Bendadaite, a new iron arsenate mineral of the arthurite group

Bendadaite, ideally Fe(2+)Fe(2)(3+)(AsO(4))(2)(OH)(2 center dot).4H(2)O, is a new member of the arthurite group It was found as a weathering product of arsenopyrite on a single hand specimen from the phosphate pegmatite Bendada. central Portugal (type locality) Co-type locality is the granite pegmatite of La via do Almerindo (Almerindo mine), Linopolis, Divmo das Laranjeiras county, Minas Gerais, Brazil Further localities are the Vein Negra mine, Copiapo province, Chile, mid-East, Bou Azzer district, Morocco, and Para Inferida yard, Fenugu Sibirt mine, Gonnosfanadiga, Medio Campidano Province, Sardinia. Italy Type bendadaite occurs as blackish green to dark brownish tufts (<0 1 mm long) and flattened radiating aggregates. in intimate association with an intermediate member of the scorodite-mansfieldite series It is monoclinic. space group P2(l/c). with a = 10 239(3) angstrom. b = 9 713(2) angstrom, c = 5 552(2) angstrom. beta = 94 11(2)degrees. = 550 7(2) angstrom(3). Z = 2 Electron-microprobe analysis yielded (wt %). CaO 0 04, MnO 0 03. CuO 006, ZnO 004. Fe(2)O(3) (total) 43 92, Al(2)O(3) 115. SnO(2) 0 10, As(2)O(5) 43 27. P(2)O(5) 1 86, SO(3) 0.03 The empirical formula is (Fe(0 52)(2+)Fe(0 32)(3+)rectangle(0 16))(Sigma 1 00)(Fe(1 89)(3+)Al(0 11))(Sigma 2 00)(As(1 87)P(0 13))(Sigma 2 00)O(8)(OH)(2 00) 4H(2)O based. CM 2(As,P) and assuming ideal 80, 2(OH), 4H2O and complete occupancy of the ferric on site by Fe(3+) and Al Optically, bendadaite is biaxial, positive, 2V(est) = 85+/-4 degrees, 2V(eale) = 88 degrees, with alpha 1 734(3). 13 1 759(3), 7 1 787(4) Pleochrosim is medium strong X pale reddish brown. Y yellowish brown, Z dark yellowish brown. absorption Z > V > X, optical dispersion weak, r > v. Optical axis plane Is parallel to (010), with X approximately parallel to a and Z nearly parallel to c Bendadaite has vitreous to sub-adamantine luster, is translucent and non-fluorescent It is brittle, shows irregular fracture and a good cleavage parallel to 1010} 3 15 0 10 g/cm(3), 3 193 g/cm3 (for the empirical formula) The five strongest powder diffraction lines [d in angstrom (I)(hkl] are 10 22 (10)(100), 7 036 (8)(110), 4 250 (5)(11 I), 2 865 (4)(311), 4 833 (3)(020,011) The d spacings are very similar to those of its Zn analogue, ojelaite The crystal structure of bendadaite was solved and refined using a crystal from the co-type locality with the composition (Fe(0 95)(2+)rectangle(0 05))(Sigma 1 00)(Fe(1 80)(3+)Al(0 20))Sigma(2 00)(As(1 48)P(0 52))(Sigma 2 00)O(8)) (OH)(2) 4H(2)O (R = 16%) and confirms an arthurite-type atomic arrangement

Glypican-3 regulates migration, adhesion and actin cytoskeleton organization in mammary tumor cells through Wnt signaling modulation

Glypican-3 (GPC3) is a proteoglycan involved in migration, proliferation and cell survival modulation in several tissues. There are many reports demonstrating a downregulation of GPC3 expression in some human tumors, including mesothelioma, ovarian and breast cancer. Previously, we determined that GPC3 reexpression in the murine mammary adenocarcinoma LM3 cells induced an impairment of their in vivo invasive and metastatic capacities together with a higher susceptibility to in vitro apoptosis. Currently, the signaling mechanism of GPC3 is not clear. First, it was speculated that GPC3 regulates the insulin-like growth factor (IGF) signaling system. This hypothesis, however, has been strongly challenged. Recently, several reports indicated that at least in some cell types GPC3 serves as a selective regulator of Wnt signaling. Here we provide new data demonstrating that GPC3 regulates Wnt pathway in the metastatic adenocarcinoma mammary LM3 cell line. We found that GPC3 is able to inhibit canonical Wnt signals involved in cell proliferation and survival, as well as it is able to activate non canonical pathway, which directs cell morphology and migration. This is the first report indicating that breast tumor cell malignant properties can be reverted, at least in part, by GPC3 modulation of Wnt signaling. Our results are consistent with the potential role of GPC3 as a metastasis suppressor.

Modulation of trans-to-cis photoisomerization and photoluminescence of 1,2-bis(4-pyridyl)ethylene or 4-styrylpyridine coordinated to fac-tricarbonyl(5-chloro-1,10-phenathroline)rhenium(I)

Photochemical and photophysical properties of fac-[Re(CO)(3)(Clphen)(trans-L)](+) complexes, Clphen = 5-chloro-1,10-phenathroline and L = 1,2-bis(4-pyridyl)ethylene, bpe, or 4-styrylpyridine, stpy, were investigated to complement the understanding of intramolecular energy transfer process in tricarbonyl rhenium(I) complexes having an electron withdrawing group attached to polypyridyl ligands. These new compounds were synthesized, characterized and the photoisomerization quantum yields were accurately determined by (1)H NMR spectroscopy. The true quantum yields for fac-[Re(CO)(3)(Clphen) (trans-bpe)](+) were constant (Phi = 0.55) at all investigated irradiation wavelengths. However, for fac-[Re(CO)(3)(Clphen)(trans-stpy)](+), similar true quantum yields were observed only at higher energy irradiation (Phi(313 nm) = 0.53 and Phi(365 nm) = 0.57), but it decreased significantly at 404 nm (Phi = 0.41). These results indicated different deactivation pathways for the trans-stpy complex photoisomerization. Quantum yields decreased as the (3)IL(trans-L) and (3)MLCT(Re -> NN) excited states become closer and the behavior was discussed in terms of the excited state energy gaps. Additionally, luminescence properties of photoproducts, fac-[Re(CO)(3)(Clphen)(cis-L)](+), were also investigated in different environments to analyze the relative energy of the (3)MLCT(Re -> Clphen) excited state for each compound. (C) 2011 Elsevier B.V. All rights reserved.

The SUMA Project: Integrating Observational Data Assessment into Space and Service Design

This presentation was offered as part of the CUNY Library Assessment Conference, Reinventing Libraries: Reinventing Assessment, held at the City University of New York in June 2014.

View of Air Conditioning and Refrigeration classroom at the New York Trade School

This black and white photograph shows classroom space of the Air Conditioning/Refrigeration Dept. empty of students. Black and white photograph.

Piano Performance at a New York Trade School Commencement

Six piano players spread across the front of the stage at the New York Trade School perform at a commencement ceremony. The graduates can be seen sitting in the first few rows of the auditorium with guests filling up the rest of the space. On the dais several administrators from the New York Trade School are shown. Black and white photograph.

Color Segmentation using LVQ-Learning Vector Quantization

This thesis aims to present a color segmentation approach for traffic sign recognition based on LVQ neural networks. The RGB images were converted into HSV color space, and segmented using LVQ depending on the hue and saturation values of each pixel in the HSV color space. LVQ neural network was used to segment red, blue and yellow colors on the road and traffic signs to detect and recognize them. LVQ was effectively applied to 536 sampled images taken from different countries in different conditions with 89% accuracy and the execution time of each image among 31 images was calculated in between 0.726sec to 0.844sec. The method was tested in different environmental conditions and LVQ showed its capacity to reasonably segment color despite remarkable illumination differences. The results showed high robustness.

Common Features in Vector Nonlinear Time Series Models

This thesis consists of four manuscripts in the area of nonlinear time series econometrics on topics of testing, modeling and forecasting nonlinear common features. The aim of this thesis is to develop new econometric contributions for hypothesis testing and forecasting in these area. Both stationary and nonstationary time series are concerned. A definition of common features is proposed in an appropriate way to each class. Based on the definition, a vector nonlinear time series model with common features is set up for testing for common features. The proposed models are available for forecasting as well after being well specified. The first paper addresses a testing procedure on nonstationary time series. A class of nonlinear cointegration, smooth-transition (ST) cointegration, is examined. The ST cointegration nests the previously developed linear and threshold cointegration. An Ftypetest for examining the ST cointegration is derived when stationary transition variables are imposed rather than nonstationary variables. Later ones drive the test standard, while the former ones make the test nonstandard. This has important implications for empirical work. It is crucial to distinguish between the cases with stationary and nonstationary transition variables so that the correct test can be used. The second and the fourth papers develop testing approaches for stationary time series. In particular, the vector ST autoregressive (VSTAR) model is extended to allow for common nonlinear features (CNFs). These two papers propose a modeling procedure and derive tests for the presence of CNFs. Including model specification using the testing contributions above, the third paper considers forecasting with vector nonlinear time series models and extends the procedures available for univariate nonlinear models. The VSTAR model with CNFs and the ST cointegration model in the previous papers are exemplified in detail,and thereafter illustrated within two corresponding macroeconomic data sets.

Leveraging new plans in AgentSpeak(PL)

In order to facilitate the development of agent-based software, several agent programming languages and architectures, have been created. Plans in these architectures are often self-contained procedures with an associated triggering event and a context condition, while any further information about the consequences of executing a plan is absent. However, agents designed using such an approach have limited flexibility at runtime, and rely on the designer’s ability to foresee all relevant situations an agent might have to handle. In order to overcome this limitation, we have created AgentSpeak(PL), an interpreter capable of performing state-space planning to generate new high-level plans. As the planning module creates new plans, the plan library is expanded, improving performance over time. However, for new plans to be useful in the long run, it is critical that the context condition associated with new plans is carefully generated. In this paper we describe a plan reuse technique aimed at improving an agent’s runtime performance by deriving optimal context conditions for new plans, allowing an agent to reuse generated plans as much as possible.

Dialectics of Diaspora Space: a Study of Contemporary, Diasporic, South Asian Fiction

In light of these continuing debates concerning immigration, national identity and belonging, re-examinations of immigrant and ethnic communities, often referred to as ‘diaspora,’ have become increasingly popular and prudent. Khachig Tololian, editor of Diaspora magazine, calls diaspora “exemplary communities of the transnational moment.”5 In an increasingly globalized world, where labor, capital, and resources are passed fluidly from continent to continent, diaspora are created by relocation or displacement of immigrant workers and their descendents.6 For these unskilled, immigrant laborers, middle class immigrants, and the children of both groups, adaptation to the culture, society, and life in a new ‘host’ country can be difficult, to say the least. So, in response to a new cultural landscape and a tenuous sense belonging, as well as to maintain a connection with a shared past, citizens of the world’s numerous diaspora replicate linguistic, cultural, and social norms, creating their own “cultural space[s]” that mirror and often replace a past relationship to their land of origin, or ‘home’.

Vector Theory of Spontaneous Lorentz Violation

Classical electromagnetism predicts two massless propagating modes, which are known as the two polarizations of the photon. On the other hand, if the Lorentz symmetry of classical electromagnetism is spontaneously broken, the new theory will still have two massless Nambu-Goldstone modes resembling the photon. If the Lorentz symmetry is broken by a bumblebee potential that allows for excitations out of the minimum, then massive modes arise. Furthermore, in curved spacetime, such massive modes will be created through a process other than the usual Higgs mechanism because of the dependence of the bumblebee potential on both the vector field and the metric tensor. Also, it is found that these massive modes do not propagate due to the extra constraints.

Forecasting accuracy and estimation uncertainty using VAR models with short- and long-term economic restrictions: a Monte-Carlo study

Using vector autoregressive (VAR) models and Monte-Carlo simulation methods we investigate the potential gains for forecasting accuracy and estimation uncertainty of two commonly used restrictions arising from economic relationships. The Örst reduces parameter space by imposing long-term restrictions on the behavior of economic variables as discussed by the literature on cointegration, and the second reduces parameter space by imposing short-term restrictions as discussed by the literature on serial-correlation common features (SCCF). Our simulations cover three important issues on model building, estimation, and forecasting. First, we examine the performance of standard and modiÖed information criteria in choosing lag length for cointegrated VARs with SCCF restrictions. Second, we provide a comparison of forecasting accuracy of Ötted VARs when only cointegration restrictions are imposed and when cointegration and SCCF restrictions are jointly imposed. Third, we propose a new estimation algorithm where short- and long-term restrictions interact to estimate the cointegrating and the cofeature spaces respectively. We have three basic results. First, ignoring SCCF restrictions has a high cost in terms of model selection, because standard information criteria chooses too frequently inconsistent models, with too small a lag length. Criteria selecting lag and rank simultaneously have a superior performance in this case. Second, this translates into a superior forecasting performance of the restricted VECM over the VECM, with important improvements in forecasting accuracy ñreaching more than 100% in extreme cases. Third, the new algorithm proposed here fares very well in terms of parameter estimation, even when we consider the estimation of long-term parameters, opening up the discussion of joint estimation of short- and long-term parameters in VAR models.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties for a lack of parsimony, as well as the traditional ones. We suggest a new procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties. In order to compute the fit of each model, we propose an iterative procedure to compute the maximum likelihood estimates of parameters of a VAR model with short-run and long-run restrictions. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank, relative to the commonly used procedure of selecting the lag-length only and then testing for cointegration.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian inflation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in different measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian in ation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in di¤erent measures of forecasting accuracy are substantial, especially for short horizons.