Processes on the emergent landscapes of biochemical reaction networks and heterogeneous cell population dynamics: differentiation in living matters.

The notion of an attractor has been widely employed in thinking about the nonlinear dynamics of organisms and biological phenomena as systems and as processes. The notion of a landscape with valleys and mountains encoding multiple attractors, however, has a rigorous foundation only for closed, thermodynamically non-driven, chemical systems, such as a protein. Recent advances in the theory of nonlinear stochastic dynamical systems and its applications to mesoscopic reaction networks, one reaction at a time, have provided a new basis for a landscape of open, driven biochemical reaction systems under sustained chemostat. The theory is equally applicable not only to intracellular dynamics of biochemical regulatory networks within an individual cell but also to tissue dynamics of heterogeneous interacting cell populations. The landscape for an individual cell, applicable to a population of isogenic non-interacting cells under the same environmental conditions, is defined on the counting space of intracellular chemical composition

Investigation of the interactions between methylene blue and intramolecular G-quadruplexes: an explicit distinction in electrochemical behavior

G-quadruplex sequences exist in eukaryotic organisms and prokaryotes, and the investigation of interactions between G-quadruplexes and small molecule ligands is important for gene therapy, biosensor fabrication, fluorescence imaging and so on. Here, we investigated the behaviour of methylene blue (MB), an electroactive molecule, in the presence of different intramolecular G-quadruplexes by electrochemical method using a miniaturized electrochemical device based on its intrinsic electrochemical property. Although the effects of MB on different intramolecular G-quadruplex structures are not obvious by circular dichroism spectroscopy, distinct differences in binding affinities of MB with different intramolecular G-quadruplexes were fast and easily observed by the electrochemical technique. At the same time, for the human telomerase G-rich sequence (HT), the diffusion current of MB changed sensitively under different ion conditions due to the formation of different conformations of HT, which indicated that our electrochemical method has the potential to study the influence of metal ions on the conformations of the G-quadruplexes with simplicity, rapid response and low cost. From all these, new stacking mechanism and rule were obtained, which were also validated by docking studies and isothermal titration calorimetry (ITC)

The Galactic extinction and reddening from the South Galactic Cap U-band Sky Survey: u band galaxy number counts and u−ru-r color distribution

We study the integral Galactic extinction and reddening based on the galaxy catalog of the South Galactic Cap U-band Sky Survey (SCUSS), where $u$ band galaxy number counts and $u-r$ color distribution are used to derive the Galactic extinction and reddening respectively. We compare these independent statistical measurements with the reddening map of \citet{Schlegel1998}(SFD) and find that both the extinction and reddening from the number counts and color distribution are in good agreement with the SFD results at low extinction regions ($E(B-V)^{SFD}<0.12$ mag). However, for high extinction regions ($E(B-V)^{SFD}>0.12$ mag), the SFD map overestimates the Galactic reddening systematically, which can be approximated by a linear relation $\Delta E(B-V)= 0.43[E(B-V)^{SFD}-0.12$]. By combing the results of galaxy number counts and color distribution together, we find that the shape of the Galactic extinction curve is in good agreement with the standard $R_V=3.1$ extinction law of \cite{ODonnell1994}

Is ultrasound combined with computed tomography useful for distinguishing between primary thyroid lymphoma and Hashimoto’s thyroiditis?

Introduction: The aim of the study is to investigate the usefulness of ultrasound combined with computed tomography (CT) for distinguishing between primary thyroid lymphoma (PTL) and Hashimoto’s thyroiditis (HT). Material and methods: The investigation was conducted retrospectively in 80 patients from January 2000 to July 2018. All patients underwent pathological tests to be classified into one of two groups: PTL group and HT group. The cut-off value of CT density was determined using receiver-operating characteristic (ROC) curve analysis. The accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of diagnosis for thyroid by CT alone, ultrasound alone, and the combination of CT plus ultrasound were calculated. Results: Of the 80 study patients, 27 patients were PTL and 53 patients were HT. Mean CT density had a sensitivity of 90.6% and a specificity of 88.9% at a cut-off value of 53.5 HU, with area under the curve (AUC) 0.88. Ultrasound combined with CT had the highest specificity, accuracy, and PPV compared with CT alone and ultrasound alone (p value &lt; 0.05). Conclusions: Features such as extremely hypoechogenicity, enhanced posterior echo, cervical lymphadenopathy in ultrasound image, and linear high-density strand signs, and very low density in CT imaging have high sensitivity and specificity in thyroid lymphoma. Therefore, ultrasound combined with CT may be useful for distinguishing between PTL and HT.

Rapid detection of porcine circovirus type 2 using a TaqMan-based real-time PCR

Porcine circovirus type 2 (PCV2) and the associated disease postweaning multisystemic wasting syndrome (PMWS) have caused heavy losses in global agriculture in recent decades. Rapid detection of PCV2 is very important for the effective prophylaxis and treatment of PMWS. To establish a sensitive, specific assay for the detection and quantitation of PCV2, we designed and synthesized specific primers and a probe in the open reading frame 2. The assay had a wide dynamic range with excellent linearity and reliable reproducibility, and detected between 102 and 1010 copies of the genomic DNA per reaction. The coefficient of variation for Ct values varied from 0.59% to 1.05% in the same assay and from 1.9% to 4.2% in 10 different assays. The assay did not cross-react with porcine circovirus type 1, porcine reproductive and respiratory, porcine epidemic diarrhea, transmissible gastroenteritis of pigs and rotavirus. The limits of detection and quantitation were 10 and 100 copies, respectively. Using the established real-time PCR system, 39 of the 40 samples we tested were detected as positive

Causal Graphical Models: Heterogeneous Data Meet Structure Identification

Causal relationship, rather than statistical association, provides the basic understanding of nature. Learning causal structure from observational data is one of the most fundamental ways to explore causality, and the process is often facilitated by causal graphical models. However, most existing approaches ignore the issue of data heterogeneity, and one only considers the case where each measured variable is a scalar. In this dissertation, we provide approaches to address heterogeneous observational data and functional measurements in causal structure learning. To address the heterogeneity problem, we propose a novel causal Bayesian network model that embeds heterogeneous samples onto a low-dimensional manifold and builds Bayesian networks conditional on the embedding. The new framework allows for more precise network inference by improving the estimation resolution from population level to observation level. Moreover, while causal Bayesian networks are in general not identifiable with purely observational data due to Markov equivalence, with the blessing of causal effect heterogeneity, we prove that the causal structure is uniquely identifiable with our proposed model under mild assumptions. Furthermore, while cycles and unmeasured confounders are inevitable in nature causal systems, we show our general model class that accommodates these structures still allows causal identification. To address the functional measurements, we develop a novel Bayesian network model for mul-tivariate functional data where the conditional independence and causal structure are represented by a directed acyclic graph. Our model is built on the strategy of adaptive basis expansion. We show a special case where the functional objects are drawn from a mixture of Gaussian processes, which allows unique causal structure identification even when the functional data are purely observational and measured with noise