Miracle-dz/UniFAR-FTUs huggingface dataset:
An autopsy study of pathology of liver in tuberculosis

Background: Hepatic tuberculosis (TB) is an uncommon manifestation of tuberculosis. The term hepatobiliary TB refers to either isolated hepatic or hepatobiliary involvement with other organ involvement Methods: A five year autopsy study conducted of 236 adult cases of tuberculosis. Clinical details of all cases were obtained from hospital records. Sections from the liver were processed routinely and histopathological findings along with Clinical & biochemical findings were analysed in detail. Results: Commonest age group affected was 31 to 40 years of age with a male: female ratio of 1.29:1. Fever was present in (56.6%). Hepatomegaly along with abdominal pain was significantly associated with Hepatic involvement by TB. Pulmonary and extra pulmonary tuberculosis was seen in 177 and 59 cases respectively. Liver involvement was mainly secondary except in one case. The spectrum of histopathological changes of the liver showed epitheliod cell granulomas in (41.5%), fatty changes in (46.6%), inflammation in (16.5%) and fibrosis in (3.8%) cases. On comparing the liver TB and non-liver TB cases, it was found that fatty changes were more common but there was no difference in the degree of fibrosis in the two groups. Comparing liver function tests, available in 213/236 cases, 61% were abnormal. Deranged AST (65%), ALT (62.05%) & ALP (57. 05%) was seen in hepatic TB cases. Conclusion: Liver involvement was mainly in the form of secondary tuberculosis. Fever, hepatomegaly, abdominal pain, fatty change and caseating granulomas along with deranged liver enzymes are significantly associated with hepatic involvement in cases of tuberculosis.
THE WHO’S WHO OF PLANT VIRUSES: A COGNITIVE APPROACH

Viral diseases in plants pose a serious threat to the plant production. Plant viruses are among the major factors that affect productivity and cause vast economic losses. Across the globe plant viruses persist to be a major threat to vegetation of fruits, vegetables, ornamental crops, etc. and their prevention and control are the major areas of concern. Over the past century, efforts have been put to understand the underlying mechanism of defense against plant viruses and reasons for this intercellular invasion. Resistance to plant viruses can be offered by several means such as conventional breeding methods, chemotherapy, thermotherapy and plant biotechnological interventions (plant tissue culture technology and production of transgenic plants). The present work is an attempt to understand the biology and mechanism of plant viral infections, the dangers posed by them and all possible alternatives for prevention. Keywords: Classification, Morphology, Transmission, Virus-free plant production, Genetic engineering.
Study of Environmental Stress Signaling in Bacillus subtilis via components of RsbR paralogues

Bacillus subtilis has found to respond the signals of environmental and metabolic stress by inducing over 40 general stress genes which are under the control of the sigma B transcription factor. Sigma B is an alternative sigma-factor in Bacillus subtilis. It mediates the response of the cell to a variety of physical insults. General stress response of Bacillus subtilis is regulated directly by a partner-switching mechanism via key protein interactions and transcription factor sigma B expression plays important role on it. Physical stress is communicated to sigmaB via a large-molecular-mass (>106-Da) structure (i.e. called the stressosome) formed by one or more members of a family of homologous proteins (RsbR, YkoB, YojH, YqhA). Signals of energy or environmental stress are conveyed to sigma B by independent pathways, each terminating with a differentially regulated serine phosphatase (i.e. for serine phosphorylation), whose activity is required to control the partner-switching regulators. In B. subtilis genome, six paralogous proteins such as YetI, YezB, YkoB, YojH, YqhA, and YtvA are found which has significant similarity to RsbR. Key word: Bacillus subtilis, RsbR, environmental stress, sigma B genes, pathways __________________________________________________*****_________________________________________________
An Optimal Approach for Intrusion Security in Cloud

Cloud computing is internet based technology that provides online resources on pay per use basis. This computing technology is dependent on virtualization. By registering with cloud providers customers can use services (soft and hard) on pay per use basis. Customers are getting services in the form of virtual machines, they interact with these virtual machines to do their jobs. In recent survey it is found that security is the top most concern associated with cloud services. Customers are always afraid of adopting clouds services as their data are stored out of their network boundary and administration. Researchers in cloud are trying to develop security models to protect cloud services from intrusions including internal and external. In this paper we have proposed an intrusion security model that uses the optimum cloud resources and increases the resource and service availability.
Optimal Resource Provosioning in Cloud Computing

Cloud computing is a technology that provides a variety of dynamically scalable and virtualized computing resources ranging from servers and storage to enterprise applications, in pay-as-you-use model via internet. Companies are able to rent resources from cloud on demand, so that their infrastructure cost can be reduced significantly. Multiple cloud users can request number of cloud services/resources simultaneously. So there must be provision that all resources are made available to requesting user in an efficient manner. However one of the major pitfalls related to this is the optimization of resources being allocated. It must be performed with the objectives of minimizing the costs associated with it, meeting customer demand and application requirements. In this paper a review of various policies for dynamic resource allocation in cloud computing is shown based on Topology Aware Resource Allocation (TARA), Linear Scheduling Strategy for Resource Allocation and Dynamic Resource Allocation for Parallel Data Processing. Moreover, significance, advantages and limitations of using Resource Allocation in Cloud computing systems is also discussed. It is believed that this paper would benefit both cloud users and researchers in overcoming the challenges faced.
On the Design of Triangular Microstrip Antenna for Wireless Communication

Microstrip patch antenna has attracted wide interest due to its important properties such as light weight ,low profile ,simple to fabricate and easy to integrate with RF devices. In this paper very small size triangular microstrip antenna is designed by using method of moments (MoM) based IE3D software for wireless communication. The proposed antenna is compact, small, simple to design and fabricate. This antenna can be used for transmission and reception of radio signal at frequency of 13.34GHz. Antenna has a good gain of 4.7858db at resonant frequency of 13.34GHz GHz has large bandwidth of 1.39GHz. Also effect of different
FORMULATION DEVELOPMENT OF MUCOADHESIVEMICROCAPSULES OF METFORMIN HYDROCHLORIDE USINGNATURAL AND SYNTHETIC POLYMERS AND IN VITROCHARACTERIZATION

The objective of this work was to develop optimized and systematically evaluate performances of mucoadhesive microcapsules of antihyperglycemic agent drug Metformin. Alginate microcapsules coated with mucoadhesive natural or synthetic polymers were prepared by Orifice-Ionic Gelation technique utilizing calcium chloride as a cross linking agent. The effect of type (natural or synthetic) and concentration of coating polymers and concentration of alginate on formulation was investigated. Prepared microcapsules were evaluated for Drug content, Entrapment efficiency, Shape, size, In vitro Mucoadhesion and In vitro release. The microcapsules obtained were spherical and free flowing. The microcapsules coated with mucoadhesive polymer Carbopol exhibited good mucoadhesive property in the in vitro Mucoadhesion test and also showed high percentage drug entrapment efficiency. The in vitro release study indicates that carbopol as coating polymer containing formulations showed controlled drug release up to 10 h from microcapsules.
Ausência de Anemia em Escolares de um Município da Região Centro-Oeste do Paraná

Anemia por deficiencia de ferro e resultado do desequilibrio negativo no balanco entre a quantidade de ferro biodisponivel absorvido na dieta e a sua necessidade orgânica, sendo que as criancas sao as mais atingidas. O objetivo do presente trabalho foi identificar a frequencia de anemia ferropriva em tres grupos de criancas que frequentam diferentes escolas municipais no municipio de Campo Mourao, com amostra composta por 55 criancas com idade entre 8 e 12 anos. Apos consentimento dos pais, foi realizado um questionario, seguido de coleta de 5 mL de sangue periferico e realizado hemograma e dosagem de ferro. Para o diagnostico de anemia, foi utilizada, como valor de referencia, a concentracao de hemoglobina <11,5g/dL. Os resultados nao evidenciaram anemia nas criancas em idade escolar.
An Efficient Trust based Routing Model using Ant-Colony Optimization for the Security of WSNs

: Trust-based leach variant routing protocols mixed with ant colony optimization techniques can be one of the good choices to design a trustable and secured wsn. Many optimization techniques like Genetic Algorithms, Fuzzy Logics, Swarm Intelligence have been utilized by researchers to develop efficient routing protocols for wsns.if we discuss about Swarm Intelligence, then Lots of research work has been done already and also lots others are going to be done mainly on 2 colonies.one is bee-colony-optimization(BCO) and another one is ant-colony-optimization(ACO) . This paper proposes ACOTRUST, a trustable leach-based routing method using ACO algorithm to optimize routing with maintaining other parameters like distribution of energy dissipation evenly throughout the sensors, energy efficiency, throughput, security of wsns.
Survey on Lazy Ensemble Methods for Improving Accuracy of Lazy Learner

— In classification, to handle test instances, supervise learning is divided in to two parts Eagar and Lazy learning. Eagar learning build decision theory form training instance and that theory is applied on all test instances. Where Lazy learning is focus on each test instance and provides local optimal solution for each test instance. Diversity is the one issue of lazy learning, it suffers from reduce diversity. It is key issue in combination approach. It is possible to merge lazy and ensemble approach for build lazy learner. Lazy ensemble is build using ensemble approach that uses multiple classifiers and combines its predictions using combination methods and find optimal label. Many approaches for ensemble like lazy stacking, lazy bagging and other methods. Stacking with Lazy learning is used for building Lazy ensemble learner provides desire accuracy. Lazy Stacking Ensemble uses different individual “type” of classifiers as base classifiers for labelling new instance. Survey can be done only on numeric datasets and Lazy stacking is outperforming then other ensemble methods when it compared with other alternative methods in terms of classification accuracy that concern with diversity
Antioxidants In Oral Health

Oral cavity is the mirror to general health as it is interconnected with other systems of the body. The human body has inbuilt defence mechanism against various disease causing agents. A free radical is one of the reactive oxygen species which causes oxidative stress and further lead to cell damage and progression to other severe diseases. Antioxidants are the first line of defence against these free radicals. Antioxidants like beta-carotene, lycopene, selenium are believed to have a preventive role against various oral diseases. This article overviews the different antioxidants available and their influence in the oral cavity.
Modeling and Design of Three-Phase Transformer less Grid-Connected PV Inverters

Three-phase transformer less inverter is widely used in low power photovoltaic (PV) grid-connected systems due to its small size, high efficiency and low cost. When no transformer is used in a grid connected photovoltaic (PV) system, a galvanic connection between the grid and PV array exists. In these conditions, dangerous leakage currents (common-mode currents) can appear through the stray capacitance between the PV array and the ground. The former, in order to create a galvanic isolation between the input and the output include a transformer (mandatory in some countries) that limits the whole system performances in terms of efficiency, weight, size and cost. On the contrary, transformer less inverters do not present any isolation and are characterized by little size, lower cost and higher efficiency (more than 2% higher). Nevertheless, the lack of transformers leads to leakage currents that can be harmful to the human body, as well as for the whole conversion system integrity. In order to avoid the leakage currents, various Transformer less inverters have been proposed using different topologies to generate constant common mode voltage. In this paper, various recently-proposed transformer less PV inverters are investigated. Their performances are compared and analyzed.
Text String Recognition from Natural Scenes by Character Descriptor Map and Stroke Width

Text detection and recognition in natural scene can give valuable information for many applications. However, getting text from images with complex background is challenging task due to less frequency of occurrence text and presence of background outliers resembling text characters. In text detection, algorithms from previous work are applied to localize text region in scene image. First, character descriptor is employed to extract structure features. Second, we tend to designed novel feature representation, stroke configuration map using character boundary and skeleton to build character structure. Our algorithm style is improved to compatible with mobile application. Developed algorithm style is compatible with the appliance of scene text extraction in good mobile devices. The Android-based demo system is developed to highlight the effectiveness of the method of scene text extraction from nearby objects. Also demo system gives the detailed information about algorithm design and performance improvement of text extraction from natural image. The demo system conjointly provides United States some insight into rule design and performance improvement of scene text extraction. The analysis results on benchmark knowledge sets demonstrate that our projected theme of text recognition is comparable the best existing ways. The evaluation results on benchmark datasets demonstrate that proposed framework outperforms in comparison to best existing ways.
Automated Flower Species Detection and Recognition from Digital Images

Automated flower species recognition has been studied for many years. Differences between these studies come from features which were extracted from the flower image, and the recognition algorithm that was used to recognize the flower species. A new automated system was adapted to detect the flower region from the image and recognize its species. Features based on color, texture, and shape were extracted from the interest part only, so the recognition accuracy is increased. New Dataset has been built which contains flowers from Jordan. The result showed a high recognition accuracy of our new dataset. In addition, our proposed system outperforms several methods on Oxfoed17 Dataset.
Risk Factors of Severe Acute Malnutrition among Under-five Children: A Hospital-based Study in Bangladesh

Background: Malnutrition is a major public health problem of developmental concern with both health and socioeconomic consequences. The study was aimed to determine the risk factors of severe acute malnutrition among under-five children. Material and Methods: This hospital-based case control study was conducted from December 2015 to April 2016 whereby a total of 196 children under 5 years (98 cases and 98 controls) whose age ranged from 6 to 60 months were included in the study, data were collected retrospectively using a questionnaire, and mid-upper arm circumference (MUAC) was taken using standard procedures. SPSS statistical software version 19 was used to analyze the dataset. Results: The mean ± standard deviation (SD) age of the cases and controls was 18.34 ± 14.6 and 16.93 ± 14.1 months, respectively, and the mean ± SD MUAC of the cases and controls was 9.36 ± 1.1 and 14.59 ± 2.2 cm, respectively. Risk factors associated with severe acute malnutrition were lack of taking balanced diet more than 3 times/day (adjusted odds ratio [AOR] = 5.355, 95% confidence interval [CI] = 1.162– 24.690), irregular hand washing with soap or other cleaning agents before feeding child (AOR = 0.317, 95% CI = 0.139–0.723), and father’s education level 0–8 class (AOR = 0.190, 95% CI = 0.054–0.676). Conclusions: Severe acute malnutrition was found to be associated with father’s education level, lack of taking balanced diet, and irregular hand washing. Interventions to reduce malnutrition and address the factors contributing to malnutrition should be a policy priority.
Secure Random Sequence based Frequency Hoping Spread Spectrum Audio Watermarking

: Digital watermarking schemes have come to be vital in the last decade because of the copyright protection applications. The watermarking algorithms counsel so distant work merely for digital picture and video only. Scutiny work gave a digital audio watermarking scheme established on range spectrum method for watermark embedding. The method doesn’t demand the early audio messenger gesture after removing watermark the early gesture is utilized merely for gesture analogy alongside the embedded audio. The Aftermath clarify that the watermark embedding is not audible but is additionally extra robust opposing the aggressions like filtering and compression. Examinations have shown that the inaudibility and robustness presentation aims can be achieved. This work displays that the counseled method provides improvements above established Range Spectrum Watermarking. Range spectrum is ought to be utilized by countless watermarking scheme as the data embedding technology. The counseled watermarking method can be effortlessly requested to usefully each audio gesture presently employing Range Spectrum, that seizes instant supremacy of the gains. Also, the method does not need each change in the detection scheme (de-embedding), and in a little cases it might be requested even to arrangements that are by now being utilized.
Deterministic Annealing Framework in MMMs-Induced Fuzzy Co-Clustering and Its Applicability

Initialization problem is a significant issue in FCM-type clustering models, in which alternative optimization is often started with random initial partitions and can be trapped into local optima caused by bad initialization. The deterministic clustering approach is a practical procedure for utilizing a robust feature of very fuzzy partitions and tries to converge the iterative FCM process to a plausible solution by gradually decreasing the fuzziness degree. In this paper, the initialization sensitivity issue is considered in multinomial mixture models-induced fuzzy co-clustering context and a new approach for implementing the deterministic annealing mechanism to fuzzy co-clustering is proposed. The advantages of the proposed approach against the conventional statistical co-clustering model are demonstrated through some numerical experiments.
PHYSIOLOGICAL, MORPHOLOGICAL, BIOCHEMICAL AND MOLECULAR BASIS OF DROUGHT TOLERANCE IN COTTON

Water is the limiting factor for plant growth, it severely affects the crop productivity. Cotton has been resistant to drought, yet it is one of the biggest factors, behind its low productivity. The current study is a comparative analysis to evaluate the physiological, morphological, biochemical and molecular parameters between two cotton varieties, FDH-786-Gossypium arboreum and CIM-496-Gossypium hirsutum, under different levels of drought stress. Plant height, root length, fresh and dry biomass and total leaf area were found to be decreased under drought stress in both the varieties, but significant reduction was observed in case of CIM-496-Gossypium hirsutum. Other morphological parameters like root, stem and leaf weight ratio and leaf area ratio were also considerably reduced in CIM-496 as compared to FDH-786. Leaf Relative water, proline and chlorophyll content, cell membrane stability, transpiration and photosynthesis rate and Stomatal conductance were also sharply decreased in CIM-496 as compared to FDH-786. Relative expression level of drought responsive genes (TPS, PIP, Gh-POD and LHCP-PSII) were observed to be expressed at higher levels in FDH-786 under different drought stress conditions. In epitome, the current study concluded with the remarks that CIM-496-Gossypium hirsutum, variety of cotton, is drought prone while FDH-786-Gossypium arboreum is drought resistant.
Stock Market Index Prediction Using Multilayer Perceptron and Long Short Term Memory Networks: A Case Study on BSE Sensex

- Stock market index is a statistical measure that quantifies the changes in a portfolio of stocks which represents a portion of the overall stock market. Prediction of stock market has been a challenging task and of great interest for scholars as the very fact that stock market is a highly volatile in its behaviour. Prediction of stock market is substantial in finance and is gathering more attention, due to the verity that if the direction of the market is predicted successfully the investors may be effectively guided. ” “Deep Learning technique is a subfield of machine learning which is concerned with algorithms necessitated by the function and structure of the brain called artificial neural networks. The most popular techniques are Multilayer Perceptron Networks, Restricted Boltzmann Machines, Convolutional Neural Networks and Long Short-Term Memory Recurrent Neural Networks. ” “This work focuses on the task of predicting the stock market Index. The objective of the project work is to develop and compare the performances of Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Networks in forecasting stock market indices. Recent ten years historical data of Bombay Stock Exchange (BSE) Sensex from the Indian Stock market has been chosen for the experimental evaluation. The Adam Optimizer is used for the training of deep neural networks. Root mean square Error (RMSE) is used to compare the performance of the prediction models. As seen from the results, the prediction is fairly accurate in both cases and MLP has outperformed LSTM model, in predicting stock market indices. Neural networks have proved to be a good technique, to forecast a chaotic time series data like stock market index. ”