Sex estimation from coxal bones using deep learning in a population balanced by sex and age.

In the field of forensic anthropology, researchers aim to identify anonymous human remains and determine the cause and circumstances of death from skeletonized human remains. Sex determination is a fundamental step of this procedure because it influences the estimation of other traits, such as age and stature. Pelvic bones are especially dimorphic, and are thus the most useful bones for sex identification. Sex estimation methods are usually based on morphologic traits, measurements, or landmarks on the bones. However, these methods are time-consuming and can be subject to inter- or intra-observer bias. Sex determination can be done using dry bones or CT scans. Recently, artificial neural networks (ANN) have attracted attention in forensic anthropology. Here we tested a fully automated and data-driven machine learning method for sex estimation using CT-scan reconstructions of coxal bones. We studied 580 CT scans of living individuals. Sex was predicted by two networks trained on an independent sample: a disentangled variational auto-encoder (DVAE) alone, and the same DVAE associated with another classifier (Crecon). The DVAE alone exhibited an accuracy of 97.9%, and the DVAE + Crecon showed an accuracy of 99.8%. Sensibility and precision were also high for both sexes. These results are better than those reported from previous studies. These data-driven algorithms are easy to implement, since the pre-processing step is also entirely automatic. Fully automated methods save time, as it only takes a few minutes to pre-process the images and predict sex, and does not require strong experience in forensic anthropology.

Virtual reality hypnosis diminishes experimental cold pain and alters autonomic responses.

Immersive virtual reality (VR) is a promising tool to reduce pain in clinical setting. Digital scripts displayed by VR disposals can be enriched by several analgesic interventions, which are widely used to reduce pain. One of these techniques is hypnosis induced through the VR script (VRH) which is facilitated by immersive environment and particularly efficient even for low hypnotizable patients. The aim of this study is to assess the efficacy of a VRH script on experimentally induced cold pain perception (intensity and unpleasantness) and physiological expression. 41 healthy volunteers had been recruited in this within-subjects study. They received 9 stimulations of 20 s (3 non-nociceptive cold; 3 low nociceptive cold and 3 highly nociceptive cold) during a VRH session of 20 min (VRH condition) or without VRH (noVRH condition). Physiological monitoring during the cold pain stimulation protocol consisted of recording heart rate, heart rate variability and respiratory frequency. Maximum cold pain intensity perception, measured through the visual analog scale (VAS) on 10, was of 3.66 ± 1.84 (VAS score/10) in noVRH condition and 2.46 ± 1.54 in VRH (Wilcoxon, p < 0.0001). Considering pain unpleasantness perception, 3.68 ± 2.06 in noVRH and 2.21 ± 1.63 in VRH (Wilcoxon, p < 0.0001). Hypnotizability negatively correlated with the decrease in VAS intensity from noVRH to VRH (Spearman r = -0.45; p = 0.0038). In our sample, we found that 31/41 volunteers (75.6%) displayed a reduction of more than 10% of their VAS pain intensity and unpleasantness scores. Trait anxiety was the best predictor of the VRH responders, as well as heart rate variability. In addition, respiratory rate was diminished under VRH in every subgroup. VRH is an effective tool to reduced pain intensity and unpleasantness in a vast majority of healthy subjects. We further indicate in this study that heart rate variability parameter RMSSD (root mean square of successive differences) is a good predictor of this effect, as well as anxiety as a personality trait (but not state anxiety). Further studies are expected to determine more precisely to whom it will be the most useful to offer tailored, non-pharmacological pain management solutions to patients.

Effect of Virtual Reality Hypnosis on Pain Threshold and Neurophysiological and Autonomic Biomarkers in Healthy Volunteers: Prospective Randomized Crossover Study.

BACKGROUND: Virtual reality hypnosis (VRH) is a promising tool to reduce pain. However, the benefits of VRH on pain perception and on the physiological expression of pain require further investigation. OBJECTIVE: In this study, we characterized the effects of VRH on the heat pain threshold among adult healthy volunteers while monitoring several physiological and autonomic functions. METHODS: Sixty healthy volunteers were prospectively included to receive nociceptive stimulations. The first set of thermal stimuli consisted of 20 stimulations at 60°C (duration 500 milliseconds) to trigger contact heat evoked potentials (CHEPs). The second set of thermal stimuli consisted of ramps (1°C/second) to determine the heat pain threshold of the participants. Electrocardiogram, skin conductance responses, respiration rate, as well as the analgesia nociception index were also recorded throughout the experiment. RESULTS: Data from 58 participants were analyzed. There was a small but significant increase in pain threshold in VRH (50.19°C, SD 1.98°C) compared to that in the control condition (mean 49.45°C, SD 1.87; P<.001, Wilcoxon matched-pairs signed-rank test; Cohen d=0.38). No significant effect of VRH on CHEPs and heart rate variability parameters was observed (all P>0.5; n=22 and n=52, respectively). During VRH, participants exhibited a clear reduction in their autonomic sympathetic tone, as shown by the lower number of nonspecific skin conductance peak responses (P<.001, two-way analysis of variance; n=39) and by an increase in the analgesia nociception index (P<.001, paired t-test; n=40). CONCLUSIONS: The results obtained in this study support the idea that VRH administration is effective at increasing heat pain thresholds and impacts autonomic functions among healthy volunteers. As a nonpharmacological intervention, VRH has beneficial action on acute experimental heat pain. This beneficial action will need to be evaluated for the treatment of other types of pain, including chronic pain.

Retinoic Acid Receptor Alpha Is Essential in Postnatal Sertoli Cells but Not in Germ Cells.

Retinoic acid signaling is indispensable for the completion of spermatogenesis. It is known that loss of retinoic acid nuclear receptor alpha (RARA) induces male sterility due to seminiferous epithelium degeneration. Initial genetic studies established that RARA acts in Sertoli cells, but a recent paper proposed that RARA is also instrumental in germ cells. In the present study, we have re-assessed the function of RARA in germ cells by genetically ablating the Rara gene in spermatogonia and their progenies using a cell-specific conditional mutagenesis approach. We show that loss of Rara in postnatal male germ cells does not alter the histology of the seminiferous epithelium. Furthermore, RARA-deficient germ cells differentiate normally and give rise to normal, living pups. This establishes that RARA plays no crucial role in germ cells. We also tested whether RARA is required in Sertoli cells during the fetal period or after birth. For this purpose, we deleted the Rara gene in Sertoli cells at postnatal day 15 (PN15), i.e., after the onset of the first spermatogenic wave. To do so, we used temporally controlled cell-specific mutagenesis. By comparing the testis phenotypes generated when Rara is lost either at PN15 or at embryonic day 13, we show that RARA exerts all of its functions in Sertoli cells not at the fetal stage but from puberty.

An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI.

Brain structure analysis in the newborn is a major health issue. This is especially the case for preterm neonates, in order to obtain predictive information related to the child development. In particular, the cortex is a structure of interest, that can be observed in magnetic resonance imaging (MRI). However, neonatal MRI data present specific properties that make them challenging to process. In this context, multi-atlas approaches constitute an efficient strategy, taking advantage of images processed beforehand. The method proposed in this article relies on such a multi-atlas strategy. More precisely, it uses two paradigms: first, a non-local model based on patches; second, an iterative optimization scheme. Coupling both concepts allows us to consider patches related not only to the image information, but also to the current segmentation. This strategy is compared to other multi-atlas methods proposed in the literature. Experiments on dHCP datasets show that the proposed approach provides robust cortex segmentation results.

Self-trapping relaxation decay investigated by time-resolved photoelectron spectroscopy.

The present work combines time-resolved photoelectron spectroscopy on isolated species with high-level data processing to address an issue which usually pertains to materials science: the electronic relaxation dynamics towards the formation of a self-trapped exciton (STE). Such excitons are common excited states in ionic crystals, silica and rare gas matrices. They are associated with a strong local deformation of the matrix. Argon clusters were taken as a model. They are excited initially to a Wannier exciton at 14 eV and their evolution towards the formation of an STE has showed an unusual type of vibronic relaxation where the electronic excitation of the cluster decreases linearly as a function of time with a 0.59 ± 0.06 eV ps-1 rate. The decay was followed for 3.0 ps, and the STE formation occurred in ∼5.1 ± 0.7 ps.

Time resolved observation of the solvation dynamics of a Rydberg excited molecule deposited on an argon cluster-I: DABCO* at short times.

This paper is a joint experimental and theoretical approach concerning a molecule deposited on a large argon cluster. The spectroscopy and the dynamics of the deposited molecule are measured using the photoelectron spectroscopy. The absorption spectrum of the deposited molecule shows two solvation sites populated in the ground state. The combined dynamics reveals that the population ratio of the two sites is reversed when the molecule is electronically excited. This work provides the timescale of the corresponding solvation dynamics. Theoretical calculation supports the interpretation. More generally, close examination of the short time dynamics (0-6 ps) of DABCO···Ar(n) gives insights into the ultrafast relaxation dynamics of molecules deposited at interfaces and provides hence the time scale for deposited molecules to adapt to their neighborhoods.

Estimation of Mueller matrices using non-local means filtering.

This article addresses the estimation of polarization signatures in the Mueller imaging framework by non-local means filtering. This is an extension of previous work dealing with Stokes signatures. The extension is not straightforward because of the gap in complexity between the Mueller framework and the Stokes framework. The estimation procedure relies on the Cholesky decomposition of the coherency matrix, thereby ensuring the physical admissibility of the estimate. We propose an original parameterization of the boundary of the set of Mueller matrices, which makes our approach possible. The proposed method is fully unsupervised. It allows noise removal and the preservation of edges. Applications to synthetic as well as real data are presented.

Joint filtering estimation of Stokes vector images based on a nonlocal means approach.

Conventional estimation techniques of Stokes images from observed radiance images through different polarization filters suffer from noise contamination that hampers correct interpretation or even leads to unphysical estimated signatures. This paper presents an efficient restoration technique based on nonlocal means, permitting accurate estimation of smoothly variable polarization signatures in the Stokes image while preserving sharp transitions. The method is assessed on simulated data as well as on real images.

Topology preserving warping of 3-D binary images according to continuous one-to-one mappings.

The estimation of one-to-one mappings is one of the most intensively studied topics in the research field of nonrigid registration. Although the computation of such mappings can be now accurately and efficiently performed, the solutions for using them in the context of binary image deformation is much less satisfactory. In particular, warping a binary image with such transformations may alter its discrete topological properties if common resampling strategies are considered. In order to deal with this issue, this paper proposes a method for warping such images according to continuous and bijective mappings while preserving their discrete topological properties (i.e., their homotopy type). Results obtained in the context of the atlas-based segmentation of complex anatomical structures highlight the advantages of the proposed approach.

A multi-level parcellation approach for brain functional connectivity analysis.

Most brain functional connectivity methods in fMRI require a brain parcellation into functionally homogeneous regions. In this work we propose a novel parcellation approach based on a spatial hierarchical clustering, that provides clusters within a multi-level framework. The method has the advantage of producing several brain parcellations rather than a single one from a fixed size-homogeneity criterion. Results obtained on real data demonstrate the relevance of the approach. Finally, a connectivity study shows the benefit of a prior multi-level parcellation of the brain.

Topology preserving warping of binary images: application to atlas-based skull segmentation.

Lots of works have been recently carried out in the field of non-rigid registration to ensure the estimation of one-to-one mappings. However, warping a binary image with such transformations may alter its discrete topological properties if common resampling strategies are considered. This paper proposes an original method for warping a binary image according to some continuous and bijective mapping, while preserving its discrete topological properties. Results obtained in the context of atlas-based segmentation highlight the interest of the approach. Indeed, the method has been successfully applied to the segmentation of skull structures from a database of 15 CT-scans, providing both geometrically and topologically satisfactory results.

Hidden Markov event sequence models: toward unsupervised functional MRI brain mapping.

RATIONALE AND OBJECTIVES: Most methods used in functional MRI (fMRI) brain mapping require restrictive assumptions about the shape and timing of the fMRI signal in activated voxels. Consequently, fMRI data may be partially and misleadingly characterized, leading to suboptimal or invalid inference. To limit these assumptions and to capture the broad range of possible activation patterns, a novel statistical fMRI brain mapping method is proposed. It relies on hidden semi-Markov event sequence models (HSMESMs), a special class of hidden Markov models (HMMs) dedicated to the modeling and analysis of event-based random processes. MATERIALS AND METHODS: Activation detection is formulated in terms of time coupling between (1) the observed sequence of hemodynamic response onset (HRO) events detected in the voxel's fMRI signal and (2) the "hidden" sequence of task-induced neural activation onset (NAO) events underlying the HROs. Both event sequences are modeled within a single HSMESM. The resulting brain activation model is trained to automatically detect neural activity embedded in the input fMRI data set under analysis. The data sets considered in this article are threefold: synthetic epoch-related, real epoch-related (auditory lexical processing task), and real event-related (oddball detection task) fMRI data sets. RESULTS: Synthetic data: Activation detection results demonstrate the superiority of the HSMESM mapping method with respect to a standard implementation of the statistical parametric mapping (SPM) approach. They are also very close, sometimes equivalent, to those obtained with an "ideal" implementation of SPM in which the activation patterns synthesized are reused for analysis. The HSMESM method appears clearly insensitive to timing variations of the hemodynamic response and exhibits low sensitivity to fluctuations of its shape (unsustained activation during task). Real epoch-related data: HSMESM activation detection results compete with those obtained with SPM, without requiring any prior definition of the expected activation patterns thanks to the unsupervised character of the HSMESM mapping approach. Along with activation maps, the method offers a wide range of additional fMRI analysis functionalities, including activation lag mapping, activation mode visualization, and hemodynamic response function analysis. Real event-related data: Activation detection results confirm and validate the overall strategy that consists in focusing the analysis on the transients, time-localized events that are the HROs. CONCLUSION: All the experiments performed on synthetic and real fMRI data demonstrate the relevance of HSMESMs in fMRI brain mapping. In particular, the statistical character of these models, along with their learning and generalizing abilities are of particular interest when dealing with strong variabilities of the active fMRI signal across time, space, experiments, and subjects.

Unsupervised learning and mapping of active brain functional MRI signals based on hidden semi-Markov event sequence models.

In this paper, a novel functional magnetic resonance imaging (fMRI) brain mapping method is presented within the statistical modeling framework of hidden semi-Markov event sequence models (HSMESMs). Neural activation detection is formulated at the voxel level in terms of time coupling between the sequence of hemodynamic response onsets (HROs) observed in the fMRI signal, and an HSMESM of the hidden sequence of task-induced neural activations. The sequence of HRO events is derived from a continuous wavelet transform (CWT) of the fMRI signal. The brain activation HSMESM is built from the timing information of the input stimulation protocol. The rich mathematical framework of HSMESMs makes these models an effective and versatile approach for fMRI data analysis. Solving for the HSMESM Evaluation and Learning problems enables the model to automatically detect neural activation embedded in a given set of fMRI signals, without requiring any template basis function or prior shape assumption for the fMRI response. Solving for the HSMESM Decoding problem allows to enrich brain mapping with activation lag mapping, activation mode visualizing, and hemodynamic response function analysis. Activation detection results obtained on synthetic and real epoch-related fMRI data demonstrate the superiority of the HSMESM mapping method with respect to a real application case of the statistical parametric mapping (SPM) approach. In addition, the HSMESM mapping method appears clearly insensitive to timing variations of the hemodynamic response, and exhibits low sensitivity to fluctuations of its shape.