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Accurate knowledge of species distributions and population dynamics is the basis for conservation biology. However, for certain species with unique life histories and very small populations, monitoring species distributions and population dynamics is extremely difficult. Recently, newly developed eDNA technology has been widely used in species monitoring, biodiversity assessments, and biomass assessments. In this research, we studied Fenneropenaeus chinensis in the Bohai Sea. We used a 0.45 μm glass fiber filter combined with a vacuum to filter 2 L seawater samples for eDNA enrichment from 54 stations in June and 60 stations in August. A DNeasy Blood and Tissue kit was used to extract the eDNA. After DNA extraction, the specific primers and probes of the mitochondrial DNA COI gene of F. chinensis were designed. Real-time quantitative PCR was used to qualitatively and quantitatively analyze the eDNA in the Bohai Sea. The temporal and spatial distribution of F. chinensis was detected by the eDNA technique. The relationship between the eDNA copy number and trawl-netted F. chinensis biomass was explored. The applicability and sensitivity of eDNA technology in marine crustacean research were verified. The results showed that the mtDNA COI gene of F. chinensis was successfully amplified in the 54 water samples collected in June, and the detection rate reached 100%. Detections occurred in only 23 of the 60 stations sampled in August. F. chinensis had a detection rate of only 38%. The copy number of eDNA obtained by real-time PCR was fitted to the density of F. chinensis captured by bottom trawling. There was not a significant positive correlation between copy number and biomass. The results obtained through this research will provide a method and theoretical basis for the application of eDNA technology in marine crustacean research. Keywords: Bohai Sea, eDNA, Fenneropenaeus chinensis, Spatial and temporal distribution, Biomass

This work is aimed to develop the key algorithms used in medical robots, which can detect alcohol use disorder from structural magnetic resonance imaging of brains. We enrolled 30 alcoholic participants and 30 nonalcoholic participants. In the algorithm stage, we suggested to use Hu moment invariant to extract global features, and use single-hidden layer neural-network as the classifier. Afterwards, we proposed a novel predator–prey adaptive-inertia chaotic particle swarm optimization algorithm to train the classifier. The ten-fold stratified cross validation showed that our method achieves a sensitivity of 90.67 ± 3.16%, a specificity of 91.33 ± 3.06%, and an accuracy of 91.00 ± 1.41%. Our results are better than genetic algorithm, firefly algorithm, and particle swarm optimization. This proposes algorithm is effective in alcoholism detection. It can be installed on medical robots.

This paper investigates the design of longitudinal autopilot with servo-elastic problems in elastic missiles. Since many previous static designs of the missile control system treat missile as an absolute rigid body which can no longer meet current actual needs, the slenderness of missile body is considered, and the model of rigid motion with elastic vibration is established for the purpose of practical applications. Moreover, in order to overcome obvious harmful effect of elastic vibration on angular velocity feedback, the amplitude and phase stabilization methods are adopted to deal with the first two modes of vibration in the angular velocity feedback loop, then through compensation for the lever arm effect and other measures to ensure the final performance of the system. Finally, the effectiveness in the design of autopilot with elastic characteristics of the proposed method combining amplitude and phase stabilization is demonstrated with a simulation.

It is of enormous significance to detect abnormal brains automatically. This paper develops an efficient pathological brain detection system based on the artificial intelligence method. We first extract brain edges by a Canny edge detector. Next, we estimated the fractal dimension using box counting method with grid sizes of 1, 2, 4, 8, and 16, respectively. Afterward, we employed the single-hidden layer feedforward neural network. Finally, we proposed an improved particle swarm optimization based on three-segment particle representation, time-varying acceleration coefficient, and chaos theory. This three-segment particle representation encodes the weights, biases, and number of hidden neuron. The statistical analysis showed the proposed method achieves the detection accuracies of 100%, 98.19%, and 98.08% over three benchmark data sets. Our method costs merely 0.1984 s to predict one image. Our performance is superior to the 11 state-of-the-art approaches.

Several resting-state neuroimaging studies have indicated abnormal regional homogeneity (ReHo) in chronic schizophrenia; however, little work has been conducted to investigate naive patients with first-episode schizophrenia (FES). Even less investigated is the association between ReHo measures and clinical symptom severity in naive patients with FES. The current study evaluated ReHo alterations in whole brain, and assessed the correlations between ReHo measures and clinical variables in naive patients with FES. Forty-four naive patients with FES and 26 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Group-level analysis was utilized to analyze the ReHo differences between FES and HC in a voxel-by-voxel manner. Severity of symptoms was evaluated using a five-factor model of the Positive and Negative Syndrome Scale (PANSS). The correlation between the severity of symptoms and ReHo map was examined in patients using voxel-wise correlation analyses within brain areas that showed a significant ReHo alteration in patients compared with controls. Compared with the healthy control group, the FES group showed a significant decrease in ReHo values in the left medial frontal gyrus (MFG), right precentral gyrus, left superior temporal gyrus (STG), left left middle temporal gyrus (MTG), left thalamus, and significant increase in ReHo values in the left MFG, left inferior parietal lobule (IPL), left precuneus, and right lentiform nucleus (LN). In addition, the correlation analysis showed the PANSS total score negatively correlated with ReHo in the right precentral gyrus and positively correlated with ReHo in the left thalamus, the positive factor positively correlated with ReHo in the right thalamus, the disorganized/concrete factor positively correlated with ReHo in left posterior cingulate gyrus (PCG), the excited factor positively correlated with ReHo in the left precuneus, and the depressed factor negatively correlated with ReHo in the right postcentral gyrus and positively correlated with ReHo in the right thalamus. Our results indicate that widespread ReHo abnormalities occurred in an early stage of schizophrenic onset, suggesting a potential neural basis for the pathogenesis and symptomatology of schizophrenia.

Alcohol use disorder (AUD) is an important brain disease. It alters the brain structure. Recently, scholars tend to use computer vision based techniques to detect AUD. We collected 235 subjects, 114 alcoholic and 121 non-alcoholic. Among the 235 image, 100 images were used as training set, and data augmentation method was used. The rest 135 images were used as test set. Further, we chose the latest powerful technique-convolutional neural network (CNN) based on convolutional layer, rectified linear unit layer, pooling layer, fully connected layer, and softmax layer. We also compared three different pooling techniques: max pooling, average pooling, and stochastic pooling. The results showed that our method achieved a sensitivity of 96.88%, a specificity of 97.18%, and an accuracy of 97.04%. Our method was better than three state-of-the-art approaches. Besides, stochastic pooling performed better than other max pooling and average pooling. We validated CNN with five convolution layers and two fully connected layers performed the best. The GPU yielded a 149× acceleration in training and a 166× acceleration in test, compared to CPU.

In order to detect cerebral microbleed more efficiently, we developed a novel computer-aided detection method based on susceptibility-weighted imaging. We enrolled five CADASIL patients and five healthy controls. We used a 20x20 neighboring window to generate samples on each slice of the volumetric brain images. The sparse autoencoder (SAE) was used to unsupervised feature learning. Then, a deep neural network was established using the learned features. The results over 10x10-fold cross validation showed our method yielded a sensitivity of 93.20±1.37%, a specificity of 93.25±1.38%, and an accuracy of 93.22±1.37%. Our result is better than Roy's method, which was proposed in 2015.

Background Facial emotion perception is impaired in schizophrenia. Although the pathology of schizophrenia is thought to involve abnormality in white matter (WM), few studies have examined the correlation between facial emotion perception and WM abnormalities in never-medicated patients with first-episode schizophrenia. The present study tested associations between facial emotion perception and WM integrity in order to investigate the neural basis of impaired facial emotion perception in schizophrenia. Methods Sixty-three schizophrenic patients and thirty control subjects underwent facial emotion categorization (FEC). The FEC data was inserted into a logistic function model with subsequent analysis by independent-samples T test and the shift point and slope as outcome measurements. Severity of symptoms was measured using a five-factor model of the Positive and Negative Syndrome Scale (PANSS). Voxelwise group comparison of WM fractional anisotropy (FA) was operated using tract-based spatial statistics (TBSS). The correlation between impaired facial emotion perception and FA reduction was examined in patients using simple regression analysis within brain areas that showed a significant FA reduction in patients compared with controls. The same correlation analysis was also performed for control subjects in the whole brain. Results The patients with schizophrenia reported a higher shift point and a steeper slope than control subjects in FEC. The patients showed a significant FA reduction in left deep WM in the parietal, temporal and occipital lobes, a small portion of the corpus callosum (CC), and the corona radiata. In voxelwise correlation analysis, we found that facial emotion perception significantly correlated with reduced FA in various WM regions, including left forceps major (FM), inferior longitudinal fasciculus (ILF), inferior fronto-occipital fasciculus (IFOF), Left splenium of CC, and left ILF. The correlation analyses in healthy controls revealed no significant correlation of FA with FEC task. Conclusions These results showed disrupted WM integrity in these regions constitutes a potential neural basis for the facial emotion perception impairments in schizophrenia.

In this paper, polypropylene (PP) melt blown non-woven fabric is treated by atmospheric He, He/O2 or He/CO2 glow discharge plasma. The variation of the surface hidrophilicity of PP sample is experimentally investigated by the surface water contact angle. The results show that the surface water contact angle firstly has a sharp decline and then up to saturation. It is worth noting that the time the water contact angle is up to saturation greatly shortens when a spot of O2 or CO2 are added into the atmospheric He. In addition, when the PP samples are treated by the atmospheric glow discharge plasma, it is necessary of selecting a right applied voltage.

In this paper, the influences of applied magnetic field on flow state during electroforming of the high-aspect-ratio (HAR) blind micro-hole were numerically analyzed using the Fluent software. The results showed that, when microelectroforming of nickel without external agitation, three vortexes could form due to the magnetohydrodynamic (MHD) effect within the HAR micro-hole with magnetic field in parallel to cathode-electrode surface, and the flow rate in the micro-hole increased with the increase of the magnetic field and current density. The MHD effect helped to enhance mass transfer during the microelectroforming of HAR microstructures.

In this paper, variable thrust angle (VTA) constant thrust rendezvous is studied. In particular, the rendezvous process is divided into in-plane motion and out-plane motion based on the relative motion dynamic model. For the in-plane motion, the calculation of thrust angle control lows is cast into a convex optimization problem by introducing a Lyapunov function subject to linear matrix inequalities. For the out-plane motion, a new algorithm of constant thrust fitting is proposed through the impulse compensation. The illustrative example is provided to show the effectiveness of the proposed control design method.

We investigate the optimum combing system of a Rician fading signal with unequal power Rician interferers and thermal noise. Based on the statistical characteristics analysis of the output SINR, we derive a close form upper bound for the symbol error probability of M-ary phase-shift keying. Simulation results demonstrate the tightness of the derived bound.

This paper addresses a novel design scheme of the limited feedback codebook when channel estimation is not ideal in a Multiple Input Single Output (MISO) system. Transmit beamforming relies on the current channel knowledge, take the estimation error into the consideration of the codebook design can improve the system performance. We propose a simple codebook design algorithm that allows the codebook to be easily modified when either the spatial correlation or the channel estimation error changes. With the lower complexity and lighter storage burden, our scheme can reach almost the same ergodic capacity performance with the Lloyd algorithm. Numerical results verify the effectiveness of our proposed scheme.