Linear discriminant analysis with misallocation in training samples

Linear discriminant analysis for a two-class case is studied in the presence of misallocation in training samples. A general appraoch to modeling of mislocation is formulated, and the mean vectors and covariance matrices of the mixture distributions are derived. The asymptotic distribution of the discriminant boundary is obtained and the asymptotic first two moments of the two types of error rate given. Certain numerical results for the error rates are presented by considering the random and two non-random misallocation models. It is shown that when the allocation procedure for training samples is objectively formulated, the effect of misallocation on the error rates of the Bayes linear discriminant rule can almost be eliminated. If, however, this is not possible, the use of Fisher rule may be preferred over the Bayes rule

Lacie phase 1 Classification and Mensuration Subsystem (CAMS) rework experiment

An experiment was designed to test the ability of the Classification and Mensuration Subsystem rework operations to improve wheat proportion estimates for segments that had been processed previously. Sites selected for the experiment included three in Kansas and three in Texas, with the remaining five distributed in Montana and North and South Dakota. The acquisition dates were selected to be representative of imagery available in actual operations. No more than one acquisition per biophase were used, and biophases were determined by actual crop calendars. All sites were worked by each of four Analyst-Interpreter/Data Processing Analyst Teams who reviewed the initial processing of each segment and accepted or reworked it for an estimate of the proportion of small grains in the segment. Classification results, acquisitions and classification errors and performance results between CAMS regular and ITS rework are tabulated

Carbon Monoxide Blocks Lipopolysaccharide-Induced Gene Expression by Interfering with Proximal TLR4 to NF-κB Signal Transduction in Human Monocytes

Carbon monoxide (CO) is an endogenous messenger that suppresses inflammation, modulates apoptosis and promotes vascular remodeling. Here, microarrays were employed to globally characterize the CO (250 ppm) suppression of early (1 h) LPS-induced inflammation in human monocytic THP-1 cells. CO suppressed 79 of 101 immediate-early genes induced by LPS; 19% (15/79) were transcription factors and most others were cytokines, chemokines and immune response genes. The prototypic effects of CO on transcription and protein production occurred early but decreased rapidly. CO activated p38 MAPK, ERK1/2 and Akt and caused an early and transitory delay in LPS-induced JNK activation. However, selective inhibitors of these kinases failed to block CO suppression of LPS-induced IL-1β, an inflammation marker. Of CO-suppressed genes, 81% (64/79) were found to have promoters with putative NF-κB binding sites. CO was subsequently shown to block LPS-induced phosphorylation and degradation of IκBα in human monocytes, thereby inhibiting NF-κB signal transduction. CO broadly suppresses the initial inflammatory response of human monocytes to LPS by reshaping proximal events in TLR4 signal transduction such as stress kinase responses and early NF-κB activation. These rapid, but transient effects of CO may have therapeutic applications in acute pulmonary and vascular injury

Bayesian Lasso-mixed quantile regression

In this paper, we discuss the regularization in linear-mixed quantile regression. A hierarchical Bayesian model is used to shrink the fixed and random effects towards the common population values by introducing an l1 penalty in the mixed quantile regression check function. A Gibbs sampler is developed to simulate the parameters from the posterior distributions. Through simulation studies and analysis of an age-related macular degeneration (ARMD) data, we assess the performance of the proposed method. The simulation studies and the ARMD data analysis indicate that the proposed method performs well in comparison with the other approaches. © 2012 Taylor & Francis

Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

In [1], we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid approach leveraging feature selection techniques and evolutionary algorithms process to optimize the selected features. Prior works solve this problem iteratively to converge to an optimal feature subset. Feature selection optimization is a non-specific domain approach. Data scientists mainly attempt to find an advanced way to analyze data n with high computational efficiency and low time complexity, leading to efficient data analytics. Thus, by increasing generated/measured/sensed data from various sources, analysis, manipulation and illustration of data grow exponentially. Due to the large scale data sets, Curse of dimensionality (CoD) is one of the NP-hard problems in data science. Hence, several efforts have been focused on leveraging evolutionary algorithms (EAs) to address the complex issues in large scale data analytics problems. Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently. In this chapter, we first provide a brief overview of previous studies that focused on solving CoD using feature extraction optimization process. We then discuss practical examples of research studies are successfully tackled some application domains, such as image processing, sentiment analysis, network traffics / anomalies analysis, credit score analysis and other benchmark functions/data sets analysis

Neoadjuvant in situ gene-mediated cytotoxic immunotherapy improves postoperative outcomes in novel syngeneic esophageal carcinoma models

Esophageal carcinoma is the most rapidly increasing tumor in the United States and has a dismal 15% 5-year survival. Immunotherapy has been proposed to improve patient outcomes; however, no immunocompetent esophageal carcinoma model exists to date to test this approach. We developed two mouse models of esophageal cancer by inoculating immunocompetent mice with syngeneic esophageal cell lines transformed by cyclin-D1 or mutant HRASG12V and loss of p53. Similar to humans, surgery and adjuvant chemotherapy (cisplatin and 5-fluorouracil) demonstrated limited efficacy. Gene-mediated cyototoxic immunotherapy (adenoviral vector carrying the herpes simplex virus thymidine kinase gene in combination with the prodrug ganciclovir; AdV-tk/GCV) demonstrated high levels of in vitro transduction and efficacy. Using in vivo syngeneic esophageal carcinoma models, combining surgery, chemotherapy and AdV-tk/GCV improved survival (P=0.007) and decreased disease recurrence (P<0.001). Mechanistic studies suggested that AdV-tk/GCV mediated a direct cytotoxic effect and an increased intra-tumoral trafficking of CD8 T cells (8.15% vs 14.89%, P=0.02). These data provide the first preclinical evidence that augmenting standard of care with immunotherapy may improve outcomes in the management of esophageal carcinoma

The Evolution and Cultural Framing of Food Safety Management Systems – Where from and Where next?

The aim of this paper is to review the development of food safety management systems (FSMS) from their origins in the 1950s to the present. The food safety challenges in modern food supply systems are explored and it is argued that there is the need for a more holistic thinking approach to food safety management. The narrative review highlights that whilst the transactional elements of how FSMS are developed, validated, implemented, monitored and verified remains largely unchanged, how organizational culture frames the operation and efficacy of FSMS is becoming more important. The evolution of a wider academic and industry understanding of both the influence of food safety culture (FS-culture) and also how such culture frames and enables, or conversely restricts the efficacy of the FSMS is crucial for consumer wellbeing. Potential research gaps worthy of further study are identified as well as recommendations given for the application of the research findings within the food industry