An integrative data-driven model simulating C. elegans brain, body and environment interactions.

The behavior of an organism is influenced by the complex interplay between its brain, body and environment. Existing data-driven models focus on either the brain or the body-environment. Here we present BAAIWorm, an integrative data-driven model of Caenorhabditis elegans, which consists of two submodels: the brain model and the body-environment model.

Transglutaminase-triggered dual gradients of mechanical and biochemical cues self-assembling peptide hydrogel for guiding MC3T3-E1 cell behaviors.

The mechanical properties and bioactive motif densities of extracellular matrix materials play crucial roles in regulating cell behaviors, such as cell adhesion, migration, proliferation, and differentiation. However, current studies on cellular responses to ECM predominantly concentrated on polymer hydrogels featuring a single factor, such as the mechanical strength, the types of bioactive motifs, and the morphology of the polymers. This limited focus may overlook the complex interplay of multiple factors.

Array Radar Three-Dimensional Forward-Looking Imaging Algorithm Based on Two-Dimensional Super-Resolution.

Radar imaging is a technology that uses radar systems to generate target images. It transmits radio waves, receives the signal reflected back by the target, and realizes imaging by analyzing the target's position, shape, and motion information. The three-dimensional (3D) forward-looking imaging of missile-borne radar is a branch of radar imaging.

Foundation Model for Advancing Healthcare: Challenges, Opportunities and Future Directions.

Foundation model, trained on a diverse range of data and adaptable to a myriad of tasks, is advancing healthcare. It fosters the development of healthcare artificial intelligence (AI) models tailored to the intricacies of the medical field, bridging the gap between limited AI models and the varied nature of healthcare practices. The advancement of a healthcare foundation model (HFM) brings forth tremendous potential to augment intelligent healthcare services across a broad spectrum of scenarios.

Rectified Binary Network for Single-Image Super-Resolution.

Binary neural network (BNN) is an effective approach to reduce the memory usage and the computational complexity of full-precision convolutional neural networks (CNNs), which has been widely used in the field of deep learning. However, there are different properties between BNNs and real-valued models, making it difficult to draw on the experience of CNN composition to develop BNN. In this article, we study the application of binary network to the single-image super-resolution (SISR) task in which the network is trained for restoring original high-resolution (HR) images.

Unveiling the multifaceted realm of human papillomavirus: a comprehensive exploration of biology, interactions, and advances in cancer management.

Human Papillomavirus (HPV), an extensive family of DNA viruses, manifests as a persistent global health challenge. Persistent HPV infection is now firmly established as a significant aetiological factor for a spectrum of malignancies. In this review, we examine the latest insights into HPV biology and its intricate relationship with the host.

3D printed VEGF-CPO biomaterial scaffold to promote subcutaneous vascularization and survival of transplanted islets for the treatment of diabetes.

Diabetes is a complex metabolic disease and islet transplantation is a promising approach for the treatment of diabetes. Unfortunately, the transplanted islets at the subcutaneous site are also affected by various adverse factors such as poor vascularization and hypoxia. In this study, we utilize biocompatible copolymers l-lactide and D,l-lactide to manufacture a biomaterial scaffold with a mesh-like structure via 3D printing technology, providing a material foundation for encapsulating pancreatic islet cells.

Brain-Derived Exosomal CircRNAs in Plasma Serve as Diagnostic Biomarkers for Acute Ischemic Stroke.

Acute ischemic stroke (AIS), commonly known as stroke, is a debilitating condition characterized by the interruption of blood flow to the brain, resulting in tissue damage and neurological deficits. Early diagnosis is crucial for effective intervention and management, as timely treatment can significantly improve patient outcomes. Therefore, novel methods for the early diagnosis of AIS are urgently needed.

Mode coupling in FM-EDFAs caused by fiber bending: theory and experiments.

Refractive index perturbation caused by erbium-doped fiber (EDF) bending is inevitable in the fabrication of erbium-doped fiber amplifiers (EDFAs). The resulting mode coupling might bring about the deviation of theoretical results from experimental data. We present a theoretical model of FM-EDFAs with mode coupling due to fiber bending and carry out a proof-of-concept experiment by a 3.

FABNet: Frequency-Aware Binarized Network for Single Image Super-Resolution.

Remarkable achievements have been obtained with binary neural networks (BNN) in real-time and energy-efficient single-image super-resolution (SISR) methods. However, existing approaches often adopt the Sign function to quantize image features while ignoring the influence of image spatial frequency. We argue that we can minimize the quantization error by considering different spatial frequency components.

Prognostic Value of the Average Lung CT Number in Patients with Acute Paraquat Poisoning.

Objective: The chest computed tomography (CT) examination is an important clinical examination in the diagnosis and monitoring of paraquat- (PQ-) induced lung injury. The aim of this study was to explore the prognostic value of the average lung CT number acquired by quantitative CT techniques in patients with acute paraquat poisoning in the early stages of the disease.

Methods: 46 patients who suffered from acute PQ poisoning in the emergency department of the Nanjing Drum Tower Hospital from January 2015 to June 2020 were enrolled in the present study.

A high-quality genome assembly highlights the evolutionary history of the great bustard (Otis tarda, Otidiformes).

Conservation genomics often relies on non-invasive methods to obtain DNA fragments which limit the power of multi-omic analyses for threatened species. Here, we report multi-omic analyses based on a well-preserved great bustard individual (Otis tarda, Otidiformes) that was found dead in the mountainous region in Gansu, China. We generate a near-complete genome assembly containing only 18 gaps scattering in 8 out of the 40 assembled chromosomes.

Optogenetics in taste research: A decade of enlightenment.

The electrophysiological function of the tongue involves complicated activities in taste sense, producing the perceptions of salty, sweet, bitter, and sour. However, therapies and prevention of taste loss arising from dysfunction in electrophysiological activity require further fundamental research. Optogenetics has revolutionized neuroscience and brought the study of sensory system to a higher level in taste.

Construction and analysis of functional brain network based on emotional electroencephalogram.

Networks play an important role in studying structure or functional connection of various brain areas, and explaining mechanism of emotion. However, there is a lack of comprehensive analysis among different construction methods nowadays. Therefore, this paper studies the impact of different emotions on connection of functional brain networks (FBNs) based on electroencephalogram (EEG).

Local Means Binary Networks for Image Super-Resolution.

The success of modern single image super-resolution (SISR) algorithms is inspired by the development of deep convolutional neural networks (CNNs). However, these CNN-based methods require considerable computation and complexity, making it impossible for these methods to perform real-time calculations in edge devices. Thus, lightweight model design has become a development trend in the super-resolution field, including pruning, quantization, and other methods.

Toward Pixel-Level Precision for Binary Super-Resolution With Mixed Binary Representation.

Binary neural network (BNN) is an effective method for reducing model computational and memory cost, which has achieved much progress in the super-resolution (SR) field. However, there is still a noticeable performance gap between a binary SR network and its full-precision counterpart. Considering that the information density in quantization features is far lower than full-precision features, we aim to improve the precision of quantization features to produce rich-enough output activations for SR task.

Fear in the Theater of the Mind: Differential Fear Conditioning With Imagined Stimuli.

Many symptoms of anxiety and posttraumatic stress disorder are elicited by fearful mental imagery. Yet little is known about how visual imagery of conditioned stimuli (CSs) affects the acquisition of differential fear conditioning. Across three experiments with younger human adults (Experiment 1: = 33, Experiment 2: = 27, Experiment 3: = 26), we observed that participants acquired differential fear conditioning to both viewed and imagined percepts serving as the CSs, as measured via self-reported fear and skin conductance responses.

Representation of Cone-Opponent Color Space in Macaque Early Visual Cortices.

In primate vision, the encoding of color perception arises from three types of retinal cone cells (L, M, and S cones). The inputs from these cones are linearly integrated into two cone-opponent channels (cardinal axes) before the lateral geniculate nucleus. In subsequent visual cortical stages, color-preferring neurons cluster into functional domains within "blobs" in V1, "thin/color stripes" in V2, and "color bands" in V4.

Efficacy and Safety of Non-Steroidal Mineralocorticoid Receptor Antagonists in Patients With Chronic Kidney Disease and Type 2 Diabetes: A Systematic Review Incorporating an Indirect Comparisons Meta-Analysis.

The non-steroidal mineralocorticoid receptor antagonists (MRAs) are promising treatments in patients with chronic kidney disease (CKD) and type 2 diabetes (T2D). We conducted a meta-analysis to explore the efficacy and safety of the non-steroidal MRAs (finerenone, apararenone, esaxerenone) and detect the differences among them. We searched several databases for eligible randomized controlled trials (RCTs) investigating non-steroidal MRAs versus placebo in patients with CKD and T2D.

Landscape of Metabolic Fingerprinting for Diagnosis and Risk Stratification of Sepsis.

Background: Sepsis and septic shock, a subset of sepsis with higher risk stratification, are hallmarked by high mortality rates and necessitated early and accurate biomarkers.

Methods: Untargeted metabolomic analysis was performed to compare the metabolic features between the sepsis and control systemic inflammatory response syndrome (SIRS) groups in discovery cohort, and potential metabolic biomarkers were selected and quantified using multiple reaction monitoring based target metabolite detection method.

Results: Differentially expressed metabolites including 46 metabolites in positive electrospray ionization (ESI) ion mode, 22 metabolites in negative ESI ion mode, and 4 metabolites with dual mode between sepsis and SIRS were identified and revealed.

Mental imagery can generate and regulate acquired differential fear conditioned reactivity.

Mental imagery is an important tool in the cognitive control of emotion. The present study tests the prediction that visual imagery can generate and regulate differential fear conditioning via the activation and prioritization of stimulus representations in early visual cortices. We combined differential fear conditioning with manipulations of viewing and imagining basic visual stimuli in humans.

A second-order accelerated neurodynamic approach for distributed convex optimization.

Based on the theories of inertial systems, a second-order accelerated neurodynamic approach is designed to solve a distributed convex optimization with inequality and set constraints. Most of the existing approaches for distributed convex optimization problems are usually first-order ones, and it is usually hard to analyze the convergence rate for the state solution of those first-order approaches. Due to the control design for the acceleration, the second-order neurodynamic approaches can often achieve faster convergence rate.

Learning lightweight super-resolution networks with weight pruning.

Single image super-resolution (SISR) has achieved significant performance improvements due to the deep convolutional neural networks (CNN). However, the deep learning-based method is computationally intensive and memory demanding, which limit its practical deployment, especially for mobile devices. Focusing on this issue, in this paper, we present a novel approach to compress SR networks by weight pruning.

Psychophysiological evidence for fear extinction learning via mental imagery.

Imagery-based extinction procedures have long been used in the treatments of fear-related conditions. The assumption is that imagery can substitute for the perceptual stimuli in the extinction process. Yet, experimental validations of this assumption have been limited in number and some have relied exclusively on measures of autonomic reactivity without consideration of conscious feelings of fear.