Showing papers by "Shanghai University published in 2023"

Identification of Smoking-Associated Transcriptome Aberration in Blood with Machine Learning Methods

Abstract: Long-term cigarette smoking causes various human diseases, including respiratory disease, cancer, and gastrointestinal (GI) disorders. Alterations in gene expression and variable splicing processes induced by smoking are associated with the development of diseases. This study applied advanced machine learning methods to identify the isoforms with important roles in distinguishing smokers from former smokers based on the expression profile of isoforms from current and former smokers collected in one previous study. These isoforms were deemed as features, which were first analyzed by the Boruta to select features highly correlated with the target variables. Then, the selected features were evaluated by four feature ranking algorithms, resulting in four feature lists. The incremental feature selection method was applied to each list for obtaining the optimal feature subsets and building high-performance classification models. Furthermore, a series of classification rules were accessed by decision tree with the highest performance. Eventually, the rationality of the mined isoforms (features) and classification rules was verified by reviewing previous research. Features such as isoforms ENST00000464835 (expressed by LRRN3), ENST00000622663 (expressed by SASH1), and ENST00000284311 (expressed by GPR15), and pathways (cytotoxicity mediated by natural killer cell and cytokine-cytokine receptor interaction) revealed by the enrichment analysis, were highly relevant to smoking response, suggesting the robustness of our analysis pipeline.

An adaptive framework to accelerate optimization of high flame retardant composites using machine learning

Abstract: Extensive machine learning methods consist of linear and nonlinear algorithms have heralded a sea change in the areas of metals, catalyst, polymers, and so on. However, most of these prevalent researches in polymer fields are focused on molecule design of polymers itself or simulation instead of composition exploration of functional polymer-based composites. The incorporation efforts of machine learning into functional polymer-based composites (in this case, flame retardancy) remain at an elementary stage. Herein, we designed an adaptive framework combining domain knowledge and machine learning to accelerate optimization of high flame retardant composites. Data resources in the adaptive framework were divided into three approaches including experiments, handbooks, and published papers, which were used to train, feedback, or predict ingeniously. The comprehensive feature engineering of flame-retardant polymer-based composites was displayed and classified detailly. Four machine learning methods consist of conventional linear regression (Lasso and Ridge), nonlinear artificial neurol networks (ANN), and their combination of Lasso, Ridge, and ANN (L-ANN) were contrasted in the run of the adaptive framework. Models of limit oxygen index (LOI) by L-ANN method were suggestive of higher accuracy in twice runs, navigating new experiments with high flame retardancy and effective prediction across different flame retardants to tackle intuition-driven trail-and-error problem. The final optimized models from the adaptive framework might be further helpful for machine intelligence of engineering of flame-retardant polymer-based composites. The proposed adaptive framework can be extended hopefully for machine intelligence design of other functional polymer-based composites.

Simultaneous Determination of Xanthine and Hypoxanthine Using Polyglycine/rGO-Modified Glassy Carbon Electrode

Abstract: A novel electrochemical sensor was developed for selective and sensitive determination of xanthine (XT) and hypoxanthine (HX) based on polyglycine (p-Gly) and reduced graphene oxide (rGO) modified glassy carbon electrode (GCE). A mixed dispersion of 7 μL of 5 mM glycine and 1 mg/mL GO was dropped on GCE for the fabrication of p-Gly/rGO/GCE, followed by cyclic voltammetric sweeping in 0.1 M phosphate buffer solution within −0.45~1.85 V at a scanning rate of 100 mV·s−1. The morphological and electrochemical features of p-Gly/rGO/GCE were investigated by scanning electron microscopy and cyclic voltammetry. Under optimal conditions, the linear relationship was acquired for the simultaneous determination of XT and HX in 1–100 μM. The preparation of the electrode was simple and efficient. Additionally, the sensor combined the excellent conductivity of rGO and the polymerization of Gly, demonstrating satisfying simultaneous sensing performance to both XT and HX.

Light subpath reservoir for interactive ray-traced global illumination

Abstract: In this paper, we propose a novel approach for sampling indirect illumination based on bidirectional path tracing and world space resampling reservoir. We cache light subpaths with high contribution in a world space grid, allowing for sampling at arbitrary vertices along the eye path. In order to find optimal samples, we continuously update the reservoirs stored in each cell according to the resampling principles. Experiments have shown that our algorithm achieves noticeable noise reduction in scenes lit by indirect light compared with path tracing, at the cost of additional time and memory consumption, while still maintaining interactive frame rates. In some special cases, our method even shows better results than a plain bidirectional path tracer, in terms of both image quality and performance.

Redox‐Active Mixed‐Linker Metal–Organic Frameworks with Switchable Semiconductive Characteristics for Tailorable Chemiresistive Sensing

Abstract: Metal-organic frameworks (MOFs), with diverse metal nodes and designable organic linkers, offer unique opportunities for the rational engineering of semiconducting properties. In this work, we report a mixed-linker conductive MOF system with both tetrathiafulvalene and Ni-bis(dithiolene) moieties, which allows the fine-tuning of electronic structures and semiconductive characteristics. By continuously increasing the molar ratio between tetrathiafulvalene and Ni-bis(dithiolene), the switching of the semiconducting behaviors from n-type to p-type was observed along with an increase in electrical conductivity by 3 orders of magnitude (from 2.88×10-7 S m-1 to 9.26×10-5 S m-1 ). Furthermore, mixed-linker MOFs were applied for the chemiresistive detection of volatile organic compounds (VOCs), where the sensing performance was modulated by the corresponding linker ratios, showing synergistic and nonlinear modulation effects.

Effect of the alkaline solution concentration on swelling performance of MX80 granular bentonite

Abstract: The influence of alkaline solution on swelling performance of granular bentonite is one of the significant factors that must be considered in the design of deep geological repository for disposing the high-level nuclear waste (HLW). In this paper, the effect of alkaline solution concentration on the swelling performance of compacted granular and powdered MX80 bentonite specimens were investigated by the swelling strain and swelling pressure tests, together with X-ray diffraction (XRD) and scanning electron microscopy (SEM) measurements. Tests results showed that the swelling strain and swelling pressure of compacted granular MX80 bentonite increased by 2.6% and 7.6%, respectively, when the alkaline solution concentration changed from 0 to 0.1 mol/L. With increasing the concentration from 0.1 mol/L, the swelling strain and swelling pressure decreased significantly. When the alkaline solution concentration changed from 0 to 1.0 mol/L, the swelling strain and swelling pressure of the granular bentonite decreased by 35.1% and 41.6%. Observed XRD and SEM results showed that effect of the alkaline solution on bentonites led to a decline in montmorillonite content, and the large layered structure was dissolved into small layered structures and particles, which caused the decline in the swelling performance of granular MX80 bentonite.

Nonlinear programming for fleet deployment, voyage planning and speed optimization in sustainable liner shipping

Abstract: <abstract> <p>Limiting carbon dioxide emissions is one of the main concerns of green shipping. As an important carbon intensity indicator, the Energy Efficiency Operational Index (EEOI) represents the energy efficiency level of each ship and can be used to guide the operations of ship fleets for liner companies. Few studies have investigated an integrated optimization problem of fleet deployment, voyage planning and speed optimization with consideration of the influences of sailing speed, displacement and voyage option on fuel consumption. To fill this research gap, this study formulates a nonlinear mixed-integer programming model capturing all these elements and subsequently proposes a tailored exact algorithm for this problem. Extensive numerical experiments are conducted to show the efficiency of the proposed algorithm. The largest numerical experiment, with 7 ship routes and 32 legs, can be solved to optimality in four minutes. Moreover, managerial insights are obtained according to sensitivity analyses with crucial parameters, including the weighting factor, unit price of fuel, Suez Canal toll fee per ship, weekly fixed operating cost and cargo load in each leg.</p> </abstract>

Finite element analysis of functionally graded magneto-electro-elastic porous cylindrical shells subjected to thermal loads

Abstract: Functionally graded materials with porosity have received increased application in satellites, space vehicles, aircraft, and other transportation systems. The multi-physics coupled modeling technique for structural response analysis remains a big challenge. This paper develops a finite element formulation based on the first-order shear deformation (FOSD) hypothesis for evaluation of the static and dynamic behavior of functionally graded magneto-electro-elastic porous (FG-MEEP) cylindrical shells under thermal loads. Four types of thermal loads such as uniform, linear, sinusoidal, and heat conduction temperature rise are included in the FE model. Furthermore, FG-U, FG-V, FG-O, and FG-X distributions of porosity is considered. The material parameters of FG-MEEP are obtained by modified power law. Two forms of material gradation in the framework of ‘B’-rich bottom and ‘F’-rich bottom are used. The correctness of the present model is verified by comparing with the results of literature. Finally, parametric studies are adopted to analyze the static and dynamic response of FG-MEEP cylindrical shell by varying functionally graded pattern, gradient index, porosity volume, temperature, porosity distribution, etc. These numerical results can serve as benchmarks for the future study of porous structures in thermal environment.

Defect and interface engineering of hexagonal Fe2O3/ZnCo2O4 n-n heterojunction for efficient oxygen evolution reaction

Abstract: This work aimed at constructing defective heterojunction to optimize the electronic structure and exploring the intrinsic mechanism of its excellent electrocatalytic performance. Specifically, Feδ+ is introduced through the cation exchange method using hexagonal interpenetrating twin Zn/Co-ZIFs as precursors. Then, the n-n heterojunction featuring oxygen vacancies at the biphasic interface of Fe2O3 and ZnCo2O4 is constructed through calcination. The obtained electrode for oxygen evolution reaction (OER) only requires 261 mV overpotential to achieve a current density of 10 mA cm−2, and shows exceptional stability at high current density, lasting for 50 h. Density functional theory calculations confirm that the construction of heterojunction can effectively optimize the d‐band center and improve the adsorption of the active center on oxygen-containing intermediates, thus optimizing the Gibbs free energy of the OER. This study provides inspiration and interface engineering strategy for the design of highly active catalysts, and enhances our understanding of the OER mechanism.

Analysis of birth rates in China with uncertain statistics

Abstract: Uncertain statistics is a set of mathematical techniques to collect, analyze and interpret data based on uncertainty theory. In addition, probability statistics is another set of mathematical techniques based on probability theory. In practice, when to use uncertain statistics and when to use probability statistics to model some quality depends on whether the distribution function of the quality is close enough to the actual frequency. If it is close enough, then probability statistics may be used. Otherwise, uncertain statistics is recommended. In order to illustrate it, this paper employs uncertain statistics, including uncertain time series analysis, uncertain regression analysis and uncertain differential equation, to model the birth rate in China, and explains the reason why uncertain statistics is used instead of probability statistics by analyzing the characteristics of the residual plot. In addition, uncertain hypothesis test is used to determine whether the estimated uncertain statistical models are appropriate.

Femtosecond laser direct writing multilayer chiral waveplates with minimal linear birefringence

Abstract: Chirality transfer from femtosecond laser direct writing in achiral transparent materials mainly originates from the interplay between anisotropic nanogratings and mechanical stress with non-parallel and non-perpendicular (oblique) neutral axes. Yet, the laser fabrication simultaneously induces non-negligible linear birefringence. For precise manipulation of circular polarization properties, as well as to unlock the full functionality, we report here a geometry-inspired multilayer method for direct writing of chiral waveplates with minimal linear birefringence. We perform a theoretical analysis of both circular and linear properties response for different multilayer configurations and achieve strong circular birefringence of up to -2.25 rad with an extinction ratio of circular birefringence to total linear birefringence of up to 5.5 dB at 550 nm. Our strategy enables the precise control of circular properties and provides a facile platform for chiral device exploration with almost no linear property existence.

High-throughput measurements of interdiffusivities in fcc Co–Ni–Ta alloys at 1373K and 1473K

Abstract: The composition-dependent interdiffusivities in the fcc Co–Ni–Ta alloys at 1373 K and 1473 K were deduced by using our newly developed numerical inverse scheme that combines two-dimensional (2D) simulations with diffusion triple experiments. This approach largely reduced the experimental efforts by analyzing only one single-phase diffusion triple at each temperature yet covering a much wider composition range than diffusion couples. The reliability of the high-throughput results was firstly verified by reproducing the experimental 2D composition profiles in each triple. In order to further validate the deduced interdiffusivities, several traditional one-dimensional (1D) diffusion couples were also devised, and the widely recognized Sauer-Freise method and Whittle-Green method were then employed to calculate the interdiffusivities for the binaries and at the intersection points within the ternary composition range, respectively. The interdiffusivities extracted from the inverse scheme agree well with those from the traditional approaches, which strongly proves the applicability of the present new scheme. Besides, the constructed main interdiffusivity planes at 1373 K and 1473 K provide an overview of the diffusion behavior in the fcc Co–Ni–Ta system and promote further kinetic studies.

Anti-Inflammatory Activity of Phenylethanoids from <i>Acanthus ilicifolius</i> var. <i>xiamenensis</i>

Abstract: Acanthus ilicifolius var. xiamenensis is a traditional herbal medicine in China. In this study, the anti-inflammatory activities of active ingredients of A. ilicifolius var. xiamenensis were investigated in RAW 264.7 cells and Freund's complete adjuvant-induced arthritic rats. Results showed that n-butanol extract exerted antiarthritic potential by reducing paw edema, arthritis score, and altered hematological and biochemical parameters in experimental rats. Phytochemical studies on n-butanol extract resulted in the isolation of five alkaloids (1–5) and five phenylethanoids (6–10). The anti-inflammatory assay of compounds 1–10 on lipopolysaccharide (LPS)-treated RAW 264.7 cells indicated that phenylethanoids 9 and 10 exhibited notable inhibitory activities. The result indicated that compounds 9 and 10 attenuated inflammation by decreasing the production of nuclear factor kappa-B (NF-κB) p65, inhibitory subunit of NF kappa B alpha, Janus kinase 2 (JAK2), signal transducer and activator of transcription 3 (STAT3), and inducible nitric oxide synthase in LPS-mediated RAW 264.7 macrophages. Phenylethanoids 9 and 10 increased the expression of interleukin-10 and endothelial nitric oxide synthase. Therefore, compounds 9 and 10 showed anti-inflammatory activity by regulation of NF-κB and JAK/STAT signaling pathways.

-Ary Distributed Arithmetic Coding for Uniform -Ary Sources<i /> <i />

Abstract: Laplacian distribution is widely used to model the differences between correlated continuous or nonbinary signals, e.g., the predictive residues of videos. Usually, distributed coding of correlated nonbinary sources, e.g., distributed video coding, is implemented by binary or nonbinary Low-Density Parity-Check (LDPC) codes. In this paper, as an alternative, we attempt using nonbinary Distributed Arithmetic Coding (DAC) to implement distributed coding of uniform nonbinary sources with Laplace-distributed correlation. To analyze $Q$ -ary DAC for uniform $Q$ -ary sources, following the methodology developed in our prior work for binary DAC, we define and deduce Coset Cardinality Spectrum (CCS) from both fixed-length and variable-length perspectives, whose physical meanings are explained in detail; while for binary DAC, our prior work totally ignored the subtle difference between these two perspectives. Compared with binary DAC, an important advantage of nonbinary DAC is that, the mapping from source symbols to coding intervals is so flexible that there are many parameters that can be tuned to achieve better performance; while for binary DAC, there are very few tunable parameters, making it very hard to achieve better performance. This paper proposes a simple method to map source symbols onto coding intervals, which results in a very lightweight codec, while possessing a good Manhattan distance distribution. Then this paper deduces the formula of path metrics for decoder design by making use of CCS. All theoretical analyses are perfectly verified by simulation results. Most important, experimental results show that $Q$ -ary DAC achieves significantly better performance than LDPC codes for distributed coding of uniform $Q$ -ary sources with Laplace-distributed correlation.

Reticular Chemistry‐Enabled Sonodynamic Activity of Covalent Organic Frameworks for Nanodynamic Cancer Therapy

Abstract: The development of covalent organic framework (COF) sonosensitizers with intrinsic sonodynamic effects is highly desirable. However, such COFs are generally constructed using small-molecule photosensitizers. Herein, we report that the reticular chemistry-based synthesis of COFs from two inert monomers yields a COF-based sonosensitizer (TPE-NN) with inherent sonodynamic activity. Subsequently, a nanoscale COF TPE-NN is fabricated and embedded with copper (Cu)-coordinated sites to obtain TPE-NN-Cu. Results show that Cu coordination can enhance the sonodynamic effect of TPE-NN, whereas ultrasound (US) irradiation for sonodynamic therapy can augment the chemodynamic efficacy of TPE-NN-Cu. Consequently, TPE-NN-Cu upon US irradiation shows high-performance anticancer effects based on mutually reinforced sono-/chemo-nanodynamic therapy. This study reveals the backbone-originated sonodynamic activity of COFs and proposes a paradigm of intrinsic COF sonosensitizers for nanodynamic therapy.

Tunable multiple plasmon-induced transparencies in multiple T-shaped cavities waveguide based on Dirac semimetals

Abstract: We study the multiple plasmon-induced transparencies (PIT) in the waveguide structure based on bulk Dirac semimetal (BDS) containing multiple side-coupled T-shaped cavities. Using the finite element method, we mainly investigate the transmission spectra, the transmission phase shifts, the delay times, and the magnetic field distributions of the two T-shaped cavities waveguide structure. It is shown that their transmission characteristics strongly depend on the geometric parameters of the waveguide structure. In addition, we discuss the effect of the Fermi level of BDS and the refractive index of the medium on the transmission spectrum of the two T-shaped cavities structure. To get more PIT peaks, we try to simulate the transmission spectrum of three T-shaped cavities waveguide structure. All the results about the multiple PIT phenomena may have some possible applications in designing lenses, high-capacity storage devices, and new filters.