Browse Articles

Effects of change in dysfunctional beliefs in avatar-based cognitive therapy for depressive symptoms: a randomized parallel trial

Abstract This study evaluated the effect of an avatar-based intervention on depressive symptoms and self-esteem. Participants (N = 151) with subclinical depressive symptoms were instructed to challenge an avatar over three sessions. While participants within the intervention group challenged their personal dysfunctional beliefs, participants in the control group challenged nonsense statements. Allocation to treatment groups was randomized. Data collection took place pre-intervention and post-intervention. Statistical analysis revealed a significant decrease in depressive symptoms, which was more pronounced for the intervention group (p < .01), as well as a significant group × time interaction for self-esteem (p < .05). The effect on depression symptom strength was large in the experimental group (d = − 1.19) and medium (d = − 0.72) in the control group, while the effect on self-esteem was moderate (d = 0.54) in the intervention and small (d = 0.29) in the control group. Our findings on symptom reduction align with prior research, while positive effects on self-esteem are a novelty. These results demonstrate the intervention’s potential for reducing the symptoms of mental illness.

Immune-mediated enterocolitis is associated with immune checkpoint inhibitors: A pharmacovigilance study from the FDA Adverse Event Reporting System (FAERS) database

Purpose Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment by demonstrating significant efficacy across multiple malignancies. However, by interfering with immune regulatory pathways, they can lead to immune-related adverse events (irAEs), including immune-mediated enterocolitis. This study aimed to evaluate the real-world risk of immune-mediated enterocolitis across different ICIs using data from the FDA’s Adverse Event Reporting System (FAERS). Methods A disproportionality analysis was conducted using FAERS data to assess the association between different ICIs and the risk of immune-mediated enterocolitis. The risk was analyzed across three ICI classes: cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitors, programmed death-1 (PD-1) inhibitors, and programmed death-ligand 1 (PD-L1) inhibitors. Results The analysis revealed significant variability in the risk of immune-mediated enterocolitis among ICIs. CTLA-4 inhibitors, particularly tremelimumab and ipilimumab, exhibited the strongest association with enterocolitis. Among PD-1 inhibitors, nivolumab demonstrated the highest risk, while PD-L1 inhibitors, including durvalumab and atezolizumab, had a lower but still notable association. Conclusions These findings underscore the need for vigilant monitoring and early intervention in patients receiving ICIs. The differential risk profile among ICIs suggests that physicians should consider enterocolitis risk when selecting and managing immunotherapy regimens.

Community environment, psychological perceptions, and physical activity among older adults

Biased perceptions of public opinion don’t define echo chambers but reveal systematic differences in political awareness

Echo chambers are widely acknowledged as a feature of online discourse and current politics: a phenomenon arising when people selectively engage with like-minded others and are shielded from opposing ideas. Various studies have operationalized the concept through studying opinions, interactions, reinforcement or group identity. Echo chambers both feed and are fed by the false consensus effect, whereby people overestimate the degree to which others share their views, with algorithmic filtering of social media also a contributing factor. Although there is strong evidence that meta-opinions - that is, people’s perceptions of others’ opinions - often fail to reflect reality, no attempt has been made to explore the space of meta-opinions, or detect echo chambers within this space. We created a new, information-theoretic method for directly quantifying the information content of meta-opinions, allowing detailed exploratory analysis of their relationships with demographic factors and underlying opinions. In a gamified survey (presented as a quiz) of 476 UK respondents, we found both the liberal left, and also people at both extremes of the left/right scale, to have more accurate knowledge of others’ opinions. Surprisingly however, we found that meta-opinions, although displaying significant false consensus effects, were not divided into any strong clusters representative of echo chambers. We suggest that the metaphor of discrete echo chambers may be inappropriate for meta-opinions: while measures of meta-opinion accuracy and its influences can reveal echo chamber characteristics where other metrics confirm their presence, the presence or absence of meta-opinion clusters is not itself sufficient to define an echo chamber. We publish both data and analysis code as supplementary material.

Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques

Breaking Structural Instability and Orbital Symmetry Mismatch in <i>p</i>-Block Metal Monochalcogenides for CO₂ Electroreduction via Noninvasive van der Waals Doping

Discursive strategies of Chinese elders in intergenerational conflicts: A critical discourse analysis of mediation television programs in China

Research on conflict discourse highlights the value of a cross-cultural perspective and the need to explore its features across different cultural contexts. However, there is limited research on conflict discourse among Chinese elders, particularly within the framework of building an Age-Friendly Society. To address this gap, this study adopts Fairclough’s dialectical-relational approach in critical discourse analysis to examine the strategies Chinese elders employ in intergenerational conflicts. Drawing on Li’s framework of conflict strategies—competition, cooperation, avoidance, and compromise—this study identifies how Chinese elders use discourse to safeguard their rights and foster mutual understanding within family interactions. The findings reveal that these four strategies serve as discursive means for elders to achieve self-fulfillment, protect their rights, and sustain family harmony amidst social change. This study offers a systematic analysis of conflict resolution strategies in intergenerational discourse, highlighting the nuanced equilibrium that elders navigate between upholding traditional values and accommodating the evolving dynamics of contemporary family life.

Stacked hybrid model for load forecasting: integrating transformers, ANN, and fuzzy logic

Abstract Modern energy management systems must include load forecasting in order for utilities to plan and optimize electricity distribution, lower operating costs, and improve grid stability. With the addition of renewable energy sources and the advancement of smart grid technology, energy systems have become increasingly complex, making accurate forecasting increasingly challenging. Conventional techniques, including regression models and ARIMA, frequently perform less well because they are unable to capture the complex multivariate relationships and temporal dependencies present in energy data. Furthermore, these techniques are prone to errors in the presence of noisy data and have scalability issues when used on big, high-dimensional datasets. This paper presents a hybrid forecasting framework that combines artificial neural networks with Time Series Transformers and Fuzzy Logic Transform in order to overcome these drawbacks. The Transformer architecture excels in capturing long-term dependencies and interdependencies between features through its self-attention mechanism. Meanwhile, FLT + ANN effectively preprocesses noisy, irregular data and models short-term nonlinear patterns. The combination of these techniques creates a robust framework capable of handling complex energy datasets while maintaining high accuracy. Extensive tests on actual energy datasets show that the suggested hybrid model outperforms both conventional and stand-alone methods. With RMSE and MAE reductions of up to 15–20%, the model outperforms baseline models such as Random Forests, Decision Trees, and Linear Regression. These findings demonstrate how the suggested paradigm has the potential to transform load forecasting and enable more intelligent, effective energy systems.

Rapid risk assessment to address emerging concerns of HPAI in raw and pasteurized milk

An outbreak of highly pathogenic avian influenza A, subtype H5N1, first reported in U.S. dairy cattle in March 2024, raised concerns of an emerging food safety threat from the virus in the milk supply. To support potential regulatory responses, we conducted a rapid assessment of the predicted risk to U.S. consumers of cow’s milk with two complementary and parallel approaches: a “bottom-up” quantitative risk assessment model that integrated data on virus levels in milk, milk consumption, and dose response; and a “top-down” epidemiological analysis that linked current novel flu illness detection to the consumption of raw and pasteurized milk. The dynamic use of the approaches accommodated rapidly evolving data in a range of risk scenarios. The risk assessment model identified pasteurization as a critical control for H5N1 in milk and highlighted the need for i) the targeted sampling of bulk tank raw milk in affected states pre-pasteurization, ii) raw milk herd surveillance and sampling, and iii) a better understanding of ingestion as a route of H5N1 infections for humans. This novel approach and the findings from this study promoted informed decision-making in an evolving outbreak investigation. This methodology can be leveraged in the conduct of future risk assessments to address emerging pathogen outbreaks that impact the food supply.

Identification of key proteins and pathways in myocardial infarction using machine learning approaches

Security importance of edge-IoT ecosystem: An ECC-based authentication scheme

Despite the many outstanding benefits of cloud computing, such as flexibility, accessibility, efficiency, and cost savings, it still suffers from potential data loss, security concerns, limited control, and availability issues. The experts introduced the edge computing paradigm to perform better than cloud computing for the mentioned issues and challenges because it is directly connected to the Internet-of-Things (IoT), sensors, and wearables in a decentralized manner to distribute processing power closer to the data source, rather than relying on a central cloud server to handle all computations; this allows for faster data processing and reduced latency by processing data locally at the ‘edge’ of the network where it’s generated. However, due to the resource-constrained nature of IoT, sensors, or wearable devices, the edge computing paradigm endured numerous data breaches due to sensitive data proximity, physical tampering vulnerabilities, and privacy concerns related to user-near data collection, and challenges in managing security across a large number of edge devices. Existing authentication schemes didn’t fulfill the security needs of the edge computing paradigm; they either have design flaws, are susceptible to various known threats—such as impersonation, insider attacks, denial of service (DoS), and replay attacks—or experience inadequate performance due to reliance on resource-intensive cryptographic algorithms, like modular exponentiations. Given the pressing need for robust security mechanisms in such a dynamic and vulnerable edge-IoT ecosystem, this article proposes an ECC-based robust authentication scheme for such a resource-constrained IoT to address all known vulnerabilities and counter each identified threat. The proof of correctness of the proposed protocol has been scrutinized through a well-known and widely used Real-Or-Random (RoR) model, ProVerif validation, and attacks’ discussion, demonstrating the thoroughness of the proposed protocol. The performance metrics have been measured by considering computational time complexity, communication cost, and storage overheads, further reinforcing the confidence in the proposed solution. The comparative analysis results demonstrated that the proposed ECC-based authentication protocol is 90.05% better in terms of computation cost, 62.41% communication cost, and consumes 67.42% less energy compared to state-of-the-art schemes. Therefore, the proposed protocol can be recommended for practical implementation in the real-world edge-IoT ecosystem.

3D printing individualized augments prosthesis and acetabular implant for the treatment of Crowe type III developmental dysplasia of the hip

Inferring internal states across mice and monkeys using facial features

Abstract Animal behaviour is shaped to a large degree by internal cognitive states, but it is unknown whether these states are similar across species. To address this question, here we develop a virtual reality setup in which male mice and macaques engage in the same naturalistic visual foraging task. We exploit the richness of a wide range of facial features extracted from video recordings during the task, to train a Markov-Switching Linear Regression (MSLR). By doing so, we identify, on a single-trial basis, a set of internal states that reliably predicts when the animals are going to react to the presented stimuli. Even though the model is trained purely on reaction times, it can also predict task outcome, supporting the behavioural relevance of the inferred states. The relationship of the identified states to task performance is comparable between mice and monkeys. Furthermore, each state corresponds to a characteristic pattern of facial features that partially overlaps between species, highlighting the importance of facial expressions as manifestations of internal cognitive states across species.

Speech-in-noise recognition as a predictor of academic achievement

Background The capacity to listen effectively greatly influences learning. Speech understanding in noise, a crucial element of listening, involves the ability to comprehend and interpret speech despite surrounding auditory distractions or background noise. While numerous studies have examined the effect of noise on academic success, there is a noticeable lack of research focusing on how a student’s skill in understanding speech amidst noise connects to their unique educational achievements and overall learning journey. This study explores the relationship between listening ability and educational achievement. Design 108 primary school aged children; 55 from Grade 3 (mean age of 8 years 9 months) and 53 from Grade 5 (mean age of 10 years 7 months) participated in this study. Children completed both a listening skills assessment, using the Sound Scouts platform and the National Assessment Program - Literacy and Numeracy (NAPLAN). Interactions between results were explored. Results A significant interaction between speech-in-noise ability and reading was seen in the combined cohort. When exploring interactions between variables within year levels, significant correlations between literacy (reading, grammar/punctuation, writing and spelling) outcomes, but not numeracy, were found in the Grade 3 children. No significant interaction between listening skills and academic achievement were observed in the Grade 5 cohort. Summary The link between speech-in noise ability and literacy development provides insight into overlapping processes in both skills and their developmental trajectories. This highlights the importance of identifying not only the role that noise itself plays in learning, but the skills that support the ability to manage classroom listening environments. This research has implications for early hearing screening programs, teaching approaches, and interventions focused on enhancing the learning environment for all students.

Optimization and induction effect evaluation of complex inducer of Aquilaria sinensis based on factorial design

Amino acids catalyse RNA formation under ambient alkaline conditions

Abstract RNA and proteins are the foundation of life and a natural starting point to explore its origins. However, the prebiotic relationship between the two is asymmetric. While RNA evolved to assemble proteins from amino acids, a significant mirror-symmetric effect of amino acids to trigger the synthesis of RNA was missing. We describe ambient alkaline conditions where amino acids, without additional chemical activators, promote RNA copolymerisation more than 100-fold, starting from prebiotically plausible ribonucleoside-2′,3′-cyclic phosphates. The observed effect is explained by acid-base catalysis, with optimal efficiency at pH values near the amine pK aH. The fold-change in oligomerisation yield is nucleobase-selective, resulting in increased compositional diversity necessary for subsequent molecular evolution and favouring the formation of natural 3′−5′ linkages. The elevated pH offers recycling of oligonucleotide sequences back to 2′,3′-cyclic phosphates, providing conditions for high-fidelity replication by templated ligation. The findings reveal a clear functional role of amino acids in the evolution of RNA earlier than previously assumed.

A location privacy protection method based on blockchain and threshold cryptography

To address privacy leakage risks arising from low collaborative user engagement, third-party trust deficits, and insufficient collaboration timeliness in location-based services (LBS), this paper proposes a dual-protection framework integrating blockchain technology and threshold cryptography for safeguarding location privacy. The framework employs asymmetric encryption with Shamir’s (t, n) secret sharing to encrypt user queries, distributing decryption key fragments to collaborative users while generating n anonymous service requests through location generalization strategies. A temporary private blockchain constructed using smart contracts ensures confidential data transmission, supported by a dynamic privacy parameter configuration system based on Byzantine fault tolerance. The framework implements a priority-response consensus mechanism through Token-based equity proof-of-stake, prioritizing service for users with higher Token values. To mitigate privacy breaches caused by unresponsive collaborators, a competitive incentive mechanism ensures timely information submission. Through ciphertext fragment verification algorithms and Lagrange interpolation-based key reconstruction, the framework enables secure query decryption and service matching in untrusted third-party environments, guaranteeing information security, integrity, and non-repudiation. Experimental validation using real-world datasets confirms the framework’s feasibility and operational effectiveness.

Female sterilization paradoxical association with premature menopause in Bihar

Abstract Premature menopause is a growing public health crisis with serious implications for women’s well-being. While premature menopause prevalence varies across India, Bihar’s rates are exceptionally high (11%), warranting specific investigation. Analyzing data from National Family Health Survey-5, this study examines state-level variations and predictors of premature menopause, focusing on the context of Bihar. Bihar’s premature menopause rates were significantly higher than in other states, even after controlling for hysterectomy, suggesting unique regional drivers. While lower education, more children, and younger age at first and last birth were risk factors across India, this is especially concerning in Bihar. I found a strong association between female sterilization and both naturally occurring and hysterectomy induced premature menopause in Bihar, despite its intended role in family planning. This paradoxical finding, coupled with the protective effect of contraceptive use, raises critical questions about the potential unintended consequences of sterilization practices in Bihar and highlights the need for comprehensive reproductive healthcare services. The study underscores the urgent need for targeted public health interventions, including investments in girls’ education, expanded reproductive healthcare options beyond sterilization, improved sterilization practices through stricter regulation and provider training, and further investigation into the complex factors driving Bihar’s high premature menopause rates.

Intracellular accumulation of amyloid-ß is a marker of selective neuronal vulnerability in Alzheimer’s disease

Abstract Defining how amyloid-β and pTau together lead to neurodegeneration is fundamental to understanding Alzheimer’s disease (AD). We used imaging mass cytometry to identify neocortical neuronal subtypes lost with AD in post-mortem brain middle temporal gyri from non-diseased and AD donors. Here we showed that L5,6 RORB+FOXP2+ and L3,5,6 GAD1+FOXP2+ neurons, which accumulate amyloid-β intracellularly from early Braak stages, are selectively vulnerable to degeneration in AD, while L3 RORB+GPC5+ neurons, which accumulate pTau but not amyloid-β, are not lost even at late Braak stages. We discovered spatial associations between activated microglia and these vulnerable neurons and found that vulnerable RORB+FOXP2+ neuronal transcriptomes are enriched selectively for pathways involved in inflammation and glycosylation and, with progression to AD, also protein degradation. Our results suggest that the accumulation of intraneuronal amyloid-β, which is associated with glial inflammatory pathology, may contribute to the initiation of degeneration of these vulnerable neurons.

Localization and characterization of cutaneous neurogenic inflammation in acute gastric mucosal injury in rats: A possible morphological explanation for visceral sensitization?

This investigation transcends traditional methodologies by providing a quantitative analysis of the dynamic relationship between visceral pathologies and neurogenic spots, employing an acute gastric mucosal injury (AGMI) rat model to map the somatotopic distribution of visceral sensitization. Through hydrochloric acid-induced plasma extravasation and Evans Blue dye (EB) marking, coupled with a geospatial grid system and multivariate statistical analysis, we identified Feature Regions (FRs) with distinct neurogenic responses. Notably, the right T10-13 dermatomere, or FR-11’, exhibited elevated levels of nociceptive neuropeptides and serotonin, indicative of its significant role in pain perception. The application of electroacupuncture at FR-11’ revealed enhanced therapeutic outcomes compared to the conventional acupoint BL-21, positioning it as a promising modality for the management of visceral pain. These findings contribute substantially to our understanding of the mechanisms underlying visceral-somatic pain and pave the way for innovative pain management interventions in clinical settings.