National external quality assessment for next-generation sequencing-based diagnostics of primary immunodeficiencies

Dutch genome diagnostic centers (GDC) use next-generation sequencing (NGS)-based diagnostic applications for the diagnosis of primary immunodeficiencies (PIDs). The interpretation of genetic variants in many PIDs is complicated because of the phenotypic and genetic heterogeneity. To analyze uniformity of variant filtering, interpretation, and reporting in NGS-based diagnostics for PID, an external quality assessment was performed. Four main Dutch GDCs participated in the quality assessment. Unannotated variant call format (VCF) files of two PID patient analyses per laboratory were distributed among the four GDCs, analyzed, and interpreted (eight analyses in total). Variants that would be reported to the clinician and/or advised for further investigation were compared between the centers. A survey measuring the experiences of clinical laboratory geneticists was part of the study. Analysis of samples with confirmed diagnoses showed that all centers reported at least the variants classified as likely pathogenic (LP) or pathogenic (P) variants in all samples, except for variants in two genes (PSTPIP1 and BTK). The absence of clinical information complicated correct classification of variants. In this external quality assessment, the final interpretation and conclusions of the genetic analyses were uniform among the four participating genetic centers. Clinical and immunological data provided by a medical specialist are required to be able to draw proper conclusions from genetic data

Anomalous gas in ESO 149-G003 : a MeerKAT-16 view

ESO 149-G003 is a close-by, isolated dwarf irregular galaxy. Previous observations with the ATCA indicated the presence of anomalous neutral hydrogen (⁠HI⁠) deviating from the kinematics of a regularly rotating disc. We conducted follow-up observations with the MeerKAT radio telescope during the 16-dish Early Science programme as well as with the MeerLICHT optical telescope. Our more sensitive radio observations confirm the presence of anomalous gas in ESO 149-G003, and further confirm the formerly tentative detection of an extraplanar HI component in the galaxy. Employing a simple tilted-ring model, in which the kinematics is determined with only four parameters but including morphological asymmetries, we reproduce the galaxy’s morphology, which shows a high degree of asymmetry. By comparing our model with the observed HI⁠, we find that in our model, we cannot account for a significant (but not dominant) fraction of the gas. From the differences between our model and the observed data cube, we estimate that at least 7–8 per cent of the HI in the galaxy exhibits anomalous kinematics, while we estimate a minimum mass fraction of less than 1 per cent for the morphologically confirmed extraplanar component. We investigate a number of global scaling relations and find that, besides being gas-dominated with a neutral gas-to-stellar mass ratio of 1.7, the galaxy does not show any obvious global peculiarities. Given its isolation, as confirmed by optical observations, we conclude that the galaxy is likely currently acquiring neutral gas. It is either re-accreting gas expelled from the galaxy or accreting pristine intergalactic material.http://mnras.oxfordjournals.orghj2022Physic

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Anomalous gas in ESO 149-G003: A MeerKAT-16 view

ESO 149-G003 is a close-by, isolated dwarf irregular galaxy. Previous observations with the ATCA indicated the presence of anomalous neutral hydrogen (H I) deviating from the kinematics of a regularly rotating disc. We conducted follow-up observations with the MeerKAT radio telescope during the 16-dish Early Science programme as well as with the MeerLICHT optical telescope. Our more sensitive radio observations confirm the presence of anomalous gas in ESO 149-G003, and further confirm the formerly tentative detection of an extraplanar H I component in the galaxy. Employing a simple tilted-ring model, in which the kinematics is determined with only four parameters but including morphological asymmetries, we reproduce the galaxy's morphology, which shows a high degree of asymmetry. By comparing our model with the observed H I, we find that in our model, we cannot account for a significant (but not dominant) fraction of the gas. From the differences between our model and the observed data cube, we estimate that at least 7-8 per cent of the H I in the galaxy exhibits anomalous kinematics, while we estimate a minimum mass fraction of less than 1 per cent for the morphologically confirmed extraplanar component

Development of a multi-entry identification key for economically important fruit fly larvae (Diptera, Tephritidae, Dacinae)

Identification of fruit fly larvae is difficult due to the limited morphological characteristics present. However, this is the stage at which fruit flies are intercepted at ports of entry through horticultural imports. Molecular tools are useful but are time-consuming and expensive compared to morphological identifications. This project aims to use available information from the literature and our own research to build a multi-entry identification key for thirteen tephritid species and species groups that are of economic concern for the European Union. Third-instar larvae were obtained from different regions and hosts. Thirteen species or representatives of species groups were obtained, including Ceratitis, Dacus, Bactrocera and Zeugodacus spp. The cephalopharyngeal skeletons were dissected out, cleared in a 10% NaOH solution, dehydrated and mounted in Euparal on glass slides. Images of at least 20 larvae/species were captured using a compound microscope fitted with a camera. Measurements were taken of the mounted mandibles and the number of tubules and their position in the anterior spiracles in relation to the cephalic skeleton were noted. Differences between morphometric parameters were tested via ANOVA and verified using discriminant function analysis. A matrix was compiled including nine characters for which significant inter-specific differentiation was preliminarily detected. The key was converted into a mobile application by LucID

Development of a multi-entry identification key for economically important fruit fly larvae (Diptera, Tephritidae, Dacinae)

Identification of fruit fly larvae is difficult due to the limited morphological characteristics present. However, this is the stage at which fruit flies are intercepted at ports of entry through horticultural imports. Molecular tools are useful but are time-consuming and expensive compared to morphological identifications. This project aims to use available information from the literature and our own research to build a multi-entry identification key for thirteen tephritid species and species groups that are of economic concern for the European Union. Third-instar larvae were obtained from different regions and hosts. Thirteen species or representatives of species groups were obtained, including Ceratitis, Dacus, Bactrocera and Zeugodacus spp. The cephalopharyngeal skeletons were dissected out, cleared in a 10% NaOH solution, dehydrated and mounted in Euparal on glass slides. Images of at least 20 larvae/species were captured using a compound microscope fitted with a camera. Measurements were taken of the mounted mandibles and the number of tubules and their position in the anterior spiracles in relation to the cephalic skeleton were noted. Differences between morphometric parameters were tested via ANOVA and verified using discriminant function analysis. A matrix was compiled including nine characters for which significant inter-specific differentiation was preliminarily detected. The key was converted into a mobile application by LucID

Structural Abnormalities in the Hair of a Patient with a Novel Ribosomopathy.

We report the biophysical characterization of hair from a patient with a de novo ribosomopathy. The patient was diagnosed with a mutation on gene RPS23, which codes for a protein which comprises part of the 40S subunit of the ribosome. The patient presents with a number of phenotypes, including hypotonia, autism, extra teeth, elastic skin, and thin/brittle hair. We combined optical microscopy, tensile tests, and X-ray diffraction experiments on hair samples obtained from the scalp of the patient to a multi-scale characterization of the hair from macroscopic to molecular length scales and observe distinct differences in the biophysical properties in the patient's hair when compared to hair from other family members. While no differences were observed in the coiled-coil structure of the keratin proteins or the structure of the intermediate filaments, the patient's hair was 22% thinner, while the Young's modulus remained roughly constant. The X-ray diffraction results give evidence that the amount of lipids in the cell membrane complex is reduced by 20%, which well accounts for the other observations. The pathologies characterized by these techniques may be used to inform the diagnosis of similar de novo mutations in the future

Working Group "Induced resistance in plants against insects and diseases": Proceedings of the meeting at Avignon, France, 10 - 13 June, 2013. "Leaping from success in the lab to success in the field".

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