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Abstract In generating adversarial examples, the conventional black-box attack methods rely on sufficient feedback from the to-be-attacked models by repeatedly querying until the attack is successful, which usually results in thousands of trials during an attack. This may be unacceptable in real applications since Machine Learning as a Service Platform (MLaaS) usually only returns the final result (i.e., hard-label) to the client and a system equipped with certain defense mechanisms could easily detect malicious queries. By contrast, a feasible way is a hard-label attack that simulates an attacked action being permitted to conduct a limited number of queries. To implement this idea, in this paper, we bypass the dependency on the to-be-attacked model and benefit from the characteristics of the distributions of adversarial examples to reformulate the attack problem in a distribution transform manner and propose a distribution transform-based attack (DTA). DTA builds a statistical mapping from the benign example to its adversarial counterparts by tackling the conditional likelihood under the hard-label black-box settings. In this way, it is no longer necessary to query the target model frequently. A well-trained DTA model can directly and efficiently generate a batch of adversarial examples for a certain input, which can be used to attack un-seen models based on the assumed transferability. Furthermore, we surprisingly find that the well-trained DTA model is not sensitive to the semantic spaces of the training dataset, meaning that the model yields acceptable attack performance on other datasets. Extensive experiments validate the effectiveness of the proposed idea and the superiority of DTA over the state-of-the-art.

BackgroundPhysical activity can alleviate negative emotions in college students by enhancing mood and cognitive functions. Yet, the mechanisms underlying these benefits remain unclear.PurposeThis study examines the association between physical activity and negative emotions—specifically, depression, anxiety, and stress—in college students. Additionally, we explore the mediating effects of psychological resilience and coping styles to offer theoretical and practical insights for mitigating students’ negative emotions.MethodsUsing a stratified random sampling approach, a total of 1,380 college students, from five universities in Hangzhou, Nanjing, and Wenzhou, were included in the analysis. The survey instruments included the Physical Activity Rating Scale, Connor-Davidson Resilience Scale, Simple Coping Style Scale, and Depression-Anxiety Stress Scale. The data were statistically analyzed using multivariate methods with IBM SPSS 25.0 and the PROCESS V3.3 plug-in.Results(1) College students engage in a low level of physical activity, with male students participating significantly more than female students (p

ObjectiveLow back pain is a prevalent and debilitating condition worldwide, with significant implications for individuals’ quality of life and productivity. The aim of this study was to assess the relationship between mood instability and the risk of developing chronic low back pain, using a rigorously designed mendelian randomization methodology.MethodThe study incorporated both univariate and multivariate mendelian randomization to analysis the causal relationship between mood instability and the risk of developing chronic low back pain. The data on mood instability from the Integrative Epidemiology Unit (IEU) opened Genome-Wide Association Studies (GWAS) project (IEU-opened GWAS project). Data on low back pain were collected from two sources: One source is the IEU open GWAS project (discovery data). Another source is a GWAS meta-analysis (replication data). Inverse variance weighted method, weighted median method, MR-Egger regression, and mendelian randomization pleiotropy residual sum and outlier method were used for mendelian randomization analysis.ResultThe univariable mendelian randomization analysis shows a statistically significant correlation between mood instability and the risk of low back pain. Several methods were performed, including inverse variance weighting (discovery data: odds ratio = 3.544, 95% confidence interval = 1.785–7.039, p = 0.000; replication data: odds ratio = 3.167, 95% confidence interval = 2.476–4.052, p = 0.000), MR-Egger (discovery data: odds ratio = 7.178, 95% confidence interval = 0.057–909.525, p = 0.429; replication data: odds ratio = 2.262, 95% confidence interval = 0.580–8.825, p = 0.246), weighted median (discovery data: odds ratio = 2.730, 95% confidence interval = 1.112–6.702, p = 0.028; replication data: odds ratio = 3.243, 95% confidence interval = 2.378–4.422, p = 0.000), MR-PRESSO (discovery data: odds ratio = 3.544, 95% confidence interval = 1.785–7.039, p = 0.001; replication data: odds ratio = 3.167, 95% confidence interval = 2.476–4.052, p = 0.000) methods. The results were consistent across these methods. The results obtained from discovery data are consistent with those obtained from discovery data. In the multivariable mendelian randomization, after adjusting for various covariates such as body mass index, current tobacco smoking, alcohol intake frequency, Total body bone mineral density, and vigorous physical activity, there is a consistent correlation between mood instability and chronic low back pain.ConclusionThis study provides robust evidence supporting a causal relationship between mood instability and the development of low back pain. Our findings suggest that addressing mood instability may play a crucial role in prevention and management strategies for individuals experiencing low back pain.

Abstract Copy–move forgery poses a significant threat to social life and has aroused much attention in recent years. Although many copy‐move forgery detection (CMFD) methods have been proposed, the most existing CMFD methods are short of adaptability in detecting images, which leads to the limitation on detection effects. To solve this problem, the paper proposes a novel keypoint‐based CMFD method: second‐keypoint matching and double adaptive filtering (SMDAF). Motivated by image matching based on keypoint, the second‐keypoint matching method is designed to match keypoints extracted from copy–move forgery images, which can be used for both the single‐CMFD and the multiple‐CMFD. Then, a double adaptive filter (DAF) based on the AdaLAM algorithm and the KANN‐DBSCAN clustering algorithm to filter wrong keypoint matches adaptively are proposed, according to the distinct distribution of keypoints in each image. Finally, the forgery regions are presented by finding their convex hulls and padding them. Compared with existing methods, extensive experiments show that the SMDAF method significantly provides more efficiency in detecting images under simulated real‐world conditions, has better robustness when facing images with different post‐treatment attacks, and is more effective in distinguishing images that look copy–move forged but are real.

Recent adversarial attack research reveals the vulnerability of learning-based deep learning models (DNN) against well-designed perturbations. However, most existing attack methods have inherent limitations in image quality as they rely on a relatively loose noise budget, i.e., limit the perturbations by Lp-norm. Resulting that the perturbations generated by these methods can be easily detected by defense mechanisms and are easily perceptible to the human visual system (HVS). To circumvent the former problem, we propose a novel framework, called DualFlow, to craft adversarial examples by disturbing the image's latent representations with spatial transform techniques. In this way, we are able to fool classifiers with human imperceptible adversarial examples and step forward in exploring the existing DNN's fragility. For imperceptibility, we introduce the flow-based model and spatial transform strategy to ensure the calculated adversarial examples are perceptually distinguishable from the original clean images. Extensive experiments on three computer vision benchmark datasets (CIFAR-10, CIFAR-100 and ImageNet) indicate that our method can yield superior attack performance in most situations. Additionally, the visualization results and quantitative performance (in terms of six different metrics) show that the proposed method can generate more imperceptible adversarial examples than the existing imperceptible attack methods.

SPX proteins sense phosphate levels in plant cells by binding to inositol polyphosphates (InsP) and suppressing the activity of PHR transcription factors. Here the authors show that when bound to InsP6, the rice SPX1 protein inhibits the activity of PHR2 by attenuating both its dimerization and DNA binding activity.

Background: This study assessed the effects of esmolol injection in patients with in-hospital cardiac arrest (IHCA) with refractory ventricular fibrillation (VF)/pulseless ventricular tachycardia (pVT).Methods: From January 2018 to December 2021, 29 patients with IHCA with refractory shockable rhythm were retrospectively reviewed. Esmolol was administered after advanced cardiovascular life support (ACLS)-directed procedures, and outcomes were assessed.Results: Among the 29 cases, the rates of sustained return of spontaneous circulation (ROSC), 24-h ROSC, and 72-h ROSC were 79%, 62%, and 59%, respectively. Of those patients, 59% ultimately survived to discharge. Four patients with cardiac insufficiency died. The duration from CA to esmolol infusion was significantly shorter for patients in the survival group (SG) than for patients in the dead group (DG) (12 min, IQR: 8.5–19.5 vs. 23.5 min, IQR: 14.4–27 min; p = 0.013). Of those patients, 76% (22 of 29) started esmolol administration after the second dose of amiodarone. No significant difference was observed in the survival rate between this group and groups administered an esmolol bolus simultaneously or before the second dose of amiodarone (43% vs. 64%, p = 0.403). Of those patients, 31% (9 of 29) were administered an esmolol bolus for defibrillation attempts ≤ 5, while the remaining 69% of patients received an esmolol injection after the fifth defibrillation attempt. No significant differences were observed in the rates of ≥ 24-h ROSC (67% vs. 60%, p = 0.73), ≥ 72-h ROSC (67% vs. 55%, p = 0.56), and survival to hospital discharge (67% vs. 55%, p = 0.56) between the groups administered an esmolol bolus for defibrillation attempts ≤ 5 and defibrillation attempts > 5.Conclusion: IHCA patients with refractory shockable rhythms receiving esmolol bolus exhibited a high chance of sustained ROSC and survival to hospital discharge. Patients with end-stage heart failure tended to have attenuated benefits from beta-blockers. Further large-scale, prospective studies are necessary to determine the effects of esmolol in patients with IHCA with refractory shockable rhythms.

Background: The COVID-19 global pandemic has posed unprecedented challenges to health care systems all over the world. The speed of the viral spread results in a tsunami of patients, which begs for a reliable screening tool using readily available data to predict disease progression.Methods: Multicenter retrospective cohort study was performed to develop and validate a triage model. Patient demographic and non-laboratory clinical data were recorded. Using only the data from Zhongnan Hospital, step-wise multivariable logistic regression was performed, and a prognostic nomogram was constructed based on the independent variables identifies. The discrimination and calibration of the model were validated. External independent validation was performed to further address the utility of this model using data from Jinyintan Hospital.Results: A total of 716 confirmed COVID-19 cases from Zhongnan Hospital were included for model construction. Men, increased age, fever, hypertension, cardio-cerebrovascular disease, dyspnea, cough, and myalgia are independent risk factors for disease progression. External independent validation was carried out in a cohort with 201 cases from Jinyintan Hospital. The area under the curve (AUC) was 0.787 (95% confidence interval [CI]: 0.747–0.827) in the training group and 0.704 (95% CI: 0.632–0.777) in the validation group.Conclusions: We developed a novel triage model based on basic and clinical data. Our model could be used as a pragmatic screening aid to allow for cost efficient screening to be carried out such as over the phone, which may reduce disease propagation through limiting unnecessary contact. This may help allocation of limited medical resources.