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Serial crystallography requires large numbers of microcrystals and robust strategies to rapidly apply substrates to initiate reactions in time-resolved studies. Here, we report the use of droplet miniaturization for the controlled production of uniform crystals, providing an avenue for controlled substrate addition and synchronous reaction initiation. The approach was evaluated using two enzymatic systems, yielding 3 µm crystals of lysozyme and 2 µm crystals of Pdx1, an Arabidopsis enzyme involved in vitamin B6 biosynthesis. A seeding strategy was used to overcome the improbability of Pdx1 nucleation occurring with diminishing droplet volumes. Convection within droplets was exploited for rapid crystal mixing with ligands. Mixing times of

Abstract Background Hip dysplasia is a condition where the acetabulum is too shallow to support the femoral head and is commonly considered a risk factor for hip osteoarthritis. The objective of this study was to develop a deep learning model to diagnose hip dysplasia from plain radiographs and classify dysplastic hips based on their severity. Methods We collected pelvic radiographs of 571 patients from two single-center cohorts and one multicenter cohort. The radiographs were split in half to create hip radiographs (n = 1022). One orthopaedic surgeon and one resident assessed the radiographs for hip dysplasia on either side. We used the center edge (CE) angle as the primary diagnostic criteria. Hips with a CE angle 25° were labeled as dysplastic, borderline, and normal, respectively. The dysplastic hips were also classified with both Crowe and Hartofilakidis classification of dysplasia. The dataset was divided into train, validation, and test subsets using 80:10:10 split-ratio that were used to train two deep learning models to classify images into normal, borderline and (1) Crowe grade 1–4 or (2) Hartofilakidis grade 1–3. A pre-trained on Imagenet VGG16 convolutional neural network (CNN) was utilized by performing layer-wise fine-turning. Results Both models struggled with distinguishing between normal and borderline hips. However, achieved high accuracy (Model 1: 92.2% and Model 2: 83.3%) in distinguishing between normal/borderline vs. dysplastic hips. The overall accuracy of Model 1 was 68% and for Model 2 73.5%. Most misclassifications for the Crowe and Hartofilakidis classifications were +/- 1 class from the correct class. Conclusions This pilot study shows promising results that a deep learning model distinguish between normal and dysplastic hips with high accuracy. Future research and external validation are warranted regarding the ability of deep learning models to perform complex tasks such as identifying and classifying disorders using plain radiographs. Level of Evidence Diagnostic level IV

Abstract Objective We aimed to assess the effect of SGLT2i on arrhythmias by conducting a meta-analysis using data from randomized controlled trials(RCTs). Background Sodium-glucose co-transporter 2 inhibitors (SGLT2i) have shown cardioprotective effects via multiple mechanisms that may also contribute to decrease arrhythmias risk. Methods We searched in databases (PubMed, Embase, Cochrane Library, and clinicaltrials.gov) up to April 2023. RCTs comparing SGLT2i with placebo were included. The effects of SGLT2i on atrial fibrillation(AF), atrial flutter(AFL), composite AF/AFL, ventricular fibrillation(VF), ventricular tachycardia(VT), ventricular extrasystoles(VES), sudden cardiac death(SCD) and composite VF/VT/SCD were evaluated. Results 33 placebo-controlled RCTs were included, comprising 88,098 patients (48,585 in SGLT2i vs. 39,513 in placebo). The mean age was 64.9 ± 9.4 years, 63.0% were male. The mean follow-up was 1.4 ± 1.1 years. The pooled-results showed that SGLT2i was associated with a significantly lower risk of AF [risk ratio(RR): 0.88, 95% confidence interval(CI) 0.78–1.00, P = 0.04] and composite AF/AFL (RR: 0.86, 95%CI 0.77–0.96, P = 0.01). This favorable effect appeared to be substantially pronounced in patients with HFrEF, male gender, dapagliflozin, and > 1 year follow-up. For SCD, only in heart failure patients, SGLT2i were found to be associated with a borderline lower risk of SCD (RR: 0.67, P = 0.05). No significant effects of SGLT2i on other ventricular arrhythmic outcomes were found. Conclusions SGLT2i lowers the risks of AF and AF/AFL, and this favorable effect appeared to be particularly pronounced in patients with HFrEF, male gender, dapagliflozin, and longer follow-up (> 1 year). SGLT2i lowers the risk of SCD only in heart failure patients. Graphical Abstract

Abstract Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and ‘speech records’ of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD.

m Umgang mit gesellschaftlichen Transformationsprozessen und strukturellen Veränderungen am Arbeitsmarkt kommt beruflicher und betrieblicher Weiterbildung eine zentrale Rolle zu. Trotz vielfältiger bildungspolitischer Bemühungen zeigt sich in Bezug auf Weiterbildungsteilnahme allerdings nach wie vor das Prinzip der „doppelten Selektivität“, wonach Personen mit höherem Bildungsabschluss auch häufiger weiterbildungsaktiv sind. Der Beitrag untersucht jene bisher nicht ausreichend erforschten betrieblichen und beschäftigungsbezogenen Aspekte, welche Weiterbildungschancen im betrieblichen Kontext befördern bzw. vermindern. Die Daten einer aktuellen Erhebung bei Beschäftigten in Niederösterreich zeigen, dass die Teilnahme an Weiterbildung z.B. vom betrieblichen Weiterbildungsmanagement und von der beschäftigungsbezogenen Position der Mitarbeiter*innen im Unternehmen abhängig ist. (Red.)

Designing power electronics converters for harsh environments is challenging due to the absence of components' performance under harsh conditions, the frequent transition and data-passing among various software, and the time-consuming and computationally heavy work flow. This paper promotes using design automation to address the aforementioned design challenges. The implementations include public-accessible component databases, automated co-action among circuit simulators and finite element simulations to perform electrical, electromagnetic and thermal co-design, and finally an exclusion-based work flow with hierarchical computation to reduce computational load. The theorized framework is exemplified on designing a real world 175 °C 1.5 kW Three-level Neutral-point-clamped dc-dc converter. A database containing the high-temperature characteristics of SiC MOSFETs and ferrites is established and shared through a web application with graphical user interface. In 310 min, the program, which includes computationally heavy 3D finite element simulation, delivers design output after evaluating the converter's electrical, electromagnetic and thermal performance under 10 million parameter sets. Finally, a 1.5 kW dc-dc converter prototype is built and tested in 175 °C ambient temperature to verify the quality of the design output.

Abstract Background Forearm and olecranon fractures are a common orthopaedic injury. This study aimed to analyse whether the incidence of forearm injury is changing and identifying trends in the number of forearm and olecranon fractures using public aggregated data in Sweden. Methods The number of forearm and olecranon fractures as defined by the number of registered diagnoses with the ICD-10 code of S52 were collected and normalized per 100,000 inhabitants and stratified per sex, age, and month. Age-adjusted incidence for forearm and olecranon fractures were calculated using the direct method. Poisson regression was used to analyse monthly, seasonal and yearly change in forearm and olecranon fracture incidence. Logistical regression was used to predict future trends of forearm and olecranon fractures. Results The findings revealed a slight decreasing trend in forearm and olecranon fractures. The average incidence rate during the study period was 333 with women having a higher incidence rate than men. More fractures occurred in the winter months. Fluctuations in the number of forearm and olecranon fractures were observed during 2020 which may be influenced by the COVID-19 pandemic. Based on current data, forearm and olecranon fractures are expected to decrease in Sweden by 2035. Conclusion This study describes the trend of forearm and olecranon fractures among individuals according to sex and age in Sweden using easily obtainable data. Trends in forearm and olecranon fractures are dependent on sex and age but generally show a decreasing trend. More precise studies are needed in order to properly quantify the specific incidence of various subtypes of forearm and olecranon fractures and associated risk factors.

The preservation of aquatic ecosystems and the availability of drinkable water sources depends on the protection of water quality. This is especially true about bodies of water such as ponds. Traditional monitoring techniques frequently require a significant investment of resources and have a restricted application, which has prompted the investigation of more effective technologies. We present a unique wireless acquisition system for monitoring real-time water quality that makes use of the Arduino (ESP32) microcontroller. This system allows us to collect data in real-time. This cutting-edge technology collects data from a variety of pond locations utilizing three individual sensors to perform remote measurements of three critical parameters: turbidity, TDS, and pH. The integration of the system with an aquatic boat enables complete sampling from the center as well as the sides of the pond, which is a significant step forward in terms of innovation. The collected information, which may include pH, turbidity, and TDS readings, is uploaded to the cloud so that it may be evaluated in real-time using the AquaSpecs app. The effectiveness of the proposed system has been proven by deployment in four ponds in Chhattisgarh; these ponds are named Birkona Pond, Budha Pond, Dagania Pond, and Kushalpur Pond. This deployment demonstrates the system’s potential for effective water quality monitoring and management. Using the proposed water quality monitoring system, it was found that the Birkona pond was the cleanest pond having average values of pH, TDS, and Turbidity were 6.23,196.75 and, 8.83 respectively. Budha pond was found highly polluted pond having average values of pH, TDS, and Turbidity were 13.30, 544.18, and, 34.89 respectively. The proposed system is also compared with recent water quality monitoring systems and achieves a higher score. Sensor errors were also very low in the proposed system when compared with traditional approaches, which makes it suitable for monitoring water quality.

Abstract Cervical cancer is the fifth most common cancer in the United Arab Emirates (UAE; HPV informatics, 2023) and the fourteenth most common in Australia (Cancer Council 2023). Despite the introduction of vaccinations and cervical screening programs in both countries, Emirati and non-Emirati women living in Ras Al Khaimah and migrant women living in Sydney still face significant health disparities when accessing cervical cancer screening services. Currently, there is a lack of literature examining the obstacles to and facilitators of cervical screening among these groups of women. This study aimed to better understand the knowledge and awareness of cervical cancer and factors that hinder or promote access to cervical screening among these groups, which is crucial if healthcare professionals and policymakers are to deliver culturally sensitive services. This study was conducted in Ras Al Khaimah (RAK) and Sydney. The results identified several barriers to cervical cancer screening participation, including lack of knowledge, emotional, cultural, religious and psychological barriers, and organizational factors. The findings have implications for policies to address these barriers and encourage women to participate in health awareness initiatives and screening services.

Abstract The empirical application of polarization and depolarization current (PDC) measurement of transformers facilitates the extraction of critical insulation-sensitive parameters. This technique, rooted in time-domain dielectric response analysis, forms the bedrock for parameterization and insulation modeling. However, the inherently time-consuming nature of polarization current measurements renders them susceptible to data corruption. This article explores deep-learning-based short-duration techniques for forecasting polarization current to address this limitation. By incorporating spatial shortcuts, the residual long short-term memory (LSTM) network facilitates the seamless propagation of spatial and temporal gradients. Furthermore, the relative forecasting assessment of the proposed residual LSTM model’s performance is made against traditional LSTM, attention LSTM, gated recurrent units (GRU), and convolutional neural network (CNN) models. Thus, optimal model selection strategies are evaluated based on their capability to capture extended dependencies and short-term information present in the data. In addition, the Monte Carlo dropout prediction is employed to estimate uncertainty in polarization current forecasts. The findings demonstrate that the proposed residual LSTM network model for polarization current forecasting yields the lowest error metrics and maintains prediction consistency over the testing duration. Thus, the proposed approach significantly reduces PDC measurement time, providing an effective means to develop proactive maintenance strategies for evaluating the insulation condition of transformers.

Workflow Scheduling is a huge challenge in cloud paradigm as many number of workflows dynamically generated from various heterogeneous resources and task dependencies in each workflow varies from each other. Therefore, if a workflow with more number of dependencies is not scheduled onto an appropriate Virtual Machine i.e. with low processing capacity which leads to delay in executing workflows and it results in increase of makespan, cost, energy consumption. In order to effectively schedule complex workflows i.e. with more task dependencies, we propose a novel multi objective workflow scheduling algorithm using Deep reinforcement Learning. Initially, priorities of all workflows calculated based on their dependencies and then calculated priorities of VMs based on electricity cost at datacenters to map workflows onto precise VMs. These priorities are fed to scheduler which uses Deep Q-Network model to dynamically schedule tasks by considering both priorities of tasks and VMs. Extensive simulations carried out on workflowsim by considering realtime scientific workflows (Montage, cybershake, Epigenomics, LIGO). Our proposed MOPWSDRL compared against existing state of art approaches i.e. Heterogeneous Earliest First Deadline, Cat Swarm Optimization, Ant Colony Optimization. Results revealed that our proposed MOPDSWRL outperforms existing state of art algorithms by minimizing makespan, energy consumption.

This study examines the effects of ultrasound-enhanced friction stir welding (USE-FSW) on the mechanical properties and microstructural characteristics of aluminum alloy AA6082-T6, commonly used in automotive, aerospace, and construction industries. The investigation included tensile and bending tests, as well as detailed microstructural evaluations using scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and energy-dispersive X-ray spectroscopy (EDS). The results indicate that USE-FSW led to an approximately 26% increase in tensile strength compared to similar samples produced by conventional friction stir welding (CFSW). Additionally, the elongation at break improved by around 52%, indicating better ductility. Flexural strength also showed a notable improvement of over 70%. Microstructural analysis revealed a finer grain structure in the stir zone, contributing to these mechanical enhancements. However, the changes in texture and grain orientation were relatively modest, as shown by EBSD and Kernel Average Misorientation (KAM) analyses. Overall, USE-FSW offers incremental improvements in weld quality and mechanical performance, making it a promising technique for producing joints with slightly enhanced strength and ductility.

The subject of the research presented in this article is the dimensional optimisation of the box structure of the main girder for overhead cranes, with the aim to reduce the main girder’s own weight. Described are the applied optimisation methods and also the problems arising during the optimisation process. Loss of shape stability is analysed in more detail, as well as the methods determined for a constructional proposal of the girder cross-section. The developed optimisation procedure is a combination of the calculations, performed in the MS Excel environment, with the simulation of a parametrically modelled girder realised using Dassault Systèmes SolidWorks software v. 2023. Using the example of optimisation of an overhead crane girder with a lifting capacity of 50 tons, the effectiveness of the proposed optimisation procedure is declared, where the existing overhead crane box girder is compared with the optimised cross-section of the box girder.

Background and purpose: Artificial intelligence (AI) has the potential to aid in the accurate diagnosis of hip fractures and reduce the workload of clinicians. We primarily aimed to develop and validate a convolutional neural network (CNN) for the automated classification of hip fractures based on the 2018 AO-OTA classification system. The secondary aim was to incorporate the model’s assessment of additional radiographic findings that often accompany such injuries. Methods: 6,361 plain radiographs of the hip taken between 2002 and 2016 at Danderyd University Hospital were used to train the CNN. A separate set of 343 radiographs representing 324 unique patients was used to test the performance of the network. Performance was evaluated using area under the curve (AUC), sensitivity, specificity, and Youden’s index. Results: The CNN demonstrated high performance in identifying and classifying hip fracture, with AUCs ranging from 0.76 to 0.99 for different fracture categories. The AUC for hip fractures ranged from 0.86 to 0.99, for distal femur fractures from 0.76 to 0.99, and for pelvic fractures from 0.91 to 0.94. For 29 of 39 fracture categories, the AUC was ≥ 0.95. Conclusion: We found that AI has the potential for accurate and automated classification of hip fractures based on the AO-OTA classification system. Further training and modification of the CNN may enable its use in clinical settings.

Background and purpose: Knowledge concerning the use AI models for the classification of glenohumeral osteoarthritis (GHOA) and avascular necrosis (AVN) of the humeral head is lacking. We aimed to analyze how a deep learning (DL) model trained to identify and grade GHOA on plain radiographs performs. Our secondary aim was to train a DL model to identify and grade AVN on plain radiographs. Patients and methods: A modified ResNet-type network was trained on a dataset of radiographic shoulder examinations from a large tertiary hospital. A total of 7,139 radiographs were included. The dataset included various projections of the shoulder, and the network was trained using stochastic gradient descent. Performance evaluation metrics, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to assess the network’s performance for each outcome. Results: The network demonstrated AUC values ranging from 0.73 to 0.93 for GHOA classification and > 0.90 for all AVN classification classes. The network exhibited lower AUC for mild cases compared with definitive cases of GHOA. When none and mild grades were combined, the AUC increased, suggesting difficulties in distinguishing between these 2 grades. Conclusion: We found that a DL model can be trained to identify and grade GHOA on plain radiographs. Furthermore, we show that a DL model can identify and grade AVN on plain radiographs. The network performed well, particularly for definitive cases of GHOA and any level of AVN. However, challenges remain in distinguishing between none and mild GHOA grades.

Human milk oligosaccharides (HMOs) are present in high numbers in milk of lactating women. They are beneficial to gut health and the habitant microbiota, but less is known about their effect on cells from the immune system. In this study, we investigated the direct effect of three structurally different HMOs on human derived macrophages before challenge with Staphylococcus aureus (S. aureus). The study demonstrates that individual HMO structures potently affect the activation, differentiation and development of monocyte-derived macrophages in response to S. aureus. 6´-Sialyllactose (6’SL) had the most pronounced effect on the immune response against S. aureus, as illustrated by altered expression of macrophage surface markers, pointing towards an activated M1-like macrophage-phenotype. Similarly, 6’SL increased production of the pro-inflammatory cytokines TNF-α, IL-6, IL-8, IFN-γ and IL-1β, when exposing cells to 6’SL in combination with S. aureus compared with S. aureus alone. Interestingly, macrophages treated with 6’SL exhibited an altered proliferation profile and increased the production of the classic M1 transcription factor NF-κB. The HMOs also enhanced macrophage phagocytosis and uptake of S. aureus. Importantly, the different HMOs did not notably affect macrophage activation and differentiation without S. aureus exposure. Together, these findings show that HMOs can potently augment the immune response against S. aureus, without causing inflammatory activation in the absence of S. aureus, suggesting that HMOs assist the immune system in targeting important pathogens during early infancy.

Summary: Virucidal filter materials were prepared by electrospinning a solution of 28 wt % poly(vinylidene difluoride) in N,N-dimethylacetamide without and with the addition of 0.25 wt %, 0.75 wt %, 2.0 wt %, or 3.5 wt % Cu(NO3)2 · 2.5H2O as virucidal agent. The fabricated materials had a uniform and defect free fibrous structure and even distribution of copper nanoclusters. X-ray diffraction analysis showed that during the electrospinning process, Cu(NO3)2 · 2.5H2O changed into Cu2(NO3)(OH)3. Electrospun filter materials obtained by electrospinning were essentially macroporous. Smaller pores of copper nanoclusters containing materials resulted in higher particle filtration than those without copper nanoclusters. Electrospun filter material fabricated with the addition of 2.0 wt % and 3.5 wt % of Cu(NO3)2 · 2.5H2O in a spinning solution showed significant virucidal activity, and there was 2.5 ± 0.35 and 3.2 ± 0.30 logarithmic reduction in the concentration of infectious SARS-CoV-2 within 12 h, respectively. The electrospun filter materials were stable as they retained virucidal activity for three months.

Rapid leaching of soluble nitrogen (N) sources in soil poses a significant challenge in agricultural practices. Therefore, gaining a comprehensive understanding of crop responses to slow-release N application rates has become crucial to contributing valuable insights to optimize N management strategies in agriculture. A field study was conducted to investigate the influence of preplant calcium cyanamide fertilizer on the growth, yield, quality, and shelf life of short-day onion. Six levels of calcium cyanamide (CaCN2, 19.8% N), 0, 90, 120, 200, 400, and 600 kg⋅ha−1 CaCN2, which are equivalent to 0, 17.82, 23.76, 39.6, 79.2, and 118.8 kg⋅ha−1 N, respectively, replicated four times were broadcasted and incorporated into the top 5 to 10 cm of soil. Using 400 kg⋅ha−1 of CaCN2 yielded noteworthy improvements in various parameters of onion growth, such as plant height, leaf count, bulb weight per plant, bulb diameter, bulb length, and overall plant weight, as indicated by the study results. The application of different levels of CaCN2 as an N source exerted a significant influence on these growth factors. Moreover, the study revealed a direct correlation between CaCN2 application levels and the storage life of onions. Specifically, the findings demonstrated that the application of 400 kg⋅ha−1 CaCN2 resulted in enhanced yield and overall onion plant growth. However, the application of 600 kg⋅ha−1 CaCN2 increased the incidences of bulb weight loss, rots, and sprouting during the 8-week storage period at room temperature. These findings provide valuable insights for onion investors and farmers in the region and offer practical recommendations for optimizing fertilizer use and storage practices to improve onion production and minimize postharvest losses.

Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.

Tibetan weedy barleys reside at the edges of qingke (hulless barley) fields in Tibet (Xizang). The spikes of these weedy barleys contain or lack a brittle rachis, with either two- or six-rowed spikes and either hulled or hulless grains at maturity. Although the brittle rachis trait of Tibetan weedy barleys is similar to that of wild barley (Hordeum vulgare ssp. spontaneum Thell.), these plants share genetic similarity with domesticated barley. The origin of Tibetan weedy barleys continues to be debated. Here, we show that most Tibetan weedy barleys originated from cross-pollinated hybridization of domesticated barleys, followed by hybrid self-pollination and recombination between Non-brittle rachis 1 (btr1) and 2 (btr2). We discovered the specific genetic ancestry of these weedy barleys in South Asian accessions. Tibetan weedy barleys exhibit lower genetic diversity than wild and Chinese landraces/cultivars and share a close relationship with qingke, genetically differing from typical eastern and western barley populations. We classified Tibetan weedy barleys into two groups, brittle rachis (BR) and non-brittle rachis (NBR); these traits align with the haplotypes of the btr1 and btr2 genes. Whereas wild barleys carry haplotype combinations of Btr1 and Btr2, each showing lower proportions in a population, the recombinant haplotype BTR2H8+BTR1H24 is predominant in the BR group. Haplotype block analysis based on whole-genome sequencing revealed two recombination breakpoints, which are present in 80.6% and 16.8% of BR accessions according to marker-assisted analysis. Hybridization events between wild and domesticated barley were rarely detected. These findings support the notion that Tibetan weedy barleys originated via recombination between Btr1 and Btr2 in domesticated barley.

Background: COVID-19 is primarily known as a respiratory illness; however, many patients present to hospital without respiratory symptoms. The association between non-respiratory presentations of COVID-19 and outcomes remains unclear. We investigated risk factors and clinical outcomes in patients with no respiratory symptoms (NRS) and respiratory symptoms (RS) at hospital admission. Methods: This study describes clinical features, physiological parameters, and outcomes of hospitalised COVID-19 patients, stratified by the presence or absence of respiratory symptoms at hospital admission. RS patients had one or more of: cough, shortness of breath, sore throat, runny nose or wheezing; while NRS patients did not. Results: Of 178,640 patients in the study, 86.4 % presented with RS, while 13.6 % had NRS. NRS patients were older (median age: NRS: 74 vs RS: 65) and less likely to be admitted to the ICU (NRS: 36.7 % vs RS: 37.5 %). NRS patients had a higher crude in-hospital case-fatality ratio (NRS 41.1 % vs. RS 32.0 %), but a lower risk of death after adjusting for confounders (HR 0.88 [0.83–0.93]). Conclusion: Approximately one in seven COVID-19 patients presented at hospital admission without respiratory symptoms. These patients were older, had lower ICU admission rates, and had a lower risk of in-hospital mortality after adjusting for confounders.

Les domaines de l'éducation (Gravelle, Frigon et Monette, 2020) et de l'enseignement supérieur (Maltais, Ness, Jungblut et Rexe, 2023) sont en mutation mondiale, confrontés à des défis croissants. À l'ère du numérique, les établissements d’enseignement doivent développer la compétence numérique des apprenants (Gouvernement du Québec, 2018, 2020b). La littérature internationale guide le déploiement des outils numériques pour la réussite éducative (Gravelle et al., 2019; Gravelle et al., 2021). L'OCDE souligne l'importance de comprendre les tendances mondiales et leur impact sur l'éducation (2019). Par exemple, l'éducation peut réduire les inégalités et encourager l'innovation numérique (OCDE, 2019). Les directions et les gestionnaires scolaires doivent promouvoir l'innovation, la compétence numérique et la sensibilisation aux risques cybernétiques (OCDE, 2019). Un financement consacré au numérique est crucial pour s’assurer de suivre un monde en mutation (Gouvernement du Québec, 2023). En somme, selon l’OCDE (2019), la gestion, la gouvernance et le financement du numérique en éducation et en enseignement supérieur sont donc des enjeux internationaux importants.

Milk analysis using mid-infrared spectroscopy (MIR) is a fast and inexpensive way of examining milk samples on a large scale for fat, protein, lactose, urea and many other novel traits. A new indicator trait for ketosis, KetoMIR, which is based on clinical ketosis diagnoses and MIR-predicted traits, was developed by the Regional State Association for Performance and Quality Inspection in Animal Breeding of Baden Württemberg in 2015. The KetoMIR result is available for each cow at milk recording during the first 120 days in milk and presented to farmers in three classes: 1 = low ketosis risk, 2 = moderate ketosis risk and 3 = high ketosis risk. The aim of the current study was to analyze the phenotypic relationships between KetoMIR and milk yield, fertility and health at the herd level. Annual herd reports from 12,909 herds with an average herd size of 27 cows were available for the analyses. Overall, the mean incidence of ketosis (KetoMIR risk class 2 or 3) at the herd level was 14.0%. Farms with the lowest ketosis risk (≤10% of cows in the herd with a moderate or high ketosis risk) differed in all variables from the farms with the highest ketosis risk (>50% of cows in the herd with a moderate or high ketosis risk). The increased ketosis risk based on KetoMIR was associated with lower average herd milk yield (-1,975 kg milk). Mean herd somatic cell count in first and higher lactations was increased by 60,500 and 134,400 cells/ml, respectively. The interval from calving to first service was prolonged by +36.5 days, as was the calving interval with +58.2 days. The newly developed KetoMIR trait may be used in ketosis prevention programs.

BackgroundHypoglycemia threatens cognitive function and driving safety. Previous research investigated in-vehicle voice assistants as hypoglycemia warnings. However, they could startle drivers. To address this, we combine voice warnings with ambient LEDs. ObjectiveThe study assesses the effect of in-vehicle multimodal warning on emotional reaction and technology acceptance among drivers with type 1 diabetes. MethodsTwo studies were conducted, one in simulated driving and the other in real-world driving. A quasi-experimental design included 2 independent variables (blood glucose phase and warning modality) and 1 main dependent variable (emotional reaction). Blood glucose was manipulated via intravenous catheters, and warning modality was manipulated by combining a tablet voice warning app and LEDs. Emotional reaction was measured physiologically via skin conductance response and subjectively with the Affective Slider and tested with a mixed-effect linear model. Secondary outcomes included self-reported technology acceptance. Participants were recruited from Bern University Hospital, Switzerland. ResultsThe simulated and real-world driving studies involved 9 and 10 participants with type 1 diabetes, respectively. Both studies showed significant results in self-reported emotional reactions (P

IntroductionMood stabilisers and other psychotropic drugs can lead to serious adverse drug events (ADEs). However, the incidence remains unknown. We aimed to (a) determine the incidence of serious ADEs in patients with bipolar or schizoaffective disorders, (b) explore the role of lithium exposure, and (c) describe the aetiology.MethodsThis study is part of the LiSIE (Lithium—Study into Effects and Side Effects) retrospective cohort study. Between 2001 and 2017, patients in the Swedish region of Norrbotten, with a diagnosis of bipolar or schizoaffective disorder, were screened for serious ADEs to psychotropic drugs, having resulted in critical, post-anaesthesia, or intensive care. We determined the incidence rate of serious ADEs/1,000 person-years (PY).ResultsIn 1,521 patients, we identified 41 serious ADEs, yielding an incidence rate of 1.9 events per 1,000 PY. The incidence rate ratio (IRR) between ADEs with lithium present and causally implicated and ADEs without lithium exposure was significant at 2.59 (95% CI 1.20–5.51; p = 0.0094). The IRR of ADEs in patients

Abstract Despite being treatable and preventable, tuberculosis (TB) affected one-fourth of the world population in 2019, and it took the lives of 1.4 million people in 2019. It affected 1.2 million children around the world in the same year. As it is an infectious bacterial disease, the early diagnosis of TB prevents further transmission and increases the survival rate of the affected person. One of the standard diagnosis methods is the sputum culture test. Diagnosing and rapid sputum test results usually take one to eight weeks in 24 h. Using posterior-anterior chest radiographs (CXR) facilitates a rapid and more cost-effective early diagnosis of tuberculosis. Due to intraclass variations and interclass similarities in the images, TB prognosis from CXR is difficult. We proposed an early TB diagnosis system (tbXpert) based on deep learning methods. Deep Fused Linear Triangulation (FLT) is considered for CXR images to reconcile intraclass variation and interclass similarities. To improve the robustness of the prognosis approach, deep information must be obtained from the minimal radiation and uneven quality CXR images. The advanced FLT method accurately visualizes the infected region in the CXR without segmentation. Deep fused images are trained by the Deep learning network (DLN) with residual connections. The largest standard database, comprised of 3500 TB CXR images and 3500 normal CXR images, is utilized for training and validating the recommended model. Specificity, sensitivity, Accuracy, and AUC are estimated to determine the performance of the proposed systems. The proposed system demonstrates a maximum testing accuracy of 99.2%, a sensitivity of 98.9%, a specificity of 99.6%, a precision of 99.6%, and an AUC of 99.4%, all of which are pretty high when compared to current state-of-the-art deep learning approaches for the prognosis of tuberculosis. To lessen the radiologist’s time, effort, and reliance on the level of competence of the specialist, the suggested system named tbXpert can be deployed as a computer-aided diagnosis technique for tuberculosis.