SparsePro: an efficient genome-wide fine-mapping method integrating summary statistics and functional annotations

Identifying causal variants from genome-wide association studies (GWASs) is challenging due to widespread linkage disequilibrium (LD). Functional annotations of the genome may help prioritize variants that are biologically relevant and thus improve fine-mapping of GWAS results. However, classical fine-mapping methods have a high computational cost, particularly when the underlying genetic architecture and LD patterns are complex. Here, we propose a novel approach, SparsePro, to efficiently conduct genome-wide fine-mapping. Our method enjoys two major innovations: First, by creating a sparse low-dimensional projection of the high-dimensional genotype data, we enable a linear search of causal variants instead of a combinatorial search of causal configurations used in most existing methods; Second, we adopt a probabilistic framework with a highly efficient variational expectation-maximization algorithm to integrate statistical associations and functional priors. We evaluate SparsePro through extensive simulations using resources from the UK Biobank. Compared to state-of-the-art methods, SparsePro achieved more accurate and well-calibrated posterior inference with greatly reduced computation time. We demonstrate the utility of SparsePro by investigating the genetic architecture of five functional biomarkers of vital organs. We show that, compared to other methods, the causal variants identified by SparsePro are highly enriched for expression quantitative trait loci and explain a larger proportion of trait heritability. We also identify potential causal variants contributing to the genetically encoded coordination mechanisms between vital organs, and pinpoint target genes with potential pleiotropic effects. In summary, we have developed an efficient genome-wide fine-mapping method with the ability to integrate functional annotations. Our method may have wide utility in understanding the genetics of complex traits as well as in increasing the yield of functional follow-up studies of GWASs. SparsePro software is available on GitHub at https://github.com/zhwm/SparsePro.

QTL-seq for the identification of candidate genes for days to flowering and leaf shape in pigeonpea

To identify genomic segments associated with days to flowering (DF) and leaf shape in pigeonpea, QTL-seq approach has been used in the present study. Genome-wide SNP profiling of extreme phenotypic bulks was conducted for both the traits from the segregating population (F2) derived from the cross combination- ICP 5529 × ICP 11605. A total of 126.63 million paired-end (PE) whole-genome resequencing data were generated for five samples, including one parent ICP 5529 (obcordate leaf and late-flowering plant), early and late flowering pools (EF and LF) and obcordate and lanceolate leaf shape pools (OLF and LLS). The QTL-seq identified two significant genomic regions, one on CcLG03 (1.58 Mb region spanned from 19.22 to 20.80 Mb interval) for days to flowering (LF and EF pools) and another on CcLG08 (2.19 Mb region spanned from 6.69 to 8.88 Mb interval) for OLF and LLF pools, respectively. Analysis of genomic regions associated SNPs with days to flowering and leaf shape revealed 5 genic SNPs present in the unique regions. The identified genomic regions for days to flowering were also validated with the genotyping-by-sequencing based classical QTL mapping method. A comparative analysis of the identified seven genes associated with days to flowering on 12 Fabaceae genomes, showed synteny with 9 genomes. A total of 153 genes were identified through the synteny analysis ranging from 13 to 36. This study demonstrates the usefulness of QTL-seq approach in precise identification of candidate gene(s) for days to flowering and leaf shape which can be deployed for pigeonpea improvement.

LeGO-LOAM-SC: An Improved Simultaneous Localization and Mapping Method Fusing LeGO-LOAM and Scan Context for Underground Coalmine

Simultaneous localization and mapping (SLAM) is one of the key technologies for coal mine underground operation vehicles to build complex environment maps and positioning and to realize unmanned and autonomous operation. Many domestic and foreign scholars have studied many SLAM algorithms, but the mapping accuracy and real-time performance still need to be further improved. This paper presents a SLAM algorithm integrating scan context and Light weight and Ground-Optimized LiDAR Odometry and Mapping (LeGO-LOAM), LeGO-LOAM-SC. The algorithm uses the global descriptor extracted by scan context for loop detection, adds pose constraints to Georgia Tech Smoothing and Mapping (GTSAM) by Iterative Closest Points (ICP) for graph optimization, and constructs point cloud map and an output estimated pose of the mobile vehicle. The test with KITTI dataset 00 sequence data and the actual test in 2-storey underground parking lots are carried out. The results show that the proposed improved algorithm makes up for the drift of the point cloud map, has a higher mapping accuracy, a better real-time performance, a lower resource occupancy, a higher coincidence between trajectory estimation and real trajectory, smoother loop, and 6% reduction in CPU occupancy, the mean square errors of absolute trajectory error (ATE) and relative pose error (RPE) are reduced by 55.7% and 50.3% respectively; the translation and rotation accuracy are improved by about 5%, and the time consumption is reduced by 2~4%. Accurate map construction and low drift pose estimation can be performed.

MPMR: Multi-Scale Feature and Probability Map for Melanoma Recognition

Malignant melanoma (MM) recognition in whole-slide images (WSIs) is challenging due to the huge image size of billions of pixels and complex visual characteristics. We propose a novel automatic melanoma recognition method based on the multi-scale features and probability map, named MPMR. First, we introduce the idea of breaking up the WSI into patches to overcome the difficult-to-calculate problem of WSIs with huge sizes. Second, to obtain and visualize the recognition result of MM tissues in WSIs, a probability mapping method is proposed to generate the mask based on predicted categories, confidence probabilities, and location information of patches. Third, considering that the pathological features related to melanoma are at different scales, such as tissue, cell, and nucleus, and to enhance the representation of multi-scale features is important for melanoma recognition, we construct a multi-scale feature fusion architecture by additional branch paths and shortcut connections, which extracts the enriched lesion features from low-level features containing more detail information and high-level features containing more semantic information. Fourth, to improve the extraction feature of the irregular-shaped lesion and focus on essential features, we reconstructed the residual blocks by a deformable convolution and channel attention mechanism, which further reduces information redundancy and noisy features. The experimental results demonstrate that the proposed method outperforms the compared algorithms, and it has a potential for practical applications in clinical diagnosis.

The Ecosystem Services and Green Infrastructure: A Systematic Review and the Gap of Economic Valuation

This study was conducted to determine the trends at the intersection of studies made on green infrastructure and ecosystem services, which have frequently become preferred in establishing urban−green space relationships in global research. Green-related concepts have frequently been used from past to present in order to neutralise the increasing pressures on urban dynamics resulting from rapid urbanisation. Green corridor, green belt, green structure, and green finger/hand concepts have been used to provide recreational opportunities, protect nature, and keep urban sprawl under control. For the last decade, however, in addition to the traditional green concepts, green infrastructure (GI) and ecosystem services (ES) have been preferred in contemporary urban planning, as they enable the integration of the ecological concerns of the landscape and the socio-political perspective. The aim of this study is to detect the trends of the green infrastructure and ecosystem services association, and to reveal these trends in the common area with the bibliometric mapping method. The economic concept and its analysing use at the intersection of green infrastructure and ecosystem services were explored with VOSviewer using the Scopus® database. Furthermore, the number of documents, which initially began with around 39,719 studies, was reduced by filtering through systematic reviews, to only three documents that met the economic valuation criteria. In this way, a lack of economic analyses, creating a serious research gap within the framework of green infrastructure and ecosystem services, was quantitatively determined.

Fault Identification Based on Local Feature Correlation

Industrial data variables show obvious high dimension and strong nonlinear correlation. Traditional multivariate statistical monitoring methods, such as PCA, PLS, CCA, and FDA, are only suitable for solving the high-dimensional data processing with linear correlation. The kernel mapping method is the most common technique to deal with the nonlinearity, which projects the original data in the low-dimensional space to the high-dimensional space through appropriate kernel functions so as to achieve the goal of linear separability in the new space. However, the space projection from the low dimension to the high dimension is contradictory to the actual requirement of dimensionality reduction of the data. So kernel-based method inevitably increases the complexity of data processing.

A Low Distortion Mesh Parameterization Mapping Method Based on Proxy Function and Combined Newton

To address the issues of low efficiencies and serious mapping distortions in current mesh parameterization methods, we present a low distortion mesh parameterization mapping method based on proxy function and combined Newton’s method in this paper. First, the proposed method calculates visual blind areas and distortion prone areas of a 3D mesh model, and generates a model slit. Afterwards, the method performs the Tutte mapping on the cut three-dimensional mesh model, measures the mapping distortion of the model, and outputs a distortion metric function and distortion values. Finally, the method sets iteration parameters, establishes a reference mesh, and finds the optimal coordinate points to get a convergent mesh model. When calculating mapping distortions, Dirichlet energy function is used to measure the isometric mapping distortion, and MIPS energy function is used to measure the conformal mapping distortion. To find the minimum value of the mapping distortion metric function, we use an optimal solution method combining proxy functions and combined Newton’s method. The experimental data show that the proposed method has high execution efficiency, fast descending speed of mapping distortion energy and stable optimal value convergence quality. When a texture mapping is performed, the texture is evenly colored, close laid and uniformly lined, which meets the standards in practical applications.

Sensory Profiles of Chocolate Drinks Made from Commercial Fermented Cocoa Powder and Unfermented Cocoa Beans

Polyphenols are the major bioactive compounds of cocoa beans. The addition of unfermented cocoa beans powder is used to enhance the functional properties of the chocolate drink. This study aimed to analyze the sensory profile of chocolate drinks made from a mixture of commercial cocoa powder and non-fermented cocoa beans by the projective mapping method. Seventy-five naive panelists tested four types of chocolate drink formulations and one benchmark (BM). The beverage formulations based on commercial cocoa powder consist of Formulations 1 (F1), F2, F3, F4 namely with the addition of 0, 10%, 20%, and 30% unfermented cocoa powder, respectively. The result showed that samples F1, F2, F3, and F4 had the dominant attributes of chocolate aroma, bitter taste, and bitter aftertaste. Whereas, BM has a dominant sensory profile of chocolate aroma, sweet taste, and sweet aftertaste. The highest value elevation (95o) is the benchmark (commercial chocolate powder drink). The next height value is in sample F1 which is located at an altitude of 20o-30o. Samples F2, F3, and F4 are the samples that have the lowest elevation (20o). Thus, the addition of cocoa powder from unfermented cocoa beans has not been accepted by consumers due to its bitter taste and bitter aftertaste.

(STUDI LITERATUR) PENGGUNAAN MODEL MIND MAP PADA PEBELAJARAN GEOGRAFI DI SEKOLAH DASAR

This article discusses the Mind Mapping method that can improve students' ability to be active in class. The purpose of this article is to find out that the Mind Mapping method can increase student activity in the classroom. Activeness here means that the role of students is very influential on learning in the current curriculum, that learning must be student center and the teacher here is only a facilitator. With the use of Mind Mapping, it is hoped that students will not feel bored with memorizing subjects, and with the use of Mind Mapping it will make it easier for students to memorize learning theories. Through mind mapping, students are expected to be able to express their opinions, be interactive, and think imaginatively with the aim of freeing students from memorization that is comfortable for them. Therefore, by using the Mind Mapping method, students can change their habits. At first they only paid attention to the teacher, but now they are required to be more active in each lesson. Keywords: Learning, Method, Mind Mapping