Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

http://www.dietcan.net/docs/Polifenoles%20en%20alimentos%20y%20biodisponibilidad.pdf

Polyphenols: food sources and bioavailability

Prospective Cohort Study

Bioavailability and bioefficacy of polyphenols in humans. I. Review of 97 bioavailability studies

Polyphenols are secondary metabolites of plants and are generally involved in defense against ultraviolet radiation or aggression by pathogens. In the last decade, there has been much interest in the potential health benefits of dietary plant polyphenols as antioxidant. Epidemiological studies and associated meta-analyses strongly suggest that long term consumption of diets rich in plant polyphenols offer protection against development of cancers, cardiovascular diseases, diabetes, osteoporosis and neurodegenerative diseases. Here we present knowledge about the biological effects of plant polyphenols in the context of relevance to human health.

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Plant Polyphenols as Dietary Antioxidants in Human Health and Disease

Flavonols are products of the flavonoid biosynthetic pathway, which also give rise to anthocyanins and condensed tannins in grapes. We investigated their presence in the berry skins of 91 grape varieties (Vitis vinifera L.), in order to produce a classification based on the flavonol profile. The presence of laricitrin 3-O-galactoside and syringetin 3-O-galactoside in red grapes is reported here for the first time. In red grapes, the main flavonol was quercetin (mean = 43.99%), followed by myricetin (36.81%), kaempferol (6.43%), laricitrin (5.65%), isorhamnetin (3.89%), and syringetin (3.22%). In white grapes, the main flavonol was quercetin (mean = 81.35%), followed by kaempferol (16.91%) and isorhamnetin (1.74%). The delphinidin-like flavonols myricetin, laricitrin, and syringetin were missing in all white varieties, indicating that the enzyme flavonoid 3',5'-hydroxylase is not expressed in white grape varieties. The pattern of expression of flavonols and anthocyanins in red grapes was compared, in order to gain information on the substrate specificity of enzymes involved in flavonoid biosynthesis.

Metabolite Profiling of Grape: Flavonols and Anthocyanins

Anthocyanins belong to the flavonoid group of polyphenolic compounds, which are responsible for the red and blue colors of plant organs such as fruits, flowers, and leaves Due to their frequent presence in plants, particularly berry fruits, vegetables, and grapes, they are key components of the human diet Interest in anthocyanins has increased widely during the past decade Numerous studies have suggested that anthocyanins have a wide range of health-promoting properties These compounds are therefore considered to be a functional food factor, which may have important implications in the prevention of chronic diseases The aim of this body of work is to investigate and review the current literature on anthocyanins, and particularly their pharmacokinetics and any health-promoting properties, in order to summarize existing knowledge and highlight any aspects that require further study and analysis

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1541-4337.12024

The Case for Anthocyanin Consumption to Promote Human Health: A Review

Forty-one apple samples, representing eight of the most widely cultivated varieties in western Europe, were collected in Trentino, Italy. Samples were extracted from fresh fruit with a mixture of acetone/water to achieve a good extraction of polyphenols, including proanthocyanidin oligomers which were analyzed by normal-phase HPLC. Up to 20 compounds including catechin, epicatechin, B2 procyanidin, hydroxycinnamates, flavonols, anthocyanins, and dihydrochalcones were analyzed by reversed-phase HPLC and LC-MS. Total polyphenol content was independently measured with an optimized Folin-Ciocalteu assay. The mean content of total polyphenols lay between 66.2 and 211.9 mg/100 g of FW depending on the variety. With chromatographic analysis, it was possible to explain the whole amount of total polyphenols measured by the FC assay. Flavanols (catechin and proanthocyanidins) are the major class of apple polyphenols (71-90%), followed by hydroxycinnamates (4-18%), flavonols (1-11%), dihydrochalcones (2-6%), and in red apples anthocyanins (1-3%).

Quantitation of polyphenols in different apple varieties.

The gut is the most extensively studied niche of the human microbiome. The aim of this study was to characterise the initial gut microbiota development of a cohort of breastfed infants (n = 192﻿) from 1 to 24 weeks of age. V4-V5 region 16S rRNA amplicon Illumina sequencing and, in parallel, bacteriological culture. The metabolomic profile of infant urine at 4 weeks of age was also examined by LC-MS. Full-term (FT), spontaneous vaginally delivered (SVD) infants’ microbiota remained stable at both phylum and genus levels during the 24-week period examined. FT Caesarean section (CS) infants displayed an increased faecal abundance of Firmicutes (p < 0.01) and lower abundance of Actinobacteria (p < 0.001) after the first week of life compared to FT-SVD infants. FT-CS infants gradually progressed to harbouring a microbiota closely resembling FT-SVD (which remained stable) by week 8 of life, which was maintained at week 24. The gut microbiota of preterm (PT) infants displayed a significantly greater abundance of Proteobacteria compared to FT infants (p < 0.001) at week 1. Metabolomic analysis of urine at week 4 indicated PT-CS infants have a functionally different metabolite profile than FT (both CS and SVD) infants. Co-inertia analysis showed co-variation between the urine metabolome and the faecal microbiota of the infants. Tryptophan and tyrosine metabolic pathways, as well as fatty acid and bile acid metabolism, were found to be affected by delivery mode and gestational age. These findings confirm that mode of delivery and gestational age both have significant effects on early neonatal microbiota composition. There is also a significant difference between the metabolite profile of FT and PT infants. Prolonged breastfeeding was shown to have a significant effect on the microbiota composition of FT-CS infants at 24 weeks of age, but interestingly not on that of FT-SVD infants. Twins had more similar microbiota to one another than between two random infants, reflecting the influence of similarities in both host genetics and the environment on the microbiota.﻿

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Evolution of gut microbiota composition from birth to 24 weeks in the INFANTMET Cohort.

https://febs.onlinelibrary.wiley.com/doi/pdf/10.1016/S0014-5793%2803%2900504-0

Fulvio Mattivi

Papers

Metabolite Profiling of Grape: Flavonols and Anthocyanins

The Case for Anthocyanin Consumption to Promote Human Health: A Review

Quantitation of polyphenols in different apple varieties.

Evolution of gut microbiota composition from birth to 24 weeks in the INFANTMET Cohort.

The stomach as a site for anthocyanins absorption from food1