Protocol for FLIP pilot study

This protocol includes a training guide to assist researchers in cataloguing food ingredients through use of the Food Label Information Program (FLIP) interface and database. It includes steps for downloading the “Food Collector App” which can scan information (including both text and image) on food labels. A step by step process is outlined for entering the data for compilation and analysis. Examples are provided for tinned food such as fruit and vegetables, and dry food packaging such as cereals

Protocol for FLIP study of project IDRC 108167 scaling-up and evaluating salt reduction policies and programs in Latin American countries

This guide assists researchers in cataloguing food ingredients by using the Food Label Information Program (FLIP) interface and database. It includes steps for downloading the “Food Collector App” which can scan information (including both text and image) on food labels. A step by step process is outlined for entering the data for compilation and analysis. Examples are provided for tinned food such as fruit and vegetables, and dry food packaging such as cereals

FLIP-LAC user guide

This is version 6.4 of the FLIP-LAC manual. The Food Labeling Information Program for Latin America (FLIP-LAC) for data collection and registration is a smartphone-based technology developed by the University of Toronto, Canada. The FLIP iPhone app is meant for quickly capturing a limited amount of information about a food product - most importantly the product barcode and photos of product. Once this information is captured, the data and the photos are later uploaded to the FLIP website where the rest of the data entry can be completed based on photos of the product

Gene Networks Driving Genetic Variation in Milk and Cheese-Making Traits of Spanish Assaf Sheep

Artículo de InvestigaciónMost of the milk produced by sheep is used for the production of high-quality cheese. Consequently, traits related to milk coagulation properties and cheese yield are economically important to the Spanish dairy industry. The present study aims to identify candidate genes and their regulators related to 14 milk and cheese-making traits and to develop a low-density panel of markers that could be used to predict an individual’s genetic potential for cheese-making efficiency. In this study, we performed a combination of the classical genome-wide association study (GWAS) with a stepwise regression method and a pleiotropy analysis to determine the best combination of the variants located within the confidence intervals of the potential candidate genes that may explain the greatest genetic variance for milk and cheese-making traits. Two gene networks related to milk and cheese-making traits were created using the genomic relationship matrices built through a stepwise multiple regression approach. Several co-associated genes in these networks are involved in biological processes previously found to be associated with milk synthesis and cheese-making efficiency. The methodology applied in this study enabled the selection of a co-association network comprised of 374 variants located in the surrounding of genes showing a potential influence on milk synthesis and cheese-making efficiency.S

Genome-wide association studies (GWAS) and post-GWAS analyses for technological traits in Assaf and Churra dairy breeds

20 p.This study aimed to perform a GWAS to identify genomic regions associated with milk and cheese-making traits in Assaf and Churra dairy sheep breeds; second, it aimed to identify possible positional and functional candidate genes and their interactions through post-GWAS studies. For 2,020 dairy ewes from 2 breeds (1,039 Spanish Assaf and 981 Churra), milk samples were collected and analyzed to determine 6 milk production and composition traits and 6 traits related to milk coagulation properties and cheese yield. The genetic profiles of the ewes were obtained using a genotyping chip array that included 50,934 SNP markers. For both milk and cheese-making traits, separate single-breed GWAS were performed using GCTA software. The set of positional candidate genes identified via GWAS was subjected to guilt-by-association-based prioritization analysis with ToppGene software. Totals of 84 and 139 chromosome-wise significant associations for the 6 milk traits and the 6 cheese-making traits were identified in this study. No significant SNPs were found in common between the 2 studied breeds, possibly due to their genetic heterogeneity of the phenotypes under study. Additionally, 63 and 176 positional candidate genes were located in the genomic intervals defined as confidence regions in relation to the significant SNPs identified for the analyzed traits for Assaf and Churra breeds. After the functional prioritization analysis, 71 genes were identified as promising positional and functional candidate genes and proposed as targets of future research to identify putative causative variants in relation to the traits under examination. In addition, this multitrait study allowed us to identify variants that have a pleiotropic effect on both milk production and cheese-related traits. The incorporation of variants among the proposed functional and positional candidate genes into genomic selection strategies represent an interesting approach for achieving rapid genetic gains, specifically for those traits difficult to measure, such as cheese-making traits.S

Primitive Cretaceous island-arc volcanic rocks in eastern Cuba : the Téneme Formation

The Téneme Formation is located in the Mayarí-Cristal ophiolitic massif and represents one of the three Cretaceous volcanic Formations established in northeastern Cuba. Téneme volcanics are cut by small bodies of 89.70 ± 0.50 Ma quarz-diorite rocks (Río Grande intrusive), and are overthrusted by serpentinized ultramafics. Téneme volcanic rocks are mainly basalts, basaltic andesites, andesites, and minor dacites, and their geochemical signature varies between low-Ti island arc tholeiites (IAT) with boninitic affinity (TiO2 < 0.4 %; high field strength elements « N-type MORB) and typical oceanic arc tholeiites (TiO2 = 0.5-0.8 %). Basaltic rocks exhibit low light REE/Yb ratios (La/Yb < 5), typical of intraoceanic arcs and are comparable to Maimón Formation in Dominican Republic (IAT, pre Albian) and Puerto Rican lavas of volcanic phase I (island arc tholeiites, Aptian to Early Albian). The mantle wedge signature of the Téneme Formation indicates a highly depleted MORB-type mantle source, without any contribution of E-MORB or OIB components. Our results suggest that Téneme volcanism represents a primitive oceanic island arc environment. If the Late Cretaceous age (Turonian or early Coniacian) proposed for Téneme Formation is correct, our results indicate that the Cretaceous volcanic rocks of eastern Cuba and the Dominican Republic are not segments of a single arc system, and that in Late Cretaceous (Albian-Campanian) Caribbean island arc development is not represented only by calc-alkaline (CA) volcanic rocks as has been suggested in previous works

PM speciation and sources in Mexico during the MILAGRO-2006 Campaign

International audienceLevels of PM10, PM2.5 and PM1 and chemical speciation of PM10 and PM2.5 were measured during the MILAGRO campaign (1st to 31st March 2006, but extended in some cases until 6th April) at four urban, one suburban, two rural background and two rural sites, with different degree of industrial influence, in the Mexico City Metropolitan Area (MCMA) and adjacent regions. PM10 and PM2.5 daily levels varied between 50?56?g/m3 and 24?46?g/m3 at the urban sites, 22?35?g/m3 and 13?25?g/m3 at the rural sites, and 75?g/m3 and 31?g/m3 at the industrial hotspot, lower than those recorded at some Asian mega-cities and similar to those recorded at other Latin American cities. At the urban sites, hourly PM2.5 and PM1 concentrations showed a marked impact of road traffic emissions (at rush hours), with levels of coarse PM remaining elevated during daytime. At the suburban and rural sites different PM daily patterns were registered according to the influence of the pollution plume from MCMA, and also of local soil resuspension. The speciation studies showed that mineral matter accounted for 25?27% of bulk PM10 at the urban sites and a higher proportion (up to 43%) at the suburban and rural sites. This pattern is repeated in PM2.5, with 15% at urban and 28% at suburban and rural sites. Carbonaceous compounds accounted for a significant proportion at the urban and industrial sites (32?46% in PM10, and 51?55% in PM2.5), markedly reduced at the suburban and rural sites (16?23% in PM10, and 30% in PM2.5). The secondary inorganic aerosols accounted for 10?20% of bulk PM10 at urban, suburban, rural and industrial sites, with a higher proportion (40%) at the industrial background site. A relatively high proportion of nitrate in rural sites was present in the coarse fraction. Typically anthropogenic elements (As, Cr, Zn, Cu, Pb, Sn, Sb, Ba, among others) showed considerably high levels at the urban sites; however levels of particulate Hg and crustal trace elements (Rb, Ti, La, Sc, Ga) were generally higher at the suburban site. Principal component analysis identified three major common factors: crustal, regional background and road traffic. Moreover, some specific factors were obtained for each site

The Lyth Bound and the End of Inflation

We derive an extended version of the well-known Lyth Bound on the total variation of the inflaton field, incorporating higher order corrections in slow roll. We connect the field variation $\Delta\phi$ to both the spectral index of scalar perturbations and the amplitude of tensor modes. We then investigate the implications of this bound for ``small field'' potentials, where the field rolls off a local maximum of the potential. The total field variation during inflation is {\em generically} of order $m_{\rm Pl}$, even for potentials with a suppressed tensor/scalar ratio. Much of the total field excursion arises in the last e-fold of inflation and in single field models this problem can only be avoided via fine-tuning or the imposition of a symmetry. Finally, we discuss the implications of this result for inflationary model building in string theory and supergravity.Comment: 10 pages, RevTeX, 2 figures (V3: version accepted for publication by JCAP

Identifying the Machine Learning Family from Black-Box Models

[EN] We address the novel question of determining which kind of machine learning model is behind the predictions when we interact with a black-box model. This may allow us to identify families of techniques whose models exhibit similar vulnerabilities and strengths. In our method, we first consider how an adversary can systematically query a given black-box model (oracle) to label an artificially-generated dataset. This labelled dataset is then used for training different surrogate models (each one trying to imitate the oracle¿s behaviour). The method has two different approaches. First, we assume that the family of the surrogate model that achieves the maximum Kappa metric against the oracle labels corresponds to the family of the oracle model. The other approach, based on machine learning, consists in learning a meta-model that is able to predict the model family of a new black-box model. We compare these two approaches experimentally, giving us insight about how explanatory and predictable our concept of family is.This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-17-1-0287, the EU (FEDER), and the Spanish MINECO under grant TIN 2015-69175-C4-1-R, the Generalitat Valenciana PROMETEOII/2015/013. F. Martinez-Plumed was also supported by INCIBE under grant INCIBEI-2015-27345 (Ayudas para la excelencia de los equipos de investigacion avanzada en ciberseguridad). J. H-Orallo also received a Salvador de Madariaga grant (PRX17/00467) from the Spanish MECD for a research stay at the CFI, Cambridge, and a BEST grant (BEST/2017/045) from the GVA for another research stay at the CFI.Fabra-Boluda, R.; Ferri Ramírez, C.; Hernández-Orallo, J.; Martínez-Plumed, F.; Ramírez Quintana, MJ. (2018). Identifying the Machine Learning Family from Black-Box Models. Lecture Notes in Computer Science. 11160:55-65. https://doi.org/10.1007/978-3-030-00374-6_6S556511160Angluin, D.: Queries and concept learning. Mach. Learn. 2(4), 319–342 (1988)Benedek, G.M., Itai, A.: Learnability with respect to fixed distributions. Theor. Comput. Sci. 86(2), 377–389 (1991)Biggio, B., et al.: Security Evaluation of support vector machines in adversarial environments. In: Ma, Y., Guo, G. (eds.) Support Vector Machines Applications, pp. 105–153. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02300-7_4Blanco-Vega, R., Hernández-Orallo, J., Ramírez-Quintana, M.J.: Analysing the trade-off between comprehensibility and accuracy in mimetic models. In: Suzuki, E., Arikawa, S. (eds.) DS 2004. LNCS (LNAI), vol. 3245, pp. 338–346. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30214-8_29Dalvi, N., Domingos, P., Sanghai, S., Verma, D., et al.: Adversarial classification. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 99–108. ACM (2004)Dheeru, D., Karra Taniskidou, E.: UCI machine learning repository (2017). http://archive.ics.uci.edu/mlDomingos, P.: Knowledge discovery via multiple models. Intell. Data Anal. 2(3), 187–202 (1998)Duin, R.P.W., Loog, M., Pȩkalska, E., Tax, D.M.J.: Feature-based dissimilarity space classification. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds.) ICPR 2010. LNCS, vol. 6388, pp. 46–55. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17711-8_5Fernández-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems. J. Mach. Learn. Res. 15(1), 3133–3181 (2014)Ferri, C., Hernández-Orallo, J., Modroiu, R.: An experimental comparison of performance measures for classification. Pattern Recognit. Lett. 30(1), 27–38 (2009)Giacinto, G., Perdisci, R., Del Rio, M., Roli, F.: Intrusion detection in computer networks by a modular ensemble of one-class classifiers. Inf. Fusion 9(1), 69–82 (2008)Huang, L., Joseph, A.D., Nelson, B., Rubinstein, B.I., Tygar, J.: Adversarial machine learning. In: Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, pp. 43–58 (2011)Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51(2), 181–207 (2003)Landis, J.R., Koch, G.G.: An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics 33, 363–374 (1977)Lowd, D., Meek, C.: Adversarial learning. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data mining, pp. 641–647. ACM (2005)Martınez-Plumed, F., Prudêncio, R.B., Martınez-Usó, A., Hernández-Orallo, J.: Making sense of item response theory in machine learning. In: Proceedings of 22nd European Conference on Artificial Intelligence (ECAI). Frontiers in Artificial Intelligence and Applications, vol. 285, pp. 1140–1148 (2016)Papernot, N., McDaniel, P., Goodfellow, I.: Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. arXiv preprint arXiv:1605.07277 (2016)Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: 2016 IEEE European Symposium on Security and Privacy (EuroS&P), pp. 372–387. IEEE (2016)Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A.: Distillation as a defense to adversarial perturbations against deep neural networks. In: 2016 IEEE Symposium on Security and Privacy (SP), pp. 582–597. IEEE (2016)Sesmero, M.P., Ledezma, A.I., Sanchis, A.: Generating ensembles of heterogeneous classifiers using stacked generalization. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 5(1), 21–34 (2015)Smith, M.R., Martinez, T., Giraud-Carrier, C.: An instance level analysis of data complexity. Mach. Learn. 95(2), 225–256 (2014)Tramèr, F., Zhang, F., Juels, A., Reiter, M.K., Ristenpart, T.: Stealing machine learning models via prediction APIs. In: USENIX Security Symposium, pp. 601–618 (2016)Valiant, L.G.: A theory of the learnable. Commun. ACM 27(11), 1134–1142 (1984)Wallace, C.S., Boulton, D.M.: An information measure for classification. Comput. J. 11(2), 185–194 (1968)Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992

One-year study of airborne sugar compounds: cross-interpretation with other chemical species and meteorological conditions

The daily evolution of seventeen sugar compounds (seven saccharides, seven alcohol-saccharides and three anhydrosaccharides) in atmospheric aerosol samples collected between 9 March 2016 and 14 March 2017 was studied in León (Spain). The main links between the concentration of sugar compounds and various chemical species, pollen, fungal spores and meteorological conditions were investigated. The results showed that, in spring, when high levels of metabolic activity of the plants occur and temperatures increase, glucose, sucrose, 2-methyl-erithritol, mannitol, arabitol and inositol, are significantly correlated with airborne pollen concentrations. Between spring and autumn, Alternaria air concentrations are significantly correlated with temperatures, arabitol and sorbitol + adonitol concentrations. Furthermore, during rainy days, Alternaria is also correlated with mannitol. In autumn, lower temperatures cause an increase in the concentrations of levoglucosan, mannosan and galactosan, probably due to the increased use of domestic heating devices. These anhydrosugars and arabinose, fructose and glucose, are significantly correlated with K, NO3−, EC, OC, Cu, Zn, Se, Pb, V and Ni, while mannosan also correlates with As, showing that these anhydrosaccharides can be emitted from different anthropogenic sources. Precipitation causes an increase in glucose and sucrose concentrations, due to the break of pollen particles that produce hundreds of fine size particles. Besides, precipitation causes an increase in arabitol concentrations, due to the release and growth of fungi.publishe