Rapid Diagnosis by Microfluidic Techniques

Pathogenic bacteria in an aqueous or airborne environments usually cause infectious diseases in hospital or among the general public. One critical step in the successful treatment of the pathogen-caused infections is rapid diagnosis by identifying the causative microorganisms, which helps to provide early warning of the diseases. However, current standard identification based on cell culture and traditional molecular biotechniques often depends on costly or time-consuming detection methods and equipments, which are not suitable for point-of-care tests. Microfluidic-based technique has recently drawn lots of attention, due to the advantage that it has the potential of providing a faster, more sensitive, and higher-throughput identification of causative pathogens in an automatic manner by integrating micropumps and valves to control the liquid accurately inside the chips. In this chapter, microfluidic techniques for serodiagnosis of amebiasis, allergy, and rapid analysis of airborne bacteria are described. The microfluidic chips that integrate microcolumns, protein microarray, or a staggered herringbone mixer structure with sample to answer capability have been introduced and shown to be powerful in rapid diagnosis especially in medical fields

BayeSED-GALAXIES I. Performance test for simultaneous photometric redshift and stellar population parameter estimation of galaxies in the CSST wide-field multiband imaging survey

The forthcoming CSST wide-field multiband imaging survey will produce seven-band photometric spectral energy distributions (SEDs) for billions of galaxies. The effective extraction of astronomical information from these massive datasets of SEDs relies on the techniques of both SED synthesis (or modeling) and analysis (or fitting). We evaluate the performance of the latest version of BayeSED code combined with SED models with increasing complexity for simultaneously determining the photometric redshifts and stellar population parameters of galaxies in this survey. By using an empirical statistics-based mock galaxy sample without SED modeling errors, we show finding that the random observational errors in photometries are more important sources of errors than the parameter degeneracies and Bayesian analysis method and tool. By using a Horizon-AGN hydrodynamical simulation-based mock galaxy sample with SED modeling errors about the star formation histories (SFHs) and dust attenuation laws (DALs), the simple typical assumptions lead to significantly worse parameter estimation with CSST photometries only. The SED models with more flexible (or complicated) forms of SFH/DAL do not necessarily lead to better estimation of redshift and stellar population parameters. We discuss the selection of the best SED model by means of Bayesian model comparison in different surveys. Our results reveal that the Bayesian model comparison with Bayesian evidence may favor SED models with different complexities when using photometries from different surveys. Meanwhile, the SED model with the largest Bayesian evidence tends to give the best performance of parameter estimation, which is more clear for photometries with larger discriminative power.Comment: Accepted for publication in ApJS (49 pages, 23 figures, 5 tables). Comments are welcome! The new version of BayeSED code, documents, and the scripts used for the performance tests presented in this work will be publicly available at https://bitbucket.org/hanyk/bayesed/, https://bayesed.readthedocs.io/, and https://github.com/hanyk/BayeSED-performance-test/, respectivel

A META-ANALYSIS OF ABNORMAL GLUCOSE METABOLISM IN FIRST-EPISODE DRUG-NAIVE SCHIZOPHRENIA

Background: Patients with schizophrenia exhibit a higher mortality rate compared with the general population. This mortality has been attributed predominantly by the high risk of type 2 diabetes mellitus in the patients. We aimed to assess the inherent risk of glucose metabolism abnormalities in first-episode drug-naïve schizophrenia. Subjects and methods: We searched English database (PubMed, EMBASE, MEDLINE, Cochrane Library databases) and Chinese database (Wan Fang Data, CBM disc, VIP, and CNKI) from their inception until Jul 2018 for case-control studies examining glucose metabolism abnormalities. Measurements, such as fasting plasma glucose levels, fasting plasma insulin levels, insulin resistance and HbA1c levels in first episode antipsychotic-naive patients were used to test for prediabetes. Standardized/weighted mean differences and 95% confidence intervals were calculated and analyzed. Results: 19 studies (13 in English and 6 in Chinese) consisting of 1065 patients and 873 controls were included. Fasting plasma glucose levels (95% CI; 0.02 to 0.29; P=0.03), 2 h plasma glucose levels after an OGTT (95% CI; 0.63 to 1.2; P<0.00001), fasting plasma insulin levels (95% CI; 0.33 to 0.73; P<0.00001), insulin resistance (95% CI; 0.29 to 0.6; P<0.00001) in patients with firstepisode schizophrenia were significant elevated. There was no significant difference in HbA1c level (95% CI; -0.34 to 0.18; P=0.54) in patients with first-episode schizophrenia compared with controls. Conclusions: This meta-analysis showed that glucose metabolism was impaired in patients with first-episode schizophrenia. Higher quality studies with larger samples are warranted to confirm these findings

Determining Singularity-Free Inner Workspace through Offline Conversion of Assembly Modes for a 3-RRR PPM

The existing singularity avoidance methods have deficiencies, such as the conditionality of the online conversion of the assembly modes (AMs) and the kinematically redundant manipulator with the predicament of the prototype design and added complexity of the mechanism. To address these issues, a method to determine a singularity-free inner workspace through offline conversion of the AMs of the 3-RRR planar parallel manipulator (PPM) is presented. Based on the geometric relations among rods of the manipulator during the occurrence of singularity, and the singular points at or near the boundary of the workspace are permitted, the AMs and ranges of the orientation angle of the moving platform corresponding to the inner singularity-free workspace are determined through a three-dimensional search method. The simulation and experimental comparisons indicate that singular-free paths related to the constant or variable orientation angle of the moving platform can be planned on the singularity-free inner workspace

MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs

In this paper, we propose a model-operator-data network (MOD-Net) for solving PDEs. A MOD-Net is driven by a model to solve PDEs based on operator representation with regularization from data. In this work, we use a deep neural network to parameterize the Green's function. The empirical risk consists of the mean square of the governing equation, boundary conditions, and a few labels, which are numerically computed by traditional schemes on coarse grid points with cheap computation cost. With only the labeled dataset or only the model constraints, it is insufficient to accurately train a MOD-Net for complicate problems. Intuitively, the labeled dataset works as a regularization in addition to the model constraints. The MOD-Net is much efficient than original neural operator because the MOD-Net also uses the information of governing equation and the boundary conditions of the PDE rather than purely the expensive labels. Since the MOD-Net learns the Green's function of a PDE, it solves a type of PDEs but not a specific case. We numerically show MOD-Net is very efficient in solving Poisson equation and one-dimensional Boltzmann equation. For non-linear PDEs, where the concept of the Green's function does not apply, the non-linear MOD-Net can be similarly used as an ansatz for solving non-linear PDEs

Research on the Model of Making a Price Match Based-on Automatic Negotiated Price for Electronic Commerce

The paper established a new sealed bargaining mechanism based on the electronic business negotiation model and considering the opaqueness of information on demand and supply. Using the supply function and demand function to analyze the behavior rule during the course of the price change, in the paper we established and proved a series of intersecting chord theorems about concave supply function and demand function, thus we got a transaction mechanism of negotiating prices that manufacturers and distributors submitted the supply and demand according to node gradually recursion algorithm after the first offer made by the e-commerce platform, And proved the negotiated price converged to the equilibrium price of supply and marketing

Complete Moment Convergence for Arrays of Rowwise -Mixing Random Variables

We investigate the complete moment convergence for maximal partial sum of arrays of rowwise -mixing random variables under some more general conditions. The results obtained in the paper generalize and improve some known ones

Does rDLPFC activity alter trust? Evidence from a tDCS study

Trust plays an important role in the human economy and people’s social lives. Trust is affected by various factors and is related to many brain regions, such as the dorsolateral prefrontal cortex (DLPFC). However, few studies have focused on the impact of the DLPFC on trust through transcranial direct current stimulation (tDCS), although abundant psychology and neuroscience studies have theoretically discussed the possible link between DLPFC activity and trust. In the present study, we aimed to provide evidence of a causal relationship between the rDLPFC and trust behavior by conducting multiple rounds of the classical trust game and applying tDCS over the rDLPFC. We found that overall, anodal stimulation increased trust compared with cathodal stimulation and sham stimulation, while the results in different stages were not completely the same. Our work indicates a causal relationship between rDLPFC excitability and trust behavior and provides a new direction for future research