bluuebunny/crossref_metadata_2025_split huggingface dataset:
Patient acceptance of teleneurology across neurologic conditions
Authors: Seigel Courtney R. , Martin Holly , Bastin Grace , Myers Laura J. , Taylor Stan , Pike Francis , Wilkinson Jayne , Williams Linda S. 
Published: February 2024

Abstract
Introduction
Patient acceptability with outpatient teleneurology has been reported within specific conditions, but less is known about acceptability across neurologic conditions. The study objective was to compare the acceptability of teleneurology between patients with various neurological conditions and determine what other factors influence acceptability.

Methods
This was a prospective study of Veterans who completed new outpatient teleneurology visits with the Department of Veterans Affairs National Teleneurology Program. Visits were conducted via video to home or video to the outpatient clinic. Patient acceptability was assessed via telephone interview two weeks post-visit. Acceptability was a summed score (3–21) of three 7-point Likert questions (higher = more acceptable). Clinical diagnosis categories were based on the neurologists’ ICD10 diagnosis code. Acceptability score was modeled using a censored Tobit model controlling for demographics, type of tele-visit, medical comorbidity, and ICD10 category.

Results
In FY 2021, 277 of 637 (43.5%) patients completed an interview with analyzable acceptability data. Of these 277, 70 (25.3%) had codes indicating headache, 46 (16.6%) movement disorder, 45 (16.2%) general symptoms, and 116 (41.9%) for all other categories. Mean patient acceptability was 18.3 (SD 3.2). There was no significant difference in scores between these groups. The only factor independently related to acceptability was medical comorbidity, with higher comorbidity associated with higher acceptability scores.

Discussion
Patients find their outpatient teleneurology experience highly acceptable independent of neurologic condition. Those with more comorbidity report higher acceptability. Use of teleneurology may be useful and acceptable across many outpatient neurologic conditions including for more medically complex patients.
Machine learning predicts the risk of osteoporosis in patients with breast cancer and healthy women
Authors: Zhao Fang , Li Chaofan , Wang Weiwei , Zhang Yu , Yao Peizhuo , Wei Xinyu , Jia Yiwei , Dang Shaonong , Zhang Shuqun 
Published: February 2024

Abstract
Objective
In this study, we investigated the effects of endocrine therapy and related drugs on the body composition and bone metabolism of patients with breast cancer. Additionally, using body composition-related indicators in machine learning algorithms, the risks of osteoporosis in patients with breast cancer and healthy women were predicted.

Methods
We enrolled postmenopausal patients with breast cancer who were hospitalized in a tertiary hospital and postmenopausal women undergoing health checkups in our hospital between 2019 and 2021. The basic information, body composition, bone density-related indicators, and bone metabolism-related indicators of all the study subjects were recorded. Machine learning models were constructed using cross-validation.

Results
Compared with a healthy population, the body composition of patients with breast cancer was low in bone mass, protein, body fat percentage, muscle, and basal metabolism, whereas total water, intracellular fluid, extracellular fluid, and waist-to-hip ratio were high. In patients with breast cancer, the bone mineral density (BMD), Z value, and T value were low and the proportion of bone loss and osteoporosis was high. BMD in patients with breast cancer was negatively correlated with age, endocrine therapy status, duration of medication, and duration of menopause, and it was positively correlated with body mass index (BMI) and basal metabolism. The parameters including body composition, age, hormone receptor status, and medication type were used for developing the machine learning model to predict osteoporosis risk in patients with breast cancer and healthy populations. The model showed a high accuracy in predicting osteoporosis, reflecting the predictive value of the model.

Conclusions
Patients with breast cancer may have changed body composition and BMD. Compared with the healthy population, the main indicators of osteoporosis in patients with breast cancer were reduced nonadipose tissue, increased risk of edema, altered fat distribution, and reduced BMD. In addition to age, duration of treatment, and duration of menopause, body composition-related indicators such as BMI and basal metabolism may be considerably associated with BMD of patients with breast cancer, suggesting that BMD status can be monitored in clinical practice by focusing on changes in the aforementioned indexes, which may provide a way to prevent preclinical osteoporosis.
Physiotherapeutische Differenzialdiagnose von Rückenschmerzen im Zusammenhang mit Endometriose
Authors: Oberegger Elisabeth , Taxer Bernhard 
Published: February 2024

Zusammenfassung
Hintergrund
Endometriose (EM) ist eines der häufigsten gynäkologischen Krankheitsbilder in unserer Gesellschaft. Die Diagnose des Krankheitsbilds dauert im Durchschnitt 7 bis 10 Jahre. Um diesen Zeitraum zu verkürzen, muss dieses Syndrom mehr Aufmerksamkeit bekommen. Das Ziel des vorliegenden Artikels ist es, Überschneidungen der beiden Krankheitsbilder EM und Low Back Pain (LBP) zu untersuchen und deren Relevanz für das physiotherapeutische Screening zu beschreiben.

Fragestellung
Welche klinischen Zeichen haben die Syndrome EM und LBP gemein und inwieweit kann das physiotherapeutische Screening gynäkologische Aspekte berücksichtigen und dementsprechend angepasst werden?

Ergebnisse
Um diese Fragestellung zu beantworten, wurden Entstehung sowie Symptome der beiden Syndrome recherchiert und auf Überschneidungen gescreent. Diese wurden dann in Bezug auf die bereits vorhandene Literatur und Fallstudien gesetzt. Die aktuelle Forschungslage zeigt Überschneidungen der beiden Krankheitsbilder hinsichtlich der Ätiologie, des Schmerzmechanismus sowie eines nicht zu unterschätzenden psychosozialen Aspekts. Die Studienlage zeigt, dass mehr Frauen als Männer von LBP und anderen chronischen Schmerzsyndromen betroffen sind. Die EM kommt fast ausschließlich bei Frauen vor und zählt wie der LBP zu den chronischen Schmerzsyndromen. So wird in der Literatur ein gemeinsamer Schmerzmechanismus der beiden Syndrome diskutiert. Die häufigste Überschneidung der beiden Krankheitsbilder zeigt sich durch das Auftreten des LBP als häufiges Symptom der EM, wobei dieser Zusammenhang durch strukturelle Ursachen sowie durch eine reflektorische Schmerzpräsentation begründet werden kann.

Schlussfolgerung
In der Physiotherapie können Hinweise auf EM in der Anamnese und physischen Untersuchung beobachtet werden. Das Berücksichtigen dieser Faktoren kann dabei helfen, die Diagnosezeit der EM zu verkürzen, indem man bei einem Verdacht auf eine gynäkologische Beteiligung bei LBP auf eine weitere Abklärung verweist. Eine umfassende Anamnese ist wichtig und sollte urologische, gynäkologische sowie sexualanamnestische Aspekte abdecken.
Changes in health-related quality of life and sleep habits after a 6-month non-randomised cluster-controlled trial among children with overweight or obesity
Authors: Kleppang Annette Løvheim , Abildsnes Eirik , Haraldstad Kristin , Stea Tonje Holte 
Published: February 2024

AbstractBeing overweight or obese can have severe negative psychological impacts and reduce health-related functioning. To improve health-related quality of life (HRQoL) and sleep habits for children with overweight or obesity, it is important to design and implement effective interventions. The aim of this study was to evaluate the effects of a 6-month family-based lifestyle intervention on HRQoL and sleep habits in Norwegian children with overweight or obesity in a primary-care setting. This 6-month, non-randomised, cluster-controlled trial included Norwegian children aged 5–13 years with overweight or obesity and their parents. A questionnaire was filled out by the parents. A total of 33 and 52 children in the control group and 41 and 78 children in the intervention group answered the HRQoL and sleep habits questions, respectively, and were included. The intervention group received individual family counselling and participated in physical activity groups and nutrition courses. The Children’s Sleep Habits Questionnaire (CSHQ) and Kidscreen-10 index were used to assess sleep habits and HRQoL. At baseline, the mean average scores for HRQoL were 50.0 [standard deviation (SD) 8.1] for the intervention group and 49.0 (SD 10.1) for the control group. For sleep habits at baseline, the mean average scores were 45.2 (SD 11.8) for the intervention group and 46.0 (SD 11.9) for the control group. No significant changes in HRQoL and sleep habits after the intervention were revealed. Overall, the family-based lifestyle intervention targeting overweight and obese children in a primary-care setting showed no significant effect on HRQoL or sleep quality.
How to track and segment fish without human annotations: a self-supervised deep learning approach
Authors: Saleh Alzayat , Sheaves Marcus , Jerry Dean , Rahimi Azghadi Mostafa 
Published: February 2024

AbstractTracking fish movements and sizes of fish is crucial to understanding their ecology and behaviour. Knowing where fish migrate, how they interact with their environment, and how their size affects their behaviour can help ecologists develop more effective conservation and management strategies to protect fish populations and their habitats. Deep learning is a promising tool to analyse fish ecology from underwater videos. However, training deep neural networks (DNNs) for fish tracking and segmentation requires high-quality labels, which are expensive to obtain. We propose an alternative unsupervised approach that relies on spatial and temporal variations in video data to generate noisy pseudo-ground-truth labels. We train a multi-task DNN using these pseudo-labels. Our framework consists of three stages: (1) an optical flow model generates the pseudo-labels using spatial and temporal consistency between frames, (2) a self-supervised model refines the pseudo-labels incrementally, and (3) a segmentation network uses the refined labels for training. Consequently, we perform extensive experiments to validate our method on three public underwater video datasets and demonstrate its effectiveness for video annotation and segmentation. We also evaluate its robustness to different imaging conditions and discuss its limitations.
Understanding upper-limb movements via neurocomputational models of the sensorimotor system and neurorobotics: where we stand
Authors: Parziale Antonio , Marcelli Angelo 
Published: February 2024

AbstractRoboticists and neuroscientists are interested in understanding and reproducing the neural and cognitive mechanisms behind the human ability to interact with unknown and changing environments as well as to learn and execute fine movements. In this paper, we review the system-level neurocomputational models of the human motor system, and we focus on biomimetic models simulating the functional activity of the cerebellum, the basal ganglia, the motor cortex, and the spinal cord, which are the main central nervous system areas involved in the learning, execution, and control of movements. We review the models that have been proposed from the early of 1970s, when the first cerebellar model was realized, up to nowadays, when the embodiment of these models into robots acting in the real world and into software agents acting in a virtual environment has become of paramount importance to close the perception-cognition-action cycle. This review shows that neurocomputational models have contributed to the comprehension and reproduction of neural mechanisms underlying reaching movements, but much remains to be done because a whole model of the central nervous system controlling musculoskeletal robots is still missing.
High-resolution prediction models for Rhipicephalus microplus and Amblyomma cajennense s.l. ticks affecting cattle and their spatial distribution in continental Ecuador using bioclimatic factors
Authors: Pérez-Otáñez Ximena , Rodríguez-Hidalgo Richar , Enríquez Sandra , Celi-Erazo Maritza , Benítez Washington , Saegerman Claude , Vaca-Moyano Franklin , Ron-Garrido Lenin , Vanwambeke Sophie O. 
Published: February 2024

AbstractIn Ecuador, the main tick species affecting cattle are Rhipicephalus microplus and Amblyomma cajennense sensu lato. Understanding their spatial distribution is crucial. To assess their distribution, data from 2895 farms visited between 2012 and 2017 were utilized. Ticks were collected during animal inspections, with each farm’s location georeferenced. Bioclimatic variables and vapor pressure deficit data were obtained from Climatologies at High resolution for the Earth´s Land Surface Areas (CHELSA) dataset. They were overlaid to develop predictive maps for each species using Random Forest (RF) models. The cross-validation results for RF prediction models showed high accuracy for both R. microplus and A. cajennense s.l. presence with values of accuracy = 0.97 and 0.98, sensitivity = 0.96 and 0.99, and specificity = 0.96 and 0.93, respectively. A carefully selected subset of bioclimatic variables was used to describe the presence of each tick species. Higher levels of precipitation had positive effect on the presence of R. microplus but a negative effect on A. cajennense s.l. In contrast, isothermality (BIO3) was more important for the presence of A. cajennense s.l. compared to R. microplus. As a result, R. microplus had a broader distribution across the country, while A. cajennense s.l. was mainly found in coastal areas with evident seasonality. The coexistence of both species in some regions could be attributed to transitional zones, whereas high altitudes limited tick presence. This information can aid in developing appropriate tick management plans, particularly considering A. cajennense s.l.’s broad host range species and R. microplus’s specificity for cattle. Moreover, the predictive models can identify areas at risk of associated challenging hemoparasite, requiring special attention and mitigation measures.
Graphic abstract
Effect of temporal resolution on calcium scoring: insights from photon-counting detector CT
Authors: Sartoretti Thomas , Mergen Victor , Dzaferi Amina , Allmendinger Thomas , Manka Robert , Alkadhi Hatem , Eberhard Matthias 
Published: February 2024

AbstractTo intra-individually investigate the variation of coronary artery calcium (CAC), aortic valve calcium (AVC), and mitral annular calcium (MAC) scores and the presence of blur artifacts as a function of temporal resolution in patients undergoing non-contrast cardiac CT on a dual-source photon counting detector (PCD) CT. This retrospective, IRB-approved study included 70 patients (30 women, 40 men, mean age 78 ± 9 years) who underwent ECG-gated cardiac non-contrast CT with PCD-CT (gantry rotation time 0.25 s) prior to transcatheter aortic valve replacement. Each scan was reconstructed at a temporal resolution of 66 ms using the dual-source information and at 125 ms using the single-source information. Average heart rate and heart rate variability were calculated from the recorded ECG. CAC, AVC, and MAC were quantified according to the Agatston method on images with both temporal resolutions. Two readers assessed blur artifacts using a 4-point visual grading scale. The influence of average heart rate and heart rate variability on calcium quantification and blur artifacts of the respective structures were analyzed by linear regression analysis. Mean heart rate and heart rate variability during data acquisition were 76 ± 17 beats per minute (bpm) and 4 ± 6 bpm, respectively. CAC scores were smaller on 66 ms (median, 511; interquartile range, 220–978) than on 125 ms reconstructions (538; 203–1050, p < 0.001). Median AVC scores [2809 (2009–3952) versus 3177 (2158–4273)] and median MAC scores [226 (0-1284) versus 251 (0-1574)] were also significantly smaller on 66ms than on 125ms reconstructions (p < 0.001). Reclassification of CAC and AVC risk categories occurred in 4% and 11% of cases, respectively, whereby the risk category was always overestimated on 125ms reconstructions. Image blur artifacts were significantly less on 66ms as opposed to 125 ms reconstructions (p < 0.001). Intra-individual analyses indicate that temporal resolution significantly impacts on calcium scoring with cardiac CT, with CAC, MAC, and AVC being overestimated at lower temporal resolution because of increased motion artifacts eventually leading to an overestimation of patient risk.
Increasing STEM Skills, Knowledge and Interest Among Diverse Students: Results from an Intensive Summer Research Program at the University of California, San Francisco
Authors: Galvez Gino , Killilea David W. , Berry Sharla , Narayanaswami Vasanthy , Fung Ellen B. 
Published: February 2024

AbstractThis study evaluates the effectiveness of the UCSF Summer Student Research Program (SSRP) in enhancing research-related skills, academic outcomes, and post-baccalaureate aspirations of underrepresented minority (URM) and non-URM undergraduate students in biomedical sciences and STEM fields. The SSRP, spanning 9 weeks, provides immersive research experiences, structured mentorship, trainings, seminars, and STEM education. Pre- and post-program survey data from eight cohorts (N = 315) were analyzed using paired-sample t-tests, MANOVA, and content analysis. Results demonstrate significant gains in critical thinking skills, research abilities, science identity, applied science skills, and readiness for a research career. Notably, participants exhibited improvements in understanding the research process, scientific thinking, science writing, and problem-solving. URM and non-URM students experienced similar gains, highlighting the program’s inclusivity. The SSRP also positively influenced students’ postgraduate aspirations. Some participants expressed heightened interest in pursuing Master of Arts, Ph.D., and M.D. degrees, indicating increased clarity and motivation towards advanced education and research careers. Furthermore, 87% of participants expressed a high likelihood of engaging in future research endeavors, underscoring the program’s sustained impact on research interest. This study underscores the transformative potential of a well-structured, intensive summer research program in significantly enhancing academic outcomes for URM and non-URM students alike. These findings align with the persistence framework, emphasizing the importance of early research experiences, active learning, and learning communities in fostering student success. The SSRP’s effectiveness in improving research skills and post-baccalaureate aspirations suggests its potential in diversifying the STEM fields, biomedical sciences and health-related professions.
Trends in abundance and occupancy of the protected water beetle Graphoderus bilineatus in the Netherlands
Authors: van Strien Arco J. , Koese Bram , Stienstra Jelle , Soldaat Leo L. , de Zeeuw Marnix 
Published: February 2024

Abstract
The European Habitats Directive prescribes Member States to report on trends in abundance and distribution of protected species. One of these species is Graphoderus bilineatus (De Geer, 1774), a middle-sized predaceous aquatic beetle, listed in Annex II and IV of the Habitats Directive. In the Netherlands, a monitoring scheme for this species has been set up to assess the national trend as well as the trend in the national Natura2000 sites. In this scheme, a selection of 1 km2 squares is surveyed in cycles of six years using a standardized field method by professional fieldworkers. In each selected square, five locations were sampled per annum (September–October) by two different observers using hand netting. The data of the first two rounds (2011–2016 and 2017–2022) have been analysed using both an occupancy model and a Poisson GLMM. We found evidence of a declining trend in occupancy as well as in population size. The decreases were stronger outside than within Natura2000 sites and also stronger than those of three other beetle species that are often found together with G. bilineatus. In addition, considerable differences between observers were detected in the data, despite the application of a standardized field method.

Implications for insect conservation
Graphoderus bilineatus is declining and the decline is stronger outside Natura2000 sites then within, most likely due to differences in the development of water quality, vegetation structure and water management. Further research is required to identify the exact causes of the decline; however water quality improvement seems essential to turn the tide. The study also shows the benefits of a monitoring scheme for trend assessments. The recent decline would probably not have been noticed based on distribution data alone.
A Procedural Constructive Learning Mechanism with Deep Reinforcement Learning for Cognitive Agents
Authors: de Lellis Rossi Leonardo , Rohmer Eric , Dornhofer Paro Costa Paula , Colombini Esther Luna , da Silva Simões Alexandre , Gudwin Ricardo Ribeiro 
Published: February 2024

AbstractRecent advancements in AI and deep learning have created a growing demand for artificial agents capable of performing tasks within increasingly complex environments. To address the challenges associated with continuous learning constraints and knowledge capacity in this context, cognitive architectures inspired by human cognition have gained significance. This study contributes to existing research by introducing a cognitive-attentional system employing a constructive neural network-based learning approach for continuous acquisition of procedural knowledge. We replace an incremental tabular Reinforcement Learning algorithm with a constructive neural network deep reinforcement learning mechanism for continuous sensorimotor knowledge acquisition, thereby enhancing the overall learning capacity. The primary emphasis of this modification centers on optimizing memory utilization and reducing training time. Our study presents a learning strategy that amalgamates deep reinforcement learning with procedural learning, mirroring the incremental learning process observed in human sensorimotor development. This approach is embedded within the CONAIM cognitive-attentional architecture, leveraging the cognitive tools of CST. The proposed learning mechanism allows the model to dynamically create and modify elements in its procedural memory, facilitating the reuse of previously acquired functions and procedures. Additionally, it equips the model with the capability to combine learned elements to effectively adapt to complex scenarios. A constructive neural network was employed, initiating with an initial hidden layer comprising one neuron. However, it possesses the capacity to adapt its internal architecture in response to its performance in procedural and sensorimotor learning tasks, inserting new hidden layers or neurons. Experimentation conducted through simulations involving a humanoid robot demonstrates the successful resolution of tasks that were previously unsolved through incremental knowledge acquisition. Throughout the training phase, the constructive agent achieved a minimum of 40% greater rewards and executed 8% more actions when compared to other agents. In the subsequent testing phase, the constructive agent exhibited a 15% increase in the number of actions performed in contrast to its counterparts.