Predicting the intensity and impact of Dreissena infestation on native unionid bivalves from Dreissena field density

1995 ◽  
Vol 52 (7) ◽  
pp. 1449-1461 ◽  
Author(s):  
A. Ricciardi ◽  
J. B. Rasmussen ◽  
F. G. Whoriskey
Keyword(s):  
Dreissena Polymorpha ◽  
Habitat Degradation ◽  
Poisson Model ◽  
Saturation Curve ◽  
Linear Regression Models ◽  
Dreissena Bugensis ◽  
Field Density ◽  
Infestation Intensity ◽  
The Mean ◽  
Large Clusters

Introduced dreissenid mussels (Dreissena polymorpha and Dreissena bugensis) foul native unionid bivalves by attaching to their shells in large clusters and may critically impair many North American unionids that are already threatened by habitat degradation. Using literature and new field data, we examined patterns of Dreissena infestation on unionids, and the relationships between Dreissena field density, infestation intensity, and unionid mortality. Linear regression models showed that Dreissena field density strongly predicts (i) the proportion of unionids colonized by dreissenids (r2 = 0.90, p < 0.0001) and (ii) the mean number of dreissenids attached to unionids (r2 = 0.81, p < 0.0001). We fitted a compound Poisson model that accounts for dreissenid clustering and predicts both the proportion of colonized unionids and the mean infestation intensity as effectively as our empirically derived models. The proportion of unionids colonized by Dreissena follows a saturation curve, increasing rapidly with Dreissena densities up to 200/m2, and reaching a plateau at 70–80% colonization. Unionid mortality (reflected by the proportion of dead unionids) is strongly correlated with Dreissena field density (r2 = 0.82, p < 0.002) at densities above 1000/m2. Our models predict that severe unionid mortality (>90%) occurs when Dreissena density and mean infestation intensity reach 6000/m2 and 100 dreissenids/unionid.

A wall jet to measure the attachment strength of zebra mussels

1995 ◽  
Vol 52 (1) ◽  
pp. 126-135 ◽  
Author(s):  
Josef Daniel Ackerman ◽  
C. Ross Ethier ◽  
Jan K. Spelt ◽  
D. Grant Allen ◽  
Catherine M. Cottrell
Keyword(s):  
Zebra Mussel ◽  
Dreissena Polymorpha ◽  
Zebra Mussels ◽  
Artificial Substrates ◽  
Environmentally Benign ◽  
Wall Jet ◽  
Dreissena Bugensis ◽  
Marine Bivalves ◽  
Attachment Strength ◽  
The Mean

A wall jet is presented as a novel means of measuring the attachment strength of zebra mussels. Attachment strength was inferred from a fluid detachment parameter (DP), defined as the nominal wall shear stress at the detachment site × mussel length2. DP varied significantly on natural and artificial substrates: in tests with 288 Dreissena bugensis (≈8–10 mm long), the mean (±SE) DP was 8.9 ± 0.9 mPa∙m2 on limestone/dolomite, 5.6 ± 0.5 mPa∙m2 on polyvinylchloride, 4.3 ± 0.4 mPa∙m2 on stainless steel, 4.2 ± 0.5 mPa∙m2 on aluminum, and 2.5 ± 0.3 mPa∙m2 on polymethylmethacrylate (Plexiglas). The attachment strength of postlarval mussels (plantigrades; <1 mm) was two orders of magnitude less than adult mussels. These results were validated with conventional tensile loadings, in which 633 Dreissena bugensis and 26 Dreissena polymorpha were pulled off substrates with a calibrated force scale. The tensile loadings results were comparable with those of marine bivalves. Good correlation between pull-off force and DP was observed. Information of this nature is useful for the implementation of environmentally benign zebra mussel controls.

The Effects of Mean of Visit-to-Visit Blood Pressure on Incident Brain Vascular Lesions and Functional-Cognitive Decline

2021 ◽  
pp. 1-12
Author(s):  
Bibek Gyanwali ◽  
Celestine Xue Ting Cai ◽  
Christopher Chen ◽  
Henri Vrooman ◽  
Chuen Seng Tan ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cognitive Decline ◽  
Hippocampal Volume ◽  
Intracranial Stenosis ◽  
Vascular Lesions ◽  
Linear Regression Models ◽  
Volume Functional ◽  
Mri Scans ◽  
The Mean

Background: Cerebrovascular disease (CeVD) is an underlying cause of cognitive impairment and dementia. Hypertension is a known risk factor of CeVD, but the effects of mean of visit-to-visit blood pressure (BP) on incident CeVD and functional-cognitive decline remains unclear. Objective: To determine the association between mean of visit-to-visit BP with the incidence and progression of CeVD [white matter hyperintensities (WMH), infarcts (cortical infarcts and lacunes), cerebral microbleeds (CMBs), intracranial stenosis, and hippocampal volume] as well as functional-cognitive decline over 2 years of follow-up. Methods: 373 patients from a memory-clinic underwent BP measurements at baseline, year 1, and year 2. The mean of visit-to-visit systolic BP, diastolic BP, pulse pressure, and mean arterial pressure were calculated. Baseline and year 2 MRI scans were graded for WMH, infarcts, CMBs, intracranial stenosis, and hippocampal volume. Functional-cognitive decline was assessed using locally validated protocol. Logistic and linear regression models with odds ratios, mean difference, and 95%confidence interval were constructed to analyze associations of visit-to-visit BP on CeVD incidence and progression as well as functional-cognitive decline. Results: Higher mean of visit-to-visit diastolic BP was associated with WMH progression. Higher tertiles of diastolic BP was associated with WMH progression and incident CMBs. There was no association between mean of visit-to-visit BP measures with incident cerebral infarcts, intracranial stenosis, change in hippocampal volume, and functional-cognitive decline. Conclusion: These findings suggest the possibility of hypertension-related vascular brain damage. Careful monitoring and management of BP in elderly patients is essential to reduce the incidence and progression of CeVD.

scholarly journals Assessing Early Heterogeneity in Doubling Times of the COVID-19 Epidemic across Prefectures in Mainland China, January–February, 2020

Epidemiologia ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 95-113 ◽  
Author(s):  
Isaac Chun-Hai Fung ◽  
Xiaolu Zhou ◽  
Chi-Ngai Cheung ◽  
Sylvia K. Ofori ◽  
Kamalich Muniz-Rodriguez ◽  
...  
Keyword(s):  
Regression Models ◽  
Doubling Time ◽  
Metropolitan Areas ◽  
Mainland China ◽  
Linear Regression Models ◽  
Case Count ◽  
Daily Time Series ◽  
The Mean ◽  
Doubling Times ◽  
Daily Time

To describe the geographical heterogeneity of COVID-19 across prefectures in mainland China, we estimated doubling times from daily time series of the cumulative case count between 24 January and 24 February 2020. We analyzed the prefecture-level COVID-19 case burden using linear regression models and used the local Moran’s I to test for spatial autocorrelation and clustering. Four hundred prefectures (~98% population) had at least one COVID-19 case and 39 prefectures had zero cases by 24 February 2020. Excluding Wuhan and those prefectures where there was only one case or none, 76 (17.3% of 439) prefectures had an arithmetic mean of the epidemic doubling time <2 d. Low-population prefectures had a higher per capita cumulative incidence than high-population prefectures during the study period. An increase in population size was associated with a very small reduction in the mean doubling time (−0.012, 95% CI, −0.017, −0.006) where the cumulative case count doubled ≥3 times. Spatial analysis revealed high case count clusters in Hubei and Heilongjiang and fast epidemic growth in several metropolitan areas by mid-February 2020. Prefectures in Hubei and neighboring provinces and several metropolitan areas in coastal and northeastern China experienced rapid growth with cumulative case count doubling multiple times with a small mean doubling time.

PARAMETER ESTIMATION ON HURDLE POISSON REGRESSION MODEL WITH CENSORED DATA

2012 ◽  
Vol 57 (1) ◽  
Author(s):  
SEYED EHSAN SAFFAR ◽  
ROBIAH ADNAN ◽  
WILLIAM GREENE
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Goodness Of Fit ◽  
Poisson Model ◽  
Likelihood Method ◽  
Poisson Regression Model ◽  
Response Variable ◽  
The Mean ◽  
Over Dispersion

A Poisson model typically is assumed for count data. In many cases, there are many zeros in the dependent variable and because of these many zeros, the mean and the variance values of the dependent variable are not the same as before. In fact, the variance value of the dependent variable will be much more than the mean value of the dependent variable and this is called over–dispersion. Therefore, Poisson model is not suitable anymore for this kind of data because of too many zeros. Thus, it is suggested to use a hurdle Poisson regression model to overcome over–dispersion problem. Furthermore, the response variable in such cases is censored for some values. In this paper, a censored hurdle Poisson regression model is introduced on count data with many zeros. In this model, we consider a response variable and one or more than one explanatory variables. The estimation of regression parameters using the maximum likelihood method is discussed and the goodness–of–fit for the regression model is examined. We study the effects of right censoring on estimated parameters and their standard errors via an example.

Abstract WMP14: Association of Systolic Blood Pressure, LDL Cholesterol, and Hgb A1c With White Matter Hyperintensity on MRI in the ACCORDION MIND Study

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Adam H de Havenon ◽  
Tanya Turan ◽  
Rebecca Gottesman ◽  
Sharon Yeatts ◽  
Shyam Prabhakaran ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Risk Factor ◽  
Regression Models ◽  
Ldl Cholesterol ◽  
Vascular Risk Factor ◽  
White Matter Hyperintensity ◽  
Vascular Risk ◽  
Linear Regression Models ◽  
Risk Factor Profile ◽  
The Mean

Introduction: While retrospective studies have shown that poor control of vascular risk factors is associated with progression of white matter hyperintensity (WMH), it has not been studied prospectively. Hypothesis: We hypothesize that higher systolic blood pressure (SBP) mean, LDL cholesterol, and Hgb A1c will be correlated with WMH progression in diabetics. Methods: This is a secondary analysis of the Memory in Diabetes (MIND) substudy of the Action to Control Cardiovascular Risk in Diabetes Follow-on Study (ACCORDION). The primary outcome was WMH progression, evaluated by fitting linear regression models to the WMH volume on the month 80 MRI and adjusting for the WMH volume on the baseline MRI. The primary predictors were the mean values of SBP, LDL, and A1c from baseline to month 80. We defined a good vascular risk factor profile as mean SBP <120 mm Hg and mean LDL <120 mg/dL. Results: We included 292 patients, with a mean (SD) age of 62.6 (5.3) years and 55.8% male. The mean number of SBP, LDL, and A1c measurements per patient was 17, 5, and 12. We identified 86 (29.4%) patients with good vascular risk factor profile. In the linear regression models, mean SBP and LDL were associated with WMH progression and in a second fully adjusted model they both remained associated with WMH progression (Table). Those with a good vascular risk factor profile had less WMH progression (β Coefficient -0.80, 95% CI -1.42, -0.18, p=0.012). Conclusions: Our data reinforce prior research showing that higher SBP and LDL is associated with progression of WMH in diabetics, likely secondary to chronic microvascular ischemia, and suggest that control of these factors may have protective effects. This study has unique strengths, including prospective serial measurement of the exposures, validated algorithmic measurement methodology for WMH, and rigorous adjudication of study data. Clinical trials are needed to investigate the effect of vascular risk factor reduction on WMH progression.

FLUCTUATIONS OF CLUSTER NUMBERS IN PERCOLATION

2002 ◽  
Vol 13 (06) ◽  
pp. 777-781
Author(s):  
DANIEL TIGGEMANN
Keyword(s):  
Power Law ◽  
Higher Moments ◽  
The Mean ◽  
Large Clusters ◽  
Small Clusters ◽  
Skewness And Kurtosis

In order to study fluctuations in percolating systems, lattices for sizes up to L = 100 000 have been simulated several thousand times using the Hoshen–Kopelman algorithm. Distributions of cluster numbers are Gaussians for small clusters and half-sided quasi-Gaussians for large clusters. The variance of cluster numbers is proportional to the mean, with power-law deviations for small clusters. Higher moments like skewness and kurtosis were also studied.

A Negative Binomial Regression Model for Accuracy Tests

2012 ◽  
Vol 36 (2) ◽  
pp. 88-103 ◽  
Author(s):  
Lai-Fa Hung
Keyword(s):  
Negative Binomial ◽  
Social Science Research ◽  
Poisson Model ◽  
Negative Binomial Regression ◽  
Science Research ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Negative Binomial Regression Model ◽  
Marginal Maximum Likelihood ◽  
The Mean

Rasch used a Poisson model to analyze errors and speed in reading tests. An important property of the Poisson distribution is that the mean and variance are equal. However, in social science research, it is very common for the variance to be greater than the mean (i.e., the data are overdispersed). This study embeds the Rasch model within an overdispersion framework and proposes new estimation methods. The parameters in the proposed model can be estimated using the Markov chain Monte Carlo method implemented in WinBUGS and the marginal maximum likelihood method implemented in SAS. An empirical example based on models generated by the results of empirical data, which are fitted and discussed, is examined.

scholarly journals Previous COVID-19 Infection and Antibody Levels After Vaccination

2021 ◽  
Vol 9 ◽  
Author(s):  
Hamad Ali ◽  
Barrak Alahmad ◽  
Abdullah A. Al-Shammari ◽  
Abdulmohsen Alterki ◽  
Maha Hammad ◽  
...  
Keyword(s):  
Neutralizing Antibodies ◽  
Individual Characteristics ◽  
Linear Regression Models ◽  
Health Authorities ◽  
Previous Infection ◽  
Public Health Authorities ◽  
The Mean ◽  
Vaccine Shortages ◽  
Antibody Levels ◽  
Ig G

Background: The emergence of new COVID-19 variants of concern coupled with a global inequity in vaccine access and distribution has prompted many public health authorities to circumvent the vaccine shortages by altering vaccination protocols and prioritizing persons at high risk. Individuals with previous COVID-19 infection may not have been prioritized due to existing humoral immunity.Objective: We aimed to study the association between previous COVID-19 infection and antibody levels after COVID-19 vaccination.Methods: A serological analysis to measure SARS-CoV-2 immunoglobulin (Ig)G, IgA, and neutralizing antibodies was performed on individuals who received one or two doses of either BNT162b2 or ChAdOx1 vaccines in Kuwait. A Student t-test was performed and followed by generalized linear regression models adjusted for individual characteristics and comorbidities were fitted to compare the average levels of IgG and neutralizing antibodies between vaccinated individuals with and without previous COVID-19 infection.Results: A total of 1,025 individuals were recruited. The mean levels of IgG, IgA, and neutralizing antibodies were higher in vaccinated subjects with previous COVID-19 infections than in those without previous infection. Regression analysis showed a steeper slope of decline for IgG and neutralizing antibodies in vaccinated individuals without previous COVID-19 infection compared to those with previous COVID-19 infection.Conclusion: Previous COVID-19 infection appeared to elicit robust and sustained levels of SARS-CoV-2 antibodies in vaccinated individuals. Given the inconsistent supply of COVID-19 vaccines in many countries due to inequities in global distribution, our results suggest that even greater efforts should be made to vaccinate more people, especially individuals without previous COVID-19 infection.

Statistical Evaluation of Total Energy Intake Based on the Number of Food Portions and Body Weight

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Rousset S ◽  
◽  
Médard S ◽  
Fleury G ◽  
Fardet A ◽  
...  
Keyword(s):  
Body Weight ◽  
Energy Expenditure ◽  
Total Energy ◽  
Energy Intake ◽  
Test Sample ◽  
Normal Weight ◽  
Total Energy Intake ◽  
Validation Dataset ◽  
Linear Regression Models ◽  
The Mean

The evaluation of food intake based on various assessment methods is critical and underreporting is frequent. The aim of the study was to develop an indirect statistical method of the total energy intake estimation based on gender, weight and the number of portions. Energy intake prediction was developed and evaluated for validity using energy expenditure measurements given by the WellBeNet app. A total of 190 volunteers with various BMIs were recruited and assigned either in the train or the test sample. The mean energy provided by a portion was evaluated by linear regression models from the train sample. The absolute values of the error between the energy intake estimation and the energy expenditure measurement were calculated for each volunteer, by subgroup and for the whole group. The performance of the models was determined using the validation dataset. As the number of portions is the only variable used in the model, the error was 30.7% and 26.5% in the train and test sample. After adding body weight in the model, the error in absolute value decreased to 8.8% and 10.8% for the normal-weight women and men, and 11.7% and 12.8% for the overweight female and male volunteers, respectively. The findings of this study indicate that a statistical approach and knowledge of the usual number of portions and body weight is effective and sufficient to obtain a precise evaluation of energy intake (about 10% of error) after a simple and brief enquiry.

Estimating Rates of Recurrent Peritonitis for Patients on CAPO

1985 ◽  
Vol 5 (1) ◽  
pp. 59-65 ◽  
Author(s):  
Edward F. Vonesh
Keyword(s):  
Risk Factors ◽  
Medical Devices ◽  
Negative Binomial ◽  
Survival Curve ◽  
Probability Model ◽  
Major Complication ◽  
Poisson Model ◽  
Negative Binomial Model ◽  
Binomial Model ◽  
The Mean

Recurrent peritonitis is a major complication of Continuous Ambulatory Peritoneal Dialysis (CAPD). As a therapy for patients with end stage renal disease, CAPD entails a continuous interaction between patient and various medical devices. The assumptions one makes regarding this interaction play an essential role when estimating the rate of recurrent peritonitis for a given patient population. Assuming that each patient has a constant rate of peritonitis, two models for evaluating the risk of recurrent peritonitis are considered. One model, the Poisson probability model, applies when the rate of peritonitis is the same from patient to patient. When this occurs, the frequency of peritoneal infections will be randomly distributed among patients (Corey, 1981). A second model, the negative binomial probability model, applies when the rate of peritonitis varies from one patient to another. In this event, the distribution of peritoneal infections will differ from patient to patient. The poisson model would be applicable when, for example, patients behave similarly with respect to their interactions with the medical devices and with potential risk factors. The negative binomial model, on the other hand, makes allowances for patient differences both in terms of their handling of routine exchanges and in their exposure to various risk factors. This paper provides methods for estimating the mean peritonitis rate under each model. In addition, “survival” curve estimates depicting the probability of remaining peritonitis free (i.e. “surviving”) over time are provided. It is shown, using data from a multi-center clinical trial, that the risk of peritonitis is best described in terms of survival curves rather than the mean peritonitis rate. For both models, the mean peritonitis rate was found to be 0.85 episodes per year. However, under the negative binomial model, the one-year survival rate, expressed as the percentage of patients remaining free of peritonitis, is 52% as compared with only 42% under the Poisson model. Moreover, the negative binomial model provided a significantly better fit to the observed frequency of peritonitis. These findings suggest that the negative binomial model provides a more realistic and accurate portrayal of the risk of peritonitis and that this risk is not nearly as high as would otherwise be indicated by a Poisson analysis.

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