Browse Articles

Utilizing a deep learning model based on BERT for identifying enhancers and their strength

An enhancer is a specific DNA sequence typically located within a gene at upstream or downstream position and serves as a pivotal element in the regulation of eukaryotic gene transcription. Therefore, the recognition of enhancers is highly significant for comprehending gene expression regulatory systems. While some useful predictive models have been proposed, there are still deficiencies in these models. To address current limitations, we propose a model, DNABERT2-Enhancer, based on transformer architecture and deep learning, designed for the recognition of enhancers (classified as either enhancer or non-enhancer) and the identification of their activity (strong or weak enhancers). More specifically, DNABERT2-Enhancer is composed of a BERT model for extracting features and a CNN model for enhancers classification. Parameters of the BERT model are initialized by a pre-training DNABERT-2 language model. The enhancer recognition task is then fine-tuned through transfer learning to convert the original sequence into feature vectors. Subsequently, the CNN network is employed to learn the feature vector generated by BERT and produce the prediction results. In comparison with existing predictors utilizing the identical dataset, our approach demonstrates superior performance. This suggests that the model will be a useful instrument for academic research on the enhancer recognition.

Spontaneous Generation of an Endogenous RORγt Agonist

Occupational respiratory symptoms and associated factors among street sweepers in low- and middle-income countries: A systematic review and meta-analysis

In low- and middle-income countries, occupational exposure continues to be a serious public health concern. Because of their working conditions, street sweepers are particularly vulnerable to health hazards, with respiratory issues being the most common. The lack of comprehensive and inconsistence evidence on occupational respiratory symptoms among street sweepers, which exacerbates this issue. This study aimed to estimate the pooled prevalence of occupational respiratory symptoms and associated factors among street sweepers in low- and middle-income countries. A comprehensive search strategy was carried out using various databases, such as PubMed, Scientific Direct, HINARI and Google Scholar, and the grey literature. The preferred reporting items for systematic reviews and meta-analyses were used. Microsoft Excel was used to extract the data, which was then transferred to STATA 14/SE software for analysis. The Joanna Briggs Institutes’ quality appraisal tool was used to ensure the quality of the included articles. A random effects model was used to estimate the pooled prevalence. In this study, a subgroup analysis was employed to examine the potential sources of heterogeneity, and sensitivity analysis was conducted to assess the effect of individual studies on the pooled results. To evaluate publication bias, the funnel plot and Egger’s regression tests were employed. The pooled prevalence of occupational respiratory symptoms among street sweepers was estimated to be 47.35% (95% CI: 34.59–60.11). Among each respiratory symptom, cough was found to be the most prevalent at 38.37% (95% CI: 28.54–48.19). In this study, past dust exposure (OR = 2.84, 95% CI: 1.06–4.61), not using facemasks (OR = 2.67, 95% CI: 1.54–3.81), having more than five years of work experience (OR = 3.68, 95% CI: 1.66–5.7), and being between the ages of 38 and 47 (OR = 2.17, 95% CI: 1.07–3.27) were found to be predictor factors with occupational respiratory symptoms among street sweepers. Furthermore, there is no evidence of publication bias for this study. The findings of this study indicated a significant prevalence of occupational respiratory symptoms among street sweepers. Therefore, it is highly recommended to focus on the provision and proper use of personal protective equipment and to enhance occupational health and safety training.

Degradable Alternating Copolymers from Living Radical Copolymerization of Natural Levoglucosenone and Dienes

Protocol: Waiting time and ways of accessing specialized health services in public hospitals in Ecuador

This study aims to determine the waiting time and the forms of access to specialized health services in public hospitals in Ecuador. A representative sample of 32 hospitals under the Ministry of Public Health was considered, with 26 selected by accessibility convenience. Data will be collected using a structured questionnaire. Patients will be asked about the number of days they waited for their medical appointments and the method used to schedule their appointments. The study distinguishes between standardized access, based on Ecuador’s formal referral and counter-referral system, and non-standardized access methods, such as personal connections or hospital staff involvement. The data of this protocol are registered and publicly accessible at: https://dx.doi.org/10.17504/protocols.io.261ge5z7wg47/v1 . We expect to identify a correlation between waiting times and the type of access to specialized medical services, with non-standardized access potentially leading to shorter waiting times. This research may highlight disparities in the system and suggest areas for improvement in equity and efficiency within the healthcare referral process. To do so, a structured survey will be used. Since a construct is not needed to determine the waiting time or the forms of access, it was not necessary to validate the instrument. However, we did validate the understanding of the questions and the response options in several places in the country. According to the results of the validation of the instrument, pollsters will be instructed to inform users about the meaning of the question on ethnic identification, which was difficult to understand in the country’s coastal areas.

Record-High-Molecular-Weight Polyesters from Ring-Opening Copolymerization of Epoxides and Cyclic Anhydrides Catalyzed by Hydrogen-Bond-Functionalized Imidazoles

Demography, commonly recorded disorders and mortality in chelonia under UK primary veterinary care: A VetCompass study

Little information has been published on the recorded disorders of pet chelonia. This cohort study aimed to report on demography, commonly recorded disorders and mortality recorded in chelonia under UK primary veterinary care in 2019. Electronic health records on a random sample of chelonia in VetCompass during 2019 were reviewed to extract species, recorded disorder and mortality data. Of 2,040 chelonia reviewed, 1,923 (94.26%) were recorded as tortoises, 74 (3.63%) terrapins and 43 (2.11%) turtles. Of the 811 (42.17%) tortoises with species information recorded, the most common species were Hermann’s tortoises (Testudo hermanni) (311, 38.35%), Horsfield’s tortoises (Testudo horsfieldii) (259, 31.94%) and Mediterranean spur-thighed tortoises (154, 18.99%). The most commonly recorded disorders were beak abnormalities (16.72%), overgrown nails (11.47%) and shell abnormalities (9.80%). The most common recorded causes of death were “disorder undiagnosed” (44.55%), dog bite (5.45%) and anorexia (3.96%). Of 178 deaths with age recorded, the median age at death was 7.32 years (IQR 2.50-15.14). Short temporal windows within the clinical records and often limited clinical workups were a frequent limitation to the precision of the recorded disorder terms. This is the largest study of chelonia to date. It has highlighted the most commonly recorded disorders of chelonia of which many are often linked to husbandry.

Development of anatomically accurate digital organ models for surgical simulation and training

Advancements in robotics and other technological innovations have accelerated the development of surgical procedures, increasing the demand for training environments that accurately replicate human anatomy. This study developed a system that utilizes the AutoSegmentator extension of 3D Slicer, based on nnU-Net, a state-of-the-art deep learning framework for automatic organ extraction, to import automatically extracted organ surface data into CAD software along with original DICOM-derived images. This system allows medical experts to manually refine the automatically extracted data, making it more accurate and closer to the ideal dataset. First, Python programming is used to automatically generate and save JPEG-format image data from DICOM data for display in Blender. Next, DICOM data imported into 3D Slicer is processed by AutoSegmentator to extract surface data of 104 organs in bulk, which is then exported in STL format. In Blender, a custom-developed Python script aligns the image data and organ surface data within the same 3D space, ensuring accurate spatial coordinates. By using Blender’s CAD functionality within this space, the automatically extracted organ boundaries can be manually adjusted based on the image data, resulting in more precise organ surface data. Additionally, organs and blood vessels that cannot be automatically extracted can be newly created and added by referencing the image data. Through this process, a comprehensive anatomical dataset encompassing all required organs and blood vessels can be constructed. The dataset created with this system is easily customizable and can be applied to various surgical simulations, including 3D-printed simulators, hybrid simulators that incorporate animal organs, and surgical simulators utilizing augmented reality (AR). Furthermore, this system is built entirely using open-source, free software, providing high reproducibility, flexibility, and accessibility. By using this system, medical professionals can actively participate in the design and data processing of surgical simulation systems, leading to shorter development times and reduced costs.

Enantiospecific Synthesis of α-Tertiary Amines: Ruthenium-Catalyzed Allylic Amination with Aqueous Ammonia

Study protocol for a randomized clinical trial evaluating the safety and efficacy of autologous adipose-derived stem cell therapy for ulcers in patients with critical limb ischemia

Background Peripheral artery disease (PAD) can develop into critical limb ischemia (CLI), which is characterized by resting pain at rest, ulcerations, or gangrene, with a high risk of amputation. The optimum course of treatment at this point is arterial revascularization, although this has a significant financial cost and is not always feasible or successful in reducing pain, healing ulcers, or preventing amputations. In situations where traditional alternatives for treating PAD have been exhausted, recent developments in cell therapy may offer a viable substitute. Objective The purpose of this study is to assess the safety and effectiveness of using expanded autologous adipose-derived stem cells (ASCs) in cellular therapy for the treatment of PAD patients who developed chronic artery ulcers. Methods An open randomized clinical trial will be carried out with two groups of twenty patients with CLI: In group 1, 2g of abdominal adipose tissue will be taken to produce ASCs. These cells will then be expanded in a lab (cell processing center) for 14–21 days before being applied to the lesion using bio-dressings and perilesional subcutaneous injections. Group 2 will receive conventional treatment with hydrogel-based dressing. There will be regular clinical assessments, supplementary tests, and photo documentation. The main efficacy outcome will be partial or complete healing of the wound. Safety outcomes will be monitored for infections, gangrene, amputations, and death. Participants will be monitored for 90 days. Cases of major amputation of the studied limb will not be included. The results will be evaluated by an independent external evaluator who is blind to the groups. Considering the high prevalence and socioeconomic consequences related to CLI and limb amputation, this study is expected to provide a positive social and financial impact on the Brazilian Unified Health System. ClinicalTrials.gov: NCT06326203.

Chiral All-Inorganic Perovskite Subnanowires

Classification of the trajectory of changes in food intake in special nursing home for oldest-old in the 6 months before death: A secondary analysis

Background For the oldest-old residents around their 90s living in facilities, quality end-of-life care is crucial. While an association between reduced food intake and death is known, specific patterns of intake changes before death are not well-documented. Aims This study aims to classify food intake changes among residents in Japan’s special nursing homes during the 6 months before death, enabling precision care for each group using routinely recorded data. Methods Sixty-nine deceased older adults from five special nursing homes were studied over 3.5 years (January 2016 to June 2020). Criteria included: at least six months’ residency before death, ability to eat orally during the study period, and death within the facility. We created a time-series dataset for 69 participants, documenting their average weekly food intake (on a scale of 0-10). Subsequently, we used cluster analysis to identify clusters of change in the average weekly food intake from the 6 months before death. Results Eligible residents’ mean age was 89.7 ±  6.7 years, and 79.7% were female. Cluster analysis classified 4 clusters of decline in food intake changes during the last 6 months before death: immediate decrease (n = 14); decrease from 1 month before death (n = 24); decrease from 3 months before death (n = 7); and gradual decrease for 6 months before death (n = 24). Conclusion This study identified four groups of food intake prior to death. Recognizing food intake clusters in practical settings can help manage and provide appropriate end-of-life care in facilities with few medical providers but many care providers.

Dynamic and Diverse Coacervate Architectures by Controlled Demembranization

Retraction: The use of machine translation algorithm based on residual and LSTM neural network in translation teaching

Inner–Outer Sheath Synergistic Shielding of Polysulfides in Asymmetric Solvent-Based Electrolytes for Stable Sodium–Sulfur Batteries

Breastfeeding related knowledge, attitudes, perceptions and practices of primary healthcare professionals in Ireland: A national cross-sectional survey

Background Global research identifies the importance of breastfeeding, including the World Health Organisation in developing recommendations and noting over 800,000 child lives would be saved each year if breastfeeding was adopted following the recommendations of WHO/UNICEF. There is limited published data exploring breastfeeding knowledge, attitudes, perceptions and practices [KAPP] of health care professionals employed in primary care. Recent Irish evidence from one local geographical area identified general practitioners and general practice nurses [GPs and GPNs] received limited formal breastfeeding education within undergraduate or postgraduate education programmes and were interested in undertaking further professional development, education and training. Methods Following ethical approval, a national cross sectional online survey using a breastfeeding [KAPP] survey instrument was completed using the Qualtrics platform. All registered GPs, GP trainees and General Practice Nurses [GPNs] in the Republic of Ireland were invited to participate. The online survey link was distributed via Ireland’s Health Service Executive health link email register via two senior HSE gatekeepers. Data collection was from June 1st 2023, to November 17th, 2023. Results A total of 662 primary health professionals participated, including 58.2% GPs, 14.2% GP trainees and 27.6% GPNs. The response rate to the survey was 10%, with approximately 6618 healthcare professionals receiving the link to the survey and 662 participating. Approximately 78% of respondents reported always recommending breastfeeding to women, and the majority (94.2%) were interested in completing further breastfeeding education. Barriers to training noted were time (84.3%), workload (62%) and financial cost (34.9%). Perceived and factual breastfeeding knowledge, perceived attitude and confidence scores with breastfeeding related issues significantly differed among the three groups. Conclusion This national study reports low engagement with a national KAPP survey. There is inadequate preparation of primary healthcare professionals both theoretically and clinically to promote, protect and support breastfeeding in the primary healthcare setting, and has important implications for supporting wellbeing and shaping population health and achieving sustainable development goals.

Catalytic Aldehyde Decarbonylase Activity by a Thiolate-Bound Iron Porphyrin: Observation of a Peroxyhemiacetal Intermediate during Catalysis

Enhancing student-centered walking environments on university campuses through street view imagery and machine learning

Campus walking environments significantly influence college students' daily lives and shape their subjective perceptions. However, previous studies have been constrained by limited sample sizes and inefficient, time-consuming methodologies. To address these limitations, we developed a deep learning framework to evaluate campus walking perceptions across four universities in China's Yangtze River Delta region. Utilizing 15,596 Baidu Street View Images (BSVIs), and perceptual ratings from 100 volunteers across four dimensions—aesthetics, security, depression, and vitality—we employed four machine learning models to predict perceptual scores. Our results demonstrate that the Random Forest (RF) model outperformed others in predicting aesthetics, security, and vitality, while linear regression was most effective for depression. Spatial analysis revealed that perceptions of aesthetics, security, and vitality were concentrated in landmark areas and regions with high pedestrian flow. Multiple linear regression analysis indicated that buildings exhibited stronger correlations with depression (β = 0.112) compared to other perceptual aspects. Moreover, vegetation (β = 0.032) and meadows (β = 0.176) elements significantly enhanced aesthetics. This study offers actionable insights for optimizing campus walking environments from a student-centered perspective, emphasizing the importance of spatial design and visual elements in enhancing students' perceptual experiences.

Subnanometric Nickel Phosphide Heteroclusters with Highly Active Ni^δ+–P^δ− Pairs for Nitrate Reduction toward Ammonia

An exploratory study of associations between judgement bias, demographic and behavioural characteristics, and detection task performance in medical detection dogs

Medical detection dogs search for diseases from remote samples (biodetection) and assist patients with chronic conditions (medical alert assistance). There is scarce information on how dogs’ decision-making tendencies relate to task performance. This study explored the relationships between medical detection dog demographics, responses in a behavioural test battery, ‘optimistic’ or ‘pessimistic’ decisions in a judgement bias task, and their performance in detection tasks. A sample of 58 trainee and trained medical detection dogs were studied in a Go/NoGo spatial judgement bias test. For trainee dogs (n = 39), training outcome (pass/fail) and trainer ratings of behavioural traits; yielding a composite score of ability in detection tasks, were used as markers of task performance. For trained biodetection dogs (n = 27), scent sensitivity and specificity scores derived during training and testing trials were used. Older dogs (p < 0.001), those showing higher ‘Confidence’ (p = 0.009), ‘Food orientation’ (p = 0.014) and ‘Playfulness’ (p = 0.005) in the test battery, and those who made more ‘optimistic’ decisions in the judgement bias task (p = 0.002), had higher detection task ability scores. For trained dogs, latency to approach ambiguous stimuli was positively correlated with scent specificity levels (n = 25, p = 0.021), suggesting that more ‘pessimistic’ dogs tended to be more specific. Our findings suggest relationships between behaviour in judgement bias tests and other learning and discrimination tasks, which may reflect underlying individual or personality differences in affective and/or cognitive processes that influence dogs’ style of searching and performance ability in medical detection tasks. Future research is needed to explore these associations further and investigate the value of judgement bias tasks in predicting later search performance in medical and other types of search dogs.