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"If anyone should know football, it's mom! She lives it everyday!"
That's a quote from Kerra Bowers' new book, "Pocketbook Coach for Moms: Football." Bowers has written a book just for moms, explaining football in a fun and interactive way, and it is in a compact book that a mom can take with her to the games.
The book has quite a bit of humor. It's simply enjoyable to read. Describing the way that moms "live' football everyday has every football mother nodding, from the "offense" she handles (trying to get things done versus trying to prevent problems), to how life and a game are common. A football team typically has one game a week, but a mom's game lasts for 16-18 hours.
So, imagine being married to a coach for 20 years and having three sons, and not knowing the ins and outs of the game. Kerra is married to Rob Bowers, head coach for the Richmond Spartans. Her three sons are playing the game.
"Football is a second language," said Bowers. "My family started talking football and they said it was no longer okay for the coach's wife to not speak 'football.'"
Describing her metamorphosis from 'clueless' to really learning it, Bowers said, "When Colby became a freshman, that's when I really had to crack down. I like watching him be in his niche. I studied. I got books, went to web sites and did research. I had Stan Kee, legendary coach from Carrollton and Scott Summers proof for me. I asked Rob questions to be sure I was understanding it. He gave me a man's answer – I wanted this from a mom's perspective! Men want to know 'when' and 'where.' Moms want to know 'why?"
Bowers said she 'thought about the book for six months.' She originally intended to do a simple set of flash cards. "But football isn't simple," she said. "The teacher in me came out and I had to break it down to level. I realized there was a real niche as a sports tutor for rookies." It took her about nine months to write.
It wasn't just football that Bowers took into consideration. Having taught for 13 years and being a former at-risk counselor, she understands children and their various stages of development. She knows the issues children and families face.
"In the 1990s, one out of three children were born to single moms. About a third of marriages end in divorce. I saw a potential way to bring moms and sons together. That's why the book is in flash card format. It has dry erase areas so your son can show you what his play is and why a play didn't work, or did."
As passionate as one can be on the subject of high school football, Bowers goes into the benefits of the game and being part of a team.
"Why do we have gangs? Because, kids need a place to belong. That's why school spirit is so important. Students that have school pride have fewer drug problems and grade problems. We need to provide memorable experiences rather than trinkets," said Bowers. "There are so many life lessons in football. Positions change week to week. If your son isn't playing, ask him why he isn't.
"Football is unique. It's the only sport I can see where they truly appreciate differences, build each other up, protect each other – literally – even if they don't like each other. It's the closest simulation to combat. There's a place for almost every boy because there's a place for almost every talent. It takes such a variety of skills and talents to use on a football team and to contribute to success."
This endeavor has been spiritual too. That has been a blessed realization to Bowers.
"God has put people in line to tell me what the next step is," she said. "I know He already knows what's happening next and I can't waste time worrying about it."
Inspirational quotes and motivations are included throughout the book, as is information regarding referees, volunteers and a complete description of the game from the field and its various parts to the offense, the defense, who the players are and what they do, to the hand signals of the referees and what the flags mean. Tough love and 'Taking Care of My Baby," from a mom (concerned about her son) to a coach. There's even a section containing advice from coaches and veteran football moms.
Bowers' book has already received recognition. She appeared on Metro Youth Sports, doing five segments for Natalie Moultrie, targeting youth high school sports.
"It brings moms and sons together and it brings teams together," Bowers continued. "One of the best early moments into this was when Colby was talking about some offensive strategy and I said, 'In the 5-2 defense…' and the expression on his face was priceless! This is why you need this book!"
The book, "Pocketbook Coach for Moms: Football," can be ordered online at www.pocketbookcoach.com. To ask questions, or for purchasing the book to use as a fundraiser for your organization, contact Kerra Bowers at email@example.com, or phone 816-419-5853.
Click here for our E-edition and read the rest of the story.
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text: I found this bottle of Port in one of my local restaurants (Nandos in Norwich) and had to take a photograph. My family (and the rest of the diners) thought I was completely mad. Old Invalid Port just tickled my sense of humor and made me remember how many implementation issues have been because a firewall is improperly configured and blocking the important port that the application needs to work.


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RSV-VAL-0010 when validating a report
Technote (troubleshooting)
Validating a report gives the error:
RSV-VAL-0010 Failed to load the report specification. CCL_ASSERT_NAMED(m_bAllRowsOptimization == FALSE, "The master dataset cannot be re-used for a second iterator if AllRows optimization was applied when the query was executed.");
Resolving the problem
Make sure that all page objects in the report have a query assigned to them and its not left blank.
Document information
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Cognos Business Intelligence
Report Studio
Software version: 10.2, 10.2.1, 10.2.1.1, 10.2.2
Operating system(s): AIX, HP-UX, Linux, Solaris, Windows
Software edition: All Editions
Reference #: 1459804
Modified date: 05 June 2017
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Tennis ace Andy Murray has cruised through to the second round of the US Open by beating Lukas Lacko from Slovakia in straight sets.
He beat his opponent 6-3 6-2 6-2 in under two hours, with temperatures in New York of up to a scorching 37C!
The British number one now faces Jamaican Dustin Brown in round two.
Murray is seeded fourth in the tournament. The 23-year-old is still looking to win his first Grand Slam title.
But there was more good news for Murray when rival Tomas Berdych, who he could have faced in the quarter finals, got knocked out.
Czech player Berdych reached this year's Wimbledon final and beat Murray in the French Open in May.


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Medical Definition of Fever with renal syndrome, hemorrhagic
Fever with renal syndrome, hemorrhagic: A set of diseases, also known as hemorrhagic fever, characterized by the abrupt onset of high fever and chills, headache, cold and cough, and pain in the muscles, joints and abdomen with nausea and vomiting followed by bleeding into the kidney and elsewhere.
Many forms of certain viruses (arboviruses, including those in the families Togaviridae, Flaviviridae, Filoviridae, and Bunyaviridae and the Hantaviruses, spread by rodents or biting insects), can cause this disorder, as can the Ebola virus.
Bioterrorism -- There has been concern about the hemorrhagic fever as a possible weapon for bioterrorism. However, the General Accounting Office, the investigative arm of the US Congress, considers hemorrhagic fever to be an "unlikely" biologic threat for terrorism, because these viruses are very difficult to obtain and process, unsafe to handle, and relatively unstable.
Last Editorial Review: 7/1/2016
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Alternatively, feel free to tweet me: @steviephil
Guest blogging policy: Please understand that I only accept guest blog posts from people I already know pretty well, so if you're thinking of getting in touch but we don't know each other (yet!) then please refrain. I don't mean that to be harsh, but I wouldn't want you to waste your time. Thanks!


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Hallelujah! Since time immemorial, mankind has dedicated magnificent buildings to the Gods. They are spiritual havens of peace, places in which things follow a different, divine rhythm: churches, cathedrals and minsters, synagogues, mosques and small pilgrimage chapels, whether Christian or non-Christian places of worship, are almost always ornately decorated and often top attractions among tourists. Cologne Cathedral, the Church of Our Lady in Dresden and Ulm Minster – many still serve as places of worship today, others are now sacred museums. But these heavenly buildings all have one thing in common: they are the most fascinating places to visit. Judaism is one of the main world religions and originated more than 4,000 years ago. Around the world, there are around 14 million people who pray in synagogues, whose God is called Yahweh, for whom Saturday, the Sabbath, is a day of rest and for whom the Star of David is the common symbol. There were also many notable figures who, in the course of history, have become role models for Christians because of their beliefs and their commitment to the church and society. Many of them are saints or have been beatified, and they are revered by the church. All of these people achieved great things and have left their mark on the world, never to be forgotten.
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Rams look to stop their free fall against Cards
Two teams, two nosedives. One will pop a parachute. The other will whisper the Rosary.
The last time we saw the St. Louis Rams against the Arizona Cardinals, both teams had crashed the NFC West penthouse. The Rams were 2-2, coming off a surprise victory over the Seattle Seahawks. Meanwhile, the Cardinals were 4-0, spinning straw into gold by claiming three triumphs by a combined nine points. Life was grand for a pair of lightweights who had learned to throw a swift jab or two. Life was good in their newfound roles.
Talk about your mayday. Since St. Louis beat Arizona 17-3 on Oct. 4 at the Edward Jones Dome, victories for either team have been more elusive than a healthy Kevin Kolb. The Rams have lost four of their last five games, a stretch that includes a tie against the San Francisco 49ers that was as satisfying as Tofu Turkey for a red fox. The Cardinals, meanwhile, have lost six consecutive, and they looked as attractive as Roseanne Barr in doing so.
Someone will find relief Sunday at University of Phoenix Stadium. Someone, anyone. That is, of course, if the Rams don't decide two knots on their record look better than one.
No matter, because the NFC West standings look much less welcoming than when these two met in Week 5. You have the 49ers at the front of the parade, the Seahawks a few steps back and the Cardinals and Rams tailing both sweeping the mess left behind.
No one said life in a free fall is glamorous.
On to Week 12…
How will the Rams respond after sleepwalking against the New York Jets?
There have been more grumbles under the Arch this past week than wet glasses in Soulard. It has become the question with no answer that satisfies: What, exactly, happened in the loss to New York last Sunday?
The Rams committed three turnovers, and the Jets none. Sam Bradford looked like Sam Badford in being outdueled by Mark Sanchez, who entered with skid marks all over his reputation. The Rams' defense was thrown under a road grader, allowing at least 24 points for the fourth consecutive game.
There were boos. There was a blowout. If you didn't know better, this was 2011, all arrows pointed down and a scorched Earth strategy was about to be enacted at Rams Park.
Yes, this team is young. Yes, the storyline has been mentioned so many times that words devoted to the narrative could fill an Encyclopedia Britannica set.
But we're past that. Or at least we should be. It's late November. This is fact: The Rams have regressed since early October.
Where's the early season spark? Where's the team that played with the 49ers at Candlestick Park but muffed multiple chances to win? Does anyone know where the Rams' minds are drifting leading into this game?
Will they play mad, like this did against the Washington Redskins and San Francisco? Or will they play uninspired, like they did against the New England Patriots and Jets?
Many questions. Precious few answers.
How will the Rams keep the pressure on rookie quarterback Ryan Lindley?
Admission: Lindley was Greek to this space before this week. Early, it was obvious that the Cardinals had issues at quarterback. After all, Kolb was mashed into tire pellets during Arizona's visit to St. Louis.
Remember that night? The Rams sacked Kolb nine times. They forced him into throws under pressure on 12 occasions. By his walk back to the locker room, Kolb was woozy, and the Cardinals' dumpster fire had begun.
Enter Lindley, who receives his first start Sunday. He completed 9 of 20 passes for 64 yards in a loss to the Atlanta Falcons last week. He's a sixth-round draft pick from San Diego State, and that's probably where a likeness to Marshall Faulk ends.
Arizona's health issues at quarterback could be St. Louis' gain. The Rams have a favorable foe behind center for a second consecutive Sunday. But as they learned last week, no matchup is won on a sheet of 8 ½ by 11.
Consider how St. Louis made Sanchez and San Francisco reserve Colin Kaepernick look like Pro Bowlers at times in the past month. The Rams haven't forced a turnover in five games, so this would be a fine time to buck the trend.
What will happen?
Are the Mayans busy? Good luck to anyone trying to sort the stars before this one. Both teams ache for a win, but both have shown a knack for playing a banana peel to their efforts to slide into the win column.
With the Rams, you should have an angry group. The last two weeks have probably tasted as sweet as vinegar around Earth City, Mo. The navy and gold is a strange bunch: One week they're up and the next they're down; one week they show signs of growth and the next they're swimming in quicksand. Who knows which personality will step off the bus in the desert?
With the Cardinals, you should have a group desperate to flip fortunes. The 4-0 start was a chunk of fool's gold. Still, their defense has some pride left. (It ranks fourth in the league in passing defense by allowing 204.7 yards per game.) Expect resistance. Expect a four-quarter game.
Both teams are desperate. Both are free fallin'.
When in doubt, go with the horse that has proven itself.
Pick: Rams 17, Cardinals 13
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While global measures of cardiovascular (CV) risk are used to guide prevention and treatment decisions, these estimates fail to account for the considerable interindividual variability in pre-clinical risk status. This study investigated heterogeneity in CV risk factor profiles and its association with demographic, genetic, and cognitive variables.
A latent profile analysis was applied to data from 727 recently postmenopausal women enrolled in the Kronos Early Estrogen Prevention Study (KEEPS). Women were cognitively healthy, within three years of their last menstrual period, and free of current or past CV disease. Education level, apolipoprotein E ε4 allele (APOE4), ethnicity, and age were modeled as predictors of latent class membership. The association between class membership, characterizing CV risk profiles, and performance on five cognitive factors was examined. A supervised random forest algorithm with a 10-fold cross-validation estimator was used to test accuracy of CV risk classification.
The best-fitting model generated two distinct phenotypic classes of CV risk 62% of women were "low-risk" and 38% "high-risk". Women classified as low-risk outperformed high-risk women on language and mental flexibility tasks (p = 0.008) and a global measure of cognition (p = 0.029). Women with a college degree or above were more likely to be in the low-risk class (OR = 1.595, p = 0.044). Older age and a Hispanic ethnicity increased the probability of being at high-risk (OR = 1.140, p = 0.002; OR = 2.622, p = 0.012; respectively). The prevalence rate of APOE-ε4 was higher in the high-risk class compared with rates in the low-risk class.
Among recently menopausal women, significant heterogeneity in CV risk is associated with education level, age, ethnicity, and genetic indicators. The model-based latent classes were also associated with cognitive function. These differences may point to phenotypes for CV disease risk. Evaluating the evolution of phenotypes could in turn clarify preclinical disease, and screening and preventive strategies.
ClinicalTrials.gov NCT00154180
Citation: Dowling NM, Gleason CE, Manson JE, Hodis HN, Miller VM, Brinton EA, et al. (2013) Characterization of Vascular Disease Risk in Postmenopausal Women and Its Association with Cognitive Performance. PLoS ONE 8(7): e68741. https://doi.org/10.1371/journal.pone.0068741
Editor: Renate B. Schnabel, University Heart Center, Germany
Received: January 5, 2013; Accepted: June 2, 2013; Published: July 17, 2013
Copyright: © 2013 Dowling et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The KEEPS-cog project was supported by grants from the National Institutes of Health (NIH) R01 AG029624, P50AG033514, and R01AG031790. The parent KEEPS trial is funded by grants from the Aurora Foundation to the Kronos Longevity Research Institute, NIH HL90639 to VMM, Mayo CTSA1 UL1 RR024150, the Einstein College of Medicine CTSA UL1 RR025750, KL2 RR025749 and TL1 RR025748, the Mayo Foundation, the United States Department of Veterans Affairs Puget Sound Health Care System, Brigham and Women's Hospital/Harvard Medical School CTSA, CTSA UL1 RR024139 and UCSF CTSA UL1 RR024131 from the National Center for Advancing Translational Sciences (NCATS), a component of the NIH and NIH Roadmap for Medical Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: Bayer HealthCare Pharmaceuticals, Inc. supplied the CLIMARA® estradiol and placebo patches and Abbott Laboratories (formerly Solvay Pharmaceuticals) provided the micronized progesterone (PROMETRIUM®) and placebo capsules. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.
Several known risk factors for the development of vascular disease have been linked to not only cardiovascular (CV) disease endpoints, but also to accelerated cognitive decline and prodromal Alzheimer's Disease (AD) [1]–[5]. Despite public awareness campaigns and the availability of well-established preventive options, CV disease remains one of the leading causes of death for women in the United States [6]–[7]. Menopause-related changes in hormonal profile may potentiate the increased risk in CV disease. Postmenopausal women are at higher risk than age-matched men, possibly due to gonadal failure and reduced gonadal steroid production [8]. Estrogens play a key role in maintaining adequate levels of high-density lipoprotein cholesterol (HDL-C), a positive influential CV health factor and a significant independent predictor of nitric oxide-dependent coronary vasodilation (as measured by flow-mediated dilation) in healthy individuals [9]–[10]. In contrast, elevated plasma levels of low density lipoprotein cholesterol (LDL-C) contributes to endothelial dysfunction and progression of coronary heart disease [11]–[12]. Endothelial dysfunction, often characterized by decreases in production of nitric oxide, is mediated not only by lipid profile, but also by other putative CV risk factors such as elevated glucose and triglyceride levels and high-sensitivity C-reactive protein [13]–[14].
The association between these classical CV disease risk factors and the magnitude of reactive hyperemia in small arteries can be observed in individuals with no history of CV disease and normal lipid profile [15]. Findings further suggest that menopause and aging also are independent risk factors for endothelial dysfunction in normotensive women [16]. Moreover, predisposing vascular disease risk factors such as central obesity, unhealthy diet and physical inactivity may synergistically increase risk for vascular disease (for example, low serum HDL-C, diabetes, and elevated blood pressure). Lower education and income are socio-economic factors often linked to increased risk of vascular disease, especially in women [17]–[18]. Genetic characteristics such as the apolipoprotein E (APOE) gene polymorphism, particularly the presence of an ε4 allele (or APOE4 isoform), are frequently linked, not only to hyperlipoproteinemia, but also to AD and cognitive impairment in non-demented adults [8], [19].
The importance of a multifactorial approach to the evaluation of both traditional and newer markers of vascular risk has been shown to augment the predictive accuracy of global estimates of CV risk [1], [20]. Thus there is an important need for increased understanding of the prevalence of these risk factors as well as their covariation. However, the assessment of a multiplicity of risk factors often involves the use of laboratory values with no "natural" cut-off between "normal" and "abnormal" levels primarily because some of these measurements (e.g., lipid levels) are continuous. Moreover, the threshold or cut-point at which a potential risk factor in the continuum of risk exposure can be considered a "true" risk is a subject of debate [21]–[22]. For instance, specific thresholds for arterial hypertension and hyperlipidaemia are on an arbitrary dichotomy [23]. It is also possible that cut-off criteria for conventional vascular risk factors may in fact vary systematically by gender, race/ethnicity or socio-cultural background. Indeed, global measures of risk based on a graded summation of factors, such as Framingham-based risk scores, have required re-calibration to improve accurate risk estimation in older women [24] and in ethnic minorities [25]. Moreover, the interactive influence of these characteristics may give rise to complex differential effects on health outcomes. Even when the assessment of vascular disease risk yields results within clinically 'acceptable' ranges there can be considerable between- and within-individual heterogeneity in the measures considered in the global evaluation of "risk."
Using a finite mixture modeling approach, [26] this study aimed at elucidating potential phenotypic heterogeneity in risk based on multiple measures of vascular disease risk obtained at baseline from a cohort of postmenopausal women enrolled in the Kronos Early Estrogen Prevention Study (KEEPS) [27]. We hypothesized that 1) co-variation among multiple manifest vascular disease risk factors could be fully explained by a discrete latent variable (latent groups) capturing heterogeneity in the sample and 2) latent group or class membership will be associated with demographic, racial/ethnic, genetic, and cognitive function variables.
Sample Description and Setting
KEEPS and KEEPS-Cog studies were reviewed and approved by Institutional Review Boards at all nine enrollment sites and at the University of Wisconsin, the KEEPS Cognitive and Affective (KEEPS-Cog) coordinating site. IRB numbers for KEEPS institutions: The central KEEPS and Phoenix KEEPS (IRB protocol by the Western IRB): STUDY NUM: 1058663 and WIRB PRO NUM: 20040792KEEPS (main study & cognitive substudy) #10-02980 and MDBHAS #11-05383. Brigham and Women's Hospital (Partners): #2004-P-002144 BWH. Mayo Clinic: 2241-04. Columbia: IRB#: AAAA-8062. Yale: 0409027022. University of Utah: 13257. Einstein/Montefiore: 04-08-213. University of Wisconsin, Madison: H-2005-0059. University of California, San Francisco (UCSF): KEEPS (main study & cognitive substudy) #10-02980. University of Washington: IRB #26702; VAPSHCS IRB #01048.
All participants provided written informed consent to participate in the main KEEPS study and in the KEEPS-cog ancillary study. The ethics committees approved the consent procedure utilized in the study. Enrollment occurred between August 2005 and July 2008 with final visits completed in 2012.
Data for this study were obtained from the multisite KEEPS and KEEPS-Cog substudy. The parent study, KEEPS, was a randomized, blinded, placebo-controlled clinical trial designed to compare the effect of 48 months of treatment with low-dose oral conjugated equine estrogen and transdermal estradiol to placebo on cardiovascular endpoints in recently menopausal women [27]–[28]. The KEEPS-Cog ancillary study aimed to evaluate the potential differential efficacy of the two forms of menopausal hormone therapy (MHT) on cognitive and mood function. Participants were recruited from nine sites across the nation. Exclusion criteria for the trial included the presence of past or current CV or cerebrovascular disease, uncontrolled hypertension, and use of lipid lowering medications. Determinations of "low risk" for CV disease were based on body mass index (BMI), blood pressure, fasting cholesterol and glucose values, tobacco use, and assessment of coronary artery calcification (CAC) measured by computerized tomography (CT). For a more detailed overview of the KEEPS study design, sample enrollment criteria, and randomization and data collection procedures, please refer to the comprehensive descriptions provided in Harman et al. [27] and Miller et al. [29].
The mixture modeling analysis used baseline (pre-randomization) data from 727 postmenopausal women, between the ages of 42 and 58, who were within 3 years of their final menstrual period. Table 1 presents a summary of the sample characteristics at study entry. In terms of demographic characteristics, the sample was predominantly non-Hispanic white (80.5%), averaged 53 (SD = 2.6) years of age, and 73.5% reported having obtained a minimum of a college degree. Additionally, of those consenting to DNA testing (N = 596), 26% had at least one APOE ε4 allele; a genetic risk marker often associated with adverse changes in cognitive functioning occurring prematurely during the aging process, [30]–[31] CV disorders in middle age, [32]–[33] and shown to interact with female gender [34]–[35]. Most participants (87%) were entirely free of CAC at baseline. The remaining 13% of the sample had CAC volume scores ranging from 0.015 to 50.
Selected anthropometric, clinical, serum, and behavioral indicators of vascular disease risk for the total sample at baseline are also summarized in Table 1. The mean body mass index (BMI) was 26.19 kg/m2 (SD = 4.31) with close to 50% of the sample with a BMI ranging from 26 to 35 kg/m2, which is considered overweight or obese as defined by published standards [36]. Approximately 33% of the sample had a waist circumference above the cut-off score for female central/abdominal obesity (>88 cm) among Caucasians in the United States [37]. Waist circumference measures for the total sample ranged from 57 to 256.5 cm (M = 83.2, SD = 15.2). A relatively small percentage of women in the sample (6.9%) self-identified as current smokers. Although mean values for other vascular disease risk factors shown in Table 1 were within 'normal' reference standards, the lower (e.g., HDL-C) or upper (e.g., total cholesterol, triglycerides, LDL-C) limit for the range of measures, in most risk factors, were slightly beyond the boundaries of 'clinically desirable levels.
Laboratory Analyses and Anthropometric Measurements
Seven vascular disease risk variables were used as surrogates for latent class membership in the analysis. These included six absolute measures (BMI, carotid artery intima-media thickness (CIMT), LDL-C, fasting blood glucose (FBG), HDL-C, and triglycerides) and a global average value of risk based on Framingham point scores (FPS) [38]. All participants underwent venous blood draws in the morning after at least 12 hours of fasting. Blood samples for lipid, glucose, and triglycerides levels measurements were sent to and analyzed by Kronos Science Laboratories (Phoenix, AZ). Blood pressure readings were taken in the morning at least 30 minutes before the blood draws or weight measurement. CIMT was measured by high-resolution B-mode ultrasound [39]. FPS were computed following standard procedures for points assignment and summation described in Wilson et al. [38] from the following six variables: 1) age, 2) systolic blood pressure, 3) diastolic blood pressure, 4) smoking, 5) HDL-C, and 6) total cholesterol. For each of these risk factors, points were assigned according to the level of associated risk.
Height (cm) and weight (kg) measurements were obtained as part of KEEPS health examination protocol and BMI was calculated as weight divided by height squared (kg/m2). Table 2 summarizes the zero-order correlations for the seven vascular disease risk variables at baseline. As expected, all variables were significantly correlated with at least one other variable in the set; with triglycerides and HDL-C having the highest inverse correlation (ρ = −0.486; p<0.002).
APOE Genotyping
APOE genotype was determined from DNA extracted from venous blood samples obtained from subjects who gave informed consent for genetic analysis. Blood samples were collected in ethylenediaminetetraacetic (EDTA) tubes during participants' health examination. DNA was amplified by polymerase chain reaction using specific primers for the APOE gene. The DNA was then sequenced and analyzed for genotype using the FinchTV program (Version 1.3; Geospiza, Inc). APOE4, as well as age, race/ethnicity, and education (as an indicator of socio-economic status-SES) were modeled as predictors of latent class membership.
Assessment of Cognitive Function
As part of the KEEPS-Cog substudy protocol, 662 participants were administered a comprehensive neuropsychological test battery by personnel trained in standardized assessment and scoring procedures. In order to efficiently analyze the cognitive functioning of study participants, a total of 25 test variables were first grouped into cognitive domains based on theoretical considerations. By summarizing our neuropsychological battery into cognitive domains, we limited capitalizing on chance associations in subsequent statistical analyses. These theoretical groupings were then tested iteratively using confirmatory factor analyses (CFA) [40]. We used multiple criteria and recommended thresholds for model selection [41]. These included: 1) comparative fit index (CFI) and Tucker-Lewis index (TLI) greater than 0.95, 2) root mean squared error of approximation (RMSEA) [42] less than 0.05, and 3) the smallest Bayesian information criterion (BIC) [43] value. All models were estimated using maximum likelihood (ML) estimation procedures with standard errors robust to non-normality of observations. The statistics software R, Version 2.15.1 (http://cran.r-project.org/) and the package lavaan, [44] Version 0.4–14, were used to fit the CFA models.
After testing a series of competing models, a bi-factor structure, [45] including a final set of 18 test variables sharing a common underlying construct, provided the best fit to the data. (For a detailed list of the tests included in the final model and an illustration of the bi-factor model, see Figure 1 and Table S1. Table S2 presents a summary of the bi-factor solution. The bi-factor model included a single broad, general construct or factor (labeled global cognition) and four specific and distinct factors uncorrelated with and varying independently of the general or global cognition factor. The four specific factors were labeled as 1) verbal learning & memory 2) auditory attention & working memory, 3) visual attention & executive function and 4) speeded language & mental flexibility. Scores on these five factors were modeled as outcomes conditioned upon latent class membership.
Eighteen variables from nine tests were used to estimate the model with a global cognitive factor capturing covariation across all variables and four independent secondary factors explaining specific shared covariations beyond that shared with other variables.
Analytical Approach
To examine sample heterogeneity or clustering, we used multiple vascular disease risk indicators as responses in a finite mixture modeling approach. As mentioned above, the vascular disease indicators included: BMI, HDL-C, LDL-C, triglycerides, FBG, CIMT, and FPS. Since the components of finite mixture densities are modeled as latent classes, the analysis is also known in the literature as latent class cluster analysis or latent profile (LP) analysis [46]–[47]. Within this modeling framework, the clusters or "classes" are not predefined; they are estimated by the model. That is, class membership is unobservable and termed latent. It is reasonable to expect that these indicators will be statistically dependent; for instance, people with elevated triglyceride levels also tend to have low HDL-C levels and other conditions such as obesity and metabolic syndrome. An underlying assumption in the LP analysis is that indicators are associated because the study population is comprised of a mixture of subpopulations or classes [48]. A related underlying principle is that as the number of classes increases the indicators become more "homogeneous" or "locally independent" within class. That is, the mutually exclusive classes derived by the LP model maximize between-group variance and minimize within-group variance.
To determine sample heterogeneity as a function of vascular disease risk, we iteratively examined the plausibility of LP models with one, two, and three-latent class solutions. Models were compared by examining multiple fit criteria: [49] 1) a comparison of an c-class solution to an (c+1)-class using a Lo-Mendel Rubin likelihood-ratio tests (LRT) [50] with the choice of the most parsimonious model, 2) BIC, and 3) overall model interpretability. We also used relative entropy as a model selection criterion and the requirement of at least 5% of the sample in each class. Relative entropy is a measure of how well the observed indicators predict class membership with values ranging from 0 to 1 and higher numbers indicating better classification. The final decision on the number of classes needed to test the mixture model hypothesis not only took into account model fit indexes, but also an observed separation of classes showing structure and response patterns that were interpretable and meaningful from a theoretical and clinical perspective.
After rigorous model fit and selection procedures for unconditional models (no covariates) using the full pre-randomized sample (N = 727), we incorporated four predictors of class membership into the model, in the same step in which the measurement model was run, and re-assessed the composition of the classes. The predictors included: 1) age in years, 2) education level (dichotomized as college degree or higher versus below a college degree), 3) APOE4 status (carriers of the ε3/4 or the ε4/4 genotype were categorized as "1;" the absence of the ε4 allele was categorized as "0"), and 4) racial/ethnic background (categorized as non-Hispanic White, non-Hispanic Black, or Hispanic). Other races/ethnicities were excluded from the analysis performed at this stage, because the number of participants in these groups was not large enough to support meaningful comparisons and ensure some prevalence of the predictor level across classes.
Upon the final model selection, each participant was allocated to the most probable latent class, that is, the class with the highest posterior probability of membership. The posterior probability is a function of the parameters of the LP model, covariates, and the participant's vascular risk profile.
Finally, estimated latent classes were modeled as explanatory variables of cognition in separate analyses; one for each of the five independent cognitive outcomes. The Wald test, a chi-square analog of an F-statistic in analysis of variance, was used to assess the significance of the association between latent classes and cognitive function. (The full model is illustrated in Figure 2.) A total of 162 cases in the pre-randomized sample (22%) had at least one missing data point on the predictors and/or cognitive outcomes. (Figure 3 shows a schematic diagram of the steps in the analysis process.).
BMI = Body mass index; CIMT = Carotid artery intima-media thickness; LDL-C = Low density lipoprotein-cholesterol; HDL-C = High density lipoprotein-cholesterol; FSG = Fasting blood glucose.
Models were estimated via full information (direct) maximum likelihood algorithms using all available data.
The nature of each latent class or "phenotype" was examined by plotting the class-specific estimated mean values against each vascular disease risk variable across classes and inspecting the characteristics of the latent class in terms of CV disease risk profile. To examine the degree to which each class membership could be impacted by demographic and genetic covariates, we used multinomial logistic regression. Odds ratios (ORs) were reported comparing the association between covariates and latent class membership. All LP models were estimated in MPLUS, Version 6.10, using full information ML methods via the expectation-maximization (EM) algorithm to handle missing data [51]–[52]. ML estimation was performed under the assumption of missing at random (MAR). [52] We estimated robust standard errors to account for the non-normality of indicator variables. Results yielding a p-value less than 0.05 were deemed statistically significant.
Cross-validation Through Supervised Machine Learning
Bias due to over-fitting is a common criticism of "in-sample" model selection in latent class modeling. We used a supervised random-forest (RF) classification algorithm with a 10-fold cross-validation estimator [53] to assess overall classification accuracy (or error rate). Latent class membership was modeled as the outcome conditioned upon vascular risk variables. Details of the RF procedure are explained elsewhere [53]–[54]. Briefly, in an attempt to reduce the bias of a "single tree" prediction of classes, ntree bootstrap samples are drawn from the total data set and for each of the samples, a classification tree is grown. The split of each node in the tree is based on a random sample of predictors. New data are predicted by aggregating the ntree classification trees (i.e., the majority votes for the classification). The accuracy or "error rate" is estimated by predicting the data not in the bootstrap sample (generally 1/3 of the sample) using the classification tree obtained with the bootstrap sample (2/3 of the sample). All these predictions are aggregated to obtain an estimate of misclassification or error rate. In our analysis, we grew a total of ntree = 1,000 trees. As part of the algorithm, RF estimates variable importance measures for each tree through permutation of variable values. Variable importance is defined as the average increase in error over all the trees (mean decrease accuracy) grown in the classifier. The analyses were performed with the randomForest [55] package in R, Version 2.15.1 (http://cran.r-project.org/).
Latent Profile Analysis
Results for the sequence of unconditional models fitted to the joint distribution of the seven CV disease risk factors indicated that a 2-class LP model adequately fit the data. As shown in Table 3, the unconditional 2-class model had the highest classification accuracy or Entropy (0.802), the lowest BIC value, and a reasonable classification of vascular disease risk patterns. The classification accuracy was substantially lower (0.684) for the 3-class model. Despite the rejection of the Lo-Mendell-Rubin LRT test in favor of the 3-class model (marginal p-value = 0.04), the separation of classes was less interpretable. Including age, education level, race/ethnicity, and APOE4 as predictors of class membership (that is, the conditional model) improved overall model fit and did not change significantly the prevalence of risk in the two classes. BIC values were lower than those obtained in the unconditional model and the Entropy for the 2-class model increased to 0.811. Additionally, the LRT test indicated that a three-class solution did not represent a significant improvement over the two-class model (p = 0.054). Therefore, a 2-class model was chosen as the best fitting model.
The patterns of estimated vascular disease risk measures and posterior probabilities assigned to each individual were used to label the latent classes. The first class was labeled "high-risk" because participants were more likely to have lower values on HDL-C and higher values on triglycerides, BMI, LDL-C, FBG, CIMT, and FPS (see Figure 4). The opposite was observed in the second class, labeled as "low-risk." That is, participants tended to have higher HDL-C levels and lower triglycerides, LDL-C, FBG, CIMT, and FPS values. The prevalence in the "high-risk" and "low-risk" class was 38% and 62%, respectively. Table 4 shows the observed and model-estimated means for all the vascular disease risk variables by latent class. Using an independent samples t-test, mean differences between groups were highly significant for all vascular disease risk variables (p-values<0.001).
BMI = Body mass index; CIMT = Carotid artery intima-media thickness; LDL-C = Low density lipoprotein-cholesterol; HDL-C = High density lipoprotein-cholesterol; FSG = Fasting blood glucose.
Predictors of Class Membership
Age, education, and race/ethnicity were predictive of class membership (see Table 5). Older age and a Hispanic background increased the probability of being in the "high-risk" class (OR = 1.140, p = 0.002; OR = 2.621, p = 0.012; respectively). Women with a college degree or above were more likely to be in the "low-risk" class (OR = 0.63, p = 0.044). The prevalence rate of women with at least one APOE-ε4 allele was also higher in the "high-risk" class compared with rates in the "low-risk" class (OR = 1.52). However, APOE4 was not predictive of class membership (p = 0.073).
Association between Latent Classes and Cognitive Function
The Wald test of parameter constraints yielded a statistically significant association between latent classes and two cognitive factor scores obtained from the bi-factor solution: 1) speeded language & mental flexibility (χ2(1df) = 6.995; p = 0.008) and 2) the general global cognition factor (χ2(1df) = 4.786; p = 0.029). The estimated mean cognitive scores were significantly better in the "low-risk" class for speeded language & flexibility (M = 0.068) and global cognition (M = 0.772) compared to those obtained in the "high-risk" class (M = −0.139; M = −0.110, respectively). In a post-hoc analysis, we estimated the effect of posterior probabilities of class membership on cognitive performance across all domains after controlling for group differences in age and education. The relationship between the probability associated with membership in the "high" risk class and performance in speeded language and flexibility tasks remained highly significant (p = 0.001). That is, the higher the probability of being in the "high" risk class, the lower the score in speeded language and flexibility. However, differences in global cognition outcomes, as a function of class probabilities and age and education covariates, were attenuated (p = 0.06). In both analyses, latent classes were not associated with performance on three specific factors in the bi-factor model, namely, verbal learning & memory, auditory attention & working memory, and visual attention & executive function.
Cross-validation through Random Forests
We used the classifications obtained through LP analysis as a dependent variable conditioned upon vascular risk and predictor variables to assess the performance of a RF algorithm at predicting class membership. A 10-fold cross-validation estimator was used to assess overall error rate. The RF algorithm yielded an estimated classification accuracy of 96%. That is, the classifier allocated individuals into the "correct" or "LP-estimated class" with approximately 4% error rate. These results support the previously obtained solution and the observed pattern of vascular disease risk measures meaningfully separating individuals into two distinct groups or phenotypes. Interestingly, the weights assigned to each vascular disease risk variable ranking their "importance" as predictors in the RF classifier (see Figure 5) corresponded closely to the observed separation of classes. For example, the four variables with the highest importance weights (HDL-C, FPS, triglycerides, and BMI) also produced the highest separation between the latent classes illustrated in Figure 4.
BMI = Body mass index; CIMT = Carotid artery intima-media thickness; LDL-C = Low density lipoprotein-cholesterol; HDL-C = High density lipoprotein-cholesterol; FSG = Fasting blood glucose; FRAM = Framingham Point scores; TRIG = Triglycerides.
A latent profile (LP) analysis, using seven clinically-relevant variables for CV disease risk obtained at baseline from a cohort of recently menopausal women enrolled in the KEEPS study, revealed two distinct classes or phenotypes, depicting low versus high CV risk. The low CV risk group was, as expected, larger, with 62% of the respondents, while the high risk group comprised the remaining 38%. Our results supported the hypothesis that genetic and demographic variables were predictive of the model-identified classes or phenotypes. An interesting finding is that latent class membership was significantly associated with performance in cognitive tasks. That is, individuals in the low CV risk group, on average, obtained significantly higher scores particularly on executive function tasks measuring speeded language and mental flexibility compared to those in the high CV risk group. The speeded language and mental flexibility factor score was composed of tests of letter and category word list generation, which have been frequently used to investigate the semantic fluency deficits related to the progression of AD [56]–[57] and ischemic vascular dementia [58]–[59].
Midlife CV risk factors are well-known non-genetic risk factors for incident AD and cognitive decline. Published studies have also shown a relationship between higher systolic blood pressure or total cholesterol and LDL-C concentrations in midlife and increased risk of cognitive impairment or AD [60]–[62]. For example, Knopman et al. [60] found an association between hypertension and decline in processing speed tasks over a 6-year period. The relationship between BMI and cognitive function appears more complex, possibly varying depending on the location of adiposity. A recent study [63] using baseline data from the seminal Women's Health Initiative (WHI) hormone trials cohort reported an inverse association between BMI and performance on the Modified Mini-Mental State examination (3MSE); a measure of global cognitive functioning. Interestingly, this association was stronger in women with smaller waist to hip ratio (WHR) (<0.78) and weaker with higher WHR and BMI measurements. These findings suggested a relationship between BMI and cognitive function conditioned upon abdominal obesity in cognitively normal older postmenopausal women. A second study using 4-year follow-up data from women enrolled in WHI Memory Study (WHIMS) [64] found significant interactions between BMI, WHR, and incident cognitive impairment and probable dementia. That is, in women with BMIs between 20 and 29.9 kg/m2, central adiposity (WHR≥0.80) was associated with an increased risk of cognitive impairment and probable dementia. Although the mechanisms underlying the complex associations between both indices of obesity and cognitive function are unclear, our study demonstrated the synergistic role of obesity, as measured by BMI, in identifying unobserved group heterogeneity.
The present study suggests that even within a relatively healthy sample of postmenopausal women at "low" CV risk, vascular disease risk factors exhibit important heterogeneity. The LP approach captured cross-sectional group differences in CV disease risk associated with demographic, genetic, and cognitive variables. To the best of our knowledge, this is the first study investigating whether model-based CV disease risk profiles or groups, based on multiple risk criteria, are associated with cognitive function in recently menopausal women. It is possible that the latent "at risk" group identified by this analysis is capturing women at increased risk for the vascular pathway to AD. The use of model-based analytical approaches to identify systematic heterogeneity and complex "within-class" inter-relationships among multiple biomarkers of risk may be more informative than using standard group-based approaches or "total" sample average scores of vascular risk variables. The accuracy and utility of single estimates of CV risk, such as FPS, can be greatly enhanced by considering additional factors that may help explain the considerable individual variability in risk that may exist in the larger population. Absolute risk in the Framingham population for a given set of factors may not be the same as that for all other populations with differing characteristics such as ethnicity. Therefore, the risk assigned by the FPS may miss a large number of individuals destined for CV events. Newer biomarkers such as CIMT and CAC scores, and both predisposing (e.g., BMI, physical inactivity, and abdominal obesity) and conditional (e.g., inflammatory markers and elevated serum triglycerides and lipoprotein) risk factors may potentially modify the magnitude of risk for individuals [65]–[66]. Our findings imply that a mixture-based approach can have potential to study the relatedness of multiple risk variables beyond single risk scores measures representing average values.
Our phenotypes portray a pattern consistent with a large number of studies showing the varying prevalence of risk factors and the underlying rates of CV disease events according to age, education level (as an indicator of SES), and race/ethnicity [67]–[72]. For example, in the Framingham study, women with less than 12 years of education had nearly a four-fold higher risk of developing CV disease than women with higher education level [69], [71]. The same study also reported a higher incidence of CV disease among postmenopausal women (up to the age of 55) than that found in younger pre-menopausal women. The Cardiovascular Health Study, a longitudinal study designed to examine risk factors for coronary artery disease in a large population of 5,201 men and women, reported that heavier weight at age 50 (i.e., a BMI ≥27) had a stronger association with prevalent CV disease in women than current weight at age 65 or older [72].
Present findings are also convergent with studies reporting a higher prevalence of vascular disease risk factors among Hispanics compared to non-Hispanic Whites [67], [73]–[76]. Risk factors observed in these studies included obesity, lower levels of physical activity, incidence of metabolic syndrome, and lipid abnormalities. The observed upward prevalence trends in APOE ε4 carriers among women in the "high risk" class in our study is also in agreement with findings from a large number of studies showing associations between APOE polymorphisms and cardiovascular risk and lipid profile phenotypes [8], [77]–[78]. Other studies have also demonstrated that a decrease in plasma estrogen levels after menopause and APOE may jointly affect lipid and triglyceride levels [79].
A limitation of the current study was the use of a selected number of CV risk factors in the LP analysis. However, these measures differentiated between distinct subclinical phenotypes and suggested a patterning of and unique co-variation in risk associated with cognitive function and demographic features. Further studies could explore the reproducibility of the results in ethnically-varied samples of postmenopausal women. Future longitudinal studies should also investigate the utility of the combination of CV disease risk variables used as surrogates for class membership in this study in predicting not only CV disease, but also cognitive impairment across multiple domains.
We acknowledge that questions remain regarding the complex nature of the interrelationships between vascular risk biomarkers included and not included in the present analysis and their dual prognostic utility for cognitive decline and CV events. Nonetheless, this study highlights the importance of a multifactorial approach to vascular disease risk. The use of an LP framework for the identification of empirically-derived qualitative phenotypes of risk based on a combination of both traditional and newer risk markers can be extremely useful in defining risk scoring systems with better prediction accuracy and clinical relevance for postmenopausal women and ethnic minority groups. Future work designed to evaluate the evolution of phenotypes could in turn contribute to the understanding of preclinical disease and the role of screening and preventive interventions.
Supporting Information
IRB numbers for KEEPS institutions: The central KEEPS and Phoenix KEEPS (IRB protocol by the Western IRB): STUDY NUM: 1058663 and WIRB PRO NUM: 20040792KEEPS (main study & cognitive substudy) #10-02980 and MDBHAS #11-05383. Brigham and Women's Hospital (Partners): #2004-P-002144 BWH. Mayo Clinic: 2241-04. Columbia: IRB#: AAAA-8062. Yale: 0409027022. University of Utah: 13257. Einstein/Montefiore: 04-08-213. Univ of Wisconsin: H-2005-0059. UCSF: KEEPS (main study & cognitive substudy) #10-02980. University of Washington IRB #26702; VAPSHCS IRB #01048.
Additional KEEPS Investigators and Staff: Albert Einstein College of Medicine: Ruth Freeman, Hussein Amin, Barbara Isaac, Maureen Magnani, Rachel Wildman. Brigham and Women's Hospital/Harvard Medical School: Maria Bueche, Marie Gerhard-Herman, Kate Kalan, Jan Lieson, Kathryn M. Rexrode, Barbara Richmond, Frank Rybicki, Brian Walsh. Columbia College of Physicians and Surgeons: Luz Sanabria, Maria Soto, Michelle P. Warren, Ralf C. Zimmerman. Kronos Longevity Research Institute: Mary Dunn, Panayiotis D.Tsitouras, Viola Zepeda. Mayo Clinic: Philip A. Araoz, Rebecca Beck, Dalene Bott-Kitslaar, Sharon L. Mulvagh, Lynne T. Shuster, Teresa G. Zais. University of California, Los Angeles, CAC Reading Center: Chris Dailing, Yanlin Gao, Angel Solano. University of California, San Francisco Medical Center: Nancy Jancar, Jean Perry, Rebecca S. Wong, Robyn Pearl, Judy Yee, Brett Elicker, Gretchen A.W. Gooding; UCSF Statistical Reading Center: Lisa Palermo. University of Southern California, Atherosclerosis Research Unit/Core Imaging and Reading Center: Yanjie Li, Mingzhu Yan. University of Utah School of Medicine: Paul N. Hopkins, M. Nazeem Nanjee, Kirtly Jones, Timothy Beals, Stacey Larrinaga-Shum. VA Puget Sound Health Care System and University of Washington School of Medicine: Pamela Asberry, SueAnn Brickle, Colleen Carney, Molly Carr, Monica Kletke, Lynna C. Smith. Yale University, School of Medicine: Kathryn Czarkowski, Lubna Pal, Linda McDonald, Mary Jane Minkin, Diane Wall, Erin Wolff (now at NIH/NICHD).
Additional Contributions: We gratefully acknowledge the dedicated efforts of all the investigators and staff at the KEEPS clinical centers, the KEEPS Data Coordinating Center at KLRI, and the NIH Institutes supporting ancillary studies. Above all, we recognize and thank the KEEPS participants for their dedication and commitment to the KEEPS research program.
Author Contributions
Conceived and designed the experiments: NMD VMM JEM HNH EAB GNP MNS MC RL GRM FN HT SMH SA. Performed the experiments: NMD VMM JEM HNH EAB GNP MNS MC RL GRM FN HT SMH. Analyzed the data: NMD. Contributed reagents/materials/analysis tools: NMD. Wrote the paper: NMD. Revised the article for intellectual content: CEG VMM JEM HNH EAB GNP MNS MC RL GRM FN HT SMH SA. Acquisition of cognitive data: CEG WW.
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- 61. Yaffe K, Barrett-Connor E, Lin F, Grady D (2002) Serum lipoprotein levels, statin use, and cognitive function in older women. Arch Neurol 59: 378–384.
- 62. Kivipelto M, Helkala EL, Laakso MP, Hanninen T, Alhainen K, et al. (2001) Midlife vascular risk factors and Alzheimer's disease in late life: a longitudinal, population based study. BMJ 2 322: 1447–1451.
- 63. Kerwin DR, Zhang Y, Kotchen JM, Epeland MA, Van Horn, L, et al (2010) The cross-sectional relationship between body mass index, waist-hip ratio, and cognitive performance in postmenopausal women enrolled in the Women's Health Initiative. J Am Geriatr Soc. 58: 1427–1432.
- 64. Kerwin DR, Gaussoin SA, Chlebowski RT, Kuller LH, Vitolins M, et al. (2011) Interaction between body mass index and central adiposity and risk of incident cognitive impairment and dementia: results from the Women's Health Initiative Memory Study. J Am Geriatr Soc. 59: 107–112.
- 65. Grundy SM, Pasternak R, Greenland P, Smith S Jr, Fuster V (1999) AHA/ACC scientific statement: assessment of cardiovascular risk by use of multiple-risk-factor assessment equations: a statement for healthcare professionals from the American Heart Association and the American College of Cardiology. J Am Coll Cardiol. 34: 1348–1359.
- 66. Naqvi TZ, Mendoza F, Rafii F, Gransar H, Guerra M, et al. (2010) High prevalence of ultrasound detected carotid atherosclerosis in subjects with low Framingham risk score: potential implications for screening for subclinical atherosclerosis. J Am Soc Echocardiogr 23: 809–815.
- 67. Shetterly SM, Rewers M, Hamman RF, Marshall JA (1994) Patterns and predictors of hypertension incidence among Hispanics and non-Hispanic whites: the San Luis Valley Diabetes Study. J Hypertens 12: 1095–1102.
- 68. Luepker RV, Rosamond WD, Murphy R, Sprafka JM, Folsom AR, et al. (1993) Socioeconomic status and coronary heart disease risk factor trends. The Minnesota Heart Study. Circulation 88: 2172–2179.
- 69. Eaker ED (1989) Psychological factors in the epidemiology of coronary heart disease in women. Psychiatric Clinics of North America 12: 167–174.
- 70. Tyroler HA (1989) Socioeconomic status in the epidemiology and treatment of hypertension. Hypertension 13: 194–197.
- 71. Dannenberg AL, Garrison RJ, Kannel WB (1988) Incidence of hypertension in the Framingham Study. Am J Public Health 78: 676–679.
- 72. Kraus JF, Borhani NO, Franti CE (1980) Socioeconomic status, ethnicity, and risk of coronary heart disease. Am J Epidemiol 111: 407–414.
- 73. Derby CA, Wildman RP, McGinn AP, Green RR, Polotsky AJ, et al. (2010) Cardiovascular risk factor variation within a Hispanic cohort: SWAN, the Study of Women's Health Across the Nation. Ethn Dis. 20: 396–402.
- 74. Harris TB, Savage PJ, Tell GS, Haan M, Kumanyika S, et al. (1997) Carrying the burden of cardiovascular risk in old age: associations of weight and weight change with prevalent cardiovascular disease, risk factors, and health status in the Cardiovascular Health Study. Am J Clin Nutr 66: 837–844.
- 75. Burchfiel CM, Hamman RF, Marshall JA, Baxter J, Kahn LB, et al. (1990) Cardiovascular risk factors and impaired glucose tolerance: The San Luis Valley Diabetes Study. Am J Epidemiol 131: 57–70.
- 76. Mitchell BD, Stern MP, Haffner SM, Hazuda HP, Patterson JK (1990) Risk factors for cardiovascular mortality in Mexican Americans and non-Hispanic whites: San Antonio Heart Study. Am J Epidemiol 131: 423–433.
- 77. Reilly M, Rader DJ (2006) Apolipoprotein E and coronary disease: a puzzling paradox. PLoS Med 3: e258.
- 78. Wilson PW, Schaefer EJ, Larson MG, Ordovas JM (1996) Apolipoprotein E alleles and risk of coronary disease: a meta-analysis. Arterioscler Thromb Vasc Biol 16: 1250–1255.
- 79. Schaefer EJ, Lamon-Fava S, Cohn SD, Schaefer MM, Ordovas JM, et al. (1994) Effects of age, gender, and menopausal status on plasma low density lipoprotein cholesterol and apolipoprotein B levels in the Framingham Offspring Study J Lipid Res. 35: 770–792.


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Michelle Williams has debuted the video to her feel good song, "Say Yes" featuring her Destiny's Child group members Beyonce and Kelly Rowland. The tune with Nigerian roots is a single from her upcoming album, Journey to Freedom, scheduled to drop in September.
RELATED: See Michelle Williams, Kelly Rowland and Beyoncé on the Set of 'Say Yes'
The trio is all smiles as they sing about who's really in charge. Check out the video above.


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Milan, 31 October 2012 – The Maire Tecnimont S.p.A. Board of Directors on today's date acknowledged Saverio Signori's and Carlo Corradini's resignations from the offices held within the company, due to personal reasons.
Based on the disclosures to the Company and to the public, on today's date Saverio Signori and Carlo Corradini do not hold directly and/or indirectly any ordinary shares of Maire Tecnimont S.p.A.
The Board of Directors wishes to thank Saverio Signori and Carlo Corradini for their qualified and professional contribution to the activities of the Board.
Futhermore, the Board of Directors appointed by co-optation Paolo Tanoni as independent director and Pierroberto Folgiero as director, the latter being currently Chief Operating Officer of Maire Tecnimont S.p.A., and they both shall hold office until the next General Assembly.
The Board of Directors has assessed that director Paolo Tanoni possesses the pre-requisites of independence envisaged by the Self-discipline Code and the Unified Finance Law (Testo Unico della Finanza).
Paolo Tanoni has more than 25 years of experience in Business and Industrial Law, Company Contracts and International Private-Business Law. He is experienced in litigation proceedings involving Brands, Patents and Models and corporate law. Paolo Tanoni has been Partner of Studio Legale Tributario associated to Ernst & Young Law, and has been in charge of the "Merger & Acquisition" Legal Department of Ernst & Young Law Global.
Pierroberto Folgiero is Chief Operating Officer of Maire Tecnimont S.p.A. entrusted with the Engineering & Construction Business of the entire Group, as well as Managing Director of both the subsidiaries Tecnimont KT S.p.A. and Tecnimont S.p.A..
The new directors' curriculum vitae are available on the Company's website www.mairetecnimont.it.
Maire Tecnimont SpA
Maire Tecnimont Group is a leading international player in Engineering & Construction, Technology & Licensing and Energy & Ventures with a focus on plant industry for the hydrocarbons (Oil & Gas, Petrochemicals, and Fertilizers) and competences in Power Generation and Infrastructure. The Group is listed on the Milan Stock Exchange and is present in over 30 countries, controls over 50 operating companies and has about 5,300 employees, half of whom are located abroad. At 31 December 2011 the Group reported revenues for €2.6 billion and a backlog of about €4.9 billion. For further information: www.mairetecnimont.it.
Public Affairs
Carlo Nicolais email@example.com
Investor Relations
Cristina Girelli
Tel +39 02 63137081
Image Building
Simona Raffaelli, Alfredo Mele
Tel +39 02 89011300


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New to Video looking for editing software.
Posted 07 June 2006 - 05:38 PM
Photoshop Elements 4.0 plus Adobe Premiere Elements 2.0. I have CS2 so a little familar with Adobe.
Posted 08 June 2006 - 12:46 AM
Have a look here Pure Motion
This small company started out as a one man show that puts out some rather good software.
Their DVDLAB Pro is nealy as good as programs costing thousands of dollars more. You can even do custom programming DVD authoring. (VM/Scripting support ) and much more.
Amphibico Phenom & EVO PRO & Navigator 900
Share Your Underwater Videos | www.flykam.com.au
Posted 09 June 2006 - 12:25 PM
If you are PC guy, I would highly recommend Adobe Premiere Elements. It is not quite as intuitive as some simpler packages but affords tons of growing room. It actually contains 90% of the Pro version but dumbed down. You can uncover the the advanced features as you progress.
The benefit of this approach is that it positions you for migrating to more professional software and workflow in the future if that is your desire (vs learning a whole new approach down the road).
Plus...it has nice integration with photoshop
Posted 09 June 2006 - 05:31 PM
Very easy to use!
Join us for an Underwater Photography Workshop at NAD Lembeh July/Aug 2016 with Doug Sloss
Blog and Photo Archive/Portfolio Site www.mikeveitchblog.com
Learn underwater photography in the ultimate classroom, Bali! or join us on a trip www.underwatertribe.com and www.baliuwphoto.com
Join us for a trip to Raja Ampat in March 2017 on the Mermaid II
Posted 10 June 2006 - 06:54 AM
With video I need to edit enough to give my Brother In Law a decent wedding video come October, so simple is good. But in Aug we go to Cocos, thought would be nice to have some video of the hammer heads.
With Photodex I have figured out how to put in "video slides" so will be fun to mix the stills with motion clips.


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Maggie Green is the author of The Kentucky Fresh Cookbook and Tasting Kentucky Favorite Recipes from the Bluegrass State. Green owns The Green Apron Company that specializes in culinary nutrition and cookbook development. As a cookbook coach, Maggie Green offers group and private cookbook coaching, virtual Cookbook Camps, webinars, and private coaching services for aspiring cookbook authors. In addition Green writes recipe chapters for nutrition books, and provides food, nutrition, cooking, and recipe content for print and digital publications.
As a cookbook editor Maggie has edited several well-known trade cookbooks including:
James Beard Award-winning cookbook, BakeWise by Shirley Corriher, The Ultrametabolism Cookbook by Dr. Mark Hyman, and the 75th Anniversary Edition of the Joy of Cooking.
Maggie served on the Medical Advisory Board for Humana's Active Outlook Program where she writes food and nutrition content for their award winning magazines.
Prior to her work in the publishing industry, Green was a personal chef for over 100 clients in the Greater Cincinnati area, and the director of a food service department at an area retirement home.
Green is a graduate of the University of Kentucky with a degree in food and nutrition and is licensed as a dietitian in Kentucky and Ohio. She is also a distinguished graduate of the Culinary Arts Management program at Sullivan University's National Center for Hospitality Studies in Louisville, so has a unique set of skills with food, nutrition, and culinary arts.
Maggie was born in Lexington, Kentucky and loves the beautiful Bluegrass state, with its rich culinary traditions and regional foods. She lives in Northern Kentucky with her husband, the best male cook she knows, and their three children.


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How tall should my stakes be for my pole beans? Are teepees the best method?
Can I use bamboo stalks for stakes..and for tomatoes too?
Anything else I need supports for?
How tall should my stakes be for my pole beans? Are teepees the best method?
Probably not the best source for your answers, but i'll try.
1) Height - How high can you reach? Pole beans will grow up and about an additional foot before gravity brings them back to the pole or whatever support you are using.
2) Teepees are fine, cattle panels, whatever - even ropes or twine suspended from a cross member will allow them to climb. Only issue I have with teepees is harvesting from the inside. Some others leave one side open so they can enter there.
3) Bamboo, metal, cedar, non-poisonous tree limbs all will make suitable poles. We used young trees cut at the ground for 8' poles when we were in N. MN. Every year we had a "mini-forest" of poplar saplings come up. We did not want them in the clearing, so one of my chores was to cut them down.
4) Tomato supports are a perpetual topic in the Tomatoes Forum. Every conceivable method is used.
Good luck.
I use Reinforcing mesh from building sites .
Only one stake 1/2 height is required to hold it up ,
And no problem picking stuff from both sides .
Good luck Ping .
Hi Bill...on #2 answer, I am not familiar at all with the other choices besides teepees. Can you show me a picture or explain a little please?
How high should the tomato stakes be?
And I'm assuming I would plant the stakes before the plants as to not damage the roots???
Ginger...I don't think I've ever seen reinforcing mesh from building sites and I didn't understand your next sentence....one half height?
Thanks for the advice!
You can get cattle panels and concrete reinforcing wire at big-box stores - Home Depot and Lowe's, and most times at Tractor Supply Co. Your local feed and tack store for large animals will also have such items. The wire comes in rolls of different lengths. Most are 4-6 feet by 25-100 feet long. This allows you to make whatever size or shape you need.
Cattle panels are much stronger, but may be a bit more awkward to manuver. Some gardeners use them in pairs like an A-Frame and grow on both sides. Being bigger and heavier, less anchoring is required.
Stakes for tomatoes (if individual for each plant) should go in right before the plant. If you are buying cages or making your own from the construction wire, can be added later. But they need to be anchored, so that is why ginger suggested a stake 1/2 the height of the cage or panel.
Anchoring is recommended for teepees or lean-to's ass well. Once they are covered with producing plants, you don't want a sudden storm to tear it out or turn it over.
I made a 4x8 foot a-frame from cedar for my polebeans, but had to anchor all four ways to prevent tearing plants from the ground with our summer storms.
If you are planning a single pole per tomato - then you will probably prune all suckers to allow a single stem until the height you want is reached. Top the plant and let it produce, but it will continue to make new suckers. If you don't prune it, you will soon have too many stems to tie to the pole.
I will try to find some of the threads in the Tomatoes Forum with pictures of various growing suspension systems.
Hope this works; http://davesgarden.com/forums/fp.php?pid=2309354
This message was edited Mar 23, 2007 4:39 PM
Ok...I follow you so far, Bill but unfortunately, I'm not allowed to view the link... :(
A few questions though...what do you mean by "top the plant"?
How far down should my tomato stake be in the ground? And then how tall out of the ground?
If I anchor all of this, won't I have strings stretched out all over the place?
And I can reach about 7 1/2 feet....without tip-toes.
There is so much to learn and I feel like I'm taking a crash course and it's becoming so overwhelming! I just want to be prepared and know exactly what to do when it's time to plant. I don't like to "wing" things... :(
My brain is really full right now so I'm going to bed and hopefully I can dream really hard so I can have some more room in the morning to absorb more info!
Thanks again for all your help,
Post a Reply to this Thread
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Artist's description:
Ink and Watercolours on Japanese hand-made paper (54cm x 41cm approx.) mounted onto Snowdon paper 59cm x 47cm approx.)
Materials used:
Ink, watercolurs, paper, water, love.
Whirl (2015)
Watercolour by Marcel Garbi
- Watercolour on Paper
- One of a kind artwork
- Size: 23.23 x 18.5 in (unframed)
- This artwork is sold unframed
- Signed on the front
- Style: Unspecified
- Subject: People and portraits
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Ink drawingby Marcel Garbi
The secrets of the Pink Sphinx
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Pupils from Padiham Primary School are the latest to book their place in a spectacular of sport after in they took victory in High Five netball tournament.
The team from Padiham competed against 23 schools from Burnley at Sir John Thursby College to finish in first place.
They will progress to SPAR Lancashire School Games county finals where they will be up against other netballers from across Lancashire.
Helen Tyson, Burnley School Games Organiser, said: "Well done to all those that played and contributed to the tournament's success, including Grace Turner of Padiham Primary for her stand out performance.
"A massive thank you to our netball coach Jane Moodie of Ribble Valley Netball Club and all the young umpires that have supported the competitions.
"I would like to congratulate Padiham for their win and wish them the best of luck for the Lancashire School Games netball finals. Burnley county netballers will be cheering them on from the side-lines."
Around 3,000 primary and secondary pupils from across the county are expected to take part in the School Games County Finals.


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Parineeti Chopra, Kalki Koechlin brighten up
Bollywood actresses Kalki Koechlin and Parineeti Chopra were out to brighten Mumbai city at their respective events. While Parineeti was spotted at the promotions of her new film, Kalki was busy choosing fashion icons. Take a look. (Photo: Varinder Chawla)
National Award winning actress Parineeti Chopra is all charged up for her next release 'Hasee Toh Phasee' opposite Siddharth Malhotra.
The onscreen couple was spotted promoting their film in Mumbai. (Photo: Varinder Chawla)
Parineeti, who has recently shed some baby fat is very happy these days as she has become thinner thanks to her rigorous workout regime.
The actress was sporting a casual look at the event, with bright red t-shirt and denims. She finished off her look with the sporty shoes and huge glares! (Photo: Varinder Chawla)
A gleeful Parineeti Chopra said: "I want to lose even more weight and look better in real life. On screen if I have to wear shorts or bikini for a scene I should have the body for that." (Photo: Varinder Chawla)
Chit-chat duo – Parineeti and Sidharth Malhotra are chatting away!
The romantic-comedy produced by Karan Johar and Anurag Kashyap is set to release on February 7. (Photo: Varinder Chawla)
Meanwhile, Bollywood's new fashionista on the block, Kalki Koechlin is out on the hunt for new fashion icons in Mumbai. The 'Yeh Jawaani Hai Deewani' actress, who has wooed her fans with her choice of dresses, looked absolutely amazing. (Photo: Varinder Chawla)
Dressed in a sheer pink dress with a satin underlay by Sailex, Kalki looked drop dead gorgeous. She accesorised her look with 'House Of Chic' earrings. We Like! (Photo: Varinder Chawla)
Kalki Koechlin shares some fashion tips! (Photo: Varinder Chawla)
Bollywood actor-turned-filmmaker Shekhar Suman is all set for the release of his maiden directorial venture 'Heartless', which stars his son Adhyayan and newcomer Ariana Ayam in the lead roles. (Photo: Varinder Chawla)
Ariana Ayam looks classy in a formal check dress. She teamed her look with black pumps and a belt. (Photo: Varinder Chawla)
Adhyayan was suave in a suit at the promotions. (Photo: Varinder Chawla)
The medical-thriller film, which also stars Shekhar Suman in a pivotal role, is partly inspired by Hollywood film 'Awake' and partly by the death of Shekhar Suman's elder son Aayush, who passed away at the age of 11 due to a heart ailment. (Photo: Varinder Chawla)


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Edit: After half day, my wife's Mac started having the same problem. I have then applied the solution that I found in the Apple
The Main Differences Between PC and a Mac
Macs have been in the news a lot lately with the announcement of the iPhone 4S and then Steve Jobs' unfortunately passing, but they have been news worthy for many years before now and they will continue to be so thanks to their ground breaking designs and world renowned software. This article is here to show you the main differences between a PC and a mac but to also quash some rumours and to explain how the same functions can occur on each of the two machines.
The first difference you will notice between a PC desktop and a mac desktop is the task bar- it's not there on the mac. Well, it is but in a different way. Mac have changed the task bar and made it more fluid and called it the dock. The doc holds all of the apps that are most often used as well as open apps and the documents folder, the apps folder, the downloads folder, and the trash bin. Instead of the start menu at the side of the screen macs have a menu bar along the top of the screen that has all of the menu information for whatever is running. For example, when no apps are being used the menu holds information about the computer, but if you run Safari it will change to show you the Safari options. Open mail and the same thing will happen.
The dock can be customised like the task bar can be. Simply drag and drop an app, folder, or file to the dock and it will slot in. If you no longer need or want it there drag and drop it to the desktop background and it will explode in a puff of dust. But this isn't the app exploding- just the shortcut.
Another different you will find between an app on a mac and a program in windows is that the three main control buttons—minimise, maximise, and close—are on the other side of the window. On a mac the red circle means close, the green one means to maximise, and the yellow one means to minimise. However, when you close an app that can continue running in the background clicking the red button won't stop it running. When you have read all of your new e-mails clicking the red button will get rid of the mail app but you will notice that the mail menu bar is still at the top of the screen. You can open and use other apps and mail will still be running in the background. If you don't want this to happen you will have to click the apps name in the menu bar and click 'quit'.
One of the main differences between a mac and a PC is the lack of a right-click button on the mouse. Some people on both sides of the fence refuse to use the other computer system because they don't know how to use it with/without the right click option. However, some mac mice come with a second button and right clicking on something will launch its secondary commands. Otherwise, holding down the command button while clicking will bring up this menu.
I hope this article has shown you the main differences between the two systems.
Related Articles


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About Bitesize Bio
Bitesize Bio started as a one-man blog in 2007, when Nick Oswald started writing articles that shared his bits and pieces of wisdom, know-how and solutions gained during his 9 years in the lab, incase anyone might find them useful.
Since then, Bitesize Bio has mushroomed into a knowledge-sharing platform where individual scientists and companies are sharing their wisdom, know-how and solutions to create what is truly the Missing Manual for Bioscientists, that is used, loved and depended-upon by hundreds of thousands of bioscientists each month.
Our remit is to cover every topic that concerns Bioscientists in the lab. Everything from how to get a technique to work better, walk-throughs to improve their basic (or complex) understanding of how a technique or process works, how to write a thesis or paper, or just how to handle their boss.
And whoever you are, we would like you to become part of this movement.
If you are a bioscientist in the lab, please…
- Our new members area will be available soon then you can register with us to ensure that can you get the most out of Bitesize Bio and really skyrocket your labwork and life, there's lots of content to explore and we help registered users to find the stuff that will really help them.
- Write for us. We have an open-door policy for writers, anyone can submit an article idea and if our Editorial Team like it, they will hold your hand through the process to create a great article and work with you to turn your personal wisdom into further articles if you require. If you are looking to springboard into a career (or sideline!) in writing you can also take this further as many have. After your first few articles are published, if you meet the standard (and you still like us) you can continue to work with us to further hone your skills with the help of our team, build a portfolio, increase your exposure on Bitesize Bio, and move through our ranks to take up further opportunities we can offer you.
If you are a marketer or company representative…
- We'd like to chat with you about how you can painlessly share your company's in-house wisdom and solutions with a hungry community of bioscientists for the benefit of your company, and of scientists everywhere. As you know, in marketing-speak that's called Content Marketing. You will find, as many others have, that there is no better place to do bioscience Content Marketing than Bitesize Bio. You can arrange for one of our team to contact you by clicking right here.


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A DEVASTATED taxi driver says he has become the victim of arsonists for the fourth time in two years.
Ken Wood, 62, of Troedle, Ponciau, was left distraught after the attack on his blue Rover at 4.30am yesterday, which he needed to travel to Birmingham later in the day for his aunt's funeral.
"This is the fourth time I have been targeted since 2010. I feel terrible about it. I would like to see CCTV cameras put up in the area," said Mr Wood.
Mr Wood's car was parked near his home when it was set on fire.
He attempted to put out the fire but the flames were too strong and the vehicle was gutted.
"My wife Pauline saw the fire and woke me up. I went out with an extinguisher and tried to put the fire out."
Some fencing was also damaged by the fire.
"I am devastated by what has happened. I needed the car to get to my aunt's funeral," said Mr Wood.
"Fortunately my daughter Jessica has kindly lent me her car so I can still go."
Mr Wood said the previous three arson attacks had been on taxis that he drove, but that the latest was on his own car.
"With the taxis, it hit my income by about £300 each time.
"I don't think it is against me personally. I think these are random incidents. It is always in the same place, the parking is secluded.
"It makes you feel uneasy, but there is nothing I can do about it."
Trine Nicholls-Smith, assistant clerk of Rhos Community Council, said: "Requests for CCTV coverage would need to be discussed by councillors and we would need a letter to be sent in addressed to the council."
A spokesman for North Wales Fire Service said a crew from Johnstown were called to the blaze and put it out. The cause was deliberate ignition.
Chief Inspector Alex Goss said: "Investigations are taking place. At this stage it is not believed to be linked to the spate in Caia Park."
Seven arrests have now been made as part of investigations into a spate of arson attacks in cars in the Caia Park area of Wrexham in recent weeks.
A North Wales police spokesman said: "Four youths were arrested on Friday and another three over the weekend. Six of them have been released on police bail pending further inquiries and one has been released with no further action to be taken.
"The arrests are not being linked to the car arson at The Orchards in the Whitegate/Hightown area of Wrexham on November 13. The investigation into that incident continues and a youth arrested in connection with that inquiry has been released on police bail."
Anyone with information is asked to call North Wales Police on 101.
See full story in the Leader


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Madge Evans Photos
Date Of Birth:
July 09, 1909
Date Of Death:
April 26, 1981 (71)
Birth Place:
New York, NY, USA
Gender: Female
Madge Evans News Feeds
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The DGF's 14th Biannual Madge Evans & Sidney Kingsley Awards at the Dramatists Guild Fund headquarters on April 4, 2016 in New York City.
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- Christopher Durang, Lisa Kron, Viola Davis & More to Receive Dramatists Guild of America Awards, 2/24 (Feb 11, 2014)
- TCG Publishes Lynn Nottage's BY THE WAY, MEET VERA STARK (Oct 8, 2013)
- Photo Flash: The Dramatists Guild Fund Awards Linda Lavin and Lynn Nottage the Madge Evans-Sidney Kingsley Award (Feb 27, 2013)
- The Dramatists Guild Fund Awards Linda Lavin and Lynn Nottage the Madge Evans-Sidney Kingsley Award (Feb 20, 2013)
- Amber Tamblyn to Lead THE MISS FIRECRACKER CONTEST on Broadway This Spring; Judith Ivey to Direct (Nov 27, 2012)
- Dramatists Guild's IN THE ROOM Series Features Tina Howe and Sarah Ruhl (May 8, 2012)
- Legendary Actress Frances SternhagenTo Be Featured In Horovitz Play and Public Interview (Jan 10, 2011)
- Segal, Orsekes, Cummings et al. Complete Cast for Broadway's THE MIRACLE WORKER (Jan 13, 2010)