An integrated decision support system based on simulation and mathematical programming of Petroleum transportation logistics

Discrete Event simulation (DES), mathematical programming (MP) and analysis of variance (ANOVA) are among the popular tools in operational research (OR) used in dynamic industry like petroleum industry. The integration of these methods even becomes more significant to managerial application in the industry. The objective of this thesis is to present an integrated decision support system by which a decision maker should be able to choose the optimal number of tanks, tank size and truck arrival rate to maximize average total profit and minimize the total transportation cost for an oil refinery terminal operations. The petroleum transportation management system (PTMS) is developed as a DSS using a discrete-event simulation program with ARENA software, mathematical linear programming (LP) with I-Log software and analysis of variance (ANOVA) with SPSS software, and these models are combined in complex program developed using visual basic software (VB). The simulation model represents the logistics operations from oil arriving to the refinery terminal to the supply points. The model process used as a decision support tool to help in evaluating and improving the comprehensive oil terminal operations. And also understanding and assessing of the different steps in a simulation process. An optimization model was formulated with the objective to minimize the total transportation cost. In the model formulation, hard constraints were considered and the linear programming (LP) technique was used. Result obtained suggests the use of certain types of trucks can reduce the operation costs, if compared to that of the current situation. The reduction of costs is due to the reduction of travelling trips as based on the problem constraints. Overall, output of this study has given positive impacts on the transportation operations. The effect of the changes can help the management of the transportation company to make efficient decisions. Multifactor ANOVA is used to determine whether different levels of the three-factors and their interactions significantly impact the oil refinery terminal's profit. ANOVA is also used to determine the flow rate of oil into the tanks station; tank and truck fill rate and a cost and revenue structure. The final step is to expand the model to cover the whole models (DES, LP and ANOVA) and create the integrated user interface. To sum up the combination of these techniques which allows evaluating the actual feasibility of supply planning considering all operations restrictions and variability of the supply logistics and the total transportation cost. In another words, a DSS have been developed to support a decision maker, who is planning to build a new facility or expand an existing oil refinery terminal, should be able to choose the optimal value for all important factors. The PTMS is able to predict with 99% confidence a set of factor levels that yields the highest average total profit

Offline signatures matching using haar wavelet subbands

The complexity of multimedia contents is significantly increasing in the current world. This leads to an exigent demand for developing highly effective systems tosatisfy human needs. Until today, handwritten signature considered an important means that is used in banks and businesses to evidence identity, so there are many works triedto develop a method for recognition purpose. This paper introduced an efficient technique for offline signature recognition depending on extracting the local feature by utilizing the haar wavelet subbands and energy. Three different setsof features are utilized by partitioning the signature image into non overlapping blocks where different block sizes are used. CEDAR signature database is used asa dataset for testing purpose. The results achieved by this technique indicate a high performance in signature recognition

OPTIMIZATION OF REAL TIME IMAGE SEGMENTATION USING EFFICIENT THRESHOLDING TECHNIQUE

The process of image segmentation is how to divide images into regions with similar properties. Threshold-based image segmentation is a multidimensional optimization problem that has been highlighted as one of the most significant image pre-processing approaches. This paper proposes an efficient technique for optimizing real time image segmentation. The approach of image thresholding may be regarded an optimization objective, and it will be discovered by using Otsu's technique in conjunction with Particle Swarm Optimization basics (PSO). For real-time validation, the suggested technique was tested on several images in real time using the PSO algorithm. The simulation results showed that, when compared to Otsu's approach, the PSO algorithm gives the most efficient outcomes in real-time applications with an improved execution time

Spatial Positioning of Immune Hotspots Reflects the Interplay between B and T Cells in Lung Squamous Cell Carcinoma

Beyond tertiary lymphoid structures, a significant number of immune-rich areas without germinal center-like structures are observed in non–small cell lung cancer. Here, we integrated transcriptomic data and digital pathology images to study the prognostic implications, spatial locations, and constitution of immune rich areas (immune hotspots) in a cohort of 935 patients with lung cancer from The Cancer Genome Atlas. A high intratumoral immune hotspot score, which measures the proportion of immune hotspots interfacing with tumor islands, was correlated with poor overall survival in lung squamous cell carcinoma but not in lung adenocarcinoma. Lung squamous cell carcinomas with high intratumoral immune hotspot scores were characterized by consistent upregulation of B-cell signatures. Spatial statistical analyses conducted on serial multiplex IHC slides further revealed that only 4.87% of peritumoral immune hotspots and 0.26% of intratumoral immune hotspots were tertiary lymphoid structures. Significantly lower densities of CD20+CXCR5+ and CD79b+ B cells and less diverse immune cell interactions were found in intratumoral immune hotspots compared with peritumoral immune hotspots. Furthermore, there was a negative correlation between the percentages of CD8+ T cells and T regulatory cells in intratumoral but not in peritumoral immune hotspots, with tertiary lymphoid structures excluded. These findings suggest that the intratumoral immune hotspots reflect an immunosuppressive niche compared with peritumoral immune hotspots, independent of the distribution of tertiary lymphoid structures. A balance toward increased intratumoral immune hotspots is indicative of a compromised antitumor immune response and poor outcome in lung squamous cell carcinoma

ASSOCIATION BETWEEN THE BASELINE GENE EXPRESSION PROFILE IN PERIAPICAL GRANULOMA AND PERIAPICAL WOUND HEALING AFTER SURGICAL ENDODONTIC TREATMENT

In this study, we have investigated the association between the baseline gene expression profile in periapical granuloma and periapical wound healing after surgical endodontic treatment. Twenty-seven patients aged between 15 and 57 years underwent periapical surgery. The retrieved periapical tissue sample was used for mRNA expression analysis of COL1A1, VTN, ITGA5, IL-4, TNF, ANGPT, VEGFA, and CTGF. All patients were recalled after 6 and 12 months for periapical healing evaluation. Healing was then correlated with baseline gene expression. Healing was observed in 15 patients at the end of 6 months, which increased to 21 patients after 12 months. Six patients showed no healing even after 12 months. Analysis of baseline expression levels of the tested genes with healing status showed the mean relative expression of VTN, VEGFA, ANGPT, TNF, and CTGF to be significantly different (p < 0.05) between the healing group (6 and 12 months) (72.99%) and the non-healing (94.42%) group. Periapical Index scores 3–5 exhibited a positive correlation with ITGA-5 expression. Overexpression of ANGPT and a strong positive correlation between ITGA5 and PAI scores in the non-healing group of patients may suggest these genes to be a potential prognostic biomarker for periapical wound non-healing after surgical endodontic treatment

Spatial Positioning of Immune Hotspots Reflects the Interplay between B and T Cells in Lung Squamous Cell Carcinoma

Beyond tertiary lymphoid structures, a significant number of immune rich areas without germinal center-like structures are observed in non-small cell lung cancer. Here, we integrated transcriptomic data and digital pathology images to study the prognostic implications, spatial locations, and constitution of immune rich areas (immune hotspots) in a cohort of 935 lung cancer patients from the TCGA. A high intratumoral immune hotspot score, which measures the proportion of immune hotspots interfacing with tumor islands, was correlated with poor overall survival in lung squamous cell carcinoma but not in lung adenocarcinoma. Lung squamous cell carcinomas with high intratumoral immune hotspot scores were characterized by consistent upregulation of B cell signatures. Spatial statistical analyses conducted on serial multiplex immunohistochemistry slides further revealed that only 4.87% of peritumoral immune hotspots and 0.26% of intratumoral immune hotspots were tertiary lymphoid structures. Significantly lower densities of CD20+CXCR5+ and CD79b+ B cells and less diverse immune cell interactions were found in intratumoral immune hotspots compared to peritumoral immune hotspots. Furthermore, there was a negative correlation between the percentages of CD8+ T cells and T regulatory cells in intratumoral but not in peritumoral immune hotspots, with tertiary lymphoid structures excluded. These findings suggest that the intratumoral immune hotspots reflect an immunosuppressive niche compared to peritumoral immune hotspots, independent of the distribution of tertiary lymphoid structures. A balance towards increased intratumoral immune hotspots is indicative of a compromised anti-tumor immune response and poor outcome in lung squamous cell carcinoma

Biomarkers for site-specific response to neoadjuvant chemotherapy in epithelial ovarian cancer: relating MRI changes to tumour cell load and necrosis.

Funder: We acknowledge funding from Cancer Research UK BIDD grant C1353/A12762 and Cancer Research UK and Engineering and Physical Sciences Research Council support to the Cancer Imaging Centre at the Institute of Cancer Research and Royal Marsden Hospital in association with the Medical Research Council and Department of Health C1060/A10334, C1060/A16464 and National Health Service funding to the National Institute for Health Research Biomedical Research Centres at Royal Marsden Hospital/Institute of Cancer Research and Cambridge, Experimental Cancer Medicine Centres, the Clinical Research Facility in Imaging, and the Cancer Research Network. We are also grateful for financial support from Addenbrooke’s Charitable Trust. The views expressed in this publication are those of the author(s) and not necessarily those of the National Health Service, the National Institute for Health Research or the Department of Health.BACKGROUND: Diffusion-weighted magnetic resonance imaging (DW-MRI) potentially interrogates site-specific response to neoadjuvant chemotherapy (NAC) in epithelial ovarian cancer (EOC). METHODS: Participants with newly diagnosed EOC due for platinum-based chemotherapy and interval debulking surgery were recruited prospectively in a multicentre study (n = 47 participants). Apparent diffusion coefficient (ADC) and solid tumour volume (up to 10 lesions per participant) were obtained from DW-MRI before and after NAC (including double-baseline for repeatability assessment in n = 19). Anatomically matched lesions were analysed after surgical excision (65 lesions obtained from 25 participants). A trained algorithm determined tumour cell fraction, percentage tumour and percentage necrosis on histology. Whole-lesion post-NAC ADC and pre/post-NAC ADC changes were compared with histological metrics (residual tumour/necrosis) for each tumour site (ovarian, omental, peritoneal, lymph node). RESULTS: Tumour volume reduced at all sites after NAC. ADC increased between pre- and post-NAC measurements. Post-NAC ADC correlated negatively with tumour cell fraction. Pre/post-NAC changes in ADC correlated positively with percentage necrosis. Significant correlations were driven by peritoneal lesions. CONCLUSIONS: Following NAC in EOC, the ADC (measured using DW-MRI) increases differentially at disease sites despite similar tumour shrinkage, making its utility site-specific. After NAC, ADC correlates negatively with tumour cell fraction; change in ADC correlates positively with percentage necrosis. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT01505829

Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer

Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls.Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls.Peer reviewe

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC