A Novel Fusion Framework Based on Adaptive PCNN in NSCT Domain for Whole-Body PET and CT Images

The PET and CT fusion images, combining the anatomical and functional information, have important clinical meaning. This paper proposes a novel fusion framework based on adaptive pulse-coupled neural networks (PCNNs) in nonsubsampled contourlet transform (NSCT) domain for fusing whole-body PET and CT images. Firstly, the gradient average of each pixel is chosen as the linking strength of PCNN model to implement self-adaptability. Secondly, to improve the fusion performance, the novel sum-modified Laplacian (NSML) and energy of edge (EOE) are extracted as the external inputs of the PCNN models for low- and high-pass subbands, respectively. Lastly, the rule of max region energy is adopted as the fusion rule and different energy templates are employed in the low- and high-pass subbands. The experimental results on whole-body PET and CT data (239 slices contained by each modality) show that the proposed framework outperforms the other six methods in terms of the seven commonly used fusion performance metrics

A Hybrid Method for Pancreas Extraction from CT Image Based on Level Set Methods

This paper proposes a novel semiautomatic method to extract the pancreas from abdominal CT images. Traditional level set and region growing methods that request locating initial contour near the final boundary of object have problem of leakage to nearby tissues of pancreas region. The proposed method consists of a customized fast-marching level set method which generates an optimal initial pancreas region to solve the problem that the level set method is sensitive to the initial contour location and a modified distance regularized level set method which extracts accurate pancreas. The novelty in our method is the proper selection and combination of level set methods, furthermore an energy-decrement algorithm and an energy-tune algorithm are proposed to reduce the negative impact of bonding force caused by connected tissue whose intensity is similar with pancreas. As a result, our method overcomes the shortages of oversegmentation at weak boundary and can accurately extract pancreas from CT images. The proposed method is compared to other five state-of-the-art medical image segmentation methods based on a CT image dataset which contains abdominal images from 10 patients. The evaluated results demonstrate that our method outperforms other methods by achieving higher accuracy and making less false segmentation in pancreas extraction

A Novel Multiinstance Learning Approach for Liver Cancer Recognition on Abdominal CT Images Based on CPSO-SVM and IO

A novel multi-instance learning (MIL) method is proposed to recognize liver cancer with abdominal CT images based on instance optimization (IO) and support vector machine with parameters optimized by a combination algorithm of particle swarm optimization and local optimization (CPSO-SVM). Introducing MIL into liver cancer recognition can solve the problem of multiple regions of interest classification. The images we use in the experiments are liver CT images extracted from abdominal CT images. The proposed method consists of two main steps: (1) obtaining the key instances through IO by texture features and a classification threshold in classification of instances with CPSO-SVM and (2) predicting unknown samples with the key instances and the classification threshold. By extracting the instances equally based on the entire image, the proposed method can ignore the procedure of tumor region segmentation and lower the demand of segmentation accuracy of liver region. The normal SVM method and two MIL algorithms, Citation-kNN algorithm and WEMISVM algorithm, have been chosen as comparing algorithms. The experimental results show that the proposed method can effectively recognize liver cancer images from two kinds of cancer CT images and greatly improve the recognition accuracy

2-Selenouridine Triphosphate Synthesis and Se-RNA Transcription

2-Selenouridine (SeU) is one of the naturally occurring modifications of Se-tRNAs (SeU-RNA) at the wobble position of the anticodon loop. Its role in the RNA-RNA interaction, especially during the mRNA decoding, is elusive. To assist the research exploration, herein we report the enzymatic synthesis of the SeU-RNA via 2-selenouridine triphosphate (SeUTP) synthesis and RNA transcription. Moreover, we have demonstrated that the synthesized SeUTP is stable and recognizable by T7 RNA polymerase. Under the optimized conditions, the transcription yield of SeU-RNA can reach up to 85% of the corresponding native RNA. Furthermore, the transcribed SeU-hammerhead ribozyme has the similar activity as the corresponding native, which suggests usefulness of SeU-RNAs in function and structure studies of noncoding RNAs, including the Se-tRNAs

9-(1,1-Dimethyl-3-oxobut­yl)adenine

The title compound, C11H15N5O, crystallizes with two independent mol­ecules in the asymmetric unit, both of which contain essentially planar imidazole and pyrimidine rings [maximum deviations = 0.002 (2) and 0.026 (2) Å, respectively, for the first mol­ecule, and 0.001 (2) and 0.025 (2) Å for the second]; the dihedral angles between the rings are 2.1 (2) and 1.7 (2)° in the two mol­ecules. The crystal structure is stabilized by inter­molecular N—H⋯N hydrogen bonds, defining chains along a, which are further linked by weak inter­molecular π–π contacts [centroid centroid distance = 3.7989 (16) Å] into planes parallel to (01)

ECPC-IDS:A benchmark endometrail cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions

Endometrial cancer is one of the most common tumors in the female reproductive system and is the third most common gynecological malignancy that causes death after ovarian and cervical cancer. Early diagnosis can significantly improve the 5-year survival rate of patients. With the development of artificial intelligence, computer-assisted diagnosis plays an increasingly important role in improving the accuracy and objectivity of diagnosis, as well as reducing the workload of doctors. However, the absence of publicly available endometrial cancer image datasets restricts the application of computer-assisted diagnostic techniques.In this paper, a publicly available Endometrial Cancer PET/CT Image Dataset for Evaluation of Semantic Segmentation and Detection of Hypermetabolic Regions (ECPC-IDS) are published. Specifically, the segmentation section includes PET and CT images, with a total of 7159 images in multiple formats. In order to prove the effectiveness of segmentation methods on ECPC-IDS, five classical deep learning semantic segmentation methods are selected to test the image segmentation task. The object detection section also includes PET and CT images, with a total of 3579 images and XML files with annotation information. Six deep learning methods are selected for experiments on the detection task.This study conduct extensive experiments using deep learning-based semantic segmentation and object detection methods to demonstrate the differences between various methods on ECPC-IDS. As far as we know, this is the first publicly available dataset of endometrial cancer with a large number of multiple images, including a large amount of information required for image and target detection. ECPC-IDS can aid researchers in exploring new algorithms to enhance computer-assisted technology, benefiting both clinical doctors and patients greatly.Comment: 14 pages,6 figure

Link Prediction Based on Extended Local Path Gain in Protein-Protein Interaction Network

Protein–protein interaction (PPI) plays key role in each cellular process of any living cell, however, almost all organisms’ PPIs are still incomplete. In this study, we firstly proposed a computational method Extended Local Path (ELP), which estimated links’ existence likelihood by integrating all their neighbours’ local paths in the network. In addition, on this basis, we extended it to Extended Local Path Gain (ELPG), which estimated gain effect when adding or deleting one potential link to the network. Applying both ELPG and ELP methods and other four recognized outstanding methods on four public PPI data of Yeast, E. coli, Fruit fly and Mouse, we demonstrated that ELPG and ELP obtained better performance under two standard measures: area under curve (AUC) and Precision. Besides, ELP and ELPG were identified as the best features for classifying existing and unknown links by using support vector machine-recursive feature elimination (SVM-RFE) for feature selection

Enhanced Artificial Enzyme Activities on the Reconstructed Sawtoothlike Nanofacets of Pure and Pr-Doped Ceria Nanocubes

In this work, a simple one-step thermal oxidation process was established to achieve a significant surface increase in {110} and {111} nanofacets on well-defined, pure and Pr-doped, ceria nanocubes. More importantly, without changing most of the bulk properties, this treatment leads to a remarkable boost of their enzymatic activities: from the oxidant (oxidase-like) to antioxidant (hydroxyl radical scavenging) as well as the paraoxon degradation (phosphatase-like) activities. Such performance improvement might be due to the thermally generated sawtoothlike {111} nanofacets and defects, which facilitate the oxygen mobility and the formation of oxygen vacancies on the surface. Finally, possible mechanisms of nanoceria as artificial enzymes have been proposed in this manuscript. Considering the potential application of ceria as artificial enzymes, this thermal treatment may enable the future design of highly efficient nanozymes without changing the bulk composition.This work has been supported by the Ministry of Science, Innovation and Universities of Spain with Reference Numbers of ENE2017-82451-C3-2-R, MAT2016-81118-P and MAT2017-87579-R. The research projects funded by the Natural Science Foundation of Shandong Province (Grant ZR2017LB028), Key R&D Program of Shandong Province (Grant 2018GSF118032), and Fundamental Research Funds for the Central Universities (Grant 18CX02125A) in China are also acknowledged. TEM/STEM data were obtained at DMEUCA node of the Spanish Unique Scientific and Technological Infrastructure (ICTS) of Electron Microscopy of Materials ELECMIM. M. Tinoco thanks the FPU Scholarship Program (Grant AP2010-3737) from Ministry of Education of Spain. H. Pan is grateful for financial support (Grant 201406140130) from the Chinese Scholarship Council to accomplish her Ph.D. study at the University of Cadiz (Spain). J. M. Gonzalez, G. Blanco, and X. Chen are also grateful for the financial support from the joint project (Proyectos Integradores, Grant PI20201) in IMEYMAT of the University of Cadiz

High drug-loaded microspheres enabled by controlled in-droplet precipitation promote functional recovery after spinal cord injury

High drug loading improves therapeutic efficacy and reduces side effects in drug delivery. Here, the authors use controlled diffusion of solvents to precipitate drug nanoparticles in polymer particles while the polymer is solidifying and demonstrate the particles for drug delivery in a spinal cord injury model. Drug delivery systems with high content of drug can minimize excipients administration, reduce side effects, improve therapeutic efficacy and/or promote patient compliance. However, engineering such systems is extremely challenging, as their loading capacity is inherently limited by the compatibility between drug molecules and carrier materials. To mitigate the drug-carrier compatibility limitation towards therapeutics encapsulation, we developed a sequential solidification strategy. In this strategy, the precisely controlled diffusion of solvents from droplets ensures the fast in-droplet precipitation of drug molecules prior to the solidification of polymer materials. After polymer solidification, a mass of drug nanoparticles is embedded in the polymer matrix, forming a nano-in-micro structured microsphere. All the obtained microspheres exhibit long-term storage stability, controlled release of drug molecules, and most importantly, high mass fraction of therapeutics (21.8-63.1 wt%). Benefiting from their high drug loading degree, the nano-in-micro structured acetalated dextran microspheres deliver a high dose of methylprednisolone (400 mu g) within the limited administration volume (10 mu L) by one single intrathecal injection. The amount of acetalated dextran used was 1/433 of that of low drug-loaded microspheres. Moreover, the controlled release of methylprednisolone from high drug-loaded microspheres contributes to improved therapeutic efficacy and reduced side effects than low drug-loaded microspheres and free drug in spinal cord injury therapy.Peer reviewe