Clozapine induced pneumonitis: a case report

Clozapine is an atypical antipsychotic used for the treatment of schizophrenia. Clozapine acts by blocking serotonin receptors in the brain, thereby reducing the symptoms of schizophrenia. Clozapine is usually restricted to the treatment of resistant cases of schizophrenia. Clozapine induced pneumonitis is a very rare adverse reaction and, one such incident in a 16-year-old Indian boy is intricated in this case report

A cross sectional study to measure prevalence of DVT in subacute and chronic spinal cord injury patients without any chemical prophylaxis

Background: Prevalence of DVT in patients with sub-acute and chronic SCI has only been reported in a limited number of studies. Knowing the incidence of thromboembolic events in the sub-acute and chronic rehabilitation phase is important to estimate disease risk and facilitate evidence based prevention. We sought to determine the prevalence of DVT in patients of subacute and chronic phases post spinal cord injury without any chemical prophylaxis.Methods: Between June 2016 and April 2018, all cases of sub-acute and chronic spinal cord injury, undergoing rehabilitation at our centre were studied. Patients with pre-existing coagulopathy/hypercoagulable state/ bleeding diathesis or on medications for these conditions, tobacco smokers, chronic alcoholics and obese individuals were excluded from the study. All patients enrolled in the study were given mechanical DVT prophylaxis and followed institutional rehabilitation protocol. They were evaluated at 3 months, 6 months and 9 months by clinical examination and CDFI for any evidence of DVT.Results: Out of 60 patients studied, 04 patients developed DVT (3 in ASIA grade A and 1 in ASIA grade B patient). 75% (3 cases) of the cases were detected in the first 3 months and only one case was detected between 3-6 moths post Spinal cord injury. The prevalence of DVT in our study, in subacute and chronic cases of spinal cord injury was 6.67%.Conclusions: Our study is in concurrence with the existing literature about the low prevalence of DVT in Southeast Asian population which doesn’t warrant DVT chemoprophylaxis in subacute and chronic SCI cases

Design of Blind Assistance System Using Refreshable Braille Display

In this paper, we demonstrate that Braille embosser is a type of device which is very useful for the initial stage Braille language learner. Here the input is provided with the help of serial port by mobile through Bluetooth module. This input is in the form of alphabetical form so the input is converted into Braille language which is displayed with the help of actuators. Blind assistance system also allows visually impaired people to do common tasks such as reading in Braille and reading documents. It is a portable device, they can carry wherever they want so that they could get information of place they are visiting with ease. As this device detect the obstacles and also determine at what distance obstacle is person can safely use this device for indoor purpose

Credibility Evaluation of User-generated Content using Novel Multinomial Classification Technique

Awareness about the features of the internet, easy access to data using mobile, and affordable data facilities have caused a lot of traffic on the internet. Digitization came with a lot of opportunities and challenges as well. One of the important advantages of digitization is paperless transactions, and transparency in payment, while data privacy, fake news, and cyber-attacks are the evolving challenges. The extensive use of social media networks and e-commerce websites has caused a lot of user-generated information, misinformation, and disinformation on the Internet. The quality of information depends upon various stages (of information) like generation of information, medium of propagation, and consumption of information. Content being user-generated, information needs a quality assessment before consumption. The loss of information is also necessary to be examined by applying the machine learning approach as the volume of content is extremely huge. This research work focuses on novel multinomial classification (based on multinoulli distribution) techniques to determine the quality of the information in the given content. To evaluate the information content a single algorithm with some processing is not sufficient and various approaches are necessary to evaluate the quality of content.  We propose a novel approach to calculate the bias, for which the Machine Learning model will be fitted appropriately to classify the content correctly. As an empirical study, rotten tomatoes’ movie review data set is used to apply the classification techniques. The accuracy of the system is evaluated using the ROC curve, confusion matrix, and MAP

Finger Millet:A "Certain" Crop for an "Uncertain" Future and a Solution to Food Insecurity and Hidden Hunger under Stressful Environments

Crop growth and productivity has largely been vulnerable to various abiotic and biotic stresses that are only set to be compounded due to global climate change. Therefore developing improved varieties and designing newer approaches for crop improvement against stress tolerance have become a priority now-a-days. However, most of the crop improvement strategies are directed toward staple cereals such as rice, wheat, maize etc., whereas attention on minor cereals such as finger millet [Eleusine coracana (L.) Gaertn.] lags far behind. It is an important staple in several semi-arid and tropical regions of the world with excellent nutraceutical properties as well as ensuring food security in these areas even during harsh environment. This review highlights the importance of finger millet as a model nutraceutical crop. Progress and prospects in genetic manipulation for the development of abiotic and biotic stress tolerant varieties is also discussed. Although limited studies have been conducted for genetic improvement of finger millets, its nutritional significance in providing minerals, calories and protein makes it an ideal model for nutrition-agriculture research. Therefore, improved genetic manipulation of finger millets for resistance to both abiotic and biotic stresses, as well as for enhancing nutrient content will be very effective in millet improvementpublishersversionPeer reviewe

Evaluation of actinomycete isolates obtained from herbal vermicompost for the biological control of Fusarium wilt of chickpea

A total of 137 actinomycetes cultures, isolated from 25 different herbal vermicomposts, were characterized for their antagonistic potential against Fusarium oxysporum f. sp. ciceri (FOC) by dual-culture assay. Of the isolates, five most promising FOC antagonistic isolates (CAI-24, CAI-121, CAI-127, KAI-32 and KAI-90) were characterized for the production of siderophore, cellulase, protease, hydrocyanic acid (HCN), indole acetic acid (IAA) and antagonistic potential against Rhizoctonia bataticola, which causes dry root rot in chickpea (three strains viz. RB-6, RB-24 and RB-115) and sorghum (one strain). All of the five FOC antagonistic isolates produced siderophore and HCN, four of them (except KAI-90) produced IAA, KAI-32 and KAI-90 produced cellulase and CAI-24 and CAI-127 produced protease. In the dual-culture assay, three of the isolates, CAI-24, KAI-32 and KAI-90, also inhibited all three strains of R. bataticola in chickpea, while two of them (KAI-32 and KAI-90) inhibited the tested strain in sorghum. When the FOC antagonistic isolates were evaluated further for their antagonistic potential in the greenhouse and wilt-sick field conditions on chickpea, 45–76% and 4–19% reduction of disease incidence were observed, respectively compared to the control. The sequences of 16S rDNA gene of the isolates CAI-24, CAI-121, CAI-127, KAI-32 and KAI-90 were matched with Streptomyces tsusimaensis, Streptomyces caviscabies, Streptomyces setonii, Streptomyces africanus and an identified species of Streptomyces, respectively using the BLAST searching. This study indicated that the selected actinomycete isolates have the potential for biological control of Fusarium wilt disease in chickpea

Design Exploration of EMBRACE Hardware Spiking Neural Network Architecture and Applications

The operation and structure of the human brain has inspired the development of next generation smart embedded computing systems. The cognitive abilities of the human brain have been partially explained by the dense and complex interconnection of neurons and synapses, where each neuron connects to thousands of other neurons and communicates through short transient pulses (spikes) along synaptic links. Brain-inspired computing paradigms such as Spiking Neural Networks (SNNs) mimic the key functions of the human brain and have the potential to offer smart and adaptive solutions for complex real world problems. The main design challenges for the realisation of practical hardware SNN systems are large scale simulation and performance measurement, compact hardware implementation, architectural scalability, application reliability, low power consumption, efficient and accurate SNN learning/training algorithms, compact implementation of complex neural models, fault tolerance and application design methodologies. This thesis contributes to the development of EMBRACE, a compact, scalable, modular, hardware SNN architecture, as an embedded computing platform. The thesis presents a prototype implementation of the EMBRACE architecture on a Xilinx Virtex-6 FPGA and demonstrates reliable, practical embedded classifier and control applications. The thesis contributes to a number of hardware SNN system design challenges such as hardware SNN simulations and performance measurement, compact hardware implementation, architectural scalability, application reliability and efficient practical application design. The research is organised in four distinct phases as follows: Simulation andPerformanceMeasurement ofHardware SNNSystems: The thesis presents EMBRACE-SysC, a SystemC simulation-based design exploration framework for Network on Chip (NoC) based hardware SNN architectures. EMBRACE-SysC incorporates performance measurement and reporting capabilities including spike communication infrastructure, neuron model validation, hardware architecture design exploration and SNN application evolution, used in later phases of this research. Architectural Techniques for Scalability: The storage of large synaptic connectivity information in hardware SNNs translates to poorly scalable, large distributed on-chip memory in hardware SNN architectures. Inspired by the modular organisation of the human brain, this thesis presents a hardware Modular Neural Tile (MNT) architecture that reduces the memory requirement of the architecture using a combination of fixed and configurable synaptic connections. The silicon footprint of the architecture is reduced by an average of 66% for practical SNN application topologies, as compared to the previously reported EMBRACE architecture. Interconnect Architecture for SNN Application Reliability: Distortion in spike timings impacts the accuracy of SNN operation by modifying the precise ring time of neurons within the SNN. The thesis presents an in-depth, simulation-based analysis of the synaptic information jitter in NoC based hardware SNNs. The thesis presents a ring topology NoC architecture using a timestamped, spike broadcast flow control technique that offers fixed spike transfer latency under various network traffic conditions, to provide reliable SNN application behaviour. Modular Application Design: Efficient implementation and training of large scale embedded applications on hardware SNN architectures poses a serious challenge due to the lack of suitable application design methodologies. _e thesis presents the modular application design of a robotic navigational controller application implemented on the EMBRACE FPGA prototype. Results indicate faster application evolution as compared to monolithic application SNNs. The stepwise knowledge integration and simplified SNN training facilitate rapid application prototyping