text:
\section{Introduction}

Named-entity recognition (NER) is the Natural Language Processing (NLP) task consisting in identifying and classifying mentions of entities in texts.
These mentions belong to a set of predefined categories, among which people, locations, and organizations are the most common.

Like the majority of NLP tasks, especially the ones relying on machine learning algorithms, manually annotated data plays a crucial role, since they are used to train and evaluate the automatic extraction systems that perform the task.
Annotated data are time and money consuming, since they usually need to be created by experts of the domain of the annotation that is going to be done.
While there is plenty of datasets for NER in English, little has been done for other languages, especially for Italian \cite{ehrmann-etal-2016-named}.\footnote{\url{https://damien.nouvels.net/resourcesen/corpora.html}}

The most common general-purpose dataset having named entity annotations is I-CAB \cite{magnini-etal-2006-cab}, created in 2006 and consisting of 525 news stories taken from the local newspaper ``L'Adige'', for a total of around 180,000 words.
Entities in I-CAB are divided into four categories (person, organization, location and geo-political entities), and the dataset is available by signing an agreement and only for research purposes.

The MEANTIME Corpus \citelanguageresource{minard-etal-2016-meantime}, developed within the NewsReader project\footnote{\url{http://www.newsreader-project.eu/}} \cite{VOSSEN201660}, consists of a total of 480 news articles on four topics, taken from Wikinews.
All the topics pertain to the financial domain, accordingly affecting the annotation and selection of the documents.
The corpus has been annotated manually at multiple levels, including entities, events, temporal information, semantic roles, and intra-document and cross-document event and entity co-reference.

The DBpedia abstract corpus\footnote{\url{http://downloads.dbpedia.org/2015-04/ext/nlp/abstracts/}} \citelanguageresource{Spasojevic:2017:DDA:3041021.3053367} contains a conversion of Wikipedia abstracts in seven languages (including Italian), with the annotations of linked entities, manually disambiguated to Wikipedia/DBpedia resources by native speakers.
Similarly, the DAWT dataset includes 13.6 million articles extracted from Wikipedia with labeled text mentions mapping to entities as well as the type of the entity.

Finally, WikiNER \citelanguageresource{Nothman2013LearningMN} is a free and multilingual dataset for NER by exploiting the text and structure of Wikipedia.
Annotations from the last three datasets rely on the Wikipedia links, whose coverage was improved by using different strategies.
For this reason, data included can be considered as silver-standard.

In this paper, we present an Italian dataset called KIND (Kessler Italian Named-entities Dataset) annotated with named entities belonging to three classes (person, location, organization), containing more than one million tokens, among with 600K are manually annotated.
The main strength of the present corpus is to be multi-domain, since it contains news articles, literature, and political speeches.
KIND is available under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license, and it is freely downloadable from Github.

In Section~\ref{sec:corpus} we describe how the texts are collected, while in Section~\ref{sec:annotation} we outline the annotation process.
In Section~\ref{sec:evaluation} we evaluate the dataset and discuss about some outcomes.
Finally, Section~\ref{sec:release} contains information on how to download and use KIND.

\section{Description of the Corpus}
\label{sec:corpus}

\begin{table*}
\begin{center}
{\setlength{\tabcolsep}{0.4em}\small
\begin{tabular}{|l|r|r|r|r|r|r|r|r|r|r|}

      \hline
      \multirow{2}{*}{Dataset} & \multirow{2}{*}{Documents} & \multirow{2}{*}{Tokens} & \multicolumn{4}{c|}{Train} & \multicolumn{4}{c|}{Test} \\
      \cline{4-11}
      & & & Total & PER & ORG & LOC & Total & PER & ORG & LOC \\
      \hline
      \hline
      Wikinews & 1,000 & 308,622 & 247,528 & 8,928 & 7,593 & 6,862 & 61,094 & 1,802 & 1,823 & 1,711\\
      \hline
      Fiction & 86 & 192,448 & 170,942 & 3,439 & 182 & 733 & 21,506 & 636 & 284 & 463 \\
      \hline
      Aldo Moro & 250 & 392,604 & 309,798 & 1,459 & 4,842 & 2,024 & 82,806 & 282 & 934 & 807\\
      \hline
      Alcide De Gasperi & 158 & 150,632 & 117,997 & 1,129 & 2,396 & 1,046 & 32,635 & 253 & 533 & 274\\
      \hline
      \hline
      Total & 1,494 & 1,044,306 & 846,265 & 14,955 & 15,013 & 10,665 & 198,041 & 2,973 & 3,574 & 3,255 \\
      \hline

\end{tabular}
\caption{Overview of the dataset}
\label{tab:overview}}
\end{center}
\end{table*}

For the construction of the dataset, we decide to use texts available in the public domain, under a license that allows both research and commercial use.
In particular we release four chapters with texts taken from: (i) Wikinews (WN) as a source of news texts from the last 20 years; (ii) some Italian fiction books (FIC)
publicly available; (iii) writings and speeches from Italian politicians Aldo Moro (AM) and (iv) Alcide De Gasperi (ADG).

Apart from Aldo Moro's writings (see Section~\ref{sec:morosilver}), all the annotations are entirely manually tagged by expert linguists .

Table~\ref{tab:overview} shows an overview of the content of the different datasets included in KIND.

\subsection{Wikinews}
\label{sec:wikinews}

Wikinews is a multi-language free-content project of collaborative journalism.
The Italian chapter contains more than 11,000 news articles,\footnote{\url{https://it.wikinews.org/wiki/Speciale:Statistiche}} released under the Creative Commons Attribution 2.5 License.\footnote{\url{https://creativecommons.org/licenses/by/2.5/}}

In building KIND, we randomly choose 1,000 articles evenly distributed in the last 20 years, for a total of 308,622 tokens.

\subsection{Literature}
\label{sec:fiction}

Regarding fiction literature, we annotate 86 book chapters from 10 books written by Italian authors, whose works are publicly available, for a total of 192,448 tokens.
The selected books are mostly novels, but there are also epistles and biographies.
The plain texts are taken from the Liber Liber website.\footnote{\url{https://www.liberliber.it/}}

In particular, we select: \textit{Il giorno delle Mésules} (Ettore Castiglioni, 1993, 12,853 tokens), \textit{L'amante di Cesare} (Augusto De Angelis, 1936, 13,464 tokens), \textit{Canne al vento} (Grazia Deledda, 1913, 13,945 tokens), \textit{1861-1911 - Cinquant'anni di vita nazionale ricordati ai fanciulli} (Guido Fabiani, 1911, 10,801 tokens), \textit{Lettere dal carcere} (Antonio Gramsci, 1947, 10,655), \textit{Anarchismo e democrazia} (Errico Malatesta, 1974, 11,557 tokens), \textit{L'amore negato} (Maria Messina, 1928, 31,115 tokens), \textit{La luna e i falò} (Cesare Pavese, 1950, 10,705 tokens), \textit{La coscienza di Zeno} (Italo Svevo, 1923, 56,364 tokens), \textit{Le cose più grandi di lui} (Luciano Zuccoli, 1922, 20,989 tokens).

In selecting works which are in the public domain, we favored texts as recent as possible, so that the model trained on this data would be efficiently applied to novels written in the last years, since the language used in these novels is more likely to be similar to the language used in the novels of our days.

\subsection{Aldo Moro's Works}
\label{sec:moro}

Writings belonging to Aldo Moro have recently been collected by the University of Bologna and published on a platform called ``Edizione Nazionale delle Opere di Aldo Moro'' \citelanguageresource{aldomorodigitale}.\footnote{\url{https://aldomorodigitale.unibo.it/}}
The project is still ongoing and, by now, it contains 806 documents for a total of about one million tokens.

In the first release of KIND, we include 392,604 tokens from the Aldo Moro's works dataset, with silver annotations (see Section~\ref{sec:morosilver}).

\subsection{Alcide De Gasperi's Writings}
\label{sec:degasperi}

Finally, we annotate 158 document (150,632 tokens) from the corpus described in \citelanguageresource{Tonelli2019PrendoLP}, spanning 50 years of European history.
The corpus is composed of a comprehensive collection of Alcide De Gasperi's public documents, 2,762 in total, written or transcribed between 1901 and 1954, and it is available for consultation on the Alcide Digitale website.\footnote{\url{https://alcidedigitale.fbk.eu/}}

\section{Annotation Process}
\label{sec:annotation}

\subsection{Data preprocessing}
\label{sec:preprocessing}

To annotate the documents, we start from plain texts.
For Wikinews, all the texts are extracted from the dumps server\footnote{\url{https://dumps.wikimedia.org/}} and cleaned from markup (e.g. text formatting) using the Bliki engine.\footnote{\url{https://github.com/axkr/info.bliki.wikipedia_parser}}
Documents from other sources (Aldo Moro's and Alcide De Gasperi's writings, along with fiction books) are already in plain text format, therefore they do not need any particular conversion.

Finally, tokens and sentences are extracted using the Tint NLP Suite \cite{alessio2018tint}.

\subsubsection{Aldo Moro's writings silver data}
\label{sec:morosilver}

As part of the activities of the project ``Edizione Nazionale delle Opere di Aldo Moro'', entities in documents from Aldo Moro's dataset have already been identified by a group of expert annotators, mainly historians and archivists.
Therefore, most of the named entities can be extracted and tagged as person, location or organization without any additional manual effort.
However, the guidelines for the annotation defined within the project are different from ours.
For example, common nouns such as ``ministro'' are tagged and linked to the referred person, even when the person's name is not included in the document (but can be inferred by the context).
For this reason, we perform a semi-automatic check of the whole corpus, following these steps:

\begin{itemize}
    \item the complete set of unique entities have been extracted from the corpus and manually checked by an expert;
    \item the entities confirmed as entities in the previous step are then searched and automatically tagged (case sensitive) in the corpus;
    \item finally, the corpus is processed with the NER module of Tint and the additional entities found in this phase are manually checked by an expert (and eventually added to the annotation).
\end{itemize}

By the application of the above-described steps, we enhance the compliance of the chapter with our guidelines.
Nevertheless, we prefer to call the annotations of this part of the dataset ``silver'' (and not ``gold''), because they are a mix of manually and automatically tagged entities.
See Section~\ref{sec:discussion} for more information.

\subsection{Annotation Tool}
To carry out the annotation of entities in KIND we choose INCEpTION \footnote{\url{https://inception-project.github.io/}} \cite{tubiblio106270} , a web-based text-annotation environment which turns out to be the most suitable for the task, since it presents several advantages.
In fact, the tool is endowed with an intuitive environment in which the labels are fully customisable.
It also includes automatic label propagation functionality, which speeds up the process of annotation conspicuously.
According to the tagging scheme we choose for the annotation (IOB), INCEpTION results as the most suitable choice also because it gives us the possibility to link the annotated spans among them, so that in a case such as ``[Paolo] [Rossi] è andato a Roma'' (``[Paolo] [Rossi] went to Rome'') I can link the name [Paolo] to the surname [Rossi], which successively will be labelled respectively as B-PER, I-PER.

\subsection{Annotation Tagging Scheme}

As said above, we used the Inside-Outside-Begining (IOB) tagging scheme, which subdivides the elements of every entity as begin-of-entity (B-ent) or continuation-of-entity (I-ent). According to this format, the entities are marked such as the first element of the ``compound'' entity is B-ent and the following ones are I-ents, as we can see in the sentence: ``La chiameranno [Fondazione]\pd{B-ORG} [Bruno]\pd{I-ORG} [Kessler]\pd{I-ORG}'' (``They will call it, [Bruno] [Kessler] [Foundation]''), where B stands for Beginning and I for Inside.
This holds true for all the named entities, such as PER (``[Sophia]\pd{B-PER} [Loren]\pd{I-PER}, famosa attrice italiana'', ``[Sophia] [Loren], famous italian actress'') or LOC ([``Via]\pd{B-LOC} [Nazionale]\pd{I-LOC} [12]\pd{I-LOC}'').
When the entity is composed of only one element, the annotation scheme treats it as it would be a first element of a compound one, with B-ent (``La rassegna è stata promossa dal [CNR]\pd{B-ORG}'', ``the exhibition was promoted by [CNR]'').
We choose to not consider nested entities as a different case (such as ``Fondazione Bruno Kessler" which it is an ORG which contains a PER entity) but to annotate only the element considered in the sentence in which it is contain.
For instance, in the sentence ``Lavora per la [Fondazione Bruno Kessler]"(``He/she works for the [Fondazione Bruno Kessler]"), ``Fondazione Bruno Kessler" would be annotated only as ORG entity.

\subsection{Person Entities}

We consider as PERSON ENTITIES (PER) those entities which refer to an individual or an animal by his/her proper name, such as in the following sentences (extracted by the present corpus) where PER is contained in square brackets: ``[Laura] vive a Roma'' (``[Laura] lives in Rome''); ``Partecipa la Torre con [Tremendo] su [Guess]'' (``The Tower participates with [Tremendo] on [Guess]'', where ``Guess'' is the horse and ``Tremendo'' the jockey).
We annotate as PER also fictitious characters, as long as they possess a proper name (e.g. Mickey Mouse), as well as proper names which refer to a group of people belonging to the same family (e.g. the Jackson). In the case there is an apposition, a title or a function preceding the proper person name, since we consider only proper names, we annotate only the proper noun and not the common noun associated, as in the case of ``papa [Giovanni] [Paolo] [II] ha viaggiato molto'' (``pope [John] [Paul] [II] has travelled a lot'').

\subsection{Organization Entities}

We consider as ORGANIZATION ENTITIES (ORG) every formally established association.
These associations can be of different types such as governmental (``Il [governo] [italiano] si è espresso a favore'', ``the [italian] [government] had spoken in favour''), commercial (``I ricavi di [Zoom] sono incrementati'', ``the revenues of [Zoom] has increased''), educational (``L'[Università] [di] [Pisa] avrà un nuovo rettore'', ``the [University] [of] [Pisa] will have a new rector''), related to media (``Si tratta di Domenico Quirico de [La] [Stampa]'', ``It is Domenico Quirico, from [La] [Stampa]''), religious (``Le posizioni della [Chiesa]  non hanno alcuna collocazione politica'', ``the views of the [Church] have no political placement''), related to sports ([Juve] - [Roma] 1 – 1), medical-scientific (``Il giovane venne soccorso dall'elicottero del [118]'', ``the boy was rescued by the helicopter of [118]''), non-governmental (such as political parties, professional regulatory or no-profit organizations), related to entertainment (``I [R.E.M.] presenteranno il loro nuovo album'', ``[R.E.M.] will present their new album''), and also brands names in general (``Creò anche la linea [Chicco] per [Artsana]'', ``He created also the [Chicco] line for [Artsana]''.
In general we can say that every time the entity is the agent of an action and it cannot be defined as a PER, the label to choose for the annotation is ORG. We will address the issue more in depth in ~\ref{sec:discussion}.

\subsection{Location Entities}

We consider as LOCATION ENTITIES (LOC) those entities referring to places defined on a geographical basis or, more in general, entities which possess a physical location and a proper name.
Therefore, we annotate as LOC nations, continents, cities but also facilities or shops, bar and restaurants if they have a proper name (e.g. ``[Torre] [Eiffel]''; ``[Bar] [Il] [Giobertino]''; ``[Stazione] [Termini]''). According to this rule, in the sentence ``Sono nato all'[ospedale] [Torregalli]'' (``I was born at [Torregalli] [hospital]'') has to be annotated, since ``Torregalli'' is the name of the hospital, but in the sentence ``l'ospedale di [Firenze] si trova in fondo a questa strada'' (``the hospital of [Florence] is a the end of this street'') only ``Firenze'' has to be annotated, as Florence is the name of the city but the hospital does not present a proper name.
We also annotate as LOC entities those places contained in a prize name or in the name of a race/competition when the award ceremony or race/competition takes place in the location described, such as in ``Premio [Roma]'', ``Coppa [Italia]'' or ``Rally [Dakar]''.

\subsection{Annotation Guidelines}

In the previous sections we described the annotation process of all the classes in detail. Beyond the above-cited examples, the annotators found some uncertain cases.
On GitHub we provide the annotation guidelines for all the classes, with dedicated paragraphs for the ambiguous cases that the annotators found, such as metonymy or the inclusion of titles and functions in the annotation of entities.

\section{Evaluation}
\label{sec:evaluation}

To evaluate the accuracy of the annotation, we use different metrics.
First of all, through inter-annotator agreement we check the effectiveness of the guidelines.
Then, we train two NER models using different algorithms: Conditional Random Fields \cite{laffertyCrf} and BERT \cite{devlin2019bert}.
The former is more common and widely used in production environments since it can be trained on traditional CPUs, while the latter represents the state-of-the-art, but it usually requires GPUs to reduce classification time.

\subsection{Inter-Annotator Agreement}

The most common way to measure inter-annotator agreement is the Cohen's kappa statistic $\kappa$ \cite{cohenskappa}, which takes into account the amount of agreement that could be expected to occur through chance.

To compute this value, two different  expert linguists are asked to annotate the same set of 15 documents randomly extracted from Wikinews.
We obtain $\kappa=0.952$, meaning an excellent agreement.

\begin{table*}
\begin{center}
{\setlength{\tabcolsep}{0.35em}\small
\begin{tabular}{|l|l|l||r|r|r||r|r|r||r|r|r||r|r|r||r|r|r|}
      \hline
      \multirow{2}{*}{Algo} & \multicolumn{2}{c||}{Dataset} &
        \multicolumn{3}{c||}{PER} & \multicolumn{3}{c||}{LOC} &
        \multicolumn{3}{c||}{ORG} &
        \multicolumn{3}{c||}{Micro} & \multicolumn{3}{c|}{Macro} \\
      \cline{2-18}
      & Train & Test & P & R & $F_1$ & P & R & $F_1$ & P & R & $F_1$
        & P & R & $F_1$ & P & R & $F_1$ \\
      \hline
      \hline
CRF & WN & WN & 0.91 & 0.92 & 0.92 & 0.85 & 0.82 & 0.83 & 0.79 & 0.71 & 0.75 & 0.85 & 0.82 & 0.83 & 0.85 & 0.82 & 0.83 \\
      \hline
CRF & AM & AM & 0.97 & 0.91 & 0.94 & 0.96 & 0.97 & 0.96 & 0.93 & 0.94 & 0.94 & 0.95 & 0.95 & 0.95 & 0.95 & 0.94 & 0.95 \\
CRF & ADG & AM & 0.95 & 0.79 & 0.86 & 0.94 & 0.62 & 0.74 & 0.61 & 0.77 & 0.68 & 0.74 & 0.71 & 0.73 & 0.83 & 0.72 & 0.76 \\
CRF & ADG & ADG & 0.92 & 0.88 & 0.90 & 0.87 & 0.69 & 0.77 & 0.80 & 0.67 & 0.73 & 0.85 & 0.72 & 0.78 & 0.86 & 0.75 & 0.80 \\
CRF & AM & ADG & 0.91 & 0.80 & 0.85 & 0.72 & 0.72 & 0.72 & 0.90 & 0.41 & 0.57 & 0.84 & 0.58 & 0.69 & 0.84 & 0.64 & 0.71 \\
      \hline
CRF & FIC & FIC & 0.81 & 0.77 & 0.79 & 0.61 & 0.76 & 0.68 & 0.74 & 0.25 & 0.37 & 0.72 & 0.66 & 0.69 & 0.72 & 0.59 & 0.61 \\
CRF & WN & FIC & 0.89 & 0.72 & 0.80 & 0.71 & 0.80 & 0.75 & 0.63 & 0.68 & 0.65 & 0.76 & 0.74 & 0.75 & 0.74 & 0.73 & 0.73 \\
CRF & WN+FIC & FIC & 0.90 & 0.78 & 0.84 & 0.73 & 0.81 & 0.77 & 0.70 & 0.66 & 0.68 & 0.79 & 0.76 & 0.78 & 0.78 & 0.75 & 0.76 \\
    \hline
      \hline
BERT & WN & WN & 0.96 & 0.96 & 0.96 & 0.88 & 0.90 & 0.89 & 0.83 & 0.82 & 0.82 & 0.89 & 0.89 & 0.89 & 0.89 & 0.89 & 0.89 \\
      \hline
BERT & AM & AM & 0.97 & 0.97 & 0.97 & 0.93 & 0.97 & 0.95 & 0.86 & 0.94 & 0.90 & 0.90 & 0.96 & 0.93 & 0.92 & 0.96 & 0.94 \\
BERT & ADG & AM & 0.93 & 0.92 & 0.92 & 0.90 & 0.54 & 0.68 & 0.53 & 0.85 & 0.65 & 0.66 & 0.74 & 0.69 & 0.79 & 0.77 & 0.75 \\
BERT & ADG & ADG & 0.96 & 0.88 & 0.91 & 0.86 & 0.83 & 0.85 & 0.75 & 0.77 & 0.76 & 0.82 & 0.81 & 0.82 & 0.86 & 0.83 & 0.84 \\
BERT & AM & ADG & 0.92 & 0.86 & 0.89 & 0.75 & 0.80 & 0.78 & 0.87 & 0.52 & 0.65 & 0.84 & 0.68 & 0.75 & 0.85 & 0.73 & 0.77 \\
      \hline
BERT & FIC & FIC & 0.94 & 0.93 & 0.94 & 0.76 & 0.85 & 0.80 & 0.77 & 0.41 & 0.54 & 0.84 & 0.80 & 0.82 & 0.82 & 0.73 & 0.76 \\
BERT & WN & FIC & 0.94 & 0.94 & 0.94 & 0.81 & 0.89 & 0.85 & 0.69 & 0.81 & 0.75 & 0.84 & 0.90 & 0.87 & 0.81 & 0.88 & 0.84 \\
BERT & WN+FIC & FIC & 0.94 & 0.94 & 0.94 & 0.81 & 0.88 & 0.84 & 0.75 & 0.85 & 0.80 & 0.85 & 0.90 & 0.88 & 0.83 & 0.89 & 0.86 \\
      \hline
\end{tabular}
}
\caption{Results of training on the KIND dataset}
\label{tab:results}
\end{center}
\end{table*}

\subsection{Experiments}
\label{sec:experiments}

Before running the experiments to train the models for the task of NER, we split the different datasets into train and test.
Typically, documents are shuffled and a subset of them is extracted to reach around 20\% of the total.
Table~\ref{tab:overview} shows how the partitioning is performed in the chapters of the dataset.
The main purpose of the fiction chapter is the possibility to train a model for NER that can be applied efficiently to literary texts in general.
For this reason, we choose to not extract the test sentences randomly in the chapter, but to use as test set the works of two authors (Guido Fabiani and Cesare Pavese), who clearly are not included in the training set, in order to avoid the possibility to have a model trained on a particular writing style.
This would allow to have a more realistic result in terms of performance.

To train the Conditional Random Fields (CRF) model, we run the implementation included in Stanford CoreNLP \cite{manning-EtAl:2014:P14-5}, already used in its NER module \cite{finkel-etal-2005-incorporating}.

We tries some sets of features choosing among the ones available in the software.
We obtain the best results with word shapes, n-grams with length 6, previous, current, and next token/lemma/class.

To enhance the classification, Stanford NER also accepts gazetteers of names labelled with the corresponding tag.
We collect a list of persons, organizations and locations from the Italian Wikipedia using some classes in DBpedia \cite{10.1007/978-3-540-76298-0_52}: \texttt{Person}, \texttt{Organization}, and \texttt{Place}, respectively.
Table~\ref{tab:gazette} shows statistics about the gazettes.

For BERT, we use an adaptation of the Bert Model with a token classification head on top, available in the transformers Python package\footnote{\url{https://github.com/huggingface/transformers}} developed by Hugging Face.\footnote{\url{https://huggingface.co/huggingface}}
The model is trained (3 epochs are enough) starting from the \texttt{bert-base-italian-cased} model.\footnote{\url{https://huggingface.co/dbmdz/bert-base-italian-cased}}

Table~\ref{tab:results} shows classification results comparing different configurations and algorithms.

\begin{center}
\begin{tabular}{|l|l|r|}
\hline
\bf Source & \bf Tag & \bf Labels \\
\hline
Wikipedia & LOC & 377,611 \\
Wikipedia & PER & 608,547 \\
Wikipedia & ORG & 84,887 \\
\hline
\end{tabular}
\captionof{table}{ Items added to the CRF NER training taken from gazettes. }
\label{tab:gazette}
\end{center}

\subsection{Discussion}
\label{sec:discussion}

We have seen above that LOC and ORG indicate different entities but there are some cases in which these two labels can overlap.
This is the case of metonymic location names, a special case which occurs when the proper name of a LOC entity is used to refer to an organization.
The most common case is when the name of a location is used to refer to a sport team (``La [Russia] ha conquistato la medaglia d'oro'', ``[Russia] have won the gold medal'') but, more in general, we can say that the cases in which the LOC entity is treated as an ORG entity include all the situations in which the LOC entity is the agent of an action, such as in the sentence ``La [Germania] si ritira dalla trattativa'' (``Germany withdrew from negotiations''), where Germany is the subject of the action of withdrawal, and it is then indicated as ORG, or ``La [Lombardia] si dice contraria al provvedimento'' (``[Lombardy] declares to be contrary to the measure''), where Lombardy is the subject as governamental body.

\subsubsection{LOC annotations in AM chapter}

As said in Section~\ref{sec:morosilver}, the chapter of KIND containing documents from Aldo Moro's work were already annotated with some named entities.
In particular, countries and regions were almost all tagged correctly and even linked to the corresponding Wikidata page.
Unfortunately, the policies used by the annotators is slightly different from the one adopted in KIND, therefore all the locations are tagged as LOC, even when they represent an organization (and therefore should be tagged as ORG in KIND).
For this reason, we performed some additional experiments using ADG and AM as train and test, respectively.
As one can see, while both models are very accurate on PER, precision and recall on LOC and ORG drop at around 0.5 in some cases.
In particular, when training on ADG and testing on AM, recall on LOC and precision on ORG are very low, while switching the datasets (training on AM and testing on ADG) will result in a low recall on ORG.
This is a result of the different policy in annotating the two datasets.

\subsubsection{Some comments about the fiction dataset}

Regarding experiments on fictional texts (FIC) test set (see Section~\ref{sec:experiments}), we try three different configurations: FIC train set, WN train set, both train sets.

In the first configuration (FIC), one can see that classification of PER and LOC is quite good, while recall on ORG is really low: this is due to the scarcity of organization mentions in fictional texts.
In the second experiment (where WN has been used as training set), the recall on ORG is higher, but the precision drops almost 10 points.
This is probably motivated by the different contexts in which organizations are mentioned in literature, w.r.t. news.
One can see that on PER and LOC tags the accuracy is higher, meaning that usually there are no relevant differences in uses in literature and news for these two categories of entities.
We finally merge the training sets of both datasets, resulting in a classification where accuracy is comparable to the one where WN is used for testing.
As a hint, we suggest to use both datasets (expecially for a correct ORG identification) when one needs to tag fictional texts.

\section{Release}
\label{sec:release}

The KIND dataset is released in open access on Github.\footnote{\url{https://github.com/dhfbk/KIND}}
Some supporting tools (such as Wikinews extraction and format conversion tools) are part of the \texttt{tint-resources} package included in Tint release.\footnote{\url{https://github.com/dhfbk/tint}}

The texts used for KIND are available with different licenses (see the single dataset description for more information), but all of them are available to use for non-commercial purposes.

The named entities annotations in WN, FIC, and AM are released under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.\footnote{\url{https://creativecommons.org/licenses/by-nc/4.0/}}
Annotations in ADG are released under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.\footnote{\url{https://creativecommons.org/licenses/by-nc-sa/4.0/}}

\section{Conclusion and Future Work}
\label{sec:conclusion}

In this paper we presented KIND, a multi-domain dataset in Italian containing more then one million tokens annotated with named entities (person, location, organization).
Part of the dataset (more than 600K tokens) contains gold annotations, manually performed by  expert linguists.
The remaining part includes around 400K tokens from Aldo Moro's works and is annotated by automatically converting an existing annotation, built using different guidelines and for different purposes.

In future, we plan to increase the number of documents, especially in the chapter that contains literature texts.
We would also like to include all the texts from Aldo Moro's works, once they are available.

\section*{Acknowledgements}

The authors thank Arianna Pergher, a student who helped during the first phase of the annotations process.

\section*{Bibliographical References}\label{reference}

\bibliographystyle{lrec2022-bib}

redpajama_set_name: RedPajamaArXiv


text:
Myeloid HIF-1 Is Protective in Helicobacter pylori–Mediated Gastritis
Pavle Matak, Mylène Heinis, Jacques R. R. Mathieu, Ross Corriden, Sylvain Cuvellier, Stéphanie Delga, Rémi Mounier, Alexandre Rouquette, Josette Raymond, Dominique Lamarque, Jean-François Emile, Victor Nizet, Eliette Touati and Carole Peyssonnaux
J Immunol April 1, 2015, 194 (7) 3259-3266; DOI: https://doi.org/10.4049/jimmunol.1401260
Pavle Matak
*INSERM, U1016, Institut Cochin, 75014 Paris, France;
†Centre National de la Recherche Scientifique, Unité Mixte de Recherche 8104, 75014 Paris, France;
‡Université Paris Descartes, Sorbonne Paris Cité, 75014 Paris, France;
Mylène Heinis
Jacques R. R. Mathieu
Ross Corriden
§Department of Pediatrics, University of California San Diego, La Jolla, CA 92093;
Sylvain Cuvellier
Stéphanie Delga
Rémi Mounier
¶Centre de Génétique et de Physiologie Moléculaire et Cellulaire, Unité Mixte de Recherche Centre National de la Recherche Scientifique 5534, Université Claude Bernard Lyon 1, Lyon, 69622 Villeurbanne Cedex, France;
Alexandre Rouquette
‖Hôpital Cochin, 75014 Paris, France;
Josette Raymond
Dominique Lamarque
#Equipe d'Accueil 4340, Université de Versailles, and Hôpital Ambroise Paré, Assistance Publique des Hôpitaux de Paris, 92104 Boulogne, France; and
Jean-François Emile
Victor Nizet
Eliette Touati
**Institut Pasteur, 75015 Paris, France
Carole Peyssonnaux
Helicobacter pylori infection triggers chronic inflammation of the gastric mucosa that may progress to gastric cancer. The hypoxia-inducible factors (HIFs) are the central mediators of cellular adaptation to low oxygen levels (hypoxia), but they have emerged recently as major transcriptional regulators of immunity and inflammation. No studies have investigated whether H. pylori affects HIF signaling in immune cells and a potential role for HIF in H. pylori–mediated gastritis. HIF-1 and HIF-2 expression was examined in human H. pylori–positive gastritis biopsies. Subsequent experiments were performed in naive and polarized bone marrow–derived macrophages from wild-type (WT) and myeloid HIF-1α–null mice (HIF-1Δmyel). WT and HIF-1Δmyel mice were inoculated with H. pylori by oral gavage and sacrificed 6 mo postinfection. HIF-1 was specifically expressed in macrophages of human H. pylori–positive gastritis biopsies. Macrophage HIF-1 strongly contributed to the induction of proinflammatory genes (IL-6, IL-1β) and inducible NO synthase in response to H. pylori. HIF-2 expression and markers of M2 macrophage differentiation were decreased in response to H. pylori. HIF-1Δmyel mice inoculated with H. pylori for 6 mo presented with a similar bacterial colonization than WT mice but, surprisingly, a global increase of inflammation, leading to a worsening of the gastritis, measured by an increased epithelial cell proliferation. In conclusion, myeloid HIF-1 is protective in H. pylori–mediated gastritis, pointing to the complex counterbalancing roles of innate immune and inflammatory phenotypes in driving this pathology.
Infection by Helicobacter pylori is the most common chronic bacterial infection of humans, affecting half the population worldwide. Persistent colonization of the gastric mucosa by H. pylori leads to chronic gastric inflammation, which may progress to peptic ulcer disease, gastric cancer, or MALT lymphoma (1). H. pylori infection is a major determinant initiating the sequence of events leading to gastric cancer, in combination with host and environmental factors. By recruiting immune cells, the bacterium triggers chronic inflammation of the gastric mucosa, and depletion of macrophages has been shown to reduce gastric pathology in H. pylori–infected mice (2), emphasizing a key role for macrophages in this process.
Macrophages have multiple functions ranging from host defense, tissue repair, to inflammatory regulation (3), but their mechanistic role in H. pylori disease remains poorly understood. Macrophages exhibit remarkable developmental and functional plasticity, with classically activated macrophages (M1) representing one extreme—the proinflammatory state—and alternatively activated macrophages (M2) representing the opposite anti-inflammatory state. Generally, the M1–M2 switch observed during the transition from acute to chronic infection may provide the host protection against destructive uncontrolled inflammation (4). An enhanced degree of M1 macrophage polarization has been described in human H. pylori–associated atrophic gastritis (5).
The hypoxia-inducible factors (HIFs) are transcription factors involved in the adaptation to low levels of oxygen, composed of an O2-regulated α-subunit and a constitutively expressed β-subunit. Hydroxylation of HIF-1 or HIF-2 by a family of prolyl hydroxylase promotes binding of the von Hippel-Lindau protein, leading to HIF ubiquitination and proteasomal degradation. Despite a high degree of sequence homology, HIF-1 and HIF-2 have been described to have nonoverlapping, and even sometimes opposing, roles (6, 7). HIF-1 is largely studied for its role during cancer development, modulating the transcription of genes involved in angiogenesis, cell survival, and invasiveness (8).
HIF factors have emerged as essential regulators of inflammation (9). The HIF-1α subunit can be stabilized in normoxia by a number of cytokines, growth factors, and other circulating molecules (e.g., NO) by differential intracellular signaling mechanisms (10). HIF-1–deficient macrophages and neutrophils have a marked reduction in ATP pools, accompanied by impairment of cell aggregation, motility, and invasiveness (11). Moreover, HIF-1 deletion in the myeloid cells is associated with the downregulation of key cytokines and chemokines, such as TNF, IL-1, IL-6, and IL-8 (12–14). HIF-1 (15) and HIF-2 (16) also extend the lifespan of neutrophils by inhibiting apoptotic pathways. In addition, studies using RNA interference showed that HIF-1α and HIF-2α, more so than NF-κB, are important in orchestrating the changes in macrophage receptor and cytokine gene expression in response to hypoxia (17).
Given the evidence supporting a role for HIF-1 signaling in inflammatory conditions, a number of recent patents have described methods to inhibit HIF-1 signaling in the treatment of inflammatory disorders (18). However, we and other groups have shown that HIF-1 is induced in phagocytes by a certain number of bacteria even under normal levels of oxygen (12, 14, 19). The activation of the HIF-1α pathway through deletion of von Hippel-Lindau protein or pharmacologic inducers supported myeloid cell production of defense factors and improved bactericidal capacity (14). Conversely, some pathogens interfere with HIF activity to improve their survival and proliferation (20, 21). Therefore, because of the dual role of HIF-1 in inflammation and bactericidal activity, there is an open question as to whether inhibiting myeloid HIF-1 would be beneficial or detrimental in chronic gastric inflammation triggered by H. pylori infection.
Cell culture, H. pylori strains, and growth conditions
SS1 (22) and B128 (23) H. pylori strains were grown on 10% blood agar under microaerophilic conditions. Bacterial lysates were obtained by passage through a French press, and protein concentrations were determined by a Bradford assay.
Bone marrow–derived macrophage infection
To isolate bone marrow–derived macrophages (BMDMs), the marrow of femurs and tibias of wild-type (WT) and HIF-1Δmyel mice were collected. Cells were plated in DMEM supplemented with 10% heat-inactivated FBS and 30% conditioned medium (a 7-d supernatant of fibroblasts from cell line L-929 stably transfected with a M-CSF expression vector). Mature adherent bone marrow cells were harvested by gentle scraping after 7 d in culture. BMDMs were incubated with 5 μg/ml bacterial extracts for 1–24 h. BMDMs were pretreated, or not, for 48 h with either 50 ng/ml IFN-γ to induce a M1 phenotype or 10 ng/ml IL-4 to induce a M2 phenotype. When specified, BMDMs were put on 0.5% oxygen pressure in a hypoxic chamber (InVivo2 500; Ruskinn), 24 h before the incubation with bacterial extracts.
Neutrophil-killing assay
To isolate neutrophils from WT and HIF-1α knockout mice, animals were injected with 1 ml 3% thioglycolate solution (BD Biosciences), and peritoneal neutrophils were collected via lavage with PBS after 4 h. The neutrophils were then counted, resuspended in RPMI 1640 with 2% FBS, and mixed with H. pylori (strain SS1) at a multiplicity of infection of 2 in 96-well plates. Plates were centrifuged at 1600 rpm to facilitate contact between the neutrophils and bacteria and incubated for 1 h at 37°C. Subsequently, samples from each well were serially diluted in water to lyse neutrophils, and the final dilutions (1:1000) were plated on BBL trypticase soy agar plates with 5% sheep blood (BD Biosciences). H. pylori colonies were counted after a 5-d incubation period at 37°C with 10% CO2.
Macrophage-killing assay
BMDMs were seeded at a density of 8 × 105 cells/well in 24-well plates the day prior to the assay. Bacteria were added at a multiplicity of infection of 10 in 350 μl assay media (RPMI 1640, 2% FBS). Plates were then centrifuged at 1600 rpm for 5 min to facilitate contact between the bacteria and the macrophages. Cells and bacteria were then incubated for 30 min at 37°C, 5% CO2, before gentamicin (100 μg/ml) was added to each well to kill extracellular bacteria. The cells were rinsed with PBS to remove extracellular dead bacteria, and 350 μl fresh media was added to each well. After an additional 2.5 h, cells were lysed via addition of Triton X-100 (0.025% final), and dilutions were plated to quantify bacteria.
Reverse transcription and real-time quantitative PCR
After cells were harvested, total RNA was extracted from cells using TRIzol (Life Technologies), according to the instructions provided by the manufacturer. Reverse transcription was performed with 100 ng total RNA, as described earlier. Quantitative PCR was performed with 1 μl 1/10 dilution of reverse-transcribed total RNA dilution, 10 μM each primer, and 2 mM MgCl2 in 1× LightCycler DNA Master SYBR Green I mix using a LightCycler apparatus (Roche Applied Science). All samples were normalized to the threshold cycle value for cyclophilin. Sequences of the primers used are presented in Table I.
Mice infection
Animal studies described in this work were reviewed and approved (Agreement P2.CP.151.10.) by the Président du Comité d'Ethique pour l'Expérimentation Animale Paris Descartes. HIF-1Δmyel mice in a C57BL/6 background have been provided by R. Johnson (University of Cambridge). Cell-specific inactivation of Hif1a in the myeloid lineage was achieved by cross-breeding LysM-Cre+/− transgenic mice (24) with HIF-1αfl/fl mice (25). In all experiments, littermates from the same breeding pair were used as controls. Mice were inoculated once by oral gavage with 100 μl suspension of 109 CFUs/ml H. pylori strain SS1 (WT, n = 14: HIF-1Δmyel, n = 15). Control groups of mice (WT, n = 7: HIF-1Δmyel, n = 11) were given peptone trypsin broth alone. The mice were killed 6 mo postinfection. Stomachs were isolated and divided into three homogenous segments, each containing antrum and body, and used for histopathologic analyses, determination of colonization, and quantitative PCR analyses.
Assessment of infection
Serum samples were obtained at 3 and 6 mo postinfection. Infection status was tested for H. pylori Ag-specific IgG Ab, as previously described by ELISA technique (26). The presence of H. pylori bacteria was also histologically confirmed by PCR and by Warthin–Starry staining.
Human biopsies
Gastric samples were obtained from patients referred at the digestive endoscopy unit of Ambroise Paré Hospital. All gastric biopsies were performed for diagnostic purpose. The diagnosis was based on histology performed on these samples. The presence of H. pylori infection (HP) was confirmed by staining with Giemsa and/or immunohistochemistry. None of the patients had any clinical evidence of atheromatous disease complication.
Patients were informed that samples could also be used for research analysis, according to French ethical laws. Collection of human tissues was declared to the French Ministry of Research (DC 2009-933-404 and DC 2009-933-406) and Commission Nationale de l'Informatique et des Libertés.
Histology and immunohistochemistry
Gastric tissue samples from noninfected and infected mice were fixed in formalin and embedded in paraffin wax. Serial 4-μm sections were cut and stained with H&E, periodic acid–Schiff, and Alcian blue using standard procedures. Tissue sections were examined for histopathologic lesions in a blinded fashion. The severity of gastritis in all groups of mice was evaluated semiquantitatively. Gastritis scores were determined according to the density of lymphocytes and plasma cells according to the Sydney classification and as previously described in the mouse model (27). This scoring is based on the standard histologic criteria used in human pathology (28). Briefly, this grading involves an evaluation of the recruitment of inflammatory cells (polymorphonuclear and mononuclear cells) in both antrum and corpus classified into four grades, as follows: none, mild, moderate, or severe.
Mouse gastric biopsies were subjected to proteinase Ag retrieval, blocking endogenous peroxidases, and immunohistochemistry with primary Ab against F4/80 (ab6640; Abcam, Cambridge, U.K.) diluted 1:200. After incubation with biotinylated secondary Ab, F4/80 was revealed with diaminobenzidine method.
For Ki67 immunofluorescence, tissues were subjected to a heat-induced epitope retrieval, incubated with rabbit polyclonal anti-Ki67 Ab (ab15580; Abcam, Cambridge, U.K.) diluted 1:100, and revealed with secondary Ab conjugated to Alexa 488.
Human gastric tissue sections were subjected to a heat-induced epitope retrieval. Sections were incubated with both rabbit polyclonal anti-HIF1a Ab (NB100-449; Novus Biologicals, Littleton, CO) diluted 1:50 and mouse monoclonal anti-human CD68 (clone KP1; Dako, Glostrup, Denmark) diluted 1:100 in PBS containing 3% BSA, 0.1% Tween 20, and 1% goat serum at 4°C overnight. The sections were washed in PBS and then incubated with secondary Ab conjugated, respectively, to Alexa 488 and Alexa 568 (Invitrogen, Paisley, U.K.) diluted 1:400.
HIF-2α was detected using rabbit polyclonal Ab (NB100-122; Novus Biologicals, Littleton, CO) with TSA Fluorescence Systems Tyramide Signal Amplification (NEL701; PerkinElmer), according to manufacturer's protocol.
Data were analyzed in Graphpad Prism 5, and the significance of experimental differences was evaluated by one-way ANOVA, followed by Newman-Keuls posthoc testing. Values in the figures are expressed as mean ± SEM. Statistical significance is indicated by symbols (*p < 0.05, **p < 0.01, ***p < 0.001).
HIF-1, but not HIF-2, is stabilized in macrophages of human H. pylori–positive gastritis biopsies
Expression of HIF-1 and HIF-2 was investigated by immunohistochemistry performed on biopsies of control patients with normal gastric mucosa (n = 5) and of gastritis patients with H. pylori infection (HP+; n = 5). Although HIF-1 was not expressed in normal gastric biopsies, HIF-1 staining was greatly enhanced in HP+ biopsies, with a higher expression in moderate than in mild gastritis. HIF-1 expression was therefore correlated to the severity of the gastritis. In particular, HIF-1 was abundantly expressed in macrophages of human H. pylori–associated gastritis biopsies (Fig. 1), as shown by a costaining of HIF-1 with CD68, a specific marker of macrophages. Conversely, HIF-2 was not stabilized in the macrophages of H. pylori–associated gastritis biopsies. Noteworthily, epithelial HIF-2 expression appeared to gradually decrease with the severity of the H. pylori–associated gastritis.
Immunostaining for HIF-1α, HIF-2α, and CD68 on sections of human normal (−) gastric biopsies and mild or moderate H. pylori positive (HP+) gastritis. One representative picture for each group (n = 5 per group). Scale bars, 50 μm. (HCX PL Fluotar L 40×/0.6 PH2, Leica DMI-3000B microscope, Leica DFC 310FX digital camera, LAS V3.8 Core software.)
Differential regulation of HIF-1 and HIF-2 by H. pylori in BMDMs
The species H. pylori comprises a large diversity of strains. Many H. pylori strains encode the cytotoxin-associated gene (Cag) pathogenicity island (PAI), which expresses a type IV secretion system, a syringe-like structure that injects virulence factors including the CagA effector protein into gastric epithelial cells (29). We examined the ability of a Cag-PAI+ H. pylori strain (B128) (22) and a Cag-PAI inactive H. pylori strain (SS1) (23) to stabilize HIF-1 ex vivo. BMDMs from WT C57BL/6 mice were incubated with bacterial extracts of B128 and SS1 for 24 h. Both strains were able to induce the transcription of HIF-1 (Fig. 2A), suggesting that the increase of HIF-1 mRNA levels in BMDMs in response to H. pylori occurs independently of Cag-PAI function. The moderate increase in HIF-1α transcriptional activation, however, correlated to a strong induction of HIF-1α at the protein level, suggestive of both transcriptional and posttranscriptional regulation (Fig. 2B). The increase in HIF-1 expression was also observed with live B128 and SS1 H. pylori strains (Supplemental Fig. 1) both at the mRNA and protein levels. Unexpectedly, HIF-2 mRNA levels drastically decreased in response to infection with both H. pylori strains. This decrease was also observed at the protein level, when the cells were preincubated for 24 h under hypoxia (Fig. 2C). For subsequent experiments, we limited our study to SS1, which is the mouse-adapted H. pylori strain (23).
Regulation of HIF-1 in BMDMs infected with SS1 or B128 H. pylori strains. (A) Quantitative RT-PCR for Hif1a and Hif2a in WT BMDMs incubated with 5 μg/ml SS1 or B128 bacterial extracts for 24 h. One representative experiment (in triplicate) of three independent experiments. (B) Western blot for HIF-1 in WT BMDMs incubated with 5 μg/ml SS1 or B128 bacterial extracts for 24 h. One representative experiment of three independent experiments. (C) HIF-2 expression in WT BMDMs challenged or not with 0.5% oxygen pressure 24 h before incubation with 5 μg/ml SS1 or B128 bacterial extracts for 24 h. One representative experiment of three independent experiments. *p < 0.05, ***p < 0.001.
HIF-1 contributes to the proinflammatory response induced by H. pylori
To assess the functional consequences of HIF-1α activation by H. pylori, we first measured the expression of proinflammatory genes in naive BMDMs from WT mice and HIF-1Δmyel mice (list of primers used in Table I). Targeted deletion of the HIF-1α gene was achieved via crosses onto a background of Cre expression driven by the lysozyme M promoter (lysM-cre), thus allowing for specific deletion of the transcription factor in the myeloid lineage, that is, monocytes/macrophages and neutrophils (11). We first confirmed the decrease in HIF-1 expression in BMDMs of HIF-1Δmyel mice compared with WT BMDMs and validated the lack of compensatory mechanism by HIF-2 (Fig. 3A). We examined the mRNA induction for the genes encoding IL-1β, IL-6, and inducible NO synthase (Nos2), whose increased expression levels have been previously reported in H. pylori–induced gastritis in humans and mice (30–34). We found that the expression of HIF-1α, Nos2, IL-1β, and IL-6 was strongly increased upon exposure of macrophages to SS1, but that this response was significantly attenuated in HIF-1α–null macrophages (Fig. 3A).
Table I. Sequence of the primers
HIF-1 dependency of SS1-induced proinflammatory cytokine mRNA expression by BMDMs. Quantitative RT-PCR for (A) Hif1a, Hif21a Nos2, IL-1β, and IL-6 and (B) Fn1 and Arg1 on WT and HIF-1Δmyel BMDMs stimulated with 5 μg/ml SS1 bacterial extracts for 24 h. BMDMs were pretreated, or not (naive), for 48 h with either 50 ng/ml IFN-γ (M1) or 10 ng/ml IL-4 (M2). Pool of three repeated experiments performed in triplicate. *p < 0.05, **p < 0.01, ***p < 0.001.
In response to IFN-γ or IL-4, macrophages undergo M1 (classical) or M2 (alternative) activation, respectively (4). We next polarized WT and HIF-1Δmyel BMDMs (Supplemental Fig. 2A, 2B), incubated them with bacterial extracts of SS1 for 24 h, and examined the relative level of HIF-1 and HIF-2, IL-1β, IL-6, and Nos2 in each cell type (Fig. 3). We found that HIF-1α was upregulated at the mRNA (Fig. 3) and protein (Supplemental Fig. 2C) level in response to stimuli that induce M1 polarization but was not altered under conditions in which macrophages undergo M2 polarization (Fig. 3A). Deletion of HIF-1 affected IL-1β and IL-6 mRNA (Fig. 3A) and protein (Supplemental Fig. 3) levels as well as Nos2 expression in M1 macrophages, but not in M2 macrophage. HIF-2 expression was strongly attenuated in M1 macrophages and upregulated in M2-polarized macrophages (Fig. 3A).
We next asked whether H. pylori could interfere with macrophage polarization. Two murine M2 markers, fibronectin (Fn1) and arginase (Arg1), were drastically decreased upon H. pylori infection in M2 macrophages (Fig. 3B). This decrease appeared to be independent of HIF-1. Altogether, these results suggest that H. pylori induced M1 polarization by stabilizing HIF-1, and prevent a switch to M2 macrophages through a HIF-1–independent mechanism.
HIF-1 contributes to the bactericidal activity against H. pylori
A hallmark feature of H. pylori gastritis and persistent infection is the pronounced accumulation of phagocytes, particularly neutrophils, in the gastric mucosa, and the ability of H. pylori to resist neutrophil killing (35). We compared the bactericidal activity of WT and HIF-1α–null neutrophils and macrophages against H. pylori, adapting an intracellular killing assay we had developed for studies of respiratory tract and skin pathogens (14) (Supplemental Fig. 4A). HIF-1 has been shown to be important for anoxia-induced neutrophil survival (15). In this study, the viability of H. pylori–infected WT and HIF-1–null neutrophils, under normoxia, was not significantly different (Supplemental Fig. 4B). HIF-1α–null neutrophils demonstrated a 66% increase in the number of surviving H. pylori SS1 compared with WT neutrophils (Fig. 4A). HIF-1α–null macrophages had ∼5-fold more surviving intracellular H. pylori SS1 compared with WT macrophages (Fig. 4B). Therefore, HIF-1α–deficient neutrophils and macrophages exhibit a killing defect against the pathogen.
Killing of SS1 by WT and HIF-1α–null neutrophils or macrophages. (A) WT and HIF-1Δmyel neutrophils were inoculated with H. pylori (SS1) at a multiplicity of infection equal to 2 for 1 h before lysis and plating on agar plates. (B) WT and HIF-1Δmyel BMDMs were inoculated with H. pylori (SS1) at a multiplicity of infection of 10 for 2.5 h hour after antibiotic treatment. Pool of three repeated experiments performed in triplicate. *p < 0.05.
No change in bacteria upon deletion of myeloid HIF-1 in a murine model of SS1-induced gastritis
Next, we used a murine model of chronic H. pylori infection to characterize the role of myeloid-HIF-1 in the induction of gastritis. WT and HIF-1Δmyel mice (on a C57BL/6 background) were inoculated with SS1 by oral gavage, as previously described (32). Control and infected mice were sacrificed 6 mo postinfection.
We first assessed the level of infection by serologic analyses. In the SS1-inoculated mice, the H. pylori Ag-specific Ab responses increased 3 and 6 mo postinfection (Fig. 5A), but no significant difference was observed between WT and HIF-1Δmyel mice. We next measured the magnitude of H. pylori colonization by a well-established approach of quantitative PCR to determining the levels of the H. pylori urease B on gastric biopsies. Urease B mRNA levels were strongly increased upon infection, but did not differ significantly between WT and HIF-1Δmyel mice. This was correlated with a similar H. pylori colonization score by direct visualization on a Warthin–Starry staining between WT and HIF-1Δmyel mice (Fig. 6A).
No change in bacterial loading, but decrease of proinflammatory genes upon deletion of myeloid HIF-1 in a murine model of SS1-induced gastritis. (A) Anti–H. pylori in the sera of WT and HIF-1Δmyel mice infected with H. pylori SS1 detected by ELISA at 0, 3, and 6 mo postinfection (left). Quantitative RT-PCR for ureB in gastric biopsies of WT and HIF-1Δmyel mice 6 mo postinfection (middle). H. pylori bacterial score performed blindly on a Warthin–Starry staining on gastric biopsies of WT and HIF-1Δmyel mice 6 mo postinfection (right). (B) Quantitative RT-PCR analysis of Nos2, IL-1β, and IL-6 mRNA level in gastric extracts of WT and HIF-1Δmyel mice. n = 7–13 mice per group. *p < 0.05, **p < 0.01, ***p < 0.001.
Gastric inflammation parameters. (A) Inflammation grading in H. pylori–infected and uninfected WT and HIF-1Δmyel mice. (B) F4/80 and HIF-1 immunostaining on gastric biopsies of WT and HIF-1Δmyel mice. Scale bars, 100 μm. (HCX FL PLAN 20×/0.4 PH1, Leica DMI-3000B microscope, Leica DFC 310FX digital camera, LAS V3.8 Core software.) One representative picture (n = 6 WT; n = 8 HIF-1Δmyel). Six measurements per individual mice were averaged for F4/80 staining. *p < 0.05
Decrease of proinflammatory genes upon deletion of myeloid HIF-1 in a murine model of SS1-induced gastritis
We also evaluated the effect of the deletion of myeloid HIF-1 on the expression of proinflammatory genes in the gastric mucosal microenvironment. A significant increase in Nos2, IL-1β, and IL-6 mRNA was observed in the gastric biopsies of infected WT mice compared with control mice. Specific deletion of HIF-1 in the myeloid lineage blunted these increases, suggesting that, at this stage of the gastritis, these inflammatory mediators were mainly produced by the myeloid lineage and were under the control of HIF-1 (Fig. 5B).
Increase of the inflammation score in HIF-1Δmyel mice
A blinded analysis to provide inflammation scores was performed by a pathologist (Fig. 6A). Surprisingly, an increase in the submucosa inflammation was observed in the antrum and fundus of the gastric tissue sections of HIF-1Δmyel–infected mice compared with WT mice. The number of plasma cells and polymorphonuclear cell infiltrates was also increased in the gastric tissue sections (antrum) of HIF-1Δmyel–infected mice compared with WT mice.
In accordance with the calculated inflammation score, the number of macrophages, as visualized by a F4/80 staining, was higher in the gastric biopsies of HIF-1Δmyel mice (Fig. 6B). HIF-1 immunohistochemistry revealed that HIF-1 seems to be mainly expressed by macrophages and that myeloid-specific HIF-1-ablation did not affect epithelial HIF signaling (Fig. 6B).
Increase of epithelial cell proliferation in HIF-1Δmyel mice
The severity of the chronic gastritis is linked to the index of epithelial cell proliferation (36, 37). To determine the degree of severity of the gastritis, we performed a KI67 immunostaining on gastric biopsies of WT and HIF-1Δmyel mice. In correspondence with the increase in inflammation in the HIF-1Δmyel mice (Fig. 6), epithelial cell proliferation (measured by the ratio of the number of KI67-positive cells in HIF-1Δmyel versus WT mice) was 2-fold higher in the antrum of HIF-1Δmyel versus WT mice (Fig. 7). HIF-1Δmyel mice developed more severe gastritis than WT mice, suggesting a protective role of myeloid HIF-1.
Increase in epithelial cell proliferation in HIF-1Δmyel mice. KI67 staining on gastric biopsies of WT and HIF-1Δmyel mice. Scale bars, 100 μm. HCX FL PLAN 20×/0.4 PH1, Leica DMI-3000B microscope, Leica DFC 310FX digital camera, LAS V3.8 Core software. In the gastritis area, the KI67 staining was quantified (normalized per the number of positive cells in the Hoechst staining) and represented as a fold change (knockout versus WT). Representative picture (left) of five stainings. *p < 0.05
Chronic inflammatory conditions, such as arthritis (38), asthma (39), and psoriasis (40), have been characterized to exhibit increased HIF-1 signaling. Given this paradigm, therapeutics for the inhibition of HIF-1 signaling have been considered for the treatment of a broad spectrum of chronic inflammatory disease states (18). Our study shows for the first time, to our knowledge, that HIF-1 is stabilized in the macrophages of human H. pylori–positive gastritis, which on its surface suggests that myeloid HIF-1 could perhaps be a therapeutic target. Chronic gastritis is characterized by mononuclear cell infiltrates, composed mainly of lymphocytes and macrophages, and has been correlated with increased levels of Nos2, and of the proinflammatory cytokines IL-6, IL-1β, and TNF-α in the gastric mucosa (30–34). Depletion of macrophages by drug-loaded liposomes ameliorated the gastritis induced by H. pylori infection (2), demonstrating the importance of macrophages in the innate immune response to H. pylori–associated gastritis.
All H. pylori isolates, irrespective of their cag PAI phenotype, induce a chronic gastritis in the host (41). We showed in this work that both the Cag+ B128 and the SS1 H. pylori strain, which lacks a functional cag-PAI, are able to upregulate HIF-1 in the macrophage. HIF-1 activation may be a convergent signaling point by H. pylori isolates for the induction of the inflammatory response in macrophages. Recent data showed that H. pylori induces HIF-1 in human gastric epithelial cells through a reactive oxygen species–dependent mechanism (42). However, HIF-1 stabilization in the innate immune cells and a putative role in these cells had not been investigated.
We found that H. pylori not only induced a prolonged M1 polarization by stabilizing HIF-1, but also prevented a switch to M2 macrophages, which play a critical role in the resolution of inflammation. The mechanism by which M2 markers are decreased remains to be investigated. Interestingly, we found that PPARγ, known to be involved in the M1 to M2 switch (43), was drastically decreased upon SS1 infection (data not shown). The decrease in HIF-2 expression by H. pylori may be a consequence of the decrease in M2 polarization (7). Imtiyaz et al. (44) showed that HIF-2 regulates some proinflammatory cytokines (IL-1β, IL-6, and IL-12) during acute and tumor inflammation. However, as HIF-2 expression was decreased by H. pylori, the inflammatory response mediated by H. pylori is unlikely dependent on HIF-2.
We found in vitro and in vivo that HIF-1 contributes to the production of Nos2, IL-1β, and IL-6. However, the net effect of the deletion of HIF-1 in the myeloid lineage was a more severe gastritis (Fig. 6), characterized by an increase of the inflammatory score and proliferation index. The increase in inflammatory cell influx may be a downstream consequence of the decrease in killing ability and cytokine production in the HIF-1Δmyel mice (Fig. 6A). Together these opposing processes may result in similar levels of colonizing H. pylori during chronic infection. Although HIF-1–deficient myeloid cells produce less cytokines, the production of superoxides by the oxidative burst was the same in both WT and HIF-1−/− macrophages (14). Therefore, even if not experimentally proven in this work, the increase in the number of inflammatory cells in the HIF-1Δmyel mice may be responsible for the worsening of the gastritis, through the production of reactive oxygen species. The increase in epithelial cell proliferation in H. pylori gastritis is regarded in the literature as one of the earliest mucosal modifications in the development of gastric adenocarcinoma. This proliferation is also a key factor in the initiation of carcinogenesis, enhancing the effects of mutagens by limiting the time for DNA repair (36, 37).
In conclusion, whereas targeting HIF-1 has been proven to be beneficial in sterile chronic inflammation, myeloid HIF-1 is protective in H. pylori–mediated gastritis, pointing to the complex counterbalancing roles of innate immune and inflammatory phenotypes in driving this pathology.
We thank Randall Johnson for providing the HIF-1Δmyel mice. We are very grateful to Valérie Michel for help with H. pylori culture and in vivo infection. We also thank Catherine Le Gall and Andrija Matak for help with tissue sections and immunohistochemistry and Sophie Vaulont for critical reading of the manuscript.
↵1 P.M. and M.H. have cofirst authorship.
This work was supported by Agence Nationale pour la Recherche Grant ANR-08-JCJC-0123 and the European Research Council under the European Community's Seventh Framework Program (FP7/2011-2015 Grant Agreement 261296).
cytotoxin-associated gene
hypoxia-inducible factor
H. pylori infection
Nos2
inducible NO synthase
pathogenicity island
Received May 16, 2014.
Accepted January 26, 2015.
Fox J. G.,
T. C. Wang
. 2007. Inflammation, atrophy, and gastric cancer. J. Clin. Invest. 117: 60–69.
Kaparakis M.,
A. K. Walduck,
J. D. Price,
J. S. Pedersen,
M. J. Pearse,
O. L. Wijburg,
R. A. Strugnell
. 2008. Macrophages are mediators of gastritis in acute Helicobacter pylori infection in C57BL/6 mice. Infect. Immun. 76: 2235–2239.
Lawrence T.,
G. Natoli
. 2011. Transcriptional regulation of macrophage polarization: enabling diversity with identity. Nat. Rev. Immunol. 11: 750–761.
Sica A.,
A. Mantovani
. 2012. Macrophage plasticity and polarization: in vivo veritas. J. Clin. Invest. 122: 787–795.
Quiding-Järbrink M.,
S. Raghavan,
M. Sundquist
. 2010. Enhanced M1 macrophage polarization in human Helicobacter pylori-associated atrophic gastritis and in vaccinated mice. PLoS One 5: e15018.
Hu C. J.,
L. Y. Wang,
L. A. Chodosh,
B. Keith,
M. C. Simon
. 2003. Differential roles of hypoxia-inducible factor 1alpha (HIF-1alpha) and HIF-2alpha in hypoxic gene regulation. Mol. Cell. Biol. 23: 9361–9374.
Takeda N.,
E. L. O'Dea,
A. Doedens,
J. W. Kim,
A. Weidemann,
C. Stockmann,
M. Asagiri,
M. C. Simon,
A. Hoffmann,
R. S. Johnson
. 2010. Differential activation and antagonistic function of HIF-alpha isoforms in macrophages are essential for NO homeostasis. Genes Dev. 24: 491–501.
Semenza G. L.
2012. Hypoxia-inducible factors in physiology and medicine. Cell 148: 399–408.
Nizet V.,
. 2009. Interdependence of hypoxic and innate immune responses. Nat. Rev. Immunol. 9: 609–617.
Dehne N.,
B. Brüne
. 2009. HIF-1 in the inflammatory microenvironment. Exp. Cell Res. 315: 1791–1797.
Cramer T.,
Y. Yamanishi,
B. E. Clausen,
I. Förster,
R. Pawlinski,
N. Mackman,
V. H. Haase,
M. Corr,
V. Nizet,
. 2003. HIF-1alpha is essential for myeloid cell-mediated inflammation. Cell 112: 645–657.
Cane G.,
A. Ginouvès,
S. Marchetti,
R. Buscà,
J. Pouysségur,
E. Berra,
P. Hofman,
V. Vouret-Craviari
. 2010. HIF-1alpha mediates the induction of IL-8 and VEGF expression on infection with Afa/Dr diffusely adhering E. coli and promotes EMT-like behaviour. Cell. Microbiol. 12: 640–653.
Mahabeleshwar G. H.,
M. A. Qureshi,
Y. Takami,
N. Sharma,
J. B. Lingrel,
M. K. Jain
. 2012. A myeloid hypoxia-inducible factor 1α-Krüppel-like factor 2 pathway regulates Gram-positive endotoxin-mediated sepsis. J. Biol. Chem. 287: 1448–1457.
Peyssonnaux C.,
V. Datta,
T. Cramer,
E. A. Theodorakis,
R. L. Gallo,
N. Hurtado-Ziola,
. 2005. HIF-1alpha expression regulates the bactericidal capacity of phagocytes. J. Clin. Invest. 115: 1806–1815.
Walmsley S. R.,
C. Print,
N. Farahi,
C. Peyssonnaux,
A. Sobolewski,
A. M. Condliffe,
A. S. Cowburn,
N. Johnson,
E. R. Chilvers
. 2005. Hypoxia-induced neutrophil survival is mediated by HIF-1alpha-dependent NF-kappaB activity. J. Exp. Med. 201: 105–115.
Thompson A. A.,
P. M. Elks,
H. M. Marriott,
S. Eamsamarng,
K. R. Higgins,
A. Lewis,
L. Williams,
S. Parmar,
G. Shaw,
E. E. McGrath,
. 2014. Hypoxia-inducible factor 2α regulates key neutrophil functions in humans, mice, and zebrafish. Blood 123: 366–376.
Fang H. Y.,
R. Hughes,
C. Murdoch,
S. B. Coffelt,
S. K. Biswas,
A. L. Harris,
H. Z. Imityaz,
E. Fredlund,
. 2009. Hypoxia-inducible factors 1 and 2 are important transcriptional effectors in primary macrophages experiencing hypoxia. Blood 114: 844–859.
Hirota S. A.,
P. L. Beck,
J. A. MacDonald
. 2009. Targeting hypoxia-inducible factor-1 (HIF-1) signaling in therapeutics: implications for the treatment of inflammatory bowel disease. Recent Pat. Inflamm. Allergy Drug Discov. 3: 1–16.
Kempf V. A.,
M. Lebiedziejewski,
K. Alitalo,
J. H. Wälzlein,
U. Ehehalt,
J. Fiebig,
S. Huber,
B. Schütt,
C. A. Sander,
S. Müller,
. 2005. Activation of hypoxia-inducible factor-1 in bacillary angiomatosis: evidence for a role of hypoxia-inducible factor-1 in bacterial infections. Circulation 111: 1054–1062.
Patel N. J.,
O. Zaborina,
L. Wu,
Y. Wang,
D. J. Wolfgeher,
V. Valuckaite,
M. J. Ciancio,
J. E. Kohler,
O. Shevchenko,
S. P. Colgan,
. 2007. Recognition of intestinal epithelial HIF-1alpha activation by Pseudomonas aeruginosa. Am. J. Physiol. Gastrointest. Liver Physiol. 292: G134–G142.
Rupp J.,
J. Gieffers,
M. Klinger,
G. van Zandbergen,
R. Wrase,
M. Maass,
W. Solbach,
J. Deiwick,
T. Hellwig-Burgel
. 2007. Chlamydia pneumoniae directly interferes with HIF-1alpha stabilization in human host cells. Cell. Microbiol. 9: 2181–2191.
Lee A.,
J. O'Rourke,
M. C. De Ungria,
B. Robertson,
G. Daskalopoulos,
M. F. Dixon
. 1997. A standardized mouse model of Helicobacter pylori infection: introducing the Sydney strain. Gastroenterology 112: 1386–1397.
Israel D. A.,
N. Salama,
C. N. Arnold,
S. F. Moss,
T. Ando,
H. P. Wirth,
K. T. Tham,
M. Camorlinga,
M. J. Blaser,
S. Falkow,
R. M. Peek Jr..
2001. Helicobacter pylori strain-specific differences in genetic content, identified by microarray, influence host inflammatory responses. J. Clin. Invest. 107: 611–620.
Clausen B. E.,
C. Burkhardt,
W. Reith,
R. Renkawitz,
I. Förster
. 1999. Conditional gene targeting in macrophages and granulocytes using LysMcre mice. Transgenic Res. 8: 265–277.
Ryan H. E.,
M. Poloni,
W. McNulty,
D. Elson,
M. Gassmann,
J. M. Arbeit,
. 2000. Hypoxia-inducible factor-1alpha is a positive factor in solid tumor growth. Cancer Res. 60: 4010–4015.
Ferrero R. L.,
P. Avé,
F. J. Radcliff,
A. Labigne,
M. R. Huerre
. 2000. Outbred mice with long-term Helicobacter felis infection develop both gastric lymphoid tissue and glandular hyperplastic lesions. J. Pathol. 191: 333–340.
Eaton K. A.,
M. J. Radin,
S. Krakowka
. 1993. Animal models of bacterial gastritis: the role of host, bacterial species and duration of infection on severity of gastritis. Zentralbl. Bakteriol. 280: 28–37.
Fenoglio-Preiser C.
1999. The Non-Neoplastic Stomach. Gastrointestinal Pathology: An Atlas and Text, 2nd Ed. Lippincott-Raven, Philadelphia, p. 153–274.
Salama N. R.,
M. L. Hartung,
A. Müller
. 2013. Life in the human stomach: persistence strategies of the bacterial pathogen Helicobacter pylori. Nat. Rev. Microbiol. 11: 385–399.
Mannick E. E.,
L. E. Bravo,
G. Zarama,
J. L. Realpe,
X. J. Zhang,
B. Ruiz,
E. T. Fontham,
R. Mera,
M. J. Miller,
P. Correa
. 1996. Inducible nitric oxide synthase, nitrotyrosine, and apoptosis in Helicobacter pylori gastritis: effect of antibiotics and antioxidants. Cancer Res. 56: 3238–3243.
Pignatelli B.,
B. Bancel,
J. Estève,
C. Malaveille,
S. Calmels,
P. Correa,
L. M. Patricot,
M. Laval,
N. Lyandrat,
H. Ohshima
. 1998. Inducible nitric oxide synthase, anti-oxidant enzymes and Helicobacter pylori infection in gastritis and gastric precancerous lesions in humans. Eur. J. Cancer Prev. 7: 439–447.
Touati E.,
V. Michel,
J. M. Thiberge,
N. Wuscher,
M. Huerre,
A. Labigne
. 2003. Chronic Helicobacter pylori infections induce gastric mutations in mice. Gastroenterology 124: 1408–1419.
Crabtree J. E.,
T. M. Shallcross,
R. V. Heatley,
J. I. Wyatt
. 1991. Mucosal tumour necrosis factor alpha and interleukin-6 in patients with Helicobacter pylori associated gastritis. Gut 32: 1473–1477.
Harris P. R.,
L. E. Smythies,
P. D. Smith,
A. Dubois
. 2000. Inflammatory cytokine mRNA expression during early and persistent Helicobacter pylori infection in nonhuman primates. J. Infect. Dis. 181: 783–786.
Allen L. A.
2007. Phagocytosis and persistence of Helicobacter pylori. Cell. Microbiol. 9: 817–828.
Cahill R. J.,
C. Kilgallen,
S. Beattie,
H. Hamilton,
C. O'Morain
. 1996. Gastric epithelial cell kinetics in the progression from normal mucosa to gastric carcinoma. Gut 38: 177–181.
Lynch D. A.,
N. P. Mapstone,
A. M. Clarke,
G. M. Sobala,
P. Jackson,
L. Morrison,
M. F. Dixon,
P. Quirke,
A. T. Axon
. 1995. Cell proliferation in Helicobacter pylori associated gastritis and the effect of eradication therapy. Gut 36: 346–350.
Giatromanolaki A.,
E. Maltezos,
N. Athanassou,
D. Papazoglou,
K. C. Gatter,
M. I. Koukourakis
. 2003. Upregulated hypoxia inducible factor-1alpha and -2alpha pathway in rheumatoid arthritis and osteoarthritis. Arthritis Res. Ther. 5: R193–R201.
Lee K. S.,
S. R. Kim,
S. J. Park,
H. S. Park,
K. H. Min,
S. M. Jin,
M. K. Lee,
U. H. Kim,
Y. C. Lee
. 2006. Peroxisome proliferator activated receptor-gamma modulates reactive oxygen species generation and activation of nuclear factor-kappaB and hypoxia-inducible factor 1alpha in allergic airway disease of mice. J. Allergy Clin. Immunol. 118: 120–127.
Rosenberger C.,
C. Solovan,
A. D. Rosenberger,
L. Jinping,
R. Treudler,
U. Frei,
K. U. Eckardt,
L. F. Brown
. 2007. Upregulation of hypoxia-inducible factors in normal and psoriatic skin. J. Invest. Dermatol. 127: 2445–2452.
Ferrero R. L.
2005. Innate immune recognition of the extracellular mucosal pathogen, Helicobacter pylori. Mol. Immunol. 42: 879–885.
Bhattacharyya A.,
R. Chattopadhyay,
E. H. Hall,
S. T. Mebrahtu,
P. B. Ernst,
S. E. Crowe
. 2010. Mechanism of hypoxia-inducible factor 1 alpha-mediated Mcl1 regulation in Helicobacter pylori-infected human gastric epithelium. Am. J. Physiol. Gastrointest. Liver Physiol. 299: G1177–G1186.
Odegaard J. I.,
R. R. Ricardo-Gonzalez,
M. H. Goforth,
C. R. Morel,
V. Subramanian,
L. Mukundan,
A. Red Eagle,
D. Vats,
F. Brombacher,
A. W. Ferrante,
A. Chawla
. 2007. Macrophage-specific PPARgamma controls alternative activation and improves insulin resistance. Nature 447: 1116–1120.
Imtiyaz H. Z.,
E. P. Williams,
M. M. Hickey,
S. A. Patel,
A. C. Durham,
L. J. Yuan,
R. Hammond,
P. A. Gimotty,
. 2010. Hypoxia-inducible factor 2alpha regulates macrophage function in mouse models of acute and tumor inflammation. J. Clin. Invest. 120: 2699–2714.
You are going to email the following Myeloid HIF-1 Is Protective in Helicobacter pylori–Mediated Gastritis
Pavle Matak, Mylène Heinis, Jacques R. R. Mathieu, Ross Corriden, Sylvain Cuvellier, Stéphanie Delga, Rémi Mounier, Alexandre Rouquette, Josette Raymond, Dominique Lamarque, Jean-François Emile, Victor Nizet, Eliette Touati, Carole Peyssonnaux
The Journal of Immunology April 1, 2015, 194 (7) 3259-3266; DOI: 10.4049/jimmunol.1401260
Diagnostic Value of Circulating Antigens in the Serum of Piglets with Experimental Acute Toxoplasmosis
Grass Carp Reovirus Nonstructural Proteins Avoid Host Antiviral Immune Response by Targeting the RLR Signaling Pathway
Respiratory Syncytial Virus Provides Protection against a Subsequent Influenza A Virus Infection
Show more INFECTIOUS DISEASE AND HOST RESPONSE

redpajama_set_name: RedPajamaCommonCrawl


Compare Interest Rates Mortgages Rates Down, Going Up?
Last week I wrote that barring something surprising in the minutes of the Fed meeting from last month, rates would probably be stable this week. As it turns out, something surprising was in the minutes, but the markets took it in stride and rates pretty much flatlined.
You can see by the red line, which tracks U.S. Treasury Bonds, that yields on the securities settled down calmly all week until Friday afternoon, when they came back up about to where they started the week.
When Treasury yields turned up on Friday afternoon most lenders re-priced to higher rates as the lenders worried what that was about. Remember, interest rates jump up, and settle down. Expect this line to bump up slightly next week.
The blue line tracks Fannie Mae's 60-day yield, essentially the wholesale price of mortgages. You can see it improved all week long, but that Friday's price does not yet reflect the slight jump in treasury yields Friday afternoon. Expect this to pop up slightly on Monday.
The above chart tracks the margin between retail rates locked and the wholesale rate. It has been above 0.400% since the beginning of 2016. You can see that it doesn't change much, but the high peaks show you where lenders got nervous about some sort of news event, and the low dips show you where lenders anticipated improving markets. This week we moved down slightly, and are sitting slightly above half-way in a range that is now pretty well-established. This means lenders have room to improve rates a little more, even if wholesale rates don't improve.
Economic reports during the first half of the week mostly fell in line with expectations, which simply underscores the stable bond yields of the week. Wednesday's FOMC minutes, which had the most potential for moving markets, were unsurprising except for the one comment I wrote about earlier this week.
Thursday's Jobless Claims caused a bit of a stir in that they were a bit lower than anticipated. Remember, rising wholesale inventories (which we saw the week before last) can lead to layoffs. A dearth of layoffs might mean retailers were confident the consumer would jump back in and buy.
But then Friday came, and Non-Farm Payrolls (new jobs created in March) came in at 98,000. The previous week 219,000 new jobs were reported, and economists expected 185,000 new jobs this week, so this was a major report. Next month's will be watched very closely.
Monday, Tuesday and Wednesday don't bring any reports that are likely to move markets, so the week could start off quietly. Thursday will bring Jobless Claims, which – given the Non-Farm Payrolls report – should be watched closely. Any number close to 250,000 should reassure investors. Much lower, and bonds could sell off, higher and stocks could sell off.
Friday is a big day, with – among others – Consumer Price Index, Core CPI, Retail Sales, Consumer Sentiment and Business Inventories.
This week also brings the first round of earnings reports for the first quarter. We now get a chance to see how corporations are doing in the new year. Strong reports could trigger a rise in stock prices, lower-than-expected earnings could trigger a selloff.
Finally, major international news can drive large movement in interest rates. This last week we fired missiles into Syria. How that plays out over the course of the next few weeks could have a much more significant impact on rates than any economic reports. Obviously, this impact and effects of this wild card is very difficult to predict.
Because of the lack of major economic reports, interest rates should follow stocks, with stable rates the first half of the week and the likelihood of improvement higher than the likelihood of interest rates going up.


You don't have to let your cellphone control you.
I found myself incapable of reading books, watching full-length movies or having long uninterrupted conversations. Social media made me angry and anxious, and even the digital spaces I once found soothing weren't helping.
Quoting the article, YouTube star Casey Neistat admits to spending nearly two hours a day on Twitter and Instagram alone. He, too, wants to quit.
If you ask Cal Newport, author of Digital Minimalism, Neistat is onto something. Newport's readers claimed they were exhausted not from any one app, but from losing control over the collective impact they had on their lives.
While the conversation around our smartphones' addictive properties isn't new, clearly, it hasn't been loud enough. Or practical enough. Because the demand for remedies is off the charts. Neistat's video got 2 million views in a week. Roose's article has 220,000 shares. Newport's book was an instant New York Times bestseller. And those three are just items of the past 30 days.
As important as this conversation is, I'm baffled by how helpless it makes us appear. After all, it's a device in our hands, not the other way around. We're in control here. In fact, you can escape your phone's toxic grasp in the next five minutes. All it takes is changing the settings and modifying your environment.
Some of what I'm going to suggest, like silencing your phone at all times, is going to sound extreme. You'll see it's really not, but only if you question your gut reaction to this idea. That reaction will be something like "I can't do this in my line of work," "I need to be reachable at all times," or "I'll miss something important," but it's probably not true. So don't let it go unchallenged.
In his book Nudge, behavioral economist and Nobel laureate Richard Thaler shares a fascinating study that shows the power of the default. In Austria, 99% of people are organ donors. In Germany, that number is just 12%. Why? Germany has an opt-in system. You have to fill in a little card and carry it in your wallet. But that takes effort, so most people never do it. Austria has an opt-out system. You're a donor by default, and most people never change it.
Humans are lazy. We'll only change the status quo if it really, really matters. Usually, it doesn't. So we don't. If your phone is silent by default, you'll find yourself changing that state much less frequently than you think you will.
On your iPhone, go to "Settings," then "Sounds," then uncheck the "Vibrate on Silent" option. Now, whenever you push the little knob on the side of your phone, it won't just mute calls and notifications, but stay silent as a rock.
This works the same on Android. With this setup, your alarms will still sound. I only ever change this when it makes sense to be interruptible, for example if I expect an important call or am out with friends and we might lose each other.
Put one more step between you and your phone's every option.
Instead of the screen lighting up when you pick it up, make yourself press a button (disable "Raise to Wake"). Instead of notifications showing up on your home screen, move all of them to the Notification Center. This way, you'll have to unlock and swipe to see them. Instead of accessing social media via their own apps, delete them and make yourself go through the browser. Instead of keeping any apps on your home screen, move them to the following pages. So you'll think about which one to use. A wallpaper with a good prompt helps.
Of course, this applies to breaking the default too: make it harder to pick up your phone. The easiest way to do that is to hide it from view. Put it in your backpack, leave it in a locker, or simply tuck it behind your laptop's screen. Since it's silent now, removing the visual cue is often enough to not grab it.
Despite what it feels like, I'd like to remind Mr. Roose and those with similar thoughts that we're all humans all the time. And we can do the things that make us special at any moment. Like taking control over our phones. Whether you're human is never in question. Will you take responsibility for it?


text:
Samar Airlines var ett tadzjikiskt flygbolag som flög både passagerare och gods inom Tadzjikistan och övriga Centralasien. De hade sitt huvudkontor i Khujand.

Flygbolaget grundades 2004 av den ryske vapenhandlaren Viktor But och den syrisk-amerikanske affärspartnern Richard Chichakli i syfte att undgå den uppdaterade versionen av International Emergency Economic Powers Act (Executive Order 13224) och Patriot Act samt andra resolutioner som var bestämda av FN:s säkerhetsråd. Samar användes primärt som en frontorganisation för de två för bland annat penningtvätt, otillåtna inköp av flygplan och transporterande av stora volymer av illegal krigsmateriel. Flygbolaget ägdes och leddes av målvakter men kontrollerades av But och Chichakli.

2007 var de intresserade av att köpa en Boeing 727-200 och en Boeing 737-200 från en flygplansförsäljare i den amerikanska delstaten Florida. Man hade gjort en första betalning där man hade fört över mer än 1,7 miljoner amerikanska dollar från flera skalbolag och frontorganisationer via Samar Airlines till banker i delstaterna New York och Utah. Syftet var att försvåra för myndigheter att spåra pengarna tillbaka till dem. När amerikanska myndigheter insåg att Chichakli var faktiskt en affärspartner till But så stoppades överföringarna av USA:s finansdepartement. But greps 2008 i Thailand medan Chichakli blev det 2013 i Australien, båda blev utlämnade till USA för att ställas inför det amerikanska rättsväsendet.

Flotta
Antonov An-12
Antonov An-28
Iljusjin Il-76

Referenser

Externa länkar
Samar Air (engelska)

Ej längre existerande asiatiska flygbolag
Frontorganisationer
Tadzjikiska flygbolag
Flygbolag bildade 2004

redpajama_set_name: RedPajamaWikipedia


text:
Concert: November 15th, Aga Khan Museum, Toronto
Autorickshaw Quintet at the Aga Khan Museum
Saturday, November 15, 8:00 p.m.
Aga Khan Museum Auditorium.
$40, $35, $30
Tickets: Buy Tickets
Mixing genres and defying expectations, the Canadian ensemble Autorickshaw incorporates contemporary jazz, funk, and folk elements with the classical and popular music of India. The group's capacity to break new ground has earned it a number of honours, including a John Lennon Songwriting Competition Grand Prize in World Music and the Touring Artist of the Year Award (both 2008).
https://www.agakhanmuseum.org/performing-arts/event/autorickshaw
By autorickshaw|May 6th, 2014|Categories: concerts|Tags: |Comments Off on Concert: November 15th, Aga Khan Museum, Toronto


Shaykh Yasir Qadhi moves on to now discuss the events that transpired between the period of Badr and that of Uhud.
The beginning is made with the elucidation of the most interesting assassination attempts made on our beloved Prophet ﷺ. Here two names come to the fore who were involved in this horrific attempt - Umayr ibn Wahab al Jumahe and Safwan ibn Umayyah. After a discussion of the way they will do this deed, Umayr went back home ,sharpened his sword and covered it with poison.what happened next? Was he successful in his misdeed? Shaykh Yasir unravels the truth.
What is the stance of the Shaykh on the saying that our Prophet SAW discriminated against the Jews? What is the truth in this? We are provided with satisfactory answers and our doubts are put to rest.
Why were the Banu Qaynuqa expelled from Madinah? What benefits were derived from such an action?
The Banu Qaynuqa were the largest of the jewish tribes and also the least harsh.
The Prophet SAW was put in such a situation to choose sides and this was unacceptable.
The Munafiqs were coming on to their own by showing their true colours.
When one of the leaders of the hypocrites came to the Prophet ﷺ and a normal conversation took place between them and he had left, the Prophet ﷺ said, "What an evil man he is".to this, Aisha RA said ,"you were just smiling and laughing with him!". The Prophet ﷺ then said "Oh Aisha have you ever known me to be crude and rude? This is the leader of the hypocrites. Verily the worst in the eyes of Allah are those whom we are forced to smile at even if our hearts curse".
Allah says, "If they do treachery to you, you can never do treachery to them".


Latitude variation is the small semi-cyclic apparent change in the declinations of stars due to the Chandler wobble and fluctuations in atmospheric refraction. Earth's polar wandering causes a maximum variation of 0.36" (equivalent to a latitude shift of about 10 meters on the ground) in a period of 432 days. Seasonal movements of air masses result in an additional variation of 0.18" ( +/-5 m) in a period of one year.


Home State Bank in Jefferson is initiating a new service to the community. The bank will present the Extraordinary Citizen award as part of its monthly Extraordinary Friday event.
This award will be given by HSB to an extraordinary Greene County resident or group who has gone above and beyond expectations by giving time and/or resources to benefit the community.
The resident or group must be nominated by another Greene County resident. Whether it was once or multiple times, the nominator should recognize the "something extraordinary" performed once or multiple times by the person/group.
A diverse panel of HSB staff will review the nominations and select the most outstanding individual or group for the award. Nominations will be compiled to be considered for future awards.
The first award will be presented on Friday, Oct. 13, during the bank's Extraordinary Friday event. The winner will receive $100 donated to the charity of his/her/their choice.


Founded in 1977, the Intentional Growth Center (IGC) is a nonprofit organization that serves as a continuing education and renewal center for both clergy and laity. Located in the historic Atkins House, IGC designs and offers courses and growth experiences at picturesque Lake Junaluska in the mountains of Western North Carolina, and in other locations throughout the country. Approximately 16 seminars and workshops are offered each year and more than 1,500 participants benefit from sessions dealing with Bible studies, ministry skills, spiritual retreats, and personal growth.
The Atkins House is the historic second home of Bishop James Atkins, the first Superintendent of Lake Junaluska Assembly, which now houses IGC's offices. Located at 959 North Lakeshore Drive, this three story house retains the charm of the early 1900's while being in the heart of the Assembly's missional activity. Along with housing the offices of IGC, the Atkins House also provides comfortable bed and breakfast facilities and a meeting room for individuals, families, or small groups.
Located in the picturesque Lake Junaluska mountains of Western North Carolina, IGC serves as a continuing education and renewal center for both clergy and laity.
IGC offers numerous growth experiences, including dozens of seminars and workshops each year that serve more than 1,800 participants.
Our goal is to increase pastoral and congregational effectiveness in order to make disciples of Jesus Christ for the transformation of the world.
At IGC, we serve those who serve God.
IGC is able to provide continuing education and spiritual enrichment to clergy and laity thanks to support from generous donors like you. Your gifts allow us to train Interim Ministers, support youth as they become faithful members of the UMC, and develop new programs to address the ever-changing needs of the church and the world.
Subscribe to our email newsletter and be among the first to hear about our offerings for continuing education, renewal, and spiritual growth. We value your privacy and will not share your information. You can opt out at any time.
To learn more, please contact us and we'll be happy to provide more information about IGC's programs, workshops and opportunities.


text:
Q: How does yacc work,can you explain it section by section? How does this tiny yacc programe work?
What I know so far:
%{...%} is definition
%% ... %% is rule,but how to interpret the rule?
and stuff after %% is function definition.
What's the section %token INTEGER between %} and %% ?
%{
#include <stdlib.h>
int yylex(void);
void yyerror(char *);
%}
%token INTEGER
%left '+' '-'
%left '*' '/'
%%
program:
program expr '\n' { printf("%d\n", $2); }
|
;
expr:
INTEGER { $$ = $1; }
| expr '*' expr { $$ = $1 * $3; }
| expr '/' expr { $$ = $1 / $3; }
| expr '+' expr { $$ = $1 + $3; }
| expr '-' expr { $$ = $1 - $3; }
;
%%
void yyerror(char *s) {
printf("%s\n", s);
}
int main(void) {
yyparse();
return 0;
}

UPDATE
What I don't understand:
program:
program expr '\n' { printf("%d\n", $2); }
|
;

A: The expr: means an expr is one of the following options, which are separated by | below.  If it can be seen as an INTEGER token, then it takes the first one.  If it can be seen as an expr followed by the * character followed by an expr then it takes the second option and so on.  $$ is the default return value, and $1 is the first token, $2 the second on and on.
So if it were parsing 5 + 6, it sees it as expr '+' expr, so it takes the 4th definition. It returns 11 as an expr, so then it matches 11 as an INTEGER token and assigns 11 as the return value.
If we were parsing a program token followed by 5 + 6, it would do the same thing to get program 11, then take the program expr rule and call the c code which would print to the screen.
Left means that operator is left associative. As in a + b + c = (a + b) + c. The operators on the same line have the same precedence, and those below it have lower precedence.
I admittedly haven't used yacc in a while so feel free to tell me I'm totally wrong.
UPDATE:
yacc generates c code, so you can put your own code directly into it.  So as it's parsing, if it sees a "program expr", then it can directly input the code in the { } into the generated code.

A: There are total five phases of compiler namely:

*

*lexical analysis

*syntax analysis

*semantic analysis (intermediate code generation-optional )

*Code optimization

*Code generation

The lex and YACC (yet another compiler compiler ) are both the Unix utilities to generate a program.lexer is responsible for matching the words in given program and when the match is found it stores the values in yylex and return in the form of tokens to lexer. yacc is basically a parser which takes the tokens and builds a tree from it to check the syntax of the program.The tokens are build by lexer and they are declared in yacc specification file.
so the y.tab.h file is included in lex program .
The above program contains a token INTEGER which is returned by lexer. The yacc program should contain a start state ...the "program" is the start state every program should contain atleast one start state... The yacc rules are in format  rule  {action}
.In above program "program expr '\n' " represents that the program can contain expression and then a newline character. i.e 5+4 'enter key' represents an expression for that we have written a expr '+' expr ;here expr can be integer in this case 5 and 4 so we include that rule also by writing expr: INTEGER
....for addition or any other role the LHS of expression is represented by $$ and  RHS by $1 ,$2 , $3...so on.hence expr: expr '+' expr { $$ = $1 + $3;} ......

redpajama_set_name: RedPajamaStackExchange


text:
Skip to page contents
Our past
Historical facts and figures
Imperial College today
Gifts and downloads
Centenary site nominated for a Webby Award
100 years - 100 stories: Building the Centenary homepage
Back to memories
Krishna Thakrar
(Electrical Engineering 1931)
regales fond memories of the Pimlico Connection and student pranks
Listen to the interview here
Krishna Thakrar: It's strange coming here. Some of it is as if time hasn't moved on at all. It's the same, but certain things have changed beyond recognition. It's amazing, it's like you're living in the present and in the past at the same time. It's wonderful.
Interviewer:	How was your life when you were here? Did you live in a hall?
KT	My first year, I lived in a house and my second and third year, I lived out in Wembley, so it was a half hour journey by tube every morning in the rush hour, but the rush hour then was not as bad as it is now. The tubes are horrible at the moment. When I left Euston station to get the tube here, I thought, gosh is this what I went through for three years? No, it wasn't this bad, I had to remind myself. But, what a wonderful three years I had, especially the first year living in the halls of residence. The gym, the squash courts, swimming pools, wonderful; it was the nicest time to get together, Wednesday afternoons. The other thing that sticks in my mind is the Pimlico Connection, where on Wednesday afternoons for a year, we went and taught at a local school, that was really, really wonderful. It gave an insight into how young people think and the problems that city schools face – it really was an eye-opener.
Interviewer:	How did it work as an eye-opener?
KT	I went to school in Africa, in Malawi and discipline was there. When I did my A-levels in Northamptonshire, there was a lot of discipline; respect for teachers was there. We found that when we went to any of the schools, it was chaos. But we also found that the teachers were over-stretched. They actually saw us as an extension to their resources and fortunately because we were engineering students here we actually did experiments and took them to the schools and the kids, they loved it. And you could see just by actually focussing on them, giving them attention, it meant that they actually enjoy it so much and it was so gratifying at the end when the students actually did a survey, they wanted us back there. So it was wonderful.
Interviewer:	And what about the parties and the bar? Did you enjoy them?
KT	Yes. Yeah we used to have regular discos in the junior common room. I don't know whether they are still here, but there should be an organisation called International Friendship for Overseas Students, and they used to have regular parties and they were fun, they really were fun. But we never forgot that we were here to study and the College made sure that we did that as well.
Interviewer:	What was the most stupid or funny thing that you did when you were here?
KT	The most stupid or funny thing was somebody who tried to do something to me, because I was always a bit of a prankster with my friends. One day a couple of the guys decided they were going to play a prank on me. I was living in Beit Hall then and they decided they were going to get here at five o'clock in the morning, on a Sunday morning, and they were going to knock on my door and run away and then they would come back and knock on my door. So two of them decided that they were going to get together, but one of them didn't turn up, so the whole plan failed. It was wonderful. And, oh yes, as a group, we actually went to learn dancing, ballroom dancing. That was crazy, we knew it was crazy but it was fun. We felt a bit like fools, but never mind, we enjoyed it. So that was something crazy we did.
Interviewer:	And what kind of pranks did you do with your friends?
KT	The only one that I can remember is on the last day, after we have finished our graduation, we were just finishing off and heading home, and we all went out for a meal. We had a few beers, and for some reason there was a group walking in front of us and one of us, I think it was me, just decided we were going to dive at their feet and grab them and that was it, we all flopped to the ground. That's what I remember. I think after a few beers, you know, you don't remember much, but those were the good old days. I don't think I would do anything like that again. I'm kind of older, I don't know about any wiser, but definitely older, and balder.
Filming by Barbara Axt Portella.
The Centenary Campaign
Through the first decade of the twenty-first century the campaign seeks to philanthropically raise £207 million from Imperial's alumni, staff and friends, and donations from charitable foundations and industry.
Where your support can make a difference
Staff and student portraits
Imperial's Centenary Year provides an opportunity to recognise and celebrate members of the Imperial community.
View staff and student portraits


text:
75th Anniversary National Pearl Harbor Remembrance Day Commemoration [Image 251 of 275]
PEARL HARBOR, HI, UNITED STATES
Photo by Lance Cpl. Miguel Rosales
A U.S. Marine with Marine Aircraft Group 24 gazes across Pearl Harbor at the Arizona Memorial during the 75th Anniversary National Pearl Harbor Remembrance Day Commemoration Dec. 7, 2016 at Joint Base Pearl Harbor-Hickam, Hawaii. Dec. 7, 2016 marks the 75th anniversary of the attack on Pearl Harbor and Oahu. The U.S. military and the State of Hawaii are hosting a series of remembrance events to honor the courage and sacrifices of Pacific Theater veterans. (U.S. Marine Corps photo by Lance Cpl. Miguel A. Rosales)
VIRIN: 161207-M-WQ429-1108
Location: PEARL HARBOR, HI, US
This work, 75th Anniversary National Pearl Harbor Remembrance Day Commemoration [Image 275 of 275], by Sgt Miguel Rosales, identified by DVIDS, must comply with the restrictions shown on https://www.dvidshub.net/about/copyright.
JBPH-H
Marine Aircraft Group 24
PH75USMC
75th National Pearl Harbor Remembrance Day
75th Anniversary National Pearl Harbor Remembrance Day Commemoration


Summerlicious and Winterlicious are two of Toronto's most popular culinary celebrations. The restaurant programs were created in 2003 to promote Toronto's unique hospitality industry, showcase Toronto's diverse cuisine and profile fine dining experiences at a range of price points.
Both Summerlicious and Winterlicious consist of a prix fixe program offered by 200 of Toronto's dining establishments. Three-course prix fixe lunch and dinner menus are offered at $23, $28, or $33 for lunch and $33, $43 or $53 for dinner (beverages, taxes and gratuities not included). In addition to the prix fixe promotion, Winterlicious also features a series of ticketed culinary experiences.