A VGGNet-based correction for satellite altimetry-derived gravity anomalies to improve the accuracy of bathymetry to depths of 6500 m

Understanding the topographic patterns of the seafloor is a very important part of understanding our planet.Although the science involved in bathymetric surveying has advanced much over the decades,less than 20%of the seafloor has been precisely modeled to date,and there is an urgent need to improve the accuracy and reduce the uncertainty of underwater survey data.In this study,we introduce a pretrained visual geometry group network(VGGNet)method based on deep learning.To apply this method,we input gravity anomaly data derived from ship measurements and satellite altimetry into the model and correct the latter,which has a larger spatial coverage,based on the former,which is considered the true value and is more accurate.After obtaining the corrected high-precision gravity model,it is inverted to the corresponding bathymetric model by applying the gravity-depth correlation.We choose four data pairs collected from different environments,i.e.,the Southern Ocean,Pacific Ocean,Atlantic Ocean and Caribbean Sea,to evaluate the topographic correction results of the model.The experiments show that the coefficient of determination(R~2)reaches 0.834 among the results of the four experimental groups,signifying a high correlation.The standard deviation and normalized root mean square error are also evaluated,and the accuracy of their performance improved by up to 24.2%compared with similar research done in recent years.The evaluation of the R^(2) values at different water depths shows that our model can achieve performance results above 0.90 at certain water depths and can also significantly improve results from mid-water depths when compared to previous research.Finally,the bathymetry corrected by our model is able to show an accuracy improvement level of more than 21%within 1%of the total water depths,which is sufficient to prove that the VGGNet-based method has the ability to perform a gravity-bathymetry correction and achieve outstanding results.

ARGA-Unet:Advanced U-net segmentation model using residual grouped convolution and attention mechanism for brain tumor MRI image segmentation

Background Magnetic resonance imaging(MRI)has played an important role in the rapid growth of medical imaging diagnostic technology,especially in the diagnosis and treatment of brain tumors owing to its non invasive characteristics and superior soft tissue contrast.However,brain tumors are characterized by high non uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature.In addition,the labeling of tumor areas is time-consuming and laborious.Methods To address these issues,this study uses a residual grouped convolution module,convolutional block attention module,and bilinear interpolation upsampling method to improve the classical segmentation network U-net.The influence of network normalization,loss function,and network depth on segmentation performance is further considered.Results In the experiments,the Dice score of the proposed segmentation model reached 97.581%,which is 12.438%higher than that of traditional U-net,demonstrating the effective segmentation of MRI brain tumor images.Conclusions In conclusion,we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.

Hyperparameter Optimization for Capsule Network Based Modified Hybrid Rice Optimization Algorithm

Hyperparameters play a vital impact in the performance of most machine learning algorithms.It is a challenge for traditional methods to con-figure hyperparameters of the capsule network to obtain high-performance manually.Some swarm intelligence or evolutionary computation algorithms have been effectively employed to seek optimal hyperparameters as a com-binatorial optimization problem.However,these algorithms are prone to get trapped in the local optimal solution as random search strategies are adopted.The inspiration for the hybrid rice optimization(HRO)algorithm is from the breeding technology of three-line hybrid rice in China,which has the advantages of easy implementation,less parameters and fast convergence.In the paper,genetic search is combined with the hybrid rice optimization algorithm(GHRO)and employed to obtain the optimal hyperparameter of the capsule network automatically,that is,a probability search technique and a hybridization strategy belong with the primary HRO.Thirteen benchmark functions are used to evaluate the performance of GHRO.Furthermore,the MNIST,Chest X-Ray(pneumonia),and Chest X-Ray(COVID-19&pneumonia)datasets are also utilized to evaluate the capsule network learnt by GHRO.The experimental results show that GHRO is an effective method for optimizing the hyperparameters of the capsule network,which is able to boost the performance of the capsule network on image classification.

A Fuzzy-based Sliding Mode Control Approach for Acceleration Slip Regulation of Battery Electric Vehicle

Due to quick response and large quantity of electric motor torque,the traction wheels of battery electric vehicle are easy to slip during the initial phase of starting.In this paper,a sliding mode control approach of acceleration slip regulation is designed to prevent the slip of the traction wheels.The wheel slip ratio is used as the state variable for the formulation of system dynamics model.The fuzzy algorithm is utilized to adjust the switch function of sliding mode controller.After stability and robustness analysis,the sliding mode control law is transferred into C code and downloaded into vehicle control unit,which is validated under wet and dry road conditions.The experimental results with a small overshoot and a quick response during starting indicate that the sliding mode controller has good control efect on the slip ratio regulation.This article proposes an acceleration slip regulation method that improves the safety during acceleration for battery electric vehicle.

Parameter Group Optimization by Combining CUBE with Surface Filtering and Its Application

Shallow water multi-beam echo sounders(MBESs)are characterized by their high resolution and high density,and MBES data processing is a hotspot in modern marine surveying.The Combined Uncertainty and Bathymetry Estimator(CUBE)is the mainstream MBES data processing algorithm,although little is known about its core theories and parameters.In this paper,the basic principle,mathematical model,key parameters,and main processing steps of CUBE are described systematically.A parameter group optimization method that combines CUBE with a surface filter is established.Additionally,an example is given that shows the steps for parameter group optimization,including selection of a typical area,parameter group testing,and comparative analysis,and the method is then applied to shallow water MBES data processing.The results show that the method can improve the accuracy and efficiency of automatic data processing effectively,and it is thus of engineering application value.

Fast Determination of Vitamin B₂Based on Molecularly Imprinted Electrochemical Sensor

Under the condition of weak acidity of pH 5.2, a sensitive vitamin B2 electrochemical sensor based on molecularly imprinted nonconducting polymer of o-aminophenol by potentiostatic polymerization in the presence of template(vitamin B2) on a glassy carbon electrode was prepared, and its performance was studied. The sensor exhibited good sensitivity and selectivity to VB2. The detection limit went down to 2.3851nM, and a linear relationship between the current incremental and the concentration was found in the range of 10~120nM. And the sensor could use in detection of VB2 real sample for a long time and show good reproducibility. The average recovery rate to VB2 was 98.41%.

Review on Offloading of Vehicle Edge Computing

Vehicle Edge Computing(VEC)is a new technology that can extend computing and storage functions to the edge of the Internet of Things systems.For limited computing power and delay-sensitive mobile applications on the Internet of Vehicles(IoV),it is important to offload computing tasks to the end of the VEC network.Still,high mobility data security and privacy resource management and the randomness of IoV brought about new problems to the offloading of VEC.To this end,this study focuses on the offloading of computing tasks in VEC.We survey principal offloading schemes and methods in the VEC field and classify the current offloading of computing tasks into different categories.We also discuss the prospect of VEC.This survey could give a reference for researchers to find and understand the essential characteristics of VEC,which helps choose the optimal solutions for the offloading of computing tasks in VEC.

Autophagy and its neuroprotection in neurodegenerative diseases

It has been suggested that protein misfolding and aggregation contribute significantly to the development of neurodegenerative diseases. Misfolded and aggregated proteins are cleared by ubiquitin proteasomal system （UPS） and by both Micro and Macro autophagy lysosomal pathway （ALP）. Autophagosomal dysfunction has been implicated in an increasing number of diseases including neurodegenerative diseases. Autophagy is a cellular self-eating process that plays an important role in neuroprotection as well as neuronal injury and death. While a decrease in autophagic activity interferes with protein degradation and possibly organelle turnover, increased autophagy has been shown to facilitate the clearance of aggregation-prone proteins and promote neuronal survival in a number of disease models. On the other hand, too much autophagic activity can be detrimental, suggesting the regulation of autophagy is critical in dictating cell fate. In this review paper, we will discuss various aspects of ALP biology and its dual functions in neuronal cell death and survival. We will also evaluate the role of autophagy in neurodegenerative diseases including Alzheimer＇s disease, Parkinson＇s disease, Huntington＇s disease, amyotrophic lateral sclerosis. Finally, we will explore the therapeutic potential of autophagy modifiers in several neurodegenerative diseases.

Intracerebroventricular transplanted bone marrow stem cells survive and migrate into the brain of rats with Parkinson's disease

In this study, 6-hydroxydopamine was stereotaxically injected into the right substantia nigra compact and ventral tegmental area of rats to establish Parkinson＇s disease models. The rats then received a transplantation of bone marrow stromal cells that were previously isolated, cultured and labeled with 5-bromo-2＇-deoxyuridine in vitro. Transplantation of the bone marrow stromal cells significantly decreased apomorphine-induced rotation time and the escape latency in the Morris water maze test as compared with rats with untreated Parkinson＇s disease. Immunohistochemical staining showed that, 5-bromo-2＇-deoxyuridine-immunoreactive cells were present in the lateral ventricular wall and the choroid plexus 1 day after transplantation. These immunoreactive cells migrated to the surrounding areas of the lateral cerebral ventricle along the corpus callosum. The results indicated that bone marrow stromal cells could migrate to tissues surround the cerebral ventricle via the cerebrospinal fluid circulation and fuse with cells in the brain, thus altering the phenotype of cells or forming neuron-like cells or astrocytes capable of expressing neuron-specific proteins. Taken together, the present findings indicate that bone marrow stromal cells transplanted intracerebroventricularly could survive, migrate and significantly improve the rotational behavior and cognitive function of rats with experimentally induced Parkinson＇s disease.

Semisupervised heterogeneous ensemble for ship target discrimination in synthetic aperture radar images

Ship detection using synthetic aperture radar(SAR)plays an important role in marine applications.The existing methods are capable of quickly obtaining many candidate targets,but numerous non-ship objects may be wrongly detected in complex backgrounds.These non-ship false alarms can be excluded by training discriminators,and the desired accuracy is obtained with enough verified samples.However,the reliable verification of targets in large-scene SAR images still inevitably requires manual interpretation,which is difficult and time consuming.To address this issue,a semisupervised heterogeneous ensemble ship target discrimination method based on a tri-training scheme is proposed to take advantage of the plentiful candidate targets.Specifically,various features commonly used in SAR image target discrimination are extracted,and several acknowledged classification models and their classic variants are investigated.Multiple discriminators are constructed by dividing these features into different groups and pairing them with each model.Then,the performance of all the discriminators is tested,and better discriminators are selected for implementing the semisupervised training process.These strategies enhance the diversity and reliability of the discriminators,and their heterogeneous ensemble makes more correct judgments on candidate targets,which facilitates further positive training.Experimental results demonstrate that the proposed method outperforms traditional tritraining.

MULTIPLICITY OF PERIODIC SOLUTIONS FOR SECOND ORDER HAMILTONIAN SYSTEMS WITH MIXED NONLINEARITIES

The multiplicity of periodic solutions for a class of second order Hamiltonian system with superquadratic plus subquadratic nonlinearity is studied in this paper.Obtained via the Symmetric Mountain Pass Lemma,two results about infinitely many periodic solutions of the systems extend some previously known results.

State-of-Health Map of Lithium-ion Batteries Established by ICE Map Approach

State-of-health(SOH) is one of the main factors for lithium-ion batteries that indicate their life information. Thus it is essential to estimate SOH accurately during the operation of lithium-ion batteries. In this paper, an SOH map is proposed to illustrate the SOH of lithium-ion batteries by an internal combustion engine(ICE) map approach. Both direct current internal resistance(DCR) and open circuit voltage(OCV) are key parameters of lithium-ion batteries, which are obtained through metering and computing. Due to serious affection by environmental temperature, temperature translation is proposed to translate DCR/OCV of different temperature into a nominal value at 25 ℃. Compared with ICE map, SOH map is illustrated by the nominal DCR and OCV, which can be looked up to get a nominal SOH. In the SOH map, a pair of the DCR and the OCV can only map out a unique SOH, which is beneficial for application in engineering practice in most cases.

Relationship of GSTM1 and GSTT1 genetic variant and markers of oxidative stress and inflammation in smokers with coronary artery disease

Objective： To investigate the role of glutathione S-transferase （GST） genetic variants and markers of oxidative stress and inflammation in smokingrelated coronary artery disease （CAD） patients. Methods： Five hundred and thirty-five Chinese CAD patients were successfully genotyped. Plasma total antioxidant status （TAOS）, glutathione, C-reactive protein （CRP）, fibrinogen（FIB） and white blood cell count （WBC） were determined to evaluate the oxidative stress and inflammatory response. Results： GSTM1-0/ GSTT1-0 subjects had a higher CRP, FIB, WBC and GSH and a lower TAOS compared to patients with wild-type GSTM1/GSTT1 genes, but there was significant difference only with regards to TAOS. Smokers with the null genotype of GSTT1 had the highest CRP and the lowest TAOS and GSH when compared to the GSTTI-1 genotype with smoking status, or the GSTT1-0 genotype with non-smoking status, or the GSTTI-I genotype with non-smoking status. However, we found no significant difference between these groups. Also, no significant interaction was observed between genotypes and smoking status in determining CRP levels. Conclusion： Our results suggest that GST polymorphisms do not modify the effect of smoking on markers of oxidative stress and inflammation in Chinese CAD patients.

An Amperometric Sensor for Sunset Yellow FCF Detection Based on Molecularly Imprinted Polypyrrole

An electrochemical method for fast detecting the concentration of sunset yellow FCF in wine samples was developed in this study. The sensor based on imprinted films which fabricated by electropolymerization of pyrrole on a glassy carbon electrode in the presence of sunset yellow FCF as the template. Comparing to the polypyrrole non-imprinted modified (NIP) electrode, the polypyrrole molecularly imprinted polymer (MIP) electrode improved the electrochemical performance of the sensor significantly. The peak current at about 0.26 V was linear with the concentration of sunset yellow FCF from 0.4 to 2 μM and from 2 to 8 μM. It can be used for more than 10 times to maintain a stable response result. The sensor had the good selectivity on sunset yellow FCF, amaranth and tartrazine, which the selection factors were 1.00, 0.80 and 0.85,respectively.

Formaldehyde Biosensor with Formaldehyde Dehydrogenase Adsorped on Carbon Electrode Modified with Polypyrrole and Carbon Nanotube

In this study, a carbon electrode electroless polymerization of polypyrrole, which formed a layer of conductive film interface,then absorped a layer of carbon nanotube particles by the way of self-assembled, is studied. The modified electrode is used to electrostatic adherence of formaldehyde dehydrogenase, with Nafion solution capped. Moreover this paper discusses the work of electrode position,? PH value and scanning speed on the influence of the electrode response, the response potential is -1.2v, the optimal PH value is 7.5, and we also review the linear range of this electrode, we discovered taht this biosensor have a good linear relationship with the concentration of 1ug/ml-360ug/ml, the Correlation coefficient is 0.9983. In addition,with the stability of this electrode tested, it can be stored 15d at4°C.

The recommendations of Chinese Parkinson’s disease and movement disorder society consensus on therapeutic management of Parkinson’s disease

Background:Parkinson’s disease(PD)is a chronic,progressive and debilitating disease,which affects over 2.5 million people in China.PD is characterized clinically by resting tremor,muscular rigidity,bradykinesia and postural instability.As the disease progresses,additional complications can arise such as non-motor and neurobehavioral symptoms.Pharmacological treatment and surgical intervention for PD have been implemented in China.Until 10 years ago,there was lack of standardization for the management of PD in different regions and among different physicians,leading to different treatment levels in different regions and different physicians.Since then,the Chinese Parkinson’s Disease and Movement Disorder Society have published three versions of guidelines for the management of PD in China,in 2006,2009 and 2014,respectively.Correspondingly,the overall level of treatment for PD in China improved.Objectives:To update the treatment guidelines based on current foreign and domestic practice guidelines and clinical evidence,and to improve the treatment options available to physicians in the management of PD.Summary:A variety of treatment recommendations in the treatment guidelines have been proposed,including physical activity and disease-modifying medication,which should be initiated at the early-stage of the disease.The principles of dosage titration should be followed to avoid acute adverse reactions to the drugs,to achieve a satisfactory clinical effect with a low dose and to reduce the incidence of long-term motor complications.Moreover,different treatment strategies should be considered at different stages of the disease.Importantly,treatment guidelines and personalized treatments should be valued equally.A set of treatment recommendations has been developed to assist physicians to improve and optimize clinical outcomes for patients with PD in China.

miRNA-181 regulates embryo implantation in mice through targeting leukemia inhibitory factor

Embryo implantation is a crucial step in mammalian reproduction.However,little is known regarding the physiological roles of microRNAs in the regulation of embryo implantation.Here we show that a minimum uterine expression of miR-181 is essential for the onset of embryo implantation.Both transient and prolonged transgenic expression of miR-181 led to impaired implantation,which can be rescued by exogenous administration of leukemia inhibitory factor(LIF).Mechanistically,miR-181 is able to directly target LIF and downregulate LIF expression,thereby inhibiting embryo implantation.We also show that miR-181 expression is regu-lated by the transcriptional factor Emx2,and the Emx2–miR-181 axis plays an importantrole in regulating embryo implantation.Taken together,these results reveal a novel function of miR-181 in embryo implantation through the regulation of LIF,and also indicate a potential link between miR-181 dysregulation and human embryo implantation defects.

Quality control of cell-based high-throughput drug screening

The pharmaceutical industry is presently suffering difficult times due to low productivity of new molecular entities.As a major source of drug leads,high-throughput screening(HTS)has been often criticized for its‘dead end’lead compounds.However,the fruitful achievements resulting from HTS technology indicate that it remains a feasible way for drug innovation.Because of increasing considerations of earlier stage ADMET(absorption,distribution,metabolism,excretion and toxicity)in drug development,cell-based HTS is highly recommended in modern drug discovery for its ability to detect more biologically relevant characteristics of compounds in living systems.This review provides a systematic and practical description of vital points for conducting high quality cell-based HTS,from assay development to optimization,compound management,data analyses,hit validation as well as lead identification.Potential problems and solutions are also covered.

Analyzing Antarctic ice sheet snowmelt with dynamic Big Earth Data

Big Earth Data—big data associated with Earth sciences—can potentially revolutionize research on climate change,sustainable development,and other issues of global concern.For example,analyzing massive amounts of satellite imagery of polar environments,which are sensitive to the effects of climate change,provides insights into global climate trends.This study proposes a method to use Big Earth Data to explore changes in snowmelt over the Antarctic ice sheet from 1979 to 2016.The method uses Zernike moments to observe melt area in Antarctica and uses the Mann-Kendall test to detect temporal changes and abnormal information about the continent’s melt area.The melting trend in the time-series data matched the changes in temperature and seasonal transitions.The results do not demonstrate significant change in the area of surface melt;however,abrupt changes in melt conditions linked to temperature changes over the Antarctic ice sheet were observed within the time series.The experiment results demonstrate that the proposed method is robust,adaptive,and capable of extracting the core features of melting snow.

Spatial and temporal assessment of the initial pattern of phytoplankton population in a newly built coastal reservoir

For decades, the main threat to the water security of a metropolis, such as the city of Shanghai, has been the rapidly growing demand for water and at the same time, the decrease in water quality, including eutrophica- tion. Therefore Shanghai shifted the preferred freshwater source to the Yangtze Estuary and constructed the Qingcaosha Reservoir, which is subject to less eutrophic water from the Yangtze River. To assess the population of phytoplankton for the first time in the newly built reservoir, this study improved an integrated method to assess the phytoplankton pattern in large-water-area reservoirs and lakes, using partial triadic analysis and Geographic Information Systems. Monthly sampling and monitoring from 10 stations in the reservoir from July 2010 to December 2011 were conducted. The study examined the common pattern of the phytoplankton population structure and determined the differences in the specific composition of the phytoplankton community during the transition period of the reservoir. The results suggest that in all but three sampling stations in the upper parts of Qingcaosha Reservoir, there was a strong common compromise in 2011. The two most important periods occurred from late summer to autumn and from winter to early spring. The former was characterized by the dominance of cyanobac- teria, whereas the latter was characterized by the dominance of both chlorophyta and diatoms. Cyanobac- teria （Microcystis spp. as the main genus） were the monopolistic dominant species in the summer after reservoir operation. The statistical analysis also indicated the necessity for regular monitoring to focus on the stations in the lower parts of the reservoir and on several limited species.