bluuebunny/crossref_metadata_2025_split huggingface dataset:
TGF-β1-induced bone marrow mesenchymal stem cells (BMSCs) migration via histone demethylase KDM6B mediated inhibition of methylation marker H3K27me3
Authors: He Qiting , Shi Jie , Liu Wei , Zhao Wei , Wang Zihao , Liu Kaiwen , Zhao Dawang , Wang Shaoyi , Guo Yongyuan , Cheng Lei , Gao Yuan 
Published: July 2022

AbstractMesenchymal stem cells (MSCs) are widely used in clinical research and therapy. Since the number of MSCs migration is extremely crucial at the lesion site, exploring the mechanisms to enhance the migration of MSCs is necessary. Therefore, this study focused on the epigenetic mechanisms in MSCs migration. TGF-β1 stimulated bone marrow mesenchymal stem cells (BMSCs) to promote cell migration at lesion sites in vitro and in vivo. The mRNA and protein levels of several migration-related genes (N cadherin, CXCR4, FN1) were enhanced. The trimethylation marker H3K27me3 recruitment on the promoter of these genes were studied to dissect the epigenetic mechanisms. TGF-β1 elevated the levels of KDM6B leading to removal of repression marker H3K27me3 in the promoter region of N cadherins and FN1. Congruently, knockdown of demethylase KDM6B substantially affected the TGF-β1 induced BMSCs migration. This promoted the down-regulation of various migration-related genes. Collectively, epigenetic regulation played an important role in BMSCs migration, and H3K27me3 was at least partially involved in the migration of BMSCs induced by TGF-β1.
Hybrid magnetorheological elastomers enable versatile soft actuators
Authors: Moreno-Mateos Miguel Angel , Hossain Mokarram , Steinmann Paul , Garcia-Gonzalez Daniel 
Published: July 2022

AbstractRecent advances in magnetorheological elastomers (MREs) have posed the question on whether the combination of both soft- and hard-magnetic particles may open new routes to design versatile multifunctional actuators. Here, we conceptualise ultra-soft hybrid MREs (≈1–10 kPa stiffness) combining experimental and computational approaches. First, a comprehensive experimental characterisation is performed. The results unravel that the magneto-mechanical performance of hybrid MREs can be optimised by selecting an adequate mixing ratio between particles. Then, a multi-physics computational framework provides insights into the synergistic magneto-mechanical interactions at the microscale. Soft particles amplify the magnetisation and hard particles contribute to torsional actuation. Our numerical results suggest that the effective response of hybrid MREs emerges from these intricate interactions. Overall, we uncover exciting possibilities to push the frontiers of MRE solutions. These are demonstrated by simulating a bimorph beam that provides actuation flexibility either enhancing mechanical bending or material stiffening, depending on the magnetic stimulation.
Mapping hydrologic alteration and ecological consequences in stream reaches of the conterminous United States
Authors: McManamay Ryan A. , George Rob , Morrison Ryan R. , Ruddell Benjamin L. 
Published: July 2022

AbstractEnvironmental flows are critical for balancing societal water needs with that of riverine ecosystems; however, data limitations often hinder the development of predictive relationships between anthropogenic modifications to streamflow regimes and ecological responses – these relationships are the basis for setting regional water policy standards for rivers. Herein, we present and describe a comprehensive dataset of modeled hydrologic alteration and consequences for native fish biodiversity, both mapped at the stream-reach resolution for the conterminous U.S. Using empirical observations of reference conditions and anthropogenically altered streamflow at over 7000 stream gauges, we developed a predictive model of hydrologic alteration, which was extended to >2.6 million stream reaches. We then used a previous nationwide assessment of ecological responses to hydrologic alteration to predict fish biodiversity loss in stream reaches resulting from streamflow modification. Validation efforts suggested hydrologic alteration models had satisfactory performance, whereas modeled ecological responses were susceptible to compounded errors. The dataset could ameliorate regional data deficits for setting environmental flow standards while providing tools for prioritizing streamflow protection or restoration.
Authentication of smart grid communications using quantum key distribution
Authors: Alshowkan Muneer , Evans Philip G. , Starke Michael , Earl Duncan , Peters Nicholas A. 
Published: July 2022

AbstractSmart grid solutions enable utilities and customers to better monitor and control energy use via information and communications technology. Information technology is intended to improve the future electric grid’s reliability, efficiency, and sustainability by implementing advanced monitoring and control systems. However, leveraging modern communications systems also makes the grid vulnerable to cyberattacks. Here we report the first use of quantum key distribution (QKD) keys in the authentication of smart grid communications. In particular, we make such demonstration on a deployed electric utility fiber network. The developed method was prototyped in a software package to manage and utilize cryptographic keys to authenticate machine-to-machine communications used for supervisory control and data acquisition (SCADA). This demonstration showcases the feasibility of using QKD to improve the security of critical infrastructure, including future distributed energy resources (DERs), such as energy storage.
Bow-tie structures of twitter discursive communities
Authors: Mattei Mattia , Pratelli Manuel , Caldarelli Guido , Petrocchi Marinella , Saracco Fabio 
Published: July 2022

AbstractBow-tie structures were introduced to describe the World Wide Web (WWW): in the direct network in which the nodes are the websites and the edges are the hyperlinks connecting them, the greatest number of nodes takes part to abow-tie, i.e. a Weakly Connected Component (WCC) composed of 3 main sectors: IN, OUT and SCC. SCC is the main Strongly Connected Component of WCC, i.e. the greatest subgraph in which each node is reachable by any other one. The IN and OUT sectors are the set of nodes not included in SCC that, respectively, can access and are accessible to nodes in SCC. In the WWW, the greatest part of the websites can be found in the SCC, while the search engines belong to IN and the authorities, as Wikipedia, are in OUT. In the analysis of Twitter debate, the recent literature focused on discursive communities, i.e. clusters of accounts interacting among themselves via retweets. In the present work, we studied discursive communities in 8 different thematic Twitter datasets in various languages. Surprisingly, we observed that almost all discursive communities therein display a bow-tie structure during political or societal debates. Instead, they are absent when the argument of the discussion is different as sport events, as in the case of Euro2020 Turkish and Italian datasets. We furthermore analysed the quality of the content created in the various sectors of the different discursive communities, using the domain annotation from the fact-checking website Newsguard: we observe that, when the discursive community is affected by m/disinformation, the content with the lowest quality is the one produced and shared in SCC and, in particular, a strong incidence of low- or non-reputable messages is present in the flow of retweets between the SCC and the OUT sectors. In this sense, in discursive communities affected by m/disinformation, the greatest part of the accounts has access to a great variety of contents, but whose quality is, in general, quite low; such a situation perfectly describes the phenomenon of infodemic, i.e. the access to “an excessive amount of information about a problem, which makes it difficult to identify a solution”, according to WHO.
The microbial carbonate factory of Hamelin Pool, Shark Bay, Western Australia
Authors: Suosaari Erica P. , Reid R. Pamela , Mercadier Christophe , Vitek Brooke E. , Oehlert Amanda M. , Stolz John F. , Giusfredi Paige E. , Eberli Gregor P. 
Published: July 2022

AbstractMicrobialites and peloids are commonly associated throughout the geologic record. Proterozoic carbonate megafacies are composed predominantly of micritic and peloidal limestones often interbedded with stromatolitic textures. The association is also common throughout carbonate ramps and platforms during the Phanerozoic. Recent investigations reveal that Hamelin Pool, located in Shark Bay, Western Australia, is a microbial carbonate factory that provides a modern analog for the microbialite-micritic sediment facies associations that are so prevalent in the geologic record. Hamelin Pool contains the largest known living marine stromatolite system in the world. Although best known for the constructive microbial processes that lead to formation of these stromatolites, our comprehensive mapping has revealed that erosion and degradation of weakly lithified microbial mats in Hamelin Pool leads to the extensive production and accumulation of sand-sized micritic grains. Over 40 km2of upper intertidal shoreline in the pool contain unlithified to weakly lithified microbial pustular sheet mats, which erode to release irregular peloidal grains. In addition, over 20 km2of gelatinous microbial mats, with thin brittle layers of micrite, colonize subtidal pavements. When these gelatinous mats erode, the micritic layers break down to form platey, micritic intraclasts with irregular boundaries. Together, the irregular micritic grains from pustular sheet mats and gelatinous pavement mats make up nearly 26% of the total sediment in the pool, plausibly producing ~ 24,000 metric tons of microbial sediment per year. As such, Hamelin Pool can be seen as a microbial carbonate factory, with construction by lithifying microbial mats forming microbialites, and erosion and degradation of weakly lithified microbial mats resulting in extensive production of sand-sized micritic sediments. Insight from these modern examples may have direct applicability for recognition of sedimentary deposits of microbial origin in the geologic record.
Administration of probiotic lactic acid bacteria to modulate fecal microbiome in feedlot cattle
Authors: Mansilla Flavia Ivana , Ficoseco Cecilia Aristimuño , Miranda María Hortencia , Puglisi Edoardo , Nader-Macías María Elena Fatima , Vignolo Graciela Margarita , Fontana Cecilia Alejandra 
Published: July 2022

AbstractModulation of animal gut microbiota is a prominent function of probiotics to improve the health and performance of livestock. In this study, a large-scale survey to evaluate the effect of lactic acid bacteria probiotics on shaping the fecal bacterial community structure of feedlot cattle during three experimental periods of the fattening cycle (163 days) was performed. A commercial feedlot located in northwestern Argentina was enrolled with cattle fed mixed rations (forage and increasing grain diet) and a convenience-experimental design was conducted. A pen (n = 21 animals) was assigned to each experimental group that received probiotics during three different periods. Groups of n = 7 animals were sampled at 40, 104 and 163 days and these samples were then pooled to one, thus giving a total of 34 samples that were subjected to high-throughput sequencing. The microbial diversity of fecal samples was significantly affected (p < 0.05) by the administration period compared with probiotic group supplementation. Even though, the three experimental periods of probiotic administration induced changes in the relative abundance of the most representative bacterial communities, the fecal microbiome of samples was dominated by the Firmicutes (72–98%) and Actinobacteria (0.8–27%) phyla, while a lower abundance of Bacteroidetes (0.08–4.2%) was present. Probiotics were able to modulate the fecal microbiota with a convergence of Clostridiaceae, Lachnospiraceae, Ruminococcaceae and Bifidobacteriaceae associated with health and growth benefits as core microbiome members. Metabolic functional prediction comparing three experimental administration periods (40, 104 and 163 days) showed an enrichment of metabolic pathways related to complex plant-derived polysaccharide digestion as well as amino acids and derivatives during the first 40 days of probiotic supplementation. Genomic-based knowledge on the benefits of autochthonous probiotics on cattle gastrointestinal tract (GIT) microbiota composition and functions will contribute to their selection as antibiotic alternatives for commercial feedlot.
Center-environment feature models for materials image segmentation based on machine learning
Authors: Han Yuexing , Li Ruiqi , Yang Shen , Chen Qiaochuan , Wang Bing , Liu Yi 
Published: July 2022

AbstractMaterials properties depend not only on their compositions but also their microstructures under various processing conditions. So far, the analyses of complex microstructure images rely mostly on human experience, lack of automatic quantitative characterization methods. Machine learning provides an emerging vital tool to identify various complex materials phases in an intelligent manner. In this work, we propose a “center-environment segmentation” (CES) feature model for image segmentation based on machine learning method with environment features and the annotation input of domain knowledge. The CES model introduces the information of neighbourhood as the features of a given pixel, reflecting the relationships between the studied pixel and its surrounding environment. Then, an iterative integrated machine learning method is adopted to train and correct the image segmentation model. The CES model was successfully applied to segment seven different material images with complex texture ranging from steels to woods. The overall performance of the CES method in determining boundary contours is better than many conventional methods in the case study of the segmentation of steel image. This work shows that the iterative introduction of domain knowledge and environment features improve the accuracy of machine learning based image segmentation for various complex materials microstructures.
Salinity of irrigation water selects distinct bacterial communities associated with date palm (Phoenix dactylifera L.) root
Authors: Shamim Azra , Sanka Loganathachetti Dinesh , Chandran Subha , Masmoudi Khaled , Mundra Sunil 
Published: July 2022

AbstractSaline water irrigation has been used in date palm (Phoenix dactylifera L.) agriculture as an alternative to non-saline water due to water scarcity in hyper-arid environments. However, the knowledge pertaining to saline water irrigation impact on the root-associated bacterial communities of arid agroecosystems is scarce. In this study, we investigated the effect of irrigation sources (non-saline freshwater vs saline groundwater) on date palm root-associated bacterial communities using 16S rDNA metabarcoding. The bacterial richness, Shannon diversity and evenness didn’t differ significantly between the irrigation sources. Soil electrical conductivity (EC) and irrigation water pH were negatively related to Shannon diversity and evenness respectively, while soil organic matter displayed a positive correlation with Shannon diversity. 40.5% of total Operational Taxonomic Units were unique to non-saline freshwater irrigation, while 26% were unique to saline groundwater irrigation. The multivariate analyses displayed strong structuring of bacterial communities according to irrigation sources, and both soil EC and irrigation water pH were the major factors affecting bacterial communities. The genera Bacillus, Micromonospora and Mycobacterium were dominated while saline water irrigation whereas contrasting pattern was observed for Rhizobium, Streptomyces and Acidibacter. Taken together, we suggest that date-palm roots select specific bacterial taxa under saline groundwater irrigation, which possibly help in alleviating salinity stress and promote growth of the host plant.
Synthesis of CuO nanoparticles stabilized with gelatin for potential use in food packaging applications
Authors: Gvozdenko A. A. , Siddiqui S. A. , Blinov A. V. , Golik A. B. , Nagdalian A. A. , Maglakelidze D. G. , Statsenko E. N. , Pirogov M. A. , Blinova A. A. , Sizonenko M. N. , Simonov A. N. , Zhukov R. B. , Kolesnikov R. O. , Ibrahim S. A. 
Published: July 2022

AbstractIn the present study, a method for the synthesis of gelatin-stabilized copper oxide nanoparticles was developed. Synthesis was carried out by direct chemical precipitation. Copper sulfate, chloride, and acetate were used as precursors for the copper oxide synthesis. Gelatin was used as a stabilizer. It was found that the formation of monophase copper oxide II only occurred when copper acetate was used as a precursor. Our results showed that particles of the smallest diameter are formed in an aqueous medium (18 ± 6 nm), and those of th largest diameter—in an isobutanol medium (370 ± 131 nm). According to the photon correlation spectroscopy data, copper oxide nanoparticles synthesized in an aqueous medium were highly stable and had a monomodal size distribution with an average hydrodynamic radius of 61 nm. The study of the pH effect on the colloidal stability of copper oxide nanoparticles showed that the sample was stable in the pH range of 6.8 to 11.98. A possible mechanism for the pH influence on the stability of copper oxide nanoparticles is described. The effect of the ionic strength of the solution on the stability of the CuO nanoparticles sol was also studied, and the results showed that Ca2+ ions had the greatest effect on the sample stability. IR spectroscopy showed that the interaction of CuO nanoparticles with gelatin occurred through the hydroxyl group. It was found that CuO nanoparticles stabilized with gelatin have a fungicidal activity at concentration equivalent 2.5 · 10−3 mol/L and as a material for food nanopackaging can provide an increase in the shelf life of products on the example of strawberries and tomatoes. We investigated the possibility of using methylcellulose films modified with CuO nanoparticles for packaging and storage of hard cheese “Holland”. The distribution of CuO nanoparticles in the methylcellulose film was uniform. We found that methylcellulose films modified with CuO nanoparticles inhibited the growth and development of QMAFAM, coliforms, yeast and mold in experimental cheese sa mples. Our research has shown that during the cheese storage in thermostat at 35 ± 1 °C for 7 days, CuO nanoparticles migrated to the product from the film. Nevertheless, it is worth noting that the maximum change in the concentration of copper in the experimental samples was only 0.12 µg/mg, which is not a toxic concentration. In general, the small value of migration of CuO nanoparticles confirms the high stability of the developed preparation. Our results indicated that the CuO nanoparticles stabilized with gelatin have a high potential for use in food packaging – both as an independent nanofilm and as part of other packaging materials.
Deep learning to distinguish Best vitelliform macular dystrophy (BVMD) from adult-onset vitelliform macular degeneration (AVMD)
Authors: Crincoli Emanuele , Zhao Zhanlin , Querques Giuseppe , Sacconi Riccardo , Carlà Matteo Maria , Giannuzzi Federico , Ferrara Silvia , Ribarich Nicolò , L’Abbate Gaia , Rizzo Stanislao , Souied Eric H. , Miere Alexandra 
Published: July 2022

AbstractInitial stages of Best vitelliform macular dystrophy (BVMD) and adult vitelliform macular dystrophy (AVMD) harbor similar blue autofluorescence (BAF) and optical coherence tomography (OCT) features. Nevertheless, BVMD is characterized by a worse final stage visual acuity (VA) and an earlier onset of critical VA loss. Currently, differential diagnosis requires an invasive and time-consuming process including genetic testing, electrooculography (EOG), full field electroretinogram (ERG), and visual field testing. The aim of our study was to automatically classify OCT and BAF images from stage II BVMD and AVMD eyes using a deep learning algorithm and to identify an image processing method to facilitate human-based clinical diagnosis based on non-invasive tests like BAF and OCT without the use of machine-learning technology. After the application of a customized image processing method, OCT images were characterized by a dark appearance of the vitelliform deposit in the case of BVMD and a lighter inhomogeneous appearance in the case of AVMD. By contrast, a customized method for processing of BAF images revealed that BVMD and AVMD were characterized respectively by the presence or absence of a hypo-autofluorescent region of retina encircling the central hyperautofluorescent foveal lesion. The human-based evaluation of both BAF and OCT images showed significantly higher correspondence to ground truth reference when performed on processed images. The deep learning classifiers based on BAF and OCT images showed around 90% accuracy of classification with both processed and unprocessed images, which was significantly higher than human performance on both processed and unprocessed images. The ability to differentiate between the two entities without recurring to invasive and expensive tests may offer a valuable clinical tool in the management of the two diseases.
Application of machine learning for inter turn fault detection in pumping system
Authors: Dutta Nabanita , Kaliannan Palanisamy , Shanmugam Paramasivam 
Published: July 2022

AbstractPump fault diagnosis is essential for the maintenance and safety of the device as it is an important appliance used in various major sectors. Fault diagnosis at the proper time can reduce maintenance costs and save energy. This article uses a Simulink model based on mathematical equations to analyze the effects of parameter estimation of three-phase induction motor-based centrifugal pumps in inter-turn fault conditions. The inter-turn fault causes a massive in, a massive increase in current, which severely affects the parameters of both motor and pump. These have been analyzed by simulation through the Matlab Simulink model. Later, the results are verified by a hardware in loop (HIL) based simulator. In this paper, machine learning (ML) based artificial neural network (ANN) and ANFIS (ANN and Fuzzy) models have been applied for fault detection. ANN and ANFIS-based models provide a satisfactory level of accuracy. These models provide accurate training and testing results. Based on root mean square error (RMSE), R2, prediction accuracy, and mean validation value, these models are compared to find out which is more suitable for this experiment. Various supervised algorithms are compared with ANN, ANFIS, and lastly, found which is the most suitable for this experiment.