Novel hydrogel obtained by chitosan and dextrin-VA co-polymerization

A novel hydrogel was obtained by reticulation of chitosan with dextrin enzymatically linked to vinyl acrylate (dextrin-VA), without cross-linking agents. The hydrogel had a solid-like behaviour with G′ (storage modulus) >> G″ (loss modulus). Glucose diffusion coefficients of 3.9 × 10−6 ± 1.3 × 10−6 cm2/s and 2.9 × 10−6 ± 0.5 × 10−6 cm2/s were obtained for different substitution degrees of the dextrin-VA (20% and 70% respectively). SEM observation revealed a porous structure, with pores ranging from 50 µm to 150 µm

WU Polyomavirus in Children with Acute Lower Respiratory Tract Infections, South Korea

In South Korea, WU polyomavirus (WUPyV) was detected in 34 (7%) of 486 children with acute lower respiratory tract infections, 3 (4.2%) of 72 asymptomatic children, and as coinfection with other respiratory viruses in 23 (67.6%) children. Although WUPyV was frequently detected, its clinical role has not been distinguished from that of coinfecting viruses

Self Hyper-parameter Tuning for Stream Recommendation Algorithms

E-commerce platforms explore the interaction between users and digital content – user generated streams of events – to build and maintain dynamic user preference models which are used to make meaningful recommendations. However, the accuracy of these incremental models is critically affected by the choice of hyper-parameters. So far, the incremental recommendation algorithms used to process data streams rely on human expertise for hyper-parameter tuning. In this work we apply our Self Hyper-Parameter Tuning (SPT) algorithm to incremental recommendation algorithms. SPT adapts the Melder-Mead optimisation algorithm to perform hyper-parameter tuning. First, it creates three models with random hyper-parameter values and, then, at dynamic size intervals, assesses and applies the Melder-Mead operators to update their hyper-parameters until the models converge. The main contribution of this work is the adaptation of the SPT method to incremental matrix factorisation recommendation algorithms. The proposed method was evaluated with well-known recommendation data sets. The results show that SPT systematically improves data stream recommendations.info:eu-repo/semantics/publishedVersio

The predictive and prognostic potential of plasma telomerase reverse transcriptase (TERT) RNA in rectal cancer patients

Background: Preoperative chemoradiotherapy (CRT) followed by surgery is the standard care for locally advanced rectal cancer, but tumour response to CRT and disease outcome are variable. The current study aimed to investigate the effectiveness of plasma telomerase reverse transcriptase (TERT) levels in predicting tumour response and clinical outcome. Methods: 176 rectal cancer patients were included. Plasma samples were collected at baseline (before CRT\ubcT0), 2 weeks after CRT was initiated (T1), post-CRT and before surgery (T2), and 4\u20138 months after surgery (T3) time points. Plasma TERT mRNA levels and total cell-free RNA were determined using real-time PCR. Results: Plasma levels of TERT were significantly lower at T2 (Po0.0001) in responders than in non-responders. Post-CRT TERT levels and the differences between pre- and post-CRT TERT levels independently predicted tumour response, and the prediction model had an area under curve of 0.80 (95% confidence interval (CI) 0.73\u20130.87). Multiple analysis demonstrated that patients with detectable TERT levels at T2 and T3 time points had a risk of disease progression 2.13 (95% CI 1.10\u20134.11)-fold and 4.55 (95% CI 1.48\u201313.95)-fold higher, respectively, than those with undetectable plasma TERT levels. Conclusions: Plasma TERT levels are independent markers of tumour response and are prognostic of disease progression in rectal cancer patients who undergo neoadjuvant therapy

Pseudomonas aeruginosa Adaptation to Lungs of Cystic Fibrosis Patients Leads to Lowered Resistance to Phage and Protist Enemies

Pathogenic life styles can lead to highly specialized interactions with host species, potentially resulting in fitness trade-offs in other ecological contexts. Here we studied how adaptation of the environmentally transmitted bacterial pathogen, Pseudomonas aeruginosa, to cystic fibrosis (CF) patients affects its survival in the presence of natural phage (14/1, ΦKZ, PNM and PT7) and protist (Tetrahymena thermophila and Acanthamoebae polyphaga) enemies. We found that most of the bacteria isolated from relatively recently intermittently colonised patients (1-25 months), were innately phage-resistant and highly toxic for protists. In contrast, bacteria isolated from long time chronically infected patients (2-23 years), were less efficient in both resisting phages and killing protists. Moreover, chronic isolates showed reduced killing of wax moth larvae (Galleria mellonella) probably due to weaker in vitro growth and protease expression. These results suggest that P. aeruginosa long-term adaptation to CF-lungs could trade off with its survival in aquatic environmental reservoirs in the presence of microbial enemies, while lowered virulence could reduce pathogen opportunities to infect insect vectors; factors that are both likely to result in poorer environmental transmission. From an applied perspective, phage therapy could be useful against chronic P. aeruginosa lung infections that are often characterized by multidrug resistance: chronic isolates were least resistant to phages and their poor growth will likely slow down the emergence of beneficial resistance mutations

Finding suitable activity clusters for decomposed process discovery

Event data can be found in any information system and provide the starting point for a range of process mining techniques. The widespread availability of large amounts of event data also creates new challenges. Existing process mining techniques are often unable to handle "big event data" adequately. Decomposed process mining aims to solve this problem by decomposing the process mining problem into many smaller problems which can be solved in less time, using less resources, or even in parallel. Many decomposed process mining techniques have been proposed in literature. Analysis shows that even though the decomposition step takes a relatively small amount of time, it is of key importance in finding a high-quality process model and for the computation time required to discover the individual parts. Currently there is no way to assess the quality of a decomposition beforehand. We define three quality notions that can be used to assess a decomposition, before using it to discover a model or check conformance with. We then propose a decomposition approach that uses these notions and is able to find a high-quality decomposition in little time. Keywords: decomposed process mining, decomposed process discovery, distributed computing, event lo

A Genome-Wide Analysis of Promoter-Mediated Phenotypic Noise in Escherichia coli

Gene expression is subject to random perturbations that lead to fluctuations in the rate of protein production. As a consequence, for any given protein, genetically identical organisms living in a constant environment will contain different amounts of that particular protein, resulting in different phenotypes. This phenomenon is known as “phenotypic noise.” In bacterial systems, previous studies have shown that, for specific genes, both transcriptional and translational processes affect phenotypic noise. Here, we focus on how the promoter regions of genes affect noise and ask whether levels of promoter-mediated noise are correlated with genes' functional attributes, using data for over 60% of all promoters in Escherichia coli. We find that essential genes and genes with a high degree of evolutionary conservation have promoters that confer low levels of noise. We also find that the level of noise cannot be attributed to the evolutionary time that different genes have spent in the genome of E. coli. In contrast to previous results in eukaryotes, we find no association between promoter-mediated noise and gene expression plasticity. These results are consistent with the hypothesis that, in bacteria, natural selection can act to reduce gene expression noise and that some of this noise is controlled through the sequence of the promoter region alon

Use of machine learning to shorten observation-based screening and diagnosis of autism

The Autism Diagnostic Observation Schedule-Generic (ADOS) is one of the most widely used instruments for behavioral evaluation of autism spectrum disorders. It is composed of four modules, each tailored for a specific group of individuals based on their language and developmental level. On average, a module takes between 30 and 60 min to deliver. We used a series of machine-learning algorithms to study the complete set of scores from Module 1 of the ADOS available at the Autism Genetic Resource Exchange (AGRE) for 612 individuals with a classification of autism and 15 non-spectrum individuals from both AGRE and the Boston Autism Consortium (AC). Our analysis indicated that 8 of the 29 items contained in Module 1 of the ADOS were sufficient to classify autism with 100% accuracy. We further validated the accuracy of this eight-item classifier against complete sets of scores from two independent sources, a collection of 110 individuals with autism from AC and a collection of 336 individuals with autism from the Simons Foundation. In both cases, our classifier performed with nearly 100% sensitivity, correctly classifying all but two of the individuals from these two resources with a diagnosis of autism, and with 94% specificity on a collection of observed and simulated non-spectrum controls. The classifier contained several elements found in the ADOS algorithm, demonstrating high test validity, and also resulted in a quantitative score that measures classification confidence and extremeness of the phenotype. With incidence rates rising, the ability to classify autism effectively and quickly requires careful design of assessment and diagnostic tools. Given the brevity, accuracy and quantitative nature of the classifier, results from this study may prove valuable in the development of mobile tools for preliminary evaluation and clinical prioritization—in particular those focused on assessment of short home videos of children—that speed the pace of initial evaluation and broaden the reach to a significantly larger percentage of the population at risk