A sensitivity analysis method for the body segment inertial parameters based on ground reaction and joint moment regressor matrices

This paper presents a method allowing a simple and efficient sensitivity analysis of dynamics parameters of complex whole-body human model. The proposed method is based on the ground reaction and joint moment regressor matrices, developed initially in robotics system identification theory, and involved in the equations of motion of the human body. The regressor matrices are linear relatively to the segment inertial parameters allowing us to use simple sensitivity analysis methods. The sensitivity analysis method was applied over gait dynamics and kinematics data of nine subjects and with a 15 segments 3D model of the locomotor apparatus. According to the proposed sensitivity indices, 76 segments inertial parameters out the 150 of the mechanical model were considered as not influent for gait. The main findings were that the segment masses were influent and that, at the exception of the trunk, moment of inertia were not influent for the computation of the ground reaction forces and moments and the joint moments. The same method also shows numerically that at least 90% of the lower-limb joint moments during the stance phase can be estimated only from a force-plate and kinematics data without knowing any of the segment inertial parameters.     

First case of V180I rare mutation in a Brazilian patient with Creutzfeldt-Jakob disease

Here, we report the first case of V180I rare mutation in a Brazilian woman whose clinical condition started with memory impairment for recent events and insomnia with 2 months of evolution, without any other alterations in neurological examination. Both the electroencephalogram (EEG) and the routine biochemical examination of cerebrospinal fluid (CSF) were normal. CSF 14-3-3 protein search was positive. Magnetic resonance imaging (MRI) of the encephalon showed findings suggestive of Creutzfeldt-Jakob disease, confirmed by sequencing of PRNP gene that reveal V180I mutation also homozygosity for methionine at codon 129 (M129M).     

Estimating and interpreting migration of Amazonian forests using spatially implicit and semi‐explicit neutral models

With many sophisticated methods available for estimating migration, ecologists face the difficult decision of choosing for their specific line of work. Here we test and compare several methods, performing sanity and robustness tests, applying to large‐scale data and discussing the results and interpretation. Five methods were selected to compare for their ability to estimate migration from spatially implicit and semi‐explicit simulations based on three large‐scale field datasets from South America (Guyana, Suriname, French Guiana and Ecuador). Space was incorporated semi‐explicitly by a discrete probability mass function for local recruitment, migration from adjacent plots or from a metacommunity. Most methods were able to accurately estimate migration from spatially implicit simulations. For spatially semi‐explicit simulations, estimation was shown to be the additive effect of migration from adjacent plots and the metacommunity. It was only accurate when migration from the metacommunity outweighed that of adjacent plots, discrimination, however, proved to be impossible. We show that migration should be considered more an approximation of the resemblance between communities and the summed regional species pool. Application of migration estimates to simulate field datasets did show reasonably good fits and indicated consistent differences between sets in comparison with earlier studies. We conclude that estimates of migration using these methods are more an approximation of the homogenization among local communities over time rather than a direct measurement of migration and hence have a direct relationship with beta diversity. As betadiversity is the result of many (non)‐neutral processes, we have to admit that migration as estimated in a spatial explicit world encompasses not only direct migration but is an ecological aggregate of these processes. The parameter m of neutral models then appears more as an emerging property revealed by neutral theory instead of being an effective mechanistic parameter and spatially implicit models should be rejected as an approximation of forest dynamics.     

Geographical structure,narrow species ranges,and Cenozoic diversification in a pantropical clade of epiphyllous leafy liverworts

The evolutionary history and classification of epiphyllous cryptogams are still poorly known. Leptolejeunea is a largely epiphyllous pantropical liverwort genus with about 25 species characterized by deeply bilobed underleaves, elliptic to narrowly obovate leaf lobes, the presence of ocelli, and vegetative reproduction by cladia. Sequences of three chloroplast regions (rbcL, trnL‐F, psbA) and the nuclear ribosomal ITS region were obtained for 66 accessions of Leptolejeunea and six outgroup species to explore the phylogeny, divergence times, and ancestral areas of this genus. The phylogeny was estimated using maximum‐likelihood and Bayesian inference approaches, and divergence times were estimated with a Bayesian relaxed clock method. Leptolejeunea likely originated in Asia or the Neotropics within a time interval from the Early Eocene to the Late Cretaceous (67.9 Ma, 95% highest posterior density [HPD]: 47.9–93.7). Diversification of the crown group initiated in the Eocene or early Oligocene (38.4 Ma, 95% HPD: 27.2–52.6). Most species clades were established in the Miocene. Leptolejeunea epiphylla and L. schiffneri originated in Asia and colonized African islands during the Plio‐Pleistocene. Accessions of supposedly pantropical species are placed in different main clades. Several monophyletic morphospecies exhibit considerable sequence variation related to a geographical pattern. The clear geographic structure of the Leptolejeunea crown group points to evolutionary processes including rare long‐distance dispersal and subsequent speciation. Leptolejeunea may have benefitted from the large‐scale distribution of humid tropical angiosperm forests in the Eocene.     

Simultaneous estimation of diet composition and calibration coefficients with fatty acid signature data

Knowledge of animal diets provides essential insights into their life history and ecology, although diet estimation is challenging and remains an active area of research. Quantitative fatty acid signature analysis (QFASA) has become a popular method of estimating diet composition, especially for marine species. A primary assumption of QFASA is that constants called calibration coefficients, which account for the differential metabolism of individual fatty acids, are known. In practice, however, calibration coefficients are not known, but rather have been estimated in feeding trials with captive animals of a limited number of model species. The impossibility of verifying the accuracy of feeding trial derived calibration coefficients to estimate the diets of wild animals is a foundational problem with QFASA that has generated considerable criticism. We present a new model that allows simultaneous estimation of diet composition and calibration coefficients based only on fatty acid signature samples from wild predators and potential prey. Our model performed almost flawlessly in four tests with constructed examples, estimating both diet proportions and calibration coefficients with essentially no error. We also applied the model to data from Chukchi Sea polar bears, obtaining diet estimates that were more diverse than estimates conditioned on feeding trial calibration coefficients. Our model avoids bias in diet estimates caused by conditioning on inaccurate calibration coefficients, invalidates the primary criticism of QFASA, eliminates the need to conduct feeding trials solely for diet estimation, and consequently expands the utility of fatty acid data to investigate aspects of ecology linked to animal diets.     

Estimating Corporate Carbon Footprints with Externally Available Data

Corporate carbon footprints (CCFs) are a core tool in greenhouse gas emissions reporting. Established approaches for CCF calculation are based on an internal perspective that requires detailed corporate information. However, many firms do not publish information about their emissions. We seek to close this data gap by estimating scope 1 and 2 CCFs from an external perspective. The study uses a regression analysis approach, using actual firm‐internally computed CCFs to assess their degree of predictability from the outside. Data were collected from 93 European companies belonging to the chemicals, construction and engineering, and industrial machinery sectors. As predictors, we use five measures that are computed with publicly available corporate data: firm size; level of vertical integration; capital intensity; centrality of production; and carbon intensity of the national energy mix. The analysis shows that significant explanatory power for the CCF can be observed for size, capital intensity, and centrality of production. The best estimation results are achieved when data from different sectors are integrated into a comprehensive all‐sector model, while accounting for sector‐specific emission intensities by means of dummy variables. With an adjusted R² value of 0.817, the proposed procedure estimates CCFs in an accurate, yet also efficient, manner. Moreover, the study enhances trust in the current CCF calculation practices by showing that their results are plausible from a third‐party perspective.     

Dealing with many correlated covariates in capture–recapture models

Capture–recapture models for estimating demographic parameters allow covariates to be incorporated to better understand population dynamics. However, high-dimensionality and multicollinearity can hamper estimation and inference. Principal component analysis is incorporated within capture–recapture models and used to reduce the number of predictors into uncorrelated synthetic new variables. Principal components are selected by sequentially assessing their statistical significance. We provide an example on seabird survival to illustrate our approach. Our method requires standard statistical tools, which permits an efficient and easy implementation using standard software.