Bayesian indicator variable selection of multivariate response with heterogeneous sparsity for multi-trait fine mapping

Variable selection has been played a critical role in contemporary statistics and scientific discoveries. Numerous regularization and Bayesian variable selection methods have been developed in the past two decades for variable selection, but they mainly target at only one response. As more data being collected nowadays, it is common to obtain and analyze multiple correlated responses from the same study. Running separate regression for each response ignores their correlation thus multivariate analysis is recommended. Existing multivariate methods select variables related to all responses without considering the possible heterogeneous sparsity of different responses, i.e. some features may only predict a subset of responses but not the rest. In this paper, we develop a novel Bayesian indicator variable selection method in multivariate regression model with a large number of grouped predictors targeting at multiple correlated responses with possibly heterogeneous sparsity patterns. The method is motivated by the multi-trait fine mapping problem in genetics to identify the variants that are causal to multiple related traits. Our new method is featured by its selection at individual level, group level as well as specific to each response. In addition, we propose a new concept of subset posterior inclusion probability for inference to prioritize predictors that target at subset(s) of responses. Extensive simulations with varying sparsity and heterogeneity levels and dimension have shown the advantage of our method in variable selection and prediction performance as compared to existing general Bayesian multivariate variable selection methods and Bayesian fine mapping methods. We also applied our method to a real data example in imaging genetics and identified important causal variants for brain white matter structural change in different regions.Comment: 29 pages, 3 figure

Death from colonic disease in epidermolysis bullosa dystrophica

BACKGROUND: Squamous cell carcinomas and renal failure were reported the causes of death in patients with recessive dystrophic epidermolysis bullosa (RDEB). Death from colonic disease in epidermolysis bullosa (EB) is never reported. CASE PRESENTATION: We demonstrate a male patient with RDEB. He suffered megacolon due to fecal impaction and died from sigmoid colon perforation with peritonitis at age 35 years. CONCLUSION: Constipation is a common clinical feature of RDEB, but fetal complications of chronic constipation are rarely reported. To the author's best knowledge, it has not been reported or recognized in the English literature previously. The aggressive assessment of constipation with fecal impaction is recommended in patients with RDEB

Decisions at the end of life: have we come of age?

Decision making is a complex process and it is particularly challenging to make decisions with, or for, patients who are near the end of their life. Some of those challenges will not be resolved - due to our human inability to foresee the future precisely and the human proclivity to change stated preferences when faced with reality. Other challenges of the decision-making process are manageable. This commentary offers a set of approaches which may lead to progress in this field

T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions

Relaxometry studies in preterm and at-term newborns have provided insight into brain microstructure, thus opening new avenues for studying normal brain development and supporting diagnosis in equivocal neurological situations. However, such quantitative techniques require long acquisition times and therefore cannot be straightforwardly translated to in utero brain developmental studies. In clinical fetal brain magnetic resonance imaging routine, 2D low-resolution T2-weighted fast spin echo sequences are used to minimize the effects of unpredictable fetal motion during acquisition. As super-resolution techniques make it possible to reconstruct a 3D high-resolution volume of the fetal brain from clinical low-resolution images, their combination with quantitative acquisition schemes could provide fast and accurate T2 measurements. In this context, the present work demonstrates the feasibility of using super-resolution reconstruction from conventional T2-weighted fast spin echo sequences for 3D isotropic T2 mapping. A quantitative magnetic resonance phantom was imaged using a clinical T2-weighted fast spin echo sequence at variable echo time to allow for super-resolution reconstruction at every echo time and subsequent T2 mapping of samples whose relaxometric properties are close to those of fetal brain tissue. We demonstrate that this approach is highly repeatable, accurate and robust when using six echo times (total acquisition time under 9 minutes) as compared to gold-standard single-echo spin echo sequences (several hours for one single 2D slice)

Iodobismuthate(III) and Iodobismuthate(III)/Iodocuprate(I) Complexes with Organic Ligands

Iodobismuthate(III) and iodobismuthate(III)/cuprate(I) complexes with chelating and bridging organic ligands are structurally characterized, including the first iodobismuthate(III) metal–organic polymers. BiIII/CuI clusters show ligand binding at copper. Diffuse reflectance spectra show UV/Vis absorptions, and DFT calculations suggest MLCT and metal–halide centered transitions

Second primary cancers among 109 000 cases of non-Hodgkin's lymphoma

An analysis of other primary cancers in individuals with non-Hodgkin's lymphoma (NHL) can help to elucidate this cancer aetiology. In all, 109 451 first primary NHL were included in a pooled analysis of 13 cancer registries. The observed numbers of second cancers were compared to the expected numbers derived from the age-, sex-, calendar period- and registry-specific incidence rates. We also calculated the standardised incidence ratios for NHL as a second primary after other cancers. There was a 47% (95% confidence interval 43–51%) overall increase in the risk of a primary cancer after NHL. A strongly significant (P<0.001) increase was observed for cancers of the lip, tongue, oropharynx*, stomach, small intestine, colon*, liver, nasal cavity*, lung, soft tissues*, skin melanoma*, nonmelanoma skin*, bladder*, kidney*, thyroid*, Hodgkin's lymphoma*, lymphoid leukaemia* and myeloid leukaemia. Non-Hodgkin's lymphoma as a second primary was increased after cancers marked with an asterisk. Patterns of risk indicate a treatment effect for lung, bladder, stomach, Hodgkin's lymphoma and myeloid leukaemia. Common risk factors may be involved for cancers of the lung, bladder, nasal cavity and for soft tissues, such as pesticides. Bidirectional effects for several cancer sites of potential viral origin argue strongly for a role for immune suppression in NHL

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Understanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource

Enhanced resistance to bacterial and fungal pathogens by overexpression of a human cathelicidin antimicrobial peptide (hCAP18/LL-37) in Chinese cabbage

The human cathelicidin antimicrobial protein hCAP18, which includes the C-terminal peptide LL-37, is a multifunctional protein. As a possible approach to enhancing the resistance to plant disease, a DNA fragment coding for hCAP18/LL-37 was fused at the C-terminal end of the leader sequence of endopolygalacturonase-inhibiting protein under the control of the cauliflower mosaic virus 35S promoter region. The construct was then introduced into Brassica rapa. LL-37 expression was confirmed in transgenic plants by reverse transcription-polymerase chain reaction and western blot analysis. Transgenic plants exhibited varying levels of resistance to bacterial and fungal pathogens. The average size of disease lesions in the transgenic plants was reduced to less than half of that in wild-type plants. Our results suggest that the antimicrobial LL-37 peptide is involved in wide-spectrum resistance to bacterial and fungal pathogen infection