Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data

Aggregating multi-subject functional magnetic resonance imaging (fMRI) data is indispensable for generating valid and general inferences from patterns distributed across human brains. The disparities in anatomical structures and functional topographies of human brains warrant aligning fMRI data across subjects. However, the existing functional alignment methods cannot handle well various kinds of fMRI datasets today, especially when they are not temporally-aligned, i.e., some of the subjects probably lack the responses to some stimuli, or different subjects might follow different sequences of stimuli. In this paper, a cross-subject graph that depicts the (dis)similarities between samples across subjects is used as a priori for developing a more flexible framework that suits an assortment of fMRI datasets. However, the high dimension of fMRI data and the use of multiple subjects makes the crude framework time-consuming or unpractical. To address this issue, we further regularize the framework, so that a novel feasible kernel-based optimization, which permits nonlinear feature extraction, could be theoretically developed. Specifically, a low-dimension assumption is imposed on each new feature space to avoid overfitting caused by the highspatial-low-temporal resolution of fMRI data. Experimental results on five datasets suggest that the proposed method is not only superior to several state-of-the-art methods on temporally-aligned fMRI data, but also suitable for dealing `with temporally-unaligned fMRI data.Comment: 17 pages, 10 figures, Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI-20

In vivo reprogramming of pancreatic acinar cells to three islet endocrine subtypes

Direct lineage conversion of adult cells is a promising approach for regenerative medicine. A major challenge of lineage conversion is to generate specific cell subtypes. The pancreatic islets contain three major hormone-secreting endocrine subtypes: insulin+ β-cells, glucagon+ α-cells, and somatostatin+ δ-cells. We previously reported that a combination of three transcription factors, Ngn3, Mafa, and Pdx1, directly reprograms pancreatic acinar cells to β-cells. We now show that acinar cells can be converted to δ-like and α-like cells by Ngn3 and Ngn3+Mafa respectively. Thus, three major islet endocrine subtypes can be derived by acinar reprogramming. Ngn3 promotes establishment of a generic endocrine state in acinar cells, and also promotes δ-specification in the absence of other factors. δ-specification is in turn suppressed by Mafa and Pdx1 during α- and β-cell induction. These studies identify a set of defined factors whose combinatorial actions reprogram acinar cells to distinct islet endocrine subtypes in vivo. DOI: http://dx.doi.org/10.7554/eLife.01846.00

Housing prices and household savings: evidence from urban China

Based on precautionary saving motives, this research develops a three-period life-cycle model to manifest the impact of housing prices on household savings in urban China. The theoretical model illustrates that the expected appreciation of housing prices at a household’s middle age leads to the increase in household savings at a household’s young age. Second, household savings at a household’s young age are positively associated with both expected educational and medical expenditures in a household’s middle age and pension expenditures at a household’s old age. Third, the expected housing prices crowd out educational and medical expenditures at a household’s middle age. With the panel data sets of China’s 31 provinces during 1996–2016, results suggest that the expected housing prices significantly interact with the current household savings. However, the influence of the expected housing prices on the current household savings is greater than that of the current household savings on the expected housing prices. Third, the expected expenditures of education, medical care and pension fuel up the current household savings. Meanwhile, the housing prices crowd out the expenditures of education, medical care and pension. Finally, data of the Urban Household Survey (UHS) over the period 2002–2007 show that the household head age has an effect of reverse U-shape on household savings. Accordingly, to prevent a housing bubble and promote household consumption, policy makers should curb housing price inflation by enacting appropriate countercyclical housing policies

The Reproducibility of Lists of Differentially Expressed Genes in Microarray Studies

Reproducibility is a fundamental requirement in scientific experiments and clinical contexts. Recent publications raise concerns about the reliability of microarray technology because of the apparent lack of agreement between lists of differentially expressed genes (DEGs). In this study we demonstrate that (1) such discordance may stem from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion, the lists become much more reproducible, especially when fewer genes are selected; and (3) the instability of short DEG lists based on P cutoffs is an expected mathematical consequence of the high variability of the t-values. We recommend the use of FC ranking plus a non-stringent P cutoff as a baseline practice in order to generate more reproducible DEG lists. The FC criterion enhances reproducibility while the P criterion balances sensitivity and specificity

Progress in molecular diagnosis and treatment of chronic mucocutaneous candidiasis

Chronic mucocutaneous candidiasis (CMC) is characterized by recurrent or persistent infections with Candida of the skin, nails, and mucous membrane. It is a rare and severe disease resulting from autoimmune defects or immune dysregulations. Nonetheless, the diagnosis and treatment of CMC still pose significant challenges. Erroneous or delayed diagnoses remain prevalent, while the long-term utility of traditional antifungals often elicits adverse reactions and promotes the development of acquired resistance. Furthermore, disease relapse can occur during treatment with traditional antifungals. In this review, we delineate the advancements in molecular diagnostic and therapeutic approaches to CMC. Genetic and biomolecular analyses are increasingly employed as adjuncts to clinical manifestations and fungal examinations for accurate diagnosis. Simultaneously, a range of therapeutic interventions, including Janus kinase (JAK) inhibitors, hematopoietic stem cell transplantation (HSCT), cytokines therapy, novel antifungal agents, and histone deacetylase (HDAC) inhibitors, have been integrated into clinical practice. We aim to explore insights into early confirmation of CMC as well as novel therapeutic options for these patients

H-ensemble: An Information Theoretic Approach to Reliable Few-Shot Multi-Source-Free Transfer

Multi-source transfer learning is an effective solution to data scarcity by utilizing multiple source tasks for the learning of the target task. However, access to source data and model details is limited in the era of commercial models, giving rise to the setting of multi-source-free (MSF) transfer learning that aims to leverage source domain knowledge without such access. As a newly defined problem paradigm, MSF transfer learning remains largely underexplored and not clearly formulated. In this work, we adopt an information theoretic perspective on it and propose a framework named H-ensemble, which dynamically learns the optimal linear combination, or ensemble, of source models for the target task, using a generalization of maximal correlation regression. The ensemble weights are optimized by maximizing an information theoretic metric for transferability. Compared to previous works, H-ensemble is characterized by: 1) its adaptability to a novel and realistic MSF setting for few-shot target tasks, 2) theoretical reliability, 3) a lightweight structure easy to interpret and adapt. Our method is empirically validated by ablation studies, along with extensive comparative analysis with other task ensemble and transfer learning methods. We show that the H-ensemble can successfully learn the optimal task ensemble, as well as outperform prior arts.Comment: AAAI 202

Ferritin light chain and squamous cell carcinoma antigen 1 are coreceptors for cellular attachment and entry of hepatitis B virus

Overexpression of squamous cell carcinoma antigen 1 (SCCA1) in hepatitis G2 (HepG2) and Chinese hamster ovary cells can increase hepatitis B virus (HBV) binding capacity by interacting with the preS1 domain of the HBV surface antigen. However, the magnitude of increase in binding capacity was higher by several orders in the former, indicating the existence of additional factor(s) produced by HepG2 cells, which facilitates HBV attachment. Ferritin light chain (FTL) was identified as the sole high hit candidate by screening human liver cDNA library using a bacterial two-hybrid system with either preS or SCCA1 as the bait. Subsequent in vitro protein–protein interaction assays confirmed the binding activity of FTL to both preS and SCCA1, as well as the formation of triple complex preS-FTL-SCCA1, and narrowed down the binding sites on FTL. In vitro overexpression of FTL could further enhance HBV attachment in both HepG2 and Chinese hamster ovary cells, which were already overexpressing SCCA1. Importantly, in vivo co-expression of human FTL and SCCA1 in mouse liver by means of tailvein hydrodynamic injection increased serum levels of HBV surface antigen transiently 24 hours post challenge with HBV-positive human sera, and a large amount of HBV core antigen-positive hepatocytes around blood vessels could be identified by immunohistochemical staining 48 hours post challenge. The data strongly suggest that FTL and SCCA1 may serve as coreceptors in HBV cellular attachment and virus entry into hepatocytes

Does graduate students' satisfaction with research laboratory affect their anxiety? Findings from a cross-sectional study at a Japanese university

This study investigates the relationship between graduate students' satisfaction with their research laboratories and their anxiety levels, using 2017 survey data from a Japanese university. Through correlation analysis and Structural Equation Modeling (SEM), this study examined how factors such as laboratory satisfaction, research outcome satisfaction, financial burden, and anxiety are interconnected. The findings reveal three key insights. First, graduate students report the highest levels of anxiety related to future prospects, employment, and economic conditions, and they are most likely to seek advice from parents or partners when experiencing anxiety. Second, satisfaction with the research laboratory significantly reduces anxiety, with the guidance methods of supervisors, interpersonal relationships, and research funding being the most influential factors. Satisfaction with research outcomes also plays a notable mediating role in this relationship. Third, seeking anxiety counseling is associated with increased anxiety levels, particularly when advice is sought from peers. These findings underscore the importance of the research laboratory environment in shaping graduate students' psychological wellbeing and provide a framework for understanding the mechanisms underlying anxiety development. This study highlights the need for universities to address laboratory dynamics and support systems to mitigate graduate student anxiety

Highway Value Iteration Networks

Value iteration networks (VINs) enable end-to-end learning for planning tasks by employing a differentiable "planning module" that approximates the value iteration algorithm. However, long-term planning remains a challenge because training very deep VINs is difficult. To address this problem, we embed highway value iteration -- a recent algorithm designed to facilitate long-term credit assignment -- into the structure of VINs. This improvement augments the "planning module" of the VIN with three additional components: 1) an "aggregate gate," which constructs skip connections to improve information flow across many layers; 2) an "exploration module," crafted to increase the diversity of information and gradient flow in spatial dimensions; 3) a "filter gate" designed to ensure safe exploration. The resulting novel highway VIN can be trained effectively with hundreds of layers using standard backpropagation. In long-term planning tasks requiring hundreds of planning steps, deep highway VINs outperform both traditional VINs and several advanced, very deep NNs.Comment: ICML 202