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## Effect of growth hormone treatment on adult height of children with idiopathic short stature. Genentech Collaborative Group. (1/13525)

BACKGROUND: Short-term administration of growth hormone to children with idiopathic short stature results in increases in growth rate and standard-deviation scores for height. However, the effect of long-term growth hormone therapy on adult height in these children is unknown. METHODS: We studied 121 children with idiopathic short stature, all of whom had an initial height below the third percentile, low growth rates, and maximal stimulated serum concentrations of growth hormone of at least 10 microg per liter. The children were treated with growth hormone (0.3 mg per kilogram of body weight per week) for 2 to 10 years. Eighty of these children have reached adult height, with a bone age of at least 16 years in the boys and at least 14 years in the girls, and pubertal stage 4 or 5. The difference between the predicted adult height before treatment and achieved adult height was compared with the corresponding difference in three untreated normal or short-statured control groups. RESULTS: In the 80 children who have reached adult height, growth hormone treatment increased the mean standard-deviation score for height (number of standard deviations from the mean height for chronologic age) from -2.7 to -1.4. The mean (+/-SD) difference between predicted adult height before treatment and achieved adult height was +5.0+/-5.1 cm for boys and +5.9+/-5.2 cm for girls. The difference between predicted and achieved adult height among treated boys was 9.2 cm greater than the corresponding difference among untreated boys with initial standard-deviation scores of less than -2, and the difference among treated girls was 5.7 cm greater than the difference among untreated girls. CONCLUSION: Long-term administration of growth hormone to children with idiopathic short stature can increase adult height to a level above the predicted adult height and above the adult height of untreated historical control children. (+info)## Capture-recapture models including covariate effects. (2/13525)

Capture-recapture methods are used to estimate the incidence of a disease, using a multiple-source registry. Usually, log-linear methods are used to estimate population size, assuming that not all sources of notification are dependent. Where there are categorical covariates, a stratified analysis can be performed. The multinomial logit model has occasionally been used. In this paper, the authors compare log-linear and logit models with and without covariates, and use simulated data to compare estimates from different models. The crude estimate of population size is biased when the sources are not independent. Analyses adjusting for covariates produce less biased estimates. In the absence of covariates, or where all covariates are categorical, the log-linear model and the logit model are equivalent. The log-linear model cannot include continuous variables. To minimize potential bias in estimating incidence, covariates should be included in the design and analysis of multiple-source disease registries. (+info)## The impact of a multidisciplinary approach on caring for ventilator-dependent patients. (3/13525)

OBJECTIVE: To determine the clinical and financial outcomes of a highly structured multidisciplinary care model for patients in an intensive care unit (ICU) who require prolonged mechanical ventilation. The structured model outcomes (protocol group) are compared with the preprotocol outcomes. DESIGN: Descriptive study with financial analysis. SETTING: A twelve-bed medical-surgical ICU in a non-teaching tertiary referral center in Ogden, Utah. STUDY PARTICIPANTS: During a 54 month period, 469 consecutive intensive care patients requiring mechanical ventilation for longer than 72 hours who did not meet exclusion criteria were studied. INTERVENTIONS: A multidisciplinary team was formed to coordinate the care of ventilator-dependent patients. Care was integrated by daily collaborative bedside rounds, monthly meetings, and implementation of numerous guidelines and protocols. Patients were followed from the time of ICU admission until the day of hospital discharge. MAIN OUTCOME MEASURES: Patients were assigned APACHE II scores on admission to the ICU, and were divided into eight diagnostic categories. ICU length of stay, hospital length of stay, costs, charges, reimbursement, and in-hospital mortality were measured. RESULTS: Mortality in the preprotocol and protocol group, after adjustment for APACHE II scores, remained statistically unchanged (21-23%). After we implemented the new care model, we demonstrated significant decreases in the mean survivor's ICU length of stay (19.8 days to 14.7 days, P= 0.001), hospital length of stay (34.6 days to 25.9 days, P=0.001), charges (US$102500 to US$78500, P=0.001), and costs (US$71900 to US$58000, P=0.001). CONCLUSIONS: Implementation of a structured multidisciplinary care model to care for a heterogeneous population of ventilator-dependent ICU patients was associated with significant reductions in ICU and hospital lengths of stay, charges, and costs. Mortality rates were unaffected. (+info)## Nonlinear tension summation of different combinations of motor units in the anesthetized cat peroneus longus muscle. (4/13525)

The purpose of this study was to examine the linearity of summation of the forces produced by the stimulation of different combinations of type identified motor units (MUs) in the cat peroneus longus muscle (PL) under isometric conditions. The muscle was fixed at its twitch optimal length, and the tension produced by the single MU was recorded during 24- and 72-Hz stimulation. The summation analysis was first carried out for MUs belonging to the same functional group, and then different combinations of fast fatigable (FF) MUs were added to the nonfatigable slow (S) and fatigue resistant (FR) group. The tension resulting from the combined stimulation of increasing numbers of MUs (measured tension) was evaluated and compared with the linearly predicted value, calculated by adding algebraically the tension produced by the individual MUs assembled in the combination (calculated tension). Tension summation displayed deviations from linearity. S and FR MUs mainly showed marked more than linear summation; FF MUs yielded either more or less than linear summation; and, when the FF units were recruited after the S and FR MUs, less than linear summation always occurred. The magnitude of the nonlinear summation appeared stimulus frequency dependent for the fatigable FF and FI group. The relationship between measured tension and calculated tension for each MU combination was examined, and linear regression lines were fitted to each set of data. The high correlation coefficients and the different slope values for the different MU-type combinations suggested that the nonlinear summation was MU-type specific. The mechanisms of nonlinear summations are discussed by considering the consequences of internal shortening and thus the mechanical interactions among MUs and shifts in muscle fiber length to a more or less advantageous portion of single MU length-tension curves. (+info)## Short-latency vergence eye movements induced by radial optic flow in humans: dependence on ambient vergence level. (5/13525)

Radial patterns of optic flow, such as those experienced by moving observers who look in the direction of heading, evoke vergence eye movements at short latency. We have investigated the dependence of these responses on the ambient vergence level. Human subjects faced a large tangent screen onto which two identical random-dot patterns were back-projected. A system of crossed polarizers ensured that each eye saw only one of the patterns, with mirror galvanometers to control the horizontal positions of the images and hence the vergence angle between the two eyes. After converging the subject's eyes at one of several distances ranging from 16.7 cm to infinity, both patterns were replaced with new ones (using a system of shutters and two additional projectors) so as to simulate the radial flow associated with a sudden 4% change in viewing distance with the focus of expansion/contraction imaged in or very near both foveas. Radial-flow steps induced transient vergence at latencies of 80-100 ms, expansions causing increases in convergence and contractions the converse. Based on the change in vergence 90-140 ms after the onset of the steps, responses were proportional to the preexisting vergence angle (and hence would be expected to be inversely proportional to viewing distance under normal conditions). We suggest that this property assists the observer who wants to fixate ahead while passing through a visually cluttered area (e.g., a forest) and so wants to avoid making vergence responses to the optic flow created by the nearby objects in the periphery. (+info)## Survival after breast cancer in Ashkenazi Jewish BRCA1 and BRCA2 mutation carriers. (6/13525)

BACKGROUND: Studies of survival following breast and ovarian cancers in BRCA1 and/or BRCA2 mutation carriers have yielded conflicting results. We undertook an analysis of a community-based study of Ashkenazi Jews to investigate the effect of three founder mutations in BRCA1 and BRCA2 on survival among patients with breast or ovarian cancer. METHODS: We collected blood samples and questionnaire data from 5318 Ashkenazi Jewish volunteers. The blood samples were tested for 185delAG (two nucleotide deletion) and 5382insC (single nucleotide insertion) mutations in BRCA1 and the 6174delT (single nucleotide deletion) mutation in BRCA2. To estimate survival differences in the affected relatives according to their BRCA1 and/or BRCA2 mutation carrier status, we devised and applied a novel extension of the kin-cohort method. RESULTS: Fifty mutation carriers reported that 58 of their first-degree relatives had been diagnosed with breast cancer and 10 with ovarian cancer; 907 noncarriers reported 979 first-degree relatives with breast cancer and 116 with ovarian cancer. Kaplan-Meier estimates of median survival after breast cancer were 16 years (95% confidence interval [CI] = 11-40) in the relatives of carriers and 18 years (95% CI = 15-22) in the relatives of noncarriers, a difference that was not statistically significant (two-sided P = .87). There was also no difference in survival times among the 126 first-degree relatives with ovarian cancer. We found no survival difference between patients with breast or ovarian cancer who were inferred carriers of BRCA1 and/or BRCA2 mutations and noncarriers. CONCLUSIONS: Carriers of BRCA1 and BRCA2 mutations appeared to have neither better nor worse survival prognosis. (+info)## Long-term results of GH therapy in GH-deficient children treated before 1 year of age. (7/13525)

OBJECTIVES: To evaluate the long-term effects of GH therapy in early diagnosed GH-deficient patients treated before 1 year of age. STUDY DESIGN: We studied all 59 patients (33 males) recorded by Association France-Hypophyse and treated with GH (0.50+/-0.15 IU/kg (S.D.) per week) before 1 year of age. Clinical presentation and growth parameters under GH treatment were analyzed. RESULTS: Neonatal manifestations of hypopituitarism were frequent: hypoglycemia (n=50), jaundice (n=25) and micropenis (n=17/33). Although birth length was moderately reduced (-0.9+/-1.4), growth retardation at diagnosis (5.8+/-3.8 months) was severe (-3.5+/-1.9 standard deviation scores (SDS)). Fifty patients (85%) had thyrotropin and/or corticotropin deficiency. After a mean duration of GH therapy of 8.0+/-3.6 years, change in height SDS was +3.11+/-2.06 S.D., exceeding 4 SDS in 19 patients. Only 9 patients (15%) did not reach a height of -2 S.D. for chronological age and 20 patients (34%) exceeded their target height. Pretreatment height SDS was independently associated with total catch-up growth. CONCLUSION: Conventional doses of GH allow normalization of height in patients with early GH deficiency and treatment. (+info)## Changes in body composition and leptin levels during growth hormone (GH) treatment in short children with various GH secretory capacities. (8/13525)

OBJECTIVE: The aim of this study was to follow changes in body composition, estimated by dual-energy X-ray absorptiometry (DXA), in relation to changes in leptin during the first year of GH therapy in order to test the hypothesis that leptin is a metabolic signal involved in the regulation of GH secretion in children. DESIGN AND METHODS: In total, 33 prepubertal children were investigated. Their mean (S.D.) chronological age at the start of GH treatment was 11.5 (1.6) years, and their mean height was -2.33 (0.38) S.D. scores (SDS). GH was administered subcutaneously at a daily dose of 0.1 (n=26) or 0.2 (n=7) IU/kg body weight. Ten children were in the Swedish National Registry for children with GH deficiency, and twenty-three children were involved in trials of GH treatment for idiopathic short stature. Spontaneous 24-h GH secretion was studied in 32 of the children. In the 24-h GH profiles, the maximum level of GH was determined and the secretion rate estimated by deconvolution analysis (GHt). Serum leptin levels were measured at the start of GH treatment and after 10 and 30 days and 3, 6 and 12 months of treatment. Body composition measurements, by DXA, were performed at baseline and 12 months after the onset of GH treatment. RESULTS: After 12 months of GH treatment, mean height increased from -2.33 to -1.73 SDS and total body fat decreased significantly by 3.0 (3.3)%. Serum leptin levels were decreased significantly at all time points studied compared with baseline. There was a significant correlation between the change in total body fat and the change in serum leptin levels during the 12 months of GH treatment, whereas the leptin concentration per unit fat mass did not change. In a multiple stepwise linear regression analysis with 12 month change in leptin levels as the dependent variable, the percentage change in fat over 12 months, the baseline fat mass (%) of body mass and GHt accounted for 24.0%, 11.5% and 12.2% of the variability respectively. CONCLUSIONS: There are significant correlations between changes in leptin and fat and endogenous GH secretion in short children with various GH secretory capacities. Leptin may be the messenger by which the adipose tissue affects hypothalamic regulation of GH secretion. (+info)Parasitic diseases in animals refer to infections caused by parasites, which are organisms that live on or inside a host organism and obtain nutrients at the host's expense. These parasites can be protozoa, helminths (worms), or arthropods such as ticks and fleas. Parasitic diseases in animals can have a significant impact on animal health and welfare, as well as on human health if the parasites are zoonotic (able to be transmitted from animals to humans). Examples of parasitic diseases in animals include: - Toxoplasmosis, caused by the protozoan Toxoplasma gondii, which can infect a wide range of animals including cats, dogs, livestock, and wildlife. - Roundworm infections, caused by various species of helminths such as Toxocara canis and Toxascaris leonina, which can infect dogs and cats and can be transmitted to humans. - Tapeworm infections, caused by various species of tapeworms such as Dipylidium caninum and Taenia solium, which can infect dogs, cats, and humans. - Flea-borne diseases, such as plague and typhus, which are caused by bacteria transmitted by fleas that feed on infected animals. Treatment of parasitic diseases in animals typically involves the use of antiparasitic drugs, although in some cases, prevention through vaccination or other measures may be more effective. It is important for veterinarians and animal owners to be aware of the risks of parasitic diseases in animals and to take appropriate measures to prevent and control them.

Hip dysplasia is a common orthopedic condition that affects dogs, particularly large and giant breed dogs. It is a developmental disorder that occurs when the hip joint does not form properly, leading to a malformation of the hip socket and the head of the femur (thigh bone). In dogs with hip dysplasia, the hip joint is unstable and can cause pain, lameness, and difficulty in movement. The severity of the condition can vary, ranging from mild to severe, and can be influenced by factors such as genetics, nutrition, and exercise. Diagnosis of hip dysplasia in dogs typically involves a physical examination, radiographs (X-rays) of the hip joint, and sometimes blood tests to rule out other conditions that may cause similar symptoms. Treatment options for hip dysplasia in dogs include medication to manage pain and inflammation, physical therapy, and in severe cases, surgery to correct the malformation of the hip joint.

**Obesity**is a medical condition characterized by excessive accumulation of body fat that increases the risk of various health problems.

Obesity is a medical condition characterized by an excessive accumulation of body fat, which increases the risk of various health problems. The World Health Organization (WHO) defines obesity as a body mass index (BMI) of 30 or higher, where BMI is calculated as a person's weight in kilograms divided by their height in meters squared. Obesity is a complex condition that results from a combination of genetic, environmental, and behavioral factors. It can lead to a range of health problems, including type 2 diabetes, heart disease, stroke, certain types of cancer, and respiratory problems. In the medical field, obesity is often treated through a combination of lifestyle changes, such as diet and exercise, and medical interventions, such as medications or bariatric surgery. The goal of treatment is to help individuals achieve and maintain a healthy weight, reduce their risk of health problems, and improve their overall quality of life.

**Body weight**refers to the total mass of an individual's body, including all organs, tissues, and fluids.

In the medical field, body weight refers to the total mass of an individual's body, typically measured in kilograms (kg) or pounds (lbs). It is an important indicator of overall health and can be used to assess a person's risk for certain health conditions, such as obesity, diabetes, and heart disease. Body weight is calculated by measuring the amount of mass that a person's body contains, which includes all of the organs, tissues, bones, and fluids. It is typically measured using a scale or other weighing device, and can be influenced by factors such as age, gender, genetics, and lifestyle. Body weight can be further categorized into different types, such as body mass index (BMI), which takes into account both a person's weight and height, and waist circumference, which measures the size of a person's waist. These measures can provide additional information about a person's overall health and risk for certain conditions.

**Birth weight**is the weight of a newborn baby at the time of delivery.

In the medical field, birth weight refers to the weight of a newborn baby at the time of delivery. It is typically measured in grams or ounces and is an important indicator of a baby's health and development. Birth weight is influenced by a variety of factors, including the mother's health, nutrition, and lifestyle, as well as the baby's genetics and gestational age. Babies who are born with a low birth weight (less than 2,500 grams or 5.5 pounds) are considered premature or small for gestational age, which can increase their risk of health problems such as respiratory distress syndrome, jaundice, and infections. On the other hand, babies who are born with a high birth weight (greater than 4,000 grams or 8.8 pounds) may be at risk for complications such as shoulder dystocia, which can lead to nerve damage or other injuries during delivery. Overall, birth weight is an important measure of a baby's health and development, and healthcare providers closely monitor it during pregnancy and delivery to ensure the best possible outcomes for both the mother and baby.

**Overweight**in the medical field refers to having a body mass index (BMI) of 25 or greater.

In the medical field, overweight is a condition where a person's body weight is greater than what is considered healthy for their height and body composition. The term "overweight" is often used interchangeably with "obesity," but they are not the same thing. The body mass index (BMI) is a commonly used tool to determine whether a person is overweight or obese. BMI is calculated by dividing a person's weight in kilograms by their height in meters squared. A BMI of 25 to 29.9 is considered overweight, while a BMI of 30 or higher is considered obese. Being overweight can increase the risk of developing a variety of health problems, including heart disease, stroke, type 2 diabetes, certain types of cancer, and osteoarthritis. Therefore, it is important to maintain a healthy weight through a balanced diet and regular physical activity.

**Cognition disorders**refer to a group of conditions that affect an individual's ability to think, reason, remember, and learn.

Cognition disorders refer to a group of conditions that affect an individual's ability to think, reason, remember, and learn. These disorders can be caused by a variety of factors, including brain injury, neurological disorders, genetic factors, and aging. Cognition disorders can manifest in different ways, depending on the specific area of the brain that is affected. For example, a person with a memory disorder may have difficulty remembering important information, while someone with a language disorder may have trouble expressing themselves or understanding what others are saying. Some common types of cognition disorders include: 1. Alzheimer's disease: A progressive neurological disorder that affects memory, thinking, and behavior. 2. Dementia: A general term used to describe a decline in cognitive function that is severe enough to interfere with daily life. 3. Delirium: A sudden onset of confusion and disorientation that can be caused by a variety of factors, including illness, medication side effects, or dehydration. 4. Aphasia: A language disorder that affects a person's ability to speak, understand, or use language. 5. Attention deficit hyperactivity disorder (ADHD): A neurodevelopmental disorder that affects a person's ability to focus, pay attention, and control impulses. 6. Learning disorders: A group of conditions that affect a person's ability to acquire and use knowledge and skills. Cognition disorders can have a significant impact on a person's quality of life, and treatment options may include medication, therapy, and lifestyle changes. Early diagnosis and intervention are important for managing these conditions and improving outcomes.

**Particulate matter**refers to tiny particles of matter, such as dust, soot, and smoke, that can be inhaled into the lungs and cause a range of health problems.

In the medical field, particulate matter (PM) refers to tiny solid or liquid particles that are suspended in the air. These particles can be inhaled into the lungs and can cause a range of health problems, including respiratory and cardiovascular diseases. PM can be classified based on their size, with smaller particles being more harmful to health. PM2.5 refers to particles with a diameter of 2.5 micrometers or less, while PM10 refers to particles with a diameter of 10 micrometers or less. These particles can penetrate deep into the lungs and even enter the bloodstream, causing inflammation and oxidative stress. Exposure to high levels of PM can increase the risk of developing conditions such as asthma, chronic obstructive pulmonary disease (COPD), lung cancer, and heart disease. It can also exacerbate existing health conditions and increase the risk of premature death. In summary, particulate matter is a type of air pollution that can have serious health consequences when inhaled. It is an important consideration in public health and environmental policy, and efforts are being made to reduce its levels in the air.

**Prenatal Exposure Delayed Effects**refers to the long-term health consequences that result from exposure to environmental or other factors during fetal development, which may not manifest until later in life.

Prenatal Exposure Delayed Effects (PEDs) refer to the long-term health effects that can occur in an individual as a result of exposure to environmental or genetic factors during pregnancy. PEDs can manifest in a variety of ways, including physical, behavioral, and cognitive impairments, and can occur even if the exposure occurred many years before the individual's birth. PEDs can result from exposure to a wide range of substances, including drugs, alcohol, tobacco, pollutants, and infections. These exposures can affect the developing fetus in various ways, including disrupting normal growth and development, altering gene expression, and causing damage to organs and systems. PEDs can also result from genetic factors, such as inherited disorders or mutations. These genetic factors can increase the risk of developing certain health conditions, such as autism, ADHD, and learning disabilities, even if the individual was not exposed to any environmental factors during pregnancy. Overall, PEDs highlight the importance of taking steps to protect pregnant women and their developing fetuses from exposure to harmful substances and environmental factors, as well as the need for ongoing monitoring and support for individuals who may be at risk for PEDs.

**Oxygen**is a gas that is essential for the survival of most living organisms and is commonly used in medical treatment to support breathing and enhance oxygen delivery to the body's tissues.

In the medical field, oxygen is a gas that is essential for the survival of most living organisms. It is used to treat a variety of medical conditions, including respiratory disorders, heart disease, and anemia. Oxygen is typically administered through a mask, nasal cannula, or oxygen tank, and is used to increase the amount of oxygen in the bloodstream. This can help to improve oxygenation of the body's tissues and organs, which is important for maintaining normal bodily functions. In medical settings, oxygen is often used to treat patients who are experiencing difficulty breathing due to conditions such as pneumonia, chronic obstructive pulmonary disease (COPD), or asthma. It may also be used to treat patients who have suffered from a heart attack or stroke, as well as those who are recovering from surgery or other medical procedures. Overall, oxygen is a critical component of modern medical treatment, and is used in a wide range of clinical settings to help patients recover from illness and maintain their health.

**Genetic predisposition to disease**refers to an individual's inherited genetic makeup that increases their risk of developing a particular disease or health condition.

Genetic predisposition to disease refers to the tendency of an individual to develop a particular disease or condition due to their genetic makeup. It means that certain genes or combinations of genes increase the risk of developing a particular disease or condition. Genetic predisposition to disease is not the same as having the disease itself. It simply means that an individual has a higher likelihood of developing the disease compared to someone without the same genetic predisposition. Genetic predisposition to disease can be inherited from parents or can occur due to spontaneous mutations in genes. Some examples of genetic predisposition to disease include hereditary breast and ovarian cancer, Huntington's disease, cystic fibrosis, and sickle cell anemia. Understanding genetic predisposition to disease is important in medical practice because it can help identify individuals who are at high risk of developing a particular disease and allow for early intervention and prevention strategies to be implemented.

**Cardiovascular diseases**refer to a group of conditions that affect the heart and blood vessels, including coronary artery disease, heart failure, and stroke.

Cardiovascular diseases (CVDs) are a group of conditions that affect the heart and blood vessels. They are the leading cause of death worldwide, accounting for more than 17 million deaths each year. CVDs include conditions such as coronary artery disease (CAD), heart failure, arrhythmias, valvular heart disease, peripheral artery disease (PAD), and stroke. These conditions can be caused by a variety of factors, including high blood pressure, high cholesterol, smoking, diabetes, obesity, and a family history of CVDs. Treatment for CVDs may include lifestyle changes, medications, and in some cases, surgery.

**HIV Infections**are caused by the Human Immunodeficiency Virus and lead to a weakened immune system, making individuals more susceptible to opportunistic infections and certain cancers.

HIV (Human Immunodeficiency Virus) infections refer to the presence of the HIV virus in the body. HIV is a retrovirus that attacks and weakens the immune system, making individuals more susceptible to infections and diseases. HIV is transmitted through contact with infected bodily fluids, such as blood, semen, vaginal fluids, and breast milk. The most common modes of transmission include unprotected sexual contact, sharing needles or syringes, and from mother to child during pregnancy, childbirth, or breastfeeding. HIV infections can be diagnosed through blood tests that detect the presence of the virus or antibodies produced in response to the virus. Once diagnosed, HIV can be managed with antiretroviral therapy (ART), which helps to suppress the virus and prevent the progression of the disease to AIDS (Acquired Immune Deficiency Syndrome). It is important to note that HIV is not the same as AIDS. HIV is the virus that causes AIDS, but not everyone with HIV will develop AIDS. With proper treatment and management, individuals with HIV can live long and healthy lives.

**Arsenic**is a toxic heavy metal that can cause a range of health problems, including skin damage, gastrointestinal issues, and cancer, when ingested or inhaled in high concentrations.

In the medical field, arsenic is a toxic heavy metal that can cause a range of health problems when ingested, inhaled, or absorbed through the skin. Arsenic is found naturally in the environment and can also be released into the air, water, and soil through human activities such as mining, smelting, and the use of certain pesticides and herbicides. Long-term exposure to arsenic can lead to a variety of health problems, including skin lesions, respiratory problems, cardiovascular disease, and cancer. Arsenic poisoning can cause symptoms such as nausea, vomiting, abdominal pain, diarrhea, and headache. In severe cases, it can lead to organ failure and death. In the medical field, arsenic poisoning is treated by removing the source of exposure and providing supportive care to manage symptoms. In some cases, chelation therapy may be used to remove arsenic from the body. It is important to note that the risk of arsenic poisoning can be reduced by avoiding exposure to contaminated water and soil, and by following safe practices when handling and disposing of arsenic-containing materials.

**Schizophrenia**is a chronic and severe mental disorder characterized by hallucinations, delusions, disorganized thinking, and abnormal behavior.

Schizophrenia is a severe mental disorder characterized by a range of symptoms that affect a person's thoughts, emotions, and behavior. These symptoms can include hallucinations (hearing or seeing things that are not there), delusions (false beliefs that are not based in reality), disorganized thinking and speech, and problems with emotional expression and social interaction. Schizophrenia is a chronic condition that can last for a lifetime, although the severity of symptoms can vary over time. It is not caused by a single factor, but rather by a complex interplay of genetic, environmental, and neurobiological factors. Treatment for schizophrenia typically involves a combination of medication, therapy, and support from family and friends. While there is no cure for schizophrenia, with proper treatment, many people are able to manage their symptoms and lead fulfilling lives.

**Weight gain**refers to an increase in body mass due to an excess of calories consumed over calories burned.

In the medical field, weight gain refers to an increase in body weight over a period of time. It can be caused by a variety of factors, including changes in diet, lack of physical activity, hormonal imbalances, certain medications, and medical conditions such as hypothyroidism or polycystic ovary syndrome (PCOS). Weight gain can be measured in kilograms or pounds and is typically expressed as a percentage of body weight. A healthy weight gain is generally considered to be 0.5 to 1 kilogram (1 to 2 pounds) per week, while an excessive weight gain may be defined as more than 0.5 to 1 kilogram (1 to 2 pounds) per week over a period of several weeks or months. In some cases, weight gain may be a sign of a more serious medical condition, such as diabetes or heart disease. Therefore, it is important to monitor weight changes and consult with a healthcare provider if weight gain is a concern.

**Disease progression**refers to the worsening or advancement of a disease over time.

Disease progression refers to the worsening or progression of a disease over time. It is a natural course of events that occurs in many chronic illnesses, such as cancer, heart disease, and diabetes. Disease progression can be measured in various ways, such as changes in symptoms, physical examination findings, laboratory test results, or imaging studies. In some cases, disease progression can be slowed or stopped through medical treatment, such as medications, surgery, or radiation therapy. However, in other cases, disease progression may be inevitable, and the focus of treatment may shift from trying to cure the disease to managing symptoms and improving quality of life. Understanding disease progression is important for healthcare providers to develop effective treatment plans and to communicate with patients about their condition and prognosis. It can also help patients and their families make informed decisions about their care and treatment options.

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**linear**function ...

###### Log-**linear** **model**

**linear**analysis General

**linear**

**model**Generalized

**linear**

**model**Boltzmann distribution Elasticity Gujarati, Damodar N.; ... A log-

**linear**

**model**is a mathematical

**model**that takes the form of a function whose logarithm equals a

**linear**combination of the ... Poisson regression for contingency tables, a type of generalized

**linear**

**model**. The specific applications of log-

**linear**

**models**... while c and the wi stand for the

**model**parameters. The term may specifically be used for: A log-

**linear**plot or graph, which is ...

**Linear** probability **model**

**linear**probability

**model**", this relationship is a particularly simple one, and allows the

**model**to be fitted by

**linear**... Aldrich, John H.; Nelson, Forrest D. (1984). "The

**Linear**Probability

**Model**".

**Linear**Probability, Logit, and Probit

**Models**. Sage ... In statistics, a

**linear**probability

**model**(LPM) is a special case of a binary regression

**model**. Here the dependent variable for ...

**models**such as the logit

**model**or the probit

**model**are more commonly used. More formally, the LPM can arise from a latent- ...

###### General **linear** **model**

**linear**

**model**and the generalized

**linear**

**model**(GLM) are two commonly used families of statistical methods to relate ... In that sense it is not a separate statistical

**linear**

**model**. The various multiple

**linear**regression

**models**may be compactly ... ISBN 0-12-471252-5. McCullagh, P.; Nelder, J. A. (1989), "An outline of generalized

**linear**

**models**", Generalized

**Linear**

**Models**, ... generalized

**linear**

**models**may be used to relax assumptions about Y and U. The general

**linear**

**model**incorporates a number of ...

###### Generalized **linear** **model**

**linear**

**model**(also an example of a general

**linear**

**model**) is

**linear**regression. In ... Such a

**model**is a log-odds or logistic

**model**. Generalized

**linear**

**models**cover all these situations by allowing for response ... The general

**linear**

**model**may be viewed as a special case of the generalized

**linear**

**model**with identity link and responses ... As most exact results of interest are obtained only for the general

**linear**

**model**, the general

**linear**

**model**has undergone a ...

###### Proper **linear** **model**

**linear**

**model**is a

**linear**regression

**model**in which the weights given to the predictor variables are ... Unit-weighted regression is the most common example of an improper

**linear**

**model**. Dawes, R. M. (1979). "The robust beauty of ... Simple regression analysis is the most common example of a proper

**linear**

**model**. ... improper

**linear**

**models**in decision making". American Psychologist. 34 (7): 571-582. doi:10.1037/0003-066X.34.7.571. S2CID ...

###### Partially **linear** **model**

**linear**

**model**to analysis collected data in 2000. So far, partially

**linear**

**model**was optimized in many other ... Zeger and Diggle applied partially

**linear**

**model**for their work. Partially

**linear**

**model**primarily contributes to the estimation ... The partially

**linear**

**model**enables and simplifies the

**linear**transformation of data (Engle, Granger, Rice and Weiss, 1986). ... A partially

**linear**

**model**is a form of semiparametric

**model**, since it contains parametric and nonparametric elements. ...

###### Generalized functional **linear** **model**

**linear**

**model**(GFLM) is an extension of the generalized

**linear**

**model**(GLM) that allows one to regress ... James (2002). "Generalized

**linear**

**models**with functional predictors". Journal of the Royal Statistical Society, Series B. 64 (3 ... Articles with short description, Short description matches Wikidata, Generalized

**linear**

**models**). ... the GFLM Functional additive

**models**Functional data analysis Functional principal component analysis Generalized

**linear**

**model**...

**Linear** no-threshold **model**

**linear**no-threshold

**model**(LNT) is a dose-response

**model**used in radiation protection to estimate stochastic health effects ... The validity of the LNT

**model**, however, is disputed, and other significant

**models**exist: the threshold

**model**, which assumes ... the supra-

**linear**

**model**is verified. It has been argued that the LNT

**model**may have created an irrational fear of radiation. ... The LNT

**model**assumes there is no lower threshold at which stochastic effects start, and assumes a

**linear**relationship between ...

###### Vector generalized **linear** **model**

**models**, one has conditional logit

**models**, nested logit

**models**, generalized logit

**models**, and ... In statistics, the class of vector generalized

**linear**

**models**(VGLMs) was proposed to enlarge the scope of

**models**catered for by ... and include 3 of the most important statistical regression

**models**: the

**linear**

**model**, Poisson regression for counts, and ... therefore this

**model**is also called the cumulative probit

**model**. In general they are called cumulative link

**models**. For ...

###### Generalized **linear** mixed **model**

**Linear**Mixed

**Models**, CRC Press Jiang, J. (2007),

**Linear**and Generalized

**Linear**Mixed

**Models**and Their Applications ... a generalized

**linear**mixed

**model**(GLMM) is an extension to the generalized

**linear**

**model**(GLM) in which the

**linear**predictor ... Generalized

**linear**mixed

**models**are a special cases of hierarchical generalized

**linear**

**models**in which the random effects are ... They also inherit from GLMs the idea of extending

**linear**mixed

**models**to non-normal data. GLMMs provide a broad range of

**models**...

###### Non-**linear** sigma **model**

**model**Chiral

**model**Little Higgs Skyrmion, a soliton in non-

**linear**sigma

**models**Polyakov action WZW

**model**Fubini-Study ... This article deals primarily with the quantization of the non-

**linear**sigma

**model**; please refer to the base article on the sigma ... The non-

**linear**Ïƒ-

**model**was introduced by Gell-Mann & LÃ©vy (1960, section 6), who named it after a field corresponding to a ... a metric often used with non-

**linear**sigma

**models**Ricci flow Scale invariance Gell-Mann, M.; LÃ©vy, M. (1960), "The axial vector ...

###### Generalized **linear** array **model**

**models**provide a structure and a computational procedure for fitting generalized

**linear**

**models**or GLMs whose

**model**matrix ... It based on the generalized

**linear**

**model**with the design matrix written as a Kronecker product. The generalized

**linear**array ... Currie, I. D.; Durban, M.; Eilers, P. H. C. (2006). "Generalized

**linear**array

**models**with applications to multidimensional ... In statistics, the generalized

**linear**array

**model**(GLAM) is used for analyzing data sets with array structures. ...

**Linear** **model** of innovation

**linear**

**model**of innovation may be found in BenoÃ®t Godin's The

**Linear**

**Model**of Innovation: The Historical ... Two versions of the

**linear**

**model**of innovation are often presented: "technology push"

**model**"market pull"

**model**From the 1950s ... Sales The

**linear**

**models**of innovation supported numerous criticisms concerning the linearity of the

**models**. These

**models**ignore ... The

**Linear**

**Model**of Innovation was an early

**model**designed to understand the relationship of science and technology that begins ...

**Linear** transform **model** (MRI)

**linear**transform

**model**is a common and widespread assumption used in the interpretation of fMRI studies. However, some ... The

**linear**transform

**model**refers to a fundamental assumption guiding the analysis of functional Magnetic Resonance Imaging ( ... Specifically, the

**model**holds that the fMRI signal is approximately proportional to a measure of local neural activity, ...

###### Standard **linear** solid **model**

**model**equivalent to the standard

**linear**solid

**model**includes a dashpot in series with the Kelvin-Voigt

**model**and is ... The standard

**linear**solid (SLS), also known as the Zener

**model**, is a method of

**modeling**the behavior of a viscoelastic material ... Often, the simpler Maxwell

**model**and the Kelvin-Voigt

**model**are used. These

**models**often prove insufficient, however; the ... The standard

**linear**solid

**model**combines aspects of the Maxwell and Kelvin-Voigt

**models**to accurately describe the overall ...

###### Hierarchical generalized **linear** **model**

**linear**

**model**is the normal conjugate hierarchical generalized

**linear**

**models**. A summary of commonly used

**models**are: ... Moreover, the generalized

**linear**mixed

**model**(GLMM) is a special case of the hierarchical generalized

**linear**

**model**. In ... There are different techniques to fit a hierarchical generalized

**linear**

**model**. Hierarchical generalized

**linear**

**model**have been ... In statistics, hierarchical generalized

**linear**

**models**extend generalized

**linear**

**models**by relaxing the assumption that error ...

**Linear**-nonlinear-Poisson cascade **model**

**linear**-nonlinear-Poisson (LNP) cascade

**model**is a simplified functional

**model**of neural spike responses. It has been ... There are three stages of the LNP cascade

**model**. The first stage consists of a

**linear**filter, or

**linear**receptive field, which ... If the nonlinearity f {\displaystyle f} is a fixed invertible function, then the LNP

**model**is a generalized

**linear**

**model**. In ... the

**linear**stage of the LNP

**model**can be generalized to a bank of

**linear**filters, and the nonlinearity becomes a function of ...

###### Non-**linear** mixed-effects **modeling** software

**modeling**, physiology-based pharmacokinetic (PBPK)

**modeling**can in some cases also be ... Nonlinear mixed-effects

**models**are a special case of regression analysis for which a range of different software solutions are ... Nonlinear mixed effects

**models**are therefore estimated according to Maximum Likelihood principles. Specific estimation methods ... SPSS at the moment does not support non-

**linear**mixed effects methods. WinBUGS is an implementation of the Metropolis-Hastings ...

###### Standard **linear** solid Q **model** for attenuation and dispersion

**models**including the above

**model**(SLS-

**model**). In order to compare the different

**models**... A standard

**linear**solid Q

**model**(SLS) for attenuation and dispersion is one of many mathematical Q

**models**that gives a ... This

**model**was compared with the Kolsky-Futterman

**model**. The Kolsky-Futterman

**model**was first described in the article ' ... The standard

**linear**solid

**model**is developed from the stress-strain relation. Using a

**linear**combination of springs and ...

###### Log-**linear** analysis

**models**are the conditional equiprobability

**model**and the mutual dependence

**model**. Each log-

**linear**

**model**can be ... Log-

**linear**analysis

**models**can be hierarchical or nonhierarchical. Hierarchical

**models**are the most common. These

**models**... The saturated

**model**is the

**model**that includes all the

**model**components. This

**model**will always explain the data the best, but ... The log-

**linear**

**models**can be thought of to be on a continuum with the two extremes being the simplest

**model**and the saturated ...

**Linear** regression

**linear**

**models**" are also called "multivariate

**linear**

**models**". These are not the same as multivariable

**linear**

**models**( ... Errors-in-variables

**models**(or "measurement error

**models**") extend the traditional

**linear**regression

**model**to allow the ... In

**linear**regression, the relationships are

**modeled**using

**linear**predictor functions whose unknown

**model**parameters are ... and a special case of general

**linear**

**models**, restricted to one dependent variable. The basic

**model**for multiple

**linear**...

**Linear** least squares

**linear**least squares problems correspond to a particularly important type of statistical

**model**called

**linear**... The approach is called

**linear**least squares since the assumed function is

**linear**in the parameters to be estimated.

**Linear**... this

**model**is still

**linear**in the unknown parameters (now just Î² 1 {\displaystyle \beta _{1}} ), so

**linear**least squares still ... More generally, one can have n {\displaystyle n} regressors x j {\displaystyle x_{j}} , and a

**linear**

**model**y = Î² 0 + âˆ‘ j = 1 n ...

**Linear** utility

**linear**utility function to that

**model**is an additive set function. This is the common case in ... Define a

**linear**economy as an exchange economy in which all agents have

**linear**utility functions. A

**linear**economy has several ... Eaves, B.Curtis (1976). "A finite algorithm for the

**linear**exchange

**model**" (PDF). Journal of Mathematical Economics. 3 (2): 197 ... Gale, David (1976). "The

**linear**exchange

**model**". Journal of Mathematical Economics. 3 (2): 205-209. doi:10.1016/0304-4068(76) ...

###### Bayesian **linear** regression

**model**evidence of the Bayesian

**linear**regression

**model**presented in this section can be used to compare competing

**linear**... Bayesian

**linear**regression is a type of conditional

**modeling**in which the mean of one variable is described by a

**linear**... ISBN 0-340-52922-9. Bayesian estimation of

**linear**

**models**(R programming wikibook). Bayesian

**linear**regression as implemented in ... The simplest and most widely used version of this

**model**is the normal

**linear**

**model**, in which y {\displaystyle y} given X {\ ...

###### Musical analysis

**linear**

**models**which "do not try to reconstitute the whole melody in order of real time succession of melodic events.

**Linear**... According to Nattiez, Boretz "seems to be confusing his own formal, logical

**model**with an immanent essence he then ascribes to ... Is it not a most impressive moment?". Formalized analyses propose

**models**for melodic functions or simulate music. Meyer ... These are in contrast to the formalized

**models**of Milton Babbitt and Boretz. ...

###### Regression toward the mean

**linear**regression is the appropriate

**model**for a set of data points whose sample correlation coefficient is ... Edward J. Dudewicz & Satya N. Mishra (1988). "Section 14.1: Estimation of regression parameters;

**Linear**

**models**". Modern ... such a line that minimizes the sum of squared residuals of the

**linear**regression

**model**. In other words, numbers Î± and Î² solve ... He quantified this trend, and in doing so invented

**linear**regression analysis, thus laying the groundwork for much of modern ...

###### Shayle R. Searle

**linear**and mixed

**models**in statistics, and published widely on the topics of

**linear**

**models**, ... Searle, S. R. (1997). "The matrix Handling of BLUE and BLUP in the mixed

**linear**

**model**".

**Linear**Algebra and Its Applications. ... Searle, S. R. (1994). "Extending some results and proofs for the singular

**linear**

**model**".

**Linear**Algebra and Its Applications. ... The statistics of

**linear**

**models**: back to basics'". Statistics and Computing. 5 (2): 103-107. doi:10.1007/BF00143939. S2CID ...

**Linear** predictor function

**Linear**

**model**

**Linear**regression Makhoul, J. (1975). "

**Linear**prediction: A tutorial review". Proceedings of the IEEE. 63 (4): 561 ... In statistics and in machine learning, a

**linear**predictor function is a

**linear**function (

**linear**combination) of a set of ... In some

**models**(standard

**linear**regression, in particular), the equations for each of the data points i = 1, ..., n are stacked ... All sorts of non-

**linear**functions of the explanatory variables can be fit by the

**model**. There is no particular need for the ...

###### Rosemary A. Bailey

**linear**

**models**in statistics. Bailey's first ... Bailey, R. A. (1994). Normal

**linear**

**models**. London: External Advisory Service, University of London. ISBN 0-7187-1176-9. Bailey ... ISBN 978-0-521-68357-9. Speed, T. P.; Bailey, R. A. (1987). "Factorial Dispersion

**Models**". International Statistical Review / ...

**Model** Summary (generalized **linear** mixed **models**)

**model**and its fit. ... Covariance Parameters (generalized

**linear**mixed

**models**). *Estimated Means: Significant Effects (generalized

**linear**mixed

**models**... Smaller values indicate better

**models**. The BIC also "penalizes" overparameterized

**models**(complex

**models**with a large number of ... Smaller values indicate better

**models**. The AICC "corrects" the AIC for small sample sizes. As the sample size increases, the ...

###### Fitting the Multiple **Linear** Regression **Model** | Introduction to Statistics | JMP

**model**, this corresponds to a

**model**that predicts more precisely. In our individual

**model**for OD, RSquare is ... Fitting the Multiple

**Linear**Regression

**Model**. Recall that the method of least squares is used to find the best-fitting line for ... Here, we fit a multiple

**linear**regression

**model**for Removal, with both OD and ID as predictors. Notice that the coefficients ... The coefficient for OD (0.559) is pretty close to what we see in the simple

**linear**regression

**model**, but its slightly higher. ...

**Linear** Mixed Effects **Models** in Agriculture | R-bloggers

**Linear**

**Models**(lm, ANOVA and ANCOVA) in Agriculture

**Linear**Mixed-Effects

**Models**This class of

**models**are used to account for ... For the dataset please look at the previous post.Just to explain the syntax to use

**linear**mixed-effects

**model**in R for cluster ... At the beginning on this tutorial we explored the equation that supports

**linear**

**model**: This equation can be seen as split into ... Once again we can use the function summary to explore our results: , summary(lme1)

**Linear**mixed-effects

**model**fit by REML Data ...

###### Algebra and Trigonometry 10th Edition Chapter 1 - 1.3 - **Modeling** with **Linear** Equations - 1.3 Exercises - Page 99 70 | GradeSaver

**Modeling**with

**Linear**Equations - 1.3 Exercises - Page 99 70 including work step by step written by community members like you. ...

**Modeling**with

**Linear**Equations - 1.3 Exercises - Page 99: 71a Previous Answer Chapter 1 - 1.3 -

**Modeling**with

**Linear**Equations ... Chapter 1 - 1.3 -

**Modeling**with

**Linear**Equations - 1.3 Exercises - Page 99: 70. Answer. 48.001 ... Chapter 1 - 1.3 -

**Modeling**with

**Linear**Equations - 1.3 Exercises - Page 99. 70 ...

###### A non-**linear** numerical wave **model** to investigate the dynamic behaviour of a large diameter monopile-based offshore wind turbine...

**linear**numerical wave

**model**to investigate the dynamic behaviour of a large diameter monopile-based offshore wind turbine ... Title: A non-

**linear**numerical wave

**model**to investigate the dynamic behaviour of a large diameter monopile-based offshore wind ... A non-

**linear**numerical wave

**model**to investigate the dynamic behaviour of a large diameter monopile-based offshore wind turbine ... The focus of the research, therefore, will be to develop a non-

**linear**numerical wave

**model**to assess the dynamic behaviour of a ...

###### Rails for **linear** roller guides: 3D **models** - SOLIDWORKS, Inventor, CATIA V5, AutoCAD, STEP, STL and many more | TraceParts

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###### 3D CAD **models** from Helix **Linear** Technologies go online with interactive product catalog built by CADENAS

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**Linear**Technologies launched an online product catalog of 3D CAD

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**Linear**Technologies go online with interactive product catalog built by CADENAS. 11.07.2018. ... Helix

**Linear**Technologies launched an online product catalog of 3D CAD

**models**for their popular configurable products, ...

###### 2302.00244] Learning Cut Selection for Mixed-Integer **Linear** Programming via
Hierarchical Sequence **Model**

**Linear**Programming via Hierarchical Sequence

**Model**. Authors: Zhihai Wang, Xijun ... Specifically, HEM consists of a two-level

**model**: (1) a higher-level

**model**to learn the number of cuts that should be selected ... Abstract: Cutting planes (cuts) are important for solving mixed-integer

**linear**programs (MILPs), which formulate a wide range ... To address this challenge, we propose a novel hierarchical sequence

**model**(HEM) to learn cut selection policies via ...

###### CBRNE - Radiation Emergencies: Overview, Terminology, Biologic Effects of Ionizing Radiation

**models**whose primary focus was containing a hazardous material release t... ...

**Linear**no-threshold

**model**. The

**linear**no-threshold

**model**(LNT), which is used by most regulatory agencies, assumes a direct and ... Whether the actual effect is

**linear**or otherwise remains unknown, and the core principle of radiation safety is to ensure that ... 1] The main challenge was adapting the existing

**models**, whose primary focus was containing a hazardous material release, to one ...

###### statsmodels.regression.**linear** **model**.OLSResults.ess - statsmodels 0.15.0 (+59)

**linear**_.

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**linearmodels**.iv.absorbing.Interaction - **linearmodels** v5.3 (+2)

**Model**Estimation *

**Linear**Factor

**Models**for Asset Pricing * System Regression

**Models**...

**linearmodels**.. iv.. absorbing.. Interaction * Clinearmodels.. iv.. absorbing.. Interaction Clinearmodels.. iv.. absorbing. ...

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**linearmodels**.iv.absorbing.Interaction(cat: ndarray , DataArray , DataFrame , ...

###### Tree islands enhance biodiversity and functioning in oil palm landscapes | Nature

**Linear**mixed-effect

**models**. We used

**linear**mixed-effect

**models**to test the effects of the experimental treatment on restoration ... Extended Data Table 1 ANOVA of the

**linear**mixed-effect

**models**for biodiversity and ecosystem functioning. Full size table. ...

**model**selection influenced only the inclusion of structural complexity and tree dominance in the

**linear**

**model**(1). Effects of ... as this improved the

**model**diagnostics before applying the respective

**linear**mixed-effect

**models**; whereas we used logarithmic ...

###### Generalised **Linear** **Models** - ANU

**linear**regression and the analysis of variance, log-

**linear**

**models**for contingency ... Additional topics may include Bayesian analysis for generalized

**linear**

**models**and generalized mixed effect

**models**. ... Generalised

**Linear**

**Models**A graduate course offered by the Rsch Sch of Finance, Actuarial Studies & App Stats. ... Course Description: This course is intended to introduce students to generalised

**linear**

**modelling**methods, with emphasis on, ...

###### Coefficient of determination R2 and intra-class correlation coefficient ICC from generalized **linear** mixed-effects **models**...

**linear**mixed-effects

**models**... Coefficient of determination R2 and intra-class correlation coefficient ICC from generalized

**linear**mixed-effects

**models**... Coefficient of determination R2 and intra-class correlation coefficient ICC from generalized

**linear**mixed-effects

**models**... Coefficient of determination R2 and intra-class correlation coefficient ICC from generalized

**linear**mixed-effects

**models**...

###### Texture Analysis and Classification With **Linear** Regression **Model** Based on Wavelet Transform - Inria - Institut national de...

**linear**regression

**model**is employed to analyze this correlation and extract texture features that characterize the samples ... In this paper, we propose a texture analysis and classification approach kith the

**linear**regression

**model**based on the wavelet ... The

**linear**regression

**model**is employed to analyze this correlation and extract texture features that characterize the samples ... Texture Analysis and Classification With

**Linear**Regression

**Model**Based on Wavelet Transform. IEEE Transactions on Image ...

###### Analysis of an arithmetic test by means of a logistic **linear** trait **model**

**models**to determine sources of ... and the goodness of fit of items to both the Rasch and the LLTM

**models**was studied. Results obtained were used to illustrate ...

###### Simple **Linear** Regression (SLR) **Model** - Help center

**linear**regression is the least squares estimator of a

**linear**regression

**model**with a single explanatory variable.... ... In statistics, simple

**linear**regression is the least squares estimator of a

**linear**regression

**model**with a single explanatory ... In other words, simple

**linear**regression fits a straight line through the set of n points in such a way that makes the sum of ... Kenney, J. F. and Keeping, E. S. (1962) "

**Linear**Regression and Correlation." Ch. 15 in Mathematics of Statistics, Pt. 1, 3rd ed ...

###### Refine **Linear** Parametric **Models**
- MATLAB & Simulink
- MathWorks Italia

**model**parameters after estimating a

**model**or constructing the

**model**with initial parameter guesses. ... Refine

**Linear**Parametric

**Models**. When to Refine

**Models**. There are two situations where you can refine estimates of

**linear**... Initial

**model**. drop-down list or type the

**model**name.. The

**model**name must be in the

**Model**Board of the System Identification ...

**models**, tfest. for idtf.

**models**, and greyest. for idgrey.

**models**.. The general syntax for refining initial

**models**is as follows ...

**Linear** programming **modeling** and solvertable - Your Assignment Helper

**model**in Excel and paste a screenshot here. Use "FORMULATEXT" in your

**model**to show calculations. ... Build a

**model**in Excel and paste a screenshot here. Use "FORMULATEXT" in your

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**model**so that SolverTable can be used to investigate these changes when the percentage increase varies from 2% to ... Provide the complete

**linear**programing formulation. Clearly specify decision variables, objective function and constraints. ...

###### Author Page for Chris Kirby :: SSRN

**Linear**Filtering for Asymmetric Stochastic Volatility

**Models**Number of pages: 9 Posted: 01 Apr 2005 ... Regime-Switching Factor

**Models**in Which the Number of Factors Defines the Regime Number of pages: 9 Posted: 01 Dec 2010 Last ... Regime-Switching Factor

**Models**in Which the Number of Factors Defines the Regime Economics Letters, Forthcoming ... Multivariate Stochastic Volatility

**Models**with Correlated Errors Number of pages: 31 Posted: 21 Jan 2006 ...

###### sklearn.**linear** **model**.PassiveAggressiveClassifier - scikit-learn 0.19.2 documentation

**linear**_

**model**. .PassiveAggressiveClassifierÂ¶. class sklearn.

**linear**_

**model**.. PassiveAggressiveClassifier. (C=1.0, fit_ ... from sklearn.

**linear**_

**model**import PassiveAggressiveClassifier ,,, from sklearn.datasets import make_classification ,,, ,,, X, y ... Fit

**linear**

**model**with Passive Aggressive algorithm.. Parameters:. X : {array-like, sparse matrix}, shape = [n_samples, n_ ... Fit

**linear**

**model**with Passive Aggressive algorithm.. Parameters:. X : {array-like, sparse matrix}, shape = [n_samples, n_ ...

###### Geographic Information System Data | Epidemic Intelligence Service | CDC

**models**: ordinary least squares; generalized

**linear**mixed

**models**; geographically weighted regression Bayesian ... Regression

**models**can be applied to spatial data to determine what independent variables might explain a spatially dependent ... Network analysis, time-series animations, map series, linked micromaps, and spatiotemporal

**modeling**are methods for evaluating ... Mean/median center Directional distribution; standard deviational ellipse;

**linear**directional mean. Identify a geographic ...

###### R: A Double Bootstrap Method for Analyzing **Linear** **Models** with
Autoregressive Errors

**Linear**

**Models**with Autoregressive Errors Documentation for package DBfit version 2.0 ... A Double Bootstrap Method for Analyzing

**Linear**

**Models**With Autoregressive Errors. dbfit. The main function for the double ... A Double Bootstrap Method for Analyzing

**Linear**

**Models**With Autoregressive Errors. boot1. First Boostrap Procedure For parameter ...

**Model** upgrading to augment **linear** **model** capabilities into nonlinear regions<...

**Model**upgrading to augment

**linear**

**model**capabilities into nonlinear regions. / Cooper, S. B.; Delli Carri, A.; Di Maio, D. ... Cooper SB, Delli Carri A, Di Maio D.

**Model**upgrading to augment

**linear**

**model**capabilities into nonlinear regions. In Nonlinear ... Cooper, S. B. ; Delli Carri, A. ; Di Maio, D. /

**Model**upgrading to augment

**linear**

**model**capabilities into nonlinear regions. ... Cooper, SB, Delli Carri, A & Di Maio, D 2016,

**Model**upgrading to augment

**linear**

**model**capabilities into nonlinear regions. in ...

###### Effect of Interventions on Influenza A (H9N2) Isolation in Hong Kong's Live Poultry Markets, 1999-2005 - Volume 13, Number 9...

**linear**

**model**(12) for the outcome variable influenza (H9N2) subtype weekly isolation counts ... Adjusted RR, associated 95% CI, and p values of Poisson generalized

**linear**

**models**for influenza (H9N2) virus isolation rates by ... To verify

**model**goodness-of-fit, 2 coauthors independently viewed residual plots and verified that the

**model**fit was adequate. ... We only retained statistically significant interaction terms in the final

**model**.. We fitted separate

**models**for chickens and ...

###### 2021 Spring | Courses | Amherst College

###### The Graduated Cylinder - Bootstrap confidence intervals on a **linear**-plateau **model** in R

**linear**-plateau

**model**in R. Crop yields go up a line, then level off at some uncertain point ... Then create the bootstraps, and fit the LP

**model**to each. But what if a

**model**fails? The purrr. package includes possibly(). ... but well fit a nonlinear

**model**known as the

**linear**-plateau (LP), or lin-plat1. Looking at the plot, notice how the relative ... set.seed(911) cotton %,% bootstraps(times = 2000) %,% mutate(

**models**= map(splits, possibly(fit_LP, otherwise = NULL)), coefs = ...

###### Regression3

- Linear regression analyses were used to develop prediction equations, the amount of predictability, and significance for static and dynamic peak back-compressive forces based on a static origin and destination average (SODA) back-compressive force. (cdc.gov)
- A linear regression model adjusted for potential confounders suggested that increased ELCE was associated with less annual cognitive decline compared with lower levels of ELCE. (medscape.com)
- Linear regression models showed that every one-unit increase in ELCE score was associated with a lower global AD pathology score (estimate, âˆ’0.057) and lower levels of tau (estimate, âˆ’0.188) and beta amyloid (estimate, âˆ’0.136). (medscape.com)

###### Statistical3

- To cap the deadly effect caused by the pandemic, we apply a statistical modelling approach to investigate and predict COVID-19 incidence. (who.int)
- Statistical forecast models play a role in predicting future epidemic threats, managing of societal, economic, cultural, and public health matters. (who.int)
- Fig. 2 be used for generating models for statistical analysis, and output can be generated in the form of reports and graphs. (who.int)

###### Cognitive1

- This paper presents an illustration of the integration of cognitive psychology and psychometric models to determine sources of item difficulty in an Arithmetic Test (AT), constructed by the authors, by means of its analysis with the LLTM. (bvsalud.org)

###### Results2

- Several approaches were attempted for harmonizing the 2003-2006 25(OH)D. A model based on RIA quality control pool data was selected because the results should be independent of any empirical trend in the sample participant data. (cdc.gov)
- Results: As workforce mobility increases, relative bias in treatment effects derived from standard models to analyze cluster-randomized trials also increases. (cdc.gov)

###### Test2

- In order to reach this aim, a group of operations required to solve the items of the test were proposed, the dimensionality was evaluated, and the goodness of fit of items to both the Rasch and the LLTM models was studied. (bvsalud.org)
- The ARIMA (11,1,11) model for the complete data set was finally selected among ARIMA models based upon the parameter test and the Box-Ljung test. (who.int)

###### Data1

- Europe use a variety of classifications to rec- publication, collaborative effort and shared ord cancer incidence, so the data in EUROCIM authorship of publications are strongly en- have been converted to ensure consistency. (who.int)

###### AFNI2

###### Approach8

- In this way the approach provides a unified framework for a range of models that have previously been used in this setting, and new more flexible variants. (nih.gov)
- This report presents a comprehensive approach to statistical modeling of post-earthquake ignitions and to data compilation for such modeling, and applies it to present day California. (buffalo.edu)
- The new approach recognizes the discrete nature of ignition counts by using generalized linear and generalized linear mixed models for the first time in this type of application. (buffalo.edu)
- In contrast, we present a unified approach that directly incorporates geometric structure into the estimation process by exploiting the joint eigenproperties of the predictors and a linear penalty operator. (nih.gov)
- Linear Mixed-Effects Modeling Approach to FMRI Group Analysis. (nih.gov)
- NTP is convening an expert panel on October 23-25, 2017, at the National Institute of Environmental Health Sciences, Research Triangle Park, NC to obtain input on specific details of its proposed approach to genomic dose-response modeling. (nih.gov)
- NTP's proposed approach is in large part consistent with an approach to genomic dose-response modeling outlined by Thomas et al. (nih.gov)
- Two additional issues, which are not immediately central to the data modeling pipeline but are critical to overall success of the genomic dose-response approach, are study design and biological interpretation of findings. (nih.gov)

###### Approaches1

- NTP will also continue to monitor the scientific literature with regard to the development of improved approaches to data modeling and analysis. (nih.gov)

###### Behavior1

- To account for this behavior, we developed an equilibrium self-association model that describes the final size distributions of apoC-II fibrils formed at different starting concentrations. (nih.gov)

###### Statistical3

- Environmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure-response relationship. (nih.gov)
- This family of models is implemented in the package dlnm within the statistical environment R. To illustrate the methodology we use examples of DLNMs to represent the relationship between temperature and mortality, using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for New York during the period 1987-2000. (nih.gov)
- Advances in statistical methods and free point and click software have made it easy to select a sample size for clustered and longitudinal designs with linear mixed models. (nih.gov)

###### Parameters1

- 15] in which biological samples are distributed over a broad dose range, which allows for more accurate estimates of model parameters. (nih.gov)

###### Theory1

- He is the first author of two books on linear model theory and practice. (nih.gov)

###### Estimate2

- It includes careful model selection and goodness-of-fit analyses, examines multiple covariates to estimate ignitions, and uses a census tract as a unit of study to enable better estimates at a finer geographic resolution. (buffalo.edu)
- This structure is inherent in the output from an increasing number of biomedical technologies, and a functional linear model is often used to estimate the relationship between the predictor functions and scalar responses. (nih.gov)

###### Time2

- Our fluorescence quenching and sedimentation velocity experiments with Alexa488-labeled apoC-II indicated a time-dependent subunit interchange for both linear and closed-loop fibrils, while dilution experiments using mature fibrils indicated a shift to smaller size distributions consistent with a reversible assembly pathway. (nih.gov)
- Model I. Const Capillary Transit Time (TT) and Varying Large Vessel TT. (nih.gov)

###### Main1

- [ 1 ] The main challenge was adapting the existing models, whose primary focus was containing a hazardous material release, to one that reflected the chaos of a large-scale disaster involving a large number of affected individuals. (medscape.com)

###### Effects1

- Here we develop the family of distributed lag non-linear models (DLNM), a modelling framework that can simultaneously represent non-linear exposure-response dependencies and delayed effects. (nih.gov)

###### Analysis1

- This paper presents an illustration of the integration of cognitive psychology and psychometric models to determine sources of item difficulty in an Arithmetic Test (AT), constructed by the authors, by means of its analysis with the LLTM. (bvsalud.org)