[Resilient Children: Evaluation of a Programme to Promote Resilience in Kindergarten and Elementary School]

In the project "Resilient Children", a resilience promotion program for kindergartens and elementary schools was directly applied and evaluated during the COVID-19-crisis.The aim of the study was to strengthen the three sources of resilience according to Grotberg (1995) I HAVE, I AM and I CAN through targeted exercises and resilience-promoting communication (transfer to everyday life). Additionally, gender differences with regard to the effect of the programme were addressed. "Resilient Children" was evaluated at the impact level (pre-post design) and process level. Eight kindergartens and three elementary schools with 125 children participated. A total of 122 teachers and 70 parents provided information about the children. The results at the impact level showed that from the parent and teacher perspective, and from the self-perspective (children), the three sources of resilience were significantly strengthened. With regard to gender differences, the results from the perspective of teachers and parents showed that girls were characterised by greater changes than boys. Compared to the girls, the physical andmental well-being of the boys improved fromthe parents' point of view. The results of the process evaluation revealed a high level of motivation and enthusiasm for participation in the programme on the part of participating children and teachers. The success of "Resilient Children" depends on the identification of the teachers with the program.

Impact of (forensic) expert opinions according to the Istanbul Protocol in Germany-results and insights of the in:Fo-project

As part of a third-party funded project, expert opinions according to the Istanbul Protocol (IP) standards were compiled in Germany on a larger scale for the first time. The assessment process was initiated for 130 project participants. Statistical analysis on numerous variables was performed to test the impact of the expert opinions, foremost of the forensic medical expert opinions, on the individuals' asylum proceedings. The variables were drawn from forensic medical expert opinions and reports of findings, questionnaires for the study participants' counsellors in the project and a query on the asylum status of the study participants. Regression analysis and bivariate analysis on two dependent variables-subjective impact on the asylum procedure from the counsellors' point of view and objective change in the asylum status-were performed to test for an influence on asylum procedures. No statistically significant results were obtained for the objective change of the study participants' asylum status. For the subjective dependent variable, a positive prediction was possible when simultaneously controlling for the independent variables introduction of a forensic medical expert opinion and highest IP grade; a negative prediction was possible when simultaneously controlling for the independent variables introduction of a forensic medical expert opinion and use of IP grading. Apart from the statistical analysis, a positive effect of the assessment on the psychosocial well-being of the study participants could be demonstrated. The results differed from other European studies which demonstrated a correlation between the objective outcome of an asylum procedure (asylum status) and, for example, specific types of violence or the number of documented injuries. Differences also occurred in the use of the plausibility grades proposed by the IP, which questions their use in cases in which the reported torture happened a relevant time ago. Therefore, compiling individually worded evaluations instead of using the IP grading system-if possible, by an experienced forensic physician-is recommended in this scenario. Still, the assessment of alleged torture experiences should follow the IP guidelines, since psychological assessments are of especially high importance in cases with healed physical injuries and since the results also demonstrated a positive effect on the psychosocial well-being of the study participants.

Alcohol use disorder as a risk factor for violent offending in a sample of female forensic-psychiatric inpatients

Introduction

Female gender is associated with a lower risk for aggressive behaviour and violent offending. Well established risk factors for aggressive behaviour are alcohol and other substance use, but previous studies focused mainly on male offenders and the general population. However, for therapeutic and prognostic reasons it is important to understand pathways to female offending.

Objectives

To examine a sample of female forensic-psychiatric inpatients regarding the association of alcohol (AUD) and other substance use disorders (SUD) with violent offending (homicide, assault, robbery).

Methods

We conducted a retrospective cohort study of 334 female patients discharged before 01.01.2019 from a secure psychiatric hospital in Germany.

Results

In total, 72% of the patients with AUD committed a violent crime, leading to admission to secure psychiatric treatment. In comparison a statistically significant lower rate (19%) of the SUD group was convicted of violent offending. Over 70% of the participants with AUD had a family history of AUD, and over 83% experienced physical violence in adulthood. We found no group differences (AUD vs. SUD) regarding aggressive behaviour during inpatient treatment.

Conclusions

According to our results, AUD compared to other SUD, is a significant risk factor for violent offending in women. A family background with AUD and a history of physical abuse might function as a risk factor for both: developing an AUD and violent offending. The comparable rates of aggression in both groups during inpatient treatment suggest that abstinence is a protective factor.

Disclosure

No significant relationships.

Opportunities for genomic selection of cheese-making traits in Montbéliarde cows

As part of the From'MIR project, traits related to the composition and cheese-making properties (CMP) of milk were predicted from 6.6 million mid-infrared spectra taken from 410,622 Montbéliarde cows (19,862 with genotypes). Genome-wide association studies of imputed whole-genome sequences highlighted candidate SNPs that were then added to the EuroG10K BeadChip, which is routinely used in genomic selection. In the present study, we (1) assessed the reliability of single-step genomic BLUP breeding values (ssEBVs) for cheese yields, coagulation traits, and casein and calcium content generated from test-day records of the first 3 lactations, (2) estimated realized genetic trends for these traits over the last decade, and (3) simulated different cheese-making breeding objectives and estimated the responses for CMP as well as for other traits currently selected in the Montbéliarde breed. To estimate the reliability of ssEBVs, the available data were split into 2 independent training and validation sets that respectively contained cows with the oldest and the most recent lactation data. The training set included 155,961 cows (12,850 with genotypes) and was used to predict ssEBVs of 2,125 genotyped cows in the validation set. We first tested 4 models that included either lactation (LACT) or test-day (TD) records from the first (1) or the first 3 (3) lactations, giving equal weight to all 50K SNP effects. Mean reliabilities were 61%, 62%, 63%, and 64% for the LACT1, LACT3, TD1, and TD3 models, respectively. Using the most accurate model (TD3), we then compared the reliabilities of 3 scenarios with: SNPs from the Illumina BovineSNP50 BeadChip only, equally weighted (50K); 50K SNPs plus additional candidate SNPs, equally weighted (50K+); and 50K and candidate SNPs with additional weight given to 7 to 14 candidate SNPs, depending on the trait (CAND). The 50K+ and CAND scenarios led to similar mean reliabilities (67%) and both outperformed the 50K scenario (64%), whereas the CAND scenario generated the less biased ssEBVs. To assess genetic trends, SNP effects were estimated with a single-step GBLUP based on the TD3 model and the 50K scenario applied to the whole population (2.6 million performance records from 190,261 cows and 423,348 animals in the pedigree, of which 21,874 were genotyped) and then applied to 50K genotypes of 21,171 males and 311,761 females. We detected a positive genetic trend for all CMP during the last decade, probably due to selection for an increase in milk protein and fat content in Montbéliarde cows. Finally, we compared the selection responses to 3 different breeding objectives: the current Montbéliarde total merit index (TMI) and 2 alternative scenarios that gave a weight of 70% to TMI and the remaining 30% to either milk casein content (TMI-COMP) or a combination of 3 CMP (TMI-Cheese). The TMI-Cheese scenario yielded the best responses for all the CMP analyzed, whereas values in the TMI-COMP scenario were intermediate, with a slight effect on other traits currently included in TMI. Based on these results, a program of genomic evaluation for CMP predicted from mid-infrared spectra was designed and implemented for the Montbéliarde breed.

A comprehensive approach for genome-wide efficiency profiling of DNA modifying enzymes

A precise understanding of DNA methylation dynamics is of great importance for a variety of biological processes including cellular reprogramming and differentiation. To date, complex integration of multiple and distinct genome-wide datasets is required to realize this task. We present GwEEP (genome-wide epigenetic efficiency profiling) a versatile approach to infer dynamic efficiencies of DNA modifying enzymes. GwEEP relies on genome-wide hairpin datasets, which are translated by a hidden Markov model into quantitative enzyme efficiencies with reported confidence around the estimates. GwEEP predicts de novo and maintenance methylation efficiencies of Dnmts and furthermore the hydroxylation efficiency of Tets. Its design also allows capturing further oxidation processes given available data. We show that GwEEP predicts accurately the epigenetic changes of ESCs following a Serum-to-2i shift and applied to Tet TKO cells confirms the hypothesized mutual interference between Dnmts and Tets.

Heterogeneity matters: Contact structure and individual variation shape epidemic dynamics

In the recent COVID-19 pandemic, mathematical modeling constitutes an important tool to evaluate the prospective effectiveness of non-pharmaceutical interventions (NPIs) and to guide policy-making. Most research is, however, centered around characterizing the epidemic based on point estimates like the average infectiousness or the average number of contacts. In this work, we use stochastic simulations to investigate the consequences of a population's heterogeneity regarding connectivity and individual viral load levels. Therefore, we translate a COVID-19 ODE model to a stochastic multi-agent system. We use contact networks to model complex interaction structures and a probabilistic infection rate to model individual viral load variation. We observe a large dependency of the dispersion and dynamical evolution on the population's heterogeneity that is not adequately captured by point estimates, for instance, used in ODE models. In particular, models that assume the same clinical and transmission parameters may lead to different conclusions, depending on different types of heterogeneity in the population. For instance, the existence of hubs in the contact network leads to an initial increase of dispersion and the effective reproduction number, but to a lower herd immunity threshold (HIT) compared to homogeneous populations or a population where the heterogeneity stems solely from individual infectivity variations.

Efficient simulation of non-Markovian dynamics on complex networks

We study continuous-time multi-agent models, where agents interact according to a network topology. At any point in time, each agent occupies a specific local node state. Agents change their state at random through interactions with neighboring agents. The time until a transition happens can follow an arbitrary probability density. Stochastic (Monte-Carlo) simulations are often the preferred-sometimes the only feasible-approach to study the complex emerging dynamical patterns of such systems. However, each simulation run comes with high computational costs mostly due to updating the instantaneous rates of interconnected agents after each transition. This work proposes a stochastic rejection-based, event-driven simulation algorithm that scales extremely well with the size and connectivity of the underlying contact network and produces statistically correct samples. We demonstrate the effectiveness of our method on different information spreading models.

Mid-infrared spectrometry prediction of the cheese-making properties of raw Montbéliarde milks from herds and cheese dairy vats used for the production of Protected Designation of Origin and Protected Geographical Indication cheeses in Franche-Comté

Franche-Comté is the primary producing region of Protected Designation of Origin cheeses in France. Normally, mid-infrared (MIR) prediction models for cheese-making property (CMP) traits are developed using individual bovine milks. However, considering the requests of all actors in the dairy sector, the present study aimed to assess the feasibility of MIR spectroscopy to develop CMP equations of Montbéliarde herd and dairy vat milks. For this purpose, 22 CMP traits were analyzed on samples collected in 2016 (half in February-March and half in May-June) from 100 commercial herds and 70 dairy vats (55 cheese dairies) located in Franche-Comté. These characteristics included 11 rennet coagulation traits and 8 lactic acidification traits measured in either soft cheese or pressed cooked cheese conditions and 3 laboratory curd yields. Models of MIR prediction for each of the 22 CMP traits were built using partial least squares regression with external validation by dividing the data set into calibration (70%) and validation (30%) sets. We confirmed that the variability of milk traits depends largely on the production scale and is higher for individual milk than for herd milk and even higher for vat milk. The best prediction models were obtained in herd milk samples for curd yields expressed in dry matter or fresh, with a coefficient of determination (R²) in external validation of 0.78 and 0.77, respectively. As with individual milk, these traits are closely related to the gross composition of the milk and therefore easier to predict by MIR spectroscopy. However, these curd yield traits were poorly predicted (R² = 0.58) in vat milk samples due to their lower variability. In herd milk samples, prediction models of other CMP traits were poorly accurate except for the ratio of the time to obtain a standard firmness to the rennet coagulation time in soft cheese or pressed cooked cheese conditions, which showed R² > 0.66 in external validation. Such trait is important in qualifying the behavior of milk during cheese production. Prediction models of other CMP traits for either herd or vat milk samples had poor accuracy, and further work is needed to improve their performance.

Seven Paradoxes of Business Process Management in a Hyper-Connected World

Business Process Management is a boundary-spanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, information systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world greatly leverages the prospects of business processes – but also boosts their complexity to a new level. We need to discuss how the BPM discipline can find new ways for identifying, analyzing, designing, implementing, executing, and monitoring business processes. In this research note, selected transformative trends are explored and their impact on current theories and IT artifacts in the BPM discipline is discussed to stimulate transformative thinking and prospective research in this field.

Cytoplasmic PPARγ is a marker of poor prognosis in patients with Cox-1 negative primary breast cancers

Background

The aim of this study was to investigate the expression of the nuclear receptor PPARγ, together with that of the cyclooxygenases Cox-1 and Cox-2, in breast cancer (BC) tissues and to correlate the data with several clinicobiological parameters including patient survival.

Methods

In a well characterized cohort of 308 primary BC, PPARγ, Cox-1 and Cox-2 cytoplasmic and nuclear expression were evaluated by immunohistochemistry. Correlations with clinicopathological and aggressiveness features were analyzed, as well as survival using Kaplan–Meier analysis.

Results

PPARγ was expressed in almost 58% of the samples with a predominant cytoplasmic location. Cox-1 and Cox-2 were exclusively cytoplasmic. Cytoplasmic PPARγ was inversely correlated with nuclear PPARγ and ER expression, but positively with Cox-1, Cox-2, and other high-risk markers of BC, e.g. HER2, CD133, and N-cadherin. Overall survival analysis demonstrated that cytoplasmic PPARγ had a strong correlation with poor survival in the whole cohort, and even stronger in the subgroup of patients with no Cox-1 expression where cytoplasmic PPARγ expression appeared as an independent marker of poor prognosis. In support of this cross-talk between PPARγ and Cox-1, we found that Cox-1 became a marker of good prognosis only when cytoplasmic PPARγ was expressed at high levels.

Conclusion

Altogether, these data suggest that the relative expression of cytoplasmic PPARγ and Cox-1 may play an important role in oncogenesis and could be defined as a potential prognosis marker to identify specific high risk BC subgroups.

Hidden Markov Modelling Reveals Neighborhood Dependence of Dnmt3a and 3b Activity

DNA methylation is an epigenetic mark whose important role in development has been widely recognized. This epigenetic modification results in heritable information not encoded by the DNA sequence. The underlying mechanisms controlling DNA methylation are only partly understood. Several mechanistic models of enzyme activities responsible for DNA methylation have been proposed. Here, we extend existing Hidden Markov Models (HMMs) for DNA methylation by describing the occurrence of spatial methylation patterns over time and propose several models with different neighborhood dependences. Furthermore, we investigate correlations between the neighborhood dependence and other genomic information. We perform numerical analysis of the HMMs applied to comprehensive hairpin and non-hairpin bisulfite sequencing measurements and accurately predict wild-type data. We find evidence that the activities of Dnmt3a and Dnmt3b responsible for de novo methylation depend on 5' (left) but not on 3' (right) neighboring CpGs in a sequencing string.

Two are better than one: HPoxBS - hairpin oxidative bisulfite sequencing

The controlled and stepwise oxidation of 5mC to 5hmC, 5fC and 5caC by Tet enzymes is influencing the chemical and biological properties of cytosine. Besides direct effects on gene regulation, oxidised forms influence the dynamics of demethylation and re-methylation processes. So far, no combined methods exist which allow to precisely determine the strand specific localisation of cytosine modifications along with their CpG symmetric distribution. Here we describe a comprehensive protocol combining conventional hairpin bisulfite with oxidative bisulfite sequencing (HPoxBS) to determine the strand specific distribution of 5mC and 5hmC at base resolution. We apply this method to analyse the contribution of local oxidative effects on DNA demethylation in mouse ES cells. Our method includes the HPoxBS workflow and subsequent data analysis using our developed software tools. Besides a precise estimation and display of strand specific 5mC and 5hmC levels at base resolution we apply the data to predict region specific activities of Dnmt and Tet enzymes. Our experimental and computational workflow provides a precise double strand display of 5mC and 5hmC modifications at single base resolution. Based on our data we predict region specific Tet and Dnmt enzyme efficiencies shaping the distinct locus levels and patterns of 5hmC and 5mC.

Comparison of Bayesian and partial least squares regression methods for mid-infrared prediction of cheese-making properties in Montbéliarde cows

Assessing the cheese-making properties (CMP) of milks with a rapid and cost-effective method is of particular interest for the Protected Designation of Origin cheese sector. The aims of this study were to evaluate the potential of mid-infrared (MIR) spectra to estimate coagulation and acidification properties, as well as curd yield (CY) traits of Montbéliarde cow milk. Samples from 250 cows were collected in 216 commercial herds in Franche-Comté with the objectives to maximize the genetic diversity as well as the variation in milk composition. All coagulation and CY traits showed high variability (10 to 43%). Reference analyses performed for soft (SC) and pressed cooked (PCC) cheese technology were matched with MIR spectra. Prediction models were built on 446 informative wavelengths not tainted by the water absorbance, using different approaches such as partial least squares (PLS), uninformative variable elimination PLS, random forest PLS, Bayes A, Bayes B, Bayes C, and Bayes RR. We assessed equation performances for a set of 20 CMP traits (coagulation: 5 for SC and 4 for PCC; acidification: 5 for SC and 3 for PCC; laboratory CY: 3) by comparing prediction accuracies based on cross-validation. Overall, variable selection before PLS did not significantly improve the performances of the PLS regression, the prediction differences between Bayesian methods were negligible, and PLS models always outperformed Bayesian models. This was likely a result of the prior use of informative wavelengths of the MIR spectra. The best accuracies were obtained for curd yields expressed in dry matter (CY_DM) or fresh (CY_FRESH) and for coagulation traits (curd firmness for PCC and SC) using the PLS regression. Prediction models of other CMP traits were moderately to poorly accurate. Whatever the prediction methodology, the best results were always obtained for CY traits, probably because these traits are closely related to milk composition. The CY_DM predictions showed coefficient of determination (R²) values up to 0.92 and 0.87, and RS_y,x values of 3 and 4% for PLS and Bayes regressions, respectively. Finally, we divided the data set into calibration (2/3) and validation (1/3) sets and developed prediction models in external validation using PLS regression only. In conclusion, we confirmed, in the validation set, an excellent prediction for CY_DM [R² = 0.91, ratio of performance to deviation (RPD) = 3.39] and a very good prediction for CY_FRESH (R² = 0.84, RPD = 2.49), adequate for analytical purposes. We also obtained good results for both PCC and SC curd firmness traits (R² ≥ 0.70, RPD ≥1.8), which enable quantitative prediction.

Simulating the Large-Scale Erosion of Genomic Privacy Over Time

The dramatically decreasing costs of DNA sequencing have triggered more than a million humans to have their genotypes sequenced. Moreover, these individuals increasingly make their genomic data publicly available, thereby creating privacy threats for themselves and their relatives because of their DNA similarities. More generally, an entity that gains access to a significant fraction of sequenced genotypes might be able to infer even the genomes of unsequenced individuals. In this paper, we propose a simulation-based model for quantifying the impact of continuously sequencing and publicizing personal genomic data on a population's genomic privacy. Our simulation probabilistically models data sharing and takes into account events such as migration and interracial mating. We exemplarily instantiate our simulation with a sample population of 1,000 individuals and evaluate the privacy under multiple settings over 6,000 genomic variants and a subset of phenotype-related variants. Our findings demonstrate that an increasing sharing rate in the future entails a substantial negative effect on the privacy of all older generations. Moreover, we find that mixed populations face a less severe erosion of privacy over time than more homogeneous populations. Finally, we demonstrate that genomic-data sharing can be much more detrimental for the privacy of the phenotype-related variants.

Lumping of degree-based mean-field and pair-approximation equations for multistate contact processes

Contact processes form a large and highly interesting class of dynamic processes on networks, including epidemic and information-spreading networks. While devising stochastic models of such processes is relatively easy, analyzing them is very challenging from a computational point of view, particularly for large networks appearing in real applications. One strategy to reduce the complexity of their analysis is to rely on approximations, often in terms of a set of differential equations capturing the evolution of a random node, distinguishing nodes with different topological contexts (i.e., different degrees of different neighborhoods), such as degree-based mean-field (DBMF), approximate-master-equation (AME), or pair-approximation (PA) approaches. The number of differential equations so obtained is typically proportional to the maximum degree k_{max} of the network, which is much smaller than the size of the master equation of the underlying stochastic model, yet numerically solving these equations can still be problematic for large k_{max}. In this paper, we consider AME and PA, extended to cope with multiple local states, and we provide an aggregation procedure that clusters together nodes having similar degrees, treating those in the same cluster as indistinguishable, thus reducing the number of equations while preserving an accurate description of global observables of interest. We also provide an automatic way to build such equations and to identify a small number of degree clusters that give accurate results. The method is tested on several case studies, where it shows a high level of compression and a reduction of computational time of several orders of magnitude for large networks, with minimal loss in accuracy.

Moment-Based Parameter Estimation for Stochastic Reaction Networks in Equilibrium

Calibrating parameters is a crucial problem within quantitative modeling approaches to reaction networks. Existing methods for stochastic models rely either on statistical sampling or can only be applied to small systems. Here, we present an inference procedure for stochastic models in equilibrium that is based on a moment matching scheme with optimal weighting and that can be used with high-throughput data like the one collected by flow cytometry. Our method does not require an approximation of the underlying equilibrium probability distribution and, if reaction rate constants have to be learned, the optimal values can be computed by solving a linear system of equations. We discuss important practical issues such as the selection of the moments and evaluate the effectiveness of the proposed approach on three case studies.

H(O)TA: estimation of DNA methylation and hydroxylation levels and efficiencies from time course data

Motivation

Methylation and hydroxylation of cytosines to form 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) belong to the most important epigenetic modifications and their vital role in the regulation of gene expression has been widely recognized. Recent experimental techniques allow to infer methylation and hydroxylation levels at CpG dinucleotides but require a sophisticated statistical analysis to achieve accurate estimates.

Results

We present H(O)TA, a software tool based on a stochastic modeling approach, which simultaneously analyzes time course data from hairpin bisulfite sequencing and hairpin oxidative bisulfite sequencing.

Availability and Implementation

: https://mosi.uni-saarland.de/HOTA.

Contact

charalampos.kyriakopoulos@uni-saarland.de or verena.wolf@uni-saarland.de.

Abstract P2-05-23: EP3-receptor expression is a prognostic marker for overall survival and progression-free survival in sporadic breast cancer

Aim: Prostaglandins are tissue hormones with a variety of biological effects and are mainly associated with infection and inflammation. However, elevated prostaglandin synthesis, especially of prostaglandin E2 (PGE2) caused by overexpression of cyclooxygenase-2 (COX-2), has also been associated with the development and progression of different kinds of cancer. Clinical trials have shown the potential of non-steroidal anti-inflammatory drugs or specific COX-2 inhibitors (coxibs) in prevention and treatment of malignant disease, as they reduce prostaglandin levels via inhibition of COX-2. Unfortunately, the clinical use of these drugs is limited due to their various side effects. PGE2 exerts its effects by signaling through four specific membrane-bound receptors, the EP-receptors 1-4. In recent research, the relevance of EP-receptors in carcinogenesis is investigated in the attempt to find a more specific target for the reduction of prostaglandin-effects without inducing side effects. This study evaluates the expression of EP3-receptor on breast cancer tissue and its correlation to progression and survival

Material and methods: A total of 277 sporadic breast cancer samples without primary distant metastases were immunohistochemically analyzed for EP3-receptor expression. Tissue samples were stained with primary anti-EP3 antibodies (monoclonal rabbit IgG). EP3-receptor-expression was quantified by the semi-quantitative immunoreactivity score (IRS); samples with an IRS ≥ 2 were scored as EP3 positive. Statistical analyses were performed with SPSS software using chi-squared test as well as Kaplan-Meier-estimates and Cox regression for survival analyses.

Results: EP3-receptor was expressed in 71.1 % of all cases. EP3-receptor expression did not correlate with clinicopathological parameters such as tumor size or lymph node status at primary diagnosis or with the expression of other immunohistochemical markers (estrogen and progesterone receptor, Her2). Distant metastasis occurred in 49.1 % of EP3 negative cases but only in 32.8 % of EP3 positive cases in an observation period of up to ten years (p = 0.03). EP3 receptor positive cases also showed significantly improved progression-free survival rates (overall [p = 0.01], ten years [p = 0.002] and five years [p = 0.04]). Furthermore, EP3-receptor positivity was associated with an improved overall survival rate (p = 0.002) and ten years survival rate (p = 0.001), whereas short-time survival rate (five years) did not differ between both EP3-groups (p = 0.10). In a multivariate analysis comparing all factors with significant influence in univariate testing, EP3-receptor could be confirmed as an independent factor.

Discussion: Our results show that EP3-receptor positivity is a relevant prognostic factor in sporadic breast cancer. Its correlation with a favorable course of disease is especially interesting as EP3 is known to regulate uPA, a well-known parameter which is – on the contrary - associated with unfavorable survival in breast cancer. Therefore, ongoing studies by our group aim to examine the correlation of EP3-receptor to the uPA/PAI1-pathway and to evaluate the possibility to target EP3-receptor in future anti-tumor therapy in breast cancer.

Citation Format: Semmlinger A, von Schönfeldt V, Wolf V, Schmoeckel E, Mayr D, Meuter A, Mahner S, Jeschke U, Ditsch N. EP3-receptor expression is a prognostic marker for overall survival and progression-free survival in sporadic breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P2-05-23.

Generalized method of moments for estimating parameters of stochastic reaction networks

BACKGROUND

Discrete-state stochastic models have become a well-established approach to describe biochemical reaction networks that are influenced by the inherent randomness of cellular events. In the last years several methods for accurately approximating the statistical moments of such models have become very popular since they allow an efficient analysis of complex networks.

RESULTS

We propose a generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model and the sample moments of population snapshot data. The proposed parameter estimation method exploits recently developed moment-based approximations and provides estimators with desirable statistical properties when a large number of samples is available. We demonstrate the usefulness and efficiency of the inference method on two case studies.

CONCLUSIONS

The generalized method of moments provides accurate and fast estimations of unknown parameters of reaction networks. The accuracy increases when also moments of order higher than two are considered. In addition, the variance of the estimator decreases, when more samples are given or when higher order moments are included.

Osteogenic differentiation capacity of human mesenchymal stromal cells in response to extracellular calcium with special regard to connexin 43

The effects of extracellular calcium on osteogenic differentiation capacity of human bone-derived mesenchymal stromal cells with special regard to connexin 43 (cx43) have been investigated by means of cell culture experiments. Mesenchymal stromal cells isolated from human cancellous bone were cultured on tissue culture plates at different calcium ion (Ca²⁺) concentrations (1.8mmoll^-1, 10mmoll^-1, 20mmoll^-1). Cell responses were evaluated by quantitative RT-PCR, immunofluorescence staining, and Lucifer Yellow fluorescence uptake experiments. It could be shown that increasing Ca²⁺ concentrations correlate with increasing cx43 and bone sialoprotein mRNA levels as well as with enhanced cx43 fluorescence signaling and matrix mineralization of the cultures as shown by von Kossa staining. Hemichannel gating - assessed by Lucifer Yellow uptake - increases with increasing extracellular Ca²⁺ concentrations suggesting that regulatory effects at the hemichannel level are calcium-dependent.

Skin metastasis, an uncommon course of prostate carcinoma: a report of two cases

Prostate cancer is one of the most common cancers among men worldwide and in the USA. Most prostate cancer progression either locally invades to seminal vesicles or metastasizes distally to bone. Skin is not a common site of metastasis for the majority of malignancies including prostate cancer. This paper reports two extremely rare cases of prostate carcinoma metastatic to the skin: a 74-year-old man previously treated with radiation for prostate cancer with cutaneous metastases to the shoulder and a 68-year-old man with prostate adenocarcinoma and cutaneous metastases to the groin. Both patients were diagnosed with skin punch biopsy and later confirmed with immunohistochemical staining for PSA and prostate specific acid phosphatase, specific for prostatic carcinoma. Although unusual, development of multiple skin lesions in patients with prostate adenocarcinoma should raise the flags of cutaneous metastases.

Infinite level-dependent QBD processes and matrix-analytic solutions for stochastic chemical kinetics

Systems of stochastic chemical kinetics are modeled as infinite level-dependent quasi-birth-and-death (LDQBD) processes. For these systems, in contrast to many other applications, levels have an increasing number of states as the level number increases and the probability mass may reside arbitrarily far away from lower levels. Ideas from Lyapunov theory are combined with existing matrix-analytic formulations to obtain accurate approximations to the stationary probability distribution when the infinite LDQBD process is ergodic. Results of numerical experiments on a set of problems are provided.

The Influence of Hydroxylation on Maintaining CpG Methylation Patterns: A Hidden Markov Model Approach

DNA methylation and demethylation are opposing processes that when in balance create stable patterns of epigenetic memory. The control of DNA methylation pattern formation by replication dependent and independent demethylation processes has been suggested to be influenced by Tet mediated oxidation of 5mC. Several alternative mechanisms have been proposed suggesting that 5hmC influences either replication dependent maintenance of DNA methylation or replication independent processes of active demethylation. Using high resolution hairpin oxidative bisulfite sequencing data, we precisely determine the amount of 5mC and 5hmC and model the contribution of 5hmC to processes of demethylation in mouse ESCs. We develop an extended hidden Markov model capable of accurately describing the regional contribution of 5hmC to demethylation dynamics. Our analysis shows that 5hmC has a strong impact on replication dependent demethylation, mainly by impairing methylation maintenance.

Method of conditional moments (MCM) for the Chemical Master Equation: a unified framework for the method of moments and hybrid stochastic-deterministic models

The time-evolution of continuous-time discrete-state biochemical processes is governed by the Chemical Master Equation (CME), which describes the probability of the molecular counts of each chemical species. As the corresponding number of discrete states is, for most processes, large, a direct numerical simulation of the CME is in general infeasible. In this paper we introduce the method of conditional moments (MCM), a novel approximation method for the solution of the CME. The MCM employs a discrete stochastic description for low-copy number species and a moment-based description for medium/high-copy number species. The moments of the medium/high-copy number species are conditioned on the state of the low abundance species, which allows us to capture complex correlation structures arising, e.g., for multi-attractor and oscillatory systems. We prove that the MCM provides a generalization of previous approximations of the CME based on hybrid modeling and moment-based methods. Furthermore, it improves upon these existing methods, as we illustrate using a model for the dynamics of stochastic single-gene expression. This application example shows that due to the more general structure, the MCM allows for the approximation of multi-modal distributions.

Approximate maximum likelihood estimation for stochastic chemical kinetics

Recent experimental imaging techniques are able to tag and count molecular populations in a living cell. From these data mathematical models are inferred and calibrated. If small populations are present, discrete-state stochastic models are widely-used to describe the discreteness and randomness of molecular interactions. Based on time-series data of the molecular populations, the corresponding stochastic reaction rate constants can be estimated. This procedure is computationally very challenging, since the underlying stochastic process has to be solved for different parameters in order to obtain optimal estimates. Here, we focus on the maximum likelihood method and estimate rate constants, initial populations and parameters representing measurement errors.

In vivo control of CpG and non-CpG DNA methylation by DNA methyltransferases

The enzymatic control of the setting and maintenance of symmetric and non-symmetric DNA methylation patterns in a particular genome context is not well understood. Here, we describe a comprehensive analysis of DNA methylation patterns generated by high resolution sequencing of hairpin-bisulfite amplicons of selected single copy genes and repetitive elements (LINE1, B1, IAP-LTR-retrotransposons, and major satellites). The analysis unambiguously identifies a substantial amount of regional incomplete methylation maintenance, i.e. hemimethylated CpG positions, with variant degrees among cell types. Moreover, non-CpG cytosine methylation is confined to ESCs and exclusively catalysed by Dnmt3a and Dnmt3b. This sequence position-, cell type-, and region-dependent non-CpG methylation is strongly linked to neighboring CpG methylation and requires the presence of Dnmt3L. The generation of a comprehensive data set of 146,000 CpG dyads was used to apply and develop parameter estimated hidden Markov models (HMM) to calculate the relative contribution of DNA methyltransferases (Dnmts) for de novo and maintenance DNA methylation. The comparative modelling included wild-type ESCs and mutant ESCs deficient for Dnmt1, Dnmt3a, Dnmt3b, or Dnmt3a/3b, respectively. The HMM analysis identifies a considerable de novo methylation activity for Dnmt1 at certain repetitive elements and single copy sequences. Dnmt3a and Dnmt3b contribute de novo function. However, both enzymes are also essential to maintain symmetrical CpG methylation at distinct repetitive and single copy sequences in ESCs.

Fast adaptive uniformisation of the chemical master equation

Within systems biology there is an increasing interest in the stochastic behaviour of biochemical reaction networks. An appropriate stochastic description is provided by the chemical master equation, which represents a continuous-time Markov chain (CTMC). The uniformisation technique is an efficient method to compute probability distributions of a CTMC if the number of states is manageable. However, the size of a CTMC that represents a biochemical reaction network is usually far beyond what is feasible. In this study, the authors present an on-the-fly variant of uniformisation, where they improve the original algorithm at the cost of a small approximation error. By means of several examples, the authors show that their approach is particularly well-suited for biochemical reaction networks.

Solving the chemical master equation using sliding windows

BACKGROUND

The chemical master equation (CME) is a system of ordinary differential equations that describes the evolution of a network of chemical reactions as a stochastic process. Its solution yields the probability density vector of the system at each point in time. Solving the CME numerically is in many cases computationally expensive or even infeasible as the number of reachable states can be very large or infinite. We introduce the sliding window method, which computes an approximate solution of the CME by performing a sequence of local analysis steps. In each step, only a manageable subset of states is considered, representing a "window" into the state space. In subsequent steps, the window follows the direction in which the probability mass moves, until the time period of interest has elapsed. We construct the window based on a deterministic approximation of the future behavior of the system by estimating upper and lower bounds on the populations of the chemical species.

RESULTS

In order to show the effectiveness of our approach, we apply it to several examples previously described in the literature. The experimental results show that the proposed method speeds up the analysis considerably, compared to a global analysis, while still providing high accuracy.

CONCLUSIONS

The sliding window method is a novel approach to address the performance problems of numerical algorithms for the solution of the chemical master equation. The method efficiently approximates the probability distributions at the time points of interest for a variety of chemically reacting systems, including systems for which no upper bound on the population sizes of the chemical species is known a priori.

Solving the chemical master equation using sliding windows

Background

The chemical master equation (CME) is a system of ordinary differential equations that describes the evolution of a network of chemical reactions as a stochastic process. Its solution yields the probability density vector of the system at each point in time. Solving the CME numerically is in many cases computationally expensive or even infeasible as the number of reachable states can be very large or infinite. We introduce the sliding window method, which computes an approximate solution of the CME by performing a sequence of local analysis steps. In each step, only a manageable subset of states is considered, representing a "window" into the state space. In subsequent steps, the window follows the direction in which the probability mass moves, until the time period of interest has elapsed. We construct the window based on a deterministic approximation of the future behavior of the system by estimating upper and lower bounds on the populations of the chemical species.

Results

In order to show the effectiveness of our approach, we apply it to several examples previously described in the literature. The experimental results show that the proposed method speeds up the analysis considerably, compared to a global analysis, while still providing high accuracy.

Conclusions

The sliding window method is a novel approach to address the performance problems of numerical algorithms for the solution of the chemical master equation. The method efficiently approximates the probability distributions at the time points of interest for a variety of chemically reacting systems, including systems for which no upper bound on the population sizes of the chemical species is known a priori.

The reliability of HER2-status determination from core-needle-biopsies and surgical specimens: A comparison of two established test methods (IHC, CISH)

e22141

Background: A milestone of breast-cancer therapy was the discovery of HER-2 entailing special targeted therapy with improved prognosis. The HER-2-status is routinely assessed through immunohistochemistry (IHC; HercepTest) showing protein over-expression and is double-checked with in-situ-hybridisation (ISH) demonstrating gene amplification in equivocal cases. It is questioned whether these methods achieve identical results in core-needle-biopsies and in excisional tumor specimens. Methods: We performed a retrospective comparative study in order to address these questions. From 01/03–06/08 we collected the HercepTest results from both core-needle-biopsy and surgical specimen of 109 breast cancer patients in our institute and compared these to newly evaluated chromogenic ISH (CISH) results for both specimen types in order to assess the reliability of HER-2- diagnosis of both methodological approaches and of specimen type. Results: We found no significant difference in the HER- 2-status determined from either needle-biopsies or surgical specimens irrespective of the test used. For the overall comparison (218 specimens) of HercepTest and CISH we found only slight, non-significant deviations. Four cases were CISH-negative in spite of HercepTest scoring of 3+. Vice versa, five out of the total of 38 (17.4%) CISH-positives did not correspond to the HercepTest results of 0 or 1+. Conclusions: Though not significant, there is some inconsistency in the HER-2-determination depending on the test-method, leaving these cases equivocal. In accordance with the literature, we therefore recommend to at least double-check samples with 2+ in the HercepTest as it is the current standard. Our data support the use of core-needle-biopsy as a reliable tissue sample for HER-2-diagnosis.

[Table: see text]