1. Introduction {#sec1}
===============

The high prevalence of pediatric obesity remains a major healthcare burden and an area of high priority for population management in pediatric care. Given the strong tracking effect of childhood obesity into adulthood \[[@B1]--[@B4]\] and the association of pediatric obesity with adverse health consequences \[[@B5]--[@B7]\], understanding behavioral factors, severity trends, and high-risk subgroups is important from both a public health and clinical perspective. Although overall pediatric obesity rates have stabilized in recent years \[[@B8]\], rates of severe obesity are high \[[@B9]\], and the fact remains that one in six US children have obesity \[[@B8], [@B10]--[@B12]\], with the burden disproportionately affecting Hispanic/Latino and black pediatric populations \[[@B13]--[@B18]\]. Newer definitions for higher order obesity delineated by the body mass index (BMI) percentage above the 95th BMI percentile provide additional classification of children with severe obesity \[[@B19], [@B20]\], where a continued upward trend has been observed nationally \[[@B9]\] and racial/ethnic disparities are evident beginning at an early age \[[@B15], [@B17]\].

The US Preventive Services Task Force and the American Association of Pediatrics recommend BMI screening for children starting at a very young age, citing evidence for early detection and management of pediatric patients with obesity \[[@B21]--[@B23]\]. At a national level, pediatric weight assessment, nutrition counseling, and counseling for physical activity are also important benchmarks in clinical practice, with implementation of standards by the National Committee for Quality Assurance in 2009 \[[@B24]\]. In 2013, the American Heart Association \[[@B19]\] recommended the use of new growth charts for children with severe obesity \[[@B20]\], with the goal of optimizing weight classification and BMI tracking above 120% of the 95th BMI percentile \[[@B20], [@B25]\]. While demographic and behavioral factors have been well documented in pediatric overweight and obesity \[[@B26]--[@B33]\], fewer studies have examined differences by obesity severity using these contemporary classification criteria. Additionally, the characterizations of dietary and lifestyle behaviors at the extremes of pediatric obesity remain understudied. A better understanding of these modifiable behaviors may lead to more targeted screening for specific risk (or protective) factors that can inform the focus of targeted intervention to decrease pediatric obesity.

Kaiser Permanente Northern California (KPNC) is a large integrated health care delivery system providing comprehensive care for an ethnically diverse population of more than three million members, of whom nearly one-fourth are children under the age of 18 years. Within KPNC, a multifaceted approach to address the growing prevalence of childhood obesity was initiated in 2001, with three main components: medical office visit interventions (screening and counseling), weight management interventions, and environmental changes \[[@B13]\]. These efforts resulted in a modest reduction in obesity prevalence in younger children, but not adolescents, between 2003--2005 and 2009-2010 \[[@B13]\]. In 2012, KPNC further implemented a population program entitled the Get Healthy Action Plan (GHAP), designed to assist pediatricians in the assessment and counseling of children and adolescents with obesity using a standardized office tool during routine well-child visits. Following measurement of height and weight, children and adolescents with BMI ≥ 95th percentile were identified by medical assistants, and the child\'s parent or the teen patient was asked to complete a brief questionnaire focused on dietary and lifestyle behaviors. Pediatricians reviewed the responses during the visit to facilitate discussion and encourage follow-up of healthy behaviors. Within 2 years, the GHAP program identified and assessed nearly 8,000 children and adolescents who met BMI criteria for obesity during their well-child visit. As such, the GHAP well-child visit encounter provides an extremely large, diverse, and real-world clinical population of children with obesity in whom obesity severity, risk behaviors, weight status, and intervention can be further examined.

The purpose of this study was to characterize the anthropometric findings, demographic factors, and behavioral correlates of obesity severity in a racial/ethnically diverse cohort of pediatric patients evaluated in the KPNC GHAP program. In addition, we wanted to use the GHAP questionnaire data as a contemporary and complete assessment of current pediatric dietary and lifestyle behaviors and trends, identifying high risk subgroups by age, gender, race/ethnicity, and weight status for future management and tracking. It is anticipated that these findings will aid in the development of targeted interventions to address specific modifiable obesogenic dietary and lifestyle behaviors in children and adolescents who have obesity or are at risk for developing obesity.

2. Material and Methods {#sec2}
=======================

This retrospective cohort study examined demographic factors and obesity severity among children assessed through the GHAP program, a population-based intervention implemented at KPNC well-child visits that utilized a pediatrician-developed (C.G.) primary care pediatric obesity assessment tool kit with medical assistant support. The study was approved by the KPNC Institutional Review Board and a waiver of informed consent was obtained due to the nature of the study.

2.1. Cohort Ascertainment, Anthropometry, and Weight Tracking {#sec2.1}
-------------------------------------------------------------

The study cohort included children and adolescents 3--17.9 years old who underwent a baseline GHAP assessment at a well-child visit between January 1, 2012, and December 31, 2013. The GHAP program was piloted in 2011 in Santa Rosa (C.G.) and subsequently scaled for broader use across more than 30 medical offices in KPNC. Pediatric Weight Management Physician Champions and Chiefs of Pediatrics for Health Plan Medical Centers were briefed on the pilot program and encouraged to adopt the tools, but participation was voluntary. Typically, medical assistants would initiate the GHAP assessment for children whose BMI was ≥95th percentile for age and sex, and, less often, physicians in some sites asked parents (or teens) to complete the questionnaire mid-way through the visit. Providers then initiated their health counseling customized to the patient\'s answers on the questionnaire. Questionnaire responses were entered into the electronic medical record.

Administrative databases were used to obtain information on age, sex, and race/ethnicity. Census block-level data were used as a proxy for socioeconomic status, with residence in a lower income neighborhood defined as living in an area with a median household income ≤ \$40,000. The electronic medical record was used to ascertain clinic-based measurements of height by stadiometer and weight using a calibrated scale. Body mass index was calculated as weight divided by height squared (kg/m^2^) and classified by BMI percentiles according to age- and gender-specific 2000 CDC growth charts \[[@B34], [@B35]\]; weight status was categorized as normal to underweight (BMI \< 85th percentile), overweight (BMI 85th to \<95th percentile), or obese (BMI ≥ 95th percentile) for age and sex \[[@B23]\]. Higher order obesity was expressed as a percentage of the 95th BMI percentile, with severe obesity defined by a threshold of BMI ≥ 120% of the 95th BMI percentile \[[@B19], [@B20], [@B25]\].

2.2. Behavior/Lifestyle Risk Factors {#sec2.2}
------------------------------------

Data on behavioral and lifestyle risk factors came from a one-page 19-item GHAP questionnaire that was completed by parents of children with obesity and/or adolescents with obesity at the time of a well-child visit (teens were given the questionnaire directly). The GHAP form included specific questions pertaining to nine dietary/lifestyle behaviors: the number of unhealthy snacks, sugar-sweetened beverages (SSBs), and juice drinks per day; the numbers of servings of fruits and vegetables per day; the frequency of eating breakfast and family meals; the number of days per week the child was physically active for at least 60 minutes outside of school physical education; the number of hours of "screen time" (time spent playing video games, using a computer, or watching television) per day; and the number of hours of sleep per night. Inadequate sleep was defined per National Sleep Foundation 2015 Consensus Guidelines \[[@B36]\] which recommends against sleep less than 8 hours per day (including naps) for children aged 3--5 and less than 7 hours per day for children aged 6--17 years. To assist parents and teens with objective reporting of dietary behaviors, specific examples were given for sugar-sweetened beverages (e.g., soda, Gatorade, Vitamin Water, Hi-C, and energy drinks) and sweet and salty snacks (cookies, candy, chips, crackers, etc.), and the serving size for fruits and vegetables was characterized as 1/2 cup (or 4 ounces). Numeric response categories were provided for each question. All behavior/lifestyle risk factors were framed as unhealthy behaviors in our analyses so that a higher percentage of each behavior reflects a higher percentage of children in that group with that unhealthy behavioral or lifestyle risk. Analyses pertaining to behavior and lifestyle risks were restricted to the 98.7% of the cohort (*n* = 7,856) for whom information was available for at least five of these nine behaviors. The age, race/ethnicity, and obesity severity profile of this slightly restricted cohort did not differ from that of the full cohort.

2.3. Statistical Analysis {#sec2.3}
-------------------------

Descriptive statistics were performed using the chi-square test for categorical variables and Student\'s *t*-test for continuous variables (Wilcoxon rank sum test for nonparametric data). Trends in severe obesity by age group were examined using the Cochrane-Armitage test. Differences in dietary and lifestyle behaviors by age group (6--11 years and 12--17 years versus 3--5 years) and by race/ethnicity (black, Hispanic, and Asian/Pacific Islander versus non-Hispanic white) or obesity severity (BMI 100--119% of the 95th BMI percentile versus BMI ≥ 120% of the 95th BMI percentile for age and sex) within age group were examined using logistic regression models that controlled for race/ethnicity and gender (age group and obesity severity differences) or age group and gender (racial/ethnic differences). All analyses were conducted using SAS Version 9.3 (SAS Institute; Cary, NC) \[[@B37]\]. Comparisons were considered statistically significant based on a *p* value of \<0.05 using two-tailed tests.

3. Results {#sec3}
==========

A total of 7956 children and adolescents aged 3--17.9 years with a BMI ≥ 95th percentile had a baseline GHAP assessment at a KPNC well-child visit during 2012-2013. Nearly half of them were females (45.5%) and there was notable racial/ethnic diversity, including 24.9% non-Hispanic white (whiteNH), 11.3% black, 43.5% Hispanic, 12.0% Asian/Pacific Islander (Asian/PI), and 8.3% other/unknown race/ethnicity. Most children were 6--11 (44.2%) or 12--17 (41.6%) years old, with only 14.2% 3--5 years old. Based on residential census block data, 14.3% of these children lived in a lower income neighborhood, with Hispanic and black children two to three times as likely as whiteNH and Asian/PI children to live in a lower income neighborhood (21.8% and 19.0% versus 6.8% and 7.5%, resp., *p* \< 0.001).

[Table 1](#tab1){ref-type="table"} shows the distribution of moderate and severe obesity by gender and race/ethnicity for preschool-aged children (3--5 years), school-aged children (6--11 years), and adolescents (12--17 years). Over one-third (37.4%) of these children with obesity met criteria for severe obesity (BMI ≥ 120% of the 95th percentile), with one-tenth found to have a BMI ≥ 140% of the 95th percentile. The proportion of children with severe obesity increased significantly by age group (31.4%, 36.1%, and 40.9%, *p* \< 0.001 for trend). Across all age groups, black and Hispanic children were more likely than whiteNH children to meet criteria for severe obesity (ages 3--5, 37.6% and 34.0% versus 24.8%, resp.; ages 6--11, 43.9% and 40.1% versus 28.9%; and ages 12--17, 50.5% and 43.2% versus 37.3%, all *p* \< 0.05). Asian/PI children in the youngest group were more likely than whiteNH children to have severe obesity (38.8% versus 24.8%, *p* \< 0.01), but rates did not differ significantly from whiteNHs in the older groups. Preschool-aged children who lived in a lower income neighborhood were also more likely than those who did not to have severe obesity (39.3% versus 29.7%, *p* \< 0.05 after adjusting for race/ethnicity and gender); however, living in a lower income neighborhood was not associated with obesity severity among older children.

[Table 2](#tab2){ref-type="table"} profiles obesity-related behaviors and lifestyle risks of children in each of the three age groups (statistical modeling found few differences in behaviors by gender, and, where differences exist, they are noted in the text). Compared to the youngest children, those in the two older age groups had higher percentages of obesogenic health behaviors, including being more likely to eat breakfast \<6 days per week, to eat meals with their family \<5 times per week, to eat ≥3 sugary or salty snacks per day, to eat \<5 servings of fruits and/or vegetables per day, to get \<5 days of exercise (at least 60 minutes outside of school) per week, and to have "screen time" ≥ 3 hours per day. Consumption of ≥2 SSBs/juice drinks per day did not differ by age, as rates were already extremely high (58%) among preschool-aged children.

Few gender differences in dietary and lifestyle risks were observed within each age group. In the youngest age group, girls were more likely than boys to have \<5 days of exercise (at least 60 minutes outside of school) per week (47.7% versus 41.6%) but tended to be less likely to have ≥3 hours of "screen time" (28.7% versus 34.0%). In the 6- to 11-year-old age group, girls were less likely than boys to have ≥3 sugary or salty snacks per day (26.0% versus 29.8%), to have ≥2 SSBs/juice drinks per day (55.6% versus 62.6%), and to have ≥3 hours of "screen time" per day (40.4% versus 45.9%), but they were more likely to have \<5 days of exercise (at least 60 minutes outside of school) per week (68.2% versus 62.3%). In the adolescent age group, girls were more likely than boys to eat breakfast \<6 times per week (63.0% versus 55.4%), but they were less likely to consume ≥2 SSBs/juice drinks per day (57.0% versus 62.2%). They were also more likely than boys to have \<5 days of exercise (at least 60 minutes outside of school) per week (74.8% versus 61.8%) and to have no days of exercise (17.7% versus 9.6%). Adolescent girls were nearly twice as likely as boys to get \<7 hours of sleep per night (11.7% versus 6.7%). Fruit and vegetable intake also varied by gender among younger children. These differences remained significant after adjusting for race/ethnicity.

[Table 3](#tab3){ref-type="table"} examines how these health-related behaviors/lifestyle risks differ by obesity severity within each age group, using moderate obesity as the reference group. Among preschool-aged children, a higher percentage of children with severe obesity ate breakfast \<6 times per week, consumed ≥2 SSBs/juice per day, and had \<5 days of exercise (at least 60 minutes outside school) per week. School-aged children with severe obesity were also more likely to eat breakfast \<6 times per week and have \<5 days of exercise (at least 60 minutes outside school) per week, in addition to limited family meals and greater "screen time." Adolescents with severe obesity shared these same unhealthy behavior trends, along with lower fruit/vegetable intake and inadequate sleep, but there was no difference in the proportion eating regular family meals when compared to adolescents with moderate obesity. These behavioral differences by obesity severity persisted after additionally controlling for residence in a lower income neighborhood (data not shown).

Significant racial/ethnic differences in obesogenic health behavior and lifestyle risks are identified in [Table 4](#tab4){ref-type="table"}. Black and Hispanic children of all age groups and younger Asian/PI children were more likely than whiteNH children to drink ≥2 SSBs/day. Selected age subsets of black and Asian/PI children were also more likely than whiteNH children to consume ≥3 sugary or salty snacks a day. Hispanic children and older black children were more likely than whiteNH children to eat breakfast \<6 times a week and have family meals \<5 days per week. Exercise level varied by race/ethnicity within age groups (consistently lower in Hispanic and younger Asian/PI children compared to whiteNH children), with low rates of exercise overall. More than half of 6--11 year olds and two-thirds of 12--17 year olds across all racial/ethnic subgroups reported getting \<5 days of exercise (at least 60 minutes outside school) per week, with large percentages getting \<3 days of exercise per week. Black children across all age groups and Asian/PI and Hispanic children among selected younger age groups were more likely than whiteNH children to have ≥3 hours of screen time per day; nearly two-thirds of adolescents in all racial/ethnic subgroups reported ≥3 hours of screen time per day. Sleep behavior also differed, with significantly greater proportion of black and Hispanic adolescents and preschool-aged black children reporting inadequate hours of sleep per night. These racial/ethnic differences persisted after additionally controlling for residence in a lower income neighborhood (data not shown).

4. Discussion {#sec4}
=============

We found that, in a racially and ethnically diverse outpatient pediatric population with moderate or severe obesity, a clinical screening questionnaire completed by parents of children with obesity or teens with obesity in the context of a well-child visit identified large numbers of preschool-aged, school-aged, and adolescent children with modifiable obesity-related dietary and lifestyle risk factors. In addition to confirming substantial opportunities for intervention with education pertaining to increased exercise and reduced screen time, SSB/juice intake, and unhealthy snack consumption, we also found that large numbers of children with obesity were not regularly having breakfast or family meals. Notably, these adverse health behaviors started at a very early age and many increased with level of BMI. The high rates of excess SSB/juice intake among preschool children (nearly two-thirds among those with severe obesity) support data from other studies demonstrating high consumption of sweetened beverages and association with adiposity in preschool-aged children \[[@B38]--[@B40]\] and underscore the need for early education and counseling of parents and caregivers \[[@B38]\]. Disparities by race/ethnicity were also seen, with greater numbers of unhealthy behaviors exhibited by young black, Hispanic, and Asian/PI children as compared to whiteNH children. Among adolescents, the largest differences were seen in black and/or Hispanic populations, where unhealthy behaviors were significantly more prevalent than in whiteNH children. Nearly two-thirds and three-fourths of black and Hispanic school-aged children and adolescents reported excess consumption of SSB/juices on a daily basis. These and other modifiable behaviors present important intervention targets for counseling during well-child visits, with tracking of behavior change through follow-up. The GHAP program also provides a future platform for examining less modifiable factors, including financial and logistical barriers, as well as broader issues pertaining to health literacy that can be addressed through additional GHAP surveys.

Our findings are consistent with data from other studies which show higher consumption of sugary drinks \[[@B29], [@B41]\]; more hours of weekday television, lower participation in vigorous exercise, and lower proportion eating breakfast among black and Hispanic children compared to whiteNH children \[[@B27], [@B30], [@B33]\], as well as higher percentages with low socioeconomic status \[[@B28], [@B30]\]. Children with low socioeconomic status have also been found to have more obesogenic behaviors \[[@B28], [@B30]\]. In our current study, obesogenic behaviors and lifestyle factors varied by obesity severity and race/ethnicity, independent of residence in a lower income neighborhood. While further examination of socioeconomic influences is warranted, these data may help direct larger sociopolitical agendas pertaining to food policy, education, school-based initiatives, and environmental influences to address challenges faced at the individual patient level.

While severe obesity and unhealthy behaviors were highly prevalent in black and Hispanic subgroups, we also found gender differences that persisted after controlling for race/ethnicity. For example, insufficient exercise was a consistent unhealthy behavior with higher prevalence among school-aged and adolescent girls compared to boys, in addition to lower frequency of daily breakfast and inadequate hours of sleep per night among adolescent girls. The high proportion of adolescents with excessive "screen time" poses an additional challenge, given that, in addition to watching television, playing video games, and using the Internet and online social media, adolescents may be required to spend time on the computer to complete homework. While these self-reported behaviors should be differentiated for counseling purposes, collectively they represent unhealthy sedentary behaviors that could contribute to obesity risk. The model of GHAP, which elegantly structures the conversation between the primary care physician and the patient and family, allows the conversation with families and teens to focus on individual challenges with regard to healthy behaviors, while lessening the tendency towards weight bias and stigma.

There are some limitations of our study to consider. First, the GHAP questionnaire responses were based on self-reported behaviors by the parent or teen, potentially subject to overreporting of healthy behaviors and underreporting of unhealthy behaviors. Future validation efforts could include a diet/physical activity log for comparison. Second, the GHAP program was restricted to children with obesity and thus these behaviors could not be compared to those of nonobese peers, including differences by race/ethnicity. However, our goal was to examine modifiable risk behaviors among children with obesity for potential intervention. Third, since these data are cross-sectional, the results can only be interpreted as associations without inferring causality. We also acknowledge that there are "less controllable" causes of obesity, including genetics, biology, and environmental factors, not addressed in the current study. Fourth, we were not able to assign socioeconomic status based on household income or parent education and had to rely on residential census block data to characterize the neighborhoods in which the children resided. Finally, while the cohort was large, it was not large enough to examine racial/ethnic and gender differences by level of obesity within each age group, which should be examined in future studies.

The strengths of our study include the comprehensive assessment of dietary and lifestyle behaviors at a well-child visit (providing a convenient benchmark for future interventions and tracking) and the ability to examine trends in obesogenic behaviors by age, race/ethnicity, and obesity severity using contemporary weight classification criteria. Knowing these trends in unhealthy obesogenic behaviors will be useful for addressing modifiable risk factors in weight management, with behavioral intervention targets in the clinical setting particularly applicable to young children who already receive frequent routine pediatric care. Furthermore, this clinic-based population study was not restricted to research subjects or a clinical trial cohort, providing an extremely large and contemporary pediatric clinic population for which future interventions can be implemented. Our study examined a racially/ethnically and socioeconomically diverse pediatric patient population drawn from more than 30 health plan clinics across a large and varied geographic area in Northern California.

In conclusion, this study provides a contemporary assessment of behavioral trends in pediatric obesity and initial data for investigating further associations between severe and/or extreme obesity and lifestyle in children and adolescents. Similar to our prior studies conducted within the KPNC pediatric population, we found that severe obesity rates were high among the subgroup of black and Hispanic children with obesity \[[@B15], [@B42]\], beginning at an early age. Given the strong tracking effect for both moderate and severe obesity in childhood \[[@B42]\], these observations underscore the need to address existing ethnic disparities and to identify young and preteen children at highest risk for sustained obesity with focus on both primary prevention and early intervention. Findings from this study will inform the development and implementation of cost-effective, targeted healthy lifestyle interventions focused on reducing obesity rates among pediatric patients in the primary care setting.

This study was funded by an internal grant from The Permanente Medical Group (TPMG) Delivery Science Program. Cheryl Green M.D. is a pediatrician and internist (Kaiser Permanente Santa Rosa Medical Center) who developed and implemented the pilot phase of the GHAP Program, prior to integration in pediatric clinics with support from TPMG Regional Health Education. The authors would also like to thank Dewey Woo M.D. and Elio Gizzi M.D. for their review of earlier versions of this paper and Joel Gonzalez for his assistance with paper preparation.

Conflict of Interests
=====================

Dr. Joan C. Lo has received research funding from Sanofi, unrelated to the current study. The remaining authors have no conflict of interests to report. The funding source (The Permanente Medical Group Delivery Science Program) had no role in the study design, data collection, analysis and interpretation of findings, writing of the report, and the decision to submit the report for publication.

######

Demographic characteristics of the pediatric GHAP cohort (row percentages provided).

  Age group                      *N*    Moderate obesity   Severe obesity
  ------------------------------ ------ ------------------ ---------------- -------------
  *Ages 3--17 years*
  Gender, %
   All                           7956   4978 (62.6%)       2134 (26.8%)     844 (10.6%)
   Male                          4333   2594 (59.9%)       1232 (28.4%)     507 (11.7%)
   Female                        3623   2384 (65.8%)       902 (24.9%)      337 (9.3%)
  Race/ethnicity, %
   WhiteNH                       1984   1345 (67.8%)       472 (23.8%)      167 (8.4%)
   Black                         899    485 (53.9%)        265 (29.5%)      149 (16.6%)
   Hispanic                      3457   2061 (59.6%)       1002 (29.0%)     394 (11.4%)
   Asian/PI                      953    632 (66.3%)        246 (25.8%)      75 (7.9%)
   Other/unknown                 663    455 (68.6%)        149 (22.5%)      59 (8.9%)
  Lower income neighborhood, %
   Yes                           1139   671 (58.9%)        326 (28.6%)      142 (12.5%)
   No                            6817   4307 (63.2%)       1808 (26.5%)     702 (10.3%)

  *Ages 3--5 years*
  Gender, %
   All                           1130   775 (68.6%)        265 (23.4%)      90 (8.0%)
   Male                          573    390 (68.1%)        133 (23.2%)      50 (8.7%)
   Female                        557    385 (69.1%)        132 (23.7%)      40 (7.2%)
  Race/ethnicity, %
   WhiteNH                       250    188 (75.2%)        50 (20.0%)       12 (4.8%)
   Black                         117    73 (62.4%)         32 (27.3%)       12 (10.3%)
   Hispanic                      527    348 (66.0%)        128 (24.3%)      51 (9.7%)
   Asian/PI                      134    82 (61.2%)         39 (29.1%)       13 (9.7%)
   Other/unknown                 102    84 (82.3%)         16 (15.7%)       2 (2.0%)
  Lower income neighborhood, %
   Yes                           206    125 (60.7%)        62 (30.1%)       19 (9.2%)
   No                            924    650 (70.3%)        203 (22.0%)      71 (7.7%)

  *Ages 6--11 years*
  Gender, %
   All                           3516   2247 (63.9%)       954 (27.1%)      315 (9.0%)
   Male                          1928   1175 (60.9%)       553 (28.7%)      200 (10.4%)
   Female                        1588   1072 (67.5%)       401 (25.3%)      115 (7.2%)
  Race/ethnicity, %
   WhiteNH                       830    590 (71.1%)        185 (22.3%)      55 (6.6%)
   Black                         376    211 (56.1%)        121 (32.2%)      44 (11.7%)
   Hispanic                      1579   946 (59.9%)        469 (29.7%)      164 (10.4%)
   Asian/PI                      439    296 (67.4%)        116 (26.4%)      27 (6.2%)
   Other/unknown                 292    204 (69.9%)        63 (21.6%)       25 (8.6%)
  Lower income neighborhood, %
   Yes                           495    299 (60.4%)        145 (29.3%)      51 (10.3%)
   No                            3021   1948 (64.5%)       809 (26.8%)      264 (8.7%)

  *Ages 12--17 years*
  Gender, %
   All                           3310   1956 (59.1%)       915 (27.6%)      439 (13.3%)
   Male                          1832   1029 (56.2%)       546 (29.8%)      257 (14.0%)
   Female                        1478   927 (62.7%)        369 (25.0%)      182 (12.3%)
  Race/ethnicity, %
   WhiteNH                       904    567 (62.7%)        237 (26.2%)      100 (11.1%)
   Black                         406    201 (49.5%)        112 (27.6%)      93 (22.9%)
   Hispanic                      1351   767 (56.8%)        405 (30.0%)      179 (13.2%)
   Asian/PI                      380    254 (66.8%)        91 (24.0%)       35 (9.2%)
   Other/unknown                 269    167 (62.1%)        70 (26.0%)       32 (11.9%)
  Lower income neighborhood, %
   Yes                           438    247 (56.4%)        119 (27.2%)      72 (16.4%)
   No                            2872   1709 (59.5%)       796 (27.7%)      367 (12.8%)

WhiteNH = non-Hispanic white; Asian/PI = Asian or Pacific Islander; Lower income neighborhood = residing in a residential census block with a median household income ≤ \$40,000.

######

Health-related behaviors and lifestyle risks of pediatric patients with obesity, by age group.

  Health behavior/lifestyle risk                     Age group
  -------------------------------------------------- ----------- --------- ---------
  Eating breakfast \<6 days a week                   28.9        34.1^a^   58.8^a^
  Eating meals with family \<5 times a week          34.9        39.4^a^   54.2^a^
  Eating \<5 servings of fruit/vegetables a day      45.1        54.8^a^   61.6^a^
   Eating \<3 servings of vegetables a day           60.2        65.6^a^   70.9^a^
   Eating \<3 servings of fruit a day                44.0        55.2^a^   63.7^a^
  Eating ≥3 sugary or salty snacks a day             24.1        28.1^a^   31.9^a^
  Drinking ≥2 sugary or juice drinks a day           58.0        59.4      59.8
  60 min exercise outside PE \<5 times a week        44.6        65.0^a^   67.6^a^
   60 min exercise \<3 times a week                  16.6        28.6^a^   36.9^a^
  TV, computer, or video games ≥3 hours a day        31.4        43.4^a^   63.5^a^
  Inadequate number of hours of sleep per night^1^   6.7         1.2^a^    8.9^a^

PE = school-based physical education or recess; TV = television. Column *N*s are approximate due to missing data.

^1^The National Sleep Foundation, 2015, consensus guidelines \[[@B36]\] recommend against sleep \<8 hours per day (including naps) for children aged 3--5 and \<7 hours per day for children aged 6--17 years; these thresholds were used to define inadequate number of hours of sleep per night.

^a^Significantly (*p* \< 0.05) different from ages 3--5 after adjusting for child\'s sex and race/ethnicity.

######

Health-related behavior and lifestyle risks of pediatric patients with obesity, by age group and obesity severity.

  Health behavior/lifestyle risk                   3--5 years   6--11 years   12--17 years
  ------------------------------------------------ ------------ ------------- -------------- --------- ------ ---------
  Eating breakfast \<6 times a week                25.5         36.5^a^       31.2           39.3^a^   55.0   64.2^a^
  Eating meals with family \<5 times a week        35.4         33.8          37.6           42.5^a^   53.1   55.9
  Eating \<5 servings of fruits/vegetables a day   45.2         45.0          54.7           54.9      59.6   64.5^a^
   Eating \<3 servings of vegetables a day         60.8         59.0          65.9           65.1      69.0   73.7^a^
   Eating \<3 servings of fruit a day              43.1         45.9          54.4           56.5      62.5   65.4
  Eating ≥3 sugary or salty snacks a day           22.9         26.8          26.7           30.5      31.1   33.1
  Drinking ≥2 sugary or juice drinks a day         54.6         65.2^a^       57.4           63.0      58.4   61.9
  60 min exercise outside PE \<5 times a week      40.7         53.0^a^       63.0           68.5^a^   66.5   69.1^a^
   60 min exercise \<3 times a week                15.1         19.8          25.7           33.9^a^   34.7   40.0^a^
  TV, computer, or video games ≥3 hours/day        29.5         35.4          40.5           48.6^a^   61.7   66.1^a^
  Inadequate number of hours of sleep/night^1^     5.4          9.4^a^        1.0            1.5       7.5    10.9^a^

PE = school-based physical education or recess; TV = television. Column *N*s are approximate due to missing data.

^1^Sleep \<8 hours per day (including naps) for ages 3--5 and \<7 hours per night for ages 6--17.

^a^Significantly (*p* \< 0.05) different from moderate obesity after adjusting for child\'s sex and race/ethnicity.

Moderate obesity = BMI 100--119% of the 95th percentile; Severe obesity = BMI ≥ 120% of the 95th percentile.

######

Health-related behaviors and lifestyle risks of pediatric patients with obesity, by age group and race/ethnicity.

  Health behavior/lifestyle risk                   3--5 years   6--11 years   12--17 years
  ------------------------------------------------ ------------ ------------- -------------- --------- ------ --------- --------- --------- ------ --------- --------- ---------
  Eating breakfast \<6 times a week                19.7         25.0          33.3^a^        37.6^a^   21.4   35.2^a^   39.5^a^   35.5^a^   48.2   70.4^a^   62.8^a^   56.0^a^
  Eating meals with family \<5 times a week        27.9         36.3          39.5^a^        30.6      28.6   55.4^a^   41.0^a^   39.2^a^   45.6   67.4^a^   57.5^a^   48.0
  Eating \<5 servings of fruits/vegetables a day   37.1         38.8          49.0^a^        52.2^a^   52.8   48.3      55.5      58.0      59.4   60.3      63.9^a^   62.5
   Eating \<3 servings of vegetables a day         58.1         56.1          60.7           61.2      66.3   58.0^a^   67.2      64.3      70.4   66.0      74.2      67.2
   Eating \<3 servings of fruit a day              35.2         41.4          47.6^a^        51.9^a^   52.3   55.3      54.2      59.4^a^   62.5   65.2      65.0      60.9
  Eating ≥3 sugary or salty snacks a day           20.7         22.6          23.1           33.8^a^   23.6   36.6^a^   27.3      29.6^a^   31.3   38.2^a^   29.8      30.7
  Drinking ≥2 sugary or juice drinks a day         44.0         67.2^a^       64.3^a^        57.1^a^   43.1   76.9^a^   64.6^a^   57.0^a^   49.4   76.8^a^   65.0^a^   50.1
  60 min exercise outside PE \<5 times a week      35.1         50.9^a^       47.0^a^        50.8^a^   55.6   58.7      67.5^a^   76.1^a^   65.1   68.1      70.1^a^   67.5
   60 min exercise \<3 times a week                10.5         14.0          19.1^a^        23.5^a^   20.8   28.0^a^   29.7^a^   40.1^a^   33.8   39.8      39.3^a^   34.4
  TV, computer, or video games ≥3 hours/day        23.8         43.5^a^       30.2           36.6^a^   37.5   46.2^a^   43.6^a^   49.5^a^   62.1   72.0^a^   61.4      63.9
  Inadequate number of hours of sleep/night^1^     4.5          17.0^a^       6.3            5.2       1.4    2.1       1.0       0.7       7.0    12.2^a^   9.7^a^    6.2

WhiteNH = non-Hispanic white; Asian/PI = Asian or Pacific Islander. PE = school-based physical education or recess; TV = television. Column *N*s are approximate due to missing data.

^1^Sleep \<8 hours per day (including naps) for ages 3--5 and \<7 hours per night for ages 6--17.

^a^Significantly (*p* \< 0.05) different from non-Hispanic whites after adjusting for child\'s sex.

[^1]: Academic Editor: Aron Weller


A report on the foreign funding of extremism in the UK was given to Downing Street last year, it has been revealed, but Theresa May is still to decide whether to make its findings public.

The Green party co-leader, Caroline Lucas, said the delay in publishing the Home Office investigation, believed to focus on Saudi Arabia, “leaves question marks over whether their decision is influenced by our diplomatic ties”.

Since the beginning of her premiership, May has sought to deepen the UK’s relationship with the Gulf, visiting Saudi Arabia as one of her first trips after triggering the formal Brexit process in March, a highly symbolic move.

The whereabouts of the report into foreign funding of extremism and radicalisation in the UK became a controversial issue in the final days of the general election after the terror attacks in Manchester and London Bridge.

It was commissioned by David Cameron and approved by May as part of a deal with the Liberal Democrats to secure the party’s support before a crucial vote on airstrikes in Syria in December 2015.

In written answers to Lucas this week, both the Home Office and Downing Street said the prime minister was personally responsible for deciding whether to release the report.

The Home Office minister Sarah Newton said: “The review into the funding of Islamist extremism in the UK was commissioned by the former prime minister and reported to the home secretary and the prime minister in 2016.

“The review has improved the government’s understanding of the nature, scale and sources of funding for Islamist extremism in the UK. Publication of the review is a decision for the prime minister.”

This week, Lucas resubmitted her parliamentary question on the review’s whereabouts to May, who wrote back to confirm ministers were still “considering advice on what is able to be published and will report to parliament with an update in due course”.

Lucas called the delay “astonishing” and said the government should reveal the advice which has prevented the publication of the report – and whether it was for diplomatic reasons. “The government is sitting on this report but refusing to publish it or give any reason for their continued secrecy,” she said.

The Brighton Pavilion MP said it was crucial to determine if the report’s delay was linked to whether it was critical of Saudi Arabia. “To defeat terror it’s vital that politicians have full view of the facts, even if they are inconvenient for the government,” she said.

During the election, the Liberal Democrat leader, Tim Farron, said Cameron had committed to publishing the report by spring 2016, but the Home Office later suggested it would never be published, calling the contents “very sensitive”.

The Labour leader, Jeremy Corbyn, and Scotland’s first minister, Nicola Sturgeon, also demanded that the report be published, while the Lib Dem foreign affairs spokesman, Tom Brake, wrote to May restating the party’s demand for it to be made public.

However, despite Farron’s insistence that publication had been part of the deal, both the home secretary, Amber Rudd, and the foreign secretary, Boris Johnson, said the report may never be seen in public.

Lucas said the terror attacks in London Bridge and the Manchester Arena said people were “quite rightly asking questions about routes to radicalisation, and the funding of terror is central to this.

“I urge Theresa May to reveal immediately whose advice they are following as to whether or not to publish this report, and to do all they can to put the facts into the public domain if it is safe to do so.”

Farron said the written statement showed the power was in May’s hands to release the report. “It is a scandal that the government are suppressing this report. The only conclusion you can draw is that they are worried about what it actually says,” he said.

“We hear regularly about the Saudi arms deals or ministers going to Riyadh to kowtow before their royal family, but yet, our government won’t release a report that will clearly criticise Saudi Arabia.

“All this government seems to care about is cosying up to one of the most extreme, nasty and oppressive regimes in the world. You would think our security would be more important, but it appears not. For that Theresa May should be ashamed of herself.”


State Farm reminds readers to review fire safety plan

October 7-13 is fire prevention week, so as the temperature cools, State Farm would like to remind readers to review their fire safety plan.

Comment

By Staff Reports

Milford Beacon

By Staff Reports

Posted Oct. 2, 2012 at 12:52 PM
Updated Oct 2, 2012 at 1:11 PM

By Staff Reports

Posted Oct. 2, 2012 at 12:52 PM
Updated Oct 2, 2012 at 1:11 PM

Wilmington, DE

October 7-13 is fire prevention week, so as the temperature cools, State Farm is reminding readers to review their fire safety plan.

Fire Extinguisher –Have an "ABC" dry chemical fire extinguisher on each level of your home, including one in the kitchen.

Smoke Alarms – Place at least one smoke alarm on every floor, including one in every occupied bedroom. In the kitchen, placing the smoke detector away from the stove will prevent false alarms. But smoke alarms won't help if they don't work; don't forget to use the change of the clocks to change the batteries and replace your smoke detectors after 10 years.

Fireplaces – Before you curl up in front of the fireplace, prepare it for the winter season. Have your chimney swept annually, and make sure you have a sturdy screen to prevent sparks from flying into the room. Also, allow fireplace and wood/pellet/coal stove ashes to cool before disposing in a metal container.

Candles – Keep lit candles away from materials that could easily catch fire. Never light candles when you're feeling drowsy—accidentally falling asleep plays a role in 12 percent of all candle fires. If you want a little bedtime glow, opt for battery-operated candles.

Have a plan – If a fire strikes, you won't have much time to react. Smoke inhalation can overwhelm a child or adult in less than two minutes. Be prepared by creating a fire evacuation plan and conducting drills.

Home Inventory – Don't wait until disaster strikes to create a home inventory. Take time now to create or update your home inventory, which should include a list of valuables, receipts/appraisals (when available) and photos.


Geoffrey Finsberg

Geoffrey Finsberg, Baron Finsberg,  (13 June 1926—8 October 1996) was a British Conservative politician.

Early life
Finsberg was educated at the City of London School and was a 'Bevin Boy' 1945-47.

Political activity
From a young age he was active in the Conservative Party and was founder chairman of Mansfield Young Conservatives 1946-47. He served as National Chairman of the Young Conservatives, 1954–57 and took senior rules in the National Union of Conservative and Unionist Associations, 1953–79 and the Greater London Area of the National Union of Conservative and Unionist Associations. He was Vice-Chairman, Conservative Party Organisation, 1975–79 and 1983–87.

Finsberg was elected a Borough Councillor in Hampstead in 1949 at just 22 years old, until 1965, and on the new London Borough of Camden 1964-74, serving as Leader, 1968-70. He was Deputy Chairman, 1969–1971, and Vice-President of the Association of Municipal Corporations, 1971-74. He stood for Parliament in Islington East in 1955 without success. He was appointed a Member of the Order of the British Empire (MBE) in the 1959 New Year Honours.

Parliamentary career
In 1970, Finsberg was elected Member of Parliament for Hampstead, serving 1970 to 1983, then for Hampstead and Highgate 1983 to 1992.  He acted as Opposition spokesman on Greater London, 1974–79; Parliamentary Under Secretary of State, Department of the Environment, 1979–81, and Department of Health and Social Security, 1981-83. He was President of the Parliamentary Assembly of the Council of Europe from 1991 to 1992. He was knighted in the 1984 New Year Honours.

He retired from Parliament at the 1992 general election, at which his seat was taken by Labour's Glenda Jackson, defeating Oliver Letwin. He was created a life peer in 1992 as Baron Finsberg, of Hampstead in the London Borough of Camden.

Outside Parliament
Finsberg was also active in business and charities: he was Controller of Personnel and Chief Industrial Relations Adviser at Great Universal Stores, 1968–79; Member, 1983–1986, and Deputy Chairman, South East Regional Board, TSB, 1986–89; Member, Post Office Users National Council, 1970–77; Member of the Council, Confederation of British Industry, 1968–79. He was Joint National Treasurer, 1993–1995, and Joint National Honorary Secretary, Council of Christians and Jews; Patron, Maccabi Association of Great Britain; Trustee, Marie Curie Cancer Foundation; a JP for Inner London from 1962.

Arms

References

External links
 Catalogue of the Finsberg papers at the Archives Division of the London School of Economics.

Category:1926 births
Category:1996 deaths
Category:Conservative Party (UK) MPs for English constituencies
Category:People educated at the City of London School
Category:Conservative Party (UK) life peers
Category:Councillors in Greater London
Category:Councillors in the London Borough of Camden
Category:Members of the Order of the British Empire
Category:English justices of the peace
Category:Jewish British politicians
Category:UK MPs 1970–1974
Category:UK MPs 1974
Category:UK MPs 1974–1979
Category:UK MPs 1979–1983
Category:UK MPs 1983–1987
Category:UK MPs 1987–1992
Category:Members of Hampstead Metropolitan Borough Council
Category:Bevin Boys
Category:Knights Bachelor


Q:

Rewrite all request to index.php (Including existing file and directory paths)?

I am working on a PHP application that I'd like to regulate file serving for various reasons. I need all requests to be passed to my index.php file. Currently I have this working for all requests except files and directories that exist. For security reasons and content protection I'd like to perform some challenges before serving files to the browser using readfile and I'd like to completely block directory access which will just show a 404 page for files and directories I don't want users to see (i.e., private content, php scripts, etc.).
This is my current HTACCESS file:
# Turn On Rewrite Engine
RewriteEngine On

# Directs all EE web requests through the site index file
RewriteCond %{REQUEST_URI} !^.*$
RewriteCond %{REQUEST_FILENAME} !-f
RewriteCond %{REQUEST_FILENAME} !-d
RewriteRule ^(.*)$ /index.php/$1 [L]

I don't no what to do with the RewriteCond that is causing files to be mapped to the index.php file if they don't exists. I'd like them to remap there whether or not they do exist.
For example, navigating to "http://www.example.com/some/real/script.php" would redirect the path to PHP regardless if it exists or not. Thanks guys in advance.

A:

This one worked for me
<IfModule mod_rewrite.c>
RewriteEngine on
RewriteBase /
# RewriteCond %{REQUEST_FILENAME} !-f
# RewriteCond %{REQUEST_FILENAME} !-d
# RewriteCond %{REQUEST_URI} !/folder
RewriteRule ^(.*)$ index.php?request=$1 [L,QSA]
</IfModule>


/*
 * Copyright (c) 2019 Arm Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#include "helpers.h"

/** Generate all the region of interests based on the image size and the anchors passed in. For each element (x,y) of the
 * grid, it will generate NUM_ANCHORS rois, given by shifting the grid position to match the anchor.
 *
 * @attention The following variables must be passed at compile time:
 * -# -DDATA_TYPE= Tensor data type. Supported data types: F16/F32
 * -# -DHEIGHT= Height of the feature map on which this kernel is applied
 * -# -DWIDTH= Width of the feature map on which this kernel is applied
 * -# -DNUM_ANCHORS= Number of anchors to be used to generate the rois per each pixel
 * -# -DSTRIDE= Stride to be applied at each different pixel position (i.e., x_range = (1:WIDTH)*STRIDE and y_range = (1:HEIGHT)*STRIDE
 * -# -DNUM_ROI_FIELDS= Number of fields used to represent a roi
 *
 * @param[in]  anchors_ptr                           Pointer to the anchors tensor. Supported data types: F16/F32
 * @param[in]  anchors_stride_x                      Stride of the anchors tensor in X dimension (in bytes)
 * @param[in]  anchors_step_x                        anchors_stride_x * number of elements along X processed per workitem(in bytes)
 * @param[in]  anchors_stride_y                      Stride of the anchors tensor in Y dimension (in bytes)
 * @param[in]  anchors_step_y                        anchors_stride_y * number of elements along Y processed per workitem(in bytes)
 * @param[in]  anchors_stride_z                      Stride of the source tensor in Z dimension (in bytes)
 * @param[in]  anchors_step_z                        anchors_stride_z * number of elements along Z processed per workitem(in bytes)
 * @param[in]  anchors_offset_first_element_in_bytes The offset of the first element in the boxes tensor
 * @param[out] rois_ptr                              Pointer to the rois. Supported data types: same as @p in_ptr
 * @param[out] rois_stride_x                         Stride of the rois in X dimension (in bytes)
 * @param[out] rois_step_x                           pred_boxes_stride_x * number of elements along X processed per workitem(in bytes)
 * @param[out] rois_stride_y                         Stride of the rois in Y dimension (in bytes)
 * @param[out] rois_step_y                           pred_boxes_stride_y * number of elements along Y processed per workitem(in bytes)
 * @param[out] rois_stride_z                         Stride of the rois in Z dimension (in bytes)
 * @param[out] rois_step_z                           pred_boxes_stride_z * number of elements along Z processed per workitem(in bytes)
 * @param[out] rois_offset_first_element_in_bytes    The offset of the first element in the rois
 */
#if defined(DATA_TYPE) && defined(WIDTH) && defined(HEIGHT) && defined(NUM_ANCHORS) && defined(STRIDE) && defined(NUM_ROI_FIELDS)
__kernel void generate_proposals_compute_all_anchors(
    VECTOR_DECLARATION(anchors),
    VECTOR_DECLARATION(rois))
{
    Vector anchors = CONVERT_TO_VECTOR_STRUCT_NO_STEP(anchors);
    Vector rois    = CONVERT_TO_VECTOR_STRUCT(rois);

    const size_t idx = get_global_id(0);
    // Find the index of the anchor
    const size_t anchor_idx = idx % NUM_ANCHORS;

    // Find which shift is this thread using
    const size_t shift_idx = idx / NUM_ANCHORS;

    // Compute the shift on the X and Y direction (the shift depends exclusively by the index thread id)
    const DATA_TYPE
    shift_x = (DATA_TYPE)(shift_idx % WIDTH) * STRIDE;
    const DATA_TYPE
    shift_y = (DATA_TYPE)(shift_idx / WIDTH) * STRIDE;

    const VEC_DATA_TYPE(DATA_TYPE, NUM_ROI_FIELDS)
    shift = (VEC_DATA_TYPE(DATA_TYPE, NUM_ROI_FIELDS))(shift_x, shift_y, shift_x, shift_y);

    // Read the given anchor
    const VEC_DATA_TYPE(DATA_TYPE, NUM_ROI_FIELDS)
    anchor = vload4(0, (__global DATA_TYPE *)vector_offset(&anchors, anchor_idx * NUM_ROI_FIELDS));

    // Apply the shift to the anchor
    const VEC_DATA_TYPE(DATA_TYPE, NUM_ROI_FIELDS)
    shifted_anchor = anchor + shift;

    vstore4(shifted_anchor, 0, (__global DATA_TYPE *)rois.ptr);
}
#endif //defined(DATA_TYPE) && defined(WIDTH) && defined(HEIGHT) && defined(NUM_ANCHORS) && defined(STRIDE) && defined(NUM_ROI_FIELDS)


Shonali Bose

Shonali Bose (born 3 June 1965) is an Indian film director, writer and film producer. Having made her feature film debut in 2005, she has since won such accolades as a National Film Award, a Bridgestone Narrative Award, and a Sundance Mahindra Global Filmmaker Award.

Bose earned her breakthrough with her first feature film, the 2005 biographical drama Amu, which was based on her own novel of the same name. The film which chronicles the attacks on Sikhs in Delhi in 1984, garnered critical acclaim and the National Film Award for Best Feature Film in English. Bose then worked as the assistant director for the 2012 war film Chittagong, which she also co-wrote.

Bose's status as a filmmaker grew following the critical and commercial success of the dramas Margarita with a Straw (2015) and The Sky Is Pink (2019). Inspired from the life of Malini Chib—her cousin and a disability rights activist, the former earned Bose a Sundance Mahindra Global Filmmaker Award and a NETPAC Award.

Bose is also an active philanthropist and supports various charitable organisations. She was married to filmmaker Bedabrata Pain, but the couple separated following the death of their son.

Early life
Shonali Bose was born on 3 June 1965 in Calcutta, West Bengal and spent most of her young adult life in Mumbai and New Delhi. She earned her Bachelor of Arts degree from Delhi University and master's degree in Political Science from Columbia University, New York. She has been an activist since time at Miranda House of the Delhi University. Bose was also involved in theater as an actor throughout school and college. Bose did not originally plan to be involved in film, however she saw it as a better outlet for activism after realizing how far removed her Ph.D studies were from activism. On her film work she says, "Film is the means with which I want to communicate ideas about social and political change."

Career

Early career and breakthrough with Amu (1999-2010)
Bose worked as an organizer for the National Lawyers Guild for about a year. She directed live community television in Manhattan before joining the MFA Directing Program at UCLA School of Theater, Film and Television. Early in her career, Bose directed such short films as The Gendarme Is Here and Undocumented, and the feature-length documentary Lifting the Veil; the productions were screened at numerous film festivals.

Bose made her feature film debut with the 2005 drama Amu; she had also written the production's screenplay. The film released in India in January to critical acclaim and positive response from the audience. Amu was screened at international film festivals in Berlin and Toronto. Bose won several national and international awards for the film, including the FIPRESCI Critics Award, the National Film Award for Best Feature Film in English and the Gollapudi Srinivas Award for Best Debut Director. Bose wrote the novel Amu, which based on the screenplay which released simultaneously with the film.

Continued critical success (2010- present)
Bose co-wrote the Bedabrata Pain directed film Chittagong, a period drama that chronicles the 1930 Chittagong armoury raid. Her 2014 production, Margarita, with a Straw, starring Kalki Koechlin as a girl with cerebral palsy, premiered at 2014 Toronto International Film Festival to critical acclaim. The film won the NETPAC Award for Best Asian Film at the event. She had begun working on the script a year after the death of her son; the film's initial draft won the Sundance Mahindra Global Filmmaker Award, at the 2012 Sundance Film Festival. Bose was inspired to make the film by her real life experiences of studying in New York, her relationship with her cousin Malini Chib who has cerebral palsy, and her and Chib's sexuality as queer and disabled people respectively. The film was originally rejected by the censor board in India, but won appeal and was released shortly after the ban on homosexuality in India was lifted.

In 2019, Bose has written and directed The Sky Is Pink, a film based on the motivational speaker Aisha Chaudhary, starring Priyanka Chopra, Farhan Akhtar and Zaira Wasim. The film was theatrically released on 11 October 2019 and was critically acclaimed.

Upcoming project
Bose has committed to write the pilot episode for an untitled television series based on Diksha Basu's debut novel The Windfall.

Personal life
She was married to Bedabrata Pain but is now separated. Bose identifies as bisexual .

Filmography

Awards

See also
 List of female film and television directors
 List of LGBT-related films directed by women

References

External links
 Official Webpage and Bio of Shonali Bose

 Images Film Festival for Women, Zimbabwe

Category:1974 births
Category:Living people
Category:20th-century Indian women writers
Category:20th-century Indian dramatists and playwrights
Category:21st-century Indian dramatists and playwrights
Category:21st-century Indian film directors
Category:21st-century Indian women writers
Category:Indian women film directors
Category:Indian women film producers
Category:Indian women screenwriters
Category:LGBT directors
Category:LGBT producers
Category:LGBT screenwriters
Category:Bisexual writers
Category:Bisexual women
Category:LGBT people from India
Category:Bengali film directors
Category:Film directors from Kolkata
Category:Film producers from Kolkata
Category:Screenwriters from Kolkata
Category:Businesswomen from West Bengal
Category:Women writers from West Bengal
Category:University of Delhi alumni
Category:Columbia University alumni
Category:UCLA Film School alumni


Post navigation

Israel – Cabernet Cobbler

People & Products

It’s kind of crazy how many people and products most of us had no clue originate from Israel. From starlets and rockers to computer programs and board games, Israeli influences are more common that you would think. Let’s take a look at some of those that are changing the world from the Holy Land:

Natalie Portman

The beautiful actress who has stolen my heart time and time again was actually born in Israel as Natalie Hershlag and moved to the U.S. in 1984, at the age of three. After wowing audiences as a 13-year-old in the 1994 Film, The Professional, Portman has gone on to star in Star Wars prequels, V for Vendetta, Black Swan, and Thor.

Max Brenner Chocolates

The “chocolate by the bald man” has its headquarters based in Ra’anana, Israel. The company was actually founded by Max Fichtman and Oded Brenner (the chocolatier of the duo). My favourite cocktail of all-time (Satisfaction Guaranteed) came from the Max Brenner restaurant and store at Caesar’s Palace in Las Vegas.

Cherry Tomatoes

No salad is truly complete without the addition of cherry tomatoes. These versatile little veggies were the product of a 12-year breeding program, overseen by labs in Israel. Highlights of the cherry tomato include an increased shelf life and optimal ripening cycle. Who would have thought it could take so long to make a baby tomato!

Gene Simmons

The KISS frontman and tongue wagging demon was born Chaim Witz in Haifa, Israel, in 1949. Along with his mother, Simmons came to the U.S. at the age of eight, speaking no English. He’s certainly come a long way, as the legendary rocker had his own reality show and even claims to have had sex with nearly 5,000 women.

SodaStream

Given my position as a top notch liquor slinger, I’ve often thought that one of these machines could come in handy, especially given I like fizz in nearly every drink I choose to make. SodaStream’s headquarters are located in Lod, Israel, which must make for some interesting flavour proposals, such as hummus and falafel!

USB Flash Drives

Where would many of us be without these pocket-sized tools? The flash drive virtually killed the floppy disk, which could only hold a fraction of the amount of information most USB’s are able to contain. The drives were developed by Israeli company M-Systems, who joined with tech giant IBM to release the product.

Haim Saban

One of the craziest phenomena of my childhood was the Mighty Morphin Power Rangers, which was the work of Israeli Haim Saban (or at least his production company). The series is still alive and kicking to this day, having gone through countless iterations. Saban is worth an estimated $3 billion, according to Forbes.

ICQ

Standing for “I Seek You,” ICQ was developed by Mirabilis, an Israeli company since absorbed by America Online (AOL) for $407 million. It was the first one-on-one online conversation program that allowed users their own account. While I didn’t use the system much, ICQ was once the hottest instant messaging service available.

Hillel Slovak

An original member of the Red Hot Chili Peppers, Slovak (born in Haifa, Israel) was there for the bands earliest successes. Sadly, Slovak died from a heroin overdose, in 1988, at the young age of 26. An inspiration for the group, he was inducted into the Rock and Roll Hall of Fame in 2012, with fellow members of the Chili Peppers.

Rummikub

This tile-based game was invented in Israel by Ephraim Hertzano and went on to be the best-selling game in the U.S. in 1977, as well as win Game of the Year awards in Germany (1980) and the Netherlands (1983). I’ve never actually played Rummikub before, but assume I’d be good, simply because the game has the word rum in it!

Israel: Cabernet Cobbler

For whatever reason, Israel seems shrouded in mystery, but the Sip Advisor likes a good mystery. We also have Israelites to thank for Windows XP, the Amazon Kindle, and modern computer printing, among many other things. Heck, they even bred the golden hamster, which has my little kitty Furious B salivating!

Sip Advisor Bar Notes (3.5 Sips out of 5):
While I enjoyed this drink, it could basically be made by combining Cabernet Sauvignon Wine and Lemon-Lime Soda. It’s a pretty simple recipe, but one I felt I needed to do with the wine I selected for these articles.


Glenn Close is your choice for the most overdue for an Oscar and it isn’t even … close. Gold Derby’s poll asking you to vote for the person who needs an Oscar the most among frequent nominees was a runaway for six-time nominee Close, even though she technically hasn’t racked up the most nominations. Will her upcoming role in “The Wife” put an end to her Oscar losing streak?

Close won our poll with 35% of the vote, while a pair of five-time nominees, Amy Adams and writer-director Christopher Nolan, rallied with 19% and 15%, respectively. Next was Annette Bening (four noms) at 9% and writer-director Paul Thomas Anderson (eight noms) at 8%.

Further down we had 6% for Michelle Williams (four noms), 5% for composer Thomas Newman (14 noms) and 2% for sound mixer Greg P. Russell (16 noms). Tying for last on this poll were writer-director Mike Leigh (seven noms) and songwriter Diane Warren (nine noms) at 1% apiece.

As for Close, it is hard to think of an actress not named Meryl Streep who had a better decade at the Oscars in the ’80s. She was nominated five times in seven years from 1982-88, for “The World According to Garp” (1982), “The Big Chill” (1983), “The Natural” (1984), “Fatal Attraction” (1987) and “Dangerous Liaisons” (1988), losing each time. Close returned decades later to claim her sixth nomination for “Albert Nobbs” (2011), for which she lost, of course, to Streep (“The Iron Lady”).

The actress has three Emmys and three Tonys under her belt but remains the most nominated living actor to never win the elusive Oscar. That could change very soon considering she has a juicy role in the upcoming film “The Wife,” for which she has earned rave reviews. She is also reportedly set to reprise her role as Norma Desmond in a movie adaptation of the “Sunset Boulevard” musical. Either film could be exactly what Close needs to finally earn the Oscar she deserves after almost four decades in the industry, and decades of coming ever so close.


Q:

On initialization of non-aggregate structures

Consider the following code:
template<size_t head,size_t ... Dims> struct make_vec {
      typedef typename make_vec<Dims...>::type type[head];
};

template<size_t head> struct make_vec<head> {typedef float type[head];};

template<size_t ... Dims>
struct vec {
    typename make_vec<Dims...>::type _data;
};

Since the class vec is an aggregate, I can use brace-enclosed-initializer lists to initialize vec.
vec<3,3> foo = {{
    {1,2,3},
    {4,5,6},
    {7,8,9}
}};

If I add a constructor to my vec class (for example to allow the use of expression-template, which is my ultimate goal), this syntax is no longer allowed, since vec would not be an aggregate anymore.
In this case, the logic thing to do would be to use std::initializer_list; however, when I compile a code which uses this construct, I get a long assembly code even with optimization  enabled (I use std::uninitialized_copy to copy from the initializer_list to my vec class data member).
I had no luck in using variadic templates to achieve the same result.
What is the best way (i.e. good runtime performance and clear syntax) to initialize user-defined fixed-size multi array types?

A:

You can write a constructor taking
typename make_vec<Dims...>::type const&

which will allow user code to use the same style of initialization as if it were an aggregate. If you're using GCC this might need a recent version, like GCC 4.7.
I have no idea about the generated code however.


Resistance to malathion and deltamethrin in Aedes aegypti (Diptera: Culicidae) from western Venezuela.
Resistance to the insecticides deltamethrin and malathion and the enzymes associated with metabolic resistance mechanisms were determined in four field populations of Aedes aegypti (L.) from western Venezuela during 2008 and 2010 using the bottle assay and the microplate biochemical techniques. For deltamethrin, mortality rates after 1 h exposure and after a 24-h recovery period were determined to calculate the 50% knock-downconcentration (KC50) and the lethal concentration (LC50), respectively. For malathion, mortality was recorded at 24 h to determine the LC50. For deltamethrin, resistance ratios of knock-down resistance and postrecovery were determined by calculating the RRKC50 and RRLC50, comparing the KC50 and LC50 values of the field populations and those of the susceptible New Orleans strain. Knock-down resistance to deltamethrin was moderate in the majority of the populations in 2008 (RRKC50 values were between 5- and 10-fold), and only one population showed high resistance in 2010 (RRKC50 > 10-fold). Moderate and high postrecovery resistance to deltamethrin was observed in the majority of the populations for 2008 and 2010, respectively. There was significantly increased expression of glutathione-S-tranferases and mixed-function oxidases. All populations showed low resistance to malathion in 2008 and 2010 with significantly higher levels of alpha-esterases for 2008 and 2010 and beta-esterases for 2008.


The Story of Rupert Lockwood: Journalist, Communist, Intellectual by Rowan Cahill

Rupert Lockwood was many things in his lifetime, variously a journalist, editor, author, pamphleteer, orator, broadcaster, historian. He was also at various times controversial, vilified, and respected. From 1939 to 1969, he was one of the best known members of the Communist Party of Australia.

During the Cold War, Lockwood was a central figure in the Royal Commission on Espionage, 1954-55, the media, ASIO, and the Menzies government doing their utmost to portray him as a spy.

Cahill has spent many years putting Lockwood’s story together, and depicts a significant and courageous journalist. His account ranges from Lockwood’s early training in the rural press of Western Victoria in the 1920s, to the Melbourne Herald in the 1930s, to the labour movement press in the 1940s and onwards.

The story told by Cahill traverses the world, including the front lines of the Spanish Civil War where Lockwood was a correspondent. It is a story filled with drama, action, intrigue, spooks, ruling class perfidy, maritime history, and details never made public before.

This work has recently earned Cahill a doctorate from the University of Wollongong, and can be freely downloaded here. A brief introduction can be read here.


On homogeneity approximation in calibration of gamma-spectrometry assessment of bulk samples.
The effect of the intrinsic inhomogeneity of a sample on the efficiency for the measurement of the sample by gamma-ray spectrometry is studied. The difference between the efficiency for the inhomogeneous sample and the efficiency for a sample made from the equivalent homogeneous matrix is evaluated.


This invention relates to a mounting tool, wherein contact springs can be fixed in place on a ridge with a press-on unit.
Contact springs are used, for example, to place adjoining components of a housing on a common reference potential. Contact springs can be disposed in a transition area between two components. The contact springs can also assume a further function, namely that of a shield against high-frequency electromagnetic waves. For mounting, strip-shaped contact springs are manually placed on a ridge of one of the two adjoining components. The contact spring is then pressed on the ridge with a plate-shaped press-on unit, so that the contact spring makes a solid connection with the ridge.
Large forces are required for such a mounting tool if it is intended to apply large contact springs over a large distance on a ridge. The mounting step is also not easy to control, since in the course of pressing on the contact spring, the contact spring can jump off partial areas of the ridge and therefore is squeezed. The result of such mounting is then no longer satisfactory.


Jonathan Ive, Apple’s Senior Vice President of Design, was interviewed by the UK’s Sunday Times where he discussed the history and future of Apple, along with many other topics.

The interview shows Ive’s perspective into the company, discussing his approach and philosophy behind designing new products:

Ive starts a new project by imagining what a new kind of product should be and what it should do. Only once he’s answered those questions does he work out what it should look like. He seeks advice in unlikely places. He worked with confectionery manufacturers to perfect the translucent jelly-bean shades of his first big hit, the original iMac. He travelled to Niigata in northern Japan to see how metalworkers there beat metal so thin, to help him create the Titanium PowerBook, the first lightweight aluminum laptop in a world of hefty black plastic slabs.

Ive also talked about manufacturers like Samsung who release devices that look strikingly similar to Apple’s. He calls this “the practice of theft of thousands of hours of struggle.”

The designer also says that Apple is working on new products, which of course he cannot say anything about. This could be a possible reference to the iWatch, which is rumoured to be focused on health and fitness applications, along with iOS 8 and future iPhones and iPads.

First page of the interview (image credit: 9to5Mac)

The massive five page interview is available from this link (a subscription is needed), and it is well worth a read. If you do not have a subscription, you can view the complete five page interview from the 9To5Mac link below.