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Many vehicles are connected to the Internet, and big data are continually created. Various studies have been conducted involving the development of artificial intelligence, machine learning technology, and big data frameworks. The analysis of smart mobility big data is essential and helps to address problems that arise as society faces increased future mobility. In this paper, we analyze application issues such as personal information leakage and data visualization due to increased data exchange in detail, as well as approaches focusing on analyses exploiting machine learning and architecture research exploiting big data frameworks, such as Apache Hadoop, Apache Spark, and Apache Kafka. Finally, future research directions and open challenges are discussed.

The mechanistic link between the complex mutational landscape ofde novomethyltransferaseDNMT3Aand the pathology of acute myeloid leukemia (AML) has not been clearly elucidated so far. A recent discovery on the catalogue of DNMT3A-destabilizing mutations throughout theDNMT3Agene as well as the oligomerization-dependent catalytic property of DNMT3A prompted us to investigate the common effect of DNMT3A-destabilizing mutations (DNMT3AINS) on the genomewide methylation patterns of AML cells. In this study, we describe the characteristics ofDNMT3AINSAML methylomes through the comprehensive computational analyses on three independent AML cohorts. As a result, we show that methylomes ofDNMT3AINSAMLs are considerably different from those ofDNMT3AR882AMLs in that they exhibit both locally disordered DNA methylation states and increased across-cell DNA methylation heterogeneity in bivalent chromatin domains. This increased epigenetic heterogeneity was functionally associated with heterogeneous expression of membrane-associated factors shaping stem cell niche, implying the diversification of the modes of leukemic stem cell-niche interactions. We also present that the level of methylation disorder at bivalent domains predicts the response of AML cells to hypomethylating agents through cell line- and patient-level analyses, which supports that the survival of AML cells depends on stochastic DNA methylations at bivalent domains. Altogether, our work provides a novel mechanistic model suggesting the genomic origin of the aberrant epigenomic heterogeneity in disease conditions.

Drug response prediction (DRP) is important for precision medicine to predict how a patient would react to a drug before administration. Existing studies take the cell line transcriptome data, and the chemical structure of drugs as input and predict drug response as IC50 or AUC values. Intuitively, use of drug target interaction (DTI) information can be useful for DRP. However, use of DTI is difficult because existing drug response database such as CCLE and GDSC do not have information about transcriptome after drug treatment. Although transcriptome after drug treatment is not available, if we can compute the perturbation effects by the pharmacologic modulation of target gene, we can utilize the DTI information in CCLE and GDSC. In this study, we proposed a framework that can improve existing deep learning-based DRP models by effectively utilizing drug target information. Our framework includes NetGP, a module to compute gene perturbation scores by the network propagation technique on a network. NetGP produces genes in a ranked list in terms of gene perturbation scores and the ranked genes are input to a multi-layer perceptron to generate a fixed dimension vector for the integration with existing DRP models. This integration is done in a model-agnostic way so that any existing DRP tool can be incorporated. As a result, our framework boosts the performance of existing DRP models, in 64 of 72 comparisons. The performance gains are larger especially for test scenarios with samples with unseen drugs by large margins up to 34% in Pearson’s correlation coefficient.

To respond to the external environmental changes for survival, bacteria regulates expression of a number of genes including transcription factors (TFs). To characterize complex biological phenomena, a biological system-level approach is necessary. Here we utilized six computational biology methods to infer regulatory network and to characterize underlying biologically mechanisms relevant to radiation-resistance. In particular, we inferred gene regulatory network (GRN) and operons of radiation-resistance bacterium Spirosoma montaniterrae DY10$$^T$$ T and identified the major regulators for radiation-resistance. Our results showed that DNA repair and reactive oxygen species (ROS) scavenging mechanisms are key processes and Crp/Fnr family transcriptional regulator works as a master regulatory TF in early response to radiation.

Phased DNA methylation states within bisulfite sequencing reads are valuable source of information that can be used to estimate epigenetic diversity across cells as well as epigenomic instability in individual cells. Various measures capturing the heterogeneity of DNA methylation states have been proposed for a decade. However, in routine analyses on DNA methylation, this heterogeneity is often ignored by computing average methylation levels at CpG sites, even though such information exists in bisulfite sequencing data in the form of phased methylation states, or methylation patterns. In this study, to facilitate the application of the DNA methylation heterogeneity measures in downstream epigenomic analyses, we present a Rust-based, extremely fast and lightweight bioinformatics toolkit called Metheor. As the analysis of DNA methylation heterogeneity requires the examination of pairs or groups of CpGs throughout the genome, existing softwares suffer from high computational burden, which almost make a large-scale DNA methylation heterogeneity studies intractable for researchers with limited resources. In this study, we benchmark the performance of Metheor against existing code implementations for DNA methylation heterogeneity measures in three different scenarios of simulated bisulfite sequencing datasets. Metheor was shown to dramatically reduce the execution time up to 300-fold and memory footprint up to 60-fold, while producing identical results with the original implementation, thereby facilitating a large-scale study of DNA methylation heterogeneity profiles. To demonstrate the utility of the low computational burden of Metheor, we show that the methylation heterogeneity profiles of 928 cancer cell lines can be computed with standard computing resources. With those profiles, we reveal the association between DNA methylation heterogeneity and various omics features. Source code for Metheor is at https://github.com/dohlee/metheor and is freely available under the GPL-3.0 license.

Patient stratification is a clinically important task because it allows us to establish and develop efficient treatment strategies for particular groups of patients. Molecular subtypes have been successfully defined using transcriptomic profiles, and they are used effectively in clinical practice, e.g., PAM50 subtypes of breast cancer. Survival prediction contributed to understanding diseases and also identifying genes related to prognosis. It is desirable to stratify patients considering these two aspects simultaneously. However, there are no methods for patient stratification that consider molecular subtypes and survival outcomes at once. Here, we propose a methodology to deal with the problem. A genetic algorithm is used to select a gene set from transcriptome data, and their expression quantities are utilized to assign a risk score to each patient. The patients are ordered and stratified according to the score. A gene set was selected by our method on a breast cancer cohort (TCGA-BRCA), and we examined its clinical utility using an independent cohort (SCAN-B). In this experiment, our method was successful in stratifying patients with respect to both molecular subtype and survival outcome. We demonstrated that the orders of patients were consistent across repeated experiments, and prognostic genes were successfully nominated. Additionally, it was observed that the risk score can be used to evaluate the molecular aggressiveness of individual patients.

본 연구는 생애과정론(life course theory)에 근거한 접근을 통해 아동기 및 청소년기 가족소득과 가족구조의 변화 추이에 대한 통시적 경험이 학업성취와 정신건강에 미치는 영향을 탐색한다. 기존 연구는 가족소득과 가족구조 요인들을 공시적으로 파악하거나 양 요인들의 시간가변성(time-variability)으로 인한 교란성(confounding)과 과도통제성(overcontrolling)의 문제를 간과하는 경향을 보여 왔다. 이와 같은 기존 연구의 취약성을 극복하기 위해 이 연구는 가족소득 및 가족구조와 관련된 자녀의 경험 지속성과 두 요인 간의 시간적 상호성에 주목한다. 이러한 차원들을 고려하기 위해 본 연구는 아동청소년패널의 초1 패널과 초4 패널 자료를 한계구조모형(marginal structural model)을 통해 분석한다. 분석결과에 따르면, 장기간 저소득 가정에서 자라거나 가족구조의 변화를 경험한 자녀들은 낮은 학업 성취도를 보이며 이러한 효과는 아동기와 청소년기를 아우르는 초기 생애 단계 전반에 걸쳐서 나타난다. 반면 한부모 가정에서 성장한 자녀는 청소년기에 낮은 학업 성취도와 높은 우울 증세를 보인다. 이와 같은 결과는 가족소득과 가족구조라는 아동 및 청소년 발달의 주요 요인들이 서로를 결정짓는 경쟁 관계라기보다는 각각 독립적으로 아동발달에 영향을 미치는 공존 관계임을 시사한다. 또한 이들 요인 각각의 효과는 생애 단계별로 달라질 수 있음을 의미한다.

Much literature on peer influence has relied on central tendency–based approaches to examine the role of peer groups. This article develops a distributional framework that (1) differentiates betwee...

The biofouling of marine organisms on a surface induces serious economic damage. One of the conventional anti-biofouling strategies is the use of toxic chemicals. In this study, a new eco-friendly oleamide-PDMS copolymer (OPC) is proposed for sustainable anti-biofouling and effective drag reduction. The anti-biofouling characteristics of the OPC are investigated using algal spores and mussels. The proposed OPC is found to inhibit the adhesion of algal spores and mussels. The slippery features of the fabricated OPC surfaces are examined by direct measurement of pressure drops in channel flows. The proposed OPC surface would be utilized in various industrial applications including marine vehicles and biomedical devices.

A number of chemicals with high industrial value can be synthesized from levulinic acid, a feasible building block readily available from cellulosic biomass. Among them, γ-valerolactone is a versatile chemical precursor for the synthesis of value-added products including bio-active molecules, bio-fuels, and carbon-based chemicals. In this study, a novel two-step chemoenzymatic conversion of levulinic acid to (R)-γ-valerolactone via 4-hydroxyvaleric acid was investigated. For that purpose, an engineered 3-hydroxybutyrate dehydrogenase (e3HBDH) with improved catalytic activity toward levulinic acid was employed in the first-step reaction, and dehydration with 1 % (v/v) sulfuric acid was applied for the lactonization of 4-hydroxyvaleric acid to γ-valerolactone in the second step. As a result, enantiomerically pure (R)-γ-valerolactone (>99 % ee) was successfully produced from the free acid form of levulinic acid with the maximum yield of approximately 100 %.

The critical problem associated with the underwater mussel adhesive catechol-based 3,4-dihydroxy-L-phenylalanine (DOPA) is its sensitivity to oxidation. To overcome this problem, mussels underwent etching in the presence of acidic pH conditions (

The quantitative characterization of the transcriptional control by histone modifications has been challenged by many computational studies, but most of them only focus on narrow and linear genomic regions around promoters, leaving a room for improvement. We present Chromoformer, a transformer-based, three-dimensional chromatin conformation-aware deep learning architecture that achieves the state-of-the-art performance in the quantitative deciphering of the histone codes in gene regulation. The core essence of Chromoformer architecture lies in the three variants of attention operation, each specialized to model individual hierarchy of transcriptional regulation involving from core promoters to distal elements in contact with promoters through three-dimensional chromatin interactions. In-depth interpretation of Chromoformer reveals that it adaptively utilizes the long-range dependencies between histone modifications associated with transcription initiation and elongation. We also show that the quantitative kinetics of transcription factories and Polycomb group bodies can be captured by Chromoformer. Together, our study highlights the great advantage of attention-based deep modeling of complex interactions in epigenomes.

Clonal hematopoiesis of indeterminate potential (CHIP), a common aging-related process that predisposes individuals to various inflammatory responses, has been reported to be associated with COVID-19 severity. However, the immunological signature and the exact gene expression program by which the presence of CHIP exerts its clinical impact on COVID-19 remain to be elucidated. In this study, we generated a single-cell transcriptome landscape of severe COVID-19 according to the presence of CHIP using peripheral blood mononuclear cells. Patients with CHIP exhibited a potent IFN-γ response in exacerbating inflammation, particularly in classical monocytes, compared to patients without CHIP. To dissect the regulatory mechanism of CHIP (+)-specific IFN-γ response gene expression in severe COVID-19, we identified DNMT3A CHIP mutation-dependent differentially methylated regions (DMRs) and annotated their putative target genes based on long-range chromatin interactions. We revealed that CHIP mutant-driven hypo-DMRs at poised cis-regulatory elements appear to facilitate the CHIP (+)-specific IFN-γ-mediated inflammatory immune response. Our results highlight that the presence of CHIP may increase the susceptibility to hyperinflammation through the reorganization of chromatin architecture, establishing a novel subgroup of severe COVID-19 patients.

Motivation Multi-omics data in molecular biology has accumulated rapidly over the years. Such data contains valuable information for research in medicine and drug discovery. Unfortunately, data-driven research in medicine and drug discovery is challenging for a majority of small research labs due to the large volume of data and the complexity of analysis pipeline. Results We present BioVLAB-Cancer-Pharmacogenomics, a bioinformatics system that facilitates analysis of multi-omics data from breast cancer to analyze and investigate intratumor heterogeneity and pharmacogenomics on Amazon Web Services. Our system takes multi-omics data as input to perform tumor heterogeneity analysis in terms of TCGA data and deconvolve-and-match the tumor gene expression to cell line data in CCLE using DNA methylation profiles. We believe that our system can help small research labs perform analysis of tumor multi-omics without worrying about computational infrastructure and maintenance of databases and tools. Availability and implementation http://biohealth.snu.ac.kr/software/biovlab_cancer_pharmacogenomics. Supplementary information Supplementary data are available at Bioinformatics online.

The quantitative characterization of the transcriptional control by histone modifications (HMs) has been challenged by many computational studies, but still most of them exploit only partial aspects of intricate mechanisms involved in gene regulation, leaving a room for improvement. We present Chromoformer, a new transformer-based deep learning architecture that achieves the state-of-the-art performance in the quantitative deciphering of the histone codes of gene regulation. The core essence of Chromoformer architecture lies in the three variants of attention operation, each specialized to model individual hierarchy of three-dimensional (3D) transcriptional regulation including (1) histone codes at core promoters, (2) pairwise interaction between a core promoter and a distal cis-regulatory element mediated by 3D chromatin interactions, and (3) the collective effect of the pairwise cis-regulations. In-depth interpretation of the trained model behavior based on attention scores suggests that Chromoformer adaptively exploits the distant dependencies between HMs associated with transcription initiation and elongation. We also demonstrate that the quantitative kinetics of transcription factories and polycomb group bodies, in which the coordinated gene regulation occurs through spatial sequestration of genes with regulatory elements, can be captured by Chromoformer. Together, our study shows the great power of attention-based deep learning as a versatile modeling approach for the complex epigenetic landscape of gene regulation and highlights its potential as an effective toolkit that facilitates scientific discoveries in computational epigenetics.

Introduction We examined the age- and sex-specific distributions of biomarkers of tobacco smoke exposure to determine the optimal cutoffs to distinguish smokers from non-smokers over the last 10 years in Korea, during which smoking prevalence and secondhand smoke (SHS) exposure declined due to changes in tobacco control policy. Methods We analyzed data from the Korea National Health and Nutrition Examination Survey on creatinine-adjusted urinary cotinine (2008–2018; 33 429 adults: 15 653 males and 17 776 females) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL; 2016–2018; 6337 adults: 3091 males and 3246 females). We determined the optimal cutoffs and confidence intervals (CIs) to distinguish smokers from non-smokers using receiver operator characteristic curve analysis and bootstrapping (1000 resamples). Results The optimal cutoff values of creatinine-adjusted urine cotinine and NNAL concentration were 20.9 ng/mg (95% CI: 20.8–21.0, sensitivity: 96.6%, specificity: 93.8%) and 8.9 pg/mg (95% CI: 8.8–8.9, sensitivity: 94.0%, specificity: 94.7%), respectively, in 2016–2018. The optimal cutoffs of both biomarkers increased with age and were higher in females than in males for NNAL concentration. In both sexes, the optimal cutoff of urine cotinine continuously declined over the study period. Conclusions The optimal cotinine cutoff declined along with smoking prevalence and levels of SHS exposure due to enforcement of tobacco control policies, including smoke-free ordinances and tax increases. Monitoring of biomarkers of tobacco exposure appears necessary for verification of smoking status and regulatory use. Implications Our results based on nationally representative data suggest that a large decrease in the optimal cutoff value of urine cotinine to distinguish smokers from non-smokers was caused by decreases in smoking prevalence and SHS exposure following enforcement of tobacco control policies over the last 10 years. We determined the optimal cutoff values of urine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), which were not previously reported in representative population in Asia, to enable more accurate estimation of exposure to tobacco smoke and proper assessment of disease risks. Gender- and age-specific differences in the optimal cutoffs require further study. Monitoring of biomarkers of tobacco smoke exposure seems necessary for verification of smoking status and regulatory use.

Identifying additional risk factors for COVID-19 severity in numerous previously healthy patients without canonical clinical risk factors remains challenging. In this study, we investigate whether clonal hematopoiesis of indeterminate potential (CHIP), a common aging-related process that predisposes various inflammatory responses, may exert COVID-19 severity. We examine the clinical impact of CHIP in 143 laboratory-confirmed COVID-19 patients. Both stratified analyses and logistic regression including the interaction between canonical risk factors and CHIP show that CHIP is an independent risk factor for severe COVID-19, especially in previously healthy patients. Analyses of 60,310 single-cell immune transcriptome profiles identify distinct immunological signatures for CHIP (+) severe COVID-19 patients, particularly in classical monocytes, with a marked increase in pro-inflammatory cytokine responses and potent IFN-γ mediated hyperinflammation signature. We further demonstrate that the enhanced expression of CHIP (+) specific IFN-γ response genes is attributed to the CHIP mutation-dependent epigenetic reprogramming of poised or bivalent cis-regulatory elements. Our results highlight a unique immunopathogenic mechanism of CHIP in the progression of severe COVID-19, which could be extended to elucidate how CHIP contributes to a variety of human infectious diseases.

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model’s interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

With the advancements of high-throughput sequencing technology, several recent studies addressed the clinical/phenotypic stratification of samples by utilizing transcriptome data. However, existing stratification methods lack efficient utilization of gene interaction information, and furthermore, handling more than 20,000 genes causes the curse of high dimensionality that hinders elucidating the linkage between genetic profiles and clinical/phenotypic differences. To overcome these challenges, we propose a network-based two-step computational framework. We first reduce dimensions of transcriptome to a few tens of dimensions by mapping transcriptome to protein interaction network followed by performing network propagation algorithm and clustering analysis. Then, each network is converted into a single numeric metric by utilizing information theoretic quantification of gene expression abnormality, which results in a single sample mapping to a metric space generated by each subnetwork in the form of vectors. The proposed network-based stratification method was used to analyses Pan-Caner dataset and Oryza sativa dataset. Extensive experiments showed that our method generates a metric space that captures data-specific biological functions and improves the stratification performance compared to existing methods. Therefore, the proposed method successfully stratified the samples, addressing the problem in the complex gene space. The proposed method is implemented in Python and available at https://github.com/Sunginyoung/net_stratification.

Motivation Characterizing cancer subclones is crucial for the ultimate conquest of cancer. Thus, a number of bioinformatic tools have been developed to infer heterogeneous tumor populations based on genomic signatures such as mutations and copy number variations. Despite accumulating evidence for the significance of global DNA methylation reprogramming in certain cancer types including myeloid malignancies, none of the bioinformatic tools are designed to exploit subclonally reprogrammed methylation patterns to reveal constituent populations of a tumor. In accordance with the notion of global methylation reprogramming, our preliminary observations on acute myeloid leukemia (AML) samples implied the existence of subclonally occurring focal methylation aberrance throughout the genome. Results We present PRISM, a tool for inferring the composition of epigenetically distinct subclones of a tumor solely from methylation patterns obtained by reduced representation bisulfite sequencing. PRISM adopts DNA methyltransferase 1-like hidden Markov model-based in silico proofreading for the correction of erroneous methylation patterns. With error-corrected methylation patterns, PRISM focuses on a short individual genomic region harboring dichotomous patterns that can be split into fully methylated and unmethylated patterns. Frequencies of such two patterns form a sufficient statistic for subclonal abundance. A set of statistics collected from each genomic region is modeled with a beta-binomial mixture. Fitting the mixture with expectation-maximization algorithm finally provides inferred composition of subclones. Applying PRISM for two AML samples, we demonstrate that PRISM could infer the evolutionary history of malignant samples from an epigenetic point of view. Availability and implementation PRISM is freely available on GitHub (https://github.com/dohlee/prism). Supplementary information Supplementary data are available at Bioinformatics online.

Cellular stages of biological processes have been characterized using fluorescence-activated cell sorting and genetic perturbations, charting a limited landscape of cellular states. Time series transcriptome data can help define new cellular states at the molecular level since the analysis of transcriptional changes can provide information on cell states and transitions. However, existing methods for inferring cell states from transcriptome data use additional information such as prior knowledge on cell types or cell-type-specific markers to reduce the complexity of data. In this study, we present a novel time series clustering framework to infer TRAnscriptomic Cellular States (TRACS) only from time series transcriptome data by integrating Gaussian process regression, shape-based distance, and ranked pairs algorithm in a single computational framework. TRACS determines patterns that correspond to hidden cellular states by clustering gene expression data. TRACS was used to analyse single-cell and bulk RNA sequencing data and successfully generated cluster networks that reflected the characteristics of key stages of biological processes. Thus, TRACS has a potential to help reveal unknown cellular states and transitions at the molecular level using only time series transcriptome data. TRACS is implemented in Python and available at http://github.com/BML-cbnu/TRACS/.

In computer graphics, fluid-like motion is an important and challenging problem with many applications. Currently, the graphical performance and resolution of the mobile phone is highly improved; therefore, mobile 3D games are more and more popular items in the field of mobile services. Yet interactive physically-based fluid simulation is still challenging for mobile platforms. Stable and fast fluid simulation methods are well developed in PC and console games, but fluid simulation and interactive fluid models still have many problems in the mobile environment. We studied and implemented physically- based models for fluids like fire and smoke effects on a Mobile 3D environment.

The human development was represented with the human development index and the availability of telecommunication service extracted from spectrum frequency monitoring all around Indonesia. In this paper, we find out the correlation between the availability of radio spectrum-related ICTs telecommunication service in the form of mobile cellular, TV broadcast, and radio broadcast with human development by using city-regency level data. We knew that telecommunication service is statistically significant and positively correlated to human development. In particular, mobile cellular service has a higher positive correlation with human development comparing to TV broadcast service, while radio broadcast service does not correlate with human development. We also suggest that policies should give more priorities to the mobile cellular technology for using spectrum frequency as this technology has the highest correlation with human development in Indonesia.

Pharmacogenomics is the study of how genes affect a person's response to drugs. Thus, understanding the effect of drug at the molecular level can be helpful in both drug discovery and personalized medicine. Over the years, transcriptome data upon drug treatment has been collected and several databases compiled before drug treatment cancer cell multi-omics data with drug sensitivity (IC 50, AUC) or time-series transcriptomic data after drug treatment. However, analyzing transcriptome data upon drug treatment is challenging since more than 20,000 genes interact in complex ways. In addition, due to the difficulty of both time-series analysis and multi-omics integration, current methods can hardly perform analysis of databases with different data characteristics. One effective way is to interpret transcriptome data in terms of well-characterized biological pathways. Another way is to leverage state-of-the-art methods for multi-omics data integration. In this paper, we developed Drug Response analysis Integrating Multi-omics and time-series data (DRIM), an integrative multi-omics and time-series data analysis framework that identifies perturbed sub-pathways and regulation mechanisms upon drug treatment. The system takes drug name and cell line identification numbers or user's drug control/treat time-series gene expression data as input. Then, analysis of multi-omics data upon drug treatment is performed in two perspectives. For the multi-omics perspective analysis, IC 50-related multi-omics potential mediator genes are determined by embedding multi-omics data to gene-centric vector space using a tensor decomposition method and an autoencoder deep learning model. Then, perturbed pathway analysis of potential mediator genes is performed. For the time-series perspective analysis, time-varying perturbed sub-pathways upon drug treatment are constructed. Additionally, a network involving transcription factors (TFs), multi-omics potential mediator genes, and perturbed sub-pathways is constructed, and paths to perturbed pathways from TFs are determined by an influence maximization method. To demonstrate the utility of our system, we provide analysis results of sub-pathway regulatory mechanisms in breast cancer cell lines of different drug sensitivity. DRIM is available at: http://biohealth.snu.ac.kr/software/DRIM/.

Epigenetic gene regulation is a major control mechanism of gene expression. Most existing methods for modeling control mechanisms of gene expression use only a single epigenetic marker and very few methods are successful in modeling complex mechanisms of gene regulations using multiple epigenetic markers on transcriptional regulation. In this paper, we propose a multi-attention based deep learning model that integrates multiple markers to characterize complex gene regulation mechanisms. In experiments with 18 cell line multi-omics data, our proposed model predicted the gene expression level more accurately than the state-of-the-art model. Moreover, the model successfully revealed cell-type-specific gene expression control mechanisms. Finally, the model was used to identify genes enriched for specific cell types in terms of their functions and epigenetic regulation.

The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:sunkim.bioinfo@snu.ac.kr

Background Although plasma free hemoglobin (fHb) test is important for assessing intravascular hemolysis, it is still dependent on the gold standard Harboe method using manual and labor‐intensive spectrometric measurements at the wavelength of 380‐415‐450 nm. We established an automated fHb assay using a routine chemistry autoanalyzer that can be tuned to a wavelength of 380‐416‐450 nm. Methods The linearity, precision, accuracy, correlation, and sample carryover of fHb measurement using TBA200FRneo method and manual Harboe method were evaluated, respectively. fHb values measured by manual Harboe method were compared with those measured by our new automated TBA200FRneo method. Results fHb measurements were linear in the range of 0.05~38.75 µmol/L by TBA200FRneo and 0.05~9.69 µmol/L by manual Harboe method. Imprecision analysis (%CV) revealed 0.9~2.8% for TBA200FRneo method and 5.3~13.6% for the manual Harboe method. Comparison analysis showed 0.9986 of correlation coefficient (TBA200FRneo = 0.970 × Harboe + 0.12). In analytical accuracy analysis, the manual Harboe method revealed about 4 times higher average total error % (12.2%) than the TBA200FRneo automated method (2.8%). The sample carryover was −0.0016% in TBA200FRneo method and 0.0038% in Harboe method. Conclusions In the measurement of fHb, the automated TBA200FRneo method showed better performance than the conventional Harboe method. It is expected that the automated fHb assay using the routine chemistry analyzer can replace the gold standard Harboe method which is labor‐intensive and need an independent spectrophotometry equipment.

Attrition of ash has a significant effect on the performance of a circulating fluidized bed (CFB) combustor, and the attrition rate coefficient Kaf is widely used to analyze the mass balance of the CFB combustor. In this study, a four-layer artificial neural network (ANN) model is developed to estimate the value of Kaf according to the chemical components of the ash based on a training database consisting of 40 sets of samples. An optimum structure comprising two hidden layers with ten neurons in each layer is adopted, and the mean square error for training and validation stages is reduced to 0.00175 and 0.13842, respectively. To verify the validity of the model, field tests are conducted in a large-scale CFB boiler by burning two types of coal with different blending ratios. The Kaf of the two kinds of coal is estimated using the trained ANN model. The ratio of the coal ash discharged via fly ash to the total coal ash decreases, while the Sauter mean diameter of the circulating material increases with an increase in the blending ratio of the coal with a smaller value of estimated Kaf; this exhibits good validity of the model.

Muconic acid (MA) is a valuable compound for adipic acid production, which is a precursor for the synthesis of various polymers such as plastics, coatings, and nylons. Although MA biosynthesis has been previously reported in several bacteria, the engineered strains were not satisfactory owing to low MA titers. Here, we generated an engineered Corynebacterium cell factory to produce a high titer of MA through 3-dehydroshikimate (DHS) conversion to MA, with heterologous expression of foreign protocatechuate (PCA) decarboxylase genes. To accumulate key intermediates in the MA biosynthetic pathway, aroE (shikimate dehydrogenase gene), pcaG/H (PCA dioxygenase alpha/beta subunit genes) and catB (chloromuconate cycloisomerase gene) were disrupted. To accomplish the conversion of PCA to catechol (CA), a step that is absent in Corynebacterium, a codon-optimized heterologous PCA decarboxylase gene was expressed as a single operon under the strong promoter in a aroE-pcaG/H-catB triple knock-out Corynebacterium strain. This redesigned Corynebacterium, grown in an optimized medium, produced about 38 g/L MA and 54 g/L MA in 7-L and 50-L fed-batch fermentations, respectively. These results show highest levels of MA production demonstrated in Corynebacterium, suggesting that the rational cell factory design of MA biosynthesis could be an alternative way to complement petrochemical-based chemical processes.

Waste animal fat is a promising feedstock to replace vegetable oil in commercial biodiesel process, however the high content of free fatty acid in waste fat makes it unfeasible to be processed with commercial base-catalytic process. Enzymatic process in supercritical fluid is a promising way to convert waste fat into biodiesel since enzyme can catalyze both esterification of free fatty acid and transesterification of triglyceride while supercritical fluid overcome mass-transfer limitation. However, the glycerol by-product needs to be separated because it might reduce the enzyme activity. Organic solvent can be used to extract the glycerol from the enzyme with destructive effect to the enzyme. Thus, the destructive effect of organic solvent on the ability of modified C.antarctica lipase B to produce biodiesel from the waste fat was investigated. And the reversibility of enzyme was tested by various ways, drowning by organic solvents, and reuse after non-solvent experiment. The activity of enzyme was considerably affected by organic solvents. The solvent-drowning test showed that the yields were similar or higher that non-solvent case. This implies that the solvent itself did not cause the permanent change in enzyme structure to decrease activity. The decrease in yield was observed in the reuse test, which is regarded to be caused by the incomplete removal of products from the first run.

In pattern recognition, the classification accuracy has a strong correlation with the selected features. Therefore, in the present paper, we applied an evolutionary algorithm in combination with linear discriminant analysis (LDA) to enhance the feature selection in a static image-based facial expressions system. The accuracy of the classification depends on whether the features are well representing the expression or not. Therefore the optimization of the selected features will automatically improve the classification accuracy. The proposed method not only improves the classification but also reduces the dimensionality of features. Our approach outperforms linear-based dimensionality reduction algorithms and other existing genetic-based feature selection algorithms. Further, we compare our approach with VGG (Visual Geometry Group)-face convolutional neural network (CNN), according to the experimental results, the overall accuracy is 98.67% for either our approach or VGG-face. However, the proposed method outperforms CNN in terms of training time and features size. The proposed method proves that it is able to achieve high accuracy by using far fewer features than CNN and within a reasonable training time.

Background Identifying preferences for stool collection devices may help increase uptake rates for colorectal cancer screening via fecal immunochemical test (FIT). This study surveyed satisfaction with different devices utilized to collect stool samples for FIT: a conventional container and a sampling bottle (Eiken OC-Sensor). Methods This cross-sectional study was conducted at the National Cancer Center, Korea. Participants aged 50–74 years who used either a conventional container or a sampling bottle to collect a stool sample for FIT were asked to complete a questionnaire designed to survey their satisfaction with the stool collection process and their intentions to undergo FIT in subsequent screening rounds. In total, 1657 participants (1224 conventional container, 433 sampling bottle) were included for analysis. Results Satisfaction with the sampling bottle was higher than that with the conventional container (79.9% vs.73.0%, p = 0.005, respectively; aOR = 1.52, 95% CI: 1.16–2.00). Participants satisfied with the sampling bottle were more likely to be female, be of younger age (50–64 years old), have higher household income, and have prior experience with FIT. Intentions to undergo subsequent screening were stronger among those given the sampling bottle than those given the conventional container (aOR = 1.78, 95% CI: 1.28–2 .48). Conclusions Satisfaction with the stool collection process was higher with the sampling bottle. However, additional studies are needed to validate whether the increased satisfaction and stronger intentions to undergo subsequent screening with the sampling bottle could actually lead to increased uptake in subsequent rounds, along with analysis of the device’s cost effectiveness. Electronic supplementary material The online version of this article (10.1186/s12885-018-4290-0) contains supplementary material, which is available to authorized users.

This study aimed to evaluate the impact of seasonal variations in climate on the performance of the fecal immunochemical test (FIT) in screening for colorectal cancer in the National Cancer Screening Program in Korea.Data were extracted from the National Cancer Screening Program databases for participants who underwent FIT between 2009 and 2010. We compared positivity rates, cancer detection rates, interval cancer rates, positive predictive value, sensitivity, and specificity for FIT during the spring, summer, fall, and winter seasons in Korea.In total, 4,788,104 FIT results were analyzed. FIT positivity rate was lowest during the summer months. In the summer, the positive predictive value of FIT was about 1.1 times (adjusted odds ratio (aOR) 1.08, 95% confidence interval (CI) 1.00-1.16) higher in the overall FIT group and about 1.3 times (aOR 1.29, 95% CI 1.10-1.50) higher in the quantitative FIT group, compared to those in the other seasons. Cancer detection rates, however, were similar regardless of season. Interval cancer risk was significantly higher in the summer for both the overall FIT group (aOR 1.16, 95% CI 1.07-1.27) and the quantitative FIT group (aOR 1.31, 95% CI 1.12-1.52). In addition, interval cancers in the rectum and distal colon were more frequently detected in the summer and autumn than in the winter.The positivity rate of FIT was lower in the summer, and the performance of the FIT screening program was influenced by seasonal variations in Korea. These results suggest that more efforts to reduce interval cancer during the summer are needed in population-based screening programs using FIT, particularly in countries with high ambient temperatures.

Marine biofouling of ship hulls and ocean structures causes enormous economic losses due to increased frictional drag. Thus, efforts have been exerted worldwide to eliminate biofouling. In addition, a strong demand exists for the development of a cost-effective and eco-friendly anti-biofouling coating technology. Thus, erucamide-polydimethylsiloxane (EP) coating is proposed in this study. EP exhibits a hydrophobic surface as the erucamide content and drag reduction effect increase. In this study, the drag reduction effect of the EP 2.5 is better than that of glass and polydimethylsiloxane (PDMS) surfaces. Moreover, the proposed EP coatings are observed to prevent the biofouling induced by bacteria (E. coli) and brown algae (Cladosiphon sp.). In addition, through a marine field test, the anti-biofouling effect of the EP surface is found to be better than the previously studied oleamide-PDMS (OP) surface. In the marine field test, the EP 2.5 demonstrates superior anti-biofouling performance for 5.5 months under real marine environment. The proposed eco-friendly EP coating method could be applicable to marine vehicles that require effective drag reduction and anti-biofouling properties.

Given that (i) levulinic acid (LA) is one of the most significant platform chemicals derived from biomass and (ii) 4-hydroxyvaleric acid (4-HV) is a potential LA derivative, the aim of this study is to achieve chemoenzymatic valorization of LA, which was obtained from agricultural wastes, to 4-HV. The thermochemical process utilized agricultural wastes (i.e., rice straw and corncob) as feedstocks and successfully produced LA, ranging from 25.1 to 65.4 mM. Additionally, formate was co-produced and used as a hydrogen source for the enzymatic hydrogenation of LA. Finally, engineered 3-hydroxybutyrate dehydrogenase from Alcaligenes faecalis (eHBDH) was applicable for catalyzing the conversion of agricultural wastes-driven LA, resulting in a maximum concentration of 11.32 mM 4-HV with a conversion rate of 48.2%. To the best of our knowledge, this is the first report describing the production of 4-HV from actual biomass, and the results might provide insights into the valorization of agricultural wastes.

Anti-biofouling surfaces are very important owing to their significant roles in microfluidic devices, biosensors and biomedical devices. However, traditional anti-biofouling surfaces could contaminate the environment. Thus, the development of environment-friendly coatings is an essential undertaking in efforts to resolve the problems associated with conventional anti-biofouling surfaces. Herein, a novel strategy inspired by the slippery surface of loach skin is proposed for the rational design of anti-biofouling surfaces. In this strategy, hydrophilic biopolymers, including chitosan, carboxymethyl cellulose, mPEG-amine (MW, 10,000 Da) and alginate, are grafted on a porous polycaprolactone (PCL) membrane. The porous PCL surface is a biomimetic surface inspired by the skin surface of a loach that secretes mucus. Previously developed antifouling surfaces were often toxic or the coating substances were easily released to the outside, and their drag reduction effects were not examined. The developed coating surface is not toxic and the coating material is not depleted to the outside. The resultant covalent biopolymer-coated surfaces (BCSs) exhibit excellent hydrophilic property and drag reduction effect in water. Especially, the additional coating of mPEG-amine on the alginate-coated surface exhibits the best drag reduction performance. In addition, the BCSs show superior anti-biofouling performance by resisting the adhesion of bacteria (Escherichia coli and Maribacter dokdonensis) and NIH3T3 fibroblasts. The proposed covalent biopolymer coatings could be potentially utilised as eco-friendly surfaces for drag reduction and anti-biofouling.