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Abstract The object scale of a small object scene changes greatly, and the object is easily disturbed by a complex background. Generic object detectors do not perform well on small object detection tasks. In this paper, we focus on small object detection based on FocusDet. FocusDet refers to the small object detector proposed in this paper. It consists of three parts: backbone, feature fusion structure, and detection head. STCF-EANet was used as the backbone for feature extraction, the Bottom Focus-PAN for feature fusion, and the detection head for object localization and recognition.To maintain sufficient global context information and extract multi-scale features, the STCF-EANet network backbone is used as the feature extraction network.PAN is a feature fusion module used in general object detectors. It is used to perform feature fusion on the extracted feature maps to supplement feature information.In the feature fusion network, FocusDet uses Bottom Focus-PAN to capture a wider range of locations and lower-level feature information of small objects.SIOU-SoftNMS is the proposed algorithm for removing redundant prediction boxes in the post-processing stage. SIOU multi-dimension accurately locates the prediction box, and SoftNMS uses the Gaussian algorithm to remove redundant prediction boxes. FocusDet uses SIOU-SoftNMS to address the missed detection problem common in dense tiny objects.The VisDrone2021-DET and CCTSDB2021 object detection datasets are used as benchmarks, and tests are carried out on VisDrone2021-det-test-dev and CCTSDB-val datasets. Experimental results show that FocusDet improves mAP@.5% from 33.6% to 46.7% on the VisDrone dataset. mAP@.5% on the CCTSDB2021 dataset is improved from 81.6% to 87.8%. It is shown that the model has good performance for small object detection, and the research is innovative.

Nuclear power is essential for sustainable energy infrastructure and economic development, necessitating materials for high-radiation environments that can facilitate visualization and observation. Conventional lead glass is inadequate for future requirements due to radiation-induced darkening, poor mechanical properties, and toxicity. Therefore, there is urgent to find new window materials that offer multi-ionization shielding (particularly against deep-penetrating gamma ray, γ, and neutron, n, radiations), desirable opto-mechanical properties, service stability against darkening, and non-toxicity. In this study, we report a family of transparent rare-earth pyrochlore ceramics LaxGd2−xZr2O7, offering unique chemo-physical properties that are ideal for robust radiation shielding windows. Remarkably, we demonstrated the capability of maintaining high transparency under heavy-dose exposure to 1000 ​kGy 60Co γ radiation. We observed the service stability against radiation darkening can be greatly enhanced with La-rich compositions, while Gd-rich compositions undergo shallow darkening that can be reversibly recovered under visible light. This behavior is attributed to mitigated oxygen migration from 48f to 8a in La-rich compositions, which have high pyrochlore phase stability and well-ordered atomic structures, and reversible oxygen migration between 48f and 8a in Gd-rich compositions, which remain active at room temperature. Our proposal and demonstration unlock ample opportunities in designing functional transparent ceramics as window materials for demanding applications in high-radiation environments.

Abstract Despite recent advancements in cancer treatment, this disease still poses a serious threat to public health. Vaccines play an important role in preventing illness by preparing the body's adaptive and innate immune responses to combat diseases. As our understanding of malignancies and their connection to the immune system improves, there has been a growing interest in priming the immune system to fight malignancies more effectively and comprehensively. One promising approach involves utilizing nanoparticle systems for antigen delivery, which has been shown to potentiate immune responses as vaccines and/or adjuvants. In this review, we comprehensively summarized the immunological mechanisms of cancer vaccines while focusing specifically on the recent applications of various types of nanoparticles in the field of cancer immunotherapy. By exploring these recent breakthroughs, we hope to identify significant challenges and obstacles in making nanoparticle-based vaccines and adjuvants feasible for clinical application. This review serves to assess recent breakthroughs in nanoparticle-based cancer vaccinations and shed light on their prospects and potential barriers. By doing so, we aim to inspire future immunotherapies for cancer that harness the potential of nanotechnology to deliver more effective and targeted treatments. Graphical abstract

We propose a broadband noise test method (10 Hz–1 MHz) using a direct power method to measure the excess noise of InGaAs/InP avalanche photodiodes (APDs) with a separate absorption, grading, charge, and multiplication structure. By this means, the impact ionization coefficient ratio (the k value) can be accurately determined. Compared with previous studies, this method can distinguish between low-frequency noise and excess noise, so for an appropriate high frequency spectral range, the excess noise can be analyzed without mixing in other noise information. Three different APDs fabricated in-house with different diameters (12, 25, and 60 µm) were investigated using the above-mentioned method under temperatures varying from −10 to −40 °C. Our findings showed that the k value of the APDs decreased as the temperature decreased. When the temperature decreased to −40 °C, the k value of the 12, 25, and 60 µm diameter diodes was as low as 0.1, 0.3, and 0.2, respectively. The measurement results are consistent with our device design and not only provide useful information for the future selection of device materials and structures but also demonstrate the feasibility of the broadband noise measurement method in the accurate determination of the k value. It indicates that low excess noise can be achieved by utilizing the temperature dependence of k.

The spatial pattern of diseased forest trees is a product of the spatial pattern of host trees and the disease itself. Previous studies have focused on describing the spatial pattern of diseased host trees, and it remains largely unknown whether an antecedent spatial pattern of host/nonhost trees affects the infection pattern of a disease and how large the effect sizes of the spatial pattern of host/nonhost trees and host size are. The results from trivariate random labeling showed that the antecedent pattern of the host ash tree, Fraxinus mandshurica, but not of nonhost tree species, impacted the infection pattern of a stem fungal disease caused by Inonotus hispidus. To investigate the effect size of the spatial pattern of ash trees, we employed the SADIE (Spatial Analysis by Distance IndicEs) aggregation index and clustering index as predictors in the GLMs. Globally, the spatial pattern (vi index) of ash trees did not affect the infection likelihood of the focal tree; however, the spatial pattern of DBH (diameter at breast height) of ash trees significantly affected the infection likelihood of the focal tree. We sampled a series of circular plots with different radii to investigate the spatial pattern effect of host size on the infection likelihood of the focal tree locally. The results showed that the location (patch/gap) of the DBH of the focal tree, rather than that of the focal tree itself, significantly affected its infection likelihood in most plots of the investigated sizes. A meta-analysis was employed to settle the discrepancy between plots of different sizes, which led to results consistent with those of global studies. The results from meta-regression showed that plot size had no significant effects.

Abstract Background Aortic dissection (AD) is a serious and fatal vascular disease. The earlier the condition of AD patients can be assessed precisely, the more scientifically controlled the patient’s condition will be. Therefore, timely and accurate diagnosis is significant for AD. Blood biomarker testing as a method of liquid biopsy can improve the diagnostic efficiency of AD. This study conducted a systematic review of the current blood diagnostic biomarkers of AD. Methods The PubMed, Cochrane Library, Web of Science, and Embase electronic databases were systematically searched from inception to January 1, 2023, using the terms “aortic dissection”, “serum”, “plasma” and “diagnosis”. Stata 12.0 software was used to perform Random effects meta-analysis was performed using Stata 12.0 software to determine the effect sizes and corresponding 95% confidence intervals. Then, a summary receiver operator characteristic (SROC) curve was drawn, and the area under the ROC curve (AUC) was calculated. Results D-dimer had the best sensitivity and AUC for AD, with values of 0.96 (95% CI: 0.93–0.98) and 0.95 (95% CI: 0.93–0.97), respectively. The sensitivity and AUC values for D-dimer with a cut-off value of 500 ng/mL were 0.97 (95% CI: 0.95–0.99) and 0.94 (95% CI: 0.92–0.96), respectively. In contrast, microRNA had a better specificity value for AD, at 0.79 (95% CI: 0.73–0.83). Conclusions D-dimer and microRNA have good accuracy in the diagnosis of AD, but the specificity of D-dimer is worse, and studies of microRNA are insufficient. The combination of different biomarkers can improve the diagnostic accuracy. Other blood biomarkers are related to the pathological progression of AD and can be selected according to pathological progress.

Underwater object detection has been shown to exhibit significant potential for exploring underwater environments. However, underwater datasets often suffer from degeneration due to uneven underwater light distribution, complex underwater environment, and crowded underwater dynamic background. Thus, object detection performance would be degraded accordingly. In this paper, a large kernel convolutional object detection network based on self-attention and long-range relationship capture is proposed. Firstly, a hybrid dilated large kernel attention mechanism is proposed, which adopts the idea of hybrid dilated convolution and combines the advantages of large kernel attention mechanism and self-attention. This attention mechanism can avoid self-attention defects while achieving self-attention adaptiveness and long-range relevance. Secondly, a feature enhancement block called residual reconstructed module is proposed, which captures long-range dependencies in the network and extracts more critical contextual information, thus solving the problem of network degradation and accuracy degradation. Thirdly, an adaptive spatial feature fusion object detection head is constructed, which can directly learn how to filter different features at different feature layers spatially; useless information is filtered out, and only useful information is kept for combination to enhance the detection capability of the network further. Finally, network for underwater object detection is proposed based on the above three techniques. Extensive experiments were conducted on the well-known datasets of RUOD, Aquarium, URPC, and MS COCO. Compared to the prior state-of-the-art methods, the experimental findings demonstrate that the proposed approach obtains the highest mAP of 88.7%, 86.5%, 98.9%, and 71.4%, respectively. This represents an improvement of 1.2, 1.5, 8.5, and 0.2 percentage, in that order. The proposed model shows the capacity to function by applying self-attention to local details, as well as the capacity to grasp global long-range relationships, prioritize essential data, and spatially filter irrelevant information.

Automatic traffic sign detection has great potential for intelligent vehicles. In recent years, traffic sign detection has made significant progress with the rise of deep learning. Detecting small traffic signs in real-world scenarios is still a challenging problem due to the complex and variable traffic environment. In this paper, a model with a small number of parameters is proposed to improve the accuracy of small traffic sign detection. Firstly, the cross-stage attention network module is proposed to enhance the feature extraction capability of the network. Secondly, a dense neck structure is proposed to make the detail information and semantic information fully fused. Finally, for the model’s loss function, SIOU with direction information is introduced to optimize the model’s training process. Tests on the challenging public datasets TT100K, CCTSDB2021, and VOC show that our approach achieves significant performance improvement with the minimum number of parameters compared to existing algorithms.

Density peak clustering (DPC) the center selection ignores the environment of samples, which can easily result in the incorrect identification of cluster centers. Additionally, a single allocation approach can easily produce the joint effect of allocation mistakes. To address the above issues, a density peaks clustering based on candidate center and multi assignment policies (DPC-CM) is proposed. Firstly, to retain cluster centers with relatively small densities, DPC-CM introduces thresholds to delineate candidate center regions in the decision graph. It also incorporates shared nearest neighbor definitions to ensure proper cluster center selection. Then, the allocation results of the cluster backbone are corrected using shared nearest neighbor information, suppressing allocation cascading errors effectively. Finally, the allocation of cluster backbone points used as the basis for label propagation also improves the allocation efficiency while taking into account the surroundings in which the sample sites are situated. The fault tolerance of the algorithm has also been enhanced by the multi-step allocation strategy. Experimental results on synthetic and UCI datasets validate that the DPC-CM algorithm outperforms comparative algorithms for clustering on complex morphological and unevenly distributed datasets.

PIN InGaAs short wavelength infrared (SWIR) focal plane array (FPA) detectors have attracted extensive attention due to their high detectivity, high quantum efficiency, room temperature operation, low dark current, and good radiation resistance. Furthermore, InGaAs FPA detectors have wide applications in many fields, such as aviation safety, biomedicine, camouflage recognition, and infrared night vision. Recently, extensive research has been conducted on the extension of the response spectrum from short wavelength infrared (SWIR) to visible light (VIS) through InP substrate removal and reserving the n-InP contact layer. However, there is little research on the absorption of InGaAs detectors in the ultraviolet (UV) band. In this paper, we present an ultra-broadband UV–VIS–SWIR 640 × 512 15 μm InGaAs FPA detector by removing the n-InP contact layer in the active area and reserving the InP contact layer around the pixels for n contact, creating incident light to be directly absorbed by the In0.53Ga0.47As absorption layer. In addition, the optical absorption characteristics of InGaAs infrared detectors with and without an n-InP contact layer are studied theoretically. The test results show that the spectral response is extended to the range of 200–1700 nm. The quantum efficiency is higher than 45% over a broad wavelength range of 300–1650 nm. The operability is up to 99.98%, and the responsivity non-uniformity is 3.28%. The imaging capability of InGaAs FPAs without the n-InP contact layer has also been demonstrated, which proves the feasibility of simultaneous detection for these three bands.

Objective: In this study, the advantages of the internal fixation configuration composed of uniplanar pedicle screws in the treatment of thoracolumbar fractures were verified by biomechanical experimental methods, which provided the basis for subsequent clinical experiments and clinical applications.Methods: A total of 24 fresh cadaveric spine specimens (T12-L2) were utilized to conduct biomechanical experiments. Two different internal fixation configurations, namely, the 6-screw configuration and the 4-screw/2-NIS (new intermediate screws) configuration, were tested using fixed-axis pedicle screws (FAPS), uniplanar pedicle screws (UPPS), and polyaxial pedicle screws (PAPS) respectively. The spine specimens were uniformly loaded with 8NM pure force couples in the directions of anteflexion, extension, left bending, right bending, left rotation, and right rotation, and the range of motion (ROM) of the T12-L1 and L1-L2 segments of the spine was measured and recorded to access biomechanical stability.Results: No structural damage such as ligament rupture or fracture occurred during all experimental tests. In the 6-screw configuration, the ROM of the specimens in the UPPS group was significantly better than that of the PAPS group but weaker than those of the FAPS group (p < 0.01). In the 4-screw/2-NIS configuration, the results were identical to the biomechanical test results for the 6-screw configuration (p < 0.01).Conclusion: Biomechanical test results show that the internal fixation configuration with UPPS can maintain the stability of the spine well, and the results are better than that of PAPS. UPPS has both the biomechanical advantages of FAPS and the superiority of easy operation of PAPS. We believe it is an optional internal fixation device for minimally invasive treatment of thoracolumbar fractures.

Background: Environmental and genetic factors are jointly involved in the development of chronic obstructive pulmonary disease (COPD). The EGLN1 gene is a major factor in upstream regulation of the hypoxia-inducible pathway. EGLN1 negatively regulates the hypoxia-inducible factors HIF-lα and HIF-2α by regulating the concentration of oxygen, mainly in a hypoxic environment. Hypoxia is a common physiologic condition during the progression of COPD, and several studies have identified genetic variants in EGLN1 as a key factor in the adaptation to hypoxic environments. However, it is still unclear whether there is an association between EGLN1 variants and the risk of developing COPD. Methods: A case-control study was conducted in the Gannan Tibetan Autonomous Prefecture, Gansu Province. A total of 292 COPD patients and 297 healthy controls were enrolled to assess the association of EGLN1 single nucleotide polymorphisms (SNPs) (rs41303095 A>G, rs480902 C>T, rs12097901 C>G, rs2153364 G>A) with COPD susceptibility. Results: The EGLN1 rs41303095 A>G, rs480902 C>T, rs12097901 C>G, and rs2153364 G>A polymorphisms were not associated with COPD susceptibility (p > 0.05). Conclusions: The EGLN1 rs41303095 A>G, rs480902 C>T, rs12097901 C>G and rs2153364 G>A polymorphisms were found in this study not to be associated with susceptibility to COPD in Gannan Tibetans.

In road tunnel fires, it is essential to keep high smoke extraction efficiency, especially for the immersed tunnels. A new-style inclined smoke barrier was proposed for the improvement of lateral smoke extraction. A series of fire scenarios were investigated with different barrier heights, fire sizes, vent aspect ratios and included angles between smoke barrier and tunnel sidewall. Results show that installing an inclined smoke barrier can improve lateral smoke extraction effectively. Overall, a higher inclined smoke barrier contributes to the lateral smoke extraction, and the smoke extraction improvement is better for those cases under smaller fire size; meanwhile the inclined smoke barrier shows a higher efficiency with the narrow and high lateral exhaust vent. The lateral smoke extraction efficiency curve can be divided into three regions along with included angles, namely the rapidly increasing region (0°

Marine organisms detection based on machine vision requires high real-time performance and accuracy. Network feature extraction is frequently made more difficult when underwater robots collect information on the seafloor because of the uneven distribution of light on the seafloor, the significant impact of water waves, and the complexity of the seafloor environment. How to detect marine organisms quickly and accurately is a great difficulty and challenge. To address the above problems, we propose a MODA (Marine Organism Detection Algorithm) based on an improved YOLOv4-tiny marine organism detection algorithm. Firstly, the Coordinate Attention module, an ultra-lightweight attention mechanism, is constructed and embedded into the backbone network to retain more information about the target of interest and enhance the feature extraction capability of the network. Secondly, the Hybrid Dilated Convolution (HDC) structure is constructed and added to the improved network to expand the feature map perception field and obtain richer semantic information to improve the network detection accuracy. Finally, a better MODA model is proposed based on the two methods mentioned above. The experimental results show that the improved model MODA improves the mAP metric from 74% to 76.62% on the URPC dataset and only increases the computational effort by 0.06 GM compared with the original YOLOv4-tiny model; the mAP metric improves from 92.37% to 98.41% on the Aquarium dataset. This improvement indicates that the MODA model is more suitable for marine organisms detection tasks.

China is suffering from serious soil and water loss due to improper land use, leading to flood and drought disasters, and industrial and agricultural reduction. The commonly used monitoring methods of soil and water loss rely on the visual interpretation of remote sensing images, which is time-consuming and laborious. In this study, deep learning semantic segmentation technology was applied to monitoring land use for soil and water conservation. Manual visual interpretation mark samples and land use products collected in recent years were combined to construct training samples for deep learning. Deep convolution neural network model based on ResNet152 and DeeplabV3+ was selected for performing land use classification. Then the soil and water loss were quantified using the Chinese Soil Loss Equation (CSLE) based on the hydrological, geographic, vegetation cover and land use information. The experimental results show that the deep learning model can quickly extract robust edge features from remote sensing images and has high pixel accuracy (83.4%). Although the model accuracy was affected by the land cover types and image quality in different study area, it can still achieve an accuracy higher than 70% in other counties through our experiment. It can further improve the information service level of soil and water conservation product, and provide a useful guideline for automatic land use interpretation and identification of soil and water conservation status based on high resolution remote sensing images.

All-inorganic CsPbBr3 perovskites exhibit great potential in optoelectronic application due to their outstanding physical properties for optoelectronics. However, the relatively low luminescence efficiency of the bulk CsPbBr3 crystal hampers its further application. Here, we report a facile method to enhance the optical efficiency of halide perovskite powder via doping the transition metal Ni ions. It is demonstrated that Ni doping results in the formation of the Cs(NiPb)Br3 alloy and also triggers the phase transition of CsPbBr3 from orthorhombic to cubic. Both the Cs(NiPb)Br3 alloy and cubic CsPbBr3 are found to contribute to the asymmetric photoluminescence (PL) spectra in the green-yellow light band. The splitting-peak fitting experiments for the overlapped PL spectrum based on the bi-Gaussian line model reveal that the Cs(NiPb)Br3 alloy dominates the contribution of the observed PL enhancement. The Ni2+ doping is also beneficial for optimizing the crystallinity of the CsPbBr3 perovskite, suppressing the nonradiative recombination and significantly enhancing the photoluminescence consequently. Our investigation provides a novel insight into the effect of environmentally friendly metal doping in perovskites.

This paper addresses the classification problem, and a semantic classification approach using neural networks is proposed. The approach embeds the theoretical findings of the axiomatic fuzzy set theory in neural networks. Complex concepts are extracted by neural networks, which means that the class description is formed analytically rather than by tuning parameters of constraint conditions. The experiments are carried out on five benchmark datasets and compared results with five other neural network-based classifiers. The experimental results show that the proposed approach produces high classification accuracy and has a better explanation.

Abstract We present a detailed investigation on the effect of functional group modulation at the edges of carbon quantum dots (CQDs) on the fluorescence from the CQDs. The CQDs attached by N, S, and P elements are synthesized via pyrolysis of a mixture of citric acid and NH3H2O, H2SO4, and H3PO4, respectively. Thus, part of –COOH at the edges of CQDs can be converted into –C=O and functional groups such as –NH2, –SO2, –HSO3, and –H2PO4 can connect to the carbon bonds. We find that the formation of the N/S/P-CQDs can reduce the amount of –COOH that attaches to the edges of sp2-conjugated π-domains located at centers of these CQDs. This effect can result in the reduction of the non-radiative recombination for electronic transition in these CQDs. As a result, the quantum yield (QY) for fluorescence from the CQDs can be efficiently enhanced. We demonstrate experimentally that the QYs for N/S/P-CQDs can reach up to 18.7%, 29.7%, and 10.3%, respectively, in comparison to 9% for these without functional group modulation. This work can provide a practical experimental approach in improving the optical properties of fluorescent CQDs.

The resolution of InGaAs FPA detectors is degraded by the electrical crosstalk, which is especially severe in high–density FPAs. We propose a guard-hole structure to suppress the electrical crosstalk in a planar-type 640 × 512 15 μm InGaAs short wavelength infrared FPA detector. For comparison, the frequently used guard ring is also prepared according to the same processing. The calculation results show that the electrical crosstalk with a guard hole is suppressed from 13.4% to 4.5%, reducing by 66%, while the electrical crosstalk with a guard ring is suppressed to 0.4%. Furthermore, we discuss the effects of the guard ring and the guard hole on the dark current, quantum efficiency, and detectivity. Experimental results show the detector with a guard-hole structure has higher performance compared with the detector with a guard-ring structure, the dark current density is reduced by 60%, the QE is increased by 64.5%, and the detectivity is increased by 1.36 times, respectively. The guard-hole structure provides a novel suppression method for the electrical crosstalk of high-density InGaAs detectors.

Recently, perovskites have garnered great attention owing to their outstanding characteristics, such as tunable bandgap, rapid absorption reaction, low cost and solution-based processing, leading to the development of high-quality and low-cost photovoltaic devices. However, the key challenges, such as stability, large-area processing, and toxicity, hinder the commercialization of perovskite solar cells (PSCs). In recent years, several studies have been carried out to overcome these issues and realize the commercialization of PSCs. Herein, the stability and photovoltaic efficiency improvement strategies of perovskite solar cells are briefly summarized from several directions, such as precursor doping, selection of hole/electron transport layer, tandem solar cell structure, and graphene-based PSCs. According to reference and analysis, we present our perspective on the future research directions and challenges of PSCs.

This paper addresses the problem of economic time series forecasting, and a new prediction method is proposed. The method fully capitalizes on the two key technologies, sparse representation, and fuzzy set theory, to handle the stock time series forecasting problems. First, sparse representation is applied to smooth the time series. Then, the fuzzy technology is used to convert time series into fuzzy series. Based on the number of the occurrence of fuzzy sets, the weights are calculated. Finally, the future value of the time series can be forecasted using the weights and inverse transformation of sparse representation. The experimental results show that the proposed method produces more accurate forecasted results.

This paper investigates the performance of the block multiple measurement vectors (BMMV) algorithm in reconstructing block joint sparse matrices. We prove that if 41) obeys block restricted isometry property with 8 K+1 , 1 That is to say, the condition 8K +l

The Li4SiO4 is a promising breeder material for future fusion reactors. Radiation induced vacancies and hydrogen isotope related impurities are the major types of point defects in this breeder material. In present study, various kinds of vacancies and hydrogen isotopes related point defects in Li4SiO4 are investigated through density functional theory (DFT) calculations. The band gap of Li4SiO4 is determined by UV-Vis diffuse reflectance spectroscopy experiments. Formation energies of all possible charge states of Li, Si and O vacancies are calculated using DFT methods. Formation energies of possible charge states of hydrogen isotopes substitution for Li and O are also calculated. We found that Li-vacancies will dominate among all vacancies in neutral charge state under radiation conditions and the O, Li, and Si vacancies (VO,VLi,VSi) are stable in charge states +2, -1, -4 for most of the range of Fermi level, respectively. The interstitial hydrogen isotopes (Hi) and substitutional HLi are stable in the charge states +1, 0 for most of the range of Fermi level, respectively. Moreover, substitutional HO are stable in +1 charge states. We also investigated the process of tritium recovery by discussing the interaction between interstitial H and Li-vacancy, O-vacancy, and found that H O + and H Li 0 are the most common H related defects during radiation process.

Hyaluronic acid (HA) concentration is an important parameter in fermentation process. Currently, carbazole assay is widely used for HA content determination in routine analysis. However, this method is time-consuming, environment polluting and has the risk of microbial contamination, as well as the results lag behind fermentation process. This paper attempted the feasibility to predict the concentration of HA in fermentation broth by using near infrared (NIR) spectroscopy in transmission mode. In this work, a total of 56 samples of fermentation broth from 7 batches were analyzed, which contained HA in the range of 2.35–9.69 g/L. Different data preprocessing methods were applied to construct calibration models. The final optimal model was obtained with first derivative using Savitzky–Golay smoothing (9 points window, second-order polynomial) and partial least squares (PLS) regression with leave-one-block-out cross validation. The correlation coefficient and Root Mean Square Error of prediction set is 0.98 and 0.43 g/L, respectively, which show the possibility of NIR as a rapid method for microanalysis and to be a promising tool for a rapid assay in HA fermentation.

Hyaluronidases are a family of enzymes that degrade hyaluronic acid (hyaluronan, HA) and widely used in many fields. A hyaluronidase producing bacteria strain was screened from the air. 16S ribosomal DNA (16S rDNA) analysis indicated that the strain belonged to the genus Bacillus, and the strain was named as Bacillus sp. A50. This is the first report of a hyaluronidase from Bacillus, which yields unsaturated oligosaccharides as product like other microbial hyaluronate lyases. Under optimized conditions, the yield of hyaluronidase from Bacillus sp. A50 could reach up to 1.5×10(4) U/mL, suggesting that strain A50 is a good producer of hyaluronidase. The hyaluronidase (HAase-B) was isolated and purified from the bacterial culture, with a specific activity of 1.02×10(6) U/mg protein and a yield of 25.38%. The optimal temperature and pH of HAase-B were 44°C and pH 6.5, respectively. It was stable at pH 5-6 and at a temperature lower than 45°C. The enzymatic activity could be enhanced by Ca2+, Mg2+, or Ni2+, and inhibited by Zn2+, Cu2+, EDTA, ethylene glycol tetraacetic acid (EGTA), deferoxamine mesylate salt (DFO), triton X-100, Tween 80, or SDS at different levels. Kinetic measurements of HAase-B towards HA gave a Michaelis constant (Km) of 0.02 mg/mL, and a maximum velocity (Vmax) of 0.27 A232/min. HAase-B also showed activity towards chondroitin sulfate A (CSA) with the kinetic parameters, Km and Vmax, 12.30 mg/mL and 0.20 A232/min respectively. Meanwhile, according to the sequences of genomic DNA and HAase-B's part peptides, a 3,324-bp gene encoding HAase-B was obtained.

Schistosomiasis is an important global public health problem, as millions of people are at risk of acquiring this infection. An ideal method for sustainable control of schistosomiasis is using a vaccine alone or in combination with drugs. In the present study, we cloned the SjGALE gene and generated the expression product in E. coli. The expression level of SjGALE during different developmental stages of S. japonicum was evaluated by real-time RT-PCR and western blotting. Immunolocalization indicated that the protein was mainly located on the tegument of the parasite. Infection of rSjGALE-immunized mice demonstrated a 34% and 49% reduction of the mean worm burden and liver egg burden, respectively, in two independent experiments, indicating immune protection. The liver egg count from each female adult worm was significantly reduced by 63% in the two trials. The cytokine profile and IgG isotype analysis demonstrated the induction of a Th1 immune profile in response to immunization with this protein, further suggesting protection against infection. In conclusion, these findings indicated that SjGALE is a potential vaccine against S. japonicum.