A computational neural model for mapping degenerate neural architectures

Degeneracy in biological systems refers to a many-to-one mapping between physical structures and their functional (including psychological) outcomes. Despite the ubiquity of the phenomenon, traditional analytical tools for modeling degeneracy in neuroscience are extremely limited. In this study, we generated synthetic datasets to describe three situations of degeneracy in fMRI data to demonstrate the limitations of the current univariate approach. We describe a novel computational approach for the analysis referred to as neural topographic factor analysis (NTFA). NTFA is designed to capture variations in neural activity across task conditions and participants. The advantage of this discovery-oriented approach is to reveal whether and how experimental trials and participants cluster into task conditions and participant groups. We applied NTFA on simulated data, revealing the appropriate degeneracy assumption in all three situations and demonstrating NTFA’s utility in uncovering degeneracy. Lastly, we discussed the importance of testing degeneracy in fMRI and the implications of applying NTFA to do so.

An Omics-Inspired Elastic Net Approach Drastically Improves Out-of-Sample Prediction and Regional Inference of Task-Based fMRI

Predicting individual differences in cognitive processes is crucial, but the ability of task-based fMRI to do so remains dubious, despite decades of costly research. We tested the ability of working-memory fMRI in predicting working-memory, using the Adolescent Brain Cognitive Development (n = 4,350). The conventionally-used mass-univariate approach led to poor out-of-sample prediction (Mean r = .1-.12). However, the multivariate ‘Elastic Net’, which draws information across brain regions, enhanced out-of-sample prediction (r = .47) by several folds. The Elastic Net also enabled us to predict cognitive performance from various tasks collected outside of the scanner, highlighting its generalizability. Moreover, using an omics-inspired approach, we combined Elastic Net with permutation, allowing us to statistically infer which brain regions contribute to individual differences while accounting for collinearity. Accordingly, our framework can build an easy-to-interpret predictive fMRI model that transfers knowledge learned from large-scale datasets to smaller samples, akin to polygenic scores in genomics.

Differences in Serbian and Libyan crane operators’ anthropometric measurements and cabin interior space modeling

BACKGROUND: Previous research has shown that the collection and analysis of crane operators’ anthropometric characteristics is very important for operators’ comfort, health and working ability and also from the aspect of performance, productivity and safety. OBJECTIVE: The first aim of this survey is to collect up-to-date anthropometric data of crane operators in Serbia and to collect those data for the first time in Libya. The second aim of this survey is to compare the collected data. The third aim is to model the interior space necessary to accommodate operators in the cabins they operate in Serbia and Libya. METHODS: Standing height, sitting height, lower leg length, upper leg length, shoulder width, hip breadth, arm length, foot length and weight data were collected of 83 Serbian and 50 Libyan crane operators. Descriptive statistics, correlation analysis and tests for differences between variables were conducted to examine differences between Serbian and Libyan crane operators and enable further modeling. The modeling of the cabin interior was done using both univariate and multivariate operators’ models from both samples. RESULTS: There are only four common correlations between variables for both samples. The only measurement without statistical differences is shoulder width. Serbian crane operators have statistically higher values of almost all measurements in comparison to Libyans. The minimal cabin interior space dimensions are 1207×1080×1884 mm for Serbian operators and 1106×1040×1790 mm for Libyan operators when using univariate approach, while multivariate approach provides more precise and comfortable accommodation within 1327×1123×1926 mm for Serbian operators and 1203×1090×1830 mm for Libyan operators. Accordingly, our survey shows that percentile models include less than the intended population proportion in the design problem with few dimensions and depends on correlations among them. CONCLUSIONS: A number of problems are solved through modeling the crane operator workspace and it has been shown that cabins with different dimensions have to be offered to Serbian and Libyan markets.

As sociedades neandertais no Barlavento algarvio: modelos preditivos com recurso aos SIG

Studies related to Neanderthal populations in the western Algarve indicate that the occupied territories were located near the coastline, where the collection of aquatic resources and the hunting of small and large animals was part of the diet of these communities. To contribute with new data for the Middle Paleolithic, an Archaeological Predictive Model was created to analyze the patterns of occupation and exploration of the territory. The present study is based on an essentially statistical, descriptive and univariate approach, using dependent variables (i.e., archaeological sites) and independent variables (e.g., geomorphological, geographic data). The results obtained indicate an occupation of the territory, close to the coastline and dispersed in the territory, linked to important valleys and rivers.

A Bayesian approach to multivariate adaptive localization in ensemble-based data assimilation with time-dependent extensions

Ever since its inception, the ensemble Kalman filter (EnKF) has elicited many heuristic approaches that sought to improve it. One such method is covariance localization, which alleviates spurious correlations due to finite ensemble sizes by using relevant spatial correlation information. Adaptive localization techniques account for how correlations change in time and space, in order to obtain improved covariance estimates. This work develops a Bayesian approach to adaptive Schur-product localization for the deterministic ensemble Kalman filter (DEnKF) and extends it to support multiple radii of influence. We test the proposed adaptive localization using the toy Lorenz'96 problem and a more realistic 1.5-layer quasi-geostrophic model. Results with the toy problem show that the multivariate approach informs us that strongly observed variables can tolerate larger localization radii. The univariate approach leads to markedly improved filter performance for the realistic geophysical model, with a reduction in error by as much as 33 %.

Multivariate drivers of diversity in temperate Australian native grasslands

Disturbance has been considered essential for maintaining biodiversity in temperate grassy ecosystems in Australia. This has been particularly well demonstrated for inter-tussock plant species in C4 Themeda-dominated grasslands in mesic environments. Disturbance is also thought crucial to maintain the structure of preferred habitat for some animals. Relationships between disturbance and diversity may be contingent on ecosystem productivity, but little is known about the generality of the disturbance-promoting-diversity paradigm across the range of temperate grasslands. To date, the disturbance-promoting-diversity paradigm has taken a univariate approach to the drivers of biodiversity; rainfall is seen as a key driver of productivity, which then drives diversity, mediated by disturbance. We argue that this framework is too simplistic as biodiversity drivers are multivariate. We suggest that the accumulation of phytomass (live and dead plant material) is an important determinant of diversity in grassy ecosystems and that phytomass accumulation is governed by multiple drivers (of which disturbance is just one). For fauna, it is structure – not biomass – that determines habitat suitability, and this can be moderated by both abiotic and biotic drivers. The assumption that there is a consistent effect of disturbance on diversity through the range of temperate grassland settings in southern Australia ignores the likelihood that biodiversity also responds to other factors such as spatial heterogeneity in the environment, resource availability and climatic variation. We developed a conceptual model of the multivariate drivers of grassland diversity that explores mechanisms underpinning patterns of species richness. Despite four decades of research, it is clear that our understanding of the multivariate drivers of diversity across the range of temperate grasslands in Australia is still incomplete. Further research into the conditions under which disturbance is required to maintain biodiversity in grasslands is integral to conservation planning in these endangered systems.