Artemisia pollen allergy in China : Component-resolved diagnosis reveals allergic asthma patients have significant multiple allergen sensitization

Background: Artemisia pollen allergy is a major cause of asthma in Northern China. Possible associations between IgE responses to Artemisia allergen components and clinical phenotypes have not yet been evaluated. This study was to establish sensitization patterns of four Artemisia allergens and possible associations with demographic characteristics and clinical phenotypes in three areas of China. Methods: Two hundred and forty patients allergic to Artemisia pollen were examined, 178 from Shanxi and 30 from Shandong Provinces in Northern China, and 32 from Yunnan Province in Southwestern China. Allergic asthma, rhinitis, conjunctivitis, and eczema symptoms were diagnosed. All patients sera were tested by ImmunoCAP with mugwort pollen extract and the natural components nArt v 1, nArt ar 2, nArt v 3, and nArt an 7. Results: The frequency of sensitization and the IgE levels of the four components in Artemisia allergic patients from Southwestern China were significantly lower than in those from the North. Art v 1 and Art an 7 were the most frequently recognized allergens (84% and 87%, respectively), followed by Art v 3 (66%) and Art ar 2 (48%). Patients from Northern China were more likely to have allergic asthma (50%) than patients from Southwestern China (3%), and being sensitized to more than two allergens increased the risk of allergic asthma, in which cosensitization to three major allergens Art v 1, Art v 3, and Art an 7 is prominent. Conclusions: Componentresolved diagnosis of Chinese Artemisia pollenallergic patients helps assess the potential risk of mugwortassociated allergic asthma.(VLID)329956

Diversity arrays technology (DArT) markers in apple for genetic linkage maps

Diversity Arrays Technology (DArT) provides a high-throughput whole-genome genotyping platform for the detection and scoring of hundreds of polymorphic loci without any need for prior sequence information. The work presented here details the development and performance of a DArT genotyping array for apple. This is the first paper on DArT in horticultural trees. Genetic mapping of DArT markers in two mapping populations and their integration with other marker types showed that DArT is a powerful high-throughput method for obtaining accurate and reproducible marker data, despite the low cost per data point. This method appears to be suitable for aligning the genetic maps of different segregating populations. The standard complexity reduction method, based on the methylation-sensitive PstI restriction enzyme, resulted in a high frequency of markers, although there was 52–54% redundancy due to the repeated sampling of highly similar sequences. Sequencing of the marker clones showed that they are significantly enriched for low-copy, genic regions. The genome coverage using the standard method was 55–76%. For improved genome coverage, an alternative complexity reduction method was examined, which resulted in less redundancy and additional segregating markers. The DArT markers proved to be of high quality and were very suitable for genetic mapping at low cost for the apple, providing moderate genome coverage

Low Molecular Weight Kappa-Carrageenan Based Microspheres for Enhancing Stability and Bioavailability of Tea Polyphenols

Tea polyphenols (TP) are a widely acknowledged bioactive natural product, however, low stability and bioavailability have restricted their application in many fields. To enhance the stability and bioavailability of TP under certain moderate conditions, encapsulation technique was applied. Kappa–Carrageenan (KCG) was initially degraded to a lower molecular weight KCG (LKCG) by H2O2, and was selected as wall material to coat TP. The obtained LKCG (Mn = 13,009.5) revealed narrow dispersed fragments (DPI = 1.14). FTIR and NMR results demonstrated that the main chemical structure of KCG remained unchanged after degradation. Subsequently, LK-CG and TP were mixed and homogenized to form LK-CG-TP microspheres. SEM images of the microspheres revealed a regular spherical shape and smooth surface with a mean diameter of 5–10 μM. TG and DSC analysis indicated that LK-CG-TP microspheres exhibited better thermal stability as compared to free TP. The release profile of LK-CG-TP in simulated gastric fluid (SGF) showed a slowly release capacity during the tested 180 min with the final release rate of 88.1% after digestion. Furthermore, in vitro DPPH radical scavenging experiments revealed that LK-CG-TP had an enhanced DPPH scavenging rate as compared to equal concentration of free TP. These results indicated that LK-CG-TP microspheres were feasible for protection and delivery of TP and might have extensive potential applications in other bioactive components

Hybrid Approach Based on Machine Learning for Hand Shape and Key Point's Estimation

In human-computer interaction and virtual truth, hand pose estimation is essential. Public dataset experimental analysis Different biometric shows that a particular system creates low manual estimation errors and has a more significant opportunity for new hand pose estimation activity. Due to the fluctuations, self-occlusion, and specific modulations, the structure of hand photographs is quite tricky. Hence, this paper proposes a Hybrid approach based on machine learning (HABoML) to enhance the current competitiveness, performance experience, experimental hand shape, and key point estimation analysis. In terms of strengthening the ability to make better self-occlusion adjustments and special handshake and poses estimations, the machine learning algorithm is combined with a hybrid approach. The experiment results helped define a set of follow-up experiments for the proposed systems in this field, which had a high efficiency and performance level. The HABoML strategy decreased analysis precision by 9.33% and is a better solution

The role of a critical left fronto-temporal network with its right-hemispheric homologue in syntactic learning based on word category information

Word category information (WCI) is proposed to be fundamental for syntactic learning and processing. However, it remains largely unclear how left-hemispheric key regions for language, including BA 44 in the inferior frontal gyrus (IFG) and superior temporal gyrus (STG), interact with their right-hemispheric homologues to support the WCI-based syntactic learning. To address this question, this study employed a unified structural equation modeling (uSEM) approach to explore both the intra- and inter-hemispheric effective connectivity among these areas, to specify the neural underpinnings of handling WCI for syntactic learning. Modeling results identified a distinctive intra-left hemispheric connection from left BA 44 to left STG, a more integrated intra-right hemispheric network, and a particular frontal right-to-left hemispheric connectivity pattern for WCI-based syntactic learning. Further analyses revealed a selective positive correlation between task performance and the lagged effect in left BA 44. These results converge on a critical left fronto-temporal language network with left BA 44 and its connectivity to left STG for WCI-based syntactic learning, which is also facilitated in a domain-general fashion by the right homologues. Together, these results provide novel insights into crucial neural network(s) for syntactic learning on the basis of WCI

One-dimensional equivalence ratio measurements by femtosecond laser filament-triggered discharge plasma spectroscopy

Equivalence ratio measurements are of great importance for combustion systems. In this paper, femtosecond laser filament-triggered discharge plasma spectroscopy is proposed for one-dimensional equivalence ratio measurements of combustion flow fields. The optical emission spectra of femtosecond laser filament-triggered discharge plasma in methane-air premixed laminar flames were measured. The spectral intensity ratios of different species show a linear correlation with the equivalence ratio. It can be demonstrated that femtosecond laser filament-triggered discharge plasma spectroscopy can be used for equivalence ratio measurements. We investigated the effect of the temperature of the methane-air mixture on the equivalence ratio measurements. We further analyzed the one-dimensional spatial intensity distributions of the plasma spectral lines. The results demonstrated the capability of femtosecond laser filament-triggered discharge plasma spectroscopy for one-dimensional equivalence ratio measurements. Finally, femtosecond laser filament-triggered discharge plasma spectroscopy was applied to a methane diffusion flame and measured the one-dimensional equivalence ratios of the diffusion flame at different heights. Femtosecond laser filament-triggered discharge plasma spectroscopy offers a new method for one-dimensional equivalence ratio measurements of combustion flow fields

Temperature measurements in heated gases and flames using carbon monoxide femtosecond two-photon laser-induced fluorescence

Nonintrusive temperature measurement is crucial in combustion research. Here, we propose a thermometric technique based on femtosecond two-photon laser-induced fluorescence of carbon monoxide (CO-fs-TPLIF). A femtosecond laser with a wavelength of 230 nm was used as an excitation source. Owing to its intrinsic broad bandwidth, dual vibrational bands of the B1Σ+ ← X1Σ+ transition of CO can be simultaneously excited. As a result, the fluorescence from the conventional bands (0,n) and the hot vibrational bands (1,n) of the B1Σ+ → A1Πu transition of CO can be simultaneously detected. Hence, the temperature-dependent Boltzmann distribution can be assessed from the relative fluorescence intensity related to different ro-vibrational states, and the temperature can be extracted from the analysis of the recorded fluorescence spectra. Two temperature calibration methods were developed, for the low-temperature range (298–1173 K), the rotational-state-associated bandwidths of the spectra were used; for the flame temperature range, the spectral intensity ratios between the hot vibrational bands (1-n) and the conventional bands (0-n) were used. The CO-fs-TPLIF thermometric technique features the advantages of a simple optical setup and the ability of one-dimensional measurements with high spatial resolution

Deep Neural Network-Based Generation of Planar CH Distribution through Flame Chemiluminescence in Premixed Turbulent Flame

Flame front structure is one of the most fundamental characteristics and, hence, vital for understanding combustion processes. Measuring flame front structure in turbulent flames usually needs laser-based diagnostic techniques, mostly planar laser-induced fluorescence (PLIF). The equipment of PLIF, burdened with lasers, is often too sophisticated to be configured in harsh environments. Here, to shed the burden, we propose a deep neural network-based method to generate the structures of flame fronts using line-of-sight CH* chemiluminescence that can be obtained without the use of lasers. A conditional generative adversarial network (C-GAN) was trained by simultaneously recording CH-PLIF and chemiluminescence images of turbulent premixed methane/air flames. Two distinct generators of the C-GAN, namely Resnet and U-net, were evaluated. The former net performs better in this study in terms of both generating snap-shot images and statistics over multiple images. For chemiluminescence imaging, the selection of the camera's gate width produces a trade-off between the signal-to-noise (SNR) ratio and the temporal resolution. The trained C-GAN model can generate CH-PLIF images from the chemiluminescence images with an accuracy of over 91% at a Reynolds number of 5000, and the flame surface density at a higher Reynolds number of 10,000 can also be effectively estimated by the model. This new method has the potential to achieve the flame characteristics without the use of laser and significantly simplify the diagnosing system, also with the potential for high-speed flame diagnostics