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

Particle Image Velocimetry (PIV) is arguably the most widely used technique to quantitatively study experimental flows \cite{westerweel1997fundamentals, raffel2018particle}. A common work-flow would consist of seeding a flow with luminescent particles and taking pairs of pictures with a known short time separation to capture an instantaneous flow state. By splitting a pair of images into (possibly overlapping) windows and cross-correlating between them allows one to infer the direction and magnitude of the flow. With modern computers and digital cameras, PIV has experienced wide adoption in the academic and industrial domains \cite{tropea2007springer}.
For cross-correlation to work properly, one has to ensure that the exposure time is short enough such that particles do not move more than a few pixels and the two images have clearly identifiable correlations \cite{westerweel1997fundamentals}. Violating this condition, for instance because of an insufficient laser intensity or a too fast flow, results in so called streaks - traces of particles.

Streaks in the PIV images are commonly considered an experimental failure since they render cross-correlation techniques less efficient or even inapplicable. When flow velocity imposes constraints for which the camera and light source at hand cannot capture instantaneous particle positions, one either has to use a higher speed camera or/and a stronger light source. Both of these solutions quickly hit financial and safety constraints, which often cannot be overcome in academia. Therefore, there exists a need for processing techniques.
Although not suitable for conventional cross-correlation methods, streak images do carry information about the flow. Whereas an image with streaks does not contain information about direction of the underlying flow, information on the mean velocity magnitude and azimuth can be inferred from streaks themselves. To the best of authors' knowledge, there are no reliable algorithms to recover quantitative information from streaks images, which motivated us to design a method for extracting information about the flow from streaks.

To this end, we applied an ensemble of Convolutional Neural Networks (CNN) trained on streak images to draw a statistical prediction of the displacement magnitude and corresponding azimuth. The potential of CNNs has long been recognized for applications related to the face and handwriting recognition \cite{lawrence1997face, simard2003best}, although their power has been fully discovered only recently when deeper (that is, with more layers) networks became feasible to train within reasonable amount of time, mostly due to the emergence of affordable Graphic Processor Units (GPUs) \cite{krizhevsky2012imagenet, karpathy2014large}. An interested reader is referred to a recent overview of the deep learning with important development milestones listed \cite{lecun2015deep}.

Before we proceed to describing methodology and results, it is worth to mention that a crucial ingredient to a success of CNNs is a suitably large training set, which a CNN is supposed to learn from without facing an over-fitting. From this perspective, streak analysis appears almost an ideal problem. Similar to PIV, we make a reasonable assumption that within a sufficiently small interrogation window velocity exhibits simple functional form (for instance, constant or linear function). This allows us to quickly generate an unlimited number of training images. Adding variability in number of streaks per window, their thickness and intensity will mimic a realistic experimental scenario sufficiently well. Therefore, we can exhaustively sample the parameter space and feed the network with as many sample as needed to attain an acceptable accuracy. The specific numbers and parameters will be discussed in the corresponding sections below.

\section{Methods}
\label{sec:methodology}

\subsection{Problem setup}

We aim at inferring the displacement and azimuth from a streak image of $N \times N$ pixels, typically as illustrated in Figure \ref{fig:expImage}.

\begin{figure}
	\includegraphics[width=\textwidth]{figs/figure0.jpg}
	\caption{Streaks image ($1024\times1024$ pixels) from a longitudinal libration experiment in a cylindrical cavity. See section \ref{experiment} for details}
	\label{fig:expImage}
\end{figure}
Similarly to how PIV is applied to a pair of images, we split the image into ($n \times n$, with n=48 in this study) sub-windows with an overlap ($50\%$ in our case). Assuming the velocity is sufficiently uniform over the ($n \times n$), we aim at determining the mean length, the displacement over the exposure time, and azimuth for each sub-window.

A closer look at the image shows that the number of streaks, their intensity and their width, vary within each sub-window. In addition, in many cases, the streaks leave the ($n \times n$) interrogation area. In order to make our CNN resilient to this variability, we generate train and validation images of ($48 \times 48$ pixels) with a random number of streaks varying between 2 and 10, a random color intensity between 90 and 255 and a random thickness between 2 and 4 pixels. The center of each streak is chosen randomly over the entire sub-window, such that streaks are allowed to go outside the image.

To generate train and validation images, we sampled displacement, $\Delta$, and asimuth, $\phi$, from uniform distributions
\begin{equation}
\Delta \sim U(1, \Delta_\textnormal{max})
\end{equation}
and
\begin{equation}
\phi \sim U(-\pi/2+\delta, \pi/2),
\end{equation}
respectively.

Here, $\phi = 0$ corresponds to the positive $x$-axis and increases clockwise.
Note that limits for $\phi$ cover only half of the circle minus $\delta = 5^{\circ}$ on one end to mitigate ambiguity with respect to the direction. Once streaks are generated, the intensity within each streak intensity is perturbed and the image is blurred with a Gaussian kernel of $3\times 3$ pixels. In this study, the maximum displacement, $\Delta_\textnormal{max}$, of a particle within an image is assumed to be half of the chosen window size, that is $r_\textnormal{max} = 24$ pixels.

Figure \ref{fig:fig1} shows a selection of streak images generated using the stated procedure. The outlined procedure enables generation of millions of images in just a few minutes on a regular PC.

\begin{figure}
\includegraphics[width=\textwidth]{figs/figure1.png}
\caption{Subset of generated streak images used for training and validation with the corresponding displacements (in pixels) and azimuth angles (in degrees) given in the titles.}
\label{fig:fig1}
\end{figure}

\subsection{Network architecture and implementation}

We aim at building a regression CNN that, given a single window image, outputs two real numbers, corresponding to the displacement and angle. To this end, we have created a network with the architecture shown in Figure \ref{fig:figcnn}. As can be seen, four convolutional units form the core of the network, each consisting of a convolutional layer, activation layer (in this case, rectified linear unit), average pooling layer used to downsample the input followed by a batch normalization layer. An increasing depth of the convolutional layers is a common choice aimed at giving a network ability to learn more complex features from the input.

To prevent overfitting and improve training speed, we used batch normalization at the end of each convolution unit \cite{ioffe2015batch} and a dropout layer with the 30\% drop fraction \cite{srivastava2014dropout}. Additionally, following common practices, the outputs are standartized and centered. The input grayscale images have a single eight bits channel and are normalized by 255 to stay in the $[0, 1]$ range.

Whereas there may exist more efficient architectures, we found that for our problem increasing depth of the network has not improved overall performance significantly, nor did increasing and/or decreasing filter sizes. Additionally, we found that, in contrast to majority of the reported CNN architectures, using the average pooling instead of the max pooling results in a slightly higher accuracy.

\begin{figure}
\includegraphics[width=\textwidth]{figs/figure_CNN.png}
\caption{Architecture of the used network. Left: sequence of layers and corresponding parameters in the brackets. Each CUNIT layer consists of four actual layers listed in the middle column. Relevant layer parameters are given on the right. Total number of learnable parameters in the network is 44370.}
\label{fig:figcnn}
\end{figure}

To increase accuracy and robustness of the prediction, we built an ensemble consisting of ten networks \cite{hinton2015distilling}. All networks have the same architecture and parameters, but were trained using different randomly chosen initial weights \cite{glorot2010understanding, he2015delving} and random shuffling to form a unique sequence of batches supplied to an optimizer. The outputs of all ensemble members are averaged to get a the final prediction and its variance, thereby providing a proxy for the uncertainty of a prediction.

Finally, the network was implemented using the PyTorch library \cite{paszke2017automatic}. The complete implementation can be found in GitHub using the link below.

\section{Results}

\subsection{Network training}

We generated one million images (e.g., Figure \ref{fig:fig1}) of which 75\% and 25\% were used for training and validation, respectively. Each network in the ensemble has been trained for 100 epochs. The ADAM optimizer with an initial learning rate value of 1e-3 was used. The learning rate was halved if no sufficient decrease in the validation loss was observed for the past 10 epochs. To make the training more resistant to potential outliers (e.g. imagine a situation when all streaks leave the window), we chose to minimize the Huber loss \cite{huber1973robust} rather than conventional least-squares loss. Huber loss represent a hybrid $L_2-L_1$ distribution with a high tolerance to the presence of long tails in the original distribution.
No pathologies between training and validation losses were observed during the training, specifically the validation loss remained a bit higher than the training loss, indicating that the network learns some generic features of the dataset. Training of each network took $\approx 90$ minutes on a single CPU-GPU system. This modest time suggests that training networks for window sizes larger that the one adopted here is feasible.

Figure \ref{fig:fig3} shows RMSE values for all networks seperately as well as the RMSE of the ensemble. It is evident that the ensemble performs up to ~20\% better than a single network. CNN ensemble achieves the accuracy of 1.05 pixels for the displacement and $8.5^{\circ}$ for the angle predictions, respectively. The latter may seem like a large error, however one should realize that having a $48 \times 48$ pixels window inevitably leads to limitations in recognizing small rotations, especially when streaks are short.

\begin{figure}
\includegraphics[width=\textwidth]{figs/figure2a.png}
\includegraphics[width=\textwidth]{figs/figure2b.png}
\caption{Top: RMSE values of individual networks (markers) and ensemble (line) for displacement $\Delta$ and angle $\phi$ calculated on the validation set. Bottom: histograms of the residuals for the displacement and angle.}
\label{fig:fig3}
\end{figure}

Figure \ref{fig:fig4} shows a random selection of validation images with the corresponding ensemble CNN predictions and true values. We see that the ensemble CNN has learned to make accurate prediction even for complex situations when some streaks overlap, join or leave the window. Additionally, Figure \ref{fig:fig5} shows nine images which produced the worst predictions of the displacement. Clearly, most of these cases are associated with situations where originally longer streaks have all been displaced outside the window or multiple streaks coincidentaly joined and formed what appears to be a longer streak. Note that such situations do not represent a failure of the CNN, but rather limitation of the window-based approach adopted in this work. Similar situations are likely to occur in experimental images and ufortunately they leave little chance for any sliding window approach to make an accurate prediction. We will discuss possible ways to mitigate this in the Conclusions section. It is worth to note that despite inaccurate displacement prediction, the angle is predicted accurately in most of these cases.

\begin{figure}
\includegraphics[width=\textwidth]{figs/figure3.png}
\caption{Images with the predicted displacement and angle depicted as magenta lines. The true values are given in titles and predicted values in brackets.}
\label{fig:fig4}
\end{figure}

\begin{figure}
\includegraphics[width=\textwidth]{figs/figure4.png}
\caption{Same as previous, but showing nine worst prediction in terms of displacement.}
\label{fig:fig5}
\end{figure}

\subsection{Application to a numerical simulation}

After confirming the excellent performance of the network on the validation set, we turn to a discrete numerical simulation (3D-DNS) of a complex thermal convection turbulent flow presented recently by \cite{plumley2016effects}. In their paper, the authors simulate rapidly rotating thermal convection in a square box, heated from below and cooled from the top with a vertical gravitational field pointing downward and a vertical axis of rotation. To validate our CNN algorithm, we used a simulation obtained for $E=10^{-7}$, where $E$ is the Ekman number representing the ratio of viscous to rotational effect, and a Raleigh number that is 90 times supercritical. Each velocity field is $192^3$. For the purpose of this study, we extracted the horizontal component of the velocity in a horizontal plane a mid-depth. Due to the limited size of the calculation each velocity component has been interpolate on a $1024\times 1024$ pixels grid.
Figure \ref{fig:fig6}-left shows the displacement map obtained from the full DNS for an arbitrary exposure time. Since the CNN will act on ($48\times48$) interrogation windows with the $50\%$ overlap, we apply a sliding window averaging with ($48\times48$) and $50\%$ overlap (Figure \ref{fig:fig6}-right). The window averaged displacement is the best reconstruction we can possibly achieve with a $48 \times 48$ pixels window and a 50\% overlap used in this study, thus all later results will be compared against it.

\begin{figure}
\includegraphics[width=\textwidth]{figs/figure5.png}
\caption{Original (left) and window averaged (right) displacement maps of the DNS \cite{plumley2016effects} used for validation.}
\label{fig:fig6}
\end{figure}

To reduce subjectivity, we generated 30 randomly seeded streak images using the original DNS velocity (Figure \ref{fig:fig6}, left) and varying streak density, intensity and thickness as formerly stated. The trained CNN ensemble was applied to all images and the resulting averaged reconstruction of both displacement and angle are shown in Figure \ref{fig:fig7}. We see that the reconstructed images recover the general structure of the flow very well. The only undesired effect is that the ensemble CNN struggles to correctly predict large displacements. When the displacement reaches half of the window size, CNN systematically predicts smaller displacements. The reason for this is not a deficiency of the CNN, but rather the fact that for displacements as large as half the window size it becomes very likely that most of the streaks go outside the window, thus a smaller displacement is a rational prediction. To solve this problem, one could apply a larger window (for instance, $64\times 64$ pixels), although this will reduce the overall resolution and may render constant velocity assumption less accurate. In real experimental settings, it may be advantageous to apply several window sizes and either choose one or combine obtained results, depending on a particular experimental setup.

\begin{figure}
\includegraphics[width=\textwidth]{figs/figure6.png}
\caption{Original (left) and window averaged (right) displacement maps of the DNS \cite{plumley2016effects} used for validation.}
\label{fig:fig7}
\end{figure}

Finally, Figure \ref{fig:fig8} shows one of the streak images generated from DNS with the true and predicted streaks for each window position. Note that while the whole velocity field is used to generate the background streak images, the overlying red streaks are reconstructed at the location of the CNN grid point using the averaged displacement and angle maps (Figure \ref{fig:fig6}) Generally, ensemble CNN performs well. There is, however, accuracy reduction in regions of large displacements for reasons described above and across areas of vertical shearing, where angle predictions become locally less accurate. Interestingly, these vertical shear regions also lead to abnormal streaks orientation (close to horizontal) when using the window averaged field values (red streak in Figure \ref{fig:fig6}).

\begin{figure}
\includegraphics[width=\textwidth]{figs/figure7.png}
\caption{Streak image generated from the DNS with true and predicted displacement and angle shown as red and yellow lines, respectively.}
\label{fig:fig8}
\end{figure}

\subsection{Application to the flow driven by longitudinal libration in laboratory experiment}\label{experiment}
Finally, we apply our algorithm to the laboratory experiment depicted in figure \ref{fig:MeisterMSC}. The apparatus consists of a straight cylinder filled with water set in rotation on a turntable at 1Hz. The cavity is 286mm high with a radius of 140mm. The bottom lead is covered with topography made of blocks ($64mm\times64mm\times 8mm$). A second motor is used to oscillate the container on the turntable, resulting in the so-called longitudinal libration. For the purpose of this experimental validation we have set the libration frequency to 1Hz and the amplitude to $34^{\circ}$.

To obtain the streak images, we use a 1W continuous diode laser to illuminate a horizontal plane of the fluid seeded with fluorescent particles. We record movies of the particles motion at a fixed exposure time of 21ms with a sampling frequency of 30 images per second. We extracted a frame corresponding to a maximum apparent amplitude of the swirling flow (\ref{fig:expImage}).

\begin{figure}
	\includegraphics[width=\textwidth]{figs/figureExperiment.jpg}
	\caption{Experimental setup of the longitudinal libration experiment \cite{MeisterMSC}.}
	\label{fig:MeisterMSC}
\end{figure}

A pre-processing step was necessary for real images to filter out the CCD matrix noise. We achieved this by a simple thresholding of an image at intensity of 50. After that, image was split into overlapping $48\times 48$ pixels windows, resulting in a total of 2400 window images. The ensemble CNN was then applied to predict displacements and angles as well as their standard deviations with the latter serving to be a proxy for the output uncertainties.

\begin{figure}
\includegraphics[width=\textwidth]{figs/figure8a.png}
\caption{Predicted displacement and angle (left) along with their standard deviations (right) inferred from the ensemble CNN for an experimental image shown in Figure \ref{fig:fig10}.}
\label{fig:fig9a}
\end{figure}

Figures \ref{fig:fig9a}-\ref{fig:fig9b} show the predictions in the form of maps and histograms, respectively. Figure \ref{fig:fig10} shows the actual experimental image we used and window-centered predictions drawn as lines. First of all, we see that experimental flow exhibits rather involved behaviour, with regions where streaks show non-linear behvaiour of the flow within a window, which violates our initial assumptions. Nonetheless, the ensemble CNN produces coherent and spatially correlated predictions (Figure\ref{fig:fig9a}) It is also interesting to analyse the standard deviation maps (Figure \ref{fig:fig9b}). Large absolute errors in the displacement map appear to be correlated with regions of fast flow. On the other hand, large uncertainties in the angle occur around regions with vertical velocity shear, as observed in DNS synthetic images, specifically where velocity seems to flip the direction.

\begin{figure}
\includegraphics[width=\textwidth]{figs/figure8b.png}
\caption{Same as above, but in a form of histograms.}
\label{fig:fig9b}
\end{figure}

In terms of performance, ensemble CNN attains a speed of seven full images (each totaling to 2400 windows) per second on an NVIDIA GoForce GTX 1070Ti powered PC. This level of performance allows us to comfortably process long sequences of images taken during experiment, which we use to study evolution of the kinetic energy. Using a more modern GPU will likely also enable real-time predictions at rates of a few dozens of images per second, which could be more relevant for industrial applications.

\begin{figure}
\includegraphics[width=\textwidth]{figs/figure9.png}
\caption{Streak image taken during an experiment overlain by the predicted displacement and angle shown as red lines.}
\label{fig:fig10}
\end{figure}

\section{Conclusions}

We presented an open-source ensemble CNN aimed at quantitative analysis of complex experimental flows using streak images. The presented approach can be applied in situations when classical PIV is inaccurate or not possible due to experimental setup or hardware limitations. The advantage of the presented approach is the ease of training/validation set generation as well as recovery of the prediction uncertainty through application of the ensemble method. We foresee that the approach may need an experiment-dependent tailoring to achieve the best performance. According to our experience, this only takes a single day on an average PC equipped with a modern GPU and should not represent a practical limitation.

The potential extensions of the presented approach may include the use of multiple window sizes applied to the same image, more complex functional parameterizations of the displacement and angle fields within a window (e.g., polynomial). Finally, a completely different approach based on an image segmentation and object localization and/or tracking can be applied to extract information about individual streaks and their evolution in time. We expect to investigate these directions in details in our future studies.

\begin{acknowledgements}
We thank authors of PyTorch, mathplotlib and h5py libraries for making them available, Meredith Plumley for sharing her DNS data, Adrian Tasistro-Hart for a number of insightful discussions on CNNs. The Jupyter Python notebooks of all programs used in this study can be found here \url{https://github.com/agrayver/streakcnn}.
\end{acknowledgements}

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I'm hoping someone can tell me why MSVCR71.dll isn't on my machine and if I can run MediaCoder without it. I get an error message when I start the application saying this file is missing. I'm running the latest XP.
It's part of the Microsoft Visual C++ runtime library. MediaCoder uses Visual C++ as part of the programming.
If you have MediaCoder currently installed, then use your "Add/Remove" programs to un-install it. Then shutdown and restart Windows. After your computer restarts, install MediaCoder again.
It's still giving me the same error--uninstalled, installed C++ runtime, rebooted, installed Media Coder, started Media Coder.
If you know how to search for a file, type in: MSVCR71.dll and search for it on your machine. There should be several copies of it. One should be in the C:\Windows\System32 directory. If not, you can copy any of the others and paste it into that directory.
I have no idea if this will help.
Appreciate the help. I may even make a donation.
Since it is there why won't MediaCoder install correctly? I still get the error message that it can't find MSVCR71.dll. What do I do now? Where do I need to place it?
Please tell me where it needs to be and I'll copy and paste.
Do not worry if your computer shows a dll missing error, You can easily download the dll file from https://unfitpc.com/msvcr71-dll and after that carefully save the file settings. It will be helpful i am sure.


hi this is john, i have a 4yrs old Rottweiler.
my purpose for buying a Rottweiler was that i needed a guard dog that will guard the home all night. but instead my Rottweiler goes into deep sleep during night and doesn't wake up even if there is suspicious sound or noise inside or outside the house.
how can i keep him awake during the night so he can guard our home?
You can't 'keep a dog awake' I'm afraid, adult dogs tend to sleep for a lot of the time… day and night.
However, if there is an intrusion, a loud noise, etc. they generally wake up! Unless your dog is sick in some way, he won't sleep through a break-in or a threat to your home or safety.
Rottweilers are very intuitive and discerning guard dogs and aren't prone to 'false alarms', indiscriminate barking or over-reaction. He should be able to recognize a real threat (not just a neighbor, a cat, or a 'trial run' that his owner has set up) and I would be very surprised if he didn't step up should the need arise. I've never known that to happen, and hopefully you will never need his services or be in real danger.
However, I would recommend having your vet check him out to make sure that he's totally healthy and strong, it's always better to be safe than sorry. Other than that, just make sure he's well fed, has enough exercise and gets lots of love and attention plus daily training (only positive, rewards-based methods though). A dog should feel like part of the family and know he is loved, he deserves that just for being there. But this will also help to ensure that he will do whatever it takes to protect the people he loves, and who love him, should the need arise.
Hi John, sorry to read that you only got a dog to guard your house. If you have a strong bond with your rottie he will guard you with his life. I have had 5 rotties and have NEVER taught them to be a guard dog, it comes naturally with them. my dogs sleep soundly during the night but if there is a noise that is unfamiliar my rottie will charge out of her dog door so fast barking it would be enough for scare anyone away.
Also rotties are what is considered "a stalker", they might let someone in but will never let them leave. There was an article in the paper over here a few years ago about an intruder who got into a house where a rottie had been left inside. When the owner arrived home hours later the intruder was sitting in a chair unable to move because the dog wouldn't let him, the instruder had wet himself he was so afraid.
Good luck with your dog and please form a bond with him.
As a TRUE rottie lover my heart aches reading your post!! Your only purpose for getting a rottie was to guard your home? At night ta boot!!Well I can tell you from experience that your dog will do his job when the need arises.My rottie NEVER got into a dog fight or showed any signs of aggression and yet when we got broken into the thief was only in my house for 32 seconds! Just long enough to be met by my loyal guard dog at the end of the hallway..(police told me this)..they got NOTHING but a nightmare for a few days I'm sure..PLEASE believe that he will be there for you when the time comes.Rotties will be even more loyal to their masters when smuthered with love..I am on my 3rd rottie who's only 5 months and already she is sooo loyal…however we treat her like our kid,NEVER lock her up and love her to death…She in return will guard us till her death…that's LOYALTY..I don't know where you live but if your that fearful to sleep at nightnight…get an alarm..between that and your rottie you be fine..Sorry to sound harsh but not everyone DESERVES to own a rottie..


text:
Dermanehzar (, also Romanized as Dermanehzār) is a village in Band-e Amir Rural District, Zarqan District, Shiraz County, Fars Province, Iran. At the 2006 census, its population was 156, in 41 families.

References

Populated places in Zarqan County

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text:
SAMPA CAUTIONS DOCILE GARBAGE COLLECTORS
Lusaka Mayor Miles Sampa has warned inactive garbage collecting companies of risking to lose their contracts in 2019 due to their tendency of not collecting waste.
Mr. Sampa said the Council would no longer tolerate companies that were not performing to their administrative expectations.
He said the council expected contracted companies to collect garbage in order to keep the environment clean as a way of preventing communicable diseases especially during the rainy season.
Mr Sampa was speaking to journalists at Kalingalinga Health Post yesterday when he urged communities to come up with Community-Based Enterprises which required them to present their profiles to the Council in order for them to be awarded contracts.
"Instead of having a centralized structure, we want now to have a community based initiative with the help of councilors and ward committees that will be collecting garbage paid for by the Council," he said.
Meanwhile, Kalingalinga ward 31 councilor Kasongo Chomba reiterated calls by the mayor for the communities to establish structures that would ensure smooth collection of garbage.
He illuminated that a tractor was procured through the ward development fund to enable the ward have a bargaining power in negotiating for the contract terms with the enterprises.
Mr. Kasongo said the tractor had eased the process of garbage collection as the community based enterprises were only required to pay for fuel and maintenance.
The Kalingalinga Ward 31 councilor said the tractor rotated throughout the community to collect the solid waste depending on the location of the aggregation points.
He further warned communities to pay for garbage collection as mandated by law and that failure to do so would result in punishments from public health authorities.
"We have seen that collecting companies are not able to do a good service because people are not paying for garbage collection so we introduced what we call Kalingalinga Sanitation Martials who go door to door to ensure that everyone has a receipt for garbage," he said.
Mr. Chomba added that drainages were also being constructed in the ward to ensure there was efficient management of liquid waste considering the swampy nature of Kalingalinga.
Calls for effective solid waste management comes after an outbreak of cholera that hit the country and recorded over 4000 cases, subsequently claiming close to 100 lives between October 2017 and March 2018.
Gabbage
Kalingalinga
Miles Sampa
Mainga Munsanje
Mainga Munsanje is a self-motivated writer, media practitioner and marketer, currently a fourth year student in Media and Communications Studies under the Department of Media and Communications at The University of Zambia. Also Chief Editor for Lusaka Star Entertainment News.
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text:
in Freedom Fighters in India
Yamuna Karjee (1898 – 1953)
by Chethan M 1.5k Views
Yamuna Karjee was a freedom fighter, an independence activist and a patriot in India, who enthusiastically participated in the Indian freedom movement.
Karjee was very inspired by the leadership of Swami Sahajanand Saraswati who initiated the peasant movement and the Kisan movement.
Yamuna Karjee had close relations with other revolutionaries and Indian leaders who participated vigorously in the peasant movement.
He was a devout patriot who participated in various Indian independence activities. He has been sentenced several times to prison for his active participation in the independence activities.
His main objective was to fight against British rule in India. He was also a popular Hindi journalist.
Yamuna Karjee Biography
Name – Yamuna Karjee
Date of Birth – 1898
Birth Place – Village of Deopar, Bihar
Date of Death – October 1953
Memorial – A University in Muzaffarpur
History of Yamuna Karjee
Karjee was born in 1898 in the small village of Deopar near Pusa, Bihar. His father, Anu Karjee, was a farmer who died when Yamuna Karjee was only 6 months old.
Karjee was attracted to the cause of the struggle for Indian freedom in his first days of school. He obtained his law degree from the Presidency of the University of Calcutta.
Karjee met several freedom fighters and the popular leader of the Indian National Congress in Calcutta as Shri. Krishna Sinha, Dr. Bidhan Chandra Roy, Rahul Sankrityan and others.
Career in Politics
After completing his studies, he received the offer of many jobs in the government. But Karjee rejected all the offers that came from the government. Yamuna Karjee has become a remarkable Hindi journalist.
He joined the editorial division of Bharat Mitra. Bharat Mitra was a Hindi weekly published in Calcutta. From 1920 to 1921, Karjee actively participated in the non-cooperation movement under the leadership of Mahatma Gandhi.
He has been an active participant in the civil disobedience movement and Salt Satyagraha. As a result of these two independence activities, Karjee was imprisoned for one year from 1929-1930.
Karjee participated as one of the contestants in the first election of Bihar and Orissa Assembly that took place in 1937.
He won the elections as one of the candidates of the Indian National Congress party. He left the Indian National Congress because of his busy commitment to various political activities.
Along with Rahul Sankatriyan and other notable political leaders, Karjee began publishing the weekly Hindi newspaper with the title Hunkar in the year 1940. Later Hunkar became the ambassador of the Kisan movement.
He was also president of Kisan Sabha and was soon elected president of the Association of Journalists.
Karjee's contribution to the abolition of the Zamindari System
Karjee was a disciple and collaborator of the abolition of the Zamindari system that was carried out under the direction of Swami Sahajanand Saraswati. Swami Sahajanand Saraswati was a great source of inspiration for all Indian citizens.
Karjee was very admired by the leadership of Sahajanand. Yamuna Karjee has done his service for the struggle for freedom, even after his departure from the Indian National Congress.
Death of Yamuna Karjee
Karjee was a great source of inspiration for all Indian citizens, even today. Karjee died in October 1953 due to cancer at the age of 55 years. After his death, the peasant movement lost control and became uncontrollable in Bihar.
A university in Muzaffarpur, Bihar was named Yamuna Karjee. His name also appears in the masterpiece of Bipan Chandra, India's struggle for freedom.
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William Hill, the UK's largest and most prolific sportsbook, entered the Australian sports betting industry in early 2015, purchasing three of the largest online sports bookmakers Down Under (Sportingbet, Tom Waterhouse and Centrebet), and rebranding/unifying them all under the one brand name – William Hill.
But the foray in to Australia was brief, selling out to CrownBet in 2018, who themselves subsequently rebranded to BetEasy.
William Hill remain a strong global brand in the online bookmaking business outside Australia, with a presence in the United Kingdom, Ireland, Canada and Nevada, United States.
Deposit local currency via one of several secure online banking methods.
Place your first wager, and it will be matched with a bonus bet, dollar for dollar, up to $700.
Use your bonus wager on a host of popular sport and racing markets.
Once a registered member, you'll be offered numerous other great bonuses and promotions on all your favourite sporting and racing events throughout the calendar year, each and every year.
The William Hill betting site interface is neatly designed, with a top menu bar expanding so punters can easily view all available sport and racing markets. Navigate to any of those sport/racing categories and you'll be able to access and choose from all the different leagues, competitions and events within that particular category.
On the left-hand side of the website, quick links are available, including direct access to special markets, quick multis, live betting events, future betting events and promotions. On the right-hand side of the website, you can quickly access your bet slip, any pending wagers you may have and the William Hill Australia blog which contains updated sport and racing tips and guides for a range of diverse competitions and events.
Navigate to any market, select your desired bet and it will automatically be added to your bet slip. Pick multiple different wagers and your selections will be displayed as single bets and also as multi-bets, with a box for the desired amount you wish to punt on each outcome, or a box to combine all your selections as a multi. Your total outlay and potential winnings are displayed, too.
On the homepage, William Hill displays featured events, next races to jump and next sporting events to take place.
William Hill's Best Tote Plus for horse racing ensures punters receive the best odds of the three Australian TABs (NSW TAB, S-TAB and TATTS) and the starting price on all win bets (and the best of the three totes on place wagers), plus an extra 5% on your winnings for win bets.
William Hill Australia has dedicated market apps for iOS, Windows and Android devices, along with a fully-fledged mobile browser website which is near identical to the applications.
Symbian OS mobile phones like Samsung, Motorola, Sony Ericsson and Nokia.
Other older phones which have access to the Internet.
Both the application and Web browser platforms are cleanly designed with quick access to banking (deposits and withdrawals), all sport and racing markets and your account settings. Download the correct app for you device by visiting the mobile website here.
William Hill accepts all major banking methods for deposits and withdrawals, including: all major credit and debit cards, PayPal, BPAY and direct wire transfers, Skrill (Moneybookers)and POLi. Aside from BPAY and direct bank transfers, all other methods are instant, with funds displayed immediately in your account after successfully completing the transaction. Withdrawals can take between 24-72 working hours. CupsBetting recommends using either a card, e-wallet account or POLi.
Before any withdrawals are requested and within 90 days of opening a William Hill Australia account, you'll need to verify you are the account holder by sending in some form of identification. The following ID documents are accepted: an active driver's licence showing current address, photo and date of birth, a copy of your passport, and a copy of a utility bill (telephone, electric, gas, sewer or water statement), received with the last three months.
PO Box 700, Parap, NT Australia, 0804.
William Hill Australia use Secure Socket Layer (SSL) digital encryption technology to protect all financial transactions and personal details. This is the same technology utilised by all major Australian banks and their online services. William Hill is licensed in the Northern Territory by the Northern Territory government and adheres to the strict online gambling laws of the country. Live betting is only available via telephone.
Featuring some of the most competitive markets of all major bookies in Australia, we recommend registering an account with William Hill Australia to make the most of your online sports betting experience.


Tried all others. Ping G400 is the best!
The PING G400 SFT fairway wood is designed to give you maximum distance and forgiveness while helping correct the miss right (for right-handed golfers) that we don't mention by name. Here's more about the club.
The PING G400 SFT fairway wood has the same tungsten back weight as other G400 fairway woods; the one that optimizes the CG location and increases MOI. The difference here is the position of the weight. The weight is shifted towards the heel, giving the club more draw-bias to help with that miss to the right (for right-handed golfers).
The face of the G400 SFT fairway wood is made from maraging steel, a thin material, that flexes twice as much for more ball speed and distance. The face also has a slight roughness that reduces the spin, adding to distance gains.
A thin, lightweight 17-4 stainless steel crown tops the G400 SFT fairway wood. The material allows weight to be moved around the perimeter of the club, increasing the MOI.
You'll also notice Turbulators on the crown. These raised ribs near the face improve aerodynamics throughout the swing as well as frame the ball at address.
The charts below show the stock loft and shaft options for the G400 SFT fairway woods.


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Home Tech & Tools The top 10 tools for journalism in 2018 (and 10 other things to bookmark)
The top 10 tools for journalism in 2018 (and 10 other things to bookmark)
We truly live in the future.
Want more digital tools for journalism delivered to your inbox every Tuesday morning? Sign up for our Try This! — Tools for Journalism newsletter.
There's no shortage of tech companies ready to promise the world to journalists in the form of the Next Great Tool.
Separating the spoons from the sporks could be someone's full-time job. And actually, it is — mine.
I've been Poynter's digital tools reporter for a year and a half, publishing news about journalism technology and training reporters from all around the world. Every Monday, I send a curated newsletter of seven or so tools, tips and tech trends directly to the email inboxes of nearly 10,000 people. In the five years before that, I built online training about tools for journalists and just about every other ink-stained topic you could imagine.
All this to say, I've sorted through plenty of purported industry saviors, time-savers and life-changers so you don't have to. Here are the tools that I liked the most this year.
Google Dataset Search / free / website
This beta from the search giant collates data sources all over the internet and makes them searchable in the ways that Google does best. For instance, you can search for data from a specific site by entering "site:" followed by a URL or hunt for exact terms by searching for them in quotation marks. Along with links to the results, the tool also shows the datasets' publication dates, who provides them, authors and descriptions. Plenty of other tools collect and share datasets, but Google is the only one that pulls them together so conveniently.
Headliner / free / website
Serial exploded the podcasting world four years ago. Since then, powerful audio storytelling from NPR, Gimlet, This American Life and other companies has propelled podcasts out of obscurity to the forefront of the media industry. So why is it the case that none of the major social networks natively support audio? Headliner solves that problem by making it exceedingly easy to turn any audio clip under 10 minutes into a video. Upload some audio and Headliner generates a waveform. Slap an image behind it and you have a social-ready audio clip in just a few minutes. The best part? They don't charge a dime for it.
Text Message Course
Prep for the Polls
This free 10-day course, from the MediaWise Voter Project, will teach you how to find reliable information on social media about the election and how to fact-check political info you see online.
Currents / free / website
Imagine being able to see what topics were performing best not only on all of your competitors' news sites, but every news site around the world (or at least every news site that uses one particular real-time analytics tool). Then imagine being able to tell the major traffic sources for those stories, what keywords people were using to find them and how that topic has historically performed. Then stop imagining, because that's what Parsely Currents does. And that's only a taste of the information it offers.
Account Analysis / free / website
It's reasonable to be skeptical of, and maybe even cynical about, your fellow Twitter users these days. When someone I don't know tweets at me, I find myself first wondering if it's a bot, or a foreign agent or some type of professional troll. If the answer appears to be yes, I turn to Account Analysis to check it out. The tool provides a series of analytics about a Twitter user, like when they most often tweet, who they tweet at and the top URLs they tend to share. If you're most often tweeting at 3 a.m. and sharing articles from notarussianspy.com, you're not getting an answer from me.
Toby / free / browser plugin
After years of flipping between Google Docs, Chrome bookmarks and just leaving every page open until my browser crashed, I found a bookmark manager that I somehow didn't realize I needed so badly. It's fair to say that Toby has vastly improved my average workday this year. A master of excess, I used to have dozens of browser tabs open across three browsers in multiple windows. Now I just drop all of my links into collections with two clicks and access them when I need them. The only issue with Toby is that it's only available for Chrome. Start.Me is a similar tool for other browsers.
Descript / various pricing plans / software
Descript, a tool that in the past I've called "magic" is a complete game-changer for editors of podcasts and short-form videos. Descript automatically transcribes uploaded audio and video files. When you edit the resulting text, the audio or video file is automatically edited to reflect that change. Descript points out bits of dead air, which users can then shorten or eliminate with a click or two. I can't imagine ever going back and editing podcasts the old way.
Privacy Badger / free / browser plugin
Privacy Badger is your force field against invisible threats on the internet. Install the plugin on your Chrome, Firefox or Opera browsers on your computer or Firefox on your Android device and Privacy Badger will block advertisers and third-party trackers from monitoring where you go and what you look at online. It's easier to set up than a typical ad blocker and, since it was created by the Electronic Frontier Foundation, it doesn't cost a cent.
Otter / free for up to 600 minutes / app and website
A little over a year ago, I rounded up the most popular automatic transcription tools, which turn recorded audio into text, and put them through the ringer. Then Otter came along and released a more accurate and less expensive tool a few months later. There's not much to dislike about it. Otter automatically transcribes audio in real time (and supplies an even more accurate transcript later). There's an app and a website version. Sharing audio and transcriptions is a breeze. Oh, and with 600 minutes free every month, you probably won't ever pay for it. We live in the future.
NewsU Online Seminar
Starting Poynt
This online seminar — led by Poynter leadership faculty Cheryl Carpenter — will give new and aspiring managers the support they need to build a sturdy foundation and grow as leaders.
Apply by: Feb. 23, 2021
Nudge / free / browser plugin
The internet is good — you're reading this! But the internet is bad — you probably have Facebook open somewhere and have already spent an hour on it today. Take control of those addictive sites with Nudge, a Chrome plugin that makes it harder to waste hours on "addictive content." The tool makes you work a little to open a website you deem addictive and will gently remind you with visual cues that you've spent too much time on it. It also blocks bits and pieces of sites, like YouTube's related videos, that are designed to suck you in. If you're so inclined, you can also delete your entire Facebook News Feed with one click. I'd call that a strong deterrent.
Lastpass / $2 per month / app, browser plugin and website
This wouldn't be a very good list without at least one information security tool. Lastpass (and other password management ilk like Dashlane and 1Password) is the most important and impactful online tool from the last few years. No more using the same password across every website. No more forgetting passwords and having to reset them. No more little pieces of paper pinned to the wall near your desk. Just sweet, sweet secure and automatically generated passwords that are available at the click of a button. When you can be safer online and lazier at the same time for the price of a cheap cup of coffee every month, there's no excuse not to do it.
Still looking for more? Here are 10 other things you should bookmark or share with a friend.
Digital Women Leaders — Are you a female-identifying person in journalism? Sign up for free mentoring from one of dozens of brilliant women in our industry.
Should I Do This Project? — It can be a struggle to decide whether to take on a new project, either personal or in the workplace. This flowchart takes seconds to complete but can save you days of time spent on a bad project.
Just Not Sorry — This Chrome extension highlights the self-defeating language — "I'm sorry," "I think," "I'm no expert" — that you might be using when you send a Gmail.
Hunter.io — The first place I go when I'm trying to find someone's email address. Type in the URL of the company of the person you're searching for and, if hunter.io doesn't already know it, it'll make a weirdly accurate guess based on the most common email patterns for that company.
I Feel Like Shit — Feeling bad? Not sure why? Here's another flowchart that will clear that problem up in a jiffy.
CityMapper — As far as I'm concerned, CityMapper is the only tool you need to find your way around a major city. Just bring an extra battery.
Email Debt Forgiveness Day — Every year, a couple dozen emails get perpetually clogged in my inbox. And then I feel bad every time I see the red circle on my email app. But, if they're still there on April 30, it's time to let them go.
Conscious Style Guide — Culture and language can change in an instant, but the books and references that guide our work take time to catch up. If you're looking for more inclusive and empowering terms than the ones you might find in your normal style guide, this one is your saving grace.
Skyscanner — Take a vacation. No, really. It's good for you and everyone around you. If you're on a journalist's budget, Skyscanner is your friend. Pick a travel location and Skyscanner will find the cheapest month and days to fly there.
Coffitivity — I'm always more productive at a coffee shop than at my desk. I always thought it was the fancy third-wave beans I buy, but this tool made me realize otherwise. It's just the gentle noises of the coffee shop itself.


[S] 2008 Hosta of the Year! Possibly the cutest little hosta out there and has set a new standard for small hostas! Almost perfectly round, blue-green leaves have great substance and form a tight mound 12"-18" across. Hosta 'Blue Mouse Ears' is thought to be a tetraploid version of 'Blue Cadet'. In summer, short scapes of lavender flowers form a neat bouquet above the plant. Very unique and sure to steal your heart!!
See Hosta 'Blue Mouse Ears' - 2008 Hosta of the Year for more information and pictures.


Tony Bennett will perform May 10 at the Rialto Square Theatre in Joliet.
The legendary Tony Bennett will perform with Antonia Bennett at 8 p.m. May 10 at the Rialto Square Theatre in Joliet.
Tickets are $54-$174. They can be purchased in person at the Rialto box office at 102 N. Chicago St. in Joliet, by phone at 800-745-3000, or online at www.rialtosquare.com.
92-year-old Tony Bennett's career has spanned more than 60 years. He has won 19 Grammy awards. In 2017, the Library of Congress presented Bennett with The Gershwin Prize. In recent years, Tony Bennett has recorded duets with an array of artists such as Lady Gaga.
Antonia Bennett is Tony Bennett's daughter and a graduate of the Berklee College of Music. She has been touring with her father and will open the performance with a collection of jazz/pop standards.
Antonia Bennett will perform with her father, Tony Bennett, at the Rialto Square Theatre in Joliet on May 10.


Newsletters & Notices: One initial notice will be distributed to properties in the vicinity of the project area. Updates will be posted on social media (Facebook, Twitter) as the project progresses.
Why is this project necessary? The station has exceeded its service life. Updated facilities are necessary to have a reliable sewer collection system.
How does this benefit the Village?
The benefits of the project will be an improved and more reliable sewer collection system. Specifically, the projects will provide an updated control system, new pumps, piping and an ability to perform maintenance while minimizing disruptions to traffic or sewer service.
Will this impact traffic and parking? There may be short-term lane reductions on Westmore/Meyers as work progress (service connection to the sanitary sewer system) and construction equipment and materials enter and leave the site. However, the primary impact will be limited to the lift station site and the west parkway (Arthur Avenue) across from the station.
Project: This is the second of two contracts associated with the full rehabilitation of the Cambria Lift Station. Phase 1 included manhole modifications, televising of the 12-inch diameter sanitary sewer line under Westmore/Meyers Road, and the installation of a permanent bypass line that will minimize traffic disruptions on Westmore/Meyers Road. The Phase 2 contract is for the replacement of pumps, piping, control panel, flow meter and air vacuum manholes, rehabilitation of the existing steel wet well, and site restoration.
4/17/19 - Thursday, 4/18, from 9 AM to 2 PM, traffic will be reduced to one lane on Westmore-Meyers Road between Wilson Avenue and Lombard Circle. A crane will require up to three traffic lanes to lift large construction materials over the power lines. Advance warning signs, arrow boards, cones, and flaggers will manage traffic.


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PUFA and CVD
By CPR St. Louis at June 26, 2011 | 4:16 pm | Print
Do Polyunsaturated Fatty Acids Protect Against Heart Disease?
There are many known risk factors associated with cardiovascular disease (CVD); such as, diet, high blood pressure, sedentary lifestyle, obesity and heredity. Two of the main biochemical risks are high plasma low-density lipoprotein cholesterol (LDL) and low plasma high-density lipoprotein cholesterol (HDL). LDLs are the major transporter for cholesterol to tissues. Circulating LDLs bind to specific LDL receptors on cell surfaces, and the LDL is internalized and degraded by the cell. Both the protein and lipid components, however, of LDLs are especially susceptible to oxidative damage by free radicals; damage to the lipid component may also ultimately result in damage to the protein component. LDLs with damaged protein coats will not be recognized by the LDL receptors of cells. These damaged LDLs, however, are phagocytized by macrophages through a different receptor, scavenger receptor A (Steinberg, 1997). The increased uptake of damaged LDLs results in vast quantities of cholesterol esters within the macrophage. At this point the macrophage is known as a foam cell. The strange accumulation of LDLs and foam cells within the walls of coronary arteries is the hallmark of cardiovascular disease. The basic pathogenesis of atherosclerosis has been reviewed (Ross, 1993).
The study of cardiovascular disease (CVD) can be separated into: 1) mechanisms governing atherosclerosis and 2) mechanisms governing blood clotting. Atherosclerosis is a degenerative disease of vascular endothelium. The initiation of the atherogenic process may begin in response to injury of endothelial cells lining vessel walls. This damage may result from a mechanical stress such as high blood pressure or from high levels of oxidized LDLs, which are known to be toxic to endothelial cells. Monocytes, which differentiate into macrophages, and T-lymphocytes adhere to the damaged area as LDLs concurrently penetrate the endothelium and accumulate within the wall of the vessel. Uptake of oxidized LDLs by macrophages is much greater than native LDLs (Steinberg, 1997). Oxidized LDLs also trigger the release of cytokines by macrophages. Cytokines play a pivotal role in mediating the atherogenic process through their chemotactic attraction of other white blood cells to the area. As more LDLs enter the area they will become oxidized by HOOH and hypochlorite produced by the additional phagocytes. A vicious cycle follows: as more oxidative damage occurs to LDLs, more phagocytes are attracted to the area, and yet more damage occurs to LDLs. The damaged LDLs are continually engulfed by macrophages via their scavenger receptor A. The accumulation of foam cells and LDLs deep to the epithelium leads to occluded vessels and impeded blood flow. The decreased speed of blood flow leads to pathological blood clotting which will cut off the blood supply to the tissue. This commonly occurs in coronary vessels. As the coronary vessels become blocked, the heart muscle becomes ischemic and death to the myocardium occurs; heart attack.
Studies on CVD cover a multitude of areas. These areas include looking at platelet function and clotting, chemicals that affect vessel diameter and blood pressure, and plasma lipids and lipoproteins. Numerous clinical and epidemiological studies have shown that these areas all appear to be influenced by dietary fatty acids (Krauss et al., 2000 and Simopoulos, 1999). In general, it has been established that a diet high in saturated fatty acids (SFAs) shows a positive correlation with the risk of CVD because of hypercholesterolemic effects or unfavorable shifts in LDL:HDL ratios. It has also been shown that the consumption of polyunsaturated fatty acids (PUFAs), provided that adjustments have been made for coconsumed SFAs, shows a negative correlation with CVD because of a hypocholesterolemic effect or favorable LDL:HDL shifts. Eicosapentaenoic acid [EPA, 20:5(n-3)] and docosahexaenoic acid [DHA, 22:6(n-3)] are PUFAs found in fish oils which have protective effects against CVD (Mori et al., 1997). 3-series prostaglandins, which are antiaggregators and vasodilators, are derived from EPA and DHA. It is possible, but unclear, that diets rich in these PUFAs exert their protective effects by lower blood pressure and decreasing platelet aggregation. Another PUFA that decreases blood pressure is -linolenic acid [GLA, 18:3(n-6)]. GLA is quickly converted into a precursor for antiaggregator and vasodilator prostaglandins. As previously mentioned, oxidized LDLs are strongly correlated with CVD. Highly unsaturated fatty acids such as EPA, DHA, and GLA are highly susceptible to lipid peroxidation. Circulating LDLs are protected from lipid peroxidation by antioxidants. One of the major antioxidants is vitamin E, mainly -tocopherol. It seems likely that dietary flavonoids (quercetin and catechin) contribute to the antioxidant defense and reduce the consumption of -tocopherol in both lipoproteins and membranes (Fremont et al., 1998). Because PUFAs are so susceptible to lipid peroxidation it could potentially increase the amount of damaged LDLs, thereby counteracting the beneficial effects of these PUFAs. In 2001, Frenoux et al. published a study in the Journal of Nutrition that looked at whether a diet high in EPA, DHA, and GLA, known to exert antihypertensive effects, is associated with impaired antioxidant status.
Frenoux et al. used 20 spontaneously hypertensive rats (SHR) and 20 normotensive Westar Kyoto rats (WKY) as subjects. The subjects were divided into four groups of 10, and were to be fed one of two diets, EPAX or Isio, for a 10 week period. Both diets consisted of 5g/100g lipid content, however only one of the diets, the EPAX diet, contained significant amounts of GLA, EPA, and DHA. Systolic blood pressures were established and blood samples were obtained.
Platelet aggregation values were determined. Both the percentage of maximum platelet aggregation and the time to reach the value were obtained. Plasma levels of -tocopherol, triacylglycerol, and total and free cholesterol concentration were determined. Total antioxidant status was determined by RBCs ability to withstand free radical-induced hemolysis. Impaired antioxidant status decreases the time 50% hemolysis (T50% hemolysis) is reached. The fatty acid composition of platelets and RBC membranes were also established. Lastly, lipid peroxidation would be determined from the VLDL-LDL fraction.
At the end of the ten weeks, the SHR rats developed hypertension. The SHR rats also had higher blood pressures than the WKY rats. The SHR rats, however, fed the EPAX diet had significantly lower blood pressures than the SHR rats fed the Isio diet. Because of previous findings by Engler et al and Narce et al, it was assumed that GLA, and not EPA and DHA, was primarily responsible for the decrease in blood pressure. GLA is known to modify the renin-angiotensin-aldosterone mechanism and increase the production of a vasodilator prostaglandin.
Total antioxidant status was improved in SHR rats fed the EPAX diet and not the Isio diet. This appears to be a direct contradiction with the fact that highly unsaturated fatty acids are susceptible to oxidation. It was hypothesized by the experimenters, based on a study by Van Den Berg et al., that perhaps increased amounts of membrane PUFAs were oxidized, but that other membrane fatty acid oxidation was subsequently decreased. The increased PUFAs in the membranes perhaps create an "oxidizable buffer". This would then explain the overall higher total antioxidant status associated with the EPAX diet. This is just one possible explanation for the increased antioxidant status. It was also determined that both the SHR and WKY rats fed the EPAX diet had lower plasma total cholesterol levels. Lower total cholesterol levels have been shown to decrease free radical production, and this could also explain the oxidative resistance in the EPAX fed SHR rats (Ohara et al.).
Overall, it appears a diet rich in GLA, EPA, and DHA produce antihypertensive effects, increase resistance to lipid peroxidation, and lower plasma lipid concentrations. These results do support that increasing these PUFAs in your diet may be a protective measure against developing CVD.
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