Microbiota and Serum Metabolic Profile Changes in Korean Native Hanwoo Steer in Response to Diet Feeding Systems

The diversity of bacteria and their function in cattle gastrointestinal tracts can influence animal welfare. Next-generation sequencing (NGS) was used to investigate microbial diversity in the feces of Hanwoo steers reared under natural grazing (GS) and housing (HS) systems. Additionally, serum metabolic parameters, such as liver and kidney markers and mineral and lipid content changes, as well as their correlation with pyrotags, were studied. A total of 6468 ± 87.86 operational taxonomic units (OTUs) were identified in both steer groups, of which 3538 ± 38.17 OTUs were from grazing steer and 2930 ± 94.06 OTUs were from GS. Chao1 index analysis revealed a higher bacterial richness in GS. The dominant bacterial taxa were Bacteroidetes and Firmicutes. GS showed lower Bacteroidetes and higher Firmicutes abundance than HS. The serum of HS showed consistent increases in gamma-glutamyl transpeptidase (γGTP), glucose (GLU), total cholesterol (T-CHO), and triglyceride (TG) levels. The impact of GS on animal health and serum metabolic markers was strongly correlated with microbiota. As shown in this study, grazing has a significant impact on the fecal microbiota at the phylum and family levels, as well as the serum biochemical metabolites of Hanwoo steers.     

A Novel Antibody-Drug Conjugate Targeting Nectin-2 Suppresses Ovarian Cancer Progression in Mouse Xenograft Models

Ovarian cancer is the fifth leading cause of cancer, followed by front line is mostly platinum agents and PARP inhibitors, and very limited option in later lines. Therefore, there is a need for alternative therapeutic options. Nectin-2, which is overexpressed in ovarian cancer, is a known immune checkpoint that deregulates immune cell function. In this study, we generated a novel anti-nectin-2 antibody (chimeric 12G1, c12G1), and further characterized it using epitope mapping, enzyme-linked immunosorbent assay, surface plasmon resonance, fluorescence-activated cell sorting, and internalization assays. The c12G1 antibody specifically bound to the C2 domain of human nectin-2 with high affinity (K_D = 2.90 × 10⁻¹⁰ M), but not to mouse nectin-2. We then generated an antibody-drug conjugate comprising the c12G1 antibody conjugated to DM1 and investigated its cytotoxic effects against cancer cells in vitro and in vivo. c12G1-DM1 induced cell cycle arrest at the mitotic phase in nectin-2-positive ovarian cancer cells, but not in nectin-2-negative cancer cells. c12G1-DM1 induced ~100-fold cytotoxicity in ovarian cancer cells, with an IC₅₀ in the range of 0.1 nM~7.4 nM, compared to normal IgG-DM1. In addition, c12G1-DM1 showed ~91% tumor growth inhibition in mouse xenograft models transplanted with OV-90 cells. These results suggest that c12G1-DM1 could be used as a potential therapeutic agent against nectin-2-positive ovarian cancers.     

Machine Learning-Based Two-Stage Data Selection Scheme for Long-Term Influenza Forecasting

One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time. Usually, vaccine production preparation must be done at least six months in advance, and accurate long-term influenza forecasting is essential for this. Although diverse machine learning models have been proposed for influenza forecasting, they focus on short-term forecasting, and their performance is too dependent on input variables. For a country’s long-term influenza forecasting, typical surveillance data are known to be more effective than diverse external data on the Internet. We propose a two-stage data selection scheme for worldwide surveillance data to construct a long-term forecasting model for influenza in the target country. In the first stage, using a simple forecasting model based on the country’s surveillance data, we measured the change in performance by adding surveillance data from other countries, shifted by up to 52 weeks. In the second stage, for each set of surveillance data sorted by accuracy, we incrementally added data as input if the data have a positive effect on the performance of the forecasting model in the first stage. Using the selected surveillance data, we trained a new long-term forecasting model for influenza and perform influenza forecasting for the target country. We conducted extensive experiments using six machine learning models for the three target countries to verify the effectiveness of the proposed method. We report some of the results.     

Targeted overexpression of protein kinase C beta2 isoform in myocardium causes cardiomyopathy

Increased cardiovascular mortality occurs in diabetic patients with or without coronary artery disease and is attributed to the presence of diabetic cardiomyopathy. One potential mechanism is hyperglycemia that has been reported to activate protein kinase C (PKC), preferentially the beta isoform, which has been associated with the development of micro- and macrovascular pathologies in diabetes mellitus. To establish that the activation of the PKCbeta isoform can cause cardiac dysfunctions, we have established lines of transgenic mice with the specific overexpression of PKCbeta2 isoform in the myocardium. These mice overexpressed the PKCbeta2 isoform transgene by 2- to 10-fold as measured by mRNA, and proteins exhibited left ventricular hypertrophy, cardiac myocyte necrosis, multifocal fibrosis, and decreased left ventricular performance without vascular lesions. The severity of the phenotypes exhibited gene dose-dependence. Up-regulation of mRNAs for fetal type myosin heavy chain, atrial natriuretic factor, c-fos, transforming growth factor, and collagens was also observed. Moreover, treatment with a PKCbeta-specific inhibitor resulted in functional and histological improvement. These findings have firmly established that the activation of the PKCbeta2 isoform can cause specific cardiac cellular and functional changes leading to cardiomyopathy of diabetic or nondiabetic etiology.     

Accuracy of duplex ultrasound in evaluating carotid artery anatomy before endarterectomy

PURPOSE: Anatomic features, such as a high carotid bifurcation (< 1.5 cm from the angle of the mandible), excessive distal extent of plaque (> 2.0 cm above the carotid bifurcation), or a small diameter (< or = 0.5 cm) redundant or kinked internal carotid artery can complicate carotid endarterectomy. In the past, arteriography was the only preoperative study capable of imaging these features. This study assessed the ability of duplex ultrasound to evaluate their presence before surgery. METHODS: A consecutive series of 20 patients who underwent 21 carotid endarterectomies had preoperative duplex ultrasound evaluations of these anatomic features. These evaluations were correlated with operative measurements from an observer blinded to the duplex findings. RESULTS: The mean difference between duplex and operative measurements for the distance between the carotid bifurcation and the angle of the mandible, the distal extent of plaque, and the internal carotid artery diameter was 0.9 cm, 0.3 cm, and 0.8 mm, respectively. The correlation coefficient between the two methods was 0.86, 0.75, and 0.59, respectively. Duplex ultrasound predicted a high carotid bifurcation, excessive distal extent of plaque, or a redundant or kinked internal carotid artery with 100% sensitivity (p < 0.05, p < 0.01, and p < 0.001, respectively). The sensitivity of duplex ultrasound in predicting a small internal carotid artery diameter was 80%. The specificity of duplex ultrasound for predicting excessive distal extent of plaque, small internal carotid artery diameter, high carotid bifurcation, and a coiled or kinked carotid artery was 92%, 56%, 100%, and 100%, respectively. CONCLUSION: Duplex ultrasound can predict the presence of anatomic features that may complicate carotid endarterectomy. Preoperative duplex imaging of these features may be helpful in patients who undergo carotid endarterectomy without preoperative arteriography.     

Solitary necrotic nodules of the liver

An experience is reported with solitary necrotic nodules of the liver, rare benign lesions described for the first time in 1983. Two patients were referred to our department because of the presence of hepatic lesions that radiology showed to be suspected secondary liver tumors. At laparotomy, both patients underwent liver resections because the lesions appeared to be malignant. Subsequent histological examination of the surgical specimens revealed that both were solitary necrotic nodules of the liver. These were the only solitary necrotic nodules found in a total of 840 operations carried out in our department since October 1981. Although they are completely benign, solitary necrotic nodules have a similar ultrasound pattern and radiological features to metastases and have been described in the literature during the follow-up of patients with other tumors. Uncertainty remains as to the etiology of these lesions, which still represent an occasional finding in liver surgery.     

Development of an ultra-HD HEVC encoder using SIMD implementation and fast encoding schemes for smart surveillance system

High efficiency video coding (HEVC) is the newest video coding standard that can support powerful video compression performance with increased picture resolution for ultrahigh definition (UHD). Compared to the previous standard, HEVC achieved a coding efficiency double with a tremendous increase in encoder computational complexity, making support of commercial applications for UHD video service difficult. Especially, optimized HEVC encoder for UHD is expected to be deployed as a key technology for an emerging smart surveillance system in Internet of Things environment. Single-instruction-multiple-data implementation on an Intel x86 processor and several fast encoding schemes were investigated for the complexity reduction of the HEVC reference model (HM) encoder. Fast encoding schemes included early termination processes and data-level parallel processing. The computational complexity of the proposed HEVC encoder was decreased by approximately 192 times compared with HM encoder with an acceptable coding loss.     

An intensive case study on kernel-based relation extraction

Relation extraction refers to a method of efficiently detecting and identifying predefined semantic relationships within a set of entities in text documents. Numerous relation extractionfc techniques have been developed thus far, owing to their innate importance in the domain of information extraction and text mining. The majority of the relation extraction methods proposed to date is based on a supervised learning method requiring the use of learning collections; such learning methods can be classified into feature-based, semi-supervised, and kernel-based techniques. Among these methods, a case analysis on a kernel-based relation extraction method, considered the most successful of the three approaches, is carried out in this paper. Although some previous survey papers on this topic have been published, they failed to select the most essential of the currently available kernel-based relation extraction approaches or provide an in-depth comparative analysis of them. Unlike existing case studies, the study described in this paper is based on a close analysis of the operation principles and individual characteristics of five vital representative kernel-based relation extraction methods. In addition, we present deep comparative analysis results of these methods. In addition, for further research on kernel-based relation extraction with an even higher performance and for general high-level kernel studies for linguistic processing and text mining, some additional approaches including feature-based methods based on various criteria are introduced.