Close packed transitions in slit-shaped pores: Density functional theory study of methane adsorption capacity in carbon

An important feature of improving lattice gas models and classical isotherms is the incorporation of a pore size dependent capacity, which has hitherto been overlooked. In this paper, we develop a model for predicting the temperature dependent variation in capacity with pore size. The model is based on the analysis of a lattice gas model using a density functional theory approach at the close packed limit. Fluid-fluid and solid-fluid interactions are modeled by the Lennard-Jones 12-6 potential and Steele's 10-4-3, potential respectively. The capacity of methane in a slit-shaped carbon pore is calculated from the characteristic parameters of the unit cell, which are extracted by minimizing the grand potential of the unit cell. The capacities predicted by the proposed model are in good agreement with those obtained from grand canonical Monte Carlo simulation, for pores that can accommodate up to three adsorbed layers. Single particle and pair distributions exhibit characteristic features that correspond to the sequence of buckling and rhombic transitions that occur as the slit pore width is increased. The model provides a useful tool to model continuous variation in the microstructure of an adsorbed phase, namely buckling and rhombic transitions, with increasing pore width. (C) 2002 American Institute of Physics.

CRIM1 Regulates the Rate of Processing and Delivery of Bone Morphogenetic Proteins to the Cell Surface

The Crim1 gene is predicted to encode a transmembrane protein containing six von Willebrand-like cysteine-rich repeats (CRRs) similar to those in the BMP-binding antagonist Chordin (Chrd). In this study, we verify that CRIM1 is a glycosylated, Type I transmembrane protein and demonstrate that the extracellular CRR-containing domain can also be secreted, presumably via processing at the membrane. We have previously demonstrated Crim1 expression at sites consistent with an interaction with bone morphogenetic proteins (BMPs). Here we show that CRIM1 can interact with both BMP4 and BMP7 via the CRR-containing portion of the protein and in so doing acts as an antagonist in three ways. CRIM1 binding of BMP4 and -7 occurs when these proteins are co-expressed within the Golgi compartment of the cell and leads to (i) a reduction in the production and processing of preprotein to mature BMP, (ii) tethering of pre-BMP to the cell surface, and (iii) an effective reduction in the secretion of mature BMP. Functional antagonism was verified by examining the effect of coexpression of CRIM1 and BMP4 on metanephric explant culture. The presence of CRIM1 reduced the effective BMP4 concentration of the media, thereby acting as a BMP4 antagonist. Hence, CRIM1 modulates BMP activity by affecting its processing and delivery to the cell surface

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Modification of MOR by desilication treatments: Structural, textural and acidic characterization

The effect of several desilication experimental parameters (base concentration, temperature and time) on the characteristics of MOR zeolite was studied. The samples were characterized by X-ray diffraction, Al-27 and Si-29 MAS-NMR, chemical analysis, and FTIR (framework vibration region). The textural characterization was made by N-2 adsorption and the acidity was evaluated by pyridine adsorption followed by FTIR and by the catalytic model reaction of n-heptane cracking. The alkaline treatments promoted the Si extraction from the zeolite framework, without considerable loss of crystallinity and, as it was envisaged, an important increase of the mesoporous structure was attained. A linear correlation between the number of framework Si per unit cell. N-Si and the asymmetric stretching wavenumber, nu(i), was observed. The acidity characterization shows that the desilicated samples exhibit practically the same acid properties than the parent HMOR zeolite. The optimum desilication conditions were those used to obtain sample M/0.2/85/2, i.e., sample treated with 0.2 M NaOH solution at 85 degrees C for 2 h.

Overproduction, crystallization and preliminary X-ray characterization of Abn2, an endo-1,5-alpha-arabinanase from Bacillus subtilis.

Acta Crystallographica F64 (2008) 636-638

Energy evaporation: the new concept of indoor systems for WPT and EH embedded into the floor

This work introduces a novel idea for wireless energy transfer, proposing for the first time the unit-cell of an indoor localization and RF harvesting system embedded into the floor. The unit-cell is composed by a 5.8 GHz patch antenna surrounded by a 13.56 MHz coil. The coil locates a device and activate the patch which, connected to a power grid, radiates to wirelessly charge the localized device. The HF and RF circuits co-existence and functionality are demonstrated in this paper, the novelty of which is also in the adoption of low cost and most of all ecofriendly materials, such as wood and cork, as substrates for electronics.

Distributed Linear Precoding and User Selection in Coordinated Multicell Systems

In this manuscript we tackle the problem of semidistributed user selection with distributed linear precoding for sum rate maximization in multiuser multicell systems. A set of adjacent base stations (BS) form a cluster in order to perform coordinated transmission to cell-edge users, and coordination is carried out through a central processing unit (CU). However, the message exchange between BSs and the CU is limited to scheduling control signaling and no user data or channel state information (CSI) exchange is allowed. In the considered multicell coordinated approach, each BS has its own set of cell-edge users and transmits only to one intended user while interference to non-intended users at other BSs is suppressed by signal steering (precoding). We use two distributed linear precoding schemes, Distributed Zero Forcing (DZF) and Distributed Virtual Signalto-Interference-plus-Noise Ratio (DVSINR). Considering multiple users per cell and the backhaul limitations, the BSs rely on local CSI to solve the user selection problem. First we investigate how the signal-to-noise-ratio (SNR) regime and the number of antennas at the BSs impact the effective channel gain (the magnitude of the channels after precoding) and its relationship with multiuser diversity. Considering that user selection must be based on the type of implemented precoding, we develop metrics of compatibility (estimations of the effective channel gains) that can be computed from local CSI at each BS and reported to the CU for scheduling decisions. Based on such metrics, we design user selection algorithms that can find a set of users that potentially maximizes the sum rate. Numerical results show the effectiveness of the proposed metrics and algorithms for different configurations of users and antennas at the base stations.

System Level Simulation and Radio Resource Management for Distributed Antenna Systems with Cognitive Radio and Multi-Cell Cooperation

4th International Conference on Future Generation Communication Technologies (FGCT 2015), Luton, United Kingdom.

Crystallization and crystallographic analysis of the apo form of the orange protein (ORP) from desulfovibrio gigas

Acta Crystallographica Section F Structural Biology and Crystallization Communications Volume 65, Part 8

Heterodimeric nitrate reductase (NapAB) from Cupriavidus necator H16: purification, crystallization and preliminary X-ray analysis

Acta Cryst. (2007). F63, 516–519

The HIF1A Functional Genetic Polymorphism at Locus +1772 Associates with Progression to Metastatic Prostate Cancer and Refractoriness to Hormonal Castration

The hypoxia inducible factor 1 alpha (HIF1a) is a key regulator of tumour cell response to hypoxia, orchestrating mechanisms known to be involved in cancer aggressiveness and metastatic behaviour. In this study we sought to evaluate the association of a functional genetic polymorphism in HIF1A with overall and metastatic prostate cancer (PCa) risk and with response to androgen deprivation therapy (ADT). The HIF1A +1772 C>T (rs11549465) polymorphism was genotyped, using DNA isolated from peripheral blood, in 1490 male subjects (754 with prostate cancer and 736 controls cancer-free) through Real-Time PCR. A nested group of cancer patients who were eligible for androgen deprivation therapy was followed up. Univariate and multivariate models were used to analyse the response to hormonal treatment and the risk for developing distant metastasis. Age-adjusted odds ratios were calculated to evaluate prostate cancer risk. Our results showed that patients under ADT carrying the HIF1A +1772 T-allele have increased risk for developing distant metastasis (OR, 2.0; 95%CI, 1.1-3.9) and an independent 6-fold increased risk for resistance to ADT after multivariate analysis (OR, 6.0; 95%CI, 2.2-16.8). This polymorphism was not associated with increased risk for being diagnosed with prostate cancer (OR, 0.9; 95%CI, 0.7-1.2). The HIF1A +1772 genetic polymorphism predicts a more aggressive prostate cancer behaviour, supporting the involvement of HIF1a in prostate cancer biological progression and ADT resistance. Molecular profiles using hypoxia markers may help predict clinically relevant prostate cancer and response to ADT.

Development of novel human cellular models for neurotoxicity studies

Dissertation to obtain master degree in Genética Molecular e Biomedicina

The HIF1A Functional Genetic Polymorphism at Locus +1772 Associates with Progression to Metastatic Prostate Cancer and Refractoriness to Hormonal Castration

The hypoxia inducible factor 1 alpha (HIF1a) is a key regulator of tumour cell response to hypoxia, orchestrating mechanisms known to be involved in cancer aggressiveness and metastatic behaviour. In this study we sought to evaluate the association of a functional genetic polymorphism in HIF1A with overall and metastatic prostate cancer (PCa) risk and with response to androgen deprivation therapy (ADT). The HIF1A +1772 C>T (rs11549465) polymorphism was genotyped, using DNA isolated from peripheral blood, in 1490 male subjects (754 with prostate cancer and 736 controls cancer-free) through Real-Time PCR. A nested group of cancer patients who were eligible for androgen deprivation therapy was followed up. Univariate and multivariate models were used to analyse the response to hormonal treatment and the risk for developing distant metastasis. Age-adjusted odds ratios were calculated to evaluate prostate cancer risk. Our results showed that patients under ADT carrying the HIF1A +1772 T-allele have increased risk for developing distant metastasis (OR, 2.0; 95%CI, 1.1-3.9) and an independent 6-fold increased risk for resistance to ADT after multivariate analysis (OR, 6.0; 95%CI, 2.2-16.8). This polymorphism was not associated with increased risk for being diagnosed with prostate cancer (OR, 0.9; 95%CI, 0.7-1.2). The HIF1A +1772 genetic polymorphism predicts a more aggressive prostate cancer behaviour, supporting the involvement of HIF1a in prostate cancer biological progression and ADT resistance. Molecular profiles using hypoxia markers may help predict clinically relevant prostate cancer and response to ADT.

Spatio-temporal analyses of the relationship between armed conflict and climate change in the eastern Africa

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.