless, distinguishing various attributes between fracture fragments and soft tissue regions in CT scans frequently poses challenges, which have received comparatively limited research attention. Besides, the cornerstone of contemporary deep learning methodologies is the availability of annotated data, while detailed CT annotations remain scarce...

This study investigates the "Negative Spillover Effect"-a conceptual framework that highlights the correlation between older adults' community safety concerns and depressive symptoms. It explores the moderating influence of the sense of freedom in this relationship. Through the analysis of data from 3408 participants in the China Labor-force Dynamics Survey, employing a two-stage least squares regression approach, the study uncovers the intricate role of the sense of freedom in influencing the depressive symptoms of older adults based on their community safety concerns...