Tuesday, January 28, 2020
The High-Protein, Low-Carb Diet: An Analysis
The High-Protein, Low-Carb Diet: An Analysis High-Protein, Low-Carb Counterattack Carbohydrate-restrictive or ketogenic diets that are rich in animal products can help some people to lose weight because they offer some short-term improvement in glucose control. But because these diets are too rich in animal products, they incur significant risks such as cancer, heart disease, and kidney disease. They are especially dangerous for diabetics because a meat-based diet increases the risks of atherosclerosis and accelerates kidney failure in people with diabetes. In a recent study, researchers found that every 5 percent increase in calories consumed from animal protein increases the risk of diabetes by 30 percent. But vegetable protein was not associated with the increased diabetes risk. How Much and What Type of Animal Products Are Permitted? A maximum of only one or two (two- to three-ounce) servings of animal products a week is recommended: One or two servings of fish per week; or One or two servings of fish plus one small serving of white meat fowl, totaling less than six ounces per week. Studies show that people eating more than two servings of fish per week have higher incidence of type 2 diabetes. There is no significant benefit from using fish in your diet; you can get omega-3 fats from supplements. No other animal products are recommended. Red meats are to be avoided completely. Studies show diabetics with high red meat intake have 50 percent higher incidence of heart disease, probably because higher level of the heme iron in red meat. Facts About Eggs and Diabetes Studies show that people eating five eggs a week or more have an increased risk of developing type 2 diabetes. Diabetics who eat more than one egg a day double their heart disease or death risk. Get Protein from Your Vegetables Human protein requirement studies in the 1950s showed that adults require 20 to 35 grams of protein per day. People who eat a vegetable-based diet have been found to consume 60 to 80 grams of protein a day, well above the minimum requirement. Protein Content from Selected Plant Foods FOOD GRAMS OF PROTEIN Almonds (3 ounces) 10 Collars (2 cups) 8 Banana 1.2 Broccoli (2 cups) 10 Brown Rice (1 cup) 5 Chick Peas (1 cup) 15 Corn (1 cup) 4.2 Lentils (2 cup) 18 Peas, frozen (1 cup) 9 Spinach, frozen (1 cup) 7 Kidney Beans (1 cup) 13 Soybeans (1 cup) 29 Sunflower seeds (1/2 cup) 13 Sesame seeds (1/2 cup) 12 Tofu (4 ounces) 11 Whole Wheat Bread (2 slices) 5 Green vegetables, legumes, and beans have more protein per calorie than meat does. They are also rich in miconutrients. Animal protein is low-nutrient food because it does not contain antioxidants or phytochemicals. So eating more plant protein is the key to increasing our micrnutrient intake. The Dangers of IGF-1 Insulin-like growth factor-1 (IGF-1) is produced by the liver in response to the pituitary-derived growth hormone. It is one of the bodys important growth promoters during fetal and childhood growth. However, in adults, higher levels of IGF-1 promote cellular replication that can accelerate the aging process and promote cancer. Elevated IGF-1 levels are associated with increased risk of all major cancers, including breast cancer, colon cancer, and prostate cancer. Lower levels of IGF-1 are associated with enhanced insulin sensitivity and enhanced life span. Protein Intake Promotes IGF-1 The composition of protein and the amount consumed also affect IGF-1 levels. Animal protein causes a larger increase in IGF-1 compared to plant protein because animal protein is more biologically complete. For people with diabetes, a relative low amount or animal protein could raise their IGF-1 level. This is the main reason why we restrict animal intakes to only six ounces per week. Plant proteins are less biologically complete. The body has to combine the amino acids for biological completenss, so they do not raise the IGF-1 level like animal proteins do. Refined Carbohydrates Promote IGF-1 Excess intake of refined carbohydrates can also have an effect on IGF-1. Insulin regulates energy metabolism and affects IGF-1 signaling by increasing the production of IGF-1 and decreasing the IGF-1-binding proteins.
Sunday, January 19, 2020
Evil in Shakespeares Macbeth - Lady Macbeth as a Second Eve :: GCSE English Literature Coursework
The Different Faces of Evil - Lady Macbeth as a Second Eve  Natural disasters in newspaper headlines, literature, video games, Hollywood movies, gapers at accidents, TV series in the afternoon - they all prove the our fascination about the evil, about death and violence. The evil in Macbeth is clearly omnipresent and an almost endless theme for different analysis. The role of Lady Macbeth is interesting on many levels of interpretation, but I shall focus on her way of being evil and her way of interacting with other characters in the play. Lady Macbeth is characterized at least as complex as her husband, although she is not the traditional tragic hero in the play. She doesn't only show the trait of being the evil but also many other, very human, traits.  The above was provided to give the student an idea of the content of the paper. The complete paper begins below.  Bosheit ist eine Art Delirium und verwirrt den Verstand.                                                            Friedrich II von Preussen  Prà ¼fe das Innere jedes beliebigen Menschen - in jedem wirst du wenigstens einen dunklen Punkt finden, den er verhà ¼llen muss. (Bernick)                                                            Ibsen, Die Stà ¼tzen der Gesellschaft III  Natural disasters in newspaper headlines, literature, video games, Hollywood movies, gapers at accidents, TV series in the afternoon - they all prove the our fascination about the evil, about death and violence. The evil in Macbeth is clearly omnipresent and an almost endless theme for different analysis. The role of Lady Macbeth is interesting on many levels of interpretation, but I concentrate on her way of being evil and her way of interacting with other characters in the play. Lady Macbeth is characterized at least as complex as her husband, although she is not the traditional tragic hero in the play. She doesn't only show the trait of being the evil but also many other, very human, traits. Her interaction with Macbeth and the other characters passes on different levels: She plays the charming serpent, she's a perfect strategist and she's probably, before her fall, the most self-confident and straight person in the play. And, she succeeds in achieving even more influence on her husband than the witches. Therefore it is worth to deal with an analysis of her conduct, which will show that she has many faces - though all of them are a sign of her evocation of the evil and, make her to a second Eve in the fall of men.
Saturday, January 11, 2020
Juvenile Offenders: Race and Ethnicity Essay
â€Å"Researchers have long observed differences in rates of serious juvenile and adult offending among ethnic and racial groups in the United States. These differences have prompted competing theoretical interpretations and public policy debates. However, conclusions about the racial differences in serious and violent juvenile offending have been reached primarily using individual-level data that, when used alone, yield incomplete results. Multilevel analyses that consider community and contextual factors have the potential to produce a fuller understanding of the meaning of these differences (, 2002).†This paper will first describe the racial distribution of serious and violent offending among juveniles in the United States. It will provide a picture of the short-term national trends for offending patterns by race and ethnicity and summarize research findings on racial and ethnic differences in chronic juvenile offending. Various explanations are given for the racial and ethnic differences. This paper will include recommendations for improving understandings of these differences and implications for guiding prevention and intervention efforts. Data from the 1998 UCR indicates that differential rates of arrest for crime are related to race (Snyder, 1999). Arrests of white juveniles (under age 18) constituted 71 percent of all juvenile arrests compared with 26 percent for black youth. American Indian or Alaska Native and Asian or Pacific Islanders account for 1 and 2 percent, respectively (Federal Bureau of Investigation, 1999). Black youth were overrepresented, given the fact that they make up 15 percent of the juvenile population compared with 79 percent white and 5 percent other races. The distribution by index crime type varies, however. Black youth accounted for 42 percent of arrests for violent crime compared with 55 percent for white youth (3 percent were youth of other races). Black youth, when compared with white youth, were most overrepresented in arrests for robbery (54 percent and 43 percent, respectively) and murder and non-negligent manslaughter (49 percent and 47 percent, respectively). Black youth were least disproportionately involved in arson arrests (18 percent and 80 percent, respectively) (Snyder, 1999; Federal Bureau of Investigation, 1999). Juvenile involvement in crime by race has been generally consistent over the past several decades (LaFree, 1995). However, the racial gap in rates of homicide widened dramatically between 1986 and 1994. Black youth were responsible for the majority of the increase in homicides by juveniles in these years â€Å"and for the majority of the decline thereafter†(Snyder and Sickmund, 1999). If all serious crime is considered, a more complex picture emerges. Between 1983 and 1992, the juvenile arrest rates for all types of violent crimes increased 82 percent among white youth and 43 percent among black youth (Snyder and Sickmund, 1995). The pattern of change was greatest for robbery and homicide arrest rates. In 1983, black youth were approximately five times more likely to be arrested for homicide than were white youth; in 1992, that ratio was more than seven to one. What is the meaning of these race-specific trends in violence? Blumstein (1995) attributed the growth of youth homicide to illicit drug markets into which youth had been recruited. Juveniles working in these markets armed themselves, and so the use of guns was â€Å"diffused†to other teenagers in the community. The notion of gun diffusion is supported by the concomitant increase in the homicide rate among black juveniles from 1986 to 1994 but has not been supported by other research (Howell, 1997). More comparative research is needed to understand racial and ethnic differences in rates of offending. In this area of research, a number of case studies were conducted in several U.S. cities in the 1980’s among youth of Hispanic ancestry. Between 1980 and 1985, homicide arrest rates for 10 to 17 year old Hispanics in New York City were more than twice those of whites (Rodriguez, 1988). In southern California, the homicide death rate for 15 to 24 year old Latino males during 1980 was more than four times the rate for white Anglo males (Valdez, Nourjah, and Nourjah, 1988). At the same time in Chicago, Latino males between ages 15 and 19 were homicide victims 4 ½ times more often than non-Latino white males (Block, 1988). These findings suggest the importance of taking ethnicity into consideration when examining youth violence data. Another factor to consider when interpreting racial and ethnic differences is the length of time and degree to which youth are involved in serious crime. UCR data are not helpful in this regard. However, a few longitudinal studies have shed some light on this issue using official data. Relying on police data from a 1945 Philadelphia cohort, Wolfgang, Figlio, and Sellin (1972) found that race and socioeconomic status were related to the frequency and seriousness of offenses. These findings were confirmed using the 1958 Philadelphia cohort. However, more data are needed to fully understand the relationship between race and chronic offending. Researchers and criminologists have long been aware of racial and ethnic differences in serious juvenile offending. Interpreting these disparities, however, is another matter; no one theory has adequately addressed the reasons for them. Criminologists have not paid enough attention to the extent to which socioeconomic disparity accounts for differences in rates of violence, even though they have tended to attribute high rates of crime to economic disadvantages. These omissions are in part due to reliance on individual-level data to identify those persons most likely to offend. However, individual-centered research is unlikely to improve understanding of the group differences. It does not take into consideration the larger socio-structural characteristics that distinguish groups and individuals. For example, the developmental life courses of blacks and whites in the United States are affected by their membership in historically distinct social and economic groups. Community-level research can be used to study this larger context and offer great potential in interpreting the meaning of racial and ethnic differences in offending. Reference Blumstein, A. 1995. Youth violence, guns, and the illicit-drug industry. Journal of Criminal Law and Criminology 86(1):10-36. Howell, J.C. 1997. Youth gang homicides, drug trafficking, and program interventions. In Juvenile Justice and Youth Violence, edited by J.C. Howell. Thousand Oaks, CA: Sage Publications, Inc., pp. 115-132. Federal Bureau of Investigation. 1999. Crime in the United States 1998. Uniform Crime Reports. Washington, DC: U.S. Department of Justice, Federal Bureau of Investigation. Snyder, H.N. 1999. Juvenile Arrests 1998. Bulletin. Washington, DC: U.S. Department of Justice, Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention. Snyder, H.N., and Sickmund, M. 1995. Juvenile Offenders and Victims: A National Report. Washington, DC: U.S. Department of Justice, Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention. Snyder, H.N., and Sickmund, M. 1999. Juvenile Offenders and Victims: 1999 National Report. Washington, DC: U.S. Department of Justice, Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention. Wolfgang, M.E., Figlio, R.M., and Sellin, T. 1972. Delinquency in a Birth Cohort. Chicago, IL: University of Chicago Press.
Friday, January 3, 2020
Absolute Error or Absolute Uncertainty Definition
Absolute error or absolute uncertainty is the uncertainty in a measurement, which is expressed using the relevant units. Also, absolute error may be used to express the inaccuracy in a measurement. Absolute error may be called approximation error. Absolute error is the difference between a measurement and a true value: E |x0 - x| Where E is absolute error, x0 is the measured value and x is the true or actual value Why Is There Error? Error is not a mistake. It simply reflects the limitations of measurement instruments. For example, if you use a ruler to measure a length, each tic on the ruler has a width. If a distance falls between marks on the ruler, you need to estimate whether the distance is closer to one mark than the other and by how much. This is error. The same measurement may be taken multiple times to gauge the range of the error. Absolute Error Example If a measurement is recorded to be 1.12 and the true value is known to be 1.00 then the absolute error is 1.12 - 1.00 0.12. If the mass of an object is measured three times with values recorded to be 1.00 g, 0.95 g, and 1.05 g, then the absolute error could be expressed as /- 0.05 g.
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