MODELING THE ROOT-KNOT NEMATODE/NUTSEDGE PEST COMPLEX: PERSPECTIVES FROM WEED SCIENCE, NEMATOLOGY AND STATISTICS

Previous research by the authors has established that southern root-knot nematode (SRKN, Meloidogyne incognita (Kofoid & White) Chitwood) and yellow and purple nutsedge (YNS, Cyperus esculentus L. and PNS, C. rotundus L.) form a pest-complex that adversely affects a wide variety of crops in the southern and western U.S. These pests appear to have co-evolved a mutually-beneficial relationship that promotes the survival of both nematodes and weeds to the detriment of crops. Traditional management has usually targeted one pest at a time, but managing this pest complex requires that all members of the complex be managed simultaneously. A series of experiments was performed to determine if this specific pest complex could be managed through crop-rotation using a non-dormant M. incognita-resistant variety of alfalfa (Medicago sativa) which can aggressively compete with, and hence decrease, occurrence of both species of nutsedges (NS), and subsequently decrease SRKN by decreasing the availability of root systems of host plants. A previous journal article discussed predicting counts of SRKN second-stage juveniles (SRKN-J2) as a function of YNS and PNS plant counts from a two-year alfalfa rotation experiment, using the Poisson distribution and a scale parameter to handle problems of overdispersion. In this paper, we examine three generalizations of the Poisson distribution that allow for the count variance being larger than the mean count: the Generalized Poisson, the Zero-Inflated Poisson (ZIP), and the Poisson Hurdle. The ZIP and Hurdle Poisson distributions both account for zero counts as a separate part of the distribution, while the Generalized Poisson incorporates a separate parameter that increases the variance relative to the mean. Different biological scenarios are presented for which each of these three general Poisson distributions might be logically appropriate. In addition, we use the alfalfa rotation data to present comparisons of fitted regression models of the three general Poisson distributions to the results from the previous analysis using the Poisson. For this data, there was no single probability distribution that worked best for all six sampling dates (three in each of the two years). This is not surprising in that over time the alfalfa rotation was, as planned, decreasing both nutsedge and nematode counts, thus presenting a moving target for the modeling process

Mortality following Traumatic Brain Injury among Individuals Unable to Follow Commands at the Time of Rehabilitation Admission: A National Institute on Disability and Rehabilitation Research Traumatic Brain Injury Model Systems Study

Severe traumatic brain injury (TBI) has been associated with increased mortality. This study characterizes long-term mortality, life expectancy, causes of death, and risk factors for death among patients admitted within the National Institute on Disability and Rehabilitation Research (NIDRR) TBI Model Systems Programs (TBIMS) who lack command following at the time of admission for inpatient TBI rehabilitation. Of the 8084 persons enrolled from 1988 and 2009, 387 from 20 centers met study criteria. Individuals with moderate to severe TBI who received inpatient rehabilitation were 2.2 times more likely to die than individuals in the U.S. general population of similar age, gender, and race, with an average life expectancy (LE) reduction of 6.6 years. The subset of individuals who were unable to follow commands on admission to rehabilitation was 6.9 times more likely to die, with an average LE reduction of 12.2 years. Relative to the U.S. general population matched for age, gender, and race/ethnicity, these non–command following individuals were more than four times more likely to die of circulatory conditions, 44 times more likely to die of pneumonia, and 38 times more likely to die of aspiration pneumonia. The subset of individuals with TBI who are unable to follow commands upon admission to inpatient rehabilitation are at a significantly increased risk of death when compared with the U.S. general population and compared with all individuals with moderate to severe TBI receiving inpatient rehabilitation. Respiratory causes of death predominate, compared with the general population

Common questions and misconceptions about protein supplementation: what does the scientific evidence really show?

Protein supplementation often refers to increasing the intake of this particular macronutrient through dietary supplements in the form of powders, ready-to-drink shakes, and bars. The primary purpose of protein supplementation is to augment dietary protein intake, aiding individuals in meeting their protein requirements, especially when it may be challenging to do so through regular food (i.e. chicken, beef, fish, pork, etc.) sources alone. A large body of evidence shows that protein has an important role in exercising and sedentary individuals. A PubMed search of “protein and exercise performance” reveals thousands of publications. Despite the considerable volume of evidence, it is somewhat surprising that several persistent questions and misconceptions about protein exist. The following are addressed: 1) Is protein harmful to your kidneys? 2) Does consuming “excess” protein increase fat mass? 3) Can dietary protein have a harmful effect on bone health? 4) Can vegans and vegetarians consume enough protein to support training adaptations? 5) Is cheese or peanut butter a good protein source? 6) Does consuming meat (i.e., animal protein) cause unfavorable health outcomes? 7) Do you need protein if you are not physically active? 8) Do you need to consume protein ≤ 1 hour following resistance training sessions to create an anabolic environment in skeletal muscle? 9) Do endurance athletes need additional protein? 10) Does one need protein supplements to meet the daily requirements of exercise-trained individuals? 11) Is there a limit to how much protein one can consume in a single meal? To address these questions, we have conducted a thorough scientific assessment of the literature concerning protein supplementation

How Important is Variability in Consumer Credit Limits?

Credit limit variability is a crucial aspect of the consumption, savings, and debt decisions of households in the United States. Using a large panel, this paper first demonstrates that individuals gain and lose access to credit frequently and often have their credit limits reduced unexpectedly. Credit limit volatility is larger than most estimates of income volatility and varies over the business cycle. While typical models of intertemporal consumption fix the credit limit, I introduce a model with variable credit limits. Variable credit limits create a reason for households to hold both high interest debts and low interest savings at the same time, since the savings act as insurance. Simulating the model using the estimates of credit limit volatility, I show that it explains all of the credit card puzzle: why around a third of households in the United States hold both debt and liquid savings at the same time. The approach also offers an important new channel through which financial system uncertainty affects household decisions

Barrier Tissue Macrophages: Functional Adaptation to Environmental Challenges

Macrophages are found throughout the body, where they have crucial roles in tissue development, homeostasis and remodeling, as well as being sentinels of the innate immune system that can contribute to protective immunity and inflammation. Barrier tissues, such as the intestine, lung, skin and liver, are exposed constantly to the outside world, which places special demands on resident cell populations such as macrophages. Here we review the mounting evidence that although macrophages in different barrier tissues may be derived from distinct progenitors, their highly specific properties are shaped by the local environment, which allows them to adapt precisely to the needs of their anatomical niche. We discuss the properties of macrophages in steady-state barrier tissues, outline the factors that shape their differentiation and behavior and describe how macrophages change during protective immunity and inflammation

Baseline characteristics of patients in the reduction of events with darbepoetin alfa in heart failure trial (RED-HF)

<p>Aims: This report describes the baseline characteristics of patients in the Reduction of Events with Darbepoetin alfa in Heart Failure trial (RED-HF) which is testing the hypothesis that anaemia correction with darbepoetin alfa will reduce the composite endpoint of death from any cause or hospital admission for worsening heart failure, and improve other outcomes.</p> <p>Methods and results: Key demographic, clinical, and laboratory findings, along with baseline treatment, are reported and compared with those of patients in other recent clinical trials in heart failure. Compared with other recent trials, RED-HF enrolled more elderly [mean age 70 (SD 11.4) years], female (41%), and black (9%) patients. RED-HF patients more often had diabetes (46%) and renal impairment (72% had an estimated glomerular filtration rate <60 mL/min/1.73 m2). Patients in RED-HF had heart failure of longer duration [5.3 (5.4) years], worse NYHA class (35% II, 63% III, and 2% IV), and more signs of congestion. Mean EF was 30% (6.8%). RED-HF patients were well treated at randomization, and pharmacological therapy at baseline was broadly similar to that of other recent trials, taking account of study-specific inclusion/exclusion criteria. Median (interquartile range) haemoglobin at baseline was 112 (106–117) g/L.</p> <p>Conclusion: The anaemic patients enrolled in RED-HF were older, moderately to markedly symptomatic, and had extensive co-morbidity.</p&gt

An Exploration of Wikipedia Contributions in the Era of COVID-19

With the declaration of COVID-19 as a pandemic by the World Health Organization and associated public health concerns and emergency measures, the lives of many came to a grinding halt. Whether due to stay-at-home orders and lockdowns, or quarantines, many were required to change their day-to-day way of life overnight. This not only changed the mechanical activities performed daily, such as driving to the office, going out to a restaurant, or attending a party, but this also changed the resultant implications of the utility we received from those activities. Utility theory posits that individuals choose what activities to perform based on some utility function, where the utility satisfies some need. As these activities to meet existing needs were disrupted, we seek to use this as an opportunity to explore how contribution in open-source communities changed, specifically the English Wikipedia. We utilize a natural experiment methodology, centered around the COVID-19 pandemic, and explore contribution patterns before and after the pandemic. Our project will contribute to the understanding of IS during unusual times, specifically in the context of open source communities that rely on volunteer time and effor

A case study in handling over-dispersion in nematode count data

Master of ScienceDepartment of StatisticsLeigh W. MurrayTraditionally the Poisson process is used to model count response variables. However, a problem arises when the particular response variable contains an inordinate number of both zeros and large observations, relative to the mean, for a typical Poisson process. In cases such as these, the variance of the data is greater than the mean and as such the data are over-dispersed with respect to the Poisson distribution due to the fact that the mean equals the variance for the Poisson distribution. This case study looks at several common and uncommon ways to attempt to properly account for this over-dispersion in a specific set of nematode count data using various procedures in SAS 9.2. These methods include but are not limited to a basic linear regression model, a generalized linear (log-linear) model, a zero-inflated Poisson model, a generalized Poisson model, and a Poisson hurdle model. Based on the AIC statistics the generalized log-linear models with the Pearson-scale and deviance-scale corrections perform the best. However, based on residual plots, none of the models appear to fit the data adequately. Further work with non-parametric methods or the negative binomial distribution may yield more ideal results