bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigating the roles of reflexes and central pattern generators in the control and modulation of human locomotion using a physiologically plausible neuromechanical model
Authors: Di Russo Andrea , Stanev Dimitar , Sabnis Anushree , Danner Simon M , Ausborn Jessica , Armand Stéphane , Ijspeert Auke 
Published: September 2023

Abstract

Objective. Studying the neural components regulating movement in human locomotion is obstructed by the inability to perform invasive experimental recording in the human neural circuits. Neuromechanical simulations can provide insights by modeling the locomotor circuits. Past neuromechanical models proposed control of locomotion either driven by central pattern generators (CPGs) with simple sensory commands or by a purely reflex-based network regulated by state-machine mechanisms, which activate and deactivate reflexes depending on the detected gait cycle phases. However, the physiological interpretation of these state machines remains unclear. Here, we present a physiologically plausible model to investigate spinal control and modulation of human locomotion. Approach. We propose a bio-inspired controller composed of two coupled CPGs that produce the rhythm and pattern, and a reflex-based network simulating low-level reflex pathways and Renshaw cells. This reflex network is based on leaky-integration neurons, and the whole system does not rely on changing reflex gains according to the gait cycle state. The musculoskeletal model is composed of a skeletal structure and nine muscles per leg generating movement in sagittal plane. Main results. Optimizing the open parameters for effort minimization and stability, human kinematics and muscle activation naturally emerged. Furthermore, when CPGs were not activated, periodic motion could not be achieved through optimization, suggesting the necessity of this component to generate rhythmic behavior without a state machine mechanism regulating reflex activation. The controller could reproduce ranges of speeds from 0.3 to 1.9 m s−1. The results showed that the net influence of feedback on motoneurons (MNs) during perturbed locomotion is predominantly inhibitory and that the CPGs provide the timing of MNs’ activation by exciting or inhibiting muscles in specific gait phases. Significance. The proposed bio-inspired controller could contribute to our understanding of locomotor circuits of the intact spinal cord and could be used to study neuromotor disorders.
A multivariate physiological model of vagus nerve signalling during metabolic challenges in anaesthetised rats for diabetes treatment
Authors: Güemes Gonzalez Amparo , Carnicer-Lombarte Alejandro , Hilton Sam , Malliaras George 
Published: September 2023

Abstract

Objective. This study aims to develop a comprehensive decoding framework to create a multivariate physiological model of vagus nerve transmission that reveals the complex interactions between the nervous and metabolic systems. Approach. Vagus nerve activity was recorded in female Sprague-Dawley rats using gold hook microwires implanted around the left cervical vagus nerve. The rats were divided into three experimental cohorts (intact nerve, ligation nerve for recording afferent activation, and ligation for recording efferent activation) and metabolic challenges were administered to change glucose levels while recording the nerve activity. The decoding methodology involved various techniques, including continuous wavelet transformation, extraction of breathing rate (BR), and correlation of neural metrics with physiological signals. Main results. Decrease in glucose level was consistently negatively correlated with an increase in the firing activity of the intact vagus nerve that was found to be conveyed by both afferent and efferent pathways, with the afferent response being more similar to the one on the intact nerve. A larger variability was observed in the sensory and motor responses to hyperglycaemia. A novel strategy to extract the BR over time based on inter-burst-interval is also presented. The vagus afferent was found to encode breathing information through amplitude and firing rate modulation. Modulations of the signal amplitude were also observed due to changes in heart rate in the intact and efferent recordings, highlighting the parasympathetic control of the heart. Significance. The analytical framework presented in this study provides an integrative understanding that considers the relationship between metabolic, cardiac, and breathing signals and contributes to the development of a multivariable physiological model for the transmission of vagus nerve signals. This work progresses toward the development of closed-loop neuro-metabolic therapeutic systems for diabetes.
Impact of fusion reactor neutronics modeling for transmutation and thermal feedback
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Authors: Bae Jin Whan , Borowiec Katarzyna , Sircar Arpan , Badalassi Vittorio 
Published: September 2023

Abstract
Fusion neutronics calculations provide important metrics pertinent to fusion device operations, such as tritium breeding ratios (TBRs) and data on heat deposition, material activation, and damage. Because of the high computational burden required to generate a high-fidelity Monte Carlo simulation of a 3D fusion device, various assumptions are made to reduce computational time by simplifying the reactor model or the calculation iteration. This paper explores the impact of fusion neutronics metrics such as the TBR and decay heat of structural materials based on assumptions of material composition in the fusion reactor and temperature modeling of materials. Results show that for compact tokamaks with high power and long operational cycles, the transmutation of structural materials is significant enough to cause a substantial change in the flux spectrum and decrease the TBR by 1.68% after 2 years of full power operation. Additionally, assuming a constant temperature and material density can impact the TBR calculations up to 3%.
Separating internal and forced contributions to near term SST predictability in the CESM2-LE
Authors: Gordon E M , Barnes E A , Davenport F V 
Published: September 2023

Abstract
An open question in the study of climate prediction is whether internal variability will continue to contribute to prediction skill in the coming decades, or whether predictable signals will be overwhelmed by rising temperatures driven by anthropogenic forcing. We design a neural network that is interpretable such that its predictions can be decomposed to examine the relative contributions of external forcing and internal variability to future regional sea surface temperature (SST) trend predictions in the near-term climate (2020–2050). We show that there is additional prediction skill to be garnered from internal variability in the Community Earth System Model version 2 Large Ensemble, even in a relatively high forcing future scenario. This predictability is especially apparent in the North Atlantic, North Pacific and Tropical Pacific Oceans as well as in the Southern Ocean. We further investigate how prediction skill covaries across the ocean and find three regions with distinct coherent prediction skill driven by internal variability. SST trend predictability is found to be associated with consistent patterns of decadal variability for the grid points within each region.
On the convex characterisation of the set of unital quantum channels
Authors: Rodriguez-Ramos Constantino , Wilmott Colin M 
Published: September 2023

Abstract
In this paper, we consider the convex structure of the set of unital quantum channels. To do this, we introduce a novel framework to construct and characterise different families of low-rank unital quantum maps. In this framework, unital quantum maps are represented as a set of complex parameters on which we impose a set of constraints. The different families of unital maps are obtained by mapping those parameters into the operator representation of a quantum map. For these families, we also introduce a scalar measuring their distance to the set of mixed-unitary maps. We consider the particular case of qutrit channels which is the smallest set of maps for which the existence of non-unitary extremal maps is known. In this setting, we show how our framework generalises the description of well-known maps such as the antisymmetric Werner–Holevo map but also novel families of qutrit maps.
Enhancing Post-Mining Lands’ Value through Sustainable Melon Cultivation: A Hydroponic Approach
Authors: Nursanto E , Widowati I , Kaswidjanti W , Supriyanta B 
Published: September 2023

Abstract
This study investigates the potential of utilizing post-mining areas for sustainable melon (Cucurbitales, Cucurbitaceae, Cucumis) cultivation, focusing on premium melon production by PT. Indominco Mandiri within its Community Development and Empowerment program. A hydroponic system, which involves the cultivation of plants in a soilless medium with a nutrient solution, was employed due to its advantages in improving plant quality and yield. Findings demonstrate that melon thrives in ex-mining conditions, reaching harvesting maturity within 2.5 months, with fruits noted for their sweetness. Additionally, melons were found to be a rich source of vitamins A, K, C, B-6, E, niacin, and essential minerals including calcium, potassium, ferum, magnesium, phosphorus, sodium, and zinc, with low sodium and no fat content. This research underscores the hydroponic system’s potential in reclaiming post-mining lands, enhancing economic viability and fostering community development and empowerment. The implications of these findings extend to environmental rehabilitation and sustainable agriculture practices, offering a feasible and productive use of post-mining land globally.
Optimal EC Value and Pruning Strategy for Enhanced Growth in Hydroponic Watermelon Cultivation
Authors: Irawati E B , Sasmita E R , Padmini O S 
Published: September 2023

Abstract
This study investigated the impact of electrical conductivity (EC) values and pruning methods on the growth and yield of hydroponic watermelon plants (Citrullus lanatus) at the Wedomartani experimental station, Sleman DIY, from May to August 2022. Using a field experiment in a split plot design, the effects of varying EC values during different plant growth phases and differing pruning practices were evaluated. EC values (in mS/cm) for the vegetative I, vegetative II, and generative phases were manipulated at 1.2;2.0;2.8, 1.5;2.1;3.1, and 1.8;2.6;3.4 respectively, while pruning involved maintaining 2 branches and 1 fruit, 3 branches and 1 fruit, and 3 branches and 2 fruits respectively. Analyses of variance (ANOVA) and Duncan’s Multiple Range Test (DMRT) at a 5% level revealed that EC significantly impacted plant height at 14, 21, and 28 days after planting (DAP), stem diameter at 28 DAP, and leaf area at 14 DAP, but not fruit weight per plant. Pruning had a significant effect on plant height at 14 DAP, with the best results obtained from maintaining 2 branches and 1 fruit. No interaction was found between EC value and pruning on watermelon plant growth and yield. These findings offer potential strategies for improving yield in hydroponic watermelon cultivation.
ITS2 Isolation in Endemic Loka Pere Banana: A New Potential DNA Barcode Marker
Authors: Rahmah Mufti Hatur , Dwivany Fenny Martha , Esyanthi Rizkita R 
Published: September 2023

Abstract
The present research aimed to investigate Loka Pere banana (Musa sp.), an endemic species with distinct characteristics, but currently underrepresented in scientific literature. Our primary goal was to isolate the Internal Transcribed Spacer (ITS2) region from the leaf and corm samples of this unique banana variant. As a methodological approach, we utilized a modified CTAB protocol to extract DNA and amplified the ITS2 region via polymerase chain reaction (PCR) using ITS-S2F and ITS-S3R primers, achieving an amplicon size approximately 500 bp long. The results showed successful isolation of the ITS2 sequences from both leaf and corm samples of Loka Pere Banana. This finding signifies the potential of the ITS2 region of the Loka Pere banana as a novel and reliable DNA barcode marker. Future research can leverage this data for more accurate molecular identification and phylogenetic analyses, enhancing our understanding of the genetic diversity among the Musa genus.
Exploration and Inhibition Test of Penicillium sp. In Vitro by Trichoderma
Authors: Yuliantoro I D , Prihatiningrum A E , Sutarman  
Published: September 2023

Abstract

Penicillium is an entomopathogenic fungus which can be used as a bio-bactericidal. Its existence in nature can be found together with other microbes including Trichoderma, a biocontrol agent that has the ability to damage the cell walls of other fungi due to the activity of the extracellular enzyme chitinase it produces. This study aims to obtain potential isolates of Penicillium biocontrol agents from vegetable growing fields and to determine their response to inhibition by Trichoderma esperellum in vitro. On PDA-chloramphenicol media, a suspension of 104 dilution was inoculated containing sample soil from vegetable crops to be isolated and purified and identified as to its species. The entomopathogenic isolates obtained were grown together with T. esperellum in dual culture and also grown in monoculture as a comparison. The isolation results obtained Penicillium sp. Pc-02. The in vitro test results showed that Trichoderma isolates could inhibit Penicillium sp. by 35.5 ± 1.9% and supported the growth of this entomopagen fungus by 15.7% at 24 and 72 hours after inoculation, respectively.
Unlocking Agricultural Potential: Soil Structure and Analysis in a Limestone Mining Site Using Artificial Intelligence and USDA Classification
Authors: Oktaviani R , Trides T , Respati L L , Pangestu I E , Ferdyan R , Simatupang N 
Published: September 2023

Abstract
This study aimed to investigate the soil structure and agricultural potential of a research area located within a limestone mining site. The sampling process was conducted using sieve analysis and hydrometer tests on soil samples collected from six points within a 1-hectare area. The results revealed that the soil structure was Silt Loam, with 48%-64% consisting of Silt, 30%-50% sand, and 1%-6% clay. The data were then classified using USDA classification to identify the potential agricultural uses of the soil. Additionally, artificial intelligence was used through ArcGIS software to prepare a regional map and analyze soil properties. The study findings can serve as a valuable reference for agro-industry activities and land-use planning in the area.
Optimizing turning parameters for improved surface quality and productivity of Al-MMC under dry, wet, and MQL conditions
Authors: Khandey Umesh , Chandra Vaibhav , Arya Vedpal , Kumar Praveen 
Published: September 2023

Abstract
The machining of Aluminum-based Metal Matrix Composites (Al-MMCs) is challenging due to their inhomogeneity, anisotropic nature, and dynamic cutting forces. In this paper, the effect of machining parameters, including cutting speed, feed rate, and depth of cut, on surface quality (Ra) and main (Normal) cutting forces (Fc) during turning of Al-MMCs under different cutting conditions (DRY, WET, and MQL) was investigated. Statistical analysis tools were used to analyze the experimental results, and ANOVA and RSM techniques were used to model the relationships between machining parameters and responses. The results showed that feed rate significantly affected both Ra and Fc for all machining conditions. The DRY machining mode was optimal for surface finish, and the MQL mode was effective in reducing cutting forces due to its cooling and lubrication properties. A feed rate of 0.03 mm rev−1 was found to be the most favourable for all cutting environments as it resulted in lower Ra and Fc values. Turning in the WET condition also produced lower Ra due to the effective flushing of chips and cleaning of the cutting zone. In contrast, turning in the MQL condition reduced cutting forces by 17% and 50% as compared with dry and Wet conditions respectively under optimal conditions. Similarly, surface roughness improves under dry conditions by 33% and 3% compared to Wet and MQL conditions.
Recent Methodological Trends in Epidemiology: No Need for Data-Driven Variable Selection?
Authors: Staerk Christian , Byrd Alliyah , Mayr Andreas 
Published: September 2023

AbstractVariable selection in regression models is a particularly important issue in epidemiology, where one usually encounters observational studies. In contrast to randomized trials or experiments, confounding is often not controlled by the study design, but has to be accounted for by suitable statistical methods. For instance, when risk factors should be identified with unconfounded effect estimates, multivariable regression techniques can help to adjust for confounders. We investigated the current practice of variable selection in 4 major epidemiologic journals in 2019 and found that the majority of articles used subject-matter knowledge to determine a priori the set of included variables. In comparison with previous reviews from 2008 and 2015, fewer articles applied data-driven variable selection. Furthermore, for most articles the main aim of analysis was hypothesis-driven effect estimation in rather low-dimensional data situations (i.e., large sample size compared with the number of variables). Based on our results, we discuss the role of data-driven variable selection in epidemiology.
3D-SMGE: a pipeline for scaffold-based molecular generation and evaluation
Authors: Xu Chao , Liu Runduo , Huang Shuheng , Li Wenchao , Li Zhe , Luo Hai-Bin 
Published: September 2023

Abstract
In the process of drug discovery, one of the key problems is how to improve the biological activity and ADMET properties starting from a specific structure, which is also called structural optimization. Based on a starting scaffold, the use of deep generative model to generate molecules with desired drug-like properties will provide a powerful tool to accelerate the structural optimization process. However, the existing generative models remain challenging in extracting molecular features efficiently in 3D space to generate drug-like 3D molecules. Moreover, most of the existing ADMET prediction models made predictions of different properties through a single model, which can result in reduced prediction accuracy on some datasets. To effectively generate molecules from a specific scaffold and provide basis for the structural optimization, the 3D-SMGE (3-Dimensional Scaffold-based Molecular Generation and Evaluation) work consisting of molecular generation and prediction of ADMET properties is presented. For the molecular generation, we proposed 3D-SMG, a novel deep generative model for the end-to-end design of 3D molecules. In the 3D-SMG model, we designed the cross-aggregated continuous-filter convolution (ca-cfconv), which is used to achieve efficient and low-cost 3D spatial feature extraction while ensuring the invariance of atomic space rotation. 3D-SMG was proved to generate valid, unique and novel molecules with high drug-likeness. Besides, the proposed data-adaptive multi-model ADMET prediction method outperformed or maintained the best evaluation metrics on 24 out of 27 ADMET benchmark datasets. 3D-SMGE is anticipated to emerge as a powerful tool for hit-to-lead structural optimizations and accelerate the drug discovery process.
Clinical severity in Parkinson’s disease is determined by decline in cortical compensation
Authors: Johansson Martin E , Toni Ivan , Kessels Roy P C , Bloem Bastiaan R , Helmich Rick C 
Published: September 2023

Abstract
Dopaminergic dysfunction in the basal ganglia, particularly in the posterior putamen, is often viewed as the primary pathological mechanism behind motor slowing (i.e. bradykinesia) in Parkinson’s disease. However, striatal dopamine loss fails to account for interindividual differences in motor phenotype and rate of decline, implying that the expression of motor symptoms depends on additional mechanisms, some of which may be compensatory in nature. Building on observations of increased motor-related activity in the parieto-premotor cortex of Parkinson patients, we tested the hypothesis that interindividual differences in clinical severity are determined by compensatory cortical mechanisms and not just by basal ganglia dysfunction.
Using functional MRI, we measured variability in motor- and selection-related brain activity during a visuomotor task in 353 patients with Parkinson’s disease (≤5 years disease duration) and 60 healthy controls. In this task, we manipulated action selection demand by varying the number of possible actions that individuals could choose from. Clinical variability was characterized in two ways. First, patients were categorized into three previously validated, discrete clinical subtypes that are hypothesized to reflect distinct routes of α-synuclein propagation: diffuse-malignant (n = 42), intermediate (n = 128) or mild motor-predominant (n = 150). Second, we used the scores of bradykinesia severity and cognitive performance across the entire sample as continuous measures.
Patients showed motor slowing (longer response times) and reduced motor-related activity in the basal ganglia compared with controls. However, basal ganglia activity did not differ between clinical subtypes and was not associated with clinical scores. This indicates a limited role for striatal dysfunction in shaping interindividual differences in clinical severity. Consistent with our hypothesis, we observed enhanced action selection-related activity in the parieto-premotor cortex of patients with a mild-motor predominant subtype, both compared to patients with a diffuse-malignant subtype and controls. Furthermore, increased parieto-premotor activity was related to lower bradykinesia severity and better cognitive performance, which points to a compensatory role.
We conclude that parieto-premotor compensation, rather than basal ganglia dysfunction, shapes interindividual variability in symptom severity in Parkinson’s disease. Future interventions may focus on maintaining and enhancing compensatory cortical mechanisms, rather than only attempting to normalize basal ganglia dysfunction.
Regional activity and effective connectivity within the frontoparietal network during precision walking with visual cueing: an fNIRS study
Authors: Le Duc Trung , Tsuyuhara Masato , Kuwamura Hiroki , Kitano Kento , Nguyen Thu Dang , Duc Nguyen Thuan , Fujita Naoto , Watanabe Tatsunori , Nishijo Hisao , Mihara Masahito , Urakawa Susumu 
Published: September 2023

Abstract
Precision walking (PW) incorporates precise step adjustments into regular walking patterns to navigate challenging surroundings. However, the brain processes involved in PW control, which encompass cortical regions and interregional interactions, are not fully understood. This study aimed to investigate the changes in regional activity and effective connectivity within the frontoparietal network associated with PW. Functional near-infrared spectroscopy data were recorded from adult subjects during treadmill walking tasks, including normal walking (NOR) and PW with visual cues, wherein the intercue distance was either fixed (FIX) or randomly varied (VAR) across steps. The superior parietal lobule (SPL), dorsal premotor area (PMd), supplementary motor area (SMA), and dorsolateral prefrontal cortex (dlPFC) were specifically targeted. The results revealed higher activities in SMA and left PMd, as well as left-to-right SPL connectivity, in VAR than in FIX. Activities in SMA and right dlPFC, along with dlPFC-to-SPL connectivity, were higher in VAR than in NOR. Overall, these findings provide insights into the roles of different brain regions and connectivity patterns within the frontoparietal network in facilitating gait control during PW, providing a useful baseline for further investigations into brain networks involved in locomotion.