A brain-to-gut signal controls intestinal fat absorption.

Although fat is a crucial source of energy in diets, excessive intake leads to obesity. Fat absorption in the gut is prevailingly thought to occur organ-autonomously by diffusion1-3. Whether the process is controlled by the brain-to-gut axis, however, remains largely unknown. Here we demonstrate that the dorsal motor nucleus of vagus (DMV) plays a key part in this process. Inactivation of DMV neurons reduces intestinal fat absorption and consequently causes weight loss, whereas activation of the DMV increases fat absorption and weight gain. Notably, the inactivation of a subpopulation of DMV neurons that project to the jejunum shortens the length of microvilli, thereby reducing fat absorption. Moreover, we identify a natural compound, puerarin, that mimics the suppression of the DMV-vagus pathway, which in turn leads to reduced fat absorption. Photoaffinity chemical methods and cryogenic electron microscopy of the structure of a GABAA receptor-puerarin complex reveal that puerarin binds to an allosteric modulatory site. Notably, conditional Gabra1 knockout in the DMV largely abolishes puerarin-induced intestinal fat loss. In summary, we discover that suppression of the DMV-vagus-jejunum axis controls intestinal fat absorption by shortening the length of microvilli and illustrate the therapeutic potential of puerarin binding to GABRA1 in fat loss.

cis-Dihydroxylation by Synthetic Iron(III)-Peroxo Intermediates and Rieske Dioxygenases: Experimental and Theoretical Approaches Reveal the Key O-O Bond Activation Step.

Dioxygen (O2) activation by iron-containing enzymes and biomimetic compounds generates iron-oxygen intermediates, such as iron-superoxo, -peroxo, -hydroperoxo, and -oxo, that mediate oxidative reactions in biological and abiological systems. Among the iron-oxygen intermediates, iron(III)-peroxo species are less frequently implicated as active intermediates in oxidation reactions. In this study, we present the combined experimental and theoretical investigations on cis-dihydroxylation reactions mediated by synthetic mononuclear nonheme iron-peroxo intermediates, demonstrating the importance of supporting ligands and metal centers in activating the peroxo ligand toward the O-O bond homolysis for the cis-dihydroxylation reactions. We found a significant ring size effect of the TMC ligand in [FeIII(O2)(n-TMC)]+ (TMC = tetramethylated tetraazacycloalkane; n = 12, 13, and 14) on the cis-dihydroxylation reactivity order: [FeIII(O2)(12-TMC)]+ > [FeIII(O2)(13-TMC)]+ > [FeIII(O2)(14-TMC)]+. Additionally, we found that only [FeIII(O2)(n-TMC)]+, but not other metal-peroxo complexes such as [MIII(O2)(n-TMC)]+ (M = Mn, Co, and Ni), is reactive for the cis-dihydroxylation of olefins. Using density functional theory (DFT) calculations, we revealed that electron transfer from the Fe dxz orbital to the peroxo σ*(O-O) orbital facilitates the O-O bond homolysis, with the O-O bond cleavage barrier well correlated with the energy gap between the frontier molecular orbitals of dxz and σ*(O-O). Further computational studies showed that the reactivity of the synthetic [FeIII(O2)(12-TMC)]+ complex is comparable to that of Rieske dioxygenases in cis-dihydroxylation, providing compelling evidence of the potential involvement of Fe(III)-peroxo species in Rieske dioxygenases. Thus, the present results significantly advance our understanding of the cis-dihydroxylation mechanisms by Rieske dioxygenases and synthetic nonheme iron-peroxo models.

Y-Shaped Backbone-Rigidified DNA Tiles for the Construction of Supersized Nondeformable Tetrahedrons for Precise Cancer Therapies.

While engineered DNA nanoframeworks have been extensively exploited for delivery of diagnostic and therapeutic regents, DNA tiling-based DNA frameworks amenable to applications in living systems lag much behind. In this contribution, by developing a Y-shaped backbone-based DNA tiling technique, we assemble Y-shaped backbone-rigidified supersized DNA tetrahedrons (RDT) with 100% efficiency for precisely targeted tumor therapy. RDT displays unparalleled rigidness and unmatched resistance to nuclease degradation so that it almost does not deform under the force exerted by the atomic force microscopy tip, and the residual amount is not less than 90% upon incubating in biological media for 24 h, displaying at least 11.6 times enhanced degradation resistance. Without any targeting ligand, RDT enters the cancer cell in a targeted manner, and internalization specificity is up to 15.8. Moreover, 77% of RDT objects remain intact within living cells for 14 h. The drug loading content of RDT is improved by 4-8 times, and RDT almost 100% eliminates the unintended drug leakage in a stimulated physiological medium. Once systemically administrated into HeLa tumor-bearing mouse models, doxorubicin-loaded RDTs preferentially accumulate in tumor sites and efficiently suppress tumor growth without detectable off-target toxicity. The Y-DNA tiling technique offers invaluable insights into the development of structural DNA nanotechnology for precise medicine.

Sohlh2 Promotes the Progression of Hepatocellular Carcinoma via TGM2-Mediated Autophagy.

Liver cancer is the third leading cause of cancer-related deaths worldwide, with hepatocellular carcinoma (HCC) accounting for 85% of liver cancer-related deaths. Autophagy controls HCC cell growth, invasion, metastasis, drug resistance, and stemness. Spermatogenesis and oogenesis basic helix-loop-helix transcription factor 2 (Sohlh2) can bind to the E-boxes in the promoter regions of target genes, which are involved in multiple neoplasms. In this study, Sohlh2 was highly expressed in HCC tissues and was related to poor prognosis. Moreover, Sohlh2 overexpression promoted the proliferation, migration, invasion, and metastasis of HCC cells in vivo and in vitro. However, Sohlh2 silencing inhibited proliferation, migration, invasion, and metastasis of HCC cells in vivo and in vitro. Mechanistically, Sohlh2 could bind to the promoter of TGM2 and enhance its transcriptional activity, thereby enhancing the autophagy of HCC cells. Furthermore, Sohlh2 protein levels were positively associated with TGM2 expression in HCC tissues. Taken together, these results demonstrate that Sohlh2 can promote HCC progression via TGM2-mediated autophagy, implying that Sohlh2 is a promising candidate for HCC treatment.

Pearcey Talbot-like plasmon: a plasmonic bottle array generation scheme.

In this Letter, a surface wave, the Pearcey Talbot-like plasmon, which has the properties of self-imaging and multiple autofocusing, is presented as a novel, to the best of our knowledge, plasmonic bottle array generation scheme. With originality, the overall structure and the partial intensity of the plasmonic bottle array can be adjusted through the initial input, and modifying the Pearcey function enables the plasmonic bottle array to exhibit self-bending characteristics, which makes particle capture and manipulation easier and more flexible. A scheme to generate the plasmon is proposed, and we prove it by the finite-difference time-domain numerical simulations.

Digital transformation and synergistic reduction in pollution and carbon Emissions--An analysis from a dynamic capability perspective.

Pollution control and carbon emission reduction pose significant challenges for developing countries, and achieving synergistic reductions in pollution and carbon emissions (SRPCs) has emerged as the optimal choice. Digitization, a prevailing trend in the current era, presents new opportunities for realizing the SRPC. We utilize data from Chinese A-share listed companies from 2007 to 2022 to investigate the impact of corporate digital transformation on the SRPC. The research reveals the following. (1) Enterprise digital transformation significantly promotes the SRPC, a conclusion validated through a series of robustness tests. However, the SRPC resulting from digital transformation is characterized by weak synergy, which is primarily observed in the coordinated reduction in air pollution and carbon emissions. (2) This effect is notably stronger for companies near environmental regulatory authorities, those receiving government environmental subsidies, and state-owned enterprises (SOEs). (3) Enhancing managerial collaborative management capabilities, promoting corporate technological innovation capabilities, and alleviating financing pressure are the primary mechanisms at play. This research provides important policy and practical insights for promoting the SRPC in the context of digitization, fostering sustainable development.

Prevalence and influencing factors of patient delay in stroke patients: a systematic review and meta-analysis.

PURPOSE: Determine the prevalence and influencing factors of patient delay in stroke patients and explore variation in prevalence by country and delayed time. METHODS: PubMed, The Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), Chinese Biomedical Database (CBM), Weipu database, and Wanfang database were comprehensively searched for observational studies from inception to April, 2023. The pooled prevalence, odds ratio (OR), and 95% confidence intervals (CI) were calculated with Stata 16.0 software. RESULTS: In total, 2721 articles were screened and data from 70 studies involving 85,468 subjects were used in meta-analysis. The pooled prevalence of patient delay in stroke patients was 59% (95% CI, 0.54-0.64). The estimates of pooled prevalence calculated for African, Asian, and European patient delay in stroke patients were 55% (0.29-0.81), 61% (0.56-0.66), and 49% (0.34-0.64).According to the patient delay time, the prevalence of 6 h, 5 h, 4.5 h, 3.5 h, 3 h and 2 h were 54% (0.47-0.61), 73% (0.61-0.86), 60% (0.49-0.71), 81% (0.68-0.93), 52% (0.42-0.62), 63% (0.19-1.07). Distance from the place of onset to the hospital > 10 km [OR=2.49, 95%CI (1.92, 3.24)], having medical insurance [OR = 0.45, 95%CI (0.26,0.80)], lack of stroke-related knowledge [OR = 1.56, 95%CI (1.08,2.26)], education level below junior high school [OR = 1.69, 95%CI (1.22,2.36)], non-emergency medical services (Non-EMS) [OR = 2.10, 95%CI (1.49,2.97)], living in rural areas [OR = 1.54, 95%CI (1.15,2.07)], disturbance of consciousness [OR = 0.60, 95%CI (0.39,0.93)], history of atrial fibrillation [OR = 0.53, 95%CI (0.47,0.59)], age ≥ 65 years [OR = 1.18, 95%CI (1.02,1.37)], National institutes of health stroke scale (NIHSS) ≤ 4 points [OR= 2.26, 95%CI (1.06,4.79)]were factors for patient delay in stroke patients. CONCLUSIONS: The prevalence of patient delay in stroke patients is high, we should pay attention to the influencing factors of patient delay in stroke patients and provide a theoretical basis for shortening the treatment time of stroke patients.

A Low Systemic Inflammatory Response Index Is Associated With Improved Survival in Adenoid Cystic Carcinoma Patients.

BACKGROUND: Predicting the long-term survival in adenoid cystic carcinoma (ACC) patients remains challenging. Inflammatory cell-based indices are emerging as prognostic indicators of oncology. PURPOSE: This study aimed to determine the associations between the preoperative systemic inflammatory response index (SIRI) and the systemic immunoinflammatory index (SII) and the 10-year survival rates in patients with ACC of the head and neck (ACCHN). STUDY DESIGN, SETTING, SAMPLE: This retrospective cohort study comprised ACCHN patients treated at the Chinese People's Liberation Army General Hospital between November 2003 and December 2020. PREDICTOR VARIABLE: The inflammatory response, assessed using the SIRI and SII, was the predictor variable. The optimal cutoff values were based on the maximum Youden index values (sensitivity ＋ specificity－1). The patients were divided into two groups each, based on the SIRI (low, ≤ 0.15) and (high, > 0.15), and SII (low, ≤ 562.8 and high, > 562.8) values. MAIN OUTCOME VARIABLE(S): Overall survival (OS), or the number of days, weeks, or months between treatment initiation and death (or the last follow-up date), was the primary outcome variable. COVARIATES: The covariates were classified as demographic (age, gender, body mass index), medical (hypertension, diabetes), inflammatory (neutrophils, lymphocytes, monocytes, platelets, lymphocyte-monocyte ratio, platelet-lymphocyte ratio, neutrophil-lymphocyte ratio), and perioperative (tumor stage, lymph node metastasis, tumor size, treatment type). ANALYSES: Descriptive, univariate, and multivariate Cox proportional risk regression analyses were performed to determine whether the SIRI and SII were independent prognostic factors for OS. Kaplan-Meier survival curves and log-rank tests were used to determine their associations with the OS. RESULTS: The study sample comprised 162 patients (mean age, 52 ± 14; males, 39.5%). The median follow-up time was 6.81 ± 0.23, and the 10-year OS rate was 7.68 ± 0.25. The low and high SIRI groups comprised 109 and 53 patients, while the low and high SII groups comprised 116 and 46 patients, respectively. SIRI was identified as a prognostic factor (P < .01; hazard ratio, 2.45; 95% confidence interval, 1.35-4.45). CONCLUSION AND RELEVANCE: The SIRI has the advantages of reproducibility, convenience, noninvasiveness, and affordability, making it a promising prognostic inflammatory index for patients with ACCHN.

Transcriptomics to evaluate the influence mechanisms of ethanol on the ester production of Wickerhamomyces anomalus with the induction of lactic acid.

Wickerhamomyces anomalus is one of the most important ester-producing strains in Chinese baijiu brewing. Ethanol and lactic acid are the main metabolites produced during baijiu brewing, but their synergistic influence on the growth and ester production of W. anomalus is unclear. Therefore, in this paper, based on the contents of ethanol and lactic acid during Te-flavor baijiu brewing, the effects of different ethanol concentrations (3, 6, and 9% (v/v)) combined with 1% lactic acid on the growth and ester production of W. anomalus NCUF307.1 were studied and their influence mechanisms were analyzed by transcriptomics. The results showed that the growth of W. anomalus NCUF307.1 under the induction of lactic acid was inhibited by ethanol. Although self-repair mechanism of W. anomalus NCUF307.1 induced by lactic acid was initiated at all concentrations of ethanol, resulting in significant up-regulation of genes related to the Genetic Information Processing pathway, such as cell cycle-yeast, meiosis-yeast, DNA replication and other pathways. However, the accumulation of reactive oxygen species and the inhibition of pathways associated with carbohydrate and amino acid metabolism may be the main reason for the inhibition of growth in W. anomalus NCUF307.1. In addition, 3% and 6% ethanol combined with 1% lactic acid could promote the ester production of W. anomalus NCUF307.1, which may be related to the up-regulation of EAT1, ADH5 and TGL5 genes, while the inhibition in 9% ethanol may be related to down-regulation of ATF2, EAT1, ADH2, ADH5, and TGL3 genes.

Blood Biomarker Detection Using Integrated Microfluidics with Optical Label-Free Biosensor.

In this study, we developed an optofluidic chip consisting of a guided-mode resonance (GMR) sensor incorporated into a microfluidic chip to achieve simultaneous blood plasma separation and label-free albumin detection. A sedimentation chamber is integrated into the microfluidic chip to achieve plasma separation through differences in density. After a blood sample is loaded into the optofluidic chip in two stages with controlled flow rates, the blood cells are kept in the sedimentation chamber, enabling only the plasma to reach the GMR sensor for albumin detection. This GMR sensor, fabricated using plastic replica molding, achieved a bulk sensitivity of 175.66 nm/RIU. With surface-bound antibodies, the GMR sensor exhibited a limit of detection of 0.16 µg/mL for recombinant albumin in buffer solution. Overall, our findings demonstrate the potential of our integrated chip for use in clinical samples for biomarker detection in point-of-care applications.

Multi-Channel Multi-Scale Convolution Attention Variational Autoencoder (MCA-VAE): An Interpretable Anomaly Detection Algorithm Based on Variational Autoencoder.

With the rapid development of industry, the risks factories face are increasing. Therefore, the anomaly detection algorithms deployed in factories need to have high accuracy, and they need to be able to promptly discover and locate the specific equipment causing the anomaly to restore the regular operation of the abnormal equipment. However, the neural network models currently deployed in factories cannot effectively capture both temporal features within dimensions and relationship features between dimensions; some algorithms that consider both types of features lack interpretability. Therefore, we propose a high-precision, interpretable anomaly detection algorithm based on variational autoencoder (VAE). We use a multi-scale local weight-sharing convolutional neural network structure to fully extract the temporal features within each dimension of the multi-dimensional time series. Then, we model the features from various aspects through multiple attention heads, extracting the relationship features between dimensions. We map the attention output results to the latent space distribution of the VAE and propose an optimization method to improve the reconstruction performance of the VAE, detecting anomalies through reconstruction errors. Regarding anomaly interpretability, we utilize the VAE probability distribution characteristics, decompose the obtained joint probability density into conditional probabilities on each dimension, and calculate the anomaly score, which provides helpful value for technicians. Experimental results show that our algorithm performed best in terms of F1 score and AUC value. The AUC value for anomaly detection is 0.982, and the F1 score is 0.905, which is 4% higher than the best-performing baseline algorithm, Transformer with a Discriminator for Anomaly Detection (TDAD). It also provides accurate anomaly interpretation capability.

Malware Identification Method in Industrial Control Systems Based on Opcode2vec and CVAE-GAN.

Industrial Control Systems (ICSs) have faced a significant increase in malware threats since their integration with the Internet. However, existing machine learning-based malware identification methods are not specifically optimized for ICS environments, resulting in suboptimal identification performance. In this work, we propose an innovative method explicitly tailored for ICSs to enhance the performance of malware classifiers within these systems. Our method integrates the opcode2vec method based on preprocessed features with a conditional variational autoencoder-generative adversarial network, enabling classifiers based on Convolutional Neural Networks to identify malware more effectively and with some degree of increased stability and robustness. Extensive experiments validate the efficacy of our method, demonstrating the improved performance of malware classifiers in ICSs. Our method achieved an accuracy of 97.30%, precision of 92.34%, recall of 97.44%, and F1-score of 94.82%, which are the highest reported values in the experiment.

Tough Hydrogels with Different Toughening Mechanisms and Applications.

Load-bearing biological tissues, such as cartilage and muscles, exhibit several crucial properties, including high elasticity, strength, and recoverability. These characteristics enable these tissues to endure significant mechanical stresses and swiftly recover after deformation, contributing to their exceptional durability and functionality. In contrast, while hydrogels are highly biocompatible and hold promise as synthetic biomaterials, their inherent network structure often limits their ability to simultaneously possess a diverse range of superior mechanical properties. As a result, the applications of hydrogels are significantly constrained. This article delves into the design mechanisms and mechanical properties of various tough hydrogels and investigates their applications in tissue engineering, flexible electronics, and other fields. The objective is to provide insights into the fabrication and application of hydrogels with combined high strength, stretchability, toughness, and fast recovery as well as their future development directions and challenges.

Blood biomarkers for post-stroke cognitive impairment: A systematic review and meta-analysis.

BACKGROUND AND PURPOSE: Post-stroke cognitive impairment (PSCI) is a frequent consequence of stroke, which affects the quality of life and prognosis of stroke survivors. Numerous studies have indicated that blood biomarkers may be the key determinants for predicting and diagnosing cognitive impairment, but the results remain varied. Therefore, this meta-analysis aims to summarize potential biomarkers associated with PSCI. METHODS: PubMed, Web of Science, Embase, and Cochrane Library were comprehensively searched for studies exploring blood biomarkers associated with PSCI from inception to 15 April 2022. RESULTS: 63 studies were selected from 4,047 references, which involves 95 blood biomarkers associated with the PSCI. We meta-analyzed 20 potential blood biomarker candidates, the results shown that the homocysteine (Hcy) (SMD = 0.35; 95 %CI: 0.20-0.49; P < 0.00001), c-reactive protein (CRP) (SMD = 0.49; 95 %CI: 0.20-0.78; P = 0.0008), uric acid (UA) (SMD = 0.41; 95 %CI: 0.06-0.76; P = 0.02), interleukin 6 (IL-6) (SMD = 0.92; 95 % CI: 0.27-1.57; P = 0.005), cystatin C (Cys-C) (SMD = 0.58; 95 %CI: 0.28-0.87; P = 0.0001), creatinine (SMD = 0.39; 95 %CI: 0.23-0.55; P < 0.00001) and tumor necrosis factor alpha (TNF-α) (SMD = 0.45; 95 %CI: 0.08-0.82; P = 0.02) levels were significantly higher in patients with PSCI than in the non-PSCI group. CONCLUSION: Based on our findings, we recommend that paramedics focus on the blood biomarkers levels of Hcy, CRP, UA, IL-6, Cys-C, creatinine and TNF-α in conjunction with neuroimaging and neuropsychological assessment to assess the risk of PSCI, which may help with early detection and timely preventive measures. At the same time, other potential blood biomarkers should be further validated in future studies.

Type I/type III IFN and related factors regulate JEV infection and BBB endothelial integrity.

BACKGROUND: Japanese encephalitis virus (JEV) remains a predominant cause of Japanese encephalitis (JE) globally. Its infection is usually accompanied by disrupted blood‒brain barrier (BBB) integrity and central nervous system (CNS) inflammation in a poorly understood pathogenesis. Productive JEV infection in brain microvascular endothelial cells (BMECs) is considered the initial event of the virus in penetrating the BBB. Type I/III IFN and related factors have been described as negative regulators in CNS inflammation, whereas their role in JE remains ambiguous. METHODS: RNA-sequencing profiling (RNA-seq), real-time quantitative PCR, enzyme-linked immunosorbent assay, and Western blotting analysis were performed to analyze the gene and protein expression changes between mock- and JEV-infected hBMECs. Bioinformatic tools were used to cluster altered signaling pathway members during JEV infection. The shRNA-mediated immune factor-knockdown hBMECs and the in vitro transwell BBB model were utilized to explore the interrelation between immune factors, as well as between immune factors and BBB endothelial integrity. RESULTS: RNA-Seq data of JEV-infected hBMECs identified 417, 1256, and 2748 differentially expressed genes (DEGs) at 12, 36, and 72 h post-infection (hpi), respectively. The altered genes clustered into distinct pathways in gene ontology (GO) terms and KEGG pathway enrichment analysis, including host antiviral immune defense and endothelial cell leakage. Further investigation revealed that pattern-recognition receptors (PRRs, including TLR3, RIG-I, and MDA5) sensed JEV and initiated IRF/IFN signaling. IFNs triggered the expression of interferon-induced proteins with tetratricopeptide repeats (IFITs) via the JAK/STAT pathway. Distinct PRRs exert different functions in barrier homeostasis, while treatment with IFN (IFN-ß and IFN-λ1) in hBMECs stabilizes the endothelial barrier by alleviating exogenous destruction. Despite the complex interrelationship, IFITs are considered nonessential in the IFN-mediated maintenance of hBMEC barrier integrity. CONCLUSIONS: This research provided the first comprehensive description of the molecular mechanisms of host‒pathogen interplay in hBMECs responding to JEV invasion, in which type I/III IFN and related factors strongly correlated with regulating the hBMEC barrier and restricting JEV infection. This might help with developing an attractive therapeutic strategy in JE.

Olver plasmon: an accelerating surface wave with various orders.

In this Letter, we introduce a new, to the best of our knowledge, class of accelerating surface plasmonic wave: the Olver plasmon. Our research reveals that such a surface wave propagates along self-bending trajectories at the silver-air interface with various orders, among which Airy plasmon is regarded as the zeroth-order one. We demonstrate a plasmonic autofocusing hot-spot by the interference of Olver plasmons and the focusing properties can be controlled. Also, a scheme for the generation of this new surface plasmon is proposed with the verification of finite difference time-domain numerical simulations.

Common carp Peptidoglycan Recognition Protein 2 (CcPGRP2) plays a role in innate immunity for defense against bacterial infections.

PGRP is a family of pattern recognition molecules of the innate immune system. PGRPs are conserved from insects to mammals and have diverse functions in antimicrobial defense. Here we cloned a common carp PGRP ortholog, CcPGRP2 containing a conserved C-terminal PGRP domain. We tested the expression levels of CcPGRP2 in the liver, spleen, kidney, foregut, midgut, and hindgut of the highest level in the liver. The expression of CcPGRP2 upregulated in common carp infected with Aeromonas hydrophila (A. hydrophila) or Staphylococcus aureus (S. aureus). Recombinant CcPGRP2 protein expressed in Escherichia coli (E. coli) system and the purified CcPGRP2 could maintain the integrity of intestinal mucosa of common carp infected with A. hydrophila. In addition, CcPGRP2 could agglutinate or bind both gram-positive and gram-negative bacteria in a Zn2+-dependent manner. CcPGRP2 has a stronger agglutination and bacterial binding ability in gram-positive bacteria than in gram-negative bacteria. It is perhaps because CcPGRP2 could bind peptidoglycan (PGN) with a higher degree to lipopolysaccharide (LPS). And CcPGRP2 shows antimicrobial activities in the presence of Zn2+. Our results of CcPGRP2 provided new insight into the function of PGRP in the innate immunity of the common carp.

Differentiation Between Internal Carotid Artery Hypoplasia and Acquired Narrowing by Neurovascular Ultrasound: Case Series and Literature Review.

Uniformly narrowed internal carotid artery (ICA) without proximal steno-occlusion or parietal anomalies is often subject to misdiagnosis due to lack of awareness. We combined our experiences of 4 cases with 29 previously published cases to form a retrospective series including 18 cases of ICA hypoplasia and 15 cases of ICA acquired narrowing. The ultrasonic manifestations of ICA acquired narrowing and ICA hypoplasia are extremely similar, but narrowed ICA without intracranial occlusion or bottle-neck-sign highly indicates ICA hypoplasia, whereas moyamoya vessels favor ICA acquired narrowing, thus promoting the understanding of and discriminability between the two on neurovascular ultrasound.

The impact of training on the inner-outer asymmetry in crowding.

Inner-outer asymmetry, where the outer flanker induces stronger crowding than the inner flanker, is a hallmark property of visual crowding. It is unclear the contribution of inner-outer asymmetry to the pattern of crowding errors (biased predominantly toward the flanker identities) and the role of training on crowding errors. In a typical radial crowding display, 20 observers were asked to report the orientation of a target Gabor (7.5° eccentricity) flanked by either an inner or outer Gabor along the horizontal meridian. The results showed that outer flanker conditions induced stronger crowding, accompanied by assimilative errors to the outer flanker for similar target/flanker elements. In contrast, the inner flanker condition exhibited weaker crowding, with no significant patterns of crowding errors. A population coding model showed that the flanker weights in the outer flanker condition were significantly higher than those in the inner flanker condition. Nine observers continued to train the outer flanker condition for four sessions. Training reduced inner-outer asymmetry and reduced flanker weights to the outer flanker. The learning effects were retained over 4 to 6 months. Individual differences in the appearance of crowding errors, the strength of inner-outer asymmetry, and the training effects were evident. Nevertheless, our findings indicate that different crowding mechanisms may be responsible for the asymmetric crowding effects induced by inner and outer flankers, with the outer flankers dominating the appearance more than the inner ones. Training reduces inner-outer asymmetry by reducing target/flanker confusion, and learning is persistent over months, suggesting that perceptual learning has the potential to improve visual performance by promoting neural plasticity.

A Lightweight Unsupervised Intrusion Detection Model Based on Variational Auto-Encoder.

With the gradual integration of internet technology and the industrial control field, industrial control systems (ICSs) have begun to access public networks on a large scale. Attackers use these public network interfaces to launch frequent invasions of industrial control systems, thus resulting in equipment failure and downtime, production data leakage, and other serious harm. To ensure security, ICSs urgently need a mature intrusion detection mechanism. Most of the existing research on intrusion detection in ICSs focuses on improving the accuracy of intrusion detection, thereby ignoring the problem of limited equipment resources in industrial control environments, which makes it difficult to apply excellent intrusion detection algorithms in practice. In this study, we first use the spectral residual (SR) algorithm to process the data; we then propose the improved lightweight variational autoencoder (LVA) with autoregression to reconstruct the data, and we finally perform anomaly determination based on the permutation entropy (PE) algorithm. We construct a lightweight unsupervised intrusion detection model named LVA-SP. The model as a whole adopts a lightweight design with a simpler network structure and fewer parameters, which achieves a balance between the detection accuracy and the system resource overhead. Experimental results on the ICSs dataset show that our proposed LVA-SP model achieved an F1-score of 84.81% and has advantages in terms of time and memory overhead.