Federated Domain Adaptation via Transformer for Multi-Site Alzheimer's Disease Diagnosis.

In multi-site studies of Alzheimer's disease (AD), the difference of data in multi-site datasets leads to the degraded performance of models in the target sites. The traditional domain adaptation method requires sharing data from both source and target domains, which will lead to data privacy issue. To solve it, federated learning is adopted as it can allow models to be trained with multi-site data in a privacy-protected manner. In this paper, we propose a multi-site federated domain adaptation framework via Transformer (FedDAvT), which not only protects data privacy, but also eliminates data heterogeneity. The Transformer network is used as the backbone network to extract the correlation between the multi-template region of interest features, which can capture the brain abundant information. The self-attention maps in the source and target domains are aligned by applying mean squared error for subdomain adaptation. Finally, we evaluate our method on the multi-site databases based on three AD datasets. The experimental results show that the proposed FedDAvT is quite effective, achieving accuracy rates of 88.75%, 69.51%, and 69.88% on the AD vs. NC, MCI vs. NC, and AD vs. MCI two-way classification tasks, respectively.

Multicenter and Multichannel Pooling GCN for Early AD Diagnosis Based on Dual-Modality Fused Brain Network.

For significant memory concern (SMC) and mild cognitive impairment (MCI), their classification performance is limited by confounding features, diverse imaging protocols, and limited sample size. To address the above limitations, we introduce a dual-modality fused brain connectivity network combining resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), and propose three mechanisms in the current graph convolutional network (GCN) to improve classifier performance. First, we introduce a DTI-strength penalty term for constructing functional connectivity networks. Stronger structural connectivity and bigger structural strength diversity between groups provide a higher opportunity for retaining connectivity information. Second, a multi-center attention graph with each node representing a subject is proposed to consider the influence of data source, gender, acquisition equipment, and disease status of those training samples in GCN. The attention mechanism captures their different impacts on edge weights. Third, we propose a multi-channel mechanism to improve filter performance, assigning different filters to features based on feature statistics. Applying those nodes with low-quality features to perform convolution would also deteriorate filter performance. Therefore, we further propose a pooling mechanism, which introduces the disease status information of those training samples to evaluate the quality of nodes. Finally, we obtain the final classification results by inputting the multi-center attention graph into the multi-channel pooling GCN. The proposed method is tested on three datasets (i.e., an ADNI 2 dataset, an ADNI 3 dataset, and an in-house dataset). Experimental results indicate that the proposed method is effective and superior to other related algorithms, with a mean classification accuracy of 93.05% in our binary classification tasks. Our code is available at: https://github.com/Xuegang-S.

Cold Plasma Irradiation Regulates Inflammation and Oxidative Stress in Human Bronchial Epithelial Cells and Human Non-Small Cell Lung Carcinoma.

Atmospheric pressure cold plasma has shown multiple biological effects of anti-bacteria and anti-cancer. In this study, the effect of atmospheric pressure cold plasma on respiratory inflammation and oxidant stress is explored. Tunicamycin was used to stimulate human bronchial epithelial cells (HBECs) and A549 cells for inflammatory response and oxidative stress, followed by atmospheric pressure cold plasma treatment. For HBECs and A549 cells, atmospheric pressure cold plasma was able to alleviate tunicamycin-induced cell proliferation inhibition, inflammation and oxidant stress, and enhance nuclear factor-erythroid-2-related factor 2 (NRF2) pathway activation. Moreover, NRF2/ARE (anti-oxidant response elements) pathway was involved in the regulation of atmospheric pressure cold plasma on tunicamycin-induced oxidative stress. These results suggest the positive effect of atmospheric pressure cold plasma on inflammation and oxidant stress of respiratory system, indicating the therapeutic potential of atmospheric pressure cold plasma for respiratory diseases.

Mono-macrophage-Derived MANF Alleviates Bacterial Myocarditis by Inhibiting NF-kappaB Activation and Myocardial Inflammation.

Bacterial myocarditis is a key cause leading to myocardial damage and cardiac dysfunction. Mesencephalic astrocyte-derived neurotrophic factor (MANF) has been found to be an anti-inflammatory factor. This study is to explore the effect of MANF on LPS-induced myocardial inflammation and macrophage differentiation. The myocarditis mouse model was constructed by LPS treatment. Myocardial damage and serum inflammatory factors were evaluated by ELISA. RT-qPCR was used to detect mRNA of M1/M2 macrophage markers. Western blot, immunohistochemical, and immunofluorescent staining were used to examine myocardial M1/M2 macrophages and NF-κB activation. Mono-macrophage-derived MANF deficiency enhanced LPS-induced inflammatory response and increased M1 macrophages in myocardium tissues, further causing more severe myocardial injury and lower survival rate of mice. Also, LPS-induced myocardial NF-κB activation was strengthened after mono-macrophage-derived MANF knockout. Mono-macrophage-derived MANF inhibits bacterial myocarditis and myocardial M1 macrophage differentiation, which is potential to be used for bacterial myocarditis treatment clinically.

Graph convolution network with similarity awareness and adaptive calibration for disease-induced deterioration prediction.

Graph convolution networks (GCN) have been successfully applied in disease prediction tasks as they capture interactions (i.e., edges and edge weights on the graph) between individual elements. The interactions in existing works are constructed by fusing similarity between imaging information and distance between non-imaging information, whereas disregarding the disease status of those individuals in the training set. Besides, the similarity is being evaluated by computing the correlation distance between feature vectors, which limits prediction performance, especially for predicting significant memory concern (SMC) and mild cognitive impairment (MCI). In this paper, we propose three mechanisms to improve GCN, namely similarity-aware adaptive calibrated GCN (SAC-GCN), for predicting SMC and MCI. First, we design a similarity-aware graph using different receptive fields to consider disease status. The labelled subjects on the graph are only connected with those labelled subjects with the same status. Second, we propose an adaptive mechanism to evaluate similarity. Specifically, we construct initial GCN with evaluating similarity by using traditional correlation distance, then pre-train the initial GCN by using training samples and use it to score all subjects. Then, the difference between these scores replaces correlation distance to update similarity. Last, we devise a calibration mechanism to fuse functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) information into edges. The proposed method is tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results demonstrate that our proposed method is useful to predict disease-induced deterioration and superior to other related algorithms, with a mean classification accuracy of 86.83% in our prediction tasks.

Augmented Multicenter Graph Convolutional Network for COVID-19 Diagnosis.

Chest computed tomography (CT) scans of coronavirus 2019 (COVID-19) disease usually come from multiple datasets gathered from different medical centers, and these images are sampled using different acquisition protocols. While integrating multicenter datasets increases sample size, it suffers from inter-center heterogeneity. To address this issue, we propose an augmented multicenter graph convolutional network (AM-GCN) to diagnose COVID-19 with steps as follows. First, we use a 3-D convolutional neural network to extract features from the initial CT scans, where a ghost module and a multitask framework are integrated to improve the network's performance. Second, we exploit the extracted features to construct a multicenter graph, which considers the intercenter heterogeneity and the disease status of training samples. Third, we propose an augmentation mechanism to augment training samples which forms an augmented multicenter graph. Finally, the diagnosis results are obtained by inputting the augmented multi-center graph into GCN. Based on 2223 COVID-19 subjects and 2221 normal controls from seven medical centers, our method has achieved a mean accuracy of 97.76%. The code for our model is made publicly.1.

Mono-macrophage-Derived MANF Protects Against Lipopolysaccharide-Induced Acute Kidney Injury via Inhibiting Inflammation and Renal M1 Macrophages.

The outburst of renal inflammatory response has been found to be a crucial cause of acute kidney injury (AKI). Attenuating the renal inflammation is an effective way for AKI treatment. Mesencephalic astrocyte-derived neurotrophic factor (MANF) has been proven to be an anti-inflammatory factor. However, the effect of MANF on renal inflammation induced by AKI is unknown. In this study, we have investigated the effect of mono-macrophage-derived MANF on AKI. We constructed the mono-macrophage-specific MANF knockout (Mø MANF-/-) mouse and used lipopolysaccharide (LPS) to induce AKI in wild-type (WT) and Mø MANF-/- mice. With mono-macrophage-specific MANF deficiency, Mø MANF-/- mice had a lower survival rate, more severe renal injury, and higher serum level of pro-inflammatory TNF-α after AKI was induced by LPS. Also, compared with WT mice, there were more M1 macrophages in renal tissues of Mø MANF-/- mice with LPS treatment, which might be attributed to the enhanced NF-κB activation in the renal microenvironment. Our study indicates the immunoregulatory role of mono-macrophage-derived MANF in the pathophysiological process of AKI, as well as the potential clinical application of MANF for AKI treatment.

Input Forces Estimation for Nonlinear Systems by Applying a Square-Root Cubature Kalman Filter.

This work presents a novel inverse algorithm to estimate time-varying input forces in nonlinear beam systems. With the system parameters determined, the input forces can be estimated in real-time from dynamic responses, which can be used for structural health monitoring. In the process of input forces estimation, the Runge-Kutta fourth-order algorithm was employed to discretize the state equations; a square-root cubature Kalman filter (SRCKF) was employed to suppress white noise; the residual innovation sequences, a priori state estimate, gain matrix, and innovation covariance generated by SRCKF were employed to estimate the magnitude and location of input forces by using a nonlinear estimator. The nonlinear estimator was based on the least squares method. Numerical simulations of a large deflection beam and an experiment of a linear beam constrained by a nonlinear spring were employed. The results demonstrated accuracy of the nonlinear algorithm.

Load Identification for a Cantilever Beam Based on Fiber Bragg Grating Sensors.

Load identification plays an important role in structural health monitoring, which aims at preventing structural failures. In order to identify load for linear systems and nonlinear systems, this paper presents methods to identify load for a cantilever beam based on dynamic strain measurement by Fiber Bragg Grating (FBG) sensors. For linear systems, the proposed inverse method consists of Kalman filter with no load terms and a linear estimator. For nonlinear systems, the proposed inverse method consists of cubature Kalman filter (CKF) with no load terms and a nonlinear estimator. In the process of load identification, the state equations of the beam structures are constructed by using the finite element method (FEM). Kalman filter or CKF is used to suppress noise. The residual innovation sequences, gain matrix, and innovation covariance generated by Kalman filter or CKF are used to identify a load. To prove the effectiveness of the proposed method, numerical simulations and experiments of the beam structures are employed and the results show that the method has an excellent performance.

Hydrogen-bonded network in the salt 4-methyl-1H-imidazol-3-ium picrate.

In the title molecular salt, C4H7N2 (+)·C6H2N3O7 (-), the phenolic proton of the starting picric acid has been transferred to the imidazole N atom. The nitro groups are twisted away from the benzene ring plane, making dihedral angles of 12.8 (2), 9.2 (4) and 29.3 (2)°. In the crystal, the component ions are linked into chains along [010] via N-H⋯O and bifurcated N-H⋯(O,O) hydrogen bonds. These chains are further linked by weak C-H⋯O hydrogen bonds into a three-dimensional network. The complex three-dimensional network can be topologically simplified into a 4-connected uninodal net with the point symbol {4.8(5)}.

Hydrogen bonding in the crystal structure of the molecular salt of pyrazole-pyrazolium picrate.

The asymmetric unit of the title organic salt [systematic name: 1H-pyrazol-2-ium 2,4,6-tri-nitro-phenolate-1H-pyrazole (1/1)], H(C3H4N2)2 (+)·C6H2N3O7 (-), consists of one picrate anion and one hydrogen-bonded dimer of a pyrazolium monocation. The H atom involved in the dimer N-H⋯N hydrogen bond is disordered over both symmetry-unique pyrazole mol-ecules with occupancies of 0.52 (5) and 0.48 (5). In the crystal, the component ions are linked into chains along [100] by two different bifurcated N-H⋯(O,O) hydrogen bonds. In addition, weak C-H⋯O hydrogen bonds link inversion-related chains, forming columns along [100].

DNA-triggered aggregation of copper, zinc superoxide dismutase in the presence of ascorbate.

The oxidative damage hypothesis proposed for the function gain of copper, zinc superoxide dismutase (SOD1) maintains that both mutant and wild-type (WT) SOD1 catalyze reactions with abnormal substrates that damage cellular components critical for viability of the affected cells. However, whether the oxidative damage of SOD1 is involved in the formation of aggregates rich in SOD1 or not remains elusive. Here, we sought to explore the oxidative aggregation of WT SOD1 exposed to environments containing both ascorbate (Asc) and DNA under neutral conditions. The results showed that the WT SOD1 protein was oxidized in the presence of Asc. The oxidation results in the higher affinity of the modified protein for DNA than that of the unmodified protein. The oxidized SOD1 was observed to be more prone to aggregation than the WT SOD1, and the addition of DNA can significantly accelerate the oxidative aggregation. Moreover, a reasonable relationship can be found between the oxidation, increased hydrophobicity, and aggregation of SOD1 in the presence of DNA. The crucial step in aggregation is neutralization of the positive charges on some SOD1 surfaces by DNA binding. This study might be crucial for understanding molecular forces driving the protein aggregation.

4-(5-Bromo-2-hydroxy-benzyl-idene-amino)-3-methyl-1H-1,2,4-triazole-5(4H)-thione.

In the title compound, C(10)H(9)BrN(4)OS, the triazole ring forms a dihedral angle of 72.05 (14)° with the benzene ring. The conformation of the mol-ecule is stabilized by intra-molecular O-H⋯·N hydrogen bonding. The crystal packing is determined by inter-molecular N-H⋯S inter-actions, a short Br⋯S contact of 3.4464 (13) Šand π-π stacking of the triazole rings and of the benzene rings (centroid-centroid distances of 3.4109 and 3.569 Å, respectively).

Bis{µ-4,4',6,6'-tetra-bromo-2,2'-[o-phenyl-enebis(nitrilo-methyl-idyne)]-diphenol-ato}-bis-[(dimethyl-formamide)cadmium(II)].

The Schiff base ligand derived from the condensation of 3,5-dibromo-salicylaldehyde and 1,2-phenyl-enediamine, in the presence of dimethyl-formamide, forms the centrosymmetric title neutral binuclear distorted complex, [Cd(2)(C(20)H(10)Br(4)N(2)O(2))(2)(C(3)H(7)NO)(2)], with the two octa-hedral Cd atoms linked by two O atoms. All bond lengths and angles show normal values.