Miracle-dz/UniFAR-FTUs huggingface dataset:
Radical Acceptance of Reality: Putting DBT®-A Skill Groups Online During the COVID-19 Pandemic: A Qualitative Study

Introduction Emotion dysregulation is a common challenge pertaining to numerous psychiatric disorders in adolescence and is associated with increased morbidity and mortality. Dialectical Behavior Therapy for Adolescents (DBT®-A) has been shown to be an effective treatment, especially in the reduction of self-harm and suicidality. Measures in relation to the COVID-19 pandemic set strict limits on physical contacts with patients. In order to continuously provide evidence-based specialized care to patients suffering from emotion dysregulation, we offered two online DBT®-A skill groups in a video-group-call format. Objective We aimed at assessing our online DBT®-A skills groups, collect according up- and downsides, and form a basis for advancement of this form of treatment provision. Also, the impact of the COVID-19 pandemic on patients was assessed. Methods A physical DBT®-A skill group was switched to a video-group-call format and a second group was initiated de novo online. After five sessions, patients engaged in structured group discussions to reflect experiences. Discussion content was analyzed via Inductive Category Formation within the Framework of Qualitative Content Analysis. Results Patients unanimously found the COVID-19 pandemic challenging, but also reported differentially on its impact. Downsides were balanced by subjective “gains” in time and a perceived reduction in stress. Technical problems of the online format were discussed, but did not limit the positive experience of still receiving treatment. Patients of both online DBT®-A skill groups valued the offer, felt connected, and reported benefits from the treatment. The transition group additionally discussed changes in structure and content of the group sessions after the switch to online meetings and reflected differential functions of the group. Discussion Although the sample size is small, and conclusions are drawn from Inductive Qualitative Content Analysis, the presented results are of interest. In our investigation, video-group-calls were both safe and beneficial for patients. This alternative to physical meetings is not only interesting for further waves of the current pandemic but also for service provision in remote areas with limited access to specialized care. Further research is needed to challenge and refine our results and to explore extensions to “basic” video-group-calls, such as “break-out sessions,” blended therapy, or real-time supervision within an online session.
Headspace gas analysis of closed radioactive waste vaults in a near surface disposal facility of Hungary

As a part of a national safety assessment research programme about the Centralised Radioactive Waste Treatment and Disposal Facility of Hungary, near Puspokszilagy village, two near surface radioactive waste container vaults were opened at 15th March 2000. This ‘A’ type vaults were closed in 1972, when they had been full. Before removing the lids sampling of the headspace gas of the vaults was performed by a special gas outlet system through the bed of concrete. One of the cells (Code: A5) contains not conditioned radioactive waste. The other one (Code: A6) contains cemented waste. Using the same method qualitative gas analyses of the two headspace gases were carried out on the spot, in the site of the disposal facility. During the gas-analyses with an OMNISTAR quadrupole mass spectrometer gas samples were collected from both cells into 7 Lvolume stainless steel bulbs with a special gas-handling system. Helium, carbon and oxygen isotope ratios, respectively, C and tritium contents of gas samples were measured in the laboratory of INR of the HAS. Compositions of the headspace gases were very similar to the ones of air. No significant hydrogen production or oxygen depletion was detected. The carbon-dioxide content was sixty-times more in the gas of A5 cell and six-times more in A6 cell compared to the air. The C and O isotope-ratios show that this surplus is the result of decomposition of organicmaterials. The specific radiocarbon activity of carbon dioxide was about 3 Bq/cm in both cells. The tritium activity-content of headspace gas was two orders of magnitude more in A5 cell (8.8 Bq/L) than in A6 cell (0.04 Bq/L). These results are in good agreement with the total tritium contents of contained waste in the individual cells. The higher He content in the vaults coming from tritium decay show that gas-migration from the cells must not be considerable.
Optimising the Loading Diversity of Rail Passenger Crowding using On-Board Occupancy Data

Crowded conditions on trains can lead to lower passenger satisfaction, discourage rail travel, result in negative economic impacts and are a factor in a number of health and safety hazards. In the UK there is an annual survey of rail passenger crowding, although the measures used do not reflect coach-by-coach variations, nor do they reflect variations across the peak period. In this MPhil thesis I investigated the application of weight-based automatic passenger counting data to deliver more even loadings on trains through the provision of new real-time and static solutions. In addition I investigated the potential benefits of such solutions in terms of reduced dwell times and reduced crowding. The overall concept proposed was to make the most of the existing available capacity; for example, so that no-one is standing when seats are available. Through analysing a large sample of air suspension data, I identified station-specific trends where some coaches were over capacity while others had spare capacity. I also conducted a critical review of academic research into on-train crowding and solutions that seek to optimise ‘loading diversity’. This study contributes to this emerging subject area in several ways: I propose two new metrics to describe inter-coach loading diversity that, unlike existing metrics, contain information relative to the capacity; I have revealed a link between the inter-coach loading diversity metrics and estimated boarding times, with trains classified as ‘very uneven’ on departure typically having dwell times of approximately five to ten seconds greater than services that were classified as being ‘even’ with a similar total number of passengers on board; and finally I have applied classification supervised learning techniques to predict the load factor for a given service and these predictors were an improvement over taking the historical average.
Hybrid graphene aerogel intermedium for bendable supercapacitor electrode

Porous structure materials indicate brilliant distribution and great absorption ability, that suggesting the potential application of aerogel state graphene based materials. Aerogel intermedium assisted method was developed and optimised to prepare bendable electrode. Hydrothermal schedule was utilised suggesting potential application at low cost and large-scale factory print issues, rather than consuming high money cost and long labour time. Nickel sulphide has been anchored onto the graphene aerogels via one-pot hydrothermal method. The membrane electrode was made with the carbon ink and aerogel intermedium on the Poly(ethylene terephthalate) (PET) plate. The bendable electrode was fabricated with intermedium and commercial carbon ink, and indicates good performance and economic consumption. The complex membrane capacitor shows the much more brilliant performance than the pure carbon membrane capacitor, with specific capacitance (31.40 mF/cm2 at 4 mV/s and 17.74 mF/cm2 at 2 mA/cm2).
Modal Identification of Dynamically Coupled Bladed Disks in Run-Up Tests

The spin test is a standard industrial practice employed for the qualification of rotor blades and disks. The expected results are the modal properties of blades and assemblages at different rotation velocities. If a significant dynamic coupling among the blades exists, global vibration modes appear, reflecting into a set of closely spaced natural frequencies for each mode family. In case of perfectly-tuned bladed disks, the circumferential structure of the mode shapes is known and can be exploited during the identification process so that traditional single-dof models may be applied. On the contrary, the mode irregularities produced by mistuning prevents the use of single-dof models requiring the development of more sophisticated approaches. In this work, we propose a multi-dof identification technique organized as follow: 1) the FRF of the bladed disk in the neighborhood of a resonance crossing is identified by the wavelet transform of the measured response; 2) the modal parameters of the system are estimated using a mixed stochastic-deterministic subspace algorithm formulated in the frequency domain. The procedure is validated using a realistic numerical simulation.Copyright © 2016 by ASME
Research on the Optimizing Model and Solving Algorithm of Assignment Plan of CRH-Emu Depot's Repair Tracks

China Railway High-speed is expanding into a large network that covers most of the middle scale cities. To manipulate the China Railway High-speed Electric Multiple Unit (CRH-Emu) train's daily inspection and repair, 45 CRH-Emu depots were established in China. Depots are equipped with several special tracks to carry out the inspection and repair. With the repair workload's rising, the number of repair-function tracks is becoming bottleneck of depots' repair capacity. In order to improve the efficiency of CRH-Emu depot's repair tracks, this paper established an integer model, with the minimal unnecessary idle time of repair tracks as the objective function, and with back-departure depot time, and the repair demand of CRH-Emu as both operational and repair constraints, the matching relationship between CRH-Emu and tracks as the track occupation constraint. To reduce the model scale, a track occupation topological graph was constructed. Based on the graph, MAX-MIN ant system was applied to obtain optimal result. By the example of solving a typical depot's tracks utilization scheme, the model and optimization method were proved to be effective. Finally, a further discussion of software for scheduling the plan was given.
Lack of ownership of mobile phones could hinder the rollout of mHealth interventions in Africa

Background: Mobile Health interventions, which require ownership of mobile phones, are being investigated throughout Africa. We estimate the percentage of individuals who own mobile phones in 33 African countries, identify a relationship between ownership and proximity to a health clinic (HC), and quantify inequities in ownership. We investigate basic mobile phone (BPs) and smartphones (SPs): SPs can connect to the internet, BPs cannot.Methods: We use nationally representative data collected in 2017--2018 from 45,823 individuals in Round 7 of the Afrobarometer surveys. We use Bayesian Multilevel Logistic regression models for our analyses.Results: 82% of individuals in the 33 countries own mobile phones: 42% BPs, 40% SPs. Individuals who live close to an HC have higher odds of ownership than those who do not (adjusted odds ratio [aOR]: 1.31, Bayesian 95% Highest Posterior Density [HPD] region: 1.24--1.39). Men, compared with women, have over twice the odds of ownership (aOR: 2.37, 95% HPD region: 1.96--2.84). Urban residents, compared with rural residents, have almost three times the odds (aOR: 2.66, 95% HPD region: 2.22--3.18) and, amongst mobile phone owners, nearly three times the odds of owning an SP (aOR: 2.67, 95% HPD region: 2.33--3.10). Ownership increases with age, peaks in 26--40 year olds, then decreases. Individuals under 30 are more likely to own an SP than a BP, older individuals more likely to own a BP than an SP. Probability of ownership decreases with the Lived Poverty Index; however, some of the poorest individuals own SPs.Conclusions: If the digital devices needed for mHealth interventions are not equally available within the population (which we have found is the current situation), rolling out mHealth interventions in Africa is likely to propagate already existing inequities in access to healthcare.
Influencing factors of anti‐SARS‐CoV‐2‐spike‐IgG antibody titers in healthcare workers: A cross‐section study

Against the background of the current COVID‐19 infection dynamics with its rapid spread of SARS‐CoV‐2 variants of concern (VOC), the immunity and the vaccine prevention of healthcare workers (HCWs) against SARS‐CoV‐2 continues to be of high importance. This observational cross‐section study assesses factors influencing the level of anti‐SARS‐CoV‐2‐spike IgG after SARS‐CoV‐2 infection or vaccination. One thousand seven hundred and fifty HCWs were recruited meeting the following inclusion criteria: age ≥18 years, PCR‐confirmed SARS‐CoV‐2 infection convalescence and/or at least one dose of COVID‐19 vaccination. anti‐SARS‐CoV‐2‐spike IgG titers were determined by SERION ELISA agile SARS‐CoV‐2 IgG. Mean anti‐SARS‐CoV‐2‐spike IgG levels increased significantly by number of COVID‐19 vaccinations (92.2 BAU/ml for single, 140.9 BAU/ml for twice and 1144.3 BAU/ml for threefold vaccination). Hybrid COVID‐19 immunized respondents (after infection and vaccination) had significantly higher antibody titers compared with convalescent only HCWs. Anti‐SARS‐CoV‐2‐spike IgG titers declined significantly with time after the second vaccination. Smoking and high age were associated with lower titers. Both recovered and vaccinated HCWs presented a predominantly good humoral immune response. Smoking and higher age limited the humoral SARS‐CoV‐2 immunity, adding to the risk of severe infections within this already health impaired collective.
Predicting Postoperative Mortality With Deep Neural Networks and Natural Language Processing: Model Development and Validation

Background Machine learning (ML) achieves better predictions of postoperative mortality than previous prediction tools. Free-text descriptions of the preoperative diagnosis and the planned procedure are available preoperatively. Because reading these descriptions helps anesthesiologists evaluate the risk of the surgery, we hypothesized that deep learning (DL) models with unstructured text could improve postoperative mortality prediction. However, it is challenging to extract meaningful concept embeddings from this unstructured clinical text. Objective This study aims to develop a fusion DL model containing structured and unstructured features to predict the in-hospital 30-day postoperative mortality before surgery. ML models for predicting postoperative mortality using preoperative data with or without free clinical text were assessed. Methods We retrospectively collected preoperative anesthesia assessments, surgical information, and discharge summaries of patients undergoing general and neuraxial anesthesia from electronic health records (EHRs) from 2016 to 2020. We first compared the deep neural network (DNN) with other models using the same input features to demonstrate effectiveness. Then, we combined the DNN model with bidirectional encoder representations from transformers (BERT) to extract information from clinical texts. The effects of adding text information on the model performance were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Statistical significance was evaluated using P<. 05. Results The final cohort contained 121,313 patients who underwent surgeries. A total of 1562 (1.29%) patients died within 30 days of surgery. Our BERT-DNN model achieved the highest AUROC (0.964, 95% CI 0.961-0.967) and AUPRC (0.336, 95% CI 0.276-0.402). The AUROC of the BERT-DNN was significantly higher compared to logistic regression (AUROC=0.952, 95% CI 0.949-0.955) and the American Society of Anesthesiologist Physical Status (ASAPS AUROC=0.892, 95% CI 0.887-0.896) but not significantly higher compared to the DNN (AUROC=0.959, 95% CI 0.956-0.962) and the random forest (AUROC=0.961, 95% CI 0.958-0.964). The AUPRC of the BERT-DNN was significantly higher compared to the DNN (AUPRC=0.319, 95% CI 0.260-0.384), the random forest (AUPRC=0.296, 95% CI 0.239-0.360), logistic regression (AUPRC=0.276, 95% CI 0.220-0.339), and the ASAPS (AUPRC=0.149, 95% CI 0.107-0.203). Conclusions Our BERT-DNN model has an AUPRC significantly higher compared to previously proposed models using no text and an AUROC significantly higher compared to logistic regression and the ASAPS. This technique helps identify patients with higher risk from the surgical description text in EHRs.
Artificial intelligence for the evaluation of peripheral artery disease using arterial Doppler waveforms to predict abnormal ankle-brachial index

Background: Patients with peripheral artery disease (PAD) are at increased risk for major adverse limb and cardiac events including mortality. Developing screening tools capable of accurate PAD identification is a necessary first step for strategies of adverse outcome prevention. This study aimed to determine whether machine analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with PAD. Methods: Consecutive patients (4/8/2015 – 12/31/2020) undergoing rest and postexercise ankle–brachial index (ABI) testing were included. Patients were randomly allocated to training, validation, and testing subsets (70%/15%/15%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict normal (> 0.9) or PAD (⩽ 0.9) using rest and postexercise ABI. A separate dataset of 151 patients who underwent testing during a period after the model had been created and validated (1/1/2021 – 3/31/2021) was used for secondary validation. Area under the receiver operating characteristic curves (AUC) were constructed to evaluate test performance. Results: Among 11,748 total patients, 3432 patients met study criteria: 1941 with PAD (mean age 69 ± 12 years) and 1491 without PAD (64 ± 14 years). The predictive model with highest performance identified PAD with an AUC 0.94 (CI = 0.92–0.96), sensitivity 0.83, specificity 0.88, accuracy 0.85, and positive predictive value (PPV) 0.90. Results were similar for the validation dataset: AUC 0.94 (CI = 0.91–0.98), sensitivity 0.91, specificity 0.85, accuracy 0.89, and PPV 0.89 (postexercise ABI comparison). Conclusion: An artificial intelligence-enabled analysis of a resting Doppler arterial waveform permits identification of PAD at a clinically relevant performance level.
Detection of Hb Yulin [β13(A10)Ala→Val, HBB: c.41C>T] by Matrix-Assisted Laser Desorption Ionization-Time-of-Flight Mass Spectrometry

Abstract We report a rare hemoglobin (Hb) variant on the β-globin gene, which was detected in a female from Yulin City, Guangxi Autonomous Region, People’s Republic of China (PRC), during routine thalassemia screening. The Hb variant remained unnoticed using capillary electrophoresis (CE) and high performance liquid chromatography (HPLC), while an additional peak was observed by matrix-assisted laser desorption ionization-time-of-flight (MALDI-TOF) mass spectrometry (MS). DNA sequencing revealed the GCC>GTC substitution at codon 13 on the β-globin gene, causing a substitution of alanine to valine. The mutation is only described in the ITHANET database but no Hb variant name and other information, so we named it Hb Yulin after the place of origin of the proband in this study. Hb Yulin is clinically silent and easily leads to misdiagnosis during hemoglobinopathies screening based on the common methods of HPLC and CE.
A phase I trial of cyclosporine for hospitalized patients with COVID-19

BACKGROUND COVID-19 remains a global health emergency with limited treatment options, lagging vaccine rates, and inadequate healthcare resources in the face of an ongoing calamity. The disease is characterized by immune dysregulation and cytokine storm. Cyclosporine A (CSA) is a calcineurin inhibitor that modulates cytokine production and may have direct antiviral properties against coronaviruses. METHODS To test whether a short course of CSA was safe in patients with COVID-19, we treated 10 hospitalized, oxygen-requiring, noncritically ill patients with CSA (starting at a dose of 9 mg/kg/d). We evaluated patients for clinical response and adverse events, measured serum cytokines and chemokines associated with COVID-19 hyperinflammation, and conducted gene-expression analyses. RESULTS Five participants experienced adverse events, none of which were serious; transaminitis was most common. No participant required intensive care unit–level care, and all patients were discharged alive. CSA treatment was associated with significant reductions in serum cytokines and chemokines important in COVID-19 hyperinflammation, including CXCL10. Following CSA administration, we also observed a significant reduction in type I IFN gene expression signatures and other transcriptional profiles associated with exacerbated hyperinflammation in the peripheral blood cells of these patients. CONCLUSION Short courses of CSA appear safe and feasible in patients with COVID-19 who require oxygen and may be a useful adjunct in resource-limited health care settings. TRIAL REGISTRATION This trial was registered on ClinicalTrials.gov (Investigational New Drug Application no. 149997; ClinicalTrials.gov NCT04412785). FUNDING This study was internally funded by the Center for Cellular Immunotherapies.
A Mobile App for Prevention of Cardiovascular Disease and Type 2 Diabetes Mellitus: Development and Usability Study

Background Cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) are posing a huge burden on health care systems worldwide. Mobile apps can deliver behavior change interventions for chronic disease prevention on a large scale, but current evidence for their effectiveness is limited. Objective This paper reported on the development and user testing of a mobile app that aims at increasing risk awareness and engaging users in behavior change. It would form part of an intervention for primary prevention of CVD and T2DM. Methods The theoretical framework of the app design was based on the Behaviour Change Wheel, combined with the capability, opportunity, and motivation for behavior change system and the behavior change techniques from the Behavior Change Technique Taxonomy (version 1). In addition, evidence from scientific literature has guided the development process. The prototype was tested for user-friendliness via an iterative approach. We conducted semistructured interviews with individuals in the target populations, which included the System Usability Scale. We transcribed and analyzed the interviews using descriptive statistics for the System Usability Scale and thematic analysis to identify app features that improved utility and usability. Results The target population was Australians aged ≥45 years. The app included 4 core modules (risk score, goal setting, health measures, and education). In these modules, users learned about their risk for CVD and T2DM; set goals for smoking, alcohol consumption, diet, and physical activity; and tracked them. In total, we included 12 behavior change techniques. We conducted 2 rounds of usability testing, each involving 5 participants. The average age of the participants was 58 (SD 8) years. Totally, 60% (6/10) of the participants owned iPhone Operating System phones, and 40% (4/10) of them owned Android phones. In the first round, we identified a technical issue that prevented 30% (3/10) of the participants from completing the registration process. Among the 70% (7/10) of participants who were able to complete the registration process, 71% (5/7) rated the app above average, based on the System Usability Scale. During the interviews, we identified some issues related to functionality, content, and language and clarity. We used the participants’ feedback to improve these aspects. Conclusions We developed the app using behavior change theory and scientific evidence. The user testing allowed us to identify and remove technical errors and integrate additional functions into the app, which the participants had requested. Next, we will evaluate the feasibility of the revised version of the app developed through this design process and usability testing.