Miracle-dz/UniFAR-FTUs huggingface dataset:
Are Smartphone Addiction. Fear of Missing Out (FoMO), and Conformity have impact for Phubbing from Millenial Generation?

This study aims to determine whether there is an influence of smartphone addiction, fear of missing out (FoMO), conformity, and demographic factors towards phubbing. The population of this research is generation Y who have communication devices and domiciles in JABODETABEK. The sample in this research is 301 people. Sampling using non-probability sampling techniques. Measuring tool used in this research is the scale of the phubbing scale instrument (PS), smartphone addiction scale-short version (SAS-SV), fear of missing out scale (FoMOS), and scale of conformity. Test the validity of measuring instrument used is the technique of confirmatory factor analysis (CFA). Hypothesis testing in this research using multiple regression analysis through IBM SPSS statistics 20. The results of this research indicate that there is a significant influence on smartphone addiction variables, fear of missing out (FoMO), conformity, and demographic factors to phubbing of 0,391 or 39,1% of the remaining 60,9% is influenced by other variables. Based on the results of the hypothesis testing of each variable that has been done, there are one variable significant influence on phubbing of smartphone addiction.
An Assessment of Beverage Management Programs in U.S. Hospitality Schools

The purpose of this study was to assess the characteristics and importance of beverage management programs within U.S. hospitality schools based on a content analysis of program websites and the attitudes of educators. The study also aimed to identify possible gaps in programs based on the linkages of offerings, attitudes of educators, and barriers to beverage education. Fifty-two U.S. hospitality schools were evaluated and educators from them were surveyed. The majority of programs offer beverage electives, but not majors or minors. The results showed no significant differences in the mean importance of beverage education characteristics among educators based on job title and years of experience. Faculty members considered incorporating industry standards into the curriculum and professional certifications more important than administrators. Overall, the few differences in attitudes among groups of educators imply that faculty and administrators generally have common goals and acknowledge similar barriers to beverage education.
Lobe growth variation and the maintenance of symmetry in foliose lichen thalli

The aim of this study was to determine how thallus symmetry could be maintained in foliose lichens when variation in the growth of individual lobes may be high. Hence, the radial growth of a sample of lobes was studied monthly, over 22 months, in 7 thalli of Parmelia conspersa (Ehrh. Ex Ach.) Ach. And 5 thalli of P. glabratula ssp fuliginosa (fr. ex Duby) Laund. The degree of variation in the total radial growth of different lobes within a thallus over 22 months varied between thalli. Individual lobes showed a fluctuating pattern of radial growth from month to month with alternating periods of fast and slow growth. Monthly variations in radial growth of different lobes were synchronized in some but not in all thalli. Few significant correlations were found between the radial growth of individual lobes and total monthly rainfall or shortwave radiation. The levels of ribitol, arabitol and mannitol were measured in individual lobes. All three polyols varied significantly between lobes within a thallus suggesting that variations in algal phostosynthesis and in the partitioning of fungal polyols may contribute to lobe growth variation. The effect on thallus symmetry of lobes which grew radially either consistently faster or slower than average was studied. Slow growing lobes were overgrown, and gaps in the perimeter were eliminated by the growth of neighbouring lobes, in approximately 7 to 9 months. However, a rapidly growing lobe, with its neighbours removed on either side, continued to grow radially at the same rate as rapidly growing control lobes. The results suggested that lobe growth variation results from a combination of factors which may include the origin of the lobes, lobe morphology and the patterns of algal cell division and hyphal elongation in different lobes. No convincing evidence was found to suggest that exchange of carbohydrate occurred between lobes which would tend to equalize their radial growth. Hence, the fluctuating pattern of lobe growth observed may be sufficient to maintain a degree of symmetry in most thalli. In addition, slow growing lobes would tend to be overgrown by faster growing neighbours thus preventing the formation of indentations in the thallus perimeter.
Running title: BCL-XL and BCL2A1 confer ABT-737 resistance

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Ants as Mechanical Transmission of Pathogenic Microorganisms in Homes of Quetta city, Balochistan

ABSTRACT Ants are very essential cause of spreading pathogenic bacteria to humans. This study was conducted for isolation of the pathogenic bacteria from external and internal parts of ants by hand picking. Total 135 samples were collected, in which 75% were contaminated with pathogens and 25% were negative. Among 9 species of ants, 11.11% carpenter ant, 5.20% pharaoh ant, 11.9 % fire ant, 6.60% weaver ant, 7.41% garden ant, 8.80% odorous ant, 5.90% pavement ant, 9.60% crazy ant and 8.14% little black ant were contaminated with bacteria. Fire ant was the most contaminated specie. The microbiological analysis identified the following microorganisms from above mentioned ants i.e. Citrobacter spp. , Pseudomonas spp. , Salmonella spp. , Clostridium spp. , Staphylococcus aureus. , Enterobacter spp. , Escherichia coli and Klebsiella spp. The examination of isolated bacteria for antimicrobial susceptibility revealed that most active antibiotics against these pathogens i.e. Colistin sulphate, Ciprofloxacin and Tetracyclines while multiple drug resistance was shown by Carbenicillin,Vancomycin, Lincomycin and Kanamycin.
Re-evaluating method-level bug prediction

Bug prediction is aimed at supporting developers in the identification of code artifacts more likely to be defective. Researchers have proposed prediction models to identify bug prone methods and provided promising evidence that it is possible to operate at this level of granularity. Particularly, models based on a mixture of product and process metrics, used as independent variables, led to the best results. In this study, we first replicate previous research on method-level bug prediction on different systems/timespans. Afterwards, we reflect on the evaluation strategy and propose a more realistic one. Key results of our study show that the performance of the method-level bug prediction model is similar to what previously reported also for different systems/timespans, when evaluated with the same strategy. However—when evaluated with a more realistic strategy—all the models show a dramatic drop in performance exhibiting results close to that of a random classifier. Our replication and negative results indicate that method-level bug prediction is still an open challenge.
Effect of Pseudomonas fluorescens Pf1 talc-based formulation under different storage periods against late leaf spot of groundnut

�� � SUMMARY In groundnut, late leaf spot caused by Cercosporidium personatum is the serious disease. The effect of storage of Pseudomonas fluorescens Pf1 talc-based formulation for different periods in the management of late leaf spot disease was assessed. Groundnut seeds were treated with Pf1 formulation (100 g/kg seed) and sprayed with Pf1 formulation (1 kg/ha) at different days of storage and challenge inoculated with pathogen. The disease intensity and population of P. fluorescens in the formulation at different days of storage were recorded. The results revealed that effective protection against the disease was observed only up to 30 days after seed treatment with Pf1 formulation and foliar application of Pf1 formulation was able to offer protection only up to 15 days.
Simple taxonomic key for identifying marine luminous bacteria

A simple key which provides faster identification of marine luminous bacteria based on 10 biochemical tests is presented. It is able to distinguish among closely related marine luminous bacteria. Using this new key, luminous bacteria of Tuticorin (India) coastal and shrimp farm environments were distinguished into 5 different species, viz., Vibrio fischeri, V harveyi, V. orientalis, V. splendidus 1 and Photobacterium leiognathi. Vibrio harveyi was the dominant species, constituting 88.66 - 90.93 % of the total luminous population followed by V. orientalis (4.43 - 4.46 %) and V. splendidus 1 (2.11 - 3.35 %).
Learning Video Object Segmentation with Visual Memory

This paper addresses the task of segmenting moving objects in unconstrained videos. We introduce a novel two-stream neural network with an explicit memory module to achieve this. The two streams of the network encode spatial and temporal features in a video sequence respectively, while the memory module captures the evolution of objects over time. The module to build a “visual memory” in video, i.e., a joint representation of all the video frames, is realized with a convolutional recurrent unit learned from a small number of training video sequences. Given a video frame as input, our approach assigns each pixel an object or background label based on the learned spatio-temporal features as well as the "visual memory" specific to the video, acquired automatically without any manually-annotated frames. The visual memory is implemented with convolutional gated recurrent units, which allows to propagate spatial information over time. We evaluate our method extensively on two benchmarks, DAVIS and Freiburg-Berkeley motion segmentation datasets, and show state-of-the-art results. For example, our approach outperforms the top method on the DAVIS dataset by nearly 6%. We also provide an extensive ablative analysis to investigate the influence of each component in the proposed framework.
Robust online face detection and tracking

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication
High dimensional classification with combined adaptive sparse PLS and logistic regression

Motivation The high dimensionality of genomic data calls for the development of specific classification methodologies, especially to prevent over‐optimistic predictions. This challenge can be tackled by compression and variable selection, which combined constitute a powerful framework for classification, as well as data visualization and interpretation. However, current proposed combinations lead to unstable and non convergent methods due to inappropriate computational frameworks. We hereby propose a computationally stable and convergent approach for classification in high dimensional based on sparse Partial Least Squares (sparse PLS). Results We start by proposing a new solution for the sparse PLS problem that is based on proximal operators for the case of univariate responses. Then we develop an adaptive version of the sparse PLS for classification, called logit‐SPLS, which combines iterative optimization of logistic regression and sparse PLS to ensure computational convergence and stability. Our results are confirmed on synthetic and experimental data. In particular, we show how crucial convergence and stability can be when cross‐validation is involved for calibration purposes. Using gene expression data, we explore the prediction of breast cancer relapse. We also propose a multicategorial version of our method, used to predict cell‐types based on single‐cell expression data. Availability and implementation Our approach is implemented in the plsgenomics R‐package.
Saccharomyces cerevisiae–Based Platform for Rapid Production and Evaluation of Eukaryotic Nutrient Transporters and Transceptors for Biochemical Studies and Crystallography

To produce large quantities of high quality eukaryotic membrane proteins in Saccharomyces cerevisiae, we modified a high-copy vector to express membrane proteins C-terminally-fused to a Tobacco Etch Virus (TEV) protease detachable Green Fluorescent Protein (GFP)-8His tag, which facilitates localization, quantification, quality control, and purification. Using this expression system we examined the production of a human glucose transceptor and 11 nutrient transporters and transceptors from S. cerevisiae that have not previously been overexpressed in S. cerevisiae and purified. Whole-cell GFP-fluorescence showed that induction of GFP-fusion synthesis from a galactose-inducible promoter at 15°C resulted in stable accumulation of the fusions in the plasma membrane and in intracellular membranes. Expression levels of the 12 fusions estimated by GFP-fluorescence were in the range of 0.4 mg to 1.7 mg transporter pr. liter cell culture. A detergent screen showed that n-dodecyl-ß-D-maltopyranoside (DDM) is acceptable for solubilization of the membrane-integrated fusions. Extracts of solubilized membranes were prepared with this detergent and used for purifications by Ni-NTA affinity chromatography, which yielded partially purified full-length fusions. Most of the fusions were readily cleaved at a TEV protease site between the membrane protein and the GFP-8His tag. Using the yeast oligopeptide transporter Ptr2 as an example, we further demonstrate that almost pure transporters, free of the GFP-8His tag, can be achieved by TEV protease cleavage followed by reverse immobilized metal-affinity chromatography. The quality of the GFP-fusions was analysed by fluorescence size-exclusion chromatography. Membranes solubilized in DDM resulted in preparations containing aggregated fusions. However, 9 of the fusions solubilized in DDM in presence of cholesteryl hemisuccinate and specific substrates, yielded monodisperse preparations with only minor amounts of aggregated membrane proteins. In conclusion, we developed a new effective S. cerevisiae expression system that may be used for production of high-quality eukaryotic membrane proteins for functional and structural analysis.
Investigation of variants of critically important antioxidant enzyme genes in patients with polycystic ovary syndrome

Aim: To investigate the possible effects of polymorphisms in genes encoding some important antioxidant enzymes such as super oxide dismutase 2 (SOD2), glutathione peroxidase 1 (GPX1), endothelial NOS (eNOS) and catalase (CAT) in patients with polycystic ovary syndrome (PCOS). Methods: Peripheral blood of 100 patients with PCOS and 100 healthy control group were collected, Polymorphisms in related genes was investigated by using polymerase chain reaction-restriction fragment length polymorphism. In addition, the related biochemical values of the patients were also investigated.Result: In our study there is no significant results for SOD2 gene but the results obtained between GPX1, eNOS and CAT genes were significant. Fasting blood sugar (FBS), insulin, triglyceride, waist circumference and dehydroepiandrosterone sulphate (DHEAS) were found to be significant with the disease, whereas follicle-stimulating hormone (FSH) was found to be effective in preventing the disease.Conclusions: These findings suggest that polymorphisms in genes encoding GPX1, eNOS and CAT enzymes may be associated with PCOS. Additionally, it is thought that the genes of FBS, triglyceride, insulin, DHEAS and waist circumference are important in the pathogenesis of the disease in the presence of homozygous mutation.
The movement of cadmium through freshwater trout branchial epithelium and its interference with calcium transport

Exposure of freshwater trout ( Salmo gairdneri ) to waterborne Cd 2+ results in accumulation of the metal in the branchial epithelial cells and its appearance in the blood. Cd 2+ apparently enters the cells via Ca 2+ channels in the apical membrane. Transfer of Cd 2+ through the basolateral membrane is probably by diffusion. Inhibition by Cd 2+ of transepithelial Ca 2+ influx is time- and Cd 2+ -concentration-dependent. The inhibition of transepithelial Ca 2+ influx is accompanied by blockage of apical Ca 2+ channels. In line with the assumption that cytosolic Cd 2+ inhibits Ca 2+ uptake by inhibiting the basolateral Ca 2+ pump, we hypothesize that the blockage of Ca 2+ channels is an indirect effect of Cd 2+ and results from a rise in cytosolic Ca 2+ level caused by inhibition of the basolateral membrane Ca 2+ pump.
Challenges in modelling isoprene and monoterpene emission dynamics of Arctic plants: a case study from a subarctic tundra heath

Abstract. The Arctic is warming at twice the global average speed, and the warming-induced increases in biogenic volatile organic compounds (BVOCs) emissions from Arctic plants are expected to be drastic. The current global models' estimations of minimal BVOC emissions from the Arctic are based on very few observations and have been challenged increasingly by field data. This study applied a dynamic ecosystem model, LPJ-GUESS, as a platform to investigate short-term and long-term BVOC emission responses to Arctic climate warming. Field observations in a subarctic tundra heath with long-term (13-year) warming treatments were extensively used for parameterizing and evaluating BVOC-related processes (photosynthesis, emission responses to temperature and vegetation composition). We propose an adjusted temperature (T) response curve for Arctic plants with much stronger T sensitivity than the commonly used algorithms for large-scale modelling. The simulated emission responses to 2 °C warming between the adjusted and original T response curves were evaluated against the observed warming responses (WRs) at short-term scales. Moreover, the model responses to warming by 4 and 8 °C were also investigated as a sensitivity test. The model showed reasonable agreement to the observed vegetation CO2 fluxes in the main growing season as well as day-to-day variability of isoprene and monoterpene emissions. The observed relatively high WRs were better captured by the adjusted T response curve than by the common one. During 1999–2012, the modelled annual mean isoprene and monoterpene emissions were 20 and 8 mg C m−2 yr−1, with an increase by 55 and 57 % for 2 °C summertime warming, respectively. Warming by 4 and 8 °C for the same period further elevated isoprene emission for all years, but the impacts on monoterpene emissions levelled off during the last few years. At hour-day scale, the WRs seem to be strongly impacted by canopy air T, while at the day–year scale, the WRs are a combined effect of plant functional type (PFT) dynamics and instantaneous BVOC responses to warming. The identified challenges in estimating Arctic BVOC emissions are (1) correct leaf T estimation, (2) PFT parameterization accounting for plant emission features as well as physiological responses to warming, and (3) representation of long-term vegetation changes in the past and the future.
Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition

The use of machine learning and computer vision methods for recognizing different plants from images has attracted lots of attention from the community. This paper aims at comparing local feature descriptors and bags of visual words with different classifiers to deep convolutional neural networks (CNNs) on three plant datasets; AgrilPlant, LeafSnap, and Folio. To achieve this, we study the use of both scratch and fine-tuned versions of the GoogleNet and the AlexNet architectures and compare them to a local feature descriptor with k-nearest neighbors and the bag of visual words with the histogram of oriented gradients combined with either support vector machines and multi-layer perceptrons. The results shows that the deep CNN methods outperform the hand-crafted features. The CNN techniques can also learn well on a relatively small dataset, Folio.
The Integrated Plant Record (IPR) analysis: Methodological advances and new insights into the evolution of European Palaeogene/Neogene vegetation

The Integrated Plant Record (IPR) vegetation analysis serves as a proxy method to derive major types of zonal palaeovegetation based on the proportion of zonal key components. This paper pursues two goals: 1) to introduce two tools (Drudge 1 and Drudge 2) to statistically determine close modern proxies for fossil plant assemblages out of the reference set of currently 505 modern vegetation units. These range from closed forests to more open (steppe) environments from Europe and Asia and are based on the correspondence in the proportion of the zonal key components (IPR Similarity) and Taxonomic Similarity (TS, genus level); and 2) to present the extension of the calibration dataset of modern zonal vegetation using the natural vegetation of Europe, the Caucasus, China, and Mongolia. The tools are tested on six Central European plant assemblages from the late Eocene to the late Pliocene. For the late Eocene to early Miocene, the results indicate a close relationship to East Asian vegetation based both on IPR Similarity and TS. For the younger sites, IPR Similarity points towards European vegetation, whereas TS still indicates closer East Asian affinity. The summary results (as presented in the Results – Mix) derived by both tools deliver modern proxy vegetation units, which are in good agreement with modern vegetation analogues proposed by traditional empirical studies. The IPR Similarity results probably reflect climate change, which results in proportions of zonal key components, i.e., leaf physiognomy of zonal forests that are more similar to modern European than to Asian vegetation from the latest early/middle Miocene onwards. Vasilis Teodoridis. Department of Biology and Environmental Studies, Faculty of Education, Charles University, Prague, Czech Republic. vasilis.teodoridis@pedf.cuni. cz Petr Mazouch. Faculty of Informatics and Statistics, University of Economics, Prague, Czech Republic. mazouchp@vse.cz Johanna Kovar-Eder. State Museum of Natural History Stuttgart, Rosenstein 1, 70191 Stuttgart, Germany. johanna.eder@smns-bw.de