Intelligent Reversible Reconfigurable Metamaterials Based on a Two-Way Shape Memory Polymer.

The development of intelligent reversible reconfigurable metamaterials has great significance in constructing three-dimensional metamaterials and introducing reversible tunability into metamaterials. Here, we introduce an intelligent metamaterial consisting of a two-way shape memory polymer (2W-SMP) ethylene vinyl acetate copolymer (EVA) actuator substrate and a patterned flexible-rigid film. Mechanical buckling of the 2W-SMP substrate was controlled by thermal stimulation. This makes it possible to afford an ability to initiate 3D structure formation or shape reconfiguration remotely in an on-demand fashion. In addition, the shape of the 2W-SMP substrate is temperature-dependent, allowing repeatable reversible deformation through temperature control after a single programming. Therefore, the electromagnetic properties of metamaterials can also be repeatedly and reversibly tuned between 9.15 and 10.82 GHz. Experimental demonstrations include the deformation and tunable electromagnetic properties of intelligent reversible reconfigurable metamaterial cells. The results create many opportunities for advanced programmable three-dimensional metamaterials.

Artificial intelligence for geoscience: Progress, challenges, and perspectives.

This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth's complexities and uncertainties pose challenges in optimization and real-world applicability. In contrast, contemporary data-driven models, particularly those utilizing machine learning (ML) and deep learning (DL), leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge. ML techniques have shown promise in addressing Earth science-related questions. Nevertheless, challenges such as data scarcity, computational demands, data privacy concerns, and the "black-box" nature of AI models hinder their seamless integration into geoscience. The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm. These models, which incorporate domain knowledge to guide AI methodologies, demonstrate enhanced efficiency and performance with reduced training data requirements. This review provides a comprehensive overview of geoscientific research paradigms, emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience. It examines major methodologies, showcases advances in large-scale models, and discusses the challenges and prospects that will shape the future landscape of AI in geoscience. The paper outlines a dynamic field ripe with possibilities, poised to unlock new understandings of Earth's complexities and further advance geoscience exploration.

Enhanced immunochromatographic assay using multifunctional gold@iridium nanoflower with colorimetric photothermal catalytic activity for the detection of staphylococcal enterotoxin B.

The development of a rapid, sensitive, and accurate screening method for staphylococcal enterotoxin B (SEB) in food is urgently needed because trace amounts of SEB can pose a serious threat to human health. Here, we developed a ultrasensitive triple-modal immunochromatographic assay (ICA) for SEB detection. The AuNFs@Ir nanoflowers exhibited enhanced colorimetric, photothermal, and catalytic performance by modulating the sharp branching structure of the gold nanoflowers and depositing high-density Ir atoms. Subsequently, the combination of AuNFs@Ir and ICA promoted colorimetric, catalytic amplified colorimetric, and photothermal-assisted quantitative detection. The results showed detection limits of 0.175, 0.0188, and 0.043 ng mL-1 in the colorimetric/photothermal/catalytic mode, which increased the sensitivity by 16.5-fold, 153.7-fold, and 67.2-fold, respectively, compared with the AuNPs-ICA. Furthermore, the proposed strategy was verified in milk, milk powder, pork, and beef successfully. This strategy improves significantly the sensitivity, accuracy, flexibility and offers an effective insight for foodborne bacterial toxin monitoring.

A highly sensitive dual-mode lateral flow immunoassay based on plasmonic hollow Ag/Au nanostars enhancing light absorption.

The conventional lateral flow immunoassay (LFIA) based on gold nanoparticles (Au NPs) is limited by low sensitivity due to the insufficient brightness of Au NPs. To address this problem, noble metal nanomaterials with localized surface plasmon resonance (LSPR) and synthetic tunability are potential signal outputs for LFIA, which can achieve better optical properties by adjusting the preparation conditions. Herein, this study prepared the hollow silver/gold nano-stars (HAg/Au NSts) as LFIA signal output via the galvanic replacement method. HAg/Au NSts with anisotropic hollow alloy nanostructures exhibit a wide visible light absorption band and great NIR thermal conversion efficiency (η = 37.32 %), which endows them with enhanced colorimetric and photothermal signals. Further, we constructed a colorimetric-photothermal (CM-PT) dual-signal HAg/Au NSts-LFIA and chose staphylococcal enterotoxin B as the target analyte. The linear range of HAg/Au NSts-LFIA is 0.19-100 ng mL-1, and the limit of detection (LOD) is up to 0.29 ng mL-1 and 0.09 ng mL-1 in the colorimetric and photothermal modes respectively. Compared with the conventional Au NPs-LFIA, HAg/Au NSts-CM/PT-LFIA effectively improved the detection performance of LFIA. In addition, HAg/Au NSts-LFIA also showed satisfactory sensitivity (vLOD = 0.78 ng mL-1) and recovery (89.06-114.74 %) in milk and pork samples. Therefore, this work provides a new shape design idea for noble metal nanomaterials in biosensor applications.

A survey on causal inference for recommendation.

Causal inference has recently garnered significant interest among recommender system (RS) researchers due to its ability to dissect cause-and-effect relationships and its broad applicability across multiple fields. It offers a framework to model the causality in RSs such as confounding effects and deal with counterfactual problems such as offline policy evaluation and data augmentation. Although there are already some valuable surveys on causal recommendations, they typically classify approaches based on the practical issues faced in RS, a classification that may disperse and fragment the unified causal theories. Considering RS researchers' unfamiliarity with causality, it is necessary yet challenging to comprehensively review relevant studies from a coherent causal theoretical perspective, thereby facilitating a deeper integration of causal inference in RS. This survey provides a systematic review of up-to-date papers in this area from a causal theory standpoint and traces the evolutionary development of RS methods within the same causal strategy. First, we introduce the fundamental concepts of causal inference as the basis of the following review. Subsequently, we propose a novel theory-driven taxonomy, categorizing existing methods based on the causal theory employed, namely those based on the potential outcome framework, the structural causal model, and general counterfactuals. The review then delves into the technical details of how existing methods apply causal inference to address particular recommender issues. Finally, we highlight some promising directions for future research in this field. Representative papers and open-source resources will be progressively available at https://github.com/Chrissie-Law/Causal-Inference-for-Recommendation.

SIRT1-Mediated HMGB1 Deacetylation Suppresses Neutrophil Extracellular Traps Related to Blood-Brain Barrier Impairment After Cerebral Venous Thrombosis.

Cerebral venous thrombosis (CVT) is a neurovascular disease with recently increasing incidence. Aseptic inflammatory responses play an important role in the pathology of CVT. Recent studies report that neutrophil extracellular traps (NETs) are major triggers of thrombosis and inflammation in stroke, but their effect on brain injury in CVT requires further validation. In this study, two CVT animal models were used to simulate superior sagittal sinus thrombosis and cortical vein thrombosis. The effects of brain tissue infiltration of NETs and the molecular mechanisms associated with NET formation were deeply explored in combination with proteomics, histology, and serology. The results showed that the cortical vein thrombosis model could be combined with more severe blood-brain barrier (BBB) disruption and showed more severe cerebral hemorrhage. Decreased Sirtuin 1 (SIRT1) expression promotes high mobility group box 1 (HMGB1) acetylation, causing increased cytosolic translocation and extracellular release, and HMGB1 can promote NET formation and recruitment. In addition, corticocerebral accumulation of NETs contributes to BBB damage. This establishes a vicious cycle between BBB damage and NET accumulation. SIRT1 mediated-HMGB1 deacetylation may play a critical role in attenuating BBB damage following CVT. This study employed a combined validation using models of venous sinus thrombosis and cortical vein thrombosis to investigate the deacetylation role of SIRT1, aiming to offer new insights into the pathological mechanisms of brain injury following CVT.

Effects of BPD tendencies and subjective well-being on NSSI in adolescents with PTSD.

Background: Severe posttraumatic stress disorder (PTSD) may lead to non-suicidal self-injury (NSSI), and borderline personality disorder (BPD) tendencies may play a role in this process. Secondary vocational students experience more social, familial and other pressures and are more vulnerable to psychological problems. Thus, we explored the effect of BPD tendencies and subjective well-being (SWB) on NSSI in secondary vocational students with PTSD. Methods: A total of 2,160 Chinese secondary vocational students in Wuhan participated in our cross-sectional investigation. The Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5), criteria for PTSD, NSSI Questionnaire, Personality Diagnostic Questionnaire-4+, subjective well-being scale, and family adaptation, partnership, growth, affection, and resolve (APGAR) Index were used. We conducted a binary logistic regression model and linear regression analysis. Results: Sex (odds ratio [OR] = 0.354, 95% confidence interval [CI] = 0.171-0.733), BPD tendencies (OR = 1.192, 95% CI = 1.066-1.333) and SWB (OR = 0.652, 95% CI = 0.516-0.824) were independent factors that predicted NSSI in secondary vocational students with PTSD. Spearman's correlation analysis showed that BPD tendencies were positively correlated with NSSI frequency (r = 0.282, P < 0.01). SWB was negatively correlated with NSSI frequency (r = -0.301, P < 0.01). The linear regression showed that BPD tendencies (ß = 0.137, P < 0.05 and ß = -0.230, P < 0.001) were significantly correlated with NSSI frequency. Spearman's correlation analysis showed that family functioning was positively correlated with SWB (r = 0.486, P < 0.01) and negatively correlated with BPD tendencies (r = -0.296, P < 0.01). Conclusion: In adolescents, PTSD in response to stressful events could lead to NSSI, and BPD tendencies promote the intensity of NSSI, while SWB diminishes its intensity. Improvement in family functioning may actively guide the development of mental health and improve SWB; such steps may constitute interventions to prevent or treat NSSI.

Proteomics analyses of acute kidney injury biomarkers in a rat exertional heat stroke model.

The frequency of exertional heat stroke (EHS) increases with the gradual elevation of global temperatures during summer. Acute kidney injury (AKI) is a common complication of EHS, and its occurrence often indicates the worsening of a patient's condition or a poor prognosis. In this study, a rat model of AKI caused by EHS was established, and the reliability of the model was evaluated by HE staining and biochemical assays. The expression of kidney tissue proteins in the EHS rats was analyzed using label-free liquid chromatography-tandem mass spectrometry. A total of 3,129 differentially expressed proteins (DEPs) were obtained, and 10 key proteins were finally identified, which included three upregulated proteins (Ahsg, Bpgm, and Litaf) and seven downregulated proteins (medium-chain acyl-CoA synthetase 2 (Acsm2), Hadha, Keg1, Sh3glb1, Eif3d, Ambp, and Ddah2). The qPCR technique was used to validate these 10 potential biomarkers in rat kidney and urine. In addition, Acsm2 and Ahsg were double-validated by Western blotting. Overall, this study identified 10 reliable biomarkers that may provide potential targets for the treatment of AKI caused by EHS.

Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition.

The teacher-free online Knowledge Distillation (KD) aims to train an ensemble of multiple student models collaboratively and distill knowledge from each other. Although existing online KD methods achieve desirable performance, they often focus on class probabilities as the core knowledge type, ignoring the valuable feature representational information. We present a Mutual Contrastive Learning (MCL) framework for online KD. The core idea of MCL is to perform mutual interaction and transfer of contrastive distributions among a cohort of networks in an online manner. Our MCL can aggregate cross-network embedding information and maximize the lower bound to the mutual information between two networks. This enables each network to learn extra contrastive knowledge from others, leading to better feature representations, thus improving the performance of visual recognition tasks. Beyond the final layer, we extend MCL to intermediate layers and perform an adaptive layer-matching mechanism trained by meta-optimization. Experiments on image classification and transfer learning to visual recognition tasks show that layer-wise MCL can lead to consistent performance gains against state-of-the-art online KD approaches. The superiority demonstrates that layer-wise MCL can guide the network to generate better feature representations. Our code is publicly avaliable at https://github.com/winycg/L-MCL.

How to prevent malicious use of intelligent unmanned swarms?

With advancements in swarm intelligence, artificial intelligence, and wireless mobile network technology, unmanned swarms such as unmanned aerial vehicles, ground vehicles, ships, and other unmanned systems are becoming increasingly autonomous and intelligent. Benefiting from these technologies, intelligent unmanned swarms are able to efficiently perform complex tasks through collaboration in various fields. However, malicious use of intelligent unmanned swarms raises concerns about the potential for significant damage to national infrastructures such as airports and power facilities. Defending against malicious activities is essential but challenging due to the swarms' abilities to perceive, understand complex environments, and make accurate decisions through multi-system collaboration. This perspective sheds light on recent research in counter-measures and provides new trends and insights on how to prevent malicious actions by intelligent unmanned swarms.

Pneumonia in Children During the 2019 Outbreak in Xiamen, China.

BACKGROUND: To understand the regional epidemiology and clinical characteristics of adenovirus pneumonia in hospitalized children during the 2019 outbreak of respiratory adenoviruses in China. METHODS: We analyzed the epidemiologic trend of adenovirus in children hospitalized for acute lower respiratory tract infections in Xiamen in 2019. Adenovirus was identified using direct fluorescent antibody detection. During the peak seasons of adenovirus epidemic, 170 adenovirus-positive specimens were obtained for molecular typing, and the clinical data were collected. RESULTS: Among the 9890 children hospitalized for acute lower respiratory tract infection, 609 (6.2%) were tested positive for adenovirus. The detection rate of adenovirus was significantly higher in boys than in grils (9.5% vs. 4.6%, P < 0.05). Adenovirus activity increased markedly between April and August with the prevalence of 7.3%-12.4%. During the outbreak season, type 7 accounted for 70.6%, followed by type 3 (28.8%) and type 4 (0.6%). Of the 155 cases of adenovirus pneumonia, the median age was 3.0 years (range: 4 month to 9 years), 153 (98.7%) had fever with a mean fever duration of 9.04 ± 5.52 days, 28 (16.5%) had wheezing, 93 (60%) showed segmental or lobar consolidation with atelectasis and 13 (8.4%) showed pleural effusion. Forty-six (29.6%) cases developed severe pneumonia, 7 (4.1%) required mechanical ventilation and 2 (1.2%) died. Younger age, longer duration of fever and higher fever spike were more frequently seen in severe cases (P < 0.05). Twenty-five (16.2%) had C-reactive protein ≥ 40 mg/L, and 91 (58.7%) had procalcitonin ≥ 0.25 mg/L. CONCLUSIONS: Adenovirus types 7 and 3 caused the outbreak of adenovirus pneumonia in community children during late spring to summer in 2019 in Xiamen. The majority of adenovirus pneumonia resembles bacterial pneumonia. The incidence of severe pneumonia was high when type 7 predominantly prevailed. Adenovirus type 7 was more common in severe cases than in nonsevere cases.

Knowledge Distillation Using Hierarchical Self-Supervision Augmented Distribution.

Knowledge distillation (KD) is an effective framework that aims to transfer meaningful information from a large teacher to a smaller student. Generally, KD often involves how to define and transfer knowledge. Previous KD methods often focus on mining various forms of knowledge, for example, feature maps and refined information. However, the knowledge is derived from the primary supervised task, and thus, is highly task-specific. Motivated by the recent success of self-supervised representation learning, we propose an auxiliary self-supervision augmented task to guide networks to learn more meaningful features. Therefore, we can derive soft self-supervision augmented distributions as richer dark knowledge from this task for KD. Unlike previous knowledge, this distribution encodes joint knowledge from supervised and self-supervised feature learning. Beyond knowledge exploration, we propose to append several auxiliary branches at various hidden layers, to fully take advantage of hierarchical feature maps. Each auxiliary branch is guided to learn self-supervision augmented tasks and distill this distribution from teacher to student. Overall, we call our KD method a hierarchical self-supervision augmented KD (HSSAKD). Experiments on standard image classification show that both offline and online HSSAKD achieves state-of-the-art performance in the field of KD. Further transfer experiments on object detection further verify that HSSAKD can guide the network to learn better features. The code is available at https://github.com/winycg/HSAKD.

Cerebral venous hemodynamic responses in a mouse model of traumatic brain injury.

Traumatic brain injury (TBI) is a serious public health problem that endangers human health and is divided into primary and secondary injuries. Previous work has confirmed that changes in cerebral blood flow (CBF) are related to the progression of secondary injury, although clinical studies have shown that CBF monitoring cannot fully and accurately evaluate disease progression. These studies have almost ignored the monitoring of venous blood flow; however, as an outflow channel of the cerebral circulation, it warrants discussion. To explore the regulation of venous blood flow after TBI, the present study established TBI mouse models of different severities, observed changes in cerebral venous blood flow by laser speckle flow imaging, and recorded intracranial pressure (ICP) after brain injury to evaluate the correlation between venous blood flow and ICP. Behavioral and histopathological assessments were performed after the intervention. The results showed that there was a significant negative correlation between ICP and venous blood flow (r = -0.795, P < 0.01), and both recovered to varying degrees in the later stages of observation. The blood flow changes in regional microvessels were similar to those in venous, and the expression of angiogenesis proteins around the impact area was significantly increased. In conclusion, this study based on the TBI mouse model, recorded the changes in venous blood flow and ICP and revealed that venous blood flow can be used as an indicator of the progression of secondary brain injury.

Clinical Analysis of the Discovery of Malignant Gynecological Tumors in the Diagnosis and Treatment of Pelvic Organ Prolapse.

Background: Clinically, malignant gynecological tumors found by chance during the diagnosis and treatment of pelvic organ prolapse (POP) are rare, and they are usually missed, leading to delayed diagnosis and treatment. The initial treatment of these tumors cannot be standardized, and, as a single surgical intervention may not be able to treat both the tumor and prolapse, secondary surgery is usually needed, affecting the quality of life of patients. Case presentation: The present study retrospectively analyzed the data of three patients who were diagnosed with malignant gynecological tumors during the diagnosis and treatment of POP. These patients were among 215 patients with POP treated in Yuncheng Central Hospital of Shanxi Province between January 2011 and May 2020. The case characteristics, surgical interventions, postoperative treatments, and follow-ups were summarized, and the characteristics of diagnosis and treatment were analyzed in the context of relevant literature. Conclusion: As long as clinicians operate in strict accordance with the standards of diagnosis and treatment, obtain a complete medical history, undertake a physical examination, and remain diligent in auxiliary examinations, following existing clinical methods and diagnosis and treatment processes, patients with POP complicated with malignant gynecological tumors can be clearly diagnosed before and during surgery. In this way, initial treatment can be standardized, and surgical methods can be selected that address both the tumor and prolapse, thereby avoiding secondary surgery and improving the patient's quality of life.

Predictive Factors for Early-Onset Seizures in Patients With Cerebral Venous Sinus Thrombosis.

Seizures are reported to be important factors contributing to poor prognosis in patients with cerebral venous sinus thrombosis (CVST). However, the predictive factors for concurrent early onset seizures in patients with CVST remain unclear. To identify the predictive factors of early seizures in patients with CVST, this study retrospectively evaluated the clinical data of patients diagnosed with CVST at two centers from January 2011 to December 2020 and analyzed the relationship between admission characteristics and early onset seizures. A total of 112 CVST patients (63 men and 49 women; mean age 39.82 ± 15.70 years) were enrolled in this study, of whom 34 (30.36%) had seizures. For patients with seizures, cerebral hemorrhage, cortical vein thrombosis, anterior superior sagittal sinus (SSS) thrombosis, middle SSS thrombosis, CVST score, modified Rankin Scale, National Institute of Health Stroke Scale (NIHSS) score, neutrophil percentage, and D-dimer level were more severe than those without seizures. Logistic regression analysis showed that cerebral hemorrhage (P = 0.002), anterior SSS thrombosis (P = 0.003), NIHSS score ≥5 (P = 0.003), and D-dimer ≥0.88 mg/L (P = 0.004) were all significant predictive factors of early-onset seizures in CVST patients. Combining the four factors further improved the predictive capability with an area under the curve of 0.871 (95% confidence interval = 0.803-0.939). Further large-scale prospective studies are required to confirm these findings.

A novel rat model for cerebral venous sinus thrombosis: verification of similarity to human disease via clinical analysis and experimental validation.

BACKGROUND: Cerebral venous sinus thrombosis (CVST) is a rare neurovascular disorder with highly variable manifestations and clinical courses. Animal models properly matched to the clinical form of CVST are necessary for elucidating the pathophysiology of the disease. In this study, we aimed to establish a rat model that accurately recapitulates the clinical features of CVST in human patients. METHODS: This study consisted of a clinical analysis and animal experiments. Clinical data for two centres obtained between January 2016 and May 2021 were collected and analysed retrospectively. In addition, a Sprague-Dawley rat model of CVST was established by inserting a water-swellable rubber device into the superior sagittal sinus, following which imaging, histological, haematological, and behavioural tests were used to investigate pathophysiological changes. Principal component analysis and hierarchical clustering heatmaps were used to evaluate the similarity between the animal models and human patients. RESULTS: The imaging results revealed the possibility of vasogenic oedema in animal models. Haematological analysis indicated an inflammatory and hypercoagulable state. These findings were mostly matched with the retrospective clinical data. Pathological and serological tests further revealed brain parenchymal damage related to CVST in animal models. CONCLUSIONS: We successfully established a stable and reproducible rat model of CVST. The high similarity between clinical patients and animal models was verified via cluster analysis. This model may be useful for the study of CVST pathophysiology and potential therapies.

Defective 2D silicon phosphide monolayers for the nitrogen reduction reaction: a DFT study.

Electroreduction of N2 is a highly promising route for NH3 production. The lack of efficient catalysts that can activate and then reduce N2 into NH3 limits this as a pragmatic application. In this work, a 2D layered group IV-V material, silicon phosphide (SiP), is evaluated as a suitable substrate for the electrochemical nitrogen reduction reaction (ENRR). To capture N2, one phosphorus (P) defect was introduced on the plane of SiP. DFT calculations found that the defective SiP monolayer (D1-SiP, which is defined by the P-defect on SiP) exhibits enormous prospects towards the ENRR because of enhanced electron conductivity, good activation on N2, lower limiting potential (UL = -0.87 V) through the enzymatic pathway, smooth charge transfer between the catalyst and the reaction species, and robust thermal stability. Importantly, D1-SiP demonstrates the suppressed activities on producing of H2 and N2H4 side-products. This research demonstrates the potential of 2D metal-free Si-based catalysts for nitrogen fixation and further enriches the study of group IV-V materials for the ENRR.

A Hierarchical Spatial-Temporal Embedding Method Based on Enhanced Trajectory Features for Ship Type Classification.

Ship type classification is an essential task in maritime navigation domains, contributing to shipping monitoring, analysis, and forecasting. Presently, with the development of ship positioning and monitoring systems, many ship trajectory acquisitions make it possible to classify ships according to their movement pattern. Existing methods of ship classification based on trajectory include classical sequence analysis and deep learning methods. However, the real ship trajectories are unevenly distributed in geographical space, which leads to many problems in inferring the ship movement mode on the original ship trajectory. This paper proposes a hierarchical spatial-temporal embedding method based on enhanced trajectory features for ship type classification. We first preprocess the trajectory and combine the port information to transform the original ship trajectory into the moored records of ships, removing the unevenly distributed points in the trajectory data and enhancing key points' semantic information. Then, we propose a Hierarchical Spatial-Temporal Embedding Method (Hi-STEM) for ship classification. Hi-STEM maps moored records in the original geographical space into the feature space and can efficiently find the classification plane in the feature space. Experiments are conducted on real-world datasets and compared with several existing methods. The result shows that our approach has high accuracy in ship classification on ship moored records. We make the source code and datasets publicly available.