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We apply the charge simulation method (CSM) in order to compute the logarithmic capacity of compact sets consisting of (infinitely) many “small” components. This application allows to use just a single charge point for each component. The resulting method therefore is significantly more efficient than methods based on discretizations of the boundaries (for example, our own method presented in Liesen et al. (Comput. Methods Funct. Theory 17, 689–713, 2017)), while maintaining a very high level of accuracy. We study properties of the linear algebraic systems that arise in the CSM, and show how these systems can be solved efficiently using preconditioned iterative methods, where the matrix-vector products are computed using the fast multipole method. We illustrate the use of the method on generalized Cantor sets and the Cantor dust.
Background
The alignment of large numbers of protein sequences is a challenging task and its importance grows rapidly along with the size of biological datasets. State-of-the-art algorithms have a tendency to produce less accurate alignments with an increasing number of sequences. This is a fundamental problem since many downstream tasks rely on accurate alignments.
Results
We present learnMSA, a novel statistical learning approach of profile hidden Markov models (pHMMs) based on batch gradient descent. Fundamentally different from popular aligners, we fit a custom recurrent neural network architecture for (p)HMMs to potentially millions of sequences with respect to a maximum a posteriori objective and decode an alignment. We rely on automatic differentiation of the log-likelihood, and thus, our approach is different from existing HMM training algorithms like Baum–Welch. Our method does not involve progressive, regressive, or divide-and-conquer heuristics. We use uniform batch sampling to adapt to large datasets in linear time without the requirement of a tree. When tested on ultra-large protein families with up to 3.5 million sequences, learnMSA is both more accurate and faster than state-of-the-art tools. On the established benchmarks HomFam and BaliFam with smaller sequence sets, it matches state-of-the-art performance. All experiments were done on a standard workstation with a GPU.
Conclusions
Our results show that learnMSA does not share the counterintuitive drawback of many popular heuristic aligners, which can substantially lose accuracy when many additional homologs are input. LearnMSA is a future-proof framework for large alignments with many opportunities for further improvements.
The classical Buscher rules d escribe T-duality for metrics and B-fields in a topologically trivial setting. On the other hand, topological T-duality addresses aspects of non-trivial topology while neglecting metrics and B-fields. In this article, we develop a new unifying framework for both aspects.
Convolutional Neural Network-based image classification models are the current state-of-the-art for solving image classification problems. However, obtaining and using such a model to solve a specific image classification problem presents several challenges in practice. To train the model, we need to find good hyperparameter values for training, such as initial model weights or learning rate. However, finding these values is usually a non-trivial process. Another problem is that the training data used for model training is often class-imbalanced in practice. This usually has a negative impact on model training. However, not only is it challenging to obtain a Convolutional Neural Network-based model, but also to use the model after model training. After training, the model might be applied to images that were drawn from a data distribution that is different from the data distribution the training data was drawn from. These images are typically referred to as out-of-distribution samples. Unfortunately, Convolutional Neural Network-based image classification models typically fail to predict the correct class for out-of-distribution samples without warning, which is problematic when such a model is used for safety-critical applications. In my work, I examined whether information from the layers of a Convolutional Neural Network-based image classification model (pixels and activations) can be used to address all of these issues. As a result, I suggest a method for initializing the model weights based on image patches, a method for balancing a class-imbalanced dataset based on layer activations, and a method for detecting out-of-distribution samples, which is also based on layer activations. To test the proposed methods, I conducted extensive experiments using different datasets. My experiments showed that layer information (pixels and activations) can indeed be used to address all of the aforementioned challenges when training and using Convolutional Neural Network-based image classification models.
Phylogenetic (i.e., leaf-labeled) trees play a fundamental role in evolutionary research. A typical problem is to reconstruct such trees from data like DNA alignments (whose columns are often referred to as characters), and a simple optimization criterion for such reconstructions is maximum parsimony. It is generally assumed that this criterion works well for data in which state changes are rare. In the present manuscript, we prove that each binary phylogenetic tree T with n ≥ 20k leaves is uniquely defined by the set Ak (T), which consists of all characters with parsimony score k on T. This can be considered as a promising first step toward showing that maximum parsimony as a tree reconstruction criterion is justified when the number of changes in the data is relatively small.
We consider Walsh’s conformal map from the exterior of a compact set E ⊆ C onto a lemniscatic domain. If E is simply connected, the lemniscatic domain is the exterior of a circle, while if E has several components, the lemniscatic domain is the exterior of a generalized lemniscate and is determined by the logarithmic capacity of E and by the exponents and centers of the generalized lemniscate. For general E, we characterize the exponents in terms of the Green’s function of Ec. Under additional symmetry conditions on E, we also locate the centers of the lemniscatic domain. For polynomial pre-images E = P−1(Ω) of a simply-connected infinite compact set Ω, we explicitly determine the exponents in the lemniscatic domain and derive a set of equations to determine the centers of the lemniscatic domain. Finally, we present several examples where we explicitly obtain the exponents and centers of the lemniscatic domain, as well as the conformal map.
Background
The Earth Biogenome Project has rapidly increased the number of available eukaryotic genomes, but most released genomes continue to lack annotation of protein-coding genes. In addition, no transcriptome data is available for some genomes.
Results
Various gene annotation tools have been developed but each has its limitations. Here, we introduce GALBA, a fully automated pipeline that utilizes miniprot, a rapid protein-to-genome aligner, in combination with AUGUSTUS to predict genes with high accuracy. Accuracy results indicate that GALBA is particularly strong in the annotation of large vertebrate genomes. We also present use cases in insects, vertebrates, and a land plant. GALBA is fully open source and available as a docker image for easy execution with Singularity in high-performance computing environments.
Conclusions
Our pipeline addresses the critical need for accurate gene annotation in newly sequenced genomes, and we believe that GALBA will greatly facilitate genome annotation for diverse organisms.
Background
An important initial phase of arguably most homology search and alignment methods such as required for genome alignments is seed finding. The seed finding step is crucial to curb the runtime as potential alignments are restricted to and anchored at the sequence position pairs that constitute the seed. To identify seeds, it is good practice to use sets of spaced seed patterns, a method that locally compares two sequences and requires exact matches at certain positions only.
Results
We introduce a new method for filtering alignment seeds that we call geometric hashing. Geometric hashing achieves a high specificity by combining non-local information from different seeds using a simple hash function that only requires a constant and small amount of additional time per spaced seed. Geometric hashing was tested on the task of finding homologous positions in the coding regions of human and mouse genome sequences. Thereby, the number of false positives was decreased about million-fold over sets of spaced seeds while maintaining a very high sensitivity.
Conclusions
An additional geometric hashing filtering phase could improve the run-time, accuracy or both of programs for various homology-search-and-align tasks.
Statistical Methods and Applications for Biomarker Discovery Using Large Scale Omics Data Set
(2023)
This thesis focuses on identifying genetic factors associated with human kidney disease progression, with three articles presented. Article I describes the identification of loci associated with UACR through trans-ethnic, European-ancestry-specific, and diabetes-specific meta-analyses. An approximate conditional analysis was performed to identify additional independent UACR-associated variants within identified loci. The genome-wide significance level of 𝛼=5×10−8 is used for both primary GWAS association and conditional analyses. However, unlike primary association tests, conditional tests are limited to specific genomic regions surrounding primary GWAS index signals rather than being applied on a genome-wide scale.
In article II, we hypothesized that the application of 𝛼=5×10−8 is overly strict and results in a loss of power. To address this issue, we developed a quasi-adaptive method within a weighted hypothesis testing framework. This method exploits the type I error (𝛼=0.05) by providing less conservative SNP specific 𝛼-thresholds to select secondary signals in conditional analysis. Through simulation studies and power analyses, we demonstrate that the quasi-adaptive method outperforms the established criterion 𝛼=5×10−8 as well as the equal weighting scheme (the Sidak-correction). Furthermore, our method performs well when applied to real datasets and can potentially reveal previously undetected secondary signals in existing data.
In article III, we extended our quasi-adaptive method to identify plausible multiple independent signals at each locus (a secondary signal, a tertiary signal, a signal of 4th, and beyond) and applied it to the publically available GWAS meta-analysis to detect additional multiple independent eGFR-associated signals. The improved quasi-adaptive method successfully identified additional novel replicated independent SNPs that would have gone undetected by applying too conservative genome-wide significance level of 𝛼=5× 10−8. Colocalization analysis based on the novel independent signals identified potentially functional genes across the kidney and other tissues.
Overall, these articles contribute to the understanding of genetic factors associated with human kidney disease progression and provide novel methods for identifying secondary and multiple independent signals in conditional GWAS analyses.
Gram-negative bacteria secrete lipopolysaccharides (LPS), leading to a host immune
response of proinflammatory cytokine secretion. Those proinflammatory cytokines are
TNF-α and IFN-γ, which induce the production of indoleamine 2,3-dioxygenase (IDO). IDO production is increased during severe sepsis, and septic shock. High IDO
levels are associated with increased mortality. This enzyme catalyzes the degradation of tryptophan (TRP) to kynurenine (KYN) along the kynurenine pathway (KP).
KYN is further degraded to kynurenic acid (KYNA). Increased IDO levels accompany
with increased levels of KYNA, which is associated with immunoparalysis.
Due to its central role, the KP is a potential target of therapeutic intervention.
The degradation of TRP to KYN by IDO was intervened by 1-Methyltryptophan (1-
MT), which is assumed to inhibit IDO. By administering 1-MT, the survival of
1-MT-treated mice suffering from sepsis increased compared to mice not treated with
1-MT. The levels of downstream metabolites such as KYN and KYNA were
expected to be decreased. Surprisingly, in healthy mice and pigs, an increase in KYNA
after 1-MT administration was reported. Those unexpected metabolite alterations after 1-MT administration, and the mode of action, were not the focus of recent
research. Hence, there is no explanation for KYNA increase, while KYN did not change.
This thesis aims to postulate a possible degradation pathway of 1-MT along the KP
with the help of ordinary differential equation (ODE) systems.
Moreover, the developed ODE models were used to determine the ability of 1-MT to
inhibit IDO in vivo. Therefore, a multiplicity of ODE models were developed, including
a model of the KP, an extension by lipopolysaccharide (LPS) administration, and 1-MT
administration.
Moreover, seven ODE models were developed, all considering possible degradation pathways of 1-MT. The most likely degradation pathway was combined with the ODE model
of LPS administration, including the inhibitory effects of 1-MT.
Those models consist of several dependent equations describing the dynamics of the KP.
For each component of the KP, one equation describes the alterations over time. Equations for TRP, KYN, KYNA, and quinolinic acid (QUIN) were developed.
Moreover, the alterations of serotonin (SER) were also included. All together belong
to the TRP metabolism. They include the degradation of TRP to SER and to KYN,
which is further degraded to KYNA and QUIN. Every degradation is catalyzed by an enzyme. Therefore, Michaelis-Menten (MM) equations were used employing the substrate
constant Km and the maximal degradation velocity Vmax. To reduce the complexity of
parameter calculation, Km values of the different enzymes were fixed to literature values.
The remaining parameters of the equations were determined so that the trajectories of
the calculated metabolite levels correspond to data. The parameters of different models were determined. To propose a degradation pathway of 1-MT leading to increased
KYNA levels, seven models were developed and compared. The most likely model was
extended to test whether the inhibitory effects of 1-MT on IDO can be determined.
Three different approaches determined the ODE model parameters of the different hypothesis of 1-MT degradation. In the first approach, ODE model parameters were fixed
to values fitted to an independent data set. In the second approach, parameters were
fitted to a subset of the data set, which was used for simulations of the different hypotheses. The third approach calculated ODE model parameters 100 times without
fixed parameters. The parameter set ending up in trajectories of the TRP metabolites,
which have the smallest distance to the data, was assumed to be the most likely. The
ODE model parameters were fitted to data measured in pigs. Two different
experimental models delivered data used in this thesis. The first experimental model
activates IDO by LPS administration in pigs. The second one combines the IDO
activation by LPS with the administration of 1-MT in pigs.
The most likely hypothesis, according to approach 1 was the degradation of 1-MT to
KYNA and TRP. For the second data set the most likely one was the direct degradation of 1-MT to KYNA. With approach 2 the most likely degradation pathways were
the combination of all degradation pathways and the degradation of 1-MT to TRP and
TRP to KYNA. With approach 3 the most likely way of KYNA increase was given by
the direct degradation of 1-MT to KYNA. In summary, the three approaches revealed
hypothesis 2, the direct degradation of 1-MT to KYNA most frequently. A cell-free
assay validated this result. This experiment combined 1-MT or TRP with or without
the enzyme kynurenine aminotransferase (KAT). KAT was already shown to degrade
TRP directly to KYNA. The levels of TRP, KYN and KYNA were measured. The
highest KYNA levels were yielded with an assay adding KAT to 1-MT, corresponding
to hypothesis 2. The models describing the inhibitory effects of 1-MT revealed that
the model without inhibitory effects of 1-MT on IDO was more likely for all three approaches.
The correctness of hypothesis 2 has to be confirmed by further in vitro experiments. It
also has to be investigated which reactions promote the degradation of 1-MT to KYNA.
The missing inhibitory properties of 1-MT on IDO, determined by the in silico ODE
models, align with previous research. It was shown that the saturation of 1-MT was too
low, e.g. in pigs, to inhibit IDO efficiently.
In this study, the first possible degradation pathway of 1-MT along the KP is proposed.
The reliability of the results depends on the quality of the experimental data, and the
season, when data were measured. Moreover, the results vary between the different
approaches of parameter fitting. Different approaches of parameter fitting have to be
included in the analysis to get more evidence for the correctness of the results.
Tafazzin is an acyltransferase with key functions in remodeling of the mitochondrial phospholipid cardiolipin (CL) by exchanging single fatty acids species in CL. Tafazzin-mediated CL remodeling determines the actual CL compositions and has been implicated in mitochondrial morphology and function. Thus, any deficiency of tafazzin leads to altered fatty acid composition of CL which is directly associated with impaired mitochondrial respiration and ATP production. Mutations in the tafazzin encoding gene TAZ, are the cause of the severe X-linked genetic disease, BARTH syndrome (BTHS).
Previous work provided first hints on a linkage of CL composition and subsequent limitations in the cellular ATP levels which may contribute to the restriction of growth. However, in C6 cells ATP levels remained unaltered due to compensatory activation of glycolysis. Moreover, it has been demonstrated that the substantial changes in CL composition are similarly resulting from knocking down either cardiolipin synthase (CRLS) or TAZ. This has also been shown in C6 glioma cells. Most notably only the knock down of TAZ, but not that of CRLS, compromised proliferation of C6 glioma cells. Therefore, a CL- independent role of TAZ in regulating cell proliferation is postulated.
In this study, any linkage of the lack of tafazzin to cellular proliferation should be investigated in more detail to allow first insight into underlying mechanisms.
The results of the current study demonstrate that the tafazzin knockout in C6 glioma cells show changes in global gene expression by applying transcriptome analysis using the- microarray Clarion S rat Affymetrix array. Out of 22,076 total number of genes detected, 1,099 genes were differentially expressed in C6 knockout cells which were either ≥2 and ≥4 fold up or down regulated genes. Furthermore, expression of selected target genes was validated using RT-qPCR. We have hypothesised that the changes in TAZ dependent gene expression is via PPAR transcription factor. According to eukaryotic promoter database (EPD) for selected target genes, exhibited at least one putative binding site for PPARG and PPARA transcription factors. However, pioglitazone and LG100268, synthetic ligands of PPARG and RXR, could not show any effect on changes in gene expression in C6 TAZ cells. Another class of cellular lipids, oxylipins were found to occur in significantly higher amounts in C6 TAZ cells compared to C6 cells which makes them candidates for mediating cellular effects and regulating gene expression via PPARs. A computational tool CiiiDER was used to for the prediction of transcription factor binding site. The transcription factors enriched in TAZ- regulated genes were found to be HOXA5 and PAX2, binding sites of which could be detected in 100 % of TAZ- regulated genes (>2-fold). By applying IPA to the differentially expressed genes we could identify lipid metabolism, and cholesterol superpathway in particular as the most affected pathway in C6 TAZ cells. This pathway consists of 20 genes, of which all (20/20) appeared to be differentially regulated in C6 TAZ cells. Of all the 20 genes, 4 of the differentially expressed genes were selected for further validation by RT-qPCR. By IPA it was possible to identify the upstream regulators that might be responsible for the differential expression of genes in C6 deficient cells. Some of the genes ACACA, HMGCR, FASN, ACSL1, 3 and, 5 identified was decreased by predicted activation and inhibition of the regulators. Further we have analysed the levels of cellular cholesterol content in C6 and C6 TAZ (w/o Δ5 and FL) cells. In C6 cells cholesterol is present more in its free form. C6 TAZ cells have increased amount of cholesterol compared to C6 cells. However, Δ5 and FL expressed C6 TAZ cells showed less amount of cholesterol.
Previous work established that knockout of tafazzin in C6 cells showed decreased cell proliferation in the absence of any changes in ATP content. To understand this phenomenon cellular senescence associated β-galactosidase in C6 and C6 TAZ cells was performed. C6 TAZ cells showed increased percentage of β-gal positive cells compared to C6 cells. Moreover, senescent associated secretory phenotype (SASP) represented by e.g. CXCL1, IL6, and IL1α was determined using RT-qPCR. Gene expression of these SASP factors was significantly upregulated in C6 TAZ cells.
Several human tafazzin isoforms exists due to alternate splicing. However, whether these isoforms differ in function and in CL remodelling activity or specificity, in particular, is unknown. The purpose of this work was to determine if specific isoforms, such as human isoform lacking exon 5 (Δ5), rat full length tafazzin (FL) and enzymatically dead full length tafazzin (H69L), can restore the wild type phenotype in terms of CL composition, cellular proliferation, and gene expression profile. Therefore, in the second part, it was demonstrated that expression of Δ5 to some extent and rat full length tafazzin can completely restore CL composition, in C6 TAZ cells which is naturally linked to the restoration of mitochondrial respiration. As expected, a comparable restoration of CL composition could not be seen after re-expressing an enzymatically dead full-length rat TAZ, (H69L; TAZ Mut). Furthermore, re-expression of the TAZ Mut largely failed to reverse the alterations in gene expression, in contrast re-expression of the TAZ FL and the Δ5 isoforms reversed gene expression to a larger extent. Moreover, only rat full length TAZ was able to reverse proliferation rate. Surprisingly, the expression of Δ5 in C6 TAZ cells did not promote proliferation of the wild type. Different effects of Δ5 and FL on CL composition and cell proliferation points to the specific and in part non-enzymatic functions of tafazzin isoforms, but this certainly requires further analysis.
Alternative splicing (AS) is a major mechanism for gene expression in eukaryotes, increasing proteome diversity but also regulating transcriptome abundance. High temperatures have a strong impact on the splicing profile of many genes and therefore AS is considered as an integral part of heat stress response. While many studies have established a detailed description of the diversity of the RNAome under heat stress in different plant species and stress regimes, little is known on the underlying mechanisms that control this temperature-sensitive process. AS is mainly regulated by the activity of splicing regulators. Changes in the abundance of these proteins through transcription and AS, post-translational modifications and interactions with exonic and intronic cis-elements and core elements of the spliceosomes modulate the outcome of pre-mRNA splicing. As a major part of pre-mRNAs are spliced co-transcriptionally, the chromatin environment along with the RNA polymerase II elongation play a major role in the regulation of pre-mRNA splicing under heat stress conditions. Despite its importance, our understanding on the regulation of heat stress sensitive AS in plants is scarce. In this review, we summarize the current status of knowledge on the regulation of AS in plants under heat stress conditions. We discuss possible implications of different pathways based on results from non-plant systems to provide a perspective for researchers who aim to elucidate the molecular basis of AS under high temperatures.
Liver diseases are important causes of morbidity and mortality worldwide. The aim of
this study was to identify differentially expressed microRNAs (miRNAs), target genes, and key
pathways as innovative diagnostic biomarkers in liver patients with different pathology and functional
state. We determined, using RT-qPCR, the expression of 472 miRNAs in 125 explanted livers from
subjects with six different liver pathologies and from control livers. ANOVA was employed to
obtain differentially expressed miRNAs (DEMs), and miRDB (MicroRNA target prediction database)
was used to predict target genes. A miRNA–gene differential regulatory (MGDR) network was
constructed for each condition. Key miRNAs were detected using topological analysis. Enrichment
analysis for DEMs was performed using the Database for Annotation, Visualization, and Integrated
Discovery (DAVID). We identified important DEMs common and specific to the different patient
groups and disease progression stages. hsa-miR-1275 was universally downregulated regardless
the disease etiology and stage, while hsa-let-7a*, hsa-miR-195, hsa-miR-374, and hsa-miR-378 were
deregulated. The most significantly enriched pathways of target genes controlled by these miRNAs
comprise p53 tumor suppressor protein (TP53)-regulated metabolic genes, and those involved in
regulation of methyl-CpG-binding protein 2 (MECP2) expression, phosphatase and tensin homolog
(PTEN) messenger RNA (mRNA) translation and copper homeostasis. Our findings show a novel
panel of deregulated miRNAs in the liver tissue from patients with different liver pathologies. These
miRNAs hold potential as biomarkers for diagnosis and staging of liver diseases.
The innate immune system relies on families of pattern recognition receptors (PRRs)
that detect distinct conserved molecular motifs from microbes to initiate antimicrobial responses.
Activation of PRRs triggers a series of signaling cascades, leading to the release of pro-inflammatory
cytokines, chemokines and antimicrobials, thereby contributing to the early host defense against
microbes and regulating adaptive immunity. Additionally, PRRs can detect perturbation of cellular
homeostasis caused by pathogens and fine-tune the immune responses. Among PRRs, nucleotide
binding oligomerization domain (NOD)-like receptors (NLRs) have attracted particular interest in the
context of cellular stress-induced inflammation during infection. Recently, mechanistic insights into
the monitoring of cellular homeostasis perturbation by NLRs have been provided. We summarize
the current knowledge about the disruption of cellular homeostasis by pathogens and focus on NLRs
as innate immune sensors for its detection. We highlight the mechanisms employed by various
pathogens to elicit cytoskeleton disruption, organelle stress as well as protein translation block, point
out exemplary NLRs that guard cellular homeostasis during infection and introduce the concept of
stress-associated molecular patterns (SAMPs). We postulate that integration of information about
microbial patterns, danger signals, and SAMPs enables the innate immune system with adequate
plasticity and precision in elaborating responses to microbes of variable virulence.
Neutrophils in Tuberculosis: Cell Biology, Cellular Networking and Multitasking in Host Defense
(2021)
Neutrophils readily infiltrate infection foci, phagocytose and usually destroy microbes. In
tuberculosis (TB), a chronic pulmonary infection caused by Mycobacterium tuberculosis (Mtb),
neutrophils harbor bacilli, are abundant in tissue lesions, and their abundances in blood correlate
with poor disease outcomes in patients. The biology of these innate immune cells in TB is complex.
Neutrophils have been assigned host-beneficial as well as deleterious roles. The short lifespan of
neutrophils purified from blood poses challenges to cell biology studies, leaving intracellular
biological processes and the precise consequences of Mtb–neutrophil interactions ill-defined. The
phenotypic heterogeneity of neutrophils, and their propensity to engage in cellular cross-talk and
to exert various functions during homeostasis and disease, have recently been reported, and such
observations are newly emerging in TB. Here, we review the interactions of neutrophils with Mtb,
including subcellular events and cell fate upon infection, and summarize the cross-talks between
neutrophils and lung-residing and -recruited cells. We highlight the roles of neutrophils in TB
pathophysiology, discussing recent findings from distinct models of pulmonary TB, and emphasize
technical advances that could facilitate the discovery of novel neutrophil-related disease
mechanisms and enrich our knowledge of TB pathogenesis
Simple Summary
Paratuberculosis is a disease which affects ruminants worldwide. Many countries have implemented certification and monitoring systems to control the disease, particularly in dairy herds. Monitoring herds certified as paratuberculosis non-suspect is an important component of paratuberculosis herd certification programs. The challenge is to detect the introduction or reintroduction of the infectious agent as early as possible with reasonable efforts but high certainty. In our study, we evaluated different low-cost testing schemes in herds where the share of infected animals was low, resulting in a low within-herd prevalence of animals shedding the bacteria that causes paratuberculosis in their feces. The test methods used were repeated pooled milk samples and fecal samples from the barn environment. Our study showed that numerous repetitions of different samples are necessary to monitor such herds with sufficiently high certainty. In the case of herds with a very low prevalence, our study showed that a combination of different sampling approaches is required.
Abstract
An easy-to-use and affordable surveillance system is crucial for paratuberculosis control. The use of environmental samples and milk pools has been proven to be effective for the detection of Mycobacterium avium subsp. paratuberculosis (MAP)-infected herds, but not for monitoring dairy herds certified as MAP non-suspect. We aimed to evaluate methods for the repeated testing of large dairy herds with a very low prevalence of MAP shedders, using different sets of environmental samples or pooled milk samples, collected monthly over a period of one year in 36 herds with known MAP shedder prevalence. Environmental samples were analyzed by bacterial culture and fecal PCR, and pools of 25 and 50 individual milk samples were analyzed by ELISA for MAP-specific antibodies. We estimated the cumulative sensitivity and specificity for up to twelve sampling events by adapting a Bayesian latent class model and taking into account the between- and within-test correlation. Our study revealed that at least seven repeated samplings of feces from the barn environment are necessary to achieve a sensitivity of 95% in herds with a within-herd shedder prevalence of at least 2%. The detection of herds with a prevalence of less than 2% is more challenging and, in addition to numerous repetitions, requires a combination of different samples.
Influenza A Virus (IAV) infection followed by bacterial pneumonia often leads to hospitalization and death in individuals from high risk groups. Following infection, IAV triggers the process of viral RNA replication which in turn disrupts healthy gut microbial community, while the gut microbiota plays an instrumental role in protecting the host by evolving colonization resistance. Although the underlying mechanisms of IAV infection have been unraveled, the underlying complex mechanisms evolved by gut microbiota in order to induce host immune response following IAV infection remain evasive. In this work, we developed a novel Maximal-Clique based Community Detection algorithm for Weighted undirected Networks (MCCD-WN) and compared its performance with other existing algorithms using three sets of benchmark networks. Moreover, we applied our algorithm to gut microbiome data derived from fecal samples of both healthy and IAV-infected pigs over a sequence of time-points. The results we obtained from the real-life IAV dataset unveil the role of the microbial families Ruminococcaceae, Lachnospiraceae, Spirochaetaceae and Prevotellaceae in the gut microbiome of the IAV-infected cohort. Furthermore, the additional integration of metaproteomic data enabled not only the identification of microbial biomarkers, but also the elucidation of their functional roles in protecting the host following IAV infection. Our network analysis reveals a fast recovery of the infected cohort after the second IAV infection and provides insights into crucial roles of Desulfovibrionaceae and Lactobacillaceae families in combating Influenza A Virus infection. Source code of the community detection algorithm can be downloaded from https://github.com/AniBhar84/MCCD-WN.
Self-similar sets with the open set condition, the linear objects of fractal geometry, have been considered mainly for crystallographic data. Here we introduce new symmetry classes in the plane, based on rotation by irrational angles. Examples without characteristic directions, with strong connectedness and small complexity, were found in a computer-assisted search. They are surprising since the rotations are given by rational matrices, and the proof of the open set condition usually requires integer data. We develop a classification of self-similar sets by symmetry class and algebraic numbers. Examples are given for various quadratic number fields.
Universal products provide an axiomatic framework to study noncommutative independences general enough to include, besides the well known "single-faced" case (i.e., tensor, free, Boolean, monotone and antimonotone independence), also more recent "multi-faced" examples like bifree independence. Questions concerning classification have been fully answered in the single-faced case, but are in general still open in the multi-faced case. In this thesis we discuss how one can use insights in the relation between universal products and their associated moment-cumulant formula as a starting point towards a combinatorial approach to (multi-faced) universal products. We define certain classes of partitions and discuss why the defining axioms are sufficient to associate to each of them a multi-faced universal product. For the two-faced case we present our result that every positive and symmetric universal product can be produced in this fashion and we outline how these results might contribute to a classification of positive and symmetric universal products.
A lot of research data has become available since the outbreak of the COVID-19
pandemic in 2019. Connecting this data is essential for the understanding of the
SARS-CoV-2 virus and the fight against the pandemic.
Amongst biological and biomedical research data, computational models targeting
COVID-19 have been emerging and their number is growing constantly. They are a
central part of the field of Systems Biology, which aims to understand the mechanisms
and behaviour of biological systems. Model predictions help to understand the
mechanisms of the novel coronavirus and the life-threatening disease it is causing.
Both biomedical research data and modelling data regarding COVID-19 have
previously been stored in separated domain-specific graph databases. MaSyMoS,
short for Management System for Models and Simulations, is a graph database for
storing simulation studies of biological and biochemical systems. The CovidGraph
project integrates research data regarding COVID-19 and the coronavirus family
from various data resources in a knowledge graph.
In this thesis, we integrate simulation models from MaSyMoS, including models
targeting COVID-19, into the CovidGraph. Therefore, we present a concept for
the integration of simulation studies and the linkage through ontology terms and
reference publications in the CovidGraph. Ultimately, we connect data from the field
of systems biology and biomedical research data in a graph database.