text:
\section{Introduction} \label{sec:intro}
The NASA/IPAC Extragalactic Database (NED\footnote{\url{https://ned.ipac.caltech.edu/}}) is a comprehensive database of multi-wavelength data for extragalactic objects that provides a systematic, ongoing fusion of information integrated from hundreds of large sky surveys and tens of thousands of research publications.
The database content includes both published information for distinct astrophysical objects such as nomenclature, positions, redshifts, photometry, diameters, images and spectra, and derived quantities such as estimates of Galactic extinction, reference-frame velocity corrections to redshifts, sizes and luminosities \citep{1988ESOC...28..335H, 2020ASPC..527....3M}.

In order to better support scientists, educators, and observatories in planning new observations, performing data analysis and modeling, making new discoveries, and preparing publications of new results, it is essential to keep the database content as current as possible. To achieve this, NED routinely reviews, extracts, and integrates data from high impact journals including Astronomy \& Astrophysics (\aap\footnote{\url{https://www.aanda.org/}}), the Astronomical Journal (\aj\footnote{\url{https://iopscience.iop.org/journal/1538-3881}}), the Astrophysical Journal (\apj\footnote{\url{https://iopscience.iop.org/journal/0004-637X}}), the Astrophysical Journal Letters (\apjl\footnote{\url{https://iopscience.iop.org/journal/2041-8205}}), the Astrophysical Journal Supplement (\apjs\footnote{\url{https://iopscience.iop.org/journal/0067-0049}}), the Monthly Notices of the Royal Astronomical Society (\mnras\footnote{\url{https://academic.oup.com/mnras}}) and Letters\footnote{\url{https://academic.oup.com/mnrasl}}, and Nature\footnote{\url{https://www.nature.com/}}. Until recently, a human expert would first scan the articles and determine if the topic of an article is appropriate to be included in the database, i.e., if the article contains data for extragalactic objects. When the topic is considered relevant, the expert would further review the content of the article to determine which of the different types of data should be extracted and integrated into the database. This is a very labor-intensive process. In 2018 alone, NED reviewed a total of 14,123 journal articles, which translated to an average of $\sim$270 articles per week. Given the increasing volume of publications and the growing volume and complexity of data therein, it is neither efficient nor sustainable to rely only on human effort to continue this work. We therefore explored applying machine learning (ML) technique to classify literature with the goals of automating and accelerating the process of selecting journal articles and data for inclusion into NED while maintaining a high level of consistency with human classifications.

This paper is organized as follows. \S\ref{sec:method} describes the method and software we selected for our ML application, and how we implemented it in our use case. \S\ref{sec:topic} discusses the sample curation and training for selecting journal articles that are of interest to NED, the performance of our best model validated against the testing sets, and its deployment into the NED production pipeline. In \S\ref{sec:content}, we extend the approach to further identify the different data content in the articles, e.g., astrometry, photometry and redshift data. We summarize our study and the results in \S\ref{sec:conclusion}.

\section{Method} \label{sec:method}
\subsection{Algorithm}\label{subsec:algorithm}
ML algorithms are generally divided into two groups: supervised and unsupervised. The classification of NED relevant articles and data calls for a supervised learning algorithm as we want to learn the mapping of features in journal articles to the decisions of human experts over the last 10-20 years, and use the learned relationship to predict the decisions on new, previously-unseen articles. We expected to have a high dimensionality in feature space ($\sim$ 1M features) as the input consists of fairly large documents ($\sim$ 0.1-1M bytes per article), and a fairly large number of them ($\sim$ several thousand articles).
Hence, we chose to focus on leveraging existing open-source software that is well-reputed to build models over text data. Additionally, the ML solution and feature extractor need to operate in a pipeline/background mode without graphical interfaces, remote connections, or human interaction.
After careful study, we chose the Stanford Classifier (SC\footnote{\url{https://nlp.stanford.edu/software/classifier.shtml}}) developed by the Stanford Natural Language Processing Group. The SC implements a Maximum Entropy classifier with Conjugate-Gradient Descent optimization (MaxECG), which is described in detail in \citet{SC2003}. The program includes an easy-to-use command-line interface with several tunable algorithms, as well as a feature extraction and mapping/encoding component. The software is well supported with a strong user base, has an open source licensing under the GNU General Public License\footnote{\url{http://www.gnu.org/licenses/old-licenses/gpl-2.0.html}}, and has also been updated and improved over the years. For the work described here, we used version 3.9.2, which was released in 2018. It is worth pointing out that our goal was not to perform a computer science experiment in search of a ``best" solution, but to apply a well-tested package that has been successfully used in other application domains. For example, the Max Entropy (MaxEnt) SC is one of four leading supervised text classification algorithms that were compared for sentiment analysis of movie recommendations in Twitter feeds \citep{Armentano2015}.

\subsection{Overview of ML Processing for NED}\label{subsec:process}
The ML process involves two major cycles: the learning (or training) cycle and the prediction cycle. Figure \ref{fig:ml} illustrates the general article classification process in NED.

\begin{figure}[ht!]
\plotone{MLProcess.pdf}
\caption{Machine learning journal article classification process in NED.\label{fig:ml}}
\end{figure}

In the learning cycle, models are constructed and evaluated using training sets and test sets that are independent subsets of the input data. Our input data are the HTML files of articles published in the astronomical journals (see \S \ref{sec:intro}), acquired from the journal websites via their ADS links, and the labels placed by human experts which we adopt as ground truth. The Stanford Classifier is used to perform feature characterization, identification and extraction on the training sets. Features can be words, parts of words, stems of words
, characters, groups of characters (letter-n-grams\footnote{An n-gram is any ordered collection of n symbols, see \url{https://en.wikipedia.org/wiki/N-gram}. Letter-n-grams use letters as the unit. Similarly, word-n-grams use words as the unit.}), and groups of words (word-n-grams). We experimented with different feature models and found the ``Chris4" word-shape\footnote{Word-shape is the pattern of characters and delimiters that are considered ``a word" by the feature extractor. Several of these, like Chris4, are built-in to the SC software, but others can be created using common regular-expression syntax. See \url{https://nlp.stanford.edu/nlp/javadoc/javanlp/edu/stanford/nlp/classify/ColumnDataClassifier.html} for more details.}, with leading or trailing 2-5 letter-n-grams, as well as whole-words, performed the best. We also learned that feature extraction is the most expensive computational activity, about 10 times the demand of the ML training itself. We therefore extract features from each article in a training set, combine them into an intermediate catalog of feature sets, and reuse them in subsequent training exercises. Positive or negative weights of the features for each class are learned in the training process based on the priors. Table \ref{tab:feature} presents some examples of the features with the weights learned in the training process. It is noticeable that the weights of the features are sensitive to the letter cases. In the examples here, the words ``galaxy", ``Galaxy", ``GALAXY" and ``GALaxy" are shown to have very different weights.

\input{tables/feature}

Multiple candidate models are trained and examined by varying the choice of the training and test sets, from which we select the best-performing model. The primary evaluation metrics we use in accessing the performance of the models are the classification accuracy and recall. Accuracy is defined as the fraction of the correct predictions (i.e., consistent with the decisions by the human experts) to the total number of predictions made:

\begin{equation}
Accuracy = \frac{T_p + T_n}{T_p + T_n + F_p + F_n} .
\end{equation}
Here $T_p$ represents the true positives where the model correctly predicts the positive class, $T_n$ represents the true negatives where the model correctly predicts the negative class, $F_p$ represents the false positives where the model incorrectly predicts the positive class, and $F_n$ represents the false negatives where the model incorrectly predicts the negative class.
Recall is defined as the fraction of actual positives that are identified correctly:

\begin{equation}
Recall = \frac{T_p}{T_p + F_n}.
\end{equation}
The initial requirement for our ML application was to minimize the human effort and accelerate the rate of classification while maintaining 90\% consistency with human classifications. By looking at the instances where the ML approach differ from the human decisions, we found that classifications by our human expert, though very accurate, typically have an error rate of $\sim$ 1\%. This led us to set our goal as maximizing the efficiency of classification at a high accuracy rate ($\sim$ 90\%) and an acceptable recall rate ($\sim$ 80-90\%).

In the prediction cycle, we extract features from new, previously unseen articles and feed them into the selected model. The model then returns a predicted label for the new article with a probability indicating the likelihood of the prediction.
In \S\ref{sec:topic} and \S\ref{sec:content}, respectively, we describe specific applications of this methodology to successfully identify articles appropriate for NED and to classify the types of data contained withing the papers (object positions, photometry, redshifts, etc.), helping expedite the data ingestion from the literature into the database.

We note that, because the classifier is trained on past publications and used to predict future publications (domain shift), it is better to accept a slightly higher rate of false positives because all the positives will be further inspected by human experts in order to clean, reformat, and validate the data for ingestion into the database. During that process, the false positives will be effectively removed and not contaminate the end result. This still saves substantial time that used to be spent examining the true negatives, allowing human resources to work on the more difficult-to-automate data validation and extraction tasks.
We are aware that NED will inevitably suffer from some incompleteness due to the false negatives. However, the main goal for this ML approach is to maximize efficiency while keeping as current as possible with the increasing flow of data from the literature. Some omissions are acceptable with an appreciable gain of processing more data into NED more quickly overall, which will be a greater benefit to a larger number of users. In addition, authors/users are generally quick to inform us if there is an important omission, and the missed data can always be added afterwards.

\section{NED-Appropriate Topics Classification}\label{sec:topic}
Our first implementation of this ML approach is to identify if the topic of an article is relevant to NED or not, i.e., NED-Appropriate Papers (NAPs) or (Not NED-Appropriate papers (NNAs). Historic data show that among the journals we reviewed, about a quarter to a third of the articles published each year contain extragalactic objects and therefore need to be selected and further categorized for inclusion into the NED database.

\subsection{Training and testing}
We selected training and testing sets from 16 years (2003-2018) of AJ articles. There were 6,987 articles in total, with 1,984 NAPs and 5,003 NNAs based on human classifications. We acquired the HTML files of these articles through the ADS API\footnote{\url{https://ui.adsabs.harvard.edu/help/api/}}. We then extracted the features from the raw HTMLs, i.e., no stripping of the HTML formatting, as this saves considerable time in processing.
For each article we extracted on the order of a million features, and it took a few days of CPU time in total to generate all the feature sets for the 6,987 articles.

From the 16 years of AJ articles, we created 100 training/testing iteration samples,
each containing the 1,984 NAPs and 1,984 randomly selected articles from the 5,003 NNAs.
Equal number of positives and negatives were chosen to avoid an imbalanced data set, which may result in better performance on the larger class and worse performance on the smaller class, and may also affect the use of regular accuracy as a evaluation metric \citep{2019arXiv190407248B}. For each iteration, we processed 10 folds of the SC-MaxECG algorithm on a randomly selected 90\% of the articles for training and 10\% for testing. Essentially, this created and tested 1,000 models with different choices of training and test sets. The resulting classifier models typically selected 720,000 features of significance and scored an accuracy ranging from 89\% to 97\%, and a recall ranging from 92\% to 96\% when evaluated on the test sets. The 100 rounds with 10 folds of ML computations used 22 CPU hours in total.

Table \ref{tab:output} provides an example output generated by one of the ML models. For each article (represented by its ADS Bibcode), the classifier outputs a label for the article, and a probability of this article belonging to this class. For example, the classifier decided that article 2012A\&A...537A..29R is 90.8\% likely to be a NAP and article 2017ApJ...841L..16V is 99.8\% likely to be a NNA.
We adopted the default probability threshold of 0.5 in SC as our decision cutoff, i.e., an article would be labelled NAP if p(NAP) $>$ 0.5 and NNA if p(NNA) $>$ 0.5.
We found that this threshold of p = 0.5 corresponds closely to the optimum point where the difference between the true positive rate and the false positive rate is maximized for the NAP/NNA classifier.
This is illustrated in Figure \ref{fig:roc} using the Receiver Operating Characteristic (ROC) curve \citep{2006PaReL..27..861F, Tharwat2021} of the best trained model, where the true positive rate is plotted against the false positive rate and the optimum point is shown to occur at a threshold of p = 0.5082. Additionally, the predictions made with this cutoff threshold do meet our goal for accuracy and recall.

\input{tables/output}

\begin{figure}[ht!]
\plotone{ROC.pdf}
\caption{Receiver Operating Characteristic (ROC) curve for the NAP/NNA classifier implemented in NED. Different probability points are labelled as well as the optimum point (OP) where the classifier discriminates the maximum true positive rate for the least false positive rate. Here the OP occurs at a true positive rate of 0.9319 and a false positive rate of 0.06473, which corresponds to a probability threshold of p = 0.5082, very close to the default p = 0.5 that we adopted.}
\label{fig:roc}
\end{figure}

During the testing, we found that 455 articles were classified opposite to NED's historical choice (i.e., false positives and false negatives) in at least one out of the 100 iterations, and 137 articles classified opposite to NED's choice in all 100 iterations. This can mean there are mistakes in the prior classification, but as the ML is ``learning" from the priors, it more likely indicates that there is an inconsistency (in the vocabulary, syntax, or both), or simply not enough information (distinguishing terms/features).

When applying the best trained model on the entire corpus of AJ articles, we were able to reproduce the human classifications to an accuracy of 97\% and a recall of 94\%. We further validated the model on other journals from the same period of time (2003-2018), and were able to reproduce the priors to an accuracy of 93\% for ApJ and ApJL, 91\% for ApJS, and 90\% for MNRAS and MNRAS Letters, individually, with a recall of $>$ 91\%  for all these journals.

We also experimented, but not elaborately, with the alternative Naive Bayes (NBy) classifier available in the SC package. NBy classifiers are known to compete favorably with MaxEnt classifiers given large training sets, i.e., $\gg$ 10,000 items in the corpus; MaxEnt classifiers perform best at general text classification with small corpora, i.e., $\lesssim$ 10,000 \citep{Potts2011, Yuret2010, 2002cs........5070P}. We evaluated the performance using computational speed, accuracy and recall. We found that MaxEnt does become slightly more expensive in computation as the number of training items increases. However, at the scale of the NED literature set (1000's), this is easily overcome by computing facilities at hand. The NBy method is problematic for small training sets, as accuracy and recall fall off quickly. In the few experiments conducted, the accuracy of the NBy classifier ranged from 40\% to 70\%, with no noticeable improvement in speed.

Other areas that were examined include: (1) Using a simpler word-shape, and/or not using letter-n-grams. This reduces the number of features substantially (factor of $\sim$ 2/3) and therefore computation. However, accuracy and recall suffered immensely, falling by more than 30\%; (2) Using only defined keywords, known by experience to be related to the selection. We confirmed that the classifiers generally did select features that include forms of the NED keywords historically used by humans in skimming articles for significant information. It was in this process where we found that letter-case was significant in weighting such features.

\subsection{Deployment to production} \label{subsec:ncsa1}
The best performing model, dubbed SC-classifier-AJ-PF1984-064 (trained on 1984 positive priors and like number of negative priors of AJ articles), was selected for deployment in the NED production pipeline and has been used to automate the identification of NED-appropriate papers among new articles published in the astronomical journals since early 2020.

Figure \ref{fig:prod} illustrates the acquisition, evaluation, and loading process in the NED production, with the ML classifier highlighted. The tool takes a list of files containing the pre-downloaded article texts (in HTML obtained from publishers' on-line archives), constructs the input needed to inform the feature extractor, invokes the feature extractor and the classifier with the configured model on the extracted features, and outputs the report and log files. The former records only the positive classifications that will be used as input to NED's existing loader program. The latter records all the classifications and the computed likelihood values, which are reserved for future testing and tuning.

\begin{figure}[ht!]
\epsscale{0.8}
\plotone{NEDProdwML.pdf}
\caption{The acquisition, evaluation and loading process in the NED production pipeline, with the ML classifier highlighted.
\label{fig:prod}}
\end{figure}

Since the performance of the selected model may degrade due to the changing of input data (e.g., shifts in research topics or journal formats can introduce new features), periodic model retraining is necessary to stay current with the astronomical literature. We set a periodic training schedule of once per year. The new training sets are from human classifications on one or two volumes per journal every 6 months, and the collected false positives detected in the data preparation stage. We deployed our model into NED production in early 2020 and collected our first batch of new human classifications in the summer of 2020. We compared the labels assigned by human experts with the predictions from the ML classifier, and present our results in Table \ref{tab:ncsa1}. It is shown that although the model was trained on pre-2019 AJ articles, the performance is still strong: the overall accuracy and recall are both at 92\% for articles published in 2020.
We plan to incorporate these new training sets in 2021/2022. Our short-term focus, however, is to expand the scope of the ML application to cover classifying types of data in the NAP articles.

\input{tables/ncsa1}

\section{Data Content Classification} \label{sec:content}
The success of the ML application to automate and accelerate the selection of NED relevant articles from astronomical journals encouraged us to further extend the approach to include the data content classifications, i.e., what types of data an article has to offer. The general methodology was the same as in our previous implementation, but was now applied to NED ``data-frames" for astrometry (positions), kinematics (redshift), photometry, and in-literature acknowledgement of NED, in addition to the appropriateness for an article to be included in NED (NAPs).

The types of data NED routinely integrates into the database contain more than the selected few above. These categories were chosen mostly due to the fact that they have the largest sample size in the database. Additionally, positions, photometry and redshift are among the most fundamental properties NED keeps for an object, and acknowledgement is an important metric used as a basis for productivity and impact by data archives such as NED.

\subsection{Training and testing}
We initially assembled a sample containing articles from all the NED core journals (A\&A, AJ, ApJ, ApJL, ApJS, MNRAS and MNRAS Letters) between the year 2004 and 2019. The NED database management system (DBMS) was used to randomly select training and test sets, based on desired criteria (NAPs or NNAs; contains specific data types or not). However, during the training/testing process, we found that articles published before 2011 were more prone to classification error. Upon examination, we found that two issues were involved. One was a significant format change in some journals' publication around 2010 (e.g., A\&A and MNRAS), which created some variations in features that compromised the performance of the model. The other was due to the fact that there were less data in NED for the years before 2011, compared to the years after. Since our goal is to have the model predict future instances accurately, it is therefore important to make sure the data the model is trained on are similar to what is expected in the near future. Hence, we revised our sample selection to include only the articles from 2011 through 2019. Table \ref{tab:sample} shows the total size of the final sample, together with the number of positive priors in each of the data categories to be modelled. It is worth pointing out that in the sample selection the NAP and acknowledgement classifications are independent, i.e., an article can acknowledge the use of NED without providing any data on objects, hence is not a NAP. The classifiers on different data types (astrometry, photometry, and kinematics) are only trained on samples that are NAP-condition-positive; that is, for example, the astrometry training set (both positives and negatives) is taken from the NAP subset.

\input{tables/sample}

For each of the 5 categories (NAP, acknowledgement, astrometry, photometry, and kinematics), we processed 20 iterations of the SC-MaxECG on randomly selected subsets from the entire sample. Each subset contains 90-95\% of the positives and an equal number of negatives in each category for training, and 5-10\% of the positives and negatives for testing. All together, we trained 100 classifier models, and it took 144 CPU hours (not including feature extraction time). Since feature extraction of individual articles and the concatenation of different feature sets from all the articles in the sample are very time-consuming, on the order of days in CPU time, we chose to do it once at the beginning and used the same combined feature file in the training of all five classifiers.

The resulting models from the 20 iterations have an accuracy range of 91-93\%, 96-98\%, 87-93\%, 88-92\% and 88-92\% for NAP, acknowledgement, astrometry, kinematics and photometry classifiers, separately. Table \ref{tab:model} presents, for the best-performed model of each classifier, the total number of articles and the number of positive priors in the subset, together with the accuracy and recall when evaluated against the respective testing set. The accuracy rates are at $>$92\% for all the classifiers, and the recall rates are at $>$93\% except for the acknowledgement category. In the case of acknowledgements, there was a shortage of positive priors due to a shift in the definition of positives for this category. ``Acknowledgement of NED" was initially defined as any mention of NED in the article text using ADS keyword extraction, or classification by ADS as having cited NED, or classification by NED team through visual inspection. The training sample was selected using the initial definition. However, we later revised the definition to only consider the NED team confirmed acknowledgements or citation, which resulted in a smaller training set and subsequently a somewhat limited yet still sufficient accuracy and recall in testing.

\input{tables/bestmodel}

\subsection{Deployment to production} \label{subsec:ncsa3}
We integrated the above-mentioned best-performing models to production at the beginning of 2021. Our initial approach was to remove the articles identified as NNA from the input of the data content classifiers, so that the data type classification would only occur on articles that were already found to be NAP. However, we found that it was considerably faster to run the entire input set (including both the NAPs and NNAs) through all classifiers, and generate recommendations/classifications based on the combined output. We therefore augmented the program to run all five classifiers simultaneously on each article, extract results and output them in the required format of the NED loader program.
During the initial deployment, we randomly selected a few volumes from different journals and had our human experts label the articles in order to assess the performance of the models. Table \ref{tab:2021} provides a few examples of the results of classification accuracy based on post-classification human verification. The total number of articles as well as the number of NAPs in each volume are listed here to caution that these are all small samples, hence the accuracy and recall rates are very sensitive to the mis-classification of even a few articles. For example, ApJ 914 contains only four articles with NED acknowledgement in the entire volume. The ML approach mis-classified three, resulting in a low recall of 25\%. Nonetheless, the classifiers are shown to perform well outside of their training domain (year 2011-2019) on these articles published in 2021. As of September 2021, this ML classifier has been in full operations for 8 months, and has reduced $\sim$ 80\% of the labor for the content classification task. This translated to a team gain of 0.4 Full-Time Equivalent (FTE) which was then focused on tasks that cannot be automated.

\input{tables/ncsa3}

As has been discussed previously in \S\ref{subsec:ncsa1}, these classifiers are trained on past publications and are being applied to publications that are new, with information/features that sometimes have not been seen before. Therefore, it is important to monitor the trend of accuracy and recall and be prepared to refresh the models with more current publications. Currently, we are keeping the periodic retraining schedule of once per year. The schedule may be changed based on the trend of the performance, which we are assessing every 6 months using human classified labels on randomly selected journal articles.

\section{Conclusion}\label{sec:conclusion}
We explored the use of ML techniques to process articles in the astronomical literature, selecting those that are appropriate for inclusion into NED, and categorizing the different types of data contained in the articles for further extraction and ingestion. We showed that this automated process can reproduce the historic classifications by human experts of articles relevant to NED and selected data types (astrometry, photometry, kinematics and acknowledgement) to an accuracy of 92\% or greater. It is worth noting that this ML approach does not completely remove human involvement in the process. Human expertise and learning are needed for marginal cases that are not resolved by existing capabilities. Further, the trained model is applied to a domain-shifted data set for predictions. Newly-emerged data content, variations in formats and representations can all contribute to deteriorating performance over time. Hence, there needs to be a continuing education program for retraining and updating the classifier models with new literature, which will require new labels identified by human experts periodically.

Overall, we have demonstrated the feasibility and value of using ML to reduce the human effort in this historically labor-intensive task while maintaining a high level of accuracy and consistency. This ML application on both subject and content classifications has been in operations since January 2021, and has gained the team about 0.4 FTE, i.e., the equivalent of 4 person-years of labor over a decade of NED operations, that we have refocused on tasks that have little or no hope of being handled with artificial intelligence or machine learning anytime soon, such as analyzing and understanding the data content well enough to clean, reformat, apply or validate metadata to prepare input to the database.

\acknowledgments
We want to acknowledge the past and continuing work of the Natural Language Processing Group at Stanford University, without which this application would have been much more difficult. We are also grateful to the American Astronomical Society Journals and IOP Publishing, the Oxford University Press, and EDP Sciences for their support in providing extensive and on-going access to their publications. We thank the anonymous referee for the critical review and comments which resulted in substantial improvements to the article. This work was funded by the National Aeronautics and Space Administration through a cooperative agreement with the California Institute of Technology.

\facilities{ADS, NED}

\software{Stanford Classifier v3.9.2, The Stanford Natural Language Processing Group, \url{https://nlp.stanford.edu/software/classifier.shtml}
}

redpajama_set_name: RedPajamaArXiv


text:
You can override any of these defaults:

```
elementOpenTag: "",
elementCloseTagWithAttr: " ",
elementCloseTagWithoutAttr: "|",

attributeTagOpen: "",
attributeTagClose: "|",
attributesSeparator: ", ",

indentString: "  ",

outputContainer: ".container",
outputFormat: "HTML",
outputRender: "default",

preset: "default",

```

## outputContainer

**Default**:

".container" (the first element with class "container")

**Type**:

string OR array

**Default example**

```javascript
<div></div>
<div class="container">This is the container</div>
<div></div>
```

**Customized example**

const config = {outputContainer: [".foo", 3]}

````javascript
<div class="foo"></div>
<div class="foo"></div>
<div class="foo"></div>
<div class="foo">This is the container</div>
<div class="foo"></div>
````

**Description**

The target element in which to render your input.

**Usage**

Start with either "." for class, "#" for id.
If you use a class but don't want to target the first of it's kind, pass an array instead of a string, with the string as the first element and the nth number as the second.
If you pass a class as a string, the nth instance parameter will default to 0.

## Tags

**Default**:
```
elementOpenTag: "" (empty string)
elementCloseTagWithAttr: " "  (single space)
elementCloseTagWithoutAttr: "|" (pipe)
attributeTagOpen: "" (empty string)
attributeTagClose: "|", (pipe)
```
**Type**: string
**Default Example**

```
a href="www.aaroncoding.com", class="batman"| The href and class are attributes
p| This paragraph has no attributes
```

**Customized Example**

```Javascript
const config = {
    elementOpenTag: "<",
    elementCloseTagWithoutAttr: ">",
    elementCloseTagWithAttr: ">",
    attributeTagOpen: "{",
    attributeTagClose: "}"

}

const input = `
<a> {href="www.aaroncoding.com", class="batman"} href and class are attributes
<p> This paragraph has no attributes
`
```

**Description**:

Contains the tags for opening and closing elements and attributes. Can be almost any string, any length.

sweetRender will start looking for the attribute open tag as soon as it sees the element close (with attributes).

## attributesSeparator

**Default**:
```
", "
```
(Single space after a comma)

**Type**:

string

**Default Example**

```
div style="margin: auto", class="foo", href="www.aaroncoding.com"| Hello World
```

**Customized Example**

```Javascript
const config = {attributesSeparator: " BAM "}

div style="margin: auto" BAM class="foo" BAM href="www.aaroncoding.com"| Hello World
```

**Description**:

When you have multiple attributes in an element, this is how you need to separate them.

**Known issues**

A single space *can* work, but you will need to be careful with spaces within a given attribute. For example inline styles with spaces

## indentString

**Default**:
```
"  "
```
(Two spaces)

**Type**: string

**Default example**:

```
div|
  ul|
    li| first list item
    li| I have a link
      a href="http://aaroncoding.com"| here
    li| third list item
p| No more lists.
```

**Customized example**

```
const config = {indentString: "-_"}

div|
-_ul|
-_-_li| first list item
-_-_li| I have a link
-_-_-_a href="http://aaroncoding.com"| here
-_-_li| third list item
p| No more lists.
```

**Description**:

The string that counts as an indent.
Can be a different number of spaces, or different string entirely.

# Presets

**Description**:

Override some of the default settings with one of the presets

## Preset: Smiley

```javascript
const config = {preset: "smiley"}

```

```
elementOpenTag: ":-)",
elementCloseTagWithAttr: " ",
elementCloseTagWithoutAttr: "(-:",

attributeTagOpen: "X-D",
attributeTagClose: "8-P",
```
example
```
:-)a href="www.aaroncoding.com", class="batman"8-P The href and class are attributes
:-)p(-: This paragraph has no attributes
```

## Preset: surprised

```javascript
const config = {preset: "surprised"}

```

```
elementOpenTag: "!",
elementCloseTagWithAttr: "!",
elementCloseTagWithoutAttr: "!",

attributeTagOpen: "!",
attributeTagClose: "!",
```
example
```
!a! !href="www.aaroncoding.com", class="batman"! The href and class are attributes
!p! This paragraph has no attributes
```

## Preset: verbose
```javascript
const config = {preset: "verbose"}

```

```
elementOpenTag: "el",
elementCloseTagWithAttr: "el",
elementCloseTagWithoutAttr: "el",

attributeTagOpen: "attr",
attributeTagClose: "attr",
```
example
```
el a el attr href="www.aaroncoding.com", class="batman" attr The href and class are attributes
el p el This paragraph has no attributes
```

## Preset: html
```javascript
const config = {preset: "html"}

```

```
elementOpenTag: "<",
elementCloseTagWithAttr: " ",
elementCloseTagWithoutAttr: ">",

attributeTagOpen: "",
attributeTagClose: ">",
```
example
```
<a href="www.aaroncoding.com", class="batman"> The href and class are attributes
<p> This paragraph has no attributes
```

redpajama_set_name: RedPajamaGithub


text:
Studio Lux's Christopher Thompson Is Elected into the Board of Trustees of the Frank Lloyd Wright Foundation
by Studio LUX
Studio Lux, an award-winning architectural lighting design firm with headquarters in Seattle, is proud to announce Christopher Thompson has been elected into the Board of Trustees of the Frank Lloyd Wright Foundation. Established by Wright in 1940, the Foundation is deeply committed to preserving its two National Historic Landmarks, Taliesin and Taliesin West, for future generations, and inspiring society though an understanding and experience of Frank Lloyd Wright's ideas, architecture and design.
Studio Lux Welcomes John Dunn as Director of LA Design Studio
Studio Lux Expands Design Leadership
Studio Lux, an award-winning architectural lighting design firm with headquarters in Seattle, is proud to announce John Dunn as Director of their Los Angeles studio. As a recognized leader in the lighting design community, John will bolster Studio Lux's ability to deliver world class expertise and value to their established and growing national and international clientele.
Energizing Frank Lloyd Wright's Taliesin West with LED Lighting – Studio Lux + Osram Sylvania
Step inside the architectural prowess of Frank Lloyd Wright's Taliesin West, as we showcase OSRAM SYLVANIA's donation and installation of innovative LED lighting products to the Energizing Taliesin West program.
5 Tips to Help You Look Better on Skype & Facetime
Studio Lux met up with KPLU's Paula Wissel to talk about how lighting affects the quality of today's online video chatting, and what this says about the lighting in your home and office.
Taking on Taliesin
Media Coverage, News
815 Pine Residential Tower
Studio Lux is pleased to be partnering with such a great team on the development of the 815 Pine Residential Tower. We cannot wait to see this project complete!
Architectural Lighting Pays Dividends
Architectural lighting designer Christopher Thompson of Studio Lux tells TheStreet's Debra Borchardt how extra attention to lighting your home pays off in value and enjoyment.
Winner of the SBID 2012 Award for Best in Technology
International Design Excellence Awards
LD+A
Click to view article here.
Click here to view article

redpajama_set_name: RedPajamaCommonCrawl


ittle monsters with big personality! There are 12 different monsters: gamer, rocker, princess, sweetie, skater, eco, birthday, peace, art, pirate, pet pal, and music pal. This assortment of 24 erasers contains 2 of each different monster.
Every monster has a long, wild mane of hair ready for you to tame. Give 'em some style or stick with their natural windblown look. These fun erasers measure about 1 1/2" from head to toe (plus another 1 1/2" or so of hair) and fit over the eraser of your standard school pencil.


text:
ISIS also spreading at Afghan-China border, says Pakistan's Foreign Office
Foreign OfficeISIS also spreading at Afghan-China border, says Pakistan's Foreign Office
ISLAMABAD, Pakistan: The Foreign Office on Thursday said the presence of ISIS was not only at Pak-Afghan border area but it was also spreading at Afghan-China border.
In his weekly media briefing in Islamabad, the Foreign Office Spokesperson Dr Faisal said Pakistan had reservations about the ungoverned areas in Afghanistan that not only posing threat to stability of Afghanistan but security of the whole region as well.
"Over 43 percent area of Afghanistan is out of the control of the Afghan government and attracts terrorist groups from across the world including ISIS", he said.
The Spokesperson said Pakistan expected that the Afghan government and Resolute Support Mission would take action to bring those territories under control.
In reply to a question, he said Pakistan placed great importance to the security of Chinese working in Pakistan and had taken strict measures regarding it.
Dr Faisal said Pakistan had received response from India regarding visit of Indian spy Kulbhushan Jadhav's mother and wife and they would come to Pakistan to meet him on December 25.
The Foreign Office Spokesperson said Pakistan had submitted a comprehensive counter memorial document before the International Court of Justice (ICJ) regarding the Kulbhushan Jadhav case covering all legal aspects.
The Spokesperson further said Indian arms buildup had pushed region into arms race fomenting instability in the whole region.
In reply to another question, he said China Pakistan Economic Corridor (CPEC) was the flagship project of Chinese Belt and Road Initiative.
"Inclusion of other Countries in the CPEC will be decided by mutual cooperation, Pakistan and China are working out modalities in this connection as many Countries have expressed their interest in joining the project", he added.
Pakistan to play India in opener of World Blind Cricket Cup
Putin worries over presence of foreign terrorists on Afghan – Central Asian borders


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The large group of Oz horses and riders dominated the Mother's Day Classic.
London and Jeff Campf take home the victory in both the Jumper Classic and the Open Welcome.
Greyscale and Shelley Campf in the Green Working winning the prestigious Alexander Perpetual Trophy.
Additional class winners not already mentioned included: Morgan Spear with Pixczar, Stephanie Haralabatos with Kohala, Erica Hass with Night Song, Diane Laycoe and Final Act, Sarah Hingson and Fremont, Jeff Campf and Jet Set, and Madison Cameron and Kantos.
Way to go team OZ!!
Thanks for a great pot luck!
I have uploaded some videos onto youtube. You can search landccameron and find them. I'll try to get better about getting everyone as the season goes on.


Any help would be appreciated. I figured it may be worth a shot here rather than trying to find some prebuilt pc that is a hit or miss.
Do you already have the VR equipment? Or is that required as well?
Where are you located / are we talking USD?
I could budget this list. My only issue is I have 0 knowledge in how to properly manage any software/system setup etc. after building.
Thank you so much! Really helpful info. I appreciate taking the time to respond as fast as you have.


I never knew Bubba Kapko. But from what I've heard, I would've liked him.
The 19-year-old 2003 Estancia High School graduate left home last August to attend Johnson & Wales University in Denver.
Bubba, or Brian if you called him by his formal name, appeared to have his whole life in front of him. College, a multitude of friends, a tight-knit family, a sense of peace and closeness to God, an outgoing personality, a terrific sense of humor and a great laugh, or so I've heard. He was athletic, playing football in high school, and was liked by everyone he met.
But in the early morning hours of Aug. 28, 2005, Bubba's life ended in a car crash, forever changing the lives of his family and friends.
You see, Bubba and four of his friends, including high school pal Kris Hartwell, were traveling back to Denver following a volleyball tournament in Boulder, Colo. It was just a group of guys having fun, doing what college kids should be doing.
According to the police report in Arvada, Colo., the accident occurred as police were chasing a stolen red Honda that, as it merged from one freeway to another, hit two other vehicles, one being the Toyota 4Runner carrying Bubba and his friends.
Bubba was the only person killed.
The death of a child — it's a thought that every parent has had to fight back against on more than one occasion. Nothing, nothing could be worse. This time for Rick and Nancy Kapko, Bubba's parents, that bad dream became reality.
So why do I bring up this sad occasion nine months after the fact?
Yesterday, I received an e-mail from Kapko family friend Cathi Hartwell.
The e-mail pointed out that one of the two people in the stolen car, Dennis White, has pleaded guilty to charges of second degree murder and criminal attempt-second degree murder.
His sentencing is set for June 16, 2006, in the courtroom of Judge Christopher Munch at the Jefferson County Court in Golden, Colo.
The sentence for this plea is 26 to 80 years. Munch, however, will consider all victim-impact statements prior to his decision.
According to Jean LoSasso, a victim witness specialist in the Jefferson County district attorney's office, letters from the public, or victim-impact statements as they are called, are encouraged to let Munch understand how this crime has affected other people's lives and any specific sentencing recommendations people might have.
Imagine for a moment that you were standing in the Kapkos' shoes. You've lost your son, or daughter for that matter. You're virtually helpless in dealing with the overall situation, while seemingly standing alone on the sidelines.
But then a community, your community, rises up and supports your seemingly lost hope.
That's what we can do and should do. Consider the few minutes it would take out of your life to write a letter of support for the Kapko family in calling for the right action against the accused in this senseless killing.
Letters, faxes or e-mails should be directed to: Judge Christopher Munch, c/o Jean LoSasso, Jefferson County District Attorney's Office, 500 Jefferson County Parkway, Golden, CO 80401. The fax number there is (303) 271-6785 and the e-mail is jlosasso@jeffco.us.
One thing I'm almost certain of is that Rick, Nancy and Bubba's two brothers, Matt and Adam, will be sitting in that courtroom that mid-June morning. Let's remind them that despite them being four states away, they are not alone.
It's what you would want, but more importantly need, if you unfortunately changed places.
The other accused, Matthew Wartena, is scheduled for a jury trial on Aug. 21, 2006. He is being charged with murder in the first degree and 10 other counts.


text:
Q: FFMPEG Failed to open codec in avformat_find_stream_info for RTSP feed I'm having an issue using FFMPEG when the version is greater than 2.8.15 (not a boundary - it's just that v3 and v4 do not work). ffprobe doesn't work either.
Version 3:
ffprobe -rtsp_transport tcp rtsp://user:password@ipaddress:5544/live0.264

returns:
[h264 @ 0xcd6e70] Overread VUI by 8 bits
[rtsp @ 0xcd3b10] Failed to open codec in av_find_stream_info
[NULL @ 0xcd6e70] Overread VUI by 8 bits
[h264 @ 0xcd6e70] Overread VUI by 8 bits
[rtsp @ 0xcd3b10] Could not find codec parameters for stream 0 (Video: h264 (High), none(progressive)): unspecified size
Consider increasing the value for the 'analyzeduration' and 'probesize' options
Input #0, rtsp, from 'rtsp://user:password@ipaddress:5544/live0.264':
  Metadata:
    title           : Session Streamed by LIBZRTSP
    comment         : live0.264
  Duration: N/A, start: 0.000000, bitrate: N/A
    Stream #0:0: Video: h264 (High), none(progressive), 90k tbr, 90k tbn, 180k tbc
    Stream #0:1: Audio: pcm_mulaw, 8000 Hz, 1 channels, s16, 64 kb/s

and
ffmpeg -f rtsp -rtsp_transport tcp -y -i "rtsp://user:password@ipaddress:5544/live0.264" -f image2 -vframes 1 -vsync 2 -pix_fmt yuvj420p "/home/james/photo_$(date +%Y.%m.%d_%H:%M:%S).jpg"

returns
[h264 @ 0x1634680] Overread VUI by 8 bits
[rtsp @ 0x16312f0] Failed to open codec in av_find_stream_info
[NULL @ 0x1634680] Overread VUI by 8 bits
[h264 @ 0x1634680] Overread VUI by 8 bits
[rtsp @ 0x16312f0] Could not find codec parameters for stream 0 (Video: h264 (High), none(progressive)): unspecified size
Consider increasing the value for the 'analyzeduration' and 'probesize' options
Guessed Channel Layout for Input Stream #0.1 : mono
Input #0, rtsp, from 'rtsp://user:password@ipaddress:5544/live0.264':
  Metadata:
    title           : Session Streamed by LIBZRTSP
    comment         : live0.264
  Duration: N/A, start: 0.000000, bitrate: N/A
    Stream #0:0: Video: h264 (High), none(progressive), 90k tbr, 90k tbn, 180k tbc
    Stream #0:1: Audio: pcm_mulaw, 8000 Hz, mono, s16, 64 kb/s
[buffer @ 0x177e510] Unable to parse option value "0x0" as image size
[buffer @ 0x177e510] Unable to parse option value "-1" as pixel format
[buffer @ 0x177e510] Unable to parse option value "0x0" as image size
[buffer @ 0x177e510] Error setting option video_size to value 0x0.
[graph 0 input from stream 0:0 @ 0x177e490] Error applying options to the filter.
Error opening filters!

Version 4 is identical, except it mentions avformat_find_stream_info instead of av_find_stream_info
But...
Version 2.8.15:
ffmpeg -f rtsp -rtsp_transport tcp -y -i "rtsp://user:password@ipaddress:5544/live0.264" -f image2 -vframes 1 -vsync 2 -pix_fmt yuvj420p "/home/james/photo_$(date +%Y.%m.%d_%H:%M:%S).jpg"

returns:
[h264 @ 0x2243e80] Overread VUI by 8 bits
    Last message repeated 5 times
Invalid UE golomb code
    Last message repeated 1 times
Guessed Channel Layout for  Input Stream #0.1 : mono
Input #0, rtsp, from 'rtsp://user:password@ipaddress:5544/live0.264':
  Metadata:
    title           : Session Streamed by LIBZRTSP
    comment         : live0.264
  Duration: N/A, start: 0.000000, bitrate: N/A
    Stream #0:0: Video: h264 (High), yuvj420p(pc, bt709), 1920x1080, 10 fps, 10 tbr, 90k tbn, 180k tbc
    Stream #0:1: Audio: pcm_mulaw, 8000 Hz, 1 channels, s16, 64 kb/s
[h264 @ 0x242bd60] Overread VUI by 8 bits
    Last message repeated 2 times
Output #0, image2, to '/home/james/photo_2019.04.04_10:17:03.jpg':
  Metadata:
    title           : Session Streamed by LIBZRTSP
    comment         : live0.264
    encoder         : Lavf56.40.101
    Stream #0:0: Video: mjpeg, yuvj420p(pc), 1920x1080, q=2-31, 200 kb/s, 10 fps, 10 tbn, 10 tbc
    Metadata:
      encoder         : Lavc56.60.100 mjpeg
Stream mapping:
  Stream #0:0 -> #0:0 (h264 (native) -> mjpeg (native))
Press [q] to stop, [?] for help
Invalid UE golomb code
frame=    1 fps=0.0 q=9.5 Lsize=N/A time=00:00:00.10 bitrate=N/A
video:146kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown

But definitely works and returns the correct photo.
For diagnostic purposes, I also have a lower res stream coming out of the camera:
rtsp://user:password@ipaddress:5544/live1.264
Which works on all versions!
Is there anyway I can see the steam info to see the difference?
Is it because the RTSP stream is invalid, but workable for v2 (and rejected for v3)?
Or is there something else wrong that I can fix by changing parameters?
Any guidance for getting this to work on a at least v3 would be greatly appreciated.
Unfortunately, these cameras are made to look like PIR alarm sensors, and are powered over Ethernet so there aren't really any other options on the market.
Thanks, let me know if you want any other info?
Update 04/04/2019 17:20 London:
I have played with the stream settings in the camera and found that the higher res stream works when I set the stream to 720p. But as soon as I switch back to 1080p it fails again. Not sure if this is helpful.

A: The camera is producing and invalid VUI, which is part of the SPS. You will need to patch ffmpeg to handle this error more gracefully, or the camera manufacture needs fix it via a firmware update.

redpajama_set_name: RedPajamaStackExchange


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Is it actually possible to love too much? If we consider for a moment love and hate as being opposite emotions, we do all no doubt agree that it is easily possible to hate too much. Hate in any degree is a dangerous and destructive emotion. Hating at all therefore constitutes hating too much.
But love is a positive emotion. Thus I question whether it could ever be possible to love too much. Being afraid to love because you are afraid of being hurt is a survival instinct.
Loving someone deeply who does not return your feelings can definitely give rise to uncomfortable emotions which are hard to cope with. Getting over a broken relationship when you still love that other person can be a very tough task indeed. But does this mean that you love too much?
If you wish to be in a relationship that is deeply loving and close then you will never achieve that state unless you do give freely the love that you would like to receive. To not give yourself that chance curtails the potential depth of your relationships. You in effect have to open your heart to that love or you will not be receptive to it.
I would for this reason argue that you could never love too much. Rather, it is possible to love another person in inappropriate circumstances. Many people "love blindly" in the sense that there is a total imbalance between logical thought and their emotional experience.
Your feelings of love and your craving for love can cloud what you are actually seeing in your relationship. Other people, who stand outside your relationship, can often see those things that you are blind to. But those other people cannot really help, because it is you who needs to somehow find a way to step back from your emotions and allow your more logical mind to play a bigger part in your daily reality.
Many women (and men too) return to relationships that are undeniably abusive. Even when the level of abuse is great they still return. We tend to think of these people as loving too much. I would still say that their love is inappropriate as opposed to being too much.
Hypnosis is a beneficial and therapeutic state of mind to employ to enable you to step back from your emotions and see things from a different perspective. Essentially if you are in a situation where your emotions have taken over to a degree that suppresses logic, then you have to seek a way in wish to redress this imbalance. Hypnosis is easy to use and swift to apply.
Hypnosis is also powerfully effective. You can access your subconscious mind as well as your conscious mind, and you can examine instinctive thoughts and behaviors so as to balance your emotional reactions and experiences. You can effectively train your mind to focus and to respond in a more appropriate manner.
Love is the most important emotion in the world, hence the saying "love makes the world go round". Fear should never be allowed to get in the way of giving loving energy into the atmosphere of this world.
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Grab yourself a free hypnosis mp3 from http://www.RoseannaLeaton.com and view her library of relationship hypnosis mp3 downloads and get help to get over a broken relationship.
Gallery | This entry was posted in Physical & Emotional Abuse and tagged abuse, breaking up, building trust, depression, divorce, hypnosis, hypnotherapy, hypnotist, kamloops, kelowna, kitsilano, love you, north vancouver, penticton, self help, sonny rosati, suicide, the love hypnotist, vernon, victoria, whistler. Bookmark the permalink.


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Synergia Financial is a full-service agency that helps individuals and businesses build a roadmap for life. Their wide range of services include all types of insurance coupled with financial planning. Rapunzel Creative redesigned the company brochure to include an infographic portraying the road of life we all have to travel. We also produced a companion Bio sheet for the company owners.


I got to meet author, Bruce Coville today! His Nina Tanleven mystery series has been an inspiration to my own writing, and I have continued to read his books ever since. In fact, I finished Monster of the Year. This week—and I didn't know he was coming to Columbus until today. I'm happy to write that he was a total gentleman and even posed for a picture with me. He made my day!


During the season of Christmas 2016, our family was in the middle of a big move. Our moving date was January 3rd and I had debated back and forth about whether or not I should decorate. I decided not to pull out a single decoration as I knew it would probably stress me out to have to pack everything away so close to our moving date. The house looked sad.
So this year, I decided I would make up for it by decorating quite a bit earlier than I normally would…and by decorating two trees (see my other look here). For our entrance and dining room, I decided on a black, white and gold theme and have been having lots of fun pulling it all together. I'll keep adding to this as I decorate more.
I found plenty of Christmas decor to suit the look I wanted. See below for sources.
And we can't forget the Reason for the Season!


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Everyone sometimes is having a hard time getting energized in the morning. There are some simple start-of-day energy tips that you can use to feel more alert and keep your weight in check.
It's important to find healthy ways to boost your energy levels.
Proteins are the workhorses of our bodies. They help persuade your brain and stomach that they're well nourished and satisfied; skimp on it and your hunger might lead you right to a bag of potato chips. In a recent study, volunteers whose daily protein consumption fell below 15 percent of their total calories were far hungrier after breakfast and ate more snacks throughout the day than those whose protein levels exceeded that amount. The extra nibbling put them on track to gain more than two pounds a month had they kept it up. Some great potein-packed breakfasts are hard boiled eggs and greek yogurt.
A square of dark chocolate has just the right amount of theobromine (a cousin to caffeine) to wake you up but not enough to cause a crash later, says chronic-fatigue specialist Jacob Teitelbaum, MD. It's also chock-full of antioxidants, may decrease heart attack risk, lifts mood-and tastes delicious.
Something crunchy make your jaw work hard, which can wake up your facial muscles, helping you feel more alert. Munch on an apple with breakfast for an energizing dose of extra fiber. Apples are loaded with soluble fibre, the key to blunting blood sugar swings.
Shaking out the sheets gets your arms moving above your heart, boosting your heart rate and invigorating your body. Also you avoid the temptation to jump again in already made bed.


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We all experience anxiety throughout our life – and there are many reasons as to why stress and anxiety can exist in our everyday lives. The most important thing to remember here is that vitamins and minerals can't make up for conditions that need professional care and help – but it can help you a lot.
Do you feel that your anxiety levels are out of control?
If you're feeling depressed, out of energy and anxious in your daily life the answer to the question above is probably yes. These are all symptoms which could be caused (or amplified) by a poor diet and nutritional intake. Being deficient in certain minerals and vitamins can affect your psychical and mental energy, your body's health along with the biochemical balance in your brain, resulting in anxiety or increasing the levels you're currently experiencing.
B vitamins are critical for your body's energy production, and vitamin B-1 is specifically known for its calming and soothing effect – which is why you'll hear it being referred to as the "anti-stress" vitamin. Several studies and extensive research shows that taking 50 milligrams of vitamin B-1 every day helps improve calmness and mood – if taken every day for two months you should start to notice the change.
Luckily for us humans, vitamin B-1 is found in almost all kinds of foods and is therefore very easy to consume and not be deficient in. If you don't like taking tablets and want a 100% natural source of these vitamins, add more whole grains and legumes to your diet.
This vitamin is essential for your neurotransmitters (chemical messengers in your brain) which ensures normal function and impacts your mood and sleep schedule. It also plays a major part in producing serotonin and norepinephrine (some of the most important neurotransmitters) which help adjust how you feel and keeps a stable mood.
The University of Maryland Medical Center published their findings on how large deficiencies of vitamin B-6 can lead to irritability, depression, anxiety, and difficulty concentrating along with the loss of short-term memory. In order to sustain a healthy environment for neurotransmitters to do their job, a dose between 1.3-2.0 milligrams is needed for adults. If you love eating chicken and fish you're in luck, these meats contain a good amount of B-6. Also, remember to add dairy to your diet, or eat legumes, brown rice, and whole grains if you're a vegetarian and want more B-6 through a natural source.
Just like vitamins, minerals are extremely important for your brain's function and for the production of neurotransmitters and hormones which affect your mental health. According to the "Indian Journey of Psychiatry", zinc helps protect your brain cells from toxins and can amplify the success and efficiency of antidepressant medications. Another essential mineral is iron (especially for women) which powers red blood cell production and delivers oxygen to your cells. Adults who suffer from constant fatigue, depression, and anxiety could also be deficient in iron, and could to their benefit add nuts, seeds, leafy greens, meats and fortified foods to their diet.
Remember: before you decide to adjust your diet or exercise routine, it's a good idea to speak to a doctor and see what they recommend. If this is something you need assistance with, don't hesitate to get in touch with Dr. Best and the Neuroscience Center.


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Construction Zone, the contractors that dried out my house, repaired the damages, and cleaned up the mess, were absolutely top notch. Everything was done to very high standards, and the individuals I came into contact with were so helpful to me. They were very professional, answered all my calls and followed up with me every step of the way, which made this transition so much easier.


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Jury rules that baby powder can cause ovarian cancer
A jury has ruled in favor of a woman that says that her use of baby powder has caused her to become diagnosed with ovarian cancer
She used the powder for over 40 years
Multinational company Johnson and Johnson has just lost their third straight trial over claims that their baby powder causes ovarian cancer.
A jury from St. Louis has awarded Deborah Giannecchini of Modesto, California over $70 million. Deborah has been diagnosed with advanced ovarian cancer back in 2012, and she blamed Johnson and Johnson's Baby Powder for causing it since she has been using the powder for her feminine hygiene for over 40 years.
They should have put a warning on the label
Photo from: wikimedia commons
The jury made the decision after about three hours of deliberation. In addition to the $70 million that Johnson and Johnson has to pay, Imerys Talc America, the supplier for the talc that's used for the baby powder, will also have to pay $2.5 million in damages.
One juror, Billie Ray, said that they should have put a warning on the label. She adds, "It seemed like Johnson & Johnson didn't pay attention, it seemed like they didn't care."
Giannecchini is extremely happy over the verdict. "I've waited for a long time for this," she shares. "I've wanted this so badly."
Carol Goodrich, spokeswoman for J&J said that the company will be appealing the verdict. "We are guided by the science, which supports the safety of Johnson's Baby Powder," she adds.
So, does talc cause cancer or not?
Talc, a type of mineral that's used as the main component in talcum powder has been used in making baby powder since at least 1894, which was when Johnson and Johnson first came out with their baby powder.
Talc, in its natural form, can sometimes contain asbestos, a substance that's known to cause cancer when inhaled. However, all talcum products have been asbestos free since the 1970s.
In terms of causing ovarian cancer however, opinions have been mixed regarding the use of talc as some studies have reported an increase in the risk of ovarian cancer, while other studies show no apparent increase whatsoever.
The International Agency for Research on Cancer (IARC) shares three important points regarding whether talc can cause cancer or not:
They classify talc that contains asbestos as "carcinogenic to humans."
On the other hand, inhaling talc without asbestos has not been found to be cancerous in humans.
However, regarding the use of talc in the genital area, the IARC classifies talc as "possibly carcinogenic to humans."
It still isn't clear if talc really is cancer causing or not. However, if you believe that talc can cause cancer, you should try and avoid using baby powder or products that contain talcum powder.
READ: Did you know that house dust can cause cancer?
Sources: bloomberg.com, foxnews.com, cancer.org
Filipino English baby powder


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Southbank Investment Research
Capital & Conflict
Exponential Investor
Buying Aim Shares
Genetics and Biotechnology
Harry Hamburg
Eoin Treacy
Sam Volkering
Nick Hubble
Nick O'Connor
Andrew Lockley
25th July 2019 Harry Hamburg
The war on cures
At the start of the year I wrote a series of five articles predicting what might happen throughout the course of 2019.
I made predictions in the following five areas:
Today, I'd like to take a look at what's been going on in the world of medicine, and how it relates to my predictions. As you'll see, it's all coming together.
Here was the crux of my article:
A strange thing happened in medicine over the course of the 20th century. Although it progressed massively in many areas, in many more it was held hostage by political beliefs.
You may think that science is the driving force of medicine. But that is simply not the case. Politics comes above science when it comes to medicine. When it comes to most things, in fact.
The 20th century's "war on drugs" meant many promising compounds were kept out of the reach of medical researchers.
The Controlled Substances Act (CSA) was drawn up in 1970. It divides drugs into four categories.
On this scale, schedule one drugs are the worst you can get. They are defied as having "no currently accepted medical use and a high potential for abuse."
And this is where the trouble starts.
In this category there is:
Diamorphine (heroin)
MDMA (ecstasy)
Psilocybin (magic mushrooms).
If a drug is in this category, it is incredibly hard to conduct medical trials with.
Remember, officially, all schedule one drugs have no medical use. If one did, it would no longer be schedule one. It would have to be re-classified.
At least that's what would happen if politics followed logic. Unfortunately, it does not. Politics is based on ideologies, not science or logic. And so the CSA is also based on ideologies and not science or logic.
For example, diamorphine is regularly prescribed for pain relief and cancer care in the UK. It clearly has medical uses.
A lot is already known about diamorphine because it is an opioid. Most prescription painkillers are. And as such, they all work in a similar way. Other than diamorphine, the most well-known opioids are probably morphine and codeine.
Yes, that's right. Codeine is an opioid. That's why you can only buy it over the counter when it's missed with paracetamol and called co-codamol. They mix it with paracetamol to stop people abusing it recreationally.
You might remember that the US is currently in the grip of an "opioid crisis". Prescription painkillers – both opioid and synthetic opioid – have become such a problem that they have actually reduced the average life expectancy in the US.
According to the CDC, 2017 was the third year in a row in which US life expectancy fell. That hasn't happened since the dual decimation of the First World War and Spanish flu in 1915-1918.
And here was the most important part:
Not only are our current painkillers flawed – in that they are killing vast swaths of the population – but current antidepressants only work in around 60% of cases.
If only scientists were free to use all the tools at their disposal to tackle the problem. Perhaps they would come up with some new "innovative" treatments instead of pushing out stronger and stronger opioid variations, and lacklustre antidepressants.
Well, it seems that enough people have been dying for policymakers to start thinking more rationally about the problem.
Over the last few years, we've seen a number of studies come out that showed the vast potential of many schedule one substances (which you'll remember, have "no currently recognised medical uses").
The interesting thing about these findings is that these illegal drugs are proving to be viable treatments for conditions that traditional treatments are failing at.
Cannabis legalises cannabis and researchers discover it is 30 times more powerful at reducing inflammation than aspirin
Since cannabis was legalised in Canada, research into its medical uses has skyrocketed. And that research is starting to pay dividends.
From the University of Guelph in Canada – where cannabis is legal:
University of Guelph researchers are the first to uncover how the cannabis plant creates important pain-relieving molecules that are 30 times more powerful at reducing inflammation than Aspirin.
The discovery unlocks the potential to create a naturally derived pain treatment that would offer potent relief without the risk of addiction of other painkillers.
"There's clearly a need to develop alternatives for relief of acute and chronic pain that go beyond opioids," said Prof. Tariq Akhtar, Department of Molecular and Cellular Biology, who worked on the study with MCB professor Steven Rothstein. "These molecules are non-psychoactive and they target the inflammation at the source, making them ideal painkillers."
The interesting thing about this discovery is that it's not a new one at all. It's been known about for over 30 years, but researchers weren't allowed to work on it because of cannabis' schedule 1 classification.
This much-needed medical use was first researched and documented more than 30 years ago
The irony here is simply astounding. For more than 30 years, science has known that cannabis possesses these pain-reliving properties – the definition of a medical use. Yet it has remained illegal because it officially has "no currently accepted medical use".
From the University of Guelph:
Known as "flavonoids," cannflavins A and B were first identified in 1985, when research verified they provide anti-inflammatory benefits that were nearly 30 times more effective gram-for-gram than acetylsalicylic acid (sold as Aspirin).
However, further investigation into the molecules stalled for decades in part because research on cannabis was highly regulated. With cannabis now legal in Canada and genomics research greatly advanced, Akhtar and Rothstein decided to analyze cannabis to understand how Cannabis sativa biosynthesizes cannflavins.
"Our objective was to better understand how these molecules are made, which is a relatively straightforward exercise these days," said Akhtar. "There are many sequenced genomes that are publicly available, including the genome of Cannabis sativa, which can be mined for information. If you know what you're looking for, one can bring genes to life, so to speak, and piece together how molecules like cannflavins A and B are assembled."
With the genomic information at hand, they applied classical biochemistry techniques to verify which cannabis genes were required to create cannflavins A and B. Their full findings were recently published in the journal  Phytochemistry. The Toronto Star also recently wrote about the research.
These findings provide the opportunity to create natural health products containing these important molecules.
"Being able to offer a new pain relief option is exciting, and we are proud that our work has the potential to become a new tool in the pain relief arsenal," said Rothstein.
Magic mushrooms could replace antidepressants within five years – The Independent
Back in June this article, and a number of others like it popped up in the UK press.
Hallucination-inducing drugs like magic mushrooms could be about to break big pharma's stranglehold on the hugely lucrative market for antidepressants, according to the head of the world's first centre for psychedelic research.
Antidepressant prescriptions have doubled in England in a decade with around seven million adults taking the drugs, and the global market is predicted to be worth $15.9bn (£12.5bn) by 2023.
At Imperial College London, Dr Robin Carhart-Harris is leading one of the first trials to test how therapy using psilocybin mushrooms, which are currently banned in the UK, compares to leading antidepressants.
While he won't prejudge the results of the study, he says participants describe a cathartic emotional "release" with psilocybin therapy – the polar opposite of antidepressants, which patients complain leave their emotions, whether positive or negative, "blunted".
It is the first of many studies planned under the banner of the new Centre for Psychedelic Research at London's Imperial College.
If you go back to the start of today's article, you can see that psilocybin (magic mushrooms) is on the list of schedule 1 drugs, which have "no currently accepted medical use".
The key thing here is that psilocybin works differently to traditional antidepressants. From the same article:
"If you ask people who are taking SSRIs chronically, they often say 'I feel blunted'," Dr Carhart-Harris told The Independent, meaning both negative and positive emotions are suppressed.
"With psilocybin therapy they say the opposite, they talk about an emotional release, a reconnection, and this key emotional centre being more responsive."
Basically, psilocybin lets you confront your demons and deal with them (which can be a "nightmarish" experience), while traditional anti-depressants make you not care about your demons. At least, that's what the researchers seem to say. Again, from The Independent:
During the therapy sessions, patients are encouraged to follow the stream of the psychedelic experience which can be extremely vivid and may require them to confront past traumas or experiences.
"We don't call it a 'bad trip'," Dr Carhart-Harris says. "We call it a 'challenging psychological experience' and we're honest with people that it can be hellish.
"It can be nightmarish, but we're prepared for this and this treatment model requires you literally face your demons."
The research is very promising, but it may still ultimately have a big mountain to climb:
"I would imagine if you had some bookmakers doing the odds, there would be strong odds on that [psychedelic therapy] will be licensed sometime in the next five to 10 years – maybe sooner," Dr Carhart-Harris says.
That could put it on a collision course with powerful interests of the pharmaceutical industry, particularly if trials show psilocybin therapy to be superior to SSRIs
"The implications of that are actually frightening to me, thinking of the power and influence of big pharma," Carhart-Harris says. "What are they going to do with that if there's this big public demand for the 'mushroom therapy', and not the Prozac?"
Well, it seems big pharma – and "the war on drugs" – may have a fight on its hands.
As I predicted at the beginning of the year, given the promising research into psilocybin as an anti-depressant, the powers that be are slowly being forced to change their tune… just as they have done with cannabis in Canada.
There are already signs that authorities may be beginning to shift. In October, US regulators gave Compass's [the company running the trials mentioned above] treatment breakthrough therapy status, a designation given to new medicines that might improve treatments for serious conditions, which means authorities will expedite their review of evidence. That decision was followed this May with Denver's vote to in effect decriminalise magic mushrooms, making it the first US city to do so, followed last week by a similar measure for several psychedelic plants in Oakland, California. Activists are pushing for a state-wide vote in Oregon next year on whether to legalise psilocybin for medical use. Meanwhile, Compass hopes to request marketing authorisation for the drug within three years, if its trials are successful. It is proceeding slowly, though, having treated around six people since trials began in January, and Malievskaia says the process could take a decade until approval. She aims to accelerate progress, with new treatment sites recently opened at Columbia University, as well as in New Orleans and the Netherlands, among others.
So it seems my predictions may have been right. The medical world really is waking up to the possibilities of new and effective treatments derived from "recreational" drugs.
And as you can see, these treatment have the possibility to change many lives for the better. Not least those at risk of becoming addicted to opioids, or those for whom traditional anti-depressants do not work.
However, what I've covered today is just one small part of what we can expect to see from the medical world in the coming year.
There are big developments going on in gene editing, biohacking and AI-enhanced treatments. My publisher Nick O'Connor explores these areas in-depth in his book: The Exponentialist.
If you want to see what the future of medicine will look like, I highly recommend you take a look at this book. You can get your hands on your own copy here.
Editor, Exponential Investor
29th January 2020 Kit Winder
What's grey, strong, stable, and boring?
What are you looking at? A computer, a phone? Where are you sitting? At home, on a train, at the office? Where did you get to where you are today? Car, bike, bus? Final question – how much of your…
Schrodinger's Tesla
Sometimes I daydream. Some of them repeat. I'm sure you all have this. One of the daydreams that recurs for me is this. The former scientist Schrodinger puts all Tesla investors into a big box. In that box is a…
What do you get if you cross the next big thing with a PR pin-up?
Kit Winder here. Your usual editor, Nick Hubble, is happily away on paternity leave, having just had his first child, so it'll be me for the next two weeks. Apologies in advance! Actually scrap that – I'm not sorry, I'm…
24th January 2020 Nick Hubble
The Green Bubble will cost you more than it earns you
Yesterday I warned you about the cost of Britain going carbon neutral by 2050. And how imposing those costs on consumers causes social unrest and other backlashes. At least it has in many places around the world. Over in Germany…
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PONSHOP receives clientele of all sorts. This patron bought $2,000 worth of merchandise today.
PONSHOP Studio and Gallery premiered its first juried art exhibition on Friday night, September 3, 2010. The "Square City" show invited artists to submit work inspired by the city within the restrictions of a two-dimensional 12"x12" to 24"x24" square. By limiting the artists to these dimensions, owners Scarlett Suhy-Pons and Gabriel Pons were able to arrange the artwork to imitate the pattern of city blocks in the gallery, so that not only the artwork, but also its presentation reflected the theme.
The juror, Bob Worthy, selected 32 pieces by 28 different artists that he felt captured and showed new ways of looking at "the city." The pieces selected ranged over a variety of media to reflect the diversity and richness of an urban environment. During the opening, an informal gallery talk was given where the PONSHOP owners thanked the artists and patrons and introduced Bob Worthy. Worthy congratulated the artists for their work, praised their creativity, and spoke on what "Square City" meant to him. He admitted how hard it was to choose three pieces for the Merit Awards and three pieces for Honorable Mentions because of the impressive level of work that was submitted.
"Alley" by Crystal Rodrigue, image collage on copper leaf, 20" x 20"
"City Lights" by Reuben Strangelove, fiberboard and LED illumination, 12" x 12"
"Lonesome Ave" by Robert Landeck, mixed media collage on canvas, 20" x 20"
"Where I Came From" by Ana Rendich, oil, 15" x 15"
"Stairway to Heaven" by Michelle Pierson, photography, 15" x 15"
"Shellshock, Livestock, This is No Oasis" by Nicholas A. Candela, mixed media, 13" x 13"
"City" by Patte Ormsby, mixed media, 24" x 24"
"Downtown City Square" by Becky Hubbard, textile, 16" x 16"
"Night Light" by Bill Firestone, acrylic on canvas, 22" x 22"
"What Lies Beneath" by Leslie Brier, mixed media,12" x 12"
In addition to these works, other featured artists include: John Earl, Bonnie Butler, Mary Mogee, Mirinda Reynolds, N.D. Jones, David Chance, Itty Bitty Press, Karen Julihn, Shannon Chance, J. Fred Perrine, Jennie Thomas, Linda Keefer, Milvi Gill, Joyce Leatherwood, Erin Kidd, Cathy Herndon, Pat Knock, and Jeff Gulick.
The opening night was a great success with a large turnout of both artists and fans. DJ Bredren rocked out on his turn tables, setting the mood for the exhibit with funky reggae music. In addition, Karma Fire performed fire eating and fire dancing in front of the shop throughout the night. She attracted huge interest with her flaming torches, something never before seen on the streets of Fredericksburg.
We would like to thank the following for their hard work in making Square City such a success: guest juror Bob Worthy, PONSHOP summer intern Kelly Penick (graphics and promotion), DJ Bredren, all of the artists that submitted work, as well as all participating artists. For more information about upcoming PONSHOP events, please email event@ponshopstudio.com. "Square City" runs until September 26.
The weekend of September 24-26th marks the First Annual Fredericksburg Via Colori® street painting festival in downtown Fredericksburg, Virginia. Via Colori® is a modern interpretation of a centuries-old tradition in which volunteer artists create large scale pastel artworks on the pavement of city streets. This event is organized by the Fredericksburg Art Commission and includes not only volunteer artists, but performers, food vendors and other extended celebrations – all coming together to support the arts education movements in the Fredericksburg area.
The streets (Sophia/Charlotte) will be tiled and each square will range in size. The squares are individually sponsored by local businesses, organizations, or other units and the proceeds after expenses are used towards art-based education programs in the Fredericksburg area community.
This year, PONSHOP owner Gabriel Pons will be the inaugural signature artist for the Fredericksburg Via Colori® festival. He will be recreating his piece "Neptune's Daughter" – originally a spray paint and stencil image of an aquatic goddess with the sea swarming above her.
The original piece was constructed in February 2010 and was inspired by a Pixies song entitled "Mr. Grieves", where the lyrics include: "…what's that floating in the water, oh Neptune's only daughter, I believe in Mr. Grieves." Gabriel will be challenged to recreate this piece on a 10ft by 10ft pavement square, using a soft chalk pastel. In the weeks leading up to the event he will be strategizing in terms of just how to recreate this image in both a different media, method, and at a much larger scale. Below are some photos from the Via Colori® workshop held Saturday August 21. Via Colori® artist Curtis Goldstein gave a two-hour demonstration to participating artists. Volunteer artists had a chance to experiment with the unique medium of pastel chalk on a street surface.
Throughout the weekend, festival goers are invited to come downtown to explore and immerse themselves in the diversity of the Fredericksburg art community. Via Colori® will utilize Sophia street and Charlotte street for the event.


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Maloti Mountains är en bergskedja i Lesotho. Den ligger i den centrala delen av landet,  km öster om huvudstaden Maseru.

Ett tropiskt höglandsklimat råder i trakten. Årsmedeltemperaturen i trakten är  °C. Den varmaste månaden är december, då medeltemperaturen är  °C, och den kallaste är juni, med  °C. Genomsnittlig årsnederbörd är  millimeter. Den regnigaste månaden är december, med i genomsnitt  mm nederbörd, och den torraste är juli, med  mm nederbörd.

Källor

Berg i Lesotho
Berg i Lesotho 2000 meter över havet eller högre

redpajama_set_name: RedPajamaWikipedia


text:
Q: Problema com OpenShift + Mentawai Estou com um problema em relação ao OpenShift. Já subi minha aplicação (java/jsp) e meu banco (postgresql) e parece estar Ok.
O problema é que utilizo o mentawai como framework mvc e a aplicação não está encontrando as lists e internacionalização i18n.
O endereço da app é este: http://artilheiropro-apro.rhcloud.com/
Já pesquisei em vários lugares mas não encontrei nada relativo a isso! Alguém poderia me ajudar?

A: Pelo que parece você não está enviando para a JSP sua lista e com isso a tag mtw na página fica quebrada.
No método correspondente que invoca sua página você deve enviar a lista posições via "output".
Exemplo:
output.setValue("posicoes", posicoes);

Esse código deverá ser colocado antes da sua consequência SUCCESS.


The Prevention, Relief & Recovery (PR&R) program is a practitioner-oriented teaching and engagement institute that conducts programs for peacebuilding, humanitarian, and conflict management professionals around the globe.
PR&R programs are usually designed for mixed groups of practitioners from the various communities involved in these complex activities. A significant number of participants come from governments and armed forces of partner nations, and are funded by US Government Security Assistance/Cooperation funds.


A rare 1940s armchair attributed to Klaus Grabe for Robert Kayton, New York. The brown plywood frame has a white plastified cord woven into it. Bauhaus trained Klaus Grabe left Germany in the 1930s for Mexico. Here Grabe worked together with fellow Bauhaus student Michael van Beuren and Morley Webb. This team was part of the MoMa Organic Design in Home Furnishings Competition and exhibition in 1941. Later in the 1940s, Grabe moved to New York, where he specialized in plywood furniture.