Multiple Model Approach Research Articles

Habitat‐mediated reef fish assemblages in coral reef tidal pools

AbstractTide pools are important environments for several fish species, as they offer essential resources such as shelter and food. Thus, understanding the relationships of the ichthyofauna with the habitat structural characteristics in tide pools is important to understand the distribution patterns of fish assemblages in shallow environments. This study investigated how the habitat structure in tide pools influences the reef fish assemblages in Southwestern Atlantic (Bahia, Brazil). During 17 months, tidal pools were evaluated through visual censuses aiming to sampling the ichthyofauna, and additionally, variables of the structural characteristics of the pools (depth, area, volume, rugosity, etc.) were measured. By using multiple statistical modeling approaches, we found that there were significant differences in diversity, abundance, and species richness at the interaction between time and space factors, with higher averages of the three variables during the warmer months across all reef banks. Moreover, at local spatial scale, greater coral‐dominated and complex pools harbored higher fish species and diversity, while algae cover was most important in shaping fish numbers. The most important variables in the structuring of the fish assemblages were the pool volume, distance from the edge of the reef, and habitat complexity, confirming our hypothesis. Resident fish species were found in pools near the out reef edge, while occasional and transient species were found in pools closer to the inland sea. Our study highlights the multi‐scale role of habitat characteristics as predictors of the fish assemblage structure in these extremely variable environments and emphasizes the need to protect these fragile and overexploited tidal pools.

Ecogeographic Divergence Linked to Dorsal Coloration in Eastern Hog-Nosed Snakes (Heterodon platirhinos)

Animal coloration can benefit fitness via its function in homeostatic regulation, communication, or camouflage. For wide-ranging taxa that are exposed to diverse climatic conditions throughout their range, spatial variation in color morphology might reflect locality-specific adaptive responses to those variable conditions. As a result, these species might vary in their color-climate associations over geographic space. Here, we integrate georeferenced photographs of adult animals with available bioclimatic data to test the hypothesis that dorsal color differences in Eastern Hog-Nosed Snakes (Heterodon platirhinos) reflect ecogeographic divergence. We first assigned each photographed snake into one of four dorsal color phases, namely, black, brown, red-orange, or yellow, and evaluated the spatial dispersion and bioclimatic niche occupancy of each phase by using multiple environmental niche modeling approaches. We then used pairwise comparisons of bioclimatic niche space to explicitly test for niche divergence among the color phases. Overall, black, brown, and red-orange phase H. platirhinos exploited different subsets of the species' geographic range and bioclimatic niche. In contrast, yellow phase snakes partly overlapped with red-orange and brown phase snakes in geographic and bioclimatic space. These findings support our hypothesis, and we discuss some of the possible functions of phase coloration.

Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios - United States, April-September 2021.

After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term projections of potential trends in COVID-19 cases, hospitalizations, and deaths, COVID-19 Scenario Modeling Hub teams used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (public health policies, such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4). Among the four scenarios, an accelerated decline in NPI adherence (which encapsulates NPI mandates and population behavior) was shown to undermine vaccination-related gains over the subsequent 2-3 months and, in combination with increased transmissibility of new variants, could lead to surges in cases, hospitalizations, and deaths. A sharp decline in cases was projected by July 2021, with a faster decline in the high-vaccination scenarios. High vaccination rates and compliance with public health prevention measures are essential to control the COVID-19 pandemic and to prevent surges in hospitalizations and deaths in the coming months.

The Impact of Nutritional Supplementation on Sweat Metabolomic Content: A Proof-of-Concept Study

Sweat is emerging as a prominent biosource for real-time human performance monitoring applications. Although promising, sources of variability must be identified to truly utilize sweat for biomarker applications. In this proof-of-concept study, a targeted metabolomics method was applied to sweat collected from the forearms of participants in a 12-week exercise program who ingested either low or high nutritional supplementation twice daily. The data establish the use of dried powder mass as a method for metabolomic data normalization from sweat samples. Additionally, the results support the hypothesis that ingestion of regular nutritional supplementation semi-quantitatively impact the sweat metabolome. For example, a receiver operating characteristic (ROC) curve of relative normalized metabolite quantities show an area under the curve of 0.82 suggesting the sweat metabolome can moderately predict if an individual is taking nutritional supplementation. Finally, a significant correlation between physical performance and the sweat metabolome are established. For instance, the data illustrate that by utilizing multiple linear regression modeling approaches, sweat metabolite quantities can predict VO2 max (p = 0.0346), peak lower body Windage (p = 0.0112), and abdominal circumference (p = 0.0425). The results illustrate the need to account for dietary nutrition in biomarker discovery applications involving sweat as a biosource.

Main sources controlling atmospheric burdens of persistent organic pollutants on a national scale

National long-term monitoring programs on persistent organic pollutants (POPs) in background air have traditionally relied on active air sampling techniques. Due to limited spatial coverage of active air samplers, questions remain (i) whether active air sampler monitoring sites are representative for atmospheric burdens within the larger geographical area targeted by the monitoring programs, and thus (ii) if the main sources affecting POPs in background air across a nation are understood. The main objective of this study was to explore the utility of spatial and temporal trends in concert with multiple modelling approaches to understand the main sources affecting polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) in background air across a nation. For this purpose, a comprehensive campaign was carried out in summer 2016, measuring POPs in background air across Norway using passive air sampling. Results were compared to a similar campaign in 2006 to assess possible changes over one decade. We furthermore used the Global EMEP Multi-media Modeling System (GLEMOS) and the Flexible Particle dispersion model (FLEXPART) to predict and evaluate the relative importance of primary emissions, secondary emissions, long-range atmospheric transport (LRAT) and national emissions in controlling atmospheric burdens of PCB-153 on a national scale. The concentrations in air of both PCBs and most of the targeted OCPs were generally low, with the exception of hexachlorobenzene (HCB). A limited spatial variability for all POPs in this study, together with predictions by both models, suggest that LRAT dominates atmospheric burdens across Norway. Model predictions by the GLEMOS model, as well as measured isomeric ratios, further suggest that LRAT of some POPs are dictated by secondary emissions. Our results illustrate the utility of combining observations and mechanistic modelling approaches to help identify the main factors affecting atmospheric burdens of POPs across a nation, which, in turn, may be used to inform both national monitoring and control strategies.

Ukrainian Migration Aspirations towards Germany: Analysis and Development Scenarios

Based on the multiple regression model and scenario approach to forecasting, the article estimates the Ukrainian migration aspirations towards Germany (the scale of migration, the economic activity of migrants, and their economic benefits). It is argued that major transformations in the gender-age structure of the German population may cause a demographic crisis and labour market imbalances. Our projections indicate the growing role of foreign human resources in the German economy. When modeling the scale of emigration from Ukraine, an integrated approach is applied, considering not only trends of pull-push factors but also special aspects of the German migration policy and the outflow of 8–10 million Ukrainian migrant workers. Given the poor statistical data on the scale of labour emigration needed for constructing reliable econometric models, the use of expert forecasting method remains the most optimal technique for assessing potential migration flows and migration systems.

Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes.

Microbiomes are complex and ubiquitous networks of microorganisms whose seemingly limitless chemical transformations could be harnessed to benefit agriculture, medicine, and biotechnology. The spatial and temporal changes in microbiome composition and function are influenced by a multitude of molecular and ecological factors. This complexity yields both versatility and challenges in designing synthetic microbiomes and perturbing natural microbiomes in controlled, predictable ways. In this review, we describe factors that give rise to emergent spatial and temporal microbiome properties and the meta-omics and computational modeling tools that can be used to understand microbiomes at the cellular and system levels. We also describe strategies for designing and engineering microbiomes to enhance or build novel functions. Throughout the review, we discuss key knowledge and technology gaps for elucidating the networks and deciphering key control points for microbiome engineering, and highlight examples where multiple omics and modeling approaches can be integrated to address these gaps.

Learning-Based Floor-Type Identification in the ZUPT-Aided Pedestrian Inertial Navigation

We present a method to identify the floor types in the pedestrian inertial navigation with the goal of adaptive estimation of navigation states under various navigation scenarios. The floor-type detection algorithm includes three major steps. First, the inertial measurement unit readouts are divided with each partition corresponding to a full human gait cycle. Second, principal component analysis is conducted on each partition to reduce dimensionality of the data. Third, the data are fed into an artificial neural network (ANN) for the floor-type identification. In this study, we identified five different floor types: walking on hard floor, walking on grass, walking on sand, walking upstairs, and walking downstairs, with an accuracy of over 99%. To verify the effects of the floor-type detection, the classification result was used to select proper parameters in the zero-velocity-update-aided pedestrian inertial navigation in the multiple model approach. Advantages of the identification approach were experimentally verified, and resulted in a reduced navigation error in the field test.

A participatory modelling approach for investigating the spread of COVID-19 in countries of the Eastern Mediterranean Region to support public health decision-making

Early on in the COVID-19 pandemic, the WHO Eastern Mediterranean Regional Office recognised the importance of epidemiological modelling to forecast the progression of the COVID-19 pandemic to support decisions guiding...

From competition to co-operation: Shifting the “one best model” perspective

Mental disorders are arguably one of the most complex and serious problems facing health practitioners today and yet their causes remain, largely, a mystery. Accordingly, there is frequent and heated debate over which of the many available models of mental disorder and their associated therapeutic interventions are likely to be most useful. The prevailing attitude toward the conceptualization of mental distress appears to be that a single superior model will emerge that neatly accounts for one or more disorder presentations: the “One Best Model” (OBM) perspective. In this article, we argue for a transition away from the OBM perspective toward a multiple model approach to psychopathology that is collaborative and pluralistic. We begin by outlining the particulars of the OBM perspective and elaborating on the problems it presents for psychopathology theory and practice. We then suggest specific ways in which this problem may be ameliorated, by adjusting how we approach the processes of modelling disorder, translating models into interventions, and applying models and interventions in practice.

Efficacy and safety of the novel glycine transporter inhibitor BI 425809 once daily in patients with schizophrenia: a double-blind, randomised, placebo-controlled phase 2 study

Cognitive impairment associated with schizophrenia predicts poor functional outcomes, but currently no approved pharmacotherapy is available. This study investigated whether the glycine transporter-1 inhibitor BI 425809 improves cognition in patients with schizophrenia. This phase 2, randomised, double-blind, placebo-controlled, parallel-group trial (81 centres, 11 countries), randomly assigned outpatients (aged 18-50 years) with schizophrenia on stable treatment to add-on once-daily oral BI 425809 2 mg, 5 mg, 10 mg, or 25 mg or placebo (1:1:1:1:2) for 12 weeks. Treatment was assigned in blocks using interactive response technology; patients, investigators, and all trial personnel were masked to group assignment. The primary endpoint was change from baseline in MATRICS Consensus Cognitive Battery (MCCB) overall composite T-score at week 12. Six predefined dose-response models were evaluated using a multiple comparison procedure and modelling approach with mixed model repeated measures to assess evidence for a non-flat dose-response relationship for cognitive improvements with BI 425809. Adverse events were monitored. Safety analyses included all randomly allocated patients who received one or more doses of trial medication; efficacy analyses included patients from this set who also had available baseline data and at least one post-baseline on-treatment measurement for the primary or secondary endpoint. This study is registered with ClinicalTrials.gov, number NCT02832037. 509 patients were randomly assigned between April 25, 2018, and Oct 4, 2019 (BI 425809 2 mg, n=85; 5 mg, n=84; 10 mg, n=85; 25 mg, n=85; placebo, n=170 444 (87%) completed the 12-week treatment. Five of six dose-response models showed a statistically significant benefit of BI 425809 over placebo (linear [t=2·55, p=0·015], linear in log [t=2·56, p=0·015]; Emax [t=2·75, p=0·0089], sigmoid Emax [t=2·98, p=0·0038], logistic [t=2·77, p=0·0085]). Pairwise comparisons showed greater mean improvement from baseline in MCCB overall composite T-score at week 12 with BI 425809 10 mg and 25 mg versus placebo (adjusted mean difference 1·98 [95% CI 0·43-3·53] for 10 mg and 1·73 [0·18-3·28] for 25 mg; standardised effect size 0·34 for 10 mg and 0·30 for 25 mg). Adverse events were balanced across groups, reported in 50 (59%) of 85 patients on BI 425809 2 mg, 44 (52%) of 84 on 5 mg, 35 (41%) of 85 on 10 mg, 36 (42%) of 85 on 25 mg, and 74 (44%) of 170 on placebo. BI 425809 improved cognition after 12 weeks in patients with schizophrenia; doses of 10 mg and 25 mg showed the largest separation from placebo. If these encouraging results are confirmed in phase 3 trials, BI 425809 could provide an effective treatment for cognitive impairment associated with schizophrenia. Boehringer Ingelheim.

Predicting the Safety Climate in Construction Sites of Saudi Arabia: A Bootstrapped Multiple Ordinal Logistic Regression Modeling Approach

Construction site accidents can be reduced through proactive steps using prediction models developed based on factors that influence the safety climate. In this study, a prediction model of the safety climate observed by construction site personnel in Saudi Arabia was developed, identifying a set of significant safety climate predictors. The model was built with data collected from 401 active construction site personnel using a bootstrapped multiple ordinal logistic regression model. The model revealed five significant predictors: supervision, guidance, and inspection; social security and health insurance; management’s commitment to safety; management’s safety justice; and coworker influence. The model can correctly predict 67% of the safety evaluations. The identified predictors present proof of the importance of safety support, commitment, and interaction in construction sites and their influence on the perceived evaluations of the safety climate by personnel. Moreover, the prediction model can help construction industry decision makers, safety policy designers, government agencies, and stakeholders to estimate the safety climate and assess the current situation. Furthermore, the model can help form a better understanding and determine areas of improvement, which can translate into higher safety performance levels.

H3.3-K27M drives neural stem cell-specific gliomagenesis in a human iPSC-derived model.

Diffuse intrinsic pontine glioma (DIPG) is an aggressive childhood tumor of the brainstem with currently no curative treatment available. The vast majority of DIPGs carry a histone H3 mutation leading to a lysine 27-to-methionine exchange (H3K27M). We engineered human induced pluripotent stem cells (iPSCs) to carry an inducible H3.3-K27M allele in the endogenous locus and studied the effects of the mutation in different disease-relevant neural cell types. H3.3-K27M upregulated bivalent promoter-associated developmental genes, producing diverse outcomes in different cell types. While being fatal for iPSCs, H3.3-K27M increased proliferation in neural stem cells (NSCs) and to a lesser extent in oligodendrocyte progenitor cells (OPCs). Only NSCs gave rise to tumors upon induction of H3.3-K27M and TP53 inactivation in an orthotopic xenograft model recapitulating human DIPGs. In NSCs, H3.3-K27M leads to maintained expression of stemness and proliferative genes and a premature activation of OPC programs that together may cause tumor initiation.

LPV system identification with multiple-model approach based on shifted asymmetric laplace distribution

ABSTRACT The robust linear parameters varying systems identification method with multiple-model approach is addressed in this paper. Various noise and outliers commonly exist in practical industrial processes and have a serious impact on data-driven system identification methods. A statistic approach is proposed in the paper where the centralised asymmetric Laplace (CAL) distribution is employed to model the noise and therefore the parameters estimation algorithm based on CAL distribution is robust to the symmetric/asymmetric noise and outliers. CAL distribution is represented as the product of exponential distribution and Gaussian distribution, and therefore the parameters estimation formulas are deduced in the expectation maximisation algorithm framework. The efficacy and the robustness of the proposed algorithm are verified by a numerical example and the continuous stirred tank reactor system experiment.

Optimal control based on multiple models approach of chaotic switched systems, application to a stepper motor

In this paper, we are interested in the control of chaotic switched systems with application to a stepper motor. The aim of the control is to determine the optimal sequence of switching instants in order to bring the chaotic behaviour of the system to a periodic one. The determination of the optimal sequence will follow two steps using an optimisation algorithm, the Hamiltonian system and the derivative of a performance criterion over the switching instants. The nonlinear switched system is modelled by a multiple linear time-invariant models approach to make possible to calculate the gradient of the cost function and therefore to determine the optimal instants of switches. Simulations are applied to a stepper motor to illustrate the results.

An evaluation of source apportionment of fine OC and PM2.5 by multiple methods: APHH-Beijing campaigns as a case study.

This study aims to critically evaluate the source apportionment of fine particles by multiple receptor modelling approaches, including carbon mass balance modelling of filter-based radiocarbon (14C) data, Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF) analysis on filter-based chemical speciation data, and PMF analysis on Aerosol Mass Spectrometer (AMS-PMF) or Aerosol Chemical Speciation Monitor (ACSM-PMF) data. These data were collected as part of the APHH-Beijing (Atmospheric Pollution and Human Health in a Chinese Megacity) field observation campaigns from 10th November to 12th December in winter 2016 and from 22nd May to 24th June in summer 2017. 14C analysis revealed the predominant contribution of fossil fuel combustion to carbonaceous aerosols in winter compared with non-fossil fuel sources, which is supported by the results from other methods. An extended Gelencsér (EG) method incorporating 14C data, as well as the CMB and AMS/ACSM-PMF methods, generated a consistent source apportionment for fossil fuel related primary organic carbon. Coal combustion, traffic and biomass burning POC were comparable for CMB and AMS/ACSM-PMF. There are uncertainties in the EG method when estimating biomass burning and cooking OC. The POC from cooking estimated by different methods was poorly correlated, suggesting a large uncertainty when differentiating this source type. The PM2.5 source apportionment results varied between different methods. Through a comparison and correlation analysis of CMB, PMF and AMS/ACSM-PMF, the CMB method appears to give the most complete and representative source apportionment of Beijing aerosols. Based upon the CMB results, fine aerosols in Beijing were mainly secondary inorganic ion formation, secondary organic aerosol formation, primary coal combustion and from biomass burning emissions.

Modeling Oil Production, CO2 Injection and Associated Storage in Depleted Oil Reservoirs: A Comparison of Approaches

The Midwest Regional Carbon Sequestration Partnership has been investigating various reservoir characterization and modeling technologies as part of its commercial-scale implementation of carbon dioxide injection for geologic storage in multiple Silurian carbonate pinnacle reefs in northern Michigan, USA. This paper compares multiple reservoir modeling approaches for history-matching oil production and CO2 injection responses, and estimating associated storage, to characterize these small spatial footprint depleted reef reservoirs. The three approaches considered are: fully compositional simulation, black-oil with pseudo-miscibility treatment, and capacitance resistance modeling (CRM). Modeling results from three reefs illustrating each modeling approach are presented, and their applicability and limitations with respect to data needs and modeling objectives are discussed.

Induced OWA Operator for Group Decision Making Dealing with Extended Comparative Linguistic Expressions with Symbolic Translation

Nowadays, decision making problems have increased their complexity and a single decision maker cannot handle these problems, with a more diverse and comprehensive view of them being necessary, which results in group decision making (GDM) schemes. The complexity of GDM problems is often due to their inherent uncertainty that is not solved just by using a group. Consequently, different methodologies has been proposed to handle it, in which, the use of the fuzzy linguistic approach stands out. Among the multiple fuzzy linguistic modeling approaches, Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) information has been recently introduced, which enhances classical linguistic modeling that is based on single terms by providing linguistic expressions in a continuous linguistic domain. Its application to decision making is quite promising, but it is necessary to develop enough operators to accomplish aggregation processes in the decision solving scheme. So far, just a small number of aggregation operators have been defined for ELICIT information. Hence, this paper aims at providing new aggregation operators for ELICIT information by developing novel OWA based operators, such as the Induced OWA (IOWA) operator in order to avoid the OWA operator needs of reordering its arguments, because ELICIT information does not have an inherent order due to its fuzzy representation. Our proposal not only consists of extending the definition of an IOWA operator for ELICIT information with crisp weights, but it is also proposed a type-1 IOWA operator for ELICIT information in which both weights and arguments are fuzzy as well as the use of ELICIT information constructing the order inducing variable to reorder the arguments. Additionally, the use of ELICIT information in GDM demands the ability to manage majority based decisions that are better represented in the IOWA operator by linguistic quantifiers. Hence, a majority-driven GDM process for ELICIT information is proposed, which it is the first proposal for fulfilling the majority solving process for GDM while using ELICIT information. Eventually, an illustrative example and a brief comparative analysis are presented in order to show the performance of the proposal and its feasibility.

Optimal control based on multiple models approach of chaotic switched systems, application to a stepper motor

In this paper, we are interested in the control of chaotic switched systems with application to a stepper motor. The aim of the control is to determine the optimal sequence of switching instants in order to bring the chaotic behaviour of the system to a periodic one. The determination of the optimal sequence will follow two steps using an optimisation algorithm, the Hamiltonian system and the derivative of a performance criterion over the switching instants. The nonlinear switched system is modelled by a multiple linear time-invariant models approach to make possible to calculate the gradient of the cost function and therefore to determine the optimal instants of switches. Simulations are applied to a stepper motor to illustrate the results.

Benthic hotspots on the northern Bering and Chukchi continental shelf: Spatial variability in production regimes and environmental drivers

Benthic biological hotspots with persistently high macrofaunal biomass exist on the highly advective continental shelf that extends from the northern Bering Sea to the northeast Chukchi Sea. Environmental factors that influence carbon export to the benthos, a key driver for hotspot formation and persistence, remain uncertain. Multiple modeling approaches were used to better understand the combined effects of biological production and physical transport processes on supplying biogenic materials to those biological hotspots. Large data sets of benthic and environmental observations were synthesized, outputs from a pan-arctic ice-ocean-biogeochemical model were analyzed, and particle tracking modeling experiments and statistical analyses were conducted. Two different biophysical mechanisms of biogenic material supply to five benthic hotspot subdomains over a latitudinal range were identified using models and verified using data synthesis. Two hotpots to the south and the north of Bering Strait and the third one in southern Barrow Canyon heavily rely on carbon supplied from upstream biological production. In contrast, the St. Lawrence Island Polynya, southwest of St. Lawrence Island in the northern Bering Sea, and the Northeast Chukchi Sea hotspots are mostly fueled by local production. Spatial statistical modeling of benthic biomass distribution generally recaptured known hotspots but also suggested the likelihood of other probable hotspots in subregions of the biologically productive Gulf of Anadyr and of the topographically controlled Herald Canyon where limited sampling has occurred. The study provides new mechanistic understandings of the oceanographic processes and biophysical interactions that produce organic matter in sea ice and in the water column that subsequently is exported to underlying benthic communities. Combining data synthesis with process-based modeling was critical in understanding the dynamics of these sympagic-pelagic-benthic ecosystems and the potential climate-change-induced ecosystem response in the Pacific Arctic region.