QUERAI – A Smart Quiz Generator

QUERAI is a website powered by an Artificial Intelligence Question & Answer quiz generator model aiming to enhance students\u27 learning experience and improve teachers\u27 qualitative work by giving them more time to deal with other activities such as assignment correction, general grading, and class preparation

Multiple imputation and maximum likelihood principal component analysis of incomplete multivariate data from a study of the ageing of port

A multivariate data matrix containing a number of missing values was obtained from a study on the changes in colour and phenolic composition during the ageing of port. Two approaches were taken in the analysis of the data. The first involved the use of multiple imputation (MI) followed by principal components analysis (PCA). The second examined the use of maximum likelihood principal component analysis (MLPCA). The use of multiple imputation allows for missing value uncertainty to be incorporated into the analysis of the data. Initial estimates of missing values were firstly calculated using the Expectation Maximization algorithm (EM), followed by Data Augmentation (DA) in order to generate five imputed data matrices. Each complete data matrix was subsequently analysed by PCA, then averaging their principal component (PC) scores and loadings to give an estimation of errors. The first three PCs accounted for 93.3% of the explained variance. Changes to colour and monomeric anthocyanin composition were explained on PC1 (79.63% explained variance), phenolic composition and hue mainly on PC2 (8.61% explained variance) and phenolic composition and the formation of polymeric pigment on PC3 (5.04% explained variance). In MLPCA estimates of measurement uncertainty is incorporated in the decomposition step, with missing values being assigned large measurement uncertainties. PC scores on the first two PCs after multiple imputation and PCA (MI+PCA) were comparable to maximum likelihood scores on the first two PCs extracted by MLPCA

Homogeneous Fermion Superfluid with Unequal Spin Populations

For decades, the conventional view is that an s-wave BCS superfluid can not support uniform spin polarization due to a gap $\Delta$ in the quasiparticle excitation spectrum. We show that this is an artifact of the dismissal of quasiparticle interactions $V_{qp}^{}$ in the conventional approach at the outset. Such interactions can cause triplet fluctuations in the ground state and hence non-zero spin polarization at "magnetic field" $h<\Delta$. The resulting ground state is a pairing state of quasiparticles on the ``BCS vacuum". For sufficiently large $V_{qp}$, the spin polarization of at unitarity has the simple form $m\propto \mu^{1/2}$. Our study is motivated by the recent experiments at Rice which found evidence of a homogenous superfluid state with uniform spin polarization.Comment: 4 pages, 3 figure

The age of data-driven proteomics : how machine learning enables novel workflows

A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. In this viewpoint we therefore point out highly promising recent machine learning developments in proteomics, alongside some of the remaining challenges

Variability in quality of white and green beans during in-pack sterilization

Non-uniformity in process quality was investigated during in-pack thermal sterilization of food products. This was accomplished through the combined application of the Monte Carlo procedure and a reliable mathematical method for process evaluation. Despite the large coefficients of variation found, the optimum quality process could be designed. The influence of the statistical variability of heating rate index on the retention of green beans color was studied and an optimum temperature range was found between 125 and 135 C. The variability in hardness of sterilized white beans, resulting from uncertainties of the combined effect of heating rate index and initial hardness of beans, was also evaluated by simulation. In this case, an optimum global temperature range between 120 and 135 C was found, independently of the rotation, F0 value and surface heat transfer coefficient assumed

Conjugated polymer nanoparticles for effective siRNA delivery to tobacco BY-2 protoplasts

<p>Abstract</p> <p>Background</p> <p>Post transcriptional gene silencing (PTGS) is a mechanism harnessed by plant biologists to knock down gene expression. siRNAs contribute to PTGS that are synthesized from mRNAs or viral RNAs and function to guide cellular endoribonucleases to target mRNAs for degradation. Plant biologists have employed electroporation to deliver artificial siRNAs to plant protoplasts to study gene expression mechanisms at the single cell level. One drawback of electroporation is the extensive loss of viable protoplasts that occurs as a result of the transfection technology.</p> <p>Results</p> <p>We employed fluorescent conjugated polymer nanoparticles (CPNs) to deliver siRNAs and knockdown a target gene in plant protoplasts. CPNs are non toxic to protoplasts, having little impact on viability over a 72 h period. Microscopy and flow cytometry reveal that CPNs can penetrate protoplasts within 2 h of delivery. Cellular uptake of CPNs/siRNA complexes were easily monitored using epifluorescence microscopy. We also demonstrate that CPNs can deliver siRNAs targeting specific genes in the cellulose biosynthesis pathway (<it>NtCesA-1a </it>and <it>NtCesA-1b)</it>.</p> <p>Conclusions</p> <p>While prior work showed that <it>NtCesA-1 </it>is a factor involved in cell wall synthesis in whole plants, we demonstrate that the same gene plays an essential role in cell wall regeneration in isolated protoplasts. Cell wall biosynthesis is central to cell elongation, plant growth and development. The experiments presented here shows that <it>NtCesA </it>is also a factor in cell viability. We show that CPNs are valuable vehicles for delivering siRNAs to plant protoplasts to study vital cellular pathways at the single cell level.</p

Effect of various growth media upon survival during storage of freeze-dried Enterococcus faecalis and Enterococcus durans

The effects of three different growth media (MRS, M17 and Lee’s) on survival during freeze-drying and subsequent storage of six strains of Enterococcus faecalis and two strains of E. durans were investigated. Methods and Results: Distinct Enterococcus spp. strains were grown on M17, MRS and Lee’s broth, freeze-dried and stored at 20 C in air under darkness. At regular intervals throughout storage, freeze-dried samples were rehydrated and then plated on M17 agar. Conclusions: A higher survival rate during storage of dried E. durans was obtained when growth occurred in MRS. The same effect was not observed, however, for the majority of E. faecalis strains, which clearly survived better in the dried state when this organism had been grown in M17 or Lee’s medium. Significance and Impact of Study: The survival of the dried Enterococcus spp. tested during storage was shown to be strain-specific and dependent on the growth medium

Effects of various sugars added to growth and drying media upon thermotolerance and survival throughout storage of freeze-dried lactobacillus delbrueckii ssp. bulgaricus

The aim of this research effort was to investigate the role of various sugar substrates in the growth medium upon thermotolerance and upon survival during storage after freeze-drying of Lactobacillus bulgaricus. Addition of the sugars tested to the growth medium, and of these and sorbitol to the drying medium (skim milk) was investigated so as to determine whether a relationship exists between growth and drying media, in terms of protection of freeze-dried cells throughout storage. The lowest decrease in viability of L. bulgaricus cells after freeze-drying was obtained when that organism was grown in the presence of mannose. However, L. bulgaricus clearly survived better during storage when cells had been grown in the presence of fructose, lactose or mannose rather than glucose (the standard sugar in the growth medium). A similar effect could not be observed in terms of thermotolerance; in this case, the growth medium supplemented with lactose was found to yield cells bearing the highest heat resistance. Supplementation of the drying medium with glucose, fructose, lactose, mannose or sorbitol led in most cases to enhancement of protection during storage, to a degree that was growth medium-dependent

Perceived Importance of Portfolios in a Smart CV after an Education Reform: An Empirical Analysis

Recent developments in recruitment processes have demonstrated that job applicants are increasingly using online Smart CVs instead of traditional approaches like hardcopy or emailing CVs. This study aims at examining perceived importance university undergraduate students of Hong Kong place or put on portfolios of Smart CVs, such as internship experience, exchange experience, scholarships & awards, participation in competitions, academic performance, and extra-curricular activities when building a Smart CV, and on investigating potential effects of the 3+3+4 academic reform in Hong Kong and admission mode. Participants were 256 undergraduate students in BBA majoring either in Information Management or in Electronic Commerce. A survey consisting of 44 items, which measured perceptions on the importance of the 6 proposed portfolios of Smart CVs, was used to collect data. Principal component analysis was used to analyze the items and 34 items were included in the final factor structure out of which 27 items got retained after subsequent reliability analysis. The 6 portfolios were positively inter-correlated. Students who were admitted under the new 4-year undergraduate curriculum using examination results of the new Hong Kong Diploma of Secondary Education (HKDSE) perceived internship experience and participation in competitions as more important in their Smart CVs, which was not the case with those who were admitted under the 3-year undergraduate curriculum using the results of the Hong Kong Advanced Level Examination (HKALE), which is no longer in use since 2012. The admission routes of students did not affect perceived importance in a Smart CV of the 6 proposed portfolios