SaaS with Spring and Docker

Ziel dieser Bachelorarbeit ist die Entwicklung einer Software as a Service Anwendung für Modelagenturen. Im Verlauf der Arbeit werden sowohl die Anforderungen als auch das darauf aufbauende Konzept behandelt. Auf diesem basierend werden die Umsetzung wichtiger Komponenten und der Betrieb in Docker erläutert, um eine lau ähige SaaS Anwendung zu erstellen. Die Arbeit setzt hierbei grundlegende Kenntnis über die Software Entwicklung und Architektur voraus.Target of this bachelor thesis is the development of a Software as a Service application for model agencies. Throughout this thesis the requirements will be discussed as well as the concept build upon it. Based on this, the implementation of important components and the operation with Docker to create a working SaaS application will be explained. The paper requires basic knowledge of software development and architecture

Erythrocyte fatty acid profiles in children are not predictive of autism spectrum disorder status: a case control study

Abstract Biomarkers promise biomolecular explanations as well as reliable diagnostics, stratification, and treatment strategies that have the potential to help mitigate the effects of disorders. While no reliable biomarker has yet been found for autism spectrum disorder (ASD), fatty acids have been investigated as potential biomarkers because of their association with brain development and neural functions. However, the ability of fatty acids to classify individuals with ASD from age/gender-matched neurotypical (NEU) peers has largely been ignored in favor of investigating population-level differences. Contrary to existing work, this classification task between ASD and NEU cohorts is the main focus of this work. The data presented herein suggest that fatty acids do not allow for classification at the individual level

Significant Association of Urinary Toxic Metals and Autism-Related Symptoms—A Nonlinear Statistical Analysis with Cross Validation

<div><p>Introduction</p><p>A number of previous studies examined a possible association of toxic metals and autism, and over half of those studies suggest that toxic metal levels are different in individuals with Autism Spectrum Disorders (ASD). Additionally, several studies found that those levels correlate with the severity of ASD.</p><p>Methods</p><p>In order to further investigate these points, this paper performs the most detailed statistical analysis to date of a data set in this field. First morning urine samples were collected from 67 children and adults with ASD and 50 neurotypical controls of similar age and gender. The samples were analyzed to determine the levels of 10 urinary toxic metals (UTM). Autism-related symptoms were assessed with eleven behavioral measures. Statistical analysis was used to distinguish participants on the ASD spectrum and neurotypical participants based upon the UTM data alone. The analysis also included examining the association of autism severity with toxic metal excretion data using linear and nonlinear analysis. “Leave-one-out” cross-validation was used to ensure statistical independence of results.</p><p>Results and Discussion</p><p>Average excretion levels of several toxic metals (lead, tin, thallium, antimony) were significantly higher in the ASD group. However, ASD classification using univariate statistics proved difficult due to large variability, but nonlinear multivariate statistical analysis significantly improved ASD classification with Type I/II errors of 15% and 18%, respectively. These results clearly indicate that the urinary toxic metal excretion profiles of participants in the ASD group were significantly different from those of the neurotypical participants. Similarly, nonlinear methods determined a significantly stronger association between the behavioral measures and toxic metal excretion. The association was strongest for the Aberrant Behavior Checklist (including subscales on Irritability, Stereotypy, Hyperactivity, and Inappropriate Speech), but significant associations were found for UTM with all eleven autism-related assessments with cross-validation <i>R</i>² values ranging from 0.12–0.48.</p></div

Prediction of ABC Total.

<p>Correlation between ABC Total value and metal excretion using linear regression (no cross-validation & cross-validation) as well as nonlinear regression (cross-validation). Only the results for the highest <i>R</i>² values are shown, but other combinations of metals frequently had similar results.</p

Fisher Discriminant Analysis of urine toxic metal data.

<p>Fig 2(a) shows the score variables and the PDF of the neurotypical participants and the participants on the autism spectrum using FDA while Fig 2(b) contains the same information derived by KFDA. The groups of the neurotypical participants and the participants on the spectrum have different distributions, however, there is significant overlap between the two groups when linear FDA is used. While there is still overlap between the two groups even for KFDA, the distributions becomes more distinct when nonlinear statistical techniques such as KFDA are used.</p

Level of toxic metals in first-morning urine.

<p>Note that for some metals (aluminum, mercury, antimony) results were often below the detection limit, so results for those metals must be interpreted cautiously.</p

Type I and Type II errors for classification of participant data into neurotypical participants and participants on the autism spectrum.

<p>Type II errors increase as smaller values are chosen for Type I errors. KFDA outperforms its linear counterpart, FDA, for all cases. Only cross-validation results are shown.</p

Median values of urinary toxic metals for ASD and control groups, normalized to the median of the control values.

<p>The bars represent the 25th and 75th percentiles.</p

Principal Component Analysis performed for the different autism measures.

<p>The Figure on the left shows that two principal components can capture the majority of the differences between the different measures. The analysis shown on the right indicates that there are two clusters with five measures each (ABC, ATEC, PDD-BI, PGI-R2, and SRS; CARS-2, SAS-Parent, Pro SAS, Raw ADOS, Adj ADOS), where there is a high correlation between the autism measures. The measure SSP seems to be distinct from either cluster.</p