Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification

Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive approach to examining abnormal brain connectivity associated with brain disorders. Graph neural network (GNN) gains popularity in fMRI representation learning and brain disorder analysis with powerful graph representation capabilities. Training a general GNN often necessitates a large-scale dataset from multiple imaging centers/sites, but centralizing multi-site data generally faces inherent challenges related to data privacy, security, and storage burden. Federated Learning (FL) enables collaborative model training without centralized multi-site fMRI data. Unfortunately, previous FL approaches for fMRI analysis often ignore site-specificity, including demographic factors such as age, gender, and education level. To this end, we propose a specificity-aware federated graph learning (SFGL) framework for rs-fMRI analysis and automated brain disorder identification, with a server and multiple clients/sites for federated model aggregation and prediction. At each client, our model consists of a shared and a personalized branch, where parameters of the shared branch are sent to the server while those of the personalized branch remain local. This can facilitate knowledge sharing among sites and also helps preserve site specificity. In the shared branch, we employ a spatio-temporal attention graph isomorphism network to learn dynamic fMRI representations. In the personalized branch, we integrate vectorized demographic information (i.e., age, gender, and education years) and functional connectivity networks to preserve site-specific characteristics. Representations generated by the two branches are then fused for classification. Experimental results on two fMRI datasets with a total of 1,218 subjects suggest that SFGL outperforms several state-of-the-art approaches

Adaptive Multimodal Neuroimage Integration for Major Depression Disorder Detection

Major depressive disorder (MDD) is one of the most common mental health disorders that can affect sleep, mood, appetite, and behavior of people. Multimodal neuroimaging data, such as functional and structural magnetic resonance imaging (MRI) scans, have been widely used in computer-aided detection of MDD. However, previous studies usually treat these two modalities separately, without considering their potentially complementary information. Even though a few studies propose integrating these two modalities, they usually suffer from significant inter-modality data heterogeneity. In this paper, we propose an adaptive multimodal neuroimage integration (AMNI) framework for automated MDD detection based on functional and structural MRIs. The AMNI framework consists of four major components: (1) a graph convolutional network to learn feature representations of functional connectivity networks derived from functional MRIs, (2) a convolutional neural network to learn features of T1-weighted structural MRIs, (3) a feature adaptation module to alleviate inter-modality difference, and (4) a feature fusion module to integrate feature representations extracted from two modalities for classification. To the best of our knowledge, this is among the first attempts to adaptively integrate functional and structural MRIs for neuroimaging-based MDD analysis by explicitly alleviating inter-modality heterogeneity. Extensive evaluations are performed on 533 subjects with resting-state functional MRI and T1-weighted MRI, with results suggesting the efficacy of the proposed method

Engineering allosteric inhibition of homoserine dehydrogenase by semi-rational saturation mutagenesis screening

Allosteric regulation by pathway products plays a vital role in amino acid metabolism. Homoserine dehydrogenase (HSD), the key enzyme for the biosynthesis of various aspartate family amino acids, is subject to feedback inhibition by l-threonine and l-isoleucine. The desensitized mutants with the potential for amino acid production remain limited. Herein, a semi-rational approach was proposed to relieve the feedback inhibition. HSD from Corynebacterium glutamicum (CgHSD) was first characterized as a homotetramer, and nine conservative sites at the tetramer interface were selected for saturation mutagenesis by structural simulations and sequence analysis. Then, we established a high-throughput screening (HTS) method based on resistance to l-threonine analog and successfully acquired two dominant mutants (I397V and A384D). Compared with the best-ever reported desensitized mutant G378E, both new mutants qualified the engineered strains with higher production of CgHSD-dependent amino acids. The mutant and wild-type enzymes were purified and assessed in the presence or absence of inhibitors. Both purified mutants maintained >90% activity with 10 mM l-threonine or 25 mM l-isoleucine. Moreover, they showed >50% higher specific activities than G378E without inhibitors. This work provides two competitive alternatives for constructing cell factories of CgHSD-related amino acids and derivatives. Moreover, the proposed approach can be applied to engineering other allosteric enzymes in the amino acid synthesis pathway

Improved Differential Analysis of Block Cipher PRIDE

In CRYPTO 2014 Albrecht \emph{et al.} brought in a 20-round iterative lightweight block cipher PRIDE which is based on a good linear layer for achieving a tradeoff between security and efficiency. A recent analysis is presented by Zhao \emph{et al.}. Inspired by their work, we use an automatic search method to find out 56 iterative differential characteristics of PRIDE, containing 24 1-round iterative characteristics, based on three of them we construct a 15-round differential and perform a differential attack on the 19-round PRIDE, with data, time and memory complexity of $2^{62}$, $2^{63}$ and $2^{71}$ respectively

New Polyketides With Anti-Inflammatory Activity From the Fungus Aspergillus rugulosa

Two new polyketide compounds, asperulosins A and B (1–2), and one new prenylated small molecule, asperulosin C (3), along with nine known compounds (4–12), were isolated and identified from a fungus Aspergillus rugulosa. Their structures were extensively elucidated via HRESIMS, 1D, and 2D NMR analysis. The absolute configurations of the new compounds were determined by the comparison of their electronic circular dichroism (ECD), calculated ECD spectra, and the detailed discussion with those in previous reports. Structurally, compounds 1 and 2 belonged to the polyketide family and were from different origins. Compound 2 was constructed by five continuous quaternary carbon atoms, which occur rarely in natural products. All of the isolates were evaluated for anti-inflammatory activity against the production of nitric oxide (NO) in lipopolysaccharide (LPS)-induced RAW264.7 cells. Among those, compounds 1 and 5 showed a significant inhibitory effect on NO production with IC50 values of 1.49 ± 0.31 and 3.41 ± 0.85 μM, respectively. Additionally, compounds 1 and 5 markedly increased the secretion of anti-inflammatory cytokine IL10 while suppressing the secretion of pro-inflammatory cytokines IL6, TNF-α, IFN-γ, MCP-1, and IL12. Besides, 1 and 5 inhibited the transcription level of pro-inflammatory macrophage markers IL6, IL1β, and TNF-α while remarkably elevating the anti-inflammatory factor IL10 and M2 macrophage markers ARG1 and CD206. Moreover, 1 and 5 restrained the expression and nuclear translocation of NF-κB, as well as its downstream signaling proteins COX-2 and iNOS. All these results suggest that 1 and 5 have potential as anti-inflammatory agents, with better or comparable activities than those of the positive control, dexamethasone

Prognostic value of N-terminal Pro–B-Type natriuretic peptide in patients with intermediate coronary lesions

BackgroundThe optimal treatment strategy for patients with coronary intermediate lesions, defined as diameter stenosis of 50–70%, remains a great challenge for cardiologists. Identification of potential biomarkers predictive of major adverse cardiovascular events (MACEs) risk may assist in risk stratification and clinical decision.MethodsA total of 1,187 patients with intermediate coronary lesions and available N-terminal pro-brain natriuretic peptide (NT-proBNP) levels were enrolled in the current study. A baseline NT-proBNP level was obtained. The primary endpoint was defined as MACEs, the composite endpoint of all-cause death and non-fatal myocardial infarction. A multivariate Cox regression model was used to explore the association between NT-proBNP level and MACE risk.ResultsThe mean age of the study cohort was 59.2 years. A total of 68 patients experienced MACE during a median follow-up of 6.1 years. Restricted cubic spline analysis delineated a linear relationship between the baseline NT-proBNP level and MACE risk. Both univariate and multivariate analyses demonstrated that an increased NT-proBNP level was associated with an increased risk of MACE [adjusted hazard ratio (HR) per doubling: 1.412, 95% confidence interval (CI): 1.022–1.952, p = 0.0365]. This association remains consistent in clinical meaningful subgroups according to age, sex, body mass index (BMI), and diabetes.ConclusionAn increased NT-proBNP level is associated with an increased risk of MACE in patients with intermediate coronary lesions and may serve as the potential biomarker for risk stratification and treatment decision guidance

Partial asynchrony of coniferous forest carbon sources and sinks at the intra-annual time scale.

As major terrestrial carbon sinks, forests play an important role in mitigating climate change. The relationship between the seasonal uptake of carbon and its allocation to woody biomass remains poorly understood, leaving a significant gap in our capacity to predict carbon sequestration by forests. Here, we compare the intra-annual dynamics of carbon fluxes and wood formation across the Northern hemisphere, from carbon assimilation and the formation of non-structural carbon compounds to their incorporation in woody tissues. We show temporally coupled seasonal peaks of carbon assimilation (GPP) and wood cell differentiation, while the two processes are substantially decoupled during off-peak periods. Peaks of cambial activity occur substantially earlier compared to GPP, suggesting the buffer role of non-structural carbohydrates between the processes of carbon assimilation and allocation to wood. Our findings suggest that high-resolution seasonal data of ecosystem carbon fluxes, wood formation and the associated physiological processes may reduce uncertainties in carbon source-sink relationships at different spatial scales, from stand to ecosystem levels

Transfer Learning from Pre-trained BERT for Pronoun Resolution

The paper describes the submission of the team "We used bert!" to the shared task Gendered Pronoun Resolution (Pair pronouns to their correct entities). Our final submission model based on the fine-tuned BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2018) ranks 14th among 838 teams with a multi-class logarithmic loss of 0.208. In this work, contribution of transfer learning technique to pronoun resolution systems is investigated and the gender bias contained in classification models is evaluated

An Integrated Analysis of GWR Models and Spatial Econometric Global Models to Decompose the Driving Forces of the Township Consumption Development in Gansu, China

The investigation of township consumption patterns has become highly significant in order to emphasize the importance of township consumption patterns in economic development and policy formulation. To attain township consumption development in underdeveloped areas is a significant way to meet the general criterion of “rich life” under China’s Rural Revitalization strategy. The primary objective of this study is to evaluate the driving forces that contribute to the development of township consumption in underdeveloped areas such as Gansu Province, China, and then scientifically design and implement a strategy for township consumption development in Gansu, all of which are related to the broader interests of rural revitalization. The study used 1233 township data of Gansu Province, China. The study integrated geographically weighted regression (GWR) and a spatial econometric global (SEG) model for data analysis and interpretation. The integration of these two models can comprehensively capture both spatial heterogeneity and spatial independence concurrently. First, we conducted integrated analyses of GWR and SEG models using consistent settings of spatial weight matrix elements, with GWR focusing on spatial heterogeneity and SEG models on spatial spillover. Second, the permanent resident population, the number of financial institution outlets, the types of townships, and the characteristics of townships had a substantial significant effect on the development of township consumption in Gansu, China. In addition, the ratio of residents with access to basic medical insurance was found to be negatively significant. The revitalization strategy for township consumption in Gansu Province, China should prioritize increasing the permanent resident population of townships, accelerating the development of township urbanization, accelerating the construction of township consumption infrastructures, and strengthening financial support from township financial institutions