NTN holds the most important share of the global marketplace for axle bearings, important automotive components that allow wheel axles to show. We're number two in global market share of constant-velocity joints (CVJs), a key part for car drivetrains.
FASTIDIOUSLY clear a brand new tissue blade with alcohol. To do this, stack two sheets of paper toweling and fold them repeatedly to create a thick pad. Dampen the floor of the pad with isopropyl alcohol or rubbing alcohol (from the pharmacy), then slowly and very fastidiously wipe the entire blade with the alcohol-dampened paper towel pad to clean it.
Make new RV curtains. These carpet-like fuzzy blue curtains are just dowdy and provides my emotional age a 3 decade increase – in the wrong direction. It is a great spot to herald some fashion and character. I want one thing long-wearing stylistically as a result of with eight window treatments & a divider curtain, this might be numerous work and a major expense. Select properly!
With a flexible work life and a pre-schooler, we purchased the van to renovate as a way to travel the nation & visit household who winter in Florida. We started working on it immediately, spending the summer season finishing our interior improve with a brand new skilled couch, roof and door liner upholstery, new lined curtains that block the light and inside paint job that offers the car a spa-like modern really feel.
I'll admit I used to be nervous about taking the plunge to color the RV inside. Vinyl surfaces and paint are usually not traditionally good pairings. The parents on the paint retailer told me there are various new paint merchandise designed especially for plastics, vinyl and cupboards. Key to success; clear surfaces, good prep and drying time…. patience in different words. Not my strong suit.


text:
The New West: How well do we know our neighbor?
CREDIT: David J Swift
By Todd Wilkinson EBS Environmental Columnist
Friends spar and become verbal foes over the fate of a couple of hundred acres of land set at the foot of a major destination ski resort that everyone loves to use, and, in the next breath, complain about its expanding size.
Bon amies, who have known each other for years dating back to the days when they first arrived in the valley as young swashbuckling outdoor athletes, barely talk to one another anymore because they disagree over the route of a proposed bike path through a national park, or guided skiing in the Forest Service backcountry.
Hunting buddies, who vote for candidates in different parties and who never bring up politics in camp, start bickering over the virtue of a local representative on the Game and Fish Commission who disses one prominent popular sportsman's organization for another with the free licenses he doles out as a perk of the job.
Do these scenarios hit close to home? They should.
It's uncanny, isn't it? Place two people side by side and ask them each to interpret a scene in front of them. Their subjective responses, which is to say, their internal biases and social values, prevent them from reaching the same conclusion.
The test applies to everything we do but when it involves the environment, the outcomes tend to produce polemical conclusions that quickly turn into political litmus tests.
For example, a pastoral meadow with cattle grazing behind barbed wire affects people different ways. Or, a grizzly bear feeding on an elk carcass. Each is beautiful in the eye of a sympathetic beholder.
But just for a moment, turn these hypotheticals on their heads: Instead of cattle in the meadow, replace them with bison and remove the fences. Rather than having the bear feasting on wapiti, swap in a Hereford or domestic sheep.
Suddenly, the crowd of onlookers parts into Republicans and Democrats, hunters and non hunters, nature lovers who, for aesthetic, business and other reasons shaped by personal conviction, will worship native species versus another kind of nature lover who regards uncontrolled, unmanaged landscapes as a threat to their economic livelihoods and by extension their personal identities.
If you think the above sounds like terrain ripe for a melee, what happens when the beloved rancher, who has said repeatedly his sole purpose in life is to raise steaks on the hoof, instead turns his spread into a golf course after the public has paid him subsidies as an unspoken tradeoff for open space.
Or imagine the kind of whispered breakfast discussions at Nora's Fish Creek Inn in Wilson, Wyoming, when an outspoken environmentalist builds a trophy home in the flood plain and wildlife winter range. The real fear ends up being the one that encourages silence in public discussions because no one wants to be labeled a hypocrite.
How many different ways can a community become fractured?
While it's easy to make sweeping judgments such as pro-environment vs. anti environment, pro business versus anti-business, pro rancher versus anti-rancher, pro Bush or Obama or Trump versus anti-Bush, anti-Obama or anti-Trump, pro military versus anti-troops, is any conclusion we draw really that simple?
We share the same communities and we allow the issues to push us toward different sides of a room as we politely exchange nods because it's the mythical western way. But behind their backs we label and extrapolate without knowing or attempting to understand the huge gray areas that color their thinking.
Only when the other person's obituary—yes, that of our perceived antagonist—appears in the local newspaper, revealing a bit of surprising biography about their life that we hadn't recognized or made an attempt to learn, that in the loss of our neighbor do we see a missed opportunity to have better connected.
I thought about this recently when I wrote an obituary. For some, this will seem like a column written in code. Only the guilty will know if it is or isn't.
Todd Wilkinson, founder of Mountain Journal (mountainjournal.org), is author of "Grizzlies of Pilgrim Creek" about famous Greater Yellowstone grizzly bear 399 featuring 150 photographs by Tom Mangelsen, available only at mangelsen.com/grizzly. His feature on the delisting of Greater Yellowstone grizzlies appears in the winter 2018 issue of Mountain Outlaw and is now on newsstands.
Related Topics:bisongrizzly bearshuntingpoliticspublic landTodd Wilkinson
Fundraising hike benefits avalanche education programs
The New West: Ryan Busse's 'Gunfight' among most important books written about guns

redpajama_set_name: RedPajamaCommonCrawl


In the summer there is even less time to fuss & fiddle with decor.
But everyone can find 10 minutes to transform a space.
This is why I look forward to our monthly series ~ Ten Minute Decorating.
That is really all the time you need when you follow our tips.
Plants & flowers are our focus this month.
I used two $5.99 plants from the grocery store to liven up a space in my kitchen.
We are just getting to the point in the restoration of our home to start decorating & styling. Yippee!!!
I chose to begin with an out of the way nook in the kitchen.
This space was to have custom cabinets like the rest of the kitchen, but then I realized this thrift store hutch would fit perfectly. ~ and what a savings too!
I think the hutch adds just the right "un-kitcheny" feel to the space and breaks up all the white, while tying in the distressed pine of the dining room table. Can you tell I am itching to decorate? Let's get back to the plants!
I played around with lots of my ironstone and gold & white pieces until I found an arrangement I loved.
The green of the moss gave me the idea that plants would do the trick.
I found an ivy & a fern in the grocery store.
Indeed I think the plants do the trick. They add a pop, carry the green of the moss balls thru the space and soften the whole look.
Repot into a larger container adding some potting soil.
If using a pot without drainage holes place small pebbles in the bottom for drainage.
Use my three secrets when re-potting or general liquid indoor plant food 1x a month.
If pot is on a wooden surface elevate it a bit with a saucer or at least folded paper towels to avoid leaving moisture rings.
Check soil a few times a week for dryness – if the top portion of your index finger comes out feeling dry water.
Move pot to the sink to water deeply & spritz lightly with the sprayer or a spray bottle to mist the leaves.
Leave pot in the sink for a while to drain before putting back on your nice furniture.
If you are vacationing for a week or two you can place pots with drainage holes in the sink filled with a few inches of water. Water well before you leave & let the plants sit in the few inches of water. The roots will absorb the water as needed while you are gone.
Next time you are at the market pick up an inexpensive plant and add a pop to your decor in 10 minutes ~ or less!
Your hutch looks beautiful. I love the white ironstone mixed with greenery. I looks perfect. You did a great job, and thanks for the tips on maintaining house plants. I think I might have to purchase a couple the next time I'm at my local grocery store. I hope that you have a wonderful day!!!
I love your hutch, it's so pretty! It's fits there perfectly!!
I am smitten with that hutch! And that it fits so perfectly into your little niche means it was meant to be. Always adore the pops of white and green.
Looks great…I think plants add so much to a room…great color, texture and not to mention healthy too!…I love your hutch…how perfectly it fits in that space!
Thanks for sharing these tips! I love the ones about replanting. My plants often die when I try to move them. And these pictures are lovely!
That hutch is perfect! You've done a great job styling with your wonderful pieces. I love the green pop.
These plants are the perfect touch to your hutch! L-O-V-E!! So quick and easy too to buy them at the grocery store! Pinned!
That hutch couldn't be styled more perfectly! And thanks so much for sharing those great tips!
The Hutch is beautiful and your tips on caring for the houseplants are very appreciated! I will be on the lookout for some greenery the next time I am grocery shopping.
Thank you Colleen! Glad to have you visit My Soulful Home. Definitely green it up!
Hi Kelly, thanks for sharing tips on caring for your household plants. I am into planting succulents right now and trying to find some help on caring for them but I will work on some of your suggestions. By the way, the hutch looks perfect the way it is….great pictures!
Hi Kelly, first I just love the podcast and your hutch is beautiful! I didn't however see in the show notes about the faux olive trees. I have actually been looking for branches, but the ones I have found have olives on them and I'm not so sure I would like that. I think I might be interested in one of the trees that you mentioned. Thanks for sharing!!
Hi Samantha, Thank you so much for listening to the Podcast! And thank you regarding my thrifted hutch. I checked with Anita & her olive trees are from Wayfair. When she check the item was out of stock so she didn't add it to the show notes. However, they will notify you of when it comes it. Here is the link: https://www.wayfair.com/Napa-Home-and-Garden–Napa-Home-and-Garden-Olive-Tree-in-Pot-DI2019-DI2019-L3717-K~NHG4342.html?csnpt=SS49-NHG4342&SSAID=687298&refid=SS687298&df=1 I will email you with it too!


Malicious actors can present themselves in a variety of ways on the internet. Automated tools can scrape information from your website, bots can probe your application for vulnerabilities, and hackers can exploit them. Using access control lists (ACLs) at the edge can help prevent the offending IP addresses they use from ever accessing your information resources.
ACLs have two parts: an ACL container and the ACL entries within it. In combination, containers and entries allow you to store a list of permissions that Varnish will use to grant or restrict access to URLs within your services.
Once you attach an ACL container to a version of your service and that service is activated, the data in the container (the ACL entries) becomes "versionless." This means that once your service is activated, any further changes to the data within, such as the addition of ACL entries, will become effective immediately.
Via Fastly's web interface or API: You can create your ACLs at the edge via the Fastly web interface or via the Fastly API. We recommend these options for most configurations that integrate websites or applications with an ACL at the edge.
Using custom VCL: You can manually create an ACL using VCL. We recommend this option only if you have simple access control requirements and can hardcode a few IP addresses in your VCL. Manually created ACLs are versioned with your services and any changes to the ACL will require changes to your VCL.
# using the Fastly API. In this example, the ACL name is office_ip_ranges.
With this VCL, access to /admin is denied for everyone by default, but the IP addresses listed in the ACL are allowed to access /admin without restriction.
TIP: Because ACL entries have a boolean option for negation, you can specify whether or not an IP address is allowed (false or 0) or blocked (true or 1).
ACL entry changes via the API don't appear in the event logs. If you use the API to add, update, or remove an ACL entry, there will be no record of it in the event logs. The only record of a change will exist when you compare service versions and view the exact point at which the ACL was associated with the service version in the first place.
ACL entry deletions are permanent. ACL entries are versionless. This means that if you delete an entry within an ACL container, that entry is permanently removed from all service versions and cannot be recovered.
ACL containers are limited to 1000 ACL entries. If you find your containers approaching this entry limit, contact us. We may be able to help you figure out an even more efficient way to do things with your ACLs at the edge.
Deleted ACL containers are only removed from the service version you're editing. ACL containers are tied to versions of services, which can be cloned and reverted. When you delete an ACL container, only the configuration of the service version you're editing will be affected. We remove the ACL entries inside a container but only for the specific service version you're editing. The ACL entries themselves are not deleted from the ACL in earlier versions of your service's configuration. This allows you to revert your configuration to a previous version in as few steps as possible.
ACLs created with custom VCL are always versioned. ACLs created with custom VCL are always tied to a service and require a new service version each time they are updated in any way. This is true for both the ACLs created using custom VCL and for any logic created to interact with those ACLs.
ACLs created with custom VCL cannot be manipulated using the API. If you create an ACL using custom VCL, that ACL must always be manipulated via custom VCL and can never be manipulated using the Fastly API. ACLs created using the API, however, can be manipulated both using the API and custom VCL.


text:
Пра́пор Терно́пільської о́бласті є офіційним символом Тернопільської області, який наслідує історичну традицію використання регіональної символіки, атрибутом місцевих органів самоврядування та виконавчої влади.

Відомості
Спершу у грудні 2001 року затверджено прямокутне полотнище з співвідношенням сторін 2:3, яке складалося з двох горизонтальних смуг — жовтої і синьої у співвідношенні 1:2. На жовтій смузі — три білих вежі з відчиненими воротами. На синій — посередині жовтий вертикальний меч руків'ям догори і горизонтальний жовтий ключ борідкою донизу і направо.

Однак після зауважень Українського геральдичного товариства щодо герба новим рішенням від 18 листопада 2003 року змінено й прапор області.

Прапор: прямокутне синє полотнище зі співвідношенням сторін 2:3, посередині покладені навхрест жовті меч і ключ, над ними — три білі вежі.

Див. також
 Герб Тернопільської області
 Тернопільська область

Джерела
  :s:Рішення Тернопільської обласної ради від 21 грудня 2001 року №317 Про герб і прапор Тернопільської області
  :s:Рішення Тернопільської обласної ради від 18 листопада 2003 року №203 Про внесення часткових змін до герба і прапора Тернопільської області
  :s:Рішення Тернопільської обласної ради від 15 березня 2014 року №1595 Про затвердження Положення і національно-визвольної символіки

Література
 Гречило, А. Сучасні символи областей України. — Київ, Львів, 2008. — С. 36-37.

Посилання
 Українське геральдичне товариство
 Українська геральдика

Символи Тернопільської області
Т

redpajama_set_name: RedPajamaWikipedia


text:
\section{Introduction}
Glioblastoma Multiforme (GBM), as the lethal primary brain tumor, has one of the worst survival rates out of all the human cancers. There are 26.5\% of patients diagnosed with GBM with an average survival rate of 2-year and median survival of 14-16 months with radiotherapy and temozolomide \cite{stupp2005radiotherapy, bleeker2012recent}. In current clinical practise, clinicians tend to estimate the survival, with their experience, by analysing the images and considering other clinical factors such as age, gender. However, researches show that these decisions and estimations are too much biased and optimistic \cite{moghtadaei2014predicting}. Therefore, early diagnosis and accurate prognostication of GBM progression demand in-depth research utilizing modern data driven technologies.

In conventional research procedures, radiological data such as magnetic resonance (MR) images are used for delineating tumor regions and extracting quantitative imaging features to estimate the survival rate, known as radiomics \cite{islam2018glioma,lao2017deep,prasanna2017radiomic}. However, recent studies \cite{gonzalez2013personalized, weller2010mgmt} find associations of gene biomarkers with the prognosis of glioma and treatment planning, which comes under genomics. Thus, current studies focus on the associations between radiomics and underlying genomics, which is known as radiogenomics. The correlations between gene biomarkers such as MGMT methylation, IDH mutations, gene microarray data, and radiomics are analyzed under radiogenomics \cite{lee2015associating,xi2018radiomics,korfiatis2016mri,incoronato2017radiogenomic,wijethilake2020radiogenomics,wijethilake2020mbec}. Nevertheless, the MGMT methylation status of GBM patients is incorporated with radiomics to analyze glioma patients' survival \cite{tixier2019preoperative}. Further, Chaddad et al. \cite{chaddad2018novel} incorporate the expression of gene markers with radiomic for survival prediction. Correspondingly, in this work, we incorporate gene expression profiles with radiomic features to produce a more predictive outcome for survival estimation.

Magnetic resonance imaging (MRI) is frequently utilized to follow-up brain tumors such as glioma, glioblastoma as a powerful noninvasive diagnostic tool. Based on different contrasts and functional features, there are several MRI modalities such as T1 or T1-contrast (spin-lattice relaxation), T2 (spin-spin relaxation), and Flair (T2 fluid-attenuated inversion recovery) \cite{tseng2017joint}. Different tissue structures and underlying anatomy are visible in different modalities. For an example, grey and white matter are visible in T1-weighted brain scan, T2 weighted brain scan locates the lesion by imaging fluids in the cortical tissue while Flair images locate the lesions through water suppression \cite{ma2018tailored}. However, the full spectrum of multi-modality MRI scans is unavailable in most cases due to the scanning cost, time, scanner availability, and patient comfort. A subset may be corruptive by excessive noise or artifacts.
Moreover, remarkable longitudinal studies like ADNI \cite{mueller2005alzheimer} requires scanning changes over time, which may exist in different acquisition protocol. In this case, remarkable modalities could be missing or corrupted in the large cohort study due to the improvement of MRI scanners or changing the observation from one tissue to another. This information missing can cause restraints in MRI analysis, diagnosis, and research studies. Thus, synthesizing the missing or corrupted modalities is essential in image related diagnosis, therapeutic planning, and research tasks such as segmentation, detection, multi-modal registration, and classification.

In this study, we use TCGA\footnote[1]{https://www.cancer.gov/tcga} (the genomic cancer atlas) dataset and design a radiogenomic model to
estimate survival duration. In the cohort, there are very few cases with all four modalities. We synthesize the missing modalities, segment the tumor regions using existing and synthesized modalities, and estimate the overall survival (OS) using the radiogenomic model. We exploit BraTS-2017 \cite{menze2015multimodal, bakas2017advancing, bakas2017segmentation} dataset to train and validate the synthesize and segmentation model.

\subsection{Related Works}
Recently, a deep convolutional neural network (DCNN) shows state-of-the-art (SOTA) performance in the biomedical applications of detection, classification, segmentation, and synthesizing. In particular, the generative adversarial network (GAN) performs outstanding performance in the task of an image to image translation.
\subsubsection{Image Synthesis}
There are several studies on designing an image synthesize model using GAN from computer vision to medical imaging applications. CVAE-GAN \cite{bao2017cvae}, a conditional variational autoencoder GAN  combines a variational auto-encoder with a GAN for synthesizing images. BicycleGAN \cite{zhu2017toward} adopts CVAE-GAN \cite{bao2017cvae} and conditional latent regressor GAN (CLR-GAN) \cite{donahue2016adversarial, dumoulin2016adversarially} for multi-modal images to single image mapping. Conditional adversarial network (cGAN) \cite{isola2017image} learns the nonlinear mapping of the input to output image and trains a loss function to apply the same generic approach to different problems synthesizing, reconstructing, and colorizing images. Dar et al \cite{dar2018image} adopt cGAN \cite{isola2017image} to translate MRI T2 weighted from source T1-weighted images. However, these studies exploit a simple, fully convolutional network (FCN) or vanilla UNet \cite{ronneberger2015u} as a generator. Moreover, most of these works exploit shallow encoder type architecture or patch discriminator instead of full encoder-decoder.

\subsubsection{Tumor Segmentation and Prognosis}
In the past years, brain tumor segmentation and overall survival prediction models have been associative remarkably. An ensemble 3D UNet and linear regression models can segment and predict survival \cite{feng2018brain}. Similarly, tumor progression is estimated using a VGG-16 based FCN and random forest models \cite{puybareau2018segmentation} and multi-modal
PixelNet and artificial neural network (ANN) \cite{islam2018glioma}. However, the prediction performance of these models is not satisfactory, as only radiomic features are used to train the model. Apart from using radiomics, survival prediction of GBM patients by using multidimensional genomic data is used \cite{kim2013identification}. Recently, the use of radiogenomics, integrating imaging with gene expression profiles proliferated. A radiogenomic model design to predict risk estimation using logistic regression model \cite{qian2018radiogenomics}. However, the study was not estimating the exact survival days.

\begin{figure}
\centering
\includegraphics[width=0.5\textwidth]{model_v4.pdf}
\caption{The workflow of synthesizing, segmentation and radiogenomic analysis in this study.}
\label{fig:workflow}
\end{figure}
\subsubsection{FCN Model}
Fully convolutional networks (FCNs) show the state-of-the-art performance of visual tasks by capturing the channel's hierarchical features and spatial information with receptive fields. "Squeeze-and-Excitation" (SE) network \cite{hu2018squeeze} performs channel-wise feature recalibration to emphasize informative features and suppress weak ones. Competitive SE architecture \cite{hu2018competitive} re-weights the feature maps with channel-wise attention for residual connection. Spatial and channel squeeze \& excitation (scSE) \cite{roy2018concurrent} in FCN applies excitation in spatial and channel-wise jointly. However, we hypothesize that squeezing weak features towards zero may increase the sparsity in deeper layers prone to poor learning \cite{uhrig2017sparsity}. Most recently, Octave convolution (OctConv) \cite{chen2019drop} introduces a separate convolution of high and low-frequency features encoded into an image that reduces the channel-wise redundancy. Low-frequency feature maps are upsampled to high-frequency maps and concatenated in the last layer to predict the output.

\begin{figure*}
\centering
\includegraphics[width=1\textwidth
]{Architecture_v9.pdf}
\caption{Proposed modules and model architecture.{(A)} Skip connection with scSE (skip-scSE) module. {(B)} Proposed decoder block. Skip-scSE module after a convolution followed by inplace ABN and a deconvolution layer at the end of each block. {(C)} Our FCN architecture with encode-decoder block of OctConv.}
\label{fig:archi_octave}
\end{figure*}

\subsection{Contributions}
In this study, we propose an FCN by leveraging OctConv and a novel decoder with a skip SE unit of spatial and channel-wise recalibration. Proposed FCN is utilized to generate missing MRI modalities and segment the tumor for TCGA using BraTS-2017 \cite{menze2015multimodal, bakas2017advancing, bakas2017segmentation} dataset. The workflow of the study is illustrated in Figure \ref{fig:workflow}. Our contributions are summarized as follows:
\begin{itemize}
\item[--] Propose an FCN architecture using octave partial convolution and skip connection with scSE (skip-scSE).
\item[--] Design a novel decoder efficiently using a skip-scSE module.
\item[--] Design a multi-modal MRI to single MRI modality synthesizing model using proposed F
CN as a generator and encoder-decoder discriminator using the cGAN learning approach.
\item[--] Generate missing modalities for the TCGA dataset using the available modality (or modalities) and predict segmentation using both available and synthesized modalities.
\item[--] Design a Radiogenomic model by combining radiomic features such as intensity, shape, and geometry with gene expression profiling data.
\end{itemize}
\section{Methods}
We propose a fully convolutional network (FCN) model with Octave convolution \cite{chen2019drop} and a novel decoder based on channel and spatial excitation. The model applies for both synthesizing MRI missing modality and tumor segmentation tasks, as shown in Figure \ref{fig:syn_seg}. The radiomic features are extracted from the segmented tumor and combined with genomic data to predict survival rate using an artificial neural network (ANN) and support vector machine (SVM) model.

\subsection{Proposed FCN}
\begin{figure}
\centering
\includegraphics[width=0.5\textwidth]{skip_scSE_v2.pdf}
\caption{Similar feature maps with original (A) scSE and the proposed (B) skip scSE. Skip scSE excite the feature maps higher range than original scSE. The feature maps are visualized from the trained models of scSE and skip-scSE.}
\label{fig:skip_scse}
\end{figure}

\subsubsection{Skip-scSE}
Spatial and channel "Squeeze \& Excitation" (scSE) \cite{roy2018concurrent, hu2018squeeze} recalibrates the feature maps to suppress the weak features and signify the essential features. Competitive inner-imaging SE \cite{hu2018competitive} enhances the learning and re-weight the channel of residual mapping to alleviate the redundancy in feature maps. Selective kernel convolution \cite{DBLP:journals/corr/abs-1903-06586} uses convolutions of different kernel sizes to enable neurons to adjust their receptive fields (RF) sizes adaptively. However, recalibrating non-significant features towards zero with scSE may increase the sparsity in deeper layers as well as reduce the parameter learning \cite{uhrig2017sparsity}. We observe that
adding skip connection with scSE retains weak features and boosts the significant features' excitation. We design the skip-scSE module by adding a skip connection and inner-imaging layers with scSE, as in Figure \ref{fig:archi_octave} (A). Inner-imaging improves the feature representation before applying channel excitation. Figure \ref{fig:skip_scse} shows that our skip-scSE boosts the excitation of the feature maps to [-0.1 to 0.4] where the range of feature with original scSE is [-0.05 to 0.20].

\subsubsection{Network Architecture}
Our FCN model consists of encoder blocks similar to UNet \cite{ronneberger2015u} with octave convolution and a novel decoder, including the skip-scSE module. Each encoder block consists of two 3x3  OctConv, followed by ReLU and max-pooling of stride 2. OctConv capable of processing higher and lower frequency information separately. For example, the high-frequency contextual features like fine details and low-frequency features like global structure, are encoded into an image. It applies group convolution to reduce channel-wise redundancy.

Skip-scSE decoder concatenates the encoder output and skip connection followed by sequential layers of 3x3 convolution, inplace activated batch-norm (ABN) \cite{rota2018place}, skip-scSE module and 4x4 deconvolution. Figure \ref{fig:archi_octave} (B) and \ref{fig:archi_octave} (C) represent the skip-scSE decoder and proposed FCN model.

\subsection{MRI-MRI translation}
\label{sec:mri_to_mri}
\begin{figure*}
\centering
\includegraphics[width=1.0\textwidth]{Archi_syn_seg_v4.pdf}
\caption{Our approaches for MRI translation and segmentation. (A) Proposed FCN uses as the generator network in cGAN; (B) Our model to segment the tumor from multi-modal MRI. Red, yellow and green color in the predictio
n represent the tumor regions of necrotic, enhancing, and edema.}
\label{fig:syn_seg}
\end{figure*}

Generative Adversarial Network (GAN) contains a Generator ($G$) and a Discriminator ($D$), two sub-network where a generator network learns to map the provided input sequence and a discriminator network distinguishes between feature maps between a target distribution and the output of generator network. We adopt proposed FCN as $G$ network in conditional GAN \cite{isola2017image} and encoder-decoder light-weight design of $D$ architecture as \cite{islam2019real} (see Figure \ref{fig:syn_seg}A).

In the training time, latent variable (random noise) $Z$ with input modalities such as $T1ce$ and $Flair$ are fed to the $G$ network which aims to translate identical modality of $\hat{T2}$ from the real contrast of $T2$. $D$ network evolves to distinguish between generated modality and real modality images. The prediction of $D$ is 1 or 0 where 1 denotes real modality and 0 denotes translated modality. Both networks $G$ and $D$ learn simultaneously in training phase by minimizing and maximizing adversarial loss respectively. We follow same objective function as pix2pix \cite{isola2017image} to optimize our model. The objective function can be formulated as-
\begin{eqnarray}\label{eq:overall_obj}
G_{obj} =\text{arg} \underset{G}{ \text{ min}} \underset{D}{ \text{ max }} \mathcal{L}_\text{$cGAN$}(G,D) + \lambda\mathcal{L}_\text{$L1$}(G)
\end{eqnarray}

where $\mathcal{L}_\text{$cGAN$}(G,D)$ is the objective function of the conditional GAN. The $G$ model, the generator, tries to minimize the objective function against the adversarial model $D$ that tries to maximize it. $\mathcal{L}_\text{$L1$}(G)$ is the distance loss, and \(\lambda\) is the adversarial factor to tune the distance loss.

\subsection{Survival Prediction}
Survival prediction is performed with genomics, radiomics, and radiogenomics. Radiomic features are extracted from the segmented tumor and fused with gene expression profiling data to estimate the OS rate.

\subsubsection{Feature Extraction}
For predicting survival rate, geometry, shape, and intensity features are extracted from segmented tumor and corresponding sub-regions inside MRI. The geometrical features we extracted are coordinates and lengths of the first axis, second axis and third axis, centroid coordinates, eigenvalues,  meridional and equatorial eccentricity for Necrosis, Tumor core and whole tumor sub-regions of the tumor by following these works\cite{islam2017fully, farag2011evaluation, ko2013tree}. Meridional eccentricity is the eccentricity measured at the section containing both the longest and the shortest axes. Equatorial eccentricity is measured at the section through the center, perpendicular to the polar axis. Figure \ref{fig:boxcount} (A) shows the measurement of the extracted geometrical features.

On the other hand, a novel shape feature, fractal dimension \cite{reishofer2018age} is calculated by box-counting inside the tumor regions such as enhancing tumor and necrosis (see Figure \ref{fig:boxcount} (B) and (C)). It calculates the geometrical complexity of the brain tumor. Kurtosis, a non-gaussianity measurement of intensity probability distribution \cite{decarlo1997meaning}, is another essential feature. Entropy and histogram serve as the intensity features. Collectively, 71 features are extracted from the segmented tumor. Besides, we have age information and 1740 gene expression profiling data for each patient.

\begin{figure}[!h]
\centering
\includegraphics[width=0.5\textwidth]{boxcount_geo_v2.pdf}
\caption{ {(A)} Geometrical features extracted from the segmentation;{(B)} Fractal analysis for Enhance tumor; {(C)} Fractal analysis for necrosis. The Box-counting method \cite{theiler1990estimating} is used to determine the fractal properties of the 3D segmented MRI.}
\label{fig:boxcount}
\end{figure}

\section{Experiments and Results}

\subsection{Dataset}
We use the TCGA GBM dataset to conduct our study. As BraTS-2017 \cite{menze2015multimodal, bakas2017advancing, bakas2017segmentation} consists of similar MRI dataset. Therefore we utilize it to train both our synthesis and segmentation models.

\subsubsection{BraTS}
We use BraTS-2017 \cite{menze2015multimodal, bakas2017advancing, bakas2017segmentation} to train our synthesis and segmentation model. It contains 285 and 46 cases in the training and validation set. Each case consists of 4 modalities, such as T1, T1c, Flair, and T2. There are 155 slices with a dimension of 240x240 in all the modalities. The MRI is annotated with three different regions of the tumor, such as necrotic and non-enhancing tumor (NCR/NET-label 1), edema (ED-label 2), and enhance tumor (ET-label 4).

\subsubsection{TCGA}
The Cancer Genome Atlas (TCGA) database consists of both MRI and genomic data. A previous study \cite{verhaak2010integrated} utilizes 202 patient cases with gene expression data, where 106 cases have corresponding MRI data. However, there is only one or two modalities of MRIs are available for most of the cases. Specifically, T2 modality is missing for the majority of the cases. There are also remarkable cases without T1 or Flair. We synthesize the missing modalities using MRI to MRI translation model (see section \ref{sec:mri_to_mri}). Moreover, the available MRIs are in different voxel sizes, spacing, and origin. We resample and register all the MRIs into equal voxel spacing similar to the BraTS-2017 dataset using ANTs library \cite{avants2011reproducible}. GBM gene expression data of TCGA is utilized to assay the glioblastoma tumor samples to get the ratio between the gene expression level to a known gene expression level  \cite{verhaak2010integrated}. The study
obtains 1740 genes with expression values after applying several filters. We exploit similar gene expression features to conduct all of our experiments.

\subsection{Implementation Details}
For both missing modality synthesis and Segmentation, the models have trained with the MRI slices of the BraTS-2017 training dataset. Learning rates are 0.0001 and 0.005 for the segmentation model and synthesis model (both generator and discriminator), respectively. The momentum and weight decay are 0.9 and 0.0001, respectively, for both and the adam algorithm is used for optimization. All the experiments are conducted with the Pytorch \cite{paszke2017automatic} deep learning framework.

\subsection{Results}
\subsubsection{Missing Modalities Generation}

\begin{figure*}
\centering
\includegraphics[width=1\textwidth]{result_visual_psnr_v3.pdf}
\caption{Quantitative and qualitative results of cross modality generation results on BraTS-2017 MRI dataset. {(A)} PSNR value comparison between our model and UNet cGAN \cite{yang2018mri} to synthesize T2 from T1c and Flair. Validation is performed on the BraTS-2017 validation dataset. {(B)} Inputs and the images generated using our model and UNet cGAN\cite{isola2017image, yang2018mri}. Red boxes present poor synthesizing performance of UNet cGAN comparing to our model.}
\label{fig:PSNR}
\end{figure*}

We generate all the missing modalities of the TCGA dataset using cGAN with our FCN for the TCGA GBM dataset. The performance of the synthesis model is calculated using the peak signal to noise ratio (PSNR) \cite{hore2010image}. We also compare the accuracy of our model with original UNet cGAN \cite{isola2017image, yang2018mri}. The quantitative and qualitative results are shown in Figure \ref{fig:PSNR} (A) and (B). The red line shows the enhanced synthesizing performance of our model comparing to UNet cGAN \cite{isola2017image, yang2018mri}. We observe that the discriminator of encoder-decoder in our model performs better than just encoder discriminator in PatchGAN \cite{li2016precomputed}.

\subsubsection{Segmentation}

\begin{table}[!h]
\begin{center}
\caption{Mean Dice score \& Hausdorff value comparison on BraTS-2017 validation set. ET, WT, TC denote as  Enhancing Tumor, Whole Tumor, Tumor Core, respectively.}
\label{table:Seg_comp}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{Model} &     \multicolumn{3}{c|}{Dice} &    \multicolumn{3}{c|}{Hausdorff} \\ \cline{2-7}  &  ET    & WT & TC & ET & WT & TC \\ \hline
Ours    &\textbf{0.7471}    & 0.8991    &\textbf{0.7991}  &\textbf{4.30}    &5.08   &6.78\\\hline
UNet scSE \cite{roy2018concurrent}        &0.7288    &0.8819     &0.7701           &5.71   & 6.37  &8.16 \\\hline
UNet \cite{ronneberger2015u}        &0.7243    &0.8776     &0.7615           &5.64    & 6.70  &8.23 \\\hline
EMMA  \cite{kamnitsas2017ensembles}    &{0.738} & \textbf{0.901}   &0.797    &{4.50} &\textbf{4.23}  &\textbf{6.56}\\ \hline
DeepLab v3 \cite{chen2018encoder} &    0.6631    & 0.8863 &    0.7834 & 4.39    & 5.62    & 8.51\\ \hline
GCN  \cite{peng2017large}   &0.7017    &0.8785    &0.7794       &5.22    &6.54   &8.37 \\ \hline
\end{tabular}
\end{center}
\end{table}

\begin{figure}
\centering
\includegraphics[width=0.48\textwidth]{Segmentation_GT_pred_v2.pdf}
\caption{Comparison between the ground truth and the obtained prediction from our model. A case is visualized randomly from cross-validation of BraTS-2017 training data. White boxes denote the improved prediction of necrotic parts by our model comparing
to other state-of-the-art segmentation models. Red, Yellow, and Green colors represent the tumor regions of Necrosis, Enhancing, Edema respectively.}
\label{fig:Segmentation_comp_brats}
\end{figure}

We use TCGA available and synthesis MRI modalities of T1c, T2, and Flair to predict tumor regions from the trained model using the BraTS-2017 training set as 4-fold cross-validation. The prediction score of the validation set of BraTS-2017 is from the ensemble of all the models as \cite{isensee2017brain}.
There are three regions in the evaluation metrics, such as enhancing tumor(ET: class label 4), tumor core (TC: class labels 1,4), and whole tumor (WT: class labels 1,2,4). Table \ref{table:Seg_comp} shows the validation results of our model compared with several state-of-the-art models including the top rank model EMMA \cite{kamnitsas2017ensembles} in BraTS-2017 challenge. Our model achieves the competitive performance with EMMA and outperforms all the other models (e.g. Deeplab v3 \cite{chen2018encoder}, GCN \cite{peng2017large}, UNet \cite{ronneberger2015u}, UNet scSE
\cite{roy2018concurrent}), in both dice and Hausdorff scores. We observe that the tumor core is a crucial region to estimate survival. Qualitative visualization of the predicted segmentation of BraTS-2017 and TCGA dataset are shown in Figure \ref{fig:Segmentation_comp_brats} and \ref{fig:Segmentation_results_tcga}, respectively.

\begin{figure}
\centering
\includegraphics[width=.48\textwidth]{Segmentation_v3.pdf}
\caption{ Visulization of the predicted segmentation for TCGA dataset using our model. The model is trained and validate with BraTS-2017 dataset and  synthesized and available modalities the TCGA data are exploited to predict the segmentation.}
\label{fig:Segmentation_results_tcga}
\end{figure}

\subsubsection{Survival Prediction}

Our radiogenomic model of SVM and ANN are evaluated with regression and classification metrics using RFE selected features of 8 radiomics, 43 genomics, and fused 51 radiogenomics. Table \ref{table:ANN_preformance} shows the mean square error (MSE) of the survival prediction days and accuracy, sensitivity, and specificity of the classification of short, medium, and long survival prediction. The maximum accuracy achieves with 84.62\% on model-3 among 4-fold cross-validation. Figure \ref{fig:OS_Shap} (A) and (B) represent the comparative performances of the ANN and SVM models and the impact of genomic features in OS estimation. The best performing ANN model consists of 6 hidden nodes (a single layer) with a ReLU activation function. As a state-of-art local explanation method, we use the Shapley Additive explanations (SHAP) \cite{lundberg2017unified}, highlighting the essential features of the best performing model. Figure \ref{fig:OS_Shap} (C) represents the mean absolute value of the SHAP values for each prominent feature according to the contribution to the model performance and each class. Our results show that the performance boosts to almost double after fusing genomic features with radiomic. We observe that the ANN model often overfit and performs poor comparing to SVM.

\begin{table*}[!h]
\begin{center}
\caption{4-fold cross validation results of the radiogenomic features using SVM. Short, medium and long survival rate are abbreviated to S., M., and L. respectively. MSE, and ACC represent mean square error, and accuracy respectively.}
\label{table:ANN_preformance}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{fold} & \multirow{2}{*}{MSE} &\multirow{2}{*}{Acc(\%)} & \multicolumn{3}{c|}{Sensitivity (\%)}  & \multicolumn{3}{c}{Specificity (\%)}\\
\cline{4-9}
& & & S & M & L &S & M & L \\
\hline
1&    25001&    50.00 & 40    &16.67&    80&    93.75&    60    &75 \\ \hline
2&    22384&    76.92 &60    &66.67&    100 &    87.50&    80    &100 \\ \hline
3&    \textbf{11731}&    \textbf{84.62} & 90    &66.67    &90&    93.75&    90    &93.75 \\ \hline
4&    13982&    73.08 &90    &16.67&    90    &87.50&    90&    81.25 \\ \hline

Avg & {18275}    & {71.15} &70&    41.67&    90    &90.63&    80&    87.50 \\ \hline
\end{tabular}
\end{center}
\end{table*}

\begin{figure*}
\centering
\includegraphics[width=1\textwidth]{OS_shap_v5.pdf}
\caption{Visualization of the performance comparison between ANN and SVM for radiomics, genomics, and radiogenomics. {(A)} Accuracy comparison between SVM and ANN. {(B)} Mean squared error for different features. ({C}) The impact of the most prominent features for the overall survival using SHAP value analysis \cite{lundberg2017unified}.}
\label{fig:OS_Shap}
\end{figure*}

\section{Discussion}
Our model achieves a significant margin in generating missing modalities and segmenting the GBM regions from the experimental results. However, the study in \cite{lee2019contrast} demonstrates that there are remarkable challenges of GAN based image translation models to learn contrast MRI (T1c) due to a lack of scalability. We also observe poor performance in synthesizing T1c from Flair. In these cases, we only exploit available modalities to predict the tumor segmentation. Further, we validate the performance improvements in segmentation by adding one by one modality, as in Table \ref{table:modality}.

To estimate the survival duration, we train regression models such as SVM, ANN with the most prominent geometric and shape features of the tumor, and combine with gene expression profiling data. We observe that gene expression profiling has a high correlation with the GBM progression and fused radiogenomic features boosts the prediction accuracy. Figure \ref{fig:OS_Shap} (C) y-axis represents the 20 most significant features of our radiogenomic model. Genomic features such as LRRC17 and LDLR demonstrate the highest impact where radiomic features such as axis coordinates of WT and TC and histogram have a remarkable influence in OS prediction. Moreover, overexpression of LRRC17, EDNRA, AHNAK2, TMSL8, and SLIT1 genes have a positive impact on the model output, leading to a more prolonged overall survival of GBM patients. High expression of the LDNRA gene impacts the model by reducing the overall survival of GBM patients. Our work verifies that fusing gene and radiomic biomarkers are significant for an accurate survival prediction of glioma patients, consistent with the previously done studies \cite{chaddad2018novel}.
One of the limitations of this study is the limited dataset since we perform regression in the overall survival prediction task. Therefore image synthesis to generate the missing MRI modalities is important to increase the population cohort in this study, further enhancing the model performance.

\begin{table}[!h]
\begin{center}
\caption{Dice score comparison for different number of input modalities for the UNet \cite{ronneberger2015u} segmentation model.}
\label{table:modality}
\begin{tabular}{|c|c|c|c|}
\hline
\multirow{2}{*}{Number of modalities} & \multicolumn{3}{c|}{Dice} \\ \cline{2-4}
& ET    & WT & TC  \\ \hline
1 modality (T1C) &0.6557 &    0.6829 &0.6872\\ \hline
2 modalities (T1C, Flair)&    0.7132&    0.8758&    0.7516 \\ \hline
3 modalities(T1C, Flair, T2)&    \textbf{0.7243}&
\textbf{0.8776}&    \textbf{0.7615} \\ \hline
\end{tabular}
\end{center}
\end{table}

\section{Conclusion}
In this work, we propose a novel FCN model for MRI missing modality synthesis, and tumor segmentation, and radiogenomic models for the overall survival estimation of GBM patients. Our FCN forms with octave convolution and skip-scSE module improve the prediction accuracy by eliminating feature redundancy and sparsity in model learning. The proposed FCN uses as a generator in a cGAN with a modified discriminator to synthesize the missing modalities. The quantitative and qualitative results indicate that our model outperforms other state-of-the-art models of synthesis and segmentation in most of the evaluation metrics. Our research demonstrates that radiogenomics is a promising strategy for OS prediction for the noninvasive management of GBM. Further, gene mutation data and other gene biomarkers can integrate with radiomic features to estimate OS in future work.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
Q: Equivalent to vlookup for strings in Python? Is there a way to search for a substring in a larger string, and then return a different substring x places left or right of the original substring?
I want to look through a string like "blahblahlink:"www.example.com"blahblah for the string "link:" and return the subsequent url.
Thanks!
Python 3, if that matters.

A: I think you should use regular expressions. There is module called re in python for that.

A: Pure Python solution would be to use index which tells you where in the string the first match occurs, then use the [start:end] slicing notation to select the string from that point:
"blahblahlink:www.example.comblahblah".index("link")
# returns 8

i = "blahblahlink:www.example.comblahblah".index("link")
"blahblahlink:www.example.comblahblah"[i+5:i+20]
# returns 'www.example.com'

redpajama_set_name: RedPajamaStackExchange


Nice Modern House Colour Design Interior #7 - was posted in July 20, 2018 at 8:14 pm. If you like to manage this picture as your laptop wallpaper, you may click the download link below or you may just right click on the photo at the top and select "Save Image As" to download the Nice Modern House Colour Design Interior #7 - or by select the "Set Desktop Background As" options if your browser has this capability. If you were no able to pick up the the most beautiful image you are looking for, you should go for "Search Column" at top right or browse different photo backgrounds which you like. This image has the picture size is 100kB and the resolution of 1024x689. Nice Modern House Colour Design Interior #7 - has been seen by 31 viewers and it is a photo from Modern House Colour Design Interior.


even though the earth is heating up there could be some relief, say recent studies. And we have to thank the Atlantic Ocean for this respite. Researchers at Leibniz Institute of Marine Sciences, Kiel, Germany, who studied the Atlantic say the oceans natural phenomenon could slow down global warming. The ocean goes through warm and cool phases, weakening future global warming trends by about 30 per cent of the prediction of the Intergovernmental Panel on Climate Changes (ipcc) fourth assessment report presented last year. That does not mean, warn the researchers, no more belt-tightening is required on greenhouse (ghg) emissions.
ipcc predicted a 0.3C rise in global temperature between 2005 and 2015. The prediction was based on ghg emissions due to anthropogenic activities. But models it used did not factor in natural phenomena, temperature variation in the Atlantic for instance.
The Leibniz research shows how temperature variations in the ocean have impacted temperatures in the Northern Hemisphere. Temperatures in the region have gone up by 0.6C from the 1970s to 2005, of which, 0.2C was because of temperature variations of the Atlantic ocean. Our finding also shows the warm cycle has peaked suggesting the effect may become less pronounced by 2015, says Vladimir Semenov, a researcher with the institute. The findings of the study were presented at the assembly of the European Geosciences Union in Vienna, Austria, in mid-April. A related study that took note of surface temperatures of the Atlantic also says that the ocean is experiencing a cooling effect and will continue to do so until 2015, which may bring down the global temperature. They also studied climate variations in the tropical Pacific ocean, which they say will remain unchanged, and not add to global temperatures.
We predict that natural climate variations in the north Atlantic and tropical Pacific may offset human induced global warming in the short-term, says Noel Keenlyside, also of the marine sciences institute, and the lead author of the paper, published in the May 1 issue of Nature.
Termed the Atlantic multidecadal oscillation, the temperature variation in the ocean has been documented for the past 150 years ever since instruments started recording sea temperatures. Studies have shown that the temperature oscillation has occurred, for sure, in the last millennium, implying it is a natural phenomenon. The oscillation has induced a similar variability in the air temperatures over the Atlantic ocean and adjacent land areas. The mechanism that drives the oscillation and its effects on the future climate, however, are not well understood. Climate scientists are working on computer simulations to gain insight into the workings of the oscillation.
Global warming is for real. We all know that. The ipcc report has clearly shown that temperatures will continue to rise because of both ghgs in the atmosphere and continuing emissions. The study under discussion doesnt dispute that. It brings to light fluctuations caused due to the natural climate variability like the one seen in the Atlantic, says Gabriele Hegerl, associate research professor at Duke University, us.
There is concern that such predictions could lead to easing up of pressure to reduce human activities that contribute to global warming. That is certain to take the edge off countries negotiating the Kyoto Protocol post 2012the year the first phase of the agreement expires. This is a problem but I hope work such as ours and others clarify to policy makers that natural variations may only temporarily mask the impacts of global warming in the short term, says Keenlyside.
Johann Jungclaus of the Max Planck Institute for Meteorology, Hamburg, Germany, and a co-author, advises caution. Some countries may use this study as an argument against steps to reduce ghg emissions. But the damage to the fight against global warming would be much bigger if short-time reversals would happen and we dont have an explanation for them.


text:
Home People Kenya Sheila Mwanyigha Biography, Age, Career, Education, Husband, Net Worth
Sheila Mwanyigha Biography, Age, Career, Education, Husband, Net Worth
You might remember her from her musical career as Nikki or from her time as co-host at Tusker Project Fame. Sheila Mwanyigha has been in the media for quite some time now as a radio and television personality. She has worked with several artists in her musical career and seeks to explore new opportunities whenever the chance arises.
Despite facing sharp criticism from the public regarding her being single and into her 40s, she continues to work hard, make money moves, and take care of herself. She receives a lot of attention since she has a curvaceous body and has in the past wowed many by her transformation after spending time working out and dedicating herself to sporty activities. This article looks at the biography of the talented Sheila Mwanyigha and what her background entails. The article will also give answers on some of the questions her fans might have given that she has not been in the limelight for a while now.
Sheila Mwanyigha Age and Place of Birth
Sheila is Kenyan by birth and has managed to keep her age details neatly under wraps all these years. She however is in her forties and is yet to share details concerning her family.
Sheila Mwanyigha Education Background
The media personality who turns heads around is a graduate, having to her name a Masters degree of Arts in Sociology from the University of Nairobi where she specialized in Medical Psychology. She however did not practice the same and found that radio was a better fit for her personality.
Sheila Mwanyigha Career Journey
Sheila came to the spotlight in 1997 under her stage name, Nikki, as a recording artist. She was primarily involved in romantic songs. She also worked with rappers as a featured artist and over her career did songs such as 'Niwe Wako Milele', 'Kipofu', 'Songs Hapa', and 'Mapenzi Tele'. Her melodious voice enchanted many and after her disappearance from the entertainment scene had many fans disappointed.
READ: Ruth Matete Biography – Age, Husband, Songs, Education and Net Worth
After music, she went into radio presenting where she found a great space for her to express herself and engage with listeners. She has often reminisced on her times as a radio show host describing it as very fulfilling and that she did not have as heavy pressures as television presented. For instance, she did not need to dress up and look spectacular when doing radio shows as her voice did all the work unlike television where appearance is a big and crucial factor for presenters and hosts.
After radio, she landed a role to co-host Tusker Project Fame, a Kenyan show that sought to hone the skills of young artists who would compete before a set of judges in competing for a lucrative music deal and a substantial amount of money on emerging victorious. The show brought up several local artists that wowed the country such as Amos and Josh, and Ruth Matete.
After Tusker Project Fame, Sheila Mwanyigha was appointed leader of East FM which is now known as Nation FM where she headed the team for a while before moving to Ebru TV. At Ebru TV, she earned the role of show presenter. She also worked as a morning show host on one of Kenya's largest media houses, NTV.
Sheila Mwanyigha Personal Life, Husband, Wedding, Children
Sheila is unmarried and has revealed that she has declined three marriage proposals on different grounds such as incompatibility. She is unbowed to societal pressures of having married at a certain age and had a family on what society terms as the 'right time'. She insists on not settling for less than what one is looking for and deserves.
In her past, she has been in a relationship with Hip Hop artist Prezzo during her time as an active recording musician. She later dated Rashid Abdalla, currently husband of Lulu Hassan. She has confessed that she was madly in love with him and was heartbroken when they had to part ways. Rashid Abdalla went abroad for further studies at the time of their courtship and on returning home, Sheila says that their relationship was not the same. He seemed drifted away from her and she attributed it to the presence of another lady in the picture. Her song, 'Niwe Wako Milele' was inspired by the incident which she says broke her and that she had to let it go. In the song, she professes her love for Rashid and confesses her pain following their separation.
Recently, Sheila Mwanyigha has staged a resurface in the music industry having released a new single, titled Pole Pole. The song is still in line with her genre of slow romantic music. She released the song on her birthday this year, 2020, expressing her excitement on the project and hopes for positive feedback from her fans who have been waiting for more of her music. Her song 'Niwe Wako Milele' was a major hit that set the bar for her future projects.
Sheila Mwanyigha Net worth and Assets
Sheila Mwanyigha runs a YouTube channel that supplements her income titled Sheila Lives Out Loud. Details on her exact net worth has not yet been established at the time of this article's publication nor the details on the assets under her name made available to the public.
Sheila Mwanyigha Contacts
A post shared by Sheila Mwanyigha (@sheilamwanyigha)


text:
Q: NullReferenceException when loading MVC view with partial data in the model A new problem for me... When I load the page, Visual Studio highlights the following line with a NullReferenceException

Layout = "~/Views/Shared/_Layout.cshtml";

I have confirmed that the targeted cshtml file does exist - this new view isn't the only one that uses it.
Any assistance in identifying and solving the problem will be greatly appreciated.
I'm getting to this page with the following jquery (it's on a <button> control):

@Model.Thread.Name

The page is supposed to use a Querystring value to allow a user to write a response to other content. The Querystring tells the system what content this new post should be associated with.
The user can also quote another user's post optionally. In this case, the system writes a quote of the other user's post into the new user's post to present it on the form. Otherwise the form should just load with an empty textarea where the user will supply the content for his/her new post.
public ActionResult New(int threadId, int quoted = 0)
{
    // Check that the user has a valid login - if not, redirect to login page.
    if (!CheckUserCookie()) return RedirectToAction("Login", "Home", new { newpost = "true", thread = threadId });

    // get medatada for the new post - user should not be able to edit this
    int UserId = Convert.ToInt32(Request.Cookies["Fudge"]["uid"]);
    bool Banned = Convert.ToBoolean(Request.Cookies["Fudge"]["ub"]);

    // don't allow the user to post if banned
    if (Banned) return RedirectToAction("Home", "Banned");

    // user is allowed to post, build the model and render the view
    NewPostViewModel Model = new NewPostViewModel
    {
        User = db.Users.FirstOrDefault(x => x.Id == UserId),
        Thread = db.Threads.FirstOrDefault(x => x.Id == threadId)
    };

    // if this post is a reply quoting another post, add the quoted post to the
    // new post's content - get it by the post id
    if (quoted >= 1)
    {
        Markdown Markdown = new Markdown();
        Post Parent = db.Posts.Include(t => t.User).FirstOrDefault(x => x.Id == quoted);
        StringBuilder sb = new StringBuilder();
        sb.AppendLine(String.Format("> *In reply to {0}*", Parent.User.Username));
        sb.AppendLine(Parent.Content);

        Model.Content = Markdown.Transform(sb.ToString());
    }
    else
    {
        // this quote isn't in reply to an existing post
        Model.Content = String.Empty;
    }

    return View(Model);
}

Here's a screenshot:


text:
Nadine Smith
September 16th, 1935 to September 20th, 2013
Nadine Marie Smith, 78, of Clyde Township, died, Friday, September 20, 2013 at home after a long illness.
She was born September 16, 1935 in Port Huron. Nadine Setter and Kenneth Smith were married September 24, 1955 in Port Huron.
Mrs. Smith was a member of the Port Huron High School Class of 1953 and served as secretary of the reunion committee. She retired from the City of Port Huron on October 1, 1995. She was a 50 year member of First Presbyterian Church.
Nadine's home and family were very important to her. She enjoyed travelling with Ken in their motor home, spending time at their home in northern Michigan and taking cruises together.
She is survived by her loving husband, Kenneth; beloved son and daughter-in-law, Brian (Brenda) Smith; two granddaughters, whom she adored and was always so proud of, Nicole V. and Brianne M. Smith; four great grandchildren, Jayden, Kaelynn, Brayden and Alivia; her brother, Arthur (Ida Mae) Setter; sister-in-law, L. Jean Setter; brother-in-law and sister-in-law, Lysle (Betty) Smith; and several special and loved nieces and nephews and great nieces and nephews.
She was preceded in death by her beloved sons, Jeffrey A. Smith and Scott A. Smith, and her brothers, Clifford E. Setter and Clyde V. (Hilda) Setter.
Funeral services will be held at 10 AM on Tuesday, September 24, 2013 (her 58th wedding anniversary) in First Presbyterian Church, 811 Wall Street, Port Huron. The family will receive friends at the church beginning at 9 AM. The Reverend Breanne Ryan will officiate.
Burial will be in Lakeside Cemetery. Pallbearers will be Ronald Arkles, Norman Fretenborough, Milan McIntyre, Richard Phillips, Gordon Shaw and Robert Warshefski.
Memorials may be made to Special Olympics Area 4, First Presbyterian Church or Blue Water Hospice. Arrangements are by Pollock-Randall Funeral Home.


text:
package helper

import (
	"log"
	"os"
)

//LogInfo the info log level
var LogInfo = log.New(os.Stdout, "INFO: ", log.Ldate|log.Ltime|log.LUTC|log.Lshortfile)

//LogWarn the warn log level
var LogWarn = log.New(os.Stdout, "WARN: ", log.Ldate|log.Ltime|log.LUTC|log.Lshortfile)

//LogError the error log level
var LogError = log.New(os.Stdout, "ERROR: ", log.Ldate|log.Ltime|log.LUTC|log.Lshortfile)

redpajama_set_name: RedPajamaGithub


text:
Cylinders, Cones and Spheres
What are Cylinders, Cones and Spheres?
Let's look at what types of 3-dimensional figures are not considered polyhedrons and what characteristics make up these solid figures.
How to find the Volume of a Cylinder
Let's cut up a cylinder to find its volume. Then you can use that to build an equation for volume of a cylinder and see how it relates to volume of a prism.
How to find the Volume of a Cone
Discover the relationship between the volume of a cylinder and cone to find the equation for volume of a cone.
How to find the Volume of a Sphere
Discover the relationship between the volume of a cylinder and sphere to find the equation for volume of a sphere.
How to find the Volume of a Hemisphere
Use the relationship between the volume of a sphere and hemisphere to find the equation for volume of a hemisphere.
How to find the Surface Area of a Cylinder
Use a net to find the surface area of a cylinder and derive an equation for the surface area of any cylinder.
How to find the Surface Area of a Cone
Use the relationship between the equations for surface area of a prism and cylinder to predict a relationship between the equations for surface area of a pyramid and cone.
How to find the Surface Area of a Sphere
Use the relationship between the surface area of a sphere and the lateral surface area of a cylinder with the same dimensions to develop an equation for the surface area of the sphere. This relationship was first discovered by Archimedes. For more information on how Archimedes discovered this relationship, please visit: https://bit.ly/2RKnue0
How to find the Surface Area of a Hemisphere
Use the relationship between the surface area of a sphere and hemisphere to find the equation for surface area of a hemisphere.


In Force: 1 April 2017. These Regulations create an obligation on prescribed persons, listed in the Schedule to the Public Interest Disclosure (Prescribed Persons) Order 2014, to report annually about the public interest disclosures which it receives from workers. It addresses the problems of a lack of consistency in approach and communication from prescribed persons on the disclosures received by them.


So, what does this all mean for frame design?
Over the past decades, frames have been designed with shorter and shorter headtubes to please the pros seeking an advantage through a lower handlebar position. Those same geometries are then offered to the masses. As you've seen in part 1 and part 2, the only problem with this approach is that it's wrong. It doesn't work for the pros, let alone for the masses.
So we need to get these headtubes longer again, and thankfully there is a way to do this without creating a problem for those riders who like their low position (out of habit or because they have the flexibility to take advantage of such a low position).
The new geometry from the Cervelo R3 and S5 series has a taller headtube so that people can put their handlebars on the correct height without needing too many spacers. At the same time the bars can still be placed low enough to fit any pro rider we have ever supported. The advantage of a longer headtube is that it increases the torsional stiffness of the frame, which in turn improves handling.
On aero frames, it also improves the aerodynamics because despite the headtube being wider than the bunch of spacers sitting atop of it if it were shorter, the shape of the headtube is much, much better and more than compensates for the increased frontal area.
So how do riders still attain the low position with the taller headtube? If you mount a -17 degree stem (that's a flat stem, so a stem that sits on the bike horizontally) on the new geometry, the handlebars sit just as low as they would with a -6 degree stem (the standard) on the old geometry. 3T, FSA and several other manufacturers now have such -17 degree stems available.
Of course, that said no pro should ever ride the R3 except for Paris-roubaix (which it was designed for originally). The aero frames are ALWAYS the fastest solution, even in mountainous terrain, so every pro taking their profession seriously and wanting to get to the finish the fastest should ride them at all times (except Roubaix).
This entry was posted on	August 8, 2011 at 15:00	and is filed under bike equipment.
What about weight though, is the R3 not a lighter frame than an S3 or S5? Plus I was always under the impression there was a lower speed limit when 'aero' on a bike comes into play?
Pros have a minimum bike weight, and the s5 hits that minimum weight too. That said, even for amateurs it would take a really nasty climb for the few grams to offset the aero advantage.
What about Rider. He was on the R5ca during the Tour while others rode the S5.
Tyler won the stage 3 TDF sprint aboard an R series frame, and indeed rode it throughout the tour. I would venture a guess that Tyler takes his profession fairly seriously. Picture confirming it is here.
Curious there were apparently no aero mass-start frames in the top 20 in the Tour GC this year.
Aero vs. conventional frame debate aside, I have to say that after incorporating the info in these bar height and width posts that it has been a big improvement for me, at least so far.
I happened to stumble on this blog just recently and decided to give these changes a try. It is rare that people in the industry challenge the conventional wisdom, but I thought the arguments made a lot of sense.
So I went and bought some narrower bars and raised my bars from their previously slammed position, and not only has my comfort been increased by miles, but I am able to ride faster for a longer period of time while eliminating comfort issues I previously had. I don't have a power meter but I would assume that I am putting out a good bit more power now.
I would say everyone should at least give these changes a try. It is worth doing as it could end up unlocking a lot of performance, and very cheaply.
Anyway, thanks for the insight Gerard!
Overall I've been happy with it comfort wise, it feels fine on long rides so thanks for all the design work!
This is very interesting Gerard and very good explained as well.
I'm sure as you say that a higher head tube is better, both for stiffness and turning.
Keep the good interesting blogs coming. Tomorrow Tour of Utah starts, will ride against your guys then.
I test rode the R3 and RS back to back and preferred RS for it's greater comfort, not least of which was being able to run bars at comfortable height without the need of extra (non-pro looking and non-aero) spaces to do so.
It seems that many companies are reluctant to increase the length of the head tube as it makes for a very odd looking bike when the top tube has a large degree of downward slope to the seat tube. Cervello gets away with it as they only have a slight downward slope. Imagine a Giant with another 20-30mm of length in the head tube. It would look silly. Given that frames are stiffer than they have ever been, and can be made considerably lighter than the UCI weight limit, I wonder why we still have the 'compact' design. Why not have a level top tube again?
I'm sorry, but I think the main problem is that when integrated headsets became de rigeour, manufacturers kept the head tube lengths the same refraining from adding the additional stack height provided and allowed by the external set.
That's certainly part of it, as I mentioned somewhere else. However, that's only a small part of the overall bar height drop that we've seen over the past 30 years. Changes in hood shape making the hoods more comfortable and therefore a more viable position also play a role.
There are many factors, but the bottom line is that the bike should be set up to put the rider in the best position, it's not the rider who should adapt him/herself to the bike.
I agree with that one-hundred percent. But, I think it's a rather large generalization to say that we all need larger headtubes. I for one agree longer headtubes would do the average to average competitive rider a great justice, but no one single person is built the same. I've fitted hundreds of cyclists to frames, many on Cervelos, and the best thing I can compare bike fitting to is a golf swing. Everyone's innate anatomy is different with some similarities and tendencies…but just because we are the same height, and have the same saddle height and arm length…does not mean that we fit the same bike.
Yes, we should raise headtubes…but I think by arguing that a 17* stem is more aero than a 6* stem when that makes less of a difference than if I have long hair or not in terms of aerodynamics…is a bit of a stretch.
I recently have lowered my stem position by 20mm, and I feel the bike handles better. It feel that when I am at high efforts, I am more comfortable and powerful…I also have lost a lot of the nagging IT band pain that I had in the higher position.
Higher isn't necessarily better for everyone. As always, I appreciate your commentary. It's a level of transparency much needed between the people making the products we remortgage our homes on. Thanks Gerard!
Hi Adam, thanks for the feedback. Not sure where you get the -17 being more aero than -6 part, that's not something I said is it? At least it's not something I meant. The point is that if people liked the "old" Cervelo geometry with their standard -6 degree stem, they can get the same position on any "new" Cervelo just by going to a -17 degree stem.
What do you think about bar height and reach in relation to weight distribution? A lot of pros ride really long and low, which puts a lot of weight over the front end, but I've noticed that a lot of US domestic pros that race a lot of technical and tight races run their bars a bit inwards and slightly higher so that they can spend more time in the drops and even out their weight distribution. So, the hands end up more under the shoulders, but aren't any lower because the bar is actually higher so they actually have more bend in their elbows. Any feedback on this notion?
First of all, I appreciate your blog, and I appreciate your work… Moreover, I'm seriously considering a S5 for my next bike.
I totally agree with what you wrote : to achieve an aero position you don't need a very low bar, you need above all to ride in the drops, with your arms bend and your shoulders low and rounded. But bar height is related to the setback of the saddle, the lenght of the top tube, and, finally, given the morphology of the rider, the mass distribution between the front and the rear wheel.
I'm wondering about a few things that I hope you will help me to clarify (sorry if it is a little complex and unclear, english isn't my mother's tongue).
Now, given all that, I could easily implement the "high forward bar" position on a 56cm S5, with a -6° 130 or 140mm stem. If I want to implement the "low bar" position, which I think is better for me given my morphology (but I could be wrong), I could either choose a 56cm frame with a -17° 100 or 110mm stem, or a 54cm frame with a -6° 120 or 130mm stem and a little spacer.
I wonder which size will handle better, given the 12mm shorter front-center of the 54cm frame ?
Sorry for writing so much !
Given those two options I would definitely suggest the 56cm for you, the 54cm will be very small. And especially as you intend to rotate forward, it is good to go with the longer front-center.
I've always thought that a taller headtube is more flexy than a shorter (my common sense says). Why you don make the frame more compact (with shorter headtube) and offer a +0, +5 stem? maybe this is more aero and stiff, isn't it?
Interesting points but my experience suggests just the opposite. Granted I no longer have much to do with racing but more than a few of our Italian bike tour clients are racers or ex-racers along with folks who've never pinned on a number. I find the trend going the other way, bikes now come in just t-shirt sizes and seem to be optimized to feel comfortable for the minute or so the customer is sitting on it in the showroom. The extended length headtubes and sloping top tubes on many North American designed bicycles make the frame no longer a diamond but a slanted rectangle. Clients renting bikes from us send us three key dimensions as to fit and I'm quite often amazed at the upright position they're in – the weight distribution is awful and you can tell on the descents…the more upright they are the more tentative they seem to be. Even worse, as Gerard pointed out, too many put their hands only on the hoods where control is less than optimum in my opinion. When I see the pros using 15 cm stems with large negative rise it tells me the bike is simply the wrong size or dimension as those seem to be chosen to appeal to the consumer in the bike shop rather than the pro who races on them. With carbon-fiber molds being expensive it seems the pro racer is doing with less-than-optimum equipment at times solely to facilitate the sales goals of the bike sponsor. This theme was pointed out in a recent issue of the Italian Bicisport magazine as well.
I have both an S5 Team and an R5VWD and beyond just Roubaix, I think that on true "mountain" days that involve a lot of climbing and demanding descending, I prefer the R5. Maybe I'm a few seconds slower on the climb on the R5 (though it sure doesn't seem that way), but I think I make up for it on the descents – the R5, IMHO, is better handling than the S5.
I don't mean to suggest that the S5 is poor handling – if it was, I would not keep it in my stable – the S5 handles just fine, thank you very much. The R5 is just something truly special in that department.
Well, the R5ca is a whole different animal altogether, it doesn't compare with any other bike in my opinion.
That different animal is referring to the ride, not the geometry. Sizes carry across seamlessly, so if you're a 58cm S3 you would also be a 58cm R5ca. Do you use any spacers on your S3 (and what angle of stem)?
It is interesting to see this topic raised and further discussed as a "new" thing.
In a late-90s issue of a cycling magazine (Cycle Sport?) Chris Boardman wrote about his road bike position. Specifically he addressed his early assumption — as a time triallist — that his road position should be as low as possible. When racing in the UK this is how he rode.
After moving to a French team on the European circuit he received advice (I've forgotten who he mentioned) to raise his handlebars and later believed this to be correct and overall very effective. The article included photos of his early position on a Cougar and later a Gan team bike with handlebars noticeably higher.
Boardman was known as a rider who did his homework, looked for effective ideas and did not follow trends. Many of his ideas from the 90s are being applied now by Sky and British Cycling with great success. His is yet more advice that should cause riders to reconsider the idea of slamming a stem to keep handlebars as low as is possible.
Hi David, thanks for your comments. I certainly don't consider it a "new" thing, in the Merckx era and before (and even a bit after) the bars were also much higher than they are now.
Yes, that is true about the older eras — I didn't mean that the idea of having higher handlebars is "new". I meant the trend of handlebars becoming too low was already noticed and being discussed at least 15 years ago — but the trend continues nonetheless.
That's the thing with discussing problems in cycling 15 years ago. Nobody was listening! Sorry, just couldn't resist.
Having said that Boardman specc'd very low head tube on his origional TT bike suggesting this was the way forward for TT'ing.
Yes — this was for TTs, with the saddle up and forward compared to road positions to have the rider position rotated at the BB and flattening the back with low shoulders. The UCI introduced saddle position restrictions for a reason. There is also a reason why this was/is an issue on TT bikes but few road riders want to jam their saddles forward for day-to-day riding and racing.
The Question is would an S5 with a normal sized headtube and no spacers be more aero than an S5 with the current long headtube and a – 17 degree stem?
I wonder if the racers at the more pointy end of events where those saved seconds maybe of greater importance are loosing out a little to gain a better fit to cyclists who would save more time by improving their position and flexability than by switch to an aero frame?
"a normal sized headtube": Comparing an S5 against older framesets with threaded headsets and including the pressed cups and nuts in the sum of the height shows that an S5 (and R5) has a historically, fairly normal headtube measurement. In the day of threaded headsets -17° stems were the absolute norm, not the exception.
"those saved seconds": In road racing the assumption that lower = more aero = faster is often based on zero facts.
Add to this Vroomen's long-standing point that one should target a drops-oriented performance position, not the hoods position many current riders focus on, and the assumption becomes weaker.
The following is vague and very thin but I don't have the time to look up references: a rider on Team Cervelo/Garmin (whatever it was at the time) spoke in an interview (cyclingnews.com?) of the sharing/learning environment on the team and how Dave Zabriskie opened his and other teammates' eyes to the fact that one could be more aero on a road bike with a higher position — this advice apparently being based on repeatable facts.


In January 2013, the government of New Zealand announced a change for the labour market access of foreign workers.
International PhD and research students at the Master's level are now allowed to take up full-time positions. Previously, these students were allowed to work at most 20 hours per week.
All international students are allowed to take up full-time positions during holiday breaks if their programme of study requests at least 120 credits and lasts at least two semesters.