RNA binding by human Norovirus 3C-like proteases inhibits proteaseactivity

AbstractA highly active, fluorescence-based, in vitro assay for human Norovirus protease from genogroup I and II viruses was optimized utilizing as little as 0.25μM enzyme, pH 7.6, and substrate:enzyme of 50–100. Activity in Tris–HCl or sodium phosphate buffers was 2-fold less than HEPES, and 2-fold lower for buffer concentrations over 10mM. Protease activity at pH 7.6 was 73% (GI) or 63% (GII) of activity at the optimal pH 9.0. Sodium inhibited activity 2–3 fold, while potassium, calcium, magnesium, and manganese inhibited 5–10 fold. Differences in efficiency due to pH, buffer, and cations were due to changes in kcat and not Km. Norovirus protease bound short RNAs representing the 3′ or 5′ ends of the virus, inhibiting protease activity (IC50 3–5μM) in a non-competitive manner. Previous reports indicated participation of the protease in the Norovirus replicase complex. The current studies provide initial support for a defined role for the viral protease in Norovirus replication

A Predictive Model for Patent Registration Time Using Survival Analysis

Abstract: The infringements and suits of patents have been increased in many technological fields. Since a patent is an intellectual property to protect inventors ’ exclusive rights of developed technologies, technological pre-occupancy is very important to the development of companies. For the technological pre-occupancy a patent registration time is surely important. In this paper we propose a predictive model of the patent registration time using survival analysis with a Weibull survival regression model and a Cox’s proportional hazard model. They are modeled on the number of international patent classifications (IPC) codes and keywords. To verify the proposed models, we perform a case study using the retrieved patent documents of ’hybrid vehicle ’ from the website of United State Patent and Trademark Office (USPTO)

Patent Data Analysis of Artificial Intelligence Using Bayesian Interval Estimation

Technology analysis is one of the important tasks in technology and industrial management. Much information about technology is contained in the patent documents. So, patent data analysis is required for technology analysis. The existing patent analyses relied on the quantitative analysis of the collected patent documents. However, in the technology analysis, expert prior knowledge should also be considered. In this paper, we study the patent analysis method using Bayesian inference which considers prior experience of experts and likelihood function of patent data at the same time. For keyword data analysis, we use Bayesian predictive interval estimation with count data distributions such as Poisson. Using the proposed models, we forecast the future trends of technological keywords of artificial intelligence (AI) in order to know the future technology of AI. We perform a case study to provide how the proposed method can be applied to real areas. In this paper, we retrieve the patent documents related to AI technology, and analyze them to find the technological trend of AI. From the results of AI technology case study, we can find which technological keywords are more important or critical in the entire structure of AI industry. The existing methods for patent keyword analysis were depended on the collected patent documents at present. But, in technology analysis, the prior knowledge by domain experts is as important as the collected patent documents. So, we propose a method based on Bayesian inference for technology analysis using the patent documents. Our method considers the patent data analysis with the prior knowledge from domain experts

An Interval Estimation Method of Patent Keyword Data for Sustainable Technology Forecasting

Technology forecasting (TF) is forecasting the future state of a technology. It is exciting to know the future of technologies, because technology changes the way we live and enhances the quality of our lives. In particular, TF is an important area in the management of technology (MOT) for R&D strategy and new product development. Consequently, there are many studies on TF. Patent analysis is one method of TF because patents contain substantial information regarding developed technology. The conventional methods of patent analysis are based on quantitative approaches such as statistics and machine learning. The most traditional TF methods based on patent analysis have a common problem. It is the sparsity of patent keyword data structured from collected patent documents. After preprocessing with text mining techniques, most frequencies of technological keywords in patent data have values of zero. This problem creates a disadvantage for the performance of TF, and we have trouble analyzing patent keyword data. To solve this problem, we propose an interval estimation method (IEM). Using an adjusted Wald confidence interval called the Agresti–Coull confidence interval, we construct our IEM for efficient TF. In addition, we apply the proposed method to forecast the technology of an innovative company. To show how our work can be applied in the real domain, we conduct a case study using Apple technology

Hypofractionated stereotactic body radiation therapy as monotherapy for intermediate-risk prostate cancer

<p>Abstract</p> <p>Background</p> <p>Hypofractionated stereotactic body radiation therapy (SBRT) has been advanced as monotherapy for low-risk prostate cancer. We examined the dose distributions and early clinical outcomes using this modality for the treatment of intermediate-risk prostate cancer.</p> <p>Methods</p> <p>Forty-one sequential hormone-naïve intermediate-risk prostate cancer patients received 35–36.25 Gy of CyberKnife-delivered SBRT in 5 fractions. Radiation dose distributions were analyzed for coverage of potential microscopic ECE by measuring the distance from the prostatic capsule to the 33 Gy isodose line. PSA levels, toxicities, and quality of life (QOL) measures were assessed at baseline and follow-up.</p> <p>Results</p> <p>All patients completed treatment with a mean coverage by the 33 Gy isodose line extending >5 mm beyond the prostatic capsule in all directions except posteriorly. Clinical responses were documented by a mean PSA decrease from 7.67 ng/mL pretreatment to 0.64 ng/mL at the median follow-up of 21 months. Forty patients remain free from biochemical progression. No Grade 3 or 4 toxicities were observed. Mean EPIC urinary irritation/obstruction and bowel QOL scores exhibited a transient decline post-treatment with a subsequent return to baseline. No significant change in sexual QOL was observed.</p> <p>Conclusions</p> <p>In this intermediate-risk patient population, an adequate radiation dose was delivered to areas of expected microscopic ECE in the majority of patients. Although prospective studies are needed to confirm long-term tumor control and toxicity, the short-term PSA response, biochemical relapse-free survival rate, and QOL in this interim analysis are comparable to results reported for prostate brachytherapy or external beam radiotherapy.</p> <p>Trial registration</p> <p>The Georgetown Institutional Review Board has approved this retrospective study (IRB 2009–510).</p

MicroRNA profiling in prostate cancer--the diagnostic potential of urinary miR-205 and miR-214.

Prostate cancer (PCa) is the most common type of cancer in men in the United States, which disproportionately affects African American descents. While metastasis is the most common cause of death among PCa patients, no specific markers have been assigned to severity and ethnic biasness of the disease. MicroRNAs represent a promising new class of biomarkers owing to their inherent stability and resilience. In the present study, we investigated potential miRNAs that can be used as biomarkers and/or therapeutic targets and can provide insight into the severity and ethnic biasness of PCa. PCR array was performed in FFPE PCa tissues (5 Caucasian American and 5 African American) and selected differentially expressed miRNAs were validated by qRT-PCR, in 40 (15 CA and 25 AA) paired PCa and adjacent normal tissues. Significantly deregulated miRNAs were also analyzed in urine samples to explore their potential as non-invasive biomarker for PCa. Out of 8 miRNAs selected for validation from PCR array data, miR-205 (p<0.0001), mir-214 (p<0.0001), miR-221(p<0.001) and miR-99b (p<0.0001) were significantly downregulated in PCa tissues. ROC curve shows that all four miRNAs successfully discriminated between PCa and adjacent normal tissues. MiR-99b showed significant down regulation (p<0.01) in AA PCa tissues as compared to CA PCa tissues and might be related to the aggressiveness associated with AA population. In urine, miR-205 (p<0.05) and miR-214 (p<0.05) were significantly downregulated in PCa patients and can discriminate PCa patients from healthy individuals with 89% sensitivity and 80% specificity. In conclusion, present study showed that miR-205 and miR-214 are downregulated in PCa and may serve as potential non-invasive molecular biomarker for PCa

Demographic and clinico-pathological characteristics of the participants for urine samples.

<p>Demographic and clinico-pathological characteristics of the participants for urine samples.</p