A MOLECULAR DYNAMICS STUDY OF SPATIAL FLUID DISTRIBUTION IN MIXED-WET SHALE NANOPORES

Mixed wet pores are abundant in shales, yet there is little to no information on the behavior of fluids confined within pores characterized by one hydrophilic and one hydrophobic surface. This mixed wettability can impact fluid storage, distribution, capillary pressures and transport through these pores. In this study, I use molecular dynamics simulations to describe the initial distribution of reservoir fluids, such as multicomponent oils and water, in pores of mixed wettability. This is an essential pre-requisite to additional studies documenting fluid transport in such pores. The molecular model of the mixed wet pore used in this study consists of kerogen separated by some distance from a clay surface. Molecular dynamics (MD) provides the spatial distribution of water and the individual hydrocarbon species, at varying values of water concentration. In a series of additional sensitivity studies, I also evaluate the impact of salinity on the distribution of these fluids for two different arrangements of charged clay surface that are denoted as hydroxyl-potassium (H-P) and potassium-potassium (P-P) surfaces. Throughout the entire study, the results from the equilibrated system indicate a high affinity between the heavy components, such as the asphaltene/resin fraction and the kerogen surfaces, which is to be expected. The more surprising result is the hydrogen bonding observed between the polar constituents in the asphaltene/resin fraction and water. This creates a situation where the asphaltene/resin fraction shows an affinity towards water and resides adjacent to the water adsorbed on to the clay surface. When this happens, the hydrophilic clay surface effectively becomes hydrophobic. A pore bounded by an asphaltene layer on one side and kerogen on the other is more oil-wetting than mixed-wet. The presence of asphaltenes can therefore expect to create conditions of modified wettability that will impact oil recovery, oil transport and distribution. Another surprising result in this work is that water forms structures that bridge between opposing surfaces of the model. These water bridges are seen to happen for water concentration values larger than 20%. In other words, water is not merely just adsorbed on to the clay surfaces, but also forms these bridge-like structures. However, when the salinity is even moderately increased, the water bridges dissipate, and water only occurs as an adsorbed phase or as a free fluid droplet. Nevertheless, I still observe a strong affinity between the asphaltene/resin fraction and water leading to a lesser degree of mixed wettability. This study is the first, to the best of my knowledge, that considers water and multicomponent hydrocarbon mixtures in mixed-wet pores with realistic surface chemistry and constitutes a necessary first-step towards additional studies related to water and hydrocarbon transport and EOR processes in shale nanopores

A time-aware LSTM approach to predict tumor size and survival month in non-small cell lung cancer

Recent advances in long short-term memory (LSTM) networks have enabled us to handle sequential and time-series data. However, some applications of LSTM networks in the healthcare domain have produced suboptimal performances, as the algorithm assumes constant elapsed times between consecutive elements of a patient health record. In reality, patient health records are heterogeneous information with irregular time intervals and different sequence lengths. The heterogeneity and temporal dynamics of the patients’ data make it challenging to analyze long-timescale progression patterns of disease when we use traditional LSTM networks. This study proposes a novel LSTM architecture, called Time-Aware LSTM with power-law decay (T-pLSTM) networks, which can capture time irregularity and long-term dependency of patients’ data. T-pLSTM can handle long-timescale patient records with irregular elapsed time by power-law forget gate and adjusted memory cell. The proposed model was tested to predict tumor size and survival month over time for non-small cell lung cancer (NSCLC) patients. The model was trained on patient records obtained from the Surveillance, Epidemiology, and End Results (SEER) Research Plus database, and its performance was evaluated by comparative analysis. The experiments using datasets with fixed and different sequence lengths showed that T-pLSTM outperformed the standard LSTM models. This result implies improvement of learning for long-term scale information with time irregularity in LSTM networks

Chromosome-scale assembly comparison of the Korean Reference Genome KOREF from PromethION and PacBio with Hi-C mapping information.

BACKGROUND:Long DNA reads produced by single-molecule and pore-based sequencers are more suitable for assembly and structural variation discovery than short-read DNA fragments. For de novo assembly, Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) are the favorite options. However, PacBio's SMRT sequencing is expensive for a full human genome assembly and costs more than $40,000 US for 30× coverage as of 2019. ONT PromethION sequencing, on the other hand, is 1/12 the price of PacBio for the same coverage. This study aimed to compare the cost-effectiveness of ONT PromethION and PacBio's SMRT sequencing in relation to the quality. FINDINGS:We performed whole-genome de novo assemblies and comparison to construct an improved version of KOREF, the Korean reference genome, using sequencing data produced by PromethION and PacBio. With PromethION, an assembly using sequenced reads with 64× coverage (193 Gb, 3 flowcell sequencing) resulted in 3,725 contigs with N50s of 16.7 Mb and a total genome length of 2.8 Gb. It was comparable to a KOREF assembly constructed using PacBio at 62× coverage (188 Gb, 2,695 contigs, and N50s of 17.9 Mb). When we applied Hi-C-derived long-range mapping data, an even higher quality assembly for the 64× coverage was achieved, resulting in 3,179 scaffolds with an N50 of 56.4 Mb. CONCLUSION:The pore-based PromethION approach provided a high-quality chromosome-scale human genome assembly at a low cost with long maximum contig and scaffold lengths and was more cost-effective than PacBio at comparable quality measurements

Segmentation of Planar Surfaces from Laser Scanning Data Using the Magnitude of Normal Position Vector for Adaptive Neighborhoods

Diverse approaches to laser point segmentation have been proposed since the emergence of the laser scanning system. Most of these segmentation techniques, however, suffer from limitations such as sensitivity to the choice of seed points, lack of consideration of the spatial relationships among points, and inefficient performance. In an effort to overcome these drawbacks, this paper proposes a segmentation methodology that: (1) reduces the dimensions of the attribute space; (2) considers the attribute similarity and the proximity of the laser point simultaneously; and (3) works well with both airborne and terrestrial laser scanning data. A neighborhood definition based on the shape of the surface increases the homogeneity of the laser point attributes. The magnitude of the normal position vector is used as an attribute for reducing the dimension of the accumulator array. The experimental results demonstrate, through both qualitative and quantitative evaluations, the outcomes’ high level of reliability. The proposed segmentation algorithm provided 96.89% overall correctness, 95.84% completeness, a 0.25 m overall mean value of centroid difference, and less than 1° of angle difference. The performance of the proposed approach was also verified with a large dataset and compared with other approaches. Additionally, the evaluation of the sensitivity of the thresholds was carried out. In summary, this paper proposes a robust and efficient segmentation methodology for abstraction of an enormous number of laser points into plane information

KOREF_S1: phased, parental trio-binned Korean reference genome using long reads and Hi-C sequencing methods

Background KOREF is the Korean reference genome, which was constructed with various sequencing technologies including long reads, short reads, and optical mapping methods. It is also the first East Asian multiomic reference genome accompanied by extensive clinical information, time-series and multiomic data, and parental sequencing data. However, it was still not a chromosome-scale reference. Here, we updated the previous KOREF assembly to a new chromosome-level haploid assembly of KOREF, KOREF_S1v2.1. Oxford Nanopore Technologies (ONT) PromethION, Pacific Biosciences HiFi-CCS, and Hi-C technology were used to build the most accurate East Asian reference assembled so far. Results We produced 705 Gb ONT reads and 114 Gb Pacific Biosciences HiFi reads, and corrected ONT reads by Pacific Biosciences reads. The corrected ultra-long reads reached higher accuracy of 1.4% base errors than the previous KOREF_S1v1.0, which was mainly built with short reads. KOREF has parental genome information, and we successfully phased it using a trio-binning method, acquiring a near-complete haploid-assembly. The final assembly resulted in total length of 2.9 Gb with an N50 of 150 Mb, and the longest scaffold covered 97.3% of GRCh38's chromosome 2. In addition, the final assembly showed high base accuracy, with Conclusions KOREF_S1v2.1 is the first chromosome-scale haploid assembly of the Korean reference genome with high contiguity and accuracy. Our study provides useful resources of the Korean reference genome and demonstrates a new strategy of hybrid assembly that combines ONT's PromethION and PacBio's HiFi-CCS

Object-Based Integration of Photogrammetric and LiDAR Data for Automated Generation of Complex Polyhedral Building Models

This research is concerned with a methodology for automated generation of polyhedral building models for complex structures, whose rooftops are bounded by straight lines. The process starts by utilizing LiDAR data for building hypothesis generation and derivation of individual planar patches constituting building rooftops. Initial boundaries of these patches are then refined through the integration of LiDAR and photogrammetric data and hierarchical processing of the planar patches. Building models for complex structures are finally produced using the refined boundaries. The performance of the developed methodology is evaluated through qualitative and quantitative analysis of the generated building models from real data

Polygenic risk score validation using Korean genomes of 265 early-onset acute myocardial infarction patients and 636 healthy controls

Background The polygenic risk score (PRS) developed for coronary artery disease (CAD) is known to be effective for classifying patients with CAD and predicting subsequent events. However, the PRS was developed mainly based on the analysis of Caucasian genomes and has not been validated for East Asians. We aimed to evaluate the PRS in the genomes of Korean early-onset AMI patients (n = 265, age <= 50 years) following PCI and controls (n = 636) to examine whether the PRS improves risk prediction beyond conventional risk factors. Results The odds ratio of the PRS was 1.83 (95% confidence interval [CI]: 1.69-1.99) for early-onset AMI patients compared with the controls. For the classification of patients, the area under the curve (AUC) for the combined model with the six conventional risk factors (diabetes mellitus, family history of CAD, hypertension, body mass index, hypercholesterolemia, and current smoking) and PRS was 0.92 (95% CI: 0.90-0.94) while that for the six conventional risk factors was 0.91 (95% CI: 0.85-0.93). Although the AUC for PRS alone was 0.65 (95% CI: 0.61-0.69), adding the PRS to the six conventional risk factors significantly improved the accuracy of the prediction model (P = 0.015). Patients with the upper 50% of PRS showed a higher frequency of repeat revascularization (hazard ratio = 2.19, 95% CI: 1.47-3.26) than the others. Conclusions The PRS using 265 early-onset AMI genomes showed improvement in the identification of patients in the Korean population and showed potential for genomic screening in early life to complement conventional risk prediction

Segmentation of Planar Surfaces from Laser Scanning Data Using the Magnitude of Normal Position Vector for Adaptive Neighborhoods

Diverse approaches to laser point segmentation have been proposed since the emergence of the laser scanning system. Most of these segmentation techniques, however, suffer from limitations such as sensitivity to the choice of seed points, lack of consideration of the spatial relationships among points, and inefficient performance. In an effort to overcome these drawbacks, this paper proposes a segmentation methodology that: (1) reduces the dimensions of the attribute space; (2) considers the attribute similarity and the proximity of the laser point simultaneously; and (3) works well with both airborne and terrestrial laser scanning data. A neighborhood definition based on the shape of the surface increases the homogeneity of the laser point attributes. The magnitude of the normal position vector is used as an attribute for reducing the dimension of the accumulator array. The experimental results demonstrate, through both qualitative and quantitative evaluations, the outcomes’ high level of reliability. The proposed segmentation algorithm provided 96.89% overall correctness, 95.84% completeness, a 0.25 m overall mean value of centroid difference, and less than 1° of angle difference. The performance of the proposed approach was also verified with a large dataset and compared with other approaches. Additionally, the evaluation of the sensitivity of the thresholds was carried out. In summary, this paper proposes a robust and efficient segmentation methodology for abstraction of an enormous number of laser points into plane information

Object based integration of photogrammetric and LiDAR data for accurate reconstruction and visualization of building models

Bibliography: p. 211-221Some pages are in colou

다양한 환경에서의 시각적 인식을 위한 비지도 도메인 적응 방법

Multi-Target Domain Adaptation,Image-to-Image Translation,Semantic Segmentation,Cross-Domain Correspondence Matching,Cross-Domain Feature ConsistencyⅠ. Introduction 1 Ⅱ. Related Work 3 Ⅲ. Proposed Method 5 3.1 Class-Wise Image Translation 6 3.1.1 High-Precision Pseudo labeling 7 3.1.2 Attribute Transfer 8 3.2 Cross-Domain Feature Consistency for Domain Alignment 9 Ⅳ. Experiments 10 4.1 Datasets 10 4.2 Implementation Details 11 4.3 Synthetic-to-Real Adaptation 11 4.4 Real-to-Real Adaptation 18 4.5 Ablation study on Cross-Domain Feature Consistency 20 Ⅴ. Conclusion 21 Ⅵ. References 22 Ⅶ. 요약문 27MasterdCollectio