Scan your old memories onto your Android phone or iPhone.
Scan 35mm film directly to your smartphone with the Smartphone Film Scanner. Additionally downloading the free lomography app enables both colour negative and colour positive film to be converted bringing your film colours to life. Whether you still shoot in 35mm or would like to relive old 35mm memories, the 35mm Smartphone Film Scanner is the gadget for you.


text:
There are often held various indoor and outdoor student activities at East European University. The students of EEU permanently participate in charitable, cultural, intellectual and cognitive activities.
Within the university it is periodically held public meetings with people who are successful and famous in different fields. During these meetings students have an opportunity to participate through question and answer modes and have interesting conversations with guests.
EEU annually takes part in Student Days and also works in close co-operation with University Sports Federation. The students of EEU are actively involved in various sport competitions which are held by University Sports Federation.
Students are also actively involved in social network. The official page of East European University frequently offers cognitive, thematic and amusing activities. The winners are given interesting and original prizes.
The students of EEU who are devoted to sports have an opportunity to enter university sport teams and achieve success in their desirable sports:
East European University has a close relationship with University Sports Federation. The students of EEU are actively involved in various sport competitions which are held by University Sports Federation. Winter Universiade 2017 was particularly successful for EEU. Within the tournament the student of university Soslan Bakaev participated in two kind of sport activities (giant slalom and cross-country skiing) from three of them and took the first prize in both competitions.
Minifooball
Every year East European University organizes minifootball matches among secondary school teams. Competitions became traditions and has been organizes for five years. Hundreds of school children take part from Georgian public and private secondary schools. The aim of the tournament is to engage young generation in sport, to fallow healthy way of life and to promote football among youngsters.

redpajama_set_name: RedPajamaCommonCrawl


Well, I don't have much to show for myself in the UFO department. I'm satisfied with my productivity, as I've started and finished several projects in the last couple months. However, there's only one item from my extensive Q3 Finish-A-long list that I actually finished. My carseat cover has been completed and well-used. Perhaps, I'll get a tutorial together for this beauty.


text:
Barnes and Noble's New Nook GlowLight Is White, Light, and Bright
Eric Limer
Filed to: ereadersFiled to: ereaders
Barnes and Noble may be going back and forth on its Android tablets, but the booksellers aren't wavering on e-ink. Meet the new Nook GlowLight, a light, white re-imagining of the front-lit reader we loved so well, back for a second shot at the throne.
Barnes & Noble Is Going to Stop Making Nook Tablets
Barnes & Noble has announced that it's going to leave manufacturing of its Color tablet…
At first glance, the new Nook GlowLight looks like a totally different gadget than its forebear, complete with a new, bulgier, roundish design, cushioned outer edges, and a bright white color that most reminiscent of a early issue Kindle. It's definitely an eye-catcher, if just a little bit Fisher-Price.
But underneath that surface, the new Nook GlowLight boasts a lot of smallish, but still impressive advancement, much like the Kindle Paperwhite 2013. The new 6.2 ounce GlowLight is a bit lighter than both the 7.3 ounce Paperwhite and its own 7 ounce predecessor. It also flashes black to reset its screen less. Way less. In fact, the new GlowLight doesn't have to flash black at all in reading mode. The more picture-heavy UI (which has a new, streamlined look on the new Nook) is a different story, but no black flashes in the middle of a thriller is clutch.
Kindle Paperwhite (2013) Review: Faster, Prettier, Still the Best
When Amazon first trotted out the Kindle Paperwhite, it was the first two-tone ereader in a long…
The new Nook GlowLight also has a higher-resolution screen than last year's model, bringing it up to par with the OG Paperwhite, if not up to (admittedly absurd) Kobo Aura HD levels. And its on-board storage of 4GB doubles that of Amazon's offering, although both can hold far more books than you could reasonably expect to read in either's lifetime. Of course, all this is on top of a front light that—in the short time we got to spend with one—seemed to put out a brighter, more even glow than the Paperwhite does.
Kobo Aura HD Review: A Beautiful Reader Screen Trapped in an Ugly Body
Everyone loves a pretty screen. The Kobo Aura HD is aiming to lead that charge in the ereader…
But the real killer feature here is price. Barnes and Noble had previously cut the price of its original Nook GlowLights to go head to head with the Kindle Paperwhite, and the new Nook starts there. It's available at your local Barnes and Noble starting today for just $120, which is the same price as a Kindle Paperwhite with ads and twenty bucks less than a Paperwhite without. Barnes and Nobles members (are there any of you out there?) get another 10 percent off.
The Nook GlowLight Gets a Price Cut to Match the Kindle Paperwhite
With the new Kindle Paperwhite on the horizon, Barnes and Noble is making a move to try and keep…
When you're coming up against Amazon, you've got one hell of a fight on pricing and ecosystem, but with $120 GlowLights, Barnes and Noble is coming out guns blazing. Good thing, too. Everybody likes a good fight.
Recent from Eric Limer
View on Blogtown USA
tIHS SOTRY NEDS 2B REEEEEELE POPALR 4 TEH NXT 2 MONHTS ABT PLS AN TY
blOgton USa is stil fien tho rite? yeaah? pls?
WAAAATHC OUT IM AL OUTAA F*KCS


text:
\section*{Introduction}

In this article, we report on an effort to use citation data to identify groups of scientific articles that may reflect widespread small-scale organization in the scientific enterprise.
We are inspired by the `Invisible College' concept, which appears to originate from a group of intellectually active individuals who held meetings around 1660 and became the
Royal Society of London in 1663~\citep{price_1966,royal_society}  but more generally refers to a relatively small self-assembled group of scientists with common scientific interests.

Importantly, there is a sense that these colleges are `in groups' with influence over prestige, research funding, and the scientific ideas of their community~\citep{price_1966}.
Thus, these groups may also advocate for or exhibit resistance
to new ideas within their domains of interest~\citep{barber_1961}.
Furthermore, while such groups may espouse idealized norms of science~\citep{merton_1957}, they are also likely to be driven by social interests such as personal recognition that influence both individual and collective behavior~\citep{barber_1962, crane_1972, hagstrom_1965}.

An important distinction has also been made between local (small) and global groups in the discussion of community~\citep[p.~112]{hull_1988}, with small groups being credited as the locus of rapid change and innovation.
Furthermore,   Price  noted, in apparent reference to Invisible Colleges, that communications are likely challenged in groups larger than 100 members~\citep{price_little_1963} and that the small `strips' at the research front of science may be at most the work of `a few hundred' persons~\citep{price_1965}; this number  is also cited in a clustering study \citep{Small1985}.

In a case study \citep{price_1966} of the Information Exchange Group No 1 \citep{green_1965} that was organized by the US National Institutes of Health to focus on electron transfer and oxidative phosphorylation,
Price and Beaver described a group of 517 members, 62\% of whom were from the United States and the rest from 27 different countries. Price and Beaver used the memos shared within Information Exchange Group No 1 as proxies for research articles and citations. They noted 1,239 authorships in 533 memos with two-author memos being the mode that was stable across a 5 year period. The majority of these authors were  were associated only with a single memo, and the top 30 authors each contributed to six or more memos.
Three conclusions were drawn from this study: first, that there existed a small nucleus of highly active researchers and others who collaborated with them only once; second, that separate groups existed within this college, and third,  that collaboration was a key feature. This valuable case study of Price and Beaver is, however, limited by examining a tiny sample of the enterprise as it existed in the first half of the decade of 1960-1970.  It is far more likely that a range of group sizes and behaviors exists now, and perhaps even then.

Thus, a natural question is how these small groups form and grow, and how to detect them
from  the bibliographic literature, which our study addresses.
Our study is motivated by two considerations.
First, the scientific enterprise has grown considerably, experienced greater globalization~\citep{wagner_2008} in the 21st century, and exhibits new features like large international collaborations such as the Human Genome Project~\citep{hgp_2001} and the Advanced LIGO project~\citep{Harry2010}. Even so, the tendency of scientists to form small groups that collaborate to advance their scientific and social interests is unlikely to have vanished and these groups may well exist within larger structures. Thus, we seek to understand organizational structures in the modern scientific enterprise and reconcile them with the observations of Price and others from the 1960s.
 Second, modern bibliographies and accessible computing make it possible to revisit the search for these small  communities of practice (and in particular invisible colleges)  at scale through community finding analyses of citation graphs.

In our use of citation data to identify and characterize putative colleges or communities of practice~\citep{cop_1991}, our working hypothesis is that such groups can be detected by identifying clusters of articles that are citation-dense, since common interests will result in citation of relevant documents especially those authored by the `in group'. Furthermore, as we noted earlier, we are particularly interested in small communities, since these may represent new ideas~\citep[p.~112]{hull_1988}.

Rather than attempting to directly identify author communities of practice, we first construct article clusters, and then examine the authors within the article clusters; the motivation for this approach is that
each community of  practice is by its nature formed around a specific research question or area,
while individual scientists may participate in multiple communities of practice based on different scientific and social interests.

We begin by identifying article clusters constructed from direct citations of articles. The use  of article-level citation patterns is motivated by the understanding that clusters defined by articles that cite each other can be more informative than clusters derived from journal-derived categories~\citep{Waltman2012,Shu2019,Milojevic2019} or from searches for words or phrases that recur in articles~\citep{Klavans2017}. We further restrict our examination to moderately-sized clusters based on the realization that  article clusters that are either very small or very large will not assist in discovering author clusters of interest.
Our pipeline (shown in Figure \ref{fig:pipeline}) shows the steps we used in this development of
author clusters.

We have chosen the field of immunology as a test case since it has existed for many years, grown and diversified over time, and exchanges discovery and methods with other biomedical areas. To identify article clusters,  we first constructed a citation graph for publications in the field of immunology within an 11 year timeframe, consisting of 2.16 million articles.
We  then used two different clustering methods, Markov Clustering \citep{dongen2000cluster}  and Graclus \citep{Dhillon2007}, which we previously used to cluster citation data within a discipline~\citep{devarakonda_2_2020}, to identify clusters that are robust to choice of clustering method.
To evaluate the approach,  we use expert annotation on a sample of the  Markov Clustering (MCL) clusters we produced that passed several stringent quality criteria (Fig.~\ref{fig:pipeline}).

These two clustering methods use different strategies and criteria.
Markov Clustering (MCL) has several desirable features:  it  is scalable, does not require pre-specification of the number of clusters to be generated, and has tunable parameters that control breadth of search and granularity of output.
Graclus is a spectral clustering method that we have previously used to construct article clusters from citation data~\citep{devarakonda_2_2020}.
In order to identify those clusters of interest, we limit our attention to those MCL clusters that have very high overlap with at least one cluster produced by Graclus:
the convergence of these two methods serves to identify those publication clusters where the citation signal is high enough to result in robustness to the choice of clustering method.
By restricting the set of clusters to a range of sizes consistent with a potential community of practice, we are able to
select publication clusters of interest that are then used to build author clusters that may represent communities of interest.

We explore the merit of this  combined approach by examining the publication clusters it produces, and use human experts to evaluate a sample of the selected clusters for thematic relatedness.
Our study shows strong concordance between the cluster conductance and expert evaluation of thematic relatedness, supporting the potential value of this protocol.
Thus, we consider this study a first step in designing and testing a pipeline that could enable large-scale identification of communities of different sizes and types, based on different search criteria. choice of clustering algorithm, and parameters.

\section*{Materials and Methods}

As a source of bibliographic data for this study, we used Scopus~\citep{scopus_ref}, as implemented in the ERNIE project~\citep{Korobskiy2019}. At the time of this analysis, our Scopus data consisted of $\sim$95 million publication records plus their cited references.
From these data, we selected publications in English, of type `article' with publication type `core', and Scopus All Science Journal Classification (ASJC) code of 2403, which corresponds to immunology, for each of the years 1985--1995.
We then  amplified the set of immunology articles thus extracted by supplementing them with articles that directly cited them and as well as by articles cited by these immunology articles.
The only constraints we imposed on the  cited or citing articles were to require that they were English publications of type `core'; in particular, the cited and citing articles were not constrained by ASJC codes.
All in all, we assembled 12 datasets (Table \ref{tab:empirical-MCL}) that consisted of (i) 11 `year-slices', each representing the set of immunology articles published in a given year (any of 1985-1995) along with their cited references and the publications
that cited them (e.g., the 1985 year-slice) and (ii) the union of data from the 11 year-slices, to create a working dataset of  2,163,683 articles that we refer to as the combined dataset. These data were stored as an annotated list of 6,846,323 pairs representing 11 years of data.

For Markov Clustering analysis, we downloaded and compiled source code for the MCL-edge software~\citep{dongen2000cluster}. After evaluating different runtime parameters, we clustered test sets using an expansion parameter of 2 (default) and an inflation parameter of 2.0 to minimize
the number of large aggregated clusters (Figure \ref{fig:fig2}). We generated clusters using the same parameters for each of the 11 individual years.  See Table \ref{tab:empirical-MCL} for empirical properties of the MCL clusters computed for the 11 year-slices and the combined dataset.

Under the same conditions, we also  generated 134,094 clusters containing 2.16 million nodes from our working dataset (above), resulting in cluster sizes of 1 (minimum), 16.2 (mean), 9 (median), and 3,956 (maximum).
For a random graph comparison, we performed 1 million reciprocal citation exchanges between randomly selected pairs of publications on these data and then ran MCL-edge on the resultant data.

To evaluate clusters and shuffled-citation clusters generated by  MCL, we measured (i) cluster conductance~\citep{Shun2016,Emmons2016,devarakonda_2_2020}, which essentially measures intra-cluster density,
and (ii) textual coherence using the Jensen-Shannon divergence~\citep{Boyack2011},  with the expectation that `good' clusters  would exhibit low conductance and high coherence, respectively.
We also clustered data from each of the 11 years using Graclus, adjusting its runtime parameter number of clusters to produce a distribution of  cluster sizes that roughly approximated those generated by MCL (Figure \ref{fig:fig2}).

\begin{table}
\begin{center}
\scalebox{0.9}{
\begin{tabular}{ccccccc}
  \hline
Dataset & num\_clusters & num\_articles & mean\_size & median\_size & mean\_cond. & mean\_coh.\\
  \hline
1985 & 10,568 & 293,086 & 27.73 & 	18 &	0.30 & 0.09 \Tstrut\\
1986 & 10,621 & 310,030 & 29.19 & 	19 &	0.32 & 0.09 \\
1987 & 10,984 & 325,661 & 29.65 & 	20 &	0.31 & 0.09 \\
1988 & 11,697 & 363,038 & 31.04 & 	21 &	0.33 & 0.09 \\
1989 & 12,401 & 397,292 & 32.04 & 	21 &	0.33 & 0.09 \\
1990 & 12,542 & 419,500 & 33.45 & 	23 &	0.34 & 0.09 \\
1991 & 13,089 & 463,581 & 35.42 & 	24 &	0.34 & 0.09 \\
1992 & 13,878 & 507,365 & 36.56 & 	25 &	0.35 & 0.09 \\
1993 & 14,135 & 542,948 & 38.41 & 	27 &	0.36 & 0.09 \\
1994 & 14,681 & 584,768 & 39.83 & 	27 &	0.38 & 0.09 \\
1995 & 15,918 & 642,686 & 40.37 & 	28 &	0.37 & 0.09 \\
combined & 134,094 & 2163683 & 16.14 & 9 &	0.70 & 0.09 \\
   \hline
\end{tabular}}
\caption{Empirical properties of the MCL  clusters computed for each year-slice as well as for the combined Immunology dataset.  We show statistics regarding size, conductance (cond.), and coherence (coh.) }
\label{tab:empirical-MCL}
\end{center}
\end{table}

To compute textual coherence, we used the titles and abstracts of all the articles in our study. On average, roughly 11\% of the publications had missing titles and/or abstracts, reducing the corpus to titles and abstracts from 1.95 million (1,955,164) articles. We first concatenated these titles and abstracts and pre-processed them by lemmatization based on parts of speech (POS) tagging, to preserve the tokens and their context.
The four POS considered were adjectives, nouns, adverbs, and verbs.
For all other types of parts of speech (including those not classified at all), the token was mapped to `noun' by default;
for example, `grow' and `growth' are two different tokens while `grow' and `growing' would be mapped to `grow'.
We then removed stop-words using a  list of 513 tokens comprising basic NLTK stop-words,
PubMed stop-words, and a select list of tokens from the top 500 most frequent words in our dataset.

For each cluster of size greater than 10 (after removing missing values), we performed a second pre-processing by filtering out those tokens that occurred only once in the entire cluster. Next, we converted all the remaining tokens by article in the cluster into a matrix of term frequencies (i.e., for each article, we had a vector of counts of all the tokens).
We also obtained a vector of counts for all the unique tokens in the cluster. Textual coherence was measured by using the Jensen-Shannon Divergence (JSD), which is used to compute the distance between two probability distributions. JSD was computed between the vector of term frequencies of the cluster and each article in the cluster using the following:
\begin{equation*}
JSD_{p,q} = \frac{1}{2}D_{KL}(p,m) + \frac{1}{2}D_{KL}(q,m)
\end{equation*}
where $m = \frac{p+q}{2}$, $p$ is the probability of a term in a document, $q$ is the probability of the same term in the cluster, and $D_{KL}$ is the Kullback-Leibler divergence, given by:
\begin{equation*}
D_{KL}(p,m) = \sum{p_{i}log(\frac{p_i}{m_i})}
\end{equation*}

We computed the textual coherence for a given cluster $X$ of size $n$ (after removing missing values) as follows.
Letting JSD\textsubscript{X} denote the arithmetic mean of all article JSD values in $X$, we define the textual coherence of $X$ to be JSD\textsubscript{X}-JSD\textsubscript{random(n)}~\citep{Boyack2011},
where JSD\textsubscript{random(n)} denotes the JSD of a  random cluster of the size $n$.

JSD\textsubscript{random(n)}  is the arithmetic mean of all the JSD values computed from  random selected sets of size $n$  from all the articles in our study.
For each value $n$, we estimate JSD\textsubscript{random(n)} by selecting 50 article subsets of size $n$ at random, and averaging the results.
The method of computing each iteration of JSD\textsubscript{random(n)} is exactly the same as the method described for JSD\textsubscript{X} above.

After completion of MCL clustering and computing conductance and coherence values, we compared a random sample of 1,000 clusters from the year 1990 and visualized the effect of shuffling citations on conductance and coherence compared to the original citation data (Figure \ref{fig:cond-coh-mcl}).

We scored each publication using a weighted citation count (Williams et al 2015, Keserci 2017) of intra-graph citations, which assigns a score to each node in a graph that consists of the number of in-graph citations it receives plus the number of citations received by its neighbors. We also computed the number of times each article in our working dataset had been cited in Scopus between publication and  July 2020.

\emph{Thematic relatedness}. Lastly, we used the expertise of two of the authors of this article (Zaka and Gallo), who are professional peer review specialists in the biomedical sciences, and highly experienced at  clustering proposals for funding based on multiple criteria such as sub-disciplines, methods, disease, and researchers.
In preliminary experiments, we provided a small number of training clusters to these evaluators representing a range of conductance values to assist in develop a common set of principles by which they would evaluate a test set. The clusters in this development set were not considered further.

For expert evaluation, we randomly selected 90 MCL clusters, each with 30--350 publications, and  with conductance values of no more than 0.5.
Since smaller clusters occur more frequently, the sample of 90  was constructed from two strata based on size to ensure representation of the larger cluster sizes.  An additional 10 clusters with conductance values greater than 0.5 were added to the sample.
The two evaluators were  each asked to evaluate 50  selected clusters (45 from the set of 90 and 5 from the set of 10) for thematic relatedness, given only the titles and abstracts for each publication in each cluster.
Using their expertise in peer review, they assigned scores on a simple scale of 1--4 where 1 represented a cluster exhibiting a single discernible scientific theme, 2 for a moderate level of thematic relatedness, 3 for poor thematic relatedness, and 4 for `unable to evaluate'.
The evaluators also annotated each cluster with keywords such as `hemophilia' or `adenosine deaminase' to indicate the theme that they discerned (Supplementary Data).

\section*{Results \& Discussion}

\emph{Markov Clustering of 2.16 million publications}. Our initial experiment was to cluster the 2.16 million publications in our combined dataset as well as separately for the 11 individual year-slices using Markov Clustering (MCL).
This experiment resulted in 134,094 clusters for the combined dataset and from 10,000-16,000 clusters for each of the individual year-slices,  as shown in Table~\ref{tab:empirical-MCL}.

Some noteworthy trends are immediately apparent.
First, the number of publications  increases monotonically each year between 1985 and 1995 and the number of clusters generated by MCL (as well as the average and median size of these clusters) similarly increases for each year.
However, while the number of clusters computed on the combined dataset is roughly ten-fold the number of clusters in individual years,  the average  and median cluster size decrease by roughly two-fold for the combined dataset (Table~\ref{tab:empirical-MCL}).

Another interesting feature is that except for the combined dataset where the mean conductance is somewhat high (0.70), the MCL clusters have very good coherence values (mean of 0.09) and  conductance values (mean of 0.30-0.38).
However, the conductance values are  of greater interest here than the coherence values, since by measuring citations they more directly assess the likely
interactions between authors.

A comparison of conductance and coherence profiles of 1000 MCL clusters from the year 1990 to random sets of publications of the same size is shown in Figure  \ref{fig:cond-coh-mcl} (Materials and Methods).
This comparison shows that random subsets of publications have much poorer conductance and coherence values, highlighting that MCL clusters are of very high quality with respect to both criteria.

We also observed that each MCL cluster in the combined dataset mapped well to a single MCL cluster in some year-slice but not as well to any MCL cluster in another year (Figure \ref{fig:fig4}).  As an example, cluster \#3780 from the combined dataset is a set of 70 publications focused on the enzyme adenosine deaminase; mutations in the gene encoding adenosine deaminase cause a severe combined immunodeficiency (SCID) phenotype. This cluster  matches best to cluster \#922 consisting of 103 publications from the 1995 year-slice with 84.3\% the first cluster (\#3780) drawn from the combined dataset found in cluster \#103 from 1995.
In comparison, the proportion of the first cluster found in the ten remaining year-slices ranged from 7.1\% to 55.7\%.
A second example provided is cluster \#122 from the combined dataset, concerning hemophilia and consisting of 134 publications; this cluster from the combined dataset matched best to cluster \#290 from 1993 of size 168 with 99.3\% of the first cluster (\#122) contained in cluster \#290  from 1993.
 In comparison again, the proportion of cluster \#122 in the best matches to clusters from the remaining ten year-slices ranged from 0\% to 28\%.
 This observation turns out to be useful in enabling a direct comparison between MCL clusters and Graclus clusters, as we now describe.

Graclus requires that the number of clusters be provided as a runtime parameter.  We found that setting this parameter to half as many clusters as those generated by MCL for a given year-slice resulted in a distribution of sizes that overlapped with MCL (Figure~\ref{fig:fig2}).
However, Graclus did not run to completion on the combined dataset when we set the runtime parameter to 67,000 clusters, or even when we set it to 30,000 clusters.
Therefore, we developed a technique to pair each MCL cluster to a Graclus cluster that maximized the overlap with the MCL cluster, computed as follows.
Specifically, we used the Jaccard Coefficient, which is the ratio between the size of the intersection and the size of the union of the two sets; this is maximized at $1.0$ (when the sets are identical), and is $0.0$ if the two sets are disjoint.
By restricting our attention to those MCL clusters that have Jaccard Coefficient greater than 0.9 to their paired Graclus cluster, we were able to  identify a subset of the MCL clusters that were also (nearly perfectly) recovered in the associated Graclus cluster, and so represent clusters that are less likely to be an artifact of the choice of clustering method.
This technique also specifically associates each MCL cluster with its nearest Graclus cluster, and so enables a comparison between MCL and Graclus clusters.
For example, an influenza virus MCL cluster (\#1189) from the combined dataset mapped best to MCL cluster \#116 from the 1988 year-slice. This cluster (\#116) from 1988, with 190 publications, matched Graclus cluster \#3164 with 194 publications.
The Jaccard Coefficient (overlap) between the two clusters was very high, equal to 0.96
(Table \ref{tab:tab3}, Row 1).

We further restricted our attention to that subset of the MCL clusters that  were  rated 1 by the evaluators.
Of the 100 clusters given to the two experts to evaluate (Materials and Methods), 77 clusters were rated 1, 18 clusters were rated 2, and 5 clusters were rated 3 (no clusters were rated 4), suggesting that the evaluators considered roughly three quarters of the clusters to be strongly themed given their knowledge and experience.
35 of these 77 MCL clusters also had Jaccard Coefficients greater than 0.9, and so were selected for further study.
These MCL clusters, 35 in all, exhibited very low conductance values of 0.01--0.17 (and in fact 32 of the 35 had conductance values of at most 0.1), suggesting exceptionally good clustering quality (Table \ref{tab:tab3}).
Interestingly, the coherence values range substantially across the 35 clusters, from as low as 0.04 to as high as 0.14, and while the higher end of this range is  good, even the top end of the range is not exceptionally good.
In general, as expected, coherence values are not as good a predictor of cluster quality as conductance.

\emph{Comparing MCL and Graclus clusters. }
Our study enables a comparison of MCL and Graclus clusters, as performed on this dataset.
 Interestingly, despite heavy overlap, the conductance values of MCL clusters often differed from those of Graclus clusters,
 with MCL clusters usually exhibiting low conductance values (median of 0.032) and Graclus clusters usually exhibiting high conductance (median of 1.0, 25 of 35 Graclus clusters had a conductance of 1.0). We traced this observation to nodes of high degree not being present in matched Graclus clusters.

For example, cluster \#198 of size 325 from the combined dataset mapped to an MCL cluster of size 328 and a Graclus cluster of size 327.
The Jaccard Coefficient for these two clusters is 0.99.
However, the two clusters differ substantially in conductance:  the MCL cluster has conductance of 0.01 whereas the conductance of the Graclus cluster is 1, which can be traced to the absence of any internal edges in the Graclus cluster (Table \ref{tab:tab3}, Row 2, Column: int.edges(g)).
Although this is a general trend, in some cases both clusters have good conductance values.
For example, cluster \#1595 of size 111 from the combined dataset mapped to an MCL cluster of size 114 and a Graclus cluster of size 112 from the 1993 year-slice. The Jaccard Coefficient for these clusters was 0.9825, and
both the MCL and Graclus clusters have very low conductance of  0.01 (Table \ref{tab:tab3}, Row 3).

Overall  for the 16,909 pairs of clusters (derived from restricting 134,089 MCL clusters in the combined dataset to  those of size 30--350), the conductance of MCL clusters was better than those of the corresponding Graclus clusters in $\sim$59.5\% of the cases, MCL and Graclus conductance were equal in $\sim$5\% of the cases,
and MCL conductance was worse than Graclus conductance in $\sim$35.5\% of the cases.
When a restriction of greater than 0.9 is placed on the Jaccard Coefficient of paired MCL and Graclus clusters (3,669 cases), the conductance of MCL clusters was better than those of corresponding Graclus clusters in $\sim$50.5\%, equal in $\sim$18.3\%, and MCL conductance was worse than Graclus conductance in $\sim$31.2\% of 3,669 pairs.

At an aggregate level, the median conductance of all  16,909 MCL clusters  was 0.165 versus 0.219 for the corresponding Graclus clusters.

When this set of 16,909  MCL clusters was reduced to those with  a Jaccard Coefficient of greater than 0.9, the median conductance for MCL was 0.072 versus 0.219 for their paired Graclus clusters.
Finally, only 6 of 16,909 MCL clusters had a conductance value of 1 ($\sim$0.04\%) compared to 4,545 Graclus clusters ($\sim$26.88\%).
 Thus, MCL clusters tend to have lower (i.e., better) conductance values compared to matched Graclus clusters.

In contrast, a comparison of coherence values for the paired MCL and Graclus clusters we evaluated shows very little difference between the two methods.
This is not surprising since the overlap between matched clusters is high (Jaccard Coefficient greater than 0.9), and coherence measures average textual similarity between members of the cluster and an overall representation of the cluster.
The significance of coherence values in interpreting cluster quality, however, is questionable for this context, where we are using article clustering in order to detect author communities; that is, at the best, high coherence would indicate that papers are examining similar questions, but would not indicate that the authors of the paper are even aware of each other, while citation-based clustering metrics, such as conductance, avoid this issue.
We also have reduced interest in  coherence values for these data, due to the average loss of 11\% of titles and abstracts across clusters (Materials and Methods).

We have found cases where further analysis of selected clusters  (and those not selected) will be necessary in order to identify author clusters, either using external edges, in-graph citation counts, or in-Scopus citation counts.
We present three edge-cases (considering intra-cluster edges only)  to illustrate this point.

Cluster \#117927 contains a single article \citep{White1989} that is focused on Staphylococcal Enterotoxin B; it has 351 external edges from other clusters in the
set of 134,094. An alternate perspective is that this cluster could be reconfigured to consist of itself and all its citing nodes to form its own community.

Cluster \#1301, focused on
non-steroidal anti-inflammatory drugs (NSAIDS), consists of 125 publications, and 123 of its articles are cited by a single article from the cluster~\citep{Cucala1987}, which is cited, in turn,
by another article in the cluster.

Finally, Cluster \#1673 consists of 108 publications, one of which~\citep{michel_1992}, a comparison of hypersensitivity vasculitis and the Henoch:Schonlein purpura, is cited by the
other 107.

In comparison, Cluster \#559 consists of 185 publications with 102 of them receiving citations from 88 nodes within the cluster.

Each of these clusters can be considered `edge-cases', and while they are generally not very common, they need to be detected and either removed from the final set of author clusters forming  putative communities of practice (or reconfigured with other clusters to represent a different  community).
Thus, further examination and possibly refinement of selected MCL clusters using citation data should be performed.

Finally, while conductance and coherence have both been proposed as quality measures for evaluating clusters, they are not directly relevant to thematic relatedness,
which requires human expert evaluation for reliable assessment. In our study,  we used human experts to evaluate  100 MCL clusters, which confirmed that the clusters
with low conductance tended to have strong thematic relatedness.
However,  as this expert evaluation was limited to only 100 MCL clusters, and each
cluster was only evaluated by one reviewer, it is premature to draw definitive conclusions about the
thematic relatedness of MCL clusters produced by this pipeline.

For each of the 35 MCL clusters produced by our pipeline, we  examined the authors of the publications in the cluster. The 35 publication clusters varied from 40--325 publications, with 199--1628 authors per cluster.
The vast majority (between 70--91\%) of the authors contribute to only one paper each in a cluster. However, the 95th percentile of authors published from 2--4 publications in the relevant cluster, while the 99th percentile
of authors publish from 2--10 publications per cluster. Thus, generally most authors participate only in minor ways in these clusters, but there are a few authors who are much more involved, a finding that is reminiscent of \cite{price_1966}.
Interestingly, the pattern of citation between authors in the 95th percentile varies from 0 to 100\% among the clusters, suggesting considerable heterogeneity in terms of collaboration and citation practice at
the upper end of productivity in these communities.

\begin{table}
\hspace*{-1cm}
\scalebox{0.8}{
\begin{tabular}{rcrrrrrccccc}
  \hline
row & match\_year & size(m) & size(g) & cond(m) & cond(g) & coh(m) & coh(g) & int.edges(m) & int.edges(g) & jc \\
  \hline
1 & 1988 & 190 & 194 & 0.06 & 1.00 & 0.11 & 0.11 & 189 & 0 & 0.96\Tstrut \\
2 & 1990 & 328 & 327 & 0.01 & 1.00 & 0.08 & 0.08 & 327 & 0 & 0.99 \\
3 & 1993 & 114 & 112 & 0.01 & 0.01 & 0.11 & 0.11 & 113 & 111 & 0.98 \\
4 & 1988 & 211 & 224 & 0.04 & 1.00 & 0.10 & 0.09 & 210 & 0 & 0.92\\
5 & 1986 & 185 & 183 & 0.02 & 1.00 & 0.04 & 0.04 & 184 & 0 & 0.98 \\
6 & 1989 & 182 & 184 & 0.05 & 1.00 & 0.05 & 0.05 & 181 & 0 & 0.97 \\
7 & 1993 & 173 & 171 & 0.01 & 1.00 & 0.10 & 0.10 & 172 & 0  & 0.98 \\
8 & 1995 & 201 & 211 & 0.06 & 1.00 & 0.09 & 0.09 & 200 & 0  & 0.91 \\
9 & 1985 & 225 & 225 & 0.01 & 1.00 & 0.10 & 0.10 & 224 & 0  & 0.98 \\
10 & 1986 & 184 & 188 & 0.02 & 1.00 & 0.10 & 0.10 & 183 & 0  & 0.96 \\
11 & 1986 & 167 & 165 & 0.00 & 1.00 & 0.13 & 0.13 & 166 & 0  & 0.99 \\
12 & 1990 & 146 & 147 & 0.02 & 1.00 & 0.10 & 0.10 & 145 & 0  & 0.97 \\
13 & 1995 & 165 & 168 & 0.02 & 1.00 & 0.11 & 0.11 & 164 & 0  & 0.96 \\
14 & 1993 & 168 & 166 & 0.03 & 1.00 & 0.13 & 0.12 & 167 & 0  & 0.98 \\
15 & 1992 & 153 & 156 & 0.05 & 1.00 & 0.08 & 0.08 & 152 & 0  & 0.94 \\
16 & 1991 & 155 & 150 & 0.17 & 1.00 & 0.12 & 0.12 & 154 & 0 & 0.96 \\
17 & 1985 & 213 & 217 & 0.08 & 1.00 & 0.09 & 0.09 & 212 & 0 & 0.95 \\
18 & 1987 & 150 & 156 & 0.03 & 1.00 & 0.08 & 0.08 & 149 & 0  & 0.94 \\
19 & 1993 & 121 & 121 & 0.02 & 0.02 & 0.07 & 0.07 & 120 & 120 & 1.00 \\
20 & 1987 & 151 & 153 & 0.04 & 1.00 & 0.08 & 0.07 & 150 & 0  & 0.96 \\
21 & 1994 & 175 & 171 & 0.09 & 1.00 & 0.13 & 0.13 & 174 & 0  & 0.93 \\
22 & 1991 & 141 & 150 & 0.12 & 1.00 & 0.06 & 0.06 & 140 & 0  & 0.90 \\
23 & 1994 & 328 & 332 & 0.05 & 1.00 & 0.14 & 0.14 & 327 & 0  & 0.95 \\
24 & 1988 & 264 & 272 & 0.05 & 1.00 & 0.09 & 0.09 & 263 & 0  & 0.95 \\
25 & 1988 & 139 & 137 & 0.02 & 1.00 & 0.05 & 0.05 & 138 & 0  & 0.97 \\
26 & 1988 & 156 & 159 & 0.03 & 1.00 & 0.10 & 0.10 & 155 & 0  & 0.96 \\
27 & 1994 & 128 & 124 & 0.10 & 1.00 & 0.08 & 0.08 & 127 & 0  & 0.97 \\
28 & 1994 & 116 & 109 & 0.15 & 0.15 & 0.07 & 0.07 & 115 & 108  & 0.92 \\
29 & 1993 & 106 & 106 & 0.01 & 0.01 & 0.02 & 0.02 & 175 & 175  & 1.00 \\
30 & 1993 & 118 & 113 & 0.06 & 0.06 & 0.11 & 0.11 & 118 & 112  & 0.93 \\
31 & 1994 &  97 &  97 & 0.01 & 0.01 & 0.08 & 0.08 & 96 & 96  & 1.00 \\
32 & 1992 &  78 &  78 & 0.00 & 0.00 & 0.13 & 0.13 & 77 & 77  & 1.00 \\
33 & 1990 &  55 &  56 & 0.02 & 0.02 & 0.08 & 0.08 & 54 & 55  & 0.98 \\
34 & 1987 &  47 &  47 & 0.02 & 0.02 & 0.08 & 0.08 & 46 & 46  & 1.00 \\
35 & 1990 &  68 &  66 & 0.08 & 0.08 & 0.08 & 0.08 & 67 & 65  & 0.94 \\
   \hline
\end{tabular}}
\caption{Features of the 35 MCL-Graclus pairs of clusters selected for thematic relatedness. \textbf{m,g} refer to MCL and Graclus respectively. size: number of publications in the cluster. cond: conductance, coh: coherence, int.edges: number of internal edges, jc: Jaccard Coefficient
for publications in paired MCL and Graclus clusters. All 35 MCL clusters received the top rating (1) by the evaluators and the Jaccard Coefficient for MCL-Graclus pairs is greater than 0.9 for all pairs (row 22 shows Jaccard Coeffficient of 0.90, but that is due to rounding). The median conductance of MCL is 0.032, the median conductance of the Graclus clusters is 1.0 (25 Graclus clusters have a conductance of 1.0).
Rows 1, 2, and 3 are discussed further in the text.
}
\label{tab:tab3}
\end{table}

Using Scopus author identifiers, we also examined the distribution of the 2,822,497 unique authors who contributed to the set of 134,094 clusters to ask how many clusters each belonged to. Roughly 61\% authored documents
in only one cluster and 89\% authored documents in 5 clusters or less with an average of 3.1 clusters per author. However, we discovered several authors associated with very large numbers of clusters (e.g., one author had  1,005 papers
distributed across 623 clusters) and  0.08\% of 2,822,497 authors contributed to 100 clusters or more. However, there are different contexts that can result in large values like these.
For example, an author may  write many papers that cite papers in different clusters, and a different  author may write several papers that are in different clusters, each of which is cited substantially, and
both authors will have papers from many clusters.
To understand these different scenarios, it is necessary to examine in-graph and in-Scopus citation counts: comparing the number of citations received versus
those made, therefore, enables the discovery of authors of influence (cited) versus those who cite influential papers without receiving many (or any) citations themselves. In the case of the author with 1,005 publications
in 623 clusters, this person authored over 2,000 publications indexed in Scopus (confirmed independently in a search of PubMed) and cited 911 articles of which 163 were self-citations. In addition these
1,005 articles were cited 193 times (Supplementary Data).

\section*{Conclusions}

This study reveals some interesting trends that suggest directions for future work.
First,  our research suggests a pipeline to identify possible communities of practice: (1) use convergent clustering, in this both Markov Clustering (MCL) and Graclus (a spectral clustering method), to  cluster the citation network, (2) find those MCL clusters within an appropriate size range that have very low conductance values and high Jaccard coefficients to their matched Graclus clusters, and (3) extract the author community for each article cluster, and  filter out those communities that are edge cases (e.g., clusters where only one author is citing the other authors, or where only one author is cited by any other author).

Although we were able to conduct an expert evaluation for only a very small sample of the clusters produced by this pipeline, our experts ranked these clusters  highly  with respect to thematic relatedness, suggesting that this pipeline could produce article clusters derived from a scientific theme. As a result, the author communities detected using the pipeline represent likely communities of practice, as was our objective.

We applied a stringent Jaccard coefficient to the article clusters we considered in order to reduce the false discovery rate (FDR). However, the conductance values even for lower Jaccard coefficients also resulted in comparably low median conductance when we examined the 100 clusters evaluated by humans. For example, the median conductance of MCL clusters was 0.03 when the Jaccard coefficient was greater than 0.9 and 0.08 when the Jaccard coefficient was relaxed to greater than 0.7. These data suggest that relaxing these constraints should be rigorously explored since it may not increase the FDR significantly. This hypothesis is also supported by the high ratings that the experts gave to MCL clusters that had low conductance but lower overlap with their paired Graclus clusters.

Despite these promising results, we are well aware of the limitations of using citation and cluster analysis to identify communities of practice. The best techniques would ideally use expert evaluation, which is unfortunately not scalable (and in our study, we only used expert evaluation for 100 of the clusters we generated). Furthermore, this study only examined immunology publications and others connected to them by citation. Other studies have shown that citation behavior can depend significantly on the field, making extrapolation of trends from one field to another premature \citep{Wallace2012,Bradley2020}. Thus, the trends in this study may not be consistently found in other research disciplines or timeframes. Our future work will characterize these initial observations, evaluate additional clustering techniques, and focus on elucidating interactions between authors within and across clusters to refine the pipeline we envision.
\clearpage

\section{Supportive Information} \vspace{3mm}Supplementary material  and code used in this study is available on our Github site~\citep{Korobskiy2019}.

\subsection*{Acknowledgments}
\vspace{3mm} We thank Vladimir Smirnov for helpful discussions on using Markov Clustering. The ERNIE project involves a collaboration with Elsevier. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or Elsevier.  We thank our Elsevier colleagues for their support of the ERNIE project.

\subsection*{authorcontributions}
\vspace{3mm}
Shreya Chandrasekharan: Conceptualization; Methodology; Investigation; Writing—Original Draft; Writing—Review and Editing. Mariam Zaka: Investigation; Writing—Review and Editing.; Stephen Gallo: Investigation; Writing—Review and Editing.Tandy Warnow: Conceptualization; Methodology; Writing—Original Draft; Writing—Review and Editing. George Chacko: Conceptualization; Methodology; Investigation; Writing—Original Draft; Writing—Review and Editing; Funding Acquisition, Resources; Supervision.

\section{Competing Interests} \vspace{3mm} The authors have no competing interests. Scopus data used in this study was available to us through a collaborative agreement with Elsevier on the ERNIE project. Elsevier personnel played no role in conceptualization, experimental design, review of results, or conclusions presented.

\section{Funding Information} \vspace{3mm} Research and development reported in this publication was partially supported by federal funds from the National Institute on Drug Abuse (NIDA), National Institutes of Health, U.S. Department of Health and Human Services, under Contract Nos. HHSN271201700053C (N43DA-17-1216) and HHSN271201800040C (N44DA-18-1216). Tandy Warnow receives funding from the Grainger Foundation.

\section{Data Availability} \vspace{3mm} Access to the bibliographic data analyzed in this study requires a license from Elsevier. Code generated for this study is freely available from our Github site~\citep{Korobskiy2019}.

\newpage

redpajama_set_name: RedPajamaArXiv


Choosing the right Secondary school for your child is very important.
This applies to children born 1st September 2007 to 31st August 2008. The national closing date is 31st October 2018. If you haven't applied yet you should still apply as soon as possible.


text:
Lamborghini has officially revealed their newest flagship super car, the 2012 Lamborghini Aventador LP700-4. This stunning new Lambo is a genuine super car, capable of reaching speeds of 217 mph on the top end, breaking to 60mph in a blinding 2.8 seconds. To put that in perspective, the Aventador hit 60 mph before you finished reading this sentence.
The design of the Aventador should feel familiar to Lamborghini fans, with trademark angles that ripple along its body in geometric symmetry. Everything from the wheels to the intakes to the back end are all given a visual upgrade over the Murcièlago this beauty replaces. Like those other recent Lambo's, the rear window is perched above the Aventador's nasty bits, providing a clear view of the fine Italian engineering within.
The engine of the Lamborghini Aventador is also a step up from the Murcièlago. The Aventador is powered by a 6.5-litre V12 engine that boosts horsepower up to 691, but it both lighter and more fuel efficient than the engine it replaces. That new engine plus an added focus on carbon fiber body construction gives the Aventador its blinding speed.
Want one of your own? 700-800 Lamborghini Aventadors will be produced each year, sold for an estimated $370k a piece. The sales release can't come soon enough, we're pumped to see the newest Lamborghini out prowling the streets.
Lamborghini Aventador Gallery
One thought on "Lamborghini Aventador"
Joanna Telli says:
Write an article on the Centenario! So cool! http://www.hypecrib.com/2016/03/lamborghini-centenario-their-newest-beast/
Lamborghini Murcielago LP670-4 SuperVeloce
Motorlust: the 12 Most Desirable Vehicles of 2012
The New Lamborghini Aventador LP 750-4 SuperVeloce Roadster Goes Topless


text:
New protocol for intestinal organoid generation developed
Posted: 13 January 2020 | Hannah Balfour (Drug Target Review) | No comments yet
A new process for producing organoids allows researchers to explore intestinal epithelium in isolation and could allow for the development of improved targeted treatments of diseases.
Researchers have created a new protocol for intestinal organoid formation using human induced pluripotent stem cells (hiPSCs). The new technique allows for intestinal epithelial cell response to drug treatment to be studied in isolation and could form the basis of disease research in inflammatory bowel disease, colon cancer and cystic fibrosis.
The study, published in Nature Communications, details how hiPSCs were given growth factor specific to intestinal cells and supported by a gel to create intestine organoids. In other organoid growing protocols, this type of organoid has to be supported by mesenchyme; however, by removing this the researchers were able to study how the epithelial cells of the intestine alone responds to medication.
Researchers at the Center for Regenerative Medicine (CReM) of Boston University and Boston Medical Center, both US, also used CRISPR technology to create a novel iPSC stem cell line that shone green when differentiated into intestinal cells. This allowed the team to follow the process of intestinal cells differentiation in vitro.
"Generating organoids in our lab allows us to create more accurate disease models, which are used to test treatments and therapies targeted to a specific genetic defect or tissue – and it's all possible without harming the patient," said Dr Gustavo Mostoslavsky, co-director of CReM, associate professor of medicine and microbiology at Boston University School of Medicine and faculty in the gastroenterology section at Boston Medical Center. "This approach allows us to determine what treatments could be most effective and which are ineffective, against a disease."
What are the pros and cons of using organoids?
Using their model, the team generated intestinal organoids from iPSCs containing a mutation that causes cystic fibrosis and corrected the mutation with CRISPR. The intestinal organoids with the mutation did not respond to a drug while the genetically corrected cells did respond, demonstrating their future potential for disease modeling and therapeutic screening applications.
"I hope that this study helps move forward our collective understanding about how diseases impact the gastrointestinal tract at the cellular level. The continual development of novel techniques in creating highly differentiated cells that can be used to develop disease models in a lab setting will pave the way for the development of more targeted approaches to treat many different diseases," concluded Mostoslavsky.
CRISPR, Disease research, Drug Targets, Organoids, Research & Development, Therapeutics
Colon cancer, Cystic fibrosis, Inflammatory bowel disease (IBD)
Boston Medical Center, Boston University School of Medicine, Center for Regenerative Medicine (CReM) of Boston University and Boston Medical Center
Dr Gustavo Mostoslavsky
By Hannah Balfour (Drug Target Review)
Chasing my Cure: Leveraging serum proteomics to identify novel therapeutic approaches and predictive biomarkers
By SomaLogic
Existing drug may improve immune response to leukaemia treatment
Uncovering ketamine's mechanisms helps identify depression treatment
S1P may be a potential target for cerebral blood alterations


text:
Discover the latest stories from our blog
Huntington Learning Center Franchise Reviews: Meet Greg Kulander
Owner of four Huntington Learning Center franchise centers opens up about the impact he has on his students' lives Greg Kulander has been passionate about education his entire life. In fact, the majority of his life has been spent in pursuing education to the highest level, teaching at the university level, and specializing in education ...
Entrepreneur Highlights Huntington Learning Center and Education Franchise Technology
Rick Grossman, Entrepreneur's Leadership Network VIP, recently highlighted Huntington Learning Center and President Anne Huntington in his feature about education franchises and their move towards new technologies. As Mr Grossman notes: "Children's-education franchises have been a growing category in recent years, and with the pandemic's impact on the marketplace, they've moved towards technology as a focal point." – Rick ...
Huntington Learning Center in Low Cost Franchises 2021 list
Huntington Learning Center, the nation's leading tutoring and test prep provider, was recently named #10 on Entrepreneur's 100 Top Low-Cost Franchises List in 2021. What is a Low Cost Franchise? A Low Cost Franchise is a recognized franchise opportunity available to aspiring business owners with a start-up investment of less than $150,000. For over 40 years, ...
Top-rated tutoring franchise launches new website to attract worthy entrepreneurs to grow the brand across the country Huntington Learning Center is ready to grow with entrepreneurs who are passionate about education and owning a business with tremendous profit and life-changing potential. Founded in 1977 by Dr. Raymond and Mrs. Eileen Huntington, Huntington Learning Center has ...
3 Reasons to Invest in a Huntington Learning Center Franchise
Why investing in the nation's premier tutoring franchise is a wise choice for entrepreneurs who want to make a difference There's never been a better time to invest in Huntington Learning Center, the nation's premier tutoring franchise. Founded in 1977 by Dr. Raymond and Mrs. Eileen Huntington, Huntington Learning Center has grown to over 300 ...


text:
olive is a topic covered in the Davis's Drug Guide.
o-liv
Monounsaturated Fatty Acid
antihypertensives
cardioprotective agents
Vallerand, April Hazard., et al. "Olive." Davis's Drug Guide, 18th ed., F.A. Davis Company, 2023. Davis's Drug Guide - OLD - USE 2.0, www.drugguide.com/ddo/view/Davis-Drug-Guide/110109/all/olive.
Vallerand AHA, Sanoski CAC, Quiring CC. Olive. Davis's Drug Guide. F.A. Davis Company; 2023. https://www.drugguide.com/ddo/view/Davis-Drug-Guide/110109/all/olive. Accessed January 30, 2023.
Vallerand, A. H., Sanoski, C. A., & Quiring, C. (2023). Olive. In Davis's Drug Guide (18th ed.). F.A. Davis Company. https://www.drugguide.com/ddo/view/Davis-Drug-Guide/110109/all/olive
Vallerand AHA, Sanoski CAC, Quiring CC. Olive [Internet]. In: Davis's Drug Guide. F.A. Davis Company; 2023. [cited 2023 January 30]. Available from: https://www.drugguide.com/ddo/view/Davis-Drug-Guide/110109/all/olive.
TY - ELEC T1 - olive ID - 110109 A1 - Sanoski,Cynthia A, AU - Vallerand,April Hazard, AU - Quiring,Courtney, BT - Davis's Drug Guide UR - https://www.drugguide.com/ddo/view/Davis-Drug-Guide/110109/all/olive PB - F.A. Davis Company ET - 18 DB - Davis's Drug Guide - OLD - USE 2.0 DP - Unbound Medicine ER -
methylphenidate (oral)


text:
China, U.S. start high-profile events to mark 30th anniversary of diplomatic ties
China and the United States on Monday kicked off a series of events to mark three decades of one of the world's most crucial bilateral relationships.
"Like a scroll of vivid historic scenes, these pictures showcase significant progress in the 30 years of China-U.S relations and inspire us," Chinese President Hu Jintao said in a congratulatory message to a photo exhibition marking the relations.
Thanks to the "ping-pong diplomacy" that melted the ice between the two nations, then U.S. President Richard Nixon paid a groundbreaking visit to China in 1972.
Former U.S. President Jimmy Carter (2nd R, front) and his wife Rosalynn Carte visit the photo exhibition marking the 30th anniversary of bilateral diplomatic ties between China and the United States in Beijing Jan. 12, 2009. The exhibition kicked off here on Monday. (Xinhua/Yao Dawei)
Photo Gallery>>>
The two countries formally established diplomatic relations on Jan. 1, 1979.
With 360 pictures recalling milestones in bilateral relations, the photo exhibition on Monday drew almost 200 people from the two countries, including key figures in forging relations like former U.S. President Jimmy Carter and former Secretary of State Henry Kissinger.
From top leaders' handshakes to U.S. swimmers' laughter at the Beijing Olympics, from McDonald's first restaurant in Beijing to China's auto show in Detroit, the pictures capture landmark moments.
"It is very nostalgic," said former U.S. ambassador to Beijing Winston Lord, a member of the U.S. delegation during Richard Nixon's groundbreaking visit to China in 1972.
"The photos brought back many warm and happy memories on working to develop harmonious relationships between the Chinese and the American people."
In a congratulatory message to the exhibition, U.S President George W. Bush said the past 30 years had "strengthened the bonds between the two countries".
Bush said he personally witnessed China's transformation over the past three decades. "In 1975 I saw a nation just beginning its journey of modernization. In 2008, I was proud to watch Beijing host a successful and thrilling Olympic games."
The yearlong photo exhibition will tour Chinese cities, before going to the United States, with the final display in the Atlanta-based Cater Center in December 2009.
Former U.S. Secretary of State Henry Kissinger (C) attends the opening ceremony of the photo exhibition marking the 30th anniversary of bilateral diplomatic ties between China and the United States in Beijing Jan. 12, 2009. The exhibition kicked off here on Monday. (Xinhua/Yao Dawei)
On Monday afternoon, a seminar to review the past three decades of bilateral ties and look to future relations began in Beijing.
The two-day seminar gathered senior Chinese diplomats, including Qian Qichen, Tang Jiaxuan, Li Zhaoxing, and all living Chinese ambassadors to Washington D.C. as well as scholars and entrepreneurs.
In addition to Carter and Kissinger, former U.S. National Security Advisors Brent Scowcroft and Zbigniew Brzezinski as well as U.S. ambassadors in Beijing attended the seminar.
"As a Chinese saying goes 'when you drink the water, think of those who dug the well', said State Councilor Dai Bingguo. "I want to pay high tribute to leaders of the older generations and all those visionaries working for the diplomatic relations between the two countries."
With U.S. President-elect Barack Obama to take office in a week, the future of bilateral ties was a hot topic at the seminar.
"China expected the new U.S. administration to show enough strategic vision and political wisdom, inherit and work from the good China legacies left by the earlier administrations," Chinese Vice Foreign Minister He Yafei said.
"Overall, the American policy with China will remain essentially the same under the Obama administration," Lord said. "If you look at what Obama has been saying about U.S.-China relations, look at what type of people he has been appointing to key foreign policy positions, these suggest great continuity."
"Obama's diplomatic team is pragmatic and values multilateral approaches," said Qin Yaqing, vice president of Chinese Foreign Affairs University. "I don't think there will be significant changes in Obama's China policy."
Later Monday, President Hu met with a U.S. delegation led by former U.S. President Carter on the sidelines of celebrations.
"As old friends of the Chinese people, you played an important role in establishing China-U.S. diplomatic ties and witnessed that historic decision," Hu told the U.S. delegation.
"For a long time, whether in office or retired, you have made unremitting efforts in building the friendship between the two nations and developing the relationship. I highly appreciate those efforts."
"For all American citizens, particularly for these leaders, this is a wonderful occasion to celebrate," Carter said.
Hu called for the two nations to review and deal with bilateral relations from a strategic and long-term perspective, and jointly respond to the global challenges.
"It is natural for the two nations to be divided on some issues as the two practice different social systems, inherit different histories and cultures, and stay in a different phase of development," Hu said.
Hu proposed that the two sides respect each other, properly address differences and problems, take into consideration each other's core interests so as to promote the long-term sound growth of bilateral ties.
Carter said, "All of us, even including Deng Xiaoping, would have been amazed 30 years ago at the great changes that took place not only within your own country, but in the relationship between our two nations."
At a reception in the Great Hall of the People Monday evening, Chinese Vice President Xi Jinping hosted about 500 guests from the two countries.
· China, U.S. start high-profile events to mark 30th anniversary of diplomatic ties


Our walks lead through areas with low traffic that are rich in greenery.
Strolling through little woodland paths or offering prayers at a Shinto shrine 😉 our walks offer many discoveries and adventures.
When a dog with a body weight of 10kg walks 4km, it will burn app. 40kcal.
That corresponds roughly to a 40 minute walk.
When the weather is nice, we go for a walk twice every day.
That is neat for those pooches that only go to the toilet outside.
A dog that is adequately walked can relax.
Sufficient exercise burns calories, relieves stress and leaves your pooch absolutely satisfied.


What is the difference between Data and Statistics? In regular conversation, both words are often used interchangeably. In the world of libraries, academia, and research there is an important distinction between data and statistics.... 15/05/2012 · Whenever we have some performance issues. First thing we will do is run this command on all the tables in our schema so all the statistics are up to date.
exec dbms_stats.set_table_prefs(user,'SALES','STALE_PERCENT','5') 2 BEST PRACTICES FOR GATHERING OPTIMIZER STATISTICS WITH ORACLE DATABASE 12C RELEASE 2 To check what preferences have been set, you can use the DBMS_STATS.GET_PREFS function.... This statistics table is created using the procedure DBMS_STATS.CREATE_STAT_TABLE. After this table is created, then you can export statistics from the data dictionary into your statistics table using the DBMS_STATS.EXPORT_*_STATS procedures.
Frequencies and Crosstabs . This section describes the creation of frequency and contingency tables from categorical variables, along with tests of independence, measures of association, and methods for graphically displaying results.
upper limit: b + (t.95)(s b) where t .95 is the value of t to use for the 95% confidence interval. The values of t to be used in a confidence interval can be looked up in a table of the t distribution.


Coalville have begun their player recruitment for next year by re-signing prop Ashley Lynds for the 2019/2020 season.
After acting early to get the coaching team confirmed, Coalville are looking to push for honours next year after a respectable top half finish in National 2 East (South), and are targeting players with previous affiliation to the club. Prop Lynds, who has been recently playing his trade at league rivals Belgrave, represented Coalville throughout age group Level, and has made host of appearances for the first team.
Coalville bolster coaching staff with the addition of Tom Blake as head of Strength & Conditioning  The Memorial Ground team add another new face as planning for 2019/2020 continues.


Housed in a noble palace dating back to the 16th century, Park Hotel is an elegant apartment hotel, situated in the heart of Hvar, a unique combination of lush Mediterranean nature, rich culture heritage and contemporary tourist appeal.
With breathtaking views to the Paklinski islands and Hvar port, the apartments are finely furnished with soft shades and elegant furniture, featuring all the facilities needed to enjoy your trip to the fullest.
When visiting Hvar, do not miss the opportunity to discover the gastronomic tradition and the treasures of culinary delicacies offered by the restaurant Park, with stunning views to the port.
Park Hotel is situated in the heart of Hvar, between the one time Rector's Palace and St. Mark church.


text:
Home Sports Australia Andy Marinos reportedly leading race to become Rugby Australia CEO
Andy Marinos reportedly leading race to become Rugby Australia CEO
SANZAAR boss Andy Marinos is in the box seat to become the next chief executive of Rugby Australia as the search for a full-time successor to Raelene Castle nears completion, the Sydney Morning Herald reports.
Multiple industry sources familiar with the recruitment process said Marinos, who has led SANZAAR since 2015, was the leading candidate for the top job, which was left vacant when Castle departed after a falling-out with the board in April. Rob Clarke has filled the CEO's role on an interim basis since then.
RA chairman Hamish McLennan is nearing a decision on a permanent replacement. He is expected to seek approval from the board to sign off on the new chief executive before the end of the year.
Marinos was one of several candidates in the running and fits the profile McLennan and his board have been searching for – an executive with experience in the sport and who understands how to manage relationships with broadcasters.
Other candidates in the mix included former NRL chief Todd Greenberg, former Cricket NSW CEO Andrew Jones, and Chris White, the boss of sport branding and sponsorship company International Quarterback.
The sources stressed that no deal had been signed and there were still other candidates in the mix but said Marinos was in a final round of meetings with key stakeholders.
Marinos, while representing Wales during his professional career, was nicknamed 'The Greek' among his teammates. He has spent his career working in rugby in Australia and South Africa and has experience leading a commercial and marketing division – an area former advertising executives McLennan and Clarke consider hugely important for growing the code's fanbase.
He also worked closely with Clarke during rugby's broadcast rights negotiations this year. Rugby sources indicated that new broadcast partners Nine Entertainment Co (owner of this masthead) and subscription service Stan were impressed by his efforts.
Nine and Stan signed a three-year $100 million deal with RA in early November.
The appointment of a CEO to Rugby Australia would mark an end to a difficult but significant year for the rugby union.
Sourced By: Sydney Morning Herald
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text:
White House offices
Budget history
Executive Office of the President of the United States
U.S. government executive agency
Seal of the Executive Office
Flag of the Executive Office
July 1, 1939; 82 years ago (1939-07-01)
U.S. Federal Government
White House, Washington, D.C.
1,800 (approximately)
$714 million[1]
Agency executive
Ron Klain, White House Chief of Staff
www.whitehouse.gov
The Executive Office of the President (EOP) comprises the offices and agencies[2] that support the work of the president at the center of the executive branch of the United States federal government. The EOP consists of several offices and agencies, such as the White House Office (the staff working directly for and reporting to the president, including West Wing staff and the president's closest advisers), the National Security Council, and the Office of Management and Budget.
The EOP is also referred to as a "permanent government", with many policy programs, and the people who implement them, continuing between presidential administrations. This is because there is a need for qualified, knowledgeable civil servants in each office or agency to inform new politicians.[3]
The civil servants who work in the Executive Office of the President are also regarded as nonpartisan and politically neutral, so that they can give impartial advice.[3]
With the increase in technological and global advancement, the size of the White House staff has increased to include an array of policy experts to effectively address various fields. There are about 1,800 positions in the EOP,[4] most of which do not require confirmation from the U.S. Senate.
The EOP is overseen by the White House chief of staff. Since January 20, 2021, that position has been held by Ron Klain, who was appointed by President Joe Biden.[5][6][7][8]
The Eisenhower Executive Office Building at night.
In 1937, the Brownlow Committee, which was a presidentially commissioned panel of political science and public administration experts, recommended sweeping changes to the executive branch of the United States government, including the creation of the Executive Office of the President. Based on these recommendations, President Franklin D. Roosevelt in 1939 lobbied Congress to approve the Reorganization Act of 1939. The Act led to Reorganization Plan No. 1,[9] which created the EOP,[10] which reported directly to the president. The EOP encompassed two subunits at its outset: the White House Office (WHO) and the Bureau of the Budget, the predecessor to today's Office of Management and Budget, which had been created in 1921 and originally located in the Treasury Department. It absorbed most of the functions of the National Emergency Council.[11] Initially, the new staff system appeared more ambitious on paper than in practice; the increase in the size of the staff was quite modest at the start. However, it laid the groundwork for the large and organizationally complex White House staff that would emerge during the presidencies of Roosevelt's successors.[12]
Roosevelt's efforts are also notable in contrast to those of his predecessors in office. During the 19th century, presidents had few staff resources. Thomas Jefferson had one messenger and one secretary at his disposal, both of whose salaries were paid by the president personally. It was not until 1857 that Congress appropriated money ($2,500) for the hiring of one clerk. By Ulysses S. Grant's presidency (1869–1877), the staff had grown to three. By 1900, the White House staff included one "secretary to the president" (then the title of the president's chief aide), two assistant secretaries, two executive clerks, a stenographer, and seven other office personnel. Under Warren G. Harding, there were thirty-one staff, although most were clerical positions. During Herbert Hoover's presidency, two additional secretaries to the president were added by Congress, one of whom Hoover designated as his press secretary. From 1933 to 1939, as he greatly expanded the scope of the federal government's policies and powers in response to the Great Depression, Roosevelt relied on his "brain trust" of top advisers, who were often appointed to vacant positions in agencies and departments, from which they drew their salaries since the White House lacked statutory or budgetary authority to create new staff positions.
After World War II, in particular during the presidency of Dwight David Eisenhower, the staff was expanded and reorganized. Eisenhower, a former U.S. Army general, had been Supreme Allied Commander during the war, and reorganized the Executive Office to suit his leadership style.[13]
Today, the staff is much bigger. Estimates indicate some 3,000 to 4,000 persons serve in EOP staff positions with policy-making responsibilities, with a budget of $300 to $400 million (George W. Bush's budget request for Fiscal Year 2005 was for $341 million in support of 1,850 personnel).[14]
Some observers have noted a problem of control for the president due to the increase in staff and departments, making coordination and cooperation between the various departments of the Executive Office more difficult.[15]
The president had the power to reorganize the Executive Office due to the 1949 Reorganization Act which gave the president considerable discretion, until 1983 when it was renewed due to President Reagan's administration allegedly encountering "disloyalty and obstruction".[15]
The chief of staff is the head of the Executive Office and can therefore ultimately decide what the president needs to deal with personally and what can be dealt with by other staff, in order to avoid wasting the time of the president.
Senior staff within the Executive Office of the President have the title Assistant to the President, second-level staff have the title Deputy Assistant to the President, and third-level staff have the title Special Assistant to the President.[16]
The core White House staff appointments, and most Executive Office officials generally, are not required to be confirmed by the U.S. Senate, although there are a handful of exceptions (e.g., the director of the Office of Management and Budget, the chair and members of the Council of Economic Advisers, and the United States Trade Representative).
The information in the following table is current as of January 20, 2021. Only principal executives are listed; for subordinate officers, see individual office pages.
Principal executive
White House Office Assistant to the President and Chief of Staff Ron Klain
National Security Council Assistant to the President for National Security Affairs Jake Sullivan
Homeland Security Council[a] Assistant to the President for Homeland Security and Counterterrorism[b] Elizabeth Sherwood-Randall
Council of Economic Advisers Chairman of the White House Council of Economic Advisers Cecilia Rouse
Council on Environmental Quality Chairman of the Council on Environmental Quality Brenda Mallory
Executive Residence Staff and Operations White House Chief Usher Robert B. Downing
National Space Council Executive Secretary of the National Space Council Chirag Parikh[17]
President's Intelligence Advisory Board Chairman of the President's Intelligence Advisory Board TBD
Office of Administration Director of the Office of Administration Anne Filipic
Office of Management and Budget Director of the Office of Management and Budget Shalanda Young (acting)
Office of National Drug Control Policy Director of National Drug Control Policy Rahul Gupta
Office of the National Cyber Director National Cyber Director Chris Inglis
Office of Science and Technology Policy Director of the Office of Science and Technology Policy Eric Lander
Office of the United States Trade Representative United States Trade Representative Katherine Tai
Office of the Vice President of the United States Chief of Staff to the Vice President Tina Flournoy
The White House Office (including its various offices listed below) is a sub-unit of the Executive Office of the President (EOP). The various agencies of the EOP are listed above.
Office of the National Security Advisor
Office of Cabinet Affairs
Office of Digital Strategy
White House Office of Communications
Office of the First Lady
Office of Management and Administration
Office of Political Affairs
Office of Presidential Personnel
Office of Scheduling and Advance
Office of the Staff Secretary
Office of White House Counsel
Oval Office Operations
White House Military Office
Joe Biden's Executive Office of the President
Office Name Term Office Name Term
White House Chief of Staff Ron Klain 2021 National Security Advisor Jake Sullivan 2021
White House Deputy Chief of Staff Jen O'Malley Dillon 2021 Deputy National Security Advisor Jonathan Finer 2021
White House Deputy Chief of Staff Bruce Reed 2021 Homeland Security Advisor Elizabeth Sherwood-Randall 2021
Counselor to the President Steve Ricchetti 2021 White House Communications Director Kate Bedingfield 2021
Jeffrey Zients 2021 Deputy White House Communications Director Pili Tobar 2021
Senior Advisor to the President Mike Donilon 2021 Kate Berner 2021
Anita Dunn 2021 White House Press Secretary Jen Psaki 2021
Director, Public Engagement Cedric Richmond 2021 Deputy Press Secretary Karine Jean-Pierre 2021
Director, Intergovernmental Affairs Julie Chavez Rodriguez 2021 Director, Speechwriting Vinay Reddy 2021
Chair, Council of Economic Advisers Cecilia Rouse 2021 Director, Digital Strategy Rob Flaherty 2021
Director, Domestic Policy Council Susan Rice 2021 Director, Legislative Affairs Louisa Terrell 2021
White House Cabinet Secretary Evan Ryan 2021 Director, Presidential Personnel Catherine M. Russell 2021
Director, Oval Office Operations Annie Tomasini 2021 White House Staff Secretary Jessica Hertz 2021
Personal Aide to the President Stephen Goepfert 2021 Director, Management and Administration Anne Filipic 2021
Personal Secretary to the President Director, Scheduling and Advance Ryan Montoya 2021
Chief of Staff to the First Lady Julissa Reynoso Pantaleón 2021 Director, Office of Science and Technology Policy Eric Lander 2021
White House Social Secretary Carlos Elizondo 2021 Director, Office of Management and Budget Shalanda Young (acting) 2021
Chief of Staff to the Vice President Hartina Flournoy 2021 United States Trade Representative Katherine Tai 2021
White House Chief Usher Robert B. Downing 2021 Director, Office of National Drug Control Policy Rahul Gupta 2021
Director, White House Military Office Maju Varghese 2021 Chair, Council on Environmental Quality Brenda Mallory 2021
(s) Indicates nominee requiring Senate confirmation.
Congress as well as the president has some control over the Executive Office of the President. Some of this authority stems from its appropriation powers given by the Constitution, such as the "power of the purse", which affects the Office of Management and Budget and the funding of the rest of federal departments and agencies. Congress also has the right to investigate the operation of the Executive Office, normally holding hearings bringing forward individual personnel to testify before a congressional committee.[3]
The Executive Office often helps with legislation by filling in specific points understood and written by experts, as Congressional legislation sometimes starts in broad terms.[3]
This table specifies the budget of the Executive Office for the years 2008–2017, and the actual outlays for the years 1993–2007.
2017 $714 million[1]
2016 $692 million[18]
2007 $2956 million[27]
Title 3 of the Code of Federal Regulations
White House Records Office
^ shares staff with the National Security Council
^ reports to the National Security Advisor
^ a b "FY 2017 Omnibus Summary – Financial Services and General Government Appropriations" (PDF). House Appropriations Committee. May 1, 2017. Retrieved May 1, 2017.
^ Harold C. Relyea (November 26, 2008). The Executive Office of the President: A Historical Overview (PDF) (Report). Washington, D.C.: Congressional Research Service. Retrieved April 17, 2018.
^ a b c d Mckeever, Robert J. (2014). A Brief Introduction to US Politics. doi:10.4324/9781315837260. ISBN 978-1315837260.
^ "The Executive Branch". whitehouse.gov. April 1, 2015. Retrieved March 5, 2021.
^ Hartnett, Cass. "Library Guides: United States Federal Government Resources: The Executive Office of the President". guides.lib.uw.edu. Retrieved January 5, 2019.
^ Trump, Donald J. (December 14, 2018). "I am pleased to announce that Mick Mulvaney, Director of the Office of Management & Budget, will be named Acting White House Chief of Staff, replacing General John Kelly, who has served our Country with distinction. Mick has done an outstanding job while in the Administration..." @realDonaldTrump. Retrieved January 5, 2019.
^ Swanson, Ian (December 14, 2018). "Trump names Mulvaney acting chief of staff". TheHill. Retrieved January 5, 2019.
^ O'Toole, Molly (December 30, 2018). "John F. Kelly says his tenure as Trump's chief of staff is best measured by what the president did not do". Los Angeles Times. Retrieved January 5, 2019.
^ Roosevelt, Franklin D. (April 25, 1939). "Message to Congress on the Reorganization Act". John T. Woolley and Gerhard Peters. The American Presidency Project. Santa Barbara: University of California. Retrieved May 6, 2011.
^ Mosher, Frederick C. (1975). American Public Administration: Past, Present, Future (2nd ed.). Birmingham: University of Alabama Press. ISBN 0817348298.
^ Roosevelt, Franklin D. (May 9, 1939). "Message to Congress on Plan II to Implement the Reorganization Act". John T. Woolley and Gerhard Peters. The American Presidency Project. Santa Barbara: University of California. Retrieved May 6, 2011. The plan provides for the abolition of the National Emergency Council and the transfer to the Executive Office of the President of all its functions with the exception of the film and radio activities which go to the Office of Education.
^ Relyea, Harold C. (March 17, 2008). "The Executive Office of the President: An Historical Overview" (PDF). Congressional Research Service. Retrieved April 14, 2008.
^ Patterson, Bradley H. (1994). "Teams and Staff: Dwight Eisenhower's Innovations in the Structure and Operations of the Modern White House". Presidential Studies Quarterly. 24 (2): 277–298. JSTOR 27551241.
^ Burke, John P. "Administration of the White House". Miller Center of Public Affairs, University of Virginia. Archived from the original on November 17, 2010. Retrieved June 6, 2009.
^ a b Ashbee, Edward (2019). US politics today. ISBN 978-1526124517. OCLC 1108740337.
^ Kumar, Martha Joynt. "Assistants to the President at 18 Months: White House Turnover Among the Highest Ranking Staff and Positions" (PDF). whitehousetransitionproject.org. Retrieved November 24, 2020.
^ "Chirag Parikh Tapped for National Space Council ExecSec". August 2, 2021.
^ "Appropriations Committee Releases Fiscal Year 2016 Financial Services Bill". House Appropriations Committee. May 24, 2016. Retrieved May 1, 2017.
^ "Appropriations Committee Releases Fiscal Year 2015 Financial Services Bill". House Appropriations Committee. July 16, 2014. Retrieved May 1, 2017.
^ "Appropriations Committee Releases Fiscal Year 2013 Financial Services Bill". House Appropriations Committee. June 5, 2012. Retrieved May 1, 2017.
^ "Appropriations Committee Releases Fiscal Year 2012 Financial Services Bill". House Appropriations Committee. June 15, 2011. Retrieved May 1, 2017.
^ "Financial Services and General Government (FSGG): FY2011 Appropriations". Congressional Research Service. July 11, 2011. Retrieved May 1, 2017.
^ "Financial Services and General Government (FSGG): FY2010 Appropriations" (PDF). Congressional Research Service. February 4, 2010. Retrieved May 1, 2017.
^ "Financial Services and General Government (FSGG): FY2009 Appropriations" (PDF). Congressional Research Service. May 12, 2009. Retrieved May 1, 2017.
^ "Financial Services and General Government (FSGG): FY2008 Appropriations". Congressional Research Service. December 20, 2007. Retrieved May 1, 2017.
^ a b c d e f g h i j k l m n o "Historical Tables, Table 4.1 – Outlays by Agency: 1962–2022". Office of Management and Budget. January 20, 2017. Retrieved June 30, 2019. [clarification needed]
Executive Office of the President Archived January 20, 2021, at the Wayback Machine
The Debate Over Selected Presidential Assistants and Advisors: Appointment, Accountability, and Congressional Oversight Congressional Research Service
Proposed and finalized federal regulations from the Executive Office of the President of the United States
Works by Executive Office of the President of the United States at Project Gutenberg
Works by or about Executive Office of the President of the United States at Internet Archive
Executive Office of the United States President
Advisory Boards (Council for Community Solutions, Corporation for National and Community Service, Initiative on Asian Americans and Pacific Islanders, President's Council of Advisors on Science and Technology, President's Intelligence Advisory Board, President's Management Advisory Board)
Executive Residence (Committee for the Preservation of the White House, Office of the Curator, Office of the Chief Usher, Office of the Chief Floral Designer, Office of the Executive Chef, Graphics and Calligraphy Office)
National Security Council (Deputies Committee)
Office of Administration (Office of Mail and Messenger Operations, Office of the Chief Financial Officer, Office of the Chief Administrative Officer)
Office of Management and Budget (Office of the Chief Performance Officer, Office of E-Government and Information Technology, Office of Federal Financial Management, Office of Information and Regulatory Affairs)
Office of Science and Technology Policy (Office of the Chief Technology Officer)
Office of the Trade Representative
Office of the Vice President (Office of the Chief of Staff)
Military Office (Communications Agency, Medical Unit, Presidential Food Service, Transportation Agency)
White House Office
Office of the Chief of Staff (Office of Senior Advisors)
Office of Communications (Office of Media Affairs, Office of Research, Office of the Press Secretary, Office of Speechwriting)
Office of the Counselor
Domestic Policy Council (Office of Faith-Based and Neighborhood Partnerships, Office of National AIDS Policy, Office of Social Innovation and Civic Participation, Rural Council)
Office of the First Lady (Office of the Social Secretary)
Office of the National Security Advisor (Homeland Security Advisor)
Office of Management and Administration (White House Operations, White House Personnel, Visitors Office)
National Trade Council
Oval Office Operations (Personal Secretary)
Presidential Innovation Fellows
Office of Public Liaison (Council on Women and Girls, Jewish Liaison, Office of Urban Affairs)
Office of the Staff Secretary (Office of the Executive Clerk, Office of Presidential Correspondence, Office of Records Management)
Office of the White House Counsel
Chief Floral Designer
China Room
Office of the Curator
Diplomatic Reception Room
Vermeil Room
State floor
Cross Hall
East Room
Family Dining Room
Grand Staircase
South Portico
State Dining Room
Chief Usher
Center Hall
East Bedroom
East Sitting Hall
Family Kitchen
Lincoln Bedroom
Lincoln Sitting Room
President's Bedroom
President's Dining Room
Private Sitting Room
Queens' Bedroom
Queens' Sitting Room
Treaty Room
Truman Balcony
West Bedroom
West Sitting Hall
Yellow Oval Room
Diet Kitchen
Linen Room
Sun Room
Work-out Room
call button
Oval Office Study
Press Briefing Room
Roosevelt Room
East Wing
Graphics and Calligraphy Office
Presidential Emergency Operations Center
White House Family Theater
Social Secretary
Visitors Office
The Ellipse
Jacqueline Kennedy Garden
Lafayette Square
Andrew Jackson statue
Marquis de Lafayette statue
North Lawn
President's Park
South Lawn
Horseshoe pit
Tennis and basketball court
White House to Treasury Building tunnel
White House vegetable garden
New Executive Office Building
White House Conference Center
Peter Parker House
Trowbridge House
Presidential Townhouse
E Street
East Executive Avenue
H Street NW
Madison Place
Pennsylvania Avenue
State Place
West Executive Avenue
Security breaches
United States articles
Pre-Columbian era
Thirteen Colonies
Continental Association
Lee Resolution
Treaty of Paris
Articles of Confederation
Confederation Period
American frontier
drafting and ratification
Federalist Era
Mexican–American War
Reconstruction era
Indian Wars
Gilded Age
Civil rights movement 1865–1896 / 1896–1954 / 1954–1968
Spanish–American War
American Century
Feminist Movement
Post-Cold War (1991–2008)
Great Recession
Outline of U.S. history
Technological and industrial
Contiguous United States
federal district
federal enclaves
Indian reservations
insular zones
minor outlying islands
populated places
Extreme points
Appalachian
Longest rivers
Red (South)
Water supply and sanitation
Independent agencies
Courts of appeals
Federal Reporter
United States Reports
National Geospatial-Intelligence Agency
Uniformed
NOAA Corps
Public Health Service Corps
Political status of Puerto Rico
Puerto Rico statehood movement
District of Columbia statehood movement
Hawaiian sovereignty movement
Anti-Americanism
Red states and blue states
Purple America
State court
Imperial Presidency
Greenhouse gas emissions by the United States
Social welfare programs
home-ownership
personal income
Great American Novel
Indigenous languages
Black American Sign Language
Plains Sign Talk
Sexuality / Adolescent Sexuality
Social class
Ages of consent
Criticism of government
Environmental movement
International rankings
Mass surveillance
Separation of church and state
Art research institutes