Unique isometric immersion into hyperbolic space

A result concerning equivalence of isometric immersions with related Gauss maps into hyperbolic space has been established

Effects of short term feeding of some marine microalgae on the microbial profile associated with Dicentrarchus labrax post larvae

AbstractThis study investigates the microbial profile and antimicrobial activity of four marine microalgae species, Tetraselmis chuii, Nannochloropsis salina, Isochrysis galbana and Chlorella salina used in aquaculture of Dicentrarchus labrax in the post larval stage to estimate which was the best algal species that could be used as a green water technique and achieving the maximum rate of growth and survival of D. labrax post larvae. The results represented a significant increase in the length and width of D. labrax at p<0.05 recorded in the case of enrichment with I. galbana followed by N. salina, and the most weight was recorded in the case of N. salina as compared with the control. Significant increase in percentage of survival of D. labrax was recorded in the case of C. salina and T. chuii (70% and 60.1%, respectively) as compared with the control (22%). The antibacterial activity (AU) of the different microalgal ethanolic extracts against fish indicator pathogens was determined. The results indicated that the ethanolic extracts of C. salina and T. chuii have the most positive records against the fish indicator pathogens (Escherichia coli, Pseudomonas aeruginosa, Vibrio damsela, Vibrio fluvialis and Aeromonas hydrophila). The current study was extended to determine the GC–MS of ethanolic extract of C. salina and T. chuii. The main constituents detected in the ethanolic extract were organic acids like hexadecanoic acid, octadecanoic acid, and an acyclic diterpene alcohol like phytol

Suppressive efficiency of Kojic acid from Aspergillus tamarii MM11 against HepG-2 cell line derived from human liver cancer

Purpose: To evaluate the antioxidant and cytotoxic properties of Kojic acid (KOJIC ACID) from Aspergillus tamarii MM11 against HepG-2 cell line derived from human liver cancer.Methods: The crude extract of A. tamarii MM11 was dissolved in a mixture of CH2Cl2/MeOH (85:15) and separation was done using silica gel chromatography using gradient size exclusion chromatograph. The non-polar oily fractions were subjected to gas chromatography-mass&nbsp; spectrometric (GC-MS) analysis. Kojic acid structure was identified by x-beam crystallography and spectroscopic methods. Total antioxidant properties of KOJIC ACID were evaluated by using 1,1-diphenyl-2- picrylhydrazyl (DPPH) against ascorbic acid as a reference. The cytotoxic activity of KOJIC ACID from A. tamarii MM11 was investigated on the human cell line of liver cancer (HepG-2) using a sulforhodamine B (SRB) assay based on a cell density determination by the measurement of cellular protein content.Result: Highly bioactive Kojic acid was isolated as the main product. A. tamarii MM11 Kojic acid showed good antioxidant activity with half-maximal inhibitory concentration of IC50 at concentrations of 10.34 compared to 6.79 μg/mL for ascorbic acid. Kojic acid also showed good cytotoxic activity against HepG-2 cell line of human liver cancer with IC50 at 6.20 compared to 3.25 μg/mL of reference drug doxorubicin.Conclusion: Kojic acid produced naturally from A. tamarii MM11 shows good antioxidant and cytotoxic activity against HepG-2 cell line derived from&nbsp; human liver cancer. These findings suggest that Kojic acid can be therapeutically used as an antitumor drug after further in vivo studies. Keywords: Aspergillus tamarii, Secondary metabolites, Kojic acid, Anticancer, Liver cance

Non-invasive index of liver fibrosis induced by alcohol, thioacetamide and schistosomal infection in mice

<p>Abstract</p> <p>Background</p> <p>Non invasive approaches will likely be increasing utilized to assess liver fibrosis. This work provides a new non invasive index to predict liver fibrosis induced in mice.</p> <p>Methods</p> <p>Fibrosis was generated by thioacetamide (TAA), chronic intake of ethanol, or infection with <it>S. mansoni </it>in 240 mice. Both progression and regression of fibrosis (after treatment with silymarin and/or praziquantel) were monitored. The following methods were employed: (i) The METAVIR system was utilized to grade and stage liver inflammation and fibosis; (ii) Determination of hepatic hydroxyproline and collagen; and (iii) Derivation of a new hepatic fibrosis index from the induced changes, and its prospective validation in a group of 70 mice.</p> <p>Results</p> <p>The index is composed of 4 serum variable including total proteins, γ-GT, bilirubin and reduced glutathione (GSH), measured in diseased, treated and normal mice. These parameters were highly correlated with both the histological stage and the grade. They were combined in a logarithmic formula, which non-invasively scores the severity of liver fibrosis through a range (0 to 2), starting with healthy liver (corresponding to stage 0) to advanced fibrosis (corresponding stage 3).Receiver operating characteristic curves (ROC) for the accuracy of the index to predict the histological stages demonstrated that the areas under the curve (AUC) were 0.954, 0.979 and 0.99 for index values corresponding to histological stages 1, 2 and 3, respectively. Also, the index was correlated with stage and grade, (0.947 and 0.859, respectively). The cut off values that cover the range between stages 0-1, 1-2 and 2-3 are 0.4, 1.12 and 1.79, respectively. The results in the validation group confirmed the accuracy of the test. The AUROC was 0.869 and there was good correlation with the stage of fibrosis and grade of inflammation.</p> <p>Conclusion</p> <p>The index fulfils the basic criteria of non-invasive marker of liver fibrosis since it is liver-specific, easy to implement, reliable, and inexpensive. It proved to be accurate in discriminating precirrhotic stages.</p

RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation

Keyword extraction is used for summarizing the content of a document and supports efficient document retrieval, and is as such an indispensable part of modern text-based systems. We explore how load centrality, a graph-theoretic measure applied to graphs derived from a given text can be used to efficiently identify and rank keywords. Introducing meta vertices (aggregates of existing vertices) and systematic redundancy filters, the proposed method performs on par with state-of-the-art for the keyword extraction task on 14 diverse datasets. The proposed method is unsupervised, interpretable and can also be used for document visualization.Comment: The final authenticated publication is available online at https://doi.org/10.1007/978-3-030-31372-2_2

What Language Model to Train if You Have One Million GPU Hours?

The crystallization of modeling methods around the Transformer architecture has been a boon for practitioners. Simple, well-motivated architectural variations can transfer across tasks and scale, increasing the impact of modeling research. However, with the emergence of state-of-the-art 100B+ parameters models, large language models are increasingly expensive to accurately design and train. Notably, it can be difficult to evaluate how modeling decisions may impact emergent capabilities, given that these capabilities arise mainly from sheer scale alone. In the process of building BLOOM--the Big Science Large Open-science Open-access Multilingual language model--our goal is to identify an architecture and training setup that makes the best use of our 1,000,000 A100-GPU-hours budget. Specifically, we perform an ablation study at the billion-parameter scale comparing different modeling practices and their impact on zero-shot generalization. In addition, we study the impact of various popular pre-training corpora on zero-shot generalization. We also study the performance of a multilingual model and how it compares to the English-only one. Finally, we consider the scaling behaviour of Transformers to choose the target model size, shape, and training setup. All our models and code are open-sourced at https://huggingface.co/bigscience .Comment: Findings of EMNLP 202

Computing probabilistic solutions of the Bernoulli random differential equation

[EN] The random variable transformation technique is a powerful method to determine the probabilistic solution for random differential equations represented by the first probability density function of the solution stochastic process. In this paper, that technique is applied to construct a closed form expression of the solution for the Bernoulli random differential equation. In order to account for the general scenario, all the input parameters (coefficients and initial condition) are assumed to be absolutely continuous random variables with an arbitrary joint probability density function. The analysis is split into two cases for which an illustrative example is provided. Finally, a fish weight growth model is considered to illustrate the usefulness of the theoretical results previously established using real data.This work has been partially supported by the Ministerio de Economía y Competitividad grant MTM2013-41765-P. Ana Navarro Quiles acknowledges the doctorate scholarship granted by Programa de Ayudas de Investigación y Desarrollo (PAID), Universitat Politècnica de València. Contratos Predoctorales UPV 2014- Subprograma 1.Casabán, M.; Cortés, J.; Navarro-Quiles, A.; Romero, J.; Roselló, M.; Villanueva Micó, RJ. (2017). Computing probabilistic solutions of the Bernoulli random differential equation. Journal of Computational and Applied Mathematics. 309:396-407. https://doi.org/10.1016/j.cam.2016.02.034S39640730

Study protocol: differential effects of diet and physical activity based interventions in pregnancy on maternal and fetal outcomes--individual patient data (IPD) meta-analysis and health economic evaluation.

© 2014 Ruifrok et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.BACKGROUND: Pregnant women who gain excess weight are at risk of complications during pregnancy and in the long term. Interventions based on diet and physical activity minimise gestational weight gain with varied effect on clinical outcomes. The effect of interventions on varied groups of women based on body mass index, age, ethnicity, socioeconomic status, parity, and underlying medical conditions is not clear. Our individual patient data (IPD) meta-analysis of randomised trials will assess the differential effect of diet- and physical activity-based interventions on maternal weight gain and pregnancy outcomes in clinically relevant subgroups of women. METHODS/DESIGN: Randomised trials on diet and physical activity in pregnancy will be identified by searching the following databases: MEDLINE, EMBASE, BIOSIS, LILACS, Pascal, Science Citation Index, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Database of Abstracts of Reviews of Effects, and Health Technology Assessment Database. Primary researchers of the identified trials are invited to join the International Weight Management in Pregnancy Collaborative Network and share their individual patient data. We will reanalyse each study separately and confirm the findings with the original authors. Then, for each intervention type and outcome, we will perform as appropriate either a one-step or a two-step IPD meta-analysis to obtain summary estimates of effects and 95% confidence intervals, for all women combined and for each subgroup of interest. The primary outcomes are gestational weight gain and composite adverse maternal and fetal outcomes. The difference in effects between subgroups will be estimated and between-study heterogeneity suitably quantified and explored. The potential for publication bias and availability bias in the IPD obtained will be investigated. We will conduct a model-based economic evaluation to assess the cost effectiveness of the interventions to manage weight gain in pregnancy and undertake a value of information analysis to inform future research. SYSTEMATIC REVIEW REGISTRATION: PROSPERO 2013: CRD42013003804.This study was funded by the National Institute for Health Research (NIHR) HTA (Health Technology Assessment) UK programme 12/01

A mean square chain rule and its application in solving the random Chebyshev differential equation

[EN] In this paper a new version of the chain rule for calculat- ing the mean square derivative of a second-order stochastic process is proven. This random operational calculus rule is applied to construct a rigorous mean square solution of the random Chebyshev differential equation (r.C.d.e.) assuming mild moment hypotheses on the random variables that appear as coefficients and initial conditions of the cor- responding initial value problem. Such solution is represented through a mean square random power series. Moreover, reliable approximations for the mean and standard deviation functions to the solution stochastic process of the r.C.d.e. are given. Several examples, that illustrate the theoretical results, are included.This work was completed with the support of our TEX-pert.Cortés, J.; Villafuerte, L.; Burgos-Simon, C. (2017). A mean square chain rule and its application in solving the random Chebyshev differential equation. Mediterranean Journal of Mathematics. 14(1):14-35. https://doi.org/10.1007/s00009-017-0853-6S1435141Calbo, G., Cortés, J.C., Jódar, L., Villafuerte, L.: Analytic stochastic process solutions of second-order random differential equations. Appl. Math. Lett. 23(12), 1421–1424 (2010). doi: 10.1016/j.aml.2010.07.011El-Tawil, M.A., El-Sohaly, M.: Mean square numerical methods for initial value random differential equations. Open J. Discret. Math. 1(1), 164–171 (2011). doi: 10.4236/ojdm.2011.12009Khodabin, M., Maleknejad, K., Rostami, K., Nouri, M.: Numerical solution of stochastic differential equations by second order Runge Kutta methods. Math. Comp. Model. 59(9–10), 1910–1920 (2010). doi: 10.1016/j.mcm.2011.01.018Santos, L.T., Dorini, F.A., Cunha, M.C.C.: The probability density function to the random linear transport equation. Appl. Math. Comput. 216(5), 1524–1530 (2010). doi: 10.1016/j.amc.2010.03.001González Parra, G., Chen-Charpentier, B.M., Arenas, A.J.: Polynomial Chaos for random fractional order differential equations. Appl. Math. Comput. 226(1), 123–130 (2014). doi: 10.1016/j.amc.2013.10.51El-Beltagy, M.A., El-Tawil, M.A.: Toward a solution of a class of non-linear stochastic perturbed PDEs using automated WHEP algorithm. Appl. Math. Model. 37(12–13), 7174–7192 (2013). doi: 10.1016/j.apm.2013.01.038Nouri, K., Ranjbar, H.: Mean square convergence of the numerical solution of random differential equations. Mediterran. J. Math. 12(3), 1123–1140 (2015). doi: 10.1007/s00009-014-0452-8Villafuerte, L., Braumann, C.A., Cortés, J.C., Jódar, L.: Random differential operational calculus: theory and applications. Comp. Math. Appl. 59(1), 115–125 (2010). doi: 10.1016/j.camwa.2009.08.061Øksendal, B.: Stochastic differential equations: an introduction with applications, 6th edn. Springer, Berlin (2007)Soong, T.T.: Random differential equations in science and engineering. Academic Press, New York (1973)Wong, B., Hajek, B.: Stochastic processes in engineering systems. Springer Verlag, New York (1985)Arnold, L.: Stochastic differential equations. Theory and applications. John Wiley, New York (1974)Cortés, J.C., Jódar, L., Camacho, J., Villafuerte, L.: Random Airy type differential equations: mean square exact and numerical solutions. Comput. Math. Appl. 60(5), 1237–1244 (2010). doi: 10.1016/j.camwa.2010.05.046Calbo, G., Cortés, J.C., Jódar, L.: Random Hermite differential equations: mean square power series solutions and statistical properties. Appl. Math. Comp. 218(7), 3654–3666 (2011). doi: 10.1016/j.amc.2011.09.008Calbo, G., Cortés, J.C., Jódar, L., Villafuerte, L.: Solving the random Legendre differential equation: Mean square power series solution and its statistical functions. Comp. Math. Appl. 61(9), 2782–2792 (2010). doi: 10.1016/j.camwa.2011.03.045Cortés, J.C., Jódar, L., Company, R., Villafuerte, L.: Laguerre random polynomials: definition, differential and statistical properties. Utilit. Math. 98, 283–293 (2015)Cortés, J.C., Jódar, L., Villafuerte, L.: Mean square solution of Bessel differential equation with uncertainties. J. Comp. Appl. Math. 309, 383–395 (2017). doi: 10.1016/j.cam.2016.01.034Golmankhaneh, A.K., Porghoveh, N.A., Baleanu, D.: Mean square solutions of second-order random differential equations by using homotopy analysis method. Romanian Reports Physics 65(2), 1237–1244 (2013)Khalaf, S.L.: Mean square solutions of second-order random differential equations by using homotopy perturbation method. Int. Math. Forum 6(48), 2361–2370 (2011)Khudair, A.R., Ameen, A.A., Khalaf, S.L.: Mean square solutions of second-order random differential equations by using Adomian decomposition method. Appl. Math. Sci. 5(49), 2521–2535 (2011)Agarwal, R.P., O’Regan, D.: Ordinary and partial differential equations. Springer, New York (2009