Search

Background: This study was conducted to evaluate the expression of Cyclin D1 in colorectal carcinoma, and to correlate Cyclin D1 positivity with the grade of the tumor along with other parameters such as age, tumor location, lymph node metastasis, and vessel invasion.Materials and Methods: The present cross-sectional study was conducted on 40 proven cases of colorectal carcinoma from October 2015 to October 2017. The tissues were fixed, processed, and stained with hematoxylin and eosin followed by immunohistochemical staining with Cyclin D1 using monoclonal antibodies.Results: 75% of the cases were conventional adenocarcinoma while the rest were mucinous adenocarcinoma. Of the total colorectal carcinomas,12.5% were Grade I, 50% were Grade II and 37.5% cases were Grade III. Cyclin D1 positivity was seen in 60% of cases with the most positivity in grade II tumors. A statistically significant correlation of Cyclin D1 was seen with age group (5th- 6th decade) and histological type of tumor. No significant correlation was seen of Cyclin D1 with lymph node involvement, depth of invasion, vascular invasion, or perineural invasion. A significant statistical correlation was seen with Crohn’s-like reaction and peritumoral lymphocytic response.Conclusions: Thus, it was concluded that a highly statistically significant correlation was seen with age group (5th- 6th decade) and histological type of tumor. No significant correlation was seen with Cyclin D1 expression with lymph node involvement, depth of invasion, vascular invasion, or perineural invasion.

Objectives: In the present study we investigated the BRCA1, Estrogen receptors status (ER), progesterone receptor status (PR) and human epidermal growth factor receptor (Her2neu) expression in selected patients with higher probability of BRCA1 positivity. The association of BRCA1 positivity and Triple Negative Breast Cancer (TNBC) status was also demonstrated. Materials and Methods: The study was conducted on 100 cases of breast cancer specimen received in department of Pathology, SGRDIMSAR, Amritsar. Histopathological typing and grading was done followed by immunohistochemistry for BRCA1, ER, PR and Her2neu. Results: All the tumors were infiltrating ductal carcinoma NOS. 4 cases were of grade I, 34 cases as grade II and 62 as grade III. ER+ PR+ Her2neu-ve cases comprised of 33% and triple negative being 38%. BRCA1 positivity was seen in 36% patients. BRCA1 positivity was significantly higher in triple negative primary tumours as compared to other groups. (23/38- 60.5% vs 13/62-20.9% respectively) with significant p =0.009. Conclusion: Triple Negative Breast Cancer (TNBC) patients are associated with poor prognostic factors and are more likely to be BRCA1 positive. Such BRCA1 identification is not only important in management of the patient for contralateral breast cancer and ovarian cancer but also blood relatives of such patients. As the relatives of such patient have 50% of increased chances of BRCA1 expression.

In this experimental work, the effect of various input parameters viz. work speed, wheel speed,abrasive material, depth of cut, concentration of cutting fluid and number of passes has been studied on thematerial removal rate of cylindrical grinded AISI. For experimentation, three levels of each variable have beenselected except wheel speed. Two levels of wheel speed have been taken. Heat treated AISI 1045 has beenconsidered as work piece material. The result reveals that number of passes followed by the type of abrasivematerial is the most significant to influence material removal rate. The optimum set of input parameters formaximizing the material removal rate has also been found.

In this present work, The effect of input parameters viz. work speed, wheel speed, abrasive material, depth of cut, concentration of cutting fluid and number of passes has been found on the surface roughness of cylindrical grinded AISI 1045 steel has been found. Three levels of each variable have been selected except wheel speed. Two levels of wheel speed have been taken. Heat treated AISI 1045 has been taken as work piece material. The result reveals that type of abrasive material is the most significant to influence surface roughness, followed by work speed. The optimum set of input parameters for minimizing the surface roughness has also been found. wheel speed, coolant flow rate and truing on Zirconia and M2 tool steel using a vitreous bond CBN wheel for high speed grinding. The author found that both normal and tangential grinding forces were reduced, but surface finish and roundness of ground parts did not improve. High coolant flow rate eliminate grinding burn on M2 steel layer and layer of Zirconia powder on wheel after grinding Zirconia. Nathan et al. (3) studied on In-process monitoring of grinding burn in the cylindrical grinding of steel. In steels burn is characterized by a visible bluish temper colour on the ground surface. The authors found that spark temperature can be considered to be good representative of grinding zone temperature and hence useful for process-monitoring purposes. Kwak (4) studied on evaluating the grinding parameters effect on geometric error by the taguchi method and developing a mathematical model by the response surface method for predicting geometric error. The author concluded that depth of cut effect more for geometric error and next was grain size. The utilization of response surface model was evaluated to select proper grinding conditions with constraints of the surface roughness and response surface model was very useful for predicting the geometric error. Sahin (5) studied wear resistance of three types of steels a low carbon steel (A1SI 1020), Carbon steel (A1SI 1340) and low alloyed steel (A1SI 5150) on a pin-on-disc type apparatus. The experiments were carried to analyze the influence of testing parameters on weight loss of various steel work pieces. An L9 orthogonal array was used. The orthogonal array, signal-tonoise ratio and analysis of variance were used to investigate the optimal testing Parameters. For AISI 1340 steel, abrasive grain size exerted the most or greatest effect on wear, but in AISI 1020 and AISI 5150 steels sliding distance exerted greatest effect on the weight loss. Kwak et al. (6) developed response surface models to predicate the grinding power and surface roughness in external cylindrical grinding of pardoned SCM 440 steel and also help in selection of grinding conditions. They concluded that due to response surface model, it is possible to predict the grinding power and the surface roughness

Potential customers of E-commerce garment businesses purchase clothes based on their personal choice as well as contemporary perception about fashion trends which is highly sublime in nature. This, coupled with the influential influx of cinema, social media and the Internet has led to rapid change in dressing preferences. E-commerce sites have a huge inventory categorized based on different attributes like colour, fabric, fit, product type and vendor. Hence, it is a tedious task for the user to find products coherent with the latest trend and appropriate according to their personal taste and global trends. The recommendation engines of these websites gives recommendations based only on a linear similarity of products, not comprehensively taking into account latest trends in fashion backed by fashion specialists, user preference or wardrobe collections. We propose an Adaptive Commodity Suggestion System called ”Match-BOT” which takes into account the trends obtained through insights provided by Google Trends, individual preferences, and similarity between user preferences. The user preferences are extracted by analyzing user purchase patterns and thus obtaining categorised product attribute preferences in the form of weights. This is an application of Content Based Learning The global trend obtained from Google Trends is then overlapped with the user preference and a user specific suggestion list is generated. The similarity between two user is calculated using Collaborative Filtering and then taken into account by including cross-user suggestions based on the degree of correlation. The output of this process would be in the form of list of suggestions of products which have been customized to society's understanding of fashion and predilections of the user and constantly updated to reflect dynamic global trends. This would result in an unparalleled browsing experience for the user and an efficient sales and successive inventory management systems.

Understanding the Learner requirements is an important aspect of any learning environment as it helps to recommend the LOs in a more personalized manner. With the growing demand for MOOCs offered by coursera, edx, etc. the learner information plays a vital role in understanding the extent to which the learners can gain out of such courses. The Learning Management Systems (LMS) across the web uses the explicit (rating, performance, etc.) and implicit feedback (LOs used) obtained through interaction with the learners to derive such information. As the requirements of the learners varies with the individual's interest and learning background, a common approach for recommending LOs may not cater the needs of all the learners. To overcome this issue, this paper proposes reinforcement learning based algorithm to analyze the learner information (derived from both implicit and explicit feedback) and generate the knowledge on the learner's requirements and capabilities inside a specific learning context. The reinforcement learning system (RILS) implemented as a part of this work utilizes the knowledge thus generated in order to recommend the appropriate LOs for the learners. The results have highlighted that the knowledge derived from the learning information analysis proved to effective in generating personalized recommendation policies that can cater the context specific requirements of the learners.

The degradation of sewage sludge in single stage and 2 phase anaerobic systems was investigated in bench-scale batch reactors. Average COD reduction efficiency in 2 phase system in this initial phase of the study was 4.5% (30 days HRT) and 3.3% (20 days HRT) higher compared to single stage system. Reducing the HRT from 30 days to 20 days had little influence in VS reduction in the two systems. The average methane content in the biogas was 61.8%, 66.5%, and 64.8% (30 days HRT) in acid reactor, methane reactor and single stage reactor, respectively, and the corresponding values were 57.7%, 66.5% and 64.9% at 20 days HRT. The microbial community analyzed by qPCR using primer/probes specific for orders of Archaea involved in methanogenesis showed that hydrogen and acetate utilizing methanogens were present in all the reactors, but relative abundance of methanogens in the acid reactor was lower compared to the methane and single stage reactors. Reducing the HRT from 30 days to 20 days decreased the relative abundance of methanogens and changed the methanogen community structure.

Demand side load prediction is one of the most challenging tasks in smart grid environment due to uncertainties between demand and supply. Hence, in order to overcome this issue, this paper presents a scheme based on machine learning and deep learning for energy load forecasting by considering the weather condition of the area. We propose a two-tier Ensemble model, which ensembles the results of machine learning model (Support Vector Machine) and deep learning models (Convolu- tional Neural Network One Dimensional and Long Term Short memory) with a simple neural network to predict the load and demand gap. Then, we train and test the model with the UMass Smart* Dataset - 2017 release by taking the readings of appliances and weather conditions. The experimental results demonstrate that the proposed scheme has a significant improvement over the existing load forecasting methods having short-term and long- term load prediction models with an overall accuracy of 95.6%