Synthesis and characterization of novel scaffold for bone tissue engineering based on Whartons´s jelly

A composite is a material made of more than one component, and the bond between the components is on a scale larger than the atomic scale. The objective of the present study was to synthesize and perform the structural characterization and biological evaluation of a new biocomposite (BCO) based on a novel combination of an organic and an inorganic phase, for bone tissue engineering applications. The organic phase consisted of Wharton´s Jelly (WJ), which was obtained from embryonic tissue following a protocol developed by our laboratory. The inorganic phase consisted of bioceramic particles (BC), produced by sintering hydroxyapatite (HA) with β- tricalcium phosphate (β-TCP), and bioactive glass particles (BG). Each phase of the BCO was fully characterized by SEM, EDS, XRD and FTIR. Biocompatibility was evaluated in vivo in the tibiae of Wistar rats (n=40). Histological evaluation was performed at 0, 1, 7, 14, 30 and 60 days. XRD showed the phases corresponding to HA and β-TCP, whereas diffractogram of BG showed it to have an amorphous structure. EDS showed mainly Si and Na, Ca, P in BG, and Ca and P in HA and β-TCP. FTIR identified bonds between the organic and inorganic phases. From a mechanical viewpoint, the composite showed high flexural strength of 40.3±0.8MPa. The synthesized BCO exhibited adequate biocompatibility as shown by formation of lamellar type bone linked by BG and BC particles. The biomaterial presented here showed excellent mechanical and biocompatibility properties for its potential clinical use.Fil: Martinez, Cristian. Universidad de Buenos Aires. Facultad de Ingenieria. Instituto de Ingeniería Biomédica; Argentina. Universidad de Buenos Aires. Facultad de Odontología. Cátedra de Anatomía Patológica; Argentina. Universidad Nacional de Cuyo. Facultad de Odontologia; ArgentinaFil: Fernández, Carlos. Universidad de Buenos Aires. Facultad de Ingenieria. Instituto de Ingeniería Biomédica; ArgentinaFil: Prado, Miguel Oscar. Comisión Nacional de Energía Atómica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ozols, Andres. Universidad de Buenos Aires. Facultad de Ingenieria. Instituto de Ingeniería Biomédica; ArgentinaFil: Olmedo, Daniel Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay; Argentina. Universidad de Buenos Aires. Facultad de Odontología. Cátedra de Anatomía Patológica; Argentin

Slow evolution of elliptical galaxies induced by dynamical friction

Many astrophysical problems, ranging from structure formation in cosmology to dynamics of elliptical galaxies, refer to slow processes of evolution of essentially collisionless self-gravitating systems. In order to determine the relevant quasi-equilibrium configuration at time t from given initial conditions, it is often argued that such slow evolution may be approximated in terms of adiabatic evolution, for the calculation of which efficient semi--analytical techniques are available. Here we focus on the slow process of evolution, induced by dynamical friction of a host stellar system on a minority component of "satellites", that we have investigated in a previous paper, to determine to what extent an adiabatic description might be applied. The study is realized by comparing directly N--body simulations of the stellar system evolution (in two significantly different models) from initial to final conditions in a controlled numerical environment. We demonstrate that for the examined process the adiabatic description is going to provide incorrect answers, not only quantitatively, but also qualitatively. The two classes of models considered exhibit generally similar trends in evolution, with one exception noted in relation to the evolution of the total density profile. This simple conclusion should be taken as a warning against the indiscriminate use of adiabatic growth prescriptions in studies of structure of galaxies.Comment: 13 pages, 5 figures, Accepted for publication in A&

Cancer cells exploit an orphan RNA to drive metastatic progression.

Here we performed a systematic search to identify breast-cancer-specific small noncoding RNAs, which we have collectively termed orphan noncoding RNAs (oncRNAs). We subsequently discovered that one of these oncRNAs, which originates from the 3' end of TERC, acts as a regulator of gene expression and is a robust promoter of breast cancer metastasis. This oncRNA, which we have named T3p, exerts its prometastatic effects by acting as an inhibitor of RISC complex activity and increasing the expression of the prometastatic genes NUPR1 and PANX2. Furthermore, we have shown that oncRNAs are present in cancer-cell-derived extracellular vesicles, raising the possibility that these circulating oncRNAs may also have a role in non-cell autonomous disease pathogenesis. Additionally, these circulating oncRNAs present a novel avenue for cancer fingerprinting using liquid biopsies

Chapter 21 Artificial intelligence and data analytics for geosciences and remote sensing theory and application

To address the limitation of conventional statistics in dealing with hyperspectral data of satellite and airborne images, two contextual analyses are introduced in this chapter. The first case study presents the development of an artificial intelligence (AI) and data analytics algorithm capable of classifying hyperspectral data to support remote sensing and geographic information systems researchers in understanding and predicting changes in natural earth processes. The classification algorithm is based on a fuzzy approach combining a decision tree classifier with a fuzzy multiple-criteria decision analysis classifier. The second case study presents the development of an AI tool that extracts features from the hyperspectral data to transform a two-dimensional (2D) satellite and airborne picture to a pseudo-3D picture to improve complexity and produce multidirectional sun-shaded pictures and their edges. Such 3D images are useful in supporting the discovery of prospective ground for mineral exploration, extraction from the earth of precious minerals or other geological materials, usually from deposits of ore, veins, lodes, seams, reefs, or placer deposits, and overall to improve the efficiency and effectiveness of mineral exploration

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

Remarkable Rates of Lightning Strike Mortality in Malawi

Livingstone's second mission site on the shore of Lake Malawi suffers very high rates of consequential lightning strikes. Comprehensive interviewing of victims and their relatives in seven Traditional Authorities in Nkhata Bay District, Malawi revealed that the annual rate of consequential strikes was 419/million, more than six times higher than that in other developing countries; the rate of deaths from lightning was 84/million/year, 5.4 times greater than the highest ever recorded. These remarkable figures reveal that lightning constitutes a significant stochastic source of mortality with potential life history consequences, but it should not deflect attention away from the more prominent causes of mortality in this rural area

Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols

© 2017, Springer-Verlag London Ltd., part of Springer Nature. Traffic classification in computer networks has very significant roles in network operation, management, and security. Examples include controlling the flow of information, allocating resources effectively, provisioning quality of service, detecting intrusions, and blocking malicious and unauthorized access. This problem has attracted a growing attention over years and a number of techniques have been proposed ranging from traditional port-based and payload inspection of TCP/IP packets to supervised, unsupervised, and semi-supervised machine learning paradigms. With the increasing complexity of network environments and support for emerging mobility services and applications, more robust and accurate techniques need to be investigated. In this paper, we propose a new supervised hybrid machine-learning approach for ubiquitous traffic classification based on multicriteria fuzzy decision trees with attribute selection. Moreover, our approach can handle well the imbalanced datasets and zero-day applications (i.e., those without previously known traffic patterns). Evaluating the proposed methodology on several benchmark real-world traffic datasets of different nature demonstrated its capability to effectively discriminate a variety of traffic patterns, anomalies, and protocols for unencrypted and encrypted traffic flows. Comparing with other methods, the performance of the proposed methodology showed remarkably better classification accuracy

Endangered edible orchids and vulnerable gatherers in the context of HIV/AIDS in the Southern Highlands of Tanzania

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens