More than the achievements of Dean Smith with North Carolina and USA basketball speak about what kind of head coach he was, the way his former players and assistants like Michael Jordan and Roy Williams speak about him tells us what kind of influential person has passed away.
Davis played under Smith in North Carolina from 1988 to 1992 before beginning a 12-year NBA career. He has been Roy Williams' assistant at Chapel Hill for the last three years.
Michael Jordan: He was more than a coach. He was my mentor, my second father. In teaching me the game of basketball, he taught me about life.
Jordan played in North Carolina for three years, from 1981 to 1984, winning the national championship in 1982 with a game winning shot against Georgetown. The popular choice for the greatest basketball player in history, Jordan is the owner of the Charlotte Hornets these days.
Phil Ford: He would always stay in contact with us, not only as student-athletes here at North Carolina, but once we graduated. He would always send us letters of encouragement when things were going bad or struggling with certain things and always let us know that he was here for us. Again, anything that he could possibly do of himself to make you better, he was willing to do.
Ford was a player at North Carolina from 1974 to 1978 before going on a decent yet short NBA career. He was an assistant coach under Smith and later Bill Guthridge from 1988 to 2000.
Marcus Paige: I never really realized how important he was to the players until I got here and got to hang out with Shammond Williams and Antawn Jamison and got to learn how important a father figure he was to them. You don't learn those things in textbooks or on ESPN, so it was kind of cool to be around people who were influenced by him and learn that way.
Paige is a Junior right now at North Carolina.
Roy Williams: It's so funny because all of our guys, when I was an assistant here, would come back and talk about how all the other guys in the NBA would make fun of that. We used to just say it was jealousy because they wanted to be part of that family.
Roy Williams has been the North Carolina head coach since 2003, winning two national championships (2005, 2009). Williams was an assistant under Smith from 1978 to 1988 before taking over the Kansas head coaching job.


Anchor Bookkeeping & Payroll PC and Richard A Chabot CPA PC and its predecessor Anchor Tax & Accounting Inc have been providing bookkeeping, payroll, tax, and consulting services in the area since 1990. Rick is a certified public accountant with over 30 years of experience in public and private accounting. We offer tax, accounting and consulting services to individuals and businesses in several industries, including property management, retail grocery and contractors.
Effective October 1, 2014 the tax preparation firm R & L Associates merged with Richard a Chabot CPA PC. R & L Associates was owned and operated in Brunswick by Ron Fluet for the past 50 years.
We welcome any questions or comments you may have. Please feel free to contact us any time.
Referrals welcomed and always appreciated.
Richard A Chabot CPA PC goes through a peer review process every three years.


text:
\section*{Introduction}
{M}agnetic Resonance Imaging (MRI) has become the standard non-invasive technique for brain tumor diagnosis over the last few decades, due to its improved soft tissue contrast \cite{deangelis2001brain, cha2006update}. Gliomas constitute $80\%$ of all malignant brain tumors originating from the glial cells in the central nervous system. Based on the aggressiveness and infiltrative nature of the gliomas the World Health Organization (WHO) broadly classified them into two categories, viz. Low-grade gliomas (LGG), consisting of low-grade and intermediate-grade gliomas (WHO grades II and III), and high-grade gliomas (HGG) or glioblastoma (WHO grade IV) \cite{louis16}. Diffuse LGG are infiltrative brain neoplasms which include histological classes astrocytomas, oligodendrogliomas, and oligoastrocytomas and World Health Organization (WHO) grade II and III neoplasms \cite{louis16}. Although LGG patients have better survival than those with HGG, the LGGs are found to typically progress to secondary GBMs and eventual death \cite{liyun13}. In both cases a correct treatment planning (including surgery, radiotherapy, and chemotherapy separately or in combination) becomes necessary, considering that an early and proper detection of the tumor grade can result in good prognosis \cite{van2012adjuvant}.

Histological grading, based on stereotactic/surgical biopsy test, is primarily used for the management of gliomas. Typically the highest grade component, among the histopathology samples obtained, is used to predict the overall tumor grade. Gliomas being heterogeneous, sometimes histopathology samples collected from different parts of the same tumor exhibit different grades. Since pathologists are not provided with the entire delineated tumor during examination, it is likely that the highest grade component may be missing in the biopsy sample. This is called the biopsy sampling error \cite{Chandrasoma89Stereotactic,glantz1991influence,jackson2001limitations}, and can potentially result in wrong clinical management of the disease. Moreover there exist several risk factors in the biopsy test, including bleeding from the tumor and brain due to the biopsy needle; causing severe migraine, stroke, coma and even death. Other associated risks involve infection or seizures \cite{field2001comprehensive,mcgirt2005independent}.

MR imaging, on the other hand, has the advantage of being able to scan the entire tumor in vivo and can demonstrate a strong correlation with histological grade. It is also not susceptible to sampling error, and inter- and intra-observer variability. In this context multi-sequence MRI plays a major role in the detection, diagnosis, and management of brain cancers in a non-invasive manner. Recent literature reports that computerized detection and diagnosis of the disease, based on medical image analysis, could be a good alternative. Decoding of tumor phenotype using noninvasive imaging is a recent field of research, known as {\it Radiomics} \cite{mitra2015medical, mitra2014integrating, gillies2015radiomics}, and involves the extraction of a large number of quantitative imaging features that may not be apparent to the human eye. An integral part of the procedure involves manual or automated delineation of the 2D region of interest (ROI) or 3D volume of interest (VOI) \cite{banerjee2016single, banerjee2016novel, banerjee2018automated, somosmitaPlos2017}, to focus attention on the malignant growth. This is typically followed by the extraction of suitable sets of hand-crafted quantitative imaging features from the ROI or VOI, to be subsequently analyzed through machine learning towards decision-making. Feature selection enables the elimination of redundant and/or less important subset(s) of features, for improvement in speed and accuracy of performance. This is particularly relevant for high-dimensional radiomic features, extracted from medical images.

Quantitative imaging features, extracted from MR images, have been investigated in literature for the assessment of brain tumors \cite{zhou2017radiomics, gillies2015radiomics}. Ref. \cite{banerjee2017fuzzieee} presents an adaptive neuro-fuzzy classifier, based on linguistic hedges (ANFC-LH), for predicting the brain tumor grade using $56$ 3D quantitative MRI features extracted from the corresponding segmented tumor volume(s). Quantitative imaging features, extracted from pre-operative gadolinium-enhanced T1-weighted MRI, were investigated for the diagnosis of \textit{meningioma} grades \cite{coroller2016d}. A study of MR imaging features was made \cite{zhao2014can} to determine those which can differentiate among grades of \textit{soft-tissue sarcoma}. The features investigated include signal intensity, heterogeneity, margin, descriptive statistics, and perilesional characteristics on images, obtained from each MR sequence. Brain tumor classification and grading, based on 2D quantitative imaging features like texture and shape (involving gray-level co-occurrence, run-length, and morphology), were also reported \cite{zacharaki2009classification}.

Although the techniques demonstrate good disease classification, their dependence on hand-crafted features requires extensive domain knowledge, involves human bias, and is problem-specific. Manual designing of features typically requires greater insight into the exact characteristics of normal and abnormal tissues, and may fail to accurately capture some important representative features; thereby hampering classifier performance. The generalization capability of such classifiers may also suffer due to the discriminative nature of the methods, with the hand-crafted features being usually designed over fixed training sets. Subsequently manual or semi-automatic localization and segmentation of the ROI or VOI is also needed to extract the quantitative imaging features \cite{banerjee2016novel, banerjee2016single}.

Convolutional Neural Networks (ConvNets) offer state-of-the-art framework for image recognition or classification \cite{he2016deep, lecun2015deep, szegedy2015going}.  ConvNet architecture is designed to loosely mimic the fundamental working of the mammalian visual cortex system. It has been shown that the visual cortex has multiple layers of abstractions which look for specific patterns in the input vision. A ConvNet is built upon a similar idea of stacking multiple layers to allow it to learn multiple different abstractions of the input data. These networks automatically learn mid-level and high-level representations or abstractions from the input training data, in the form of convolution filters that are updated during the training process. They work directly on raw input (image) data, and learn the underlying representative features of the input which are hierarchically complex, thereby ruling out the need for specialized hand-crafted image features. Moreover ConvNets require no prior domain knowledge and can automatically learn to perform any task just by working through the training data.

However training a ConvNet from scratch is generally difficult because it essentially requires large training data, along with the significant expertise to select an appropriate model architecture for proper convergence. In medical applications data is typically scarce, and expert annotation is expensive. Training a deep ConvNet requires huge computational and memory resources, thereby making it extremely time-consuming. Repetitive adjustments in architecture and/or learning parameters, while avoiding overfitting, make deep learning from scratch a tedious, time-consuming, and exhaustive procedure. Transfer learning offers a promising alternative, in case of inadequate data, to fine tune a ConvNet pre-trained on a large set of available labeled images from some other category \cite{oquab2014}. This helps in speeding up convergence, while lowering computational complexity during training \cite{tajbakhsh2016, phan2016}.

The adoption rate of ConvNets in medical imaging  has been on the rise \cite{Greenspan2016_guest_survey}. However given the insufficiency of medical image data, it often becomes difficult to use deeper and more complex networks. Application of ConvNets in gliomas have been mostly reported for the segmentation of abnormal regions from 2D or 3D MRIs \cite{Pereira2016, zikic14, Urban2014_brats, kamni17, Davy2015_brats, lyksb15}. Automated detection and extraction of High Grade Gliomas (HGG) was performed using ConvNets \cite{baner18b}. The two-stage approach first identified  the presence of HGG, followed by a bounding box based tumor localization in each ``abnormal'' MR slice. As part of the Computer-Aided Detection system, Classification and Detection ConvNet architectures were employed. Experimental results demonstrated that the  CADe system, when used as a preliminary step before segmentation, can allow improved delineation of tumor region while reducing false positives arising in normal areas of the brain. Recently Yang \textit{et al.}\cite{yang2018glioma} explored the role of deep learning and transfer learning for accurate grading of gliomas, using conventional and functional MRIs. They used a private Chinese hospital database containing $113$ pathologically confirmed glioma patients, of which there were $52$ LGG and $61$ HGG samples. The AlexNet and GoogLeNet were trained from scratch, and fine-tuned from models that had been pre-trained on the large natural image database ImageNet. Testing on the $20\%$ heldout data, randomly selected at patient-level, resulted in maximum test accuracy ($90\%$) by GoogLeNet. Radiomics has also been employed \cite{cho2018classification} for grading of gliomas into LGG and HGG, with the MICCAI BraTs 2017 dataset\cite{bakas2017advancing} being used for training and testing of models.

In addition to tumor grading, the prediction of 1p/19q codeletion status serves as a crucial molecular biomarker towards prognosis in LGG. It is found to be related to longer survival, particularly for oligodendrogliomas which are more sensitive to chemotherapy. Such noninvasive prediction through MRI can, therefore, lead to avoiding invasive biopsy or surgical procedures. Predicting 1p/19q status in LGG from MR images using ConvNet was reported \cite{akkus2017predicting}. The network was first trained on a brain tumor patient database from the Mayo Clinic, containing a total of $159$ LGG cases ($57$ non-deleted and $102$ codeleted) and having preoperative postcontrast-$T1$ and $T2$ images\cite{erickson2017data}. The model was also trained and tested on $477$ 2D MRI slices extracted from the $159$ patients, with $387$ slices being used for training and $90$ slices ($45$ non-deleted and $45$ codeleted) during testing. A test accuracy of $87.7\%$ was obtained.

Studies by the TCGA have established that LGGs can be grouped into three robust molecular classes on the basis of $IDH1/2$ mutations and $1p/19q$ co-deletion. The variants have been reported to differ with respect to tumor margins and internal homogeneity. The $T2$, $FLAIR$ mismatch sign was found to be associated with a survival profile similar to that of the $IDH$-mutant $1p/19q$-non-codeleted glioma subtype \cite{patel17}, and more favorable to that of the $IDH$-wild type gliomas  (which present outcome similar to WHO grade IV glioblastomas).

In this paper we exhaustively investigate the behaviour and performance of ConvNets, with and without transfer learning, for noninvasive studies of gliomas, involving (A) detection and grade prediction (into low- (Grades II and III) and high- (Grade IV) brain tumors (LGG and HGG), and (B) classification of LGG with/without 1p/19q codeletion, from multi-sequence MRI.

Tumors are typically heterogeneous, depending on cancer subtypes, and contain a mixture of structural and patch-level variability. Prediction of the grade of a tumor may thus be based on either the image patch containing the tumor, or the 2D MRI slice  containing the image of the whole brain including the tumor, or the 3D MRI volume encompassing the full image of the head enclosing the tumor. While in the first case only the tumor patch is necessary as input, the other two cases require the ConvNet to learn to localize the ROI (or VOI) followed by its classification. Therefore, the first case needs only classification while the other two cases additionally require detection or localization. Since the performance and complexity of ConvNets depend on the difficulty level of the problem and the type of input data representation, we introduce three kinds viz. i) Patch-based, ii) Slice-based, and iii) Volume-based data,  from the original MRI dataset by introducing the sliding window concept, and employ these over the two experiments. Three ConvNet models are developed corresponding to each case, and trained from scratch. We also compare two state-of-the-art  ConvNet architectures, viz.  VGGNet \cite{simonyan2014very} and ResNet \cite{he2016deep}, with parameters pre-trained on ImageNet using transfer learning (via fine-tuning).

The main contributions of this research are listed below.
\begin{itemize}
	\item Adaptation of deep learning to Radiomics, for the non-invasive prediction of tumor grade followed determination of the 1p/19q status in Low-Grade Gliomas, from multi-sequence MR images of the brain.

	\item Prediction of the grade of brain tumor without manual segmentation of tumor volume, or manual extraction and/or selection of features.

	\item Conceptualization of ``Augmented intelligence'', with the application of deep learning for assisting doctors and radiologists towards decision-making while minimizing human bias and errors.

	\item Development of novel ConvNet architectures viz. PatchNet, SliceNet, and VolumeNet for tumor detection and grade prediction, based on MRI patches, MRI slices, and multi-planar volumetric MR images, respectively.

	\item New framework for applying existing pre-trained deep ConvNets models on multi-channel MRI data using transfer learning. The technique can be further extended to tasks of localization and/or segmentation on different MRI data.
\end{itemize}

\section*{Results}

The ConvNet models were developed using TensorFlow, with Keras in Python. The experiments were performed on the Intel AI DevCloud platform, having a cluster of Intel Xeon Scalable processors. The quantitative and qualitative evaluation of the results are elaborated below.

\subsection*{Quantitative evaluation}
We use (i) leave-one-patient-out (LOPO), and (ii) holdout (or independent) test dataset for model validation. While only one sample is used for testing in the LOPO scheme, at each iteration, the remaining are employed for training the ConvNets. The process iterated over each patient. Although LOPO test scheme is computationally expensive, it allows availability of more data as required for ConvNets training. LOPO testing is robust and well-suited to our application, with results being generated for each individual patient. Therefore, in cases of misclassification, a patient sample may be further investigated. In holdout or independent testing scheme either a portion of the training data that has never been used for training or a separate test dataset is used during model validation.

Training and validation performance of the three ConvNets were measured using the following two metrics.

$$
Accuracy= \frac{TP + TN}{TP + FP + TN + FN},~~~~~~~~~
F_1 Score = 2 \times \frac{precision \times recall}{precision + recall}.
$$

$Accuracy$ is the most intuitive performance measure and provides the ratio of correctly predicted observations to the total observations. $F_1  Score$ is the weighted average of $Precision$ and $Recall$, which are defined as $\frac{TP}{TP + FP}$ and $\frac{TP}{TP+FN}$, with $TP$, $TN$, $FP$, and $FN$ indicating the numbers of true positive, true negative, false positive and false negative detections. In the presence of imbalanced data one typically prefers $F_1 Score$ over $Accuracy$ because the former considers both false positives and false negatives during computation.

\subsubsection*{Case study-A: Classification of low/high grade gliomas}
The dataset preparation schemes, discussed in Section \textbf{Dataset preparation}, were used to create the three separate training and testing data sets. The ConvNet models PatchNet, SliceNet, VolumeNet, were trained on the corresponding datasets using Stochastic Gradient Descent (SGD) optimization algorithm with learning rate $=0.001$ and momentum = $0.9$, using mini-batches of size $32$ samples generated from the corresponding training dataset. A small part of the training set ($20\%$) was used for validating the ConvNet model after each training epoch, for parameter selection and detection of overfitting.

Since deep ConvNets entail a large number of free trainable parameters, the effective number of training samples were artificially enhanced using real-time data augmentation -- through some linear transformation such as random rotation ($0^o - 10^o$), horizontal and vertical shifts, horizontal and vertical flips. Also we used Dropout layer with a dropout rate of $0.5$ in the fully connected layers and Batch-Normalization to control the overfitting. After each epoch, the model was validated on the corresponding validation dataset.

Training and validation $Accuracy$ and loss, and $F_1 Score$ on the validation dataset, are presented in Fig.~\ref{fig:fig1} for the three proposed ConvNets (PatchNet, SliceNet, and VolumeNet), trained from scratch, along with that for the two pre-trained ConvNets (VGGNet, and ResNet) fine-tuned on the TCGA-GBM and TCGA-LGG datasets. The plots demonstrate that VolumeNet gives the highest classification performance during training, reaching maximum accuracy on the training set ($100 \%$) and the validation set ($98\%$) within just $20$ epochs. Although the performance of PatchNet and SliceNet is quite similar on the validation set (PatchNet - $90 \%$, SliceNet - $92 \%$), it is observed that SliceNet achieves better accuracy ($94\%$) on the training set. The performance of two the pre-trained models (VGGNet and ResNet) exhibit similar results, with both achieving around $85\%$ accuracy on the validation set. All the networks reached a plateau after the 50th epoch. This establishes the superiority of the 3D volumetric level processing of VolumeNet.

\begin{figure*}[]
	\centering
	\includegraphics[width=1.0\textwidth]{Fig1.pdf}
	\caption{Comparative performance of the networks.}
	\label{fig:fig1}
\end{figure*}

\textbf{LOPO testing results}: After training, the networks were evaluated on the holdout test set employing majority voting. Each patch or slice from the test dataset was from a single test patient in the LOPO framework, and was categorized as HGG or LGG. The class with maximum number of slices or patches correctly classified was indicative of the grade of the tumor. In case of equal votes the patient was marked as ``ambiguous''.
\begin{table}[]
	\centering
	\caption{Comparative LOPO test performance}
	\label{tab:tab1}
	{\scriptsize\begin{tabular}{|l|c|c|c|c|}
		\hline
		\multicolumn{1}{|c|}{ConvNets} & Classified   & Misclassified & Ambiguous  & Accuracy          \\ \hline
		PatchNet                       & 242          & 39          & 4          & 84.91 \%          \\ \hline
		SliceNet                       & 257          & 26          & 2          & 90.18 \%          \\ \hline
		\textbf{VolumeNet}             & \textbf{277} & \textbf{8}  & \textbf{0} & \textbf{97.19 \%} \\ \hline
		VGGNet                         & 239          & 40          & 6         & 83.86 \%          \\ \hline
		ResNet                         & 242          & 42          & 1          & 84.91 \%          \\ \hline
	\end{tabular}}
\end{table}

The LOPO testing scores are displayed in Table. \ref{tab:tab1}. VolumeNet is observed to achieve the best LOPO test accuracy ($97.19 \%$), with zero ``ambiguous'' cases as compared to the other four networks. SliceNet is also found to provide good LOPO test accuracy ($90.18\%$). Both the pre-trained models show similar LOPO test accuracy as PatchNet. This is interesting because it demonstrates that with a little fine-tuning one can achieve a test accuracy similar to that by the patch-level ConvNet trained from scratch on a specific dataset. Therefore fine-tuning of a few more intermediate layers can lead to very high test scores with little training.

Table \ref{tab:tab3} compares the proposed ConvNets with existing shallow learning models in literature, used for the same application but requiring additional feature extraction and/or selection from manually segmented ROI/VOI, in terms of classification accuracy. Ref. \cite{banerjee2017fuzzieee} reports the performance achieved by seven standard classifiers, viz. i) Adaptive Neuro-Fuzzy Classifier (ANFC), ii) Naive Bayes (NB), iii) Logistic Regression (LR), iv) Multilayer Perceptron (MLP), v) Support Vector Machine (SVM), vi) Classification and Regression Tree (CART), and vii) $k$-nearest neighbors ($k$-NN), on the BraTS 2015 dataset (a subset of TCGA-GBM and TCGA-LGG datasets) consisting of $200$ HGG and $54$ LGG patient cases, each having $56$ three-dimensional quantitative MRI features manually extracted. On the other hand, the ConvNets leverage the learning capability of deep networks for automatically extracting relevant features from the data.

	\begin{table}[]
		\centering
		\caption{Comparative accuracy of deep and shallow classifiers.}
		\label{tab:tab3}
		{\scriptsize\begin{tabular}{|c|c|l|}
			\hline
			Classifier & Accuracy (\%) & \multicolumn{1}{c|}{Details}                                                                                \\ \hline
			PatchNet   & $84.91$ & \begin{tabular}[c]{@{}l@{}}Trained and tested on MRI patches of size $32 \times 32$, having $3$ ($3 \times 3$) convolution ($8$, $16$, $32$ filters), and \\single FC ($16$ neurons) layers, with $50$ epochs.\end{tabular}                                                                                \\ \hline
			SliceNet   & $90.18$ & \begin{tabular}[c]{@{}l@{}}Trained and tested on MRI slices of size $200 \times 200$, having $4$ ($3 \times 3$) convolution ($16$, $32$, $64$, $128$ filters),\\and single FC ($64$ neurons) layers, with $50$ epochs.\end{tabular}                                                                                \\ \hline
			\textbf{VolumeNet}  & \textbf{97.19}         & \begin{tabular}[c]{@{}l@{}}Trained and tested on multi-planar MRI slices of size $200 \times 200$ having three parallel ConvNets each with\\$3$ ($3 \times 3$) convolution ($8$, $16$, $32$ filters), and single FC ($32$ neurons) layers, with $10$ epochs.\end{tabular}                                                                                \\ \hline
			VGGNet     & 83.86         & \begin{tabular}[c]{@{}l@{}}Trained on ImageNet dataset, fine-tuned and tested on MRI slices of size $200 \times 200$

			\end{tabular}                                                                   \\ \hline
			ResNet     & 84.91         & \begin{tabular}[c]{@{}l@{}}Trained on ImageNet dataset, fine tuned and tested on MRI slices of size $200 \times 200$.\end{tabular}                                                                   \\ \hline
			ANFC-LH    & 85.83         & \begin{tabular}[c]{@{}l@{}}Trained on manually extracted $23$ quantitative MRI features, based on 10 fuzzy rules.\end{tabular}                                                                                \\ \hline
			NB         & 69.48         & \begin{tabular}[c]{@{}l@{}}Trained on manually extracted $23$ quantitative MRI features.\end{tabular}                                                                                \\ \hline
			LR         & 72.07         & \begin{tabular}[c]{@{}l@{}}Trained on manually extracted $23$ quantitative MRI features based on multinomial logistic regression model with \\a ridge estimator.\end{tabular}           \\ \hline
			MLP        & 78.57         & \begin{tabular}[c]{@{}l@{}}Trained on manually extracted $23$ quantitative MRI features using single hidden layer with $23$ neurons, learning\\rate = $0.1$, momentum = $0.8$.\end{tabular} \\ \hline
			SVM        & 64.94         & \begin{tabular}[c]{@{}l@{}}Trained on manually extracted $23$ quantitative MRI features, LibSVM with RBF kernel, cost = 1, gamma = 0.\end{tabular}                                         \\ \hline
			CART       & 70.78         & \begin{tabular}[c]{@{}l@{}}Trained on manually extracted $23$ quantitative MRI features using minimal cost-complexity pruning.\end{tabular}                                              \\ \hline
			$k$-NN       & 73.81         & \begin{tabular}[c]{@{}l@{}}Trained on manually extracted $23$ quantitative MRI features, accuracy averaged over scores for $k = 3, 5, 7$.\end{tabular}                                       \\ \hline
		\end{tabular}}
\end{table}

\textbf{Testing results on holdout dataset}: Trained networks were also tested on an independent test dataset (MICCAI BraTS 2017 database) as discussed in Section \textbf{Brain tumor data}. The confusion matrix for each of the networks are shown in Fig.~\ref{fig:fig2}. VolumeNet performs the best on the holdout set (accuracy = $95.00\%$). The other two models PatchNet and SliceNet, which were trained from scratch, also demonstrate good classification performance. On the other hand, the fine-tuned models VGGNet and ResNet perform poorly on the independent test dataset. Comparison is made with two recently reported methods\cite{yang2018glioma, cho2018classification}, used for the same problem on same or different datasets. The comparison results are given in Table \ref{tab:tab4}. It is observed that VolumeNet performed the best on the holdout dataset, achieving $7\%$ improvement in the accuracy as compared to state-of-the-art method \cite{cho2018classification} using the same dataset. Note that cross-validation was used in the compared models \cite{yang2018glioma, cho2018classification} for evaluating performance, with training done on a part of the same data on whose holdout portion testing was evaluated. On the other hand, we trained our models on a different dataset (from TCIA) and tested it on the BraTs dataset. This validates the robustness of our models as compared to existing methods.

\begin{table}[]
	\centering
	\caption{Comparative test performance with state-of-the-art methods on holdout dataset.}
	\label{tab:tab4}
	\scriptsize\begin{tabular}{|c|c|c|l|c|}
		\hline
		\multicolumn{2}{|c|}{Model}                                                                                & Accuracy & Dataset                          & Type                                 \\ \hline
		\multirow{3}{*}{\begin{tabular}[c]{@{}c@{}}Proposed models\\ trained from  scratch\end{tabular}} & PatchNet  & $82\%$   & \multirow{5}{*}{BraTs 2017}      & \multirow{7}{*}{Deep learning based} \\ \cline{2-3}
		& SliceNet  & $86\%$   &                                  &                                      \\ \cline{2-3}
		& VolumeNet & $95\%$   &                                  &                                      \\ \cline{1-3}
		\multicolumn{1}{|l|}{\multirow{2}{*}{Fine-tuned models}}                                         & VGGNet    & $68\%$   &                                  &                                      \\ \cline{2-3}
		\multicolumn{1}{|l|}{}                                                                           & ResNet    & $72\%$   &                                  &                                      \\ \cline{1-4}
		\multirow{2}{*}{Yang et al. \cite{yang2018glioma}}                                            & AlexNet   & $85\%$   & \multirow{2}{*}{Private dataset} &                                      \\ \cline{2-3}
		& GoogLeNet & $90\%$   &                                  &                                      \\ \hline
		\multicolumn{2}{|c|}{Cho et al. \cite{cho2018classification}}                                                    & $88\%$   & Brats 2017                       & Radiomics based                      \\ \hline
	\end{tabular}
\end{table}

\begin{figure*}[]
	\centering
	\includegraphics[width=1.0\textwidth]{Fig2.pdf}
	\caption{Confusion matrix for classification performance of the five models on MICCAI BraTs 2017 dataset.}
	\label{fig:fig2}
\end{figure*}

\subsubsection*{Case study-B: Classification of LGG with/without 1p/19q codeletion}
We trained the best performing model, i.e. VolumeNet, for the classification of LGG with/without 1p19q codeletion. The Mayo Clinic database used for the task contains $T1C$ and $T2$ MRI sequences for each patient. VolumeNet was trained on the multi-planar volumetric database, preprocesed from the raw 3D brain volume images as described in Section \textbf{Dataset preparation}. Training performance of the network, in terms of training and validation $Accuracy$ \& $loss$, are presented in Fig.~\ref{fig:fig3}. Comparison was made with a state-of-the-art method\cite{akkus2017predicting}, which is also based on deep learning and uses same dataset. The test performance on the holdout dataset is reported in Table {\color{blue}4}. Here again our model VolumeNet achieves $9\%$ more accuracy than the compared method, over the same dataset. The improvement is due to the incorporation of volumetric information through multi-planar MRI slices.

\begin{figure}
	\centering
	\begin{minipage}{0.5\textwidth}
		\centering
		\includegraphics[width=1.0\textwidth]{Fig3.pdf}
		\captionof{figure}{Traning performance of VolumeNet for the classification of LGG with/without 1p/19q codeletion.}\label{fig:fig3}
	\end{minipage}
	\begin{minipage}{0.45\textwidth}
		\centering
		\captionsetup{type=table}
		\label{tab:1p19q}
		\begin{tabular}{|c|c|c|}
			\hline
			& VolumeNet & Akkus et al. \cite{akkus2017predicting}\\ \hline\hline
			Sensitivity & 94\%      & 93\%         \\ \hline
			Specificity & 100\%     & 82\%         \\ \hline
			Accuracy    & \textbf{97\%}      & 88\%         \\ \hline
		\end{tabular}
		\captionof{table}{Comparative test performance of VolumeNet on the holdout set.}
	\end{minipage}
\end{figure}

\subsection*{Runtime analysis}
The total time required for training each network for $100$ epochs is presented in Table~\ref{tab:tab2}, averaged over several runs. This further corroborates that multi-planar and slice level processing can learn to generalize better than a patch level network, although at the expense of higher computational time.
{
\begin{table}[]
	\centering
	\caption{Comparative training time}
	\label{tab:tab2}
	{\scriptsize\begin{tabular}{|l|l|l|}
		\hline
		ConvNet   & \multicolumn{1}{c|}{\begin{tabular}[c]{@{}c@{}}Time\\ $(Mean \pm SD)$\end{tabular}} & Training type \\ \hline
		PatchNet  & $10.75 \pm 0.05$ min                                                                 & from scratch  \\ \hline
		SliceNet  & $65.95 \pm 0.02$ min                                                                & from scratch  \\ \hline
		VolumeNet	& $132.48 \pm 0.05$ min                                                                                & from scratch  \\ \hline
		VGGNet	& $8.56 \pm 0.03$    min                                                                                & fine-tuning   \\ \hline
		ResNet	& $12.14 \pm 0.03$    min                                                                                & fine-tuning   \\ \hline
	\end{tabular}}
\end{table}}

\subsection*{Qualitative evaluation}
The ConvNets were next investigated through visual analysis of their intermediate layers. The performance of a ConvNet depends on the convolution kernels, which are the feature extractors from the unsupervised learning process. Visualizing the output of any convolution layer can help determine the description of the learned kernels. Fig.~\ref{fig:fig4} illustrates the intermediate convolution layer outputs (after ReLU activation) of the proposed SliceNet architecture on sample MRI slices from an HGG patient.

\begin{figure*}[]
	\centering
	\includegraphics[width=1.0\textwidth]{Fig4.pdf}
	\caption{(a) Four sequences of an MRI slice from a sample HGG patient from TCIA~\cite{clark2013cancer} (under a Creative Commons Attribution 3.0 Unported. Full terms at https://creativecommons.org/licenses/by/3.0/). Intermediate layer outputs/feature maps, generated by SliceNet, at diferent levels by (b) Conv1, (c) Conv2, (d) Conv3 and (e) Conv4.}
	\label{fig:fig4}
\end{figure*}

The visualization of the first convolution layer activations (or feature maps) [Fig.~\ref{fig:fig4}(b)] indicates that the ConvNet has learned a variety of filters to detect edges and distinguish between different brain tissues like white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), skull and background. Most importantly, some of the filters could isolate the ROI (or the tumor); on the basis of which the whole MRI slice may be classified.

Most of the feature maps generated by the second convolution layer [Fig.~\ref{fig:fig4}(c)] mainly highlight the tumor region and its subregions; like enhancing tumor structures, surrounding cystic/necrotic components and the edema region of the tumor. Thus the filters in the second convolution layer learn to extract deeper features from the tumor by focusing on the ROI (or tumor).

The texture and shape of the tumor get enhanced in the feature maps generated from the third convolution layer [Fig.~\ref{fig:fig4}(d)]. For example, small, distributed, irregular tumor cells get enhanced (one of the most important tumor grading criteria called ``CE-Heterogeneity'' \cite{chekhun2013tumor}). Finally the last layer [Fig.~\ref{fig:fig4}(e)] extracts detailed information about more discriminating features, by combining these to produce a  clear distinction between images of different types of tumors.

\section*{Discussion}

An exhaustive study was made to demonstrate the effectiveness of Convolutional Neural networks for non-invasive, automated detection and grading of brain tumors from multi-sequence MR images. Three novel ConvNet architectures were developed for distinguishing between HGG and LGG. Three level ConvNet architectures were designed to handle images at patch, slice and multi-planar modes. This was followed by exploring transfer learning for the same task, by fine-tuning two existing ConvNet models. The scheme for incorporating volumetric tumor information, using multi-planar MRI slices, achieved the best test accuracy of $97.19 \%$ in the LOPO mode and $95\%$ on the holdout dataset for the classification of LGG/HGG (Case study A). In Case study B an accuracy of $97\%$ was obtained for the classification of LGG/HGG (case study A). In case study B an accuracy of $97.00\%$ was obtained for the classification of LGG with/without 1p/19q codeletion status on the holdout dataset.

Visualization of the intermediate layer outputs/feature maps demonstrated the role of kernels/filters in the convolution layers in automatically learning to detect tumor features closely resembling different tumor grading criteria. It was also observed that existing ConvNets, trained on natural images, performed adequately just by fine-tuning their final convolution layer on the MRI dataset. This investigation allows us to conclude that deep ConvNets could be a feasible alternative to surgical biopsy for brain tumors.

Diagnosis from histopathological images is also considered to be the ``gold standard" in this domain. HGGs are characterized by the presence of pseudopallisading necrosis (necrotizing cell-devoid region radially surrounded by lined-up tumor cells) and microvascular proliferation (enlarged new blood vessels in the tissue) \cite{mousa15}. The LGGs, on the other hand, exhibit a visual smoothness with cells spread evenly throughout the tissue. Automated glioma grading, through image analysis of the slides, serves to complement the efforts of clinicians in categorizing into low- (Grade II) and high- (Grade III, IV) gliomas (LGG and HGG).

\section*{Methods}

In this section we provide a brief description of the data preparation at three levels of resolution, followed by an introduction to convolutional neural networks and transfer learning.

\subsection*{Brain tumor data}
For the classification of low/high grade gliomas (Case study A), the models were trained on the TCGA-GBM \cite{scarpace9radiology} and TCGA-LGG \cite{pedano2016radiology} datasets downloaded from The Cancer Imaging Archive (TCIA) \cite{clark2013cancer}. The testing was on an independent set, i.e. brain tumor dataset from MICCAI BraTS 2017 \cite{bakas2017advancing, menze2015multimodal} competition containing images of low grade glioma (LGG) and high grade glioma (HGG). The TCGA GBM and LGG datasets consists of $262$ and $199$ samples respectively, whereas the BraTs 2017 database contains $210$ HGG and $75$ LGG samples. Each patient scan has four sequences, encompassing the native ($T1$), post-contrast enhanced $T1$-weighted ($T1C$), $T2$-weighted ($T2$), and $T2$ Fluid-Attenuated Inversion Recovery ($FLAIR$) volumes.

The ConvNet model used for classification of 1p/19q codeletion status in LGG (Case study B) was trained on the brain tumor patient database from Mayo Clinic, containing a total of $159$ LGG patients ($57$ non-deleted and $102$ codeleted) having preoperative postcontrast-$T1$ and $T2$ images downloaded from TCIA\cite{erickson2017data}. A total of $30$ samples ($15$ non-deleted and $10$ codeleted) were randomly selected from the data at the beginning, as a test set, and was never shown to the ConvNet during its training. The remaining $129$ samples were used for training the model.

Sample images of the two glioma grades (LGG/HGG), and LGG with and without 1p/19q codeletion are shown in Fig.~\ref{fig:fig5}(a), (b), respectively. It can be observed from the figure that it is very hard to discriminate in each case, based only on the phenotypes visible to the human eye. Hence abstract features learned by the deep layers of a ConvNet are expected to be helpful in noninvasively differentiating between them. Besides, the use of large public domain datasets can allow more clinical impact as compared to controlled and dedicated prospective image acquisitions.

\begin{figure}[]
	\centering
	\includegraphics[width=0.8\textwidth]{Fig5.pdf}
	\caption{Sample MR image sequences from TCIA \cite{clark2013cancer} (under a Creative Commons Attribution 3.0 Unported. Full terms at https://creativecommons.org/licenses/by/3.0/) of (a) low/high grade gliomas, and (b) low-grade glioma with and without 1p/19q codeletion.}
	\label{fig:fig5}
\end{figure}

The datasets were aligned to the same anatomical template, skull-stripped, bias field corrected and interpolated to $1mm^3$ voxel resolution.

\subsection*{Dataset preparation}\label{dataset_preparation}
Although the TCGA-GBM and TCGA-LGG datasets consist MRI volumes, we cannot propose a 3D ConvNet model for the classification problem; mainly because the dataset has only $262$  HGG and $199$ LGG patients data, which is considered as inadequate to train a 3D ConvNet with a huge number of trainable parameters. Another problem with the dataset is its imbalanced class distribution i.e. about $35.72\%$ of the data comes from the LGG class. Therefore we formulate 2D ConvNet models based on the MRI patches (encompassing the tumor region) and slices, followed by a multi-planar slice-based ConvNet model that incorporates the volumetric information as well.

Applying ConvNet directly on the MRI slice could require extensive downsampling, thereby resulting in loss of discriminative details. The tumor can be lying anywhere in the image and can be of any size (scale) or shape. Therefore classifying the tumor grade from patches is easier, because the ConvNet learns to localize only within the extent of the tumor in the image.  Thereby the ConvNet needs to learn only the relevant details without getting distracted by irrelevant details. However it may lack spatial and neighborhood details of the tumor, which may adversely influence grade prediction. Although classification based on the 2D slices and patches often achieves good accuracy, the incorporation of volumetric information from the dataset can enable the ConvNet to perform better.

Along these lines, we propose schemes to prepare three different sets viz.  (i) patch-based, (ii) slice-based, and (iii) multi-planar volumetric, from the TCIA datasets.

\subsubsection*{Patch-based dataset} The slice with the largest tumor region is first identified. Keeping this slice in the middle, a set of slices before and after it are considered for extracting 2D patches containing the tumor regions using a bounding-box.  This bounding-box is marked, corresponding to each slice, based on the ground truth image. The enclosed image region is then extracted.

\begin{figure}[tbp]
	\centering
	\includegraphics[width=0.4\textwidth]{Fig6.pdf}
	\caption{Ten T2-MR patches extracted from contiguous slices from an LGG patient from TCIA \cite{clark2013cancer} (under a Creative Commons Attribution 3.0 Unported. Full terms at https://creativecommons.org/licenses/by/3.0/).}
	\label{fig:fig6}
\end{figure}

We use a set of $20$ slices for extracting the patches. In case of MRI volumes from HGG (LGG) patients, four (ten) 2D patches [with a skip over $5$ ($2$) slices] are extracted for each of the MR sequences. Therefore a total of $210 \times 4 = 840$ HGG and $75 \times 10 = 750$ LGG patches, with four channels each, constitute this dataset. Although the classes are still not perfectly balanced, this ratio is found to be good enough in the scenario of this enhanced dataset.

In spite of significant dissimilarity visible between contiguous MRI slices at a global level, there may be little difference exhibited at the patch level. Therefore patches extracted from contiguous MRI slices look similar, particularly for LGG cases. Fig.~\ref{fig:fig6} depicts a set of $10$ patches extracted from contiguous MR slices of an LGG patient. This can lead to overfitting in the  ConvNet. To overcome this problem we introduce a concept of static augmentation by randomly changing the perfect bounding-box coordinates by a small amount ($\in\{-5, 5\}$ pixels) before extracting the patch. This results in improved learning and convergence of the network.

\subsubsection*{Slice-based dataset}
Complete 2D slices, with visible tumor region, are extracted from the MRI volume. The slice with the largest tumor region, along with a set of $20$ slices before and after it, are extracted from the MRI volume in a sequence similar to that of the patch-based approach. While for HGG patients $4$  (with a skip over $5$) slices are extracted, in the case of LGG patients $10$ (with a skip of $2$) slices are used.

\subsubsection*{Multi-planar volumetric dataset} Here 2D MRI slices are extracted along all three anatomical planes, viz. axial ($X$-$Z$ axes), coronal ($Y$-$X$ axes), and sagittal ($Y$-$Z$ axes), in a manner similar to that described above.

\subsection*{Convolutional neural networks}\label{ConvNets}
Convolutional Neural Networks (ConvNets) can automatically learn low-level, mid-level and high-level abstractions from input training data in the form of convolution filter weights, that get updated during the training process by backpropagation. The inputs percolating through the network are the responses of convoluting the images with various filters. These filters act as detectors of simple patterns like lines, edges, corners, from spatially contiguous regions in an image. When arranged in many layers, the filters can automatically detect prevalent patterns while blocking irrelevant regions. Parameter sharing and sparsity of connection are the two main concepts that make ConvNets easier to train with a small number of weights as compared to dense fully connected layers. This reduces the chance of overfitting, and enables learning translation invariant features. Some of the important concepts, in the context of ConvNets are next discussed.

\subsubsection*{Layers} The fundamental constituents of a ConvNet consist of the input, convolution, activation, pooling and fully-connected layers. The input layer receives a multi-channel brain MRI patch/slice denoted by $I \in \mathbb{R}^{m \times w \times h}$, where $m$ is the number of channels, $w$ and $h$ represent the resolution of the image. The convolutional layer takes the image or feature maps as input, and performs the convolution operation between the input and each of the filters to generate a set of activation maps. The output feature map dimension, from a convolution layer, is  calculated as $w_{out}/h_{out} = \frac{(w_{in}/h_{in} - F + 2P)}{S} + 1$,	where $w_{in}$ and $h_{in}$ are the width and height of the input image, $w_{out}$ and $h_{out}$ are the width and height of the effective output. Here $P$ denotes the input padding, the stride $S=1$, and $F$ is the kernel size of the neurons in a particular layer. Output responses of the convolution and fully connected layers pass through some nonlinear activation function, such as a Rectified Linear Unit (ReLU) \cite{glorot2011deep}, for transforming the data. ReLU, defined as $f(a) = max(0, a)$, is a popular activation function for deep neural networks due to its computational efficiency and reduced likelihood of vanishing gradient. The pooling layer follows each convolution layer to typically reduce computational complexity by downsampling of the convoluted response maps. Max pooling enables selection of the maximum feature response in local neighborhoods, and thereby enhances translation invariance. The features learned through a series of convolutional and pooling layers are eventually fed to a fully-connected layer, typically a Multilayer Perceptron. Additional layers like Batch-Normalization \cite{ioffe2015batch} reduce initial covariate shift. Dropout \cite{srivastava2014dropout} is used as regularizer to learn a better representation of the data.

\subsubsection*{Loss} The cost function for the ConvNets is chosen as binary cross-entropy (for a two-class problem) as
\begin{equation}
L_C = -\frac{1}{n}\sum_{i=1}^{n}\left\{y_i \log(f_i) + (1 - y_i) \log(1 - f_i)\right\},
\end{equation}
where $n$ is the number of samples, $y_i$ is the true label of a sample and $f_i$ is its  predicted label.

\subsection*{Transfer learning}\label{transfer_learning}
Typically the early layers of a ConvNet learn low-level image features, which are applicable to most vision tasks. The later layers, on the other hand, learn high-level features which are more application-specific. Therefore, shallow fine-tuning of the last few layers is usually sufficient for transfer learning. A common practice is to replace the last fully-connected layer of the pre-trained ConvNet with a new fully-connected layer, having as many neurons as the number of classes in the new target application. The rest of the weights, in the remaining layers, of the pre-trained network are retained. This corresponds to training a linear classifier with the features generated in the preceding layer. However, when the distance between the source and target applications is significant then one may need to induce deeper fine-tuning. This is equivalent to training a shallow neural network with one or more hidden layers. An effective strategy \cite{tajbakhsh2016} is to initiate fine-tuning from the last layer, and then incrementally include deeper layers in the tuning process until the desired performance is achieved.

\section*{ConvNets for Brain tumor Grading \label{brain_tumor_grading_using_ConvNets}}
This section introduces the three ConvNet architectures, trained on the three level brain tumor MR data sets, along with a brief description of the fine tuning of existing models.

\subsection*{Three level architectures}
We propose three ConvNet architectures, named PatchNet, SliceNet, and VolumeNet, which are trained from scratch on the three datasets prepared as detailed in Section \textbf{Dataset preparation}. This is followed by transfer learning and fine-tuning of these networks. The ConvNet architectures are illustrated in Fig.~\ref{fig:fig7}. PatchNet is trained on the patch-based dataset, and provides the probability of a patch belong to HGG or LGG. SliceNet gets trained on the slice-based dataset, and predicts the probability of a slice being from HGG or LGG. Finally VolumeNet is trained on the multi-planar volumetric dataset, and predicts the grade of a tumor from its 3D representation using the multi-planar 3D MRI data.

As reported in literature \cite{szegedy2015going}, smaller size convolutional filters produce better regularization due to the smaller number of trainable weights; thereby allowing construction of deeper networks without losing too much information in the layers. We use filters of size $(3 \times 3)$ for our ConvNet architectures. A greater number of filters, involving deeper convolution layers, allows for more feature maps to be generated. This compensates for the decrease in size of each feature map caused by ``valid'' convolution and pooling layers. Due to the complexity of the problem and bigger size of the input image, the SliceNet and VolumeNet architectures are deeper as compared to the PatchNet.

\begin{figure*}[tbp]
	\centering
	\includegraphics[width=0.85\textwidth]{Fig7.pdf}
	\caption{Three level ConvNet architectures (a) PatchNet, (b) SliceNet, and (c) VolumeNet, sample MRIs are from TCIA database \cite{clark2013cancer} (under a Creative Commons Attribution 3.0 Unported. Full terms at https://creativecommons.org/licenses/by/3.0/).}
	\label{fig:fig7}
\end{figure*}

\subsection*{Fine-tuning}

Pre-trained VGGNet (16 layers), and ResNet (50 layers) architectures, trained on the ImageNet dataset, are employed for transfer learning. Even though ResNet is deeper than VGGNet, the model size of ResNet is substantially smaller due to the usage of global average pooling rather than fully-connected layers. Transferring from the non-medical to the medical image domain is achieved through fine-tuning of the last convolutional block of each model, along with the fully-connected layer (top-level classifier). Fine-tuning of a trained network is achieved by retraining on the new dataset, while involving very small weight updates. The adopted procedure is outlined below.

\begin{itemize}
	\item Instantiate the convolutional base of the model and load its pre-trained weights.
	\item  Replace the last fully-connected layer of the pre-trained ConvNet with a new fully-connected layer, having single neuron with sigmoid activation.
	\item Freeze the layers of the model up to the last convolutional block.
	\item Finally retrain the last convolution block and the fully-connected layers using Stochastic Gradient Descent (SGD) optimization algorithm with a very slow learning rate.
\end{itemize}

Since the base models were trained on RGB images, and accept single input with three channels, we train and test them on the slice-based dataset involving three MR sequences ($T1C$, $T2$, $FLAIR$). The $T1C$ sequence was found to perform better than $T1$, when used in conjunction with $T2$ and $FLAIR$. Although running either of these two models from scratch is very expensive, particularly on CPU, the concept of fine-tuning just the last few layers could be easily accomplished.

redpajama_set_name: RedPajamaArXiv


Many people panic when faced with pain, or at the very least consider pain to be a bad thing.
In Integrated Healing, we consider pain to be a messenger, asking us not to do a particular activity until we sort out a related dysfunction.
It is our experience, and that of many people we speak with, that the site of the pain is frequently not at it's source – for example, an unaligned spine may cause hip, knee or ankle pain (to name but 3 common areas.) Unfortunately, if you work on the hip, because that is where the pain is, relief will only be temporary at best, because what is being treated is a symptom, and not the root cause. In Integrated Healing we test the body to find the root cause of the pain and focus the healing wherever the client's body tells us it needs it. It takes away the guesswork and any need for a prescriptive approach.


text:
Nicole Uphoff (born 25 January 1967) is a German equestrian who competes in the sport of dressage. She won four gold medals in individual and team competition at the 1988 and 1992 Summer Olympics. Riding her star horse, Rembrandt, Uphoff also won numerous other international competitions, including the World Equestrian Games and the European Dressage Championships.

Personal life

Uphoff first married international show jumper Otto Becker in the early 1990s, but the pair separated in late 2007 and later divorced. In January 2004, Uphoff gave birth to her first child, a son named Patrick. Long-time boyfriend Travis Morgan was the baby's father.

Competitive career
In 1985, Uphoff, riding Rembrandt, began to compete at events for young riders, and in 1986, the pair began working with Uwe Schulten-Baumer, a well-known dressage coach. In 1987, they began to compete, and win, at the international level. They rose to the top of the dressage world with unheard-of speed, due to Rembrandt's elegance and expression in the ring. Their rise to fame resulted in a 1988 Olympic nomination. Uphoff switched trainers four months before the Games began, leaving Schulten-Baumer to work with Harry Boldt, the German national coach.

Rembrandt and Uphoff were Olympic Champions twice, in 1988 in Seoul and 1992 in Barcelona. Both times they took both individual gold and a gold medal with the German team. The Barcelona games resulted in Rembrandt being titled a "living work of art" by one German sports commentator. During the last two years of his career, Rembrandt began to decline slightly, and he missed the team nomination to the 1996 Summer Olympics. Uphoff used her right as defending champion to compete as an individual, and they were again part of the German delegation in Atlanta, where they placed 14th. This placement was despite being pulled from competition before the last round, due to an injury, at which point they was in 8th place. Uphoff and Rembrandt also took both individual and team gold at the first World Equestrian Games in 1990. In 1993, he was injured by a kick from another horse during a victory lap at the German Championships, but recovered from the blow to take individual silver at the 1994 World Equestrian Games. The German team, including Uphoff, again took gold.

Uphoff and Rembrandt also competed at many other international-level events. In 1989, Rembrandt made his European Dressage Championships debut at Mondorf, Luxembourg, where they took both individual and team gold. It was the first time that the gold medal-winning German team (which had also won the previous 12 team golds) had been made up of four female riders. In 1991, at the Championships in Donaueschingen, Germany, the pair took silver in the Grand Prix Special, while again riding to gold as part of the German team. The silver medal was the first defeat in three years for the pair, and was dealt by another Schulten-Baumer student, Isabell Werth on Gigolo FRH. At the 1995 Championships, held in Mondorf, Luxembourg, Uphoff again assisted the German team to a gold medal, while taking 5th individually. The pair also won seven times at the Stuttgart German Masters international show, the last in 1995. This was a record number of wins until 2010, when Werth won for an eighth time, and Uphoff remains the only rider to have won seven masters titles on a single horse (Werth rode three different horses to her eight victories).

In 1996, Uphoff and Rembrandt gave a farewell show at his official retirement at the Stuttgart Indoor Show. Rembrandt spent the rest of his life at Uphoff's farm. On 30 October 2001, he was euthanized after his health deteriorated quickly and he was unable to rise. Rembrandt's transitions, particularly from passage to extended trot back to passage, have been called "unequalled". He was also known for his spookiness, however, to the point that crowds hushed themselves when he was performing. He is known as a "legendary" dressage horse, and mounted upon Rembrandt, Nicole Uphoff pioneered an era of female-dominated dressage competitions. In 2009, Uphoff donated Rembrandt's saddle to the equestrian museum at CHIO Aachen in Germany.

After the 1996 Olympics and Rembrandt's retirement, Uphoff faded from the dressage world for several years, unable to find a mount that could win in international competition. However, in early 1998, she began riding Borbet Rubinstein and soon returned to international prominence, winning the 1999 CDI Frankfurt. She also began training several of Rubinstein's offspring. In 1999, she appeared on German television, giving dressage commentary, and was named the German Sportswoman of the Year. There was speculation that she may make a bid for the 2000 Olympic team. She did not compete in the 2000 Olympics, and by early 2013, she had officially retired from competition.

References

External links

1967 births
Living people
Olympic gold medalists for West Germany
Olympic gold medalists for Germany
Equestrians at the 1988 Summer Olympics
Equestrians at the 1992 Summer Olympics
Equestrians at the 1996 Summer Olympics
Olympic equestrians of West Germany
Olympic equestrians of Germany
German female equestrians
German dressage riders
Sportspeople from Duisburg
Olympic medalists in equestrian
Medalists at the 1992 Summer Olympics
Medalists at the 1988 Summer Olympics
20th-century German women

redpajama_set_name: RedPajamaWikipedia


text:
DTN News: U.S. Department of Defense Contracts Dated November 28, 2011
Source: U.S. DoD issued No. 982-11 November 28, 2011
(NSI News Source Info) WASHINGTON - November 28, 2011: U.S. Department of Defense, Office of the Assistant Secretary of Defense (Public Affairs) Contracts issued November 28,2011 are undermentioned;
AAI, Corp., Goose Creek, S.C. (N61340-12-D-5101); Aero Simulation, Inc.*, Tampa, Fla. (N61340-12-D-5102); The Boeing Co., St. Louis, Mo. (N61340-12-D-5103); CAE USA, Inc., Tampa, Fla. (N61340-12-D-5104); Computer Science Corp., Falls Church, Va. (N61340-12-D-5105); Cubic Simulation Systems, Inc., Orlando, Fla. (N61340-12-D-5106); Indra Systems, Inc., Winter Park, Fla. (N61340-12-D-5107); FlightSafety International, Inc., Broken Arrow, Okla. (N61340-12-D-5108); J.F. Taylor, Inc.*, Lexington Park, Md. (N61340-12-D-5109); L-3 Communications Corp., Arlington, Texas (N61340-12-D-5110); Lockheed Martin Global Training and Logistics, Orlando, Fla. (N61340-12-D-5111); QinetiQ North America, Inc., Huntsville, Ala. (N61340-12-D-5112); Rockwell Collins Simulation & Training Solutions, Cedar Rapids, Iowa (N61340-12-D-5113); Science Applications International Corp., McLean, Va. (N61340-12-D-5114); Symvionics, Inc.*, Arcadia, Calif. (N61340-12-D-5115); Kratos Technology & Training Solutions, Inc., San Diego, Calif. (N61340-12-D-5116); Thales USA Defense & Security, Inc., Arlington, Va. (N61340-12-D-5117); and Veraxx Engineering*, Chantilly, Va. (N61340-12-D-5118), are each being awarded a contract under the Training Systems Contract (TSC) III LOT I multiple award indefinite-delivery/indefinite-quantity contract. Aero Simulation, Inc.*, Tampa, Fla. (N61340-12-D-5201); CymSTAR, L.L.C.*, Broken Arrow, Okla. (N61340-12-D-5202); Environmental Tectonics Corp.*, Southampton, Pa. (N61340-12-D-5203); J.F. Taylor, Inc.*, Lexington Park, Md. (N61340-12-D-5204); Logistics Services International, Inc.*, Jacksonville, Fla. (N61340-12-D-5205): Opinicus Corp.*, Lutz, Fla. (N61340-12-D-5206); Symvionics, Inc.*, Arcadia, Calif. (N61340-12-D-5207); TJ Drafting & Design, Inc.*, Christmas, Fla. (N61340-12-D-5208); Triton Joint Venture, L.L.C.*, Orlando, Fla. (N61340-12-D-5209); and Veraxx Engineering*, Chantilly, Va. (N61340-12-D-5210) are each being awarded a contract under the TSC III LOT II multiple award indefinite-delivery/indefinite-quantity contract. These multiple award contracts provide for training systems design, development, production, test and evaluation, delivery, modification, and support. Included within this contract is associated instructional systems development products and training systems support, which includes interim contractor support or contractor engineering and technical services and provisioning for the initial support period. The scope of the TSC III multiple award contract is intended to encompass the training systems requirements of Naval Air Systems Commands Aviation Training Systems Program Office, as well as the Program Directorates for Aviation; Surface; Undersea; and Cross-Warfare/International at the Naval Air Warfare Center Training System Division. The aggregate not-to-exceed amount for these multiple award contracts, inclusive of options, is $2,000,000,000, and the companies will have the opportunity to complete for delivery orders under their respective LOTs. Work will be performed at each awardee's facility site, or at existing or future training sites. All orders are expected to be placed by November 2016, and all performance must be complete by November 2022. Contract funds will not expire at the end of the current fiscal year. These contracts were competitively procured via a multiple award electronic request for proposals, and 35 offers were received for LOT I. LOT II was procured as a 100 percent small business set-aside, and 27 offers were received. The Naval Air Warfare Center Training System Division, Orlando, Fla., is the contracting activity.
BREMCOR, Arlington, Va., is being awarded a $21,028,650 modification under a previously awarded firm-fixed-price, indefinite-delivery/indefinite-quantity contract (N62470-06-D-4611) to exercise option five for base operation support services at Naval Station Guantanamo Bay, Cuba. The work to be performed includes, but not limited to, the following functions: port operations, base support vehicles and equipment, elevator maintenance, equipment maintenance, utilities, environmental, transportation, bachelor housing, fire alarms, wastewater, steam, and facility management. Work also provides service calls consisting of repairs of base facilities, utilities, transportation, and fire alarm systems. The total contract amount after exercise of this option will be $115,046,778. Work will be performed in Guantanamo Bay, Cuba, and is expected to be completed November 2012. Contract funds will not expire at the end of the current fiscal year. The Naval Facilities Engineering Command, Southeast, Jacksonville, Fla., is the contracting activity.
The Boeing Co., St. Louis, Mo., is being awarded a $17,262,617 firm-fixed-price order against a previously issued basic ordering agreement (N00019-11-G-0001) for the design, development, and installation of engineering change proposal (ECP) 618 retrofit kits for eight Royal Malaysian Air Force F/A-18 aircraft under the Foreign Military Sales Program. This contract action also includes training for ECP 618 and ECP 624, and the installation of other systems that are part of the Malaysian upgrade. Work will be performed in St. Louis, Mo. (70 percent), and Butterworth, Malaysia (30 percent), and is expected to be completed in April 2015. Contract funds will not expire at the end of the current fiscal year. The Naval Air Systems Command, Patuxent River, Md., is the contracting activity.
L-3 Communications Link Simulation & Training Division, Arlington, Texas, is being awarded a $13,826,160 modification to a previously awarded firm-fixed price, indefinite-delivery/indefinite-quantity contract (N00019-07-D-0100) for logistics services in support of the C-9 aircraft located at the Naval Air Station (NAS) Whidbey Island, Wash.; the Marine Corps Air Station (MCAS) Cherry Point, N.C.; the Joint Reserve Base (JRB) Fort Worth, Texas; and McGuire Air Force Base (AFB), N.J. Services to be provided include site support, inventory management, depot-level support, drop-in maintenance, component repair and overhaul of government-owned inventory, contractor field teams, and replacement of government-owned inventory. Work will be performed at NAS Whidbey Island, Wash. (43 percent); MCAS Cherry Point, N.C. (25 percent), JRB Fort Worth, Texas (17 percent); and McGuire AFB, N.J. (15 percent). Work is expected to be completed in July 2012. Contract funds will not expire at the end of the current fiscal year. Funds will be obligated on individual delivery orders as they are issued. The Naval Air Systems Command, Patuxent River, Md., is the contracting activity.
Insitu, Inc., Bingen, Wash., is being awarded an $11,994,145 modification to a previously awarded firm-fixed-price contract (N00019-11-C-0061) to exercise an option for operational and maintenance services in support of the ScanEagle Unmanned Aerial Systems. These services will provide electro-optical/infrared and mid-wave infrared imagery in support of Marine Corps operations in Operation Enduring Freedom to provide real-time imagery and data. Work will be performed in Bingen, Wash., and is expected to be completed in January 2012. Contract funds in the amount of $11,994,145 will expire at the end of the current fiscal year. The Naval Air Systems Command, Patuxent River, Md., is the contracting activity.
Keystone Prepositioning Services, Inc., Bala Cynwyd, Pa., is being awarded an $8,991,957 firm-fixed-price contract for the operation and maintenance of three of Military Sealift Command's government-owned Maritime Prepositioning Force ships: USNS Sgt. Matej Kocak, USNS Pfc. Eugene A. Obregon, and USNS Maj. Stephen W. Pless. The ships will continue to support at-sea prepositioning of equipment and supplies and surge-sealift requirements for the Department of Defense. This contract includes options, which, if exercised, would bring the cumulative value of this contract to $47,403,888. The contract includes four one-year option periods and an annual award fee of up to $125,000 per ship. The contractor can also earn up to $50,000 annually per ship for efforts that result in a reduction in ship energy consumption. Work will be performed at sea worldwide, and is expected to be completed in September 2012. Completion date with all option periods exercised will be September 2016. Contract funds will expire at the end of the current fiscal year. This contract was competitively procured with more than 50 proposals solicited via solicitations posted to the Military Sealift Command, Navy Electronic Commerce Online and Federal Business Opportunities websites, with and six offers received. The Military Sealift Command, Washington, D.C., is the contracting activity (N00033-12-C-3116).
United Launch Services, L.L.C., Littleton, Colo., is being awarded a $150,000,000 firm-fixed-price contract. This undefinitized contractual action is to provide Evolved Expendable Launch Vehicle launch services in support of the fifth Wideband Global Satellite Communication mission. The location of performance is United Launch Services, Littleton, Colo. The Space and Missile Systems Center, El Segundo, Calif., is the contracting activity (FA8811-11-C-0001).
Supply Core, Inc.*, Rockford, Ill., was issued a modification on contract SPM500-05-D-BP06. The award is a firm-fixed-price contract with a maximum $32,000,000 maintenance, repair, and operations prime vendor for the Japan region, award of an eight-month bridge contract. There are no other locations of performance. Using services are Army, Navy, Air Force, Marine Corps and federal civilian agencies. There were six responses to the Web-solicitation. Type of appropriation is fiscal 2012 Defense Working Capital Funds. The date of performance completion is July 31, 2012. The Defense Logistics Agency Troop Support, Philadelphia, Pa., is the contracting activity.
Infrastructure Defense Technologies, L.L.C.*, Belvidere, Ill., was issued a modification exercising option year one on contract SPM8EG-11-D-0003. The award is a fixed-price with economic price adjustment, long-term indefinite quantity contract with a maximum $28,300,468 for metal revetments. There are no other locations of performance. Using services are Army, Navy, Air Force, and Marine Corps. There were three responses to the Defense Logistics Agency Internet Bid Board System solicitation. Type of appropriation is fiscal 2012 Defense Capital Revolving Funds. The date of performance completion is Dec. 28, 2012. The Defense Logistics Agency Troop Support, Philadelphia, Pa., is the contracting activity.
Oregon Freeze Dry*, Albany, Ore., was awarded a firm-fixed-price contract with a maximum $13,085,154 for granola with milk and blueberries and granola with milk and banana for unitized group ration express. There are no other locations of performance. Using services are Army and Marine Corps. There was one response to the Web solicitation. Type of appropriation is fiscal 2012 Defense Working Capital Funds. The date of performance completion is Nov. 28, 2014. The Defense Logistics Agency Troop Support, Philadelphia, Pa., is the contracting activity (SPM-3S1-12-D-Z195).
DEFENSE INFORMATION SYSTEMS AGENCY
On Nov. 22, Claraview, a division of Teradata, Reston, Va., was awarded an indefinite-delivery/indefinite-quantity contract not-to-exceed ceiling price of $27,016,520 for continuation of system life cycle support services for data management/enterprise data warehouse/business intelligence environments. The period of performance is for a base period of Jan. 1, 2012 through May 31, 2012 and four one-year option periods. The period of performance for option year one is June 1, 2012 through May 31, 2013; option year two is June 1, 2013 through May 31, 2014; option year three is June 1, 2014 through May 31, 2015; and option year four is June 1, 2015 through May 31, 2016. Performance will be at Defense Commissary Agency, Fort Lee, Va. The solicitation was issued as a full and open competitive action pursuant to 10 U.S.C. 2304, 41 U.S.C. 253, and FAR 6.101(b). Solicitation HC1028-10-R-2038 was posted at Defense Information Technology Contracting Organization Contracting Opportunities website and posted on the Federal Business Opportunities website Aug. 30, 2010, four offers were received. During the subsequent corrective action, two offers were re-evaluated. Claraview is a large business. The Defense Information Technology Contracting Organization, Scott Air Force Base, Ill., is the contracting activity (HC1028-12-D-0003).
DEFENSE HUMAN RESOURCE ACTIVITY
HP Enterprise Services, L.L.C., Herndon, Va., is being awarded an estimated $22,638,387 firm-fixed price, requirements contract for the Defense Manpower Data Center's user support. Work will be performed worldwide and is expected to be completed in November 2012. Contract funds will be obligated on individual task orders that will be placed in fiscal 2012. This requirement was advertised on the Federal Business Opportunities website on a sole-source basis. One bid was received. The Defense Human Resources Activity Procurement Support Office, Alexandria, Va., is the contracting activity (H98210-12-D-0001).
*Small business
*Link for This article compiled by Roger Smith from reliable sources
U.S. DoD issued No. 982-11 November 28, 2011
*Speaking Image - Creation of DTN News ~ Defense Technology News
*This article is being posted from Toronto, Canada By DTN News ~ Defense-Technology News
dtnnews@ymail.com
COPYRIGHT (C) DTN NEWS DEFENSE-TECHNOLOGY NEWS
Labels: Contract, Contracts, DoD, DTN Defense-Technology News, DTN Logos, DTN News, DTN News Images, U.S. Department Of Defense, U.S. DoD, Washington
DTN News - OSHKOSH DEFENSE NEWS: New Oshkosh Defense Virtual Trainer Improves Safety, Reduces Military Vehicle Training Costs
Virtual vehicle training decreases cost by 20 percent or more
Source: DTN News - - This article compiled by Roger Smith from reliable sources Oshkosh Defense
(NSI News Source Info) TORONTO, Canada / OSHKOSH, Wis - November 28, 2011:
Oshkosh Defense, a division of Oshkosh Corporation (NYSE:OSK), has expanded its military training capabilities with the new Oshkosh Virtual Trainer. This virtual training element makes the overall vehicle training program safer and more cost-effective. Oshkosh will unveil its first virtual training module for the Heavy Expanded Mobility Tactical Truck (HEMTT) M978 Tanker at the annual Interservice/Industry Training, Simulation and Education Conference (I/ITSEC) Nov. 28–Dec. 1 in Orlando, Fla.
"In today's traditional HEMTT Tanker training curriculum, soldiers receive a limited amount of hands-on experience for critical procedures such as fueling and refueling," said Dan Albrecht, senior training manager for Oshkosh Defense. "The Oshkosh Virtual Trainer provides life-like training in a safe, virtual environment, which provides soldiers with the skills they need to perform the actual tasks. In addition to enhancing soldiers' training experience, we estimate that adding a virtual element to our comprehensive program will reduce training costs by 20 percent or more per participant."
In a recent pilot of the inaugural Oshkosh Virtual Trainer, the HEMTT Tanker module was shown to reduce training time by two days compared to the traditional classroom approach – an efficiency that equates to a 20 percent or more cost savings per participant. During the training, 12 students were able to train simultaneously and demonstrate skill proficiency in the virtual environment before performing tasks on the actual vehicle. For example, students are able to practice fueling and refueling procedures, and recovering from a fuel spill in the virtual world, without being exposed to the real-life hazards. The interactive skill-building using virtual training is not achieved in the traditional training courses where students learn primarily from static course materials and instructor demonstrations.
In addition to reducing direct training expenses, the Oshkosh Virtual Trainer helps prevent wear and tear on the U.S. government's demonstration vehicles and reduce costly damage to these vehicles.
The Oshkosh Virtual Trainer HEMTT Tanker module will be incorporated into training courses for U.S. Army personnel in May.
"We are excited to introduce our new virtual training capabilities to soldiers in May, and look forward to expanding the program to include more vehicle modules in the future," Albrecht said.
In addition to its new virtual-training capability, Oshkosh Defense also provides hands-on, classroom and simulation training for operators and mechanics as part of its full life-cycle services and support on all Oshkosh Heavy and Medium Tactical Wheeled Vehicles. The company received a Best Programs (red) ribbon award from Military Training Technology for the 2011 Top Simulation and Training Companies competition.
The HEMTT Tanker Virtual Task Trainer will be on display and Oshkosh representatives will be available at I/ITSEC at DiSTI booth #2481.
About Oshkosh Defense
Oshkosh Defense, a division of Oshkosh Corporation, is an industry-leading global designer and manufacturer of tactical military trucks and armored wheeled vehicles, delivering a full product line of conventional and hybrid vehicles, advanced armor options, proprietary suspensions and vehicles with payloads that can exceed 70 tons. Oshkosh Defense provides a global service and supply network including full life-cycle support and remanufacturing, and its vehicles are recognized the world over for superior performance, reliability and protection. For more information, visit www.oshkoshdefense.com.
About Oshkosh Corporation
Oshkosh Corporation is a leading designer, manufacturer and marketer of a broad range of specialty access equipment, commercial, fire & emergency and military vehicles and vehicle bodies. Oshkosh Corporation manufactures, distributes and services products under the brands of Oshkosh®, JLG®, Pierce®, McNeilus®, Medtec®, Jerr-Dan®, Oshkosh Specialty Vehicles, Frontline™, SMIT™, CON-E-CO®, London® and IMT®. Oshkosh products are valued worldwide in businesses where high quality, superior performance, rugged reliability and long-term value are paramount. For more information, visit www.oshkoshcorporation.com.
®, TM All brand names referred to in this news release are trademarks of Oshkosh Corporation or its subsidiary companies.
*Link for This article compiled by Roger Smith from reliable sources Oshkosh Defense
Labels: DTN Defense-Technology News, DTN Logos, DTN News, HEMTT, Oshkosh, Oshkosh Corporation, Oshkosh Defense, U. S. Armed Forces
DTN News - IRAN NEWS: Enemy Of Iranian State / Iran Threatens To Bomb Turkey If U.S. Or Israel Attacks Its Nuclear Installations
*Telegraph View: The expulsion of Britain's ambassador to Iran has provided a vivid insight into the mindset of the Islamic regime
Source: DTN News - - This article compiled by Roger Smith from reliable sources Telegraph - UK / Daily Mail - UK
(NSI News Source Info) TORONTO, Canada - November 28, 2011: By urging the expulsion of Britain's ambassador, Iran's parliament has provided a vivid insight into the mindset of the Islamic Republic's leadership. Their first thought as they retreat further into isolation and economic malaise is to credit Britain with prime responsibility for all their problems.
For over a century, British statecraft was indeed the dominant factor in Iranian politics, causing the downfall of leaders who were thought to threaten London's interests. But the era when a string of Royal Navy bases guaranteed that the Gulf was a British lake ended several generations ago. However, the old men who misrule Iran still claim to detect a British hand behind every significant event.
The latest trigger for their suspicion appears to have been the Treasury's (overdue) decision to exclude Iran from transactions with the UK financial system. Meanwhile, another round of UN sanctions is approaching as Iran's efforts to acquire nuclear weapons capability become increasingly obvious.
While the regime may well ignore the parliamentary vote, it clearly believes that Britain will be the guiding force behind this diplomatic drive. When millions marched in Tehran to protest against Mahmoud Ahmadinejad's victory in the rigged elections of 2009, that too was blamed on the British. Ayatollah Ali Khamenei, the obdurate "Supreme Leader", claimed on that occasion that Britain was the regime's "greatest enemy".
Perhaps we should rejoice in the status that all this accords us. Perception can amount to reality in international affairs, so the fact that Iran vastly overestimates our national power should cause us to be more assertive. And if Tehran's ossified, brutal leaders regard us as their foremost opponent, that is an honour indeed.
*Iran Threatens To Bomb Turkey If U.S. Or Israel Attacks Its Nuclear Installations
Iran will bomb Turkey if the U.S. or Israel tries to destroy its nuclear installations, a senior military commander warned today.
General Amir Ali Hajizadeh, head of the aerospace division of the powerful Revolutionary Guard, threatened to target Nato's missile defence shield in the neighbouring country.
The system which Turkey only agreed to install in September, is designed to prevent Iranian missile attacks on Israel.
Warning: Iran's Supreme Leader Ayatollah Ali Khamenei, seen meeting veterans in Tehran today, said plans to attack Turkey was part of Iran's strategy of respinding ¿to threats with threats¿
map2.jpg
Preparing: Khamenei, flanked by military and Revolutionary Guards commanders, reviews female members of the Basij militia in Tehran today
Provoking: Masked members of Iran's paramilitary Basij militia parade in front the former U.S embassy
The warning is part of is part of a new strategy devised by the Supreme Leader Ayatollah Ali Khamenei that consists of responding 'to threats with threats'.
General Hajizadeh said: 'Should we be threatened, we will target NATO's missile defense shield in Turkey and then hit the next targets.'
Tensions have been rising between Iran and the West since the release of a report earlier this month by the International Atomic Energy Agency.
VOTE TO EXPEL UK AMBASSADOR
Iranian lawmakers today voted to expel Britain's ambassador to Tehran in a new outbreak of hostility towards its old imperial foe.
To a chorus of 'death to England' shouts, parliamentarians approved a bill requiring reduced diplomatic relations due to London's support of upgraded U.S. sanctions.
During an open session broadcast live by state radio, 171 out of 196 MPs present voted for in favour of the measure.
Unlike the U.S., which has had no ambassador in Iran since the 1979 hostage crisis, Britain has maintained full diplomatic relations with the Islamic Republic.
However, Iranians have long been suspicious of the UK in large part due to Britain's long-held influence on its rulers and economy prior to the Islamic revolution. Britain's Foreign Office today said the decision to order the country's ambassador, Dominick John Chilcott, to leave Tehran was regrettable.
'This unwarranted move will do nothing to help the regime address their growing isolation, or international concerns about their nuclear program and human rights record,'a spokesman said.
'If the Iranian government acts on this, we will respond robustly in consultation with our international partners.'
It said for the first time that Tehran was suspected of conducting secret experiments whose sole purpose was the development of nuclear arms.
The U.S. and its Western allies suspect Iran of trying to produce atomic weapons, and Israel, which views Tehran as an existential threat, has warned of a possible strike on Iran's nuclear programme.
Iran says its programme is for peaceful purposes.
A military installation in the Turkish town of Kurecik, some 435 miles west of the Iranian border, has been designated as the radar site, according to Turkish government officials.
General Hajizadeh said the United States also plans to install similar stations in Arab states, which has spurred Iran to alter its military defence strategy.
Iran's Supreme Leader Ayatollah Ali Khamenei, who has the final say on all state matters, is also commander in chief of Iran's armed forces.
Another senior Guard commander, Yadollah Javani, threatened that Tehran will target Israel's nuclear facilities should the Jewish state attack Iran.
'If Israel fires a missile at our nuclear facilities or vital installations, it should know that Israel's nuclear centres will be the target of our missiles,' the semiofficial ISNA news agency quoted him as saying.
Also Saturday, the chief of Iran's elite Quds Force said he doesn't fear assassination and is ready for 'martyrdom.'
The comments by Quds Force commander Brigadeer General Ghassem Soleimani were published in several Iranian newspapers.
The Quds Force is the special foreign operations unit of the country's powerful Revolutionary Guard, and Soleimani is a key figure in Iran's military establishment but rarely speaks in public.
Fervour: Young militia members flash victory signs in front of an anti-U.S. mural painted on a wall in Tehran
Ready for war: Soldiers on Tehran's streets has become an increasingly common sight in recent weeks
Menacing: Balaclava-wearing members of the Basij militia were out in force during an anti-U.S. protest on Friday
Debate: Iran's parliament today as it approved a bill to reduce Tehran's diplomatic relations with London and withdraw the country's ambassador to Britain
Tensions have increased in recent weeks between Iran and the U.S., with several American neoconservatives urging the Obama administration to use covert action against Iran and kill some of its top officials, including Soleimani.
The force has been accused by the Americans of involvement in an alleged plot to assassinate the Saudi ambassador to Washington. Two men, including an alleged member of Iran's Quds Force, have been charged in New York federal court in the case.
Iran has dismissed the American claims as a 'foolish plot', saying U.S. officials have offered no proof.
Read more: http://www.dailymail.co.uk/news/article-2066959/Iran-threatens-bomb-Turkey-U-S-Israel-attack-nuclear-installations.html#ixzz1ezOmU7pO
Disclaimer statement Whilst every effort has been made to ensure the accuracy of the information supplied herein, DTN News ~ Defense-Technology News cannot be held responsible for any errors or omissions. Unless otherwise indicated, opinions expressed herein are those of the author of the page and do not necessarily represent the corporate views of DTN News ~ Defense-Technology News
Telegraph - UK / Daily Mail - UK
Labels: DTN Defense-Technology News, DTN Logos, DTN News, Financial Support, Iran, Nuclear Power Plants, U.K.
DTN News: U.S. Department of Defense Contracts Dat...
DTN News - OSHKOSH DEFENSE NEWS: New Oshkosh Defen...
DTN News - IRAN NEWS: Enemy Of Iranian State / Ira...

redpajama_set_name: RedPajamaCommonCrawl


Maricris Millares had been working for Costa cruises for eight years when, on the night of January 13, the Costa Concordia ran aground and capsized just off the coast of the Isola del Giglio.
A 30-year old Filipina cabin stewardess, Millares was one of a crew of one thousand on board (together with 3,200 passengers). Almost half were from Indonesia (170) and the Philippines (296).
Their ordeal was compounded by the fact that many of them had to spend several days being put up in a hotel at Rome airport. Millares says she was several days without documents, money, clothes or even shoes, before priests from the Sant'Egidio Community arrived to help her.
Without passports or money, they "could not leave the hotel they were booked in," the ambassador of the Philippines to the Holy See, Mercedes Tuason, pointed out.
However, they had left the ship with "nothing else beyond their clothes," said Father Paolo Cristiano, a Rome-based priest who oversees the Sant'Egidio Community's work in the Philippines.
Fr Cristiano said the Filipino and Indonesian workers are among the "angels" who saved many of the passengers on board. While the captain, Francesco Schettino, allegedly left the ship and refused to board it again, most of them stayed until the last moment.
Millares says a man gave her and her boyfriend Michael his two children as he went back inside to look for his wife. "We were the last to leave. Our lifeboat carried only about 30 people", against a capacity of 150. The man, his wife and the children eventually reunited on shore.
Stuck in the airport hotels without passports or visas, the crew were unable to go to church so a group of Filipino priests celebrated mass at the the airport's Church of Our of Lady of Loreto, bringing them rosaries and scapulars.
Fr Cristiano arranged for three truckloads of jackets and shoes to be sent to the hotels where the workers were lodged at Rome's Fiumicino airport.
"The company has stated its commitment to give compensation to the crew. The repatriation is expected to be done soon after the negotiations between the crew and the company are completed," a spokesman of the Indonesian embassy in Rome, Musrifun Lajawa, told Indonesia's official news agency Antara on January 16.
The last of the Indonesian crew members arrived back in Bali on January 21. Likewise, all but one of the Filipino workers are now back in Manila, one injured man remaining in an Italian hospital.
Morti tre senza dimora in giorno: "inaccettabile, aumentare i rifugi"


These images, created by Alex Wild, have been released into the public domain. They can be used in any context, including commercial, without prior permission.
High magnification photograph of the fore and hind wings of a Mexican honey wasp. Note the hooks, called hamuli, that connect the wings during flight. Specimen from the University of Texas Insect Collection.
Portrait of a red imported fire ant, Solenopsis invicta. This species arrived to the southeastern United States from South America in the 1930s. Specimen from Brackenridge Field Laboratory, Austin, Texas, USA. Public domain image by Alex Wild, produced by the University of Texas "Insects Unlocked" program.
A young Camponotus queen ant prepares to depart on a mating flight.


Being faithful to one's duties of everyday life and doing them well, seeking to do even ordinary things with extraordinary love for the greater glory of God.
Participating in the Holy Mass with due preparation and greater attention. Perhaps in addition to Sunday, consider attending one additional weekday Mass.
Making a soul-searching, honest and humble confession at our regularly scheduled times, on our Lenten Penance Day (Friday, March 2) or by making an appointment with one of the priests.
Exploring the many resources on Formed.org (Parish code: PBGWTD) or choosing some spiritual reading to stir your heart towards holiness.
Paying more attention to the observance of silence, in order to listen to the voice of God – perhaps by signing up for one or more hours during the Forty Hours weekend or visiting the Adoration Chapel at Holy Cross.
Meditating on the Passion of Jesus in the Four Gospels (Mt 26 and 27; Mk 14 and 15; Lk 22 and 23; John 18 and 19).
Frequent praying of the five sorrowful mysteries of the Rosary and the Divine Mercy Chaplet.
Practicing the spiritual and corporal works of mercy, especially noting the Parish Almsgiving opportunities.
Trying to be kind, gentle and humble to one another, especially those to whom we struggle to be patient.
Everyone 14 years of age or older is bound to abstain from meat on Ash Wednesday, all the Fridays of Lent and Good Friday. Everyone 18 or older, and under 59 years of age, is bound to fast on Ash Wednesday and Good Friday.