Miracle-dz/UniFAR-FTUs huggingface dataset:
Are KIR-HLA Polymorphisms Relevant for the Outcome of Chronic Infected Hepatitis C Virus Patients?

Natural killer (NK) cells play an important role in viral clearance and in the elimination of neoplastic cells. Recent cellular and genetic studies revealed that NK cells modulate the immune response against hepatitis C virus (HCV). Improving our understanding of NK cells could help us gain further insight into the virus-host interaction and the mechanisms of viral persistence. Previous studies mention the role of Killer cell Immunoglobulin-like Receptors (KIRs) and their Human Leukocyte Alleles (HLA) ligands in disease progression of chronic HCV infected patients. Specific HLA class I and class II alleles may influence the susceptibility or resistance to persistent HCV infection. In the last few years, progress has been made in the link between KIR-HLA gene polymorphisms and susceptibility to immune-mediated diseases such as chronic HCV infection. Many studies have shown the importance of NK cells in the outcome of chronic HCV patients. The immune response against HCV is affected by certain KIR genes and HLA genes. There is a great potential for the use of KIR genes as a prognostic tool for the development of complications such as liver cirrhosis and HCC.
Anti-oxidative, Anti-inflammatory, Anti-diabetic and Anti-ACE Hydrolysates Derived from Fish Protein: A Review of Multifunctional Peptides

Bioactive compounds have widely studied to reduce risk of cardiovascular disease on human’s health. In recent years, much attention has been paid to the existence of peptides with biological activities and hydrolysates derived from foods. This paper presents a review of the oxidative stress and hypertension that occurred in the human and food system. Antioxidant and antihypertension peptides isolated from fish sources may be used as functional ingredients in food formulations to promote consumer’s health and improve the shelf life of food products. In addition, the antioxidant and antihypertension peptides derived from various food proteins, particularly from fish, have been discussed. Furthermore, the bioactive peptides may exist as multifunctional peptides which can exert more than one biological activity.
Maximizing Individual Interest in Social Activity Planning

Recent study shows that a person will join a social get-together development if the activity is interesting, and if a few partners too join the development as mates. The composition has shown that the interests of a man and the social coziness among allies can be reasonably gotten and mined from relational cooperation destinations. In any case, even with the more than two sorts of information by and large open, social get-together practices still ought to be encouraged physically, and the system is dreary and dull for customers, especially for a far reaching social get-together development, due to troubles of social accessibility and the distinctions of possible interests among allies. To address the above fundamental need, this paper proposes to actually pick and recommend potential members of a social gettogether development, which could be extraordinarily accommodating for individual to individual correspondence destinations as a regard included organization. We initially arrange another issue, named Willingness mAximization for Social grOup (WASO). This paper points out that the course of action gained by an insatiable figuring is likely going to be gotten in an area perfect course of action. Thusly, we layout another randomized figuring to reasonably and successfully deal with the issue. Given the open computational spending arranges, the proposed count can in a perfect world assign the advantages and find an reply with a gauge extent. We complete the proposed count in Facebook, and the customer consider displays that social get-togethers obtained by the proposed count by and large beat the courses of action physically composed by customers.
Design and Implementation of Rank Based ACO approach for Multipath Routing Mechanism with Load Balancing in Ad hoc Network

: Ad-Hoc wireless networks are self-organizing multi-hop wireless networks. Ad-Hoc networks can quickly and inexpensively be set up as needed Therefore, they are highly applicable in many fields such as emergency deployments and community networking. Mobile ad hoc networks have some challenges like the design of an efficient routing protocol. The multipath routing protocol with load balancing provides a solution for the congestion network and increases its capacity. To consider that the use of multiple paths simultaneously for transmission data allows to improve the network performance, we propose a protocol LB-AOMDV (Load Balancing-AOMDV), a solution to achieve better load balancing mechanism. In this paper, we also apply the Ant Colony optimization technique to the routing problem, after that we apply a extension of ACO that is ranked based ACO approach. We refer to the new modified protocol as the LB-AOMDV Rank Based Ant Colony Optimization algorithm . The main goal in the design of the protocol will be reduce the routing overhead, response time, end-to-end delay and increase the performance .
In vitro pancreatic lipase inhibition potential of commonly used Indian spices

Obesity is the excessive accumulation of fat that may have deleterious health effects. One of the most approached methods of treating obesity is inhibition of pancreatic lipase activity. Pancreatic lipase breaks down triglycerides into fatty acids which then get absorbed through the duodenal mucosa. A pancreatic lipase inhibitor prevents the formation of fatty acids and hence prevents any accumulation of fats in the body. The aim of this study is to evaluate the 3 Indian spices and their mixture for inhibitory activity against pancreatic lipase. In the present study, three Indian spices namely, Nigella sativa, Trigonella foenum-graecum, Trachyspermum ammi and their mixture (1:1:1 combination) were evaluated for their inhibitory effect on porcine pancreatic lipase enzyme using in vitro assay. The inhibitory activity was demonstrated by all the 3 Indian spices and their mixtures which can play a beneficial role in the treatment of obesity.
Automatic Test Case Generation Using Genetic Algorithm

— Software testing is most effort consuming phase in software development. One would like to minimize the efforts and maximize the number of faults detected. Hence test case generation may be treated as an optimization problem. One of the major difficulties in software testing is the automatic generation of test data that satisfy a given adequacy criterion. Generating test cases automatically will reduce cost and efforts significantly. In this paper, test case data is generated automatically using Genetic Algorithms and results are compared with Random Testing. It is observed that Genetic Algorithms outperforms Random
Soil Health Issues for Sustainability of South Asian Agriculture

Even though, the economic and agricultural growth in South Asia has improved in recent times, still food security and malnutrition is the biggest concern in these countries. Poverty in Asia is influenced by food insecurity, population densities, small land holdings and climate changes which also influence nutritional status. The present paper focuses on the concerns related to food, nutritional security and soil health issues in South Asian countries with special emphasis on cause and effects of soil related constraints and strategies to be adopted to manage and alleviate the problem. Some of the important soil health management strategies viz., site specific nutrient management (SSNM), integrated nutrient management (INM), crop residue recycling, and use of compost, vermicompost, and soil health card based interventions are promising in South Asian condition. National level policy interventions are needed to promote sustainable use of soil and water resources include prohibiting residue burning, reducing deforestation, promoting integrated farming systems and facilitating payments for ecosystem services. A wide spread adoption of these measures can improve soil quality and agronomic productivity of fragile and degraded ecosystems.
Content based Image Retrieval using Multiview Alignment Hashing

— In this paper, content based image retrieval is implemented using multiview hashing techniques. The implementation consists of three steps preprocessing, features extraction , classification. Preprocessing step consists of color to gray conversion and histogram equalization, feature extraction consist of Histogram of Oriented gradients (HOG) for shape detection, Local Binary pattern (LBP) for texture description and color histogram for color similarity. Hashing is a simple , popular and efficient method for nearest neighbor search in large-scale data spaces by high-dimensional feature descriptors into a similarity preserving Hamming space with a low dimension. For most hashing methods,. The proposed method is systematically evaluated on three data sets: Caltech-256; CIFAR-10; and CIFAR-20, and the results show that our method outperform than individual techniques.
Gender Recognition and Age-group Prediction: A Survey

—Over the recent years, a great deal of effort has been made to age estimation & gender recognition from face images. It has been reported that age can be accurately estimated under controlled environment such as frontal faces, no expression, and static lighting conditions. However, it is not straightforward to achieve the same accuracy level in real-world environment because of considerable variations in camera settings, facial poses, and illumination conditions. In this paper, we discuss different method to estimate age and gender predication.
A Review on Storage and Task Scheduling in Heterogeneous Hadoop Clusters

The task scheduling algorithm for homogeneous Hadoop clusters is incapable of proper utilization of resources in heterogeneous clusters. To overcome this issue, an adaptive task scheduling algorithm has been proposed. With adaptive task scheduling we aim for better resource utilization by dynamically adjusting the workload at runtime. Also we are making the storage of data resource aware so that the load is balanced according to processing capabilities. With the adaptive strategy task trackers can self-regulate the tasks while maintaining performance and providing stability and scalability. The adaptive approach promises higher efficiency and better performance.
Working Memory and Speech Recognition Performance in Noise: Implications for Classroom Accommodations

Purpose: The aim of this study was to compare children’s performance on speech recognition and working memory tasks with two noise source configurations: back and side. Method: Children with normal hearing between the ages of 8-10 years of age participated in this study. Working memory and speech recognition in noise were administered in a counterbalanced manner across listening conditions. Results: Speech recognition performance in noise was significantly poorer when presented at 180 than from 90 degrees azimuth. There was no effect of noise source configuration on working memory performance. However, working memory performance in noise, regardless of position, were significantly poorer compared to quiet. No relationship was present between auditory working memory in noise and speech recognition in noise, when noise was presented at 90° azimuth. Conclusion: Children use perceptual cues and cognitive resources based on the difficulty of the task and audibility of the signal, Cognitive resources are largely called upon when listening conditions are more adverse and tasks become complex.
CO-DIAGNOSABILITY APPROACH OF DISCRETE EVENT SYSTEMS WITHOUT GLOBAL MODEL BASED ON PETRI NETWORKS AND INTERNAL SUCCESSION EVENTS

Diagnosability ensures that the global model of a centralized system, will always be able to diagnose a predetermined set of faults previously listed unambiguously. However co-diagnosability guarantee that these faults are diagnosed in a decentralized manner using multiple local diagnosticians. The co-diagnosability property is stronger than diagnosability, because if a system is Co-diagnosable it is diagnosable; while a diagnosable system does not necessarily ensure the co-diagnosability of this system. The challenge of decentralized diagnosis approaches is to perform multiple local diagnostics and verify that they are equivalent to the global one without the need for a global model. This paper proposes an approach to obtain a decentralized co-diagnosable diagnosis structure of the discrete event system, without the use of a global model, based on internal succession events of local diagnosticians and Petri networks.
Modified Milch Technique : A Safe and Painless Method for Reduction of Anterior Shoulder Dislocation

Background: The shoulder is the most commonly dislocated major joint in the body and this dislocation causes severe and agonizing pain. Prompt and painless reduction forms the cornerstone of treatment. We studied the efficacy of the Milch technique in the reduction of anterior dislocation of shoulder. Materials: Between September 2011 and March 2014, 60 cases of anterior dislocation of shoulder were reduced by Milch technique in C.S Hospital Shivamogga and Dhruva Orthopaedic Clinic Shivamogga and the pain experienced by the patient was quantified by Visual Analogue Score. Results: Reduction was successful in 56 cases (93.3%). The mean Visual Analogue score was 1.82. There were no complications. Conclusion: We conclude that the Milch technique is a very effective and relatively painless method for reducing anterior dislocation of the shoulder.
Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks

: Easygoing affiliations give a virtual stage to clients to reveal themselves to individuals if all else fails. These structures allow clients to present specific of them and interface with their mates. Client proﬁle and family relationship relations are really private. It is possible to expect fragile information passed on in released data inactively by utilizing data mining procedures. In this we take a gander at how to dispatch affirmation strikes using released online gathering data to find undisclosed private information about individuals, including their political affiliations. By then we devise possible sanitation strategies that may be found in various conditions. By then, the vitality of these systems by executing them with a dataset from a Facebook web gathering application and needing to use techniques for total finding to learn fragile parts of the illuminating record. In like manner, also this shows the possible results where the method for both neighborhood and get-together estimation may be obliged with the sanitation techniques as portrayed.
DESIGN AND IMPLEMENTATION OF A MODEL FOR HAZE REMOVAL USING IMAGE VISIBILITY RESTORATION TECHNIQUE

Images plays an important role in the real world, images are used for describing the changes in the environment. Images are captured in open environment due to the bad weather or atmosphere images are not a clear. Images acquired in bad weather, such as the fog and haze, are extremely degraded by scattering of an atmosphere, and decreases contrast. The bad weather not only lead to variant of the visual outcome of image, but also to the difficulty of the post processing of the image. Images captured during adverse weather conditions frequently feature degraded visibility and undesirable color cast effects. The presence of suspended particles like haze, fog and mist in the atmosphere deteriorates quality of captured images. In this paper, we have proposed a dark channel prior and contrast limited adaptive histogram equalization technique, it is based on adaptive histogram equalization. The dark channel prior technique is helpful to clear the hazy images. Removing haze effects on image is a challenging and meaningful task for image processing and computer vision applications. In this work we remove haze from hazy image, and improve the quality of an image and then at last we obtain restored enhance haze-free image with clear visibility. The proposed technique is designed and implemented in MATLAB.
Analysis of Financial Time Series Data Using Adaptive Neuro Fuzzy Inference System (ANFIS)

The aim of this research is to analyze ANFIS performance for prediction of financial time series data. Financial time series data is usually characterized by volatility clustering, persistence, and leptokurtic data behavior. The financial time series data are usually non-stationary and non-linear. ARIMA has a good performance to predict linear time series data, but its performance is decreasing when applied to predict non-linear times series data. ANFIS as one of hybrid models which composes neural network (NN) and fuzzy system is expected to be able to predict the financial time series data more accurate. Many research conclude that the effectiveness of ANFIS depend on the input selection, the membership function (MF) selection and the rule generation. In this study, the input variables of ANFIS are selected based on preprocessing of original data by using Subset ARIMA model. The rule bases of ANFIS are generated based on a linear Sugeno fuzzy model. The consequent parameters are identified by using least square method and premise parameters are adapted by using gradient descent. For practical assessment of ANFIS performance, ANFIS method is implemented to analyze the Indonesia inflation monthly data from January 1970 up to December 2012.
Construction of Irregular LDPC Code Using ACE Algorithm

-The Low Density Parity Check (LDPC) codes have been widely acclaimed in recent years for their near-capacity performance, they have not found their way into many important applications. For some cases, this is due to their increased decoding complexity relative to the classical coding techniques. For other cases, this is due to their inability to reach very low bit error rates at low signal-to-noise ratios (SNRs), a consequence of the error rate floor phenomenon associated with iterative LDPC decoders. The construction of finite-length irregular LDPC codes with low error floors is currently an attractive research problem A new metric, called extrinsic message degree (EMD), measures cycle connectivity in bipartite graphs of LDPC codes. Using an easily computed estimate of EMD, we propose a Viterbi-like algorithm that selectively avoids small cycle clusters that are isolated from the rest of the graph. , a simple but powerful heuristic design algorithm, the approximate cycle extrinsic message degree (ACE) constrained design algorithm, has recently been proposed. This algorithm is different from conventional girth conditioning by emphasizing the connectivity as well as the length of cycles.
The Antihyperalgesic Activity of Lacosamide and Amitriptyline in Combination in Diabetes Induced Neuropathy

The term diabetes mellitus describes a metabolic disorder of multiple etiologies characterized by chronic hyperglycemia with disturbances of carbohydrate, fat and protein metabolism resulting from defects in insulin secretion, insulin action, or both. The effects of diabetes mellitus include long term damage, dysfunction and failure of various organs. Diabetes mellitus is a major and growing health problem in most countries and an important cause of prolonged ill health and early death. Diabetes has now become a global public health burden with worldwide incidence of 5% in the general population. The long term effects of diabetes mellitus include progressive development of the specific complications of retinopathy with potential blindness, nephropathy that may lead to renal failure, and/or neuropathy with risk of foot ulcers, amputation, Charcot joints, and features of autonomic dysfunction, including sexual dysfunction. One of the most common diabetes-related complication is diabetic neuropathy which affects nearly 50% to 66% of all patients with diabetes and affects individuals with both type 1 and type 2 diabetes. Tricyclic antidepressants are considered the first-line choice of treatment for chronic pain associated with diabetic neuropathy. Neuropathic pain generally responds more quickly than depression to tricyclic antidepressants and often with one third to one half the dosage administered for depression. Lacosamide is a novel chemical entity with anticonvulsant and analgesic properties that is being developed to treat epilepsy and neuropathic pain conditions. Nandita Avinash Ranbhise*, Prof. D.G. Joshi
Anterior Cruciate Ligament Reconstruction Comparison between Single Bundle and Double Bundle-Double Tunnel Techniques: Pilot Study

This study aim was to compare results of single bundle and double bundle double tunnel surgical procedures for anterior cruciate ligament reconstruction. All anterior cruciate ligament reconstruction procedures used an allograft and were performed by the same surgeon. Patients were enrolled from April to November 2016 and two postoperative short term follow-up visits (one and 3 months) where completed. Patient ́s clinical examination included Pivot Shift, KT1000, Tegner-Lysholm and International Knee Documentation Committee (IKDC) questionnaires before surgery, after surgery (except questionnaires) and at each visit. Comparison between preoperative results and three months clinical follow-up showed statistical differences in KT1000 values and side to side KT1000 difference, despite the type of surgery. The effect of type of surgery within time was significantly different between single bundle and double bundle double tunnel for side to side KT1000 difference. Analysis for Tegner-Lysholm and IKDC score improvement showed statistical difference in double bundle double tunnel group, as early as three months. Patients recovered despite the type of anterior cruciate ligament reconstruction technique used; however, a tendency towards better results with double bundle double tunnel procedure is clear; this has to be further confirmed in a larger study with longer term evaluation.
Emotion Recognition: Detecting Emotions from Textual Documents, Blogs and Audio Files

: With the rapid growth of the World Wide Web and due to increasing amount of textual information such as blogs, discussion forums, review sites available on the web. Now a days the area of emotion detection is considered as an active research field. Textual data has proven to be the main tool of communication in human-machine interaction and the use of social networking sites is one of the approaches for putting views of user. In order to make it as human and real as possible, this communication is constantly improving. In this paper, we propose a novel approach to recognize emotions based on keyword spotting technique along with the concept of ontology. An emotion estimation from the textual documents, data entered by user on blogs like twitter, along with the audio is taken and emotions is extracted from it. Because text, either written or spoken, is the unrivaled way to express one‘s feelings and emotions, it is the best quality to design for building efficient emotion recognition systems. However, researches on building text-based emotion recognition systems are progressing slowly compared to face or voice based systems. In order to improve the textual methods of communication such as text files, textual documents, tweets, it is needed to analyse the emotion of user by studying the input text and emotions of the user.