| 1. |
- Abbafati, Cristiana, et al.
(författare)
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- 2020
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Tidskriftsartikel (refereegranskat)
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| 2. |
- Faux, Pierre, et al.
(författare)
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Neanderthal introgression in SCN9A impacts mechanical pain sensitivity
- 2023
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Ingår i: Communications Biology. - 2399-3642. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- The Nav1.7 voltage-gated sodium channel plays a key role in nociception. Three functional variants in the SCN9A gene (encoding M932L, V991L, and D1908G in Nav1.7), have recently been identified as stemming from Neanderthal introgression and to associate with pain symptomatology in UK BioBank data. In 1000 genomes data, these variants are absent in Europeans but common in Latin Americans. Analysing high-density genotype data from 7594 Latin Americans, we characterized Neanderthal introgression in SCN9A. We find that tracts of introgression occur on a Native American genomic background, have an average length of similar to 123kb and overlap the M932L, V991L, and D1908G coding positions. Furthermore, we measured experimentally six pain thresholds in 1623 healthy Colombians. We found that Neanderthal ancestry in SCN9A is significantly associated with a lower mechanical pain threshold after sensitization with mustard oil and evidence of additivity of effects across Nav1.7 variants. Our findings support the reported association of Neanderthal Nav1.7 variants with clinical pain, define a specific sensory modality affected by archaic introgression in SCN9A and are consistent with independent effects of the Neanderthal variants on Nav1.7 function. Neanderthal-derived variants in the SCN9A gene (encoding the voltage gated sodium channel, Nav1.7) are associated with enhanced experimental mechanical pain sensitivity in modern humans.
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| 3. |
- Gomez L, Ana Maria, 1993-, et al.
(författare)
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Determination of the Plasma Delay Time in PIPS detectors for fission fragments at the LOHENGRIN spectrometer
- 2023
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Ingår i: 15<sup>th</sup> International Conference on Nuclear Data for Science and Technology (ND2022). - : EDP Sciences.
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Konferensbidrag (refereegranskat)abstract
- The VElocity foR Direct particle Identification spectrometer (VERDI) is a 2E-2v fission spectrometer that allows the measurement of the total mass distribution of secondary fission fragments with a resolving power of 1-2 u. It consists of two time-of-flight (ToF) arms, with one Micro Channel Plate (MCP) detector and up to 32 Silicon PIPS (Passive Implanted Planar Silicon) detectors per arm. The MCPs provide the start timing signals and the PIPS detectors provide both the energy and the stopping ToF signals. In real conditions, the PIPS signals are affected by the formation of plasma from the interaction between the heavy ions and the detector material. The plasma contributes to a reduction in signal amplitude, resulting in a Pulse Height Defect (PHD), and introduces a signal delay, known as Plasma Delay Time (PDT). An experiment to characterize the PDT and PHD was performed at the LOHENGRIN recoil separator of the Institut Laue Langevin (ILL). Characteristic fission fragments from the 239Pu(n,f) reaction were separated based on their A/Q and E/Q ratios, allowing the measurement of a wide range of energies from 21 to 110 MeV and masses between 80 and 149 u. Six PIPS detectors were characterized to study their individual responses to the PDT and PHD effects. The signals were recorded in a digital acquisition system to completely exploit the offline analysis capabilities. Achieved combined timing and energy resolutions for fission fragments varied between 72(2) ps and 100(4) ps and 1.4% - 2% (FWHM), respectively. Preliminary PHD and PDT data are presented from the masses A=85, 95, 130 and 143. The PHD trends are strongly correlated with both the ion energy and mass. The PDT, on the other hand, shows a strong variation as a function of the ion kinetic energy but a smaller dependence on the ion mass.
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| 4. |
- Hering, Alessa, et al.
(författare)
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Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
- 2023
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Ingår i: IEEE Transactions on Medical Imaging. - : Institute of Electrical and Electronics Engineers (IEEE). - 0278-0062 .- 1558-254X. ; 42:3, s. 697-712
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Tidskriftsartikel (refereegranskat)abstract
- Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https:// learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.
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| 5. |
- Jones, Benedict C, et al.
(författare)
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To which world regions does the valence-dominance model of social perception apply?
- 2021
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Ingår i: Nature Human Behaviour. - : Springer Science and Business Media LLC. - 2397-3374. ; 5:1, s. 159-169
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Tidskriftsartikel (refereegranskat)abstract
- Over the past 10 years, Oosterhof and Todorov's valence-dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov's methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov's original analysis strategy, the valence-dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence-dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 5 November 2018. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.7611443.v1 .
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| 6. |
- Rydhmer, Klas, et al.
(författare)
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Photonic sensors reflect variation in insect abundance and diversity across habitats
- 2024
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Ingår i: Ecological Indicators. - 1470-160X. ; 158
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Tidskriftsartikel (refereegranskat)abstract
- To mitigate ongoing insect biodiversity declines, there is a need for efficient yet accurate monitoring methods. The use of traditional catch-based survey methods is constrained both by costs and need for expertise for manual taxonomic identification. Emerging methods, such as eDNA and robotic sorting, have the potential to reduce workload but still require resource-intensive sample collection in the field. Recently, remote sensing methods such as photonic sensors have shown promise for recording large numbers of insect observations. However, accurately determining species composition in collected data remains a challenge. In this study, we investigated the potential of photonic sensors for quantifying species richness of flying insects in the field and at five sites and compared the results with estimates based on conventional Malaise traps. Firstly, we evaluated two unsupervised clustering methods using a library of measured insect signals from 42 known species. Secondly, we correlated estimated number of clusters in data recorded at five sites with species richness assessment of catches from Malaise traps. This study is based on 84,770 library- and 238,584 field individual insect recordings. Our results demonstrate that both clustering methods perform well and reflect estimates obtained by Malaise traps, indicating the potential of automated insect biodiversity monitoring. This offers the possibility of more efficient but still accurate methods for studying insect biodiversity with broader temporal and spatial coverage.
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