scholarly journals Lessons Learned from Spectranomics: Wet Tropical Forests

2020 ◽  
pp. 105-120
Author(s):  
Roberta E. Martin
Keyword(s):  
Tropical Forests ◽  
Spectral Properties ◽  
Lessons Learned ◽  
Time Varying ◽  
Plant Canopy ◽  
Functional Changes ◽  
Biodiversity Science ◽  
Scalable Methods ◽  
Changes Over Time ◽  
Conservation And Management

AbstractOne of the major struggles for biodiversity science is how to measure biodiversity at scales relevant for conservation and management, particularly in wet tropical forests where vast, largely inaccessible landscapes and enormous taxonomic variation make field-based approaches alone infeasible, and current Earth-observing satellites are unable to detect compositional differences or forest functional changes over time. The Spectranomics approach was developed to link plant canopy functional traits to their spectral properties with the objective of providing time-varying, scalable methods for remote sensing (RS) of forest biodiversity. In this chapter we explain key components of Spectranomics and highlight some of the major lessons learned over the past decade as we developed the program in tropical forests sites around the world.

Selective Linear Segmentation for Detecting Relevant Parameter Changes*

2020 ◽  
Author(s):  
Arnaud Dufays ◽  
Elysee Aristide Houndetoungan ◽  
Alain Coën
Keyword(s):  
Time Series ◽  
Hedge Fund ◽  
Expectation Maximization Algorithm ◽  
Model Parameters ◽  
Relevant Parameter ◽  
Time Varying ◽  
Long Time ◽  
Model Selection Uncertainty ◽  
Penalized Likelihood Approach ◽  
Changes Over Time

Abstract Change-point (CP) processes are one flexible approach to model long time series. We propose a method to uncover which model parameters truly vary when a CP is detected. Given a set of breakpoints, we use a penalized likelihood approach to select the best set of parameters that changes over time and we prove that the penalty function leads to a consistent selection of the true model. Estimation is carried out via the deterministic annealing expectation-maximization algorithm. Our method accounts for model selection uncertainty and associates a probability to all the possible time-varying parameter specifications. Monte Carlo simulations highlight that the method works well for many time series models including heteroskedastic processes. For a sample of fourteen hedge fund (HF) strategies, using an asset-based style pricing model, we shed light on the promising ability of our method to detect the time-varying dynamics of risk exposures as well as to forecast HF returns.

scholarly journals Body weight at 10 years of age and change in body composition between 8 and 10 years of age were related to survival in a longitudinal study of 39 Labrador retriever dogs

2019 ◽  
Vol 61 (1) ◽  
Author(s):  
Johanna Christina Penell ◽  
David Mark Morgan ◽  
Penny Watson ◽  
Stuart Carmichael ◽  
Vicki Jean Adams
Keyword(s):  
Body Composition ◽  
Body Weight ◽  
End Of Life ◽  
Fat Mass ◽  
Lean Mass ◽  
Coat Colour ◽  
Overweight And Obesity ◽  
Time Varying ◽  
Cohort Study Design ◽  
Changes Over Time

Abstract Background Overweight and obesity have been adversely associated with longevity in dogs but there is scarce knowledge on the relation between body composition and lifespan. We aimed to investigate the effects of body composition, and within-dog changes over time, on survival in adult Labradors using a prospective cohort study design. The dogs had a median age of 6.5 years at study start and were kept in similar housing and management conditions throughout. The effects of the various predictors, including the effect of individual monthly-recorded change in body weight as a time varying covariate, were evaluated using survival analysis. Results All dogs were followed to end-of-life; median age at end-of-life was 14.0 years. Body composition was measured annually with dual-energy x-ray absorptiometer (DEXA) scans between 6.2 and 17.0  years. All 39 dogs had DEXA recorded at 8, 9 and 10 years of age. During the study the mean (± SD) percent of fat (PF) and lean mass (PL) was 32.8 (± 5.6) and 64.2 (± 5.5) %, respectively, with a mean lean:fat ratio (LFR) of 2.1 (± 0.6); body weight (BW) varied from 17.5 to 44.0 kg with a mean BW change of 9.9 kg (± 3.0). There was increased hazard of dying for every kg increase in BW at 10 years of age; for each additional kg of BW at 10 years, dogs had a 19% higher hazard (HR = 1.19, P = 0.004). For the change in both lean mass (LM) and LFR variables, it was protective to have a higher lean and/or lower fat mass (FM) at 10 years of age compared to 8 years of age, although the HR for change in LM was very close to 1.0. For age at study start, older dogs had an increased hazard. There was no observed effect for the potential confounders sex, coat colour and height at shoulders, or of the time-varying covariate. Conclusions These results suggest that even rather late-life control efforts on body weight and the relationship between lean and fat mass may influence survival in dogs. Such “windows of opportunity” can be used to develop healthcare strategies that would help promote an increased healthspan in dogs.

Time-Varying Delay Estimation Applied to the Surface Electromyography Signals Using the Parametric Approach

2018 ◽  
Vol 17 (02) ◽  
pp. 1850015 ◽  
Author(s):  
Gia Thien Luu ◽  
Abdelbassit Boualem ◽  
Tran Trung Duy ◽  
Philippe Ravier ◽  
Olivier Butteli
Keyword(s):  
Parametric Method ◽  
Optimization Technique ◽  
Likelihood Estimation ◽  
Model Parameters ◽  
Fiber Direction ◽  
Time Varying ◽  
Time Varying Delay ◽  
Power Spectral ◽  
Changes Over Time ◽  
Varying Delay

Muscle Fiber Conduction Velocity (MFCV) can be calculated from the time delay between the surface electromyographic (sEMG) signals recorded by electrodes aligned with the fiber direction. In order to take into account the non-stationarity during the dynamic contraction (the most daily life situation) of the data, the developed methods have to consider that the MFCV changes over time, which induces time-varying delays and the data is non-stationary (change of Power Spectral Density (PSD)). In this paper, the problem of TVD estimation is considered using a parametric method. First, the polynomial model of TVD has been proposed. Then, the TVD model parameters are estimated by using a maximum likelihood estimation (MLE) strategy solved by a deterministic optimization technique (Newton) and stochastic optimization technique, called simulated annealing (SA). The performance of the two techniques is also compared. We also derive two appropriate Cramer–Rao Lower Bounds (CRLB) for the estimated TVD model parameters and for the TVD waveforms. Monte-Carlo simulation results show that the estimation of both the model parameters and the TVD function is unbiased and that the variance obtained is close to the derived CRBs. A comparison with non-parametric approaches of the TVD estimation is also presented and shows the superiority of the method proposed.

scholarly journals Viral phylodynamics and the search for an ‘effective number of infections’

2010 ◽  
Vol 365 (1548) ◽  
pp. 1879-1890 ◽  
Author(s):  
Simon D. W. Frost ◽  
Erik M. Volz
Keyword(s):  
Exponential Growth ◽  
Initial Phase ◽  
Epidemiological Data ◽  
Time Varying ◽  
Effective Number ◽  
Epidemiological Models ◽  
Effective Population ◽  
Coalescence Rate ◽  
Changes Over Time ◽  
Over Time

Information on the dynamics of the effective population size over time can be obtained from the analysis of phylogenies, through the application of time-varying coalescent models. This approach has been used to study the dynamics of many different viruses, and has demonstrated a wide variety of patterns, which have been interpreted in the context of changes over time in the ‘effective number of infections’, a quantity proportional to the number of infected individuals. However, for infectious diseases, the rate of coalescence is driven primarily by new transmissions i.e. the incidence, and only indirectly by the number of infected individuals through sampling effects. Using commonly used epidemiological models, we show that the coalescence rate may indeed reflect the number of infected individuals during the initial phase of exponential growth when time is scaled by infectivity, but in general, a single change in time scale cannot be used to estimate the number of infected individuals. This has important implications when integrating phylogenetic data in the context of other epidemiological data.

scholarly journals Inspecting Gradual and Abrupt Changes in Emotion Dynamics With the Time-Varying Change Point Autoregressive Model

2020 ◽  
Vol 36 (3) ◽  
pp. 492-499
Author(s):  
Casper J. Albers ◽  
Laura F. Bringmann
Keyword(s):  
Negative Affect ◽  
Positive Affect ◽  
Change Point ◽  
A Priori ◽  
Ar Model ◽  
Time Varying ◽  
Abrupt Changes ◽  
Emotion Dynamics ◽  
Changes Over Time ◽  
Over Time

Abstract. Recent studies have shown that emotion dynamics such as inertia (i.e., autocorrelation) can change over time. Importantly, current methods can only detect either gradual or abrupt changes in inertia. This means that researchers have to choose a priori whether they expect the change in inertia to be gradual or abrupt. This will leave researchers in the dark regarding when and how the change in inertia occurred. Therefore in this article, we use a new model: the time-varying change point autoregressive (TVCP-AR) model. The TVCP-AR model can detect both gradual and abrupt changes in emotion dynamics. More specifically, we show that the inertia of positive affect and negative affect measured in one individual differs qualitatively in how it changes over time. Whereas the inertia of positive affect increased only gradually over time, negative affect changed both in a gradual and abrupt fashion over time. This illustrates the necessity of being able to model both gradual and abrupt changes in order to detect meaningful quantitative and qualitative differences in temporal emotion dynamics.

scholarly journals Bioinformatics as a Tool for the Structural and Evolutionary Analysis of Proteins

2020 ◽  
Author(s):  
Edna María Hernández-Domínguez ◽  
Laura Sofía Castillo-Ortega ◽  
Yarely García-Esquivel ◽  
Virginia Mandujano-González ◽  
Gerardo Díaz-Godínez ◽  
...  
Keyword(s):  
Biological Molecules ◽  
Evolutionary Analysis ◽  
Evolutionary Processes ◽  
Regulation Of Expression ◽  
Computational Mathematics ◽  
Functional Changes ◽  
Sequencing Technologies ◽  
Metabolomic Data ◽  
Changes Over Time

This chapter deals with the topic of bioinformatics, computational, mathematics, and statistics tools applied to biology, essential for the analysis and characterization of biological molecules, in particular proteins, which play an important role in all cellular and evolutionary processes of the organisms. In recent decades, with the next generation sequencing technologies and bioinformatics, it has facilitated the collection and analysis of a large amount of genomic, transcriptomic, proteomic, and metabolomic data from different organisms that have allowed predictions on the regulation of expression, transcription, translation, structure, and mechanisms of action of proteins as well as homology, mutations, and evolutionary processes that generate structural and functional changes over time. Although the information in the databases is greater every day, all bioinformatics tools continue to be constantly modified to improve performance that leads to more accurate predictions regarding protein functionality, which is why bioinformatics research remains a great challenge.

Time‐varying spectral properties of chaotic otoacoustic emissions

1990 ◽  
Vol 88 (S1) ◽  
pp. S17-S17
Author(s):  
Douglas H. Keefe ◽  
Edward M. Burns ◽  
Robert Ling
Keyword(s):  
Otoacoustic Emissions ◽  
Spectral Properties ◽  
Time Varying

scholarly journals Lessons Learned From Food & Fitness About Building Successful Partnerships: Focus, Capacity, and Sustainability

2018 ◽  
Vol 19 (1_suppl) ◽  
pp. 115S-124S ◽  
Author(s):  
Laurie Lachance ◽  
Martha Quinn ◽  
Theresa Kowalski-Dobson
Keyword(s):  
The United States ◽  
Community Partnerships ◽  
Lessons Learned ◽  
Children And Families ◽  
Sustainable Change ◽  
Community Residents ◽  
Implementation Period ◽  
Changes Over Time ◽  
Locally Grown ◽  
Over Time

Approaches undertaken by the Food & Fitness (F&F) community partnerships demonstrate that engaging community residents in the process of creating systems change strengthens the ability of neighborhoods, organizations, and institutions to foster and sustain those changes over time. The F&F partnerships were established to increase access to locally grown food and safe places for physical activity for children and families in communities with inequities across the United States. A critical focus of this initiative has been to use community-determined approaches to create changes in policies, infrastructures, and systems that will lead not only to change but also to sustainable change that positively influences health equity. During the 9 years of the initiative, lessons were learned about the fundamental elements that built the foundation for success across all partnership work. Data were extracted from the systems and policy change tracking forms related to efforts for all F&F sites over the entire implementation period (2009-2016). Documentation related to both the process and outcomes of the efforts were qualitatively analyzed to determine factors related to success. The following factors have emerged from our analyses and uncover a deeper understanding of what actions and factors were critical for the work: focus of the work over time, capacity built in the partnerships, and sustainability of the work and outcomes.

The Challenges of Rural Electrification in Canada

2020 ◽  
pp. 239-253
Author(s):  
Alastair Lucas
Keyword(s):  
Social Justice ◽  
Yukon Territory ◽  
Lessons Learned ◽  
Rural Electrification ◽  
Public Power ◽  
Private Power ◽  
Energy Justice ◽  
Changes Over Time ◽  
Geographical Economic ◽  
Over Time

There have been significant energy justice issues in the development of Canadian rural electrification (RE), particularly in the provinces of Alberta, Ontario, and the Yukon territory. In an historical approach, investigative questions concerning the nature of the problem, the discourse, solutions canvassed, and changes over time, are asked. These questions frame matters of distributive justice and social justice. Lessons learned include: (i) geographical, economic, and political context can determine the just allocation of RE benefits and burdens; and (ii) public vs private approaches to RE have developed in different provinces. However, there is little evidence that successful distributional and social justice results in RE were very different in Alberta (private power) and Ontario (public power). Legal instruments, particularly Rural Electrification Association co-operatives, played an important role in Alberta.

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