Den Finkenbach wieder besucht – wie lange dauert das Larvalstadium des Bachneunauges Lampetra planeri (Bloch, 1784)?

2021 ◽  
Vol 58 ◽  
pp. 88-102
Author(s):  
Volker Salewski
Keyword(s):  
Visual Analysis ◽  
Generalized Additive Models ◽  
Age Groups ◽  
Information Criterion ◽  
Additive Models ◽  
Larval Life ◽  
Brook Lamprey ◽  
Age Cohorts ◽  
Lampetra Planeri ◽  
High Degree

Im November 1989 und von März bis November 1990 wurden im Finkenbach im hessischen Odenwald monatlich Larven des Bachneunauges Lampetra planeri gefangen und vermessen. Ziel war es, anhand von Längenfrequenzen die Anzahl von Altersklassen und damit die Dauer des Larvalstadiums bestimmen zu können. Anhand einer visuellen Analyse der Längenverteilungen wurde 1990 auf eine Dauer des Larvalstadiums von etwas über sechs Jahren, in einigen Fällen vielleicht auch ein Jahr länger, geschlossen. Die 1989/90 erhobenen Daten wurden 2020 mittels generalisierter additiver Modelle erneut ausgewertet. Anhand des Akaike-Informationskriteriums wurde für jeden Monat das Modell bestimmt, das die Anzahl vorhandener Größenklassen am besten beschrieb. Mit diesen Modellen konnten die Einschätzungen von 1990 im Wesentlichen bestätigt werden. Allerdings ist die Annahme, dass das Larvalstadium etwas über sechs Jahre dauert, mit großen Unsicherheiten behaftet. Die Anzahl von Larven in den höheren Längenbereichen ist zu gering, um hier robuste Schlüsse zur Anzahl von Größen- und damit Altersklassen zuzulassen. Weiterhin ist bei anderen Neunaugenarten auch experimentell nachgewiesen, dass die Metamorphose in einem unterschiedlichen Alter einsetzen kann. Das Wachstum von Neunaugenlarven und damit das Alter, in dem die Metamorphose einsetzt, ist von den Verhältnissen in den Gewässern abhängig. Daher wäre es interessanter, den Einfluss von Umweltbedingungen auf das Wachstum in einer Zeit des Klimawandels zu untersuchen, anstatt sich nur auf die Frage des Zeitpunkts der Metamorphose zu beschränken. Finkenbach revisited – how long is the duration of larval life in the Brook Lamprey? Abstract: In November 1989 and from March to November 1990, larvae of the brook lamprey Lampetra planeri were caught and measured monthly in the Finkenbach-River in the Hessian Odenwald in Germany. The aim was to analyse length frequencies to determine the number of age cohorts and thus the duration of the larval life. Based on a purely visual analysis of the length distributions, it was concluded that the larval life lasted a little over six years, and in some cases perhaps a year longer. The data collected in 1989/90 were re-analysed in 2020 with generalized additive models. Using the Akaike information criterion, the model that best described the number of existing size classes was determined for each month. With these models, the assessment was similar compared to the visual analysis in 1990. However, the assumption that the larval stage lasts a little over six years includes a high degree of uncertainty. The number of larvae in the higher length ranges is too low to allow robust conclusions about the numbers of size-cohorts and thus age groups. Furthermore, it has been experimentally shown in other lamprey species that metamorphosis can begin at different ages. The growth of lamprey larvae and thus the age at which they enter metamorphosis depends on environmental conditions. Therefore, it would be more interesting to examine the influence of these conditions on growth in a time of global warming, instead of restricting analyses to the question of the exact age of metamorphosis.

The Relationship between Mobility and COVID-19 in Germany: Modeling Case Occurrence using Apple's Mobility Trends Data

2021 ◽  
Author(s):  
Mark David Walker ◽  
Mihály Sulyok
Keyword(s):  
Generalized Additive Models ◽  
Information Criterion ◽  
Additive Models ◽  
Online Data ◽  
German Government ◽  
Explanatory Variables ◽  
Mobility Data ◽  
Community Mobility ◽  
The Relationship ◽  
Potential Use

Abstract Background Restrictions on social interaction and movement were implemented by the German government in March 2020 to reduce the transmission of coronavirus disease 2019 (COVID-19). Apple's “Mobility Trends” (AMT) data details levels of community mobility; it is a novel resource of potential use to epidemiologists. Objective The aim of the study is to use AMT data to examine the relationship between mobility and COVID-19 case occurrence for Germany. Is a change in mobility apparent following COVID-19 and the implementation of social restrictions? Is there a relationship between mobility and COVID-19 occurrence in Germany? Methods AMT data illustrates mobility levels throughout the epidemic, allowing the relationship between mobility and disease to be examined. Generalized additive models (GAMs) were established for Germany, with mobility categories, and date, as explanatory variables, and case numbers as response. Results Clear reductions in mobility occurred following the implementation of movement restrictions. There was a negative correlation between mobility and confirmed case numbers. GAM using all three categories of mobility data accounted for case occurrence as well and was favorable (AIC or Akaike Information Criterion: 2504) to models using categories separately (AIC with “driving,” 2511. “transit,” 2513. “walking,” 2508). Conclusion These results suggest an association between mobility and case occurrence. Further examination of the relationship between movement restrictions and COVID-19 transmission may be pertinent. The study shows how new sources of online data can be used to investigate problems in epidemiology.

scholarly journals SAT0587 MACHINE-LEARNING DERIVED ALGORITHMS FOR OUTCOMES PREDICTION IN RHEUMATIC DISEASES: APPLICATION TO RADIOGRAPHIC PROGRESSION IN EARLY AXIAL SPONDYLOARTHRITIS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1252.2-1253
Author(s):  
R. Garofoli ◽  
M. Resche-Rigon ◽  
M. Dougados ◽  
D. Van der Heijde ◽  
C. Roux ◽  
...  
Keyword(s):  
Machine Learning ◽  
Radiographic Progression ◽  
Generalized Additive Models ◽  
Regression Trees ◽  
Information Criterion ◽  
Additive Models ◽  
Super Learner ◽  
Additive Regression ◽  
Selection Operator ◽  
Lasso Method

Background:Axial spondyloarthritis (axSpA) is a chronic rheumatic disease that encompasses various clinical presentations: inflammatory chronic back pain, peripheral manifestations and extra-articular manifestations. The current nomenclature divides axSpA in radiographic (in the presence of radiographic sacroiliitis) and non-radiographic (in the absence of radiographic sacroiliitis, with or without MRI sacroiliitis. Given that the functional burden of the disease appears to be greater in patients with radiographic forms, it seems crucial to be able to predict which patients will be more likely to develop structural damage over time. Predictive factors for radiographic progression in axSpA have been identified through use of traditional statistical models like logistic regression. However, these models present some limitations. In order to overcome these limitations and to improve the predictive performance, machine learning (ML) methods have been developed.Objectives:To compare ML models to traditional models to predict radiographic progression in patients with early axSpA.Methods:Study design: prospective French multicentric cohort study (DESIR cohort) with 5years of follow-up. Patients: all patients included in the cohort, i.e. 708 patients with inflammatory back pain for >3 months but <3 years, highly suggestive of axSpA. Data on the first 5 years of follow-up was used. Statistical analyses: radiographic progression was defined as progression either at the spine (increase of at least 1 point per 2 years of mSASSS scores) or at the sacroiliac joint (worsening of at least one grade of the mNY score between 2 visits). Traditional modelling: we first performed a bivariate analysis between our outcome (radiographic progression) and explanatory variables at baseline to select the variables to be included in our models and then built a logistic regression model (M1). Variable selection for traditional models was performed with 2 different methods: stepwise selection based on Akaike Information Criterion (stepAIC) method (M2), and the Least Absolute Shrinkage and Selection Operator (LASSO) method (M3). We also performed sensitivity analysis on all patients with manual backward method (M4) after multiple imputation of missing data. Machine learning modelling: using the “SuperLearner” package on R, we modelled radiographic progression with stepAIC, LASSO, random forest, Discrete Bayesian Additive Regression Trees Samplers (DBARTS), Generalized Additive Models (GAM), multivariate adaptive polynomial spline regression (polymars), Recursive Partitioning And Regression Trees (RPART) and Super Learner. Finally, the accuracy of traditional and ML models was compared based on their 10-foldcross-validated AUC (cv-AUC).Results:10-fold cv-AUC for traditional models were 0.79 and 0.78 for M2 and M3, respectively. The 3 best models in the ML algorithm were the GAM, the DBARTS and the Super Learner models, with 10-fold cv-AUC of: 0.77, 0.76 and 0.74, respectively (Table 1).Table 1.Comparison of 10-fold cross-validated AUC between best traditional and machine learning models.Best modelsCross-validated AUCTraditional models M2 (step AIC method)0.79 M3 (LASSO method)0.78Machine learning approach SL Discrete Bayesian Additive Regression Trees Samplers (DBARTS)0.76 SL Generalized Additive Models (GAM)0.77 Super Learner0.74AUC: Area Under the Curve; AIC: Akaike Information Criterion; LASSO: Least Absolute Shrinkage and Selection Operator; SL: SuperLearner. N = 295.Conclusion:Traditional models predicted better radiographic progression than ML models in this early axSpA population. Further ML algorithms image-based or with other artificial intelligence methods (e.g. deep learning) might perform better than traditional models in this setting.Acknowledgments:Thanks to the French National Society of Rheumatology and the DESIR cohort.Disclosure of Interests:Romain Garofoli: None declared, Matthieu resche-rigon: None declared, Maxime Dougados Grant/research support from: AbbVie, Eli Lilly, Merck, Novartis, Pfizer and UCB Pharma, Consultant of: AbbVie, Eli Lilly, Merck, Novartis, Pfizer and UCB Pharma, Speakers bureau: AbbVie, Eli Lilly, Merck, Novartis, Pfizer and UCB Pharma, Désirée van der Heijde Consultant of: AbbVie, Amgen, Astellas, AstraZeneca, BMS, Boehringer Ingelheim, Celgene, Cyxone, Daiichi, Eisai, Eli-Lilly, Galapagos, Gilead Sciences, Inc., Glaxo-Smith-Kline, Janssen, Merck, Novartis, Pfizer, Regeneron, Roche, Sanofi, Takeda, UCB Pharma; Director of Imaging Rheumatology BV, Christian Roux: None declared, Anna Moltó Grant/research support from: Pfizer, UCB, Consultant of: Abbvie, BMS, MSD, Novartis, Pfizer, UCB

scholarly journals Severe volcanic SO2 exposure and respiratory morbidities in the Icelandic population – a register study

2019 ◽  
Author(s):  
Hanne Krage Carlsen ◽  
Unnur Valdimarsdóttir ◽  
Haraldur Briem ◽  
Francesca Dominici ◽  
Ragnhildur Gudrun Finnbjornsdottir ◽  
...  
Keyword(s):  
Respiratory Disease ◽  
Respiratory Health ◽  
Generalized Additive Models ◽  
Tertiary Care ◽  
Age Groups ◽  
Medical Doctor ◽  
Emergency Department Visits ◽  
Additive Models ◽  
Care Utilization ◽  
Icelandic Population

AbstractBackgroundThe Holuhraun volcanic eruption September 2014 to February 2015 emitted large amounts of sulfur dioxide (SO2).ObjectivesThe aim of this study was to determine the association between volcanic SO2 gases on general population respiratory health in the Icelandic capital area some 250 km from the eruption site.MethodsRespiratory health outcomes were: asthma medication dispensing (AMD) from the Icelandic Medicines Register, medical doctor consultations in primary care (PCMD) and hospital emergency department visits (HED) in Reykjavík (population: 215 000) for respiratory disease from 1 January 2010 to 31 December 2014. The associations between daily counts of health events,and daily mean SO2 concentration and high SO2 levels (24-hour mean SO2>125µg/m3) were analyzed using generalized additive models.ResultsAfter the eruption began, AMD was higher than before (129.4 vs. 158.4 individuals per day, p<0.05). Increases in SO2 concentration were associated with an estimated increase in AMD by 1.05% (95% CI 0.48 - 1.62%) per 10 µg/m3 at lag 0-2, 1.51% (95% CI 0.63 – 2.40%) increase in individuals under 18 years of age. PCMD for respiratory causes increased by 1.52% (95% CI 1.04 -2.00%) per 10 µg/m3 SO2 at lag 0-2. For HED, only effect estimated for individuals aged 64 years and older were significantly increased, by 1.541% (95% CI 0.02-3.07%) per 10 µg/m3 SO2 at lag 0-2. Following days with SO2 levels above 125 µg/m3, AMD and PCMD were increased in all age groups, in AMD mostly so in individuals under 18 by 20.4%(95%CI 4.8 – 23.4%), and adult PCMD visits by 24.1%(95%CI 16.8 – 31.3%). HED was significantly increased in elderly by 26.3% (95%CI 5.56-47.0).DiscussionHigh levels of volcanic SO2 are associated with increases in dispensing of AMD, and health care utilization in primary and tertiary care. Individuals with prevalent respiratory disease may be particularly susceptible.FundingThe study was funded by the Icelandic Ministry of Health.

scholarly journals Distinctive trajectories of COVID-19 epidemic by age and gender: a retrospective modeling of the epidemic in South Korea

2020 ◽  
Author(s):  
Xinhua Yu ◽  
Jiasong Duan ◽  
Yu Jiang ◽  
Hongmei Zhang
Keyword(s):  
South Korea ◽  
Elderly People ◽  
Negative Binomial ◽  
Generalized Additive Models ◽  
Age Groups ◽  
Additive Models ◽  
Age And Gender ◽  
Epidemic Dynamics ◽  
And Gender ◽  
Gender Groups

AbstractObjectivesElderly people had suffered disproportional burden of COVID-19. We hypothesized that males and females in different age groups might have different epidemic trajectories.MethodsUsing publicly available data from South Korea, daily new COVID-19 cases were fitted with generalized additive models, assuming Poisson and negative binomial distributions. Epidemic dynamics by age and gender groups were explored with interactions between smoothed time terms and age and gender.ResultsA negative binomial distribution fitted the daily case counts best. Interaction between the dynamic patterns of daily new cases and age groups was statistically significant (p<0.001), but not with gender group. People aged 20-39 years led the epidemic processes in the society with two peaks: one major peak around March 1 and a smaller peak around April 7, 2020. The epidemic process among people aged 60 or above was trailing behind that of younger people with smaller magnitude. After March 15, there was a consistent decline of daily new cases among elderly people, despite large fluctuations of case counts among young adults.ConclusionsAlthough young people drove the COVID-19 epidemic in the whole society with multiple rebounds, elderly people could still be protected from virus infection after the peak of epidemic.

scholarly journals Impact of biometeorological conditions and air pollution on influenza-like illnesses incidence in Warsaw

2021 ◽  
Author(s):  
Katarzyna Lindner-Cendrowska ◽  
Peter Bröde
Keyword(s):  
Generalized Additive Models ◽  
Age Groups ◽  
Health Resources ◽  
Additive Models ◽  
Temperate Climate ◽  
Climate Index ◽  
Climate Change Scenarios ◽  
Atmospheric Conditions ◽  
Thermal Climate ◽  
Pm2.5 And Pm10

AbstractIn order to assess the influence of atmospheric conditions and particulate matter (PM) on the seasonally varying incidence of influenza-like illnesses (ILI) in the capital of Poland—Warsaw, we analysed time series of ILI reported for the about 1.75 million residents in total and for different age groups in 288 approximately weekly periods, covering 6 years 2013–2018. Using Poisson regression, we predicted ILI by the Universal Thermal Climate Index (UTCI) as biometeorological indicator, and by PM2.5 and PM10, respectively, as air quality measures accounting for lagged effects spanning up to 3 weeks. Excess ILI incidence after adjusting for seasonal and annual trends was calculated by fitting generalized additive models. ILI morbidity increased with rising PM concentrations, for both PM2.5 and PM10, and with cooler atmospheric conditions as indicated by decreasing UTCI. While the PM effect focused on the actual reporting period, the atmospheric influence exhibited a more evenly distributed lagged effect pattern over the considered 3-week period. Though ILI incidence adjusted for population size significantly declined with age, age did not significantly modify the effect sizes of both PM and UTCI. These findings contribute to better understanding environmental conditionings of influenza seasonality in a temperate climate. This will be beneficial to forecasting future dynamics of ILI and to planning clinical and public health resources under climate change scenarios.

scholarly journals Standardization of landing efficiency for opilio crab in the western Bering Sea by using of generalized additive models

2021 ◽  
Vol 201 (2) ◽  
pp. 359-370
Author(s):  
I. S. Chernienko
Keyword(s):  
Bering Sea ◽  
Space Form ◽  
Generalized Additive Models ◽  
Information Criterion ◽  
Additive Models ◽  
Significant Difference ◽  
Akaike Criterion ◽  
Explained Variance ◽  
Modal Values ◽  
Delay Difference

Generalized additive models are applied for standardization of daily landing per unit effort (LPUE) for opilio crab using the data of fishery statistics for the West Bering Sea fishery zone in 2003–2020. A set of 12 models with various combinations of predictors was examined and the best model with the smallest value of Akaike criterion was selected (information criterion Akaike 21743, explained variance 58.6 %). The selected model reflects the effect of depth, distance from the coast, daily effort and tensor product of geographic coordinates and day of the year. LPUE was standardized using the selected model by substituting median values of nominal predictors and modal values of categorical predictors. Then the crab stock was estimated using the state-space form of Deriso-Schnute delay-difference model. The estimates based on both standardized and nominal indices are compared and a significant difference between them is found: the stock is assessed as 23,040 t with nominal indices but as 17,070 t using the standardized indices.

scholarly journals Sex Differences in the Nonlinear Association Between BMI and LDL Cholesterol in Middle-Aged and Older Adults: Results from two Nationally Representative Surveys of China

2021 ◽  
Author(s):  
Haibin Li ◽  
Jiahui Ma ◽  
Deqiang Zheng ◽  
Xia Li ◽  
Xiuhua Guo ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Sex Differences ◽  
Generalized Additive Models ◽  
Low Density Lipoprotein ◽  
Age Groups ◽  
Density Lipoprotein ◽  
Additive Models ◽  
Middle Aged ◽  
Nutrition Survey ◽  
Nationally Representative

Abstract Background: The association between body mass index (BMI) and low-density lipoprotein cholesterol (LDL-C) in middle-aged and older man and women was understudied. We aimed to explore whether there were sex differences in this relationship in a large sample of Chinese adults.Methods: Participants in the China Health and Retirement Longitudinal Study (CHARLS, 2011-2012) (n=7485) and the China Health and Nutrition Survey (CHNS, 2009) (n=4788) were cross-sectionally investigated. Generalized additive models with a smooth function for BMI and a smooth-factor interaction for BMI with sex were performed and stratified by age and metabolic syndrome. Segment linear splines regressions were fitted to calculate the slopes with the different breakpoints.Results: Among the 12273 participants aged 45 to 75 years, 5780 (47.1%) were males. The nonlinear relationship between BMI and LDL-C was observed in females and males (P interaction <0.001). The slopes of the BMI and LDL-C association changed (P <0.001) at BMI 22.5 kg/m2 in females and 27.5 kg/m2 in males. Below these BMI breakpoints, LDL-C increased 2.14 (95% CI: 1.42 to 2.86) and 1.77 (95% CI: 1.43 to 2.11) mg/dL per kg/m2, respectively. In females, there was a plateau at BMI values of 22.5-27.5 kg/m2, and then gradually increased after a BMI of 27.5 kg/m2. However, LDL-C declined -1.84 (95% CI: -3.01 to -0.66) mg/dL per kg/m2 above BMI of 27.5 kg/m2 in males. The pattern of sex and BMI-LDL-C association was similar in all age groups but modified by the number of metabolic syndrome criteria.Conclusions: The BMI and LDL-C relationship was inverted U-shaped in males and approximately linear in females.

Modeling tree diameters using mixtures of skewed Student’s t and related distributions

2020 ◽  
Vol 50 (10) ◽  
pp. 1039-1049
Author(s):  
Mahdi Teimouri ◽  
Rafał Podlaski
Keyword(s):  
Visual Analysis ◽  
Size Structure ◽  
Information Criterion ◽  
Scientific Basis ◽  
Forest Patches ◽  
Breast Height ◽  
Student’S T ◽  
Log Normal ◽  
High Degree ◽  
Selected Functions

The size structure of tree populations making up a forest stand provides a scientific basis for evaluating the forest resources and scheduling future silviculture treatments. We evaluated the efficiency of the skewed Student’s t (ST) model to predict the distributions of various complex diameter at breast height (DBH) data. The ST model was compared with finite mixtures of selected functions (gamma, log-normal, and Weibull). Additionally, two scenarios were employed to determine the number of components: (1) the use of the Bayesian information criterion and (2) visual analysis of the histogram of the DBH relative frequencies. The ST model demonstrated the highest degree of flexibility for fitting the DBH distributions. It outperformed other competitors in modeling the DBH variables in terms of all implemented scenarios. It possesses a high degree of flexibility. This model should be used to fit the DBH distributions of multigeneration forest patches with diverse size structures.

scholarly journals Decline of depressive symptoms in Europe: differential trends across the lifespan

2020 ◽  
Author(s):  
Johannes Beller ◽  
Enrique Regidor ◽  
Lourdes Lostao ◽  
Alexander Miething ◽  
Christoph Kröger ◽  
...  
Keyword(s):  
Older Adults ◽  
Depressive Symptoms ◽  
Generalized Additive Models ◽  
Age Groups ◽  
Additive Models ◽  
European Countries ◽  
Multilevel Regression ◽  
Middle Aged ◽  
General Decline ◽  
Middle Aged Adults

Abstract Purpose We examined changes in the burden of depressive symptoms between 2006 and 2014 in 18 European countries across different age groups. Methods We used population-based data drawn from the European Social Survey (N = 64.683, 54% female, age 14–90 years) covering 18 countries (Austria, Belgium, Denmark, Estonia, Finland, France, Germany, Great Britain, Hungary, Ireland, The Netherlands, Norway, Poland, Portugal, Slovenia, Spain, Sweden, Switzerland) from 2006 to 2014. Depressive symptoms were measured via the CES-D 8. Generalized additive models, multilevel regression, and linear regression analyses were conducted. Results We found a general decline in CES-D 8 scale scores in 2014 as compared with 2006, with only few exceptions in some countries. This decline was most strongly pronounced in older adults, less strongly in middle-aged adults, and least in young adults. Including education, health and income partially explained the decline in older but not younger or middle-aged adults. Conclusions Burden of depressive symptoms decreased in most European countries between 2006 and 2014. However, the decline in depressive symptoms differed across age groups and was most strongly pronounced in older adults and least in younger adults. Future studies should investigate the mechanisms that contribute to these overall and differential changes over time in depressive symptoms.

scholarly journals Model Selection in Continuous Test Norming With GAMLSS

Assessment ◽  
2017 ◽  
Vol 26 (7) ◽  
pp. 1329-1346 ◽  
Author(s):  
Lieke Voncken ◽  
Casper J. Albers ◽  
Marieke E. Timmerman
Keyword(s):  
Model Selection ◽  
Sample Size ◽  
Akaike Information Criterion ◽  
Reference Group ◽  
Generalized Additive Models ◽  
Selection Procedure ◽  
Information Criterion ◽  
Exponential Model ◽  
Additive Models ◽  
Model Selection Procedure

To compute norms from reference group test scores, continuous norming is preferred over traditional norming. A suitable continuous norming approach for continuous data is the use of the Box–Cox Power Exponential model, which is found in the generalized additive models for location, scale, and shape. Applying the Box–Cox Power Exponential model for test norming requires model selection, but it is unknown how well this can be done with an automatic selection procedure. In a simulation study, we compared the performance of two stepwise model selection procedures combined with four model-fit criteria (Akaike information criterion, Bayesian information criterion, generalized Akaike information criterion (3), cross-validation), varying data complexity, sampling design, and sample size in a fully crossed design. The new procedure combined with one of the generalized Akaike information criterion was the most efficient model selection procedure (i.e., required the smallest sample size). The advocated model selection procedure is illustrated with norming data of an intelligence test.

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