FedGST:Federated Graph Spatio-Temporal Framework for Brain Functional Disease Prediction

Currently, most medical institutions face the challenge of training a unified model using fragmented and isolated data to address disease prediction problems. Although federated learning has become the recognized paradigm for privacy-preserving model training, how to integrate federated learning with fMRI temporal characteristics to enhance predictive performance remains an open question for functional disease prediction. To address this challenging task, we propose a novel Federated Graph Spatio-Temporal (FedGST) framework for brain functional disease prediction. Specifically, anchor sampling is used to process variable-length time series data on local clients. Then dynamic functional connectivity graphs are generated via sliding windows and Pearson correlation coefficients. Next, we propose an InceptionTime model to extract temporal information from the dynamic functional connectivity graphs on the local clients. Finally, the hidden activation variables are sent to a global server. We propose a UniteGCN model on the global server to receive and process the hidden activation variables from clients. Then, the global server returns gradient information to clients for backpropagation and model parameter updating. Client models aggregate model parameters on the local server and distribute them to clients for the next round of training. We demonstrate that FedGST outperforms other federated learning methods and baselines on ABIDE-1 and ADHD200 datasets.</p

FedGST:Federated Graph Spatio-Temporal Framework for Brain Functional Disease Prediction

Currently, most medical institutions face the challenge of training a unified model using fragmented and isolated data to address disease prediction problems. Although federated learning has become the recognized paradigm for privacy-preserving model training, how to integrate federated learning with fMRI temporal characteristics to enhance predictive performance remains an open question for functional disease prediction. To address this challenging task, we propose a novel Federated Graph Spatio-Temporal (FedGST) framework for brain functional disease prediction. Specifically, anchor sampling is used to process variable-length time series data on local clients. Then dynamic functional connectivity graphs are generated via sliding windows and Pearson correlation coefficients. Next, we propose an InceptionTime model to extract temporal information from the dynamic functional connectivity graphs on the local clients. Finally, the hidden activation variables are sent to a global server. We propose a UniteGCN model on the global server to receive and process the hidden activation variables from clients. Then, the global server returns gradient information to clients for backpropagation and model parameter updating. Client models aggregate model parameters on the local server and distribute them to clients for the next round of training. We demonstrate that FedGST outperforms other federated learning methods and baselines on ABIDE-1 and ADHD200 datasets.</p

Back to the Starting Point: on the Simulation of Initial Magnetic Fields and Spin Periods of Non-accretion Pulsars

Neutron stars (NSs) play essential roles in modern astrophysics. Magnetic fields and spin periods of newborn (zero age) NSs have large impact on the further evolution of NSs, which are however poorly explored in observation due to the difficulty of finding newborn NSs. In this work, we aim to infer the magnetic fields and spin periods (Bi and Pi) of zero-age NSs from the observed properties of NS population. We select non-accretion NSs (NANSs) whose evolution is solely determined by magnetic dipole radiation. We find that both Bi and Pi can be described by log-normal distribution and the fitting sensitively depends on our parameters.Comment: 8 pages, 5 figures, accepted for publication in Ap

CriticBench: Evaluating Large Language Models as Critic

Critique ability are crucial in the scalable oversight and self-improvement of Large Language Models (LLMs). While many recent studies explore the critique ability of LLMs to judge and refine flaws in generations, how to comprehensively and reliably measure the critique abilities of LLMs is under-explored. This paper introduces CriticBench, a novel benchmark designed to comprehensively and reliably evaluate four key critique ability dimensions of LLMs: feedback, comparison, refinement and meta-feedback. CriticBench encompasses nine diverse tasks, each assessing the LLMs' ability to critique responses at varying levels of quality granularity. Our extensive evaluations of open-source and closed-source LLMs reveal intriguing relationships between the critique ability and tasks, response qualities, and model scales. Datasets, resources and evaluation toolkit for CriticBench will be publicly released at https://github.com/open-compass/CriticBench

Characterization and expression analysis of four members genes of flavanone 3-hydroxylase families from Chamaemelum nobile

Chamaemelum nobile is a traditional Chinese herbal medicine, whose secondary metabolites used in the pharmacology of Chinese medicine. Among them, the flavonoids have great research value. Flavanone 3-hydroxylase (F3H) is one of the core enzymes in the early steps of flavonoid biosynthesis. This study aimed to elucidate the structures, functions, and expression levels of F3H families from C. nobile. Four members of the F3H family were screened from C. nobile transcriptome data and performed bioinformatics analysis. Results showed that CnF3H1~4 had a high similarity with the other F3H plants, and all genes contained two conserved isopenicillin N synthase-like and oxoglutarate/iron-dependent dioxygenase domains. Further analysis revealed that the four CnF3H proteins contained some differences in binding sites. The results of secondary and 3-D structures displayed that the composition and proportion of the four CnF3H secondary structures were basically the same, and their 3D structures were consistent with the secondary structures. The phylogenetic tree displayed that CnF3H2, CnF3H3, and CnF3H4 were grouped with Asteraceae. The expression patterns of CnF3Hs in the roots, stems, leaves, and flowers of C. nobile were evaluated using the value of RPKM. The results indicated that CnF3Hs had significant difference in the expression of different tissues. Especially, CnF3H1~3 and CnF3H4 had the highest expression levels in the flowers and roots, respectively. Hence, CnF3Hs played a significant role in the flavonoid metabolism

catena-Poly[[[diaqua­iron(II)]-μ-pyridine-2,5-dicarboxyl­ato-[tetra­aqua­iron(II)]-μ-pyridine-2,5-dicarboxyl­ato] tetra­hydrate]

In the crystal structure of the title compound, {[Fe2(C7H3NO4)2(H2O)6]·4H2O}n, there are two types of coordination for the FeII atoms. One FeII atom is in a distorted octa­hedral N2O4 environment, with two chelating rings from the pyridine­dicarboxyl­ate ligands and two O atoms from the water mol­ecules, while the other is in a distorted octa­hedral O6 environment with two O atoms from the pyridine­dicarboxyl­ate ligands and four O atoms from the water mol­ecules. Both FeII atoms lie on crystallographic centers of symmetry. The complex possesses an infinite chain structure running along the [101] direction. These chains are inter­connected by the uncoordinated water mol­ecules through O—H⋯O hydrogen bonds

A transformative route to nanoporous manganese oxides of controlled oxidation states with identical textural properties

Nanoporous nanocrystalline metal oxides with tunable oxidation states are crucial for controlling their catalytic, electronic, and optical properties. However, previous approaches to modulate oxidation states in nanoporous metal oxides commonly lead to the breakdown of the nanoporous structure as well as involve concomitant changes in their morphology, pore size, surface area, and nanocrystalline size. Herein, we present a transformative route to nanoporous metal oxides with various oxidation states using manganese oxides as model systems. Thermal conversion of Mn-based metal-organic frameworks (Mn-MOFs) at controlled temperature and atmosphere yielded a series of nanoporous manganese oxides with continuously tuned oxidation states: MnO, Mn3O 4, Mn5O8, and Mn2O3. This transformation enabled the preparation of low-oxidation phase MnO and metastable intermediate phase Mn5O8 with nanoporous architectures, which were previously rarely accessible. Significantly, nanoporous MnO, Mn3O4, and Mn5O8 had a very similar morphology, surface area, and crystalline size. We investigated the electrocatalytic activity of nanoporous manganese oxides for oxygen reduction reaction (ORR) to identify the role of oxidation states, and observed oxidation state-dependent activity and kinetics for the ORR.close5