text: Meta Battle Subway PokeBase - Pokemon Q&A Login Register All Activity Questions Unanswered Tags Users Ask a Question Rules Chat Does switching out after using Wish heal the Pokémon you switch to? If so, how much HP is restored? 0 votes 2,765 views This is something I want to know. Lets say Vaporeon (base 130 HP) used Wish and then switched out to Shuckle. Would Shuckle's HP be restored, and if yes, would it be restored by half is max or by more (because Shuckle's max HP is lower)? healing hp switching asked Apr 27, 2014 by Pimplup Please log in or register to add a comment. Please log in or register to answer this question. 2 Answers 1 vote Best answer seconding everything pinsir said, but I'm not sure what "always does" means. wish will always heal half of the wish users hp. meaning that if you have a vaporeon with max 130 hp and switch to shuckle, the shuckle will get 65 hp restored. answered Apr 27, 2014 by Dr Dude selected Apr 27, 2014 by Pimplup Wish always heals half of the "HP bar" no matter what commented Apr 27, 2014 by Mosmic Power Oh, thanks, #Mega Pinsir. commented Apr 27, 2014 by Pimplup Your welcome commented Apr 27, 2014 by Mosmic Power "Generation V Wish now heals for half of the user's maximum HP rather than the recipient's." Source: http://bulbapedia.bulbagarden.net/wiki/Wish It will be half the life bar of the target for Gen IV or previous. commented Apr 28, 2014 by Malachite Oh, thanks, Malachite, for clearing that up. Well, that answers my question! commented Apr 28, 2014 by Pimplup Well, that was what i tried to explain in the example :P, but guess it was a bit messy commented Apr 29, 2014 by Dr Dude Please log in or register to add a comment. 1 vote Yes, shuckle will be healed as long as it has some damage. If shuckle is fully healed(in hp) then wish will fail. Also, if you use wish then your Pokemon faints, then wish won't activate. Also, wish will heal the same amount of hp as it always does. Hope this helps! answered Apr 27, 2014 by Mosmic Power Please log in or register to add a comment. Related questions If you Wish and switch out, do you have to attack first before you are healed or do you get healed immediately? How much does regenerator heal when the pokemon is switched out? How Much HP Does Cheek Pouch Restore? Do leftovers heal 1/12th total HP, or 1/12th current HP? What moves slowly heal HP? Would I be able to heal when using Wish, while getting inflicted with Heal Block the very turn after? Does Core Minior change back to Shell Minior if its HP is Restored? Does the ability Unburden work after you switch that Pokemon out? Useful Meta Posts Vote for Sun/Moon moveset questions How to change your username How to add a user avatar Why was my post hidden? The points system Question etiquette Flags vs Downvotes Anime FAQ


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R4 3DS cards are a godsend especially for people who want to get the most out of their Nintendo 3DS portable gaming consoles.
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subset: commoncrawl


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This is how 4 referees rated Alexandra:
|Hourly Rate
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I am a 40 year old mother of three who went in to teaching as a mature student seven years ago. My main reason for the change in career was because after having my own children I was fascinated with their different learning styles. I found when teaching them at home what worked with one child did not for another. Through studying education I realised that there are many different ways children learn, kinesthetic, auditory and visual being the main ones and that it is important that they are given opportunities to learn in the best way for them.
I qualified with a PGCE in KS1 and KS2 from Hertfordshire University in 2005 and since then I have worked as a Primary school teacher with experience in KS1 but mainly KS2. I have been at the same school for 5 years now and have consistently been observed and rated an 'Outstanding' teacher. I have also qualified as a 1:1 tutor and since 2007 have tutored children privately and through the school I work at with great success.
Initially my first conversation is with the parents to see what they want to achieve from the tuition, sometimes it is passing an exam or increasing their level and sometimes it is confidence in a subject (which usually produces the other two outcomes). Then on the initial session I ask the child what they think they are good at in the subject and what they would like to be better at. From these conversations I set up three learning objectives and a success criterial for each objective. I am very happy for a parent to join the first session to be involved in these learning objectives. Over ten weeks we concentrate on these learning objectives with the hope that after that period of time the child feels confident in these areas of the subject and a marked improvement can be seen. I then review with tha parent and child, additional learning objectives can be set up or if all that the parent and child wanted to achieve has been achieved then the sessions can end. Lessons will usually follow the same format as a lesson in school a fun and interactive starter, main teaching which will be focused on a childs individual learning style and then a plenary which will pull together what has been learnt and check the child's understanding.
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|Message view
|« Date » · « Thread »
|« Date » · « Thread »
|"Graham Allwood" <graham.allw...@eborsolutions.com>
|RE: Multiple logs
|Fri, 03 Dec 2004 11:12:20 GMT
I don't know if this helps much but do you know that the thread id can be included in the log output - using the patternlayout component anyway. If doesn't give you separate output but I suppose you log to a file you could then analyze it further in Excel. Regards Graham _____ From: GROSPELIER David [mailto:firstname.lastname@example.com] Sent: 03 December 2004 10:59 To: email@example.com Subject: Multiple logs Hi, I want to generate a log file for every thread in my application. My application is multithread and I want to know which thread does which action. Today, I've only one log file but the log entries are mixed and can't find which thread is concerned. Any help is appreciated! Thanks. David Grospelier Chef de projets Sword - www.sword.fr
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Thanks and goodbye for now. We're done for another day. Thanks for joining us for Morning Express, if you have any news tips or events you'd like to share for tomorrow or later in the week, please email Angus at email@example.com. See you back here tomorrow!
Fires: the new fire we reported earlier near Castlemaine at Barkers Creek is now under control and there is no threat to communities, according to the CFA. Full alert listings are here
Trains: there's reports of a delay on the Alamein, Belgrave and Lilydale lines of up to 10 minutes due to a signalling issue at Burnley
Traffic: here's another picture from this morning's truck crash at Kilmore, where a B-double truck rolled over and blocked the main (corrected) access to Kilmore, dumping a load of scrap metal everywhere.
A B-double has rolled dropping its load of scrap metal blocking the main road out of the town of Kilmore. pic.twitter.com/KoRWSTQm6m — Victoria Police (@VictoriaPolice) February 11, 2014
This just in from 3AW's Neil Mitchell regarding rebel MP Geoff Shaw, Member for Frankston
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EXCLUSIVE: The video @3AWNeilMitchell says raises questions about Geoff Shaw's evidence http://t.co/pKJuN9hJ9e pic.twitter.com/IfW21dL5oG — 3AW Melbourne (@3AW693) February 11, 2014
Free talk: Singer/songwriter Mark Seymour - the frontman for Hunters & Collectors – is giving a free talk about his career, the music industry and risk, creativity and the art of songwriting. He'll also perform three songs (we're guessing "Thrown Your Arms Around Me" will be one of them). It's at 5.30pm to 6.30pm at RMIT University, Building 80, 427-433 Swanston Street, Level 2, Room 7. Bookings are essential on 9925 1717 (and ask them for directions while you're at it). More details
Regional trains: some V/Line trains to Eaglehawk and Bendigo will be replaced by coaches today due to fire damage on the line. If you're travelling towards Bendigo or east to Gipopsland keep an eye on the V/Line site here
OK, we hear you... (see tweet) no more smoky photos for today. (But we can't make any promises about tomorrow.)
Yes, it's smokey everwhere and sunrise is red everywhere. Don't need pics showing me what everyone is seeing. — Weekly editor (@Weeklyeditor) February 11, 2014
Traffic: Freeways are looking OK, with Eastlink and Mornington Peninsula Link good, the Monash, the Princes and the Calder moderate inbound and the West Gate Freeway running solidly but flowing after the earlier breakdown on the bridge outbound, which has been cleared.
Foodie class: Tonight, Anthony Kumnick from Greenvale Farm and Micah Hewitt from Hudsons Meat will deconstruct a whole pig and share their tips on how to prepare it in their Hands On Pork Class – 6.30pm at Hobba, 428 Malvern Road, bookings through 0417 669 724.
Back to top
Here's the full story on that dramatic roadside birth early this morning in Dandenong, when police got a bit of a surprise after a (low-speed) pursuit. Caroline Zielinkski reports
@StellaSpoons @theage Given the circumstances, a very clever name for the baby is in order... — Rob Stahl (@Robtrinity) February 11, 2014
Traffic: that breakdown outbound on the West Gate Bridge has been reported as cleared, but expect some delays as traffic gets moving again.
What the Morwell fires looked like through night-vision goggles, via Ambulance Victoria's helicopter paramedics.
Our helicopter MICA flight paramedics use night vision goggles.This pic of the Morwell fire was taken through them pic.twitter.com/1rlJuCIVuL — Ambulance Victoria (@AmbulanceVic) February 11, 2014
Another eerie bushfire sunrise shot (via Instagram)...
Top police officer nabbed: 3AW's breakfast crew report that former Victoria Police Chief Commissioner Christine Nixon was caught by police yesterday driving an unregistered car.
Nixon, of Blairgowrie, was stopped on the Mornington Peninsula Freeway in Rosebud after her car was pinged by automatic number plate recognition and has been fined $722.
3AW reporter Yvette Gray says Nixon has confirmed she was the police officer in question, saying it was an "oversight".
The story initially broke after a caller rang into 3AW's Rumor File segment.
Police would not confirm details except to say: "Police were operating an automatic number plate recognition site on the Mornington Peninsula Fwy at Rosebud yesterday afternoon, and around 12pm a 60-year-old Blairgowrie woman was detected driving an unregistered vehicle. The vehicle was intercepted and she was issued with a fine of $722."
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Traffic: Very poor visibility on the roads around Kilmore and Wallan (where that truck rolled earlier today)...
@mmmhotbreakfast with visibility or lack there of on the hume near kilmore & wallan, please everyone drive carefully pic.twitter.com/1FAZGKeZcO — Dani Kelly (@ThePrincessDani) February 11, 2014
Rumour: 3AW's Rumour File is reporting a "former high level cop" was picked up driving an unregistered car yesterday. We'll keep you posted...
Traffic: we're seeing reports of a plantation fire beside the Calder Freeway between Faraday and Harcourt (not far from Castlemaine). If you know more email us at firstname.lastname@example.com or tweet us at @theage
Traffic: Caroline Zielinski has some more on the B-double rollover at Kilmore, which has blocked the only road into the fire-affected town - here's her story
B double rolled at Kilmore has blocked last remaining road out of town @thetodayshow #vicfires pic.twitter.com/VH9pwpOtM9 — Justine Mackenzie (@justinemack9) February 11, 2014
Only in Melbourne: Coffee expert Matt Holden tackles one of those "only in Melbourne" arguments – what's the difference between a flat white and a café latte? The most obvious one is that a flat white is served in a cup, and a latte in a glass (duh). But is that it? Click through to his column hereBack to top
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As citizens of Madison, we are writing this letter to urge Madisonians to consider seriously the impressive record of (Democratic candidate) Astri Baillie as they vote for members of the Borough Council. Her character, experience, energy and vision make her the best candidate for the position. In our many years as voters who have never missed an election, we have come to know that most candidates for council are people who care for the common good . To appreciate this, one has to go no farther than read the fine statements of this year's candidates in the Oct. 11 issue of the Madison Eagle. Nevertheless, we firmly believe that Astri stands out among the candidates. She is truly exceptional and she will have a very strong impact as a new council member. She has a rich record of community service: president of the local chapter of the League of Women Voters; member and president of the Zoning Board of Adjustment; trustee of the Museum of Early Trades and Crafts; member of the Domes tic Violence Crisis Response Team. As a lifelong resident of Madison, she knows the issues in depth, and she proposes solid non-partisan approaches.
We met Astri and John, her husband, through our children, who are classmates wit h two of their children. We have had the opportunity to see them as parents, as friends, and as citizens. We are constantly impressed with Astri's love for the Madison community, her understanding of the issues, her unselfish generosity, her energy and her contagious enthusiasm.
We are glad to see a great number of Madisonians supporting her candidacy. She deserves a serious consideration by all voters.
GEORGIA N. and
Woodland Road


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Multi-state modeling is often employed to describe the progression of a disease process. In epidemiological studies of certain diseases, the disease state is typically only observed at periodic clinical visits, producing incomplete longitudinal data. In this paper we consider fitting semi-Markov models to estimate the persistence of human papillomavirus (HPV) type-specific infection in studies where the status of HPV type(s) is assessed periodically. Simulation study results are presented indicating the semi-Markov estimator is more accurate than an estimator currently used in the HPV literature. The methods are illustrated using data from the HIV Epidemiology Research Study (HERS).
Persistent HPV infection is considered to drive the progression of cervical neoplasia to cervical cancer [1, 2], the second most frequently occurring cancer in women worldwide. HPV persistence is associated with high-grade cervical intraepithelial neoplasia (CIN 2/3), or pre-cancerous lesions of grades 2 or 3, which may develop into invasive cervical cancer (ICC) [1]. Thus, HPV persistence is important as a clinical marker and endpoint in clinical trials [1] and in screening to identify women who are at highest risk of high grade pre-cancerous lesions and cervical cancer [1, 3]. Characterizing the persistence of HPV infections is also important in studying the natural disease history of cervical cancer.
Despite its importance, there is no consensus regarding what exactly constitutes a "persistent" infection [1, 4]. Conceptually HPV persistence is defined as the length of time during which an individual is infected with an HPV infection, i.e., the duration the infection persists. Because a woman can become infected with one or more types of HPV, persistence is often defined in terms of the duration of a type specific infection. Unfortunately, it is impossible to observe the duration of a type specific infection. Clinical trials and natural history studies of HPV typically produce incomplete data where type-specific HPV infection is observed intermittently at a sequence of discrete time points (study visits), resulting in unobservable exact times of transition between infection states (i.e., "panel data") [5, 6]. Even if infection status could be monitored continuously, HPV tests are typically based on viral load being above or below a detection limit. Test results below a limit of detection do not distinguish between the infection being cleared versus remaining latent in some reservoir in the body. As a result of these issues, investigators typically resort to an operational definition of persistence as a surrogate for the true unobservable underlying continuous infection process. The aim of this paper is the development of a method for estimating the distribution of type-specific persistence from longitudinal HPV test results based on whatever particular operational definition is adopted by investigators.
This paper is motivated by HERS, a longitudinal study of 1310 women in the U.S. from 1993 - 2000 who either had HIV without an AIDS-defining condition (1987 Centers for Disease Control and Prevention case definition) or were at risk of HIV infection due to injection drug use or high-risk sexual behavior. There were a maximum of 15 study visits per participant, each occurring at fixed scheduled visits approximately six months apart. Each visit included a gynecological exam, cervicovaginal lavage to collect samples for detecting HPV DNA, and Papanicolaou test screening. In an analysis of longitudinal HPV data from the first 10 visits of HERS, Koshiol et al. [7] adopted an operational definition of HPV persistence defined as the time between the date of the first type-specific positive visit and the date of the first of two consecutive visits negative for the same HPV type. The requirement of two consecutive negative tests allows for possible misclassification of test results. In particular, a single intermittent type-specific HPV-negative test is not generally believed to necessarily be indicative of a woman clearing infection and then becoming reinfected with the same HPV type [4]. Rather a single intermittent type-specific negative test may represent a false negative result, perhaps due to transient suppression of viral load below the level of detection. On the other hand, two consecutive negative tests results for the same HPV type suggest the infection may have cleared.
Estimating HPV persistence is challenging for several reasons. A method for estimating HPV persistence needs to be sufficiently flexible to accommodate various operational definitions of persistence. A persistence estimator must also accommodate the incomplete data typically produced in clinical trials and natural history studies of HPV. In these settings, HPV infection may be missing at certain time points; for example, a subject may skip a clinic visit during the study for unknown reasons, the specimen drawn may be of insufficient quality to perform a valid lab test, or the lab test result may be inconclusive. Even in the absence of missed study visits or drop out, some type-specific HPV infections may still be present at the conclusion of the study, such that some accommodation for right censoring is necessary. If the operational definition of persistence allows for an HPV positive individual to clear infection and subsequently acquire a new HPV infection, a persistence estimator would also need to accommodate the possibility of repeated infections within an individual over time.
Current analytical approaches typically employed in estimating time of persistence from panel data entail using standard survival analysis methods, which we call "empirical estimators" (EEs). In the absence of right censoring, EEs reduce to observed proportions. For example, if there are no repeated infections, the probability of a particular HPV type persisting for at least time t is estimated by the proportion of study individuals infected with that type where the infection lasted t or longer. In the presence of right censoring, these simple estimators are extended using the Kaplan-Meier method. To deal with intermittent missing HPV results, EEs often explicitly or implicitly assume that an individual is HPV positive when a study visit is missed following a visit where an HPV positive result occurred [7, 8, 9, 10, 11, 12]. EEs sometimes exclude individuals with consecutively missing HPV results [7, 9]. Intuitively, such assumptions and exclusions will lead to bias or inefficiency when estimating the duration of infection. Indeed, a simulation study is described below confirming this intuition.
As an alternative to EEs, in this paper we consider fitting semi-Markov models to estimate type specific HPV persistence from longitudinal HPV data. Markov models are often used in modeling multi-state disease processes. However, the Markov assumption may not be appropriate in modeling HPV since the probability of transitioning between states may depend on the elapsed time in the current state. For example, the likelihood that an HPV type-specific infection will not clear is known to increase with the amount of infection time [10]. When future transitions depend upon the time spent in the current state, the stochastic process is classified as semi-Markov. In the HPV setting, semi-Markov models allow for the possibility that the probability of clearing an HPV type-specific infection may depend on how long an individual has been HPV positive.
Recently, Kang and Lagakos [13] developed methods for fitting continuous-time semi-Markov multi-state models to HPV panel data. Their methods were illustrated with a model of the natural history of oncogenic genital HPV infection in women using data from the placebo arm of an HPV vaccine trial. While Kang and Lagakos avoid the usual Markov assumption employed in multi-state modeling, their proposed methods require some strong assumptions which arise from modeling time as continuous. For instance, their methods require assuming specific "guarantee time" for transitions from particular states, i.e., a-priori specification of minimum times an individual must remain in a state (such as HPV infection) before transitioning to other states. Kang and Lagakos also make parametric assumptions about the transition time distributions.
In this paper we consider discrete-time semi-Markov models for estimating type specific HPV persistence. Discrete-time models have advantages over continuous-time models, such as not requiring the specification of guarantee times or parametric distributional assumptions. Discrete time semi-Markov models have been applied in the HPV setting previously, using either Bayesian or random effects modeling [14, 15]. Here we consider fitting discrete-time semi-Markov models using nonparametric frequentist methods that do not require specification of prior distributions or parametric assumptions as in Bayesian and random effects modeling.
The outline of the remaining sections is as follows. In Section 2 an EE of type-specific HPV persistence is illustrated using panel data. In Section 3 a maximum-likelihood estimator (MLE) of type-specific HPV persistence is proposed using a semi-Markov two-state discrete-time model. Section 4 describes a simulation study comparing the MLE and EE in settings similar to HERS. Section 5 extends the two-state model from Section 3 to a more general three-state model. In Section 6 the different estimators are applied to data from HERS. Section 7 concludes with a discussion.
In this section, we present an illustrative example of an EE motivated by Koshiol et al. [7]. Suppose we observe five subjects in one of two observable states (0=HPV type negative or 1=HPV type positive) at seven time points T0,…,T6. The following could occur:
where * represents a missing response. In the last column of the table above i denotes an estimate of the duration of HPV type infection for individual i. Following Koshiol et al. [7], the duration estimates assume intermittent negative responses (i.e., a single zero preceded and followed by a one) and single missing responses following a positive response are actually positive responses. The EE of the probability HPV type infection persists at least t time points equals , where I[·] is the usual indicator function which equals 1 if · is true and 0 otherwise. The estimated times i rely on a key assumption: HPV type positive individuals with a single missing visit would have tested HPV type positive if the visit had not been missed. Intuitively one might expect such an estimator to be biased. Indeed simulations studies in Section 4 below provide empirical evidence showing the EE is biased. In the Appendix a proof is given showing the EE is not in general a consistent estimator of the duration of infection, even if there are no missed visits.
As an alternative to the empirical estimator, in this section we consider maximum-likelihood estimation of HPV type-specific persistence using a semi-Markov two-state discrete-time model for individuals starting in the same infection-free state.
Let X(·) = {Xτ : τ {0, 1, 2,…}} denote a discrete-time stochastic process with state space S = {0, 1} where Xτ = 0 denotes HPV type negative and Xτ = 1 denotes HPV type positive at time τ. Assume X0 = 0, i.e., all individuals are HPV type negative at time 0. Let Yi {0, 1} denote the i-th state visited by the stochastic process, and let Ti {1, 2,…} denote the i-th sojourn time (i.e., the amount of time that an individual stays in Yi−1 before transitioning to Yi). Thus, X(·) is equivalent to {Y1, T1, Y2,…,Yi, Ti,…}. Assume X(·) is a time-homogeneous semi-Markov process [17, 18] such that for j ≠ k and i = 1, 2,…
i.e., the probability of transitioning to state k after sojourn time t in state j is independent of the history of the process. Let pjk(t) = P{Yi+1 = k, Ti+1 = tYi = j} for j ≠ k and let pjj(t) denote the conditional probability of remaining in state j after sojourn time t, i.e., pjj(t) = 1 − Σj≠kpjk(t).
A special case of a semi-Markov process is when future transitions depend only upon the current state, independent of time. The stochastic process is classified as Markov if
Let pjk = P{Xi+1 = kXi = j}. The Markov property (2) implies pjk(t) = pjk(1 − pjk)t−1, i.e., the sojourn time follows a geometric distribution.
Under the Markov assumption, the stochastic process is governed by fewer parameters (p01 and p10) than under the semi-Markov assumption. Thus, Markov models are easier to fit, but may not be flexible enough to adequately model complex processes. For example, there is evidence that the probability of clearing an HPV type infection depends on how long an individual has been infected [10]. On the other hand, there may be no reason to believe that the probability of acquiring an HPV type infection depends on how long the individual has been uninfected. Following Kang and Lagakos [13], for now we assume that the stochastic process leaving state 1 (HPV type positive) is semi-Markov and leaving state 0 (HPV type negative) is Markov [13]. That is, we assume (1) holds for j = 1, k = 0 and that (2) holds for j = 0, k = 1. Models relaxing this assumption are considered in Section 5.
Typically in HPV studies an individual's disease process is only observed until some time point at which follow-up ends. Let nt represent the total number of possible observed time points after study entry. Under the assumption above, the observable process X(·) is characterized by the (nt + 1)-dimensional vector p = (p01, p10(1), p10(2),…,p10(nt − 1), p1+(nt)) where in general
for j {0, 1}. Let the random variable M (with realization m) denote the total number of states visited by time nt such that YM is the state occupied at nt. Let tM+ denote the sojourn time in YM at nt, i.e., tM+ is the amount of time an individual has occupied YM at the end of the study. Let x = (x0, x1,…,xnt) denote the path up to time nt and let πx(p) = P{(X0, X1,…,Xnt) = x}. Then
Below we show that HPV type-specific persistence at any particular time point may be written as a function of p. Here the operational definition of persistence discussed in Section 1 is adopted. However, other definitions of persistence could also be used provided the estimand can be written as a function of the transition probabilities.
Let ϕj(p) denote the probability an individual is HPV type positive for j units of time followed by two HPV type negative tests allowing for single intermittent negative results. For example, if j = 1 then the probability HPV persists 1 unit of time is
where X(0:n) (X0, X1,…,Xn). Similarly, for j = 2 and j = 3, the probabilities HPV persists 2 or 3 units of time equal
respectively. More generally, define for x(0:j+2) [0, 1]j+3, a vector of length j + 3 zeroes and ones,
That is, ηx(0:j+2) is an indicator function that equals 1 if an individual becomes HPV type positive at time 1 and does not have two consecutive HPV type negative results until time j + 2; otherwise, ηx(0:j+2) equals zero. Then for j = 1, 2,…
and the probability type-specific HPV persists at least j units of time is
Maximum likelihood methods can be used to draw inference regarding the estimands of interest, e.g., (6). One challenge in the analysis of longitudinal HPV studies is missing data. That is, x may not be completely observable for some individuals. Assuming the missing data mechanism is missing at random (MAR) [19] (i.e., given the observed data, the missingness mechanism does not depend on the unobserved data), the likelihood contribution for an individual is obtained by summing over all possible trajectories consistent with that individual's observed data. For individual i, let αix = 1 if the path x is consistent with the observed data for that individual, and 0 otherwise. Revisiting the example in Section 2, for subject 1, α1(0000100) = 1 and α1x = 0 for all x ≠ (0000100); for subject 5, α5(0001100) = α5(0001000) = 1 and α5x = 0 otherwise.
Under MAR the likelihood can be written as
where the product is over all individuals and the sum is over all possible paths of length nt + 1. Let Ω denote the set of p satisfying the constraints
The MLE of p is obtained by maximizing the log-likelihood, log L(p), over the parameter space Ω. The MLE of p in turn gives rise to the MLE of functions of p (e.g., ϕj(p)) due to the invariance property of maximum likelihood [20].
Standard numerical methods for maximizing functions with linear equality and inequality constraints can be used to maximize log L(p) over Ω. Many of these optimizers are readily available in software. For example, the SAS Version 9.2 IML procedure (SAS, Inc., Cary, North Carolina) offers a number of optimization subroutines for maximizing continuous non-linear functions subject to linear equality and inequality constraints. In the results below, the NLPQN( ) function, a (dual) quasi-Newton algorithm, was used to maximize log L(p) over Ω.
Confidence intervals (CIs) for inference regarding the transition probabilities (as well as functions thereof such as (6)) can be obtained using profile likelihood [21]. A likelihood ratio test can be used to assess whether a simpler model assuming both states are Markov adequately fits the data. In particular, the likelihood can be maximized under the full model described above and under the null model H0 : p10(j) = {1 − p10(1)}j−1 p10(1) for j = 1,…, nt − 1. Under the H0, the corresponding likelihood ratio test statistic will have approximately a χ2 distribution with nt − 2 degrees of freedom.
A simulation study was conducted to assess the bias of the EE and semi-Markov MLE of persistence described above. For the simulation study, time of persistence was defined as the time from first HPV type-positive result until the time of the first of two consecutive HPV type-negative results. Data sets, each with a sample size of 500 individuals, were randomly generated to be similar to the HERS data analyzed by Koshiol et al. [7]. The number of possible study visits was 10 (including study entry, such that nt = 9) with two visits per year. The stochastic process leaving state 0 (HPV negative) was Markov and leaving state 1 (HPV positive) was semi-Markov. A complete set of study visits ranging from visit 0 (study entry) to visit 9 was first created for each subject. Next, to construct a pattern of missing responses attributable to drop out, a woman was right-censored with probability 0.05 at visits 2-8 and probability 0.10 at visit 9. Intermittent missing response probabilities were 0.12, 0.09, 0.11, 0.10, 0.11, 0.11, 0.08, and 0.09 for study visits 2-9, respectively.
Simulations were done under three different transition probability scenarios based on fitting the two-state model from Section 3 to the HERS data for HPV types 16 (scenario I), 53 (scenario II), and 18 (scenario III). For scenario I, the transition probabilities were p =(0.016, 0.653, 0.151, 0.085, 0.0001, 0.0001, 0.028, 0.0001, 0.0001, 0.083). Based on equation (6), under scenario I, the probabilities HPV persists at least j = 1,…, 7 units of time (corresponding to 0.5 to 3.5 years) were 0.36, 0.21, 0.12, 0.12, 0.11, 0.09, and 0.08. For scenario II, p =(0.038, 0.526, 0.225, 0.094, 0.059, 0.0001, 0.014, 0.0001, 0.042, 0.042). For scenario III, p =(0.015, 0.570, 0.247, 0.076, 0.0001, 0.024, 0.0001, 0.0001, 0.0001, 0.081).
For each scenario, 1000 data sets were generated. For each simulated data set, the MLE and EE of the probability of HPV infection persisting at least t years (t {0.5, 1.0,…, 3.5}) were evaluated. The MLE was computed based on equation (6). Here and in the sequel the EE was evaluated using the Kaplan-Meier estimator as described in Koshiol et al. [7]. In particular, to compute the EE the duration of individual type-specific infections was calculated as the time between the first HPV positive visit and the first of two consecutive HPV negative visits. The previous HPV result was carried forward for single intermittent missing HPV results. HPV infections followed by a single HPV negative result at an individual's last study visit were censored at the last visit. Six months was added to the duration for HPV infections that were positive at the final visit. Women with missing HPV results at two or more consecutive visits were excluded.
The average estimates over the 1000 simulations for scenario I are presented in Figure 1. As expected, the EE tended to over-estimate the probability of HPV persistence. On the other hand, the MLEs were approximately unbiased. Results from scenarios II and III were similar (not shown). For each simulated data set, 95% profile likelihood-based CIs associated with the MLEs were calculated. Empirical coverage for each scenario was calculated by the proportion of simulations where the CI overlapped the true probability HPV persists at least j = 1,…, 7 units of time. The empirical coverage of the profile likelihood-based CIs given in Table 1 indicates approximate nominal coverage.
In the semi-Markov model developed in Section 3, a woman can occupy one of two possible states: HPV type negative (state 0) or HPV type positive (state 1), where state 0 is assumed to be Markov and state 1 is allowed to be semi-Markov. Extensions of this two-state model are considered in this section. Note the two-state model makes no distinction between (i) women who have never been infected during the study and (ii) women who have been infected during the study but subsequently cleared infection. For both (i) and (ii) the probability of infection in the two-state model equals p01, i.e., no distinction is made between the probability of the initial HPV infection (after time 0) and the probability of subsequent infections. To allow for such a distinction, the two-state model can be extended to a three-state model with state space S = {0*, 0, 1}, where state 0* denotes being HPV type negative with no prior type-specific infection (since time 0), 0 denotes being HPV type negative after an HPV type infection, and as before 1 denotes HPV type positive. Assume all individuals are in state 0* at time 0, that states 0* and 0 are Markov, and that individuals may transition from states 0* to 1, from 1 to 0, and from 0 to 1. Then the likelihood development and inferential procedures for the three-state model are analogous to those in Section 3, except there is one additional parameter to estimate, namely the probability of transition from state 0* to 1, denoted by p0*1.
The two-state model can be viewed as a special case of the three-state model where p01 = p0*1. Thus the three-state model can be used to assess the fit of the two-state model using a likelihood ratio test comparing the two models. Under the null hypothesis the two-state model holds, i.e., p01 = p0*1, the likelihood ratio test statistic will have approximately a χ2 distribution with 1 degree of freedom.
The three-state model can be generalized even further by letting state 0 be semi-Markov. Likelihood development and inference are again similar to Section 3, except the single parameter p01 is replaced by nt − 1 parameters p01(1), p01(2),…, p0+(nt − 1). Note the transition probabilities from state 0 to state 1 are only identifiable from the observable data for sojourn times up to nt − 2 since all individuals are assumed to begin in state 0*. For the estimands defined in Section 3.2, p01 is replaced by p01 (1). Based on this more general three-state model a likelihood ratio test can be employed to assess whether the simpler three-state model assuming state 0 is Markov adequately fits the data, with the test statistic having approximately a χ2 distribution with nt − 3 degrees of freedom (assuming nt > 3). Further generalization of the three state model allowing for state 0* to be semi-Markov is more difficult as the duration a woman has occupied state 0* prior to the start of a study will in general not be known.
In this section we analyze data from the HERS cohort described in Section 1.1. Women were included in this analysis if they were HIV seropositive at baseline and had two or more study visits, HPV DNA results at study entry, a cervix, and no cervical treatment in the past 6 months prior to enrollment. Only data from the first 10 visits were analyzed. At each visit in HERS women were tested for 26 possible HPV types. For this analysis, HPV type 53, 16, and 18 infections were analyzed separately. HPV type 53 was analyzed because it was the most common individual type among HIV positive women enrolled in the study [7]; HPV types 16 and 18 are the two most common cancer-associated HPV types worldwide [22]. Each type-specific analysis only included women who were HPV type-specific negative at study enrollment. For instance, the HPV type 16 analysis included only the 524 women who were not infected with HPV 16 at study enrollment. Similarly, there were 516 (500) women who were not infected with HPV type 18 (53) at enrollment.
Type-specific persistence was estimated from the HERS data using the EE and the semi-Markov models. The EE was computed as described above. In order to fit the discrete time semi-Markov models to the HERS data, visit times were rounded to the nearest six month scheduled time point; visit times were not rounded for calculating the EE. The two-state model from Section 3 and the three-state models from Section 5 were fit separately for each type. For type 16 the three-state model assuming states 0* and 0 are Markov and state 1 is semi-Markov provided a better fit than the two-state model (likelihood ratio test p-value < 0.001). Similar results were obtained for type 53 (p-value < 0.001) and type 18 (p-value < 0.001). For type 16 there was no improvement in fit by allowing state 0 to be semi-Markov (p-value = 0.23). Similar results were obtained for type 53 (p-value = 0.12). For type 18 the more general three-state model provided a slightly better fit (p-value = 0.01), suggesting the probability of reinfection with type 18 may be time dependent. Estimates of type-specific persistence for the best fitting models are given in Table 2 for all types and in Figure 2 for type 16. As expected, estimates using the EE are higher than from the semi-Markov models.
This research was motivated by an analysis of the HERS cohort to estimate HPV type-specific persistence. Our results suggest EEs may result in overestimation of persistence of HPV type infections. In general, EEs are not consistent, and simulation studies demonstrate substantial bias of EEs in finite samples. Alternatively, using discrete-time semi-Markov models, we consider a maximum likelihood-based estimator of HPV type-specific persistence. If the model assumptions are correct, the resulting MLEs will in general be consistent estimators of persistence; simulation studies indicate the MLEs are approximately unbiased. Comparison of the EE and MLE applied to the HERS data indicates the bias of the EE can be large in practice.
There are several appealing aspects of the MLE of HPV type-specific persistence. First, this estimator requires no parametric distributional assumptions or a priori specification of guarantee times. Second, the underlying semi-Markov model allows the probability of clearing an HPV type infection to possibly depend on the time infected with that type. Third, the MLE gives valid large sample inference when the HPV infection model is correctly specified and the missing data mechanism is MAR. Simulation results suggest the MLE is approximately unbiased in finite sample settings similar to HERS. Finally, the method is flexible with respect to estimands. Namely, an estimator of any definition of persistence can be constructed provided the estimand can be written as a function of the transition probability estimates. In other words, if an investigator wishes to consider more than one persistence definition (e.g., time until first of two negative tests compared to time until first negative test), the MLEs of both types of persistence are easily computed as functions of the MLEs of the transition probabilities.
The proposed method depends on time being sufficiently discrete, which can be achieved by rounding or coarsening of observation times for studies that do not have planned visit schedules. This discretization may lead to bias or loss of precision. However, without coarsening the observation times, the number of parameters in the semi-Markov model may become prohibitively large. Thus, this method may be best suited for studies with regularly scheduled visits (e.g., clinical trials), providing a non-parametric approach to summarizing the observable discrete infection data from such studies. If, on the other hand, the goal is to extrapolate from the observable data to the underlying unobservable continuous infection process, then the methods proposed by Kang and Lagakos [13] may be more appropriate. By treating time as continuous, the Kang and Lagakos method does not require discrete visit times. Their method does however require certain strong assumptions, such as parametric distributions and guarantee times, that the discrete time model does not require.
There are many possible avenues of further methodological research related to estimating HPV persistence. For instance, both the proposed method and the Kang and Lagakos approach assume all individuals are HPV type negative at baseline. However, in longitudinal studies of HPV, a woman may already be infected at the first study visit, i.e., at study enrollment. Further research is needed to allow for such "prevalent infections." Additional research is also needed on methods to combine data across HPV studies, where visit schedules and patient characteristics may differ between studies.
Finally we note that while motivated by HPV, the discrete time semi-Markov model could be applied to other settings where recurrent infections or re-activations occur, such as malaria, herpes, or parasitic infection [23, 24, 25], and only panel data is available on the infection/activation state.
Contract/grant sponsor: NCI R01 CA114773-05
Here we sketch a proof that the EE is not, in general, a consistent estimator of the duration of infection. Consider the case where there are three follow-up time points (i.e., nt = 3) and there is no missing data. The table below shows the eight possible observed data patterns and for each pattern the corresponding estimated duration of HPV infection used in computing the EE (as described in Sections 2 and 4).
|x = (x0, x1, x2, x3)
Here, for j {1, 2, 3}, we let j+ denote scenarios where the duration of infection is right censored in the sense that from the observed data the duration is known to be at least j units of time and possibly longer. For example, for x = (0111), the duration of infection is at least 3 time units. Because duration of infection is defined as the time between the first type-specific HPV positive visit and the first of two consecutive type-specific HPV negative visits, for x = (0101) the duration of infection is also at least 3 time units. Note in the last row that x = (0000) does not have an estimated duration of HPV infection since individuals with this observed data pattern are never infected during follow-up and thus contribute no information about the duration of infection.
As mentioned in Section 4, Koshiol et al. added one time unit to the estimated duration of infection for HPV infections that were positive at the final visit. This convention is not employed in the table above, but were this convention adopted the proof below remains unchanged.
Now consider estimating the probability an infection is of duration 1 time unit (i.e., an individual is HPV positive for one time unit followed by two consecutive negative tests). Under the semi-Markov model, this probability equals ϕ(1) = p10(1)(1 − p01). Below we show that the EE of the probability an infection is of duration 1 does not in general converge in probability to ϕ(1). In particular, for a data set of individuals with three follow-up time points and no missing data, the Kaplan-Meier estimator of the probability of an infection having duration 1 equals
where the summation is over all individuals. The numerator of (9) is the number of individuals with an infection of duration 1 time unit and the denominator of (9) is the number of individuals with an infection of duration at least 1 time unit. By the weak law of large numbers and Slutsky's theorem,
Clearly the right side of (10) does not in general equal ϕ(1) = p10(1)(1 − p01). In fact, the right side of (10) will always be less than ϕ(1) provided 0 < p01 < 1.
To illustrate the extent of the bias that can occur by using the EE, suppose p01 = 0.016 and p10(1) = 0.653. Then the probability an infection is of duration 1 equals ϕ(1) = 0.643. Yet as the sample size tends to infinity, the EE (1) will converge in probability to 0.218.
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Frankly, that's not easy. However, a few weeks ago we watched a 6-foot-8 power forward out of Reno (Nev.) High and he's got a chance to fit the bill.
Olek Czyz, a native of Poland, runs with the Las Vegas Prospects. Basically, he forces you to notice his game because he's athletic and nasty rolled into a strong power forward's body.
As a junior, Czyz averaged 19 points and 9 rebounds, a major boost from the year before. Some programs saw him but few prioritized him.
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Czyz has an attitude and not a bad one mind you, but an aggressive no-non-sense approach. At the Cactus Classic, he tried to dunk everything in sight. He's very athletic, a good leaper and plays with that edge you can't quantify in the box score.
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Czyz isn't merely a dunker. He can step out and play facing the basket too.
Washington State was the first to catch on and they offered. The Cougars, Arizona State, Pepperdine and Santa Clara are on him the hardest. Oregon and USC have been in touch and Washington too. "Nevada is interested a lot," Czyz said. "They haven't (offered) yet which is weird but they talk to me all the time."
If he can replicate his intensity and results from the Cactus Classic, we could be talking about a legitimate high-major prospect.
Czyz hasn't made any unofficial visits to date. He goes to Washington State in three weeks and Arizona State this month plus a basketball camp at Pepperdine. You can bet those programs aren't going to let him off campus without pressing for a commitment.


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Burglars using drones to spy on rural homes
It has been revealed that criminals are using drones in remote areas of the country to assist in their effort to break into homes.
Resdients of Littleton, Tipperary, a small village less than 10km from Thurles, spoke of spotting remote-controlled low flying gadgets moving through the sky at night, in the area which is already blighted by burglaries.
The use of these drones is illegal, with some locals saying they are prepared to shoot the devices down.
Numerous homes in Littleton have been targeted by burglars in the past year.
Both US and British authorities have warned of the growing threat of drone use, while gardai say that anyone who spots a suspicious device should call it in without delay.
New legislation makes flying a drone in the dark an illegal activity, while it is also against the law to carry out surveillance on a person's property.
Garage owner Micheál Clohessy told the Irish Daily Mail that he spotted a drone flying over his Littleton premises twice last November.
"It was about eight o'clock at night. We were out and we spotted it in the sky, we followed its tracking path for about ten minutes but it disappeared.
"One of my neighbours saw it then a few nights after but we've had no sight of it since then.
"It's sinister at that hour of night. You wouldn't fly it at night otherwise for the risk of losing it but I'd say any farmer who sees one over his land will shoot it down.
"At the height they're at you'd take one down handy enough."
John Tully, Littleton resident, stated that these drones are a "big problem".
He said: "They're using them to fly over your premises. It's very common, they're checking out what's in the yards, how they will get in, how they won't get in."
Drone expert Steve Slade said that the use of drones at night is "reckless" behaviour.
"If you haven't been flying during the day and you're not aware of where the pylons are, the telephone poles are, the towers are – because not all towers are lit, they are meant to be but they're not so if you're flying at any kind of height at night I would think it's very reckless and it's putting people on the ground at risk."
An October 2014 report by experts at Birmingham University on the impact of drones warned of the "serious" threat posed by those using Remotely Piloted Aircraft for criminal purposes.
The report said that drones were "a potentially new and useful tool to those of criminal intent."
It further stated: "For criminals, RPA have significant potential for providing situational awareness of a property or area. They are ideal lookouts for burglars, train robbers, and poachers."
In August, Brazilian authorities arrested four men who were thought to have robbed several banks, implementing the use of a drone in the process.
Local media reported that the men used a DJI Phantom Drone to spy on bank employees and their families, as well as to take photos and videos which were uncovered by police.


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*Position : Big Data/Hadoop Lead Developer * *Location : Warren, NJ (May have to be in Warren for 1st week only )* *Duration : 6+ Months * *Interview : Phone / Skype GC, USC Only* *Job Requirement* *3 areas he is focusing on: * 1- Not just a Sr. Developer. Candidates need to have technical lead and Architecture experience. 2 - Candidates need to have JAVA Data experience. Experience with ETL tools, previous experience with moving data, storing data, moving / joining tables, pipe-lining data through different streams. Have previous experience moving data and organizing in data base. This group is all about moving data throughout the organization. Different data to Data bases to Data bases to files. Need to know how data is extracted transformed and loaded (ETL) .They should have worked with some form of data movement and should know the internals of how to move efficiently data between a source and a target. 3 - Must have solid/strong Spark experience. Regards , Kailash Negi VSG Business Solutions 221 Cornwell Dr Bear, DE 19701 Email ID : *kail...@vsgbusinesssolutions.com* <kail...@vsgbusinesssolutions.com> Phone: 302-261-3207 x 102 -- You received this message because you are subscribed to the Google Groups "American Vendor--IT Consulting" group. To unsubscribe from this group and stop receiving emails from it, send an email to sap-vendor+unsubscr...@googlegroups.com. To post to this group, send email to email@example.com. Visit this group at https://groups.google.com/group/sap-vendor. For more options, visit https://groups.google.com/d/optout.


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Stepanni is a character from Step. He wears a blue hoodie with a red arrow on it. He enjoys all genres of music and loves to dance! He doesn't speak, but talks through his actions. He is mortal enemies with Exil.
Stepanni first appears in the game Step! He is the playable main character. In this game, he must dance across the world to find and defeat Exil.
Fantendo Smash Bros. Online
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text: Products New Strategy Helps Customize TDFS October 30, 2012 (PLANSPONSOR.com) - Lyxor developed a new approach to target-date funds (TDFs) to ensure proper retirement benefits. By PLANSPONSOR staff email@example.com | October 30, 2012 Page 1 of 2 View Full Article In Lyxor Asset Management's white paper, "How to Design Target-Date Funds," the firm's research arm, Lyxor Research, details an approach to TDFs that takes into account allocation, portfolio theory and exposure.As the firm points out, as people live longer, investors will increasingly need to save sufficiently for retirement. In terms of pension investment, the default funds offered by defined contribution (DC) plans are proving popular. Investors are particularly attracted to TDFs (designed to suit investors who will retire around a similar date) given their simple and straightforward approach to saving.TDFs are designed to collect the clients' savings throughout their entire working life, in order to maximize benefits in retirement. These funds' allocation process takes the investor's lifecycle into account. In DC pension plans, younger participants invest, on average, 27% of their assets in such investment vehicles.Lyxor's approach calls for TDFs to be aligned with modern portfolio theory, not general industry consensus. Generally, these funds share the same allocation behavior. They are mostly invested in equities at inception, when investors are young, whereas the allocation will be more heavily weighted toward bonds and cash as the clients approach retirement. This behavior corresponds to popular advice in the financial industry, but Lyxor said it lacked theoretical foundations until now. Modern portfolio theory says that optimal investment decisions should not take the horizon into account. Accordingly, investors should hold a constant proportion of equities and bonds over time, which Lyxor said contradicts the general behavior of the industry. Therefore, the allocation processes of TDFs remained unclearly motivated.Investors should hold a stable proportion of their "human capital" in equities over time. Lyxor research explains this industry practice by the future income profile of investors. The research calls for investors to invest a stable proportion of their "human capital" in equities over time. So-called human capital is defined as the current capital plus the present value of future savings. Human capital would outweigh current capital for young investors, but both would converge as retirement approaches. As such, Lyxor said the portfolio should be overweighed in equities for young investors, in terms of current capital. Prev1 2Next View Full Article Tagged: Defined-Contribution, Plan-Design, Target-Date related articles related research most e-mailed UpFrontWell-Versed ERISA Litigators From plansponsor.com DC Plan Sponsors Feel Responsible for Retirement ReadinessDCIIA Calls on Sponsors to Use Automatic Tools More RobustlyTrustee Found Liable for Diversification Failures DC Industry Providers Voice Fiduciary Rule FrustrationTax Changes Could Entice Small Businesses to Offer Retirement Plans ESOPs Still a Significant Share of Retirement Plans UpFrontWell-Versed ERISA Litigators From plansponsor.com 2013 PLANSPONSOR DC Survey: Plan Benchmarking UpFrontWell-Versed ERISA Litigators From plansponsor.com Guidelines for Keeping HSAs Not Subject to ERISAA Look at Proposals to Shore Up Social SecurityMany Employees Would Choose Student Loan Assistance Over 401kLongevity Annuities Can Improve DC Plan Participant Retirement WealthAICPA Proposes Framework for Valuation of Certain Financial Instruments go to news topic


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New book
"synopsis" may belong to another edition of this title.
£ 3.53
Within U.S.A.
Book Description HarperCollins Canada, Limited. PAPERBACK. Book Condition: New. 0006485472 12+ Year Old paperback book-Never Read-may have light shelf or handling wear-has a price sticker or price written inside front or back cover-publishers mark-Good Copy- I ship FAST with FREE tracking!!!! * I am a reputable seller with excellent feedback- selling online since 2006*. Bookseller Inventory # SKU000033272
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Book Description HarperCollins Canada, Limited, 2000. Paperback. Book Condition: New. book. Bookseller Inventory # 0006485472
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The Telegraph this morning is reporting that millions of pensioners will get less than they were expecting when their Winter Fuel Allowance arrives in a few weeks' time. The bung - paid to millionaires and paupers alike - will be "reduced this year in line with plans announced by Alistair Darling, the former Labour chancellor, in his last budget", writes Robert Winnett. This means £200 rather than £250 for younger pensioners, and £300 rather than £400 for those over eighty. A nasty surprise, that, especially as George Osborne's words - and the general reporting of the Comprehensive Spending Review - implied that there would be no change.
What Osborne actually said was this:
We also keep the universal benefits for pensioners, in recognition of the fact many have worked hard and saved hard all their lives. Free eye tests; free prescription charges; free bus passes; free TV licenses for the over 75s; and Winter Fuel Payments will remain exactly as budgeted for by the previous Government - as promised.
I thought at the time that this sounded like a particularly sneaky cut - and Tweeted that it seemed like bad politics not to warn pensioners that their cherished allowance was being reduced by as much as a quarter. The difference between the two rates originates in Gordon Brown's love of political gestures: technically, for the past couple of years pensioners were given an extra, specially-announced "top-up" of the basic £200 or £300 rate, as a sort of Christmas present by a generous Labour government. Crucially, for technical reasons the calculations in Alistair Darling's March budget this year assumed the basic rather than the bonus rate of WFA, even though it was widely assumed that the bonus would be re-announced in due course.
So while George Osborne appeared to be saying that the WFA would be the same as last year, as far as pensioners are concerned it will be less. That is, at least, the natural meaning of his words, and how the Telegraph reported it.
But on Monday Steve Webb, Lib Dem minister of state in the Department of Work and Pensions, stood up in the House of Commons and said quite plainly:
In winter 2010, the winter fuel payment will continue to be paid at the higher rate of £250 or £400, according to family circumstances. Decisions about the rates for future winters will be taken as part of the annual Budget cycle, as normal.
Was this wrong? It's hard to believe that a minister would give false information to the House unless he himself - and his whole Department - had themselves been misled. Yet that would seem to be the implication of George Osborne's announcement. If he had intended to keep the higher rate to which pensioners have become accustomed, after all, would he not have said so unambiguously, if only to point out his largesse?
It's all rather confusing, especially since DirectGov is still telling pensioners that the rates will be the same as last year. I guess they'll just have to wait and see.


text: Cast Kendall Schmidt - Kendall Knight James Maslow - James Diamond Carlos Pena, Jr. - Carlos Garcia Logan Henderson - Logan Mitchell Ciara Bravo - Katie Knight Retrieved from "http://nickelodeon.wikia.com/wiki/The_Big_Time_Rush_Boogie?oldid=110870" Ad blocker interference detected! Wikia is a free-to-use site that makes money from advertising. We have a modified experience for viewers using ad blockers Wikia is not accessible if you've made further modifications. Remove the custom ad blocker rule(s) and the page will load as expected. Categories: Candidates for deletion


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Hi everyone on this blog i want talk about resident evil retribution connection with the game okay.
first i know you guys do not very like RE movies i Understand becausse they ruin the stories and the chracthers like chris redfield became so pussy and naive,jill more rude and little bitchis,carlos more look like arabian instead mexican,and well except wesker,in hte movie he more cool then in the game.
What i want to talk about is capcom make the face character in game is more look like in the movies.
1.wheen they chosse wenworth miller to become chris redfield in afterlife chris face in the game RE 5 chance drasticaly from RE Remake & Umbrella Chronivles to be more look like Wenworth miller.
2.Carlos I DOnt Know.
3.Leon Because The Rumours Is Jansen Akles Will Potray Leon In Retribution Capcom Keep Leon Face In ORC,But Because Jansen Akles Is Not Avalaible The Chance To Johan Urb Like Chris Capcom Leon Face Drasticaly From RE 4,DC,ORC,Even CG Movie TO more ugly Face Who Looks LIke Johan Urb.Am I Right?
3.Jill Face More Look Like Siena guilory in ORC.
4,Only Claire Not Have Big CChance To Her FAce Only IN CG MOvies.
well that's my opinion What About You Guys.
Note:WHen Damnation OUT?


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-Quick listening example to continue improvement of recognizing musical instrumentation.
-Continue work on music for liturgies. Review all songs covered, as well as introduce new songs requested by Mr. Griffin.
Piano review
-Review keys on piano (middle C, "natural notes," sharps/flats, half/whole steps). Let students know of SmartBoard activity on Friday to allow them to prepare for activity.
Liturgy Review
-Review all music covered from Wednesday's liturgy. Ensure students knew all songs for hymns, as well as parts of mass. Answer any questions students may have regarding songs sung.
Vocal Warm-Up
-Vocal warm-up using slide whistle. Students imitate sounds produced on slide whistle by teacher using their "singing" voice, following patterns that go high and low. Teacher monitors students breathing, posture, diction, etc.
Rhythmic Math
-Continue work on knowing musical rhythms. Review all rhythms covered, relating them to sizes of pizza covered from weeks ago. When comfortable, using SmartBoard, write math equations on board using rhythms. Students solve all problems given by teacher to solidify knowledge of rhythms.


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This B and B is in a lovely setting on 47 acres on a hill sitting back from the road (Route 50) a few miles east of Middleburg, VA. It started as a one room log cabin in 1805 and was enlarged by the middle 1800s. There are 8 bedrooms-we were in the Violet room on the second floor with the bathroom shared with the adjoining room (bathroom doors could be locked from both sides). The furnishings were simple--a very comfortable king sized bed, two lamps, a heater and robes were available to wear. There is also a large lovely porch with chairs in the front.
On the first floor is a sitting room with a TV, books and magazines, a love seat and chairs. The breakfast room could seat 16 guests--it was a weekend so an excellent breakast was served including french toast and 2 kinds of quiche as well as cereal, fruit, juice, breads, etc. In addition, they provide many kinds of snacks, juice and soft drinks at any time during the day--a very nice touch. On the grounds, there is a hot tub, pool and a number of trails around the property. In a separate building, they have banquet faciilites and there was a wedding rehearsal and dinner the night we stayed.
Briar Patch participated in the program--B and Bs for Vets, where active and retired military veterans can stay a free night on Veterans Day weekend--a wonderful way to give back to those who served in the military. We stayed one night as part of that program and liked Briar Patch so much we booked 2 nights with friends for the spring.
The only negative was that the free high speed wifi did not work for us--we were told in advance that it was slow, but we were unable to use our tablets fo get on the internet.
This is a great place to spend time and explore the lovely surrounding areas of northern Virginia. We look forward to our return.
- Official Description (provided by the hotel):
- Nestled in the foothills of the Blue Ridge Mountains & located near Middleburg in the heart of VA horse & wine country yet only 20 minutes from Dulles Airport & about an hour from Washington, DC, Briar Patch Bed & Breakfast Inn is a historic farm on 47 rolling acres with majestic mountain views. It is a great venue whether you are considering a romantic weekend getaway in one of our nine bedrooms, experiencing one of our fun cooking class weekends, holding a wedding in our event building for up to 200 guests, hosting your favorite organization's fundraiser, having a meeting in a unique location, or planning a party. Our location and amenities, including our beautiful pool will make your weekend or event one to remember.Stay in one of our eight bedrooms in the main house or combine two rooms to create your own private suite. For even more privacy, stay in our one bedroom cottage with a fully equipped kitchen. Hold your wedding, family celebration, business meeting, group picnic, or off-site retreat in our indoor facility – The Fox Den at Briar Patch – for up to 200 guests or on our beautiful grounds. ... more less
- Also Known As:
- Briar Patch Hotel Middleburg
- Briar Patch Bed And Breakfast