bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of Levodopa and Environmental Setting on Gait and Turning Digital Markers Related to Falls in People with Parkinson's Disease
Authors: Shah Vrutangkumar V. , McNames James , Carlson‐Kuhta Patricia , Nutt John G. , El‐Gohary Mahmoud , Sowalsky Kristen , Mancini Martina , Horak Fay B. 
Published: October 2022

ABSTRACTBackgroundIt is unknown whether medication status (off and on levodopa) or laboratory versus home settings plays a role in discriminating fallers and non‐fallers in people with Parkinson's disease (PD).ObjectivesTo investigate which specific digital gait and turning measures, obtained with body‐worn sensors, best discriminated fallers from non‐fallers with PD in the clinic and during daily life.MethodsWe recruited 34 subjects with PD (17 fallers and 17 non‐fallers based on the past 6 month's falls). Subjects wore three inertial sensors attached to both feet and the lumbar region in the laboratory for a 3‐minute walking task (both off and on levodopa) and during daily life activities for a week. We derived 24 digital (18 gait and 6 turn) measures from the 3‐minute walk and from daily life.ResultsIn clinic, none of the gait and turning measures collected during on levodopa state were significantly different between fallers and non‐fallers. In contrast, digital measures collected in the off levodopa state were significantly different between groups, (average turn velocity, average number of steps to complete a turn, and variability of gait speed, P < 0.03). During daily life, the variability of average turn velocity (P = 0.023) was significantly different in fallers than non‐fallers. Last, the average number of steps to complete a turn was significantly correlated with the patient‐reported outcomes.ConclusionsDigital measures of turning, but not gait, were different in fallers compared to non‐fallers with PD, in the laboratory when off medication and during a daily life.
The Benefits of Switching to a Healthy Diet on Metabolic, Cognitive, and Gut Microbiome Parameters Are Preserved in Adult Rat Offspring of Mothers Fed a High‐Fat, High‐Sugar Diet
Authors: Kendig Michael D. , Hasebe Kyoko , Tajaddini Aynaz , Kaakoush Nadeem O. , Westbrook R. Frederick , Morris Margaret J. 
Published: October 2022

ScopeMaternal obesity increases the risk of health complications in children, highlighting the need for effective interventions. A rat model of maternal obesity to examine whether a diet switch intervention could reverse the adverse effects of an unhealthy postweaning diet is used.Methods and resultsMale and female offspring born to dams fed standard chow or a high‐fat, high‐sugar “cafeteria” (Caf) diet are weaned onto chow or Caf diets until 22 weeks of age, when Caf‐fed groups are switched to chow for 5 weeks. Adiposity, gut microbiota composition, and place recognition memory are assessed before and after the switch. Body weight and adiposity fall in switched groups but remain significantly higher than chow‐fed controls. Nonetheless, the diet switch improves a deficit in place recognition memory observed in Caf‐fed groups, increases gut microbiota species richness, and alters β diversity. Modeling indicate that adiposity most strongly predicts gut microbiota composition before and after the switch.ConclusionMaternal obesity does not alter the effects of switching diet on metabolic, microbial, or cognitive measures. Thus, a healthy diet intervention lead to major shifts in body weight, adiposity, place recognition memory, and gut microbiota composition, with beneficial effects preserved in offspring born to obese dams.
CIABNet: Category imbalance attention block network for the classification of multi‐differentiated types of esophageal cancer
Authors: Li Min , Chen Chen , Cao Yanzhen , Zhou Panyun , Deng Xin , Liu Pei , Wang Yunling , Lv Xiaoyi , Chen Cheng 
Published: October 2022

AbstractBackgroundEsophageal cancer has become one of the important cancers that seriously threaten human life and health, and its incidence and mortality rate are still among the top malignant tumors. Histopathological image analysis is the gold standard for diagnosing different differentiation types of esophageal cancer.PurposeThe grading accuracy and interpretability of the auxiliary diagnostic model for esophageal cancer are seriously affected by small interclass differences, imbalanced data distribution, and poor model interpretability. Therefore, we focused on developing the category imbalance attention block network (CIABNet) model to try to solve the previous problems.MethodsFirst, the quantitative metrics and model visualization results are integrated to transfer knowledge from the source domain images to better identify the regions of interest (ROI) in the target domain of esophageal cancer. Second, in order to pay attention to the subtle interclass differences, we propose the concatenate fusion attention block, which can focus on the contextual local feature relationships and the changes of channel attention weights among different regions simultaneously. Third, we proposed a category imbalance attention module, which treats each esophageal cancer differentiation class fairly based on aggregating different intensity information at multiple scales and explores more representative regional features for each class, which effectively mitigates the negative impact of category imbalance. Finally, we use feature map visualization to focus on interpreting whether the ROIs are the same or similar between the model and pathologists, thus better improving the interpretability of the model.ResultsThe experimental results show that the CIABNet model outperforms other state‐of‐the‐art models, which achieves the most advanced results in classifying the differentiation types of esophageal cancer with an average classification accuracy of 92.24%, an average precision of 93.52%, an average recall of 90.31%, an average F1 value of 91.73%, and an average AUC value of 97.43%. In addition, the CIABNet model has essentially similar or identical to the ROI of pathologists in identifying histopathological images of esophageal cancer.ConclusionsOur experimental results prove that our proposed computer‐aided diagnostic algorithm shows great potential in histopathological images of multi‐differentiated types of esophageal cancer.
Avian Predation on Juvenile and Adult Lost River and Shortnose Suckers: An Updated Multi-Predator Species Evaluation
Authors: Evans Allen F. , Payton Quinn , Banet Nathan , Cramer Bradley M. , Kelsey Caylen , Hewitt David A. 
Published: October 2022

Abstract
Previous research suggests that predation by piscivorous colonial waterbirds may negatively influence the survival of Lost River Suckers (LRS) Deltistes luxatus and Shortnose Suckers (SNS) Chasmistes brevirostris in the Upper Klamath Basin (UKB), USA. However, estimates of predation from past studies, which were based on suckers with PIT tags, represent minimum estimates of sucker mortality because analyses did not account for the proportion of tags that were consumed by birds and deposited beyond their breeding colony. To address this uncertainty, we fed PIT-tagged suckers to American white pelicans Pelecanus erythrorhynchos to estimate deposition probabilities. A hierarchical Bayesian model was then used to estimate predation rates (percentage of available tagged fish that were consumed) on juvenile suckers that were released as part of the Sucker Assisted Rearing Program (SARP) and on wild juvenile and adult LRS and SNS during 2009–2020. Pelican deposition probabilities were estimated to be 0.47 (95% credible interval = 0.36–0.60), indicating that for every 100 tags consumed, 47 tags on average were deposited on breeding colonies by birds. Deposition-corrected estimates of predation rates were approximately two times greater than those previously reported and ranged annually from 4.3% (95% credible interval = 2.9–6.7%) to 8.5% (6.3–12.7%) on SARP juvenile suckers, from 4.3% (0.9–13.2%) to 10.5% (3.8–24.5%) on wild juvenile suckers, and from 0.1% (&lt;0.1–0.3%) to 7.2% (2.8–16.4%) on adult suckers, depending on species and location. Results suggest that predation by colonial waterbirds, although not the original cause of sucker declines, was a substantial source of sucker mortality in some years. Future studies should consider models that jointly estimate both predation and survival and models that include environmental factors that potentially influence sucker susceptibility to avian predators in the UKB.
Numerical approximations for the nonlinear time fractional reaction–diffusion equation
Authors: Liu Haiyu , Lü Shujuan 
Published: October 2022

AbstractIn this article, we propose an implicit pseudospectral scheme for nonlinear time fractional reaction–diffusion equations with Neumann boundary conditions, which is based upon Gauss–Lobatto–Legendre–Birkhoff pseudospectral method in space and finite difference method in time. A priori estimate of numerical solution is given firstly. Then the existence of numerical solution is proved by Brouwer fixed point theorem and the uniqueness is obtained. It is proved rigorously that the fully discrete scheme is unconditionally stable and convergent. Furthermore, we develop a modified scheme by adding correction terms for the problem with nonsmooth solutions. Numerical examples are given to verify the theoretical analysis.
Integrated nonlinear suboptimal control‐and‐estimator based on the state‐dependent differential Riccati equation approach
Authors: Korayem Moharram Habibnejad , Lademakhi Naeim Yousefi 
Published: October 2022

AbstractThis paper is devoted to the theory and application of a combined nonlinear suboptimal control and estimator based on the state‐dependent differential Riccati equation (SDDRE), aiming finite‐time tracking of manipulators that possess flexible joints. Though measuring the angular positions of links is possible for flexible joint manipulators (FJMs), it is not feasible to measure the changes in the position angles of actuators. In addition, the existence of measurement noise and the influence of unknown workspace disturbance are always unavoidable. Therefore, combining an estimator with a controller could result in full‐state feedback for the system. Designing the proposed integrated estimator algorithm includes two parts: The first part is the theoretical extension of the SDDRE approach expressed with nonlinear state variables and inputs of the system in a finite‐time horizon. In the second part, enhancing and upgrading software and hardware interface, the proposed procedure for FJMs is modeled and simulated. Then, the experimental implementation is applied in the online closed‐loop form.
Measurement experimental set‐up for thermal characterization of low thickness polymers
Authors: Salhi Imane , Jay Jacques , Belhora Fouad , Hajjaji Abdelowahed 
Published: October 2022

AbstractThe characterization of the thermal properties of polymers represents a fundamental step in improving materials for energy harvesting processes. Thermal conductivity, for example, is measured by measuring heat flux and temperature difference. This study seeks to design and fabricate an experimental set‐up to measure the thermal conductivity of low‐thickness polymers at room temperature. It is unique in that it is an affordable and lightweight instrument developed using electrical resistance as a heater and a limited number of components. Error analysis and reproducibility analysis were carried out for the thermal conductivity. The instrument was found to be reliable in making thermal conductivity measurements with an average of ±7%.
Industrial cutting waste granite dust reinforced cardanol benzoxazine/epoxy resin hybrid composites for high‐voltage electrical insulation applications
Authors: Saravanan Veera Sena Vellapandi , Arumugam Hariharan , Mohamed Mydeen Khajanajimudeen , Krishnasamy Balaji , Mohamed Iqbal Mohamedmustafa , Muthukaruppan Alagar 
Published: October 2022

AbstractAn attempt has been made to develop hybrid composites from benzoxazine monomer (C‐ddm) hybridized with DGEBA epoxy resin (EP) and reinforced with varying weight percentages (20 wt%, 40 wt%, 60 wt%, 80 wt% and 100 wt%) of glycidoxypropyltrimethoxy‐ silane (GPTMS) functionalized granite dust (GD) obtained from industrial granite cutting and polishing process in order to utilize them for electrical insulation applications. The thermal stability of granite dust reinforced poly(EP‐co‐C‐ddm) composites was studied by TGA analysis. Among the composites samples studied, 100 wt% GD reinforced poly(EP‐co‐C‐ddm) composites possess better thermal stability than that of other neat matrices and composites. Among the composites prepared using varying weight percentages of functionalized GD reinforcement, it was observed that 80 wt% GD reinforced poly(EP‐co‐C‐ddm) composites possesses better hydrophobic character than that of other neat matrices and composites. The value of LOI calculated for neat matrix (poly[EP‐co‐C‐ddm]) and 20 wt%, 40 wt%, 60 wt%, 80 wt% and 100 wt% GD reinforced composites was found to be 22, 28, 34, 40, 43 and 45 respectively. The 80 wt% GD reinforced poly(EP‐co‐C‐ddm) composites possess the higher values of tensile strength and flexural strength of 47 MPa and 140 MPa, respectively than those of their samples. The values of electrical surface resistivity and electrical volume resistivity of all the neat matrices and GD reinforced polybenzoxazine composites were found to be in the order of 1012 and 1013 respectively. The values of dielectric strength obtained from break down voltage (BDV) for neat matrix [poly(EP‐co‐C‐ddm)] and 20 wt%, 40 wt%, 60 wt%, 80 wt% and 100 wt% of GD reinforced poly(EP‐co‐C‐ddm) composites are 15.0, 15.5, 16.5, 17.0, 17.0 and 17.0 kV/mm, respectively. Data obtained from thermal stability, hydrophobic behavior and dielectric studies it was inferred that the hybrid polymer composites developed in the present work can be conveniently used in the form insulators, sealants, adhesives and matrices where application demands at high‐performance industrial and engineering applications.
Future‐proofing photovoltaics module reliability through a unifying predictive modeling framework
Authors: Springer Martin , Jordan Dirk C. , Barnes Teresa M. 
Published: October 2022

AbstractSolar energy, especially photovoltaics (PV), plays a significant role in the global energy transition required for decarbonization. Technological advancements in increasing efficiencies coupled with cost reductions through economies of scale made PV a cost‐competitive energy source set to exceed the milestone of 1 TW globally deployed capacity in 2022. However, ongoing downward price pressure coupled with increasing service life expectations creates tremendous challenges for reliability engineers in ensuring the safe and reliable operation of PV modules and systems over the anticipated service life. Today's reliability research efforts aim for module lifetimes of up to 50 years. Still, our current tools fall short in accurately assessing degradation mechanisms and failure modes over such extended periods. We argue that the well‐established PV reliability learning cycle needs to be accelerated to keep up with the rapid technological advancements and high expectations that are put on PV and its role in the global energy transition. In this article, we explore the evolution of the PV reliability learning cycle and highlight the significance that predictive modeling capabilities will have on future PV module reliability. We propose creating a unifying modeling framework—which once established will enable the holistic assessment of PV module reliability, accelerate the PV reliability learning cycle, and bring us closer to quantitative service life predictions of PV modules.
Pisum sativum has no competitive responses to neighbors: A case study in (non)reproducible plant biology
Authors: Mobley Mariah L. , Kruse Audrey S. , McNickle Gordon G. 
Published: October 2022

AbstractPlant–plant competition is ubiquitous in nature. However, studying below ground behavior of roots has always posed certain difficulties. Pea (Pisum sativum L.) has become a common study species for questions about how plant roots respond to neighboring plant roots and barriers in soil. However, published results point in several different directions. This has sometimes been interpreted as pea having sophisticated context dependent responses that can change in complex ways depending on its surroundings, but it could also just point to small statistical power resulting in type I or II statistical errors. To explore this further, here, we combine the result of five new experiments with published results to examine 18 unique experiments from 10 different studies and 6 cultivars of pea for a total of 254 replicate plants. We used a Bayesian hierarchical meta‐analysis approach to estimating the likely effect size from the available data, as well as quantify heterogeneity among different experiments, studies and cultivars. The posterior distributions show that, at the coarsest possible scale of total root production, it is unlikely that P. sativum root growth is influenced by either neighbors or pot volume that varies primarily by depth. We find no evidence of publication bias and conclude that this is simply due to statistical sampling error and the scientific method combined with frequentist statistics operating as intended. We suggest that further work on pea should consider repeating experiments that reported finer scale root plasticity at the rhizosphere scale or consider exploring different pot geometries such as volume that varies by depth or width. We also suggest that more diversity in study species are needed to better understand the neighbor‐volume response.
Molecular determinants of tetrahydrocannabinol binding to the glycine receptor
Authors: Alvarez Lautaro D. , Alves N. R. Carina 
Published: October 2022

AbstractThe recognition of Cannabis as a source of new compounds suitable for medical use has attracted strong interest from the scientific community in its research, and substantial progress has accumulated regarding cannabinoids' activity; however, a thorough description of their molecular mechanisms of action remains a task to complete. Highlighting their complex pharmacology, the list of cannabinoids' interactors has vastly expanded beyond the canonical cannabinoid receptors. Among those, we have focused our study on the glycine receptor (GlyR), an ion channel involved in the modulation of nervous system responses, including, to our interest, sensitivity to peripheral pain. Here, we report the use of computational methods to investigate possible binding modes between the GlyR and Δ9‐tetrahydrocannabinol (THC). After obtaining a first pose for the THC binding from a biased molecular docking simulation and subsequently evaluating it by molecular dynamic simulations, we found a dynamic system with an identifiable representative binding mode characterized by the specific interaction with two transmembrane residues (Phe293 and Ser296). Complementarily, we assessed the role of membrane cholesterol in this interaction and positively established its relevance for THC binding to GlyR. Lastly, the use of restrained molecular dynamics simulations allowed us to refine the description of the binding mode and of the cholesterol effect. Altogether, our findings contribute to the current knowledge about the GlyR‐THC mode of binding and propose a new starting point for future research on how cannabinoids in general, and THC in particular, modulate pain perception in view of its possible clinical applications.
Monitoring a labeled degree‐corrected stochastic block model
Authors: Abossedgh Sara , Saghaei Abbas , Amiri Amirhossein 
Published: October 2022

AbstractThe stochastic block model (SBM) is a random graph model that focuses on partitioning the nodes into blocks or communities. A degree‐corrected stochastic block model (DCSBM) considers degree heterogeneity within nodes. Investigation of the type of edge label can be useful for studying networks. We have proposed a labeled degree‐corrected stochastic block model (LDCSBM), added the probability of the occurrence of each edge label, and monitored the behavior of this network. The LDCSBM is a dynamic network that varies over time; thus, we applied the monitoring process to both the US Senate voting network and simulated networks by defining structural changes. We used the Shewhart control chart for detecting changes and studied the effect of Phase I parameter estimation on Phase II performance. The efficiency of the model for surveillance has been evaluated using the average run length for estimated parameters.
Reliability‐based design optimization of a car body using dimension‐ reduced Chebyshev polynomial
Authors: Li Feng , Zhou Yichen , Wei Tonghui , Li Hongfeng 
Published: October 2022

AbstractA reliability‐based design optimization (RBDO) method of a car body is presented on basis of dimension‐reduced Chebyshev polynomial method (DCM). To improve calculation efficiency and save computational time, complex models are often approximated by metamodels in reliability analysis. Traditional metamodels require a large number of sample points, which is time‐consuming. To improve the efficiency, DCM is proposed to approximate the performance function of the car body. First, the performance function is decomposed by the dimension‐reduction method into a sum of univariate functions, which are then fitted through Chebyshev polynomials. The reliability of the car body is predicted by the Taylor expansion method and the fourth‐moment method. Finally, the result of RBDO is obtained using an improved adaptive genetic algorithm. The proposed method saves on the calculation time with high precision. Besides, the improved adaptive genetic algorithm reduces the number of iterations in the car body optimization and improves the efficiency.
Long‐duration remote underwater videos reveal that grazing by fishes is highly variable through time and dominated by non‐indigenous species
Authors: Magneville Camille , Leréec Le Bricquir Marie‐Lou , Dailianis Thanos , Skouradakis Grigorios , Claverie Thomas , Villéger Sébastien 
Published: October 2022

AbstractIn the marine environment, fish contribute to key ecological processes such as controlling food‐webs through top‐down impacts, especially on algae. To date, the assessment of fish grazing activity has mostly been performed using short‐term (<1 h) censuses by divers or remote cameras which do not allow estimating the variability of grazing rate within and between days. However, understanding the temporal variation of fish activity and hence contribution of species to ecosystem functioning is of particular interest in the context of biological invasion. Here, using long‐duration remote underwater cameras, we recorded fish abundance and grazing events over three consecutive days in October 2019 in a shallow Mediterranean ecosystem from northern Crete. This novel approach allowed us to assess temporal variation of abundance and grazing activity of the two native (Sarpa salpa and Sparisoma cretense) and the two non‐indigenous fish species (Siganus rivulatus and Siganus luridus). Non‐indigenous Siganus rivulatus was the most common species in the studied coastal habitat, followed by the two native species while the non‐indigenous Siganus luridus was scarce. Overall, the non‐indigenous S. rivulatus and the native S. salpa are responsible for more than 90% of the recorded grazing activity with similar bite rates between the two species. More than 70% of the grazing activity arose in grazing pulses in the afternoon, supporting the diel feeding hypothesis according to which feeding is greater in the afternoon when nutritive quality of macrophytes is the highest. In addition, some of the highest peaks in grazing activity were driven by a few individuals. Hence, surveys of only abundance could not provide accurate estimates of herbivory. Last, Siganus rivulatus presence did not significantly affect grazing activity of the native Sarpa salpa. Our results demonstrate that long‐duration remote underwater videos are a useful tool to accurately assess the contribution of fishes to ecosystem functioning.