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HOME >> BIOLOGY >> NEWS
Scientists urge new approaches to plant research
...EAST LANSING Mich. -- You'd be amazed at how much you can learn from ...In a paper published this week in the journal Science a Michig... Metabolism of plants provides humans with fiber fuel food and thera...However Last and co-author Ron Milo of the Weizmann Institute of Scie...
http://www.bio-medicine.org/inc/biomed/biology-news.asp
http://feeds.bio-medicine.org/latest-biology-news
Date:6/29/2012
EAST LANSING, Mich. -- You'd be amazed at how much you can learn from a plant.
In a paper published this week in the journal Science, a Michigan State University professor and a colleague discuss why if humans are to survive as a species, we must turn more to plants for any number of valuable lessons.
"Metabolism of plants provides humans with fiber, fuel, food and therapeutics," said Robert Last, an MSU professor of biochemistry and molecular biology. "As the human population grows and nonrenewable energy sources diminish, we need to rely increasingly on plants and to increase the sustainability of agriculture."
However, Last and co-author Ron Milo of the Weizmann Institute of Science point out that despite decades of plant genetic engineering, there are relatively few types of commercial products originating from this body of work.
"This is in part because we do not understand enough about the vastly complex set of metabolic reactions that plants employ," Last said. "It's like designing and building a bridge armed only with satellite images of existing bridges."
The authors say that perhaps the best approach is to bring together a variety of disciplines not just plant scientists to study how plants operate.
They also suggest looking hard at what brought plants to the place they are today evolution.
"We think that understanding design principles of plant metabolism will be aided by considering how hundreds of millions of years of evolution has led to well-conserved examples of metabolic pathways," Last said.
One of the amazing aspects of plant metabolism is this: It must continuously strike a balance between evolving to meet an ever-changing environment while maintaining the internal stability needed to carry on life as it knows it.
In addition, the authors point out that plants experiment with specialized (also called secondary) metabolism which can produce novel chemicals that are used to defend against pathogens and herbivores.
"Humans benefit from this 'arms race' because some of these compounds have important therapeutic properties," Last said. "Unfortunately, design principles are not so well studied in these rapidly evolving metabolic processes. Using new approaches, including considering optimality principles, will lead to advances in medicinal chemistry as well as creating more and healthier food."
Last is Barnett Rosenberg chair of Biochemistry and Molecular Biology and Plant Biology. Co-author Milo is a professor of plant sciences at Israel's Weizmann Institute of Science.
Contact: Tom Oswald
tom.oswald@cabs.msu.edu
Source:Eurekalert
Related biology news :
1. Stanford scientists develop gene therapy approach to grow blood vessels in ischemic limbs
2. Queens scientists seek vaccine for Pseudomonas infection
3. Scientists produce eye structures from human blood-derived stem cells
4. American Society of Plant Biologists honors early career women scientists
5. Brandeis scientists win prestigious prize for circadian rhythms research
6. Scientists discover new method of proton transfer
7. Salk scientists open new window into how cancers override cellular growth controls
8. WileyChina.com - Now Featuring Bespoke Pages for China's Life Scientists
9. Scientists win $2 million to study new pathway in development and maintenance of lymphoma
10. UGA scientists reveal genetic mutation depicted in van Goghs sunflower paintings
11. Genetic mutation depicted in van Goghs sunflower paintings revealed by scientists

redpajama_set_name: RedPajamaCommonCrawl


With the change of seasons, it's important that you consider things to look out for to keep your pets safe. Your dogs and cats rely on you to provide a safe environment so keep these 5 tips in mind during the Autumn months.
Cold Weather – some dogs are not equipped to handle the cold weather. If your dog is susceptible to the cold weather, get her a dog sweater. We recommend that you keep you cats indoors but if you do have an outdoor cat, make sure that they are in the house at night and also keep them indoors during the bitter cold weather. As pets are domesticated animals, most are not equipped to handle extreme cold temperatures.
Piles of Leaves – with piles of leaves come fleas and ticks. Keep your pet save with flea and tick repellant so that they can play in the leaves without worrying about the health issues associated with these pests.
Less Sunlight – during the Fall and Winter months, the number of hours of sunlight diminishes. During the dark hours, your pet is more likely to be injured by a car or cyclist who cannot see your cat or dog. There are LED collars and reflective harness and collars that can help keep your pets visible. Invest in one for year round protection.
Halloween – Halloween is a fun time of the year for children, however, can create a hazardous environment for your pets. Please keep the Halloween candy out of reach from your cats and dogs. Costumes can also contain toxic material that can be harmful to your pets when chewed or swallowed. When in doubt, ensure that these items are not accessible to your pets.
Wild Mushrooms – many types of poisonous mushrooms grow during the Fall. If your pet ingests a mushroom or anything else you think is poisonous, immediately call the ASPCA Animal Poison Control Center at (888) 426-4435.
As a pet owner, pet safety should be one of your top priorities when looking out for your cats and dogs. Be diligent and keep an eye on those things in the environment that could be harmful to your pets.


Live coverage of the countdown and launch of an Ariane 5 rocket with the Star One D1 and JCSAT 15 communications satellites. Text updates will appear automatically below; there is no need to reload the page. Follow us on Twitter.
Arianespace's live video stream begins at 2010 GMT (3:20 p.m. EST). Please ensure your browser is updated with the latest version of Flash.


Something you and I would least expect – Facebook with killer ads. Apparently, Facebook had been producing notable marketing materials lately. This recent ad by Facebook features people assembling in movie characters, taking photos while they mingle around camera. See, the fact that marketing sometimes cannot be a linear effort sees it diverse its application using unique approaches. In this context, we still see very real, very down to earth creative direction accompanied by a rather straightforward message: "We are not alone." I believe the message they are trying to commute hasn't dilute at all. Instead, it emphasizes that every minute, every second, there are someone out there who is the exact fit for you or your community. And that is happening all on Facebook.


text:
\section{Introduction}
\label{sec:introduction}

As we continue to assemble, and publish increasingly larger collections of biomedical data, the quality of data tended to drop
\cite{Clifford2009,10.1145/3411764.3445518}. Perhaps more importantly, the labels associated with the data also drop in quality as the database size increases. These issues arise because the resources required to hand curate the data and labels scale with the data volume
\cite{DBLP:journals/corr/abs-2007-10502}. Moreover, larger datasets were often collected serendipitously, or for other reasons (e.g., electronic medical records, or other non-research medical data). Data are therefore not coded or labelled appropriately for the scientific question that is being asked by a researcher at a later date.

One potential solution to this issue is the use of transfer learning
\cite{8583799,9498418,LIANG2020104964}
and domain adaptation \cite{5640675} to pre-train models on  more ``trustworthy'' data. Of course, this may ``bake-in'' the biases of the original data, which tend to be skewed towards the well-funded researchers that collected the original data. In particular, the bias is often away from people of color, women, and other minorities, particularly those in Low and Middle Income Countries (LMICs) \cite{10.1093/jamia/ocaa094,10.3389/frai.2020.561802}.

While we, and others, have demonstrated the utility of transfer learning and domain adaptation to leverage large biomedical databases for new tasks, ranging from sleep staging to eye tracking \cite{9144416,9360436,9079571,Nasiri2020,Li2021,10.1093/sleep/zsab027}, to the best of our knowledge, no published work has yet to emerge which incorporates the potential of  physiological models to boost the performance of a learning algorithm. That is, if a suitable model existed from which we were able to generate new ``realistic'' patient data, over a range of conditions, we could significantly augment a small database of under-represented individuals, with similar data, simultaneously improving performance and reducing bias towards the small and relatively homogenous
source data.

In this work, we used a well-known computationally efficient yet realistic model of the electrocardiogram (ECG) to generate a large dataset (over 180,000) ``recordings'' of a subtle cardiac abnormality largely unrelated to the arrhythmia database (of over 70,000 subjects) on which a deep convolutional neural network (DCNN) was trained and validated. Using transfer learning, the network was adapted to the artificial ECG, and then to a much smaller database of only 36 individuals to test the hypothesis that realistic artificial data can significantly boost the performance of a DNN on databases comprising limited population numbers.

\section{Background}

\subsection{Synthetic data}
A closely related area of research to the work proposed here is synthetic data generation, which is receiving increasing attention of late, is that of synthetic data generation. The majority of articles appear to have focused on synthetic medical data generation have focused on imaging and electronic medical records
\cite{Goncalves2020,
Dube2014,
Kim2015,
Buczak2010,
Walonoski2018,
Chen2019,
Sankaranarayanan2018,
Yang2018,
Zhang2019,
Tucker2020,
Chen2021,
Mahmood2018,
Tang2021,
Yale2020,
Nie2018,
Ive2020,
Prakosa2012,
Moniz2009,
Benaim2020}.
In fact, examples of synthetic data generation to improve classifier performance are too numerous to effectively review, and are not the focus of this research. While synthetic data are useful for boosting data representation from minority classes, or improving the robustness of applying data driven approaches such as machine learning to a variety of problems, there are inherent dangers. First, there are few guarantees that the artificial data are generated in a manner consistent with reality. The choices of the researcher largely drive the distribution of augmentation.
More recent work in the use of generative artificial neural networks has `automated' these choices somewhat, but at the cost of lacking generalization outside the distribution of the source data. Conversely, a model-based approach provides for the possibility of generating realistic (and physiologically bounded) data outside the observation distribution.

\subsection{Artificial physiological data}
Again, a survey of artificial physiological data generation is outside the scope of this article. The reader is referred to several recent surveys on the selection, utility and validity of physiological models
\cite{Kerkhof1999,
Pruett2020,
Bighamian2021}. The key point is that clinicians, working with experts in machine learning, find that a model-based approach is still of utility in medicine
\cite{Sarma2020}. Even though big data/deep learning approaches are starting to outperform domain-expert driven approaches to feature creation, clinical teams have a deep distrust of brute-force data-driven models. Ultimately, a clinician finds it hard to be comfortable with a black box (lacking explanation of the reasons for the inclusion of the certain features).
Model-based approaches allow the development team to test a wide variety of possible `patients', particularly edge cases or rare pathological scenarios. Such stress tests of the code are critical for building confidence. While it is possible to claim  that \emph{eventually} clinicians will come to accept modern machine learning in the same way that they have accepted other key black box technology (such as bedside arrhythmia alarms).  However, acceptance is much more likely if the developed algorithms allow some form of reasoning that map back to traditional (compartmental) physiological models.

In this work we propose one further use of artificial models - to generate (pre-) training data for deep neural networks, in order to learn specifics of a rare pathology (or a pathology that is expensive and time-consuming to capture).

\subsection{Clinical domain example}

The example medical condition on which we  demonstrate the utility of the proposed approach was chosen to be post-traumatic stress disorder (PTSD), since data on PTSD patients is difficult to collect.
PTSD is a psychiatric disorder occurring in individuals who had experienced/witnessed terrifying events for a prolonged duration. These events included but were not limited to wars, physical and sexual abuse, traffic accidents, or natural disasters like earthquakes. Symptoms included negative thoughts, social distancing, and avoiding places, activities, and people that served as reminders of the event. In most cases, the individuals recovered from the experience in a few weeks or months, needing support from family and friends. Some cases were more severe and required therapeutic intervention.

In the United States, war veterans, in particular, were observed to develop symptoms and treatment costs were USD\$8300 on average per veteran annually \cite{ptsdexpense}. Early detection could lead to an early start in the recovery cycle, delayed treatment of the disorder would prolong suffering and may reduce treatment efficacy \cite{Maguen2014ptsdtreatment}. Delaying treatment may also cause the symptoms to become worse and result in a poor quality of life \cite{delayptsdtreat}.

In hospitals, PTSD assessments are performed as structured clinical interviews by experienced healthcare workers. The clinician-administered PTSD scale (CAPS-5) is the gold standard for PTSD diagnosis and consists of a 30-item questionnaire that can take from 45-60 minutes to complete \cite{Jiang2020}. The CAPS-5 can be used to make a current PTSD diagnosis (past month), lifetime PTSD diagnosis (worst month) or a PTSD assessment over the past week, depending on the period for which the individual is examined. The response to each item on the questionnaire is noted as a severity score on a scale of 0-4. Zero corresponds to the absence of symptoms and four corresponds to the highest level of severity. The responses in the interview are highly subjective and can vary depending on the openness or consistency of the subject and the skill of the clinician/rater. The overall process for PTSD assessment is time-consuming and burdensome on the healthcare workers, aside from the existence of
variability in the subject's responses or rater's skill in administering the survey.
Improving the screening test for PTSD with machine learning would reduce the burden on the healthcare providers and shorten the latency in the time to treatment for individuals who had PTSD.

Previous studies have trained machine learning algorithms to
classify PTSD status from the responses for self-reported PTSD surveys. Wshah \textit{et al.}~\cite{Wshah2019} trained an ensemble of classifiers on a reduced set of responses from the PTSD checklist-5 (PCL-5) that were collected through smartphones. Ilhan \textit{et al.}~\cite{Ilhan2015} used a similar approach of training a classifier on responses to a questionnaire similar to the PCL-5. The authors used feature selection to remove uninformative questions, which improved classification performance. Jiang \textit{et al.}~\cite{Jiang2020} trained a random forest classifier on the responses to the self-administered interview for the diagnostic statistical manual for mental disorders (DSM-5). They achieved an accuracy, a sensitivity, a specificity, a positive predictive value (PPV), a negative predictive value (NPV) and an area under receiver operating characteristic (AUROC) curve, all above 0.9 using the top 14 response features determined using the Gini impurity \cite{Breiman1984gini}. Their results suggested that structured interviews for PTSD screening could be abbreviated without losing accuracy, thus reducing the burden on clinicians.
The
inter-subject variability
in the responses
to the questionnaire and inter-rater variability
remained high, however. Other more objective features have been sought. Marmar \textit{et al.}~\cite{Marmar2019} extracted features from speech recorded from a group of veterans with or without PTSD to determine PTSD status. Speech signals were easily recorded and transmitted. The 18 most important features for PTSD classification were determined through `shaving' and used to train a random forest classifier. Schultebrauks \textit{et al.}~\cite{schultebraucks2020} trained a deep belief network (DBN) on features extracted from audio and video recorded for individuals who had undergone a traumatic event and were being evaluated for PTSD.

With respect to cardiovascular data,  Reinertsen \textit{et al.}~\cite{Reinertsen2017ptsd} demonstrated that heart rate variability, recorded at the cardiovascular nadir (during deepest sleep) could identify PTSD withing a cohort of 72 subjects with an AUC of 0.86. Cakmak \textit{et al.}~\cite{Cakmak_9333654} demonstrated that circadian rhythm changes, measured by a wrist-worn research watch are predictive of post-trauma outcomes in a cohort of 1618 post-trauma patients. The highest cross-validated performance of research watch-based features (derived from pulse and accelerometer) was achieved for classifying participants with pain interference (AUC=0.70). A survey-based model achieved an AUC of 0.77, and the fusion of research watch features and ED survey metrics improved the AUC to 0.79.

In this study,  we trained an end-to-end DCNN on multi-lead ECG data to classify PTSD in order to demonstrate the strength of the association of myocardial electrical disturbances with PTSD, and to identify whether an appropriate electro-physiological model of the heart can provide a significant boost in classifier performance. Our aim was not to surpass  state-of-the-art results (because there is no public PTSD database with sufficient ECG), but merely to identify to what degree electrophysiology is connected with PTSD beyond the current traditional markers (and how we can optimally select data or models to identify subtle markers of PTSD).
The motivation for this is that PTSD has been shown to be associated with arrhythmogenesis \cite{Vaccarino2013ptsd} and increased T wave alternans (TWA) \cite{LAMPERT20151000}.
However, our target population consisted of only 36 individuals. To prevent the model from overfitting on this small dataset, and to improve generalization, the DCNN was pre-trained on over 70,000 ECGs taken from the PhysioNet Challenge 2021, as well as a novel dataset of artificial ECGs with varying levels of TWA amplitudes, generated specifically for this work.

\section{Methods}

\subsection{Data}
\label{ssec:data}
Three data sets were used to train the
DCNN for a binary task of classifying a subject as having PTSD or not.
Figure \ref{fig:artifecg_pipeline} summarizes the generation of the artificial ECG with TWAs, Section \ref{ssec:artif_ecg}, and the training of the DCNN using each of the 3 data-sets explained in the following sections.

\subsubsection{Arrhythmia Data: The PhysioNet/ Computing in Cardiology (CinC) Challenge 2021}
The first data-set used in this work (to pre-train the DNN) is the twelve-lead ECGs from the PhysioNet/CinC Challenge 2021 ECG database, which  consists of over 70,000 labeled twelve-lead ECGs collected from 6 different hospitals across 3 continents \cite{Reyna_2021b,Perez_Alday_2021,Physionet}.
From hereon, the PhysioNet/CinC Challenge 2021 ECG data are referred to as the real \emph{arrhythmia data}.
The recordings exhibit one or more of twenty-five labeled different arrhythmias, including atrial arrhythmias, ventricular arrhythmias and normal sinus rhythm. Since PTSD is associated with arrhythmogenesis \cite{Rosman2019}, we expect hat arrhythmia data will have some relationship with the PTSD label, which is discoverable by a deep neural network.

\subsubsection{Model-based artificial ECG}
\label{ssec:artif_ecg}
The second data-set consists of artificial ECG generated with varying amplitudes of TWA. Similar to Clifford \textit{et al.}~\cite{Clifford2006artifecga,Clifford2005artifecgb,Clifford2007artifecgc,Clifford2010}, the morphologies of these artificial ECGs were derived using a least-square fit of Gaussian parameters (in the VCG representation) to normal subjects in the Physikalisch-Technische Bundesanstalt database (PTBDB) \cite{BousseljotKreiselerSchnabel+1995+317+318}. The intuition behind this approach was that any continuous function
can be approximated arbitrarily well with a finite number of Gaussian functions \cite{BenArie95,McSharry2003}. The average beats for each subject were estimated from the VCG recordings in the [X, Y, Z] orthogonal vector directions. Gaussian functions with varying amplitude and standard deviations were placed at different instants during the beat's cardiac cycle to best approximate the average beat morphology in each VCG recording by minimizing the squared error.
The functionality for the least-square estimate of Gaussian parameters fitted to a VCG beat was implemented as part of the open-source electrophysiological toolbox \cite{SameniOSET}. The Dower transform was then applied to the artificial VCG to generate the twelve-lead ECG representation \cite{Dower1980}. The ECG simulator could accurately generate abnormalities such as TWAs of varying amplitudes. For details on the generation of the artificial ECG with TWAs refer to \cite{Clifford2010} and \cite{Sadiq_2021}. The code for the ECG simulator is available as part of an open-source toolbox \cite{Vest2017Cardiotool}.

Elevated TWAs
were associated with elevated levels of stress by Lampert \textit{et al.}~\cite{LAMPERT20151000}. However, no digital database of significant size existed for ECGs labeled with TWAs. For the purpose of this study we generated artificial ECGs with or without TWAs. The morphologies of the artificial ECGs were derived from 47 normal subjects in the PTBDB. The HRs for the ECGs was varied from 60-110 beats per minute (bpm) in increments of 2, the upper limit for the HRs was kept at 110\,bpm instead of 100\,bpm since individuals with PTSD were known to have slightly elevated HRs than normal. The BR was varied between 12-20 respirations per minute (rpm) in increments of 1. For ECGs with TWAs, the TWA amplitude was varied between 20 to 100\,$\mu$V in increments of 1\,$\mu$V. To augment the artificial ECG data random perturbations were added to the Gaussian parameters for amplitude and standard deviation, used for estimating the ECG morphology in a recording. For each of the 47 subjects used, a maximum of $\pm$4.5\% percent of the parameter value was used to set the limits of a uniform distribution centered at the original parameter value and a number uniformly sampled from this distribution was assigned to the respective parameter.

The limit of $\pm$4.5\% was determined empirically. For each of the 47 subjects the range of HRs were determined for which the QTc interval remained within the normal physiological range of between 360\,ms to 440\,ms \cite{Clifford:2006:AMT:1213221}. The QT interval was corrected using the `Bazett' correction~\cite{bazett1920analysis}. Next, the percentage of the value of the amplitude or standard deviation parameter used to limit the distribution of perturbations was increased from 1\% to 10\% in increments of 1\% before fine tuning. The perturbation was uniformly sampled from each distribution. For a maximum perturbation of at most $\pm$4.5\% of the parameter value, more than 95\% of the QTc intervals were within the normal range specified above. Therefore perturbations were added within the $\pm$4.5\% of the parameter value range. The additional check on the QTc interval was added so the artificial ECGs generated with perturbations would resemble ECGs from normal individuals with realistic characteristics. The model-based artificial ECG data was referred to as artificial TWA data. In addition electrode movement
and muscle artifact
noise from the noise stress test database on PhysioNet was added to train the model to be robust to noise for significant TWA detection \cite{Moody_1984}. Electrode movement and muscle artifact noise was added in equal proportion to each ECG window for a signal to noise ratio (SNR) between 15 to 30 dB. Baseline wander
was not added as it was easily removed through median filtering. A total of 180,000 artificial ECGs were generated, half of which had TWAs and half did not. For pre-training the DCNN, the artificial ECGs were divided into 10 folds in a stratified manner. Eight folds were used for training, 1 fold for validation and 1 for testing. The weights with the lowest error on the validation fold were used for evaluating the test set classification accuracy. The network was trained as described in Section \ref{ssec:dnn}.

\subsubsection{PTSD data and preprocessing}
The PTSD data consisted of 12 subjects with PTSD and 24 controls. The data was sampled at 1000 Hz, consistent with previous algorithms that used deep neural networks trained on ECG data to detect cardiac arrhythmias
\cite{Fu2020}. The data consisted of single channel Lead I ECG and the amplitude resolution of the data was measured to be 1.32$\times$10\textsuperscript{-6} mV. Hence the data was high resolution both in the temporal and spatial domains.

Each subjects recording in the PTSD data-set was segmented into 16-second windows with 20\% overlap. Fifty 16-second windows with a signal quality index (SQI) of 1 were randomly selected for each individual. The signal quality index by Li \textit{et al.}~\cite{Li2014sqi} was used to identify clean ECG analysis windows that were minimally affected by noise \cite{Sadiq_2021}. The algorithm used two sets of fiducial point detectors, one being robust to noise and the second being sensitive to noise. When the fiducial points detected by either detector were consistent the signal was considered to be clean, in case of disagreement between the detections the signal was considered noisy. Baseline wander was removed using the 2 stage median filter by de Chazal \textit{et al.}~\cite{deChazal2004}.

\begin{figure}[tbh]

         \centering
         \includegraphics[width=\textwidth]{./fig/non_eps/flowdiag_e.jpg}
         \caption{}

     \caption{A flowchart illustrating the process of generation of artificial ECG with TWAs. Steps 1a and 1b: 47 real VCG records were acquired from the PTBDB on PhysioNet. Step 2: Least-square estimates for Gaussians were derived for the average beat in the X, Y and Z VCG leads for each record. Steps 3 and 4: The derived Gaussian estimates were used for generating the artificial VCG with TWAs. Step 5: The Dower transform was applied to the VCG to obtain 12 ECG, lead I was used in the subsequent analysis, Step 6: Muscle artifact (MA) and electrode movement (EM) noise from the NSTDB on PhysioNet were added to the artificial ECG before training the classifier so it would be robust to noise. Steps 7 and 8: The artificial TWA data was used in combination with the real arrhythmia data and PTSD data to train a deep convolutional neural network (DCNN) for PTSD classification

     }
        \label{fig:artifecg_pipeline}
\end{figure}

\subsection{Deep convolutional neural network architecture and training}
\label{ssec:dnn}

\subsubsection{Network architecture}
\label{sssec:mdl}
The
DCNN used was the same as that developed by
Zhao \textit{et al.}~\cite{Zhao2020}. The network was selected as overall it was the second best algorithm at the PhysioNet challenge in 2020. In addition the network was the top performing algorithm for the category of algorithms that learned all the features from the input ECG and did not require evaluation of any additional hand-crafted features to be presented as an input. The architecture is
given in figure \ref{fig:resnet}a.

\begin{figure}[tbh]
     \centering
     \begin{subfigure}[b]{0.4\textwidth}
         \centering
         \includegraphics[width=0.55\textwidth]{./fig/non_eps/resnet.jpg}
         \caption{}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{0.4\textwidth}
         \centering
         \includegraphics[width=0.55\textwidth]{./fig/non_eps/resnet_resb.jpg}
         \caption{
         }
     \end{subfigure}
     \caption{(a) ResNet model pre-trained on the real arrhythmia data and used for classifying as PTSD. (b) Residual block.
     }
        \label{fig:resnet}
\end{figure}

The following models were trained to classify the PTSD ECG data using the data-sets described in Section \ref{ssec:data}.

\begin{itemize}
\item \textbf{Baseline (BL) model}: Hand-crafted features and logistic regression were used as the baseline against which deep learning approaches were compared. A logistic regression classifier was trained on features extracted from TWAs measured using the modified moving average (MMA) method. To reject noise-triggered TWA, a surrogate statistical test \cite{Nemati2011twa}, provided as part of the open source PhysioNet Cardiovascular Signal Processing Toolbox \cite{Vest2017Cardiotool} was used. The TWAs were measured in 60 beat windows with 50\% overlap. TWAs were considered statistically significant compared to the noise threshold from the surrogate test with a p-value $\leq 0.05$. The significant TWA detections were divided by HR decile into the following bins: [30,60), [60,70), [70,80), [80,90), [90,100) and [100,110], measured in bpm. For each individual the mean significant TWA amplitude was computed in each bin and used as a feature vector. The AUROC curve was computed for leave one (subject) out testing, i.e. one subject was held out for testing while the features from the remaining subjects were used for training the logistic regression classifier. The process of training and testing the classifier was repeated so that each subject was considered as the test subject.
\textbf{Only the target PTSD data was used for training the logistic regression classifier.}

\item \textbf{Model 1:} The weights of the ResNet model based on Zhao's approach were randomly initialized. Only the 8\textsuperscript{th} residual block (ResB) and the fully connected (FC) layer weights were trained using the PTSD data. Training the 8\textsuperscript{th} ResB and FC layer allowed direct comparison with model 6 to determine if pre-training of the layers before the 8\textsuperscript{th} ResB
with the
real arrhythmia data and the artificial TWA data improved classification performance on the target PTSD data. \textbf{No real arrhythmia data or artificial TWA data were used for transfer learning; only the target PTSD data were used to train and test.}

\item \textbf{Model 2:} The weights of the ResNet model based on Zhao's approach were randomly initialized and the complete model was trained on the artificial TWA data. The trained classifier
was used to directly classify the PTSD data. \textbf{No real arrhythmia data or PTSD data were used for training the model.}

\item \textbf{Model 3:} The weights of the ResNet architecture were pre-trained on the
real arrhythmia
data, after which transfer learning was performed by training the 7\textsuperscript{th} ResB, 8\textsuperscript{th} ResB and the FC layer using the PTSD data only. \textbf{No artificial TWA data was used for pre-training.}

\item \textbf{Model 4:} The weights of the ResNet model based on Zhao's approach were randomly initialized and the model was trained on the artificial TWA data. A transfer learning step was then performed to train only the
8\textsuperscript{th} ResB and the FC layer using the PTSD data. \textbf{No real arrhythmia data was used for pre-training.}

\item \textbf{Model 5:} The weights of the ResNet architecture were pre-trained on the real arrhythmia data. Transfer learning was performed on the 7\textsuperscript{th} ResB, 8\textsuperscript{th} ResB and the FC layer with the artificial TWA data for classifying TWA presence. \textbf{No PTSD data was used - only pre-training with the real arrhythmia data and the artificial TWA data were used with a transfer learning step.}

\item \textbf{Model 6:} The weights of the ResNet architecture were pre-trained on the real arrhythmia data. Transfer learning was performed on the 7\textsuperscript{th} ResB , 8\textsuperscript{th} ResB and FC layer using the artificial TWA data. A second transfer learning step was then performed to train only the 8\textsuperscript{th} ResB and FC layer using the PTSD data. \textbf{All data-sets were used and two transfer learning steps were used.}
\end{itemize}
Figure \ref{fig:three graphs} illustrates
the data-sets used for training each DCNN model
The network layers enclosed in blue were trained on the
real arrhythmia data, the network layers enclosed in green were pre-trained on the artificial TWA data and the network layers enclosed in yellow were trained on the PTSD data. In model 6, the first transfer learning step on the artificial TWA data was considered a stepping stone before performing transfer learning on the PTSD data. If model 6 outperformed model 4, additionally training on the artificial TWA data was beneficial for PTSD classification. Evaluating the performance of model 1 would determine if pre-training on the real arrhythmia data and artificial TWA data was important for accurate PTSD classification. Model 5 was completely trained on the real arrhythmia data after which transfer learning was performed on the 7\textsuperscript{th} ResB, 8\textsuperscript{th} ResB and the FC layer, with the artificial TWA data. The trained model 5 was used to classify the PTSD data, to determine if transfer learning on the PTSD data was an important step to correctly classify subjects as suffering from PTSD or not.

\subsubsection{Network training}
The ResNet architecture was trained for 50 epochs with an ADAM optimizer. The learning rate was initially set to 0.003 and was reduced by a factor of 0.1 after the 20th and 40th epoch. The batch size was kept at 64. After pre-training on the real arrhythmia data the final sigmoid function layer was replaced to have 2 outputs. When classifying the artificial
TWA data and PTSD data all the input leads except lead I were reduced to zero.
To account for the class imbalance when training on the PTSD data, 25 of the available 50 windows for each control subject not being tested, were used to constitute the training data.

\begin{figure}[tbh]
\centering
\begin{subfigure}{0.205\textwidth}
  \includegraphics[width=\linewidth]{fig/non_eps/resnet_mdl1ee.jpg}
  \caption{ResNet model 1}
\end{subfigure}\hfil
\begin{subfigure}{0.2\textwidth}
  \includegraphics[width=\linewidth]{fig/non_eps/resnet_mdl2n.jpg}
  \caption{ResNet model 2}
\end{subfigure}\hfil
\begin{subfigure}{0.2\textwidth}
  \includegraphics[width=\linewidth]{fig/non_eps/resnet_mdl3ee.jpg}
  \caption{ResNet model 3}
\end{subfigure}

\medskip
\begin{subfigure}{0.2\textwidth}
  \includegraphics[width=\linewidth]{fig/non_eps/resnet_mdl4eee.jpg
  \caption{ResNet model 4}
\end{subfigure}\hfil
\begin{subfigure}{0.2\textwidth}
  \includegraphics[width=\linewidth]{fig/non_eps/resnet_mdl4ee.jpg}
  \caption{ResNet model 5}
\end{subfigure}\hfil
\begin{subfigure}{0.2\textwidth}
  \includegraphics[width=\linewidth]{fig/non_eps/resnet_mdl6ee.jpg}
  \caption{ResNet model 6}
\end{subfigure}

\caption{How each deep convolutional neural network model was trained is illustrated. The layers of the network trained on the real arrhythmia data are enclosed in blue. The layers of the network trained on the artificial TWA data are enclosed in green. The layers of the network trained on the PTSD data are enclosed in yellow.}
\label{fig:three graphs}
\end{figure}

\subsection{Evaluation methodology: Leave one out testing}
\label{sssec:loot}
Given the limited number of subjects in the PTSD data-set, the performance was evaluated on unseen data with the following leave-one-out testing (LOOT) approach. One subject was held out as the test subject and was not used for training or validation. The remaining subjects were used for training and validation. Ten percent of each subject's data in the training set was used for validation. The classifier was trained until the validation loss was observed to increase for three consecutive cycles. The classifier with the lowest error on the validation data was used for classifying the test subject. The procedure was repeated to test each subject and the fraction of windows for each subject classified as PTSD was used to compute the AUROC curve. The accuracy, balanced accuracy and F1 score for the optimal operating point on the receiver operating characteristic (ROC) curve was computed for each model. Due to class imbalance in the PTSD data the balanced accuracy was computed as the un-weighted average of sensitivity and specificity. The optimal operating point was computed as follows, a line with a slope of 1 started at the top left corner of the plot and was lowered until it intersected the ROC curve, the point at which the line first intersected was considered the optimal operating point. Defining the slope to be 1 resulted in the equal importance of the sensitivity and specificity, so the determination of the optimal point on the ROC curve was not affected by the imbalance in classes.

\subsubsection{Evaluating trained models on artificial TWA data}
Each of the models trained during LOOT was used to evaluate the classification performance on the artificial TWA data. This step was performed to determine wehether the models could still generalize back to. larger cohort, or whether they had overfit on the relatively small numbers of PTSD subjects. The mean and variance for the AUROC curve, sensitivity and specificity with LOOT were reported for models 1-6 and the baseline model mentioned in section \ref{sssec:mdl}. A $\chi^2$ test was performed between the classification labels for the artificial TWA data from model 6 and the true labels.

\subsubsection{Leave one out cross validation}
\label{sssec:loocv}
The classification achieved through LOOT was expected to be lower than the classification performance achieved given a larger number of data samples. The training and validation data sets consisted of ECG windows from the same individuals, therefore the classifier would overfit to particular trends in the ECGs for the individuals forming the training and validation data. The trained classifier was therefore not expected to generalize as well to ECG data from unseen individuals.
In addition, the performance for the best model with LOOT with respect to AUROC curve was evaluated using leave one out cross validation (LOOCV). The classification achieved through this approach was an optimistic estimate since it was not tested on unseen data and validation set loss is usually slightly lower than test set loss. However, given a sufficient number of samples and the distribution of samples in the validation and test sets being similar, the validation loss was expected to be similar to the test set loss. The ECG windows from one individual were considered as the validation data and the remaining individuals were used for training the classifier. The classifier with the lowest error on the validation data was used to classify each window of the validation data as PTSD or control. The procedure was repeated for each individual in the data-set and the fraction of windows for each individual classified as PTSD was used to compute the AUROC curve.
The overall training and evaluation of the deep neural networks is summarized in Table \ref{tbl:loocv}.

\subsubsection{Statistical tests}
A $\chi^2$ statistical test was performed to determine significant differences between AUROC curves.
Half of labels in the PTSD data-set were randomly assigned as PTSD and the remaining half as control before the LOOCV was performed. The AUROC curve was computed for each of the individuals and averaged. The process of randomly assigning labels and estimating the average AUROC curve was repeated 100 times to determine if the AUROC curve evaluated from the original data was statistically significantly higher than an AUROC curve
evaluated
from the lowest error on the validation data.

\begin{figure}[tbh]
     \centering

         \label{fig:y equals x}

     \caption
     ROC curve for LOOT for each of the deep learning models and the baseline approach for classification of the PTSD data. Classification performance was expected to improve when different individuals formed the validation and training data.
     }
     \label{fig:aucloocv}
\end{figure}

\section{Results}
\label{sec:reslt}
Table \ref{tbl:loocv} summarizes the PTSD classification performance
of each model described in Section \ref{sssec:mdl}.
Note that the highest performance scores are bolded.
Models 4 and 6 provided the best performance results in terms of AUC, F1 and BAcc. It is notable that the only consistent element in these models is the use of the artificial data and the PTSD data for training. However, when no real data was used, either for pre-training, or as a target, the results were poor. Model 2, which uses only artificial data simply classifies all data as `normal'. The ROC curves for LOOT with the PTSD data for each model is plotted in figure \ref{fig:aucloocv}. Classification performance achieved through training and testing model 6 proved accurate PTSD classification from ECG data could be achieved.

\begin{table}[tbh]
\centering
\caption{The AUROC curve (AUC), accuracy (Acc.), F1 score (F1) and balanced accuracy (BAcc.) for LOOT of models BL, 1, 2, 3, 4, 5 and 6 given in Section \ref{ssec:dnn}. `Arrhythmia data' and `Artificial data'
refers to the real arrhythmia data from the 2021 PhysioNet challenge and the artificial TWA data respectively. Highest performances are bolded.
}
\begin{tabular}{|l|l|l|l|l|l|l|l|}
\hline
Model \# & Arrhythmia data & Artificial data & PTSD data & AUC & Acc. & F1
& BAcc. \\ \hline
BL       & No & No & Yes & 0.56 & 0.67 & 0.57 & 0.67 \\ \hline
1        & No & No & Yes & 0.49 & 0.64 & 0.24 & 0.52\\ \hline
2        & No & Yes & No & 0.50 & 0.67 & 0.00 & 0.50\\ \hline
3        & Yes & No & Yes & 0.71 & 0.61 & 0.36 & 0.71\\ \hline
4        & No & \bf{Yes} & Yes & 0.74 & 0.69 & \bf{0.65} & \bf{0.73}\\ \hline
{5}        & {Yes} & \bf{Yes} & {No} & {0.63} & \bf{0.75} & {0.52} & {0.67} \\ \hline
6        & Yes & \bf{Yes} & Yes & \bf{0.77} & {0.72} & 0.64 & \bf{0.73} \\ \hline
\end{tabular}
\label{tbl:loocv}
\end{table}

\begin{table}[tbh]
\centering
\begin{tabular}{|l|l|l|l|}
\hline
Model \# & AUC $\pm$ $\sigma^2$  & Sensitivity $\pm$ $\sigma^2$ & Specificity $\pm$ $\sigma^2$ \\ \hline
BL      & 0.45 $\pm$ 0.001 & 0.39 $\pm$ 0.002 & 0.93 $\pm$ 0.00005 \\ \hline
1       & 0.62 $\pm$ 0.002 & 0.79 $\pm$ 0.004 & 0.43 $\pm$ 0.008 \\ \hline
2       & 1 & 1 & 1 \\ \hline
3       & 0.60 $\pm$ 0.0002 & 0.71 $\pm$ 0.02 & 0.45 $\pm$ 0.02 \\ \hline
4       & 0.85 $\pm$ 0.01 & 0.71 $\pm$ 0.04 & {0.89 $\pm$ 0.005}\\ \hline
{5}     & {1} & {1} & {1}  \\ \hline
6       & {0.98 $\pm$ 0.003} & {0.93 $\pm$ 0.002} & 0.93 $\pm$ 0.0006  \\ \hline
\end{tabular}
\caption{The mean and variance ($\sigma^2$) for the AUROC curve (AUC), sensitivity and specificity of classifying the artificial TWA data using LOOT are reported for the models given in section \ref{sssec:mdl}. For models 2 and 5, not trained on the PTSD data, the classification metrics for the test fold of the artificial TWA data are reported.}
\label{tbl:artif_twa}
\end{table}

Table \ref{tbl:artif_twa} summarizes classification results for the completely trained models described in section \ref{sssec:mdl} on the artificial TWA dat
. Models 1 and 3 achieved average AUROC curves of 0.62 and 0.60, respectively, for classifying the artificial TWA data. Model 3 achieved a sensitivity of 0.71, suggesting identifying TWAs was an important feature learned from the PTSD data for accurate classification. Models 4 and 6 classified the artificial TWA data with average AUROC curves of 0.85 and 0.98 respectively. Model 6 particularly classified the artificial TWA data accurately, suggesting after the transfer learning on the PTSD data, TWA detection was important for PTSD classification. The $\chi^2$ test yielded the output from each trained model 6 was independent of the true label. However, given the large number of ECGs in the artificial TWA data-set, the test was over-powered, clearly indicated by the average AUROC of 0.98 with a small variance in table \ref{tbl:artif_twa}.

\section{Discussion}
The results in table \ref{tbl:loocv} clearly indicate that pre-training on a large database of ECGs (over 70,000) with varying arrhythmias and other clinical abnormalities (Model 3), or a large artificial database (of 180,000) ECGs (Model 4), provides a significant boost in performance over both standard and DNN-based baseline approaches (model BL, and Model 1) for the chosen classification problem. Specifically, in the case of pre-training on real arrhythmia data alone, the AUC rose from 0.56 to 0.71, and to 0.74 for pre-training on artificial TWA data alone. Pre-training on artificial data led to a boost for all metrics, providing the highest F1 and BAcc. (Using real arrhythmia data only led to a drop in Acc. and F1 over the BL model.) Pre-training on both the real arrhythmia data and artificial data led to the largest increase in AUC, although it led to inferior results (in F1 and BAcc) compared to excluding the real arrhythmia data. Importantly, this indicates that the artificial data was the key component in boosting performance.

It is interesting to note that when the real PTSD data was not used (Model 2), significant performance reduction was observed, as expected, even though the artificial data that was tuned to exhibit similar characteristics to the PTSD cohort. This indicates that the artificial data does not closely match the distributions of the real target data. Therefore, the artificial data is likely to be providing a coarse tuning of the deep neural network on a very broad range of simulated TWA data, allowing the network to focus in on  features related to PTSD. In addition, pre-training on real arrhythmia data leads to a network that recognizes and differentiates between rhythms, although this somewhat biases the classifier towards predicting patients are `normal'.
This is consistent with the literature, where TWAs and arrhythmias have been shown to be important markers of PTSD, but with varying specificity \cite{Vaccarino2013ptsd,LAMPERT20151000}.

It is also important to note that when the DNN trained on all data using transfer learning (model 6) is back-tested on the artificial TWA data, the performance drops only slightly (from an AUC of 1.0 to 0.98 - see table \ref{tbl:artif_twa}). This provides evidence that using artificial data does not cause a loss of generalization in the model even though the final target data included only 36 subjects.

\section{Conclusion}
The work presented in this article demonstrates the utility of a realistic model to significantly boost training and test performance on a small dataset. Our results indicate that the model was the single most important part of the transfer learning process, boosting performance by more than either the source (arrhythmia) data, or the target (PTSD) data.

This result is significant for several reasons.
First, in the biological sciences, and healthcare in particular, it is common to be resource limited, and only have a small collection of high-quality data. Large volume collection can be prohibitive because of costs, legal/privacy barriers, social resistance to data acquisition, or the remoteness of the population.  Secondly, many diseases are quite rare, and it is impracticable to assemble large databases amenable to machine learning.
Thirdly, even if a large database can be collected, they are typically difficult to curate, and often the quality of the labels drops as the volume increases. Fourthly, large databases tend to disadvantage under-represented minorities, or individuals from resource-constrained areas such as the rural US, or LMICs in general.

It is interesting to consider that the principle of data augmentation using artificial data could be applied to similar problems to improve classification performance on the target domain data, provided the model used for data generation accurately captured the features characteristic of the target domain data.  Finally, we note that because the model employed is computationally efficient, the artificial database can be created on-the-fly, and stored in memory, thus reducing storage costs if needed.

We note a key limitation of the study is that \emph{because} our target dataset is relatively small and imbalanced (only 36 individuals), further work is required to identify if the proposed methodology will generalize beyond the population under consideration. (However, the back-testing on artificial data presents some evidence to support the idea of generalization.) To address this, we are developing similar approaches on other cardiac conditions where larger numbers of target data are available. By subsampling such data, we can test the idea presented in this work.

It is interesting to consider that the principle of data augmentation using artificial data could be applied to similar problems to improve classification performance on the target domain data, provided the model used for data generation accurately captures the features characteristic of the target domain data.  Finally, we note that because the model employed is computationally efficient, the artificial database can be created on-the-fly, and stored in memory, thus reducing storage costs if needed.
Given that data sourcing, transfer and preservation are costly, and compute is relatively cheap, our approach could have enormous potential in the biological sciences. Moreover, the burden and ethical considerations of collection of data from humans and animals for medical research are considerable, and increasingly the focus of attention. Ideally, approaches such as the ones presented here might usher in a new era of in-silico experimentation in a manner akin to the switch over to nuclear weapons testing in the 1990's by most countries.

\bibliographystyle{unsrt}

redpajama_set_name: RedPajamaArXiv


text:
Q: No package nginx-plus-module-brotli available error - Amazon Linux 2 LTS I want to install Brotli following the documentation, on Amazon Linux 2 LTS.
yum install nginx-plus-module-brotli

But I am getting:
No package nginx-plus-module-brotli available.

Does this mean that the documentation is outdated (package has a new name?)... or is there something I am missing?
How can I debug this situation?
Thanks.

redpajama_set_name: RedPajamaStackExchange


With business today, we experience lots of cyber liability. It's not just limited to your website anymore. With credit card information being hacked, it is more important than ever to manage your risk. In this resource, we cover digital exposures and safeguarding critical data.


text:
"Welcome to South Africa where you can do this to a woman and get a R500 bail"
A South African woman, Palesa Ya Seponono, has reacted after a court granted bail to her baby daddy who she accused of assaulting her.
LIB earlier reported that Seponono had to get stitches on her face after the suspect, Thabo Lietsoso Sempe, allegedly attacked her with a broken bottle.
It was gathered that Welkom Magistrate Court on Thursday, November 24, granted R500 bail to the suspect.
Taking to Facebook, the mother of one said the world has failed her.
"Welcome to South Africa where you can do this to a woman and get a R500 bail wow," she wrote.
"The world can fail me but God will never fail me shame even in these situations he is still my Lord and savior. God work overtime to save my life, the opportunity is yours." she added.
Former commissioner, Dayo Adeneye humbled, forced to make Eba in London (video)
VC of UNICAL reacts after a 300L student died at the school's health centre over N200 medical card
We have recorded the lowest robbery incidents in decades
Woman bails out her husband after he was arrested for beating her.


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You don't have to doubt why people prefer playing in dominoqqinstead of going to a land based casino. As you can see, many people are busy with their work and can no longer travel just to go to a casino. However, with their devices, they can already place their bets and play their favorite casino games.
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The money is there. That's the issue. For each of those conversations the money was there, not here. There's enough money out there; but out there doesn't move businesses, projects, and ideas here – wherever here may be.
This is a story that includes the poor and the rich, two four letter words that overly simplify the world because every issue is about individuals, but simplifications must be made to make conversations happen.
Whidbey Island is not alone. Plenty of people are trying to pay their bills by starting businesses.
Ideally, starting a business is a very American idea. The majority of wealthy people got their money from inheritances or working in finance; but many of the minority built their wealth by working. They succeeded in clearing the realistic hurdle that blocks many idealistic plans.
Getting a business started isn't easy. It takes money to make money and the poor don't have money, by definition. It is possible to build up from only a few hundred dollars, but those stories seem about as common as hearing about lottery ticket winners. The next most logical step is to borrow money. The rich have money, by definition, and many will make it available at a price. So, one way to become less poor is to make someone else more rich. Granted, that price reflects the potential losses from failed loans, and that price doesn't always cover those losses.
As a consultant, one of the most common conversations I have is how someone is going to fund their idea. Now, here I have to show some discretion because appearances are important and I don't want to reveal the reality behind confident facades. But then, I suspect we all know people wearing a bold front that's propped up by hope, faith, or debt. That facade may be worn far past the establishment of the business because sole-proprietorships are fragile and not protected like corporations. Their owners know how easy it is to fall back down there.
The island is a good place to see the process in action. Anonymity is harder to maintain. Storefronts opening and closing are associated with people instead of businesses. If a store opens, folks know who is running it. If it closes, we lament the loss for the person more than the loss of the business. Conventional wisdom claims that the main reason young businesses don't survive is because they didn't have enough money at the start. They were under-capitalized, just as the poor are under-funded.
The same is true with advocacies and non-profits. Great ideas, but insufficient funds for the work that needs to be done.
One of the topics that may come up at next week's real estate talk is similar. Affordability is directly a function of assets, debts, income, and expenses. Finding safe, clean, and sustainable housing is much easier with more money. A home means a more stable life, a chance to become part of a non-transient community, and legitimacy. With a fixed address a person can be more reliable, more likely to get a job, more likely to be healthier and have lower health care costs, and potentially have lower stress levels. Without the house, there may not be the job, there may be a wide variety of health issues, and there's less money available for prevention or treatment.
So, it sounds simple. Get people into houses, any kind of houses. There are several people and organizations working to help, but the very fact that housing is expensive means the solution requires money. The money, and to some extent the houses, are there; but it's not where it is needed.
Thanks to crowdsourcing and social media, there are probably more sources of funds that ever. Whidbey Island is also populated with charitable people.
Each source has its own set of rules, guidelines, goals, procedures, and limitations. To some extent, they exist because the traditional source of borrowing money from a bank is less popular than before. Banks are more cautious because of recent losses. Faith in banks has also diminished for several reasons, including scandals like the one at Wells Fargo, and personal experiences during the foreclosure crisis. I can't compare to the situation before the Great Recession (the Second Great Depression), but I do know many people who have an aversion to being beholden to a bank, or any debt. If they can't buy it for cash, they won't buy it; and that includes houses.
That 46% of the wealth could do a lot more work, but much of it is held in financial instruments like stocks. Billionaires rarely make billions from paychecks. Billionaires are created by stock appreciation. As wealth inequality grows, more money flows out of the economy and into tax havens. The rich get richer and the poor have less available to help them become less poor – unless they borrow from the rich making the rich richer.
I'm one of the lucky ones. I live in a supportive community, and have received generosity beyond what I imagined. I'm in a much better situation than I was even two years ago. And yet, a few months of delayed projects and compensations could mean I'd have to sell my house and move – somewhere more affordable. I doubt that will happen, but I notice it and then look around at how many others are in less comfortable situations.
A few decades ago and relative to today, banks were seen as a source of affordable funds, government provided more services, social mobility was greater, wealth and income inequality was much lower. From what I witness, the new sources don't compensate for the loss of the old supports. If you find that "It should be easier" you're probably right. But, until something changes, this is the new normal – and be thankful for those who decide that generosity isn't measured by profit and loss.
This entry was posted in Uncategorized and tagged affordable housing, Charity, entrepreneur, Entrepreneurship, Great Depression, Great Recession, housing, income, Income Inequality, inventions, non-profit, philanthropy, poverty, real estate, Second Great Depression, sustainability, wealth, Wealth Inequality, Whidbey Island. Bookmark the permalink.


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Can Sharks Have Bounce Back Season?
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NBA Losing To COVID
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Titans Rush Into the Playoffs With Win over Texans
Glenn Kaplan 1 year ago
The Tennessee Titans needed to win on the road against the Houston Texans in order to clinch a playoff spot. Derrick Henry busted a 53-yard touchdown with 2:54 remaining in regulation to give him the rushing title for the season and his performance also helped Tennessee win the game over Houston by a score of 35-14.
Henry finished the game with 211 yards rushing on 32 carries and three touchdowns. He missed last week's game for the Titans with a hamstring injury. It did not look like he was dealing with one at all in this game. Derrick Henry continues to be a work horse for this football team. He is one of the reasons why they are going to playoffs for the second time in three seasons.
The Texans scored on the opening drive of the game on a Duke Johnson 1-yard rushing touchdown. He finished with 12 yards rushing on four carries and a score. Johnson also caught five passes for 45 yards receiving.
On the next drive for Tennessee, Ryan Tannehill threw a 51-yard touchdown pass to A.J. Brown. Tannehill completed 13-of-20 passes for 198 yards and two touchdowns. He also rushed for 20 yards on five carries.,
Marcus Mariota also completed one pass for 24 yards receiving in this game for the Tennessee Titans.
A.J. Brown finished the game with four receptions for 124 yards receiving and a touchdown. He is the first rookie since Michael Thomas in 2016 to do that for the New Orleans Saints.
MyCole Pruitt caught a one-yard touchdown pass for Tennessee in the second-quarter from Ryan Tannehill. He caught two passes for 13 yards receiving and a touchdown.
Corey Davis caught four passes for 44 yards receiving.
Henry scored on a 3-yard touchdown run for the Tennessee Titans to start the third-quarter.
A.J. McCarron scored on a 1-yard touchdown run for Houston on the next drive. McCarron started for Deshaun Watson in this game in order for Watson to rest for next week. A.J. McCarron completed 21-of-36 passes for 225 yards and one interception. He also rushed for 39 yards on five carries and a score.
DeAndre Hopkins also did not play in this game for the Houston Texans due to rest. DeAndre Carter was the leading receiver in this game for Houston with six receptions for 65 yards receiving.
Jordan Akins caught six passes for 54 yards receiving for the Texans.
Taiwan Jones was the leading rusher in this game for Houston with 40 yards rushing on nine attempts. He also caught one pass for nine yards receiving.
Carlos Hyde rushed for 13 yards on four carries.
Steven Mitchell caught two passes for 37 yards receiving.
Henry also scored on a 1-yard touchdown run for Tennessee in the fourth-quarter and the rest was history.
The Tennessee Titans (9-7) will be on the road against the New England Patriots (12-4) for Wild Card Weekend.
The Houston Texans (10-6) will host the Buffalo Bills (10-6) on Wild Card Weekend.
Cowboys Beat Redskins; Dallas Eliminated From Playoff Contention Anyways
Sun Dec 29 , 2019
The Tennessee Titans needed to win on the road against the Houston Texans in order to clinch a playoff spot. Derrick Henry busted a 53-yard touchdown with 2:54 remaining in regulation to give him the rushing title for the season and his performance also helped Tennessee win the game over […]
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The Nest by Cynthia D'Aprix Sweeney
The Nest by Cynthia D'Aprix Sweeney is backordered and will ship as soon as it is back in stock.
Hardcover with DJ in Very Good (VG) condition.
An instant Globe and Mail bestseller.
On a wintry afternoon in New York City, Melody, Beatrice and Jack Plumb gather to confront their charismatic and reckless older brother, Leo, who has just been released from rehab. Leo's bad behaviour, culminating in a car crash while under the influence—a nineteen-year-old waitress beside him—has endangered the Plumbs' joint trust fund, or "the Nest," as they've taken to calling it. The four siblings are at very different places in their lives, but all believe that this money will solve a host of self-inflicted problems and their consequences. And until Leo's accident, they'd been mere months away from receiving it.
Can Leo get the Plumbs out of this mess, as he's always been able to do for himself before? Or will the Plumb siblings have to do without the money and the future lives they've envisioned? As the siblings grapple with family tensions, old histories and the significant emotional and financial cost of the accident, Sweeney introduces an unforgettable cast of supporting characters: Leo's stalwart ex-girlfriend who now thinks that maybe, just maybe, he is capable of change; the waitress whose life was shattered in the accident and the Iraqi war veteran who falls in love with her; and a retired, grieving firefighter with a very big secret.
Tender, funny and deftly written, The Nest explores what money does to relationships, what happens to our ambitions over the course of our lives, and the fraught but unbreakable ties we have with our families.


We campaign for an integrated healthcare system which reduces inequalities in health and is accountable to the communities it serves. We are affiliated to the Labour Party. Until 1980 we were called the Socialist Medical Association.
We arrange conferences. We publish policies and reports. We provide a platform for debate on health issues. We are not a doctrinaire organisation. Our members disagree about many things. This debate informs our contributions to the development of the Labour Party's Health Policy. Our Annual General Meeting and the Central Council decide our policies according to our constitution, More details are in our Annual Reports .
This is a small organisation, but we have useful resources. Chiefly we have our members – more than 1000 across the UK. They include doctors, nurses, therapists, dentists, managers, pharmacists, academics, scientists, patients and citizens. We have nine active branches. Organisations can affiliate, and Unite, GMB, and a number of local Labour Parties and union branches do. If you want to join there is a membership form here.
Our database of contacts – about 35000 – is the key to our network which spans health, social care and politics.


Latest Northeast Frontier Railway Recruitment 2018-2019 Notifications - Apply online application form for current job vacancies and get all upcoming Northeast Frontier Railway posts at www.nfr.indianrailways.gov.in updated on April 19th 2019. Candidates seeking for the Northeast Frontier Railway jobs can get the complete information on this page.
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Akira takes Akari, Aika, and Alice for training with a customer and picks up a honeymoon couple for a morning-long gondola tour that ends up being a full-day chore. Akira purposely entraps the three trainees in a maze formed by the rising tide and several low-lying bridges. By working together, Akari and her friends eventually find a hidden way out, while the husband learns a lesson about patience and pride.
When Alice graduates from middle school, she's happy that she can now spend more time practicing with Aika and Akari. Athena invites Alice on a picnic on a canal through the countryside outside Neo-Venezia, and has Alice treat her as a customer. Athena praises Alice's skills as an undine, and Alice responds that she is training with an excellent mentor. As they pass through a lock, Alice is startled when an undine wishes her good luck. While waiting for the lock to fill, Alice tells Athena that she's confident in all of her undine skills except singing canzone, and Athena tells her conveying a love for singing is more important than any technical skill. When they reach the Hill of Hope, the destination of the Single promotion exam, Akari, Aika, the owners of Orange Planet, and a representative from the Gondola Association are waiting for them. After an exam on canzone, Athena takes off both of Alice's gloves, promoting her straight from Pair to Prima (the first undine ever to do so), and gives her the title Orange Princess.
Akari spends the night with Alice at Orange Company. While there, Akari meets Athena, Alice's mentor and roommate, the third of the so-called Three Water Fairies. She also meets Alice's kitten, Maa-kun, who Alice keeps hidden from Athena because pets are forbidden in the company dorms. During the day, while Alice is at school, Maa-kun runs away but is finally found at dusk by Akari and Athena, who knew about the cat all along.
After a day of practice, Akari, Aika, and Alice return to Aria Company and find their three mentors talking together. Alicia tells the trainees that they once did similar combined practice, but have seldom all met together since becoming primes (full undines). In flashbacks, Alicia and Akira as singles are shown befriending the extremely clumsy Athena and discovering her amazing talent as a singer. While escorting Aika and Alice home, Akari begins to cry when she realizes they three will also part ways one day.
When Ai visits Akari for Neo-Venezia's Carnevale, she helps out at Aria Company. Before the festival starts, she sees President Aria leave by himself, and Akari tells her he'll be gone for the duration. On the first day of celebrations, Akari and Ai meet Casanova, the mysterious masked master of ceremonies of the Carnevale named after the historical figure. Later, on Ai's last day on Aqua, while talking with Aika and Alice they spot one of Casanova's attendants. They chase after him but Akari and Ai get separated from the others. Eventually they confront Casanova, who invites them to join his cheering procession for a while. When he eventually stops them from following any further, he gives them each a jasmine flower before revealing that President Aria is one of his attendants, and that he is Cait Sith, the legendary king of Aqua's cats. When Aika and Alice finally find Akari and Ai, they claim that Casanova is a fairy.
When Akari learns there will be a meteor shower that night, she, Aika, and Alice make plans to watch it. As they do so, they meet Al, a gnome (an underground regulator of Aqua's gravity) on a shopping trip to the planet's surface, who invites them to lunch. In return a blushing Aika invites him to join them that night. That evening, Alice bows out, leaving just Aika, Al, and Akari. Because Piazza San Marco is too crowded and bright, they search through the narrow streets for a better view. For courage through the darker passages, Akari repeats her chant from Casanova's procession. Eventually, Aika takes them onto the roof of a Himeya Company office building, with an unobstructed panorama of the night-time city and the meteors. When Akari searches the rooftops for another vantage, Aika realizes she's alone with Al and gets flustered, and blames him as a gravity regulator for attracting all the meteors that burn up in the atmosphere. As they walk home, Al reminds Aika that some meteors don't burn up but survive to reach the surface.
Mr. Mailman borrows Akari and her gondola for a day so he can complete his rounds while his gondola is repaired. In the morning, they collect mail from canal-side postboxes, then after lunch deliver the mail—and along the way help a boy deliver in person a letter to his former teacher at her wedding, apologizing for behaving badly to her because he was upset over her leaving. At the end of the day, Mr. Mailman explains that Neo-Venezians prefer the inconvience of mail because a letter, being not as quickly written as e-mail, can carry the sender's heart, and so can become a treasure, and furthermore it can be a link between the past and present. Akari says that letters are like Neo-Venezia, in that you can touch the creators' hearts through them, and Mr. Mailman tells her that she has been painted with "the color of Neo-Venezia". The next morning, Akari is moved when she receives his hand-written thank-you note.
The next morning, Aika, Alice, and Akatsuki demand to know whether the news about Alicia is true, and Akari says yes, Alicia is retiring to get married and work for the Gondola Association. Alicia had delayed this until Akari became a Prima, and Akari blames herself for not becoming more reliable before now. Aika and Alice worry about Akari, but she says she'll be fine. The next several days are busy, as Alicia hands the running of Aria Company over to Akari. When Akari eventually breaks down crying, Alicia admits that Akari has long had the skills to be a Prima but she'd delayed the exam because she didn't want to leave Akari. Mr. Mailman, now retired himself, hires Akari for a tour, during which he offers to refer customers to her. At Alicia's retirement ceremony, all the undines of the city participate. The following morning, Akari sees Aria Company filled with memories of Alicia and her friends, then opens the reception-area shutter to let in her new beginning. In an afterword, Akari tells Ai what happens to the other characters—including Athena becoming an opera singer, leaving Akira the undisputed top Prima—ending with Ai's first morning as a new Pair for Aria Company.
Akari receives a mysterious message, which directs her to a secluded bay where she meets Aika and Alice following similar messages. Together, they find Alicia and Akira, who sent the messages, and they spend the day in special training and relaxed swimming.
During the annual flooding of Neo-Venezia, Akari is caught in a rain-shower away from home and shelters in her friend Aika's room at Himeya Company. Later, Aika gets in a fight with her strict mentor, Akira, and leaves to stay the night with Akari at Aria Company. In a flashback, Aika remembers her first meeting with Akari's mentor, Alicia, showing why she admires Alicia and wants to become an undine (gondolier).
During rowing practice on the first warm day of early spring, Aika, Akari, and Alice find a box in the base of a statue. Inside it is a note that sends them on a treasure hunt through the streets of Neo-Venezia. One clue leads them to Caffè Florian on Piazza San Marco that's the Venetian cafe (transported to Aqua) where cafe latte was supposedly invented, where Akari chats with a customer who calls himself an expert at enjoying the Piazza. After the shadow from the Campanile has moved far enough the three can find their next clue, he prays they discover a wonderful treasure. Their final clue leads them to a beautiful hillside view of Neo-Venezia, described as a "treasure in your heart". When they return the clues, so others can find this treasure for themselves, they notice marks indicating they are not the first to do so. Back at the Piazza, Alicia introduces the man as the cafe's owner, and he greets Akari as an "Expert in Happiness." In response to Akari's e-mail about the day, Ai calls Neo-Venezia a big treasure chest.
In the first half of a two-part episode, Alicia takes Akari to an island to visit a reproduction of a Japanese Inari shrine. When Akari buys some inari sushi, the saleswoman warns that if she meets any fox spirits, not to go with them. After getting separated from Alicia during a sunshower, Akari meets a wedding procession of fox spirits, and one holds out his hand to her. Frightened, she gives him her inari sushi, and they leave her. In the second half, Alicia takes Akari on a picnic to look for spring at a special place she knows on another island. After walking through a forest, they follow an old railway, but at a fork in the line they chose the wrong way. Just as they are about to turn back, they find an abandoned rail-car beneath a large blooming cherry tree. While they admire it, Alicia tells a story about the importance of making mistakes in serendipity.
When Akari asks Alicia why Aria Company doesn't have a distinctive palina, or pole for hitching boats, Alicia doesn't know and suggests that Akari design one. Looking for inspiration, Akari searches the company archives and finds sketches for a palina design. Grandma Akino visits while in the city on business and tells her the sketch was made by one of her first employees, and was never executed because the company colors hadn't been decided. Akari comes up with a design in blue (for the sea and sky) and white, based on the old design and current company uniforms, and paints it herself. As they admire the new palina, Alicia says that one day, after she retires, Akari as a Prima will one day look at it with a junior employee of her own—at which time the palina will represent Akari as she is now. Akari replies that, no, Alicia is also part of the palina, and the original designer is as well. Ai writes that this means reading an old e-mail of theirs would let someone meet the two of them as they were at that time.
Two weeks after Alice's promotion to Prima, Akira hears some Himeya Singles gossip about how Aika, the heir to the company, is handling "losing" to a competitor. Akira worries over whether Aika is holding up, but has difficulty talking about it with her until Aika mentions she was present at Alice's promotion. Aika assures Akira she's been motivated by the competition and asks to be trained hard. Akira sets the date for Aika's Prima exam in a month, and says she won't forgive her if she, as the company's future queen, fails. Meanwhile, Alice is settling into her working life as an undine, but has little free time. She realizes it's been several days since she last saw Akari and Aika, at the moon-gazing, but can think of no excuse to call her friends, even though she misses them and is lonely. When Akari and Aika pay a surprise visit with a pizza, Akari tells Alice that being a friend is reason enough.
Akari tells Aika that practice has gotten lonely without Alice, and Aika apologizes that she can't practice either anymore: she's been promoted to Prima, with the title Rosen Queen, and will be managing a new Himeya Company branch office. When Akari tells Alicia, she wonders why Aika cried over this, and in response Alicia sets Akari's Prima exam for the next day. That night, Akari is too nervous to sleep and practices rowing until very late. In the morning it's raining, but Akari asks that the exam not be postponed, and by afternoon it has cleared enough to hold it. As Akari guides Alicia through the city, a number of friends greet her, and Akatsuki and Woody cheer her on from the Rialto Bridge. Alicia directs Akari down a very narrow, difficult channel, which she successfully negotiates, then gives Akari the choice of their final destination, which will determine what kind of Prima she'll be. Akari takes her to Aria Company, where Alicia thanks Akari for coming into her life and takes off Akari's glove, promoting her to Prima with the title Aquamarine, then says she has something important to tell her.
A young girl, Ai, arrives at Aria Company and demands that Akari give her a gondola tour of the city of Neo-Venezia. When Akari protests that, as a single (journeyman), she is not allowed to guide customers without a supervisor, Ai insists she be taken as a "friend" instead of a "customer," and Akari gives in. As they tour the city, Akari realises from Ai's reactions that it is a miracle to like something you had previously disliked.
Akari is asked to deliver an old letter. With the help of Woody, a sylph (airborn deliveryman), she find its destination but discovers it is now under water. She and Alicia read the letter and learn that it is from a woman writing to her husband who was away working on the terraforming of Mars from before the oceans rose and the planet was renamed Aqua.
While training in a strait with difficult currents, Akari and Aika witness the impressive rowing skills of a younger girl, Alice. She bluntly refuses their attempts at friendship, but is troubled by Akari's sincere smile, which is unlike those of other undines. The next day, Alice meets Akari and Akatsuki, an apprentice salamander (weather controller), and joins them first on Akari's gondola tour of Neo-Venezia, then on Akatsuki's tour of the floating weather station where he lives, becoming friends with Akari in the process.
In the first half of a two-part episode, Akari and Alicia give the Aria Company building a spring cleaning. President Aria, the company president and mascot, is eager to help but gets in the way instead and, depressed, he runs away. In the second half, President Aria emulate his favourate cat superhero from a comic book, as he both fails and succeeds helping other cats and a young boy.
Akari, Aika, and Alice visit Akino, the legendary founder of Aria Company, in retirement in the countryside. Akino asks for the girls' help with domestic chores, and Aika believes they are tests that will make them great undines. That night, Akino reveals that the secret to becoming a good undine is to enjoy every moment.
Akari decides to visit the first bridge built in Neo-Venezia. When she finds it, it is full of cats, including Cait Sith, the century-old king of the city's cats. When Akari crosses the bridge, she time-travels back to before Neo-Venezia was built, where she witnesses the arrival of flowing water to the region, a result of the terraforming of Mars.
Ai makes a surprise visit to Aria Company during the New Years break. While there, she meets Akari's various friends and inadvertantly embarrassed Akari by disclosing personal secrets and gossip that Akari had included in e-mails to Ai. When President Aria randomly runs off, Akari and Ai give chase and are led to a glimpse of Neo-Venezia as it existed in the past. The two interpret this as formal welcome to Ai by the planet Aqua.
When Alice leaves Orange Planet, Athena notices she doesn't stay with the other Pairs but goes off alone. After practice Akari and Aika visit Alice and Athena's dorm room, and when it becomes late without their noticing, Athena invites them to stay the night. That evening, Alice notices that Athena is more clumsy than usual and accuses her of being happy, and Athena says it's because Alice seems to be having fun with her friends. Alice receives an invitation to a party with other Pairs, but turns it down despite Akari's urging because she thinks they're jealous of her talent as a younger undine. Athena tells her that people are like mirrors, and that they reflect her fear of being rejected, which she sees as envy. After watching Maa go to great effort to climb onto a bed to bite President Aria's belly, Alice resolves to try to smile more and attend the party.
In the first half of a two-part episode, on a summer day hot enough to see mirages on the ocean, Akari goes shopping in the afternoon. Through the heatwaves, she sees the street suddenly empty of people and thinks she has wandered into a dream. She follows President Aria into a cool cafe where only iced milk is served, because it's a refuge for the city's cats where humans usually cannot go. When Akari leaves, she sees Cait Sith tip his hat to her, and then the cafe turns into a boarded-up building and the people return. In the second half, Akari buys a nightshine windchime, a summer specialty of Aqua with a noctilucent clapper. For the month its glow lasts, she takes it everywhere with her, and at night holds tea parties with President Aria in her gondola, using it as their light. When it begins flickering out, Alicia and Akari follow Neo-Venezian custom and row out into bay to let the clapper drop into the water when it dies. When Akari's does so, it leaves behind a rare tear-shaped crystal, and Akari begins crying.
As Akari eats lunch alone on a hot midsummer day, she is joined by Akira, then Akatsuki, and then Al. As Akatsuki bickers with Akira, Al claims he was always hotheaded like that, and tells a story about Akatsuki, Woody, and himself when they all were boys, shown in a flashback: one time in a playground, they don't let two new kids play with them, but when one jumps off a swing further than Akatsuki can, he challenges that kid to a slide race, during which Akatsuki crashes and concedes defeat. Back in the present, Alicia (who overhears the story) says it brings back memories, and reveals the two kids were Akira and herself. Akari says that it's nice, the way the lives of the other four circle around and meet again, inside the large circle that is Aqua, and that she feels left out because she's been in Neo-Venezia for only a short time. Akari reassures Akari that she's already inside their circle of friends, at the center. As the episode ends, Athena is shown rowing by, then several other characters meet in a series of chance encounters.


text:
Telegram from Herbert S. Bryant to Dispatch Agent
ID: SIA2010-0561
Creator: Bryant, Herbert S 1878-1950
Form/Genre: Document
Citation: Smithsonian Institution Archives, Record Unit 305, Box 899, Folder: 170221
Telegram from Herbert S. Bryant, director of the Division of Correspondence and Documents for the U.S. National Museum, to Howard Fyfe, United Dispatch Agent, New York City, regarding a wooden case addressed to his attention for the Smithsonian Institution, Division of Birds. Bryant asks if the box has arrived. This box contains bird skins and one skeleton collected and sent by S. Dillon Ripley from Ceylon where he was serving with the Office of Strategic Services during World War II.
Ripley, Sidney Dillon 1913-2001
Fyfe, Howard
United States National Museum Dept. of Biology Division of Birds
United States National Museum
Office of Correspondence and Documents
United Dispatch, Inc
Office of Strategic Services
Historic Images of the Smithsonian
Future Smithsonian Secretary S. Dillon Ripley, formerly assistant curator in the Division of Birds, volunteered for service during World War II with the Office of Strategic Services, predecessor to the Central Intelligence Agency. Stationed in Ceylon, now Sri Lanka, he continued his ornithological passion with bird watching in his free time. As an avid bird collector, Ripley maintained contact with the Smithsonian and donated bird specimens he collected there to the United States National Museum, Division of Birds. Having traveled widely in the South Pacific before the war, he knew the geography, natural history and languages of the region.
See Neg. Numbers SIA2010-0556, SIA2010-0557, SIA2010-0558, SIA2010-0559, SIA2010-0560, SIA2010-0555, SIA2010-0562 relating to the shipment by S. Dillon Ripley of bird skins from Ceylon, now Sri Lanka, to the Smithsonian during World War II.
Contained within
Smithsonian Institution Archives, Record Unit 305, Box 899, Folder: 170221
Institutional History Division, Smithsonian Archives, 600 Maryland Avenue, SW, Washington, D.C. 20024-2520, SIHistory@si.edu
Collecting and collections
Smithsonian Institution--Employees
SIA2010-0561
Number of Images: 1; Color: Color; Size: 7.94w x 10.21h; Type of Image: Document; Medium: Paper


Her wavy hair was pulled back into a high ponytail, and a tank top covered her leotard.
I never sweat and my body temperature drops as soon as I strip down to a leotard and tights after my warm up.
I'll have your pink leotard ready for you as soon as you come home, dear.
Bulgaria's Silvia Mitova was one of those truly great gymnasts who most certainly would have gained greater glory had she sported a different leotard.
Dressing in a red leotard, hanging out at bars drinking Shirley Temples is not exciting.
Lisa walked into the gym, dressed in a leotard and sweats, her hair in a messy ponytail.
Her face and neck are moist with sweat, and she has pulled a pink practice tutu over her leotard and tights.
She wore a royal-blue leotard and long, navy-blue high-heeled boots.
Everyone else was wearing a leotard or something like that.
So he wears a leotard and flies around in a red cape.
There she was, barely recognizable, a diva dressed in a leotard, her hair drawn into a knot.
She had her hair up and was dressed in a black leotard with pink tights.
She motioned for two women, one with frosted hair and the other wearing a leotard that had a lightning bolt sewn across the front, to come over.
My costume, a red leotard with a flowing black see-through skirt, was destroyed.
She hung up her leotard in 1998 and started class at the University of Maryland, but soon realized that gymnastics was not quite out of her system.
Smith gave Mandy not only a new leotard, but tights and ballet shoes too.
She was wearing a pink leotard and this frilly pink tutu.
It looked like a leotard with little dangling fringes at the bottom and sequins everywhere.
She became a household name through her TV show, Relaxing with Roma where she famously sported a leopard-print leotard which became her trademark.