Physicochemical characterization and antioxidant activity of polysaccharides from Chlorella sp. by microwave-assisted enzymatic extraction

Microwave-assisted enzymatic extraction (MAEE) was used for the separation of polysaccharides from micro-Chlorella. The extraction condition of MAEE was optimized by Box-Behnken design and response surface methodology. Results showed that the optimal condition for the extraction of Chlorella sp. crude polysaccharides (CSCP) was at 50°C for 2.3 h with 380 W of microwave power and 0.31% of enzyme dosage. Under the optimal extraction condition, the extraction yield of CSCP reached 0.72%. Similarly, the α-amylase modification conditions of the CSCP were also optimized, in which the 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging rate was used as the response value. The scavenging rate of DPPH free radicals was 17.58% when enzyme dosage was 271 U/g at 51°C for 14 min. Moreover, the enzyme-modified CSCP presented a typical heteropolysaccharide mainly including glucose (48.84%), ribose (13.57%) and mannose (11.30%). MAEE used in this work achieved a high extraction yield of CSCP, which provides an efficient method for the extraction of CSCP from Chlorella sp

Transport evidence of asymmetric spin-orbit coupling in few−-layer superconducting 1Td−-MoTe2_2

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) MX2 (M=W, Mo, Nb, and X=Te, Se, S) with strong spin-orbit coupling (SOC) possess plenty of novel physics including superconductivity. Due to the Ising SOC, monolayer NbSe$_2$ and gated MoS$_2$ of 2H structure can realize the Ising superconductivity phase, which manifests itself with in-plane upper critical field far exceeding Pauli paramagnetic limit. Surprisingly, we find that a few-layer 1Td structure MoTe$_2$ also exhibits an in-plane upper critical field ($H_{c2,//}$) which goes beyond the Pauli paramagnetic limit. Importantly, the in-plane upper critical field shows an emergent two-fold symmetry which is different from the isotropic $H_{c2,//}$ in 2H structure TMDCs. We show that this is a result of an asymmetric SOC in 1Td structure TMDCs. The asymmetric SOC is very strong and estimated to be on the order of tens of meV. Our work provides the first transport evidence of a new type of asymmetric SOC in TMDCs which may give rise to novel superconducting and spin transport properties. Moreover, our findings mostly depend on the symmetry of the crystal and apply to a whole class of 1Td TMDCs such as 1Td-WTe$_2$ which is under intense study due to its topological properties.Comment: 34 pages, 12 figure

Multi-terminal transport measurements of MoS2 using a van der Waals heterostructure device platform

Atomically thin two-dimensional semiconductors such as MoS2 hold great promise in electrical, optical, and mechanical devices and display novel physical phenomena. However, the electron mobility of mono- and few-layer MoS2 has so far been substantially below theoretically predicted limits, which has hampered efforts to observe its intrinsic quantum transport behaviours. Potential sources of disorder and scattering include both defects such as sulfur vacancies in the MoS2 itself, and extrinsic sources such as charged impurities and remote optical phonons from oxide dielectrics. To reduce extrinsic scattering, here we developed a van der Waals heterostructure device platform where MoS2 layers are fully encapsulated within hexagonal boron nitride, and electrically contacted in a multi-terminal geometry using gate-tunable graphene electrodes. Magneto-transport measurements show dramatic improvements in performance, including a record-high Hall mobility reaching 34,000 cm2/Vs for 6-layer MoS2 at low temperature, confirming that low-temperature performance in previous studies was limited by extrinsic interfacial impurities rather than bulk defects in the MoS2. We also observed Shubnikov-de Haas oscillations for the first time in high-mobility monolayer and few-layer MoS2. Modeling of potential scattering sources and quantum lifetime analysis indicate that a combination of short-ranged and long-ranged interfacial scattering limits low-temperature mobility of MoS2.Physic

Automatic Sleep Stage Classification Based on Convolutional Neural Network and Fine-Grained Segments

Sleep stage classification plays an important role in the diagnosis of sleep-related diseases. However, traditional automatic sleep stage classification is quite challenging because of the complexity associated with the establishment of mathematical models and the extraction of handcrafted features. In addition, the rapid fluctuations between sleep stages often result in blurry feature extraction, which might lead to an inaccurate assessment of electroencephalography (EEG) sleep stages. Hence, we propose an automatic sleep stage classification method based on a convolutional neural network (CNN) combined with the fine-grained segment in multiscale entropy. First, we define every 30 seconds of the multichannel EEG signal as a segment. Then, we construct an input time series based on the fine-grained segments, which means that the posterior and current segments are reorganized as an input containing several segments and the size of the time series is decided based on the scale chosen depending on the fine-grained segments. Next, each segment in this series is individually put into the designed CNN and feature maps are obtained after two blocks of convolution and max-pooling as well as a full-connected operation. Finally, the results from the full-connected layer of each segment in the input time sequence are put into the softmax classifier together to get a single most likely sleep stage. On a public dataset called ISRUC-Sleep, the average accuracy of our proposed method is 92.2%. Moreover, it yields an accuracy of 90%, 86%, 93%, 97%, and 90% for stage W, stage N1, stage N2, stage N3, and stage REM, respectively. Comparative analysis of performance suggests that the proposed method is better, as opposed to that of several state-of-the-art ones. The sleep stage classification methods based on CNN and the fine-grained segments really improve a key step in the study of sleep disorders and expedite sleep research

Feature Extraction of Marine Water Pollution Based on Data Mining

The ocean occupies more than two-thirds of the earth’s area, providing a lot of oxygen and materials for human survival and development. However, with human activities, a large number of sewage, plastic bags, and other wastes are discharged into the ocean, and the problem of marine water pollution has become a hot topic in the world. In order to extract the characteristics of marine water pollution, this study proposed K-means clustering technology based on cosine distance and discrimination to study the polluted water. In this study, the polygonal area method combined with six parameters of water quality is used to analyze the marine water body anomalies, so as to realize the rapid and real-time monitoring of marine water body anomalies. At the same time, the cosine distance method and discrimination are used to classify marine water pollutants, so as to improve the classification accuracy. The results show that the detection rate of water quality anomalies is more than 88.2%, and the overall classification accuracy of water pollution is 96.3%, which proves the effectiveness of the method. It is hoped that this study can provide timely and effective data support for the detection of marine water bodies

An optimized digital watermarking algorithm in wavelet domain based on differential evolution for color image.

In this paper, a new color watermarking algorithm based on differential evolution is proposed. A color host image is first converted from RGB space to YIQ space, which is more suitable for the human visual system. Then, apply three-level discrete wavelet transformation to luminance component Y and generate four different frequency sub-bands. After that, perform singular value decomposition on these sub-bands. In the watermark embedding process, apply discrete wavelet transformation to a watermark image after the scrambling encryption processing. Our new algorithm uses differential evolution algorithm with adaptive optimization to choose the right scaling factors. Experimental results show that the proposed algorithm has a better performance in terms of invisibility and robustness

A dynamic and resource sharing virtual network mapping algorithm

Network virtualization can effectively establish dedicated virtual networks to implement various network functions. However, the existing research works have some shortcomings, for example, although computing resource properties of individual nodes are considered, node storage properties and the network topology properties are usually ignored in Virtual Network (VN) modelling, which leads to the inaccurate measurement of node availability and priority. In addition, most static virtual network mapping methods allocate fixed resources to users during the entire life cycle, and the users’ actual resource requirements vary with the workload, which results in resource allocation redundancy. Based on the above analysis, in this paper, we propose a dynamic resource sharing virtual network mapping algorithm named NMA-PRS-VNE, first, we construct a new, more realistic network framework in which the properties of nodes include computing resources, storage resources and topology properties. In the node mapping process, three properties of the node are used to measure its mapping ability. Second, we consider the resources of adjacent nodes and links instead of the traditional method of measuring the availability and priority of nodes by considering only the resource properties, so as to more accurately select the physical mapping nodes that meet the constraints and conditions and improve the success rate of subsequent link mapping. Finally, we divide the resource requirements of Virtual Network Requests (VNRs) into basic sub-requirements and variable sub-variable requirements to complete dynamic resource allocation. The former represents monopolizing resource requirements by the VNRs, while the latter represents shared resources by many VNRs with the probability of occupying resources, where we keep a balance between resource sharing and collision among users by calculating the collision probability. Simulation results show that the proposed NMA-PRS-VNE can increase the average acceptance rate and network revenue by 15% and 38%, and reduce the network cost and link pressure by 25% and 17%

Efficacy and safety of transurethral resection of bladder tumour combined with chemotherapy and immunotherapy in bladder-sparing therapy in patients with T1 high-grade or T2 bladder cancer: a protocol for a randomized controlled trial

Abstract Background Bladder cancer is the tenth most common cancer worldwide. For patients with T1 high-grade or T2 bladder cancer, radical cystectomy is recommended. However, radical cystectomy is associated with various complications and has a detrimental impact on the quality of life. Bladder-sparing therapy has been widely explored in patients with muscle-invasive bladder cancer, and whether a combination of transurethral resection of bladder tumour (TURBT) with chemotherapy and immunotherapy shows definite superiority over TURBT plus chemotherapy is still a matter of debate. The aim of this study is to investigate the efficacy and safety of TURBT combined with chemotherapy and immunotherapy in bladder-sparing therapy in patients with T1 high-grade or T2 bladder cancer who are unwilling or unsuitable to undergo radical cystectomy. Methods An open-label, multi-institutional, two-armed randomized controlled study will be performed with 86 patients with T1 high-grade or T2 bladder cancer meeting the eligibility criteria. Participants in the experimental group (n = 43) will receive TURBT combined with chemotherapy (GC: gemcitabine 1000 mg/m2 on the 1st day and the 8th day, cisplatin 70 mg/m2 on the 2nd day, repeated every 21 days) and immunotherapy (toripalimab 240 mg on the 5th day, repeated every 21 days), and those in the control group (n = 43) will receive TURBT plus chemotherapy (GC). The primary outcome is pathological response, and the secondary outcomes include progression-free survival, overall survival, toxicities, and quality of life. Discussion To the best of our knowledge, this is the first study to evaluate the efficacy and safety of TURBT combined with GC regimen and toripalimab in bladder-sparing therapy in patients with T1 high-grade or T2 bladder cancer. The expected benefit is that the combination of TURBT with chemotherapy and immunotherapy would be more effective than TURBT plus chemotherapy without compromising the quality of life and increasing the toxicity. Trial registration ChiCTR2200060546, chictr.org.cn, registered on June 14, 2022

Multi-Granularity Graph Convolution Network for Major Depressive Disorder Recognition

Major depressive disorder (MDD) is the most common psychological disease. To improve the recognition accuracy of MDD, more and more machine learning methods have been proposed to mining EEG features, i.e. typical brain functional patterns and recognition methods that are closely related to depression using resting EEG signals. Most existing methods typically utilize threshold methods to filter weak connections in the brain functional connectivity network (BFCN) and construct quantitative statistical features of brain function to measure the BFCN. However, these thresholds may excessively remove weak connections with functional relevance, which is not conducive to discovering potential hidden patterns in weak connections. In addition, statistical features cannot describe the topological structure features and information network propagation patterns of the brain’s different functional regions. To solve these problems, we propose a novel MDD recognition method based on a multi-granularity graph convolution network (MGGCN). On the one hand, this method applies multiple sets of different thresholds to build a multi-granularity functional neural network, which can remove noise while fully retaining valuable weak connections. On the other hand, this method utilizes graph neural network to learn the topological structure features and brain saliency patterns of changes between brain functional regions on the multi-granularity functional neural network. Experimental results on the benchmark datasets validate the superior performance and time complexity of MGGCN. The analysis shows that as the granularity increases, the connectivity defects in the right frontal(RF) and right temporal (RT) regions, left temporal(LT) and left posterior(LP) regions increase. The brain functional connections in these regions can serve as potential biomarkers for MDD recognition