55 research outputs found
Variation of postharvest quality attributes and aroma development of Xiaobai apricot during storage at near freezing temperature
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High-Throughput and Accurate Determination of Transgene Copy Number and Zygosity in Transgenic Maize: From DNA Extraction to Data Analysis
No full textIt is vital to develop high-throughput methods to determine transgene copy numbers initially and zygosity during subsequent breeding. In this study, the target sequence of the previously reported endogenous reference gene hmg was analyzed using 633 maize inbred lines, and two SNPs were observed. These SNPs significantly increased the PCR efficiency, while the newly developed hmg gene assay (hmg-taq-F2/R2) excluding these SNPs reduced the efficiency into normal ranges. The TaqMan amplification efficiency of bar and hmg with newly developed primers was calculated as 0.993 and 1.000, respectively. The inter-assay coefficient of variation (CV) values for the bar and hmg genes varied from 1.18 to 2.94%. The copy numbers of the transgene bar using new TaqMan assays were identical to those using dPCR. Significantly, the precision of one repetition reached 96.7% of that of three repetitions of single-copy plants analyzed by simple random sampling, and the actual accuracy reached 95.8%, confirmed by T1 and T2 progeny. With the high-throughput DNA extraction and automated data analysis procedures developed in this study, nearly 2700 samples could be analyzed within eight hours by two persons. The combined results suggested that the new hmg gene assay developed here could be a universal maize reference gene system, and the new assay has high throughput and high accuracy for large-scale screening of maize varieties around the world
Underwater Target Detection Utilizing Polarization Image Fusion Algorithm Based on Unsupervised Learning and Attention Mechanism
No full textSince light propagation in water bodies is subject to absorption and scattering effects, underwater images using only conventional intensity cameras will suffer from low brightness, blurred images, and loss of details. In this paper, a deep fusion network is applied to underwater polarization images; that is, the underwater polarization images are fused with intensity images using the deep learning method. To construct a training dataset, we establish an experimental setup to obtain underwater polarization images and perform appropriate transformations to expand the dataset. Next, an end-to-end learning framework based on unsupervised learning and guided by an attention mechanism is constructed for fusing polarization and light intensity images. The loss function and weight parameters are elaborated. The produced dataset is used to train the network under different loss weight parameters, and the fused images are evaluated based on different image evaluation metrics. The results show that the fused underwater images are more detailed. Compared with light intensity images, the information entropy and standard deviation of the proposed method increase by 24.48% and 139%. The image processing results are better than other fusion-based methods. In addition, the improved U-net network structure is used to extract features for image segmentation. The results show that the target segmentation based on the proposed method is feasible under turbid water. The proposed method does not require manual adjustment of weight parameters, has faster operation speed, and has strong robustness and self-adaptability, which is important for research in vision fields, such as ocean detection and underwater target recognition
Volatile Compounds Analysis and Biomarkers Identification of Four Native Apricot (Prunus armeniaca L.) Cultivars Grown in Xinjiang Region of China
No full textFlavor (odor and taste) have a significant role in the consumer’s acceptance, and volatile compounds are responsible for the odor of apricots. In the present work, headspace solid-phase microextraction with gas chromatography coupled to tandem mass spectrometry (HS-SPME-GC-MS/MS) together with multivariate analysis, i.e., partial least square discrimination analysis (PLS-DA), were applied to construct the volatile fingerprints and biomarkers of apricots in Xinjiang, China. As a result, a total of 63 volatile substances were identified in the fruits of four apricot cultivars, seven of which were considered to serve as volatile biomarkers, which are damascenone for Dabaiyou apricots; acetophenone, myrcenol and 7-hexadecenal for Luopuhongdaike apricots; 2,4-dimethyl-cyclohexanol for You apricots; eucalyptol and salicylaldehyde for Xiaobai apricots. Moreover, Xiaobai apricots were richer in soluble sugars, organic acids and total phenolic and total flavonoid content than the other three apricot varieties. This work helps to characterize the volatile profiles and biomarkers of different apricot cultivars while providing theoretical guidance for developing apricot-flavored foods in practical production
A micromachined piezoelectric microgripper for manipulation of micro/nanomaterials
No full textMicro/nanomaterials and devices have attracted great interest in recent years because of their extensive application prospects in almost all kinds of fields. However, the manipulations of the material at the micro/nanoscale, such as the separation or transfer of a micro/nano-object in the process of assembling micro/nanodevices, are quite difficult. In this paper, we present a micromachined micro-gripper made of photoresist material (SU-8) and driven by piezoelectric Pb(Mg, Nb) O-3-PbTiO3 single crystal pieces. In order to keep two grasping jaws of the micro-gripper operating in the same plane at the micro/nanometer scale, a fine circular flexure hinge was fabricated for elastically connecting them together. After introducing the interface effect, the relationship between the opening stroke of two jaws and the applied voltage was developed and then confirmed by finite element simulation. The micro-gripper was finally installed on a six degree of freedom stage for performing a pick-up, release, and transfer manipulation of a 2 mu m ZnO micro-fiber. The presented piezoelectric micro-gripper shows a great potential for the precise manipulation of a single piece of micro/nanomaterial for micro/nanodevices' assembling. Published by AIP Publishing.National Natural Science Foundation of China [51132001]; Beijing Municipal Science and Technology Projects [Z131100003213020, Z151100003715003]SCI(E)ARTICLE68
Clinical Insight-Augmented Multi-View Learning for Alzheimer’s Detection in Retinal OCTA Images
No full textAlzheimer’s disease (AD) poses a significant globalchallenge, with a notable absence of accessible and cost-effectivediagnostic tools for widespread AD detection. The retina, mirroringthe brain in anatomy and physiology, has emerged asa potential avenue for rapid AD identification through retinalimaging. The current retinal image-based AD detection methodsusually focus primarily on the macular area, but ignore thepotential value that the optic disc region may have for thedetection task. In this study, we leverage both macular- anddisc-centered OCTA images and propose a multi-region fusionframework for AD detection. Based on clinical evidence, weintegrate handcrafted features into the framework to improvemodel performance and interpretability. Specifically, vascularmorphological parameters extracted from the macular and discregions are used as input to a revalued KNN model to improvepredictive capabilities. Furthermore, recognizing the significanceof extracting and utilizing complementary information from themacular and optic disc regions, we propose an uncertaintyguidedstrategy based on Dempster-Shefer Theory (DST) tofuse knowledge from different regions. This approach considerseach region’s forecast quality and significantly improves theeffectiveness and robustness of the model. Through comparativeanalysis with existing methods, we have demonstrated that ourmethod outperforms the state-of-the-art ones and provides morevaluable pathological evidence for the association between retinalvascular changes and AD
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