One of the most controversial promises of Donald Trump's presidential campaign was the one to finish building the wall or fence along the US – Mexico border. The reason for this promise was to help stop the uncontrolled flow of illegal aliens into the US.
Trump also raised the ire of many liberals when he characterized many illegals as being criminals. They call him a hater and racist when in fact, he was speaking the truth. There are tons of reports of crimes committed by illegal aliens. I've personally seen entire family friendly neighborhoods turned into crime infested slums that weren't safe for anyone to walk or even live in their own homes. I've watched as the property values plummeted as a direct result of illegal aliens moving into the neighborhoods, often with as many as 30-40 illegals in each 3-4 bedroom house. I've seen how the flood of illegals crossing the border have transformed large areas of desert into trash covered danger zones. I know ranchers who carry a gun with them at all times and even sleep with a loaded gun under their pillows. One rancher sent his wife and daughter to live with relatives in a city further north away from the border because of the dangerous conditions created by illegal aliens and drug traffickers. I know law enforcement officers who say that over 75% of the crime they encounter is directly or indirectly related or caused by illegal aliens.
Of course, the Obama administration had a habit of turning illegal aliens convicted of felonies, back out on the streets of America to victimize more American citizens. Under Obama, Mexico deported more illegals than the US did. In 2014, Mexico deported 107,199 illegals compared to the US deporting only 104,688 illegals.
Much to the dismay and horror of many liberals, Donald Trump won the electoral vote and is now the President of the United States and he has been taking action on many of his campaign promises, including completing the border wall or fence.
Congress approved the construction of a border fence over a decade ago, but it was never completed due to lack of funding and liberal Democrats taking control of Congress in 2008.
Perhaps the most efficient section of border fence that has been completed is the nearly 100 miles of border security fence stretching out into the desert from Yuma, Arizona. Prior to the construction of the Yuma fence, Border Patrol agents arrested an average of 800 illegals per day. Since the construction of the fence, they only arrest an average of 15 illegal aliens per day.
I wish I could get this message to President Trump, but here is how he can expand the efficient Yuma border fences across to Texas and the Pacific Ocean, with little labor cost.
Most construction projects spend over 50% of their total costs on labor. What if Trump could build the rest of the border fence without it costing the nation any more than they are spending now?
The US military has construction units that build airports, military bases and entire cities in other parts of the world. What if Trump used our military construction units to build the border wall/fence? These men and women are already being paid by the taxpayers so why not put them to work for our own national protection instead of having them building in other countries?
That would leave the main cost of the fence to be material. Reducing the labor to nearly nothing would save billions of dollars and keep our military personnel here in America instead of abroad.
At the moment, everyone is wondering where Trump plans to get the money from to pay for the border wall/fence. He has vowed to make Mexico pay, but using our military labor, would ease that burden of who is going to pay and how much.
Once the border wall/fence is built, I suggest that Trump use US military personnel to supplement the Border Patrol in maintaining the security along the entire length of the fence. I'm sure many Americans would prefer to see our military men and women protecting America's border instead of the border in other countries that hate America like Iraq and Afghanistan.
I believe this is a win-win solution, if only I would have the chance to share this suggestion with President Trump.


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Home Entertainment Instagram Adds New Features
Amid backlash over the intrusion of algorithmic, recommended content into Instagram's feed, Instagram today introduced a number of new features designed to make it easier for users to keep up with their real-world friends. The company is now rolling out several significant changes, including most notably an addition called Notes — a feature Meta had considered turning into a Twitter competitor, according to a recent news report. With Notes, users can update their friends using just text and emoji, adding a different format for social updates beyond the images and videos Instagram is best known for. Other new features are also rolling out to Stories and will introduce new ways to share with groups.
Of all the new features being announced, Instagram Notes is perhaps the most interesting as it adds a way to communicate with others using just text. While that's obviously reminiscent of a platform like Twitter, the current implementation has a much different user interface. In Instagram, users can leave notes by going to the top of their inbox, then selecting the followers they follow back (aka mutuals) or others from their existing "Close Friends" list. They'll then type out the note itself using 60 characters of just text or emoji. The note will appear at the top of friends' inboxes for 24 hours and replies will arrive as DMs.
Image Credits: Instagram
Instagram said that during testing it found people appreciated having a way to start conversations in a lightweight way.
So while the format itself differs from Twitter's real-time feed, the use case for Notes could have some overlap as the company described the feature as a way for users to share "what they're up to" or ask for recommendations. Twitter today prompts users for similar input. When you go to compose a tweet, for example, the app asks you to share "What's happening?" And like Notes, it has a contained text input limit. (Though that limit will now grow substantially, Twitter owner Elon Musk said.)
Meanwhile, The New York Times reported last week how Meta was considering turning Instagram Notes, which has been in testing for many months, into a more fully fledged Twitter rival to capitalize on the chaos at Twitter following Elon Musk's acquisition. The report said the company had been weighing whether Notes should even be its own standalone app or another feed inside Instagram. For the time being, however, it appears Instagram is launching Notes as is.
Another set of new features targets Instagram Stories.
One is an update to the "Add Yours" feature launched last year, which encourages others to participate in your trend by sharing their own variation. Now, Instagram is testing an update where you can specifically invite friends to participate by tapping "pass it on" when you see a trend you think they'd like. This feature is meant to combat one of the bigger threats from TikTok where users replicate trends, whether dances or skits or AI effects set to music, by posting their own take.
Instagram is also now testing "Candid," a way for friends to share Stories that are only visible to others who also share their own Candids. This feature is an obvious competitor to BeReal, which also locks friends' content behind a blurred screen until you also post. And like BeReal, Candid sends out daily notification reminders. (TikTok is trying a similar feature with its TikTok Now posts that appear in users' feeds.)
This isn't the first time Instagram has tried to take on BeReal, which has been gaining a following among younger Gen Z users. The company earlier this year tested other features including one called IG Candid Challenges, which is similar to what's now become Candid. It also more shamelessly duped BeReal with a dual camera feature it simply called Dual.
Instagram says users can capture a Candid from the Stories camera, the multi-author Story at the top of the feed, or from the daily notification reminder.
Two other features focus on improvements to group sharing.
The new "Group Profiles" are a new type of profile on Instagram for sharing posts and stories with friends. Content shared to a Group Profile is shared with group members instead of your followers and gets posted only to the Group Profile, not your own profile. This seems to respond to how many younger people are already using Instagram — to post content to groups for their school, for example, or around some sort of theme. Before, these accounts would be managed by only select people with the account login who may curate content from submissions. Group Profiles could prompt greater participation as they reduce the barrier to posting.
Collaborative Collections are another new way to connect with a group of friends. In this case, the idea is to allow a group to connect over a shared interest by saving posts to a new "collaborative collection" in a group or via 1-to-1 direct messages (DMs). Users can add to a collaborative collection by saving a post they come across in their feed or by sharing it with a friend over DM, then saving it from there.
It's essentially an expansion of the more than five-year-old Collections feature, but one that helps you build that collection with others. This could be useful for gathering together travel ideas for a group trip or sharing recipes, among other things.
The new features were announced by Mark Zuckerberg on Instagram itself.
The company confirmed to TechCrunch that Notes will be coming to both iOS and Android users, while the rest of the features are still in early-stage tests. Group Profiles are testing in Canada, Chile and Taiwan, while the other features are in testing with a small percentage of people around the world, we're told. The one exception is Collaborative Collections — in this case if you're in the test group and start a collection with someone who's not in the test and invite someone new, they will then be automatically added to the test.
"Connecting with others is why people come to Instagram," the Meta blog post stated — an acknowledgement, of sorts, of the backlash the app saw from users who are unhappy with the irrelevant and intrusive content in their Instagram feeds. This culminated in Instagram actually rolling back some changes after Kylie Jenner and other celebs publicly complained about the app trying to be too much like TikTok. The company decided to pause tests of full-screen posts and decrease the amount of recommended content as a result of users' complaints.
The new set of features refocuses on social sharing with friends and seems to be a better move in terms of acknowledging what people actually want from Instagram — to connect with friends, not to just be entertained, as on TikTok.
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redpajama_set_name: RedPajamaCommonCrawl


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Complex Symmetry of Truncated Composition Operators
Directed Project Contributors: Project Contributors: Ruth Jansen, Rebecca K. Rousseau
An N x N matrix is a representation of a linear function, called an operator, from RN to itself. For a given operator, each different basis representation of RN creates a different N x N matrix that represents that same operator. An operator is called complex symmetric if there is some basis for which the matrix representation is symmetric (possibly with both real- and complex-valued entries). This idea has also studied on infinite vector spaces such as the Hardy space and operators on those spaces, such as composition operators [2, 4]. Here, we define a truncated composition operator by considering only a finite-dimensional portion of a composition operator, so that we can represent it with a matrix and investigate any resulting complex symmetry.
Symmetric matrices, whose entries form a mirror image across the diagonal, are a familiar concept in the study of linear algebra and its applications. The concept of a complex symmetric operator is similar in nature and has been shown to have a wide variety of interesting applications within operator theory (Garcia, Putinar [2]). An operator is complex symmetric if it has a symmetric matrix representation with respect to some orthonormal basis. This is distinct from a self-adjoint matrix, in which entries across the diagonal are the complex conjugate of one another rather than identical.
For methods and research, please view our article in Rose-Hulman Undergraduate Mathematics Journal (Volume 18, Issue 1, Article 3).
[1] Levon Balayan and Stephan Ramon Garcia. (2010). Unitary Equivalence to a Complex Symmetric Matrix: Geometric Criteria. Operators and Matrices, 4(1): 53-76.
[2] Stephen Ramon Garcia and Mihai Putinar. (2005). Complex Symmetric Operators and Applications. Transactions of the American Mathematical Society 358(3): 1285-1315.
[3] Carl Cowen and Barbara MacCluer. (1995). Composition Operators on Spaces of Analytic Functions. CRC Press, Inc.
[4] Sivaram K. Narayan, Daniel Sievewright, and Derek Thompson (2016). Complex Symmetric Composition Operators on H2. Journal of Mathematical Analysis and Applications, 443(1): 625-630.
[5] James E. Tener. (2010). Unitary Equivalence to a Complex Symmertric Matrix. Pomona College, Claremont, CA.


http://charlotteskitchendiary.com/111.zip Q- What is Rhinoplasty?
http://charlotteskitchendiary.com/2018/11/01/a-foodie-guide-self-catering-luxury-in-the-lake-district/ A- Rhinoplasty is also known as nose reshaping, nose job, it is a surgical process that is performed to change the shape of the nose or to improve its function due to trauma or birth defects. Rhino means Nose, Plasty means to shape or form. Surgeon makes 3-D changes on nose which instantly reflects on other areas of the nose.
http://charlotteskitchendiary.com/tag/heritage-bundt/ Q- Who all needs Rhinoplasty?
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http://charlotteskitchendiary.com/author/charlotte/feed A- Nasal growth is usually complete by the age of 15 in girls and boys by the age of 16. So the best time to do cosmetic rhinoplasty is late adolescence or early adulthood. Cosmetic rhinoplasty becomes challenging with age. All other factors come into picture .Beyond the age of 60 it is not less likely to be recommended, due to poor healing responses. However, functional rhinoplasty can be done later in life to cure heath problems.
Buy Phentermine Reddit Q- What are the pre-operative measures?
A- Surgeon will determine what needs to be done; will go over the realistic expectations of the procedure. And to the Patients it is recommended to not take any medications that may inhibit blood clotting during surgery or may result in any complication post surgery. Surgeons recommend to smokers to not to smoke for at least one month prior to the operation and a month afterwards since smoking slows down the healing process.
Buy Xanax Turkey Q- What is the procedure of Rhinoplasty?
Buy Liquid Alprazolam A- Rhinoplasty is done on same-day basis. Most of surgeons use general anesthesia because there tends to be bleeding in back of throat which may be a problem to semi conscious patient.
Otherwise Rhinoplasty is of 2 types.
Depending upon patient's situation, surgeons may make an incision across the bottom of the nose between two nostrils; this is open technique, whereas in close technique incision is made in the lining of the nostrils.
After this, rearrangement of cartilage and in some cases break and rearrange bone is also done to reshape the nose. A splint is usually placed on the nose for one week.
Q- Is Post procedure and touch ups required after the surgery?
A- Since rhinoplasty is complicated procedure and nose swells during the operation, there are chances patients may need to have some post surgery touch-ups.
Order Valium Online Legal Q- How much time it will take for recovery?
A- The nose will be noticeably swollen and moderate bruising will be there around eyes and nose that would last up to two weeks. Nose shouldn't be blown. Patient should avoid strenuous activity for a month at least. ENT Specialist will let you know when a person can get back to their normal routine. Nose will look refined over the following months, once the splint comes off after the surgery even though minor traces of swelling will disappear with time.
Buy Zolpidem Usa Q- What is the surgery cost?
http://charlotteskitchendiary.com/tag/tuscany/feed/ A- The surgery cost of Rhinoplasty depends upon how much alteration the patient want. It varies from 75,000 to 1,00,000 INR depends upon the room category and services opt by the patient.
Q- How much time will it take to perform surgery?
A- Mostly the procedure takes approximately 2 hours of time. The patient will get back to his work with ENT Surgeon recommendations.
Cheap Zolpidem Q- What is an alternatives of Rhinoplasty Surgery?
A- There is none, but yes if the problem is regarding breathing alternative such as nasal strips and physician recommended treatments can be done.
Q- What are the associated risks and limitations?


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James Harden: 'I Want My Step-Back to Be One of Those Moves That Last Forever'
by Marcel Mutoni August 26, 2019
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'He's a Great Player': Damian Lillard Drops 61 on the Warriors
James Harden is determined to make his infamous step-back move one of those that "last forever" in the minds of NBA fans.
In fact, Harden may break out those one-legged joints he's been unveiling during summer time pickup games next season.
For game. @jharden13 #knowyourbigpicture #familyforever #theblueprint
A post shared by Darico "Rico" Hines (@ricohinesbball) on Aug 18, 2019 at 3:49pm PDT
The 2018 MVP says he will continue to be an innovator on the court.
James Harden on the one-legged stepback 3s he's been launching in pickup runs: "With basketball, you have to be creative. This is my 11th year, and every single year I want to get better. I don't want to stay the same. You've got to find ways to keep growing." pic.twitter.com/Uk3zgJu6pG
— Tim MacMahon (@espn_macmahon) August 24, 2019
Per ESPN:
"I'm always trying to be creative," Harden told ESPN after giving away bicycles to dozens of children at Houston's Tuffly Park as part of his annual charity weekend. "I'm always trying to get better — at basketball, life, businesswise. I'm always trying to find ways to be impactful. With basketball, you have to be creative. This is my 11th year, and every single year I want to get better. I don't want to stay the same. You've got to find ways to keep growing."
Harden, whose step-back has emerged as arguably the league's most lethal weapon over the past few years, said he doesn't know whether he's yet confident enough in the one-legged variation to use it in games that count. But that goal has been in Harden's mind as he made the shot a focus of his summer work.
"I'm not sure; it's something that I work on," Harden said when asked if he'll use the one-legged, step-back 3 this season. "But you know how Mike [Jordan] has his fadeaway and Dirk [Nowitzki] has his one-leg and [Kareem Abdul-Jabbar] had the sky hook, I want my step-back to be one of those moves that last forever. So when I travel around the world and I see little kids that [say], 'Hey James, I got a step-back!' — I love to see that.
"It's me being a creator and me being an innovator and paving the way in basketball in my own way, doing it how I want to do it, and that's what it's all about. As a little kid playing in these parks, that's what I imagined, that's what I dreamed of. Now it's coming to reality, so it's pretty cool."
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Metabolite profiling, the simultaneous quantification of multiple metabolites in an experiment, is becoming increasingly popular, particularly with the rise of systems-level biology. The workhorse in this field is gas-chromatography hyphenated with mass spectrometry (GC-MS). The high-throughput of this technology coupled with a demand for large experiments has led to data pre-processing, i.e. the quantification of metabolites across samples, becoming a major bottleneck. Existing software has several limitations, including restricted maximum sample size, systematic errors and low flexibility. However, the biggest limitation is that the resulting data usually require extensive hand-curation, which is subjective and can typically take several days to weeks.
We introduce the TargetSearch package, an open source tool which is a flexible and accurate method for pre-processing even very large numbers of GC-MS samples within hours. We developed a novel strategy to iteratively correct and update retention time indices for searching and identifying metabolites. The package is written in the R programming language with computationally intensive functions written in C for speed and performance. The package includes a graphical user interface to allow easy use by those unfamiliar with R.
TargetSearch allows fast and accurate data pre-processing for GC-MS experiments and overcomes the sample number limitations and manual curation requirements of existing software. We validate our method by carrying out an analysis against both a set of known chemical standard mixtures and of a biological experiment. In addition we demonstrate its capabilities and speed by comparing it with other GC-MS pre-processing tools. We believe this package will greatly ease current bottlenecks and facilitate the analysis of metabolic profiling data.
Metabolite profiling has become a powerful tool to address biological problems. It has been used in a wide range of applications, such as discovering the effects of herbicides in plants , drugs in medical fields [2, 3], or molecular physiology and functional genomics [4–6].
GC-MS is by far the most widely applied analytical method as it provides an efficient way to quantify hundreds of metabolites in a single sample run [7–9]. In addition, robust extraction protocols exist and there is continued development of data evaluation methods. See  for a introduction to metabolomics using GC-MS.
The relatively low cost and high-throughput of GC-MS instruments has led to data pre-processing becoming a major bottleneck in metabolite profiling, particularly with recent increases in experiment size. The typical GC-MS pre-processing approach consists of peak detection and mass spectra deconvolution, retention time alignment across different samples and a normalisation step to remove systematic variation in the data. Finally, the peak information is collected and transformed into a data matrix for statistical analysis. Many software tools are available that can perform said steps, either using a non-targeted approach, i.e., all the extracted peaks are used as relevant information, or a metabolite-targeted approach, where detected peaks are searched in existing compound databases, matching their retention time and spectra. Examples of GC-MS pre-processing software solutions include AMDIS , Leco ChromaTOF , MetAlign , XCMS  and Tagfinder .
Although deconvolution is a well accepted method, biological complexity of samples, e.g., co-eluting compounds, causes errors in the deconvolution algorithm that result in partially deconvoluted or mixed mass spectra tags (MSTs). A widely accepted alternative to avoid this inconvenience is the extraction of peak apex intensities for selected masses . This method has the advantage of being both less computationally intensive and less error susceptible. In spite of that, existing tools have other limitations such as restrictions to maximum sample number, long run-time and complicated peak identification. The latter typically requires manual inspection and curation that may take weeks to complete for an average experiment.
Our aim was to develop a flexible and automated tool that can pre-process data, identify and quantify metabolite levels for even very large numbers of samples in a targeted manner, whilst keeping processing time to a minimum. Our approach uses peak apex intensities to avoid deconvolution errors, a metabolite reference library based on selective masses and retention time indices , conversion and correction from retention time (RT) to retention time index (RI), RI updating and metabolite identification using multiple correlated masses [15, 17].
Our software is implemented in the R language with computationally intensive functions programmed in C for speed and performance. The software is available as an R-package within the framework of the Bioconductor project .
This section describes our GC-MS pre-processing approach (Figure 1). In short, a sample definition file and a reference library are required to identify the files to process and to provide the metabolite information to be searched for. Mass trace apices are extracted from every chromatogram and stored in text files. At the same time, RT is converted to RI. Selective masses from the reference library are searched in a three-step RI updating procedure. Every file, either chromatogram or peak list, is processed one at a time, allowing the analysis of a large number of samples. Also, since the most time consuming algorithms are written in C, the overall processing time, from raw chromatograms to a metabolite data matrix, is very short.
TargetSearch flow chart. TargetSearch pre-processing flow chart for the analysis of GC-MS data.
There are three types of input that the user must supply: chromatogram files, sample description and a reference library.
GC-MS chromatograms must be first exported to the widely accepted and platform-independent NetCDF file format. This is usually performed by using the platform-specific GC-MS software (e.g. Leco ChromaTOF). Baseline correction and smoothing could be performed before exporting the chromatograms, but as this is not always done, we implemented these steps so they can be performed by TargetSearch.
The "sample description" file contains a list of sample names, sample files and sample groups, which is used for the downstream analyses.
Finally, the "reference library" file contains the information on the MSTs that will be searched for in the chromatograms. Required information is the metabolite name, expected RI, selective masses, most abundant masses, spectra and RI deviation. The file format can be tab-delimited text containing said columns or the commonly used NIST MS Search Software text format . We developed a novel iterative approach to RI assignment to take advantage of the fact that RI variation decreases in the following order: between experiment >> between samples >> between masses from the same metabolite. Thus, we use selective masses to identify the experiment RI from the library RI, following that use this to find the sample RI and finally use this to identify co-eluting masses for quantification. TargetSearch can be used with freely available public metabolite libraries, for example, in [16, 20], with commercial libraries, for example,  or in-house developed or curated libraries.
Initially, TargetSearch performs baseline correction and identifies the local apex intensities from the mass traces in all chromatograms. The baseline correction algorithm is based on  and should be performed if the exported chromatograms were not baseline corrected by the vendor software. This is controlled by the user. After that, TargetSearch finds the retention time of the RI markers, internal standard compounds like alkanes or fatty acid methyl esters, and converts the retention time to RI using a linear interpolation . The RI marker definition, time window and m/z values, have to be provided by the user in the same way as for other software [12, 15, 24].
In addition, a report is created to allow the rapid assessment of RI marker outliers. This is an essential step which can be cumbersome and time-consuming using other methods. The generated report contains a separate plot for each retention index marker in which sample number is plotted against the found retention time. Outliers are highlighted on a per-day basis using a 3σ cut-off (or other defined cut-off). Here, measurement days are defined in the "sample description" file or can be detected automatically using hierarchical clustering. This report allows a fast assessment of outliers allowing the user to quickly decide whether the sample should be removed from the data set or not. It is also possible to manually edit the found retention time, but this is in our experience rarely necessary.
Finally, TargetSearch creates a tab delimited file per chromatogram file which contains a peak list, retention times and converted RIs. For efficiency, it is possible to start subsequent steps from these saved files by selecting "Apex Data" instead of "NetCDF Data".
Metabolites are identified in three steps. First, for every metabolite, selective masses are searched for in a given time window around the expected RI, returning the median RI of all selective masses. The expected RI of the respective metabolite is updated to that value. In the second step, TargetSearch searches the selected masses using the updated RI and a given time deviation, usually smaller than the one used in the first step. The intensities of the selected masses are normalised to the median of the day, and then used to extract other masses with correlated apex profiles. The masses for which the Pearson correlation coefficient is above a given threshold are taken as metabolite markers and their RIs are averaged on a per sample basis. This average RI is taken to represent the exact position where the metabolite elutes in the respective sample. Finally, using this exact RI and a much smaller RI window, TargetSearch searches again for the top masses and the apex intensities are returned.
The aim of TargetSearch is to return a metabolite profile that is directly interpretable with little additional manual curation. This is achieved by using many correlated masses for accurate metabolite identification and quantification. TargetSearch details the set of masses that were used for each metabolite. However, as metabolites with similar selective masses and RIs can be present in metabolite libraries it is necessary to reduce redundancy. TargetSearch does this by selecting peaks for which the RI gap is smaller than a defined cut-off and computing the Pearson correlation between them. When two or more metabolites within such a time-group are tightly correlated only the one with more correlated masses is retained. The potential ambiguity is given in the metabolite identity information. This mostly occurs with large, comprehensive libraries which contain many metabolites that elute at the same time and are thus not distinguishable with any approach, or metabolites that are not in the samples, but whose RI and selective masses match one metabolite that indeed is there. Such ambiguity is largely avoided in the case that a curated or more selective library is used.
where N is the total number of masses. If one mass is missing, then the intensity of that mass is zero.
At the end, three matrices are given. One contains the so called metabolite information. This consists of metabolite names; number of correlating masses and the actual correlating mass values, the library RI, found RI and the deviation, spectra similarity score, and the number of samples in which a given metabolite was found. The other two matrices contain the normalised intensities and the RIs, where columns are samples and rows metabolites.
In order to facilitate the use of TargetSearch for users unfamiliar with R, we provide a simple to use graphical user interface (GUI). A screenshot can be seen in Figure 2. Many parameters that would be set by calling TargetSearch functions can be set here before running the complete analysis. After the analysis is finished, the results are stored in a set of text files that contain the matrices described in the section above. To invoke the GUI, use the R-command TargetSearchGUI().
TargetSearch grahical user interface. A simple GUI for TargetSearch. Here, the user can set all the parameters, import or manually edit the sample files, retention markers definition and library for and run the analysis in one go.
In order to validate and demonstrate our method, we performed pre-processing analyses of both a standard mixture experiment and a biological example dataset. The standard mixture dataset allows the performance of TargetSearch to be objectively assessed by comparing the results against the known concentration ranges, whilst the analysis of a biological dataset and the resulting comparison to manual data curation provides an example of the performance on an actual experiment. The standard mixture experiment is described in the example in order to demonstrate the analysis workflow, whilst only the interpretation and comparison of results is presented for the biological dataset.
The following section describes the analysis of the standard mixture experiment containing 27 samples using TargetSearch. It is worth noting that these samples were measured in another laboratory (RIKEN Plant Science Center, Japan), which shows that TargetSearch can work for other data and not only for data generated by our GC-MS platform. Each sample consisted of a mixture of 44 chemical standards at different proportions that were measured as described by Kusano et al. . All chemicals used for GC-TOF/MS analysis were purchased from Wako (Tokyo, Japan) or Sigma-Aldrich (Tokyo, Japan). See Additional file 1 for a detailed list of the standard proportions. The chromatograms are available for download via DROP Met at http://prime.psc.riken.jp. We provide a detailed R-script (Additional file 2) to perform the analysis of the 27 samples. See the TargetSearch vignette or the user manual for further options not covered here.
The first step in GC-MS data pre-processing is to import the chromatogram NetCDF files to TargetSearch. In order to do that, the user must provide the sample ("samples.txt") and retention index ("rimLimits.txt") markers definitions, as described in the user input subsection (see Additional files 3 and 4). The following R-code shows how both definition files are imported.
The parameters set the directories where the chromatograms are located and where the extracted peak list files will be saved. After that, the user needs to perform baseline correction and peak identification. Obviously, baseline correction is needed here because these chromatograms have not already been corrected. Otherwise this step is optional.
The RIcorrect function performs both algorithms and is the most time consuming step of the whole pre-processing analysis. In this stage it is possible to specify several parameters: massRange parameter limits the mass values (m/z) that will be included in the analysis, smoothing Window parameter is the number of raw data points that will be averaged in order to remove noise, IntThreshold defines the minimum apex intensity to be listed, baseline indicates that baseline correction will be performed, and baseline.opts is a list object that contains the parameters passed to the baseline algorithm.
The function returns a matrix (RImatrix) which contains the retention times of the RI markers (rows) found in every sample (columns) and creates a peak list tab delimited file per chromatogram as described in the peak identification section.
This took around 13 minutes to run on an Intel® Core™2 CPU, 2.13 GHz, 2Gb of RAM computer. It should be mentioned that performing baseline correction doubles the running time in comparison to the running time of already corrected chromatograms, so is very useful to select baseline correction when exporting the chromatograms with the vendor software.
As mentioned before, an essential step is to look for RI markers outliers. This can be easily done by the function FameOutliers which creates a separate plot for each marker and reports whether or not outliers have been found (Figure 3). In this example, we did not find any outliers, but we did observe a strong so-called "day effect" when comparing the measurement day number 2 against days 3 and 4.
Retention index markers report. Retention time of the first four retention index markers in the standard mixture experiment. Samples and retention times are represented in the x and y-axis, respectively. Different days of measurement are indicated by numbers 2, 3 and 4. A so-called day effect is observed between days 2, and 3 and 4, manifested as a retention time shift.
The next step is to import the library file (Additional file 5), which is done by the function ImportLibrary. Useful options are TopMasses, which takes the top N most intense masses from the reference spectrum to be searched for, and RI_dev sets the RI deviations. The RI deviations can be equal for all metabolites (a vector of length three), or individual ones (a matrix of three columns and as many rows as metabolites) for fine tuning, i.e., it is possible to set specific RI deviations per metabolite.
The metabolite search is performed in three steps as explained in the library search subsection, where a smaller window is used in every following step. Function medianRILib searches the average RI of the selective masses and returns an updated library object. Function sampleRI searches for the selective masses in the samples using the updated average RIs and returns a matrix where each element (i, j) represents the average RI of metabolite i in sample j. Finally, function peakFind searches again for the top masses and returns the apex intensities and RIs. The three step search is performed by the following R-code.
The object peakData object has two slots containing all apex raw intensities and RIs of all the masses that were searched for in matrix form. The apex matrix can in principle be used to perform further statistical analysis, by either using R or other statistical tools. In addition, we provide two functions, Profile and ProfileCleanUp, that create a metabolite profile out of the raw apex data.
The first function makes the metabolite profile and the second one looks for possible ambiguities, which are controlled by the parameters timeSplit and r_thresh. They define the RI gap cut-off that is used to look for metabolites whose RI distance is less than this gap and the correlation threshold, respectively. A conceptually similar approach so-called "time groups" is implemented in the TagFider software . A summary of the obtained metabolite profile is shown in Table 1 (see Additional file 6 for the full profile). We could identify 43 of the 44 standards. All metabolites have at least seven correlating masses, except maltose that has only four. The similarity score (1) was over 790 for every metabolite except histidine (443), fructose-6-phosphate (661) and maltose (716). Only one ambiguous assignment was found: ribose and arabinose. This may have happened because both metabolites have very similar RIs, the same selective masses and similar spectra. One way to resolve such ambiguity can be setting smaller RI deviations and/or using different selective masses.
Summary of the standard mixture metabolite profiling.
The summarised metabolite profile of the chemical standards (see Additional file 6 for full information). Name: metabolite name; Mass count: the number of correlating masses; RI: averaged RI; Score: spectrum similarity according to (1); RI dev; RI deviation from the expected RI; Sample Count: the number of samples in which the metabolite was found; Corr Coef: the correlation coefficient between metabolite input concentration and abundance estimation.
Since the chemical standards were measured using different proportions (Additional file 1), we examined the correlation between the abundance estimation with the input concentration in order to further confirm the TargetSearch results. There is a clear correlation for all the metabolites (Table 1, Additional file 7). Most of the metabolites (29) had a correlation coefficient greater than 0.9 and the lowest coefficient was 0.69. Here we showed that with little effort we can perform a metabolite analysis that can be used with little manual curation needed (only 4 of 44 has to be manually checked).
After GC-MS pre-processing, the user can visually compare the averaged metabolite spectra with the reference spectra. Our software include two functions to do so. plotSpectra and plotAllSpectra. They plot a spectra contrast between averaged metabolite spectra, obtained by computing the median intensity of every mass across the samples, and reference spectra for a given metabolite (Figure 4). This allows the user to quickly validate the results by visually inspecting the contrasted spectra. In addition, the metabolite spectra can also be exported as a MSP format file (function writeMSP), which can be imported into NIST for further comparisons against other metabolite libraries.
Comparison between identified metabolite spectra and reference spectra. Comparison example between the averaged (blue) and reference spectra (red) of glycolic acid (right) and histidine (left). This type of comparisons can be used to assess the quality of individual identified peaks. In this example, histidine may need a closer inspection to elucidate what causes the difference.
We additionally provide a function to visualise chromatographic peaks in a given sample, which may also help to identify metabolites. See the TargetSearch vignette for examples.
The biological dataset consisted of wild-type Col-0 Arabidopsis thaliana seedling samples. Three week old seedlings, grown on solid MS medium with 1% sucrose, were kept for 4h under either continuous light or darkness. Metabolites were extracted from pools of seedlings in a total of four biological replicates. Extraction and derivatisation of metabolites from leaves using GC-MS were performed as outlined by Lisec et al. . GC-MS data were obtained using an Agilent 7683 series autosampler (Agilent Technologies GmbH, Waldbronn, Germany), coupled to an Agilent 6890 gas chromatograph - Leco Pegasus 2 time-of-flight mass spectrometer (LECO, St. Joseph, MI, USA). Identical chromatogram acquisition parameters were used as those previously described by Weckwerth et al. .
A similar pre-processing analysis as the one described for the standard mixture dataset was performed. We used a fatty acid methyl esters (FAMEs) as RI marker standards (Additional file 8) and an in-house reference library composed of 153 metabolites (Additional file 9). This library was manually curated and in addition to known metabolites includes several unknown metabolites that have been observed in previous experiments.
After running TargetSearch, we manually compared the final profile with the deconvoluted peak profiles obtained with LECO (see Additional file 10 for full profile with manual annotations). The profile list contained 138 entries in total, of which 131 metabolites were unambiguously assigned. The 7 ambiguous assignments corresponded to 14 metabolites (2 metabolites per entry). The remaining 8 metabolites (of the 153) were neither present in the final profile nor in the chromatograms as confirmed by manual inspection. Based on our previous experience, we routinely only consider metabolites to be present if at least 3 correlating masses at the correct RI are identified (this excludes the duplicate isotope pairs that are often observed to correlate). Taking this into account, we would consider 101 metabolites to be identified in this experiment. We thus checked these manually and found that 96 were correctly assigned (these were all assigned a similarity score above 600), 1 ambiguity was wrongly resolved, i.e., the correct metabolite was not the one suggested; 1 ambiguity could not be resolved manually, due to similar RIs and reference spectra; 1 metabolite was not found in the chromatograms; and 2 metabolites were not found but 2 peaks (unknowns) were found at their expected retention time. The later could be anticipated in the profile since the similarity score reported by TargetSearch was below 400.
Although we would not routinely work with the remaining 37 entries (38 metabolites in total) that did not meet our correlating masses minimum, we additionally manually inspected these. Their classification was as follows: 31 metabolites, including 1 ambiguity, were not found in the chromatograms; 5 metabolites were only found in a few of the chromatograms at low abundance and 2 metabolites were present but overloaded. In summary, 96 of 101 metabolites found by TargetSearch were correctly assigned. Similarly, 31 of 38 metabolites that we considered to have too few correlating masses to be present were manually confirmed not to be present in the chromatograms. Just 5 metabolites were found by our software but could not be confirmed to sufficient confidence by manual curation, whilst 7 metabolites were not reported by TargetSearch but were present in the chromatograms. This gives over 91% of assignment accuracy, counting the true positives and true negatives ratio. The results are illustrated in Figure 5.
Comparison between identified metabolites by TargetSearch and manual curation. The venn diagrams show the number of metabolites considered to be either present or not present by TargetSearch in comparison to the manual curation. Left hand side: 96 metabolites identified by TargetSearch that were confirmed by manual curation. Right hand side: 31 metabolites not identified by TargetSearch and confirmed by manual curation. Both diagrams: 5 metabolites found by our software but not confirmed by manual curation, whilst 7 metabolites were not reported by TargetSearch but were present in the chromatograms.
Based on this example analysis and our experience with several other biological datasets, errors can be made by TargetSearch in the following cases: metabolites with similar selective masses and spectra that elute at similar retention times; low abundant metabolites may cause spurious correlations, whilst overloaded peaks often elude the peak detection algorithm or disrupt correlation; finally, metabolites not present in the library that elute in the place where another metabolite is expected, as occurred in two cases in this analysis.
Many of these issues can be solved by changing the parameter definitions and in general the resulting dataset is more objectively linked to these parameters than the decisions involved in a typical manual curation. Metabolite identification issues for example can be solved by increasing the intensity threshold during the peak detection stage, or by changing the correlation cut-off. We also note that most of these errors are inherent to metabolite profiling per se rather than being specific to our software and are thus also addressed by other technical aspects of the experiment (e.g. saturation/sample overloading or low-abundance).
We developed this package with the intention to process as many chromatograms as possible in a single run and to be fast. To asses performance, we compared the processing speed of TargetSearch with other GC-MS pre-processing software: XCMS and Tagfinder. First, we wanted to see what was the maximum number of chromatograms that can be processed by said tools. TargetSearch could process our largest experiment - 1025 baseline corrected chromatograms (Caldana et al., unpublished), taking around 6 hours to get a final data matrix (Intel® Core™2 CPU, 2.13 GHz, 2Gb of RAM). However, neither XCMS nor Tagfinder were able to process anywhere near that number of chromatograms with the same computer. We then reduced the number of chromatograms to 200 so we could compare the processing times. TargetSearch proved to be much faster than XCMS and Tagfinder. Results are summarised in Table 2.
Performance comparison of pre-processing analysis using Tagfinder, XCMS and TargetSearch.
All parameters were set to default in order to obtain the maximum number of samples, but only 1025 samples were available. Times were compared using 200 samples. Before running this analysis, Tagfinder intensity threshold was set to 200 to allow the 200 samples to be imported. Time is expressed in seconds. Peak import is not necessary in XCMS and TargetSearch. RI correction is performed together with Peak extraction in TargetSearch.
Here we presented the TargetSearch software for the data pre-processing of GC-MS based metabolite profiling experiments. Our method includes a novel strategy to iteratively correct and update retention times and uses efficient criteria for searching and identifying metabolites. This massively reduces the time from data acquisition to biological interpretation, particularly as good quality data can be directly obtained with limited need for manual curation. Another advantage over other software, is the ability to analyse virtually any number of samples, at least over 1000, which otherwise is a major limitation of existing software, whilst maintaining a fast processing speed. We also provide a GUI intended to assist users not familiar with R. Using a dilution mixture dataset we found that TargetSearch accurately identified the chemical components and reported abundance estimates which very well reproduced the input concentrations. Using a real biological experiment, the accuracy in terms of automated profiling was over 90%. These results show that TargetSearch greatly facilitates the analysis of metabolic profiling data and creates substantial time savings allowing scientists to focus more on the biology under investigation.
The authors acknowledge Aenne Eckhardt, Gudrun Wolter and Anjte Boltze for running samples. Alexander Erban, Alexander Luedemann and Joachim Kopka for useful discussion. Thomas Degenkolbe, Szymon Jozefczuck and Gareth Catchpole for commenting on the method. ACI acknowledges MECESUP. Makoto Kobayashi is thanked for GC-MS measurements. Funding was provided by Max Planck Society, Max Planck Institute for Molecular Plant Physiology. MAH was supported by FP6 "AGRON-OMICS" (contract LSHG-CT-2006-037704).
MAH initiated and supervised the project and developed an initial method with HR. ACI improved and developed the majority of the method and the R package. CC tested and contributed to the method. JL contributed R scripts and developed the graphic user interface. MK performed GC-MS analysis. LW discussed and supervised the project. ACI drafted the manuscript, with additions from all authors. All authors read and approved the final manuscript.
Leco ChromaTOF, St. Joseph, MI, USA[http://www.leco.com].


text:
Q: Why does this Python subprocess command only work when shell=True on Windows? I have a python script that involves multiple subprocess.call commands. I wrote the script on a Mac, and it runs perfectly. I just tried running it on Windows and I am baffled by an error.
The following command to call ImageMagick returns "exit status 4":
file1 = "D:/Temp/OCR_test/sample/images/crops/time_0011.png"
subprocess.call(['convert', file1, '-resize', '200%', file1])

Changing the command to the following works:
subprocess.call(['convert', file1, '-resize', '200%', file1], shell=True)

I'm a little wary of using shell=True because of the warnings in the documentation.
I also need the command to work on both Mac and Windows, and I am confused as to why it wouldn't work on Windows (I checked and the command does work using Windows CMD).
Interestingly, the following line worked earlier in the script (where file, lat_crop1, and croplat are defined variables):
subprocess.call(['ffmpeg', '-loglevel', 'panic', '-i', file, '-vf', lat_crop1, '-n', croplat])

I read this SO question and tried all the suggestions (shlex, variations of my command, etc...), but I still get the same result.
Anyone have any idea how I could modify that line so it can work without shell=True?
Also, what does "exit status 4" mean? I Googled and read so much documentation but found nothing about it.
EDIT: Based on the information provided in the answer, I changed to the command that was not working to subprocess.call(['mogrify', file1, '-resize', '200%', file1]) and that runs successfully in Python on Windows. Luckily ImageMagick provides mogrify as an alternative to convert.

A: I suspect you're calling C:\Windows\System32\convert.exe (NTFS/FAT partition converter) instead of imagemagick.
When shell=True, the path finds the convert.bat or convert.cmd script from imagemagick, but without it, the path can only find the .exe file, which is a completely different program, and you get error 4: invalid parameter.
In that particular case, it doesn't work even with an executable, because the "wrong" convert is located in a system path. shell=False only searches in system paths (python subprocess Popen environment PATH?). So that's bad luck that a program named convert is located in the system path.
Try to explicitly add .bat extension like this:
subprocess.call(['convert.bat', file1, '-resize', '200%', file1])

To know which executables are likely to be run you can type:
where convert

in a command prompt.
In your case (an executable), that could be workarounded by passing the absolute path of the executable you want to run.
Another way would be to copy/rename ImageMagick convert to imconvert. Which program calls itself convert and doesn't expect conflicts anyway ?
Or in that case, it's legitimate to leave shell=True, with a nice comment explaining that Microsoft left a confusing (and seldom used convert program in a system path for us to trip into)
Solutions are not pretty, at least there are some.

redpajama_set_name: RedPajamaStackExchange


text:
Ady Mariska (Hadad, 1888. február 26. – Budapest, 1977. február 4.) magyar költő, elbeszélő. Ady Endre unokatestvére volt.

Élete

Budapesten szerzett tanítói oklevelet, írói munkásságát novellával és tárcával kezdte; az önálló társadalmi létet kereső és kiharcoló nő hangját szólaltatta meg. Az első világháborúban elvesztette férjét, Landt Lajost, s a három árvával magára maradt özvegy versekben panaszolja el a háború súlyos következményeit. Lírai képalkotásában Ady Endrét követi. Írásai főleg erdélyi lapokban, így a Csíki Lapok, Ellenzék, Magyar Nép, Pásztortűz, Székely Nép, Szilágy, Vasárnapi Ujság, Zord Idő hasábjain jelentek meg. Az Ady életrajz számára értékes családi emlékezései Kovalovszky Miklós Emlékezések Ady Endréről (I. Budapest, 1961) c. munkájában is szerepelnek. Tagja volt a Kemény Zsigmond Társaságnak.

Kötetben szépirodalmi munkái

 Sok ború – kevés derű (elbeszélések, rajzok, Zilah, 1907)
 Én az őszben járok (versek, Székelykeresztúr, 1924; 2. bőv. kiad. Kolozsvár)

Források

 Ki kicsoda a magyar irodalomban? Tárogató könyvek

Magyar költők
Magyar írók
Magyar nők
1888-ban született személyek
1977-ben elhunyt személyek

redpajama_set_name: RedPajamaWikipedia


- by certified cheque, money order or bank draft made out to "Spring Hockey".
All registrations must be accompanied by FULL payment and will be considered complete after the payment is validated.
- The program is cancelled prior to commencement. Please allow four weeks for receipt of all eligible refunds.
No Refund will be issued after all teams are formed and the season starts.
In all cases of withdrawal from the program, the registrant is responsible for advising the office as well as the coach of his/her intent to withdraw. All refund cheques will be mailed to registrant within four weeks.


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The brewery in Ipswich where Notch brews their beer is kinda like what we used to operate out of when we first got started. It's a shared space that multiple businesses can use while they are getting started. Chris shares the space with other brewers and soda makers.
This is a picture of a dry erase board that really interested me for some reason. That's Chris's arm (he's the brewer) and he's translating all the hieroglyphics. The bottom right corner shows the production schedule and which tanks are reserved for brewing. The rest of the board is a map of the floor plan with all the holding tanks. This is where beer is stored and fermented until it's ready to be bottled, or put in a keg. Each tank is labeled with whose beer it is, what kind of beer it is and the initials of the person who used the tank last. So if the person who used the tank last didn't clean it properly and it affects a batch, they can hold that person responsible. I love systems like this.
If you want to taste the results of all this planning, come by the restaurant tonight, we'll be launching Notch Session Ale. 8pm to midnight at the restaurant (7 Holyoke). Beer is $3. Chris will pour.
What does a whoopie pie taste like at midnight?


This lovely, creamy, savory soup enhances the delicate garlic-asparagus flavor of the scapes. You may use the garlic scape flowers as well.
This is a truly easy, incredibly delicious pie that looks like it takes much more effort than it actually does. Once you start eating it, none of that matters, either.
This is a lovely, earthy fall salad that uses lemon juice and truffle oil to make a delicious vinaigrette. If you don't have truffle oil, you can substitute a good quality extra-virgin olive oil.
This beautiful, light dish is a celebration of spring flavors. Feel free to substitute any vegetables in season, including ramps, green garlic (in place of the cloves), and morels (instead of white button mushrooms).


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text:
Die Liste der Naturdenkmale in Hontheim nennt die im Gemeindegebiet von Hontheim ausgewiesenen Naturdenkmale (Stand 8. August 2013).

Einzelnachweise

Hontheim
!Naturdenkmale


The website domain has been duly purchased and links made, so you can now see the BMSS website at www.bmssonline.com.
We've also secured www.bmssonline.co.uk, as we are substantially a UK based modelling society.
Hopefully, we will have the website fuly up and running (With a secure members' pages set) by the end of the week.


The 2019 National RSL & Services Clubs Conference will be held in the Hunter Valley Wine Country at The Crowne Plaza Resort - 14th - 17th April.
As a collective of RSL Licensed Clubs & RSL Licensed Sub Branches across the state jurisdictions of Queensland, New South Wales and Victoria we all share common themes, goals and challenges as contemporary RSL hospitality venues. At this year's conference delegates we will come together to hear from business leaders and industry experts in an effort to gain that competitive edge in an ever changing hospitality market place.
As community entities, RSL & Services Clubs are assumed to be well engaged in their local communities. But how well do our clubs engage? Do we expect the community to come to us, or is a more pro-active process likely to lead to greater awareness and success?
What does 'community engagement' actually mean and how can it shape our businesses and strengthen our profile?
There will be a special guest speaker, Gail O'Brien AO the wife of late celebrated cancer surgeon, Professor Chris O'Brien AO. A health professional of 40 years, Gail trained in physiotherapy and is skilled in a diverse range of therapies including orthopaedic rehabilitation, gerontology, and women's health.
You will also hear from club managers and CEO's who have successfully implemented very effective campaigns in support of important charities or community projects by engaging their members and local community.
An 'engaging' Conference, in the beautiful Hunter Valley, with fantastic social events and networking, as well as strong educational content.
Hopefully the 2019 National RSL & Services Clubs Conference will provide some valuable insights and ideas for everyone to take away and to make you better equipped for Engaging your Community!
So close to Sydney yet truly a world away, the Hunter Valley is the birthplace of Australian wine. Wine Country – as the Hunter Valley is known – is a dynamic wine region with more than 150 premium wine producers and 60 tempting restaurants.
Come a few days early and explore some of the great wineries, restaurants and cafes that the Hunter Valley has to offer! Newcastle is only a short drive away if you want to take a day trip to the stunning beaches or take a walk on the newly built Anzac Bridge, which attracts thousands of people a day to take in the breathe taking scenery!


text:
Chief Atahm School is an immersion school working to revive the Secwepemc language. It is located on the Adams Lake reserve near the town of Chase, British Columbia. The school is operated by parents.

References

External links
Chief Atahm School

Secwepemc
First Nations education
First Nations organizations in British Columbia


I am overjoyed and expectant for all that is ahead for 100cameras in 2018! We have recently launched our Snapshot Project platform as an avenue to serve more kids worldwide with each new year. By packaging up our model, curriculum and tools, and the lessons we have learned from the field for close to a decade to now, we are planning to work with 20 new communities next year alone through equipping passionate creatives to empower kids and communities around the world that are close to their hearts.
It is quickly becoming a favorite highlight of my work to connect with leaders across four continents who will be spearheading Snapshot Projects across our world. From a seventeen year old leading some of her high school photography club members to travel and work with Rohingya refugees in Bangladesh to a retired local doctor utilizing her love of photography to give kids a platform to process their stories in the midst of a confusing time in Venezuela to a Mom and her two teenage girls that just completed a trek to reach the Q'eros tribe in the mountains of Peru, I am convinced each Snapshot Project leader represents the most incredible threads that are found in humanity across our great earth. These examples represent just a few of our inspiring leaders and their stories, and we are excited to be introducing each to you over the next few weeks.
On behalf of ONA Bags and 100cameras, we invite you to be encouraged with us as we follow along their journeys to empower kids they care about to process and tell their stories through photography in a way that impacts how they view themselves and their roles in their communities. As each project is completed, the students' images will be for sale at 100cameras.org, and 100% of the proceeds will fund the most pressing needs in their communities, enabling them to see the impact of their contribution.
When you purchase a product with ONA Bags, you are supporting the Storytelling Fund which will help directly further our mission through these Snapshot Projects, and we are truly thankful for your involvement and support!
Learn more at 100cameras.org, and view/purchase the students' photos in their online shop. Help share the story of 100cameras and the students' perspectives by following on Instagram, Facebook, and Twitter.


On May 16, 2018 Venable partner Sung Hwang will moderate the panel "The Basics of Condominium Offering Plans and the Role of New York State Attorney General's Office – Real Estate Finance Bureau" at the New York County Lawyers Association. He will be joined by NYCLA's Asian Practice Committee for an updated look at the New York State Attorney General's Office requirements for condominium offering plans, a discussion from the attorney's perspective, and information on what banks look for in an offering plan to get a new project ready for mortgage lending.
Venable will be co-sponsoring the program with NYCLA's Asian Practice Committee, the Asian American Bar Association of New York, and the Korean American Lawyers Association of Greater New York.
David W. Lu, Seiden & Schein, P.C.


McCann uses 21st century technology, but our customer-oriented service philosophy is grounded in 44 years of experience. We are meeting the challenges faced in today's fast-paced, ever-changing world of assessment while continuing the tradition of fair, valid assessment of superior quality.
Describes the anatomy and physiology of the human body, as well as disorders and treatment of the various body systems.
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Focuses on the application of management theory to the operations of claim departments, including the management functions of planning, controlling, organizing and leading.
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11/7/09: Updated below the legend with an opinion of Ralph Peters. Thank you, Ralph. A timely message!
Our hearts are heavy... Our prayers and thoughts are with our sons and daughters and their sons and daughters at Ft. Hood.
Be sure to read Greyhawk's initial reporting on those who were killed yesterday... and be sure to check back at Mudville Gazette often for updates.
On Thursday afternoon, a radicalized Muslim US Army officer shouting "Allahu Akbar!" committed the worst act of terror on American soil since 9/11. And no one wants to call it an act of terror or associate it with Islam.
What cowards we are. Political correctness killed those patriotic Americans at Ft. Hood as surely as the Islamist gunman did. And the media treat it like a case of non-denominational shoplifting.
When the terrorist posts anti-American hate-speech on the Web; apparently praises suicide bombers and uses his own name; loudly criticizes US policies; argues (as a psychiatrist, no less) with his military patients over the worth of their sacrifices; refuses, in the name of Islam, to be photographed with female colleagues; lists his nationality as "Palestinian" in a Muslim spouse-matching program, and parades around central Texas in a fundamentalist playsuit — well, it only seems fair to call this terrorist an "Islamist terrorist."
But the president won't. Despite his promise to get to all the facts. Because there's no such thing as "Islamist terrorism" in ObamaWorld.
And the Army won't. Because its senior leaders are so sick with political correctness that pandering to America-haters is safer than calling terrorism "terrorism."
And the media won't. Because they have more interest in the shooter than in our troops — despite their crocodile tears.
Maj. Nadal Malik Hasan planned this terrorist attack and executed it in cold blood. The resulting massacre was the first tragedy. The second was that he wasn't killed on the spot.
Hasan survived. Now the rest of us will have to foot his massive medical bills. Activist lawyers will get involved, claiming "harassment" drove him temporarily insane. There'll be no end of trial delays. At best, taxpayer dollars will fund his prison lifestyle for decades to come, since our politically correct Army leadership wouldn't dare pursue or carry out the death penalty.
Maj. Hasan will be a hero to Islamist terrorists abroad and their sympathizers here. While US Muslim organizations decry his acts publicly, Hasan will be praised privately. And he'll have the last laugh.
But Hasan isn't the sole guilty party. The US Army's unforgivable political correctness is also to blame for the casualties at Ft. Hood.
Given the myriad warning signs, it's appalling that no action was taken against a man apparently known to praise suicide bombers and openly damn US policy. But no officer in his chain of command, either at Walter Reed Army Medical Center or at Ft. Hood, had the guts to take meaningful action against a dysfunctional soldier and an incompetent doctor.
Had Hasan been a Lutheran or a Methodist, he would've been gone with the simoon. But officers fear charges of discrimination when faced with misconduct among protected minorities.
Now 12 soldiers and a security guard lie dead. 31 soldiers were wounded, 28 of them seriously. If heads don't roll in this maggot's chain of command, the Army will have shamed itself beyond moral redemption.
There's another important issue, too. How could the Army allow an obviously incompetent and dysfunctional psychiatrist to treat our troubled soldiers returning from war? An Islamist whacko is counseled for arguing with veterans who've been assigned to his care? And he's not removed from duty? What planet does the Army live on?
For the first time since I joined the Army in 1976, I'm ashamed of its dereliction of duty. The chain of command protected a budding terrorist who was waving one red flag after another. Because it was safer for careers than doing something about him.
Get ready for the apologias. We've already heard from the terrorist's family that "he's a good American." In their world, maybe he is.
A disgruntled Muslim soldier murdered his officers way back in 2003, in Kuwait, on the eve of Operation Iraqi Freedom. Recently? An American mullah shoots it out with the feds in Detroit. A Muslim fanatic attacks an Arkansas recruiting station. A Muslim media owner, after playing the peace card, beheads his wife. A Muslim father runs over his daughter because she's becoming too Westernized.
Muslim terrorist wannabes are busted again and again. And we're assured that "Islam's a religion of peace."
I guarantee you that the Obama administration's non-response to the Ft. Hood attack will mock the memory of our dead.
Ralph Peters' latest novel is "The War After Armageddon."
FORT HOOD, Texas — Former President George W. Bush and his wife, Laura, visited wounded soldiers and their families near the site of the worst mass shooting on an Army post in the United States.
AND WHERE IS OUR ESTEEMED PRESIDENT???
Thanks for sharing. I have never been to this web page but it has a ton of infomation.


text:
Worthy fights : a memoir of leadership in war and peace / Leon Panetta ; with Jim Newton
Panetta, Leon E., 1938-
New York : Penguin Press, 2014.
498 pages, 32 unnumbered pages of plates : illustrations ; 25 cm
Newton, Jim, 1963-
pt. I. Politics and progress. "A better life" -- "Look at yourself in the mirror" -- "You did what was right" -- "No more excuses" -- "Working for us" -- "It's the right fight" -- "If the White House is falling apart ..." -- "We thought you would cave -- pt. II. Protect and defend. "The combatant commander in the War on Terrorism" -- "Tell it like it is ... our national security depends on it" -- "Disrupt, dismantle, defeat" -- "Everywhere in the world" -- "Go in and get Bin Laden" -- "To be free, we must also be secure" -- "A new defense strategy for the twenty-first century" -- "In together, out together" -- "I cannot imagine the pain" -- Leadership or crisis.
Leon Panetta's first career, beginning as an army intelligence officer and including 16 years in the House of Representatives, lasted thirty-five years and culminated in his role as Clinton's budget czar and White House chief of staff. He then "retired" to establish the Panetta Institute with his wife of fifty years, Sylvia; to serve on the Iraq Study Group; and to protect his beloved California coastline. But in 2009, he accepted what many said was a thankless task: returning to public office as the director of the CIA, taking it from a state of turmoil after the Bush-era torture debates and moving it back to the vital center of America's war against Al Qaeda, including the campaign that led to the killing of Osama bin Laden. And then, in the wake of bin Laden's death, Panetta became the U.S. Secretary of Defense, inheriting two troubled wars in a time of austerity and painful choices. Worthy Fights is a reflection of Panetta's values. It is imbued with the frank, grounded, and often quite funny spirit of a man who never lost touch with where he came from: his family's walnut farm in beautiful Carmel Valley, California. It is also a testament to a lost kind of political leadership, which favors progress and duty to country over partisanship. Panetta is a Democrat who pushed for balanced budgets while also expanding care for the elderly and sick; a devout Catholic who opposes the death penalty but had to weigh every drone strike from 2009 through 2011. Throughout his career, Panetta's polestar has been his belief that a public servant's real choice is between leadership or crisis. Troubles always come about through no fault of one's own, but most can be prevented with courage and foresight.
United States. Central Intelligence Agency—Officials and employees—Biography
Cabinet officers—United States—Biography
War on Terrorism, 2001-2009
United States—Foreign relations—2009-2017


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BLOOMINGTON, Minn. – October 2, 2018 – Continuing to build exposure for its student-athletes and programs, the Women's League of the Western Collegiate Hockey Association (WCHA) is once again partnering with FOX Sports North and FOX Sports Wisconsin for multi-game coverage during the 2018-19 campaign. The WCHA's 20th anniversary season will feature nine of its regular season contests televised across the region's preeminent sports networks.
The 2018-19 WCHA package begins Friday, Oct. 19 and includes the most regular season games in the three years of this partnership. Bemidji State University, the University of Minnesota Duluth, Minnesota State University, Ohio State University, St. Cloud State University and the University of Wisconsin will all be featured.
Highlighting the women's schedule for "College Hockey on FOX Sports North" is a pair of games on Saturday, Jan. 19 as part of the network's 13th Annual Hockey Day Minnesota event – a 1:00 p.m. outdoor game between host Bemidji State and Minnesota State, along with a 4:00 p.m. contest between visiting Ohio State and Minnesota Duluth.
Telecasts from St. Cloud will be produced by the award-winning crew of Husky Productions, while Minnesota State's home games are made possible in part by the partnership between Maverick Athletics and the renowned BLC Studios, a division of Bethany Lutheran College.
The full 2018-19 WCHA schedule on FOX Sports North and FOX Sports Wisconsin follows.
Jan. 19 Saturday Minnesota State vs. Bemidji State 1:00 p.m.
All games televised on FOX Sports North will be available in high definition, and will be streamed live via the FOX Sports app. The FOX Sports app, available to iOS and Android devices, provides live streaming video of FOX Sports content along with highlights and instant alerts for scores and key plays. Fans can download the mobile app for free from the Apple App Store and Google Play. Streaming on the FOX Sports app is also available on connected devices, including on AppleTV, Android TV, FireTV, Roku and Xbox One.
Information on channel locations for FOX Sports North/ PLUS can be found here.
FOX Sports North reaches more than 2 million homes throughout Minnesota, Wisconsin, Iowa and North and South Dakota and telecasts nearly 2,600 hours of locally produced programming per year. For more information about the Emmy Award-winning regional sports network: www.foxsportsnorth.com.
The Women's League of the Western Collegiate Hockey Association is celebrating 20 Years of Excellence during 2018-19 anniversary season. The nation's premier women's hockey conference, WCHA member institutions have won a record 16 national championships since the league's inception in 1999 (including 15 of a possible 18 NCAA titles), while producing seven Patty Kazmaier Memorial Award winners, 98 All-Americans and numerous Olympic and national team members. The seven-team, NCAA Division 1 conference consists of: Bemidji State University (Beavers), the University of Minnesota (Golden Gophers), the University of Minnesota Duluth (Bulldogs), Minnesota State University (Mavericks), Ohio State University (Buckeyes), St. Cloud State University (Huskies) and the University of Wisconsin (Badgers). For more information, visit www.wcha.com.


Most fertilizers today are produced in large-scale, centralized facilities and then shipped to remote/rural areas. Due to the long-distance transportation, many rural farmers often pay 2-3 times the world price for their fertilizers. As they have limited income, these farmers often become dependent on the cheapest, synthetic varieties that over time degrade and acidify their soil. Mr. Kibuchi, for example, a rural farmer in Kenya, has seen his harvest yields decrease by almost 50% in the past 20 years.
We use technology (developed at MIT) to downsize/decentralize fertilizer production, making it possible to deploy on a village level profitably using local resources/labor. Imagine low-cost, small-scale and portable reactor units that can be latched onto tractors and shipping containers, and enable the on-site conversion of agricultural residues into a carbon-rich intermediate in 2 hours without external energy input. This intermediate is then mixed with our proprietary recipes to complete the nutrient requirements. Our product is a liming agent, which reverses soil acidify and improves yields. At the same price that farmers pay for their fertilizers, their yields can improve by 30% and income by 50%. In addition to benefits to farmers, our process, by virtue of creating profitable village-based fertilizer operations, also creates livelihoods/jobs in rural decentralized areas. Finally, our product is carbon-negative: the more farmers purchase/use it, the more CO2 we mitigate.
Mr. Kibuchi is a rice farmer in Mwea, Kenya who owns a one-acre land. For the past 20 years, he has been dependent on imported DAP fertilizer, and has noticed that his crop yield almost halved due to increasing soil acidity and leaching. Last year, he started using our Safi Sarvi, and noticed an immediate improvement in his crop growth (rate, flowering, and harvest yield) compared to before. After 2 seasons, he has seen his harvest and income increase by 30%, which was sufficient to allow him to send his 2 children to school and invest in a new tractor. So far we have impacted more than 1,000 farmers like Mr. Kibuchi. In Kenya alone, there are an estimated 3 million such farmers, spending about USD45 million/year on ineffective fertilizer. In addition to social benefits, due to the carbon-rich nature of our fertilizer, each ton applied sequesters 1.7 tons of CO2 equivalent from the atmosphere. So far we have sequestered about 5,700 tons of CO2 and managed 7,200 tons of local waste.
Initially, as we prove our self-operated pilot cases, we rely primarily on grants (60%) and earned income from local operations (40%) to iterate our process. As we replicate in more villages, we expect these operations to be completely supported by earned income and make a $60,000/year/village profit. Once we prove this profitability, we will start franchising the solution out to farmers' cooperatives and local agricultural NGOs to achieve faster scale, by providing training/equipment in exchange for 10% of the profits from these franchisees as our income. To help us scale beyond Kenya into other countries, we will also seek impact financing (~$500,000) in 2019 to accelerate the process; we estimate that this equity financing can be paid back in 5 years with 4x return.
Co-founder Samuel came from a rural Kenyan farming family, and grew up witnessing how farms in his village were degraded by the dependency of synthetic fertilizers. He studied agriculture in university, wanting to improve rural agriculture in his village and beyond. In 2013, Samuel met co-founder Kevin, who has been working since 2011 on a low-cost, decentralized biomass reactor design. The duo initially co-founded a prior company that sold millions of low-toxin mosquito coils to the Kenyan market. After this initial work, Samuel brought local insight of rural farmers from his background and developed various proprietary nutrient recipes into the Safi Sarvi product that they love, while Kevin completed the design of the reactors as part of his PhD thesis at MIT. The co-founders incorporated the company in 2015 and now has grown it to more than 1,000 customers and 7 full-time employees.
Dorine: Thanks for your comments --- and congratulations on your project (I have definitely heard of SimGas in Kenya before). We'd love to talk further about working together in Kenya --- just shoot me an email at trashiscash at mit dot edu.
To answer your questions --- We use slightly different feedstock than you do. Biodigesters typically require wet feedstock such as manure, organic waste, etc. We work best with dry, fibrous feedstock such as agricultural and forestry residues. Our process is thermochemical whose advantage is that it is about 100 times faster than biological pathways (it takes us about 30-90 minutes to turn organic waste into fertilizer, rather than weeks'-months' time).
Hi Kevin, thanks for getting back and explaining your technology.
30-90m is very fast! I understand the value of speed in this process when you produce fertilizer in bulk, to be able to serve as many farmers as possible.
For a domestic biogas digester (one digester per farmer), once the digestion process is up and running (indeed 3-4 weeks after installation), it produces fertilizer (and biogas) continuously and on a daily basis, for about 10-20 years (as long as the farmer keeps feeding the digester regularly with organic waste). So speed isn't an issue when it's made fresh and available every day, right in the backyard of the farmer. I'll shoot you an email, I hope to learn more from you!


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Boil the potatoes in 2 cups water, when nearly done, add the peas and corn cook. I used frozen vegetables so it took about 5 minutes.
Drain most of the water add the spinach and could for 5-7 minutes, add salt and pepper and mash making sure that there is only a little water in the pan.
Add some olive oil or butter.


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Our Massage therapy team is available to do Sports/Deep Tissue Release, Relaxation Massage, and Pregnancy Massage. We offer Manual Lymphatic Drainage, and Anti-cellulite treatments.
This amazing inch loss treatment consists of 3 gels and a cream, applied in a 2-stage process, and it continues to work for up to 12 hours after the treatment! There are no bulky bandages, and it is truly effective (but not for pregnant women, or those breastfeeding).
Treatments are 1 to 1.5 hour and cost £80 per session. Discounts are offered for courses. Ask for details.
This type of massage consists of five basic strokes: effleurage (stroking), petrissage (kneading), friction, tapotement (percussion), and vibration and was developed by a Swedish Physiologist in the 19th Century. Alleviate stress, ease aching muscles, and revive the senses with this powerful massage.
A non-invasive drainage massage to decrease the appearance of Cellulite, or improve lymphatic drainage.
The lymphatic system is a complex network of vessels and ducts that move fluid throughout the body and it is responsible for moving toxins away from healthy cells and carrying germ-fighting materials to cells when they are under attack. The lymphatic system does not have its own pumping mechanism and therefore, this type of therapy is intended to help the body produce a free-flowing lymphatic system.
A lymphatic drainage massage primarily focuses on specific lymph nodes and points of the body, as well as the natural flow of the lymphatic system. This process will reduce blockages of the lymphatic system, which in turn promotes a healthier body.
We also have an incredible new IPC (intermittent pneumatic compression) machine, which assists lymphatic drainage (especially in those with lymphoedema, or those performing in high level sport who need something as an adjunct to their sports massages). It also helps to improve the look of cellulite – ask us about the combined treatments of gel + IPC for sports release, or for added cellulite reduction.
A gentle, luxurious massage, where you are in control of the depth of the muscle release.
Anyone can benefit from a sports massage, including people in physically demanding jobs and those with other emotional, occupational and postural stress, or those who just enjoy a deep tissue massage.
Remedial massage uses a base of Swedish massage techniques to achieve balance.
Although many clients who consult a remedial massage therapist have pain as a result of specific injuries (whiplash, falls, sports injuries, etc.), repetitive strain injuries (RSI) and other work-related problems, the vast majority originate in postural alignment problems that sometimes go back to childhood and adolescence.
A gentle and safe treatment for mums to be.
A treatment done on your feet to promote balance and wellbeing.


text:
FIENDS WITH BENEFITS
Britain's worst mum Karen Matthews 'thinks she deserves free bus pass for putting new home town on map'
Christy Cooney
Updated: 14 Jul 2019, 18:00
Karen Matthews thinks she deserves a free bus pass and other perks for putting her new home town on the map, a local source has claimed.
Matthews served four years in jail for staging the kidnapping of her own daughter in 2008, and was relocated after her release in 2012.
Matthews is reported to think she deserves to receive free perks for putting her new hometown on the mapCredit: Splash News
Matthews served four years in jail for staging the kidnap of her nine-year-old daughter ShannonCredit: Channel 5
She was later reportedly spotted working in a local charity shop and standing outside a Jobcentre Plus looking for work.
Newspapers are not allowed to reveal Matthews's new location, but word is said to have spread after she was recognised by locals.
Now Matthews is reported to believe bars, cafes, and restaurants should be handing out freebies to her for raising the town's profile.
A source in the town told the Daily Star: "She says nobody would have heard of it if it wasn't for her."
Matthews has reportedly changed her name and altered her appearance, but has still been spotted.
The source added: "Karen is spotted all the time despite changing her appearance.
"She thinks she brings tourists in.
"She believes this benefits trade and the local economy.
"Karen thinks she should get something back in return, like a free bus pass or free goods.
"She thinks she has been good for the town.
"The way she keeps going on about it you'd think she expected to be granted the freedom of the place.''
In February 2008, Matthews's nine-year-old daughter Shannon was reported missing from the town of Dewsbury in West Yorkshire.
The search for her quickly became a major missing person police operation.
Shannon was found alive and well in March at the home of 39-year-old Michael Donovan, uncle to Karen's boyfriend at the time.
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Shannon and Donovan were later found to have plotted the kidnapping in order to claim a £50,000 reward.
Both were given eight years in prison, with Karen later being released after serving half her sentence.
Matthews has reportedly been seen outside a local job centre and working in a charity shopCredit: Splash News
A source claimed Matthews says her presence benefits the local economyCredit: Splash News