bluuebunny/crossref_metadata_2025_split huggingface dataset:
Off- to in-season body composition adaptations in elite male and female endurance and power event athletics competitors: an observational study
Authors: Pettersson Stefan , Kalén Anton , Gustafsson Mikael , Grau Stefan , Caspers Andreas 
Published: April 2024

Abstract
Background
Monitoring elite athletes’ body composition (BC) is vital for health and optimizing performance in sports emphasizing leanness, such as athletics. This study aims to investigate and compare sex- and event-specific off-to in-season BC changes in endurance and power event athletics competitors.

Methods
Elite male and female endurance athletes (> 800 m runners; n = 21) and power event athletes (sprinters, jumpers; n = 32) underwent dual-energy X-ray absorptiometry (DXA) scans for whole and regional lean mass (LM), fat mass (FM), bone mineral content (BMC), and density (BMD) during off-season (September-October) and in-season (April-May). Linear mixed models tested between-group off-season differences in BC, within-group off-season to in-season changes, and between-group differences in change. To assess meaningful or least significant changes (LSC) in BC, DXA precision errors were determined from two consecutive total body scans in a subsample of athletes (n = 30).

Results
Male athletes (n = 26) gained significantly (p < 0.05) more body mass (BM; mean difference 1.5 [95% confidence interval (CI):0.5–2.4] kg), LM (843 [95% CI:-253:1459] g), and trunk LM (756 [-502:1156] g) than female athletes (n = 27). The proportion of changes in athlete’s BC exceeding the LSC threshold for LM and trunk LM were 70% and 65% in males, and 48% and 26% in females. Significant (p < 0.05) within-group off-season to in-season increases in LM were found for male endurance and power athletes, and female power athletes. All groups significantly increased BMD (p < 0.05). Only male and female power athletes had significant in- to-off-season increases in BMC. 80% of all athletes who had a meaningful increase in BMC belonged to the power event group. No significant within- or between group change in FM was observed.

Conclusions
The present study found that male athletes gained more BM, LM and trunk LM than females. Within-group increases in regional and whole-body LM and BMC were predominantly found among power event competitors. Incorporating individual meaningful changes alongside traditional statistics provided additional insights into sex and event-group differences. Future research on elite athletic event groups should include DXA measurements closer to major outdoor-season competitions, coupled with site-specific measures (ultrasound, MRI) for better detection of subtle changes in LM and FM.
A new multi-criteria decision making method for the selection of construction contractors using interval valued fuzzy set
Authors: Nithya N. S. , Thota Srinivasarao , Rathour Laxmi , Shanmugasundaram P. 
Published: April 2024

Abstract
Objective
This article introduces a novel approach called Digital Weighted Multi Criteria Decision Making (DWMCDM) that employs interval valued fuzzy sets to select the best contractor for building projects. The contractor is chosen based on the pre-qualification and bid evaluation phases. In the first phase, the distance between the actual and required skills of the significant criteria is determined, and it is then converted into digital weighted distances to identify the maximum number of criteria related to the specific project of each contractor. The second step ranks the best contractor based on the bid price and digital weighted distances.

Results
The suggested technique integrates the pre-qualification and bid review phases to address project award delays and other restrictions. Finally, a real-world application is addressed to demonstrate the applicability of the proposed approach to any type of interval valued fuzzy inputs.
Laparoscopic extraction of a symptomatic upper abdominal pedunculated parietal peritoneal lipoma arising intermittent abdominal pain: a case report
Authors: Dohmoto Yu , Akashi Yoshimasa , Ogawa Koichi , Enomoto Tsuyoshi , Ohara Yusuke , Owada Yohei , Oda Tatsuya 
Published: April 2024

Abstract
Introduction
Lipomas arising in the parietal peritoneum are rare, and some of them cause abdominal pain due to torsion of the pedunculated peritoneum. We encountered a case of parietal peritoneal lipoma arising upper peritoneum. In this report, we describe the detail of clinical presentation and discuss its potential pathogenesis and treatment strategy.

Case presentation
45 year-old Japanese female patient presented with long-lasting intermittent pain in the left upper abdominal region. Abdominal imaging showed a well-defined fatty mass measuring 40 mm in size, suggesting a parietal peritoneal lipoma. Laparoscopy revealed a tumor with a twisted peduncle; however, no adhesion of the surrounding tissues and ischemic changes were visible. The tumor was easily removed by dissection of the tumor pedicle.

Conclusion
Parietal peritoneal lipoma often shows pedunculated form and it causes abdominal pain by the torsion of tumor pedicle. Therefore, this type of lipoma should be considered a more aggressive surgery.
Learning symmetry-aware atom mapping in chemical reactions through deep graph matching
Authors: Astero Maryam , Rousu Juho 
Published: April 2024

AbstractAccurate atom mapping, which establishes correspondences between atoms in reactants and products, is a crucial step in analyzing chemical reactions. In this paper, we present a novel end-to-end approach that formulates the atom mapping problem as a deep graph matching task. Our proposed model, AMNet (Atom Matching Network), utilizes molecular graph representations and employs various atom and bond features using graph neural networks to capture the intricate structural characteristics of molecules, ensuring precise atom correspondence predictions. Notably, AMNet incorporates the consideration of molecule symmetry, enhancing accuracy while simultaneously reducing computational complexity. The integration of the Weisfeiler-Lehman isomorphism test for symmetry identification refines the model’s predictions. Furthermore, our model maps the entire atom set in a chemical reaction, offering a comprehensive approach beyond focusing solely on the main molecules in reactions. We evaluated AMNet’s performance on a subset of USPTO reaction datasets, addressing various tasks, including assessing the impact of molecular symmetry identification, understanding the influence of feature selection on AMNet performance, and comparing its performance with the state-of-the-art method. The result reveals an average accuracy of 97.3% on mapped atoms, with 99.7% of reactions correctly mapped when the correct mapped atom is within the top 10 predicted atoms.Scientific contributionThe paper introduces a novel end-to-end deep graph matching model for atom mapping, utilizing molecular graph representations to capture structural characteristics effectively. It enhances accuracy by integrating symmetry detection through the Weisfeiler-Lehman test, reducing the number of possible mappings and improving efficiency. Unlike previous methods, it maps the entire reaction, not just main components, providing a comprehensive view. Additionally, by integrating efficient graph matching techniques, it reduces computational complexity, making atom mapping more feasible.
Anti-interrupted-sampling repeater jamming method based on frequency agility waveform and sparse recovery
Authors: Ji Yunhao , Wei Shan , Lu Yaobing 
Published: April 2024

AbstractInterrupted-sampling repeater jamming (ISRJ) is a type of intra-pulse coherent jamming that poses a significant threat to radar detection and tracking of targets. This paper proposes an ISRJ suppression method based on frequency agile waveform and sparse recovery, starting from the temporal discontinuity and modulation characteristics of ISRJ. This method is particularly suitable for scenarios with high jamming duty ratio (JDR) and high jammer sampling duty ratio (SDR). By dividing the transmitted waveform into sub-pulses with different carrier frequencies and applying a two-round block sparse algorithm, the method accurately recovers three parameters of ISRJ, achieving effective jamming identification, reconstruction, and cancellation. Additionally, a target detection technique based on robust sparse recovery is proposed, significantly improving the stability and accuracy of target detection. Comparative experimental results conducted in three scenarios confirm the effectiveness and superiority of this method under high JDR and SDR conditions.
Comparison between high-flow nasal oxygen (HFNO) alternated with non-invasive ventilation (NIV) and HFNO and NIV alone in patients with COVID-19: a retrospective cohort study
Authors: da Cruz Amanda Pereira , Martins Gloria , Martins Camila Marinelli , Marques Victoria , Christovam Samantha , Battaglini Denise , Robba Chiara , Pelosi Paolo , Rocco Patricia Rieken Macedo , Cruz Fernanda Ferreira , dos Santos Samary Cynthia , Silva Pedro Leme 
Published: April 2024

Abstract
Background
Non-invasive respiratory support (conventional oxygen therapy [COT], non-invasive ventilation [NIV], high-flow nasal oxygen [HFNO], and NIV alternated with HFNO [NIV + HFNO] may reduce the need for invasive mechanical ventilation (IMV) in patients with COVID-19. The outcome of patients treated non-invasively depends on clinical severity at admission. We assessed the need for IMV according to NIV, HFNO, and NIV + HFNO in patients with COVID-19 according to disease severity and evaluated in-hospital survival rates and hospital and intensive care unit (ICU) lengths of stay.

Methods
This cohort study was conducted using data collected between March 2020 and July 2021. Patients ≥ 18 years admitted to the ICU with a diagnosis of COVID-19 were included. Patients hospitalized for < 3 days, receiving therapy (COT, NIV, HFNO, or NIV + HFNO) for < 48 h, pregnant, and with no primary outcome data were excluded. The COT group was used as reference for multivariate Cox regression model adjustment.

Results
Of 1371 patients screened, 958 were eligible: 692 (72.2%) on COT, 92 (9.6%) on NIV, 31 (3.2%) on HFNO, and 143 (14.9%) on NIV + HFNO. The results for the patients in each group were as follows: median age (interquartile range): NIV (64 [49–79] years), HFNO (62 [55–70] years), NIV + HFNO (62 [48–72] years) (p = 0.615); heart failure: NIV (54.5%), HFNO (36.3%), NIV + HFNO (9%) (p = 0.003); diabetes mellitus: HFNO (17.6%), NIV + HFNO (44.7%) (p = 0.048). > 50% lung damage on chest computed tomography (CT): NIV (13.3%), HFNO (15%), NIV + HFNO (71.6%) (p = 0.038); SpO2/FiO2: NIV (271 [118–365] mmHg), HFNO (317 [254–420] mmHg), NIV + HFNO (229 [102–317] mmHg) (p = 0.001); rate of IMV: NIV (26.1%, p = 0.002), HFNO (22.6%, p = 0.023), NIV + HFNO (46.8%); survival rate: HFNO (83.9%), NIV + HFNO (63.6%) (p = 0.027); ICU length of stay: NIV (8.5 [5–14] days), NIV + HFNO (15 [10–25] days (p < 0.001); hospital length of stay: NIV (13 [10–21] days), NIV + HFNO (20 [15–30] days) (p < 0.001). After adjusting for comorbidities, chest CT score and SpO2/FiO2, the risk of IMV in patients on NIV + HFNO remained high (hazard ratio, 1.88; 95% confidence interval, 1.17–3.04).

Conclusions
In patients with COVID-19, NIV alternating with HFNO was associated with a higher rate of IMV independent of the presence of comorbidities, chest CT score and SpO2/FiO2.
Trial registration ClinicalTrials.gov identifier: NCT05579080.
Immediate inflammatory response to mechanical circulatory support in a porcine model of severe cardiogenic shock
Authors: Gregers Emilie , Frederiksen Peter H. , Udesen Nanna L. J. , Linde Louise , Banke Ann , Povlsen Amalie L. , Larsen Jeppe P. , Hassager Christian , Jensen Lisette O. , Lassen Jens F. , Schmidt Henrik , Ravn Hanne B. , Heegaard Peter M. H. , Møller Jacob E. 
Published: April 2024

Abstract
Background
In selected cases of cardiogenic shock, veno-arterial extracorporeal membrane oxygenation (V-A ECMO) is combined with trans valvular micro axial flow pumps (ECMELLA). Observational studies indicate that ECMELLA may reduce mortality but exposing the patient to two advanced mechanical support devices may affect the early inflammatory response. We aimed to explore inflammatory biomarkers in a porcine cardiogenic shock model managed with V-A ECMO or ECMELLA.

Methods
Fourteen landrace pigs had acute myocardial infarction-induced cardiogenic shock with minimal arterial pulsatility by microsphere embolization and were afterwards managed 1:1 with either V-A ECMO or ECMELLA for 4 h. Serial blood samples were drawn hourly and analyzed for serum concentrations of interleukin 6 (IL-6), IL-8, tumor necrosis factor alpha, and serum amyloid A (SAA).

Results
An increase in IL-6, IL-8, and SAA levels was observed during the experiment for both groups. At 2–4 h of support, IL-6 levels were higher in ECMELLA compared to V-A ECMO animals (difference: 1416 pg/ml, 1278 pg/ml, and 1030 pg/ml). SAA levels were higher in ECMELLA animals after 3 and 4 h of support (difference: 401 ng/ml and 524 ng/ml) and a significant treatment-by-time effect of ECMELLA on SAA was identified (p = 0.04). No statistical significant between-group differences were observed in carotid artery blood flow, urine output, and lactate levels.

Conclusions
Left ventricular unloading with Impella during V-A ECMO resulted in a more extensive inflammatory reaction despite similar end-organ perfusion.
Dual modality prompt learning for visual question-grounded answering in robotic surgery
Authors: Zhang Yue , Fan Wanshu , Peng Peixi , Yang Xin , Zhou Dongsheng , Wei Xiaopeng 
Published: April 2024

AbstractWith recent advancements in robotic surgery, notable strides have been made in visual question answering (VQA). Existing VQA systems typically generate textual answers to questions but fail to indicate the location of the relevant content within the image. This limitation restricts the interpretative capacity of the VQA models and their ability to explore specific image regions. To address this issue, this study proposes a grounded VQA model for robotic surgery, capable of localizing a specific region during answer prediction. Drawing inspiration from prompt learning in language models, a dual-modality prompt model was developed to enhance precise multimodal information interactions. Specifically, two complementary prompters were introduced to effectively integrate visual and textual prompts into the encoding process of the model. A visual complementary prompter merges visual prompt knowledge with visual information features to guide accurate localization. The textual complementary prompter aligns visual information with textual prompt knowledge and textual information, guiding textual information towards a more accurate inference of the answer. Additionally, a multiple iterative fusion strategy was adopted for comprehensive answer reasoning, to ensure high-quality generation of textual and grounded answers. The experimental results validate the effectiveness of the model, demonstrating its superiority over existing methods on the EndoVis-18 and EndoVis-17 datasets.
Correlation of lesion severity with bacterial changes in Treponeme-Associated Hoof Disease from free-roaming wild elk (Cervus canadensis)
Authors: Wilson-Welder Jennifer H. , Han Sushan , Bayles Darrell O. , Alt David P. , Kanipe Carly , Garrison Kyle , Mansfield Kristin G. , Olsen Steven C. 
Published: April 2024

Abstract
Background
Treponeme-Associated Hoof Disease (TAHD) is a polybacterial, multifactorial disease affecting free-ranging wild elk (Cervus canadensis) in the Pacific Northwest. Previous studies have indicated a bacterial etiology similar to digital dermatitis in livestock, including isolation of Treponema species from lesions. The lesions appear to progress rapidly from ulcerative areas in the interdigital space or along the coronary band to severe, ulcerative, necrotic, proliferative lesions under-running the hoof wall, perforating the sole, and contributing to hoof elongation, deformity, and overgrowth. Eventually the lesions undermine the laminal structure leading to sloughing of the hoof horn capsule. The objective of this study was to characterize the bacterial communities associated with hoof lesions, which were categorized into 5 stages or disease grade severities, with 0 being unaffected tissue and 4 being sloughed hoof capsule. We also wanted to determine if the etiology of TAHD through morphological changes was dominated by Treponema, as observed in hoof diseases in livestock.

Results
The bacterial 16S rRNA gene was sequenced from 66 hoof skin biopsy samples representing 5 lesion grades from samples collected by Washington Department of Fish and Wildlife as part of a voluntary hunter program. Analysis of the relative abundance of bacterial sequences showed that lesions were dominated by members of the bacterial phyla Proteobacteria, Firmicutes, Spirochaetes, Bacteroidetes and Actinobacteria. In lesion samples, members of the genus Treponema, Porphyromonas, and Mycoplasma increased with lesion severity. Association analysis indicated frequent identification of Treponema with Porphyromonas, Bacteroides and other anaerobic Gram-positive cocci.

Conclusions
The bacterial 16S rRNA gene sequencing confirmed the presence of Treponema species at all stages of TAHD lesions, treponeme specie-specific PCR and histopathology, indicating that the morphological changes are a continual progression of disease severity with similar bacterial communities. Association and abundance of these other pathogenic genera within lesions may mean synergistic role with Treponema in hoof disease pathogenesis. Characterizing bacteria involved in lesion development, and their persistence during disease progression, provides evidence for science-based management decisions in TAHD infected elk populations.
Optimizing evidence-based practice implementation: a case study on simulated patient protocols in long-term opioid therapy
Authors: Green Ellen , Hamm Megan , Gowl Catherine , Van Deusen Reed , Liebschutz Jane M. , Wilson J. Deanna , Merlin Jessica 
Published: April 2024

Abstract
Background
Substantial work has been done to update or create evidence-based practices (EBPs) in the changing health care landscape. However, the success of these EBPs is limited by low levels of clinician implementation.

Objective
The goal of this study is to describe the use of standardized/simulated patient/person (SP) methodology as a framework to develop implementation bundles to increase the effectiveness, sustainability, and reproducibility of EBPs across health care clinicians.

Design
We observed 12 clinicians’ first-time experiences with six unique decision-making algorithms, developed previously using rigorous Delphi methods, for use with patients exhibiting concerning behaviors associated with long-term opioid therapy (LTOT) for chronic pain. Clinicians were paired with two SPs trained to portray individuals with one of the concerning behaviors addressed by the algorithms in a telehealth environment. The SP evaluations were followed by individual interviews, guided by the Consolidated Framework for Implementation Research (CFIR), with each of the clinician participants.

Participants
Twelve primary care clinicians and 24 SPs in Western Pennsylvania.

Main measurement
The primary outcome was identifying likely facilitators for the successful implementation of the EBP using the SP methodology. Our secondary outcome was to assess the feasibility of using SPs to illuminate likely implementation barriers and facilitators.

Results
The SP portrayal illuminated factors that were pertinent to address in the implementation bundle. SPs were realistic in their portrayal of patients with concerning behaviors associated with LTOT for chronic pain, but clinicians also noted that their patients in practice may have been more aggressive about their treatment plan.

Conclusions
SP simulation provides unique opportunities for obtaining crucial feedback to identify best practices in the adoption of new EBPs for high-risk patients.

Setting
Zoom simulated patient evaluations.
A design of power prediction algorithm based on health assessment optimized neural network
Authors: Xie Xin , Huang Feng , He Chengjin , Zhou Huaan , Hu Feiyu , Zeng Bin , Huang Lingxiang 
Published: April 2024

AbstractWind power prediction holds significant value for the stability of the electrical grid when wind power is connected to the grid. Using neural networks for wind power prediction may have some limitations, such as slow speed and low accuracy. This paper proposes to enhance the power prediction accuracy and speed by optimizing the neural network through health assessment wind turbines. Firstly, based on wind turbine actual operating data, a health assessment is conducted to obtain a health matrix of wind turbine. Then, by calculating the weights of the matrix, the power prediction strategy of the network is optimized. Following that, matrix approximation hyperparameters are utilized to expedite the optimization process. Finally, some tests are conducted on neural network power prediction, act as optimized back propagation (BP) neural network and whale swarm algorithm–support vector regression (WSA-SVR) neural networks are employed for wind power prediction. Results show noticeable optimization: after optimizing the BP network, power prediction accuracy increased by about 40%, and prediction speed rose by about 20%; after optimizing the WSA-SVR network, power prediction accuracy improved by 10%, and prediction speed surged by about 45%. Further analysis shows that this method can improve the accuracy and speed of most neural network wind power prediction algorithms.
A Better Estimate of Internally Generated Intangible Capital
Authors: Iqbal Aneel , Rajgopal Shiva , Srivastava Anup , Zhao Rong 
Published: April 2024

Internally developed intangibles are not included in reported assets under U.S. generally accepted accounting principles. The omission of this increasingly important class of assets reduces the usefulness and relevance of financial statement analysis, conducted with reported values of equity and assets. Recent studies overcome this deficiency by capitalizing research and development (R&D) expenses to estimate the value of knowledge capital and by capitalizing selling, general, and administrative (SG&A) expenses to estimate the value of organization capital. Those two estimates are then added to reported values for financial statement analysis. For convenience, many studies rely on two rules of thumb and assume them to be equally applicable in all instances: (1) 30% of SG&A and 100% of R&D expenses are investments, and (2) the useful life of SG&A and R&D investments is three and five years, respectively. We propose a more flexible approach by estimating the capitalization and amortization parameters on an industry–year–specific basis. Our modified values of total assets and equity, inclusive of the value of capitalized intangibles, exhibit greater association with future returns and investments compared with as-reported values and values estimated with uniform rules of thumb. We provide a better estimate of intangible capital for the consumers of financial statements. This paper was accepted by Ranjani Krishnan, accounting. Funding: A. Srivastava and R. Zhao acknowledge financial support from the Social Sciences and Humanities Research Council of Canada. A. Srivastava acknowledges financial support from the Canada Research Chairs Program of the Government of Canada. A. Iqbal acknowledges financial support from the Canadian Securities Institute Research Foundation and Chartered Professional Accountants (Alberta) Education Foundation. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.01703 .