Miracle-dz/UniFAR-FTUs huggingface dataset:
Cytoplasmic redox imbalance in the thioredoxin system activates Hsf1 and results in hyperaccumulation of the sequestrase Hsp42 with misfolded proteins

Cells employ multiple systems to maintain homeostasis when experiencing environmental stress. For example, the folding of nascent polypeptides is exquisitely sensitive to proteotoxic stressors including heat, pH and oxidative stress, and is safeguarded by a network of protein chaperones that concentrate potentially toxic misfolded proteins into transient assemblies to promote folding or degradation. The redox environment itself is buffered by both cytosolic and organellar thioredoxin and glutathione pathways. How these systems are linked is poorly understood. Here, we determine that specific disruption of the cytosolic thioredoxin system resulted in constitutive activation of the heat shock response in Saccharomyces cerevisiae and accumulation of the sequestrase Hsp42 into an exaggerated and persistent juxtanuclear quality control (JUNQ) compartment. Terminally misfolded proteins also accumulated in this compartment in thioredoxin reductase (TRR1)-deficient cells, despite apparently normal formation and dissolution of transient cytoplasmic quality control (CytoQ) bodies during heat shock. Notably, cells lacking TRR1 and HSP42 exhibited severe synthetic slow growth exacerbated by oxidative stress, signifying a critical role for Hsp42 under redox challenged conditions. Finally, we demonstrated that Hsp42 localization patterns in trr1Δ cells mimic those observed in chronically aging and glucose-starved cells, linking nutrient depletion and redox imbalance with management of misfolded proteins via a mechanism of long-term sequestration.
Dysregulated DnaB unwinding induces replisome decoupling and daughter strand gaps that are countered by RecA polymerization

Repair of DNA damage begins with the elicitation of targeted cellular responses to restore the genome. In E. coli, major products of DNA damage result in the buildup of single-stranded DNA (ssDNA) that is rapidly bound by cooperative filamentation of RecA to initiate the SOS response. The replicative helicase, DnaB, is a central component of the replisome, unwinding duplex DNA in concert with Pol III template dependent synthesis. Interestingly, helicase unwinding is heavily regulated, and the unwinding rate can be reduced by over 10-fold if DnaB becomes decoupled from Pol III. However, if DnaB is dysregulated by mutations that enforce a faster more constricted conformation, unwinding can continue independently, generating excess ssDNA resulting in severe cellular stress. This surplus ssDNA can stimulate RecA recruitment for recombinational repair or activation of SOS to increase the available repair protein pool. To better understand the consequences of dysregulated unwinding, we combined targeted dnaB mutations with an inducible plasmid-based RecA filament inhibition strategy to examine the dependencies on RecA in counteracting decoupling. We find that RecA filamentation is instrumental for processing daughter strand gaps left behind from decoupled unwinding and synthesis to prevent DNA breaks. Without functional RecA filaments, dnaB mutant strains had a greater burden from endogenous damage but without a compensatory increase in mutagenesis. Overall, RecA plays a critical role in strain survival by processing DNA gaps and protecting from breaks caused by dysregulated or interrupted helicase activity in vivo. AUTHOR SUMMARY Coupled DNA unwinding and synthesis is a genomic protection strategy used during DNA replication to prevent excessive buildup of labile single-stranded DNA (ssDNA). The helicase and polymerase enzymes have evolved multidimensional regulation tactics to maintain this connection despite different individual kinetic rates and differential responses to genomic obstacles. For one, the DnaB helicase in E. coli can alter its hexameric ring structure by dilating to slow down or constricting to speed up DNA unwinding. Here, we have utilized persistently constricted mutants of DnaB in vitro or genomically edited dnaB in vivo to induce decoupling in the replisome. Constricted DnaB mutants limit total leading strand synthesis by Pol III, indicating that lost kinetic regulation between these enzymes results in inefficient replication. Using an inducible plasmid-based system to disrupt Rad51 filamentation in vivo, we show that RecA is responsible for the increased mutagenesis, a filamented cellular phenotype, and mitigating DNA breaks from excess ssDNA caused by decoupling. These results reveal a role for RecA filamentation in mediating excess ssDNA resulting from decoupling to maintain survival and adaptation.
Cycle-consistent Masked AutoEncoder for Unsupervised Domain Generalization

Self-supervised learning methods undergo undesirable performance drops when there exists a significant domain gap between training and testing scenarios. Therefore, unsupervised domain generalization (UDG) is proposed to tackle the problem, which requires the model to be trained on several different domains without supervision and generalize well on unseen test domains. Existing methods either rely on a cross-domain and semantically consistent image pair in contrastive methods or the reconstruction pair in generative methods, while the precious image pairs are not available without semantic labels. In this paper, we propose a cycle cross-domain reconstruction task for unsupervised domain generalization in the absence of paired images. The cycle cross-domain reconstruction task converts a masked image from one domain to another domain and then reconstructs the original image from the converted images. To preserve the divergent domain knowledge of decoders in the cycle reconstruction task, we propose a novel domain-contrastive loss to regularize the domain information in reconstructed images encoded with the desirable domain style. Qualitative results on extensive datasets illustrate our method improves the state-of-the-art unsupervised domain generalization methods by average +5.59%, +4.52%, +4.22%, +7.02% on 1%, 5%, 10%, 100% PACS, and +5.08%, +6.49%, +1.79%, +0.53% on 1%, 5%, 10%, 100% DomainNet, respectively.
Provable Robustness against Wasserstein Distribution Shifts via Input Randomization

Certified robustness in machine learning has primarily focused on adversarial perturbations with a fixed attack budget for each sample in the input distribution. In this work, we present provable robustness guarantees on the accuracy of a model under bounded Wasserstein shifts of the data distribution. We show that a simple procedure that randomizes the input of the model within a transformation space is provably robust to distributional shifts under that transformation. Our framework allows the datum-specific perturbation size to vary across different points in the input distribution and is general enough to include fixed-sized perturbations as well. Our certificates produce guaranteed lower bounds on the performance of the model for any shift (natural or adversarial) of the input distribution within a Wasserstein ball around the original distribution. We apply our technique to certify robustness against natural (non-adversarial) transformations of images such as color shifts, hue shifts, and changes in brightness and saturation. We obtain strong performance guarantees for the robust model under clearly visible shifts in the input images. Our experiments establish the non-vacuousness of our certificates by showing that the certified lower bound on a robust model’s accuracy is higher than the empirical accuracy of an undefended model under a distribution shift. We also show provable distributional robustness against adversarial attacks. Moreover, our results also imply guaranteed lower bounds (hardness result) on the performance of models trained on so-called “unlearnable” datasets that have been poisoned to interfere with model training. We show that the performance of a robust model is guaranteed to remain
Explaining Temporal Graph Models through an Explorer-Navigator Framework

While Graph Neural Network (GNN) explanation has recently received significant attention, existing works are generally designed for static graphs. Due to the prevalence of temporal graphs, many temporal graph models have been proposed, but explaining their predictions still remains to be explored. To bridge the gap, in this paper, we propose a Temporal GNN Explainer (T-GNNExplainer) method. Specifically, we regard a temporal graph as a sequence of temporal events between nodes. Given a temporal prediction of a model, our task is to find a subset of historical events that lead to the prediction. To handle this combinatorial optimization problem, T-GNNExplainer includes an explorer to find the event subsets with Monte Carlo Tree Search (MCTS), and a navigator that learns the correlations between events and helps reduce the search space. In particular, the navigator is trained in advance and then integrated with the explorer to speed up searching and achieve better results. To the best of our knowledge, T-GNNExplainer is the first explainer tailored for temporal graph models. We conduct extensive experiments to evaluate the performance of T-GNNExplainer. Experimental results demonstrate that T-GNNExplainer can achieve superior performance with up to ⇠50% improvement in Area under Fidelity-Sparsity Curve.
On The Specialization of Neural Modules

A number of machine learning models have been proposed with the goal of achieving systematic generalization: the ability to reason about new situations by combining aspects of previous experiences. These models leverage compositional architectures which aim to learn specialized modules dedicated to structures in a task that can be composed to solve novel problems with similar structures. While the compositionality of these architectures is guaranteed by design, the modules specializing is not. Here we theoretically study the ability of network modules to specialize to useful structures in a dataset and achieve systematic generalization. To this end we introduce a minimal space of datasets motivated by practical systematic generalization benchmarks. From this space of datasets we present a mathematical definition of systematicity and study the learning dynamics of linear neural modules when solving components of the task. Our results shed light on the difficulty of module specialization, what is required for modules to successfully specialize, and the necessity of modular architectures to achieve systematicity. Finally, we confirm that the theoretical results in our tractable setting generalize to more complex datasets and non-linear architectures.
Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting

Recently many deep models have been proposed for multivariate time series (MTS) forecasting. In particular, Transformer-based models have shown great potential because they can capture long-term dependency. However, existing Transformer-based models mainly focus on modeling the temporal dependency (cross-time dependency) yet often omit the dependency among different variables (cross-dimension dependency), which is critical for MTS forecasting. To ﬁll the gap, we propose Crossformer, a Transformer-based model utilizing cross-dimension dependency for MTS forecasting. In Crossformer, the input MTS is embedded into a 2D vector array through the Dimension-Segment-Wise (DSW) embedding to preserve time and dimension information. Then the Two-Stage Attention (TSA) layer is proposed to efﬁciently capture the cross-time and cross-dimension dependency. Utilizing DSW embedding and TSA layer, Crossformer establishes a Hierarchical Encoder-Decoder (HED) to use the information at different scales for the ﬁnal forecasting. Extensive experimental results on six real-world datasets show the effectiveness of Crossformer against previous state-of-the-arts.
Warping the Space: Weight Space Rotation for Class-Incremental Few-Shot Learning

Class-incremental few-shot learning, where new sets of classes are provided sequentially with only a few training samples, presents a great challenge due to catastrophic forgetting of old knowledge and overfitting caused by lack of data. During finetuning on new classes, the performance on previous classes deteriorates quickly even when only a small fraction of parameters are updated, since the previous knowledge is broadly associated with most of the model parameters in the original parameter space. In this paper, we introduce WaRP, the weight space rotation process, which transforms the original parameter space into a new space so that we can push most of the previous knowledge compactly into only a few important parameters. By properly identifying and freezing these key parameters in the new weight space, we can finetune the remaining parameters without affecting the knowledge of previous classes. As a result, WaRP provides an additional room for the model to effectively learn new classes in future incremental sessions. Experimental results confirm the effectiveness of our solution and show the improved performance over the state-of-the-art methods.
Nonlinear response topology optimization using equivalent static loads—case studies

ABSTRACT Nonlinear structural optimization is fairly expensive and difficult, because a large number of nonlinear analyses is required due to the large number of design variables involved in topology optimization. In element density based topology optimization, the low density elements create mesh distortion and the updating of finite element material with low density elements has a severe effect on the optimization results in the next cycles. In order to overcome these difficulties, the equivalent static loads method for nonlinear response structural optimization (ESLSO) primarily used for size and shape optimization has been applied to topology optimization. The nonlinear analysis is performed with the given loading conditions to calculate equivalent static loads (ESLs) and these ESLs are used to perform linear response optimization. In this paper, the authors have presented the results of five case studies with material, geometric and contact nonlinearities showing good agreement and providing justification of the proposed method.
Assessing the adoption of regenerative agricultural practices in Eastern Africa

Food security remains a challenge for Africa and regenerative agricultural practices can play an important role in building resilience while sustaining agricultural production and preserving diversity of ingredients and rich biodiversity. This study conducted surveys in eight regions in East Africa, four in Rwanda and four in Kenya, to understand how circular principles are applied in agriculture, potential drivers of the adoption of regenerative agriculture and its contribution to household food security. A binary logistic regression model was used to capture the influence of independent variables on the adoption of regenerative agricultural practices and determining factors of household food security. The findings suggest that farming households engage in less than four different practices, mainly in less labor-intensive practices such as crop rotation and intercropping. Household food security increased with an increased number of regenerative agricultural practices applied by the household and increased frequency of farm visits by extension agents. Regenerative agriculture practices, when applied in combinations, have the potential to increase household food security. This study recommends designing scaling-up pathways to accelerate the transition to regenerative agriculture and build a more resilient food system in East Africa.
Dietary diversity and possible sarcopenia among older people in China: a nationwide population-based study

Background Sarcopenia is a common geriatric disease. Many dietary factors may contribute to the development of sarcopenia. Few studies have been conducted on dietary diversity and sarcopenia in Chinese older adults. Among a nationwide sample, the objective of this study is to assess the association between the dietary diversity score (DDS) and the prevalence of possible sarcopenia. We considered the different patterns of dietary diversity in relation to possible sarcopenia. Methods We conducted this analysis utilizing the cross-sectional data from the 2012, 2014, and 2018 waves of the Chinese longitudinal healthy longevity survey (CLHLS). A standard developed by the Asian Working Group for Sarcopenia 2019 (AWGS2019) was used to assess the possibility of sarcopenia. On the basis of the DDS generated by previous studies, we have constructed four new indicators as follows: total diet, animal-based diet, plant-based diet, and plant-based diet without the consumption of legume products and nuts. We used the generalized estimation equation (GEE) model to evaluate the associations between the DDS of the total diet, animal-based diet, plant-based diet, and plant-based diet without the intake of legume products and nuts and possible sarcopenia. These associations were statistically adjusted for a variety of potential confounders. Sensitivity analysis was performed by excluding some participants who were long-term bedridden, had Alzheimer's disease, or were terminally ill. Results The analysis included 6,624 participants (mean age 83.4 years at baseline). In our study, we found that participants with a higher DDS of the total diet (OR = 0.62; 95% CI: 0.51–0.77), animal-based diet (OR = 0.62; 95% CI: 0.49–0.79), and plant-based diet (OR = 0.64;95% CI: 0.51–0.80) were at a lower risk of developing sarcopenia. In sensitivity analyses, the associations remained unchanged. Conclusion Taking a diversified diet, including animal foods, may reduce the risk of developing sarcopenia. According to the findings of this study, adopting a diversified diet might reduce the risk of sarcopenia for older adults.
COVID-19 mRNA vaccination is associated with IgA nephropathy: an analysis of the Japanese adverse drug event report database

Purpose: Coronavirus disease 2019 (COVID-19) mRNA vaccines are used worldwide to prevent severe symptoms of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. IgA nephropathy (IgAN) is the most common form of glomerular injury after COVID-19 vaccination; however, because of the low frequency of such events, only a few reports have been published. A large pharmacovigilance database of real-world spontaneous adverse event (AE) reports is essential for evaluating the drug-associated safety signals regarding rare AEs. Herein, we aimed to investigate the frequency of IgAN after the COVID-19 vaccination, using the Japanese Adverse Drug Event Report (JADER) database. Methods: Data on drug-associated AEs reported between April 2004 and May 2022 were obtained from the JADER database on the Pharmaceuticals and Medical Devices Agency website. To evaluate the safety signals for the targeted AEs, reporting odds ratios (RORs), information components (ICs), and their 95% confidence intervals (CIs) were calculated using two-by-two contingency tables. Results: A total of 697,885 cases were included in the analysis. Safety signals were detected for IgAN (ROR: 6.49, 95% CI: 4.38–9.61; IC: 2.27, 95% CI: 1.70–2.83). Of 30 cases for IgAN associated with COVID-19 mRNA vaccines, 16 had information available on time to onset. Of the 16 cases, 11 occurred ≤2 days after vaccination, and two occurred >28 days after vaccination. Conclusion: These results suggest that, compared with other drugs, COVID-19 vaccination is associated with a higher frequency of IgAN. Monitoring of gross hematuria following COVID-19 vaccination should be needed.
Capturing the impact employees have on their coworkers and leaders: a holistic approach on health-specific support behavior from employees

Support is a valuable resource for ensuring employee health in the workplace. However, research on health-specific support behavior (i.e., support specifically targeting concrete health aspects) has only concentrated on either leader behavior (e.g., healthy leadership styles) or support provided by employees for specific health issues (e.g., healthy eating or smoking cessation). Although the importance of employee health has been well established, the examination of a wider range of potential health-specific support behaviors from employees provided for their colleagues and leaders has been neglected. To understand employee health-specific support behavior, we adapted an existing health-oriented leadership questionnaire to cover support for colleagues (PeerCare) and their leaders (LeaderCare). Capturing the employee perspective with a sample of 347 employees, the results confirmed a delineation of health-oriented scales (factor, convergent, and discriminant validity). By testing health-specific support behavior processes at work, the positive effects of PeerCare on general health were demonstrated. Contrary to expectations, existing health effects are outweighed when leaders provide health-specific support behavior to their employees (StaffCare). However, the results imply that the health-specific support behavior practices of different actors reinforce each other: the effects of StaffCare and PeerCare enhance each other, and StaffCare has a strong influence on LeaderCare. Remarkably, SelfCare has a key role in this process. The open questions and implications regarding the effects of the different health-specific support behavior measurements are discussed.
The role of the gut microbiota in gastric cancer: the immunoregulation and immunotherapy

Gastric cancer (GC) is one of the most common cancers, leading to the deaths of millions of people worldwide. Therefore, early detection and effective therapeutic strategies are of great value for decreasing the occurrence of advanced GC. The human microbiota is involved not only in the maintenance of physiological conditions, but also in human diseases such as obesity, diabetes, allergic and atopic diseases, and cancer. Currently, the composition of the bacteria in the host, their functions, and their influence on disease progression and treatment are being discussed. Previous studies on the gut microbiome have mostly focused on Helicobacter pylori (Hp) owing to its significant role in the development of GC. Nevertheless, the enrichment and diversity of other bacteria that can modulate the tumor microenvironment are involved in the progression of GC and the efficacy of immunotherapy. This review provides systematic insight into the components of the gut microbiota and their application in GC, including the specific bacteria of GC, their immunoregulatory effect, and their diagnostic value. Furthermore, we discuss the relationship between the metabolism of microbes and their potential applications, which may serve as a new approach for the diagnosis and treatment of GC.
Memory Gym: Partially Observable Challenges to Memory-Based Agents in Endless Episodes

Memory Gym introduces a unique benchmark designed to test Deep Reinforcement Learning agents, specifically comparing Gated Recurrent Unit (GRU) against Transformer-XL (TrXL), on their ability to memorize long sequences, withstand noise, and generalize. It features partially observable 2D environments with discrete controls, namely Mortar Mayhem, Mystery Path, and Searing Spotlights. These originally finite environments are extrapolated to novel endless tasks that act as an automatic curriculum, drawing inspiration from the car game “I packed my bag”. These endless tasks are not only beneficial for evaluating efficiency but also intriguingly valuable for assessing the effectiveness of approaches in memory-based agents. Given the scarcity of publicly available memory baselines, we contribute an implementation driven by TrXL and Proximal Policy Optimization. This implementation leverages TrXL as episodic memory using a sliding window approach. In our experiments on the finite environments, TrXL demonstrates superior sample efficiency in Mystery Path and outperforms in Mortar Mayhem. However, GRU is more efficient on Searing Spotlights. Most notably, in all endless tasks, GRU makes a remarkable resurgence, consistently outperforming TrXL by significant margins.