Teratogenic effects of carbamazepine on embryonic eye development in pregnant mice

Background: Carbamazepine is an antiepileptic drug used widely for the treatment of epileptic seizures and neuropathic pain. Several malformations in humans, mainly neural tube defects, have been reported as a consequence of its use during pregnancy. The association between maternal use of carbamazepine and congenital eye malformations is not very well understood. Objective: The purpose of this study was to examine this association after intraperitoneal injection of carbamazepine during the period of organogenesis in mice. Methods: Balb/c timed-pregnant mice were divided into 4 experimental and control groups. Two experimental groups received daily intraperitoneal injections of 15mg/kg (group I) or 30mg/kg (group II) of carbamazepine on gestational days 6 to 15. Two control groups received normal saline or Tween 20 (polysorbate 20). Dams underwent Cesarean section on gestational day 18 and embryos were harvested. External examination for eye malformations, routine histological processing of malformed fetuses to study eye morphology, and skeletal staining were performed. Results: The mean weight and crown-rump of the fetuses in both experimental groups were significantly reduced compared with those of the control groups. Various malformations were detected such as brachygnathia, calvarial deformity, vertebral deformity, short tail, and brachydactyly. Premature opening of one or both eyes with mild to severe exophthalmos occurred in the 2 experimental groups. Deformed lens, retinal folds with undeveloped layers, and corneal folds with absence of surface epithelium were detected in both experimental groups. Conclusions: This study, to the best of our knowledge, showed for the first time that intraperitoneal administration of carbamazepine at clinically comparable doses during organogenesis can induce several eye malformations in mice. The implication of these results needs to be considered when carbamazepine is administered during human pregnancy. © 2010 Informa UK Ltd

Development of the Persian version of gross motor function measure-88 (GMFM-88): A study of reliability

The aims of this study were to cross-culturally translate and equivalence of Gross Motor Function Measure 88 (GMFM-88) in to Persian and to evaluate its reliability in the 50 children with Cerebral Palsy (CP). Our investigation was a none-experimental and methodological study which developed a Persian translation of GMFM-88 based on International Quality of Life Assessment (IQLA) guidelines. Inter-rater reliability was perfumed by comparison of scores recorded by two expert physiotherapists in a blind pattern while inter-rater reliability was assessed by comparison of scores recorded by an expert physiotherapist in two continuous weeks. Intra-class Correlation Coefficient (ICC) was used to evaluate both reliabilities. Additionally internal consistency was calculated using Cronbach's alpha coefficient. The ICC was 0.99 for both inter-rater reliability and intra-rater reliability with 95 Confidence Interval (CI) = 0.99-1. Cronbach�s alpha coefficients for all dimensions of GMFM-88 were ranged 0.78-0.94, which showed an acceptable internal consistency. The Persian version of GMFM-88 which indicated high internal consistency is a reliable instrument to quantifying gross motor function in children with CP and to following efficacy of various rehabilitation and medical treatments in these patients. © 2015 Academic Journals Inc

Synthetic Data Generation and Defense in Depth Measurement of Web Applications

Measuring security controls across multiple layers of defense requires realistic data sets and repeatable experiments. However, data sets that are collected from real users often cannot be freely exchanged due to privacy and regulatory concerns. Synthetic datasets, which can be shared, have in the past had critical flaws or at best been one time collections of data focusing on a single layer or type of data. We present a framework for generating synthetic datasets with normal and attack data for web applications across multiple layers simultaneously. The framework is modular and designed for data to be easily recreated in order to vary parameters and allow for inline testing. We build a prototype data generator using the framework to generate nine datasets with data logged on four layers: network, file accesses, system calls, and database simultaneously. We then test nineteen security controls spanning all four layers to determine their sensitivity to dataset changes, compare performance even across layers, compare synthetic data to real production data, and calculate combined defense in depth performance of sets of controls

CLort: High Throughput and Low Energy Network Intrusion Detection on IoT Devices with Embedded GPUs

While IoT is becoming widespread, cyber security of its devices is still a limiting factor where recent attacks (e.g., the Mirai bot-net) underline the need for countermeasures. One commonly-used security mechanism is a Network Intrusion Detection System (NIDS), but the processing need of NIDS has been a significant bottleneck for large dedicated machines, and a show-stopper for resource-constrained IoT devices. However, the topologies of IoT are evolving, adding intermediate nodes between the weak devices on the edges and the powerful cloud in the center. Also, the hardware of the devices is maturing, with new CPU instruction sets, caches as well as co-processors. As an example, modern single board computers, such as the Odroid XU4, come with integrated Graphics Processing Units (GPUs) that support general purpose computing. Even though using all available hardware efficiently is still an open issue, it has the promise to run NIDS more efficiently.In this work we introduce CLort, an extension to the well-known NIDS Snort that a) is designed for IoT devices b) alleviates the burden of pattern matching for intrusion detection by offloading it to the GPU. We thoroughly explain how our design is used as part of the latest release of Snort and suggest various optimizations to enable processing on the GPU. We evaluate CLort in regards to throughput, packet drops in Snort, and power consumption using publicly available traffic traces.\ua0CLort achieves up to 52% faster processing throughput than its CPU counterpart. CLort can also analyze up to 12% more packets than its CPU counterpart when sniffing a network.\ua0Finally, the experimental evaluation shows that CLort consumes up to 32% less energy than the CPU counterpart, an important consideration for IoT devices

Effectiveness of entropy-based features in high-and low-intensity DDoS attacks detection

DDoS attack detection using entropy-based features in network traffic has become a popular approach among researchers in the last five years. The use of traffic distribution features constructed using entropy measures has been proposed as a better approach to detect Distributed Denial of Service (DDoS) attacks compared to conventional volumetric methods, but it still lacks in the generality of detecting various intensity DDoS attacks accurately. In this paper, we focus on identifying effective entropy-based features to detect both high- and low-intensity DDoS attacks by exploring the effectiveness of entropy-based features in distinguishing the attack from normal traffic patterns. We hypothesise that using different entropy measures, window sizes, and entropy-based features may affect the accuracy of detecting DDoS attacks. This means that certain entropy measures, window sizes, and entropy-based features may reveal attack traffic amongst normal traffic better than the others. Our experimental results show that using Shannon, Tsallis and Zhou entropy measures can achieve a clearer distinction between DDoS attack traffic and normal traffic than Rényi entropy. In addition, the window size setting used in entropy construction has minimal influence in differentiating between DDoS attack traffic and normal traffic. The result of the effectiveness ranking shows that the commonly used features are less effective than other features extracted from traffic headers

Security of data science and data science for security

In this chapter, we present a brief overview of important topics regarding the connection of data science and security. In the first part, we focus on the security of data science and discuss a selection of security aspects that data scientists should consider to make their services and products more secure. In the second part about security for data science, we switch sides and present some applications where data science plays a critical role in pushing the state-of-the-art in securing information systems. This includes a detailed look at the potential and challenges of applying machine learning to the problem of detecting obfuscated JavaScripts

A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed

The potassium channel blocker, dalfampridine diminishes ouabain-induced arrhythmia in isolated rat atria

The aim of the present experiment was to investigate the possible antiarrhythmic effects of dalfampridine in ouabain-induced arrhythmia in rats. Twenty-four male rats including the control and dalfampridine-incubated (100 µM to 10 mM) ouabain-stimulated (40 µM) groups were used. After induction of anesthesia, the atria were isolated and the time of onset of arrhythmia and asystole were recorded. The contractile force of atria was also measured. Dalfampridine at concentration of 1 mM significantly postponed the onset of arrhythmia and asystole compared to control group (p �.05). Ouabain significantly increased the atrial beating rate in control group (p �.05), while pretreatment of isolated atria with dalfampridine reversed this effect. Incubation of isolated atria with ouabain did not alter the contractile force in both control- and dalfampridine-treated groups (p >.05). It is concluded that dalfampridine might possess antiarrhythmic properties in reducing the atrial arrhythmias. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group