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Gene expression has inherent stochasticity resulting from transcription's burst manners. Single-cell snapshot data can be exploited to rigorously infer transcriptional burst kinetics, using mathematical models as blueprints. The classical telegraph model (CTM) has been widely used to explain transcriptional bursting with Markovian assumptions. However, growing evidence suggests that the gene-state dwell times are generally non-exponential, as gene-state switching is a multi-step process in organisms. Therefore, interpretable non-Markovian mathematical models and efficient statistical inference methods are urgently required in investigating transcriptional burst kinetics. We develop an interpretable and tractable model, the generalized telegraph model (GTM), to characterize transcriptional bursting that allows arbitrary dwell-time distributions, rather than exponential distributions, to be incorporated into the ON and OFF switching process. Based on the GTM, we propose an inference method for transcriptional bursting kinetics using an approximate Bayesian computation framework. This method demonstrates an efficient and scalable estimation of burst frequency and burst size on synthetic data. Further, the application of inference to genome-wide data from mouse embryonic fibroblasts reveals that GTM would estimate lower burst frequency and higher burst size than those estimated by CTM. In conclusion, the GTM and the corresponding inference method are effective tools to infer dynamic transcriptional bursting from static single-cell snapshot data.

Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution(SISR) and obtained remarkable performance. However, most of the existing CNN-based SISR methods tend to produce over-smoothed outputs and miss some textural details. To address these issues, we propose a wavelet-based asymmetric convolution network (WACN). Different from conventional CNN methods that directly infer HR images, our approach firstly learns to predict the LR’s corresponding series of HR’s wavelet coefficients before reconstructing HR images from them. This helps to capture more structural information in images to preserve texture information and avoid artifacts. To enhance the ability of feature extraction, we propose an asymmetric convolution block (ACB) structure to form a very deep network. In the training phase, ACB can provide different receptive fields to enrich feature information. In the inference phase, ACB’s asymmetric convolution kernel can be equivalently fused into the standard square-kernel layers, such that no extra computational burdens are introduced in the inference phase. Furthermore, we propose a variance-based channel attention (VCA) mechanism to adaptively rescale channel-wise features by considering interdependencies among channels. Extensive experimental results demonstrate the superiority of the proposed WACN in comparison with the state-of-the-art methods.

Crabs may elicit effects on wetland carbon (C), nitrogen (N), and phosphorus (P) concentrations and associated ecological stoichiometry. In this study, we assessed effects of crabs on carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emissions; soil C, N, and P concentrations; and stoichiometry in upper and mid-tidal flats of an estuarine wetland in China. The results showed that averaged CO2, CH4, and N2O fluxes were greater in the upper and mid-tidal flats in the presence of crabs, being 46.4, 66.7, and 69.7% and 53.6, 143, and 73.1% greater than control, respectively. Mixed model analyses showed overall positive relationships between wetland soil CO2 CH4 and N2O emissions (F = 4.65, P = 0.033; F = 42.42, P = 0.042 and F = 10.2, P = 0.0018, respectively) in the presence of crabs, taking into account season, flooding intensity, and plot effects. This may be related to the direct effects of respiration and the indirect effects of feeding, excretion, and disturbance of soil on microorganisms and/or plant roots. There were no effects of crabs on total C or N concentrations, whereas decreased soil total P concentrations, especially in the upper-tidal flats (P = 0.04). Crab presence was positively associated with soil C/P and N/P ratios (P

The WAAM molding process is complex, and the morphology, microstructure, and mechanical properties of the printed parts are affected by multiple factors. In this study, the heat input in the process of WAAM research has made the comprehensive elaboration. The numerical simulation method is used to simulate the molten pool in the process of WAAM, and the force, heat transfer, and velocity of the molten pool are analyzed. Path planning is also an important part of WAAM research, and there are many methods for additive manufacturing path planning, all of which are aimed at improving the dimensional accuracy and performance of the printed parts. Reasonable path planning can also be used for additive components with complex shapes and structures. This study comprehensively expounds on the research on WAAM path planning by scholars in recent years.

The haze in remote sensing images can cause the decline of image quality and bring many obstacles to the applications of remote sensing images. Considering the non-uniform distribution of haze in remote sensing images, we propose a single remote sensing image dehazing method based on the encoder–decoder architecture, which combines both wavelet transform and deep learning technology. To address the clarity issue of remote sensing images with non-uniform haze, we preliminary process the input image by the dehazing method based on the atmospheric scattering model, and extract the first-order low-frequency sub-band information of its 2D stationary wavelet transform as an additional channel. Meanwhile, we establish a large-scale hazy remote sensing image dataset to train and test the proposed method. Extensive experiments show that the proposed method obtains greater advantages over typical traditional methods and deep learning methods qualitatively. For the quantitative aspects, we take the average of four typical deep learning methods with superior performance as a comparison object using 500 random test images, and the peak-signal-to-noise ratio (PSNR) value using the proposed method is improved by 3.5029 dB, and the structural similarity (SSIM) value is improved by 0.0295, respectively. Based on the above, the effectiveness of the proposed method for the problem of remote sensing non-uniform dehazing is verified comprehensively.

This paper conducts an unprecedented research that thoroughly defines the concept of creative hubs as an urban mechanism that emerges from the need of effectively leveraging local resources to better facilitate creative activities and ultimately improve local economics. Building a conceptual framework that articulates the three essential components (the 3Ps) of creative hubs: people, place and planning, this study further analyzes the creative-hub distribution in the city of Toronto. Using a mapping approach to illustrate how different creative hubs scatter, it is observed that there exist congregations of institutional-level and district-level creative hubs along the north-south and east-west direction respectively in the City of Toronto. Finally, a case study on Liberty Village is conducted to scrutinize how a creative hub achieve [sic] its functional value basing [sic] on its people, place, and planning policies.

We focus on the range migration (RM) and Doppler frequency migration (DFM) corrections in the long-time coherent integration, and a fast detection method based on two-dimensional trilinear autocorrelation function is proposed for the maneuvering target with jerk motion. This proposed method can integrate the echoes’ energy into peaks in a three-dimensional parameter space coherently and estimate the target’s radial range, acceleration, and jerk simultaneously by the peak detection technique. Then through the estimations of radial range, acceleration, and jerk, the radial velocity can be obtained through one-dimensional parameter searching. Finally, RM and DFM can be compensated simultaneously, and the target can be detected through the constant false alarm technique. This proposed method can strike a good balance between the computational complexity and detection performance. Experiments with the simulation and real measured radar data are conducted to verify the proposed method.

Objective: Tubal ectopic pregnancy (TEP) remains the most common cause of maternal morbidity and mortality in the early months of pregnancy. The aim of this study is to perform the correlation between PROKRs and pro-inflammatory genes and explore the role of novel genes in pathogenesis of TEP. Methods: Here, quantitative real time PCR and immunohistochemistry were used to assess the expression of the novel genes in 120 TEP patients and 30 age-matched non-TEP patients. The correlation between PROKRs and pro-inflammatory genes were analyzed by Pearson correlation coefficient. Univariate and multivariate Cox regression analyses were used to assess the risk prediction rate of novel genes. Receiver operating characteristic was used to assess the performance of our model. Results: PROKRs (PROKR1 and PROKR2) and pro-inflammatory genes (TNF-α, IL-6, and IL-8) expression levels significantly enhanced in TEP patients, and significantly positive correlation with pro-inflammatory genes for PROKRs. A multivariate Cox regression analysis demonstrated that 2 genes (PROKR2 and IL8) had significant diagnostic value, which were associated with the occurrence and development of TEP. Conclusion: Our data further denote that dysregulation of PROKR2 and IL-8 were risk factor and played an important role in the pathogenesis of TEP.

Crabs may elicit effects on wetland carbon (C), nitrogen (N), and phosphorus (P) concentrations and associated ecological stoichiometry. In this study, we assessed effects of crabs on carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emissions; soil C, N, and P concentrations; and stoichiometry in upper and mid-tidal flats of an estuarine wetland in China. The results showed that averaged CO2, CH4, and N2O fluxes were greater in the upper and mid-tidal flats in the presence of crabs, being 46.4, 66.7, and 69.7% and 53.6, 143, and 73.1% greater than control, respectively. Mixed model analyses showed overall positive relationships between wetland soil CO2 CH4 and N2O emissions (F = 4.65, P = 0.033; F = 42.42, P = 0.042 and F = 10.2, P = 0.0018, respectively) in the presence of crabs, taking into account season, flooding intensity, and plot effects. This may be related to the direct effects of respiration and the indirect effects of feeding, excretion, and disturbance of soil on microorganisms and/or plant roots. There were no effects of crabs on total C or N concentrations, whereas decreased soil total P concentrations, especially in the upper-tidal flats (P = 0.04). Crab presence was positively associated with soil C/P and N/P ratios (P

The extended factored approach (EFA) is believed to be one of the most efficient and practical space–time adaptive processing (STAP) algorithms for clutter suppression in an airborne radar system. However, it cannot effectively work in the airborne radar system with large antenna array for the huge computational cost and the lack of training sample. To solve these problems, a bi-iterative algorithm based on the persymmetric covariance matrix estimation is proposed in this paper. Firstly, the clutter covariance matrix is estimated by using the original data, the constructed spatial transformed data, the constructed temporal transformed data and the constructed spatial–temporal transformed data. Secondly, the spatial weight vector in EFA is decomposed as the Kronecker products of two short weight vectors. Finally, the bi-iterative algorithm is exploited to obtain the desired weight vectors. Thus, the improving EFA with small training sample demanding is realized. Experimental results demonstrate the effectiveness of the proposed method under small training sample support.

Serious hexavalent chromium [Cr(VI)] pollution has continuously threatened ecological security and public health. Microorganism-assisted remediation technology has strong potential in the treatment of environmental Cr(VI) pollution due to its advantages of high efficiency, low cost, and low secondary pollution. Sporosarcina saromensis M52, a strain with strong Cr(VI) removal ability, isolated from coastal intertidal zone was used in this study. Scanning electron microscopy coupled with energy dispersive X-ray analysis indicated M52 was relatively stable under Cr(VI) stress and trace amount of Cr deposited on the cell surface. X-ray photoelectron spectroscopy and X-ray diffraction analyses exhibited M52 could reduce Cr(VI) into Cr(III). Fourier transform infrared spectroscopy showed the bacterial surface was mainly consisted of polysaccharides, phosphate groups, carboxyl groups, amide II (NH/CN) groups, alkyl groups, and hydroxyl groups, while functional groups involving in Cr(VI) bio-reduction were not detected. According to these characterization analyses, the removal of Cr(VI) was primarily depended on bio-reduction, instead of bio-adsorption by M52. Genome analyses further indicated the probable mechanisms of bio-reduction, including the active efflux of Cr(VI) by chromate transporter ChrA, enzymatic redox reactions mediated by reductases, DNA-repaired proteases ability to minimize the ROS damage, and the formation of specific cell components to minimize the biofilm injuries caused by Cr(VI). These studies provided a theoretical basis which was useful for Cr(VI) remediation, especially in terms of increasing its effectiveness. The main finding of the work M52 realized the bioremediation of Cr(VI) majorly through bio-reduction, including Cr(VI) efflux, chromate reduction, DNA repair, and the formation of specific cell components, instead of bio-adsorption.

Biochar can be a soil amendment that increases nutrient retention and carbon (C) sequestration in rice paddy systems. However, biochar can lose nutrient during its production processes so modifications such as coating with the nitrogen (N) that can slowly release nutrients to soil following application are necessary. Dynamic changes in paddy soil microbial communities affect the biogeochemical cycling of soil nutrients; however, the effects of addition of N-enriched biochar on paddy soil microorganisms and nutrient stoichiometry are unclear. Here, we investigated the effects of N-enriched biochar on soil bacteria and fungi community structure and on carbon (C), N, phosphorous (P), and iron (Fe) stoichiometry in subtropical paddy soils. The soil concentrations of TC, TN and TP increased by 0.27–18.55%, 1.31–18.15% and 10.35–54.24% respectively under the nitrogen rich biochar treatment. Under N-enriched biochar application, ratios of N/P and C/P decreased, while P-fixation capacity increased; the decrease in N/P ratio indicated that rice productivity was N-limited. The quantity of soil fungi increased by 42.07% and 10.89% in early and late rice, respectively in the treatment group applying 4t ha−1 of N-enriched biochar. Relative abundance of the bacteria Bacillus, Geobacter, and Sideroxydans decreased with N-enriched biochar, whereas that of Thiobacillus and Thermomonas increased; relative abundance of the fungi Spicellomyces and Crustoderma increased with biochar. Relative abundance of the bacteria Bacteroides was negatively correlated with TC and Fe3+ (P

The conversion of paddy cropping to vegetable production and other crops is a common agricultural practice, driven by economic benefits. However, soil carbon emissions and nutrient stoichiometric responses to this type of land-use change remain unclear. In this study, carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emissions, Soil Organic Carbon (SOC), total nitrogen (TN), and total phosphorus (TP) concentrations and their stoichiometric relationships were measured over one year, following conversion of paddy fields to vegetable and jasmine crops 10 years ago. Soil CO2 emissions increased by 1.16 and 2.35 times after conversion to vegetable and jasmine production, respectively, and soil CH4 and N2O emissions decreased by 52 and 66%, and 12.3 and 42.5%, respectively. Global warming potential increased by 0.81 and 1.67 times, respectively; the increases in total C emissions were associated with decreases in SOC concentrations. TN decreased by 27% in jasmine soils following conversion from paddy cropping, and TP concentrations increased by 11% in vegetable soils. Carbon soil stocks increased by 19% in vegetable soils and decreased by 11% in jasmine soils, due to changes in SOC concentration and bulk density. Soil CO2 and total C emissions were negatively correlated with soil TN concentration and C:P ratio (p

In general, the space-time adaptive processing (STAP) can achieve excellent clutter suppression and moving target detection performance in the airborne multiple-input multiple-output (MIMO) radar for the increasing system degrees of freedom (DoFs). However, the performance improvement is accompanied by a dramatic increase in computational cost and training sample requirement. As one of the most efficient dimension-reduced STAP methods, the extended factored approach (EFA) transforms the full-dimension STAP problem into several small-scale adaptive processing problems, and therefore alleviates the computational cost and training sample requirement. However, it cannot effectively work in the airborne MIMO radar since sufficient training samples are unavailable. Aiming at the problem, a fast iterative method using persymmetry covariance matrix estimation in the airborne MIMO radar is proposed. In this method, the clutter covariance matrix is estimated by the original data and the constructed data. Then, the spatial weight vector in EFA is decomposed into the Kronecker product of two short-weight vectors. The bi-iterative algorithm is exploited to obtain the desired weight vectors. Simulation results demonstrate the effectiveness of our proposed method.

Combined with two different types of image dehazing strategies based on image enhancement and atmospheric physical model, respectively, a novel method for gray-scale image dehazing is proposed in this paper. For image-enhancement-based strategy, the characteristics of its simplicity, effectiveness, and no color distortion are preserved, and the common guided image filter is modified to match the application of image enhancement. Through wavelet decomposition, the high frequency boundary of original image is preserved in advance. Moreover, the process of image dehazing can be guided by the image of scene depth proportion directly estimated from the original gray-scale image. Our method has the advantages of brightness consistency and no distortion over the state-of-the-art methods based on atmospheric physical model. Particularly, our method overcomes the essential shortcoming of the abovementioned methods that are mainly working for color image. Meanwhile, an image of scene depth proportion is acquired as a byproduct of image dehazing.

A novel method was developed for simultaneous determination of 10 progestin residues in fish by ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS) combined with a modified quick, easy, cheap, effective, rugged, and safe (QuEChERS) method. The homogenized samples were dispersed by water, extracted with acetonitrile, and then purified by QuEChERS reagent. The concentrated analytes were detected by UPLC-Q-TOF/MS. High linearities (R2 > 0.995) and recoveries (85.71–117.08%) at three spiked levels (5, 10, and 20 ng/g) and low relative standard deviation values (n = 7) and limits of detection (0.23–0.66 ng/g) were obtained. This method is simple, rapid, reliable, sensitive, and efficient and can be used for monitoring of progestin residues in fish. This method provides a strong guarantee to deal with food emergencies for the laboratory, provides technical support for the screening and quantitative detection of progesterone in fish, and provides technical support for the food safety of aquatic products.

This paper proposes a new robotic fish which avoids the complex mechanical structure and reduces the model complexity comparing to the existing bioinspired robotic fish, giving rise to a semibiomimetic robotic fish. The generalized Lagrange equation is adopted to establish the dynamic model of the robotic fish. The controllability of the system is analyzed, upon which a trajectory tracking control algorithm is designed by using the feedback linearization technique. The simulation results show that the dynamic model adopted in this paper can achieve better control performance.

Ship motions are usually employed to analyze water transportation system. Interpolation of ship motions can be used for estimating the lost ship movement information, which is important for analyzing water traffic. Although some methods are proposed to interpolate ship trajectory, these methods are needed to improve interpolation accuracy. In addition, the course and speed of ship are not interpolated in previous studies. This article proposes a novel estimation algorithm for interpolating ship motions. First, the bilateral filtering is used to smooth the navigation trajectories, then a novel interpolation approach forecasts the vessel positions during the interpolation time period by taking into account the dynamic information of Automatic Identification System (AIS), and then forms the interpolation track of the moving vessel. In order to validate the proposed method, the AIS data of Yangtze River is collected from the AIS stations and the proposed restoration methods are verified by MATLAB under simulation. The results show that the proposed method can effectively remove the inaccurate ship motion data and interpolate the lost ship motion data.

When the mathematical model of projects selection is established in the technological innovation, the conventional methods have deficiencies in dealing with the fuzzy uncertainty. To improve the mathematical model, in this paper, Neuro-fuzzy decision tree(Neuro-FDT) is introduced to the research on the innovation projects selection . Fuzzy decision trees are powerful, top-down, hierarchical search methodology to extract human interpretable classification rules. However, they are very poor in classification accuracy. Neural networks-fuzzy decision tree improves FDT's classification accuracy and extracts more accuracy human interpretable classification rules. The fuzzy rules enable a decision-maker to decide the optimal projects selection of technological innovation. Comparing with the conventional methods, the mathematical model of neuro-fuzzy decision tree can be easily established by fully utilizing the information of projects. The result of the positive research indicated that this mathematical model is very valid for innovation projects selection and it will have a good application prospect in this area.

In many parts of the world, deteriorating environmental conditions have led policy makers to develop policies and programs aimed at promoting conservation practices on lands devoted to agriculture. Such programs have been studied by environmental economists, but little research has been done on the usefulness of strategically varying the conservation contract's length. This paper uses theory and simulation to investigate the optimal contract length of land conservation programs when a policy maker tries to maximize the present discounted value of the stream of environmental benefits from the program. We find that contract length should vary with characteristics of the ecological processes that yield benefits from land retirement. Optimal contracts are longer when the environmental benefits in question things like woodland biodiversity take time to develop. However, it is not typically optimal to have the indefinitely lived contracts favored by some conservation groups, or even to offer contracts as long as the maturation period for the environmental services in question. In general, the optimal contract length depends on the trade off between an ecological effect (increasing the environmental benefits from one farmer) and an enrollment effect (increasing the number of farmers enrolled). Our findings also suggest that non-ecological regional characteristics (such as turnover rate and average farm income) could play an important role in the design of conservation programs.

In many parts of the world, deteriorating environmental conditions have led policy makers to develop policies and programs aimed at promoting conservation practices on lands devoted to agriculture. Such programs have been studied by environmental economists, but little research has been done on the usefulness of strategically varying the conservation contract's length. This paper uses theory and simulation to investigate the optimal contract length of land conservation programs when a policy maker tries to maximize the present discounted value of the stream of environmental benefits from the program. We find that contract length should vary with characteristics of the ecological processes that yield benefits from land retirement. Optimal contracts are longer when the environmental benefits in question things like woodland biodiversity take time to develop. However, it is not typically optimal to have the indefinitelylived contracts favored by some conservation groups, or even to offer contracts as long as the maturation period for the environmental services in question. In general, the optimal contract length depends on the trade off between an ecological effect (increasing the environmental benefits from one farmer) and an enrollment effect (increasing the number of farmers enrolled). Our findings also suggest that non-ecological regional characteristics (such as turnover rate and average farm income) could play an important role in the design of conservation programs.