Miracle-dz/UniFAR-FTUs huggingface dataset:
Wnt signaling regulates pancreatic β cell proliferation

There is widespread interest in defining factors and mechanisms that stimulate proliferation of pancreatic islet cells. Wnt signaling is an important regulator of organ growth and cell fates, and genes encoding Wnt-signaling factors are expressed in the pancreas. However, it is unclear whether Wnt signaling regulates pancreatic islet proliferation and differentiation. Here we provide evidence that Wnt signaling stimulates islet β cell proliferation. The addition of purified Wnt3a protein to cultured β cells or islets promoted expression of Pitx2, a direct target of Wnt signaling, and Cyclin D2, an essential regulator of β cell cycle progression, and led to increased β cell proliferation in vitro. Conditional pancreatic β cell expression of activated β-catenin, a crucial Wnt signal transduction protein, produced similar phenotypes in vivo, leading to β cell expansion, increased insulin production and serum levels, and enhanced glucose handling. Conditional β cell expression of Axin, a potent negative regulator of Wnt signaling, led to reduced Pitx2 and Cyclin D2 expression by β cells, resulting in reduced neonatal β cell expansion and mass and impaired glucose tolerance. Thus, Wnt signaling is both necessary and sufficient for islet β cell proliferation, and our study provides previously unrecognized evidence of a mechanism governing endocrine pancreas growth and function.
Microdistribution of the polymorphic snail Littorina saxatilis (Olivi) in a patchy rocky shore habitat

Abstract Galician populations of the marine snail Littorina saxatilis have two main morphs on exposed rocky shores. The ridged and banded morph inhabits the barnacle (Chthamalus stellatus) dominated upper shore, while the smooth and unbanded morph is mainly found in the lower-shore zone of blue mussels (Mytilus galloprovincialis). The distribution of the two morphs overlaps in the mid shore where hybrids are also present. The mid-shore habitat is a mixture of barnacle and mussel patches. We hypothesised that the different architectural complexity of barnacles and mussels would affect the distribution of the two morphs. Fractal dimensions were used to describe the substratum complexity of 96 small patches from three sites with different proportions of barnacles and mussels. Increased proportions of mussels in a patch increased the fractal dimension, and thus surface complexity. Snail abundance decreased with increased substratum complexity in the smooth and unbanded morph and in the hybrids but not in the .
Impact of High-Skilled Migration to the UK on the Source Countries (EU8) Economies

The majority of studies into the economic effects of high-skilled migration focus on aggregate impact on the economic output in the countries of destination. The economic impact of migration of the highly qualified on the economies of the countries of their origin has been examined less. This qualitative research aims to address that gap by identifying the economic effects of high-skilled migration on Central and Eastern Europe, the region which faces many long-term challenges to its economic development. We use the available data from the UK International Passenger Survey for the 2004-2016 period to test whether the outflow of highly qualified workers from the EU8 countries to the UK is detrimental or beneficial for the growth of sending economies in the short and long term. In order to test these hypotheses, econometric time series analysis methods of structural vector autoregression and cointegration were applied. Our results have shown a positive short-term effect of brain outflow on regions’ GDP and wage growth as well as unemployment; on the other hand, we presented empirical evidence in support of the hypothesis of the negative long-term effect of high-skilled migration on EU8 countries’ GDP and wage growth as well as unemployment. These results are fairly robust to imply that a negative view on high-skilled migration from EU8 is broadly consistent with the previous findings of “harmful brain drain” scholars.
Allorecognition elements on a urochordate histocompatibility locus indicate unprecedented extensive polymorphism

Tissue contacts between different colonies of the cosmopolitan urochordate Botryllus schlosseri resulted either in fusion between allogenic blood vessels or in the formation of rejections, characterized by necrotic areas. This colony specificity is manifested by a single, highly polymorphic mendelian locus, with codominantly expressed alleles, called the tunicate fusibility/histocompatibility (Fu/HC) haplotype. Two colonies sharing even one allele at the Fu/HC locus may fuse; rejecting colonies share no alleles. Previous studies have revealed up to 100 alleles on the Fu/HC locus in different natural populations. We studied Fu/HC polymorphism in three populations (> 1000 colonies) along the Mediterranean coast of Israel, 12–36 km apart. By using the protocol of colony allorecognition assays (CAAS) on 444 tests within localities, 58–306 alleles per locality were calculated. We also estimate a total of 479–560 Fu/HC alleles in the three Israeli populations. An additional 103 interpopulation CAAs resulted in zero fusion, an outcome which contradicts previous findings showing 4.4–12.0% fusions between more remote B. schlosseri populations such as those from Israel versus Monterey, California, and California versus Japan assays. We propose that the recorded unprecedented Fu/HC polymorphism is maintained through overdominant selection, which is promoted by the gregarious settlement of Fu/HC-compatible larvae and by the subsequent threat of germ cell parasitism following colony fusion.
Kinematic Analysis of the Metamorphic Anthropomorphic Robotic Hand with Gear and Four-Bar Linkage Based Transmission

This paper represents robotic hand which combines metamorphic and anthropomorphic principles. Solid model of the hand is introduced and then the actuation mechanism is discussed. Kinematic and static analysis of the hand and fingers are developed. Afterwards, grasping potential is briefly reviewed so that the possible force accumulated at the fingertips could be approximated and compared with other robotic hands. In the end, evaluation of the proposed robotic hand is addressed. Keywords—metamorphic, anthropomorphic, robotic hand, gear, four-bar linkage, kinematics.
Productivity Dynamics in the Arab Economies: The Challenges of Generating Multifactor Productivity Estimates at Industry Level

The growth gap between the Arab countries and the rest of the world has been increasing since the 1990s, despite the economic and political transition in the Arab world. Against this background, the analysis of the empirics of growth in this part of the world becomes significant. Our attempts to quantify and examine the growth dynamics in the Arab world point to the lack of data for undertaking quality research. Reviewing the available information on three chosen countriesEgypt, Morocco and Tunisia from different international data sets, we explore the creation of an Arab KLEMS dataset which would allow examination of empirics of growthboth for the economy and sub sectors of the economy. In this light, we highlight the challenges that need to be overcome if multifactor productivity estimates can be created for three chosen countriesEgypt, Morocco and Tunisia. JEL classification: o4, o14 and o57
Structure and expression profile of the cinnamyl alcohol dehydrogenase gene and its association with lignification in the sorghum (Sorghum bicolor (L.) Moench) bmr-6 mutant

1) National Institute of Livestock and Grassland Science, 768 Senbonmatsu, Nasushiobara, Tochigi 329-2793, Japan 2) Division of BioResources, Frontier Science Research Center, University of Miyazaki, 1-1 Gakuen Kibanadai-nishi, Miyazaki, Miyazaki 889-2192, Japan 3) Department of Animal and Grassland Science, Faculty of Agriculture, University of Miyazaki, 1-1 Gakuen Kibanadai-nishi, Miyazaki, Miyazaki 889-2192, Japan
Design of a Nucleic Acid Biosensor Using COMSOL Multiphysics

The detection of target biomarkers (proteins, DNA or antigens) from biological samples such as serum, urine or blood in high-throughput manner with high specificity is the starting point of developing effective therapy for many diseases including cancers. Currently available bio sensing techniques that are involving in the detection of biomarker molecules in patients’ samples do not have the adequate sensitivity and limit of detection to be effectively detect early stage of cancer. Especially, the limit of detection of the current biosensing techniques varies from molar (M) to few nano/pico (10) molar and this limit is insufficient for early detection of cancer. To address these issues, we proposed a lowcost and high-throughput technique based on dielectrophoresis (DEP). In this work, we have used the COMSOL software to design electrodes which were used in DEP based biomarker detection.
FACE RECOGNITION: LITERATURE REVIEW WITH EMPHASIS ON DEEP LEARNING

Under the broad umbrella of object recognition, Face recognition is one area with active research for last few decades mainly due to its applications and the challenges in the environment where they are used. The face recognition as a biometric authentication can work without much cooperation of human. The current face recognition techniques perform well in constrained environment but performance degrades in unconstrained environment as the images captured may vary in resolution, illumination, pose, occlusion and expressions. This sometimes makes intra class variance more than the inter class variance and leads to misclassification. In this article, an overview of some of the strategies adopted by the researchers to overcome the challenges like pose variation, low resolution and occlusion in unconstrained environment has been discussed. Moreover, this paper reviews the use of deep learning in face recognition to achieve accuracy at par with humans in image classification tasks. We also discuss the challenges with Deep learning and the strategies adopted to overcome them. This paper provide an up-to-date review of face recognition techniques.
Host Utilization Algorithm to Reduce Energy Consumption in Cloud Computing

The utilization of distributed computing server farm is developing quickly to fulfill the large increment required for systems administration, High-Performance Computing(HPC) as well as stockpiling assets for executing business and logical applications. The process of Virtual Machine (VM) solidification is inclusive of VMs getting relocated in order to make use of less physical servers. As a result, it enables the shut down or lowpower mode of more number of servers which enhances the vitality utilization effectiveness, working expense and CO2 discharge. An urgent advance in VM union is have over-burden discovery, which endeavors to foresee whether a physical server is going to be oversubscribed with VMs. On the contrary to usual studies which performed utilization of CPU being the standalone indicator for host overload, a multiple correlation host overload detection algorithm was proposed in the recent study by considering a lot of factors in this regard. A higher load balance model was introduced in this text for the general public cloud, supported by the concept of cloud partitioning, in addition to a switch mechanism used to strategize differently under different scenarios. The IP address is generally shared by a true server and carbo balance. In this regard, the load balancer considers the interface developed with IP address which accepts request packets and the packets are directed to the selected servers. With an aim to improve the efficiency in public cloud environment, the algorithm employed the sport theory in the load balancing strategy.
HYBRID MODEL FOR TWITTER DATA SENTIMENT ANALYSIS BASED ON ENSEMBLE OF DICTIONARY BASED CLASSIFIER AND STACKED MACHINE LEARNING CLASSIFIERS-SVM, KNN AND C5.0

Social Networking sites like Twitter and Facebook has offered the possibility to users to express their opinion on various topics and events. Opinion mining is a technique to find the sentiment of people about these topics, which can be useful in decision support. Various government policies can also be monitored by doing the sentiment analysis of related tweets. The objective of this research is to enhance the accuracy of twitter sentiment classification. The paper proposes a framework for a hybrid approach with an ensemble of stacked machine learning algorithms and dictionary based classifier. Sentiment Score extracted from dictionary based classifier is added as additional feature in the feature set. Three machine learning algorithms SVM, KNN and C5.0 are stacked to build an ensemble by using two Meta learners RF and GLM. Real time manually labeled tweets based on “Clean India Mission” an Indian government policy is used for implementation of the model. Proposed model is compared with different machine learning and ensemble classifiers. Proposed hybrid model recorded higher accuracy of 0.9066377 for 5 fold cross validation and 0.9124793 for 10 fold cross validation as compared to 0.8667328 in case of stacked ensemble of SVMRadial, KNN and C5.0 by using RF as Meta classifier. RF Meta classifier performed better as compared to GLM in all stacked based ensemble. Proposed model also recorded higher accuracy as compared to machine learning classifiers-SVM, Naïve Bayes, Decision Tree, Random forest and Maximum Entropy. The contribution of the research is to enhance the accuracy of stacked based ensemble classifiers for twitter sentiment classification by using additional sentiment score provided by dictionary based classifier.
Responses of bryophytes to simulated environmental change at Latnjajaure, northern Sweden

Abstract We simulated a predicted environmental change in a subarctic-alpine plant community to study short-term growth in Hylocomium splendens, and three-year effects in abundance changes of the five most common bryophytes at Latnjajaure, northern Sweden. The experiment had a factorial design with increased temperature and nutrients, alone and in combination. Moss growth was measured during the 1995 growing season, and we measured species abundance before and after three years of environmental perturbation. The combined treatments of fertilizer and temperature enhancement caused a decreased growth in length and dry weight in H. splendens. There was a significant decrease in abundance of Rhytidium rugosum in the combined temperature and fertilizer treatment. The other four common bryophyte species (Aulacomnium turgidum, Dicranum groenlandicum, Hylocomium splendens, and Ptilidium ciliare) showed weaker, but not significant trends in the same direction. As the bryophytes are an important component of arctic and subarctic vegetation, we expect that they will play a key role in the impact of anticipated Global Change on the ecosystems.
Improvement of the SCOR model by the use of the Performance Measurement system and an aggregation approach based on the NonAdditive Fuzzy Sugeno Integral: A case study for the Selection of Automotive Suppliers

This study aims to extend the SCOR model used in the supply chain (SC) context; we propose to extend here the proposed approaches for expressing the overall performance of an SC. The aggregation of appropriate Key Performance Indicators (KPI) in the global performance formula is based on a Sugeno integral operator, according to the fuzzy set theory, in order to deal with the nonlinearity of this model, makes data ambiguous in the process of multicriteria decision-making. Therefore, this work aims to help managers to select a suitable supplier in the Supply Chain context. The approach is used to evaluate the best contractors by using the Sugeno Integral to deal with the interrelationships aspects between KPI. Key-Words: Performance aggregation; Sugeno integral; Supply chain; SCOR model
Adaptive Invariant Risk Minimization for Molecule Property Prediction

The current race to identify promising repurposing drug candidates for COVID-19 highlights a significant limitation of existing property prediction algorithms. Since their accuracy tightly depends on the access to large amounts of in-domain training data, these algorithms provide limited utility today when high-throughput screening is lacking. At the time of writing, there are only 48 drugs and 880 fragments with measured in-vitro SARS-CoV-2 activity in the public domain [6]. This scenario is not unique to the current pandemic, but is also common for initial stages of novel therapeutic development where limited screening data has to inform on-going experimental exploration.
A Live Fuel Moisture Content Product from Landsat TM Satellite Time Series for Implementation in Fire Behavior Models

Live Fuel Moisture Content (LFMC) contributes to fire danger and behavior, as it affects fire ignition and propagation. This paper presents a two layered Landsat LFMC product based on topographically corrected relative Spectral Indices (SI) over a 2000–2011 time series, which can be integrated into fire behavior simulation models. Nine chaparral sampling sites across three Landsat-5 Thematic Mapper (TM) scenes were used to validate the product over the Western USA. The relations between field-measured LFMC and Landsat-derived SIs were strong for each individual site but worsened when pooled together. The Enhanced Vegetation Index (EVI) presented the strongest correlations (r) and the least Root Mean Square Error (RMSE), followed by the Normalized Difference Infrared Index (NDII), Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI). The relations between LFMC and the SIs for all sites improved after using their relative values and relative LFMC, increasing r from 0.44 up to 0.69 for relative EVI (relEVI), the best predictive variable. This relEVI served to estimate the herbaceous and woody LFMC based on minimum and maximum seasonal LFMC values. The understory herbaceous LFMC on the woody pixels was extrapolated from the surrounding pixels where the herbaceous vegetation is the top layer. Running simulations on the Wildfire Analyst (WFA) fire behavior model demonstrated that this LFMC product alone impacts significantly the fire spatial distribution in terms of burned probability, with average burned area differences over 21% after 8 h burning since ignition, compared to commonly carried out simulations based on constant values for each fuel model. The method could be applied to Landsat-7 and -8 and Sentinel-2A and -2B after proper sensor inter-calibration and topographic correction.
Significant variation in sensitivity to a DMI fungicide in field populations of Fusarium graminearum

Fusarium head blight caused by Fusarium graminearum (Fg) is a destructive disease on cereals around the world. Intensive application of fungicides on cereals has selected for resistance in many cereal pathogens. Two hundred and twenty-four isolates of Fg from 13 different German field populations, 22 isolates from the UK and six reference isolates from Italy and the USA were tested for sensitivity to propiconazole. Propiconazole sensitivity showed a high heritability (H2 = 0·97), with mean EC50 values ranging from 5·4 to 62·2 mg L-1. Nearly 80% of the variance for propiconazole sensitivity was distributed within field populations, suggesting significant potential for selection of resistant isolates at the field scale. Sequence analysis revealed that the CYP51A and CYP51C genes carried several non-synonymous substitutions but the CYP51B protein was not polymorphic. The non-synonymous substitutions were distributed randomly among populations and were not correlated with fungicide sensitivity. Although not conclusive, the findings suggest that genes outside of the CYP51 family may make the most important contribution to DMI resistance in F. graminearum.
Life in Moving Fluids: The Physical Biology of Flow

Both a landmark text and reference book, Steven Vogel's "Life in Moving Fluids" has also played a catalytic role in research involving the applications of fluid mechanics to biology. In this revised edition, Vogel continues to combine humor and clear explanations as he addresses biologists and general readers interested in biological fluid mechanics, offering updates on the field over the last dozen years and expanding the coverage of the biological literature. His discussion of the relationship between fluid flow and biological design now includes sections on jet propulsion, biological pumps, swimming, blood flow, and surface waves, and on acceleration reaction and Murray's law. This edition contains an extensive bibliography for readers interested in designing their own experiments.
In last few decades we have seen the evolution of rovers from a stationary Lander to a solar-powered rover with actuated arms and now a car-size rover with a mini nuclear reactor carrying out exploration task in hostile mars

6793 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: A2979109119/2019©BEIESP DOI: 10.35940/ijeat.A2979. 109119 ABSTRACT--This paper presents the design, architecture, and constructions of a planetary autonomous exploration vehicle platform, which can be used to develop and test Artificial Intelligence, based software and generate a large dataset for the training of neural networks. Rovers will be at the frontier of planetary exploration, capable of executing tasks without human supervision in a harsh and unpredictable environment, and to do so it requires real-time command execution to keep it away from a risky situation. Due to the limitations imposed by communication latency and small window to communicate through deep space satellites, existing mars rovers are semiautonomous. To develop AI-based software for the rover, a lowcost alternative of a planetary rover is required to generate data from different types of sensors and actuators for a long duration of time and perform all possible scenarios and actions. Presently this task is done using simulation or replicas of the actual rovers used in planetary missions which are very costly. The proposed rover design is a low-cost alternative, capable of powering, driving varieties of sensors, scale up to new hardware and record data as specified by the user. It can also be used to test the newly developed algorithm before being tested on an actual rover. This platform can be used as a simulation platform for software as the proposed platform is directly in contact with the environmental factors.
NEW TECHNIQUE FOR DUPLICATION THE REMOTE DATABASES BASED ON MULTI-AGENTS

Most enterprises use multiple sites to store remote data. Therefore, it is important to centralize the data in real time with efficient systems that duplicate the data to a central site. In this paper, I present a new approach to duplicate the data stored in multiple remote sites using multi-agents. These facilitate the transfer of data and schema of a database automatically and quickly, we exploit the communication between multiple agents to duplicate data and schema. A prototype has been developed using multiple mobile agents to extract the Relational-Object schema and corresponding data from a remote site, and then make a comparison with the schema from the central site to duplicate the data. The experimental results were highly significant, demonstrating that the proposed method is feasible and effective.