Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Aspergillus fumigatus Infection on Antibody Immune Response to Newcastle Disease Virus in Broiler Chickens.

Aspergillus fumigatus infection might predispose birds to other respiratory infections with other pathogens such as Newcastle Disease Virus (NDV). This study aimed to investigate the incidence of Aspergillus fumigatus in commercial farms and its histopathological effects on respiratory organs and to evaluate the immunosuppressive effect of aspergillosis on NDV vaccinated birds. Aspergillus fumigatus was isolated from feedstuff and broilers in farms with respiratory manifestation. Twenty NDV-vaccinated broiler chickens of 10 days old were experimentally infected by feeding on feedstuff contaminated with Aspergillus fumigatus. Twenty vaccinated broilers but not fed the contaminated diet were used as the control group. Clinical signs, histopathological changes, NDV antibody levels in infected birds were recorded. Clinically, infected birds showed respiratory distress, dyspnea, gasping, ruffled feathers, green watery diarrhea, anorexia, lethargy, and unilateral drooping of wing. Histopathological changes were observed as disseminated granulomatous foci in the affected lungs, with caseous necrosis and leukocytes infiltration. The antibody immune response against NDV significantly reduced in infected birds compared with that of non-infected broilers. It is concluded, that Aspergillus fumigatus infection suppresses the immune responses and predisposes the broilers to other microbial infections, leading to considerable economic losses in the poultry industry.
SCS-CN method revisited in perspective of strange data

The Soil Conservation Service Curve Number (SCS-CN) method2,3 is popularly used for estimation of direct runoff for a given rainfall event from small agricultural watersheds. Most of the commercial watershed models such as CREAMS,4 AGNPS,5 EPIC,6 and SWAT7 employ this methodology because of its simplicity, ease of use, lesser input data requirements, use of major runoff producing characteristics, widespread acceptance, and significant infrastructure and institutional momentum for this procedure within Natural Resource Conservation Service (NRCS). 8
Improving the Performance of SBR WWTP under the Effect of Organic Shock Load using Stoat Software

There is a substantial interest for using process models in wastewater treatment plant design. The process models are used to verify that the designed process or the effluents would meet the standard guidelines. Wastewater treatment plant (WWTP) models are used for improving the treatment process to solve treatment malfunctioning for plant robust operations [1]. Sequence batch reactors (SBR) systems can eradicate growth of most of the filamentous microorganisms, by controlling the operation of SBR cycle to overcome the hydraulic and organic shock load problems [2].
Role of cancer antigen 125 and risk of malignancy index 3 to differentiate benign and malignant female pelvic masses followed by histopathological correlation

Introduction: Pelvic mass is a worldwide female health problem with serious consequences in mortality, morbidity, and cost to the society. The present study was designed to see the role of serum Cancer Antigen 125 (CA125) and Risk of Malignancy Index 3 (RMI3) to differentiate benign and malignant pelvic masses followed by histopathological correlation. Materials and Method: In this prospective study 70 cases of female with pelvic mass were included. Serum CA125 with other clinical covariates were measured and RMI3 was calculated. The findings were histopathologically correlated. 70 normal healthy females served as controls. Statistical analysis was done by using unpaired t-test and Fisher’s exact test. Results: Serum CA125 and RMI3 were significantly elevated in 92.3% of malignant cases and only in 3.5% of benign cases. Mean of RMI3 and CA125 was 5191.6 and 608.1 U/mL in malignant cases, where 39.0 and 20.9 U/mL in benign cases respectively. The sensitivity and specificity of both CA125 (cutoff of 35U/mL) and RMI3 (cutoff of 200) for predicting malignancy was 96.49% and 92.31% respectively with 98.21% positive predictive and 85.71% negative predictive value. Conclusion: Serum CA125 and RMI3 were increased significantly in females with malignant pelvic mass in comparison to benign cases. Value of CA125 >35 U/mL and RMI3 >200 in predicting malignancy revealed modest diagnostic accuracy. Therefore combining RMI3 and CA125 helps to assess pelvic masses pre-operatively and to refer patients to gynaecological oncology center for appropriate management.
Graph Analytics for Big Data

In the era of big data, interest in anal ysis and extraction of information from large data graphs is increasing apace. This paper examines the field of graph analytics from so ewhat of a query processing point of view. Whether it be deter mination of shortest paths or finding patterns in a data graph matching a query graph, the issue is to find interesting characteris tics or information content from graphs. Many of the associated difficu lty can be abstracted to problems on paths or problems on patt erns. Unfortunately, seemingly simple problems, such as f inding patterns in a data graph matching a query graph are surprisingly difficult. In addition, the iterative nature of alg orithms in this field makes the simple MapReduce style of parallel and de alt out processing less effective. Still, the need to provi de answers even for very large graphs is driving the research. Adva nce, trends and directions for future research are presented.
IN VITRO ANTIOXIDANT ACTIVITY OF METHANOLIC EXTRACT OF CALLICARPA ARBOREA LEAVES

Callicarapa arborea, commonly known as Beautyberry Tree belongs to Verbenaceae family. It is widely used in traditional medicine for treatment of jaundice, fever, headache, stomachache, skin and scorpion bites diseases. The fresh leaves of the plant were washed, air dried in shade, ground to powder and subjected to cold maceration using methanol as per the standard procedure, condensed by subjecting to a rotary evaporator and lyophilized. The methanolic extract prepared was analyzed for antioxidant activity by three different in vitro methods; namely (1) DPPH free radical scavenging activity, (2) Ferric reducing antioxidant potential (FRAP) assay and (3) Total phenolic contents by spectrophotometeric method. In the radical scavenging measurement method of DPPH and ferric iron reducing ability assay, the antioxidant activities were 9.78±0.12 mg and 12.81±0.64 mg Trolox equivalent respectively per gram of dried leaves.
Marqueurs prédictifs de réponse aux inhibiteurs de BRAF dans le mélanome cutané métastatique

Les therapies ciblees ont ameliore la survie des patients atteints de melanome cutane metastatique, toutefois certains patients echappent au traitement. La reponse au traitement met en jeu des parametres dependants du patient, de la biologie de la tumeur, et de la survenue de mecanismes de resistance largement etudies.Le but de ce travail est d’etudier deux marqueurs mesurables en pratique clinique lors du suivi des patients, pour leur potentiel predictif de reponse aux inhibiteurs de BRAF en monotherapie, l’un biologique et l’autre pharmacologique : la charge allelique mutee (CAM), definie par le rapport mute/total, des oncogenes BRAF et NRAS, et la concentration plasmatique de vemurafenib (CPV). Nous rapportons pour la premiere fois la prevalence et les mecanismes du desequilibre allelique des oncogenes BRAF et NRAS, defini par une CAM elevee (>60%) ou faible (≤30%), par opposition a une CAM « heterozygote » equilibree (entre 30 et 60%). Des arguments in vitro et in vivo soutiennent l’hypothese qu’une CAM BRAFV600E >60% puisse etre associee a une meilleure reponse aux inhibiteurs de BRAF. La CAM de NRAS ne semble pas etre un marqueur pronostique au moment du diagnostic de melanome, toutefois sa valeur predictive sur la reponse aux inhibiteurs de MEK sera interessante a etudier.Nous montrons pour la premiere fois l’impact du dosage de la CPV lors du suivi des patients traites en monotherapie. Une CPV basse serait predictive d’une progression ; ainsi, la CPV semble etre un marqueur predictif original de reponse, pour lequel la valeur seuil ainsi que les mecanismes pharmacogenetiques impliques restent a determiner.
STUDY OF FIXED DOSE COMBINATIONS IN A TERTIARY CARE HOSPITAL

ABSTRACT Objective: To study and analyze the pattern of fixed dose combinations (FDCs) in medicine outpatient clinic. Methods: The study is prospective cross-sectional study was conducted for 2 months in medicine outpatient clinic in tertiary care hospital. After taking approval from Institutional Ethics Committee, the data were collected on every working day of the hospital. Prescriptions from the patients coming to medicine outpatient clinic outpatient department were taken and required information is filled in data record form. Results: A total of 83 prescriptions were included in the study. Out of 287 drugs, 111, i.e., 39.92% were fixed dose formulations. As per drug category analysis, nutritional supplements were used (47.74%). While 27.02% and 9.03% of FDCs were given for cardiovascular complains and respiratory complains, respectively. FDCs used for other conditions were 16.21%. About 29.72% of total FDCs are included in approved list of FDCs by Drugs Controller General of India (DCGI), November 2014. Conclusion: 39.92% drugs are FDCs out of total prescribed drugs. Nutritional supplements are prescribed in higher concentrations. Out of total FDCs only 29.72% were included in DCGI approved list of FDCs and only four FDCs are found in the WHO list of essential drugs. Keywords : Fixed dose combinations, Prescriptions, Approved, Drugs Controller General of India.
A Finite Difference Technique for Singularly Perturbed Two-Point Boundary value Problem using Deviating Argument

In this paper, we have presented a finite difference technique to solve singularly perturbed two-point boundary value problem using deviating argument. We have replaced the given second order boundary value problem by an asymptotically equivalent first order differential equation with deviating argument. We have applied a fourth order finite difference approximation for the first derivative and obtained a tridiagonal system. Then, we have solved this tridiagonal system efficiently by discrete invariant imbedding algorithm. This method is iterative on the deviating argument. We have presented numerical results of several model examples to support the proposed method. Index Terms — Singularly perturbed two-point boundary value problem, Boundary layer, Deviating argument, Finite difference, Taylor series, Tridiagonal system. ——————————  ——————————
A survey on Unequal Clustering Protocol based on Fuzzy approach for Heterogeneous Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have increased due significance for applications requiring remote detecting. Wireless Sensor Networks have gained due importance for applications which require remote sensing such as Intrusion Detection, Weather Forecasting. The major challenges faced are related to energy and routing. The sensor nodes being battery operated devices of limited resources, techniques like data aggregation, selective node activation and energy efficient routing are adopted with main focus of saving energy. The objective is to prolong life of the network but also distributing the load evenly among the nodes. Several approaches have been suggested in literature most of the methods applicable to homogeneous WSNs only This paper proposes a fuzzy logic based unequal clustering protocols for WSNs in heterogeneous settings. Four choice criteria for choosing bunch heads are contribution to the fuzzy logic which gives two yields rather than one. This novel element makes the proposed technique viable.
Deep learning approach for diabetic retinopathy screening

Purpose Diabetic retinopathy (DR) is the major cause of blindness in the working-age population. With an increasing number of diabetic patients worldwide, automated screening tools become indispensable. Recent progress in machine learning and image analysis enables efficient automated screening. Methods DreamUp Vision uses state-of the art technology based on deep-learning. Our algorithm was trained on over 70,000 labeled retinal images. Images were graded by ophthalmologists as follows: 0 (no retinopathy), 1 (mild non proliferative DR), 2 (moderate non proliferative DR), 3 (severe non proliferative DR) and 4 (proliferative retinopathy). Each patient in the dataset is represented by two images of left and right eyes. Grading is done for each eye image separately. Our algorithm performs quick and reliable detection of anomalies in retinal images, diagnoses their stage of diabetic retinopathy and provides the location of the anomalies detected in the pictures. We consider a patient as referable if the DR stage is between 2 and 4, otherwise we consider the patient as non-referable. We evaluate our model on over 10,000 fundus images from 5,000 patients taken from the Kaggle DR Detection Challenge dataset, provided by California Healthcare Foundation. Results Our algorithm achieves an area under the receiver operating characteristic curve AUROC of 0.946 with 96.2% sensitivity (95% CI: 95.8–96.5) and 66.6% specificity (95% CI: 65.7–67.5) for identifying referable DR on the Kaggle dataset. Conclusions The performances we have obtained enable a reliable automated DR screening. As the amount of available labeled data grows and given our technology's ability to learn from labeled images, we believe that significant performance improvement can be achieved. The same process can be applied to the detection of other eye diseases as well.
Evaluating Kernel Effect on Performance of SVM Classification using Satellite Images

— Support Vector Machine (SVM), a statistical learning algorithm has proved its excellencies in almost every domain towards of Image classification on various data formats. However, satellite images are more complex due to its number of bands that really contribute to image classification problem. Supervised approaches for satellite images needs more precise, fast and efficient machine learning models. Even though, SVM is non-parametric binary classifier, but could be mould to resolve multi-class classification problems. SVM comprise of superior machine learning algorithms group based upon the mean field theory, which basically uses quadratic optimization technique. SVM kernels are used to demarcate optimum hyperplane in non-linear problem space, which in return generalize to unseen samples with least error among all possible boundaries separating two classes. Kernel efficiency varies with SVM implementations like C-SVC, nu-SVC, nu-SVR, single class SVM, and CS-SVM. Use of density estimation function along with various norms, have great impact on SVM kernel performance for classification problems. Kernel tricks very well handle the problem of mixed pixels, which is vital affecting source on classification accuracies. Research illustrate that SVM kernel type sigmoid perform better than other SVM kernel types like Polynomial, Radial Basic, and Linear kernel at fixed training data size of 300 pixel/class. It is most appropriate in situations where the training sample data are difficult to collect, as it works better even with small number of training samples. Perhaps, 75 to 100 pixels per class are recommended for accuracy assessment of classification.
mapreduce: Fine-Grain Incremental Processing In Big Data Mining

I 2 MAPREDUCE: Fine-Grain Incremental Processing in big data mining a novel incremental processing extension to Map Reduce, the most widely used framework for mining big data. As compare with the high-tech work on In coop, I 2 MapReduce has its own advantages (i) It prefers key-value pair level incremental processing to perform instead of task level re-computation, (ii) It supports one-step computation along with more sophisticated iterative computation, which is extensively used in data mining applications, and (iii) It reduce I/O overhead for accessing preserved fine-grain computation states by incorporating the set of novel techniques. I evaluate I 2 MAPREDUCE: Fine-Grain Incremental processing in big data mining using a one-step algorithm and four iterative algorithms with assorted computation characteristics. Experimental results on Amazon EC2 show significant performance improvements of I 2 MapReduce compared to both plain and iterative MapReduce performing re-computation.
Education and Stress Management Strategies on Levels of Burden in Caregivers of Chronic Mental Illness Individuals

India, the second most populated rural areas of the world, with a population of 1.02 billion, is a land of contrasts [1,2]. Advances in medical science have resulted in a concomitant increase in the life span of individuals with disabilities [3]. Vagaries of caregivers were observed quite commonly among the patients of mental upset. In India, unlike in the West, the folks have always remained the major source of concern for the mentally ill even in the past, since there have been never adequate number of hospital beds for these patients. The degree of burden on the caregivers has dramatically increased as the ongoing shift of patient attention from institutions to the residential district [4]. There is a shortfall in trained work force sources in the rehabilitation of chronic mental illness patients. Successful management of mental malady in the community relies significantly on an informal or nonprofessional network of caregivers [5]. Caregivers face mental ill health as a direct result of their caring role and experience higher rates of mental ill health than the normal population. The care givers are reported to have 50% more health problems, 70% more prescribed medicines, 25% more to hospitals or emergency rooms, 23% more levels of stress hormones, sleep deprivation & poor eating habits than noncaregivers. These requirements can bring significant levels of stress for the caregiver and can move Abstract
Intelligent Home Security System using illumination sensitive background model

: Home security has been a concern of worldwide. The need of security systems is considered as one of the important aspects of our modern life, people are using such systems to be aware about anything could be happen in their places like their houses when they are away, these system also let them feel more secure and that is what the people really need. As the technology is developing, rich home based security systems are implemented to ensure safety and security of. Video surveillance is an important area of computer vision research, its applications including both outdoor and indoor automated surveillance systems Home security system is an essential mean of protecting our home from illegal invasion. A conventional home security system consists of a Closed Circuit Television, CCTV and burglar alarm which can be replaced by computer based systems with the capability of smarter detection and alert system. In the context of smart home environments, the In-House Video Surveillance systems have as main goal to control the safety and the security of materials and of people living in a domestic environment. This paper provides a comparative approach between tracking learning and detection algorithm with illumination sensitive background model. Further a face recognition module is implemented which has the capability of identify intruder. On finding the intruder, the system sends an email on the owner mail id with images of the intruders so that the further action can be initiated.
Immunogenicity and Safety of Combined Tetanus, Reduced Diphtheria, Acellular Pertussis Vaccine when Co-Administered with Quadrivalent Meningococcal Conjugate and Human Papillomavirus Vaccines in Healthy Adolescents

Objective: Current United States immunization recommendations for adolescents include vaccines against tetanus, diphtheria and pertussis (Tdap), human papillomavirus (HPV), and Neisseria meningitidis serogroups A, C, W-135, and Y. In this Phase IV study, we primarily investigated the impact of concomitant administration of a quadrivalent meningococcal CRM197-conjugate vaccine (MenACWY-CRM) with Tdap and HPV vaccines, in terms of immunogenicity to Tdap antigens and overall reactogenicity. Methods: A total of 801 healthy adolescents aged 10-18 years were randomized to one of two groups to receive either MenACWY-CRM or a placebo, co-administered with Tdap and a quadrivalent HPV vaccine (HPV4). Antibody responses to the Tdap antigens, as well as to meningococcal serogroups A, C, W-135, and Y, were assessed at one month post-vaccination. Safety and adverse events were monitored throughout the study. Results: One month post-vaccination, 95% and 99% of subjects in the MenACWY-CRM group had seroprotective antibody levels (≥1.0 IU/mL) against the diphtheria and tetanus toxoids, respectively, compared with 82% and 98% in the placebo group. Ratios of geometric mean concentrations of antibodies against pertussis antigens pertussis toxin, filamentous hemagglutinin and pertactin for the MenACWY-CRM group versus placebo were 1.01, 0.84, and 0.82, respectively. Predetermined non-inferiority criteria for immunological responses against all Tdap antigens were met. Co-administration of a single dose of MenACWY-CRM was well tolerated and elicited robust antibody responses against the four meningococcal serogroups, with 77%, 84%, 95% and 86% of subjects having seroprotective human complement serum bactericidal activity (titers ≥8) against serogroups A, C, W-135, and Y, respectively, one month post-vaccination. Conclusions: Collectively, these results demonstrate that the MenACWY-CRM, Tdap and HPV4 vaccines can be administered at the same visit without compromising Tdap immune responses or increasing reactogenicity. [The study is registered with ClinicalTrials.gov, number NCT01424644].
Access Control Allowance and SecureData Storage in Cloud with Authority

: Cloud computing is a revolutionary computing environment, which allows user a flexible, on-demand, and low-cost usage of computing resources, but the data is outsourced to some cloud servers, and various privacy concerns emerge from it.Attribute-based Encryption (ABE) is a cryptographic conducting tool to guarantee data owner’s direct control over their data in public cloud storage. ABE is an encryption method used by the user to store the data in the cloud. ABE is a public-key based one to many encryption methodologies which allows users to encrypt and decrypt data based on user attributes Different plans taking into account the Ciphertext Policy attribute based encryption have been proposed to secure the storage in cloud.Nonetheless, most work spotlights on the information substance security and the access control, while less consideration is paid to the benefit control and the identity security. In this paper focused on better access control permissions for each file based on the attribute of user. Attributes are efficiently utilized for encrypting the valuable data