Application of A Hand-Held LiDAR Scanner for the Urban Cadastral Detail Survey in Digitized Cadastral Area of Taiwan Urban City

The cadastral detail data is used for overlap analysis with digitized graphic cadastral maps to solve the problem of inconsistencies between cadastral maps and the current land situation. This study investigated the feasibility of a handheld LiDAR scanner to collect 3D point clouds in an efficient way for a detail survey in urban environments with narrow and winding streets. Then, urban detail point clouds were collected by the handheld LiDAR scanner. After point cloud filtering and the ranging systematic error correction that was determined by a plane-based calibration method, the collected point clouds were transformed to the TWD97 cadastral coordinate system using control points. The land detail line data were artificially digitized and the results showed that about 97% error of the digitized detail positions was less than 15 cm compared to the check points surveyed by a total station. The results demonstrated the feasibility of using a handheld LiDAR scanner to perform an urban cadastral detail survey in digitized graphic areas. Therefore, the handheld LiDAR scanner could be used for the production of the detail lines for urban cadastral detail surveying for digitized cadastral areas in Taiwan.

Shared Genetic Liability and Causal Associations Between Major Depressive Disorder and Cardiovascular Diseases

Major depressive disorder (MDD) is phenotypically associated with cardiovascular diseases (CVD). We aim to investigate mechanisms underlying relationships between MDD and CVD in the context of shared genetic variations. Polygenic overlap analysis was used to test genetic correlation and to analyze shared genetic variations between MDD and seven cardiovascular outcomes (coronary artery disease (CAD), heart failure, atrial fibrillation, stroke, systolic blood pressure, diastolic blood pressure, and pulse pressure measurement). Mendelian randomization analysis was used to uncover causal relationships between MDD and cardiovascular traits. By cross-trait meta-analysis, we identified a set of genomic loci shared between the traits of MDD and stroke. Putative causal genes for MDD and stroke were prioritized by fine-mapping of transcriptome-wide associations. Polygenic overlap analysis pointed toward substantial genetic variation overlap between MDD and CVD. Mendelian randomization analysis indicated that genetic liability to MDD has a causal effect on CAD and stroke. Comparison of genome-wide genes shared by MDD and CVD suggests 20q12 as a pleiotropic region conferring risk for both MDD and CVD. Cross-trait meta-analyses and fine-mapping of transcriptome-wide association signals identified novel risk genes for MDD and stroke, including RPL31P12, BORSC7, PNPT11, and PGF. Many genetic variations associated with MDD and CVD outcomes are shared, thus, pointing that genetic liability to MDD may also confer risk for stroke and CAD. Presented results shed light on mechanistic connections between MDD and CVD phenotypes.

Leveraging the Mendelian disorders of the epigenetic machinery to systematically map functional epigenetic variation

Although each Mendelian Disorder of the Epigenetic Machinery (MDEM) has a different causative gene, there are shared disease manifestations. We hypothesize that this phenotypic convergence is a consequence of shared epigenetic alterations. To identify such shared alterations, we interrogate chromatin (ATAC-seq) and expression (RNA-seq) states in B cells from three MDEM mouse models (Kabuki [KS] type 1 and 2 and Rubinstein-Taybi type 1 [RT1] syndromes). We develop a new approach for the overlap analysis and find extensive overlap primarily localized in gene promoters. We show that disruption of chromatin accessibility at promoters often disrupts downstream gene expression, and identify 587 loci and 264 genes with shared disruption across all three MDEMs. Subtle expression alterations of multiple, IgA-relevant genes, collectively contribute to IgA deficiency in KS1 and RT1, but not in KS2. We propose that the joint study of MDEMs offers a principled approach for systematically mapping functional epigenetic variation in mammals.

Single-Cell RNA Sequencing Reveals Cellular and Transcriptional Changes Associated With M1 Macrophage Polarization in Hidradenitis Suppurativa

Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease characterized by recurrent abscesses, nodules, and sinus tracts in areas of high hair follicle and sweat gland density. These sinus tracts can present with purulent drainage and scar formation. Dysregulation of multiple immune pathways drives the complexity of HS pathogenesis and may account for the heterogeneity of treatment response in HS patients. Using transcriptomic approaches, including single-cell sequencing and protein analysis, we here characterize the innate inflammatory landscape of HS lesions. We identified a shared upregulation of genes involved in interferon (IFN) and antimicrobial defense signaling through transcriptomic overlap analysis of differentially expressed genes (DEGs) in datasets from HS skin, diabetic foot ulcers (DFUs), and the inflammatory stage of normal healing wounds. Overlap analysis between HS- and DFU-specific DEGs revealed an enrichment of gene signatures associated with monocyte/macrophage functions. Single-cell RNA sequencing further revealed monocytes/macrophages with polarization toward a pro-inflammatory M1-like phenotype and increased effector function, including antiviral immunity, phagocytosis, respiratory burst, and antibody-dependent cellular cytotoxicity. Specifically, we identified the STAT1/IFN-signaling axis and the associated IFN-stimulated genes as central players in monocyte/macrophage dysregulation. Our data indicate that monocytes/macrophages are a potential pivotal player in HS pathogenesis and their pathways may serve as therapeutic targets and biomarkers in HS treatment.

Multi-Scenario Model of Plastic Waste Accumulation Potential in Indonesia Using Integrated Remote Sensing, Statistic and Socio-Demographic Data

As a significant contributor of plastic waste to the marine environment, Indonesia is striving to construct a national strategy for reducing plastic debris. Hence, the primary aim of this study is to create a model for plastic waste quantity originating from the mainland, accumulated in estuaries. This was achieved by compiling baseline data of marine plastic disposal from the mainland via comprehensive contextualisation of data generated by remote sensing technology and spatial analysis. The parameters used in this study cover plastic waste generation, land cover, population distribution, and human activity identification. These parameters were then used to generate the plastic waste disposal index; that is, the distribution of waste from the mainland, flowing through the river, and ultimately accumulating in the estuary. The plastic waste distribution is calculated based on the weighting method and overlap analysis between land and coastal areas. The results indicate that 0.6% of Indonesia, including metropolitan cities, account for the highest generation of plastic waste. Indicating of plastic releases to the ocean applied by of developing three different scenarios with the highest estimation 11.94 tonnes on a daily basis in an urban area, intended as the baseline study for setting priority zone for plastic waste management.

Cell type-specific signal analysis in EWAS

Hundreds of epigenome-wide association studies (EWAS) have been performed, successfully identifying replicated epigenomic signals in processes such as ageing and smoking. Despite this progress, it remains a major challenge in EWAS to detect both cell type-specific and cell type confounding effects impacting study results. One way to identify these effects is through eFORGE (experimentally derived Functional element Overlap analysis of ReGions from EWAS), a published tool that uses 815 datasets from large-scale mapping studies to detect enriched tissues, cell types and genomic regions. Here, I show that eFORGE analysis can be extended to EWAS differentially variable positions (DVPs), identifying target cell types and tissues. In addition, I also show that eFORGE tissue-specific enrichment can be detected for sites below EWAS significance threshold. I develop on these and other analysis examples, extending our knowledge of eFORGE cell type- and tissue-specific enrichment results for different EWAS.

Understory biomass estimation based on Structure from Motion data by Multi-layered forest drone survey

<p>Understory vegetation is an important part of evapotranspiration from forest floor. Forest management changes the forest structure and then affects the understory vegetation biomass (UVB). Quantitative measurement and estimation of  UVB is a step cannot be ignored in the study of forest ecology and forest evapotranspiration. However, large-scale biomass measurement and estimation is challenging. In this study, Structure from Motion (SfM) was adopted simultaneously at two different layers in a plantation forest made by Japanese cedar and Japanese cypress to reconstruct forest structure from understory to above canopy: i) understory drone survey in a 1.1h sub-catchment to generate canopy height model (CHM) based on dense point clouds data derived from a manual low-flying drone under the canopy; ii) Above-canopy drone survey in whole catchment (33.2 ha) to compute canopy openness data based on point clouds of canopy derived from an autonomous flying drone above the canopy. Combined with actual biomass data from field harvesting to develop regression models between the CHM and UVB, which was then used to map spatial distribution of  UVB in sub-catchment. The relationship between UVB and canopy openness data was then developed by overlap analysis. This approach yielded high resolution understory over catchment scale with a point cloud density of more than 20 points/cm<sup>2</sup>. Strong coefficients of determination (R-squared = 0.75) of the cubic model supported prediction of UVB from CHM, the average UVB was 0.82kg/m<sup>2</sup> and dominated by low ferns. The corresponding forest canopy openness in this area was 42.48% on average. Overlap analysis show no significant interactions between them in a cubic model with weak predictive power (R-squared < 0.46). Overall, we reconstructed the multi-layered structure of the forest and provided models of UVB. Understory survey has high accuracy for biomass measurement, but it’s inherently difficult to estimate UVB only based on canopy openness result.</p>

STEM Abstracting and Indexing (A&I) Tool Overlap Analysis in 2020: An Open Science Informed Approach Amid Pandemic Budgets

Objectives: Compare journal coverage of abstract and indexing tools commonly used within academic science and engineering research. Methods: Title lists of Compendex, Inspec, Reaxys, SciFinder, and Web of Science were provided by their respective publishers. These lists were imported into Excel and the overlap of the ISSN/EISSNs and journal titles was determined using the VLOOKUP command, which determines if the value in one cell can be found in a column of other cells. Results: There is substantial overlap between the Web of Science’s Science Citation Index Expanded and the Emerging Sources Citation Index, the largest database with 17,014 titles, and Compendex (63.6%), Inspec (71.0%), Reaxys (67.0%), and SciFinder (75.8%). SciFinder also overlaps heavily with Reaxys (75.9%). Web of Science and Compendex combined contain 77.6% of the titles within Inspec. Conclusion: Flat or decreasing library budgets combined with increasing journal prices result in an unsustainable system that will require a calculated allocation of resources at many institutions. The overlap of commonly indexed journals among abstracting and indexing tools could serve as one way to determine how these resources should be allocated.