Gastrointestinal-Sparing Effects of Novel NSAIDs in Rats with Compromised Mucosal Defence

Nonsteroidal anti-inflammatory drugs are among the most commonly used prescription and over-the-counter medications, but they often produce significant gastrointestinal ulceration and bleeding, particularly in elderly patients and patients with certain co-morbidities. Novel anti-inflammatory drugs are seldom tested in animal models that mimic the high risk human users, leading to an underestimate of the true toxicity of the drugs. In the present study we examined the effects of two novel NSAIDs and two commonly used NSAIDs in models in which mucosal defence was expected to be impaired. Naproxen, celecoxib, ATB-346 (a hydrogen sulfide- and naproxen-releasing compound) and NCX 429 (a nitric oxide- and naproxen-releasing compound) were evaluated in healthy, arthritic, obese, and hypertensive rats and in rats of advanced age (19 months) and rats co-administered low-dose aspirin and/or omeprazole. In all models except hypertension, greater gastric and/or intestinal damage was observed when naproxen was administered in these models than in healthy rats. Celecoxib-induced damage was significantly increased when co-administered with low-dose aspirin and/or omeprazole. In contrast, ATB-346 and NCX 429, when tested at doses that were as effective as naproxen and celecoxib in reducing inflammation and inhibiting cyclooxygenase activity, did not produce significant gastric or intestinal damage in any of the models. These results demonstrate that animal models of human co-morbidities display the same increased susceptibility to NSAID-induced gastrointestinal damage as observed in humans. Moreover, two novel NSAIDs that release mediators of mucosal defence (hydrogen sulfide and nitric oxide) do not induce significant gastrointestinal damage in these models of impaired mucosal defence

Neurofibromatosis

Neurofibromatosis (NF) is one of the most common genetic disorders. Inherited in an autosomal dominant fashion, this phacomatosis is classified into two genetically distinct subtypes characterized by multiple cutaneous lesions and tumors of the peripheral and central nervous system. Neurofibromatosis type 1 (NF1), also referred to as Recklinghausen's disease, affects about 1 in 3500 individuals and presents with a variety of characteristic abnormalities of the skin and the peripheral nervous system. Neurofibromatosis type 2 (NF2), previously termed central neurofibromatosis, is much more rare occurring in less than 1 in 25 000 individuals. Often first clinical signs of NF2 become apparent in the late teens with a sudden loss of hearing due to the development of bi-or unilateral vestibular schwannomas. In addition NF2 patients may suffer from further nervous tissue tumors such as meningiomas or gliomas. This review summarizes the characteristic features of the two forms of NF and outlines commonalities and distinctions between NF1 and NF2

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

Attention or instruction: do sustained attentional abilities really differ between high and low hypnotisable persons?

Previous research has suggested that highly hypnotisable participants (‘highs’) are more sensitive to the bistability of ambiguous figures—as evidenced by reporting more perspective changes of a Necker cube—than low hypnotisable participants (‘lows’). This finding has been interpreted as supporting the hypothesis that highs have more efficient sustained attentional abilities than lows. However, the higher report of perspective changes in highs in comparison to lows may reflect the implementation of different expectation-based strategies as a result of differently constructed demand characteristics according to one’s level of hypnotisability. Highs, but not lows, might interpret an instruction to report perspective changes as an instruction to report many changes. Using a Necker cube as our bistable stimulus, we manipulated demand characteristics by giving specific information to participants of different hypnotisability levels. Participants were told that previous research has shown that people with similar hypnotisability as theirs were either very good at switching or maintaining perspective versus no information. Our results show that highs, but neither lows nor mediums, were strongly influenced by the given information. However, highs were not better at maintaining the same perspective than participants with lower hypnotisability. Taken together, these findings favour the view that the higher sensitivity of highs in comparison to lows to the bistability of ambiguous figures reflect the implementation of different strategies

The Calcitonin and Glucocorticoids Combination: Mechanistic Insights into Their Class-Effect Synergy in Experimental Arthritis

PMCID: PMC3564948This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Astrobiological Complexity with Probabilistic Cellular Automata

Search for extraterrestrial life and intelligence constitutes one of the major endeavors in science, but has yet been quantitatively modeled only rarely and in a cursory and superficial fashion. We argue that probabilistic cellular automata (PCA) represent the best quantitative framework for modeling astrobiological history of the Milky Way and its Galactic Habitable Zone. The relevant astrobiological parameters are to be modeled as the elements of the input probability matrix for the PCA kernel. With the underlying simplicity of the cellular automata constructs, this approach enables a quick analysis of large and ambiguous input parameters' space. We perform a simple clustering analysis of typical astrobiological histories and discuss the relevant boundary conditions of practical importance for planning and guiding actual empirical astrobiological and SETI projects. In addition to showing how the present framework is adaptable to more complex situations and updated observational databases from current and near-future space missions, we demonstrate how numerical results could offer a cautious rationale for continuation of practical SETI searches.Comment: 37 pages, 11 figures, 2 tables; added journal reference belo

Electrical conductivity during incipient melting in the oceanic low-velocity zone

International audienceThe low-viscosity layer in the upper mantle, the asthenosphere, is a requirement for plate tectonics1. The seismic low velocities and the high electrical conductivities of the asthenosphere are attributed either to subsolidus, water-related defects in olivine minerals2, 3, 4 or to a few volume per cent of partial melt5, 6, 7, 8, but these two interpretations have two shortcomings. First, the amount of water stored in olivine is not expected to be higher than 50 parts per million owing to partitioning with other mantle phases9 (including pargasite amphibole at moderate temperatures10) and partial melting at high temperatures9. Second, elevated melt volume fractions are impeded by the temperatures prevailing in the asthenosphere, which are too low, and by the melt mobility, which is high and can lead to gravitational segregation11, 12. Here we determine the electrical conductivity of carbon-dioxide-rich and water-rich melts, typically produced at the onset of mantle melting. Electrical conductivity increases modestly with moderate amounts of water and carbon dioxide, but it increases drastically once the carbon dioxide content exceeds six weight per cent in the melt. Incipient melts, long-expected to prevail in the asthenosphere10, 13, 14, 15, can therefore produce high electrical conductivities there. Taking into account variable degrees of depletion of the mantle in water and carbon dioxide, and their effect on the petrology of incipient melting, we calculated conductivity profiles across the asthenosphere for various tectonic plate ages. Several electrical discontinuities are predicted and match geophysical observations in a consistent petrological and geochemical framework. In moderately aged plates (more than five million years old), incipient melts probably trigger both the seismic low velocities and the high electrical conductivities in the upper part of the asthenosphere, whereas in young plates4, where seamount volcanism occurs6, a higher degree of melting is expected