Miracle-dz/UniFAR-FTUs huggingface dataset:
Dormancy Breaking Studies of Dodder (Cuscuta spp.) was Problem in Greenhouse Tomato

Tomato cultivation was in the first place with 619.877 tons in the glass greenhouse in the Mediterranean region. Tomato production was ranked first with 437 tons while cucumber production ranked second with 48 tons in Kahramanmaras (Anonymous 2015a). Many problems such as diseases, weeds and pests are encountered in greenhouse tomato cultivation. One of the most important of these problems is the weeds. One of the foreign weeds, Dodder ( Cuscuta spp.) is a parasite plant and the most difficult to control. When you do not control with Dodder ( Cuscuta spp.), economic loss occurs 100% in tomato yield and quality. It has been diagnosed that it is a dodder ( Cuscuta campestris Yunck.) in tomato growing in the glass greenhouse of the Agriculture Faculties. Since the dodder seeds have dormancy, various attempts have been made to break this dormancy. Methods of breaking dormancy on dodder seeds; low temperature application was carried out at 3.5°C for 80 days, in deep freezer at -200 0 C for 24 hours, in pure water for 72 hours, kept at room temperature 26 0 C for 284 days and 1% Sulfuric acid (H 2 SO 4 ) was applied to the seeds of the dodder for 3 minutes. From these applications dodder seed germination was observed only in sulfuric acid applications.
Measurement Errors in Range-Based Localization Algorithms for UAVs: Analysis and Experimentation

Localizing ground devices (GDs) is an important requirement for a wide variety of applications, such as infrastructure monitoring, precision agriculture, search and rescue operations, to name a few. To this end, unmanned aerial vehicles (UAVs) or drones offer a promising technology due to their flexibility. However, the distance measurements performed using a drone, an integral part of a localization procedure, incur several errors that affect the localization accuracy. In this paper, we provide analytical expressions for the impact of different kinds of measurement errors on the ground distance between the UAV and GDs. We review three range-based and three range-free localization algorithms, identify their source of errors, and analytically derive the error bounds resulting from aggregating multiple inaccurate measurements. We then extend the range-free algorithms for improved accuracy. We validate our theoretical analysis and compare the observed localization error of the algorithms after collecting data from a testbed using ten GDs and one drone, equipped with ultra wide band (UWB) antennas and operating in an open field. Results show that our analysis closely matches with experimental localization errors. Moreover, compared to their original counterparts, the extended range-free algorithms significantly improve the accuracy.
Genetic variability within accessions of the B73 maize inbred line

The maize inbred line B73 has been extensively studied at the molecular level. Researchers now have access to the genome sequence of B73 as well as to databases of biallelic and multiallelic markers where functional poly- morphisms between B73 and a public inbred line population can be compared to relate genotypes to phenotypes. This indicates the importance to determine the genetic fidelity of the germplasm during preservation and propa- gation processes, in particular, when seeds of reference inbred lines such as B73 are maintained. The aim of this study was to assess the genetic uniformity among three different sources of the B73 inbred line by means of 75 Simple Sequence Repeats (SSRs). The three B73 sources showed homozygosis; however, some accessions dif- fered greatly from the expected locus size predicted at the reference B73 genomic sequence. A particular haplo- type was prevalent in the USDA accession PI550473. The error rate of the allele size determination was estimated. The genotyping technique used in this work allowed the separation of alleles of ± 2 bp range difference within the same electrophoresis run, whereas allele size estimations between experiments, within the laboratory, differed in ± 4 bp range difference. Besides experimental errors in genotyping, the putative cause of differences among ac- cessions could be attributed to seed contamination and genetic drift. The B73 accessions evaluated in our work can be shared among laboratories to precise genotyping and phenotyping of maize inbred lines.
Accurate Extraction of Mountain Grassland From Remote Sensing Image Using a Capsule Network

Due to an increasing demand of animal husbandry in arid (semiarid) area in China, grassland monitoring based on big data has become a proliferating research focus in recent years. However, the spectral of grass is interfered by topographic relief or that of forest in remote sensing image classification, leading to confusing pixels. In this letter, Tangbula grassland in the middle section of the Tianshan Mountains in Xinjiang (China) was selected as the research area. Upon analysis, we developed a novel composite multifeature deep learning method of capsule network to realize rapid and high-precision remote sensing recognition of the mountainous grassland, through combining the spectral bands with all the extracted features [normalized difference vegetation index (NDVI), topographic, and texture]. Using the new method, the accuracy of the grassland and overall classifications reached the highest values of 91.60% and 96.13%, respectively, greater than those of 85.58% and 92.18%, respectively, of normal classifications without input from texture and topographic features. Compared with the other methods, the method we applied is better than support vector machine (SVM), random forest, and artificial neural network in terms of grassland extraction and classification accuracy.
The Effect of Virtual Reality Programs on Upper Extremity Function in Stroke Patients : A Meta-Analysis

This study examined the effect size of virtual reality programs on the upper extremity function of stroke patients through a meta-analysis. Databases, such as the Research Information Sharing Service, the Korean Studies Information Service System, the National Library of Korea, the Korean Citation Index, and the National Digital Science Library, were used. Previous articles were surveyed for virtual reality programs between January 2010 and June 2019. A meta-analysis was performed by selecting the final 14 studies based on the PICO standard. The RoB and RoBANS tools were used as quality assessment tools for randomized and non-randomized control trials, respectively. The CMA 3.0 program was used to calculate the effect size of each study. Sub-group analysis, meta-regression analysis, and publication bias were performed. The total effect size of the virtual reality programs on the upper extremity function was Hedges's g=0.390 (95% CI: 0.192~0.587) (p<.05). The virtual reality program positively affects the upper extremity function of stroke patients. Therefore, the development of various virtual reality programs and industry-academia cooperation technology for stroke patients is required in accordance with the fourth industry. Randomized control trials and detailed upper extremity function studies for virtual reality programs will be needed in follow-up studies.
Bayesian Inference of Other Minds Explains Human Choices in Group Decision Making

To make decisions in a social context, humans have to predict the behavior of others, an ability that is thought to rely on having a model of other minds known as theory of mind. Such a model becomes especially complex when the number of people one simultaneously interacts is large and the actions are anonymous. Here, we show that in order to make decisions within a large group, humans employ Bayesian inference to model the “mind of the group,” making predictions of others’ decisions while also considering the effects of their own actions on the group as a whole. We present results from a group decision making task known as the Volunteers Dilemma and demonstrate that a Bayesian model based on partially observable Markov decision processes outperforms existing models in quantitatively explaining human behavior. Our results suggest that in group decision making, rather than acting based solely on the rewards received thus far, humans maintain a model of the group and simulate the group’s dynamics into the future in order to choose an action as a member of the group.
The in vitro toxicity of nitrile and epithionitrile derivatives of glucosinolates from rutabaga in human and bovine liver cells

Previous evidence suggests that select nitrile and epithionitrile derivatives of glucosinolates can cause liver disease in cows grazing on brassica forage crops. A toxic incidence in New Zealand in cattle grazing brassica led us to investigate the direct in vitro hepatotoxicity and possible inhibition of the ABCG2 transporter of five nitrile compounds. In this study, we investigated 1-cyano-2-hydroxy-3-butene (CHB, epithionitrile derivative of progoitrin), 1-cyano-2-hydroxy-3,4-epithiobutane (CHEB, nitrile derivative of progoitrin), 3-butenenitrile (nitrile from sinigrin), 4-pentenenitrile (nitrile from gluconapin), and 5-hexenenitrile (nitrile from glucobrassicanapin). Cell viability was assessed following 24- and 72-hr treatments with the 5 different compounds using the MTT assay (HepG2 cells and bovine primary liver cells). Additionally, ABCG2 transporter function was assessed. The results showed that none of the tested compounds caused cytotoxicity at concentrations up to 2 mM for 24hr. Over 72-hr the maximum concentration was 20 μM but no reduction in cell viability was observed. No inhibition of the ABCG2 transporter occured at concentrations up to 1 mM. Overall this study suggests that direct or secondary toxicity due to selected nitrile or epithionitrile derivatives of these glucosinolates was not the cause of the toxic event in cattle.
Extensive mitochondrial population structure and haplotype-specific phenotypic variation in the Drosophila Genetic Reference Panel

The Drosophila Genetic Reference Panel (DGRP) serves as a valuable resource to better understand the genetic landscapes underlying quantitative traits. However, such DGRP studies have so far only focused on nuclear genetic variants. To address this, we sequenced the mitochondrial genomes of >170 DGRP lines, identifying 229 variants including 21 indels and 7 frameshifts. We used our mitochondrial variation data to identify 12 genetically distinct mitochondrial haplotypes, thus revealing important population structure at the mitochondrial level. We further examined whether this population structure was reflected on the nuclear genome by screening for the presence of potential mito-nuclear genetic incompatibilities in the form of significant genotype ratio distortions (GRDs) between mitochondrial and nuclear variants. In total, we detected a remarkable 1,845 mito-nuclear GRDs, with the highest enrichment observed in a 40 kb region around the gene Sex-lethal (Sxl). Intriguingly, downstream phenotypic analyses did not uncover major fitness effects associated with these GRDs, suggesting that a large number of mito-nuclear GRDs may reflect population structure at the mitochondrial level rather than actual genomic incompatibilities. This is further supported by the GRD landscape showing particular large genomic regions associated with a single mitochondrial haplotype. Next, we explored the functional relevance of the detected mitochondrial haplotypes through an association analysis on a set of 259 assembled, non-correlating DGRP phenotypes. We found multiple significant associations with stress- and metabolism-related phenotypes, including food intake in males. We validated the latter observation by reciprocal swapping of mitochondrial genomes from high food intake DGRP lines to low food intake ones. In conclusion, our study uncovered important mitochondrial population structure and haplotype-specific metabolic variation in the DGRP, thus demonstrating the significance of incorporating mitochondrial haplotypes in geno-phenotype relationship studies.
Process Factors Affecting Reperfusion Time in Patients Presenting with ST-segment Elevation Myocardial Infarction (STEMI)

PROCESS FACTORS AFFECTING REPERFUSION TIME IN PATIENTS PRESENTING WITH ST-SEGMENT ELEVATION MYOCARDIAL INFARCTION (STEMI) By Richard R. Wall MSRS, RT(R)(CT)(CI), RCIS A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2020. Dissertation Chair: Jeffrey S. Legg Ph.D. Associate Professor and Chair, Department of Radiation Sciences An ST-segment myocardial infarction (STEMI) occurs when the blood flow to the myocardium is suddenly and completely blocked causing the myocardium to become ischemic. A STEMI is a life-threatening condition that necessitates emergent medical treatment. Research has shown that longer reperfusion times are associated with negative patient outcomes. Therefore, time is critical in the treatment of a STEMI. The purpose of this study was to analyze the process factors involved with the identification and treatment of a STEMI and to develop statistical models to determine which factors have a statistically significant impact on reperfusion times at both the overall and individual facility levels. Retrospective data, covering a three-year period, was collected from four hospitals in the Las Vegas area. A total of 647 cases were analyzed using multiple
The community structure of functional brain networks exhibits scale-specific patterns of variability across individuals and time

The network organization of the human brain varies across individuals, changes with development and aging, and differs in disease. Discovering the major dimensions along which this variability is displayed remains a central goal of both neuroscience and clinical medicine. Such efforts can be usefully framed within the context of the brain’s modular network organization, which can be assessed quantitatively using powerful computational techniques and extended for the purposes of multi-scale analysis, dimensionality reduction, and biomarker generation. Though the concept of modularity and its utility in describing brain network organization is clear, principled methods for comparing multi-scale communities across individuals and time are surprisingly lacking. Here, we present a method that uses multi-layer networks to simultaneously discover the modular structure of many subjects at once. This method builds upon the well-known multi-layer modularity maximization technique, and provides a viable and principled tool for studying differences in network communities across individuals and within individuals across time. We test this method on two datasets and identify consistent patterns of inter-subject community variability, demonstrating that this variability – which would be undetectable using past approaches – is associated with measures of cognitive performance. In general, the multi-layer, multi-subject framework proposed here represents an advancement over current approaches by straighforwardly mapping community assignments across subjects and holds promise for future investigations of inter-subject community variation in clinical populations or as a result of task constraints.
Probe Selection for 5G Massive MIMO Base Station Over-the-Air Testing

A sectored multiprobe anechoic chamber is seen as a promising method to carry out the radiated testing of massive multiple-input–multiple-output (MIMO) base station (BS), and realistic channel environment reconstruction is crucial for the performance assessment of wireless devices, especially for the testing of beamforming procedures, such as beam acquisition, tracking, and so on. This letter proposes a novel probe selection algorithm and corresponding device rotation scheme to enable more accurate channel emulation with specific probe antennas. On the other hand, within the acceptable threshold of error, the number of required probe antennas can be reduced, which will mitigate setup complexity and the number of hardware resources. In the simulation, to verify the robustness of the proposed probe configuration, several metrics are examined with different numbers of selected probes, in terms of spatial correlation and power angular spectrum emulation. The simulation results show that the proposed selection and rotation algorithms perform well in synthesizing the accurate target propagation environment on the BS side.
Identification of Triticum aestivum L., Triticum spelta L. and Triticum durum DESF. genotypes on the HMW-GS base

The main goal of this work was to identify genotypes of three wheat species (Triticum aestivum L., Triticum spelta L., Triticum durum DESF.) on the basis of individual high-molecular-weight glutenin subunits (HMW-GS) and to predict their technological quality. Detection of HMW-GS was carried out by the standard reference method ISTA SDS-PAGE and the Glu-score was calculated according to the catalogue of alleles for HMW-GS. Among the common wheat varieties the highest Glu-score (10) was determined for the cultivars Axis, Istra and Solara. The most frequently occurring HMW-GS in genotypes of Triticum aestivum L. were 0; 7 + 9; 5 + 10. On the other hand, in the spelt wheat the highest frequency of HMW-GS was detected for 2*; 6 + 8; 2 + 12. The Glu-score for Triticum spelta L. genotypes ranged from 6 to 8. Among the Triticum durum DESF. cultivars, up to 71% were characterized by Glu-score 4, which predetermines them for special baking purposes. The most frequent combination of HMW-GS in durum wheat was 0 and 7 + 8. Thus, SDS PAGE of HMW-GS can be used for identification, differentiation and characterization of different species of wheat and for prediction of bread-making quality of wheat.
The terrestrial mammals of Mozambique: Integrating dispersed biodiversity data

Background: The most comprehensive synopsis of the mammal fauna of Mozambique was published in 1976, listing 190 species of terrestrial mammals. Up-to-date knowledge of the country’s biodiversity is crucial to establish the baseline information needed for conservation and management actions.Objectives: The aim of this article was to present a list of terrestrial mammal species reported from Mozambique, based on primary occurrence data.Method: We integrated existing knowledge, from dispersed sources of biodiversity data: the Global Biodiversity Information Facility portal, natural history collections, survey reports and literature. Data were updated and manually curated. However, none of the specimens upon which occurrences are based was directly observed. To partly overcome this impediment, we developed a species selection process for specimen data. This process produced the country’s species checklist and an additional list of species with questionable occurrence in the country.Results: From the digital and non-digital sources, we compiled more than 17 000 records. The data integrated resulted in a total of 217 mammal species (representing 14 orders, 39 families and 133 genera) with supported occurrence in Mozambique and 23 species with questionable reported occurrence in the country.Conclusion: The diversity of species accounted for is considerable as more than 70% of species present in the southern African subregion are found in Mozambique. We consider that the current number of mammal species in Mozambique is still underestimated. The methodological approach for species selection for specimen data can be adapted to update species checklists of crucial importance to countries facing similar lack of knowledge regarding their biodiversity.
Assessment of Genetic Diversity in Robusta Coffee Using Morphological Characters

ABSTRACT Assessing the level of genetic variation that exist in any breeding population is important as this has significant implications for selection and variety development. The objectives of the present study were to determine the variation among 71 Robusta coffee genotypes based on 21 traits and to determine the genetic relationships between the genotypes. The 71 genotypes were established in 2013 as clones raised from single node cuttings and planted in a randomized complete block design with four blocks, with five plants per genotype per block. There was variation in all the traits assessed for the different Robusta coffee genotypes as evidenced by the coefficient of variation and frequencies of the quantitative and qualitative traits, respectively. Principal Component Analysis (PCA) was performed to obtain information on the relative importance of the morphological characters assessed, and cluster analysis was used to determine relationships among the genotypes evaluated. The first and second principal components accounted for 39.2% and 18.9% of the total variability, respectively. PCA revealed that height, stem diameter, span, number of laterals per tree, diameter of laterals, and number of nodes per lateral were the principal characters to discriminate the Robusta coffee genotypes examined. Cluster analysis grouped the Robusta coffee genotypes into five clusters as revealed by the dendrogram. The diversity among the Robusta coffee genotypes in both quantitative and qualitative traits revealed by this study can be used for trait improvement through selection and hybridization.
Extracellular Acidification Activates cAMP Responsive Element Binding Protein via Na+/H+ Exchanger Isoform 1–Mediated Ca2+ Oscillation in Central Nervous System Pericytes

Objective—We have previously shown that Na+/H+ exchanger isoform 1 (NHE1) plays an important role in Ca2+ signaling and cell proliferation in human central nervous system (CNS) pericytes. The aims of the present study were to elucidate how NHE1–induced Ca2+ signaling during acidosis is transformed into cellular responses in CNS pericytes. Methods and Results—Human CNS pericytes were cultured, and the activation of cAMP responsive element–binding protein (CREB) was evaluated by Western blotting analysis, immunofluorescence, and luciferase assays. In human CNS pericytes, low extracellular Na+ or low pH generated Ca2+ oscillation and subsequently phosphorylated Ca2+/calmodulin-dependent kinase II (CaMKII) and CREB in a time-dependent manner. Focal cerebral ischemia was applied using photothrombotic distal middle cerebral artery occlusion in mice, and the phosphorylation of CREB and the production of interleukin-6 were observed in pericytes migrating into the peri-infarct penumbra during the early phase after.
Electromagnetic-Based Correction of Bio-Integrated RFID Sensors for Reliable Skin Temperature Monitoring

Bio-integrated wireless sensors in the form of conformable plaster, based on the Radiofrequency Identification (RFID) communication, have been recently proposed for the battery-less measurement of the human skin temperature. However, the response of the Integrated Circuit (IC) transponder is sensitive to the strength of the interrogating power. Indeed, high power produces artifacts on the sampled temperature up to 2 °C when the mutual position between reader and sensors, as well as the emitted power, can not be carefully controlled. Hence, a reliable adoption of this technology in real cases is challenging and still in question. A combined macro-scale electromagnetic-thermal model is here introduced to predict and correct the above artifact so that the temperature measurement becomes insensitive to the RF power collected by the IC. The method is based on the new generation RFID ICs with on-chip temperature sensor that are also capable to give back the strength of the collected RF power. The model is validated in controlled conditions and then applied for different skin temperature measurements on human body. An average accuracy of ±0.25 °C, compared with a reference calibrated thermocouple, was demonstrated in the considered tests.