text:
\section{Introduction}
\IEEEPARstart{U}{nderstanding} the underlying details of glomerular morphology through renal biopsy evaluation provides insights into various renal disorders~\cite{greenberg2009primer,rayat2007glomerular,puelles2015counting}. The golden standard of characterizing glomeruli is a manual estimation via advanced imaging techniques~\cite{puelles2015counting}. However, manual quantification of morphometric parameters requires exhaustive resources and is not scalable. In recent years, there has been a paradigm shift towards automatic glomerular instance segmentation, which aims to provide instance-level, pixel-wise annotation for each glomerulus driven by Convolutional Neural Networks (CNNs)~\cite{gallego2018glomerulus, bukowy2018region}. The de facto standard method of instance segmentation of glomeruli, and more broadly the kidney, is Mask-RCNN~\cite{peng2019extent,macdonald2020improved}, an end-to-end pipeline which performs detection and instance segmentation simultaneously~\cite{he2017mask}. Since the end-to-end architecture of Mask-RCNN is designed for natural images (e.g., $\approx$ 1000$\times$1000 pixels), both downsampling and tiling are utilized in order to leverage processing speeds and fit modern GPU memory when Mask-RCNN is applied to high resolution WSI (e.g., $>$ 10000$\times$10000 pixels on 40x). However, a loss of information is often associated with the downsampling process that is inherent to the end-to-end framework of Mask-RCNN. In particular, a single glomerulus from a WSI can be more than 1,000$\times$1,000 pixels in image resolution, which yields significant information loss when the corresponding features maps are downsampled to the 28$\times$28 resolution via the end-to-end Mask-RCNN segmentation head~\cite{peng2019extent}, as demonstrated in {\color{red} Fig.~\ref{Fig.1}}. Thus, the prevalent end-to-end instance segmentation method might not be the best solution for high resolution WSI. When re-imagining glomerular instance segmentation for high resolution WSI, an intuitive idea of addressing the tradeoff between resolution and accuracy would be the fundamental separation of detection and segmentation. In this detect-then-segment manner, detection could be performed on downsampled tiles for computational efficiency, while segmentation could be conducted on high-resolution images as unrelated pixels are excluded by detection. Inspired by this rationale, we aim to explore if the end-to-end or detect-then-segment framework is optimal for high-resolution WSI objects in the context of renal pathology.

\begin{figure*}[t]
\begin{center}
\includegraphics[width=1.0\textwidth]{{Figures/Fig1.pdf}}
\end{center}
\caption{
  This figure showcases the end-to-end Mask-RCNN instance segmentation pipeline in blue arrows, and the proposed detect-then-segment framework in black arrows. In our proposed method, the two-stage detect-then-segment strategy is used, where detection will first occur on downsampled images, and then segmentation is performed on high resolution objects.
}
\label{Fig.1}
\end{figure*}

In the current study, we propose a detect-then-segment framework for glomerular instance segmentation in order to more broadly improve current instance segmentation techniques when applied to high-resolution WSI. In our study, we utilize two distinct high-resolution segmentation networks for semantic segmentation, and we use Mask-RCNN for instance glomerular detection. A central focus of our study is to compare our proposed detect-then-segment framework to the performance of the end-to-end Mask-RCNN pipeline on high resolution WSI. In addition, we conduct extensive analyses to ascertain the best detect-then-segment strategy through two of the most widely used segmentation backbones (U-Net and DeepLab\textunderscore v3), six unique resolutions (512$\times$512, 256$\times$256, 128$\times$128, 64$\times$64, 32$\times$32, and 28$\times$28), and two distinct color spaces (RGB and LAB). To the best of our knowledge, no previous studies have comprehensively evaluated glomerular segmentation performance comparing detect-then-segment and end-to-end strategies.

To evaluate the performance of these two distinct segmentation frameworks, we divided our experiments into two scenarios: 1) manual detection, and 2) automatic detection. In the first scenario --- labeled as ``manual detection" --- manual detection results (bounding boxes) were used to evaluate the segmentation performance in our detect-then-segment framework. Then, in the second scenario --- labeled as ``automatic detection" --- automatic detection results from the same Mask-RCNN detection head were used to compare segmentation performance across end-to-end and detect-then-segment strategies. The key difference in our automatic detection phase is the use of either an end-to-end Mask-RCNN segmentation head or an additional high-resolution segmentation head for glomerular instance-level segmentation. Decoupling detection and segmentation allows for more freedom in understanding how to improve the segmentation of glomeruli, and more broadly, high resolution WSI objects of similar character.

For the manual detection phase, we trained the segmentation networks of our proposed detect-then-segment method using 704 manually traced training glomerular images from 42 biopsy samples. Meanwhile, 98 validation glomerular images, 147 internal testing images, and 385 external testing images were manually extracted from 7, 7, and 5 WSI images respectively to evaluate segmentation performance. The original resolution of our glomeruli image data was of $\approx$ 1000$\times$1000 pixels. To be compatible with our GPU memory, we first scaled all input images down to the resolution of 512$\times$512. Then, according to our procedure, we further scaled down the input images to the resolutions of 512$\times$512, 256$\times$256, 128$\times$128, 64$\times$64, 32$\times$32, and 28$\times$28 to train and evaluate two of the most widely used segmentation backbones, U-Net and DeepLab\textunderscore v3.

For the automatic detection experiments, we compared performance between the end-to-end Mask-RCNN segmentation and our proposed detect-then-segment approach using automatic detection results. To do so, Mask-RCNN was trained and validated on the same biopsy WSI images as the manual detection experiments. From the 4 internal testing WSI, 120 glomeruli were correctly detected (IOU $>$ 0.5 compared with true bounding box) from the detection head in Mask-RCNN. Then, we directly applied the trained Mask-RCNN segmentation head as well as our trained segmentation models (U-Net and DeepLab\textunderscore v3) from our first phase to the same 120 glomerular detected images to compute the final segmentation performance when the detection is fairly provided. The experiments show that our detect-then-segment framework, under the automatic detection scenarios, achieves a DSC value of 0.953, whereas Mask-RCNN provides a lower DSC value of 0.902.

Our contributions, as listed below, do not claim algorithmic novelty over prior arts but rather investigate the problems overlooked in previous works:
 \begin{itemize}
  \item Proposing a new detect-then-segment glomerular instance segmentation framework by performing instance detection and semantic segmentation on different resolutions with a coarse-to-fine design to avoid extreme downsampling for high resolution glomerular segmentation in renal pathology WSI.
  \item Evaluating if the de facto end-to-end design or our detect-then-segment approach is optimal for segmenting glomeruli in high resolution WSI.
  \item Performing extensive analyses by varying the image resolution, color space, and segmentation framework in segmenting previously detected glomeruli image objects. This comprehensive analysis allows us to provide an extensive quantitative reference for other researchers to select the optimized segmentation approaches for glomeruli, or other biological objects of similar character, on high-resolution WSI.
\end{itemize}

\begin{figure*}[t]
\centering
\includegraphics[width=1\textwidth]{{Figures/Method_UP.pdf}}
\caption{
   This figure demonstrates an abstraction of the proposed detect-then-segment methodology used in our experiments. Each row indicates a different trial, where a previously detected glomerulus is downsampled to the distinct dimensions of 512$\times$512, 256$\times$256, 128$\times$128, 64$\times$64, 32$\times$32, and 28$\times$28. Then, these downsampled images are passed through a segmentation network. Two of the most prevalent segmentation backbones (U-Net and Deeplab\textunderscore v3) are used as the segmentation networks in this study. The predicted masks are produced, and then upsampled back to the initial and original resolution --- which in our study was 512$\times$512 --- of the glomerulus for a fair DSC comparison. Additionally, in the trial utilizing a U-Net backbone, we separately evaluated the images in both a RGB and LAB color space so as to understand the effect of color space on segmentation performance. In the Deeplab\textunderscore v3 trial, we only evaluated the best performing color space from our U-Net experiment. Overall, this figure shows the segmentation networks evaluated across six different resolutions, two unique color spaces, and two distinct segmentation backbones.
}
\label{Fig.2}
\end{figure*}

\section{Related Works}
The introduction of WSI demonstrates a shift towards computer-aided diagnosis (CAD) techniques to more accurately characterize critical objects. The use of WSI, and its associated analysis techniques, has been shown to be effective and even expanding in the field of renal pathology~\cite{sarwar2019physician}. To properly distinguish and characterize different glomeruli within renal biopsy samples, modern deep learning techniques of detection and segmentation have been utilized. Several studies have shown the great accuracy by which CNNs are able to properly detect and localize glomeruli within sample images~\cite{bukowy2018region, uchino2020classification, pavinkurve2019deep, temerinac2017detection}.  Similarly, CNNs have also been able to accurately segment glomeruli, allowing for normal and sclerosed glomeruli to be properly distinguished~\cite{altini2020semantic, kannan2019segmentation, gadermayr2017segmenting, gadermayr2017cnn, ginley2019computational, ginley2020neural}. Other studies have further combined the process of the detection and segmentation of glomeruli~\cite{bueno2020glomerulosclerosis}. Of course, the end-goal of deep learning in renal imaging is its application in CAD. In this regard, several studies have also shown the ability to perform diagnoses based on the preliminary quantification and characterization of glomerular data through deep learning~\cite{barros2017pathospotter,marsh2018deep}. Common to all of the above studies is the application of CNNs to localize, detect, segment, or characterize glomeruli to better understand renal pathology. The uniqueness of our research presents itself by identifying the specific techniques that work best for high resolution glomeruli data, rather than using common solutions to the niche field of renal imaging. Our paper analyzes several factors that work best in the specific context of instance segmentation of high resolution glomerular data, and other biological objects of similar character and size. Additionally, we further propose a pipeline that is different than the conventional end-to-end instance segmentation tactics that are often used in medical imaging and computer vision, so as to yield better and more accurate results.

\section{Method}

Generally, the methodology followed in this study can be broken down into two major steps: 1) detection and 2) segmentation. Within detection, we discuss our approach towards manual and automatic detection of glomeruli; on the other hand, within segmentation, we demonstrate how we comprehensively analyzed our detect-then-segment framework, and the steps taken to compare it to a classic end-to-end Mask-RCNN pipeline. Our detect-then-segment approach can be seen visually in {\color{red} Fig.~\ref{Fig.2}}.

 \subsection{Detection}

In the manual detection portion of our study, the manually traced bounding boxes for glomeruli are used to provide the ideal detection results. In order to introduce more background variation and avoid the problematic situation in which the glomeruli is always in the middle of the detection, we randomly expanded the detection bounding boxes to 1.5 times the longest dimension of the manual boxes with random center shift. We ensured that the image still contained the complete glomerulus.

In the automatic detection portion, Mask-RCNN~\cite{he2017mask} was employed as the detection method. The Feature Pyramid Network (FPN)~\cite{lin2017feature} with ResNet-101~\cite{he2016deep} is used as the feature extraction backbone. The default Mask-RCNN implementation (\url{https://github.com/facebookresearch/maskrcnn-benchmark}) was used during training. For all training and testing within detection, the original high-resolution WSI (0.25 $\mu m$ per pixel) was downsampled to a lower resolution (4 $\mu m$ per pixel), given the size of a glomerulus~\cite{puelles2011glomerular} as well as its ratio within a patch. Then, we randomly tiled the image patches (where each patch contained at least one glomerulus with 512$\times$512 pixels) as experimental images for our detection networks. Eventually, we formed a cohort with 7040 training images with manual segmentation masks for training the Mask-RCNN glomerular detection.

\subsection{Segmentation}

\subsubsection{Manual Detection}
In this study, a standard implementation of the U-Net and DeepLab\textunderscore v3 architectures was used to perform segmentation on the glomeruli image data in the manual detection phase of our experiment. In particular, U-Net and DeepLab\textunderscore v3 were trained with the preprocessed images as described in \textsection{3.1}. The input image data for both segmentation frameworks contained 3 input channels (RGB or LAB), and the output data contained 2 classes (foreground and background).

Limited by GPU memory, all original image resolution glomeruli ($>$ 1000$\times$1000) were initially scaled down to 512$\times$512. Then, this input image dataset was further scaled down to the sizes of 512$\times$512, 256$\times$256, 128$\times$128, 64$\times$64, 32$\times$32, and 28$\times$28. Once these images were downsampled, the training images were further represented in either the standard RGB or LAB image space. The LAB image space was evaluated as it was recently shown to confer the best performance for basic image classification tasks by reducing image channel-wise correlation~\cite{gowda2018colornet}. Data augmentation was also performed for image segmentation, where 50\% of the training images were altered through channel shuffling, translation, rotation, sheer, left-to-right flipping, and Gaussian blur.

Two of the most prevalent segmentation backbones (U-Net and DeepLab\textunderscore v3) were employed in this study. Briefly, the U-Net architecture is an end-to-end Fully Convolutional Network (FCN). In terms of general network architectures, U-Net can be divided into two major portions: 1) encoder, and 2) decoder. The encoder contains both convolutional and max pooling layers which obtain greater context of the input image through downsampling, allowing for the encoding of the input image into feature representations at multiple different resolutions. The second path is the decoder, which symmetrically expands and upsamples the input image. This allows for precise localization using bilinear interpolation, and effectively rescales the feature map to the original image size~\cite{ronneberger2015u}. Similarly, DeepLab\textunderscore v3 also has encoder and decoder stages. However, in its encoder phase, DeepLab\textunderscore v3 utilizes Atrous, or dilated, convolution to obtain greater context of the input image. The decoder phase then follows to create and rescale the feature map of the original image~\cite{he2017mask}. Through the manually detected glomerular images, we evaluated the performance of U-Net and DeepLab\textunderscore v3 with the aforementioned designs.

\subsubsection{Automatic Detection}

Finally, in the automatic detection phase of our experiment, the trained Mask-RCNN network was performed on all testing images to achieve 120 glomerular detection bounding boxes from 5 WSI biopsies in downsampled images. Then, the bounding box coordinates were upscaled to the original image resolution ($>$ 1000$\times$1000 pixels) to crop the corresponding glomeruli and the masks in highest resolution. Furthermore, both DeepLab\textunderscore v3 and U-Net pretrained models were also applied on the same group of 120 images, which were downscaled to each of the tested resolutions (512$\times$512, 256$\times$256, 128$\times$128, 64$\times$64, 32$\times$32, and 28$\times$28). After corresponding predicted masks were generated, they were upsampled to the initial resolution to calculate the mean and median DSC scores from the manual masks.

\subsection{Data Analysis}

The DSC was primarily used to evaluate the performance of segmentation. To begin, in our manual detection experimentation --- which comprised of the 704 training, 98 validation, 147 internal testing, and 385 external testing images --- we evaluated the performance of U-Net and DeepLab\textunderscore v3 segmentation across six different resolutions (512$\times$512, 256$\times$256, 128x129, 64$\times$64, 32$\times$32, 28$\times$28) and two different color spaces (RGB and LAB). In particular, for each epoch within the segmentation process for each resolution, DSC values were developed for the validation and testing data. For each tested resolution, the best epoch was selected via the highest DSC for the validation dataset, and the generated model in that epoch was saved. Then, these generated, predicted masks for each tested resolution were upsampled to a 512$\times$512 resolution to be compared against the initial ground truth mask data (which is also of 512$\times$512 resolution) for the validation and testing images. Mean and median DSC, as well as standard deviation, were computed again for these upsampled image sets for each resolution.

Throughout our study, in the specific context of our manual detection phase results, we draw a distinction between the terms of ``sample space" and ``512 space". We define ``sample space" as the evaluation of the predicted images in each of the six tested resolutions (512$\times$512, 256$\times$256, 128$\times$128, 64$\times$64, 32$\times$32, and 28$\times$28) against the corresponding downsampled input images to produce a preliminary DSC value across six distinct resolutions. We similarly define ``512 space" as the evaluation of the predicted images across the six tested resolutions which are then upsampled to the original size of the input image --- which in our study was 512$\times$512 as established earlier --- and then compared to the original resolution 512$\times$512 input images to produce a fair DSC score. This can be seen visually in the latter columns of {\color{red} Fig.~\ref{Fig.2}}.

Furthermore, in our automatic detection experimentation, which comprised a cohort of 120 glomerular images in original resolution ($>$ 1000$\times$1000), we similarly applied the detect-then-segment framework which was directly compared to Mask-RCNN through the use of mean and median DSC, as well as the standard deviation of the DSC data. Through a similar process in the manual detection phase, we applied Mask-RCNN to our cohort of input images, and produced relevant statistics for the DSC scores. Additionally, after applying both U-Net and DeepLab\textunderscore v3 to each of the six tested resolutions of the input image and producing corresponding predicted masks, such predicted masks were then upsampled and compared to the original resolution of the input image ($>$ 1000$\times$1000) for a fair DSC comparison.

\section{Experimental Design}

\subsection{Dataset}
WSI from renal needle biopsies and human kidney nephrectomy tissues were utilized for analysis. The kidney needle biopsy was routinely processed, paraffin embedded, and 2 $\mu m$ thickness sections cut and stained with hematoxylin and eosin (HE), periodic acid–-Schiff (PAS) or Jones. The human nephrectomy tissues were acquired from noncancerous tissue from patients with cancer. The tissue was routinely processed, paraffin embedded, and 3 $\mu m$ thickness sections cut and stained with PAS. The data was deidentified, and studies were approved by the Institutional Review Board (IRB). For the purposes of training and testing, the high resolution WSI (0.25 $\mu m$ per pixel) was downsampled to a lower resolution (4 $\mu m$ per pixel). Then, patches were identified which contained glomeruli in its original resolution ($>$ 1000$\times$1000 pixels). Images of glomeruli, as well as their manually traced ground truth masks, were then collected. In this study, these input images served as our previously detected glomerular images upon which segmentation then was performed. Eventually, we formed a cohort with 704 training, 98 validation, and 147 internal testing images. Additionally, a group of 385 images was used as external testing data. The training, validation, and testing data were used in our manual detection experimentation. Finally, a separate cohort of 120 images with Mask-RCNN detected glomeruli was used to directly evaluate the performance of our proposed framework relative to Mask-RCNN. This set of 120 images was derived from 5 different patients with WSI of the kidney tissue, and was utilized in our automatic detection experimentation.

\begin{table*}
\caption{DSC scores collected for the internal testing data using both U-Net and DeepLab\textunderscore v3 in 512 Space.}

\centering
\begin{tabular}{|c|c|l|l|l|l|l|l|}
\hline
\multicolumn{2}{|c|}{Image Resolution}                                                        & \multicolumn{1}{c|}{512$\times$512}                             & \multicolumn{1}{c|}{256$\times$256}                            & \multicolumn{1}{c|}{128$\times$128}                            & \multicolumn{1}{c|}{64$\times$64}                              & \multicolumn{1}{c|}{32$\times$32}                              & \multicolumn{1}{c|}{28$\times$28}                              \\ \hline
\multirow{2}{*}{U-Net}       & \begin{tabular}[c]{@{}c@{}}Mean Dice \\ $\pm$ Std Dev\end{tabular} & \begin{tabular}[c]{@{}l@{}}0.909 \\ $\pm$ 0.099\end{tabular} & \begin{tabular}[c]{@{}l@{}}0.936\\ $\pm$ 0.073\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textbf{0.940}\\ \textbf{$\pm$ 0.051}\end{tabular} & \begin{tabular}[c]{@{}l@{}}0.920\\ $\pm$ 0.047\end{tabular} & \begin{tabular}[c]{@{}l@{}}0.878\\ $\pm$ 0.066\end{tabular} & \begin{tabular}[c]{@{}l@{}}0.853\\ $\pm$ 0.072\end{tabular} \\ \cline{2-8}
                             & Median Dice                                                    & 0.948                                                    & \textbf{0.961}                                                   & 0.957                                                   & 0.936                                                   & 0.897                                                   & 0.872                                                   \\ \hline
\multirow{2}{*}{DeepLab\_v3} & \begin{tabular}[c]{@{}c@{}}Mean Dice \\ $\pm$ Std Dev\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textbf{0.948}\\ \textbf{$\pm$} \textbf{0.062}\end{tabular}  & \begin{tabular}[c]{@{}l@{}}0.947\\ $\pm$ 0.033\end{tabular} & \begin{tabular}[c]{@{}l@{}}0.935\\ $\pm$ 0.048\end{tabular} & \begin{tabular}[c]{@{}l@{}}0.907\\ $\pm$ 0.059\end{tabular} & \begin{tabular}[c]{@{}l@{}}0.840\\ $\pm$ 0.080\end{tabular} & \begin{tabular}[c]{@{}l@{}}0.817\\ $\pm$ 0.090\end{tabular} \\ \cline{2-8}
                             & Median Dice                                                    & \textbf{0.963}                                                    & 0.959                                                   & 0.948                                                   & 0.925                                                   & 0.861                                                   & 0.843                                                   \\ \hline
\end{tabular}
\label{table1}
\end{table*}
\begin{table*}
\caption{
DSC scores collected for the external testing data using both U-Net and DeepLab\textunderscore v3 in 512 space.}
\centering
\begin{tabular}{|c|c|l|l|l|l|l|l|}
\hline
\multicolumn{2}{|c|}{Image Resolution}                                                        & \multicolumn{1}{c|}{512$\times$512}                             & \multicolumn{1}{c|}{256$\times$256}                             & \multicolumn{1}{c|}{128$\times$128}                             & \multicolumn{1}{c|}{64$\times$64}                               & \multicolumn{1}{c|}{32$\times$32}                               & \multicolumn{1}{c|}{28$\times$28}                               \\ \hline
\multirow{2}{*}{U-Net}       & \begin{tabular}[c]{@{}c@{}}Mean Dice \\ $\pm$ Std Dev\end{tabular} & \begin{tabular}[c]{@{}l@{}}0.816 \\ $\pm$ 0.141\end{tabular} & \begin{tabular}[c]{@{}l@{}}0.899 \\ $\pm$ 0.063\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textbf{0.902} \\ \textbf{$\pm$} \textbf{0.072}\end{tabular} & \begin{tabular}[c]{@{}l@{}}0.873 \\ $\pm$ 0.101\end{tabular} & \begin{tabular}[c]{@{}l@{}}0.845 \\ $\pm$ 0.086\end{tabular} & \begin{tabular}[c]{@{}l@{}}0.815 \\ $\pm$ 0.105\end{tabular} \\ \cline{2-8}
                             & Median Dice                                                    & 0.853                                                    & 0.917                                                    & \textbf{0.918}                                                    & 0.897                                                    & 0.867                                                    & 0.838                                                    \\ \hline
\multirow{2}{*}{DeepLab\_v3} & \begin{tabular}[c]{@{}c@{}}Mean Dice \\ $\pm$ Std Dev\end{tabular} & \begin{tabular}[c]{@{}l@{}}0.918\\ $\pm$ 0.071\end{tabular}  & \begin{tabular}[c]{@{}l@{}}\textbf{0.922}\\ \textbf{$\pm$} \textbf{0.062}\end{tabular}  & \begin{tabular}[c]{@{}l@{}}0.911 \\ $\pm$ 0.068\end{tabular} & \begin{tabular}[c]{@{}l@{}}0.887\\ $\pm$ 0.087\end{tabular}  & \begin{tabular}[c]{@{}l@{}}0.812\\ $\pm$ 0.122\end{tabular}  & \begin{tabular}[c]{@{}l@{}}0.810\\ $\pm$ 0.088\end{tabular}  \\ \cline{2-8}
                             & Median Dice                                                    & 0.931                                                    & \textbf{0.934}                                                    & 0.928                                                    & 0.906                                                    & 0.845                                                    & 0.834                                                    \\ \hline
\end{tabular}
\label{table2}
\end{table*}

\subsection{Experimental Design}
Our study was split into two distinct phases: manual detection and automatic detection. For our manual detection experimentation, 704 images were randomly chosen as testing images, while the remaining 98 images were used for validation. Additionally, 147 internal testing images were utilized alongside 385 external testing images from an independent cohort. On the other hand, for our automatic detection experimentation, 120 images, kept in their original resolutions ($>$ 1,000$\times$1,000 pixels), were used to evaluate the performance of our detect-then-segment framework relative to Mask-RCNN.

The U-Net, DeepLab\textunderscore v3, and Mask-RCNN pipelines were deployed on a typical workstation with Intel Xeon CPU 2.2 GHz, 13 GB RAM, 33 GB Disk Space, 12 GB NVIDIA Tesla K80 GPU, and CUDA 10.1. For our manual detection experimentation, the hyper-parameters of the U-Net and DeepLab\textunderscore v3 pipelines were 150 epochs, a batch size of 4, a learning rate of 0.0001, a color space argument (RGB or LAB, depending on the trial), as well as a scale argument, which was altered to test the performance of segmentation across six distinct resolutions. Additionally, an Adam Optimizer was used to adaptively alter the learning rate, with beta values ranging from 0.9 to 0.999.

\subsubsection{Manual Detection}
Within our manual detection experimentation, manually detected glomerular images were processed with 2 distinct color spaces, 6 different image resolutions, and 2 unique segmentation backbones. We began with a U-Net segmentation framework, where the RGB and LAB color spaces were studied. Within each color space, 6 resolutions were tested through the U-Net pipeline: 1) 512$\times$512, 2) 256$\times$256, 3) 128$\times$128, 4) 64$\times$64, 5) 32$\times$32, and 6) 28$\times$28. For each trial, 150 epochs were run for all training, validation, and testing images. Additionally, within each epoch, a DSC value was generated for the validation and testing images. For each resolution, the epoch with the highest DSC value for the validation data was recorded and its generated model was saved. With this generated model, predicted masks were created at each of the six resolutions analyzed for the validation and testing data. This process was repeated yet again for the LAB color space. After this experiment was completed for the U-Net framework, the previously described methodology was also repeated for the DeepLab\textunderscore v3 framework, but the only color space analyzed for DeepLab\textunderscore v3 was the best performing color space in the U-Net trial. If the difference in performance between the two color spaces was negligible in the U-Net trial, then we defaulted to RGB to analyze the DeepLab\textunderscore v3 framework. This is because utilizing the RGB color space is standard, and the introduction of the LAB color space in our study was due to recent findings that show that the LAB color space produces better results for image classification tasks by reducing image channel-wise correlation~\cite{gowda2018colornet}.

Once all predicted masks were generated, the performance of the U-Net and DeepLab\textunderscore v3 segmentation networks was then analyzed by upsampling the predicted masks for the validation and testing images, and comparing them back to the original 512$\times$512 ground truth mask. Mean and median DSC scores were computed as a result of the upsampling.

\subsubsection{Automatic Detection}

In the automatic detection phase, the performance of our detect-then-segment framework was directly compared against Mask-RCNN through a cohort of 120 glomeruli images that were kept in their original resolution ($>$ 1000$\times$1000 pixels). In doing so, we utilized the model files that were generated for each of the six resolutions for both U-Net and DeepLab\textunderscore v3 as described in \textsection{4.2.1}. In particular, we first downsampled the 120 glomerular images to the scales of 512$\times$512, 256$\times$256, 128$\times$128, 64$\times$64, 32$\times$32, and 28$\times$28. Then, we procured each model file produced at the corresponding resolutions in the U-Net trial with the RGB color space. We then generated the predicted masks for each of the six downsampled resolutions of the 120 glomerular images utilizing the U-Net model file. We repeated this process for DeepLab\textunderscore v3 in the RGB color space. All predicted image sets were then upsampled back to the original size of the glomeruli images ($>$ 1,000$\times$1,000 pixels). Mean and median DSC values were then generated to evaluate performance. Furthermore, we also used a standard Mask-RCNN implementation to generate predicted masks at the corresponding original resolutions ($>$ 1,000$\times$1,000 pixels) of the glomeruli images. We then compared mean, median, and standard deviation DSC values to investigate which methodology performed best.

\subsection{Evaluation Metrics and Statistical Methods}

DSC was the primary statistic used to evaluate segmentation performance. In particular, mean, median, and standard deviation of DSC were generated for analysis. Additionally, to evaluate statistical significance between each resolution and different methods, the Wilcoxon Rank Sum test was used with a significance threshold of either p $<$ 0.05 or p $<$ 0.01. A notched box plot was generated to visually demonstrate median DSC data, as well as the results of Wilcoxon Rank Sum test. Additionally, bar graphs were generated to show mean and standard deviation DSC data. Similarly, to demonstrate the relation between the performance of segmentation within each resolution in both LAB and RGB color space, DSC values for the U-Net trial were summarized in data tables.

\section{Results}

Our results presented in this section are divided into two central subsections to explore each aspect of our experimentation: 1) manual detection and 2) automatic detection.

\subsection{Manual Detection}

The first aspect of our study is manual detection, wherein we analyzed the key factors of segmentation, which include: segmentation backbones, input image resolutions, as well as color spaces. This phase of our study allowed us to better assess the conditions in which a detect-then-segment framework performs most optimally in the context of high resolution WSI.

\begin{figure*}
\begin{center}
\includegraphics[width=1.0\textwidth]{{Figures/Good_Average_BadUNET.pdf}}
\end{center}
\caption{
   This figure demonstrates three categories of good, average, and bad performance which is defined by DSC. The background of each comparison is the ground truth input, and the overlaid image is the predicted mask. This specific figure shows the results of U-Net.
}
\label{Fig.3}
\end{figure*}

\begin{figure*}
\begin{center}
\includegraphics[width=1.0\textwidth]{{Figures/Good_Average_BadDeep.pdf}}
\end{center}
\caption{
       This figure demonstrates three categories of good, average, and bad performance which is defined by DSC. The background of each comparison is the ground truth input, and the overlaid image is the predicted mask. This specific figure shows the results of DeepLab\textunderscore v3.
}
\label{Fig.4}
\end{figure*}

\begin{figure*}
\begin{center}
\includegraphics[width=1.0\textwidth]{{Figures/Notch.pdf}}
\end{center}
\caption{
     This figure demonstrates the notched box plots of each resolution for both internal and external testing data of the RGB color space. The left bar graph summarizes the results for the U-Net trial; the right graph summarizes the results for DeepLab\textunderscore v3. The legend and asterisks demonstrates results of computing the Wilcoxon Rank Sum Test on the specified resolutions. The median DSC values are derived by evaluating the ground truth mask in 512$\times$512 resolution, relative to the predicted mask in 512$\times$512 resolution for a fair comparison.
}
\label{Fig.5}
\end{figure*}

\begin{figure*}
\begin{center}
\includegraphics[width=1.0\textwidth]{{Figures/UNET.pdf}}
\end{center}
\caption{
     The above figure shows the mean and standard deviation for both internal and external testing images evaluated in both sample space and 512 space. Similar to how we define ``512 space", the term ``sample space" refers to the process by which the predicted masks that are produced across six distinct resolutions (512$\times$512, 256$\times$256, 128$\times$128, 64$\times$64, 32$\times$32, and 28$\times$28) are evaluated against the corresponding downsampled input image across the same six resolutions. We re-evaluated such images in 512 space for a fair DSC comparison through upsampling, as discussed earlier. As shown, 64$\times$64, 32$\times$32, and 28$\times$28 declined greatly in accuracy when comparing DSC in 512 space relative to DSC in sample space.
}
\label{Fig.6}
\end{figure*}

\begin{figure*}
\begin{center}
\includegraphics[width=1.0\textwidth]{{Figures/DeepLab_V3-1.pdf}}
\end{center}
\caption{
The above figure shows the mean data and standard deviation for both internal and external testing images evaluated in both sample space and 512 space. As shown, 64$\times$64, 32$\times$32, and 28$\times$28 declined greatly in accuracy when comparing DSC in 512 space relative to DSC in sample space.
}
\label{Fig.7}
\end{figure*}

\begin{table*}
\caption{Performance of RGB and LAB color spaces on internal testing data using U-Net in 512 space.}
\centering
\begin{tabular}{|c|l|l|l|l|l|l|l|}
\hline
\multicolumn{2}{|c|}{{\color[HTML]{000000} \textbf{Image Resolution}}}                                                                                & {\color[HTML]{000000} 512$\times$512}                                                  & {\color[HTML]{000000} 256$\times$256}                                                  & {\color[HTML]{000000} 128$\times$128}                                                  & {\color[HTML]{000000} 64$\times$64}                                                    & {\color[HTML]{000000} 32$\times$32}                                                    & {\color[HTML]{000000} 28$\times$28}                                                    \\ \hline
{\color[HTML]{000000} }                                        & {\color[HTML]{000000} \begin{tabular}[c]{@{}l@{}}Mean Dice \\ $\pm$ Std Dev\end{tabular}} & {\color[HTML]{000000} \begin{tabular}[c]{@{}l@{}}0.909 \\ $\pm$ 0.099\end{tabular}} & {\color[HTML]{000000} \begin{tabular}[c]{@{}l@{}}0.936 \\ $\pm$ 0.073\end{tabular}} & {\color[HTML]{000000} \begin{tabular}[c]{@{}l@{}}\textbf{0.940} \\ \textbf{$\pm$ 0.051}\end{tabular}} & {\color[HTML]{000000} \begin{tabular}[c]{@{}l@{}}0.920 \\ $\pm$ 0.047\end{tabular}} & {\color[HTML]{000000} \begin{tabular}[c]{@{}l@{}}0.878 \\ $\pm$ 0.066\end{tabular}} & {\color[HTML]{000000} \begin{tabular}[c]{@{}l@{}}0.853 \\ $\pm$ 0.072\end{tabular}} \\ \cline{2-8}
\multirow{-2}{*}{{\color[HTML]{000000} \textbf{RGB}}}    & {\color[HTML]{000000} Median Dice}                                                    & {\color[HTML]{000000} 0.948}                                                    & {\color[HTML]{000000} \textbf{0.961}}                                                    & {\color[HTML]{000000} 0.957}                                                    & {\color[HTML]{000000} 0.936}                                                    & {\color[HTML]{000000} 0.897}                                                    & {\color[HTML]{000000} 0.872}                                                    \\ \hline

{\color[HTML]{000000} }                                        & {\color[HTML]{000000} \begin{tabular}[c]{@{}l@{}}Mean Dice \\ $\pm$ Std Dev\end{tabular}} & {\color[HTML]{000000} \begin{tabular}[c]{@{}l@{}}0.917 \\ $\pm$ 0.081\end{tabular}} & {\color[HTML]{000000} \begin{tabular}[c]{@{}l@{}}0.936 \\ $\pm$ 0.068\end{tabular}} & {\color[HTML]{000000} \begin{tabular}[c]{@{}l@{}}\textbf{0.940} \\ \textbf{$\pm$ 0.050}\end{tabular}} & {\color[HTML]{000000} \begin{tabular}[c]{@{}l@{}}0.925 \\ $\pm$ 0.045\end{tabular}} & {\color[HTML]{000000} \begin{tabular}[c]{@{}l@{}}0.872 \\ $\pm$ 0.069\end{tabular}} & {\color[HTML]{000000} \begin{tabular}[c]{@{}l@{}}0.863 \\ $\pm$ 0.066\end{tabular}} \\ \cline{2-8}
\multirow{-2}{*}{{\color[HTML]{000000} \textbf{LAB}}}    & {\color[HTML]{000000} Median Dice}                                                    & {\color[HTML]{000000} 0.946}                                                    & {\color[HTML]{000000} \textbf{0.961}}                                                    & {\color[HTML]{000000} 0.957}                                                    & {\color[HTML]{000000} 0.941}                                                    & {\color[HTML]{000000} 0.896}                                                    & {\color[HTML]{000000} 0.882}                                                    \\ \hline

\end{tabular}
\label{table3}
\end{table*}

\subsubsection{U-Net VS DeepLab\textunderscore v3}

We first present that both U-Net and DeepLab\textunderscore v3 confer particular advantages over one another across the six tested resolutions in the context of glomerular image data. {\color{green} Tab.~\ref{table1} and \ref{table2}} present DSC values for internal and external data for U-Net and DeepLab\textunderscore v3 in the RGB color space. Both tables show us that DeepLab\textunderscore v3 would perform better than U-Net for larger resolutions, such as 512$\times$512, 256$\times$256, and 128$\times$128, but would under-perform relative to U-Net for smaller image resolutions, such as 64$\times$64, 32$\times$32, and 28$\times$28. As shown, there is no clear, consistent framework that achieved the best DSC results for all trials. However, both U-Net and DeepLab\textunderscore v3 show distinct advantages --- DeepLab\textunderscore v3 tends to perform better for larger resolutions, whereas U-Net confers higher DSC values for smaller resolutions.
\subsubsection{Image Resolution}

In the trial utilizing a U-Net framework, the resolution in RGB space with the highest median DSC value of 0.961 was 256$\times$256, which was statistically different relative to 64$\times$64, 32$\times$32, and 28$\times$28. On the other hand, for the external testing dataset in RGB space, the resolution with the highest median DSC value of 0.918 was 128$\times$128, which was significantly different relative to 28$\times$28, 32$\times$32, 64$\times$64, and 512$\times$512. A similar analysis was performed on the mean values and standard deviation of the internal and external testing DSC data of the RGB color space in the U-Net trial. For both internal and external data, 128$\times$128 was the resolution with highest mean DSC values in both the sample space and the upscaled 512 space. Additionally, {\color{red} Fig.~\ref{Fig.6}} further shows the resolutions of 64$\times$64, 32$\times$32, and 28$\times$28 experience the greatest decline in performance when comparing the DSC in sample space to the DSC in 512 space.

Considering the trial that used a DeepLab\textunderscore v3 framework, the highest median DSC value for internal data was 0.963, which occurred in the 512$\times$512 resolution. This particular resolution was significantly greater relative to 128$\times$128, 64$\times$64, 32$\times$32, and 28$\times$28. Similarly, for the external data, the highest median DSC value was 0.934 which occurred in 256$\times$256 space. This resolution was statistically greater relative to the resolutions of 128$\times$128, 64$\times$64, 32$\times$32, and 28$\times$28. Analyzing the mean data for DeepLab\textunderscore v3, it is clear that the highest mean DSC in the internal data occurred in the 256$\times$256 resolution when evaluating DSC in the sample space, whereas the highest mean DSC in 512 space occurred for the 512$\times$512 resolution.  For the external data, the highest mean DSC value occurred in the 256$\times$256 resolution when evaluating dice in the sample and 512 space. Similar to U-Net, the highest difference in mean DSC between the sample space and 512 space occurred in the resolutions of 64$\times$64, 32$\times$32, and 28$\times$28. The comparison of mean data can be seen visually in {\color{red} Fig.~\ref{Fig.7}}.

\begin{table*}
\caption{Results of U-Net and DeepLab\textunderscore v3 models during our automatic detection phase, as well as Mask-RCNN.}
\centering
\begin{tabular}{cllllllll}
\cline{1-7} \cline{9-9}
\multicolumn{1}{|c|}{Resolution}                                                                   & \multicolumn{1}{c|}{512$\times$512}                                                  & \multicolumn{1}{c|}{256$\times$256}                                                  & \multicolumn{1}{c|}{128$\times$128}                                                  & \multicolumn{1}{c|}{64$\times$64}                                                    & \multicolumn{1}{c|}{32$\times$32}                                                    & \multicolumn{1}{c|}{28$\times$28}                                                    & \multicolumn{1}{l|}{} & \multicolumn{1}{c|}{\begin{tabular}[c]{@{}c@{}}Mask \\ RCNN\end{tabular}}     \\ \cline{1-7} \cline{9-9}
\multicolumn{1}{|c|}{\begin{tabular}[c]{@{}c@{}}\textbf{U-Net:}\\Mean Dice $\pm$ \\ Std Dev\end{tabular}}      & \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}0.935 $\pm$ \\ 0.062\end{tabular}} & \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textbf{0.947 $\pm$} \\ \textbf{0.037}\end{tabular}} & \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}0.935 $\pm$ \\ 0.035\end{tabular}} & \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}0.903 $\pm$ \\ 0.041\end{tabular}} & \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}0.833 $\pm$\\ 0.060\end{tabular}}  & \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}0.791 $\pm$ \\ 0.067\end{tabular}} & \multicolumn{1}{l|}{} & \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}0.902 $\pm$ \\ 0.038\end{tabular}} \\ \cline{1-7} \cline{9-9}
\multicolumn{1}{|c|}{\begin{tabular}[c]{@{}c@{}}\textbf{U-Net:}\\ Median Dice\end{tabular}}                 & \multicolumn{1}{l|}{0.956}                                                    & \multicolumn{1}{l|}{\textbf{0.957}}                                                    & \multicolumn{1}{l|}{0.945}                                                    & \multicolumn{1}{l|}{0.914}                                                    & \multicolumn{1}{l|}{0.845}                                                    & \multicolumn{1}{l|}{0.798}                                                    & \multicolumn{1}{l|}{} & \multicolumn{1}{l|}{0.908}                                                    \\ \cline{1-7} \cline{9-9}
\multicolumn{1}{l}{}                                                                               &                                                                               &                                                                               &                                                                               &                                                                               &                                                                               &                                                                               &                       &                                                                               \\ \cline{1-7} \cline{9-9}
\multicolumn{1}{|c|}{\begin{tabular}[c]{@{}c@{}}\textbf{DeepLab\_v3:}\\ Mean Dice $\pm$\\ Std Dev\end{tabular}} & \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textbf{0.953 $\pm$} \\ \textbf{0.027}\end{tabular}} & \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}0.941 $\pm$ \\ 0.034\end{tabular}} & \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}0.919 $\pm$\\ 0.043\end{tabular}}  & \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}0.876 $\pm$ \\ 0.053\end{tabular}} & \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}0.771 $\pm$ \\ 0.067\end{tabular}} & \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}0.750 $\pm$ \\ 0.067\end{tabular}} & \multicolumn{1}{l|}{} & \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}0.902 $\pm$ \\ 0.038\end{tabular}} \\ \cline{1-7} \cline{9-9}
\multicolumn{1}{|c|}{\begin{tabular}[c]{@{}c@{}}\textbf{DeepLab\_v3:}\\ Median Dice\end{tabular}}           & \multicolumn{1}{l|}{\textbf{0.961}}                                                    & \multicolumn{1}{l|}{0.950}                                                    & \multicolumn{1}{l|}{0.931}                                                    & \multicolumn{1}{l|}{0.891}                                                    & \multicolumn{1}{l|}{0.782}                                                    & \multicolumn{1}{l|}{0.753}                                                    & \multicolumn{1}{l|}{} & \multicolumn{1}{l|}{0.908}                                                    \\ \cline{1-7} \cline{9-9}
\end{tabular}
\label{table4}
\end{table*}
The trend in the median DSC data and the results of the Wilcoxon Rank Sum Test can be seen in {\color{red} Fig.~\ref{Fig.5}}.

\subsubsection{Image Color Space}

The conferred accuracy of using either the LAB or RGB color spaces were found to be negligible in the trial utilizing a U-Net framework. {\color{green} Tab.~\ref{table3}} shows the results of the RGB and LAB color spaces on the internal dataset using U-Net. Both RGB and LAB give almost the same best DSC values, which is bolded. Therefore, the rest of the analysis in this paper is focused on the effectiveness of segmentation in the RGB color space, as the results are generalizable due to the similarity of segmentation performance between the RGB and LAB color spaces. In particular, the DeepLab\textunderscore v3 trial, as stated in the methodology, only performs its segmentation in the RGB color space, due to the results of U-Net.

\subsection{Automatic Detection}

\subsubsection{Detect-then-Segment Framework VS End-to-End Mask-RCNN}
To begin, {\color{green} Tab.~\ref{table4}} demonstrates the results of applying our U-Net and DeepLab\textunderscore v3 models on the cohort of 120 images during our automatic detection phase, and generating predicted masks by downsampling the input images to six distinct resolutions. By evaluating the difference in performance between our proposed detect-then-segment pipeline relative to the standard end-to-end Mask-RCNN framework, we found that both U-Net and DeepLab\textunderscore v3 showed better mean and median DSC values for the resolutions of 512$\times$512, 256$\times$256, and 128$\times$128 (p $<$ 0.01, Wilcoxon Rank Sum). In particular, at best, our framework provides a mean DSC value of 0.953 via the DeepLab\textunderscore v3 backbone operating on a previously detected glomerulus of 512$\times$512 resolution, whereas Mask-RCNN produced a mean DSC value of 0.902.

\section{Discussion}
First, in our experimentation with manual detection, we comprehensively searched and tested for the best detect-then-segment strategy. The results demonstrate that: 1) utilizing a higher resolution does not necessarily confer the best segmentation results; 2) the resolutions of 128$\times$128 and 256$\times$256 consistently demonstrated the best segmentation results; 3) lower resolutions (64$\times$64, 32$\times$32, and 28$\times$28) experience the greatest loss of accuracy when comparing DSC in sample space relative to 512 space; 4) DeepLab\textunderscore v3 yields better results in higher resolutions (512$\times$512, 256$\times$256, and 128$\times$128), whereas U-Net performs most optimally in lower resolutions (64$\times$64, 32$\times$32, and 28$\times$28); and 5) neither the LAB nor RGB color space give rise to better segmentation results relative to one another. Briefly, our results in \textsection{5.1.2} demonstrate that the resolutions of 128$\times$128 and 256$\times$256 consistently demonstrated the best DSC results, and the particular resolution of 512$\times$512 would actually yield relatively lower segmentation results, especially for median DSC. Additionally, the lower resolutions --- namely 64$\times$64, 32$\times$32, and 28$\times$28 --- consistently experienced a great loss in accuracy when analyzing the effectiveness of segmentation in 512 space. Finally, the results demonstrate that DeepLab\textunderscore v3 and U-Net perform most optimally in different ranges of resolutions. When considering RGB and LAB color spaces, we found there was no discernible effect or advantage of color space on the segmentation of high resolution glomerular images.

Further, we show that our proposed detect-then-segment pipeline is superior to the conventional end-to-end Mask-RCNN framework. Our summarized results show that the image resolutions of 512$\times$512, 256$\times$256, and 128$\times$128, in both U-Net and DeepLab\textunderscore v3 in RGB space are significantly better than that of Mask-RCNN. Of course, the most optimal result was achieved through DeepLab\textunderscore v3, with a mean DSC of 0.953 which occured in 512$\times$512 space. However, both U-Net and DeepLab\textunderscore v3 showed the same trend in the data. Overall, through our automatic detection trial, we conclude that utilizing a detect-then-segment framework across 512$\times$512, 256$\times$256, or 128$\times$128 will provide better segmentation results compared to the typical Mask-RCNN pipeline. Additionally, through our manual detection trial, we demonstrate that the most optimal detect-then-segment strategy involves utilizing a DeepLab\textunderscore v3 framework on larger resolution input images (256$\times$256 and 128$\times$128), in either the RGB or Lab color space.

One key advantage of our research is our analysis of several critical factors of segmentation. In particular, our efforts strive to understand what works best for high resolution renal WSI, as opposed to practicing the standard end-to-end methods that are popular in computer vision. By studying the effect of color space, resolution, and segmentation backbone on the characterization of WSI through glomeruli data, we are better able to understand how to improve current segmentation networks that operate on high resolution images so as to yield better results. Another key advantage is how our study definitively shows that our proposed framework can yield a clear advantage in accuracy over the standard end-to-end instance segmentation methods in the context of high resolution renal WSI. Overall, our data provides a unique and important view towards new methodologies that show better results in high resolution imaging.

However, there are some important limitations to our study. First, the focus of this study was in the context of renal pathology and glomerular data. However, we expect the findings will be generalizable for other objects in renal pathology as the scaling issues are similar. Another limitation includes the fundamental restraints of the GPU when processing large scale images. In our study, the largest resolution photo on which we trained our data was 512$\times$512. In particular, we had to downsample the original resolution of the glomerular data due to the memory limitations of the GPU in order to efficiently conduct our study.

A central way by which this study may be improved is by diversifying the dataset so as to include other biological objects, such as veins, arteries or tubules. Doing so would help solve the problem of a lack of generalizability with respect to our results, and would allow for the significance of our conclusions to be more widespread.

\section{Conclusions}
Overall our experimentation through manual and automatic detection phases lead us to a three-fold conclusion: 1) a detect-then-segment framework is more effective than an end-to-end pipeline in the context of high resolution renal WSI; 2) the performance of a detect-then-segment framework is most optimal with a DeepLab\textunderscore v3 segmentation backbone operating on a 512$\times$512 resolution for previously detected glomerular input images; and 3) utilizing either RGB or LAB color spaces for previously detected glomerular input images does not yield a particular advantage over the other in a detect-then-segment framework. To conclude, our research paves the way towards further discussion and analysis in understanding effective and more nuanced methodologies that are more accurate than the current framework by which we characterize high resolution images of glomeruli, and biological objects of similar character, on large-scale WSI.

\section*{Acknowledgment}
This work was supported in part by NIH NIDDK DK56942(ABF).

\ifCLASSOPTIONcaptionsoff
  \newpage
\fi
\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
Samsung H200 HD Camcorder Is On Pre-Order
Posted: Jan 9 2010, 7:00am CST | by Susan McGlaun , Updated: Aug 10 2010, 10:53am CDT , in Shopping Tips
Premiering at CES 2010 Samsung's new H200 camcorder offers high-performance with full 1920x1080/60i HD recording. It features an impressive 37mm wide-angle lens, which offers the consumer a versatile 20x optical zoom. This lens allows you to fit more of their desired scene into the frame. It also has Smart Optical Image Stabilization which is superior to image stabilizing technologies found in other camcorders.
The H200 2.7" swiveling, touch-screen LCD boasts a 230K-pixel resolution and adds to the user experience by giving consumers uncomplicated and easy access to all of the camcorders various controls and settings. The H200 does not feature internal SSD memory and requires the use of a SD/SDHC memory card.
The Samsung H200 also allows the user the ability to take pictures with 4.7 megapixels. Smart Auto mode, which automatically selects the needed scene mode which will produce the best possible pictures and video for the scenario they are in. It also has time lapse mode so users can program the new H200 camcorders to record a single image at intervals of one, three or five seconds and then save each image as one single movie file at 720p resolution.
The Samsung H200 is available for pre-order on Amazon for $399.99.
I4U News brings your daily shopping tips of new releases, pre-orders, hot deals and unique gadgets. Read the latest Shopping Tips now.
Susan McGlaun
Susan is the editor of the shopping guide on I4U News. She is finding for our readers the best deals and the most interesting products to buy. She lives for finding a great deal that can save our readers serious money. For every season Susan is reporting about the best gift ideas including Back-to-School and of course the Holidays.
Susan can be contacted directly at susan@i4u.com.

redpajama_set_name: RedPajamaCommonCrawl


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Medicine Club started its journey in 31 January 1981 from Mymensingh Medical College.
The concept of Medicine Club was defined to Mymensingh Medical College for several years. It was also in 1987 when they started to spread their concept to other medical colleges. The first attempted took place in Rajshahi Medical College in 1990. But they were successful for the first time in Sir Solimullah Medical College in 1990. Barisal Sher-E-Bangla Medical College unit was the next one to start in 1992. Attempts were also made in Dhaka Medical College & Sylhet M.A.G. Osmani Medical College in 1992. The first central conference of Medicine Club was held in 1992 at Mymensingh Medical College. Three active units (MMC unit, SSMC unit & SBMC unit ) took part in that conference.
At the end of 1996,some new units were installed. These are Rajshahi Medical College unit, Comilla Medical College unit & Bangladesh Medical College unit. Faridpur Medical College was included in Medicine Club family in 1998. At the end of 1998, Khulna Medical College unit started their journey. Dinajpur Medical College was brought into view in1999. Rangpur Medical College unit & Bogra Medical College unit started in January 2000. In the mean time Pioneer Medical College unit & Gonoshasthaya Samaj Vittik Medical College unit have startes their noble action of Medicine Club.


text:
All in News Features
Too scared to win?
Barack Obama must fight for his principles, or he'll give away the keys to the White House
By JEFF INGLIS | August 13, 2008
Credit: Dale Stephanos
The video shows a meeting of Barack Obama's campaign staff. A progressive activist arrives to pitch in, but her eyes glaze over amid Democratic-establishment polling reports and move-to-the-center cliché-spouting. Not quite two minutes go by before she interrupts to explain Obama's connections to big corporations and neo-conservative foreign-policy advisers. "He'll promise to rock the boat, but he won't sink it," she warns, insisting that the campaign return to the strong, eloquent, principled stands Obama took in the primary.
Her argument wins over those in the room, but before switching strategies, one of the ex-establishment groupies has a question: "Do we still work for Obama?" The progressive's answer: "No! He works for us. He always did."
Sure, it's just the opening skit of the most recent Liberty News TV episode, a progressive news-and-commentary program written and filmed in Portland and distributed on public-access cable channels nationwide. The Illinois senator and his campaign staff need to sit up and take notice anyway, not because it's a suggestion of a path to victory, but because the clip lays out his only path to victory.
There are a lot of people giving Obama advice about what he should do to beat John McCain. (See "Winning at the Grassroots Level" for a list of books offering similar advice for progressive activists.) But only one of them is offering advice based on an actual analysis of long-term voting and polling data to determine what voters really really want. And what they want is not someone who follows the polls and gets pushed around by the media, but someone who knows what he believes, says so, and stands up for it even in the face of criticism.
In his primary campaign, Obama staked out the progressive, aggressive, principled high ground, and attracted millions of passionate supporters. Having created the movement, and having been selected as its head, he should now follow his people — which almost certainly means doing something more dangerous than any major candidate has ever done: ditching the party establishment.
The people who back Obama may be energetic young progressives, but they are not unlike the vast majority of Americans when it comes to what they look for in a candidate. Glenn Hurowitz, a longtime progressive activist, explains in his book Fear and Courage in the Democratic Party that a major factor determining any voter's choice is whether the candidate fights well (a characteristic described in polling data as being a "strong leader").
That trait, Hurowitz argues, trumps most other concerns — even differences of opinion on major policy questions (though not party affiliation). His book, based on a new analysis of 40 years of election and polling data, suggests that the reason the far-right conservative movement has risen to control the American political system is not due to any particular intelligence or ability on the part of right-wing activists, who espouse positions vastly divergent from most Americans' values. The rise of the right has happened because Democrats and progressives refuse to stand and fight for what they believe in.
1 | 2 | 3 | 4 | 5 | next >
Topics: News Features , Barack Obama, Election Campaigns, Elections and Voting, More , Barack Obama, Election Campaigns, Elections and Voting, Politics, U.S. Politics, U.S. Presidential Election, Political Parties, U.S. Democratic Party Politics, George W. Bush, Al Gore, Less
ARTICLES BY JEFF INGLIS
INVISIBLE PAYWALL | December 12, 2013
The Lewiston Sun Journal's online paywall launched last week, and nobody really noticed.
A MEDITATION FOR NEWCOMERS TO BUYING ART | December 04, 2013
In fact, art buying is — or can be — a lot like meditation. For starters, it's best to approach it with a relaxed body and mind.
COUNCIL PROTECTS CLINIC PATIENTS, INVITES LAWSUIT | November 20, 2013
Maine law already prevents protesters from blocking a patient's path to the door of a clinic, bans threats made toward clinic staff or patients (including by telephone), and outlaws excessive noise aimed at disturbing the occupants of a clinic or health-care facility. PPNNE has hired a Portland police officer to patrol the sidewalk nearby, to help enforce those laws, but problems remain.
GETTING SPUN | November 14, 2013
The sequence of subsequent events shows not only how good Michaud and his team are at co-opting the news cycle, but also how vulnerable Maine's mainstream media is to a professional operation.
MARGINALIZED MAINSTREAM | October 17, 2013
Amid a weeks-long deluge of pontification and punditry in newspapers every day and around the clock on dozens of TV channels, who would have thought that the key to the shutdown dilemma would be a five-minute video of procedural business on the floor of the US House of Representatives?
See all articles by: JEFF INGLIS
Mrs. Warren goes to Washington
BOOK EXCERPT: Wild West Broadway: Busing, the Bulger Brothers, Kevin White, and the Boston Globe
Whitey's Southie: A tour of Bulger's haunts
Inside Whitey's head


Handmade in Texas since 1883. Dunn is a sleek roper featuring supple Florence buffalo leather. Pull strap overlays and a single scroll beneath the collar separate this boot from the rest of the pack. All toe, heel and width options available.


text:
Best Headphones and Earphones of 2021 in India
From Apple to Nothing, here are our top picks among headphones and earphones for 2021.
By Ali Pardiwala | Updated: 25 November 2021 16:17 IST
The OnePlus Buds Pro brings enjoyable sound and ANC, for just under Rs. 10,000
Source: OnePlus. Design: Akhil Arora/Gadgets 360
The AirPods Max is luxurious, but is best suited for Apple devices
The Nothing Ear 1 looks impressive and sounds good too
Realme offers affordable ANC with the Buds Q2
The best headphones and earphones of 2021 come from a diverse list of brands, covering different fit and styles, as well as varied price segments. However, a quick look at our list will show you that most of our picks are true wireless earphones (TWS), showing just how popular this type of wireless headset has become. That said, our list also covers options from other design styles, including over-ear headphones, neckband earphones, and our favourite pick among the less common but still popular audiophile category.
As before, we've selected our favourite picks across different price segments, paying close attention to value-for-money as a key purchase factor. Interestingly, most of the options here also include active noise cancellation (ANC) as a feature, showing just how accessible and commonplace the once premium technology has now become. Here are our top picks among headphones and earphones for 2021 in India.
Best overall headset of 2021: OnePlus Buds Pro
Our choice for the most impressive headset of 2021 is the OnePlus Buds Pro, the company's newest true wireless earphones. Priced at Rs. 9,999, the OnePlus Buds Pro is a considerable step up over the budget options in the range, in terms of features and performance. Although an impressive overall package, it's the fun, energetic sound that really helps these earphones stand apart, and makes this quite easily the best pair of true wireless earphones you can buy for less than Rs. 10,000 right now.
Best Active Noise Cancelling Headphones and Earphones
Although the charging case looks a bit plain, and some features such as Dolby Atmos work only when the OnePlus Buds Pro are paired with OnePlus smartphones, the overall experience is fairly uniform and good across source devices. The design and quality of the active noise cancellation are good as well, making this the standout headset for 2021.
is available in India for Rs. 59,900. Although very expensive and essentially built and best suited for use with iOS devices, these headphones offer a premium experience in every way, including the design and comfort, the quality of the active noise cancellation, the ease of use and connectivity, and the quality of the sound.
Best True Wireless Earphones You Can Buy
As with other Apple AirPods products, the AirPods Max is best suited to use with Apple devices, with the connectivity features and Lightning charging port only really making sense for users who have an iPhone - or perhaps also an iPad or a Mac computer. Apple's Smart Case which is bundled with the headphones is also a bit silly in its design, but the AirPods Max more than makes up for its flaws with luxurious design and comfort, very good active noise cancellation, and superb sound driven by Apple's Adaptive EQ technology. This is a premium headset like no other.
. Priced at Rs. 2,299, the Realme Buds Wireless 2 offers a lot of features for the price, including active noise cancellation, app support through the Realme Link app, Google Fast Pair, and support for the LDAC Bluetooth codec. This makes it a full-featured wireless headset with very good sound for the price.
The Best Movies on Netflix
Although the design isn't quite as refined as we would have hoped for due to cables that are a bit long, and battery life that is fairly average, the sound quality and feature set makes up for these small drawbacks. Although many might prefer the convenience and comfort of the true wireless form factor, the Realme Buds Wireless 2 offers excellent value and sound quality for those willing to go with the traditional neckband style.
is exactly that. This pair of wired audiophile earphones is priced at Rs. 1,899, and offers beginners and budget audiophile buyers an excellent listening experience for the price. The unique styling, detachable cable, detailed sound, and ease of use with compatible source devices and DACs alike make this a worthwhile piece of kit.
The Best Budget Audiophile Kit for Apple Music Lossless Audio
There's really nothing to complain about with the KZ ZSN Pro X, making this a great headset to consider if you're on a budget and want good sound quality. It works particularly well for high-resolution audio, so if you are just getting started with Apple Music's high-resolution audio tracks, then this is the ideal headset under Rs. 2,000.
offer a similar experience with Android devices. Priced at Rs. 9,999, the Pixel Buds A-Series works well with Google Assistant, and uses Google Fast Pair to link the earphones to your Google account on Android. The earphones look good, are comfortable, and offer good all-round performance with music and calls.
Apple AirPods Max vs Sony WH-1000XM4
Although you can use the Google Pixel Buds A-Series with non-Android devices, the user experience is considerably reduced in terms of features and capabilities. The lack of active noise cancellation is also a big drawback here, particularly given that the earphones have a proper in-ear fit, and similarly priced options do have ANC. However, it's a headset well-equipped for use with Android, and is one of our top picks of the year for this reason.
is our top pick among affordable true wireless earphones for this reason; at Rs. 2,499, this headset offers ANC, app support, decent sound quality, fairly good battery life, and excellent value for money.
Eight Mind Blowing Headphones That Redefine the Word 'Flagship'
We did find the soundstage a bit narrow, the highs a bit shrill, and the low-latency mode a bit ineffective, but the Realme Buds Q2 gets it right in the matters that count, particularly in offering a lot of bang for your buck.
. Although launched at Rs. 5,999, the earphones are now available for Rs. 6,999. However, they are still good value for money thanks to good features such as active noise cancellation, app support, and Qi wireless charging, apart from the excellent see-through design and modern styling.
Dolby Atmos Music in India: Everything You Need to Know
The sound is balanced and detailed, and the excellent design also extends to the charging case. There are some issues including the lack of voice assistant support, and a somewhat inadequate attack and drive in the sound, but the Nothing Ear 1 scores big points on its design and features, making it a worthwhile pick among the many launches this year.
, the Gadgets 360 podcast for an exclusive wide-ranging interview, as he talks about the 5G push, Make in India, Realme GT series and Book Slim, and how stores can improve their standing. Orbital is available on Spotify, Gaana, JioSaavn, Google Podcasts, Apple Podcasts, Amazon Music and wherever you get your podcasts.
The Best Phones Under Rs. 30,000 in India
The 10 Best Web Series and TV Shows of 2022


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"OECD Guidelines grievance procedure could be improved"
Category: Research
"The effectiveness of the grievance procedures at National Contact Points (NCPs) of countries that endorse the OECD Guidelines could be improved." This is a key conclusion reached in the study by Sander van 't Foort, who will receive his doctorate today from Nyenrode Business University. He formulated nine criteria that could increase the procedures' effectiveness, including transparency, accessibility and legitimacy. He also examined whether these grievance procedures could lead to agreements between the parties involved. In addition, he recommends revising the OECD Guidelines and adapting them to current times. "Climate change, biodiversity improvement and Artificial Intelligence (AI) are not included in the current OECD Guidelines for Multinational Enterprises," Van 't Foort says.
Countries that are members of the OECD (a partnership between 37 countries aimed at shaping social and economic policy) have adopted the organization's Guidelines relating to corporate social responsibility. If a party (often a multinational company) does not adhere to these Guidelines, those affected can reach out (often through trade unions or NGOs) to a so-called National Contact Point (NCP) in their country. Since 1984, all countries that endorse the OECD Guidelines are required to have an NCP. An NCP handles such complaints in a so-called specific instance procedure (SIP). In his research, Van 't Foort examined the effectiveness of these NCP procedures. He studied the procedures in various countries.
Nine criteria
Van 't Foort states that the effectiveness of the SIP is determined by the extent to which parties reach an agreement. He found that despite the increase in the number of complaints submitted to NCPs worldwide, the number of agreements reached between parties is actually decreasing. Furthermore, various sources have criticized the effectiveness of the NCPs. This criticism pertains mainly to the accessibility of the procedure and the fact that it does not always result in compensation for the victims.
In his study, Van 't Foort formulated nine criteria that could increase effectiveness. One such criterion is accessibility. If this is improved, effectiveness increases. "Oftentimes victims have no money or resources, or there is a language barrier to carrying out the procedure. An NCP can facilitate the process in such cases. In the Netherlands, for instance, the NCP covers travel costs and translation fees for victims," Van 't Foort explains. Other criteria that could increase effectiveness are: visibility, transparency, accountability, equitability, compatibility with the OECD Guidelines, impartiality, legitimacy and predictability.
Not legally binding, but effective nonetheless
There are several ways in which the SIP can help to resolve conflicts about corporate social responsibility. "Examples include creating a sense of ownership among multinationals, fostering an environment that is conducive to constructive solutions and granting legitimacy to the claimants," Van 't Foort says. He researched several best practices, such as a case in which a Congolese subsidiary of Heineken had been accused of collective dismissals based on unjustified grounds. A group of 168 former employees submitted a complaint to the Dutch NCP.
Although the NCP does not issue legally binding decisions, but instead only recommendations, this procedure nevertheless resulted in compensation for the claimants. In addition, Heineken has committed to developing new policies for doing business in unstable countries and conflict zones. This case shows that the SIP can be an effective tool in helping trade unions, NGOs, citizens and companies to solve conflicts in a constructive manner. "And that's why now is the perfect time to update the OECD Guidelines," Van 't Foort says.
Summary dissertation Sander van 't Foort (166 KB)
Nyenrode Healthcare and Public Domain
Overview - PhD graduations
Sander van 't Foort
Collaboration as a strategic asset
Government and Corporate Governance - MBA module Public & Private
Part-time | Dutch | Start Autumn, 2023
Risk and Finance - MBA module Public & Private
Part-time | Dutch | Start Spring 2023
Digitalization and Boardroom Dynamics - MBA module Business & IT
Part-time | English | March 28, 2023


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'Dangerous Infringement': Gun-Rights Group Launches Legal Challenge Against Dem Gov's Weapons Ban
Dem Rep Carolyn Maloney Calls for Ban on 'Machine Gun Magazines'
Alex Griswold
Rep. Carolyn Maloney (D., N.Y.) called for a ban on "machine gun magazines" during a press conference Tuesday.
"If guns made this country safer, we would be the safest country on earth, but we are far from it," Maloney told reporters outside of the U.S. Capitol Building.
The congresswoman, who represents New Yorkers in Manhattan, Brooklyn, and Queens, called for a multi-pronged approach to gun control in the wake of the Feb. 14 mass shooting in Parkland, Florida.
"We need to really act in a number of ways. We need to pass an assault weapon ban, we need to ban the massacre machine gun magazines, and we need comprehensive background checks," she continued. "These are all bills and efforts that Democrats have supported."
It's not clear what Maloney meant by "machine gun magazines," which could have been a botched reference to bump stocks or high-capacity magazines, both of which have been targeted by Democratic legislation in recent years.
Fully-automatic machine guns have been effectively banned in the United States since the passage of the 1986 Firearm Owners' Protection Act. While it is technically possible to purchase pre-1986 machine guns, it takes a mountain of paperwork and approval from the Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF), and prices from private sales can go into the tens of thousands of dollars.
Published under: Carolyn Maloney , Gun Control , Guns


1) THE INTAKE MANIFOLD MUST BE THOROUGHLY CLEANED AND FREE OF ALL DEBRIS BEFORE INSTALLATION ON THE REMANUFACTURED ENGINE.
Debris such as old piston, ring or valve material can be trapped in the intake, plenum, or EGR system. The debris can and will be pulled into the replacement engine causing the replacement engine to fail.
DO NOT GLASS BEAD, SAND BLAST OR USE ANY TYPE OF MEDIA THAT COULD BECOME TRAPPED IN THE INTAKE.
2) AFTER INSTALLING THE ENGINE, ROTATE THE CRANKSHAFT A MINIMUM OF FOUR COMPLETE REVOLUTIONS BEFORE ENGAGING THE STARTER.
Nuts, bolts, washers or other small parts that may have been dropped into the intake manifold or intake ports can severely damage the replacement engine. If the engine cannot be rotated by hand, inspect for foreign objects in combustion chamber.
The preferred method is to use a pressurized system to push clean oil through the entire oiling system.
A distributor type engine can be primed with a priming tool and drill motor to rotate the oil pump shaft. The technician must make sure that oil flows to the top of the engine and into the rocker arms.
A distributor-less engine should be primed before the drive belt and sparkplugs are installed. First, disable the fuel and ignition systems and crank the engine in short durations until oil pressure is achieved. Second, crank an additional two minutes in short durations to insure the oiling system is purged of air.
Check and adjust oil level after priming and purging is complete.
Oil coolers need to be replaced and the cooler lines cleaned of all debris.
Fill cooling system to the proper level with the correct mixture of coolant and water.
4) INSTALLER MUST DIAGNOSE, REPAIR AND INSURE PROPER OPERATION OF ENGINE RELATED SYSTEMS AND SUB SYSTEMS. FAILURE TO MAKE REPAIRS TO THESE COMPONENTS MAY RESULT IN ENGINE FAILURE.
Fan (motor, wiring, relays, switches or clutch).
Air induction (air cleaner and inlet hoses).
Starting, charging and ignition systems, computer controls, and all associated wiring, relays and sensors.
For any additional technical assistance during engine installation or after start up, please call the customer service department.


In its latest efforts to address America's growing problem with prescription drugs – specifically opiates, the Food and Drug Administration (FDA) announced this week that it will now require its "black box" warning label on all immediate-release opioid prescription painkillers – including Percocet and Vicodin, and more than 220 branded and generic drugs. The boxed warning is the strongest and most serious type of warning.
Immediate-release opioid painkillers are among the most commonly prescribed and used in the United States. They account for 90 percent of all prescribed opioid painkillers.
This change comes nearly three years after the FDA approved similar warning to time-released opioid painkillers, such as OxyContin. Now, both immediate and long-acting formulations will have labels highlighting the risks of abuse, addiction, overdose and even death. The new FDA label specifications also spell out instructions to doctors on how to administer the drugs to patients.
The new label, however, does not include a maximum dose level – or recommend lower daily doses of opioids.
The Fastest Growing Addiction in America.
Prescription pill addiction is the fastest growing addiction in the United States. Families across the country are experiencing heartbreak and tragedy resulting from addiction: Damaged relationships, financial ruin, health emergencies, job loss, failed marriages, overdose and death. Addiction and overdose from opiates – specifically prescription painkillers – has become one of the most devastating public health crises facing our nation.
Roughly 48 million Americans – or 1 in 5 – report that they have abused a prescription drug at least once in their life. According to the Center for Disease Control, someone dies from a prescription drug overdose every 19 minutes.
Since 1999, the number of people complaining of pain has remained consistent across the U.S. – however, the sales of prescription opioid pain pills has quadrupled. Even worse? So has the number of deaths caused by opioid overdoses.
Overdose deaths are now the leading cause of death from injuries in the United States.
People who become addicted to prescription pain pills are roughly 40 times more likely to use heroin – as nearly 4 out of every 5 people addicted to heroin started with prescription painkillers.
Will Label Really Make a Difference?
We commend federal health regulators in their efforts to fight an epidemic of abuse and overdose death tied to prescription painkillers. However, it draws the question: Will it really make a difference?
The large uptick in the number of people prescribed and using immediate-release opioid prescription pills happened within the past 15 years. In 2012, the United Nations' World Drug Report detailed the non-medical use of prescription painkillers "extremely problematic." So why has it taken so long for these labels?
Not all prescription pill users are getting high on painkillers. In fact, there are many people using prescription medications to combat chronic pain. For those in chronic pain, the warning labels may come as a reminder as to the dangers of the drugs. However, for those using opioid painkillers to get high, it's not likely the the warning labels will even be read – let alone effective. Most people in active addiction looking to avoid painful withdrawal isn't likely to sit down and look over an insert that came with the pill bottle – They're just going take the pill.
The new warning labels are not perfect, however, they are a start and an attempt to make a difference – even if that difference is very small. These labels won't solve our drug overdose crisis or opioid addiction, but they're a step in the right direction on a long road ahead.


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What is Headless CMS – a 2 Minute Guide
Nov 25, 2021 | Tech
"Accelerating your go-to-market strategy and maximizing ROI by serving omnichannel content with a Headless CMS"
When you circle around choosing a Headless CMS, the options and combinations available out there might seem overwhelming. On the technical side, the development team needs to choose a CMS that fits into their technology stack, enabling them to work with their preferred frameworks. However, there is no dearth of options as well in the Marketing side. Take into consideration to ensure that a Headless CMS is both – operationally fulfilling, and future-proof.
What is a Headless CMS?
A headless CMS is a kind of content management system where the 'body' of the content repository is decoupled from the presentation layer head. The reason it is called Headless is because the presentation layer is separated from the body.
The term "Headless" comes from the concept of chopping the "head" (the front end, i.e. the website) off the "body" (the back-end, i.e. the content repository). A headless CMS remains with an interface to add content and a RESTful API (JSON, XML) to deliver content wherever you need it. Due to this approach, a headless CMS does not care about how and where your content gets displayed. A headless CMS has only one focus: storing and delivering structured content.
Why is it Essential to Embrace Headless CMS?
Data is unified into a Single Headless Content Hub
This content infrastructure eliminates the endless work of copy-pasting, enabling enterprises to unify all the content under a single centralized content hub, making editing more lucrative. You just need to change the copy in one place, and it applies that change everywhere the content is located.
Content-First Always
So, editors traditionally had to wait until the development of a website so that they could start populating it. With headless CMS, the front end and the CMS are separate entities, therefore the content team can start populating the website as soon as the wireframe is ready. The development work can also begin simultaneously for a stronger collaborative and dynamically paced process.
Flexibility for the Development Team
Since the front end is separate in a Headless CMS, it makes the actual system for managing content via a dynamic API separate. Therefore, rendering full flexibility to the developers in terms of the framework and languages used in the frontend. This enables the designers and developers to trick their creative sting and think outside the box and be more dynamic than developing on a traditional CMS.
Ease of Content Editing
Another benefit of using this CMS is the simplicity of in the user interface for content editors. Like Gather Content, web pages appear as content items and can be easily edited, added or removed. The usability of a seamless user interface means minimal training must be given to your less tech savvy departments.
Point is: It is not limited to websites
A headless CMS can deliver your content through an API directly to where you need it. Because of the headless approach the content can be used on an iOS app, Android app, TV Apps, eCommerce sites as well as any platform and technology you can think of (yes and even a Windows Phone App) and is therefore a powerful option for mobile and web developers.
Mercedes-Benz Ready To Launch Local Assembled EV Cars in India 2022


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A single pulse of diffuse contaminants alters the size distribution of natural phytoplankton communities
Baho, Didier Ludovic; Pomati, Francesco; Leu, Eva; Hessen, Dag Olav; Moe, S. Jannicke; Norberg, Jon; Nizzetto, Luca
1709431.pdf (1.822Mb)
Institutt for biovitenskap [1143]
CRIStin høstingsarkiv [14985]
Science of the Total Environment. 2019, 683, 578-588, DOI: https://doi.org/10.1016/j.scitotenv.2019.05.229
The presence of a multitude of bioactive organic pollutants collectively classified as pharmaceuticals and personal care products (PPCPs) in freshwaters is of concern, considering that ecological assessments of their potential impacts on natural systems are still scarce. In this field experiment we tested whether a single pulse exposure to a mixture of 12 pharmaceuticals and personal care products, which are commonly found in European inland waters, can influence the size distributions of natural lake phytoplankton communities. Size is one of the most influential determinants of community structure and functioning, particularly in planktonic communities and food webs. Using an in-situ microcosm approach, phytoplankton communities in two lakes with different nutrient levels (mesotrophic and eutrophic) were exposed to a concentration gradient of the PPCPs mixture at five levels. We tested whether sub-lethal PPCPs doses affect the scaling of organisms' abundances with their size, and the slope of these size spectra, which describe changes in the abundances of small relative to large phytoplankton. Our results showed that a large proportion (approximately 80%) of the dataset followed a power-law distribution, thus suggesting evidence of scale invariance of abundances, as expected in steady state ecosystems. PPCPs were however found to induce significant changes in the size spectra and community structure of natural phytoplankton assemblages. The two highest treatment levels of PPCPs were associated with decreased abundance of the most dominant size class (nano-phytoplankton: 2–5 μm), leading to a flattening of the size spectra slope. These results suggest that a pulse exposure to PPCPs induce changes that potentially lead to unsteady ecosystem states and cascading effects in the aquatic food webs, by favoring larger non-edible algae at the expense of small edible species. We propose higher susceptibility due to higher surface to volume ratio in small species as the likely cause of these structural changes.
This item's license is: Attribution-NonCommercial-NoDerivatives 4.0 International


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News Sports Business Opinion Lifestyle Obituaries E-Edition Legals
Supreme Court blocks Harris County vote-by-mail plan
Chuck Lindell
Amid a simmering partisan fight over voting by mail during the pandemic, the Texas Supreme Court stepped in Wednesday to block Harris County from sending vote-by-mail applications to every registered voter in the county.
The court's nine Republican justices gave no reason for the stay, noting only that it was a temporary measure until the court rules on a petition challenging the legality of the planned mailing.
Despite Wednesday's stay, county officials had already agreed to refrain from mass mailing vote-by-mail applications for the Nov. 3 election as part of a separate legal challenge filed Monday by Texas Attorney General Ken Paxton.
Under that agreement, Harris County Clerk Chris Hollins said he would not send unsolicited vote-by-mail applications until a hearing can be held on Paxton's request for a state District Court order to block the mailings.
That hearing is expected to be held before Sept. 9, Hollins told the Supreme Court.
"Thus, this proceeding and the request for emergency, temporary relief provide no added value, are superfluous, and do not warrant this Court's time, attention, and resources," lawyers for Hollins told the court.
Hollins, a Democrat, has said he hopes to make it easier to cast a ballot by mail by those who qualify, but Paxton, a Republican, argues that the county clerk lacks the authority to send applications en masse because state law limits voting by mail to specific voters, including those 65 and older and those with a disability.
Wednesday's action by the Supreme Court was the result of a separate challenge by conservative activist Steven Hotze of Houston, the Harris County GOP and Sharon Hemphill, a Republican candidate for district judge in Harris County.
Their petition, also filed Monday, described Harris as "a rogue clerk who is abusing the application to vote by mail process and compromising the integrity of elections in Harris County."
The Hotze action also argued that bypassing lower courts with a direct petition to the Supreme Court was justified because preparations for the November election are underway.
State Republican leaders have fought efforts to expand voting by mail during the pandemic, arguing that the Legislature created policies that favor in-person voting as more secure while allowing only limited exceptions for those who need to cast an absentee ballot.
Democrats, arguing that voters should not have to choose between their health and their right to vote, have filed state and federal lawsuits seeking to let every voter decide whether to cast a ballot by mail or in person during the pandemic.
The Texas Supreme Court rejected the Democrats' claims in May, ruling that a lack of immunity to COVID-19 is not a disability that entitles voters to a mail-in ballot under state law.
A federal appeals court heard arguments Monday, but has not yet ruled, on the Democrats' claim that restricting mail-in voting to those under 65 is improper age discrimination.
Meantime, Texas Democrats accused Republicans of hypocrisy after reports that President Donald Trump's campaign sent unsolicited vote-by-mail applications to supporters in Texas.
"It seems if Republicans had their way, the only requirement for Texans to cast a mail-in ballot would be, 'Are you voting for Donald Trump?'" party spokesman Abhi Rahman said. "Voting by mail is safe, secure and convenient. It's time to end this debate and expand voting by mail to every eligible Texan, once and for all."
© 2021 www.caller.com. All rights reserved.


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Using this app you can view any Whatsapp registered number's Profile Picture, Status, Last seen and Online activity. It can be used for an unknown number of services.
You can view all your contacts last seen dates and status messages on the same place. This is the best amazing app to view any friends profile pictures.


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NTU scientists hit the target
Published on : 19-Jul-2013
The search for new drugs, including those for cancer, is set to speed up thanks to a new research technique invented by scientists at the Nanyang Technological University (NTU).
Named the "Cellular Thermal Shift Assay" (CETSA), scientists can now know for sure if a drug had reached its target protein in the body, which is a critical step in determining the effectiveness of most medicines.
Presently, scientists can only hypothesise if a drug has indeed reached its target protein, leading to expensive and prolonged drug development process. CETSA will help scientists take out much of the usual trial-and-error guesswork from the drug development equation.
This research breakthrough was recently published in Science, one of the world's top scientific journals.
Most drugs operate by binding to one or more proteins, which 'blocks' the proteins' function. Scientists around the world face two common bottlenecks: how to identify the right proteins to target and how to design drug molecules which are able to efficiently seek out and bind to these proteins.
CETSA's inventor, Professor Pär Nordlund from NTU's School of Biological Sciences, said their new method will not only ease the two bottlenecks, but also has important applications in monitoring a patient's progress, for example, during cancer treatment.
"With CETSA, we can in principle determine which drug and treatment regime is most effective at targeting the proteins in the tumour in cancer patients, and monitor when resistance is developing," says Prof Nordlund.
Science 5 July 2013
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