scholarly journals Disentangling the effect of climatic and hydrological predictor variables on benthic macroinvertebrate distributions from predictive models

Hydrobiologia ◽  
2021 ◽  
Author(s):  
Katie Irving ◽  
Sonja C. Jähnig ◽  
Mathias Kuemmerlen
Keyword(s):  
Stream Flow ◽  
Model Performance ◽  
Predictor Variables ◽  
Distribution Models ◽  
Future Studies ◽  
The Difference ◽  
Improved Performance ◽  
Species Specific ◽  
Macroinvertebrate Species ◽  
Hydrological Variables

AbstractLotic freshwater macroinvertebrate species distribution models (SDMs) have been shown to improve when hydrological variables are included. However, most studies to date only include data describing climate or stream flow-related surrogates. We assessed the relative influence of climatic and hydrological predictor variables on the modelled distribution of macroinvertebrates, expecting model performance to improve when hydrological variables are included. We calibrated five SDMs using combinations of bioclimatic (bC), hydrological (H) and hydroclimatic (hC) predictor datasets and compared model performance as well as variance partition of all combinations. We investigated the difference in trait composition of communities that responded better to either bC or H configurations. The dataset bC had the most influence in terms of proportional variance, however model performance was increased with the addition of hC or H. Trait composition demonstrated distinct patterns between associated model configurations, where species that prefer intermediate to slow-flowing current conditions in regions further downstream performed better with bC–H. Including hydrological variables in SDMs contributes to improved performance, it is however, species-specific and future studies would benefit from hydrology-related variables to link environmental conditions and diverse communities. Consequently, SDMs that include climatic and hydrological variables could more accurately guide sustainable river ecosystem management.

scholarly journals Testing the Influence of Seascape Connectivity on Marine-Based Species Distribution Models

2021 ◽  
Vol 8 ◽  
Author(s):  
Giorgia Cecino ◽  
Roozbeh Valavi ◽  
Eric A. Treml
Keyword(s):  
Species Distribution ◽  
Larval Dispersal ◽  
Species Distribution Models ◽  
Temporal Dynamics ◽  
Explanatory Power ◽  
Model Performance ◽  
Boosted Regression Trees ◽  
Distribution Models ◽  
Flexible Tool ◽  
Species Specific

Species distribution models (SDMs) are commonly used in ecology to predict species occurrence probability and how species are geographically distributed. Here, we propose innovative predictive factors to efficiently integrate information on connectivity into SDMs, a key element of population dynamics strongly influencing how species are distributed across seascapes. We also quantify the influence of species-specific connectivity estimates (i.e., larval dispersal vs. adult movement) on the marine-based SDMs outcomes. For illustration, seascape connectivity was modeled for two common, yet contrasting, marine species occurring in southeast Australian waters, the purple sea urchin, Heliocidaris erythrogramma, and the Australasian snapper, Chrysophrys auratus. Our models illustrate how different species-specific larval dispersal and adult movement can be efficiently accommodated. We used network-based centrality metrics to compute patch-level importance values and include these metrics in the group of predictors of correlative SDMs. We employed boosted regression trees (BRT) to fit our models, calculating the predictive performance, comparing spatial predictions and evaluating the relative influence of connectivity-based metrics among other predictors. Network-based metrics provide a flexible tool to quantify seascape connectivity that can be efficiently incorporated into SDMs. Connectivity across larval and adult stages was found to contribute to SDMs predictions and model performance was not negatively influenced from including these connectivity measures. Degree centrality, quantifying incoming and outgoing connections with habitat patches, was the most influential centrality metric. Pairwise interactions between predictors revealed that the species were predominantly found around hubs of connectivity and in warm, high-oxygenated, shallow waters. Additional research is needed to quantify the complex role that habitat network structure and temporal dynamics may have on SDM spatial predictions and explanatory power.

scholarly journals Hospital acquired pressure injury prediction in surgical critical care patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jenny Alderden ◽  
Kathryn P. Drake ◽  
Andrew Wilson ◽  
Jonathan Dimas ◽  
Mollie R. Cummins ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Model Performance ◽  
Routine Care ◽  
Predictor Variables ◽  
Classification Algorithms ◽  
Data Set ◽  
Icu Patients ◽  
Pressure Injury ◽  
Surgical Icu ◽  
Hospital Acquired

Abstract Background Hospital-acquired pressure injuries (HAPrIs) are areas of damage to the skin occurring among 5–10% of surgical intensive care unit (ICU) patients. HAPrIs are mostly preventable; however, prevention may require measures not feasible for every patient because of the cost or intensity of nursing care. Therefore, recommended standards of practice include HAPrI risk assessment at routine intervals. However, no HAPrI risk-prediction tools demonstrate adequate predictive validity in the ICU population. The purpose of the current study was to develop and compare models predicting HAPrIs among surgical ICU patients using electronic health record (EHR) data. Methods In this retrospective cohort study, we obtained data for patients admitted to the surgical ICU or cardiovascular surgical ICU between 2014 and 2018 via query of our institution's EHR. We developed predictive models utilizing three sets of variables: (1) variables obtained during routine care + the Braden Scale (a pressure-injury risk-assessment scale); (2) routine care only; and (3) a parsimonious set of five routine-care variables chosen based on availability from an EHR and data warehouse perspective. Aiming to select the best model for predicting HAPrIs, we split each data set into standard 80:20 train:test sets and applied five classification algorithms. We performed this process on each of the three data sets, evaluating model performance based on continuous performance on the receiver operating characteristic curve and the F1 score. Results Among 5,101 patients included in analysis, 333 (6.5%) developed a HAPrI. F1 scores of the five classification algorithms proved to be a valuable evaluation metric for model performance considering the class imbalance. Models developed with the parsimonious data set had comparable F1 scores to those developed with the larger set of predictor variables. Conclusions Results from this study show the feasibility of using EHR data for accurately predicting HAPrIs and that good performance can be found with a small group of easily accessible predictor variables. Future study is needed to test the models in an external sample.

Dichotomization, Partial Correlation, and Conditional Independence

1996 ◽  
Vol 21 (3) ◽  
pp. 264-282 ◽  
Author(s):  
András Vargha ◽  
Tamás Rudas ◽  
Harold D. Delaney ◽  
Scott E. Maxwell
Keyword(s):  
Conditional Independence ◽  
Partial Correlation ◽  
Statistical Significance ◽  
Conventional Wisdom ◽  
Predictor Variables ◽  
Lower Power ◽  
Partial Correlations ◽  
Current Article ◽  
The Difference ◽  
The Relationship

It was recently demonstrated that performing median splits on both of two predictor variables could sometimes result in spurious statistical significance instead of lower power. Not only is the conventional wisdom that dichotomization always lowers power incorrect, but the current article further demonstrates that inflation of apparent effects can also occur in certain cases where only one of two predictor variables is dichotomized. In addition, we show that previously published formulas claiming that correlations are necessarily reduced by bivariate dichotomization are incorrect. While the magnitude of the difference between the correct and incorrect formulas is not great for small or moderate correlations, it is important to correct the misunderstanding of partial correlations that led to the error in the previous derivations. This is done by considering the relationship between partial correlation and conditional independence in the context of dichotomized predictor variables.

Effect of Tobacco on Lipoprotein Profile: A Comparative study among Smokers and Chewers

2021 ◽  
pp. 4973-4976
Author(s):  
Roshan Kumar Jha ◽  
Ranjit S. Ambad ◽  
Priya Koundal ◽  
Akansha Singh
Keyword(s):  
Risk Factors ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Total Cholesterol ◽  
Sample Processing ◽  
Exclusion Criteria ◽  
Future Studies ◽  
Area Of Interest ◽  
Significant Difference ◽  
The Difference

It has been proved that tobacco is one of the cholesterol dependent risk factors pathogenically, and in addition with other risk factors it may lead to coronary heart disease. Thus, a strong interaction exists between hypercholesterolemia and tobacco ingesting in the genesis of coronary heart disease. The aim of this study was to study the effect of tobacco smoking and chewing and compare its effect on lipoproteins. 60 subjects were included in the study, and were grouped into 3 three groups, tobacco smokers, tobacco chewers and tobacco non-abusers. Each group comprises 20 participants: selected on the basis of inclusion and exclusion criteria. Proper sampling and sample processing methods were employed to evaluate lipid profile. Total cholesterol and triglycerides levels were increased in smokers in comparison to non-smokers/non-chewers, and the differences were significant p<0.0001. HDL level was decreased in smokers as compared to non-smokers/non-chewers and the difference was statistically significant p<0.0001. Total cholesterol and LDL levels were increased in smokers in comparison to chewers. HDL level was decreased in chewers as compared to chewers. There was no significant association in any of the parameters. Present study observed increased and significant p<0.0001 differences in levels of total cholesterol and triglycerides while, HDL levels were decreased significantly p<0.0001, and also observed there was no significant difference among tobacco smokers and chewers. This may be a new area of interest for future studies.

scholarly journals An alternative way to evaluate chemistry-transport model variability

2017 ◽  
Vol 10 (3) ◽  
pp. 1199-1208 ◽  
Author(s):  
Laurent Menut ◽  
Sylvain Mailler ◽  
Bertrand Bessagnet ◽  
Guillaume Siour ◽  
Augustin Colette ◽  
...  
Keyword(s):  
Transport Model ◽  
Model Performance ◽  
Error Statistics ◽  
Chemistry Transport Model ◽  
Temporal Correlations ◽  
Time Period ◽  
The Difference ◽  
Model Variability ◽  
Spatial Locations ◽  
Statistical Indicators

Abstract. A simple and complementary model evaluation technique for regional chemistry transport is discussed. The methodology is based on the concept that we can learn about model performance by comparing the simulation results with observational data available for time periods other than the period originally targeted. First, the statistical indicators selected in this study (spatial and temporal correlations) are computed for a given time period, using colocated observation and simulation data in time and space. Second, the same indicators are used to calculate scores for several other years while conserving the spatial locations and Julian days of the year. The difference between the results provides useful insights on the model capability to reproduce the observed day-to-day and spatial variability. In order to synthesize the large amount of results, a new indicator is proposed, designed to compare several error statistics between all the years of validation and to quantify whether the period and area being studied were well captured by the model for the correct reasons.

scholarly journals Can Intentional Distractions Affect Endurance Performance Positively?

2021 ◽  
Vol 4 (1) ◽  
pp. 15
Author(s):  
Roger Couture
Keyword(s):  
Perceived Exertion ◽  
Endurance Performance ◽  
Residual Effects ◽  
Ratings Of Perceived Exertion ◽  
Future Studies ◽  
Performance Time ◽  
Performance Change ◽  
Confounding Variables ◽  
Improved Performance ◽  
Active Distraction

Distractions are often associated with negative outcomes however, distractions can also benefit people. Using the hypothesis of internal-external distractions in the competition for cue, this study examined the effects of an active (controlled) and passive (uncontrolled) distraction on three endurance tasks. Participants (N=42), aged 20 to 23 years were assigned to three groups. Tasks and conditions were counterbalanced across groups to minimize the residual effects of fatigue, learning an intervention and other confounding variables. Performance time, heart rate, ratings of perceived exertion and perceived fatigue were measured. Results showed that active distraction significantly improved performance and lowered Rate of Perceived Exertion in one task. As expected, the active distraction group was the least accurate for estimating time spent. Passive distraction caused minimal performance change. More investigation is needed to understand why an active distraction only affected one trial. Future studies should delve into means for better understanding the hypothesis of competition for cue.

scholarly journals Vocal markers of Autism Spectrum Disorder: assessing the generalizability of machine learning models

2021 ◽  
Author(s):  
Astrid Rybner ◽  
Emil Trenckner Jessen ◽  
Marie Damsgaard Mortensen ◽  
Stine Nyhus Larsen ◽  
Ruth Grossman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Model Performance ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Future Studies ◽  
Out Of Sample ◽  
Biobehavioral Markers ◽  
Similar Task ◽  
Machine Learning Models

Background: Machine learning (ML) approaches show increasing promise to identify vocal markers of Autism Spectrum Disorder (ASD). Nonetheless, it is unclear to what extent such markers generalize to new speech samples collected in diverse settings such as using a different speech task or a different language. Aim: In this paper, we systematically assess the generalizability of ML findings across a variety of contexts. Methods: We re-train a promising published ML model of vocal markers of ASD on novel cross-linguistic datasets following a rigorous pipeline to minimize overfitting, including cross-validated training and ensemble models. We test the generalizability of the models by testing them on i) different participants from the same study, performing the same task; ii) the same participants, performing a different (but similar) task; iii) a different study with participants speaking a different language, performing the same type of task. Results: While model performance is similar to previously published findings when trained and tested on data from the same study (out-of-sample performance), there is considerable variance between studies. Crucially, the models do not generalize well to new similar tasks and not at all to new languages. The ML pipeline is openly shared. Conclusion: Generalizability of ML models of vocal markers - and more generally biobehavioral markers - of ASD is an issue. We outline three recommendations researchers could take in order to be more explicit about generalizability and improve it in future studies.

scholarly journals Distilling Deep Structural Facial Relationships for FAU Intensity Estimation

2021 ◽  
Author(s):  
Yingruo Fan ◽  
Jacqueline CK Lam ◽  
Victor On Kwok Li
Keyword(s):  
Structural Characteristics ◽  
Model Performance ◽  
Training Model ◽  
Model Parameters ◽  
Intensity Estimation ◽  
Structural Relationships ◽  
Proposed Model ◽  
Comparable Performance ◽  
Knowledge Distillation ◽  
The Difference

<div> <div> <div> <p>Facial emotions are expressed through a combination of facial muscle movements, namely, the Facial Action Units (FAUs). FAU intensity estimation aims to estimate the intensity of a set of structurally dependent FAUs. Contrary to the existing works that focus on improving FAU intensity estimation, this study investigates how knowledge distillation (KD) incorporated into a training model can improve FAU intensity estimation efficiency while achieving the same level of performance. Given the intrinsic structural characteristics of FAU, it is desirable to distill deep structural relationships, namely, DSR-FAU, using heatmap regression. Our methodology is as follows: First, a feature map-level distillation loss was applied to ensure that the student network and the teacher network share similar feature distributions. Second, the region-wise and channel-wise relationship distillation loss functions were introduced to penalize the difference in structural relationships. Specifically, the region-wise relationship can be represented by the structural correlations across the facial features, whereas the channel-wise relationship is represented by the implicit FAU co-occurrence dependencies. Third, we compared the model performance of DSR-FAU with the state-of-the-art models, based on two benchmarking datasets. Our proposed model achieves comparable performance with other baseline models, though requiring a lower number of model parameters and lower computation complexities. </p> </div> </div> </div>

scholarly journals Ba incorporation in benthic foraminifera

2017 ◽  
Author(s):  
Lennart J. de Nooijer ◽  
Anieke Brombacher ◽  
Antje Mewes ◽  
Gerald Langer ◽  
Gernot Nehrke ◽  
...  
Keyword(s):  
Trace Elements ◽  
Benthic Foraminifera ◽  
General Trend ◽  
Seawater Chemistry ◽  
Minor And Trace Elements ◽  
Foraminiferal Species ◽  
Element Partitioning ◽  
The Difference ◽  
Species Specific ◽  
New Applications

Abstract. Barium (Ba) incorporated in the calcite of many foraminiferal species is proportional to the concentration of Ba in seawater. Since the open ocean concentration of Ba closely follows seawater alkalinity, foraminiferal Ba/Ca can be used to reconstruct the latter. Alternatively, Ba/Ca from foraminiferal shells can also be used to reconstruct salinity in coastal settings where seawater Ba concentration corresponds to salinity as rivers contain much more Ba than seawater. Incorporation of a number of minor and trace elements is known to vary (greatly) between foraminiferal species and application of element/Ca ratios thus requires the use of species-specific calibrations. Here we show that calcite Ba/Ca correlates positively and linearly with seawater Ba/Ca in cultured specimens of two species of benthic foraminifera, Heterostegina depressa and Amphistegina lessonii. The slopes of the regression, however, vary 2–3 fold between these two species (0.33 and 0.78, respectively). This difference in Ba-partitioning resembles the difference in partitioning of other elements (Mg, Sr, B, Li and Na) in these foraminiferal taxa. A general trend across element partitioning for different species is described, which may help developing new applications of trace elements in foraminiferal calcite in reconstructing past seawater chemistry.

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