text:
Q: Blowfish code should be equivalent but is not We have a class which wraps BouncyCastle (actually SpongyCastle for Android) Blowfish to encrypt data to stream:
public class BlowfishOutputStream extends OutputStream
{
    private final OutputStream os;
    private final PaddedBufferedBlockCipher bufferedCipher;

Our original code encrypted a whole byte array before writing to the output stream in a single operation
public void write(byte[] raw, int offset, int length) throws IOException
{
    byte[] out = new byte[bufferedCipher.getOutputSize(length)];
    int result = this.bufferedCipher.processBytes(raw, 0, length, out, 0);
    if (result > 0)
    {
        this.os.write(out, 0, result);
    }
}

When sending images (ie large amount of data at once) it results in two copies being retained in memory at once.
The following code is meant to be equivalent but is not, and I do not know why. I can verify that data is being sent (sum of c2 is equivalent to the length) but an intermediate process when it is received on our server discards the image before we get to see what arrives. All I know at this stage is that when the initial code is used, the response is received and the included images can be extracted, when the replacement code is used the response is received (and accepted) but images do not appear to be extracted.
public void write(byte[] raw, int offset, int length) throws IOException
{
    // write to the output stream as we encrypt, not all at once.
    final byte[] inBuffer = new byte[Constants.ByteBufferSize];
    final byte[] outBuffer = new byte[Constants.ByteBufferSize];
    ByteArrayInputStream bis = new ByteArrayInputStream(raw);
    // read into inBuffer, encrypt into outBuffer and write to output stream
    for (int len; (len = bis.read(inBuffer)) != -1;)
    {
        int c2 = this.bufferedCipher.processBytes(inBuffer, 0, len, outBuffer, 0);
        this.os.write(outBuffer, 0, c2);
    }
}

Note that the issue is not due to a missing call to doFinal, as this is called when the stream is closed.
public void close() throws IOException
{
    byte[] out = new byte[bufferedCipher.getOutputSize(0)];
    int result = this.bufferedCipher.doFinal(out, 0);
    if (result > 0)
    {
        this.os.write(out, 0, result);
    }
    *nb try/catch omitted*
}

A: Confirmed, although ironically the issue was not with the images but in previous data, but that data was writing the complete raw byte array and not just the range specified. The equivalent code for encrypting the byte array on the fly is:
@Override
public void write(byte[] raw, int offset, int length) throws IOException
{
    // write to the stream as we encrypt, not all at once.
    final byte[] inBuffer = new byte[Constants.ByteBufferSize];
    final byte[] outBuffer = new byte[Constants.ByteBufferSize];
    int readStart = offset;
    // read into inBuffer, encrypt into outBuffer and write to output stream
    while(readStart<length)
    {
        int readAmount = Math.min(length-readStart, inBuffer.length);
        System.arraycopy(raw, readStart, inBuffer, 0, readAmount);
        readStart+=readAmount;
        int c2 = this.bufferedCipher.processBytes(inBuffer, 0, readAmount, outBuffer, 0);
        this.os.write(outBuffer, 0, c2);
    }
}


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I love November, it's my favourite month of the year, Why?, well for many reasons, firstly it brings the promise of December which is basically the Friday of months, we'll get there in ten days, but for now lets focus on No-vem-baah!, the chill folks, these lovely cold mornings, nippy afternoons and cool nights are such a fun weather to live in especially for folks close to the equator, who have a forever kind of summer most months of the year, Next is winter vegetables are here and they are saturated with colour, health and nutrients and give culinary aficionados like me a reason to get my rusty wheels up and running again, thinking of all the yummy dishes that I could create with them and finally Straw-be-rries!!!, enough said.
Now cooler days call for cosying up under the blanket,catching eighty winks (because its too cool for forty, you see!) at 3 pm, along warmer foods that give the tummy the same effect that a blanket gives the body and what could be more comforting than a hot bowl of chicken soup?, toss in some corn and a ladle of beer and you are on your way to a blissfully cozy snooze.
In a large pan or soup pot heat the olive oil over medium high heat.
Add the chicken pieces and cook the chicken for about 5 to 7 minutes, until cooked through and no longer pink.
To the pot, add the garlic and chopped onion and cook for another 3 minutes until the onion becomes translucent, stirring occasionally.
Season with cumin, salt and pepper, then add the milk and the chicken broth and bring to a boil. Add the cream-style corn, corn kernels and stir.
Simmer for another 10 minutes, stirring occasionally.
Garnish with chopped green onions and coriander leaves and serve topped with cheese if preferred.


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text:
Валентина Алексеевна Сочнева (род. 9 июня 1936 года, Казань) — советский, российский педагог, математик, директор физико-математической школы «Квант» (с 1972 года), доцент Института математики и механики им. Н. И. Лобачевского Казанского федерального университета.

Биография
В 1953 году Валентина Сочнева была принята без экзаменов на механико-математический факультет Казанского Университета (как победительница математической олимпиады). В 1966 году защитила диссертацию «О решениях дифференциальных уравнений в частных производных в пространствах Жевре». В том же году В. А. Сочнева сделала доклад на международном конгрессе математиков в Москве на тему «Solutions of general linear systems of partial differential equations, analytic with respect to one variable». С 1969 года преподает математику в Казанском Университете (курсы лекций «Методы математической физики», «Элементы теории вероятностей и математической статистики», «Математический анализ для физиков», «Дифференциальные уравнения и вариационное исчисление», «Основы векторного и тензорного анализа»). Научные работы Валентины Сочневой относятся к области уравнений в частных производных, обобщение теоремы Ковалевской. Своим учителем Валентина Сочнева считает казанского математика В. Р. Фридлендера — одного из основателей олимпиадного движения в Республике Татарстан.

В 1972 году В. А. Сочнева организовала Летнюю физико-математическую школу «Квант» при Казанском государственном университете. Валентина Алексеевна руководит школой «Квант» более 40 лет. За 1972—2013 годы более 6000 школьников из школ Татарстана получили углубленные знания в области физики, химии, математики, биологии, проходя обучение в ЛФМШ Квант. Более того, в школе Квант В. А. Сочнева воспитала ряд педагогов, оказавших впоследствии заметное влияние на организацию школ для одаренных детей в городе Казани, в том числе экспериментальной школы-лицея № 1 и Академического колледжа, позднее объединённых в Лицей им. Н. И. Лобачевского при Казанском (Приволжском) федеральном университете, Специализированного Олимпиадно-Научного Центра (школа СОлНЦе).

Известные ученики Валентины Алексеевны Сочневой:
 Александр Балакин, доктор физико-математических наук, профессор, Институт физики КФУ, отделение физики, кафедра теории относительности и гравитации
 Игорь Григорьев — организатор и директор Экспериментальной школы-лицея № 1 при КГУ, создатель педагогической программы «Организация космических разведчиков».
 Искандер Калимуллин, доктор физико-математических наук, КФУ
 Людмила Лазарева — преподаватель математики, подготовила ряд призёров Всероссийских математических олимпиад, руководитель Республиканского Центра для одаренных детей.
 Андрей Поминов, заместитель Министра образования и науки Республики Татарстан
 Ибрагим Фаткуллин, associated professor University of Arizona
 Павел Шмаков — директор Академического колледжа при КГУ, директор Лицея им. Лобачевского при Казанском университете (2011—2013), создатель и директор школы СОлНЦе (г. Казань).

В. А. Сочнева и её ученики создали при Казанском университете заочную физико-математическую школу и Малый Университет для школьников Татарстана, организовали проведение научных конференций школьников.
Валентина Алексеевна Сочнева является членом Попечительского Совета Специализированного научно-олимпиадного центра «СОлНЦе», членом Ассоциации «Женщины в науке и образовании». Более 50 лет Валентина Алексеевна была членом жюри республиканской математической олимпиады школьников Татарстана. В. А. Сочнева — лауреат фонда Дмитрия Зимина Династия 2006, 2008 год — Номинация «Наставник будущих ученых» 2009 год — Номинация «Учитель, воспитавший ученика».

Награды, почётные звания
 2008 год Звание Заслуженный учитель Российской Федерации
 Почётное звание Заслуженный преподаватель КФУ
 Почётное звание Заслуженный учитель Республики Татарстан

Публикации
 Калачева Н. В., Сочнева В. А. Математика: задачи, упражнения, практикум. Учебно-методическое пособие. Казань: КФУ, 2012. — 100с.
 Кац Б. А., Киндер М. И., Сочнева В. А. Задачи математической олимпиады школьников Татарстана. 2011—2012 уч. год. — Казань: Изд-во «Печатный Двор», 2012. — 35 с.
 V. A. Sochneva, V. R. Fridlender A generalization of the Goursat problem Izv. Vyssh. Uchebn. Zaved. Mat., 1986, no. 8, 63-70
 N. M. Zobin, V. A. Sochneva, V. R. Fridlender An algebraically closed field Izv. Vyssh. Uchebn. Zaved. Mat., 1977, no. 2, 37-48
 V. A. Sochneva, V. R. Fridlender Uniqueness spaces of Cauchy and Goursat problems Izv. Vyssh. Uchebn. Zaved. Mat., 1974, no. 7, 83-90
 V. A. Sochneva, V. R. Fridlender The Cauchy problem for multidimensional linear systems over Gevrey spaces Mat. Sb. (N.S.), 79(121):2 (1969), 264—292
 V. A. Sochneva Solutions of general linear systems of partial differential equations, analytic with respect to one variable Izv. Vyssh. Uchebn. Zaved. Mat., 1967, no. 2, 67-73
 V. A. Sochneva, V. R. Fridlender Gevrey solutions of partial differential equations Izv. Vyssh. Uchebn. Zaved. Mat., 1966, no. 1, 139—146
 V.A.Sochneva, Solution of general linear sistems of partial differential equations over Gevrey spases, Dokl.Akad.Nauk SSSR 166(1966),41-44 = Soviet Math. 7(1966),39-42.MR 32 #7916
 В. А. Сочнева, «Методы математической физики», ч.2, 1975 г.учебное пособие, изд-во КГУ, 100стр
 А. В. Аминова, В. А. Сочнева, « Методы математической физики», ч. 1, 1978 г., учебное пособие, изд-во КГУ, 164стр.
 Е. А. Широкова, В. А. Сочнева, «Уравнения математической физики»,Изд-во КГУ, 2010, учебное пособие, 52стр.
 Сочнева В. А. «Краткий конспект лекций по математике с элементами теории вероятностей и математической статистики». Учебное пособие для студентов гуманитарных факультетов. Казань: Изд-во КГУ, 2007. 78 с.

 Примечания

 Ссылки
 Ассоциация «Женщины в науке и образовании».  // Редактор-составитель Н. А. Винокурова. Москва, 2008.
 Зульфия Таразова Время завидовать пожилым // Молодежь Татарстана
 П. Шмаков, Н. Иваницкая''.  Всё для института, все для института // Газета «Первое сентября».

Выпускники Казанского университета
Преподаватели Казанского университета
Заслуженные учителя Республики Татарстан

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A Camp Fire's destruction in northern California left rescue teams sifting through debris for human remains, Tim Craig reported for the Washington Post. "Butte Co. Sheriff just announced the number of persons unaccounted for in the Camp Fire is now 631. (Jumped by 500 today). Confirmed death toll is 63, after the remains of 7 more people found within past day or so," Craig added on Twitter.
Elsewhere in the LA Times, Nicole Santa Cruz and Marisa Gerber write about camp fire evacuees who were made homeless by flames and now face hardship, disease, and desperation.
Dale then wrote a Perspective piece for the Washington Post revealing that It's easy to fact check Trump's lies [because] he tells the same ones all the time.
Julia Edwards Ainsley, Courtney Kube, and Carol Lee at NBC News report that the White House weighed booting an Erdogan foe from the U.S. just to appease Turkey. Maybe in return, Erdogan can get the White House some water for their thirst.
Annie Gowen did some digging and found documents that show that Taxpayers' cost for Trump Jr.'s business trip to India nearly $100K. Don't miss her story in the Washington Post.
Speaking of spending, Heidi Przybyla at NBC News reveals that the U.S. Marshals Service is spending millions on Betsy DeVos' security in an unusual arrangement. "PLS TUNE IN SOON: This is way more than the $3.5 million Pruitt spent that caused an uproar," she tweeted.
First, Aruna Viswanatha and Ryan Dube reported at the Wall Street Journal that the U.S. Is Optimistic It Will Prosecute WikiLeaks Founder Julian Assange.
Then, Matt Zapotosk and Devlin Barrett at the Washington Post filed news that Julian Assange had been charged, based on what prosecutors revealed in an inadvertent court filing.
Ashley Feinberg told us in a tweet, "I wrote about Tim Miller and the 'Pod Save America' guys." Her piece–What Did 'Pod Save America' Expect?–is up now on the Huffington Post. "The only conceivable reason to ally yourself with someone like Miller is to show the world how tolerant and reasonable you are," Ryan Cooper quoted from Feinberg's piece.
Elsewhere in Washington D.C., freshman congresswoman-elect Alexandria Ocasio-Cortez reportedly got in a closed-door fight with New Jersey Rep. Frank Pallone, a veteran lawmaker, over climate change, Anthony Adragna, John Bresnahan, and Zack Colman report at Politico.
And the update from Georgia is that Stacey Abrams' team is preparing to push for a new vote in the much-contested gubernatorial race, according to Bill Barrow and Kate Brumback's reporting for the AP.
"Two Navy SEALs & two Marine Raiders have just been charged with murder, obstruction of justice, burglary and other charges in the grisly strangulation of Army Staff Sgt. Logan Melgar in Mali last June," Spencer Ackerman tweeted. Ackerman reported on the story alongside Kevin Maurer at the Daily Beast.
A man shouted 'Heil Hitler, Heil Trump' and did the Nazi salute during a performance of "Fiddler on the Roof" In Baltimore. Christina Tkacik, Sarah Meehan, and Lillian Reed reported on the disturbing news for the Baltimore Sun.
For a look at How Facebook's Leaders Fought Through Crisis, Sheera Frenkel, Nicholas Confessore, Cecilia Kang, and Matthew Rosenberg published "The result of months of reporting" with Delay, Deny and Deflect in the New York Times. "You've seen the fallout from Facebook's problems. Now here's the full story of what happened inside," Jack Nicas encouraged.
Yesterday, TMZ broke the very sad news that Kim Porter died at 47. Porter was best known as Diddy's ex-girlfriend and mother to his 4 kids. "OMG," Candace Amos reacted.
Yesterday, we asked: Who holds the record of being the American who has spent the most time (665 days) in outer space?
Answer: That would be Peggy Whitson. Read more about her journey here.
Congrats to exactly no one since not one of you tweeted the correct answer. Better luck today, eh?
Your question of the day for today is…The main character in which novel is born at exactly the same moment India gains independence from Great Britain?


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text:
"""
Este processamento gera uma tabulação de idiomas de publicação de cada artigo
da coleção SciELO.
Formato de saída:
"PID","ISSN","título","área temática","ano de publicação","tipo de documento","idiomas","pt","es","en","other","pt-es","pt-en","en-es","exclusivo nacional","exclusivo estrangeiro","nacional + estrangeiro"
"""
import argparse
import logging
import codecs

import utils

logger = logging.getLogger(__name__)

def _config_logging(logging_level='INFO', logging_file=None):

    allowed_levels = {
        'DEBUG': logging.DEBUG,
        'INFO': logging.INFO,
        'WARNING': logging.WARNING,
        'ERROR': logging.ERROR,
        'CRITICAL': logging.CRITICAL
    }

    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')

    logger.setLevel(allowed_levels.get(logging_level, 'INFO'))

    if logging_file:
        hl = logging.FileHandler(logging_file, mode='a')
    else:
        hl = logging.StreamHandler()

    hl.setFormatter(formatter)
    hl.setLevel(allowed_levels.get(logging_level, 'INFO'))

    logger.addHandler(hl)

    return logger

class Dumper(object):

    def __init__(self, collection, issns=None, output_file=None):

        self._ratchet = utils.ratchet_server()
        self._articlemeta = utils.articlemeta_server()
        self.collection = collection
        self.issns = issns
        self.output_file = codecs.open(output_file, 'w', encoding='utf-8') if output_file else output_file
        header = [u"PID",u"ISSN",u"título",u"área temática",u"ano de publicação",u"tipo de documento",u"idiomas",u"pt",u"es",u"en",u"other",u"pt-es",u"pt-en",u"en-es",u"exclusivo nacional",u"exclusivo estrangeiro",u"nacional + estrangeiro"]
        self.write(','.join(header))

    def write(self, line):
        if not self.output_file:
            print(line.encode('utf-8'))
        else:
            self.output_file.write('%s\r\n' % line)

    def run(self):
        for item in self.items():
            self.write(item)

    def items(self):

        if not self.issns:
            self.issns = [None]

        for issn in self.issns:
            for data in self._articlemeta.documents(collection=self.collection, issn=issn):
                logger.debug('Reading document: %s' % data.publisher_id)
                yield self.fmt_csv(data)

    def fmt_csv(self, data):
        know_languages = set(['pt', 'es', 'en'])
        languages = set(data.languages())
        line = []
        line.append(data.publisher_id)
        line.append(data.journal.scielo_issn)
        line.append(data.journal.title)
        line.append(','.join(data.journal.subject_areas))
        line.append(data.publication_date[0:4])
        line.append(data.document_type)
        line.append(','.join(languages))
        line.append('1' if 'pt' in languages else '0')  # PT
        line.append('1' if 'es' in languages else '0')  # ES
        line.append('1' if 'en' in languages else '0')  # EN
        line.append('1' if len(languages.difference(know_languages)) > 0 else '0')  # OTHER
        line.append('1' if 'pt' in languages and 'es' in languages and len(languages) == 2 else '0')  # PT-ES
        line.append('1' if 'pt' in languages and 'en' in languages and len(languages) == 2 else '0')  # PT-EN
        line.append('1' if 'es' in languages and 'en' in languages and len(languages) == 2 else '0')  # ES-EN
        line.append('1' if 'pt' in languages and len(languages) == 1 else '0')  # Exclusivo Nacional
        line.append('1' if not 'pt' in languages and len(languages) > 0 else '0')  # Exclusivo Estrangeiro
        line.append('1' if 'pt' in languages and len(languages) > 1 else '0')  # Nacional + Estrangeiro

        joined_line = ','.join(['"%s"' % i.replace('"', '""') for i in line])

        return joined_line

def main():

    parser = argparse.ArgumentParser(
        description='Dump languages distribution by article'
    )

    parser.add_argument(
        'issns',
        nargs='*',
        help='ISSN\'s separated by spaces'
    )

    parser.add_argument(
        '--collection',
        '-c',
        help='Collection Acronym'
    )

    parser.add_argument(
        '--output_file',
        '-r',
        help='File to receive the dumped data'
    )

    parser.add_argument(
        '--logging_file',
        '-o',
        help='Full path to the log file'
    )

    parser.add_argument(
        '--logging_level',
        '-l',
        default='DEBUG',
        choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'],
        help='Logggin level'
    )

    args = parser.parse_args()
    _config_logging(args.logging_level, args.logging_file)
    logger.info('Dumping data for: %s' % args.collection)

    issns = None
    if len(args.issns) > 0:
        issns = utils.ckeck_given_issns(args.issns)

    dumper = Dumper(args.collection, issns, args.output_file)

    dumper.run()

redpajama_set_name: RedPajamaGithub


text:
package com.alorma.github.presenter.repo.tags;

import com.alorma.github.sdk.bean.info.RepoInfo;
import core.datasource.SdkItem;
import core.repository.GenericRepository;
import rx.Observable;

public class GetTagsCountUseCaseImpl implements GetTagsCountUseCase {
  private final GenericRepository<RepoInfo, Integer> genericRepository;

  public GetTagsCountUseCaseImpl(GenericRepository<RepoInfo, Integer> genericRepository) {

    this.genericRepository = genericRepository;
  }

  @Override
  public Observable<SdkItem<Integer>> getTagsCount(RepoInfo repoInfo) {
    return genericRepository.execute(new SdkItem<>(repoInfo));
  }
}


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text:
Reducing the Future Risk of Trauma: On the Integration of Global Disaster Policy within Specific Health Domains and Established Fields of Practice
Rural Versus Urban Patients: Benchmarking the Outcomes of Patients with Acute Myocardial Infarction in Shanxi, China from 2013 to 2017
Xu, F.
Cohen, S. A.
Greaney, M. L.
Greene, G. W.
The Association between US Adolescents' Weight Status, Weight Perception, Weight Satisfaction, and Their Physical Activity and Dietary Behaviors
Furong Xu
1,*,
Steven A. Cohen
Mary L. Greaney
Geoffrey W. Greene
Department of Kinesiology, University of Rhode Island, Independence Square II, Kingston, RI 02881, USA
Health Studies Program, University of Rhode Island, Independence Square II, Kingston, RI 02881, USA
Department of Nutrition and Food Sciences, University of Rhode Island, Fogarty Hall, Kingston, RI 02881, USA
Int. J. Environ. Res. Public Health 2018, 15(9), 1931; https://doi.org/10.3390/ijerph15091931
Received: 17 August 2018 / Revised: 2 September 2018 / Accepted: 2 September 2018 / Published: 5 September 2018
(This article belongs to the Section Health Behavior, Chronic Disease and Health Promotion)
Background: It remain unclear that the association between weight status, weight perception, weight satisfaction and the clustering of physical activity (PA) and dietary behaviors in adolescents. Method: A cross-sectional analysis of National Health and Nutrition Examination Survey and the US Department of Agriculture's Food Patterns Equivalents 2007–2014 data from adolescents aged 12–17 years (n = 2965) was conducted. Multivariable logistic regression models adjusted for demographic characteristics examined the association between weight status, weight perception, weight satisfaction, and the four created lifestyle groups (healthier behaviors, healthier diet only, physically active only, unhealthier behaviors). Results: Males with obesity were more likely to be in the healthier diet only group than males with a normal weight (OR = 1.90, 95% CI: 1.02, 3.52). Similar patterns were found in males who perceived themselves as being overweight or having obesity (OR = 2.09, 95% CI: 1.09, 3.99) and males with obesity who perceived their weight status accurately (OR = 2.33, 95% CI: 1.12, 4.88). Female respondents who were satisfied with their weight were 59% less likely to be in the healthier diet only group than healthier behaviors group compared with females who were weight dissatisfied (OR = 0.41, 95% CI: 0.23, 0.75). This pattern was not observed in males. Conclusions: Clustering PA and dietary behaviors were associated with weight status and weight perception for males but not females. Weight satisfaction was associated with clustered PA and dietary behaviors for females. These findings are important for obesity prevention policies and programs to better address adolescents' obesity and reduce health disparities in this population.
weight perception; weight satisfaction; physical activity; diet quality
Currently 20.6% of US adolescents aged 12–19 have obesity [1]. This high prevalence of obesity and its associated short- and long-term health risks [1,2] make addressing obesity in this population a public health priority. Obesity among adolescents is often attributed to inadequate physical activity (PA) and a poor diet [3,4,5,6,7,8], and obesity prevention efforts often focus on increasing PA and promoting healthy diets to promote energy balance [7,8,9,10,11]. However, most available studies examining the relationships between lifestyle behaviors and obesity among adolescents focus on either PA or dietary behaviors [4,5,6,7,8]. Although an understanding of factors associated with both PA and dietary behaviors is needed to inform obesity prevention efforts for adolescents, research examining these behaviors simultaneously is lacking.
Weight perception, the way in which individuals view their body weight, may be associated with adolescents PA and dietary behaviors [12]. Some evidence suggests that adolescents with self-perceived overweight or obesity, regardless of weight perception accuracy, are less physically active than those who perceive themselves as being normal weight [6,13,14,15]. They also are more likely to make unhealthier dietary choices [15,16,17]. However, other studies have had inconsistent results [18,19]. For example, Wong & Leatherdale found that adolescents who perceived themselves as being overweight or having obesity are not necessarily physically inactive when compared with their normal weight counterparts [18], and Fredrickson and colleagues found that adolescents' awareness of being overweight was not associated with their dietary choices [19].
Weight satisfaction, how one feels about his/her weight, also maybe associated with adolescents' PA and dietary behaviors [20]. Research examining the relationship between weight satisfaction, PA and dietary behaviors among adolescents is limited and inconsistent [19,21,22]. Therefore, there is a need to examine this relationship among a nationally representative sample of US adolescents. Thus, the purpose of the present study was to examine the association between adolescents' weight status, weight perception, weight satisfaction and the clustering of PA and dietary behaviors. Given that associations could differ by sex, we examined these relationships in males and females respectively.
The current study was a cross-sectional analysis of data from the National Health and Nutrition Examination Survey (NHANES) and the US Department of Agriculture's Food Patterns Equivalents (USDA-FPE) 2007–2014 datasets. NHANES data were collected via questionnaires (e.g., demographics, weight perception or satisfaction, 24-h dietary recall) or measured (e.g., height and weight) with response rates vary from 70–80% [23,24]. The USDA-FPE data examined in this study were the dietary components assessed via 24-h dietary recalls [24,25]. The two datasets were merged using participants' study identification numbers and days of 24-h recalled dietary data. The present study was exempted by University of Rhode Island Institutional Review Board as the data used for the study are de-identified.
2.1. Analytic Sample
A total of 40,617 respondents were abstracted from NHANES and the USDA-FPE 2007–2014 datasets. Of these respondents, 3920 were 12–17 years of age. Of these, 3041 had complete data for all examined variables including two days of dietary recalls. Of the 3041 respondents, 2.5% (n = 76) were underweight based on body mass index (BMI, <5th percentile for BMI), and were excluded due to possible underweight-related psychological or physical pathology issues [26] as well as the small sample size. The final analytic sample included 2965 adolescents.
2.2. Weight Status
BMI was calculated using height and weight that was measured at the Mobile Examination Center (MEC). BMI was used to determine weight status according to 2000 Centers for Disease Control and Prevention growth charts (0–20 years): obesity (≥95th percentile), overweight (≥85th percentile but <95th percentile), normal weight (≥5th percentile but normal <85th percentile, and underweight (<5 the percentile) [27].
2.3. Weight Perception and Weight Perception Accuracy
Respondents' weight perception was measured by a single-item question that asked them how they considered their weight with the response options of fat or overweight, about right weight, and thin [23]. Weight perception accuracy (accurate, inaccurate) was determined by comparing perceived and measured weight status (normal, overweight, obese). If the two measures were concordant, weight perception accuracy was considered to be accurate and if the two measures were discordant weight perception accuracy was deemed inaccurate [6].
2.4. Weight Satisfaction
Weight satisfaction was measured one of two ways depending on respondents' age. For respondents aged 12–15 years, it was assessed by a single item that asked what they would like to do about their weight with the response options of: (1) lose weight, (2) gain weight, (3) stay the same or (4) not trying to do anything about weight [23]. Respondents who wanted to "gain or lose weight" were classified as being weight dissatisfied while those who reported that they were trying to "stay the same or were not trying to do anything about their weight" were reported as being weight satisfied. Respondents aged 16–17 were asked whether they would like to change their weight with the response options of: (1) weigh more, (2) weigh less or (3) weigh the same [23], and those who reported that they would like to weigh more or less were classified as being weight dissatisfied while those who reported that they would like to weigh same were classified as being weight satisfied.
2.5. Physical Activity
PA was measured in three domains (work, travel, and recreational) using the Global Physical Activity Questionnaire [23]. The metabolic equivalent of task (MET) minutes of moderate to vigorous PA per week in each domain was determined and then summed to calculate total PA time per week [23,28]. Respondents who participated in ≥1680 MET-minutes of PA per week were classified as meeting current PA recommendations, all others were classified as not meeting PA recommendations [29].
2.6. Diet Quality
Diet quality was determined using the National Cancer Institute's simple Healthy Eating Index 2015 (HEI-2015) scoring algorithm [30,31], which is a reliable and valid measure of diet quality [24,30]. The HEI-2015 uses information from two 24-h dietary recalls conducted by NHANES to assess 13 dietary components (e.g., saturated fats, whole grains, etc.) [30]. The 13 components were summed to create total diet quality scores (range 0 to 100) with a higher score indicating better diet quality and greater adherence to the 2015–2020 Dietary Guidelines for Americans [31]. Participants were stratified into tertiles based on their total dietary quality scores: (1) lower quality diet (scores <42.1); (2) intermediate quality diet (scores 42.1–51.7); (3) healthier diet (scores >51.7). The top tertile was viewed as being indicative of a healthier diet whereas another two tertiles (lower quality diet and intermediate quality diet) were considered to indicate eating a less healthful diet.
2.7. Lifestyle Groups
Respondents were classified into four lifestyle groups based on their PA and dietary behaviors [29,31]: Group 1, healthier behaviors: meeting PA recommendations and eating a healthier diet; Group 2, healthier diet only: eating a healthier diet but not meeting PA recommendations; Group 3, physically active only: meeting PA recommendations but eating a less healthful diet; and Group 4, unhealthier behaviors: not meeting PA recommendations and eating a less healthful diet.
2.8. Demographic Characteristics
Examined demographic characteristics included age, sex, race/ethnicity (non-Hispanic White, non-Hispanic Black, Mexican American, other Hispanic, others) and parental education levels (high school or less, college or above) [25]. Family income and family size was used to calculate the poverty-to-income ratio that was then used to determine if the household income was at/above (≥1) or below (<1) federal poverty level [32]. NHANES has sampling design to assure reliable estimates of various US population subgroups thus participants are representative of the US population [24,33].
2.9. Statistical Analysis
The MEC exam 2-year weights were used for all analyses [33]. Descriptive results were obtained for the sample and are presented as mean ± standard error for continuous variables and frequencies and proportions for categorical variables. For the sex-specific prevalence of weight status, weight perception, weight perception accuracy, and weight satisfaction by the lifestyle groups, p-values for continuous variables were obtained by performing PROC SURVEYREG, and p-values for categorical variable were obtained by performing PROC SURVEYLOGISTC to perform the adjusted analyses, adjusting for age, race/ethnicity, parental education levels, and whether respondents lived at/above or below the poverty line. Bonferroni corrections were conducted for all multiple comparisons.
To examine the association between weight status, weight perception, weight perception accuracy, and weight satisfaction (independent variables) and the four lifestyle groups (dependent variable), multivariable logistic regression models were performed with the use of PROC SURVEYLOGISTIC, with GLOGIT link (multinomial logistic model) with Group 1, healthier behaviors group, being the reference category to estimate the adjusted odds ratios. All models were adjusted for age, race/ethnicity, parental education level, and poverty status, and stratified by sex. All analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and significance was set at p < 0.05.
Respondents' mean age was 14.5 years, approximately half (49.7%) were females, 42.3% were racial/ethnic minorities, 39.6% had a parent with a high school education or less, 19.4% lived below the poverty line (see Table 1). About a quarter (20.8%) of respondents were classified as having obesity, 21.9% perceived themselves as being overweight or having obesity, 26% accurately perceived their weight as being overweight or obese, 55.8% were dissatisfied with their weight, 85.2% of respondents with obesity were dissatisfied with their weight.
In regards to the examined behaviors, 32.9% had diets that were classified as being a healthier diet and 54.9% met current PA recommendations. About one-fifth (18.6%) of respondents were classified as being in Group 1, the healthier behaviors group, indicating that they met current PA recommendations and ate a healthier diet (see Table 2). More females than males were in Group 2, healthier diet only group (17.2% females vs. 11.4% males, p < 0.001), and Group 4, unhealthier behaviors group (36.9% vs. 24.7%, p < 0.001), whereas more males were in Group 3, physically active only group (44.5% males vs. 27.9% females, p < 0.001) (see Table 2).
As shown in Table 3, Figure 1 and Figure 2, there were differences in weight status by lifestyle groups. A greater proportion of normal measured/perceived weight males as well as weight satisfied females were in Group 1, the healthier behaviors group, than Group 2, healthier diet only group, while males who perceived themselves as being overweight or having obesity and females who were weight dissatisfied were more likely to be in Group 2, the healthier diet only group, than Group 1, healthier behaviors group. Moreover, males with self-perceived overweight or obesity and females with obesity were more likely to be in Group 2, the healthier diet only group, than Group 3, the physically active only group.
Comparing Group 1, the healthier behaviors group, to the other three lifestyle groups by weight categories, males with overweight or obesity were more likely to be in Group 2, the healthier diet only group, than males with normal weight (see Table 4). More specifically, males with obesity were 90% more likely to be in Group 2, the healthier diet only group, than Group 1, healthier behaviors group, compared to males of normal weight (OR = 1.90, 95% CI: 1.02, 3.52). Similarly, males who perceived themselves as being overweight or having obesity (OR = 2.09, 95% CI: 1.09, 3.99) and those who accurately perceived themselves as having obesity (OR = 2.33, 95% CI: 1.12, 4.88) were more likely to be in Group 2 than Group 1. Males who accurately perceived themselves as being overweight were more likely to be in Group 2, the healthier diet only group (OR = 3.87, 95% CI: 1.01, 15.69) or Group 4, unhealthier behaviors group (OR = 3.15, 95% CI: 1.00, 10.04) than males who accurately perceived themselves as being normal weight (Table 4). No statistical difference was observed between males who were satisfied with their weight and those who were weight dissatisfied.
For females, there was no significant association between weight status, weight perception or weight perception accuracy by lifestyle groups. Females who were satisfied with their weight were 59% less likely to be in Group 2, healthier diet only group, than Group 1, the healthier behaviors group, compared with those who were dissatisfied with their weight (OR = 0.41, 95% CI: 0.23, 0.75).
The present study used a nationally representative sample of adolescents to examine differences in four lifestyle groups characterized by the clustering of PA and dietary behaviors by weight status, weight perception, and weight satisfaction. Study results extended previous research findings by documenting that males with overweight or obesity or self-perceived overweight or obesity were less likely to meet PA recommendations than males with normal weight but diet quality did not differ [34]. Results of the current study also determined that females who were satisfied with their weight were more likely to meet PA recommendations and have a healthful diet than those who were dissatisfied with their weight. These findings indicate that lifestyle behaviors for males differ by weight status and weight perception whereas lifestyle behaviors for females differ by weight satisfaction.
In the present study, 54.9% of respondents were classified as meeting current PA recommendations, 32.9% had healthier diets. However, only 18.6% of the sample met PA recommendations and ate a more healthful diet. Although these finding are consistent with previous studies examining PA and/or diet quality among adolescents [4,11,35,36], no prior studies to our knowledge, examined the clustering of PA and dietary behaviors using a representative sample of US adolescents. Moreover, no identified studies assessed diet quality using the HEI-2015, which examines adherence to 2015–2020 dietary Guidelines for Americans [30]. The HEI-2015 differs from prior versions in assessed components and scoring, but, the average diet quality score of 47.5 in the current study is comparable to HEI-2010 mean score of 48.4 found in adolescents aged 12–18 from NHANES 2011–2012 [37]. Although the upper tertile (HEI-2015 scores >51.7) was defined as being a healthier diet for this study, according to the original HEI rating system, scores 51–80 indicate a diet that needs improvement [38]. Furthermore, the present study revealed sex differences across lifestyle groups: more females than males were in the healthier diet only group and the unhealthier behaviors group whereas more males than females were in the physically active only group. This finding is consistent with prior research that had found males to be more physically active than females [6,14] but differs from findings from other research who found that males had better dietary quality than females [35,39]. However, such results should be interpreted with caution because the clustering of PA and dietary behaviors were not examined in these studies [6,14,35,39].
Study findings indicate that males with overweight or obesity or who perceived themselves as being overweight or having obesity were more likely to consume a healthier diet but less likely to meet the PA recommendations than males with a normal weight or perceived their weight to be normal. However, similar results were not found among females. This finding differs from that of prior studies who have found that adolescents with self-perceived overweight or obesity were physically inactive [6,14,15] but not related to dietary choices [40], although comparisons between these studies should be made with caution considering that the current study examined individuals' PA and dietary behavior cluster. Moreover, the present study found that males who accurately perceived themselves as being overweight were more likely have unhealthier lifestyle behaviors. These findings suggest that for males recognizing oneself as being overweight may be a barrier to participating in PA and eating a healthier diet but this might not the case for males with obesity. These findings also suggest that sex differences in the relationship of weight status, weight perception and lifestyle behaviors should be considered while tailoring suitable obesity prevention or intervention strategies to address the unique needs of males and females with obesity in different lifestyle behavior groups.
Additionally, although the percentage of adolescents who were weight satisfied in this study is comparable to other studies [19,40], no difference in weight satisfaction by sex were observed as other studies have found [19,40]. This could be due to the use of a single item assessing weight satisfaction in the current study compared to other research [19,40]. Furthermore, differences in weight satisfaction by lifestyle groups were observed only among females. Females who satisfied with their weight were more likely in the healthier behaviors group, meeting PA recommendations and eating a healthier diet, than females who dissatisfied with their weight. Fredrickson and colleagues also have found that weight satisfaction is associated with healthy behaviors such as meeting the daily fruit recommendations but not related to daily servings of vegetables or PA [19]. Nevertheless, the present study extends previous research by examining PA and overall diet quality in tandem, which provides important insights for obesity prevention efforts and support the idea that weight satisfaction may serve as motivating factor for females engaging in healthy behaviors.
Study strengths include the use of a nationally representative sample that is racially/ethnically diverse (42.3% identifying as racial/ethnic minorities) and parents with less than a college education (39.6%), to examine the association of weight status, weight perception, weight satisfaction, and the clustering of PA and dietary behaviors. An additional study strength is use of measured height and weight to determine weight status as well as the use of 24-h recall methodology instead of a food frequency questionnaire. This also is the first study using HEI-2015 as an overall measure of dietary quality in adolescents. Study limitations include its cross-sectional design which does not allow for causality evaluation, weight satisfaction assessed using a single-item, and PA measured using a self-reported instrument. In addition, healthier PA was defined as meeting guidelines but since no guidelines exist for HEI-2015, healthier was defined as the upper tertile. This resulted in a greater proportion of adolescents meeting the PA cutoff for healthier than the dietary cutoff for healthier, thus results must be interpreted with caution. Nevertheless, all the measures are widely used and the study utilized validated instruments [24,28,29,30].
There were differences in weight status and weight perception by lifestyle group, and these differences were largely driven by PA for males and diet quality for females. Males with overweight or obesity were more likely not to meet PA recommendations but tended to have a higher quality diet than males with normal weight. Similar pattern was also observed in males with self-perceived overweight or obesity. Whereas females who were weight satisfied were more likely to meet the PA recommendation and eat a healthier diet than females who were dissatisfied with their weight. These results indicate that the importance of weight status, weight perception, and weight satisfaction in the adoption of healthier lifestyle behaviors particularly for individuals with overweight or obesity. Our findings also suggest that examining PA and dietary behavior cluster instead of either/or in the present study provides important insights on lifestyle behaviors difference that can be used to frame future research or interventions.
F.X., M.L.G. and S.A.C. designed the study. F.X. drafted the manuscript and F.X., M.L.G., S.A.C. and G.W.G. reviewed and provided edits and critical feedback. F.X. conducted all analyses with assistance from S.A.C. All authors approved the final version of the manuscripts.
The authors declare no conflict of interest regarding this paper.
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Figure 1. Lifestyle groups by weight status in males (n = 1504). Group 1: healthier behaviors; Group 2: healthier diet only; Group 3: physically active only; Group 4: unhealthier behaviors; a. Group 1 differed from Group 2 with a p < 0.05; b. Group 2 differed from Group 4 with a p < 0.05.
Figure 2. Lifestyle groups by weight status in females (n = 1461). Group 1: healthier behaviors; Group 2: healthier diet only; Group 3: physically active only; Group 4: unhealthier behaviors; a. Group 2 differed from Group 3 with a p < 0.05.
Table 1. Sample characteristics stratified by sex (n = 2965).
n = 2965
Gender, n (weighted %)
Males 1504 (50.3) - - -
Females 1461 (49.7) - - -
Age (years) (mean ± SE) 14.5 ± 0.0 14.5 ± 0.1 14.6 ± 0.1 0.409
Ethnicity, n (weighted %)
Non-Hispanic White 827 (57.7) 435 (57.4) 392 (57.9) 0.804
Non-Hispanic Black 748 (14.1) 378 (14.0) 370 (14.2) 0.856
Mexican American 695 (13.7) 346 (13.8) 349 (13.5) 0.787
Other Hispanic 342 (6.5) 171 (6.6) 171 (6.4) 0.772
Others 353 (8.0) 174 (8.1) 179 (7.9) 0.857
Parent education level, n (weighted %)
High school or less 1420 (39.6) 719 (38.9) 701 (40.3) 0.524
College or above 1451 (60.4) 736 (61.1) 715 (59.7) 0.524
Poverty to income ratio, n (weighted %)
<1.0 793 (19.4) 394 (19.0) 399 (19.9) 0.589
≥1.0 1941 (80.6) 994 (81.0) 947 (80.1) 0.589
BMI (kg/m2) 23.5 ± 0.1 23.4 ± 0.2 23.6 ± 0.2 0.446
Weight status, n (weighted %)
Normal weight 1765 (62.1) 905 (60.6) 860 (63.6) 0.308
Overweight 521 (17.1) 254 (17.6) 267 (16.6) 0.604
Obesity 679 (20.8) 345 (21.8) 334 (19.8) 0.291
Perceived weight status, n (weighted %)
Thin 196 (5.5) 128 (7.3) 68 (3.6) <0.001 *
Normal 2055 (72.6) 1093 (75.6) 962 (69.6) <0.001 *
Overweight/Obese 714 (21.9) 283 (17.1) 431 (26.8) <0.001 *
Accurate weight perception #
Normal 1481 (74.1) 760 (76.3) 721 (72.0) 0.072
Overweight 148 (6.4) 46 (4.3) 102 (8.3) 0.012 *
Obese 471 (19.6) 217 (19.4) 254 (19.7) 0.898
Weight satisfaction, n (weighted %)
Satisfied 938 (44.2) 471 (46.0) 467 (42.6) 0.191
Dissatisfied 1358 (55.8) 634 (54.0) 724 (57.4) 0.191
Weight satisfaction by weight category, n (weighted %)
Satisfied 727(61.6) 343(61.6) 384(61.6) 0.993
Dissatisfied 474(38.4) 225(38.4) 249(38.4) 0.993
Satisfied 134(33.8) 80(45.0) 54(21.9) <0.001 *
Dissatisfied 313(66.2) 132(55.0) 181(78.1) <0.001 *
Satisfied 77(14.8) 48(18.7) 29(10.6) 0.079
Physical activity (PA)
Total PA (MET-minutes/week) 2798.1 ± 85.2 3350.7 ± 121.7 2239.8 ± 92.1 <0.001 *
Met PA recommendation $, n (weighted %) 1491 (54.9) 900 (63.8) 591 (45.9) <0.001 *
Sedentary (sitting) in minutes/day 498.1 ± 5.5 489.1 ± 7.1 507.1 ± 6.4 0.026 *
Diet quality score (HEI 2015)
Total diet quality (mean ± SE) 47.5 ± 0.3 46.9 ± 0.4 48.1 ± 0.4 0.016 *
Tertile classification of total diet quality score, n (weighted %)
First tertile (<42.1) 978 (34.3) 517 (34.7) 461 (34.0) 0.741
Second tertile (42.1–51.7) 978 (32.7) 518 (34.6) 460 (30.8) 0.108
Third tertile (>51.7) 1009 (32.9) 469 (30.7) 540 (35.2) 0.023 *
Note: Data are present as weighted mean ± standard error (SE) unless otherwise specified; $ accumulated ≥1680 MET minutes/week for 12–17 years old, HEI = Health Eating Index; # weigh perception accuracy defined as consistency of perceived and measured weight status. If the two measures were concordant, weight perception accuracy was considered to be accurate; * p < 0.05.
Table 2. Lifestyle groups stratified by sex (n = 2965).
Proportion Difference between Females and Males (95% CI)
Groups, n (weighted %) n = 2965 n = 1504 n = 1461 n = 2965
Group 1: Healthier behaviors 515 (18.6) 282 (19.3) 233 (18.0) −1.27 (−4.84, 2.31) 0.481
Group 2: Healthier diet only 494 (14.3) 187 (11.4) 307 (17.2) 5.75 (2.56, 8.94) <0.001 *
Group 3: Physically active only 976 (36.3) 618 (44.5) 358 (27.9) −16.63 (−20.1, −13.15) <0.001 *
Group 4: Unhealthier behaviors 980 (30.8) 417 (24.7) 563 (36.9) 12.14 (8.48, 15.81) <0.001 *
Note: Data are present as weighted mean ± standard error unless otherwise specified; CI = confidence interval; * p < 0.05.
Table 3. Associations between weight perception, weight satisfaction and lifestyle groups (n = 2965).
Group 1: Healthier Behaviors
Group 2: Healthier Diet Only
Group 3: Physically Active Only
Group 4: Unhealthier Behaviors
Overall p Value
Males (n = 1504) n = 282 (19.3%) n = 187 (11.4%) n = 618 (44.5%) n = 417 (24.7%)
Weight status
Normal weight 173 (65.9) 92 (48.7) a 386 (62.1) 254 (59.2) c 0.017 *
Overweight 43 (12.8) 37 (20.6) 109 (20.1) 65 (15.6) 0.041 *
Obese 66 (21.4) 58 (30.7) 123 (17.7) 98 (25.2) 0.038 *
Perceived weight status
Thin 18 (5.0) 16 (7.8) 58 (7.9) 36 (7.9) 0.258
Normal 210 (78.1) 121 (66.4) a 466 (79.3) 296 (71.0) 0.022 *
Overweight/Obese 54 (16.9) 50 (25.8) a 94 (12.8) b 85 (21.1) 0.005 *
Accurate weight perception
Normal 150 (79.3) 73 (61.7) a 323 (81.5) b 214 (71.4) 0.007 *
Overweight 7 (3.2) 9 (6.9) 13 (2.5) 17 (6.9) 0.048 *
Obese 41 (17.5) 38 (31.5) 75 (16.0) 63 (21.7) 0.045 *
Weight satisfaction
Satisfied 94 (53.0) 54 (43.8) 193 (44.7) 130 (43.9) 0.468
Dissatisfied 119 (47.0) 94 (56.2) 255 (55.3) 166 (56.1) 0.468
Females (n = 1461) n = 233 (18.0%) n = 307 (17.2%) n = 358 (27.9%) n = 563 (36.9%)
Normal weight 145 (68.0) 165 (56.3) 216 (68.8) 334 (60.9) 0.222
Overweight 42 (16.7) 58 (15.7) 74 (17.0) 93 (16.6) 0.956
Obese 46 (15.3) 84 (28.1) 68 (14.2) b 136 (22.5) 0.007 *
Thin 9 (3.4) 16 (4.7) 15 (2.8) 28 (3.8) 0.538
Normal 168 (73.7) 181 (59.2) 250 (75.4) b 363 (68.1) 0.023 *
Overweight/Obese 56 (23.0) 110 (36.1) 93 (21.8) b 172 (28.1) 0.049 *
Overweight 15 (8.2) 30 (11.1) 25 (7.8) 32 (7.4) 0.863
Obese 35 (15.5) 63 (28.3) 49 (13.5) 107 (22.9) 0.011 *
Satisfied 88 (52.0) 80 (29.1) a 110 (43.5) 189 (43.8) c 0.009 *
Dissatisfied 103 (48.0) 175 (70.9) a 178 (56.5) 268 (56.2) c 0.009 *
Note: Data are presented as n (weighted %); p-value for continuous variables was obtained by performing PROC SURVEYREG, and p-value for category variable was obtained by performing PROC SURVEYLOGISTC to perform the adjusted analyses, adjusted for age, race, parental education level and poverty status; Bonferroni corrections were used for all multiple comparisons; a Group 1 differed from Group 2 with a p < 0.05; b Group 2 differed from Group 3 with a p < 0.05; c Group 2 differed from Group 4 with a p < 0.05; * p < 0.05.
Table 4. Adjusted odd ratios of lifestyle groups related to weight status, weight perception, and weight satisfaction (n = 2965).
Group 2: Healthier Diet Only vs. Group 1: Healthier Behaviors
Group 3: Physically Active Only vs. Group 1: Healthier Behaviors
Group 4: Unhealthier Behaviors vs. Group 1: Healthier Behaviors
Males (n = 1504) Adjusted ORs (95% CI) p Value Adjusted ORs (95% CI) p Value Adjusted ORs (95% CI) p Value
Normal weight 1.0 (REF) 1.0 (REF) 1.0 (REF)
Overweight 2.33 (1.15, 4.68) 0.016* 1.73 (1.00, 3.00) 0.046 * 1.21 (0.67, 2.19) 0.528
Obese 1.90 (1.02, 3.52) 0.038* 0.85 (0.50, 1.44) 0.537 1.14 (0.69, 1.86) 0.605
Normal 1.0 (REF) 1.0 (REF) 1.0 (REF)
Thin 0.89 (0.34, 2.32) 0.805 1.43 (0.71, 2.88) 0.308 1.45 (0.71, 2.95) 0.301
Overweight/Obese 2.09 (1.09, 3.99) 0.024* 0.82 (0.49, 1.38) 0.444 1.37 (0.87, 2.16) 0.164
Overweight 3.87 (1.01, 15.69) 0.047 * 1.09 (0.33, 3.63) 0.886 3.15 (1.00, 10.04) 0.048 *
Obese 2.33 (1.12, 4.88) 0.022 * 0.90 (0.48, 1.68) 0.74 1.18 (0.66, 2.10) 0.571
Dissatisfied 1.0 (REF) 1.0 (REF) 1.0 (REF)
Satisfied 0.69 (0.40, 1.19) 0.174 0.73 (0.46, 1.15) 0.165 0.75 (0.45, 1.24) 0.256
Females (n = 1461)
Overweight 1.05 (0.51, 2.17) 0.885 1.02 (0.51, 2.05) 0.953 1.06 (0.56, 2.00) 0.856
Obese 1.97 (0.96, 4.04) 0.058 0.92 (0.51, 1.66) 0.779 1.52 (0.85, 2.73) 0.153
Overweight/Obese 1.53 (0.83, 2.84) 0.167 0.84 (0.51, 1.38) 0.472 1.25 (0.75, 2.10) 0.378
Overweight 1.41 (0.48, 4.20) 0.525 0.86 (0.39, 1.87) 0.69 0.94 (0.37, 2.36) 0.889
Obese 1.80 (0.87, 3.72) 0.105 0.79 (0.41, 1.53) 0.477 1.42 (0.74, 2.70) 0.28
Satisfied 0.41 (0.23, 0.75) 0.003 * 0.74 (0.43, 1.27) 0.269 0.74 (0.41, 1.34) 0.307
Note: ORs = odds ratios; REF = reference; Adjusted ORs were obtained by PROC SURVEYLOGISTIC using the generalized logit model with the LINKG = LOGIT option (multinomial logistic model), adjusted for age, race/ethnicity, parental education level, poverty status; * p < 0.05.
Xu, F.; Cohen, S.A.; Greaney, M.L.; Greene, G.W. The Association between US Adolescents' Weight Status, Weight Perception, Weight Satisfaction, and Their Physical Activity and Dietary Behaviors. Int. J. Environ. Res. Public Health 2018, 15, 1931. https://doi.org/10.3390/ijerph15091931
Xu F, Cohen SA, Greaney ML, Greene GW. The Association between US Adolescents' Weight Status, Weight Perception, Weight Satisfaction, and Their Physical Activity and Dietary Behaviors. International Journal of Environmental Research and Public Health. 2018; 15(9):1931. https://doi.org/10.3390/ijerph15091931
Xu, Furong, Steven A. Cohen, Mary L. Greaney, and Geoffrey W. Greene. 2018. "The Association between US Adolescents' Weight Status, Weight Perception, Weight Satisfaction, and Their Physical Activity and Dietary Behaviors" International Journal of Environmental Research and Public Health 15, no. 9: 1931. https://doi.org/10.3390/ijerph15091931


text:
What Is Lame Duck President and What Can Trump Do If He Loses Election?
An outgoing president continues to hold the same power for the next two months and if Trump loses, it would be interesting to see the decisions he takes
November 4, 2020 22:45 +08
Despite projections favoring Democratic nominee Joe Biden to win the 2020 U.S. Presidential Election, incumbent President Donald Trump has a favorable outcome. While Biden is leading with 238 electoral college votes against Trump's 213, the race is still on as millions of votes are yet to be counted. There is a chance that the lead can flip by Friday and Trump can surge to the 270-mark. But if the trend continues and Biden becomes the President-elect, what's going to happen to Trump and his policies?
The answer isn't simple. For argument's sake, if we assume that Trump will lose the election, he will still be the president for the next two months until the midday of inauguration on January 20, 2021. During the period when a president is due to vacate the White House is called a lame duck. The outgoing president is expected to do nothing but enjoy the last few days at the White House. However, historically, it has never happened. The president still holds the power as the commander-in-chief and the executive power to do anything including launching a nuclear missile.
Even if Donald Trump loses, he will have all the power in the next two months to make crucial decisions Official WH Photos by Joyce Boghosian
Past History
A president's job doesn't end on the election night and history tells us that none of the outgoing presidents relaxed for the next two months. President George HW Bush after serving just one term as the President lost his re-election against Bill Clinton in 1992. But HW Bush used his last two months to issue presidential pardons to six officials of the Ronald Reagan administration. The former officials were charged for their involvement in the Iran-Contra scandal. The officials included former Defense Secretary Caspar W Weinberger.
Clinton too pushed his agenda in his last two months before George W Bush took over in 2000 after a controversial election that was decided by the U.S. Supreme Court. Clinton tried to broker a peace deal between Israel and Palestine. He also signed an agreement with North Korea to limit missile development. But both the deals broke apart later and still are two crucial world peace issues today.
Bush, on his part, bailed out two carmakers General Motors and Chrysler who were facing bankruptcy due to the 2008 financial crisis. With a total of $17 billion, he saved the two. He also pushed through environmental "midnight regulations", making it easier for the industries to look past environmental laws before Barack Obama took office.
Former President Barack Obama sanctioned Russian officials and organizations for election interference in his last two months at the White House Barack Obama Presidential Library/obamalibrary.gov
Obama's contributions in the last two months were not specifically aimed at the U.S. Instead, he sought to take action against alleged Russian interference in the 2016 Presidential election. He issued multiple sanctions against Russian officials and organizations. Besides, he expelled over 30 Russian officials from the U.S. Apart from that, he took an important decision that blocked a missile deal with Saudi Arabia to stop furthering its agenda in Yemen.
How Would A Lame Duck Trump Do?
While it is too early to say who will be the next U.S. President, if it is assumed that Biden wins the election, Trump will still hold a lot of power. Trump isn't shy about issuing executive orders. Since January 20, 2017, Trump has issued a total of 193 executive orders that include some massive undoing of the Obama administration. Those include reversing the Affordable Care Act (Obama Care), suspending visas from Muslim majority countries, sanctions on Venezuela among many others. He also announced his decision to withdraw the U.S. from Iran nuclear agreement, Paris Climate Change Agreement and the World Health Organization (WHO).
While the formal withdrawal process has completed for the Iran nuclear deal and Paris Agreement, if Trump loses, it will be up to Biden to either continue with the WHO withdrawal or undo his decision. But the President has hinted that he would not agree to a peaceful transition of power. Instead, he would fight until January 20, 2021, to keep his power and take Biden to the Supreme Court if necessary.
Documentary filmmaker Michael Moore said that the next two months would be hell even if Donald Trump loses his reelection Wikimedia Commons
Michael Moore, a documentary filmmaker, said that the next two months would be "hell" if Trump loses. "You don't even want to think of the fact that a soldier who goes with him everywhere with a briefcase is still going to be with him for two-and-a-half months, with the nuclear codes. Just that alone — it might be something we have to fix in the future," Moore told Moviemaker.
So, even if — by any chance — Trump loses the election, he will not shy away from taking key decisions. The top agenda could be issuing the second Coronavirus stimulus package. But considering Trump's short history in politics, nothing is guaranteed. He might make a decision that could stun even his GOP followers.
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text:
The First Lady's Day One of the Obama Administration
The morning started with a trip to the National Cathedral for service and prayer and, later, White House tours. The President and First Lady looked amazing fresh for two people who spent the night before attending ten Inaugural Balls, but hell, you're only elected the first black American, multi-ethnic "Leader of the Free World once, right? (Unless you get two terms. Then you get twoesies!) Live it up! (Minneapolis Star-Tribune)
Wonder what Michelle wore? Click below to the jump!
(Gawd, it's so odd to be preoccupied by her clothes, considering she's a brilliant, fascinating person in her own right, but until she starts doing her First Lady thing — and she's chosen military families and the horrid D.C. school system to adopt, methinks — this is what we've got! FIRST 100 DAYS FASHION WATCH!)
The cut is very retro. The print appears to be oriental in nature. Do we likey? Do we not likey? I love a fifties throwback and I wish I could get a better, close-up picture of the pattern, but these snippets will have to do for now.
Check out more First 100 Day pictures on … you guessed it … the First 100 Days Flickr page!
The Hope Faireys
Extreme Obama: Hennessy 44
24 thoughts on "The First Lady's Day One of the Obama Administration"
Me likey her dress.
Josey says:
Kitten Heels, please!!!!!!!
Kitten heels? she spent all day in high heels, walked several city blocks and went to ten balls. I think she earned the flats. Lovely print of the dress and the cut was so feminine. Michelle will never be boring.
The outfit is cute. I agree that after all day in heels and little sleep she earned the right to wear flats today.
Redstar says:
Don't like it. also didn't like the ball gown. I think she needs to wear a lower waist. This dress and the ball gown skirts tend to swallow her up.
BTW, new blog design looks great!
Andrea Williams says:
I did NOT like the print because it just seemed too busy. The flats were ok because after being on her feet all inaugural yesterday she needed to rest them. Even when I'm not into an outfit I like Michelle on levels beyond her wardrobe. I want US as a people to respect her beyond her fashion sense. As we all know the enemies of the world Ann coulter, Rush L., etc… are out wanting them to fail and hence the country to fail. So beyond fashion dos and donts I just love who and what she represents to black women/women of color who frequently are viewed as unicorns in terms of what is o.k. in the larger society. I'm just grinning ear to ear and channeling my inner Michelle as I go about my daily life.
rawdawgbuffalo says:
they are adorable, but This aint the Mountain Top or Promised Land – yet
SDG says:
Nice retro cut. Don't like the pattern at all. Too much going on. I'm sure her "feets" are thanking her for those flats today. With all the balls and walking in heels for hours. Relief at last.
dkan71 says:
Hate the dress. Hate the June Cleaver cut of it and hate hate hate the flats on her.
Nonie says:
The image on the dress is wisteria!http://latimesblogs.latimes.com/alltherage/2009/01/michelle-obam-5.html
My mistake – not wisteria: http://www.baltimoresun.com/entertainment/news/bal-lifestyle-firstlady0121,0,6069945.storyThe first lady wore a Tracy Feith retro-inspired dress to the church service. The dress, which Feith calls "Yoka Yoka Garden," looks like a vintage frock pulled from the costume department of TV's Mad Men, with its slim bodice, full skirt and three-quarter sleeves. The floral print on a black cotton fabric features a pattern of greens, lavenders and white cranes with a bit of gold.
I don't like the dress, but then again, I'm not a fan of this kind of design. Reminds me of the dress she wore when they announced Biden was the VP. The best pictures, though, are the ones of them opening The White House to the people. To-the-people.
Kells says:
I like the dress and I think it's cute even with flats. The evening gown was a flop. Know how I know. Cause Oprah didn't have anything to say about it on her show today.
Michele Davis says:
I love the dress. I also think she will inspire shoe designers to do a little more with flats. Not everyone wears heels.
Lady M says:
I like it, but I think it would look even better with a lower waistline/torso, especially since she's got long legs. Same with her dress last night. I like the flats. Comfortable but cute.
anon58 says:
I have a weird crazy thought which I hate to even write its so out there….is it possible michelle obama is pregnant? i'd say 'no' based on something she said awhile back about being through with that but the last two days the clothes have been very waist-y…they make her look a little thick which isn't usually her look or her style….a friend of mine had the same thought.
The A says:
Ladies – PLEASE!! let's not get sucked into this shallow area of the gender pool. It's time to change our country starting with ourselves.We have a brilliant complex woman like Michelle Obama as our First Lady. Why would you want to reduce her to a plastic fashion icon?!Yes, she is conservatively stylish but clothes are not her main focus. All The Working Mothers In The House Git Yo Hands Up!!The woman shops @ White House Black Market and J Crew. Although this is part of her appeal, you should also know this is a message to tell the fashion industry that she isn't their savior. If she wears something you like – then consider it a bonus.First you diss ReRe, then you nitpick with Michelle.Your claws are so unbecoming AND you are really scratching up your own faces.and why no criticism of Laura Bush's outfit?
and don't get me started on the pregnancy rumorsThis falls under the none of your business category.
What is it with people thinking MO's prego?! She is NOT pregnant. Maybe her waistlines are high or whatever, but there is NO baby bump or anything else . The two of them are past their prime and now is really not the best time for them to have another child. I know someone's gonna be in here like, "oh my mama had me when she was 50" or whatever but seriously…BO's got enough on his hands… and then some. Give MO a break. I agree with The A. I liked ReRe's hat, and she was the best person to sing at that event. I don't think any of the younger stars would have been appropriate. Let's see how Rhianna and Beyonce sound like in 40+ years.
oci says:
the dress is ugly as sin, but for some reason, it works. this woman is really fashion's wet dream. i love her!
politicallyincorrect says:
See BW sit here and every othe website and obsess about Michelle's fashion has already gotten old and its only been 2 days. I would like to see how some of these people who are complaining dress like.
DBB says:
I would agree. I think the dress is a bit of an UG MO, but somehow she makes it look fabulous, flats and all. Now, I do worry about the obsession with the clothes, but I can't help myself. My DH even pointed out to me that if 'others' were talking more about her clothes than her intellect, I would be the first one to 'get my feminist attitude on', but for some reason I am drawn to this woman. I think that for the first time in my adult life I have a woman that I inspires all of my own complex pieces. I am inspired by Jullian Malveux (sp?) as an academician, by Beyonce's fitness, by Kerri Washington's beauty, by many many others for different reasons. But Michelle Obama inspires me to be a better wife, mother, professional, to get my tired BEhind up at 5:00am and work out, and yes even a better dresser. Those types of role models are hard to come by.BTW, when she came out Inauguration day in that gold and green I just wanted to sing I was so tickled. She is wonderful, so let us just enjoy having the most visible Black woman in the world who is 'representing" on all cylinders.
Swenja says:
I am no expert, so I so I am curious about what it is about the dress that makes it inappropriate for a church service (or the flats, either)?


After a big night out that started with amazing pizza at Dizzy's , followed by a game of battleshots (surprisingly lethal) and back to Dizzy's for more drinks and dancing around til very early in the morning I was feeling a little sorry for myself. So in my very hung over state I carted everyone off to Clarke's diner on Bree for a hangover curing lunch. And oh how wonderfully Clarke's performed. I was struggling to function in my hungover state and thus choosing from their lovely lunch menu was proving to be a challenge. We finally opted for the chicken mayo buttermilk sandwich and an amazing Clarke's cheese burger to share between two of us. The rest of the table opted for burgers. But the oddest order of the day goes to- a box of corn flakes and a black label- I was at a loss for words- he however was very happy.
Here is my beautiful sandwich. It was absolutely huge, absolutely delicious and only cost me R55. good choice 🙂 The bread was amazing- I'm not sure what it was some kind of light fluffy white bread with a hard crispy crust- amazing! Inside my amazing sandwich was wonderful chicken breast chicken mayo, avocado and buttermilk coleslaw with green and red cabbage. The coleslaw made for some seriously messy eating- but the taste explosion was well worth the messy fingers and dripping buttermilk. Amazing sandwich- will definitely be back! But be warned- sandwiches are only served from 11am til 5pm.
And Here is my beautiful Clarke's cheese burger for R55- you can branch out and add bacon- but the burger is rich enough!! The bun is brioche- which is amazing and truly decadent. And the burger patty is incredibly and always served medium rare- just how it should be. So brioche, perfectly cooked burger patty and gooey melted cheese- absolute perfection- and bye bye hangover!
Update: We've been back to Clarke's many times for their amazing burgers but the other day we branched out! We tried Clarke's Mac and Cheese and oooh darling – it is cheesy! And a bowl of Mac and Cheese for R40 is great for all budgets. The Mac and Cheese can be upgraded with extra bacon or baby tomatoes but truly it is delicious all on its own!
We also tried the Hail Pizza Sandwich which is an open baked sandwich with slow cooked tomato ragout topped with mozzarella and pepperoni sausage for R55. And is pretty damn delicious!
Opening times: Monday 7am til 5pm, Tuesday to Saturday- 7am til late. Sundays 8am til 3pm.


He's young, he's green, he comes of age in a cynical world and isn't afraid to take the fight to anyone that he finds corrupt, even if that means operating above the law.
He's writer Grant Morrison and artist Rags Morales' rebooted new Superman, in the Man of Steel's first major updating of the iconic character in 25 years.
The new, crusading Superman debuts Sept. 7th from DC Comics in Action Comics #1, what the the publisher calls the "cornerstone" of its "New 52" line-wide relaunch of their superhero universe.
Newsarama and its publishing partners present an exclusive six-page preview of the historic 40-page debut issue, the first Action Comics #1 in over 73 years.
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text:
Q: Data analysis for inconsistent string formatting I have this task that I've been working on, but am having extreme misgivings about my methodology.
So the problem is that I have a ton of excel files that are formatted strangely (and not consistently) and I need to extract certain fields for each entry. An example data set is

My original approach was this:

*

*Export to csv

*Separate into counties

*Separate into districts

*Analyze each district individually, pull out values

*write to output.csv

The problem I've run into is that the format (seemingly well organized) is almost random across files. Each line contains the same fields, but in a different order, spacing, and wording. I wrote a script to correctly process one file, but it doesn't work on any other files.
So my question is, is there a more robust method of approaching this problem rather than simple string processing? What I had in mind was more of a fuzzy logic approach for trying to pin which field an item was, which could handle the inputs being a little arbitrary. How would you approach this problem?
If it helps clear up the problem, here is the script I wrote:
# This file takes a tax CSV file as input
# and separates it into counties
# then appends each county's entries onto
# the end of the master out.csv
# which will contain everything including
# taxes, bonds, etc from all years

#import the data csv
import sys
import re
import csv

def cleancommas(x):
  toggle=False
  for i,j in enumerate(x):
    if j=="\"":
      toggle=not toggle
    if toggle==True:
      if j==",":
        x=x[:i]+" "+x[i+1:]
  return x

def districtatize(x):
  #list indexes of entries starting with "for" or "to" of length >5
  indices=[1]
  for i,j in enumerate(x):
    if len(j)>2:
      if j[:2]=="to":
        indices.append(i)
    if len(j)>3:
      if j[:3]==" to" or j[:3]=="for":
        indices.append(i)
    if len(j)>5:
      if j[:5]==" \"for" or j[:5]==" \'for":
        indices.append(i)
    if len(j)>4:
      if j[:4]==" \"to" or j[:4]==" \'to" or j[:4]==" for":
        indices.append(i)
  if len(indices)==1:
    return [x[0],x[1:len(x)-1]]
  new=[x[0],x[1:indices[1]+1]]
  z=1
  while z<len(indices)-1:
    new.append(x[indices[z]+1:indices[z+1]+1])
    z+=1
  return new
  #should return a list of lists. First entry will be county
  #each successive element in list will be list by district

def splitforstos(string):
  for itemind,item in enumerate(string):      # take all exception cases that didn't get processed
    splitfor=re.split('(?<=\d)\s\s(?=for)',item)  # correctly and split them up so that the for begins
    splitto=re.split('(?<=\d)\s\s(?=to)',item)    # a cell
    if len(splitfor)>1:
      print "\n\n\nfor detected\n\n"
      string.remove(item)
      string.insert(itemind,splitfor[0])
      string.insert(itemind+1,splitfor[1])
    elif len(splitto)>1:
      print "\n\n\nto detected\n\n"
      string.remove(item)
      string.insert(itemind,splitto[0])
      string.insert(itemind+1,splitto[1])

def analyze(x):
  #input should be a string of content
  #target values are nomills,levytype,term,yearcom,yeardue
  clean=cleancommas(x)
  countylist=clean.split(',')
  emptystrip=filter(lambda a: a != '',countylist)
  empt2strip=filter(lambda a: a != ' ', emptystrip)
  singstrip=filter(lambda a: a != '\' \'',empt2strip)
  quotestrip=filter(lambda a: a !='\" \"',singstrip)
  splitforstos(quotestrip)
  distd=districtatize(quotestrip)
  print '\n\ndistrictized\n\n',distd
  county = distd[0]
  for x in distd[1:]:
    if len(x)>8:
      district=x[0]
      vote1=x[1]
      votemil=x[2]
      spaceindex=[m.start() for m in re.finditer(' ', votemil)][-1]
      vote2=votemil[:spaceindex]
      mills=votemil[spaceindex+1:]
      votetype=x[4]
      numyears=x[6]
      yearcom=x[8]
      yeardue=x[10]
      reason=x[11]
      data = [filename,county,district, vote1, vote2, mills, votetype, numyears, yearcom, yeardue, reason]
      print "data",data
    else:
      print "x\n\n",x
      district=x[0]
      vote1=x[1]
      votemil=x[2]
      spaceindex=[m.start() for m in re.finditer(' ', votemil)][-1]
      vote2=votemil[:spaceindex]
      mills=votemil[spaceindex+1:]
      votetype=x[4]
      special=x[5]
      splitspec=special.split(' ')
      try:
        forind=[i for i,j in enumerate(splitspec) if j=='for'][0]
        numyears=splitspec[forind+1]
        yearcom=splitspec[forind+6]
      except:
        forind=[i for i,j in enumerate(splitspec) if j=='commencing'][0]
        numyears=None
        yearcom=splitspec[forind+2]
      yeardue=str(x[6])[-4:]
      reason=x[7]
      data = [filename,county,district,vote1,vote2,mills,votetype,numyears,yearcom,yeardue,reason]
      print "data other", data
    openfile=csv.writer(open('out.csv','a'),delimiter=',', quotechar='|',quoting=csv.QUOTE_MINIMAL)
    openfile.writerow(data)

# call the file like so: python tax.py 2007May8Tax.csv
filename = sys.argv[1] #the file is the first argument
f=open(filename,'r')
contents=f.read() #entire csv as string
#find index of every instance of the word county
separators=[m.start() for m in re.finditer('\w+\sCOUNTY',contents)] #alternative implementation in regex

# split contents into sections by county
# analyze each section and append to out.csv
for x,y in enumerate(separators):
  try:
    data = contents[y:separators[x+1]]
  except:
    data = contents[y:]
  analyze(data)

A:
is there a more robust method of approaching this problem rather than simple string processing?

Not really.

What I had in mind was more of a fuzzy logic approach for trying to pin which field an item was, which could handle the inputs being a little arbitrary. How would you approach this problem?

After a ton of analysis and programming, it won't be significantly better than what you've got.
Reading stuff prepared by people requires -- sadly -- people-like brains.
You can mess with NLTK to try and do a better job, but it doesn't work out terribly well either.
You don't need a radically new approach.  You need to streamline the approach you have.
For example.
  district=x[0]
  vote1=x[1]
  votemil=x[2]
  spaceindex=[m.start() for m in re.finditer(' ', votemil)][-1]
  vote2=votemil[:spaceindex]
  mills=votemil[spaceindex+1:]
  votetype=x[4]
  numyears=x[6]
  yearcom=x[8]
  yeardue=x[10]
  reason=x[11]
  data = [filename,county,district, vote1, vote2, mills, votetype, numyears, yearcom, yeardue, reason]
  print "data",data

Might be improved by using a  named tuple.
Then build something like this.
data = SomeSensibleName(
    district= x[0],
    vote1=x[1], ... etc.
)

So that you're not creating a lot of intermediate (and largely uninformative) loose variables.
Also, keep looking at your analyze function (and any other function) to pull out the various "pattern matching" rules.  The idea is that you'll examine a county's data, step through a bunch of functions until one matches the pattern; this will also create the named tuple.  You want something like this.
for p in ( some, list, of, functions ):
    match= p(data)
    if match:
        return match

Each function either returns a named tuple (because it liked the row) or None (because it didn't like the row).

redpajama_set_name: RedPajamaStackExchange


text:
The Gardner Heist
By Richard Lacayo March 25, 2009
To mark the anniversary last week of the spectacular 1990 robbery at the Isabella Stewart Gardner Museum in Boston — among other things the thieves made off with Vermeer's The Concert, seen above, three Rembrandts and a Manet — I made my way through The Gardner Heist, a new book by Ulrich Boser, a journalist who was drawn into the case four years ago. At the time Boser was thinking about doing a profile of Harold Smith, an insurance claims adjuster who had made a name for himself as a specialist in stolen art and jewelry. No surprise, the Gardner theft, which remains the biggest unsolved art crime, was a preoccupation for Smith and he had followed many leads over the years.
But Smith was 78 when he met Boser and died a few months later. Boser obtained access to Smiths files and dove into the case, which for the most part meant digging into the rotating collection of suspects whose names have been floated repeatedly over the years. He follows the trail to Britain and Ireland. The IRA is often suspected to have either played a role in the robbery or taken possession later of the art. At this point, as often happens with stolen art, it may now have disappeared into gangland, to be used as collateral in gun running and drug deals.
The dust jacket promises that Boser has identified the robbers but has he? In the last chapter Boser abruptly circles back to a suspect he looked into earlier in the book, a Boston hood named David Turner who's now doing a 38 year stretch in prison for trying to rob an armored car depot. The Gardner was robbed by two men posing as a cops. Boser shows some photographs of various suspects in the case to a witness who saw what appeared to be two policemen waiting in a car outside museum that night, 19 long years ago. The witness says he thinks he recognizes Turner. Thinks? For a time Boser also communicates with Turner in prison and thinks he detects "an undertone of vanity" in his voice when he asks him questions over the phone about the Gardner thefts, which, by the way, Turner denies having had anything to do with.
There's other evidence that points to Turner, plausible yes but none of it conclusive. And even if he's one of the pair who robbed the Gardner that night, it doesn't appear that he still controls the stolen art. If he did, he would almost certainly use the promise of its return as leverage to get his sentence reduced for the armored car crime. But if Boser doesn't convince you that he's cracked the case, he persuades you that it's a swamp of intricacies. As somebody once said to Jack Nicholson, "It's Chinatown, Jake".


You will enjoy the charm of a fifties decor in a friendly atmosphere that serves only the finest local ingredients and welcomes you like family. You are treated with attentive care in a sparkling clean environment. As chef, Bill carefully oversees the preparation of each dish with an discerning eye. He uses only the freshest seafood, meats and garden herbs presented in generous portions. Bill's love of food and hospitable charm had early beginnings. He was born in Greece, twenty miles south of Sparta, in a home filled with the aroma of delicious, freshly prepared meals served with love.
Fresh, simply prepared food, warmed with camaraderie. Those two qualities have characterized each of the restaurants Bill and Carole Polimenakos have owned in their rich twenty year history of serving good food to others.
The family tradition continues. Together, Bill, Carole and son Pano bring you a dining experience of exceptional quality. We hope to see you soon and have the opportunity to serve you.