Development and antimicrobial activity of composite edible films of chitosan and nisin incorporated with perilla essential oil-glycerol monolaurate emulsions.

Aquatic products are highly susceptible to spoilage, and preparing composite edible film with essential oil is an effective solution. In this study, composite edible films were prepared using perilla essential oil (PEO)-glycerol monolaurate emulsions incorporated with chitosan and nisin, and the film formulation was optimized by response surface methodology. These films were applied to ready-to-eat fish balls and evaluated over a period of 12 days. The films with the highest inhibition rate against Staphylococcus aureus were acquired using a polymer composition of 6 µL/mL PEO, 18.4 µg/mL glycerol monolaurate, 14.2 mg/mL chitosan, and 11.0 µg/mL nisin. The fish balls coated with the optimal edible film showed minimal changes in appearance during storage and significantly reduced total bacterial counts and total volatile basic nitrogen compared to the control groups. This work indicated that the composite edible films containing essential oils possess ideal properties as antimicrobial packaging materials for aquatic foods.

Comprehensive Analysis of a Platelet- and Coagulation-Related Prognostic Gene Signature Identifies CYP19A1 as a Key Tumorigenic Driver of Colorectal Cancer.

BACKGROUND: The intricate interplay between the platelet-coagulation system and the progression of malignant tumors has profound therapeutic implications. However, a thorough examination of platelet and coagulation markers specific to colorectal cancer (CRC) is conspicuously absent in the current literature. Consequently, there is an urgent need for further exploration into the mechanistic underpinnings of these markers and their potential clinical applications. METHODS: By integrating RNA-seq data and clinicopathological information from patients with CRC in the cancer genome atlas, we identified genes related to the platelet-coagulation system using weighted gene co-expression networks and univariate Cox analysis. We established a prognostic risk model based on platelet- and coagulation-related genes using Lasso Cox regression analysis and validated the model in two independent CRC cohorts. We explored potential biological functional disparities between high-risk and low-risk groups through comprehensive bioinformatics analysis. RESULTS: Our findings indicate that colorectal cancer patients classified as high-risk generally exhibit poorer prognoses. Moreover, the model's risk scores were associated with the differential composition of the immune tumor microenvironment, suggesting its applicability to infer immunotherapy responsiveness. Cellular functional experiments and animal experiments indicated that CYP19A1 expression in CRC influences malignant phenotype and platelet activation. CONCLUSIONS: In summary, we present a novel platelet- and coagulation-related risk model for prognostic assessment of patients with CRC and confirm the important role of CYP19A1 in promoting malignant progression of CRC.

Current progress of anti­PD­1/PDL1 immunotherapy for glioblastoma (Review).

Glioblastoma (GBM) is the most common central nervous system malignancy in adults. GBM may be classified as grade IV diffuse astrocytoma according to the 2021 World Health Organization revised classification of central nervous system tumors, which means it is the most aggressive, invasive, undifferentiated type of tumor. Immune checkpoint blockade (ICB), particularly anti­programmed cell death protein­1 (PD­1)/PD­1 ligand­1 immunotherapy, has been confirmed to be successful across several tumor types. However, in GBM, this treatment is still uncommon and the efficacy is unpredictable, and <10% of patients show long­term responses. Recently, numerous studies have been conducted to explore what factors may indicate or affect the ICB response rate in GBM, including molecular alterations, immune expression signatures and immune infiltration. The present review aimed to summarize the current progress to improve the understanding of immunotherapy for GBM.

Engineered Core-Shell SiC@SiO2 Nanofibers for Enhanced Electromagnetic Wave Absorption Performance.

To enable SiC material to achieve high electromagnetic wave (EMW) absorption performance, solving its impedance mismatch with EMW is necessary. Therefore, a novel approach is proposed for the precise control of impedance matching by adjusting the shell thickness of SiO2 nanolayers on the surface of SiC nanofibers (NFs). High-angle annular dark field-scanning transmission electron microscopy (HAADF-STEM) reveals the atomic scale oxidation process of SiC, providing fresh insights into the oxidation mechanism. By oxidizing to construct a heterogeneous core-shell structure nanofiber (NF) can effectively lock the incident EMW inside the NF through the generated charges gathered at the interface, forming an electronic barrier that prevents the outward propagation of EMWs. The produced SiC@SiO2 NFs-3 exhibits exceptional EMW absorption properties, including an impressive minimum reflection loss (RLmin) of -53.09 dB and a broad maximum effective absorption bandwidth (EABmax) of 8.85 GHz. These findings not only deepen understanding of the oxidation mechanism of SiC but also offer valuable insights for further enhancing the EMW absorption capabilities of SiC materials, paving the way for their application in advanced EMW technologies.

Construction of heterogeneous MOF-on-MOF for highly efficient gaseous iodine sequestration under static conditions.

Considering the unexpected nuclear power waste emission and potential nuclear leakage, the exploration of robust materials for the effective capture and storage of radioactive iodine is of great importance but still remains a challenge. In this work, we report the rational synthesis of functionalized NH2-UiO-66-on-ZIF-67 architecture to enhance the static adsorption and retention of volatile iodine. Such MOF-on-MOF heterostructures was fabricated through seeding ZIF-67 core on the surface of NH2-UiO-66 satellite via a facile polyvinylpyrrolidone (PVP) regulated internal extended growth strategies. NH2-UiO-66-on-ZIF-67 exhibited unique core-satellite structure, which significantly promotes the binding interactions with iodine through synergizing of the N-rich imidazole moieties and surface functionalized amino groups within the porosity channels. As a result, the as fabricated NH2-UiO-66-on-ZIF-67 achieves enhanced mass diffusion and high capture capacity of 3600 mg/g for iodine vapor under static sorption conditions. Moreover, water vapor in humid conditions (relative humidity of 18 %) has almost no effect on the static iodine adsorption performance of the material. This study sheds light on a reliable MOF-on-MOF hybrid strategy for effective radioiodine treatment to ensure the safety nuclear waste management.

Embedding Reverse Electron Transfer Between Stably Bare Cu Nanoparticles and Cation-Vacancy CuWO4.

Cu nanoparticles (NPs) have attracted widespread attention in electronics, energy, and catalysis. However, conventionally synthesized Cu NPs face some challenges such as surface passivation and agglomeration in applications, which impairs their functionalities in the physicochemical properties. Here, the issues above by engineering an embedded interface of stably bare Cu NPs on the cation-vacancy CuWO4 support is addressed, which induces the strong metal-support interactions and reverse electron transfer. Various atomic-scale analyses directly demonstrate the unique electronic structure of the embedded Cu NPs with negative charge and anion oxygen protective layer, which mitigates the typical degradation pathways such as oxidation in ambient air, high-temperature agglomeration, and CO poisoning adsorption. Kinetics and in situ spectroscopic studies unveil that the embedded electron-enriched Cu NPs follow the typical Eley-Rideal mechanism in CO oxidation, contrasting the Langmuir-Hinshelwood mechanism on the traditional Cu NPs. This mechanistic shift is driven by the Coulombic repulsion in anion oxygen layer, enabling its direct reaction with gaseous CO to form the easily desorbed monodentate carbonate.

[Early identification of acute kidney injury in children with primary nephrotic syndrome]. / å„¿ç«¥åŽŸå‘æ€§è‚¾ç— ç»¼åˆå¾å¹¶å‘æ€¥æ€§è‚¾æŸä¼¤çš„æ—©æœŸè¯†åˆ«.

OBJECTIVES: To investigate the incidence and risk factors for acute kidney injury (AKI) in children with primary nephrotic syndrome (PNS), as well as the role of neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) in the early identification of AKI in these children. METHODS: A prospective collection of clinical data from children hospitalized with PNS at the Children's Hospital of the Capital Institute of Pediatrics from January 2021 to October 2022 was conducted. The children were divided into two groups based on the presence of AKI: the AKI group (47 cases) and the non-AKI group (169 cases). The risk factors for AKI in children with PNS were identified by multivariate logistic regression analysis. Urinary KIM-1 and NGAL levels were compared between the AKI and non-AKI groups, as well as among the different stages of AKI. RESULTS: The incidence of AKI in children with PNS was 21.8%. Multivariate logistic regression analysis revealed that steroid-resistant nephrotic syndrome, gastrointestinal infections, and heavy proteinuria were independent risk factors for AKI in these children with PNS (P<0.05). Urinary KIM-1 and NGAL levels were higher in the AKI group compared to the non-AKI group (P<0.05), and the urinary NGAL and KIM-1 levels in the AKI stage 2 and stage 3 subgroups were higher than those in the AKI stage 1 subgroup (P<0.017). CONCLUSIONS: KIM-1 and NGAL can serve as biomarkers for the early diagnosis of AKI in children with PNS. Identifying high-risk populations for AKI in children with PNS and strengthening the monitoring of related risk factors is of significant importance.

Adapting Few-Shot Classification via In-Process Defense.

Most few-shot learning methods employ either adaptive approaches or parameter amortization techniques. However, their reliance on pre-trained models presents a significant vulnerability. When an attacker's trigger activates a hidden backdoor, it may result in the misclassification of images, profoundly affecting the model's performance. In our research, we explore adaptive defenses against backdoor attacks for few-shot learning. We introduce a specialized stochastic process tailored to task characteristics that safeguards the classification model against attack-induced incorrect feature extraction. This process functions during forward propagation and is thus termed an "in-process defense." Our method employs an adaptive strategy, effectively generating task-level representations, enabling rapid adaptation to pre-trained models, and proving effective in few-shot classification scenarios for countering backdoor attacks. We apply latent stochastic processes to approximate task distributions and derive task-level representations from the support set. This task-level representation guides feature extraction, leading to backdoor trigger mismatching and forming the foundation of our parameter defense strategy. Benchmark tests on Meta-Dataset reveal that our approach not only withstands backdoor attacks but also shows an improved adaptation in addressing few-shot classification tasks.

Local artifacts amplification for deepfakes augmentation.

With the rapid and continuous development of AIGC, It is becoming increasingly difficult to distinguish between real and forged facial images, which calls for efficient forgery detection systems. Although many detection methods have noticed the importance of local artifacts, there has been a lack of in-depth discussion regarding the selection of locations and their effective utilization. Besides, the traditional image augmentation methods that are widely used have limited improvements for forgery detection tasks and require more specialized augmentation methods specifically designed for forgery detection tasks. In this paper, this study proposes Local Artifacts Amplification for Deepfakes Augmentation, which amplifies the local artifacts on the forged faces. Furthermore, this study incorporates prior knowledge about similar facial features into the model. This means that within the facial regions defined in this work, forged features exhibit similar patterns. By aggregating the results from all facial regions, the study can enhance the overall performance of the model. The evaluation experiments conducted in this research, achieving an AUC of 93.40% and an Acc of 87.03% in the challenging WildDeepfake dataset, demonstrate a promising improvement in accuracy compared to traditional image augmentation methods and achieve superior performance on intra-dataset evaluation. The cross-dataset evaluation also showed that the method presented in this study has strong generalization abilities.

Modeling X chromosome inactivation using t5iLA naive human pluripotent stem cells.

BACKGROUND: X chromosome inactivation (XCI) is a critical epigenetic event for dosage compensation of X-linked genes in female mammals, ensuring developmental stability. A robust in vitro model is required for mimicking XCI during the early stages of embryonic development. This methodology article introduces an advanced framework for the in-depth study of XCI using human pluripotent stem cells (hPSCs). By focusing on the transition between naive and primed pluripotent states, we highlight the role of long non-coding RNA X-inactive specific transcript (XIST) and epigenetic alterations in mediating XCI. RESULTS: Our methodology enables the distinction between naive and primed hESCs based on XIST expression and the activity of X-linked reporters, facilitating the investigation of XCI initiation and maintenance. Through detailed experimental procedures, we demonstrate the utility of our hESC lines in modeling the process of human XCI, including the establishment of conditions for random XCI induction and the analysis of X chromosome reactivation. METHODS: The study outlines a comprehensive approach for characterizing the X chromosome status in hPSCs, employing dual fluorescent reporter hESC lines. These reporter lines enable real-time tracking of XCI dynamics through differentiation processes. We detailed protocols for the induction of X chromosome reactivation and inactivation, as well as the X status characterization methods including cultivation of hESCs, flow cytometric analysis, RNA fluorescence in situ hybridization (FISH), and transcriptome sequencing, providing a step-by-step guide for researchers to investigate XCI mechanisms in vitro. CONCLUSIONS: This article provides a detailed, reproducible methodology for studying XCI mechanisms in vitro, employing hPSCs as a model system. It presents a significant advance in our ability to investigate XCI, offering potential applications in developmental biology, disease modeling, and regenerative medicine. By facilitating the study of XCI dynamics, this methodological framework paves the way for deeper understanding and manipulation of this fundamental biological process.

Image-Level Adaptive Adversarial Ranking for Person Re-Identification.

The potential vulnerability of deep neural networks and the complexity of pedestrian images, greatly limits the application of person re-identification techniques in the field of smart security. Current attack methods often focus on generating carefully crafted adversarial samples or only disrupting the metric distances between targets and similar pedestrians. However, both aspects are crucial for evaluating the security of methods adapted for person re-identification tasks. For this reason, we propose an image-level adaptive adversarial ranking method that comprehensively considers two aspects to adapt to changes in pedestrians in the real world and effectively evaluate the robustness of models in adversarial environments. To generate more refined adversarial samples, our image representation enhancement module leverages channel-wise information entropy, assigning varying weights to different channels to produce images with richer information content, along with a generative adversarial network to create adversarial samples. Subsequently, for adaptive perturbation of ranking, the adaptive weight confusion ranking loss is presented to calculate the weights of distances between positive or negative samples and query samples. It endeavors to push positive samples away from query samples and bring negative samples closer, thereby interfering with the ranking of system. Notably, this method requires no additional hyperparameter tuning or extra data training, making it an adaptive attack strategy. Experimental results on large-scale datasets such as Market1501, CUHK03, and DukeMTMC demonstrate the effectiveness of our method in attacking ReID systems.

A Membrane-Anchoring Self-Assembling Peptide Allows Bioorthogonal Coupling of Type-I AIEgens for Pyroptosis-Induced Cancer Therapy.

Enrichment of photosensitizers (PSs) on cancer cell membranes via bioorthogonal reactions is considered to be a very promising therapeutic modality. However, azide-modified sugars-based metabolic labeling processes usually lack targeting and the labeling speed is relatively slow. Moreover, it has been rarely reported that membrane-anchoring pure type-I PSs can induce cancer cell pyroptosis. Here, we report an alkaline phosphatase (ALP) and cholecystokinin-2 receptor (CCK2R) dual-targeting peptide named DBCO-pYCCK6, which can selectively and rapidly self-assemble on cancer cell membrane, and then bioorthogonal enrich type-I aggregation-induced emission luminogens (AIEgen) PSs (SAIE-N3) on the cell membrane. Upon light irradiation, the membrane-anchoring SAIE-N3 could effectively generate type-I reactive oxygen species (ROS) to induce gasdermin E (GSDME)-mediated pyroptosis. In vivo experiments demonstrated that the bioorthogonal combination strategy of peptide and AIEgen PSs could significantly inhibit tumor growth, which is accompanied by CD8+ cytotoxic T cell infiltration. This work provides a novel self-assembly peptide-mediated bioorthogonal reaction strategy to bridge the supramolecular self-assembly and AIE field through strain-promoted azide-alkyne cycloaddition (SPAAC) and elucidates that pure type-I membrane-anchoring PSs can be used for cancer therapy via GSDME-mediated pyroptosis.

Inspector for Face Forgery Detection: Defending Against Adversarial Attacks From Coarse to Fine.

The emergence of face forgery has raised global concerns on social security, thereby facilitating the research on automatic forgery detection. Although current forgery detectors have demonstrated promising performance in determining authenticity, their susceptibility to adversarial perturbations remains insufficiently addressed. Given the nuanced discrepancies between real and fake instances are essential in forgery detection, previous defensive paradigms based on input processing and adversarial training tend to disrupt these discrepancies. For the detectors, the learning difficulty is thus increased, and the natural accuracy is dramatically decreased. To achieve adversarial defense without changing the instances as well as the detectors, a novel defensive paradigm called Inspector is designed specifically for face forgery detectors. Specifically, Inspector defends against adversarial attacks in a coarse-to-fine manner. In the coarse defense stage, adversarial instances with evident perturbations are directly identified and filtered out. Subsequently, in the fine defense stage, the threats from adversarial instances with imperceptible perturbations are further detected and eliminated. Experimental results across different types of face forgery datasets and detectors demonstrate that our method achieves state-of-the-art performances against various types of adversarial perturbations while better preserving natural accuracy. Code is available on https://github.com/xarryon/Inspector.

Increased tensile strength induced by the precipitation of nanocrystals for welding joints of Zr-based amorphous alloys.

Zr-based amorphous alloys have attracted intensive attention for applications because of their excellent mechanical property. However, the welding process is inevitable for some special cases, such as the obtain of large size structure parts. It is significant to clarify the influence of introduced welding joints on mechanical properties in Zr-based amorphous alloys. Herein, the increased tensile strength of welding joints in Zr-based amorphous alloys is demonstrated by choosing a suitable initial temperature of Cu cooling fixtures for pulsed laser welding. It is found that an optimized tensile strength is observed when the initial temperature is -20 °C. With the decrease of the initial temperature from 10 to -30 °C, the tensile strength shows a trend of first increasing and then decreasing. Combined with the characterization of microstructures, it can be concluded that the increased tensile strength results from the precipitation of nanocrystals in the heat affected zone. Thus, our results provide a method to improve the mechanical property by controlling the microstructures of the heat affected zone in welding joints of Zr-based amorphous alloys.

GazeForensics: DeepFake detection via gaze-guided spatial inconsistency learning.

DeepFake detection is pivotal in personal privacy and public safety. With the iterative advancement of DeepFake techniques, high-quality forged videos and images are becoming increasingly deceptive. Prior research has seen numerous attempts by scholars to incorporate biometric features into the field of DeepFake detection. However, traditional biometric-based approaches tend to segregate biometric features from general ones and freeze the biometric feature extractor. These approaches resulted in the exclusion of valuable general features, potentially leading to a performance decline and, consequently, a failure to fully exploit the potential of biometric information in assisting DeepFake detection. Moreover, insufficient attention has been dedicated to scrutinizing gaze authenticity within the realm of DeepFake detection in recent years. In this paper, we introduce GazeForensics, an innovative DeepFake detection method that utilizes gaze representation obtained from a 3D gaze estimation model to regularize the corresponding representation within our DeepFake detection model, while concurrently integrating general features to further enhance the performance of our model. Experimental results demonstrate that our proposed GazeForensics method performs admirably in terms of performance and exhibits excellent interpretability.

Quantitative Assessment and Regulation of Passage and Entrance Attraction Efficiency of Vertical-Slot Fishway on Heishuihe River in Southwest China.

Fish passage facilities are essential for restoring river connectivity and protecting ecosystems, effectively balancing economic and ecological benefits. Systematic and comprehensive monitoring, assessment, and optimized management are therefore crucial. This study quantitatively evaluated the entire upstream migration process of fish from the downstream river to the entrance and exit of the fishway and investigated the upstream movement patterns of fish under various environmental factors. A total of 19 fish species were monitored in the Heishuihe River downstream of the dam, with 15 species reaching the fishway entrance and 12 species successfully passing through it. The entrance attraction and passage rates of the vertical-slot fishway at the Songxin hydropower station were 15.7% and 40.42%, respectively. The best upstream performance was observed in May, with fish demonstrating better upstream timing and speed during nighttime compared to daytime. Specifically, the highest entrance attraction efficiency was recorded at a flow rate of 6-7 m3/s and a temperature of 19-20 °C, while the optimal passage efficiency was observed at a flow rate of 0-0.5 m3/s and a temperature of 17-20 °C. Additionally, a multifactorial Cox proportional hazards regression model was constructed to identify key factors influencing the probability of fishway entrance attraction and successful passage. The model elucidated the impact patterns of these key factors on fish upstream migration, ultimately generating an alignment diagram for prediction and control. This study provides a theoretical foundation and data support for developing optimized operational schedules for fishways. The findings offer a more comprehensive and systematic approach for monitoring and evaluating fish passage facilities, serving as a scientific basis for ecological restoration and fish conservation in this region and similar areas.

Tubal replacement of cervix for treatment of type II vaginal atresia complicated with cervical dysplasia: a case report.

Vaginal atresia is a rare obstructive disease of the reproductive tract. It is characterized by the absence or underdevelopment of the vaginal canal and results in various clinical manifestations. Hysterectomy can physically and mentally burden young female patients with a congenital cervix and complete vaginal atresia. This report presents a case of type II vaginal atresia complicated by cervical dysplasia in a female patient >10 years of age. Our team opted to preserve the patient's uterus, innovated a fallopian tube transplantation technique, and performed cervicovaginal reconstruction using natural channels instead of the cervical canal. The patient experienced menarche within the first 2 weeks postoperatively, and follow-up at 6 months revealed no abnormalities.

Enhanced photoelectrochemical water splitting performance of α-Fe2O3 photoanodes through Co-modification with Co single atoms and g-C3N4.

The practical application of α-Fe2O3 in water splitting is hindered by significant charge recombination and slow water oxidation. To address this issue, a CoSAs-g-C3N4/Fe2O3 (CoSAs: cobalt single atoms) photoanode was fabricated in this study through the co-modification of CoSAs and g-C3N4 to enhance photoelectrochemical (PEC) water splitting. The coupling between g-C3N4 and α-Fe2O3 resulted in the formation of a heterojunction, which provided a strong built-in electric field and an additional driving force to mitigate charge recombination. Moreover, g-C3N4 served as a suitable carrier for single atoms, which effectively anchored CoSAs through N/C coordination. The highly dispersed CoSAs provided abundant active sites, which further promoted surface holes extraction and oxidation kinetics, resulting in higher PEC performance and photostability. This study indicates the benefits of these collaborative strategies and provides more efficient designs for solar energy conversion in PEC systems.

Genetic Architecture and Analysis Practices of Circulating Metabolites in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program.

Circulating metabolite levels partly reflect the state of human health and diseases, and can be impacted by genetic determinants. Hundreds of loci associated with circulating metabolites have been identified; however, most findings focus on predominantly European ancestry or single study analyses. Leveraging the rich metabolomics resources generated by the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program, we harmonized and accessibly cataloged 1,729 circulating metabolites among 25,058 ancestrally-diverse samples. We provided recommendations for outlier and imputation handling to process metabolite data, as well as a general analytical framework. We further performed a pooled analysis following our practical recommendations and discovered 1,778 independent loci associated with 667 metabolites. Among 108 novel locus - metabolite pairs, we detected not only novel loci within previously implicated metabolite associated genes, but also novel genes (such as GAB3 and VSIG4 located in the X chromosome) that have putative roles in metabolic regulation. In the sex-stratified analysis, we revealed 85 independent locus-metabolite pairs with evidence of sexual dimorphism, including well-known metabolic genes such as FADS2 , D2HGDH , SUGP1 , UTG2B17 , strongly supporting the importance of exploring sex difference in the human metabolome. Taken together, our study depicted the genetic contribution to circulating metabolite levels, providing additional insight into the understanding of human health.

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. Generally, the distribution of features in the network for SISR is more complex than those in recognition models for preserving the abundant image information, e.g., texture, color, and details. To enhance the representation ability of BNN, we explore a novel activation-rectified inference (ARI) module that achieves a more complete representation of features by combining observations from different quantitative perspectives. The activations are divided into several parts with different quantification intervals and are inferred independently. This allows the binary activations to retain more image detail and yield finer inference. In addition, we further propose an adaptive approximation estimator (AAE) for gradually learning the accurate gradient estimation interval in each layer to alleviate the optimization difficulty. Experiments conducted on several benchmarks show that our approach is able to learn a binary SISR model with superior performance over the state-of-the-art methods. The code will be released at https://github.com/jwxintt/Rectified-BSR.