text:
\section{Introduction}

The spatial resolution of images acquired by fluorescence microscopy refers to the shortest distance at which two fluorescent entities are perceived separately by the camera system. As a consequence of the light diffraction phenomena, lens with a uniformly illuminated circle aperture generate patterns known as Airy disks \citep{renz2013fluorescence}, namely the fluorescent emitters to be imaged are represented as blobs and not as isolated spots of light. The ability of the microscope to distinguish two relatively close entities is bounded by the well-known diffraction limit \citep{zheludev2008diffraction}, that represents an intrinsic constraint of the resolution of the optical acquisition device. More precisely, according to the Abbe's Criterion, the smallest resolvable distance by a light microscope corresponds roughly to half the optical wavelength, that is about 200 nm \citep{abbe1873beitrage}, thus compromising the direct observation of structures at nanoscale such as proteins, microtubules, mitochondria and less complex molecules.

In the last decade, super-resolution microscopy techniques have revolutionized light microscopy biological imaging allowing biologists to see beyond the diffraction limit \citep{sahl2013super}. Among these techniques, in 2014, the pioneering works on STimulated Emission Depletion (STED) microscopy by Stefan W. Hell, and the development of Single-Molecule Localization Microscopy (SMLM) by Eric Betzig and William E. Moerner, have been awarded the Nobel Prize in Chemistry.

In particular, SMLM techniques, such as Photo-Activated Localisation Microscopy (PALM) \citep{betzig2006imaging,hess2006ultra} and STochastic Optical Reconstruction Microscopy (STORM) \citep{rust2006sub}, provide high-precision molecule localization by sequentially activating and imaging a small percentage of photoswitchable fluorophores (emitters). \\
These molecules absorb and emit light of a specific wavelength. Therefore, by imaging a sparse set of activated emitters at a time, it is possible to identify their precise location in the Field of View (FOV). First, these SMLM techniques provide a stack of diffraction-limited frames, containing blobs (Airy disks), modelled by Gaussian Point Spread Functions (PSFs) \citep{rossmann1969point}. Then, each frame is analyzed separately with the aim of providing high precision localization maps of the emitters. After the individual processing is performed, all the frames are re-combined together to finally obtain a unique super-resolved image overcoming the diffraction limit.

The sparser the set of activated emitters per frame is, the more precise the localization is. However, considering sparse frames takes a longer acquisition time thus limiting the ability to capture fast dynamics within live specimens.
Conversely, a high density of activated emitters negatively affects the quality of the super-resolved image in terms of localization precision. Indeed, localization in high-density settings, that are characterized by overlapping PSFs, represents a challenging task for all the existing sophisticated localization software tools \citep{sage2015quantitative,bernhem2018smlocalizer,davis2020rainbowstorm}.

Approaches based on diverse rationale, from blinking statistics \citep{cox2012bayesian,dertinger2009fast,gustafsson2016fast} and standard Gaussian fitting or centroid estimation \citep{henriques2010quickpalm}, to subtraction of the model PSF \citep{gordon2004single,serge2008dynamic,qu2004nanometer} or deconvolution with sparsity-promoting constraints \citep{holden2011daostorm,hugelier2016sparse,gazagnes2017high,solomon2019sparcom,min2014falcon}, have been proposed to handle high-density data.

According to the deconvolution approaches with sparsity-promoting priors, for each frame, the emitters localization maps are viewed as the critical points of a least squares $\ell_0$-penalized objective. In particular, in \citep{soubies2015continuous}, the authors define the Continuous Exact $\ell_0$ (CEL0) regularizer, which has been proven to be effective in the field of SMLM with high-density data \citep{gazagnes2017high}. This CEL0-based method solves, through an iterative scheme, a continuous non-convex optimization problem whose objective function is a weighted sum of an $\ell_{2}$ fidelity term and the CEL0 regularizer. Furthermore, positivity constraints are also added to the model since molecules locations must be reconstructed. Despite its state-of-the-art performances in retrieving the localization maps, the accuracy of the method strictly depends on the choice of the parameter balancing the $\ell_{2}$ and the sparsity-promoting terms, thus drastically limiting its usage in real experiments.

More recently, a deep learning-based approach that exploits a Convolutional Neural Network (CNN) has been developed \citep{nehme2018deep}. The method, called DeepSTORM, is trained on synthetic (artificially generated) frames, and tested directly on experimental data, thus avoiding to collect a huge amount of training samples related to the particular experiment under study. Despite being a fast and parameter-free super-resolution microscopy algorithm able to manage high-density data, DeepSTORM is not designed to reconstruct high precision localization maps.

In the present paper, we propose a method tailored to perform the localization, named DeepCEL0, that bridges the gap between deep learning-based and regularized deconvolution approaches. Basically, we exploit the skeleton of the network architecture underneath DeepSTORM using the CEL0 penalty as part of the loss function for training the net. We further add positivity constraints through the insertion of a RELU layer in the network architecture. The strength of DeepCEL0 consists in embedding and joining the main advantages of the two standard methods, thus supplying, on the one hand, a quite fast and parameter-free deep-learning algorithm, like DeepSTORM, and, on the other hand, an efficient algorithm providing high precision localization maps, like CEL0. We report quantitative and qualitative molecule localization results on the simulated and real datasets from IEEE ISBI SMLM challenge \footnote{\url{http://bigwww.epfl.ch/smlm/}}. We also compare the performances achieved by DeepCEL0 with those obtained by the two baseline state-of-the-art methods, CEL0 and DeepSTORM.

\input{method}
\input{results}

\section{Conclusion}

In this paper, we present a deep learning-based method called DeepCEL0 for precise single molecule localization in high density fluorescence microscopy settings. The proposed method brings together the benefits of two well-known standard methods in the field, i.e., DeepSTORM and CEL0, introducing a network architecture with two main novelties: a continuous $\ell_{0}$-penalized training loss function and the adoption of positivity constraints on the solution through a ReLu layer. Compared to the standard methods, numerical results show how DeepCEL0 can provide very high precision localization maps, without detriment to computational cost. Moreover, the method is parameter-free and can be easily tested and applied on real data after a training phase on only synthetic data. The promising results make the methods easy to perform in disparate real applications exploiting fluorescence microscopy.

\section*{Funding}
P.C. and E.L.P. have been partially supported by the GNCS-INDAM project 2020 \textit{"Ottimizzazione per l'apprendimento automatico e apprendimento automatico per l'ottimizzazione".

\input{main_bio.bbl}

\end{document}

\section{Mathematical Background}

\subsection{A mathematical image formation model for PALM/STORM acquisitions}
We now recast the problem of finding high precision localization maps as a standard image super resolution inverse problem. The image acquisition model we develop is used in the experimental section to construct reliable synthetic data simulating PALM/STORM diffraction-limited acquisitions. Let $\mathbf{Y} \in \mathbb{R}_{+}^{M \times M }$ be a frame acquired by PALM/STORM techniques representing a sparse set of activated molecules on a coarse pixel-grid of dimension $M \times M$. Let $\mathbf{X} \in \mathbb{R}_{+}^{N \times N}$ be the localization map referred to the diffraction-limited PALM/STORM acquisition, that is a HR image defined on a $L$-time thinner pixel-grid such that $N=LM$, with $L$ a non-negative integer termed as magnification factor.

In the following, for the sake of the readability of the manuscript, given a generic 2D image $\mathbf{Z}$ of dimension $m \times n$ we denote by $\overrightarrow{\mathbf{Z}}$ its vectorized version of dimension $mn \times 1$.

The acquisition of the LR image $\overrightarrow{\mathbf{Y}}$ is modelled through the following discrete degradation process:

\begin{equation} \label{eq:inverse_problem2}
    \overrightarrow{\mathbf{Y}} = \mathcal{N}(\mathbf{S}_{L} \mathbf{K} \overrightarrow{\mathbf{X}}),
\end{equation}

where $\mathcal{N}$ models the presence of Poisson noise and Additive White Gaussian (AWG) noise, which are typical aberrations in fluorescence imaging \citep{garini1999signal,waters2009accuracy,jezierska2012poisson}. \\
In order to simplify the model, we remark that the Poisson noise is a data dependent noise which can be well approximated as AWG noise. Therefore, in the following, we restrict to AWG as noise aberration affecting the LR image $\overrightarrow{\mathbf{Y}}$, and then:

\begin{equation} \label{eq:inverse_problem}
    \overrightarrow{\mathbf{Y}} = \mathbf{S}_{L} \mathbf{K} \overrightarrow{\mathbf{X}} + \eta,
\end{equation}
where $\eta \in \mathbb{R}^{M^{2}}$ is a realization of a Gaussian random variable with mean zero and standard deviation $\sigma_{\eta}$.

The presence of the typical Airy disk patterns (blobs) in the LR acquisition is modelled through Gaussian PSFs. Therefore, in our model (Eq. \eqref{eq:inverse_problem}) we assume the operator $\mathbf{K} \in \mathbb{R}^{N^{2} \times N^{2}}$ denotes the discretization of a convolution with a Gaussian kernel $\textit{k} : \mathbb{R}^{2} \to \mathbb{R}$ with mean equal to zero and standard deviation $\sigma_{\textit{k}}$, which is defined as follows:

\begin{equation} \label{eq:PSF}
    \textit{k} (x,y) := \dfrac{1}{\sigma_{\textit{k}} \sqrt{2 \pi}} \text{exp} \left( - \dfrac{x^{2}+ y^{2}}{2\sigma_{\textit{k}}^{2}}\right).
\end{equation}

The operator $\mathbf{S}_{L} \in \mathbb{R}^{M^{2} \times N^{2}}$ in Eq. \eqref{eq:inverse_problem} refers to the discretization of the downsampling operator linking the HR localization map represented on a fine pixel grid to the PALM/STORM acquired image defined on a $L$-time coarser pixel grid.

\subsection{A deconvolution approach with a sparsity-promoting prior for single molecule localization}

The problem in Eq. \eqref{eq:inverse_problem} is well-known to be ill-posed, meaning that, given $\overrightarrow{\mathbf{Y}}$, it is not feasible to retrieve $\overrightarrow{\mathbf{X}}$ by simply inverting the degradation described by $\mathbf{S}_{L}\mathbf{K}$, due to the lack of uniqueness and stability of the solution.
Since, for each frame, the related localization map is sparse, a quite common approach to overcome this instability, consists in forcing the solution to satisfy some sparsity constraints through the $\ell_{0}$ functional. More precisely, an estimate $\overrightarrow{\mathbf{X}}^{*}$ of the unknown $\overrightarrow{\mathbf{X}}$ can be viewed as the solution of the following $\ell_{0}$-regularized optimization problem:

\begin{equation} \label{eq:l0-optimization}
    \overrightarrow{\mathbf{X}}^{*} \in \underset{{\bm{x}} \in \mathbb{R}^{N^2}}{\arg \min} \dfrac{1}{2} \lVert \mathbf{S}_{L} \mathbf{K} {\bm{x}} - \overrightarrow{\mathbf{Y}} \rVert^{2}_{2} + \lambda \lVert {\bm{x}} \rVert_{0} + \mathbf{1}_{\geq 0} ({\bm{x}}),
\end{equation}

where $\lVert \cdot \rVert_{0} : \mathbb{R}^{N^2} \to \mathbb{R}$ denotes the $\ell_{0}$ functional defined as:

\begin{equation} \label{eq:l0_penalizer}
\lVert {\bm{x}} \rVert_{0}:=\sum_{i=1}^{N^2} | {\bm{x}}_{i} |_{0} \quad    \text{with} \quad                | {\bm{x}}_{i} |_{0}:= \begin{cases}
                        0 & {\bm{x}}_{i} = 0  \\
                        1 & {\bm{x}}_{i} \neq 0,
                          \end{cases}
\end{equation}

which counts the number of non-zero elements of ${\bm{x}} \in \mathbb{R}^{N^2}$. Moreover, $\lambda >0$ is the trade-off parameter measuring the level of sparsity of $ \overrightarrow{\mathbf{X}}^{*}$, whereas $\mathbf{1}_{\geq 0}(\cdot)$ is formally the characteristic function of the positive octant of $\mathbb{R}^{N^2}$ constraining the computed estimation $\overrightarrow{\mathbf{X}}^{*}$ to have positive entries and $\lVert \cdot \rVert_{2}$ is the $\ell_{2}$-norm.\\
In \citep{gazagnes2017high} the authors propose to replace the $\ell_{0}$ penalization \eqref{eq:l0_penalizer} by its continuous relaxation $\Phi_{\text{CEL0}} : \mathbb{R}^{N^2} \to \mathbb{R}$, named as CEL0 penalizer \citep{soubies2015continuous}, which reads:

\begin{align}
    \Phi_{\text{CEL0}}({\bm{x}}) := \sum_{i=1}^{N^{2}} \lambda_{\text{CEL0}} -  \dfrac{\lVert {\bm{c}}_{i} \rVert}{2} \left( |{\bm{x}}_{i}| - \dfrac{\sqrt{2\lambda_{\text{CEL0}}}}{\lVert {\bm{c}}_{i} \rVert} \right)^{2} \mathbf{1}_{V_{i}},
\end{align}

where by $\mathbf{1}_{V_{i}}$ we denote the characteristic function of the set $V_{i}:= \lbrace {\bm{x}}_{i}\in\mathbb{R} \big{|} \lvert{\bm{x}}_{i}\rvert < \frac{\sqrt{2\lambda_{\text{CEL0}}}}{\lVert {\bm{c}}_{i} \rVert} \rbrace$; by $c_{i}$ we denote the $i$-th column of the matrix $\mathbf{S}_{L} \mathbf{K}$ for $i=1 \dots N^{2}$, whereas the positive scalar $\lambda_{\text{CEL0}}$ balances the strength of the sparsity induced by the CEL0 penalizer.
Therefore, given a PALM/STORM acquisition $\overrightarrow{\mathbf{Y}}$, the CEL0-based method estimates the high precision localization map $\overrightarrow{\mathbf{X}}^{*}$ by solving the following optimization problem:

\begin{equation} \label{eq:CEL0_minimization}
\overrightarrow{\mathbf{X}}^{*} \in \underset{{\bm{x}} \in \mathbb{R}^{N^2}}{\arg \min} \dfrac{1}{2} \lVert \mathbf{S}_{L} \mathbf{K} {\bm{x}} - \overrightarrow{\mathbf{Y}} \rVert^{2}_{2} + \Phi_{\text{CEL0}}({\bm{x}}) + \mathbf{1}_{\geq 0} ({\bm{x}}).
\end{equation}

For its numerical solution, in \citep{gazagnes2017high} the authors make use of the iterative reweighted $\ell_{1}$ (IRL1) strategy \citep{ochs2015iteratively} which is tailored to handle nosmooth noconvex optimization problems.

\subsection{A deep learning based method for single molecule localization}

In \citep{nehme2018deep} the authors have introduced a fast and parameter-free deep learning-based method termed as DeepSTORM, which makes use of a CNN to provide a HR counterpart of the LR frames acquired by PALM/STORM techniques. In particular, given an outer training set of $K$ 2D-image pairs $\lbrace \left(\mathbf{Y}_{k}, \mathbf{X}_{k}\right) \rbrace_{k=1 \dots K}$, where $\mathbf{Y}_{k} \in \mathbb{R}^{M \times M}$ denotes the LR input and $\mathbf{X}_{k} \in \mathbb{R}^{N \times N}$ denotes its HR counterpart, and a particular encoder-decoder architecture $f_{\theta}$ with weights $\theta$, DeepSTORM training involves the following $\ell_{1}$-regularized loss function:

\begin{equation} \label{eq:loss_deepStorm}
    \dfrac{1}{K} \left( \sum_{k=1}^{K} \lVert g \ast f_{\theta}(\text{NN}(\mathbf{Y}_{k})) - g \ast \mathbf{X}_{k} \rVert^{2}_{F} + \lVert \overrightarrow{f_{\theta}(\text{NN}(\mathbf{Y}_{k}))} \rVert_{1} \right),
\end{equation}

where by NN we denote the coarse Nearest Neighbour super resolution algorithm resampling a LR image on a $L$-times finer grid by simply replicating the pixel values; by $\ast$ we refer to the discrete convolution with a 2D Gaussian kernel $g$ whose standard deviation is equal to 1. Moreover, by $\lVert \cdot \rVert_{F}$ and  $\lVert \cdot \rVert_{1}$ we denote the Frobenius norm and the $\ell_{1}$-norm, respectively.

Once provided the final set of trained weights $\theta^{*}$ and a PALM/STORM acquisition $\mathbf{Y}$, an approximation $\mathbf{X}^{*}$ of the HR localization map $\mathbf{X}$ is obtained by computing $f_{\theta^{*}}(\text{NN}(\mathbf{Y}))$.

Roughly speaking, DeepSTORM approximates the map linking the coarse Nearest Neighbour HR approximations of the PALM/STORM LR frames to their high-quality HR counterparts.

As far as the training set is concerned, one of the main novelties of DeepSTORM is that it can provide good performances on real images even if it is trained only on a synthetic dataset created using an ImageJ plug-in called ThunderStorm \citep{ovesny2014thunderstorm}.

\section{Method}

\subsection{A CEL0 regularized loss function}

Our proposal combines the learning-based approach DeepSTORM and the sparsity-constrained deconvolution approach CEL0. In particular, we aim at providing a method which preserves the main advantages of the both aforementioned approaches, namely the ability of CEL0 to retrieve high precision localization maps and the fast and parameter-free computation provided by DeepSTORM. Therefore, following the idea of \citep{nehme2018deep}, we could train a CNN architecture $f_{\theta}$ based on the following regularized loss function:

\begin{equation} \label{eq:loss_storm_CEL0}
    \dfrac{1}{K} \Bigg{(}\sum_{k=1}^{K} \lVert g \ast f_{\theta}(\text{NN}(\mathbf{Y}_{k})) - g \ast \mathbf{X}_{k} \rVert^{2}_{2} +  \Phi_{\text{CEL0}}(\overrightarrow{f_{\theta}(\text{NN}(\mathbf{Y}_{k}))}) + \mathbf{1}_{\geq 0} (\overrightarrow{f_{\theta}(\text{NN}(\mathbf{Y}_{k}))})\Bigg{)},
\end{equation}

where $\lbrace \left(\mathbf{Y}_{k}, \mathbf{X}_{k}\right) \rbrace_{k=1 \dots K}$ is a prefixed training set of $K$ LR-HR 2D-image pairs of size $M\times M$ and $N \times N$, respectively, whilst NN referrers to the Nearest Neighbour upscaling of factor equal to $L$.

Differently from \citep{nehme2018deep}, the loss in Eq. \eqref{eq:loss_storm_CEL0} presents two main differences:  the $\ell_{1}$-regularizer is replaced by the CEL0 regularizer in order to guarantee better sparse reconstructions and the characteristic function of the positive octant is supplied to enforce positivity constraints on the computed solution. Unfortunately, the latter leads to a noncontinuous functional which can be difficult to train. To overcome this issue, we neglect the characteristic function term, thus training $f_{\theta}$ w.r.t. the following loss function:

\begin{equation} \label{eq:loss_storm_CEL02}
    \dfrac{1}{K} \Bigg{(}\sum_{k=1}^{K}  \lVert g \ast f_{\theta}(\text{NN}(\mathbf{Y}_{k})) - g \ast \mathbf{X}_{k} \rVert^{2}_{2} +  \Phi_{\text{CEL0}}(\overrightarrow{f_{\theta}(\text{NN}(\mathbf{Y}_{k}))})\Bigg{)}.
\end{equation}

To force positivity constraints on the reconstructed solutions, we consider a CNN architecture $f_{\theta}$ which is a slightly modified version of the CNN architecture used in \citep{nehme2018deep}. In the following paragraphs, we describe in detail the skeleton of the architecture and the synthetic training set considered in our experimentation.

\subsection{A positivity-promoting Deep Architecture}

The considered CNN architecture, denoted as $f_{\theta}$ in Eq. \eqref{eq:loss_storm_CEL02}, is a modified version of the one initially proposed in \citep{nehme2018deep}. For the sake of brevity, we call convolutional layer the composition of convolutional filters with a batch normalization layer \citep{ioffe2015batch} followed by a ReLU non-linearity \citep{glorot2011deep} as activation function. The original architecture is an encoder-decoder and it is composed of seven convolutional layers. Each layer uses $3\times 3$ kernels of different depth equal to 32, 64, 128 and 512, respectively. In the encoder part, the filters' depth increases, and a $2\times 2$ max-pooling is used as downscaling operator to compress the features. In the decoder part, the layers are interleaved with a nearest neighbour upsampling operator and the filters' depth decreases. At the end of the network, another layer is added to compute the pixel-wise prediction. This layer is a $1\times 1$ convolutional filter. In the original implementation, this last layer uses a linear activation function, i.e., the identity. In order to induce positivity constraints to the computed solution, we replace the activation layer with ReLU.

\subsection{Synthetic training set and implementation notes}

In the experimental section, we evaluate how our method performs when dealing with an upsampling factor $L$ equal to $4$. According to this choice, we now describe the considered synthetic training set $\lbrace \left(\mathbf{Y}_{k}, \mathbf{X}_{k}\right) \rbrace_{k=1 \dots K}$. In the following, we refer to $\mathbf{Y}_{k}$ as the input image and to $\mathbf{X}_{k}$ as the target image, respectively.
As well as for DeepSTORM \citep{nehme2018deep}, we generate a synthetic dataset made up of $20$ high density images containing 6 emitters per $\mu m^{2}$. The emitters are positioned on a FOV of size $64 \times 64$ pixels such that each pixel has size of 100 nm. We extract from these high density images $K=10000$ patches of size $26\times 26$. By projecting the emitter positions on a 4-times thinner pixel grid, we build the target images $\mathbf{X}_{k}$ of size $104 \times 104$.

The input images are constructed accounting for the corruptions affecting the acquired diffraction-limited data to be reconstructed, that are the standard deviation of the Gaussian PSF modelling the Airy patterns $\sigma_{\textit{k}}$ and the noise standard deviation $\sigma_{\eta}$. Therefore, $\mathbf{Y}_{k}$ for $k= 1 \dots K$ are obtained by blurring the $26\times 26$ patches with a discrete Gaussian kernel and by adding noisy components. Finally, these corrupted synthetic patches are upsampled by a factor equals 4 through the NN interpolation algorithm, and then used as input of the network. \\
We stress that, in real applications, the standard deviation of the PSF considered, if unknown, can be estimated using the Abbe's criterion which requires the light wavelength and the numerical aperture of the optical device used for acquiring the experimental data under study. Furthermore, the amount of noise can be either calculated directly from the microscope and detector characteristics or even through several mathematical techniques (\textit{see} \citep{paul2010automatic,mandracchia2020fast} and references therein).

We use this synthetic dataset to train the proposed DeepCEL0 by minimizing the loss function defined in Eq. \eqref{eq:loss_storm_CEL02}. We train the network for $100$ epochs on batches containing $16$ samples using Adam optimizer and setting the learning rate equal to $0.001$. \\
In \eqref{eq:loss_storm_CEL02}, we set $g$ as a fixed Gaussian kernel, whose standard deviation equal to 1. Moreover, we point out that the contribution of the CEL0 regularization term can be weighted using the parameter $\lambda_{\text{CEL0}}$. It is worth noting that in this deep learning-based framework the parameter $\lambda_{\text{CEL0}}$ has not the same meaning of the regularization parameter in the deconvolution approach. \eqref{eq:CEL0_minimization}. Indeed, in the former case it does not directly correspond to a degree of sparsity of the computed solution. In our experiments, we tested different values for $\lambda_{\text{CEL0}}$ but we observed that setting it to $100$ provides outstanding results for all the tests.  The network is implemented in TensorFlow and the code is available at https://github.com/sedaboni/DeepCEL0.

\section{Results}

\subsection{Performance Evaluation and competitors} \label{sec:Performance Evaluation}

We assess the quality of the high resolution localization maps provided by our method and the competitors both on synthetic and real PALM/STORM images through quality metrics and visual inspections. When the ground-truth (GT) images are available, the performances are evaluated by pairing the GT molecules with the estimated ones: a match between a GT and an estimated molecule is created when the distance between their localizations is lower than a set tolerance $\delta$, whose standard values, if expressed in terms of pixels, are 2, 4 and 6 \citep{sage2015quantitative}. Such a tolerance $\delta$ is chosen lower than the Full-width at Half Maximum (FWHM) of the estimated Gaussian PSF modelling the Airy pattern. In the following, the matched estimated molecules up to the given tolerance are defined as True Positive (TP) molecules; the remaining estimated molecules are referred to as False Positive (FP) molecules; and finally, the GT molecules with no match are categorized as False Negative (FN) molecules. Beyond the estimated molecules, it is important to take into account the pixels of the GT images not corresponding to any molecule, which are labelled as True Negative (TN) molecules.
The performances are assessed by computing the following evaluation metrics:
\begin{align}
     \text{Jaccard} (\%) & =  \frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}} \times 100, \\
     \text{Sensitivity}   (\%) & = \frac{\text{TP}}{\text{TP} + \text{FN}} \times 100, \\
     \text{Specificity}   (\%) & = \frac{\text{TN}}{\text{TN} + \text{FP}} \times 100.
\end{align}

In order to quantify the level of corruption in the simulated data, we consider the signal-to-noise ratio (SNR) given by the following formula:

\begin{equation}
    \text{SNR}\text{(dB)}:= 10 \log_{10} \left( \dfrac{\lVert \mathbf{S} \mathbf{H} \overrightarrow{\mathbf{X}}\rVert_{2}^{2}}{\lVert \mathbf{S} \mathbf{H} \overrightarrow{\mathbf{X}} - \overrightarrow{\mathbf{Y}} \rVert_{2}^{2}} \right)
\end{equation}

where $\overrightarrow{\mathbf{X}}$ is the real vectorized molecule localization map and $\overrightarrow{\mathbf{Y}}$ is the LR counterpart.

We compare our approach with the state-of-the-art algorithms CEL0 and DeepSTORM on both high density synthetic and real SMLM PALM/STORM low resolution images whose level of corruption ranges from SNR = 15dB to SNR = 10 dB.

\subsection{Localization on synthetic test images}

DeepSTORM algorithm could provide a fast SR image reconstruction, although it is not tailored to provide high precision localization maps of the emitters. Conversely, CEL0 is designed to localize the molecules but presents two main flaws: the method is strongly dependent on the choice of the regularization parameter and the overall computation process is largely slower than the one provided by DeepSTORM.\\
Once the network has been trained, the proposed DeepCEL0 and DeepSTORM share a comparable computational time for SR image reconstruction. Moreover, as we demonstrate in the following, DeepCEL0 is also able to effectively localize the molecules.

First of all, to validate the localization performances of our proposal, we construct three different synthetic test scenarios as GT images, simulating high resolution fluorescence microscopy localization maps on a FOV of $512 \times 512$ pixels of size 25 nm. The two first scenarios, referred as Test 1a and Test 2a, represent two molecules, arranged on the 256th column, at distance of 25 nm and 75 nm, respectively. The other scenario, referred as Test 3a, shows four molecules disposed on a circle of a radius equal to 125 nm. Two molecules are arranged on the 256th column whereas the others are arranged on the 256th row. Once set $L=4$, by down-sampling the GT images according to the model in Eq. \eqref{eq:inverse_problem}, we simulate the acquisition of $128 \times 128$ LR diffraction-limited frames, where the PSF is modelled by a Gaussian function whose FWHM is equal to 258.21 nm, that is $\sigma_{\textit{k}} \approx $ 110 nm.

In the lower panel of Fig. \ref{fig:sintetic_reconstruction}, we report the close-ups (x10 zooming) of the Region of Interest (ROI), namely the central zone of the $512 \times 512$ images where the synthetic spots are located, for the GT and Nearest Neighbour (NN), DeepSTORM, DeepCEL0 and CEL0 super resolved reconstructions. We remark that all the reconstructions reported have been normalized in the interval [0,1].\\
The zooms related to these methods are highlighted by blue, purple, green, red and yellow boxes, respectively. It is noteworthy that in the purple box the NN $\times 4$ upsampled image is reported in order to visualize the LR image at the same dimensions of the GT image. For the CEL0 method, we depict two different reconstructions obtained by setting two different regularization parameters for each test case.  \\
In the upper panel of Fig. \ref{fig:sintetic_reconstruction}, we draw the line profiles corresponding to the 256th column of the $512\times512$ GT, NN and DeepCEL0 images.

For all the three tests, the two molecules arranged on the 256th column correspond to the two peaks in the line profile of the GT image (\textit{see} the blue line and blue box), whereas we observe they are completely overlapped and indistinguishable in the diffraction limited NN upsampled image's line profile (\textit{see} purple line and purple box).

As qualitative results, in Test 1a, Test 2a and Test 3a, DeepCEL0 is able to well approximate the positions of the two molecules (\textit{see} the red dashed line and red box). In all the three tests considered, the synthetic molecules are placed at a distance which is largely smaller than FWHM. In particular, Test 3a is the most challenging scenario since the number of molecules to estimate is greater than two. Also in this case, DeepCEL0 provides broadly better performances than DeepSTORM and more stable performances with respect to the CEL0 method.
Indeed, DeepSTORM (\textit{see} green box) does not separate the two synthetic molecules and provides a large number of FP molecules, thus confirming it is not designed to produce accurate localization maps \citep{nehme2018deep}.
CEL0, as expected, provides highly accurate localization maps, but, as we can observe, its performances are highly sensitive to the choice of the regularization parameter. Furthermore, it is remarkable how small changes of the regularization parameter produce largely different reconstructions and how different scenarios require different optimal parameters.

Finally,  the results provided by DeepCEL0 have been computed by using the same architecture trained setting $\lambda_{\text{CEL0}}=100$. Therefore, these tests prove the effectiveness of our approach retrieving highly precise localization maps and more stability with respect to the choice of the hyperparameter $\lambda_{\text{CEL0}}$, if compared to the CEL0 method.

\subsection{Localization on IEEE ISBI simulated dataset}

We now consider a more realistic dataset provided by the 2013 IEEE ISBI SMLM challenge. The dataset is a stack of 361 different frames used as GT localization maps in our analysis. A total number of 81049 emitters are counted: for each frame 217 fluorophores are activated on average. The frames simulate realistic high density acquisitions of 8 tubes of diameter size equal to 30 nm depicted on a FOV of $256 \times 256$ pixels of size 25 nm. We simulate LR diffraction-limited acquisitions by applying the image formation model in Eq. \eqref{eq:inverse_problem} setting $L=4$, thus representing the GT scenarios on a coarser grid of $64 \times 64$ pixels of size 100 nm. The PSF is modelled by using a Gaussian function of FWHM = 258.21 nm ($\approx$ $\sigma_{\textit{k}} = 110$ nm). We further corrupt the LR frames by adding three different realization of Gaussian noise, such that SNR = 10 dB, 12 dB, 15 dB, respectively referred to as Test 2b, Test 3b, Test 4b. Finally, we consider a more realistic scenario, referred to as Test 1b, where the frames are corrupted by Gaussian noise components of different standard deviations.

The localization performances of CEL0, DeepSTORM and DeepCEL0 are assessed for the four tests by using the evaluation metrics introduced in Section \ref{sec:Performance Evaluation}. In Table \ref{tab:1}, we report the results expressed in terms of Jaccard index for $\delta$ set as 2,4,6 pixels, corresponding to a tolerance of 50,100,150 nm, respectively. The sensitivity and specificity values, instead, are computed only with respect to the tolerance $\delta = 2$, which expresses a more faithful compliance of the reconstructions compared to the GT.

DeepSTORM and DeepCEL0 models are trained on synthetic images (\textit{see} Sub-Section 3.3) such that the downsampling factor $L$ and the PSF are set as above and the Gaussian noise corruption leads to an SNR value equals 15 dB on average. These two trained models are used for all the four tests considered. For the sake of a fair comparison, we estimate the CEL0 regularization parameter by a trial and error procedure sampling 30 values over the range $[1e-3,1]$ on randomly chosen 8 frames among the stacked ones. In the following, we refer to the best regularization parameter for CEL0 as the one maximazing the Jaccard index with tolerance $\delta$ equals 2.

In Table \ref{tab:1}, we report for each test (Test 1b, 2b, 3b, 4b) the results of CEL0, DeepSTORM and DeepCEL0. For what concerns CEL0, in the first row referred to each test, we draw the results obtained with respect to the best regularization parameters, whereas in the second row we show the results obtained by choosing as regularization parameter the best one obtained for Test 4b.

We inspect in depth Test 2b, the most challenging one due to a low SNR value. In Fig. \ref{fig:localization}, we provide the localization maps of the reconstructions for the frame 2.  Green circles indicate the TP molecules, whereas red crosses highlight the FP molecules. In Fig. \ref{fig:ISBI}, we report the sum of all the stacked reconstructed SR frames provided by the competing methods and of the stacked GT and LR frames. In particular, for GT and all the methods, above the magenta line we report the activated molecules as white spots, whereas below the magenta line we depict the normalized images. The three close-ups show three region of interest where the tubulins seems completely overlapped in the LR acquisition because of the diffraction limit.

As a general comment, on the one side, DeepSTORM struggles to provide high precision localization maps: very low Jaccard values for all the tests and tolerances are obtained. For all the tests, the sensitivity reaches the highest possible value but at the expense of a very low specificity, meaning that this result is completely biased since it provides a very huge number of FP molecules (more than 50000). As an example, in Test 2b, this aspect is evident from Fig. \ref{fig:localization} and Fig. \ref{fig:ISBI} where we provide the localization maps for a single frame and for the sum of the stacked SR frames. On the other side, CEL0 reaches the best performances on Test 4b and competing performances on Test2b, but poor results on Test 1b and Test 2b that represent the most realistic and challenging scenarios. Conversely to the competing methods, DeepCEL0 provides satisfying results for all the tests with respect to the Jaccard, sensitivity and specificity indexes thus confirming the effectiveness of the novelties introduced, namely the addition of positivity constraints and the usage of a CEL0 regularized loss function. In particular, the highest value of sensitivity and specificity entails that DeepCEL0 reaches the largest number of TP molecules and reduces the number of FP molecules. Moreover, Fig. \ref{fig:localization} and Fig. \ref{fig:ISBI} confirm once again how our proposal can better provide localization maps with less FP molecules than CEL0 solutions.

Finally, an interesting aspect to underline is that, even if DeepCEL0 is trained on images with SNR equals 15 dB on average, the trained models seem stable at varying level of corruption (Test 1b, Test 2b, Test 3b). This stability is not still valid for CEL0 which shows a strong dependence on the choice of the regularization parameter as highlighted by the results in Table \ref{tab:1}.

\input{fig1}

\begin{table*}[!t]

\begin{center}
\caption{\textbf{Performance evaluation for localization on IEEE ISBI simulated dataset in terms of Jaccard Index, sensitivity and specificity.} The first and second best Jaccard Index values are highlighted in red and blue, respectively.}
\scalebox{1}{
\begin{tabular}{c| c|ccc|c|c}\thickhline
 \textbf{Test} &  \textbf{Method}   &  \multicolumn{3}{c|}{\textbf{Jaccard } ($\%$)} & \textbf{Sensitivity}($\%$)  & \textbf{Specificity} ($\%$) \\

 &  & $\delta=2$ & $\delta=4$ & $\delta=6$ & $\delta=2$ & $\delta=2$  \\
\thickhline
   \multirow{5}{*}{\textbf{1b}} & \multirow{2}{*}{CEL0} & \textcolor{blue}{41.64}   & \textcolor{blue}{49.46}   & \textcolor{blue}{51.65}   &    56.22  & 99.88    \\
   &                      &  35.94  & 43.93   &  46.70   &  67.74  &  99.71   \\
& DeepSTORM                        &  0.38   & 0.38    & 0.38    &    100.00 & 13.17    \\
& DeepCEL0                         & \textcolor{red}{58.96}   & \textcolor{red}{68.94}   & \textcolor{red}{71.55}  &    70.89  & 99.95
 \\\thickhline
\multirow{5}{*}{\textbf{2b}} & \multirow{2}{*}{CEL0} & \textcolor{blue}{42.29}   & \textcolor{blue}{50.21}   & \textcolor{blue}{51.48}   &    49.08  & 99.95    \\
&                      & 17.13   & 28.64   & 33.16    &    51.15  & 99.34    \\
& DeepSTORM             &  0.38   & 0.38    & 0.38    &    100.00 & 13.34    \\
& DeepCEL0              & \textcolor{red}{54.83}   & \textcolor{red}{68.49}   & \textcolor{red}{71.37}   &    65.44  & 99.94
 \\ \thickhline
  \multirow{5}{*}{\textbf{3b}} & \multirow{2}{*}{CEL0} & \textcolor{red}{62.17}   & \textcolor{blue}{63.60}   & \textcolor{blue}{64.32}   &    65.90  & 99.98    \\
                            &                       & 48.19   & 50.46   & 51.38   &    73.73  & 99.82    \\
& DeepSTORM                 &  0.38   & 0.38    & 0.38    &    100.00 & 13.53    \\
& DeepCEL0                  & \textcolor{blue}{61.13}   & \textcolor{red}{69.66}   & \textcolor{red}{71.55}   &    69.59  & 99.95
 \\\thickhline
 \multirow{4}{*}{\textbf{4b}} & CEL0 & \textcolor{red}{68.27}   & \textcolor{red}{70.33}   & \textcolor{blue}{71.02}   &    78.34  & 99.95    \\
& DeepSTORM                        &  0.38   & 0.38    & 0.38    &    100.00 & 12.65    \\
& DeepCEL0                         & \textcolor{blue}{61.48}   & \textcolor{blue}{69.80}   & \textcolor{red}{72.33}   &    70.12  & 99.96

\end{tabular} \label{tab:1}
}
\end{center}

\end{table*}

\input{fig_loc}
\input{fig3}

\subsection{Localization on IEEE ISBI real dataset}

As final evaluation test, we compare DeepCEL0 with CEL0 and DeepSTORM on a real high density dataset of tubulins that is part of the 2013 IEEE ISBI SMLM challenge. The dataset refers to a real acquisition stack representing a field of view of $128 \times 128$ pixels of size 100 nm over 500 different consecutive frames. The PSF is modelled by using a Gaussian function of FWHM = 351.8 nm, that is  $\sigma_{\textit{k}} \approx$ 150 nm \citep{chahid2014echantillonnage}. We aim at representing the acquired LR frames on a finer pixel grid of a factor $L=4$, thus achieving a final resolution of 25 nm as pixel size.
CEL0 reconstruction has been obtained by choosing the regularization parameter equal to 0.5 as suggested by \citep{bechensteen2019new}. For all the learning-based models, we trained the networks on a training set of synthetic images (\textit{see} Sub-section 3.3) by a Gaussian blur modelled with the above underlined PSF.
In Fig. \ref{fig:ISBI_real}, we report the normalized LR image, i.e, the sum of all the stacked LR frames. Moreover we depict the reconstructions provided by all the considered competitors. Above the green line we show the image with the activated molecules as white spots, whereas, below the yellow line the normalized images are reported. Finally, three close-ups are considered to better underline the differences between the competing methods. All the considerations highlighted for the simulated scenarios are still valid in this case. We here appreciate the influence of the L0 regularization both in CEL0 and DeepCEL0 reconstructions. Indeed, they look sharper than the solution provided by DeepSTORM. Finally, if compared with CEL0, DeepCEL0 better separates the diverse tubulins and better preserves the continuous shapes.

\input{fig4}

redpajama_set_name: RedPajamaArXiv


New Jersey Sen. Cory Booker became the latest senator to throw his hat into the 2020 ring Friday morning, when he with a video posted to Twitter. Booker attained national fame as the mayor of Newark, New Jersey, for seven years. As a senator, he has been a vocal member of the Senate Judiciary Committee.
Booker, who was raised in the affluent suburbs of northern New Jersey, has often cited his parents, Cary and Carolyn, as his inspiration. They were among the first black executives at IBM and desegregated their mostly white neighborhood.
Booker received his bachelor's and master's degrees from Stanford University, where he also played as a tight end for the football team. After graduation, he was awarded the prestigious Rhodes scholarship to study at the University of Oxford, before returning to the U.S. to get his law degree at Yale Law School.
After working as a public-interest attorney and housing advocate in Newark, Booker decided to run for the city's municipal council in 1998. He managed to topple a long-time incumbent and, at age 29, become the youngest-ever member of the council, where he gained notoriety for fasting outside a housing project to denounce the intensifying crime and drug use in some of Newark's neighborhoods.
After launching an unsuccessful bid in 2002 against incumbent mayor Sharpe James, Booker ran again in 2006 and defeated deputy mayor Ronald Rice. During his tenure as mayor, Booker was praised for attracting large companies to Newark and revamping the city's downtown. However, he was also criticized by local residents and officials for appearing out-of-touch and focusing on his national image.
After longtime New Jersey Senator Frank Lautenberg died in the summer 2013, Booker ran to fill his seat and defeated a little-known Republican mayor in a special election. In 2014, he won reelection to serve a full term in the Senate. Booker is notably the only vegan in the Senate.
Booker has made criminal justice reform and the decriminalization of marijuana integral issues of his agenda in the Senate. Pointing to the disproportionate incarceration of minorities for marijuana-related offenses, he crafted the Marijuana Justice Act in the summer of 2017.
Booker is one of the chief architects of the First Step Act, a landmark bill that President Trump signed into law in December after rare overwhelming bipartisan support in Congress. The legislation increases investment in programs to curb recidivism among federal prisoners and modifies several sentencing laws, including mandatory minimum sentences for nonviolent drug offenders. Although he hopes to tout it as a signature achievement of his career in Congress, Booker acknowledged that the bill is just one stepping stone to overhaul the criminal justice system.
Although he has a liberal voting record in the Senate and has been a staunch critic of the Trump administration, Booker has faced criticism by some progressives for receiving large donations from pharmaceutical corporations and banks. His 2020 campaign will not accept donations from corporate PACs or lobbyists.
In what he touted as his "Spartacus" moment, Booker claimed to have released confidential committee documents during the contentious confirmation process for Justice Brett Kavanaugh. His Republican colleagues accused him of grandstanding and said the documents had already been cleared.
In a November interview with the New York Post, the president said Booker "ran Newark into the ground" and falsely accused him of not living in the city when he was mayor.
During the 2016 campaign, Mr. Trump claimed to know more about Booker than the New Jersey senator knew about himself. "If Cory Booker is the future of the Democratic Party, they have no future! I know more about Cory than he knows about himself," Mr. Trump tweeted in July of that year.


On December 5, 2017, changes to the Employment Insurance Act that were introduced in Bill C-44, the Budget Bill Implementation Act, 2017, No. 1, came into place. Employers may find the new changes will require amending existing leave policies and collective agreements to reflect the impact on top‑ups, or supplementary benefits to benefits claimants.
The changes apply to new claims for maternity, parental, and caregiver leave.
Employment Insurance (EI) maternity benefits provide up to 15 weeks of benefits for birth mothers and surrogates. The new changes will allow the benefits to be claimed back 12 weeks prior to the expected due date, up from eight weeks.
up to 61 weeks over a period of up to 18 months at 33 percent average weekly earnings (to a maximum of $346 per week), ending 78 weeks after the child is born or placed with the family.
To be eligible for parental benefits, employees must have accumulated 600 hours of insurable employment in the previous year – this remains unchanged.
The Canada Labour Code and the Employment Insurance Regulations have been amended to reflect the extended leave period. This means workers in federally‑regulated companies will be automatically eligible to elect either option. Ontario has also committed to amending its Employment Standards Act to extend job protections to those who elect 18‑month leaves, so provincially‑regulated workplaces will soon follow suit.
The EI Family Supplement has also been enhanced in the 2017 Budget to reflect the extended benefit period. The Family Supplement provides additional income support to eligible claimants while they are receiving EI benefits. To be eligible, a claimant must have an annual family net income of less than or equal to $25,921, have one or more children under the age of 18, and receive the Canada Child Benefit.
Benefits for individuals who leave work to care for a family member have also changed. Family Caregiver benefits allow for 15 weeks within one year of benefits to care for a family member who is 18 years of age or older and critically ill, and 35 weeks within a year of benefits to care for a child (under 18 years of age) who is critically ill. The caregiver provision for children has been amended to extend benefits to caregivers who are extended family or individuals considered to be "like family". Compassionate Care benefits allow for 26 weeks of benefits for Canadians who are off work to care for a family member who has a serious medical condition with a significant risk of death. Amendments extend eligibility to nurse practitioners and doctors.
Employers should review the terms of their employment contracts, benefit plans, internal policies, and collective agreements to assess the implications . If your agreements and policies top‑up EI benefits for the duration of parental leave, ensure you're meeting new obligations for extended leave. Consider too that planning for maternity leave with your employees may start earlier and have different budget implications. Provincially‑regulated companies should continue to watch this space for amendments in Ontario as they are announced.
This summarizes the main changes – employers should review the legislation itself before changing policies. The legislation is found here.


text:
Photo story: 17 of 30
Long-Term Projects, 2nd prize
Women on Bam-e Tehran, the 'Roof of Tehran', look out over the capital. One wears a dressing following plastic surgery. Iran has one of the highest rates of nose surgery in the world.
Women smoke shisha in a café in Tehran.
Women are made up in a beauty salon.
A woman bathes in the Persian Gulf with her son.
Women wearing chadors cross the Varzaneh Bridge in the province of Isfahan. In this region, white is traditionally worn to ward off evil.
A sex worker lies on her bed.
Women play pool.
Sadiq sits with his dog in his living room in Los Angeles, USA.
Members of a family at home in Yazd, central Iran.
Young women practice the violin.
Siavash, a tattooist, smokes a cigarette.
Young men and women in a swimming pool at a party in Tehran.
Women dance together at a party.
Mothers of men killed in the 1980-88 Iran-Iraq War pray at their sons' graves, surrounded by their portraits.
A cleric and his family walk on Valiasr Street, one of Tehran's main commercial thoroughfares.
Women smoke on the balcony of a private home.
A rock band plays in Isfahan, central Iran.
Female relatives of men killed in the 1980-88 Iran-Iraq War offer prayers at a memorial site in Khuzestan, near the Iraqi border.
Female members of the Basij militia's Ashura putting their guns away after a ceremony. The militia took part in a military parade to mark national Basij week at a Revolutionary Guards military base.
A boy rides a bike in a housing estate in the Ekbatan district of Tehran.
Ashraf, 30, sits in the passenger seat of a classic car after her wedding reception.
Clerics leave a mosque in the city of Qom, southwest of Tehran. Qom is a holy city for Shia Muslims, and an important center of Shia scholarship.
Two young people watch films in a private apartment.
Serial killer Mohammad Bijeh is hanged after receiving a public flogging. Together with an accomplice he had kidnapped and murdered between 19 and 22 people, mostly children.
A young Iranian woman sings at an event for Iranian refugees, in Los Angeles, USA.
Mina Siegel, who emigrated from Iran to the US in 1973 to pursue her graduate studies, now leads an active retirement after a successful career, and lives in New York. She takes pride in her contribution to the life of the Iranian community in New York City.
Two young couples sit in a tent smoking shisha.
A young man practices parkour, a sport that uses the urban landscape as an obstacle course.
Tara and Soroush, two Iranian students, hold hands on their wedding day in downtown Chicago, USA, while a taxi driver prays on the sidewalk.
A mural depicting the Ayatollah Khomeini, the figurehead of Iran's Islamic Revolution.
Nature,3rd prize
2017 Photo Contest, Long-Term Projects, Stories, 2nd prize
Since the Islamic Revolution in Iran in 1979, the country has followed a strictly conservative theocratic line. Western cultural influences have been severely restricted. An estimated 60 percent of Iran's population is under 30 and have little knowledge of their country before the revolution, yet all the trappings of Western youth and modernity are now beamed into homes via the internet and (as yet still illegal) satellite television. Daily, millions of young people engage in activities that are officially illegal and can carry severe penalties. The Basij—a volunteer militia—polices public morals, on the lookout for such offences as women showing too much hair, or couples inappropriately holding hands.
The photographer was born and raised in Iran and has been photographing his country for 15 years. He aims to document parts of Iran's complex society showing less-observed areas of daily life. He also turned his focus on some Iranian immigrants in the US, to see how they had kept their language, culture and traditions alive.
Between 2006 and 2008, he turned his lens on the wars in both Georgia and Lebanon. In 2009, Hossein moved to Afghanistan and documented American ground operations and the lives o...
Family Love 1993-2014
Darcy Padilla

redpajama_set_name: RedPajamaCommonCrawl


text:
Hong Kong Wetland Park – world-class ecotourism park
Katy | Updated: April 23, 2019 | Nature & Outdoors | No Comments
Hong Kong Wetland Park Background
Hong Kong Wetland Park is the first of its in kind Hong Kong. It is a blend of tourism, conservation and education. The park is a great place for tourists to experience and learn more about Hong Kong nature as showcases the diversity of Hong Kong's wetland ecosystem. It is also a nice place for a great day out with friends and family.
Hong Kong Wetland Park is located in the north-west of the New Territories, near Tin Shui Wai. It is an ecological mitigation area (EMA) for the wetlands lost due to the Tin Shui Wai New Town development project.
Date back to 1998, the Agriculture and Fisheries Department (now AFCD) and the Hong Kong Tourist Association (now the Hong Kong Tourism Board) did a research. The Hong Kong Wetland Park could work as a buffer for the Mai Po Nature Reserve. They finally decided to turn the land into Hong Kong's first eco-tourism project, namely the Hong Kong Wetland Park. The construction of the Wetland Park costed about HK$ 500 million and was opened to the public on 20 May 2006.
Hong Kong Wetland Park Review
The 61-hectare Wetland Park comprises of two parts: the Wetland Reserve of 60 hectares (150-acre), and the Wetland Interactive World, a visitor centre of 10,000 square metres (110,000 sq. ft.).
The Wetland Interactive World has five exhibition galleries which let you understand different aspects of wetlands:
1) What are Wetlands? – an overview on the functions and the importance of wetlands to wildlife.
2) Living Wetlands – the introduction of wetlands in Northern tundra, Tropical swamps and in Hong Kong.
3) Human Culture: explains the relationship between wetlands and human-being
4) Wetlands Challenge – let you understand the global threat of wetlands through interactive games.
5) Viewing Gallery – provide the best view of Hong Kong Wetland Park, also an indoor place for bird-watching.
There are also a theatre for film showing and an indoor play area for kids.
Hong Kong Wetland Park Animals and Outdoor Walks
The Wetland Reserve gives you closer access to the habitats and wildlife. The Reserve is organized into six areas:
1) Steam Walk – showcases a model of stream habitat in Hong Kong.
2) Succession Walk – demonstrates how an open water turns into a wet woodland.
3) Mangrove Broad Walk – the floating bridge rises and falls as the tides comes and goes. You can find fiddler crabs and mudskippers here.
4) Bird Hides – let you observe birds at the riverside, fishponds and mudflat. The Park has recorded over 200 species.
5) Wildside Walk – you can find woodland, grassland, seasonal ponds and reed bed here.
6) Butterfly Garden – a great place to observe butterflies
Actually, at the entrance of this outdoor walk, you will first see Pui Pui's Home. Pui Pui is a female saltwater crocodile. She has become a local celebrity since its first appearance at Shan Pui River, Yuen Long in 2003. She has moved permanently to the Hong Kong Wetland Park in 2006. Nobody knows where Pui Pui came from as crocodile is not common in Hong Kong. It is estimated that the rare species was a runaway pet of illegal imports. Some people think that Pui Pui was abandoned by its owner due to its growing shape. Anyway, don't forget to say hello to her when you drop by.
Best time to visit Hong Kong Wetland Park
The best time to visit the Park would be from autumn to spring. It is because migratory birds which breed in northern China, Mongolia and Siberia fly a long distance to their wintering grounds in Southeast Asia and Australasia every year. Hong Kong Wetland Park and the nearby Mai Po Inner Deep Bay wetlands act as important stop-over sites, which provide migratory birds with shelters and food.
Join the Bird Watching Festival organized by the Park every year from November to March/ April. A series of activities would be launched.
You may need a pair of binoculars for bird watching , maybe a pair for kids as well.
Don't miss the ecological guided tours. They are led by trained volunteers during Saturdays, Sundays and Hong Kong public holidays.
Take your cap, sunglasses and sun lotion. Wear clothes in long sleeves so as to avoid mosquitoes and bugs.
Don't wear bright color in order to avoid stressing birds and other wildlife.
Make sure you bring enough water as it may take you half day to whole day to walk around the whole park.
There is a fast food restaurant and a souvenir shop for you to rest and shop.
Hong Kong Wetland Park Entrance Fee
Adult Ticket: HK $ 30, Concessionary Ticket: HK $15, you can also apply for the Multi-entry Passes.
How to get to Wetland Park Hong Kong
Take the MTR West Rail to Tin Shui Wai Station. Change to 705 or 706 Light Rail and get off at Wetland Park Station. Walk for 5 minutes to get there.
From Hong Kong Island: Take the Bus 967 from MTR Admiralty Station Exit B to Wetland Park. Walk for 5 minutes to get there.
From New Territories East: Take the Bus 276B from MTR Sheung Shui Station Exit C to Wetland Park. Walk for 5 minutes to get there.
Address: Hong Kong Wetland Park, Wetland Park Road, Tin Shui Wai, New Territories, Hong Kong
Opening Hours: 10:00am – 5:00pm (Ticket selling: 9:30am – 4:00pm); Tuesdays is closed (except Public Holidays)
Official website of Hong Kong Wetland Park: https://www.wetlandpark.gov.hk/en
What are your favourite scenic spots and animals in the Park? Feel free to share with us.
Thank you for reading this post. Please get in touch if you have any questions or comments. Please like us on Facebook and share our posts.
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Tags:hong kong nature, hong kong wetland park, hong kong wildlife
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text:
Keytruda
AsiDNA
$34M Funding Helps Speed Up New Class of Cancer Diagnostics
by Diana Campelo Delgado
Click here to subscribe to the Ovarian Cancer News Today Newsletter!
InterVenn Biosciences has raised $34 million to commercialize a new diagnostic tool for ovarian cancer, based on the identification of circulating proteins containing sugar molecules (glycoproteins). The company expects to validate the diagnostic tool intended for immediate clinical use and drive rapidly expanding partnership projects. The funding will also help accelerate the use of glycoproteins in other cancer indications. "I'm humbled but inspired by the task of driving glycoproteomics as a new diagnostic nomenclature to stratify patients, classify disease, and spawn new medical innovation," John Leite, PhD, chief business officer of InterVenn, said in a press release. Protein glycosylation — the addition of sugar molecules, or glycans — is the most common and complex form of post-translational protein modification that cells use to adapt to environmental changes and other challenges. Abnormal protein glycosylation has been implicated in important steps of disease biology, including in cancer development, autoimmunity, and aging. As such, researchers can examine glycans and glycosylated proteins in patients to discover uniquely informative disease biomarkers. While glycoproteins have been difficult to study in the past, InterVenn has developed a platform that overcomes prior challenges. Its proprietary platform, VISTA, combines artificial intelligence and mass spectrometry (a technique that allows the identification of a substance's components based on their mass) to analyze tumor-associated changes in circulating glycoproteins. This biomarker research product may be used in the pursuit of new solutions for over a dozen different oncology indications, including ovarian, pancreatic, liver, prostate, and kidney cancer. "InterVenn e
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Tagged Biomarkers, clinical trial, diagnostic, glycoproteins, glycoproteomic, InterVenn Biosciences, VISTA platform, VOCAL trial.
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Celebrate the Year of the Goat and share Chinese Zodiac predictions for the new year. The backgrounds on display are bold and full of life.
Spark up 2015 with Lifestyle templates and Leisure backgrounds for free, as you make a positive start to a brand new year through free New Year 2015 backgrounds for Microsoft PowerPoint 2010 PC and Mac.


text:
Swift's silver mine is an alleged silver mine whose existence is part of the folklore of the Appalachian Mountains.  The mine was supposedly discovered in 1760 by an Englishman named Jonathan Swift. The uncertainty of its location is part of the folklore of its existence, with locations ascribed to eastern Kentucky, southwest Virginia or eastern Tennessee.

Legend
The legend of Swift's silver mine is based on accounts given in the journal of an Englishman named Jonathan Swift. Swift claimed to have preceded Daniel Boone into Kentucky, coming to the region in 1760 on a series of mining expeditions. The journal recounts how a wounded bear led Swift to a vein of silver ore in a cave, and how that for the next nine years, he made annual treks back to the site of the mine, carrying out "silver bars and minted coins." An article in an 1886 edition of Harper's Magazine tells how Swift supposedly buried a good deal of the treasure at various locations:

Later, amid numerous obstacles that included Indian attacks, and a mutiny by his crew, Swift walled up the cave and discontinued his mining operation. He left his journal in the possession of a Mrs. Renfro, the widow of one Joseph Renfro of Bean's Station, Tennessee, in whom he was purported to have a romantic interest. Before Swift could return to the mine, he was stricken blind and was unable to locate it again.

Variations

Settlers in Wise County, Virginia, believed that the mine was located on or around Stone Mountain, and that local Indians knew the location of the mine. According to the settlers, an Indian chief named Benge once said that "if the pale face knew what he knew they could shoe their horses cheaper with silver than with iron." They maintain that a captured settler named Hans G. Frenchman was taken to the mine by the Indians. He marked its location, and later escaped his captors and revealed the location of the mine to Swift. In this version of the story, Swift and Frenchman took only enough silver to buy two horses, and on a return trip, were unable to locate the mine.

Another variation along these lines holds that Swift was taken to the mine by a Frenchman named "Monday" or "Monde". In this version, Swift and Monde are driven from the mine by an Indian attack, and Swift kills Monde for fear that he will reveal the location of the mine to others. Later, when Swift attempts to return to the mine, Monde's hand covers the compass so he cannot tell which direction to proceed.

Solomon Mullins, or "Money-making Sol" (born 1782, died 1858) was rumored to have discovered Swift's mines near Pine Mountain in Southwest Virginia.  Solomon lived in what was then known as Holly Creek, but is now Clintwood, Virginia.  Solomon melted the silver down and used one of his slaves to "strike" for him (striking: the head of a hammer is heated until it is a plastic state and struck over a good coin. The coin, being harder than the softened head of the hammer is, therefore, imprinted. The hammer is then allowed to cool thus producing the die). To finish the process, or to imprint the counterfeit coins with official markings, Sol and his slave would relocate to the privacy afforded by caves located on cliffs adjacent Sol's farm. To this day, the cliffs are known as "Sol's Cliffs".

According to local legend, Solomon's "counterfeit" money used more silver, and was worth more, than the official currency at the time.  Apparently, Sol mixed the pure silver with other lesser metals to make his money.  Solomon never disclosed where he obtained the pure silver, but many people speculated that he found the silver in one of the many caves on Pine Mountain close to his farm.

Once, Solomon was caught by a U.S. detective while at work in the cliffs. Reportedly, realizing his predicament, he ordered the man to help with his work, saying "Grab a hammer and strike this." He hoped the action, if taken, would make the detective complicit; regardless of the story's veracity, it did not do Sol any good. In early 1837, Solomon and two of his 10 children, Peter and Spencer, were brought to trial and were charged with making counterfeit coins. Solomon was found guilty, but fled Virginia before being sentenced. He reportedly died in 1858 and never revealed the location of his source of silver.

Each year in Wolfe County, Kentucky, there is a Swift Silver mine festival in the county seat of Campton, where locals believe the mine may be located near Swift Creek.

Expeditions
John Filson is the first person known to have referenced the mine following Swift's death. In 1788, Filson claimed a tract of land supposed to have included a silver mine worked by "a certain man named Swift." Filson disappeared, taking with him any knowledge he may have had as to the mine's location.

Kentucky pioneer James Harrod may also have believed in Swift's silver mine. According to Harrod's wife, a man named Bridges said he had found the mine, and asked Harrod for his help in developing it. Despite the fact that Harrod and Bridges had a dispute over land some years previous, these two and another man entered the wilderness of Kentucky in 1792, purportedly in search of the mine. Harrod did not return from the trip, and although his body was never found, his wife maintained that Bridges had used the story of the mine to lure him into the woods to murder him.

When Judge John Haywood was working on a history of the area around Clear Creek in Kentucky, he observed two "ancient" furnaces that he believed may have been used by Swift.

In 1854, Professor David Dale Owen was dispatched to Bell County, Kentucky, as part of a geological survey of the state. As part of the survey, Owen examined a location reputed by the locals to be the site of Swift's silver mine. Guided by an explorer named Benjamin Herndon, Owen examined the area and found that it contained "some accidental minerals sparingly disseminated, such as sulphuret of zinc and lead – which proved on examination to be hydrated silicate of alumina." Owen's survey did not find any significant deposits of silver ore in the area.

Skepticism
Geological evidence casts doubt on the existence of Swift's silver mine. In two centuries, subsequent mining and excavation of the area said to have contained the mine have not yielded a single vein of silver ore.

Regarding the journal
Skeptical writer Joe Nickell pointed out that there are numerous versions of the journal upon which the legend of Swift's Silver Mine is based. The best known and most complete of these contains portions plagiarized from a history of Kentucky dating later than the supposed time frame of the journal itself. Because this version is filled with references to Freemasonry and moral allusions, some historians dismiss the entire tale as an allegory.

Regarding Swift
There is the question of Swift himself. No proof exists that there ever was a John or Jonathan Swift who mined silver in Kentucky. (James Dougherty gives alternative first names of "George", "William", and "Tom".) The man many considered to be the Jonathan Swift of legend is known to have been alive many years after the purported death of the fabled miner. Nothing is known of this man's ancestry, and what little is known about him personally has been handed down through tradition. If the birth date given in Swift's journal is to be believed, the known Swift would have been an incredible 112 years old at the time of his death.

If there was an actual Jonathan Swift, there is some disagreement as to his character. Tradition holds that he was an established Indian trader, but some historians came to believe he was a pirate who preyed on Spanish merchants and made his frequent trips into the wilderness not to retrieve his treasure, but to hide and coin it. A resident of Laurel County, Kentucky, named William Reams held that following Swift's visit to the mine in 1769 – the last trip recorded in the journal – he and his accomplices agreed not to claim any of the treasure they had hidden in the area until 1790. When they did return, Swift was overcome by the sight of the wealth and killed the other members of the party while they slept. Following this action, he was struck blind and unable to recover the treasure.

Nickell contends that the pirate theory raises more questions than it answers. He contends that a journey into Kentucky was a dangerous undertaking, and that Swift could just as easily set up a clandestine coining operation in the backwoods of Virginia or aboard his ship. He also questions why Swift would take the time to produce such a detailed journal to cover his story.

See also
Lost mines

Notes

References

Further reading

Swift's Silver Mine References from RootsWeb

Appalachia
Lost mines
Silver mines in the United States

redpajama_set_name: RedPajamaWikipedia


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text:
American Poetry: The Nineteenth Century, Vol.… | Folio | TinyCat |
American Poetry: The Nineteenth Century, Vol. 1: Philip Freneau to Walt Whitman
by John Hollander (Editor)
Hardcover, 1993
American Poetry Series {Library of America}
Schroll Collection
Library of America (1993), 1099 pages
In nineteenth-century America, poetry was, part of everyday life, as familiar as a hymn, a love song, a patriotic exhortation. American Poetry: The Nineteenth Century reveals the vigor and diversity of a tradition embracing solitary visionaries and congenial storytellers, humorists and dissidents, songwriters and philosophers. These two volumes reassess America's poetic legacy with a comprehensive sweep that no previous anthology has attempted. This second volume follows the evolution of American poetry from the monumental mid-century achievements of Herman Melville and Emily Dickinson to the modernist stirrings of Stephen Crane and Edwin Arlington Robinson. The cataclysm of the Civil War - reflected in fervent antislavery protests, in marching songs and poetic calls to arms, and in muted post bellum expressions of grief and reconciliation - ushered in a period of accelerating change and widening regional perspectives. Among the unfamiliar pleasures to be savored in this volume are the penetrating meditations of the reclusive Frederick Goddard Tuckerman, the eloquent lyricism of Emma Lazarus, the mournful, superbly crafted fine simple verse of Trumbull Stickney. Here too are the pioneering African-American poets (Frances Harper, Albery Allson Whitman, Paul Laurence Dunbar); popular humorists (James Whitcomb Riley, Eugene Field); writers embodying America's newfound cosmopolitanism (Edith Wharton, George Santayana); and extravagant self-mythologizing figures who could have existed nowhere else, like the actress Adah Isaacs Menken and the frontier poet Joaquin Miller. Parodies, dialect poems, song lyrics, and children's verse evoke the liveliness of an era when poetry was accessible to all. Here are poems that played a crucial role in American public life, whether to arouse the national conscience (Edwin Markham's "The Man with the Hoe") or to memorialize the golden age of the national pastime (Ernest Lawrence Thayer's "Casey at the Bat"). An entire section of this volume is devoted to American Indian poetry in nineteenth-century versions, making available - some for the first time since their initial publication - an astonishing range of translations and adaptations: Ojibwa healing rituals, the songs of the Ghost Dance religion, Zuni mythological narratives, chants from the Kwakiutl Winter Ceremonial. Also included is a generous selection from America's rich heritage of anonymous folk songs, ballads, and hymns. Unprecedented in its textual authority, the anthology includes newly researched biographical sketches of each poet, a year-by-year chronology of poets and poetry from 1800 to 1900, and extensive notes.… (more)
American poetry19th century
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text:
New York Times' vision of Ukrainian "democracy"
Bill Van Auken
The New York Times, which from the outset of the Ukrainian crisis has tailored its coverage slavishly to the line of the US State Department, spelled out its vision for a "democratic Ukraine" Wednesday in an editorial entitled "A Critical Election in Ukraine."
Even as the country descends ever more dangerously into bloody civil war, the Times editorial board claimed to detect signs that "may bode well for the presidential election scheduled for Sunday."
The main signs, according to the editorial, are Russian President Vladimir Putin's failure to endorse the autonomy referenda held earlier this week in eastern Ukraine, and the pullback of Russian troops from the Ukrainian border.
The implication is clearly that the problems in Ukraine are entirely of Moscow's making. This is the propaganda line it has pursued for months, most notoriously in its publication of a front-page story, accompanied by photographic "evidence" supplied by the State Department and the Kiev regime, asserting that Russian special operations troops were directing the protests in eastern Ukraine. The so-called evidence was quickly exposed as fabricated and the story a lie, but this has done nothing to shift the Times coverage, which has the unmistakable stench of war propaganda.
In its latest editorial, the newspaper comments, "It is crucial for the vote to be accepted by all sides so Moscow can stop referring to the interim administration as the 'illegitimate regime in Kiev.'"
This is a rather odd reason to hold an election—to alter the way a government is depicted by the government of another country. The formulation begs the question as to what is the legitimacy of the Kiev regime, which was brought to power through the overthrow of an elected president by means of violent protests, orchestrated by Washington and its Western European allies and led by extreme nationalist and neo-fascist forces.
The Times acknowledges that the election is a contest between "chocolate king" Petro Poroshenko, the recently released "gas princess" Yulia Tymoshenko and the banker Sergey Tigipko, all of whom are members of "the clique of very rich businessmen who have been at the root of the corruption of the Ukrainian government" and who "made their fortunes in the chaotic privatization of state assets" following the dissolution of the Soviet Union. In other words, the Ukrainian people are being asked to choose between a trio of criminals and parasites who enriched themselves at their expense through the theft of state property.
The front-runner Poroshenko, according to the Times, "has political strengths" that include backing the coup that toppled President Viktor Yanukovych, supporting the turn to the European Union and having been "deeply involved in Ukrainian politics from the outset." In other words, he is a deeply corrupt billionaire who appears prepared to subordinate himself to US policy aims.
Additional rays of hope are detected by the Times' editors in the activities of Rinat Akhmetov, Ukraine's richest billionaire, and Igor Kolomoisky, who ranks second or third in terms of the accumulation of ill-gotten wealth.
Kolomoisky, who the Kiev junta appointed as governor of Dnipropetrovsk Oblast, is praised for putting up his own money for "bounties" placed on the heads of "terrorists," i.e., those in the east who have staged protests against the coup regime in Kiev. Kolomoisky, a mafia businessman who has in the past organized gangs of armed thugs to physically seize rival companies, has been implicated as an organizer of the horrific massacre carried out by fascist elements in Odessa earlier this month.
Akhmetov, who is said to be worth $11.6 billion, began his career, according to a documentary study of Donetsk, as a "mafia thug." A British historian specializing in Ukraine, Andrew Wilson, characterized him as an "enforcer" for a Donetsk crime boss who used extortion and physical violence to seize control of former state-owned property.
Akhmetov is lionized by the Times for "sending his steel and mining workers to recover occupied buildings in Mariupol and Makeyevka." The editorial adds: "Mr. Akhmetov's employees—and there are nearly 300,000 of them—evidently heeded his warning that their livelihoods would be uncertain under Russian control."
In the same edition of the paper containing the editorial, the Times carried a news report asserting that the anti-Kiev protesters in eastern Ukraine had faced "an unaccustomed wave of anger from residents" and that steelworkers had "easily wrested control of the port city of Mariupol last week under the direction of Ukraine's richest man, Rinat Akhmetov, who owns the mills where they work."
It went on to report that, "On Tuesday, workers at a steel mill in Mariupol and at a metalwork facility in the city of Yenakiyeve … left work at noon to listen to speeches in support of Ukrainian unity."
The newspaper characterized these events as "potentially giving an enormous lift to the provisional government in Kiev."
The Times report was notably short on detail, in large measure because the events it describes never happened. Rather they were gleaned from releases issued by the billionaire Akhmetov's press agents.
The occupied administration building in Mariupol was not "recovered" by miners and steelworkers; it was gutted in a brutal assault by the Ukrainian military and Right Sector fascists in which at least 20 civilians were murdered in a hail of automatic weapons and tank fire.
Those newspapers that were present in Mariupol and other Donbas areas where Akhmetov owns factories and mills described a very different scene than the one promoted by the Times .
Reporting from Akhmetov's Ilyich steelworks in Mariupol, the Washington Post said, "nobody stepped outside the factory gate when the plant whistle sounded at noon" for an announced protest.
"Alexei Primenko, 63, a locksmith in the steel plant's main office, was one of those who shrugged off the demonstration," the Post reported. "He said workers pay lip service to Akhmetov's calls for pro-Ukrainian support because they owe their salaries to him. But Primenko said their hearts are not in it. 'What's the demonstration going to give me?' he said."
The Financial Times was even blunter. "An appeal by Ukraine's richest man for hundreds of thousands of workers to protest against pro-Russian militants was largely ignored on Tuesday, in a setback for authorities in Kiev who hoped to build support for the presidential election on Sunday," the London financial newspaper reported.
According to the FT report, "turnout at the rallies was low and a number of workers in attendance said they supported the insurgents" opposing the Kiev regime. It described one "rally" of a few hundred workers outside the Donetsk Enakievo Metallurgical Works, a factory that employs 6,000. Addressed by the factory director, the "rally" took the form of a "team meeting" and lasted less than 10 minutes, with no political statements made.
"Afterwards, workers milling around said they had come to the meeting voluntarily but most said they did not share their boss Mr Akhmetov's views on the militants," the FT reported.
"Akhmetov is a businessman. He is looking after his business interests, it's natural," Stas, a worker, told the newspaper. He added that the plant director had been "wise" to avoid putting any "political accent" on the brief meeting.
"Ninety-nine per cent of the workers are against the Kiev authorities," Vladimir Sadovoy, the head of the factory workers' union told the newspaper. "Some want to be part of Russia, others want to be part of Ukraine, others want to be independent. But everyone is against the Kiev authorities, absolutely."
Once probed, the New York Times vision of "democracy" is revealed as so much smoke and mirrors. Behind the lies and state propaganda, however, the content of this vision is itself significant, consisting of a multi-billionaire oligarch ordering "his" workers to action, like some feudal lord sending his serfs into battle.
The newspaper's turn to the Ukrainian oligarchs as that country's saviors is clearly connected to the Times' orientation toward the financial oligarchy and the wealthy and privileged layers that surround it within the United States itself.
What the reports from eastern Ukraine show, however, is that all of the confusion and nationalist agitation notwithstanding, the working class is not some docile servant of this or that oligarch, but rather is hostile to the entire corrupt ruling strata and determined to defend its own interests. It is the independent political struggle of this class alone that can provide a progressive way out of the imperialist-instigated crisis in Ukraine.
UkraineThe 2014 coup in Ukraine


The Black-capped Kingfisher is not as common as the other species of the Kingfisher. These birds are skittish and do not permit a close approach. To fill most of the frame would have taken at least 800 mm but I had a maximum reach of 400 mm. Nevertheless the light was good and the bird appositely positioned.


I have always held a fascination with being quoted… Somethings have been stated so well they resonate through time… I find I enjoy quotes and psalms… I enjoy quotes which make me think… some quotes even manage to capture an essence of life… here are some of my favorite quotes……..
This entry was posted in Blogroll, Conversations, Imagination, Inspiration, Journeys, Life, Quotes, Thoughts, Writing. Bookmark the permalink.
🙂 Thanks for tagging me. I'll update the quotes sometime in the eve today. For now, I have a trip to catch.
BEAUTIFUL quotes! Am I gonna take some of them? Oh, that's for sure. I have the perfect pictures for some of them.
Dear enreal, you have really a quote treasure herein your post. My favourite is Mr. Einstein. Blessings.
((rainer)) Thanks… I could go on forever… words when put together just right can bring you to a place of reflection and contemplation… don't you think?
(((nikhil))) I love that one too!
Wow that quote by Einstein moved me to tears. It sums up the purpose of my life. To simplify in order to clarify in order to understand.
Yes words do have incredible power and with tone they have even more power. I thought about what word I would add to your list and the first word that came to my mind was "awareness".
Nice quotes. Who said a great scientist can't be a great philosopher? Einstein proved otherwise.
Sorry , enreal . I don't have a stock of quotes .
But I want to say something .
Every quote is valid within a context .
There are times when they clear up fog or give a direction .
I enjoyed reading all the comments! As you already know I love quotes too!
It was very difficult for me to narrow it down to a few, because my list continues to grow! Alas, here are a few that I count among my favorites. (you've seen some of them in my posts).
I see the DIVINE in you dear one…thank you for your light!
(((SE))) Can't wait! Have a great day!
Oh I am going to have fun with this….