Adaptive Superpixel for Active Learning in Semantic Segmentation

Learning semantic segmentation requires pixel-wise annotations, which can be time-consuming and expensive. To reduce the annotation cost, we propose a superpixel-based active learning (AL) framework, which collects a dominant label per superpixel instead. To be specific, it consists of adaptive superpixel and sieving mechanisms, fully dedicated to AL. At each round of AL, we adaptively merge neighboring pixels of similar learned features into superpixels. We then query a selected subset of these superpixels using an acquisition function assuming no uniform superpixel size. This approach is more efficient than existing methods, which rely only on innate features such as RGB color and assume uniform superpixel sizes. Obtaining a dominant label per superpixel drastically reduces annotators' burden as it requires fewer clicks. However, it inevitably introduces noisy annotations due to mismatches between superpixel and ground truth segmentation. To address this issue, we further devise a sieving mechanism that identifies and excludes potentially noisy annotations from learning. Our experiments on both Cityscapes and PASCAL VOC datasets demonstrate the efficacy of adaptive superpixel and sieving mechanisms

Learning Debiased Classifier with Biased Committee

Neural networks are prone to be biased towards spurious correlations between classes and latent attributes exhibited in a major portion of training data, which ruins their generalization capability. We propose a new method for training debiased classifiers with no spurious attribute label. The key idea is to employ a committee of classifiers as an auxiliary module that identifies bias-conflicting data, i.e., data without spurious correlation, and assigns large weights to them when training the main classifier. The committee is learned as a bootstrapped ensemble so that a majority of its classifiers are biased as well as being diverse, and intentionally fail to predict classes of bias-conflicting data accordingly. The consensus within the committee on prediction difficulty thus provides a reliable cue for identifying and weighting bias-conflicting data. Moreover, the committee is also trained with knowledge transferred from the main classifier so that it gradually becomes debiased along with the main classifier and emphasizes more difficult data as training progresses. On five real-world datasets, our method outperforms prior arts using no spurious attribute label like ours and even surpasses those relying on bias labels occasionally.Comment: Conference on Neural Information Processing Systems (NeurIPS), New Orleans, 202

Active Learning for Semantic Segmentation with Multi-class Label Query

This paper proposes a new active learning method for semantic segmentation. The core of our method lies in a new annotation query design. It samples informative local image regions (e.g., superpixels), and for each of such regions, asks an oracle for a multi-hot vector indicating all classes existing in the region. This multi-class labeling strategy is substantially more efficient than existing ones like segmentation, polygon, and even dominant class labeling in terms of annotation time per click. However, it introduces the class ambiguity issue in training since it assigns partial labels (i.e., a set of candidate classes) to individual pixels. We thus propose a new algorithm for learning semantic segmentation while disambiguating the partial labels in two stages. In the first stage, it trains a segmentation model directly with the partial labels through two new loss functions motivated by partial label learning and multiple instance learning. In the second stage, it disambiguates the partial labels by generating pixel-wise pseudo labels, which are used for supervised learning of the model. Equipped with a new acquisition function dedicated to the multi-class labeling, our method outperformed previous work on Cityscapes and PASCAL VOC 2012 while spending less annotation cost

Attenuating MKRN1 E3 ligase-mediated AMPKα suppression increases tolerance against metabolic stresses in mice

The 5′ adenosine monophosphate-activated protein kinase (AMPK) is an essential energy sensor in the cell, which, at low energy levels, instigates the cellular energy-generating systems along with suppression of the anabolic signaling pathways. The activation of AMPK through phosphorylation is a well-known process; however, activation alone is not sufficient, and knowledge about the other regulatory networks of post-translational modifications connecting the activities of AMPK to systemic metabolic syndromes is important, which is still lacking. The recent studies on Makorin Ring Finger Protein 1 (MKRN1) mediating the ubiquitination and proteasome-dependent degradation of AMPKa implicate that the post-translational modification of AMPK, regulating its protein homeostasis, could impose significant systemic metabolic effects (Lee et al. Nat Commun 9:3404). In this study, MKRN1 was identified as a novel E3 ligase for both AMPKα1 and α2. Mouse embryonic fibroblasts, genetically deleted for Mkrmn1, and Ampkα1 and α2, became stabilized with the suppression of lipogenesis pathways and an increase in nutrient consumption and mitochondria regeneration. Of note, the Mkrn1 knockout mice fed normal chow displayed no obvious phenotypic defects or abnormality, whereas the Mkrn1-null mice exhibited strong tolerance to metabolic stresses induced by high-fat diet (HFD). Thus, these mice, when compared with the HFD-induced wild type, were resistant to obesity, diabetes, and non-alcoholic fatty liver disease. Interestingly, in whole-body Mkrn1 knockout mouse, only the liver and white and brown adipose tissues displayed anincrease in the active phosphorylated AMPK levels, but no other organs, such as the hypothalamus, skeletal muscles, or pancreas, displayed such increases. Specific ablation of MKRN1 in the mouse liver using adenovirus prevented HFD-induced lipid accumulation in the liver and blood, implicating MKRN1 as a possible therapeutic target for metabolic syndromes, such as obesity, type II diabetes, and fat liver diseases. This study would provide a crucial perspective on the importance of post-translational regulation of AMPK in metabolic pathways and will help researchers develop novel therapeutic strategies that target not only AMPK but also its regulators

Multi-dimensional histone methylations for coordinated regulation of gene expression under hypoxia

Hypoxia increases both active and repressive histone methylation levels via decreased activity of histone demethylases. However, how such increases coordinately regulate induction or repression of hypoxia-responsive genes is largely unknown. Here, we profiled active and repressive histone tri-methylations (H3K4me3, H3K9me3, and H3K27me3) and analyzed gene expression profiles in human adipocyte-derived stem cells under hypoxia. We identified differentially expressed genes (DEGs) and differentially methylated genes (DMGs) by hypoxia and clustered the DEGs and DMGs into four major groups. We found that each group of DEGs was predominantly associated with alterations in only one type among the three histone tri-methylations. Moreover, the four groups of DEGs were associated with different TFs and localization patterns of their predominant types of H3K4me3, H3K9me3 and H3K27me3. Our results suggest that the association of altered gene expression with prominent single-type histone tri-methylations characterized by different localization patterns and with different sets of TFs contributes to regulation of particular sets of genes, which can serve as a model for coordinated epigenetic regulation of gene expression under hypoxia.111Ysciescopu

Sleep disturbances, depressive symptoms, and cognitive efficiency as determinants of mistakes at work in shift and non-shift workers

IntroductionShift work is known to reduce productivity and safety at work. Previous studies have suggested that a variety of interrelated factors, such as mood, cognition, and sleep, can affect the performance of shift workers. This study aimed to identify potential pathways from depression, sleep, and cognition to work performance in shift and non-shift workers.Material and methodsOnline survey including the Center for Epidemiologic Studies Depression Scale (CES-D), Cognitive Failure Questionnaire (CFQ), and Pittsburgh Sleep Quality Index (PSQI), as well as two items representing work mistakes were administered to 4,561 shift workers and 2,093 non-shift workers. A multi-group structural equation model (SEM) was used to explore differences in the paths to work mistakes between shift and non-shift workers.ResultsShift workers had higher PSQI, CES-D, and CFQ scores, and made more mistakes at work than non-shift workers. The SEM revealed that PSQI, CES-D, and CFQ scores were significantly related to mistakes at work, with the CFQ being a mediating variable. There were significant differences in the path coefficients of the PSQI and CES-D between shift and non-shift workers. The direct effects of sleep disturbances on mistakes at work were greater in shift workers, while direct effects of depressive symptoms were found only in non-shift workers.DiscussionThe present study found that shift workers made more mistakes at work than non-shift workers, probably because of depressed mood, poor sleep quality, and cognitive inefficiency. Sleep influences work performance in shift workers more directly compared to non-shift workers

Shift schedules and circadian preferences: the association with sleep and mood

ObjectWe explored the circadian preferences of non-shift workers (non-SWs) and various types of shift workers (SWs), and the associations of these preferences with sleep and mood.MethodsIn total, 4,561 SWs (2,419 women and 2,142 men aged 37.00 ± 9.80 years) and 2,093 non-SWs (1,094 women and 999 men aged 37.80 ± 9.73 years) completed an online survey. Of all SWs, 2,415 (1,079 women and 1,336 men aged 37.77 ± 9.96 years) reported regularly rotating or fixed schedules (“regular SWs”), and 2,146 (1,340 women and 806 men aged 36.12 ± 9.64 years) had irregular schedules (“irregular SWs”). Of the regular SWs, 2,040 had regularly rotating schedules, 212 had fixed evening schedules, and 163 had fixed night schedules. All participants completed the Morningness-Eveningness Questionnaire (MEQ) exploring circadian preferences, the short form of the Center for Epidemiological Studies-Depression Scale (CES-D) evaluating depression, the Insomnia Severity Index (ISI), and the Epworth Sleepiness Scale (ESS).ResultsCompared to non-SWs, SWs had lower MEQ scores, i.e., more eveningness, after controlling for age, gender, income, occupation, and weekly work hours (F = 87.97, p < 0.001). Irregular SWs had lower MEQ scores than regular SWs (F = 50.89, p < 0.001). Among regular SWs, the MEQ scores of fixed evening and fixed night SWs were lower than those of regularly rotating SWs (F = 22.42, p < 0.001). An association between the MEQ and ESS scores was apparent in non-SWs (r = −0.85, p < 0.001) but not in SWs (r = 0.001, p = 0.92).ConclusionSWs exhibited more eveningness than non-SWs; eveningness was particularly prominent in SWs with irregular or fixed evening/night shifts. Eveningness was associated with sleepiness only in non-SWs, but not in SWs

Динаміка та аналіз виробничого травматизму та професійних захворювань в Україні

Кожного року в Україні на виробництві травмується понад 10 тис. людей, з них гине понад 600 осіб. Оптимістична, на перший погляд, статистика, за якою травматизм на виробництві за роки незалежності України зменшився в десять разів, виявляється не такою вже й оптимістичною, коли аналізуються конкретні цифри

Comprehensive Proteome Profiling of Platelet Identified a Protein Profile Predictive of Responses to An Antiplatelet Agent Sarpogrelate

Sarpogrelate is an antiplatelet agent widely used to treat arterial occlusive diseases. Evaluation of platelet aggregation is essential to monitor therapeutic effects of sarpogrelate. Currently, no molecular signatures are available to evaluate platelet aggregation. Here, we performed comprehensive proteome profiling of platelets collected from 18 subjects before and after sarpogrelate administration using LC-MS/MS analysis coupled with extensive fractionation. Of 5423 proteins detected, we identified 499 proteins affected by sarpogrelate and found that they strongly represented cellular processes related to platelet activation and aggregation, including cell activation, coagulation, and vesicle-mediated transports. Based on the network model of the proteins involved in these processes, we selected three proteins (cut-like homeobox 1; coagulation factor XIII, B polypeptide; and peptidylprolyl isomerase D) that reflect the platelet aggregation-related processes after confirming their alterations by sarpogrelate in independent samples using Western blotting. Our proteomic approach provided a protein profile predictive of therapeutic effects of sarpogrelate. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.1

A Protein Profile of Visceral Adipose Tissues Linked to Early Pathogenesis of Type 2 Diabetes Mellitus

Adipose tissue is increasingly recognized as an endocrine organ playing important pathophysiological roles in metabolic abnormalities, such as obesity, cardiovascular disease, and type 2 diabetes mellitus (T2DM). In particular, visceral adipose tissue (VAT), as opposed to subcutaneous adipose tissue, is closely linked to the pathogenesis of insulin resistance and T2DM. Despite the importance of VAT, its molecular signatures related to the pathogenesis of T2DM have not been systematically explored. Here, we present comprehensive proteomic analysis of VATs in drug-naïve early T2DM patients and subjects with normal glucose tolerance. A total of 4,707 proteins were identified in LC-MS/MS experiments. Among them, 444 increased in abundance in T2DM and 328 decreased. They are involved in T2DM-related processes including inflammatory responses, peroxisome proliferator-activated receptor signaling, oxidative phosphorylation, fatty acid oxidation, and glucose metabolism. Of these proteins, we selected 11 VAT proteins that can represent alteration in early T2DM patients. Among them, up-regulation of FABP4, C1QA, S100A8, and SORBS1 and down-regulation of ACADL and PLIN4 were confirmed in VAT samples of independent early T2DM patients using Western blot. In summary, our profiling provided a comprehensive basis for understanding the link of a protein profile of VAT to early pathogenesis of T2DM. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.1