| 1. |
- Yang, Shangchen, et al.
(författare)
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Genomic investigation of the Chinese alligator reveals wild-extinct genetic diversity and genomic consequences of their continuous decline
- 2023
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Ingår i: Molecular Ecology Resources. - : Wiley. - 1755-098X .- 1755-0998. ; 23:1, s. 294-311
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Tidskriftsartikel (refereegranskat)abstract
- Critically endangered species are usually restricted to small and isolated populations. High inbreeding without gene flow among populations further aggravates their threatened condition and reduces the likelihood of their long-term survival. Chinese alligator (Alligator sinensis) is one of the most endangered crocodiles in the world and has experienced a continuous decline over the past c. 1 million years. In order to identify the genetic status of the remaining populations and aid conservation efforts, we assembled the first high-quality chromosome-level genome of Chinese alligator and explored the genomic characteristics of three extant breeding populations. Our analyses revealed the existence of at least three genetically distinct populations, comprising two breeding populations in China (Changxing and Xuancheng) and one breeding population in an American wildlife refuge. The American population does not belong to the last two populations of its native range (Xuancheng and Changxing), thus representing genetic diversity extinct in the wild and provides future opportunities for genetic rescue. Moreover, the effective population size of these three populations has been continuously declining over the past 20 ka. Consistent with this decline, the species shows extremely low genetic diversity, a large proportion of long runs of homozygous fragments, and mutational load across the genome. Finally, to provide genomic insights for future breeding management and conservation, we assessed the feasibility of mixing extant populations based on the likelihood of introducing new deleterious alleles and signatures of local adaptation. Overall, this study provides a valuable genomic resource and important genomic insights into the ecology, evolution, and conservation of critically endangered alligators.
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| 2. |
- Alvén, Jennifer, 1989, et al.
(författare)
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A Deep Learning Approach to MR-less Spatial Normalization for Tau PET Images
- 2019
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Ingår i: Medical Image Computing and Computer Assisted Intervention : MICCAI 2019 - 22nd International Conference, Proceedings - MICCAI 2019 - 22nd International Conference, Proceedings. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783030322458 - 9783030322441 ; 11765 LNCS, s. 355-363
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Konferensbidrag (refereegranskat)abstract
- The procedure of aligning a positron emission tomography (PET) image with a common coordinate system, spatial normalization, typically demands a corresponding structural magnetic resonance (MR) image. However, MR imaging is not always available or feasible for the subject, which calls for enabling spatial normalization without MR, MR-less spatial normalization. In this work, we propose a template-free approach to MR-less spatial normalization for [18F]flortaucipir tau PET images. We use a deep neural network that estimates an aligning transformation from the PET input image, and outputs the spatially normalized image as well as the parameterized transformation. In order to do so, the proposed network iteratively estimates a set of rigid and affine transformations by means of convolutional neural network regressors as well as spatial transformer layers. The network is trained and validated on 199 tau PET volumes with corresponding ground truth transformations, and tested on two different datasets. The proposed method shows competitive performance in terms of registration accuracy as well as speed, and compares favourably to previously published results.
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| 3. |
- Campbell, William A, et al.
(författare)
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Zebrafish lacking Alzheimer presenilin enhancer 2 (Pen-2) demonstrate excessive p53-dependent apoptosis and neuronal loss.
- 2006
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Ingår i: Journal of neurochemistry. - : Wiley. - 0022-3042. ; 96:5, s. 1423-40
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Tidskriftsartikel (refereegranskat)abstract
- Gamma-secretase cleavage, mediated by a complex of presenilin, presenilin enhancer (Pen-2), nicastrin, and Aph-1, is the final proteolytic step in generating amyloid beta protein found in brains of Alzheimer's disease patients and Notch intracellular domain critical for proper neuronal development. Here, we employ the zebrafish model to study the role of Pen-2 in neuronal survival. We found that (i) knockdown of Pen-2 using antisense morpholino led to a reduction of islet-1 positive neurons, (ii) Notch signaling was reduced in embryos lacking Pen-2 or other gamma-secretase components, (iii) neuronal loss in Pen-2 knockdown embryos is not as a result of a lack of neuronal precursor cells or cell proliferation, (iv) absence of Pen-2 caused massive apoptosis in the whole animal, which could be suppressed by simultaneous knockdown of the tumor suppressor p53, (v) loss of islet-1 or acetylated tubulin positive neurons in Pen-2 knockdown embryos could be partially rescued by knockdown of p53. Our results demonstrate that knockdown of Pen-2 directly induces a p53-dependent apoptotic pathway that contributes to neuronal loss and suggest that Pen-2 plays an important role in promoting neuronal cell survival and protecting from apoptosis in vivo.
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| 4. |
- Makkie, Milad, et al.
(författare)
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Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analytics
- 2019
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Ingår i: Neurocomputing. - : Elsevier. - 0925-2312 .- 1872-8286. ; 325, s. 20-30
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, analyzing task-based fMRI (tfMRI) data has become an essential tool for understanding brain function and networks. However, due to the sheer size of tfMRI data, its intrinsic complex structure, and lack of ground truth of underlying neural activities, modeling tfMRI data is hard and challenging. Previously proposed data modeling methods including Independent Component Analysis (ICA) and Sparse Dictionary Learning only provided shallow models based on blind source separation under the strong assumption that original fMRI signals could be linearly decomposed into time series components with corresponding spatial maps. Given the Convolutional Neural Network (CNN) successes in learning hierarchical abstractions from low-level data such as tfMRI time series, in this work we propose a novel scalable distributed deep CNN autoencoder model and apply it for fMRI big data analysis. This model aims to both learn the complex hierarchical structures of the tfMRI big data and to leverage the processing power of multiple GPUs in a distributed fashion. To deploy such a model, we have created an enhanced processing pipeline on the top of Apache Spark and Tensorflow, leveraging from a large cluster of GPU nodes over cloud. Experimental results from applying the model on the Human Connectome Project (HCP) data show that the proposed model is efficient and scalable toward tfMRI big data modeling and analytics, thus enabling data-driven extraction of hierarchical neuroscientific information from massive fMRI big data.
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