neural coding

In this paper, we introduce in-depth the analysis of CNNs and ViT architectures in medical images, with the goal of providing insights into subsequent research direction. In particular, the origins of deep neural networks should be explainable for medical images, but there has been a paucity of studies on such explainability in the aspect of deep neural network architectures. Therefore, we investigate the origin of model performance, which is the clue to explaining deep neural networks, focusing on the two most relevant architectures, such as CNNs and ViT...

Weakly supervised temporal action localization aims to locate the temporal boundaries of action instances in untrimmed videos using video-level labels and assign them the corresponding action category. Generally, it is solved by a pipeline called "localization-by-classification", which finds the action instances by classifying video snippets. However, since this approach optimizes the video-level classification objective, the generated activation sequences often suffer interference from class-related scenes, resulting in a large number of false positives in the prediction results...

Spatial transcriptomics (ST), containing gene expression with fine-grained (i.e., different windows) spatial location within tissue samples, has become vital in developing innovative treatments. Traditional ST technology, however, rely on costly specialized commercial equipment. Addressing this, our article aims to creates a cost-effective, virtual ST approach using standard tissue images for gene expression prediction, eliminating the need for expensive equipment. Conventional approaches in this field often overlook the long-distance spatial dependencies between different sample windows or need prior gene expression data...

Intraoperative imaging techniques for reconstructing deformable tissues in vivo are pivotal for advanced surgical systems. Existing methods either compromise on rendering quality or are excessively computationally intensive, often demanding dozens of hours to perform, which significantly hinders their practical application. In this paper, we introduce Fast Orthogonal Plane (Forplane), a novel, efficient framework based on neural radiance fields (NeRF) for the reconstruction of deformable tissues. We conceptualize surgical procedures as 4D volumes, and break them down into static and dynamic fields comprised of orthogonal neural planes...

Protein-ligand binding affinity prediction plays an important role in the field of drug discovery. Existing deep learning-based approaches have significantly improved the efficiency of protein-ligand binding affinity prediction through their excellent inductive bias capability. However, these methods only focus on fragmented three-dimensional data, which truncates the integrity of pocket data, leading to the neglect of potential long-range interactions. In this paper, we propose a dual-stream framework, with amino acid sequence assisting the atomic data fusion for graph neural network (termed SadNet), to fuse both 3D atomic data and sequence data for more accurate prediction results...

From deciding which meal to prepare for our guests to trading-off the pro-environmental effects of climate protection measures against their economic costs, we often must consider the consequences of our actions for the well-being of others (welfare). Vexingly, the tastes and views of others can vary widely. To maximize welfare according to the utilitarian philosophical tradition, decision makers facing conflicting preferences of others should choose the option that maximizes the sum of subjective value (utility) of the entire group...

Computer representations of three-dimensional (3D) geometries are crucial for simulating systems and processes in engineering and science. In medicine, and more specifically, biomechanics and orthopaedics, obtaining and using 3D geometries is critical to many workflows. However, while many tools exist to obtain 3D geometries of organic structures, little has been done to make them usable for their intended medical purposes. Furthermore, many of the proposed tools are proprietary, limiting their use. This work introduces two novel algorithms based on Generalized Regression Neural Networks (GRNN) and 4 processes to perform mesh morphing and overclosure adjustment...

Graph convolutional networks (GCNs) have been widely used in skeleton-based action recognition. However, existing approaches are limited in fine-grained action recognition due to the similarity of interclass data. Moreover, the noisy data from pose extraction increase the challenge of fine-grained recognition. In this work, we propose a flexible attention block called channel-variable spatial-temporal attention (CVSTA) to enhance the discriminative power of spatial-temporal joints and obtain a more compact intraclass feature distribution...

Subarachnoid hemorrhage (SAH) is a devastating cerebrovascular event associated with high mortality and significant morbidity. Recent studies have highlighted the emerging role of ferroptosis, a novel form of regulated cell death, in the pathogenesis of SAH. Circular RNAs (circRNAs), have been found to play essential roles in various cellular processes, including gene regulation and disease pathogenesis. The expression profile of circRNAs in neural tissues, particularly in the brain, suggests their critical role in synaptic function and neurogenesis...

BACKGROUND: Direct potable reuse (DPR) involves adding purified wastewater that has not passed through an environmental buffer into a water distribution system. DPR may help address water shortages and is approved or is under consideration as a source of drinking water for several water-stressed population centers in the United States, however, there are no studies of health outcomes in populations who receive DPR drinking water. Our objective was to determine whether the introduction of DPR for certain public water systems in Texas was associated with changes in birth defect prevalence...

Performance during perceptual decision-making exhibits an inverted-U relationship with arousal, but the underlying network mechanisms remain unclear. Here, we recorded from auditory cortex (A1) of behaving mice during passive tone presentation, while tracking arousal via pupillometry. We found that tone discriminability in A1 ensembles was optimal at intermediate arousal, revealing a population-level neural correlate of the inverted-U relationship. We explained this arousal-dependent coding using a spiking network model with a clustered architecture...

UNLABELLED: Prior lesion, noninvasive-imaging, and intracranial-electroencephalography (iEEG) studies have documented hierarchical, parallel, and distributed characteristics of human speech processing. Yet, there have not been direct, intracranial observations of the latency with which regions outside the temporal lobe respond to speech, or how these responses are impacted by task demands. We leveraged human intracranial recordings via stereo-EEG to measure responses from diverse forebrain sites during (i) passive listening to /bi/ and /pi/ syllables, and (ii) active listening requiring /bi/-versus-/pi/ categorization...

ZNF143 is a sequence-specific DNA binding protein that regulates the expression of protein-coding genes and small RNA molecules. In humans, ZNF143 interacts with HCFC1, a transcriptional cofactor, to regulate the expression of downstream target genes, including MMACHC , which encodes an enzyme involved in cobalamin ( cbl ) metabolism. Mutations in HCFC1 or ZNF143 cause an inborn error of cobalamin metabolism characterized by abnormal cbl metabolism, intellectual disability, seizures, and mild to moderate craniofacial abnormalities...

Circular RNAs (circRNAs) have surfaced as important non-coding RNA molecules in biology. Understanding interactions between circRNAs and RNA-binding proteins (RBPs) is crucial in circRNA research. Existing prediction models suffer from limited availability and accuracy, necessitating advanced approaches. In this study, we propose CRIECNN (Circular RNA-RBP Interaction predictor using an Ensemble Convolutional Neural Network), a novel ensemble deep learning model that enhances circRNA-RBP binding site prediction accuracy...

The neural circuits of reward processing and interval timing (including the perception and production of temporal intervals) are functionally intertwined, suggesting that it might be possible for momentary reward processing to influence subsequent timing behavior. Previous animal and human studies have mainly focused on the effect of reward on interval perception, whereas its impact on interval production is less clear. In this study, we examined whether feedback, as an example of performance-contingent reward, biases interval production...

BACKGROUND: Stem cell therapy is a promising therapeutic strategy. In a previous study, we evaluated tumorigenicity by the stereotactic transplantation of neural stem cells (NSCs) and embryonic stem cells (ESCs) from experimental mice. Twenty-eight days later, there was no evidence of tumor formation or long-term engraftment in the NSCs transplantation group. In contrast, the transplantation of ESCs caused tumor formation; this was due to their high proliferative capacity...

MOTIVATION: Personalized cancer treatments require accurate drug response predictions. Existing deep learning methods show promise but higher accuracy is needed to serve the purpose of precision medicine. The prediction accuracy can be improved with not only topology but geometrical information of drugs. RESULTS: A novel deep learning methodology for drug response prediction is presented, named Hi-GeoMVP. It synthesizes hierarchical drug representation with multi-omics data, leveraging graph neural networks and variational autoencoders for detailed drug and cell line representations...

Convolutional neural networks (CNNs) have significantly advanced various fields; however, their computational demands and power consumption have escalated, posing challenges for deployment in low-power scenarios. To address this issue and facilitate the application of CNNs in power constrained environments, the development of dedicated CNN accelerators is crucial. Prior research has predominantly concentrated on developing low precision CNN accelerators using code generated from high-level synthesis (HLS) tools...

The application of wearable magnetoencephalography using optically-pumped magnetometers has drawn extensive attention in the field of neuroscience. Electroencephalogram system can cover the whole head and reflect the overall activity of a large number of neurons. The efficacy of optically-pumped magnetometer in detecting event-related components can be validated through electroencephalogram results. Multivariate pattern analysis is capable of tracking the evolution of neurocognitive processes over time. In this paper, we adopted a classical Chinese semantic congruity paradigm and separately collected electroencephalogram and optically-pumped magnetometer signals...

Neuronal ensembles are groups of neurons with correlated activity associated with sensory, motor, and behavioral functions. To explore how ensembles encode information, we investigated responses of visual cortical neurons in awake mice using volumetric two-photon calcium imaging during visual stimulation. We identified neuronal ensembles employing an unsupervised model-free algorithm and, besides neurons activated by the visual stimulus (termed "onsemble"), we also find neurons that are specifically inactivated (termed "offsemble")...