Predicting the catalytic sites of isopenicillin N synthase (IPNS) related non-haem iron-dependent oxygenases and oxidases (NHIDOX) through a structural superimposition and molecular docking approach

Isopenicillin N synthase (IPNS) related Non-haem iron-dependent oxygenases and oxidases (NHIDOX) demonstrated a striking structural conservativeness, even with low protein sequence homology. It is evident that these enzymes have an architecturally similar catalytic centre with active ligands lining the reactive pocket. Deacetoxycephalosporin C synthase (DAOCS), isopenicillin N synthase (IPNS), deacetylcephalosporin C synthase (DACS), clavaminate synthase 1 and 2 (CAS1 and 2) are important bacterial enzymes that catalyze the formation of β-lactam antibiotics belonging to this enzyme family. Most plant enzyme members within this subfamily namely flavonol synthase (FLS), leucoanthocyanidin dioxygenase (LDOX), anthocyanidin synthase (ANS), 1-aminocyclopropane-1-carboxylic acid oxidase (ACCO), gibberellin 20-oxidase (G20O), desacetoxyvindoline-4-hydroxylase (D4H), flavanone 3β-hydroxylase (F3H), and hyoscyamine 6β-hydroxylase (H6H) are involved in catalyzing the biosyntheses of plant secondary metabolites. With the advancement of protein structural analysis software, it is possible to predict the catalytic sites of protein that shared a structural resemblance. By exploiting the superimposition model of DAOCS-IPNS, DAOCS-IPNS-CAS, G20O-LDOX, FLS-LDOX, ACCO-LDOX, D4H-LDOX, F3H-LDOX and H6H-LDOX model; a computational protocol for predicting the catalytic sites of proteins is now made available. This study shows that without the crystallized or nuclear magnetic resonance (NMR) structures of most NHIDOX enzyme, the plausible catalytic sites of protein can be forecasted using this structural bioinformatics approach.Keywords: Enzyme, catalytic sites, isopenicillin N synthase, ligand

First Report from the Asian Rotavirus Surveillance Network

Rotavirus remains the most common cause of severe, dehydrating diarrhea among children worldwide. Several rotavirus vaccines are under development. Decisions about new vaccine introduction will require reliable data on disease impact. The Asian Rotavirus Surveillance Network, begun in 2000 to facilitate collection of these data, is a regional collaboration of 36 hospitals in nine countries or areas that conduct surveillance for rotavirus hospitalizations using a uniform World Health Organization protocol. We summarize the Network's organization and experience from August 2001 through July 2002. During this period, 45% of acute diarrheal hospitalizations among children 0–5 years were attributable to rotavirus, higher than previous estimates. Rotavirus was detected in all sites year-round. This network is a novel, regional approach to surveillance for vaccine-preventable diseases. Such a network should provide increased visibility and advocacy, enable more efficient data collection, facilitate training, and serve as the paradigm for rotavirus surveillance activities in other regions

CHInese medicine NeuroAiD efficacy on stroke recovery - Extension study (CHIMES-E): A multicenter study of long-term efficacy

© 2015 S. Karger AG, Basel. Background: The CHInese Medicine NeuroAiD Efficacy on Stroke recovery (CHIMES) study was an international randomized double-blind placebo-controlled trial of MLC601 (NeuroAiD) in subjects with cerebral infarction of intermediate severity within 72 h. CHIMES-E (Extension) aimed at evaluating the effects of the initial 3-month treatment with MLC601 on long-term outcome for up to 2 years. Methods: All subjects randomized in CHIMES were eligible for CHIMES-E. Inclusion criteria for CHIMES were age ≥18, baseline National Institute of Health Stroke Scale of 6-14, and pre-stroke modified Rankin Scale (mRS) ≤1. Initial CHIMES treatment allocation blinding was maintained, although no further study treatment was provided in CHIMES-E. Subjects received standard care and rehabilitation as prescribed by the treating physician. mRS, Barthel Index (BI), and occurrence of medical events were ascertained at months 6, 12, 18, and 24. The primary outcome was mRS at 24 months. Secondary outcomes were mRS and BI at other time points. Results: CHIMES-E included 880 subjects (mean age 61.8 ± 11.3; 36% women). Adjusted OR for mRS ordinal analysis was 1.08 (95% CI 0.85-1.37, p = 0.543) and mRS dichotomy ≤1 was 1.29 (95% CI 0.96-1.74, p = 0.093) at 24 months. However, the treatment effect was significantly in favor of MLC601 for mRS dichotomy ≤1 at 6 months (OR 1.49, 95% CI 1.11-2.01, p = 0.008), 12 months (OR 1.41, 95% CI 1.05-1.90, p = 0.023), and 18 months (OR 1.36, 95% CI 1.01-1.83, p = 0.045), and for BI dichotomy ≥95 at 6 months (OR 1.55, 95% CI 1.14-2.10, p = 0.005) but not at other time points. Subgroup analyses showed no treatment heterogeneity. Rates of death and occurrence of vascular and other medical events were similar between groups. Conclusions: While the benefits of a 3-month treatment with MLC601 did not reach statistical significance for the primary endpoint at 2 years, the odds of functional independence defined as mRS ≤1 was significantly increased at 6 months and persisted up to 18 months after a stroke.Link_to_subscribed_fulltex

Establishment of the nasal microbiota in the first 18 months of life: Correlation with early-onset rhinitis and wheezing.

BACKGROUND: Dynamic establishment of the nasal microbiota in early life influences local mucosal immune responses and susceptibility to childhood respiratory disorders. OBJECTIVE: The aim of this case-control study was to monitor, evaluate, and compare development of the nasal microbiota of infants with rhinitis and wheeze in the first 18 months of life with those of healthy control subjects. METHODS: Anterior nasal swabs of 122 subjects belonging to the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) birth cohort were collected longitudinally over 7 time points in the first 18 months of life. Nasal microbiota signatures were analyzed by using 16S rRNA multiplexed pair-end sequencing from 3 clinical groups: (1) patients with rhinitis alone (n = 28), (2) patients with rhinitis with concomitant wheeze (n = 34), and (3) healthy control subjects (n = 60). RESULTS: Maturation of the nasal microbiome followed distinctive patterns in infants from both rhinitis groups compared with control subjects. Bacterial diversity increased over the period of 18 months of life in control infants, whereas infants with rhinitis showed a decreasing trend (P < .05). An increase in abundance of the Oxalobacteraceae family (Proteobacteria phylum) and Aerococcaceae family (Firmicutes phylum) was associated with rhinitis and concomitant wheeze (adjusted P < .01), whereas the Corynebacteriaceae family (Actinobacteria phylum) and early colonization with the Staphylococcaceae family (Firmicutes phylum; 3 weeks until 9 months) were associated with control subjects (adjusted P < .05). The only difference between the rhinitis and control groups was a reduced abundance of the Corynebacteriaceae family (adjusted P < .05). Determinants of nasal microbiota succession included sex, mode of delivery, presence of siblings, and infant care attendance. CONCLUSION: Our results support the hypothesis that the nasal microbiome is involved in development of early-onset rhinitis and wheeze in infants

Fuzzy association rules vs fuzzy associative patterns in defending against web service attacks

This paper presents a fuzzy association rule-based intrusion detection and prevention (FAR IDP) system that is implemented within an e-commerce Web service-based application. This system compares the effectiveness and efficiency of using 20 fuzzy association rules compared to 366 fuzzy associative patterns (FAP) to determine whether to definitely grant access to normal transaction, probably deny access for suspicious transaction or certainly deny access to transactions which may contain malicious inputs or XML content. Experimental results from our FAR IDP system have demonstrated that both rules-based and pattern-based algorithms are able to detect, prevent and predict Web service attacks such as SQL injection, XML injection, DoS and SOAP oversized close to real-time, with detection accuracy of not lower than 99%. There is also a slight difference in terms of time; the transaction time for FAP is almost doubled that of FAR's in ms. Additionally, with a transaction time of less than 0.25ms and a detection accuracy of greater than 99%, our FAR IDP has outperformed many other fuzzy and Web service-based IDP systems

Policy-enhanced ANFIS model to counter SOAP-related attacks

Business Intelligence or e-commerce applications are increasingly built on the Web Service platform. Thus, SOAP-related attacks have a higher chance of occurring at the Application Layer. Although active research has been on-going in Host and Network-based intrusion detection and intrusion prevention areas, they are not adequate to countermeasure the attacks occurring at the Application Layer. This is detrimental, especially for e-commerce where sensitive and huge amount of business-related information are being exposed over the Internet. Consequently, in this paper, a policy-enhanced fuzzy model with adaptive neuro-fuzzy inference system features is introduced. Transactions generated by simulation reveal that SOAP-related attacks at the Application Layer can be detected and prevented by validating input values, input field lengths, and SOAP size using our model to classify the possibilities of granting or denying access to the backend application or database. Restricting the inputs using business policies further strengthens the model to be able to achieve detection accuracy of 99% and false positive rate of only 1%. Thus, our model has significantly contributed to an added layer of security protection for Web Service-based e-commerce applications. (C) 2012 Elsevier B.V. All rights reserved

Defending against XML-related attacks in e-commerce applications with predictive fuzzy associative rules

Security administrators need to prioritise which feature to focus on amidst the various possibilities and avenues of attack, especially via Web Service in e-commerce applications. This study addresses the feature selection problem by proposing a predictive fuzzy associative rule model (FARM). FARM validates inputs by segregating the anomalies based fuzzy associative patterns discovered from five attributes in the intrusion datasets. These associative patterns leads to the discovery of a set of 18 interesting rules at 99% confidence and subsequently, categorisation into not only certainly allow/deny but also probably deny access decision class. FARM's classification provides 99% classification accuracy and less than 1% false alarm rate. Our findings indicate two benefits to using fuzzy datasets. First, fuzzy enables the discovery of fuzzy association patterns, fuzzy association rules and more sensitive classification. In addition, the root mean squared error (RMSE) and classification accuracy for fuzzy and crisp datasets do not differ much when using the Random Forest classifier. However, when other classifiers are used with increasing number of instances on the fuzzy and crisp datasets, the fuzzy datasets perform much better. Future research will involve experimentation on bigger data sets on different data types

Discovering fuzzy association rule patterns and increasing sensitivity analysis of XML-related attacks

Most active research in Host and Network-based Intrusion Detection (ID) and Intrusion Prevention (IP) systems are only able to detect and prevent attacks of the computer systems and attacks at the Network Layer. They are not adequate to countermeasure XML-related attacks. Furthermore, although research have been conducted to countermeasure Web application attacks, they are still not adequate in countering SOAP or XML-based attacks. In this paper, a predictive fuzzy association rule model aimed at segregating known attack patterns (such as SQL injection, buffer overflow and SOAP oversized payload) and anomalies is developed. First, inputs are validated using business policies. The validated input is then fed into our fuzzy association rule model (FARM). Consequently, 20 fuzzy association rule patterns matching input attributes with 3 decision outcomes are discovered with at least 99% confidence. These fuzzy association rule patterns will enable the identification of frequently occurring features, useful to the security administrator in prioritizing which feature to focus on in the future, hence addressing the features selection problem. Data simulated using a Web service e-commerce application are collected and tested on our model. Our model's detection or prediction rate is close to 100% and false alarm rate is less than 1%. Compared to other classifiers, our model's classification accuracy using random forests achieves the best results with RMSE close to 0.02 and time to build the model within 0.02 s for each data set with sample size of more than 600 instances. Thus, our novel fuzzy association rule model significantly provides a viable added layer of security protection for Web service and Business Intelligence-based applications

PeANFIS-FARM Framework in defending against Web Service attacks

Internet-enabled Web Service (WS) applications, such as e-commerce, are facing eXtensible Markup Language (XML)-related security threats. However, network and host-based intrusion (ID) and prevention (IP) systems and Web Service Security (WSS) standards are inadequate in countering against these threats. This paper presents a framework to mitigate XML/SOAP attacks. Our framework comprises of two intelligent models: the policy-enhanced adaptive neuro-fuzzy inference system (PeANFIS) and fuzzy association rule mining (FARM) model. Performance evaluation of each model indicates detection rate of greater than 99% and false alarm rate of less than 1%. In this paper, we aim to help the security administrator to decide which model to implement depending on the context of the situation. We present rule-based cases as examples to guide design and implementation decisions. Our future work shall see the implementation of the PeANFIS-FARM framework on a wider scale and in cloud computing