bluuebunny/crossref_metadata_2025_split huggingface dataset:
Elucidating the impacts of trees on landslide initiation throughout a typhoon: Preferential infiltration, wind load and root reinforcement
Authors: Zhuang Yu , Xing Aiguo , Petley Dave , Jiang Yuehua , Sun Qiang , Bilal Muhammad , Yan Jinkai 
Published: August 2023

AbstractTyphoon‐induced landslides are widespread, damaging and deadly. In this study, we elucidated their spatiotemporal features through investigations in Southeast coastal China and proposed a numerical model that involves typhoon wind load, preferential infiltration and root reinforcement. The wind load is calculated through a combination of the autoregressive method and a multi‐degree‐of‐freedom tree swaying mode, transmitting to the slope through the wind–tree interaction. Taproots of trees are modelled as piles, and root–soil interfaces are modelled as preferential infiltration boundaries. Using the numerical model, we quantitatively assessed the impacts of wind load, rainfall infiltration and root reinforcement on the slope stability and compared results with cases in Waipaoa and Wairoa (New Zealand). Results suggest that the impacts of trees depend heavily on meteorological and geological conditions. Trees play a destabilizing role in Southeast China during a typhoon but a stabilizing role in the cases in New Zealand. For slopes in Southeast China with a thick soil layer (>root depth), strong wind load and preferential flow resulting from root–soil interfaces, rather than slope surface infiltration, significantly decrease the slope stability in a short time. Slope failure occurred in all scenarios that account for the preferential infiltration, and its combination with a Force 14 typhoon wind load can fail the slope at 10 h after the typhoon initiation. Differently, tree roots in cases in New Zealand can penetrate through the thin soil layer (<1 m) and provide considerable additional resistance to stabilize the slope.
Biomechanical Stimulation of Muscle Constructs Influences Phenotype of Bone Constructs by Modulating Myokine Secretion
Authors: Suresh Kumar Harshini , Barnett Edwina N. , Fowlkes John L. , Kalaitzoglou Evangelia , Annamalai Ramkumar T. 
Published: August 2023

ABSTRACTDiabetes is a chronic metabolic disorder that can lead to diabetic myopathy and bone diseases. The etiology of musculoskeletal complications in such metabolic disorders and the interplay between the muscular and osseous systems are not well understood. Exercise training promises to prevent diabetic myopathy and bone disease and offer protection. Although the muscle‐bone interaction is largely biomechanical, the muscle secretome has significant implications for bone biology. Uncoupling effects of biophysical and biochemical stimuli on the adaptive response of bone during exercise training may offer therapeutic targets for diabetic bone disease. Here, we have developed an in vitro model to elucidate the effects of mechanical strain on myokine secretion and its impact on bone metabolism decoupled from physical stimuli. We developed bone constructs using cross‐linked gelatin, which facilitated osteogenic differentiation of osteoprogenitor cells. Then muscle constructs were made from fibrin, which enabled myoblast differentiation and myotube formation. We investigated the myokine expression by muscle constructs under strain regimens replicating endurance (END) and high‐intensity interval training (HIIT) in hyperglycemic conditions. In monocultures, both regimens induced higher expression of Il15 and Igf1, whereas END supported more myoblast differentiation and myotube maturation than HIIT. When co‐cultured with bone constructs, HIIT regimen increased Glut4 expression in muscle constructs more than END, supporting higher glucose uptake. Likewise, the muscle constructs under the HIIT regimen promoted a healthier and more matured bone phenotype than END. Under static conditions, myostatin (Mstn) expression was significantly downregulated in muscle constructs co‐cultured with bone constructs compared with monocultures. Together, our in vitro co‐culture system allowed orthogonal manipulation of mechanical strain on muscle constructs while facilitating bone‐muscle biochemical cross‐talk. Such systems can provide an individualized microenvironment that allows decoupled biomechanical manipulation, help identify molecular targets, and develop engineered therapies for metabolic bone disease. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC. on behalf of American Society for Bone and Mineral Research.
Identifying vulnerability to human trafficking in Bangladesh: An ecosystem approach using weak‐signal analysis
Authors: van der Vink Gregory E. , Carlson Katherine N. , Phillips Erica , Szeto Sabrina H. , Park Jeffrey , Jackson Michael E. 
Published: August 2023

AbstractWe present an ecosystem approach to analyze open‐source data to identify populations vulnerable to human trafficking and to reveal underlying causal relationships. In the case of Bangladesh, our analysis suggests combinations of indicators that are highly predictive of human trafficking. The traditional narrative that poverty and unemployment are the main drivers for human trafficking may be an oversimplification. We find many areas where vulnerability is highest within lower‐middle to middle‐class societies with (a) moderate levels of income and education, (b) adherence to traditional gender norms of a male‐dominated patriarchal society, and (c) access to an urban center.
Deep learning‐based detection and classification of lumbar disc herniation on magnetic resonance images
Authors: Zhang Weicong , Chen Ziyang , Su Zhihai , Wang Zhengyan , Hai Jinjin , Huang Chengjie , Wang Yuhan , Yan Bin , Lu Hai 
Published: August 2023

AbstractBackgroundThe severity assessment of lumbar disc herniation (LDH) on MR images is crucial for selecting suitable surgical candidates. However, the interpretation of MR images is time‐consuming and requires repetitive work. This study aims to develop and evaluate a deep learning‐based diagnostic model for automated LDH detection and classification on lumbar axial T2‐weighted MR images.MethodsA total of 1115 patients were analyzed in this retrospective study; both a development dataset (1015 patients, 15 249 images) and an external test dataset (100 patients, 1273 images) were utilized. According to the Michigan State University (MSU) classification criterion, experts labeled all images with consensus, and the final labeled results were regarded as the reference standard. The automated diagnostic model comprised Faster R‐CNN and ResNeXt101 as the detection and classification network, respectively. The deep learning‐based diagnostic performance was evaluated by calculating mean intersection over union (IoU), accuracy, precision, sensitivity, specificity, F1 score, the area under the receiver operating characteristics curve (AUC), and intraclass correlation coefficient (ICC) with 95% confidence intervals (CIs).ResultsHigh detection consistency was obtained in the internal test dataset (mean IoU = 0.82, precision = 98.4%, sensitivity = 99.4%) and external test dataset (mean IoU = 0.70, precision = 96.3%, sensitivity = 97.8%). Overall accuracy for LDH classification was 87.70% (95% CI: 86.59%–88.86%) and 74.23% (95% CI: 71.83%–76.75%) in the internal and external test datasets, respectively. For internal testing, the proposed model achieved a high agreement in classification (ICC = 0.87, 95% CI: 0.86–0.88, P < 0.001), which was higher than that of external testing (ICC = 0.79, 95% CI: 0.76–0.81, P < 0.001). The AUC for model classification was 0.965 (95% CI: 0.962–0.968) and 0.916 (95% CI: 0.908–0.925) in the internal and external test datasets, respectively.ConclusionsThe automated diagnostic model achieved high performance in detecting and classifying LDH and exhibited considerable consistency with experts' classification.
Computational pathology in cancer diagnosis, prognosis, and prediction – present day and prospects
Authors: Verghese Gregory , Lennerz Jochen K , Ruta Danny , Ng Wen , Thavaraj Selvam , Siziopikou Kalliopi P , Naidoo Threnesan , Rane Swapnil , Salgado Roberto , Pinder Sarah E , Grigoriadis Anita 
Published: August 2023

AbstractComputational pathology refers to applying deep learning techniques and algorithms to analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led to an explosion in innovation in computational pathology, ranging from the prospect of automation of routine diagnostic tasks to the discovery of new prognostic and predictive biomarkers from tissue morphology. Despite the promising potential of computational pathology, its integration in clinical settings has been limited by a range of obstacles including operational, technical, regulatory, ethical, financial, and cultural challenges. Here, we focus on the pathologists’ perspective of computational pathology: we map its current translational research landscape, evaluate its clinical utility, and address the more common challenges slowing clinical adoption and implementation. We conclude by describing contemporary approaches to drive forward these techniques. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Post‐operative flank irradiation using conformal versus highly conformal radiotherapy techniques for paediatric renal tumours: Results from the French registry PediaRT
Authors: Le Quellenec Gaelle , Bernier‐Chastagner Valerie , Sellami Noura , Helfre Sylvie , Satragno Camilla , Leseur Julie , Escande Alexandre , Jolnerovski Maria , Noel Georges , Missohou Fernand , Claude Line , Cantaloube Marie , Laprie Anne , Huchet Aymeri , Scouarnec Cyrielle , Guimard Gregory , Muracciole Xavier , Paul Julie , Supiot Stéphane , Jouglar Emmanuel ,   the Groupe Français de Radiothérapie Pédiatrique
Published: August 2023

AbstractPurposeThree‐dimensional conformal RT (3D‐RT) techniques are gold standard for post‐operative flank radiotherapy (RT) in paediatric renal tumours. Recently, highly conformal RT (HC‐RT) techniques have been implemented without comparative clinical data. The main objective of this multicentre study was to compare locoregional control (LRC) in children treated either with HC‐RT or 3D‐RT techniques.MethodsPatients treated with post‐operative flank RT for renal tumour registered in the national cohort PediaRT between March 2013 and September 2019 were included. Treatment and follow‐up data, including toxicities and outcomes, were retrieved from the database. LRC was calculated, and dose reconstruction was performed in case of an event.ResultsSeventy‐nine patients were included. Forty patients were treated with HC‐RT and 39 with 3D‐RT. Median follow‐up was 4.5 years. Three patients had locoregional failure (LRF; 4%). HC‐RT was not associated with a higher risk of LRF. Three‐year LRC were 97.4% and 94.7% in the HC‐RT and 3D‐RT groups, respectively. The proportion of planning target volumes receiving 95% or more of the prescribed dose did not significantly differ between both groups (HC‐RT 88%; 3D‐RT 69%; p = .05). HC‐RT was better achieving dose constraints, and a significant mean dose reduction was observed in the peritoneal cavity and pancreas associated with lower incidence of acute gastrointestinal toxicity.ConclusionLRF after post‐operative flank RT for renal tumours was rare and did not increase using HC‐RT versus 3D‐RT techniques. Dose to the pancreas and the peritoneal cavity, as well as acute toxicity, were reduced with HC‐RT compared to 3D‐RT.
Tyrosine kinase inhibitor therapy in pediatric sarcoma: Prognostic implications of pulmonary metastatic cavitation
Authors: Morakote Wipawee , Adams Lisa C. , Ramasamy Shakthi K. , Spunt Sheri L. , Baratto Lucia , Liang Tie , Daldrup‐Link Heike E. 
Published: August 2023

AbstractPurposesThis study aims to ascertain the prevalence of cavitations in pulmonary metastases among pediatric and young adult patients with sarcoma undergoing tyrosine kinase inhibitor (TKI) therapy, and assess whether cavitation can predict clinical response and survival outcomes.MethodsIn a single‐center retrospective analysis, we examined chest computed tomography (CT) scans of 17 patients (median age 16 years; age range: 4–25 years) with histopathologically confirmed bone (n = 10) or soft tissue (n = 7) sarcoma who underwent TKI treatment for lung metastases. The interval between TKI initiation and the onset of lung nodule cavitation and tumor regrowth were assessed. The combination of all imaging studies and clinical data served as the reference standard for clinical responses. Progression‐free survival (PFS) was compared between patients with cavitating and solid nodules using Kaplan–Meier survival analysis and log‐rank test.ResultsFive out of 17 patients (29%) exhibited cavitation of pulmonary nodules during TKI therapy. The median time from TKI initiation to the first observed cavitation was 79 days (range: 46–261 days). At the time of cavitation, all patients demonstrated stable disease. When the cavities began to fill with solid tumor, 60% (3/5) of patients exhibited progression in other pulmonary nodules. The median PFS for patients with cavitated pulmonary nodules after TKI treatment (6.7 months) was significantly longer compared to patients without cavitated nodules (3.8 months; log‐rank p‐value = .03).ConclusionsCavitation of metastatic pulmonary nodules in sarcoma patients undergoing TKI treatment is indicative of non‐progressive disease, and significantly correlates with PFS.
Identification of risk factors for adverse drug reactions in a pharmacovigilance database
Authors: Attelind Sofia , Eriksson Niclas , Sundström Anders , Wadelius Mia , Hallberg Pär 
Published: August 2023

AbstractIntroductionIn addition to identifying new safety signals, pharmacovigilance databases could be used to identify potential risk factors for adverse drug reactions (ADRs).ObjectiveTo evaluate whether data mining in a pharmacovigilance database can be used to identify known and possible novel risk factors for ADRs, for use in pharmacovigilance practice.MethodExploratory data mining was performed within the Swedish national database of spontaneously reported ADRs. Bleeding associated with direct oral anticoagulants (DOACs)‐rivaroxaban, apixaban, edoxaban, and dabigatran‐was used as a test model. We compared demographics, drug treatment, and clinical features between cases with bleeding (N = 965) and controls who had experienced other serious ADRs to DOACs (N = 511). Statistical analysis was performed by unadjusted and age adjusted logistic regression models, and the random forest based machine‐learning method Boruta.ResultsIn the logistic regression, 13 factors were significantly more common among cases of bleeding compared with controls. Eleven were labelled or previously proposed risk factors. Cardiac arrhythmia (e.g., atrial fibrillation), hypertension, mental impairment disorders (e.g., dementia), renal and urinary tract procedures, gastrointestinal ulceration and perforation, and interacting drugs remained significant after adjustment for age. In the Boruta analysis, high age, arrhythmia, hypertension, cardiac failure, thromboembolism, and pharmacodynamically interacting drugs had a larger than random association with the outcome. High age, cardiac arrhythmia, hypertension, cardiac failure, and pharmacodynamically interacting drugs had odds ratios for bleeding above one, while thromboembolism had an odds ratio below one.ConclusionsWe demonstrated that data mining within a pharmacovigilance database identifies known risk factors for DOAC bleeding, and potential risk factors such as dementia and atrial fibrillation. We propose that the method could be used in pharmacovigilance for identification of potential ADR risk factors that merit further evaluation.
Prevalence of thyroid dysfunction during first trimester of pregnancy: A descriptive cross‐sectional study
Authors: Sharma Sandip K. , Gurung Geeta , Katuwal Neeta , Joshi Padam R. , Chaurasia Hemu , Lamsal Sunita 
Published: August 2023

AbstractObjectivesThyroid dysfunction carries large burden during pregnancy and untreated thyroid dysfunction is associated with poor maternal and foetal outcome. The exact burden of thyroid dysfunction during pregnancy in Nepal is not well established. This is descriptive cross‐sectional study done in tertiary care centre of Nepal to know burden of thyroid dysfunction and establish possible need of universal thyroid function screening during pregnancy.MethodsA descriptive cross‐sectional study was conducted from 4 September 2020 to 3 September 2021 on pregnant women attending to ANC clinic during their first trimester after obtaining ethical approval. Among eligible patients, after taking informed consent, a total of 385 pregnant women were included in the study. Thyroid function status was assessed by measuring serum levels of thyroid stimulating hormone (TSH), free thyroxine (FT4), and free tri‐iodothyronine (FT3). Women with pre‐existing thyroid disorders were excluded. Serum thyroid function was used to detect thyroid disorder based on American Thyroid Association 2011 criteria. A convenience sampling method was used. Point estimate and 95% confidence interval (CI) were calculated.ResultsAmong 385 pregnant women, 152 (39.48%) (39.20–39.70, 95% CI) had thyroid disorders. Among thyroid disorders (n = 152), 111 (28.83%) had subclinical hypothyroidism, 37 (9.61%) overt hypothyroidism, 3 (0.78%) subclinical hyperthyroidism and 1 (0.26%) had overt hyperthyroidism.ConclusionAround four in every 10 pregnant women had thyroid disorder. Thyroid dysfunction is easily detectable and can be effectively, inexpensively treated, which can prevent adverse maternal and foetal outcome. Thus, we should consider universal screening of pregnant women for thyroid disorder especially in a country like Nepal where there is a high prevalence of undiagnosed thyroid disorder.
On the sample complexity of observers for unknown linear systems with biased dynamics estimations
Authors: Ding Xuda , Wang Han , He Jianping , Chen Cailian , Guan Xinping 
Published: August 2023

AbstractObservers have broad applications in power systems, whereas observer models are hard to obtain when the system is unknown. This article considers the observer design problems for unknown noisy linear time‐invariant systems based on biased dynamics estimations. Unlike the unbiased methods, biased methods have stronger generalization ability, which benefits obtaining stable estimations for noisy systems. However, the biased estimation's influence on observer design still needs to be investigated. To analyze the influence, we exploit the impact of estimation bias‐variance trade‐off to observer design. Specifically, we propose a support vector regression (SVR) based estimator to provide biased estimations for the system identification of unknown linear systems. The sample complexity results of SVR with bias‐variance trade‐offs are analyzed and used for observer design and performance analysis. Then, a stable observer gain design algorithm is developed based on biased estimation. The observation performance is evaluated by the mean square observation error, which is shown to be adjustable by tuning the trade‐off between bias and variance, thus achieving higher scalability than the unbiased methods. Finally, observing performance analysis demonstrates the influence of the bias‐variance trade‐off for the observer. Extensive simulation validations are conducted to verify the computed estimation error and performance optimality with different bias‐variance trade‐offs and noise settings.
Practical trajectory tracking of output constrained random nonlinear systems
Authors: Yin Ruijie , Wu Zhaojing 
Published: August 2023

AbstractThis article studies practical trajectory tracking of output constrained multi‐input and multi‐output random nonlinear systems. First, the definition of ultimately bounded in the mean square and its Lyapunov criterion are given. Then, by proposing barrier Lyapunov function into the backstepping control design, constraint can be tackled and kept in the given region. Stability analysis is performed to the closed‐loop error system and practical trajectory tracking is achieved, meanwhile the second‐order moment of tracking error can be made arbitrarily small. A simulation example of a mechanical arm demonstrates the effectiveness of this control scheme.
Evaluation of multi‐user multiple‐input multiple‐output digital beamforming algorithms in B5G/6G low Earth orbit satellite systems
Authors: Dakkak M. Rabih , Riviello Daniel Gaetano , Guidotti Alessandro , Vanelli‐Coralli Alessandro 
Published: August 2023

SummarySatellite communication systems will be a key component of 5G and 6G networks to achieve the goal of providing unlimited and ubiquitous communications and deploying smart and sustainable networks. To meet the ever‐increasing demand for higher throughput in 5G and beyond, aggressive frequency reuse schemes (i.e., full frequency reuse), combined with digital beamforming techniques to cope with the massive co‐channel interference, are recognized as a key solution. Aimed at (i) eliminating the joint optimization problem among the beamforming vectors of all users, (ii) splitting it into distinct ones, and (iii) finding a closed‐form solution, we propose a beamforming algorithm based on maximizing the users' signal‐to‐leakage‐and‐noise ratio served by a low Earth orbit satellite. We investigate and assess the performance of several beamforming algorithms, including both those based on channel state information at the transmitter, that is, minimum mean square error and zero forcing, and those only requiring the users' locations, that is, switchable multi‐beam. Through a detailed numerical analysis, we provide a thorough comparison of the performance in terms of per‐user achievable spectral efficiency of the aforementioned beamforming schemes, and we show that the proposed signal to‐leakage‐plus‐noise ratio beamforming technique is able to outperform both minimum mean square error and multi‐beam schemes in the presented satellite communication scenario.
Measuring sustainable ergonomics: A hybrid multi‐criteria perspective on ergonomics indicators
Authors: Sarbat Irem , Ozmehmet Tasan Seren 
Published: August 2023

AbstractDecision making process comprises complexity due to holistically managed requirements. Since this process has diverse effects on stakeholders as decision makers, undoubtedly, the decisions become more significant for organizations having critical decisions in today's globalized sustainable world. If these decisions influence not only providing human well‐being but also improving the quality of working life through measuring the performance of business processes, their results should be monitored more systematically. Through this viewpoint, in this paper, the applicability of ergonomics indicators, as the fundamental part of a systematic approach integrating ergonomics and sustainability to monitor and evaluate processes of organizations, is queried within a hybrid multi‐criteria perspective, including five evaluation criteria consolidated for this paper, through a designed survey by asking practitioners as potential users of such an approach. The used methods, that is, BWM and PROMETHEE, enable presenting a base study to decide which ergonomics indicators are necessary for developing a systematic approach, indicating the prioritization of these indicators to be used as candidates for the approach. Results show that 15 ergonomics indicators can be candidates for facilitating sustainable process performance measurement in ergonomics. Importantly, this paper proposes guidance in making decisions for achieving well‐managed processes in organizations through ergonomics indicators evaluated on a hybrid multi‐criteria perspective.