Bayesian spatial+: A joint model perspective

A common phenomenon in spatial regression models is spatial confounding. This phenomenon occurs when spatially indexed covariates modeling the mean of the response are correlated with a spatial effect included in the model. spatial+ Dupont et al. (2022) is a popular approach to reducing spatial confounding. spatial+ is a two-stage frequentist approach that explicitly models the spatial structure in the confounded covariate, removes it, and uses the corresponding residuals in the second stage. In a frequentist setting, there is no uncertainty propagation from the first stage estimation determining the residuals since only point estimates are used. Inference can also be cumbersome in a frequentist setting, and some of the gaps in the original approach can easily be remedied in a Bayesian framework. First, a Bayesian joint model can easily achieve uncertainty propagation from the first to the second stage of the model. In a Bayesian framework, we also have the tools to infer the model's parameters directly. Notably, another advantage of using a Bayesian framework we thoroughly explore is the ability to use prior information to impose restrictions on the spatial effects rather than applying them directly to their posterior. We build a joint prior for the smoothness of all spatial effects that simultaneously shrinks towards a high smoothness of the response and imposes that the spatial effect in the response is a smoother of the confounded covariates' spatial effect. This prevents the response from operating at a smaller scale than the covariate and can help to avoid situations where there is insufficient variation in the residuals resulting from the first stage model. We evaluate the performance of the Bayesian spatial+ via both simulated and real datasets

Bayesian nonparametric generative modeling of large multivariate non-Gaussian spatial fields

Multivariate spatial fields are of interest in many applications, including climate model emulation. Not only can the marginal spatial fields be subject to nonstationarity, but the dependence structure among the marginal fields and between the fields might also differ substantially. Extending a recently proposed Bayesian approach to describe the distribution of a nonstationary univariate spatial field using a triangular transport map, we cast the inference problem for a multivariate spatial field for a small number of replicates into a series of independent Gaussian process (GP) regression tasks with Gaussian errors. Due to the potential nonlinearity in the conditional means, the joint distribution modeled can be non-Gaussian. The resulting nonparametric Bayesian methodology scales well to high-dimensional spatial fields. It is especially useful when only a few training samples are available, because it employs regularization priors and quantifies uncertainty. Inference is conducted in an empirical Bayes setting by a highly scalable stochastic gradient approach. The implementation benefits from mini-batching and could be accelerated with parallel computing. We illustrate the extended transport-map model by studying hydrological variables from non-Gaussian climate-model output

Liesel: A Probabilistic Programming Framework for Developing Semi-Parametric Regression Models and Custom Bayesian Inference Algorithms

Liesel is a probabilistic programming framework focusing on but not limited to semi-parametric regression. It comprises a graph-based model building library, a Markov chain Monte Carlo (MCMC) library with support for modular inference algorithms combining multiple kernels (both implemented in Python), and an R interface (RLiesel) for the configuration of semi-parametric regression models. Each component can be used independently of the others, e.g. the MCMC library also works with third-party model implementations. Our goal with Liesel is to facilitate a new research workflow in computational statistics: In a first step, the researcher develops a model graph with pre-implemented and well-tested building blocks as a base model, e.g. using RLiesel. Then, the graph can be manipulated to incorporate new research ideas, before the MCMC library can be used to run and analyze a default or user-defined MCMC procedure. The researcher has the option to combine powerful MCMC algorithms such as the No U-Turn Sampler (NUTS) with self-written kernels. Various tools for chain post-processing and diagnostics are also provided. Considering all its components, Liesel enables efficient and reliable statistical research on complex models and estimation algorithms. It depends on JAX as a numerical computing library. This way, it can benefit from the latest machine learning technology such as automatic differentiation, just-in-time (JIT) compilation, and the use of high-performance computing devices such as tensor processing units (TPUs)

Adsorption geometry and electronic structure of iron phthalocyanine on Ag surfaces: A LEED and photoelectron momentum mapping study

We present a comprehensive study of the adsorption behavior of iron phthalocyanine on the low-index crystal faces of silver. By combining measurements of the reciprocal space by means of photoelectron momentum mapping and low energy electron diffraction, the real space adsorption geometries are reconstructed. At monolayer coverage ordered superstructures exist on all studied surfaces containing one molecule in the unit cell in case of Ag(100) and Ag(111), and two molecules per unit cell for Ag(110). The azimuthal tilt angle of the molecules against the high symmetry directions of the substrate is derived from the photoelectron momentum maps. A comparative analysis of the momentum patterns on the substrates with different symmetry indicates that both constituents of the twofold degenerate FePc lowest unoccupied molecular orbital are occupied by charge transfer from the substrate at the interface

Cellular expression, trafficking, and function of two isoforms of human ULBP5/RAET1G

Background: The activating immunoreceptor NKG2D is expressed on Natural Killer (NK) cells and subsets of T cells. NKG2D contributes to anti-tumour and anti-viral immune responses in vitro and in vivo. The ligands for NKG2D in humans are diverse proteins of the MIC and ULBP/RAET families that are upregulated on the surface of virally infected cells and tumours. Two splicing variants of ULBP5/RAET1G have been cloned previously, but not extensively characterised. Methodology/Principal Findings: We pursue a number of approaches to characterise the expression, trafficking, and function of the two isoforms of ULBP5/RAET1G. We show that both transcripts are frequently expressed in cell lines derived from epithelial cancers, and in primary breast cancers. The full-length transcript, RAET1G1, is predicted to encode a molecule with transmembrane and cytoplasmic domains that are unique amongst NKG2D ligands. Using specific anti-RAET1G1 antiserum to stain tissue microarrays we show that RAET1G1 expression is highly restricted in normal tissues. RAET1G1 was expressed at a low level in normal gastrointestinal epithelial cells in a similar pattern to MICA. Both RAET1G1 and MICA showed increased expression in the gut of patients with celiac disease. In contrast to healthy tissues the RAET1G1 antiserum stained a wide variety or different primary tumour sections. Both endogenously expressed and transfected RAET1G1 was mainly found inside the cell, with a minority of the protein reaching the cell surface. Conversely the truncated splicing variant of RAET1G2 was shown to encode a soluble molecule that could be secreted from cells. Secreted RAET1G2 was shown to downregulate NKG2D receptor expression on NK cells and hence may represent a novel tumour immune evasion strategy. Conclusions/Significance: We demonstrate that the expression patterns of ULBP5RAET1G are very similar to the well-characterised NKG2D ligand, MICA. However the two isoforms of ULBP5/RAET1G have very different cellular localisations that are likely to reflect unique functionality

Adenylyl Cyclase Plays a Regulatory Role in Development, Stress Resistance and Secondary Metabolism in Fusarium fujikuroi

The ascomycete fungus Fusarium fujikuroi (Gibberella fujikuroi MP-C) produces secondary metabolites of biotechnological interest, such as gibberellins, bikaverin, and carotenoids. Production of these metabolites is regulated by nitrogen availability and, in a specific manner, by other environmental signals, such as light in the case of the carotenoid pathway. A complex regulatory network controlling these processes is recently emerging from the alterations of metabolite production found through the mutation of different regulatory genes. Here we show the effect of the targeted mutation of the acyA gene of F. fujikuroi, coding for adenylyl cyclase. Mutants lacking the catalytic domain of the AcyA protein showed different phenotypic alterations, including reduced growth, enhanced production of unidentified red pigments, reduced production of gibberellins and partially derepressed carotenoid biosynthesis in the dark. The phenotype differs in some aspects from that of similar mutants of the close relatives F. proliferatum and F. verticillioides: contrary to what was observed in these species, ΔacyA mutants of F. fujikuroi showed enhanced sensitivity to oxidative stress (H2O2), but no change in heavy metal resistance or in the ability to colonize tomato tissue, indicating a high versatility in the regulatory roles played by cAMP in this fungal group

Integrated genome and transcriptome sequencing identifies a noncoding mutation in the genome replication factor DONSON as the cause of microcephaly-micromelia syndrome

While next-generation sequencing has accelerated the discovery of human disease genes, progress has been largely limited to the "low hanging fruit" of mutations with obvious exonic coding or canonical splice site impact. In contrast, the lack of high-throughput, unbiased approaches for functional assessment of most noncoding variants has bottlenecked gene discovery. We report the integration of transcriptome sequencing (RNA-seq), which surveys all mRNAs to reveal functional impacts of variants at the transcription level, into the gene discovery framework for a unique human disease, microcephaly-micromelia syndrome (MMS). MMS is an autosomal recessive condition described thus far in only a single First Nations population and causes intrauterine growth restriction, severe microcephaly, craniofacial anomalies, skeletal dysplasia, and neonatal lethality. Linkage analysis of affected families, including a very large pedigree, identified a single locus on Chromosome 21 linked to the disease (LOD > 9). Comprehensive genome sequencing did not reveal any pathogenic coding or canonical splicing mutations within the linkage region but identified several nonconserved noncoding variants. RNA-seq analysis detected aberrant splicing in DONSON due to one of these noncoding variants, showing a causative role for DONSON disruption in MMS. We show that DONSON is expressed in progenitor cells of embryonic human brain and other proliferating tissues, is co-expressed with components of the DNA replication machinery, and that Donson is essential for early embryonic development in mice as well, suggesting an essential conserved role for DONSON in the cell cycle. Our results demonstrate the utility of integrating transcriptomics into the study of human genetic disease when DNA sequencing alone is not sufficient to reveal the underlying pathogenic mutation

Drivers of genetic diversity in secondary metabolic gene clusters within a fungal species

Drivers of genetic diversity in secondary metabolic gene clusters within a fungal speciesFilamentous fungi produce a diverse array of secondary metabolites (SMs) critical for defense, virulence, and communication. The metabolic pathways that produce SMs are found in contiguous gene clusters in fungal genomes, an atypical arrangement for metabolic pathways in other eukaryotes. Comparative studies of filamentous fungal species have shown that SM gene clusters are often either highly divergent or uniquely present in one or a handful of species, hampering efforts to determine the genetic basis and evolutionary drivers of SM gene cluster divergence. Here, we examined SM variation in 66 cosmopolitan strains of a single species, the opportunistic human pathogen Aspergillus fumigatus. Investigation of genome-wide within-species variation revealed 5 general types of variation in SM gene clusters: nonfunctional gene polymorphisms; gene gain and loss polymorphisms; whole cluster gain and loss polymorphisms; allelic polymorphisms, in which different alleles corresponded to distinct, nonhomologous clusters; and location polymorphisms, in which a cluster was found to differ in its genomic location across strains. These polymorphisms affect the function of representative A. fumigatus SM gene clusters, such as those involved in the production of gliotoxin, fumigaclavine, and helvolic acid as well as the function of clusters with undefined products. In addition to enabling the identification of polymorphisms, the detection of which requires extensive genome-wide synteny conservation (e.g., mobile gene clusters and nonhomologous cluster alleles), our approach also implicated multiple underlying genetic drivers, including point mutations, recombination, and genomic deletion and insertion events as well as horizontal gene transfer from distant fungi. Finally, most of the variants that we uncover within A. fumigatus have been previously hypothesized to contribute to SM gene cluster diversity across entire fungal classes and phyla. We suggest that the drivers of genetic diversity operating within a fungal species shown here are sufficient to explain SM cluster macroevolutionary patterns.National Science Foundation (grant number DEB-1442113). Received by AR. U.S. National Library of Medicine training grant (grant number 2T15LM007450). Received by ALL. Conselho Nacional de Desenvolvimento Cientı´fico e 573 Tecnológico. Northern Portugal Regional Operational Programme (grant number NORTE-01- 0145-FEDER-000013). Received by FR. Fundação de Amparo à Pesquisa do 572 Estado de São Paulo. Received by GHG. National Institutes of Health (grant number R01 AI065728-01). Received by NPK. National Science Foundation (grant number IOS-1401682). Received by JHW. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Chronic Hepatitis B Virus Infection: The Relation between Hepatitis B Antigen Expression, Telomere Length, Senescence, Inflammation and Fibrosis.

BACKGROUND: Chronic Hepatitis B virus (HBV) infection can lead to the development of chronic hepatitis, cirrhosis and hepatocellular carcinoma. We hypothesized that HBV might accelerate hepatocyte ageing and investigated the effect of HBV on hepatocyte cell cycle state and biological age. We also investigated the relation between inflammation, fibrosis and cell cycle phase. METHODS: Liver samples from patients with chronic HBV (n = 91), normal liver (n = 55) and regenerating liver (n = 15) were studied. Immunohistochemistry for cell cycle phase markers and HBV antigens was used to determine host cell cycle phase. Hepatocyte-specific telomere length was evaluated by quantitative fluorescent in-situ hybridization (Q-FISH) in conjunction with hepatocyte nuclear area and HBV antigen expression. The effects of induced cell cycle arrest and induced cellular senescence on HBV production were assessed in vitro. RESULTS: 13.7% hepatocytes in chronic HBV had entered cell cycle, but expression of markers for S, G2 and M phase was low compared with regenerating liver. Hepatocyte p21 expression was increased (10.9%) in chronic HBV and correlated with liver fibrosis. Mean telomere length was reduced in chronic HBV compared to normal. However, within HBV-affected livers, hepatocytes expressing HBV antigens had longer telomeres. Telomere length declined and hepatocyte nuclear size increased as HBV core antigen (HBcAg) expression shifted from the nucleus to cytoplasm. Nuclear co-expression of HBcAg and p21 was not observed. Cell cycle arrest induced in vitro was associated with increased HBV production, in contrast to in vitro induction of cellular senescence, which had no effect. CONCLUSION: Chronic HBV infection was associated with hepatocyte G1 cell cycle arrest and accelerated hepatocyte ageing, implying that HBV induced cellular senescence. However, HBV replication was confined to biologically younger hepatocytes. Changes in the cellular location of HBcAg may be related to the onset of cellular senescence