Divergent response of blue carbon components to wetland types and hydrological effects in typical estuarine wetlands of Jiaozhou Bay, China.

As ecosystems subject to periodic tides, estuarine wetlands have a significant capacity to sequester carbon over time. Understanding the distribution patterns of soil carbon components and identifying the key factors influencing these patterns are key to gaining insight into the function of "blue carbon" in coastal wetlands. To clarify the response of soil carbon components to wetland types and hydrological effects in estuarine wetlands, the typical estuarine wetlands in Jiaozhou Bay, China were selected as the study area, and the soil organic carbon (SOC), dissolved organic carbon (DOC), microbial biomass carbon (MBC), soil inorganic carbon (SIC) and dissolved inorganic carbon (DIC) under different wetland types and hydrological effects were investigated. The results showed that the SOC, SIC, and MBC contents were significantly influenced by the wetland types. The SOC and MBC contents were as follows: mudflat (GT) > Phragmites australis wetland (PA) > Suaeda salsa wetland (SS). The overall content of SIC was highest in PA, followed by GT and SS. Hydrological effects had significant influence on the soil MBC, DOC and DIC contents. With the increase hydrological effects, the soil MBC content decreased by 38.89%-72.22%, while the DOC and DIC contents increased by 15.13%-19.89% and 13.41%-86.70%, respectively. The results of the correlation analysis and structural equation model indicated that wetland types and hydrological effects directly or indirectly (through changes in soil pH, bulk density, water content, and salinity) drove the changes in soil carbon contents in estuarine wetlands. Altogether, our findings implied that the alterations of wetland types and hydrological effects will affect the blue carbon function of estuarine wetlands. In the future, for accurate assessment of a blue carbon budget for estuarine wetlands, the differences in wetland types and hydrological effects of different areas should be considered.

A Face Detection and Standardized Mask-Wearing Recognition Algorithm.

In the era of coronavirus disease (COVID-19), wearing a mask could effectively protect people from the risk of infection and largely reduce transmission in public places. To prevent the spread of the virus, instruments are needed in public places to monitor whether people are wearing masks, which has higher requirements for the accuracy and speed of detection algorithms. To meet the demand for high accuracy and real-time monitoring, we propose a single-stage approach based on YOLOv4 to identify the face and whether to regulate the wearing of masks. In this approach, we propose a new feature pyramidal network based on the attention mechanism to reduce the loss of object information that can be caused by sampling and pooling in convolutional neural networks. The network is able to deeply mine the feature map for spatial and communication factors, and the multi-scale feature fusion makes the feature map equipped with location and semantic information. Based on the complete intersection over union (CIoU), a penalty function based on the norm is proposed to improve positioning accuracy, which is more accurate at the detection of small objects; the new bounding box regression function is called Norm CIoU (NCIoU). This function is applicable to various object-detection bounding box regression tasks. A combination of the two functions to calculate the confidence loss is used to mitigate the problem of the algorithm bias towards determinating no objects in the image. Moreover, we provide a dataset for recognizing faces and masks (RFM) that includes 12,133 realistic images. The dataset contains three categories: face, standardized mask and non-standardized mask. Experiments conducted on the dataset demonstrate that the proposed approach achieves mAP@.5:.95 69.70% and AP75 73.80%, outperforming the compared methods.

MSIA-Net: A Lightweight Infrared Target Detection Network with Efficient Information Fusion.

In order to solve the problems of infrared target detection (i.e., the large models and numerous parameters), a lightweight detection network, MSIA-Net, is proposed. Firstly, a feature extraction module named MSIA, which is based on asymmetric convolution, is proposed, and it can greatly reduce the number of parameters and improve the detection performance by reusing information. In addition, we propose a down-sampling module named DPP to reduce the information loss caused by pooling down-sampling. Finally, we propose a feature fusion structure named LIR-FPN that can shorten the information transmission path and effectively reduce the noise in the process of feature fusion. In order to improve the ability of the network to focus on the target, we introduce coordinate attention (CA) into the LIR-FPN; this integrates the location information of the target into the channel so as to obtain more expressive feature information. Finally, a comparative experiment with other SOTA methods was completed on the FLIR on-board infrared image dataset, which proved the powerful detection performance of MSIA-Net.

3D-printed polyether-ether-ketone/n-TiO2 composite enhances the cytocompatibility and osteogenic differentiation of MC3T3-E1 cells by downregulating miR-154-5p.

The object was to enhance the bioactivity of pure polyether-ether-ketone (PEEK) by incorporating nano-TiO2 (n-TiO2) and investigate its potential mechanism. PEEK/n-TiO2 composite was manufactured using a 3D PEEK printer and characterized by scanning electron microscopy (SEM), 3D profiler, energy-dispersive spectroscopy, and Fourier-transform infrared (FT-IR) analyses. Cytocompatibility was tested using SEM, fluorescence, and cell counting kit-8 assays. Osteogenic differentiation was evaluated by osteogenic gene and mineralized nodule levels. The expression of the candidate miRNAs were detected in composite group, and its role in osteogenic differentiation was studied. As a results the 3D-printed PEEK/n-TiO2 composite (Φ = 25 mm, H = 2 mm) was successfully fabricated, and the TiO2 nanoparticles were well distributed and retained the nanoscale size of the powder. The Ra value of the composite surface was 2.69 ± 0.29, and Ti accounted for 22.29 ± 12.09% (in weight), and FT-IR analysis confirmed the characteristic peaks of TiO2. The cells in the composite group possessed better proliferation and osteogenic differentiation abilities than those in the PEEK group. miR-154-5p expression was decreased in the composite group, and the inhibition of miR-154-5p significantly enhanced the proliferation and osteogenic differentiation abilities. In conclusion, 3D-printed PEEK/n-TiO2 composite enhanced cytocompatibility and osteogenic induction ability by downregulating miR-154-5p, which provides a promising solution for improving the osteointegration of PEEK.

Global reorganization of deep-sea circulation and carbon storage after the last ice age.

Using new and published marine fossil radiocarbon (14C/C) measurements, a tracer uniquely sensitive to circulation and air-sea gas exchange, we establish several benchmarks for Atlantic, Southern, and Pacific deep-sea circulation and ventilation since the last ice age. We find the most 14C-depleted water in glacial Pacific bottom depths, rather than the mid-depths as they are today, which is best explained by a slowdown in glacial deep-sea overturning in addition to a "flipped" glacial Pacific overturning configuration. These observations cannot be produced by changes in air-sea gas exchange alone, and they underscore the major role for changes in the overturning circulation for glacial deep-sea carbon storage in the vast Pacific abyss and the concomitant drawdown of atmospheric CO2.

Deglacial Subantarctic CO2 outgassing driven by a weakened solubility pump.

The Subantarctic Southern Ocean has long been thought to be an important contributor to increases in atmospheric carbon dioxide partial pressure (pCO2) during glacial-interglacial transitions. Extensive studies suggest that a weakened biological pump, a process associated with nutrient utilization efficiency, drove up surface-water pCO2 in this region during deglaciations. By contrast, regional influences of the solubility pump, a process mainly linked to temperature variations, have been largely overlooked. Here, we evaluate relative roles of the biological and solubility pumps in determining surface-water pCO2 variabilities in the Subantarctic Southern Ocean during the last deglaciation, based on paired reconstructions of surface-water pCO2, temperature, and nutrient utilization efficiency. We show that compared to the biological pump, the solubility pump imposed a strong impact on deglacial Subantarctic surface-water pCO2 variabilities. Our findings therefore reveal a previously underappreciated role of the solubility pump in modulating deglacial Subantarctic CO2 release and possibly past atmospheric pCO2 fluctuations.

BRefine: Achieving High-Quality Instance Segmentation.

Instance segmentation has been developing rapidly in recent years. Mask R-CNN, a two-stage instance segmentation approach, has demonstrated exceptional performance. However, the masks are still very coarse. The downsampling operation of the backbone network and the ROIAlign layer loses much detailed information, especially from large targets. The sawtooth effect of the edge mask is caused by the lower resolution. A lesser percentage of boundary pixels leads to not-fine segmentation. In this paper, we propose a new method called Boundary Refine (BRefine) that achieves high-quality segmentation. This approach uses FCN as the foundation segmentation architecture, and forms a multistage fusion mask head with multistage fusion detail features to improve mask resolution. However, the FCN architecture causes inconsistencies in multiscale segmentation. BRank and sort loss (BR and S loss) is proposed to solve the problems of segmentation inconsistency and the difficulty of boundary segmentation. It is combined with rank and sort loss, and boundary region loss. BRefine can handle hard-to-partition boundaries and output high-quality masks. On the COCO, LVIS, and Cityscapes datasets, BRefine outperformed Mask R-CNN by 3.0, 4.2, and 3.5 AP, respectively. Furthermore, on the COCO dataset, the large objects improved by 5.0 AP.

Evidence for massive methane hydrate destabilization during the penultimate interglacial warming.

The stability of widespread methane hydrates in shallow subsurface sediments of the marine continental margins is sensitive to temperature increases experienced by upper intermediate waters. Destabilization of methane hydrates and ensuing release of methane would produce climatic feedbacks amplifying and accelerating global warming. Hence, improved assessment of ongoing intermediate water warming is crucially important, especially that resulting from a weakening of Atlantic meridional overturning circulation (AMOC). Our study provides an independent paleoclimatic perspective by reconstructing the thermal structure and imprint of methane oxidation throughout a water column of 1,300 m. We studied a sediment sequence from the eastern equatorial Atlantic (Gulf of Guinea), a region containing abundant shallow subsurface methane hydrates. We focused on the early part of the penultimate interglacial and present a hitherto undocumented and remarkably large intermediate water warming of 6.8 °C in response to a brief episode of meltwater-induced, modest AMOC weakening centered at 126,000 to 125,000 y ago. The warming of intermediate waters to 14 °C significantly exceeds the stability field of methane hydrates. In conjunction with this warming, our study reveals an anomalously low δ13C spike throughout the entire water column, recorded as primary signatures in single and pooled shells of multitaxa foraminifers. This extremely negative δ13C excursion was almost certainly the result of massive destabilization of methane hydrates. This study documents and connects a sequence of climatic events and climatic feedback processes associated with and triggered by the penultimate climate warming that can serve as a paleoanalog for modern ongoing warming.

Hydrological control of river and seawater lithium isotopes.

Seawater lithium isotopes (δ7Li) record changes over Earth history, including a ∼9‰ increase during the Cenozoic interpreted as reflecting either a change in continental silicate weathering rate or weathering feedback strength, associated with tectonic uplift. However, mechanisms controlling the dissolved δ7Li remain debated. Here we report time-series δ7Li measurements from Tibetan and Pamir rivers, and combine them with published seasonal data, covering small (<102 km2) to large rivers (>106 km2). We find seasonal changes in δ7Li across all latitudes: dry seasons consistently have higher δ7Li than wet seasons, by -0.3‰ to 16.4‰ (mean 5.0 ± 2.5‰). A globally negative correlation between δ7Li and annual runoff reflects the hydrological intensity operating in catchments, regulating water residence time and δ7Li values. This hydrological control on δ7Li is consistent across climate events back to ~445 Ma. We propose that hydrological changes result in shifts in river δ7Li and urge reconsideration of its use to examine past weathering intensity and flux, opening a new window to reconstruct hydrological conditions.

An Efficient Lightweight SAR Ship Target Detection Network with Improved Regression Loss Function and Enhanced Feature Information Expression.

It is difficult to identify the ship images obtained by a synthetic aperture radar (SAR) due to the influence of dense ships, complex background and small target size, so a deep learning-based target detection algorithm was introduced to obtain better detection performance. However, in order to achieve excellent performance, most of the current target detection algorithms focus on building deep and high-width neural networks, resulting in bloated network structure and reduced detection speed, which is not conducive to the practical application of target detection algorithms. Thereby, an efficient lightweight network Efficient-YOLO for ship detection in complex situations is proposed in the present work. Firstly, a new regression loss function ECIOU is proposed to enhance the detection boxes localization accuracy and model convergence speed. Secondly, We propose the SCUPA module to enhance the multiplexing of picture feature information and the model generalization performance. Thirdly, The GCHE module is proposed to strengthen the network's ability to extract feature information. At last, the effectiveness of our method is tested on the specialized ship dataset: SSDD and HRSID datasets. The results show that Efficient-YOLO outperforms other state-of-the-art algorithms in accuracy, recall and detection speed, with smaller model complexity and model size.

Dynamic gesture recognition based on 2D convolutional neural network and feature fusion.

Gesture recognition is one of the most popular techniques in the field of computer vision today. In recent years, many algorithms for gesture recognition have been proposed, but most of them do not have a good balance between recognition efficiency and accuracy. Therefore, proposing a dynamic gesture recognition algorithm that balances efficiency and accuracy is still a meaningful work. Currently, most of the commonly used dynamic gesture recognition algorithms are based on 3D convolutional neural networks. Although 3D convolutional neural networks consider both spatial and temporal features, the networks are too complex, which is the main reason for the low efficiency of the algorithms. To improve this problem, we propose a recognition method based on a strategy combining 2D convolutional neural networks with feature fusion. The original keyframes and optical flow keyframes are used to represent spatial and temporal features respectively, which are then sent to the 2D convolutional neural network for feature fusion and final recognition. To ensure the quality of the extracted optical flow graph without increasing the complexity of the network, we use the fractional-order method to extract the optical flow graph, creatively combine fractional calculus and deep learning. Finally, we use Cambridge Hand Gesture dataset and Northwestern University Hand Gesture dataset to verify the effectiveness of our algorithm. The experimental results show that our algorithm has a high accuracy while ensuring low network complexity.

An image super-resolution reconstruction model based on fractional-order anisotropic diffusion equation.

The image denoising model based on anisotropic diffusion equation often appears the staircase effect while image denoising, and the traditional super-resolution reconstruction algorithm can not effectively suppress the noise in the image in the case of blur and serious noise. To tackle this problem, a novel model is proposed in this paper. Based on the original diffusion equation, we propose a new method for calculating the adaptive fidelity term and its coefficients, which is based on the relationship between the image gradient and the diffusion function. It is realized that the diffusion speed can be slowed down by adaptively changing the coefficient of the fidelity term, and it is proved mathematically that the proposed fractional adaptive fidelity term will not change the existence and uniqueness of the solution of the original model. At the same time, washout filter is introduced as the control item of the model, and a new model of image super-resolution reconstruction and image denoising is constructed. In the proposed model, the order of fractional differential will be determined adaptively by the local variance of the image. And we give the numerical calculation method of the new model in the frequency domain by the method of Fourier transform. The experimental results show that the proposed algorithm can better prevent the staircase effect and achieve better visual effect. And by introducing washout filter to act as the control of the model, the stability of the system can be improved and the system can converge to a stable state quickly.

Mechanical Properties of Dental Alloys According to Manufacturing Process.

The purpose of this study is to investigate the effect of the fabrication method of dental prosthesis on the mechanical properties. Casting was produced using the lost wax casting method, and milling was designed using a CAD/CAM program. The 3D printing method used the SLS technique to create a three-dimensional structure by sintering metal powder with a laser. When making the specimen, the specimen was oriented at 0, 30, 60, and 90 degrees. All test specimens complied with the requirements of the international standard ISO 22674 for dental alloys. Tensile strength was measured for yield strength, modulus of elasticity and elongation by applying a load until fracture of the specimen at a crosshead speed of 1.5 ± 0.5 mm/min (n = 6, modulus of elasticity n = 3). After the tensile test, the cross section of the fractured specimen was observed with a scanning electron microscope, and the statistics of the data were analyzed with a statistical program SPSS (IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY, USA: IBM Corp.) and using Anova and multiple comparison post-tests (scheffe method). The yield strength was the highest at 1042 MPa at an angle of 0 degrees in the specimen produced by 3D printing method, and the elongation was the highest at 14% at an angle of 90 degrees in the specimen produced by 3D printing method. The modulus of elasticity was the highest at 235 GPa in the milled specimen. In particular, the 3D printing group showed a difference in yield strength and elongation according to the build direction. The introduction of various advanced technologies and digital equipment is expected to bring high prospects for the growth of the dental market.

Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4.

To solve the problems of low accuracy, low real-time performance, poor robustness and others caused by the complex environment, this paper proposes a face mask recognition and standard wear detection algorithm based on the improved YOLO-v4. Firstly, an improved CSPDarkNet53 is introduced into the trunk feature extraction network, which reduces the computing cost of the network and improves the learning ability of the model. Secondly, the adaptive image scaling algorithm can reduce computation and redundancy effectively. Thirdly, the improved PANet structure is introduced so that the network has more semantic information in the feature layer. At last, a face mask detection data set is made according to the standard wearing of masks. Based on the object detection algorithm of deep learning, a variety of evaluation indexes are compared to evaluate the effectiveness of the model. The results of the comparations show that the mAP of face mask recognition can reach 98.3% and the frame rate is high at 54.57 FPS, which are more accurate compared with the exiting algorithm.

Sea level and deep-sea temperature reconstructions suggest quasi-stable states and critical transitions over the past 40 million years.

Sea level and deep-sea temperature variations are key indicators of global climate changes. For continuous records over millions of years, deep-sea carbonate microfossil-based δ18O (δc) records are indispensable because they reflect changes in both deep-sea temperature and seawater δ18O (δw); the latter are related to ice volume and, thus, to sea level changes. Deep-sea temperature is usually resolved using elemental ratios in the same benthic microfossil shells used for δc, with linear scaling of residual δw to sea level changes. Uncertainties are large and the linear-scaling assumption remains untested. Here, we present a new process-based approach to assess relationships between changes in sea level, mean ice sheet δ18O, and both deep-sea δw and temperature and find distinct nonlinearity between sea level and δw changes. Application to δc records over the past 40 million years suggests that Earth's climate system has complex dynamical behavior, with threshold-like adjustments (critical transitions) that separate quasi-stable deep-sea temperature and ice-volume states.

Characterization of rare earth elements present in coal ash by sequential extraction.

Although it has recently been reported that notable amounts of rare earth elements (REEs) are present in the residual coal ash, little is currently known regarding the association of these elements with the coal ash matrix, thereby limiting the potential for extraction of REEs from coal ash. In this study, we analyzed the binding characteristics of REEs within coal ash via sequential extraction and examined REE recovery during a coal ash recycling process. Major components of coal ash were found to be mineral oxides, mainly composed of Si, Fe, Al, and Ca, and residual carbons. Bottom and fly ashes were found to contain 185.8 mg/kg and 179.2 mg/kg of REEs, respectively. Tessier sequential extraction confirmed that 85 % of REEs are included in the residual fraction of both bottom and fly ashes. Furthermore, BCR sequential extraction revealed that 60-70 % of REEs are contained within the residual fraction, thereby indicating that REEs are strongly bound in both bottom and fly ashes and the use of very strong acids is required for the thorough extraction of REEs from coal ash. Additionally, it was found that 46.3 % of REEs can be recovered from the wastewaters produced during the process of coal ash-derived zeolite synthesis.

Akt and calcium-permeable AMPA receptor are involved in the effect of pinoresinol on amyloid ß-induced synaptic plasticity and memory deficits.

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders characterized by memory deficits. Although no drug has given promising results, synaptic dysfunction-modulating agents might be considered potential candidates for alleviating this disorder. Pinoresinol, a lignan found in Forsythia suspensa, is a memory-enhancing agent with excitatory synaptic activation. In the present study, we tested whether pinoresinol reduces learning and memory and excitatory synaptic deficits in an amyloid ß (Aß)-induced AD-like mouse model. Pinoresinol enhanced hippocampal long-term potentiation (LTP) through calcium-permeable AMPA receptor, which was mediated by Akt activation. Moreover, pinoresinol ameliorated LTP deficits in amyloid ß (Aß)-treated hippocampal slices via Akt signaling. Oral administration of pinoresinol ameliorated Aß-induced memory deficits without sensory dysfunction. Moreover, AD-like pathology, including neuroinflammation and synaptic deficit, were ameliorated by pinoresinol administration. Collectively, pinoresinol may be a good candidate for AD therapy by modulating synaptic functions.

Evaluation of the Milling Accuracy of Zirconia-Reinforced Lithium Silicate Crowns Fabricated Using the Dental Medical Device System: A Three-Dimensional Analysis.

This study aimed to analyze the milling accuracy of lithium disilicate and zirconia-reinforced silicate crown fabricated using chairside computer-aided design/manufacturing (CAD/CAM) system. Mandibular left first premolar was selected for abutment. A master model was obtained for digital impression using an intraoral scanner, and crowns were designed using a CAD software design program. Amber Mill (AM), IPS e max CAD (IPS), and CELTRA DUO (CEL) were used in the CAD/CAM system, and a total 45 crowns (15 crowns each for AM, IPS, and CEL) was fabricated. Milling accuracy was analyzed with respect to trueness, measured by superimposing CAD design data and scan data through a three-dimensional program to compare the outer and inner surfaces and internal and external parts, thereby acquiring both quantitative and qualitative data. Data were analyzed using the non-parametric test and Kruskal-Wallis H test. In addition, the Mann-Whitney U test was used by applying the level of significance (0.05/3 = 0.016) adjusted by post-analysis Bonferroni correction. All the measured parts of the lithium disilicate and zirconia-reinforced silicate crowns showed statistically significant differences (p < 0.05). The lithium disilicate (AM and IPS) materials showed superior milling accuracy than the zirconia-reinforced lithium silicate (CEL) materials.

Influence of Orthodontic Anchor Screw Anchorage Method on the Stability of Artificial Bone: An In Vitro Study.

This study aims to compare the torque values for various lengths of the titanium-based orthodontic anchor screw (OAS), different anchorage methods and varying artificial bone densities after predrilling. Furthermore, the effects of these parameters on bone stability are evaluated. A total of 144 OASs were prepared with a diameter of 1.6 mm and heights of 6, 8 and 10 mm. Artificial bones were selected according to their density, corresponding to Grades 50, 40 and 30. Torque values for the automatic device and manual anchorage methods exhibited a statistically significant difference for the same-sized OAS, according to the bone density of the artificial bones (p < 0.05). However, when insertion torque was at the maximum rotations, there was no significant difference in the torque values for the Grade 30 artificial bone (p > 0.05). When the torque values of both anchorage methods were statistically compared with the mean difference for each group, the results of the manual anchorage method were significantly higher than those of the automatic device anchorage method (p < 0.05). A statistically significant difference was observed in the bone stability resulting from different OAS anchorage methods and artificial bone lengths. These findings suggest that the automatic anchorage method should be used when fixing the OAS.

Development of biocatalysts immobilized on coal ash-derived Ni-zeolite for facilitating 4-chlorophenol degradation.

A new type of biocatalyst was developed to facilitate the biochemical decomposition of 4-chlorophenol (4-CP) in this study. Oxydoreductases that catalyze the initial steps of 4-CP biodegradation were immobilized on a synthetic inorganic enzyme support. Type-X zeolite, a high-surface area support, was synthesized from coal fly ash, on which nickel ions were plated by impregnation (Ni-zeolite), followed by the effective immobilization (77.5% immobilization yield) of recombinant monooxygenase (CphC-I), dioxygenase (CphA-I), and flavin reductase (Fre) isolated from Pseudarthrobacter chlorophenolicus A6 and Escherichia coli K-12, respectively. The retained catalytic activity of the enzymes immobilized on Ni-zeolite was as high as 64% of the value for the corresponding free enzymes. The Michaelis-Menten kinetic parameters vmax and KM of the immobilized enzymes were determined to be 0.20 mM·min-1 and 0.44 mM, respectively. These results are expected to provide useful information with respect to the development of novel enzymatic treatments for phenolic hydrocarbon contaminants.