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Abstract In the western United States, much of the annual precipitation falls during relatively few storm events. When precipitation is measured as daily (or hourly, etc.) accumulations, these events appear as sequences of various durations. We describe a trivariate probability distribution and assert that it provides a natural description for the durations, magnitudes, and maximum intensities of such events. We show that this distribution, in its most straightforward application, indeed describes precipitation events composed of daily observations at most long-term weather stations across the western United States, allowing for a flexible assessment of specific event probabilities and return periods with respect to their three defining components. This characterization opens the door to further understanding of risk associated with out-of-sample meteorological events.

Abstract Bone conduction implants enable patients to hear via vibrations transmitted to the skull. The main constraint of current bone conduction implants is their maximum output force level. Stimulating closer to the cochlea is hypothesized to increase efficiency and improve force transfer, addressing this limitation. This study evaluated stimulation at four positions in human cadaveric specimens: the cochlear promontory, the posterior wall of the outer ear canal, the lateral semi-circular canal, and the standard Bone-Anchored Hearing Aid (Baha) location. To assess potential hearing sensation, three objective measures were simultaneously recorded. For intracochlear pressure and promontory velocity, stimulating at the lateral semi-circular canal and promontory results in the highest response, with a gain of up to 20 dB. Ear canal pressure shows less conclusive results, with significant differences at only a few frequencies. These findings suggest that stimulation closer to the cochlea offers higher efficiency, which could benefit patients needing higher output force levels than currently available or those eligible for electro-vibrational stimulation, e.g. a cochlear implant combined with a bone conduction device.

Abstract Background As one of the main goals of hip and knee replacements is to improve patients’ health-related quality of life, a meaningful evaluation can be achieved by calculating minimal important changes (MICs) for improvements in patient-reported outcome measures (PROMs). This study aims at providing MICs adjusted for patient characteristics for EQ-5D-3L index score improvements after hip and knee replacements. It adds to existing literature by relying on a large national sample and precise clustering algorithms, and by employing a state-of-the-art methodology for the calculation of improved adjusted MICs. Methodology A retrospective observational study was conducted using the publicly available National Health Service (NHS) PROMs dataset for primary hip and knee replacements. We used information on 252,331 hip replacements and 279,668 knee replacements from all NHS-funded providers in England between 2013 and 2020. Clusters of patients were created based on pre-operative EQ-VAS, depression status, and sex. Unstratified and stratified estimates for meaningful EQ-5D-3L improvements were obtained through anchor-based predictive MICs corrected for the proportion of improved patients and the reliability of transition ratings. Results Stratifying patients showed that MICs varied across subgroups based on pre-operative EQ-VAS, depression status, and sex. MICs were larger for patients with worse pre-operative EQ-VAS scores, while patients with better pre-operative scores required smaller MICs to achieve a meaningful change. We show how after stratification the percentage of patients achieving their stratified MIC was better in line with the actual share of improved patients. Larger MICs were found for patients with depression and for female patients. MICs calculated for knee replacements were consistently lower than those for hip replacements. Conclusions Our findings show the importance of adjusting MICs for patients’ characteristics and should be considered for quality-related choices and policy initiatives.

Abstract Atmospheric rivers (ARs) are narrow corridors of intense water vapor transport, shaping precipitation, floods, and economies. Temporal clustering of ARs tripled losses compared to isolated events, yet the reasons behind this clustering remain unclear. AR orientation further modulates hydrological impacts through terrain interaction. Here we identify unique ARs over the North Pacific and Western U.S. and utilize Cox regression and composite analysis to examine how six major climate modes influence temporal clustering of unique ARs and orientation during extended boreal winter (November to March). Results show that climate modes condition temporal clustering of unique ARs. The Pacific-North American weather pattern strongly modulates the clustering over the Western U.S. from early to late winter. The quasi-biennial oscillation and Pacific decadal oscillation affect late winter clustering, while the Arctic oscillation dominates early winter. Climate modes also strongly influence AR orientation, with ENSO particularly affecting the orientation of temporally clustered ARs.

Abstract Hope is a vital coping mechanism, enabling individuals to effectively confront life’s challenges. This study proposes a technique employing Natural Language Processing (NLP) tools like Linguistic Inquiry and Word Count (LIWC), NRC-emotion-lexicon, and vaderSentiment to analyze social media posts, extracting psycholinguistic, emotional, and sentimental features from a hope speech dataset. The findings of this study reveal distinct cognitive, emotional, and communicative characteristics and psycholinguistic dimensions, emotions, and sentiments associated with different types of hope shared in social media. Furthermore, the study investigates the potential of leveraging this data to classify different types of hope using machine learning algorithms. Notably, models such as LightGBM and CatBoost demonstrate impressive performance, surpassing traditional methods and competing effectively with deep learning techniques. We employed hyperparameter tuning to optimize the models’ parameters and compared their performance using both default and tuned settings. The results highlight the enhanced efficiency achieved through hyperparameter tuning for these models.

Abstract Background Radical prostatectomies can be performed using open retropubic, laparoscopic, or robot-assisted laparoscopic surgery. The literature shows that short-term outcomes (in particular, inpatient complications) differ depending on the type of procedure. To date, these differences have only been examined and confirmed in isolated cases based on national routine data. Methods The data was based on the Swiss Medical Statistics from 2016 to 2018 from a national survey of administrative data from all Swiss hospitals. Cases with the coded main diseases neoplasm of the prostate (ICD C61) and the main treatments of laparoscopic (CHOP 60.5X.20) or retropubic (CHOP 60.5X.30) radical prostatectomies were included, resulting in a total sample size of 8,593 cases. Results A procedure-related complication occurred in 998 cases (11.6%). By surgical procedure, complication rates were 10.1% for robotic-assisted laparoscopic radical prostatectomy 9.0% for conventional laparoscopic radical prostatectomy and 17.1% for open retropubic radical prostatectomy (p

Summary: Migratory cells respond to graded concentrations of diffusible chemoattractants in vitro, but how complex tissue geometries in vivo impact chemotaxis is poorly understood. To address this, we studied the Drosophila border cells. Live-imaged border cells varied in their chemotactic migration speeds, which correlated positionally with distinct architectures. We then developed a reduced mathematical model to determine how chemoattractant distribution is affected by tissue architecture. Larger extracellular volumes locally dampened the chemoattractant gradient and, when coupled with an agent-based motion of the cluster, reduced cell speeds. This suggests that chemoattractant levels vary by tissue architectures, informing cell migration behaviors locally, which we tested in vivo. Genetically elevating chemoattractant levels slowed migration in specific architectural regions, while mutants with spacious tissue structure rescued defects from high chemoattractant levels, promoting punctual migration. Our results highlight the interplay between tissue geometry and the local distribution of signaling molecules to orchestrate cell migration.

Abstract Introduction H‐index is a widely used metric quantifying a researcher's productivity and impact based on an author's publications and citations. Though convenient to calculate, h‐index fails to incorporate collaborations and interrelationships between physicians into its assessment of academic impact, leading to limited insight into grouped networks. We present social network analysis as a tool to measure relationships between physicians and quantify their academic impact. Methods A bibliometric multicenter analysis was conducted on physician faculty from 129 US ACGME accredited otolaryngology programs who have publications with a physician co‐author in the field. Using web searches, 2494 physician faculty were identified. Scopus IDs, h‐indices, and publication data for these physicians were identified using multiple Elsevier APIs queried in December 2023. Publications with multiple otolaryngology physician co‐authors were included. Network and sub network maps were generated using Gephi and analyzed with custom R scripts. Centrality measures (degree, PageRank, betweenness centralities) quantified collaboration propensity. Non‐parametric correlation analysis between centrality measures and h‐index was conducted. Sankey diagrams were plotted using ggplot2. Results A co‐authorship network of 2259 physicians was constructed. Physicians were visualized as nodes with collaborations as links. Centrality measures correlated strongly with h‐index (h‐index vs. degree centrality: r2 = 0.62, h‐index vs. PageRank: r2 = 0.55, h‐index vs. betweenness centrality: r2 = 0.55; p

The book of the prophet Hosea (meaning: 'he saves') was written in the 8thCentury CE towards the end of the northern Kingdom at a time when Judah and Israel we're still relatively intact. The prophet Amos warned the people of Yahweh that they needed to accept the judgement of God who is just and realise that the Assyrians would most likely annihilate them. Hosea entered the picture and most of his utterances revolved around the fact that Israel was unfaithful, and this is the hallmark of his writing. The book of Hosea casts a gaudy light upon the ethical condition of Ephraim (Israel during its final years at a time when they adopted the debased practices of their Canaanite neighbours. Hosea found himself in a position to proclaim the truth of what would happen to the people who had lost their faith in themselves and also in Yahweh, but this was not necessarily Yahweh’s final word. Hosea displayed great insight concerning the truth of God's mercy which was critical then as it is today. Hosea (5:6) warns "They will go with their flocks and herds to seek Yahweh, but they will not find Him; He has withdrawn from them". Later in Hosea (5:15) Yahweh warns "I will go away and return to My place until they acknowledge their guilt and seek My face; in their affliction they will earnestly seek Me". God requires repentance from people and atonement with Him as Lord of all. This article pursues the issue of God’s apostasy or "withdrawing" from His people. We may seek the Lord and not find Him because our attitude may be wrong, and how we seek Him is not acceptable – we are called to seek Him with a sincere and contrite heart and with all our soul (Deuteronomy 4:29). What is the theological exegesis of the selected verse and what were and are the dogmatic realities expressed in it. In addition, what can we in our current troubled world learn from the passage and what causes God to withdraw from us? What can Hosea teach the legal profession today? Though we may be dark as can be in spirit, and living in the shadows, God is by contrast an ever intensely radiant light holding out for the restoration of His people if they repent and place their faith and trust in Him.

Abstract Potency assessment of monoclonal antibodies or corresponding biosimilars in cell-based assays is an essential prerequisite in biopharmaceutical research and development. However, cellular bioassays are still subject to limitations in sample throughput, speed, and often need costly reagents or labels as they are based on an indirect readout by luminescence or fluorescence. In contrast, whole-cell Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) Mass Spectrometry (MS) has emerged as a direct, fast and label-free technology for functional drug screening being able to unravel the molecular complexity of cellular response to pharmaceutical reagents. However, this approach has not yet been used for cellular testing of biologicals. In this study, we have conceived, developed and benchmarked a label-free MALDI-MS based cell bioassay workflow for the functional assessment of complement-dependent cytotoxicity (CDC) of Rituximab antibody. By computational evaluation of response profiles followed by subsequent m/z feature annotation via fragmentation analysis and trapped ion mobility MS, we identified adenosine triphosphate and glutathione as readily MS-assessable metabolite markers for CDC and demonstrate that robust concentration–response characteristics can be obtained by MALDI-TOF MS. Statistical assay performance indicators suggest that whole-cell MALDI-TOF MS could complement the toolbox for functional cellular testing of biopharmaceuticals.

IntroductionThe 2024 Voice AI Symposium, hosted by the Bridge2AI-Voice Consortium in Tampa, FL, featured two keynote speeches that addressed the intersection of voice AI, healthcare, ethics, and law. Dr. Rupal Patel and Dr. Nicholson Price provided insights into the advancements, applications, and challenges of AI-driven voice tools in healthcare. The symposium aimed to advance cross-disciplinary collaboration and establish frameworks for the ethical use of AI technologies in healthcare.MethodsThe keynote speeches, delivered on May 1st and 2nd, were 30 minutes each, followed by 10-minutes Q&A sessions. The audio was recorded and transcribed using Whisper (v7.13.1). Content summaries were generated with the aid of ChatGPT (v4.0), and the authors reviewed and edited the final transcripts to ensure accuracy and clarity.ResultsDr. Rupal Patel’s keynote, “Reflections and New Frontiers in Voice AI”, explored the potential of voice AI for early detection of health conditions, monitoring disease progression, and promoting non-invasive global health management. She highlighted innovative uses beyond traditional applications, such as examining menopause-related symptoms. Dr. Nicholson Price’s keynote, “Governance for Clinical Voice AI”, addressed the regulatory and ethical challenges posed by AI in healthcare. He stressed the need for context-aware systems and dynamic legal frameworks to address liability and accountability.ConclusionsThe 2024 Voice AI Symposium highlighted the transformative potential of voice AI for early detection, health monitoring, and reducing healthcare disparities. It also underscored the importance of dynamic governance to address the ethical and regulatory challenges of deploying AI in clinical settings.

BackgroundA clinical dashboard is a data-driven clinical decision support tool visualizing multiple key performance indicators in a single report while minimizing time and effort for data gathering. Studies have shown that including patient-reported outcome measures (PROMs) in clinical dashboards supports the clinician’s understanding of how treatments impact patients’ health status, helps identify changes in health-related quality of life at an early stage, and strengthens patient-physician communication. ObjectiveThis study aims to determine design components for clinical dashboards incorporating PROMs to inform software producers and users (ie, physicians). MethodsWe conducted interviews with software producers and users to test preselected design components. Furthermore, the interviews allowed us to derive additional components that are not outlined in existing literature. Finally, we used inductive and deductive coding to derive a guide on which design components need to be considered when building a clinical dashboard incorporating PROMs. ResultsA total of 25 design components were identified, of which 16 were already surfaced during the literature search. Furthermore, 9 additional components were derived inductively during our interviews. The design components are clustered in a generic dashboard, PROM-related, adjacent information, and requirements for adoption components. Both software producers and users agreed on the primary purpose of a clinical dashboard incorporating PROMs to enhance patient communication in outpatient settings. Dashboard benefits include enhanced data visualization and improved workflow efficiency, while interoperability and data collection were named as adoption challenges. Consistency in dashboard design components is preferred across different episodes of care, with adaptations only for disease-specific PROMs. ConclusionsClinical dashboards have the potential to facilitate informed treatment decisions if certain design components are followed. This study establishes a comprehensive framework of design components to guide the development of effective clinical dashboards incorporating PROMs in health care practice.

IntroductionThe 2024 Voice AI Symposium, presented by the Bridge2AI-Voice Consortium, featured deep-dive educational workshops conducted by experts from diverse fields to explore the latest advancements in voice biomarkers and artificial intelligence (AI) applications in healthcare. Through five workshops, attendees learned about topics including international standardization of vocal biomarker data, real-world deployment of AI solutions, assistive technologies for voice disorders, best practices for voice data collection, and deep learning applications in voice analysis. These workshops aimed to foster collaboration between academia, industry, and healthcare to advance the development and implementation of voice-based AI tools.MethodsEach workshop featured a combination of lectures, case studies, and interactive discussions. Transcripts of audio recordings were generated using Whisper (Version 7.13.1) and summarized by ChatGPT (Version 4.0), then reviewed by the authors. The workshops covered various methodologies, from signal processing and machine learning operations (MLOps) to ethical concerns surrounding AI-powered voice data collection. Practical demonstrations of AI-driven tools for voice disorder management and technical discussions on implementing voice AI models in clinical and non-clinical settings provided attendees with hands-on experience.ResultsKey outcomes included the discussion of international standards to unify stakeholders in vocal biomarker research, practical challenges in deploying AI solutions outside the laboratory, review of Bridge2AI-Voice data collection processes, and the potential of AI to empower individuals with voice disorders. Additionally, presenters shared innovations in ethical AI practices, scalable machine learning frameworks, and advanced data collection techniques using diverse voice datasets. The symposium highlighted the successful integration of AI in detecting and analyzing voice signals for various health applications, with significant advancements in standardization, privacy, and clinical validation processes.DiscussionThe symposium underscored the importance of interdisciplinary collaboration to address the technical, ethical, and clinical challenges in the field of voice biomarkers. While AI models have shown promise in analyzing voice data, challenges such as data variability, security, and scalability remain. Future efforts must focus on refining data collection standards, advancing ethical AI practices, and ensuring diverse dataset inclusion to improve model robustness. By fostering collaboration among researchers, clinicians, and technologists, the symposium laid a foundation for future innovations in AI-driven voice analysis for healthcare diagnostics and treatment.

BackgroundWhile the effectiveness of patient-reported outcome measures (PROMs) as an intervention to impact patient pathways has been established for cancer care, it is unknown for other indications. We assessed the cost-effectiveness of a PROM-based monitoring and alert intervention for early detection of critical recovery paths following hip and knee replacement.Methods and findingsThe cost-effectiveness analysis (CEA) is based on a multicentre randomised controlled trial encompassing 3,697 patients with hip replacement and 3,110 patients with knee replacement enrolled from 2019 to 2020 in 9 German hospitals. The analysis was conducted with a subset of 546 hip and 492 knee replacement cases with longitudinal cost data from 24 statutory health insurances. Patients were randomised 1:1 to a PROM-based remote monitoring and alert intervention or to a standard care group. All patients were assessed at 12-months post-surgery via digitally collected PROMs. Patients within the intervention group were additionally assessed at 1-, 3-, and 6-months post-surgery to be contacted in case of critical recovery paths. For the effect evaluation, a PROM-based composite measure (PRO-CM) was developed, combining changes across various PROMs in a single index ranging from 0 to 100. The PRO-CM included 6 PROMs focused on quality of life and various aspects of physical and mental health. The primary outcome was the incremental cost-effectiveness ratio (ICER). The intervention group showed incremental outcomes of 2.54 units PRO-CM (95% confidence interval (CI) [0.93, 4.14]; p = 0.002) for patients with hip and 0.87 (95% CI [-0.94, 2.67]; p = 0.347) for patients with knee replacement. Within the 12-months post-surgery period the intervention group had less costs of 376.43€ (95% CI [-639.74, -113.12]; p = 0.005) in patients with hip, and 375.50€ (95% CI [-767.40, 16.39]; p = 0.060) in patients with knee replacement, revealing a dominant ICER for both procedures. However, it remains unclear which step of the multistage intervention contributes most to the positive effect.ConclusionsThe intervention significantly improved patient outcomes at lower costs in patients with hip replacements when compared with standard care. Further it showed a nonsignificant cost reduction in knee replacement patients. This reinforces the notion that PROMs can be utilised as a cost-effective instrument for remote monitoring in standard care settings.Trial registrationRegistration: German Register for Clinical Studies (DRKS) under DRKS00019916.

Abstract Extreme weather and climate events can have substantial impacts on society and the environment. Compound extremes (two or more extreme events occurring simultaneously or successively) may exert even larger impacts than individual events. Here we examine physical drivers behind variability in hydrometeorological (precipitation and temperature) compound extremes on subseasonal-to-seasonal timescales. Observational evidence presented here through composite analysis indicates that compound extreme frequency is linked to the Madden-Julian oscillation, a unique type of organized tropical convection varying primarily on subseasonal-to-seasonal timescales. The linkage between Madden-Julian oscillation and compound extremes is largely dependent on ENSO phases, which can be seen through different magnitudes or changes in sign of the canonical relationship conditioned on ENSO states. Similarly, the Madden-Julian oscillation can interrupt the canonical ENSO-compound extreme relationship. Our results suggest a potential route to improve subseasonal-to-seasonal prediction of western US compound extremes by considering the combined effect of both Madden-Julian oscillation and ENSO.

Abstract Background Body contouring surgery after massive weight loss is associated with different risk factors. Wound healing disorders and seromas commonly occur postoperatively. Bariatric interventions lead to massive weight loss with excess skin and soft tissue. In this study, perioperatively collected laboratory markers of this special patient population were analyzed. Methods Fifty-nine patients were analyzed retrospectively regarding bariatric surgery, weight loss, body contouring surgery, laboratory markers, and complication rates. Results Body contouring surgery (n = 117) was performed in 59 patients. Weight loss was achieved after gastric bypass (40.1%), gastric banding (33.9%), or sleeve gastrectomy (26.0%), with an average of 69.2 kg. The most common body contouring procedure included abdominoplasty (n = 50), followed by thigh lift (n = 29), mammaplasty (n = 19), brachioplasty (n = 14), and upper body lift (n = 5). Analysis of laboratory markers revealed no exceptional and clinically relevant variations. Correlation analysis revealed associations between resection weight, amount of drain fluid, and particular laboratory markers. Conclusion Analysis of perioperative laboratory markers in this special patient population after massive weight loss did not indicate clinically relevant risk factors regardless of the type of bariatric or body contouring surgery. Body contouring surgeries after bariatric interventions prove to be safe and low risk concerning perioperative laboratory markers and postoperative hospitalization.

Abstract Study goal We compared the survival rates of women with breast cancer (BC) detected within versus outside the mammography screening program (MSP) “donna”. Methods We merged data from the MSP with the data from corresponding cancer registries to categorize BC cases as within MSP (screen-detected and interval carcinomas) and outside the MSP. We analyzed the tumor stage distribution, tumor characteristics and the survival of the women. We further estimated hazard ratios using Cox-regressions to account for different characteristics between groups and corrected the survival rates for lead-time bias. Results We identified 1057 invasive (ICD-10: C50) and in-situ (D05) BC cases within the MSP and 1501 outside the MSP between 2010 and 2019 in the Swiss cantons of St. Gallen and Grisons. BC within the MSP had a higher share of stage I carcinoma (46.5% vs. 33.0%; p

Abstract Corrosion results in large costs and environmental impact but can be controlled by thin oxide films that passivate the metal surfaces and hinder further oxidation or dissolution in an aqueous environment. The structure, chemistry, and thickness of these oxide films play a significant role in determining their anti-corrosion properties and the early-stage oxidation dynamics affect the properties of the developed oxide. Here, we use in situ X-ray Photoelectron Spectroscopy (XPS) to study the early-stage oxidation of a Ni-Cr-Mo alloy at room temperature and up to 400 °C. Cr and Mo begin to oxidize immediately after exposure to O2, and Cr3+, Mo4+, and Mo6+ oxides are formed. In contrast, Ni does not contribute significantly to the oxide film. A self-limiting oxide thickness, which did not depend on temperature below 400 °C, is observed. This is attributed to the consumption of available Cr and Mo near the surface, which results in an enrichment of metallic Ni under the oxide. The self-limited oxide thickness is 6–8 Å, which corresponds to 3–4 atomic layers of cations in the oxide. At 400 °C, sublimation of Mo6+ oxide is observed, resulting in the formation of an almost pure layer of Cr2O3 on the alloy surface. Lastly, a mechanism is presented that explains the formation of the bi-layer oxide structure observed for Ni-Cr-Mo alloys, which involves the enhanced migration of hexavalent Mo ions in the electric field, which drives mass transport during oxidation according to both the Cabrera Mott model and the Point Defect Model.

Thisstudy introduces a new model for detecting insults in Roman Urdu, filling an important gap in natural language processing (NLP) for low-resource languages. The transliterated nature of Roman Urdu also poses specific challenges from a computational linguistics perspective, including non-standardized grammar, variation in spellings for the same word, and high levels of code-mixing with English, which together make automated insult detection for Roman Urdu a highly complex problem. To address these problems, we created a large-scale dataset with 46,045 labeled comments from social media websites such as Twitter, Facebook, and YouTube. This is the first dataset for insult detection for Roman Urdu that was created and annotated with insulting and non-insulting content. Advanced preprocessing methods such as text cleaning, text normalization, and tokenization are used in the study, as well as feature extraction using TF–IDF through unigram (Uni), bigram (Bi), trigram (Tri), and their unions: Uni+Bi+Trigram. We compared ten machine learning algorithms (logistic regression, support vector machines, random forest, gradient boosting, AdaBoost, and XGBoost) and three deep learning topologies (CNN, LSTM, and Bi-LSTM). Different models were compared, and ensemble ones were proven to give the highest F1-scores, reaching 97.79%, 97.78%, and 95.25%, respectively, for AdaBoost, decision tree, TF–IDF, and Uni+Bi+Trigram configurations. Deeper learning models also performed on par, with CNN achieving an F1-score of 97.01%. Overall, the results highlight the utility of n-gram features and the combination of robust classifiers in detecting insults. This study makes strides in improving NLP for Roman Urdu, yet further research has established the foundation of pre-trained transformers and hybrid approaches; this could overcome existing systems and platform limitations. This study has conscious implications, mainly on the construction of automated moderation tools to achieve safer online spaces, especially for South Asian social media websites.

Depression, a serious mental health disorder, requires accurate classification for effective intervention. Existing methods often fail to capture nuanced emotional and linguistic cues, leading to suboptimal classification of depression severity. This study bridges this gap by leveraging content-based approaches (N-grams) and context-based methods (Sentence Transformers), alongside advanced transformer-based models, to classify mild, moderate, and severe depression using text data sourced from Reddit. By demonstrating the effectiveness of modern NLP techniques in capturing subtle contextual variations, this research highlights the potential of transformer-based models to enhance depression severity detection. The proposed framework offers a scalable and adaptable solution for real-world mental health diagnostics and early intervention systems.

With the rapid expansion of social media, detecting offensive language has become critically important for healthy online interactions. This poses a considerable challenge for low-resource languages such as Roman Urdu which are widely spoken on platforms like Facebook. In this paper, we perform a comprehensive study of offensive language detection models on Roman Urdu datasets using both Machine Learning (ML) and Deep Learning (DL) approaches. We present a dataset of 89,968 Facebook comments and extensive preprocessing techniques such as TF-IDF features, Word2Vec, and fastText embeddings to address linguistic idiosyncrasies and code-mixed aspects of Roman Urdu. Among the ML models, a linear kernel Support Vector Machine (SVM) model scored the best performance, with an F1 score of 94.76, followed by SVM models with radial and polynomial kernels. Even the use of BoW uni-gram features with naive Bayes produced competitive results, with an F1 score of 94.26. The DL models performed well, with Bi-LSTM returning an F1 score of 98.00 with Word2Vec embeddings and fastText-based Bi-RNN performing at 97.00, showcasing the inference of contextual embeddings and soft similarity. The CNN model also gave a good result, with an F1 score of 96.00. The CNN model also achieved an F1 score of 96.00. This study presents hybrid ML and DL approaches to improve offensive language detection approaches for low-resource languages. This research opens up new doors to providing safer online environments for widespread Roman Urdu users.

Background and Objectives: Toxic epidermal necrolysis (TEN) and Stevens–Johnson syndrome (SJS) are rare yet life-threatening dermatologic conditions characterized by severe skin and mucous membrane involvement. Accurate prognostic systems are crucial for clinical management to assess disease severity and predict outcomes. The primary objective of this study was to assess the epidemiological characteristics and clinical outcomes of patients with Stevens–Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), and SJS/TEN overlap over a 17-year period at a specialized burn center. The secondary objectives were to evaluate the performance of existing prognostic scoring systems (SCORTEN, Re-SCORTEN, and ABCD-10) in predicting mortality and to propose a novel classification tree model to improve mortality prediction. Materials and Methods: A 17-year retrospective study at a burn center included 68 patients with SJS, SJS/TEN overlap, or TEN. Demographic, clinical, laboratory data, and prognostic scores (SCORTEN, Re-SCORTEN, ABCD-10) were collected and analyzed for associations with mortality. A classification tree was created to detect unknown determinants of SJS/TEN mortality. Results: The drug most frequently associated with the occurrence of SJS/TEN was metamizole. The mortality rate was 51%. Affected body surface area, platelet count, and serum blood urea nitrogen differed significantly between survivors and non-survivors. Regarding the scoring systems, only the Re-SCORTEN showed reliable differentiation for these groups. A classification tree model achieved an accuracy of 89% in predicting the mortality risk. In the ROC curve analysis, the AUC values were 0.88 for the classification tree, 0.66 for Re-SCORTEN, 0.61 for SCORTEN, and 0.56 for ABCD-10. Conclusions: This study explores mortality predictors in SJS/TEN via a classification tree model, highlighting potential factors for further investigation. While cautioning against immediate clinical application due to data constraints, the findings underscore the need for larger studies to validate and refine prediction models in this context.

This article delves into the parallel natures of both the divine law and animal rights within the religious contexts of the Old and New Testaments. Employing a comparative approach, the paper inspects the developing perspectives on the treatment of animals in biblical scriptures, shedding light on the ethical and legal dimensions embedded in religious teachings. The study investigates key subtopics such as sacrificial rituals and moral imperatives, providing a nuanced understanding of the historical development of attitudes towards animals. Additionally, the analysis of this article encompasses ethical considerations, legal implications, and societal attitudes towards animals, drawing connections between ancient laws and contemporary discourse. Moreover, this article will look at the contemporary needs of society and how religious discourse and juristic principles can be applied to ensure the safety and rights of animals.

Robotic devices have recently enhanced cochlear implantation by improving precision resulting in reduced intracochlear damage during electrode insertion. This study aimed to gain first insights into the expected dimensions of the cone-like workspace from the posterior tympanotomy towards the round window membrane.This retrospective chart review analyzed ten postoperative CT scans of adult patients who were implanted with a CI in the past ten years. The dimensions of the cone-like workspace were determined using four landmarks (P1–P4). In the anteroposterior range, P1 and P2 were defined on the edge of the bony layer over the facial nerve and chorda tympani nerve, respectively. In the inferosuperior range, P3 was defined on the bony edge of the incus buttress and P4 was obtained at a distance of 0.45 mm between the facial nerve and the chorda tympani nerve. After selecting the landmarks, the calculations of the dimensions of the surgical access space were done in a standardized coordinate system and presented using descriptive statistics.The cone-like space is limited by two maximal angles, α and β. The average angle α of 19.84 (±3.55) degrees defines the angle towards the round window membrane between P1 and P2. The second average angle β of 53.56 (±10.29) degrees defines the angle towards the round window membrane between P3 and P4. Based on the angles the mean anteroposterior range of 2.25 (±0.42) mm and mean inferosuperior range of 6.73 (±2.42) mm. The distance from the posterior tympanotomy to the round window membrane was estimated at 6.05 (±0.71) mm.These findings present data on the hypothetical maximum workspace in which a future robotically steered insertion tool can be positioned for an optimal automated electrode insertion. A larger sample size is necessary before generalizing these dimensions to a population. Further research including preoperative CT scans is needed for planning robotic-steered cochlear implantation.

INTRODUCTION: Quantifying antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and neutralising antibodies may help to understand protection at the individual and population levels. Determination of neutralising antibodies using classical virus neutralisation tests (VNT) is considered the gold standard, but they are costly and time-intensive. Enzyme-linked immunosorbent assay (ELISA)-based surrogate VNTs (sVNT) or anti-SARS-CoV-2 spike protein receptor binding domain immunoglobulins (anti-S-RBD Ig) may be suitable alternatives to VNTs. We aimed to (a) explore the correlations between anti-S-RBD Ig, VNT, and sVNT measurements and (b) describe humoral immunity against SARS-CoV-2 after vaccination, natural infection, and vaccine breakthrough infection in healthy blood donors. METHODS: We measured total anti-SARS-CoV-2 Ig in 5714 serum samples from 2748 healthy individuals visiting the Swiss Red Cross Blood Donation Centre in Basel from 03/2020 to 04/2022. We used the Elecsys® Anti-SARS-CoV-2 immunoassay (Roche) against the N- and S-receptor binding domain (RBD) proteins. In a subset of 548 samples from 123 donors, we conducted sVNTs against the Wuhan wild-type SARS-CoV-2 (SARS-CoV-2 Neutralizing Antibodies Detection Kit; Adipogen™). In 100 samples from 40 donors, we correlated sVNT and VNTs against the wild-type (D614G WU1) virus. Surveys were sent to the blood donors to collect data on their SARS-CoV-2 infection and vaccination status. Using this data, donors were categorised as “vaccination only”, “infection before vaccination”, “post-vaccine breakthrough infection”, and “natural infection only”. RESULTS: Our longitudinal observation study cohort consisted of 50.7% males with a median age of 31 years (range 18–75 y). Anti-SARS-CoV-2 N protein positivity rates per month indicate 57.1% (88/154) of the cohort was infected up to 04/2022. No differences in seropositivity were found between sexes, age groups, blood types (AB0 or RhD), and cytomegalovirus serostatus. We observed a high correlation between anti-S-RBD Ig and inhibition percentage (Spearman’s ρ = 0.92, Kendall’s τ = 0.77, p

Abstract Water years (WY) 2017 and 2023 were anomalously wet for California, each alleviating multiyear drought. In both cases, this was unexpected given La Niña conditions, with most seasonal forecasts favoring drier‐than‐normal winters. We analyze over seven decades of precipitation and snow records along with mid‐tropospheric circulation to identify recurring weather patterns driving California precipitation and Sierra Nevada snowpack. Tropical forcing by ENSO causes subtle but important differences in these wet weather patterns, which largely drives the canonical seasonal ENSO‐precipitation relationship. However, the seasonal frequency of these weather patterns is not strongly modulated by ENSO and remains a primary source of uncertainty for seasonal forecasting. Seasonal frequency of ENSO‐independent weather patterns was a major cause of anomalous precipitation in WY2017, record‐setting snow in WY2023, and differences in precipitation outcome during recent El Niño winters 1983, 1998, and 2016. Improved understanding of recurrent atmospheric weather patterns could help to improve seasonal precipitation forecasts.

Abstract Medication adherence is vital for patients suffering from Chronic Obstructive Pulmonary Disease (COPD) to mitigate long-term consequences. The impact of poor medication adherence on inferior outcomes like exacerbations leading to hospital admissions is yet to be studied using real-world data. Using Swiss claims data from 2015-2020, we group patients into five categories according to their medication possession ratio. By employing a logistic regression, we quantify each category’s average treatment effect of the medication possession ratio on hospitalized exacerbations. 13,557 COPD patients are included in the analysis. Patients with high medication adherence (daily medication reserve of 80% to 100%) are 51% less likely to incur exacerbation following a hospital stay than patients with the lowest medication adherence (daily medication reserve of 0% to 20%). The study shows that medication adherence varies strongly among Swiss COPD patients. Furthermore, high medication adherence immensely decreases the risk of hospitalized exacerbations.

The mortality of patients with MI has significantly decreased in recent decades, mainly due to early reperfusion therapy with a probability of surviving of more than 90% if the patient reaches the hospital [...]

Abstract Background As patient-reported outcomes (PROs) gain prominence in hip and knee arthroplasty (HA and KA), studies indicate PRO variations between genders. Research on the specific health domains particularly impacted is lacking. Hence, we aim to quantify the gender health gap in PROs for HA/KA patients, differentiating between general health, health-related quality of life (HrQoL), physical functioning, pain, fatigue, and depression. Methods The study included 3,693 HA patients (1,627 men, 2,066 women) and 3,110 KA patients (1,430 men, 1,680 women) receiving surgery between 2020 to 2021 in nine German hospitals, followed up until March 2022. Questionnaires used were: EQ-VAS, EQ-5D-5L, HOOS-PS, KOOS-PS, PROMIS-F-SF, PROMIS-D-SF, and a joint-specific numeric pain scale. PROs at admission, discharge, 12-months post-surgery, and the change from admission to 12-months (PRO-improvement) were compared by gender, tested for differences, and assessed using multivariate linear regressions. To enable comparability, PROs were transformed into z-scores (standard deviations from the mean). Results Observed differences between genders were small in all health domains and differences reduced over time. Men reported significantly better health versus women pre-HA (KA), with a difference of 0.252 (0.224) standard deviations from the mean for pain, 0.353 (0.243) for fatigue (PROMIS-F-SF), 0.327 (0.310) for depression (PROMIS-D-SF), 0.336 (0.273) for functionality (H/KOOS-PS), 0.177 (0.186) for general health (EQ-VAS) and 0.266 (0.196) for HrQoL (EQ-5D-5L). At discharge, the gender health gap reduced and even disappeared for some health dimensions since women improved in health to a greater extent than men. No gender health gap was observed in most PRO-improvements and at month 12. Conclusions Men experiencing slightly better health than women in all health dimensions before surgery while experiencing similar health benefits 12-months post-surgery, might be an indicator of men receiving surgery inappropriately early, women unnecessarily late or both. As studies often investigate the PRO-improvement, they miss pre-surgery gender differences, which could be an important target for improvement initiatives in patient-centric care. Moreover, future research on cutoffs for meaningful between-group PRO differences per measurement time would aid the interpretation of gender health disparities. Trial registration German Register for Clinical Trials, DRKS00019916, 26 November 2019.

Abstract Background The Genetic Frontotemporal Initiative Staging Group has proposed clinical criteria for the diagnosis of prodromal frontotemporal dementia (FTD), termed mild cognitive and/or behavioral and/or motor impairment (MCBMI). The objective of the study was to validate the proposed research criteria for MCBMI-FTD in a cohort of genetically confirmed FTD cases against healthy controls. Methods A total of 398 participants were enrolled, 117 of whom were carriers of an FTD pathogenic variant with mild clinical symptoms, while 281 were non-carrier family members (healthy controls (HC)). A subgroup of patients underwent blood neurofilament light (NfL) levels and anterior cingulate atrophy assessment. Results The core clinical criteria correctly classified MCBMI vs HC with an AUC of 0.79 (p < 0.001), while the addition of either blood NfL or anterior cingulate atrophy significantly increased the AUC to 0.84 and 0.82, respectively (p < 0.001). The addition of both markers further increased the AUC to 0.90 (p < 0.001). Conclusions The proposed MCBMI criteria showed very good classification accuracy for identifying the prodromal stage of FTD.

Patients seek quality healthcare because they trust their doctors and the healthcare system. However, the use of AI models in medical consultations has undermined this trust. AI systems typically depend on accurate and large volumes of data for training, but in cases of insufficient or incorrect data, this can lead to incomplete or flawed outputs. The inaccuracies in the response generated by AI systems may result in biased outcomes, compromising patient care and further eroding the trust patients place in the healthcare system. In this paper, we describe an innovative approach to distinguishing between responses generated by AI and those written by human doctors during health consultations, using an efficient neural network. As part of our feature extraction approach, we converted text into numerical representations via word-level tokenization, mapping to integer sequences. This allows the neural network to efficiently process text while preserving semantic structure and handling a large vocabulary with fixed sequence lengths. Through rigorous experimentation and evaluation, we showcase the effectiveness and reliability of our proposed neural network architecture, MedXNet, in accurately classifying diverse responses encountered in health consultations. For the classification approach, we combined BiLSTM, Transformer, and CNN layers to capture local and global dependencies in sequence inputs and a dense layer that was fully connected with dropout regularization and softmax activation. We compared MedXNet performance with different RNNs, including LSTM, Bi-LSTM, GRU, and 1D-CNN, across three datasets of increasing complexity. Dataset A represents simple data, dataset B introduces greater complexity, and dataset C poses the highest level of challenge. Our findings revealed that MedXNet outperforms the others with an accuracy of 98.74% on dataset A. Although the accuracy of MedXNet decreased on B, it remains the top performer. With 94.63% accuracy, MedXNet still achieves the highest accuracy in dataset C. Based on these findings, MedXNet demonstrated robustness across a wide range of data complexity levels, making it an ideal classification tool for doctor-written and AI-generated text in health consultations. This can enhance the trust patients have in the responses they receive during online medical consultations.

Identifying and categorizing proper nouns in text, known as named entity recognition (NER), is crucial for various natural language processing tasks. However, developing effective NER techniques for low-resource languages like Urdu poses challenges due to limited training data, particularly in the nastaliq script. To address this, our study introduces a novel data augmentation method, “contextual word embeddings augmentation” (CWEA), for Urdu, aiming to enrich existing datasets. The extended dataset, comprising 160,132 tokens and 114,912 labeled entities, significantly enhances the coverage of named entities compared to previous datasets. We evaluated several transformer models on this augmented dataset, including BERT-multilingual, RoBERTa-Urdu-small, BERT-base-cased, and BERT-large-cased. Notably, the BERT-multilingual model outperformed others, achieving the highest macro F1 score of 0.982%. This surpassed the macro f1 scores of the RoBERTa-Urdu-small (0.884%), BERT-large-cased (0.916%), and BERT-base-cased (0.908%) models. Additionally, our neural network model achieved a micro F1 score of 96%, while the RNN model achieved 97% and the BiLSTM model achieved a macro F1 score of 96% on augmented data. Our findings underscore the efficacy of data augmentation techniques in enhancing NER performance for low-resource languages like Urdu.

Abstract Promoters are important in catalysis, but the atomistic details of their function and particularly their role in reaction instabilities such as kinetic phase transitions and oscillations are often unknown. Employing hydrogen oxidation as probe reaction, a Rh nanotip for mimicking a single Rh nanoparticle and field electron microscopy for in situ monitoring, we demonstrate a La-mediated local catalytic effect. The oscillatory mode of the reaction provides a tool for studying the interplay between different types of reaction pacemakers, i.e., specific local surface atomic configurations that initiate kinetic transitions. The presence of La shifts the bistable reaction states, changes the oscillation pattern and deactivates one of two pacemaker types for the La-free surface. The observed effects originate from the La-enhanced oxygen activation on the catalyst. The experimental observations are corroborated by micro-kinetic model simulations comprising a system of 25 coupled oscillators.

Abstract Background Plasma biomarkers reflecting the pathology of frontotemporal dementia would add significant value to clinical practice, to the design and implementation of treatment trials as well as our understanding of disease mechanisms. The aim of this study was to explore the levels of multiple plasma proteins in individuals from families with genetic frontotemporal dementia. Methods Blood samples from 693 participants in the GENetic Frontotemporal Dementia Initiative study were analysed using a multiplexed antibody array targeting 158 proteins. Results We found 13 elevated proteins in symptomatic mutation carriers, when comparing plasma levels from people diagnosed with genetic FTD to healthy non-mutation controls and 10 proteins that were elevated compared to presymptomatic mutation carriers. Conclusion We identified plasma proteins with altered levels in symptomatic mutation carriers compared to non-carrier controls as well as to presymptomatic mutation carriers. Further investigations are needed to elucidate their potential as fluid biomarkers of the disease process.

Abstract Background The self-perceived health status of patients undergoing total hip and knee arthroplasty (THA and TKA) might differ post-operatively due to gender, age, or comorbidities. Patient-reported outcome measures (PROMs) such as the EQ-5D-5L measure the self-perceived health status. This study investigates whether the index score of the EQ-5D-5L is a valid tool for interpreting gender differences in outcomes for patients undergoing THA and TKA. Methods Routine and PROM-data of elective primary THA or TKA patients in two German hospitals between 2016 and 2018 were analyzed. Univariate analysis with Pearson’s chi-square was conducted to identify control variables for gender. To quantify the association between gender and the EQ-5D-5L dimensions, a cumulative odds ordinal logistic regression with proportional odds was conducted. Results Two thousand three hundred sixty-eight​​ THA patients (m = 978; f = 1390) and 1629 TKA patients (m = 715; f = 914) were considered. The regression analysis of the individual EQ-5D-5L dimensions showed that female gender was significantly associated with better self-care (THA and TKA) and better post-operative mobility (THA). In contrast, male gender was significantly associated with less pain/discomfort (TKA) and less anxiety/depression (THA) pre-surgery and 3-months post-surgery. Conclusion Our results confirmed that the self-perceived health status improved after surgery. However, due to the different associations of gender to the individual dimensions of the EQ-5D-5L, the weighted index score clouds the comparability between patients with different gender undergoing THA or TKA. Therefore, we argue to use the individual five dimensions for health status analysis, to reveal relevant additional information.

Recently, there has been significant interest in applying machine-learning (ML) techniques to the automated analysis of X-ray scattering experiments, due to the increasing speed and size at which datasets are generated. ML-based analysis presents an important opportunity to establish a closed-loop feedback system, enabling monitoring and real-time decision-making based on online data analysis. In this study, the incorporation of a combined one-dimensional convolutional neural network (CNN) and multilayer perceptron that is trained to extract physical thin-film parameters (thickness, density, roughness) and capable of taking into account prior knowledge is described. ML-based online analysis results are processed in a closed-loop workflow for X-ray reflectometry (XRR), using the growth of organic thin films as an example. Our focus lies on the beamline integration of ML-based online data analysis and closed-loop feedback. Our data demonstrate the accuracy and robustness of ML methods for analyzing XRR curves and Bragg reflections and its autonomous control over a vacuum deposition setup.

Many criteria must be taken into account while selecting the best renewable energy source (RES), which necessitates a sophisticated multi-criteria decision-making (MCDM) procedure. Conflicting norms, as well as insufficient and inaccurate information, make this endeavour challenging. The theory of moderator intuitionistic fuzzy set (MIFS), a generalization of intuitionistic fuzzy sets (IFSs) has been developed to handle these uncertainties. The MIFS also helps to get a higher degree of precision in the uncertain behaviours due to the moderator parameter. In this paper, first some distance measures are presented for MIFSs, and then, on the basis of the proposed distances, we suggest a TOPSIS technique for choosing the best RES inside MIFS architecture. The efficacy of the proposed MIFS-based TOPSIS technique is put to the test by comparing different wind generating systems in a case study. The findings of the study are then compared in order to demonstrate that the suggested method is superior to the existing ones.

In this article, the author deals with the formation of Caffa by the end of the fourteenth century as the leading thalassopoliteia in the Black Sea region, when the head of the city of Caffa became “the ruler of the whole Black Sea” at the same time. The author refers to Liber Gazariae (1341), Caffa’s Statuta (1316, 1449), Caffa’s notarial acts from the fourteenth–fifteenth centuries, Massaria Caffae 1381, 1424, Italian pilot charts, Byzantine periploi from the fourteenth–fifteenth centuries, Italian nautical charts from the fourteenth–fifteenth centuries, and materials of archaeological excavations in Feodosia port. Following the study, the author reveals the essence of the thalassopoliteia of Caffa in the pre-modern world which involved the establishment of Caffa’s territorial power over the entire coastline of the Crimean Peninsula in the form of the “Great Chora”; control over maritime communications, requirements of obligatory calls to the port of Caffa for all vessels sailing the Black Sea; creation of a maritime logistics network in the form of ports, harbours, berths, and anchorages for the export of local products, which was served by ships, loaders, and the metrological service of Caffa’s port; introduction of the semaphore signaling system within the “Great Chora” of Caffa ensuring safety of navigation in the Black Sea; neutralisation of piracy, corsairing, and maritime actions of hostile countries, reimbursement of damages to affected citizens of Caffa; creation of a naval chain of fortresses and fortifications with locations of fast-moving battle triremes; development of shipbuilding and ship repair infrastructure both in the port of Caffa and in its subordinate ports; formation of the institution of Caffa’s thalassopoliteia, a special “maritime identity”, the birth of the idea of “contract with the sea”.

Abstract Background In the absence of medical necessity, opting for caesarean sections exposes mothers and neonates to increased risks of enduring long-term health problems and mortality. This ultimately results in greater economic burden when compared to the outcomes of spontaneous vaginal births. In Switzerland around 33% of all births are by caesarean section. However, the rate of caesarean sections without medical indication is still unknown. Therefore, we devise an identification strategy to differentiate caesarean sections without medical indication using routine data. In addition, we aim to categorize the influencing factors for women who undergo spontaneous vaginal births as opposed to those with caesarean sections without medical indication. Method We use Swiss Federal Statistics data including 98.3% of all women giving birth from 2014 to 2018. To determine non-medically indicated caesarean sections in our dataset, we base our identification strategy on diagnosis-related groups, diagnosis codes, and procedure classifications. Subsequently, we compare characteristics of women who give birth by non-medically CS and external factors such as the density of practicing midwives to women with spontaneous vaginal birth. Logistic regression analysis measures the effect of factors, such as age, insurance class, income, or density of practicing midwives on non-medically indicated caesarean sections. Results Around 8% of all Swiss caesarean sections have no medical indication. The regression analysis shows that higher age, supplemental insurance, higher income, and living in urban areas are associated with non-medically indicated caesarean sections, whereas a higher density of midwives decreases the likelihood of caesarean sections without medical indication. Conclusions By identifying non-medically indicated caesarean sections using routine data, it becomes feasible to gain insights into the characteristics of impacted mothers as well as the external factors involved. Illustrating these results, our recommendation is to revise the incentive policies directed towards healthcare professionals. Among others, future research may investigate the potential of midwife-assisted pregnancy programs on strengthening spontaneous vaginal births in absence of medical complications.

Nocturia is a highly prevalent condition associated with poor quality of life and increased adverse health outcomes. Our study investigates the prevalent condition of nocturia and its profound impact on individuals’ quality of life and adverse health outcomes. We explore the correlation between nocturia and various comorbidities in a southeast Michigan population by analyzing data from 1023 participants who completed self-administered questionnaires during the 2021 Michigan Institute of Urology’s Men’s Health Event; 892 participants responded to the nocturia questionnaire. Categorizing participants based on the severity of nocturia into three groups—no nocturia, nocturia once per night, and multiple nocturia episodes per night—we employed Pearson’s Chi-square test to examine the relationship between nocturia severity and the presence of comorbidities. Our findings demonstrate significant associations between increasing episodes of nocturnal voids and the prevalence of hypertension, non-insulin-dependent diabetes and anxiety. Additionally, arthritis and colorectal polyps exhibit notable links with multiple nocturia episodes, while participants with a family history of hypertension show decreased prevalence as nocturnal voids increase, and a family history of myocardial infarction is notably associated with nocturia, particularly in the nocturia once per night group. Our research highlights the widespread impact of nocturia and its strong associations with diverse comorbidities, underscoring the importance of further investigation to comprehend the underlying mechanisms and enhance patient outcomes.

Objective To evaluate the Descemet membrane endothelial keratoplasty (DMEK) preparation performance of trainee surgeons in an ex vivo human donor cornea DMEK wet lab simulation setting.Methods Human donor corneoscleral rims unsuitable for transplantation were obtained from Moorfields Lions Eye Bank. At the wet lab, graft stripping was performed by scoring the peripheral endothelium. The trypan blue positive cells (TBPC) and cell density (cells/mm2—reticule count) were counted manually before and after stripping. The procedural time, peripheral and central tears and complete peel-off were also recorded and analysed.Results Eight trainee surgeons attended the wet lab each attempting three DMEKs. Between the first and last attempts a significant decrease was seen in the procedural time (17.6 min vs 10.6 min (p

Study region: Raised bog “Königsdorfer-Weidfilz”, Bavaria, Germany. Study focus: This study investigates effects of different rewetting scenarios on water levels in raised bog peat under varying climatic conditions. We apply physically-based models with high temporal and spatial resolutions to compare seasonal and annual water levels. The results were evaluated to determine the significance of these water level changes. Based on these water levels, a qualitative assessment was conducted to determine the percentage of areas that are more or less likely contributing to climate change through greenhouse gas emissions. New hydrological insights: Our study demonstrates the potential for investigating the rewetting of small peatland areas using high-resolution three-dimensional hydrological models. By utilizing a partially rewetted raised bog as a case study, we successfully modeled areas with different drainage states. Our results indicate that the areas rewetted in the respective scenarios behave similarly to the areas that have already been rewetted on site. Our study highlights that additional rewetting measures have a positive impact on reducing climate-active areas with low water levels in raised bogs. When combined with natural vegetation succession and changes in soil properties resulting from the formation of a new functional acrotelm layer after rewetting, these changes further enhance the effectiveness of the rewetting process. Although the influence of relevant dry periods after rewetting remains significant, our results suggest that the resilience of the peatland increases.

Abstract INTRODUCTION We aimed to expand the range of the frontotemporal dementia (FTD) phenotypes assessed by the Clinical Dementia Rating Dementia Staging Instrument plus National Alzheimer's Coordinating Center Behavior and Language Domains (CDR plus NACC FTLD). METHODS Neuropsychiatric and motor domains were added to the standard CDR plus NACC FTLD generating a new CDR plus NACC FTLD‐NM scale. This was assessed in 522 mutation carriers and 310 mutation‐negative controls from the Genetic Frontotemporal dementia Initiative (GENFI). RESULTS The new scale led to higher global severity scores than the CDR plus NACC FTLD: 1.4% of participants were now considered prodromal rather than asymptomatic, while 1.3% were now considered symptomatic rather than asymptomatic or prodromal. No participants with a clinical diagnosis of an FTD spectrum disorder were classified as asymptomatic using the new scales. DISCUSSION Adding new domains to the CDR plus NACC FTLD leads to a scale that encompasses the wider phenotypic spectrum of FTD with further work needed to validate its use more widely. Highlights The new Clinical Dementia Rating Dementia Staging Instrument plus National Alzheimer's Coordinating Center Behavior and Language Domains neuropsychiatric and motor (CDR plus NACC FTLD‐NM) rating scale was significantly positively correlated with the original CDR plus NACC FTLD and negatively correlated with the FTD Rating Scale (FRS). No participants with a clinical diagnosis in the frontotemporal dementia spectrum were classified as asymptomatic with the new CDR plus NACC FTLD‐NM rating scale. Individuals had higher global severity scores with the addition of the neuropsychiatric and motor domains. A receiver operating characteristic analysis of symptomatic diagnosis showed nominally higher areas under the curve for the new scales.

Abstract A lack of biomarkers that detect drug-induced liver injury (DILI) accurately continues to hinder early- and late-stage drug development and remains a challenge in clinical practice. The Innovative Medicines Initiative’s TransBioLine consortium comprising academic and industry partners is developing a prospective repository of deeply phenotyped cases and controls with biological samples during liver injury progression to facilitate biomarker discovery, evaluation, validation and qualification. In a nested case–control design, patients who meet one of these criteria, alanine transaminase (ALT) ≥ 5 × the upper limit of normal (ULN), alkaline phosphatase ≥ 2 × ULN or ALT ≥ 3 ULN with total bilirubin > 2 × ULN, are enrolled. After completed clinical investigations, Roussel Uclaf Causality Assessment and expert panel review are used to adjudicate episodes as DILI or alternative liver diseases (acute non-DILI controls). Two blood samples are taken: at recruitment and follow-up. Sample size is as follows: 300 cases of DILI and 130 acute non-DILI controls. Additional cross-sectional cohorts (1 visit) are as follows: Healthy volunteers (n = 120), controls with chronic alcohol-related or non-alcoholic fatty liver disease (n = 100 each) and patients with psoriasis or rheumatoid arthritis (n = 100, 50 treated with methotrexate) are enrolled. Candidate biomarkers prioritised for evaluation include osteopontin, glutamate dehydrogenase, cytokeratin-18 (full length and caspase cleaved), macrophage-colony-stimulating factor 1 receptor and high mobility group protein B1 as well as bile acids, sphingolipids and microRNAs. The TransBioLine project is enabling biomarker discovery and validation that could improve detection, diagnostic accuracy and prognostication of DILI in premarketing clinical trials and for clinical healthcare application.

FlexPES is a soft X-ray beamline on the 1.5 GeV storage ring at MAX IV Laboratory, Sweden, providing horizontally polarized radiation in the 40–1500 eV photon energy range and specializing in high-resolution photoelectron spectroscopy, fast X-ray absorption spectroscopy and electron–ion/ion–ion coincidence techniques. The beamline is split into two branches currently serving three endstations, with a possibility of adding a fourth station at a free port. The refocusing optics provides two focal points on each branch, and enables either focused or defocused beam on the sample. The endstation EA01 at branch A (Surface and Materials Science) is dedicated to surface- and materials-science experiments on solid samples at ultra-high vacuum. It is well suited not only to all flavours of photoelectron spectroscopy but also to fast (down to sub-minute) high-resolution X-ray absorption measurements with various detectors. Branch B (Low-Density Matter Science) has the possibility to study gas-phase/liquid samples at elevated pressures. The first endstation of this branch, EB01, is a mobile setup for various ion–ion and electron–ion coincidence techniques. It houses a versatile reaction microscope, which can be used for experiments during single-bunch or multi-bunch delivery. The second endstation, EB02, is based on a rotatable chamber with an electron spectrometer for photoelectron spectroscopy studies on primarily volatile targets, and a number of peripheral setups for sample delivery, such as molecular/cluster beams, metal/semiconductor nanoparticle beams and liquid jets. This station can also be used for non-UHV photoemission studies on solid samples. In this paper, the optical layout and the present performance of the beamline and all its endstations are reported.

Abstract We introduce a novel Natural Language Processing (NLP) task called guilt detection, which focuses on detecting guilt in text. We identify guilt as a complex and vital emotion that has not been previously studied in NLP, and we aim to provide a more fine-grained analysis of it. To address the lack of publicly available corpora for guilt detection, we created VIC, a dataset containing 4622 texts from three existing emotion detection datasets that we binarized into guilt and no-guilt classes. We experimented with traditional machine learning methods using bag-of-words and term frequency-inverse document frequency features, achieving a 72% f1 score with the highest-performing model. Our study provides a first step towards understanding guilt in text and opens the door for future research in this area.

Abstract A cochlear implant is a neuroprosthetic device that can restore speech perception for people with severe to profound hearing loss. Because of recent evolutions, a growing number of people with a cochlear implant have useful residual acoustic hearing. While combined electro-acoustic stimulation has been shown to improve speech perception for this group of people, some studies report limited adoption rates. Here, we present electro-vibrational stimulation as an alternative combined stimulation strategy that similarly targets the full cochlear reserve. This novel strategy combines the electrical stimulation by the cochlear implant with low-frequency bone conduction stimulation. In a first evaluation of electro-vibrational stimulation, speech perception in noise was assessed in 9 subjects with a CI and symmetrical residual hearing. We demonstrate a statistically significant and clinically relevant improvement for speech perception in noise of 1.9 dB signal-to-noise ratio. This effect was observed with a first prototype that provides vibrational stimulation to both ears with limited transcranial attenuation. Future integration of electro-vibrational stimulation into one single implantable device could ultimately allow cochlear implant users to benefit from their low-frequency residual hearing without the need for an additional insert earphone.

In this paper, we further develop a novel, efficient approach to the problem of signal detection against background noise based on a nonlinear residual functional called the neuron-like criterion function (NCF). A detailed comparison of the NCF-based technique and the conventional correlation criterion function (CCF)-based matched-signal detection is performed. For this purpose, we calculated the detection performance curves for both techniques and found the range of the problem parameters in which the NCF-based detector shows a certain advantage. The latter consists of achieving a fixed value of detection probability at a lower threshold value of the input signal-to-noise ratio (SNR) compared to the CCF-based detector. Special attention is given to the practically important scenario of receiving a weak signal against the background of non-stationary noise with a certain trend (positive or negative) of its intensity. For these two specific cases, modified NCFs are given, which are then used for computer simulation. For both broadband and narrow-band signals, the quantitative bounds of the most effective use of the derived NCFs are established and interpreted. The real sea noise data obtained from two underwater acoustic arrays, one stationary on the sea bottom and the other towed near the sea surface, are used for experimental validation. The experimental data processing results confirm the simulation results and make it possible to demonstrate the advantage of the NCF if the noise intensity shows a significant trend over the signal observation interval. The latter case obviously corresponds to the use of the towed array in the coastal area.