Weakly supervised conditional random fields model for semantic segmentation with image patches.

Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model. As a result, it is often very time-consuming and labor-intensive, yet still subject to manual errors and subjective inconsistency. To tackle such difficulties, a weakly supervised ISS approach is proposed, in which the challenging problem of label inference from image-level to pixel-level will be particularly addressed, using image patches and conditional random fields (CRF). An improved simple linear iterative cluster (SLIC) algorithm is employed to extract superpixels. for image segmentation. Specifically, it generates various numbers of superpixels according to different images, which can be used to guide the process of image patch extraction based on the image-level labeled information. Based on the extracted image patches, the CRF model is constructed for inferring semantic class labels, which uses the potential energy function to map from the image-level to pixel-level image labels. Finally, patch based CRF (PBCRF) model is used to accomplish the weakly supervised ISS. Experiments conducted on two publicly available benchmark datasets, MSRC and PASCAL VOC 2012, have demonstrated that our proposed algorithm can yield very promising results compared to quite a few state-of-the-art ISS methods, including some deep learning-based models

Blood Eosinophils and Clinical Outcomes in Patients With Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Propensity Score Matching Analysis of Real-World Data in China

Background and Objective: Elevated eosinophils in chronic obstructive pulmonary disease (COPD) are recognized as a biomarker to guide inhaled corticosteroids use, but the value of blood eosinophils in hospitalized exacerbations of COPD remains controversial. This study aimed to evaluate the accuracy of eosinophils in predicting clinical outcomes in acute exacerbation of COPD (AECOPD).Methods: We analyzed data from the acute exacerbation of chronic obstructive pulmonary disease inpatient registry (ACURE) study, which is an ongoing nationwide multicenter, observational real-world study in patients admitted for AECOPD. Data collected between January 2018 and December 2019 in 163 centers were first reviewed. The eligible patients were divided into eosinophilic and non-eosinophilic groups, according to blood eosinophil with 2% of the total leukocyte count as the threshold. Propensity score (PS) matching was performed to adjust for confounders.Results: A total of 1,566 patients (median age: 69 years; 80.3% male) were included and 42.7% had an eosinophilic AECOPD. Eosinophil count <2% was associated with the development of respiratory failure and pneumonia. After PS matching, 650 pairs in overall patients, 468 pairs in patients with smoking history and 177 pairs in patients without smoking were selected, respectively. Only in patients with smoking history, the non-eosinophilic AECOPD was associated with longer median hospital stays (9 vs. 8 days, P = 0.034), higher dosage of corticosteroid use, higher economic burden of hospitalization, and poorer response to corticosteroid therapy compared to the eosinophilic AECOPD. No significant difference was found in patients without smoking. Eosinophil levels had no relationship with the change of COPD Assessment Test scores and readmissions or death after 30 days.Conclusion: Elevated eosinophils were associated with better short-term outcomes only in patients with a smoking history. Eosinophil levels cannot be confidently used as a predictor alone for estimating prognosis

Safe-commit analysis to facilitate team software development

Software development teams exchange source code in shared repositories. These repositories are kept consistent by having developers follow a commit policy, such as “Pro-gram edits can be committed only if all available tests suc-ceed. ” Such policies may result in long intervals between commits, increasing the likelihood of duplicative develop-ment and merge conflicts. Furthermore, commit policies are generally not automatically enforceable. We present an analysis-based algorithm to identify com-mittable changes that can be released early, without caus-ing failures of existing tests, even in the presence of failing tests in a developer’s local workspace! The algorithm can support relaxed commit policies that allow early release of changes, reducing the potential for merge conflicts. In experiments using several versions of Daikon with failing tests, 3 newly enabled commit policies were shown to allow a significant percentage of changes to be committed.

New risk score for predicting progression of membranous nephropathy

Abstract Background Patients with Idiopathic membranous nephropathy (IMN) have various outcomes. The aim of this study is to construct a tool for clinicians to precisely predict outcome of IMN. Methods IMN patients diagnosed by renal biopsy from Shanghai Ruijin Hospital from 2009.01 to 2013.12 were enrolled in this study. Primary outcome was defined as a combination of renal function progression [defined as a reduction of estimated glomerular filtration rate (eGFR) equal to or over 30% comparing to baseline], ESRD or death. Risk models were established by Cox proportional hazard regression analysis and validated by bootstrap resampling analysis. ROC curve was applied to test the performance of risk score. Results Totally 439 patients were recruited in this study. The median follow-up time was 38.73 ± 19.35 months. The enrolled patients were 56 (15–83) years old with a male predominance (sex ratio: male vs female, 1:0.91). The median baseline serum albumin, eGFR-EPI and proteinuria were 23(8–43) g/l, 100.31(12.81–155.98) ml/min/1.73 m2 and 3.98(1.50–22.98) g/24 h, respectively. In total, there were 36 primary outcomes occurred. By Cox regression analysis, the best risk model included age [HR: 1.04(1.003–1.08), 95% CI from bootstrapping: 1.01–1.08), eGFR [HR: 0.97 (0.96–0.99), 95% CI from bootstrapping: 0.96–0.99) and proteinuria [HR: 1.09 (1.01–1.18), 95% CI from bootstrapping: 1.02–1.16). One unit increasing of the risk score based on the best model was associated with 2.57 (1.97–3.36) fold increased risk of combined outcome. The discrimination of this risk score was excellent in predicting combined outcome [C statistics: 0.83, 95% CI 0.76–0.90]. Conclusions Our study indicated that older IMN patients with lower eGFR and heavier proteinuria at the time of renal biopsy were at a higher risk for adverse outcomes. A risk score based on these three variables provides clinicians with an effective tool for risk stratification.https://deepblue.lib.umich.edu/bitstream/2027.42/147736/1/12967_2019_Article_1792.pd

Implicit Regularization and Convergence for Weight Normalization

Normalization methods such as batch [Ioffe and Szegedy, 2015], weight [Salimansand Kingma, 2016], instance [Ulyanov et al., 2016], and layer normalization [Baet al., 2016] have been widely used in modern machine learning. Here, we study the weight normalization (WN) method [Salimans and Kingma, 2016] and a variant called reparametrized projected gradient descent (rPGD) for overparametrized least-squares regression. WN and rPGD reparametrize the weights with a scale g and a unit vector w and thus the objective function becomes non-convex. We show that this non-convex formulation has beneficial regularization effects compared to gradient descent on the original objective. These methods adaptively regularize the weights and converge close to the minimum l2 norm solution, even for initializations far from zero. For certain stepsizes of g and w , we show that they can converge close to the minimum norm solution. This is different from the behavior of gradient descent, which converges to the minimum norm solution only when started at a point in the range space of the feature matrix, and is thus more sensitive to initialization.Comment: NeurIPS 202

Integrated glycomic analysis of ovarian cancer side population cells

Additional file 2. The category of representative lectins for glycan profiling

Viral Paratransgenesis in the Malaria Vector Anopheles gambiae

Paratransgenesis, the genetic manipulation of insect symbiotic microorganisms, is being considered as a potential method to control vector-borne diseases such as malaria. The feasibility of paratransgenic malaria control has been hampered by the lack of candidate symbiotic microorganisms for the major vector Anopheles gambiae. In other systems, densonucleosis viruses (DNVs) are attractive agents for viral paratransgenesis because they infect important vector insects, can be genetically manipulated and are transmitted to subsequent generations. However, An. gambiae has been shown to be refractory to DNV dissemination. We discovered, cloned and characterized the first known DNV (AgDNV) capable of infection and dissemination in An. gambiae. We developed a flexible AgDNV-based expression vector to express any gene of interest in An. gambiae using a two-plasmid helper-transducer system. To demonstrate proof-of-concept of the viral paratransgenesis strategy, we used this system to transduce expression of an exogenous gene (enhanced green fluorescent protein; EGFP) in An. gambiae mosquitoes. Wild-type and EGFP-transducing AgDNV virions were highly infectious to An. gambiae larvae, disseminated to and expressed EGFP in epidemiologically relevant adult tissues such as midgut, fat body and ovaries and were transmitted to subsequent mosquito generations. These proof-of-principle data suggest that AgDNV could be used as part of a paratransgenic malaria control strategy by transduction of anti-Plasmodium peptides or insect-specific toxins in Anopheles mosquitoes. AgDNV will also be extremely valuable as an effective and easy-to-use laboratory tool for transient gene expression or RNAi in An. gambiae

Fibroblast Growth Factor 21 Deficiency Attenuates Experimental Colitis-Induced Adipose Tissue Lipolysis

Aims. Nutrient deficiencies are common in patients with inflammatory bowel disease (IBD). Adipose tissue plays a critical role in regulating energy balance. Fibroblast growth factor 21 (FGF21) is an important endocrine metabolic regulator with emerging beneficial roles in lipid homeostasis. We investigated the impact of FGF21 in experimental colitis-induced epididymal white adipose tissue (eWAT) lipolysis. Methods. Mice were given 2.5% dextran sulfate sodium (DSS) ad libitum for 7 days to induce colitis. The role of FGF21 was investigated using antibody neutralization or knockout (KO) mice. Lipolysis index and adipose lipolytic enzymes were determined. In addition, 3T3-L1 cells were pretreated with IL-6, followed by recombinant human FGF21 (rhFGF21) treatment; lipolysis was assessed. Results. DSS markedly decreased eWAT/body weight ratio and increased serum concentrations of free fatty acid (FFA) and glycerol, indicating increased adipose tissue lipolysis. eWAT intracellular lipolytic enzyme expression/activation was significantly increased. These alterations were significantly attenuated in FGF21 KO mice and by circulating FGF21 neutralization. Moreover, DSS treatment markedly increased serum IL-6 and FGF21 levels. IL-6 pretreatment was necessary for the stimulatory effect of FGF21 on adipose lipolysis in 3T3-L1 cells. Conclusions. Our results demonstrate that experimental colitis induces eWAT lipolysis via an IL-6/FGF21-mediated signaling pathway