Data Processing Pipeline Research Articles

Science Product Pipelines and Archive Architecture for the DART Mission

On 2022 September 26, the Double Asteroid Redirection Test (DART) mission was the first successful demonstration of a kinetic impactor for planetary defense. The DART mission utilized a novel autonomous data processing pipeline architecture to quickly produce and analyze the quality of raw and calibrated images from the camera mounted on board the spacecraft. Optimization of the data processing pipeline allowed the final 150 images prior to impact to be calibrated and delivered to the Investigation Team and the press within 15 minutes of acquisition. A data quality analysis pipeline allowed for rapid identification of detector misconfigurations, missing data, and other adverse events. DART data products, along with data from LICIACube and data from ground observatories, used common file formats to facilitate the development of analysis and archiving software. This architecture is described for future missions with large volumes of data and an emphasis on quick-turnaround applications such as planetary defense.

Target-based georeferencing of terrestrial radar images using TLS point clouds and multi-modal corner reflectors in geomonitoring applications

Terrestrial Radar Interferometry (TRI) is widely adopted in geomonitoring applications due to its capability to precisely observe surface displacements along the line of sight, among other key characteristics. As its deployment grows, TRI is also increasingly used to monitor smaller and more dispersed geological phenomena, where the challenge is their precise localization in 3d space if the pose of the radar interferometer is not known beforehand. To tackle this challenge, we introduce a semi-automatic target-based georeferencing method for precisely aligning TRI data with 3d point clouds obtained using long-range Terrestrial Laser Scanning (TLS). To facilitate this, we developed a multi-modal corner reflector (mmCR) that serves as a common reference point recognizable by both technologies, and we accompanied it with a semi-automatic data-processing pipeline, including the algorithms for precise center estimation. Experimental validation demonstrated that the corner reflector can be localized within the TLS data with a precision of 3–5 cm and within the TRI data with 1–2 dm. The targets were deployed in a realistic geomonitoring scenario to evaluate the implemented workflow and the achievable quality of georeferencing. The post-georeferencing mapping uncertainty was found to be on a decimeter level, matching the state-of-the-art results using dedicated targets and achieving more than an order of magnitude lower uncertainty than the existing data-driven approaches. In contrast to the existing target-based approaches, our results were achieved without laborious visual data inspection and manual target detection and on significantly larger distances, surpassing 2 km. The use of the developed mmCR and its associated data-processing pipeline extends beyond precise georeferencing of TRI imagery to TLS point clouds, allowing for alternatively georeferencing using total stations, mapping quality evaluation as well as on-site testing and calibrating TRI systems within the application environment.

Probing atmospheric escape through metastable He I triplet lines in 15 exoplanets observed with SPIRou

For several years, the metastable helium triplet line has been successfully used as a tracer to probe atmospheric escape in transiting exoplanets. This absorption in the near-infrared (1083.3 nm) can be observed from the ground using high-resolution spectroscopy, providing new constraints on the mass-loss rate and the temperature characterizing the upper atmosphere of close-in exoplanets. The aim of this work is to search for the He triplet signature in 15 transiting exoplanets – ranging from super-Earths to ultrahot Jupiters – observed with SPIRou, a high-resolution (R ~ 70 000) near-infrared spectropolarimeter at the CFHT, in order to bring new constraints or to improve existing ones regarding atmospheric escape through a homogeneous study. We developed a full data processing and analysis pipeline to correct for the residual telluric and stellar contributions. We then used two different 1D models based on the Parkerwind equations and nonlocal thermodynamic equilibrium (NLTE) radiative transfer to interpret the observational results. We confirm published He triplet detections for HAT-P-11 b, HD 189733 b, and WASP-69 b. We tentatively detect the signature of escaping He in HD 209458 b, GJ 3470 b, and WASP-76 b. We report new constraints on the mass-loss rate and temperature for our three detections and set upper limits for the tentative and nondetections. We notably report improved constraints on the mass-loss rate and temperature of the escaping gas for TOI-1807 b, and report a nondetection for the debated atmospheric escape in GJ 1214 b. We also conducted the first search for the He signature in GJ 486 b since its discovery and report a nondetection of the He triplet. Finally, we studied the impact of important model assumptions on our retrieved parameters, notably the limitations of 1D models and the influence of the H/He ratio on the derived constraints.

'Where have my patients gone?': A simulation study on real-world data processing in Clinical Data Warehouses

ObjectiveTo access Electronic Health Record (EHR) data, hospitals have implemented Clinical Data Warehouses (CDWs) using Extract Transform and Load (ETL) processes. While ETL performances are typically evaluated individually, our study examines the cumulative impact of ETLs on data availability. MethodsUsing a real multi-hospital CDW as a case study, we modeled EHR data processing from the software sources to the CDW's data store. We simulated a scenario where researchers aimed to reconstruct breast cancer care trajectories using EHR data. We calculated the size and characteristics of the data store population, and compared them to the original population. ResultsEHR data are recorded in various software depending on data category, hospital, and year, each requiring specific series of ETLs for integration in the CDW. Despite acceptable transfer rates for each ETL (range 73 %-100 %), cumulative losses led to study populations in the data store being up to 90 % smaller than anticipated when researchers required data exhaustivity for patients. Population size decreased steeply with the more data categories required. No difference was found in population characteristics between the data store and the original cohorts. Discussion & ConclusionResearchers should scrutinize data availability in CDWs as missing data could result from outsourced care, incomplete input, or underperforming ETLs. Integrating more data sources in CDWs increases the number of data routes, necessitating time for ETL implementation and maintenance, and increases data loss risks. Though commonly perceived as a “black box”, data transformation can significantly influence the reliability of populations studied in CDWs. Public interest SummaryTo access data generated during care, researchers build Clinical Data Warehouses (CDWs). CDWs are infrastructures composed of a series of processing steps to extract the data from the data source, transform it according to the needs and load it into a data store. Usually, the performances of these processing steps are evaluated one a time. However, each data point goes through a series of processing steps before being made available for research. In this study, we aim to evaluate the impact of the entire data processing pipeline on the availability of data points in a CDW by simulating a study on breast cancer and evaluating the impact on the size and the characteristics of the final cohort. The cumulative losses of the processing steps resulted in a population 90 % smaller than anticipated. The characteristics of the final population showed no difference to those of the original cohort.

X-Ray Source Classification Using Machine Learning: A Study with EP-WXT Pathfinder LEIA

X-ray observations play a crucial role in time-domain astronomy. The Einstein Probe (EP), a recently launched X-ray astronomical satellite, emerges as a forefront player in the field of time-domain astronomy and high-energy astrophysics. With a focus on systematic surveys in the soft X-ray band, EP aims to discover high-energy transients and monitor variable sources in the universe. To achieve these objectives, a quick and reliable classification of observed sources is essential. In this study, we developed a machine learning classifier for autonomous source classification using data from the EP-WXT Pathfinder—Lobster Eye Imager for Astronomy (LEIA) and EP-WXT simulations. The proposed Random Forest classifier, built on selected features derived from light curves, energy spectra, and location information, achieves an accuracy of approximately 95% on EP simulation data and 98% on LEIA observational data. The classifier is integrated into the LEIA data processing pipeline, serving as a tool for manual validation and rapid classification during observations. This paper presents an efficient method for the classification of X-ray sources based on single observations, along with implications of most effective features for the task. This work facilitates rapid source classification for the EP mission and also provides valuable insights into feature selection and classification techniques for enhancing the efficiency and accuracy of X-ray source classification that can be adapted to other X-ray telescope data.

Correcting Fabry-Pérot etalon effects in solar observations

Context. Data processing pipelines of Fabry–Pérot interferometers (FPI) must take into account the side effects these devices introduce in the observations. Interpretation of these observations without proper correction can lead to inaccurate or false results, with consequent impact on their physical interpretation. Corrections typically require prior knowledge of the properties of the etalon and the way they affect the incoming light in order to calibrate the data successfully. Aims. We have developed an algorithm to derive etalon properties from flat-field observations and tested its applicability and accuracy using simulated observations and real measurements. Methods. We employed analytical expressions of the transmission profiles for FPIs in collimated and telecentric configurations to derive their expected impact on the observations. These analytical expressions allowed us to develop a customized optimization algorithm capable of inferring the properties of the etalon from the observations. The algorithm’s performance has been tested on simulated observations with an etalon in collimated and telecentric setups employing various noise levels and spectral samplings. Additionally, we explored how tilting the etalon in a telecentric configuration influences the algorithm’s effectiveness. Lastly, we also applied the algorithm to a set of real flat-field observations taken with the high-resolution telescope of the Polarimetric and Helioseismic Imager on board the Solar Orbiter mission (HRT-SO/PHI). Results. The algorithm is able to retrieve the gain and etalon induced transmission velocity shifts (cavity map), with an average accuracy ranging between 0.4% and 0.1% for the former and between 120 m s−1 and 30 m s−1 for the latter. Both reducing the noise level and increasing the spectral sampling of the observations proved to greatly increase the algorithm’s performance, as expected. Results also suggest that determination of the observed object from the data is possible but an additional error between 40 m s−1 and 10 m s−1 is to be expected in the inferred cavity map. Furthermore, we show that neglecting the asymmetries arising from either tilts of the etalon or imperfections in the telecentrism can lead to large errors when determining the gain. Tests with HRT-SO/PHI data have verified the applicability of the algorithm in real cases. Conclusions. Our presented method enabled us to derive the transmission profile of FPIs from observations of collimated and telecentric configurations. It has proven to be robust against the presence of noise and limited spectral line sampling. The results reported here also show the importance of accounting for the asymmetries arising in real telecentric mounts when interpreting the results of real instruments.

The FRB-searching Pipeline of the Tianlai Cylinder Pathfinder Array

This paper presents the design, calibration, and survey strategy of the Fast Radio Burst (FRB) digital backend and its real-time data processing pipeline employed in the Tianlai Cylinder Pathfinder Array. The array, consisting of three parallel cylindrical reflectors and equipped with 96 dual-polarization feeds, is a radio interferometer array designed for conducting drift scans of the northern celestial semi-sphere. The FRB digital backend enables the formation of 96 digital beams, effectively covering an area of approximately 40 square degrees with the 3 dB beam. Our pipeline demonstrates the capability to conduct an automatic search of FRBs, detecting at quasi-real-time and classifying FRB candidates automatically. The current FRB searching pipeline has an overall recall rate of 88%. During the commissioning phase, we successfully detected signals emitted by four well-known pulsars: PSR B0329+54, B2021+51, B0823+26, and B2020+28. We report the first discovery of an FRB by our array, designated as FRB 20220414A. We also investigate the optimal arrangement for the digitally formed beams to achieve maximum detection rate by numerical simulation.

Simulated LC-MS Data Set for Assessing the Metabolomics Data Processing Pipeline Implemented into MVAPACK.

Metabolomics commonly relies on using one-dimensional (1D) 1H NMR spectroscopy or liquid chromatography-mass spectrometry (LC-MS) to derive scientific insights from large collections of biological samples. NMR and MS approaches to metabolomics require, among other issues, a data processing pipeline. Quantitative assessment of the performance of these software platforms is challenged by a lack of standardized data sets with "known" outcomes. To resolve this issue, we created a novel simulated LC-MS data set with known peak locations and intensities, defined metabolite differences between groups (i.e., fold change > 2, coefficient of variation ≤ 25%), and different amounts of added Gaussian noise (0, 5, or 10%) and missing features (0, 10, or 20%). This data set was developed to improve benchmarking of existing LC-MS metabolomics software and to validate the updated version of our MVAPACK software, which added gas chromatography-MS and LC-MS functionality to its existing 1D and two-dimensional NMR data processing capabilities. We also included two experimental LC-MS data sets acquired from a standard mixture andMycobacterium smegmatiscell lysates since a simulated data set alone may not capture all the unique characteristics and variability of real spectra needed to assess software performance properly. Our simulated and experimental LC-MS data sets were processed with the MS-DIAL and XCMSOnline software packages and our MVAPACK toolkit to showcase the utility of our data sets to benchmark MVAPACK against community standards. Our results demonstrate the enhanced objectivity and clarity of software assessment that can be achieved when both simulated and experimental data are employed since distinctly different software performances were observed with the simulated and experimental LC-MS data sets. We also demonstrate that the performance of MVAPACK is equivalent to or exceeds existing LC-MS software programs while providing a single platform for processing and analyzing both NMR and MS data sets.

GPU-accelerated OCT imaging: Real-time data processing and artifact suppression for enhanced monitoring of 3D bioprinted tissues and vascular-like networks

Optical coherence tomography (OCT) imaging technology has significant advantages in in situ and noninvasive monitoring of biological tissues. However, it still faces the following challenges: including data processing speed, image quality, and improvements in three-dimensional (3D) visualization effects. OCT technology, especially functional imaging techniques like optical coherence tomography angiography (OCTA), requires a long acquisition time and a large data size. Despite the substantial increase in the acquisition speed of swept source optical coherence tomography (SS-OCT), it still poses significant challenges for data processing. Additionally, during in situ acquisition, image artifacts resulting from interface reflections or strong reflections from biological tissues and culturing containers present obstacles to data visualization and further analysis. Firstly, a customized frequency domain filter with anti-banding suppression parameters was designed to suppress artifact noises. Then, this study proposed a graphics processing unit (GPU)-based real-time data processing pipeline for SS-OCT, achieving a measured line-process rate of 800[Formula: see text]kHz for 3D fast and high-quality data visualization. Furthermore, a GPU-based real-time data processing for CC-OCTA was integrated to acquire dynamic information. Moreover, a vascular-like network chip was prepared using extrusion-based 3D printing and sacrificial materials, with sacrificial material being printed at the desired vascular network locations and then removed to form the vascular-like network. OCTA imaging technology was used to monitor the progression of sacrificial material removal and vascular-like network formation. Therefore, GPU-based OCT enables real-time processing and visualization with artifact suppression, making it particularly suitable for in situ noninvasive longitudinal monitoring of 3D bioprinting tissue and vascular-like networks in microfluidic chips.

Maiolica seen by Vis–NIR hyperspectral imaging spectroscopy: the application of an ultraportable camera at the Museo Nazionale del Bargello

An ultraportable hyperspectral camera operating in the Vis–NIR range (400–1000 nm) was used in this study for the non-invasive analysis of a selection of Italian maiolica wares and sherds from the Museo Nazionale del Bargello in Florence, Italy. The studied objects included authentic archaeological sherds, nineteenth-century forgeries, and ceramics of uncertain origin. The primary aim of this study was to evaluate the efficacy of hyperspectral imaging (HSI) spectroscopy combined with multivariate analysis for examining highly refractive and three-dimensional glazed ceramic artefacts within the constraints of a museum environment, including imperfect lighting conditions. Two data processing pipelines were tested: one based on principal component analysis (PCA) with score plots and the other on spectral angle mapper (SAM) classification obtained through the ENVI Spectral Hourglass Wizard (ENVI-SHW). The analyses enabled the discrimination between original parts and/or restored parts and additions. Among others, a complex assemblage of original sherds, restored parts, and non-ceramic parts was observed in a Renaissance pastiche vase. Furthermore, the method enabled the identification of the various chromophores providing clues on the artistic colour palette. In particular, the identification of chromium in the blue and green glazes of two objects of uncertain chronology suggested that they were not made before the nineteenth century. The acquisition of a larger and more detailed database of HSI data on maiolica glazes is strongly encouraged to improve the applicability of this method in authenticity, art-historical and archaeological studies.

Real-Time Precision in 3D Concrete Printing: Controlling Layer Morphology via Machine Vision and Learning Algorithms

3D concrete printing (3DCP) requires precise adjustments to parameters to ensure accurate and high-quality prints. However, despite technological advancements, manual intervention still plays a prominent role in this process, leading to errors and inconsistencies in the final printed part. To address this issue, machine learning vision models have been developed and utilized to analyze captured images and videos of the printing process, detecting defects and deviations. The data collected enable automatic adjustments to print settings, improving quality without the need for human intervention. This work first examines various techniques for real-time and offline corrections. It then introduces a specialized computer vision setup designed for real-time control in robotic 3DCP. Our main focus is on a specific aspect of machine learning (ML) within this system, called speed control, which regulates layer width by adjusting the robot motion speed or material flow rate. The proposed framework consists of three main elements: (1) a data acquisition and processing pipeline for extracting printing parameters and constructing a synthetic training dataset, (2) a real-time ML model for parameter optimization, and (3) a depth camera installed on a customized 3D-printed rotary mechanism for close-range monitoring of the printed layer.

Automated pipeline processing X-ray diffraction data from dynamic compression experiments on the Extreme Conditions Beamline of PETRAIII.

Presented and discussed here is the implementation of a software solution that provides prompt X-ray diffraction data analysis during fast dynamic compression experiments conducted within the dynamic diamond anvil cell technique. It includes efficient data collection, streaming of data and metadata to a high-performance cluster (HPC), fast azimuthal data integration on the cluster, and tools for controlling the data processing steps and visualizing the data using the DIOPTAS software package. This data processing pipeline is invaluable for a great number of studies. The potential of the pipeline is illustrated with two examples of data collected on ammonia-water mixtures and multiphase mineral assemblies under high pressure. The pipeline is designed to be generic in nature and could be readily adapted to provide rapid feedback for many other X-ray diffraction techniques, e.g. large-volume press studies, in situ stress/strain studies, phase transformation studies, chemical reactions studied with high-resolution diffraction etc.

UWLPIPE: Ultra-wide Bandwidth Low-frequency Pulsar Data Processing Pipeline

For real-time processing of ultra-wide bandwidth low-frequency pulsar baseband data, we designed and implemented an ultra-wide bandwidth low-frequency pulsar data processing pipeline (UWLPIPE) based on the shared ringbuffer and GPU parallel technology. UWLPIPE runs on the GPU cluster and can simultaneously receive multiple 128 MHz dual-polarization VDIF data packets preprocessed by the front-end FPGA. After aligning the dual-polarization data, multiple 128M subband data are packaged into PSRDADA baseband data or multi-channel coherent dispersion filterbank data, and multiple subband filterbank data can be spliced into wideband data after time alignment. We used the Nanshan 26 m radio telescope with the L-band receiver at 964 ∼ 1732 MHz to observe multiple pulsars. Finally, we processed the data using DSPSR software, and the results showed that each subband could correctly fold out the pulse profile, and the wideband pulse profile accumulated by multiple subbands could be correctly aligned.

Synthetic orbit uncertainty generation through regression analysis of historical Conjunction Data Messages

In the last decades, the Earth-orbiting population of both active and non-active objects has grown significantly, leading to a substantial increase in number of possible in-orbit collisions. It is therefore crucial to monitor the orbit of space resident objects to assess in advance the threat of risky conjunctions. Within this framework, the 18th Space Defense Squadron (SDS) is consistently updating the orbit of thousands of tracked objects by processing observations of the U.S. Space Surveillance Network (SSN). The determined orbital data is continuously maintained in the Special Perturbation (SP) catalogue and used by the 19th SDS to issue close approach warnings to satellite operators around the globe in the form of Conjunction Data Messages (CDM). The Flight Dynamics (FD) group of the German Space Operation Centre (GSOC) receives on regular basis a subset of the SP catalogue data along with CDMs associated to the fleet of its controlled satellites. The SP ephemerides are in fact provided without any covariance information preventing any computation of the Probability of Collision (Pc). In GSOC FD we are implementing a service to link a series of synthetic orbital error covariance matrices to a given SP ephemeris by statistically analyzing historical CDMs of past events. More than 30 GB of past conjunction data are processed to extract state vector, covariance matrix and object size parameter of already encountered secondary objects. The orbital errors of these last are subsequently categorized and divided into orbital classes to decouple the high correlation the covariance has with respect to solar flux, object dimension, altitude of perigee, eccentricity and orbit inclination. The classification aims at collecting similar CDMs regarding the aforementioned dependencies, and approximates the predicted 1-sigma position errors in the orbital frame by optimal curve-fitting techniques. By evaluation of the curve fitting coefficients of a requested orbit class a covariance matrix can be generated for any prediction time in upcoming CDM refinements and other analyses. The work discusses the limiting cases of the classification approach, bringing possible solutions to the scenario of empty classes. An in-depth characterization of the parameters that affect the orbital errors is in fact performed to individualize the neighboring class that provides the closest and most meaningful covariance timeline. Successively, the effect of using synthetic covariance in a conjunction risk assessment is also explored, adapting the problem on real operations. Lastly, the entire data processing pipeline and how the described service fits into the GSOC Flight Dynamics System (FDS) framework is described.

PATH-11. FEASIBILITY OF BRAIN TUMOR CLASSIFICATION BY ENZYMATIC DNA METHYLATION SEQUENCING ANALYSIS OF CELL-FREE DNA OBTAINED FROM CEREBROSPINAL FLUID

Abstract BACKGROUND Array-based DNA methylation profiling is the gold standard for molecular classification of brain tumors. However, it relies on significant amount of input DNA extracted from surgical tissue, thus limiting its use for tumors where biopsy is challenging or limited in quantity. Cell-free tumor DNA (cfDNA) in cerebrospinal fluid (CSF) presents alternative opportunities for brain tumor diagnosis and disease monitoring following treatment. Novel enzymatic DNA methylation sequencing (EM-seq) methods may allow us to overcome input DNA limitations and accurately quantify methylation from cfDNA. METHODS We performed methylation sequencing using the NEBNext EM-seq kit on cfDNA from archival CSF samples collected from three brain tumor patients with confirmed histopathological diagnoses. Variable amounts of input cfDNA (0.1ng-10ng) were tested. We utilized the methylseq pipeline for data processing. For tumor classification, data was limited to CpG sites overlapping the MethylationEPIC array before analysis using MNP-Flex, a modified platform-agnostic version of the Heidelberg methylation classifier RESULTS Using the EM-seq method, genomic coverage for 10 and 1ng input DNA samples (average: 46x and 26x, respectively) was sufficient for generating global methylation profiles. Samples with 0.1ng input showed an average coverage of only 5.32x due to high levels of read duplication. However, methylation levels for CpG sites with at least 5x coverage were highly correlated across varying input DNA amounts, suggesting that lower input cfDNA could still be used for tumor classification based on relevant CpG sites. CONCLUSIONS Using the MNP-Flex classifier, which was originally trained with methylation array data from tumor tissue, we successfully predicted brain tumor types (both high-grade glioma and medulloblastoma) with cfDNA methylation data down to only 0.1ng of input cfDNA, matching diagnosis based on tissue methylation and histopathology in this pilot study. Further classification of additional tumor types using CSF cfDNA is required to confirm the clinical utility of this platform.

Improving platelet-RNA-based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification.

Liquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community on which methods are the most effective or how to process the data. To circumvent this, we performed a large-scale study using various machine-learning techniques. First, we took a closer look at existing datasets and filtered out some patients to assert data collection quality. The final data collection included platelet RNA samples acquired from 1397 cancer patients (17 types of cancer) and 354 asymptomatic, presumed healthy, donors. Then, we assessed an array of different machine-learning models and techniques (e.g., feature selection of RNA transcripts) in pan-cancer detection and multiclass classification. Our results show that simple logistic regression performs the best, reaching a 68% cancer detection rate at a 99% specificity level, and multiclass classification accuracy of 79.38% when distinguishing between five cancer types. In summary, by revisiting classical machine-learning models, we have exceeded the previously used method by 5% and 9.65% in cancer detection and multiclass classification, respectively. To ease further research, we open-source our code and data processing pipelines (https://gitlab.com/jopekmaksym/improving-platelet-rna-based-diagnostics), which we hope will serve the community as a strong baseline.

Using a Combination of Novel Research Tools to Understand Social Interaction in the Drosophila melanogaster Model for Fragile X Syndrome.

Fragile X syndrome (FXS), the most common monogenic cause of inherited intellectual disability and autism spectrum disorder, is caused by a full mutation (>200 CGG repeats) in the Fragile X Messenger Ribonucleoprotein 1 (FMR1) gene. Individuals with FXS experience various challenges related to social interaction (SI). Animal models, such as the Drosophila melanogaster model for FXS where the only ortholog of human FMR1 (dFMR1) is mutated, have played a crucial role in the understanding of FXS. The aim of this study was to investigate SI in the dFMR1B55 mutants (the groups of flies of both sexes simultaneously) using the novel Drosophila Shallow Chamber and a Python data processing pipeline based on social network analysis (SNA). In comparison with wild-type flies (w1118), SNA analysis in dFMR1B55 mutants revealed hypoactivity, fewer connections in their networks, longer interaction duration, a lower ability to transmit information efficiently, fewer alternative pathways for information transmission, a higher variability in the number of interactions they achieved, and flies tended to stay near the boundaries of the testing chamber. These observed alterations indicate the presence of characteristic strain-dependent social networks in dFMR1B55 flies, commonly referred to as the group phenotype. Finally, combining novel research tools is a valuable method for SI research in fruit flies.

An Image Quality Evaluation and Masking Algorithm Based On Pretrained Deep Neural Networks

With the growing amount of astronomical data, there is an increasing need for automated data processing pipelines, which can extract scientific information from observation data without human interventions. A critical aspect of these pipelines is the image quality evaluation and masking algorithm, which evaluate image qualities based on various factors such as cloud coverage, sky brightness, scattering light from the optical system, point-spread-function size and shape, and read-out noise. Occasionally, the algorithm requires masking of areas severely affected by noise. However, the algorithm often necessitates significant human interventions, reducing data processing efficiency. In this study, we present a deep-learning-based image quality evaluation algorithm that uses an autoencoder to learn features of high quality astronomical images. The trained autoencoder enables automatic evaluation of image quality and masking of noise affected areas. We have evaluated the performance of our algorithm using two test cases: images with point spread functions of varying full width half magnitude, and images with complex backgrounds. In the first scenario, our algorithm could effectively identify variations of the point spread functions, which can provide valuable reference information for photometry. In the second scenario, our method could successfully mask regions affected by complex regions, which could significantly increase the photometry accuracy. Our algorithm can be employed to automatically evaluate image quality obtained by different sky surveying projects, further increasing the speed and robustness of data processing pipelines.

Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics

Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines’ suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline’s performance across criteria and datasets, to inform future best practices in functional connectomics.

Implementing ABCD studyⓇ MRI sequences for multi-site cohort studies: Practical guide to necessary steps, preprocessing methods, and challenges

Large multi-site studies that combine magnetic resonance imaging (MRI) data across research sites present exceptional opportunities to advance neuroscience research. However, scanner or site variability and non-standardised image acquisition protocols, data processing and analysis pipelines can adversely affect the reliability and repeatability of MRI derived brain measures. We implemented a standardised MRI protocol based on that used in the Adolescent Brain Cognition Development (ABCD)Ⓡ study in two sites, and across four MRI scanners.Twice repeated measurements of a single healthy volunteer were obtained in two sites and in four 3T MRI scanners (vendors: Siemens, Philips, and GE). Imaging data included anatomical scans (T1 weighted, T2 weighted), diffusion weighted imaging (DWI) and resting state functional MRI (rs-fMRI). Standardised containerized pipelines were utilised to pre-process the data and different image quality metrics and test-retest variability of different brain metrics were evaluated.The implementation of the MRI protocols was possible with minor adjustments in acquisition (e.g. repetition time (TR), higher b-values) and exporting (DICOM formats) of images due to different technical performance of the scanners. This study provides practical insights into the implementation of standardised sequences and data processing for multisite studies, showcase the benefits of containerised preprocessing tools, and highlights the need for careful optimisation of multisite image acquisition.