Specific Patterns Research Articles

Self-Organizing a Latent Hierarchy of Sketch Patterns for Controllable Sketch Synthesis.

Encoding sketches as Gaussian mixture model (GMM)-distributed latent codes is an effective way to control sketch synthesis. Each Gaussian component represents a specific sketch pattern, and a code randomly sampled from the Gaussian can be decoded to synthesize a sketch with the target pattern. However, existing methods treat the Gaussians as individual clusters, which neglects the relationships between them. For example, the giraffe and horse sketches heading left are related to each other by their face orientation. The relationships between sketch patterns are important messages to reveal cognitive knowledge in sketch data. Thus, it is promising to learn accurate sketch representations by modeling the pattern relationships into a latent structure. In this article, we construct a tree-structured taxonomic hierarchy over the clusters of sketch codes. The clusters with the more specific descriptions of sketch patterns are placed at the lower levels, while the ones with the more general patterns are ranked at the higher levels. The clusters at the same rank relate to each other through the inheritance of features from common ancestors. We propose a hierarchical expectation-maximization (EM)-like algorithm to explicitly learn the hierarchy, jointly with the training of encoder-decoder network. Moreover, the learned latent hierarchy is utilized to regularize sketch codes with structural constraints. Experimental results show that our method significantly improves controllable synthesis performance and obtains effective sketch analogy results.

TrustGNN: Graph Neural Network-Based Trust Evaluation via Learnable Propagative and Composable Nature.

Trust evaluation is critical for many applications such as cyber security, social communication, and recommender systems. Users and trust relationships among them can be seen as a graph. Graph neural networks (GNNs) show their powerful ability for analyzing graph-structural data. Very recently, existing work attempted to introduce the attributes and asymmetry of edges into GNNs for trust evaluation, while failed to capture some essential properties (e.g., the propagative and composable nature) of trust graphs. In this work, we propose a new GNN-based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs into a GNN framework for better trust evaluation. Specifically, TrustGNN designs specific propagative patterns for different propagative processes of trust, and distinguishes the contribution of different propagative processes to create new trust. Thus, TrustGNN can learn comprehensive node embeddings and predict trust relationships based on these embeddings. Experiments on some widely-used real-world datasets indicate that TrustGNN significantly outperforms the state-of-the-art methods. We further perform analytical experiments to demonstrate the effectiveness of the key designs in TrustGNN.

Learning Constrained Dynamic Correlations in Spatiotemporal Graphs for Motion Prediction.

Human motion prediction is challenging due to the complex spatiotemporal feature modeling. Among all methods, graph convolution networks (GCNs) are extensively utilized because of their superiority in explicit connection modeling. Within a GCN, the graph correlation adjacency matrix drives feature aggregation, and thus, is the key to extracting predictive motion features. State-of-the-art methods decompose the spatiotemporal correlation into spatial correlations for each frame and temporal correlations for each joint. Directly parameterizing these correlations introduces redundant parameters to represent common relations shared by all frames and all joints. Besides, the spatiotemporal graph adjacency matrix is the same for different motion samples, and thus, cannot reflect samplewise correspondence variances. To overcome these two bottlenecks, we propose dynamic spatiotemporal decompose GC (DSTD-GC), which only takes 28.6% parameters of the state-of-the-art GC. The key of DSTD-GC is constrained dynamic correlation modeling, which explicitly parameterizes the common static constraints as a spatial/temporal vanilla adjacency matrix shared by all frames/joints and dynamically extracts correspondence variances for each frame/joint with an adjustment modeling function. For each sample, the common constrained adjacency matrices are fixed to represent generic motion patterns, while the extracted variances complete the matrices with specific pattern adjustments. Meanwhile, we mathematically reformulate GCs on spatiotemporal graphs into a unified form and find that DSTD-GC relaxes certain constraints of other GC, which contributes to a better representation capability. Moreover, by combining DSTD-GC with prior knowledge like body connection and temporal context, we propose a powerful spatiotemporal GCN called DSTD-GCN. On the Human3.6M, Carnegie Mellon University (CMU) Mocap, and 3D Poses in the Wild (3DPW) datasets, DSTD-GCN outperforms state-of-the-art methods by 3.9%-8.7% in prediction accuracy with 55.0%-96.9% fewer parameters. Codes are available at https://github.com/Jaakk0F/DSTD-GCN.

SIAT: A systematic inter-component communication real-time analysis technique for detecting data leak threats on Android

This paper presents the design and implementation of a systematic Inter-Component Communications (ICCs) dynamic Analysis Technique (SIAT) for detecting privacy-sensitive data leak threats. SIAT’s specific approach involves the identification of malicious ICC patterns by actively tracing both data flows and implicit control flows within ICC processes during runtime. This is achieved by utilizing the taint tagging methodology, a technique utilized by TaintDroid. As a result, it can discover the malicious intent usage pattern and further resolve the coincidental malicious ICCs and bypass cases without incurring performance degradation. SIAT comprises two key modules: Monitor and Analyzer. The Monitor makes the first attempt to revise the taint tag approach named TaintDroid by developing the built-in intent service primitives to help Android capture the intent-related taint propagation at multi-level for malicious ICC detection. Specifically, we enable the Monitor to perform systemwide tracking of intent with five abstraction functionalities embedded in the interactive workflow of components. By analyzing the taint logs offered by the Monitor, the Analyzer can build the accurate and integrated ICC patterns adopted to identify the specific leak threat patterns with the identification algorithms and predefined rules. Meanwhile, we employ the patterns’ deflation technique to improve the efficiency of the Analyzer. We implement the SIAT with Android Open Source Project and evaluate its performance through extensive experiments on a particular dataset consisting of well-known datasets and real-world apps. The experimental results show that, compared to state-of-the-art approaches, the SIAT can achieve about 25% ∼200% accuracy improvements with 1.0 precision and 0.98 recall at negligible runtime overhead. Apart from that, the SIAT can identify two undisclosed cases of bypassing that prior technologies cannot detect and quite a few malicious ICC threats in real-world apps with lots of downloads on the Google Play market.

Modern Approach in Pattern Recognition Using Circular Fermatean Fuzzy Similarity Measure for Decision Making with Practical Applications

The circular Fermatean fuzzy (CFF) set is an advancement of the Fermatean fuzzy (FF) set and the interval-valued Fermatean fuzzy (IVFF) set which deals with uncertainty. The CFF set is represented as a circle of radius ranging from 0 to 2 with the center at the degree of association (DA) and degree of nonassociation (DNA). If multiple people are involved in making decisions, the CFF set, as an alternative to the FF and IVFF sets, can deal with ambiguity more effectively by encircling the decision values within a circle rather than taking an average. Using algorithms, a pattern can be observed computationally or visually. Machine learning algorithm utilizes pattern recognition as an instrument for identifying patterns and also similarity measure (SM) is a beneficial pattern recognition tool used to classify items, discover variations, and make future predictions for decision making. In this work, we introduce the CFF cosine and Dice similarity measures (CFFDMs and CFFSMs), and their properties are studied. Unlike traditional approaches of decision making, which emphasize a single number, the proposed CFFSMs observe the pattern over the circular region to help in dealing with uncertainty more effectively. We introduce an innovative decision-making method in the FF setting. Available bank loans and applicants’ eligibility levels are represented as CFF set using their FF criteria and are taken as loan patterns and customer eligibility patterns. The loan is allocated to the applicant by measuring the CFFCSM and CFFDSM between the two patterns. Also, laptops are suggested to the customers by measuring the similarity between specification pattern and requirement pattern. The correctness and consistency of the proposed models are ensured by comparison analysis and graphical simulations of the input and similarity CFFNs.

Response and assembly of abundant and rare taxa in Zaopei under different combination patterns of Daqu and pit mud: from microbial ecology to Baijiu brewing microecosystem

The quality and aroma of strong-flavor Baijiu are mainly dependent on Daqu, pit mud (PM), and the interaction of both. However, little is known about how their combination patterns affect the microbiome and metabolome of Zaopei, especially the metabolic function of rare taxa. Here, an experiment on industrial size was designed to assess the effects of 6 combinations (3 kinds of Daqu × 2 kinds of PM) on the composition and assembly of different taxa, as well as the flavor profile. The results showed that Zaopei’s microbiota was composed of a few abundant taxa and enormous rare taxa, and rare bacterial and abundant fungal subcommunities were significantly affected by combination patterns. The assembly processes of abundant/rare taxa and bacterial/fungal communities were distinct, and environmental changes mediated the balance between stochastic and deterministic processes in rare bacteria assembly. Furthermore, specific combination patterns improved the flavor quality of Zaopei by enhancing the interspecies interaction, which was closely related to rare taxa, especially rare bacteria. These findings highlighted that rare bacteria might be the keystone in involving community interaction and maintaining metabolic function, which provided a scientific foundation for better understanding and regulating the brewing microbiota from the viewpoint of microbial ecology.

Antipsychotic drugs selectively decorrelate long-range interactions in deep cortical layers.

Psychosis is characterized by a diminished ability of the brain to distinguish externally driven activity patterns from self-generated activity patterns. Antipsychotic drugs are a class of small molecules with relatively broad binding affinity for a variety of neuromodulator receptors that, in humans, can prevent or ameliorate psychosis. How these drugs influence the function of cortical circuits, and in particular their ability to distinguish between externally and self-generated activity patterns, is still largely unclear. To have experimental control over self-generated sensory feedback, we used a virtual reality environment in which the coupling between movement and visual feedback can be altered. We then used widefield calcium imaging to determine the cell type-specific functional effects of antipsychotic drugs in mouse dorsal cortex under different conditions of visuomotor coupling. By comparing cell type-specific activation patterns between locomotion onsets that were experimentally coupled to self-generated visual feedback and locomotion onsets that were not coupled, we show that deep cortical layers were differentially activated in these two conditions. We then show that the antipsychotic drug clozapine disrupted visuomotor integration at locomotion onsets also primarily in deep cortical layers. Given that one of the key components of visuomotor integration in cortex is long-range cortico-cortical connections, we tested whether the effect of clozapine was detectable in the correlation structure of activity patterns across dorsal cortex. We found that clozapine as well as two other antipsychotic drugs, aripiprazole and haloperidol, resulted in a strong reduction in correlations of layer 5 activity between cortical areas and impaired the spread of visuomotor prediction errors generated in visual cortex. Our results are consistent with the interpretation that a major functional effect of antipsychotic drugs is a selective alteration of long-range layer 5-mediated communication.

Patient-Physician Relationship in Irritable Bowel Syndrome: Review on Empathy and Stigma.

Irritable bowel syndrome (IBS) lacks a clear understanding of the disease's pathogenesis and effective treatments thus producing frustration among providers and patients, leading to the stigmatization of the disease and the patients with the syndrome. A literature search was performed to make a hermeneutical review on empathic patient-provider communication and IBS. The relationship is defined by partners being dependent on one another in the pursuit of obtaining good outcomes. It is a unique interaction depending not only on the individual qualities of each partner but also on the specific patterns of the patient-physician synergy. Empathy is crucial for any relationship. It helps to recognize the other as the other of myself, a person like me. Meanwhile, stigmatization results from identifying and labelling human differences and stereotyping persons who are linked to undesirable characteristics. IBS is at high risk of stigmatization in various contexts and settings including health care, causing patients and physicians misconceptions and distress, which in turn leads to the worsening of the disease in patients and burnout in physicians. Narrative-based medicine helps create a holistic perspective of a patient's problems and health, thus providing a tool for an empathic doctor-patient relationship that fosters mutual understanding and helps patients with IBS make sense of symptoms, increases their ability to manage their IBS in a psychologically flexible manner, subsequently helping them maintain their quality of life.

A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer's severity using multitask feature extraction.

Alzheimer's disease is the most prevalent form of dementia, which is a gradual condition that begins with mild memory loss and progresses to difficulties communicating and responding to the environment. Recent advancements in neuroimaging techniques have resulted in large-scale multimodal neuroimaging data, leading to an increased interest in using deep learning for the early diagnosis and automated classification of Alzheimer's disease. This study uses machine learning (ML) methods to determine the severity level of Alzheimer's disease using MRI images, where the dataset consists of four levels of severity. A hybrid of 12 feature extraction methods is used to diagnose Alzheimer's disease severity, and six traditional machine learning methods are applied, including decision tree, K-nearest neighbor, linear discrimination analysis, Naïve Bayes, support vector machine, and ensemble learning methods. During training, optimization is performed to obtain the best solution for each classifier. Additionally, a CNN model is trained using a machine learning system algorithm to identify specific patterns. The accuracy of the Naïve Bayes, Support Vector Machines, K-nearest neighbor, Linear discrimination classifier, Decision tree, Ensembled learning, and presented CNN architecture are 67.5%, 72.3%, 74.5%, 65.6%, 62.4%, 73.8% and, 95.3%, respectively. Based on the results, the presented CNN approach outperforms other traditional machine learning methods to find Alzheimer severity.

Abstract 2513: MAGE-A3, MAGE-A4, and PD-L1 protein show co-expression in colon, lung and bladder cancer

Abstract PD-L1 programmed death ligand 1/CD274 has been shown to be an excellent immune therapy target for many tumors. Currently, studies have shown PD-L1in combination with other immune therapy targets can improve patient outcomes generating the need for new targets. One candidate is the cancer testis antigen Melanoma Antigen Gene Family (MAGE-A) because these genes have limited expression in normal tissues and high expression in cancer. Clinical trials have already begun targeting MAGE-A3 and MAGE-A4 proteins in multiple tumor types. Protein assessment of individual MAGE-A family members has been challenging due to sequence homology as high as ninety-five percent between members. Previously, with the use of CytoSections two highly specific antibodies for immunohistochemistry to MAGE-A3 (clone OTI1H1) and MAGE-A4 (clone OTI2C1) were found. In this study, the MAGE-A3 and MAGE-A4 antibodies were screened with PD-L1 on colon, lung, and bladder cancers. Results show detection of both MAGE-A3 and MAGE-A4 in 25% of colon; 30% of lung; and 20% of bladder cancers. More than 70% of the lung and bladder cancers were positive for MAGE-A4 but only six of twenty-four colon cancers were positive for MAGE-A4. The results show 60 percent of the positive for MAGEA3 or MAGEA4 in colon, lung, and bladder cancers were also positive for PD-L1. This study suggests that MAGE-A3 and MAGE-A4 have tumor specific expression patterns and a potential to be a co-immunotherapy target with PD-L1. Citation Format: Rachel Gonzalez, YiChen Guo, Jina Yom, Bailey Gilmore, Eden Zewdu, Xi-Andy Han, Dehe Kong, Tianli Qu, Qi Ren, Xiaomin Hu, Ranran Zhang, Zhaohui Wu, Alex Strom, Xuan Liu, Wei Fu. MAGE-A3, MAGE-A4, and PD-L1 protein show co-expression in colon, lung and bladder cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2513.

CircTLK1: A novel regulator involved in VSMC phenotypic switching and the developmental process of atherosclerosis.

Phenotypic switching of vascular smooth muscle cells (VSMCs) is essential for atherosclerosis development. Circular RNA (circRNA) is a specific non-coding RNA that is produced as a closed-loop structure in mammals, and its specific expression pattern is closely related to its cell type and tissue. To clarify the roles of circTLK1 in VSMC phenotypic switching, we performed qRT-PCR, immunoblotting, and immunostaining. qRT-PCR revealed that circTLK1 was upregulated in both mouse models of atherosclerosis invivo and PDGF (platelet-derived growth factor)-BB-induced VSMCs invitro. Furthermore, the overexpression of circTLK1 promoted PDGF-BB-induced VSMC phenotypic switching. Conversely, experiments performed invivo demonstrate that the knockdown of SMC-specific circTLK1 led to a reduction in the development of atherosclerosis. The relationship between circTLK1 and miR-513a-3p and Krüppel-like factor 4 (KLF4) was detected by RNA immunoprecipitation (RIP), luciferase reporter assay, RNA pull-down, and RNA fluorescence insitu hybridization (RNA FISH). Mechanistically, circTLK1 acted as a sponge for miR-513a-3p, leading to the upregulation of KLF4, a key transcription factor for phenotypic switching. Targeting the circTLK1/miR-513a-3p/KLF4 axis may provide a potential therapeutic strategy for atherosclerosis.

Adaptive HELLO protocol for vehicular networks

In vehicular networks, the update of car Firmware Over The Air (FOTA) is becoming a challenging issue and it mainly relies on topology discovery of neighbouring nodes. Topology discovery in mobile wireless networks is usually done by using HELLO messages. Due to mobility, topology changes occur frequently and must be quickly discovered to avoid routing failures. Since the optimal HELLO frequency depends on parameters that are subject to changes (e.g., speed of nodes, density of nodes), it must be dynamically adjusted to obtain the best trade-off between the network load and the freshness of routing tables. Existing solutions assume random mobility, constant node density and average speed, which do not hold in vehicular networks because vehicles follow specific trajectory patterns (the roads) and density and speed evolve as a function of time (rush hour vs non-rush hour) and area (urban, rural, highway). In this paper, we first draw the specific features of a vehicular network at different times and spaces by analysing real datasets and then propose a dynamic neighbour discovery protocol, Vehicular Adaptive Neighbour discovery Protocol (VANP). VANP is a fully-distributed protocol that sends beacons at an optimal frequency without knowing it a priori. The objective is to reduce the frequency at which HELLO messages are sent to save bandwidth and energy while still preserving the quality of the neighbour discovery. Through extensive simulations run on real datasets, we show that the optimal HELLO frequency can be reached by maintaining a constant optimal turnover, independent from the speed of the nodes and by aiming at this turnover, nodes automatically use the optimal HELLO frequency. Results show that VANP allows the discovery of relevant neighbours by missing at most two neighbours over all scenarios and reducing the number of HELLO messages up to twice, hence saving bandwidth and energy.

Redesigned Nylon 6 Variants with Enhanced Recyclability, Ductility, and Transparency.

Geminal (gem-) disubstitution in heterocyclic monomers is an effective strategy to enhance polymer chemical recyclability by lowering their ceiling temperatures. However, the effects of specific substitution patterns on the monomer's reactivity and the resulting polymer's properties are largely unexplored. Here we show that, by systematically installing gem-dimethyl groups onto ϵ-caprolactam (monomer of nylon 6) from the α to ϵ positions, both the redesigned lactam monomer's reactivity and the resulting gem-nylon 6's properties are highly sensitive to the substitution position, with the monomers ranging from non-polymerizable to polymerizable and the gem-nylon properties ranging from inferior to far superior to the parent nylon 6. Remarkably, the nylon 6 with the gem-dimethyls substituted at the γ position is amorphous and optically transparent, with a higher Tg (by 30 °C), yield stress (by 1.5 MPa), ductility (by 3×), and lower depolymerization temperature (by 60 °C) than conventional nylon 6.

Redesigned Nylon 6 Variants with Enhanced Recyclability, Ductility, and Transparency

AbstractGeminal (gem−) disubstitution in heterocyclic monomers is an effective strategy to enhance polymer chemical recyclability by lowering their ceiling temperatures. However, the effects of specific substitution patterns on the monomer's reactivity and the resulting polymer's properties are largely unexplored. Here we show that, by systematically installing gem‐dimethyl groups onto ϵ‐caprolactam (monomer of nylon 6) from the α to ϵ positions, both the redesigned lactam monomer's reactivity and the resulting gem‐nylon 6’s properties are highly sensitive to the substitution position, with the monomers ranging from non‐polymerizable to polymerizable and the gem‐nylon properties ranging from inferior to far superior to the parent nylon 6. Remarkably, the nylon 6 with the gem‐dimethyls substituted at the γ position is amorphous and optically transparent, with a higher Tg (by 30 °C), yield stress (by 1.5 MPa), ductility (by 3×), and lower depolymerization temperature (by 60 °C) than conventional nylon 6.

DO PEOPLE SAVE ENOUGH FOR TOMORROW? THE COMPARATIVE STUDY ON WESTERN BALKANS

Due to the increase in life expectancy and the decline in the birth rate, which has an impact on the increase in the proportion of the older population, there is a need to develop a rational pension strategy. This is a behavioral economic analysis of voluntary pension funds in the Western Balkans. The main goal of the research is to determine whether fund users behave according to the well-known Richard Thaler model or have a specific pattern of behavior. According to Thaler’s model, people make financial decisions with limited rationality. They are impatient and short-sighted, which is why they focus on the present rather than the future. Younger employees mainly opt for actions that give them immediate gratification rather than planning for the future. More money flows into pension funds when people are approaching retirement, not when they are in their best working and financial position. Another peculiarity of the Western Balkans is that they have gone through a long period of transition, a period of instability, and that long-term savings were not on the agenda for the average family. The paper is based on a hypothesis that should determine whether users of voluntary pension funds in Western Balkans countries (Serbia, North Macedonia, Montenegro) increase a portion of their salary savings as they approach the retirement period. Analysing the content of the statistical reports collected in the pension funds was used as a method of data collection. A regression analysis was performed to examine the established model. Model creation and calculations were performed in IBM Amos 25 and SPSS 25. A significance level was set to α=0.05 (Hair et al., 2019). The results for Serbia, showed significant values for all three R coefficients. R square values were taken into account. Based on the results, the predictor variable (number of users by age) has significantly explained the variability of “amounts of accumulated funds by age” in all three countries of the Western Balkans. The fund users in all three countries behave similarly, paying significantly higher amounts into the fund after the age of forty. Beneficiaries aged between 20 and 39, members of a very productive labour cohort, pay lower, often symbolic amounts into the pension funds. We are talking about relatively young funds in their infancy, which are unstable or insufficiently stable. A more stable and efficient structure can be observed in the case of Serbian voluntary pension funds.

Sex Differences in the Role of Multimorbidity on Poststroke Disability: The Taiwan Stroke Registry.

Multimorbidity is common in patients who experience stroke. Less is known about the effect of specific multimorbidity patterns on long-term disability in patients with stroke. Furthermore, given the increased poststroke disability frequently seen in female vs male patients, it is unknown whether multimorbidity has a similar association with disability in both sexes. We assessed whether specific multimorbidity clusters were associated with greater long-term poststroke disability burden overall and by sex. In the Taiwan Stroke Registry, an ongoing nationwide prospective registry, patients with first-ever ischemic stroke were enrolled; this analysis is restricted to those individuals surviving to at least 6 months poststroke. Using a hierarchical clustering approach, clusters of prestroke multimorbidity were generated based on 16 risk factors; the algorithm identified 5 distinct clusters. The association between clusters and 12-month poststroke disability, defined using the modified Rankin Scale (mRS), was determined using logistic regression models, with additional models stratified by sex. The longitudinal association between multimorbidity and functional status change was assessed using mixed-effects models. Nine-thousand eight hundred eighteen patients with first-ever ischemic stroke were included. The cluster with no risk factors was the reference, "healthier" risk group (N = 1,373). Patients with a cluster profile of diabetes, peripheral artery disease (PAD), and chronic kidney disease (CKD) (N = 1882) had significantly greater disability (mRS ≥ 3) at 1 month (OR [95% CI] = 1.36 [1.13-1.63]), 3 months (OR [95% CI] = 1.27 [1.04-1.55]), and 6 months (OR [95% CI] = 1.30 [1.06-1.59]) but not at 12 months (OR [95% CI] = 1.16 [0.95-1.42]) than patients with a healthier risk factor profile. In the sex-stratified analysis, the associations with this risk cluster remained consistent in male patients (OR [95% CI] = 1.42 [1.06-1.89]) at 12 months, who also had a higher comorbidity burden, but not in female patients (OR [95% CI] = 0.95 [0.71-1.26]), who had higher proportions of severe strokes and severe disability (p-interaction = 0.04). Taiwanese patients with multimorbidity, specifically the concurrent presence of diabetes, PAD, and CKD, had higher odds of a worse functional outcome in the first 6 months poststroke. Clusters of multimorbidity may be less informative for long-term disability in female patients. Further studies should evaluate other mechanisms for worse disability in female patients poststroke.

Computationally inferred cell-type specific epigenome-wide DNA methylation analysis unveils distinct methylation patterns among immune cells for HIV infection in three cohorts.

Epigenome-wide association studies (EWAS) have identified CpG sites associated with HIV infection in blood cells in bulk, which offer limited knowledge of cell-type specific methylation patterns associated with HIV infection. In this study, we aim to identify differentially methylated CpG sites for HIV infection in immune cell types: CD4+ T-cells, CD8+ T-cells, B cells, Natural Killer (NK) cells, and monocytes. Applying a computational deconvolution method, we performed a cell-type based EWAS for HIV infection in three independent cohorts (Ntotal = 1,382). DNA methylation in blood or in peripheral blood mononuclear cells (PBMCs) was profiled by an array-based method and then deconvoluted by Tensor Composition Analysis (TCA). The TCA-computed CpG methylation in each cell type was first benchmarked by bisulfite DNA methylation capture sequencing in a subset of the samples. Cell-type EWAS of HIV infection was performed in each cohort separately and a meta-EWAS was conducted followed by gene set enrichment analysis. The meta-analysis unveiled a total of 2,021 cell-type unique significant CpG sites for five inferred cell types. Among these inferred cell-type unique CpG sites, the concordance rate in the three cohorts ranged from 96% to 100% in each cell type. Cell-type level meta-EWAS unveiled distinct patterns of HIV-associated differential CpG methylation, where 74% of CpG sites were unique to individual cell types (false discovery rate, FDR <0.05). CD4+ T-cells had the largest number of unique HIV-associated CpG sites (N = 1,624) compared to any other cell type. Genes harboring significant CpG sites are involved in immunity and HIV pathogenesis (e.g. CD4+ T-cells: NLRC5, CX3CR1, B cells: IFI44L, NK cells: IL12R, monocytes: IRF7), and in oncogenesis (e.g. CD4+ T-cells: BCL family, PRDM16, monocytes: PRDM16, PDCD1LG2). HIV-associated CpG sites were enriched among genes involved in HIV pathogenesis and oncogenesis that were enriched among interferon-α and -γ, TNF-α, inflammatory response, and apoptotic pathways. Our findings uncovered computationally inferred cell-type specific modifications in the host epigenome for people with HIV that contribute to the growing body of evidence regarding HIV pathogenesis.

Microbiome changes through the ontogeny of the marine sponge Crambe crambe

BackgroundPoriferans (sponges) are highly adaptable organisms that can thrive in diverse marine and freshwater environments due, in part, to their close associations with internal microbial communities. This sponge microbiome can be acquired from the surrounding environment (horizontal acquisition) or obtained from the parents during the reproductive process through a variety of mechanisms (vertical transfer), typically resulting in the presence of symbiotic microbes throughout all stages of sponge development. How and to what extent the different components of the microbiome are transferred to the developmental stages remain poorly understood. Here, we investigated the microbiome composition of a common, low-microbial-abundance, Atlantic-Mediterranean sponge, Crambe crambe, throughout its ontogeny, including adult individuals, brooded larvae, lecithotrophic free-swimming larvae, newly settled juveniles still lacking osculum, and juveniles with a functional osculum for filter feeding.ResultsUsing 16S rRNA gene analysis, we detected distinct microbiome compositions in each ontogenetic stage, with variations in composition, relative abundance, and diversity of microbial species. However, a particular dominant symbiont, Candidatus Beroebacter blanensis, previously described as the main symbiont of C. crambe, consistently occurred throughout all stages, an omnipresence that suggests vertical transmission from parents to offspring. This symbiont fluctuated in relative abundance across developmental stages, with pronounced prevalence in lecithotrophic stages. A major shift in microbial composition occurred as new settlers completed osculum formation and acquired filter-feeding capacity. Candidatus Beroebacter blanensis decreased significatively at this point. Microbial diversity peaked in filter-feeding stages, contrasting with the lower diversity of lecithotrophic stages. Furthermore, individual specific transmission patterns were detected, with greater microbial similarity between larvae and their respective parents compared to non-parental conspecifics.ConclusionsThese findings suggest a putative vertical transmission of the dominant symbiont, which could provide some metabolic advantage to non-filtering developmental stages of C. crambe. The increase in microbiome diversity with the onset of filter-feeding stages likely reflects enhanced interaction with environmental microbes, facilitating horizontal transmission. Conversely, lower microbiome diversity in lecithotrophic stages, prior to filter feeding, suggests incomplete symbiont transfer or potential symbiont digestion. This research provides novel information on the dynamics of the microbiome through sponge ontogeny, on the strategies for symbiont acquisition at each ontogenetic stage, and on the potential importance of symbionts during larval development.

Investigating female versus male differences in white matter neuroplasticity associated with complex visuo-motor learning

Magnetic resonance imaging (MRI) has increasingly been used to characterize structure–function relationships during white matter neuroplasticity. Biological sex differences may be an important factor that affects patterns of neuroplasticity, and therefore impacts learning and rehabilitation. The current study examined a participant cohort before and after visuo-motor training to characterize sex differences in microstructural measures. The participants (N = 27) completed a 10-session (4 week) complex visuo-motor training task with their non-dominant hand. All participants significantly improved movement speed and their movement speed variability over the training period. White matter neuroplasticity in females and males was examined using fractional anisotropy (FA) and myelin water fraction (MWF) along the cortico-spinal tract (CST) and the corpus callosum (CC). FA values showed significant differences in the middle portion of the CST tract (nodes 38–51) across the training period. MWF showed a similar cluster in the inferior portion of the tract (nodes 18–29) but did not reach significance. Additionally, at baseline, males showed significantly higher levels of MWF measures in the middle body of the CC. Combining data from females and males would have resulted in reduced sensitivity, making it harder to detect differences in neuroplasticity. These findings offer initial insights into possible female versus male differences in white matter neuroplasticity during motor learning. This warrants investigations into specific patterns of white matter neuroplasticity for females versus males across the lifespan. Understanding biological sex-specific differences in white matter neuroplasticity may have significant implications for the interpretation of change associated with learning or rehabilitation.

Differentially Expressed Genes in Atopic Dermatitis: Bioinformatics analysis of pooled microarray gene expression datasets in Gene Expression Omnibus

Introduction:&#x0D; Atopic dermatitis (AD) is a chronic and refractory inflammatory skin disease characterized by relapsing eczematous and pruritic skin lesions. Understanding the specific gene expression patterns associated with AD is crucial for advancing diagnosis and targeted treatment development. Using bioinformatics methods, candidate genes and biological pathways involved in AD pathogenesis were identified based on gene expression profiles in the Gene Expression Omnibus (GEO) database.&#x0D; Materials and Methods:&#x0D; A comprehensive analysis of four pooled transcriptomic datasets obtained from the Gene Expression Omnibus (GEO) database were conducted. Differential gene expression analysis was performed using the GEO2R. The differentially expressed genes (DEGs) between lesion skin of AD patients and normal skin of individuals were analyzed using the Gene Ontology (GO) term enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) network to explore the functional roles of these genes.&#x0D; Results:&#x0D; Among the patient-level gene expression datasets, we identified 133 shared DEGs, consisting of 48 upregulated genes and 85 downregulated genes. GO analyses revealed these DEGs to be significantly enriched in biological processes including inflammatory responses, cytokine-mediated signaling pathway. These DEGs were also enriched in the KEGG pathway, including viral protein interaction with cytokine and cytokine receptor, C-type lectin receptor signaling pathway, cytokine-cytokine receptor interaction, JAK-STAT signaling pathway, and Adipocytokine signaling pathway.&#x0D; Conclusion:&#x0D; By comparing with other studies using the same method, we found that in addition to the already confirmed pathways such as inflammatory response, different studies have found changes in different hub genes and metabolic pathways, which prompts us to develop individualized treatments for AD.