CsEXL3 regulate mechanical harvest-related droopy leaves under the transcriptional activation of CsBES1.2 in tea plant.

Due to a labor shortage, the mechanical harvesting of tea plantations has become a focal point. However, mechanical harvest efficiency was hampered by droopy leaves, leading to a high rate of broken tea shoots and leaves. Here, we dissected the genetic structure of leaf droopiness in tea plants using genome-wide association studies (GWAS) on 146 accessions, combined with transcriptome from two accessions with contrasting droopy leaf phenotypes. A set of 16 quantitative trait loci (QTLs) containing 54 SNPs and 34 corresponding candidate genes associated with droopiness were then identified. Among these, CsEXL3 (EXORDIUM-LIKE 3) from Chromosome 1 emerged as a candidate gene. Further investigations revealed that silencing CsEXL3 in tea plants resulted in weaker vascular cell malformation and brassinosteroid-induced leaf droopiness. Additionally, brassinosteroid signal factor CsBES1.2 was proved to participate in CsEXL3-induced droopiness and vascular cell malformation via using the CsBES1.2-silencing tea plant. Notably, CsBES1.2 bound on the E-box of CsEXL3 promoter to transcriptionally activate CsEXL3 expression as CUT&TAG based ChIP-qPCR and ChIP-seq suggested in vivo as well as EMSA and Y1H indicated in vitro. Furthermore, CsEXL3 instead of CsBES1.2 decreased lignin content and the expressing levels of lignin biosynthesis genes. Overall, our findings suggest that CsEXL3 regulates droopy leaves, partially through the transcriptional activation of CsBES1.2, with the potential to improve mechanical harvest efficiency in tea plantations.

Machine learning-enhanced echocardiography for screening coronary artery disease.

BACKGROUND: Since myocardial work (MW) and left atrial strain are valuable for screening coronary artery disease (CAD), this study aimed to develop a novel CAD screening approach based on machine learning-enhanced echocardiography. METHODS: This prospective study used data from patients undergoing coronary angiography, in which the novel echocardiography features were extracted by a machine learning algorithm. A total of 818 patients were enrolled and randomly divided into training (80%) and testing (20%) groups. An additional 115 patients were also enrolled in the validation group. RESULTS: The superior diagnosis model of CAD was optimized using 59 echocardiographic features in a gradient-boosting classifier. This model showed that the value of the receiver operating characteristic area under the curve (AUC) was 0.852 in the test group and 0.834 in the validation group, with high sensitivity (0.952) and low specificity (0.691), suggesting that this model is very sensitive for detecting CAD, but its low specificity may increase the high false-positive rate. We also determined that the false-positive cases were more susceptible to suffering cardiac events than the true-negative cases. CONCLUSIONS: Machine learning-enhanced echocardiography can improve CAD detection based on the MW and left atrial strain features. Our developed model is valuable for estimating the pre-test probability of CAD and screening CAD patients in clinical practice. TRIAL REGISTRATION: Registered as NCT03905200 at ClinicalTrials.gov. Registered on 5 April 2019.

Piezoelectric deformable mirror driven by unimorph actuator arrays on multi-spatial layers.

In order to reduce the cost of the piezo actuator array deformable mirror (DM), a piezoelectric DM driven by unimorph actuator arrays on multi-spatial layers is proposed. The actuator density can be multiplied by increasing the spatial layers of the actuator arrays. A low-cost DM prototype with 19 unimorph actuators located on three spatial layers is developed. The unimorph actuator can generate a wavefront deformation up to 11 µm at an operating voltage of 50 V. The DM can reconstruct typical low-order Zernike polynomial shapes accurately. The mirror can be flattened to 0.058 µm in RMS. Furthermore, a focal spot close to Airy spot is obtained in the far field after the aberrations of the adaptive optics testing system being corrected.

Identification of QTL controlling volatile terpene contents in tea plant (Camellia sinensis) using a high-aroma 'Huangdan' x 'Jinxuan' F1 population.

Aroma is an important factor affecting the character and quality of tea. The improvement of aroma trait is a crucial research direction of tea plant breeding. Volatile terpenes, as the major contributors to the floral odors of tea products, also play critical roles in the defense responses of plants to multiple stresses. However, previous studies have largely focused on the aroma formation during the manufacture of tea or the comparison of raw tea samples. The mechanisms causing different aroma profiles between tea cultivars have remained underexplored. In the current study, a high-density genetic linkage map of tea plant was constructed based on an F1 population of 'Huangdan' × 'Jinxuan' using genotyping by sequencing. This linkage map covered 1754.57 cM and contained 15 linkage groups with a low inter-marker distance of 0.47 cM. A total of 42 QTLs associated with eight monoterpene contents and 12 QTLs associated with four sesquiterpenes contents were identified with the average PVE of 12.6% and 11.7% respectively. Furthermore, six candidate genes related to volatile terpene contents were found in QTL cluster on chromosome 5 by RNA-seq analysis. This work will enrich our understanding of the molecular mechanism of volatile terpene biosynthesis and provide a theoretical basis for tea plant breeding programs for aroma quality improvement.

Comprehensive analysis and expression profiles of the AP2/ERF gene family during spring bud break in tea plant (Camellia sinensis).

BACKGROUND: AP2/ERF transcription factors (AP2/ERFs) are important regulators of plant physiological and biochemical metabolism. Evidence suggests that AP2/ERFs may be involved in the regulation of bud break in woody perennials. Green tea is economically vital in China, and its production value is significantly affected by the time of spring bud break of tea plant. However, the relationship between AP2/ERFs in tea plant and spring bud break remains largely unknown. RESULTS: A total of 178 AP2/ERF genes (CsAP2/ERFs) were identified in the genome of tea plant. Based on the phylogenetic analysis, these genes could be classified into five subfamilies. The analysis of gene duplication events demonstrated that whole genome duplication (WGD) or segmental duplication was the primary way of CsAP2/ERFs amplification. According to the result of the Ka/Ks value calculation, purification selection dominated the evolution of CsAP2/ERFs. Furthermore, gene composition and structure analyses of CsAP2/ERFs indicated that different subfamilies contained a variety of gene structures and conserved motifs, potentially resulting in functional differences among five subfamilies. The promoters of CsAP2/ERFs also contained various signal-sensing elements, such as abscisic acid responsive elements, light responsive elements and low temperature responsive elements. The evidence presented here offers a theoretical foundation for the diverse functions of CsAP2/ERFs. Additionally, the expressions of CsAP2/ERFs during spring bud break of tea plant were analyzed by RNA-seq and grouped into clusters A-F according to their expression patterns. The gene expression changes in clusters A and B were more synchronized with the spring bud break of tea plant. Moreover, several potential correlation genes, such as D-type cyclin genes, were screened out through weighted correlation network analysis (WGCNA). Temperature and light treatment experiments individually identified nine candidate CsAP2/ERFs that may be related to the spring bud break of tea plant. CONCLUSIONS: This study provides new evidence for role of the CsAP2/ERFs in the spring bud break of tea plant, establishes a theoretical foundation for analyzing the molecular mechanism of the spring bud break of tea plant, and contributes to the improvement of tea cultivars.

The unrevealed 3D morphological evolution of annealed nanoporous thin films.

Nanoporous materials (sub-10 nm in diameter) have potential applications in chips, biosensors, thermoelectrics, desalination and other fields due to their large surface-to-volume ratio. Thermal annealing is a preferred technique to precisely control the ultra-fine nanopore size. Here, the 3D morphological evolution of a membrane with periodic nanopores by thermal annealing is studied. It is found that the evolution is determined by the combination of the membrane thickness, the initial nanopore radius and the periodic length of the porous pattern, rather than the previously suggested ratio between the membrane thickness and pore radius. High-temperature annealing experiments and molecular dynamics simulations are performed to confirm the rationality of the newly proposed model. Energy analysis demonstrates that surface energy minimization is the driving force of the morphological evolution. The local minimum of energy in the new model provides the possibility of thermal stability of nanoporous silicon as a thermoelectric material. This study provides guidance for the mass production of nanoporous membranes with high-temperature annealing.

Influence of Phase Transitions on Diffusive Molecular Transport Across Biological Membranes.

Phase transitions of lipid bilayer membranes should affect passive transport of molecules. While this hypothesis has been used to design drug-releasing thermosensitive liposomes, the effect has yet to be quantified. Herein, we use time-resolved second harmonic light scattering to measure transport of a molecular cation across membranes of unilamellar liposomes composed of the total lipid extract of E. coli from 9 °C to 36 °C, in which two distinct phase transitions (gel to liquid-disordered phase) have been identified. While the transport rate slowly increases with temperature as a diffusion process, dramatic jumps are observed at 14.7 °C and 27.6 °C, the known phase transitions. The transport rate constant measured as (7.3±0.8)×10-3  s-1 in the liquid-disordered phase at 36 °C is 35-times faster than (2.1±0.2)×10-4  s-1 of the gel phase at 9 °C. For the mixed-phase between these two phases, the measured rates are consistent with a structure of gel domains among a liquid-disordered bulk.

Quantitative Modeling of Electron Dynamics and the Effect of Diffusion in Photosensitized Semiconductor Nanocomposites.

Photosensitized semiconducting nanomaterials have received considerable attention because of their applications in photocatalytic and photoelectronic devices. In such systems, photoexcited electrons with sufficiently high energies can be injected into the conduction band (CB) of an adjacent semiconductor. These excited electrons are subjected to various physical processes that can lead to their annihilation before exercising their catalytic/electric functions, and the efficiency of the photosensitized functions depends on the quantity of CB electrons produced and how long they remain near the surface region of the semiconductor. The rise and decay of photoexcited electrons in the semiconductor CB can be probed with transient IR absorption (TA), which was first demonstrated by Lian and co-workers. Results from various laboratories have since revealed that electrons appear in the CB following the excitation of the photosensitizer in tens to hundreds of femtoseconds and that the decay of the CB electrons typically exhibits multiple exponentials on varying ultrafast time scales. The size of the semiconductor nanoparticle appears to influence the diffusion of the CB electrons and thus their lifetimes. In all studies reported, the observed multiexponential decays have been analyzed and interpreted using purely phenomenological models, in which the individual decays were intuitively assigned to one specific relaxation or loss process. In reality, however, each exponential decay can be a convolution of multiple physical processes. In this Account, we report a universally applicable physical model, constructed by including all known electron dynamic processes, to quantitatively account for the multiexponential decays. We characterize the model as universal, as it can be used to analyze our own TA measurements, as well as data acquired in other laboratories. In our study of TiO2 nanorods photosensitized by Ag platelets, we demonstrate that each of the observed triple-exponential decays corresponds to a convolution of several physical decay processes occurring on similar time scales. The rate of each of the processes can be deconvoluted and determined to construct a complete, physically based model to assess the most important question: How many CB electrons are near the semiconductor surface region and what is their lifetime?The size of the semiconductor is an important consideration. Intuitively, as the semiconductor volume increases, there is more room for CB electrons to diffuse around, which increases their lifetime as annihilation occurs primarily at the surface. Indeed, Tachiya and co-workers previously reported that this lifetime increases with particle size. Nevertheless, while CB electrons live longer in the bulk of the particle, they are only useful when they are at the surface. Overall, what really matters is the CB electrons near the surface region, where the photosensitized functions actually occur. In applying our model to analyze the previously reported size-dependent Au/TiO2 results, we successfully reproduced the observation that larger semiconductor nanoparticles lengthen the lifetime of CB electrons because of diffusion into the bulk. More importantly, however, our model reveals that the size of the semiconductor has almost no influence on the retention of CB electrons near the semiconductor surface. This information is only revealed when all physical processes are quantitatively taken into account for the observed electron dynamics, which is not feasible with a phenomenological approach.

Characterization of white-light non-diffracting beams generated using a deformable mirror.

White-light non-diffraction beams such as Airy beam and Bessel beam have potential applications in multispectral imaging and micromanipulation. Generation of white-light Airy beam and Bessel beam with high quality and high efficiency still remains challenging for conventional diffractive or refractive optics which suffers from significant chromatic dispersion. In this paper, both high-quality white-light Airy beam and Bessel beam are generated using a deformable mirror by modulating the incident LED beam with tunable cubic and conical wavefronts. The main lobe of the generated white-light non-diffraction beams does not suffer from chromatic dispersion along the propagation. The results also show that the generation of the white-light Bessel beam has higher requirements for spatial coherence than white-light Airy beams. Our work expands the understanding of the white-light non-diffraction beams and paves the way for the applications.

Indole Facilitates Antimicrobial Uptake in Bacteria.

Indole signaling in bacteria plays an important role in antibiotic resistance, persistence, and tolerance. Here, we used the nonlinear optical technique, second-harmonic light scattering (SHS), to examine the influence of exogenous indole on the bacterial uptake of the antimicrobial quaternary ammonium cation (qac), malachite green. The transport rates of the antimicrobial qac across the individual membranes of Escherichia coli and Pseudomonas aeruginosa, as well as liposomes composed of the polar lipid extract of E. coli, were directly measured using time-resolved SHS. Whereas exogenous indole was shown to induce a 2-fold increase in the transport rate of the qac across the cytoplasmic membranes of the wild-type bacteria, it had no influence on a knockout strain of E. coli lacking the tryptophan-specific transport protein (Δmtr). Likewise, indole did not affect the transport rate of the qac diffusing across the liposome membrane. Our findings suggest that indole increases the bacterial uptake of antimicrobials through an interaction with the Mtr permease.

Ag nanoplatelets as efficient photosensitizers for TiO2 nanorods.

The lifetime for injecting hot electrons generated in Ag nanoplatelets to nearby TiO2 nanorods was measured with ultrafast transient IR absorption to be 13.1 ± 1.5 fs, which is comparable to values previously reported for much smaller spherical Ag nanoparticles. Although it was shown that the injection rate decreases as the particle size increases, this observation can be explained by the facts that (1) the platelet has a much larger surface to bulk ratio and (2) the platelet affords a much larger surface area for direct contact with the semiconductor. These two factors facilitate strong Ag-TiO2 coupling (as indicated by the observed broadened surface plasmon resonance band of Ag) and can explain why Ag nanoplatelets have been found to be more efficient than much smaller Ag nanoparticles as photosensitizers for photocatalytic functions. The fast injection rate, together with a stronger optical absorption in comparison with Au and dye molecules, make Ag nanoplatelets a preferred photosensitizer for wide bandgap semiconductors.

Non-Volatile Metabolic Profiling and Regulatory Network Analysis in Fresh Shoots of Tea Plant and Its Wild Relatives.

There are numerous non-volatile metabolites in the fresh shoots of tea plants. However, we know little about the complex relationship between the content of these metabolites and their gene expression levels. In investigating this, this study involved non-volatile metabolites from 68 accessions of tea plants that were detected and identified using untargeted metabolomics. The tea accessions were divided into three groups from the results of a principal component analysis based on the relative content of the metabolites. There were differences in variability between the primary and secondary metabolites. Furthermore, correlations among genes, gene metabolites, and metabolites were conducted based on Pearson's correlation coefficient (PCC) values. This study offered several significant insights into the co-current network of genes and metabolites in the global genetic background. Thus, the study is useful for providing insights into the regulatory relationship of the genetic basis for predominant metabolites in fresh tea shoots.

Determination of bacterial surface charge density via saturation of adsorbed ions.

Bacterial surface charge is a critical characteristic of the cell's interfacial physiology that influences how the cell interacts with the local environment. A direct, sensitive, and accurate experimental technique capable of quantifying bacterial surface charge is needed to better understand molecular adaptations in interfacial physiology in response to environmental changes. We introduce here the method of second-harmonic light scattering (SHS), which is capable of detecting the number of molecular ions adsorbed as counter charges on the exterior bacterial surface, thereby providing a measure of the surface charge. In this first demonstration, we detect the small molecular cation, malachite green, electrostatically adsorbed on the surface of representative strains of Gram-positive and Gram-negative bacteria. Surprisingly, the SHS-deduced molecular transport rates through the different cellular ultrastructures are revealed to be nearly identical. However, the adsorption saturation densities on the exterior surfaces of the two bacteria were shown to be characteristically distinct. The negative charge density of the lipopolysaccharide coated outer surface of Gram-negative Escherichia coli (6.6 ± 1.3 nm-2) was deduced to be seven times larger than that of the protein surface layer of Gram-positive Lactobacillus rhamnosus (1.0 ± 0.2 nm-2). The feasibility of SHS-deduced bacterial surface charge density for Gram-type differentiation is presented.

Silencing circRNA LRP6 down-regulates PRMT1 to improve the streptozocin-induced pancreatic ß-cell injury and insulin secretion by sponging miR-9-5p.

Due to the sedentary lifestyles of people, the number of obese people is increasing alarmingly, which leads to the high prevalence of diabetes mellitus (DM). It was reported that circularRNA (circRNA) LRP6 was upregulated in HG-treated mesangial cells, and it could regulate high glucose-induced cell injury via sponging miR-205. Thus, the aim of this study was to explore the underlying pathogenesis of DM. Streptozocin (STZ) was used to stimulate the in vitro model of pancreatic ß-cell injury. Then, quantitative reverse transcription polymerase chain reaction (RT-qPCR) and methyl thiazolyl tetrazolium (MTT) assay were used to evaluate the expression of circLRP6 and the cell viability in STZ-challenged INS-1 cells, respectively. After knocking down circLRP6, the cell viability and apoptosis were respectively measured by MTT and TdT-mediated dUTP nick-end labeling (TUNEL) staining, and insulin release and oxidative stress were respectively measured by enzyme-linked immunosorbent assay (ELISA) and corresponding kits. After the interactions among circLRP6, PRMT1, and miR-9-5p were predicted and confirmed, the above mentioned assays were conducted again. The expression of circLRP6 was elevated while cell viability was decreased after INS-1 cells were exposed to STZ. Silencing circLRP6 resulted in an increase in the cell viability, a decrease in the cell apoptosis, together with more insulin release. The circLRP6/miR-9-5p/PRMT1 regulatory network was then confirmed, which affected the cell viability, apoptosis, insulin release, and oxidative stress in STZ-challenged INS-1 cells. In conclusion, this study first provides evidence that the circLRP6/miR-9-5p/PRMT1 regulatory network can affect STZ-induced cell viability, oxidative stress, and insulin secretion in INS-l cells, which can further impact the progression of diabetes.

PAIP 2019: Liver cancer segmentation challenge.

Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal of the platform is to construct a high-quality pathology learning data set that will allow greater accessibility. The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assisted Intervention Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets. The goal of the challenge was to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs). Additionally, the PAIP of this year attempted to address potential future problems of AI applicability in clinical settings. In the challenge, participants were asked to use analytical data and statistical metrics to evaluate the performance of automated algorithms in two different tasks. The participants were given the two different tasks: Task 1 involved investigating Liver Cancer Segmentation and Task 2 involved investigating Viable Tumor Burden Estimation. There was a strong correlation between high performance of teams on both tasks, in which teams that performed well on Task 1 also performed well on Task 2. After evaluation, we summarized the top 11 team's algorithms. We then gave pathological implications on the easily predicted images for cancer segmentation and the challenging images for viable tumor burden estimation. Out of the 231 participants of the PAIP challenge datasets, a total of 64 were submitted from 28 team participants. The submitted algorithms predicted the automatic segmentation on the liver cancer with WSIs to an accuracy of a score estimation of 0.78. The PAIP challenge was created in an effort to combat the lack of research that has been done to address Liver cancer using digital pathology. It remains unclear of how the applicability of AI algorithms created during the challenge can affect clinical diagnoses. However, the results of this dataset and evaluation metric provided has the potential to aid the development and benchmarking of cancer diagnosis and segmentation.

Metabolite signatures of diverse Camellia sinensis tea populations.

The tea plant (Camellia sinensis) presents an excellent system to study evolution and diversification of the numerous classes, types and variable contents of specialized metabolites. Here, we investigate the relationship among C. sinensis phylogenetic groups and specialized metabolites using transcriptomic and metabolomic data on the fresh leaves collected from 136 representative tea accessions in China. We obtain 925,854 high-quality single-nucleotide polymorphisms (SNPs) enabling the refined grouping of the sampled tea accessions into five major clades. Untargeted metabolomic analyses detect 129 and 199 annotated metabolites that are differentially accumulated in different tea groups in positive and negative ionization modes, respectively. Each phylogenetic group contains signature metabolites. In particular, CSA tea accessions are featured with high accumulation of diverse classes of flavonoid compounds, such as flavanols, flavonol mono-/di-glycosides, proanthocyanidin dimers, and phenolic acids. Our results provide insights into the genetic and metabolite diversity and are useful for accelerated tea plant breeding.