Synthesis, structure, and field emission properties of sulfur-doped nanocrystalline diamond

The synthesis and properties of sulfur-doped nanocrystalline diamond films were investigated. The films were deposited by hot-filament chemical vapor deposition on Mo substrates using methane, hydrogen, and hydrogen disulfide. The nanocrystalline nature of the material arises from the induction of continuous secondary nucleation in the chemical vapor deposition environment. Complementary characterization tools were employed in order to obtain a comprehensive and coherent understanding of the correlations between the structural and electronic properties. In particular, sulfur-doped nanocrystalline diamond films show n-type Hall conductivity, enhanced field emission properties, and insensitivity to ion radiation. It was found that n-type doping of the tetragonally-bonded carbon matrix together with a nano network of trigonally-bonded carbon are crucial elements for enhanced field emission from nanocrystalline diamond. These conclusions and the corresponding supporting evidence are discussed.     

Linear microstrip surface coil for MR imaging of the rat spinal cord at 4.7 T

A new design of RF coil based on a quasi-transverse electromagnetic field is described. The coil was developed for the acquisition of MR images of the rat spinal cord at 4.7 T. Different materials for the construction of the coil were tested, and the best results were obtained with Teflon. The design of the microstrip coil enables the investigator to change the length of the coil in a longitudinal direction and yields a relatively high signal-to-noise ratio due to the restricted field of view. Low RF field penetration depth also helps in suppressing motion artifacts generated by, e.g., breathing or heartbeats.     

Academic trajectories of newcomer immigrant youth.

Immigration to the United States presents both challenges and opportunities that affect students' academic achievement. Using a 5-year longitudinal, mixed-methods approach, we identified varying academic trajectories of newcomer immigrant students from Central America, China, the Dominican Republic, Haiti, and Mexico. Latent class growth curve analysis revealed that although some newcomer students performed at high or improving levels over time, others showed diminishing performance. Multinomial logistic regressions identified significant group differences in academic trajectories, particularly between the high-achieving youth and the other groups. In keeping with ecological–developmental and stage–environment fit theories, School Characteristics (school segregation rate, school poverty rate, and student perceptions of school violence), Family Characteristics (maternal education, parental employment, and household structure), and Individual Characteristics (academic English proficiency, academic engagement, psychological symptoms, gender, and 2 age-related risk factors, number of school transitions and being overaged for grade placement) were associated with different trajectories of academic performance. A series of case studies triangulate many of the quantitative findings as well as illuminate patterns that were not detected in the quantitative data. Thus, the mixed-methods approach sheds light on the cumulative developmental challenges that immigrant students face as they adjust to their new educational settings. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

p21WAF1/Cip1 expression is associated with cell differentiation but not with p53 mutations in squamous cell carcinomas of the larynx

The anti-metastatic effect of Z-100, an immunomodulatory arabinomannan extracted from Mycobacterium tuberculosis, was investigated in mice bearing B16 melanoma cells. Treatment of BF10 mice implanted with high metastatic B16F10 melanoma cells with a 10 mg/kg dose of Z-100 resulted in the reduction of experimental pulmonary metastasis as compared with that of BF10 mice treated with saline. The number of pulmonary metastatic colonies in BF1 mice (mice implanted with low metastatic B16F1 melanoma cells) was greatly increased after the inoculation of CD4+ CD11b+ CD281+ TCR alphabeta+ type 2 T cells (F10-Th2 cells) derived from BF10 mice, while only a few metastatic colonies were demonstrated in lungs of BF1 mice inoculated with naive CD4+ T cells. However, the numbers of metastatic colonies in BF1 mice were not increased when they were inoculated with the F10-Th2 cell fraction derived from Z-100-treated BF10 mice and the generation of F10-Th2 cells in BF10 mice was effectively suppressed by the Z-100 treatment. These results suggest that Z-100 inhibits pulmonary metastasis of B16 melanoma through the regulation of tumor-associated Th2 cells, which are a key cell in the acceleration of tumor metastasis.     

Can porous polymeric scaffolds be functionalized by stem cells leading to osteogenic differentiation? A systematic review of in vitro studies

The aim of this study was to analyze the influence of nanoporous structure of polymeric biomaterials on the in vitro osteogenic induction of human stem cells. An electronic search in three databases (MEDLINE, SCOPUS, and Web of Science) was performed for articles that were published before May 2018. In vitro studies were included if they met the following criteria: (1) the use of polymeric scaffolds (natural or synthetic); (2) the co-culture of human stem cells with the scaffold; and (3) cell viability, proliferation, and osteogenic differentiation assays. The main characteristics of the published studies were summarized, and a quality assessment tool was used to analyze methodological features. Eighty-eight potential articles were firstly retrieved. Thirteen were eligible for qualitative analysis. Only three studies characterized cell stemness. Nanostructure of the scaffolds showed a significant influence on viability, proliferation, and osteogenic differentiation of human stem cells. Combination of porosity between 72 and 93% and a large range diameter between 50 and 224 μm resulted in more remarkable cellular proliferation and differentiation. Porous polymeric scaffolds can be functionalized by stem cells leading to osteogenic induction. High standards of laboratory practice and accurate methodological reporting are essential for the credibility of the results.     

Learning Structure from Data and Its Application to Ozone Prediction

In this paper we propose an algorithm for structure learning in predictive expert systems based on a probabilistic network representation. The idea is to have the simplest structure (minimum number of links) with acceptable predictive capability. The algorithm starts by building a tree structure based on measuring mutual information between pairs of variables, and then it adds links as necessary to obtain certain predictive performance. We have applied this method for ozone prediction in México City, where the ozone level is used as a global indicator for the air quality in different parts of the city. It is important to predict the ozone level a day, or at least several hours in advance, to reduce the health hazards and industrial losses that occur when the ozone reaches emergency levels. We obtained as a first approximation a tree-structured dependency model for predicting ozone in one part of the city. We observe that even with only three parameters, its estimations are acceptable.A causal network representation and the structure learning techniques produced some very interesting results for the ozone prediction problem. Firstly, we got some insight into the dependence structure of the phenomena. Secondly, we got an indication of which are the important and not so important variables for ozone forecasting. Taking this into account, the measurement and computational costs for ozone prediction could be reduced. And thirdly, we have obtained satisfactory short term ozone predictions based on a small set of the most important parameters.     

From latex specifications to parallel codes

The advent of multicore systems, joined to the potential acceleration of the graphics processing units, has given us a low cost computation capability unprecedented. The new systems alleviate some well-known important architectural problems at the expense of a considerable increment of the programmability wall. Heterogeneity, at both the architectural and programming levels, poses a great challenge to programmers. As a contribution, we propose a development methodology for the automatic source-to-source transformation on specific domains. This methodology is successfully instantiated as a framework to solve Dynamic Programming problems. As a result of applying our framework, the end user (a physicist, a mathematician or a biologist) can express her problem through a latex equation and automatically derive efficient parallel codes for current homogeneous or heterogeneous architectures. The approach allows an easy portability to new emergent architectures.     

A cluster-DEE-based strategy to empower protein design

The Medical and Pharmaceutical industries have shown high interest in the precise engineering of protein hormones and enzymes that perform existing functions under a wide range of conditions. Proteins are responsible for the execution of different functions in the cell: catalysis in chemical reactions, transport and storage, regulation and recognition control. Computational Protein Design (CPD) investigates the relationship between 3-D structures of proteins and amino acid sequences and looks for all sequences that will fold into such 3-D structure. Many computational methods and algorithms have been proposed over the last years, but the problem still remains a challenge for Mathematicians, Computer Scientists, Bioinformaticians and Structural Biologists. In this article we present a new method for the protein design problem. Clustering techniques and a Dead-End-Elimination algorithm are combined with a SAT problem representation of the CPD problem in order to design the amino acid sequences. The obtained results illustrate the accuracy of the proposed method, suggesting that integrated Artificial Intelligence techniques are useful tools to solve such an intricate problem.     

Efficient implementation of the implicit error correction nVoD schema

Implicit Error Correction is a near Video-on-Demand (nVoD) schema that trades bandwidth utilization for initial playback delay while potentially allowing an infinite number of users. Additionally, it provides error protection without further bandwidth increase by exploiting the implicit redundancy of nVoD protocols, using linear combinations of the segments transmitted in a given time slot. This paper introduces a new implementation that outperforms the original schema by adding two new mechanisms: redundancy channels and feedback error correction.