bluuebunny/crossref_metadata_2025_split huggingface dataset:
Projecting the impact of an ebola virus outbreak on endangered mountain gorillas
Authors: Zimmerman Dawn M. , Hardgrove Emily , Sullivan Sara , Mitchell Stephanie , Kambale Eddy , Nziza Julius , Ssebide Benard , Shalukoma Chantal , Cranfield Mike , Pandit Pranav S. , Troth Sean P. , Callicrate Taylor , Miller Philip , Gilardi Kirsten , Lacy Robert C. 
Published: April 2023

AbstractEbola virus is highly lethal for great apes. Estimated mortality rates up to 98% have reduced the global gorilla population by approximately one-third. As mountain gorillas (Gorilla beringei beringei) are endangered, with just over 1000 individuals remaining in the world, an outbreak could decimate the population. Simulation modeling was used to evaluate the potential impact of an Ebola virus outbreak on the mountain gorilla population of the Virunga Massif. Findings indicate that estimated contact rates among gorilla groups are high enough to allow rapid spread of Ebola, with less than 20% of the population projected to survive at 100 days post-infection of just one gorilla. Despite increasing survival with vaccination, no modeled vaccination strategy prevented widespread infection. However, the model projected that survival rates greater than 50% could be achieved by vaccinating at least half the habituated gorillas within 3 weeks of the first infectious individual.
Using machine learning to predict student retention from socio-demographic characteristics and app-based engagement metrics
Authors: Matz Sandra C. , Bukow Christina S. , Peters Heinrich , Deacons Christine , Dinu Alice , Stachl Clemens 
Published: April 2023

AbstractStudent attrition poses a major challenge to academic institutions, funding bodies and students. With the rise of Big Data and predictive analytics, a growing body of work in higher education research has demonstrated the feasibility of predicting student dropout from readily available macro-level (e.g., socio-demographics or early performance metrics) and micro-level data (e.g., logins to learning management systems). Yet, the existing work has largely overlooked a critical meso-level element of student success known to drive retention: students’ experience at university and their social embeddedness within their cohort. In partnership with a mobile application that facilitates communication between students and universities, we collected both (1) institutional macro-level data and (2) behavioral micro and meso-level engagement data (e.g., the quantity and quality of interactions with university services and events as well as with other students) to predict dropout after the first semester. Analyzing the records of 50,095 students from four US universities and community colleges, we demonstrate that the combined macro and meso-level data can predict dropout with high levels of predictive performance (average AUC across linear and non-linear models = 78%; max AUC = 88%). Behavioral engagement variables representing students’ experience at university (e.g., network centrality, app engagement, event ratings) were found to add incremental predictive power beyond institutional variables (e.g., GPA or ethnicity). Finally, we highlight the generalizability of our results by showing that models trained on one university can predict retention at another university with reasonably high levels of predictive performance.
DUSP6 is a memory retention feedback regulator of ERK signaling for cellular resilience of human pluripotent stem cells in response to dissociation
Authors: Yoo Dae Hoon , Im Young Sam , Oh Ji Young , Gil Dayeon , Kim Yong-Ou 
Published: April 2023

AbstractCultured human pluripotent stem cells (hPSCs) grow as colonies that require breakdown into small clumps for further propagation. Although cell death mechanism by single-cell dissociation of hPSCs has been well defined, how hPSCs respond to the deadly stimulus and recover the original status remains unclear. Here we show that dissociation of hPSCs immediately activates ERK, which subsequently activates RSK and induces DUSP6, an ERK-specific phosphatase. Although the activation is transient, DUSP6 expression persists days after passaging. DUSP6 depletion using the CRISPR/Cas9 system reveals that DUSP6 suppresses the ERK activity over the long term. Elevated ERK activity by DUSP6 depletion increases both viability of hPSCs after single-cell dissociation and differentiation propensity towards mesoderm and endoderm lineages. These findings provide new insights into how hPSCs respond to dissociation in order to maintain pluripotency.
Disruptions of default mode network and precuneus connectivity associated with cognitive dysfunctions in tinnitus
Authors: Rosemann Stephanie , Rauschecker Josef P. 
Published: April 2023

AbstractTinnitus is the perception of a ringing, buzzing or hissing sound “in the ear” without external stimulation. Previous research has demonstrated changes in resting-state functional connectivity in tinnitus, but findings do not overlap and are even contradictory. Furthermore, how altered functional connectivity in tinnitus is related to cognitive abilities is currently unknown. Here we investigated resting-state functional connectivity differences between 20 patients with chronic tinnitus and 20 control participants matched in age, sex and hearing loss. All participants underwent functional magnetic resonance imaging, audiometric and cognitive assessments, and filled in questionnaires targeting anxiety and depression. Significant differences in functional connectivity between tinnitus patients and control participants were not obtained. However, we did find significant associations between cognitive scores and functional coupling of the default mode network and the precuneus with the superior parietal lobule, supramarginal gyrus, and orbitofrontal cortex. Further, tinnitus distress correlated with connectivity between the precuneus and the lateral occipital complex. This is the first study providing evidence for disruptions of default mode network and precuneus coupling that are related to cognitive dysfunctions in tinnitus. The constant attempt to decrease the tinnitus sensation might occupy certain brain resources otherwise available for concurrent cognitive operations.
Association between maternal postpartum depressive symptoms, socioeconomic factors, and birth outcomes with infant growth in South Africa
Authors: Ricci Hannah , Nakiranda Regina , Malan Linda , Kruger Herculina S. , Visser Marina , Ricci Cristian , Faber Mieke , Smuts Cornelius M. 
Published: April 2023

AbstractThis study aimed to investigate the association between maternal postpartum depressive symptoms, household demographic, socioeconomic, and infant characteristics with infant physical growth, and how these factors correlate to determine latent factors. This study was based on the baseline data of a 6-month randomised controlled trial aimed at providing an egg a day to infants aged 6 to 9-months from a low socioeconomic community in South Africa. Information collected on household demographic, socioeconomic, and infant characteristics was by face-to-face structured interviews, and trained assessors took anthropometric measurements. The Edinburgh Postnatal Depression Scale (EPDS) was used to assess maternal postpartum depressive symptoms. The analysis was based on 428 mother-infant pairs. Total EPDS score and its subscales score were not associated with stunting or underweight risk. However, a three- to four-fold increased risk of stunting and underweight, respectively was observed for premature birth. Low birthweight was associated with an estimated six-fold increased risk of underweight and stunting. Being female was associated with about 50% reduced risk of stunting and underweight. In conclusion, more robust studies are needed to substantiate these findings, with more awareness creation on the consequences of LBW and prematurity on the physical growth of infants from resource-limited settings.
The unraveling of balanced complexes in metabolic networks
Authors: Langary Damoun , Küken Anika , Nikoloski Zoran 
Published: April 2023

AbstractBalanced complexes in biochemical networks are at core of several theoretical and computational approaches that make statements about the properties of the steady states supported by the network. Recent computational approaches have employed balanced complexes to reduce metabolic networks, while ensuring preservation of particular steady-state properties; however, the underlying factors leading to the formation of balanced complexes have not been studied, yet. Here, we present a number of factorizations providing insights in mechanisms that lead to the origins of the corresponding balanced complexes. The proposed factorizations enable us to categorize balanced complexes into four distinct classes, each with specific origins and characteristics. They also provide the means to efficiently determine if a balanced complex in large-scale networks belongs to a particular class from the categorization. The results are obtained under very general conditions and irrespective of the network kinetics, rendering them broadly applicable across variety of network models. Application of the categorization shows that all classes of balanced complexes are present in large-scale metabolic models across all kingdoms of life, therefore paving the way to study their relevance with respect to different properties of steady states supported by these networks.
Sensor-cable-probe and sampler for early detection and prediction of dry matter loss and real-time corn grain quality in transport and storage
Authors: Nunes Camila Fontoura , Coradi Paulo Carteri , Jaques Lanes Beatriz Acosta , Teodoro Larissa Pereira Ribeiro , Teodoro Paulo Eduardo 
Published: April 2023

AbstractTaking into account that the transport of grains can be carried out over long distances and that the mass of grains during transport often has high moisture content, there may be risks of heat and moisture transfer and heating of the grains mass, proving quanti-qualitative losses. Thus, this study aimed to validate a method with probe system for real-time monitoring of temperature, relative humidity and carbon dioxide in the grain mass of corn during transport and storage to detect early dry matter losses and predict possible changes on the grain physical quality. The equipment consisted of a microcontroller, system's hardware, digital sensors to detect air temperature and relative humidity, a non-destructive infrared sensor to detect CO2 concentration. Real-time monitoring system determined early and satisfactorily in an indirect way the changes in the physical quality of the grains confirming by the physical analyses of electrical conductivity and germination. The equipment in real-time monitoring and the application of Machine Learning was effective to predict dry matter loss, due to the high equilibrium moisture content and respiration of the grain mass on the 2-h period. All machine learning models, except support vector machine, obtained satisfactory results, equaling the multiple linear regression analysis.
Trophic state resilience to hurricane disturbance of Lake Yojoa, Honduras
Authors: Fadum J. M. , Waters M. N. , Hall E. K. 
Published: April 2023

AbstractCyclones are a poorly described disturbance in tropical lakes, with the potential to alter ecosystems and compromise the services they provide. In November 2020, Hurricanes Eta and Iota made landfall near the Nicaragua-Honduras border, inundating the region with a large amount of late-season precipitation. To understand the impact of these storms on Lake Yojoa, Honduras, we compared 2020 and 2021 conditions using continuous (every 16 days) data collected from five pelagic locations. The storms resulted in increased Secchi depth and decreased algal abundance in December 2020, and January and February 2021, and lower-than-average accumulation of hypolimnetic nutrients from the onset of stratification (April 2021) until mixus in November 2021. Despite the reduced hypolimnetic nutrient concentrations, epilimnetic nutrient concentrations returned to (and in some cases exceeded) pre-hurricane levels following annual water column turnover in 2021. This response suggests that Lake Yojoa’s trophic state had only an ephemeral response to the disturbance imposed by the two hurricanes, likely due to internal input of sediment derived nutrients. These aseasonal storms acted as a large-scale experiment that resulted in nutrient dilution and demonstrated the resilience of Lake Yojoa’s trophic state to temporary nutrient reductions.
Effect of the new silicon-based agent on the symptoms of interstitial pneumonitis
Authors: Shimada Masato , Koyama Yoshihisa , Kobayashi Yuki , Kobayashi Hikaru , Shimada Shoichi 
Published: April 2023

AbstractInterstitial pneumonia (IP) is a collective term for diseases whose main lesion is fibrosis of the pulmonary interstitium, and the prognosis associated with acute exacerbation of these conditions is often poor. Therapeutic agents are limited to steroids, immunosuppressants, and antifibrotic drugs, which and have many side effects; therefore, the development of new therapeutic agents is required. Because oxidative stress contributes to lung fibrosis in IP, optimal antioxidants may be effective for the treatment of IP. Silicon (Si)-based agents, when administered orally, can continuously generate a large amount of antioxidant hydrogen in the intestinal tract. In this study, we investigated the effect of our Si-based agent on methotrexate-induced IP, using the IP mouse models. Pathological analysis revealed that interstitial hypertrophy was more significantly alleviated in the Si-based agent-treated group than in the untreated group (decreased by about 22%; P < 0.01). Moreover, additional morphological analysis demonstrated that infiltration of immune cells and fibrosis in the lungs were significantly inhibited by treatment with the Si-based agent. Furthermore, Si-based agent reduced oxidative stress associated with IP by increasing blood antioxidant activity. (increased by about 43%; P < 0.001). Taken together, these results suggest that Si-based agents can be effective therapeutic agents for IP.
Factors affecting digital technology access in vocational education
Authors: Habibi Akhmad , Sofyan Sofyan , Mukminin Amirul 
Published: April 2023

AbstractIf policies are not thoroughly designed, technology integration may fail. As a result, users’ perceptions of technology, especially access to digital technology, are critical for technology integration in education. This study aimed to develop and validate a scale to model factors affecting digital technology access for instructional use in Indonesian vocational schools. The study also reports the structural model of the path analysis and tests of differences based on geographical areas. A scale adapted from prior studies was established, validated, and examined for its validity and reliability. A total of 1355 responses were measurable; partial least squares structural equation modeling (PLS-SEM) and t-test procedures were applied for the data analysis. The findings informed that the scale was valid and reliable. For the structural model, the strongest relationship emerged between motivational access and skills access, while the lowest existed between material access and skills access. However, motivational access has an insignificant effect on instructional use. The t-test results show that geographical areas were significantly different regarding all involved variables.
Influence of domain walls thickness, density and alignment on Barkhausen noise emission in low alloyed steels
Authors: Neslušan M. , Pitoňák M. , Minárik P. , M. Tkáč  , Kollár P. , Životský O. 
Published: April 2023

AbstractThis study deals with the characterization of low alloyed steels of different yield strengths (varying in the range of 235–1100 MPa) via Barkhausen noise emission. The study investigates the potential of this technique to distinguish among the low alloyed steels and all significant aspects contributing to Barkhausen noise, such as the residual stress state, microstructure expressed in terms of dislocation density, grain size, prevailing phase, as well as associated aspects of the domain wall substructure (domain wall thickness, energy, their spacing and density in the matrix). Barkhausen noise in the rolling as well as transversal direction grows along with the yield strength (up to 500 MPa) and the corresponding grain refinement of ferrite. As soon as the martensite transformation occurs in a high strength matrix, this evolution saturates, and remarkable magnetic anisotropy is developed when Barkhausen noise in the transversal direction grows at the expense of the rolling direction. The contribution of residual stresses as well as the domain wall thickness is only minor, and the evolution of Barkhausen noise is driven by the density of the domain walls and their realignment.
Persistent currents and electronic properties of Mandelbrot quantum rings
Authors: Tehrani Davood Haji Taghi , Solaimani M. 
Published: April 2023

AbstractIn this study, we investigate the persistent current, and electronic energy levels of Mandelbrot quantum rings. For this purpose, three types of Mandelbrot quantum rings are proposed. Furthermore, Mandelbrot equation is generalized by introducing parameter m, which makes Mandelbrot’s shape more symmetric by adding new branches to it, on the other hand, the iteration parameter M, controls geometrical deficiencies. We explain the procedure needed to form these structures, including a padding scheme, then we solve the resulting two-dimensional Schrodinger equation using the central finite difference method with uniform distribution of the mesh points. Thereafter, we obtain the persistent current in different situations including different Mandelbrot orders and quantum ring shapes. We show that the persistent current can have different shapes and intensities by changing the described geometrical parameters of Mandelbrot quantum rings. We explain this phenomenon by considering symmetries in the potential, and consequently the wavefunction.
Buried object characterization by data-driven surrogates and regression-enabled hyperbolic signature extraction
Authors: Yurt Reyhan , Torpi Hamid , Kizilay Ahmet , Koziel Slawomir , Pietrenko-Dabrowska Anna , Mahouti Peyman 
Published: April 2023

AbstractThis work addresses artificial-intelligence-based buried object characterization using FDTD-based electromagnetic simulation toolbox of a Ground Penetrating Radar (GPR) to generate B-scan data. In data collection, FDTD-based simulation tool, gprMax is used. The task is to estimate geophysical parameters of a cylindrical shape object of various radii, buried at different positions in the dry soil medium simultaneously and independently of each other. The proposed methodology capitalizes on a fast and accurate data-driven surrogate model developed for object characterization in terms of its vertical and lateral position, and the size. The surrogate is constructed in a computationally efficient manner as compared to methodologies using 2D B-scan image. This is achieved by operating at the level of hyperbolic signatures extracted from the B-scan data through linear regression, which effectively reduces the dimensionality and the size of data. The proposed methodology relies on reducing of 2D B-scan image to 1D data including variation of reflected electric fields’ amplitudes with respect to the scanning aperture. The input of the surrogate model is the extracted hyperbolic signature obtained through linear regression executed on the background subtracted B-scan profiles. The hyperbolic signatures encode information about the geophysical parameters of the buried object, including depth, lateral position, and radius, all of which can be extracted using proposed methodology. Parametric estimation of the object radius and the estimation of the location parameters simultaneously is a challenging problem. Applying the application of processing steps on B-scan profiles incurs high computational costs, which is a limitation of the current methodologies. The metamodel itself is rendered using a novel deep-learning-based modified multilayer perceptron (M2LP) framework. The presented object characterization technique is favourably benchmarked against the state-of-the-art regression techniques, including Multilayer Perceptron (MLP), Support Vector Regression Machine (SVRM), and Convolutional Neural Network (CNN). The verification results demonstrate the average mean absolute error of 10 mm, and the average relative error of 8 percent, both corroborating the relevance of the proposed M2LP framework. In addition, the presented methodology provides a well-structured relation between the geophysical parameters of object and the extracted hyperbolic signatures. For the sake of supplementary verification under realistic scenarios, it is also applied for scenarios involving noisy data. The environmental and internal noise of the GPR system and their effect is analyzed as well. Furthermore, the proposed surrogate modeling approach is validated using measurement data, which is indicative of suitability of the approach to handle physical measurements as data sources.
Patterns and driving factors of ecological stoichiometry in system of deadwood and soil in mountains forest ecosystem
Authors: Błońska Ewa , Piaszczyk Wojciech , Lasota Jarosław 
Published: April 2023

AbstractThe aim of our research was to identify the factors that most strongly determine the C, N and P cycles in the deadwood—soil system in mountains forest ecosystems. We assumed that the climatic conditions resulting from the location in the altitude gradient and rate of deadwood decomposition most strongly determine the C/N/P stoichiometry. A climosequence approach comprising north (N) and south (S) exposure along the altitudinal gradient (600, 800, 1000 and 1200 m a.s.l.) was set up. Spruce logs at different decomposition stages (III, IV and V) were selected for the analysis in Babiogórski National Park (southern Poland). We calculated the C/N/P stoichiometry for deadwood and soil samples to reflect the nutrient availability. Our research indicates a very strong influence of the location conditions in the altitude gradient on the C/N/P stoichiometry. The GLM analysis confirmed the importance of high elevation in shaping the C, N and P content. A strong correlation was confirmed between P content, N content and C/N ratio. A higher C/N/P ratio was found in deadwood compared to soil, regardless of location. Decaying wood is an important source of N and P and the degree of decomposition made a significant contribution to explaining the variability of C, N and P content. The obtained results indicate the need to leave deadwood in forest ecosystems in order to improve biogeochemical cycles. Deadwood, by having a beneficial effect on many components of the forest ecosystem, will improve its biodiversity and, consequently, its stability.
Future-proofing ecosystem restoration through enhancing adaptive capacity
Authors: Frietsch Marina , Loos Jacqueline , Löhr Katharina , Sieber Stefan , Fischer Joern 
Published: April 2023

AbstractSocial-ecological ecosystem restoration involves interacting challenges, including climate change, resource overexploitation and political instability. To prepare for these and other emerging threats, we synthesized key restoration and social-ecological systems literature and derived three guiding themes that can help to enhance the adaptive capacity of restoration sites: (i) work with the existing system, (ii) create self-sustaining, adaptive systems, and (iii) foster diversity and participation. We propose a two-step approach and provide an example from Rwanda detailing the application of these principles. While site-specific activities have to be designed and implemented by local practitioners, our synthesis can guide forward-thinking restoration practice.