The Amazing Race Repeated Update Q-Learning VS. Q-Learning

In this paper, we will conduct an experiment that aims to compare the performance of two reinforcement learning algorithms, the Repeated Update Q-learning algorithm (RUQL) [1] and the Q-learning algorithm(QL) [5]. A simulated version of a robot crawler developed by [6] will be used in this experiment, it is shown in figure (1). An investigation study about the difference in performance between RUQL and Q-learning algorithm (QL) [5] is discussed in this paper. Several trials and tests were conducted to estimate the difference in the crawler’s movement using both algorithms. Additionally, a detailed description of the Markovian decision processes (MDPs) elements [2] is introduced, MDP model includes states, actions and rewards for the task in hand. The parameters that were used and tuned in this experiment will be mentioned and the reasons for choosing their values will be explained.  Finally, the source code for the crawler robot was modified in order to implement RUQL and Q-Learning (QL) algorithms, Eclipse [3] and Java SE Development Kit 8 (JDK) [4] are used for this purpose. After running the crawler robot simulation, the results drawn from the experiment showed that RUQL significantly outperforms the traditional QL.  &nbsp

Stent Fracture after Everolimus-Eluting Stent Implantation

Compared with bare-metal stents, drug-eluting stents (DES) have greatly reduced the risk of in-stent restenosis (ISR) by inhibiting neointimal growth. Nevertheless, DES are still prone to device failure, which may lead to cardiac events. Recently, stent fracture (SF) has emerged as a potential mechanism of DES failure that is associated with ISR. Stent fracture is strongly related to stent type, and prior reports suggest that deployment of sirolimus eluting stents (SES) may be associated with a higher risk of SF compared to other DES. Everolimus eluting stents (EESs) represent a new generation of DES with promising results. The occurrence of SF with EES has not been well established. The present paper describes two cases of EES fracture associated with ISR

Matrix Metalloproteinase Proteolysis of the Myelin Basic Protein Isoforms Is a Source of Immunogenic Peptides in Autoimmune Multiple Sclerosis

Matrix metalloproteinases (MMPs) play a significant role in the fragmentation of myelin basic protein (MBP) and demyelination leading to autoimmune multiple sclerosis (MS) and experimental autoimmune encephalomyelitis (EAE). The classic MBP isoforms are predominantly expressed in the oligodendrocytes of the CNS. The splice variants of the single MBP gene (Golli-MBP BG21 and J37) are widely expressed in the neurons and also in the immune cells. The relative contribution of the individual MMPs to the MBP cleavage is not known.To elucidate which MMP plays the primary role in cleaving MBP, we determined the efficiency of MMP-2, MMP-8, MMP-9, MMP-10, MMP-12, MT1-MMP, MT2-MMP, MT3-MMP, MT4-MMP, MT5-MMP and MT6-MMP in the cleavage of the MBP, BG21 and J37 isoforms in the in vitro cleavage reactions followed by mass-spectroscopy analysis of the cleavage fragments. As a result, we identified the MMP cleavage sites and the sequence of the resulting fragments. We determined that MBP, BG21 and J37 are highly sensitive to redundant MMP proteolysis. MT6-MMP (initially called leukolysin), however, was superior over all of the other MMPs in cleaving the MBP isoforms. Using the mixed lymphocyte culture assay, we demonstrated that MT6-MMP proteolysis of the MBP isoforms readily generated, with a near quantitative yield, the immunogenic N-terminal 1-15 MBP peptide. This peptide selectively stimulated the proliferation of the PGPR7.5 T cell clone isolated from mice with EAE and specific for the 1-15 MBP fragment presented in the MHC H-2(U) context.In sum, our biochemical observations led us to hypothesize that MT6-MMP, which is activated by furin and associated with the lipid rafts, plays an important role in MS pathology and that MT6-MMP is a novel and promising drug target in MS especially when compared with other individual MMPs

Interbilayer-crosslinked multilamellar vesicles as synthetic vaccines for potent humoral and cellular immune responses

available in PMC 2011 September 1Vaccines based on recombinant proteins avoid the toxicity and antivector immunity associated with live vaccine (for example, viral) vectors, but their immunogenicity is poor, particularly for CD8+ T-cell responses. Synthetic particles carrying antigens and adjuvant molecules have been developed to enhance subunit vaccines, but in general these materials have failed to elicit CD8+ T-cell responses comparable to those for live vectors in preclinical animal models. Here, we describe interbilayer-crosslinked multilamellar vesicles formed by crosslinking headgroups of adjacent lipid bilayers within multilamellar vesicles. Interbilayer-crosslinked vesicles stably entrapped protein antigens in the vesicle core and lipid-based immunostimulatory molecules in the vesicle walls under extracellular conditions, but exhibited rapid release in the presence of endolysosomal lipases. We found that these antigen/adjuvant-carrying vesicles form an extremely potent whole-protein vaccine, eliciting endogenous T-cell and antibody responses comparable to those for the strongest vaccine vectors. These materials should enable a range of subunit vaccines and provide new possibilities for therapeutic protein delivery.Ragon Institute of MGH, MIT and HarvardBill & Melinda Gates FoundationUnited States. Dept. of Defense (contract W911NF-07-D-0004)National Institutes of Health (U.S.) (P41RR002250)National Institutes of Health (U.S.) (RC2GM092599

Special issue on real‐time behavioral monitoring in IoT applications using big data analytics

Real-time social multimedia level threat monitoring is becoming harder, due to higher and rapidly increasing data induction. Data induction through electric smart devices is greater compared to information processing capacity. Nowadays, data becomes humongous even coming from the single source. Therefore, when data emanates from all heterogeneous sources distributed over the globe makes data magnitude harder to process up to a needed scale. Big data and Deep learning have become standard in providing well-known solutions built-up using algorithms and techniques in resolving data matching issues. Now, with the involvement of sensors and automation in generating data obscures everything, predicting results to overcome a current era of ever enhancing demands and getting real-time visualization brings the need of feature like human behavior mode extraction to overcome any future threats. Big data analytics can bring the opportunity of predicting any misfortune even before they happen. Map reduce feature of big data supports massive data oriented process execution using distributed processing. Real-time human feature identification and detection can occur through sensors and internet sources. A behavioral prediction can further classify the information collected for introducing enhanced security extents. Real-time sensor devices are producing 24/7-hour data for further processing recording each event. IoT-based sensors can support in behavioral analysis model of a human. Real-time human behavioral monitoring based on image processing and IoT using big data analytics

Age-sex differences in the global burden of lower respiratory infections and risk factors, 1990-2019 : results from the Global Burden of Disease Study 2019

BACKGROUND: The global burden of lower respiratory infections (LRIs) and corresponding risk factors in children older than 5 years and adults has not been studied as comprehensively as it has been in children younger than 5 years. We assessed the burden and trends of LRIs and risk factors across all age groups by sex, for 204 countries and territories. METHODS: In this analysis of data for the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we used clinician-diagnosed pneumonia or bronchiolitis as our case definition for LRIs. We included International Classification of Diseases 9th edition codes 079.6, 466-469, 470.0, 480-482.8, 483.0-483.9, 484.1-484.2, 484.6-484.7, and 487-489 and International Classification of Diseases 10th edition codes A48.1, A70, B97.4-B97.6, J09-J15.8, J16-J16.9, J20-J21.9, J91.0, P23.0-P23.4, and U04-U04.9. We used the Cause of Death Ensemble modelling strategy to analyse 23 109 site-years of vital registration data, 825 site-years of sample vital registration data, 1766 site-years of verbal autopsy data, and 681 site-years of mortality surveillance data. We used DisMod-MR 2.1, a Bayesian meta-regression tool, to analyse age-sex-specific incidence and prevalence data identified via systematic reviews of the literature, population-based survey data, and claims and inpatient data. Additionally, we estimated age-sex-specific LRI mortality that is attributable to the independent effects of 14 risk factors. FINDINGS: Globally, in 2019, we estimated that there were 257 million (95% uncertainty interval [UI] 240-275) LRI incident episodes in males and 232 million (217-248) in females. In the same year, LRIs accounted for 1·30 million (95% UI 1·18-1·42) male deaths and 1·20 million (1·07-1·33) female deaths. Age-standardised incidence and mortality rates were 1·17 times (95% UI 1·16-1·18) and 1·31 times (95% UI 1·23-1·41) greater in males than in females in 2019. Between 1990 and 2019, LRI incidence and mortality rates declined at different rates across age groups and an increase in LRI episodes and deaths was estimated among all adult age groups, with males aged 70 years and older having the highest increase in LRI episodes (126·0% [95% UI 121·4-131·1]) and deaths (100·0% [83·4-115·9]). During the same period, LRI episodes and deaths in children younger than 15 years were estimated to have decreased, and the greatest decline was observed for LRI deaths in males younger than 5 years (-70·7% [-77·2 to -61·8]). The leading risk factors for LRI mortality varied across age groups and sex. More than half of global LRI deaths in children younger than 5 years were attributable to child wasting (population attributable fraction [PAF] 53·0% [95% UI 37·7-61·8] in males and 56·4% [40·7-65·1] in females), and more than a quarter of LRI deaths among those aged 5-14 years were attributable to household air pollution (PAF 26·0% [95% UI 16·6-35·5] for males and PAF 25·8% [16·3-35·4] for females). PAFs of male LRI deaths attributed to smoking were 20·4% (95% UI 15·4-25·2) in those aged 15-49 years, 30·5% (24·1-36·9) in those aged 50-69 years, and 21·9% (16·8-27·3) in those aged 70 years and older. PAFs of female LRI deaths attributed to household air pollution were 21·1% (95% UI 14·5-27·9) in those aged 15-49 years and 18·2% (12·5-24·5) in those aged 50-69 years. For females aged 70 years and older, the leading risk factor, ambient particulate matter, was responsible for 11·7% (95% UI 8·2-15·8) of LRI deaths. INTERPRETATION: The patterns and progress in reducing the burden of LRIs and key risk factors for mortality varied across age groups and sexes. The progress seen in children younger than 5 years was clearly a result of targeted interventions, such as vaccination and reduction of exposure to risk factors. Similar interventions for other age groups could contribute to the achievement of multiple Sustainable Development Goals targets, including promoting wellbeing at all ages and reducing health inequalities. Interventions, including addressing risk factors such as child wasting, smoking, ambient particulate matter pollution, and household air pollution, would prevent deaths and reduce health disparities. FUNDING: Bill & Melinda Gates Foundation

Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

<p>Abstract</p> <p>Background</p> <p>RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM) approach was used to quantitatively model RNA interference activities.</p> <p>Results</p> <p>Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (<it>N</it>-grams) and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative.</p> <p>Conclusion</p> <p>The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall <it>t</it>-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid sequences can be found at the following site: <url>ftp://scitoolsftp.idtdna.com/SEQ2SVM/</url>.</p

A haplotype map of allohexaploid wheat reveals distinct patterns of selection on homoeologous genomes

BACKGROUND: Bread wheat is an allopolyploid species with a large, highly repetitive genome. To investigate the impact of selection on variants distributed among homoeologous wheat genomes and to build a foundation for understanding genotype-phenotype relationships, we performed population-scale re-sequencing of a diverse panel of wheat lines. RESULTS: A sample of 62 diverse lines was re-sequenced using the whole exome capture and genotyping-by-sequencing approaches. We describe the allele frequency, functional significance, and chromosomal distribution of 1.57 million single nucleotide polymorphisms and 161,719 small indels. Our results suggest that duplicated homoeologous genes are under purifying selection. We find contrasting patterns of variation and inter-variant associations among wheat genomes; this, in addition to demographic factors, could be explained by differences in the effect of directional selection on duplicated homoeologs. Only a small fraction of the homoeologous regions harboring selected variants overlapped among the wheat genomes in any given wheat line. These selected regions are enriched for loci associated with agronomic traits detected in genome-wide association studies. CONCLUSIONS: Evidence suggests that directional selection in allopolyploids rarely acted on multiple parallel advantageous mutations across homoeologous regions, likely indicating that a fitness benefit could be obtained by a mutation at any one of the homoeologs. Additional advantageous variants in other homoelogs probably either contributed little benefit, or were unavailable in populations subjected to directional selection. We hypothesize that allopolyploidy may have increased the likelihood of beneficial allele recovery by broadening the set of possible selection targets

WGS-based telomere length analysis in Dutch family trios implicates stronger maternal inheritance and a role for RRM1 gene

Telomere length (TL) regulation is an important factor in ageing, reproduction and cancer development. Genetic, hereditary and environmental factors regulating TL are currently widely investigated, however, their relative contribution to TL variability is still understudied. We have used whole genome sequencing data of 250 family trios from the Genome of the Netherlands project to perform computational measurement of TL and a series of regression and genome-wide association analyses to reveal TL inheritance patterns and associated genetic factors. Our results confirm that TL is a largely heritable trait, primarily with mother’s, and, to a lesser extent, with father’s TL having the strongest influence on the offspring. In this cohort, mother’s, but not father’s age at conception was positively linked to offspring TL. Age-related TL attrition of 40 bp/year had relatively small influence on TL variability. Finally, we have identified TL-associated variations in ribonuclease reductase catalytic subunit M1 (RRM1 gene), which is known to regulate telomere maintenance in yeast. We also highlight the importance of multivariate approach and the limitations of existing tools for the analysis of TL as a polygenic heritable quantitative trait