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A recent advance in the disorder prediction field is the development of the quality assessment (QA) scores. QA scores complement the propensities produced by the disorder predictors by identifying regions where these predictions are more likely to be correct. We develop, empirically test and release a new QA tool, QUARTERplus, that addresses several key drawbacks of the current QA method, QUARTER. QUARTERplus is the first solution that utilizes QA scores and the associated input disorder predictions to produce very accurate disorder predictions with the help of a modern deep learning meta-model. The deep neural network utilizes the QA scores to identify and fix the regions where the original/input disorder predictions are poor. More importantly, the accurate QUATERplus’s predictions are accompanied by easy to interpret residue-level QA scores that reliably quantify their residue-level predictive quality. We provide these interpretable QA scores for QUARTERplus and 10 other popular disorder predictors. Empirical tests on a large and independent (low similarity) test dataset show that QUARTERplus predictions secure AUC = 0.93 and are statistically more accurate than the results of twelve state-of-the-art disorder predictors. We also demonstrate that the new QA scores produced by QUARTERplus are highly correlated with the actual predictive quality and that they can be effectively used to identify regions of correct disorder predictions. This feature empowers the users to easily identify which parts of the predictions generated by the modern disorder predictors are more trustworthy. QUARTERplus is available as a convenient webserver at http://biomine.cs.vcu.edu/servers/QUARTERplus/ .

Genome-scale mechanistic models of pathways are gaining importance for genomic data interpretation because they provide a natural link between genotype measurements (transcriptomics or genomics data) and the phenotype of the cell (its functional behavior). Moreover, mechanistic models can be used to predict the potential effect of interventions, including drug inhibitions. Here, we present the implementation of a mechanistic model of cell signaling for the interpretation of transcriptomic data as an R/Bioconductor package, a Cytoscape plugin and a web tool with enhanced functionality which includes building interpretable predictors, estimation of the effect of perturbations and assessment of the effect of mutations in complex scenarios., This work is supported by grants SAF2017-88908-R from the Spanish Ministry of Economy and Competitiveness and PT17/0009/0006 and PI20/01305 from the ISCIII, both co-funded with European Regional Development Funds (ERDF) as well as H2020 Programme of the European Union grants Marie Curie Innovative Training Network “Machine Learning Frontiers in Precision Medicine” (MLFPM) (GA 813533) and “ELIXIR-EXCELERATE fast-track ELIXIR implementation and drive early user exploitation across the life sciences” (GA 676559) to JD. Funding for open access charge: from the Spanish Ministry of Economy and Competitiveness / SAF2017-88908-R.

A fundamental biological question is how diverse and complex signaling and patterning is controlled correctly to generate distinct tissues, organs, and body plans, but incorrectly in diseased cells and tissues. Signaling pathways important for growth control have been identified, but to identify the mechanisms their transient and context-dependent interactions encode is more difficult. Currently computational systems biology aims to infer the control mechanisms by investigating quantitative changes of gene expression and protein concentrations, but this inference is difficult in nature. We propose it is desirable to explicitly simulate events and orders of gene regulation and protein interactions, which better elucidate control mechanisms, and report a method and tool with three examples. The Drosophila wing model includes Wnt, PCP, and Hippo pathways and mechanical force, incorporates well-confirmed experimental findings, and generates novel results. The other two examples illustrate the building of three-dimensional and large-scale models. These examples support that reconstructed spatiotemporal distributions of key signaling events help elucidate growth control mechanisms. As biologists pay increasing attention to disordered signaling in diseased cells, to develop new modeling methods and tools for conducting new computational studies is important.

Commercial fluorescence microscope stands and fully automated XYZt fluorescence imaging systems are generally beyond the limited budgets available for teaching and outreach. We have addressed this problem by developing “Microscopi”, an accessible, affordable, DIY automated imaging system that is built from 3D printed and commodity off-the-shelf hardware, including electro-mechanical, computer and optical components. Our design features automated sample navigation and image capture with a simple web-based graphical user interface, accessible with a tablet or other mobile device. The light path can easily be switched between different imaging modalities. The open source Python-based control software allows the hardware to be driven as an integrated imaging system. Furthermore, the microscope is fully customisable, which also enhances its value as a learning tool. Here, we describe the basic design and demonstrate imaging performance for a range of easily sourced specimens.

The blood-brain barrier (BBB) is the major obstacle for brain drug delivery and limits the treatment options for central nervous system diseases. To circumvent the BBB, we introduce focused ultrasound-mediated intranasal brain drug delivery (FUSIN). FUSIN utilizes the nasal route for direct nose-to-brain drug administration, bypassing the BBB and minimizing systemic exposure to the major organs, such as heart, lung, liver, and kidney [1]. It also uses transcranial ultrasound energy focused at a targeted brain region to induce microbubble cavitation, enhancing the transport of intranasally administered agents at the FUS-targeted brain location. FUSIN is unique because it can achieve noninvasive and localized brain drug delivery with minimized systemic toxicity to other major organs. The goal of this paper is to provide a detailed protocol for FUSIN delivery to the mouse brain.•FUSIN delivery utilizes the nose-to-brain pathway for brain drug delivery.•FUSIN utilizes FUS combined with microbubble to significantly enhance the delivery efficiency of intranasally administered drugs to the FUS targeted brain regions.•FUSIN achieves efficient brain delivery with minimized systemic exposure in the major organs., Graphical abstract Image, graphical abstract

The optokinetic response (OKR) is an effective behavioural assay to investigate functional vision in zebrafish. The rapid and widespread use of gene editing, drug screening and environmental modulation technologies has resulted in a broader need for visual neuroscience researchers to access affordable and more sensitive OKR, contrast sensitivity (CS) and visual acuity (VA) assays. Here, we demonstrate how 2D- and 3D-printed, striped patterns or drums coupled with a motorised base and microscope provide a simple, cost-effective but efficient means to assay OKR, CS and VA in larval-juvenile zebrafish. In wild-type, five days post-fertilisation (dpf) zebrafish, the 2D or 3D set-ups of 0.02 cycles per degree (cpd) (standard OKR stimulus) and 100% black-white contrast evoked equivalent responses of 24.2±3.9 or 21.8±3.9 saccades per minute, respectively. Furthermore, although the OKR number was significantly reduced compared to the 0.02 cpd drum (p OKR, VA and CS of zebrafish larvae can be efficiently measured using 2D- or 3D-printed striped drums. For data consistency the luminance of the OKR light source, the time of day when the analysis is performed, and the order of presentation of VA and CS drums must be considered. These simple methods allow effective and more sensitive analysis of functional vision in zebrafish.

Codon optimization in protein-coding sequences (CDSs) is a widely used technique to promote the heterologous expression of target genes. In codon optimization, a combinatorial space of nucleotide sequences that code a given amino acid sequence and take into account user-prescribed forbidden sequence motifs is explored to optimize multiple criteria. Although evolutionary algorithms have been used to tackle such complex codon optimization problems, evolutionary codon optimization tools do not provide guarantees to find the optimal solutions for these multicriteria codon optimization problems. We have developed a novel multicriteria dynamic programming algorithm, COSMO. By using this algorithm, we can obtain all Pareto-optimal solutions for the multiple features of CDS, which include codon usage, codon context, and the number of hidden stop codons. User-prescribed forbidden sequence motifs are rigorously excluded from the Pareto-optimal solutions. To accelerate CDS design by COSMO, we introduced constraints that reduce the number of Pareto-optimal solutions to be processed in a branch-and-bound manner. We benchmarked COSMO for run-time and the number of generated solutions by adapting selected human genes to yeast codon usage frequencies, and found that the constraints effectively reduce the run-time. In addition to the benchmarking of COSMO, a multi-objective genetic algorithm (MOGA) for CDS design was also benchmarked for the same two aspects and their performances were compared. In this comparison, (i) MOGA identified significantly fewer Pareto-optimal solutions than COSMO, and (ii) the MOGA solutions did not achieve the same mean hypervolume values as those provided by COSMO. These results suggest that generating the whole set of the Pareto-optimal solutions of the codon optimization problems is a difficult task for MOGA.

Graphical abstract, Post-translational modifications of proteins expand their functional diversity, regulating the response of cells to a variety of stimuli. Among these modifications, phosphorylation is the most ubiquitous and plays a prominent role in cell signaling. The addition of a phosphate often affects the function of a protein by altering its structure and dynamics. However, these alterations are often difficult to study and the functional and structural implications remain unresolved. New approaches are emerging to overcome common obstacles related to the production and manipulation of these samples. Here, we summarize the available methods for phosphoprotein purification and phosphomimetic engineering, highlighting the advantages and disadvantages of each. We propose a general workflow for protein phosphorylation analysis combining computational and biochemical approaches, building on recent advances that enable user-friendly and easy-to-access Molecular Dynamics simulations. We hope this innovative workflow will inform the best experimental approach to explore such post-translational modifications. We have applied this workflow to two different human protein models: the hemeprotein cytochrome c and the RNA binding protein HuR. Our results illustrate the usefulness of Molecular Dynamics as a decision-making tool to design the most appropriate phosphomimetic variant.

The capacity of an organism to alter its phenotype in response to environmental perturbations changes over developmental time and is a process determined by multiple genes that are co-expressed in intricate but organized networks. Characterizing the spatiotemporal change of such gene networks can offer insight into the genomic signatures underlying organismic adaptation, but it represents a major methodological challenge. Here, we integrate the holistic view of systems biology and the interactive notion of evolutionary game theory to reconstruct so-called systems evolutionary game networks (SEGN) that can autonomously detect, track, and visualize environment-induced gene networks along the time axis. The SEGN overcomes the limitations of traditional approaches by inferring context-specific networks, encapsulating bidirectional, signed, and weighted gene-gene interactions into fully informative networks, and monitoring the process of how networks topologically alter across environmental and developmental cues. Based on the design principle of SEGN, we perform a transcriptional plasticity study by culturing Euphrates poplar, a tree that can grow in the saline desert, in saline-free and saline-stress conditions. SEGN characterize previously unknown gene co-regulation that modulates the time trajectories of the trees' response to salt stress. As a marriage of multiple disciplines, SEGN shows its potential to interpret gene interdependence, predict how transcriptional co-regulation responds to various regimes, and provides a hint for exploring the mass, energetic, or signal basis that drives various types of gene interactions.

ProTExA is a web-tool that provides a post-processing workflow for the analysis of protein and gene expression datasets. Using network-based bioinformatics approaches, ProTExA facilitates differential expression analysis and co-expression network analysis as well as pathway and post-pathway analysis. Specifically, for a given set of protein-gene expression data across samples, ProTExA: (1) performs statistical analysis and filtering to highlight the differentially expressed proteins-genes, (2) performs enrichment analysis to identify top-scored pathways, (3) generates pathway-to-pathway and pathway-to-gene networks (4) generates protein and gene co-expression networks using a variety of methodologies, and (5) applies clustering methodologies to identify sub-networks of co-expressed proteins-genes. The proposed web-tool is a simple yet informative tool, towards understanding and exploitation of protein and gene expression datasets, especially for those that do not have the expertise and local resources to replicate specific analyses in the context of collaborative and scientific data exchanging.

Determining the composition of bacterial communities beyond the level of a genus or species is challenging because of the considerable overlap between genomes representing close relatives. Here, we present the mSWEEP method for identifying and estimating the relative abundances of bacterial lineages from plate sweeps of enrichment cultures. mSWEEP leverages biologically grouped sequence assembly databases, applying probabilistic modelling, and provides controls for false positive results. Using sequencing data from major pathogens, we demonstrate significant improvements in lineage quantification and detection accuracy. Our method facilitates investigating cultures comprising mixtures of bacteria, and opens up a new field of plate sweep metagenomics.

The COVID-19 pandemic is expanding at an unprecedented rate. As a result, diagnostic services are stretched to their limit, and there is a clear need for the provision of additional diagnostic capacity. Academic laboratories, many of which are closed due to governmental lockdowns, may be in a position to support local screening capacity by adapting their current laboratory practices. Here, we describe the process of developing a SARS-Cov2 diagnostic workflow in a conventional academic Containment Level 2 (CL2) laboratory. Our outline includes simple SARS-Cov2 deactivation upon contact, the methods for a quantitative real-time reverse transcriptase PCR (qRT-PCR) detecting SARS-Cov2, a description of process establishment and validation, and some considerations for establishing a similar workflow elsewhere. This was achieved under challenging circumstances through the collaborative efforts of scientists, clinical staff, and diagnostic staff to mitigate to the ongoing crisis. Within 14 days, we created a validated COVID-19 diagnostics service for healthcare workers in our local hospital. The described methods are not exhaustive, but we hope may offer support to other academic groups aiming to set up something comparable in a short time frame.

Current bioinformatics workflows for PIWI-interacting RNA (piRNA) analysis focus primarily on germline-derived piRNAs and piRNA-clusters. Frequently, they suffer from outdated piRNA databases, questionable quantification methods, and lack of reproducibility. Often, pipelines specific to miRNA analysis are used for the piRNA research in silico. Furthermore, the absence of a well-established database for piRNA annotation, as for miRNA, leads to uniformity issues between studies and generates confusion for data analysts and biologists. For these reasons, we have developed WIND (Workflow for pIRNAs aNd beyonD), a bioinformatics workflow that addresses the crucial issue of piRNA annotation, thereby allowing a reliable analysis of small RNA sequencing data for the identification of piRNAs and other small non-coding RNAs (sncRNAs) that in the past have been incorrectly classified as piRNAs. WIND allows the creation of a comprehensive annotation track of sncRNAs combining information available in RNAcentral, with piRNA sequences from piRNABank, the first database dedicated to piRNA annotation. WIND was built with Docker containers for reproducibility and integrates widely used bioinformatics tools for sequence alignment and quantification. In addition, it includes Bioconductor packages for exploratory data and differential expression analysis. Moreover, WIND implements a "dual" approach for the evaluation of sncRNAs expression level quantifying the aligned reads to the annotated genome and carrying out an alignment-free transcript quantification using reads mapped to the transcriptome. Therefore, a broader range of piRNAs can be annotated, improving their quantification and easing the subsequent downstream analysis. WIND performance has been tested with several small RNA-seq datasets, demonstrating how our approach can be a useful and comprehensive resource to analyse piRNAs and other classes of sncRNAs.

Surface Plasmon Resonance imaging (SPRi) was used to determine the presence and strength of binding of IgG, IgM and IgA against the Receptor Binding Domain (RBD) of SARS-CoV-2 in sera of 102 CoViD-19 and non-CoViD-19 patients. The SPRi assay simultaneously measures the antibody isotype levels and the strength of binding to the RBD of ultimate 384 patient samples in one run. It turns out that during the course of the disease, the IgG levels and strength of binding increased while generally the IgM and IgA levels go down. Recovered patients all show high strength of binding of the IgG type to the RBD protein. The anti-RBD immunoglobulins SPRi assay provides additional insights in the immune status of patients recovering from CoViD-19. This new high throughput method can be applied for the assessment of the quality of the immune reaction of healthy individuals to SARS-CoV-2 and its mutants in vaccination programs.•Surface Plasmon Resonance imaging is an unprecedented technology for high throughput screening of antibody profiling of CoViD19 patients.•Fingerprinting of isotypes IgM, IgG and IgA can be performed for 384 patients in one run.•An affinity maturation effect was shown for patients recovering from CoViD19., Graphical abstract Image, graphical abstract

Highlights • Several challenges must be addressed when performing context-specific model extraction. • We propose a framework for efficient extraction/analysis of genome scale metabolic models. • We highlight the impact of choice of model extraction method (MEM) on the output models. • We highlight the impact of thresholding rules and values on the output models. • Cyp51 knockout mice diet experiment data are used as a case study., Omics data can be integrated into a reference model using various model extraction methods (MEMs) to yield context-specific genome-scale metabolic models (GEMs). How to chose the appropriate MEM, thresholding rule and threshold remains a challenge. We integrated mouse transcriptomic data from a Cyp51 knockout mice diet experiment (GSE58271) using five MEMs (GIMME, iMAT, FASTCORE, INIT an tINIT) in a combination with a recently published mouse GEM iMM1865. Except for INIT and tINIT, the size of extracted models varied with the MEM used (t-test: p-value 90%) in PC1 for FASTCORE. In iMAT, each of the three factors explained less than 40% of the variability within PC1, PC2 and PC3. Among all the MEMs, FASTCORE captured the most of the true variability in the data by clustering samples by gender. Our results show that for the efficient use of MEMs in the context of omics data integration and analysis, one should apply various MEMs, thresholding rules, and thresholding values to select the MEM and its configuration that best captures the true variability in the data. This selection can be guided by the methodology as proposed and used in this paper. Moreover, we describe certain approaches that can be used to analyse the results obtained with the selected MEM and to put these results in a biological context.

Graphical abstract How to get error bars on the ratiometric calcium estimator? The two measurements areas (region of interest, ROI, on the cell body and background measurement region, BMR, outside of the cell) are displayed on the frame corresponding to one actual experiment. Two measurements, one following an excitation at 340 nm and the other following an excitation at 380 nm are performed (at each ’time point’) from each region. The result is a set of four measures: adu340 (from the ROI), adu340,B (from the BMR), adu380 and adu380,B.. The fact that the measurements as well as the subsequent quantities derived from them are random variable realization is conveyed throughout the figure by the use of Gaussian probability densities. The densities from the BMR are ’tighter’ because there are much more pixels in the BMR than in the ROI (the standard deviations of the densities shown on this figure have been enlarged for clarity, but their relative size has been preserved, the horizontal axis in black always starts at 0). The key result of the paper is that the standard deviation of the four Gaussian densities corresponding to the raw data (bottom of the figure) can be reliably estimated from the data alone, egσ380≈Gadu380+V, where V is the product of the CCD chip gain squared by the number of pixels in the ROI by the CCD chip read-out variance. The paper explains how to compute the standard deviation of the derived distributions obtained at each step of the calcium concentration estimation., The ratiometric fluorescent calcium indicator Fura-2 plays a fundamental role in the investigation of cellular calcium dynamics. Despite of its widespread use in the last 30 years, only one publication (Joucla et al., 2010)) proposed a way of obtaining confidence intervals on fitted calcium dynamic model parameters from single ‘calcium transients’. Shortcomings of this approach are its requirement for a ‘3 wavelengths’ protocol (excitation at 340 and 380 nm as usual plus at 360 nm, the isosbestic point) as well as the need for an autofluorence / background fluorescence model at each wavelength. Here, we propose a simpler method that eliminates both shortcommings:1.a precise estimation of the standard errors of the raw data is obtained first,2.the standard error of the ratiometric calcium estimator (a function of the raw data values) is derived using both the propagation of uncertainty and a Monte-Carlo method.Once meaningful standard errors for calcium estimates are available, standard errors on fitted model parameters follow directly from the use of nonlinear least-squares optimization algorithms.

Prostate cancer (PCa) is the most frequent malignancy in men worldwide after lung cancer. The gold standard for diagnosing prostate cancer is prostate biopsy. There are two main approaches for diagnosing PCa: transrectal biopsy and transperineal biopsy. In Indonesia, transperineal approach is rarely done as a part of diagnostic work up. The most known method of transperineal biopsy is the fan technique, which is a difficult technique to perform, particularly when directing the needle to the target accurately. For this reason, we develop an affordable bioplastic VY probe mounted needle guide device ($10) to provide precise needle insertion during the biopsy. In this study, we assessed the objective and subjective factors of the newly developed VY device.•Basic knowledge and operation technique were equalized by holding a prostate biopsy training using fan technique and VY device to 20 first-year Residents•Participants were randomized and devided into two groups to perform fan technique and VY technique based on the training given•Subjective factors (usefulness, the easiness, and preference) and objective factors (duration of puncture to needle visualization on USG, biopsy duration time, and the ability to reach the target tissue) were evaluated., Graphical abstract Image, graphical abstract

Li-ion battery electrodes, such as widely used graphite anodes, may have anisotropic tortuosity due to the non-equiaxed shape of the active material particles and the post-casting calendaring process. Such anisotropy can be ignored in conventional electrodes because all the macroscopic ion transport occurs along the electrode thickness, making the ion transport effectively one dimensional. However, the anisotropy becomes important to consider with three-dimensional architectures, such as those generated by laser-patterning. COMSOL MultiphysicsⓇ is one of the leading tools used by the Li-ion battery modeling community to simulate the electrochemical dynamics of the Li-ion batteries. However, in its current implementation of the underlying model equations, the Li-ion battery module in COMSOL 5.4 cannot be used to simulate the electrochemical behavior of an electrode with anisotropic tortuosity. In this work, we show how the current implementation can be modified to simulate the anisotropic case.•The existing Li-ion battery model in COMSOL 5.4 was extended to account for the anisotropy in the electrode tortuosity•The extended model is necessary to accurately simulate the electrochemical dynamics of 3D architectures such as Highly ordered laser-patterned electrode (HOLE)•The testing and validation results are included in this work, Graphical abstract Image, graphical abstract

There has been a growing interest in the association between indoor air quality (IAQ) with an increase in the time spent at home. However, there is still a lack of evidence on the impact of IAQ on occupants' health and well-being in the long term. This study aimed to develop a field-based longitudinal study design to evaluate the IAQ level and daily symptoms of adults and children living in different types of buildings over one year. We proposed vital principles to be considered when recruiting the study participants so that potential confounders, such as age, underlying diseases, and the geographic area would be either removed in advance or matched between different building types. We suggested collecting exposure and outcome data in three categories: lifestyle and housing environment, IAQ measurement, and occupants' health. We presented web-based survey tools for collecting housing and health data, and the frequency of data collection varied from weekly to six-month intervals. We developed two different models using a generalized mixed model for modeling the association between housing environment, IAQ, and human health. The current study design could be applied for future studies on the association between built environment and health, regardless of the type of buildings.•A real-time indoor air quality monitor was used to monitor indoor air parameters every 5 mins over one year.•A simple web-based survey tool was developed to collect data for occupants' daily symptoms in the long term.•A binomial generalized linear mixed model and a Poisson generalized linear mixed model was developed to evaluate the association between indoor air parameters, and building types and daily symptoms., Graphical abstract Image, graphical abstract

A miniaturized optoelectronic sensor is demonstrated that measures total protein concentration in serum and urine with sensitivity and accuracy comparable to gold-standard methods. The sensor is comprised of a vertical cavity surface emitting laser (VCSEL), photodetector and other custom optical components and electronics that can be hybrid packaged into a portable, handheld form factor. In conjunction, a custom fluorescence assay has been developed based on the protein-induced fluorescence enhancement (PIFE) phenomenon, enabling real-time sensor response to changes in protein concentration. Methods are described for the following:•Standard curves: Used to determine the sensitivity, dynamic range, and linearity of the VCSEL biosensor/PIFE assay system in buffer as well as in human blood and urine samples.•Comparison of VCSEL biosensor performance with a benchtop fluorimetric microplate reader.•Accuracy of the VCSEL biosensor/PIFE assay system: Evaluated by comparing sensor measurements with gold-standard clinical laboratory measurements of total protein in serum and urine samples from patients with diabetes., Graphical abstract Image, graphical abstract

Evidence based crop diversification requires modelling for crops that are currently neglected or underutilised. Crop model calibration is a lengthy and resource consuming effort that is typically done for a particular variety or a set of varieties of a crop. Whilst calibration data are widely available for major crops, such data are rarely available for underutilised crops due to limited funding for detailed field data collection and model calibration. Subsequently, the lack of evidence on their performance will lead to the lack of interest from the policy and regulatory communities to include these crops in the agricultural development plans. In order to motivate further research into the use of state of the art techniques in modelling for less known crops, we have developed and validated an ideotyping technique that approximates the crop modelling parameters based on already calibrated crops of different lineage. The method has been successfully tested for hemp (Cannabis sativa L.) based on a well-known crop model. In this paper we present the method and provide an impetus on the way forward to further develop such methods for modelling the performance of minor crops and their varieties.•The approach works based on modelling the performance of hemp using the knowledge from an existing model that was developed for sugar cane.•The customisation uses one of the most prominent models (AquaCrop) to approximate growth coefficients for hemp (Cannabis sativa L.).•A sequential procedure was used to approximate the phenological stages in the growth model that performs well in the calibration and validation steps., Graphical abstract Image, graphical abstract

We present an automated method to determine oil release from lipid-based materials. Oil-release tests can provide information regarding the ability to retain oil within the structuring network of lipid-based materials. This test provides a first insight into the stability of these materials and their possible applications in food, cosmetic, and pharmaceutical products. The method presented features a simple setup comprising a camera that automatically captures images of the evolution of the oil stain released from lipid-based materials placed on a filter paper. Image postprocessing is performed with two custom-made scripts developed for the freeware application ImageJ. The scripts allow direct calculation of the oil-stain area from all images stored in a folder returning as output numerical values in a table. This method was shown to be:•inexpensive, as the employed tools and equipment are available in most laboratories both in academia and industry,•self-running, as the method automatically captures images at predefined time intervals for a certain time span,•practical, as manual-image analysis is unnecessary (200 images can be automatically analyzed in 3 min)., Graphical abstract Image, graphical abstract

Lipids are a very heterogeneous class of biomolecules with distinct structures and functions. Total lipids (TLs) obtained from natural sources are regularly further separated into lipid subclasses, with the two major ones being the polar lipids (PLs) and neutral lipids (NLs). Traditional analytical methods for fractionating TLs into NLs, PLs, and their subclasses, usually comprise difficult, costly and time-consuming steps. Instead, several benefits and applications are derived by implementing a novel one-step semi-preparative and reversed-phase HPLC-analysis for separating TLs into all kinds of lipid subclasses. This method allows a one-step separation/fractionation of several subclasses of bio-functional PLs (i.e. phospholipids, glycolipids, phenolic compounds, N-acyl-homoserine-lactones, etc.) and NLs (i.e. triacylglycerols, fatty acids, esters, etc.) from TL-extracts of a natural source, prior to further testing them for their bio-functionality (i.e. in bioassays/cell models) and structure-activity relationships (i.e. LC-MS/GC-MS).•This method can be applied in several natural sources, such as animal and marine sources, plants, microorganisms of biotechnological and agricultural interest, foods, beverages and related products, and by-products.•This method can also be applied for separating specific bio-functional lipids from complex medical and pharmaceutical samples (i.e. cells, tissues, blood, plasma, liposomes, etc.), either for evaluating their role in diseases (i.e. PAF/PAF-like molecules) or by elucidating their protective roles (i.e. PLs rich in ω3 PUFA) for supplements and nutraceuticals’ applications., Graphical abstract Image, graphical abstract

Date palm (Phoenix dactylifera L; Arecaceae) is one of a few fruit trees that can remarkably grow in dessert agroecosystems that are characterized by extreme temperature fluctuations. Due to increasing demands for dates in the global market and commercial cultivation in many countries, the tree is currently under extensive research in many countries, particularly to improve the germplasm using different molecular tools. Most molecular techniques largely depend on good quality DNA in significant quantities, which are highly compromised by the presence of various contaminants in DNA. The traditional cetyltrimethylammoniumbromide (CTAB) based method has failed to produce good quality DNA from date palm due to hard fibrous nature of tissue. On the basis of previous studies, commercial DNA extraction kits are not economical although they are very effective. Therefore, we have developed an improved DNA extraction protocol by modifying the original CTAB method to produce extra pure DNA in large quantities. The novel method has been validated using different quality testing approaches. This cost-effective method can be used successfully for DNA extraction from date palm. Moreover, this improved method may have potential for DNA extraction from other palms that have similar leaf texture to date palm leaves, but this method needs to be tested for other palms before being used. The improved method has following key modifications:•Grinding of plant tissue in liquid nitrogen and subsequent lysis of cells in CTAB buffer that has increased concentration of ß-mercaptoethanol•Repeated steps of chloroform: IAA extraction and ethanol washing•Addition of RNase A before the DNA precipitation step, Graphical Abstract Image, graphical abstract

We introduce Stata and R codes to estimate the household smoke-exposure risk (SER) variable using cooking fuel- and cooking place-related information obtained from country-level demographic and health survey (DHS) data. Two categories of cooking fuels (smoke-producing and non-smoke producing fuels) and two categories of cooking places (indoor and outdoor) are used to estimate the household SER. Finally, household SER is classified into four levels of risk: high (cooking indoor using smoke-producing fuels), medium (cooking outdoor using smoke-producing fuels), low (cooking indoor using non-smoke-producing fuels), and very low (cooking outdoor using non-smoke-producing fuels). An example of a household SER calculation using the DHS data and codes is provided for clarification. The available DHS data of over 90 countries contain both cooking fuel- and cooking place-related information, so the method of estimating household SER would be the same for these countries.•Household-level cooking fuel and cooking place data can be used to estimate household SER.•This paper illustrates an estimation technique for household smoke-exposure risk (SER) using demographic and health survey (DHS) data.•This method can be used to estimate household SER data for any country on the DHS country list., Graphical abstract Image, graphical abstract

Conventional light and electron microscopy are the most widely used techniques for examining plant reproductive tissues; however, they are time-consuming or expensive. The anther is the male part of the plant reproductive system. Structural changes drive development, and any structural defect may lead to an increase in fertility or cause sterility; thus, quick detection of structural changes is crucial in reproductive biology. We optimized an existing low-temperature SEM alternative to examine the internal structure of hydrated, fresh-frozen anthers. In contrast with the original technique, our method does not require precooling adhesion (ethanol to fix the specimen), and the cryo-sectioning can be conducted at atmospheric pressure. In addition to enabling the differentiation between aerial and liquid-filled intercellular spaces, this method is expected to facilitate the detection of quick (during a day) developmental changes in plant reproductive tissues, which is a current challenge using conventional approaches.•This method allows the high-throughput imaging of fresh-frozen plant reproductive samples collected every 10 min, which is important for developmental studies.•The cryo-images of samples with thickness ranging from 0.2 to 3 mm can be well-preserved at 800X magnification.•This method does not require chemical processing, critical point drying, customized cryo-accessories, controlled temperature cold stages, or metal coating. This simplified method does not require highly skilled personnel, and it is suitable in most microscopy laboratories., Graphical abstract Image, graphical abstract

The monitoring of marine biotoxins (MBTs) in seawater is presented as an alternative strategy to determine their presence and the possible implications in the ecosystem. For this, an analytical method based on hydrophilic interaction liquid chromatography coupled to high resolution mass spectrometry (HILIC-HRMS) has been developed to identify and quantify some hydrophilic MBTs in seawater: saxitoxin (STX), decarbamoyl-saxitoxin (dcSTX), neosaxitoxin (NeoSTX), gonaytoxin-2,3 (GTX-2,3) and tetrodotoxin (TTX), which are responsible of gastrointestinal and central nervous system distress in humans when are consumed via seafood. Particulate and filtrate portion were analyzed separately in order to characterize the extracellular toxins dissolved in the water and those present in the particulate. Ultrasound assisted solid-liquid extraction with methanol was used for the isolation of the MBTs from particulate and solid phase extraction using silica cartridges for the filtrate. Extraction procedure was the most critical step during the analytical method due to the high polarity of the toxins and the absolute recoveries obtained ranged from 15 to 47 % in the filtrate and 26 to 71 % in the particulate portions. Limits of detection of the method ranged from 0.5 to 5 µg/L in the filtrate portion and from 3.1 to 62 µg/L in the particulate portion.•Saxitoxins and tetrodotoxins have been analysed by using HILIC-HRMS.•UAE with methanol and SPE with silica cartridges have been employed for the extractions of the polar MBTs from seawater., The work was supported by the Spanish Ministry of Economy and competitiveness through the project PLAS-MED (CTM2017-89701C3-1-R). The authors would like to express their deepest gratitude to Dr. Roser Chaler and Dori Fanjul, because of their technical support during the HPLC-HRMS analyses.

Pinyon and juniper expansion into sagebrush ecosystems is one of the major challenges facing land managers in the Great Basin. Effective pinyon and juniper treatment requires maps that accurately and precisely depict tree location and degree of woodland development so managers can target restoration efforts for early stages of pinyon and juniper expansion. However, available remotely sensed layers that cover a regional spatial extent lack the spatial resolution or accuracy to meet this need. Accuracy can be improved using object-based image analysis methods such as automated feature extraction, which has proven successful in accurately classifying land cover at the site-level but to date has yet to be applied to regional extents due to time and computational limitations. Using Feature Analyst™, we implement our framework with 1-m2 reference imagery provided by National Agricultural Imagery Program to classify conifers across Nevada and northeastern California. Our resulting binary conifer map has an overall accuracy of 86%. We discuss the advantages to accuracy and precision our framework provides compared to other classification methods. ● This framework allows automated feature extraction for large quantities of data and very high spatial resolution imagery ● It leverages supervised learning ● It results in high accuracy maps for regional spatial extents, Graphical abstract Image, graphical abstract

Time series data about when heating is on and off in homes can be useful for research on building energy use and occupant behaviours, particularly data at room level and at a granularity of minutes. Direct methods which measure the temperature of radiators and other heaters can be effective at producing such data, but are expensive. Indirect methods, which infer heating on- and off-times from ambient room temperature data, can be cheaper but produce more error-prone data. Existing indirect methods have however utilised relatively simple prediction algorithms based on changes in ambient temperature between closely adjacent time points. In the method presented here we have implemented several refinements to this approach:•An Artificial Neural Network algorithm is applied to the prediction task: a deep, dilated convolutional network.•A wider range of input features is utilised to base predictions upon: ambient room temperature and humidity, and external temperature and humidity.•Predictions for each time point are based on data from a wider, 600-minute, time window.•We evaluate model performance on a dataset with 10 min granularity and achieve mean precision and recall during the heating season of >=0.74 for individual time points, and >=0.82 for full heating events, outperforming comparator methods., Graphical abstract Image, graphical abstract

The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

Pharmacokinetic (PK) predictions of new chemical entities are aided by prior knowledge from other compounds. The development of robust algorithms that improve preclinical and clinical phases of drug development remains constrained by the need to search, curate and standardise PK information across the constantly-growing scientific literature. The lack of centralised, up-to-date and comprehensive repositories of PK data represents a significant limitation in the drug development pipeline.In this work, we propose a machine learning approach to automatically identify and characterise scientific publications reporting PK parameters from in vivo data, providing a centralised repository of PK literature. A dataset of 4,792 PubMed publications was labelled by field experts depending on whether in vivo PK parameters were estimated in the study. Different classification pipelines were compared using a bootstrap approach and the best-performing architecture was used to develop a comprehensive and automatically-updated repository of PK publications. The best-performing architecture encoded documents using unigram features and mean pooling of BioBERT embeddings obtaining an F1 score of 83.8% on the test set. The pipeline retrieved over 121K PubMed publications in which in vivo PK parameters were estimated and it was scheduled to perform weekly updates on newly published articles. All the relevant documents were released through a publicly available web interface (https://app.pkpdai.com) and characterised by the drugs, species and conditions mentioned in the abstract, to facilitate the subsequent search of relevant PK data. This automated, open-access repository can be used to accelerate the search and comparison of PK results, curate ADME datasets, and facilitate subsequent text mining tasks in the PK domain.

Surveying people via the Internet presents both opportunities and challenges. The opportunities include quick access to respondents all over the world enabling fast realization of surveys at low costs. The challenges include a lack of control over both the sampling process and research implementation, as well as low response rates. This article describes a method of obtaining an appropriate sample of people for survey research utilising the power of the Internet, while simultaneously allowing the researcher to minimise risk thanks to enhanced control over the sampling process and the implementation of the survey. The proposed method could be used mainly in economic and social surveys; it allows researchers to reach selected groups of respondents and to conduct surveys on a global scale.•The proposed sampling method uses LinkedIn's network structure to quickly reach a dispersed population•Creating a list of units belonging to the study population resembles the snowball method, though the units are selected for the sample by the researcher and not indicated by the respondents., Graphical abstract Image, graphical abstract

Continuous slug flow in microreactors are featured by alternative presence of regulate segments of immiscible phases in microchannel or capillaries with lateral dimensions below 1 mm. Due to the high interfacial area and short diffusive distance therein, such microreactors have been widely applied in chemical engineering processes that are sensitive to mass transfer. Therefore, mass transfer rates in microreactors have long been broadly investigated via either typical offline or online methods. Compared to these conventional methods, the colorimetric technique based on the oxidation of resazurin with oxygen enables direct determination of physical mass transfer rates. However, this technique was currently applied only to the gas-liquid system in microreactors, and mostly in rectangular channels due to the simplicity in image processing. Based on this, the current paper showed a demo where the colorimetric technique using resazurin was adapted to a liquid-liquid system for the mass transfer study of flowing droplets within a slug flow capillary. Experimental tips and tricks were summarized, and a sliced color-concentration calibration strategy was proposed to balance analyzing efficiency and accuracy., Graphical abstract Image, graphical abstract

Highlights • Hedgerow network mapping in a continental Mediterranean mountain using GIS and Remote Sensing. • Identify the different levels of surface soil moisture provided by images from the satellite Sentinel-2. • Analyse of the evolution of hedgerow area from 1956 to the present time from aerial pictures and satellite images and their correlation with surface soil moisture and slope. • A classification of hedgerow networks in the Mediterranean region (Central System, Spain)., The present paper addresses the methodology carried out to obtain a new classification of hedgerow networks in the Mediterranean region using GIS and Remote Sensing. A new methodology has been developed due to the need to establish a typification of the hedgerows in this sector of southern Europe, where the variable conditioning the localization, area and type of these landscapes is degree of surface soil moisture. In the continental Mediterranean context, this age-old agricultural system is closely linked to access to, and management of, water resources, which are vital in the dry season. For this reason, we mapped the hedgerow network of a continental Mediterranean mountain, establishing different levels of surface soil moisture provided by images from the satellite Sentinel-2. The results render three types of hedgerows: moist, semi-moist and dry, each one presenting clearly differentiated localizations and characteristics. To this can be added the analysis of the evolution of their area from 1956 to the present time from aerial pictures and satellite images and their correlation with surface soil moisture and slope. We present results on this tested method from the central sector of the Iberian Peninsula, Spain.•The presented procedure focuses on a new classification of hedgerow networks in the Mediterranean region based on the degree of surface soil moisture.•The method stablishes different levels of surface soil moisture provided by images from the satellite Sentinel-2.•The procedure allows us to analyze and correlate the evolution of Mediterranean hedgerows with surface soil moisture., Graphical abstract Image, graphical abstract

Bisphenol A (BPA) is a highly-produced organic compound of anthropogenic origin with a wide-range use and is ubiquitously present in both living organisms and the environment. A previously published analytical method for testing of the free (aglycone) BPA in foodstuffs was simplified and optimized for sheep muscle tissue, kidney and liver, by using only a single MISPE purification step allied with HPLC-FLD analysis. The recovery rates and RSD values over a concentration range of 1–10 µg/kg were in the range of 67‒86% and 3‒34%, respectively, while linearity in the matrix, represented by the r2, was ≥0.999. LOD values were 0.5‒1 µg/kg and LOQ values were 1 µg/kg. The analytical method used is a contribution to the field of veterinary toxicology and food testing and also proved to be applicable for other food-producing animal species, e.g. pigs, poultry and freshwater fish. The MISPE sorbent material for testing of BPA was reusable, with up to five re-use cycles without significant loss in performance characteristics.•The paper reports on a rapid determination of free (aglycone) BPA in tissues of food-producing animals by HPLC-FLD and presents a substantial improvement of analytical performance with regard to time- and cost-savings, as well as environmental protection.•MISPE is an advanced analytical technology, and the results proved that in a single and simplified SPE step it had good performance characteristics such as selectivity, recovery and precision, allied with low LOD and LOQ values.•It was proved that MISPE BPA cartridges could be used at least five times without significant loss of methodological recovery and precision., Graphical abstract Image, graphical abstract

Microbes interact in a multitude of ways with host plants, can dwell as endophytes within plants causing no apparent disease, and often provide benefits to their host. Observing microorganism distribution and colonization is a prerequisite for interactive research. To this end, we describe use of fluorescent staining for microorganism labeling and highlight its simplicity, and efficiency. Fluorescein can quickly bind to Beauveria bassiana spores, producing bright green fluorescence that can be observed even inside plant tissues. This method provides an intuitive visual image that can be utilised for subsequent data acquisition and statistical analysis.•Our protocol depends on binding of fluorescein diacetate (FDA) specifically to microorganisms. The fungus hydrolyses and metabolises FDA in cells to produce bright green fluorescent products. This fluorescent signal can easily penetrate plant epidermis and be detected by fluorescence microscopy.•FDA, which itself does not emit light, will emit a fluorescent signal only when combined with B. bassiana. Concomitant genetic testing of the fungal ITS confirmed the high level of the fluorescent staining method for detection of B. bassiana.•Compared with the previous green fluorescent protein (GFP) labeling methods, this protocol improved the labeling efficiency of microorganisms and simplifies the process., Graphical abstract Image, graphical abstract

Decision-making is one of the most important management functions and a critical task for managers. The tools that support decision makers in making decisions are Multi-criteria Decision Making/Aid/Analysis (MCDM/MCDA) methods. Since most decisions are made under conditions of uncertainty, the fuzzy MCDM/MCDA methods are particularly important as they allow capturing the uncertainty and imprecision of the information used in making decisions. This method is the Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy PROMETHEE), and its extension in the form of New Easy Approach to Fuzzy PROMETHEE (NEAT F-PROMETHEE). However, the unavailability of software using the NEAT F-PROMETHEE method significantly reduces its ease of use and may discourage potential users and researchers considering using it in their studies. Therefore, to facilitate the use of this MCDA method, the article presents the implementation of NEAT F-PROMETHEE in the MATLAB environment. Moreover, the verification of the developed implementation and its application in the management decision-making problem is presented, together with the analysis of the operation of the mapping correction function used in NEAT F-PROMETHEE. The results obtained with NEAT F-PROMETHEE were compared with the results of the Fuzzy PROMETHEE method which did not apply correction. The analysis shows that the correction applied in NEAT F-PROMETHEE allows obtaining results with a smaller error than the non-corrected implementations of PROMETHEE Fuzzy. Therefore, a more accurate solution of the decision problem is obtained.•improving the process of mapping fuzzy numbers in the Fuzzy PROMETHEE method•implementing a correction mechanism while mapping trapezoidal fuzzy numbers, Graphical abstract Image, graphical abstract

Movement analysis provides a vast amount of data, which, frequently, are not used in the clinical decision-making process. For example, traditional gait data visualization is based on a time-based display of joint angles, but part of the information is lost when these time-series are averaged across different gait strides. Horizon graph is a data display method that increases the density of time-series data by horizontally dividing and layering multiple filled line graphs. This higher data density increases the amount of information displayed in the same graph and, consequently, enables visual data comparisons between multiple time series. Horizon graph of kinematic data allows displaying several cycles of different joints and their respective continuous symmetry ratio between sides. The aim of this work is to introduce the Horizon graph as a method to analyze kinematic gait data and help to characterize its symmetry. Examples of Horizon graph application to running is offered. Horizon graph may prove to be a useful clinical tool to visualize kinematic time-series and facilitate their clinical interpretation.•Continuous gait time series is a powerful tool for clinical analysis.•Horizon graph, higher data density graph, increases the information displayed.•Horizon graph is a clinical tool to visualize kinematic curves., Graphical abstract Image, graphical abstract

Drug repurposing techniques allow existing drugs to be tested against diseases outside their initial spectrum, resulting in reduced cost and eliminating the long time-frames of new drug development. In silico drug repurposing further speeds up the process either by proposing drugs suitable to invert the transcriptomic profile of a disease or by indicating drugs based on their common targets or structural similarity with other drugs with similar mode of action.Such methods usually return a number of potential repurposed drugs that need to be tested against the disease in in vitro, pre-clinical and clinical studies. Thus, it is crucial to have a more sophisticated candidate drug ranking in order to start testing from the most promising chemical substances. As a means to enhance the above decision process, we present CoDReS (Composite Drug Reranking Scoring), a drug (re-)ranking web-based tool, which combines an initial drug ranking (i.e. repurposing score or hypothesis/potentiality score) with a functional score of each drug considered in conjunction with the disease under study as well as with a structural score derived from potential drugability violations. Furthermore, a structural similarity clustering is applied on the considered drugs and a handful of structural exemplars are suggested for further in vitro and in vivo validation. The user is able to filter the results further, through structural similarity examination of the candidate drugs with drugs that have failed against the queried disease where related clinical trials have been carried out.CoDReS is publicly available online at http://bioinformatics.cing.ac.cy/codres. Keywords: Drug discovery, Drug ranking, Data mining, Cheminformatics

RNA splicing may generate different kinds of splice junctions, such as linear, back-splice and fusion junctions. Only a limited number of programs are available for detection and quantification of splice junctions. Here, we present Assembling Splice Junctions Analysis (ASJA), a software package that identifies and characterizes all splice junctions from high-throughput RNA sequencing (RNA-seq) data. ASJA processes assembled transcripts and chimeric alignments from the STAR aligner and StringTie assembler. ASJA provides the unique position and normalized expression level of each junction. Annotations and integrative analysis of the junctions enable additional filtering. It is also appropriate for the identification of novel junctions. ASJA is available at https://github.com/HuangLab-Fudan/ASJA. Keywords: RNA splicing, Splice junctions, RNA-seq, Circular RNA

Agent-based modelling methodologies offer a number of advantages when it comes to socio-ecological systems research. In particular, they enable experiments to be conducted that are not practical or feasible to conduct in real world settings; they can capture heterogeneity in agent circumstances, knowledge, behaviour, and experiences; and they facilitate a multi-scale, causal understanding of system dynamics. However, developing detailed, empirically informed agent-based models is typically a time and resource intensive activity. Here, we describe a detail-rich, ethnographically informed agent-based model of a Nepalese smallholder village that was created for the purpose of studying the impact of multiple stressors on mountain communities. In doing so, we aim to make the model accessible to other researchers interested in simulating such communities and to provide inspiration for other socio-ecological system modellers.•The model is described using the ODD protocol.•The number of replicate runs required for experiments is discussed, and the model validation and sensitivity analysis processes that have been conducted are explained.•Suggestions are made for how the model can practically be used and for how model outputs can be analysed., Graphical abstract Image, graphical abstract

Unmanned Aerial Vehicles (UAVs) have started to receive more attention in the ecological field in the past 15 years, as they provide very high-resolution imagery that ranges from meters to millimeters. Very high-resolution multispectral imagery obtained from UAVs can help in assessing and monitoring native desert vegetation. Thus, this study use UAVs to develop a method to estimate the biomass and carbon stock of native desert shrubs. The method integrates different techniques and software to monitor native plants' coverage, biomass, and carbon stock. The techniques used in this work are also applicable for other native desert shrubs in the region and could support ecosystem managers in assessing and monitoring arid ecosystems and restoration and revegetation programs. A three-stage image and data management are discussed, including: (1) fieldwork and image acquisition using UAVs, (2) image pre-processing, and (3) image processing using different techniques and software.•Determining shrub biomass is not restricted to multispectral data only but could be applicable for RGB data since it mainly depends on the DSM and DTM.•Allometric parameters could help in estimating desert shrub biomass which could be measured easily and rapidly using UAV imagery.•SVM Supervised classification could help in distinguishing between native shrubs and grasses., Graphical abstract Image, graphical abstract

The study of higher cognitive processes often relies on the manipulation of bottom-up stimulus characteristics such as exposure time. While several software exist that can schedule the onset and offset time of a visual stimulus, the actual exposure time depends on several factors that are not easy to control, resulting in undesired variability within and across studies. Here we present VISTO, a simple device built on the Arduino platform that allows one to measure the exact onset and offset of a visual stimulus, and to test its synchronization with a trigger signal. The device is used to measure the profile of luminance waveforms in arbitrary analog/digital (AD) units, and the implications of these luminance profiles are discussed based on a model of information accumulation from visual exposure. Moreover, VISTO can be calibrated to match the brightness of each experimental monitor. VISTO allows for control of stimulus timing presentation, both in classical laboratory settings and in more complex settings as technology allows to use new display devices or acquisition equipment. In sum, VISTO allows one to:•measure the profile of luminance curves.•determine the exposure time of a visual stimulus.•measure the synchronization between a trigger signal and a visual stimulus., Graphical abstract Image, graphical abstract

Sustainability Transitions (ST) is a complex phenomenon, encompassing environmental, societal and economic aspects. Its study requires a proper investigation, with the identification of a robust indicator and the definition of a suitable method of analysis. To identify the most informative geographical boundaries for analysing ST pathways, we consider the Carbon Emission Intensity (CEI) and estimate a four-level growth model to study its pattern over time for all the EU regions. We apply this model to a novel longitudinal dataset that covers CEI data of European regions at four different geographical scales (state, areas, regions, and provinces) over a nine-year timespan. This approach aims at supporting the decision-makers in developing more effective sustainability transitions policies across Europe, especially focusing on regions and overcoming the well-known “one-size fits all” approach.•The unconditional growth model has been applied to a multi-level structure considering four levels, defined by three geographical scales and time.•The ideal structure of the model would have required five levels, but the sample size of the dataset made the application computationally unfeasible;•The application of the model allowed to identify patterns of stability and change over time of the variable amongst different geographical units., Graphical abstract Image, graphical abstract

Highlights • The Grey and Vector autoregressive models are coupled to improve their accuracy. • Five economic and demographic parameters are included in the new hybrid model. • This new model is a reliable forecasting tool for assessing energy demand., Forecasting energy demand in general, and electricity demand in particular, requires the developing reliable forecasting tools that can be used to monitor the evolution of consumers’ energy needs more accurately. The proposed new hybrid GM(1,1)-VAR(1) model is meant for that purpose. The latter is based on the Grey and Vector autoregressive approaches, and makes it possible to predict future demand, by taking into account economic and demographic determinants with an exponential growth trend. With an associated APE of 1.5, a MAPE of 1.628%, and an RMSE of 15.42, this new model thus presents better accuracy indicators than hybrid models of the same nature. Also, it proves to be as accurate as some recent hybrid artificial intelligence models. The model is thus a reliable forecasting tool that can be used to monitor the evolution of energy demand.•The Grey and Vector autoregressive models are coupled to improve their accuracy.•Five economic and demographic parameters are included in the new hybrid model.•This new model is a reliable forecasting tool for assessing energy demand., Graphical abstract Image, graphical abstract

The detection of NREM-REM sleep cycles in human sleep data (i.e., polysomnographically assessed sleep stages) enables fine-grained analyses of ultradian variations in sleep microstructure (e.g., sleep spindles, and arousals), or other amplitude- and frequency-specific electroencephalographic features during sleep. While many laboratories have software that is used internally, reproducibility requires the availability of open-source software. Therefore, we here introduce the ‘SleepCycles’ package for R, an open-source software package that identifies sleep cycles and their respective (non-) rapid eye movement ([N]REM) periods from sleep staging data. Additionally, each (N)REM period is subdivided into parts of equal duration (percentiles), which may be useful for further fine-grained analyses. The detection criteria used in the package are, with some adaptations, largely based on criteria originally proposed by Feinberg and Floyd (1979). The latest version of the package can be downloaded from the Comprehensive R Archives Network (CRAN).•The package ‘SleepCycles’ for R allows to identify sleep cycles and their respective NREM and REM periods from sleep staging results.•Besides the cycle detection, NREM and REM periods are also split into parts of equal duration (percentiles) thereby allowing for a better temporal resolution across the night and comparisons of sleep cycles with different durations amongst different night recordings., Graphical abstract Image, graphical abstract

Routine generation of large Rb-Sr age datasets has been limited by the time-consuming requirement of chromatographic isolation of Sr from Rb to avoid the isobaric interference of 87Rb+ on 87Sr+. Recently, direct measurement of interference-free 87Sr/86Sr has become possible with the advent of tandem quadrupole inductively coupled plasma mass spectrometry (ICP-MS/MS). Here, we describe a method that uses ICP-MS/MS to measure 87Sr/86Sr combined with a standard addition calibration for precise determination of Rb/Sr, allowing efficient generation of Rb-Sr ages. The method is demonstrated on a suite of ~50 to 90 Myrs (million years) old celadonite samples that are dated with age uncertainties typically of ±2 to 5 Myrs provided the sample Rb/Sr is >2. A method to separate the phyllosilicate fraction from the bulk sample, because it commonly has a higher Rb/Sr more suitable for dating, using differential settling is also described.•Differential settling allows high Rb/Sr phyllosilicates to be separated from other phases•ICP-MS/MS allows 87Sr/86Sr measurement without chromatographic isolation of Sr+ from Rb+•Single-spike standard addition ICP-MS was used for Rb/Sr measurement, Graphical abstract Image, graphical abstract

This paper describes the reduction in memory and computational time for the simulation of complex radiation transport problems with the discrete ordinate method (DOM) model in the open-source computational fluid dynamics platform OpenFOAM. Finite volume models require storage of vector variables in each spatial cell; DOM introduces two additional discretizations, in direction and wavelength, making memory a limiting factor. Using specific classes for radiation sources data, changing the store of fluxes and other minor changes allowed a reduction of 75% in memory requirements. Besides, a hierarchical parallelization was developed, where each node of the standard parallelization uses several computing threads, allowing higher speed and scalability of the problem. This architecture, combined with optimization of some parts of the code, allowed a global speedup of x15. This relevant reduction in time and memory of radiation transport opens a new horizon of applications previously unaffordable.

This article adapts an existing experimental protocol for assessing individuals’ ability to transfer knowledge across instrumental and pavlovian learning stages. The protocol (Transfer of Control using differential outcomes learning) is adapted to fit social contexts wherein the pavlovian learning phase is modulated so that individuals are able to observe, and potentially learn from, the stimulus associated with reinforcing outcomes presented to another (observable) individual. Transfer of Control concerns participants combining knowledge of learned instrumental and pavlovian (stimulus, response, outcome) associations in order to ground the learning of new associations. The article describes the theoretical and procedural underpinnings of a novel Social Transfer of Control methodology. The use of such a methodology is two-fold: i) to serve as a guide to researchers interested in evaluating how individuals can learn from others in a partially observable setting, i.e. when behavioural and reinforcing outcome information is hidden, and bring to bear this knowledge on their own instrumental decision making; ii), to facilitate investigation of the routes of cognitive and emotional empathy, with potential applications for educational and clinical settings.•Three stage Transfer of Control behavioural methodology is adapted to include a social (pavlovian) learning stage.•Hypotheses can be tested that concern learning rewarding instrumental responses achieved by observation of others’ emotionally expressive reactions to differentially rewarding outcomes.•Methodological and validation considerations for evaluating the above are presented, Graphical abstract Image, graphical abstract

This study proposes a novel approach that investigates the dynamic dependency among exchange rates by extending time-varying copulas' parameters following an autoregressive moving average (ARMA) process. The process consists of an autoregressive part that explains the effect of the previous parameters and a forcing variable that measures the dependence structure between marginal variables. We apply this model to the daily data of the exchange rates of five Asian countries with the strongest economies before and during the 2020 pandemic, namely CNY/USD, IDR/USD, INR/USD, JPY/USD, and KRW/USD. The ARIMA-GARCH model was used to model the exchange rates data and estimate the dynamic dependence using time-varying copulas with the extended parameters. The dynamic dependencies between Chinas and the four countries' exchange rates before and during the 2020 pandemic was evidenced. Moreover, India is the country whose exchange rate has been most strongly affected by the pandemic. Some of the highlights of the proposed approach are:•This paper provides two algorithms to investigate the dynamic dependencies among exchange rates data during a crisis and forecast the data using time-varying copulas with the extended parameters.•There are four extended time-varying copulas' parameters which can measure the dynamic dependencies between variables.•The computation procedure is easy to implement., Graphical Abstract Image, graphical abstract