Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulation of Weibel-Palade Body Exocytosis by -Synuclein in Endothelial Cells

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Fine-scale spatial and temporal genomic variation among Dungeness crab Cancer magister larval recruits in the California Current Ecosystem

Dynamic marine environments can shape complex spatial and temporal patterns in the population connectivity of marine species, and this is often exemplified in species with long larval phases. Here, we used a genotyping-by-sequencing (GBS) approach to examine fine-scale spatial and temporal genomic variation among Dungeness crab Cancer magister larval recruits sampled in the California Current Ecosystem. Specifically, we compared samples collected during expectedand late-season recruitment time periods within 2 consecutive years (2017 and 2018) at 2 sites in Oregon, USA (Yaquina Bay and Coos Bay). Evidence was found for high gene flow between the expectedand late-season recruits within each year and at both sites based on 1389 neutral loci. In contrast, strong genetic differentiation was observed between these 2 groups within each year and at both sites based on variation at 2 putatively adaptive loci. Contrary to prediction, the magnitude of genetic differentiation between these 2 seasonal groups was greater in 2017 when the Pacific Decadal Oscillation was stronger, upwelling was weaker, and the spring transition was later. Spatial genetic variation was not observed within 2017 or 2018. Comparing across years, expectedand late-season groups were differentiated at putatively adaptive loci. Interestingly, strong genetic differentiation was also observed between late-season groups across years. We found no evidence for cohesive larval dispersal among recruits based on genetic related ness estimates. Overall, our findings provide evidence for high connectivity within Dungeness crab, but suggest that selective pressures and ocean conditions influence the genetic composition of larval recruits both intraand inter-annually.
Charles Darwin University Genome-wide screen identifies new candidate genes associated with artemisinin susceptibility in Plasmodium falciparum in Kenya Borrmann,

Citation for published version (APA): Borrmann, S. , Straimer, J., Mwai, L. , Abdi, A. , Rippert, A. , Okombo, J., Muriithi, S. , Sasi, P., Kortok, M. M. , Lowe, B. , Campino, S. , Assefa, S. , Auburn, S. , Manske, M. , Maslen, G. , Peshu, N. , Kwiatkowski, D. , Marsh, K. , Nzila, A. , & Clark, T. (2013). Genome-wide screen identifies new candidate genes associated with artemisinin susceptibility in Plasmodium falciparum in Kenya. Scientific Reports, 3, 1-10. [3318]. https://doi.org/10.1038/srep03318
Model Reference Adaptive System with Finite-Set for Encoderless Control of PMSGs in Micro-Grid Systems

In micro-grid systems, wind turbines are essential power generation sources. The direct-driven surface-mounted permanent-magnet synchronous generators (SMPMSGs) in variable-speed wind generation systems (VS-WGSs) are promising due to their high efficiency/power density and the avoidance of using a gearbox, i.e., regular maintenance and noise are averted. Usually, the main goal of the control system for SMPMSGs is to extract the maximum available power from the wind turbine. To do so, the rotor position/speed of the SMPMSG must be known. Those signals are obtained by the help of an incremental encoder or speed transducer. However, the system reliability is remarkably reduced due to the high failure rate of these mechanical sensors. To avoid this problem, this paper presents a model reference adaptive system with finite-set (MRAS-FS) observer for encoderless control of SMPMSGs in VS-WGSs. The motif of the presented MRAS-FS observer is taken from the direct-model predictive control (DMPC) principle, where a certain number of rotor position angles are utilized to estimate the stator flux of the SMPMSG. Subsequently, a new optimization criterion (also called quality or cost function) is formulated to select the best rotor position angle based on minimizing the error between the estimated and reference value of the stator flux. Accordingly, the traditional fixed-gain proportional-integral regulator generally employed in the classical MRAS observers is not needed. The proposed MRAS-FS observer is validated experimentally, and its estimation response has been compared with the conventional MRAS observer under different conditions. In addition to that, the robustness of the MRAS-FS observer is tested at mismatches in the parameters of the SMPMSG.
A NOVEL SELF-TAUGHT LEARNING FRAMEWORK USING SPATIAL PYRAMID MATCHING FOR SCENE CLASSIFICATION

Abstract. Remote sensing earth observation images have a wide range of applications in areas like urban planning, agriculture, environment monitoring, etc. While the industrial world benefits from availability of high resolution earth observation images since recent years, interpreting such images has become more challenging than ever. Among many machine learning based methods that have worked out successfully in remote sensing scene classification, spatial pyramid matching using sparse coding (ScSPM) is a classical model that has achieved promising classification accuracy on many benchmark data sets. ScSPM is a three-stage algorithm, composed of dictionary learning, sparse representation and classification. It is generally believed that in the dictionary learning stage, although unsupervised, one should use the same data set as classification stage to get good results. However, recent studies in transfer learning suggest that it might be a better strategy to train the dictionary on a larger data set different from the one to classify.In our work, we propose an algorithm that combines ScSPM with self-taught learning, a transfer learning framework that trains a dictionary on an unlabeled data set and uses it for multiple classification tasks. In the experiments, we learn the dictionary on Caltech-101 data set, and classify two remote sensing scene image data sets: UC Merced LandUse data set and Changping data set. Experimental results show that the classification accuracy of proposed method is compatible to that of ScSPM. Our work thus provides a new way to reduce resource cost in learning a remote sensing scene image classifier.
Relevance of Invasive Testing in Era of Non-Invasive Testing for Prenatal Chromosomal Abnormalities

Prenatal screening for chromosomal abnormalities has two components i.e. prenatal screening (maternal serum screening and cell-free fetal DNA screening) and prenatal diagnosis (chorionic villus sampling, amniocentesis, and cordocentesis). Prenatal testing in the past decade is evolving towards non-invasive methods to determine the chromosome abnormality disorders in the fetus without incurring the risk of miscarriage. Conventional tools for prenatal screening included maternal age, maternal serum markers, ultrasound marker (nuchal thickness), and their combinations. With the increased risk of screening test patients were offered diagnostic tests (chorionic villus sampling, amniocentesis, and cordocentesis). After the availability of noninvasive prenatal tests for commercial use in 2011, a great marketing drive is there to establish it as a master tool for prenatal testing. However various society guidelines i.e. ACOG, RCOG, and ISUOG have clearly stated that cell-free fetal DNA based noninvasive prenatal tests is a screening test, not a diagnostic test. In the succeeding paragraph, we will review current trends in the field of cell-free fetal DNA noninvasive prenatal tests and the relevance of invasive testing in the context of noninvasive prenatal tests. Noninvasive prenatal tests does not entirely replace invasive prenatal testing procedures. Positive noninvasive prenatal tests findings must be confirmed by diagnostic tests based on an invasive sample source, mainly chorionic villus sampling or amniocentesis due to false positive and false negative reports of cell-free fetal DNA based tests. Continuing research and development efforts are focused on overriding noninvasive prenatal tests limitations. Recent studies show that procedure-associated risks in the case of prenatal invasive testing are very low as compared to previous studies. Prenatal invasive testing will remain as the backbone of prenatal diagnostic testing until the limitation of noninvasive prenatal tests is overcome.
NEIGHBOUR-BASED DOMAIN ADAPTATION FOR INVESTIGATION OF TRANSFERABLE ABILITY OF PREVIOUSLY LABELED DATA FOR LAND-COVER CLASSIFICATION OF AERIAL IMAGES

Abstract. Existing land-cover classification methods are usually based on adequate labelled data. But annotating enough training samples is hard and time-consuming. Therefore, we need to investigate how existing labelled data can help to increase land-cover classification. Source labelled data are proposed to be selected by calculating the target center of reliable target pseudo-labelled data for each class in this paper. Then we augment the training dataset with reliable target pesudo-labeled data and selected source labelled data to improve the quality and quantity of training dataset. We also investigate the amount of source labelled data that should be selected and the number of limited target labelled data that can produce good transfer learning performance. The UC Merced dataset is employed as the target dataset to evaluate the proposed approach while the NWPU-RESISC45 dataset is considered as the source labelled data. The experimental results show that selected source labelled data and reliable target pesudo-labeled data may improve the land-cover classification performance if selected source labelled data and reliable target pesudo-labeled data are augmented with the limited target labelled data respectively.
Are Urban Communities in Successional Stasis? A Case Study on Epiphytic Lichen Communities

Urban areas may contain a wide range of potential habitats and environmental gradients and, given the many benefits to human health and well-being, there is a growing interest in maximizing their biodiversity potential. However, the ecological patterns and processes in urban areas are poorly understood. Using a widely applicable ecological survey method, we sampled epiphytic lichen communities, important bioindicators of atmospheric pollution, on host Quercus trees in urban parks of London, UK, to test if common patterns relating to lichen diversity are mirrored in urban green spaces. We found lichen diversity to be dependent on host species identity, and negatively related to local tree crowding. In addition, we found a strong negative effect of tree size on lichen diversity, leaving large trees as unexploited niches. A novel network analysis revealed the presence of only pioneer communities, showing the lichen communities are being held in successional stasis, likely due to the heritage effects of SO2 emissions and current nitrogen pollution and particulate emissions. Our study highlights that jointly assessing species richness, community structure and the successional stage can be key to understanding diversity patterns in urban ecosystems. Subsequently, this may help best determine the optimum conditions that will facilitate biodiversity increase within cities.
Interactive comment on “ High resolution global grids of revised Priestley-Taylor and Hargreaves-Samani coefficients for assessing ASCE-standardized reference crop evapotranspiration and solar radiation ” by Vassilis G

Comment R2.1: This study analyses the global relationship between ETo annual maps obtained by means of three different methods (ASCE PM, P-T and H-S), with the purpose of determining the accuracy of the methods based on poor data availability (P-T and H-S). In addition, the manuscript includes a calibration exercise to obtain revised coefficients at the global scale for P-T and H-S equations based on the obtained ASCE
WHO recommendation on multiple micronutrient supplementation during pregnancy

This recommendation updates and supersedes the WHO recommendation found in the WHO ANC guideline issued in 2016 (1). The evidence is derived from trials using multiple micronutrient supplements (MMS) containing 13 to 15 micronutrients (including iron and folic acid [IFA]) and the widely available United Nations International Multiple Micronutrient Antenatal Preparation (UNIMMAP), which contains 15 micronutrients, including 30 mg of iron and 0.4 mg of folic acid. As the evidence was mainly derived from lowand middle-income countries, its applicability to highincome countries or to populations not at risk of micronutrient deficiencies – for example, due to an adequate diet and food fortification programmes – is unclear. Research in this context therefore includes: controlled clinical trials in which early pregnancy ultrasound is used to establish gestational age with certainty**, with assessment of critical maternal and perinatal outcomes, and follow-up of infants sustained into childhood; and where programmes of MMS are being considered, implementation research to establish the impact of switching from iron and folic acid supplements to MMS, including evaluation of acceptability, feasibility, sustainability, equity and cost-effectiveness. Many MMS contain 30 mg or less of elemental iron and WHO recommends antenatal iron and folic acid supplements containing 60 mg of elemental iron in populations where anaemia is a severe public health problem (a prevalence of 40% or higher) (2). Therefore, countries should consider their population magnitude and distribution of anaemia, its nutritional determinants (i.e. iron deficiency), as
Utilization of Industrial Wastes in Road Construction: A Review

Now a day’s disposal of different wastes, mainly industrial wastes is a major problem. Since these wastes are nonbiodegradable and very fine in particle size results to various environmental threats. As the number of industries and factories are rising drastically, there is a need to know the benefits of waste materials which can be recycled and reused in any other forms. The most suitable method of utilization of these industry waste materials is adopting them in construction. In many of the research works it is proved that the waste materials produced by industries can be utilized in road pavements. Large quantity of wastes is produced in these establishments. The pollution and disposal of waste material can be partly reduced by using in construction. Many Researchers have developed the specifications of road construction using industrial wastes which in turn can be possible to return higher economics [1]. The pollution and disposal problems can be minimized by utilizing these materials in road constructions. These days coarse and fine aggregates are replaced partially with industrial waste materials. Taking into considerations that natural materials are getting exhausted in nature and quantity is decreasing gradually also the extraction of good quality materials are very economic, scientists are looking forward for the improvement and utilization of industrial waste materials in road construction. A review on some of the most suitable methods are discussed in this paper. [2].
Impact of dried seaweed meal on heat-stressed lactating dairy cattle

Twenty-four lactating Holstein cows were used to determine the production response to the inclusion of brown seaweed in the basal diet during summer heat stress. Cows were blocked by lactation number, days in milk, and energy-corrected milk and then allotted to either a control or control + brown seaweed diet. Cattle on the brown seaweed diet were fed 4 ounces per cow per day for 7 days , and then 2 ounces per cow per day for 14 days, before the start of the experiment. All cattle were housed in a tie-stall barn, fed individually, and milked twice daily. Cows fed brown seaweed produced more (P<0.01) milk (77.6 vs 73.8 lb) and milk protein than controls did. But the addition of brown seaweed did not reduce respiration rates, rectal temperature, or rear-udder skin temperature. This indicated a similar heat-stress response for treated and control cows. Other studies have shown a reduction in respiration rates and body temperature when stressed cattle were fed brown seaweed. Further investigation is necessary to determine the factors that resulted in the observed milk and milk-protein responses in this study.
The first Mesozoic Derbidae (Homoptera: Fulgoroidea) from Cretaceous Burmese amber

1 Zoological Institute, Russian Academy of Sciences, Universitetskaya Emb. 1, St.-Petersburg 199034, Russia. E-mail: alexandr.emeljanov@zin.ru. 2 Borissiak Paleontological Institute, Russian Academy of Sciences, Profsoyuznaya St. 123, Moscow, 117647, Russia. E-mail: dshh@narod.ru. 1 Зоологический институт РАН, Университетская наб. 1, Санкт-Петербург 199034, Россия. 2 Палеонтологический институт им. А. А. Борисяка РАН, ул. Профсоюзная 123, Москва 117647, Россия.
Increased Genetic Diversity via Gene Flow Provides Hope for Acacia whibleyana, an Endangered Wattle Facing Extinction

In this paper we apply a conservation genomics approach to make evidence-based management recommendations for Acacia whibleyana, an endangered shrub endemic to Eyre Peninsula, South Australia. We used population genomic analysis to assess genetic connectivity, diversity, and historical inbreeding across all known stands of the species sampling remnant stands, revegetated stands of unknown origin, and a post-fire seedling cohort. Our results indicate a degree of historical connectivity across the landscape, but habitat loss and/or pollinator community disruption are potential causes of strong genetic structure across the remnant stands. Remnant stands had low genetic diversity and showed evidence of historical inbreeding, but only low levels of intra-stand relatedness indicating that risks of contemporary inbreeding are low. Analysis of a post-fire first generation cohort of seedlings showed they likely resulted from intra-stand matings, resulting in reduced genetic diversity compared to the parents. However, admixed seedlings in this cohort showed an increase in heterozygosity relative to likely sources and the non-admixed seedlings of the same stand. Assisted inter-stand gene flow may prove an effective management strategy to boost heterozygosity and corresponding increases in adapting capacity in this endangered species.
Cross-cultural Adaptation of the Body Satisfaction and Global Self Perception Questionnaire for Subjects with Non-specific Musculoskeletal Disorders

Background: Assessing patient’s cognitions and emotions about their physical body is an important part of rehabilitation planning, as musculoskeletal disorders can lead to a change in bodily perceptions. Methods: This study explores the validity and reliability of the Body Satisfaction and Global Self-Perception Questionnaire (QSCPGS)] in the Spanish population. In addition to calculating Cronbach’s alpha, we conducted Exploratory Factor Analysis and Confirmatory Factor Analyses to test the scale’s validity. We then conducted a test–retest and longitudinal measurement invariance test to explore our measure’s reliability. Results: Our final sample consisted of 147 subjects with non-specific musculoskeletal disorders (M = 32 years, SD = 15.06), who provided two data points in a 30-day period. A reduced, four-factor model fit the data better [(χ2 = 92.51*; df = 71; df = χ2/df = 1.30; RMSEA= .04; CFI = .95; TLI = .94; SRMR = .05; WRMR = .84)] than any other model. The re-test validity analyses revealed that the four-factor model was stable over time. Finally, the reduced scale correlated with the SF-36 Quality of Life inventory and participants’ BMI. Conclusions: The QSCPGSe (Spanish version QSCPGS) is a reliable and effective tool for measuring body image perceptions that are more accurate than the original scale.
Optimal Model for Energy Management Strategy in Smart Building with Energy Storage Systems and Electric Vehicles

The aim of this work was to develop an optimal model for an energy management strategy in a real micro-grid, which involves a smart building, a photovoltaic system with storage, and a plug-in full electric vehicle. A controller based on a mathematical algorithm was the core of each strategy, which directly acted on a relay board managing the interconnection between the different elements comprising the micro-grid. The development of an optimization model involving binary variables required an efficient code that achieved solutions in a short time. The analyzed case-study corresponded to the solar energy research center (CIESOL) smart building, a bioclimatic building, that is located at the University of Almería (Spain), designated to research in renewable energies. Using the methodologies described in this work, the total cost of the smart building energy consumption was minimized by decreasing the power supplied from the grid, especially at peak hours. Highlighting the use of a simple model that provided better performance than the current state of the art methodologies. The optimal model for energy management strategy demonstrated the advantages of using classical optimization techniques to solve this specific optimization problem, compared to a rule-based controller. The linear modeling was capable of producing a simple algorithm with less code development and a reduction in the computational effort.
Implementing a Data Mining Solution Approach to Identify the Valuable Customers for Facilitating Electronic Banking

Nowadays, the banking system is known as one of the inherent sectors of customer relationship management systems. Its main advantage is to redesign a more responsive organization to satisfy the customers. The banking system aims to improve the structure of organizations to provide a better customer service through a set of automated and integrated processes. The final goal is to collect and reprocess the personal information of customers. To handle this dilemma, a number of new techniques in data mining provide a powerful tool to explore customers’ information regarding a set of data and tools for customer relationship management. Accordingly, the customers’ classification and coordination of banking system are the main challenging issues of today's world. These reasons motivate the attempts of this study to apply a composition of neural network by considering the C4.5 decision tree and the k-closest neighbor method as a variant of core boosting methodology with maximal strategy. To validate the proposed solution approach, a case study of Ansar Bank in Iran is utilized. From the results, it is observed that the proposed method provides a competitive output with the rate of 95% for the customers’ classification. It also outperforms other existing methods with the rate of C4.5 decision tree, neural network, Naive Bayes and KNN with the rate of 1.04%. The main finding of this research is to propose an algorithm with the error rate of 1.9% and error squared of 0.72% as the best performance among other methods from the literature.