Browse Articles

Probing quantum floating phases in Rydberg atom arrays

Automatic classification of mobile apps to ensure safe usage for adolescents

The integration of mobile devices into adolescents’ daily lives is significant, making it imperative to prioritize their safety and security. With the imminent arrival of fast internet (6G), offering increased bandwidth and reduced latency compared to its predecessor (5G), real-time streaming of high-quality video and audio to mobile devices will become feasible. To effectively leverage the fast internet, accurately classifying Mobile Applications (M-APPs) is crucial to shield adolescents from inappropriate content, including violent videos, pornography, hate speech, and cyberbullying. This work introduces an innovative approach utilizing Deep Learning techniques, specifically Attentional Convolutional Neural Networks (A-CNNs), for classifying M-APPs. The goal is to secure adolescent mobile usage by predicting the potential negative impact of M-APPs on adolescents. The proposed methodology employs multiple Machine and Deep Learning (M/DL) models, but A-CNNs based on Bidirectional Encoder Representations from Transformers embeddings outperformed other models, achieving an average accuracy of 88.74% and improving the recall from 99.33% to 99.65%.

A compliant metastructure design with reconfigurability up to six degrees of freedom

Abstract Compliant mechanisms with reconfigurable degrees of freedom are gaining attention in the development of kinesthetic haptic devices, robotic systems, and mechanical metamaterials. However, available devices exhibit limited programmability and form-customizability, restricting their versatility. To address this gap, we propose a metastructure concept featuring reconfigurable motional freedom and tunable stiffness, adaptable to various form factors and applications. These devices incorporate passive flexures and actively stiffness-changing rods to modify kinematic freedom. A rational design pipeline informs the flexures’ topological arrangements, geometric parameters, and control signals based on targeted mobilities, enabling the creation of unitary joints with up to six degrees of freedom. Our demonstrative application examples include a wrist device that has an effective stiffness of 0.370 Nm/deg (unlocked state, 5% displacement) to 2.278 Nm/deg (locked state, 1% displacement) to enable dynamic joint mobility control, a haptic thimble device (2.27-52.815 Nmm−1 at 1% displacement) that mimics the sensation of touching physical materials ranging from soft gel to metal surfaces, and a wearable device composed of multiple joints tailored for the arm and hand to augment haptic experiences or facilitate muscle training. We believe the presented method can help democratize compliant metastructures development and expand their versatility for broader contexts.

A phase I/II study of adoptive immunotherapy using donor liver graft-derived NK cell-enriched immune cells to prevent severe infection after liver transplantation

Bloodstream infections (BSIs) are significant postoperative complications associated with high mortality rates after liver transplantation (LT). Natural killer (NK) cells, which are key components of the innate immune system, have demonstrated potential to combat both infections and cancer. The use of activated NK cells to mitigate post-LT infections, particularly BSIs, has attracted considerable interest. We conducted a single-arm Phase I/II clinical trial to evaluate the safety and efficacy of transfusing donor liver-derived NK cells into LT recipients. Patients were administered a single infusion of these NK cells three days post-LT. The primary endpoint was BSI incidence. This study was terminated in 19 patients because of the high incidence of BSIs. Of the 19 patients receiving immunotherapy, six (31.5%) developed BSIs within one month of LT. No adverse events were directly related to NK cell infusion. Acute rejection was noted in seven patients (36.8%). After infusion, NK cell activity in the recipient’s peripheral blood remained stable. In conclusion, this clinical trial did not reach the primary endpoint. This could be attributed to a significant percentage of patients presenting with high immunological risk. Nonetheless, the infusion procedure demonstrated a favorable safety profile without serious adverse events.

Oxygen-Driven Atom Transfer Radical Polymerization

Development and validation of an automated machine for self-injury assessment via young Koreans’ natural writings

Self-injury is common in all countries, and 20% of South Korean youths experience self-injury. One of the barriers to assessment and treatment planning is the tendency of young self-injurers to conceal their identities. Following a new stream of research that uses online text data to assess psychological symptoms as they are described in online posts, this study developed a computerized machine that can analyze South Korean self-injurers’ writing in assessing their self-injury severity. Based on 16,645 online posts, Study 1 developed a machine called the Korean Self-Injurious Text Reviewer (K-SITR) using Latent Dirichlet Allocation topic modeling and machine learning. The K-SITR’s text-assessment results were statistically indistinguishable from those of professional counselors. Study 2 confirmed the validity of the K-SITR through a survey of 47 young Koreans who had experienced self-injury. Results showed that the K-SITR scores converged with participants’ self-injury frequency and duration and discriminated from other heterogenous factors. The K-SITR also had incremental validity over two popular self-injury questionnaires. This study provides a new measure that may reduce the tendency of young self-injurers to self-conceal compared to traditional direct-item questionnaires.

Diverse Behaviors of N₂ on Mo Centers Bearing POCOP-Pincer Ligands and the Role of π-Electron Configuration in Regulating the Pathway of N₂ Activation

Sexual and reproductive health needs of women with severe mental illness in low- and middle-income countries: A scoping review

Background This scoping review aimed to understand the extent and type of evidence in relation to sexual and reproductive health needs of women with severe mental illness (SMI) in low- and middle-income countries (LMIC) and to summarise those needs. Methods Inclusion criteria were 1) focus on sexual and reproductive health needs 2) women or girls with SMI, professionals, caregivers of women with SMI and community members 3) study set in a LMIC 4) peer reviewed literature (no restriction on study date or design). Studies were identified from comprehensive searches of Medline, EMBASE, CINAHL and PsycINFO (to July 2023). Results The review included 100 papers. Most studies were cross-sectional and set in hospital outpatient departments. Only 20 of 140 LMIC countries were included in this review and only 15 studies were set-in low-income countries (LIC). Included studies often had multiple focus areas and were grouped by frequency of topic into categories of HIV (prevalence, risk behaviour and knowledge), other sexually transmitted infections (STIs), sexual function, contraception use and family planning, sexual violence, fertility, pregnancy and postpartum. Included studies indicated women with SMI have worse outcomes and worse sexual and reproductive health compared to both women without SMI and men with SMI. Women with SMI were shown to have higher rates of HIV and low levels of contraception knowledge and use, with little advice offered by professionals. Conclusions This review highlights the need for a greater diversity of study methodology, robustness of ethical and consensual reporting when researching vulnerable populations and for further research on interventions and models of care aimed at addressing stigma, discrimination and improving the sexual and reproductive health of women with SMI. Future research should better represent the breadth of LMIC, investigate cultural adaptability of interventions and consider sexual health needs across the life course.

Signalling pathways involved in urotensin II induced ventricular myocyte hypertrophy

Sustained pathologic myocardial hypertrophy can result in heart failure(HF); a significant health issue affecting a large section of the population worldwide. In HF there is a marked elevation in circulating levels of the peptide urotensin II(UII) but it is unclear whether this is a result of hypertrophy or whether the high levels contribute to the development of hypertrophy. The aim of this study is to investigate a role of UII and its receptor UT in the development of cardiac hypertrophy and the signalling molecules involved. Ventricular myocytes isolated from adult rat hearts were treated with 200nM UII for 48hours and hypertrophy was quantified from measurements of length/width (L/W) ratio. UII resulted in a change in L/W ratio from 4.53±0.10 to 3.99±0.06; (p<0.0001) after 48hours. The response is reversed by the UT-antagonist SB657510 (1μM). UT receptor activation by UII resulted in the activation of ERK1/2, p38 and CaMKII signalling pathways measured by Western blotting; these are involved in the induction of hypertrophy. JNK was not involved. Moreover, ERK1/2, P38 and CaMKII inhibitors completely blocked UII-induced hypertrophy. Sarcoplasmic reticulum (SR) Ca2+-leak was investigated in isolated myocytes. There was no significant increase in SR Ca2+-leak. Our results suggest that activation of MAPK and CaMKII signalling pathways are involved in the hypertrophic response to UII. Collectively our data suggest that increased circulating UII may contribute to the development of left ventricular hypertrophy and pharmacological inhibition of the UII/UT receptor system may prove beneficial in reducing adverse remodeling and alleviating contractile dysfunction in heart disease.

Scalable and energy-efficient task allocation in industry 4.0: Leveraging distributed auction and IBPSO

Industry 4.0 has transformed manufacturing with the integration of cutting-edge technology, posing crucial issues in the efficient task assignment to multi-tasking robots within smart factories. The paper outlines a unique method of decentralizing auctions to handle basic tasks. It also introduces an improved variant of the improved Binary Particle Swarm Optimization (IBPSO) algorithm to manage complicated tasks that require multi-robot collaboration. The main contributions we make are: the design of an auction decentralization algorithm (AOCTA) which allows for an efficient and flexible task distribution in dynamic contexts, the optimization of coalition formation in complex jobs by using IBPSO and improves the efficiency of energy and decreases the cost of computation as well as thorough simulations that show that our proposed method significantly surpasses conventional methods for efficiency, task completion rates in terms of energy usage, task completion rate, and scaling of the system. This research contributes to the development of smart manufacturing through providing an effective solution that aligns with the sustainability objectives and addresses operational efficiency as well as environmental impacts. Addressing the challenges posed by dynamic task allocation in distributed multi-robot systems, these advanced technologies provide a comprehensive solution, facilitating the evolution of innovative manufacturing systems.

Therapeutic blockade of CCL17 in obesity-exacerbated osteoarthritic pain and disease

Objectives We previously reported that CCL17 gene-deficient mice are protected from developing pain-like behaviour and exhibit less disease in destabilization of medial meniscus (DMM)-induced OA, as well as in high-fat diet (HFD)-exacerbated DMM-induced OA. Here, we explored if therapeutic neutralization of CCL17, using increasing doses of a neutralizing monoclonal antibody (mAb), would lead to a dose-dependent benefit in these two models. Design DMM-induced OA was initiated in male mice either fed with a control diet (7% fat) or 8 weeks of a 60% HFD, followed by therapeutic intraperitoneal administration (i.e. when pain is evident) of an anti-CCL17 mAb (B293, 25mg/kg, 5mg/kg or 1mg/kg) or isotype control (BM4; 25mg/kg). Pain-like behaviour and arthritis were assessed by relative static weight distribution and histology, respectively. The effects of B293 (25mg/kg) on HFD-induced metabolic changes, namely oral glucose tolerance test, insulin tolerance test and liver triglyceride levels, were examined. Results Therapeutic administration of B293 results in a dramatic amelioration of DMM-induced OA pain-like behaviour and the inhibition of disease progression, compared to BM4 (isotype control) treatment. A similar therapeutic effect was observed in HFD-exacerbated OA pain-like behaviour and disease. B293 treatment did not alter the measured HFD-induced metabolic changes. Conclusions Based on the data presented, CCL17 could be a therapeutic target in OA patients with joint injury alone or with obesity.

Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system

Centrifugal compressors are widely used in the oil and natural gas industry for gas compression, reinjection, and transportation. Fault diagnosis and identification of centrifugal compressors are crucial. To promptly monitor abnormal changes in compressor data and trace the causes leading to these data anomalies, this paper proposes a security monitoring and root cause tracing method for compressor data anomalies. Additionally, it presents an intelligent system design method for fault tracing in compressors and localization of faults from different sources. This method starts from petrochemical big data and consists of three parts: fault dynamic knowledge graph construction, instrument data sliding fault-tolerant filtering, and the fusion and reasoning of fault dynamic knowledge graph and instrument data variation monitoring. The results show that this method effectively overcomes the problems of false alarms and missed alarms based on fixed threshold alarm methods, and achieves 100% classification of two types of faults: non starting of the drive machine and low oil pressure by constructing a PCA (Principal Component Analysis)—SPE (Square Prediction Error)—CNN (Convolutional Neural Network) classifier. Combined with dynamic knowledge graph and NLP (Natural Language Processing) inference, it achieves good diagnostic results.

Moroccan natural products for multitarget-based treatment of Alzheimer’s disease: A computational study

Alzheimer’s disease is a neurodegenerative disorder that impairs neurocognitive functions. Acetylcholinesterase, Butyrylcholinesterase, Monoamine Oxidase B, Beta-Secretase, and Glycogen Synthase Kinase Beta play central roles in its pathogenesis. Current medications primarily inhibit AChE but fail to halt or reverse disease progression due to the multifactorial nature of Alzheimer’s. This underscores the necessity of developing multi-target ligands for effective treatment. This study investigates the potential of phytochemical compounds from Moroccan medicinal plants as multi-target agents against Alzheimer’s disease, employing computational approaches. A virtual screening of 386 phytochemical compounds, followed by an assessment of pharmacokinetic properties and ADMET profiles, led to the identification of two promising compounds, naringenin (C23) and hesperetin (C24), derived from Anabasis aretioides. These compounds exhibit favourable pharmacokinetic profiles and strong binding affinities for the five key targets associated with the disease. Density functional theory, molecular dynamics simulations, and MM-GBSA calculations further confirmed their structural stability, with a slight preference for C24, exhibiting superior intermolecular interactions and overall stability. These findings provide a strong basis for further experimental research, including in vitro and in vivo studies, to substantiate their potential efficacy in Alzheimer’s disease.

The astonishing scientists who starved to protect plants during the Second World War

Universal parent-focused child sexual abuse prevention: A quasi-experimental protocol

Background Child sexual abuse (CSA) is a significant public health concern, and there is a lack of universal, evidence-based primary prevention interventions that extend beyond a focus solely on children. Parents remain a consistently underutilized target for primary prevention efforts aimed at mitigating CSA despite their unique relationship and close proximity to their children. CSA risk is not confined to any specific demographic, and its effects on affected children are well-documented, significantly impacting numerous dimensions of their wellbeing. Thus, there is a clear and urgent need to address this gap in prevention strategies. Methods This study will use a quasi-experimental design (target N = 412) to examine potential gains in CSA-related awareness and intentions to use protective behaviors among parents who participate in a universal parent-focused CSA prevention workshop, Smarter Parents. Safer Kids., compared to those who do not. Participants in both the control (n = 206) and experimental group (n = 206) will complete 3 survey assessments: Survey 0 (baseline), Survey 1 (1-month), and Survey 2 (3-month follow-up). The experimental group will participate in a Smarter Parents. Safer Kids. workshop between the Surveys 0 and 1. We will use data collected from the baseline to measure potential mediators of CSA-related awareness and intention to use protective and preventive behaviors. In adjacent efforts to enhance the curriculum’s reach with future dissemination and implementation, we will also explore the impact of recruitment materials and strategies on parental engagement. Conclusion Results of this study will advance efforts to implement parent-focused CSA prevention with a universal audience.

Affective polarization in a word: Open-ended and self-coded evaluations of partisan affect

The literature finds that partisanship drives negative emotional evaluations of out-partisans. Yet, scholars base these insights on measures–like thermometers, candidate evaluations, and social-distance measures–that discount the sentiment attached to individuals’ negative attitudes. We introduce a unique measure of affect capturing the motivation underpinning partisans’ attitudes. Our measure asks respondents for one-word to describe voters in their party and the opposing party. Then respondents code the sentiment behind their word choice themselves. Together, our measure produces qualitative and quantitative measures of respondents’ affect. We find that our self-coded open-ended measure has strong face validity and correlates strongly with existing affect measures. This measure advances our understating of partisan affect by allowing scholars a window into respondents’ state of mind. Scholars can easily apply our measure’s procedure beyond partisanship to other groups of interest.

Impacts of host phylogeny, diet, and geography on the gut microbiome of rodents

Mammalian gut microbial communities are thought to play a variety of important roles in health and fitness, including digestion, metabolism, nutrition, immune response, behavior, and pathogen protection. Gut microbiota diversity among hosts is strongly shaped by diet as well as phylogenetic relationships among hosts. Although various host factors may influence microbial community structure, the relative contribution may vary depending on several variables, such as taxonomic scales of the species studied, dietary patterns, geographic location, and gut physiology. The present study focused on 12 species of rodents representing 3 rodent families and 3 dietary guilds (herbivores, granivores, and omnivores) to evaluate the influence of host phylogeny, dietary guild and geography on microbial diversity and community composition. Colon samples were examined from rodents that were collected from 7 different localities in Texas and Oklahoma which were characterized using 16S rRNA gene amplicon sequencing targeting the V1-V3 variable regions. The microbiota of colon samples was largely dominated by the family Porphyromonadaceae (Parabacteriodes, Coprobacter) and herbivorous hosts harbored richer gut microbial communities than granivores and omnivores. Differential abundance analysis showed significant trends in the abundance of several bacterial families when comparing herbivores and granivores to omnivores, however, there were no significant differences observed between herbivores and granivores. The gut microbiotas displayed patterns consistent with phylosymbiosis as host phylogeny explained more variation in gut microbiotas (34%) than host dietary guilds (10%), and geography (3%). Overall, results indicate that among this rodent assemblage, evolutionary relatedness is the major determinant of microbiome compositional variation, but diet and to a lesser extent geographic provenance are also influential.

The impact mechanism of geopolitical risks on ESG performance: The moderating effects of investor attention and government subsidies

The impact of geopolitical risks (GPR) on enterprises is significant, yet the existing literature lacks a comprehensive understanding of how GPR affects environmental, social, and governance (ESG) performance. This study addresses this gap by analysing data from Chinese enterprises over the period 2009 to 2021. It empirically examines the impact of GPR on ESG performance and explores the underlying mechanisms. Specifically, the analysis considers the roles of investor attention and government subsidies as moderating factors. The results indicate that GPR inhibits corporate ESG performance. State-owned enterprises are found to mitigate these adverse effects, while privately-owned enterprises tend to exacerbate them. Mechanism tests reveal that GPR negatively impacts ESG performance by increasing financing constraints and reducing financial performance. Furthermore, increased investor attention and government subsidies can alleviate the negative effects of GPR on ESG performance. These findings offer valuable insights for organisations, governments, and stakeholders, enabling them to better respond to GPR and achieve sustainable development.

Air pollution and brain damage: what the science says

Prevalence of keratoconus and keratoconus suspect, and their characteristics on corneal tomography in a population-based study

Keratoconus (KC) is a progressive corneal disorder resulting in severe visual impairment. We aimed to determine the prevalence and corneal tomographic characteristics of KC and keratoconus suspect (KCS) in a population-based study, and to construct discrimination models with or without corneal tomography. A total of 1,544 eyes (822 participants aged ≥35 years) were evaluated using data from the Yamagata Study (2015–2017). Systemic and ophthalmological examinations including corneal tomography with swept-source anterior segment optical coherence tomography (AS-OCT) were conducted to determine the prevalence and corneal tomographic characteristics of KC and KCS. In addition, data on 766 eyes were used to construct discrimination models with or without corneal tomography. In results, KC was diagnosed in six (0.85%) participants, and KCS was diagnosed in 27 (1.46%) participants. The values including corneal power, keratometric cylinder, corneal central and thinnest thickness, corneal asymmetry, higher-order irregularity, and their inter-eye differences were associated with KC and KCS. The areas under the receiver operating characteristic curves for the three multivariate discrimination models (without corneal tomography, with corneal tomography, and without corneal tomography + inter-eye difference models) for participants with KC or KCS were 0.848, 1.000, and 0.930, respectively. When corneal tomography is unavailable, inter-eye differences in corneal parameters may be useful screening tools for KC and KCS.