3,307 research outputs found
Perancangan Sistem Informasi Pengelola Barang/Inventaris Di Jc Komp
No full textInventory information system is a system used to enter inventory data into the database, so that there are no errors in input, output data, and reporting based on the desired data. based on surveys and interviews with jc comp personnel, information was obtained that the existing system in the jc comp warehouse section is still manual. therefore, the system that will be created by the author is the result of a replication of the existing system in the jc comp warehouse section. in addition to the process of input and output of goods, this information system is also equipped with features for creating data reports, input and output of goods, and searching for goods data by item name. with the inventory information system is expected to be useful for the warehouse parts jc comp. By implementing this system in the jc comp warehouse, it is hoped that it can reduce errors that may occur. this system is also expected to further speed up the process of input, output, and report generation, which in turn will help the jc comp warehouseSistem Informasi Persediaan Barang adalah sebuah sistem yang digunakan untuk memasukkan data-data persediaan barang ke dalam database, sehinggga tidak terjadi kesalahan dalam input, output data, dan pembuatan laporan berdasarkan data yang diinginkan. Berdasarkan survey dan wawancara dengan bagian personalia Jc Komp, didapatkan informasi bahwa sistem yang ada dibagian gudang Jc Komp masih manual. Oleh karena itu, sistem yang akan dibuat oleh penulis adalah hasil replikasi dari sistem yang telah ada dibagian gudang Jc Comp. Selain proses input dan output barang, pada sistem informasi ini juga dilengkapi fitur pembuatan laporan data, input, dan output barang, dan pencarian data barang berdasarkan nama barang. Dengan adanya Sistem Informasi persediaan barang ini diharapkan dapat bermanfaat bagi bagian gudang Jc Komp. Dengan diterapkannya sistem ini pada bagian gudang Jc Comp, maka diharapkan dapat mengurangi kesalahan-kesalahan yang mungkin terjadi. Sistem ini juga diharapkan dapat lebih mempercepat proses input, output, dan pembuatan laporan yang pada akhirnya dapat membantu bagian gudang Jc Komp
Estimating the impact of influenza vaccination and antigenic drift on influenza-related morbidity and mortality in England & Wales using hidden Markov models
No full textInfluenza causes substantial morbidity and mortality in some influenza sea-
sons, especially among the elderly. Influenza seasons dominated by circula-
tion of influenza A/H3N2 virus tend to result in more morbidity and mor-
tality than seasons dominated by influenza A/H1N1 or influenza B viruses.
Influenza viruses undergo constant mutation, called antigenic drift, which
is largely driven by host immunity. It has been shown that antigenic drift
in influenza A/H3N2 virus proceeds in a punctuated, as opposed to contin-
uous, fashion. A cluster of antigenically similar influenza A/H3N2 viruses
appears to remain dominant for between 1 and 8 influenza seasons before
being supplanted by a new cluster. Influenza seasons when a new cluster
becomes dominant may result in higher morbidity and mortality than other
seasons. Influenza vaccine effectiveness varies between influenza seasons be-
cause of the different subtypes in circulation and the degree of antigenic
match between vaccine and circulating variants. In each influenza season in
recent years, over 70% of the population of England & Wales aged > 65 has
been vaccinated, though the impact of this high coverage on population level
morbidity and mortality is unknown. Multivariate time series models were
fitted to reports of laboratory confirmed influenza, sentinel general practi-
tioner (GP) consultations for influenza-like-illness, and all deaths registered
to underlying pneumonia or influenza in England & Wales from 1975/76 to
2004/05. The models successfully distinguish influenza
- attributable GP
consultations and deaths from GP consultations and deaths that would be
expected in the absence of influenza. This distinction is made jointly by
the laboratory reports and the non-laboratory confirmed surveillance data.
It is not possible to use the multivariate time series models to quantify
the average effect of the appearance of a new cluster of influenza A/H3N2
virus variants, or vaccine impact, on influenza
- attributable morbidity or
mortality in the data analyzed. Reasons for this are discu
A variance components factor model for genetic association studies: a Bayesian analysis.
No full textStudies of gene-trait associations for complex diseases often involve multiple traits that may vary by genotype groups or patterns. Such traits are usually manifestations of lower-dimensional latent factors or disease syndromes. We illustrate the use of a variance components factor (VCF) model to model the association between multiple traits and genotype groups as well as any other existing patient-level covariates. This model characterizes the correlations between traits as underlying latent factors that can be used in clinical decision-making. We apply it within the Bayesian framework and provide a straightforward implementation using the WinBUGS software. The VCF model is illustrated with simulated data and an example that comprises changes in plasma lipid measurements of patients who were treated with statins to lower low-density lipoprotein cholesterol, and polymorphisms from the apolipoprotein-E gene. The simulation shows that this model clearly characterizes existing multiple trait manifestations across genotype groups where individuals' group assignments are fully observed or can be deduced from the observed data. It also allows one to investigate covariate by genotype group interactions that may explain the variability in the traits. The flexibility to characterize such multiple trait manifestations makes the VCF model more desirable than the univariate variance components model, which is applied to each trait separately. The Bayesian framework offers a flexible approach that allows one to incorporate prior information
Amenable L-2-Theoretic Methods and Knot Concordance
No full textWe reveal new structures in the topological knot concordance group. As a key ingredient, we develop obstructions using L-2-theoretic methods for amenable groups in Strebel's class recently introduced by Orr and the author. Concerning (h)-solvable knots, which are defined in terms of certain Whitney towers of height h in bounding 4-manifolds, we show the following: for any n>1, there are (n)-solvable but non-(n. 5)-solvable (and therefore nonslice) knots, which are not detected by prior methods using Cochran-Orr-Teichner L-2-signature obstructions as well as Levine algebraic obstructions and Casson-Gordon invariants.X1197sciescopu
Dynamics of Network Formation Processes in the Co-Author Model
No full textThis article studies the dynamics in the formation processes of a mutual consent network in game theory setting: the Co-Author Model. In this article, a limited observation is applied and analytical results are derived. Then, 2 parameters are varied: the number of individuals in the network and the initial probability of the links in the network in its initial state. A simulation result shows a finding that is consistent with an analytical result for a state of equilibrium while it also shows different possible equilibria.Dynamics, Network, Game Theory, Model,Simulation, Equilibrium, Complexity
High-level polyomavirus JC viruria following long-term steroid therapy
No full textCASE REPORT JC virus is a highly seroprevalent ubiquitous polyomavirus which is acquired at an early age through respiratory or oral route, Thereafter JCV establishes persistent, but mainly asymptomatic, infections in various tissues, including the genitourinary tract and brain Corresponding author Cristina Costa, MD S.C.D.U. Virologia Azienda Ospedaliero-Universitaria San Giovanni Battista di Torino Via Santena, 9 -10126 Torino E-mail: [email protected] increasing with age, with adult prevalence rate often between 15% and 60
Engineering Framework to Utilize Miniaturized Charpy Type SE(B) Specimens to Predict Jc of Full Sized Specimens
No full textAbstractThis paper introduces our experience of using miniature Charpy type SE(B) specimen in obtaining fracture toughness Jc of a material in the ductile to brittle transition temperature (DBTT) region. Width W x thickness B of 2 x 2 mm, 3 x 3 mm and 10 x 10 mm were chosen as miniature specimens and 25 x 25 mm were chosen as full sized specimen. 0.55% carbon steel JIS S55C, whose tensile to yield stress ratio σTS/σYS was equal to 1.8 was chosen as a material to simulate a degraded (embrittled) material in the DBTT region. Focus was placed on whether cleavage fracture could be predicted for these miniaturized specimens. Another focus was placed on whether the Jc of full sized specimen is predictable from the test results of the miniature sized specimens, in case cleavage fracture were observed. The results showed that the modified Ritch-Knott-Rice (RKR) failure criterion (which predicts the onset of cleavage fracture when the crack opening stress measured at 4 times the crack-tip opening displacement exceeds this σ22c) could predict whether cleavage fracture would occur or not. Another finding was that, in case cleavage fracture was observed though, the critical value σ22c in the modified RKR failure criterion was independent of specimen size, and thus, Jc of the full sized specimen is predictable from the miniature specimen test results, though M = (W-a)σYS/Jc was smaller than ASTM E1921 requirement of 30. Here, a and σYS are crack length and yield strength, respectively
Mendelian randomisation and cardiovascular disease
No full textBackground and aims: Homocysteine (Hcy), C-reactive protein (CRP), and Lipoprotein-
associated phospholipase A2 (Lp-PLA2) have been associated with a high risk of cardiovascular
disease. If casual, they are expected to provide additional tools for prevention. Utilising the unique
properties of genetic variants (randomly allocated and unmodifiable), they could be used as
unconfounded proxies of environmental exposures to investigate disease aetiology, known as
Mendelian randomisation. Herein I conduct a series of Mendelian randomisation experiments to:
(i) investigate the role of Hyc in stroke; (ii) judge causality of CRP in cardiovascular disease; (iii)
investigate the validity of Lp-PLA2 as a therapeutic target in coronary heart disease (CHD) and;
(iv) describe how the integration of cis-acting variants and their cognate proteins can be used to
dissect causal pathways.
Methods: For aim (i), I conducted synthesis research of published and unpublished studies
investigating the MTHFR/C677T variant, Hcy and stroke. For aims (ii) to (iv), a series of
prospective collaborations conducting de novo genotyping for CRP and PLA2G7 genes, were
established using European-based cardiovascular genetic studies of adults.
Results: The meta-analyses of studies on MTHFR/C677T-Hcy-Stroke to 2003, showed that
subjects with the TT genotype had on average 1.93 µmol/L higher levels of Hcy compared with
subjects with CC genotype, and an odds ratio (OR) of stroke of 1.26 (95%Cl: 1.14,1.40). An
update analyses to 2008 showed that in both MTHFR-Hcy and MTHFR-stroke associations,
studies in Asia had the largest effect, followed by Europe with intermediate effect, and lower or
negligible effect in the Americas and Australasia. Analysis on CRP, indicated that subjects
homozygous for the T-allele of CRP/+1444C>T variant despite having 0.68 mg/L higher levels of
CRP, had no increase in risk of myocardial infarction (OR of 1.01 [95CI: 0.74,1.38]). A tagging-
haplotype approach showed a gradual increase on CRP levels by haplotype, but no effect on
CHID, diabetes or stroke. Analysis of the seven PLA2G7 tagging-SNPs showed that the best
variant (rs1051931) had small to moderate effects on the Lp-PLA2 activity (up to 7% relative
differences). No genetic signal with CHD was observed for any PLA2G7 variant, despite some
comparisons including up to 8412 CHID cases. A description of the proof of principle, illustrating
how to utilise cis-acting variants and their cognate proteins to distinguish causal from non-causal
effects among correlated blood proteins was presented, using as an example 6 proteins that have
been associated with CHD risk.
Conclusions: Genetic evidence reported in this document suggested that the MTHFR effect is
more pronounced in geographic regions associated with low folate intake. Genetic studies on
CRP presented in this document indicated that CRP is unlikely to be causal in cardiovascular
disease. A genetic approach using common variants in PLA2G7 had a reduced ability to confirm
or reject Lp-PLA2 as a valid drug target. Finally, integration of cis-acting variants with their
cognate proteins seems to be an effective way of dissecting biological pathways
Bayesian modelling in genetic association studies
No full textBayesian Model Selection Approaches are flexible methods that can be utilised to investigate
Genetic Association studies in greater detail; enabling us to more accurately
pin-point locations of disease genes in complex regions such as the MHC, as well as
investigate possible causal pathways between genes, disease and intermediate phenotypes.
This thesis is split into two distinct parts. The first uses a Bayesian Multivariate
Adaptive Regression Spline Model to search across many highly correlated variants to
try to determine which are likely to be the truly causal variants within complex genetic
regions and also how each of these variants influences disease status. Specifically, I
consider the role of genetic variants within the MHC region on SLE. The second part
of the thesis aims to model possible disease pathways between genes, disease, intermediate
phenotypes and environmental factors using Bayesian Networks, in particular
focussing upon Coronary Heart disease and numerous blood biomarkers and related
genes.
Bayesian Multivariate Adaptive Regression Spline Model:
Genetic association studies have the problem that often many genotypes in strong linkage
disequilibrium (LD) are found to be associated with the outcome of interest. This
makes it difficult to establish the actual SNP responsible.
The aim of this part of the thesis is to investigate Bayesian variable selection methods
in regions of high LD. In particular, to investigate SNPs in the major histocompatibility
complex (MHC) region associated with systematic lupus erythematosus (SLE).
Past studies have found several SNPs in this region to be highly associated with SLE
but these SNPs are in high LD with one another.
It is desirable to search over all possible regression models in order to find those SNPs
that are most important in the prediction of SLE. The Bayesian Multivariate Adapative
Regression Splines (BMARS) model used should automatically correct for nearby associated
SNPs, and only those directly associated should be included in the model. The
BMARS approach will also automatically select the most appropriate disease model
for each directly associated variant.
It was found that there appear to be 3 separate SNP signals in the MHC region that
show association with SLE. The rest of the associations found using simple Frequentist
tests are likely to be due to LD with the true signal.
Bayesian Networks for Genetic Association Studies:
Coronary Heart Disease (CHD) is one of many diseases that result from complicated
relationships between both genetic and environmental factors. Identifying causal factors
and developing new treatments that target these factors is very difficult. Changes in
intermediate phenotypes, or biomarkers, could suggest potential causal pathways, although
these have a tendency to group amongst those patients with higher risk of CHD
making to difficult to distinguish independent causal relationships. I aim to model
disease pathways allowing for intermediate phenotypes as well as genetic and environmental
factors. Statistical methodology was developed using directed acyclic graphs (DAGs). Disease
outcomes, genes, intermediate phenotypes and possible explanatory variables were
represented as nodes in a DAG. Possible models were investigated using Bayesian regression
models, based upon the underlying DAG, in a reversible jump MCMC framework.
Modelling the data this way allows us to distinguish between direct and indirect
effects as well as explore possible directionality of relationships. Since different DAGs
can belong to the same equivalence class, some directions of association may become
indistinguishable and I am interested in the implications of this.
I investigated the integrated associations of genotypes with multiple blood biomarkers
linked to CHD risk, focusing particularly on relationships between APOE, CETP
and APOB genotypes; HDL- and LDL- cholesterol, triglycerides, C-reactive protein,
fibrogen and apolipoproteins A and B.
Overview:
I will begin by introducing the topics of genetics, statistics and directed acyclic graphs
with a background on each (Chapters 2,3 and 4 respectively). Chapter 5 will then detail
the analysis and results of the BMARS model. The analysis and results of Bayesian
networks for genetic association studies will then be covered in Chapter 6
Simultaneous Analysis of All SNPs in Genome-Wide and Re-Sequencing Association Studies
No full textTesting one SNP at a time does not fully realise the potential of genome-wide association studies to identify multiple causal variants, which is a plausible scenario for many complex diseases. We show that simultaneous analysis of the entire set of SNPs from a genome-wide study to identify the subset that best predicts disease outcome is now feasible, thanks to developments in stochastic search methods. We used a Bayesian-inspired penalised maximum likelihood approach in which every SNP can be considered for additive, dominant, and recessive contributions to disease risk. Posterior mode estimates were obtained for regression coefficients that were each assigned a prior with a sharp mode at zero. A non-zero coefficient estimate was interpreted as corresponding to a significant SNP. We investigated two prior distributions and show that the normal-exponential-gamma prior leads to improved SNP selection in comparison with single-SNP tests. We also derived an explicit approximation for type-I error that avoids the need to use permutation procedures. As well as genome-wide analyses, our method is well-suited to fine mapping with very dense SNP sets obtained from re- sequencing and/or imputation. It can accommodate quantitative as well as case-control phenotypes, covariate adjustment, and can be extended to search for interactions. Here, we demonstrate the power and empirical type-I error of our approach using simulated case-control data sets of up to 500 K SNPs, a real genome-wide data set of 300 K SNPs, and a sequence-based dataset, each of which can be analysed in a few hours on a desktop workstation
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