Android applications All are actually written in Java programming language, with Google’s Android API, which is similar to JDK. Couple of years back Android has provided much needed boost and today…
Android applications All are actually written in Java programming language, with Google’s Android API, which is similar to JDK. Couple of years back Android has provided much needed boost and today…
1. Happy Dog Inc. produces three types of dog food. Puppy
Blend is produced for dogs that are less than a year old,
Adult Blend for dogs between 1 and 8 years old, and
Geriatric Blend for dogs older than 8 years. Each blend,
sold in 5-pound bags, has a unique recipe that requires,
among other ingredients, exact quantities of certain raw
a. Formulate a linear programming model that pro-
duces as many bags of dog food as possible without
exceeding the demand or the available supply of raw
b. Reformulate the linear programming model if the
company is now interested in maximizing their profit
(price – raw material cost) from dog food produc-
tion. Assume that Puppy Blend sells for $9.50 per
bag. Adult Blend sells for $8.50 per bag, and Geriatric
Blend sells for $9.00 per bag. Further, chicken costs
$2.50 per pound, fish meal costs $1.25 per pound.
and soy flour costs $2.00 per pound. How does this
new information change your linear programming
can help with
file errors. Write three
Python examples that actually generate file errors on your computer and catch the errors with try: except: blocks. Include the code and output for each example in your post.
Describe how you might deal with each error if you were writing a large production program. These descriptions should be general ideas in English, not actual Python code.
I Have Your answer to this problem in thequot;Millen. pyquot; like !`
Recall that amp; Python list can contain any type of element , even another list. As a result, we can have list of lists, licks of lists
of lists , and so on . with ever- increasing levels of nesting . This problem intends to put a stop In that sort of nonsense .
Complete the Flatten ! ! function , which takes a single list as its argument . The function examines its list argument and*
RECURSIVELY’ generates (and returns ; a new single- level ( non – nested; list that contains every element from the original
list, in the same onder it appeared in that list . For example , the list 12. 14. 61 . $1 contains a list as one of its elements !
Flatten ! ! will process this list and return the new single- level list 12. 4 6. $1. where the inner list has been eliminated!
and its elements have replaced the original sublist
The possible recursive strategy resembles the following*
. If the current list is an empty list . simply return an empty list ( This is your base case )
. Otherwise !
. If the first element of the current list is a list, return the result of flattening that list plus the result of flattening the
reminder of the list . ( This is your first recursive case )*
. Otherwise , return a list containing just the first element plus the result of flattening the remainder of the list . ( This
Is your second recursive case )
Hint : Python has a special function named is instance !’ that can be extremely helpful here. isinstance !’ lakes
two arguments . amp; value to examine , and the name of a Python type (in this case . List) . It reams True or False
depending on whether the first argument belongs In the specified type . For example , the function call is instance ( 12 .
int ) returns True . while the function call amp; singtance ! ‘ 3 . 14 ‘, Float ; returns False.
Flatten ! ! ! !
Flatten $ 1 21 1
\Flatteni13 . 5 . 1 . 01 1
\13 . 6 . 1 . 01
\Flatten ! ! 151 1 1\
Flatten 1 1 1 . 3 . 5 . 171 . 5 . 11 1 1
\11 . 3 . 5 . 7 . 5 . 111
flatten ! ! !$1 . 32 . 36 . 55 , 193. 541 . 50 .
15 . 32 , 36 . 55, 53 . 59 . 98 , BB ]
Flatten ! ! ! !131 1 1 1 1\
Flatten $ 165 , 152 . 411 . 17. 124. 351 . 931 .
165 , 52 . 41 . 7, 14. 35, 93, 73, 33 . 5^ ^ ^` .`
73 . 33 . 91. 14. 163, 24 . 1 127 , 381 . 178 .
$3 , 24 , 27 , 89 , 78 , 75, 13, 31 , 841
75 . 131 . 311 . 841 1 1\
THE COGNITIVE IMPLICATIONS OF OBESITY AND NUTRITION IN
CHILDHOOD Naiman A.Khan, Lauren B. Raine, Sharon M.Donovan, and Charles H.Hillman ABSTRACT
The prevalence of childhood obesity in the United States has tripled since the 1980s and is strongly linked to the early onset of several metabolic diseases. Recent studies indicate that lower cognitive function may be another complication of childhood obesity. This review considers the research to date on the role of obesity and nutrition on childhood cognition and brain health. Although a handful of studies point to a maladaptive relationship between obesity and aspects of cognitive control, remarkably little is known regarding the impact of fat mass on brain development and cognitive function. Further, missing from the literature is the role of nutrition in the obesity-cognition interaction. Nutrition may directly or indirectly influence cognitive performance via several pathways including provision of key substrates for optimal brain health, modulation of gut microbiota, and alterations in systemic energy balance. However, in the absence of malnutrition, the functional benefits of specific nutrient intake on particular cognitive domains are not well characterized. Here, we examine the literature linking childhood obesity and cognition while considering the effects of nutritional intake. Possible mechanisms for these relationships are discussed and suggestions are made for future study topics. Although childhood obesity prevalence rates in some developed countries have recently stabilized, significant disparities remain among groups based on sex and socioeconomic status. Given that the elevated prevalence of pediatric overweight and obesity may persist for the foreseeable future, it is crucial to develop a comprehensive understanding of the influence of obesity and nutrition on cognition and brain health in the pediatric population. The obesity epidemic continues to affect a remarkably high proportion of the world’s population. It is projected that the percentage of adults who are overweight or obese around the world could increase from 33% in 2005 to 58% by 2030 (Kelly, Yang, Chen, Reynolds, He, 2008). In the United States, Corresponding author: Naiman A. Khan, University of Illinois, 313 Louise Freer Hall, MC-052 906 S. Goodwin Avenue, Urbana, IL 61801, 51 over 30% of adults are obese and estimates of childhood overweight (85th percentile BMI-for-age) and obesity (95th percentile BMI-for-age) prevalence stand at approximately 32% and 17%, respectively (Ogden, Carroll, Kit, Flegal, 2012). Consequently, the estimated deaths attributed to obesity and its comorbidities have increased by 77% since 1991 (Mokdad, Marks, Stroup, Gerberding, 2004). Given that childhood obesity is projected to nearly double over the next 20 years, accumulation of excess fat mass has become the most pressing public health concern among children in the developed world (Wang, Beydoun, Liang, Caballero, Kumanyika, 2008). Although elevated BMI has been associated with adverse neurocognitive outcomes in adults, the impact of obesity on cognitive health in childhood remains unknown (Hildreth, Pelt, Schwartz, 2012; Jeong, Nam, Son, Son, Cho, 2005; Stanek et al., 2011). Current knowledge is equivocal and is based on studies that have widely divergent methodologies. Furthermore, the majority of studies in children examining the obesity-cognition interaction fail to assess nutritional intake, which might have independent or synergistic effects on both obesity and cognition. Although the human brain develops rapidly over the first 2 years of life, functional development of the hippocampus and frontal lobe—regions involved in relational memory and cognitive control (inhibition, working memory, and cognitive flexibility)— continues throughout childhood (Bryan et al., 2004; Johnson, 2001; Lenroot Giedd, 2006; Thatcher, 1991). This protracted functional development may provide critical periods for impact by behavioral factors such as diet. However, the degree to which nutritional intake influences cognitive function throughout the lifespan may also be influenced by the preexisting nutritional and health status of the individual. CHILDHOOD OBESITY: PREVALENCE AND CLINICAL IMPLICATIONS Overweight and obesity are clinically defined as excessive fat accumulation that may impair health (Caterson Gill, 2002). Given the high costs of fat mass assessment techniques, weight adjusted for height [expressed as body mass index (BMI), calculated as weight in kilograms divided by square of height in meters] is most commonly used for identifying overweight and obesity. Due to the variability in height and weight that occurs during growth, assessment of a child’s BMI necessitates comparison to a reference population of the same gender and age (Ebbeling Ludwig, 2008). Following the rapid rise in childhood obesity in the United States during the 1980s and 1990s (Ogden, Flegal, Carroll, Johnson, 2002), during which obesity increased three-fold, significant increases between 1999-2000 and 2009-2010 were only seen at the highest BMI cut-off and among adolescent males (Ogden et al., 2012). Although a possible link between public health campaigns and 52 the recent stabilization of obesity prevalence has been suggested in countries including the United States, Switzerland, and Sweden, the reasons for this stabilization remain unclear (Ebbeling Ludwig, 2008; Pe´neau et al., 2009). Nevertheless, in the United States, obesity remains a major public health threat since over 9% of all infants and toddlers have a high weight-forrecumbent length and 12% of 2-5 year-olds are considered obese. In addition, the BMI distribution has continually shifted to the right since the 1980s, suggesting that the severity of overweight has increased substantially (Stifel Averett, 2009). These trends have resulted in a school-aged population that is significantly more obese than their historical counterparts and has a considerably higher risk for earlier onset of chronic disease. The causes of childhood obesity have been the subject of considerable debate and are covered elsewhere (Harrison et al., 2011). Currently accepted theories implicate the interaction between genetic predisposition and social trends toward higher caloric intake and reduced energy expenditure (Moreno, Pigeot, Ahrens, 2011). Parental obesity doubles the risk of adult obesity among both obese and nonobese children (Whitaker, Wright, Pepe, Seidel, Dietz, 1997). Evidence from epigenetics (i.e., the study of stable inheritance of gene expression that occurs without modifications in underlying DNA sequence) indicates that genomes interact with environmental signals to affect subsequent health and disease risk (Jime´nez-Chillaro´n et al., 2012; Wu Suzuki, 2006). Epigenetic mechanisms include DNA methylation, histone modifications and, more recently, a variety of noncoding RNAs (Jime´nez-Chillaro´n et al., 2012). The impact of such interactions may occur during and/or after intrauterine development. For example, fetal overnutrition as a consequence of maternal obesity may be implicated in the rise of childhood obesity (Danielzik, Langnase, Mast, Spethmann, Muller, 2002). Also, infants born to women with gestational diabetes have significantly higher fat mass than infants of women without gestational diabetes (Catalano, Thomas, Huston Presley, Amini, 2003). Maternal consumption of a high fat diet during gestation is related to subsequent excess fat accumulation in rat pups (Wu Suzuki, 2006), independent of offspring diet. Collectively, there is converging evidence supporting the role of early environmental programming in the development of childhood obesity. Although childhood obesity often persists into adulthood, the pathological processes of obesity-related morbidities begin in childhood (Biro Wien, 2010). Obesity is strongly associated with a constellation of metabolic disorders marked by abdominal obesity, glucose intolerance, dyslipidemia, high blood pressure, and elevated proinflammatory markers (Despre´s et al., 2008; Huang, Ball, Franks, 2007). The early stages of atherosclerosis, the leading cause of cardiovascular and cerebrovascular events, can appear in utero, during infancy, or throughout childhood (Napoli et al., 1997, 1999). 53 OBESITY, NUTRITION, AND COGNITION Data from adult studies indicates that obesity may also cause structural changes in the brain. In a study in which diffusion tensor imaging (DTI) was used, scientists found among healthy adults that BMI was negatively related to white matter integrity in the corpus callosum and fornix fibers (Stanek et al., 2011). In a longitudinal study, increasing BMI during the onset of menopause was associated with a 15% decrease in cerebral gray matter volume in women after controlling for cardiovascular health markers (Soreca et al., 2009). Obesity and elevated markers of cardiovascular disease increase the risk for incidence of dementia later in life (Fitzpatrick et al., 2009; Whitmer, Gunderson, Barrett-Connor, Quesenberry, Yaffe, 2005). The evidence for the negative role of obesity in cognitive decline is compelling. However, knowledge of how obesity affects cognition during childhood has only emerged over the last decade, and as such is limited. COGNITIVE IMPLICATIONS OF OBESITY IN CHILDHOOD Studies assessing the impact of obesity on cognition vary in the age of children studied (prepubertal or pubertal) and outcomes evaluated (neuroelectric or academic achievement). Most have relied on BMI as the primary measure of obesity, neglecting the influence of body composition or fat distribution. This is a significant limitation, as body composition in children differs by age, gender, and stage of sexual maturity (Ahmed, Ong, Dunger, 2009; Bacha, Saad, Gungor, Janosky, Arslanian, 2003; Heyward Wagner, 2004). Additionally, individuals with excess central adiposity have a substantially higher risk for developing insulin resistance and metabolic syndrome (Despre´s, 2006). To our knowledge, only one study has assessed event-related brain potentials (ERPs; see Chapter 3) among children with or without insulin resistance (Tascilar et al., 2011). Tascilar et al. (2011) investigated alterations in the P3-ERP among 10- to 11-year-olds. The P3 is a positive-going, endogenous ERP component that occurs approximately 300- 800 ms after stimulus onset (Hillman, Kamijo, Pontifex, 2012). In contrast to their healthy weight counterparts, the obese group of children had smaller P3 amplitude and longer P3 latency indicating a decrease in the allocation of attentional resources, and slower cognitive processing/stimulus evaluation speed, respectively. Furthermore, the obese group of children with insulin resistance had smaller P3 amplitude and longer latency compared to the obese group of children without insulin resistance. Kamijo, Khan et al. (2012) assessed cross-sectional relationships between direct measures of adiposity (percent fat mass and central adiposity), cognitive control, and scores on the Wide Range Achievement Test 3rd edition (WRAT3). Following adjustment of confounding variables (age, gender, IQ, SES, VO2max) percent body fat negatively predicted reading and spelling, but not arithmetic. 54 However, central adiposity negatively predicted performance on all three WRAT3 components. These findings suggest that insulin resistance and fat distribution are associated with cognitive ability in prepubertal children. Obesity, assessed using BMI-for-age, has been found to be negatively related to cognitive control in children as well (Kamijo, Pontifex et al., 2012a, 2012b; Li, Dai, Jackson, Zhang, 2008). Using ERPs, Kamijo, Pontifex et al. (2012a) showed that obese children exhibit lower response accuracy in a NoGo task requiring inhibitory control. Specifically, overweight children failed to display the typical frontal distribution for the NoGo P3 relative to the Go P3, indicating that obese status in childhood is negatively and selectively associated with prefrontal inhibitory control. Another aspect of cognitive control, action monitoring, has recently been investigated in relation to obesity. The error-related negativity (ERN) is a neuroelectric measure used to reflect the action monitoring system and larger ERN amplitude and longer reaction time following error detection is indicative of improved cognitive control (Falkenstein, Hohnsbein, Hoormann, Blanke, 1991; Gehring, Goss, Coles, Meyer, Donchin, 1993). It was recently demonstrated that obese children exhibit smaller ERN amplitude and lower post-error response accuracy compared to their healthy weight counterparts Kamijo, Pontifex et al. (2012b). Such a finding points to a maladaptive relationship between obesity and aspects of cognitive control and action monitoring processes. Among adolescents, extreme obesity (i.e., BMI-for-age 99th percentile) was related to impairments in attention, mental flexibility, and disinhibition (Lokken, Boeka, Austin, Gunstad, Harmon, 2009). Li et al. (2008) assessed relationships between academic performance, cognitive functioning, and BMI among a nationally representative sample of 2,519 children aged 8-16 years. Visuospatial organization and general mental ability was negatively related to BMI after controlling for demographics, lifestyle factors, and lipid profiles. Although BMI was not related to academic achievement, overweight children had lower working memory performance as well as lower average scores of the series of four tests (block-design and digit-span subtests of the Wechsler Intelligence Scale for Children, and the reading and arithmetic sections of the Wide Range Achievement Test). Collectively, the aforementioned studies suggest that obesity is implicated in lower performance on cognitive control tasks. However, the impact of obesity on academic achievement has been a controversial topic, and on this front, the evidence has been inconclusive. Scholastic Outcomes and Obesity in Childhood There is a paucity of representative datasets that evaluate obesity along with a wide range of school outcomes. The Early Childhood Longitudinal Study—Kindergarten Class (ECLS-K) examined the link between change in 55 OBESITY, NUTRITION, AND COGNITION overweight status and school outcomes among a national sample of U.S. elementary school children (Datar Sturm, 2006). Datar and coworkers assessed changes in weight status over the first 4 years of schooling (kindergarten to 3rd grade). Their findings indicated that girls who moved from normal to overweight status were likely to score lower on standardized mathematics and reading tests, higher on teacher-reported externalizing behavior problems, and lower on teacher ratings of self-control compared to girls who were never overweight. However, there were no differences between girls who were never overweight and those who remained overweight. Additionally, there were no significant findings for boys on the measure of academic performance. A follow-up cross-sectional analysis at 3rd grade showed that the differences between overweight and non-overweight children on math and reading disappeared when individual characteristics were adjusted for (SES, mother’s education, etc.) (Judge Jahns, 2007). The most recent study related to the ECLS-K (kindergarten to 5th grade) considered all five waves of the study and their findings differed by BMI group (never obese, later onset, and persistent), time point (kindergarten, 1st, 3rd, and 5th grades), and gender (Gable, Krull, Chang, 2012). Girls who displayed later onset of obesity performed more poorly on math assessments at first and third grade. These effects were mediated by interpersonal skills and were accompanied with higher internalizing behaviors. Considering the work by Li et al. (2008), it appears that weight status has a cross-sectional relationship with lower academic performance. However, longitudinal data remain limited and the results from the ECLS-K studies do not implicate obesity as a causal factor in diminished academic outcomes in children. Recent evidence suggests that the relationship between obesity and cognition may not be unidirectional. In other words, aspects of cognition may instead determine obesity. Graziano, Calkins, and Keane (2010) investigated the role of emotion regulation and sustained attention and inhibitory control in development of obesity among 2-year-olds. Poorer inhibitory control at 2 years was predictive of obese status at 5.5 years. This relationship persisted even after controlling for BMI at 2 years, suggesting that poorly developed selfregulation skills may contribute to the development of pediatric obesity. Guxens et al. (2009) conducted a longitudinal study among 421 Spanish preschool children to assess whether cognition at age 4 would predict changes in BMI at age 6. Children with higher scores of general cognition at age 4 were less likely to be overweight at 6 years of age. After adjusting for maternal education and BMI, children with higher general cognition at 4 years were more likely to maintain a healthy weight status between ages 4 and 6 years. Interestingly, Guxens et al. (2009) did not observe cross-sectional relationships between cognitive function scores and BMI at age 4. An MRI study among 83 young females (18-19 years) showed that weight gain was related to low gray matter (GM) volume in regions implicated in inhibitory control. The 56 authors concluded that abnormalities in regional GM volumes, but not WM volumes, increase the risk for future weight gain (Yokum, Ng, Stice, 2011). However, it should be noted that other studies suggest no relationship between weight status and cognitive performance (Gunstad et al., 2008; LeBlanc et al., 2012). Gunstad et al. (2008) failed to find any association between weight status and several markers of cognitive performance (including cognitive control, verbal memory, and attention) among a healthy sample of 6- to 19-year-olds (N ¼ 478). Similarly, Leblanc et al. (2012) found no impact of obesity on standardized academic tests among 1963 fourth to sixth graders. Overall, the evidence for the negative influence of childhood obesity on cognitive function remains equivocal and thus controversial. However, it is recognized that obesity in adulthood is associated with poorer cognitive outcomes later in life including increased risk for dementia (Fitzpatrick et al., 2009; Whitmer et al., 2005). Although the mechanism remains unknown, insulin resistance appears to play a significant role in this pathology (Hildreth et al., 2012). Given that the majority of overweight or obese individuals are insulin resistant (Stefan et al., 2008), identification of modifiable risk factors in childhood could reduce the incidence of cognitive impairment later in life. Most studies in children utilize cross-sectional designs, lack adjustment of key covariates, and rely exclusively on BMI. These limitations notwithstanding, there is growing cross-sectional evidence suggesting that obesity has a weak negative association with cognitive health. Additional research is needed to determine which cognitive processes have greater susceptibility to the effects of overall adiposity and fat distribution. NUTRITIONAL EFFECTS ON COGNITION In vitro studies demonstrate that nutrients function as substrates for energy, form precursors for neurotransmitters, and serve in pathways involved in cell signaling and gene transcription in the brain. However, much of what is known in vivo is based on animal studies assessing cognitive decline. This focus on aged models has limited the knowledge on the role of nutrition in cognitive function in childhood. Nutritional effects on brain health may involve the composition of the gut microbiota, as well as dietary components. The Gut Microbiota The bidirectional signaling between the gastrointestinal tract and the brain is vital for maintaining homeostasis and is regulated by the neural [both central (CNS) and enteric nervous systems (ENS)], hormonal and immunological components. The human gut is home to trillions of microorganisms that 57 OBESITY, NUTRITION, AND COGNITION influence host health and disease, including, among others, diet and nutrition, obesity, intestinal diseases, and cancer (Flint, Scott, Louis, Duncan, 2012). Pertinent to this review, growing evidence supports a key role for the gut microbiota in childhood obesity (Karlsson et al., 2012) and in brain development, including learning and anxiety (Manco, 2012), suggesting that the gut microbiota could be a central mediator, although to date this has not been directly investigated. A recent study demonstrated that the gut microbial species differed between preschool children (age 4-5 years) with excessive body weight (n¼ 20) versus normal weight (n ¼ 20). The amount of Enterobacteriaceae was significantly higher in those with excessive body weight. In contrast, A. muciniphila-like bacteria and Desulfovibrio were more abundant in children with healthy BMI. Further, there was a trend for decreased bacterial diversity in children with excessive body weight (Karlsson et al., 2012). Evidence from animal models provides further insight into the link between gut microbiota and brain development. An essential role for the microbiota in brain development was demonstrated by comparing mice with a conventional microbiota to germ-free mice, which displayed increased motor activity and reduced anxiety and altered expression of genes involved in longterm potentiation in brain regions implicated in motor control and anxietylike behavior (Bravo et al., 2011; Heijtz et al., 2011; Neufeld, Kang, Bienenstock, Foster, 2011). Importantly, a critical window exists after which microbial colonization did not reverse the abnormal behavioral phenotype (Heijtz et al., 2011). Additionally, provision of a single lactobacillus species (L. rhamnosus JB-1) reduced anxiety- and depressionrelated behaviors in mice, which did not occur in vagotomized mice, identifying the vagus as a major modulator of communication between gut microbes and the brain (Bravo et al., 2011). Thus, future investigations are needed to define whether differences in the microbiome between lean and obese children impacts brain development and cognitive function. Neurodevelopment and Nutrient Deficiencies The development of the brain occurs through several overlapping processes (migration, myelination, and synaptogenesis) that proceed at varying velocities from early gestation into childhood (Lenroot Giedd, 2006). Over the first 2 years of life, the brain achieves 80% of its adult weight (Dekaban Sadowsky, 1978). This rapid early development of the brain in relation to the rest of the body emphasizes the need for optimal nutritional intake during pregnancy and early postnatal life. Extensive reviews on nutrients necessary for healthy neurodevelopment have been presented elsewhere (Benton, 2010; Bryan et al., 2004; Georgieff Innis, 2005; Rao Georgieff, 2007). However, some micronutrients play especially crucial roles in hippocampal and prefrontal growth and function (Georgieff, 2007). 58 Neurodevelopment processes provide critical periods of growth during which the brain is especially sensitive to nutritional insult. For example, the closing of the neural tube, which occurs 21-28 days into gestation, requires adequate levels of folate—an essential B vitamin (Benton, 2010). Approximately 300,000 newborns worldwide are affected by neural tube defects (NTDs) often manifesting in the form of spina bifida and anencephaly (Gardiner et al., 2008). Epidemiological evidence shows a decline in the prevalence of NTDs since the U.S. food supply was fortified with folic acid in 1998 (Williams et al., 2002). Maternal intake of folate during early pregnancy has been linked to higher scores on the Peabody Picture Vocabulary Test III (PPVT-III), a test of receptive language that predicts overall intelligence, in children at 3 years (Villamor, Rifas-Shiman, Gillman, Oken, 2012). However, additional studies are needed to determine whether folate supplementation in childhood enhances performance on specific aspects of cognition and memory. In addition to folate, lower intake of choline during pregnancy has also been suggested to affect risk for NTDs (Shaw et al., 2009). Choline has wideranging functions including neurotransmitter synthesis, cell structure integrity, and conversion to methyl donor betaine (Benton, 2010; Zeisel Da Costa, 2009). Deficiency of choline during the final stages of gestation in rodents results in poorer memory as an adult (Zeisel Niculescu, 2006). However, among pregnant women, supplementing with phosphatidyl choline, the main dietary source of choline, from 18 weeks to 90 days postpartum did not result in enhanced cognitive abilities (short-term visuospatial memory, long-term episodic memory, language development, and global development) in their children at 10-12 months of age (Cheatham et al., 2012). Given that 80% of the women supplemented in this study already met their daily choline recommendation at time of supplementation, it remains unknown whether supplementing pregnant women with chronically lower intake of choline would enhance infant brain development. In addition, it is possible that a longer follow-up period would have revealed lateemerging effects. Therefore, additional supplementation studies are needed to elucidate the role of choline in cognitive development in infancy and childhood. Vitamin B12 plays an important role in fatty acid metabolism and its deficiency causes impaired myelination and demyelination of the spinal cord and the brain (Dror Allen, 2008; Healton, Savage, Brust, Garrett, Lindenbaum, 1991). Several mechanisms have been proposed for this effect including reduced phosphatidylcholine synthesis, elevated homocysteine, imbalance of neurotrophic and neurotoxic cytokines, and accumulation of lactate in brain cells (Dror Allen, 2008). Given that myelination is most active in the first 6 months of life, the brain may be especially susceptible to B12 deficiencies early in life (Lo¨vblad et al., 1997). However, the evidence 59 OBESITY, NUTRITION, AND COGNITION for impaired neurological development due to vitamin B12 deficiency in humans is largely based on case studies, and thus the long-term impact of suboptimal intake of vitamin B12 on cognitive development remains unknown. The role of vitamin D in brain development and function has also been gaining support over the past decade. Early life deficiency has been linked to neuropsychiatric disorders, such as schizophrenia, and deficiencies in adulthood are known to exacerbate Parkinson’s disease, Alzheimer’s disease, depression, and cognitive decline (Cui, Groves, Burne, Eyles, McGrath, 2013). The discovery that the brain synthesizes the active form of vitamin D, and expression of vitamin D receptors in the hippocampus suggests it may modulate proteins involved in learning and memory (Langub, Herman, Malluche, Koszewski, 2001). Although the evidence in early life is limited to rodent models, gestational vitamin D deficiency appears to cause permanent damage by altering the ratio of neural stem cell proliferation to programmed cell death in the brain (Levenson Figueiroˆa, 2008). Considering the surprisingly high prevalence of vitamin D deficiency among pregnant women (5% among white and 29% among black women) and newborns (10% and white and 47% among black neonates) in the United States (Bodnar et al., 2007), it is crucial that researchers elucidate whether vitamin D deficiency alters cognitive function in childhood. Among minerals, iron deficiency is the most common gestational micronutrient deficiency (Rao Georgieff, 2007; Stoltzfus, 2001). Perinatal iron deficiency has been shown to alter the neurochemical profile of the rat hippocampus resulting in impairments in energy status, neurotransmission, and myelination (Rao, Tkac, Townsend, Gruetter, Georgieff, 2003). Decrements in memory and learning have also been observed as a function of iron deficiency. Neonatal piglets consuming an iron deficient diet displayed lower acquisition on a hippocampal-dependent spatial T-maze task (Rytych et al., 2012). Among humans, newborns with low amounts of cord ferritin exhibit lower performance on mental and psychomotor tests at 5 years of age (Tamura et al., 2002). Another key mineral, Zinc, is a co-factor in enzymes that mediate protein and nucleic acid synthesis (Sandstead, 1985). Children born to zinc deficient mothers show decreased preferential looking behavior suggesting that zinc deficiency selectively affects hippocampal function (Merialdi et al., 2004). In addition to micronutrient inadequacies, protein/energy malnutrition between the third trimester and 2 months of postnatal life has enduring detrimental effects on global deficits in motor control and language development (Grantham-McGregor Baker-Henningham, 2005). In summary, brain structures displaying rapid growth during early childhood such as the hippocampus and cortex appear especially vulnerable to nutritional insult (Georgieff, 2007; Gotlieb, Biasini, Bray, 1988; Pollitt Gorman, 1994). 60 Nutrients and Cognitive Function in Children Children in developed countries rarely present with gross nutritional inadequacies, and supplementation in the absence of clinical deficiency remains a controversial topic. Nevertheless, the search for nutrients that enhance cognitive performance during growth is important because functional development of the hippocampus and frontal lobe continues throughout childhood (Bryan et al., 2004; Johnson, 2001; Lenroot Giedd, 2006; Thatcher, 1991). Breast milk is the optimal form of nutrition for the infant and its nutrient profile provides the basis for current nutrient recommendations for children younger than 2 years (IOM (US), Panel on Macronutrients, 2005). However, by 6 months of age, half of infants are consuming fortified infant formula (Li, Darling, Maurice, Barker, Grummer-Strawn, 2005). Furthermore, most studies of breastfed infants lack cognitive assessment of the child or mother. Few randomized controlled trials have been conducted and much of the evidence is based on observational studies. According to a meta-analysis, breast-fed infants may score 2-5 points higher on cognitive developmental tests compared to their non-breast-fed counterparts (Anderson, Johnstone, Remley, 1999). This difference may be even higher for children who are born preterm (Lucas, Morley, Cole, Lister, Leeson-Payne, 1992). After stratifying children based on breastfeeding duration and gestation status, preterm infants who were breast-fed performed better on naming vocabulary, pattern recognition, and picture similarities subscales at 5 years of age (Quigley et al., 2012). It remains unclear whether these positive effects can be attributed to specific nutrients in breast milk since maternal and lifestyle characteristics often confound the findings (Qawasmi, Landeros-Weisenberger, Leckman, Bloch, 2012; Von Kries, Koletzko, Sauerwald, Von Mutius, 2002). Long chain polyunsaturated fatty acids (LCPUFAs) have been extensively studied for their role in brain development and cognitive function (Eilander, Hundscheid, Osendarp, Transler, Zock, 2007; Georgieff Innis, 2005). Among LCPUFAs, both DHA and arachidonic acid (AA) are preferentially accumulated in the forebrain during the third trimester and first 2 years of life (Lauritzen, Jorgensen, Olsen, Straarup, Michaelsen, 2005; Martinez, 1992). Docosahexaenoic acid (DHA), in particular, plays a crucial role in maintaining cortical neuronal integrity (Joardar, Sen, Das, 2006; McNamara et al., 2010). Deficiency in DHA results in abnormalities in neurons, glial cells, oligodendrocytes, myelin, and nerve endings (Bourre, 2006). Although DHA and AA can be derived from their essential fatty acid precursors, alinolenic (ALA) and linoleic acid (LA), this conversion is not efficient in the human fetus and breast milk varies in its concentration of LCPUFA (Cetin Koletzko, 2008; Hoffman, Boettcher, Diersen-Schade, 2009; Jensen Lapillonne, 2009; Uauy, Mena, Wegher, Nieto, Salem, 2000). Therefore, it 61 OBESITY, NUTRITION, AND COGNITION was hypothesized that supplementing infant formula with DHA and AA would improve cognitive function in children. Supplementation of preterm infants with DHA improved visual acuity and short-term global development (O’Connor et al., 2001; SanGiovanni, Parra-Cabrera, Colditz, Berkey, Dwyer, 2000). However, the overall results thus far have been inconclusive and a recent meta-analysis concluded that LCPUFA supplementation did not significantly improve early cognition (Campoy, Escolano-Margarit, Anjos, Szajewska, Uauy, 2012; Qawasmi et al., 2012). Interestingly, among school children with learning disabilities and developmental disorders, supplementation with DHA has been shown to improve cognitive function (Kirby, Woodward, Jackson, 2010), indicating the potential benefits of supplementation during school-aged years. Supplementing infant formula with micronutrients (e.g., iron) has yielded mixed results as well. Among infants without iron deficiency, supplementing with iron does not improve and may even be related to lower cognitive function in those with elevated hemoglobin levels at 10 years of age (Lozoff, Castillo, Clark, Smith, 2012). Conversely, providing a cocktail of micronutrients along with DHA has been shown to improve verbal learning but not general intelligence or attention (Osendarp et al., 2007). Therefore, current findings suggest that nutritional status of the child (deficient or adequate) may play an important role in determining cognitive outcomes as a function of supplementation. In addition, future research should examine the efficacy of supplementation of combinations of nutrients rather than provision of a single nutrient for cognitive and brain health. Dietary components such as high saturated fats and sugars may also have a detrimental impact on brain function (Das, 2010), although much of this evidence is derived from animal models. Brain derived neurotrophin factor (BDNF) appears to function at the crossroads of cognitive and metabolic regulation (Go´mez-Pinilla, 2008). BDNF modulates insulin resistance and glucose metabolism and deletion or polymorphisms of BDNF are related to abnormal hippocampal function among rodent and human studies (Egan et al., 2003; Go´mez-Pinilla, 2008; Nakagawa et al., 2002; Tonra et al., 1999). Exposure to a diet high in saturated fat and sucrose, independent of obesity, was related to decreased BDNF in the hippocampus and poorer learning and memory performance (Molteni, Barnard, Ying, Roberts, GomezPinilla, 2002). However, conclusive evidence for specific nutrients that can enhance cognitive function in children has been elusive thus far. Some studies have considered nutrition composition of meals and cognitive function. Breakfast omission has been linked to poorer performance on learning and memory among school-aged children and adolescents (Pollitt Mathews, 1998; Rampersaud, 2009). Modifying nutritional composition of meals may also affect short-term cognitive performance. School-aged children consuming 62 breakfasts with low glycemic index (GI) may perform better on attentional tasks when compared to no breakfast or breakfast with high GI (Cooper, Bandelow, Nute, Morris, Nevill, 2012; Ingwersen, Defeyter, Kennedy, Wesnes, Scholey, 2007). CONCLUSION Emerging evidence suggests that cognitive and brain health may be profoundly affected by weight status and nutritional intake. Obese children have been shown to exhibit lower performance on cognitive control tasks and findings from ERP studies indicate that overweight and nonoverweight children have differential underlying brain activity during task performance. In addition to weight status, central adiposity and insulin resistance are associated with lower cognitive control in children (Kamijo, Khan et al. 2012; Tascilar et al., 2011). Insulin resistance has already been implicated in adult cognitive impairment; however, additional work in children is needed to elucidate insulin’s role in brain function through the lifespan. Regarding academic achievement, there are no definitive prospective studies demonstrating a causal relationship between weight status and diminished academic achievement in children. However, cross-sectional studies indicate a negative association with obesity and academic achievement (Li et al., 2008). Therefore, additional longitudinal studies are needed to determine whether obesity is a determinant of lower academic achievement. The rapid development of the brain over the first 2 years of life makes it particularly susceptible to nutrient deficiencies (Georgieff, 2007). This is evident from observations of decrements in brain development and function among animals and humans following perinatal or early nutrient deficiencies. However, there remains remarkably little known about the effects of overall diet or particular nutrients on specific aspects of cognitive control among children without nutrient deficiencies. Emerging data suggest the potential for the gut microbiome to mediate brain development and childhood obesity risk. Considering that the gut microbiota is highly susceptible to nutrient intake as well, future studies should investigate how nutrients targeting the microbiome may modulate both obesity and cognition. Despite wide-scale intervention efforts, childhood obesity remains a major public health threat in much of the developed world (De Onis, Blo¨ssner, Borghi, 2010; Ogden et al., 2012). Given that this trend may persist for the foreseeable future (Wang et al., 2008), it is essential that researchers develop a comprehensive understanding of the cognitive implications of nutrition and obesity.
itemName= input(quot;Enter the name of item purchased: quot;)
yrPurch = int(input(quot;Enter the year purchased: quot; ))
estCost = float(input(quot;Enter cost of item: quot;))
Our (Time Warner’s) only competitor is District X currently provides bundled services
at $84.95. We are currently charging a 10% premium over their price, but there are unsubstantiated rumors that they are contemplating a 10% price increase. We don’t know their cost structure, so we don’t know whether their potential price increase is driven by cost increases or is merely a strategic move on their part.
Historically, when we both charge the same price, our market share is about 65%. When we charge a 10 percent premium over their price, our market share declines to about 60%. It appears that in those instances where they have charged a 10% premium over our price, our market share is about 70%. Please provide a recommendation regarding whether we should maintain our current price or reduce our price to $84.95.
Please factor into your recommendation that we pay programming fees to providers that amount to $32.50 for each subscriber. In addition, maintenance, service and billing costs are about $7.60 per subscriber. At present, there are about 110,000 households in the relevant area.
1. Draw the payoff matrix (note that Time Warner and District x are the only ones in the market.
2. Based on the payoff matrix what recommendation would you give regarding whether Time Warner should maintain its current price or reduce it to $84.95? Explain your answer.
tool and sensitivity analysis to plan refinery operations and to improve profitability. As a manager you will be required to analyze situations quantitatively to minimize costs or maximize profit.
What other considerations might a manager at Chevron have to account for while planning production in such a complex environment?
Python 3.5 test
A loop can also be known as a
2 points Saved Answer
Abstracts a program’s various tasks or function into…
Approving the ﬁnal version of web design March 25. 2015 [mplernentation of responsive features into the design March 30. 2015 Programming of a new administration system April 30~ 2015 Testing May…
Task 1 . Define an elegant class
Design an elegant class for pupils who are good
at arithmetic operations , such as addition
subtraction multiplication , and division . The
data type of operands may be integer or float .
For example , the add methods can be defined as
following . They are overloading methods
blic int add intox into the float addsint x
float y )
public float add float
public float add float
return x ty !
return ( float ( x + y )
of properties , constructors and methods of Thi
class Pupil as shown in Figure
Must be home – kan
Figure I Model for Pupil
Popular. Because of increasing demand, each manager has been lobbying to the Headquarters for purchasing an automatic programming machine that would mechanize part of their operations. Unfortunately, these machines have huge capacity and none of the divisions of Wixon seem to have enough demand to justify purchase of these machines. The managers and the Headquarters have agreed to purchase these machines on a cost sharing agreement. At present they are considering four different options. The incremental contributions of Premier and Popular divisions for each of the four machines and the respective cost of the machines are as follows:
Cost of Machine
I tried all my effort in doing it
but I still got errors in my code and the form is not working as well.
Week 5 Homework Questions
Answer the following questions:
1. Describe the graphical coordinate system in Java. Where is the origin? What
units apply to the x,y coordinates?
printReport – invoke getCity to retrieve the user input for the city. Once you have the city, get the distance to it by calling getDistance. Try to open the file (Hint: try-catch, file may not exist, place the file in the right place). Read the file (Hint: you don’t know how many lines are in it, so peak into it before you read; find where months begin – the month starts with a number, not a String, so peak for integers; don’t forget to move the cursor to the next line when you encounter an integer). Keep track of total trips for each month, and all months together (Hint: running totals). Print out a charges for the chosen destination for each month (Hint: a charge for each trip is made up of 2 cents charged for each mile of the total miles for the trip (both directions) plus a $10 consuing fee) as well as a grand total for the whole period. Format your output (Hint: assume number of trips can be in the double digits, consuing fees in the triple digits, mileage and total revenue can have up to 4 digits plus 2 digits precision)
Write a class definition for Date, an object type that contains three integers,year, month and day. This class
should provide two constructors. The first should take no parameters. The second should take parameters named year, month and day, and use them to initialize the instance variables.Write a main method that creates a new Date object named birthday. The new object should contain your birthdate. You can use either constructor
Write a method squareOfSymbols that displays a solid rectangle of symbols whose height
width are specified in integer parameter height and width respectively. And this method also
receive a third parameter of type char called fillCharacter. For example, if height is 5, weight is
4, and the fillCharacter is *, the method should display
Incorporate this method into an application that reads an integer value for side from the user
and outputs the asterisks with the squareOfAsterisks method.
Write methods that accomplish each of the following tasks:
A. Write a method called divideQuotient that calculates the integer part of the quotient when
integer a is divided by integer b.
B. Write a method called divideRemainder that calculates the integer remainder when integer
a is divided by integer b.
C. Use the methods developed in parts (a) and (b) to write a method displayDigits that
receives an integer between 1 and 99999 and displays it as a sequence of digits, separating
each digit by two spaces. For example, the integer 4562 should appear as 4 5 6 2
D. Also write a method called displayReverseDigits that is similar to (c), but it displays digits in
reverse order. For example, 2 6 5 4
Incorporate the methods into an application that inputs an integer and calls displayDigits by
passing the method the integer entered. Display the resus.
Write an application that simulates coin tossing. Let the program toss a coin each time the user
chooses the Toss Coin menu option. Count the number of times each side of the coin appears.
Display the resus. The program should call a separate method flip that takes no arguments
and returns a value from a Coin enum (HEADS and TAILS). [Note: If the program realistically
simulates coin tossing, each side of the coin should appear approximately half the time.]
3 on page 719 which creates a Point class with 2 instance variables; the xCoordinate and yCoordinate. It should have a defau constructor and a values constructor. Also include a set method that sets both attributes, a get method for each attribute, and a method that redefines toString() to print the attributes as follows.
point: (x, y)
Do number 4 on page 719 which creates a Circle class that extends the Point class above. It adds the radius, circumference, and area instance variables. It should have a defau constructor and a values constructor (circumference and area will be 0.0). Include a set method that sets the coordinates and radius attributes, get methods that get each of the 3 new attributes, and a method that redefines toString() to print all attributes. Also, include methods to calculate the circumference and area of the circle. Use pie = 3.141593. Make sure your methods don’t repeat the code already written in the Point class.
Circumference = 2pier
Area = pier2
Do number 5 on page 719 which creates a Cylinder class that extends the Circle class above. It adds the height, surfaceArea, and volume instance variables. It should have a defau constructor and a values constructor (circumference and circleArea will be calculated from the Circle class, surfaceArea and volume will be 0.0). Include a set method for the center point coordinates, radius, height, circumference, and circleArea attributes (circumference and circleArea will be calculated from the Circle class), get methods that get each of the 3 new attributes, and a method that redefines toString() to print all attributes. Also, include methods to calculate the surfaceArea and volume of the cylinder. Make sure your methods don’t repeat the code already written in the Circle class.
surfaceArea = 2 * circleArea + circleCircumference * cylinderHeight
volume = circleArea * cylinderHeight
1.In the values constructor and the set method you’ll need to call the methods to calculate circumference and area so that they have values to use in calculating the surface area and volume.
2.The methods in the Circle class to calculate circumference and area will need to return those values for use in the Cylinder class.
Create a client program that uses all 3 of the classes created above. Make it do the following in this order:
1.Instantiate point1 with the defau constructor.
2. Instantiate point2 with the values constructor.
3. Use the Point class print method to print point1 and point2.
4. Call the set method to set the x and y coordinates for point1.
5. Use the get methods to get the attributes for point1 and print them in the client (not with the print method).
6. Instantiate circle1 with the defau constructor.
7. Instantiate circle2 with the values constructor.
8. Call the methods to calculate the circumference and area for circle2.
9. Use the Circle class print method to print attributes for circle1 and circle2.
10. Use the set method to set the coordinates and radius for circle1.
11. Call the methods to calculate the circumference and area for circle1.
12. Use the get methods to get the attributes for circle1 and print them in the client (not with the print method).
13. Instantiate cylinder1 with the defau constructor.
14. Instantiate cylinder2 with the values constructor.
15. Call the methods to calculate the surfaceArea and volume for cylinder2.
16. Use the Cylinder class print method to print the attributes for cylinder1 and cylinder2
17. Call the set method to set the attributes for cylinder1.
18. Call the methods to calculate the surfaceArea and volume for cylinder1.
19.Use the get methods to get the attributes for cylinder1 and print them in the client (not with the print method).
program will be a string of the form:
operand1 operator operand2
where operand1 and operand2 are non-negative integers and operator is a single-character operator, which is either +, -, or *. You may assume that there is a space between each operand and the operator. You may further assume that the input is a valid mathematical expression, i.e. your program is not responsible for the case where the user enters gibberish.
Your function will return an integer, such that the returned value is equal to the value produced by applying the given operation to the given operands.
calc(5 + 10) # 15
Can only use find and rfind or the slice operators. Cannot use split or eval.
that is good for programming or networking and database and why do you think
the IT forum is good for you?
would be much helpful to follow the format and guidelines based off the first phase since this is an intro programming class and haven’t practiced with advanced techniques
requires JAVA programming only. A modular and an internally source code documented file
is required for completion as highlighted in the submission section of the document. Please follow all instructions and requirements as noted in the file, game of chance.
The files RandomLicensePlate.java and Documentation give an example on how your source code should be internally documented.
Part B: Short Answer (80 marks in total) Note: whenever you are asked to provide an example, it should be assumed that the example is to be
Python code. Question 1: (10 marks) – Analysis and…
one https://www.coursehero.com/tutors-problems/Python-Programming/9085391-I-am-very-new-to-Python-and-am-trying-to-solve-this-classic-data-minin/ . Attached are the program specs. Someone help. If you need my very little code to get started, I will post it up.
Python 3.5.1 (v3.5.1:37a07cee5969, Dec 6 2015, 01:38:48) [MSC v.1900 32 bit
(Intel)] on win32
Type quot;copyrightquot;, quot;creditsquot; or quot;license()quot; for more information….
In mathematics, the notation n! represents the factorial of the
imageFile). Parameter imageFile holds the name of the image that will be manipulated. We will use W to denote the width and H to denote the height of the image.
reduceWidth(double x) Returns a new Picture whose width is âx Ã W â. Note that the type
of this method must be Picture. Your method must use the algorithm described earlier to reduce the image width. And your method must use the static method minCostVC to compute the vertical cut.
minCostVC(int M): Returns a min-cost vertical cut. Type of this method must be array list of integers. Note that if M has n rows, the the returned array list has exactly 2n integers.