In the neuroscience research field, specific for medical imaging analysis, how to mining more latent medical information from big medical data is significant for us to find the solution of diseases. In this review, we focus on neuroimaging data that is functional Magnetic Resonance Imaging (fMRI) which non-invasive techniques, it already becomes popular tools in the clinical neuroscience and functional cognitive science research. After we get fMRI data, we actually have various software and computer programming that including open source and commercial, it's very hard to choose the best software to analyze data. What's worse, it would cause final result imbalance and unstable when we combine more than software together, so that's why we want to make a pipeline to analyze data. On the other hand, with the growing of machine learning, Python has already become one of very hot and popular computer programming. In addition, it is an open source and dynamic computer programming, the communities, libraries and contributors fast increase in the recent year. Through this review, we hope that can make neuroimaging data analysis more easy, stable and uniform base the one platform system.

In recent years discrete element method (DEM) has gradually been applied to the traditional fluidization simulation of fine particles in micro fluidized bed (MFB). The application of DEM in simulating fast fluidization of fine particles in MFB has not yet received attention. This article presents a drag model that relies on the surrounding environment of particles, namely particle circumstance-dependent drag modle or PCDD model. Fast fluidization in MFB of fine particles is simulated using DEM based on the PCDD model. It is found that the gas-solid two-phase flow in MFB meets the common law of fast fluidization in circulating fluidized bed (CFB) to a certain extent, and also presents a special law that is significantly different from that in CFB. Simulations indicate that the local structure in MFB exhibits particle aggregation which is a natural property of fast fluidized, forming a structure where continuous dilute phase and dispersed concentrated phase coexist. The formation and fragmentation of particle clusters in different local regions of the bed have time synchronization. There exists strong effect of solid back-mixing in MFB, leading to relatively low outlet solid flux. The gas back-mixing effect is, however, not so distinct. The axial porosity shows a monotonically increasing distribution with the bed height, but does not strictly follow the single exponential distribution. The solid volume fraction at the bottom of the bed is significantly lower than the correlated value in CFB. The axial heterogeneous distribution of the cross-sectional average porosity in the lower half of the bed is also weakened. The radial porosity shows a distribution pattern of higher in the central region and lower in the sidewall region. Compared with the correlation results in CFB, the porosity near the central region in MFB is relatively low, while the porosity near the sidewall region is relatively high. The geometric size of the container, especially the radial size, is much smaller than that of a CFB. The relative area of contact between the MFB wall and particles is larger, and the wall friction factor becomes more significant. This may be the main reason for the time synchronization of particle agglomeration, severe particle back-mixing, low outlet solid flux, monotonically increasing axial porosity and weak core-annular structure of radial porosity.

For massive multiple-input multiple-output (MIMO) communications, the minimum mean square error (MMSE) scheme provides close to optimal recognition but involves inverting the high-dimensional matrix. A Gauss-Seidel (GS) detector based on conjugate gradient and Jacobi iteration (CJ) joint processing is introduced to address this problem. Firstly, the signal is initialized with the best of the three initialization regimes for faster algorithm convergence. Secondly, the signal is processed together with CJ. Finally, the pre-processed result is transferred to the GS detector. The simulation results indicate that the proposed iterative algorithm has a lower BER performance than the GS and improved iterative scheme based on GS in channels with different correlation levels.

To overcome the difficulty of separating bastnaesite from fluorite through the flota-tion technique, the present study examined the suitability of sodium alginate (SA) as a depressant in the flotation process. The effect of SA on the flotation separation of bastnaesite and fluorite was evaluated using micro-flotation tests, zeta potential meas-urements, adsorption density measurements, Fourier infrared spectroscopy, and X-ray photoelectron spectroscopy. The micro-flotation results showed that SA exerted a strongly detrimental effect on fluorite flotation, while slightly affecting bastnaesite flotation. The surface chemistry results revealed that the -COO- and HO- functional groups in SA coordinated with Ca2+ on the fluorite surface, which induced hydro-philicity and hindered the adsorption in the subsequent collector. However, the inter-action of SA with the bastnaesite surface was marginal and did not affect the anchor-ing of the collector on the surface of bastnaesite. Based on these results, the present study proposes a possible model for the interaction of SA on the surfaces of the two minerals, laying a foundation for the flotation separation of bastnaesite from fluorite with SA as an environmentally benign depressant.

Compressed Sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically Generative Adversarial Networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning recon-struction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study we present a novel GAN-based model that delivers superior performance without escalating model complexity. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby fo-cusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies af-firmed the efficacy of the modified modules within the network. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent sta-bility but also outperforming other networks in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.

Cervical cancer is a worldwide public health problem with a high rate of illness and mortality among women. In this study, we proposed a novel framework based on Faster RCNN-FPN ar-chitecture for the detection of abnormal cervical cells in cytology images from cancer screening test. We extended the Faster RCNN-FPN model by infusing deformable convolution layers into the feature pyramid network (FPN) to improve scalability. Furthermore, we introduced a global contextual aware module alongside the Region Proposal Network (RPN) to enhance the spatial correlation between the background and the foreground. Extensive experimentations with the proposed deformable and global context aware (DGCA) RCNN were carried out using the cer-vical image dataset of “Digital Human Body" Vision Challenge from the Alibaba Cloud TianChi Company. Performance evaluation based on the mean average precision (mAP) and receiver operating characteristic (ROC) curve has demonstrated considerable advantages of the proposed framework. Particularly, when combined with tagging of the negative image samples using tra-ditional computer-vision techniques, 6-9% increase in mAP has been achieved. The proposed DGCA-RCNN model has potential to become a clinically useful AI tool for automated detection of cervical cancer cells in whole slide images of Pap smear.

p-Hydroxyphenylpyruvate dioxygenase (HPPD) is not only the useful molecular target in treating life-threatening tyrosinemia type I, but also an important target for chemical herbicides. A combined in silico structure-based pharmacophore and molecular docking based virtual screening were performed to identify novel potential HPPD inhibitors. The complex based pharmacophore model (CBP) with 0.721 of ROC used for screening compound showed remarkable ability to retrieve known active ligands from decoy molecule. The ChemDiv database was screened using CBP-Hypo2 as a 3D query, and the best-fit hits subjected to molecular docking with two methods of LibDock and CDOCKER in Accelrys Discovery Studio 2.5(DS 2.5) to discern interactions with key residues at the active site of HPPD. 4 Compounds with top rank in HipHop model and well-known binding model were finally chosen as identification of lead compounds with potentially inhibitory effects on active site of target. The results provided powerful insight to the development of novel HPPD inhibitors herbicides using computational techniques.

The requirements for using noncyanide imitation gold plating as decorative electroplating are increasing; thus, continuously improving the quality of the coating of the imitation gold plating and optimizing the coating process have become the current priority. In this work, hydroxyethylidene diphosphonic acid (HEDP) was used as the main complexing agent, CuSO4·5H2O, ZnSO4·7H2O and NaSnO3·3H2O were the main salts, and NaOH and anhydrous sodium carbonate were used as the buffers to prepare the electroplating solution. Using sodium citrate (SC), sodium potassium tartrate (SS), sodium gluconate (SG), and glycerol (Gl) as four additives, the effects of the number of carboxyl groups on the properties of a Cu-Zn-Sn alloy coating were compared. The electrochemical analysis showed that Cu-Zn-Sn alloy codeposition occurred at -0.50 V vs. Hg|HgO. The scanning electron microscopy (SEM) results showed that the particle size of the coatings obtained with carboxyl-containing additives was more uniform than that obtained with the electroplating solution without additives. The X-ray fluorescence spectrometry (XRF) analysis revealed that the composition of the Cu-Zn-Sn alloy coating obtained by using SC as an additive in the electroplating solution was 89.75 wt% Cu, 9.61 wt% Zn, and 0.64 wt% Sn, and the color of the coating was golden yellow. The X-ray diffraction (XRD) pattern showed that the coating was a mixture of Cu, Cu5Zn8, CuSn, Cu6Sn5, and CuZn phases. The analysis of the electroplating solution by UV, IR and NMR spectroscopy methods indicates that the additives improve the coating by affecting the complexation reaction of metal ions. These results can provide technical and theoretical guidance for developing Cu-Zn-Sn ternary alloy electrodeposition technology with the new cyanide-free HEDP alkaline electroplating system.

In this work, a ZIF-67/MWCNTs/Nafion sensor platform was constructed based on the good adsorption capacity of ZIF-67, the electrical conductivity of multiwalled carbon nanotubes (MWCNTs) and the excellent chemical stability of Nafion for the detection of Cu2+ in water. Meanwhile, the modified materials were characterized by scanning electron microscopy (SEM), Transmission electron microscopy (TEM), BET specific surface area test, X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD) and Fourier transform infrared spectrometry (FT-IR). Cyclic voltammetry (CV), electrochemical impedance (EIS), and square wave solvation voltammetry (SWSV) electrochemical methods were used to perform applied test studies on ZIF-67/MWCNTs/Nafion/GCE. The results show that ZIF-67/MWCNTs/Nafion/GCE has high sensitivity (57.5 μA/μM) and a low limit of detection (15.0 nM) for the electrochemical detection of Cu2+ ions in an electrochemical sensing system. It has high adsorption selectivity for Cu2+, and the recovery of Cu2+ in real water reached 98.6%-103%. The modified electrode has good repeatability, reproducibility, anti-interference, and stability, which makes this sensing platform can be practically applied to the detection of domestic water.

When existing at emulsion interface and continuous phase, dispersibility of water-soluble flexible polymer chains has obvious effect on rheology and dielectric properties of whole emulsion. CNF Pickering emulsion is a good system to research these properties with respect to their microscopic phase structure, dielectric and rheological properties by using cellulose nanofibers (CNF) as water-soluble Pickering emulsifier, liquid Paraffin as oil phase and DDAB as a cationic auxiliary surfactant. The CNF- and DDAB-contents were systematically varied while the water to paraffin oil ratio remained constant to discern the influence of the Pickering emulsifiers. Polarized optical microscopic images revealed that the droplets have tendency to become smaller size for higher CNF content, but bigger size for higher DDAB content, which was proved by the fluorescent analysis for CNF dispersibility with varying DDAB content. The dielectric damping exhibits a minimum, whose value decreases with increasing DDAB- and CNF-content. Increasing the DDAB-content promotes solubilization of CNFs in the aqueous phase and will increase the overall viscosity and yield points. Similarly, a higher CNF-content leads to a higher viscosity and yield point, but at high DDAB-contents the viscosity function exhibits an S-shape at intermediate CNF-content. To evaluate the results further they were compared with CNF-dispersions (without oil-phase), which showed a surfactant effect slightly on maximum stress but strongly on yield stress τy, indicating DDAB can promote the formation of CNF network rather than the viscosity of whole system.

The information of seismic damage of buildings in SAR images of different time phase, especially in SAR images after earthquake, is easily disturbed by other factors, which affects the accuracy of information discrimination. In order to identify and evaluate the distribution information of the seismic damage accurately and make full use of the abundant texture features in the SAR image. The conventional method of change detection based on texture features usually takes the pixel as the calculating unit. In this paper, a method of texture feature change detection of SAR images based on watershed segmentation algorithm is proposed. Based on the optimization of texture feature parameters, the feature parameters are segmented by the watershed segmentation algorithm, and the feature object image is obtained. This method introduces the idea of object oriented, and carries out the calculation of the difference map at the object level, Finally, the classification threshold value of different types of seismic damage types is selected, and the recognition of building damage is achieved. Taking the ALOS data before and after the earthquake in Yushu as an example to verify the effectiveness of the method, the overall accuracy of the building extraction is 88.9%, Compared with pixel-based methods, it is proved that the proposed method is effective.

Sinojackia Hu. comprises five to eight Chinese endemic species with high ornamental and medicinal value. However, the generic limits, interspecific relationships and evolutionary history of the genus remain unresolved. In this study, we newly sequenced and assembled three plastomes of S. oblongicarpa and compared them with those of the other congeneric species to explore the taxonomic delimitation of the species and the evolutionary history of the genus. Plastomes structure of Sinojackia species were extremely conserved in terms of number of genes, sequence length and GC content. Codon usage patterns revealed that natural selection may be the main factor shaping codon usage bias. Our phylogenetic tree shows that Sinojackia is monophyletic and can be divided into two clades. Sinojackia oblongicarpa as a distinct species is supported for it is distantly related to S. sarcocarpa. The evolutionary analysis of morphological features indicates that woody mesocarp is an ancestral feature, while mesocarp undeveloped, spongy and fleshy are the later derived. Sinojackia originated in Central-Southeast China during the early Miocene. In this period, it experienced elevated diversification and migrated from the Central-Southeast China to Hunan Province and Sichuan Province with the development of the Asian monsoon and East Asian flora. Sinojackia experienced elevated diversification at intraspecies levels that mainly occurred in the Quaternary. Glacial-interglacial interactions with the monsoon climate may provide favorable expansion conditions for Sinojackia on a small-scale.

After decades of studies, the pathogenesis of Autism Spectrum Disorder (ASD) remains unclear and there still lackseffective treatment strategies for core symptoms. Our recent work, combined with others’ research in a diversity ofmodels and clinical trials, suggested a concise, but potentially rational theory that can illustrate the key of the pathogenesis and treatment of ASD from the aspect of damage control.

A new type of artificial block is proposed by modifying the traditional block. Based on the physical model experiment, the wave reflection and run-up characteristics of the new ecological blocks under regular waves were examined, and the influence of the height of the "planting trough" border on the hydrodynamic properties was analyzed. The results indicate that the wave reflection and run-up would decrease with a decrease in the breakwater slope. As wave height increases, wave run-up increases, and the reflection coefficient falls. Wave reflection and run-up increase with an increasing wave period. The relative depth of the water has a smaller impact on the wave reflection than the wave run-up. The reflection coefficient of the new ecological blocks is consistently lower than that of the conventional and continues to decrease as the border of the "planting trough" is raised. The raised borders prevent water loss in the trough, resulting in no significant improvement in the wave run-up performance of the new blocks. The reflection coefficient and the relative run-up R/H gradually increase with the average wave breaking parameter ξ increase. The crude permeability coefficients for the new blocks, New Heights 1 and New Heights 2, are 0.53, 0.53, and 0.54, respectively.