Surface-enhanced Raman scattering nanotag of tunable ZIF-8 shell-encapsulated magnetic core-plasmonic satellites for disentangling chemical enhancement from electromagnetic enhancement.

To enhance the stability of Raman reporters, these reporters were trapped in a metal organic framework (MOF) exoskeleton that was grown and compressed on Fe3O4@Au core-satellites, producing recyclable surface-enhanced Raman scattering (SERS) nanotags. Furthermore, encapsulation of Raman reporters in the assembled MOF-based nanocomposites was divided into two types of patterns, pre-enrichment and post-enrichment, in order to disentangle chemical enhancement of charge transfer (CT) from electromagnetic enhancement (EM) in SERS. Hence, to demonstrate the effect of encapsulation, a typical non-thiolated Raman reporter, for example crystal violet (CV) trapped in a core-satellite nanoassembly-based zeolitic imidazolate framework (ZIF-8) shell, was selected. The results suggest that stability and Raman intensity are remarkably improved. Moreover, the pattern of incorporation of CV into the ZIF-8 shell with tunable shell thickness can contribute to the disentangling of CT effects from EM effects.

A highly sensitive DNAzyme-based SERS biosensor for quantitative detection of lead ions in human serum.

Lead ions (Pb2+), one form of the toxic heavy metal, have drawn significant attention due to their harmful effects on human health and the environment. Although many analytical techniques have been developed over the past few decades, the development of a sensitive, selective, and rapid method to detect Pb2+ remains a challenge. In this work, we developed a sensitive surface-enhanced Raman scattering (SERS) biosensor for highly sensitive detection of Pb2+ by using DNAzyme-modified Fe3O4@Au@Ag nanoparticles (Fe3O4@Au@Ag NPs). Firstly, the thiolated 5'-Cy3 DNA probe was modified on the surface of Fe3O4@Au@Ag NPs, which hybridized with the Pb2+-specific DNAzyme to form a SERS biosensor, and the Cy3 labels were used to detect Pb2+. In the presence of Pb2+, the DNAzyme cleaves the Cy3-labeled DNA probe, leading to the release of Cy3-labeled DNA probe from the Fe3O4@Au@Ag NPs. Therefore, the Raman intensity of the Cy3 labels decreases. The proposed biosensor exhibited excellent linearity in the range from 0.01 to 1.0 nM, with a limit of detection for Pb2+ of 5 pM. It features superior selectivity to Pb2+ over other interfering metal ions and good application in the determination of Pb2+ in tap water and human serum samples. The SERS biosensor provides a novel' simple and sensitive method for detection of Pb2+ and sheds new light on the design and synthesis of analogous SERS biosensors for the detection of other heavy metal ions.

Au@Ag core-shell nanoparticles for microRNA-21 determination based on duplex-specific nuclease signal amplification and surface-enhanced Raman scattering.

A novel surface-enhanced Raman scattering (SERS) analysis strategy has been designed combining Au@DTNB@Ag core-shell nanoparticles (DTNB attachment on gold nanoparticles, then encapsulated in Ag shell nanoparticles named as ADANPs) and duplex-specific nuclease signal amplification (DSNSA) platform. Firstly, ADANPs and magnetic substrate of Fe3O4 nanoparticles were covalently attached to the 3'- and 5'- end of capture probe (CP) targeting miRNA-21. Upon the addition of target miRNA-21, these heteroduplexes were specifically cleaved by DSN and resulted in ADANPs that were released from the surface of Fe3O4 nanoparticles (Fe3O4 NPs). At the same time, miRNA-21 remained intact and can rehybridize another DNA probe to trigger the signal-amplifying reaction. Based on this principle, the developed SERS method exhibited good linearity in the range 0 to 1 nM for miRNA-21 with a limit of detection (LOD) of 0.084 fM and has an ability to differentiate even a single-base mismatched sequence on the target sequence or other miRNA sequence. The results provide a novel SERS method which can successfully been applied to the miRNA-21 detection in human serum. Graphical abstract a shows the synthesis of Fe3O4 NPs and the conjugation of Au@DTNB@Ag NPs (ADANPs) for the detection of miRNA-21, b shows the operating principle of DSN-assisted signal amplification strategy for miRNA detection based on Fe3O4@CP@ADA NPs.

A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation.

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

Cube-like Fe3O4@SiO2@Au@Ag magnetic nanoparticles: a highly efficient SERS substrate for detection of pesticide.

As a novel SERS nanocomposities, cube-like Fe3O4@SiO2@Au@Ag magnetic nanoparticles have been synthesized for the first time. Cube-like α-Fe2O3 NPs with uniform size can be achieved by optimizing reaction temperature and time. Firstly, the cube-like Fe3O4@SiO2 with good dispersity were achieved by calcining α-Fe2O3@SiO2 NPs in hydrogen atmosphere at 360 °C for 2.5 h, followed by self-assembling PEI shell via sonication. Furthermore, the Au@Ag particles can be densely assembled on the Fe3O4@SiO2 NPs to form the Fe3O4@SiO2@Au@Ag composite structure via strong Ag-N interaction. The obtained nanocomposites exhibit an excellent surface-enhanced Raman (SERS) behavior, reflected from low detection of limit (p-ATP) at 5×10-14 M level. Moreover, these nanocubes are used for detection of thiram and the detection limit can reach up to 5×10-11 M, while the rule of U.S. Environmental Protection Agency specifies that the residue in fruit must be lower than 7 ppm. Hence, the resulting substrate with high SERS activity has great practical potential applications in rapid detection of chemical, biological and environment pollutants with a simple portable Raman instrument at trace level.

A Forecasting Model Based on High-Order Fluctuation Trends and Information Entropy.

Most existing high-order prediction models abstract logical rules that are based on historical discrete states without considering historical inconsistency and fluctuation trends. In fact, these two characteristics are important for describing historical fluctuations. This paper proposes a model based on logical rules abstracted from historical dynamic fluctuation trends and the corresponding inconsistencies. In the logical rule training stage, the dynamic trend states of up and down are mapped to the two dimensions of truth-membership and false-membership of neutrosophic sets, respectively. Meanwhile, information entropy is employed to quantify the inconsistency of a period of history, which is mapped to the indeterminercy-membership of the neutrosophic sets. In the forecasting stage, the similarities among the neutrosophic sets are employed to locate the most similar left side of the logical relationship. Therefore, the two characteristics of the fluctuation trends and inconsistency assist with the future forecasting. The proposed model extends existing high-order fuzzy logical relationships (FLRs) to neutrosophic logical relationships (NLRs). When compared with traditional discrete high-order FLRs, the proposed NLRs have higher generality and handle the problem caused by the lack of rules. The proposed method is then implemented to forecast Taiwan Stock Exchange Capitalization Weighted Stock Index and Heng Seng Index. The experimental conclusions indicate that the model has stable prediction ability for different data sets. Simultaneously, comparing the prediction error with other approaches also proves that the model has outstanding prediction accuracy and universality.

Facile Fabrication of Large-Scale Silver Nanowire Bilayer Films and Its Application as Sensitive and Reproducible Surface-Enhanced Raman Scattering Substrates.

The homogeneous surface-enhanced Raman scattering (SERS) active hot spots on a SERS substrate is the most crucial factor in ensuring their application as reproducible and ultrasensitive sensing platforms. In this paper, we report on a simply shaking-assisted liquid­liquid (water-chloroform) interfacial assembly process for fabricating aligned Ag nanowire (AgNW) bilayer films on solid substrates. A scalable fabrication process can be easily realized by using a large size of container. These AgNW bilayer films can be used as ideal SERS active substrates for chemical and biomolecular detection with highly sensitivity and excellent reproducibility. Significantly, sensitive and quantitative detection of carbaryl with a detection limit of 0.1 ppm using these SERS substrates to demonstrate potential applications for environmental pollutant analysis.

DeAnnCNV: a tool for online detection and annotation of copy number variations from whole-exome sequencing data.

With the decrease in costs, whole-exome sequencing (WES) has become a very popular and powerful tool for the identification of genetic variants underlying human diseases. However, integrated tools to precisely detect and systematically annotate copy number variations (CNVs) from WES data are still in great demand. Here, we present an online tool, DeAnnCNV (Detection and Annotation of Copy Number Variations from WES data), to meet the current demands of WES users. Upon submitting the file generated from WES data by an in-house tool that can be downloaded from our server, DeAnnCNV can detect CNVs in each sample and extract the shared CNVs among multiple samples. DeAnnCNV also provides additional useful supporting information for the detected CNVs and associated genes to help users to find the potential candidates for further experimental study. The web server is implemented in PHP + Perl + MATLAB and is online available to all users for free at http://mcg.ustc.edu.cn/db/cnv/.

Hierarchically assembled NiCo@SiO2@Ag magnetic core-shell microspheres as highly efficient and recyclable 3D SERS substrates.

The hierarchically nanosheet-assembled NiCo@SiO2@Ag (NSA) core-shell microspheres have been synthesized by a layer-by-layer procedure at ambient temperature. The mean particle size of NSA microspheres is about 1.7 µm, which is made up of some nanosheets with an average thickness of ∼20 nm. The outer silver shell surface structures can be controlled well by adjusting the concentration of Ag(+) ions and the reaction times. The obtained NSA 3D micro/nanostructures show a structure enhanced SERS performance, which can be attributed to the special nanoscale configuration with wedge-shaped surface architecture. We find that NSA microspheres with nanosheet-assembled shell structure exhibit the highest enhancement efficiency and high SERS sensitivity to p-ATP and MBA molecules. We show that the detection limits for both p-ATP and MBA of the optimized NSA microsphere substrates can approach 10(-7) M. And the relative standard deviation of the Raman peak maximum is ∼13%, which indicates good uniformity of the substrate. In addition, the magnetic NSA microspheres with high saturation magnetization show a quick magnetic response, good recoverability and recyclability. Therefore, such NSA microspheres may have great practical potential applications in rapid and reproducible trace detection of chemical, biological and environment pollutants with a simple portable Raman instrument.

Environmental taxes, technological innovation and firm performance: Evidence from China's manufacturing firms.

Based on panel data from 2011 to 2019 for heavily polluting listed firms in the manufacturing industry, this paper examines the impact of environmental taxes on technological innovation and firm performance using the propensity score matching (PSM) and differences-in-differences (DID) methods. The empirical results show the following: (i) Firm performance and innovation quantity are positively affected by environmental taxes. The average effects of environmental taxes on firm performance and innovation quantity are 1.28 and 0.219, respectively. However, environmental taxes have no significant impact on innovation quality. (ii) A mechanism analysis reveals that innovation quantity plays a significant partial mediating role in the positive effect of environmental taxes on firm performance. (iii) Heterogeneity analysis shows that different environmental tax rates lead to a variation in innovation quantity and firm performance across regions. The positive effect of environmental taxes on innovation quantity is only confirmed in high-tax and low-tax areas. Meanwhile, high environmental taxes are related to better firm performance. Based on the research, policy recommendations are put forward to optimise environmental taxes, such as improving the environmental tax system and coordinating environmental tax and innovation policies.

Environmental taxes, enterprise innovation, and environmental total factor productivity-effect test based on Porter's hypothesis.

Under the increasingly severe environmental constraints, improving environmental total factor productivity (ETFP) is the fundamental way for the sustainable development of heavily polluting enterprises. Based on 3463 panel data of A-share listed companies in China from 2011 to 2019, this paper employs Porter's hypothesis (PH) framework to explore the impact of environmental tax (EN_T) on enterprise innovation and environmental total factor productivity for the heavily polluting manufacturing industry using the propensity score matching (PSM) method. The empirical results show the following. (i) Environmental taxes positively affect enterprise innovation (EI) and environmental total factor productivity (ETFP). (ii) Mechanism analysis verifies a partial mediating effect for EI between EN_T and ETFP. (iii) Regional heterogeneity analysis illustrates the differences in the impact of environmental taxes on innovation quality. (iv) Individual heterogeneity analysis shows that the "strong Porter hypothesis" is only valid for large-scale enterprises. The results are of great importance for both government and enterprises to improve the EN_T system and optimize the allocation of resources in realistic practice.

A simple and highly efficient route to the synthesis of NaLnF4-Ag hybrid nanorice with excellent SERS performances.

This paper reports the synthesis of a new class of NaLnF(4)-Ag (Ln = Nd, Sm, Eu, Tb, Ho) hybrid nanorice and its application as a surface-enhanced Raman scattering (SERS) substrate in chemical analyses. Rice-shaped NaLnF(4) nanoparticles as templates are prepared by a modified hydrothermal method. Then, the NaLnF(4) nanorice particles are decorated with Ag nanoparticles by magnetron sputtering method to form NaLnF(4)-Ag hybrid nanostructures. The high-density Ag nanogaps on NaLnF(4) can be obtained by the prolonging sputtering times or increasing the sputtering powers. These nanogaps can serve as Raman 'hot spots', leading to dramatic enhancement of the Raman signal. The NaLnF(4)-Ag hybrid nanorice is found to be robust and is an efficient SERS substrate for the vibrational spectroscopic characterization of molecular adsorbates; the Raman enhancement factor of Rhodamine 6G (R6G) absorbed on NaLnF(4)-Ag nanorice is estimated to be about 10(13). Since the produced NaLnF(4)-Ag hybrid nanorice particles are firmly fastened on a silicon wafer, they can serve as universal SERS substrates to detect target analytes. We also evaluate their SERS performances using 4-mercaptopyridine (Mpy), and 4-mercaptobenzoic acid (MBA) molecules, and the detection limit for Mpy and MBA is as low as 10(-12) M and 10(-10) M, respectively, which meets the requirements of the ultratrace detection of analytes. This simple and highly efficient approach to the large-scale synthesis of NaLnF(4)-Ag nanorice with high SERS activity and sensitivity makes it a perfect choice for practical SERS detection applications.

Ethical Risk Factors and Mechanisms in Artificial Intelligence Decision Making.

While artificial intelligence (AI) technology can enhance social wellbeing and progress, it also generates ethical decision-making dilemmas such as algorithmic discrimination, data bias, and unclear accountability. In this paper, we identify the ethical risk factors of AI decision making from the perspective of qualitative research, construct a risk-factor model of AI decision making ethical risks using rooting theory, and explore the mechanisms of interaction between risks through system dynamics, based on which risk management strategies are proposed. We find that technological uncertainty, incomplete data, and management errors are the main sources of ethical risks in AI decision making and that the intervention of risk governance elements can effectively block the social risks arising from algorithmic, technological, and data risks. Accordingly, we propose strategies for the governance of ethical risks in AI decision making from the perspectives of management, research, and development.

A graphene oxide-based hairpin probe coupling duplex-specific nuclease signal amplification for detection and imaging of mRNA in living cells.

In situ imaging of mRNA in living cells can help to monitor the real time mRNA expression and also useful for diagnosis and prognosis of the diseases. In this study, a new strategy was designed for simple, sensitive, and selective platform to detect the mRNA levels by combining a hairpin probe-graphene oxide (HP1/GO) and duplex-specific nuclease signal amplification (DSNSA). Initially, the DNA probe was adsorbed on the surface of GO to protect it from enzymatic digestion. Then, the target mRNA (T1) was hybridized with a partial hairpin probe which formed a duplex. Finally, under the action of DSN nuclease, the ssDNA in the DNA/RNA hybrid was selectively cleaved and produced small fragments. Then, T1 triggered the next reaction cycle, constituting a new circular exponential amplification. Here, we conclude that this assay is highly sensitive for the detection of target mRNA with the lower detection limit of 1 fM under optimal conditions. Furthermore, this strategy was successfully used for imaging of mRNA in living cells.

A "turn-off" SERS aptasensor based DNAzyme-gold nanorod for ultrasensitive lead ion detection.

It is great significance to precisely monitor lead (II) ions (Pb2+) for environment protection and human health monitoring. We designed a sensitive detection strategy for sensitive and selective determination of Pb2+, based on a Pb2+-specific DNAzyme as the catalytic unit, Cy3-labeled DNA modified gold nanorods (AuNRs) as SERS reporter. Firstly, AuNRs surface were employed as a platform for the immobilization of thiolated probe DNA, and then hybridized with DNAzyme catalytic beacons. By taking advantage of DNAzyme digest, a molecular beacon, causes a "turn-off" SERS signal by disrupting the labeled probes. Under the optical conditions, the DNAzyme-AuNRs sensor system exhibited high sensitivity, acceptable stability and reproducibility with a wide linear range from 0.5 to 100 nM (R2 = 0.9973), and an ultra-low detection limit of 0.01 nM. The proposed strategy has additional advantages of being less time-consuming, low-cost and remote query, and avoids the interference of some metals such as Fe3+, Cd2+, Ba2+, Cu2+, Zn2+. The SERS biosensor system has been successfully applied for detecting Pb2+ in real samples with a satisfactory result. The result indicated that the proposed sensing strategy not only enriches SERS platform of monitoring Pb2+ but also exhibits potential for the point-of-care diagnostic application of the clinical screening in complicated biological samples.

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network.

In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method.

Controlled synthesis of Au-Fe3O4 hybrid hollow spheres with excellent SERS activity and catalytic properties.

Au-Fe3O4 hybrid hollow spheres have been successfully synthesized by a one-pot process via the hydrothermal treatment of FeCl3, HAuCl4, citrate, urea, and polyacrylamide (PAM). The amount of Au nanoparticles located in the hybrid hollow spheres can be tuned by changing the molar ratio of Au/Fe precursors. A possible synthetic mechanism of the Au-Fe3O4 hybrid hollow spheres has been proposed. The obtained hybrids exhibit not only a superior surface-enhanced Raman scattering (SERS) sensitivity, but also an excellent catalytic activity. The detection limit of the Au-Fe3O4 hybrid hollow spheres (the Au/Fe molar ratio is 0.2, Au-Fe3O4-0.2) for R6G can reach up to 10(-10) M, which can meet the required concentration level for ultratrace detection of analytes using SERS. Furthermore, the catalytic experiments of the Au-Fe3O4-0.2 hybrid hollow spheres demonstrate that the model of 4-nitrophenol (4-NP) molecules can be degraded within 3 min and the catalytic activity can be recovered without sharp activity loss in six runs, which indicates their superior catalytic degradation activity. The reason may be due to the highly efficient partial charge transfer between Au and Fe3O4 at the nanoscale interface. The results indicate that the bifunctional Au-Fe3O4 hybrid hollow spheres can serve as promising materials in trace detection and industrial waste water treatment.

Controlled synthesis of Au-loaded Fe3O4@C composite microspheres with superior SERS detection and catalytic degradation abilities for organic dyes.

Bifunctional Au-loaded Fe3O4@C composite microspheres were controllably synthesized by coating of Au nanoparticles (NPs) on the surface of the poly(diallyldimethylammonium chloride) (PDDA) functionalized Fe3O4@C microspheres. The amount of Au loading can be effectively tuned by altering the feeding amounts of solution Au NPs or further growth. The obtained Au-loaded Fe3O4@C composite microspheres exhibit both superior surface-enhanced Raman scattering (SERS) sensitivity and catalytic degradation activity for organic dyes. The SERS signal intensity of methylene blue (MB) distinctly enhances with the increase of Au loading, which endows increased Raman 'hot spots' and provides a significant enhancement of the Raman signal through electromagnetic (EM) field enhancements. Furthermore, the catalytic experiments of the Fe3O4@C@Au composite microspheres with the highest Au loading demonstrate that the model organic dye of MB molecules could be degraded within 10 min and the catalytic activity could be recovered without sharp activity loss in six runs, which indicates their superior catalytic degradation activity. The reason could be mainly ascribed to the synergistic effects of small size of Au NPs, the good adsorption behavior of carbon layers and the excellent dispersivity of the composite microspheres induced by the sandwiched carbon layers. The results indicate that the bifunctional Au-loaded Fe3O4@C composite microspheres could be served as promising materials in wastewater treatment.

A facile strategy for obtaining fresh Ag as SERS active substrates.

A facile strategy has been reported to obtain on-line fresh Ag as surface-enhanced Raman scattering (SERS) active substrates by making AgCl nanoparticles exposed to the laser beam of Raman spectrometer. The composition and morphology of AgCl nanoparticles were characterized by X-ray diffraction (XRD), UV-visible (UV-vis) spectroscopy and scanning electron microscopy (SEM). The laser-driven evolvement and possible formation mechanism of cubic AgCl nanoparticles to Ag/AgCl composites were also investigated by transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDS) and high-resolution transmission electron microscopy (HRTEM). Raman measurements demonstrate that the fresh Ag nanoparticles with a few defects have a prominent SERS sensitivity to probe molecules, such as the 4-mercaptopyridine (4-Mpy) and 4-aminothiophenol (PATP) molecules. The SERS intensity of 4-Mpy and PATP increases up to the maximum when the laser irradiation time is prolonged to 50s, which corresponds to the defect extent and the proportion of fresh Ag in the Ag/AgCl composites. This work provides a simple, efficient and feasible approach for obtaining on-line fresh Ag as SERS substrates.