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Due to a lack of awareness of the signs and methods for prevention, skin cancer is one of the most deadly types of cancer, and the death rate has dramatically increased. Therefore, in order to stop the spread of cancer, early identification at an early stage is essential. There are other varieties of skin cancer, but melanoma is the most dangerous one. However, if discovered early, melanoma patients have a 96% survival rate with straightforward and affordable therapies. The goal of the project is to identify and categorize different types of skin cancer using machine learning and image processing techniques. Melanoma skin cancer poses a serious and dangerous risk to people. Due to the direct link between melanoma skin cancer and fatalities, early detection of this disease is crucial for patients. Melanoma skin cancer is fully treatable if caught in its early stages. In this study, early melanoma skin cancer detection and classification are performed utilizing a variety of algorithms, including the K-means clustering method, neural networks, K-Nearest Neighbour, and Navie Bays, etc.

We have developed a syringolin-based chemical probe and explored its utility for the profiling of metabolite extracts as potent inhibitors of the 20S proteasome. Activity-guided fractionation by competitive labeling allowed us to isolate and identify glidobactin A and C as well as luminmycin A from a Burkholderiales strain. The natural products exhibited unique subunit specificities for the proteolytic subunits of human and mouse constitutive and immunoproteasome in the lower nanomolar range. In particular, glidobactin C displayed an unprecedented β2/β5 coinhibition profile with single-digit nanomolar potency in combination with sufficiently high cell permeability. These properties render glidobactin C a promising live cell proteasome inhibitor with potent activity against human breast cancer cell lines and comparably low immunotoxicity., Competitive profiling with a syringolin A probe allows us to find subunit specific proteasome inhibitors “in the haystack” of complex metabolite extracts.

Social Media has an impact on different aspects of our life. It has revolutionized the way people communicate and socialize on the web. It is an undeniable force in modern society. It has the power to force necessary changes. Social networking platforms such as Facebook, Twitter, Instagram allows users to interact with the world, to express their view on different topics in society. Social media influence in political campaigns has increased tremendously, it plays an important role in electoral politics, political polling, so it is necessary to perform sentiment analysis on political topics to get the public opinion. This technical paper focuses mainly on analysis of political tweets using sentiment analysis. In this paper, we performed sentiment analysis on different political topics in India and analysed the overall sentiments regarding those topics, using naive bayes machine learning algorithm and classified those tweets as positive and negative.

Biomineralization is an active, biologically governed process of mineral formation, established early on in the history of life. The appearance of biomineralizing organisms heavily influenced the course of evolution, leading to the development of the large diversity of the extant taxa. Yet, we are still only beginning to grasp the intricate, genetically regulated mechanisms involved. Since prokaryotic organisms were the first to emerge from the primordial environments, we investigated bacteria-mineral interactions using titration and gas diffusion systems adapted to emulate conditions, which may have facilitated the development of biomineralization initially. By screening the minerals and bacteria from titration experiments with scanning electron microscopy, we discovered a broad spectrum of behavioral strategies employed by bacteria confronted with calcification, which fell into three main categories: (1) evasion of mineralization by the formation of the biofilm, (2) random embedding into the mineral, and (3) control over the mineral shape during its formation. The latter phenomenon we termed pseudo-biomineralization. Our experiments indicate that pseudo-biomineralization is an active process obligatorily reliant on the external calcifying conditions and allowing considerable degree of control over mineral shape, thus producing structures reminiscent of true biominerals. Here, we describe this notion for the first time, thus providing vital insight into the genesis of a transitional stage to calcium carbonate-based biomineralization systems.

Introduction: Exfoliation syndrome (ES) is associated with a variety of findings in the angle of the anterior chamber of the eyes. We undertook the present study to record such findings with the help of gonioscopy. Materials and Methods: Complete ocular examination including visual acuity, slit-lamp examination, applanation tonometry, dilated fundus examination, gonioscopy, and perimetry was done, and the findings recorded in all the patients of ES detected from among 2000 consecutive patients above the age of 40 from the outpatient department of a rural hospital in the western Indian state of Maharashtra. Results: A total of 2000 patients were examined of which 62 (3.1%) were detected as having ES. On gonioscopy, angle was found to be open in 56 (90%) and occludable in 6 (10%) patients. Deep pigmentation of trabecular meshwork (TM) was seen in 54 (87%) patients. Sampaolesi line was seen in all the 56 (90%) patients with angles open and was not seen in the 6 patients who had occludable angles. Exfoliative material was seen in 2 (3%) patients. Laxity of the zonules was detected in 8 (13%) patients, but their angle was open. The average intraocular pressure (IOP) of the study group was 19.4 mmHg with 18 (29%) patients having IOP >21 mmHg. Fundus examination was normal in all the patients. The visual field examination showed glaucomatous field defects in one patient. Conclusions: Open angles showing dense pigmentation of the TM and the presence of Sampaolesi line were the most common findings on gonioscopy in a large majority of patients with ES. Sampaolesi line was not seen in patients having occludable angles.

BACKGROUND Meibomian Gland Dysfunction (MGD) is widely prevalent condition that is associated with evaporative dry eye disease. Many a times, patients are not symptomatic, so the condition goes unnoticed in early stages. The aim of the study is to investigate the association of MGD in relation to age, occupation and lid hygiene in rural population. MATERIALS AND METHODS In our prospective observational study, 100 patients in each case and control group were enrolled and detailed personal history including occupational, medical and drug history was recorded. Symptoms and visual acuity of patients was recorded followed by examination of lids, adnexa, ocular surface and surrounding nasal and paranasal area. MGD was graded as per the quality and expressibility of meibomian gland secretion. RESULTS Prevalence of MGD increases with advancing age. Chi-square test is suggestive of strong association with increasing age and MGD (x2=58.3; P

For the success of any software system thorough testing and verification is very important. The acceptance of the software by the users and the term for usage depends on how well it works. For this purpose testing is an essential step. However, testing to find defects or bugs is time- consuming, expensive and has a high possibility of human error. In Automated testing, software tools are used to run detailed, repetitive, and data-intensive tests automatically. In this work we focus on identifying best tool for testing a Dynamic and Configurable web app. We provide a thorough literature survey of the various testing tools available for testing MEAN (Mongo Express Angular Node) applications. As a part of this work an Automation Test Suite is developed to test Dynamic and Configurable MEAN application.

In Image processing, it becomes very interesting to recognize the human gesture for general life. Facial expressions analysis plays a significant role for human computer interaction. In this paper both genetic and data mining algorithm is given. In data mining algorithm the focus is given on facial expression regions such as eyebrow, eye and mouth. Then facial characteristics points are calculated which are given as a input to decision tree which gives output as emotion. In genetic algorithm, preprocessing is applied on input image. Features are extracted and optimized values are calculated. Thus emotions are classified using neural networks.

The paper presents an effective monochrome image colorization technique using Thepade's transform error vector rotation algorithms with Slant and Hartley transforms in RGB, YIQ and YCbCr color spaces. Vector quantization is used to generate a color pallet which colors a target monochrome image. Two error vector sequences (Slant & Hartley) represented by binary numbers based on Slant and Hartley transforms are used in Thepade's transform error vector rotation algorithms. The proposed method works in two stages. In first stage color pallet is generated using the source (color) image from which color traits need to be taken using Thepade's transform error vector rotation algorithms and then colors are transferred to a target (monochrome) image using generated color pallet. The quality of colorization depends on the source color image chosen and the target monochrome image which is to be colored. As there are no objective criteria for qualitative analysis of the proposed method, here the grayscale version of original color image is recolored using proposed technique and as quality comparison criteria, the mean squared error between original color image and recolored image is calculated. Five different codebook (color pallet) sizes give variations of proposed colorization method. Using recolorization of 15 different images, The experimental results show that higher color pallet sizes give better colorization in RGB and YIQ color space and lower color pallet sizes give better colorization in YCbCr color space.

There are number of Psychological researches related to behavioral aspects of human being of organizational field. Presents research is about police and local body Administration of organizational field. To study police Employees psychologically become necessary because of suicide case on Duty, Demolishing, antisocial activities, aggressiveness of seniors and these results in indicate imbalance of low order. Exploitation of citizens treating inferiorly, job in satisfaction, Corruption, various scandals, sexual Harassment by some civil employees of local body administration, force to study psychologically above said is to focus on mood states of employees and life satisfaction. It is necessary to study their state of mind psychologically. Thus researcher selected the Aurangabad area of Maharashtra region in India. 400 respondents from police and civil employees were selected by random method with criterion of equal matching method. There are four hypotheses were formulated, a) The level of life satisfaction would be high in civil employees than policeman. b) The life satisfaction will be more in urban employees than rural employees serving in police and civil departments. c) There will be significant differences in mood states of police and civil employees. d) There would be significant differences in mood states of urban and rural employees serving in police and civil depts. Researcher has used life satisfaction scale designed and prepared by Q.G. Alam & Dr. Ramji Srivastava and Mood States Questionnaire by Dr. M. Bhargva and Dr. Kapoor has used for data collection. Two-way ANOVA was applying for data analysis, all results were significant on 0.05 and 0.01 level. Hence it concluded that nature of employment and area of residence are influence on life satisfaction and mood states.