bluuebunny/crossref_metadata_2025_split huggingface dataset:
Empirical modeling and prediction of neuronal dynamics
Authors: Fisco-Compte Pau , Aquilué-Llorens David , Roqueiro Nestor , Fossas Enric , Guillamon Antoni 
Published: April 2024

AbstractMathematical modeling of neuronal dynamics has experienced a fast growth in the last decades thanks to the biophysical formalism introduced by Hodgkin and Huxley in the 1950s. Other types of models (for instance, integrate and fire models), although less realistic, have also contributed to understand neuronal dynamics. However, there is still a vast volume of data that have not been associated with a mathematical model, mainly because data are acquired more rapidly than they can be analyzed or because it is difficult to analyze (for instance, if the number of ionic channels involved is huge). Therefore, developing new methodologies to obtain mathematical or computational models associated with data (even without previous knowledge of the source) can be helpful to make future predictions. Here, we explore the capability of a wavelet neural network to identify neuronal (single-cell) dynamics. We present an optimized computational scheme that trains the ANN with biologically plausible input currents. We obtain successful identification for data generated from four different neuron models when using all variables as inputs of the network. We also show that the empiric model obtained is able to generalize and predict the neuronal dynamics generated by variable input currents different from those used to train the artificial network. In the more realistic situation of using only the voltage and the injected current as input data to train the network, we lose predictive ability but, for low-dimensional models, the results are still satisfactory. We understand our contribution as a first step toward obtaining empiric models from experimental voltage traces.
Beam narrowing test: a motor index of post-stroke motor evaluation in an aged rat model of cerebral ischemia
Authors: Pinosanu Leonard Radu , Boboc Ianis Kevyn Stefan , Balseanu Tudor Adrian , Gresita Andrei , Hermann Dirk M. , Popa‐Wagner Aurel , Catalin Bogdan 
Published: April 2024

AbstractEach year, 15 million people worldwide suffer from strokes. Consequently, researchers face increasing pressure to develop reliable behavioural tests for assessing functional recovery after a stroke. Our aim was to establish a new motor performance index that can be used to evaluate post-stroke recovery in both young and aged animals. Furthermore, we validate the proposed procedure and recommend the necessary number of animals for experimental stroke studies. Young (n = 20) and aged (n = 27) Sprague–Dawley rats were randomly assigned to receive either sham or stroke surgery. The newly proposed performance index was calculated for the post-stroke acute, subacute and chronic phases. The advantage of using our test over current tests lies in the fact that the newly proposed motor index test evaluates not only the performance of the unaffected side in comparison to the affected one but also assesses overall performance by taking into account speed and coordination. Moreover, it reduces the number of animals needed to achieve a statistical power of 80%. This aspect is particularly crucial when studying aged rodents. Our approach can be used to monitor and assess the effectiveness of stroke therapies in experimental models using aged animals.
Hydrologic interpretation of machine learning models for 10-daily streamflow simulation in climate sensitive upper Indus catchments
Authors: Mushtaq Haris , Akhtar Taimoor , Hashmi Muhammad Zia ur Rahman , Masood Amjad , Saeed Fahad 
Published: April 2024

AbstractMachine learning for hydrologic modeling has seen significant recent development and has been suggested as a valuable augmentation to physical hydrological modeling, especially in data-scarce catchments. In Pakistan, surface water flows predominantly originate from the transboundary Upper Indus sub-catchments of Chenab, Jhelum, Indus, and Kabul rivers. These catchments have large drainage areas, climate-driven streamflows, high variations in elevation, and limited streamflow gauge coverage. Hence, using machine learning models for data-driven river flow modeling may be well-suited for these catchments. However, hydrologic interpretability of machine learning models is important for the practical use of such models for these catchments. Thus, the current study besides evaluating the potential of three machine learning models (XGBOOST, Classification and Regression Trees(CART), and RandomForest) for streamflow simulation also focused on the hydrologic interpretation of machine learning models using SHapley Additive exPlananations (SHAP). All of these models performed well and the range of $$\textrm{R}^2$$

R
2

and Nasche-Efficiency for all three models lies between 0.61 to 0.90. Moreover, SHAP correctly identified minimum temperature as the most critical feature in glacier-fed Indus and Chenab catchments. It also provides logical insights into interactions between minimum temperature and precipitation for the indus and Chenab catchment. The findings of this study strongly illustrate the usefulness of SHAP analysis in interpreting the behavior of data-scarce high-elevation climate-sensitive catchments using tree-based machine learning models.
Correction of the spring-dashpot-slider model
Authors: Führer Florian , Brendel Lothar , Wolf Dietrich E. 
Published: April 2024

AbstractThe spring-dashpot-slider is a common way to include solid friction for discrete element method simulations of granular matter. However, the most popular model that is currently in use has a number of problems, including the spontaneous creation of energy. The main cause for these problems is the discontinuous evolution of the spring displacement. In this paper, we derive a differential equation for the displacement that yields a continuous time evolution, that fixes the problems of the discontinuous model and is simpler to implement.
Differential DNA methylation associated with delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage: a systematic review
Authors: Klepinowski Tomasz , Pala Bartłomiej , Pettersson Samuel D. , Łątka Kajetan , Taterra Dominik , Ogilvy Christopher S. , Sagan Leszek 
Published: April 2024

Abstract
Recent studies suggest that differential DNA methylation could play a role in the mechanism of cerebral vasospasm (CVS) and delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH). Considering the significance of this matter and a lack of effective prophylaxis against DCI, we aim to summarize the current state of knowledge regarding their associations with DNA methylation and identify the gaps for a future trial. PubMed MEDLINE, Scopus, and Web of Science were searched by two authors in three waves for relevant DNA methylation association studies in DCI after aSAH. PRISMA checklist was followed for a systematic structure. STROBE statement was used to assess the quality and risk of bias within studies. This research was funded by the National Science Centre, Poland (grant number 2021/41/N/NZ2/00844). Of 70 records, 7 peer-reviewed articles met the eligibility criteria. Five studies used a candidate gene approach, three were epigenome-wide association studies (EWAS), one utilized bioinformatics of the previous EWAS, with two studies using more than one approach. Methylation status of four cytosine-guanine dinucleotides (CpGs) related to four distinct genes (ITPR3, HAMP, INSR, CDHR5) have been found significantly or suggestively associated with DCI after aSAH. Analysis of epigenetic clocks yielded significant association of lower age acceleration with radiological CVS but not with DCI. Hub genes for hypermethylation (VHL, KIF3A, KIFAP3, RACGAP1, OPRM1) and hypomethylation (ALB, IL5) in DCI have been indicated through bioinformatics analysis. As none of the CpGs overlapped across the studies, meta-analysis was not applicable. The identified methylation sites might potentially serve as a biomarker for early diagnosis of DCI after aSAH in future. However, a lack of overlapping results prompts the need for large-scale multicenter studies. Challenges and prospects are discussed.
Interleukin-6 drives endothelial glycocalyx damage in COVID-19 and bacterial sepsis
Authors: Drost Carolin Christina , Rovas Alexandros , Osiaevi Irina , Schughart Klaus , Lukasz Alexander , Linke Wolfgang A. , Pavenstädt Hermann , Kümpers Philipp 
Published: April 2024

AbstractDamage of the endothelial glycocalyx (eGC) plays a central role in the development of vascular hyperpermeability and organ damage during systemic inflammation. However, the specific signalling pathways for eGC damage remain poorly defined. Aim of this study was to combine sublingual video-microscopy, plasma proteomics and live cell imaging to uncover further pathways of eGC damage in patients with coronavirus disease 2019 (COVID-19) or bacterial sepsis. This secondary analysis of the prospective multicenter MICROCODE study included 22 patients with COVID-19 and 43 patients with bacterial sepsis admitted to intermediate or intensive care units and 10 healthy controls. Interleukin-6 (IL-6) was strongly associated with damaged eGC and correlated both with eGC dimensions (rs=0.36, p = 0.0015) and circulating eGC biomarkers. In vitro, IL-6 reduced eGC height and coverage, which was inhibited by blocking IL-6 signalling with the anti-IL-6 receptor antibody tocilizumab or the Janus kinase inhibitor tofacitinib. Exposure of endothelial cells to 5% serum from COVID-19 or sepsis patients resulted in a significant decrease in eGC height, which was attenuated by co-incubation with tocilizumab. In an external COVID-19 cohort of 219 patients from Massachusetts General Hospital, a previously identified proteomic eGC signature correlated with IL-6 (rs=-0.58, p < 0.0001) and predicted the combined endpoint of 28-day mortality and/or intubation (ROC-AUC: 0.86 [95% CI: 0.81–0.91], p < 0.001). The data suggest that IL-6 may significantly drive eGC damage in COVID-19 and bacterial sepsis. Our findings provide valuable insights into pathomechanisms of vascular dysfunction during systemic inflammation and highlight the need for further in vivo studies.
Comparisons of songbirds on sale across online and physical markets in Indonesia
Authors: Grimwood Emily I. , Menner Jochen K. , Marsden Stuart J. 
Published: April 2024

AbstractUnsustainable wildlife trade is a leading threat to biodiversity, not least in Southeast Asia where serious overexploitation of songbirds has precipitated the ‘Asian Songbird Crisis’. While the nature of bird trade in physical markets is fairly well studied, the growing online trade in birds is far less understood, in terms of diversity and traits of birds on offer. Here, online trade, monitored across twelve broad spectrum Indonesian bird-selling Facebook groups over a period of six months in 2022, is compared to published data from physical markets, and from a machine learning web-scrape. Nearly 2,000 individuals of 190 Indonesian species were recorded from Facebook, with 9.5% of species being categorised as Threatened (IUCN 2022), 15.8% protected under Indonesian law, and 17.4% regarded as priority taxa according to the Asian Songbird Trade Specialist Group. These represent similar proportions of species to those from physical market surveys, although physical markets had more individuals of protected species than did Facebook groups. Bird family composition did not correlate between online and physical platforms, with the former dominated by Muscicapidae and the latter by Estrildidae. Controlling for trade volume, online groups had higher species richness than physical markets, although the difference was not significant. Bird ‘communities’ on offer in the individual Facebook groups were both similar to each other, and distinct from those in physical markets, although there was a geographical signature especially in the latter. Results highlight the importance of monitoring online trade as, while there are substantial differences in types of birds sold, it contains a similarly high number of species of conservation concern to physical markets.
Sphagnan in Sphagnum-dominated peatlands: bioavailability and effects on organic matter stabilization
Authors: Bryan Lauren , Shaw Rachel , Schoonover Erik , Koehl Alexis , DeVries-Zimmerman Suzanne , Philben Michael 
Published: April 2024

AbstractThe unique pectin-like carbohydrate “sphagnan” has been shown to protect organic matter from microbial decomposition in Sphagnum-dominated peatlands. However, the bioavailability of sphagnan has not been evaluated, and it is not known if it persists or continues to affect decomposition processes over the long timescales of peat formation. To address this, we assessed the connection between sphagnan content and organic matter decomposition rates in a temperate peatland near Fennville, MI, USA. We compared the effects of sphagnan over two timescales: (1) a short-term litter incubation assay using mosses from different peatland microtopographies; and (2) oxic and anoxic incubation assays of peat collected from multiple depths within the peat profile, reflecting a natural long-term decomposition continuum. On both timescales, we hypothesized that higher sphagnan content would be associated with lower decomposition rates, and that sphagnan would be selectively preserved compared to bulk C and other carbohydrates. The litter decomposition experiment supported both hypotheses, as higher sphagnan content was associated with lower mass loss, and sphagnan content increased due to selective preservation. In the peat, we observed weak but significant correlations between the relative abundance of sphagnan (as a fraction of total non-cellulosic sugars) and both aerobic and anaerobic respiration rates. This relationship was stronger in cores collected from hollow microtopographies than those from hummocks. However, there was not a significant relationship between respiration rates and the total (C-normalized) sphagnan content. Sphagnan content increased with depth in the peat profile, indicating selective preservation compared to bulk C. Additionally, we observed the accumulation of non-cellulosic glucose in the deep peat, likely derived from microbial exopolysaccharides. Together, these results indicate that sphagnan persists in the catotelm and continues to contribute to the long-term stabilization of organic matter in Sphagnum-rich peatlands, although the weak relationship with respiration indicates that its influence is relatively minor.
Lost in the Forest: Encoding categorical variables and the absent levels problem
Authors: Smith Helen L. , Biggs Patrick J. , French Nigel P. , Smith Adam N. H. , Marshall Jonathan C. 
Published: April 2024

AbstractLevels of a predictor variable that are absent when a classification tree is grown can not be subject to an explicit splitting rule. This is an issue if these absent levels are present in a new observation for prediction. To date, there remains no satisfactory solution for absent levels in random forest models. Unlike missing data, absent levels are fully observed and known. Ordinal encoding of predictors allows absent levels to be integrated and used for prediction. Using a case study on source attribution of Campylobacter species using whole genome sequencing (WGS) data as predictors, we examine how target-agnostic versus target-based encoding of predictor variables with absent levels affects the accuracy of random forest models. We show that a target-based encoding approach using class probabilities, with absent levels designated the highest rank, is systematically biased, and that this bias is resolved by encoding absent levels according to the a priori hypothesis of equal class probability. We present a novel method of ordinal encoding predictors via principal coordinates analysis (PCO) which capitalizes on the similarity between pairs of predictor levels. Absent levels are encoded according to their similarity to each of the other levels in the training data. We show that the PCO-encoding method performs at least as well as the target-based approach and is not biased.
The challenge of change: understanding the role of habits in university students’ self-regulated learning
Authors: David Louise , Biwer Felicitas , Crutzen Rik , de Bruin Anique 
Published: April 2024

AbstractStudy habits drive a large portion of how university students study. Some of these habits are not effective in fostering academic achievement. To support students in breaking old, ineffective habits and forming new, effective study habits, an in-depth understanding of what students’ study habits look like and how they are both formed and broken is needed. Therefore, in this study, we explored these aspects among first-year university students in six focus group discussions (N = 29). Using a thematic analysis approach, we clustered the data in five themes: Goals Matter, Balancing Perceived Efficiency and Effectiveness when Studying, Navigating Student Life: from Structured Routines to Self-Regulation Challenges, the Quest for Effective Habits with Trying to Break Free From the Screen as subtheme, and the Motivation Roller Coaster. Findings suggest that students had different study habits depending on their goals. Students had quite accurate metacognitive knowledge about effective learning strategies for long-term learning, but often used other learning strategies they deemed most efficient in reaching their goals. Students indicated intentions to change, but did not prioritize change as their current habits enabled them to pass exams and change was not perceived as adding value. Fluctuations in motivation and transitioning to a self-regulated life hampered students’ intentions to form new and break old habits. Next to insights into factors affecting students’ behavioral change intentions, the findings suggest the importance of aligning assessment methods with life-long learning and supporting students in their long-term academic goal setting to prioritize study habits which target lasting learning to optimally foster their self-regulated learning.
Vision-state Fusion: Improving Deep Neural Networks for Autonomous Robotics
Authors: Cereda Elia , Bonato Stefano , Nava Mirko , Giusti Alessandro , Palossi Daniele 
Published: April 2024

AbstractVision-based deep learning perception fulfills a paramount role in robotics, facilitating solutions to many challenging scenarios, such as acrobatic maneuvers of autonomous unmanned aerial vehicles (UAVs) and robot-assisted high-precision surgery. Control-oriented end-to-end perception approaches, which directly output control variables for the robot, commonly take advantage of the robot’s state estimation as an auxiliary input. When intermediate outputs are estimated and fed to a lower-level controller, i.e., mediated approaches, the robot’s state is commonly used as an input only for egocentric tasks, which estimate physical properties of the robot itself. In this work, we propose to apply a similar approach for the first time – to the best of our knowledge – to non-egocentric mediated tasks, where the estimated outputs refer to an external subject. We prove how our general methodology improves the regression performance of deep convolutional neural networks (CNNs) on a broad class of non-egocentric 3D pose estimation problems, with minimal computational cost. By analyzing three highly-different use cases, spanning from grasping with a robotic arm to following a human subject with a pocket-sized UAV, our results consistently improve the R$$^{2}$$

2

regression metric, up to +0.51, compared to their stateless baselines. Finally, we validate the in-field performance of a closed-loop autonomous cm-scale UAV on the human pose estimation task. Our results show a significant reduction, i.e., 24% on average, on the mean absolute error of our stateful CNN, compared to a State-of-the-Art stateless counterpart.
Patient Engagement with Conversational Agents in Health Applications 2016–2022: A Systematic Review and Meta-Analysis
Authors: Cevasco Kevin E. , Morrison Brown Rachel E. , Woldeselassie Rediet , Kaplan Seth 
Published: April 2024

Abstract
Clinicians and patients seeking electronic health applications face challenges in selecting effective solutions due to a high market failure rate. Conversational agent applications (“chatbots”) show promise in increasing healthcare user engagement by creating bonds between the applications and users. It is unclear if chatbots improve patient adherence or if past trends to include chatbots in electronic health applications were due to technology hype dynamics and competitive pressure to innovate. We conducted a systematic literature review using Preferred Reporting Items for Systematic reviews and Meta-Analyses methodology on health chatbot randomized control trials. The goal of this review was to identify if user engagement indicators are published in eHealth chatbot studies. A meta-analysis examined patient clinical trial retention of chatbot apps. The results showed no chatbot arm patient retention effect. The small number of studies suggests a need for ongoing eHealth chatbot research, especially given the claims regarding their effectiveness made outside the scientific literatures.
Exposing and explaining fake news on-the-fly
Authors: de Arriba-Pérez Francisco , García-Méndez Silvia , Leal Fátima , Malheiro Benedita , Burguillo Juan Carlos 
Published: April 2024

AbstractSocial media platforms enable the rapid dissemination and consumption of information. However, users instantly consume such content regardless of the reliability of the shared data. Consequently, the latter crowdsourcing model is exposed to manipulation. This work contributes with an explainable and online classification method to recognize fake news in real-time. The proposed method combines both unsupervised and supervised Machine Learning approaches with online created lexica. The profiling is built using creator-, content- and context-based features using Natural Language Processing techniques. The explainable classification mechanism displays in a dashboard the features selected for classification and the prediction confidence. The performance of the proposed solution has been validated with real data sets from Twitter and the results attain 80% accuracy and macro F-measure. This proposal is the first to jointly provide data stream processing, profiling, classification and explainability. Ultimately, the proposed early detection, isolation and explanation of fake news contribute to increase the quality and trustworthiness of social media contents.