A Data Model for Documents' Electronic Archiving

Authors present in this paper a model that can be used to organize the data in an electronic archive. It will be explained, first of all, the archive concept, the electronic archive concept and the document management concept. It will be presented, afterwards, an object oriented model proposed by the authors of the storage data in an electronic archive.archive, electronic archive, document management, object oriented data model.

The IDENTIFY study: the investigation and detection of urological neoplasia in patients referred with suspected urinary tract cancer - a multicentre observational study

Objective To evaluate the contemporary prevalence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC] and renal cancer) in patients referred to secondary care with haematuria, adjusted for established patient risk markers and geographical variation. Patients and Methods This was an international multicentre prospective observational study. We included patients aged ≥16 years, referred to secondary care with suspected urinary tract cancer. Patients with a known or previous urological malignancy were excluded. We estimated the prevalence of bladder cancer, UTUC, renal cancer and prostate cancer; stratified by age, type of haematuria, sex, and smoking. We used a multivariable mixed-effects logistic regression to adjust cancer prevalence for age, type of haematuria, sex, smoking, hospitals, and countries. Results Of the 11 059 patients assessed for eligibility, 10 896 were included from 110 hospitals across 26 countries. The overall adjusted cancer prevalence (n = 2257) was 28.2% (95% confidence interval [CI] 22.3–34.1), bladder cancer (n = 1951) 24.7% (95% CI 19.1–30.2), UTUC (n = 128) 1.14% (95% CI 0.77–1.52), renal cancer (n = 107) 1.05% (95% CI 0.80–1.29), and prostate cancer (n = 124) 1.75% (95% CI 1.32–2.18). The odds ratios for patient risk markers in the model for all cancers were: age 1.04 (95% CI 1.03–1.05; P < 0.001), visible haematuria 3.47 (95% CI 2.90–4.15; P < 0.001), male sex 1.30 (95% CI 1.14–1.50; P < 0.001), and smoking 2.70 (95% CI 2.30–3.18; P < 0.001). Conclusions A better understanding of cancer prevalence across an international population is required to inform clinical guidelines. We are the first to report urinary tract cancer prevalence across an international population in patients referred to secondary care, adjusted for patient risk markers and geographical variation. Bladder cancer was the most prevalent disease. Visible haematuria was the strongest predictor for urinary tract cancer

Big Data ETL Process and Its Impact on Text Mining Analysis for Employees&rsquo; Reviews

Big data analysis is challenging in the current context for enterprises that would like to apply these capabilities in the human resource sector. This paper will show how an organization can take advantage of the current or former employees&rsquo; reviews that are provided on a constant basis on different sites, so that the management can adjust or change business decisions based on employees&rsquo; wishes, dissatisfaction or needs. Considering the previously mentioned challenge on big data analysis, this research will first provide the best practice for the collection and transformation of the data proposed for analysis. The second part of this paper presents the extraction of two datasets containing employee reviews using data scraping techniques, the analysis of data by using text mining techniques to retrieve business insights and the comparison of the results for these algorithms. Experimental results with Na&iuml;ve Bayes, Logistic Regression, K-Nearest Neighbor and Support Vector Machine for employee sentiment prediction showed much better performances for Logistic Regression. Three out of the four analyzed algorithms performed better for the second, triple-size dataset. The final aim of the paper is to provide an end-to-end solution with high performance and reduced costs

Big Data ETL Process and Its Impact on Text Mining Analysis for Employees’ Reviews

Big data analysis is challenging in the current context for enterprises that would like to apply these capabilities in the human resource sector. This paper will show how an organization can take advantage of the current or former employees’ reviews that are provided on a constant basis on different sites, so that the management can adjust or change business decisions based on employees’ wishes, dissatisfaction or needs. Considering the previously mentioned challenge on big data analysis, this research will first provide the best practice for the collection and transformation of the data proposed for analysis. The second part of this paper presents the extraction of two datasets containing employee reviews using data scraping techniques, the analysis of data by using text mining techniques to retrieve business insights and the comparison of the results for these algorithms. Experimental results with Naïve Bayes, Logistic Regression, K-Nearest Neighbor and Support Vector Machine for employee sentiment prediction showed much better performances for Logistic Regression. Three out of the four analyzed algorithms performed better for the second, triple-size dataset. The final aim of the paper is to provide an end-to-end solution with high performance and reduced costs

Cognitive and Behavioral Factors Predicting the Decision to Vaccinate against COVID-19 in Clinical Psychiatric Population—A Cross-Sectional Survey

The spread of the COVID-19 virus created more than a medical crisis, while it also negatively affected the mental health of the general population. This context increased the vulnerability of the psychiatric population. While research interest highly targeted vaccine hesitancy and acceptance, many studies focused on trust issues—both in vaccine efficacy and in communication with authorities. Less is known about the psychological underpinnings of the COVID vaccination decision, specifically in the high-uncertainty circumstances due to the novelty of the virus. In a cross-sectional study, we investigated the predictive value of several cognitive (perceived risk, vulnerability, uncertainty, and trust in one’s decision) and behavioral (previous vaccinations, social media use, and practicing preventive behavior) factors, for the vaccination decision against COVID-19, for 252 psychiatric inpatients (data collected between September 2021 and February 2022). Demographics and diagnostics were also considered. We found a significant relationship between the “Perceived risk of vaccination” and the choice of vaccination (χ2(2, N = 252) = 58.59, p ≤ 0.001), and between the “Trust in own decision to vaccinate” and the decision to vaccinate (χ2(2, N = 252) = 31,5, p ≤ 0.001). The overall regression model was statistically significant (χ2 (9, N = 252) = 97.1, p p χ2(1, N = 252) = 2.74, p > 0.05) in this special population. No other behavioral factors, diagnosis, or demographics were significant as predictors, for the clinical psychiatric population surveyed, except the educational level. Implications for future vaccination acceptance of this special population are discussed

Missorted surface proteins accumulate in the sortase mutant AR01.

<p>A. Wild type <i>S</i>. <i>pyogenes</i> D471, sortase mutant AR01, complemented AR01+pAR107, and a passaged AR01 variant, were diluted from overnight cultures 1:100 into fresh media (containing spectinomycin for AR01+pAR107), grown to log phase, fixed, attached to glass cover slides, and permeabilized with PlyC. The cells were blocked and incubated with pentaglycine-biotin (5G-biotin) in the presence or absence of purified <i>S</i>. <i>aureus</i> sortase A. The slides were washed, and pentaglycine-biotins molecules (attached to LPXTG motifs by sortase) were detected using FITC-streptavidin. DAPI was used to visualize DNA. The scale bar represents 1 μm. Note that expression of sortase from pAR107 is lower than the wild type, explaining the presence of intact LPXTG motifs. B. The cell wall fraction of log-phase D471 cells (solubilized with PlyC in PBS 30% raffinose) was separated by SDS-PAGE. Duplicate gels were stained with GelCode blue or processed for Western blot using the 10B6 monoclonal antibody. The most prominent bands on the stained gel correspond to M protein.</p

The sortase mutant is highly sensitive to LL-37.

<p><b>A</b> Wild type D471, original sortase mutant AR01, sortase complemented AR01+pAR107, and passaged AR01 (day 6), were grown to OD₆₀₀ 0.5, diluted 1:10⁵ in TH+Y (containing spectinomycin for AR01+pAR107) and mixed with LL-37, scrambled peptide, or TH+Y alone. The strains were incubated stationary in a 96-well plate for 16 h at 37°C, and the final OD₆₀₀ was measured. The final OD₆₀₀ value is presented as a fraction of the value obtained in the absence of peptides (defined as 100%). Experiments were repeated 3 times in triplicates, and a representative experiment is shown; error bars represent standard deviation. P-values for samples showing significant growth inhibition were calculated using t-test. <b>B</b> Cultures prepared in a similar manner were rotated in microfuge tubes for 3 h at 37°C, and then serially diluted and plated for CFU quantification. The CFU ratio treated/untreated is presented. Experiments were repeated 3 times in duplicates, and the results from all 3 experiments are shown; error bars represent standard deviation. P-values were calculated using t-test.</p

The sortase mutant, but not the passaged strain, displays increased membrane permeability.

<p>Wild type D471, sortase mutant AR01 (original stock), complemented AR01+pAR107, and a low-M passaged variant of AR01, were diluted 1:50 from an overnight culture and grown to OD₆₀₀ 0.5. The cells were incubated with 5 mM SYTOX green for 30 min at room temperature, washed with PBS, and immediately imaged. The scale bar represents 2 μm. Additional images are presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140784#pone.0140784.s008" target="_blank">S8 Fig</a>.</p

The <i>S</i>. <i>pyogenes</i> sortase mutant is under selective pressure leading to the loss of M protein expression.

<p><b>A</b> Wild type D471, sortase mutant AR01 (original stock), and complemented AR01+pAR107, were sonicated to separate the streptococcal chains into single cells, and subsequently plated. Single colonies were picked and grown in TH+Y (containing spectinomycin for AR01+pAR107) at 37°C overnight. Each day, a seed culture was transferred to a new tube for overnight growth, and a sample of the original culture was separated into supernatant and cell pellet, and stored at -80°C until use; the cultures were passaged in this manner six times. The cell pellets were lysed with PlyC. M protein was quantified by capture ELISA as described in the Materials and Methods section. The results represent 13 repeats for D471, 24 repeats for AR01, and 11 repeats for AR01+pAR107. <b>B</b> The relative amount of SfbI in AR01 supernatant and cell lysate was similarly determined by capture ELISA; the results represent 15 repeats. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140784#pone.0140784.s006" target="_blank">S6 Fig</a> for <i>sfbI</i>-negative control.</p

Passage of the sortase mutant AR01 in TH+Y results in loss of M protein expression.

<p><b>A</b> Wild type <i>S</i>. <i>pyogenes</i> D471, sortase mutant AR01, complemented AR01+pAR107, and 10 separate variants of AR01 that were passaged 6 times in TH+Y, were diluted from an overnight culture 1:100 into fresh media (containing spectinomycin for AR01+pAR107), grown to log phase, fractionated into supernatant, wall (solubilized with PlyC in PBS 30% raffinose), and spheroplast pellet, and examined by Western blot. The monoclonal antibody 10B6 was used to detect M protein, specific serum was used to detect SfbI, and the monoclonal antibody 3A1 was used to detect cytoplasmic glyceraldehyde 3-phosphate dehydrogenase (GAPDH), as loading control. <b>B</b> Cells grown in a similar manner were fixed, and processed for fluorescence microscopy as described in the Materials and Methods section. Specific antibodies were used to label M protein (red) and SfbI (green). The cell wall was stained with WGA marina blue (blue). Images were obtained using deconvolution immunofluorescence and Nomarski microscopy. Deconvolution images are presented as maximum intensity projections, composed of all the Z-sections. Additional images are presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140784#pone.0140784.s004" target="_blank">S4 Fig</a>.</p