bluuebunny/crossref_metadata_2025_split huggingface dataset:
Meeting sustainable development goals via robotics and autonomous systems
Authors: Guenat Solène , Purnell Phil , Davies Zoe G. , Nawrath Maximilian , Stringer Lindsay C. , Babu Giridhara Rathnaiah , Balasubramanian Muniyandi , Ballantyne Erica E. F. , Bylappa Bhuvana Kolar , Chen Bei , De Jager Peta , Del Prete Andrea , Di Nuovo Alessandro , Ehi-Eromosele Cyril O. , Eskandari Torbaghan Mehran , Evans Karl L. , Fraundorfer Markus , Haouas Wissem , Izunobi Josephat U. , Jauregui-Correa Juan Carlos , Kaddouh Bilal Y. , Lewycka Sonia , MacIntosh Ana C. , Mady Christine , Maple Carsten , Mhiret Worku N. , Mohammed-Amin Rozhen Kamal , Olawole Olukunle Charles , Oluseyi Temilola , Orfila Caroline , Ossola Alessandro , Pfeifer Marion , Pridmore Tony , Rijal Moti L. , Rega-Brodsky Christine C. , Robertson Ian D. , Rogers Christopher D. F. , Rougé Charles , Rumaney Maryam B. , Seeletso Mmabaledi K. , Shaqura Mohammed Z. , Suresh L. M. , Sweeting Martin N. , Taylor Buck Nick , Ukwuru M. U. , Verbeek Thomas , Voss Hinrich , Wadud Zia , Wang Xinjun , Winn Neil , Dallimer Martin 
Published: June 2022

AbstractRobotics and autonomous systems are reshaping the world, changing healthcare, food production and biodiversity management. While they will play a fundamental role in delivering the UN Sustainable Development Goals, associated opportunities and threats are yet to be considered systematically. We report on a horizon scan evaluating robotics and autonomous systems impact on all Sustainable Development Goals, involving 102 experts from around the world. Robotics and autonomous systems are likely to transform how the Sustainable Development Goals are achieved, through replacing and supporting human activities, fostering innovation, enhancing remote access and improving monitoring. Emerging threats relate to reinforcing inequalities, exacerbating environmental change, diverting resources from tried-and-tested solutions and reducing freedom and privacy through inadequate governance. Although predicting future impacts of robotics and autonomous systems on the Sustainable Development Goals is difficult, thoroughly examining technological developments early is essential to prevent unintended detrimental consequences. Additionally, robotics and autonomous systems should be considered explicitly when developing future iterations of the Sustainable Development Goals to avoid reversing progress or exacerbating inequalities.
An unexpected dual-emissive luminogen with tunable aggregation-induced emission and enhanced chiroptical property
Authors: Zhang Xinyu , Liu Huiqing , Zhuang Guilin , Yang Shangfeng , Du Pingwu 
Published: June 2022

AbstractIn the literature, organic materials with both aggregation-induced emission (AIE) and aggregation-caused quenching (ACQ) effects that can emit with multiple bands both in the solution and aggregated state are rarely reported. Herein we report a novel chiral dual-emissive bismacrocycle with tunable aggregation-induced emission colors. A facile four-step synthesis strategy is developed to construct this rigid bismacrocycle, (1,4)[8]cycloparaphenylenophane (SCPP[8]), which possesses a 1,2,4,5-tetraphenylbenzene core locked by two intersecting polyphenylene-based macrocycles. The luminescent behavior of SCPP[8] shows the unique characteristics of both ACQ effect and AIE effect, inducing remarkable redshift emission with near white-light emission. SCPP[8] is configurationally stable and possesses a novel shape-persistent bismacrocycle scaffold with a high strain energy. In addition, SCPP[8] displays enhanced circularly polarized luminescence properties due to AIE effect.
MicroRNA-21 promotes pancreatic β cell function through modulating glucose uptake
Authors: Liu Ruiling , Liu Cuilian , He Xiaozhen , Sun Peng , Zhang Bin , Yang Haoran , Shi Weiyun , Ruan Qingguo 
Published: June 2022

AbstractPancreatic β cell dysfunction contributes to the pathogenesis of type 2 diabetes. MiR-21 has been shown to be induced in the islets of glucose intolerant patients and type 2 diabetic mice. However, the role of miR-21 in the regulation of pancreatic β cell function remains largely elusive. In the current study, we identify the pathway by which miR-21 regulates glucose-stimulated insulin secretion utilizing mice lacking miR-21 in their β cells (miR-21βKO). We find that miR-21βKO mice develop glucose intolerance due to impaired glucose-stimulated insulin secretion. Mechanistic studies reveal that miR-21 enhances glucose uptake and subsequently promotes insulin secretion by up-regulating Glut2 expression in a miR-21-Pdcd4-AP-1 dependent pathway. Over-expression of Glut2 in knockout islets results in rescue of the impaired glucose-stimulated insulin secretion. Furthermore, we demonstrate that delivery of miR-21 into the pancreas of type 2 diabetic db/db male mice is able to promote Glut2 expression and reduce blood glucose level. Taking together, our results reveal that miR-21 in islet β cell promotes insulin secretion and support a role for miR-21 in the regulation of pancreatic β cell function in type 2 diabetes.
The NEON Daily Isotopic Composition of Environmental Exchanges Dataset
Authors: Finkenbiner Catherine E. , Li Bonan , Spencer Lindsey , Butler Zachariah , Haagsma Marja , Fiorella Richard P. , Allen Scott T. , Anderegg William , Still Christopher J. , Noone David , Bowen Gabriel J. , Good Stephen P. 
Published: June 2022

AbstractThe National Ecological Observatory Network (NEON) provides open-access measurements of stable isotope ratios in atmospheric water vapor (δ2H, δ18O) and carbon dioxide (δ13C) at different tower heights, as well as aggregated biweekly precipitation samples (δ2H, δ18O) across the United States. These measurements were used to create the NEON Daily Isotopic Composition of Environmental Exchanges (NEON-DICEE) dataset estimating precipitation (P; δ2H, δ18O), evapotranspiration (ET; δ2H, δ18O), and net ecosystem exchange (NEE; δ13C) isotope ratios. Statistically downscaled precipitation datasets were generated to be consistent with the estimated covariance between isotope ratios and precipitation amounts at daily time scales. Isotope ratios in ET and NEE fluxes were estimated using a mixing-model approach with calibrated NEON tower measurements. NEON-DICEE is publicly available on HydroShare and can be reproduced or modified to fit user specific applications or include additional NEON data records as they become available. The NEON-DICEE dataset can facilitate understanding of terrestrial ecosystem processes through their incorporation into environmental investigations that require daily δ2H, δ18O, and δ13C flux data.
Real-world sensor dataset for city inbound-outbound critical intersection analysis
Authors: Mon Ei Ei , Ochiai Hideya , Komolkiti Patrachart , Aswakul Chaodit 
Published: June 2022

AbstractThis paper reports the release of dataset that describes the critical city inbound-outbound intersection in the central business district’s Sathorn area of Bangkok, Thailand. The available traffic volume and occupancy are captured by sensors located on the Sathorn area’s links. Induction loop coil sensors and thermal and CCTV cameras have been installed at the approaching links of the critical Sathorn-Surasak intersection. Traffic volume data have been collected from the CCTV cameras every 5 s from 2016 to 2019, and occupancy and volume data have been collected from the loop coil sensors every 5 s from May to September 2016 during a social experiment that was part of Sathorn Model project. Occupancy and volume data have also been collected from the thermal cameras every 5 s from May to June 2016. The dataset provides temporal and spatial coverage of Sathorn Road’s primary urban areas, including weekdays, weekends, and public holidays. This dataset can be resources for research on traffic state estimation, traffic light control optimization, and the analysis of critical intersections.
Scaling up SoccerNet with multi-view spatial localization and re-identification
Authors: Cioppa Anthony , Deliège Adrien , Giancola Silvio , Ghanem Bernard , Van Droogenbroeck Marc 
Published: June 2022

AbstractSoccer videos are a rich playground for computer vision, involving many elements, such as players, lines, and specific objects. Hence, to capture the richness of this sport and allow for fine automated analyses, we release SoccerNet-v3, a major extension of the SoccerNet dataset, providing a wide variety of spatial annotations and cross-view correspondences. SoccerNet’s broadcast videos contain replays of important actions, allowing us to retrieve a same action from different viewpoints. We annotate those live and replay action frames showing same moments with exhaustive local information. Specifically, we label lines, goal parts, players, referees, teams, salient objects, jersey numbers, and we establish player correspondences between the views. This yields 1,324,732 annotations on 33,986 soccer images, making SoccerNet-v3 the largest dataset for multi-view soccer analysis. Derived tasks may benefit from these annotations, like camera calibration, player localization, team discrimination and multi-view re-identification, which can further sustain practical applications in augmented reality and soccer analytics. Finally, we provide Python codes to easily download our data and access our annotations.
Insulin signaling shapes fractal scaling of C. elegans behavior
Authors: Arata Yukinobu , Shiga Itsuki , Ikeda Yusaku , Jurica Peter , Kimura Hiroshi , Kiyono Ken , Sako Yasushi 
Published: June 2022

AbstractFractal scaling in animal behavioral activity, where similar temporal patterns appear repeatedly over a series of magnifications among time scales, governs the complex behavior of various animal species and, in humans, can be altered by neurodegenerative diseases and aging. However, the mechanism underlying fractal scaling remains unknown. Here, we culturedC. elegansin a microfluidic device for 3 days and analyzed temporal patterns ofC. elegansactivity by fractal analyses. The residence-time distribution ofC. elegansbehaviors shared a common feature with those of human and mice. Specifically, the residence-time power-law distribution of the active state changed to an exponential-like decline at a longer time scale, whereas the inactive state followed a power-law distribution. An exponential-like decline appeared with nutrient supply in wild-type animals, whereas this decline disappeared in insulin-signaling-defectivedaf-2anddaf-16mutants. The absolute value of the power-law exponent of the inactive state distribution increased with nutrient supply in wild-type animals, whereas the value decreased indaf-2anddaf-16mutants. We conclude that insulin signaling differentially affects mechanisms that determine the residence time in active and inactive states inC. elegansbehavior. In humans, diabetes mellitus, which is caused by defects in insulin signaling, is associated with mood disorders that affect daily behavioral activities. We hypothesize that comorbid behavioral defects in patients with diabetes may be attributed to altered fractal scaling of human behavior.
Cranial defect and pneumocephalus are associated with significant postneurosurgical positional brain shift: evaluation using upright computed tomography
Authors: Yoshida Keisuke , Toda Masahiro , Yamada Yoshitake , Yamada Minoru , Yokoyama Yoichi , Tsutsumi Kei , Fujiwara Hirokazu , Kosugi Kenzo , Jinzaki Masahiro 
Published: June 2022

AbstractOnly few studies have assessed brain shift caused by positional change. This study aimed to identify factors correlated with a large postneurosurgical positional brain shift (PBS). Sixty-seven patients who underwent neurosurgical procedures had upright computed tomography (CT) scan using settings similar to those of conventional supine CT. The presence of a clinically significant PBS, defined as a brain shift of ≥ 5 mm caused by positional change, was evaluated. The clinical and radiological findings were investigated to identify factors associated with a larger PBS. As a result, twenty-one patients had a clinically significant PBS. The univariate analysis showed that supratentorial lesion location, intra-axial lesion type, craniectomy procedure, and residual intracranial air were the predictors of PBS. Based on the multivariate analysis, craniectomy procedure (p < 0.001) and residual intracranial air volume (p = 0.004) were the predictors of PBS. In a sub-analysis of post-craniectomy patients, PBS was larger in patients with supratentorial craniectomy site and parenchymal brain injury. A large craniectomy area and long interval from craniectomy were correlated with the extent of PBS. In conclusion, patients who undergo craniectomy and those with residual intracranial air can present with a large PBS. In post-craniectomy patients, the predisposing factors of a large PBS are supratentorial craniectomy, presence of parenchymal injury, large skull defect area, and long interval from craniectomy. These findings can contribute to safe mobilization among postneurosurgical patients and the risk assessment of sinking skin flap syndrome.
Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction
Authors: Heng Seah Yi , Ridwan Wanie M. , Kumar Pavitra , Ahmed Ali Najah , Fai Chow Ming , Birima Ahmed Hussein , El-Shafie Ahmed 
Published: June 2022

AbstractSolar energy serves as a great alternative to fossil fuels as they are clean and renewable energy. Accurate solar radiation (SR) prediction can substantially lower down the impact cost pertaining to the development of solar energy. Lately, many SR forecasting system has been developed such as support vector machine, autoregressive moving average and artificial neural network (ANN). This paper presents a comprehensive study on the meteorological data and types of backpropagation (BP) algorithms used to train and develop the best SR predicting ANN model. The meteorological data, which includes temperature, relative humidity and wind speed are collected from a meteorological station from Kuala Terrenganu, Malaysia. Three different BP algorithms are employed into training the model i.e., Levenberg–Marquardt, Scaled Conjugate Gradient and Bayesian Regularization (BR). This paper presents a comparison study to select the best combination of meteorological data and BP algorithm which can develop the ANN model with the best predictive ability. The findings from this study shows that temperature and relative humidity both have high correlation with SR whereas wind temperature has little influence over SR. The results also showed that BR algorithm trained ANN models with maximum R of 0.8113 and minimum RMSE of 0.2581, outperform other algorithm trained models, as indicated by the performance score of the respective models.
In vitro triple coculture with gut microbiota from spondyloarthritis patients is characterized by inter-individual differences in inflammatory responses
Authors: Beterams Annelore , Calatayud Arroyo Marta , De Paepe Kim , De Craemer Ann-Sophie , Elewaut Dirk , Venken Koen , Van de Wiele Tom 
Published: June 2022

AbstractSpondyloarthritis is a group of chronic inflammatory diseases that primarily affects axial or peripheral joints and is frequently associated with inflammation at non-articular sites. The disease is multifactorial, involving genetics, immunity and environmental factors, including the gut microbiota. In vivo, microbiome contributions are difficult to assess due to the multifactorial disease complexity. In a proof-of-concept approach, we therefore used a triple coculture model of immune-like, goblet and epithelial cells to investigate whether we could detect a differential impact from spondyloarthritis- vs. healthy-derived gut microbiota on host cell response. Despite their phylogenetic resemblance, flow cytometry-based phenotypic clustering revealed human-derived gut microbiota from healthy origin to cluster together and apart from spondyloarthritis donors. At host level, mucus production was higher upon exposure to healthy microbiota. Pro-inflammatory cytokine responses displayed more inter-individual variability in spondyloarthritis than in healthy donors. Interestingly, the high dominance in the initial sample of one patient of Prevotella, a genus previously linked to spondyloarthritis, resulted in the most differential host response upon 16 h host-microbe coincubation. While future research should further focus on inter-individual variability by using gut microbiota from a large cohort of patients, this study underscores the importance of the gut microbiota during the SpA disease course.
Designing grazing susceptibility to land degradation index (GSLDI) in hilly areas
Authors: Minea Gabriel , Ciobotaru Nicu , Ioana-Toroimac Gabriela , Mititelu-Ionuș Oana , Neculau Gianina , Gyasi-Agyei Yeboah , Rodrigo-Comino Jesús 
Published: June 2022

AbstractEvaluation of grazing impacts on land degradation processes is a difficult task due to the heterogeneity and complex interacting factors involved. In this paper, we designed a new methodology based on a predictive index of grazing susceptibility to land degradation index (GSLDI) built on artificial intelligence to assess land degradation susceptibility in areas affected by small ruminants (SRs) of sheep and goats grazing. The data for model training, validation, and testing consisted of sampling points (erosion and no-erosion) taken from aerial imagery. Seventeen environmental factors (e.g., derivatives of the digital elevation model, small ruminants’ stock), and 55 subsequent attributes (e.g., classes/features) were assigned to each sampling point. The impact of SRs stock density on the land degradation process has been evaluated and estimated with two extreme SRs’ density scenarios: absence (no stock), and double density (overstocking). We applied the GSLDI methodology to the Curvature Subcarpathians, a region that experiences the highest erosion rates in Romania, and found that SRs grazing is not the major contributor to land degradation, accounting for only 4.6%. This methodology could be replicated in other steep slope grazing areas as a tool to assess and predict susceptible to land degradation, and to establish common strategies for sustainable land-use practices.
HIF-1α-mediated augmentation of miRNA-18b-5p facilitates proliferation and metastasis in osteosarcoma through attenuation PHF2
Authors: Luo Peng , Zhang Yan-dong , He Feng , Tong Chang-jun , Liu Kai , Liu He , Zhu Shi-zhuang , Luo Jian-zhou , Yuan Bing 
Published: June 2022

AbstractExtensive evidence has explored the involvement of microRNAs (miRNAs) in osteosarcoma (OS). Limitedly, the concrete function of microRNA-18b-5p (miR-18b-5p) in OS remains unexplored and largely elusive. Here, we validated that miR-18b-5p significantly elevated in OS via analyzing the data from GEO database. The results showed that miR-18b-5p was overexpressed in human OS tissues and cell lines. The clinical evidence suggested that high level of miR-18b-5p was negatively correlated with the poor prognosis of OS. Meanwhile, miR-18b-5p upregulation facilitated the proliferation and metastasis of OS cells in vitro and in vivo. The mechanism exploration demonstrated that miR-18b-5p acted as a potential inhibitor of PHF2, a tumor suppressor gene, at post-transcriptional level. Moreover, hypoxia induced gene expression of miR-18b-5p was clarified to be transcriptionally mediated by HIF-1α. The clinicopathological analysis in samples of OS patients further supported that miR-18b-5p had a positive correlation with HIF-1α expression, and negative correlation with PHF2. Collectively, the present study uncovered a new molecular mechanism of OS tumorigenesis and development and miR-18b-5p might be a prognostic biomarker and potential therapeutic target for OS treatment.
Effects of thickness reduction in cold rolling process on the formability of sheet metals using ANFIS
Authors: Xie Yichen , Wu Yuping , Jalali Arman , Zhou Huajie , Amine Khadimallah Mohamed 
Published: June 2022

AbstractCold rolling has detrimental effect on the formability of sheet metals. It is, however, inevitable in producing sheet high quality surfaces. The effects of cold rolling on the forming limits of stretch sheets are not investigated comprehensively in the literature. In this study, a through experimental study is conducted to observe the effect of different cold rolling thickness reduction on the formability of sheet metals. Since the experimental procedure of such tests are costly, an artificial intelligence is also adopted to predict effects of cold thickness reduction on the formability of the sheet metals. In this regard, St14 sheets are examined using tensile, metallography, cold rolling and Nakazima’s hemi-sphere punch experiments. The obtained data are further utilized to train and test an adaptive neural network fuzzy inference system (ANFIS) model. The results indicate that cold rolling reduces the formability of the sheet metals under stretch loading condition. Moreover, the tensile behavior of the sheet alters considerably due to cold thickness reduction of the same sheet metal. The trained ANFIS model also successfully trained and tested in prediction of forming limits diagrams. This model could be used to determine forming limit strains in other thickness reduction conditions. It is discussed that determination of forming limit diagrams is not an intrinsic property of a chemical composition of the sheet metals and many other factors must be taken into account.
A novel GAN-based regression model for predicting frying oil deterioration
Authors: Ye Kai , Wang Zhenyu , Chen Pengyuan , Piao Yangheran , Zhang Kuan , Wang Shu , Jiang Xiaoming , Cui Xiaohui 
Published: June 2022

AbstractFrying is a common food processing method because fried food is popular with consumers for its attractive colour and crisp taste. What’s concerning is that the complex physical and chemical reactions occurring during deep frying are harmful to the well-being of people. For this reason, researchers proposed various detecting methods to assess frying oil deterioration. Some studies design sensor probe, others utilize spectroscopic related methods. However, these methods all need the participating of professionals and expensive instruments. Some of the methods can only function on a fixed temperature. To fix the defects of the above models, in this study, we make use of recent advances in machine learning, specifically generative adversarial networks (GAN). We propose a GAN-based regression model to predict frying oil deterioration. First, we conduct deep frying experiments and record the values of indexes we choose under different temperature and frying time. After collecting the data, we build a GAN-based regression model and train it on the dataset. Finally, we test our model on the test set and analyze the experimental results. Our results suggest that the proposed model can predict frying oil deterioration without experiments. Our model can be applied to other regression problems in various research areas, including price forecasting, trend analysis and so on.