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 Biochemistry is applied to medicine, dentistry, and veterinary medicine. In food science, biochemists research ways to develop abundant and inexpensive sources of nutritious foods, determine the chemical composition of foods, develop methods to extract nutrients from waste products, or invent ways to prolong the shelf life food products. In agriculture, biochemists study the interaction of herbicides with plants. They examine the structure-activity relationships of compounds, determine their ability to inhibit growth, and evaluate the toxicological effects on surrounding life. Other industries offer unlimited opportunities for biochemists in the areas of basic research, quality assurance, pollution monitoring & control, regulatory affairs and the application of technology Colleges and universities employ the majority of biochemists as teachers or researchers in schools sciences, medicine, engineering, pharmacy, dentistry, veterinary medicine, and agriculture. Industries that produce pharmaceuticals, agricultural chemicals, foods, feeds, and consumer products also employ biochemists in research as well as in areas outside the lab such as marketing, management, science information, technical writing, and editing. Biotechnology companies employ biochemists in research quality control, clinical research, manufacturing, and information systems with applications to the environment, energy, human health care, agriculture, and animal health. Some biochemists work in hospitals.Companies Which recruit Students: Biocon  National Dairy Research Institute  Dr. Reddy’s Laboratory  Strand Life Sciences  Ranbaxy  Bangalore Genei  Many Pharmaceutical Company International companies:  Sigma-Aldrich  Fisher Scientific  Qiagen  New England Biolabs  Bio-Rad Laboratories  Millipore  BioGenex India Life Sciences  Genotypic Technologies Pvt. Ltd.  Magene Life Sciences  Transgene Biotek Ltd.  Monsanto India Ltd.  Syngene International Ltd.  Orchid Pharmaceuticals  Sun Pharmaceutical Industries Ltd  Various universities and degree colleges for teaching and research  Multinational seed and chemical: Monsanto, Dow chemicals, Bayer, Syngenta, Mahyco, Gardha, etc.  Kribhco, NABARD, NFL, Agriculture insurance etc. Government organisationsYou can do M. Phil or Phd if you are interested in research or lectureship. You should have atleast 55% in Masters.In some university you be required to undergo a written test and/or interview. For some universities it is a must to clear the UGC - NET exam to become a research fellow in India. So select the university where you would like to pursue and check the requirements for the same.Phd is offered in two modes:Part Time and Full TimePart Time: A candidate has to attend classes on weekends.Full Time: A candidate has to attend regular classes(5–6 days a week).So, you can decide which mode would you prefer.for doing Phd, you must have master degree and you should have atleast 55% in Masters. And you already have completed masters. you need to qualify entrance exam such as, NET/SLET/GATE/CSIR and interviewetcIn some university you be required to undergo their separate written test and interview. you can do it from:Delhi University,University of Hydrabad,Jadavpur University,Jawaharlal Nehru University,Lovely Professional University,Jamia Mililia Islamia University,Banaras Hindu University,Aligarh Muslim University,University of Mumbai, Christ university, MS Ramaiah University,The Maharaj Sayaji Rao University, Anna University, Indira Gandhi National Open University (New Delhi)

Why is the normal distribution important?Many (but not all!) variables in fields such as psychology tend to have normally distributed scores.The fairly widespread occurrence of normally distributed scores may be due to random processes that tend to generate normal distributions.The sampling distribution for statistics such as M (sample mean) tend to be normally distributed (even when the distribution of individual X scores is not perfectly normal). This is based on the Central Limit Theorem, which was developed using imaginary normally distributed scores. Monte Carlo simulations show that even when assumptions about distribution shape for individual scores are violated, sampling distributions may still be fairly normal (Sawilowsky & Blair, 1992.) However, there are limits to this robustness against violations of assumptions.The shape of the sampling distribution is normal for statistics such as M when population standard deviation sigma is known and other assumptions are met. The shape of the distribution is modified normal; i.e., a t distribution, when population standard deviation sigma is not known and sample standard deviation SD is used to estimate it. The sampling distribution is the basis for construction of statistical significance tests and Confidence Intervals (in other words, the statistical techniques taught in most introductory courses and widely relied on in research reports).However, keep in mind that not all X variables have normally distributed scores; and that not all sample statistics have normally shaped sampling distributions. In these situations, different analyses not taught in introductory courses are often needed, and tools such as bootstrapping are sometimes used.The first part of the answer describes a process that can generate a normal distribution.The second part provides examples of a few common distribution shapes that are non-normal.PART 1: CERTAIN KINDS OF CHANCE OR RANDOMNESS CAN GENERATE NORMAL DISTRIBUTIONSHow can normally distributed scores arise in gambling situations?The normal or Gaussian distribution was developed partly as a way to think about outcomes in gambling. Gamblers have always been interested in probability. For example, what is the probability of getting ‘heads’ when a fair coin is tossed? If the coin is fair, the probability of heads = 50% or .50; the probability of tails also is 50% or .50 Gamblers also wanted to know things such as: What is the probability of getting 6 heads in a row?Each coin toss is independent of other coin tosses, and that means we can work out the probability of a series of outcomes by multiplication. Given that the proportion of heads = .50 for each toss, the probability of getting heads 6 times in a row is .5 * .5 * .5 * 5 * .5 * .5 = (.5)6 = .015625 or about 1.6%. In other words, this is a very unlikely outcome. This outcome can happen only one way (heads on all 6 tosses).Other outcomes can happen several ways, for example we can obtain 3 head (H) and 3 tails (T) in many sequences such as: HHHTTT, HTHTHT, THHTHH, and so forth. Because there are so many ways to obtain 3 heads, that outcome has a higher probability than HHHHHH. (Formally, this situation can be described in terms such as Bernoulli trials and binomial distribution; some introductory statistics books include an entire chapter about the binomial distribution).A good way to visualize coin toss outcomes is through simulation. To visualize what happens, look at this on-line animation demonstration using diagrams of a device called a quincunx or Galton board:https://www.mathsisfun.com/data/quincunx.html. Some science museums have physical models of these. A screen shot appears below. I encourage you to go to this link and run the simulation yourself.The quincunx is set up the following way. Tiny balls are dropped into it from the top (perhaps through a funnel, not shown in the Figure 5.21 diagram). At the bottom, there is a set of bins, in this case, 7 bins. There are pegs represented by circles; From the top down, there is one peg in the first row, 2 pegs in the second row, down to 6 pegs in the last row.You can see a physical model that does the same thing as this computer model in this Youtube video: https://www.youtube.com/watch?v=6YDHBFVIvIs .Imagine this situation from the perspective of the tiny ball (if you can). The ball drops straight down and hits the first peg. It can be deflected either toward the right side or the left side of the page in your book. By chance, the ball should go right 50% of the time and left 50% of the time.Now the ball hits a second peg. Again, it can go right or left. By the time the ball reaches the bottom and falls into one of the bins, it has hit 6 pegs. Each peg represents one random event (for example, one coin toss) that has two possible outcomes; right or left; heads or tails.Now let’s imagine that this represents the outcomes of a series of six coin tosses. Balls that end up in the far-right hand bin (and there will not be many of them) have gone right 6 times, and if going right represents “Heads”, these balls represent getting 6 heads in a row. The bin on the far left of the page represents 0 heads (or 6 tails). The bins in between the two extreme bins represent outcomes that include 3 heads, 3 tails; 4 heads, 2 tails; and so forth.If we put one ball into the top and let it drop through, that represents the outcome for one gambler doing six coin tosses. That ball will end up in just one of the 7 bins.We see something more interesting if we line up 201 gamblers and have each one put a ball into the top. We are now doing a simulation to find out: among the 201 gamblers, what percent of them get 6 heads? What percent get 3 heads and 3 tails? And so forth.The outcome of this simulation appears at the bottom of Figure 5.21. Most gamblers ended up in the bins near the middle; they got outcomes such as 3 heads / 3 tails or 4 heads/ 2 tails. Small numbers of gamblers end up in the bin at the far left (0 heads) and the far right (6 heads). Among these 201 gamblers, six heads was a rare outcome; it has a low probability, on the order of 1.5%.The outcome is represented by the number of balls in each of the 7 bins (see bottom of Figure 5.21. This is analogous to a histogram where X = number of heads and Y = number of gamblers. What does this distribution look like? If you said ‘a normal distribution’, you are correct. You can run this simulation yourself and put in numbers larger than 201. The larger the number of tiny balls (or imaginary gamblers) you run through the simulation, the more closely the frequency distribution of outcomes will approximate normal. Also, the distribution will look more nearly normal if there are 10 or 100 rows of pegs instead of only 6 rows.If you are a gambler, the moral of the story is, don’t bet on getting 6 heads in a row. It’s very unlikely to happen.Why Real World Variables Such as Height May Have Approximately Normally Distributed OutcomesSo what does this have to do with height? Let’s suppose that there are 6 genes that influence height. Let’s also suppose that each gene has two different forms (alleles) and that one form causes a person to be taller, the other causes a person to be shorter. Let’s suppose that at conception, the probability of getting the “tall” versus “short” allele is 50%/ 50%. We can make the analogy that getting the tall allele for each gene is like getting Heads on each coin toss. Very few people will get all 6 tall alleles and end up being very tall, or 0 tall alleles and end up being very short; most people will receive some of both alleles, and end up being moderate in height, with a height distribution that resembles the frequencies for coin toss sequences in Figure 5.21.Of course, this is not an accurate description of factors that influence height or stature. There may be more than 400 genes that influence height (Boston Children’s Hospital, Oct. 5, 2014). Some genes may have more than two forms or alleles. The selection of alleles an individual child may inherit depends on the alleles their parents have. In addition, environmental factors such as malnutrition affect adult stature. (Look up “stature” if you want to know additional interesting things about factors that affect height.) However, human heights are fairly normally distributed, even if the process is far more complicated than illustrated by the quincunx or Galton board example.Formally, a mathematician would say: in the limit, as N (of balls or gamblers) increases, this distribution of outcomes converges to a normal distribution. Mathematicians (including DeMoivre in 1733, LaPlace in 1783, Adrain in 1808, and Gauss in 1809) worked out the mathematical details (that is, the equations in section 5.10 and the properties of the normal distribution). Quetelet (1835) was the first to notice that this distribution shape approximately matches the outcomes for some measurements of human characteristics such as height.This, as Wigner (1960) pointed out, is a dramatic example of “the unreasonable effectiveness of mathematics”. The normal or Gaussian distribution was developed first (in response to interest in gambling). The observation that heights, and scores on many other variables, have approximately this distribution shape, came later. (Keep in mind that not all variables have normal distributions, and in fact, empirical distributions in real data are rarely very close to normal, Micceri, 1989).PART 2: MANY VARIABLES ARE NOT NORMALLY DISTRIBUTEDAn example of a common type of variable that is NOT normally distributed: scores for a behavior frequency, such as, how many times have you used marijuana in the past month? Questions like this often have many responses of “0” and a scattering of other responses, as in this figure adapted from Wagner et al. (2015).When you want to predict this type of variable in multiple regression, special types of analysis should be used such as Poisson or negative binomial regression (zero inflated or not zero inflated). Some of these models can predict use vs non use, and then, varying amounts of use among users. It is possible that predict use vs non use differ from variables that predict amount of use.Many other distribution shapes are possible; here are a few additional examples.When you want to predict scores for a quantitative variable that is non-normally distributed, methods different from linear regression are often needed. For example, to predict scores for the bimodal distribution of degree of agreement ratings in the figure above, you could treat this as a categorical variable with 5 response options and use a form of logistic regression.This material is based on a draft for 3rd edition revisions to: Warner, R. (2012) Applied Statistics: From Bivariate Through Multivariate Techniques. Thousand Oaks: Sage.REFERENCESBoston Children’s Hospital (Oct. 5, 2014). Number of genes linked to height revealed by study. Retrieved from Number of genes linked to height revealed by studyMaths Zone at Cambridge Science Festival ( 2013). Demonstration of the Galton Board from the Large Maths Outreach and Careers Kit developed by the Institute of Mathematics and its Applications as part of the National HE STEM Programme. Retrieved from: https://www.youtube.com/watch?v=6YDHBFVIvIsMicceri, T. (1989). The unicorn, the normal curve, and other improbable creatures. Psychological Bulletin, 105, 156-166.Sawilowsky, S. S., & Blair, R. C. (1992). A more realistic look at the robustness and Type II error properties of the t test to departures from population normality. Psychological Bulletin, 111(2), 352-360.Wagner, B., Riggs, P., & Mikulich-Gilberton, S. (2015). The importance of distribution-choice in modeling substance use data: A comparison of negative binomial, beta binomial, and zero-inflated distributions. American Journal of Drug and Alcohol Abuse, 41(6), 489-497.Wigner, E. P. (1960). The unreasonable effectiveness of mathematics in the natural sciences. Communications on Pure and Applied Mathematics, 13, 1-14.

THC and CBD have been proven or heavily shown to assist with reducing or stopping seizures and epileptic seizures caused by genetic disorders, reducing or stopping neurodegenerative diseases such as Alzheimer’s, stroke, glutamate toxicity, MS, Parkinson’s disease and alcohol abuse, reducing peripheral neuropathic pain caused by spinal cord injury, rheumatoid arthritis, cancer pain and tumor growth, assisting with cancer cell death, the inhibition of metastasis, and has been shown to reduce anxiety, stress and PTSD symptoms. Ongoing trials also show CBD to help with substance abuse for morphine, heroin, methamphetamine and alcohol, as well as aid in symptoms caused by autism.Over the last two decades, THC and CBD have been shown to have incredible anti-seizure activity, reducing and sometimes stopping seizures entirely in both animals and humans affected by intractable seizure disorders such as Dravet syndrome and Lennox-Gastaut syndrome. Case studies and anecdotal reports also suggest that THC and CBD are an effective treatment for children with drug-resistant forms of epilepsy. Currently, the National Institute on Drug Abuse is collaborating with the National Institute on Neurological Disorders and Stroke to evaluate CBD’s (not THC’s, as it’s still a Schedule I substance) effects on epilepsy. In addition, GW Pharmaceuticals is testing Epidiolex, a purified CBD extract intended for the treatment of pediatric epilepsy.CBD has been shown to have neuroprotective properties in cell cultures of several neurodegenerative diseases, including Alzheimer’s, stroke, glutamate toxicity, multiple sclerosis (MS), Parkinson’s disease and neurodegeneration caused by alcohol abuse. Nabiximols, which contain about 50% THC and 50% CBD, is an approved drug throughout most of Europe for the treatment of spasticity associated with MS. Clinical trials for the same drug are currently underway in the US. Further trials have confirmed CBD’s use in reducing Parkinson’s disease.Clinical trials conducted throughout Europe have confirmed that Nabiximols, which contain about 50% THC and 50% CBD, reduce peripheral neuropathic pain, rheumatoid arthritis and cancer pain. In addition, Nabiximols are approved in Canada for the treatment of neuropathic pain in MS and cancer pain unresponsive to opioid therapy.There are numerous reports of CBD showing anti-tumor effects in cell cultures and animals. These studies have found reduced cell viability, increased cancer cell death, decreased tumor growth and inhibition of metastasis. Dr. Sean McAllister of the Pacific Medical Center in San Francisco has received numerous grants by the National Institute of Health to fund his research into CBD’s anticancer properties for decades. In 2007, he discovered CBD kills breast cancer cell proliferation and metastasis, and destroys malignant tumors by switching off expression of the ID-1 gene, a protein that plays a major role as a cancer cell conductor. At 2014’s annual summer conference of the International Cannabinoid Research Society, CBD was described as “the most efficacious inducer of apoptosis” in prostate cancer, among others.Additionally, THC induces apoptosis (cell death) in C6 glioma cells, an aggressive form of brain cancer. This discovery was made by Cristina Sanchez at Complutense University in Madrid back in 1998, and peer-reviewed studies in several countries later would reaffirm this discovery and add THC to the list of cannabinoids that confer a direct antitumoral effect. In 2006, a Spanish team led by Manuel Guzman injected THC directly into the tumors of nine hospitalized patients with glioblastoma who failed to respond to standard brain cancer therapies, and found that the THC treatment significantly reduced tumor cell proliferation in every subject. A year later, Harvard University scientists reported that THC slows tumor growth in lung cancer and significantly reduces the ability of cancer to spread.THC was found to actively target cancerous cells while leaving healthy cells unscathed. Chemotherapy drugs, by contrast, are highly toxic and damage the brain and body indiscriminately, in hopes of killing more cancerous cells than healthy ones. THC, among other cannabinoids, is becoming a new class of anticancer drugs.CBD has shown therapeutic efficiency regarding anxiety and stress in numerous animals, reducing both behavioral and physiological, such as heart rate, measures of stress and anxiety. CBD has been shown to reduce the anxiety in patients with social anxiety subjected to a stressful public speaking task. In a clinical trial regarding post-traumatic stress disorders, CBD improved “consolidation of extinction learning,” or in other words, forgetting of traumatic memories. CBD appears to alter the serotonin receptor 1a signalling, though more research is needed.More recently, CBD has been shown to reduce substance abuse disorders for patients addicted to morphine, heroin, methamphetamine and alcohol. The National Institute for Drug Abuse is continuing multiple clinical trials for other commonly abused substances as well, and in October of 2017 reported their first heroin addict completely clean solely through CBD treatments.In 2018, studies aimed at understanding THC and CBD’s relationship with autism finally became more common. One study examined sixty children in Israel suffering from autism. The parents, over the course of a few months, administered 20:1 CBD:THC pills, and 61% of parents felt their child’s symptoms were improved. Anxiety was reduced in 39% of the subjects, and communication between the parents and children were improved in 47% of the cases. Additional studies are being conducted, including a much larger double-blinded placebo controlled study, to ascertain any further relations between cannabinoids and autism.Karniol IG, Shirakawa I, Takahashi RN, Knobel E, Musty RE. Effects of delta9-tetrahydrocannabinol and cannabinol in man. Pharmacology. (1975)Consroe P and Wolkin A. Cannabidiol-Antiepileptic Drug Comparisons and Interactions in Experimentally Induced Seizures in Rats. J Pharmacol Exp Ther. (1977)RL Hawks et al. The Constituents of Cannabis and the Disposition and Metabolism of Cannabinoids. NIDA Res Monogr. (1982)Guimaraes et al. Antianxiety Effect of Cannabidiol in Elevated Plus-Maze. Psychopharmacology. (1990)Herkenham et al. 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