Root and canal morphology of the permanent teeth in medieval and current French population.

OBJECTIVE: This work describes and compares the root and root canal morphology of a medieval population dating from the 8th to 10th century from the southwest of France, and a current French population. DESIGN: The root morphology of 579 teeth from 70 medieval individuals was analyzed using cone beam computed tomography, and compared with 690 teeth from a current French population of 329 individuals. The Vertucci classification was used to describe the root canal configuration. RESULTS: In the medieval population, the maxillary first premolar usually had one root. In contrast, in the current population this tooth predominantly had two roots, and the three-root form had appeared. Mandibular canine with two roots was observed in 5.7% of cases, and in the current population this form was found in 1.6% but the difference was not significative. The greatest variability between the two populations in terms of root canal configuration was in one-rooted maxillary first and second premolars, the mandibular canines, and the distal roots of the mandibular first molars. Differences in root numbers and canal configurations of the maxillary molars investigated among the two populations were not significant. CONCLUSIONS: This study indicated that the upper first premolar of the current population tended to have more than one root, while this tooth type of the medieval group mostly appeared with only one. For the root canal configuration, studies in the upper premolars, lower canines and first molars of the current population apparently revealed a significant simplification compared with the ancient group.

Old hearts for modern investigations: CT and MR for archaeological human hearts remains.

INTRODUCTION: Among 800 burials dated between the 15th and 18th centuries and found in the center of Rennes (Brittany, France), a collection of five heart-shaped lead urns was discovered. This material was studied using classical methods (external study, autopsy and histology), and also modern imaging like computed tomography (CT), magnetic resonance (MR) before and after coronary opacification. The aim of this manuscript is to describe different steps of ancient soft tissues study, especially using imaging techniques. METHODS: The study gathered various specialists: anthropologists, archeologists, forensic pathologists, radiologists, pathologic physicians, and physicists. Imaging techniques were performed, before and after coronary opacification. Finally, hearts were autopsied and different histological samples were analyzed. RESULTS: Only heart n°2 was too damaged to be studied. Heart n°3 was considered as normal using all investigation techniques. The study of Hearts n°s 4 and 5 revealed dilated cardiomyopathy while Heart n°1 showed important signs of diffuse hypertrophic cardiomyopathy. Different fibro lipid plaques were identified using imaging techniques, and were confirmed by histology. CONCLUSIONS: The study of archeological soft tissues using modern imaging is possible if the material is well-preserved. This type of research can uncover principal findings, allowing scientists to establish diseases of ancient times.

GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended (GLEAM-X) I: Survey Description and Initial Data Release.

We describe a new low-frequency wideband radio survey of the southern sky. Observations covering 72-231MHz and Declinations south of +30° have been performed with the Murchison Widefield Array "extended" Phase I I configuration over 2018-2020 and will be processed to form data products including continuum and polarisation images and mosaics, multi-frequency catalogues, transient search data, and ionospheric measurements. From a pilot field described in this work, we publish an initial data release covering 1,447 deg2 over 4 h≤ RA≤ 13 h, -32.7° ≤ Dec ≤ -20.7°. We process twenty frequency bands sampling 72-231 MHz, with a resolution of 2'-45″, and produce a wideband source-finding image across 170-231MHz with a root-mean-square noise of 1.27 ± 0.15 mJy beam-1. Source-finding yields 79,124 components, of which 71,320 are fitted spectrally. The catalogue has a completeness of 98% at ~ 50 mJy, and a reliability of 98.2% at 5σ rising to 99.7% at 7σ. A catalogue is available from Vizier; images are made available on the GLEAM-X VO server and SkyView. This is the first in a series of data releases from the GLEAM-X survey.

MRI-based automated computer classification of probable AD versus normal controls.

Automated computer classification (ACC) techniques are needed to facilitate physician's diagnosis of complex diseases in individual patients. We provide an example of ACC using computational techniques within the context of cross-sectional analysis of magnetic resonance images (MRI) in neurodegenerative diseases, namely Alzheimer's dementia (AD). In this paper, the accuracy of our ACC methodology is assessed when presented with real life, imperfect data, i.e., cohorts of MRI with varying acquisition parameters and imaging quality. The comparative methodology uses the Jacobian determinants derived from dense deformation fields and scaled grey-level intensity from a selected volume of interest centered on the medial temporal lobe. The ACC performance is assessed in a series of leave-one-out experiments aimed at separating 75 probable AD and 75 age-matched normal controls. The resulting accuracy is 92% using a support vector machine classifier based on least squares optimization. Finally, it is shown in the Appendix that determinants and scaled grey-level intensity are appreciably more robust to varying parameters in validation studies using simulated data, when compared to raw intensities or grey/white matter volumes. The ability of cross-sectional MRI at detecting probable AD with high accuracy could have profound implications in the management of suspected AD candidates.

MR-based neurological disease classification methodology: application to lateralization of seizure focus in temporal lobe epilepsy.

Classification approaches for neurological diseases tend to concentrate on specific structures such as the hippocampus (HC). The hypothesis for the novel methodology presented in this work is that pathologies will impact large tissue areas with detectable variations of T1-weighted MR signal intensity and registration metrics. The technique is applied to lateralization of seizure focus in 127 patients with intractable temporal lobe epilepsy (TLE), in which the site of seizure onset was determined by comprehensive evaluation (69 with left MTL seizure focus (SF) (group "L") and 58 with right SF (group "R")). The method analyses large, non-specific Volumes of Interest (VOI) centered on the left and right medial temporal lobes (MTL) (55 x 82 x 80 voxels) in pre-processed scans aligned in stereotaxic space. Extracted VOIs are linearly and nonlinearly registered to a reference target image. Principal Components Analyses of (i) the normalized intensity and (ii) the trace, a measure of local volume change, are used to generate a multidimensional reference space from a set of 152 neurologically healthy subjects. VOIs from TLE patients, processed in a similar fashion, are projected in this space, and leave-one-out, forward stepwise linear discriminant analysis of the eigencoordinate distributions is used for classification. Following manual MRI volumetric analysis, 80 patients had HC atrophy (group "HA") ipsilateral to the SF (42 with left SF or "LHA", and 38 with right or "RHA"), and the remaining 47 had normal HC volumes (group "HNV") (27 with left SF or "LNV", and 20 with right SF or "RNV"). The automated method was 100% accurate at separating "HA" vs. "HNV", "LHA" vs. "RHA", and "LNV" vs "RNV". It was also 96% accurate at separating "L" vs. "R". Our results indicate that MR data projected in multidimensional feature domains can lateralize SF in epilepsy patients with a high accuracy, irrespective of HC volumes. This single-scan, practical and objective method holds promise for the pre-surgical evaluation of TLE patients.

Predicting clinical variable from MRI features: application to MMSE in MCI.

The ability to predict a clinical variable from automated analysis of single, cross-sectional T1-weighted (T1w) MR scans stands to improve the management of patients with neurological diseases. We present a methodology for predicting yearly Mini-Mental Score Examination (MMSE) changes in Mild Cognitive Impairment (MCI) patients. We begin by generating a non-pathological, multidimensional reference space from a group of 152 healthy volunteers by Principal Component Analyses of (i) T1w MR intensity of linearly registered Volumes of Interest (VOI); and (ii) trace of the deformation fields of nonlinearly registered VOIs. We use multiple regression to build linear models from eigenvectors where the projection eigencoordinates of patient data in the reference space are highly correlated with the clinical variable of interest. In our cohort of 47 MCI patients, composed of 16 decliners, 26 stable and 5 improvers (based on MMSE at 1 yr follow-up), there was a significant difference (P = 0.0003) for baseline MMSE scores between decliners and improvers, but no other differences based on age or sex. First, we classified our three groups using leave-one-out, forward stepwise linear discriminant analyses of the projection eigencoordinates with 100% accuracy. Next, we compared various linear models by computing F-statistics on the residuals of predicted vs actual values. The best model was based on 10 eigenvectors + baseline MMSE, with predicted yearly changes highly correlated (r = 0.6955) with actual data. Prospective study of an independent cohort of patients is the next logical step towards establishing this promising technique for clinical use.

Whole-brain voxel-based statistical analysis of gray matter and white matter in temporal lobe epilepsy.

Volumetric MRI studies based on manual labeling of selected anatomical structures have provided in vivo evidence that brain abnormalities associated with temporal lobe epilepsy (TLE) extend beyond the hippocampus. Voxel-based morphometry (VBM) is a fully automated image analysis technique allowing identification of regional differences in gray matter (GM) and white matter (WM) between groups of subjects without a prior region of interest. The purpose of this study was to determine whole-brain GM and WM changes in TLE and to investigate the relationship between these abnormalities and clinical parameters. We studied 85 patients with pharmacologically intractable TLE and unilateral hippocampal atrophy and 47 age- and sex-matched healthy control subjects. The seizure focus was right sided in 40 patients and left sided in 45. Student's t test statistical maps of differences between patients' and controls' GM and WM concentrations were obtained using a general linear model. A further regression against duration of epilepsy, age of onset, presence of febrile convulsions, and secondary generalized seizures was performed with the TLE population. Voxel-based morphometry revealed that GM pathology in TLE extends beyond the hippocampus involving other limbic areas such as the cingulum and the thalamus, as well as extralimbic areas, particularly the frontal lobe. White matter reduction was found only ipsilateral to the seizure focus, including the temporopolar, entorhinal, and perirhinal areas. This pattern of structural changes is suggestive of disconnection involving preferentially frontolimbic pathways in patients with pharmacologically intractable TLE.

Appearance-based segmentation of medial temporal lobe structures.

A new paradigm for the characterization of structure appearance is proposed, based on a combination of gray-level MRI intensity data and a shape descriptor derived from a priori principal components analysis of 3D deformation vector fields. Generated without external intervention, it extends into 3D more classic, 2D manual landmark-based shape models. Application of this novel concept led to a method for the segmentation of medial temporal lobe structures from brain magnetic resonance images. The strategy employed for segmentation aims at synthesizing, using the appearance model, a deformation field that maps a new volume onto a reference target. Any information defined on the reference can then be propagated back on the new volume instance, thereby achieving segmentation. The proposed method was tested on a data set of 80 normal subjects and compared against manual segmentation as well as automated segmentation results from ANIMAL, a nonlinear registration and segmentation technique. Experimental results demonstrated the robustness and flexibility of the new method. Segmentation accuracy, measured by overlap statistics, is marginally lower (< 2%) than ANIMAL, while processing time is six times faster. Finally, the applicability of this concept toward shape deformation analysis is presented.

Ranking stormwater control strategies under uncertainty: the River Cam case study.

Monte Carlo simulations taking uncertainty in model parameters into account were performed on a river water quality model. The simulation results were used to rank wastewater treatment plant control strategies according to their impacts on river water quality. This impact is estimated by the maximum ammonium concentration and by the duration of dissolved oxygen concentration below 4 g/m3 at the downstream boundary of the system. The strategies were classified according to the previous criteria using 4 ranking methods, one of them being based on the concept of stochastic dominance. Results are presented for a case study based on a 10 km stretch of the River Cam as it passes through the city of Cambridge in Eastern England. It was found that ranking was robust in face of uncertainty in the parameter values for the control strategies considered as being superior in terms of river water quality impacts.