Childhood anxious solitude/withdrawal (AS) predicts social anxiety disorder symptoms (SAS) in childhood and adolescence. However, the nature and timing of transactions between AS and SAS across development and the impact of ecological transitions is poorly understood. This investigation modeled cross-lagged effects between AS and SAS from 4th to 7th grade (approximately 9-12 years of age), to evaluate increased transactions after the middle school transition (MST) in the fall of 6th grade and thereafter. Biological sex differences in transactions were also tested. Participants were 230 American children (57% girls), half of whom were oversampled for AS. Peers nominated children for AS and children self-reported SAS in 4th through 7th grade. Results of a multigroup (biological sex) auto-regressive cross-lagged panel model revealed both stability in AS and SAS from 4th through 7th grade, as well as significantly more transactions between AS and SAS after the MST and during the first two years of middle school than during the last two years of elementary school. AS predicted increases in SAS just after the MST (spring 5th to fall 6th grade), and during the first year (fall to spring 6th grade) and second year (fall to spring 7th grade) of middle school. Conversely, SAS predicted an increase in AS from the spring of 6th grade to the fall of 7th grade. Most transactions occurred for both sexes, but several sex-specific transactions are also described. Results support a transactional model of AS and SAS co-development in early adolescence and the importance of ecological transitions.
Private equity (PE) investment in US hospitals has attracted substantial policy and research attention, but empirical work has been limited by fragmented and inconsistent transaction data. We aimed to construct a more comprehensive and validated dataset of PE ownership of US hospitals and to provide a practical guide for using these data in research. We integrated 6 major commercial deal databases to identify PE investments in US hospitals from 2000 to 2024. We filtered transactions to PE-related hospital deals, matched targets to American Hospital Association (AHA) and the Centers for Medicare & Medicaid Services (CMS) hospital identifiers, manually verified uncertain matches, reconciled duplicate transactions across sources, expanded system-level deals to constituent hospitals, and verified deal and exit dates. We identified 141 unique PE deals involving 555 unique short-term acute care hospitals, corresponding to 721 hospital-deal observations. The 6 databases differed substantially in deal coverage, deal type, and whether transactions were reported at the hospital or system level. Reliance on a single source would therefore omit many valid deals and could produce biased or incomplete analytic samples. We also found that linking transactions to stable hospital identifiers required substantial manual verification due to system-level transactions, inconsistent reporting, and identifier changes over time. Accurate study of PE ownership in hospitals requires multisource data construction, transparent validation, and careful linkage to stable hospital identifiers. This harmonized dataset and workflow provide infrastructure for more accurate, transparent, and replicable research on PE ownership in the US hospital sector.
Internet of Vehicles (IoV) and IoT environment require decentralized platforms that can support a high number of transactions and provide high security and privacy assurance. This study suggests a reputation-aware, zero-knowledge proof (ZKP) based, dynamically sharded smart contract system that is able to process scalable and privacy-preserving transactions. The suggested architecture highly incorporates dynamic sharding, ZKP-based verification, decentralized smart contracts and reputation-based selection of leaders to jump over the scalability, trusting and performance limitations of traditional blockchain systems. There are also extensive experimental assessments that occur within 100-1000 transactions per second (tps) and batch sizes of 10, 30, 50, and 100. Findings indicate that the given framework demonstrates the ability to scale throughput linearly to about 1000 tps, and Enhanced Fabric and Ethereum reach throughput saturation at 140-150 and 15-20 tps, respectively. The proposed system has an average latency of less than 500 ms at an arrival rate of 1000 tps whereas at the same rate, baseline approaches have a latency of over 8000 ms with larger batch sizes. The success rate of the transaction is always above 97, which is due to the isolation of reputation and adaptive scheduling of shards. Moreover, the framework decreases 40-50% and 45-50% the computation overhead and the cost of communication respectively, over heavyweight baseline schemes. These results show that the synergistic implementation of ZKP, dynamic sharding, decentralized smart contracts, and reputation-aware control are a scalable solution with high throughput IoT and IoV applications that is efficient and secure.
Linker histone H1, the most abundant chromatin protein, condenses chromatin, modulates DNA transactions such as transcription and DNA replication/repair, and participates in differentiation, development, and tumorigenesis. While recent studies indicate that nucleosomes are clustered as condensed chromatin domains in higher eukaryotic cells, how histone H1 mechanically condenses chromatin remains unclear. Here, using a combination of direct visualization of single-H1 molecules in living human cells and multiscale molecular dynamics simulations, we demonstrate that the majority of H1 behaves like a liquid inside chromatin domains, rather than binding stably to nucleosomes as suggested by the traditional model. H1 functions as a liquid-like "glue," mediating dynamic multivalent electrostatic interactions between nucleosomes within chromatin domains. Consistently, rapid depletion of H1.2 leads to decondensed chromatin domains both in cells and in silico. Our findings suggest that the H1 "glue" condenses chromatin domains while keeping them fluid and accessible, thereby supporting essential DNA transactions.
Prior research established positive ties between intimate partners' self-esteem (SE) and support interactions, but evidence on whether intraindividual changes in SE and supportive dyadic coping are associated over time remains scarce. The present study tested prominent theories suggesting a reciprocal prospective relationship between SE, one's own and partner's supportive dyadic coping using 14 waves of nationally representative data totaling 13,683 young adults from Germany. We used random intercept cross-lagged panel models for data analysis. Our findings revealed significant reciprocal within-person transactions between SE, one's own and partner's dyadic coping over time. We conclude that high SE may boost positive support interactions in intimate relationships. At the same time, giving and receiving positive support in times of stress may strengthen intimate partners' SE on the long run. Overall, our findings provide robust evidence that people's support interactions and SE development go hand in hand within the context of intimate partner relationships.
Regulatory pathways strongly influence how drugs are developed, financed, and sold. This article compares the FDA's 505(b)(1) and 505(b)(2) routes, showing how the latter reduces both the development risk and the probability of postapproval transactions, and identifies strategies that can restore attractive, risk-adjusted investment returns.
Equipment utilization in hospitals often remains below 60% due to departmental fragmentation and information asymmetry. Hospitals of all sizes face pressure to optimize equipment investments, yet coordinated sharing mechanisms remain underutilized. We evaluated an equipment allocation center (EAC) that combines centralized coordination with economic incentives to improve equipment sharing across departments. We analyzed equipment sharing data from a 560-bed tertiary hospital in eastern China for the calendar year 2024. The EAC maintains 120 pooled devices while providing real-time tracking of 1367 items across 29 clinical departments. An economic mechanism charges borrowing departments depreciation-based fees while crediting lending departments with 80% of these fees as revenue. We assessed operational performance, network topology, and economic outcomes stratified by department size and specialty. The EAC coordinated 1558 equipment transactions with 100% departmental participation. Equipment utilization improved from 52.3% to 78.6%, a gain of 26.3 percentage points. Average response time was 40 minutes for routine requests; emergency fulfillment reached 98.5%. Annual direct benefits totaled 1.36 million Chinese Yuan (CNY), with estimated procurement avoidance of 5.09 million CNY. The network showed hub-spoke topology; without the central hub, 17 of 29 departments would lose sharing access entirely. Small departments achieved the highest benefit-per-bed ratios: the intensive care unit with 16 beds realized 5600 CNY per bed annually. Centralized equipment coordination with economic incentives markedly improves utilization and yields considerable financial returns. The model particularly benefits smaller departments by enabling equipment access without ownership requirements. Hub-based architecture creates operational efficiency but also coordination dependency requiring contingency planning.
Block verification is very important in order to get people to agree on blockchain systems that use Proof-of-Work (PoW). In platforms like Ethereum, miners don't get paid directly for verifying transactions. This is called the Verifier's Dilemma since miners have to choose between putting their computing power toward honest verification or more profitable mining activities. This paper provides a comprehensive analysis of the Verifier's Dilemma and its consequences for the fairness and efficacy of decentralized networks, especially those that provide nascent Internet of Things (IoT) security frameworks. Data from around 200,000 smart contracts has been utilized to model the costs of verification in the actual world. This data includes the CPU execution times of the contracts. Gaussian Mixture Models is used to improve these data distributions, and XGBoost is used to estimate CPU time from Used Gas values. This made it possible to simulate verification behavior in a realistic way. Three mitigation strategies-parallelization, deliberate invalid-block insertion, and the integration of ommer blocks are analyzed in order to determine their efficacy in diminishing miners' incentives to avoid verification. Our results show that the severity of the Verifier's Dilemma is greatly affected by the choice of mitigation method and other factors like miner hash power and ommer block rates. These findings not only help us understand how verification works in PoW blockchains, but they also help us design secure and durable blockchain-based IoT systems, where integrity, transparency, and strong consensus are all important.
Online fraud cases have increased significantly and globally during the past decade. In Malaysia, a total of 36,936 fraud cases were reported in 2023, involving a total loss of $12.8 billion per year. Online commercial transactions, including but not limited to financial borrowing, were among the most reported fraud cases. Financial insecurity, social isolation, and loneliness are some of the multiple factors that predispose an individual to be susceptible to fraud-related activities. Victims of online fraud have been found to be older and female and to have higher scores on the impulsivity measures of urgency and sensation seeking, and higher addiction measures. The impact of fraud on the victims can lead to both physical and mental health problems and adverse mental health outcomes. This report describes 2 cases of brief psychotic disorder that were associated with stress from being victimized by online fraud-related behavior. Both cases involved individuals experiencing psychological distress following an online fraud.
Despite 'Swimmers Itch' having been first reported in the Transactions of the Royal Society of South Australia in 1941, and shortly afterwards in the New Zealand Medical Journal in 1944, there remain few publications in the Australasian dermatology journals.
DNA-protein crosslinks (DPCs) are toxic DNA lesions that block all DNA transactions including replication and transcription, and the consequences of impaired DNA-protein crosslink repair (DPCR) are severe. At the cellular level, impaired DPCR leads to the formation of double strand breaks, genomic instability, and cell death, while at the organismal level, it is associated with cancer, aging, and neurodegeneration. Despite its importance, the mechanisms of DPCR at the organismal level are largely unknown. Proteases play a central role in DPCR, as they remove proteinaceous part of the DPCs, while the peptide remnant crosslinked to DNA is subsequently removed by other repair factors. We characterized the role of putative protease ACRC/GCNA (ACidic Repeat Containing/Germ Cell Nuclear Antigen) in DPCR at the organismal level. For this purpose, we have created new animal models with CRISPR/Cas system: two zebrafish lines with inactive Acrc. We were able to overcome the early embryonic lethality caused by Acrc inactivation by injecting Acrc-WT messenger RNA and have created a viable animal model to study the role of Acrc in adult tissues. We identified histone H3, topoisomerases 1 and 2, Dnmt1, Parp1, Polr3a, and Mcm2 as putative DPC substrates of Acrc. We have shown that Acrc is essential for vertebrate development, and that the mechanism behind it is DPC removal.
The rapid growth of Internet of Things (IoT) devices in smart grids and industrial control systems means that the global state has attained a level of technological evolution. Yet this growth has also created an enormous attack surface with millions of vulnerable endpoints, thus revealing inherent weaknesses of traditional security models. These conventional systems are fraught with data integrity challenges, single points of failure, and no proactive defense against new, adaptive cyber-physical threats. To overcome these limitations, this paper presents "Causio-TwinChain," a new security model that synergistically integrates three leading-edge technologies to establish a proactive, self-diagnostic, and tamper-resistant security framework for critical IoT infrastructure. A digital twin is a virtual replica that can monitor physical devices in real time via sandboxing. A permissioned blockchain provides an immutable, tamper-proof ledger for all device data and transactions, ensuring data integrity and auditability. Two kinds of machine-learning engines form the core intelligence: contrastive Learning, which detects subtle anomalies by modeling normal operations; and structural causal Learning, which diagnoses root causes of security incidents and predicts their potential impact. The model's superior efficacy is demonstrated on an industrial IoT dataset. Causio-TwinChain yielded a 15.3% higher F1-score in novel attack detection, and reduced the mean time for incident diagnosis by 68% compared to benchmark intrusion detection systems. This model reduced the false-positive rate by 22%, demonstrating its robustness in noisy environments. Moving beyond mere attack detection to explainable diagnosis and predictive mitigation, this work establishes a new benchmark for building proactive, resilient, and self-healing security frameworks that safeguard the most critical IoT applications and enhance trust and continuity in operational services.
The pool of free intracellular Mg2+ varies among tissues with the highest concentration measured in muscle tissue and the lowest measured in immune cells and in the brain. Here we investigate the impact of free Mg2+ on the fidelity of human DNA ligase I (LIG1). LIG1 is the major DNA ligase and is required to complete DNA replication, recombination and repair pathways. Biallelic hypomorphic variants of LIG1 cause immunodeficiency-96. We employed steady-state kinetics to compare fidelity of LIG1 towards a damaged nucleobase at the 3'- hydroxyl side of a nicked DNA substrate. The fidelity for discrimination between a damaged and undamaged nick increases by 21-fold when the free Mg2+ concentration is decreased from 1.0 to 0.2 mM. This has important implications for neurodegenerative and immune diseases, because the brain and the immune system are reported to have free Mg2+ concentration in the range from 0.2 to 0.4 mM. We examined a recently characterized minor variant of LIG1, K845N, which has a protective effect in Huntington's disease, and found that the fidelity of K845N LIG1 is also enhanced as free Mg2+ decreases. This increase in fidelity is mainly due to the increased release of the AMP-DNA intermediate from a pro-mutagenic DNA substrate. A model is proposed whereby the fidelity of DNA transactions is sensitive to the availability of the Mg2+ cofactor for DNA ligation and therefore ligation fidelity may vary between tissues.
Virtual live streaming enables consumers to engage with virtual anchors, facilitating product information acquisition and online transactions. Despite its promising prospects, the field currently grapples with insufficient purchase intention. Anthropomorphizing virtual anchors in such contexts is common, yet the uncanny valley effect can undermine consumer acceptance. Drawing on mind perception and anthropomorphism theories, we explore factors influencing purchase intention in virtual live streaming. Analyzing data from 197 Taobao virtual live streaming consumers, we find that utility and responsiveness positively affect perceived agency, while friendliness and empathy enhance perceived experience. Moreover, perceived agency and experience positively affect purchase intention. Anthropomorphism strengthens the link between utility/responsiveness and perceived agency but weakens the association between friendliness and perceived experience. Our findings offer insights for both research and practice, though limitations are acknowledged and discussed.
Stroke remains a leading cause of long-term disability worldwide, and rehabilitation is essential for recovery. Although artificial intelligence (AI)-related technologies have received growing attention in stroke rehabilitation, the knowledge structure and thematic evolution of this interdisciplinary field remain unclear. To conduct a bibliometric analysis of AI-related research in stroke rehabilitation from 2005 to 2024 and map publication trends, major contributors, thematic clusters, and emerging topics. Relevant publications were retrieved from the Web of Science Core Collection (WoSCC), including SCI-Expanded and SSCI, on November 30, 2024. Only English-language articles and review articles published between January 1, 2005, and November 30, 2024 were included. A total of 3436 records were analyzed using CiteSpace 6.4.R1 Basic, GraphPad Prism 10.1.2, and biblioshiny in R. Analyses covered publication trends, collaboration networks, journal distribution, keyword co-occurrence, clustering, and burst detection. Publication output increased markedly over time, with the United States contributing the largest number of publications. The Swiss Federal Institutes of Technology Domain was among the leading institutions, and Rocco Salvatore Calabrò was among the most productive and highly cited authors. Core publication venues included the Journal of NeuroEngineering and Rehabilitation and IEEE Transactions on Neural Systems and Rehabilitation Engineering. The literature mainly focused on virtual reality, upper-limb rehabilitation, rehabilitation robotics, machine learning, cognitive rehabilitation, and transcranial direct current stimulation. Recent burst terms, including machine learning, artificial intelligence, and deep learning, indicated growing attention to data-driven rehabilitation approaches. AI-related research in stroke rehabilitation has expanded substantially, with increasing emphasis on adaptive, data-driven, and technology-assisted approaches. This study provides a descriptive overview of the field's major trajectories, emerging gaps, and interdisciplinary directions, and may help inform future research and translational exploration.
We study the behavior of an institution that broadcasts reputational signals to facilitate trust in a population. Using an online marketplace as a motivating example, we develop a theoretical model in which buyers and sellers are matched on a platform to engage in transactions involving moral hazard: After receiving payment, sellers may either faithfully deliver goods or renege. Although buyers do not observe a seller's true strategy-good-faith or bad-faith-the platform broadcasts binary reputation signals about sellers. Buyers condition their purchase decisions on these signals, sellers adapt their strategies over time, and the resulting market composition determines the platform's commission revenue and players' welfare. Our analysis reveals a second layer of moral hazard at the institutional level. Because revenue depends on transaction volume, the platform has an incentive to inflate ratings, making good-faith and bad-faith sellers more difficult to distinguish. This distortion is self-limiting, however: Excessive inaccuracy erodes buyer trust and collapses trade. When signal accuracy is costless, the platform maximizes profit by perfectly identifying good sellers while tolerating some false positives. When accuracy is costly, the platform has an incentive to actively erode signal quality, even at a cost. If the platform can also set commission fees, higher fees are accompanied by stronger incentives to maintain accuracy. These results clarify when institutional incentives align with, or diverge from, the welfare of buyers and good-faith sellers who rely on reputational information.
To explore the impact, barriers, and facilitators of routinely sharing clinic visit recordings with patients in diverse clinical settings. We conducted a multiple-case study of three early-adopter clinics in the U.S.: a primary care clinic in Michigan and an oncology clinic in Texas that shared audio recordings, and a neurology clinic in Arizona that shared video recordings. From March 2016 to January 2017, we conducted semi-structured interviews with clinicians, patients, care partners, and administrators (≥18 years, English-speaking), and direct observation of patients using their recordings. Transcripts were analyzed using framework analysis to identify cross-cutting themes. Three coders independently reviewed all transcripts, and a medical anthropologist audited key analytic stages. We interviewed 67 stakeholders (32 patients, 10 care partners, 15 clinicians, and 10 administrators). Across sites, stakeholders reported that recordings improved patients' recall, understanding, and communication. Patients also used recordings for reflection on their performance in visits and planning, while care partners described reduced anxiety and enhanced involvement. Clinicians reported improved visit interactions, and some used recordings for self-assessment. Key factors influencing implementation included clinic culture, institutional support, workflow logistics, data security, and patient characteristics. Concerns were limited and focused primarily on data privacy. A conceptual framework summarizing themes related to barriers, facilitators, use, and impact of routine recording in healthcare was developed. Routinely sharing visit recordings can enhance patient-centered communication and care partner engagement while supporting clinician performance. Successful implementation depends on aligning institutional culture, privacy safeguards, and workflow integration. Sharing visit recordings was acceptable and beneficial across stakeholders. The practice of sharing recordings revealed that clinic visit interventions are more than just transactions of medical information-they promote emotional support, self-reflection, and family engagement.
Rising quantum hazards and flaws in conventional encryption make cloud-based healthcare data security harder. Quantum-Secure HealthChain, a new architecture using blockchain and quantum computing, improves medical data security, patient privacy, and data fidelity. To prevent quantum attacks, the proposed system uses Quantum Key Distribution (QKD) for safe cryptographic key exchange and quantum-resistant encryption. Blockchain technology secures medical records, while multi-layered encryption ensures data privacy. Quantum Biometric Authentication improves access control using quantum entanglement and biometric data. Key generation, encryption, blockchain storage, authentication, and decryption are system process steps. Experimental evaluation focuses on encryption speed, resource economy, throughput, and scalability using simulated healthcare data. Experimental data demonstrate system strength and efficiency. Encryption and decryption perform consistently for 1 to 100 MB data sizes with negligible overhead. Throughput can reach 105 transactions per second under normal demand; CPU (82%) and memory (210 MB) utilization are low. Scalability studies show linear expansion lets the system handle increased data volumes and user demands without sacrificing performance. Security study confirms quantum attack, data corruption, and unauthorized access resistance. Quantum-Secure HealthChain offers a revolutionary method to cloud-based healthcare system security. Blockchain-quantum computing integration ensures strong authentication, safe key exchange, and quantum-resistant encryption. Its security, scalability, and efficiency make it a future-ready platform for safe medical data management, reducing quantum computing hazards.
In the context of smart cities, Non-Fungible Tokens (NFTs) are transforming digital art markets by enabling secure, decentralized transactions. As NFT trading grows, incorporating intelligence and adaptability becomes crucial-making Machine Learning (ML) integration essential. However, existing models, particularly Cooperative Game Theoretic Trading (CoGTT) frameworks, underutilize ML across all trading phases. Key gaps include limited real-time adaptability, suboptimal negotiation strategies, and inadequate buyer-seller matchmaking. This research addresses these gaps by integrating ML into a three-phase CoGTT framework-ML-augmented Naive Trading, Min-Max Price Negotiation, and Equilibrium-Based Trading-to enhance decision-making and pricing. The methodology applies ML algorithms such as decision trees, clustering, and reinforcement learning (Q-learning) within a public blockchain-based simulation environment using smart contracts. The simulation uses a customized dataset reflecting both market dynamics and artist credibility. The dataset is synthetically generated to emulate an NFT marketplace while maintaining controlled experimental conditions, which may limit direct applicability to volatile real-world markets. Zero-knowledge proofs (ZKPs) are employed to preserve privacy. ZKPs are employed to preserve privacy. A comparative analysis of ML models for NFT price estimation and strategic bidding demonstrates the effectiveness of combining predictive algorithms with reinforcement learning. Linear Regression and Random Forest models both accurately estimate NFT prices, with Random Forest achieving higher real-time prediction accuracy (R2 = 0.9920). K-Means clustering effectively segments market participants to support targeted negotiation, achieving a silhouette score of 0.8178. Integrating Q-learning with Random Forest enables dynamic bidding strategies that minimize the gap between recommended and actual prices. The discrete action set (decrease, stay, increase) supports interpretable, real-time bid adjustments. These findings highlight the potential for ML-driven NFT trading systems to support scalable, privacy-compliant digital marketplaces in smart cities, aligning trading behavior with market demands through automated, data-driven processes.
DNA ends generated by double-strand breaks are vulnerable intermediates that must be rapidly recognized, protected, and resolved to preserve genome integrity. We present optical tweezers (OT)-Curtains, a single-molecule method inspired by DNA curtains that uses a custom branched DNA substrate containing multiple accessible ends for simultaneous observation on dual-trap OT coupled to confocal fluorescence microscopy. Eliminating DNA surface anchoring, facilitating rapid protein and buffer exchange, and offering the possibility for force-free experiments, OT-Curtains overcomes common limitations of flow-stretch-based methods. OT-Curtains allows real-time visualization and quantification of end recognition, protection, resection, and cleavage at several DNA ends in parallel. We demonstrate compatibility with well-studied DNA-binding systems by monitoring Ku-mediated DNA break recognition, AddAB-mediated DNA break resection, ParB-mediated DNA condensation, and KpnI-mediated DNA cleavage. We show that kinetic and mechanistic parameters can be extracted from the data under defined forces and solution conditions. OT-Curtains offers an accessible and multiplexed route to interrogate DNA-end transactions central to double-stranded DNA break repair pathways and telomere biology, as well as a general framework for benchmarking proteins acting at DNA ends.