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The separation and recognition of fructose with a large linear detection range, low cost and less time consuming are more concerned in sugar industry production and monitoring of sugar intake in daily diet. In this study, monodisperse and fluorescent microspheres of poly (DVB-co-PBA) were synthesized by a one-step solvothermal reaction. The morphology, surface chemical properties, thermal stability and hydrophilicity of poly(DVB-co-PBA) microspheres were characterized by scanning electron microscopy, Fourier transform infrared spectroscopy, thermogravimetric analysis and water contact angle measurement, respectively. The fluorescent poly(DVB-co-PBA) microspheres with structured morphology, narrow diameter distribution and thermal stability could be applied for separation and recognition of sugar. A dual-model spectral detection of D-fructose was established with a large linear range (fluorescence measurement of 0.5 ~ 10 mg mL−1 and UV–VIS detection of 15 ~ 415 mg mL−1). Further, for the extended application of poly(DVB-co-PBA) microspheres, we investigated the applicability of synthetic materials in neutral water and doped the poly(DVB-co-PBA) microspheres with three types of fluorescent nanomaterials by coloring.

Missing covariates often occur in biomedical studies with survival outcomes. Multiple imputation via chained equations (MICE) is a semi-parametric and flexible approach that imputes multivariate data by a series of conditional models, one for each incomplete variable. When applying MICE, practitioners tend to specify the conditional models in a simple fashion largely dictated by the software, which could lead to suboptimal results. Practical guidelines for specifying appropriate conditional models in MICE are lacking. Motivated by a study of time to hip fractures in the Women's Health Initiative Observational Study using accelerated failure time models, we propose and experiment with some rationales leading to appropriate MICE specifications. This strategy starts with specifying a joint model for the variables involved. We first derive the conditional distribution of each variable under the joint model, then approximate these conditional distributions to the extent which can be characterized by commonly used regression models. We propose to fit separate models to impute incomplete variables by the failure status, which is key to generating appropriate MICE specifications for survival outcomes. The proposed strategy can be conveniently implemented with all available imputation software that uses fully conditional specifications. Our simulation results show that some commonly used simple MICE specifications can produce suboptimal results, while those based on the proposed strategy appear to perform well and be robust toward model misspecifications. Hence, we warn against a mechanical use of MICE and suggest careful modeling of the conditional distributions of variables to ensure proper performance.

In this study, a surface chemical-modified rice husk biochar with abundant amino groups and disulfide bonds for the removal of cadmium was prepared using cystamine dihydrochloride as a modification ligand and glutaraldehyde as a crosslinker. The biochars were characterized by Fourier transform infrared spectrometry (FTIR), elemental analysis, scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), thermogravimetry analysis (TGA), and nitrogen sorption (BET) before and after modification. The adsorption properties of the modified biochars for Cd (II) were investigated in detail via adsorption isotherm models, adsorption kinetics models, and selective adsorption experiments. The surfaces of the cystamine-modified biochars with granular nanopolymers of sufficient functional groups of primary amine and disulfide linkage rendered the biochar surface more conducive to electrostatic attraction and surface complexation. The theoretical maximum adsorption capacity of the modified biochars (81.02 mg g&minus, 1) was almost 10-fold greater than that of the raw biochars (8.347 mg g&minus, 1) for Cd (II). Besides, the cystamine-modified biochars had a better affinity for Cd (II) compared to other heavy metals (Zn, As, Cd, Co, Ni, Cr), showing six-fold greater affinity for Cd (II) than Zn2+. The results of this study indicate that the modification of biochars derived from rice husks shows great potential in the removal of Cd (II) from contaminated water.

We report the use of biochar and Fe3O4 nanoparticles as co-stabilizers for oil-in-water (o/w) Pickering emulsion. The emulsion is subsequently used to prepare magnetic tetracycline-imprinted biochar composite microspheres (MMIPMs) with good uniformity and high selectivity. The MMIPMs were characterized by scanning electron microscopy (SEM), Brunner-Emmet-Teller (BET) measurements, Fourier transform infrared spectrometry (FT-IR), X-ray diffraction (XRD), vibrating sample magnetometer (VSM) and thermogravimetry analysis (TGA). The adsorption properties of tetracycline to the MMIPMs were investigated using different adsorption experiments including adsorption kinetic experiment, equilibrium binding experiment, selectivity evaluation and competitive adsorption tests. The theoretical maximum adsorption capacity of the MMIPMs (15.45 mg g&minus, 1) was greater than that of the raw biochar (2.10 mg g&minus, 1) and non-imprinted biochar composite microspheres (3.39 mg g&minus, 1) for tetracycline. Further, the MMIPMs were used as adsorbent for magnetic solid phase extraction (SPE) for the extraction of tetracycline present in drinking water, milk, fish and chicken samples. Under optimal conditions, the results showed good recovery yield ranging from 88.41% to 106.29% with a relative standard deviation (RSD) ranging from 0.35% to 6.83%, respectively.

Yanqin Sun,1,* Bingshuang Hu,2,* Yanhong Wang,3 Zhen Li,1 Jingfang Wu,4 Yunchu Yang,4 Yue Wei,5 Xiaofeng Peng,5 Hongling Chen,5 Rongqi Chen,5 Pingyan Jiang,5 Sixian Fang,5 Zhiwu Yu,6 Linlang Guo4 1Department of Pathology, Guangdong Medical University, Dongguan, China; 2Department of Radiotherapy, Zhongshan People’s Hospital, Zhongshan, China; 3Department of Gynecology, Jinxiang People’s Hospital, Jining, China; 4Department of Pathology, Zhujiang Hospital, Southern Medical University, Guangzhou, China; 5College of Pharmacy, Guangdong Medical University, Dongguan, China; 6Division of Laboratory Science, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China *These authors contributed equally to this work Objective: microRNAs are regulatory molecules regarded as important in the pathogenesis of different types of tumors. microRNA-216a (miR-216a-5p) has been identified as a tumor suppressor in multiple malignancies. However, the role of miR-216a-5p in the pathogenesis of small cell lung cancer (SCLC) remains obscure. The objective of this study was to investigate the role of the miR-216a-5p/Bcl-2 axis in SCLC pathogenesis. Materials and methods: All the experimental methods used were as follows: microarray analysis, cell culture, transient, and stable gene transfection; real-time fluorescence PCR; Western blot; flow cytometry for cell cycle analysis; in vitro proliferation assay; in vitro wound healing experiment; in vivo xenograft model in nude mice; and dual luciferase reporter assay. All statistical analyses were carried out using GraphPad Prism 7 software. Statistical significance was analyzed by Student’s t-test or one-way ANOVA. P

Within the context of California's public report of coronary artery bypass graft (CABG) surgery outcomes, we first thoroughly review popular statistical methods for profiling healthcare providers. Extensive simulation studies are then conducted to compare profiling schemes based on hierarchical logistic regression (LR) modeling under various conditions. Both Bayesian and frequentist's methods are evaluated in classifying hospitals into ‘better’, ‘normal’ or ‘worse’ service providers. The simulation results suggest that no single method would dominate others on all accounts. Traditional schemes based on LR tend to identify too many false outliers, while those based on hierarchical modeling are relatively conservative. The issue of over shrinkage in hierarchical modeling is also investigated using the 2005–2006 California CABG data set. The article provides theoretical and empirical evidence in choosing the right methodology for provider profiling.

Missing covariates often occur in biomedical studies with survival outcomes. Multiple imputation via chained equations (MICE) is a semi-parametric and flexible approach that imputes multivariate data by a series of conditional models, one for each incomplete variable. When applying MICE, practitioners tend to specify the conditional models in a simple fashion largely dictated by the software, which could lead to suboptimal results. Practical guidelines for specifying appropriate conditional models in MICE are lacking. Motivated by a study of time to hip fractures in the Women's Health Initiative Observational Study using accelerated failure time models, we propose and experiment with some rationales leading to appropriate MICE specifications. This strategy starts with specifying a joint model for the variables involved. We first derive the conditional distribution of each variable under the joint model, then approximate these conditional distributions to the extent which can be characterized by commonly used regression models. We propose to fit separate models to impute incomplete variables by the failure status, which is key to generating appropriate MICE specifications for survival outcomes. The proposed strategy can be conveniently implemented with all available imputation software that uses fully conditional specifications. Our simulation results show that some commonly used simple MICE specifications can produce suboptimal results, while those based on the proposed strategy appear to perform well and be robust toward model misspecifications. Hence, we warn against a mechanical use of MICE and suggest careful modeling of the conditional distributions of variables to ensure proper performance.