BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection

Graph anomaly detection (GAD) has gained increasing attention in recent years due to its critical application in a wide range of domains, such as social networks, financial risk management, and traffic analysis. Existing GAD methods can be categorized into node and edge anomaly detection models based on the type of graph objects being detected. However, these methods typically treat node and edge anomalies as separate tasks, overlooking their associations and frequent co-occurrences in real-world graphs. As a result, they fail to leverage the complementary information provided by node and edge anomalies for mutual detection. Additionally, state-of-the-art GAD methods, such as CoLA and SL-GAD, heavily rely on negative pair sampling in contrastive learning, which incurs high computational costs, hindering their scalability to large graphs. To address these limitations, we propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE). We extract a subgraph (graph view) centered on each target node as node context and transform it into a dual hypergraph (hypergraph view) as edge context. These views are encoded using graph and hypergraph neural networks to capture the representations of nodes, edges, and their associated contexts. By swapping the context embeddings between nodes and edges and measuring the agreement in the embedding space, we enable the mutual detection of node and edge anomalies. Furthermore, we adopt a bootstrapped training strategy that eliminates the need for negative sampling, enabling BOURNE to handle large graphs efficiently. Extensive experiments conducted on six benchmark datasets demonstrate the superior effectiveness and efficiency of BOURNE in detecting both node and edge anomalies

Successional change in species composition alters climate sensitivity of grassland productivity.

Succession theory predicts altered sensitivity of ecosystem functions to disturbance (i.e., climate change) due to the temporal shift in plant community composition. However, empirical evidence in global change experiments is lacking to support this prediction. Here, we present findings from an 8-year long-term global change experiment with warming and altered precipitation manipulation (double and halved amount). First, we observed a temporal shift in species composition over 8 years, resulting in a transition from an annual C3 -dominant plant community to a perennial C4 -dominant plant community. This successional transition was independent of any experimental treatments. During the successional transition, the response of aboveground net primary productivity (ANPP) to precipitation addition magnified from neutral to +45.3%, while the response to halved precipitation attenuated substantially from -17.6% to neutral. However, warming did not affect ANPP in either state. The findings further reveal that the time-dependent climate sensitivity may be regulated by successional change in species composition, highlighting the importance of vegetation dynamics in regulating the response of ecosystem productivity to precipitation change

A potential brain functional biomarker distinguishing patients with Crohn’s disease with different disease stages: a resting-state fMRI study

BackgroundThe previous studies have demonstrated that patients with Crohn’s disease in remission (CD-R) have abnormal alterations in brain function. However, whether brain function changes in patients with Crohn’s disease in activity (CD-A) and the relationship with CD-R are still unclear. In this study, we aimed to investigate whether the different levels of disease activity may differentially affect the brain function and to find the brain functional biomarker distinguishing patients with different disease stages by measuring the amplitude of low frequency fluctuations (ALFF).Methods121 patients with CD and 91 healthy controls (HCs) were recruited. The clinical and psychological assessment of participants were collected. The criteria for the disease activity were the Crohn’s disease activity index (CDAI) scores. CD-R refers to CD patients in remission which the CDAI score is less than 150. Conversely, CD-A refers to CD patients in activity which the CDAI score is ≥150. The ALFF was compared among three groups by performing one-way analysis of variance, followed by a post hoc two-sample t-test. Differences among the groups were selected as seeds for functional connectivity analyses. We also investigated the correlation among clinical, psychological scores and ALFF. Binary logistic regression analysis was used to examine the unique contribution of the ALFF characteristics of the disease stages.ResultsThere were widespread differences of ALFF values among the 3 groups, which included left frontal pole (FP_L), right supramarginal gyrus (SG_R), left angular gyrus (AG_L), right cingulate gyrus (CG_R), right intracalcarine cortex (IC_R), right parahippocampal gyrus (PG_R), right lingual gyrus (LG_R), right precuneous cortex (PC_R), left occipital fusiform gyrus (OFG_L). Significant brain regions showing the functional connections (FC) increased in FP_L, SG_R, PC_R and OFG_L between CD-A and HCs. The erythrocyte sedimentation rate had a negative correlation with the ALFF values in PC_R in the patients with CD. The phobic anxiety values had a negative correlation with the ALFF values in OFG_L. The psychoticism values had a negative correlation with ALFF values in the IC_R. And the hostility values had a positive correlation with the ALFF values in CG_R. Significant brain regions showing the FC increased in FP_L, SG_R, CG_R, PG_R, LG_R and OFG_L between CD-R and HCs. In binary logistic regression models, the LG_R (beta = 5.138, p = 0.031), PC_R (beta = 1.876, p = 0.002) and OFG_L (beta = 3.937, p = 0.044) was disease stages predictors.ConclusionThe results indicated the significance of the altered brain activity in the different disease stages of CD. Therefore, these findings present a potential identify neuroimaging-based brain functional biomarker in CD. Additionally, the study provides a better understanding of the pathophysiology of CD

Mesenchymal stromal cells pretreated with pro‐inflammatory cytokines promote skin wound healing through VEGFC ‐mediated angiogenesis

n/

Functional Gene Array-Based Ultrasensitive and Quantitative Detection of Microbial Populations in Complex Communities.

While functional gene arrays (FGAs) have greatly expanded our understanding of complex microbial systems, specificity, sensitivity, and quantitation challenges remain. We developed a new generation of FGA, GeoChip 5.0, using the Agilent platform. Two formats were created, a smaller format (GeoChip 5.0S), primarily covering carbon-, nitrogen-, sulfur-, and phosphorus-cycling genes and others providing ecological services, and a larger format (GeoChip 5.0M) containing the functional categories involved in biogeochemical cycling of C, N, S, and P and various metals, stress response, microbial defense, electron transport, plant growth promotion, virulence, gyrB, and fungus-, protozoan-, and virus-specific genes. GeoChip 5.0M contains 161,961 oligonucleotide probes covering >365,000 genes of 1,447 gene families from broad, functionally divergent taxonomic groups, including bacteria (2,721 genera), archaea (101 genera), fungi (297 genera), protists (219 genera), and viruses (167 genera), mainly phages. Computational and experimental evaluation indicated that designed probes were highly specific and could detect as little as 0.05 ng of pure culture DNAs within a background of 1 μg community DNA (equivalent to 0.005% of the population). Additionally, strong quantitative linear relationships were observed between signal intensity and amount of pure genomic (∼99% of probes detected; r > 0.9) or soil (∼97%; r > 0.9) DNAs. Application of the GeoChip to a contaminated groundwater microbial community indicated that environmental contaminants (primarily heavy metals) had significant impacts on the biodiversity of the communities. This is the most comprehensive FGA to date, capable of directly linking microbial genes/populations to ecosystem functions.IMPORTANCE The rapid development of metagenomic technologies, including microarrays, over the past decade has greatly expanded our understanding of complex microbial systems. However, because of the ever-expanding number of novel microbial sequences discovered each year, developing a microarray that is representative of real microbial communities, is specific and sensitive, and provides quantitative information remains a challenge. The newly developed GeoChip 5.0 is the most comprehensive microarray available to date for examining the functional capabilities of microbial communities important to biogeochemistry, ecology, environmental sciences, and human health. The GeoChip 5 is highly specific, sensitive, and quantitative based on both computational and experimental assays. Use of the array on a contaminated groundwater sample provided novel insights on the impacts of environmental contaminants on groundwater microbial communities

The Atypical Effective Connectivity of Right Temporoparietal Junction in Autism Spectrum Disorder: A Multi-Site Study

Social function impairment is the core deficit of autism spectrum disorder (ASD). Although many studies have investigated ASD through a variety of neuroimaging tools, its brain mechanism of social function remains unclear due to its complex and heterogeneous symptoms. The present study aimed to use resting-state functional magnetic imaging data to explore effective connectivity between the right temporoparietal junction (RTPJ), one of the key brain regions associated with social impairment of individuals with ASD, and the whole brain to further deepen our understanding of the neuropathological mechanism of ASD. This study involved 1,454 participants from 23 sites from the Autism Brain Imaging Data Exchange (ABIDE) public dataset, which included 618 individuals with ASD and 836 with typical development (TD). First, a voxel-wise Granger causality analysis (GCA) was conducted with the RTPJ selected as the region of interest (ROI) to investigate the differences in effective connectivity between the ASD and TD groups in every site. Next, to obtain further accurate and representative results, an image-based meta-analysis was implemented to further analyze the GCA results of each site. Our results demonstrated abnormal causal connectivity between the RTPJ and the widely distributed brain regions and that the connectivity has been associated with social impairment in individuals with ASD. The current study could help to further elucidate the pathological mechanisms of ASD and provides a new perspective for future research

Evaluation of Stability and Biocompatibility of Chitosan/Sodium Tripolyphosphate and Chitosan/Flaxseed Gum Composite Nanoparticles Loaded with Bighead Carp Peptides

Chitosan nanoparticle is becoming an excellent carrier for the delivery of bioactive components due to the advantages of simple preparation, low cost and high biocompatibility. Previous studies have shown that chitosan/sodium tripolyphosphate (CS/TPP) and chitosan/flaxseed gum (CS/FG) nanoparticles loaded with bighead carp peptides (BCP) have the advantages of small particle size, high encapsulation rate and significant slow-release effect. This study explored the effects of ionic strength, pH, simulated digestion and storage time on the preparation of chitosan/sodium tripolyphosphate (CS/TPP-BCP) and chitosan/flaxseed gum (CS/FG-BCP) nanoparticles, and evaluated the extracellular lactate dehydrogenase content and antioxidant capacity in vivo of Caco-2 cells treated with the chitosan nanoparticles and their cellular uptake. The results showed that the two kinds of chitosan nanoparticles were stable under acidic conditions and sensitive to a solution with opposite charges. The stability of the nanoparticles loaded with bighead peptides was higher than that of free peptides and both nanoparticles showed higher biocompatibility and cell uptake

Climate warming accelerates temporal scaling of grassland soil microbial biodiversity.

Determining the temporal scaling of biodiversity, typically described as species-time relationships (STRs), in the face of global climate change is a central issue in ecology because it is fundamental to biodiversity preservation and ecosystem management. However, whether and how climate change affects microbial STRs remains unclear, mainly due to the scarcity of long-term experimental data. Here, we examine the STRs and phylogenetic-time relationships (PTRs) of soil bacteria and fungi in a long-term multifactorial global change experiment with warming (+3 °C), half precipitation (-50%), double precipitation (+100%) and clipping (annual plant biomass removal). Soil bacteria and fungi all exhibited strong STRs and PTRs across the 12 experimental conditions. Strikingly, warming accelerated the bacterial and fungal STR and PTR exponents (that is, the w values), yielding significantly (P < 0.001) higher temporal scaling rates. While the STRs and PTRs were significantly shifted by altered precipitation, clipping and their combinations, warming played the predominant role. In addition, comparison with the previous literature revealed that soil bacteria and fungi had considerably higher overall temporal scaling rates (w = 0.39-0.64) than those of plants and animals (w = 0.21-0.38). Our results on warming-enhanced temporal scaling of microbial biodiversity suggest that the strategies of soil biodiversity preservation and ecosystem management may need to be adjusted in a warmer world

Two DsbA Proteins Are Important for Vibrio parahaemolyticus Pathogenesis

Bacterial pathogens maintain disulfide bonds for protein stability and functions that are required for pathogenesis. Vibrio parahaemolyticus is a Gram-negative pathogen that causes food-borne gastroenteritis and is also an important opportunistic pathogen of aquatic animals. Two genes encoding the disulfide bond formation protein A, DsbA, are predicted to be encoded in the V. parahaemolyticus genome. DsbA plays an important role in Vibrio cholerae virulence but its role in V. parahaemolyticus is largely unknown. In this study, the activities and functions of the two V. parahaemolyticus DsbA proteins were characterized. The DsbAs affected virulence factor expression at the post-translational level. The protein levels of adhesion factor VpadF (VP1767) and the thermostable direct hemolysin (TDH) were significantly reduced in the dsbA deletion mutants. V. parahaemolyticus lacking dsbA also showed reduced attachment to Caco-2 cells, decreased β-hemolytic activity, and less toxicity to both zebrafish and HeLa cells. Our findings demonstrate that DsbAs contribute to V. parahaemolyticus pathogenesis

CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have not been publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. Manual segmentations of the myocardium and chambers of all the subjects are also provided within the dataset. Scripts of state-of-the-art reconstruction algorithms were also provided as a point of reference. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community. Researchers can access the dataset at https://www.synapse.org/#!Synapse:syn51471091/wiki/.Comment: 14 pages, 8 figure