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Thompson et al., 2023 (Generalized models for quantifying laterality using functional transcranial Doppler ultrasound. Human Brain Mapping, 44(1), 35–48) introduced generalised model‐based analysis methods for determining cerebral lateralisation from functional transcranial Doppler ultrasound (fTCD) data which substantially decreased the uncertainty of individual lateralisation estimates across several large adult samples. We aimed to assess the suitability of these methods for increasing precision in lateralisation estimates for child fTCD data. We applied these methods to adult fTCD data to establish the validity of two child‐friendly language and visuospatial tasks. We also applied the methods to fTCD data from 4‐ to 7‐year‐old children. For both samples, the laterality estimates from the complex generalised additive model (GAM) approach correlated strongly with the traditional methods while also decreasing individual standard errors compared to the popular period‐of‐interest averaging method. We recommend future research using fTCD with young children consider using GAMs to reduce the noise in their LI estimates. [ABSTRACT FROM AUTHOR]

We consider how analysis of brain lateralization using functional transcranial Doppler ultrasound (fTCD) data can be brought in line with modern statistical methods typically used in functional magnetic resonance imaging (fMRI). Conventionally, a laterality index is computed in fTCD from the difference between the averages of each hemisphere's signal within a period of interest (POI) over a series of trials. We demonstrate use of generalized linear models (GLMs) and generalized additive models (GAM) to analyze data from individual participants in three published studies (N = 154, 73 and 31), and compare this with results from the conventional POI averaging approach, and with laterality assessed using fMRI (N = 31). The GLM approach was based on classic fMRI analysis that includes a hemodynamic response function as a predictor; the GAM approach estimated the response function from the data, including a term for time relative to epoch start (simple GAM), plus a categorical index corresponding to individual epochs (complex GAM). Individual estimates of the fTCD laterality index are similar across all methods, but error of measurement is lowest using complex GAM. Reliable identification of cases of bilateral language appears to be more accurate with complex GAM. We also show that the GAM‐based approach can be used to efficiently analyze more complex designs that incorporate interactions between tasks. [ABSTRACT FROM AUTHOR]

The prediction models of geographical distribution of the plant species are probabilistic and static models. They determinate the mathematical equations governing on geographical distribution of species with their current environment and environmental factors effective on the distribution of species. The aim of the current Study is to review the performance of Generalized Linear Model (GLM) and Generalized Additive Model )GAM(in determination of the relationship between vegetation and environmental factors in Polour rangeland. Stratified random sampling method was used. Five dominant species include Astragalus ochrodeucus, Ferula gumosa, Thymus kotschyanus,Onobrychis Cornata and Agropyron Sp were identified. the studied environmental factors were including 13 soil characteristics, 3 topographic factors and 3 climatic factors. The analyses were performed using Presence-Absence and GRASP package of R software. Also for evaluating the performance of prediction model , the statistical coefficients including AUC, AIC, RMSE and R2 were used. The results showed that the highest R2 in GLM model is related to presence of Agropyron Sp with 0.98 and also the lowest RMSE and AIC is related to Astragalus ochrodeucus with 0.29 and 12 respectively. Also in GAM model the highest R2 is related to Thymus kotschyanus with 0.98 and the lowest RMSE and AIC is related to Astragalus ochrodeucus and Ferula gumosa with 0.22 and 18.12 respectively. .. Also the highest AUC in GLM model is related to Onobrychis Cornata and in GAM model is related to Agropyron Sp with 0.86.Given the value of protecting and valuable plant species studied, the results of these models can be used in programs of conservation and improvement of the tourist area.

Abstract: Historical longline catch per unit effort (CPUE) constitutes the major time series used in tuna stock assessment to follow the trend in abundance since the beginning of the large-scale tuna fisheries. The efficiency and species composition of a longline fishing operations essentially depends on the overlap in the vertical and spatial distribution between hooks and species habitat. Longline catchability depends on the vertical distribution of hooks and the aim of our paper was to analyse principal factors affecting the deviation of observed longline hook depths from predicted values. Since observed hook depth is usually shallower than predicted, this deviation is called longline shoaling. We evaluate the accuracy of hook depth distribution estimated from a theoretical catenary model commonly used in longline CPUE standardizations. Temperature-depth recorders (TDRs) were deployed on baskets of a monitored longline. Mainline shapes and maximum fishing depths were similar to gear configurations commonly used to target both yellowfin and bigeye tuna by commercial longliners in the central part of the South Pacific Ocean. Our working hypothesis assumes that the maximum fishing depth reached by the mainline depends on the gear configuration (sag ratio, mainline length per basket), the fishing tactics (bearing of the setting) and environmental variables characterizing water mass dynamics (wind stress, current velocity and shear). Based on generalized additive models (GAMs) simple transformations are proposed to account for the non-linearity between the shoaling and explanatory variables. Then, generalized linear models (GLMs) were fit to model the effects of explanatory variables on the longline shoaling. Results indicated that the shoaling (absolute as well as relative) was significantly influenced by (1) the shape of the mainline (i.e., the tangential angle), which is the strongest predictor, and (2) the current shear and the direction of setting. Geometric forcing (i.e. transverse versus in-line) between the environment and the longline set is shown for the first time from in situ experimental fishing data. Results suggest that a catenary model that does not take these factors into consideration provides a biased estimate of the vertical distribution of hooks and must be used with caution in CPUEs standardization methods. Since catchability varies in time and space we discuss how suitable data could be routinely collected onboard commercial fishing vessels in order to estimate longline catchability for stock assessments. [Copyright &y& Elsevier]

Abstract: Commonly, the most costly part of geomorphological distribution modelling studies is gathering the data. Thus, guidance for researchers concerning the quantity of field data needed would be extremely practical. This paper scrutinises the relationship between the sample size (the number of observations varied from 20 to 600) and the predictive ability of the generalized linear model (GLM), generalized additive model (GAM), generalized boosting method (GBM) and artificial neural network (ANN) in two data settings, i.e., independent and split-sample approaches. The study was performed using empirical data of periglacial processes from an area of 600 km2 in northernmost Finland at grid resolutions of 1 ha (100×100 m) and 25 ha (500×500 m). A rather sharp increase in the predictive ability of the models was observed when the number of observations increased from 20 to 100, and the level of robust predictions was reached with 200 observations. The result indicates that no more than a few hundred observations are needed in geomorphological distribution modelling at a medium scale resolution (ca. 0.01–1 km2). [Copyright &y& Elsevier]

The article presents a study that explores how digital elevation model (DEM) leads to increase uncertainty in results from several modeling procedure developed for the prediction of absence or presence of vegetation species. Under the investigation, species spatial-distribution models for two types of predictor variables that are derived from DEM were developed. The models created were then compared against each other and against the original DEM model throughout the modeling process. Results suggest that DEM error affects all the modeling procedure and results, that include the statistical significance of environmental variables, species response curves in generalized additive models, prediction accuracy, and spatial extent of predictions.