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Although gut microbiome dysbiosis has been associated with inflammatory bowel disease (IBD), the relationship between the oral microbiota and IBD remains poorly understood. This study aimed to identify unique microbiome patterns in saliva from IBD patients and explore potential oral microbial markers for differentiating Crohn’s disease (CD) and ulcerative colitis (UC). A prospective cohort study recruited IBD patients (UC: n = 175, CD: n = 127) and healthy controls (HC: n = 100) to analyze their oral microbiota using 16S rRNA gene sequencing. Machine learning models (sparse partial least squares discriminant analysis (sPLS-DA)) were trained with the sequencing data to classify CD and UC. Taxonomic classification resulted in 4041 phylotypes using Kraken2 and the SILVA reference database. After quality filtering, 398 samples (UC: n = 175, CD: n = 124, HC: n = 99) and 2711 phylotypes were included. Alpha diversity analysis revealed significantly reduced richness in the microbiome of IBD patients compared to healthy controls. The sPLS-DA model achieved high accuracy (mean accuracy: 0.908, and AUC: 0.966) in distinguishing IBD vs. HC, as well as good accuracy (0.846) and AUC (0.923) in differentiating CD vs. UC. These findings highlight distinct oral microbiome patterns in IBD and provide insights into potential diagnostic markers.

Background and AimsCrohn’s disease (CD) has a complex polygenic etiology with high heritability. We keep putting an effort to identify novel variants associated with susceptibility to CD through a genome-wide association study (GWAS) in large Korean populations.MethodsGenome-wide variant data from 902 Korean patients with CD and 72,179 controls were used to assess the genetic associations in a meta-analysis with previous Korean GWAS results from 1,621 patients with CD and 4,419 controls. Epistatic interactions between CD-risk variants of interest were tested using a multivariate logistic regression model with an interaction term.ResultsWe identified two novel genetic associations with the risk of CD nearZBTB38and within the leukocyte immunoglobulin-like receptor (LILR) gene cluster (P−8), with highly consistent effect sizes between the two independent Korean cohorts. CD-risk variants in the LILR locus are known quantitative trait loci (QTL) for multiple LILR genes, of whichLILRB2directly interacts with various ligands including MHC class I molecules. The LILR lead variant exhibited a significant epistatic interaction with CD-associated regulatory variants forTAP2involved in the antigen presentation of MHC class I molecules (P=4.11×10−4), showing higher CD-risk effects of theTAP2variant in individuals carrying more risk alleles of the LILR lead variant (OR=0.941,P=0.686 in non-carriers; OR=1.45,P=2.51×10−4in single-copy carriers; OR=2.38,P=2.76×10−6in two-copy carriers).ConclusionsThis study demonstrated that genetic variants at two novel susceptibility loci and the epistatic interaction between variants in LILR andTAP2loci confer risk of CD.

We demonstrate use of a complex constraint based on the interaction of x-rays with matter for reconstructing images from coherent X-ray diffraction. We show the complementary information provided by the phase and magnitude of the reconstructed wavefield greatly improves the quality of the resulting estimate of the transmission function of an object without the need for a priori information about the object composition.

Previous literature posits that CEOs with inside debt holdings are more likely to engage in corporate social responsibility (CSR) since they respond to bondholders’ demands as their appetite for risk decreases. This study examines whether a CEO’s risk concerns moderate the effect of CEO inside debt holdings on the firm’s CSR performance. We find that the positive relationship between CEO inside debt and CSR is weakened when CEOs are close to retirement, or when firms face less product market competition within their respective industries. Our results also suggest that the effect of CEO inside debt, as well as the moderating effect of CEO retirement and low competition, differs between internal and external stakeholder CSR. Furthermore, we find that the moderating effect of CEO retirement is strongly supported in propensity score matching analysis and alternative model specification, whereas that of low competition is weakly supported. Overall, this study has important policy implications for board members, managers, investors, and regulators as it highlights that CEOs’ compensation structure and risk concerns can be determinants of corporate sustainability policies.

Almost half of patients show no primary or secondary response to monoclonal anti-tumor necrosis factor α (anti-TNF) antibody treatment for inflammatory bowel disease (IBD). Thus, the exact mechanisms of a non-durable response (NDR) remain inadequately defined. We used our genome-wide genotype data to impute expression values as features in training machine learning models to predict a NDR. Blood samples from various IBD cohorts were used for genotyping with the Korea Biobank Array. A total of 234 patients with Crohn’s disease (CD) who received their first anti-TNF therapy were enrolled. The expression profiles of 6294 genes in whole-blood tissue imputed from the genotype data were combined with clinical parameters to train a logistic model to predict the NDR. The top two and three most significant features were genetic features (DPY19L3, GSTT1, and NUCB1), not clinical features. The logistic regression of the NDR vs. DR status in our cohort by the imputed expression levels showed that the β coefficients were positive for DPY19L3 and GSTT1, and negative for NUCB1, concordant with the known eQTL information. Machine learning models using imputed gene expression features effectively predicted NDR to anti-TNF agents in patients with CD.

Background/Aims Risk prediction models using a deep neural network (DNN) have not been reported to predict the risk of advanced colorectal neoplasia (ACRN). The aim of this study was to compare DNN models with simple clinical score models to predict the risk of ACRN in colorectal cancer screening. Methods Databases of screening colonoscopy from Kangbuk Samsung Hospital (n=121,794) and Kyung Hee University Hospital at Gangdong (n=3,728) were used to develop DNN-based prediction models. Two DNN models, the Asian-Pacific Colorectal Screening (APCS) model and the Korean Colorectal Screening (KCS) model, were developed and compared with two simple score models using logistic regression methods to predict the risk of ACRN. The areas under the receiver operating characteristic curves (AUCs) of the models were compared in internal and external validation databases. Results In the internal validation set, the AUCs of DNN model 1 and the APCS score model were 0.713 and 0.662 (p0.1). Conclusions Simple score models for the risk prediction of ACRN are as useful as DNN-based models when input variables are limited. However, further studies on this issue are warranted to predict the risk of ACRN in colorectal cancer screening because DNN-based models are currently under improvement.

As cyberattacks become more intelligent, the difficulty increases for traditional intrusion detection systems to detect advanced attacks that deviate from previously stored patterns. To solve this problem, a deep learning-based intrusion detection system model has emerged that analyzes intelligent attack patterns through data learning. However, deep learning models have the disadvantage of having to re-learn each time a new cyberattack method emerges. The time required to learn a large amount of data is not efficient. In this paper, an experiment was conducted using the Leipzig Intrusion Detection Data Set (LID-DS), which is a host-based intrusion detection data set released in 2018. In addition, in order to evaluate and improve the performance of the system, a host-based intrusion detection model consisting of pre-processing, vector-to-image processing, training and testing steps is proposed. In the training and testing steps, a Siamese Convolutional Neural Network (Siamese-CNN) is constructed using the few-shot learning method, which shows excellent performance by learning a small amount of data. Siamese-CNN determines whether the attack type is the same based on the similarity score of each cyberattack sample converted to an image. The accuracy was calculated using the few-shot learning technique. The performance of the Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN are compared to confirm the performance of Siamese-CNN. As a result of measuring the accuracy, precision, recall, and F1-score indicators, it was confirmed that the recall of the Siamese-CNN model proposed in this study increased by about 6% compared to the Vanilla-CNN model.

This article reports on the results of online discussions held in February-March 2022 to share different perspectives on the impact of the COVID-19 pandemic crisis upon interculturalism. Organized under the title “The Role of Interculturalism in the Time of the COVID-19 Pandemic”, the editors of INContext invited scholars from various academic backgrounds. The aim of this discussion was not to provide practical solutions nor to draw a coherent picture of the current crisis provoked by the pandemic. Rather, it endeavored to test the possibility of analyzing this crisis in terms of interculturalism, thereby seeking the possibility of broadening the approach in general. There has been a large amount of scholarly research on the impact of the COVID-19 pandemic upon global community in various academic fields. In order for intercultural studies to make a contribution to this task, it is urgent to encourage active exchange of opinions among those whose academic interests lie in cultural communications and interactions. This article first summarizes some notable academic discussions on intercultural studies, including politics, education, art, and iterature. Comparing the opinions submitted for the discussions is the basis of the present research before drawing any significant academic implications. Among those who provided their opinions through various channels, five scholars were chosen to represent different aspects of the topic: the psychological aspect of the COVID-19 crisis; the implications of cyber literature access for youtheducation; the efforts of a regional community to maintain its creative spirit in arts and culture; and the crisis of liberalism in international relations. The contribution from these five scholars provided the basis for the case studies presented here and analyzed through an intercultural lens for the impact of the COVID-19 pandemic on humanity. Just as the scope of intercultural studies is shaped and enriched by contributions from many academic fields, the participants in the online discussions cited here also entertain the hope that this article will help INContext shape the agenda in the field of interculturalism and thereby spearhead academic endeavors to include divergent perspectives in the quest to understand humankind and the diverse cultural manifestations which are its reflection.

This study investigates whether a target firm's corporate social responsibility (CSR) performance creates value for shareholders. Our results indicate that target CSR performance that is stronger than that of the acquirer firm yields higher premiums for target shareholders. In addition, the positive valuation effect of CSR is more pronounced when well‐governed acquirers conduct the takeover. Our evidence is robust to several sensitivity tests. These results imply that favorable market reactions to the target's CSR are due to the market expectation that well‐governed acquirers reward the target's shareholders with a fair CSR value in merger and acquisition (M&A) transactions. Consistent with the stakeholder theory that maximizes stakeholder value, investing in CSR creates value in M&A transactions.

Background/Aims: We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal neoplasia (ACRN) in asymptomatic adults, based on which colorectal cancer screening could be customized. Methods: We collected data on 26 clinical and laboratory parameters, including age, sex, smoking status, body mass index, complete blood count, blood chemistry, and tumor marker, from 70,336 first-time colonoscopy screening recipients. For reference, we used a logistic regression (LR) model with nine variables manually selected from the 26 variables. A deep neural network (DNN) model was developed using all 26 variables. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the models were compared in a randomly split validation group. Results: In comparison with the LR model (AUC, 0.724; 95% confidence interval [CI], 0.684 to 0.765), the DNN model (AUC, 0.760; 95% CI, 0.724 to 0.795) demon strated significantly improved performance with respect to the prediction of ACRN (p < 0.001). At a sensitivity of 90%, the specificity significantly increased with the application of the DNN model (41.0%) in comparison with the LR model (26.5%) (p < 0.001), indicating that the colonoscopy workload required to detect the same number of ACRNs could be reduced by 20%. Conclusions: The application of DNN to big clinical data could significantly improve the prediction of ACRNs in comparison with the LR model, potentially realizing further customization by utilizing large quantities and various types of biomedical information.

There is a global focus on adding renewable energy sources to the mix of energy supplies. In this study, the grid connections for large-scale offshore wind farms in areas that have high penetration of renewable energy sources were examined. System strength evaluation considering the interaction of wind farms and inverter-based resources (IBRs) was performed; the fault current was then analyzed to determine their contribution to the total fault current at a bus level. These studies revealed that the interaction between offshore wind farms and IBRs may make the power system weaker, and it is possible that fault current contributions from offshore wind farms can violate the capacity limit of existing circuit breakers. The results of steady-state analysis were verified through case studies focused on the southwest area of the Korea Electric Power Corporation (KEPCO) system where large-scale offshore wind farms are planned to be established and connected. Power system planners will benefit from the results of this study with a better understanding of the factors to consider when integrating large-scale wind farms in areas with high penetration of renewables.

Spinel Cobalt Vanadate CoV$_2$O$_4$ has been grown on (001) SrTiO$_3$ substrates. Using torque magnetometry experiments, we find that the previously observed temperature induced anisotropy change, where the easy axis changes from the out of plane [001] direction to a biaxial anisotropy with planar easy axes, occurs in a gradual second-order structural phase transition. This work characterizes this transition and the magnetic anisotropies in the (001), (100), and (-110) rotation planes, and explores their field dependence up to 30~T. Below 80~K, hysteretic features appear around the hard axes, i.e., the out-of-plane direction in (-110) and (010) rotations and the planar directions in (001) rotations. This is due to a Zeeman Energy that originates from the lag of the magnetization with respect to the applied magnetic field as the sample is rotated. The appearance of the hysteresis, which persist up to very high fields, shows that the anisotropy at low temperature is rather strong. Additionally, field dependent distortions to the symmetry of the torque response in increasing applied fields shows that magnetostriction plays a large role in determining the direction and magnitude of the anisotropy., Main text: 9 pages and 6 figures; supplemental materials: 9 pages and 10 figures

Description, Ultrafast laser–excited nanoparticle images reveal nonequilibrium kinetics–driven unusual surface ordering at high temperature., Ultrafast light-matter interactions enable inducing exotic material phases by promoting access to kinetic processes blocked in equilibrium. Despite potential opportunities, actively using nonequilibrium kinetics for material discovery is limited by the poor understanding on intermediate states of driven systems. Here, using single-pulse time-resolved imaging with x-ray free-electron lasers, we found intermediate states of photoexcited bismuth nanoparticles that showed kinetically reversed surface ordering during ultrafast melting. This entropy-lowering reaction was further investigated by molecular dynamics simulations to reveal that observed kinetics were thermodynamically buried in equilibrium, which emphasized the critical role of electron-mediated ultrafast free-energy modification in inducing exotic material phases. This study demonstrated that ultrafast photoexcitations of electrons provide an efficient strategy to induce hidden material phases by overcoming thermodynamic barriers via nonequilibrium reaction pathways.

We employ femtosecond X-ray absorption spectroscopy of [Ru(m-bpy)

Phase recovery is a well-known inverse problem prevalent across science disciplines and attracts active research interests to develop a number of theoretical and experimental methods. Recent developments in artificial intelligence have further prompted research activities in processing the experimentally collected imperfect data, but applications have been limited to slow-varying data such as real images. Experimental noise present in largely fluctuating diffraction data, in particular, adds practical challenges to hamper consistent phase recovery. Here, we introduce a convolutional neural-network assisted $k$-space denoising method that can directly manage noisy diffraction signals. It showed superior performance on denoising the diffraction data, which promote improved phase recovery from noise-buried single-pulse diffraction signals obtained by the x-ray free-electron laser. Adapting our method to general diffraction data can expand boundaries of interpretable data and enhance observability of faint objects with weak signals.

Background/Aims: In ulcerative colitis (UC) patients, Escherichia coli Nissle 1917 (EcN) is equivalent to mesalazine for preventing disease relapse; however, evidence of the ability of EcN to increase health-related quality of life or induce remission remains scarce. We investigated the efficacy of EcN as an add-on therapy for UC.Methods: In this multicentre, double-blind, randomised, placebo-controlled study, a total of 133 UC patients were randomly assigned to receive either EcN or placebo once daily for 8 weeks. Inflammatory bowel disease questionnaire (IBDQ) scores (primary endpoint) and clinical remission and response rates (secondary endpoints) were compared (Clinical trial registration number: NCT04969679).Results: In total, 118 patients (EcN, 58; placebo, 60) completed the study. The number of patients reaching the primary endpoint did not differ between the EcN and placebo groups (30 [51.7%] vs. 31 [51.7%]; per-protocol analysis, p = 1.0; intention-to-treat analysis, p = 0.86). However, significantly fewer patients in the EcN group exhibited a decreased IBDQ score (1 [1.7%] vs. 8 [13.3%]; per-protocol analysis, p = 0.03; intention- to-treat analysis, p = 0.02). Moreover, a significantly higher number of patients in the EcN group displayed clinical response at 4 weeks (23 [39.7%] vs. 13 [21.7%], p = 0.04) and endoscopic remission at 8 weeks (26 [46.4%] vs. 16 [27.1%], p = 0.03).Conclusions: Although the number of patients reaching the primary endpoint did not differ between the EcN and placebo groups, EcN was found to be safe and effective in preventing the exacerbation of IBDQ scores and achieving clinical responses and endoscopic remission in patients with mild-to-moderate UC.

In humans, inactivating mutations in MLL4, which encodes a histone H3-lysine 4-methyltransferase, lead to Kabuki syndrome (KS). While dwarfism is a cardinal feature of KS, the underlying etiology remains unclear. Here we report that Mll4 regulates the development of growth hormone-releasing hormone (GHRH)-producing neurons in the mouse hypothalamus. Our two Mll4 mutant mouse models exhibit dwarfism phenotype and impairment of the developmental programs for GHRH-neurons. Our ChIP-seq analysis reveals that, in the developing mouse hypothalamus, Mll4 interacts with the transcription factor Nrf1 to trigger the expression of GHRH-neuronal genes. Interestingly, the deficiency of Mll4 results in a marked reduction of histone marks of active transcription, while treatment with the histone deacetylase inhibitor AR-42 rescues the histone mark signature and restores GHRH-neuronal production in Mll4 mutant mice. Our results suggest that the developmental dysregulation of Mll4-directed epigenetic control of transcription plays a role in the development of GHRH-neurons and dwarfism phenotype in mice., Mutations in the MLL4 gene can cause Kabuki syndrome, whose underlying molecular mechanisms are unclear. Here, the authors show that Mll4 epigenetically regulates the transcriptional program leading to the formation of GHRH-neurons in the developing mouse hypothalamus.

Genetic differences may be associated with the response to tyrosine kinase inhibitor (TKI) in patients with chronic myeloid leukemia (CML). In this study, we identified genetic alterations between rapid and slow responders (BCR/ABL1 International Scale at 6 months: ≤0.1 % vs. 0.1 %) of TKI treatment in chronic phase CML patients. Our analyses involved single nucleotide polymorphism (SNP), a Genome Wide Association Study and a Network-wide Association Study (NetWAS). Seventy-two patients from 16 institutions were enrolled and treated with a TKI, nilotinib. Gene Set Analysis identified genetic alterations in pathways related to the differentiation, proliferation, and activity of various innate immune cells. The NetWAS analysis found that genes associated with natural killer (NK) cells (PTPRCAP, BLNK, HCK, ARHGEF11, GPR183, TRPV2, SHKBP1, CD2) showed significant differences between rapid and slow responders of nilotinib. However, we found no significantly different genetic alterations according to the response in the SNP analysis. In conclusion, we found that rapidity of response to TKI was associated with pathway-associated genetic alterations in immune cells, particularly with respect to NK cell activity. These results suggested that the innate immune system at initial diagnosis had an important role in treatment response in patients with CML.

Background This study is to evaluate the accuracy of machine learning for differentiation between optic neuropathies, pseudopapilledema (PPE) and normals. Methods Two hundred and ninety-five images of optic neuropathies, 295 images of PPE, and 779 control images were used. Pseudopapilledema was defined as follows: cases with elevated optic nerve head and blurred disc margin, with normal visual acuity (> 0.8 Snellen visual acuity), visual field, color vision, and pupillary reflex. The optic neuropathy group included cases of ischemic optic neuropathy (177), optic neuritis (48), diabetic optic neuropathy (17), papilledema (22), and retinal disorders (31). We compared four machine learning classifiers (our model, GoogleNet Inception v3, 19-layer Very Deep Convolution Network from Visual Geometry group (VGG), and 50-layer Deep Residual Learning (ResNet)). Accuracy and area under receiver operating characteristic curve (AUROC) were analyzed. Results The accuracy of machine learning classifiers ranged from 95.89 to 98.63% (our model: 95.89%, Inception V3: 96.45%, ResNet: 98.63%, and VGG: 96.80%). A high AUROC score was noted in both ResNet and VGG (0.999). Conclusions Machine learning techniques can be combined with fundus photography as an effective approach to distinguish between PPE and elevated optic disc associated with optic neuropathies.

Colorectal cancer (CRC) is the third common malignant tumor and the second most responsible for worldwide cancer deaths. Also, RNAseq technology has been used for two purposes: to find exonic regions on the genome and quantify the expression level of the gene. We tried to validate the pipeline for identifying somatic variants from RNA-seq data, mainly following GATK4 somatic calling pipelines with some optimizing modifications. It is intended to examine whether somatic mutations driven from RNAseq data are associated with the biological behaviors of the individual colorectal cancer cells. We found that key genes (i.e., tumor suppressor genes such as APC and p53) are mostly mutated by T to C (in the non-coding region) or C to T (in the coding region) transitions, so that causes missense and nonsense mutations. However, highly mutated genes did not show significant expressional changes compared to the normal tissue. Otherwise, genes related to the metastatic potentials were observed to have highly increased expression. Our results can substantially verify the reliability of the somatic mutation calling approach using RNAseq data to call cancer-driving mutation and confirm an increase of extracellular matrix metabolism in the CRC. Citation Format: Ji Won Choi, Kwangsung Ahn, Sangsoo Kim, Dong-Il Park, Soo-kyung Park. RNA-seq based somatic variant calling and gene expression analysis reveals tumor heterogeneity and metastatic potential in colorectal cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6253.

In Korea, subtype B is the predominant variant of HIV-1, but full genome sequencing and analysis of its viral variants are lacking. We performed near full-length genome (NFLG) sequencing and phylogenetic and recombination analyses of fifty plasma samples from HIV-positive men who have sex with men (MSM) from a Korea HIV/AIDS cohort study. Viral genomes were amplified and the near-full-length sequences were determined using next-generation sequencing (NGS) and Sanger sequencing. We focused on the HIV-1 subtype classification and identification of HIV recombinants. Twelve HIV-1 NFLGs were determined: ten were subtyped as pure HIV-1 subtype B and two recombinant strains as a common subtype CRF07_BC, and a novel subtype CRF43_02G recombined with CRF02_AG again, or a new CRF02_AG and subtype G recombinant. For the ten NFLGs determined by NGS, “the novel recombinant emerged at approximately 2003 and the other nine subtype B about 2004 or 2005”. This is the first report analyzing HIV-1 NFLG, including recombinants and clinical characteristics, by subtype among MSM in Korea. Our results provide novel insights for understanding the recombinants in the HIV-1 epidemic in Korea.

The structures as building blocks for designing functional nanomaterials have fueled the development of versatile nanoprobes to understand local structures of noncrystalline specimens. Progress in analyzing structures of individual specimens with atomic scale accuracy has been notable recently. In most cases, however, only a limited number of specimens are inspected lacking statistics to represent the systems with structural inhomogeneity. Here, by employing single-particle imaging with X-ray free electron lasers and algorithms for multiple-model 3D imaging, we succeeded in investigating several thousand specimens in a couple of hours and identified intrinsic heterogeneities with 3D structures. Quantitative analysis has unveiled 3D morphology, facet indices, and elastic strain. The 3D elastic energy distribution is further corroborated by molecular dynamics simulations to gain mechanical insight at the atomic level. This work establishes a route to high-throughput characterization of individual specimens in large ensembles, hence overcoming statistical deficiency while providing quantitative information at the nanoscale.

To lead the world's fifth-generation mobile communication networks (5G) market, China introduced several policies to support 5G industry development that will impact telecommunication operators, the main implementers in this industry. Thus, this study examines the impact of the government's 5G policy announcements on telecommunication operators' firm value in China, where the state exerts a strong influence on industry development. We find that government policy announcement in general affects telecommunication operators' stock returns negatively, and when the government announces policies with a higher level of interference, the decline in firm value is more pronounced. To understand the comprehensive impact of 5G technology on telecommunication operators, we also examine the effect of institution-driven corporate technology R&D and investment activities on firm value. We find that the firms' 5G activities also decrease their firm value, and this effect is more significant than government policy announcements. These results imply that the market has a negative evaluation of the introduction of 5G technology due to its immaturity and uncertainty. This study provides a basis for understanding the market's views on 5G technology and development policies.

As the microbiome is composed of a variety of microbial interactions, it is imperative in microbiome research to identify a microbial sub-community that collectively conducts a specific function. However, current methodologies have been highly limited to analyzing conditional abundance changes of individual microorganisms without considering group-wise collective microbial features. To overcome this limitation, we developed a network-based method using nonnegative matrix factorization (NMF) to identify functional meta-microbial features (MMFs) that, as a group, better discriminate specific environmental conditions of samples using microbiome data. As proof of concept, large-scale human microbiome data collected from different body sites were used to identify body site-specific MMFs by applying NMF. The statistical test for MMFs led us to identify highly discriminative MMFs on sample classes, called synergistic MMFs (SYMMFs). Finally, we constructed a SYMMF-based microbial interaction network (SYMMF-net) by integrating all of the SYMMF information. Network analysis revealed core microbial modules closely related to critical sample properties. Similar results were also found when the method was applied to various disease-associated microbiome data. The developed method interprets high-dimensional microbiome data by identifying functional microbial modules on sample properties and intuitively representing their systematic relationships via a microbial network.

Research on interlocal collaboration in economic development occupies one of the largest bodies of institutional collective action studies. Collaboration risks are embedded in transaction costs, and they impede forming an effective network operation among diverse actors to enhance economic development. Further, interest in a network boundary (e.g., through internal or external networks) is relatively marginalized. Collaborative network effects—both informal and formal—on economic development are investigated by analyzing the responses to the 2004 National Survey of Economic Development and Planning Officials from 382 municipalities in U.S. metropolitan areas. Based on intracity and intercity boundaries, interesting findings were discerned concerning the two different hypothesized network effects on economic development. Internal networks have an inverse U-curve shape with economic development, and external networks have a positive relationship with economic development. Further, a formal network f...

Sea bass can regulate the concentration of Na+, K+, and Cl-, among other ions, in their blood, skin, gills, and kidney. Therefore, the salinity of the water does not have a great influence on their metabolism, and sea bass can live in both sea and freshwater in accordance with the salt concentration. Most salinity control occurs in the gills, primarily through the control of chloride cells present there. The concentration of ions in the blood is controlled by the cotransporter Na+ / K+ / 2Cl- (NKCC) in the chloride cell, and the subunits of Na+ / K+ ATPase (NKA) function to maintain homeostasis. The expression of NKA is regulated by subunits of the protein FXYD, allowing the sea bass to survive in compliance with the salinity. In this way, it is possible for sea bass to live in sea and freshwater by controlling the salinity of its body using functions of various channels, proteins, and genes present in the chloride cells of sea bass. In this study, we investigated recent studies of salt control mechanisms in sea bass and their application.

Despite critical roles of the hypothalamic arcuate neurons in controlling the growth and energy homeostasis, the gene regulatory network directing their development remains unclear. Here we report that the transcription factors Dlx1/2 and Otp coordinate the balanced generation of the two functionally related neurons in the hypothalamic arcuate nucleus, GHRH-neurons promoting the growth and AgRP-neurons controlling the feeding and energy expenditure. Dlx1/2-deficient mice show a loss-of-GHRH-neurons and an increase of AgRP-neurons, and consistently develop dwarfism and consume less energy. These results indicate that Dlx1/2 are crucial for specifying the GHRH-neuronal identity and, simultaneously, for suppressing AgRP-neuronal fate. We further show that Otp is required for the generation of AgRP-neurons and that Dlx1/2 repress the expression of Otp by directly binding the Otp gene. Together, our study demonstrates that the identity of GHRH- and AgRP-neurons is synchronously specified and segregated by the Dlx1/2-Otp gene regulatory axis., In the hypothalamus, the arcuate nucleus (ARC) contains AgRP-neurons that regulate energy balance as well as GHRH-neurons that regulate linear growth. Here, the authors looked at how the transcription factors Dlx1/2 and Otp link development of AgRP- and GHRH-neurons.

Immunoassays are analytical methods using antibody-specific reactions to analyze samples. Due to recent developments in antibody technology, the scope of potential samples has expanded to not only proteins, but also low molecular-weight compounds, carbohydrates, lipids, and microorganisms. Immunoassays have the advantage of being highly sensitive, capable of detecting small amounts, and thus have potential for application in biosensors. Immunoassays using magnetic beads have been developed and can be converted to more diverse platforms than the existing limited well plate-based assay. Furthermore, magnetic bead immunoassays detect analytical samples more quickly, and are becoming one of the most suitable immunoassay tools applicable to biosensors. However, their development requires optimization for the improvement of detection ability for specific samples. Therefore, we propose a guideline for solving detection problems occurring in magnetic bead immunoassay optimization processes. It is aimed to be a good reference, enabling researchers performing such optimization more quickly and efficiently

Microfocusing of hard X-ray free-electron laser pulses using Kirkpatrick–Baez mirrors at the nano-crystallography and coherent imaging hutch of the Pohang Accelerator Laboratory X-ray Free-Electron Laser facility is reported., The Pohang Accelerator Laboratory X-ray Free-Electron Laser (PAL-XFEL) is a recently commissioned X-ray free-electron laser (XFEL) facility that provides intense ultrashort X-ray pulses based on the self-amplified spontaneous emission process. The nano-crystallography and coherent imaging (NCI) hutch with forward-scattering geometry is located at the hard X-ray beamline of the PAL-XFEL and provides opportunities to perform serial femtosecond crystallography and coherent X-ray diffraction imaging. To produce intense high-density XFEL pulses at the interaction positions between the X-rays and various samples, a microfocusing Kirkpatrick–Baez (KB) mirror system that includes an ultra-precision manipulator has been developed. In this paper, the design of a KB mirror system that focuses the hard XFEL beam onto a fixed sample point of the NCI hutch, which is positioned along the hard XFEL beamline, is described. The focusing system produces a two-dimensional focusing beam at approximately 2 µm scale across the 2–11 keV photon energy range. XFEL pulses of 9.7 keV energy were successfully focused onto an area of size 1.94 µm × 2.08 µm FWHM.

Next-generation sequencing (NGS) technology has become a trend in the genomics research area. There are many software programs and automated pipelines to analyze NGS data, which can ease the pain for traditional scientists who are not familiar with computer programming. However, downstream analyses, such as finding differentially expressed genes or visualizing linkage disequilibrium maps and genome-wide association study (GWAS) data, still remain a challenge. Here, we introduce a dockerized web application written in R using the Shiny platform to visualize pre-analyzed RNA sequencing and GWAS data. In addition, we have integrated a genome browser based on the JBrowse platform and an automated intermediate parsing process required for custom track construction, so that users can easily build and navigate their personal genome tracks with in-house datasets. This application will help scientists perform series of downstream analyses and obtain a more integrative understanding about various types of genomic data by interactively visualizing them with customizable options.

As next-generation sequencing technologies have advanced, enormous amounts of whole-genome sequence information in various species have been released. However, it is still difficult to assemble the whole genome precisely, due to inherent limitations of short-read sequencing technologies. In particular, the complexities of plants are incomparable to those of microorganisms or animals because of whole-genome duplications, repeat insertions, and Numt insertions, etc. In this study, we describe a new method for detecting misassembly sequence regions of Brassica rapa with genotyping-by-sequencing, followed by MadMapper clustering. The misassembly candidate regions were cross-checked with BAC clone paired-ends library sequences that have been mapped to the reference genome. The results were further verified with gene synteny relations between Brassica rapa and Arabidopsis thaliana. We conclude that this method will help detect misassembly regions and be applicable to incompletely assembled reference genomes from a variety of species.