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Abstract Strain-specific probiotics can present antioxidant activity and reduce damage caused by oxidation. Streptococcus alactolyticus strain FGM (S. alactolyticus strain FGM) isolated from the chicken cecum shows potential probiotic properties which have been previously demonstrated. However, the antioxidant properties of S. alactolyticus strain FGM remain unknown. In this view, cell-free supernatant (CFS), intact cells (IC) and intracellular extracts (CFE) of strain FGM and 3 strains of Lactobacillus (LAB) were prepared, and their scavenging capacities against DPPH, hydroxyl radicals and linoleic acid peroxidation inhibitory were compared in this study. The effects of strain FGM cell-free supernatant (FCFS) on NO production, activity of SOD and GSH-Px in RAW264.7 cells and LPS-induced RAW264.7 cells were analyzed. The metabolites in the supernatant were quantitated by N300 Quantitative Metabolome. It was shown that the physicochemical characteristics of CFS to scavenge DPPH, hydroxyl radicals, and linoleic acid peroxidation inhibitory were significantly stronger than that of IC and CFE in the strain FGM (P

Abstract Background The present study aimed to investigate the prevalence and molecular characterization of extended-spectrum β-lactamase (ESBL)—producing Escherichia coli (E. coli) isolated from dairy cattle with endometritis in China. The prevalence of ESBL-producing E. coli in sample was detected using ChromID ESBL agar, and genotyping of the ESBL producers was performed by PCR and DNA sequencing. Results The results revealed that the proportion of positive pathogens tested was 69.76% (180/258) in samples obtained from cows diagnosed with clinical endometritis, with E. coli accounting for 170 out of the 180 positive samples. The infection rate of isolated E. coli was 39.14% (101/258), and co-infections with other pathogens were prevalent. Furthermore, among the 158 E. coli isolates, 50 strains were identified as ESBL producers, with TEM and CTX-M prevalence rates at 78.00% and 32.00%, respectively. Drug sensitivity experiments indicated that 50 isolates of ESBL- producing E. coli were multidrug resistance (MDR), with 48.0% of them exhibiting positive results for both the class 1 integron gene and five gene cassettes associated with resistance to trimethoprim (dfr1 and dfrA17) and aminoglycosides (aadA1, aadA5, and dfrA1), respectively. Conclusion This investigation demonstrated a substantial prevalence and heightened level of antimicrobial resistance among ESBL-producing E. coli isolates derived from dairy cattle infected with endometritis in China.

ABSTRACT: Phillygenin (PHI) and Baicalin (Bai) are the major chemical ingredients extracted from Forsythia suspensa and Scutellaria baicalensis, respectively. The mixture of Forsythia suspensa and Scutellaria baicalensis according to the theories of Traditional Chinese Veterinary Medicine, compounded formulation can effectively exert heat-clearing and detoxifying effect, but the synergistic anti-IBV activity of PHI combined with Bai was unclear. Here, the protection of PHI combined with Bai on avian infectious bronchitis virus (IBV) M41 infection and the change of respiratory microbiota and metabolomics profiles in broilers that infected with IBV were investigated. According to the experimental findings, the combination of PHI and Bai effectively alleviated broilers’ slowing-growth weight and respiratory symptoms. This was accompanied by a reduction in viral copies and histopathological changes, as well as an increase of antiviral protein (G3BP1) level in tracheas and anti-IBV antibody levels in serum. In addition, 16s RNA sequencing revealed that IBV infection significantly changed respiratory microbiota composition at different taxonomic levels and respiratory metabolism composition in broilers. Interestingly, PHI combined with Bai modulated the composition of respiratory microfloras, especially the abundance of Firmicutes and Lactobacillaceae were upregulated, as well as the abundance of Proteobacteria was downregulated. The metabolomics results indicated that PHI combined with Bai involved in glucose, lipids, amino acids and nucleotide metabolism during IBV infection. In summary, PHI combined with Bai exhibited a synergistic effect on preventing infectious bronchitis (IB), with the protection being closely associated with the composition of respiratory microbiota and metabolites. Therefore, adding the mixture of PHI and Bai to the chicken drinking water is recommended to prevent and control IB in clinical.

Abstract Background To investigate the risk factors for new vertebral compression fractures (NVCFs) after percutaneous kyphoplasty (PKP) for osteoporotic vertebral compression fractures (OVCFs) and to create a nomogram to predict the occurrence of new postoperative fractures. Methods This was a retrospective analysis of the clinical data of 529 OVCF patients who received PKP treatment in our hospital from June 2017 to June 2020. Based on whether there were new fractures within 2 years after surgery, the patients were divided into a new fracture group and a nonnew fracture group. Univariate and multivariate analyses were used to determine the risk factors for the occurrence of NVCFs after surgery. The data were randomly divided into a training set (75%) and a testing set (25%). Nomograms predicting the risk of NVCF occurrence were created based on the results of the multivariate analysis, and performance was evaluated using receiver operating characteristic curves (ROCs), calibration curves, and decision curve analyses (DCAs). A web calculator was created to give clinicians a more convenient interactive experience. Results A total of 56 patients (10.6%) had NVCFs after surgery. The univariate analysis showed significant differences in sex and the incidences of cerebrovascular disease, a positive fracture history, and bone cement intervertebral leakage between the two groups (P

The use of seawater and sea sand as replacements for fresh water and river sand in the preparation of seawater and sea sand concrete can effectively address issues such as high transportation costs, extended construction periods, and resource wastage. Nevertheless, in northern coastal areas, the problem of concrete durability in the complex and changing marine environment is more prominent. Research on the durability of seawater sea sand concrete is beneficial to the widening of its application range. To investigate the impact of glass fiber (GF) and polyvinyl alcohol fiber (PVA) with different blending methods on the seawater freeze–thaw resistance of seawater sea sand concrete (SSC), corresponding specimens were prepared, and seawater freeze–thaw cycling tests were conducted. By adopting the slow-freezing method and combining macro-structure and micro-morphology, the damage mechanism and the deterioration law of fiber-reinforced SSC under seawater freezing and thawing were investigated. The results indicate that, macroscopically, the incorporation of GF and PVA can effectively mitigate the damage to the matrix and reduce the effects of external erosive substances on the rate of strength loss, the rate of mass loss, and the relative dynamic elastic modulus. After 75 cycles, the SSC with a total volume doping of 0.3% and a blending ratio of 1:1 showed a 41.23% and 27.55% reduction in mass loss and strength loss, respectively, and a 29.9% improvement in relative dynamic elastic modulus compared with the basic group. Microscopic analysis reveals that the combined effect of freezing and expansion forces, the expansive substances generated by seawater intrusion into the interior of the matrix, and salt crystallization all weaken the bond between aggregate and mortar, leading to accelerated deterioration of the concrete. The incorporation of fibers enables the matrix to become denser and improves its crack-resistant properties, resulting in a better durability than that of the basic group. The damage prediction model established by the NSGM(1,N) model of gray system theory exhibits high accuracy and is suitable for long-term prediction, accurately predicting the damage of seawater sea sand concrete under seawater freeze–thaw coupling.

Refined lithology identification is an essential task, often constrained by the subjectivity and low efficiency of classical methods. Computer-aided automatic identification, while useful, has seldom been specifically geared toward refined lithology identification. In this study, we introduce Rock-ViT, an innovative machine learning approach. Its architecture, enhanced with supervised contrastive loss and rooted in visual Transformer principles, markedly improves accuracy in identifying complex lithological patterns. To this end, we have collected public datasets and implemented data augmentation, aiming to validate our method using sandstone as a focal point. The results demonstrate that Rock-ViT achieves superior accuracy and effectiveness in the refined lithology identification of sandstone. Rock-ViT presents a new perspective and a feasible approach for detailed lithological analysis, offering fresh insights and innovative solutions in geological analysis.

Semantic segmentation of remote sensing (RS) images is vital in various practical applications, including urban construction planning, natural disaster monitoring, and land resources investigation. However, RS images are captured by airplanes or satellites at high altitudes and long distances, resulting in ground objects of the same category being scattered in various corners of the image. Moreover, objects of different sizes appear simultaneously in RS images. For example, some objects occupy a large area in urban scenes, while others only have small regions. Technically, the above two universal situations pose significant challenges to the segmentation with a high quality for RS images. Based on these observations, this paper proposes a Mask2Former with an improved query (IQ2Former) for this task. The fundamental motivation behind the IQ2Former is to enhance the capability of the query of Mask2Former by exploiting the characteristics of RS images well. First, we propose the Query Scenario Module (QSM), which aims to learn and group the queries from feature maps, allowing the selection of distinct scenarios such as the urban and rural areas, building clusters, and parking lots. Second, we design the query position module (QPM), which is developed to assign the image position information to each query without increasing the number of parameters, thereby enhancing the model’s sensitivity to small targets in complex scenarios. Finally, we propose the query attention module (QAM), which is constructed to leverage the characteristics of query attention to extract valuable features from the preceding queries. Being positioned between the duplicated transformer decoder layers, QAM ensures the comprehensive utilization of the supervisory information and the exploitation of those fine-grained details. Architecturally, the QSM, QPM, and QAM as well as an end-to-end model are assembled to achieve high-quality semantic segmentation. In comparison to the classical or state-of-the-art models (FCN, PSPNet, DeepLabV3+, OCRNet, UPerNet, MaskFormer, Mask2Former), IQ2Former has demonstrated exceptional performance across three publicly challenging remote-sensing image datasets, 83.59 mIoU on the Vaihingen dataset, 87.89 mIoU on Potsdam dataset, and 56.31 mIoU on LoveDA dataset. Additionally, overall accuracy, ablation experiment, and visualization segmentation results all indicate IQ2Former validity.

An increasing amount of evidence indicates that Baicalin (Bai, a natural glycosyloxyflavone compound) exhibits an antiviral effect against avian viruses. However, it remains unclear if the antiviral effect of Bai against infectious bronchitis virus (IBV) is exerted indirectly by modulating respiratory tract microbiota and/or their metabolites. In this study, we investigated the protection efficacy of Bai in protecting cell cultures and broilers from IBV infection and assessed modulation of respiratory tract microbiota and metabolites during infection. Bai was administered orally to broilers by being mixed in with drinking water for seven days. Ultimately, broilers were challenged with live IBV. The results showed that Bai treatment reduced respiratory tract symptoms, improved weight gain, slowed histopathological damage, reduced virus loads and decreased pro-inflammation cytokines production. Western blot analysis demonstrated that Bai treatment significantly inhibited Toll-like receptor 7 (TLR7), myeloid differentiation factor 88 (MyD88) and nuclear factor kappa-B (NF-κB) expression both in cell culture and cells of the trachea. Bai treatment reversed respiratory tract microbiota dysbiosis, as shown by 16S rDNA sequencing in the group of broilers inoculated with IBV. Indeed, we observed a decrease in Proteobacteria abundance and an increase in Firmicutes abundance. Metabolomics results suggest that the pentose phosphate pathway, amino acid and nicotinamide metabolism are linked to the protection conferred by Bai against IBV infection. In conclusion, these results indicated that further assessment of anti-IBV strategies based on Bai would likely result in the development of antiviral molecule(s) which can be administered by being mixed with feed or water.

A total of 66 sets of pullout specimens were prepared to investigate the bonding properties of basalt fiber-reinforced polymer reinforcement (hereinafter referred to as BFRP) with seawater sand concrete (hereinafter referred to as SSC). The volume dosages of mono-doped glass fibers and mono-doped polypropylene fibers were 0.1%, 0.2%, and 0.3%; the total volume dosage was set to be constant at 0.3%; and the doping ratios of the hybrid fibers were 1:2, 1:1, and 2:1. The effect on the bonding performance of BFRP reinforcement with SSC was studied on the condition of the diameter D of the BFRP reinforcement being 12 mm; the bond length of SSC being 3D, 5D, and 7D; and the surface characteristics of the reinforcement being sandblasted and threaded. The research showed that due to internal cracks in the matrix, salt crystals in the pores, chloride salts with high brittleness and expansion, as well as sulfate corrosion products such as “Frederick salts” in SSC, the concrete became brittle, resulting in more brittle splitting failures during the pullout test. Doped fibers can increase the ductility effect of concrete, but the bonding effect between the threaded fiber reinforcement and the SSC was not as good as that of the sandblasting group. When the bond length was 5D, the bonding effect between the BFRP reinforcement and SSC was the best, and the bonding performance of the experimental group with doped fibers was better than that of the threaded group. Finally, by combining the ascending segment of the Malvar model with the descending segment of the improved BPE model, a constitutive relationship model suitable for the bond–slip curve between BFRP reinforcement and SSC was fitted, which laid a theoretical foundation for future research on SSC.

Abstract Coronavirus disease 2019 (COVID-19) continues to constitute an international public health emergency. Vaccination is a prospective approach to control this pandemic. However, apprehension about the safety of vaccines is a major obstacle to vaccination. Amongst health professionals, one evident concern is the risk of antibody-dependent enhancement (ADE), which may increase the severity of COVID-19. To explore whether ADE occurs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and increase confidence in the safety of vaccination, we conducted a meta-analysis to investigate the relationship between post-immune infection and disease severity from a population perspective. Databases, including PubMed, EMBASE, Chinese National Knowledge Infrastructure, SinoMed, Scopus, Science Direct, and Cochrane Library, were searched for articles on SARS-CoV-2 reinfection published until 25 October 2021. The papers were reviewed for methodological quality, and a random effects model was used to analyse the results. Heterogeneity was assessed using the I 2 statistic. Publication bias was evaluated using a funnel plot and Egger’s test. Eleven studies were included in the final meta-analysis. The pooled results indicated that initial infection and vaccination were protective factors against severe COVID-19 during post-immune infection (OR = 0.55, 95%CI = 0.31–0.98). A subgroup (post-immune infection after natural infection or vaccination) analysis showed similar results. Primary SARS-CoV-2 infection and vaccination provide adequate protection against severe clinical symptoms after post-immune infection. This finding demonstrates that SARS-CoV-2 may not trigger ADE at the population level.

Robust aiding of inertial navigation systems in GNSS-denied environments is critical for the removal of accumulated navigation error caused by the drift and bias inherent in inertial sensors. One way to perform such an aiding uses matching of geophysical measurements, such as gravimetry, gravity gradiometry or magnetometry, with a known geo-referenced map. Although simple in concept, this map-matching procedure is challenging: The measurements themselves are noisy, their associated spatial location is uncertain, and the measurements may match multiple points within the map (i.e., non-unique solution). In this paper, we propose a probabilistic multiple-hypotheses tracker to solve the map-matching problem and allow robust inertial navigation aiding. Our approach addresses the problem both locally, via probabilistic data association, and temporally by incorporating the underlying platform kinematic constraints into the tracker. The estimated platform position from the output of map matching is then integrated into the navigation state using an unscented Kalman filter. Additionally, we present a statistical measure of local map information density — the map feature variability — and use it to weight the output covariance of the proposed algorithm. The effectiveness and robustness of the proposed algorithm are demonstrated using a navigation scenario involving gravitational map matching.

Permeable pavements can decrease the volume of stormwater, thereby mitigating the risk of flooding and reducing the urban heat island effect. This study investigated the influence of incorporating granulated blast-furnace slag (GBFS), hydroxypropyl methylcellulose (HPMC), and polypropylene plastic textile fiber (PPTF) on the mechanical properties and water permeability of pervious concrete. Orthogonal tests were employed to conduct the analysis. The findings indicate that the pervious concrete with GBFS, HPMC, and PPTF (termed GBFS-HPMC/fiber pervious concrete) exhibited the highest cubic compressive strength, ultimate tensile strength, and flexural strength. These values were 25.22 MPa, 3.36 MPa, and 5.39 MPa, respectively. The standard deviations for cubic compressive strength, split tensile strength, flexural strength, water permeability coefficient, and porosity, as calculated using SPSS, were 1.57, 0.1, 1.17, 0.35, and 0.4, respectively. Scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) were used to analyze the microstructure and compositional combinations of the pervious concrete. The analyses revealed that the calcium-silicate-hydrate (C-S-H) gel, produced by GBFS hydration, enhanced the bonding within the interfacial transition zone (ITZ) and between the fibers and aggregates. Additionally, the anchoring and supporting effects of the PPTF in the matrix contributed to stabilizing the overall matrix structure. Lastly, a gray correlation analysis was applied to optimize the admixture. The findings indicate that following the optimization, the cubic compressive strength increased by 7.2%, splitting tensile strength by 2.1%, and flexural strength by 2.5%. In summary, the mechanical properties of pervious concrete improved after optimizing the admixture.

Currently, there are no particularly effective biomarkers to distinguish between latent tuberculosis infection (LTBI) and active pulmonary tuberculosis (PTB) and evaluate the outcome of TB treatment. In this study, we have characterized the changes in the serum metabolic profiles caused by Mycobacterium tuberculosis (Mtb) infection and standard anti-TB treatment with isoniazid–rifampin–pyrazinamide–ethambutol (HRZE) using GC-MS and LC-MS/MS. Seven metabolites, including 3-oxopalmitic acid, akeboside ste, sulfolithocholic acid, 2-decylfuran (4,8,8-trimethyldecahydro-1,4-methanoazulen-9-yl)methanol, d-(+)-camphor, and 2-methylaminoadenosine, were identified to have significantly higher levels in LTBI and untreated PTB patients (T0) than those in uninfected healthy controls (Un). Among them, akeboside Ste and sulfolithocholic acid were significantly decreased in PTB patients with 2-month HRZE (T2) and cured PTB patients with 2-month HRZE followed by 4-month isoniazid-rifampin (HR) (T6). Receiver operator characteristic curve analysis revealed that the combined diagnostic model showed excellent performance for distinguishing LT from T0 and Un. By analyzing the biochemical and disease-related pathways, we observed that the differential metabolites in the serum of LTBI or TB patients, compared to healthy controls, were mainly involved in glutathione metabolism, ascorbate and aldarate metabolism, and porphyrin and chlorophyll metabolism. The metabolites with significant differences between the T0 group and the T6 group were mainly enriched in niacin and nicotinamide metabolism. Our study provided more detailed experimental data for developing laboratory standards for evaluating LTBI and cured PTB.

Benefiting from a high quantum efficiency, low thermal emittance, and large absorption coefficient, InxGa1−xAs is an excellent group III–V compound for negative electron affinity (NEA) photocathodes. As the emission layer, InxGa1−xAs, where x = 0.15, has the optimal performance for detection in the near-infrared (NIR) region. Herein, an NEA In0.15Ga0.85As photocathode with Al0.63Ga0.37As as the buffer layer is designed in the form of a transmission mode module. The electronic band structures and optical properties of In0.15Ga0.85As and Al0.63Ga0.37As are calculated based on density functional theory. The time response characteristics of the In0.15Ga0.85As photocathode have been fully investigated by changing the photoelectron diffusion coefficient, the interface recombination velocity, and the thickness of the emission layer. Our results demonstrate that the response time of the In0.15Ga0.85As photocathode can be reduced to 6.1 ps with an incident wavelength of 1064 nm. The quantum efficiency of the In0.15Ga0.85As photocathode is simulated by taking into account multilayer optical thin film theory. The results indicate that a high quantum efficiency can be obtained by parameter optimization of the emission layer. This paper provides significant theoretical support for the applications of semiconductor photocathodes in the near-infrared region, especially for the study of ultrafast responses in the photoemission process.

The coupling effects of surface plasmon resonance (SPR) from metamaterials induce variation in both the frequency and intensity of plasmonic modes. Here, we report an angular-dependent THz modulator with hybrid metal–graphene metastructures. The metastructures composed of the period gold split-rod arrays on top of a monolayer graphene, which show redshift modulation in the THz region with an increasing incident angle due to the strong out-of-plane magnetic flux introduced by the clockwise circular current at the oblique incidence. By utilizing graphene-based actively tunable conductor with ion-gel electrical gating, the THz transmission can be significantly modified. The modulation depth of the hybrid metal–graphene metastructure modulator can reach ~37.6% at 0.62 THz with a gate voltage of −3 V. The theoretical modeling of transmitted dependency on frequency and incident angle is demonstrated at different Fermi energies, which fits well with the experimental results. This hybrid device can offer a useful method for THz applications (such as angle sensors or angular-resolved spectroscopy), where angle-dependent modulation is needed.

Abstract Background Bloodstream infection (BSI) caused by Staphylococcus aureus (S. aureus) can be life-threatening and pose a great challenge to infection control and clinical treatment. However, little information exists regarding the characterization of S. aureus in BSI patients in Shandong, China. To identify the clonality, virulence genes, and antibiotic resistance of S. aureus in blood, a total of 101 nonrepetitive blood isolates were collected. The antibiotic resistance phenotypes were determined, and virulence genes were analyzed with polymerase chain reaction (PCR). Finally, the genetic relatedness was investigated with Staphylococcus chromosomal cassette mec (SCCmec) typing for methicillin-resistant S. aureus (MRSA) isolates, Staphylococcal protein A (spa), and multilocus sequence typing (MLST) for all of 101 isolates. Results Of the 101 S. aureus isolates, 24 MRSA isolates and 77 methicillin-susceptible S. aureus (MSSA) isolates were identified. Overall, MRSA isolates had higher resistance rates than MSSA isolates when exposed to any of the 15 antibiotics tested in this study except for trimethoprim/sulfamethoxazole. Among the 17 virulence genes tested in this study, hla, hld, and hlg could be detected in all isolates. MRSA isolates were more likely to carry seb and hlb genes, while MSSA isolates were more likely to carry seg and sei genes. Thirty-five sequence types (STs) and 49 spa types were identified, of which ST59-t437 and ST398-t571 were the most abundant. These two genotypes were also the most abundant ST-spa types in MRSA and MSSA isolates, but their abundances shifted over time, with ST398-t571 being the predominant genotype from 2016 to 2017, and ST59-t437 from 2018 to 2020. Besides, all the ST59-t437 isolates harbored hlgb gene, whereas most (88.9%) ST398-t571 did not. In addition, twenty-four MRSA isolates were subject to SCCmec typing. SCCmec IVa was the most prevalent SCCmec type, and all the ST59-t437 MRSA isolates were SCCmec IVa. We also observed 15 new STs, and some of them were MRSA. Conclusion These findings provide additional observations and epidemiological data for blood S. aureus isolates, which can improve future infection-control measures and aid in potential clinical treatments in hospitals and other clinical settings.

Abstract Satellite image processing has been widely used in recent years in a number of applications such as land classification, Identification transfer, resource exploration, super‐resolution image, etc. Due to the orbital location, revision time, quick view angle limitations, and weather impact, the satellite images are challenging to manage. There are many types of resolution, such as spatial, spectral, and temporal. Still, in our case, we concentrated on spatial image resolution to super resolve the images from low‐resolution images. For remote sensing image super‐resolution fast wavelet‐based super‐resolution (FWSR), we propose a novel, fast wavelet‐based plexus framework that performs super‐resolution convolutional neural network (SRCNN)‐like extraction of features based on three hidden layers. First, wavelet sub‐band images are combined into a pre‐defined full‐scale data training factor, including approximation and interchangeable stand‐alone units (frequency sub‐bands). Second, to speed up image recovery, mapping the sub‐band image of the wavelet is then measured using its approximate image. Third, the added sub‐pixel layer at the end of the network model is intended to reproduce image quality using a plexus framework. The approximation sub‐band images obtained after discrete wavelet transform wavelet decomposition are used as input rather than the original image because of their high‐frequency data and preserved characteristics. Five current super‐resolution neural network approaches are compared with the proposed technique and tested on three pubic satellite image datasets and two benchmark datasets. The experimental findings are well compared qualitatively and quantitatively.

Semantic segmentation of remote-sensing (RS) images is one of the most fundamental tasks in the understanding of a remote-sensing scene. However, high-resolution RS images contain plentiful detailed information about ground objects, which scatter everywhere spatially and have variable sizes, styles, and visual appearances. Due to the high similarity between classes and diversity within classes, it is challenging to obtain satisfactory and accurate semantic segmentation results. This paper proposes a Dynamic High-Resolution Network (DyHRNet) to solve this problem. Our proposed network takes HRNet as a super-architecture, aiming to leverage the important connections and channels by further investigating the parallel streams at different resolution representations of the original HRNet. The learning task is conducted under the framework of a neural architecture search (NAS) and channel-wise attention module. Specifically, the Accelerated Proximal Gradient (APG) algorithm is introduced to iteratively solve the sparse regularization subproblem from the perspective of neural architecture search. In this way, valuable connections are selected for cross-resolution feature fusion. In addition, a channel-wise attention module is designed to weight the channel contributions for feature aggregation. Finally, DyHRNet fully realizes the dynamic advantages of data adaptability by combining the APG algorithm and channel-wise attention module simultaneously. Compared with nine classical or state-of-the-art models (FCN, UNet, PSPNet, DeepLabV3+, OCRNet, SETR, SegFormer, HRNet+FCN, and HRNet+OCR), DyHRNet has shown high performance on three public challenging RS image datasets (Vaihingen, Potsdam, and LoveDA). Furthermore, the visual segmentation results, the learned structures, the iteration process analysis, and the ablation study all demonstrate the effectiveness of our proposed model.

The paper considers the problem of tracking a moving target using a pair of cooperative bearing-only mobile sensors. Sensor trajectory optimisation plays the central role in this problem, with the objective to minimize the estimation error of the target state. Two approximate closed-form statistical reward functions, referred to as the Expected Rényi information divergence (RID) and the Determinant of the Fisher Information Matrix (FIM), are analysed and discussed in the paper. Being available analytically, the two reward functions are fast to compute and therefore potentially useful for longer horizon sensor trajectory planning. The paper demonstrates, both numerically and from the information geometric viewpoint, that the Determinant of the FIM is a superior reward function. The problem with the Expected RID is that the approximation involved in its derivation significantly reduces the correlation between the target state estimates at two sensors, and consequently results in poorer performance.

Abstract The object identification within an image captured during rough weather conditions (such as haze, fog) poses difficulty due to the reduction of an image. The rough weather conditions lead not only to the variation of the image's visual effect but also to the disadvantage of post‐processing of an image. Furthermore, it causes inconvenience of all types of instruments that rely on optical imaging, such as satellite remote‐sensing systems, aerial photo systems, outdoor monitoring systems, and object identification systems, respectively. Hence, the improvement and restorement of the visual effects and enhanced post‐processing are needed. This research introduces a new image enhancement approach for image dehazing based on dark channel prior and piecewise linear transformation; also, the histogram equalisation technique, i.e. contrast limited adaptive histogram equalisation is applied. A dark channel prior is well known for its simplicity and productivity. In this work, the dark channel prior to a new angle is analysed in the first step, where average patch sizes are estimated for the computation of haze densities. Furthermore, the sky is approximated up to 5–10% of the hazy images, which has a good effect in removing the haze from the image. Using the dark channel, the proposed algorithm significantly boosted the effects of the dark images as well as reduced the influence of haze and noise. Eventually, for colour correction, the piecewise linear transformation technique is applied, which enhances the colour close to the original image. Experimental results demonstrate that the proposed method significantly improves the visibility of the algorithm on dark remote‐sensing images as well as on hazy natural images.

Infectious bronchitis (IB) causes significant economic losses to commercial chicken farms due to the failures of vaccine immunization or incomplete protection. In this study, we evaluated the combination effect of Shegandilong (SGDL) granule (a traditional Chinese veterinary medicine) and doxycycline on the prevention of IBV infection and injury in the respiratory tract in broilers. A total of 126, 7-day-old broilers were randomly divided into four groups after vaccination. Group I served as a control. Broilers in Group II were given doxycycline, and Group III was given SGDL granule through drinking water. Broilers in Group IV were given SGDL granule and doxycycline by drinking water. Broilers in all groups were challenged with IBV through intraocular and intranasal routes at day 28. Results showed that the anti-IBV antibody level was higher in group IV compared with the level in other groups. Immunohistochemistry and ELISA results showed that an increase of immunoglobulin A (IgA) was observed in the trachea with the maximum level observed at day 14. In addition, SGDL granule + doxycycline effectively inhibited IBV replication and stopped IBV propagation from the trachea to the lung; modulated the mRNA expressions of IL-1β, IL-6, TNF-α, and IFN-γ; and extenuated the histopathology lesions in trachea and lung. These data imply that a combination of SGDL granule and doxycycline is effective in preventing IBV infection and respiratory tract injury in broilers.

In this study, graphene oxide (GO) was prepared by an improved Hummers’ method, and the graphene oxide–carbon fiber (GO-CF) hybrid fibers were prepared by electrophoretic deposition (EPD) with the above GO. The microstructure and mechanical properties of GO, CF and GO-CF cement matrix composites were studied by X-ray diffraction (XRD), SEM and mechanical tests. X-ray diffraction (XRD) and SEM results showed that the surface modification of CF with GO could effectively improve the dispersion of CF in the cement matrix, the template action of GO providing nucleation sites for cement hydration, which significantly enhanced the interface bond between CF and the cement matrix. The mechanical properties test results showed that the early compressive strength and flexural strength of cement matrix composites were significantly improved by GO. Adding CF into the cement matrix significantly improved the flexural strength, although the compressive strength was not improved, even exhibiting a downward trend when the CF content exceeded 0.5%. The flexural and compressive strength of GO-CF cement matrix composites were clearly improved, its 7 d flexural strength and compressive strength increased by 30.89% and 17.56%, respectively, and its 28 d flexural strength and compressive strength increased by 38.37% and 14.32%, respectively. The research results indicate that a new method was provided which used GO as a dispersant and surface modifier to improve the dispersion of chopped CF in a cement matrix, and the interaction between CF and the cement matrix interface could also be applied to the functional aspects of cement matrix composites.

ABSTRACTp-Aminosalicylic acid (PAS) is an important second-line antibiotic for treating multidrug-resistant tuberculosis (MDR-TB). Due to gastrointestinal disturbance and intolerance, its potent and efficacy in the treatment of extensively drug-resistant (XDR)-TB commonly are poor. Thus, it is important to reveal the mechanism of susceptibility and resistance of Mycobacterium tuberculosis (Mtb) to this drug. Herein, we screened and established PAS-resistant (PASr) folC mutated and un-mutated Mtb strains, then utilized a multi-omics (genome, proteome, and metabolome) analysis to better characterize the mechanisms of PAS resistance in Mtb. Interestingly, we found that promotion of SAM-dependent methyltransferases and suppression of PAS uptake via inhibiting some drug transport associated membrane proteins were two key pathways for the folC mutated strain evolving into the PASr Mtb strain. However, the folC un-mutated strain was resistant to PAS via uptake of exogenous methionine, mitigating the role of inhibitors, and promoting DfrA, ThyA and FolC expression. Beyond these findings, we also found PAS resistance in Mtb might be associated with the increasing phenylalanine metabolism pathway. Collectively, our findings uncovered the differences of resistant mechanism between folC mutated and un-mutated Mtb strains resistant to PAS using multi-omics analysis and targeting modulators to these pathways may be effective for treatment of PASr Mtb strains.

Bacterial biofilms are believed to be principal virulence factors for many localized chronic infectious diseases. Escherichia coli is one of the most common microbial pathogens and frequently causes biofilm-associated opportunistic infections, such as diarrhea, endometritis and mastitis. Cinnamomum camphora essential oil (CCEO) has shown potential in treating intractable chronic endometritis in dairy cows. There is little scientific evidence regarding the effect of CCEO on bacterial biofilms. The objective of this study was to investigate the effect of CCEO on E. coli biofilm formation and how CCEO affects E. coli in suspension and in a biofilm. CCEO killed all clinical E. coli strains in either planktonic or biofilm state isolated from dairy cows with clinical endometritis. The minimum inhibitory concentration (MIC) for 90% of the organisms was 4.297 μL/mL, the minimum bactericidal concentration for 90% of the organisms was 6.378 μL/mL, the minimum biofilm inhibitory concentration for 90% of the organisms was 6.850 μL/mL, and the minimum biofilm eradication concentration (MBEC) for 90% of the organisms was 8.467 μL/mL. The MBECs were generally two times higher than the MICs. Flow cytometry analysis confirmed that significant bacterial killing occurred during the first 1 h after exposure to subinhibitory concentrations of CCEO. In addition, CCEO exerted a significant inhibitory effect on E. coli biofilm formation, and bacterial killing occurred during the first 30 min of exposure to subinhibitory biofilm concentrations of CCEO. The biofilm yield of E. coli was significantly reduced after CCEO treatment, along with an increased dead/live microbial ratio in biofilms compared with that in the non-treated control, as confirmed by scanning electron microscopy images and confocal laser scanning microscopy images. These data revealed that CCEO efficiently kills E. coli during planktonic growth and biofilm formation.

Cloud recognition is a basic task in ground meteorological observation. It is of great significance to accurately identify cloud types from long-time-series satellite cloud images for improving the reliability and accuracy of weather forecasting. However, different from ground-based cloud images with a small observation range and easy operation, satellite cloud images have a wider cloud coverage area and contain more surface features. Hence, it is difficult to effectively extract the structural shape, area size, contour shape, hue, shadow and texture of clouds through traditional deep learning methods. In order to analyze the regional cloud type characteristics effectively, we construct a China region meteorological satellite cloud image dataset named CRMSCD, which consists of nine cloud types and the clear sky (cloudless). In this paper, we propose a novel neural network model, UATNet, which can realize the pixel-level classification of meteorological satellite cloud images. Our model efficiently integrates the spatial and multi-channel information of clouds. Specifically, several transformer blocks with modified self-attention computation (swin transformer blocks) and patch merging operations are used to build a hierarchical transformer, and spatial displacement is introduced to construct long-distance cross-window connections. In addition, we introduce a Channel Cross fusion with Transformer (CCT) to guide the multi-scale channel fusion, and design an Attention-based Squeeze and Excitation (ASE) to effectively connect the fused multi-scale channel information to the decoder features. The experimental results demonstrate that the proposed model achieved 82.33% PA, 67.79% MPA, 54.51% MIoU and 70.96% FWIoU on CRMSCD. Compared with the existing models, our method produces more precise segmentation performance, which demonstrates its superiority on meteorological satellite cloud recognition tasks.

Autumn phenology, commonly represented by the end of season (EOS), is considered to be the most sensitive and crucial productivity indicator of alpine and cold grassland in the Qinghai-Tibetan Plateau. Previous studies typically assumed that the rates of EOS changes remain unchanged over long time periods. However, pixel-scale analysis indicates the existence of turning points and differing EOS change rates before and after these points. The spatial heterogeneity and controls of these turning points remain unclear. In this study, the EOS turning point changes are extracted and their controls are explored by integrating long time-series remote sensing images and piecewise regression methods. The results indicate that the EOS changed over time with a delay rate of 0.08 days/year during 1982–2015. The rates of change are not consistent over different time periods, which clearly highlights the existence of turning points. The results show that temperature contributed most strongly to the EOS changes, followed by precipitation and insolation. Furthermore, the turning points of climate, human activities (e.g., grazing, economic development), and their intersections are found to jointly control the EOS turning points. This study is the first quantitative investigation into the spatial heterogeneity and controls of the EOS turning points on the Qinghai-Tibetan Plateau, and provides important insight into the growth mechanism of alpine and cold grassland.

Flexoelectric effect is the coupling of the inhomogeneous deformation and electric polarization, which exists widely in dielectrics. At present, although some scholars have carried out the experimental research on the measurement of the flexoelectric coefficient under impact loading, there is still no research on the variation law of flexoelectric effect under different impact velocities. In this letter, the voltage variations of barium titanate (BT) samples at different impact velocities are studied. The result shows that when the impact velocity is above 230 m/s, the increasing amplitude of the maximum voltage generated by the flexoelectric effect becomes larger, and when the impact velocity is below 200 m/s, the maximum voltage change is gentle. Finally, the longitudinal flexoelectric coefficient of BT is calculated as 33.57×10-6 C/m, which is consistent with the published measured value. This can provide theoretical guidance for the preparation of dynamic sensors based on flexoelectric effect in the future. Keywords: Flexoelectricity, Impact speed, Voltage

Background/Aims: Vascular endothelial growth factor (VEGF) has been demonstrated to play a pivotal role in the regulation of angiogenesis in ovarian follicular development, particularly during the preovulatory period. Although numerous studies have shown that interleukin-6 (IL-6) is one of the major inducing factors that regulate the expression of VEGF in non-ovarian cells, whether it involved in regulating the expression of VEGF in normal ovarian granulosa cells is still unknown. The aim of this study was to elucidate the mechanisms underlying the effect of IL-6 on FSH-induced VEGF expression in bovine granulosa cells derived from large follicles. Methods: VEGF mRNA expression in granulosa cells after IL-6 with/without inhibitors treatment was analyzed by RT-qPCR. Phosphorylation levels of ERK1/2 and STAT3 proteins induced by IL-6 were analyzed by western blotting. The protein levels produced by granulosa cells were detected by ELISA. Results: High concentration of IL-6 (10ng/ml) can significantly up-regulate FSH-induced VEGF gene and protein expression levels in granulosa cells, and also promote the VEGF upstream regulators HIF-1α and COX2 mRNA expression. VEGF expression levels were significantly decreased after specifically blocking HIF-1α and COX2 by using inhibitors. The up-regulation effect of IL-6 on FSH-induced VEGF expression in granulosa cells mainly through activating the JAK/STAT3 signaling pathway, which can be impaired by JAK inhibitors. Conclusion: IL-6 can promote FSH-induced VEGF expression in granulosa cells, which is mainly achieved by increasing the expression of HIF-1α and COX2.This promoting effect is mediated by activating the JAK/STAT3 pathway. Moreover, there may be a synergistic relationship between FSH and IL-6 in the regulation of VEGF expression.

Abstract This research was conducted to verify the structural and functional characteristics of mast cells in the electroacupuncture (EA) effects on bradycardia. First, we examined the mast cell density at PC 6, adjacent acupoint LU 7, and a non-acupoint. We tested the effects of EA at PC 6 on heart rate (HR) and blood pressure (BP) in rabbits with pituitrin-induced bradycardia. We also injected sodium cromolyn (Cro), a mast cell membrane stabilizer, at PC 6 30 min before EA to investigate if it affected the EA effects. The results showed that in both PC 6 and LU 7, the mast cell densities were higher than in the non-acupoint (P

With the remarkable development and progress of earth-observation techniques, remote sensing data keep growing rapidly and their volume has reached exabyte scale. However, it’s still a big challenge to manage and process such huge amounts of remote sensing data with complex and diverse structures. This paper designs and realizes a distributed storage system for large-scale remote sensing data storage, access, and retrieval, called RSIMS (remote sensing images management system), which is composed of three sub-modules: RSIAPI, RSIMeta, RSIData. Structured text metadata of different remote sensing images are all stored in RSIMeta based on a set of uniform models, and then indexed by the distributed multi-level Hilbert grids for high spatiotemporal retrieval performance. Unstructured binary image files are stored in RSIData, which provides large scalable storage capacity and efficient GDAL (Geospatial Data Abstraction Library) compatible I/O interfaces. Popular GIS software and tools (e.g., QGIS, ArcGIS, rasterio) can access data stored in RSIData directly. RSIAPI provides users a set of uniform interfaces for data access and retrieval, hiding the complex inner structures of RSIMS. The test results show that RSIMS can store and manage large amounts of remote sensing images from various sources with high and stable performance, and is easy to deploy and use.

This paper considers the two-dimensional (2D) anchorless localization problem for sensor networks in global positioning system (GPS)-denied environments. We present an efficient method, based on the multidimensional scaling (MDS) algorithm, in order to estimate the positions of the nodes in the network using measurements of the inter-node distances. The proposed method takes advantage of the mobility of the nodes to address the location ambiguity problem, i.e., rotation and flip ambiguity, which arises in the anchorless MDS algorithm. Knowledge of the displacement of the moving node is used to produce an analytical solution for the noise-free case. Subsequently, a least squares estimator is presented for the noisy scenario and the associated closed-form solution derived. The simulations show that the proposed algorithm accurately and efficiently estimates the locations of nodes, outperforming alternative methods.

There is an urgent need for new immunodominant antigens to improve the diagnosis of tuberculosis (TB) and the efficacy of the TB vaccine to control the disease worldwide. In this study, we evaluated the diagnostic potential of a novel Mycobacterium tuberculosis (MTB)-specific antigen, Rv2351c, from region of difference (RD) 7 of the MTB genome, and investigated the potency of the vaccine by identifying its immunological function in human and animal immunological experiments. Twenty T-cell epitopes were identified using TEpredict and prediction tools from the Immune Epitope Database and Analysis Resource. A total of 159 subjects, including 61 patients with pulmonary TB, 38 patients with no TB and 55 healthy donors, were recruited and analyzed with an enzyme-linked immunospot (ELISpot) assay. The ELISpot assay using Rv2351c to detect TB infection, as compared with bacteriological tests as the gold standard, had a sensitivity and specificity of 61.4% (35/57) and 91.4% (85/93), respectively. The ELISpot assay using Rv2351c had a good conformance (κ=0.554) as compared with the bacteriological test. Rv2351c also elicited a potent cellular immune response with a high expression of cytokines (IFN-γ (4978±596.7 μg/mL) and IL-4 (68.3±15.5 μg/mL)) and a potent humoral immune response with a high concentration of IgG (1:2.2 × 106), IgG1 (1:4.5 × 105) and IgG2a (1:1.6 × 106) in immunized BALB/c mice. In addition, the ratio of IgG2a/IgG1 indicated that Rv2351c induced cellular immunity in the mice. The results of this study indicated that Rv2351c is an antigen with good immunogenicity that may potentially be used to develop diagnostic techniques and new TB vaccines.Emerging Microbes & Infections (2017) 6, e48; doi:10.1038/emi.2017.34; published online 7 June 2017

Background/Aims: IL-6 is one of the main cytokines in regulating ovarian follicular development and ovulation. However, the factors that regulate IL-6 expression in follicles are still unclear. The aim of this study was to elucidate the mechanisms underlying the effect of IL-1α on IL-6 expression in granulosa cells. Methods: IL-6 expression after IL-1α with/without inhibitors treatment was analyzed by RT-qPCR and ELISA. The phosphorylation of proteins induced by IL-1α was analyzed by western blot. The intracellular cAMP level was assayed by immunoassay kit. Results: IL-1α has a dose-dependent effect on IL-6 expression in granulosa cells. This promoting effect can be significantly attenuated by Erk, c-Jun, p38 and IκB proteins inhibitors, respectively. Moreover, the phosphorylation levels of Erk, c-Jun, p38 and IκBα proteins were significantly increased after IL-1α treatment. In addition, we also found that IL-1α not only reversed the cAMP attenuated IL-6 expression， but also increased IL-1α mRNA expression in granulosa cells. Conclusion: The regulation of IL-1α on IL-6 expression is mediated by activation of MAPKs and NF-κB signaling pathways. Moreover，IL-1α may regulate the ovulation-related genes expression in granulosa cells by an autocrine and/or paracrine manner.

To precisely evaluate the retrofitting effectiveness of Carbon Fiber Reinforced Plastic (CFRP) sheets on the impact response of reinforced concrete (RC) T-beams, a non-linear finite element model was developed to simulate the structural response of T-beams with CFRP under impact loads. The numerical model was firstly verified by comparing the numerical simulation results with the experimental data, i.e., impact force, reaction force, and mid-span displacement. The strengthening effect of CFRP was analyzed from the section damage evaluation. Then the impact force, mid-span displacement, and failure mode of CFRP-strengthened RC T-beams were studied in comparison with those of un-strengthened T-beams. In addition, the influence of the impact resistance of T-beams strengthened with FRP was investigated in terms of CFRP strengthening mode, CFRP strengthening sizes, CFRP layers and FRP material types. The numerical simulation results indicate that the overall stiffness of the T-beams was improved significantly due to external CFRP strips. Compared with the un-strengthened beam, the maximum mid-span displacement of the CFRP-strengthened beam was reduced by 7.9%. Additionally, the sectional damage factors of the whole span of the CFRP-strengthened beam were reduced to less than 0.3, indicating that the impact resistance of the T-beams was effectively enhanced.

Semantic segmentation in high-resolution remote-sensing (RS) images is a fundamental task for RS-based urban understanding and planning. However, various types of artificial objects in urban areas make this task quite challenging. Recently, the use of Deep Convolutional Neural Networks (DCNNs) with multiscale information fusion has demonstrated great potential in enhancing performance. Technically, however, existing fusions are usually implemented by summing or concatenating feature maps in a straightforward way. Seldom do works consider the spatial importance for global-to-local context-information aggregation. This paper proposes a Learnable-Gated CNN (L-GCNN) to address this issue. Methodologically, the Taylor expression of the information-entropy function is first parameterized to design the gate function, which is employed to generate pixelwise weights for coarse-to-fine refinement in the L-GCNN. Accordingly, a Parameterized Gate Module (PGM) was designed to achieve this goal. Then, the single PGM and its densely connected extension were embedded into different levels of the encoder in the L-GCNN to help identify the discriminative feature maps at different scales. With the above designs, the L-GCNN is finally organized as a self-cascaded end-to-end architecture that is able to sequentially aggregate context information for fine segmentation. The proposed model was evaluated on two public challenging benchmarks, the ISPRS 2Dsemantic segmentation challenge Potsdam dataset and the Massachusetts building dataset. The experiment results demonstrate that the proposed method exhibited significant improvement compared with several related segmentation networks, including the FCN, SegNet, RefineNet, PSPNet, DeepLab and GSN.For example, on the Potsdam dataset, our method achieved a 93.65% F 1 score and 88.06% I o U score for the segmentation of tiny cars in high-resolution RS images. As a conclusion, the proposed model showed potential for object segmentation from the RS images of buildings, impervious surfaces, low vegetation, trees and cars in urban settings, which largely varies in size and have confusing appearances.

China is experiencing severe PM2.5 (fine particles with a diameter of 2.5 μg or smaller) pollution problem. Little is known, however, about how the increasing concentration trend is spatially distributed, nor whether there are some areas that experience a stable or decreasing concentration trend. Managers and policymakers require such information to make strategic decisions and monitor progress towards management objectives. Here, we present a pixel-based linear trend analysis of annual PM2.5 concentration variation in China during the period 1999−2016, and our results provide guidance about where to prioritize management efforts and affirm the importance of controlling coal energy consumption. We show that 87.9% of the whole China area had an increasing trend. The drastic increasing trends of PM2.5 concentration during the last 18 years in the Beijing−Tianjin−Hebei region, Shandong province, and the Three Northeastern Provinces are discussed. Furthermore, by exploring regional PM2.5 pollution, we find that Tarim Basin endures a high PM2.5 concentration, and this should have some relationship with oil exploration. The relationship between PM2.5 pollution and energy consumption is also discussed. Not only energy structure reconstruction should be repeatedly emphasized, the amount of coal burned should be strictly controlled.

The rapid growth in the volume of remote sensing data and its increasing computational requirements bring huge challenges for researchers as traditional systems cannot adequately satisfy the huge demand for service. Cloud computing has the advantage of high scalability and reliability, which can provide firm technical support. This paper proposes a highly scalable geospatial cloud platform named the Geospatial Data Cloud, which is constructed based on cloud computing. The architecture of the platform is first introduced, and then two subsystems, the cloud-based data management platform and the cloud-based data processing platform, are described. ––– This paper was presented at the First Scientific Data Conference on Scientific Research, Big Data, and Data Science, organized by CODATA-China and held in Beijing on 24-25 February, 2014.

Abstract Background Psoroptes cuniculi can parasitise the ear canal of the rabbit, and cause the afflicted animals to cease feeding and become severely debilitated, sometimes resulting in death. In this study, we examined the oxidative status and inflammatory level of the peripheral blood of rabbits infested with Psoroptes cuniculi and investigated the pathogenesis of this disease. Methods A total of 24 rabbits were divided into a healthy rabbit group and two infested rabbit groups. After weighing the rabbits, approximately 5 ml of blood was obtained from each animal. Then, the blood serum was extracted and used to assess the levels of antioxidant enzymes and inflammatory factors. Results Compared to the healthy rabbits, the activities of catalase and glutathione-S-transferase and the level of malonyldialdehyde were increased, but the activity of superoxide dismutase was reduced in the infested rabbits. At the same time, a variety of inflammatory cells were activated, and the levels of inflammatory factors such as prostaglandin E2, interleukin-6, interleukin-8 and transforming growth factor-β1 were increased in peripheral blood. Conclusion Animal acariasis was associated with immunosuppressive disorders and inflammatory reaction. These results advance our understanding of the pathogenesis of Psoroptes cuniculi infestation in rabbits and can help guide the effectual treatment of this disease in clinics.

We propose an iterative nonlinear estimator based on the technique of variational Bayesian optimization. The posterior distribution of the underlying system state is approximated by a solvable variational distribution approached iteratively using evidence lower bound optimization subject to a minimal weighted Kullback-Leibler divergence, where a penalty factor is considered to adjust the step size of the iteration. Based on linearization, the iterative nonlinear filter is derived in a closed-form. The performance of the proposed algorithm is compared with several nonlinear filters in the literature using simulated target tracking examples.

Temperature, solar radiation and water are major important variables in ecosystem models which are measurable via wireless sensor networks (WSN). Effective data analysis is necessary to extract significant spatial and temporal information. In this work, information regarding the long term variation of seasonal field environment conditions is explored using Hilbert-Huang transform (HHT) based analysis on the wireless sensor network data collection. The data collection network, consisting of 36 wireless nodes, covers an area of 100 square kilometres in Yanqing, the northwest of Beijing CBD, in China and data collection involves environmental parameter observations taken over a period of three months in 2011. The analysis used the empirical mode decomposition (EMD/EEMD) to break a time sequence of data down to a finite set of intrinsic mode functions (IMFs). Both spatial and temporal properties of data explored by HHT analysis are demonstrated. Our research shows potential for better understanding the spatial-temporal relationships among environmental parameters using WSN and HHT.

Golay complementary waveforms can, in theory, yield radar returns of high range resolution with essentially zero sidelobes. In practice, when deployed conventionally, while high signal-to-noise ratios can be achieved for static target detection, significant range sidelobes are generated by target returns of nonzero Doppler causing unreliable detection. We consider signal processing techniques using Golay complementary waveforms to improve radar detection performance in scenarios involving multiple nonzero Doppler targets. A signal processing procedure based on an existing, so called, Binomial Design algorithm that alters the transmission order of Golay complementary waveforms and weights the returns is proposed in an attempt to achieve an enhanced illumination performance. The procedure applies one of three proposed waveform transmission ordering algorithms, followed by a pointwise nonlinear processor combining the outputs of the Binomial Design algorithm and one of the ordering algorithms. The computational complexity of the Binomial Design algorithm and the three ordering algorithms are compared, and a statistical analysis of the performance of the pointwise nonlinear processing is given. Estimation of the areas in the Delay–Doppler map occupied by significant range sidelobes for given targets are also discussed. Numerical simulations for the comparison of the performances of the Binomial Design algorithm and the three ordering algorithms are presented for both fixed and randomized target locations. The simulation results demonstrate that the proposed signal processing procedure has a better detection performance in terms of lower sidelobes and higher Doppler resolution in the presence of multiple nonzero Doppler targets compared to existing methods.

We propose an alternative waveform scheme built on mutually-orthogonal complementary sets for a distributed multistatic radar. Our analysis and simulation show a reduced frequency band requirement for signal separation between antennas with centralized signal processing using the same carrier frequency. While the scheme can tolerate fluctuations of carrier frequencies and phases, range sidelobes arise when carrier frequencies between antennas are significantly different.

DarwinTree (http://www.darwintree.cn) provides an integrated bioinformatics platform that supports all phases of the analytical pathway for phylogenetic study from data collections, phylogenetic tree constructions, visualization of the tree of life, and web-based rendering to specific application services & data mining. First, it is a repository for sequence records that form the basic data unit of all phylogenetic studies. Second, it is a workbench that aids the management, quality assurance, and analysis of phylogenetic data. Third, it provides a community of phylogenetic application services with tree reconstruction and related data mining. This paper provides a brief introduction to the key elements of DarwinTree, discusses their functional capabilities, details the database features, phylogeny pipeline, data mining tools, and specific application services available to support it, and concludes with future prospects.

Information geometry enables a deeper understanding of the methods of statistical inference. In this paper, the problem of nonlinear parameter estimation is considered from a geometric viewpoint using a natural gradient descent on statistical manifolds. It is demonstrated that the nonlinear estimation for curved exponential families can be simply viewed as a deterministic optimization problem with respect to the structure of a statistical manifold. In this way, information geometry offers an elegant geometric interpretation for the solution to the estimator, as well as the convergence of the gradient-based methods. The theory is illustrated via the analysis of a distributed mote network localization problem where the Radio Interferometric Positioning System (RIPS) measurements are used for free mote location estimation. The analysis results demonstrate the advanced computational philosophy of the presented methodology.

Because negative pressure can stimulate vascular proliferation, improve blood circulation and promote osteogenic differentiation of bone marrow stromal cells, we investigated the therapeutic effect of negative pressure on femoral head necrosis (FHN) in a rabbit model. Animals were divided into four groups (n = 60/group): [1] model control, [2] core decompression, [3] negative pressure and [4] normal control groups. Histological investigation revealed that at 4 and 8 weeks postoperatively, improvements were observed in trabecular bone shape, empty lacunae and numbers of bone marrow hematopoietic cells and fat cells in the negative pressure group compared to the core decompression group. At week 8, there were no significant differences between the negative pressure and normal control groups. Immunohistochemistry staining revealed higher expression of vascular endothelial growth factor (VEGF) and bone morphogenetic protein-2 (BMP-2) in the femoral heads in the negative pressure group compared with the core decompression group. Transmission electron microscopy revealed that cell organelles were further developed in the negative pressure group compared with the core decompression group. Microvascular ink staining revealed an increased number of bone marrow ink-stained blood vessels, a thicker vascular lumen and increased microvascular density in the negative pressure group relative to the core decompression group. Real-time polymerase chain reaction revealed that expression levels of both VEGF and BMP-2 were higher in the negative pressure group compared with the core decompression group. In summary, negative pressure has a therapeutic effect on FHN. This effect is superior to core decompression, indicating that negative pressure is a potentially valuable method for treating early FHN.