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The process of mining and ore crushing generates a lot of dust. Bag filter is an effective equipment to remove particulate matter from dusty airflow. However, there is a problem of uneven distribution of air volume in the filter bag during operation. The existing way of adding deflectors in bag filters increases the complexity of its structure. In order to solve the problem of uneven distribution of mass flow of middle box lower inlet filter bag, a method of filter bag mass flow even distribution by adjusting selected bags’ permeability is proposed. Firstly, realizable k-ε equation model is selected to simulate the flow field of model bag filter. Then, the numerical simulation results are compared with model experimental results, which verifies the correctness of the simulation method. After that, a geometric model of filter bag with 160 filter bags installed in the middle box is established. Flow field characteristics, mass flow distribution and uniformity of the filter bag at the face velocity of 0.5～1.2 m/min are analyzed by numerical simulation. Finally, taking the uneven distribution of filter bag mass flow at 1 m/min face velocity as an example problem, a method to reduce the permeability of filter bag at the outlet side with large mass flow is designed. According to the Darcy formula and pressure drop calculation formula of filter material, relationship between permeability and filter material thickness, fiber diameter and filling ratio are given. The results show that under this research model, the increase of face velocity will make the mass flow distribution of the filter bag more uneven, and the mass flow of the filter bags on the outlet side of the bag filter will gradually increase. Reducing the permeability of 8−10 rows of filter bags in the area with high flow rates from 2.0×10−11 m2 to 1.7×10−11 m2 can reduce the root mean square deviation of mass flow distribution of filter bags from 0.158 to 0.099. The increase in the initial pressure drop of filter bag is 6.1%, proving this method is feasible. In the process of making filter material, the permeability of filter material can be adjusted, so it is feasible to make the mass flow distribution of filter bag uniform by adjusting the permeability of filter bag in selected area.

In the field of labor protection, the real-time monitoring of workers’ physical health status has received a lot of attention. Because breathing contains a lot of physiological information, the development of wearable devices with sensing functions for monitoring the wearer’s respiratory status has become a research hotspot. Traditional commercial respiratory monitoring devices are expensive, bulky, and not easy to be carried, limiting their use in front-line production sites, such as high dust mass concentration mines’ operating environments. In view of this situation, based on the piezoelectric properties of polyvinylidene fluoride (PVDF), a flexible PVDF nanofiber membrane with sensing properties was prepared using electrospinning technology. The high permeability carbon fiber felt and PVDF nanofiber membrane were designed as a sandwich structure. The sensing module was embedded in the mask. The sensor module converted the mechanical energy generated by the respiratory airflow into electrical energy. Through the collection and analysis of electrical signal data, the real-time monitoring of respiratory intensity and respiratory frequency was completed. The results show that the piezoelectric signals generated by the mask embedded with the sensing module are significantly different under different breathing states. Under the three breathing states of low intensity (15 L/min), medium intensity (50 L/min) and high intensity (85 L/min), the output piezoelectric voltage peaks are 300, 700 and 1 100 mV, respectively, and the frequency of voltage peaks is 18, 16 and 14 times/min, respectively. Therefore, based on the analysis of the peak voltage and its frequency of the piezoelectric signal, the real-time monitoring of respiratory parameters such as the wearer's respiratory intensity and respiratory frequency can be realized. In addition, the piezoelectric properties of PVDF nanofiber membranes were determined to be related to the content of β-phase crystals by XRD, FT-IR, TG and other analysis methods, which provided a basis for further improving the piezoelectric properties of PVDF nanofiber membranes. The developed mask with sensing function can be used for the real-time respiratory monitoring of mine workers’ working conditions, and the feedback to managers when workers’ working conditions are abnormal to facilitate some timely treatment measures.

Abstract Background The presence of Staphylococcus aureus in the bloodstream can lead to the development of sepsis; however, the severity and risk factors of the systemic inflammatory response to Staphylococcus aureus bloodstream infections were unclear. This study is aimed to build a model to predict the risk of sepsis in children with Staphylococcus aureus bloodstream infections. Methods A retrospective analysis of hospitalized pediatric patients diagnosed with Staphylococcus aureus bloodstream infections was performed between January 2013 and December 2019. Each patient was assessed using the pediatric version of the Sequential Organ Failure Assessment score (pSOFA) within 24 h of blood culture collection. A nomogram based on logistic regression models was constructed to predict the risk factors for sepsis in children with Staphylococcus aureus bloodstream infections. It was validated using the area under the receiver-operating characteristic curve (AUC). Results Of the 94 patients included in the study, 35 cases (37.2%) developed sepsis. The pSOFA scores ranged from 0 to 8, with 35 patients having a pSOFA score of ≥ 2. Six children (6.4%) died within 30 days, who were all from the sepsis group and had different pSOFA scores. The most common organs involved in sepsis in children with staphylococcal bloodstream infections were the neurologic system (68.6%), respiratory system (48.6%), and coagulation system (45.7%). Hospital-acquired infections (adjusted odds ratio [aOR], 3.0; 95% confidence interval [CI], 1.3–7.2), implanted catheters (aOR, 10.4; 95% CI, 3.8–28.4), procalcitonin level ≥ 1.7 ng/mL (aOR, 15.4; 95% CI, 2.7–87.1), and underlying diseases, especially gastrointestinal malformations (aOR, 14.0; 95% CI, 2.9–66.7) were associated with Staphylococcus aureus sepsis. However, methicillin-resistant Staphylococcus aureus infection was not a risk factor for sepsis. The nomogram had high predictive accuracy for the estimation of sepsis risk, with an AUC of 0.85. Conclusions We developed a predictive model for sepsis in children with Staphylococcus aureus infection.

Abstract Most of the shape classification methods are based on a single closed contour. However, practical shapes always have complex contours, for example, a combination of multiple open contours. How to accurately identify complex shapes is an unsolved problem. In this research, a novel method is proposed to classify complex shapes. The proposed method firstly encodes a complex shape to an angle code and a sparsity code, then input these codes to a 1‐D CNN for extracting features and classification. Experiments on two datasets show this novel method is superior in terms of classification accuracy. These two datasets are practical shape dataset collected by this paper on internet and MPEG‐7 CE‐1 Part B. The proposed method achieves higher classification accuracy than compared methods. In order to show the performance of the proposed method on each class, the accuracy on each class is analyzed. Ablation experiment is conducted to show the contribution of each module in the network. The result shows that each module is meaningful in the network, because without any module the accuracy drops.

Wheat (Triticum aestivum L.) is one of the most important food crops worldwide and provides the staple food for 40% of the world’s population. Increasing wheat production has become an important goal to ensure global food security. The grain yield of wheat is a complex trait that is usually influenced by multiple agronomically important traits. Thus, the genetic dissection and discovery of quantitative trait loci (QTL) of wheat-yield-related traits are very important to develop high-yield cultivars to improve wheat production. To analyze the genetic basis and discover genes controlling important agronomic traits in wheat, a recombinant inbred lines (RILs) population consisting of 180 RILs derived from a cross between Xinong822 (XN822) and Yannong999 (YN999), two well-adapted cultivars, was used to map QTL for plant height (PH), spike number per spike (SNS), spike length (SL), grain number per spike (GNS), spike number per plant (SN), 1000- grain weight (TGW), grain length (GL), grain width (GW), length/width of grain (GL/GW), perimeter of grain (Peri), and surface area of grains (Sur) in three environments. A total of 64 QTL were detected and distributed on all wheat chromosomes except 3A and 5A. The identified QTL individually explained 2.24–38.24% of the phenotypic variation, with LOD scores ranging from 2.5 to 29. Nine of these QTL were detected in multiple environments, and seven QTL were associated with more than one trait. Additionally, Kompetitive Allele Specific PCR (KASP) assays for five major QTL QSns-1A.2 (PVE = 6.82), QPh-2D.1 (PVE = 37.81), QSl-2D (PVE = 38.24), QTgw-4B (PVE = 8.78), and QGns-4D (PVE = 13.54) were developed and validated in the population. The identified QTL and linked markers are highly valuable in improving wheat yield through marker-assisted breeding, and the large-effect QTL can be fine-mapped for further QTL cloning of yield-related traits in wheat.

Abstract Maritime surveillance systems have been commonly exploited in vessel traffic services. The maritime visual information can be obtained through shore‐borne, ship‐borne or air‐borne cameras. However, the obtained visual data often suffers from blur effects due to the shaky imaging devices in wild conditions (e.g. wind, waves and currents). It is necessary to develop image stabilisation methods to promote visual quality. In this work, we propose a two‐step image stabilisation method by introducing the blind deblurring technology. In particular, we first propose a hybrid regularised blur kernel estimation method to robustly and accurately capture the motion trajectory of imaging devices during shaking. Once the estimation of motion trajectory is obtained, we then present a robust non‐blind deblurring method in the second step to implement restoration of latent sharp image (i.e. image stabilisation). All non‐convex and non‐smooth minimisation problems related to blur kernel estimation and non‐blind deblurring are effectively solved using the alternating direction method of multipliers‐based numerical methods. Extensive experiments on both synthetic and realistic blurred images have been performed. All experimental results have demonstrated the satisfactory performance of our image stabilisation method under different imaging conditions. Moreover, the comparisons on other state‐of‐the‐art methods have also indicated that our method could provide a good representation of promoting object detection in vision‐enabled maritime surveillance systems.

Abstract Background The clinical significance of group B streptococcus (GBS) was different among different clonal complexes (CCs), accurate strain typing of GBS would facilitate clinical prognostic evaluation, epidemiological investigation and infection control. The aim of this study was to construct a practical and facile CCs prediction model for S. agalactiae. Methods A total of 325 non-duplicated GBS strains were collected from clinical samples in Xinhua Hospital, Shanghai, China. Multilocus sequence typing (MLST) method was used for molecular classification, the results were analyzed to derive CCs by Bionumeric 8.0 software. Antibiotic susceptibility test was performed using Vitek-2 Compact system combined with K-B method. Multiplex PCR method was used for serotype identification. A total of 45 virulence genes associated with adhesion, invasion, immune evasion were detected by PCR method and electrophoresis. Three types of features, including antibiotic susceptibility (A), serotypes (S) and virulence genes (V) tests, and XGBoost algorithm was established to develop multi-class CCs identification models. The performance of proposed models was evaluated by the receiver operating characteristic curve (ROC). Results The 325 GBS were divided into 47 STs, and then calculated into 7 major CCs, including CC1, CC10, CC12, CC17, CC19, CC23, CC24. A total of 18 features in three kinds of tests (A, S, V) were significantly different from each CC. The model based on all the features (S&A&V) performed best with AUC 0.9536. The model based on serotype and antibiotic resistance (S&A) only enrolled 5 weighed features, performed well in predicting CCs with mean AUC 0.9212, and had no statistical difference in predicting CC10, CC12, CC17, CC19, CC23 and CC24 when compared with S&A&V model (all p > 0.05). Conclusions The S&A model requires least parameters while maintaining a high accuracy and predictive power of CCs prediction. The established model could be used as a promising tool to classify the GBS molecular types, and suggests a substantive improvement in clinical application and epidemiology surveillance in GBS phenotyping.

The cement industry’s intricate production process, including kiln heating and fossil fuel use, contributes 5–8% of global CO2 emissions, marking it as a significant carbon emitter in construction. This study focuses on quantifying CO2 capture potential in blended cement systems through the utilisation of phenolphthalein and thermalgravimetric methodologies. Its primary objective is to assess the CO2 absorption capacity of these blended systems’ pastes. Initial evaluation involves calculating the carbon capture capacity within the paste, subsequently extended to estimate CO2 content in the resultant concrete products. The findings indicate that incorporating ground granulated blast-furnace slag (GGBS) or an ettringite-based expansive agent did not notably elevate carbonation depth, irrespective of their fineness. Conversely, the introduction of fly ash (FA) notably augmented the carbonation depth, leading to a substantial 36.4% rise in captured CO2 content. The observed distinctions in carbonation behaviour primarily stem from variances in pore structure, attributable to distinct hydration characteristics between GGBS and FA. Thermal analysis confirms the increased stabilisation of CO2 in FA blends, highlighting the crucial influence of material composition on carbonation and emission reduction. Incorporating both GGBS and FA notably diminishes binder emissions, constituting almost half of PC-concrete emissions. Initially, 60% GGBS shows lower emissions than 50% FA, but when considering CO2 capture, this emission dynamic significantly changes, emphasising the intricate influence of additives on emission patterns. This underscores the complexity of evaluating carbonation-induced emissions in cementitious systems.

Previous reports have shown that heterologous boosting with the AD5-vectored COVID-19 vaccine Convidecia based on a primary series of two doses of inactivated vaccine induces increasing immune responses. However, the immune persistence until 6 months after the heterologous prime-boost immunization was limited. Participants were from two single-center, randomized, controlled, observer-blinded trials, which involved individuals of 18–59 years of age and over 60 years of age. Eligible participants who previously primed with one dose or two doses of CoronaVac were stratified and randomly assigned to inoculate a booster dose of Convidecia or CoronaVac. Neutralizing antibodies against a live SARS-CoV-2 prototype virus and Delta and Omicron (B.1.1.529) variants, pseudovirus neutralizing antibodies against Omicron BA.4/5 variants, and anti-SARS-CoV-2 RBD antibodies at month 6 were detected, and the fold decreases and rate difference were calculated by comparing the levels of antibodies at month 6 with the peak levels at month 1. The neutralizing antibody titers against prototype SARS-CoV-2, RBD-specific IgG antibodies, and the Delta variant in the heterologous regimen of the CoronaVac plus Convidecia groups were significantly higher than those of the homologous prime-boost groups. In three-dose regimen groups, the geometric mean titers (GMTs) of neutralizing antibodies against prototype SARS-CoV-2 were 30.6 (95% CI: 25.1; 37.2) in the heterologous boosting group versus 6.9 (95% CI: 5.6; 8.6) in the homologous boosting group (p < 0.001) at month 6 in participants aged 18–59 years, and in the two-dose regimen, the neutralizing antibody GMTs were 8.5 (95% CI: 6.2; 11.7) and 2.7 (2.3 to 3.1) (heterologous regimen group versus CoronaVac regimen group, p < 0.001). Participants aged over 60 years had similar levels of neutralizing antibodies against the prototype, with GMTs of 49.1 (38.0 to 63.6) in the group receiving two doses of CoronaVac plus one dose of Convidecia versus 9.4 (7.7 to 11.4) in the group receiving three doses of CoronaVac (p < 0.001) and 11.6 (8.4 to 16.0) in the group receiving one dose of CoronaVac and one dose of Convidecia versus 3.3 (2.7 to 4.0) in the group receiving two doses of CoronaVac (p < 0.001). Compared with day 14, over sixfold decreases in neutralizing antibody GMTs were observed in the heterologous groups of the three- or two-dose regimen groups of younger and elderly participants, while in the homologous regimen groups, the GMTs of neutralizing antibodies decreased about fivefold in the two age groups. The heterologous prime-boost regimen with two doses of CoronaVac and one dose of Convidecia was persistently more immunogenic than the regimen of the homologous prime-boost with three doses of CoronaVac.

Species of Anteon Jurine, 1807 are a large group of parasitoids attacking leafhoppers, which are important insect pests. Despite their great potential in pest biological control, the taxonomy and biology of these parasitoids are far from clear. Sexual dimorphism is extreme in Anteon species and has hampered the taxonomy of these parasitoids, resulting in many species described based on a single sex. In this paper, we employed an integrated taxonomic approach for delimitating species, combining morphological examinations with DNA barcoding, to investigate Anteon species from China. In total, 53 COI sequences representing 29 species of Anteon were obtained and analyzed. On the basis of both morphology and DNA barcoding, five new species of Anteon were discovered and described: A. clariclypeum sp. nov., A. maguanense sp. nov., A. parafidum sp. nov., A. shaanxianum sp. nov., and A. shandonganum sp. nov. The neotype of A. claricolle Kieffer is designated. The sexual association of six species was confirmed by DNA barcoding, which led to the synonymy of Anteon liui Xu, Olmi & He 2010, new syn., under Anteon meifenganum Olmi, 1991. Keys to species of Anteon from the Oriental and Eastern Palaearctic are updated to contain the five new species. Our study demonstrates that DNA barcoding is a potent tool for tackling the taxonomic challenges in parasitoids with extreme sexual dimorphism.

Abstract Background Recent investigations of the meninges have highlighted the importance of the dura layer in central nervous system immune surveillance beyond a purely structural role. However, our understanding of the meninges largely stems from the use of pre-clinical models rather than human samples. Methods Single-cell RNA sequencing of seven non-tumor-associated human dura samples and six primary meningioma tumor samples (4 matched and 2 non-matched) was performed. Cell type identities, gene expression profiles, and T cell receptor expression were analyzed. Copy number variant (CNV) analysis was performed to identify putative tumor cells and analyze intratumoral CNV heterogeneity. Immunohistochemistry and imaging mass cytometry was performed on selected samples to validate protein expression and reveal spatial localization of select protein markers. Results In this study, we use single-cell RNA sequencing to perform the first characterization of both non-tumor-associated human dura and primary meningioma samples. First, we reveal a complex immune microenvironment in human dura that is transcriptionally distinct from that of meningioma. In addition, we characterize a functionally diverse and heterogenous landscape of non-immune cells including endothelial cells and fibroblasts. Through imaging mass cytometry, we highlight the spatial relationship among immune cell types and vasculature in non-tumor-associated dura. Utilizing T cell receptor sequencing, we show significant TCR overlap between matched dura and meningioma samples. Finally, we report copy number variant heterogeneity within our meningioma samples. Conclusions Our comprehensive investigation of both the immune and non-immune cellular landscapes of human dura and meningioma at single-cell resolution builds upon previously published data in murine models and provides new insight into previously uncharacterized roles of human dura.

With the rapid development of the global economy and trade, the number of ships serving ports in China is increasing continuously. Port traffic is becoming busier, and ship behavior is more complex and changeable. The analysis of ship behavior patterns in port waters has become an urgent problem to improve the efficiency and safety of port areas. In this paper, through the full integration of ship trajectory and port geographic information, the behavior chain of a single ship across the whole process of entering and exiting the port is identified. The traffic complexities and dynamics can be further analyzed by grouping the movement patterns of large ships. Based on graph theory, the port areas can be described as a transportation network in which functional areas are nodes and fairways between different areas are edges. The traffic can be analyzed through the network structure characteristics, such as node degree, node weight, and edge weight, and by their similarities and differences. This methodology provides a quantitative analysis for exploring the behavior patterns of large ships as well as the various traffic complexities. A case study in Tianjin Port has been conducted to verify the proposed model. The results show that it can accurately analyze a ship behavior’s regularity, occasion, and correlation. It provides a theoretical reference for the port to schedule and formulate emergency plans.

The diffusion phenomena taking place in complex networks are usually modelled as diffusion process, such as the diffusion of diseases, rumors and viruses. Identification of diffusion source is crucial for developing strategies to control these harmful diffusion processes. At present, accurately identifying the diffusion source is still an opening challenge. In this paper, we define a kind of diffusion characteristics that is composed of the diffusion direction and time information of observers, and propose a neural networks based diffusion characteristics classification framework (NN-DCCF) to identify the source. The NN-DCCF contains three stages. First, the diffusion characteristics are utilized to construct network snapshot feature. Then, a graph LSTM auto-encoder is proposed to convert the network snapshot feature into low-dimension representation vectors. Further, a source classification neural network is proposed to identify the diffusion source by classifying the representation vectors. With NN-DCCF, the identification of diffusion source is converted into a classification problem. Experiments are performed on a series of synthetic and real networks. The results show that the NN-DCCF is feasible and effective in accurately identifying the diffusion source.

Shadow removal is an important issue in the field of motion object surveillance and automatic control. Although many works are concentrated on this issue, the diverse and similar motion patterns between shadows and objects still severely affect the removal performance. Constrained by the computational efficiency in real-time monitoring, the pixel feature based methods are still the main shadow removal methods in practice. Following this idea, this paper proposes a novel and simple shadow removal method based on a differential correction calculation between the pixel values of Red, Green and Blue channels. Specifically, considering the fact that shadows are formed because of the occlusion of light by objects, all the reflected light will be attenuated. Hence there will be a similar weakening trends in all Red, Green and Blue channels of the shadow areas, but not in the object areas. These trends can be caught by differential correction calculation and distinguish the shadow areas from object areas. Based on this feature, our shadow removal method is designed. Experiment results verify that, compared with other state-of-the-art shadow removal methods, our method improves the average of object and shadow detection accuracies by at least 10% in most of the cases.

Puccinia triticina, which is the causative agent of wheat leaf rust, is widely spread in China and most other wheat-planting countries around the globe. Cultivating resistant wheat cultivars is the most economical, effective, and environmentally friendly method for controlling leaf rust-caused yield damage. Exploring the source of resistance is very important in wheat resistance breeding programs. In order to explore more effective resistance sources for wheat leaf rust, the resistance of 112 wheat accessions introduced from the U.S. National Plant Germplasm System were identified using a mixture of pathogenic isolates of THTT, THTS, PHTT, THJT and THJS which are the most predominant races in China. As a result, all of these accessions showed high resistance at seedling stage, of which, ninety-nine accessions exhibited resistance at adult plant stage. Eleven molecular markers of eight effective leaf rust resistance genes in China were used to screen the 112 accessions. Seven effective leaf rust resistance genes Lr9, Lr19, Lr24, Lr28, Lr29, Lr38 and Lr45 were detected, except Lr47. Twenty-three accessions had only one of those seven effective leaf rust resistance gene. Eleven accessions carried Lr24+Lr38, and 7 accessions carried Lr9+Lr24+Lr38, Lr24+Lr38+Lr45, Lr24+Lr29+Lr38 and Lr19+Lr38+Lr45 respectively. The remaining seventy-one accessions had none of those eight effective leaf rust resistance genes. This study will provide theoretical guidance for rational utilization of these introduted wheat accessions directly or for breeding the resistant wheat cultivars.

ABSTRACT Emerging hypervirulent carbapenem-resistant Klebsiella pneumoniae (hv-CRKP) is a severe public health problem worldwide. To assess the cooccurrence of CRKP and hv-CRKP, a total of 1,181 CRKP isolates were collected from 2009 to 2018, covering their initial occurrence to outbreaks. Overall, two major capsular serotypes, namely, wzi209-CRKP and K14.K64-CRKP, were identified as being prevalent in pediatric and adult patients, respectively. Most isolates carried blaKPC, and the blaKPC-carrying hybrid plasmid IncFII-IncR, which was stable and transferable, was identified. The conjugation region (traN/traC) of IncFII-IncR was found to be variable, and the genes were used as markers to identify the transmission of strains among patient groups in this study. Notably, hv-CRKP was characterized by screening for four virulence genes (rmpA, iroN, terW, and rmpA2) in all 977 blaKPC-carrying K14.K64-CRKP and wzi209-CRKP strains. Two virulence types, namely, rmpA/iroN/terW/rmpA2 positive and terW/rmpA2 positive, were found. The corresponding virulence plasmids Vir1, i.e., nonconjugative IncFIB(k)-IncHI1B, and Vir2, i.e., conjugative antibiotic-resistant IncFIB-IncHI1B, were further characterized. Both Vir1 and Vir2 were stable, and the transferability of Vir2 was significantly higher than that of IncFII-IncR. However, none of the Vir1- or Vir2-carrying strains exhibited the hypervirulent phenotype. Meanwhile, hv-CRKP (terW/rmpA2 positive) was found in late 2018 among wzi209-CRKP strains. The corresponding Vir2-related fragment was characterized as chromosomally integrated, which dramatically enhanced the virulence of wzi209-CRKP. Transmission of hv-CRKP among patient groups was also confirmed according to virulence elements. Taken together, CRKP and hv-CRKP occurred on a large scale. Plasmids and their derivatives played an important role on this process. Surveillance and intervention of hv-CRKP are urgently needed. IMPORTANCE Currently, an increasing number of hv-CRKP strains have been reported and pose a substantial threat to public health worldwide, because these strains are considered to be simultaneously hypervirulent, carbapenem resistant, and transmissible. In this study, we provided a complete transition process of CRKP and hv-CRKP from their early emergence to outbreak in 10 years. We identified two epidemic groups, K14.K64 (wzi64)-CRKP and wzi209-CRKP, in adult and pediatric patients, respectively. K14.K64 (wzi64)-CRKP was widely present, while wzi209-CRKP was rarely reported as an epidemic type. We discovered a large scale of hv-CRKP transmission from CRKP and determined the importance of antibiotic resistance and virulence plasmids and their derivatives for the transition of CRKP and hv-CRKP. Two virulence plasmids coexist in out hospital, but neither of them enhanced virulence. Notably, we found a newly emerged type of CRKP, hypervirulent wzi209-CRKP, which had dramatically enhanced virulence, making it a great threat to human health.

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 provides a highly efficient and flexible genome editing technology with numerous potential applications ranging from gene therapy to population control. Some proposed applications involve the integration of CRISPR/Cas9 endonucleases into an organism’s genome, which raises questions about potentially harmful effects to the transgenic individuals. One example for which this is particularly relevant are CRISPR-based gene drives conceived for the genetic alteration of entire populations. The performance of such drives can strongly depend on fitness costs experienced by drive carriers, yet relatively little is known about the magnitude and causes of these costs. Here, we assess the fitness effects of genomic CRISPR/Cas9 expression in Drosophila melanogaster cage populations by tracking allele frequencies of four different transgenic constructs that allow us to disentangle ‘direct’ fitness costs due to the integration, expression, and target-site activity of Cas9, from fitness costs due to potential off-target cleavage. Using a maximum likelihood framework, we find that a model with no direct fitness costs but moderate costs due to off-target effects fits our cage data best. Consistent with this, we do not observe fitness costs for a construct with Cas9HF1, a high-fidelity version of Cas9. We further demonstrate that using Cas9HF1 instead of standard Cas9 in a homing drive achieves similar drive conversion efficiency. These results suggest that gene drives should be designed with high-fidelity endonucleases and may have implications for other applications that involve genomic integration of CRISPR endonucleases.

Objective: This study was developed to assess the in vivo antimicrobial activity of specific drugs using a model system consisting of Caenorhabditis elegans (C. elegans) infected with Carbapenem-resistant Klebsiella pneumoniae (CRKP) in an effort to identify promising drugs for CRKP-infected patient treatment.Methods: A C. elegans-CRKP liquid assay platform was developed and used to conduct limited in vivo screening for antimicrobial agents with potential activity against CRKP. Time curves for 10 different concentrations of tested antimicrobial agents were tested in this model system at 0, 2, 6, 8, and 12 h after treatment. The protective effects of these different antimicrobial agents were compared at different time points. Furthermore, ten CRKP strains samples were isolated from clinical specimens to demonstrate the applicability of the nematode model method, and two typical clinical cases are presented.Results: CRKP bacteria were sufficient to induce C. elegans death in a dose- and time-dependent fashion, while effective antimicrobial agents improved the survival of these nematodes in a dose-dependent manner. Notably, PB and TGC exhibited robust antibacterial protection within 12 h even at low tested concentrations, and clear efficacy remained evident for high doses of CAZ at this same time point as mediators of improved nematode survival. The results of C. elegans model method were well consistent with that using the Kirby-Bauer method in 10 CRKP strains samples, and two typical clinical cases showed applicability, reliability and efficacy of C. elegans model method.Conclusion: Overall, nematode models in drug sensitivity testing have shown advantages in clinical settings. Our results highlight the value of C. elegans model systems as tools for the simultaneous screening of different agents for in vivo antibacterial efficacy and are deserved further study.

Filter media may encounter aerosols mixed with solid and oil ingredients from various sources, such as industries, transportation, and households, in the air purification process, while the influence such oil content has on the loading performance of single-stage and two-stage filtration systems is under-reported. Thus, this study aims to evaluate oil fraction effects on the loading performance of single-stage and two-stage filtration systems. First, to reveal the oil–solid mixed particle deposition mechanisms, the filter media parameters, i.e., specific cake resistance ε and cake porosity K2, were tested, indicating that a slight amount of oil can increase the dust holding capacity (DHC) of filters by forming a more porous cake, while an excess of oil results in reduced DHC by forming impermeable liquid films on the solid skeleton. Further two-stage experimental results indicate that the effectiveness of a pre-stage filter can be significantly affected by the properties of incoming aerosol and main-stage filters. The utilization of a pre-stage filter unintentionally deteriorated the service lifetime of the main-stage filter when challenged with contaminants with certain oil particles. This counter-intuitive negative phenomenon is due to the special loading behaviors of oil–solid mixed particles. The existing pre-stage filters allow a higher fine oil particle fraction to reach the main-stage downstream, while the induced cake filtration scenario leading to a film clogging scenario adversely reduced the lifetime of the main-stage filter. The findings suggest that the feasibility of a pre-stage in the filtration system requires compressive evaluations according to the specific oil-coated contaminants.

The complexity and changefulness of inland navigation environment in space and time makes it hard to guarantee the applicability and accuracy of existing ship speed models. In this paper, a novel method for inland ship speed modelling under complex and changeful navigation environment is proposed. Firstly, an unsupervised machine learning algorithm, Density-Based Spatial Clustering of Application with Noise (DBSCAN), is utilized to cluster the environmental data including water level, water speed, wind speed and wind direction, to get the segment division information, which greatly helps reduce the influence of other uncertain environmental factors on the speed model. Then, Generalized Regression Neural Network (GRNN) is tailored and employed to build the ship speed estimation model with multiple input variables. Finally, a detailed case study of a ship sailing in the Yangtze River trunk line is conducted to validate the proposed methods. The results show that the ship speed model established based on machine learning methods works effectively in speed estimation and analysis. Moreover, compared with other regression methods and neural networks, the proposed GRNN model has the best performance in ship speed modelling.

Abstract Background Klebsiella pneumoniae bloodstream infection (Kp-BSI) is a serious threat to pediatric patients. The objective of this study was to explore the risk factors, validate the prediction efficiency of pediatric Sequential Organ Failure Assessment (SOFA) and establish better early predictors of mortality in pediatric patients with Kp-BSI. Methods All children diagnosed with Kp-BSI were included in this retrospective cohort study from January 2009 to June 2019. Basic characteristics, symptoms and physical examinations, treatments, laboratory statistics, and SOFA at the onset of Kp-BSI were recorded. The Cox proportional hazard model and receiver operating characteristic curves were used to assess the association between the variables and the 90-day mortality and their predictive value. DeLong’s test of receiver operating characteristic curves and integrated discrimination improvement index were used to determine the improvement in predictive capacity of the modified SOFA models. A predictive score was developed using multivariate logistic regression. Results Of the 146 children enrolled, 33 (22.6%) patients died within 90 days. Hospitalization in the last 6 months, intra-abdominal source of infection, presence of organ failure, and altered levels of blood biomarkers, including C-reactive protein, albumin, and lactate were significant risk factors for 90-day mortality. The area under the curve (AUC) of SOFA for predicting 90-day mortality was 0.80 (95% CI 0.71–0.89). Moreover, we found that a prediction model combining SOFA with two other parameters, namely hospitalization in the last 6 months and intra-abdominal source of infection, was better at predicting mortality (AUC = 0.89, 95% CI 0.82–0.96; sensitivity = 0.86; specificity = 0.84). According to this novel risk model, we defined three statistically different groups: low-risk, medium-risk and high-risk groups, with an observed 90-day mortality of 5.4, 35.7, and 72.0%, respectively. With reference to the low-risk patients, the medium-risk and high-risk groups had a higher mortality, with hazard ratios of 8.36 (95% CI 3.60–27.83) and 20.27 (95% CI 7.47–54.95), respectively. Conclusions The modified SOFA may be better than the original score to predict 90-day mortality in pediatric patients with Kp-BSI. Future prospective studies are required to validate this novel scoring system in external cohorts.

BackgroundHeterologous boost vaccination has been proposed as an option to elicit stronger and broader, or longer-lasting immunity. We assessed the safety and immunogenicity of heterologous immunization with a recombinant adenovirus type-5-vectored Coronavirus Disease 2019 (COVID-19) vaccine (Convidecia, hereafter referred to as CV) and a protein-subunit-based COVID-19 vaccine (ZF2001, hereafter referred to as ZF).Methods and findingsWe conducted a randomized, observer-blinded, placebo-controlled trial, in which healthy adults aged 18 years or older, who have received 1 dose of Convidecia, with no history of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, were recruited in Jiangsu, China. Sixty participants were randomly assigned (2:1) to receive either 1 dose of ZF2001 or placebo control (trivalent inactivated influenza vaccine (TIV)) administered at 28 days after priming, and received the third injection with ZF2001 at 5 months, referred to as CV/ZF/ZF (D0-D28-M5) and CV/ZF (D0-M5) regimen, respectively. Sixty participants were randomly assigned (2:1) to receive either 1 dose of ZF2001 or TIV administered at 56 days after priming, and received the third injection with ZF2001 at 6 months, referred to as CV/ZF/ZF (D0-D56-M6) and CV/ZF (D0-M6) regimen, respectively. Participants and investigators were masked to the vaccine received but not to the boosting interval. Primary endpoints were the geometric mean titer (GMT) of neutralizing antibodies against wild-type SARS-CoV-2 and 7-day solicited adverse reactions. The primary analysis was done in the intention-to-treat population. Between April 7, 2021 and May 6, 2021, 120 eligible participants were randomly assigned to receive ZF2001/ZF2001 (n = 40) or TIV/ZF2001 (n = 20) 28 days and 5 months post priming, and receive ZF2001/ZF2001 (n = 40) or TIV/ZF2001 (n = 20) 56 days and 6 months post priming. Of them, 7 participants did not receive the third injection with ZF2001. A total of 26 participants (21.7%) reported solicited adverse reactions within 7 days post boost vaccinations, and all the reported adverse reactions were mild, with 13 (32.5%) in CV/ZF/ZF (D0-D28-M5) regimen, 7 (35.0%) in CV/ZF (D0- M5) regimen, 4 (10.0%) in CV/ZF/ZF (D0-D56-M6) regimen, and 2 (10.0%) in CV/ZF (D0-M6) regimen, respectively. At 14 days post first boost, GMTs of neutralizing antibodies in recipients receiving ZF2001 at 28 days and 56 days post priming were 18.7 (95% CI 13.7 to 25.5) and 25.9 (17.0 to 39.3), respectively, with geometric mean ratios of 2.0 (1.2 to 3.5) and 3.4 (1.8 to 6.4) compared to TIV. GMTs at 14 days after second boost of neutralizing antibodies increased to 107.2 (73.7 to 155.8) in CV/ZF/ZF (D0-D28-M5) regimen and 141.2 (83.4 to 238.8) in CV/ZF/ZF (D0-D56-M6) regimen. Two-dose schedules of CV/ZF (D0-M5) and CV/ZF (D0-M6) induced antibody levels comparable with that elicited by 3-dose schedules, with GMTs of 90.5 (45.6, 179.8) and 94.1 (44.0, 200.9), respectively. Study limitations include the absence of vaccine effectiveness in a real-world setting and current lack of immune persistence data.ConclusionsHeterologous boosting with ZF2001 following primary vaccination with Convidecia is more immunogenic than a single dose of Convidecia and is not associated with safety concerns. These results support flexibility in cooperating viral vectored and recombinant protein vaccines.Trial registrationStudy on Heterologous Prime-boost of Recombinant COVID-19 Vaccine (Ad5 Vector) and RBD-based Protein Subunit Vaccine; ClinicalTrial.gov NCT04833101.

Earth’s aurora is a luminescent phenomenon generated by the interaction between magnetospheric precipitating particles and the upper atmosphere; it plays an important role in magnetosphere–ionosphere (M-I) coupling. The transpolar arc (TPA) is a discrete auroral arc distributed in the noon-midnight direction poleward of the auroral oval and connects the dayside to the nightside sectors of the auroral oval. Studying the seasonal variation of TPA events can help us better understand the long-term variation of the interaction between the solar wind, the magnetosphere, and M-I coupling. However, a statistical study of the seasonal variation of TPA incidence has not previously been carried out. In this paper, we have identified 532 TPA events from the IMAGE database (2000–2005) and the Polar database (1996–2002), and calculated the incidence of TPA events for different months. We find a semiannual variation in TPA incidence. Clear peaks in the incidence of TPAs occur in March and September; a less pronounced peak appears in November. We also examine seasonal variation in the northward interplanetary magnetic field (IMF) over the same time period. The intensity and occurrence rate of the northward IMF exhibit patterns similar to that of the TPA incidence. Having studied IMF Bz before TPA onset, we find that strong and steady northward IMF conditions are favorable for TPA formation. We suggest that the semiannual variation observed in TPA incidence may be related to the Russell–McPherron (R-M) effect due to the projection effect of the IMF By under northward IMF conditions.

The strategy of ecological priority and green development in China has made the fuel consumption of inland ships receive unprecedented attentions. Reliable fuel consumption prediction is the vital basis of navigation planning, energy supervision, and efficiency optimization. In this article, a cargo ship sailing on the Yangtze River trunk line was taken as the research object. A comprehensive fitting analysis of inland ship fuel consumption was conducted, and a prediction method was proposed. First, the multi-source data including ship navigation status and environment information were collected by multi-source sensors. Second, to conduct a detailed analysis of the collected data, the authors proposed data pre-processing and trajectory segmentation methods and analyzed the correlation between multi-source variables and fuel consumption. Third, a Back Propagation Neural Network with double hidden layers (DBPNN) was tailored to build a fuel consumption prediction model. Fourth, the developed model was validated using real ship measurement data. Different input variables were selected for fuel consumption prediction, and the results showed that after adding the variables of environmental feature including water level, water speed, wind speed, wind angle, and route segment, the prediction error RMSE (root mean square error) and MAE (mean absolute error) were reduced by 35.31% and 30.30%, respectively, while the R2 (R-squared) increased to 0.9843. What's more, compared with other ANNs (artificial neural networks) such as Elman, RBF (radial basis function), three support vector regression (SVR) models, random forest regression (RFR) model, GRNN (generalized regression neural network), RNN (recurrent neural network), GRU (gated recurrent unit) and LSTM (long short-term memory) the proposed DBPNN model showed better performance in fuel consumption prediction.

Abstract Background Casticin, an isoflavone compound extracted from the herb Fructus Viticis, has demonstrated anti-inflammatory and anticancer activities and properties. The aim of this study was to investigate the effects and mechanisms of casticin in nasopharyngeal carcinoma (NPC) cells and to determine its potential for targeted use as a medicine. Methods NPC cells were used to perform the experiments. The CCK‑8 assay and colony formation assays were used to assess cell viability. Flow cytometry was used to measure the cell cycle and apoptosis analysis (annexin V/PI assay). A three-dimensional (3D) tumour sphere culture system was used to characterize the effect of casticin on NPC stem cells. In silico molecular docking prediction and high-throughput KINOME scan assays were used to evaluate the binding of casticin to phosphoinositide 3-kinase (PI3K), including wild-type and most of mutants variants. We also used the SelectScreen assay to detect the IC50 of ATP activity in the active site of the target kinase. Western blotting was used to evaluate the changes in key proteins involved cell cycle, apoptosis, stemness, and PI3K/protein kinase B (AKT) signalling. The effect of casticin treatment in vivo was determined by using a xenograft mouse model. Results Our results indicate that casticin is a new and novel selective PI3K inhibitor that can significantly inhibit NPC proliferation and that it induces G2/GM arrest and apoptosis by upregulating Bax/BCL2 expression. Moreover, casticin was observed to affect the self-renewal ability of the nasopharyngeal carcinoma cell lines, and a combination of casticin with BYL719 was observed to induce a decrease in the level of the phosphorylation of mTORC1 downstream targets in BYL719-insensitive NPC cell lines. Conclusion Casticin is a newly emerging selective PI3K inhibitor with potential for use as a targeted therapeutic treatment for nasopharyngeal carcinoma. Accordingly, casticin might represent a novel and effective agent against NPC and likely has high potential for combined use with pharmacological agents targeting PI3K/AKT.

Streptococcus agalactiae [also known as group B Streptococcus (GBS)] is a tremendous threat to young infants. Eighty pediatric GBS infection cases were enrolled from a teaching hospital in Shanghai between 2009 and 2020; among them, 72.5% (58/80) were diagnosed with bloodstream infection (BSI). Sequence types (STs) and serotypes of associated GBS strains were identified, and most of the Ib/clonal complex (CC)12 (86.7%, 13/15) strains caused BSIs, which was significantly higher than that of the genetically related clone Ib/CC10 (20%, 2/10; p < 0.05). Ib/CC12 BSI (30.8%) mortality was significantly higher than that of non-Ib/CC12 BSI (2.2%; p < 0.05). Virulence genes associated with adhesion, invasion, and immune evasion were detected using polymerase chain reaction. The fbsA and gbsPC1 positive rates of Ib/CC12 strains was higher than that of non-Ib/CC12 strains, whereas cpsIaJ, cpsJ, cpsI, and cpsG positive rates were lower than those of non-Ib/CC12 (p < 0.05). In in vitro studies, the Ib/CC12 strains had strong invasiveness in RAW264.7 cells, but less invasiveness in human umbilical vein endothelial cells, human brain microvascular endothelial cells, and human mammary epithelial cells when compared to other two clones. In the in vivo model, the Ib/CC12 GBS invaded the circulation system more rapidly after intraperitoneal injection, was more difficult to eradicate by phagocytes, and caused significantly higher mortality than Ib/CC10 and III/ST17 (p < 0.05). Genome analysis showed that the Ib/CC12 strains had two clustered regularly interspaced short palindromic repeat-Cas systems and carried more antibiotic resistant genes, which conferred resistance to macrolides, clindamycin, aminoglycosides, and tetracycline. The Ib/CC12 strains had 45 unique annotated genes compared to that of Ib/CC10, including the pathogen-related toxin/antitoxin system, PezA/T. In conclusion, Ib/CC12 is an emerging hypervirulent multiresistant GBS clone that causes invasive and fatal infections in pediatric patients. The prevention and control of Ib/CC12 GBS infection should be emphasized.

Abstract In the current COVID‐19 pandemic, wearing masks and hand disinfection are widely adopted hygiene practices. Alcohol‐based sanitizers are commonly used for hand disinfection, however, the alcohol vapors can dissipate the charges on electrostatic filters. In the present study, the effects of alcohol vapors from alcohol‐based sanitizers during hand disinfection on the in‐use masks are studied. The results show that the negative effects are not significant for nonelectrostatic cotton masks or N95 respirators with multiple charged layers, but noticeable for surgical masks. After five rounds of hand disinfection, the filtration efficiencies of the filtering materials of the surgical masks decrease by more than 8% for 400 and 500 nm particles and by 3.7 ± 1.8% for 1 µm particles, the effective filtration efficiency of the surgical masks worn by the volunteers (with leakage considered) decreases by about 5% for ambient aerosol. In another process to imitate intensive disinfection procedures by healthcare workers, a 30 min surface cleaning process using alcohol‐based sanitizer is performed, and the effective efficiency of the N95 respirators worn by the volunteers decreases by nearly 9%. The simple practice of avoiding vapor during hand disinfection could mitigate the effects of alcohol vapor, which is demonstrated on two brands of surgical masks.

An amendment to this paper has been published and can be accessed via the original article.

Automatic identification system (AIS) is an important part of perfecting terrestrial networks, radar systems and satellite constellations. It has been widely used in vessel traffic service system to improve navigational safety. Following the explosion in vessel AIS data, the issues of data storing, processing, and analysis arise as emerging research topics in recent years. Vessel trajectory compression is used to eliminate the redundant information, preserve the key features, and simplify information for further data mining, thus correspondingly improving data quality and guaranteeing accurate measurement for ensuring navigational safety. It is well known that trajectory compression quality significantly depends on the threshold selection. We propose an Adaptive Douglas-Peucker (ADP) algorithm with automatic thresholding for AIS-based vessel trajectory compression. In particular, the optimal threshold is adaptively calculated using a novel automatic threshold selection method for each trajectory, as an improvement and complement of original Douglas-Peucker (DP) algorithm. It is developed based on the channel and trajectory characteristics, segmentation framework, and mean distance. The proposed method is able to simplify vessel trajectory data and extract useful information effectively. The time series trajectory classification and clustering are discussed and analysed based on ADP algorithm in this paper. To verify the reasonability and effectiveness of the proposed method, experiments are conducted on two different trajectory data sets in inland waterway of Yangtze River for trajectory classification based on the nearest neighbor classifier, and for trajectory clustering based on the spectral clustering. Comprehensive results demonstrate that the proposed algorithm can reduce the computational cost while ensuring the clustering and classification accuracy.

Reliable and accurate prediction of time series plays a crucial role in the maritime industry, such as economic investment, transportation planning, port planning, design, and so on. The dynamic growth of maritime time series has the predominantly complex, and nonlinear and non-stationary properties. To guarantee high-quality prediction performance, we propose to first adopt the empirical mode decomposition (EMD) and ensemble EMD (EEMD) methods to decompose the original time series into high- and low-frequency components. The low-frequency components can be easily predicted directly through traditional neural network (NN) methods. It is more difficult to predict high-frequency components due to their properties of weak mathematical regularity. To take advantage of the inherent self-similarities within high-frequency components, these components will be divided into several continuous small (overlapping) segments. The grouped segments with high similarities are then selected to form more proper training datasets for traditional NN methods. This regrouping strategy can assist in enhancing the prediction accuracy of high-frequency components. The final prediction result is obtained by integrating the predicted high- and low-frequency components. Our proposed three-step prediction frameworks benefit from the time series decomposition and similar segments grouping. The experiments on both port cargo throughput and vessel traffic flow have illustrated its superior performance in terms of prediction accuracy and robustness.

Wheat stripe (yellow) rust is a worldwide disease that seriously reduces wheat grain yield and quality. Adult-plant resistance (APR) to stripe rust is generally more durable but usually controlled by multiple genes with partial resistance. In this study, a recombinant inbred line population was developed from a cross between a Chinese wheat landrace, Tutoumai, with APR to stripe rust, and a highly susceptible wheat cultivar, Siyang 936. The population was genotyped by genotyping-by-sequencing and phenotyped for APR to stripe rust in four consecutive field experiments. Three QTLs, QYr.sdau-1BL, QYr.sdau-5BL, and QYr.sdau-6BL, were identified for APR to stripe rust, and explained 8.0–21.2%, 10.1–22.7%, and 11.6–18.0% of the phenotypic variation, respectively. QYr.sdau-1BL was further mapped to a 21.6 Mb region using KASP markers derived from SNPs identified by RNA-seq of the two parents. In the QYr.sdau-1BL region, 13 disease-resistance-related genes were differently expressed between the two parents, and therefore were considered as the putative candidates of QYr.sdau-1BL. This study provides favorable gene/QTL and high-throughput markers to breeding programs for marker-assisted selection of the wheat stripe rust APR genes.

The Qinghai–Tibet Plateau (QTP) is a major “river source” and “ecological source” in China, as well as South Asia and Southeast Asia, and is a typical plateau region. Studying the evolution characteristics and ecological effects of the production-living-ecological space (PLES) of the QTP is of great practical significance and theoretical value for strengthening its ecological construction and environmental protection. Based on 30 m × 30 m land use/cover data of the QTP at five time-points of 1980, 1990, 2000, 2010, and 2020, this paper investigates the PLES evolution characteristics, transfer characteristics, eco-environmental response, and influencing factors of the eco-environmental quality index (EEQI) in the region of China of the QTP from 1980 to 2020 by land use transfer matrix, eco-environmental response model, hot spot analysis, and geographically weighted regression (GWR). The results show that: (1) from 1980 to 2020, the ecological space of the QTP decreased, while the production and living space saw an increase. The PLES pattern of the QTP showed a clear shift from 2000 to 2010, while there was no significant change from 1980 to 2000 and from 2010 to 2020. (2) From 1980 to 2020, the EEQI of the QTP decreased from 0.5634 in 1980 to 0.5038 in 2010, and then increased to 0.5044 in 2020, showing a changing trend of first decreasing and then increasing; the degradation of grassland ecological space to other ecological space was the main cause leading to ecological environment deterioration. (3) From 1980 to 2000, the EEQI was high in the midwestern and southeastern parts of the QTP, presenting a double-center distribution. From 2010 to 2020, the EEQI decreased in the western part, while the high value area in the eastern part increased significantly, obviously low in the west and high in the east. The spatial variation characteristics of hot and cold spots and EEQI are generally similar. (4) Natural ecological and socioeconomic factors have significant differences on the spatial distribution of EEQI in the QTP, and natural ecological factors are the main driving factors, with topographic relief having the strongest effect on EEQI as a natural ecological factor, and population density having the strongest effect as a socioeconomic factor.

An analytical algorithm is proposed to extract model parameters in a single-diode lumped-parameter equivalent circuit of solar cells. In the calculation process, six relevant factors in the datasheet are necessary to establish a set of transcendental equations. This set of equations is analyzed and solved to acquire accurate values for the model parameters, by retaining equation items with important physical significance. Furthermore, based on simulations and reconstruction experiments, verifications show the accuracy and universality of the algorithm. Therefore, the proposed algorithm can be regarded as a valid solution for estimating the model parameters of a single-diode lumped-parameter circuit of solar cells.

Ichneumonidae is one of the largest families of insects with a mega-diversity of specialized morphological and biological characteristics. We newly sequenced 92 mitochondrial genomes of ichneumonid wasps and found that they have a conserved base composition and a lower evolutionary rate than that of other families of parasitic Hymenoptera. There are 38 types of gene order in the ichneumonid mitochondrial genome, with 30 novel types identified in 104 ichneumonids. We also found that the rearrangement events occur more frequently in Ophioniformes than in Ichneumoniformes and Pimpliformes. Furthermore, the higher Ophioniformes and their relative lineages shared the transposition of trnL2 to trnI-trnQ-trnM tRNA cluster. We confirmed five higher-level groupings of Ichneumonidae: Brachycyrtiformes, Ichneumoniformes, Ophioniformes, Pimpliformes and Xoridiformes. Two formerly unplaced subfamilies, Eucerotinae and Microleptinae, were placed in Brachycyrtiformes and Ichneumoniformes, respectively. The results will improve our understanding of the diversity and evolution of Ichneumonidae.

Abstract Background Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been proved to be a useful tool for identification of pathogens directly isolated from blood cultures in clinical microbiology laboratories. β-Lactam antibiotics are commonly used for treatment of bloodstream infections caused by Enterobacteriaceae strains, and carbapenem is the superlative class of β-lactam antibiotics. Since the carbapenem resistance rate of Enterobacteriaceae strains raised year by year, efficient detection of carbapenemase activity and timely delivery of carbapenem susceptibility reports of Enterobacteriaceae strains isolated from blood cultures is important for clinicians. Methods We used 64 simulated blood cultures to establish the method of MALDI-TOF MS based ertapenem hydrolysis assay. The cutoff value of logRQ calculated from the peaks intensity of ertapenem and its hydrolysate was first set to identify the strains with carbapenemase activity. Then, we detected and calculated the logRQ values of 385 Enterobacteriaceae strains from positive clinical blood cultures to distinguish the carbapenemase producers and noncarbapenemase producers. Results The mean logRQ value of 32 noncarbapenemase producers was − 0.85 ± 0.14 in simulated blood cultures, while the logRQ value of 32 carbapenemase producers was 0.87 ± 0.55. Thus, the cutoff value of logRQ was set at − 0.45 with sensitivity of 100% and specificity of 100%. In 385 clinical positive blood cultures, the logRQ values of all carbapenem-susceptible Enterobacteriaceae strains (81.3%, 313/385) were

Abstract This paper investigates the multi-cell coordinated beamforming (MCBF) design for secure simultaneous wireless information and power transfer (SWIPT) in both centralized and distributed manners. In each cell, one transmitter serves multiple information receivers (IRs) and energy receivers (ERs) with the non-linear energy harvesting (EH) model. Meanwhile, several eavesdroppers (Eves) intend to intercept the confidential information transmitted for IRs. To achieve a secure transmission, the artificial noise (AN) is embedded in the transmit signals of each transmitter. The proposed design is formulated into a power-minimization problem to guarantee the IRs’ information and ERs’ energy requirements while avoiding the information being intercepted by Eves. Since the problem is non-convex and not easy to solve, a solution method based on semi-definition relaxation (SDR) is proposed and the global optimum is proved to be guaranteed with full channel state information (CSI). We further present a distributed AN-aided MCBF for the system by using alternating direction method of multipliers (ADMM), with which each transmitter is able to calculate its own beamforming vectors and AN covariance matrix based on its local CSI. Simulation results show that our proposed distributed design converges to the global optimum obtained by the centralized one. It is also shown that by employing AN, the total required power of the system is reduced and the effect of AN on the system performance decreases with increment of transmit antennas. Compared with traditional linear EH model, optimizing the system under the non-linear EH one avoids false output power at the ERs and saves power at the transmitter.

Automatic identification systems (AISs) serve as a complement to radar systems, and they have been installed and widely used onboard ships to identify targets and improve navigational safety based on a very high-frequency data communication scheme. AIS networks have also been constructed to enhance traffic safety and improve management in main harbors. AISs record vessel trajectories, which include rich traffic flow information, and they represent the foundation for identifying locations and analyzing motion features. However, the inclusion of redundant information will reduce the accuracy of trajectory clustering; therefore, trajectory data mining has become an important research direction. To extract useful information with high accuracy and low computational costs, trajectory mapping and clustering methods are combined in this paper to explore big data acquired from AISs. In particular, the merge distance (MD) is used to measure the similarities between different trajectories, and multidimensional scaling (MDS) is adopted to construct a suitable low-dimensional spatial expression of the similarities between trajectories. An improved density-based spatial clustering of applications with noise (DBSCAN) algorithm is then proposed to cluster spatial points to acquire the optimal cluster. A fusion of the MD, MDS, and improved DBSCAN algorithms can identify the course of trajectories and attain a better clustering performance. Experiments are conducted using a real AIS trajectory database for a bridge area waterway and the Mississippi River to verify the effectiveness of the proposed method. The experiments also show that the newly proposed method presents a higher accuracy than classical ones, such as spectral clustering and affinity propagation clustering.

An effective ship scheduling strategy is critical for the efficiency of waterway transportation, especially in restricted one-way waterways. By treating ship traffic as a distributed system, a novel Ship Self-Organizing Cooperation algorithm (SSOCA) is proposed to evaluate the effects of self-organizing cooperation between ships. An assumption is made that overtaking is not allowed under the given safety requirement. Observing that traffic efficiency is influenced by speed differences, two types of delay times, wait time and navigation time as increased by speed reductions, are applied to evaluate traffic efficiency. The mathematical model of delay time is inferred in different entry sequences subsequently. Taking advantage of the delay model, each ship makes a decision regarding its own sequence to find the local optimum iteratively. An Arena-based ship traffic model is constructed. The simulation results indicate that average delay time for ships is decreased in comparison with the First Come First Served (FCFS) model. That advantage exists for different combinations of traffic flow parameters. Moreover, a balance between efficiency and computation is also achieved by distributing the computational burden to each ship.

Abstract Relapse and metastasis of nasopharyngeal carcinoma (NPC) are presumably attributed to cancer stem cells (CSCs). In recent years, chimeric antigen receptor (CAR)-modified immune effector cells have been shown to have impressive antitumour efficacy. In this study, we aimed to identify appropriate tumour-associated antigens predominantly expressed on NPC stem cells (NPCSCs) and determine their suitability for CAR-engineered cytokine-induced killer (CIK) cell therapy against NPC. By investigating the expression patterns of potential target antigens (ROR1, 5T4 and CAIX) in NPC, we found that the oncofetal antigen 5T4 was predominately expressed in NPC cell lines and tissues but absent in non-cancerous nasopharyngeal tissues. Moreover, significantly enhanced expression of 5T4 in NPC spheroids revealed its relationship with putative NPCSCs. Hence, we designed a CAR construct (5T4-28Z) specific for 5T4 and generated CAR-transduced CIK cells. Our results showed that the artificial CAR was efficiently expressed on the surface of CIK cells and that no native phenotypes were altered by the gene transduction. Functional assays revealed that 5T4-28Z-CIK cells possessed both CAR-mediated and CAR-independent anti-NPC activity and were capable of efficiently attacking NPC cells, especially NPCSC-like cells in vitro, suggesting that they might serve as an attractive tool for developing efficient therapies against NPC.

Wireless power transfer is able to provide sustainable and relatively stable energy supply for battery-powered wireless sensor networks. This paper investigates how to optimally design a wireless powered sensor network with minimal power requirements. To this end, we formulate an optimization problem to minimize the total energy consumption at two remote radio units (RRUs) by jointly optimizing energy beamforming and time assignment, where the circuit energy consumption including basic circuit and information processing energy consumption at sensors is taken into account in order to achieve a more practical and general system design. To solve this non-convex optimization problem, an efficient solution method is presented on the basis of variable substitutions and semidefinite relaxation technique. We analyze the optimality of our proposed solution method. When the number of sensors is not more than four, the rank-one constraint is always guaranteed. When it is larger than four, we show that with our proposed solution method via simulations, an approximate global optimal result can be achieved. Simulation results also show that by jointly optimizing the energy beamforming and time assignment, the system required power can be greatly reduced, while the energy beamforming has greater effect than time assignment on the proposed system. Moreover, it is shown that for fixed non-zero circuit energy consumption (including the static part and the dynamic part), the total energy consumption at the RRUs almost linearly increases with the increment of transmission rate requirement. Besides, the total energy consumption at two RRUs caused by different numbers of enjoyers and collaborators is also discussed.

The Rao-Blackwellized particle filtering (RBPF) offers a general tracking framework with linear/nonlinear state space models, which outperforms the standard particle filtering in nonlinear and non-Gaussian tracking scenarios. Unfortunately, in conventional radar systems, the observations contain no information about the linear part of target state. In these cases, the RBPF algorithm fails to catch the real trajectories, because we cannot obtain the enough information to correctly update the linear part of target state in the tracking procedure. To overcome such an issue, this paper proposes a Kalman estimation-based BRPF (KE-BRPF) algorithm. In KE-RBPF, the correlation between linear and nonlinear parts of target state is investigated. Benefitting from such investigation, we derive a new set of formulea to present the correlation in terms of means and variances. By utilizing these formulas, our KE-RBPF algorithm correctly tracks the linear part of target state based on the nonlinear one. Finally, the simulation results verify that, our KE-RBPF performs better than other state-of-the-art tracking methods in nonlinear and non-Gaussian radar tracking scenarios, with at least 18% reduction in terms of the means and central tendency of error of tracking root-mean-square-error.

This paper investigates the optimal energy beamforming and time assignment in radio frequency (RF) energy harvesting (EH) wireless powered sensor networks for smart cities, where sensor nodes (SNs) first harvest energy from a sink node, and then transmit their collected data to the sink node via time-division-multiple-access (TDMA) manner by using the harvested energy. In order to achieve green system design, we formulate a problem to minimize the energy requirement of the sink node to support transmission between the sink node and the SNs under data amount constraint and EH constraint. For practical design, the energy consumed by circuit and information processing is also considered. Since the problem is non-convex, we use semidefinite relaxation (SDR) method to relax it into a convex optimization problem and then solve it efficiently. We theoretically prove that when the number of SNs are not greater than two, the relaxed problem guarantees rank-one constraint and when the number of SNs exceeds two, our obtained results are very close to the optimal ones. Simulation results show that when the data amount is relatively small, the energy consumed by circuit and information processing affects the system performance greatly, but for a relatively large data amount, the energy requirement of the sink node on its own signal processing is affected very limited and the system energy requirement is dominated by the transmit power consumption at the SNs. Furthermore, we also discuss the effects of the other parameters on the system performance, which provide some useful insights in future smart city planning.

This article contains data on research conducted in “A double standard model for allocating limited emergency medical service vehicle resources ensuring service reliability” (Liu et al., 2016) [1]. The crash counts were sorted out from comprehensive crash records of over one thousand major signalized intersections in the city of Chicago from 2004 to 2010. For each intersection, vehicular crashes were counted by crash severity levels, including fatal, injury Types A, B, and C for major, moderate, and minor injury levels, property damage only (PDO), and unknown. The crash data was further used to rank intersections by equivalent injury crash frequency. The top 200 intersections with the highest number of crash occurrences identified based on crash frequency- and severity-based scenarios are shared in this brief. The provided data would be a valuable source for research in urban traffic safety analysis and could also be utilized to examine the effectiveness of traffic safety improvement planning and programming, intersection design enhancement, incident and emergency management, and law enforcement strategies.

A functioning gene drive system could fundamentally change our strategies for the control of vector-borne diseases by facilitating rapid dissemination of transgenes that prevent pathogen transmission or reduce vector capacity. CRISPR/Cas9 gene drive promises such a mechanism, which works by converting cells that are heterozygous for the drive construct into homozygotes, thereby enabling super-Mendelian inheritance. Although CRISPR gene drive activity has already been demonstrated, a key obstacle for current systems is their propensity to generate resistance alleles, which cannot be converted to drive alleles. In this study, we developed two CRISPR gene drive constructs based on the nanos and vasa promoters that allowed us to illuminate the different mechanisms by which resistance alleles are formed in the model organism Drosophila melanogaster. We observed resistance allele formation at high rates both prior to fertilization in the germline and post-fertilization in the embryo due to maternally deposited Cas9. Assessment of drive activity in genetically diverse backgrounds further revealed substantial differences in conversion efficiency and resistance rates. Our results demonstrate that the evolution of resistance will likely impose a severe limitation to the effectiveness of current CRISPR gene drive approaches, especially when applied to diverse natural populations.

Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system.

Increased vascular permeability leading to acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) is central to the pathogenesis of heatstroke. Protease-activated receptor 1 (PAR1), the receptor for thrombin, plays a key role in disruption of endothelial barrier function in response to extracellular stimuli. However, the role of PAR1 in heat stress-induced endothelial hyper-permeability is unknown. In this study, we measured PAR1 protein expression in heat-stressed human umbilical venous endothelial cells (HUVECs), investigated the influences of PAR1 on endothelial permeability, F-actin rearrangement, and moesin phosphorylation by inhibiting PAR1 with its siRNA, neutralizing antibody (anti-PAR1), specific inhibitor(RWJ56110), and Xuebijing injection (XBJ), a traditional Chinese medicine used for sepsis treatment, and evaluated the role of PAR1 in heatstroke-related ALI/ARDS in mice by suppressing PAR1 with RWJ56110, anti-PAR1and XBJ. We found that heat stress induced PAR1 protein expression 2h after heat stress in endothelial cells, caused the release of endothelial matrix metalloprotease 1, an activator of PAR1, after 60 or 120 min of heat stimulation, as well as promoted endothelial hyper-permeability and F-actin rearrangement, which were inhibited by suppressing PAR1 with RWJ56110, anti-PAR1 and siRNA. PAR1 mediated moesin phosphorylation, which caused F-actin rearrangement and disruption of endothelial barrier function. To corroborate findings from in vitro experiments, we found that RWJ56110 and the anti-PAR1 significantly decreased lung edema, pulmonary microvascular permeability, protein exudation, and leukocytes infiltrations in heatstroke mice. Additionally, XBJ was found to suppress PAR1-moesin signal pathway and confer protective effects on maintaining endothelial barrier function both in vitro and in vivo heat-stressed model, similar to those observed above with the inhibition of PAR1. These results suggest that PAR1 is a potential therapeutic target in heatstroke.

The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluations.

Protein aggregation in vivo has been extensively associated with a large spectrum of human diseases. On the other hand, mechanistic insights into protein aggregation in vitro were incomplete due to the inability in solubilizing insoluble proteins for high-resolution biophysical investigations. However, a new avenue may be opened up by our recent discovery that previously-thought insoluble proteins can in fact be solubilized in salt-free water. Here we use this approach to study the NMR structural and dynamic properties of an insoluble SH3 mutant with a naturally-occurring insertion of Val22 at the tip of the diverging turn. The obtained results reveal: 1) regardless of whether the residue is Val, Ala, Asp or Arg, the insertion will render the first hNck2 SH3 domain to be insoluble in buffers. Nevertheless, all four mutants could be solubilized in salt-free water and appear to be largely unfolded as evident from their CD and NMR HSQC spectra. 2) Comparison of the chemical shift deviations reveals that while in V22-SH3 the second helical region is similarly populated as in the wild-type SH3 at pH 2.0, the first helical region is largely unformed. 3) In V22-SH3, many non-native medium-range NOEs manifest to define non-native helical conformations. In the meanwhile a small group of native-like long-range NOEs still persists, indicating the existence of a rudimentary native-like tertiary topology. 4) Although overall, V22-SH3 has significantly increased backbone motions on the ps-ns time scale, some regions still own restricted backbone motions as revealed by analyzing (15)N relaxation data. Our study not only leads to the establishment of the first high-resolution structural and dynamic picture for an insoluble protein, but also shed more light on the molecular events for the nonhierarchical folding mechanism. Furthermore, a general mechanism is also proposed for in vivo protein aggregation triggered by the genetic mutation and posttranslational modification.