Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding

Deep learning-based fault diagnosis (FD) approaches require a large amount of training data, which are difficult to obtain since they are located across different entities. Federated learning (FL) enables multiple clients to collaboratively train a shared model with data privacy guaranteed. However, the domain discrepancy and data scarcity problems among clients deteriorate the performance of the global FL model. To tackle these issues, we propose a novel framework called representation encoding-based federated meta-learning (REFML) for few-shot FD. First, a novel training strategy based on representation encoding and meta-learning is developed. It harnesses the inherent heterogeneity among training clients, effectively transforming it into an advantage for out-of-distribution generalization on unseen working conditions or equipment types. Additionally, an adaptive interpolation method that calculates the optimal combination of local and global models as the initialization of local training is proposed. This helps to further utilize local information to mitigate the negative effects of domain discrepancy. As a result, high diagnostic accuracy can be achieved on unseen working conditions or equipment types with limited training data. Compared with the state-of-the-art methods, such as FedProx, the proposed REFML framework achieves an increase in accuracy by 2.17%-6.50% when tested on unseen working conditions of the same equipment type and 13.44%-18.33% when tested on totally unseen equipment types, respectively

M^2-3DLaneNet: Multi-Modal 3D Lane Detection

Estimating accurate lane lines in 3D space remains challenging due to their sparse and slim nature. In this work, we propose the M^2-3DLaneNet, a Multi-Modal framework for effective 3D lane detection. Aiming at integrating complementary information from multi-sensors, M^2-3DLaneNet first extracts multi-modal features with modal-specific backbones, then fuses them in a unified Bird's-Eye View (BEV) space. Specifically, our method consists of two core components. 1) To achieve accurate 2D-3D mapping, we propose the top-down BEV generation. Within it, a Line-Restricted Deform-Attention (LRDA) module is utilized to effectively enhance image features in a top-down manner, fully capturing the slenderness features of lanes. After that, it casts the 2D pyramidal features into 3D space using depth-aware lifting and generates BEV features through pillarization. 2) We further propose the bottom-up BEV fusion, which aggregates multi-modal features through multi-scale cascaded attention, integrating complementary information from camera and LiDAR sensors. Sufficient experiments demonstrate the effectiveness of M^2-3DLaneNet, which outperforms previous state-of-the-art methods by a large margin, i.e., 12.1% F1-score improvement on OpenLane dataset

Qualitative and Quantitative Analysis of Five Indoles or Indazole Amide Synthetic Cannabinoids in Suspected E-Cigarette Oil by GC-MS

Objective To establish the GC-MS qualitative and quantitative analysis methods for the synthetic cannabinoids, its main matrix and additives in suspicious electronic cigarette （e-cigarette） oil samples. Methods The e-cigarette oil samples were analyzed by GC-MS after diluted with methanol. Synthetic cannabinoids, its main matrix and additives in e-cigarette oil samples were qualitatively analyzed by the characteristic fragment ions and retention time. The synthetic cannabinoids were quantitatively analyzed by using the selective ion monitoring mode. Results The linear range of each compound in GC-MS quantitative method was 0.025-1 mg/mL, the matrix recovery rate was 94%-103%, the intra-day precision relative standard deviations （RSD） was less than 2.5%, and inter-day precision RSD was less than 4.0%. Five indoles or indazole amide synthetic cannabinoids were detected in 25 e-cigarette samples. The main matrixes of e-cigarette samples were propylene glycol and glycerol. Additives such as N,2,3-trimethyl-2-isopropyl butanamide （WS-23）, glycerol triacetate and nicotine were detected in some samples. The content range of synthetic cannabinoids in 25 e-cigarette samples was 0.05%-2.74%. Conclusion The GC-MS method for synthesizing cannabinoid, matrix and additive in e-cigarette oil samples has good selectivity, high resolution, low detection limit, and can be used for simultaneous qualitative and quantitative analysis of multiple components; The explored fragment ion fragmentation mechanism of the electron bombardment ion source of indole or indoxamide compounds helps to identify such substances or other compounds with similar structures in cases

Characterizing the Blood Oxygen Level-Dependent Fluctuations in Musculoskeletal Tumours Using Functional Magnetic Resonance Imaging

This study characterized the blood oxygen level-dependent (BOLD) fluctuations in benign and malignant musculoskeletal tumours via power spectrum analyses in pre-established low-frequency bands. BOLD MRI and T1-weighted imaging (T1WI) were collected for 52 patients with musculoskeletal tumours. Three ROIs were drawn on the T1WI image in the tumours’ central regions, peripheral regions and neighbouring tissue. The power spectrum of the BOLD within each ROI was calculated and divided into the following four frequency bands: 0.01–0.027 Hz, 0.027–0.073 Hz, 0.073–0.198 Hz, and 0.198–0.25 Hz. ANOVA was conducted for each frequency band with the following two factors: the location of the region of interest (LoR, three levels: tumour “centre”, “peripheral” and “healthy tissue”) and tumour characteristic (TC, two levels: “malignant” and “benign”). There was a significant main effect of LoR in the frequencies of 0.073–0.198 Hz and 0.198–0.25 Hz. These data were further processed with post-hoc pair-wise comparisons. BOLD fluctuations at 0.073–0.198 Hz were stronger in the peripheral than central regions of the malignant tumours; however, no such difference was observed for the benign tumours. Our findings provide evidence that the BOLD signal fluctuates with spatial heterogeneity in malignant musculoskeletal tumours at the frequency band of 0.073–0.198 Hz

CXCR5+PD-1+ follicular helper CD8 T cells control B cell tolerance

Many autoimmune diseases are characterized by the production of autoantibodies. The current view is that CD4+ T follicular helper (Tfh) cells are the main subset regulating autoreactive B cells. Here we report a CXCR5+PD1+ Tfh subset of CD8+ T cells whose development and function are negatively modulated by Stat5. These CD8+ Tfh cells regulate the germinal center B cell response and control autoantibody production, as deficiency of Stat5 in CD8 T cells leads to an increase of CD8+ Tfh cells, resulting in the breakdown of B cell tolerance and concomitant autoantibody production. CD8+ Tfh cells share similar gene signatures with CD4+ Tfh, and require CD40L/CD40 and TCR/MHCI interactions to deliver help to B cells. Our study thus highlights the diversity of follicular T cell subsets that contribute to the breakdown of B-cell tolerance

Fatty Acid Binding Protein 5 (FABP5) Promotes Aggressiveness of Gastric Cancer Through Modulation of Tumor Immunity.

PurposeGastric cancer (GC) is the second most lethal cancer globally and is associated with poor prognosis. Fatty acid-binding proteins (FABPs) can regulate biological properties of carcinoma cells. FABP5 is overexpressed in many types of cancers; however, the role and mechanisms of action of FABP5 in GC remain unclear. In this study, we aimed to evaluate the clinical and biological functions of FABP5 in GC.Materials and methodsWe assessed FABP5 expression using immunohistochemical analysis in 79 patients with GC and evaluated its biological functions following in vitro and in vivo ectopic expression. FABP5 targets relevant to GC progression were determined using RNA sequencing (RNA-seq).ResultsElevated FABP5 expression was closely associated with poor outcomes, and ectopic expression of FABP5 promoted proliferation, invasion, migration, and carcinogenicity of GC cells, thus suggesting its potential tumor-promoting role in GC. Additionally, RNA-seq analysis indicated that FABP5 activates immune-related pathways, including cytokine-cytokine receptor interaction pathways, interleukin-17 signaling, and tumor necrosis factor signaling, suggesting an important rationale for the possible development of therapies that combine FABP5-targeted drugs with immunotherapeutics.ConclusionsThese findings highlight the biological mechanisms and clinical implications of FABP5 in GC and suggest its potential as an adverse prognostic factor and/or therapeutic target

A de novo Genome of a Chinese Radish Cultivar

AbstractHere, we report a high-quality draft genome of a Chinese radish (Raphanus sativus) cultivar. This draft contains 387.73Mb of assembled scaffolds, 83.93% of the scaffolds were anchored onto nine pseudochromosomes and 95.09% of 43 240 protein-coding genes were functionally annotated. 184.75Mb (47.65%) of repeat sequences was identified in the assembled genome. By comparative analyses of the radish genome against 10 other plant genomes, 2 275 genes in 780 gene families were found unique to R. sativus. This genome is a good reference for genomic study and of great value for genetic improvement of radish

Metabolomics in the Development and Progression of Dementia: A Systematic Review

Dementia has become a major global public health challenge with a heavy economic burden. It is urgently necessary to understand dementia pathogenesis and to identify biomarkers predicting risk of dementia in the preclinical stage for prevention, monitoring, and treatment. Metabolomics provides a novel approach for the identification of biomarkers of dementia. This systematic review aimed to examine and summarize recent retrospective cohort human studies assessing circulating metabolite markers, detected using high-throughput metabolomics, in the context of disease progression to dementia, including incident mild cognitive impairment, all-cause dementia, and cognitive decline. We systematically searched the PubMed, Embase, and Cochrane databases for retrospective cohort human studies assessing associations between blood (plasma or serum) metabolomics profile and cognitive decline and risk of dementia from inception through October 15, 2018. We identified 16 studies reporting circulating metabolites and risk of dementia, and six regarding cognitive performance change. Concentrations of several blood metabolites, including lipids (higher phosphatidylcholines, sphingomyelins, and lysophophatidylcholine, and lower docosahexaenoic acid and high-density lipoprotein subfractions), amino acids (lower branched-chain amino acids, creatinine, and taurine, and higher glutamate, glutamine, and anthranilic acid), and steroids were associated with cognitive decline and the incidence or progression of dementia. Circulating metabolites appear to be associated with the risk of dementia. Metabolomics could be a promising tool in dementia biomarker discovery. However, standardization and consensus guidelines for study design and analytical techniques require future development