Analysis of CKM-Favored Quasi-Two-Body B→D(R→)KπB \to D (R\to) K \pi Decays in PQCD Approach

LHCb Collaboration studied the resonant structure of $B_s\to \overline{D}^0K^-\pi^+$ decays using the Dalitz plot analysis technique, based on a data sample corresponding to an integrated luminosity of $3.0{\rm fb}^{-1}$ of $pp$ collision. The $K^-\pi^+$ components have been analyzed in the amplitude model, where the decay amplitude is modeled to be the resonant contributions with respect to the intermediate resonances $K^*(892)$, $K_0^*(1430)$ and $K_2^*(1430)$. Motivated by the experimental results, we investigate the color-favored quasi-two-body $B \to \overline{D}^0K\pi$ decays in the framework of the perturbative QCD (PQCD) approach. We calculate the the branching fractions by introducing the appropriate wave functions of $K\pi$ pair. Our results are in agreement well the available data, and others can be tested in LHCb and Belle-II experiments. Using the narrow-width-approximation, we also extract the branching fractions of the corresponding two-body $B\to \overline D R$ decays, which agree to the previous theoretical calculations and the experimental data within the errors. There are no $CP$ asymmetries in these decays in the standard model, because these decays are all governed by only the tree operators.Comment: 18 pages, 1 figure

Clinical Features and Genetic Analysis of 20 Chinese Patients with X-Linked Hyper-IgM Syndrome

X-linked hyper-IgM syndrome (XHIGM) is one type of primary immunodeficiency diseases, resulting from defects in the CD40 ligand/CD40 signaling pathways. We retrospectively analyzed the clinical and molecular features of 20 Chinese patients diagnosed and followed up in hospitals affiliated to Shanghai Jiao Tong University School of Medicine from 1999 to 2013. The median onset age of these patients was 8.5 months (range: 20 days–21 months). Half of them had positive family histories, with a shorter diagnosis lag. The most common symptoms were recurrent sinopulmonary infections (18 patients, 90%), neutropenia (14 patients, 70%), oral ulcer (13 patients, 65%), and protracted diarrhea (13 patients, 65%). Six patients had BCGitis. Six patients received hematopoietic stem cell transplantations and four of them had immune reconstructions and clinical remissions. Eighteen unique mutations in CD40L gene were identified in these 20 patients from 19 unrelated families, with 12 novel mutations. We compared with reported mutation results and used bioinformatics software to predict the effects of mutations on the target protein. These mutations reflected the heterogeneity of CD40L gene and expanded our understanding of XHIGM

Nonlinear Model-Based Method for Clustering Periodically Expressed Genes

Clustering periodically expressed genes from their time-course expression data could help understand the molecular mechanism of those biological processes. In this paper, we propose a nonlinear model-based clustering method for periodically expressed gene profiles. As periodically expressed genes are associated with periodic biological processes, the proposed method naturally assumes that a periodically expressed gene dataset is generated by a number of periodical processes. Each periodical process is modelled by a linear combination of trigonometric sine and cosine functions in time plus a Gaussian noise term. A two stage method is proposed to estimate the model parameter, and a relocation-iteration algorithm is employed to assign each gene to an appropriate cluster. A bootstrapping method and an average adjusted Rand index (AARI) are employed to measure the quality of clustering. One synthetic dataset and two biological datasets were employed to evaluate the performance of the proposed method. The results show that our method allows the better quality clustering than other clustering methods (e.g., k-means) for periodically expressed gene data, and thus it is an effective cluster analysis method for periodically expressed gene data

Efficient Jacobian-Based Inverse Kinematics With Sim-to-Real Transfer of Soft Robots by Learning

This paper presents an efficient learning-based method to solve the inverse kinematic (IK) problem on soft robots with highly non-linear deformation. The major challenge of efficiently computing IK for such robots is due to the lack of analytical formulation for either forward or inverse kinematics. To address this challenge, we employ neural networks to learn both the mapping function of forward kinematics and also the Jacobian of this function. As a result, Jacobian-based iteration can be applied to solve the IK problem. A sim-to-real training transfer strategy is conducted to make this approach more practical. We first generate a large number of samples in a simulation environment for learning both the kinematic and the Jacobian networks of a soft robot design. Thereafter, a sim-to-real layer of differentiable neurons is employed to map the results of simulation to the physical hardware, where this sim-to-real layer can be learned from a very limited number of training samples generated on the hardware. The effectiveness of our approach has been verified on pneumatic-driven soft robots for path following and interactive positioning

Gambaran Kadar Kalsium Pada Pasien Penyakit Ginjal Kronik Stadium 5 Non Dialisis

: Calcium is the largest mineral in the body and is necessary in most biological processes. The body\u27s calcium levels are influenced by a variety of renal disorders, one of which is chronic kidney disease. Chronic kidney disease is a pathophysiological process with diverse etiology, resulting in a progressive decline in renal function, and generally end up with kidney failure (stage 5 / end stage). This study aims to describe the levels of calcium in patients with non dialysis stage 5 chronic kidney disease. The method used in this study was a descriptive study conducted from December 2015-January 2016 at two hospitals, Prof. Dr. R. D Kandou hospital and Advent Teling hospital in Manado. The samples were blood samples of all patients with non-dialysis CKD stage 5 in the period and criteria set determined by non-probability sampling types consecutive sampling. Examination serum calcium using O-Cresolphthalein Complexon method. The result obtained 22 (62.9%) were decreased calcium levels (hypocalcemia), 12 (34.3%) calcium levels nomal and 1 (2.9%) with increased levels of calcium (hypercalcemia). The results of this study concluded that most of the non-dyalisis stage 5 chronic kidney disease patients (62,9%) were decline in calcium levels

Nonlinear-Model-Based Analysis Methods for Time-Course Gene Expression Data

Microarray technology has produced a huge body of time-course gene expression data and will continue to produce more. Such gene expression data has been proved useful in genomic disease diagnosis and drug design. The challenge is how to uncover useful information from such data by proper analysis methods such as significance analysis and clustering analysis. Many statistic-based significance analysis methods and distance/correlation-based clustering analysis methods have been applied to time-course expression data. However, these techniques are unable to account for the dynamics of such data. It is the dynamics that characterizes such data and that should be considered in analysis of such data. In this paper, we employ a nonlinear model to analyse time-course gene expression data. We firstly develop an efficient method for estimating the parameters in the nonlinear model. Then we utilize this model to perform the significance analysis of individually differentially expressed genes and clustering analysis of a set of gene expression profiles. The verification with two synthetic datasets shows that our developed significance analysis method and cluster analysis method outperform some existing methods. The application to one real-life biological dataset illustrates that the analysis results of our developed methods are in agreement with the existing results

Research and Development of Automatic Detection Instrument for Stored Grain Fungi

Fungus is one of the primary factors endangering the safety of grain storage.The rapid detection of fungi on stored grain in early stage is an effective measure to prevent and control the fungal multiplication and ensure food security.In 2018,an industry standard “LS/T 6132 Inspection of Grain and Oil—Storage fungal examination—Enumeration spores of fungi” was promulgated and implemented in the grain industry.In this study,we developed an automatic detector for the detection of fungi in grain storage to promote the application of this industry standard.During the development of the detector,we built a fungal spore image library based on a large number of stored grain fungal spore pictures,and developed a fungal spore image recognition software using neural network algorithm.By optimizing the auto focusing algorithm of the microscopic imaging system,the fungal spore image under the microscope can be automatically focused and photographed.And the image recognition software was used to recognize and count the spores of the stored grain fungal automatically.This detector can realize the automatic detection of fungi on stored grain and reduce the probability of mistaken in personnel operation and identification

State Observer Design for Delayed Genetic Regulatory Networks

Genetic regulatory networks are dynamic systems which describe the interactions among gene products (mRNAs and proteins). The internal states of a genetic regulatory network consist of the concentrations of mRNA and proteins involved in it, which are very helpful in understanding its dynamic behaviors. However, because of some limitations such as experiment techniques, not all internal states of genetic regulatory network can be effectively measured. Therefore it becomes an important issue to estimate the unmeasured states via the available measurements. In this study, we design a state observer to estimate the states of genetic regulatory networks with time delays from available measurements. Furthermore, based on linear matrix inequality (LMI) approach, a criterion is established to guarantee that the dynamic of estimation error is globally asymptotically stable. A gene repressillatory network is employed to illustrate the effectiveness of our design approach