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Investigating the impact of train-induced vibration loads on soil hydraulic properties, this study conducted experiments using a self-designed indoor soil seepage platform that incorporates vibration loads. The experiments were complemented with scanning electron microscopy to analyze the influence of train-induced vibration loads on soil hydraulic conductivity and its evolutionary characteristics under different vibration frequencies. The experimental results indicated that as the vibration frequency increases from no vibration (0 Hz) to 20 Hz, the time required for the soil volumetric moisture content to reach its peak and stabilize decreases rapidly. However, after the vibration frequency exceeds 20 Hz, the rate at which the time required for the volumetric moisture content to reach its peak and stabilize decreases slows down. Furthermore, the soil pore water pressure increases with the increase in vibration frequency. At a vibration frequency of 80 Hz, the peak value of pore water pressure increases by 105% compared to the non-vibration state, suggesting that higher vibration frequencies promote the development and acceleration of soil pore moisture migration. Additionally, as the vibration frequency increases, the soil hydraulic conductivity initially experiences a rapid increase, with a growth rate ranging from 40.1 to 47.4%. However, after the frequency exceeds 20 Hz, this growth rate significantly decreases, settling to only 18.6% to 7.8%. When the soil was subjected to a vibration load, the scanning electron microscopy test revealed alterations in its pore structure. Micropores and small pores transformed into macropores and mesopores. Additionally, the microstructural parameters indicated that vibration load decreased the complexity of soil pores, thereby speeding up the hydraulic conduction process. This, in turn, affected the hydraulic properties of the soil and established a relationship between pore structure complexity and soil hydraulic properties. [ABSTRACT FROM AUTHOR]

It is known that the Invariant Subspace Problem for Hilbert spaces is equivalent to the statement that all minimal non-trivial invariant subspaces for a universal operator are one dimensional. In this paper, we characterize all linear fractional composition operators and their adjoints that have universal translates on the space S 2 (D) . Moreover, we characterize all adjoints of linear fractional composition operators that have universal translates on the Hardy space H 2 (D) . In addition, we consider the minimal invariant subspaces of the composition operator C φ a on S 2 (D) , where ϕa(z) = az + 1 − a, a ∈ (0, 1). Finally, some relationships between complex symmetry and universality for bounded linear operators and commuting pairs of operators on a complex separable, infinite dimensional Hilbert space are explored. [ABSTRACT FROM AUTHOR]

As a crucial task in Computer Vision, object detection has substantially improved in recent years, with the aid of deep learning and increasingly abundant datasets. However, compared with natural image detection, medical CT images require more precision due to the obvious clinical implications. Detecting multiple lesions or clusters with relatively few training samples and indistinctive feature representation is extremely problematic. In this paper, we propose comprehensive improvements to the original YOLOv3, such as data augmentation, feature attention enhancement and feature complementarity enhancement to increase general lesion area detection performance. Ablation studies use the open DeepLesion dataset to validate these improvements and confirm the effectiveness of each amendment. Comparisons between state-of-the-art counterparts demonstrated that the proposed lesion object detector has enhanced salient accuracy (under two commonly used metrics) and an exceptional speed-accuracy trade-off. The proposed model achieved 57.5% mAP and 85.07% sensitivity at 4 false positives (FPs) per image, while running at reliable 35.6 frames per second (FPS). These findings indicate that the proposed detector is more practicable than other currently available computer aided diagnostics (CAD). [ABSTRACT FROM AUTHOR]

In the present research, novel lanthanide coordination compounds [DyL(PhCOO)(CH3OH)](ClO4)2·(CH3OH)2 (1) were characterized by the compression of 2,6-diformyl-4-methyl-phenol (dmp) and 1,3-diamino-2-propanol using benzoate as the secondary ligand, where L indicates the deprotonated macrocyclic ligand. Through the high structural rigidity driven by the coordination of the macrocyclic ligand formed by condensation in methanol solution and sodium benzoate with Dy(ClO4)3·6H2O, compound 1 exhibits outstanding cyan-emitting fluorescence performance and potential applications as a fluorescent material. Additionally, hyaluronic acid (HA)/ carboxymethyl chitosan (CMCS) hydrogels were prepared with loaded resveratrol metal-organic complexes according to the synthetic chemical approach. In biological study, we evaluated the effect of hydrogels on oxidative stress on human dermal fibroblasts. Examined by molecular docking simulation, the results showed that the binding interactions were from the phenol group, the carboxyl group and also the “-N=” group, indicating Dy metal complex has excellent biological capability. [ABSTRACT FROM AUTHOR]

Deploying convolutional neural networks (CNNs) on mobile devices is difficult due to the limited memory and computation resources. We aim to design efficient neural networks for heterogeneous devices including CPU and GPU, by exploiting the redundancy in feature maps, which has rarely been investigated in neural architecture design. For CPU-like devices, we propose a novel CPU-efficient Ghost (C-Ghost) module to generate more feature maps from cheap operations. Based on a set of intrinsic feature maps, we apply a series of linear transformations with cheap cost to generate many ghost feature maps that could fully reveal information underlying intrinsic features. The proposed C-Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks. C-Ghost bottlenecks are designed to stack C-Ghost modules, and then the lightweight C-GhostNet can be easily established. We further consider the efficient networks for GPU devices. Without involving too many GPU-inefficient operations (e.g., depth-wise convolution) in a building stage, we propose to utilize the stage-wise feature redundancy to formulate GPU-efficient Ghost (G-Ghost) stage structure. The features in a stage are split into two parts where the first part is processed using the original block with fewer output channels for generating intrinsic features, and the other are generated using cheap operations by exploiting stage-wise redundancy. Experiments conducted on benchmarks demonstrate the effectiveness of the proposed C-Ghost module and the G-Ghost stage. C-GhostNet and G-GhostNet can achieve the optimal trade-off of accuracy and latency for CPU and GPU, respectively. MindSpore code is available at https://gitee.com/mindspore/models/pulls/1809, and PyTorch code is available at https://github.com/huawei-noah/CV-Backbones. [ABSTRACT FROM AUTHOR]

Quantum dialogue (QD) is a quantum communication mode which enables two communication parties to exchange their secret messages simultaneously. In this paper, we propose a measurement-device-independent quantum dialogue (MDI-QD) protocol using the polarization–spatial-mode hyperentanglement. The protocol can eliminate the security loopholes related to measurement devices and the information leakage. Comparing with previous MDI-QD protocol, our MDI-QD protocol has higher channel capacity. Two communication parties can exchange 4 bits of messages per hyperentangled photon pair. Moreover, for promoting its practical application, we try to use the practical linear-optical partial hyperentangled Bell-state measurement in our MDI-QD protocol, with which the parties can exchange 3 bits of messages per hyperentangled photon pair. Our high-capacity MDI-QD protocol has application potential in future quantum communication field. [ABSTRACT FROM AUTHOR]

Segmentation of the liver from abdominal CT images is an essential step for computer-aided diagnosis and surgery planning. The U-Net architecture is one of the most well-known CNN architectures which achieved remarkable successes in both medical and biological image segmentation domain. However, it does not perform well when the target area is small or partitioned. In this paper, we propose a novel architecture, called dense feature selection U-Net (DFS U-Net), which addresses this challenging problem. Specifically, The Hounsfield unit values were windowed in a range to exclude irrelevant organs, and then use the pre-processed data to train our proposed DFS U-Net model. To further improve the segmentation accuracy of the small region and disconnected regions of interests with limited training datasets, we improve the loss function by adding a parameter to the formula. With respect to the ground truth, the Dice score ratio can reach over 94.9% for the liver. Our experimental results demonstrate its potential in clinical usage with high effectiveness, robustness and efficiency. [ABSTRACT FROM AUTHOR]

The molecular mechanism of flower development and sexual differentiation, which is an intricate process, still unclear in Vitis plants. In this study, we performed the transcriptome analysis by high-throughput sequencing to measure gene expression at three stages in male, female, and hermaphrodite grape flowers, and in combination with the fluctuation of hormones (ABA, IAA, BR, GA3, and JA) to understand the gene function and molecular basis of developmental processes in Vitis flowers. The results showed that more than 581 million clean reads were generated from all samples and average 83% reads were mapped on the Vitis vinifera genome. More, quite a large number of genes were differentially expressed between different stages within and across cultivars and various transcription factor families involved were identified in flower development. The genes belonging to MYB, bHLH, ERF, NAC, and WRKY have more numbers than other TF families. Furthermore, we analyzed the correlation of those hormones with 165 DEGs (including 36 TF genes). The results suggested that those genes might play important roles in sex differentiation during flower development and a unique opportunity to increase the efficiency of crossbreeding in Vitis plant. [ABSTRACT FROM AUTHOR]

In this paper, we study complex symmetric C0-semigroups on the Bergman space A2(ℂ+) of the right half-plane ℂ+. In contrast to the classical case, we prove that the only involutive composition operator on A2(ℂ+) is the identity operator, and the class of J-symmetric composition operators does not coincide with the class of normal composition operators. In addition, we divide semigroups {ϕt} of linear fractional self-maps of ℂ+ into two classes. We show that the associated composition operator semigroup {Tt} is strongly continuous and identify its infinitesimal generator. As an application, we characterize Jσ-symmetric C0-semigroups of composition operators on A2(ℂ+). [ABSTRACT FROM AUTHOR]

Incremental sheet forming (ISF) has received increasing attention with its advantages of economy and flexibility for small batch sheet metal parts. However, the excessive thinning may be resulted at large forming angles for complex parts, which cannot meet the design requirements and seriously impedes the industrial application of ISF. Although the multistage incremental sheet forming (MISF) has been proposed to overcome the dilemma of excessive thinning in ISF, the design of the optimal strategy which can control material flow and improve thickness distribution is a critical issue. In this paper, three promising multistage two point incremental sheet forming (MTPIF) path strategies, i.e., parallel line strategy, variable angle strategy, and stretch-bend-assisted strategy, are selected to investigate the influence of tool paths on thickness distributions, material flow, and geometric error. Firstly, experimental thickness distributions of truncated pyramids formed with three path strategies and single-stage TPIF strategy are compared. Meanwhile, the theoretical models are introduced to illustrate the difference of thickness distributions both at the wall and the corner. Compared with single-stage TPIF strategy, MTPIF path strategies studied in this paper are effective to improve the thickness distribution of truncated pyramids at both the component wall and the corner. Through the theoretical analysis, it is found that the thickness distribution at the wall can be controlled by adjusting intermediate forming angles. Meanwhile, the intermediate shape in circumferential direction has significant influence on the thickness distribution for complex parts. Then, the material flows of three path strategies at different forming stages are analyzed. It is found that the materials at the flange as well as transition arcs are deformed into wall to increase the thickness distribution. Finally, considering both thickness distributions and geometric errors, the variable angle strategy is found as the effective MTPIF strategy for the studied process. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of image matting for transparent objects. Existing approaches often require tedious capturing procedures and long processing time, which limit their practical use. In this paper, we formulate transparent object matting as a refractive flow estimation problem, and propose a deep learning framework, called TOM-Net, for learning the refractive flow. Our framework comprises two parts, namely a multi-scale encoder-decoder network for producing a coarse prediction, and a residual network for refinement. At test time, TOM-Net takes a single image as input, and outputs a matte (consisting of an object mask, an attenuation mask and a refractive flow field) in a fast feed-forward pass. As no off-the-shelf dataset is available for transparent object matting, we create a large-scale synthetic dataset consisting of 178 K images of transparent objects rendered in front of images sampled from the Microsoft COCO dataset. We also capture a real dataset consisting of 876 samples using 14 transparent objects and 60 background images. Besides, we show that our method can be easily extended to handle the cases where a trimap or a background image is available. Promising experimental results have been achieved on both synthetic and real data, which clearly demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

A rare case of a 15-year-old male patient with neuromuscular scoliosis with forced vital capacity (FVC) below 30%, who went through a successful surgery without any pulmonary complications, is reported herein. The patient had obvious asymmetric shoulders and poor exercise tolerance. The Cobb's angle of the main thoracic curve was 62.8°, and FVC in sitting position was 18% of predictive value. After skull traction and pulmonary rehabilitation, the FVC was still below 30%, and he finally went through surgery under this serious condition. By early pulmonary rehabilitation using home ventilator, he successfully recovered without any pulmonary complications. The patient had complete symptom remission and no deterioration of Cobb's angle was found during follow-up. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of reconstructing the surface shape of transparent objects. The difficulty of this problem originates from the viewpoint dependent appearance of a transparent object, which quickly makes reconstruction methods tailored for diffuse surfaces fail disgracefully. In this paper, we introduce a fixed viewpoint approach to dense surface reconstruction of transparent objects based on refraction of light. We present a simple setup that allows us to alter the incident light paths before light rays enter the object by immersing the object partially in a liquid, and develop a method for recovering the object surface through reconstructing and triangulating such incident light paths. Our proposed approach does not need to model the complex interactions of light as it travels through the object, neither does it assume any parametric form for the object shape nor the exact number of refractions and reflections taken place along the light paths. It can therefore handle transparent objects with a relatively complex shape and structure, with unknown and inhomogeneous refractive index. We also show that for thin transparent objects, our proposed acquisition setup can be further simplified by adopting a single refraction approximation. Experimental results on both synthetic and real data demonstrate the feasibility and accuracy of our proposed approach. [ABSTRACT FROM AUTHOR]

Objective: To explore whether segmentation and 3D modeling are more accurate in the preoperative detection of the neurovascular relationship (NVR) in patients with trigeminal neuralgia (TN) compared to MRI fast imaging employing steady-state acquisition (FIESTA). Method: Segmentation and 3D modeling using 3D Slicer were conducted for 40 patients undergoing MRI FIESTA and microsurgical vascular decompression (MVD). The NVR, as well as the offending vessel determined by MRI FIESTA and 3D Slicer, was reviewed and compared with intraoperative manifestations using SPSS. Results: The k agreement between the MRI FIESTA and operation in determining the NVR was 0.232 and that between the 3D modeling and operation was 0.6333. There was no significant difference between these two procedures (χ = 8.09, P = 0.088). The k agreement between the MRI FIESTA and operation in determining the offending vessel was 0.373, and that between the 3D modeling and operation was 0.922. There were significant differences between two of them (χ = 82.01, P = 0.000). The sensitivity and specificity for MRI FIESTA in determining the NVR were 87.2 % and 100 %, respectively, and for 3D modeling were both 100 %. Conclusion: The segmentation and 3D modeling were more accurate than MRI FIESTA in preoperative verification of the NVR and offending vessel. This was consistent with surgical manifestations and was more helpful for the preoperative decision and surgical plan. [ABSTRACT FROM AUTHOR]

Chlorella sp. TLD 6B, a microalgae growing in the Taklamakan Desert, Xinjiang of China, is a good model material for studying the physiological and environmental adaptation mechanisms of plants in their arid habitats, as its adaptation to the harsh desert environment has led to its strong resistance. However, when using real-time quantitative polymerase chain reaction (RT-qPCR) to analyze the gene expression of this algae under abiotic stress, it is essential to find the suitable endogenous reference genes so to obtain reliable results. This study assessed the expression stability of 9 endogenous reference genes of Chlorella sp. TLD 6B under four abiotic stresses (drought, salt, cold and heat). These genes were selected based on the analysis results calculated by the three algorithmic procedures of geNorm, NormFinder, and BestKeeper, which were ranked by refinder. Our research showed that 18S and GTP under drought stress, 18S and IDH under salt stress, CYP and 18S under cold stress, GTP and IDH under heat stress were the most stable endogenous reference genes. Moreover, UBC and 18S were the most suitable endogenous reference gene combinations for all samples. In contrast, GAPDH and α-TUB were the two least stable endogenous reference genes in all experimental samples. Additionally, the selected genes have been verified to be durable and reliable by detecting POD and PXG3 genes using above endogenous reference genes. The identification of reliable endogenous reference genes guarantees more accurate RT-qPCR quantification for Chlorella sp. TLD 6B, facilitating functional genomics studies of deserts Chlorella as well as the mining of resistance genes. [ABSTRACT FROM AUTHOR]

Purpose: To design excellent polypeptide-based gene vectors and determine the gene delivery efficiency. Methods: Polypeptides (designated as xPolyK, xPolyK-R1 and xPolyK-R2), comprising the DNA condensing and buffering peptide HKH as well as cell penetrating peptide (CPP) R were obtained by the oxidative polymerization of CHKHC and CRC at different molar ratios in 4 mL phosphate-buffered saline (PBS) containing 30% ( v/v) DMSO at room temperature for 96 h. The cytotoxicity of vectors was studied by MTT assay. Moreover, particle size, zeta potential and morphology along with the in vitro transfection efficiency and cellular uptake of vector/plasmid DNA (pDNA) complexes were characterized at various w/w ratios to determine their potential in gene therapy. Results: All the vectors presented excellent ability of binding and condensing pDNA, additionally with low cytotoxicity. Simultaneously, transfection efficiency of the vectors appeared apparent dependence on the vector composition. The distinct correlation between the content of CRC with the transfection efficiency demonstrated the effective improvement in transfection efficacy by the oxidative polymerization. Particularly, xPolyK-R2 possessed the highest transfection efficiency at a w/w ratio of 50. Furthermore, xPolyK-R2 also presented the best cellular uptake capability demonstrated by confocal microscopy and flow cytometry. Conclusions: Bioreducible polypeptides incorporating with proper amount of CPP are promising as effective non-viral gene vectors in gene therapy. [ABSTRACT FROM AUTHOR]

Based on the dual source cumulative rotation technique in the time-domain proposed by Zeng and MacBeth (1993), a new algebraic processing technique for extracting shear-wave splitting parameters from multi-component VSP data in frequency-dependent medium has been developed. By using this dual source cumulative rotation technique in the frequency-domain (DCTF), anisotropic parameters, including polarization direction of the shear-waves and timedelay between the fast and slow shear-waves, can be estimated for each frequency component in the frequency domain. It avoids the possible error which comes from using a narrow-band filter in the current commonly used method. By using synthetic seismograms, the feasibility and validity of the technique was tested and a comparison with the currently used method was also given. The results demonstrate that the shear-wave splitting parameters frequency dependence can be extracted directly from four-component seismic data using the DCTF. In the presence of larger scale fractures, substantial frequency dependence would be found in the seismic frequency range, which implies that dispersion would occur at seismic frequencies. Our study shows that shear-wave anisotropy decreases as frequency increases. [ABSTRACT FROM AUTHOR]

In this manuscript, we studied the thermal properties of hundred-watt fiber laser oscillator by real-time in-situ distributed temperature measurement. Optical frequency domain reflectometry (OFDR) was introduced to measure the temperature distribution of gain fiber core. The fiber laser oscillator operated at 1080 nm and the wavelength of detecting signal from OFDR was ~1550 nm. The maximum output power of this fiber oscillator was 100 W. The fiber core temperature distributions in experiment agree well with our theoretical simulation. The temperature measurement of gain fiber core in oscillator has always been a problem because the backward laser from the oscillator may reduce the signal-to-noise ratio in OFDR. To the best of our knowledge, this is the first temperature distribution measurement of fiber core in hundred-watt oscillator. By the experimental measurement and theoretical model, we also analyzed the thermal properties of laser oscillator respectively pumped by 915 nm and 976 nm LD sources. We found fiber laser oscillator pumped by 976 nm LD sources experienced not only higher maximum thermal load but also higher average thermal load than that pumped by 915 nm LD sources at the same level output power. We also analyzed the fiber core temperature of other components in system, such as combiners and fiber Bragg gratings (FBG). These results are meaningful for us to improve the thermal design and management in fiber lasers. [ABSTRACT FROM AUTHOR]

Segmenting the pancreas from abdominal CT scans is challenging since it often takes up a relatively small region. Researchers suggested leveraging coarse-to-fine approaches to cope with this challenge. However, the coarse-scaled segmentation and the fine-scaled segmentation are either trained separately utilizing the coordinates located by the coarse-scaled segmentation mask to crop the fine-scaled segmentation input, or trained jointly utilizing the coarse-scaled segmentation mask to enhance the fine-scaled segmentation input. We argued that these two solutions are complementary to some extent and can promote each other to improve the performance of pancreas segmentation. In addition, the backbone in the coarse-scaled segmentation and fine-scaled segmentation is mostly based on UNet or UNet-like networks, where the multi-scale features transmitted from the encoder to the decoder have not been explored for vertical calibration before. In this paper, we propose a cascaded multi-scale feature calibration UNet (CMFCUNet) for pancreas segmentation where the multi-scale features in the backbone of each scaled segmentation are calibrated vertically in a pixel-wise fashion. Besides, the coarse-scaled segmentation and the fine-scaled segmentation are connected by leveraging a designed dual enhancement module (DEM). Experiments are first conducted on the public NIH pancreas dataset. First, when leveraging CMFCUNet, our method increased by over 3% on the Jaccard index (JI) and nearly 1% on dice similarity coefficient (DSC) which surpasses all existing pancreas segmentation approaches. In addition, our experiments demonstrate that CMFCUNet improved the coarse-to-fine segmentation framework and outperformed the mainstream coarse-to-fine pancreas segmentation approaches. Furthermore, we also conducted ablation studies to analyze the effectiveness of the backbone (MFCUNet) and the DEM. In addition to the experiments on the NIH dataset, we also experimentally demonstrate the excellent generalization of our method on the MSD pancreas dataset. [ABSTRACT FROM AUTHOR]

Deep learning has demonstrated remarkable performance in the medical domain, with accuracy that rivals or even exceeds that of human experts. However, it has a significant problem that these models are "black-box" structures, which means they are opaque, non-intuitive, and difficult for people to understand. This creates a barrier to the application of deep learning models in clinical practice due to lack of interpretability, trust, and transparency. To overcome this problem, several studies on interpretability have been proposed. Therefore, in this paper, we comprehensively review the interpretability of deep learning in medical diagnosis based on the current literature, including some common interpretability methods used in the medical domain, various applications with interpretability for disease diagnosis, prevalent evaluation metrics, and several disease datasets. In addition, the challenges of interpretability and future research directions are also discussed here. To the best of our knowledge, this is the first time that various applications of interpretability methods for disease diagnosis have been summarized. [ABSTRACT FROM AUTHOR]

Nitrogen management plays important roles in high-yielding rice production. In this two-year study, four rice cultivars were used to investigate the effects of the nitrogen application regime on yields, yield components, photosynthetic characteristics, and the dry matter accumulation and transformation of rice. Under a split-plot design, the main plots were assigned to five nitrogen application strategies, of which strategies 0 (N1), 165 (N2), 195 (N3), and 225 kg N ha−1 (N4) were applied as follows: 50% at basal, 35% at tillering and 15% at panicle initiation. In addition, strategy N5 was applied with 195 kg N ha−1 by 40% at basal, 15% at green-returning, 25% at the tillering stage, 15% at the panicle initiation stage, and 5% at the spikelet differentiation stage. Each main plot was sub-plotted by four experimental rice cultivars. Our results showed that all four cultivars obtained the highest yield with 195 kg N ha−1application. The nitrogen treatments seemed to have a smaller effect on the number of spikelets per panicle and 1000-grains weight, but more influence on the number of panicles and seed-setting rate and eventually affected grain yield. Under the N5 treatment, high yield was obtained due to more effective panicles, more spikelets per panicle and a high seed-setting rate. Additionally, the N5 treatment caused a longer leaf stay-green duration and higher photosynthetic potential, and the leaf area decreased slower at the late stage. The N4 treatment increased the dry matter formation at the jointing stage and the N3 treatment accelerated the dry matter accumulation between the jointing and heading stages. The results suggested that under 195 kg N ha−1 nitrogen amount, a higher post-anthesis biomass could be gained, and the N5 treatment showed greater advantages from heading to maturity than the other treatments in the aspects of more dry matter accumulation at the late growth stage, a coordinated organ growth ratio, and a higher matter translocation rate from the stem and leaf to the panicle, thus benefiting yield formation. [ABSTRACT FROM AUTHOR]

Exploiting the optical/magnetic bifunctional upconversion nanoparticles (UCNPs) with small size has become a trend in the high-resolution biological imaging. Here, we report the simultaneous size manipulation and color tuning in Er3+-sensitized perovskite KZnF3 UCNPs via Mn2+ doping. It was found that the introduction of Mn2+ into the KZnF3 host matrix facilitates the reduction of the nanoparticle size (from 1 μm to 50 nm). Under the excitation of both 980 and 808 nm continuous wave (CW) laser, these KZnF3:Er3+/Mn2+ (1/x mol%) UCNPs can efficiently generate tunable upconversion luminescence (UCL) and achieve pure red UCL as the Mn2+ concentration reaches 30 mol%. It is suggested that the energy transfer (ET) processes between Er3+ and Mn2+ ions contribute to the single-band red UC emission, which was well supported by investigating the dependence of UCL intensity on the pump power and the decay lifetimes of 4S3/2 and 4F9/2 states of Er3+ ions. In addition, the KZnF3:Er3+/Mn2+ UCNPs have been used as the T1-weighted magnetic resonance imaging (MRI) agents, which exhibits a high optical contrast with the background. These features make the KZnF3:Er3+/Mn2+ UCNPs promising for excellent dual-modal agents in UCL imaging and MRI. [ABSTRACT FROM AUTHOR]

We experimentally investigate the effect of post-deposition annealing on the charge distribution of a metal-oxide-semiconductor capacitor with a TiN/HfO2/SiO2/Si gate structure. We decoupled interfacial charges at the SiO2/Si and HfO2/SiO2 interfaces; bulk charges in HfO2; and the dipole formation at the HfO2/SiO2 interface. The interfacial charges at the HfO2/SiO2 interface decreased and the dipole increased after H2 or N2 annealing. Oxygen dangling bonds are the physical origin of the charges at the HfO2/SiO2 interface. The interfacial charges at the SiO2/Si interface and the bulk charges in HfO2 are almost unchanged. [ABSTRACT FROM AUTHOR]

Flower color variegation has been observed in many plant species. However, pink flowers on the white-blooming hawthorn trees found by our group earlier have never been reported. To better understand the differentially expressed genes (DEGs) in variegated hawthorn flowers, white and pink flowers at different developmental stages (S1 and S2) underwent transcriptome sequencing separately. Approximately 34.28 Gb of high-quality data were obtained and assembled into 100,013 unigenes with an average length of 706.93 bp. These unigenes were further subjected to functional annotation and biochemical pathway analysis, and DEGs of two types of hawthorn flowers at different developmental stages were studied. Based on the enrichment analysis of DEGs, eight anthocyanin-modified enzyme genes or other enzyme genes that indirectly affect anthocyanin synthesis (5AT, 3GGT, and AI, β-Glu, two Aux/IAAs, two PODs), eight structural genes (UFGT, DFR, CHI, two F3Hs, and three PALs), and three transcription factors (one MYB and two bHLHs) were also identified. We randomly selected 15 genes, and the trends in the expression levels of these genes in the organs of white and pink flowers at different developmental stages were verified by quantitative real-time PCR. Mass sequence data obtained by RNA-seq of variegated hawthorn flowers provided basic sequence information and a unique opportunity to uncover the genetic mechanisms underlying flower color variegation. [ABSTRACT FROM AUTHOR]

Key message: An efficient mannose selection system was established for transformation of Indica cultivar IR58025B . Different selection pressures were required to achieve optimum transformation frequency for different PMI selectable marker cassettes. Abstract: This study was conducted to establish an efficient transformation system for Indica rice, cultivar IR58025B. Four combinations of two promoters, rice Actin 1 and maize Ubiquitin 1, and two manA genes, native gene from E. coli (PMI-01) and synthetic maize codon-optimized gene (PMI-09) were compared under various concentrations of mannose. Different selection pressures were required for different gene cassettes to achieve corresponding optimum transformation frequency (TF). Higher TFs as 54 and 53 % were obtained when 5 g/L mannose was used for selection of prActin-PMI-01 cassette and 7.5 g/L mannose used for selection of prActin-PMI-09, respectively. TFs as 67 and 56 % were obtained when 7.5 and 15 g/L mannose were used for selection of prUbi-PMI-01 and prUbi-PMI-09, respectively. We conclude that higher TFs can be achieved for different gene cassettes when an optimum selection pressure is applied. By investigating the PMI expression level in transgenic calli and leaves, we found there was a significant positive correlation between the protein expression level and the optimal selection pressure. Higher optimal selection pressure is required for those constructs which confer higher expression of PMI protein. The single copy rate of those transgenic events for prActin-PMI-01 cassette is lower than that for other three cassettes. We speculate some of low copy events with low protein expression levels might not have been able to survive in the mannose selection. [ABSTRACT FROM AUTHOR]