Heat-Insulated Regenerated Fibers with UV Resistance: Silk Fibroin/Al2O3 Nanoparticles.

The various wastes generated by silkworm silk textiles that are no longer in use are increasing, which is causing considerable waste and contamination. This issue has attracted widespread attention in countries that use a lot of silk. Therefore, enhancing the mechanical properties of regenerated silk fibroin (RSF) and enriching the function of silk are important directions to expand the comprehensive utilization of silk products. In this paper, the preparation of RSF/Al2O3 nanoparticles (NPs) hybrid fiber with different Al2O3 NPs contents by wet spinning and its novel performance are reported. It was found that the RSF/Al2O3 NPs hybrid fiber was a multifunctional fiber material with thermal insulation and UV resistance. Natural light tests showed that the temperature rise rate of RSF/Al2O3 NPs hybrid fibers was slower than that of RSF fibers, and the average temperature rose from 29.1 °C to about 35.4 °C in 15 min, while RSF fibers could rise to about 40.1 °C. UV absorption tests showed that the hybrid fiber was resistant to UV radiation. Furthermore, the addition of Al2O3 NPs may improve the mechanical properties of the hybrid fibers. This was because the blending of Al2O3 NPs promoted the self-assembly of ß-sheets in the RSF reaction mixture in a dose-dependent manner, which was manifested as the RSF/Al2O3 NPs hybrid fibers had more ß-sheets, crystallinity, and a smaller crystal size. In addition, RSF/Al2O3 NPs hybrid fibers had good biocompatibility and durability in micro-alkaline sweat environments. The above performance makes the RSF/Al2O3 NPs hybrid fibers promising candidates for application in heat-insulating and UV-resistant fabrics as well as military clothing.

Node-downloadable frequency transfer system based on a mode-locked laser with over 100 km of fiber.

To meet the requirements of time-frequency networks and enable frequency downloadability for nodes along the link, we demonstrated the extraction of stable frequency signals at nodes using a mode-locked laser under the condition of 100 km laboratory fiber. The node consists of a simple structure that utilizes widely used optoelectronic devices and enables plug-and-play applications. In addition, the node can recover frequency signals with multiple frequencies, which are useful for scenarios that require different frequencies. Here, we experimentally demonstrated a short-term frequency instability of 2.83 × 10-13@1 s and a long-term frequency instability of 1.18 × 10-15@10,000 s at the node, which is similar to that at the remote site of the frequency transfer system. At the same time, frequency signals with different frequencies also achieved stable extraction with the same performance at the node. Our results can support the distributed application under large-scale time-frequency networks.

High-definition image transmission through dynamically perturbed multimode fiber by a self-attention based neural network.

We implement faithful multimode fiber (MMF) image transmission by a self-attention-based neural network. Compared with a real-valued artificial neural network (ANN) based on a convolutional neural network (CNN), our method utilizes a self-attention mechanism to achieve a higher image quality. The enhancement measure (EME) and structural similarity (SSIM) of the dataset collected in the experiment improved by 0.79 and 0.04; the total number of parameters can be reduced by up to 25%. To enhance the robustness of the neural network to MMF bending in image transmission, we use a simulation dataset to prove that the hybrid training method is helpful in MMF transmission of a high-definition image. Our findings may pave the way for simpler and more robust single-MMF image transmission schemes with hybrid training; SSIM on datasets under different disturbances improve by 0.18. This system has the potential to be applied to various high-demand image transmission tasks, such as endoscopy.

Ketamine exerts dual effects on the apoptosis of primary cultured hippocampal neurons from fetal rats in vitro.

Ketamine, a noncompetitive N-methyl D-aspartate (NMDA) receptor antagonist, is widely used in pediatric clinical practice. The neuroprotective and neurotoxic effects of ketamine on brain neurons during development remain controversial. The reason may be related to the different concentrations of ketamine used in practice and the small range of concentrations used in previous studies. In this study, cultured hippocampal neurons were treated with ketamine in a wide range of concentrations to comprehensively observe the effects of different concentrations of ketamine on neurons. We demonstrated that low concentrations of ketamine (10 µM, 100 µM and 1000 µM) promoted neuronal survival (p < 0.05) and reduced neuronal apoptosis (p < 0.05) compared with those of the control group. High concentrations of ketamine (2000 µM, 2500 µM and 3000 µM) reduced neuronal survival (p < 0.05) and promoted neuronal apoptosis (p < 0.05). The p38 MAPK inhibitor SB203580 reduced neuronal apoptosis induced by high concentrations of ketamine (2500 µM) (p < 0.05). Our findings indicate that ketamine exerts a dual effect on the apoptosis of primary cultured fetal rat hippocampal neurons in vitro and that the neurotoxic effects of ketamine are related to activation of the p38 MAPK signaling pathway.

Rapid diagnosis of cervical cancer based on serum FTIR spectroscopy and support vector machines.

Cervical cancer is one of the most common malignant tumors among female gynecological diseases. This paper aims to explore the feasibility of utilizing serum Fourier Transform Infrared (FTIR) spectroscopy, combined with machine learning and deep learning algorithms, to efficiently differentiate between healthy individuals, hysteromyoma patients, and cervical cancer patients. In this study, serum samples from 30 groups of hysteromyoma, 36 groups of cervical cancer, and 30 healthy groups were collected and FTIR spectra of each group were recorded. In addition, the raw datasets were averaged according to the number of scans to obtain an average dataset, and the raw datasets were spectrally enhanced to obtain an augmentation dataset, resulting in a total of three sets of data with sizes of 258, 96, and 1806, respectively. Then, the hyperparameters in the four kernel functions of the Support Vector Machine (SVM) model were optimized by grid search and leave-one-out (LOO) cross-validation. The resulting SVM models achieved recognition accuracies ranging from 85.0% to 100.0% on the test set. Furthermore, a one-dimensional convolutional neural network (1D-CNN) demonstrated a recognition accuracy of 75.0% to 90.0% on the test set. It can be concluded that the use of serum FTIR spectroscopy combined with the SVM algorithm for the diagnosis of cervical cancer has important medical significance.

The Size Effect of Silver Nanoparticles on Reinforcing the Mechanical Properties of Regenerated Fibers.

Regenerated silk fibroin (RSF), made from discarded silk cocoons, can be processed into regenerated silk fibers by a simple, inexpensive, and environmentally friendly wet-spinning process. However, the breaking strength and toughness of most RSF fibers are lower than those of natural silk. In this study, Ag nanoparticles (NPs) of different sizes were introduced into RSF to form RSF/AgNPs hybrid fibers by wet spinning. The effects of AgNPs of different sizes on the mechanical properties and structure of the hybrid fibers were investigated. The results demonstrated that the mechanical properties of hybrid fibers were significantly improved, especially the breaking strain, after the addition of four different sizes of AgNPs. With the reduction in AgNPs size (2-60 nm), the breaking strength and breaking strain of hybrid fibers tended to increase. The results showed that the hybrid fibers containing 2 nm AgNPs were remarkable, with excellent mechanical properties and toughness, and the breaking strain reached 138.27%, which was far greater than blank RSF fibers (15.02%) and even natural silk (about 21%). The S-FTIR and WAXD showed that, compared with the larger AgNPs, the smaller AgNPs contributed more to the formation of silk fibroin ß-sheet and crystallinity, and reduced the ß-crystallite size. This study is helpful to understand the relationship between the size of nanoparticles and the mechanical properties of hybrid fibers.

The Photothermal Conversion and UV Resistance of Silk Fabrics Being Achieved through Surface Modification with C@SiO2 Nanoparticles.

With the improvement in people's living standards, the development and application of smart textiles are receiving increasing attention. In this study, a carbon nanosurface was successfully coated with a SiO2 layer to form C@SiO2 nanomaterials, which improved the dispersion of carbon nanomaterials in an aqueous solution and enhanced the absorption of light by the carbon nanoparticles. C@SiO2 nanoparticles were coupled on the surface of silk fabric with the silane coupling agent KH570 to form C@SiO2 nanosilk fabric. The silk fabric that was subjected to such surface modification was endowed with a special photothermal function. The results obtained with scanning electron microscopy (SEM), energy dispersive spectrometer (EDS), and infrared spectroscopy (FTIR) showed that C@SiO2 nanoparticles were successfully modified on the surface of the silk fabric. In addition, under the irradiation of near-infrared light with a power of 20 W and a wavelength of 808 nm, the C@SiO2 nanosilk fabric experienced rapid warming from 23 °C to 60 °C within 30 s. After subjecting the functional fabric to hundreds of photothermal experiments and multiple washes, the photothermal efficiency remained largely unchanged and proved to be durable and stable. In addition, the thermogravimetric (TG) analysis results showed that the C@SiO2 nanoparticles contributed to the thermal stability of the silk fabric. The UV transmittance results indicated that C@SiO2 nanofabric is UV-resistant. The silk modification method developed in this study is low-cost, efficient, and environmentally friendly. It has some prospects for future applications in the textile industry.

The Study of Alanine Transaminase Activity in Tissues of Silkworm (Bombyx mori) via Direct Analysis in Real-Time (DART) Mass Spectrometry.

Alanine transaminase (ALT) is an important amino acid-metabolizing enzyme in silkworm Bombyx mori L., and is mainly involved in transferring glutamate to alanine (serving as an essential precursor in silk protein synthesis) through transamination. Therefore, it is generally believed that silk protein synthesis in the silk gland and the cocoon quantity increase with the increase in ALT activity to a certain extent. Here, a novel analytical method was developed to determine the ALT activity in several key tissues of Bombyx mori L. including the posterior silk gland, midgut, fat body, middle silk gland, trachea and hemolymph, by combining the direct-analysis-in-real-time (DART) ion source with a triple-quadrupole mass spectrometer. In addition, a traditional ALT activity assay, the Reitman-Frankel method, was also used to measure ALT activity for comparison. The ALT activity results obtained via the DART-MS method are in good agreement with those obtained via the Reitman-Frankel method. However, the present DART-MS method provides a more convenient, rapid and environmentally friendly quantitative method for ALT measurement. Especially, this method can also monitor ALT activity in different tissues of Bombyx mori L. in real time.

Edge detection in single multimode fiber imaging based on deep learning.

We propose a new edge detection scheme based on deep learning in single multimode fiber imaging. In this scheme, we creatively design a novel neural network, whose input is a one-dimensional light intensity sequence, and the output is the edge detection result of the target. Different from the traditional scheme, we can directly obtain the edge information of unknown objects by using this neural network without rebuilding the image. Simulation and experimental results show that, compared with the traditional method, this method can get better edge details, especially in the case of low sampling rates. It can increase the structural similarity index of edge detection imaging from 0.38 to 0.62 at the sampling rate of 0.6%. At the same time, the robustness of the method to fiber bending is also proved. This scheme improves the edge detection performance of endoscopic images and provides a promising way for the practical application of multimode fiber endoscopy.

Using FADOF to eliminate the background light influence in ghost imaging.

The high solar background during the day adversely affects the long distance daytime operations of ghost imaging. It is extremely hard to distinguish the signal light from the background noise light after they are both converted to voltage or current signals by the bucket detector, so spectral filtering before the detector is quite important. In this work, a Faraday anomalous dispersion optical filter (FADOF) is used in eliminating the background light influence in ghost imaging. Results of lab experiment show that the background light noise tolerance of the ghost imaging with FADOF is at least 18 times bigger than that with a 10 nm optical filter. The method has simple structure, great performance and great algorithms compatibility.

Cooperatively Routing a Truck and Multiple Drones for Target Surveillance.

With the development of drone technology, drones have been deployed in civilian and military fields for target surveillance. As the endurance of drones is limited, large-scale target surveillance missions encounter some challenges. Based on this motivation, we proposed a new target surveillance mode via the cooperation of a truck and multiple drones, which enlarges the range of surveillance. This new mode aims to rationally plan the routes of trucks and drones and minimize the total cost. In this mode, the truck, which carries multiple drones, departs from its base, launches small drones along the way, surveils multiple targets, recycles all drones and returns to the base. When a drone is launched from the truck, it surveils multiple targets and flies back to the truck for recycling, and the energy consumption model of the drone is taken into account. To assist the new problem-solving, we developed a new heuristic method, namely, adaptive simulated annealing with large-scale neighborhoods, to optimize truck and drone routes, where a scoring strategy is designed to dynamically adjust the selection weight of destroy operators and repair operators. Additionally, extensive experiments are conducted on several synthetic cases and one real case. The experimental results show that the proposed algorithm can effectively solve the large-scale target surveillance problem. Furthermore, the proposed cooperation of truck and drone mode brings new ideas and solutions to targets surveillance problems.

An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship.

Airship-based Earth observation is of great significance in many fields such as disaster rescue and environment monitoring. To facilitate efficient observation of high-altitude airships (HAA), a high-quality observation scheduling approach is crucial. This paper considers the scheduling of the imaging sensor and proposes a hierarchical observation scheduling approach based on task clustering (SA-TC). The original observation scheduling problem of HAA is transformed into three sub-problems (i.e., task clustering, sensor scheduling, and cruise path planning) and these sub-problems are respectively solved by three stages of the proposed SA-TC. Specifically, a novel heuristic algorithm integrating an improved ant colony optimization and the backtracking strategy is proposed to address the task clustering problem. The 2-opt local search is embedded into a heuristic algorithm to solve the sensor scheduling problem and the improved ant colony optimization is also implemented to solve the cruise path planning problem. Finally, extensive simulation experiments are conducted to verify the superiority of the proposed approach. Besides, the performance of the three algorithms for solving the three sub-problems are further analyzed on instances with different scales.

A Coordinated Vehicle-Drone Arc Routing Approach Based on Improved Adaptive Large Neighborhood Search.

Through urban traffic patrols, problems such as traffic congestion and accidents can be found and dealt with in time to maintain the stability of the urban traffic system. The most common way to patrol is using ground vehicles, which may be inflexible and inefficient. The vehicle-drone coordination maximizes utilizing the flexibility of drones and addresses their limited battery capacity issue. This paper studied a vehicle-drone arc routing problem (VD-ARP), consisting of one vehicle and multiple drones. Considering the coordination mode and constraints of the vehicle-drone system, a mathematical model of VD-ARP that minimized the total patrol time was constructed. To solve this problem, an improved, adaptive, large neighborhood search algorithm (IALNS) was proposed. First, the initial route planning scheme was generated by the heuristic rule of "Drone-First, Vehicle-Then". Then, several problem-based neighborhood search strategies were embedded into the improved, adaptive, large neighborhood search framework to improve the quality of the solution. The superiority of IALNS is verified by numerical experiments on instances with different scales. Several critical factors were tested to determine the effects of coordinated traffic patrol; an example based on a real road network verifies the feasibility and applicability of the algorithm.

A Dynamic Task Scheduling Method for Multiple UAVs Based on Contract Net Protocol.

Unmanned aerial vehicles are becoming promising platforms for disaster relief, such as providing emergency communication services in wireless sensor networks, delivering some living supplies, and mapping for disaster recovery. Dynamic task scheduling plays a very critical role in coping with emergent tasks. To solve the multi-UAV dynamic task scheduling, this paper constructs a multi-constraint mathematical model for multi-UAV dynamic task scheduling, involving task demands and platform capabilities. Three objectives are considered, which are to maximize the total profit of scheduled tasks, to minimize the time consumption, and to balance the number of scheduled tasks for multiple UAVs. The multi-objective problem is converted into single-objective optimization via the weighted sum method. Then, a novel dynamic task scheduling method based on a hybrid contract net protocol is proposed, including a buy-sell contract, swap contract, and replacement contract. Finally, extensive simulations are conducted under three scenarios with emergency tasks, pop-up obstacles, and platform failure to verify the superiority of the proposed method.

Asymmetric Arc Routing by Coordinating a Truck and Multiple Drones.

Unmanned Aerial Vehicles, commonly known as drones, have been widely used in transmission line inspection and traffic patrolling due to their flexibility and environmental adaptability. To take advantage of drones and overcome their limited endurance, the patrolling tasks are parallelized by concurrently dispatching the drones from a truck which travels on the road network to the nearby task arc. The road network considered in previous research is undirected; however, in reality, the road network usually contains unidirectional arcs, i.e., the road network is asymmetric. Hence, we propose an asymmetric coordinated vehicle-drones arc routing mode for traffic patrolling. In this mode, a truck travelling on an asymmetric road network with multiple drones needs to patrol multiple task arcs, and the drones can be launched and recovered at certain nodes on the truck route, making it possible for drones and the truck to patrol the task in parallel. The total patrol time is the objective function that needs to be minimized given the time limit constraints of drones. The whole problem can be considered as an asymmetric arc routing problem of coordinating a truck and multiple drones. To solve this problem, a large-scale neighborhood search with simulated annealing algorithm (LNS-SA) is proposed. Finally, extensive computation experiments and a real case are carried out. The experimental results show the efficiency of the proposed algorithm. Moreover, a detailed sensitivity analysis is performed on several drone-parameters of interest.

[Determination of organophosphorus pesticide residues in milk of cows and sheep by ultra-high-performance liquid chromatography- tandem mass spectrometry combined with passing type purification method].

OBJECTIVE: A method for the rapid determination of dichlorvos, trichlorfon, fenthion, fenthion-sulfone, fenthion-sulfoxide, fenthion-oxon, fenthion-oxon-sulfone, fenthion-oxon-sulfoxide, phoxim, propetamphos, malathion, diazinon and coumaphos 13 common organophosphorus pesticides and their metabolites poison residues in milk of cows and sheep by ultra-high-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS) with passing type solid phase extraction(SPE) purification was developed. METHODS: After centrifugation at 4 ℃, the milk was purified by passing type SPE with acetonitrile precipitating protein and determined by UPLC-MS/MS in electrospray positive ion mode(ESI+) and multi-reaction monitoring scanning(MRM), external standard method for quantitative analysis with matrix matching standard curve. RESULTS: The recoveries of 13 target compounds were between 81.5% and 107.5% and relative standard deviation was between 1.24% and 6.23% at three spiked levels of 5, 10, 20 µg/L. The detection limits of 13 target compounds were between 0.015 and 0.15 µg/L, and the quantitative limits were between 0.05 and 0.50 µg/L. No organophosphorus pesticide residues were detected in 20 samples of cows and sheep milk. CONCLUSION: The method has the advantages of good linear independence, low detection limit, high precision and accuracy, and can be used for daily monitoring of milk and related products.

Clustering-based CLEAN algorithm in ghost imaging with sparse spatial frequencies.

When insufficient samples in the spatial frequency domain could be effectively compensated by the modified CLEAN algorithm, a novel aperture-synthetic scheme of ghost imaging takes advantage of a superior speed of modulation and an enhancement on the spatial resolution. However, there still exist some imperfections in the modified CLEAN reconstructions, such as the fact that some omitted scatter noise still remains or the object contour may be incomplete. Therefore, we optimize the modified CLEAN algorithm by proposing a density clustering algorithm to overcome these drawbacks and improve the visual quality.

Compressive endoscopic imaging with complementary light modulation.

We propose an effective endoscopic imaging method utilizing compressive sensing (CS) theory on the basis of complementary light modulation of a spatial light modulator. Both the simulated and the experimental results show that complementary compressive sensing (CCS) always needs less time to obtain better work than conventional CS with normal modulation at the same sampling rate. First, the speed of CCS is at least twice as fast as CS. Second, in comparison with CS, CCS can improve the signal-to-noise ratio of the reconstructed image by 49.7%, which indicates that this method is of great significance to endoscopic applications in terms of image fidelity and denoising performance.

Cell membrane-encapsulated magnetic nanoparticles for enhancing natural killer cell-mediated cancer immunotherapy.

Natural killer (NK) cells have exhibited therapeutic potential for various malignant tumors. However, the cytotoxic effect of NK cells is relatively weak and less specific compared to other immunotherapy approaches such as chimeric antigen receptor T-Cell (CART) therapy, constituting a great challenge for adoptive immunotherapy. Here, we report cell membrane-encapsulated magnetic nanoparticles for activating NK cells and enhancing anti-tumor effects. Magnetic nanoparticles were coated with silicon dioxide (SiO2), and cancer cell membranes were mixed with Fe3O4@SiO2 to construct cancer cell membrane coated Fe3O4@SiO2 magnetic nanoparticles (CMNPs). The functionalized nanoparticles bearing cancer-specific antigens on the surface effectively stimulated NK cells by enhancing expression of surface activating receptors and boosting anti-tumor function through the secretion of soluble cytotoxic effectors. To conclude, the biomimetic magnetic nanoparticles offer a versatile and powerful tool to present tumor-specific antigens, priming anti-tumor capability, which is promising to enhance NK cell-based adoptive cancer immunotherapy.

Raman spectroscopic study of cervical precancerous lesions and cervical cancer.

Early detection of cervical lesions, accurate diagnosis of cervical lesions, and timely and effective therapy can effectively avoid the occurrence of cervical cancer or improve the survival rate of patients. In this paper, the spectra of tissue sections of cervical inflammation (n = 60), CIN (cervical intraepithelial neoplasia) I (n = 30), CIN II (n = 30), CIN III (n = 30), cervical squamous cell carcinoma (n = 30), and cervical adenocarcinoma (n = 30) were collected by a confocal Raman micro-spectrometer (LabRAM HR Evolution, Horiba France SAS, Villeneuve d'Ascq, France). The Raman spectra of six kinds of cervical tissues were analyzed, the dominant Raman peaks of different kinds of tissues were summarized, and the differences in chemical composition between the six tissue samples were compared. An independent sample t test (p ≤ 0.05) was used to analyze the difference of average relative intensity of Raman spectra of six types of cervical tissues. The difference of relative intensity of Raman spectra of six kinds of tissues can reflect the difference of biochemical components in six kinds of tissues and the characteristic of biochemical components in different kinds of tissues. The classification models of cervical inflammation, CIN I, CIN II, CIN III, cervical squamous cell carcinoma, and cervical adenocarcinoma were established by using a support vector machine (SVM) algorithm. Six types of cervical tissues were classified and identified with an overall diagnostic accuracy of 85.7%. This study laid a foundation for the application of Raman spectroscopy in the clinical diagnosis of cervical precancerous lesions and cervical cancer.