bluuebunny/crossref_metadata_2025_split huggingface dataset:
E-scooter driving under the acute influence of alcohol—a real-driving fitness study
Authors: Zube Katharina , Daldrup Thomas , Lau Michael , Maatz Rüdiger , Tank Anne , Steiner Irina , Schwender Holger , Hartung Benno 
Published: February 2022

Abstract
Purpose
To assess the effects of alcohol on the ability to drive an e-scooter, driving tests reflecting real-life situations accompanied by medical examinations focusing on balance were conducted at different blood alcohol concentrations (BACs).

Methods
Fifty-seven subjects who consumed alcohol (28 female, 29 male) and 6 consistently sober subjects (3 female, 3 male) participated in the study. Alcohol was administered on a fixed schedule, and the individual drinking quantity was individually calculated in advance using the Widmark formula. Repeated runs through a fixed course were performed. Following each ride, a blood sample was taken for BAC determination, and medical tests were performed.

Results
Even at low BACs (0.21–0.60 g/kg), subjects showed a significant decrease in driving performance, to approximately 60% of the initial level. Differences in driving performance at different BAC ranges were observed for different obstacles, especially for the narrowing track, gate passage, slalom, and driving in circles obstacles. Furthermore, worse Romberg and Unterberger test results were correlated with worse driving performance. It cannot be assumed that learning effects during the study had a relevant effect, as shown in the comparison of the driving performance of the alcohol-consuming group with that of the control group. Sex-specific differences were not found.

Discussion
Significant deteriorations in driving performance at BACs below 1.10 g/kg confirmed alcohol-related risk potential when using e-scooters. At this time, these findings may lead to the assumption of “relative driving impairment” in Germany. The Romberg and Unterberger tests could be considered a complementary investigation method for the assessment of e-scooter driving impairment.

Conclusion
Even at rather low BACs between 0.21 and 0.40 g/kg, there was a significant deterioration in driving performance under the influence of alcohol compared to sober, which highlights the negative effects of alcohol on e-scooter driving.
Automated Rock Quality Designation Using Convolutional Neural Networks
Authors: Alzubaidi Fatimah , Mostaghimi Peyman , Si Guangyao , Swietojanski Pawel , Armstrong Ryan T. 
Published: February 2022

AbstractMineral and hydrocarbon exploration relies heavily on geological and geotechnical information extracted from drill cores. Traditional drill-core characterization is based purely on the subjective expertise of a geologist. New technologies can provide automatic mineral analysis and high-resolution drill core images in a non-destructive manner. However, automated rock mass characterization presents a significant challenge due to its lack of generalization and robustness. To date, the automated estimation of rock quality designation (RQD), a key parameter for rock mass classification, is based mostly on digital image processing techniques with significant user biases. Alternatively, we propose using computer vision and machine learning-based algorithms for drill core characterization using drill core images to determine the RQD. A convolutional neural network (CNN) is used to detect and classify intact and non-intact cores, and to filter out empty tray areas and non-rock objects present in the core trays. The model calculates the length of the detected intact cores and estimates the RQD. We train the CNN model with thousands of sandstone core images from different drill holes in South Australia. The proposed method is tested on 540 sandstone core rows and 90 limestone core rows (~ 1 m each), which produces average error rates of 2.58% and 3.17%, respectively.
Frequency dependency of therapeutic efficacy in dorsal root ganglion stimulation for neuropathic pain
Authors: Piedade G. S. , Gillner S. , McPhillips P. S. , Vesper J. , Slotty P. J. 
Published: February 2022

Abstract

Background
The influence of the stimulation frequency on the outcomes of dorsal root ganglion stimulation (DRG-S) to treat pain is not well understood. It is assumed that specific neural components dedicated to different tasks in the DRG can be preferably influenced at specific frequencies. The identification of frequencies designed for the type of pain and the ratio of neuropathic versus nociceptive pain might improve overall pain control and open new indications in DRG-S.

Method
We report on a randomized double-blind clinical trial with a crossover design. Patients with a permanent DRG-S system underwent phases of stimulation with 20 Hz, 40 Hz, 60 Hz, 80 Hz, and sham in a randomized order. Each phase lasted for 4 days and was followed by a 2-day washout period. Pain intensity and quality of life were assessed with visual analog scale (VAS), McGill Pain Questionnaire (MPQ), EQ-5D, and Beck Depression Inventory (BDI). Analgesics intake was assessed.

Results
Overall 19 patients were included in the study. CRPS was the most frequent pain etiology (7). Five patients had a PainDetect score of 12 or lower at baseline. The mean VAS before the system was implanted was 8.6 and 3.9 at the baseline. Pain intensity was reduced to 3.7 by the stimulation with 20 Hz but increased with higher frequencies reaching 5.8 at 80 Hz. A significant difference among the groups was shown over all variables examined (VAS, MPQ, EQ-5D, BDI). The best results were seen at 20 Hz for all variables, including the smallest increase in pain medication consumption.

Conclusions
The choice of the stimulation frequency shows a clear influence on pain reduction and quality of life. Lower stimulation frequencies seem to be most effective in neuropathic pain. Further studies are required to determine whether specific frequencies should be preferred based on the condition treated.
Comparison of Machine Learning Methods Using Spectralis OCT for Diagnosis and Disability Progression Prognosis in Multiple Sclerosis
Authors: Montolío Alberto , Cegoñino José , Garcia-Martin Elena , Pérez del Palomar Amaya 
Published: February 2022

AbstractMachine learning approaches in diagnosis and prognosis of multiple sclerosis (MS) were analysed using retinal nerve fiber layer (RNFL) thickness, measured by optical coherence tomography (OCT). A cross-sectional study (72 MS patients and 30 healthy controls) was used for diagnosis. These 72 MS patients were involved in a 10-year longitudinal follow-up study for prognostic purposes. Structural measurements of RNFL thickness were performed using different Spectralis OCT protocols: fast macular thickness protocol to measure macular RNFL, and fast RNFL thickness protocol and fast RNFL-N thickness protocol to measure peripapillary RNFL. Binary classifiers such as multiple linear regression (MLR), support vector machines (SVM), decision tree (DT), k-nearest neighbours (k-NN), Naïve Bayes (NB), ensemble classifier (EC) and long short-term memory (LSTM) recurrent neural network were tested. For MS diagnosis, the best acquisition protocol was fast macular thickness protocol using k-NN (accuracy: 95.8%; sensitivity: 94.4%; specificity: 97.2%; precision: 97.1%; AUC: 0.958). For MS prognosis, our model with a 3-year follow up to predict disability progression 8 years later was the best predictive model. DT performed best for fast macular thickness protocol (accuracy: 91.3%; sensitivity: 90.0%; specificity: 92.5%; precision: 92.3%; AUC: 0.913) and SVM for fast RNFL-N thickness protocol (accuracy: 91.3%; sensitivity: 87.5%; specificity: 95.0%; precision: 94.6%; AUC: 0.913). This work concludes that measurements of RNFL thickness obtained with Spectralis OCT have a good ability to diagnose MS and to predict disability progression in MS patients. This machine learning approach would help clinicians to have valuable information.
Twitter-aided decision making: a review of recent developments
Authors: Zhang Yihong , Shirakawa Masumi , Wang Yuanyuan , Li Zhi , Hara Takahiro 
Published: February 2022

AbstractTwitter is one of the largest online platforms where people exchange information. In the first few years since its emergence, researchers have been exploring ways to use Twitter data in various decision making scenarios, and have shown promising results. In this review, we examine 28 newer papers published in last five years (since 2016) that continued to advance Twitter-aided decision making. The application scenarios we cover include product sales prediction, stock selection, crime prevention, epidemic tracking, and traffic monitoring. We first discuss the findings presented in these papers, that is how much decision making performance has been improved with the help of Twitter data. Then we offer a methodological analysis that considers four aspects of methods used in these papers, including problem formulation, solution, Twitter feature, and information transformation. This methodological analysis aims to enable researchers and decision makers to see the applicability of Twitter-aided methods in different application domains or platforms.
Shifts in microbial stoichiometry upon nutrient addition do not capture growth-limiting nutrients for soil microorganisms in two subtropical soils
Authors: Rosinger Christoph , Keiblinger Katharina M. , Rousk Johannes , Sandén Hans 
Published: February 2022

AbstractMicrobial stoichiometry has become a key aspect in ecological research as shifts in microbial C:N, C:P and N:P ratios upon nutrient addition are presumed to give insight into relative nutrient limitations for soil microorganisms–with far-reaching implications for biogeochemical processes. However, this expectation has never been tested against direct methods of microbial growth responses to nutrient addition. We therefore manipulated a subtropical grassland and forest soil with multifactorial C-, N- and P-additions during 30 days to induce changes in limiting resources and evaluated the resulting soil microbial growth rates, microbial biomass stoichiometry, potential enzyme activities and microbial community composition. Our results show that microbial stoichiometric shifts upon nutrient addition ambiguously predict growth-limiting nutrients for soil microbes. For example, P- and NP-addition to the grassland soil significantly shifted the microbial N:P ratio, which suggests increased N- relative to P-limitation. Microbial growth responses however indicated that soil microbes remained C limited. The same applies for the forest soil, where P-, CN-, NP- and CNP-additions shifted the microbial N:P ratio, yet microbial growth remained C limited. This indicates that microorganisms can immobilize N and P for storage when C is the main limiting nutrient, and that intracellular storage of N and P is responsible for the observed shifts in microbial stoichiometry. Moreover, our data imply that shifts in microbial C:N ratios do not necessarily indicate shifts in microbial community composition and suggest that soil microorganisms–when subject to resource pulses–are stoichiometrically quite plastic.
Quantification of periaortic adipose tissue in contrast-enhanced CT angiography: technical feasibility and methodological considerations
Authors: Mamopoulos Apostolos T. , Freyhardt Patrick , Touloumtzidis Aristotelis , Zapenko Alexander , Katoh Marcus , Gäbel Gabor 
Published: February 2022

AbstractTo examine the feasibility of the quantification of abdominal periaortic fat tissue (PaFT) (tissue within − 45 to − 195 HU) in enhanced CT-angiographies compared to unenhanced CT-scans and identify methodological issues affecting its clinical implementation. Using OsirixMD, PaFT volume and mean HU value were retrospectively measured within a 5 mm periaortic ring in paired unenhanced and enhanced abdominal aortic CT-scans. The correlation between PaFT values was examined in a derivation cohort (n = 101) and linear regression analysis produced correction factors to convert values from enhanced into values from unenhanced CTs. The conversion factors were then applied to enhanced CTs in a different validation cohort (n = 47) and agreement of corrected enhanced values with values from unenhanced scans was evaluated. Correlation between PaFT Volume und Mean HU from enhanced and unenhanced scans was very high (r > 0.99 and r = 0.95, respectively, p < 0.0001 for both). The correction factors for PaFT Volume and Mean HU were 1.1057 and 1.0011. Potential confounding factors (CT-kilovoltage, slice thickness, mean intraluminal contrast density, aortic wall calcification, longitudinal variation of intraluminal contrast density, aortic diameter) showed no significant effect in a multivariate regression analysis (p > 0.05). Bland-Altman analysis of corrected enhanced and unenhanced values showed excellent agreement and Passing–Bablok regression confirmed minimal/no residual bias. PaFT can be quantified in enhanced CT-angiographies very reliably. PaFT Volume scores are very consistently slightly underestimated in enhanced scans by about 10%, while the PaFT Mean HU value remains practically constant and offers distinct methodological advantages. However, a number of methodological issues remain to be addressed.
Nest-site selection influences offspring sex ratio in green turtles, a species with temperature-dependent sex determination
Authors: Heredero Saura L. , Jáñez-Escalada L. , López Navas J. , Cordero K. , Santidrián Tomillo P. 
Published: February 2022

Abstract
Climate change threatens species with temperature-dependent sex determination as further warming could result in extremely biased sex ratios or offspring of only one sex. Among the possible adaptations of sea turtles to climate change, are behavioral responses toward nesting in cooler areas. We analyzed nesting patterns of East Pacific green turtles (Chelonia mydas) in Costa Rica to determine the occurrence of nest-site selection and how this could influence primary sex ratios (PSR). Green turtles exhibited nest-site repeatability. Nests placed by the same individual were generally closer (mean distance: 237.4 m) than other nests on the beach (mean distance: 411.0 m) and this repeatability was maintained in different nesting seasons. Additionally, turtles tended to place late nests closer to each other than their early nests, suggesting an adjusting nesting behavior throughout the nesting season. A great majority of nests were placed in the vegetation (80.9%) and within this zone, turtles preferred nesting under trees (78%) than in grass areas (28%), where temperatures were cooler and PSR were less female biased. Mean nest temperature (°C) during the thermosensitive period and mean PSR were 30.7 ± 1.2 °C and 79 ± 4%, respectively. Most years were female-biased or extremely female-biased but there was approximately one male-biased year in the decade. Although many nests produced 100% females, some male hatchlings were produced every year, even during the extreme 2015–2016 El Niño event. The preference of green turtles for nesting in shaded areas could help to mitigate the negative impacts of climate change unless temperatures in shaded areas rose above the male producing temperatures.
Electrophysiological evaluation and 18-month follow-up of two regimens with aflibercept for neovascular age-related macular degeneration
Authors: Schroeder Marion , Kjellström Ulrika , Lövestam-Adrian Monica 
Published: February 2022

AbstractPurposeTo compare two aflibercept treatment regimens and the electrophysiological outcome concerning cone and rod function in age-related macular degeneration (nAMD) over 18 months.Methods41 patients with treatment-naïve nAMD were randomized 1:1 to either arm 1 or 2. Arm 1 received three consecutive monthly aflibercept injections, followed by bimonthly treatment until week 52. Thereafter, a treat-and-extend (TAE) regimen was applied. Arm 2 was treated according to a TAE protocol throughout the 18-month follow-up. We assessed visual acuity (VA), central retinal thickness (CRT), injection rate and interval, and evaluated cone and rod function with full-field and multifocal electroretinography (ffERG, mERG).ResultsThere were no statistically significant differences in mean baseline VA, lesion type, age, gender, or symptom duration between the two arms. During the 18-month follow-up, mean VA improved in arm 1 (n = 19) from 63.5 ± 10.5 to 69.1 ± 9.2 letters;p = 0.098; and in arm 2 (n = 20) from 66.8 ± 13.6 to 73.9 ± 9.0 letters;p = .002. In both arms, mean CRT was significantly reduced;p < 0.000. At month 18, we found no significant difference in the number of injections or injection intervals between groups. Arm 1 had received 11.3 ± 1.7 injections vs. 10.9 ± 2.0 in arm 2. The mean injection interval was 9.2 ± 3.4 weeks vs. 9.5 ± 3.1, with 52% (n = 10) on the maximum 12-week interval in arm 1, and 50% (n = 10) in arm 2. The combined rod-cone a-wave amplitude significantly decreased over time;p = 0.043. The isolated rod b-wave amplitude showed a statistically significant decline;p = 0.026. The overall mERG amplitude and implicit time remained unchanged over time;p = 0.878 vs.p = 0.922. The central ring 1 mERG amplitude improved;p = 0.041, with an unaffected implicit time.ConclusionsAfter 18 months, both treatments arms have received a similar number of injections at comparable intervals. Electrophysiological evaluation shows no signs of toxicity concerning cone function. But ffERGs for the combined and isolated rod response have declined, possibly reflecting either toxic effects of the drug to rods or the natural course of the disease itself.
Perception of online feedback and its impact on cognitive and emotional engagement with feedback
Authors: Mayordomo Rosa M. , Espasa Anna , Guasch Teresa , Martínez-Melo Montserrat 
Published: February 2022

AbstractOnline feedback plays a key role in learning, but this requires that students engage with feedback. Some authors identify students’ perception of feedback as relevant to understanding engagement. To measure the effect that perceived feedback (specifically its valence) has upon engagement with feedback, a quasi-experimental study has been carried out between two groups of master’s students (191 students, 91.6% women, with a mean age of 30.6 years): a group in which students had the possibility to resubmit an assignment after feedback and another group in which students only received feedback at the end of the assignment. Results show there are no significant differences between both groups regarding the perception of the feedback. However, the results point to the importance of feedback valence perception in resubmission situation. In this situation, a significant relationship between emotional engagement and cognitive engagement with feedback was identified depending on how the feedback was perceived. The main conclusions are the need of incorporating opportunities for resubmission to promote engagement with feedback, the relevance of the perception of feedback, and the role of emotional engagement plays in relation to cognitive engagement with feedback.
Social learning as an underlying mechanism for sustainability in neglected communities: The Brazilian case of the Bucket Revolution project
Authors: Bonatti Michelle , Erismann Carla , Askhabalieva Ayna , Borba Juliano , Pope Kamila , Reynaldo Renata , Eufemia Luca , Turetta Ana Paula , Sieber Stefan 
Published: February 2022

AbstractIn neglected communities, waste and organic residues are not only a vector of several problems, like diseases and water pollution, but also a contributor to increasing forms of vulnerability and marginalization. At the same time, these communities also have presented innovative local initiatives and transformative learning about natural resources management that can be a vehicle for achieving more sustainable food systems. In the south of Brazil, community-based organic residue management has shown an extraordinary potential to improve food security and livelihoods for (≈1600) community members of a vulnerable urban territory. In this context, the overall objective of this article is (a) To better understand what Social Learning (SL) processes related to successful organic residues management in neglected communities exist and (b) To identify what knowledge systems are created in one empirical case. The study case is based on a communitarian waste management project, the Bucket Revolution Project (BRP). The analytical framework builds upon social learning theory and its triple-loop process focusing on four specific phenomena. The applied mixed-methods approach was made in four steps: 1. a focus group to investigate collective community issues; 2. semi-structured interviews to investigate specific and individual issues in the context of the BRP; 3. social media analysis to better understand the BRP narratives; and finally 4. participant observation in community and institutional meetings. Mainly using MaxQda software and coding indicators of SL, the data show that “Diversity of knowledge integration” is the most identified SL indicator in the interviews (52%). For BRP, identity development, community conditions improvement, and environment understanding are three key components of the knowledge system enhanced through an underlying process of social learning. Furthermore, the study also shows that there are endogenous and exogenous social learning processes at work.
Modeling tillage and manure application on soil phosphorous loss under climate change
Authors: Wang Zhaozhi , Zhang Tiequan , Tan Chin Sheng , Xue Lulin , Bukovsky Melissa , Qi Zhiming 
Published: February 2022

AbstractPhosphorus (P) losses from non-point sources into receiving water bodies play a significant role in eutrophication. Given their failure to adequately control eutrophication in the Lake Erie, conservation recommendations for agricultural watersheds should be reconsidered, particularly under climate change. Using the Environmental Policy Integrated Climate model, the potential impacts on crop yield, surface runoff, tile drainage, and relevant dissolved reactive phosphorus (DRP) losses from manure-amended corn-soybean rotation plots in the Lake Erie basin were estimated for six tillage methods with different mixing efficiencies and manure broadcast application. These were investigated under twelve different regional and global future climate simulations. Tillage alone proved to have only a minor impact on mean corn yield (± 2%). Climate change led to large uncertainties under the single tillage treatment. As a result of the combined effects of biogeochemical processes (e.g., supply) and hydrological (e.g., transport), strong negative relationships (R2 = 0.98) were found between tillage mixing efficiency and DRP loss in surface runoff, tile drainage, and total DRP loss. The impacts of combined manure application (broadcast) and tillage on crop yield and flow volume were similar as those of tillage alone. With respect to total DRP losses, the effects of labile P content change outweighed those of surface runoff or tile drainage change (hydrologic). This resulted in a change in total DRP losses ranging from − 60% to + 151%, with being closely correlated with decreasing tillage mixing efficiency (R2 = 0.94) from moldboard to no-till. Therefore, rotational tillage should be considered for DRP loss reduction and energy saving.