162 research outputs found
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 200Â blood samples (50Â 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 16Â microsatellite loci. Results showed that all studied
microsatellite markers were highly polymorphic in all breeds. Overall, a
total of 147Â alleles were detected using the 16Â microsatellite loci. The
average number of alleles per locus was 7.52Â (0.49), 7.35Â (0.54), 6.3Â (0.44),
and 6Â (0.38) for the Arab-Barb, Barb, Arabian, and English Thoroughbred
breeds, respectively. The observed heterozygosities ranged from 0.63Â (0.03)
in the English Thoroughbred to 0.72 in the Arab-Barb breeds, whereas the
expected heterozygosities were between 0.68Â (0.02) in the English
Thoroughbred and 0.73Â 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
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