Enhancing Health Benefits of Tomato by Increasing its Antioxidant Contents through Different Techniques: A Review

Tomato is known to be a great dietary source of antioxidant lycopene which is found to be linked with reduced risk of life-threatening diseases like heart attack and cancers. Antioxidants delay the aging process by mopping up reactive free radicals from cells, those if present may damage our DNA and other vital cellular organelles. Antioxidant metabolites are a group of vitamins, carotenoids, phenolic compounds, and phenolic acids that can provide effective protection against Reactive Oxygen Species (ROS) by neutralizing free radicals, which are unstable molecules linked to the development of many degenerative diseases and medical conditions. There are pre and postharvest techniques available in the literature and these when adopted by the researchers showed significant progress in enhancing antioxidant contents of tomato fruit. In addition, there are various biochemical and genetic modification approaches to improve the expression of several antioxidant enhancing phytonutrients, enzymes and genes in tomato fruit. Trichoderma enriched bio-fertilizer application in tomato enhanced ascorbic acid under the treatment of 100% bio-fertilizer and beta-carotene was increased under 75% Bio-Fertilizer+25% N whereas elevated lycopene contents were observed in case of recommended dose of NPK. Various omics approaches like genomics, transcriptomics, miRNAomics, proteomics, and metabolomics have emerged as extremely helpful tools for the plant scientists in improving the beta-carotene, lycopene and antioxidant levels resulting in highly desirable new tomato cultivars.  Thus, in light of immense advantages of these techniques, the present study was undertaken to collect all the necessary information about different techniques employed by numerous researchers to increase the antioxidant contents in tomato and to document here the optimized experimental conditions that can be beneficial for future studies in this field. However, still in-depth genome wide studies are needed for better understanding and further enhancement of traits like flavor, quality and antioxidant contents in context to rapidly changing and uncertain climate.Keywords: Antioxidants; tomato; lycopene; β-carotene; reactive oxygen species (ROS)

Circulation of Dengue Serotypes in Local Population of District Lahore, Pakistan

Infection with dengue virus (DENV) is considered as serious public health issues internationally as amounted to 2.5 billion people are at infection risk throughout the world. Dengue is now endemic in Pakistan. Till now, no licensed vaccine is available against dengue virus infection. The main purpose of this study was to find out differences in the levels of IgM and IgG on gender basis as well as distribution of dengue serotypes in the local population of district Lahore, Pakistan. Fifteen blood samples including 3 control sample were collected from dengue infected patients and statistical results showed significantly higher mean levels of IgM (1.12 + 0.09) and IgG (2.07 + 0.56) antibodies in patients as compared to control groups for IgM (0.34 + 0.05) and IgG (0.10 + 0.05) antibodies respectively. Statistical results on Gender base showed significantly higher mean levels of IgM (1.10 + 0.19) in males and of IgG (2.27 + 0.74) in females. The enveloped gene of 1.5 kb was successfully amplified through polymerase chain reaction and cloned in ?TZ57R/T. The cloned gene was then confirmed through restriction digestion. Out of 15 dengue samples, only 3 dengue samples were successfully amplified using polymerase chain reaction which all belongs to serotype 2 In future, it may contribute in development of treatment to dengue infection with dengue virus type 2 which is more prevalent in district Lahor

An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs

We combined clinical, cytokine, genomic, methylation and dietary data from 43 young adult monozygotic twin pairs (aged 22-36 years, 53% female), where 25 of the twin pairs were substantially weight discordant (delta body mass index > 3 kg m(-2)). These measurements were originally taken as part of the TwinFat study, a substudy of The Finnish Twin Cohort study. These five large multivariate datasets (comprising 42, 71, 1587, 1605 and 63 variables, respectively) were jointly analysed using an integrative machine learning method called group factor analysis (GFA) to offer new hypotheses into the multi-molecular-level interactions associated with the development of obesity. New potential links between cytokines and weight gain are identified, as well as associations between dietary, inflammatory and epigenetic factors. This encouraging case study aims to enthuse the research community to boldly attempt new machine learning approaches which have the potential to yield novel and unintuitive hypotheses. The source code of the GFA method is publically available as the R package GFA.Peer reviewe

Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets

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Matrix and Tensor Factorization Methods for Toxicogenomic Modeling and Prediction

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Learning with multiple pairwise kernels for drug bioactivity prediction

Motivation: Many inference problems in bioinformatics, including drug bioactivity prediction, can be formulated as pairwise learning problems, in which one is interested in making predictions for pairs of objects, e.g. drugs and their targets. Kernel-based approaches have emerged as powerful tools for solving problems of that kind, and especially multiple kernel learning (MKL) offers promising benefits as it enables integrating various types of complex biomedical information sources in the form of kernels, along with learning their importance for the prediction task. However, the immense size of pairwise kernel spaces remains a major bottleneck, making the existing MKL algorithms computationally infeasible even for small number of input pairs. Results: We introduce pairwiseMKL, the first method for time- and memory-efficient learning with multiple pairwise kernels. pairwiseMKL first determines the mixture weights of the input pairwise kernels, and then learns the pairwise prediction function. Both steps are performed efficiently without explicit computation of the massive pairwise matrices, therefore making the method applicable to solving large pairwise learning problems. We demonstrate the performance of pairwiseMKL in two related tasks of quantitative drug bioactivity prediction using up to 167 995 bioactivity measurements and 3120 pairwise kernels: (i) prediction of anticancer efficacy of drug compounds across a large panel of cancer cell lines; and (ii) prediction of target profiles of anticancer compounds across their kinome-wide target spaces. We show that pairwiseMKL provides accurate predictions using sparse solutions in terms of selected kernels, and therefore it automatically identifies also data sources relevant for the prediction problem.Peer reviewe