“You feel like a slave owner 1/2 the time and a slave the other half!!!! ”

Work/Life Balance

Career Opportunities

Comp & Benefits

Senior Management

Current Employee - District Manager in Tampa, FL

Current Employee - District Manager in Tampa, FL

I have been working at Dollar General

Doesn't Recommend

Disapproves of CEO

Doesn't Recommend

Disapproves of CEO

Pros

Company Car is good You will meet some really hard working people who truly try to do a good job

Cons

Run, run, run!!! Store Level- You are expected to get Store Managers to run a store that is open for 12-13 hours a day, 7 days a week on 115 hours. Not possible! Most of these Store Mangers work well over 70 hours a week for $30,000. But if you happen to get a Store Manager that is lazy, or is incompetent HR will back them...all they have to do is make a complaint about you. True or not that Store Manager is now untouchable for fear of lawsuit. Meanwhile, the rest of your Managers are busting their butts to get it all done and the squeaky wheels do nothing. Equipment-Antiquated equipment, computers, buildings...everything! Nothing ever works correctly. The only way to fix it is to go and buy the part or router or whatever yourself and hope you can get reimbursed. One store-in Florida-had no air for over 5 months. They wonder why sales are down...the chocolates are dripping off the shelves, the loaves of bread have condensation in them...disgusting! Another store has a 6 inch wide crack in the floor...a sinkhole will swallow that building, I hopes its after hours. Corporate Level-Hours and hours of your time is wasted in conference calls about stuff that has no chance of working. No one responds to you from tech support for computer problems for days on end. Facility services almost don't exist. Nasty emails arrive daily and nightly. Your phone will ring from 5:30am until after midnight every day. I had to start leaving my phone in another room so I could get 4-5 hours of sleep a night. They expect you as a DM to get your managers ($30,000 a year) and your hourly employees (min. wage) to work miracles while corporate executives take the private company jet back and forth to work from home every week. It's insanity. Overall, DG is a terrible place to work. You will be treated poorly and in turn you will be expected to treat those under you the same way. It sucks to lose a $90,000 a year salary, but it was that or my soul. I'm glad I have made the decision to jump ship...wonder what my boss will say when I tell him! Wish me luck!

As a manager, your life becomes Dollar General and after you give them your heart, soul and family life, they decide you are too old and defame you so they can fire you and put someone one third your age and no experience to replace you. Run don't walk away from a job opportunity with Dollar General.

Advice to Management

Stop pretending you really care about your people. ACTUALLY appreciate the really good and dedicated people who work for you.


kitchen can storage rack can storage for pantry can storage rack pantry can storage rack pantry medium size of pull can storage for pantry pantry can organizer can storage for pantry pantry can storag.


Introduction {#s1}
============

The geographical distribution of DF across the world has rapidly expanded in recent decades [@pone.0081887-Phillips1], [@pone.0081887-WHO1]. Globalization and modern lifestyle changes have increased the number of international travellers around the world. Importation of DF cases has become an important means by which the dengue virus (DENV) spreads to local *Aedes aegypti* and *Aedes albopictus* mosquitoes, particularly in currently non-endemic countries [@pone.0081887-Degallier1], [@pone.0081887-Hu1]. Although DF is not naturally endemic in Australia, the DF mosquito *Ae. aegypti* is common in northern Queensland and outbreaks can occur when the DENV is transmitted to the local mosquito population by infected international travellers [@pone.0081887-Queensland1]. Cairns experiences the highest number of DF cases in 2009 in Australia. Reappearance occurred in 1981 after a 26 year absence in Australia [@pone.0081887-Kay1]. A significant increase has been noted in the number of imported DF cases and the frequency of outbreaks since the international airport was opened in 1984 in Cairns [@pone.0081887-Ritchie1]--[@pone.0081887-Knope1]. Annual outbreaks occur when imported DF cases infect the local *Ae. aegypti* mosquito population, setting up autochthonous DENV transmission [@pone.0081887-Queensland1]. Therefore, it is important to evaluate the effect of numbers of imported DF cases on the incidence of autochthonous DF in Cairns, Australia [@pone.0081887-BannisterTyrrell1].

DENV is transmitted by mosquitoes of the genus *Ae. aegypti* and a number of previous studies have demonstrated that weather conditions can strongly influence breeding, maturation period, virus replication, density and life cycle of *Ae. aegypti* mosquitoes [@pone.0081887-Martens1], [@pone.0081887-Focks1]. DENV transmission is thus highly dependent on weather factors, including temperature, rainfall and relative humidity [@pone.0081887-Hu2]--[@pone.0081887-Racloz1]. There is growing evidence that climate change may impact on DF emergence [@pone.0081887-Martens1], [@pone.0081887-Hu2], [@pone.0081887-Nicholls1]--[@pone.0081887-Hales1]. Recent studies have indicated that climate change does not just increase temperature but also increases the frequency and intensity of extreme weather events such as heatwaves, hurricanes and tropical cyclones, and drought [@pone.0081887-McMichael1]. However, uncertainty regarding the impacts of extreme weather events on DF incidence still exists and there is a need for further investigations [@pone.0081887-Banu1].

The current study aimed to examine the relationship between the numbers of imported DF cases, weather factors, inter-month climate variations and autochthonous DF incidence, and to facilitate our understanding of the potential effects of imported DF cases and weather variability events on autochthonous DF incidence in Cairns.

Materials and Methods {#s2}
=====================

Study Site and Data Collection {#s2a}
------------------------------

The study site was located at Cairns (latitude −16.55, longitude 145.46), a tropical city that is popular for domestic and foreign tourists, in the north of Queensland, Australia. The highest mean temperature (31°C) occurs in the period of December, January and February. The lowest mean temperature (17--18°C) is observed in the period of June, July and August. The monthly total rainfall is between 279 and 455 mm [@pone.0081887-Bureauof1], making Cairns one of the wettest cities in Australia. The population size was 150,920 in the 2010 Census [@pone.0081887-AustralianBureauof1], making it the second largest Australian city (after Townsville) in the north of Queensland.

The data on numbers of notified DF cases in Cairns (the area based on Cairns Public Health Unit, which include Port Douglas and Innisfail areas). The data included both autochthonous DF cases and imported DF cases between 1^st^ January 2000 and 31^st^ December 2009 (Note, cases with an unknown country of origin were removed), which were provided by Queensland Health (the Queensland state government department of health). DF cases were confirmed by positive test results based on DF NS1 antigen, IgM antibody and PCR tests from clinical laboratories [@pone.0081887-Queensland1]. As DF is a notifiable disease, it is a legal requirement that positive test results are reported by laboratories to the Queensland Health, where data are archived by the Communicable Diseases Unit. Imported DF cases were defined as confirmed DF cases with a travel history to DF endemic countries within 12 days. Autochthonous DF cases had no travel history [@pone.0081887-Queensland1]. Daily climate data (including daily minimum temperature (MT), daily relative humidity (RH) and daily rainfall) were obtained at Cairns Aero from the Australian Bureau of Meteorology between 1^st^ January 2000 and 31^st^ December 2009. Monthly standard deviations were calculated for daily MT, daily rainfall and daily RH in each month for providing information on within-month climate variation. Annual population size in Cairns was obtained from Australian Bureau of Statistics.

Statistical Analysis and Modelling {#s2b}
----------------------------------

Generalised linear regression models with Poisson and negative binomial link have been widely applied to count data analysis in epidemiology. Count data often display over-dispersion due to excess zeroes and/or unobserved heterogeneity [@pone.0081887-Cameron1]. Poisson regression models entail a key assumption that the variance of the outcome is equal to its mean, and thus are not suited to the over-dispersed count data. The classical negative binomial model, which allows the variance to be greater than the mean, is typically used to account for over-dispersion. However, the negative binomial model lacks the ability to account for an excess in zero counts [@pone.0081887-Rose1]. To account for excess zeroes, hurdle models and zero-inflated models have been commonly used [@pone.0081887-Rose1]--[@pone.0081887-Jalava1]. The zero-inflated model treats excess zeroes as "structural" and allows for additional "sampling" zeroes, whereas the hurdle model only accommodates excess sampling zeroes. Some research showed that zero-inflated and hurdle models often perform similarly [@pone.0081887-Rose1], [@pone.0081887-Gray1]. Model choice between zero-inflated and hurdle models may be more dependent on the study objective or sampling design [@pone.0081887-Rose1].

The monthly DF data exhibited both over-dispersion and excess zeroes. The hurdle model is more appropriate for data with too many zeroes and scarce cases [@pone.0081887-Jalava2]. Here, a time-series negative-binomial hurdle model (TNBH) was chosen in this case study. A hurdle model is composed of two parts: a binary response model generating the zero cases and a truncated-at-zero count model generating positive counts [@pone.0081887-Mullahy1]. A hurdle model is given bywhere *y~t~* represents the number of autochthonous DF cases at time period *t*; *β* and *δ* are the model regression coefficients; *f~count~* represents the count process with a negative binomial density; *f~zero~* corresponds to the hurdle part using a logistic regression. Let *u~t~* be the mean of the autochthonous DF count distribution. In this study, *u~t~* and *λ~t~* can be expressed as:

where *x~it~* represents *i*th independent variable at time period *t*, including monthly imported DF cases, monthly MT, monthly rainfall, monthly RH and monthly standard deviation for daily RH (SDRH); *x~6~* ~(*t−*1)~ and *x~7~* ~(*t*−1)~ represent monthly standard deviations of daily MT (SDMT) and daily rainfall (SDR) at a lag of 1 month, respectively. ln(*y~t−1~*) and ln(*y~t−2~*) are autoregressive terms at lag of 1 and 2 months (AR(1) and AR(2)), which deal with autocorrelation of the residuals. *P~t~* is population size at time period *t*. Adjustment was made for the population size by including an offset term ln(*P~t~*). The season term is a categorical variable (wet season: November--April), dry season: May--October) in order to adjust for the seasonal pattern. Log-transformations were required for the number of imported cases, lagged autochthonous DF cases, rainfall and SDR to normalize these variables, which were highly skewed.

Several statistics were calculated to assess goodness of fit of the model. The Vuong non-nested test was used to compare the TNBH model with a negative binomial model [@pone.0081887-Vuong1]. We also compared the TNBH with a Poisson model, a negative binomial model and a Poisson hurdle model using the Akaike Information Criterion (AIC) [@pone.0081887-Akaike1].

Results {#s3}
=======

Descriptive Analysis {#s3a}
--------------------

During 2000--2009, there were total of 86 imported DF cases and 1865 autochthonous DF cases in Cairns. The countries of origin associated with the largest total numbers of imported DF cases were Papua New Guinea (27.9%), Indonesia (20.9%), Thailand (10.5%), Philippines (5.8%) and Cambodia (4.7%) in the study period ([Figure 1](#pone-0081887-g001){ref-type="fig"}). However, the total number of imported DF cases from Indonesia was larger than that of Papua New Guinea between 2004 and 2009. [Figure 2](#pone-0081887-g002){ref-type="fig"} showed a strong seasonal epidemic pattern of the number of monthly autochthonous DF cases during the study period. The largest numbers of monthly imported DF cases were reported in April and May ([Figure 2](#pone-0081887-g002){ref-type="fig"}), although seasonal patterns were not as clear as for autochthonous DF cases.

![The percentages of imported DF cases from overseas between 1^st^ January 2000 and 31^st^ December 2009 in Cairns, Australia.](pone.0081887.g001){#pone-0081887-g001}

![Boxplots of the numbers of autochthonous DF cases and imported DF cases for each month across the years 2000 to 2009 in Cairns, Australia.](pone.0081887.g002){#pone-0081887-g002}

Over the period 2000--2009, the mean monthly RH was 89.8%, ranging from 72.3--98.0%. The monthly rainfall ranged from 0.0--44.4 mm with an average of 5.5 mm. The mean monthly MT was 21°C and fluctuated between 14.3 and 24.9°C. The mean monthly SDRH, SDR and SDMT were 8.5% (range 3.1--17.9%), 10.3 mm (range 0.0--61.1mm) and 1.8°C (range 0.5--4.2°C.), respectively. The pairwise scatter plot with smoother spline depicts the crude relationships between all the selected variables ([Figure 3](#pone-0081887-g003){ref-type="fig"}).

![Pairwise scatterplot of monthly autochthonous DF cases and selected explanatory variables.\
ADF is monthly autochthonous DF cases. LIMDF is log-transformed monthly imported DF cases. Lrainfall is log-transformed monthly rainfall. SDRH is monthly standard deviation of daily relative humidity. LSDR is log-transformed monthly standard deviation of daily rainfall at a lag of one month. LSDMT is monthly standard deviation of daily minimum temperature at a lag of one month.](pone.0081887.g003){#pone-0081887-g003}

Lagged weather effects were identified by cross-correlation analysis. [Figure 4](#pone-0081887-g004){ref-type="fig"} shows the cross-correlations between monthly autochthonous DF cases and selected climate variables. Significant cross-correlations at lags of 0--1 and 5--7 months were observed among log-transformed rainfall, MT and log-transformed SDR. Autochthonous DF cases were positively associated with RH at a lag of 0 months, and were negatively associated with RH at lags of 4--9 months. Autochthonous DF cases were negatively associated with SDMT at a lag of zero month. No lagged effects were observed between SDRH and autochthonous DF cases. We only included weather variables at lags of 0 or 1 month in the TNBH model because of DF biologic plausibility. The effects of lag of 4--9 months were considered as a seasonal variation.

![Plots of cross-correlations between number of monthly autochthonous DF cases and monthly climate variables during 2000 to 2009 in Cairns, Australia.\
ADF is monthly autochthonous DF cases. SDRH is monthly standard deviation of daily relative humidity. SDMT is monthly standard deviation of daily minimum temperature. ln(SDR) is log-transformed monthly standard deviation of daily rainfall. The two dash lines are critical values for cross-correlation (at the 5% level).](pone.0081887.g004){#pone-0081887-g004}

Time Series Hurdle Model {#s3b}
------------------------

In this study, 60% of the observations on monthly autochthonous DF cases were zero values. The mean and variance of the numbers of monthly autochthonous DF cases were 15.5 and 2221, respectively. The data exhibited both over-dispersion and excess zeroes. Our results indicated that the TNBH model was flexible and suitable to our data based on AIC and Vuong non-nested test.

The selected variables and the final TNBH model were assessed by cross-correlation functions, the Vuong non-nested test and the AIC. The results indicated that the final TNBH model had the lowest AIC value (AIC = 421.12) in comparison with the time-series Poisson model (AIC = 817.9), the time-series negative binomial model (AIC = 442.9) and the time-series Poisson hurdle model (AIC = 695). The Vuong non-nested test also showed the superiority of the TNBH over the time-series negative binomial model (p\<0.000). The residuals of the TNBH model appeared to fluctuate randomly around zero and with little autocorrelation after accounting for the AR(1) and AR(2).

[Table 1](#pone-0081887-t001){ref-type="table"} showed the estimated parameters, RR, OR and 95% CI for the negative binomial component and the zero hurdle component of the TNBH model. After adjustment for seasonality, population size and autocorrelation at lags of 1--2 months in the TNBH, the results of the negative binomial component showed that monthly autochthonous DF incidence was significantly and positively associated with log-transformed monthly imported DF cases (RR:1.52; 95% CI: 1.01--2.28), monthly MT (RR: 2.28; 95% CI: 1.77--2.93), monthly RH (RR: 1.21; 95% CI: 1.06--1.37), and monthly SDRH (RR: 1.27; 95% CI: 1.08--1.50). There was a statistically significant negative relationship between incidence of monthly autochthonous DF and log-transformed monthly rainfall (RR: 0.50; 95% CI: 0.31--0.81). Moreover, monthly MT (OR: 1.64; 95% CI: 1.01--2.67) was significantly related to the occurrence of monthly autochthonous DF cases in the zero hurdle model component.

10.1371/journal.pone.0081887.t001

###### The estimated parameters, RR, OR and 95% confidence intervals for the count negative binomial portion and zero hurdle portion of the TNBH model.

![](pone.0081887.t001){#pone-0081887-t001-1}

  Lag months        Parameter       Negative binomial partRR (95% CI)   Zero hurdle partOR (95% CI)
  ------------ ------------------- ----------------------------------- -----------------------------
  1                   AR(1)                 4.29 (3.18, 5.79)               7.25 (2.42, 21.69)
  2                   AR(2)                 0.50 (0.39, 0.64)                0.64 (0.29, 1.44)
  0             ln(importedcases)           1.52 (1.01, 2.28)                1.32 (0.36, 4.76)
  0             ln(rainfall)(mm)            0.50 (0.31, 0.81)                1.75 (0.58, 5.30)
  0                  RH (%)                 1.21 (1.06, 1.37)                1.04 (0.86, 1.25)
  0                  MT (°C)                2.28 (1.77, 2.93)                1.64 (1.01, 2.67)
  0                 SDRH (%)                1.27 (1.08, 1.50)                1.23 (0.90, 1.68)
  1               ln(SDR) (mm)              1.09 (0.77, 1.54)                1.54 (0.65, 3.69)
  1                 SDMT (°C)               1.16 (0.59, 2.29)                0.58 (0.16, 2.12)

AR is autoregressive term. MT is monthly minimum temperature. SDRH is monthly standard deviation of daily relative humidity. ln(SDR) is log-transformed monthly standard deviation of daily rainfall. SDMT is monthly standard deviation of daily minimum temperature.

Discussion {#s4}
==========

Our study concluded that observed incidence of autochthonous DF was significantly positively associated with the number of imported DF cases, but that the occurrence of autochthonous DF counts was associated with MT rather than the current number of imported DF cases. After accounting for incorporating imported DF cases, the study confirmed that local weather factors, including MT, rainfall and RH, strongly influenced the observed incidence of autochthonous DF in Cairns, Australia.

There was a significantly positive relationship between numbers of imported DF cases and autochthonous DF incidence in Cairns in the study. Approximately 90% of imported DF cases were from the countries in Pacific region and Southeast Asia. DF is endemic to countries in this region, including Papua New Guinea, Indonesia, Thailand, Philippines and Cambodia, which include popular holiday destinations for Australians, and where many Australians have family or work-related links. Our study indicated that imported DF cases strongly influenced autochthonous DF cases, and the occurrence of autochthonous DF was not associated with imported DF cases in the hurdle part. These findings could support the suggestion that imported DF cases play a significant role in influencing numbers of local DF cases under favourable weather conditions.

Our results revealed that MT and RH were significantly, positively associated with incidence of autochthonous DF. Temperature and RH have been demonstrated as the main determinants of DENV transmission and influence the life cycle of *Ae. aegypti*, like many previous studies [@pone.0081887-Bi1]--[@pone.0081887-Lambrechts1]. Increasing temperatures lead to an increase in feeding frequency and shorter gonotrophic cycle, pupae development period, virus replication and extrinsic incubation period for *Ae. aegypti* [@pone.0081887-Watts1]--[@pone.0081887-Descloux1], and thus enhance the risk of DF outbreaks. Increasing RH also favours oviposition, blood-feeding, longevity, virus propagation and density of *Ae. aegypti* [@pone.0081887-Thu1]--[@pone.0081887-Wu2].

Rainfall was also found to be negatively associated with autochthonous DF incidence in the study. Many researches have indicated that increased rainfall can increase the number of outdoor larval breeding places of *Ae. aegypti* and can therefore increase the density of mosquitos and enhance the risk of DENV transmission [@pone.0081887-Descloux1], [@pone.0081887-Thu1], [@pone.0081887-Nakhapakorn1]. On the other hand, some studies have reported a negative relationship between rainfall and DF cases, which has been explained by larvae and eggs being washed away by massive rainfall [@pone.0081887-Foo1]--[@pone.0081887-Halide1]. Previous research indicated that many container types were suitable to *Ae. Aegypti* breeding sites in wet season in Cairns, such as pot plant base, pot plant pot, bucket, black plastic, small plastic container, palm frond and bromeliad, and that limited resources in containers significantly negatively influenced the abundance and size of *Ae. aegypti* in Cairns [@pone.0081887-Williams1]. Cairns typically has a tropical monsoon climate with abundant amounts of rainfall in the form of frequent thunderstorms in the wet season [@pone.0081887-Sturman1]. Here, the result could be explained that heavy rainfalls might immediately wash away larvae from some subterranean sites and small, shallow containers. Moreover, frequent thunderstorms might flush away some resources from containers to lead to restriction of growth and development of larvae and reduction of the adult population of *Ae. aegypti* in Cairns.

Our result revealed that within-month variability in daily RH played a significant positive role in autochthonous DF incidence in Cairns. In general, RH variation is highly dependent on the fluctuations of temperature, precipitation and evaporation. Therefore, RH variation might be derived by the fluctuations of other weather factors, particularly heavy rainfall. Our study suggested that SDRH could be considered as a small-scale climate index to capture within-month climate variation, particularly in extreme weather events within a month in Cairns.

The study had several known limitations. First, underreporting would have been likely if people infected by DF had subclinical conditions and did not seek medical attention. Misclassification among the imported, autochthonous DF cases and other arboviral infections (eg., chikungunya) could also be possible. Second, a number of potential confounders (ie., virus strain, mosquito population densities and survival, population immunity and social factors) may affect the assessment of the relationship between weather variability and the DENV transmission. More detailed risk assessment may also be required. Additionally, previous study showed that the key determinants of observed DF incidence included temperature, rainfall and humidity. However, the advantage of our study was that the relationship between weather variables and autochthonous DF incidence was examined under accounting for imported DF cases and excess zeroes.

In summary, our research showed that monthly imported DF cases, local weather variables and inter-month relative humidity variability were strongly associated with incidence of monthly autochthonous DF in Cairns between January 2000 and December 2009. Imported DF cases might be a key driver in wet season. The result also indicated that increasing monthly RH fluctuation enhanced the risk of DENV transmission in Cairns. The results could provide new insights for the future control, prediction and prevention of the risk of DENV transmission. Local health authorities may give a close surveillance of imported DF cases and control local *Ae. aegypti* during favourable weather conditions for DENV transmission.

We thank the Queensland Department of Health, Australian Bureau of Meteorology, and Australian Bureau of Statistics for providing the data on notified dengue fever cases, climate, and population growth, respectively.

[^1]: **Competing Interests:**The authors have declared that no competing interests exist.

[^2]: Conceived and designed the experiments: XH WH. Analyzed the data: XH WH GW AC. Wrote the paper: XH WH GW AC.


export  interface IListItem {
    [key: string]: any;
    id: string;
    title: string;
    modified: Date;
    created: Date;
    modifiedby: string;
    createdby: string;
}


Q:

How can i install perl 5.10.1 in ubuntu 18.04 LTS?

I am not able to install Perl 5.10.1 in ubuntu 18.04. I am trying to install it via perlbrew but I am getting some error.
perlbrew install perl-5.10.1

apt-get install perlbrew
perlbrew init
perlbrew install perl-5.10.1
perlbrew use perl-5.10.1

I have to downgrade the Perl version since I am in need of these for my applications backend
*Error Message:
Test Summary Report
-------------------
makefile:850: recipe for target '_test_tty' failed
make[2]: *** [_test_tty] Error 255
make[2]: Leaving directory '/root/perl5/perlbrew/build/perl-5.10.1/perl-5.10.1'
makefile:864: recipe for target '_test' failed
make[1]: *** [_test] Error 2
make[1]: Leaving directory '/root/perl5/perlbrew/build/perl-5.10.1/perl-5.10.1'
makefile:967: recipe for target 'test_harness' failed
make: *** [test_harness] Error 2
##### Brew Failed #####*

A:

If you update the patchperl component of your perlbrew to the latest version, it should work. See this issue for more information. Try this:
$ perlbrew install-patchperl  # <-- install latest version of patchperl
$ perlbrew install perl-5.10.1


<?xml version="1.0" encoding="utf-8"?>
<resources>

	<string name="hello">I am the first activity!</string>
	<string name="app_name">Explicit Intents Demo</string>
	<string name="other">I am the other activity!</string>

</resources>


S 3 bedroom, 2 full bathroom home in the heart of Fontana on a nice and quiet street. this home features a ...

Perfect family home, 4 bedrooms 2 baths. home features open floor plan with counter bar in the kitchen for those busy mornings getting the day started...

Standard sale! gorgeous upgraded home, turn key just on the market, this beautiful one story home...

Completely remodeled shows great,house offers 3 bedroom ,2 bath ,new painting inside and outside ,central ac and heating ,new roof,new windows, new doors, large living room with modern recessed lights and new laminated floors, large family room with new recess lights , fireplace and new laminated floors,bedrooms with new carpet and new doors ,kitchen offers new wood cabinets with self closing doors and quartz counter tops,new sink with modern faucet and garbage disposal, new tile floors,bathroom with new tile walls with modern design,also new tile floor new cabinets and toilette,2 car garage,big front and back yard with sprinklers,fuit trees,excellent and quiet neighborhood close to schools...

Charming single level home, with bright, open kitchen, boasting plenty of cabinetry and counter space, eat in dining, welcoming living room with warm brick fireplace...

Garage is converted for potential positive income. standard sale...

Wow wow wow,,this newer home is a beauty. move in ready already upgraded as it is a newer build...

Amazing single story home is centrally located in Fontana. it ...

Perfect large and strategic parcel (just over 2 acres) for the savvy investor who knows how to piece and assemble land for its highest and best use...

7.7 acres flat lot-level which is an excellent opportunity for builder, developer or investors...

Corner lot. great location, right across from the Fontana court house. bottom level has a deli. ...

We have all been there what should we do buy or rent?Lets take a look:

13oct

Testimonials

"Sean was able to secure the perfect deal in the sale of our house. Not only did we get what we thought the house was worth, but we paid low escrow fees and we were able to stay in our house (as renters) until we could find a new home. Sean understands the challenges of selling and buying at the same time while raising a family and was very flexible. "

MLS® Disclaimer

Based on information from California Regional Multiple Listing Service, Inc. as of December 13, 2017 3:45 PM. This information is for your personal, non-commercial use and may not be used for any purpose other than to identify prospective properties you may be interested in purchasing. Display of MLS data is usually deemed reliable but is NOT guaranteed accurate by the MLS. Buyers are responsible for verifying the accuracy of all information and should investigate the data themselves or retain appropriate professionals. Information from sources other than the Listing Agent may have been included in the MLS data. Unless otherwise specified in writing, Broker/Agent has not and will not verify any information obtained from other sources. The Broker/Agent providing the information contained herein may or may not have been the Listing and/or Selling Agent.

Based on information from CARETS as of December 13, 2017 3:45 PM. The information being provided by CARETS is for the visitor's personal, noncommercial use and may not be used for any purpose other than to identify prospective properties visitor may be interested in purchasing. The data contained herein is copyrighted by CARETS, CLAW, CRISNet MLS, i-Tech MLS, PSRMLS and/or VCRDS and is protected by all applicable copyright laws. Any dissemination of this information is in violation of copyright laws and is strictly prohibited.

Any property information referenced on this website comes from the Internet Data Exchange (IDX) program of CRISNet MLS and/or CARETS. All data, including all measurements and calculations of area, is obtained from various sources and has not been, and will not be, verified by broker or MLS. All information should be independently reviewed and verified for accuracy. Properties may or may not be listed by the office/agent presenting the information.


Introduction {#Sec1}
============

DNA methylation is a type of epigenetic modification of post-replicative DNA, where a methyl residue is covalently added to the cytosine nucleotides. This dynamic process is catalysed by DNA methyltransferases and is essential for all mammalian cells. It has been shown that human tissues have each its own specific methylation pattern which contributes to tissue-specific transcription pattern and thereby to tissue development and specific functions^[@CR1]^. The uterine inner lining, the endometrium, is a unique tissue because it undergoes histologically and functionally distinguished cyclic phases of growth and atrophy under the control of ovarian steroid hormones estrogen and progesterone. The proper functioning of the endometrium is needed to support the implantation of the embryo in the mid-secretory phase of the menstrual cycle. Endometrial receptivity or 'window of implantation' (WOI) has some inter-individual variation in the timing but occurs approximately a week after ovulation on cycle days 19--24.

Transcriptome studies have demonstrated a myriad of changes in endometrial gene expression during the transition from pre-receptive to receptive phase^[@CR2],\ [@CR3]^, and a specific transcriptome signature has been detected that is now used to determine the individual WOI and aid in selecting the best day for embryo transfer in women undergoing *in vitro* fertilization^[@CR4]^. Although the endometrial function is believed to be under epigenetic control^[@CR5]^, less is known about how endometrial DNA methylation pattern changes throughout the menstrual cycle, what impact it has on gene expression, and whether aberrations in methylation pattern could lead to altered endometrial function. According to recent studies, the endometrial methylome might indeed be dynamic throughout the menstrual cycle^[@CR6],\ [@CR7]^, correlate with changes in the transcriptome^[@CR6],\ [@CR7]^ and also play a role in the pathogenesis of endometrial disorders by affecting the expression of genes relevant for maintaining proper endometrial function^[@CR6],\ [@CR8]--[@CR10]^. However, none of the previous studies have used genome-wide technologies to target directly the establishment of endometrial receptivity, therefore, we lack an understanding on how global DNA methylation changes and concomitant changes in gene expression occurring in a limited time-frame could contribute to controlling endometrial receptivity.

In order to better understand how DNA methylation changes might modify endometrial receptivity or the susceptibility to endometrial pathologies, we need a more thorough understanding on the normal endometrial methylome that corresponds to the restructuring of the endometrial tissue. We hypothesized that the transcriptomic changes observed in endometrial tissue around the time of embryo implantation are at least partially caused by changes in global DNA methylation pattern. Therefore, the aim of the present study was to use genome-wide technologies to characterize the endometrial methylome in pre-receptive and receptive endometrium sampled from the same individual within the same menstrual cycle. To find differentially methylated sites with higher confidence and obtain more robust results, we used a combination of three analysis methods, and to evaluate the potential effect of DNA methylation on gene expression, we tested for correlation between DNA methylation and gene expression levels. Finally, pathway analysis was used to put the findings into a wider biological context.

Results {#Sec2}
=======

General profiling {#Sec3}
-----------------

We studied the genome-wide DNA methylation profiles in endometrial biopsies from two time-points, pre-receptive (LH + 2) and receptive (LH + 8), in one menstrual cycle from 17 healthy, fertile-aged women. Of the 437,022 CpGs remaining for analysis after quality control, 19% (83,728) were consistently hypermethylated (β \> 0.8), while 33% (145,385) were hypomethylated (β \< 0.2) in both pre-receptive and receptive time-points.

To test for differences in methylation value distributions between genomic regions, we carried out pairwise comparisons using the Kolmogorov-Smirnov test (for all comparisons presented here, p \< 2.2 × 10^−16^). With regards to genomic location, CpG sites in CpG islands (CGIs) showed relatively lower methylation levels than CpG sites located in shelves (regions spanning 2--4 kb up- and downstream of the CpG islands), whereas the methylation levels of sites in CpG shores (regions spanning \<2 kb up- and downstream of the CpG islands) followed a more uniform distribution, both in pre-receptive and receptive time-points (Fig. [1a](#Fig1){ref-type="fig"}). CpG sites in TSS1500 (−200 to −1,500 bases upstream of the transcription start site, TSS) showed slightly higher methylation levels compared to TSS200 (up to −200 bases upstream of TSS) regions of the gene promoters (Fig. [1b](#Fig1){ref-type="fig"}). On average, promoter regions exhibited lower methylation levels than gene body regions, supporting the claim that genomic regions involved in active transcription are hypomethylated resulting in accessibility to transcription factors^[@CR1]^. Overall, the methylation profiles of samples from pre-receptive and receptive endometrium were relatively similar, with no great-magnitude changes (Fig. [2](#Fig2){ref-type="fig"}).Figure 1Methylation levels in pre-receptive (cyan, left) and receptive (orange, right) endometrium represented as split beanplots. The width of the plot represents the distribution of data, the black line shows the mean methylation value in group, while the dashed black line represents the overall average methylation level. (**a**) According to location (relative to CpG islands). The x-axis denotes the CpG island location while the y-axis denotes methylation β-values (0 to 1). (**b**) According to the region functional categories. The x-axis denotes the functional group while the y-axis denotes methylation β-values (0 to 1). CpGs annotated to multiple gene locations are labelled as 'Others', and CpGs with unknown annotations are labelled as 'Unknown'. Figure 2Density plot of DNA methylation levels (as β values) for pre-receptive (LH + 2) and receptive (LH + 8) endometrium samples from 17 women.

Differential methylation {#Sec4}
------------------------

For differential methylation analysis, we used a combination of three different methods to increase the possibility of identifying true positive results. Single CpG-level analysis resulted in 53,371 (12.2% of total) differentially methylated CpGs using RnBeads, 28,994 (6.6%) using Wilcoxon's signed rank test and 55,086 (12.6%) using seqlm (all analyses were adjusted for age). The intersect of the three analysis methods resulted in 22,272 CpGs (5.1%) associated with 5,979 genes as differentially methylated between pre-receptive and receptive endometrium (Supplementary Figure [2](#MOESM1){ref-type="media"}) and were considered as the most likely set of truly differentially methylated CpGs (Supplementary Table [1](#MOESM2){ref-type="media"}). The same set of CpGs was used in all further single CpG site-level analyses. Changes in methylation levels included both increased (n = 18,820 CpG sites; 4.3% of all CpGs; 84.5% from differentially methylated CpGs; delta-β mean = 0.059, median = 0.057) and decreased (n = 3,452 CpG sites, 0.8% of all CpGs, 15.5% of differentially methylated CpGs; delta-β mean = −0.052, median = −0.051) methylation in receptive phase samples. A total of 842 CpG sites had a delta-β absolute value more than 0.1. The top ten sites with the largest methylation differences between pre-receptive and receptive endometrium are shown on Fig. [3](#Fig3){ref-type="fig"}. Clustering analysis using the 22,272 differentially methylated CpGs resulted in two main branches that divided the analysed samples according to menstrual cycle phase (pre-receptive and receptive). The first branch included all pre-receptive phase samples, except for one which clustered together with receptive phase samples. In addition, three receptive phase samples also clustered in the first branch (Supplementary Figure [3](#MOESM1){ref-type="media"}).Figure 3CpG-level differential methylation analysis results. Methylation levels of top 10 CpG sites differentially methylated between pre-receptive and receptive endometrium. Each plot represents a single CpG site and the gene it was annotated to. Upper panel (orange) -- higher methylation in receptive endometrium; lower panel (light blue) -- lower methylation in receptive endometrium.

The region level analysis of all CpGs revealed 2,026 significant differentially methylated regions (DMRs; defined as at least 3 differentially methylated CpGs within a 500 bp window) (False Discovery Rate adjusted p-value, FDR \< 0.05; Supplementary Table [2](#MOESM2){ref-type="media"}), of which 1,650 exhibited increased (associated with 1,217 genes) and 376 decreased (associated with 276 genes) methylation in receptive phase samples. 48 genes were present in both lists, depending on the location of the DMR. The most significant DMRs included CpGs in the 'Open Sea' region \~31 kb downstream from *IGF2*, in the 'Body' region of *PDLIM2* and the 3′ UTR region of *ZMIZ1*. *ZMIZ1* was also one of the genes highlighted in site-level analysis (Fig. [3](#Fig3){ref-type="fig"}).

We also examined the location of differentially methylated CpG sites and regions in relation to gene sub-regions (TSS200, TSS1500, 5′ UTR, 1^st^ Exon, Gene body, 3′ UTR) and CpG islands (N_Shelf, N_shore, CpG island, S_Shelf, S_Shore, remaining sequences termed as 'Open Sea'). Figure [4a and b](#Fig4){ref-type="fig"} represent the distribution of DMRs and differentially methylated CpGs. It can be clearly seen that gene body region exhibits highest differential methylation in both region and site level analyses. However, differential methylation mapped to multiple locations (represented as 'Others') was more common (up to 21% for DMRs related to increased methylation in receptive phase) in region level analysis than the site level analysis. This could be owing to the fact that methylation levels of nearby CpGs from multiple locations were spatially correlated and grouped into a single DMR. Large proportion of these differentially methylated regions/sites could not be annotated to known gene sub-regions (shown as 'Unknown') and only a negligible portion of them were located in promoter (TSS200 and TSS1500) and other genomic regions (5′ UTR, 3′ UTR and 1^st^ Exon). Regarding localization relative to CpG island, majority (up to 60%) of differentially methylated regions/sites were located in 'Open Sea'. Comparing to the overall distribution of all analysed sites (n = 437,022), the distribution of differentially methylated CpG sites was significantly different for both in relation to gene-subregions and CGIs ($\documentclass[12pt]{minimal}
                \usepackage{amsmath}
                \usepackage{wasysym}
                \usepackage{amsfonts}
                \usepackage{amssymb}
                \usepackage{amsbsy}
                \usepackage{mathrsfs}
                \usepackage{upgreek}
                \setlength{\oddsidemargin}{-69pt}
                \begin{document}$$\chi $$\end{document}$ ^2^ p-value for both \< 2.2 × 10^−16^). This was characterized by under-representation in CGIs (10.7% of significant *vs*. 31.6% of all CpGs) and TSSs (9.5% of significant *vs*. 21.1% of all CpGs), and over-representation in 'Open Sea' (59.0% of significant *vs*. 35.4% of all CpGs), gene body (39.2% of significant *vs*. 31.0% of all CpGs) and 'Unknown' (30.6% of significant *vs*. 23.3% of all CpGs) regions.Figure 4Location of differentially methylated sites and regions in relation to functional subregions and CpG islands. (**a**) Region-level analysis. (**b**) CpG-level analysis.

Correlation between methylation and gene expression {#Sec5}
---------------------------------------------------

To characterize the potential effect of methylation status on gene expression levels, we used RNA sequencing data to evaluate the expression change of differentially methylated genes in the same samples. For the correlation analysis, only significantly differentially methylated CpG sites with an absolute delta-β value \> 0.1 were used. In addition, we used only Illumina annotated CpGs and transcript pairs, which excluded all CpGs in 'Open sea' and resulted in 464 genes and 531 CpGs in total for analysis (altogether 546 pairs, as some CpGs were annotated to more than one gene). Correlation analysis showed 169 significantly correlated gene-CpG site pairs \[that is 157 (34%) of tested genes and 168 (32%) of tested sites\] (permutation p-value \< 0.05) (Supplementary Table [3](#MOESM2){ref-type="media"}). Overall, the average proportion of significantly correlated CpGs was around 30%, but showed significant variation across different regions ranging from 22% in the 1^st^ Exon to 38% in the 5′ UTR (Table [1](#Tab1){ref-type="table"}). The proportion of positive and negative correlations also varied in different regions, negative correlations being more common in the 5′ UTR and 1^st^ Exon, while positive correlations were more prevalent in the Body region (Table [1](#Tab1){ref-type="table"}), consistent with the 'DNA methylation paradox'^[@CR11]^.Table 1Correlations between CpG site methylation and gene expression.RegionDifferentially methylated CpGs in region (n)CpGs correlated with gene expression n (%)Positively correlated CpGs n (%)Negatively correlated CpGs n (%)5′ region14545 (31.0%)20 (44.4%)25 (55.6%)  1st exon184 (22.2%)1 (25.0%)3 (75.0%)   TSS200164 (25.0%)2 (50.0%)2 (50.0%)  TSS1500389 (23.7%)6 (66.7%)3 (33.3%)   5′ UTR7328 (38.4%)11 (39.3%)17 (60.7%)Body401124 (30.9%)70 (56.5%)54 (43.5%)   Body353109 (30.9%)62 (56.9%)47 (43.1%)  3′ UTR4815 (31.3%)8 (53.3%)7 (46.7%)

Strongest negative correlations were observed for *ARL15*, *EPB41L2*, *ZNF516*, *WSB1*, *CDK6*, *TRPM1*, *RASSF8*, *AQP11*, *DENND2D* and *MAPK14* (Supplementary Table [3](#MOESM2){ref-type="media"}). Strongest positive correlations were observed for *ANTXR2*, *CTTN*, *CAMTA2*, *TMEM45A*, *SNX29*, *C1S*, *FYN*, *ANKRD55*, *KLF7* and *AKAP13* (Supplementary Table [3](#MOESM2){ref-type="media"}).

Gene Ontology (GO) and pathway analyses {#Sec6}
---------------------------------------

In order to characterize the genes annotated to differentially methylated sites and regions, gene ontology and pathway analyses using g:Profiler^[@CR12]^ and PANTHER^[@CR13],\ [@CR14]^ were carried out, and g:Profiler results were aggregated using GOsummaries^[@CR14]^. In site-level analyses, we used the 22,272 differentially methylated CpGs, and the gene ontology analyses were performed separately for 1,464 and 5,196 genes associated with lower and higher methylation levels in receptive endometrium, respectively (according to CpG annotation). 681 genes were present in both categories, depending on CpG annotation. As shown in Fig. [5a](#Fig5){ref-type="fig"}, in site-level analyses, the genes affected by decreased methylation were mainly associated with immune response regulation and cell activation and adhesion, while genes associated with increased methylation were related to extracellular matrix organization, cellular signalling, regulation and development (Supplementary Table [4](#MOESM2){ref-type="media"}). This is largely mirrored by region-level analyses of DMRs, involving 1,206 genes associated with increased methylation and 275 with decreased methylation in receptive phase, respectively, which show that processes related to extracellular matrix and cellular adhesion are most affected by differential methylation (Fig. [5b](#Fig5){ref-type="fig"}, Supplementary Table [5](#MOESM2){ref-type="media"}). To functionally annotate the genes showing correlation between site-level methylation and gene expression (72 negative and 85 positive correlations), we used gene ontology analysis, which showed that positively correlated genes are related to extracellular matrix organization (*ITGAE*, *LAMA4*, *NID1*, *TGFB3*, *COL4A2*, *ADAMTS1*, *VCAM1*, and *COL6A2*) and immune response (*FYN*, *BCL3*, *PVR*, *JAK3*, *IL1RL1*, *RFTN1*, *MYO1G*, *CXCL13*, and *C1S*), while no enrichment in biological terms was seen for negative correlations (Fig. [5c](#Fig5){ref-type="fig"}, Supplementary Table [6](#MOESM2){ref-type="media"}).Figure 5Pathway analysis of genes mapped to significantly differentially methylated sites. (**a**) CpG-level analyses. 'Increased' and 'decreased' methylation stand for methylation status in receptive endometrium relative to pre-receptive endometrium; (**b**) Region-level (DMR) analyses. 'Increased' and 'decreased' methylation stand for methylation status in receptive endometrium relative to pre-receptive endometrium; (**c**) For genes showing positive correlation between gene expression and methylation. No enrichment for biological terms was seen among negative correlations. The barplot shows the --log10 (p-values) for most significantly enriched pathways and GO terms. For full lists, please see Supplementary Tables [4](#MOESM2){ref-type="media"}--[6](#MOESM2){ref-type="media"}).

PANTHER pathway analyses for the same gene lists showed enrichment in 16 pathways in site-level analysis, including VEGF signalling, oxytocin receptor mediated signalling, endothelin signalling, angiogenesis, integrin signalling, EGFR signalling, Wnt signalling, GnRH receptor and chemokine/cytokine signalling mediated inflammation pathways (for details see Supplementary Table [7](#MOESM2){ref-type="media"}). No enrichment was seen in region-level analysis; however, genes for which we observed correlation between methylation and gene expression were enriched for integrin signalling pathway genes.

Discussion {#Sec7}
==========

The current paper describes the methylation landscape in pre-receptive and receptive endometrium of healthy fertile-aged women within one menstrual cycle, showing multiple small-scale changes that correlate well with changes in gene expression.

Previously it has been shown that the endometrial methylome is dynamic and changes throughout the menstrual cycle^[@CR7],\ [@CR8]^. However, these studies have compared different women with different menstrual cycle phases, thereby raising the question of how many of the described changes are due to true biological changes and not inter-individual variability^[@CR7],\ [@CR8]^. Furthermore, although the dynamic nature of endometrial methylome has been demonstrated, no study has used precisely timed tissue samples to investigate the methylation changes taking place at the time endometrial receptivity is established. Our study is the first to use precisely dated and histologically confirmed endometrial biopsies taken from the same women within the same menstrual cycle to eliminate inter-individual and inter-cycle variability. Such design targets the transition from pre-receptive to receptive phase of the endometrium to better characterize the potential methylation changes taking place during this limited period that could help to unravel the biological mechanisms responsible for endometrial receptivity. In our dataset, the comparison of methylation profiles showed no large-degree differences between early- and mid-secretory endometrium. However, we detected small-scale changes in methylation in a number of CpG sites. Since various methods use slightly different statistical approaches for detecting differential methylation, we used three methods and considered only those sites differentially methylated that were identified by all used methods. This way the methods are likely to complement each other and thus improve the reliability of our results.

Both site- and region-level analysis identified CpGs annotated to *ZMIZ1* as one of the top significantly differentially methylated genes. ZMIZ1 is a transcription factor regulator that among others regulates the androgen receptor, Smad3/4 and p53 signalling, the latter has also been associated with endometrial receptivity^[@CR15],\ [@CR16]^. Differentially methylated sites were also mapped to several genes with known function in endometrial receptivity and embryo implantation, including *PAEP*, *MAP3K5*, *ENPEP*, *GPX3*, *ARID5B*, *AOX1*, and *ANXA4* ^[@CR17]^. Furthermore, ontology and pathway analyses of the genes annotated to differentially methylated sites/regions highlighted several pathways with established role in endometrial receptivity, such as immune response, Wnt signalling, angiogenesis and VEGF signalling, cell adhesion and extracellular matrix remodelling^[@CR18]^.

Previous studies exploring the endometrial methylome have reported sites in or near *FAM181A*, *UXT*, *KRT34*, *KRTAP17-1*, *LASS3*, *CCL4*, *ST6GAL1*, *ZNF143*, *CYSLTR2*, *TDGF1*, *RANBP3L*, *SNORD109A*, *TRIM74*, *ACOT2*, *WT1*, *TCEAL4*, *MPP7*, *CASP8*, *PTPRN2* and *HCP5* as differentially methylated between the early- and mid-secretory phases^[@CR7],\ [@CR8]^. Our study confirmed the differential methylation of *KRTAP17-1*, *CASP8*, *RANBP3L*, *WT1*, *MPP7*, *PTPRN2*, and *HCP5*. Not much is known about the roles of *KRTAP17-1*, *RANBP3L*, *MPP7*, *HCP5* and *PTPRN2* in endometrial biology. However, *CASP8* has been shown to be among the genes dysregulated in women with chronic endometritis and impaired receptivity^[@CR19]^, and IVF treatment failure^[@CR20]^, while WT1 is associated with decidualization in rat endometrial stromal cells^[@CR21]^, and is downregulated during WOI in polycystic ovary syndrome patients^[@CR22]^. These lines of evidence support their role among the genes modifying the microenvironment within the receptive endometrium. Interestingly, *PTPRN2* was also among the genes that show a correlation between methylation and gene expression in our study, as two CpGs annotated to *PTPRN2* were negatively correlated with gene expression. Despite different study designs and relatively small overlaps, the aforementioned seven genes have been identified as differentially methylated between early- and mid-secretory endometrium in more than one study^[@CR7],\ [@CR8]^, proposing them as interesting candidates for further investigation.

We also correlated the differentially methylated CpGs with the greatest absolute changes in methylation levels with corresponding transcript levels and observed numerous correlations. There is no consensus on the extent of change in methylation needed to impact gene expression, as it probably depends on multiple additional regulatory factors and also on whether whole tissue or distinct cellular subpopulations are studied. However, small absolute changes in methylation have previously been found to associate with gene expression both on whole tissue^[@CR7]^ and cell population^[@CR23]^ level. Correlation analysis of methylation and gene expression levels revealed both positive and negative correlations in varying proportions depending on the genomic region. This is in accordance with recent studies showing that methylation can affect gene expression in both directions^[@CR24],\ [@CR25]^. However, as expected, we observed more negative correlations in the 5′ UTR while positive correlations were more common in the gene Body region. This is consistent with the 'DNA methylation paradox', whereby methylation of the transcribed region and region of transcription initiation have opposite effects on gene expression^[@CR11]^. The proportion of negative and positive correlations is somewhat different from what Houshdaran *et al*.^[@CR7]^ showed, as in their study, positive correlations were substantially more prevalent (70% positive vs 30% negative). Furthermore, the absolute number of observed correlations is also different between our study (169 correlations) and Houshdaran's (40 correlations)^[@CR7]^. However, it should be pointed out that the methodology used for methylation and gene expression profiling was different in our and Houshdaran's study, and we used a paired study design, which could be the source for discordances and makes it difficult to compare the results.

Gene ontology and pathway analyses indicated that genes with a correlation between methylation and gene expression were related to extracellular matrix organization, integrin signalling and immune response, which are all important for endometrial function, and establishment of receptivity via tissue remodelling and modifying maternal immunity to facilitate implantation of the semi-allogenic embryo^[@CR18]^. Genes related to extracellular matrix organization and immune response with positive correlation between methylation and expression levels included those that have previously been associated with endometrial receptivity, decidualization and embryo implantation either in humans or animal models, such as *TGFB3* ^[@CR26]^, *ADAMTS1* ^[@CR27]^, *VCAM1* ^[@CR28]^, *IL1RL1* (also known as *ST2*)^[@CR29]^, *CXCL13* ^[@CR30]^ and *BCL3* ^[@CR31]^. Interestingly, a direct link between *BCL3* methylation and expression has also been shown in mouse endometrial cells^[@CR31]^. Although negative correlations were not enriched for any specific biological terms, they also involved genes linked with processes associated with endometrial receptivity, such as *CDK6* ^[@CR32]^, *PTCH1* ^[@CR33]^, *TDO2* ^[@CR34]^, and *ETS2* ^[@CR35]^. However, the observed statistical correlations need additional functional studies to determine the causal effect of methylation change on gene expression level.

Strengths, limitations and future directions {#Sec8}
--------------------------------------------

The current study is the first using a study design targeting specifically the pre-receptive and receptive phases of the endometrium, and large-scale genome-wide approach to characterize the endometrial tissue methylome and its correlation with gene expression. By investigating samples from two time-points from the same women within the same cycle and evaluating methylation and gene expression within the same sample, we reduce inter-individual and inter-cycle variability and provide insight into the potential biological effects of methylation changes relevant for establishing endometrial receptivity and maintaining endometrial function. For methylome profiling we used the Illumina HumanMethylation450 array, one of the most comprehensive and high-resolution arrays for this purpose, while for obtaining gene expression data, we used RNA sequencing, which is more specific and sensitive, and with a broader dynamic range for quantifying gene expression levels compared to array technology. Due to the fact that several methods are available for differential methylation analysis, with no proper benchmark, we also used multiple analysis methods for detecting site-level differential methylation, enabling to select differentially methylated sites with higher confidence. In addition, since only site-level analysis ignores potential correlation between sites and can provide redundant results, we also evaluated region-level differential methylation, which offers improved statistical power^[@CR36]^ and sensitivity^[@CR37]^.

When interpreting the results of our study, it must be borne in mind that the sample size was rather limited (a total of 34 biopsies from 17 women for differential methylation analyses, and 14 biopsies from 7 women for methylation-gene expression correlation), which means replication in a larger dataset is required. Our study had 60% power to detect (at a nominal significance level of 0.05) CpG level absolute delta-$\documentclass[12pt]{minimal}
                \usepackage{amsmath}
                \usepackage{wasysym}
                \usepackage{amsfonts}
                \usepackage{amssymb}
                \usepackage{amsbsy}
                \usepackage{mathrsfs}
                \usepackage{upgreek}
                \setlength{\oddsidemargin}{-69pt}
                \begin{document}$$\beta $$\end{document}$ changes equal to or larger than \~0.2.

Furthermore, we studied endometrial whole tissue biopsies that contain various cell types (stroma, epithelium, immune cells etc), each with potentially distinct methylation patterns, which are 'diluted' in whole tissue samples; therefore, methylation profiling of distinct endometrial cell populations separated by cell sorting or other methods is warranted and highly anticipated. If such a dataset becomes available for endometrial tissue or cells, it would also be interesting to consider the histone modifications around differentially methylated sites and regions to further understand the epigenetic regulation of gene expression in the endometrium.

Conclusion {#Sec9}
==========

Our study offers insight into the methylation pattern and correlation between methylation and gene expression during pre-receptive and receptive phase in the human endometrium, showing that the overall methylome remains relatively stable during this stage of the menstrual cycle, with small-scale changes affecting only 5% of the studied sites. The generalized results of our analyses indicate that extracellular matrix organization and immune response are the most likely pathways regulated by methylation changes. Altogether, these results provide another piece of the puzzle for understanding the molecular mechanisms governing endometrial biology and receptivity and highlight the need for similar studies in distinct endometrial cell populations.

Material and Methods {#Sec10}
====================

Ethics statement {#Sec11}
----------------

The study was approved by the Ethics Review Committee of the University of Tartu, Estonia (permission no 221/M-31). An informed consent was signed by all women before tissue collection and all methods were carried out in accordance with relevant guidelines and regulations.

Patient characteristics {#Sec12}
-----------------------

Endometrial biopsies (17 paired biopsies, a total of 34 biopsies) were obtained from 17 healthy fertile-aged volunteers (≤35 years; average ± standard deviation 30.1 ± 3.4) with average body mass index 23.6 ± 4.4. All women selected for the study reported regular menstrual cycles (25--35 days) and were clinically examined for the absence of hormonal dysbalance and/or uterine pathologies. The women self-reported to be non-smokers with no previous infertility records and had at least one live-born child. No participants had taken hormonal medications at least three months before entering the study. Endometrial tissue biopsy was obtained using Pipelle catheter (Laboratoire CCD, Paris, France) on day two and eight ( ± 1 day) after the LH surge (LH + 2 and LH + 8, respectively) within one natural cycle. These two time-points in the early- and mid-secretory endometrial phase correspond to the pre-receptive and receptive endometrium, respectively. Before taking the biopsy, the occurrence of ovulation was confirmed by ultrasound. LH surge was identified using commercial LH kits (BabyTime® hLH urine cassette, Pharmanova). Part of the collected endometrial tissue was stored in formaldehyde for histological confirmation of endometrial phase, while the rest was frozen at −80 °C in RNAlater (Ambion Inc., Austin, TX, USA). The endometrial phase (early secretory for pre-receptive time-point and mid-secretory for receptive time-point) was histologically confirmed for all biopsies included in this study.

DNA extraction and DNA methylation measurement {#Sec13}
----------------------------------------------

Genomic DNA was isolated from approximately 20 mg of endometrial tissue using AllPrep DNA/RNA/miRNA Universal Kit (Qiagen, Venlo, The Netherlands) according to manufacturer's original protocol. DNA hybridization to Infinium HumanMethylation 450 K BeadChip (Illumina, San Diego, CA, USA) was performed at USC Epigenome Center (Los Angeles, CA, USA) according to manufacturer's specifications. Raw intensity files in IDAT format were used for all following analysis steps.

RNA extraction and sequencing {#Sec14}
-----------------------------

For total RNA extraction, up to 30 mg of tissue was homogenized in the presence of QIAzol reagent (Qiagen) and processed using miRNeasy Mini kit (Qiagen), following manufacturer's protocol. Purified RNA quality (all RIN \> 7.5) was evaluated using Bioanalyzer (Agilent Technologies, Waldbronn, Germany). To perform transcriptome sequencing, cDNA libraries were generated from \~1 μg of endometrial total RNA using Illumina TruSeq technology (Illumina), following cDNA quality control with Bioanalyzer. RNA sequencing (RNA-seq) was performed at the Estonian Genome Center Core Facility using Illumina paired-end 100 bp sequencing technology according to manufacturer's specifications. The sequenced data was trimmed and adapters removed with Trimmomatic-0.32^[@CR38]^. Reads were quality filtered with FASTQ quality filter tool from FASTX-Toolkit v.0.0.14 and mapped with TopHat2^[@CR39]^ on Human genome version 19. The transcript counts were extracted with HTSeq-count script^[@CR40]^ from mapped data and further processed with Bioconductor package edgeR, which is designed for the analysis of count-based \[count-per-million (CPM)\] expression data^[@CR41]^. The CPM values provided by edgeR were used for further correlation analysis and the CPM values for the transcripts used in correlation analyses (see below) are given in Supplementary Table [8](#MOESM2){ref-type="media"}. No additional filters for CPM values were used.

RNA-seq results were selectively confirmed by quantitative real-time PCR. Details of the differential expression analysis results, which are a part of a larger endometrial transcriptome dataset, will be presented in a separate paper (Suhorutshenko *et al*. in preparation).

Normalization of methylation data {#Sec15}
---------------------------------

Data quality control and preprocessing were performed using the Bioconductor package RnBeads ver. 1.1.8^[@CR42]^. The methylation β-value (ratio of methylated probe intensity over total intensity, ranging from 0 to 1) for each CpG probe was calculated according to Illumina's formula β = m/(m + u + 100), where 'm' stands for methylated probe intensity and 'u' for unmethylated probe intensity. The methylumi-implemented Illumina scaling normalization was used, which fits with our data according to clustering (Supplementary Figure [1](#MOESM1){ref-type="media"}) \[<https://www.bioconductor.org/packages/release/bioc/html/methylumi.html>\]. Probes targeting the last 3 bases of sequence that overlaps with a single nucleotide polymorphism (SNP) were filtered out, as were cross-reactive probes^[@CR43]^. In the first filtering step, 4,823 sites were removed because they overlap with SNPs, 30,378 probes were removed because their sequences were non-specific and have a high likelihood of cross-hybridization, and 1,703 probes were removed because the RnBeads 'GreedyCut' algorithm identified them as unreliable measurements across samples. In total, 36,904 probes were removed during initial filtering. In the second filtering step (includes the normalization procedure) a total of 11,651 probes were removed, 10,287 of which were located on sex chromosomes and the rest were context-specific non-CpG probes. At the end of filtering, 437,022 out of 485,577 probes remained for subsequent analysis.

Genomic annotation of CpGs {#Sec16}
--------------------------

The genomic regions for the CpG sites were annotated using the annotation file provided by Illumina. For the location relative to a gene, the following categories were used: TSS1500 (1,500 bp upstream from transcription start site -- TSS), TSS200 (200 bp upstream from TSS), 1^st^ Exon, 5′ UTR (5′ untranslated region), Body (gene body), and 3′ UTR (3′ untranslated region). For the location relative to a CpG island (CGI), we used the following categories: island (CGI), S_Shore and N_Shore (up to 2 kb up- and downstream of the CGI), S_Shelf and N_Shelf (2--4 kb up- and downstream of the CGI), OpenSea (all others). When analysing the correlation between DNA methylation and gene expression, TSS1500, TSS200, 5′ UTR and first exon were grouped as the '5' region', whereas gene body and 3′ UTR were grouped into 'gene body'. Due to alternative transcription start sites and several genes in one region, 327 (0.07%) CpGs in total and 13 (7.7%) sites among the significantly correlated ones were assigned multiple annotations. To test for differences in methylation value distributions between genomic regions, we carried out pairwise comparisons using the Kolmogorov-Smirnov test.

Differential methylation analysis {#Sec17}
---------------------------------

For differential methylation analysis, three different approaches were used to increase the probability of achieving true positive results. Combining information from multiple methods can reduce the proportion of false positive findings and generalize the results with higher confidence, thereby increasing the reliability of the results. To make the results comparable and because the M-value is more statistically valid for differential methylation analyses^[@CR44]^, all differential methylation analyses were conducted using M-values (defined as log2 ratio of methylated and unmethylated probe intensities) calculated with lumi R package^[@CR45]^. All differential methylation analyses were adjusted for age due to the effect it has on methylation levels^[@CR46]^.

For single CpG level differential methylation analysis, we used RnBeads^[@CR42]^, seqlm^[@CR37]^, and since we detected a slightly abnormal distribution in our data, also Wilcoxon signed-rank test. False discovery rate (FDR) adjusted p-value \< 0.05 was considered as the statistical significance threshold. In the seqlm analysis, no limiting criteria were defined and all CpG sites with a FDR \< 0.05 were extracted to make reasonable comparison with other methods. Eventually, the intersection between the three sets of significant differentially methylated CpGs generated by used programs was determined to define the most likely set of truly differentially methylated sites.

In addition to site-level analyses, we also performed region-level analysis using seqlm to detect differentially methylated regions (DMRs). In this analysis, DMR search criteria were the following: at least 3 consecutive differentially methylated CpGs (FDR \< 0.05) within a 500 bp window.

To compare the distribution (in relation to gene subregions and CGIs) of differentially methylated CpG sites to the overall distribution of all analysed CpGs (n = 437,022) on the array, we used the $\documentclass[12pt]{minimal}
                \usepackage{amsmath}
                \usepackage{wasysym}
                \usepackage{amsfonts}
                \usepackage{amssymb}
                \usepackage{amsbsy}
                \usepackage{mathrsfs}
                \usepackage{upgreek}
                \setlength{\oddsidemargin}{-69pt}
                \begin{document}$$\chi $$\end{document}$ ^2^ test.

Correlation between methylation and gene expression {#Sec18}
---------------------------------------------------

For the correlation analysis between RNA-seq and differential methylation analysis results, we used 7 individuals from the original dataset (n = 17) for whom both DNA methylation and RNA-seq data was available for both time-points (7 biopsies from pre-receptive and 7 biopsies from receptive time-point). Only those CpGs that were significantly differentially methylated in all three analyses with an absolute delta-β values \> 0.1 were used for correlation analysis. CPM values provided by edgeR^[@CR41]^ and significantly differentially methylated site β-values from RnBeads were used. We only evaluated *cis*-correlations, therefore we tested for correlations between a given CpG and the gene to which it was annotated. Spearman's rank correlation coefficient was used to calculate correlations and the permutation p-values were used to evaluate the significance. For the significantly correlated CpG-gene pairs, if some region of gene of interest contained more than one CpG site, a median correlation value was calculated by region, e.g. 5′ UTR, 3′ UTR, Body, 1^st^ Exon, TSS200 and TSS1500. For example, CpGs in the *C1QTNF7* gene were located in 3 different regions (TSS200, Body and 5′ UTR). One CpG is located in the 5′ UTR, while Body and TSS200 regions contained two sites each. The median correlation was calculated separately for the *C1QTNF7* TSS200 and *C1QTNF7* Body.

Gene ontology (GO) and pathway analyses {#Sec19}
---------------------------------------

GO enrichment analysis was performed for the genes mapped to significantly differentially methylated CpGs and DMRs, and for the differentially methylated genes with significant correlation with gene expression using the web tools g:Profiler^[@CR12]^ and PANTHER (v11.1)^[@CR13]^. For graphic representation of the g:Profiler analyses we aggregated the results using the Bioconductor package GOsummaries^[@CR14]^ which internally uses g:Profiler with numerous filtering criteria in order to achieve non-redundant summaries. p-values from the g:Profiler analysis (corrected for multiple testing using the g:SCS algorithm implemented in g:profiler)^[@CR12]^ were used for depicting the pathway analysis results.

Accession codes {#Sec20}
---------------

All of the Illumina HumanMethylation450 DNA methylation data are available at the GEO database (accession number GSE90060).

Electronic supplementary material
=================================

 {#Sec21}

Supplementary Figures Supplementary Dataset 1

Viktorija Kukushkina and Vijayachitra Modhukur contributed equally to this work.

**Electronic supplementary material**

**Supplementary information** accompanies this paper at doi:10.1038/s41598-017-03682-0

**Publisher\'s note:** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The authors of the study would like to express their gratitude to all the women participating in this study, and to Dr. Peeter Karits, Dr. Elle Talving, Katrin Kepp and Silva Kasela who contributed to this project. This research was funded by grants IUT34-16 and IUT34-4 from the Estonian Ministry of Education and Research, by Enterprise Estonia, grant no EU48695, by the EU FP7-PEOPLE-2012-IAPP grant SARM (grant no 324509), by EU-FP7 Eurostars Program (grant NOTED, EU41564) and by the European Commission Horizon 2020 research and innovation programme under grant agreement 692065 (project WIDENLIFE).

V.K. and V.M. performed the statistical analyses, interpreted the results and prepared the manuscript. M.S. performed the wet-lab experiments, interpreted the results and commented on the manuscript. A.V.-M. coordinated sample collection, analysed and interpreted clinical data, and commented on the manuscript. M.P., R.M., N.R., S.A., F.J.E., J.V., K.Z. and A.S. advised on the analytic strategy, interpreted the results and commented in the manuscript. T.L.-P. coordinated the study, reviewed and interpreted the results, and prepared the manuscript.

Competing Interests {#FPar1}
===================

The authors declare that they have no competing interests.


New Rochelle, NY, May 30, 2017-A comprehensive review of four decades of national homicide data show important gender differences and trends among homicide victims and offenders in the U.S., related to prevalence and the characteristics of the crimes and the men and women involved. The article "Gender Differences in Patterns and Trends in U.S. Homicide, 1976-2015" is published in Violence and Gender, a peer-reviewed journal from Mary Ann Liebert, Inc., publishers. The article is available free on the Violence and Gender website.

Coauthors James Alan Fox and Emma Fridel, Northeastern University, Boston, MA, examined the FBI's Supplementary Homicide Reports database covering 1976-2015. They report gender differences among victims and offenders based on characteristics such as their age and race, the weapon used and circumstances of the crime, and the victim-offender relationship. Among the most striking and important findings were the trends in intimate partner homicide. The researchers discuss how factors such as the ongoing efforts to reduce domestic violence, greater availability of social and legal interventions for women victimized by intimate partner violence, reduced stigma associated with being a victim of domestic violence, and the Brady Handgun Prevention Act of 1993 may have all had an effect on the observed trends.

"This study by James Fox and Emma Fridel, 'Gender Differences in Patterns and Trends in U.S. Homicide, 1976-2015,' is a must read! These researchers studied homicide data over nearly four decades, and one of their most 'striking' findings concerned the reduction in male intimate partner victimization homicides. You will find their explanation for this reduction absolutely fascinating," says Editor-in-Chief Mary Ellen O'Toole, PhD, Forensic Behavioral Consultant and Senior FBI Profiler/Supervisory Special Agent (ret.) and currently, Director of the Forensic Sciences Program, George Mason University, Fairfax, VA.

###

About the Journal

Violence and Gender is the only peer-reviewed journal focusing on the understanding, prediction, and prevention of acts of violence. Through research papers, roundtable discussions, case studies, and other original content, the Journal critically examines biological, genetic, behavioral, psychological, racial, ethnic, and cultural factors as they relate to the gender of perpetrators of violence. Led by Editor-in-Chief Mary Ellen O'Toole, PhD, Forensic Behavioral Consultant and Senior FBI Profiler/Criminal Investigative Analyst (ret.), Violence and Gender explores the difficult issues that are vital to threat assessment and prevention of the epidemic of violence. Violence and Gender is published quarterly online with Open Access options and in print, and is the official journal of The Avielle Foundation. Complete tables of content and a sample issue may be viewed on the Violence and Gender website.

About the Publisher

Mary Ann Liebert, Inc., publishers is a privately held, fully integrated media company known for establishing authoritative peer-reviewed journals in many promising areas of science and biomedical research, including Cyberpsychology, Behavior, and Social Networking and Journal of Child and Adolescent Psychopharmacology. Its biotechnology trade magazine, GEN (Genetic Engineering & Biotechnology News), was the first in its field and is today the industry's most widely read publication worldwide. A complete list of the firm's 80 journals, books, and newsmagazines is available on Mary Ann Liebert, Inc., publishers website.


Louisiana senators hear grim predictions of budget impact

BATON ROUGE — Gov. John Bel Edwards’ administration has presented to senators a grim picture of the fallout from the House-proposed version of next year’s budget, with the governor threatening a veto if anything resembling the spending plan reaches his desk.

Over dozens of hours of hearings, agency leaders say they’d have to release prisoners early, shutter health programs and damage critical services in the financial year that begins July 1. House Republican leaders who drew up the plan call that scare tactics and say state government can’t afford the spending trajectory proposed by the Democratic governor.


Reviews
(20)

Reviews From Guests

Aziz is excellent in communication: always fast, reliable and direct answers. His apartment was cosy, very clean and in a great location: you may spend your day walking to top Hamburg's destinations with all the hectic crowds and come back to have a great sleep in his quiet home-like apartment in a real Hamburger neighborhood, not a touristy area, which guarantees you geniun experience of the great Northern German city. :)

Aziz has a beautiful apartment located right in the heart of Hamburg. You are steps away from all the cool clubs and amazing restaurants. I highly recommend you visit and if you do Aziz is a great option.

What a fantastic place to stay. Aziz is a great host, and we probably could not have chosen a bettter place to stay.
The flat is really lovely, and so central.
We would definitely try and stay there again on the next trip to Hamburg.

Aziz's Aunt was a perfect guest, extremely clean and she left the apartment in a perfect state. Being well travelled, she is also very interesting to talk to. She was the first guest I actually received a present from! I can highly recommend her to anyone!

I did not had the chance to meet Aziz and his aunt personally but I was very happy to hear his aunt really liked her stay in my flat. I enjoyed communicating with Aziz and I am sure that we will meet in Hamburg soon and that the contact will go on. Mücke


Lasting Valentines

Grandparents make a huge impact on their grandchildren’s lives. Although all my grandparents lived on another continent and I never met them, they passed on a godly heritage through their faith and prayers. In turn, I want to pass on a Christian heritage to my grandchildren and future generations I will never meet.

One way to bless your grandchildren is to give them a Valentine’s Day gift of prayer. That simply means you spend extra time praying for them. You could use Scripture verses such as these and insert their names in the blanks as you pray.

Lord, I pray that _____ will seek first Your Kingdom and Your righteousness (Matthew 6:33).

If you send your grandchildren a card by mail, e-mail, or text them, tell them you’re praying for them. You could include the specific verses you prayed.

Thank You, Lord, for our grandchildren. Help us to grow in our love for You and to model godly behavior before them. May we as a family glorify You in all we do. We love You and pray in Jesus’s name. Amen.

Lydia E. Harris is happily married to Milt, her sweetheart for 48 years. She has two married children, five grandkids, and is the author of a Bible study, "Preparing My Heart for Grandparenting" (AMG Publishers, 2010 release). She's also a tea enthusiast and writes the column "A Cup of Tea with Lydia."


well first off do you want a light or dark skin tone? how deailed do you want the models to be?

I fall into a dark holeAnd I can't come outDo you know if Merlin did existOr Frodo wore the ringDid Corum kill the godsOr where's the wonderlandWhich young Alice had seenOr was it just a dreamI knew the answersNow they're lost for me

tbh i dont my beastmen only have dark skin cuz i hate the bright scheme. but maybe mixing w.e skin tone you use with leviathan purple then washing it on may do the trick

I fall into a dark holeAnd I can't come outDo you know if Merlin did existOr Frodo wore the ringDid Corum kill the godsOr where's the wonderlandWhich young Alice had seenOr was it just a dreamI knew the answersNow they're lost for me


Gordon Owen

Gordon Owen (born 14 June 1959) is an English former professional footballer.

Career

A much travelled player, Owen was born in Barnsley and began his career at Sheffield Wednesday, featuring in the club's promotion winning season under Jack Charlton in 1979-80. After falling out of favour at the club, spending spells on loan at Rotherham United, Doncaster Rovers and Chesterfield, he left to sign for Cardiff City in August 1983. His only season at Ninian Park saw him finish as the club's top scorer with 18 goals in all competitions but at the end of the season he decided to leave the club as he wanted to return north, signing for his hometown side Barnsley, again finishing as top scorer in his first season.

Owen left Barnsley the following year, signing for Bristol City where he played over 50 times before, after a short loan spell at Hull City, he signed for Mansfield Town. He would later finish his professional career at Blackpool, having loan spells at Carlisle United and Exeter City while at the club, before moving into non-league football.

References

External links

Category:1959 births
Category:Living people
Category:Sportspeople from Barnsley
Category:English footballers
Category:Sheffield Wednesday F.C. players
Category:Rotherham United F.C. players
Category:Doncaster Rovers F.C. players
Category:Chesterfield F.C. players
Category:Cardiff City F.C. players
Category:Barnsley F.C. players
Category:Bristol City F.C. players
Category:Hull City A.F.C. players
Category:Mansfield Town F.C. players
Category:Blackpool F.C. players
Category:Carlisle United F.C. players
Category:Exeter City F.C. players
Category:English Football League players
Category:Association football wingers


Their study in Neuroimaging: Clinical provides proof-of-concept that imaging can help track neural functioning in this population, known to be affected by the virus and potentially by the treatments meant to keep HIV at bay.

The issue of neural dysfunction in the HIV-positive population is significant, says Georgetown neuroscientist Xiong Jiang, PhD, the study’s senior investigator.

“About half of people living with HIV are affected by HIV-associated neurocognitive disorders, or HAND, and we expect this condition will escalate as the current HIV-positive generation ages,” he says.

But testing for cognitive decline that is not yet obvious is difficult. “There is no one clinical screening tool that has been found to be appropriately sensitive or specific for HAND yet,” says co-author Mary Young, MD, director of Georgetown University Medical Center’s HIV Women’s Program. “Therefore, there has been great interest in developing biomarkers of overall brain health and disease in HIV to streamline diagnosis and monitoring.”

Young collaborated with Jiang on a potential biomarker based on fMRI techniques Jiang and his collaborators had developed, which have been shown to be sensitive to subtle behavioral changes.

To test whether these fMRI techniques might be capable of detecting and assessing neural injury due to HIV before symptoms occur, Jiang and Young studied 28 women (15 who were HIV-positive, with an average age of 50) from the National Institutes of Health-funded Washington, DC Metropolitan Interagency Women’s HIV Study (WIHS) cohort. WIHS at Georgetown, led by Young since 1993, includes 300 participants and is one of nine national sites designed to follow HIV infected women along with a control population.

In this proof-of-concept study, the researchers used face processing and its associated brain region, fusiform face area, or FFA, as the mean to probe neural injury in HIV. They found that the neural specificity in the FFA – estimated via fMRI-adaptation, an established technique, and local regional heterogeneity analysis, or Hcorr, a novel technique – is reduced in the HIV-positive participants. “FFA neurons were not as finely tuned in this group, compared to uninfected participants,” Jiang says.

“We are very excited about this finding because Hcorr, which can estimate neural specificity rapidly, holds the promise to serve as a research tool to examine neural injury and a clinical tool to assess and monitor HAND progression. It could also be useful for drug development as it is safe and non-invasive,” says Jiang.

Currently, there are no ways to treat HAND other than to control HIV replication, Young says “Developing defined treatments guided by biomarkers would be the next step. Imaging techniques like Hcorr could help identify dysfunction before deficits are in place,” she says. “Based on the nature of those deficits, we could then try treatments used in other conditions and or begin to develop specific compounds to study. In the meantime imaging could help identify a group that might need additional psychosocial supports to successfully navigate older age.”

The researchers say their findings also support the hypothesis that HIV-positive individuals are aging faster compared with a non-infected population.

“Our data, demonstrating the decrease in neural?specificity before the onset of behavioral symptoms, is in line with reports from aging studies and provides supports?to the accelerated aging theory,” Jiang says.

Source

The study was supported by a NEW Investigator Award from the District of Columbia Developmental Center for AIDS Research (P30AI087714), the Women’s Interagency HIV Study, and grants from the NIH (5U01AI069494 and 5P30HD040677-13).


Churston railway station

Churston railway station is on the Dartmouth Steam Railway, a heritage railway in Torbay, Devon, England. It is situated beside the main road to Brixham and close to the villages of Churston Ferrers and Galmpton.

Steam trains depart several times a day in accordance with seasonal timetables issued by the Dartmouth Steam Railway and Riverboat Company with services north to Goodrington Sands and Paignton and south to Greenway Halt and Kingswear.

History

Before preservation
The Dartmouth and Torbay Railway from Paignton railway station to Churston was opened for passengers on 14 March 1861 and for goods traffic on 1 April 1861.  The station was known as Brixham Road at the time, and the line was extended to Kingswear railway station on 16 August 1864.  The initial single platform was supplemented by a second in 1865.  The Dartmouth and Torbay Railway was always operated by the South Devon Railway Company and was amalgamated with it on 1 January 1872. This was only short lived as it was in turn amalgamated into the Great Western Railway on 1 February 1876.

In the meantime the station had changed its name to Churston when an independent branch line had been opened by the Torbay and Brixham Railway to serve the latter town on 28 February 1868. The station was now a junction but the goods shed had to be moved to a new site alongside the Brixham line to make room for a short bay platform to accommodate the Brixham trains.  Sidings were added to allow for the goods traffic handled on the branch, including a busy trade in fish. The Torbay and Brixham Railway was taken over by the Great Western Railway on 1 January 1883.

The lines had been built using the  broad gauge, but on 21 May 1892 were closed for the weekend to be converted to  standard gauge. The following year saw the platforms lengthened and a new signal box constructed.  The platforms were further lengthened and a new signal box opened on 9 February 1913 to control the now extended crossing loop.

The Great Western Railway was nationalised into British Railways on 1 January 1948. The Brixham branch closed on 13 May 1963, but the Kingswear service continued but Sunday trains no longer called at Churston after the 1967 summer season. General freight traffic was withdrawn on 14 June 1965 although coal was still handled until 4 December 1967. The crossing loop was taken out of use on 20 October 1968 and the signal box closed.

In preservation
The line was sold to the Dart Valley Light Railway plc on 30 December 1972, which operated another nearby railway at .  Since then Churston has become an important centre for engineering on the railway.

The signal box was reopened in 1979 to control a new crossing loop, albeit with electric multiple-aspect signals, and the following year the former Brixham bay platform was relaid.  In 1981 the turntable from Goodrington was moved to a position alongside the Brixham junction at Churston.  The signal box was closed again in 1991 when control of the whole line was transferred to Britannia Crossing at Kingswear. A locomotive workshop was built behind the Up (towards Paignton) platform in 1993 and the station building restored and reopened. The Brixham bay platform was then covered by a carriage workshop in 1996.

Plans
The Association of Train Operating Companies included Brixham one of fourteen towns that, based on 2009 data, would benefit from a new railway service. This would be an extension of the First Great Western service from  to  on to Churston, which would then act as a railhead for Brixham. It would also serve other housing developments in the area since the opening of the steam railway, and may require the doubling of that line between  and .

Description
The main entrance is onto the platform mainly used by trains towards Kingswear.  Signalling allows trains to use this in both directions but in practice trains towards Paignton depart from the opposite platform, which is reached by a footbridge. The station buildings are built of solid masonry with a large canopy integral with the roof. The local paper described them on the opening day "as unarchitectural as any Goth could wish".

Alongside the main platform, at the Paignton end, a modern workshop stands on the site of the platform once used by Brixham trains; this is used for carriage repairs. On the same side, beyond the modern bridge carrying the road to Brixham, are some sidings and the turntable. Opposite the carriage workshop, behind the Paignton platform, is another workshop which is used for heavy repairs to locomotives.

The former Railway Hotel at the end of the station approach road is now a 4-star guest house.

Cultural references
Agatha Christie lived nearby, and set some stories in and around the area. Churston is the 'C' in The A.B.C. Murders.

Churston station was the location for the opening scene in The System, a 1964 film.  Several of the main characters arrive at the last minute and jump onto a Diesel Multiple Unit that is just pulling out towards Kingswear. After the opening credits, they then get off a different train at Brixham.

The station also appears (in colour) in the 1939 film Sons of the Sea.

Services
A seasonal service of steam hauled trains operates between  and .

References

Further reading

External links
  Dartmouth Steam Railway and Riverboat Company Timetables

Category:Former Great Western Railway stations
Category:Heritage railway stations in Devon
Category:1861 establishments in England
Category:Railway stations opened in 1861