Life Data Research Articles

What determines participation in sport for older adults in England: A multilevel analysis of Active Lives data.

Physical inactivity within an ageing population is an ongoing public health concern for policymakers. Engagement in sport forms a foundation of policy designed to encourage physical activity participation and improve health and wellbeing. This study aimed to (i) understand the extent to which older adults participate in sport and the (ii) correlates that predict this involvement within an English population sample of older adults. A further aim was (iii) to examine the extent in which sports participation may vary due to the opportunity provided across Active Partnerships in England. To address this, a multi-level analysis framed through COM-B was conducted of the 2021 English Active Lives dataset (i.e., during the COVID-19 pandemic). The Active Lives survey provides population-level insight into sport, exercise, and physical activity participation across England. It samples upwards of n = 180,000 participants beyond the age of 16 years and asks questions on factors that influence participation. Our findings drawn from a sample of n = 68,808 older adults (i.e., >60-years of age) indicate that when accounting for variation across regions sports participation was significantly predicted by age (β = -.246, p = .040) and multiple deprivation (β = .706, p = .030). Further, our analysis suggests sports participation across regions is associated with changes in the perceptions of opportunity to participate (β = -28.70, p = .001). As the UK transitions from the COVID-19 pandemic, findings have implications for the promotion of sports participation for older adults, in that local, regional, and national stakeholders must do more to change perceptions of social and physical opportunity within an ageing population. This may be achieved through adaptations to the recreational sporting landscape, raising awareness, and supportive policy changes on a national level.

Electronic health intervention to manage symptoms of immunotherapy in patients with cancer (SOFIA): Results from a randomized controlled pilot trial.

For patients receiving immune checkpoint inhibitors, early detection of immune-related adverse events (irAEs) is critical for one's safety. To this end, a smartphone app (SOFIA) was developed that featured the assessment of electronic patient-reported outcomes (ePROs) focusing on irAEs as well as a set of comprehensive supportive information. Its feasibility and preliminary efficacywere evaluated in a randomized controlled trial (RCT). Patients who received immune checkpoint inhibition therapy were randomly assigned to an intervention group (IG) or a control group (CG; care as usual). During the 12-week intervention period, IG patients used SOFIA to report twice weekly ePROs and receive cancer- and immunotherapy-relevant contents. Before a patient's next clinical visit, the physician in charge was given the ePRO reports. The primary objective was to test the feasibility of SOFIA. Furthermore, the preliminary efficacy of SOFIA for health-related quality of life (HRQOL), psychosocial outcomes, and medical data was examined. Clinical outcomes were assessed at baseline (T0), post-intervention (T1), and a 3-month follow-up (T2). Seventy-one patients were randomized to the IG (n=34) or the CG (n=37). SOFIA showed high feasibility and acceptance. At T1, patients in the IG reported significantly better HRQOL and role functioning and less depression, distress, and appetite loss. No significant differences were revealed regarding medical data, the utilization of supportive care services, or survival. SOFIA showed high feasibility and acceptance and improved HRQOL and psychosocial outcomes. These results suggest further evaluation of efficacy in a large-scale confirmatory multicenter RCT.

Feasibility of using an automated call service to collect quality of life and functional outcome data in trauma patients

ObjectivesFollowing up trauma patients after discharge, to evaluate their subsequent quality of life and functional outcomes, is notoriously difficult, time consuming, and expensive. Automated systems are a conceptually attractive solution....

MotGen: a closed-loop bacterial motility control framework using generative adversarial networks.

Many organisms' survival and behavior hinge on their responses to environmental signals. While research on bacteria-directed therapeutic agents has increased, systematic exploration of real-time modulation of bacterial motility remains limited. Current studies often focus on permanent motility changes through genetic alterations, restricting the ability to modulate bacterial motility dynamically on a large scale. To address this gap, we propose a novel real-time control framework for systematically modulating bacterial motility dynamics. We introduce MotGen, a deep learning approach leveraging Generative Adversarial Networks to analyze swimming performance statistics of motile bacteria based on live cell imaging data. By tracking objects and optimizing cell trajectory mapping under environmentally altered conditions, we trained MotGen on a comprehensive statistical dataset derived from real image data. Our experimental results demonstrate MotGen's ability to capture motility dynamics from real bacterial populations with low mean absolute error in both simulated and real datasets. MotGen allows us to approach optimal swimming conditions for desired motility statistics in real-time. MotGen's potential extends to practical biomedical applications, including immune response prediction, by providing imputation of bacterial motility patterns based on external environmental conditions. Our short-term, in-situ interventions for controlling motility behavior offer a promising foundation for the development of bacteria-based biomedical applications. MotGen is presented as a combination of Matlab image analysis code and a machine learning workflow in Python. Codes are available at https://github.com/bgmseo/MotGen, for cell tracking and implementation of trained models to generate bacterial motility statistics.

Research on smart city public health detection system and improvement technology based on intelligent multi-objective.

With the increase of urban population density, urban sanitation becomes more severe; urban sanitation has important influence on public health. Therefore, in order to realize the detection of public health in smart cities, the research will use cutting-edge scientific and technological methods to improve urban environmental health, so as to promote the realization of public health achievements. This study introduces public health detection and optimizationtechnologies for smart cities. Firstly, a data detection system for urban public health environment was established using sensors and intelligent multi-objective technology to evaluate the water quality, air quality, and noise level of the city. Then, an intelligent garbage management system based on Tensor-flow was constructed to achieve efficient garbage collection and treatment. Finally, an intelligent traffic management system was developed to monitor and regulate urban traffic flow. The results of the simulation experiment demonstrated that the life data detection system was operationally stable, with a high success rate of 98%. Furthermore, its accuracy in detecting residents' living environment data was above 95%, the maximum relative error was only 0.0465, making it a reliable and efficient tool. The waste recycling system achieved a minimum accuracy of 83.6%, the highest accuracy rate was 95.3%, making it capable of sorting and recycling urban waste effectively. Additionally, the smart traffic management system led to a 20% reduction in traffic congestion rates, 20 tonnes less tailpipe emissions and an improvement in public health and well-being. In summary, the plan proposed in this study aims to create a more comfortable, safe, and healthy urban public health environment, while providing theoretical support for environmental health management in smart cities.

DNA barcoding elucidates ecological dynamics regulating the diversity of Theretra, Hübner 1819 (Lepidoptera: Sphingidae) from northernmost Western Ghats

The study investigates the distribution and diversity of the old-world moth genus Theretra from the family Sphingidae in the Indian subcontinent. Through extensive data collection and molecular analysis from the Northernmost Western Ghats (Nashik District), seven species of Theretra were identified: T. alecto, T. castanea, T. clotho, T. gnoma, T. nessus, T. oldenlandiae, and T. sumatrensis.Molecular clustering identifies genetically similar specimens, which further helps to recognise similar ecological niches and the associated ecological drivers regulating the distribution pattern of similar specimens. A dataset of 196 published records from Barcode of Life Data Systems (BOLD), including outgroup and the sequences generated in the present study for the Indian species of Theretra, were compiled in a dataset ‘THEREIND’. The crucial role of monsoon and elevation in the diversity and distribution of these moths was comprehended using DNA barcoding and sequence clustering on BOLD. The comparisons suggested a strong correlation with either monsoon or elevation or both.In the purview of the sixth mass extinction and the first true extinction of insects, adequate information on the diversity and the factors affecting it would provide fundamental information to insinuate conservation strategies required for coping with continuous climatic changes.

Detailed DNA barcoding of mayflies in a small European country proved how far we are from having comprehensive barcode reference libraries.

Mayflies (Ephemeroptera) are among the crucial water and habitat quality bioindicators. However, despite their intensive long-term use in various studies, more reliable mayfly DNA barcode data have been produced in a negligible number of countries, and only ~40% of European species had been barcoded with less than 50% of families covered. Despite being carried out in a small area, our study presents the second-most species-rich DNA reference library of mayflies from Europe and the first comprehensive view from an important biodiversity hotspot such as the Western Carpathians. Within 1153 sequences, 76 morphologically determined species were recorded and added to the Barcode of Life Data System (BOLD) database. All obtained sequences were assigned to 97 BINs, 11 of which were unique and three represented species never barcoded before. Sequences of 16 species with high intraspecific variability were divided into 40 BINs, confirming the presence of cryptic lineages. Due to the low interspecific divergence and the non-existing barcoding gap, sequences of six species were assigned to three shared BINs. Delimitation analyses resulted in 79 and 107 putative species respectively. Bayesian and maximum-likelihood phylogenies confirmed the monophyly of almost all species and complexes of cryptic taxa and proved that DNA barcoding distinguishes almost all studied mayfly species. We have shown that it is still sufficient to thoroughly investigate the fauna of a small but geographically important area to enrich global databases greatly. In particular, the insights gained here transcend the local context and may have broader implications for advancing barcoding efforts.

Deep learning in the classification and recognition of cardiac activity patterns

Electrocardiography is an examination performed frequently in patients experiencing symptoms of heart disease. Upon a detailed analysis, it has shown potential to detect and identify various activities. In this article, we present a deep learning approach that can be used to analyze ECG signals. Our research shows promising results in recognizing activity and disease patterns with nearly 90% accuracy. In this paper, we present the early results of our analysis, indicating the potential of using deep learning algorithms in the analysis of both onedimensional and two–dimensional data. The methodology we present can be utilized for ECG data classification and can be extended to wearable devices. Conclusions of our study pave the way for exploring live data analysis through wearable devices in order to not only predict specific cardiac conditions, but also a possibility of using them in alternative and augmented communication frameworks.

Abstract 4699: Evaluating extracellular matrix invasion and cytotoxicity of natural killer cells in a novel co-culture assay

Abstract Background: The complex tumor microenvironment (TME) in solid tumors presents unique challenges for lymphocytes used in cancer immunotherapy. The extracellular matrix (ECM) in TME can limit the recruitment of lymphocytes and restrict access to cancer cells. This study utilized Matrigel to study the kinetics of natural killer cell invasion and cytotoxicity. We hypothesized that the kinetics of tumor cell killing by the NKs would depend on the distance for invasion and rely on matrix-metalloproteinases (MMPs) for remodeling the ECM. Methods: Increasing volumes (50-100 µL/well) of Matrigel (6 mg/mL) representing increasing distance for invasion was layered over MCF7-red target tumor cells expressing nuclear-localized mKate2 (red fluorescent protein). NK92 (Creative Bioarray) or eGFP-NK92 (BPS Bioscience) were seeded over the solidified Matrigel layer at E:T of 3:1. The broad spectrum MMP inhibitor, Ilomostat (GM6001, 2 µM and 10 µM), was added to the wells to investigate to role of MMPs. Label-free impedance and live cell imaging data was collected in real time on xCELLigence RTCA eSight to evaluate NK cell invasion and cytotoxicity. Percent cytolysis was calculated on the RTCA software from normalized cell impedance readings and imaging data using the formula [(1-(treated/untreated)*100]. Results: Impedance reading increases as the MCF7-red cells settle, attach, and proliferate. The readings plateau as the cells reach confluence. The addition of Matrigel modulates the impedance signature of MCF7 cells. Importantly, Matrigel delays cytolysis by NK92 cells added at 24h. The delay increases with increasing volume of Matrigel representing increasing invasion distance. The time to achieve 60% killing with respect to controls (KT60) was 67, 76 and 89 hours for 50, 75 and 100 µL/well, respectively. Consistent with the role of MMPs in invasion, percent cytolysis calculated independently from normalized impedance readings and live cell imaging data confirmed both delayed and reduced target cell killing by GFP-NK92 cells in the presence of Ilomostat. Interestingly, only a few GFP-NK92 were detected by live cell imaging when there was increased target cell death. These included highly active NK cells that made multiple contacts with different MCF7 red targets in clusters suggesting serial killing activity. Conclusions: The observations that only a few NK92 cells are detected during the killing of susceptible target cells and that cytolysis is not abrogated by the inhibition of MMPs suggest a potential role for distal effectors like cytokines, independent of invasion. This is a novel 2.5D assay that demonstrates the potential of RTCA eSight platform for studying the invasion and cytotoxicity function of various immune cells through both real-time impedance recording and live-cell time-lapse imaging simultaneously. (For Research Use Only. Not for use in diagnostic procedures. RA45236.3472453704) Citation Format: Jyothi Thyagabhavan Mony, Xiaoyu Zhang. Evaluating extracellular matrix invasion and cytotoxicity of natural killer cells in a novel co-culture assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4699.

Abstract 3248: Metabolic rewiring shifts engineered TCR T cell bioenergetics to enhance cytotoxicity

Abstract Background: Adoptive T cell-based transfer has shown tremendous success in hematological cancers. However, an important obstacle to using cell and gene therapy for solid tumors is the aggressive tumor microenvironment (TME). To significantly improve prognosis of subjects undergoing immunotherapy, it is essential to incorporate unique methods to drive T cell energy production and metabolism towards empowering T cells to dismantle the suppressive TME. This can promote anti-tumor effects and persistence of the transfused engineered T cells. In the present study we present a simple metabolic preconditioning process that improves T cell performance and bioenergetics. Methods: Using the Agilent xCELLigence RTCA eSight, and Seahorse XF Analyzer we assessed the killing efficiency, and bioenergetics of engineered T-cells after pretreating with Arginine. Briefly, engineered TCR T-cells against MART-1 were generated by transducing CD3+ T-cells (Hemacare, Seattle, WA) with retrovirus SAMEN-DMF5 with a CD34 marker gene, against MART-1. The TCR T cells were pre-conditioned in 6mM Arginine for 7 days, followed by a killing assay using MART-1 expressing melanoma cell line as target cells (624.38) at the E:T ratio of 5:1. Target melanoma cells, 624.38 cells, were engineered to express a red-fluorescent nuclear protein. The comparison was made with transduced T-cells grown in RPMI (no added amino acid supplementation, and non-transduced T cells. The cytotoxicity assay was assessed using real-time impedance and live cell imaging data collected on xCELLigence RTCA eSight. Concurrently, the metabolic profiling of the engineered T-cells was examined by the XF96 Analyzer. Results: Supplementing the growth medium with 6 mM Arginine significantly increased the potency of MART-1 engineered T cells (up to 6-fold) and bioenergetic studies revealed increased spare respiratory capacity, basal respiration, and ATP production via mitochondrial respiration. Conclusions: Arginine pre-conditioning improved TCR T cell potency through metabolic rewiring favoring mitochondrial respiration. For Research Use Only. Not for use in diagnostic procedures RA45236.3483333333 Citation Format: Rashmi Pillai, Xiaoyu Zhang, Yama Abassi. Metabolic rewiring shifts engineered TCR T cell bioenergetics to enhance cytotoxicity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3248.

Abstract 7434: Deployment of a visualization tool directly linked to the CellEngine cytometry analysis software to accelerate analysis of complex cytometry data sets in the context of ongoing clinical trials

Abstract Introduction High-dimension cytometry panels have become a hallmark of clinical phase 1 and 2 trials. These panels can provide hundreds of readouts and need to be combined with subject metadata (e.g., health data, demographics, timepoint, and dose) to provide insights on the identification of biomarkers. Several bioinformatics tools are available to interpret these complex datasets; however, these usually require significant expertise in data manipulation, which sometimes forces the person doing the data interpretation to outsource the data visualization and statistical analysis to a bioinformatics team. As a consequence, the scientist can be one step-removed from the “raw” data, critically preventing the interpretation of biomarker effects in the context of the raw staining profiles and associated quality controls (e.g. in-run controls). Here, we show how a new tool integrated with the CellEngine cytometry analysis software can accelerate data interpretation without removing the scientist from the raw data. Methods The visualization tool pulls live data from CellEngine, displaying both study- and cohort-level information in summary views as well as the sample-level staining profiles. As a live link, any changes made in CellEngine, such as ones to gating and compensation, or the addition of new samples during an ongoing study, are automatically reflected in the dashboard. This link also gives the ability to consult staining profiles and performance of controls directly within the tool without having to switch between applications. CellEngine is capable of analyzing 10,000s of samples at once, and thus can handle the largest of studies. Results Here, we show how the visualization tool was used to accelerate the data interpretation of two different flow cytometry assays. In the first example, lymphodepletion and immune system reconstitution in patients undergoing CAR-T cell therapy was monitored using the tool. The live update of analysed data within the tool, coupled with the ability to verify the performance of the two controls ran with each sample, accelerated the data interpretation and improved the data quality. In the second example, we show how the software was used to follow cytokines responses across different timepoints in a vaccination program where samples were analysed using a polyfunctional intra-cellular cytokine staining panel. The tool greatly accelerated the interrogation of this complex data set and allowed the identification of trends within the different T cell responses. Conclusion This new tool can help maximize the utility of high-dimensional cytometry in clinical trial analysis while allowing to keep the crucial link between graphical representation of large number of samples and the data associated with each individual flow cytometry staining profile. Citation Format: Damien Montamat-Sicotte, Zach Bjornson, Dean Franckaert, Nicholas Dupuis, Eustache Paramithiotis. Deployment of a visualization tool directly linked to the CellEngine cytometry analysis software to accelerate analysis of complex cytometry data sets in the context of ongoing clinical trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7434.

UCOMB-real life data: treatment strategies for chronic urticaria patients with comorbidities

Background There is a lack of real-life safety data on treatment options for chronic urticaria in the presence of comedication and comorbidities. Methods We present a single-center UCARE pilot study of 212 outpatients with chronic urticaria. Patients were divided into three groups according to different CU therapies according to international guidelines. Results Of 212 patients, 108 (mean age 48.9 years, 71.3% female) had 59 comorbidities, including cardiovascular, autoimmune and malignant diseases. Patients were followed for a mean of 24.6 months (SD ± 21.3). Urticaria therapies were divided into three groups: A: 105 (97.2%) with omalizumab and 2nd generation antihistamines), B: 16 patients (14.8%): dual therapy with antihistamines and cyclosporine in 10 (9.3%), montelukast in five (4. 6%), dapsone in four (3.7%), hydroxychloroquine in one patient (0.9%), C: 12 (11.1%) patients received a third drug for 4.9 months (SD ± 3.2) and one quadruple therapy (2.1 months). 10 out of 12 (83.3%) patients received montelukast, two (16.7%) cyclosporine, two (16.7%) dapsone and one (8.3%) hydroxychloroquine as a third drug for chronic urticaria. Conclusions Combining treatment modalities for chronic urticaria and comorbidities are available and feasible with a good safety profile.

Effects of Mindful Eating in Patients with Obesity and Binge Eating Disorder.

Binge eating disorder (BED) is a psychiatric illness related to a high frequency of episodes of binge eating, loss of control, body image dissatisfaction, and suffering caused by overeating. It is estimated that 30% of patients with BED are affected by obesity. "Mindful eating" (ME) is a promising new eating technique that can improve self-control and good food choices, helping to increase awareness about the triggers of binge eating episodes and intuitive eating training. To analyze the impact of ME on episodes of binge eating, body image dissatisfaction, quality of life, eating habits, and anthropometric data [weight, Body Mass Index (BMI), and waist circumference] in patients with obesity and BED. This quantitative, prospective, longitudinal, and experimental study recruited 82 patients diagnosed with obesity and BED. The intervention was divided into eight individual weekly meetings, guided by ME sessions, nutritional educational dynamics, cooking workshops, food sensory analyses, and applications of questionnaires [Body Shape Questionnaire (BSQ); Binge Eating Scale (BES); Quality of Life Scale (WHOQOL-BREF)]. There was no dietary prescription for calories, carbohydrates, proteins, fats, and fiber. Patients were only encouraged to consume fewer ultra-processed foods and more natural and minimally processed foods. The meetings occurred from October to November 2023. To carry out inferential statistics, the Shapiro-Wilk test was used to verify the normality of variable distribution. All variables were identified as non-normal distribution and were compared between the first and the eighth week using a two-tailed Wilcoxon test. Non-Gaussian data were represented by median ± interquartile range (IQR). Additionally, α < 0.05 and p < 0.05 were adopted. Significant reductions were found from the first to the eighth week for weight, BMI, waist circumference, episodes of binge eating, BSQ scale score, BES score, and total energy value (all p < 0.0001). In contrast, there was a significant increase in the WHOQOL-BREF score and daily water intake (p < 0.0001). ME improved anthropometric data, episodes of binge eating, body image dissatisfaction, eating habits, and quality of life in participants with obesity and BED in the short-term. However, an extension of the project will be necessary to analyze the impact of the intervention in the long-term.

Predictive factors for dose reduction and discontinuation of nintedanib in fibrotic interstitial lung diseases: Real life data

Nintedanib, an intracellular inhibitor targeting multiple tyrosine kinases, has emerged as a standard treatment for various fibrotic lung diseases. Despite its efficacy, side effects such as nausea, diarrhea, and hepatotoxicity often lead to dose reduction or discontinuation. In this retrospective analysis at an university hospital's interstitial lung disease clinic, we aimed to identify baseline characteristics associated with dose adjustment or treatment discontinuation. Of the 58 patients included, 41.4% maintained the full nintedanib dose, while 31.0% required dosage reduction, and 27.6% discontinued treatment due to adverse events, predominantly gastrointestinal and hepatotoxic effects. Multivariate analysis revealed body surface area (BSA) as an independent and significant baseline risk factor (adjusted Odds Ratio [aOR] 0.22), suggesting a 78% decreased chance of requiring dose modification for every decimal point increase in BSA. A BSA cutoff of ≤1.73 m [2] exhibited a sensitivity of 73% and specificity of 91.7%, with significant impact on one-year survival under full-dose treatment (p < 0.001). Lower BSA was associated with early onset adverse effects, particularly gastrointestinal, supporting the need for regular clinical monitoring. The study emphasizes the importance of recognizing baseline factors to ensure the safety and tolerability of nintedanib, thereby preventing the progression of pulmonary fibrosis. These findings contribute to the evolving understanding of nintedanib management in fibrotic interstitial lung diseases, guiding clinicians in personalized treatment approaches.

Search for high-creep-strength welding conditions considering HAZ shape factors for 2 1/4Cr–1Mo steel

The creep rupture life of ferritic heat-resistant steel weld joints is limited by Type IV cracking that occurs in the heat-affected zone (HAZ), whose shape affects creep damage accumulation. In this study, we address the inverse problem of extending the creep rupture life of weld joints by controlling HAZ shape via welding conditions. As reported separately, we have developed a workflow that predicts weld joint creep rupture life from the predicted HAZ shape from welding conditions and have implemented it in the material design system. Using this workflow, we presented a tandem Bayesian model for predicting the creep rupture life from welding conditions via the geometric features of HAZ shapes (HAZ shape factors), which are considered to determine the creep rupture life. The prediction model of a HAZ shape factor from welding conditions was formed by Gaussian process regression. The prediction model of the creep rupture life was formed by Bayesian linear regression. These models were probabilistically connected by Bayesian statistical mathematics. An algorithm to increase the creep rupture life was developed to search for welding conditions. This method was applied to a 2 1/4Cr–1Mo heat-resistant steel weld joint simulated with a plate I-bevel three-layer gas tungsten arc welding. The number of welding conditions combination reaches 78=5764801\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${7}^{8}=5764801$$\\end{document}. Start from 49 initial HAZ shape factors and 22 creep rupture life data, we performed forward calculations of 20 rupture lives to find welding conditions that can improve the creep rupture life by 12% over the initial data.

Updated distribution of Zoropsis spinimana (Dufour, 1820; Araneae: Zoropsidae) in Germany and novel insights into its ecology based on a citizen science survey

In Germany, Zoropsis spinimana (Dufour, 1820) is an introduced, likely synanthropic spider species. Here, we report the results of a nationwide mapping appeal conducted by the citizen science platform NABU-naturgucker.de, used to assemble live distributional data for the species in Germany. With the help of media interest in this species, we gathered a valuable dataset and a large image gallery of the species. In just five weeks, we received more than 15,000 records, representing a 2.3-fold increase in occupied territory compared to previous knowledge. By analyzing the data in detail, we obtained novel insights into the ecology and eco-geography of Z. spinimana in Germany, including information on prey, coloration, potential predators, altitudinal distribution and temporal appearance, along with two cases of accidental human translocation.

Sequence effects on the life estimation of welded tubular structures made of S355J2H under uniaxial fatigue loading

The use of hollow sections to form lightweight structures is widespread in common steel processing industries such as crane, commercial vehicle, steel bridge and agricultural machinery construction. The hollow sections are mainly designed as truss or frame structures, in some cases using high-strength and higher-strength steels in order to achieve optimum utilization of the component and material. A new collection of fatigue life data covering sequence effects and the accuracy of the linear damage accumulation is presented. Effects of the shape of the applied load spectra and sequence effects of different amplitudes have been investigated. This document covers tubes of 4 to 8 mm thickness made by low-carbon or mild steel S355J2H. In general, it was found that the spectrum shape and the loading sequence have an influence on the service life. Depending on the shape of the spectrum, random tests tended to lead to shorter service lives than tests with block-loading sequences. An influence of overloads was also found for the tests with interspersed overloads. Typical maximum linear damage sums taken from recommendations and codes of 0.2 or sometimes 0.5 are exceeded for all spectra investigated and in some of the cases even significantly above 1.0. Transferability of the recommendations to component-type structures like tubular joints needs revision to lift its lightweight potential. Using stress concentration factors (SCF) from finite element analysis, typical strength values for the structural and effective notch stress concepts are checked. All joints investigated show a significantly higher strength compared to the IIW recommendations using the structural stress approach or compared to the DVS 0905 with the effective notch stress approach.

Health-Related Quality of Life (HRQoL) Data From KEYNOTE-412: Chemoradiotherapy (CRT) with or Without Pembrolizumab (pembro) in Patients (pts) with Locally Advanced Head and Neck Squamous Cell Carcinoma (LA HNSCC)

Health-Related Quality of Life (HRQoL) Data From KEYNOTE-412: Chemoradiotherapy (CRT) with or Without Pembrolizumab (pembro) in Patients (pts) with Locally Advanced Head and Neck Squamous Cell Carcinoma (LA HNSCC)

Online and Ex Situ Damage Characterization Techniques for Fiber-Reinforced Composites under Ultrasonic Cyclic Three-Point Bending.

With ultrasonic fatigue testing (UFT), it is possible to investigate the damage initiation and accumulation from the weakest link of the composite material in the very high cycle fatigue (VHCF) regime in a shorter time frame than conventional fatigue testing. However, the thermal influence on the mechanical fatigue of composites and the scatter in fatigue data for composites under ultrasonic cyclic three-point bending loading still need to be investigated. In this study, we conducted interrupted constant-amplitude fatigue experiments on a carbon-fiber satin-fabric reinforced in poly-ether-ketone-ketone (CF-PEKK) composite material. These experiments were carried out using a UFT system, which operates at a cyclic frequency of 20 kHz with a pulse-pause sequence. Various parameters, such as the CF-PEKK specimen's surface temperature, acoustic activity, and the ultrasonic generator's input resonance parameters, were measured during cyclic loading. During experiment interruption, stiffness measurement and volumetric damage characterization in the CF-PEKK specimens using 3D X-ray microscopy (XRM) were performed. The locations of damage initiation and accumulation and their influence on the changes in in situ parameters were characterized. Under fixed loading conditions, damage accumulation occurred at different locations, leading to scattering in fatigue life data. Further, the damage population decreased from the surface to the bulk of the composite material.

Thyroid Eye Disease: Pilot Study Comparison Between Patients in United States-Based and India-Based Practices

PurposeThyroid eye disease (TED) phenotype varies by ethnicity/race and genetic/environmental factors. This study compared demographic and clinical characteristics of TED patients from the US and India. DesignObservational pilot study Methods64 patients with TED ages ≥18 years old with active disease (onset of symptoms ≤18 months or presenting clinical activity score (CAS) ≥4) were recruited between March and October 2021 from clinical practices in the United States (Philadelphia, PA) (n= 30) and India (Hyderabad, India) (n=34). Data collection at baseline and 3 months included clinical measurements, thyroid disease history, and photographs. Ocular symptoms and quality of life data were obtained by phone interview. CAS was calculated using the standard 7-point scale. ResultsThere was no statistically significant difference in age, TED duration, or smoking status between patient groups. Both groups had good vision, a low rate of optic neuropathy, and comparable exophthalmometry. US patients were predominantly female (86.7% (26/30)), with a history of hyperthyroidism (96% 29/30). In comparison, Indian patients were 52.9% (18/34, p=0.004) female; underlying thyroid disease was distributed between hyperthyroidism (52%), hypothyroidism (17.6%) and euthyroid (9.7%). Mean (SD) CAS in the US cohort was double the score in the India cohort (4.2 (1.7) vs 1.65 (1.7), respectively) (P<0.0001). However, patients at both sites experienced subjective symptoms of TED at a similar frequency. ConclusionsObserved differences in TED between US and Indian patients warrant further investigation to elucidate differences in pathogenesis, disease expression, or other factors that may influence TED in diverse populations.