Browse Articles

Microdroplet Surface Drives and Accelerates Proton-Controlled, Size-Dependent Nitrate Photolysis

Cataract induction in an arthropod reveals how lens crystallins contribute to the formation of biological glass

Lenses are vital components of well-functioning eyes and are crafted through the precise arrangement of proteins to achieve transparency and refractive ability. In addition to optical clarity for minimal scatter and absorption, proper placement of the lens within the eye is equally important for the formation of sharp, focused images on the retina. Maintaining these states is challenging due to dynamic and substantial post-embryonic eye and lens growth. Here, we gain insights into required processes through exploring the optical and visual consequences of silencing a key lens constituent in Thermonectus marmoratus sunburst diving beetle larvae. Using RNAi, we knocked down Lens3, a widely expressed cuticular lens protein during a period of substantial growth of their camera-type principal eyes. We show that lens3 RNAi results in the formation of opacities reminiscent of vertebrate lens ‘cataracts’, causing the projection of blurry and degraded images. Consequences of this are exacerbated in low-light conditions, evidenced by impaired hunting behaviour in this visually guided predator. Notably, lens focal lengths remained unchanged, suggesting that power and overall structure are preserved despite the absence of this major component. Further, we did not detect significant shifts in the in-vivo refractive states of cataract-afflicted larvae. This in stark contrast with findings in vertebrates, in which form-deprivation or the attenuation of image contrast, results in the dysregulation of eye growth, causing refractive errors such as myopia. Our results provide insights into arthropod lens construction and align with previous findings which point towards visual input being inconsequential for maintaining correctly focused eyes in this group. Our findings highlight the utility of T. marmoratus as a tractable model system to probe the aetiology of lens cataracts and refractive errors.

Unraveling Ultrafast Li-Ion Dynamics in the Solid Electrolyte LiTi₂(PS₄)₃ by NMR down to Cryogenic Temperatures

Trend and decomposition analysis of factors influencing teenage pregnancy and motherhood in Nigeria, 2003–2018

Background Nigeria is among the countries with a high burden of Teenage Pregnancy and Motherhood (TPM) in sub-Saharan Africa. The adverse effect of TPM on young girls is enormous and often compromises their future socioeconomic advancement, including education. Limited number of studies have assessed the trends in TPM and the decomposition of its contributing factors. This study aimed to assess the levels, trends, and drivers of changes in TPM, between 2003 and 2018, in Nigeria. Methods This study used a cross-sectional design with four consecutive rounds (2003, 2008, 2013, and 2018) of Nigeria Demographic and Health Survey datasets. Women aged 20–49 years who had ever terminated pregnancy, reported at least one childbirth or stillbirth before attaining age 20, were analysed. The outcome variable was having experienced TPM as a teenager. Data were analysed using trend and multivariate decomposition analyses at a 5% significance level. Results The prevalence of TPM was 56.1%, ranging from 64.7% in 2003 to 55.7% in 2018. Overall, the prevalence of TPM decreased significantly by 10.7% over the studied period (p < 0.001). The change was due to a composite of a positive significant effect of the net compositional change (126%) and a negative effect of the net behavioural change (26%). The identified significant drivers of shift in TPM due to changes in the composition of women included current age, educational level, employment status, timing of marriage, age at first sexual intercourse, contraceptive use, ethnicity, and region of residence. Due to the change in behaviour, TPM reduced by 20% among South-South residents compared with their North-Central counterparts. However, TPM increased by 260% among teens who had their first sexual initiation. Conclusions The TPM prevalence remained high in Nigeria, though a decreasing trend was observed within the studied period. Government and other stakeholders should focus pragmatic interventions on the identified drivers of TPM change over the last two decades in their efforts to alleviate TPM in Nigeria.

A Nitrilium-Type <i>N</i> -Heterocyclic Aryne

Cloud-edge collaborative data anomaly detection in industrial sensor networks

Industrial sensor networks exhibit heterogeneous, federated, large-scale, and intelligent characteristics due to the increasing number of Internet of Things (IoT) devices and different types of sensors. Efficient and accurate anomaly detection of sensor data is essential for guaranteeing the system’s operational reliability and security. However, existing research on sensor data anomaly detection for industrial sensor networks still has several inherent limitations. First, most detection models usually consider centralized detection. Thus, all sensor data have to be uploaded to the control center for analysis, leading to a heavy traffic load. However, industrial sensor networks have high requirements for reliable and real-time communication. The heavy traffic load may cause communication delays or packets lost by corruption. Second, there are complex spatial and temporal features in industrial sensor data. The full extraction of such features plays a key role in improving detection performance. Nevertheless, the majority of existing methodologies face challenges in simultaneously and comprehensively analyzing both features. To solve the limitations above, this paper develops a cloud-edge collaborative data anomaly detection approach for industrial sensor networks that mainly consists of a sensor data detection model deployed at individual edges and a sensor data analysis model deployed in the cloud. The former is implemented using Gaussian and Bayesian algorithms, which effectively filter the substantial volume of sensor data generated during the normal operation of the industrial sensor network, thereby reducing traffic load. It only uploads all the sensor data to the sensor data analysis model for further analysis when the network is in an anomalous state. The latter based on GCRL is developed by inserting Long Short-Term Memory network (LSTM) into Graph Convolutional Network (GCN), which can effectively extract the spatial and temporal features of the sensor data for anomaly detection. The proposed approach is extensively assessed through experiments using two public industrial sensor network datasets compared with the baseline anomaly detection models. The numerical results demonstrate that the proposed approach outperforms the existing state-of-the-art models.

Low-Energy Photoelectron Spectroscopy and Scattering from Aqueous Solutions and the Role of Solute Surface Activity

Ethanol alters mechanosensory habituation in C. elegans by way of the BK potassium channel through a novel mechanism

In this research, we investigated how alcohol modulates the simplest form of learning, habituation, in Caenorhabditis elegans. We used our high throughput Multi-Worm Tracker to conduct a large scale study of more than 21,000 wild-type worms to assess the effects of different concentrations of alcohol on habituation of the well-characterized tap withdrawal response. We found that the effect of alcohol on habituation of this reversal response to a repeated mechanosensory stimulus (taps) differed depending on the component of the reversal response assessed. Interestingly, when we examined habituation of response probability on and off alcohol we discovered that alcohol switched the predominant response to tap from a backward reversal to a brief forward movement. Because the large conductance potassium (BK) channel has been shown to be important for the effect of alcohol on behaviour in a variety of organisms, including C. elegans, we investigated whether the C. elegans BK channel ortholog, SLO-1, mediated the effects of alcohol on habituation. We tested several different strains of worms with mutations in slo-1 along with wild-type controls; null mutations in slo-1 made animals resistant to alcohol induced changes in habituation. However, a mutation in the putative ethanol binding site on SLO-1 did not disrupt the impact of ethanol on habituation. Finally, by degrading SLO-1 in different parts of the nervous system we found that the function of SLO-1 in ethanol’s impact on habituation is likely distributed throughout the neural circuit that responds to tap. Based on these results, our main conclusions are 1) ethanol is not a general facilitator or inhibitor of habituation but rather a complex modulator, 2) SLO-1 is critical for the effect of ethanol on habituation, 3) ethanol is interacting (directly or indirectly) with SLO-1 through a novel unidentified mechanism to influence how animals respond to repeated taps.

Theoretical Insights into the Resonant Suppression Effect in Vibrational Polariton Chemistry

Machine learning driven biomarker selection for medical diagnosis

Recent advances in experimental methods have enabled researchers to collect data on thousands of analytes simultaneously. This has led to correlational studies that associated molecular measurements with diseases such as Alzheimer’s, Liver, and Gastric Cancer. However, the use of thousands of biomarkers selected from the analytes is not practical for real-world medical diagnosis and is likely undesirable due to potentially formed spurious correlations. In this study, we evaluate 4 different methods for biomarker selection and 5 different machine learning (ML) classifiers for identifying correlations—evaluating 20 approaches in all. We found that contemporary methods outperform previously reported logistic regression in cases where 3 and 10 biomarkers are permitted. When specificity is fixed at 0.9, ML approaches produced a sensitivity of 0.240 (3 biomarkers) and 0.520 (10 biomarkers), while standard logistic regression provided a sensitivity of 0.000 (3 biomarkers) and 0.040 (10 biomarkers). We also noted that causal-based methods for biomarker selection proved to be the most performant when fewer biomarkers were permitted, while univariate feature selection was the most performant when a greater number of biomarkers were permitted.

Atmospheric Pressure Synthesis of Ultrathin Monoclinic FeCr₂S₄ Crystals with Robust Antiferromagnetism

Bridged Boranoanthracenes: Precursors for Free Oxoboranes through Aromatization-Driven Oxidative Extrusion

Digital transformation and corporate ESG performance: Research based on a capability-motivation dual framework

This study systematically examines the impact mechanism and heterogeneous characteristics of digital transformation (DT) on corporate environmental, social, and governance (ESG) performance, using panel data from Chinese A-share listed companies from 2010–2022. The research constructs a “capability-motivation” analytical framework based on resource dependence theory and agency theory, yielding the following conclusions. First, DT has a significant positive effect on corporate ESG performance, which is robustly verified through instrumental variables method and system GMM, among others. Second, DT enhances corporate ESG performance through the dual pathways of alleviating financing constraints (resource effect) and reducing agency costs (governance effect). Third, this promotional effect varies significantly across different lifecycle stages, being more prominent in mature and declining companies but not significant in growth-stage companies. Finally, the positive impact of DT on ESG performance is stronger in state-owned enterprises than in non-state-owned enterprises. This study integrates scattered findings in existing literature through a “capability-motivation” dual framework, providing a more systematic explanation for understanding the relationship between DT and ESG, and offering theoretical foundations and practical implications for companies to formulate differentiated digital-ESG strategies.

Correction to “Chemoselective Silver-Catalyzed Nitrene Transfer: Tunable Syntheses of Azepines and Cyclic Carbamimidates

Interpretation of ATR-FTIR spectra of dental adhesives throughout simultaneous polymerization and solvent loss

This study developed new Fourier transform infrared (FTIR) spectroscopy methods to assess effects of drying level on the composition and polymerization kinetics of One‐Step® (OS), OptibondTM Universal (OU) and G‐Bond (GB) dental adhesives. 5 μL of each adhesive were placed in turn on an FTIR, Attenuated Total Reflectance (ATR) accessory, operating at 37ºC. Spectra were generated before, during and after light-curing (20 s, 1000 mW/cm2, 450−470 nm) at 10 s after placement or following 300 s of passive drying (n = 3). Individual spectra of solvents, monomers and fillers, combined with spectral change upon polymerization, were used to generate model spectra and quantify component levels versus time up to 300 s after start of light exposure. Polymerization rates and maximum degree of conversion were derived using a combination of polymer and monomer peaks at 1480 and 1320 cm-1. Inferential analyses included Kruskal-Wallis/Mann-Whitney U using a significance level of 5%. Initial acetone levels were 65, 48 and 50% in OS, OU and GB, respectively, whilst curing at 10 versus 300 s gave final acetone levels of 35, 20 and 32% versus 0, 0 and 10%. With earlier light exposure, monomer reaction rate was reduced but continued for longer leading to final conversions of 88, 86 and 40% versus 61, 66 and 77% for OS, OU and GB, respectively. The FTIR techniques developed could monitor process kinetics and demonstrate the large, highly significant effects of drying method on final polymerized dental adhesive composition and polymerization level.

Reference values of urinary metabolites of organophosphate in healthy Iranian adults

Organophosphorus pesticides are widely used in agriculture in Iran; we evaluated exposure to these pesticides among Iranian adults. Pesticide-specific urinary metabolites were used as biomarkers for exposure to various pesticides, including organophosphorus insecticides. The aim of the study was to estimate reference values (RV95) and their relationships with the measured factors. We used the 95th percentile as the basis for deriving these reference values. The analysis included descriptive statistics and multiple linear regression, conducted using Python software. We measured metabolites for Chlorpyrifos (TCP: 2-isopropyl-4-methyl-6-hydroxypyrimidine), Diazinon (IMPY: 2-isopropyl-4-methyl-6-hydroxypyrimidine), and Malathion (Malathion dicarboxylic acid) in 490 healthy Iranian adults. Additionally, we recorded age, gender, wealth index, and body composition parameters including body fat, muscle mass, visceral fat, and BMI. Fasting urine sampling, along with body composition and demographic measurements, were conducted. Urine samples were subsequently analyzed. The Chlorpyrifos, Diazinon, and Malathion Reference Value (RV95) levels ranged from ND-24.9 µg/L (RV95: 2.8 µg/L, 2.9 µg/gcrt), ND-64.36 µg/L (RV95: 8.6 µg/L, 9.3 µg/gcrt), and ND-47.69 µg/L (RV95: 9.8 µg/L, 8.2 µg/gcrt), respectively. Diazinon (IMPY) and Malathion (Malathion dicarboxylic acid) showed no significant relationship between their urinary levels and demographic features. However, visceral fat percentage had a significant inverse correlation with urinary levels of Chlorpyrifos (TCP) (P = 0.038). Other factors such as age, sex, visceral fat, BMI, and wealth index showed no significant relationship with urinary levels (P &gt; 0.05). Non-zero levels were found in 98.8% of adults’ urine samples for this metabolite. The reference value of this pesticide metabolite in urine could be helpful for policymakers in assessing the level of exposure among Iranians.

Enantioselective β-C(sp ³ )–H Nucleophilic Tosylation of Native Amides: A Synthetic Platform for Chiral Methyl Stereocenters

Epidemiology and clinical management of acute diarrhoea in dogs under primary veterinary care in the UK

Background Acute diarrhoea is a common canine veterinary presentation in the UK. This study aimed to report the incidence, demographic risk factors and clinical management for acute diarrhoea diagnosed under primary veterinary care in the UK in 2019. Methods A cohort study design with a cross-sectional analysis was applied to anonymised VetCompass clinical data. Risk factor analysis used multivariable logistic regression modelling. Results The analysis included a random sample of 1,835 confirmed incident acute diarrhoea cases in 2019 from an overall study population of 2,250,417 dogs. After accounting for subsampling, the estimated one-year incidence risk for acute diarrhoea in dogs overall was 8.18% (95% CI: 7.83–8.55). Of the first acute diarrhoea event in 2019 for the 1,835 cases, 1473 (80.27%) had only one physical visit for veterinary care related to the acute diarrhoea. The most common comorbid clinical signs with acute diarrhoea included vomiting (n = 812, 44.25%), reduced appetite (508, 27.68%) and lethargy (444, 24.20%). Overall, 538 (29.32%) cases were recorded as haemorrhagic diarrhoea. The most common clinical managements were probiotics (n = 1094, 59.62%), dietary management (807, 43.98%), antibiosis (701, 38.20%) and maropitant (441, 24.03%). Six breeds showed increased odds of acute diarrhoea compared with crossbred dogs: Maltese (OR 2.17, 95% CI 1.25–3.77), Miniature Poodle (OR 2.17, 95% CI 1.19–3.95), Cavapoo (OR 2.07, 95% CI 1.32–3.25), German Shepherd Dog (OR 1.69, 95% CI 1.29–2.22), Yorkshire Terrier (OR 1.51, 95% CI 1.15–1.98) and Cockapoo (OR 1.36, 95% CI 1.05–1.74). The odds of diagnosis increased in dogs aged under 3 years and dogs aged over 9 years, compared to dogs aged 4–5 years. Conclusions This study confirms acute diarrhoea as a common clinical condition in dogs managed under primary veterinary care, with 1-in-12 dogs diagnosed each year. The identified breed predispositions suggest some genetic element to the condition. The clinical outcomes following veterinary care appear to be very positive, with over 80% of acute diarrhoea cases not receiving a second veterinary visit. However, antibiotic use remained frequent, despite years of recommendation to the contrary and raises concerns about unnecessary antibiotic therapy for this condition.

Design and Structural Elucidation of Glycopeptide Fibrils: Emulating Glycosaminoglycan Functions for Biomedical Applications

Buca della Iena and Grotta del Capriolo: New chronological, lithic, and faunal analyses of two late Mousterian sites in Central Italy

New radiocarbon, lithic, faunal, and documentary analyses of two sites, Buca della Iena and Grotta del Capriolo, located in Tuscany (Central Italy) and excavated in the late 1960s’, are presented. The new analyses significance will be evaluated within the late Neanderthal occupation in the northwestern Italian peninsula and provide insights into their demise. Reassessment of stratigraphical and fieldwork documentation identified areas of stratigraphic reliability, supporting robust interpretations. Radiocarbon dating reveals broadly contemporaneous occupations at both sites between 50–40 ka cal BP, with Buca della Iena showing occupation from approximately 47 to 42.5 ka cal BP. Lithic analyses demonstrate the consistent application of the same chaîne opératoire across both sites. Faunal analyses indicate that carnivores, particularly Crocuta spelaea , were the dominant accumulating agents in Buca della Iena, while limited preservation at Grotta del Capriolo prevents detailed taxonomic determination. However, hominin presence at both sites is evidenced by cut-marked bones. This study provides new perspectives on the Middle-to-Upper Palaeolithic transition in the northwestern Italian peninsula.