Effect of Plant Originated Coagulants and Chymosin on Ovine Milk Coagulation

The coagulation of ewe's milk was studied by using plant source coagulants namely the artichoke, Cynara scolymus L. cv. Blanca, and latex from the fig tree (Ficus carica L.). A turbidimetric method was used to evaluate and compare the coagulation properties of the novel coagulants with chymosin treated samples. Syneresis capacity and sensory evaluation of resultant cheese samples were studied and it was found that both cynara and chymosin produced sigmoidal increase in turbidity to the milk with three distinct phases. The coagulation kinetics was affected substantially by both coagulants. Plant coagulant induced shorter gelation time compared to chymosin however required longer time for restructuration (end of coagulation). The coagulum obtained with the latex of Ficus carica had a higher yield, owing to its high water retention capacity. With the exception of color, the overall sensory attributes did not show significant differences among coagulants.     

Urinary Tract Tumor Organoids Reveal Eminent Differences in Drug Sensitivities When Compared to 2-Dimensional Culture Systems

Generation of organoids from urinary tract tumor samples was pioneered a few years ago. We generated organoids from two upper tract urothelial carcinomas and from one bladder cancer sample, and confirmed the expression of cytokeratins as urothelial antigens, vimentin as a mesenchymal marker, and fibroblast growth factor receptor 3 by immunohistochemistry. We investigated the dose response curves of two novel components, venetoclax versus {"type":"entrez-nucleotide","attrs":{"text":"S63845","term_id":"400540","term_text":"S63845"}}S63845, in comparison to the clinical standard cisplatin in organoids in comparison to the corresponding two-dimensional cultures. Normal urothelial cells and tumor lines RT4 and HT1197 served as controls. We report that upper tract urothelial carcinoma cells and bladder cancer cells in two-dimensional cultures yielded clearly different sensitivities towards venetoclax, {"type":"entrez-nucleotide","attrs":{"text":"S63845","term_id":"400540","term_text":"S63845"}}S63845, and cisplatin. Two-dimensional cultures were more sensitive at low drug concentrations, while organoids yielded higher drug efficacies at higher doses. In some two-dimensional cell viability experiments, colorimetric assays yielded different IC₅₀ toxicity levels when compared to chemiluminescence assays. Organoids exhibited distinct sensitivities towards cisplatin and to a somewhat lesser extent towards venetoclax or {"type":"entrez-nucleotide","attrs":{"text":"S63845","term_id":"400540","term_text":"S63845"}}S63845, respectively, and significantly different sensitivities towards the three drugs investigated when compared to the corresponding two-dimensional cultures. We conclude that organoids maintained inter-individual sensitivities towards venetoclax, {"type":"entrez-nucleotide","attrs":{"text":"S63845","term_id":"400540","term_text":"S63845"}}S63845, and cisplatin. The preclinical models and test systems employed may bias the results of cytotoxicity studies.     

Semisupervised online learning of hierarchical structures for visual object classification

Multimedia Tools and Applications - One of the main challenges in hierarchical object classification is the derivation of the correct hierarchical structure. The classic way around the problem is...     

Orthographic and morphological processing for English–Arabic statistical machine translation

Much of the work on statistical machine translation (SMT) from morphologically rich languages has shown that morphological tokenization and orthographic normalization help improve SMT quality because of the sparsity reduction they contribute. In this article, we study the effect of these processes on SMT when translating into a morphologically rich language, namely Arabic. We explore a space of tokenization schemes and normalization options. We also examine a set of six detokenization techniques and evaluate on detokenized and orthographically correct (enriched) output. Our results show that the best performing tokenization scheme is that of the Penn Arabic Treebank. Additionally, training on orthographically normalized (reduced) text then jointly enriching and detokenizing the output outperforms training on enriched text.     

Bayesian hybrid generative discriminative learning based on finite Liouville mixture models

Recently hybrid generative discriminative approaches have emerged as an efficient knowledge representation and data classification engine. However, little attention has been devoted to the modeling and classification of non-Gaussian and especially proportional vectors. Our main goal, in this paper, is to discover the true structure of this kind of data by building probabilistic kernels from generative mixture models based on Liouville family, from which we develop the Beta-Liouville distribution, and which includes the well-known Dirichlet as a special case. The Beta-Liouville has a more general covariance structure than the Dirichlet which makes it more practical and useful. Our learning technique is based on a principled purely Bayesian approach which resulted models are used to generate support vector machine (SVM) probabilistic kernels based on information divergence. In particular, we show the existence of closed-form expressions of the Kullback-Leibler and Rényi divergences between two Beta-Liouville distributions and then between two Dirichlet distributions as a special case. Through extensive simulations and a number of experiments involving synthetic data, visual scenes and texture images classification, we demonstrate the effectiveness of the proposed approaches.     

Variational Bayesian inference for infinite generalized inverted Dirichlet mixtures with feature selection and its application to clustering

We developed a variational Bayesian learning framework for the infinite generalized Dirichlet mixture model (i.e. a weighted mixture of Dirichlet process priors based on the generalized inverted Dirichlet distribution) that has proven its capability to model complex multidimensional data. We also integrate a “feature selection” approach to highlight the features that are most informative in order to construct an appropriate model in terms of clustering accuracy. Experiments on synthetic data as well as real data generated from visual scenes and handwritten digits datasets illustrate and validate the proposed approach.     

On Bayesian analysis of a finite generalized Dirichlet mixture via a Metropolis-within-Gibbs sampling

In this paper, we present a fully Bayesian approach for generalized Dirichlet mixtures estimation and selection. The estimation of the parameters is based on the Monte Carlo simulation technique of Gibbs sampling mixed with a Metropolis-Hastings step. Also, we obtain a posterior distribution which is conjugate to a generalized Dirichlet likelihood. For the selection of the number of clusters, we used the integrated likelihood. The performance of our Bayesian algorithm is tested and compared with the maximum likelihood approach by the classification of several synthetic and real data sets. The generalized Dirichlet mixture is also applied to the problems of IR eye modeling and introduced as a probabilistic kernel for Support Vector Machines.