text:
Q: Is there a way in bash script to have an option to give an argument but it shouldn't a must? I have a scenario where i would like to assign an option a default value but a user can decide to give it another argument:
Here is an example
 check_param() {

        for arg in "$@"; do
                shift
                case "$arg" in
                        "--force") set -- "$@" "-f" ;;
                        "--type") set -- "$@" "-t" ;;
                        "--help") set -- "$@" "-h" ;;
                        "--"*) echo "Unknown parameter: " $arg; show_help; exit 1 ;;
                        *) set -- "$@" "$arg"
                esac
        done

        # Standard Variables
        force=0
        type="daily"

        OPTIND=1
        while getopts "hft:v" opt
        do
                case "$opt" in
                        "f")    force=1 ;;
                        "t")    type=${OPTARG} ;;
                        "h")    show_help; exit 0 ;;
                        "?")    show_help; exit 1 ;;
                esac
        done
        shift $(expr $OPTIND - 1) # remove options from positional parameters

From the above example, i would like when the user gives the parameter -t without any argument to apply the default value which is daily , and the user can also use parameter -t with any other argument and that will be checked later in code.
The problem is now the parameter -t must be given an argument due to the colon, but i kinda need for it to do both, with or without argument.
Thanks in advance for any explanations or links to any article that can help.
So according to a suggestion i got Here is the test result
check_param() {

        ## Standard Variablen der Parameter
        force=0
        type="daily.0"

        ## Break down the options in command lines for easy parsing
        ## -l is to accept the long options too
        args=$(getopt -o hft::v -l force,type::,help -- "$@")
        eval set -- "$args"

        ## Debugging mechanism
        echo ${args}
        echo "Number of parameters $#"
        echo "first parameter $1"
        echo "Second parameter $2"
        echo "third parameter $3"

        while (($#)); do
                case "$1" in
                -f|--force) force=1; ;;
                -t|--type) type="${2:-${type}}"; shift; ;;
                -h|--help) show_help; exit 0; ;;
                --) shift; break; ;;
                *) echo "Unbekannter Parameter"; exit 1; ;;
                esac
                shift
        done
echo ${type}

}

check_param $@

echo ${type}

The output:
sh scriptsh -t patch.0
-t '' -- 'patch.0'
Number of parameters 4
first parameter -t
Second parameter
third parameter --
daily.0
daily.0

It still didn't assign the value patch to the variable type

A:
Is there a way in bash script to have an option to give an argument but it shouldn't a must?

Yes, there is a way.
getopts does not supports optional arguments. So... you can:

*

*roll your own bash library for parsing arguments or

*use another tool that has support for optional arguments.

A common tool is getopt that should be available on any linux.
args=$(getopt -o hft::v -l force,type::,help -- "$@")
eval set -- "$args"
while (($#)); do
   case "$1" in
   -f|--force) force=1; ;;
   -t|--type) type="${2:-default_value}"; shift; ;;
   -h|--help) echo "THis is help"; exit; ;;
   --) shift; break; ;;
   *) echo "Error parsgin arguments"; exit 1; ;;
   esac
   shift
done

getopt handles long arguments and reorders arguments, so you can ./prog file1 -t opt and ./prog -t opt file1 with same result.


text:
GISST
THE LEIPZIG INTERNATIONAL SCHOOL MAGAZINE
EY 3 Graduation 2020
Back to Kindergarten
LIK distance learning evaluation
CAS Project: Garden Journal – a way to combine graphic design skills with love for nature
CAS Project: Garden Stage – from vision to reality in one school year
Equipping students for effective lifelong learning
For the joy of it! A student's experience of athletics at LIS
Sports Field Upgrade
Grade 2 Swimming Lessons
Three LIS winners in Leipzig Art Competition
LIS at Nacht der Kunst
Every Storm Runs Out of Rain – A People's Journal of a Pandemic
Leipzig Lake and Art-Scene Tour
Alumni Interview Tomasz Droszcz – Class of 2007
Alumni Wine and Tapas Evening
Alumni Interview Paula Rauschendorf – Class of 2012
GREASE is the word!
Cedric M. Thompson·
ArtsDramaMusicSecondary
Last year Grease celebrated its 40 anniversary, with fans still hopelessly devoted to this cult musical about the boys and girls of Rydell High.
Though Grease implies many complex things, it is actually about the ordinary, everyday lives of a group of teenagers. Their chief worries are whether or not they'll have a date to the dance and can they get the car.
Based on a hit Broadway musical about the love affairs of 1950s high school students, Grease has been capturing the hearts of fans for four decades. For three performances in March 2019, students from Leipzig International School took to the gritty, industrial stage that is Werk 2, Halle 2 in Connewitz, to tell us more.
With #MeToo awareness in the foreground, one doesn't take on a musical like Grease without thinking twice, perhaps thrice. Having settled on performing the high school version ("cleaned up", so to speak) of the popular musical, I felt compelled to read extensively to be sure about my direction as I set about bringing this show to the LIS community.
Scott Miller, in his 2006 book, Sex, Drugs, Rock & Roll, and Musicals, reminds us that 1959 was a pivotal moment in American cultural history, when rock and roll was giving birth to the Sexual Revolution and everything in American culture was about to be turned upside down. "Grease", he claims, "is really the story of America's tumultuous crossing over from the 50s to the 60s, overthrowing repression and tradition for freedom and adventure (and a generous helping of cultural chaos), a time when the styles and culture of the disengaged and disenfranchised became overpowering symbols of teenage power and autonomy. Originally a rowdy, dangerous, over-sexed, and insightful piece of alternative theatre, Grease was inspired by the rule-busting success of Hair and shows like it, rejecting the trappings of other Broadway musicals for a more authentic, more visceral, more radical theatrical experience that revealed great cultural truths about America."
©Anna Fahrenkamp
This year LIS brought you Grease to reveal some of these cultural truths. At the end of the day, Grease is about authenticity, the watchword of that first rock and roll generation. Miller urges us to understand what Sandra Dee represented. She wasn't happy in her real life because she was never allowed to be herself – to be authentic – and Sandy Dumbrowski suffers the same problem. Sandra Dee represents not just strict morality in Grease, but the entire manufactured mainstream culture of 1950s America, a culture the kids of Grease reject.
In Grease, Sandy ultimately assimilates into the greaser community, rejecting her parents' world view. To quote Miller once more, "Grease isn't just about Sandy saying goodbye to her false idols; it's also about America saying goodbye to the false idols of the 1950s, saying goodbye to the turning of its collective blind eye away from the hidden horrors of the decade: rampant racism, sexism, homophobia, teen pregnancy and abortion, prescription drug abuse in the suburbs, and so much more. Sandy has to face herself and find her own authenticity but, so too, does America." So, too, the LIS community.
The subtle nod to a DDR aesthetic is deliberate and calculated for the sexual-cultural revolution was not confined to America; inauthenticity is a global phenomenon, and teenagers – those potent caldrons of dreams and desire – are remarkably the same around the world.
Sure, the Trabi mightn't have the same prowess as the American cars of the 1950s, but I am pretty sure that those who were lucky to possess a Trabant have lingering salacious coming-of-age memories. Our Trabi, Achtung Baby, has a distinguished provenance, having been used to promote a series of U2 concerts in Berlin. How fitting and privileged, tonight, for us to have had this iconic car as Greased Lightening. WATCH OUT BABY.
Thanks to the brilliance and dedication of both our musical director, Paul R. Foulkes, and our choreographer Anna-Maria Bernardt, we are able to bring you back to the 1950's for a little rock 'n roll. And, naturally, this production would not have been possible without the dedication of Marisa Di Teresa (Costume Design) and Chelsea Vincent (Wardrobe Assistant), Roisin Ní Leathlobhair (Vocal Coach & Production Assistant), and Berivan Kernich (Vocal Coach). The biggest applause, however, goes to the cast who had the guts to audition for the show and then sacrifice the time to attend rehearsals.
Four decades have passed since the Rydell High gang twisted, hand jived and crooned their way into the hearts of teenagers the world over – and this year we hope they left you hopelessly devoted to Grease, to authenticity, and to the music programme at LIS. Grease is the WORD!
Cedric M. Thompson, Stage Director March, 2019
Leipzig International School Productions
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Cedric M. Thompson
Secondary Teacher - Music / PSHE
Cedric Thompson is an English Language & Literature teacher and an Integrated Arts specialist who especially enjoys using music and theatre arts to enhance linguistic skills. Effectively a TCI, Cedric was born in South Africa and emigrated to Canada in 1984, where he continued his studies and worked for ten years in both the Catholic School system and the (public) Toronto School Board. He has been teaching internationally for the past 17 years, sharing his skills in Zimbabwe, Turkey, Uganda, Germany, as well as Ukraine.
Kontakt | Datenschutz-Bestimmungen | Impressum
Parent Association Leipzig International School

redpajama_set_name: RedPajamaCommonCrawl


A federal judge has postponed the sentencing date for convicted former Pilot Flying J President Mark Hazelwood until Sept. 26.
On Feb. 15, a jury found Hazelwood guilty of witness tampering and conspiracy to commit wire and mail fraud for his leadership role in a massive scheme to cheat truckers out of their diesel fuel rebates.
Hazelwood had asked for a four-month postponement of his original Aug. 22 sentencing date after he fired his attorney.


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Contagion reflects the values of a prevalent segment of our society: those dominated by the idea that science is the only hope for our salvation. Modernists still thrive, despite the postmodern evidence that for every cure for polio, there is an advance in weapons of mass destruction. They think that Nature—and its priests, our scientists—alone can rescue us from ourselves.
Steven Soderbergh (Traffic, Ocean's Eleven), the director of Contagion, is known for going back and forth between "serious" films and "big budget" ones. This movie toes the line between the two, racing through the super-charged story of a plague ravaging the world (26 million people killed in six weeks), but providing diverse sub-plots involving everything from underprivileged children in the mountains above Hong Kong to a father-daughter relationship in the cold of a Minnesota winter. Even with its rapid pace, the story is a cogent one. A killer virus creates social havoc because it spreads so rapidly and is so lethal, and a team of experts seeks to find out how to stop it. The film provides a textbook example of how the Centers for Disease Control and the World Health Organization use the various means at their disposal to track the virus, discover its origin and develop a vaccine. In this film, however, the entire process is speeded up because the disease is so vicious, and a number of scientists have to break the rules in order to save the human race.
Excellent performances from an all-star cast bring the characters into sharp focus and provide them with deep viewer sympathy in a very short time. Most fully developed and most moving is the frightened father, Mitch Emhoff, portrayed by Matt Damon. Mitch spends much of the film protecting his daughter from exposure to the disease after the death of his wife and son early on. Mitch's wife, played mostly in flashbacks by Gwyneth Paltrow, is the first to get the disease and dies early on; a subplot of her unfaithfulness contributes to the roundness of Damon's character without needing a lot of exposition. This scarcity of backstory does not usually work, though, for the other characters in the film. Kate Winslet's character, for example, died without enough opportunity to care sufficiently about her, though what Winslet did in such a short period of time on screen was extraordinary.
The plot points causing the breakthroughs that eventually win the day are well-spaced and very clever. Elliott Gould's curmudgeonly old professor illegally continues his work after he has been told to destroy the samples of the virus that he has been given. His rebellion against the system brings to the table an important piece of the puzzle of the nature of the virus, as well as a clear example of the philosophy of salvation contained in Contagion. Another tireless researcher, played by the astounding Jennifer Ehle, makes the emotional, self-sacrificial decision that wins the day. Both Gould and Ehle play rugged individuals, totally dedicated to their science, and here is where the reality of the film breaks down: science and science alone—practiced by the researcher sometimes as lone hero, sometimes as team player—rescues mankind.
One of the earmarks of a deficient film is its inability to hold the viewer throughout the entirety of its story. Halfway through my first viewing, I suddenly became aware of an absurdity in Contagion: I was watching a film about the endangerment of the entire human race, but I had yet to see one person praying, one reference to religion, one bow to the irrepressible tendency of human beings to seek God in a crisis.
In fact, Contagion depicts just the opposite. There are two Christian symbols in the whole movie, and both are ironic and condescending. In a brief sighting, an out-of-focus nun, a nurse, leaves the side of a dying man, clearly overwhelmed by the situation and too nervous and/or frightened to help him. Enter an almost comatose Winslet, lying in the bed next to the dying patient, who, though she herself is dying and delirious, seeks to be the Good Samaritan and give him her blanket. The CDC will provide not only the knowledge needed to stop the virus, but also the humanity to care for those in need. No place for the clergy or the church here, thank you very much.
The second concise reference to religion is only a shot of a cross atop an open air school in the village above Hong Kong, signaling both that school, i.e. science, has replaced the church, and that knowledge will win the day, not religion. A third possible reference is unclear, but is just as problematic, if intended. In a quick montage showing the devastation of the disease, there is a brief sighting of what appears to be an empty church, not a full one with people seeking God's help.
Contagion portrays the whole race as dog-eat-dog, as the virus spreads and the social fabric unravels. Only the scientists keep their cool, reach out to their fellow human beings, take risks that sometimes cost their lives and finally save the day. Richard Dawkins, rejoice. You have a friend in the makers of Contagion. Poor God; absent again, just when we needed Him.
2011; PG-13: Graphic hospital scenes. DVD to be released January, 2012.


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text:
package com.tek271.funj;

import com.google.common.collect.Lists;
import org.junit.Test;

import java.util.List;
import java.util.Properties;

import static org.junit.Assert.assertEquals;

public class ReflectionToolsTest {
	private static final String NOT_EXIST = "propertyNameWhichDoesNotExist";
	private final Zoo zoo = Zoo.createZoo(1);

	@Test(expected = IllegalArgumentException.class)
	public void getPropertyValueFailsWhenNullObject() {
		ReflectionTools.getPropertyValue(null, "a");
	}

	@Test(expected = IllegalArgumentException.class)
	public void getPropertyValueFailsWhenPropertyNameIsNull() {
		ReflectionTools.getPropertyValue(this, null);
	}

	@Test(expected = IllegalArgumentException.class)
	public void getPropertyValueFailsWhenPropertyNameIsEmpty() {
		ReflectionTools.getPropertyValue(this, "");
	}

	@Test(expected = IllegalArgumentException.class)
	public void getPropertyValueFailsWhenPropertyNameIsBlank() {
		ReflectionTools.getPropertyValue(this, "  ");
	}

	@Test(expected = IllegalArgumentException.class)
	public void getPropertyValueFailsWhenPropertyNameIsNotValid() {
		ReflectionTools.getPropertyValue(this, NOT_EXIST);
	}

	@Test
	public void getPropertyCanAccessFields() {
		Integer id = ReflectionTools.getPropertyValue(zoo, "id");
		assertEquals(1, id.intValue());
	}

	@Test
	public void getPropertyCanAccessGetter() {
		String city = ReflectionTools.getPropertyValue(zoo, "city");
		assertEquals("city_1", city);
	}

	@Test
	public void getPropertyCanAccessNestedObjects() {
		String color = ReflectionTools.getPropertyValue(zoo, "cat.color");
		assertEquals("color_1", color);
	}

	@Test
	public void getPropertyCanAccessNestedIndexedObjects() {
		Zoo.Dog dog = ReflectionTools.getPropertyValue(zoo, "dogs[0]");
		assertEquals(zoo.dogs.get(0), dog);
	}

	@Test
	public void getPropertyCanAccessNestedIndexedObjectsFields() {
		String bark = ReflectionTools.getPropertyValue(zoo, "dogs[1].bark");
		assertEquals("bark_2", bark);
	}

	@Test(expected = IllegalArgumentException.class)
	public void checkPropertyExistsFailsIfPropertyDoesNotExist() {
		ReflectionTools.checkPropertyExists(zoo, NOT_EXIST);
	}

	@Test(expected = IllegalArgumentException.class)
	public void checkPropertyExists_IterableFailsIfPropertyDoesNotExist() {
		List<Zoo> list = Lists.newArrayList(zoo);
		ReflectionTools.checkPropertyExists(list, NOT_EXIST);
	}

	@Test
	public void checkPropertiesExistChecksMultipleProperties() {
		ReflectionTools.checkPropertiesExist(zoo, "id", "city");
	}

	@Test(expected = IllegalArgumentException.class)
	public void checkPropertiesFailsIfAnyPropertyNameDoesNotExist() {
		ReflectionTools.checkPropertiesExist(zoo, "id", "cityXX");
	}

	@Test
	public void callStaticCallsSimpleMethodWithNoArgs() {
		Properties actual = ReflectionTools.callStatic("System.getProperties");
		assertEquals(System.getProperties(), actual);
	}

	@Test
	public void callStaticHandlesOptionalParenthesis() {
		Properties actual = ReflectionTools.callStatic("System.getProperties()");
		assertEquals(System.getProperties(), actual);
	}

	@Test
	public void callStaticWillCallMethodWithArgs() {
		String fullFunctionName = this.getClass().getName() + ".add";
		Integer actual = ReflectionTools.callStatic(fullFunctionName, 10, 20);
		assertEquals(30, actual.intValue());
	}

	@SuppressWarnings("UnusedDeclaration")
	public static int add(int a, int b) {
		return a+b;
	}

	@Test
	public void callStaticWithClassWillCallMethodWithArgs() {
		Integer actual = ReflectionTools.callStatic(this.getClass(), "add", 10, 20);
		assertEquals(30, actual.intValue());
	}

	@Test
	public void callMethod() {
		Integer actual = ReflectionTools.callMethod(this, "subt", 10, 4);
		assertEquals(6, actual.intValue());
	}

	@SuppressWarnings("UnusedDeclaration")
	public int subt(int a, int b) {
		return a-b;
	}

}

redpajama_set_name: RedPajamaGithub


text:
package web

import (
	"encoding/json"
	"encoding/xml"
	"fmt"
	"io"
	"io/ioutil"

	"gopkg.in/yaml.v2"
)

type Marshaller func(v interface{}) ([]byte, error)

func MarshallerFor(contentType string) (m Marshaller, err error) {
	switch contentType {
	case MimeHtml, MimePlain:
		m = marshalText
	case MimeJson, MimeJavascript:
		m = json.Marshal
	case MimeXml, MimeXml2:
		m = xml.Marshal
	case MimeYaml, MimeYaml2:
		m = yaml.Marshal
	default:
		err = fmt.Errorf("unable to encode unrecognized contentType=%s", contentType)
		return
	}
	return
}

func marshalText(value interface{}) ([]byte, error) {
	switch value.(type) {
	case string:
		s, _ := value.(string) // TODO: Cleanup/revisit
		return []byte(s), nil
	case []byte:
		b, _ := value.([]byte) // TODO: Cleanup/revisit
		return b, nil
	}
	return []byte{}, fmt.Errorf("unable to transform value=%T to []byte", value)
}

func DecodeJson(src io.Reader, value interface{}) error {
	decoder := json.NewDecoder(src)
	if err := decoder.Decode(value); err != nil {
		return err
	}
	return nil
}
func DecodeXml(src io.Reader, value interface{}) error {
	decoder := xml.NewDecoder(src)
	if err := decoder.Decode(value); err != nil {
		return err
	}
	return nil
}
func DecodeYaml(src io.Reader, value interface{}) error {
	in, err := ioutil.ReadAll(src)
	if err != nil {
		return err
	}
	if err := yaml.Unmarshal(in, value); err != nil {
		return err
	}
	return nil
}


text:
\section{Introduction}

Multi-state models are stochastic models which generalize a wide class of survival scenarios, from unidimensional survival models to multi-event models such as competing risks models or repeated events \cite{andersen competing, kneib}. In the multi-state framework, events  are the states of the process, and  their respective occurrences are transitions between  the state of departure and the state of interest.   The uncertainty associated to transitions is modelled via transition probabilities or, equivalently, transition intensities.  The latter are analogue to hazard functions in the field of survival analysis. Multi-state models are especially useful in medical research because they provide  a natural setting for dealing with   the natural history of complex diseases \cite{Le}.

The so-called illness-death model \cite{andersen} is one of the simplest and most studied multi-state models. It has three states:  an initial state, an illness-related state and a death state. The process starts in the initial state from which it can progress to the illness transient state or to death, which is an absorbent state. Death is also accessible from the illness state.  This model is particularly useful to the study of chronic diseases \cite{vejakama}, cancer progression \cite{armero illness-death} or cardiovascular diseases \cite{kuhn} in which there is a considerable risk of death over time.

The Cox proportional hazards model \cite{cox prop} is the most popular regression tool in the survival framework to model hazard functions associated to survival times. It expresses hazard functions as the product of a time-dependent baseline hazard function and the exponential of a regression term including covariates and latent elements. The popularity of this model is primarily due two reasons. First, due to  the interpretability of hazard ratios to evaluate differences in the risk of the event of interest among the different covariate levels.  Second, because under the frequentist paradigm that objective does not require to make any assumptions about the baseline hazard function. From a Bayesian reasoning, a model for the baseline risk function needs to be specified \cite{christensen}, either parametrically or semi-parametrically  \cite{lazaro}. In particular, when we work only with covariates and use a Weibull baseline risk function, the overall risk function  will also be Weibull.   This property is the basis of the corespondence between  the Weibull Cox proportional hazards model and the Weibull accelerated failure time (AFT) regression model \cite{collett}.

The Bayesian paradigm provides a flexible framework for statistical inferences and generation of knowledge. Under this framework any measure of interest is subject to uncertainty: not only random variables  but also parameters, hypothesis, models, etc. On the other hand, the Bayesian inferential process via the Bayes' theorem allows to sequentially update previous knowledge of those measures using new information. The procedure is conceptually simple: the elicitation of a  prior distribution  for all uncertainties in the model, the computation of the likelihood function for the data obtained, and the estimation of the posterior distribution which   updates the relevant knowledge. This posterior is the starting point to approximate the posterior distribution of any measure of interest, such as sojourn times, transition and occupation probabilities, and cumulative incidence functions.

Regression survival models can include not only covariates  but also latent effects  that account for some  non-explained heterogeneity between groups of the target population.    In particular, the existence of differences among spatial regions is especially common in epidemiological studies. Uncontrolled risk factors may be relevant to explain high or low risks of disease in some regions, leading to this heterogeneity. We focus in this paper on lattice data, i.e. data for a finite number of sub-regions of a larger one. Often neighbouring regions can be expected to be similar and thus random effects with a spatial correlation structure can be assumed.

There is  a plenty of models for assessing  spatial correlation in the statistical literature. Conditional autoregressive (CAR) models \cite{besag} and their variants based on a neighbourhood definition of the correlation have been widely used in disease mapping. In particular, the model proposed by Besag, York and Mollié (BYM) in 1991 \cite{bym} has been postulated as the main choice over the past decades to deal with counts assuming a Poisson process. It is defined by means of two random effects, the first based on the neighbourhood structure and thus summarizing the spatial correlation between regions, and a second unstructured accounting for heterogeneity among regions. Leroux et al. (1999) \cite{leroux} propose an alternative specification for the precision matrix of the spatially distributed random effects that better distinguishes between spatial dependence and dispersion effects. Under this model random effects are defined as a mixture of independent and spatially correlated scenarios. Some authors assessed the behaviour of spatial models inside the survival framework such as Banerjee, Wall and Carlin (2003) \cite{banerjee frailty}, comparing different models without random effects (usually referred as frailties in the survival setting), with non-spatial frailties and with a CAR frailty.

Regarding illness-death models, not only spatial correlation can be modelled, but also correlation between the three transitions, resulting in a multivariate model for random effects. In this regard, Carlin and Banerjee (2003) \cite{carlin multivariate} proposed a multivariate CAR model for spatially correlated survival times. However, and despite its interest there are few studies considering spatial components in the illness-death model framework. The most remarkable research work in this direction is Nathoo and Dean (2007) \cite{nathoo}  in which various structures for  region-specific random effects are proposed, with especial attention  to the comparison of different baseline functions such as Weibull distributions, piecewise-exponential forms and cubic B-splines.

We propose a Bayesian methodological framework to deal with spatially-correlated random effects within the illness-death scenario. In particular, a multivariate version of the Leroux model is used to jointly model that spatial correlation  as well as  the correlation between  the transition survival times. The Bayesian procedure involving the approximation of   the relevant  posterior distribution is done via the integrated nested Laplace approximation (INLA), which in general provides accurate estimations and reduces the computational time compared to Markov chain Monte Carlo (MCMC) methods. In the context of the proposed methodological framework, the computation of posterior outcomes such as  sojourn times, transition and occupation probabilities and cumulative incidence functions   results in   natural outcomes. Moreover, those quantities can be mapped providing rich information about the spatial distribution of illness and death in terms of probabilities that may have important clinical implications as interpreted by clinicians and epidemiologists. We apply this model to a real-world study involving recurrent hip fracture in old people. Data come from the PREV2FO cohort of patients from the Comunitat Valenciana (Spain) aged 65 and over who have been discharged from hospital after a hip fracture. They include  individual baseline information of those patients  and their progression over time. In addition to these individual characteristics, Health Areas where patients belong to are included to assess geographical differences in their corresponding risks and transition probabilities.

This paper is structured as follows: illness-death model is presented in Section \ref{sec:illness-death model}; the methodological framework regarding  the proposed spatial illness-death model is described in Section \ref{sec:bayes-illness-death model}, including the sampling model in Subsection \ref{subsec:sampling}, Bayesian inference and prior specification in Subsection \ref{subsec:prior}, and some posterior outcomes in Subsection \ref{subsec:outcomes}. Section \ref{sec:appli} includes the analysis of the study of recurrent hip fracture, in particular, Subsection \ref{subsec:prev2fo} presents the PREV2FO cohort, Subsection \ref{subsec:posterior} includes some relevant results regarding posterior inference of the parameters of the model, and Subsection \ref{subsec:measures} includes the results regarding relevant measures of the process such as cumulative incidences and transition probabilities. Finally, we present a discussion in Section \ref{sec:discu}.

\section{Illness-death models}\label{sec:illness-death model}

Illness-death models are the  most popular multi-state models \cite{andersen}. In their simplest version they comprise three states: an initial state (1), an illness-related state (2), and a death state (3). The death state is absorbent and accessible directly from the initial state or through the intermediate and transient state  defined by the illness. Figure \ref{diag} depicts the model, including transitions between the states.

\begin{figure}[H]
  \centering
  \includegraphics[width=3cm]{Figures/diag_multistate.pdf}
  \caption{Illness-death model with initial state (1), transient illness state (2) and death state (3). Arrows between the states represent the corresponding transitions.  }\label{diag}
\end{figure}

From a probabilistic framework, an illness-death model is defined as a stochastic process $\{Z(t), t>0\}$ in continuous time $t$  which takes as values the possible states where individuals can be. In particular,  $S=\{1,2,3\}$ is the state space  and $C=\{1\rightarrow2, \,1\rightarrow3, \,2\rightarrow3\}$  the set which includes all possible transitions of the process. We assume a semi-Markovian \cite{meira smmr} structure of the process whose  evolution  from the initial state to  disease or to death only depends on the history of the process through the current state; but the transition from   disease to   death will depend not only on the present situation of the process but also on how long it has been in the initial state before jumping to the disease state.

The random behaviour of our illness-death model is determined by the initial distribution of the process, $\textrm{P}(Z(0)=i)$ $\forall i \in S$ (usually $\textrm{P}(Z(0)=1)=1$ because it is assumed that at $t=0$ individuals are in state $1$),  and the so-called transition probabilities between states defined as:
\begin{align}
&p_{1j}(s,t)= \textrm{P}(Z(t)=j\mid Z(s)=1), \quad s \leq t, \,\,j=2,3, \label{trans_prob1}\\
&p_{23}(s,t \mid t_{12})=\textrm{P}(Z(t)=j\mid Z(s)=1, T_{12}=t_{12}), \quad s \leq t_{12} \leq t, \nonumber
\end{align}

\noindent where $T_{12}$ is the time  the process spends in state $1$ before entering into state $2$.
 Transition probabilities provide intuitive and easily interpretable information about the problem of interest but are difficult to model.  For this reason, the statistical modelling usually relies on transition intensities, which are much less intuitive but easier to model. They   account for the instantaneous hazard of progression to state $j$ conditional on the current state $i$ as follows
\begin{align}
h_{ij}(t) = & \lim_{\Delta t \rightarrow 0}\dfrac{p_{ij}(t, t +\Delta t)}{\Delta t}. \label{trans_intens1}
\end{align}
Notice that transition $2\rightarrow3$ needs to add the condition $T_{12}=t_{12}$ to the subsequent transition intensity.

In the case of our illness-death model, transition probabilities are computed from transition intensities as indicated below  (Armero \textit{et al.}, 2016) \cite{armero illness-death}:
\begin{align}
&p_{11}(s,t)=\exp{\Big\{-\int_s^t \,(h_{12}(u)+h_{13}(u)) \mbox{d}u \Big\}}\nonumber\\ \label{trans_prob2}
&p_{22}(s,t \mid t_{12})=\exp{\Big\{-\int_s^t h_{23}(u-t_{12}|t_{12})\mbox{d}u \Big\}} \nonumber\\
&p_{12}(s,t )=\int_s^t p_{11}(s,u) h_{12}(u)p_{22}(u,t| u) \mbox{d}u \nonumber\\
&p_{13}(s,t)=1-p_{11}(s,t)-p_{12}(s,t)\nonumber\\
&p_{23}(s,t \mid t_{12})=1-p_{22}(s,t)\nonumber\\
&p_{33}(s,t)=1,
\end{align}

The equivalence between some concepts from the world of stochastic processes and of survival analysis is quite natural for the given setting since the transition time from $i$ to $j$ can be seen as the survival time between the initiating event $i$ and the  entrance in the state of interest $j$, $T_{ij}$.  In this regard,
 transition intensities     in the stochastic framework (\ref{trans_intens1})  are equivalent to hazard functions in  the survival setting as follows
 \begin{align}
h_{ij}(t) = & \lim_{\Delta t \rightarrow 0}\dfrac{\textrm{P}(t\leq T_{ij}< t+\Delta t \mid T_{ij} \geq t)}{\Delta t}. \label{trans_intens2}
 \end{align}

Transition intensities can be naturally modelled using Cox proportional hazard models \cite{cox prop}, which expresses hazard functions by means of the product of a baseline hazard function and an exponential  regression term with covariates, random effects or   any other element that can provide information on the variable of interest.

\section{Bayesian spatial illness-death modelling}\label{sec:bayes-illness-death model}

We propose a Bayesian spatial   illness-death model for a   finite spatial lattice data   with a set of local neighbourhoods defined by geographic vicinity between the sites of the target region. This model includes the joint modelling of the three relevant survival times of the illness-death model associated to each site as well as a spatial structure, based on the Leroux model \cite{leroux}, which connects the survival process of the different sites of the spatial domain. The notation of the Bayesian content that we will introduce from now on considers all probabilities and derived concepts to be conditional because all the parameters and hyperparameters on which they depend have probability distributions.

\subsection{Sampling model}\label{subsec:sampling}

Let $h_{ij}^{(k)}(t\mid \boldsymbol{\theta})$ denote the conditional hazard function  for survival time $T_{ij}$ associated to transition $i\rightarrow j \in C$ at time $t$ for an individual from region $k$, $k=1, \ldots, K$  which we express through the Cox model \cite{cox prop}
\begin{align}
&h_{ij}^{(k)}(t \mid \boldsymbol{\theta}, \boldsymbol{\psi}) = h_{ij,0}(t\mid \boldsymbol{\theta})\,\exp\{\eta_{ij}^{(k)}\}, \label{eqn:Cox}\\
&\eta_{ij}^{(k)}=\boldsymbol{x}^{\prime} \boldsymbol{\beta}_{ij}  + b_{ij}^{(k)}, \nonumber
\end{align}
\noindent where $\boldsymbol{\theta}$ is the vector of parameters  and hyperparameters of the model, $\boldsymbol{\psi}$ the vector of all random effects, $h_{ij,0}(t\mid \boldsymbol{\theta})$  a baseline hazard function,   and $\eta_{ij}$  a regression term defined in term of covariates $\boldsymbol{x}=(x_1, \ldots, x_L)^{\prime}$, a vector of regression coefficients $\boldsymbol{\beta}_{ij}=(\beta_{ij,1}, \ldots, \beta_{ij,L})^{\prime}$ and a random effect  $b_{ij}^{(k)}$ associated with transition   $i\rightarrow j$  in region $k$. Baseline hazard functions can be approached in several ways. From parametric models such as the Weibull, undoubtedly the traditional and most widely used model in biometric applications, to more flexible modelling such as piecewise constant functions or B-splines (Ibrahim, Chen and Sinha, 2001) \cite{ibrahim}. In our case, we propose a full parametric approach via Weibull baseline hazard functions defined as $h_{ij,0}(t \mid \boldsymbol{\theta})=\alpha_{ij}\lambda_{ij}t^{\alpha_{ij}-1}$.

Random effects $b_{ij}^{(k)}$ in (\ref{eqn:Cox}) depend on the different model transitions as well as of the different sites in the target region. Let $\boldsymbol{B}$ be a matrix which comprises all random effects:

\begin{equation}
\boldsymbol{\mathrm{B}}=\left(
  \begin{array}{ccc}
    b_{12}^{(1)} & \quad b_{13}^{(1)} & \quad b_{23}^{(1)} \\
    \vdots       & \quad \vdots       & \quad \vdots \\
    b_{12}^{(k)} & \quad b_{13}^{(k)} & \quad b_{23}^{(k)} \\
    \vdots       & \quad \vdots       & \quad \vdots \\
    b_{12}^{(K)} & \quad b_{13}^{(K)} & \quad b_{23}^{(K)} \\
  \end{array}
\right),
\end{equation}

 \noindent $\boldsymbol{\mathrm{B}}(,i)$   the $i$-th column of the matrix $\boldsymbol{\mathrm{B}}$, and   $vec(\boldsymbol{\mathrm{B}})=[\boldsymbol{\mathrm{B}}(,1)^{\prime}, \boldsymbol{\mathrm{B}}(,2)^{\prime}, \boldsymbol{\mathrm{B}}(,3)^{\prime}]^{\prime}$   a column vector $(3K \times 1)$ including each of the columns of matrix $\boldsymbol{\mathrm{B}}$. We assume a conditional multivariate Gaussian Markov random field for the random effects   $vec(\boldsymbol{\mathrm{B}})$ with a mean vector whose elements are all zero and a matrix of variances-covariances $\Sigma$
\begin{equation}
(vec(\boldsymbol{\mathrm{B}})\mid \boldsymbol \Sigma) \sim N_{3K}(0, \boldsymbol{\Sigma}).
\end{equation}
\noindent It is worth mentioning that $vec(\boldsymbol{\mathrm{B}})$ is precisely the set of random effects that we have generically represented as $\boldsymbol \psi$ above.

The structure of $\boldsymbol \Sigma$ includes both  multivariate dependence   between the illness-death transitions of a given site (between columns of $\boldsymbol{B}$)  and   spatial dependence for each transition of the model (within columns of $\boldsymbol{B}$) in the form
\begin{equation}
\boldsymbol{\Sigma}= \boldsymbol{\Sigma}_{between} \otimes \boldsymbol{\Sigma}_{within},
\end{equation}

\noindent where $\otimes$ represents the Kronecker product.

Models for the spatial   variability  $\boldsymbol{\Sigma}_{within}$   have a wider range of options: from the simplest independent scenario to the conditional autoregressive (CAR) models \cite{besag} and their variants (intrinsic CAR, proper CAR, Besag York \& Mollié, Leroux model). The proposal by Besag, York \& Mollié (BYM) \cite{bym} has been widely used for disease mapping in the epidemiological literature over the past decades. Part of its popularity remains in its interpretability, as it   consists of   a random effect, which considers spatial correlation between regions according to a neighbourhood structure, and an unstructured random effect accounting for heterogeneity among regions. With the BYM model, however, only the sum of both sets of random effects is identifiable, failing thus to identify both random effects separately \cite{eberly}. The Leroux model \cite{leroux} circumvents this problem since there is only one set of random effects defined in terms of a mixture of independent and spatially-dependent elements that allows to assess the intensity of them as follows
\begin{equation}
\boldsymbol{\Sigma}_{within}=\Big(\tau\,[(1-\gamma)\boldsymbol{\mathrm{I}}+\gamma(\boldsymbol{\mathrm{D}}-\boldsymbol{\mathrm{W}})]\Big)^{-1},
\end{equation}

\noindent where $\tau$ is a dispersion hyperparameter, $\boldsymbol{\mathrm{I}}$ the identity matrix, $\boldsymbol{\mathrm{D}}$ a diagonal matrix    whose non-zero elements   on the diagonal are the number of neighbours in the corresponding  site, $\boldsymbol{\mathrm{W}}$ an adjacency matrix, i.e, $\boldsymbol{\mathrm{W}}_{kl}=1$ if sites $k$ and $l$ are neighbours, $k\neq l$, and 0 otherwise, and hyperparameter $\gamma \in [0, 1]$ determines how matrices $\boldsymbol{\mathrm{I}}$ and $\boldsymbol{\mathrm{D}}-\boldsymbol{\mathrm{W}}$
are combined. A value of $\gamma=0$ simplifies  to an independent random effects model without spatial patterns, whilst $\gamma=1$ corresponds to an intrinsic CAR model.

We model the variance-covariance matrix  $\boldsymbol{\Sigma}_{between}$ of the times between the three  transitions as
\begin{equation}
\boldsymbol{\Sigma}_{between}=\left(
  \begin{array}{ccc}
    \dfrac{1}{\tau_{12}}                                     & \quad \dfrac{\rho_{(12)(13)}}{\sqrt{\tau_{12} \tau_{13}}}        & \quad \dfrac{\rho_{(12)(23)}}{\sqrt{\tau_{12} \tau_{23}}}  \\ \\
    \dfrac{\rho_{(12)(13)}}{\sqrt{\tau_{12} \tau_{13}}}      & \quad \dfrac{1}{\tau_{13}}                                       & \quad \dfrac{\rho_{(13)(23)}}{\sqrt{\tau_{13} \tau_{23}}}  \\ \\
    \dfrac{\rho_{(12)(23)}}{\sqrt{\tau_{12} \tau_{23}}}      & \quad \dfrac{\rho_{(13)(23)}}{\sqrt{\tau_{13} \tau_{23}}}              & \quad \dfrac{1}{\tau_{23}} \\
  \end{array}
\right).
\end{equation}

This matrix includes two types of hyperparameters: $\tau_{ij}$, the marginal precision of the random effects associated to transition $i \rightarrow j$, and $\rho_{(ij)(i'j')}$, the correlation between the random effects on transitions $i \rightarrow j$ and $i' \rightarrow j'$.

Note that for identifiability reasons we fix $\tau=1$ for the multivariate version of the Leroux model, so that the covariance matrix $\boldsymbol{\Sigma}_{between}$ becomes the one capturing dispersion.

\subsection{Bayesian inference and prior specification}\label{subsec:prior}

A Bayesian approach based on the integrated nested Laplace approximation (INLA) \cite{rue inla}  has been considered to estimate the posterior distribution   of all the quantities of interest of the model. Bayesian inference combines  prior knowledge of all unknown parameters and hyperparameters $\boldsymbol{\theta}$ of the model in probabilistic terms throught the prior distribution with the likelihood function obtained from data $\mathcal D$ by means of the Bayes' theorem to derive the joint posterior distribution $\pi(\boldsymbol{\theta}, \boldsymbol{\psi} \mid \mathcal D)$  of the  parameters and hyperparameters $\boldsymbol{\theta}$  and random effects $\boldsymbol{\psi}$. As models get more complex, it is harder to find an analytic expression for those posterior distributions and computational methods are required to approach them. The most popular procedures are Markov chain Monte Carlo (MCMC) methods \cite{mcmc} which, in most cases, imply large computational times to ensure convergence of the estimations. Alternatively, INLA is a fast and accurate option. It uses  Laplace approximations to obtain the approximated marginal posterior distribution of the parameters, hyperparameters and  latent terms of the sampling model. Survival models, including Cox proportional hazards models, can be adapted and implemented in INLA because they can be expressed in terms of Gaussian Markov random field (GMRF) models \cite{martino}. In particular, competing risks models \cite{niekerk competing}, and illness-death models as an extension of them, can be approached using INLA. It also allows the inclusion of gaussian random effects in the regression term of the Cox proportional hazards model, and thus the proposed spatial illness-death model is naturally approachable with INLA.

As just discussed, we need to complete the Bayesian model with a prior distribution for all parameters and hyperparameters $\boldsymbol{\theta}$ of the sampling model. We have considered a framework of prior independence between the different elements in $\boldsymbol{\theta}$. The shape parameters $\alpha_{ij}$ of the baseline hazard functions in (\ref{eqn:Cox}) were assumed to follow a penalized complexity prior (PC prior) as described in INLA documentation (See \texttt{inla.doc("pc.alphaw")} for a detailed definition). Those PC priors consider an exponential as the base model, i.e. a Weibull model with $\alpha_{ij}=1$, and penalize the departure from this exponential model. The more general Weibull model would be preferred only if there exists enough evidence supporting it \cite{niekerk weibull}. Meanwhile, the scale parameter for the baseline transition intensities, $\lambda_{ij}$, has not a prior by itself but through considering an intercept $\beta_{ij,0}=log(\lambda_{ij})$ which follows, as well as the regression coefficients $\beta_{ij,l}$, a gaussian distribution with mean 0 and precision $0.001$.

The covariance matrix including correlation between transitions, $\boldsymbol{\Sigma}_{between}$, was assumed to follow an inverse Wishart distribution, or equivalently, a Wishart distribution for the precision matrix $\boldsymbol{\Sigma}_{between}^{-1}$. The Wishart distribution is a multivariate generalization of the gamma distribution \cite{gelman bda}. It is specially relevant when modelling correlated normal random effects as it is a conjugated prior distribution  for the precision matrix from multivariate normal distributions, being the most common choice when inferring covariance matrixes. In our case, the prior values  for the Wishart parameters were those provided by default in the INLA specification of the model for correlated random effects, i.e, $\boldsymbol{\Sigma}_{betwen}^{-1} \sim \textrm{Wishart}_3(\nu=7, \boldsymbol{\mathrm{R}}=\boldsymbol{\mathrm{I}})$, where $\nu=7$ are the degrees of freedom. Wishart distribution with the identity as the scale matrix is typically set as a relatively uninformative prior. Note however that several authors have discussed its appropriateness and some alternatives have been proposed. For instance, this prior specification might not be appropriated in the presence of parameters with small variances, resulting in a strongly informative prior distribution \cite{schuurman}. On the other hand, separation-strategies decompose the covariance matrix into variance and correlation components, being it possible to specify separate priors for each component. Correlation matrix obtained after this separation may be assumed to follow an inverse Wishart distribution\cite{omalley}, or a Lewandowski-Kurowicka-Joe \cite{lewandowski}, for instance. Meanwhile, many positive priors can be set for variances such as truncated-normal, half-normal, half-Cauchy or uniform distributions \cite{gelman bda}.

A non-informative uniform prior, $\textrm{U}(0,1)$ was assumed for the mixture parameter $\gamma$ of the  Leroux modelling.   To define the multivariate Leroux model for random effects we used the \texttt{rgeneric} latent effect. Using this mechanism, latent effects can be implemented in INLA via R \cite{gomez-rubio}. Despite they are not specifically applied to survival models, some multivariate versions of random effects have already been defined using this method, such as intrinsic multivariate CAR latent effects, and collected in the \texttt{INLAMSM} package for \texttt{R} \cite{palmi}.

\subsection{Posterior outcomes}\label{subsec:outcomes}

The posterior distribution $\pi(\boldsymbol{\theta}, \boldsymbol{\psi} \mid \mathcal D)$  contains all updated information on the random behaviour of the illness-death population.   Nevertheless, it provides unclear practical  evidence about   the prognostic clinical status for a patient over time. Compound measures such as sojourn times distributions, transition and occupation probabilities and cumulative incidences functions \cite{meira biometrical, touraine} mix the time-evolution information from the illness-death setting as well as the risk-variation among regions. They are specially relevant   in order to gain insight into the clinical setting. From a statistical point of view, posterior inferences of those quantities of interest is straightforward as they are indeed defined as functions of the aforementioned parameters and effects. We introduce some of these  measures of performance and discuss their posterior estimation.

\subsubsection{Sojourn times.}

Sojourn time in state $i$ for an individual living in the site $k$ refers to the time an individual remains in that state without leaving. Possibly, the most interesting sojourn time in illness-death models  corresponds to the initial state. It is defined in terms of the conditional survival function as follows
\begin{align}
S_{1}^{(k)}(t\mid \boldsymbol{\theta}, \boldsymbol{\psi})& =P(T^{(k)}>t \mid \boldsymbol{\theta}, \boldsymbol{\psi}),  \\
& = \mbox{exp}\Big(- \int_{0}^{t}\,(h_{12}^{(k)}(u\mid \boldsymbol{\theta}, \boldsymbol{\psi})+h_{13}^{(k)}(u\mid \boldsymbol{\theta}, \boldsymbol{\psi}))\,\mbox{d}u \Big) \nonumber
\end{align}
\noindent where $T^{(k)}=\mbox{min}\{T_{12}^{(k)}, T_{13}^{(k)}\}$. Because
sojourn time in state $1$ depends on $(\boldsymbol{\theta}, \boldsymbol{\psi})$, the subsequent posterior distribution
$\pi(S_{1}^{(k)}(t\mid \boldsymbol \theta, \boldsymbol \psi)\mid \mathcal D)$, $\forall t$, can be easily approximate from a simulated sample of the posterior  $\pi(\boldsymbol{\theta}, \boldsymbol{\psi} \mid \mathcal D)$.

\subsubsection{Transition and occupation probabilities.}

Transition probabilities depend on the parameters through the subsequent hazard functions according to (\ref{trans_prob2}). Therefore, their posterior distribution $\pi(p_{ij}^{(k)}(s,t\mid \boldsymbol{\theta}, \boldsymbol{\psi} \mid \mathcal D)$ associated with an individual in region $k$ will be also computed from a simulated sample of $\pi(\boldsymbol{\theta}, \boldsymbol{\psi} \mid \mathcal D)$. Occupation probabilities refers to   probabilities associated to the presence of the process in each of the different states at a given time $t$. They can be expressed as transition probabilities $\pi(p_{ij}^{(k)}(0,t\mid \boldsymbol{\theta}, \boldsymbol{\psi} \mid \mathcal D)$, and consequently its posterior distribution could be also approximated  from an approximate sample from $\pi(\boldsymbol{\theta}, \boldsymbol{\psi} \mid \mathcal D)$.

\subsubsection{Cumulative incidence functions.}

Cumulative incidence functions are more frequently used in competing risks environments but  they are also useful for illness-death models, specially when illness is relevant by itself and not only as an intermediate state between the initial state and death. They can be defined equivalently to the competing scenario for survival times $T_{12}^{(k)}$ and $T_{13}^{(k)}$ as follows

\begin{align}
F_{12}^{(k)}(t\mid \boldsymbol{\theta}, \boldsymbol{\psi})= & P(T^{(k)} \leq t, \delta^{(k)}=1  \mid \boldsymbol{\theta}, \boldsymbol{\psi}),\\
F_{13}^{(k)}(t\mid \boldsymbol{\theta}, \boldsymbol{\psi})= & P(T^{(k)} \leq t, \delta^{(k)}=0  \mid \boldsymbol{\theta}, \boldsymbol{\psi}),
\end{align}

\noindent where $\delta^{(k)}$ is the indicator function with value 1 if $T_{12}^{(k)}<T_{13}^{(k)}$ and 0 otherwise. They can be interpreted as the probability at time $t$ of having moved directly from the initial state 1 to state $j$, $j=2,3$, keeping this sense of accumulation as its name suggests. Cumulative incidence regarding the illness state 2 is highly informative because it indicates how many individuals are expected to suffer the illness. It can also be directly compared with the transition probability from state 1 to 2,   which indicates the expected rate of patients who experienced illness and are still alive. Cumulative incidence functions are also expressed in terms of $(\boldsymbol{\theta}, \boldsymbol{\psi})$ as
\begin{equation}
F_{1j}^{(k)}(t\mid \boldsymbol{\theta}, \boldsymbol{\psi})=\int_0^t h_{1j}^{(k)}(s)\exp\Big\{-\int_0^s (h_{12}^{(k)}(u)+h_{13}^{(k)}(u) \textrm{d}u\Big\} \, \textrm{d}s, \,\,j=1,2.
\end{equation}
Consequently, the posterior distribution  of each of  these cumulative incidences,  $\pi(F_{1j}^{(k)}(t\mid \boldsymbol{\theta}, \boldsymbol{\psi})\mid \mathcal D)$, can also be approached by simulated samples of the posterior distribution $\pi(\boldsymbol{\theta}, \boldsymbol{\psi} \mid \mathcal D)$.

\section{A study of recurrent hip fractures in elderly patients}\label{sec:appli}

Clinical settings involving progression of non-terminal diseases, repeated events and populations with a considerable competing risk of death are the main scenarios where multi-state models can be applied. We illustrate here the application of the previous  model  on a study of recurrent hip fracture.

\subsection{The PREV2FO cohort}\label{subsec:prev2fo}

We analyse the PREV2FO cohort, a population-based cohort comprising patients aged 65 years and older discharged after hospitalization for an osteoporotic hip fracture in the Valencia Region (Spain) from January 1, 2008, to December 31, 2015 \cite{llopis}. The Valencia Region is an autonomous community of Spain, with a population of roughly 5 million people (10\% of the Spanish population). The region provides universal healthcare services through the Valencia Health System (VHS) which is an extensive network of public hospitals, primary care centers, and other public resources managed autonomously by the regional government. It is divided in 24 Health Areas, each one corresponding to the administrative area of influence of a public hospital from the VHS.

Patients were followed after the index fracture until death or end of study (December 31, 2016), accounting for recurrent hip fractures during the follow-up period. Figure \ref{diag_hip} shows a diagram  of this process as and illness-death model with an initial state of discharge after a first hip fracture (F), an intermediate state that accounts for discharge after a refracture (R), and   the state of death (D). From a clinical point of view there is a possibility of more than one refracture. We have dismissed this possibility because in our study only a reduced number of the patients suffered from them.

In order to define a basic patient profile we have considered sex,  age at the discharge and the Health Area in which patients were hospitalized as covariates. The study involved $34\hspace*{0.03cm}491$  patients discharged alive after hip fracture, 25$\hspace*{0.03cm}$807 (74.8$\%$)
were women and 8$\hspace*{0.03cm}$684 (25.2$\%$) men. Regarding age, 12.4$\%$ of patients were under
75 years old, 43.6$\%$ between 75 and 85 years old, 40.6$\%$ between 85 and 94 years
old, and 3.4$\%$ were over 95 years old. The mean age at the first  fracture was 83.4
years (IQR: 79.0-88.3). Patients were followed a median time of 5.0 years (IQR:
3.0-7.0 years).

\begin{figure}[H]
  \centering
  \includegraphics[width=5.5cm]{Figures/diag_hip.pdf}
  \caption{Illness-death model with an initial state of hip fracture, a recurrent hip fracture state and a death state.}\label{diag_hip}
\end{figure}

Survival times from state $F$ to $D$, from $F$ to $R$, and from $R$ to $D$ in the Health Area $k$, $k=1, \ldots, 28$, $T^{(k)}_{FD}$, $T^{(k)}_{FR}$ and $T^{(k)}_{RD}$, respectively are modelled by means of a Bayesian spatial Cox proportional hazards model as proposed in previous section.

\subsection{Posterior distribution}\label{subsec:posterior}

We present the  approximated posterior  distribution  sequentially, first the parameters, then the hyperparameters and finally the random effects.   Table \ref{tab_param} summarizes the approximate posterior marginal  distribution of all parameters  of the model. Estimations of the shape parameters of the baseline risk functions, $\alpha_{FD}$, $\alpha_{FR}$, and $\alpha_{RD}$, indicate decreasing hazards over time, specially for the risks of death without and after refracture. $\alpha_{F\!R}$ is closer to 1 which is the threshold which changes the behaviour of the Weibull hazard functions, from increasing to decreasing. Women and men showed no relevant differences in the risk of recurrent hip fracture (E$(\beta_{FR, Woman} \mid \mathcal D)=0.021$), whereas women showed lower mortality risks as compared to men (E$(\beta_{FD, Woman} \mid \mathcal D)<0$, E$(\beta_{RD, Woman} \mid \mathcal D)<0 $). Age was found as a risk factor for refracture and for death without and after refracture.

\begin{table}[h]
\centering
\caption{Summary of the approximate posterior distribution of the parameters from an illness-death model with a multivariate-Leroux model for random effects (I). Transition-related parameters: shape of Weibull distribution and regression coefficients.}
\begin{tabular}{c|c|cccc}
\hline
 Time &\textrm{Parameter}   & Mean & SD & 2.5\% & 97.5\%   \\
  \hline
 From $F$ to $R$ & $\alpha_{F\!R}$  & 0.921 & 0.016 & 0.891 & 0.953    \\
  &$\lambda_{F\!R}$    & 0.028 & 0.005 & 0.018 & 0.040   \\
  & $\beta_{F\!R, \,Woman} $ & 0.021 & 0.050 & -0.076 & 0.119     \\
  & $\beta_{F\!R, \,Age}$  & 0.024 & 0.003 & 0.018 & 0.030    \\
  \hline
  From $F$ to $D$&$\alpha_{F\!D}$  & 0.776 & 0.005 & 0.766 & 0.786      \\
  &$\lambda_{F\!D}$  & 0.335 & 0.054 & 0.238 & 0.460   \\
  &$\beta_{F\!D, \,Woman}$   & -0.510 & 0.017 & -0.543 & -0.477   \\
  &$\beta_{F\!D, \,Age}$   & 0.070 & 0.001 & 0.068 & 0.073 \\
   \hline
From $R$ to $D$& $\alpha_{R\!D}$  & 0.628 & 0.016 & 0.597 & 0.659  \\
&  $\lambda_{R\!D}$  & 0.593 & 0.131 & 0.374 & 0.897 \\
&  $\beta_{R\!D, \,Woman}$  &  -0.634 & 0.065 & -0.761 & -0.505   \\
 & $\beta_{R\!D, \,Age}$  & 0.049 & 0.005 & 0.040 & 0.059 \\
\hline
\end{tabular}
\label{tab_param}
\end{table}

Figure \ref{baseline} shows the posterior expectation of the baseline hazard function associated to each of the three survival times. Note that  risks of death after recurrent hip fracture are higher than those of death without refracture. Transition intensity from fracture to refracture is notably lower than transitions to death. Note that  baseline hazard functions are indeed the hazard functions for the reference values of predictors: average-aged men from a Health Area with random effect equal to 0. Baseline functions suggest higher hazards during the first year, including the hazard of refracture, despite it cannot be appreciated graphically. It results in a sharper increase in the cumulative incidence of those events, as well as greater increases or decreases in the transition probabilities during the initial follow-up.

\begin{figure}
  \centering
  \includegraphics[width=9cm]{Figures/baseline_hazards.pdf}
  \caption{Posterior mean of the baseline hazard function  for each survival transition: from initial hip fracture to recurrent hip fracture, from initial hip fracture to death without refracture and from recurrent hip fracture to death. Horizontal axis indicates years from initial fracture, for transitions from initial fracture to refracture or death without refracture, whilst years from refracture for the transition from refracture to death.}\label{baseline}
\end{figure}

Table \ref{tab_param_re} presents a summary of the approximate posterior marginal distribution of the hyperparameters of the spatial illness-death model, all them associated to the  variability   between the  transition survival times and within the different Health Areas in the Valencia Region. The estimation of the parameter $\gamma$ from the Leroux model is  $0.841$ thus indicating that the mixture of an independent scenario and an intrinsic CAR model lends toward the second (Figure \ref{gamma}). A 95\% credible interval excludes lower values suggesting a relevant spatial correlation between areas. Correlation parameters between transitions showed posterior distributions not only including 0 but also zero-centered, which indicates irrelevant correlation parameters thus indicating an uncorrelated scenario. The highest value however was estimated for the correlation between death without refracture and death after refracture, $\rho_{(F\!D)(R\!D)}$, showing a slight correlation between both types of mortality. Uncertainty about random effects is given by precision parameters $\tau$. Higher precision estimations indicate lower variability among random effects. Although the magnitude of the three is very similar, ordered from least to most uncertainty we have random effects on transitions of death without refracture, refracture, and death after refracture.

\begin{table}
\centering
\caption{Summary of the approximate posterior distribution of the hyperparameters from the illness-death model estimated with a multivariate-Leroux model for the spatial random effects. $\gamma$ parameter from the Leroux model, precision of the random effects, and correlation between transitions times.}
\begin{tabular}{rrrrr}
  \hline
 \textrm{Parameter}   & Mean & SD & 2.5\% & 97.5\%  \\
  \hline
  $\gamma$ & 0.841 & 0.101 & 0.591 & 0.973 \\
  $\tau_{F\!R}$ & 14.257 & 4.620 & 7.197 & 25.185 \\
  $\tau_{F\!D}$ & 19.896 & 5.595 & 10.915 & 32.737 \\
  $\tau_{R\!D}$ & 11.743 & 4.181 & 5.386 & 21.625 \\
  $\rho_{(F\!R)(F\!D)}$ & -0.044 & 0.181 & -0.388 & 0.315 \\
  $\rho_{(F\!R)(R\!D)}$ & -0.076 & 0.178 & -0.415 & 0.275 \\
  $\rho_{(F\!D)(R\!D)}$ & 0.109 & 0.164 & -0.217 & 0.423 \\
   \hline
\end{tabular}
\label{tab_param_re}
\end{table}

\begin{figure}
  \centering
  \includegraphics[width=8cm]{Figures/gamma_param.pdf}
  \caption{Approximated posterior distribution of the $\gamma$ parameter from an illness-death model with multivariate-Leroux random effects.}\label{gamma}
\end{figure}
Figure \ref{maps_re} displays the posterior mean of the random effects associated to each transition time and Health Area of the Valencia Region. Health Areas coloured red indicate a higher risk of experiencing the event of interest compared to the overall average for all areas. Areas shaded in yellow indicate the opposite. The random effects associated with the three survival times of the illness model from the same Health Area do not always behave the same. We can observe some areas with positive random effects in the three survival times considered, but also some cases where the effects show negative relationships. There are some particular areas with a particular spatial  pattern. This is the case of Requena-Utiel (the most western Health Area) and Denia (located at the cape in the east of the Valencia Region). The first shows lower risk of recurrent hip fracture and higher risks of death without and after refracture. The latter shows the opposite scenario, higher risk of refracture and lower mortality. Both cases illustrate negative association between the risk of refracture and mortality, whilst positive association between both risks of death.

\begin{figure}
  \centering
  \includegraphics[width=15cm]{Figures/Maps_re_multiLeroux_wishart1.pdf}
  \vspace{-2cm}
  \caption{Posterior  mean of the region-specific random effects by Health Area of the Valencia Region, from an illness-death model with multivariate-Leroux random effects.}\label{maps_re}
\end{figure}

\subsection{Outcome measures of the hip fracture process}\label{subsec:measures}

The examination of the raw estimations provided by the posterior distribution $\pi(\boldsymbol{\theta}, \boldsymbol{\psi} \mid \mathcal D)$ contains full information about the differences in the risk of each outcome. Nevertheless, it provides unclear evidence about which will be the prognostic for a patient with a hip fracture in each particular Health Area or what would be the general evolution of the survival time transitions in the target population. Information regarding time-evolution from the illness-death setting  and the variation in the risk among Areas are indeed combined in posterior distributions for cumulative incidences and for transition probabilities.

The cumulative incidence of a hip refracture at time $t$ can be interpreted as the probability of having a hip fracture at a time  before $t$ without having died before that time, as death plays a major role censoring refracture. Note that  higher risk of death leads to the observation of fewer refractures. Therefore, two Areas with the same risk of refracture could show different incidences of refracture depending on the risk of death. Figure \ref{maps_CI} shows the posterior mean of the cumulative incidence of refracture for  80-year-old women and men  in the different Health Areas of the Valencia Region at $t=1,2, \ldots,5$ years after a first hip fracture. In broad terms, higher incidences of recurrent hip fracture are estimated for those regions with higher risk of refracture as it is expected. Those differences become more visible after some years from the initial fracture. The Health Area of Requena-Utiel (the most western region) shows a particular low incidence despite its not so low risk, which can be related to being the region with the highest risk of death without refracture. Men show lower incidences of refracture due to their increased risk of death, as we found no differences in the risk of refracture compared with women. Men reach the same incidence values than women with a delay of 1-2 years approximately.

\begin{figure}
  \centering
  \includegraphics[width=14cm]{Figures/CI_refrac_maps.pdf}
  \caption{Posterior mean of the cumulative incidence of refracture in 80-year-old women and men at $t=1,2, \ldots,5$ years after a first fracture by Health Area.}\label{maps_CI}
\end{figure}

\begin{figure}
  \centering
  \includegraphics[width=14cm]{Figures/pFR_maps.pdf}
  \caption{Posterior mean of the transition probability from fracture to refracture ($p_{F\!R}$) in 80-year-old women and men at $t=1,2, \ldots,5$ years after a first fracture by Health Area.}\label{maps_pFR}
\end{figure}

\begin{figure}
  \centering
  \includegraphics[width=14cm]{Figures/pFD_maps.pdf}
  \caption{Posterior mean of the total-death probability ($p_{F\!D}$) in 80-year-old women and men at $t=1,2, \ldots,5$ years after a first fracture by Health Area.}\label{maps_pFD}
\end{figure}

\begin{figure}
  \centering
  \includegraphics[width=14cm]{Figures/pRD_maps.pdf}
  \caption{Posterior mean of the probability of death after refracture ($p_{R\!D}$) in 80-year-old women and men at $t=1,2, \ldots,5$ years after a refracture by Health Area.}\label{maps_pRD}
\end{figure}

Regarding  transition probabilities from fracture to refracture (Figure \ref{maps_pFR}), they are  also higher for women, as they are more likely to experience a refracture than men. It shows an increasing trend during 2 and 4 years from the initial fracture for men and women, respectively. After this time, the probability of being refractured and alive remains stable, as the number of patients at risk of refracture decreases and the mortality after refracture offsets the number of new refractures.

Mortality is higher in men for both, total mortality and after recurrent hip fracture only. Women approximately reach at 4 years the same mortality than men at 2 years (Figure \ref{maps_pFD}). This difference is even higher for death after refracture. Women reach at 5 years after refracture the same mortality rates than men only 2 years after refracture (Figure \ref{maps_pRD}).

The number of patients who die after a refracture represent a low fraction of the total mortality. Cumulative incidence of refracture indicates that less than 10\% of women experience a refracture 5 years the initial fracture (even lower in men). Thus, the spatial pattern of the total-death probability (Figure \ref{maps_pFD}) is similar to that showed by the random effects on the risk of death without refracture (Figure \ref{maps_re}).

Probability of death is higher for those patients with a recurrent hip fracture. Mortality one year after refracture is similar to that expected two years after the initial fracture. Its spatial pattern is also different with respect to that shown by total-mortality, and is identical to that shown by the respective random effects on transition from refracture to death in Figure \ref{maps_re}. This is due to the fact that the probability of death after refracture is the only one which depends exclusively on one transition intensity, in particular, the transition intensity from refracture to death.

\section{Discussion}\label{sec:discu}

The potential and usefulness of illness-death models are highly increased after combining it with spatial information modelled by multivariate random effects associated to a set of spatial units. Differences among regions can be studied under this joint framework, in addition to progression of individuals over time. Despite correlation between transitions was not observed to be relevant in our real-world study, the possibility of modelling it jointly with the spatial correlation from a model based on a neighbourhood structure, such as the Leroux model, defines an extensive number of options depending on the needs of the clinical framework. Moreover, one might be interested in using more complex multi-state models with additional states and transitions. It would be analogous as the concept throughout our work regarding how random effects are included in transition intensities is quite general and thus, it is not restricted to illness-death models.

The usage of the integrated nested Laplace approximation (INLA) is another strong point of our work. Computational time is highly reduced compared to MCMC methods, and the introduction of gaussian random effects in the regression terms is something natural in INLA. However, it is not a popular choice when assessing multi-state models yet, although this in the near future might change given its benefits. Multi-state modelling in INLA remains unexplored and further work regarding this issue would be of high interest.

Finally, assessing transition probabilities and cumulative incidences instead of only analysing the estimations of random effects on transition intensities provides a deeper understanding of the clinical or epidemiological problem. In fact, risk assessment alone seldom provides predictive information regarding the prognostic of a patient given some specific characteristics. Meanwhile, trajectories expressed in terms of probabilities are dynamic and interpretable outcomes which are essential to reach an individualized care and that can be determinant to clinicians and policy makers.

\subsection*{Declaration of competing interests}
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

\subsection*{Funding}
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been funded by Instituto de Salud Carlos III (ISCIII) through the projects [PI14/00993, PI18/01675], ``RD16/0001/0011 - Red Temática de Servicios de Salud Orientados a Enfermedades Crónicas (REDISSEC)", and ``RD21/0016/0006 - Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud (RICAPPS)", and co-funded by the European Union. FLC was funded by Instituto de Salud Carlos III (ISCIII) [grant number FI19/00190], and co-funded by the European Union. CA was partially funded by Ministerio de Ciencia e Innovaci\'on (MCI, Spain) [grant number PID2019-106341GB-I00].

redpajama_set_name: RedPajamaArXiv


This study day is aimed at radiographers from both diagnostic and therapeutic disciplines, but is also suitable for all interested healthcare professionals & researchers (both quantitative and qualitative). The day will cover many topics including Barriers & Facilitators to Participation in Research, Beatson Cancer Charity – Our Contribution to Research at The Beatson and have a Special Guest speaker - Sheila Hassan, President of The Society of Radiographers.
Lynne Omar, Research Radiographer, Omar Medical Ltd.
If you are interested in attending this Beatson Bootcamp, please email Lorraine Whyte here.


text:
Paul Castellano's House
Staten Island, New York (NY), US
Paul Castellano, the alleged Mafia godfather, who was according to legend killed on orders from rival John Gotti at Sparks Steak House in December 1985, lived here. Born, Constantino "Paul" Castellano, Paul was also known as "The Howard Hughes of the Mob" and "Big Paulie" (or "PC" to his family). As an American crime boss he succeeded Carlo Gambino as head of the Gambino crime family. Castellano was killed in an unsanctioned assassination on December 16, 1985, ordered by John Gotti, who subsequently became boss. Baddabing, badaboom just like that Gotti had usurped the big man himself, taking control of the family.
Paul Castellano had an estimated net worth equal to $20 million at the height of his power in the early 80s which is the same as around $50 million today, and this estimate does not include his shadow assets so often hoarded by big time crime bosses. In 1985 he was assassinated in an unsanctioned hit by John Gotti. This story has been featured in several films including by Richard C. Sarafian in the 1996 HBO movie Gotti, Abe Vigoda on the NBC TV movie Witness to the Mob in 1998, Sam Coppola in the 2001 TV movie The Big Heist, and Chazz Palminteri on the 2001 TNT TV movie Boss of Bosses. Paul Castellano passed away on December 16, 1985 at 70 years old from multiple gunshot wounds.
In his book, Sammy "The Bull" Gravano would later admit that in a typical year mob bosses like Paul and John Gotti would earn $10-15 million every year but would obscure these assets, sometimes hiding cash or using offshore accounts. In 1981, Castellano spent over a million dollars to build an extremely lavish 17-room mansion on Staten Island which later became known as The White House. The house has an Olympic-sized swimming pool, English garden and top-of-the-line Carrara marble throughout. In order to pay for his lavish lifestyle, Castellano began demanding 15% cuts of his capos' earnings, up from the standard 10% a decision that proved fatal. At the time many of his capos were struggling to make ends meet, so Paul's lavish mansion plus the increase in monthly tribute sewed the seeds that would eventually cause him to be assassinated.
Homes - Celebrity - Misc
By: BermudaBreeze
Anthony Lenza's House
Frank Cali's House (Deceased)
Drug kingpin Frank Matthews' House (former)
Billiou–Stillwell–Perine House
Ernest Flagg's House (Former)
Robin Quivers' House (former)
Corleone Mansion
Godfather Mansion for Sale
Abandoned Highway
Vanderbilt Family Mausoleum


Soft enamel. Black pin with rubber clutch.
Measures (h) 26 x (w) 28 mm.
Exclusive collection from Pins & Pongs and Nuglybird.
Andrew Seetoh also known as Nuglybird, is a designer, a father and a friend. With his keen interest in people, he wants to creates experiences that will hopefully be meaningful moments.


text:
What is the G20?
Wed, Jul 05, 2017 - 2:08 PM
[BERLIN] The Group of 20 leading industrialised and emerging powers hold annual summits on economic governance, with wars and crises often overtaking the official agenda.
Grouping the United States and other G7 members with giants such as China, India and Russia, it represents two thirds of the world population, three quarters of world trade and about 80 per cent of economic output.
The other members are Argentina, Australia, Brazil, Britain, Canada, France, Germany, Indonesia, Italy, Japan, Mexico, Saudi Arabia, South Africa, South Korea, Turkey and the European Union.
The G20, with its ministerial-level beginnings in 1999, first met for a summit in 2008 in Washington to discuss ways to achieve balanced and sustainable world economic growth.
Critics charge that the "self-appointed" club undermines the role of the international bodies, such the United Nations, International Monetary Fund (IMF) and World Bank.
SEE ALSO: Are China's relations with Japan entering a new era?
The grouping has often been a magnet for protesters, including at this year's meet in Hamburg, Germany.
At the 2010 summit in Toronto, thousands of anti-capitalist protesters clashed with Canadian police, leaving dozens injured on both sides and sparking mass arrests.
The list of G20 members does not strictly reflect the world's 20 biggest economies. Some non-members such as Switzerland have bigger economies than, for example, member Argentina.
The leaders mostly meet behind closed doors and lack the power to enforce binding decisions or rules.
The heads of global and regional bodies are invited to the summits - among them the chiefs of the UN, IMF, World Bank, WTO, African Union, Apec and Southeast Asia's Asean.
Rotating G20 summits also invite guest nations, including "permanent guest" Spain and, this year, partner countries the Netherlands, Norway and Singapore.
Next year's G20 host will be Argentina.


A natural account of dependence in terms of modality and existence is that one thing x will depend on another thing y just in case it is necessary that y exists if x exists (or in the symbolism of modal logic, □(Ex→Ey).
He is going to criticise this view (which he traces back to Aristotle and Husserl). It immediately seems possible that there might be counterexamples. x might depend on y, but not necessarily depend on y. Necessities may not produce dependence.


text:
Q: RMI UnmarshalException As I am coding RMI system, I ran into problem with exception at client side
Exception in thread "main" java.rmi.UnmarshalException: error unmarshalling return; nested exception is:
    java.lang.ClassNotFoundException: com.cs.entity.LectureCourse
    at sun.rmi.server.UnicastRef.invoke(UnicastRef.java:178)
    at java.rmi.server.RemoteObjectInvocationHandler.invokeRemoteMethod(RemoteObjectInvocationHandler.java:178)
    at java.rmi.server.RemoteObjectInvocationHandler.invoke(RemoteObjectInvocationHandler.java:132)
    at $Proxy1.getNamedClass(Unknown Source)
    at com.cs.test.ClientCRUD.main(ClientCRUD.java:40)

as com.cs.entity.LectureCourse is a class that should be downloaded by client at runtime using RMI codebase feature I have checked to make sure

*

*the codebase at server VM argument is set correctly and

*the client VM option -Djava.security.policy= is also correctly set.

*-Djava.rmi.server.hostname is also used to make sure registry get things correctly

When I ran client and server at same node it works but exception is displayed at client when separate physical machine holds server and client separately.
Does anyone saw this problem before? Please help...
@EJP
Following is the code I used to start RMI registry (inside tomcat 7 contextInitialized)
        final String value = "http://192.168.1.11:8080/csl/";
        System.out.println(value);
        System.setProperty("java.rmi.server.codebase", value);

        registry = LocateRegistry.createRegistry(8899)

and this is the code I used to lookup registry in Client
    System.setSecurityManager(new RMISecurityManager());

    final String HOST = "192.168.1.11";
    final int port = 8899;
    final Registry registry = LocateRegistry.getRegistry(HOST, port);

I think they work correctly but the error frustrates me a lot
I think I find where the problem is. as mentioned before, RMI registry was started at Tomcat 7 contextInitialized method which should be invoked at starting up of the web project. it turns out the same piece of code for RMI server works pretty well if it is in an independent main Application. according to my several tests in different RMI server configurations, I concluded that

*

*Tomcat 7 contextInitialized method does something different then ordinary java main program

*I am not sure but codebase seems work only if the URL(I am using webserver) indicates to a specific jar file rather then directory. (this could be my misunderstanding of the tutorial or something like that)

So the question turns out to be why Tomcat 7 does not start RMI registry differently? I am still working on that

A:
Tomcat 7 contextInitialized method does something different then ordinary java main program

Of course it does. But without knowing what specific expectations you have, it is impossible to comment further.

I am not sure but codebase seems work only if the URL(I am using webserver) indicates to a specific jar file rather then directory. (this could be my misunderstanding of the tutorial or something like that)

It works a lot better because there is only one download, and you don't have to sprinkle .class files all over your codebase server: just deploy one or more JAR files.
If you're expecting RMI to load a JAR file from a directory without being specifically told to do so, it won't. If you don't name a JAR file it will look for .class files.

redpajama_set_name: RedPajamaStackExchange


One part of the Interstim therapy that takes getting used to is the fact that you have a little device inside your body that will set off security systems and that the device can potentially be ruined by some security systems. In knowing this, Interstim provides all implanted patients with a nice little medical card that states that "I have a device that may set off your airport/security system." Since getting my device implanted, I have had to use this medical card in situations that require me to pass through security systems. Some security guards at these security checkpoints have been more than professional, some downright rude.
1. Present medical card, explain to the guard that you have an implanted medical device that will trigger security and could be damaged by certain systems.
2. Ask for a pat down versus going through the security system.
2. Ask for the handheld scanner (no magnets). I am not even sure if you can go through those huge walk through devices safely. I never have since being implanted.
3. Place your hand over the implant. Advise the security guard that scanning directly on the device will wreck the device, ask them to scan around.
4. After going through security, turn the device back on.
I am hopeful that as the Interstim surgery and device become more prominently known that the training of security personnel in accommodations for security systems while increase.


The socio-economic discourse of not only our state, but also country is shaped by the thoughts, ideas and actions of our youth. It is this young generation which shoulders the responsibility of finding novel solutions to various problems afflicting our society. Issues such as agrarian crisis, growing economic disparity, gender inequality and continuous climatic deterioration, if remain untackled, would be the biggest cause of worry for our generations to come.
We at ACT recognise this dilemma and believe that the only solution is to facilitate an increased participation of the youth in the democratic discourse.
Thus, using cutting edge technology, Yuva-Maha-Samvaad is our pioneering attempt to provide the young citizens a structured platform for voicing their opinions and raising critical questions pertaining to various problems existing in our society. Through Yuva-Maha-Samvaad, we intend to enhance the youth's participation in nation-building by facilitating dialogues and opening channels of discussions with key stakeholders and decision makers.
Through these discussions we aim to create and provide a MahaYuva VISION DOCUMENT to the government to act upon. This document will be formed by incorporating the suggestions received from the students before the event and during discussion with CM. This will enable the decision makers to understand the aspirations of the younger generation and consequently fulfil them by implementing relevant solutions and policy level changes.
Under Yuva-Maha-Samvaad, to encourage an increased contribution of youngsters in matters of governance and decision making, the honourable CM of Maharashtra Shri Devendra Fadnavis, through digital interactions, would be connecting, conversing and brainstorming with more than 5 lakh students across 1000+ colleges in the state.
Theme: WINGS OF FIRE: EMPLOYMENT OPPORTUNITIES FOR A NEW MAHARASHTRA.
To foster the spirit of democratic participation amongst the youth of Maharashtra, ACT introduced a unique initiative called Maha-Digital-Samvaad, a digital platform for directly communicating with the students across different colleges in various cities of the state.
Over the last two months, our team successfully executed three such sessions of Maha-Digital-Samvaad, the biggest amongst them taking place on January 25 this year, commemorating Republic Day. This episode of the Maha-Digital- Samvaad witnessed Adv. Dhanraj Vanjari, (ex ACP Mumbai) interacting with hundreds of students at more than 500 locations situated across different colleges of Maharashtra.
We believe, this platform called Digital Samvaad should be leveraged to its maximum advantage for providing the college students of Maharashtra a chance to raise questions and voice their opinions on matters of governance and decision making. Thus, Yuva-Maha-Samvaad has been initiated which is directed towards achieving this goal.


text:
Brad Pitt, Angelina Jolie "Calling Off Divorce" And "Rebuilding Family"?
By Gossip Cop Staff	| 12:06 pm, June 2, 2018
Are Brad Pitt and Angelina Jolie "calling off" their divorce? A new report claims the estranged spouses have reconciled and are "rebuilding" their family. Gossip Cop, however, can debunk the inaccurate story.
A headline in the latest edition of Globe exclaims, "Brad & Angie Hot Again!" According to this "world exclusive," Pitt and Jolie have "mended fences in a sizzling reunion between the sheets." The supermarket tabloid alleges the real reason their divorce hasn't been finalized is because the stars "couldn't bring themselves to sign the papers." Instead, contends the magazine, "the warring exes had a steamy, clothes-ripping reconciliation."
Notably, it's not said just when this alleged reunion occurred. But a so-called "insider" is quoted as saying, "It was exciting and explosive, and it settled old scores and made them realize they weren't done with each other." Was this supposed source in the room with them? Perhaps hiding under the bed? Or in the closet? In any case, the purported tipster goes on to assert, "They care for each other and they want to work things out."
It's even alleged Pitt dropped a "bombshell admission" to an unnamed pal, in which he declared, "I love my wife!" The outlet and its "insider" further claim Jolie has given him a key to her home so he can "come and go as he pleases," and that they've had "several hookups in the last few weeks." The questionable snitch adds, "It's safe to say the divorce is off!"
The story is filled out with claims about a future "second commitment ceremony." It's also said Pitt will have "the kids over the summer," and he's "making plans for visits to London," where Jolie will be shooting Maleficent 2." But this tale about how they've "decided to give [their] love another try" is entirely wrong and filled with falsehoods.
All six kids will actually be spending the summer with Jolie as she films the Maleficent sequel in the UK, as first reported by People. Meanwhile, Pitt is committed to being in Los Angeles so he can shoot Quentin Tarantino's Once Upon A Time In Hollywood. It's also impossible for them to have had "hookups" over the last few weeks. Pitt was seen in LA last week, but Jolie has been with the children in England. In fact, she's been in London since at least May 17, so she certainly hasn't been having rendezvous with Pitt in California.
And opposed to "calling off" their divorce, the exes are still moving forward with a permanent split. Their plans to dissolve the marriage, however, have been held up because Pitt and Jolie are still at odds over a custody agreement. In fact, E! Online recently described her as "furious" over the situation. That's a far cry from getting back together.
Conclusion: The Globe claims Jolie and Pitt are "calling off" their divorce, but they're actually still negotiating custody arrangements. While the tabloid alleges the former couple has recently been engaging in "hookups," they've really been on separate continents. The magazine also wrongly asserts the kids are spending the summer in Los Angeles when the reputable People has already reported they'll be in London. As a result, Gossip Cop has determined this report is completely false.
Angelina Jolie Brad Pitt Fact Check The Globe
Russian, Ale. "Angelina Jolie Films Maleficent 2 in London - with All Six Kids Going to School on Set!" People, 31 May 2018.
Sinha-Roy, Piya. "Leonardo DiCaprio & Quentin Tarantino Talk 'Once Upon a Time in Hollywood'." Entertainment Weekly, 24 Apr. 2018.
"Brad Pitt Keeps on Smiling in LA amid Custody Battle Drama with Ex Angelina Jolie." Daily Mail Online, 25 May 2018
Waddell, Lily. "Angelina Jolie Enjoys Doting on Children Knox, 9, and Shiloh, 11, at Legoland Windsor amid Custody Battle with Brad Pitt." Daily Mail Online, 26 May 2018.
Dupre, Elyse. "Brad & Angelina Still at Odds Over Custody of Kids." E! Online, 24 May 2018.
Brad Pitt, Jennifer Aniston Movie Rumors
Jennifer Aniston Upset About Brad Pitt Seeing "Angelina Jolie Lookalike"?
Jennifer Aniston To Set "Record Straight" About Brad Pitt, Justin Theroux?
Angelina Jolie Worried About Brad Pitt Using Her Past Drug Scandal To Win Custody Of Kids?
Brad Pitt Prevented George Clooney From Getting 'Once Upon A Time In Hollywood' Role?
Truth About Leonardo DiCaprio Helping Brad Pitt Find Love


text:
Q: Behind the scenes activity on python prompt load-up What happens behind the scenes when we invoke the python prompt?
I am more interested in understanding how do the methods/functions like print, import and the likes load up in the interpreter?
EDIT: Some clarification on my question:
When we type python on our unix prompt/windows console and Hit enter, what are the libraries that get loaded. My specific interest is as to how the keywords like print and import are made available to the user.

A: Like you , I am very interested by the underlying mechanisms of Python.
I think you'll love this series:
http://tech.blog.aknin.name/category/my-projects/pythons-innards/

A: There are too many levels to that question. Here's a very rough sketch.
There's the whole C-level interpreter initialization, a bunch of in-the-interpreter tasks, reading the environment and options, customization of the interpreter session. All that defines what you see when you run python.
I know there's a good description of the whole process somewhere.


text:
Türkic Genetics
Contents Pazyryk Genetics Contents Amerin Genetics Sources
Tamgas
Language Genetics
Wikipedia Ogur and Oguz
Klyosov A. Türkic DNA genealogy
Alinei M. Kurgan Culture Mesolith Datelines
Ephthalite Dateline Alan Dateline
Scytho-Iranian theory Ossetian Genetics Fake Scythian language Real Scythian Word List Zelenchuk Inscription
Etruscan Genetics Eastern Huns Genetics Fake Scythian Word List
Cavalli-Sforza
In most cases I have a record where the page came from. It is possible that the source site moved or does not exist any more. All of the maps are accessible from their original source, and no credit is clamed here. This map comes from the Internet, http://home.att.net/~eugenics/GeneMap.htm, and is duplicated here because it is a rare (up until now) illustration, and it puts in a proper perspective the anthropological findings of the last century, helping to explain why there is so much sincere confusion and intentional misrepresentation in the attribution of the archeological monuments. In a way it is unique for today's science in placing the North Türkic breed on the human genetic map. There could be different maps, using different selection of the criteria, different coordinate system, or differing scale, and they would carry a somewhat different message. They will enlighten the different genetic faucets of the similarities and differences inherent to the group identified asTürkic, and its various components. We may not share the notions of the authors of the page, and admit that there may be varying interpretations of the statistical facts, but the purpose of the illustration is to show the genetic relationship of the Türkic people within the human diversity.
http://www.balzan.it/english/pb1999/cavalli/paper.htm
http://home.att.net/~eugenics/GeneMap.htm
http://www.turkgenealogy.com/turkhistory/history_of_Turksurnames.htm
Human Genome Diversity Project
Following is a graphic representation of the genetic differences between 42 population groups. This data is fromThe History and Geography of Human Genes by Cavalli-Sforza, Menozzi and Piazza. Gypsies, Jews, Samaritans and a few other population groups have not been considered because of their complex histories, but research is progressing on their racial make-ups. Also note that there are four major races, not three, and that northeastern Asians are closer genetically to Caucasians than they are to southeast Asians. We unfortunately tend to lump all Asians together, but they are genetically very different. Northeast Asians tend to have superior intelligence, given that they survived the Wurm glaciation about 10,000 years ago, similar to northwest Caucasians or Aryans. Below the following figure descriptions of some of the cryptic racial categories as used by Cavalli-Sforza. Also note that if you are interested in Caucasians, they are again broken down into 26 racial groups (See the above book). Theoretically, racial groups can be broken down all the way to the individual (except for identical twins) to make up about 6 billion racial groups. See Jensen, chapter 12 at this web site for an explanation of racial groups and what they mean.
Mongol: Nomadic people of Mongolia.
Tibetan: People of Tibet.
Eskimo: Peoples inhabiting the Arctic coastal regions of North America, Geenland and northeast Siberia.
Na-Dene: North American Indian language.
Uralic: Language family that comprises the Finno-Uric and Samoyedic subfamilies [named after the Ural mountains].
North Turkic: Turkey.
Ainu: A separate indigenous people that live in Japan.
South Dravidian: A language spoken by peoples in southern India and northern Sri Lanka.
Chukchi: Northeast Siberia
Lapp: Nomadic herding people in northern Scandinavian countries.
Basque: A people inhabiting north central Spain (said to be the most homogeneous racial group found by Cavalli-Sforza).
Sardinian: Sardinia, an island of Italy.
Thai: A people of Thailand.
Polynesian: A division of Oceania including scattered islands of the central and southern Pacific Ocean roughly between New Zealand, Hawaii, and Easter Island.
Melanesian: Islands northeast of Australia and south of the equator.
Khmer: A people of Cambodia.
Micronesian: A division of Oceania in the western Pacific Ocean comprising islands east of the Philippines and north of the equator.
Malaysian: Southern Malay Peninsula and the northern part of the island of Borneo.
Berber: North Africa.
San: Nomadic hunting people of southwest Africa.
Mbuti: African pygmies.
Bantu: linguistically related central and southern Africans.
Nilo-Saharan: linguistically related sub-Saharan Africans from Nigeria to Kenya
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