Federated Graph Anomaly Detection via Contrastive Self-Supervised Learning.

Attribute graph anomaly detection aims to identify nodes that significantly deviate from the majority of normal nodes, and has received increasing attention due to the ubiquity and complexity of graph-structured data in various real-world scenarios. However, current mainstream anomaly detection methods are primarily designed for centralized settings, which may pose privacy leakage risks in certain sensitive situations. Although federated graph learning offers a promising solution by enabling collaborative model training in distributed systems while preserving data privacy, a practical challenge arises as each client typically possesses a limited amount of graph data. Consequently, naively applying federated graph learning directly to anomaly detection tasks in distributed environments may lead to suboptimal performance results. We propose a federated graph anomaly detection framework via contrastive self-supervised learning (CSSL) federated CSSL anomaly detection framework (FedCAD) to address these challenges. FedCAD updates anomaly node information between clients via federated learning (FL) interactions. First, FedCAD uses pseudo-label discovery to determine the anomaly node of the client preliminarily. Second, FedCAD employs a local anomaly neighbor embedding aggregation strategy. This strategy enables the current client to aggregate the neighbor embeddings of anomaly nodes from other clients, thereby amplifying the distinction between anomaly nodes and their neighbor nodes. Doing so effectively sharpens the contrast between positive and negative instance pairs within contrastive learning, thus enhancing the efficacy and precision of anomaly detection through such a learning paradigm. Finally, the efficiency of FedCAD is demonstrated by experimental results on four real graph datasets.

Towards net zero: A technological review on the potential of space-based solar power and wireless power transmission.

The global need for energy is increasing at a high rate and is expected to double or increase by 50%, according to some studies, in 30 years. As a result, it is essential to look into alternative methods of producing power. Solar photovoltaic (PV) power plants utilize the sun's clean energy, but they're not always dependable since they depend on weather patterns and requires vast amount of land. Space-based solar power (SBSP) has emerged as the potential solution to this issue. SBSP can provide 24/7 baseload carbon-free electricity with power density over 10 times greater than terrestrial alternatives while requiring far less land. Solar power is collected and converted in space to be sent back to Earth via Microwave or laser wirelessly and used as electricity. However, harnessing its full potential necessitates tackling substantial technological obstacles in wireless power transmission across extensive distances in order to efficiently send power to receivers on the ground. When it comes to achieving a net-zero goal, the SBSP is becoming more viable option. This paper presents a review of wireless power transmission systems and an overview of SBSP as a comprehensive system. To introduce the state-of-the-art information, the properties of the system and modern SBSP models along with application and spillover effects with regard to different sectors was examined. The challenges and risks are discussed to address the key barriers for successful project implementation. The technological obstacles stem from the fact that although most of the technology is already available none are actually efficient enough for deployment so with, private enterprises entering space race and more efficient system, the cost of the entire system that prevented this notion from happening is also decreasing. With incremental advances in key areas and sustained investment, SBSP integrated with other renewable could contribute significantly to cross-sector decarbonization.

Personalized Federated Graph Learning on Non-IID Electronic Health Records.

Understanding the latent disease patterns embedded in electronic health records (EHRs) is crucial for making precise and proactive healthcare decisions. Federated graph learning-based methods are commonly employed to extract complex disease patterns from the distributed EHRs without sharing the client-side raw data. However, the intrinsic characteristics of the distributed EHRs are typically non-independent and identically distributed (Non-IID), significantly bringing challenges related to data imbalance and leading to a notable decrease in the effectiveness of making healthcare decisions derived from the global model. To address these challenges, we introduce a novel personalized federated learning framework named PEARL, which is designed for disease prediction on Non-IID EHRs. Specifically, PEARL incorporates disease diagnostic code attention and admission record attention to extract patient embeddings from all EHRs. Then, PEARL integrates self-supervised learning into a federated learning framework to train a global model for hierarchical disease prediction. To improve the performance of the client model, we further introduce a fine-tuning scheme to personalize the global model using local EHRs. During the global model updating process, a differential privacy (DP) scheme is implemented, providing a high-level privacy guarantee. Extensive experiments conducted on the real-world MIMIC-III dataset validate the effectiveness of PEARL, demonstrating competitive results when compared with baselines.

Cyber-agricultural systems for crop breeding and sustainable production.

The cyber-agricultural system (CAS) represents an overarching framework of agriculture that leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and scalable cyberinfrastructure (CI) in both breeding and production agriculture. We discuss the recent progress and perspective of the three fundamental components of CAS - sensing, modeling, and actuation - and the emerging concept of agricultural digital twins (DTs). We also discuss how scalable CI is becoming a key enabler of smart agriculture. In this review we shed light on the significance of CAS in revolutionizing crop breeding and production by enhancing efficiency, productivity, sustainability, and resilience to changing climate. Finally, we identify underexplored and promising future directions for CAS research and development.

A review on self-healing featured soft robotics.

Soft robots are becoming more popular because they can solve issues stiff robots cannot. Soft component and system design have seen several innovations recently. Next-generation robot-human interactions will depend on soft robotics. Soft material technologies integrate safety at the material level, speeding its integration with biological systems. Soft robotic systems must be as resilient as biological systems in unexpected, uncontrolled situations. Self-healing materials, especially polymeric and elastomeric ones, are widely studied. Since most currently under-development soft robotic systems are composed of polymeric or elastomeric materials, this finding may provide immediate assistance to the community developing soft robots. Self-healing and damage-resilient systems are making their way into actuators, structures, and sensors, even if soft robotics remains in its infancy. In the future, self-repairing soft robotic systems composed of polymers might save both money and the environment. Over the last decade, academics and businesses have grown interested in soft robotics. Despite several literature evaluations of the soft robotics subject, there seems to be a lack of systematic research on its intellectual structure and development despite the rising number of articles. This article gives an in-depth overview of the existing knowledge base on damage resistance and self-healing materials' fundamental structure and classifications. Current uses, problems with future implementation, and solutions to those problems are all included in this overview. Also discussed are potential applications and future directions for self-repairing soft robots.

Non-Invasive Portable Technologies for Monitoring Breast Cancer Related Lymphedema to Facilitate Telehealth: A Scoping Review.

Breast cancer related lymphedema (BCRL) is a common, debilitating condition that can affect up to one in five breast cancer surviving patients (BCSP). BCRL can significantly reduce the quality of life (QOL) of patients and poses a significant challenge to healthcare providers. Early detection and continuous monitoring of lymphedema is crucial for the development of client-centered treatment plans for post-cancer surgery patients. Therefore, this comprehensive scoping review aimed to investigate the current technology methods used for the remote monitoring of BCRL and their potential to facilitate telehealth in the treatment of lymphedema. Initially, five electronic databases were systematically searched and analyzed following the PRISMA flow diagram. Studies were included, specifically if they provided data on the effectiveness of the intervention and were designed for the remote monitoring of BCRL. A total of 25 included studies reported 18 technological solutions to remotely monitor BCRL with significant methodological variation. Additionally, the technologies were categorized by method of detection and wearability. The findings of this comprehensive scoping review indicate that state-of-the-art commercial technologies were found to be more appropriate for clinical use than home monitoring, with portable 3D imaging tools being popular (SD 53.40) and accurate (correlation 0.9, p 0.05) for evaluating lymphedema in both clinic and home settings with expert practitioners and therapists. However, wearable technologies showed the most future potential for accessible and clinical long-term lymphedema management with positive telehealth outcomes. In conclusion, the absence of a viable telehealth device highlights the need for urgent research to develop a wearable device that can effectively track BCRL and facilitate remote monitoring, ultimately improving the quality of life for patients following post-cancer treatment.

A small molecule potent IRAK4 inhibitor abrogates lipopolysaccharide-induced macrophage inflammation in-vitro and in-vivo.

Increasing evidence supports vanillin and its analogs as potent toll-like receptor signaling inhibitors that strongly attenuate inflammation, though, the underlying molecular mechanism remains elusive. Here, we report that vanillin inhibits lipopolysaccharide (LPS)-induced toll-like receptor 4 activation in macrophages by targeting the myeloid differentiation primary-response gene 88 (MyD88)-dependent pathway through direct interaction and suppression of interleukin-1 receptor-associated kinase 4 (IRAK4) activity. Moreover, incubation of vanillin in cells expressing constitutively active forms of different toll-like receptor 4 signaling molecules revealed that vanillin could only able to block the ligand-independent constitutively activated IRAK4/1 or its upstream molecules-associated NF-κB activation and NF-κB transactivation along with the expression of various proinflammatory cytokines. A significant inhibition of LPS-induced IRAK4/MyD88, IRAK4/IRAK1, and IRAK1/TRAF6 association was evinced in response to vanillin treatment. Furthermore, mutations at Tyr262 and Asp329 residues in IRAK4 or modifications of 3-OMe and 4-OH side groups in vanillin, significantly reduced IRAK4 activity and vanillin function, respectively. Mice pretreated with vanillin followed by LPS challenge markedly impaired LPS-induced IRAK4 activation and inflammation in peritoneal macrophages. Thus, the present study posits vanillin as a novel and potent IRAK4 inhibitor and thus providing an opportunity for its therapeutic application in managing various inflammatory diseases.

An integrated P2P framework for E-learning.

The focus of this paper is to design and develop a Peer-to-Peer Presentation System (P2P-PS) that supports E-learning through live media streaming coupled with a P2P shared whiteboard. The participants use the "ask doubt" feature to raise and resolve doubts during a session of ongoing presentation. The proposed P2P-PS system preserves causality between ask doubt and its resolution while disseminating them to all the participants. A buffered approach is employed to enhance the performance of P2P shared whiteboard, which may be used either in tandem with live media streaming or in standalone mode. The proposed system further extends P2P interactions on stored contents (files) built on top of a P2P file sharing and searching module with additional features. The added features allow the creation of mash-up presentations with annotations, posts, comments on audio, video, and PDF files as well as a discussion forum. We have implemented the P2P file sharing and searching system on the de Bruijn graph-based overlay for low latency. Extensive experiments were carried out on Emulab to validate the P2P-PS system using 200 physical nodes.

Motifs enable communication efficiency and fault-tolerance in transcriptional networks.

Analysis of the topology of transcriptional regulatory networks (TRNs) is an effective way to study the regulatory interactions between the transcription factors (TFs) and the target genes. TRNs are characterized by the abundance of motifs such as feed forward loops (FFLs), which contribute to their structural and functional properties. In this paper, we focus on the role of motifs (specifically, FFLs) in signal propagation in TRNs and the organization of the TRN topology with FFLs as building blocks. To this end, we classify nodes participating in FFLs (termed motif central nodes) into three distinct roles (namely, roles A, B and C), and contrast them with TRN nodes having high connectivity on the basis of their potential for information dissemination, using metrics such as network efficiency, path enumeration, epidemic models and standard graph centrality measures. We also present the notion of a three tier architecture and how it can help study the structural properties of TRN based on connectivity and clustering tendency of motif central nodes. Finally, we motivate the potential implication of the structural properties of motif centrality in design of efficient protocols of information routing in communication networks as well as their functional properties in global regulation and stress response to study specific disease conditions and identification of drug targets.

Discrete diffusion models to study the effects of Mg2+ concentration on the PhoPQ signal transduction system.

BACKGROUND: The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This modeling method requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational overhead. This objective motivates the use of new methods that can transform the problem from energy and affinity based modeling to information theory based modeling. To achieve this, we transform all dynamics within the cell into a random event time, which is specified through an information domain measure like probability distribution. This allows us to use the "in silico" stochastic event based modeling approach to find the molecular dynamics of the system. RESULTS: In this paper, we present the discrete event simulation concept using the example of the signal transduction cascade triggered by extra-cellular Mg2+ concentration in the two component PhoPQ regulatory system of Salmonella Typhimurium. We also present a model to compute the information domain measure of the molecular transport process by estimating the statistical parameters of inter-arrival time between molecules/ions coming to a cell receptor as external signal. This model transforms the diffusion process into the information theory measure of stochastic event completion time to get the distribution of the Mg2+ departure events. Using these molecular transport models, we next study the in-silico effects of this external trigger on the PhoPQ system. CONCLUSIONS: Our results illustrate the accuracy of the proposed diffusion models in explaining the molecular/ionic transport processes inside the cell. Also, the proposed simulation framework can incorporate the stochasticity in cellular environments to a certain degree of accuracy. We expect that this scalable simulation platform will be able to model more complex biological systems with reasonable accuracy to understand their temporal dynamics.

Chromatin remodeling in silico: a stochastic model for SWI/SNF.

Beside their contribution in DNA packaging, histone-core particles modulate the transcription machinery access to the DNA through dynamic chromatin structure. Chromatin remodeling complexes perturb such modulations through diverse mechanisms. SWI/SNF is a well-studied highly conserved chromatin remodeling complex that is ubiquitous across eukaryotes. Rigorous study of experimental observations suggests randomness in dynamics of SWI/SNF in cis chromatin remodeling process. In this work we propose a stochastic computational model that captures such fluctuations. We incorporate the physiological properties of the process through parametric microevents. Each microevent is then associated with a stochastic model that couples its random temporal and spatial dynamics with the energy landscape of the remodeling process. We further show that DNA sequence stacks and friction force have negligible effect on chromatin remodeling. Our approach shows a promising approximation to the force impinged on the DNA by the SWI/SNF complex. We validate our model predictions with several experimental data sets. The proposed model suggest that the in cis translocation rate of histone-core particle follows a Gamma distribution. By carefully analyzing the simulation results we conjecture that SWI/SNF chromatin remodeling has low energy efficiency (<0.30). We use our model to recapitulate the dynamics of the parallel remodeling processes that occur in close proximity across a typical eukaryotic genome. Our results suggest that the orchestrated chromatin remodeling makes few kilobase-pairs of the DNA accessible to the transcription machinery in a timely manner.

Maintaining defender's reputation in anomaly detection against insider attacks.

We address issues related to establishing a defender's reputation in anomaly detection against two types of attackers: 1) smart insiders, who learn from historic attacks and adapt their strategies to avoid detection/punishment, and 2) naïve attackers, who blindly launch their attacks without knowledge of the history. In this paper, we propose two novel algorithms for reputation establishment--one for systems solely consisting of smart insiders and the other for systems in which both smart insiders and naïve attackers are present. The theoretical analysis and performance evaluation show that our reputation-establishment algorithms can significantly improve the performance of anomaly detection against insider attacks in terms of the tradeoff between detection and false positives.

Comparing 2-nt 3' overhangs against blunt-ended siRNAs: a systems biology based study.

In this study, we formulate a computational reaction model following a chemical kinetic theory approach to predict the binding rate constant for the siRNA-RISC complex formation reaction. The model allowed us to study the potency difference between 2-nt 3' overhangs against blunt-ended siRNA molecules in an RNA interference (RNAi) system. The rate constant predicted by this model was fed into a stochastic simulation of the RNAi system (using the Gillespie stochastic simulator) to study the overall potency effect. We observed that the stochasticity in the transcription/translation machinery has no observable effects in the RNAi pathway. Sustained gene silencing using siRNAs can be achieved only if there is a way to replenish the dsRNA molecules in the cell. Initial findings show about 1.5 times more blunt-ended molecules will be required to keep the mRNA at the same reduced level compared to the 2-nt overhang siRNAs. However, the mRNA levels jump back to saturation after a longer time when blunt-ended siRNAs are used. We found that the siRNA-RISC complex formation reaction rate was 2 times slower when blunt-ended molecules were used pointing to the fact that the presence of the 2-nt overhangs has a greater effect on the reaction in which the bound RISC complex cleaves the mRNA.