Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospects

Recent zero-shot learning (ZSL) approaches have integrated fine-grained analysis, i.e., fine-grained ZSL, to mitigate the commonly known seen/unseen domain bias and misaligned visual-semantics mapping problems, and have made profound progress. Notably, this paradigm differs from existing close-set fine-grained methods and, therefore, can pose unique and nontrivial challenges. However, to the best of our knowledge, there remains a lack of systematic summaries of this topic. To enrich the literature of this domain and provide a sound basis for its future development, in this paper, we present a broad review of recent advances for fine-grained analysis in ZSL. Concretely, we first provide a taxonomy of existing methods and techniques with a thorough analysis of each category. Then, we summarize the benchmark, covering publicly available datasets, models, implementations, and some more details as a library. Last, we sketch out some related applications. In addition, we discuss vital challenges and suggest potential future directions.Comment: 9 pages, 1 figure, 4 table

SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detection

This paper provides a novel framework for single-domain generalized object detection (i.e., Single-DGOD), where we are interested in learning and maintaining the semantic structures of self-augmented compound cross-domain samples to enhance the model's generalization ability. Different from DGOD trained on multiple source domains, Single-DGOD is far more challenging to generalize well to multiple target domains with only one single source domain. Existing methods mostly adopt a similar treatment from DGOD to learn domain-invariant features by decoupling or compressing the semantic space. However, there may have two potential limitations: 1) pseudo attribute-label correlation, due to extremely scarce single-domain data; and 2) the semantic structural information is usually ignored, i.e., we found the affinities of instance-level semantic relations in samples are crucial to model generalization. In this paper, we introduce Semantic Reasoning with Compound Domains (SRCD) for Single-DGOD. Specifically, our SRCD contains two main components, namely, the texture-based self-augmentation (TBSA) module, and the local-global semantic reasoning (LGSR) module. TBSA aims to eliminate the effects of irrelevant attributes associated with labels, such as light, shadow, color, etc., at the image level by a light-yet-efficient self-augmentation. Moreover, LGSR is used to further model the semantic relationships on instance features to uncover and maintain the intrinsic semantic structures. Extensive experiments on multiple benchmarks demonstrate the effectiveness of the proposed SRCD.Comment: 10 pages, 5 figure

Attribute-Aware Representation Rectification for Generalized Zero-Shot Learning

Generalized Zero-shot Learning (GZSL) has yielded remarkable performance by designing a series of unbiased visual-semantics mappings, wherein, the precision relies heavily on the completeness of extracted visual features from both seen and unseen classes. However, as a common practice in GZSL, the pre-trained feature extractor may easily exhibit difficulty in capturing domain-specific traits of the downstream tasks/datasets to provide fine-grained discriminative features, i.e., domain bias, which hinders the overall recognition performance, especially for unseen classes. Recent studies partially address this issue by fine-tuning feature extractors, while may inevitably incur catastrophic forgetting and overfitting issues. In this paper, we propose a simple yet effective Attribute-Aware Representation Rectification framework for GZSL, dubbed $\mathbf{(AR)^{2}}$, to adaptively rectify the feature extractor to learn novel features while keeping original valuable features. Specifically, our method consists of two key components, i.e., Unseen-Aware Distillation (UAD) and Attribute-Guided Learning (AGL). During training, UAD exploits the prior knowledge of attribute texts that are shared by both seen/unseen classes with attention mechanisms to detect and maintain unseen class-sensitive visual features in a targeted manner, and meanwhile, AGL aims to steer the model to focus on valuable features and suppress them to fit noisy elements in the seen classes by attribute-guided representation learning. Extensive experiments on various benchmark datasets demonstrate the effectiveness of our method.Comment: 11 pages, 6 figure

Mendelian randomization supports genetic liability to hospitalization for COVID-19 as a risk factor of pre-eclampsia

BackgroundPre-eclampsia and eclampsia are among the major threats to pregnant women and fetuses, but they can be mitigated by prevention and early screening. Existing observational research presents conflicting evidence regarding the causal effects of coronavirus disease 2019 (COVID-19) on pre-eclampsia risk. Through Mendelian randomization (MR), this study aims to investigate the causal effect of three COVID-19 severity phenotypes on the risk of pre-eclampsia and eclampsia to provide more rigorous evidence.MethodsTwo-sample MR was utilized to examine causal effects. Summary-level data from genome-wide association studies (GWAS) of individuals of European ancestry were acquired from the GWAS catalog and FinnGen databases. Single-nucleotide polymorphisms associated with COVID-19 traits at p < 5 × −8 were obtained and pruned for linkage disequilibrium to generate instrumental variables for COVID-19. Inverse variance weighted estimates were used as the primary MR results, with weighted median and MR-Egger as auxiliary analyses. The robustness of the MR findings was also evaluated through sensitivity analyses. Bonferroni correction was applied to primary results, with a p < 0.0083 considered significant evidence and a p within 0.083–0.05 considered suggestive evidence.ResultsCritical ill COVID-19 [defined as hospitalization for COVID-19 with either a death outcome or respiratory support, OR (95% CI): 1.17 (1.03–1.33), p = 0.020] and hospitalized COVID-19 [defined as hospitalization for COVID-19, OR (95% CI): 1.10 (1.01–1.19), p = 0.026] demonstrated suggestive causal effects on pre-eclampsia, while general severe acute respiratory syndrome coronavirus 2 infection did not exhibit a significant causal effect on pre-eclampsia. None of the three COVID-19 severity phenotypes exhibited a significant causal effect on eclampsia.ConclusionsOur investigation demonstrates a suggestive causal effect of genetic susceptibility to critical ill COVID-19 and hospitalized COVID-19 on pre-eclampsia. The COVID-19 severity exhibited a suggestive positive dose–response relationship with the risk of pre-eclampsia. Augmented attention should be paid to pregnant women hospitalized for COVID-19, especially those needing respiratory support

Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: A comparative risk assessment

Background: High blood pressure, blood glucose, serum cholesterol, and BMI are risk factors for cardiovascular diseases and some of these factors also increase the risk of chronic kidney disease and diabetes. We estimated mortality from cardiovascular diseases, chronic kidney disease, and diabetes that was attributable to these four cardiometabolic risk factors for all countries and regions from 1980 to 2010. Methods: We used data for exposure to risk factors by country, age group, and sex from pooled analyses of population-based health surveys. We obtained relative risks for the effects of risk factors on cause-specific mortality from meta-analyses of large prospective studies. We calculated the population attributable fractions for each risk factor alone, and for the combination of all risk factors, accounting for multicausality and for mediation of the effects of BMI by the other three risks. We calculated attributable deaths by multiplying the cause-specific population attributable fractions by the number of disease-specific deaths. We obtained cause-specific mortality from the Global Burden of Diseases, Injuries, and Risk Factors 2010 Study. We propagated the uncertainties of all the inputs to the final estimates. Findings: In 2010, high blood pressure was the leading risk factor for deaths due to cardiovascular diseases, chronic kidney disease, and diabetes in every region, causing more than 40% of worldwide deaths from these diseases; high BMI and glucose were each responsible for about 15% of deaths, and high cholesterol for more than 10%. After accounting for multicausality, 63% (10·8 million deaths, 95% CI 10·1-11·5) of deaths from these diseases in 2010 were attributable to the combined effect of these four metabolic risk factors, compared with 67% (7·1 million deaths, 6·6-7·6) in 1980. The mortality burden of high BMI and glucose nearly doubled from 1980 to 2010. At the country level, age-standardised death rates from these diseases attributable to the combined effects of these four risk factors surpassed 925 deaths per 100 000 for men in Belarus, Kazakhstan, and Mongolia, but were less than 130 deaths per 100 000 for women and less than 200 for men in some high-income countries including Australia, Canada, France, Japan, the Netherlands, Singapore, South Korea, and Spain. Interpretation: The salient features of the cardiometabolic disease and risk factor epidemic at the beginning of the 21st century are high blood pressure and an increasing effect of obesity and diabetes. The mortality burden of cardiometabolic risk factors has shifted from high-income to low-income and middle-income countries. Lowering cardiometabolic risks through dietary, behavioural, and pharmacological interventions should be a part of the global response to non-communicable diseases. Funding: UK Medical Research Council, US National Institutes of Health. © 2014 Elsevier Ltd

Neural Basis of the Emotional Conflict Processing in Major Depression: ERPs and Source Localization Analysis on the N450 and P300 Components

Objects: Effective psychological function requires that cognition is not affected by task-irrelevant emotional stimuli in emotional conflict. Depression is mainly characterized as an emotional disorder. The object of this study is to reveal the behavioral and electrophysiological signature of emotional conflict processing in major depressive disorder (MDD) using event-related potentials (ERPs) and standardized low-resolution brain electromagnetic tomography (sLORETA) analysis.Method: We used a face–word Stroop task involving emotional faces while recording EEG (electroencephalography) in 20 patients with MDD and 20 healthy controls (HCs). And then ERPs were extracted and the corresponding brain sources were reconstructed using sLORETA.Results: Behaviorally, subjects with MDDs manifested significantly increased Stroop effect when examining the RT difference between happy incongruent trials and happy congruent trials, compared with HC subjects. ERP results exhibited that MDDs were characterized by the attenuated difference between P300 amplitude to sad congruent stimuli and sad incongruent stimuli, as electrophysiological evidence of impaired conflict processing in subjects with MDD. The sLORETA results showed that MDD patients had a higher current density in rostral anterior cingulate cortex (rostral ACC) within N450 time window in response to happy incongruent trials than happy congruent stimuli. Moreover, HC subjects had stronger activity in right inferior frontal gyrus (rIFG) region in response to incongruent stimuli than congruent stimuli, revealing successful inhibition of emotional distraction in HCs, which was absent in MDDs.Conclusion: Our results indicated that rostral ACC was implicated in the processing of negative emotional distraction in MDDs, as well as impaired inhibition of task-irrelevant emotional stimuli, relative to HCs. This work furnishes novel behavioral and neurophysiological evidence that are closely related to emotional conflict among MDD patients

Preparation and Characterization of Nanoparticles Made from Co-Incubation of SOD and Glucose

The attractive potential of natural superoxide dismutase (SOD) in the fields of medicine and functional food is limited by its short half-life in circulation and poor permeability across the cell membrane. The nanoparticle form of SOD might overcome these limitations. However, most preparative methods have disadvantages, such as complicated operation, a variety of reagents—some of them even highly toxic—and low encapsulation efficiency or low release rate. The aim of this study is to present a simple and green approach for the preparation of SOD nanoparticles (NPs) by means of co-incubation of Cu/Zn SOD with glucose. This method was designed to prepare nanoscale aggregates based on the possible inhibitory effect of Maillard reaction on heating-induced aggregation during the co-incubation. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) results indicated that the Maillard reaction occurred during the co-incubation process. It was found that enzymatically active NPs of Cu/Zn SOD were simultaneously generated during the reaction, with an average particle size of 175.86 ± 0.71 nm, and a Zeta potential of −17.27 ± 0.59 mV, as established by the measurement of enzymatic activity, observations using field emission scanning electron microscope, and analysis of dynamic light scattering, respectively. The preparative conditions for the SOD NPs were optimized by response surface design to increase SOD activity 20.43 fold. These SOD NPs showed storage stability for 25 days and better cell uptake efficacy than natural SOD. Therefore, these NPs of SOD are expected to be a potential drug candidate or functional food factor. To our knowledge, this is the first report on the preparation of nanoparticles possessing the bioactivity of the graft component protein, using the simple and green approach of co-incubation with glucose, which occurs frequently in the food industry during thermal processing

Image3_Mendelian randomization supports genetic liability to hospitalization for COVID-19 as a risk factor of pre-eclampsia.pdf

BackgroundPre-eclampsia and eclampsia are among the major threats to pregnant women and fetuses, but they can be mitigated by prevention and early screening. Existing observational research presents conflicting evidence regarding the causal effects of coronavirus disease 2019 (COVID-19) on pre-eclampsia risk. Through Mendelian randomization (MR), this study aims to investigate the causal effect of three COVID-19 severity phenotypes on the risk of pre-eclampsia and eclampsia to provide more rigorous evidence.MethodsTwo-sample MR was utilized to examine causal effects. Summary-level data from genome-wide association studies (GWAS) of individuals of European ancestry were acquired from the GWAS catalog and FinnGen databases. Single-nucleotide polymorphisms associated with COVID-19 traits at p −8 were obtained and pruned for linkage disequilibrium to generate instrumental variables for COVID-19. Inverse variance weighted estimates were used as the primary MR results, with weighted median and MR-Egger as auxiliary analyses. The robustness of the MR findings was also evaluated through sensitivity analyses. Bonferroni correction was applied to primary results, with a p ResultsCritical ill COVID-19 [defined as hospitalization for COVID-19 with either a death outcome or respiratory support, OR (95% CI): 1.17 (1.03–1.33), p = 0.020] and hospitalized COVID-19 [defined as hospitalization for COVID-19, OR (95% CI): 1.10 (1.01–1.19), p = 0.026] demonstrated suggestive causal effects on pre-eclampsia, while general severe acute respiratory syndrome coronavirus 2 infection did not exhibit a significant causal effect on pre-eclampsia. None of the three COVID-19 severity phenotypes exhibited a significant causal effect on eclampsia.ConclusionsOur investigation demonstrates a suggestive causal effect of genetic susceptibility to critical ill COVID-19 and hospitalized COVID-19 on pre-eclampsia. The COVID-19 severity exhibited a suggestive positive dose–response relationship with the risk of pre-eclampsia. Augmented attention should be paid to pregnant women hospitalized for COVID-19, especially those needing respiratory support.</p

Table1_Mendelian randomization supports genetic liability to hospitalization for COVID-19 as a risk factor of pre-eclampsia.xlsx

BackgroundPre-eclampsia and eclampsia are among the major threats to pregnant women and fetuses, but they can be mitigated by prevention and early screening. Existing observational research presents conflicting evidence regarding the causal effects of coronavirus disease 2019 (COVID-19) on pre-eclampsia risk. Through Mendelian randomization (MR), this study aims to investigate the causal effect of three COVID-19 severity phenotypes on the risk of pre-eclampsia and eclampsia to provide more rigorous evidence.MethodsTwo-sample MR was utilized to examine causal effects. Summary-level data from genome-wide association studies (GWAS) of individuals of European ancestry were acquired from the GWAS catalog and FinnGen databases. Single-nucleotide polymorphisms associated with COVID-19 traits at p −8 were obtained and pruned for linkage disequilibrium to generate instrumental variables for COVID-19. Inverse variance weighted estimates were used as the primary MR results, with weighted median and MR-Egger as auxiliary analyses. The robustness of the MR findings was also evaluated through sensitivity analyses. Bonferroni correction was applied to primary results, with a p ResultsCritical ill COVID-19 [defined as hospitalization for COVID-19 with either a death outcome or respiratory support, OR (95% CI): 1.17 (1.03–1.33), p = 0.020] and hospitalized COVID-19 [defined as hospitalization for COVID-19, OR (95% CI): 1.10 (1.01–1.19), p = 0.026] demonstrated suggestive causal effects on pre-eclampsia, while general severe acute respiratory syndrome coronavirus 2 infection did not exhibit a significant causal effect on pre-eclampsia. None of the three COVID-19 severity phenotypes exhibited a significant causal effect on eclampsia.ConclusionsOur investigation demonstrates a suggestive causal effect of genetic susceptibility to critical ill COVID-19 and hospitalized COVID-19 on pre-eclampsia. The COVID-19 severity exhibited a suggestive positive dose–response relationship with the risk of pre-eclampsia. Augmented attention should be paid to pregnant women hospitalized for COVID-19, especially those needing respiratory support.</p