Browse Articles

Correction: Research on the supply-demand balance evaluation and driving mechanism of community public service facilities

Multiplexed detection of febrile infections using CARMEN

Abstract Detection and diagnosis of bloodborne pathogens are critical for patients and for preventing outbreaks, yet challenging due to these diseases’ nonspecific initial symptoms. We previously advanced CRISPR-based Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (CARMEN) technology for simultaneous detection of pathogens on numerous samples. Here, we develop three CARMEN panels that target viral hemorrhagic fevers, mosquito-borne viruses, and sexually transmitted infections, collectively identifying 23 pathogens. We use deep learning to design CARMEN assays with enhanced sensitivity and specificity, validating them and evaluating their performance on synthetic targets, spiked healthy normal serum samples, and patient samples for Neisseria gonorrhoeae in the United States and for Lassa and mpox virus in Nigeria. Our results show multiplexed CARMEN assays match or outperform individual assay RT-qPCR in sensitivity, with matched specificity. These findings underscore CARMEN’s potential as a highly effective tool for accurate pathogen detection for clinical diagnosis and public health surveillance.

Characteristics and environmental benefits of CO2 mineralization using a recyclable chelating agent in concrete manufacturing

Accuracy of AI-based binary classification for detecting malocclusion in the mixed dentition stage

Background Malocclusion is a common anomaly and is frequently observed in children and adults. Early detection and treatment of malocclusion is necessary to prevent and minimize complications. Therefore, developing a tool to check dentition at an early stage and motivate patients themselves to visit the dentist is required. Objective This study aimed to examine the feasibility of building an AI model that can detect malocclusion in children during the mixed dentition stage. Methods This study was conducted as a feasibility study using cross-sectional data. Subjects were recruited from panelists registered with Macromill, Inc. (approximately 1.3 million registered in 2021). A total of 519 elementary school children (275 boys and 244 girls in Grades 3–6) were included in this study. Questionnaire data and tooth alignment images of the children were collected. The dataset was created, and AI-based binary classification models for malocclusion were developed using an automated machine learning platform (DataRobot) to construct three algorithms for determining malocclusion (deep bite, maxillary protrusion, and crowding). Using a test dataset, the model’s performance was assessed through sensitivity, specificity, accuracy, precision, F1 score, receiver operating characteristic (ROC) curves, and area under the ROC curves (AUC). Results Three dental images were used for all model building, and questionnaire data used all four questions about oral habits (Q1: mouth open during the day, Q2: sleep with mouth open, Q3: have difficulty eating hard foods, Q4: prefer soft foods) for the deep bite classification model, Q1 and Q3 for the maxillary protrusion classification model, and Q1 and Q4 for the crowding classification model. The maxillary protrusion and crowding classification models showed moderate accuracy (AUC > 0.70), and the deep bite classification model showed high accuracy (AUC > 0.90). The permutation importance showed that dental image was the highest contributing factor in each model. Furthermore, while questionnaire data on oral habits were not an important factor in determining deep bite, these questionnaire data were an important factor in determining maxillary protrusion and crowding. Also, statistical analysis of the association between malocclusion and these oral habits revealed a significant association between maxillary protrusion or crowding and the presence or absence of oral habits. Conclusion For the detection of malocclusion in mixed dentition, AI-based binary classification models are a promising approach as a screening tool.

Rational electrolyte solvent screening for high-energy lithium metal batteries at low temperatures

Reimagining invasive weeds through a preliminary antibacterial and phytochemical evaluation of Ipomoea purpurea (L.) Roth floral and seed ethereal extracts

Dynamics of temporal influence in polarised networks

In social networks, it is often of interest to identify the most influential users who can successfully spread information to others. This is particularly important for marketing (e.g., targeting influencers for a marketing campaign) and to understand the dynamics of information diffusion (e.g., who is the most central user in the spreading of a certain type of information). However, different opinions often split the audience and make the network polarised, with fragmented structure. In polarised networks, information becomes siloed within communities in the network, and the most influential user within a network might not be the most influential across all communities. Additionally, influential users and their influence may change over time as users may change their opinion or choose to decrease or halt their engagement on the subject. In this work, we aim to study the temporal dynamics of users’ influence in fragmented social networks. We compare the stability of influence ranking using temporal centrality measures, while extending them to account for community structure across a number of network evolution behaviours. We show that we can successfully aggregate nodes into influence bands, and how to aggregate centrality scores to analyse the influence of communities over time. A modified version of the temporal independent cascade model and the temporal degree centrality perform the best in this setting, as they are able to reliably isolate nodes into their bands.

Spectroscopic insights into the near-earth didymos-dimorphos binary system following the double asteroid redirection test (DART) mission impact

The structure-dependent effects of newly synthesised cationic gemini surfactants against yeast cells

Designing influenza virus-derived cell-penetrating peptides for antigen delivery: Integrating uptake efficiency, safety, and receptor targeting

Successful antigen delivery is of paramount importance for successful vaccination. Cell-penetrating peptides (CPPs) offer a highly effective means of delivering antigens, nucleic acids, and other drug compounds to cells. However, their mechanisms of action remain poorly understood and require further investigation. This study sought to identify novel CPPs within the influenza virus proteome using computational screening methods for vaccine and antigen delivery. CPP candidates were screened from major influenza proteins using CellPPD, C2Pred, and PreTP-EL. Their efficiencies in uptake, physicochemical properties, and safety profiles were assessed using MLCPP, ProtParam, IEDB, ToxinPred, and AllerTop. Structural properties were assessed using AlphaFold, and binding interactions with the lung-targeting sialic acid analog LSTc were investigated using molecular docking and molecular dynamics simulations. Of the CPPs discovered, PB1-derived peptides, especially PB1−1 (RGDTQIQTRR), exhibit high membrane permeability and strong affinity for sialic acid receptors, along with low predicted toxicity and promising intracellular delivery capacity. PB1−1 forms a stable complex with LSTc, which pointed towards its potential for receptor-mediated lung targeting. The identified influenza-derived CPPs have strong therapeutic potential owing to their high predicted uptake efficiency, good safety profiles, and capacity for binding to lung-specific sialic acid receptors, suggesting their suitability for targeted vaccine or antigen delivery. These peptides take advantage of viral-mimetic entry pathways, including clathrin/caveolae-mediated endocytosis and direct membrane permeation, to efficiently deliver therapeutic cargo into cells. Cumulatively, our results suggest that influenza-derived CPPs, particularly PB1−1, may be suitable candidates for respiratory therapy and vaccine delivery. However, given the purely computational scope, the results should be considered hypothesis-generating and require experimental validation in vitro and in vivo.

Mechanism of RPA phosphocode priming and tuning by CDK1/WEE1 signaling circuit

Unraveling the relative impact of material and optical stochastic effects on EUV LWR

Abstract Extreme ultraviolet (EUV), with a 13.5 nm wavelength and 92 eV, produces high-resolution patterns but becomes more sensitive to stochastic effects because of its lower photon density compared to ArF and KrF, which have energies less than 10 eV. Therefore, controlling the stochastic effect is getting attention as EUV becomes mandatory to obtain ultrafine patterns. Unfortunately, various stochastic terms come from mask roughness, light-source, and photoresist materials, but each stochastic term is entangled and hard to decouple experimentally. Herein, we performed pattern shape simulation, separated each stochastic effect, and obtained patterns. Among the mask pattern roughness, photon-induced optical stochastics, and photoresist-induced material stochastics, material stochastics shows the most significant improvement on pattern break risk (2.5 nm widened failure-free window). In addition, the dispersion of photons caused by optical stochastic effects or mask pattern roughness plays a role in offsetting the excessive degradation of patterns due to material stochastic effects. Our simulation-based approach clarifies the role of each stochastic effect in pattern formation and guides process conditions and the novel photoresist development.

Disposal practices of cigarettes and electronic nicotine products among adults, findings from Wave 6 (2021) of the PATH Study

Background Tobacco product waste is environmentally hazardous but preventable. Therefore, it is important to understand tobacco disposal behaviors among those using tobacco products. Objectives To explore self-reported disposal practices of cigarette butts and electronic nicotine products (ENP) components among adults (aged 18+). Methods We used nationally representative cross-sectional data from Wave 6 (2021; n = 29,516) of the Population Assessment of Tobacco and Health (PATH) Study among adults who used cigarettes (manufactured and/or roll-your-own) and/or ENP every day, some days, or in the past 30 days. Results In 2021, 89.7% (95% CI: 88.7, 90.6) of adults who smoked manufactured cigarettes usually disposed of cigarette butts in landfills (in an ash tray, in a cigarette disposal, or in the trash). Most adults who usually disposed of butts in landfills smoked daily (73.1%; 95% CI: 71.3, 74.9) and smoked an average of 14.6 cigarettes per day. Among those who used ENP, most adults usually disposed of the components in landfills (disposable devices: 83.1%; 95% CI: 80.5, 85.4; empty pods and cartridges: 85.4%; 95% CI: 82.4, 87.9; coils and atomizers: 71.2%; 95% CI: 68.1, 74.1; batteries: 48.5%; 95% CI: 45.2, 51.9; e-liquid containers: 70.9%; 95% CI: 67.1, 74.5; and leftover or unused e-liquid: 50.5%; 95% CI: 45.8, 55.2). Recycling as a usual practice was limited for people who used ENP- for disposable devices: 6.0% (95%CI: 4.6, 7.8); empty pods and cartridges: 5.0% (95% CI: 3.7, 6.8); coils and atomizers: 11.7% (95% CI: 9.4, 14.5); batteries: 23.7% (95% CI: 20.9, 26.8); and e-liquid containers: 18.0% (95% CI: 15.1, 21.3). Discussion These findings demonstrate the large scope of tobacco product waste disposal in the United States and may inform efforts to address tobacco product waste management, such as environmental impact assessments and consumer education about proper disposal of tobacco products.

A resource to empirically establish drug exposure records directly from untargeted metabolomics data

Abstract Despite extensive efforts, extracting medication exposure information from clinical records remains challenging. To complement this approach, here we show the Global Natural Product Social Molecular Networking (GNPS) Drug Library, a tandem mass spectrometry (MS/MS) based resource designed for drug screening with untargeted metabolomics. This resource integrates MS/MS references of drugs and their metabolites/analogs with standardized vocabularies on their exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data, supporting flexible summarization at multiple ontology levels to align with different research goals. We demonstrate its application by stratifying participants in a human immunodeficiency virus (HIV) cohort based on detected drug exposures. We uncover drug-associated alterations in microbiota-derived N -acyl lipids that are not captured when stratifying by self-reported medication use. Overall, GNPS Drug Library provides a scalable resource for empirical drug screening in clinical, nutritional, environmental, and other research disciplines, facilitating insights into the ecological and health consequences of drug exposures. While not intended for immediate clinical decision-making, it supports data-driven exploration of drug exposures where traditional records are limited or unreliable.

Associations between neighborhood socioeconomic status with depressive symptoms, and psychological distress among Asian American adults in New York City

Abstract The relationship between neighborhood socioeconomic status (NSES) with mental health among Asian Americans (AA) is underexamined. We sought to determine whether NSES is associated with symptoms of psychological distress or depression among AA residents of New York City (NYC). We examined 4,557 Chinese, Asian from the Indian Subcontinent (ISC), or Other Asian participants of the NYC Community Health Survey, 2018–2020. Participants self-reported psychological distress using the (Kessler-6 (K6)) and depressive symptoms using the Patient Health Questionnaire-8 (PHQ8), with higher scores indicating worse mental health. The neighborhood was defined by residence in one of 55 districts. We constructed a NSES factor score from neighborhood levels of: unemployment, poverty, high rent burden, and a college or greater education. NSES was categorized into tertiles. Hierarchical linear models assessed associations between NSES and mental health, adjusted for individual level age, sex, income, education, nativity, body mass index, current drinking, current smoking, and physical activity. Among NYC AA residents, 52% were women, 45% were 25–44 years old, 25% had less than high school education, and 56% lived in poverty. Living in a high compared with low NSES tertile associated with a lower PHQ8 scores (beta: -0.65; 95% CI: -1.10,-0.19) among Chinese NYC residents and a higher K6 score (beta 1.27; 95% CI: 0.59,1.95) among Asians from the ISC NYC residents. In NYC, living in low NSES neighborhoods was associated with less depression symptoms among Chinese Americans, and greater psychological distress among Asians from the ISC. These results underscore that neighborhood context is associated with mental health and that aggregation of AA into one group can obscure important associations.

Prevalence and risk factors of developing cardiac arrhythmia in patients presenting to the emergency department with electrical injuries

Background Cardiac arrhythmias following electrical shocks are significant concerns in emergency medicine, yet predictive factors remain unclear. Objective This study aimed to investigate the prevalence and risk factors of cardiac arrhythmias in patients presenting with electrical injuries to the emergency department. Methods In this retrospective study conducted between January 2019 and December 2023, we analysed 189 patients aged ≥18 years who presented with electrical injuries. Patients were divided into two groups based on whether or not they developed an arrhythmia. Demographics, clinical characteristics, and laboratory parameters were compared between groups. Results Cardiac arrhythmia developed in 21.2% (n = 40) of patients. The arrhythmia group showed significantly higher mean age (32.4 ± 16.7 vs 26.5 ± 14.8 years, p = 0.023) and high-voltage exposure rates (≥1000 V) (p = 0.015). Multivariate analysis identified age (OR: 1.02, 95% CI: 1.01–1.05), CK > 850 U/L (OR: 1.32, 95% CI: 1.17–1.81), troponin >250 ng/mL (OR: 1.23, 95% CI: 1.09–1.72), lactate >2.1 mmol/L (OR: 2.51, 95% CI: 1.67–5.91), and high voltage (OR: 2.03, 95% CI: 1.64–5.39) as independent risk factors. ROC analysis showed high voltage (AUC: 0.804) as the strongest predictor of developing arrhythmia. Conclusion This study demonstrates that high voltage exposure, advanced age, and elevated biomarkers are significant predictors of developing arrhythmia in patients with electrical injuries. These findings may guide clinical decision-making regarding cardiac monitoring in the emergency department.

An unfinished Pompeian construction site reveals ancient Roman building technology

Cell envelope maintenance by PhoP is essential for <i>Mycobacterium tuberculosis</i> methylglyoxal resistance

During Mycobacterium tuberculosis infections bacteria are engulfed by macrophages, a main line of defense against invading pathogens. Upon activation, macrophages increase glycolysis, producing the antibacterial aldehyde methylglyoxal. To test whether bacterial methylglyoxal resistance is required for robust infections, we sought to identify M. tuberculosis defense mechanisms against methylglyoxal. We identified phoP mutants were among the most highly sensitive strains to methylglyoxal in vitro. phoP mutants are highly attenuated in mice but a phoP mutant was even more attenuated in mice that accumulate methylglyoxal. We further found phoP bacilli were more permeable to methylglyoxal and accumulated glycated proteins. Suppressor mutations in the fatty acid β-oxidation genes fadE25 or fixB restored impermeability and resistance to methylglyoxal to a phoP mutant. Together, our data show that an important role for PhoP is to provide M. tuberculosis resistance to methylglyoxal toxicity in vivo by regulating cell envelope integrity.

Microstructure and hot corrosion behavior of slurry silicon-aluminide coating modified by chromizing and chromium plating on superalloy Rene-80

BloomSec: Scalable and privacy-preserving searchable encryption for cloud environments

The utilization of on-demand remote cloud services provides a flexible way to fulfill the demands of emerging resource-intensive applications. However, migrating data to the cloud also introduced security threats, including unauthorized access and information theft. To resolve this issue, the existing solutions encrypt information locally before uploading it to the server. This process provides information protection with the limitation of non-searchable data. To overcome this limitation, searchable encryption has emerged as a promising cryptographic technique. Some existing searchable encryption techniques are facing data leakage issues by exposing search queries or data to the cloud service provider. Another class of existing searchable schemes introduces processing cost or communication overhead for the data user. The recent searchable solution that is both secure and efficient for data users is Labeled Searchable Encryption (LSE). However, LSE cannot manage large datasets effectively and introduces communication overhead on the data user side. To ensure that Secure Searchable Encryption (SSE) can meet the demands of modern data-driven applications without compromising security and performance, this study aims to investigate and develop novel approaches to enhance the efficiency and security of SSE for large datasets. Experimental findings have proved that the proposed BloomSec is much more efficient and scalable than the classic method of Labeled Searchable Encryption (LSE), consuming significantly less overhead for users, which makes it practically useful for a large dataset without compromising security.