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In order to obtain endophytic selenium-rich yeast from Lycium barbarum with strong ability producing alcohol and aroma, and then promote the selenium-rich fermentation products and improve the flavor, this study took the endophytic yeast of L. barbarum stored in the laboratory as experimental materials, and the yeast with strong selenium-rich ability was obtained through the initial screening of selenium-resistant method and red selenium methods. Atmospheric room temperature plasma (ARTP) technology was used to obtain mutant strains and through TTC medium, Duchenne tubule fermentation, alcohol production, ester and acid production, tolerance and other experiments to screen out the target strains of aroma enhancement and high alcohol production. The content of selenium in yeast cell and fermentation liquid was determined by hydride generation atomic fluorescence spectrometry. The results showed that M1 Saccharomyces cerevisiae and M16 Clavispora lusitaniae were obtained by primary selenium screening, the mutagenic strain M1-5 of S. cerevisiae with higher alcohol production capacity and the mutagenic strain M16-28 of C. lusitaniae with higher aroma production capacity were obtained through ARTP mutagenesis. The alcohol production capacity of M1-5 increased by 38.07% compared with M1, and the ester production capacity of M16-28 increased by 107.62% compared with M16, and the genetic stability was good. The selenium contents in the intracellular and fermentation broth of the M1-5 strains were 204.8 and 1158 μg/L, respectively, and in M16-28 were 666.45μg/L and 1830 μg/L, respectively. Therefore, the selenium enrichment capacity of M16-28 was higher than that of M1-5, and the selenium content of fermentation liquid was extremely significantly higher than that of yeast cells (P

In order to investigate the characterisation and biological activity of the leaf protein extract of Lycium barbarum, alkaline extraction and acid precipitation method was used to extract Lycium barbarum leaf protein extract. And the physicochemical properties, functional properties, structural characterisation and bioactivities of Lycium barbarum leaf protein extract were analysed by the methods of water holding capacity (WA), oil-holding property (FA), thermal stability (TM), solubility (PS), froth capability (FC), emulsification capability (EC), and other indexes as well as by the sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), scanning electron microscopy (SEM), ultraviolet-visible spectroscopy (UV), Fourier infraredspectroscopy (FT-IR), and X-ray Diffraction (XRD), and the in vitro radical scavenging assays and enzyme inhibition assays. The results showed that the total amino acid content of Lycium barbarum leaf protein extract reached 344.00±10.49 mg/100 g, the molecular weight ranged in 40 to 55 kDa, the water WA and FA property were 2.70 and 3.43 g/g, and the denaturation temperature was 85.73 ℃. With the increase of pH, the PS, FC, EC and froth stability (FS) of Lycium barbarum leaf protein extract showed a tendency of first decreasing and then increasing, while emulsion stability (ES) was exactly the opposite. The surface of Lycium barbarum leaf protein extract had tightly connected pores and was in the form of lumps of uneven sizes, with characteristicabsorption peaks near 270 nm, appearing amide A, B, Ⅰ, Ⅱ, Ⅲ bands, and a complete triplehelix structure. The scavenging rate of superoxide anion and hydroxyl radical was 37.73% and 35.38% at the concentration of 10 mg/mL of Lycium barbarum leaf protein extract, respectively. The IC50 of α-glucosidase and α-amylase was 9.88 and 21.09 mg/mL, respectively. In conclusion, the Lycium barbarum leaf protein extract was characterised and structurally stable, had certain in vitro antioxidant capacity and could inhibit the activity of enzymes related to blood glucose metabolism. This experiment provides theoretical basis for the development, utilisation and deep processing of Lycium barbarum leaf protein extract.

Abstract Wheat head detection and counting using deep learning techniques has gained considerable attention in precision agriculture applications such as wheat growth monitoring, yield estimation, and resource allocation. However, the accurate detection of small and dense wheat heads remains challenging due to the inherent variations in their size, orientation, appearance, aspect ratios, density, and the complexity of imaging conditions. To address these challenges, we propose a novel approach called the Oriented Feature Pyramid Network (OFPN) that focuses on detecting rotated wheat heads by utilizing oriented bounding boxes. In order to facilitate the development and evaluation of our proposed method, we introduce a novel dataset named the Rotated Global Wheat Head Dataset (RGWHD). This dataset is constructed by manually annotating images from the Global Wheat Head Detection (GWHD) dataset with oriented bounding boxes. Furthermore, we incorporate a Path-aggregation and Balanced Feature Pyramid Network into our architecture to effectively extract both semantic and positional information from the input images. This is achieved by leveraging feature fusion techniques at multiple scales, enhancing the detection capabilities for small wheat heads. To improve the localization and detection accuracy of dense and overlapping wheat heads, we employ the Soft-NMS algorithm to filter the proposed bounding boxes. Experimental results indicate the superior performance of the OFPN model, achieving a remarkable mean average precision of 85.77% in oriented wheat head detection, surpassing six other state-of-the-art models. Moreover, we observe a substantial improvement in the accuracy of wheat head counting, with an accuracy of 93.97%. This represents an increase of 3.12% compared to the Faster R-CNN method. Both qualitative and quantitative results demonstrate the effectiveness of the proposed OFPN model in accurately localizing and counting wheat heads within various challenging scenarios.

Granary evolves over time, adopting various climate responsive strategies and accordingly producing different grain storage environments. Meanwhile, as a major grain-growing area since ancient times, the North China Plain has a huge demand for granaries. Therefore, this paper investigates the historical vicissitudes of grain storage environment in North China Plain. Four granaries built during different historical periods (ranging from AD 583 to AD 2002) were selected. While their architectural features were obtained via field surveys and literature review, additional information was collected from on-site measurements to mathematically simulate the grain storage environments created by them. Results show that the grain storage environment has not been significantly improved, as the number of hours for safe grain storage has increased only by 2.36% in the past 1400 years. Further analysis revealed that these historical granaries adopted two different strategies (temperature-first vs. humidity-first) that failed to create a desirable grain storage environment, thus calling for a deliberate trade-off between air temperature and relative humidity. The present study not only deepens our understanding of historical granaries typical in the North China Plain but also provides valuable practical implications for the design and construction of modern granaries.

Pest infestation poses significant threats to grain storage due to pests’ behaviors of feeding, respiration, excretion, and reproduction. Efficient pest detection and control are essential to mitigate these risks. However, accurate detection of small grain pests remains challenging due to their small size, high variability, low contrast, and cluttered background. Salient pest detection focuses on the visual features that stand out, improving the accuracy of pest identification in complex environments. Drawing inspiration from the rapid pest recognition abilities of humans and birds, we propose a novel Cascaded Aggregation Convolution Network (CACNet) for pest detection and control in stored grain. Our approach aims to improve detection accuracy by employing a reverse cascade feature aggregation network that imitates the visual attention mechanism in humans when observing and focusing on objects of interest. The CACNet uses VGG16 as the backbone network and incorporates two key operations, namely feature enhancement and feature aggregation. These operations merge the high-level semantic information and low-level positional information of salient objects, enabling accurate segmentation of small-scale grain pests. We have curated the GrainPest dataset, comprising 500 images showcasing zero to five or more pests in grains. Leveraging this dataset and the MSRA-B dataset, we validated our method’s efficacy, achieving a structure S-measure of 91.9%, and 90.9%, and a weighted F-measure of 76.4%, and 91.0%, respectively. Our approach significantly surpasses the traditional saliency detection methods and other state-of-the-art salient object detection models based on deep learning. This technology shows great potential for pest detection and assessing the severity of pest infestation based on pest density in grain storage facilities. It also holds promise for the prevention and control of pests in agriculture and forestry.

To improve the protein dissolution rate and the quality of fresh Lycium barbarum pulp (LBP), we optimized the slit dual-frequency ultrasound-assisted pulping process, explored the dissolution kinetics of Lycium barbarum protein (LBPr), and established a near-infrared spectroscopy in situ real-time monitoring model for LBPr dissolution through spectral information analysis and chemometric methods. The results showed that under optimal conditions (dual-frequency 28–33 kHz, 300 W, 31 min, 40 °C, interval ratio 5:2 s/s), ultrasonic treatment not only significantly increased LBPr dissolution rate (increased by 71.48 %, p

Abstract Emulating the auditory sense is a significant challenge in terms of both integration and energy consumption for handling complicated spatiotemporal information. Here, we demonstrate how to utilize the chaotic dynamics of a threshold switching memristor, which usually acts as a leaky integrate and fire neuron in the neuromorphic network, to encode the frequency and amplitude in auditory information. We fabricate a Pd/Nb/NbOx/Nb/Pd memristor dominated by the Poole–Frankel conduction mechanism, set its state at the edge of chaos, and stimulate it using periodic perturbations. The memristor's responses to the perturbation frequencies can be categorized into three zones. Two are phase locking with linear phase–frequency relationships, and one has a hyperbolic spike number–frequency relationship. The memristor system also enables intensity coding and tonotopy by modulating the response spikes in either the locked phase or spike number. Based on the emulation of these two features, the memristor system demonstrates sound location and frequency mixing. Our study suggests a novel routine for handling the auditory and visual senses using threshold‐switching memristor arrays to enhance the efficiency of neuromorphic networks.

In this study, the slit dual-frequency ultrasound-assisted pulping of fresh Lycium barbarum fruit was optimized to improve the dissolution of polysaccharides. The microscopic mechanism of polysaccharide dissolution was explored through establishing polysaccharides dissolution kinetics model and visualizing the multi-physical fields during ultrasonic process, and an in situ real-time monitoring model was established by the relationship between the chemical value and spectral information collected by near-infrared spectroscopy. The results showed that, under optimal conditions, treatment with ultrasound (28–33 kHz, 250 W, 30 min) not only significantly promoted the dissolution rate of polysaccharides in Lycium barbarum pulp (LBPPs, increased by 43.64 %, p

Maternally inherited obligate endosymbionts codiverge with their invertebrate hosts and reflect their host’s evolutionary history. Whiteflies (Hemiptera: Aleyrodidae) harbor one obligate endosymbiont, Candidatus Portiera aleyrodidarum (hereafter Portiera). Portiera was anciently acquired by whitefly and has been coevolving with its host ever since. Uncovering the divergence of endosymbionts provides a fundamental basis for inspecting the coevolutionary processes between the bacteria and their hosts. To illustrate the divergence of Portiera lineages across different whitefly species, we sequenced the Portiera genome from Aleyrodes shizuokensis and conducted a comparative analysis on the basic features and gene evolution with bacterial genomes from five whitefly genera, namely Aleurodicus, Aleyrodes, Bemisia, Pealius, and Trialeurodes. The results indicated that Portiera from Bemisia possessed significantly larger genomes, fewer coding sequences (CDSs), and a lower coding density. Their gene arrangement differed notably from those of other genera. The phylogeny of the nine Portiera lineages resembled that of their hosts. Moreover, the lineages were classified into three distinct genetic groups based on the genetic distance, one from Aleurodicus (Aleurodicinae), one from Bemisia (Aleyrodinae), and another from Aleyrodes, Pealius, and Trialeurrodes (Aleyrodinae). Synonymous and nonsynonymous rate analyses, parity rule 2 plot analyses, neutrality plot analyses, and effective number of codons analyses supported the distinction of the three genetic groups. Our results indicated that Portiera from distant hosts exhibit distinct genomic contents, implying codivergence between hosts and their endosymbionts. This work will enhance our understanding of coevolution between hosts and their endosymbionts.

As insect infestation is the leading factor accounting for nutritive and economic losses in stored grains, it is important to detect the presence and number of insects for the sake of taking proper control measures. Inspired by the human visual attention mechanism, we propose a U-net-like frequency-enhanced saliency (FESNet) detection model, resulting in the pixelwise segmentation of grain pests. The frequency clues, as well as the spatial information, are leveraged to enhance the detection performance of small insects from the cluttered grain background. Firstly, we collect a dedicated dataset, GrainPest, with pixel-level annotation after analyzing the image attributes of the existing salient object detection datasets. Secondly, we design a FESNet with the discrete wavelet transformation (DWT) and the discrete cosine transformation (DCT), both involved in the traditional convolution layers. As current salient object detection models will reduce the spatial information with pooling operations in the sequence of encoding stages, a special branch of the discrete wavelet transformation (DWT) is connected to the higher stages to capture accurate spatial information for saliency detection. Then, we introduce the discrete cosine transform (DCT) into the backbone bottlenecks to enhance the channel attention with low-frequency information. Moreover, we also propose a new receptive field block (NRFB) to enlarge the receptive fields by aggregating three atrous convolution features. Finally, in the phase of decoding, we use the high-frequency information and aggregated features together to restore the saliency map. Extensive experiments and ablation studies on our dataset, GrainPest, and open dataset, Salient Objects in Clutter (SOC), demonstrate that the proposed model performs favorably against the state-of-the-art model.

For grain storage and protection, grain pest species recognition and population density estimation are of great significance. With the rapid development of deep learning technology, many studies have shown that convolutional neural networks (CNN)-based methods perform extremely well in image classification. However, such studies on grain pest classification are still limited in the following two aspects. Firstly, there is no high-quality dataset of primary insect pests specified by standard ISO 6322-3 and the Chinese Technical Criterion for Grain and Oil-seeds Storage (GB/T 29890). The images of realistic storage scenes bring great challenges to the identification of grain pests as the images have attributes of small objects, varying pest shapes and cluttered backgrounds. Secondly, existing studies mostly use channel or spatial attention mechanisms, and as a consequence, useful information in other domains has not been fully utilized. To address such limitations, we collect a dataset named GP10, which consists of 1082 primary insect pest images in 10 species. Moreover, we involve discrete wavelet transform (DWT) in a convolutional neural network to construct a novel triple-attention network (FcsNet) combined with frequency, channel and spatial attention modules. Next, we compare the network performance and parameters against several state-of-the-art networks based on different attention mechanisms. We evaluate the proposed network on our dataset GP10 and open dataset D0, achieving classification accuracy of 73.79% and 98.16%. The proposed network obtains more than 3% accuracy gains on the challenging dataset GP10 with parameters and computation operations slightly increased. Visualization with gradient-weighted class activation mapping (Grad-CAM) demonstrates that FcsNet has comparative advantages in image classification tasks.

Wolfberry (Lycium barbarum L.) is a nutritious and medicinal fruit, and deeply processed products of wolfberry needs to be improved. In this study, nutrition, bioactive compounds, and hepaprotective activity were explored in wolfberry vinegar (WFV). The contents of nutrients including total sugar and protein in WFV samples were 2.46 and 0.27 g/100 mL, respectively. Total phenolic and flavonoid contents in WFV were 2.42 mg GAE/mL and 1.67 mg RE/mL, respectively. p-Hydroxybenzoic acid and m-hydroxycinnamic acid were the main polyphenols in WFV. The antioxidant activity of WFV were 20.176 mM Trolox/L (ABTS), 8.614 mM Trolox/L (FRAP), and 26.736 mM Trolox/L (DPPH), respectively. In addition, WFV treatment effectively alleviated liver injury by improving histopathological changes and reducing liver biochemical indexes in CCl4-treated mice. WFV alleviated oxidative damage by inhibiting oxidative levels and increasing antioxidant levels. These results suggest that WFV can be utilized as a functional food to prevent oxidative liver injury.

Little is known about how stimuli of different intensities result in different behavioral outcomes in C. elegans. In this study, the authors demonstrate how distinct signal patterns, involving different glutamate receptors, in a single interneuron AIB can encode differential behavioral outputs depending on the stimulus intensity

Continuous network design problem (CNDP) is searching for a transportation network configuration to minimize the sum of the total system travel time and the investment cost of link capacity expansions by considering that the travellers follow a traditional Wardrop user equilibrium (UE) to choose their routes. In this paper, the CNDP model can be formulated as mathematical programs with complementarity constraints (MPCC) by describing UE as a non-linear complementarity problem (NCP). To address the difficulty resulting from complementarity constraints in MPCC, they are substituted by the Fischer-Burmeister (FB) function, which can be smoothed by the introduction of the smoothing parameter. Therefore, the MPCC can be transformed into a well-behaved non-linear program (NLP) by replacing the complementarity constraints with a smooth equation. Consequently, the solver such as LINDOGLOBAL in GAMS can be used to solve the smooth approximate NLP to obtain the solution to MPCC for modelling CNDP. The numerical experiments on the example from the literature demonstrate that the proposed algorithm is feasible.

Vibrio cholerae and Vibrio vulnificus are two most reported foodborne Vibrio pathogens related to seafood. Due to global ocean warming and an increase in seafood consumption worldwide, foodborne illnesses related to infection of these two bacteria are growing, leading to food safety issues and economic consequences. Molecular detection methods targeting species-specific genes are effective tools in the fight against bacterial infections for food safety. In this study, a duplex detection biosensor based on isothermal recombinase polymerase amplification (RPA) and a three-segment lateral flow strip (LFS) has been established. The biosensor used lolB gene of Vibrio cholerae and empV gene of Vibrio vulnificus as the detection markers based on previous reports. A duplex RPA reaction for both targets were constructed, and two chemical labels, FITC and DIG, of the amplification products were carefully tested for effective and accurate visualization on the strip. The biosensor demonstrated good specificity and achieved a sensitivity of 101 copies per reaction or one colony forming unit (CFU)/10 g of spiked food for both bacteria. Validation with clinical samples showed results consistent with that of real-time polymerase chain reaction. The detection process was simple and fast with a 30-min reaction at 37 °C and visualization on the strip within 5 min. With little dependence on laboratory settings, this biosensor was suitable for on-site detection, and the duplex system enabled simultaneous detection of the two important foodborne bacteria. Moreover, the principle can be extended to healthcare and food safety applications for other pathogens.

The localization system of low-cost autonomous vehicles such as autonomous sweeper requires a highly lateral localization accuracy as the vehicle needs to keep a near lateral-distance between the side brush system and the road curb. Existing methods usually rely on a global navigation satellite system that often loses signal in a cluttered environment such as sweeping streets between high buildings and trees. In a GPS-denied environment, map-based methods are often used such as visual and LiDAR odometry systems. Apart from heavy computation costs from feature extractions, they are too expensive to meet the low-price market of the low-cost autonomous vehicles. To address these issues, we propose a mono-vision based lateral localization system of an autonomous sweeper. Our system relies on a fish-eye camera and precisely detects road curbs with a deep curb detection network. Curbs locations are then referred to as straightforward marks to control the lateral motion of the vehicle. With our self-recorded dataset, our curb detection network achieves 93% pixel-level precision. In addition, experiments are performed with an intelligent sweeper to prove the accuracy and robustness of our proposed approach. Results demonstrate that the average lateral distance error and the maximum invalid rate are within 0.035 m and 9.2%, respectively.

This paper presents the design of a hybrid system named the Mooring Buoys Observation System for Benthic with Electro Optical Mechanical Cable (MBOSBC) for long-term mooring buoy observation in deep oceans. MBOSBC is comprised of three main modules: a surface buoy, a benthic node, and an Electro-Optical-Mechanical (EOM) cable. The Surface buoy provides energy for the entire system, the Benthic node is used to observe scientific phenomena on the seabed, and the Electro-Optical-Mechanical (EOM) cable connects the sea surface buoy and the benthic observation node, serving as the information and power transmission link. This paper provides design criteria for a single point mooring buoy using an EOM cable. The environmental load of the buoy under varying wind, wave, and current conditions is analyzed and the mooring hydrodynamic force of EOM is calculated. After calculating the load of the EOM cable under extreme marine conditions, the hydrodynamic force needed for the system is analyzed. After physically testing the strength of the designed EOM cable, a near-shore test of MBOSBC was carried out, in order to verify that the system has the expected function.

In order to enhance the adsorptivity of viscose-based activated carbon fibers (VACFs) for low-concentration Cu(II), sonication-assisted zinc chloride reactivation process and sonication-assisted-pickling regeneration process were studied in this research. This study aims to prepare a modified VACFs with high Cu(II) adsorptivity and recycling performance at the same time. As 10 wt% ZnCl _2 used as activator, specific surface area, micropore volume and mesopore volume of VACFs increase by 59.33%, 37.35% and 813.87% respectively, besides, the Cu(II) adsorptivity was improved by 2.04 times through the reactivation process. Based on the sonication-assisted-pickling regeneration process, the regeneration rate was changed from 96.85% to 107.81% during 1–5 recycling times.