Acute Administration of Clozapine and Risperidone Altered Dopamine Metabolism More in Rat Caudate than in Nucleus Accumbens: A Dose-Response Relationship

The present study compares the extrapyramidal and neurochemical effects of clozapine and risperidone in rat caudate (corpus striatum) and nucleus accumbens (ventral striatum) dose-dependently. Animals injected with clozapine (2.5, 5.0 and 10.0 mg/kg IP) or risperidone (1.0, 2.5 and 5.0 mg/kg IP) in acute were sacrificed 1 h later to collect brain samples. Extrapyramidal side effects (EPS) in terms of locomotor activity and catalepsy were monitored in each animal after the drug or vehicle administration. Maximum cataleptic potentials were found only at high doses of clozapine (10.0 mg/kg; 60%) and risperidone (5.0 mg/kg; 100%). Neurochemical estimations were carried out by HPLC-EC. Both drugs at all doses significantly (p<0.01) increased the concentration of homovanillic acid (HVA), a metabolite of DA, in the caudate nucleus and decreased in nucleus accumbens. Levels of Dihydroxyphenylacetic acid (DOPAC) significantly (p<0.01) increased in the caudate by clozapine administration and decreased in the nucleus accumbens by the administration of both drugs in a dose-dependent manner. 5-Hydroxyindoleacetic acid (5-HIAA), the predominant metabolite of serotonin significantly decreased in the caudate and nucleus accumbens in a similar fashion. Levels of tryptophan (TRP) were remained insignificant in caudate and nucleus accumbens by the injections of two drugs. In caudate, clozapine and risperidone administrations significantly (p<0.01) decreased HVA/DA ratio and increased DOPAC/DA ratio in nucleus accumbens at all doses. The findings suggest the evidence for DA/5-HT receptor interaction as an important link in the lower incidence of EPS. The possible role of serotonin1A receptors in the pathophysiology of schizophrenia is also discussed

Effects of chronic mild stress on apomorphine induced behavioral sensitization in different brain regions of rats in relation to serotonin change

Background: The impacts of unpredictable stressors have influence on neurochemical and behavioral parameters in laboratory animals. Stress induced behavioral changes particularly those associated with anxiety like behavior may activate topographically organized mesolimbic cortical serotonergic system. This study was designed to investigate the influence of unpredictable stress on behavioral and neurochemical parameters in apomorphine treated rats.Methods: Initially, the animals were divided into two groups as Unstressed and stressed (uncontrollable chronic mild stress or UCMS). Both groups of animals were subdivided into two groups; i.e. saline and apomorphine administrated animals at dose 1.0 mg/kg. Behavioral manipulations was observed by monitoring the locomotor activity and exploratory activity. Neurochemical estimation of 5-hydroxytryptamine (5-HT) was done by High performance liquid chromatography (HPLC).  Animals were decapitated 24hr post apomorphine injection and different regions of brain (dorsal and ventral striatum), of animals were collected and stored at -70°C.Results: This preclinical study showed that the UCMS induced hypophagia were promoted in apomorphine administrated animals. Apomorphine induced hyperlocomotion were more prominent in unstressed animals than that of stressed groups.  It implies that apomorphine is effective in the retrieval from UCMS induced depressive symptoms in rats. Neurochemical study showed decreased level of 5-HT in unstressed animals than stressed animals in response to apomorphine administration.Conclusion: This study, therefore establish the relation between stress and addiction at behavioral as well as neurochemical level to better understand the idea whether intolerable stress promotes addiction

An Automated Text Mining Approach for Classifying Mental-Ill Health Incidents from Police Incident Logs for Data-Driven Intelligence

Data-driven intelligence can play a pivotal role in enhancing the effectiveness and efficiency of police service provision. Despite of police organizations being a rich source of qualitative data (present in less formally structured formats, such as the text logs), little work has been done in automating steps to allow this data to feed into intelligence-led policing tasks, such as demand analysis/prediction. This paper examines the use of police incident logs to better estimate the demand of officers across all incidents, with particular respect to the cases where mental-ill health played a primary part. Persons suffering from mental-ill health are significantly more likely to come into contact with the police, but statistics relating to how much actual police time is spent dealing with this type of incident are highly variable and often subjective. We present a novel deep learning based text mining approach, which allows accurate extraction of mental-ill health related incidents from police incident logs. The data gained from these automated analyses can enable both strategic and operational planning within police forces, allowing policy makers to develop long term strategies to tackle this issue, and to better plan for day-today demand on services. The proposed model has demonstrated the cross-validated classification accuracy of 89.5% on the real dataset

Rhetorical Devices and Stylistic Features of Qur'anic Grammar

The language and style of the Qur’an have attracted a large amount of work by Western scholars. Yet there are two neglected areas: balāgha in Arabic (rhetoric), and certain aspects of Qur’anic style. This chapter sheds some light on both. Balāgha was developed to understand the finest aspects of Qur’anic Arabic. It is important in Arabic education, even in secondary schools, but not in the Western tradition of teaching Arabic. ʿIlm al-maʿanī (the study of meanings) is the most neglected part of balāgha. A summary is given here of its main topics, which may help to remedy some serious misunderstandings of the language and effect of the Qur’an. Whereas Western works on style have largely concentrated on figurative language, jinās, sajʿ, etc., other aspects of style are discussed that are more informative of the way the Qur’an presents its message and makes it effective

Sūrat Maryam (Q. 19): Comforting Muḥammad

Mary is a very important figure in the Qur'an, in which she is mentioned by name 34 times. In addition to passing references, such as in Q. 23:50 and Q. 66:12, two important Qur'anic pericopes are devoted to her: Q. 3:33–50 and Q. 19:16–36. Over and above this, Sura 19 is named after her. Much has been written about this sura, with recent scholarship giving particular attention to its structure. Identifying the structure within the sura is of course important, especially in view of the way the material is presented in the muṣḥaf, which sets out the entire sura in continuous text, without any divisions or paragraphs apart from the markers at the end of each verse. Structural analysis can be useful in terms of identifying main subject topics of a given sura, but there is a danger that overreliance on structural analysis can focus too heavily on form, thereby overlooking the message and purpose that runs throughout each sura. Form and literary analysis are important but only insofar as they indicate topics and meanings and show the purpose of the entire sura. In this article, I seek to undertake a structural analysis of Sūrat Maryam which identifies a different structure from those outlined by previous scholars. This structural analysis is based in, and led by, the study of the theme and purpose of this sura, which I maintain is to provide comfort and reassurance for the Prophet Muḥammad

Automatic extraction of retinal features to assist diagnosis of glaucoma disease

Glaucoma is a group of eye diseases that have common traits such as high eye pressure, damage to the Optic Nerve Head (ONH) and gradual vision loss. It affects the peripheral vision and eventually leads to blindness if left untreated. The current common methods of diagnosis of glaucoma are performed manually by the clinicians. Clinicians perform manual image operations such as change of contrast, zooming in zooming out etc to observe glaucoma related clinical indications. This type of diagnostic process is time consuming and subjective. With the advancement of image and vision computing, by automating steps in the diagnostic process, more patients can be screened and early treatment can be provided to prevent any or further loss of vision. The aim of this work is to develop a system called Glaucoma Detection Framework (GDF), which can automatically determine changes in retinal structures and imagebased pattern associated with glaucoma so as to assist the eye clinicians for glaucoma diagnosis in a timely and effective manner. In this work, several major contributions have been made towards the development of the automatic GDF consisting of the stages of preprocessing, optic disc and cup segmentation and regional image feature methods for classification between glaucoma and normal images. Firstly, in the preprocessing step, a retinal area detector based on superpixel classification model has been developed in order to automatically determine true retinal area from a Scanning Laser Ophthalmoscope (SLO) image. The retinal area detector can automatically extract artefacts out from the SLO image while preserving the computational effciency and avoiding over-segmentation of the artefacts. Localization of the ONH is one of the important steps towards the glaucoma analysis. A new weighted feature map approach has been proposed, which can enhance the region of ONH for accurate localization. For determining vasculature shift, which is one of glaucoma indications, we proposed the ONH cropped image based vasculature classification model to segment out the vasculature from the ONH cropped image. The ONH cropped image based vasculature classification model is developed in order to avoid misidentification of optic disc boundary and Peripapillary Atrophy (PPA) around the ONH of being a part of the vasculature area. Secondly, for automatic determination of optic disc and optic cup boundaries, a Point Edge Model (PEM), a Weighted Point Edge Model (WPEM) and a Region Classification Model (RCM) have been proposed. The RCM initially determines the optic disc region using the set of feature maps most suitable for the region classification whereas the PEM updates the contour using the force field of the feature maps with strong edge profile. The combination of PEM and RCM entitled Point Edge and Region Classification Model (PERCM) has significantly increased the accuracy of optic disc segmentation with respect to clinical annotations around optic disc. On the other hand, the WPEM determines the force field using the weighted feature maps calculated by the RCM for optic cup in order to enhance the optic cup region compared to rim area in the ONH. The combination of WPEM and RCM entitled Weighted Point Edge and Region Classification Model (WPERCM) can significantly enhance the accuracy of optic cup segmentation. Thirdly, this work proposes a Regional Image Features Model (RIFM) which can automatically perform classification between normal and glaucoma images on the basis of regional information. Different from the existing methods focusing on global features information only, our approach after optic disc localization and segmentation can automatically divide an image into five regions (i.e. optic disc or Optic Nerve Head (ONH) area, inferior (I), superior(S), nasal(N) and temporal(T)). These regions are usually used for diagnosis of glaucoma by clinicians through visual observation only. It then extracts image-based information such as textural, spatial and frequency based information so as to distinguish between normal and glaucoma images. The method provides a new way to identify glaucoma symptoms without determining any geometrical measurement associated with clinical indications glaucoma. Finally, we have accommodated clinical indications of glaucoma including the CDR, vasculature shift and neuroretinal rim loss with the RIFM classification and performed automatic classification between normal and glaucoma images. Since based on the clinical literature, no geometrical measurement is the guaranteed sign of glaucoma, the accommodation of the RIFM classification results with clinical indications of glaucoma can lead to more accurate classification between normal and glaucoma images. The proposed methods in this work have been tested against retinal image databases of 208 fundus images and 102 Scanning Laser Ophthalmoscope (SLO) images. These databases have been annotated by the clinicians around different anatomical structures associated with glaucoma as well as annotated with healthy or glaucomatous images. In fundus images, ONH cropped images have resolution varying from 300 to 900 whereas in SLO images, the resolution is 341 x 341. The accuracy of classification between normal and glaucoma images on fundus images and the SLO images is 94.93% and 98.03% respectively

Design and Implementation of Power System Optimization Using Particles Swarm Algorithms

This study aims to develop a power system optimization simulation environment for one of Electrical Supply Grid in Peshawar Region where theoretical, calculated and collected data is used and proposes enhancements in the distribution system as per the results of the simulation of the Particle Swarm Optimizer and Newton Rhapson Algorithm on it. The simulation framework is cross-evaluated on the IEEE-30 Bus bar system and compared with eminent researches in this field. The results are plotted and tabulated first as a comparison and then as a proposed model for Peshawar Region’s selected substations and the involved grid

A Taxonomy on Misbehaving Nodes in Delay Tolerant Networks

Delay Tolerant Networks (DTNs) are type of Intermittently Connected Networks (ICNs) featured by long delay, intermittent connectivity, asymmetric data rates and high error rates. DTNs have been primarily developed for InterPlanetary Networks (IPNs), however, have shown promising potential in challenged networks i.e. DakNet, ZebraNet, KioskNet and WiderNet. Due to unique nature of intermittent connectivity and long delay, DTNs face challenges in routing, key management, privacy, fragmentation and misbehaving nodes. Here, misbehaving nodes i.e. malicious and selfish nodes launch various attacks including flood, packet drop and fake packets attack, inevitably overuse scarce resources (e.g., buffer and bandwidth) in DTNs. The focus of this survey is on a review of misbehaving node attacks, and detection algorithms. We firstly classify various of attacks depending on the type of misbehaving nodes. Then, detection algorithms for these misbehaving nodes are categorized depending on preventive and detective based features. The panoramic view on misbehaving nodes and detection algorithms are further analyzed, evaluated mathematically through a number of performance metrics. Future directions guiding this topic are also presented