k-generalized Fibonacci numbers which are concatenations of two repdigits

We show that the k-generalized Fibonacci numbers that are concatenations of two repdigits have at most four digits

The Lab4P consortium of probiotics attenuates atherosclerosis in LDL receptor deficient mice fed a high fat diet and causes plaque stabilization by inhibiting inflammation and several pro-atherogenic processes

Scope Previous studies showed that Lab4 probiotic consortium plus Lactobacillus plantarum CUL66 (Lab4P) reduced diet-induced weight gain and plasma cholesterol levels in C57BL/6J mice fed a high fat diet (HFD). The effect of Lab4P on atherosclerosis is not known and was therefore investigated. Methods and results Atherosclerosis-associated parameters were analyzed in LDL receptor deficient mice fed HFD for 12 weeks alone or supplemented with Lab4P. Lab4P increased plasma HDL and triglyceride levels and decreased LDL/VLDL levels. Lab4P also reduced plaque burden and content of lipids and macrophages, indicative of dampened inflammation, and increased smooth muscle cell content, a marker of plaque stabilization. Atherosclerosis arrays showed that Lab4P altered the liver expression of 19 key disease-associated genes. Lab4P also decreased the frequency of macrophages and T-cells in the bone marrow. In vitro assays using conditioned media from probiotic bacteria demonstrated attenuation of several atherosclerosis-associated processes in vitro such as chemokine-driven monocytic migration, proliferation of monocytes and macrophages, foam cell formation and associated changes in expression of key genes, and proliferation and migration of vascular smooth muscle cells. Conclusion This study provides new insights into the anti-atherogenic actions of Lab4P together with the underlying mechanisms and supports further assessments in human trials

Pro-atherogenic actions of signal transducer and activator of transcription 1 serine 727 phosphorylation in LDL receptor deficient mice via modulation of plaque inflammation

Atherosclerosis is a chronic inflammatory disorder of the vasculature regulated by cytokines. We have previously shown that extracellular signal-regulated kinase-1/2 (ERK1/2) plays an important role in serine 727 phosphorylation of signal transducer and activator of transcription-1 (STAT1) transactivation domain, which is required for maximal interferon-γ signaling, and the regulation of modified LDL uptake by macrophages in vitro. Unfortunately, the roles of ERK1/2 and STAT1 serine 727 phosphorylation in atherosclerosis are poorly understood and were investigated using ERK1 deficient mice (ERK2 knockout mice die in utero) and STAT1 knock-in mice (serine 727 replaced by alanine; STAT1 S727A). Mouse Atherosclerosis RT² Profiler PCR Array analysis showed that ERK1 deficiency and STAT1 S727A modification produced significant changes in the expression of 18 and 49 genes, respectively, in bone marrow-derived macrophages, with 17 common regulated genes that included those that play key roles in inflammation and cell migration. Indeed, ERK1 deficiency and STAT1 S727A modification attenuated chemokine-driven migration of macrophages with the former also impacting proliferation and the latter phagocytosis. In LDL receptor deficient mice fed a high fat diet, both ERK1 deficiency and STAT1 S727A modification produced significant reduction in plaque lipid content, albeit at different time points. The STAT1 S727A modification additionally caused a significant reduction in plaque content of macrophages and CD3 T cells and diet-induced cardiac hypertrophy index. In addition, there was a significant increase in plasma IL-2 levels and a trend toward increase in plasma IL-5 levels. These studies demonstrate important roles of STAT1 S727 phosphorylation in particular in the regulation of atherosclerosis-associated macrophage processes in vitro together with plaque lipid content and inflammation in vivo, and support further assessment of its therapeutical potential

A cluster based collaborative filtering method for improving the performance of recommender systems in ecommerce

Rapid growth of E-commerce has made a huge number of products and services accessible to the users. The vast variety of options makes it difficult for the users to finalize their decisions. Recommender systems aim at offering the most suitable items to the users. To do this, recommender systems use data about user’s behaviour and interest (in the past) and characteristics of items. In addition to the data, recommender systems employ machine learning algorithms to build sophisticated models to predict the user’s behaviour in the future. In this thesis, two new methods are proposed for recommender systems both of which consist of two phases: offline and online. In the offline phase, users are clustered based on their similarities; and in the online phase, items which are interesting for a user’s cluster members are recommended to that user. The first proposed method, CFGA, is based on collaborative filtering technique, uses genetic algorithm to cluster users in the offline phase. The fitness function takes into account the users’ ratings and rating times. In the online phase, the ratings of the target user for each item is calculated from the ratings of his or her cluster members to that item. Items with ratings above a threshold are considered interesting for the user and are recommended to him or her. The method is evaluated with two data sets from Movielens for which experimental results show that CFGA is more accurate than several existing recommendation methods. However, there are a couple of existing methods that outperform CFGA. The second method is a hybrid method which combines collaborative filtering and demographic recommendation algorithms. Similarly to CFGA, the second method uses genetic algorithm for clustering users. However, the fitness function, in addition to users’ ratings, incorporates demographic information about users (age, occupation, and sex). Experimental results show that the hybrid method outperforms not only CFGA, but also all existing similar methods

Investigation of the actions of Resveratrol on atherosclerosis development using in vitro and in vivo model systems

Background: Atherosclerosis continues to be a major contributor to cardiovascular disease (CVD), which is one of the leading causes of morbidity and mortality globally. The current pharmacological strategies targeting hyperlipidaemia, such as statins, have shown limited effectiveness in combating cardiovascular risk and have other issues. Considering the limitations associated with statins and other pharmacotherapies, alternative avenues need to be sought. Nutraceuticals, such as resveratrol (RSV), have been highlighted as potential candidates for atherosclerosis prevention and treatment due to its demonstrated ability to modify several atherogenic risk factors and its excellent safety profile. Unfortunately, its effects on the full range of atherosclerotic processes along with the underlying molecular mechanisms are not fully understood. Therefore, the main aims of this study were to investigate the effects of RSV on key cellular processes associated with atherosclerosis development in vitro and to elucidate its effects on atherosclerosis progression in a mouse model system. Methods: Various in vitro assays were carried out using different cell lines and primary cell cultures to investigate the effect of RSV treatment on a range of key cellular processes associated with atherosclerosis development. Furthermore, to investigate the effect of RSV on atherosclerotic plaque progression in vivo, 8-week-old male low-density lipoprotein receptor-deficient (LDLR-/-) mice were fed either a high-fat diet (HFD) or HFD-supplemented with RSV for 12 weeks. This was followed by a comprehensive analysis of risk factors associated with disease initiation and progression, such as plasma lipid profile and staining of resident cells (e.g. macrophages, T-cells and smooth muscle cells (SMCs)) in the plaque. Results: RSV attenuated several key atherosclerosis-associated processes in vitro, such as monocyte migration towards monocyte chemoattractant protein-1(MCP-1), reactive oxygen species (ROS) production in all investigated cell types, and foam cell formation. Furthermore, RSV reduced human aortic smooth muscle cells (HASMCs) invasion and enhanced their proliferation, and exhibited anti-inflammatory actions. Regarding in vivo progression study, mice that received RSV-supplemented HFD for 12 weeks showed an improvement in plasma lipid profile, attenuation of plaque inflammation and enhancement markers of plaque stability. Furthermore, additional investigation on liver samples showed that RSV has the ability to reduce steatosis. Conclusion: The findings from this study provide valuable insights into the anti-atherogenic actions of RSV and implicate it as a potential nutraceutical candidate that could be used globally as a part of ongoing atherosclerotic CVD prevention and management strategies due to the lack of undesirable side effects and the comparatively low cost compared to standard pharmacological medications. The potential of RSV should be investigated however in large clinical trials

Automatic text classification using bag of words and bag of concepts based representations

Automatic Text Classification (ATC) is one of the most important tasks in data mining for organizing information and knowledge discovery. The goal of ATC is to alleviate the need of manually organizing large collections of text documents, which is done by assigning one or more predefined categories to a given textual document via applying appropriate natural language processing techniques. Overall, the classification process involves three components: text pre-processing, text representation and the classifier which is built using one of the Machine Learning (ML) algorithms. In general, all existing text representations are based on the Bag-of-Words (BOW) and Bag-of-Concepts (BOC) models and their variations. The BOW representation model ignores the semantic connections between words by breaking terms into their constituent words, and synonymous words are considered as independent words with no semantic association. The BOW limitations are addressed by using concepts as features in BOC model to represent text in ATC systems. The aim of this work is to investigate and assess the effect of communally available text representation models on the performance of ATC system, in term of the accuracy of the classification and the efficiency of implementation. To achieve that, both BOW and BOC representation models are used with the ATC system and Wikipedia as a knowledge base is utilized to provide concepts. In addition, different strategies that use both words and concepts to build combined models are reviewed and compared to BOW and BOC representation models. Moreover, two languages are used to evaluate these representation models in their ATC system, which are English and Arabic. For Arabic ATC system, different variations of BOW representation models are compared which is a result of different methods that used in text pre-processing component. Furthermore, WordNet as KBs is used to provide concepts to represent Arabic text in the ATC system. This is then followed by attempts to enrich text representation by combining the features of both BOW and BOC models, in order to further enhance the performance of the ATC. Our investigation has resulted in the development of two new strategies, namely Adding Unmapped Concepts (AUC) and Using Concepts for Terms which do not appear in the Document (CTD). Both developed strategies improve ATC systems’ performance in comparison with BOW and BOC representation models. They also bring text classification to a qualitatively new level of performance when compared to other strategies. In addition, CTD developed strategy reduced the time and memory required compared to other strategies used to enrich text representation in ATC systems. The results of our experiments show that text representation is a key element affecting the performance of both English and Arabic ATC systems, and the developed strategies show improvement in both languages in ATC systems. Furthermore, using Wikipedia concepts to build BOC model for Arabic ATC shows more efficiency for representing text than BOW model which does not line with what has been stated in English ATC. The reason behind that is the complex nature of the Arabic language which contains rich morphology and a large degree of the inflections and derivations. In addition, Arabic suffers from poor a morphological tool which makes Wikipedia concepts better features to represent text

Exploring the effect of lexical inferencing and dictionary consultation on undergraduate EFL students' vocabulary acquisition.

This study compares how lexical inferencing and dictionary consultation affect L2 vocabulary acquisition. Sixty-one L1 Arabic undergraduates majoring in English language read target words in semi-authentic English reading materials and were either asked to guess their meaning or look it up in a dictionary. A pre- and delayed post-test measured participants' knowledge of target words and overall vocabulary size. The results show a significant and comparable learning effect for both vocabulary learning strategies (VLS), with a higher pre-test vocabulary size related to a larger learning effect for both VLS. In addition, the better participants were at guessing correctly, the better they learned words through inferencing. The results suggest that both VLS are equally effective for our learner group and that learners' overall vocabulary size influences the amount of learning that occurs when using these VLS

Monitoring modified lipoprotein uptake and macropinocytosis associated with macrophage foam cell formation

Macrophage foam cell formation plays a crucial role in the initiation and progression of atherosclerosis. Macrophages uptake native and modified low density lipoprotein (LDL) through either receptor-dependent or receptor-independent mechanisms to transform into lipid laden foam cells. Foam cells are involved in the formation of fatty streak that is seen during the early stages of atherosclerosis development and therefore represents a promising therapeutic target. Normal or modified lipoproteins labeled with fluorescent dyes such as 1,1′-dioctadecyl-3-3-3′,3′-tetramethylindocarbocyanine perchlorate (Dil) are often used to monitor their internalization during foam cell formation. In addition, the fluorescent dye Lucifer Yellow (LY) is widely used as a marker for macropinocytosis activity. In this chapter, we describe established methods for monitoring modified lipoprotein uptake and macropinocytosis during macrophage foam cell formation