Graphical reduction and analysis small-angle neutron scattering program: GRASP

GRASP is a scientific software application designed for the graphical inspection, reduction and analysis of multidetector data produced by the small-angle neutron scattering (SANS) instruments at the Institut Laue-Langevin and other neutron sources around the world. The first developments of GRASP began more than 20 years ago and were written in MATLAB, allowing rapid development of scientific code, with much of the data handling, matrix manipulation, mathematical tools, user interface and graphical tools integrated at a high level in the underlying MATLAB platform. By their very nature, multidimensional data are often best appreciated in graphical form. GRASP deals with many of the diverse requirements for data reduction and analysis of SANS data using a general set of tools and reduction algorithms suited to 2D multidetector data. A further fundamental architectural inclusion is a third dimension of data manipulation, thereby easily allowing parametric analysis and cross referencing of series data such as composition, kinetic measurements, temperature, magnetic field, angle or time of flight, often considered as a single 'measurement'. This article serves as a reference document for users of the software, and outlines the architecture and strategy of the program. An overview of some of the features, capabilities, peripheral user modules and neutron scattering tools is presented.

Rethinking data treatment: The sucrose preference threshold for anhedonia in stress-induced rat models of depression

BACKGROUND

Exposing rats to repeated unpredictable stressors is a popular method for modelling depression. The sucrose preference test is used to assess the validity of this method, as it measures a rat´s preference for a sweet solution as an indicator of its ability to experience pleasure. Typically, if stressed rats show a lower preference compared to unstressed rats, it is concluded they are experiencing stress-induced anhedonia.

METHODS

While conducting a systematic review, we identified 18 studies that used thresholds to define anhedonia and to distinguish "susceptible" from "resilient" individuals. Based on their definitions, researchers either excluded "resilient" animals from further analyses or treated them as a separate cohort. We performed a descriptive analysis to understand the rationale behind these criteria.

RESULTS

we found that the methods used for characterizing the stressed rats were largely unsupported. Many authors failed to justify their choices or relied exclusively on referencing previous studies. When tracing back the method to its origins, we converged on a pioneering article that, although employed as a universal evidence-based justification, cannot be regarded as such. What is more, through a simulation study, we provided evidence that removing or splitting data, based on an arbitrary threshold, introduces statistical bias by overestimating the effect of stress.

CONCLUSION

Caution must be exercised when implementing a predefined cut-off for anhedonia. Researchers should be aware of potential biases introduced by their data treatment strategies and strive for transparent reporting of methodological decisions.

Rapid and Reliable Data Treatment for the Control of Food Chemical Contaminants by LC-HRMS

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is considered an unavoidable extension of low-resolution LC-MS/MS that stretches the capabilities of multi-residue analysis of chemical contaminants in food. However, LC-HRMS acquisitions generate a massive amount of information available for data processing with supplier software that still miss critical calculation features and adapted reporting tools. Consequently, routine laboratories are still reluctant to switch from LC-MS/MS to LC-HRMS, the latter is still perceived as a cumbersome and demanding technology. In that context, we propose a four-step LC-HRMS workflow to speed-up data processing in situations of multi-residue multi-matrix analysis with the goal to maximize the time spent on data interpretation rather than on data formatting. The first three steps of the workflow imply specific settings on the Orbitrap HRMS associated software (TraceFinder^TM) while the fourth step is the novelty i.e. a newly coded R-script capable to translate a raw export file into a comprehensive .xlsx report file in a few seconds. As recommended by various international guidelines and in some official methods, standard addition-based applications are fully embedded in this reporting tool whilst still being the main bottleneck of supplier's software. The reporting tool also allows appropriate data formatting, filtering, and color-coding options to provide a clear picture of compounds being detected or not, and those requiring specific attention due to unmet quality control criteria as required by European legislation (European Commission SANTE 11312/2021). It is hoped that additional functionalities compatible with R scripts will be soon fully embedded in the supplier's software for easier data interpretation and reporting.

Binary Simplification as an Effective Tool in Metabolomics Data Analysis

Metabolomics aims to perform a comprehensive identification and quantification of the small molecules present in a biological system. Due to metabolite diversity in concentration, structure, and chemical characteristics, the use of high-resolution methodologies, such as mass spectrometry (MS) or nuclear magnetic resonance (NMR), is required. In metabolomics data analysis, suitable data pre-processing, and pre-treatment procedures are fundamental, with subsequent steps aiming at highlighting the significant biological variation between samples over background noise. Traditional data analysis focuses primarily on the comparison of the features' intensity values. However, intensity data are highly variable between experimental batches, instruments, and pre-processing methods or parameters. The aim of this work was to develop a new pre-treatment method for MS-based metabolomics data, in the context of sample profiling and discrimination, considering only the occurrence of spectral features, encoding feature presence as 1 and absence as 0. This "Binary Simplification" encoding (BinSim) was used to transform several benchmark datasets before the application of clustering and classification methods. The performance of these methods after the BinSim pre-treatment was consistently as good as and often better than after different combinations of traditional, intensity-based, pre-treatments. Binary Simplification is, therefore, a viable pre-treatment procedure that effectively simplifies metabolomics data-analysis pipelines.

Harmonization and Visualization of Data from a Transnational Multi-Sensor Personal Exposure Campaign

Use of a multi-sensor approach can provide citizens with holistic insights into the air quality of their immediate surroundings and their personal exposure to urban stressors. Our work, as part of the ICARUS H2020 project, which included over 600 participants from seven European cities, discusses the data fusion and harmonization of a diverse set of multi-sensor data streams to provide a comprehensive and understandable report for participants. Harmonizing the data streams identified issues with the sensor devices and protocols, such as non-uniform timestamps, data gaps, difficult data retrieval from commercial devices, and coarse activity data logging. Our process of data fusion and harmonization allowed us to automate visualizations and reports, and consequently provide each participant with a detailed individualized report. Results showed that a key solution was to streamline the code and speed up the process, which necessitated certain compromises in visualizing the data. A thought-out process of data fusion and harmonization of a diverse set of multi-sensor data streams considerably improved the quality and quantity of distilled data that a research participant received. Though automation considerably accelerated the production of the reports, manual and structured double checks are strongly recommended.

THORONDOR: a software for fast treatment and analysis of low-energy XAS data

THORONDOR is a data treatment software with a graphical user interface (GUI) accessible via the browser-based Jupyter notebook framework. It aims to provide an interactive and user-friendly tool for the analysis of NEXAFS spectra collected during in situ experiments. The program allows on-the-fly representation and quick correction of large datasets from single or multiple experiments. In particular, it provides the possibility to align in energy several spectral profiles on the basis of user-defined references. Various techniques to calculate background subtraction and signal normalization have been made available. In this context, an innovation of this GUI involves the usage of a slider-based approach that provides the ability to instantly manipulate and visualize processed data for the user. Finally, the program is characterized by an advanced fitting toolbox based on the lmfit package. It offers a large selection of fitting routines as well as different peak distributions and empirical ionization potential step edges, which can be used for the fit of the NEXAFS rising-edge peaks. Statistical parameters describing the goodness of a fit such as χ² or the R-factor together with the parameter uncertainty distributions and the related correlations can be extracted for each chosen model.

Influence of multiplexing conditions on artefact signal and the signal-to-noise ratio in the decoded data in Hadamard transform ion mobility spectrometry

In Hadamard transform ion mobility spectrometry (HT IMS), the signal-to-noise ratio is always lower for non-modified pseudorandom sequences than for modified sequences. Since the use of non-modified modulating pseudorandom sequences is strategically preferable from a duty cycle standpoint, we investigated the change in the interference signal when transitioning from non-modified modulating sequences to sequences modified by the addition of 1,3,5 and 7 zeros. The interfering signal in HT IMS with modified pseudorandom sequences was shown to be mainly random noise for all the cases except for modifying by incorporation of 1 zero. For standard samples of tetraalkylammonium halides, modulation by non-modified pseudorandom sequences is beneficial in the case of small numbers of averaged spectra (below ∼40 averaged spectra compared to any modified pseudorandom sequences except for 1 zero modified and below ∼200 averaged spectra compared to signal averaging ion mobility spectrometry) and worsens the signal-to-noise ratio in the case of large numbers of averaged spectra. Contrarily, modulation by modified pseudorandom sequences is beneficial for any number of averaged spectra, except for very small ones (below 15 averaged spectra compared to modulation by non-modified sequences). Pseudorandom sequence modified with 1 zero incorporation is beneficial in the case of below ∼400 averaged spectra compared to any modified and non-modified pseudorandom sequences. The signal-to-noise ratio in conventional signal averaging mode ion mobility spectrometry is affected by random noise, whereas the HT IMS with non-modified pseudorandom sequences was demonstrated to be primarily affected by a systematic noise-like artefact signal. Because noise-like artefact signals were found to be reproducible, predicting models for interference signals could be generated to improve signal-to-noise ratio. This is significant because non-modified modulating sequences are limited by their poor signal-to-noise ratio. This improvement would increase the viability of non-modified modulating sequences which are preferred because of their higher sample utilization efficiency.

Quality Control and Pre-Analysis Treatment of the Environmental Datasets Collected by an Internet Operated Deep-Sea Crawler during Its Entire 7-Year Long Deployment (2009-2016)

Deep-sea environmental datasets are ever-increasing in size and diversity, as technological advances lead monitoring studies towards long-term, high-frequency data acquisition protocols. This study presents examples of pre-analysis data treatment steps applied to the environmental time series collected by the Internet Operated Deep-sea Crawler “Wally” during a 7-year deployment (2009–2016) in the Barkley Canyon methane hydrates site, off Vancouver Island (BC, Canada). Pressure, temperature, electrical conductivity, flow, turbidity, and chlorophyll data were subjected to different standardizing, normalizing, and de-trending methods on a case-by-case basis, depending on the nature of the treated variable and the range and scale of the values provided by each of the different sensors. The final pressure, temperature, and electrical conductivity (transformed to practical salinity) datasets are ready for use. On the other hand, in the cases of flow, turbidity, and chlorophyll, further in-depth processing, in tandem with data describing the movement and position of the crawler, will be needed in order to filter out all possible effects of the latter. Our work evidences challenges and solutions in multiparametric data acquisition and quality control and ensures that a big step is taken so that the available environmental data meet high quality standards and facilitate the production of reliable scientific results.

MELC Genomics: A Framework for De Novo Genome Assembly

The development of next-generation sequencing platforms increased substantially the capacity of data generation. In addition, in the past years, the costs for whole genome sequencing have been reduced that made it easier to access this technology. As a result, the storage and analysis of the data generated became a challenge, ushering in the development of bioinformatic tools, such as programs and programming languages, able to store, process, and analyze this huge amount of information. In this article, we present MELC genomics, a framework for genome assembly in a simple and fast workflow.