GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels

Since annotating medical images for segmentation tasks commonly incurs expensive costs, it is highly desirable to design an annotation-efficient method to alleviate the annotation burden. Recently, contrastive learning has exhibited a great potential in learning robust representations to boost downstream tasks with limited labels. In medical imaging scenarios, ready-made meta labels (i.e., specific attribute information of medical images) inherently reveal semantic relationships among images, which have been used to define positive pairs in previous work. However, the multi-perspective semantics revealed by various meta labels are usually incompatible and can incur intractable "semantic contradiction" when combining different meta labels. In this paper, we tackle the issue of "semantic contradiction" in a gradient-guided manner using our proposed Gradient Mitigator method, which systematically unifies multi-perspective meta labels to enable a pre-trained model to attain a better high-level semantic recognition ability. Moreover, we emphasize that the fine-grained discrimination ability is vital for segmentation-oriented pre-training, and develop a novel method called Gradient Filter to dynamically screen pixel pairs with the most discriminating power based on the magnitude of gradients. Comprehensive experiments on four medical image segmentation datasets verify that our new method GCL: (1) learns informative image representations and considerably boosts segmentation performance with limited labels, and (2) shows promising generalizability on out-of-distribution datasets

Effect of aramid core-spun yarn on impact resistance of aramid/epoxy composite

Introduction: The surface of aramid filament is smooth, which is a great defect for impact resistance and composite molding of aramid/epoxy composite. In this study, a new type of yarn—aramid core-spun yarn is introduced to the fabrication of compositematerials. It increases the friction among yarns and optimizes the performance of yarns.Methods: To verify the improvement of yarn in the composite material, the hand lay-up process is used, and the first layer and the fourth layer are replaced by core-spun yarns in a four-layer composite configuration.Results and Discussion: The energy absorption, and the damage of the impacted surface and the back surface are evaluated through the drop weight impact test. The yarn pull-out test can reflect the internal friction of fabric. The results show that the average energy absorption of new yarn in the first layer is 10 J cm2/g more than that in the fourth layer at a 90°/45°/-45°/0° configuration after the normalization, but the conclusion is contrary when the structure is -45°/0°/90°/45°. Under the structure of 90°/45°/-45°/0°, the damaged area of the fabric is larger when the aramid core-spun yarn is laid on the first layer, while a contrary result can be found for the structure of -45°/0°/90°/45°. The fundamental research will provide design ideas and supports for aramid composite

Sample-efficient Multi-objective Molecular Optimization with GFlowNets

Many crucial scientific problems involve designing novel molecules with desired properties, which can be formulated as a black-box optimization problem over the discrete chemical space. In practice, multiple conflicting objectives and costly evaluations (e.g., wet-lab experiments) make the diversity of candidates paramount. Computational methods have achieved initial success but still struggle with considering diversity in both objective and search space. To fill this gap, we propose a multi-objective Bayesian optimization (MOBO) algorithm leveraging the hypernetwork-based GFlowNets (HN-GFN) as an acquisition function optimizer, with the purpose of sampling a diverse batch of candidate molecular graphs from an approximate Pareto front. Using a single preference-conditioned hypernetwork, HN-GFN learns to explore various trade-offs between objectives. We further propose a hindsight-like off-policy strategy to share high-performing molecules among different preferences in order to speed up learning for HN-GFN. We empirically illustrate that HN-GFN has adequate capacity to generalize over preferences. Moreover, experiments in various real-world MOBO settings demonstrate that our framework predominantly outperforms existing methods in terms of candidate quality and sample efficiency. The code is available at https://github.com/violet-sto/HN-GFN.Comment: NeurIPS 202

Non-alcoholic fatty liver disease is associated with a worse prognosis in patients with heart failure: A pool analysis

Background and aimsNon-alcoholic fatty liver disease (NAFLD) is associated with a higher risk of heart failure (HF) than those without NAFLD. However, the prognostic impact of NAFLD in HF is still controversial. This meta-analysis aimed to explore the association between NAFLD and the risk of adverse outcomes in patients with HF.MethodsWe searched multiple electronic databases (Embase, PubMed, and Google Scholar) for potentially related studies up to June 30, 2022. Cohort studies reported multivariable adjusted relative risks and 95% confidence intervals (CIs) of adverse outcomes in HF patients with NAFLD comparing those without NAFLD were included for analysis.ResultsA total of six studies involving 12,374 patients with HF were included for analysis, with a median follow-up duration of 2.5 years. The pooled analysis showed that HF patients with NAFLD were associated with a significantly increased risk of major composite adverse outcomes (HR 1.61, 95% CI 1.25-2.07), all-cause mortality (HR 1.66, 95% CI 1.39-1.98), and HF hospitalization or re-hospitalization (HR 1.71, 95% CI 1.03-2.86).ConclusionNAFLD is associated with a worse prognosis in patients with HF. Effective screening and treatment strategies are needed to improve the prognosis in HF patients with NAFLD

X-ray Polarimetry of the accreting pulsar 1A~0535+262 in the supercritical state with PolarLight

The X-ray pulsar 1A 0535+262 exhibited a giant outburst in 2020, offering us a unique opportunity for X-ray polarimetry of an accreting pulsar in the supercritical state. Measurement with PolarLight yielded a non-detection in 3-8 keV; the 99% upper limit of the polarization fraction (PF) is found to be 0.34 averaged over spin phases, or 0.51 based on the rotating vector model. No useful constraint can be placed with phase resolved polarimetry. These upper limits are lower than a previous theoretical prediction of 0.6-0.8, but consistent with those found in other accreting pulsars, like Her X-1, Cen X-3, 4U 1626-67, and GRO J1008-57, which were in the subcritical state, or at least not confidently in the supercritical state, during the polarization measurements. Our results suggest that the relatively low PF seen in accreting pulsars cannot be attributed to the source not being in the supercritical state, but could be a general feature.Comment: accepted for publication in Ap

Predictive Value of Novel Inflammation-Based Biomarkers for Pulmonary Hypertension in the Acute Exacerbation of Chronic Obstructive Pulmonary Disease

Recently, there has been an increasing interest in the potential clinical use of several inflammatory indexes, namely, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic-immune-inflammation index (SII). This study aimed at assessing whether these markers could be early indicators of pulmonary hypertension (PH) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). A total of 185 patients were enrolled in our retrospective study from January 2017 to January 2019. Receiver operating characteristic curve (ROC) and area under the curve (AUC) were used to evaluate the clinical significance of these biomarkers to predict PH in patients with AECOPD. According to the diagnostic criterion for PH by Doppler echocardiography, the patients were stratified into two groups. The study group consisted of 101 patients complicated with PH, and the control group had 84 patients. The NLR, PLR, and SII values of the PH group were significantly higher than those of the AECOPD one (p<0.05). The blood biomarker levels were positively correlated with NT-proBNP levels, while they had no significant correlation with the estimated pulmonary arterial systolic pressure (PASP) other than PLR. NLR, PLR, and SII values were all associated with PH (p<0.05) in the univariate analysis, but not in the multivariate analysis. The AUC of NLR used for predicting PH was 0.701 and was higher than PLR and SII. Using 4.659 as the cut-off value of NLR, the sensitivity was 81.2%, and the specificity was 59.5%. In conclusion, these simple markers may be useful in the prediction of PH in patients with AECOPD