LTNE thermoconvective instability in Newtonian rotating layer under magnetic field utilizing nanoparticles

Journal of Thermal Analysis and Calorimetry - In the present framework, the simultaneous effect of rotation, magnetic field, heat source and local thermal non-equilibrium (LTNE) is investigated on...     

Nephroprotective properties of chitosan/sodium lignosulfonate/Au nanoparticles in streptozotocin-induced nephropathy in mice: Introducing a novel therapeutic drug for the treatment of nephropathy

In this study, we report the green synthesis of nontoxic, stable, and small size gold nanoparticle by using chitosan/sodium lignosulfonate hydrogel with capping/reducing ability for the synthesis of CS/NaLS/Au NPs. The prepared bio-nanocomposite were characterized by advanced physicochemical techniques like Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Energy Dispersive X-ray spectroscopy (EDX) and X-ray Diffraction (XRD) study. It has been established that CS/NaLS/Au NPs have a spherical shape with a mean diameter from 20 to 30 nm. Diabetes was induced by administration of 60 mg/kg of streptozotocin (STZ) intraperitoneally in 100 mature male mice and they were randomly divided into 5 groups. The negative control group received normal saline and treatment groups received glibenclamide with dose 0.5 mg/kg and 10 and 40 μg/kg of CS/NaLS/Au NPs through gavage for 50 days. In addition, one group considered as positive control (in treated-diabetic). On the last day, serum levels of samples blood glucose, urea and creatinine were measured. After tissue processing, 5 μm sections of the kidneys were prepared and they were stained by periodic acid Schiff (PAS) and used for stereological analysis. In the antioxidant test, the IC50 of CS/NaLS/Au NPs and BHT against DPPH free radicals were 117 and 86 µg/mL, respectively. In the cellular and molecular part of the recent study, the treated cells with CS/NaLS/Au NPs were assessed by MTT assay for 48 h about the cytotoxicity properties on normal (HUVEC) cell line. The increased levels of blood glucose and urea were decreased (p < 0.05) significantly in CS/NaLS/Au NPs-treated groups as compared to the untreated diabetic. The kidney weight, kidney volume (Volume of cortex, medulla, glomerulus, proximal and distal tubules, collecting ducts, loop of Henle, interstitial tissues, and vessels) and kidney structures length (length of proximal and distal tubules, collecting ducts, loop of Henle, and vessels) decreased significantly (p < 0.05) after treatment with high dose of CS/NaLS/Au NPs (p < 0.05). According to the obtained results, CS/NaLS/Au NPs can regulates the levels of blood glucose and urea and inhibits from kidney damages in STZ-induced diabetic mice. This study suggested CS/NaLS/Au NPs as an antidiabetic and nephroprotective drug in the developing countries.     

Designing DNA cage-based immuno-fluorescence strategy for rapid diagnosis of clinical cervical cancer tissues

Exploiting a tissue diagnosis method to abstain the involuted operating and consume valuable reagents while realizing high-speed and inexpensive pathological grading technology to supply a better scheme for cancer therapy is a significant method of cancers detection. A promising immuno-fluorescence strategy was rationally designed and synthesized by loading ruthenium complex into cervical cancer-targeted DNA-cage, which was well used to realize high-speed and inexpensive diagnosis of clinical ce...     

Network analysis based on low-rank method for mining information on integrated data of multi-cancers

The noise problem of cancer sequencing data has been a problem that can’t be ignored. Utilizing considerable way to reduce noise of these cancer data is an important issue in the analysis of gene co-expression network. In this paper, we apply a sparse and low-rank method which is Robust Principal Component Analysis (RPCA) to solve the noise problem for integrated data of multi-cancers from The Cancer Genome Atlas (TCGA). And then we build the gene co-expression network based on the integrated data after noise reduction. Finally, we perform nodes and pathways mining on the denoising networks. Experiments in this paper show that after denoising by RPCA, the gene expression data tend to be orderly and neat than before, and the constructed networks contain more pathway enrichment information than unprocessed data. Moreover, learning from the betweenness centrality of the nodes in the network, we find some abnormally expressed genes and pathways proven that are associated with many cancers from the denoised network. The experimental results indicate that our method is reasonable and effective, and we also find some candidate suspicious genes that may be linked to multi-cancers.     

Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients

Paraquat (PQ) poisoning seriously harms the health of humanity. An effective diagnostic method for paraquat poisoned patients is a crucial concern. Nevertheless, it's difficult to identify the patients with low intake of PQ or delayed treatment. Here, a new efficient diagnostic approach to integrate machine learning and gas chromatography-mass spectrometry (GC–MS), named GEE, is proposed to identify the PQ poisoned patients. First, GC–MS provides the original data that efficiently identified the paraquat-poisoned patients. According to the high dimensionality of the original data, in the second stage, the chaos enhanced grey wolf optimization (EGWO) is adopted to search the optimal feature sets to improve the accuracy of identification. Finally, the extreme learning machine (ELM) is used to identify the PQ poisoned patients. To efficiently evaluate the proposed method, four measures were used in our experiments and comparisons were made with six other methods. The PQ-poisoned patients and robust volunteers can be well identified by GEE and the values of AUC, accuracy, sensitivity and specificity were 95.14%, 93.89%, 94.44% and 95.83%, respectively. Our experimental results demonstrated that GEE had better performance and might serve as a novel candidate diagnosis of PQ-poisoned patients.     

An Ito–Taylor weak 3.0 method for stochastic dynamics of nonlinear systems

A new framework for development of order 3.0 weak Taylor scheme towards stochastic modeling and dynamics of coupled nonlinear systems is presented. The proposed method is derived by including third order multiple stochastic integral terms of Ito–Taylor expansion and developing them for a wide class of stochastic nonlinear systems. For computing the system responses of linear and a wide class of nonlinear structural systems, the use of lower order integration schemes is sufficient. But for highly non-linear stochastically driven systems like base isolated hysteretic systems and degrading stochastic systems the evaluation of higher order terms is necessary. Additionally, the use of higher order integration schemes for stochastic dynamics of higher dimensional nonlinear systems remains a challenge due to the arising mathematical complexities with the increase in the number of DOFs (degrees-of-freedom) which really necessitates the development of the proposed algorithm. The proposed algorithm is verified using a representative class of coupled nonlinear system in presence and absence of nonlinear degradation and hysteretic oscillators. The efficiency of the proposed numerical scheme over classical integration schemes is demonstrated through a practical engineering problem. Finally, an automated extension of the proposed algorithm is presented by generalizing it for a system of N-DOFs.     

Origins of Boosted Charge Storage on Heteroatom-Doped Carbons

Although tremendous efforts have been devoted to understanding the origin of boosted charge storage on heteroatom-doped carbons, none of the present studies has shown a whole landscape. Herein, by both experimental evidence and theoretical simulation, it is demonstrated that heteroatom doping not only results in a broadened operating voltage, but also successfully promotes the specific capacitance in aqueous supercapacitors. In particular, the electrolyte cations adsorbed on heteroatom-doped carbon can effectively inhibit hydrogen evolution reaction, a key step of water decomposition during the charging process, which broadens the voltage window of aqueous electrolytes even beyond the thermodynamic limit of water (1.23 V). Furthermore, the reduced adsorption energy of heteroatom-doped carbon consequently leads to more stored cations on the heteroatom-doped carbon surface, thus yielding a boosted charge storage performance.     

Size-dependent dynamics of a FG Nanobeam near nonlinear resonances induced by heat

This study explores heat-induced nonlinear vibration of a functionally graded (FG) capacitive nanobeam within the framework of nonlocal strain gradient theory (NLSGT). The elastic FG beam, which is firstly deflected by a DC voltage, is driven to vibrate about its deflected position by a periodic heat load. The nano-structure, which consists of a clamped-clamped nanobeam, is modeled assuming Euler–Bernoulli beam assumption which accounts for the nonlinear von-Karman strain and the electrostatic and intermolecular forcing. To simulate the static and dynamic responses, a model reduction procedure is carried out by employing the Galerkin method. The method of Averaging as a regular semi-analytic perturbation method is applied to obtain governing equations of the steady-state responses. With the purpose of establishing the validity of the solution, a Shooting technique in conjunction with the Floquet theory is used to capture the periodic motions and then examine their stability. The nonlinear resonance frequency of the FG nanobeam near its fundamental natural frequency (primary resonance) and near principal parametric resonance is investigated while the emphasis is placed on studying the effect of various parameters including DC voltage, amplitude of the periodic heat source, material index, damping ratio, and small scale parameters. The main objective of this study is to model a miniature structure which can be used as either a sensitive remote temperature sensor or a high-efficiency thermal energy harvester.     

Proton conductivity study on three CS/IL@fle-MOF membranes

The low-cost, high specific surface area and porosity, controlled pore size, and chemical properties of metal–organic framework (MOF) materials have attracted much attention in the exploration of proton conduction. The method of chemically modifying MOF structures or introducing conductive medium into the holes can effectively improve the proton conductivities of the materials. Here, the structural tunability of ionic liquid (IL) and flexible MOF (fle-MOF) materials are matched to give full play to the conductivity of IL, the framework support, and the microporous effect of MOFs, which achieves the synergistic effect of performance and expands the temperature range of proton transfer. Three kinds of CS/IL@fle-MOF membranes were prepared by combining three fle-MOFs with 1-carboxymethyl-3-methylimidazole (CMMIM) in different proportions to obtain 15 pieces of membranes. The comparative analyses show that CS/IL@fle-MOF membranes have excellent proton conduction performance at a wider temperature range (263–353 K) and lower relative humidity (75% RH). Among them, the proton conductivities of CS/CMMIM@MIL-88A-25% and CS/CMMIM@MIL-88B-125% are up to 1.33 and 1.42 S cm⁻¹ at 75% RH and 353 K, respectively; whereas those of CS/CMMIM@MIL-53(Fe)-75% and CS/CMMIM@MIL-88B-125% reach up to 2.1 × 10⁻³ and 1.28 × 10⁻³ S cm⁻¹ at 75% RH and 263 K, respectively. The E_a of CS/CMMIM@fle-MOFs is in the range of 0.1–0.5 eV, suggesting that the proton transport follows predominantly the typical Grotthuss transfer mechanism. The results of this study indicate that the CS/CMMIM@fle-MOF membranes combinations offer great potential for the design of composite porous proton-conducting materials.     

Efficient Gaussian sample specific network marker discovery and drug enrichment analysis validation

Identifying stable gene markers at an individual level can help to understand the genetic mechanisms of each individual patient and accomplish personalized medicine. In this paper, we propose an efficient framework to identify sample-specific markers. Gene expression data first is transformed to a corresponding likelihood matrix to alleviate inherent noise besides adding population information to each sample. Then those significantly differential genes or gene pairs are further mapped to a STRING network for analysis by assuming that the likelihood of each gene or gene pairs in the control group follows a Gaussian distribution. The proposed method is applied to three benchmark datasets including lung adenocarcinoma, kidney renal clear cell carcinoma, and uterine corpus endometrial carcinoma. It is found that disease gene markers identified by the proposed methods outperform the previous sample-specific network (SSN) method in both subtyping and survival analysis. Furthermore, we exploit the application of the subtype markers in following drug selection. The difference of the enriched drug set may reflect some underlying mechanisms of the subtypes and shed light on selecting appropriate drugs for each cancer subtype.