VoxRec : hybrid convolutional neural network for active 3D object recognition

Deep Neural Network methods have been used to a variety of challenges in automatic 3D recognition. Although discovered techniques provide many advantages in comparison with conventional methods, they still suffer from different drawbacks, e.g., a large number of pre-processing stages and time-consuming training. In this paper, an innovative approach has been suggested for recognizing 3D models. It contains encoding 3D point clouds, surface normal, and surface curvature, merge them to provide more effective input data, and train it via a deep convolutional neural network on Shapenetcore dataset. We also proposed a similar method for 3D segmentation using Octree coding method. Finally, comparing the accuracy with some of the state-of-the-art demonstrates the effectiveness of our proposed method

Cloud-based automated clinical decision support system for detection and diagnosis of lung cancer in chest CT

Lung cancer is a major cause for cancer-related deaths. The detection of pulmonary cancer in the early stages can highly increase survival rate. Manual delineation of lung nodules by radiologists is a tedious task. We developed a novel computer-aided decision support system for lung nodule detection based on a 3D Deep Convolutional Neural Network (3DDCNN) for assisting the radiologists. Our decision support system provides a second opinion to the radiologists in lung cancer diagnostic decision making. In order to leverage 3-dimensional information from Computed Tomography (CT) scans, we applied median intensity projection and multi-Region Proposal Network (mRPN) for automatic selection of potential regionof-interests. Our Computer Aided Diagnosis (CAD) system has been trained and validated using LUNA16, ANODE09, and LIDC-IDR datasets; the experiments demonstrate the superior performance of our system, attaining sensitivity, specificity, AUROC, accuracy, of 98.4%, 92%, 96% and 98.51% with 2.1 FPs per scan. We integrated cloud computing, trained and validated our Cloud-Based 3DDCNN on the datasets provided by Shanghai Sixth People’s Hospital, as well as LUNA16, ANODE09, and LIDC-IDR. Our system outperformed the state-of-the-art systems and obtained an impressive 98.7% sensitivity at 1.97 FPs per scan. This shows the potentials of deep learning, in combination with cloud computing, for accurate and efficient lung nodule detection via CT imaging, which could help doctors and radiologists in treating lung cancer patients

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey

Deep gesture interaction for augmented anatomy learning

Augmented reality is very useful in medical education because of the problem of having body organs in a regular classroom. In this paper, we propose to apply augmented reality to improve the way of teaching in medical schools and institutes. We propose a novel convolutional neural network (CNN) for gesture recognition, which recognizes the human's gestures as a certain instruction. We use augmented reality technology for anatomy learning, which simulates the scenarios where students can learn Anatomy with HoloLens instead of rare specimens. We have used the mesh reconstruction to reconstruct the 3D specimens. A user interface featured augment reality has been designed which fits the common process of anatomy learning. To improve the interaction services, we have applied gestures as an input source and improve the accuracy of gestures recognition by an updated deep convolutional neural network. Our proposed learning method includes many separated train procedures using cloud computing. Each train model and its related inputs have been sent to our cloud and the results are returned to the server. The suggested cloud includes windows and android devices, which are able to install deep convolutional learning libraries. Compared with previous gesture recognition, our approach is not only more accurate but also has more potential for adding new gestures. Furthermore, we have shown that neural networks can be combined with augmented reality as a rising field, and the great potential of augmented reality and neural networks to be employed for medical learning and education systems

Open su(4)-invariant spin ladder with boundary defects

The integrable su(4)-invariant spin-ladder model with boundary defect is studied using the Bethe ansatz method. The exact phase diagram for the ground state is given and the boundary quantum critical behavior is discussed. It consists of a gapped phase in which the rungs of the ladder form singlet states and a gapless Luttinger liquid phase. It is found that in the gapped phase the boundary bound state corresponds to an unscreened local moment, while in the Luttinger liquid phase the local moment is screened at low temperatures in analogy to the Kondo effect.Comment: Revtex 9 pages, published in PR

Cosmic Rays during BBN as Origin of Lithium Problem

There may be non-thermal cosmic rays during big-bang nucleosynthesis (BBN) epoch (dubbed as BBNCRs). This paper investigated whether such BBNCRs can be the origin of Lithium problem or not. It can be expected that BBNCRs flux will be small in order to keep the success of standard BBN (SBBN). With favorable assumptions on the BBNCR spectrum between 0.09 -- 4 MeV, our numerical calculation showed that extra contributions from BBNCRs can account for the $^7$Li abundance successfully. However $^6$Li abundance is only lifted an order of magnitude, which is still much lower than the observed value. As the deuteron abundance is very sensitive to the spectrum choice of BBNCRs, the allowed parameter space for the spectrum is strictly constrained. We should emphasize that the acceleration mechanism for BBNCRs in the early universe is still an open question. For example, strong turbulent magnetic field is probably the solution to the problem. Whether such a mechanism can provide the required spectrum deserves further studies.Comment: 34 pages, 21 figures, published versio

Triphosphate reorientation of the incoming nucleotide as a fidelity checkpoint in viral RNA-dependent RNA Polymerases

The nucleotide incorporation fidelity of the viral RNA-dependent RNA polymerase (RdRp) is important for maintaining functional genetic information but, at the same time, is also important for generating sufficient genetic diversity to escape the bottlenecks of the host's antiviral response. We have previously shown that the structural dynamics of the motifDloop are closely related to nucleotide discrimination. Previous studies have also suggested that there is a reorientation of the triphosphate of the incoming nucleotide, which is essential before nucleophilic attack from the primer RNA 3-hydroxyl. Here, we have used 31PNMRwith poliovirus RdRp to show that the binding environment of the triphosphate is different when correct versus incorrect nucleotide binds.Wealso show that amino acid substitutions at residues known to interact with the triphosphate can alter the binding orientation/environment of the nucleotide, sometimes lead to protein conformational changes, and lead to substantial changes in RdRp fidelity. The analyses of other fidelity variants also show that changes in the triphosphate binding environment are not always accompanied by changes in the structural dynamics of the motif D loop or other regions known to be important for RdRp fidelity, including motif B. Altogether, our studies suggest that the conformational changes in motifs B and D, and the nucleoside triphosphate reorientation represent separable, "tunable" fidelity checkpoints

Nucleobase but not sugar fidelity is maintained in the Sabin I RNA-dependent RNA polymerase

The Sabin I poliovirus live, attenuated vaccine strain encodes for four amino acid changes (i.e., D53N, Y73H, K250E, and T362I) in the RNA-dependent RNA polymerase (RdRp). We have previously shown that the T362I substitution leads to a lower fidelity RdRp, and viruses encoding this variant are attenuated in a mouse model of poliovirus. Given these results, it was surprising that the nucleotide incorporation rate and nucleobase fidelity of the Sabin I RdRp is similar to that of wild-type enzyme, although the Sabin I RdRp is less selective against nucleotides with modified sugar groups. We suggest that the other Sabin amino acid changes (i.e., D53N, Y73H, K250E) help to re-establish nucleotide incorporation rates and nucleotide discrimination near wild-type levels, which may be a requirement for the propagation of the virus and its efficacy as a vaccine strain. These results also suggest that the nucleobase fidelity of the Sabin I RdRp likely does not contribute to viral attenuation

Contribution of biomimetic collagen-ligand interaction to intrafibrillar mineralization

Contemporary models of intrafibrillar mineralization mechanisms are established using collagen fibrils as templates without considering the contribution from collagen-bound apatite nucleation inhibitors. However, collagen matrices destined for mineralization in vertebrates contain bound matrix proteins for intrafibrillar mineralization. Negatively charged, high\u2013molecular weight polycarboxylic acid is cross-linked to reconstituted collagen to create a model for examining the contribution of collagen-ligand interaction to intrafibrillar mineralization. Cryogenic electron microscopy and molecular dynamics simulation show that, after cross-linking to collagen, the bound polyelectrolyte caches prenucleation cluster singlets into chain-like aggregates along the fibrillar surface to increase the pool of mineralization precursors available for intrafibrillar mineralization. Higher-quality mineralized scaffolds with better biomechanical properties are achieved compared with mineralization of unmodified scaffolds in polyelectrolyte-stabilized mineralization solution. Collagen-ligand interaction provides insights on the genesis of heterogeneously mineralized tissues and the potential causes of ectopic calcification in nonmineralized body tissues