246 research outputs found
Simultaneous in vivo positron emission tomography and magnetic resonance imaging
Positron emission tomography (PET) and magnetic resonance imaging (MRI) are widely used in vivo imaging technologies with both clinical and biomedical research applications. The strengths of MRI include high-resolution, high-contrast morphologic imaging of soft tissues; the ability to image physiologic parameters such as diffusion and changes in oxygenation level resulting from neuronal stimulation; and the measurement of metabolites using chemical shift imaging. PET images the distribution of biologically targeted radiotracers with high sensitivity, but images generally lack anatomic context and are of lower spatial resolution. Integration of these technologies permits the acquisition of temporally correlated data showing the distribution of PET radiotracers and MRI contrast agents or MR-detectable metabolites, with registration to the underlying anatomy. An MRI-compatible PET scanner has been built for biomedical research applications that allows data from both modalities to be acquired simultaneously. Experiments demonstrate no effect of the MRI system on the spatial resolution of the PET system and <10% reduction in the fraction of radioactive decay events detected by the PET scanner inside the MRI. The signal-to-noise ratio and uniformity of the MR images, with the exception of one particular pulse sequence, were little affected by the presence of the PET scanner. In vivo simultaneous PET and MRI studies were performed in mice. Proof-of-principle in vivo MR spectroscopy and functional MRI experiments were also demonstrated with the combined scanner
Magnification-independent Histopathological Image Classification with Similarity-based Multi-scale Embeddings
The classification of histopathological images is of great value in both
cancer diagnosis and pathological studies. However, multiple reasons, such as
variations caused by magnification factors and class imbalance, make it a
challenging task where conventional methods that learn from image-label
datasets perform unsatisfactorily in many cases. We observe that tumours of the
same class often share common morphological patterns. To exploit this fact, we
propose an approach that learns similarity-based multi-scale embeddings (SMSE)
for magnification-independent histopathological image classification. In
particular, a pair loss and a triplet loss are leveraged to learn
similarity-based embeddings from image pairs or image triplets. The learned
embeddings provide accurate measurements of similarities between images, which
are regarded as a more effective form of representation for histopathological
morphology than normal image features. Furthermore, in order to ensure the
generated models are magnification-independent, images acquired at different
magnification factors are simultaneously fed to networks during training for
learning multi-scale embeddings. In addition to the SMSE, to eliminate the
impact of class imbalance, instead of using the hard sample mining strategy
that intuitively discards some easy samples, we introduce a new reinforced
focal loss to simultaneously punish hard misclassified samples while
suppressing easy well-classified samples. Experimental results show that the
SMSE improves the performance for histopathological image classification tasks
for both breast and liver cancers by a large margin compared to previous
methods. In particular, the SMSE achieves the best performance on the BreakHis
benchmark with an improvement ranging from 5% to 18% compared to previous
methods using traditional features
Allelic Share among Two Taxonomically Related \u3cem\u3eElymus\u3c/em\u3e Species, Hexaploidy \u3cem\u3eE. nutans\u3c/em\u3e Griseb and Tetraploid \u3cem\u3eE. burchan‐buddae\u3c/em\u3e (Nevski) Tzvelev
Laminar Entry Flow of Herschel-Bulkley Fluids in a Circular Pipe
Advances in Fluid Mechanics135-144AFME
Comparative venom gland transcriptome analysis of the scorpion Lychas mucronatus reveals intraspecific toxic gene diversity and new venomous components
<p>Abstract</p> <p>Background</p> <p><it>Lychas mucronatus </it>is one scorpion species widely distributed in Southeast Asia and southern China. Anything is hardly known about its venom components, despite the fact that it can often cause human accidents. In this work, we performed a venomous gland transcriptome analysis by constructing and screening the venom gland cDNA library of the scorpion <it>Lychas mucronatus </it>from Yunnan province and compared it with the previous results of Hainan-sourced <it>Lychas mucronatus</it>.</p> <p>Results</p> <p>A total of sixteen known types of venom peptides and proteins are obtained from the venom gland cDNA library of Yunnan-sourced <it>Lychas mucronatus</it>, which greatly increase the number of currently reported scorpion venom peptides. Interestingly, we also identified nineteen atypical types of venom molecules seldom reported in scorpion species. Surprisingly, the comparative transcriptome analysis of Yunnan-sourced <it>Lychas mucronatus </it>and Hainan-sourced <it>Lychas mucronatus </it>indicated that enormous diversity and vastly abundant difference could be found in venom peptides and proteins between populations of the scorpion <it>Lychas mucronatus </it>from different geographical regions.</p> <p>Conclusions</p> <p>This work characterizes a large number of venom molecules never identified in scorpion species. This result provides a comparative analysis of venom transcriptomes of the scorpion <it>Lychas mucronatus </it>from different geographical regions, which thoroughly reveals the fact that the venom peptides and proteins of the same scorpion species from different geographical regions are highly diversified and scorpion evolves to adapt a new environment by altering the primary structure and abundance of venom peptides and proteins.</p
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