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The 2015 Mw 7.8 Gorkha, Nepal Earthquake triggered in excess of 20,000 landslides across 14 districts of Central and Western Nepal. Whilst the instantaneous impact of these landslides was significant, the ongoing effect of the earthquake on changing the potential for rainfall-triggered landsliding in the months and years that followed has remained poorly understood and challenging to predict. To provide insight into how landsliding has evolved since the earthquake, and how it has impacted those living in the affected area, a detailed time-series landslide mapping campaign was undertaken to monitor the evolution of coseismic landslides and the initiation of new post-seismic landslides. This was supplemented by numerical modelling to simulate the future potential reactivation and runout of landslides as debris flows under monsoon rainfall, identifying locations potentially at risk. This analysis shows that landslide hazard was higher in November 2019 as compared to immediately after the 2015 earthquake, with a considerable portion of the landscape being impacted by landsliding. We show that, while pre-existing landslides continued to pose the majority of hazard in the aftermath of the earthquake, a significant number of landslides also occurred in new locations. We discuss the value of this type of analysis in informing the reconstruction and management of settlements at risk by summarizing how this work was integrated into the project Durable Solutions II, that supported communities at risk from landslides. Finally, we consider how such data could be used in future to inform risk sensitive land-use planning and disaster recovery, and to mitigate the impacts of future landsliding in Nepal and beyond.

Abstract This review, commissioned by the Research Councils UK Living With Environmental Change (LWEC) programme, concerns research on the impacts on health and social care systems in the United Kingdom of extreme weather events, under conditions of climate change. Extreme weather events considered include heatwaves, coldwaves and flooding. Using a structured review method, we consider evidence regarding the currently observed and anticipated future impacts of extreme weather on health and social care systems and the potential of preparedness and adaptation measures that may enhance resilience. We highlight a number of general conclusions which are likely to be of international relevance, although the review focussed on the situation in the UK. Extreme weather events impact the operation of health services through the effects on built, social and institutional infrastructures which support health and health care, and also because of changes in service demand as extreme weather impacts on human health. Strategic planning for extreme weather and impacts on the care system should be sensitive to within country variations. Adaptation will require changes to built infrastructure systems (including transport and utilities as well as individual care facilities) and also to institutional and social infrastructure supporting the health care system. Care sector organisations, communities and individuals need to adapt their practices to improve resilience of health and health care to extreme weather. Preparedness and emergency response strategies call for action extending beyond the emergency response services, to include health and social care providers more generally.

Citizen science for disaster risk reduction (DRR) holds huge promise and has demonstrated success in advancing scientific knowledge, providing early warning of hazards, and contributed to the assessment and management of impacts. While many existing studies focus on the performance of specific citizen science examples, this paper goes beyond this approach to present a systematic global mapping of citizen science used for DRR in order to draw out broader insights across diverse methods, initiatives, hazards and country contexts. The systematic mapping analyzed a total of 106 cases of citizen science applied to DRR across all continents. Unlike many existing reviews of citizen science initiatives, relevance to the disaster risk context led us to ‘open up’ our mapping to a broader definition of what might constitute citizen science, including participatory research and narrative-based approaches. By taking a wider view of citizen science and opening up to other disciplinary practices as valid ways of knowing risks and hazards, we also capture these alternative examples and discuss their relevance for aiding effective decision-making around risk reduction. Based on this analysis we draw out lessons for future research and practice of citizen science for DRR including the need to: build interconnections between disparate citizen science methods and practitioners; address multi-dimensionality within and across hazard cycles; and develop principles and frameworks for evaluating citizen science initiatives that not only ensure scientific competence but also attend to questions of equity, responsibility and the empowerment of those most vulnerable to disaster risk.

Deep learning models have been used for a variety of image processing tasks. However, most of these models are developed through supervised learning approaches, which rely heavily on the availability of large-scale annotated datasets. Developing such datasets is tedious and expensive. In the absence of an annotated dataset, synthetic data can be used for model development; however, due to the substantial differences between simulated and real data, a phenomenon referred to as domain gap, the resulting models often underperform when applied to real data. In this research, we aim to address this challenge by first computationally simulating a large-scale annotated dataset and then using a generative adversarial network (GAN) to fill the gap between simulated and real images. This approach results in a synthetic dataset that can be effectively utilized to train a deep-learning model. Using this approach, we developed a realistic annotated synthetic dataset for wheat head segmentation. This dataset was then used to develop a deep-learning model for semantic segmentation. The resulting model achieved a Dice score of 83.4% on an internal dataset and Dice scores of 79.6% and 83.6% on two external datasets from the Global Wheat Head Detection datasets. While we proposed this approach in the context of wheat head segmentation, it can be generalized to other crop types or, more broadly, to images with dense, repeated patterns such as those found in cellular imagery.

Introduction: The intracellular Ca2+ sensor stromal interaction molecule 1 (STIM1) is thought to play a critical role in enamel development, as its mutations cause Amelogenesis Imperfecta (AI). We recently established an ameloblast-specific (AmelX-iCre) Stim1 conditional deletion mouse model to investigate the role of STIM1 in controlling ameloblast function and differentiation in vivo (Stim1 cKO). Our pilot data (Said et al., J. Dent. Res., 2019, 98, 1002–1010) support our hypothesis for a broad role of Stim1 in amelogenesis. This paper aims to provide an in-depth characterization of the enamel phenotype observed in our Stim1 cKO model.Methods: We crossed AmelX-iCre mice with Stim1-floxed animals to develop ameloblast-specific Stim1 cKO mice. Scanning electron microscopy, energy dispersive spectroscopy, and micro- CT were used to study the enamel phenotype. RNAseq and RT-qPCR were utilized to evaluate changes in the gene expression of several key ameloblast genes. Immunohistochemistry was used to detect the amelogenin, matrix metalloprotease 20 and kallikrein 4 proteins in ameloblasts.Results: Stim1 cKO animals exhibited a hypomineralized AI phenotype, with reduced enamel volume, diminished mineral density, and lower calcium content. The mutant enamel phenotype was more severe in older Stim1 cKO mice compared to younger ones and changes in enamel volume and mineral content were more pronounced in incisors compared to molars. Exploratory RNAseq analysis of incisors’ ameloblasts suggested that ablation of Stim1 altered the expression levels of several genes encoding enamel matrix proteins which were confirmed by subsequent RT-qPCR. On the other hand, RT-qPCR analysis of molars’ ameloblasts showed non-significant differences in the expression levels of enamel matrix genes between control and Stim1-deficient cells. Moreover, gene expression analysis of incisors’ and molars’ ameloblasts showed that Stim1 ablation caused changes in the expression levels of several genes associated with calcium transport and mitochondrial kinetics.Conclusions: Collectively, these findings suggest that the loss of Stim1 in ameloblasts may impact enamel mineralization and ameloblast gene expression.

Abstract Background Gene co-expression networks (GCNs) are not easily comparable due to their complex structure. In this paper, we propose a tool, Juxtapose, together with similarity measures that can be utilized for comparative transcriptomics between a set of organisms. While we focus on its application to comparing co-expression networks across species in evolutionary studies, Juxtapose is also generalizable to co-expression network comparisons across tissues or conditions within the same species. Methods A word embedding strategy commonly used in natural language processing was utilized in order to generate gene embeddings based on walks made throughout the GCNs. Juxtapose was evaluated based on its ability to embed the nodes of synthetic structures in the networks consistently while also generating biologically informative results. Evaluation of the techniques proposed in this research utilized RNA-seq datasets from GTEx, a multi-species experiment of prefrontal cortex samples from the Gene Expression Omnibus, as well as synthesized datasets. Biological evaluation was performed using gene set enrichment analysis and known gene relationships in literature. Results We show that Juxtapose is capable of globally aligning synthesized networks as well as identifying areas that are conserved in real gene co-expression networks without reliance on external biological information. Furthermore, output from a matching algorithm that uses cosine distance between GCN embeddings is shown to be an informative measure of similarity that reflects the amount of topological similarity between networks. Conclusions Juxtapose can be used to align GCNs without relying on known biological similarities and enables post-hoc analyses using biological parameters, such as orthology of genes, or conserved or variable pathways. Availability A development version of the software used in this paper is available at https://github.com/klovens/juxtapose

To explain how cartilage appeared in different parts of the vertebrate body at discrete times during evolution, we hypothesize that different embryonic populations co-opted expression of a core gene regulatory network (GRN) driving chondrocyte differentiation. To test this hypothesis, laser-capture microdissection coupled with RNA-seq was used to reveal chondrocyte transcriptomes in the developing chick humerus and ceratobranchial, which are mesoderm- and neural crest-derived, respectively. During endochondral ossification, two general types of chondrocytes differentiate. Immature chondrocytes (IMM) represent the early stages of cartilage differentiation, while mature chondrocytes (MAT) undergo additional stages of differentiation, including hypertrophy and stimulating matrix mineralization and degradation. Venn diagram analyses generally revealed a high degree of conservation between chondrocyte transcriptomes of the limb and head, including SOX9, COL2A1, and ACAN expression. Typical maturation genes, such as COL10A1, IBSP, and SPP1, were upregulated in MAT compared to IMM in both limb and head chondrocytes. Gene co-expression network (GCN) analyses of limb and head chondrocyte transcriptomes estimated the core GRN governing cartilage differentiation. Two discrete portions of the GCN contained genes that were differentially expressed in limb or head chondrocytes, but these genes were enriched for biological processes related to limb/forelimb morphogenesis or neural crest-dependent processes, respectively, perhaps simply reflecting the embryonic origin of the cells. A core GRN driving cartilage differentiation in limb and head was revealed that included typical chondrocyte differentiation and maturation markers, as well as putative novel “chondrocyte” genes. Conservation of a core transcriptional program during chondrocyte differentiation in both the limb and head suggest that the same core GRN was co-opted when cartilage appeared in different regions of the skeleton during vertebrate evolution.

Similarities and differences in the associations of biological entities among species can provide us with a better understanding of evolutionary relationships. Often the evolution of new phenotypes results from changes to interactions in pre-existing biological networks and comparing networks across species can identify evidence of conservation or adaptation. Gene co-expression networks (GCNs), constructed from high-throughput gene expression data, can be used to understand evolution and the rise of new phenotypes. The increasing abundance of gene expression data makes GCNs a valuable tool for the study of evolution in non-model organisms. In this paper, we cover motivations for why comparing these networks across species can be valuable for the study of evolution. We also review techniques for comparing GCNs in the context of evolution, including local and global methods of graph alignment. While some protein-protein interaction (PPI) bioinformatic methods can be used to compare co-expression networks, they often disregard highly relevant properties, including the existence of continuous and negative values for edge weights. Also, the lack of comparative datasets in non-model organisms has hindered the study of evolution using PPI networks. We also discuss limitations and challenges associated with cross-species comparison using GCNs, and provide suggestions for utilizing co-expression network alignments as an indispensable tool for evolutionary studies going forward.

Abstract Background Gene set analysis is a well-established approach for interpretation of data from high-throughput gene expression studies. Achieving reproducible results is an essential requirement in such studies. One factor of a gene expression experiment that can affect reproducibility is the choice of sample size. However, choosing an appropriate sample size can be difficult, especially because the choice may be method-dependent. Further, sample size choice can have unexpected effects on specificity. Results In this paper, we report on a systematic, quantitative approach to study the effect of sample size on the reproducibility of the results from 13 gene set analysis methods. We also investigate the impact of sample size on the specificity of these methods. Rather than relying on synthetic data, the proposed approach uses real expression datasets to offer an accurate and reliable evaluation. Conclusion Our findings show that, as a general pattern, the results of gene set analysis become more reproducible as sample size increases. However, the extent of reproducibility and the rate at which it increases vary from method to method. In addition, even in the absence of differential expression, some gene set analysis methods report a large number of false positives, and increasing sample size does not lead to reducing these false positives. The results of this research can be used when selecting a gene set analysis method from those available.

Gene set analysis methods are widely used to provide insight into high-throughput gene expression data. There are many gene set analysis methods available. These methods rely on various assumptions and have different requirements, strengths and weaknesses. In this paper, we classify gene set analysis methods based on their components, describe the underlying requirements and assumptions for each class, and provide directions for future research in developing and evaluating gene set analysis methods.

Introduction: Delta checks use two specimen test results taken in succession in order to detect test result changes greater than expected physiological variation. One of the most common and serious errors detected by delta checks is specimen mix-up errors. The positive and negative predictive values of delta checks for detecting specimen mix-up errors, however, are largely unknown. Materials and Methods: We addressed this question by first constructing a stochastic dynamic model using repeat test values for five analytes from approximately 8000 inpatients in Calgary, Alberta, Canada. The analytes examined were sodium, potassium, chloride, bicarbonate, and creatinine. The model simulated specimen mix-up errors by randomly switching a set number of pairs of second test results. Sensitivities and specificities were then calculated for each analyte for six combinations of delta check equations and cut-off values from the published literature. Results: Delta check specificities obtained from this model ranged from 50% to 99%; however the sensitivities were generally below 20% with the exception of creatinine for which the best performing delta check had a sensitivity of 82.8%. Within a plausible incidence range of specimen mix-ups the positive predictive values of even the best performing delta check equation and analyte became negligible. Conclusion: This finding casts doubt on the ongoing clinical utility of delta checks in the setting of low rates of specimen mix-ups.