VPS18-regulated vesicle trafficking controls the secretion of pectin and its modifying enzyme during pollen tube growth in Arabidopsis.

In eukaryotes, homotypic fusion and vacuolar protein sorting (HOPS) as well as class C core vacuole/endosome tethering (CORVET) are evolutionarily conserved membrane tethering complexes that play important roles in lysosomal/vacuolar trafficking. Whether HOPS and CORVET control endomembrane trafficking in pollen tubes, the fastest growing plant cells, remains largely elusive. In this study, we demonstrate that the four core components shared by the two complexes, Vacuole protein sorting 11 (VPS11), VPS16, VPS33, and VPS18, are all essential for pollen tube growth in Arabidopsis thaliana and thus for plant reproduction success. We used VPS18 as a representative core component of the complexes to show that the protein is localized to both multivesicular bodies (MVBs) and the tonoplast in a growing pollen tube. Mutant vps18 pollen tubes grew more slowly in vivo, resulting in a significant reduction in male transmission efficiency. Additional studies revealed that membrane fusion from MVBs to vacuoles is severely compromised in vps18 pollen tubes, corroborating the function of VPS18 in late endocytic trafficking. Furthermore, vps18 pollen tubes produce excessive exocytic vesicles at the apical zone and excessive amounts of pectin and pectin methylesterases in the cell wall. In conclusion, this study establishes an additional conserved role of HOPS/CORVET in homotypic membrane fusion during vacuole biogenesis in pollen tubes and reveals a feedback regulation of HOPS/CORVET in the secretion of cell wall modification enzymes of rapidly growing plant cells.

Effects of school myopia management measures on myopia onset and progression among Chinese primary school students.

BACKGROUND: Schools play an organizational role in managing myopia-related behavioral habits among students. We evaluated the effects of school myopia management measures on myopia onset and progression in a school-based prospective 1-year observational study. METHODS: In total, 8319 children from 26 elementary schools were included. Online questionnaire completed by a parent, in which school myopia management experience including outdoor activities in recess or physical education class, teachers' supervision, and teaching facilities. Variables were defined as implemented well or poorly, according to the Comprehensive Plan to Prevent Myopia among Children and Teenagers. Children underwent ophthalmic examinations, and the incidence and progression of myopia from 2019 to 2020 were estimated. Multilevel logistic regression models were constructed to analyze the association between school management measures and myopia development in 8,9 years and 10,11 years students. RESULTS: From 2019 to 2020, the incidence of myopia among primary school students was 36.49%; the mean difference of spherical equivalent in myopic children was - 0.29 ± 1.22 diopters. The risk of incident myopia was reduced by 20% in 8,9 years participants with well-implemented class recess compared with those with poorly implemented class recess (adjusted odds ratio [aOR]: 0.80, p = 0.032). PE outdoor time was significantly associated with myopia incidence in 10,11 years students (aOR: 0.76, p = 0.043). Compared with poorly implemented reading and writing posture, desk and chair height, 10,11 participants with well-implemented desk and chair height were less likely to have rapid myopic progression (p = 0.029, p = 0.022). CONCLUSION: In Shanghai, children's myopia is associated with better implementation of school myopia management measures. The present findings suggest that outdoor activities during class recess or PE class, providing suitable desks and chairs, and adequate instruction in reading and writing postures might protect against pathological eye growth. An age-specific myopia prevention and control programs in school is of primary importance.

Convolutional Neural Networks for Classifying Cervical Cancer Types Using Histological Images.

Cervical cancer is the most common cancer among women worldwide. The diagnosis and classification of cancer are extremely important, as it influences the optimal treatment and length of survival. The objective was to develop and validate a diagnosis system based on convolutional neural networks (CNN) that identifies cervical malignancies and provides diagnostic interpretability. A total of 8496 labeled histology images were extracted from 229 cervical specimens (cervical squamous cell carcinoma, SCC, n = 37; cervical adenocarcinoma, AC, n = 8; nonmalignant cervical tissues, n = 184). AlexNet, VGG-19, Xception, and ResNet-50 with five-fold cross-validation were constructed to distinguish cervical cancer images from nonmalignant images. The performance of CNNs was quantified in terms of accuracy, precision, recall, and the area under the receiver operating curve (AUC). Six pathologists were recruited to make a comparison with the performance of CNNs. Guided Backpropagation and Gradient-weighted Class Activation Mapping (Grad-CAM) were deployed to highlight the area of high malignant probability. The Xception model had excellent performance in identifying cervical SCC and AC in test sets. For cervical SCC, AUC was 0.98 (internal validation) and 0.974 (external validation). For cervical AC, AUC was 0.966 (internal validation) and 0.958 (external validation). The performance of CNNs falls between experienced and inexperienced pathologists. Grad-CAM and Guided Gard-CAM ensured diagnoses interpretability by highlighting morphological features of malignant changes. CNN is efficient for histological image classification tasks of distinguishing cervical malignancies from benign tissues and could highlight the specific areas of concern. All these findings suggest that CNNs could serve as a diagnostic tool to aid pathologic diagnosis.

Ion Separation Together with Water Purification via a New Type of Nanotube: A Molecular Dynamics Study.

We propose a CNT-based concentric twin tube (CTT) as nanochannels for both water purification and ion separation at the nanoscale. In the model, a source reservoir dealing with the solution connects three containers via the CTT that has three subchannels for mass transfer. Before entering the three subchannels, the solution in the separating zone will form three layers (the aqua cations, water, and the aqua anions, respectively) by applying a charged capacitor with the two electrodes parallel to the flow direction of the solution. Under an electric field with moderate intensity, the three subchannels in the CTT have stable configurations for mass transfer. Since the water and the two types of aqua ions are collected by three different containers, the present model can realize both ion separation and water purification. The mass transfer in the subchannels will be sped up by an external pressure exerted on the solution in the source reservoir. The physical properties of the model, e.g., water purification speed, are analyzed with respect to the effects of the electric field, the size of CTT, and the concentration of solute, such as NaCl.

Characterizing an Uncertainty Diagram and Kirkwood-Dirac Nonclassicality Based on Discrete Fourier Transform.

In this paper, we investigate an uncertainty diagram and Kirkwood-Dirac (KD) nonclassicality based on discrete Fourier transform (DFT) in a d-dimensional system. We first consider the uncertainty diagram of the DFT matrix, which is a transition matrix from basis A to basis B. Here, the bases A, B are not necessarily completely incompatible. We show that for the uncertainty diagram of the DFT matrix, there is no "hole" in the region of the (nA,nB) plane above and on the line nA+nB=d+1. Then, we present where the holes are in the region strictly below the line and above the hyperbola nAnB=d. Finally, we provide an alternative proof of the conjecture about KD nonclassicality based on DFT.

Multilevel metabolic engineering of Bacillus licheniformis for de novo biosynthesis of 2-phenylethanol.

Due to its pleasant rose-like scent, 2-phenylethanol (2-PE) has been widely used in the fields of cosmetics and food. Microbial production of 2-PE offers a natural and sustainable production process. However, the current bioprocesses for de novo production of 2-PE suffer from low titer, yield, and productivity. In this work, a multilevel metabolic engineering strategy was employed for the high-level production of 2-PE. Firstly, the native alcohol dehydrogenase YugJ was identified and characterized for 2-PE production via genome mining and gene function analysis. Subsequently, the redirection of carbon flux into 2-PE biosynthesis by combining optimization of Ehrlich pathway, central metabolic pathway, and phenylpyruvate pathway enabled the production of 2-PE to a titer of 1.81 g/L. Specifically, AroK and AroD were identified as the rate-limiting enzymes of 2-PE production through transcription and metabolite analyses, and overexpression of aroK and aroD efficiently boosted 2-PE synthesis. The precursor competing pathways were blocked by eliminating byproduct formation pathways and modulating the glucose transport system. Under the optimal condition, the engineered strain PE23 produced 6.24 g/L of 2-PE with a yield and productivity of 0.14 g/g glucose and 0.13 g/L/h, respectively, using a complex medium in shake flasks. This work achieves the highest titer, yield, and productivity of 2-PE from glucose via the phenylpyruvate pathway. This study provides a promising platform that might be widely useful for improving the production of aromatic-derived chemicals.

Nuclear DNA replicates during zygote development in Arabidopsis and Torenia fournieri.

The progression of the cell cycle is continuous in most cells, but gametes (sperm and egg cells) exhibit an arrest of the cell cycle to await fertilization to form a zygote, which then continues through the subsequent phases to complete cell division. The phase in which gametes of flowering plants arrest has been a matter of debate, since different phases have been reported for the gametes of different species. In this study, we reassessed the phase of cell-cycle arrest in the gametes of two species, Arabidopsis (Arabidopsis thaliana) and Torenia fournieri. We first showed that 4', 6-diamidino-2-phenylindole staining was not feasible to detect changes in gametic nuclear DNA in T. fournieri. Next, using 5-ethynyl-2'-deoxyuridine (EdU) staining that detects DNA replication by labeling the EdU absorbed by deoxyribonucleic acid, we found that the replication of nuclear DNA did not occur during gamete development but during zygote development, revealing that the gametes of these species have a haploid nuclear DNA content before fertilization. We thus propose that gametes in the G1 phase participate in the fertilization event in Arabidopsis and T. fournieri.

Self-assembly for preparing nanotubes from monolayer graphyne ribbons on a carbon nanotube.

Graphyne nanotube (GNT), as a promising one-dimensional carbon material, attracts extensive attention in recent years. However, the synthesis of GNT is still challenging even in the laboratory. This study reveals the feasibility of fabricating a GNT by self-assembling a monolayer graphyne (GY) ribbon on a carbon nanotube (CNT) via theoretical and numerical analysis. Triggered by the van der Waals force from the CNT, a GY ribbon near the tube first winds upon the tube and then conditionally self-assembles to form a GNT. The self-assembly process and result are heavily influenced by the ambient temperature, which indicates the thermal vibration of the nanosystem. Molecular dynamic simulation results address the temperature range conducive to successful self-assembly. Different types of GNTs, e.g.α-,ß-, andγ-GNTs with specified chirality (armchair, zigzag, and chiral), length, and radius, can be obtained via self-assembly by controlling the geometry of the GY ribbons and temperature. The present theoretical understanding is helpful for fabricating GNTs with predefined morphology.

GDF11 knockdown downregulates SMURF1 to inhibit breast cancer progression by activation of p53 and inactivation of ERα signaling.

Breast cancer (BC) is a prevalent neoplasm that occurs in women all over the world. Growth and differentiation factor 11 (GDF11) plays an essential role in cancer progression. This study focused on investigating the biological role and underlying mechanisms of GDF11 in BC. We detected the expression of GDF11 in 27 patients with BC and BC cell lines. Kaplan-Meier plotter was employed to analyze the relationship between GDF11 expression and overall survival (OS) of BC patients. The proliferative, migratory, invasive, and apoptotic abilities of T47D cells were examined. Correlation analysis of GDF11 with Smad ubiquitination regulatory factor 1 (SMURF1) was conducted. The association between GDF11 and the p53 pathway was analyzed by western blot and PFT-α (a p53 inhibitor)-mediated rescue assays. A brief analysis of the role of estrogen receptor alpha (ERα) signaling in BC progression was performed. The results showed that GDF11 was increased in BC tissues and cell lines, and the high expression of GDF11 was associated with the poor OS of BC patients. GDF11 knockdown inhibited the proliferation, migration, and invasion of T47D cells, but promoted cell apoptosis. Meanwhile, the GDF11 knockdown reduced the SMURF1 expression and invoked the p53 pathway activation. SMURF1 overexpression and PFT-α partially blocked the effects of GDF11 knockdown. In addition, GDF11 knockdown and SMURF1 silencing inhibited the activation of the ERα signaling pathway. In summary, GDF11 was involved in the progression of BC by regulating SMURF1-mediated p53 and ERα pathways, opening up a new way for BC treatment.

Asbestos-Induced Lung Cancer-Associated SNP rs13383928 Regulates PTH2R Expression in Lung Tissue.

BACKGROUND: Lung cancer is one of the most common malignant tumors, and asbestos exposure was suggested to contribute to a proportion of lung cancer cases. Previous genome-wide gene-environment interaction analysis reported that rs13383928 was associated with asbestos-related lung cancer. However, the mechanism of this association was still unclear. METHODS: In the present study, we retrieved the genotype data from the 1,000 Genomes Project on single-nucleotide polymorphisms (SNPs) surrounding rs13383928 and analyzed the linkage disequilibrium (LD) pattern of this region. Further functional genomics analyses were performed. RESULTS: The result indicated that no other SNPs were in LD with rs13383928, suggesting that rs13383928 is the causal one. The following dual luciferase assay disclosed that the T allele of rs13383928 presented significantly higher enhancer activity than G in lung cells, thus verifying that this SNP was functional in the lung. Through chromosome conformation capture, the PTH2R (parathyroid hormone 2 receptor)promoter was identified to interact with the segment surrounding rs13383928. By chromatin immunoprecipitation, it was observed that the region spanning rs13383928 could bind transcription factor FOXJ2 (forkhead box J2). CONCLUSION: Our functional genomics evidence supports a link between rs13383928 and asbestos-related lung cancer through regulating PTH2R.

Effects of Tearing Conditions on the Crack Propagation in a Monolayer Graphene Sheet.

The path of crack propagation in a graphene sheet is significant for graphene patterning via the tearing approach. In this study, we evaluate the fracture properties of pre-cracked graphene during the tearing process, with consideration of the effects of the aspect ratio, loading speed, loading direction, and ambient temperatures on the crack propagation in the monolayer sheet. Some remarkable conclusions are drawn based on the molecular dynamic simulation results, i.e., a higher loading speed may result in a complicated path of crack propagation, and the propagation of an armchair crack may be accompanied by sp carbon links at high temperatures. The reason for this is that the stronger thermal vibration reduces the load stress difference near the crack tip and, therefore, the crack tip can pass through the sp link. A crack propagates more easily along the zigzag direction than along the armchair direction. The out-of-plane tearing is more suitable than the in-plane tearing for graphene patterning. The path of crack propagation can be adjusted by changing the loading direction, e.g., a rectangular graphene ribbon can be produced by oblique tearing. This new understanding will benefit the application of graphene patterning via the tearing approach.

A competitive PCR-based method to detect a single copy of T-DNA insertion in transformants.

Gene function studies benefit from the availability of mutants. In plants, Agrobacterium-mediated genetic transformation is widely used to create mutants. These mutants, also called transformants, contain one or several transfer-DNA (T-DNA) copies in the host genome. Quantifying the copy number of T-DNA in transformants is beneficial to assess the number of mutated genes. Here, we developed a competitive polymerase chain reaction (PCR)-based method to detect a single copy of a T-DNA insertion in transformants. The competitor line BHK- -1 that contains a single copy of competitor BHK- (BHK, Basta, Hygromycin, Kanamycin-resistant genes) was crossed with test transformants and the genomic DNA of F1 plants was subjected to competitive PCR. By analyzing the gray ratio between two PCR products, we were able to determine whether or not the test transformants contained a single copy of T-DNA insertion. We also generated the control lines BHK±1:1 and BHK±2:1 , which contain the target (BHK+ ) and competitor (BHK- ) in a ratio of 1:1 and 2:1, respectively. The ratios of their PCR products are useful references for quantitative analysis. Overall, this method is reliable and simple in experimental manipulations and can be used as a substitute for Southern-blot analysis to identify a single copy of T-DNA insertion in transformants.

Efficiency of CNT-based rotation transmission nanosystem in water.

Carbon nanotubes (CNTs) have been widely used as the motor and rotor in a rotational transmission nanosystem (RTnS), whose function is to transfer the input rotational frequency of the motor into the output frequency of the rotor through motor-rotor interactions. A wide range of techniques has been explored to achieve a CNT-based RTnS with a stable and adjustable transmission. In this work, a CNT-based rotor is partly immersed into a water box and the associated water-rotor interaction leads to effective manipulation of the transmission efficiency of RTnS. Molecular dynamics simulations are performed on this new RTnS to investigate the dynamic response of the rotor and the local flow field near the water-rotor interface. Various parameters, including ambient temperature, tubes' radii, and volume fractions of water in the box (Vf) are examined for their effects on the rotational transmission efficiency. This study offers useful guidelines for the design of stable RTnS with controllable transmission efficiency.

Bonding few-layered graphene via collision with high-speed fullerenes.

Graphene, as a typical two-dimensional material, is popular in the design of nanodevices. The interlayer relative sliding of graphene sheets can significantly affect the effective bending stiffness of the few-layered graphene. For restricting the relative sliding, we adopted the atomic shot peening method to bond the graphene sheets together by ballistic C60 fullerenes from its two surfaces. Collision effects are evaluated via molecular dynamics simulations. Results obtained indicate that the fullerenes' incident velocity has an interval, in which the graphene sheet can be bonded after collision while no atoms on the fullerenes escaping from the graphene ribbon after collision. The limits of the interval increase with the layer number. Within a few picoseconds of collision, a stable carbon network is produced at an impacted area. The graphene sheets are bonded via the network and cannot slide relatively anymore. Conclusions are drawn to show the way of potential applications of the method in manufacturing a new graphene-based two-dimensional material that has a high out-of-plane bending stiffness.

Position effects of the graphene-origami actuators on the rotation of a CNT nanomotor.

This study designs a carbon nanotube (CNT)-based rotary nanomotor actuated by four graphene origami (G-ori) drivers with adjustable positions. When the drivers' tips have different contact states with the CNT rotor at a finite temperature, the rotor has different rotational states due to different interaction strength between the rotor and the tips. Using the molecular dynamics simulation approach, we study the effects of the drivers' position, such as the gaps between the rotor and the drivers' tips and their layout angles. Numerical results indicate that both the stable rotational frequency (SRF) and the rotational direction change with the layout angles. In an interval from -40° to -25°, the SRF increases monotonously. There also exists an angle interval in which the G-ori drivers fail to actuate the rotor's rotation. The gap offset leads to different SRF of the same rotor. Hence, one can design a rotary nanomotor with controllable rotation, which is critical for its applications in a nanomachine.

A method for designing tunable chiral mechanical carbon networks for energy storage.

A method is proposed for designing tunable chiral nano-networks using partly hydrogenated graphene ribbons and carbon nanotubes (CNTs). In the network, the hydrogenated graphene ribbons (HGRs) act as basic components, which connect each other via CNT joints. Each component contains two HGR segments and an internal graphene joint (G-J2) or CNT joint (CNT-J2). Since the two HGR segments are hydrogenated at opposite surfaces, they may wind in chiral about the internal joint to form a scroll (G-J2-scroll or CNT-J2-scroll) or about the two end joints to form CNT-J4-scrolls. In general, a G-J2-scroll is formed more easily than both a CNT-J4-scroll and a CNT-J2-scroll. Because of scrolling, the surface energy is reduced. This reduction is converted to and stored as deformation potential energy. By means of molecular-dynamics simulations, we studied the final configurations of two types of networks from the same components, the maximum shrinkage, and their capacity of energy storage for potential application of energy storage or as large-deformable components in a nano-device. The results indicate that the network reaches a stable state when the shrinkage reaches 70% of the two in-plane dimensions.

Self-Regulated Particle Swarm Multi-Task Optimization.

Population based search techniques have been developed and applied to wide applications for their good performance, such as the optimization of the unmanned aerial vehicle (UAV) path planning problems. However, the search for optimal solutions for an optimization problem is usually expensive. For example, the UAV problem is a large-scale optimization problem with many constraints, which makes it hard to get exact solutions. Especially, it will be time-consuming when multiple UAV problems are waiting to be optimized at the same time. Evolutionary multi-task optimization (EMTO) studies the problem of utilizing the population-based characteristics of evolutionary computation techniques to optimize multiple optimization problems simultaneously, for the purpose of further improving the overall performance of resolving all these problems. EMTO has great potential in solving real-world problems more efficiently. Therefore, in this paper, we develop a novel EMTO algorithm using a classical PSO algorithm, in which the developed knowledge transfer strategy achieves knowledge transfer between task by synthesizing the transferred knowledges from a selected set of component tasks during the updating of the velocities of population. Two knowledge transfer strategies are developed along with two versions of the proposed algorithm. The proposed algorithm is compared with the multifactorial PSO algorithm, the SREMTO algorithm, the popular multifactorial evolutionary algorithm and a classical PSO algorithm on nine popular single-objective MTO problems and six five-task MTO problems, which demonstrates its superiority.

Evolutionary Multi-Objective One-Shot Filter Pruning for Designing Lightweight Convolutional Neural Network.

Deep neural networks have achieved significant development and wide applications for their amazing performance. However, their complex structure, high computation and storage resource limit their applications in mobile or embedding devices such as sensor platforms. Neural network pruning is an efficient way to design a lightweight model from a well-trained complex deep neural network. In this paper, we propose an evolutionary multi-objective one-shot filter pruning method for designing a lightweight convolutional neural network. Firstly, unlike some famous iterative pruning methods, a one-shot pruning framework only needs to perform filter pruning and model fine-tuning once. Moreover, we built a constraint multi-objective filter pruning problem in which two objectives represent the filter pruning ratio and the accuracy of the pruned convolutional neural network, respectively. A non-dominated sorting-based evolutionary multi-objective algorithm was used to solve the filter pruning problem, and it provides a set of Pareto solutions which consists of a series of different trade-off pruned models. Finally, some models are uniformly selected from the set of Pareto solutions to be fine-tuned as the output of our method. The effectiveness of our method was demonstrated in experimental studies on four designed models, LeNet and AlexNet. Our method can prune over 85%, 82%, 75%, 65%, 91% and 68% filters with little accuracy loss on four designed models, LeNet and AlexNet, respectively.

Differential Evolution Based Layer-Wise Weight Pruning for Compressing Deep Neural Networks.

Deep neural networks have evolved significantly in the past decades and are now able to achieve better progression of sensor data. Nonetheless, most of the deep models verify the ruling maxim in deep learning-bigger is better-so they have very complex structures. As the models become more complex, the computational complexity and resource consumption of these deep models are increasing significantly, making them difficult to perform on resource-limited platforms, such as sensor platforms. In this paper, we observe that different layers often have different pruning requirements, and propose a differential evolutionary layer-wise weight pruning method. Firstly, the pruning sensitivity of each layer is analyzed, and then the network is compressed by iterating the weight pruning process. Unlike some other methods that deal with pruning ratio by greedy ways or statistical analysis, we establish an optimization model to find the optimal pruning sensitivity set for each layer. Differential evolution is an effective method based on population optimization which can be used to address this task. Furthermore, we adopt a strategy to recovery some of the removed connections to increase the capacity of the pruned model during the fine-tuning phase. The effectiveness of our method has been demonstrated in experimental studies. Our method compresses the number of weight parameters in LeNet-300-100, LeNet-5, AlexNet and VGG16 by 24×, 14×, 29× and 12×, respectively.

Ideal Oscillation of a Hydrogenated Deformable Rotor in a Gigahertz Rotation-Translation Nanoconverter at Low Temperatures.

It was discovered that large-amplitude axial oscillation can occur on a rotor with an internally hydrogenated deformable part (HDP) in a rotation-translation nanoconverter. The dynamic outputs of the system were investigated using molecular dynamics simulations. When an input rotational frequency (100 GHz > ω > 20 GHz) was applied at one end of the rotor, the HDP deformed under the centrifugal and van der Waals forces, which simultaneously led to the axial translation of the other end of the rotor. Except at too high an input rotational frequency (e.g., >100 GHz), which led to eccentric rotation and even collapse of the system, the present system could generate a periodic axial oscillation with an amplitude above 0.5 nm at a temperature below 50 K. In other ranges of temperature and amplitude, the oscillation dampened quickly due to the drastic thermal vibrations of the atoms. Furthermore, the effects of the hydrogenation scheme and the length of HDP on the equilibrium position, amplitude, and frequency of oscillation were investigated. The conclusions can be applied to the design of an ideal nano-oscillator based on the present rotation-translation converter model.