Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces

Charting cortical growth trajectories is of paramount importance for understanding brain development. However, such analysis necessitates the collection of longitudinal data, which can be challenging due to subject dropouts and failed scans. In this paper, we will introduce a method for longitudinal prediction of cortical surfaces using a spatial graph convolutional neural network (GCNN), which extends conventional CNNs from Euclidean to curved manifolds. The proposed method is designed to model the cortical growth trajectories and jointly predict inner and outer cortical surfaces at multiple time points. Adopting a binary flag in loss calculation to deal with missing data, we fully utilize all available cortical surfaces for training our deep learning model, without requiring a complete collection of longitudinal data. Predicting the surfaces directly allows cortical attributes such as cortical thickness, curvature, and convexity to be computed for subsequent analysis. We will demonstrate with experimental results that our method is capable of capturing the nonlinearity of spatiotemporal cortical growth patterns and can predict cortical surfaces with improved accuracy.Comment: Accepted as oral presentation at IPMI 201

Infant Cognitive Scores Prediction With Multi-stream Attention-based Temporal Path Signature Features

There is stunning rapid development of human brains in the first year of life. Some studies have revealed the tight connection between cognition skills and cortical morphology in this period. Nonetheless, it is still a great challenge to predict cognitive scores using brain morphological features, given issues like small sample size and missing data in longitudinal studies. In this work, for the first time, we introduce the path signature method to explore hidden analytical and geometric properties of longitudinal cortical morphology features. A novel BrainPSNet is proposed with a differentiable temporal path signature layer to produce informative representations of different time points and various temporal granules. Further, a two-stream neural network is included to combine groups of raw features and path signature features for predicting the cognitive score. More importantly, considering different influences of each brain region on the cognitive function, we design a learning-based attention mask generator to automatically weight regions correspondingly. Experiments are conducted on an in-house longitudinal dataset. By comparing with several recent algorithms, the proposed method achieves the state-of-the-art performance. The relationship between morphological features and cognitive abilities is also analyzed

Genetic diversity in Tunisian horse breeds

This study aimed at screening genetic diversity and differentiation in four horse breeds raised in Tunisia, the Barb, Arab-Barb, Arabian, and English Thoroughbred breeds. A total of 200 blood samples (50 for each breed) were collected from the jugular veins of animals, and genomic DNA was extracted. The analysis of the genetic structure was carried out using a panel of 16 microsatellite loci. Results showed that all studied microsatellite markers were highly polymorphic in all breeds. Overall, a total of 147 alleles were detected using the 16 microsatellite loci. The average number of alleles per locus was 7.52 (0.49), 7.35 (0.54), 6.3 (0.44), and 6 (0.38) for the Arab-Barb, Barb, Arabian, and English Thoroughbred breeds, respectively. The observed heterozygosities ranged from 0.63 (0.03) in the English Thoroughbred to 0.72 in the Arab-Barb breeds, whereas the expected heterozygosities were between 0.68 (0.02) in the English Thoroughbred and 0.73 in the Barb breeds. All FST values calculated by pairwise breed combinations were significantly different from zero (p  <  0.05) and an important genetic differentiation among breeds was revealed. Genetic distances, the factorial correspondence, and principal coordinate analyses showed that the important amount of genetic variation was within population. These results may facilitate conservation programs for the studied breeds and enhance preserve their genetic diversity

Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project

Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune diseases in humans, done by mean of Indirect ImmunoFluorescence (IIF)method, and performed by analyzing patterns and fluorescence intensity. This paper introduces the AIDA Project (autoimmunity: diagnosis assisted by computer) developed in the framework of an Italy-Tunisia cross-border cooperation and its preliminary results. A database of interpreted IIF images is being collected through the exchange of images and double reporting and a Gold Standard database, containing around 1000 double reported images, has been settled. The Gold Standard database is used for optimization of aCAD(Computer AidedDetection) solution and for the assessment of its added value, in order to be applied along with an Immunologist as a second Reader in detection of autoantibodies. This CAD system is able to identify on IIF images the fluorescence intensity and the fluorescence pattern. Preliminary results show that CAD, used as second Reader, appeared to perform better than Junior Immunologists and hence may significantly improve their efficacy; compared with two Junior Immunologists, the CAD system showed higher Intensity Accuracy (85,5% versus 66,0% and 66,0%), higher Patterns Accuracy (79,3% versus 48,0% and 66,2%), and higher Mean Class Accuracy (79,4% versus 56,7% and 64.2%)

Preliminary results of the project A.I.D.A. (Auto Immunity: Diagnosis Assisted by computer)

In this paper, are presented the preliminary results of the A.I.D.A. (Auto Immunity: Diagnosis Assisted by computer) project which is developed in the frame of the cross-border cooperation Italy-Tunisia. According to the main objectives of this project, a database of interpreted Indirect ImmunoFluorescence (IIF) images on HEp 2 cells is being collected thanks to the contribution of Italian and Tunisian experts involved in routine diagnosis of autoimmune diseases. Through exchanging images and double reporting; a Gold Standard database, containing around 1000 double reported IIF images with different patterns including negative tests, has been settled. This Gold Standard database has been used for optimization of a computing solution (CADComputer Aided Detection) and for assessment of its added value in order to be used along with an immunologist as a second reader in detection of auto antibodies for autoimmune disease diagnosis. From the preliminary results obtained, the CAD appeared more powerful than junior immunologists used as second readers and may significantly improve their efficacy

Structure and microstructure evolution of Al-Mg-Si alloy processed by equal-channel angular pressing

An ultrafine grained Al–Mg–Si alloy was prepared by severe plastic deformation using the equal-channel angular pressing (ECAP) method. Samples were ECAPed through a die with an inner angle of F = 90° and outer arc of curvature of ¿ = 37° from 1 to 12 ECAP passes at room temperature following route Bc. To analyze the evolution of the microstructure at increasing ECAP passes, X-ray diffraction and electron backscatter diffraction analyses were carried out. The results revealed two distinct processing regimes, namely (i) from 1 to 5 passes, the microstructure evolved from elongated grains and sub-grains to a rather equiaxed array of ultrafine grains and (ii) from 5 to 12 passes where no change in the morphology and average grain size was noticed. In the overall behavior, the boundary misorientation angle and the fraction of high-angle boundaries increase rapidly up to 5 passes and at a lower rate from 5 to 12 passes. The crystallite size decreased down to about 45 nm with the increase in deformation. The influence of deformation on precipitate evolution in the Al–Mg–Si alloy was also studied by differential scanning calorimetry. A significant decrease in the peak temperature associated to the 50% of recrystallization was observed at increasing ECAP passes.Peer ReviewedPreprin