[**Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions**]{}

 [and]{}  [**Jia-Jyun Tien$^{c}$**]{}

[**Abstract**]{}

The aim of this paper is to investigate estimation and inference on a low-dimensional causal parameter in the presence of high-dimensional controls in an instrumental variable quantile regression. The estimation and inference are based on the Neyman-type orthogonal moment conditions, that are relatively insensitive to the estimation of the nuisance parameters. The Monte Carlo experiments show that the econometric procedure performs well. We also apply the procedure to reinvestigate two empirical studies: the quantile treatment effect of 401(k) participation on accumulated wealth, and the distributional effect of job-training program participation on trainee earnings.

Keywords: instrumental variable, quantile regression, treatment effect, LASSO,

double machine learning.

[*JEL Classification: C21; C26.*]{}

------------------------------------------------------------------------

Correspondence: Jau-er Chen. E-mail: jechen@tiu.ac.jp   Address: 1-13-1 Matobakita Kawagoe, Saitama 350-1197, Japan.

This version: September 2019. We are grateful to Masayuki Hirukawa, Tsung-Chih Lai, and Hsin-Yi Lin for discussions and comments. This paper has benefited from presentations at the 2nd International Conference on Econometrics and Statistics (EcoSta 2018), and the Ryukoku University. The authors declare no conflict of interest. The usual disclaimer applies.

Funding: This research was partly funded by the personal research fund from Tokyo International University, and financially supported by the Center for Research in Econometric Theory and Applications (Grant no. 107L900203) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.

Introduction
============

Model selection and variable selection are widely discussed in the area of prediction. Much less attention, however, has been paid to the modification of prediction methods under the context of causal machine learning in economics, cf. Athey (2017) and Athey (2018). As one of the pioneering papers, within the linear framework of instrumental variable estimation, Belloni et al. (2014) proposed a double-selection procedure to correct for an omitted variable bias in a high-dimensional framework. Constructing a general framework encompassing results from the aforementioned Belloni’s paper, Chernozhukov et al. (2015) and Chernozhukov et al. (2018a) proposed a unified procedure, double/debiased machine learning (DML), which remains valid for nonlinear or semi-nonparametric models. The aim of this paper is to investigate estimation and inference on a low-dimensional causal parameter in the presence of high-dimensional controls in an instrumental variable quantile regression. In particular, our procedure follows the idea outlined by Chernozhukov et al (2018b). To the best of our knowledge, the present study is the first to investigate Monte Carlo performance and empirical studies of the double machine learning procedure within the framework of instrumental variable quantile regressions. The Monte Carlo experiments show that our econometric procedure performs well.

Causal machine learning has been actively studied in economics in recent years, which are based on two approaches: the double machine learning, cf. Chernozhukov et al. (2018), and the generalized random forests, cf. Athey, Tibshirani and Wager (2019). Chen and Hsiang (2019) investigate the generalized random forests model using instrumental variable quantile regression. In contrast to the DML for instrumental variable quantile regressions, their econometric procedure yields a measure of variable importance in terms of heterogeneity among control variables. Although related to our paper, Chen and Hsiang (2019) do not consider the setting of high-dimensional controls.

We apply the proposed procedure to empirically investigate causal quantile effects of the 401(k) participation on net financial assets. Our empirical results signify that the 401(k) participants with low savings propensity are more associated with the nonlinear income effect, which complements the findings concluded in Chernozhukov et al. (2018a) and Chiou et al. (2018). Another empirical example of the job training program participation is investigated as well.

The rest of the paper is organized as follows. The model specification and estimation procedure are introduced in Section 2. Section 3 presents Monte Carlo experiments. Section 4 presents two empirical applications. Section 5 concludes the paper.

The Model
=========

We briefly review the conventional instrumental variable quantile regression (IVQR), and then the IVQR within the framework of high-dimensional controls. Our DML procedure for the IVQR is introduced in this section, which is constructed based on a tentative procedure suggested by Chernozhukov et al. (2018b).

The Inverse Quantile Regression as a GMM Estimator
--------------------------------------------------

The following conditional moment restriction yields an IVQR estimator. $$\mathbb{P}[Y \leq q(\tau,D,X)|X,Z ]= \tau,$$ where $q(\cdot)$ is the structural quantile function, $\tau$ stands for the quantile index, $D$, $X$ and $Z$ are, respectively, the target variable, control variables and instruments. Condition (1) and linear structural quantile specification leads to the following unconditional moment restriction $$\mathbb{E}[(\tau - \mathbf{1}(Y - D'\alpha - X'\beta \leq 0)\Psi]=0$$ where $$\Psi:= \Psi(X, Z)$$ is a vector of a function of instruments and control variables. The parameters depend on the quantile of interest, but we suppress the $\tau$ associated with $\alpha$ and $\beta$ for simplicity of presentation. Equation (2) leads to a particular moment condition for doing partialling out: $$g_{\tau}(V,\alpha;\beta,\delta) = (\tau - \mathbf{1}(Y \leq D'\alpha + X'\beta)\Psi(\alpha,\delta(\alpha)))$$ with “instrument” $$\Psi(\alpha,\delta(\alpha)) := (Z - \delta(\alpha)X)$$ $$\delta(\alpha) = M(\alpha)J^{-1}(\alpha),$$ where $\delta$ is a matrix parameter, $$M(\alpha) = \mathbb{E}[ZX'f_{\varepsilon}(0|X,Z)],\ J(\alpha) = \mathbb{E}[XX'f_{\varepsilon}(0|X,Z)]$$ and $f_{\varepsilon}(0|X,Z)$ is the conditional density of $\epsilon = Y - D'\alpha - X'\beta(\alpha)$ with $\beta(\alpha)$ defined by $$\mathbb{E}[(\tau - \mathbf{1}(Y \leq D'\alpha + X'\beta(\alpha))X]=0.$$

We construct the grid search interval for $\alpha$ first and profile out the coefficient for each $\alpha$ in the interval on the exogenous variable by equation (5). That is, $$\hat{\beta}(a) = \arg \min_{b \in \mathcal{B}} \frac{1}{N}\sum_{i=1}^N \rho_{\tau}(Y_i - D_{i}'a - X_{i}'b).$$ We build sample counterpart of the population moment condition based on equations (2)–(5). That is, $$\hat{g}_{N}(a) = \frac{1}{N}\sum_{i=1}^N g(V_i,a,\hat{\beta}(a),\hat{\delta}(a)),$$ where $$\hat{\delta}(a) = \widehat{M}(a)\widehat{J}^{-1}(a)$$ for $$\widehat{M}(a) = \frac{1}{Nh_N}\sum_{i=1}^N Z_i X_i'K_{h_N}\big(Y_i - D_i'a-X_i'\hat{\beta}(a)\big)$$ $$\widehat{J}(a) = \frac{1}{Nh_N}\sum_{i=1}^N X_i X_i'K_{h_N}\big(Y_i - D_i'a-X_i'\hat{\beta}(a)\big)$$ where $K_{h_N}$ is a kernel function with bandwidth $h_N$. We thus can solve for the parameters through optimizing the GMM criterion function. Specifically, $$\hat{\alpha}(\tau) = \arg \min_{a \in \mathcal{A}} N\hat{g}_N(a)'\widehat{\Sigma}(a,a)^{-1}\hat{g}_N(a)$$ $$\widehat{\Sigma}(a_1,a_2) = \frac{1}{N}\sum_{i=1}^N g\big(V_i,a_1,\hat{\beta}(a_1)\big)g\big(V_i,a_2,\hat{\beta}(a_2)\big)'$$ where $\widehat{\Sigma}(a_1,a_2)$ is a weighting matrix used in the GMM estimation. Notice that the estimator $\hat{\alpha}$ based on the inverse quantile regression (i.e. IVQR) is first-order equivalent to the estimator defined by the GMM.

Estimation with High-dimensional Controls
-----------------------------------------

We modify the procedure introduced in Subsection 2.1 in order to deal with a dataset of high-dimensional control variables. We construct the grid search interval for $\alpha$ and profile out the coefficients on exogenous variable using the $L_1$-norm penalized quantile regression estimator: $$\hat{\beta}(a) = \arg \min_{b \in \mathcal{B}} \frac{1}{n}\sum_{i=1}^N \rho_{\tau}(Y_i - D_i'a - X_i'b) + \lambda\sum_{j=1}^{dim(b)} |b_j|.$$ In addition, we estimate $$\widehat{M}(a) = \frac{1}{Nh_N}\sum_{i=1}^N Z_i X_i'K_{h_N}\big(Y_i - D_i'a-X_i'\hat{\beta}(a)\big)$$ $$\widehat{J}(a) = \frac{1}{Nh_N}\sum_{i=1}^N X_i X_i'K_{h_N}\big(Y_i - D_i'a-X_i'\hat{\beta}(a)\big).$$

We also do dimension reduction on $J$ because of the large dimension of $X$. In particular, we implement the following regularization. $$\hat{\delta}_j(a) = \arg \min_{\delta} \frac{1}{2} \delta'\hat{J}(a)\delta - \hat{M}_j(a)\delta + \vartheta||\delta||_1.$$ The regularization above does a weighting LASSO for each instrument variable on control variables, and consequently the $L_1$ norm optimization obeys the Karush-Kuhn-Tucker condition $$||\hat{\delta}_j(a)'\hat{J}(a) - \hat{M}_j(a)||_{\infty} \leq \vartheta, \quad \forall j.$$ After implementing the double machine learning procedure outlined above for the IVQR, we now can solve for the low-dimensional causal parameter $\alpha$ through optimizing the GMM defined as follows. The sample counterpart of the moment condition $$\hat{g}_N(a) = \frac{1}{N} \sum_{i=1}^N \big(\tau - \mathbf{1}\big(Y_i - D_i'a -X_i'\hat{\beta}(a) \leq 0\big)\big)\Psi(a,\hat{\delta}(a)).$$ Accordingly, $$\hat{\alpha} = \arg \min_{a \in \mathcal{A}} N\hat{g}_N(a)'\widehat{\Sigma}(a,a)^{-1}\hat{g}_N(a).$$ More importantly, the aforementioned double machine learning procedure (DML-IVQR hereafter) satisfies the Neyman orthogonality conditions, cf. Chernozhukov et al. (2018b).

Weak-Identification Robust Inference
------------------------------------

Under the regularity conditions listed in Chernozhukov and Hansen (2008), the asymptotic normality of the GMM estimator with a nonsmooth objective function is guaranteed. We have $$\sqrt{n}\hat{g}_N(a) \stackrel{d} \longrightarrow  N(0,\Sigma(a,a)).$$ Consequently, it leads to $$N\hat{g}_N(a)'\widehat{\Sigma}(a,a)^{-1}\hat{g}_N(a) \stackrel{d} \longrightarrow  \chi^2_{dim(Z)}.$$ We define $$W_N\equiv N\hat{g}_N(a)'\widehat{\Sigma}(a,a)^{-1}\hat{g}_N(a).$$ It then follows that a valid $(1-p)$ percent confidence region for the true parameter, $\alpha_0$, may be constructed as the set $$CR := \{\alpha \in \mathcal{A}:W_N (\alpha) \leq c_{1-p}\},$$ where $c_{1-p}$ is the critical point such that $$P[\chi^2_{dim(Z)} > c_{1-p}]=p,$$ and $\mathcal{A}$ can be numerical approximated by the grid $\{\alpha_j,j=1,...,J\}$.

Algorithms for L1-norm Penalized Quantile Optimization
------------------------------------------------------

The suggested double machine learning algorithm involves solving L1-norm optimization which is a nontrivial task. Researchers often represent the L1-norm penalized quantile objective function as a linear programming problem. Specifically, $$\underset{\theta_0 \in \mathcal{R}, \theta\in \mathcal{R}^{p}}{\text{minimize}}
\sum_{i=1}^N  \rho_{\tau}(Y_i - \theta_0 - W_i'\theta)+\lambda\Vert\theta\Vert_1$$

$$\begin{aligned}
& \underset{\theta_0 \in \mathcal{R},\theta\in \mathcal{R}^{p},\xi\in \mathcal{R}^{n}}{\text{minimize}}
& & \sum_{i=1}^N  \{\tau(\xi)_{+}+(1-\tau)(\xi)_{-}\}+ \lambda\Vert\theta\Vert_1  \\
& \text{subject to}
& & \theta_0 +x_i'\theta+\xi_i =y_i, \;  i = 1, \ldots, n.
\end{aligned}$$

$$\begin{matrix}
z & := & [ & \theta_0^{+} & \theta_0^{-} & (\theta^{+})' & (\theta^{-})' & (\xi^{+})' & (\xi^{-})' & ]' \\
c & := & [ & 0 & 0 & \pmb{0}' & \pmb{0}' & \tau \pmb{1}' & (1-\tau) \pmb{1}' & ]'
\\
a & := & [ & 0 & 0 & \pmb{1}' & \pmb{1}' & \pmb{0}' & \pmb{0}' & ]'
\\
A & := & [ & \pmb{1}' & -\pmb{1}' & X & -X &   I' & -I' & ]
\\
b & := & Y,
\end{matrix}$$

where $\theta = [\alpha', \beta']'$ and $W = [D', X']'$.

However, it turns out that the computation is challenging and time-consuming. For instance, it often meets the singular design within the high dimensional framework. As an alternative, we utilize the algorithm developed by Yi and Huang (2017) who use the Huber loss function to approximate the quantile loss function. In the equation (12), $\rho_{\tau}$ is not differentiable, and $$\rho_{\tau}(t)=(1-\tau)t_{-}+\tau t_{+}=\frac{1}{2} {|t|+(2\tau-1)t}.$$ Since $h_{\tau}(t) \rightarrow |t|$ as $\tau \rightarrow 0^{+}$, where $h_{\tau}(t)$ is the Huber loss function of t defined in Yi and Huang (2017), we have $\rho_{\tau}(t)\approx \frac{1}{2}{h_{\tau}(t)+(2\tau-1)t}$ for small $\tau$. Therefore the equation (12) can be approximated by $$\underset{\theta_0 \in \mathcal{R}, \theta\in \mathcal{R}^{p}}{\text{minimize}}
\sum_{i=1}^N  h_{\tau}(Y_i - \theta_0 - W_i'\theta)+(2\tau-1)(Y_i - \theta_0 - W_i'\theta)+\lambda\Vert\theta\Vert_1.$$ The optimization above stands for the Huber approximation. This optimization problem is more computationally feasible for the sake of the differentiability of the loss function.

Monte Carlo Experiments
=======================

We evaluate the finite-sample performance, in terms of RMSE and MAD, of the double machine learning for the IVQR. The following data generating process is modified from the one considered in Chen and Lee (2018).

$$\begin{bmatrix}
    u_i \\
    \epsilon_i\\

\end{bmatrix}
\sim N\left(0,\begin{bmatrix}
    1  & 0.3 \\
    0.3  & 1\\

\end{bmatrix}\right)\\
\medskip$$ $$\begin{bmatrix}
    x_i \\
    z_i\\
    v_i\\
\end{bmatrix}
\sim N (0,I)\\
\medskip$$ $$Z_i = z_i+ v_{i} + x_{i}$$ $$D_i = \Phi(z_i +\epsilon_i)$$ $$X_i = \Phi(x_i)$$ $$Y_i=1+ D_i + X_i^T {\mbox{\boldmath $1$}}+ D_i*u_i,$$ where $\Phi(\cdot)$ is the cumulative distribution function of a standard normal random variable. Consequently, $$\alpha(\tau)=1+F_\epsilon^{-1}(\tau),$$ where $F_\epsilon(\cdot)$ is the cumulative distribution function of $\epsilon$.

Partialing out and nonPartialing out $Z$ on $X$
-----------------------------------------------

We focus on comparing MAD and RMSE resulting from different models under the exact specification (10 control variables). po-GMM stands for doing partialing out $Z$ on $X$. GMM stands for doing no partialing out $Z$ on $X$. Table 1 shows that doing partialing out $Z$ on $X$ leads to an efficiency gain across quantiles especially when sample size is moderate.

  ------------------------- -------- -------- -------- -------- --

  $\alpha_{0.1}$(po-GMM)     0.1888   0.1510   0.1219   0.0950
  $\alpha_{0.1}$(GMM)        0.4963   0.2559   0.1631   0.1138
  $\alpha_{0.25}$(po-GMM)    0.1210   0.0966   0.0812   0.0654
  $\alpha_{0.25}$(GMM)       0.1782   0.1179   0.0963   0.0754
  $\alpha_{0.5}$(po-GMM)     0.0989   0.0716   0.0689   0.0436
  $\alpha_{0.5}$(GMM)        0.1436   0.1016   0.0801   0.0542
  $\alpha_{0.75}$(po-GMM)    0.1374   0.1066   0.0828   0.0676
  $\alpha_{0.75}$(GMM)       0.2403   0.1710   0.1146   0.0848
  $\alpha_{0.90}$(po-GMM)    0.2437   0.1839   0.1391   0.1067
  $\alpha_{0.90}$(GMM)       0.8483   0.5340   0.3481   0.1967
  ------------------------- -------- -------- -------- -------- --

  : Partiailing out and nonPartialing out Z on X

\[table 1\]

The date generating process considers ten control variables. po-GMM stands for doing partialing out $Z$ on $X$. GMM stands for doing no partialing out $Z$ on $X$.

\[table:simDisimCoefNewDef\]

IVQR with High-dimensional Controls
-----------------------------------

We now evaluate the finite-sample performance of the IVQR with high-dimensional controls. The data generating process involves 100 control variables with an approximate sparsity structure. In particular, the exact model (true model) depends only on 10 relevant control variables out of the 100 controls. GMM uses 100 control variables without regularization. Table 2 shows that the RMSE and MAD stemmed from the DML-IVQR are close to those from the exact model. In addition, Figure 1 plots distributions of the IVQR estimator with/without double machine learning. The DML-IVQR stands for the double machine learning for the IVQR with high-dimensional controls. Histograms signify that the DML-IVQR estimator is more efficient and less biased than the IVQR using many control variables. Since a weak-identification robust inference procedure results naturally form the IVQR, cf. Chernozhukov and Hansen (2008), we construct the robust confidence regions for the GMM and the DML-IVQR estimators. Figure 2 signifies that, across quantiles, the weak-identification (or weak-instrument) robust confidence region based on the DML-IVQR is relatively sharp. The Monte Carlo experiments show that the DML-IVQR procedure performs well.

  ---------------------------- -------- -------- -------- -------- --

  $\alpha_{0.1}$(GMM)           0.7648   0.6645   0.3917   0.3442
  $\alpha_{0.1}$(exact-GMM)     0.1888   0.1510   0.1219   0.0950
  $\alpha_{0.1}$(DML-IVQR)      0.3112   0.2389   0.1376   0.1085
  $\alpha_{0.25}$(GMM)          0.2712   0.2212   0.1646   0.1361
  $\alpha_{0.25}$(exact-GMM)    0.1210   0.0966   0.0812   0.0654
  $\alpha_{0.25}$(DML-IVQR)     0.1562   0.1254   0.0991   0.0804
  $\alpha_{0.5}$(GMM)           0.1627   0.1234   0.1038   0.0754
  $\alpha_{0.5}$(exact-GMM)     0.0989   0.0716   0.0689   0.0436
  $\alpha_{0.5}$(DML-IVQR)      0.1168   0.0846   0.0775   0.0510
  $\alpha_{0.75}$(GMM)          0.3421   0.2806   0.1747   0.1452
  $\alpha_{0.75}$(exact-GMM)    0.1374   0.1066   0.0828   0.0676
  $\alpha_{0.75}$(DML-IVQR)     0.1495   0.1167   0.0930   0.0741
  $\alpha_{0.9}$(GMM)           0.9449   0.8032   0.4320   0.3681
  $\alpha_{0.9}$(exact-GMM)     0.2437   0.1839   0.1391   0.1067
  $\alpha_{0.9}$(DML-IVQR)      0.3567   0.2608   0.1649   0.1231
  ---------------------------- -------- -------- -------- -------- --

  : IVQR with High-dimensional Controls

\[table 2\]

\[figur\]

  ---------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------
   ![Histograms of the IVQR Estimator with/without the DML](figure/1.png "fig:"){width="68mm"}    ![Histograms of the IVQR Estimator with/without the DML](figure/25.png "fig:"){width="68mm"}
   ![Histograms of the IVQR Estimator with/without the DML](figure/75.png "fig:"){width="68mm"}   ![Histograms of the IVQR Estimator with/without the DML](figure/9.png "fig:"){width="68mm"}
  ---------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------

![Histograms of the IVQR Estimator with/without the DML](figure/5.png){width="68mm"}

Notice: DML-IVQR results are plotted in green. Results from the GMM with many controls are in orange.

\[figur\]

![Weak-Instrument Robust Inference: DML-IVQR versus GMM](figure/hdmfull.png)

Empirical Applications
======================

Quantile treatment effects of 401(k) participation on accumulated wealth
------------------------------------------------------------------------

We reinvestigate impact of the 401(k) participation on accumulated wealth. Total wealth or net financial asset is the outcome variable $Y$. Treatment variable $D$ is a binary variable standing for participation in the 401(k) plan. Instrument $Z$ is an indicator for being eligible to enroll in the 401(k) plan. The vector of covariates $X$ consists of income, age, family size, married, an IRA individual retirement account, a defined benefit status indicator, a home ownership indicator and the different education-year indicator variables. The data consists of 9915 observations.

Following the regression specification in Chernozhukov and Hansen (2004), Table 3 presents quantile treatment effects obtained from different estimation procedures which have been defined in the previous section including IVQR, po-GMM and GMM. The corresponding results are similar. As to the high-dimensional analysis, we create 119 technical control variables including those constructed by the polynomial bases, interaction terms, and cubic splines (thresholds). To ensure each basis has equal length, we utilize the minimax normalization for all technical control variables. Consequently, we use the plug-in method to determine the value of penalty when doing the LASSO under the moment condition, and tune the penalty in the quantile L1-norm objective function based on the Huber approximation by 5-fold cross validation. The DML-IVQR also implements feature normalization of the outcome variable for the sake of computational efficiency. To make the estimated treatment effects across different estimation procedures roughly comparable, Table 4 shows the effect obtained through the DML-IVQR multiplied by the standard deviation of the outcome variable. Weak identification/instrument robust inference on quantile treatment effects are depicted in Figures 4 and 5. Yet, the robust confidence interval widens as the sample size becomes fewer at the upper quantiles; estimated quantile treatment effects are significantly different from zero. We could use the result from the DML-IVQR as a data-driven robustness check on those summarized in the Table 3.

Tables 5 and 6 present the selected important variables across different quantiles. The approximate sparsity is asymmetric across the conditional distribution in the sense that the number of selected variables decreases as the quantile index $\tau$ increases. However, it hinges on the relatively small number of observations at the upper quantiles as well. Our empirical results also signify that the 401(k) participants with low savings propensity are more associated with the nonlinear income effect than those with high savings propensity, which complements the results concluded in Chernozhukov et al. (2018a) and Chiou et al. (2018). In this particular example, $\tau$ captures the rank variable which governs the unobservable heterogeneity: savings propensity. Small values of $\tau$ represent participants with low savings propensity. The nonlinear income effects, across quantile ranging from (0, 0.5\], are picked up by the selected variables such as $\max(0, inc-0.2)$, $\max(0, inc^2-0.2)$,$\max(0, inc^3-0.2)$ and etc. Technical variables in terms of age, education, family size, and income are more frequently selected. In addition, these four variables are also identified as important variables in the context of the generalized random forests, cf. Chen and Hsiang (2019).

[l\*[6]{}[c]{}r]{} Quantiles &0.1 &0.15 &0.25 &0.5 &0.75 &0.85 &0.9\
TW(IVQR) & 4400 & 5300 & 4900 & 6700 & 8000 & 8300 & 10800\
TW(po-GMM) & 4400 & 5100 & 4900 & 6300 & 8200 & 7500 & 9100\
TW(GMM) & 4400 & 5200 & 4800 & 6300 & 8400 & 8000 & 8700\

NFTA(IVQR) & 3600 & 3600 & 3700 & 5700 & 13200 & 15800 & 17700\
NFTA(po-GMM) & 3500 & 3600 & 3700 & 5600 & 13900 & 15800 & 17700\
NFTA(GMM) & 3500 & 3600 & 3700 & 5700 & 13900 & 16100 & 18200\

\[table 3\]

[c]{}

![Effects of Participation in the 401(K) on Total Wealth (TW) and Net Financial Assets (NFTA) respectively.](figure/hq_tw.png "fig:")\
![Effects of Participation in the 401(K) on Total Wealth (TW) and Net Financial Assets (NFTA) respectively.](figure/hq_nfta.png "fig:")\

\[figur\]

[l\*[6]{}[c]{}r]{} Quantiles & 0.1 & 0.15 & 0.25 & 0.5 & 0.75 & 0.85 & 0.9\
NFTA(std-DML-IVQR $\times$63522) & 3176 & 3049 & 3303 & 5844 & 18802 & 26298 & 28076\
TW(std-DML-IVQR $\times$111529) & 2453 &3011 & 3457 & 7695 & 15056 & 18736 & 16394\
NFTA(std-DML-IVQR) & 0.05 &0.048 & 0.052 & 0.092 & 0.296 & 0.414 & 0.442\
TW(std-DML-IVQR) & 0.022 &0.027 & 0.031 & 0.069 & 0.135 & 0.168 & 0.147\

We create 119 technical control variables including those constructed by the polynomial bases, interaction terms, and cubic splines (thresholds). The DML-IVQR estimates the distributional effect which signifies an asymmetric pattern similar to the one identified in Chernozhukov and Hansen (2004).

\[table 4\]

\[figur\]

  ---------------------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------------------
   ![Weak Instrument Robust Inference, P401(K) on TW with hqreg L1-norm](figure/hq_tw15.png "fig:"){width="68mm"}   ![Weak Instrument Robust Inference, P401(K) on TW with hqreg L1-norm](figure/hq_tw25.png "fig:"){width="68mm"}
   ![Weak Instrument Robust Inference, P401(K) on TW with hqreg L1-norm](figure/hq_tw75.png "fig:"){width="68mm"}   ![Weak Instrument Robust Inference, P401(K) on TW with hqreg L1-norm](figure/hq_tw85.png "fig:"){width="68mm"}
  ---------------------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------------------

![Weak Instrument Robust Inference, P401(K) on TW with hqreg L1-norm](figure/hq_tw50.png){width="68mm"}

\[figur\]

  -------------------------------------------------------------------------------------------------------------------- --------------------------------------------------------------------------------------------------------------------
   ![Weak Instrument Robust Inference, P401(K) on NFTA with hqreg L1-norm](figure/hq_nfta15.png "fig:"){width="68mm"}   ![Weak Instrument Robust Inference, P401(K) on NFTA with hqreg L1-norm](figure/hq_nfta25.png "fig:"){width="68mm"}
   ![Weak Instrument Robust Inference, P401(K) on NFTA with hqreg L1-norm](figure/hq_nfta75.png "fig:"){width="68mm"}   ![Weak Instrument Robust Inference, P401(K) on NFTA with hqreg L1-norm](figure/hq_nfta85.png "fig:"){width="68mm"}
  -------------------------------------------------------------------------------------------------------------------- --------------------------------------------------------------------------------------------------------------------

![Weak Instrument Robust Inference, P401(K) on NFTA with hqreg L1-norm](figure/hq_nfta50.png){width="68mm"}

[|l|[c]{}|]{} Quantile &Selected Variables\
0.15 & $ira$, $educ$, $educ^2$ , $age*ira$, $age*inc$, $fsize*educ$, $fsize*hmort$\
& $ira*educ$, $ira*inc$, $hval*inc$, $marr$, $male$, $i4$, $a3$\
&$twoearn$, $marr*fsize$, $pira*inc$, $max(0, age^3-0.2)$\
&$max(0, educ^2-0.4)$, $max(0, educ-0.2)$, $max(0, age^2-0.4)$\
0.25 & $ira$, $age*fsize$, $age*ira$,$age*inc$\
& $fsize*educ$, $ira*educ$, $ira*inc$\
& $hval*inc$, $marr$, $male$, $i3$, $twoearn$, $marr*fsize$\
& $pira*inc$, $twoearn*fsize$, $max(0,inc-0.2)$\
0.5 & $inc^2$, $age*fsize$, $age*ira$, $age*inc$\
& $fsize*educ$, $ira*educ$, $ira*hval$, $ira*inc$\
& $hval*inc$, $male$, $a1$, $a3$ , $pira*inc$, $twoearn*age$, $twoearn*fsize$\
& $twoearn*hmort$, $twoearn*educ$, $max(0, educ-0.6)$\
0.75 & $inc$, $ira$, $age*ira$, $ age*hval$\
& $age*inc$, $educ*inc$, $hval*inc$, $pira*inc$, $pira*age$

\
0.85 & $inc$, $ira$, $age*hval$, $age*inc$, $ira*educ$\
& $educ*inc$, $hval*inc$, $pira*inc$, $pira*hval$\

Selected variables across $\tau$, tuned via cross validation.

$ira$: individual retirement account (IRA), $inc$: income, $fsize$: family size, $hequity$: home equity, $hva$l: home value, $educ$: education years, $marr$: married, $smcol$: college, $db$: defined benefit pension, $hown$: home owner, $hmort$: home mortgage, $a1$: less than 30 years old, $a2$: 30-35 years old, $a3$: 36-44 years old, $a4$: 45-54 years old, $a5$: 55 years old or older, $i1$: $<\$10K$, $i2$: $\$10-20K$, $i3$: $\$20-30K$, $i4$: $\$30-40K$, $i5$: $\$40-50K$, $i6$: $\$50-75K$, $i7$: $\$75K+$.

\[table 5\]

[|l|[c]{}|]{} Quantile &Selected Variables\
0.15 & $ira$, $educ^2$, $fsize^3  $, $hval^3$, $educ^3$, $age*educ$, $age*hmort$\
& $age*inc $, $fsize*hmort$, $fsize*inc $, $ira*educ$ , $ira*inc$\
&$hval*inc$, $marr$, $db$, $male$, $i2$, $i3$\
&$i4$, $i5$, $twoearn$, $marr*fsize$\
& $pira*inc$, $pira*educ$, $twoearn*inc$, $twoearn*ira$\
& $max(0,age^3-0.2)$, $max(0,age^2-0.2)$, $max(0,age-0.6)$\
&$max(0,inc^3-0.2)$, $max(0,inc^2-0.2)$, $max(0,educ-0.2)$\
0.25 & $ira$, $hmort$, $age*hmort$, $age*inc$, $fsize*hmort$, $fsize*inc$\
& $ira*educ$, $ira*inc$, $hval*inc$, $db$, $smcol$, $male$\
& $i2$, $i3$, $i4$, $i5$, $a2$, $a3$\
& $twoearn$, $pira*inc$ ,$ pira*age$\
& $pira*fsize$, $twoearn*inc$, $ twoearn*ira$\
& $twoearn*hmort$, $max(0, age^2-0.2)$\
& $max(0, age-0.6)$, $max(0, inc^2-0.2)$, $max(0, inc-0.4)$\
&$max(0, inc-0.2)$, $max(0, educ-0.2)$\
0.5 & $age$, $ira$, $age*fsize$, $ age*ira$, $ age*inc$\
& $fsize*educ $, $  fsize*hmort$, $ira*educ$, $ ira*inc$, $hval*inc$, $hown$\
& $male$, $i3$, $i4$, $a1$, $a2$, $a4$,$pira*inc$\
&$pira*fsize$, $twoearn*inc$, $ twoearn*fsize$\
& $twoearn*hmort$, $twoearn*educ$, $max(0,inc-0.2)$\
0.75 & $ira$, $age*inc$, $hval*inc$, $ pira*inc$, $pira*age$

\
0.9 & $ira$, $ age*inc$, $ educ*inc$, $  hval*inc$, $ pira*inc$\

Selected variables across $\tau$, tuned via cross validation.

$ira$: individual retirement account (IRA), $inc$: income, $fsize$: family size, $hequity$: home equity, $hva$l: home value, $educ$: education years, $marr$: married, $smcol$: college, $db$: defined benefit pension, $hown$: home owner, $hmort$: home mortgage, $a1$: less than 30 years old, $a2$: 30-35 years old, $a3$: 36-44 years old, $a4$: 45-54 years old, $a5$: 55 years old or older, $i1$: $<\$10K$, $i2$: $\$10-20K$, $i3$: $\$20-30K$, $i4$: $\$30-40K$, $i5$: $\$40-50K$, $i6$: $\$50-75K$, $i7$: $\$75K+$.

\[table 5\]

Effects of subsidized training on male and female trainee earnings
------------------------------------------------------------------

Abadie, Angrist and Imbens (2002) use the Job Training Partnership Act (JTPA) data to estimate the quantile treatment effect of job training on the earning distribution. The data are from Title II of the JTPA in early 1990’s, which consist of 11,204 samples, 5,102 of them are male, and 6,102 of them are female. In estimation, they take thirty-month earnings as the outcome variable, enrollment for JTPA service as the treatment variable, and a randomized o er of JTPA enrollment as the instrumental variable. The control variables include the binary variables of black and Hispanic applicants, high-school graduates, married applicants, 5 age-group, AFDC receipt (for women), whether the applicant worked at least 12 weeks in the 12 months preceding random assignment, the dummies for the original recommended service strategy (classroom, OJT/JSA, other) and a dummy for whether earnings data are from the second follow-up survey.

Table 7 presents quantile treatment effects for male and female groups respectively obtained from several estimation procedures including IVQR, po-GMM, and GMM. As to the high-dimensional analysis, we create 85 technical control variables including those constructed by the polynomial bases, interaction terms, and cubic splines (thresholds). Table 8 shows the quantile treatment effect obtained through the DML-IVQR. Table 7 together with the existing findings in the literature suggest that for female only, job training program generates significantly positive treatment effect on earnings at 0.5 and 0.75 quantiles. The DML-IVQR signifies similar results, which can be confirmed by the identification-robust confidence intervals depicted in Figures 6 and 7. The selected variables are collected in the online appendix[^1]. Thus, the existing empirical conclusions in the literature is reassured by the IVQR using double machine learning procedure.

[l\*[6]{}[c]{}r]{} Quantiles &0.1 &0.15 &0.25 &0.5 &0.75 &0.85 &0.9\
Male(IVQR) & 0 & -200 & 400 & 500 & 3300 & 3100 & 1700\
Male(po-GMM) & 0 & -100 & 500 & 1900 & 5000 & 6800 & 7800\
Male(GMM) & 0 & -100 & 500 & 1600 & 5100 & 5800 & 7200\

Female(IVQR) & 0 & 0 & 400 & 1600 & 2500 & 1900 & 1400\
Female(po-GMM) & 0 & 200 & 700 & 3300 & 5200 & 6500 & 6900\
Female(GMM) & 100 & 200 & 700 & 3200 & 5200 & 6500 & 6900\

\[table 7\]

[l\*[6]{}[c]{}r]{} Quantiles & 0.1 & 0.15 & 0.25 & 0.5 & 0.75 & 0.85 & 0.9\
Male(std-DML-IVQR $\times$19400) & 0 & -97 & -97 & 0 & 2735 & 2735 & 488\
Female(std-DML-IVQR $\times$13400) & 0 & 67 & 335 & 1274 & 2213 & 1676 & 268\
Male(std-DML-IVQR) &0 & -0.005 & -0.005 & 0 & 0.14 & 0.14 & 0.025\
Female(std-DML-IVQR) & 0 &0.005 & 0.025 & 0.095 & 0.165 & 0.125 & 0.02\

We create 85 technical control variables including those constructed from the polynomial bases, interaction terms, and cubic splines (thresholds).

\[table 8\]

\[figur\]

  ---------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------
   ![Weak Instrument Robust Inference. The male group.](figure/hqreg_male15.png "fig:"){width="68mm"}   ![Weak Instrument Robust Inference. The male group.](figure/hqreg_male25.png "fig:"){width="68mm"}
   ![Weak Instrument Robust Inference. The male group.](figure/hqreg_male75.png "fig:"){width="68mm"}   ![Weak Instrument Robust Inference. The male group.](figure/hqreg_male85.png "fig:"){width="68mm"}
  ---------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------

![Weak Instrument Robust Inference. The male group.](figure/hqreg_male50.png){width="68mm"}

\[figur\]

  -------------------------------------------------------------------------------------------------------- --------------------------------------------------------------------------------------------------------
   ![Weak Instrument Robust Inference. The female group.](figure/hqreg_female15.png "fig:"){width="68mm"}   ![Weak Instrument Robust Inference. The female group.](figure/hqreg_female25.png "fig:"){width="68mm"}
   ![Weak Instrument Robust Inference. The female group.](figure/hqreg_female75.png "fig:"){width="68mm"}   ![Weak Instrument Robust Inference. The female group.](figure/hqreg_female85.png "fig:"){width="68mm"}
  -------------------------------------------------------------------------------------------------------- --------------------------------------------------------------------------------------------------------

![Weak Instrument Robust Inference. The female group.](figure/hqreg_female50.png){width="68mm"}

Conclusion
==========

The performance of a debiased/double machine learning algorithm within the framework of high-dimensional IVQR is investigated. The simulation results signify that the proposed procedure performs more efficiently than those based on the conventional estimator with many controls. Furthermore, we evaluate the corresponding weak-identification robust confidence interval of the low-dimensional causal parameter. Given a large number of technical controls, we reinvestigate quantile treatment effects of the 401(k) participation on accumulated wealth and then highlight the non-linear income effects driven by the the savings propensity.

Abadie A. Angrist J. and G. Imbens. 2002.  “Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings,” [*Econometrica*]{}, 70(1): 91–117.

Athey, S. 2017. “Beyond Prediction: Using Big Data for Policy Problems,” [*Science*]{}, 355: 483–485.

Athey, S.  2018. “The Impact of Machine Learning on Economics,” working paper, Stanford GSB.

Athey, S., Tibshirani, J., and S. Wager. 2019.“Generalized Random Forests,” [*The Annals of Statistics*]{}, 47(2): 1148–1178.

Belloni, A., Chernozhukov V., and C. Hansen. 2014. “High-Dimensional Methods and Inference on Structural and Treatment Effects,” [*Journal of Economic Perspectives*]{}, 28: 29–50.

Chen, J.-E. and C.-W. Hsiang. 2019.“Causal Random Forests Model using Instrumental Variable Quantile Regression,” working paper, Center for Research in Econometric Theory and Applications, National Taiwan University.

Chen, L.-Y., and S. Lee.  2018. “Exact Computation of GMM Estimators for Instrumental Variable Quantile Regression Models,” [*Journal of Applied Econometrics*]{}, forthcoming.

Chernozhukov, V., and C. Hansen.  2004. “The Impact of 401(k) Participation on the Wealth Distribution: An Instrumental Quantile Regression Analysis,” [*Review of Economics and Statistics*]{}, 86: 735–751.

Chernozhukov, V. and C. Hansen. 2008. “Instrumental Variable Quantile Regression: A Robust Inference Approach,” [*Journal of Econometrics*]{}, 142: 379–398.

Chernozhukov, V., Hansen C., and M. Spindler. 2015.“Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach,” [*Annual Review of Economics*]{}, 7: 649–688.

Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., and J. Robins. 2018a.“Double/debiased Machine Learning for Treatment and Structural Parameters,” [*Econometrics Journal*]{}, 21: C1–C68.

Chernozhukov, V., Hansen, C., and K. Wüthrich. 2018b.  “Instrumental Variable Quantile Regression,” [*Handbook of Quantile Regression*]{}.

Chiou, Y.-Y., Chen, M.-Y., and J.-E. Chen.  2018. “Nonparametric Regression with Multiple Thresholds: Estimation and Inference,” [*Journal of Econometrics*]{}, 206(2): 472–514.

Yi, C., and J. Huang. 2017.  “Semismooth Newton Coordinate Descent Algorithm for Elastic-net Penalized Huber Loss Regression and Quantile Regression,” [*Journal of Computational and Graphical Statistics*]{}, 26(3): 547–557.

[^1]: Selected variables for the male group: <https://github.com/FieldTien/DML-QR/blob/master/Empirical_work/hqreg_data/selected_male.csv>; selected variables for the female group: <https://github.com/FieldTien/DML-QR/blob/master/Empirical_work/hqreg_data/selected_female.csv>


Stock removal

Stock removal is the process of removing material (stock) from a workpiece. Stock removal processes include:

Machining
Milling
Turning
Drilling
Grinding
Filing
Broaching
Shaping
Planing
Sawing

References

Notes

Bibliography
.

Category:Metalworking terminology


Like this item?

This is an Imperial Walker sculpture I made from recycled computer parts and other scrap metal materials. The Imperial Walker, also known as the AT-AT is an iconic figure from the original Star Wars trilogy movies. It is a static sculpture, meaning there are no moveable parts although the piece appears to be in mid-stride.

The main body is composed of power supply boxes from old computers, the head from floppy drive housings, legs and feet from various scrap metal. The entire piece has been welded together using the MIG welding process. Two coats of cold galvanizing primer are applied followed by a coat of varied grays and finished with two coats of protective gloss. The whole sculpture was randomly "attacked" with the welding arc to simulate battle scars.

This AT-AT stands at 18 inches, measures 17-1/2 inches long and 6 inches wide. He weighs about 15 pounds.

A quick note: this is NOT a toy! Not only is it heavy, but it is made of metal and has sharp edges and corners. Please do not let children play with it!


id: dsq-747510243
date: 2008-08-03T05:25:11.0000000-07:00
name: Craig Laparo
avatar: https://disqus.com/api/users/avatars/Craig Laparo.jpg
message:
I'm a student in the Google Summer of Code program, working for Dojo (http://dojotoolkit.org) and I came across your awesome regular expression for catching HTML tags.  I'd like to use it in my code, if it's ok with you.  Do you have a CLA?


The present paper discusses a general expression for determining the minimum sample size (plants) for a given number of seeds or vice versa for capturing multiple allelic diversity. The model considers sampling from a large 2 k-ploid population under a broad range of mating systems. Numerous expressions/results developed for germplasm collection/regeneration for diploid populations by earlier workers can be directly deduced from our general expression by assigning appropriate values of the corresponding parameters. A seed factor which influences the plant sample size has also been isolated to aid the collectors in selecting the appropriate combination of number of plants and seeds per plant. When genotypic multiplicity of seeds is taken into consideration, a sample size of even less than 172 plants can conserve diversity of 20 alleles from 50,000 polymorphic loci with a very large probability of conservation (0.9999) in most of the cases.

Infertility affects 15% couples attempting pregnancy and in 40–50% of these cases the male partner has qualitative or quantitative abnormalities of sperm production. Microdeletions in the azoospermia factor (AZF) region on the long arm of the Y chromosome are known to be associated with spermatogenic failure and have been used to define three regions on Yq (AZFa, AZFb and AZFc) which are critical for spermatogenesis and are recurrently deleted in infertile males. Semen analysis was carried out on one hundred and twenty five infertile males with oligozoospermia and azoospermia. Cytogenetic analysis was done for all the cases and in all cytogenetically normal cases (n = 83) microdeletion analysis was carried out on DNA extracted from peripheral blood using PCR. The sequence tagged sites (STS) primers sY84, sY86 (AZFa); sY127, sY134 (AZFb); sY254, sY255 (AZFc) were used for each case. Eight of the eighty three cases (9.63%) showed deletion of at least one of the STS markers. Correlation of phenotype with microdeletion was done in each case to determine any phenotype association with deletion of particular AZF locus. Based on the present study, the frequency of microdeletion in the Indian population is 9.63%. This study emphasizes the need for PCR analysis for determining genetic aetiology in cases with idiopathic severe testiculopathy.

Amino acid sequence analysis corresponding to the PPE proteins in H37Rv and CDC 1551 strains of theMycobacterium tuberculosis genomes resulted in the identification of a previously uncharacterized 225 amino acid-residue common region in 22 proteins. The pairwise sequence identities were as low as 18%. Conservation of amino acid residues was observed at fifteen positions that were distributed over the whole length of the region. The secondary structure corresponding to this region is predicted to be a mixture of a-helices and β-strands. Although the function is not known, proteins with this region specific to mycobacterial species may be associated with a common function. We further observed another group of 20 PPE proteins corresponding to the conserved C-terminal region comprising 44 amino acid residues with GFxGT and PxxPxxW sequence motifs. This region is preceded by a hydrophobic region, comprising 40–100 amino acid residues, that is flanked by charged amino acid residues. Identification of conserved regions described above may be useful to detect related proteins from other genomes and assist the design of suitable experiments to test their corresponding functions. Amino acid sequence analysis corresponding to the PE proteins resulted in the identification of tandem repeats comprising 41-43 amino acid residues in the C-terminal variable regions in two PE proteins (Rv0978 and Rv0980). These correspond to the AB repeats that were first identified in some proteins of theMethanosarcina mazei genome, and were demonstrated as surface antigens. We observed the AB repeats also in several other proteins of hitherto uncharacterized function inArchaea andBacteria genomes. Some of these proteins are also associated with another repeat called the C-repeat or the PKD-domain comprising 85 amino acid residues. The secondary structure corresponding to the AB repeat is predicted mainly as 4 β-strands. We suggest that proteins with AB repeats inMycobacterium tuberculosis and other genomes may be associated as surface antigens. TheM. leprae genome, however, does not contain either the AB or C-repeats and different proteins may therefore be recruited as surface antigens in theM. leprae genome compared to theM. tuberculosis genome.

Neurotrophins and their receptors of the Trk family play a critical role in proliferation, differentiation and survival of the developing neurons. There are reports on their expression in neoplasms too, namely, the primitive neuroectodermal tumours of childhood, and in adult astrocytic gliomas. The involvement of Trk receptors in tumour pathogenesis, if any, is not known. With this end in view, the present study has examined 10 tumour biopsy samples (identified as astrocytoma, pilocytic astrocytoma and glioblastoma) and peritumoral brain tissue of adult patients, for the presence of Trk A and Trk B receptors, by immunohistochemistry. The nature of the tumour samples was also confirmed by their immunoreactivity (IR) to glial fibrillary acidic protein. In the peritumoral brain tissue, only neurons showed IR for Trk A and Trk B. On the contrary, in the tumour sections, the IR to both receptors was localized in the vast majority of glia and capillary endothelium. There was an obvious pattern of IR in these gliomas: high levels of IR were present in the low-grade (type I and II) astrocytoma; whereas in the advanced malignant forms (WHO grade IV giant cell glioblastoma and glio-blastoma multiforme) the IR was very weak. These findings suggest that Trk A and Trk B are involved in tumour pathogenesis, especially in the early stage, and may respond to signals that elicit glial proliferation, and thus contribute to progression towards malignancy.

Mus terricolor I, II and III are the three chromosomal species which differ in stable autosomal short-arm heterochromatin variations established in homozygous condition. Analysis of meiosis in the laboratory-generated F1 male hybrids from crosses (both ways) betweenM. terricolor I and II and betweenM. terricolor I and III shows high frequencies of pairing abnormalities at pachytene. The backcross (N3 generation) male hybrids betweenM. terricolor I and II have meiotic abnormalities as in the F1male hybrids, though to a lesser extent. They show difference in pairing abnormalities in the different karyotypic forms; the backcross hybrids heterozygous for the heterochromatic short arms have more anomalies compared to the homokaryotypic hybrids. This suggests a negative influence of the heterochromatin heterozygosity in meiotic pairing. The results indicate a role for heterochromatin variations in the development of a reproductive barrier in the speciatingM. terricolor complex.

Embryos excised from seeds of six generations (P1, P2, F1, BC1, BC2 and F2) of a cross WH 283 x WH 533 were cultured on modified MS medium already inoculated with secondary sporidia ofNeovossia indica. Significant variations for callusing response (CR) (54.55–75.55%) were observed among generations but the presence or absence ofN. indicia did not affect callusing response. A clear inhibition zone (IZ) was formed around each embryo showing callusing. The diameter of IZ varied significantly among generations and was maximum in the resistant genotype, WH 283 (3.60 cm). Fresh weight and dry weight of calli, initiated from embryo cultured and inoculated withN. indica, varied significantly among generations. Coefficient of infection as well as percentage of infection reflected the overdominance of susceptibility. Generation mean analysis showed that the three parameter model was adequate for diameter of IZ only. Six-parameter model showed that additive (in presence ofN. indica), additive and additive x dominance (in absence ofN. indica) effects were also significant. Complementary type of epistasis for fresh weight of calli and dominance, and dominance x dominance effects for dry weight of calli were observed in the presence ofN. indica. Magnitude of additive effects was higher for diameter of IZ in three parameter model. Therefore, selection might assist in improving this trait and thus indirectly help in attaining the resistance towardsN. indica.

Exposure of bovine pulmonary artery smooth muscle plasma membrane suspension with the oxidant H2O2 (1 mM) stimulated Ca2+ATPase activity. We sought to determine the role of matrix metalloprotease-2 (MMP-2) in stimulating Ca2+ATPase activity by H2O2 in the smooth muscle plasma membrane. The smooth muscle membrane possesses a Ca2+-dependent protease activity in the gelatin containing zymogram having an apparent molecular mass of 72 kDa. The 72 kDa protease activity was found to be inhibited by EGTA, 1: 10-phenanthroline, a2-macroglobulin and tissue inhibitor of metalloprotease-2 (TIMP-2) indicating that the Ca2+-dependent 72 kDa protease is the MMP-2. Western immunoblot studies of the membrane suspension with polyclonal antibodies of MMP-2 and TIMP-2 revealed that MMP-2 and TIMP-2, respectively, are the ambient matrix metalloprotease and the corresponding tissue inhibitor of metalloprotease in the membrane.

In addition to increasing the Ca2+ATPase activity, H2O2 also enhanced the activity of the smooth muscle plasma membrane associated protease activity as evidenced by its ability to degrade14C-gelatin. The protease activity and the Ca2+ATPase activity were prevented by the antioxidant, vitamin E, indicating that the effect produced by H2O2 was due to reactive oxidant species(es). Both basal and H2O2 stimulated MMP-2 activity and Ca2+ATPase activity were inhibited by the general inhibitors of matrix metalloproteases: EGTA, 1: 10-phenanthroline, α2-macroglobulin and also by TIMP-2 (the specific inhibitor of MMP-2) indicating that H2O2 increased MMP-2 activity and that subsequently stimulated Ca2+ATPase activity in the plasma membrane. This was further confirmed by the following observations: (i) adding low doses of MMP-2 or H2O2 to the smooth muscle membrane suspension caused submaximal increase in Ca2+ATPase activity, and pretreatment with TIMP-2 prevents the increase in Ca2+ATPase activity; (ii) combined treatment of the membrane with low doses of MMP-2 and H2O2 augments further the Ca2+ATPase activity caused by the respective low doses of either H2O2 or MMP-2; and (iii) pretreatment with TIMP-2 prevents the increase in Ca2+ATPase activity in the membrane caused by the combined treatment of MMP-2 and H2O2.

In order to evaluate the modulatory effects of manganese, high fat diet fed and alloxan diabetic rats were taken and the changes in the glucose oxidation, glycerol release and effects of manganese on these parameters were measured from adipose tissue. An insulin-mimetic effect of manganese was observed in the adipose tissue in the controls and an additive effect of insulin and manganese on glucose oxidation was seen when Mn2+ was addedin vitro. The flux of glucose through the pentose phosphate pathway and glycolysis was significantly decreased in high fat fed animals. Although thein vitro addition of Mn2+ was additive with insulin when14CO2 was measured from control animals, it was found neither in young diabetic animals (6–8 weeks old) nor in the old (16 weeks old). Both insulin and manganese caused an increased oxidation of carbon-1 of glucose and an increase of its incorporation into14C-lipids in the young control animals; the additive effect of insulin and manganese suggests separate site of action. This effect was decreased in fat fed animals, diabetic animals and old animals. Manganese alone was found to decrease glycerol in both the control and diabetic adipose tissue inin vitro incubations. The results of the effects of glucose oxidation, lipogenesis, and glycerol release in adipose tissue of control and diabetic animals of different ages are presented together with the effect of manganese on adipose tissue from high fat milk diet fed animals.

We have demonstrated the presence of a Ca2+-dependent/calmodulin-stimulated protein kinase (PK) in chloronema cells of the mossFunaria hygrometrica. The kinase, with a molecular mass of 70,000 daltons (PK70), was purified to homogeneity using ammonium sulphate fractionation, DEAE-cellulose chromatography, and calmodulin (CaM)-agarose affinity chromatography. The kinase activity was stimulated at a concentration of 50 (AM free Ca2+, and was further enhanced 3–5-fold with exogenously added 3–1000 nm moss calmodulin (CaM). Autophosphorylation was also stimulated with Ca2+ and CaM. Underin vitro conditions, PK70 phosphorylated preferentially lysine-rich substrates such as HIIIS and HVS. This PK shares epitopes with the maize Ca2+-dependent/calmodulin-stimulated PK (CCaMK) and also exhibits biochemical properties similar to the maize, lily, and tobacco CCaMK. We have characterized it as a moss CCaMK.

An anaerobic methylotrophic methanogenic enrichment culture, with sustained metabolic characteristics, including that of methanation for over a decade, was the choice of the present study on interspecies interactions. Growth and methanation by the enrichment were suppressed in the presence of antibiotics, and no methanogen grown on methanol could be isolated using stringent techniques. The present study confirmed syntrophic metabolic interactions in this enrichment with the isolation of a strain ofPseudomonas sp. The organism had characteristic metabolic versatility in metabolizing a variety of substrates including alcohols, aliphatic acids, amino acids, and sugars. Anaerobic growth was favoured with nitrate in the growth medium. Cells grown anaerobically with methanol, revealed maximal nitrate reductase activity. Constitutive oxidative activity of the membrane system emerged from the high-specific oxygen uptake and nitrate reductase activities of the aerobically and anerobically grown cells respectively. Cells grown anaerobically on various alcohols effectively oxidized methanol in the presence of flavins, cofactor FAD and the methanogenic cofactor F420, suggesting a constitutive alcohol oxidizing capacity. In cells grown anaerobically on methanol, the rate of methanol oxidation with F420 was three times that of FAD. Efficient utilization of alcohols in the presence of F420 is a novel feature of the present study. The results suggest that utilization of methanol by the mixed culture would involve metabolic interactions between thePseudomonas sp. and the methanogen(s). Methylotrophic, methanogenic partnership involving an aerobe is a novel feature hitherto unreported among anaerobic syntrophic associations and is of ecological significance.

This study examines mycelial colonization of common soil fungi by bradyrhizobia and an azorhizobial strain, resulting in the forming of biofilms. The effects of the fungal exudates on a bradyrhizobial strain have also been investigated. Bradyrhizobia gradually colonized the mycelia for about 18 days, after which the biofilm structures collapsed with the release of the rhizobial cell clusters to the culture medium. The azorhizobial strain showed differential colonization of the mycelia. In general, there were no considerable mycotoxin effects of the fungal exudates on the bradyrhizobial strain used, instead the rhizobial strain utilized the exudates as a source of nutrition. This study indicates that the present microbial association with biofilm formation has important implications in the survival of rhizobia under adverse soil conditions devoid of vegetation. Further, it could have developed an as yet unidentified nitrogen fixing system that could have contributed to the nitrogen economy of soils.


/* $Id$ */
/* //////////////////////////////////////////////////////////////////////////
//                                                                         //
// This code is Copyright (c) 2005 LizardTech, Inc, 1008 Western Avenue,   //
// Suite 200, Seattle, WA 98104.  Unauthorized use or distribution         //
// prohibited.  Access to and use of this code is permitted only under     //
// license from LizardTech, Inc.  Portions of the code are protected by    //
// US and foreign patents and other filings. All Rights Reserved.          //
//                                                                         //
////////////////////////////////////////////////////////////////////////// */
/* PUBLIC */

#ifndef NITFIMAGEMANAGER_H
#define NITFIMAGEMANAGER_H

// lt_lib_base
#include "lt_base.h"

// lt_lib_mrsid_core
#include "lti_geoImageReader.h"
#include "lti_imageStageManager.h"

LT_BEGIN_NAMESPACE(LizardTech)

class LTFileSpec;
class LTIOStreamInf;

class NITFImageReader;

class NITFFileHeader;
class NITFImageSegment;
class NITFGraphicSegment;
class NITFLabelSegment;
class NITFTextSegment;
class NITFDataSegment;
class NITFResSegment;
class NITFFileMetadata;
class NITFSecurityMetadata;

/**
 * class for reading an NITF file
 *
 * This class provides support for reading an NITF file, containing
 * one or more image segments.
 *
 * This class is used in conjunction with the NITFReader class.
 *
 * THIS CLASS SUBJECT TO SIGNIFICANT CHANGE IN SUBSEQUENT RELEASES.
 */
class NITFImageManager : public LTIImageStageManager
{
   LTI_REFERENCE_COUNTED_BOILERPLATE(NITFImageManager);
public:
   /**
    * initialization
    * Creates an NITFImageManager object from the given file.
    *
    * @param fileSpec  the NITF file to be opened
    * @param useWorldFile indicates world file to be used for geo coords (applies to the first image segment only)
    */
   LT_STATUS initialize(const LTFileSpec& fileSpec, bool useWorldFile=true);

   // for LizardTech internal use only
   // must be called before initialize
   LT_STATUS setTileMaskList(const lt_uint32* tileMaskList, lt_uint32 tileMaskListLen);

   /**
    * creates a reader for a single segment
    *
    * This function is used to create an NITFImageReader for a
    * single given image segment, specified by number.
    *
    * The \a reader is allocated and initialized by this function,
    * but the caller has ownership of the object.
    *
    * @param reader  the created segment reader
    * @param idx     index of the image segment (one-based index)
    * @return status code
    */
   LT_STATUS createReader(NITFImageReader*& reader, lt_uint32 idx);

   // LTIImageStageManager -- imageNumber is zero-based index
   LT_STATUS createImageStage(lt_uint32 imageNumber,
                              LTIImageStage *&imageStage);

   /**
    * COMRAT compliance control
    *
    * This function is used to control whether or not the COMRAT
    * field is to be read.  The 2500C specification requires
    * this field be present; the 2500B Note 2 specification does
    * not.  This function is used to indicate which version
    * of the specification this file adheres to.
    *
    * The default is false, i.e. 2500C formatting.
    *
    * This function must be called prior to initialize().
    *
    * @param use2500B set to true for 2500B/Note2 formatting
    */
   void setCompat_2500B_N2(bool use2500B);

   /**
    * COMRAT compliance setting
    *
    * Returns the 2500B compatability setting; see the
    * setCompat_2500B_N2() function for details.
    *
    * @return true iff 200B / Note 2 formatting is being used
    */
   bool getCompat_2500B_N2() const;

   lt_int64 getFileSize() const;

   /**
    * returns FileHeader metadata
    */
   const NITFFileHeader* getFileHeader() const;

   /**
    * returns number of image segments
    */
   lt_uint32 getNumImageSegments() const;

   // not for general use
   const NITFImageSegment* getImageSegment(lt_uint32 num) const;

   /**
    * returns the IID1 field of the given image segment
    */
   const char* getImageSegmentIID1(lt_uint32 num) const;

   /**
    * returns the number of graphic segments
    */
   lt_uint32 getNumGraphicSegments() const;

   // not for general use
   const NITFGraphicSegment* getGraphicSegment(lt_uint32 num) const;

   /**
    * returns the number of graphic segments
    */
   lt_uint32 getNumLabelSegments() const;

   // not for general use
   const NITFLabelSegment* getLabelSegment(lt_uint32 num) const;

   /**
    * returns the number of graphic segments
    */
   lt_uint32 getNumTextSegments() const;

   // not for general use
   const NITFTextSegment* getTextSegment(lt_uint32 num) const;

   /**
    * returns the number of graphic segments
    */
   lt_uint32 getNumDataSegments() const;

   // not for general use
   const NITFDataSegment* getDataSegment(lt_uint32 num) const;

   /**
    * returns the number of graphic segments
    */
   lt_uint32 getNumResSegments() const;

   // not for general use
   const NITFResSegment* getResSegment(lt_uint32 num) const;

   /**
    * returns NITF version information
    */
   const char* getVersionString() const;

   LTFileSpec* getFileSpec() const;

   /**
    * returns the file-level metadata
    */
   const NITFFileMetadata* getFileMetadata() const;

   /**
    * returns file-level security metadata
    */
   const NITFSecurityMetadata* getSecurityMetadata() const;

private:
    LT_STATUS fixMetadata(LTIImageStage *mos);

   LTFileSpec* m_fileSpec;
   LTIOStreamInf* m_stream;
   lt_int64 m_fileSize;
   bool m_useWorldFile;

   NITFFileHeader* m_fileHeader;
   NITFImageSegment** m_imageSegments;
   NITFGraphicSegment** m_graphicSegments;
   NITFLabelSegment** m_labelSegments;
   NITFTextSegment** m_textSegments;
   NITFDataSegment** m_dataSegments;
   NITFResSegment** m_resSegments;
   lt_uint32 m_numImageSegments;
   lt_uint32 m_numGraphicSegments;
   lt_uint32 m_numLabelSegments;
   lt_uint32 m_numTextSegments;
   lt_uint32 m_numDataSegments;
   lt_uint32 m_numResSegments;

   char* m_versionString;

   bool m_compat_2500B_N2;

   const lt_uint32* m_tileMaskList;
   lt_uint32 m_tileMaskListLen;
};

LT_END_NAMESPACE(LizardTech)

#endif // NITFIMAGEMANAGER_H


If the rise of Cryptokitties taught us anything, it’s that right now gaming is literally one of the biggest mass adoption use-cases for blockchain technology. Despite the slow nature and lack of scalability on the Ethereum network, this single game still managed to be wildly popular. As scalability improves, many more use-cases will become possible, but the precedent has been set for gaming to be one of the biggest applications of this tech for many years to come. Not to mention the continually growing global gaming market, particularly in Asia. So for that reason, I am going to review 3 of the best projects I have found today.

Some key factors in my analysis are

Scalability — It is no good to be stuck on a network that cannot handle a single game

Worldwide Appeal — Companies targeting the growth of gaming in asia, particularly China

Tokenomics — Is the project’s associated token likely to rise in value? What are the mechanisms that will give the token value, and is there a benefit to holding it?

Traditional ICO metrics such as hard cap, supply sold, market positioning etc.

Team — What background experience do the main team bring to the table

Below is a review of each, and a conclusion on which one I’d rather put my money into. I will leave out lots of info for the sake of brevity, or else this article would just be too long. This will be a long one, so bear with me or scroll down for the Pablo Cryptobar Conclusion.

1. Yumerium (YUM)

The Blockchain-Based, Incentivised, Open Gaming Platform.

Sale: 26th September 2018–10th October 2018

Hardcap: $20 Million

Percent of supply sold: 39%

Website, Twitter, Telegram.

Yumerium, developed by Subdream Studios, are trying to target the gaming economy, specifically focusing on the developers and the development of VR gaming, along with the financial and accessibility problems that come with it.

Although they will be including the decentralised developer crowdfunding approach, to minimise such things as fees from traditional crowdfunding sites, they seem to be focusing on the ‘Pay to Play’ aspect of VR to bridge a gap between developers and gamers. They will be initially using the Ethereum virtual machine for their smart contracts, which does present scalability issues, but are looking to be as chain-agnostic as possible in order to port to a faster network if necessary.

• The Yumerium Team

There are 16 members to the Korean based Yumerium team. Included in the team are many years of experience within the VR gaming industry. Their founder Jihan Jung is the CEO of two other VR gaming companies; Subdream Studios, the leading VR company in Korea, and COLOPL NI — a US-based VR focused development study branching from the Japanese giant COLOPL. So it is safe to say, he has great experience in the field of VR gaming.

Alongside Jung, their CTO Francisco Martin (also the CTO of Subdream Studios) again has great experience in the world of VR and game development. Martin is currently a game developer at Virtual World Arcade, a company that is attempting to bring VR to the masses by bringing arcade and mobile VR events all around the world.

• Key Features

Yumerium’s parent company, Subdream Studios, are the owners of VR Plus — the biggest VR franchise in Korea (2nd biggest online gaming market in the world).

They are targeting the VR side of gaming and VR development, which is cool in its own right and gives a unique window for VR devs to have an ease of access element into new game technology and creationism.

This team is betting on the success of the VR market, which is more niche than the whole online/mobile gaming industry, comprising less than 10% of the whopping $138 billion market at around $9.5 billion. This number is expected to only grow to 22.9 billion by the end of 2020.

The ingame token reward bounty is one of the unique concepts specific to Yumerium. Developers can set in game goals or ‘achievements’ into their games and when users play the game and achieve a specific criteria, the bounty automatically unlocks the smart contract prize to be sent to the user. Doing quests or unlocking achievements for tokens is definitely a great idea in my eyes. But it is not yet specified on the amounts, uniqueness and availability of these bounties, or where the tokens themselves come from i.e. the Yumerium foundation, or the devs personal funding.

• Bonus Structure

Yumerium has a strong bonus structure for the earliest and biggest buyers. Unfortunately at this time the best early bird presale bonuses are gone, although volume bonuses are still available it seems (they are separate). This is a favourable method of selling out a project, however it does not bode well for future token price on secondary markets and leaves public buyers at a very large bonus deficit.

The background for this project is solid and has potential to be a strong player in the VR world, especially with support of its parent company. It is likely that buying on exchanges after the sale would be more profitable and a better risk to reward, given the bonus setup and how late it is now.


/**
 * Range selector for [[DateAxis]].
 */

/**
 * ============================================================================
 * IMPORTS
 * ============================================================================
 * @hidden
 */
import { RangeSelector, IRangeSelectorEvents } from "./RangeSelector";
import { registry } from "../../core/Registry";
import { DateAxis } from "../../charts/axes/DateAxis";
import { AxisRenderer } from "../../charts/axes/AxisRenderer";
import { ITimeInterval } from "../../core/defs/ITimeInterval";
import { DateFormatter } from "../../core/formatters/DateFormatter";
import * as $time from "../../core/utils/Time";
import * as $type from "../../core/utils/Type";
import * as $array from "../../core/utils/Array";

/**
 * ============================================================================
 * REQUISITES
 * ============================================================================
 * @hidden
 */

export interface IDateAxisRangeSelectorPeriod {
	name: string;
	interval: ITimeInterval | "ytd" | "max";
}

/**
 * Defines events for [[RangeSelector]].
 */
export interface IDateAxisRangeSelectorEvents extends IRangeSelectorEvents {

	/**
	 * Invoked when pre-defined period is selected (button clicked).
	 *
	 * @since 4.10.3
	 */
	periodselected: {
		interval: ITimeInterval;
		startDate: Date;
	}

}

/**
 * ============================================================================
 * MAIN CLASS
 * ============================================================================
 * @hidden
 */

/**
 * Creates a control used to select date range and preset periods for
 * a [[DateAxis]].
 *
 * ```TypeScript
 * let selector = new am4plugins_rangeSelector.DateAxisRangeSelector()
 * selector.container = document.getElementById("selectordiv");
 * selector.axis = chart.xAxes.getIndex(0);
 * ```
 * ```JavaScript
 * var selector = new am4plugins_rangeSelector.DateAxisRangeSelector()
 * selector.container = document.getElementById("selectordiv");
 * selector.axis = chart.xAxes.getIndex(0);
 * ```
 *
 * @see {@link https://www.amcharts.com/docs/v4/tutorials/plugin-range-selector/} for more information on how to use this plugin
 * @important
 * @todo JSON example
 */
export class DateAxisRangeSelector extends RangeSelector {

	/**
	 * Defines available events.
	 */
	public _events!: IDateAxisRangeSelectorEvents;

	/**
	 * Reference to target axis.
	 *
	 * @ignore
	 */
	public _axis: DateAxis<AxisRenderer>;

	/**
	 * Holds references to various HTML elements control consists of.
	 */
	protected _elements: {
		wrapper?: HTMLElement;
		rangeWrapper?: HTMLElement;
		fromTitle?: HTMLElement;
		fromInput?: HTMLInputElement;
		toTitle?: HTMLElement;
		toInput?: HTMLInputElement;
		periodWrapper?: HTMLElement;
		periodTitle?: HTMLElement;
		periodButtons?: HTMLElement[];
	} = {
			periodButtons: []
		};

	/**
	 * List of pre-defined period buttons.
	 */
	protected _periods: IDateAxisRangeSelectorPeriod[];

	/**
	 * Date format to use for input fields.
	 */
	protected _inputDateFormat = "yyyy-MM-dd";

	/**
	 * Date formatter.
	 */
	protected _dateFormatter: DateFormatter;

	/**
	 * Timeout to use to delay zooming of axis (so it does not happen on
	 * every keystroke in input fields).
	 */
	private _zoomTimeout: any;

	/**
	 * Number of milliseconds to wait after last keystroke in date input field
	 * before zooming the axis.
	 *
	 * @default 500
	 */
	public zoomDelay: number = 500;

	/**
	 * Constructor
	 */
	constructor() {
		super();
		this.className = "DateAxisRangeSelector";
		this._disposers.push(this._language);
		this.invalidate();
		this.applyTheme();
	}

	/**
	 * (Re)draws the control.
	 *
	 * @ignore Exclude from docs
	 */
	public validate(): void {
		this.draw();
		super.validate();
	}

	/**
	 * Adds events to the axis.
	 */
	protected prepAxis(): void {
		this._disposers.push(this.axis.events.on("selectionextremeschanged", (ev) => {
			this.updateRangeInputs();
		}));
		this._disposers.push(this.axis.events.on("extremeschanged", (ev) => {
			this.updateRangeInputs();
		}));
	}

	/**
	 * Draws the control.
	 *
	 * @ignore
	 */
	public draw(): void {

		super.draw();

		const tabindex = this.tabindex;

		// Range wrapper
		if (!this._elements.rangeWrapper) {
			this._elements.rangeWrapper = document.createElement("div");
			this._elements.rangeWrapper.className = this.classPrefix + "-range-wrapper";
			this._elements.wrapper.appendChild(this._elements.rangeWrapper);
		}

		// From title
		if (!this._elements.fromTitle) {
			this._elements.fromTitle = document.createElement("span");
			this._elements.fromTitle.className = this.classPrefix + "-title " + this.classPrefix + "-from-title";
			this._elements.fromTitle.innerHTML = this.language.translateAny("From %1", undefined, "");
			this._elements.rangeWrapper.appendChild(this._elements.fromTitle);
		}

		// From input
		if (!this._elements.fromInput) {
			this._elements.fromInput = <HTMLInputElement>document.createElement("input");
			this._elements.fromInput.type = "text";
			this._elements.fromInput.className = this.classPrefix + "-from-input";

			if (tabindex) {
				this._elements.fromInput.setAttribute("tabindex", tabindex.toString());
			}

			this._elements.rangeWrapper.appendChild(this._elements.fromInput);

			this._elements.fromInput.addEventListener("keyup", () => {
				this.updateZoom()
			});
		}

		// To title
		if (!this._elements.toTitle) {
			this._elements.toTitle = document.createElement("span");
			this._elements.toTitle.className = this.classPrefix + "-title " + this.classPrefix + "-to-title";
			this._elements.toTitle.innerHTML = this.language.translateAny("To %1", undefined, "");
			this._elements.rangeWrapper.appendChild(this._elements.toTitle);
		}

		// To input
		if (!this._elements.toInput) {
			this._elements.toInput = <HTMLInputElement>document.createElement("input");
			this._elements.toInput.type = "text";
			this._elements.toInput.className = this.classPrefix + "-to-input";
			if (tabindex) {
				this._elements.toInput.setAttribute("tabindex", tabindex.toString());
			}
			this._elements.rangeWrapper.appendChild(this._elements.toInput);

			this._elements.toInput.addEventListener("keyup", () => {
				this.updateZoom()
			});
		}

		// Period buttons
		if (this.periods.length) {

			// period wrapper
			if (!this._elements.periodWrapper) {
				this._elements.periodWrapper = document.createElement("div");
				this._elements.periodWrapper.className = this.classPrefix + "-period-wrapper";
				this._elements.wrapper.appendChild(this._elements.periodWrapper);

				// Period title
				if (!this._elements.periodTitle) {
					this._elements.periodTitle = document.createElement("span");
					this._elements.periodTitle.className = this.classPrefix + "-title " + this.classPrefix + "-period-title";
					this._elements.periodTitle.innerHTML = this.language.translateAny("Zoom");
					this._elements.periodWrapper.appendChild(this._elements.periodTitle);
				}

				// Period buttons
				$array.each(this.periods, (period) => {
					const button = document.createElement("button");
					button.className = this.classPrefix + "-period-button";
					button.innerHTML = period.name;
					if (tabindex) {
						button.setAttribute("tabindex", tabindex.toString());
					}
					this._elements.periodButtons.push(button);
					this._elements.periodWrapper.appendChild(button);

					button.addEventListener("click", () => {
						this.setPeriodInterval(period.interval);
					});

				});

			}
		}

		this.dispatchImmediately("drawn", {
			type: "drawn"
		});

		this.updateRangeInputs();

	}

	/**
	 * Updates input values based on the current zoom selection of the axis.
	 *
	 * @ignore
	 */
	public updateRangeInputs(): void {
		if (this._elements.fromInput && this._elements.toInput && this.axis) {
			const axis = this.axis;
			const formatter = this.dateFormatter;
			const minZoomed = axis.minZoomed + $time.getDuration(axis.mainBaseInterval.timeUnit, axis.mainBaseInterval.count) * 0.5;
			this._elements.fromInput.value = formatter.format(minZoomed, this.inputDateFormat);
			this._elements.toInput.value = formatter.format(new Date(axis.maxZoomed), this.inputDateFormat);
		}
	}

	/**
	 * Zooms axis according to input fields.
	 *
	 * @ignore
	 */
	public updateZoom(): void {
		if (this._zoomTimeout) {
			clearTimeout(this._zoomTimeout);
		}
		this._zoomTimeout = setTimeout(() => {
			let start = this._elements.fromInput.value;
			let end = this._elements.toInput.value;
			if ((start.length < this.inputDateFormat.length) || (end.length < this.inputDateFormat.length)) {
				return;
			}
			let startDate = this.dateFormatter.parse(start, this.inputDateFormat);
			let endDate = this.dateFormatter.parse(end, this.inputDateFormat);

			if (startDate && endDate) {
				this.axis.zoomToDates(startDate, endDate);
			}
		}, this.zoomDelay);
	}

	/**
	 * Zooms the axis to a preset time interal or `"ytd"` or `"max"`.
	 *
	 * @param  interval  Interval
	 */
	public setPeriodInterval(interval: ITimeInterval | "ytd" | "max"): void {
		let date;
		const group = this.getGroupInterval(this.axis.baseInterval);
		if (interval == "max") {
			date = new Date(this.axis.groupMin[group] || this.axis.min);
		}
		else if (interval == "ytd") {
			date = new Date(this.axis.groupMin[group] || this.axis.min);
			date = new Date(this.axis.groupMax[group] || this.axis.max);
			$time.round(date, "year", 1);
		}
		else if ($type.isObject(interval)) {
			date = new Date(this.axis.groupMax[group] || this.axis.max);
			$time.add(date, interval.timeUnit, -1 * interval.count);
		}

		if (date) {
			this.zoomToDates(date);
		}

		this.dispatchImmediately("periodselected", {
			interval: interval,
			startDate: date
		});
	}

	private getGroupInterval(interval: ITimeInterval): string {
		return interval.timeUnit + interval.count;
	}

	/**
	 * Zooms the axis using start date.
	 *
	 * @param  date  Start date
	 */
	public zoomToDates(date: Date): void {
		const axis = this.axis;
		const group = this.getGroupInterval(axis.baseInterval);
		const min = axis.groupMin[group] || axis.min;
		const max = axis.groupMax[group] || axis.max;
		axis.keepSelection = true;
		axis.zoom({ start: (date.getTime() - min) / (max - min), end: 1 });
	}

	/**
	 * Getters and setters
	 */

	/**
	 * A list of pre-defined periods to show buttons for.
	 *
	 * @param  value  Periods
	 */
	public set periods(value: IDateAxisRangeSelectorPeriod[]) {
		this._periods = value;
		this.invalidate();
	}

	/**
	 * @return Periods
	 */
	public get periods(): IDateAxisRangeSelectorPeriod[] {
		if (!this._periods) {
			this._periods = [
				{ name: this.language.translateAny("%1M", undefined, "1"), interval: { timeUnit: "month", count: 1 } },
				{ name: this.language.translateAny("%1M", undefined, "3"), interval: { timeUnit: "month", count: 3 } },
				{ name: this.language.translateAny("%1M", undefined, "6"), interval: { timeUnit: "month", count: 6 } },
				{ name: this.language.translateAny("%1Y", undefined, "1"), interval: { timeUnit: "year", count: 1 } },
				{ name: this.language.translateAny("YTD"), interval: "ytd" },
				{ name: this.language.translateAny("MAX"), interval: "max" },
			]
		}
		return this._periods;
	}

	/**
	 * A [[DateFormatter]] instance to use.
	 *
	 * If not set, control will inherit one from the target axis.
	 *
	 * @param  value  Formatter
	 */
	public set dateFormatter(value: DateFormatter) {
		this._dateFormatter = value;
		this.invalidate();
	}

	/**
	 * @return Formatter
	 */
	public get dateFormatter(): DateFormatter {
		if (!this._dateFormatter) {

			// Maybe use one from axis?
			if (this._axis) {
				this._dateFormatter = this._axis.dateFormatter;
			}
			else {
				this._dateFormatter = new DateFormatter();
				this._disposers.push(this._dateFormatter);
			}
		}

		return this._dateFormatter;
	}

	/**
	 * An format to use for the date input fields.
	 *
	 * If not set, it will use `dateFormat` from the [[DateFormatter]] object.
	 *
	 * @default "yyyy-MM-dd"
	 * @param  value  Date format
	 */
	public set inputDateFormat(value: string) {
		if (this._inputDateFormat != value) {
			this._inputDateFormat = value;
			this.invalidate();
		}
	}

	/**
	 * @return Date format
	 */
	public get inputDateFormat(): string {
		if (this._inputDateFormat) {
			return this._inputDateFormat;
		}
		else if (this.dateFormatter && $type.isString(this.dateFormatter.dateFormat)) {
			return this.dateFormatter.dateFormat;
		}
		else {
			return "yyyy-MM-dd";
		}
	}

}

/**
 * Register class in system, so that it can be instantiated using its name from
 * anywhere.
 *
 * @ignore
 */
registry.registeredClasses["DateAxisRangeSelector"] = DateAxisRangeSelector;


Immunocytochemical localization of ingested kidney bean (Phaseolus vulgaris) lectins in rat gut.
The binding of ingested kidney bean (Phaseolus vulgaris) lectins to the luminal surface of the rat gut was investigated by an indirect immunofluorescence method in which the primary antiserum was monospecific for kidney bean globulin lectins. The major lectin-positive sites were found to be the non-crypt regions of villi in the proximal region of the small intestine; exactly the same regions displayed extensive disruption of microvilli. Lectins were not localized in the brush border regions of ileal villi and no microvillus abnormalities were detected in this region of the gut. The luminal surface of the caecum was strongly lectin-positive and here also microvillus disruption was evident. Low levels of lectins were detected on the luminal surface of the colon; no microvillus abnormalities were detected in this region.


What a difference a couple of years makes. For the Conservatives, cutting is the new spending and, in the words of Ed Fast, trade is the new stimulus.

The Canadian response to the financial crisis has been to grow domestic demand, aided by low interest rates, and re-orient trade relations to the emerging new world economic order.

The first course has been successful but households are now tapped out and interest rates are set to rise. The fate of the Canadian economy and, in all likelihood, the Conservative government rests on boosting exports that are still below pre-recession levels.

[np-related]

Those hopes rely on Canada concluding a comprehensive economic and trade agreement (CETA) with the European Union. This is not just because it would boost existing two-way trade of $92.1-billion by an estimated 20%, but also because the reforms needed to strike the CETA in areas like intellectual property, government procurement, labour mobility and agriculture will be required if the Conservatives are to make good on their ambitious trade agenda with other countries like Japan, India and South Korea.

“It’s foundational for the government,” said Jason Langrish, executive director of the Canada-Europe Roundtable for Business. “Even though the CETA might not deliver the longer-term export gains that some deals in Asia might, we need the reforms inherent in CETA to move forward with a wide-ranging trade agenda.”

The problem for the government is that the deal has hit a number of speed-bumps, some political and some technical.

The Tories have woken up late to the fact that their silence on the progress of the CETA has allowed their opponents to fill the information vacuum

The NDP has long (and with some justification) criticized the government’s lack of transparency on the trade talks. Former trade critic Peter Julian also expressed concerns about European demands for patent term restoration, which would add one to three years to the life of drug patents to compensate for the period when they are being tested and approved. He has argued it could increase drug prices for provincial governments.

But the party has a new leader, Thomas Mulcair, and a new trade critic, B.C. MP Don Davies, and there are no clear signals yet whether they will oppose the CETA, or back it with reservations, (The deal is considered broadly favourable for Quebec, so Mr. Mulcair will have to tread lightly).

While the NDP position is ambiguous, the Conservatives have noted that opposition to the CETA is rallying around leftist civil society and labour groups like the Council of Canadians and the CAW, who are stirring up discontent at municipal level by suggesting that under the deal, all local procurement will have to be offered up for tender internationally. (Mr. Fast has re-assured municipalities that they can continue to prefer local companies, if projects are below a certain threshold or deal with goods and services excluded from CETA).

The Tories have woken up late to the fact that their silence on the progress of the CETA has allowed their opponents to fill the information vacuum. They intend to rectify the situation later this week, when ministers will fan out across the country to sell the deal to Canadians, claiming it will benefit every region. Government sources say they will be spelling out those benefits and attempting to dispel the “falsehoods” raised by their opponents. The government will have been encouraged by a new study conducted by the Asia-Pacific Foundation that suggested 69% of Canadians are in favour of a free trade deal with the EU, compared to less than 50% for China, India and South Korea.

Aside from political opportunism, there are substantive problems that could yet derail a deal. Perhaps the biggest threat originates from so-called “rules of origin” guidelines, under which the Europeans are insisting that 60% of the final value of any product must be created in Canada. Given the heavily integrated nature of Canadian and American manufacturing process, this presents major challenges, particularly for the automotive and processed food industries, whose products tend to criss-cross the border.

It is no small irony that supply management, so often the target of free trade advocates, does not appear to be a major sticking point. There is agricultural protectionism on both sides, so the Europeans are not calling for wholesale reform of the practice of sky-high tariffs on imported chicken, eggs and dairy products. More likely, there will be a tweaking of the system that will allow more European cheese to enter Canada tariff-free.

As is usual with cases involving the departments of International Trade or Foreign Affairs, Canadians learned more from visiting dignitaries than their own government. The Danish Trade Minister, Pia Olsen Dyhr, was in Ottawa to hold talks on bi-lateral trade and the CETA. She said getting a good agreement is more important than getting a fast agreement, but added that 75% of the issues have already been finalized.

From the government’s point of view, a deal in 2012 is crucial, particularly given the prospect of new provincial governments in Quebec City and Victoria who may be less sympathetic to the trade agenda.

For the Conservatives, CETA represents the next generation of comprehensive deals and the surest way of ensuring that black ink becomes the new red when writing future budgets.

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Mesoscopic reflectance angular domain spectroscopic imaging.
The advancement of angular domain imaging in mesoscopic reflectance multispectral imaging is reported. The key component is an angular filter array that performs the angular filtration of the back-scattered photons and generates image contrast due to the variances in tissue optical properties. The proposed modality enables multispectral imaging of subsurface features for samples too thick for transillumination angular domain spectroscopic imaging (ADSI) approaches. The validation was carried out with tissue-mimicking phantoms with multiple absorptive features embedded below the surface. Multispectral images in the range of 666 to 888 nm clearly revealed the location of the features with the background scattering levels up to 20 cm⁻¹. The shape of the features was recoverable at depths of up to three to four times the transport mean free path. The spatial resolution was <1 mm and the field-of-view was larger than 2.5 cm x 30. cm. Furthermore, the attenuation spectra of measured absorptive features were successfully extracted. Target detectability and imaging quality with different background scattering levels, target depths, and illumination focal depths were discussed, as well as the capability of ADSI in reflectance optical mesoscopic imaging and its potential applications.


The Ashton Kutcher biopic Jobs is coming to Blu-ray and DVD on Nov. 26; feel like getting a combo pack with both formats for free? How about a $100 gift card good for Apple products? Yeah, we thought that'd get your attention. For a chance to win one or both, just enter Mac|Life's giveaway on Facebook! Read on for details.

Something tells me Larry Ellison didn't rush out to see "Jobs" this weekend. But maybe he should have.

Last week, the Oracle CEO praised his longtime friend in a candid interview with Charlie Rose on "CBS This Morning," and painted a dreary picture of Apple in the process.

"They will not be nearly so successful because he's gone. He was brilliant. I mean, our Edison. He was our Picasso. He was an incredible inventor. ... We saw Apple with Steve Jobs (raises a finger high into the air). We saw Apple without Steve Jobs (lowers the finger). We saw Apple with Steve Jobs (raises his finger again). Now, we're gonna see Apple without Steve Jobs (holds the finger in the air for a moment before dropping it again)."

It may be true that Apple flourished when Steve Jobs came back and floundered during the decade he was gone, but to say Apple won't survive without him is to trivialize his impact on the design, direction and dogma of the company he founded.