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A 29-year-old woman who underwent laser in situ keratomileusis (LASIK) for myopic astigmatism in both eyes presented with severe pain, photophobia and decreased visual acuity in the left eye eight days after surgery. Examination revealed severe anterior uveitis with fibrinous exudates in the anterior chamber, flap edema and epithelial bullae. Laboratory investigations for uveitis were negative and the patient required systemic and intensive topical steroids with cycloplegics to control the inflammation. This case demonstrates that severe anterior uveitis may develop after LASIK and needs prompt and vigorous management for resolution.

Breast cancer is the most common invasive cancer in women and as per the data in 2008, this deadly cancer was responsible for 458,503 deaths worldwide in 2008 [1]. Several researches are on-going for identifying therapeutic strategies for breast cancer. Breast cancer biology is complex and breast cancer stem cells that are often resistant to conventional therapies like chemotherapy [2] increases the complexity as it has been reported that at even early stages of the disease, a portion of the breast cancer cells may have eloped to the bone marrow facilitated by the mesenchymal stem cells [3] and remain dormant becoming active later thereby causing recurrence or advancement of the disease. Natural Killer (NK) cell based Autologous Immune Enhancement Therapy (AIET) which has been administered for different types of cancers [4,5] represents a potential option, as NK cells being a part of innate immunity help in tackling circulating cancer cells [6] and cancer stem cells [7] thereby helping to prevent metastasis. Herein we report our experience of NK cell based AIET in a patient of stage III A breast cancer (inflammatory type) diagnosed three months post-partum. A 29 year old female with history of pain and tenderness in the left breast post-partum was investigated in October 2012 and the investigations revealed the presence of infiltrating ductal carcinoma (pT3 N2a Mx- Stage III A) (T4bN2M0) and the cancer was ER positive, PR negative, Her2neu negative, Ki67 positive (86% of the tumour cells) and EGFR, Cytokeratin 5 negative. The patient underwent three cycles of pre-operative chemotherapy (from October 2012 to December 2012) using Doxorubicin, Docetaxel and Cyclophosphamide followed by left modified radical mastectomy (December 2012) and then three cycles of post-operative chemotherapy (from January to February 2013). The patient simultaneously underwent 12 transfusions of NK cell based AIET from November 2012 to February 2013 planned in accordance with the chemotherapy cycles. Approximately 200-220 ml of peripheral blood (PB) was withdrawn for the first three cycles (three transfusions in one cycle – nine transfusions in total) and then for the 10th transfusion, only 40 ml of PB was withdrawn as the patient's general health condition was low. 185 ml of PB was withdrawn for the 11th and 12th transfusions. For each AIET transfusion, the NK cells isolated from peripheral blood monononuclear cells (PBMNCSs) were culture-expanded in vitro based on earlier described protocols [8, 9] for 10-12 days before being infused to the patient. Chemotherapy has earlier been reported to cause a decline in NK cell number and function [10]. It was observed in the present case that, with progressively increasing number of chemotherapy cycles, there was a gradual decline in the in vitro growth expansion of the NK cells, though the number of NK cells isolated from the peripheral blood remained fairly constant. After AIET and chemotherapy, the patient underwent radiotherapy with 5400cGy in 27 fractions. The patient is under follow-up. The decrease in NK cell expansion potential with progressive chemotherapy implies a weakened immune system after chemotherapy. This experience could be an eye opener to understand the potential weakness of the immune system with the present therapies enable us undertake researches to come out with more targeted therapies which shall not compromise the immune system thereby plugging the loopholes to prevent metastases and to yield a better prognosis.

The abnormal tissues which are originated from the brain cells are known as brain tumor. It may be cancerous tumor or non-cancerous tumor; these can cause pressure inside the skull to increase the tumors. This damages the brain and become life-threatening. The main cause of brain tumor is still unknown. The early diagnosis is required otherwise it increases the mortality rate. The segmentation of tumor part is essential to identify the affected area in the brain. In this study, an effective method for Brain Tumor Segmentation (BTS) is presented. The BTS system uses Discrete Wavelet Transform (DWT), K-Means Clustering (KMC) algorithm and morphological operations for segmentation. Firstly, the input brain images are given to DWT for decomposition and it produces lower and higher frequency sub-band coefficients. Then the Inverse DWT (IDWT) is applied to reconstruct the image. The reconstructed image is given to KMC technique for segmentation. Then the unwanted regions are removed by morphological operations to detect the brain tumor in the given input image. [ABSTRACT FROM AUTHOR]

Byline: N. Ramkumar, P. Subramanian, M. Rajmohan This paper focuses on the inbound logistics segment in the supply chain of automobile companies, which are known to be highly complex. This has always affected key business functions in the supply chain with respect to cost, coordination, and material delay issues. During third party logistics service provider (TPLs) selection, practitioners have started to concentrate on coordination, integration, and cooperation as some of the key areas to improve SC performance. In this context, we propose a model for selection of TPLs network with an objective to create a favourable environment for improving coordination and integration using analytic hierarchy process (AHP) and technique for order preference by similarity to ideal situation (TOPSIS) approach. A case of an automobile company is used to explain the model and potential expected benefits are discussed. A comparative analysis on decision-making certainty between the classical AHP and TOPSIS approach is also discussed.

Byline: M. Rajmohan, P. Shahabudeen Multi Depot Vehicle Routing Problem with Time Windows (MDVRPTW) is one of the major logistics problems In this paper a two phase heuristics is proposed in which the customers are assigned to the depot in the first phase The routes are constructed with modified ant colony optimisation procedure and are improved using 2-Opt procedure in phase two The proposed heuristic has been applied to Cordeau MDVRPTW benchmark problems It is found that the proposed heuristic gives promising and better results, on an average, as compared to those solutions given by the existing heuristic for the MDVRPTW.

Growing industrial demand in present competitive scenario has raised the need for new materials with high strength to weight ratio to replace the conventional materials. This work discusses the multi‐objective optimization of wear performance of plain epoxy, epoxy/E‐glass fiber and epoxy/E‐glass fiber/carbon particles composites prepared by simple hand lay‐up process with clamp loading. Wear test carried in pin on disc setup, with design of experiments based on response surface methodology with speed, load and sliding distance as numerical factors at three levels and sample as categorical. Optimization carried, aiming towards reduction of performance measures like Weight loss (WL) and Coefficient of friction (μ) followed by development of an artificial neural model for predicting the wear performances. The experimental and the artificial neural network (ANN) predicted values are found very close and thus the ANN model developed can predict the WL and coefficient of friction both within and across the design space. POLYM. COMPOS., 40:2812–2818, 2019. © 2018 Society of Plastics Engineers [ABSTRACT FROM AUTHOR]

The evaluation and selection of strategies for sustainable development in manufacturing supply chains are critical tasks for the decision‐maker because of the complexity involving various criteria. This paper deals with the prioritization of critical sustainability criteria and the sustainability strategy selection (SSS) using an integrated weighting approach. This approach consists of the analytic hierarchy process (AHP), the subjective and objective weight integrated approach (SOWIA) and the technique for order preference by similarity to an ideal solution (TOPSIS) and is found to be effective for solving this multi‐criteria decision‐making problem. The proposed methodology is applied to solve a real‐time supply chain decision‐making issue of an Indian manufacturing industry. From a sustainability perspective, the results indicate the second strategy set as the best among the given alternatives, with an objective factor of 0.5. The robustness of the results has been analysed to identify the uncertainty in the final ranking. The consistency of the final ranking has been evaluated based on different objective factor values. The novelty of this paper pertains to addressing the most relevant SSS problem involved in a manufacturing supply chain and in establishing a hybrid approach for solving it. This approach offers a flexible provision for decision‐makers to incorporate the appropriate level of subjectivity and objectivity into their decisions based on their degree of expertise. This study is unique and carries a high originality value, as it offers a new structured and flexible approach to solving the SSS problem. [ABSTRACT FROM AUTHOR]

Efficacy of the multi-robot systems depends on proper sequencing and optimal allocation of robots to the tasks. Focuses on deciding the optimal allocation of set-of-robots to a set-of-tasks with precedence constraints considering multiple objectives. Taguchi's design of experiments based parameter tuned genetic algorithm (GA) is developed for generalised task allocation of single-task robots to multi-robot tasks. The developed methodology is tested for 16 scenarios by varying the number of robots and number of tasks. The scenarios were tested in a simulated environment with a maximum of 20 robots and 40 multi-robot foraging tasks. The tradeoff between performance measures for the allocations obtained through GA for different task levels was used to decide the optimal number of robots. It is evident that the tradeoffs occur at 20 per cent of performance measures and the optimal number of robot varies between 10 and 15 for almost all the task levels. This method shows good convergence and found that the precedence constraints affect the optimal number of robots required for a particular task level. [ABSTRACT FROM AUTHOR]