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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

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.

Device vulnerabilities emerge one after another in the Internet of thing environment, the attackers attack vulnerabilities on several low-level devices simultaneously by multi-step attack method to trigger the vulnerabilities on other high-level devices to damage or control the information system. Considering the correlation between device vulnerabilities, we proposed a method based on attack graph to evaluate vulnerability risk in order to ensure Internet of thing network security. First, according to the type, version, and other relevant information of device vulnerabilities in the Internet of thing environment, hidden Markov model can be used to model the association between device states. Second, analyze the possible attacks on the vulnerabilities on the device, and generate the attack graph according to the correlation between the device states and the relevant information of the vulnerabilities in the device. Finally, the vulnerabilities are objectively and accurately evaluated according to the attack graph. The experiments results show that the proposed method can map the relationship between devices more accurately and objectively and improve the efficiency and accuracy of the vulnerability evaluation. [ABSTRACT FROM AUTHOR]

Polymeric β‐nucleating agents (β‐NAs) have the advantages of good thermal stability, low toxicity, and relatively superior compatibility with isotactic polypropylene (iPP). However, endowing polymeric β‐NAs with high efficiency remains a challenge. Herein, we report a strategy that incorporates ionic monomers into polymeric β‐NAs to significantly enhance their nucleation efficiency. Sodium dimethyl 5‐sulfonatoisophthalate, an ionic monomer, is introduced into a liquid crystalline polyester NA (PBDPS), and the resulting ionomer is named PBDPSi. The attractive interaction between the ion pairs acts as physical crosslinks and stabilizes the active nucleation center of the ordered benzene rings. Moreover, the ionic monomer modifies the viscosity of the liquid crystalline NA and improves the dispersibility of PBDPSi in the iPP matrix. In addition, it enhances the relative growth rate of β‐spherulites of iPP. When 5% sulfonate monomer is introduced into the NA, PBDPSi exhibits the optimal β‐nucleation effect, and the maximum value (0.97) of the relative content of β‐iPP crystals (kβ) is obtained. Compared with PBDPS, PBDPSi shows better β‐nucleation performance, especially at low concentrations. The incorporation of 0.1 wt% PBDPSi into iPP provides a kβ value of 0.41, which is almost four times that obtained with PBDPS. [ABSTRACT FROM AUTHOR]

In this article, the molecular-weight dependence of the phase structure, rheological behaviour and thermal stability of a main-chain liquid crystalline polyester (PBDPS) with strong π-π interaction was investigated in details by differential scanning calorimetry, polarising optical microscopy, wide-angle X-ray diffraction, dynamic oscillatory rheological measurements and thermogravimetric analysis. While the molecular weight of PBDPS is 5.2k, it shows smectic B and smectic A phases prior to isotropic phase. With increasing molecular weight, smectic B phase of PBDPS gradually disappeared. Moreover, the smecitc B to smectic A (Tlc) and smectic A to isotropic (Ti) phase transition temperatures decreased as increased molecular weight. The rheology results suggested that fluid type of PBDPSs changed from Newtonian fluid to pseudo-plastic fluid as the molecular weight of PBDPS increased. In addition, the zero-shear viscosity (η0) of PBDPS showed a power law relationship () with molecular weight of PBDPS. All the PBDPSs showed good thermal stability with an initial decomposition temperature higher than 360°C. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study is to establish a prognostic model to predict postrecurrence survival (PRS) probability after initial resection of hepatocellular carcinoma (HCC)., Study Design: Patients with recurrent HCC after curative resection were identified through a multicenter consortium (training cohort, TC); data were from a separate institution were used as validation cohort (VC). The α-fetoprotein (AFP) tumor burden score (ATS) was defined as the distance from the origin on a 3-dimensional Cartesian coordinate system that incorporated 3 variables: largest tumor diameter ( x axis), number of tumors ( y axis), and ln AFP ( z axis). ATS was calculated using the Pythagorean theorem: ATS 2 = (largest tumor diameter) 2 + (number of tumors) 2 + (ln AFP) 2 , where ATS d and ATS r represent ATS at the time of initial diagnosis and at the time of recurrence, respectively. The final model was ATS m = ATS d + 4 × ATS r . Predictive performance and discrimination of the ATS model were evaluated and compared with traditional staging systems., Results: The ATS model demonstrated strong predictive performance of PRS in both the TC (area under the curve [AUC] 0.70) and VC (AUC 0.71). An ATS-based nomogram was able to stratify patients accurately into low- and high-risk categories relative to PRS (TC: ATS m ≤ 27, 74.9 months vs. ATS m ≥ 28, 23.3 months; VC: ATS m ≤ 27, 59.4 months vs. ATS m ≥ 28, 15.1 months; both p < 0.001). The ATS model predicted PRS among patients undergoing curative or noncurative treatment of HCC recurrence (both p < 0.05). Of note, the ATS model outperformed the Barcelona Clinic Liver Cancer (BCLC), China Liver Cancer (CNLC), and American Joint Committee on Cancer (AJCC) staging systems relative to 1-, 2-, 3-, 4- and 5-year PRS (AUC 0.70, vs. BCLC, AUC 0.50, vs. CNLC, AUC 0.54, vs. AJCC, AUC 0.51)., Conclusions: The ATS model had excellent prognostic discriminatory power to stratify patients relative to PRS., (Copyright © 2023 by the American College of Surgeons. Published by Wolters Kluwer Health, Inc. All rights reserved.)