Front propagation and arrival times in networks with application to neurodegenerative diseases

Many physical, epidemiological, or physiological dynamical processes on networks support front-like propagation, where an initial localized perturbation grows and systematically invades all nodes in the network. A key question is then to extract estimates for the dynamics. In particular, if a single node is seeded at a small concentration, when will other nodes reach the same initial concentration? Here, motivated by the study of toxic protein propagation in neurodegenerative diseases, we present and compare three different estimates for the arrival time in order of increasing analytical complexity: the linear arrival time, obtained by linearizing the underlying system; the Lambert time, obtained by considering the interaction of two nodes; and the nonlinear arrival time, obtained by asymptotic techniques. We use the classic Fisher-Kolmogorov-Petrovsky-Piskunov equation as a paradigm for the dynamics and show that each method provides different insight and time estimates. Further, we show that the nonlinear asymptotic method also gives an approximate solution valid in the entire domain and the correct ordering of arrival regions over large regions of parameters and initial conditions.

Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints

Compositional disorder induces myriad captivating phenomena in perovskites. Target-driven discovery of perovskite solid solutions has been a great challenge due to the analytical complexity introduced by disorder. Here, we demonstrate that an unsupervised deep learning strategy can find fingerprints of disordered materials that embed perovskite formability and underlying crystal structure information by learning only from the chemical composition, manifested in $$({{\rm{A}}}_{1-{\rm{x}}}{{\rm{A}}^{\prime} }_{{\rm{x}}}){{\rm{BO}}}_{3}$$ ( A 1 − x A ′ x ) BO 3 and $${\rm{A}}({{\rm{B}}}_{1-{\rm{x}}}{{\rm{B}}^{\prime} }_{{\rm{x}}}){{\rm{O}}}_{3}$$ A ( B 1 − x B ′ x ) O 3 formulae. This phenomenon can be capitalized to predict the crystal symmetry of experimental compositions, outperforming several supervised machine learning (ML) algorithms. The educated nature of material fingerprints has led to the conception of analogical materials discovery that facilitates inverse exploration of promising perovskites based on similarity investigation with known materials. The search space of unstudied perovskites is screened from ~600,000 feasible compounds using experimental data powered ML models and automated web mining tools at a 94% success rate. This concept further provides insights on possible phase transitions and computational modelling of complex compositions. The proposed quantitative analysis of materials analogies is expected to bridge the gap between the existing materials literature and the undiscovered terrain.

 A Multi-paradigmatic Interdisciplinary Framework for Human Service Systems

Increasingly, human service systems are complicated by interprofessional spaces, quickening technological change, and social uncertainty. New guides built on existing research, practice, and interdisciplinary knowledge can lead practitioners through these complexities. Targeted toward an interdisciplinary audience, this article introduces four mechanisms to navigate the practical realities of human services organizations. The first, paradigms of organizational analysis, centers on embedded assumptions within human services organizations and their implications. The second, an organizational health paradigm, focuses on organizational health and functioning. The third, an ethical paradigm, incorporates interdisciplinary ethics across various disciplines. The final integrates these mechanisms along four practical pillars of human services systems: policy, organizations, community, and planning/evaluation that incorporate context, focus, and application of organizational practice activities. This framework aims to reduce analytical complexity, comprehensively guide practitioners in understanding contemporary human services systems, and apply these integrated dimensions across policy, organization, community, and planning/evaluation in human services settings.

Analytical Performance of a Standardized Kit for Mass Spectrometry-based Measurements of Human Glycosaminoglycans

Glycosaminoglycans (GAGs) are long linear sulfated polysaccharides implicated in processes linked to disease development such as mucopolysaccharidosis, respiratory failure, cancer, and viral infections, thereby serving as potential biomarkers. A successful clinical translation of GAGs as biomarkers depends on the availability of standardized GAG measurements. However, owing to the analytical complexity associated with the quantification of GAG concentration and structural composition, a standardized method to simultaneously measure multiple GAGs is missing. In this study, we sought to characterize the analytical performance of a ultra-high-performance liquid chromatography coupled with triple-quadrupole tandem mass spectrometry (UHPLC-MS/MS)-based kit for the quantification of 17 GAG disaccharides. The kit showed acceptable linearity, selectivity and specificity, accuracy and precision, and analyte stability in the absolute quantification of 15 GAG disaccharides. In native human samples, here using urine as a reference matrix, the analytical performance of the kit was acceptable for the quantification of CS disaccharides. Intra- and inter-laboratory tests performed in an external laboratory demonstrated robust reproducibility of GAG measurements showing that the kit was acceptably standardized. In conclusion, these results indicated that the UHPLC-MS/MS kit was standardized for the simultaneous measurement of GAG disaccharides allowing for comparability of measurements and enabling translational research.SummaryAnalytical performance of a kit for standardized GAG measurements, based on an established UHPLC-MS/MS method

From BIDS-Formatted EEG Data to Sensor-Space Group Results: A Fully Reproducible Workflow With EEGLAB and LIMO EEG

Reproducibility is a cornerstone of scientific communication without which one cannot build upon each other’s work. Because modern human brain imaging relies on many integrated steps with a variety of possible algorithms, it has, however, become impossible to report every detail of a data processing workflow. In response to this analytical complexity, community recommendations are to share data analysis pipelines (scripts that implement workflows). Here we show that this can easily be done using EEGLAB and tools built around it. BIDS tools allow importing all the necessary information and create a study from electroencephalography (EEG)-Brain Imaging Data Structure compliant data. From there preprocessing can be carried out in only a few steps using EEGLAB and statistical analyses performed using the LIMO EEG plug-in. Using Wakeman and Henson (2015) face dataset, we illustrate how to prepare data and build different statistical models, a standard factorial design (faces ∗ repetition), and a more modern trial-based regression approach for the stimulus repetition effect, all in a few reproducible command lines.

Multiple spatial behaviours govern social network positions in a wild ungulate

The structure of wild animal social systems depends on a complex combination of intrinsic and extrinsic drivers. Population structuring and spatial behaviour are key determinants of individuals’ observed social behaviour, but quantifying these spatial components alongside multiple other drivers remains difficult due to data scarcity and analytical complexity. We used a 43-year dataset detailing a wild red deer population to investigate how individuals’ spatial behaviours drive social network positioning, while simultaneously assessing other potential contributing factors. Using Integrated Nested Laplace Approximation (INLA) multi-matrix animal models, we demonstrate that social network positions are shaped by two-dimensional landscape locations, pairwise space sharing, individual range size, and spatial and temporal variation in population density, alongside smaller but detectable impacts of a selection of individual-level phenotypic traits. These results indicate strong, multifaceted spatiotemporal structuring in this society, emphasising the importance of considering multiple spatial components when investigating the causes and consequences of sociality.Authorship StatementGFA conceived the study, analysed the data, and wrote the manuscript, advised by JAF. AM and SM collected the data. JAF, JMP, THCB, and DN commented on the manuscript.Data Accessibility StatementThe code used here is available at https://github.com/gfalbery/Spocial_Deer. On acceptance, the data will be uploaded to the same repo, which will be archived on Zenodo.

On Analytical Complexity of Antiderivatives

It is shown that the class of all functions of two variables of finite analytical complexity is not closed under integration. It also follows that the class of all functions of finite analytical complexity in the case of three or more variables is not closed under integration. For the case of three or more variables explicit examples of finite complexity functions with infinite complexity antiderivatives are constructed

Analytic Correlation Filtration: A New Tool to Reduce Analytical Complexity of Metabolomic Datasets

Metabolomics generates massive and complex data. Redundant different analytical species and the high degree of correlation in datasets is a constraint for the use of data mining/statistical methods and interpretation. In this context, we developed a new tool to detect analytical correlation into datasets without confounding them with biological correlations. Based on several parameters, such as a similarity measure, retention time, and mass information from known isotopes, adducts, or fragments, the algorithm principle is used to group features coming from the same analyte, and to propose one single representative per group. To illustrate the functionalities and added-value of this tool, it was applied to published datasets and compared to one of the most commonly used free packages proposing a grouping method for metabolomics data: ‘CAMERA’. This tool was developed to be included in Galaxy and will be available in Workflow4Metabolomics (http://workflow4metabolomics.org). Source code is freely available for download under CeCILL 2.1 license at https://services.pfem.clermont.inra.fr/gitlab/grandpa /tool-acf and implement in Perl.

Environmental DNA and metabarcoding for the study of amphibians and reptiles: species distribution, the microbiome, and much more

In the last decade, eDNA and metabarcoding have opened new avenues to biodiversity studies; amphibians and reptiles are animals for which these new approaches have allowed great leaps forward. Here we review different approaches through which eDNA can be used to study amphibians, reptiles and many more organisms. eDNA is often used to evaluate the presence of target species in freshwaters; it has been particularly useful to detect invasive alien amphibians and secretive or rare species, but the metabarcoding approach is increasingly used as a cost-effective approach to assess entire communities. There is growing evidence that eDNA can be also useful to study terrestrial organisms, to evaluate the relative abundance of species, and to detect reptiles. Metabarcoding has also revolutionized studies on the microbiome associated to skin and gut, clarifying the complex relationships between pathogens, microbial diversity and environmental variation. We also identify additional aspects that have received limited attention so far, but can greatly benefit from innovative applications of eDNA, such as the study of past biodiversity, diet analysis and the reconstruction of trophic interactions. Despite impressive potential, eDNA and metabarcoding also bear substantial technical and analytical complexity; we identify laboratory and analytical strategies that can improve the robustness of results. Collaboration among field biologists, ecologist, molecular biologists, and bioinformaticians is allowing fast technical and conceptual advances; multidisciplinary studies involving eDNA analyses will greatly improve our understanding of the complex relationships between organisms, and our effectiveness in assessing and preventing the impact of human activities.