bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantitative flow ratio or angiography for the assessment of non-culprit lesions in acute coronary syndromes, a randomized trial
Authors: Ullrich-Daub Helen , Olschewski Maximilian , Schnorbus Boris , Belhadj Khelifa-Anis , Köhler Till , Vosseler Markus , Münzel Thomas , Gori Tommaso 
Published: July 2024

Abstract
Background
Patients undergoing percutaneous coronary intervention for acute coronary syndromes often have multivessel disease (MVD). Quantitative flow ratio (QFR) is an angiography-based technology that may help quantify the functional significance of non-culprit lesions, with the advantage that measurements are possible also once the patient is discharged from the catheterization laboratory.

Aim
Our two-center, randomized superiority trial aimed to test whether QFR, as compared to angiography, modifies the rate of non-culprit lesion interventions (primary functional endpoint) and improves the outcomes of patients with acute coronary syndromes and MVD (primary clinical endpoint).

Methods
In total, 202 consecutive patients (64 [56–71] years of age, 160 men) with STEMI (n = 69 (34%)), NSTEMI (n = 94 (47%)), or unstable angina (n = 39 (19%)) and MVD who had undergone successful treatment of all culprit lesions were randomized 1:1 to angiography- vs. QFR-guided delayed revascularization of 246 non-culprit stenoses (1.2/patient).

Results
The proportion of patients assigned to percutaneous intervention was not different between groups (angiography group: 45 (45%) vs. QFR: 56 (55%), P = 0.125; relative risk = 0.80 (0.60–1.06)). At 12 months, a primary clinical endpoint event (composite of death, nonfatal myocardial infarction, revascularization, and significant angina) occurred in 24 patients (angiography-guided) and 23 patients (QFR-guided; P = 0.637, HR = 1.16 [0.63–2.15]). None of its components was different between groups.

Discussion
QFR guidance based on analysis of images from the primary intervention was not associated with a difference in the rate of non-culprit lesion staged revascularization nor in the 12-month incidence of clinical events in patients with acute coronary syndromes and multivessel disease.

Trial registration number
ClinicalTrials.gov Registry (NCT04808310).

Graphical abstract
Postoperative hip bracing reduces kinesiophobia in patients undergoing hip arthroscopy: a randomized-controlled trial
Authors: Fischer Maximilian , Nonnenmacher Lars , Sobau Christian , Zimmerer Alexander 
Published: July 2024

Abstract
Introduction
Detailed postoperative rehabilitation protocols after hip arthroscopy for femoroacetabular impingement syndrome (FAIS) are still a matter of debate. Adjunctive hip bracing represents a promising tool to improve early patients’ mobilization. To present, the effect of hip brace therapy on postoperative functional outcomes and specific patient individual psychologic factors remains controversially discussed. Consequently, we aimed to report postoperative outcomes focusing on hip function, pain and kinesiophobia between braced and unbraced patients.

Materials and methods
A prospective, randomized-controlled trial was conducted, including patients undergoing hip arthroscopy for FAIS. After exclusion, a final study cohort of 36 patients in the intervention group (postoperative hip brace) and 36 patients in the control group (no hip brace) were compared for kinesiophobia (Tampa Scale of Kinesiophobia), pain (Visual analog scale) and joint function (International Hip Outcome Tool-12) within the first six postoperative months.

Results
Hip arthroscopy significantly improved all patient-reported outcomes in both groups. Intergroup analysis revealed significantly lower levels of kinesiophobia in braced patients at 6-months follow up (30.7 vs. 34.1, p = 0.04) while not negatively affecting pain and joint function. No intra- and postoperative complications occurred within both groups.

Conclusions
This study could demonstrate that bracing after hip arthroscopy can positively influence kinesiophobia, while the brace did not negatively impact postoperative pain and quality of life. Thus, hip bracing could be a viable assistive therapy in the postoperative rehabilitation phase after hip arthroscopy.
Establishing minimal clinically important differences and patient acceptable symptom state thresholds following birmingham hip resurfacing
Authors: Pasqualini Ignacio , Huffman Nickelas , Emara Ahmed K. , Klika Alison K. , McLaughlin John P. , Mesko Nathan , Brooks Peter J. , Piuzzi Nicolas S. 
Published: July 2024

Abstract
Introduction
Birmingham Hip Resurfacing (BHR) has emerged as a compelling and innovative alternative to total hip arthroplasty (THA), especially among young, active patients. However, the Minimal Clinically Important Difference (MCID) and the Patient Acceptable Symptom State (PASS) thresholds have not yet been determined for patients undergoing BHR. Therefore, the current study aimed to (1) determine the MCID and PASS thresholds for both the Hip disability and Osteoarthritis Outcome Score (HOOS)-Pain and HOOS physical function shortform (PS), for patients who underwent BHR; and (2) identify factors influencing the achievement of MCID and PASS for HOOS-Pain and HOOS-PS.

Methods
Prospectively collected data from patients undergoing BHR was analyzed. Patients with osteoarthritis and completed preoperative and 1-year postoperative PROMs were included. Distribution-based and anchored-based approaches were used to estimate MCID and PASS, respectively. The optimal cut-off point for PASS thresholds was calculated using the Youden index.

Results
MCID for HOOS-Pain and PS were calculated to be 9.2 and 9.3, respectively. The PASS threshold for HOOS-Pain and PS were ≥ 77.7 and ≥ 87.3, respectively. The current study identified several factors affecting postoperative achievement of thresholds. Baseline Mental Component Summary (MCS) scores were a predictor for achieving MCID for postoperative HOOS-Pain, achieving MCID for postoperative HOOS-PS, achieving PASS for postoperative HOOS-Pain, and achieving PASS for postoperative HOOS-PS. Furthermore, baseline HOOS-Pain was a significant predictor for achieving MCID for postoperative HOOS-PS, achieving PASS for postoperative HOOS-Pain, and achieving PASS for postoperative HOOS-PS.

Conclusions
MCID and PASS thresholds were established for HOOS-Pain and PS domains following BHR with most patients achieving these clinically meaningful benchmarks. Additionally, several factors affecting achievement of MCID and PASS were identified, including modifiable risk factors that may allow clinicians to implement optimization strategies and further improve outcomes.
The impact of virtual and augmented reality on presence, user experience and performance of Information Visualisation
Authors: Gronowski Ashlee , Arness David Caelum , Ng Jing , Qu Zhonglin , Lau Chng Wei , Catchpoole Daniel , Nguyen Quang Vinh 
Published: July 2024

AbstractThe fast growth of virtual reality (VR) and augmented reality (AR) head-mounted displays provides a new medium for interactive visualisations and visual analytics. Presence is the experience of consciousness within extended reality, and it has the potential to increase task performance. This project studies the impact that a sense of presence has on data visualisation performance and user experience under AR and VR conditions. A within-subjects design recruited 38 participants to complete interactive visualisation tasks within the novel immersive data analytics system for genomic data in AR and VR, and measured speed, accuracy, preference, presence, and user satisfaction. Open-ended user experience responses were also collected. The results implied that VR was more conducive to efficiency, effectiveness, and user experience as well as offering insight into possible cognitive load benefits for VR users.
Climate change and Arctic charr (Salvelinus alpinus) in North America: modelling possible changes in range with different climate scenarios and interspecific interactions
Authors: Bommersbach Cassandra K. , Grenier Gabrielle , Gendron Haley , Harris Les N. , Janjua M. Yamin , Mandrak Nicholas E. , Tallman Ross F. 
Published: July 2024

AbstractOne of the greatest challenges for researchers today is understanding climate-change impacts on fish populations, particularly in vulnerable and understudied ecosystems such as the Canadian Arctic. Among other impacts, northern fishes will undergo thermal stress as atmospheric and sea surface temperatures are projected to rise globally. Models that consider how both environmental factors such as temperature and potential species interactions will impact population extirpation and species’ range contraction can help project the future distribution of a species in the face of a warming climate. Here, we investigate the climate-change impacts of rising temperatures and the potential northward distributional shift of brook charr (Salvelinus fontinalis) on Arctic charr (Salvelinus alpinus), Canada’s northernmost freshwater fish species. Specifically, we used a logistic regression model to establish baseline relationships between the current distribution of Arctic charr and the variables, degree-days (a key climate variable), geographical location, and brook charr occurrence. We developed the model applying the expected changes in degree-days to 2050 (25–50% increase from the average of 1976–2005) and 2080 (50–100% increase) while incorporating the historical distribution of Arctic charr to estimate the change in Arctic charr distribution over that time. We found that growing degree-days, longitude, latitude, and brook charr occurrences correctly classified 93% of Arctic charr historical occurrences in Canada. We estimate that in a high-carbon scenario, where degree-days are expected to increase by 50 to 100%, Arctic charr range is projected to decrease by 18% in Canada by 2051–2080 and decrease even further by 3% with the presence of brook charr. The Canadian high Arctic may provide refuge for Arctic charr, likely maintaining temperatures optimal for species persistence. Regardless, management that considers the climate stresses on Arctic charr populations will be important to preserve this highly valued resource that is pivotal for food security and traditional ways of life for northerners.
Symplectic Quantization III: Non-relativistic Limit
Authors: Gradenigo Giacomo , Livi Roberto , Salasnich Luca 
Published: July 2024

AbstractFirst of all we shortly illustrate how the symplectic quantization scheme (Gradenigo and Livi, Found Phys 51(3):66, 2021) can be applied to a relativistic field theory with self-interaction. Taking inspiration from the stochastic quantization method by Parisi and Wu, this procedure is based on considering explicitly the role of an intrinsic time variable, associated with quantum fluctuations. The major part of this paper is devoted to showing how the symplectic quantization scheme can be extended to the non-relativistic limit for a Schrödinger-like field. Then we also discuss how one can obtain from this non-relativistic theory a linear Schrödinger equation for the single-particle wavefunction. This further passage is based on a suitable coarse-graining procedure, when self-interaction terms can be neglected, with respect to interactions with any external field. In the Appendix we complete our survey on symplectic quantization by discussing how this scheme applies to a non-relativistic particle under the action of a generic external potential.
Deep Model-Based Reinforcement Learning for Predictive Control of Robotic Systems with Dense and Sparse Rewards
Authors: Antonyshyn Luka , Givigi Sidney 
Published: July 2024

AbstractSparse rewards and sample efficiency are open areas of research in the field of reinforcement learning. These problems are especially important when considering applications of reinforcement learning to robotics and other cyber-physical systems. This is so because in these domains many tasks are goal-based and naturally expressed with binary successes and failures, action spaces are large and continuous, and real interactions with the environment are limited. In this work, we propose Deep Value-and-Predictive-Model Control (DVPMC), a model-based predictive reinforcement learning algorithm for continuous control that uses system identification, value function approximation and sampling-based optimization to select actions. The algorithm is evaluated on a dense reward and a sparse reward task. We show that it can match the performance of a predictive control approach to the dense reward problem, and outperforms model-free and model-based learning algorithms on the sparse reward task on the metrics of sample efficiency and performance. We verify the performance of an agent trained in simulation using DVPMC on a real robot playing the reach-avoid game. Video of the experiment can be found here: https://youtu.be/0Q274kcfn4c.
Death associated protein like 1 acts as a novel tumor suppressor in melanoma by increasing the stability of P21 protein
Authors: Liu Xiaoyan , Hu Xiaojuan , Jing Meiyu , Huang Lijin , You Yaqi , Zhang Yaru , Li Ke , Tu Yunhai , Liu Youjia , Chen Xiaogang , Su Jianzhong , Hejtmancik J. Fielding , Hou Ling , Ma Xiaoyin 
Published: July 2024

Abstract
Melanoma is a primary malignant tumor with high lethality, which occurs in the skin and eye tissues, while the molecular mechanisms of melanomagenesis remain largely unknown. Here, we show that death-associated protein-like 1 (DAPL1) expression is lower in melanoma tissues than in paracancerous tissues or nevus tissues, and Uveal melanoma patients with lower DAPL1 expression have a poorer survival rate than those with higher expression of DAPL1. Overexpression of DAPL1 inhibits proliferation of cultured melanoma cells, whereas knockdown of DAPL1 increases cell proliferation. Tumor transplantation experiment results also demonstrate that DAPL1 inhibits tumorigenesis of melanoma cells both in subretinal and subcutaneous tissues of nude mice in vivo. Finally, DAPL1 inhibits proliferation of melanoma cells by increasing the protein level of P21 via decreasing the ubiquitin mediated degradation of P21 and promoting its stability. Conversely, knockdown of P21 neutralizes the effects of inhibition of DAPL1 on melanoma cell proliferation and enhances the severity of melanoma tumorigenesis. These results suggest that DAPL1 is a novel melanoma tumor suppressor gene and thus a potential therapeutic target for melanoma.
A model for identifying potentially inappropriate medication used in older people with dementia: a machine learning study
Authors: Hu Qiaozhi , Zhao Mengnan , Teng Fei , Lin Gongchao , Jin Zhaohui , Xu Ting 
Published: July 2024

Abstract
Background
Older adults with dementia often face the risk of potentially inappropriate medication (PIM) use. The quality of PIM evaluation is hindered by researchers' unfamiliarity with evaluation criteria for inappropriate drug use. While traditional machine learning algorithms can enhance evaluation quality, they struggle with the multilabel nature of prescription data.

Aim
This study aimed to combine six machine learning algorithms and three multilabel classification models to identify correlations in prescription information and develop an optimal model to identify PIMs in older adults with dementia.

Method
This study was conducted from January 1, 2020, to December 31, 2020. We used cluster sampling to obtain prescription data from patients 65 years and older with dementia. We assessed PIMs using the 2019 Beers criteria, the most authoritative and widely recognized standard for PIM detection. Our modeling process used three problem transformation methods (binary relevance, label powerset, and classifier chain) and six classification algorithms.

Results
We identified 18,338 older dementia patients and 36 PIMs types. The classifier chain + categorical boosting (CatBoost) model demonstrated superior performance, with the highest accuracy (97.93%), precision (95.39%), recall (94.07%), F1 score (95.69%), and subset accuracy values (97.41%), along with the lowest Hamming loss value (0.0011) and an acceptable duration of the operation (371s).

Conclusion
This research introduces a pioneering CC + CatBoost warning model for PIMs in older dementia patients, utilizing machine-learning techniques. This model enables a quick and precise identification of PIMs, simplifying the manual evaluation process.
Leader-in-Extremis: Neil Armstrong in der Filmbiographie „Aufbruch zum Mond“ – ein Fallbeispiel für Führung bei Gefahr für „Leib und Leben“
Authors: West-Leuer Beate 
Published: July 2024

ZusammenfassungLeadership-in-Extremis bedeutet Führung bei Gefahr für „Leib und Leben“. Dies gilt auch für die bemannte Raumfahrt, was in Chazelles Filmbiographie „Aufbruch zum Mond“ (2018) anschaulich dokumentiert wird. Die Wiederaufnahme des Mondlandungsprogramms durch die NASA ist für die Autorin Anlass, die Merkmale von Führung in Extremsituationen zu untersuchen und zu Neil Armstrongs Führungsstil im Apolloprojekt in Beziehung zu setzen. Abschließende Reflexionen gelten der Notwendigkeit interventionstechnischer Anpassungen im Coaching von Führungskräften in Extremsituationen. Aufgrund nationaler und internationaler Eskalationen von gewaltsamen und kriegerischen Konflikten können primär- bis tertiärpräventive Coachingangebote sinnvoll sein.
Modelling the influence of tool wear on shape errors in milling using a hybrid soft-sensor
Authors: Denkena Berend , Wichmann Marcel , Rokicki Markus , Stürenburg Lukas 
Published: July 2024

Abstract
The wear of cutting tools during milling processes not only constrains the volume of material that can be removed by a tool but also results in a progressive deterioration of the quality of the workpiece. Although there are established methodologies for predicting tool wear, there is a paucity of knowledge regarding the impact of tool wear on shape error. The authors present a data-driven soft sensor to model the effect of tool wear on shape error, obviating the need for direct tool wear measurement. To evaluate this approach, a milling experiment was conducted, wherein process forces, spindle current, and resulting shape error were measured. Furthermore, a geometrical cutting simulation was conducted in order to obtain cutting conditions, including the volume of material removed. This study examines the contribution of these features to the prediction performance of the proposed soft sensor. Additionally, the transferability from models trained on different tools is investigated to ascertain the impact of tool wear variance on prediction performance. Prediction experiments demonstrate that a soft-sensor based on a combination of simulation and process monitoring data enables a model trained on data from multiple milling tools to account for wear and predict shape error well under varying wear scenarios. The approach presented here has been demonstrated to result in a reduction of the prediction error of up to 60% compared to an average baseline prediction.
Investigation of the Circular Transcriptome in Alzheimer’s Disease Brain
Authors: Gao Yulan , Xu Si-Mei , Cheng Yuning , Takenaka Konii , Lindner Grace , Janitz Michael 
Published: July 2024

AbstractCircular RNAs (circRNAs) are a subclass of non-coding RNAs which have demonstrated potential as biomarkers for Alzheimer’s disease (AD). In this study, we conducted a comprehensive exploration of the circRNA transcriptome within AD brain tissues. Specifically, we assessed circRNA expression patterns in the dorsolateral prefrontal cortex collected from nine AD-afflicted individuals and eight healthy controls. Utilising two circRNA detection tools, CIRI2 and CIRCexplorer2, we detected thousands of circRNAs and performed a differential expression analysis. CircRNAs which exhibited statistically significantly differential expression were identified as AD-specific differentially expressed circRNAs. Notably, our investigation revealed 120 circRNAs with significant upregulation and 1325 circRNAs displaying significant downregulation in AD brains when compared to healthy brain tissue. Additionally, we explored the expression profiles of the linear RNA counterparts corresponding to differentially expressed circRNAs in AD-afflicted brains and discovered that the linear RNA counterparts exhibited no significant changes in the levels of expression. We used CRAFT tool to predict that circUBE4B had potential to target miRNA named as hsa-miR-325-5p, ultimately regulated CD44 gene. This study provides a comprehensive overview of differentially expressed circRNAs in the context of AD brains, underscoring their potential as molecular biomarkers for AD. These findings significantly enhance our comprehension of AD’s underlying pathophysiological mechanisms, offering promising avenues for future diagnostic and therapeutic developments.
RCM based optimization of maintenance strategies for marine diesel engine using genetic algorithms
Authors: Tripathi Ankush , Hari Prasad M. 
Published: July 2024

AbstractIn the modern world the availability of the machinery for any industry is of utmost importance. It is the right maintenance at right time which keeps these machineries available for their jobs. The primary goal of maintenance is to avoid or mitigate consequences of failure of equipment. There are various types of maintenance schemes available such as breakdown maintenance, preventive maintenance, condition based maintenance etc. Out of all these schemes Reliability Centred Maintenance (RCM) is most recent one and the application of which will enhance the productivity and availability. RCM ensures better system uptime along with understanding of risk involved. RCM has been used in various industries, however, it is very less explored and utilized in marine operations.Hence in the present study maintenance schemes of a marine diesel engine has been considered for optimization using RCM.Failure Modes and Effects Analysis and Fault Tree Analysis (FTA)are some of the basic steps involved in RCM. Due to the scarcity of reliability data particularly in the marine environment some of the components data had to be estimated based on the operating experience. As FTA is based on binary state perspective, assuming the system exist in either functioning or failed state, some of the components (whose performance varies with time and degrades) cannot be modeled using FTA. Hence, in this paper reliability modeling of performance degraded components is dealt with Markov models and the required data is evaluated from condition monitoring techniques. After obtaining the availability of the marine diesel engine, based on the importance ranking, critical components have been obtained for optimizing the maintenance schedules. In this paper genetic algorithm approach has been used for optimization. The results obtained have been compared and new maintenance scheme has been proposed.