Developing a quantitative adulteration discrimination model for forest-grown Panax notoginseng using near-infrared spectroscopy with a dual-branch network.

The ecological cultivation of herbs is gaining popularity due to its superior quality and greater economic value, with increasing applications in functional foods. However, profit-driven motives have led to the mislabeling of conventionally grown herbs as ecologically cultivated. Species consistency presents new challenges in authenticity identification, especially in detecting adulteration.

Chemical profiles of allspice (Pimenta dioica (L.) Merr.) water and ethanol extracts and their antivirus and radical scavenging activities.

Chemical compositions of the allspice water and ethanol extracts were characterized using UPLC-HRMS/MS. A total of 20 compounds were tentatively identified, with 12 reported for the first time in allspice. Both extracts dose-dependently inhibited the binding of SARS-CoV-2 spike protein to ACE2.

[Relapsed immune thrombotic thrombocytopenic purpura secondary to systemic lupus erythematosus: a case report and literature review].

To review the diagnosis and treatment process of a patient with relapsed immune thrombotic thrombocytopenic purpura (iTTP) secondary to systemic lupus erythematosus (SLE) and conduct a review of relevant literature, so as to provide a reference basis for the diagnosis and treatment of this disease. We retrospectively analyzed the diagnosis and treatment process of a patient with relapsed iTTP secondary to SLE who was admitted to the First Affiliated Hospital of Soochow University, and conducted a review of literature. When the patient's iTTP relapsed, it was accompanied by moderate lupus activity, hypofibrinogenemia, renal insufficiency, and pulmonary infection.

Arsenene-Vanadene nanodots co-activate Apoptosis/Ferroptosis for enhanced chemo-immunotherapy.

Triple-Negative Breast Cancer (TNBC) represents a highly aggressive subtype of breast cancer with an unfavorable prognosis, characterized by minimal immune infiltration and pronounced immune suppression, resulting in a limited response to immunotherapy. In this study, a multifunctional Arsenene-Vanadene nanodot (AsV) drug delivery system is introduced, which responds to the tumor microenvironment by releasing arsenic and vanadium. Arsenic undergoes oxidation to generate highly toxic trivalent arsenic, which induces apoptosis in tumor cells while utilizing apoptotic cell debris to transiently activate the immune system.

Development and validation of a risk prediction model for frailty in older nappers.

Background: Frailty among older adults has received widespread attention from society, especially among nappers. The objective of this study was to develop a frailty prediction model for nappers.

Methods: The data source was the China Health and Retirement Longitudinal Study, with a cohort of 1830 older nappers.

Proper Early Weaning from Ventilator Assistance Influences the Overall Survival of Patients with Severe Burns: A Case-Control Study.

Background: Ventilator assistance is crucial in the treatment of severely burned patients. However, there is no consensus on the optimal duration of ventilator assistance, and whether it has an impact on the short - and long-term prognosis of severely burned patients is still unclear.

Methods: We incorporated 185 severely burned patients in our study.

Industrial by-products of tiger nut starch as a source of cellulose nanocrystals for biodegradable packaging materials.

The development of eco-friendly, biodegradable nanomaterials is essential for promoting the sustainable utilization of industry by-products from tiger nut starch. This study focuses on the extraction of cellulose nanocrystals (TN-CNC) from tiger nut starch by-products through acid hydrolysis, as well as evaluation of their effects on the characteristics of starch-based biodegradable packaging. The TN-CNC was identified as having a rod-like morphology, exhibiting high crystallinity (CI = 87.

Preparation of various aminooligosaccharides using a novel chitinase and its CBM fusion mutants.

Aminooligosaccharides, including N-acetyl chitooligosaccharides (N-acetyl COSs), chitooligosaccharides (COSs), and partially acetylated chitooligosaccharides (paCOSs) have gained significant attention owing to their diverse bioactivities. Thus, the preparation of aminooligosaccharides particularly paCOSs, is a prospective way to reuse the huge amount of crustacean waste for high value-added products. In this study, a novel GH family 18 chitinase (MyChi54) suitable for aminooligosaccharides preparation was identified and characterized.

Loteprednol etabonate alleviates NLRP3 inflammasome-associated inflammatory diseases in mice by suppressing the transcription of IL-1β.

Excessive activation of the NLRP3 inflammasome leads to cellular inflammation and tissue damage. Finding an inhibitor of its activation is urgent need for NLRP3 inflammasome-associated inflammatory diseases. In this study, we identified Loteprednol etabonate (LE), a well-known anti-inflammatory drug for ocular conditions, as a potent inhibitor of NLRP3 inflammasome activation through screening an FDA-approved drug library.

Bioinspired programmed antibiofilm strategies for accelerated wound healing via spatiotemporally controlled enzyme nanoreactors.

Biofilms, protected by their dense, self-produced matrix, pose a significant clinical challenge due to their antibiotic resistance, leading to persistent infections and delayed wound healing, particularly in diabetic patients. Tailored to the biofilm life cycle, a double-layered nanoreactor was developed for rapid and complete antibiofilm therapy. The inner layer, cross-linked with poly(allylamine hydrochloride) (PAH)/phosphate, dimeric indocyanine green (dICG), and bromothymol blue (BTB), shields glucose oxidase (GOx) and β-glucanase (β-DEX) from unfavorable environment.

[ E6 improves vinorelbine-induced immunosuppression in zebrafish through its metabolites acetic acid and propionic acid].

Objectives: To explore the mechanism of E6 for improving vinorelbine-induced immunosuppression in zebrafish.

Methods: The intestinal colonization of E6 labeled by fluorescein isothiocyanate (FITC) in zebrafish was observed under fluorescence microscope. In a zebrafish model of vinorelbine-induced immunosuppression, the immunomodulatory activity of E6 was assessed by analyzing macrophage and neutrophil counts in the caudal hematopoietic tissue (CHT), the number of T-lymphocyte, and the expressions of interleukin-12 (IL-12) and interferon-γ (IFN-γ).

Constitutively active glucagon receptor drives high blood glucose in birds.

As the body's primary source of energy, the maintenance of blood glucose is indispensable for overall health and metabolic homeostasis. It is predominantly regulated by the glucagon receptor family which is highly conserved in vertebrates. Compared to other vertebrates, avian blood glucose levels are relatively high, yet its regulatory mechanisms have remained obscure for more than a century.

Diffusion Models in Low-Level Vision: A Survey.

Deep generative models have gained considerable attention in low-level vision tasks due to their powerful generative capabilities. Among these, diffusion model-based approaches, which employ a forward diffusion process to degrade an image and a reverse denoising process for image generation, have become particularly prominent for producing high-quality, diverse samples with intricate texture details. Despite their widespread success in low-level vision, there remains a lack of a comprehensive, insightful survey that synthesizes and organizes the advances in diffusion model-based techniques.

PolyGraph: a Graph-based Method for Floorplan Reconstruction from 3D Scans.

The task of reconstructing indoor floorplans has become an increasingly popular subject, offering substantial benefits across various applications such as interior design, virtual reality, and robotics. Despite the growing interest, existing approaches frequently encounter challenges due to high computational costs and sensitivity to errors in primitive detection. In this paper, we introduce PolyGraph, a new computational framework that combines a deep-learning based primitive detection network with an optimization-based reconstruction algorithm to facilitate high-quality reconstruction results.

A Nonlinear Noise-Resistant Zeroing Neural Network Model for Solving Time-Varying Quaternion Generalized Lyapunov Equation and Applications to Color Image Processing.

The time-varying Lyapunov equation (TVLE) plays a crucial role in control design and system stability. However, there has been limited research conducted on the time-varying generalized Lyapunov equation in the quaternion field. To tackle the time-varying quaternion generalized Lyapunov equation, a nonlinear noise-resistant zeroing neural network (NNR-ZNN) model with a novel power activation function (NPAF) is devised.

RCM: A Neural Policy Model With Reconstruction Mechanism to Construct a Solution for the Agile Satellite Scheduling Problem.

The agile Earth observation satellite scheduling problem (AEOSSP) with time-dependent transition time is a combinatorial optimization challenge. Due to its NP-hardness, problem-tailored methods are sensitive to instances and require massive computational overhead. Recently, deep reinforcement learning (DRL) models have shown promise in efficiently addressing the AEOSSP.

PhysMLE: Generalizable and Priors-Inclusive Multi-task Remote Physiological Measurement.

Remote photoplethysmography (rPPG) has been widely applied to measure heart rate from face videos. To increase the generalizability of the algorithms, domain generalization (DG) attracted increasing attention in rPPG. However, when rPPG is extended to simultaneously measure more vital signs (e.

Interpretable Optimization-Inspired Deep Network for Off-Grid Frequency Estimation.

The accuracy of on-grid frequency estimation methods suffers from the quantization error of discrete grids. In this article, a deep unfolded network for off-grid frequency estimation is proposed, dubbed OGFreq. In the OGFreq, there exist two kinds of variables.

Learning Decision Boundaries for Multidimensional Anomaly Detection.

In this article, we attempt to study a problem of identifying outliers from multiple dimensions, terming it as multidimensional anomaly detection. This task assumes that each sample corresponds to heterogeneous discriminative spaces, where each space characterizes distinct semantic information along one dimension. Consequently, the abnormalities exhibited by a sample will vary under these semantically different dimensions.

Implicit-explicit Integrated Representations for Multi-view Video Compression.

With the increasing consumption of 3D displays and virtual reality, multi-view video has become a promising format. However, its high resolution and multi-camera shooting result in a substantial increase in data volume, making storage and transmission a challenging task. To tackle these difficulties, we propose an implicit-explicit integrated representation for multi-view video compression.

Instruct-ReID++: Towards Universal Purpose Instruction-Guided Person Re-identification.

Recently, person re-identification (ReID) has witnessed fast development due to its broad practical applications and proposed various settings, e.g., traditional ReID, clothes-changing ReID, and visible-infrared ReID.

AUCPro: AUC-Oriented Provable Robustness Learning.

The current studies of provable robustness for deep neural networks (DNNs) usually assume that the class distribution is overall balanced. However, in real-world applications especially for safety-sensitive systems, the class distribution often exhibits a long-tailed property. It is well-known that the Area Under the ROC Curve (AUC) is a more proper metric for long-tailed learning problems.

Contrastive Learning Guided Fusion Network for Brain CT and MRI.

Medical image fusion technology provides professionals with more detailed and precise diagnostic information. This paper introduces a new efficient CT and MRI fusion network, CLGFusion, based on a contrastive learning-guided network. CLGFusion includes two encoding branches at the feature encoding stage, enabling them to interact and learn from each other.

Hierarchically Optimized Multiple Instance Learning With Multi-Magnification Pathological Images for Cerebral Tumor Diagnosis.

Accurate diagnosis of cerebral tumors is crucial for effective clinical therapeutics and prognosis. However, limitations in brain biopsy tissues and the scarcity of pathologists specializing in cerebral tumors hinder comprehensive clinical tests for precise diagnosis. To address these challenges, we first established a brain tumor dataset of 3,520 cases collected from multiple centers.