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Surgical procedures may affect the changes in gastric contents and volume, resulting in a potential risk of pulmonary aspiration. We aimed to assess the differences in preoperative and postoperative gastric contents and volume in patients undergoing endoscopic endonasal transsphenoidal surgery using ultrasound and determine the potential factors associated with intraoperative volume changes. This prospective study enrolled 71 adult patients diagnosed with pituitary adenoma. Semi-quantitative (Perlas score: grade 0, 1, or 2) and quantitative (cross-sectional area, CSA) ultrasound scans of the gastric antrum were performed before anaesthetic induction and postoperatively in semirecumbent and semirecumbent-right lateral positions. The postoperative median (interquartile [range]) antral CSA in both positions were 3.5 cm2 (3.3–3.7 [2.9–12.1]) and 3.7 cm2 (3.4–4.6 [2.9–26.6]), significantly higher than the preoperative values of 3.3 cm2 (3.1–3.4 [2.7–3.9]) and 3.4 cm2 (3.2–3.7 [2.8–5.7]), respectively (P=0.001). The postoperative estimated gastric volume was 20.2 ml (2.2–42.7 [0–316.7]), significantly larger than the preoperative value of 14.5 ml (0–32.6 [0–66.4]) (P=0.001). Seven (9.9%) patients showed significant volume changes, from preoperative grade 0 to postoperative grade 2 (estimated volume >1.5 ml.kg-1) (P=0.02). Logistic regression analysis revealed that gastroesophageal reflux, intraoperative irrigation volume, and anaesthesia duration were independent risk factors for significant intraoperative volume changes (PTrial registration: This prospective observational study was registered at http://www.chictr.org.cn (ChiCTR2100045110) on 7 April 2021.

Background The objective of this study was to investigate genetic variations and the relationships between these genetic variations and clinicopathological features of high-recurrence risk papillary thyroid carcinoma in a southern Chinese population. Methods One hundred sixty-eight patients of high-recurrence risk papillary thyroid carcinoma were recruited for this study from 2017 to 2018. Formalin-fixed paraffin-embedded tissue and the data of clinicopathological characteristics were all collected and analyzed from these patients. We used next-generation sequencing technology to investigate the targeted gene mutations and gene fusions of the pathology specimens. Results The frequency of candidate tumor driver gene mutation was 85.1% in 143 patients, including BRAF V600E mutation in 119 patients(70.8%), RET fusion in 13 patients(7.7%), TERT promoter mutations in 11 patients(6.5%), RAS (HRAS, NRAS, KRAS) gene mutations in 10 patients(6.0%), and other mutations involving TP53, PIK3CA, AKT1, PTEN and NTRK1. Concomitant presence of more than two genetic aberrations was seen in 27 patients (16.1%). Our study showed that BRAF V600E mutation is highly correlated with conventional PTC (p BRAF V600E and TERT promoter mutation duet was associated with older patient age (> 45, p = 0.003) and higher disease stage of III or IV (p = 0.002). RAS gene and BRAF V600E co-mutations were only seen in multifocal PTC (p = 0.015). Conclusion In our high-recurrence risk PTC cohort, most patients had more than one driver gene aberration. Coexistence of BRAF V600E with TERT promoter mutations or with RAS mutations were significantly correlated with worse clinicopathological characteristics.

Fault diagnosis of rotating machinery is of great significance to ensure the reliable operation of machinery and equipment. During the operation of mechanical equipment, there will be many noise sources that interfere with the vibration signal, and destroy the characteristic value of the vibration signal, which is difficult to fault diagnosis. Therefore, this paper proposes a multiscale convolutional neural network (MS-CNN) fault diagnosis algorithm. The algorithm has convolution kernels of multiple scales, which can extract the characteristics of vibration signals containing noise sources from different scales. Tested with a public data set containing noise, the fault diagnosis rate can be as high as 96% when the signal- to-noise ratio (the ratio of useful signal power S to noise power N) is -10 dB. Compared with the other four algorithms, the results show the superiority of MS-CNN.

In the field of knowledge graph reasoning, path reasoning based on reinforcement learning avoids using random walking methods and the inefficient search, but what follows is the false path problem. The amount of false paths is more than that of correct ones. The agent would usually reach the correct entity from the wrong paths first, and be more inclined to them in subsequent exploration. We propose to use curriculum learning to solve this problem: assuming that in the environment corresponding to the simple samples, the proportion of correct paths and the quality of paths are higher. The agent counters the sensitivity of RL models to false paths in the strategy by learning the basics knowledge out of simple sample sets. After a comprehensive evaluation on three KG datasets, our method is highly versatile and improves performance in knowledge-based question answering with almost no additional training time. And taking MINERVA as the baseline, the MRR index has increased by 1.3%, 3.7% on datasets WN18RR, NELL-995 respectively.

The convolutional neural networks (CNNs), such as U-Net, have shown competitive performance in automatic extraction of buildings from very high-resolution (VHR) remotely sensed imagery. However, due to the unstable multi-scale context aggregation, the insufficient combination of multi-level features, and the lack of consideration about semantic boundary, most existing CNNs produce incomplete segmentation for large-scale buildings and result in predictions with huge uncertainty at building boundaries. This paper presents a novel network embedded a special boundary-aware loss, called Boundary-aware Refined Network (BARNet), to address the gap above. The unique property of BARNet is the gated-attention refined fusion unit (GARFU), the denser atrous spatial pyramid pooling (DASPP) module, and the boundary-aware (BA) loss. The performance of BARNet is tested on two popular benchmark datasets that include various urban scenes and diverse patterns of buildings. Experimental results demonstrate that the proposed method outperforms several state-of-the-art (SOTA) benchmark approaches in both visual interpretation and quantitative evaluations.

A new dynamic model for a two-input two-path split torque transmission system which considers meshing error, time-varying meshing stiffness, and meshing-in impact is proposed. Time-varying meshing stiffness and meshing-in impact of each gear pair are accurately calculated based on tooth contact analysis and loaded tooth contact analysis. Equivalent displacements of eccentricity error and installation error along the meshing line of second- and third-stages gears are derived. The modified tooth surface of a third-stage double-helical gear is obtained by optimizing the amplitude of static loaded transmission error and meshing-in impact via nondominated sorting genetic algorithm-II (NSGA-II). Influence of modification on load sharing and dynamic load characteristics of split torque transmission system is investigated. The results indicate that the system’s dynamic meshing force increases when meshing-in impact is accounted for, which is unfavorable for the transmission. Following the modification of a double-helical gear, the dynamic load characteristics of the split torque transmission system are significantly improved, while its load sharing characteristics are improved to a certain extent.

Low-resolution face recognition is a challenging problem in the face recognition algorithm. Most face recognition algorithms have a significant reduction in recognition accuracy when facing low-resolution faces. In this paper, we propose a DCR(Deep Coupled ResNet) model based on super-resolution reconstruction, named SR-DCR model, to tackle this challenging. Firstly, we designed a super-resolution face reconstruction model based on the facial feature. The "perceptual loss + classification loss" is used as the loss function of the reconstructed network. And the facial feature is used to constrain the reconstruction process to make the reconstructed face more favorable for recognition. Then a series of improvements and adjustments were made to the DCR model. Improved the network structure of the main network, and its cross-layer connection method was changed to a close connection, which further promoted feature propagation in the network and enhanced feature reusability; The loss function of the main network is improved, and the improved "triplet loss" is used as a loss function to directly optimize the facial feature between high resolution and low-resolution, further enhancing the high and low resolution. The aggregation between facial features; at the same time, the training method of the DCR model is adjusted, and the steps of joint training between the main network and branch network are added to further optimize DCR network parameters. Finally, the super-resolution reconstruction and the improved DCR model are combined to recognize low-resolution faces. The performance of the proposed algorithm is compared with that of other advanced methods on LFW and SCFace datasets. Experimental results show that the algorithm improves the recognition rate by 3.5% based on DCR. Effectively improve the accuracy of face recognition at low resolution.

As an inevitable phenomenon in most optical remote-sensing images, the effect of shadows is prominent in urban scenes. Shadow detection is critical for exploiting shadows and recovering the distorted information. Unfortunately, in general, automatic shadow detection methods for urban aerial images cannot achieve satisfactory performance due to the limitation of feature patterns and the lack of consideration of non-local contextual information. To address this challenging problem, the global-spatial-context-attention (GSCA) module was developed to self-adaptively aggregate all global contextual information over the spatial dimension for each pixel in this paper. The GSCA module was embedded into a modified U-shaped encoder–decoder network that was derived from the UNet network to output the final shadow predictions. The network was trained on a newly created shadow detection dataset, and the binary cross-entropy (BCE) loss function was modified to enhance the training procedure. The performance of the proposed method was evaluated on several typical urban aerial images. Experiment results suggested that the proposed method achieved a better trade-off between automaticity and accuracy. The F1-score, overall accuracy, balanced-error-rate, and intersection-over-union metrics of the proposed method were higher than those of other state-of-the-art shadow detection methods.

Tarihsel olarak Hindistan denizaşırı Britanya İmparatorluğu’nun kurulması ve sürdürülmesinde çok önemli bir rol oynamıştır. Hindistan'ın fethi aracılığıyla İngilizler sadece büyük maddi zenginlik elde etmek ve sanayi sermayesi birikimini tamamlamakla kalmamış, aynı zamanda sömürgeci yayılmasını kolaylaştırmak için büyük bir yetenekli insan kaynağı havuzu da temin etmiştir. Günümüzde, Çin diasporasının ardından en büyük ikinci diasporayı oluşturan denizaşırı Hintlilerin toplam sayısı 30 milyonu aşmış durumdadır. Bu muazzam göçmen grubu ve denizaşırı dağılımı, İngilizlerin Hindistan'ı fethi ve insan kaynaklarının temini ile Hindistan'a ihracatından ayrılmazdı. İngiliz sömürge yöneticileri, askere alma, hizmet personelinin işe alımı, suçluların sürgün edilmesi ve senetli kölelik yoluyla, Hint insan gücünü sömürgeci çıkarlarına hizmet etmek için diğer kolonilere aktarmıştır. Bu yayılmaya çok sayıda Hintli iş adamı da eşlik etmiştir. Bütün bu Hintli göçmenler bugünkü Hint diasporasının temelini ve yapısını küresel ölçekte ortaya koymaktadır., Historically, India played a crucial role in the establishment and maintenance of the British Empire overseas. Through the conquest of India, the British not only acquired great material wealth and secured the accumulation of industrial capital, but also obtained a sizeable pool of skilled human resources to facilitate its colonial expansion. At present, the total number of overseas Indians has exceeded 30 million, making up the second largest Diaspora after that of China. This massive immigrant group and its overseas distribution were inseparable from the British conquest of India and its borrowing and export of human resources to India. Through conscription, the recruitment of service personnel, the exile of criminals and the utilization of indentured labour, the British colonial rulers transferred Indian manpower to other colonies to serve their colonialist interests. This expansion was also accompanied by the movement of a large number of Indian businessmen, and all these Indian immigrants would lay the basic foundation and structure of today's global Indian Diaspora.

High temperature at anthesis is one of the most serious stress factors for rice (Oryza sativa L.) production, causing irreversible yield losses and reduces grain quality. Illustration of thermotolerance mechanism is of great importance to accelerate rice breeding aimed at thermotolerance improvement. Here, we identified a new thermotolerant germplasm, SDWG005. Microscopical analysis found that stable anther structure of SDWG005 under stress may contribute to its thermotolerance. Dynamic transcriptomic analysis totally identified 3559 differentially expressed genes (DEGs) in SDWG005 anthers at anthesis under heat treatments, including 477, 869, 2335, and 2210 for 1, 2, 6, and 12 h, respectively, however, only 131 were regulated across all four-time-points. The DEGs were divided into nine clusters according to their expressions in these heat treatments. Further analysis indicated that some main gene categories involved in heat-response of SDWG005 anthers, such as transcription factors, nucleic acid and protein metabolisms related genes, etc. Comparison with previous studies indicates that a core gene-set may exist for thermotolerance mechanism. Expression and polymorphic analysis of agmatine-coumarin-acyltransferase gene OsACT in different accessions suggested that it may involve in SDWG005 thermotolerance. This study improves our understanding of thermotolerance mechanisms in rice anthers during anthesis, and also lays foundation for breeding thermotolerant varieties via molecular breeding.

Additional file 1.

Background: Inserting a J-tip guide-wire into a vein’s lumen is often difficult when using the Seldinger or modified Seldinger technique for central venous catheterization (CVC) in newborns. This study was designed to compare the efficacy and safety of guide-wire insertion using the combination of a short peripheral intravenous cannula with a straight-tip guide-wire vs. a needle with a J-tip guide-wire for ultrasound-guided (USG) cannulation of the internal jugular vein (IJV) in newborns using an in-plane technique. Methods: One hundred and thirty newborn patients (weight, 1.4-5.2 kg) scheduled for selective or emergency surgery, were randomly assigned to either the needle group (combined with a J-tip guide-wire) or cannula group (combined with a straight-tip guide-wire). The primary outcome was the rate of successful guide-wire insertion on the first attempt. The puncture attempts, catheter placement attempts, and mechanical complications were also compared between the groups. Results: The rate of successful guide-wire insertion on the first attempt was higher in the cannula group (97%) than in the needle group (76%) ( p 2 = 11.233). Moreover, fewer insertion attempts were needed in the cannula group (1.0 ± 0.2) than in the needle group (1.7 ± 1.1) ( p Conclusion: The short peripheral cannula combined with a straight-tip guide-wire was superior to the needle combined with a J-tip guide-wire for USG newborn IJV catheterization in terms of successful guide-wire insertion on the first attempt and overall number of insertion attempts.

Laminitis is regarded as an important underlying cause of lameness disorders, yet its specific pathogenesis remains unclear. Consistent histological changes in cows with laminitis consist in the degradation of the basement membrane (BM). The breakdown of BM is accomplished by numerous proteases, especially MMP-9 and MMP-2. This study recruited 45 cows according to veterinary diagnostic criteria and divided them into three groups: subclinical laminitis cows (SCL, n = 15), chronic laminitis cows (CL, n = 15), and healthy cows (CON, n = 15). After blood samples were collected from the jugular vein, the serum was separated and frozen at –80°C. The serum samples were analyzed by gelatin zymography and reverse zymography to evaluate the activities of MMP-9, MMP-2, TIMP-1, and TIMP-2. In the SCL group, the activity of MMP-9 significantly increased (P

Atomic Compton scattering effect significantly contributes to low-energy electronic recoils below its k-shell energy for the direct dark matter detection. Searches on ADM models, dark photon models, leptophilic dark matter models as well as the conventional WIMPs for background understandings are vitally required to clarify the effect. We employed the relativistic impulse approximation (RIA) together with the ab initio Multi-Configuration Dirac-Fock (MCDF) theory to obtain the atomic Compton scattering for Germanium (Ge) Silicon (Si) and Xenon (Xe) atoms. Comparisons on low momentum transfer regions with our calculations for Ge and Si are achieved. In addition, millicharged dark matter particles estimated by RIA in the atomic ionization for Ge and Xe have been evaluated. A factor-of-two discrepancy on the incoherent-scattering factor (a.k.a. scattering function) near 100 eV/c momentum transfer with the Ge system between our calculation and the latest version of Geant4 (10.07.02) simulation data is observed. Plans on the experimental verification and the perspectives of the atomic Compton scattering effect for the direct detections will be discussed.

Clustered countercurrent-flow micro-channel reactors (C-CFMCR) with varying magnification times were applied for the preparation of Ni-doped manganese dioxide (Ni-MnO2) via co-precipitation processes. Effects of the feeding flow rate of reactants and the concentration of the doping agent on the doping processes were investigated. The C-CFMCR, which is of intensified micromixing efficiency as compared with conventional stirred reactors (STR), played a crucial role in facilitating the uniform nucleation and growth of MnO2 crystals and then the doping of Ni. As a result, the prepared Ni-MnO2 had a decreased particle size and a more uniform microporous structure. EDS analysis showed an even distribution of Ni2+ within the nanocomposites and thus enhanced electrochemical performance of the MnO2 composite materials as supercapacitor electrodes was achieved. The 1.0 at% Ni-MnO2 nanocomposites prepared under optimal conditions exhibited the highest specific capacitance of ~ 389.6 F g−1 at a current density of 1 A g−1 and showed excellent cycling stability with 79.3% retention of the initial capacitance after 5000 charge/discharge cycles in 2 M KOH aqueous solution. The as-assembled Ni-MnO2//activated carbon (AC) asymmetric supercapacitor displayed a wide operating voltage (0–1.6 V), high energy and power densities (13.3 W h kg−1 and 187.52 W kg−1 at 0.25 A g−1, respectively), and a stable cycling behavior (83.8% capacitance retention after 1000 cycles at 1 A g−1). In addition, only a weak scaling-up effect of C-CFMCR on the co-precipitation process was observed, suggesting that C-CFMCR is a prospective technique for the continuous and large-scale production of nanoparticles.

Lipocalins play a key role in regulating biological functions such as modulation of cell growth and metabolism, binding of cell-surface receptors, nerve growth and regeneration, and regulating of immune responses. Identifying and analyzing plant lipocalins has become one of the important issues in the study of lipocalin family. Traditional methods such as protein structure analysis, cell localization and phylogenetic studies are complex and very expensive, which makes current exploration progress of plant lipocalins still slow compared with deep learning methods. In this paper, based on convolutional neural network, we constructed a deep learning model called 'LCNet', which has sensitivity and specificity for plant lipocalin genes of 0.953 and 0.941 respectively. In addition, we further verified the prediction performance of LCNet model by studying the similarities and differences of gene relative expression levels between lipocalin genes already identified biologically in Oryza and the genes predicted as Oryza lipocalin by LCNet model during the process of absorbing and transporting PCB18. This combination of deep learning and biological experiments has high precision, simple operation and low cost, which can reduce the workload of biologists and can be extended to other proteins to solve similar problems.

A In this paper, we are aimed at discussing problems of distribution network planning and inventory management in a multiple-retailing supply chain. We study the distribution model and inventory management model, integrating both models to analyse the optimal decisions on maximum profit. The main methodology we used is literature study and model formulations. Through the research, we finally present the optimal distribution and inventory decision.

Smart transportation has a significantly impact on city management and city planning, which has received extensive attentions from academic and industrial communities. Different from omni-directional sensing system, as a directional sensing system, the multimedia-directional sensor network holds the special coverage scheme, which is usually used for smart cities, smart transportation, and harsh environment surveillance, for instance, nuclear-pollution regions where are inhospitable for people. This paper advances Virtual Stream Artificial Fish-swarm based Coverage-Enhancing Algorithm (VSAFCEA) as a coverage-enhancing means in multimedia directional sensor networks. Firstly, a concept of virtual streams, based on traditional artificial fish-swarm algorithm, is proposed. Then, the traditional behaviors of fishes in artificial fish-swarm algorithm are modified and expanded with several new behaviors. Finally, the presented VSAFCEA is adopted for coverage-enhancing issue in the situation of directional sensor networks with rotational direction-adjustable model. With a sequence of steps of artificial fishes in virtual stream, the presented VSAFCEA can figure out the approximation to the highest area coverage rate. Based on comparison of these simulation results (results of presented VSAFCEA and that of other typical coverage-enhancing ways in directional sensor networks), the conclusion can be drawn that VSAFCEA could attain higher area coverage rate of directional sensor networks with fewer iterative computing times.

Annual teosinte, the ancestor of cultivated maize (Zea mays ssp. mays), is a valuable germplasm for enhancing the genetic diversity and adaptability of maize to various environment stimuli. However, comprehensive transcriptomic and genomic resources are unavailable in public databases, which significantly impede the identification and utilization of favourable genes or alleles in teosinte. In this study, we globally sequenced the transcriptomes of six teosinte accessions by Illumina paired-end sequencing. A high-quality teosinte transcriptome was de novo assembled with an average length of 770 bp and 63.99% of annotated unigenes. We found approximately 75% of the genes were highly conserved between maize and teosinte. Moreover, we also found 1516 unigenes were specifically expressed in teosinte, of which 84 unigenes were supported by gene models of four plant species, and 571 unigenes were located in the intergenic regions of maize genome, showing evidence-based expressed presence/absence variations (ePAVs). Furthermore, we also identified 99 unigenes with strong selection signals and 57 unigenes with >1 Ka/Ks ratios, suggesting that these genes might be under strong selection during maize domestication and improvement. Additionally, 11 286 teosinte unigene-derived primer pairs were developed for amplifying simple sequence repeat (SSR) loci. This study provides a comprehensive transcriptome of teosinte, a subset of genes that are highly conserved or diverged during maize domestication and improvement, and a massive set of available SSR primer pairs. These results facilitate the investigation of the comparative genomics and molecular domestication of teosinte and the utilization of teosinte germplasm for maize improvement.

Vehicle targets in unmanned aerial vehicle (UAV) images are generally small, so a significant amount of detailed information on targets may be lost after neural computing, which leads to the poor performances of the existing recognition algorithms. Based on convolutional neural networks that utilize the YOLOv3 algorithm, this article focuses on the development of a quick automatic vehicle detection method for UAV images. First, a vehicle dataset for target recognition is constructed. Then, a novel YOLOv3 vehicle detection framework is proposed according to the following characteristics: The vehicle targets in the UAV image are relatively small and dense. The average precision (AP) increased by 5.48%, from 92.01% to 97.49%, which still remains the rather high processing speed of the YOLO network. Finally, the proposed framework is tested using three datasets: COWC, VEDAI, and CAR. The experimental results demonstrate that our method had a better detection capability.

Thirty-five pairs of SSR primers were used for genetic diversity analysis and DNA fingerprinting of 31 hybrid rice core parental lines developed in central- and southern-China using one japonica rice line and three inbred rice lines as the check varieties. The average number of alleles (Na) per SSR locus was 4.02, with a range of two to eight, the effective number of alleles (Ne) was 83.16 with a mean of 2.38, ranging from 1.19 to 4.66. The polymorphic information content (PIC) ranged from 0.16 to 0.79, with an average number of 0.52. The results of the cluster analysis indicated that the check varieties viz., one japonica rice and three inbred rice, were clustered into two groups with similarity coefficients of 0.62 and 0.71 respectively indicating their relatedness. Thirty-one hybrid rice parental lines were clustered into 6 groups according to their different types, pedigrees and regions of development with similarity coefficients of approximately 0.76. The highest genetic similarity coefficient (0.94) was observed between Y58S and C815S, and the lowest (0.63) was observed between Quan9311A and Peiai64S. The purity of one hybrid rice cultivar was tested using characteristic marker and the field test, and it was demonstrated that the purities obtained using the two methods were similar. This research will be helpful for rice breeding, new cultivar registration and seed production.

Background: A growing body of research has suggested that tetrahydrobiopterin (BH4) responsive phenotype can be predicted by the phenylalanine hydroxylase (PAH) genotype in patients with phenylketonuria (PKU), but data concerning the association between genotype and BH4 responsiveness are scarce in China. Methods: A total of 165 PKU patients from China who had undergone a 24-h loading test with BH4 administration were recruited. Genotyping was performed by the next-generation sequencing (NGS) technique. Using the predicted residual PAH activity, we analyzed the association between genotype and BH4-responsiveness. Results: Among the 165 patients, 40 patients (24.24%) responded to BH4. A total of 74 distinct mutations were observed, including 13 novel mutations. The mutation p.R241C was most frequently associated with response. Two known mutations (p.A322T and p.Q419R) and two novel mutations (p.L98V and IVS3-2A>T) were first reported as responsive to BH4. Residual PAH activity of at least 12.5% was needed for responsive genotypes. Conclusion: Genotype-based predictions of BH4-responsiveness are only for selecting potential responders. Accordingly, it is necessary to test potential responders with a long-term BH4 challenge.

The differentiation and subsequent development of plant tissues or organs are tightly regulated at multiple levels, including the transcriptional, posttranscriptional, translational, and posttranslational levels. Transcriptomes define many of the tissue-specific gene expression patterns in maize, and some key genes and their regulatory networks have been established at the transcriptional level. In this study, the sequential window acquisition of all theoretical spectra-mass spectrometry technique was employed as a quantitative proteome assay of four representative maize tissues, and a set of high-confidence proteins was identified. Integrated analysis of the proteome and transcriptome revealed that protein abundance was positively correlated with mRNA level with weak to moderate correlation coefficients, but the abundance of key proteins for function or architecture in a given tissue was closely tempospatially regulated at the transcription level. A subset of differentially expressed proteins, specifically tissue-specific highly expressed proteins, was identified, for example, reproductive structure and flower development-related proteins in tassel and ear, lipid and fatty acid biosynthetic process-related proteins in immature embryo, and inorganic substance and oxidation reduction responsive proteins in root, potentially revealing the physiology, morphology, and function of each tissue. Furthermore, we found many new proteins in specific tissues that were highly correlated with their mRNA levels, in addition to known key factors. These proteome data provide new perspective for understanding many aspects of maize developmental biology. Raw proteomics data are available via ProteomeXchange with identifier PXD008464.

Argonaute (AGO) proteins play a key role in regulation of gene expression through small RNA-directed RNA cleavage and translational repression, and are essential for multiple developmental processes. In the present study, 17 AGO genes of maize (Zea mays L., ZmAGOs) were identified using a Hidden Markov Model and validated by rapid amplification of cDNA ends assay. Subsequently, quantitative PCR revealed that expressions of these genes were higher in reproductive than in vegetative tissues. AGOs presented five temporal and spatial expression patterns, which were likely modulated by DNA methylation, 5'-untranslated exons and microRNA-mediated feedback loops. Intriguingly, ZmAGO18b was highly expressed in tassels during meiosis. Furthermore, in situ hybridization and immunofluorescence showed that ZmAGO18b was enriched in the tapetum and germ cells in meiotic anthers. We hypothesized that ZmAGOs are highly expressed in reproductive tissues, and that ZmAGO18b is a tapetum and germ cell-specific member of the AGO family in maize.

Multimedia directional sensor network is one kind of directional sensing systems, whose coverage scheme is quite different from the omnidirectional sensing system. And it is often used in atrocious environmental surveillance, such as nuclear contaminative areas, where people can hardly arrive. In this paper, a genetic simulated annealing-based coverage-enhancing algorithm GSACEA is proposed as a coverage-enhancing method in multimedia directional sensor networks. Firstly, GSACEA combines the genetic algorithm and simulated annealing algorithm into an algorithm with new architecture. Then, the proposed GSACEA is applied for the purpose of coverage-enhancing in the case of directional sensor networks with rotational direction-adjustable model. Finally, after series actions of genetic simulated annealing, the proposed method can find the approximate solution to the best area coverage rate. And according to the results of simulations, which compared the proposed method with several other classic coverage-enhancing methods in directional sensor networks, it could be concluded that GSACEA can achieve the highest area coverage rate of directional sensor networks and reduce the iterative computing times simultaneously. Copyright © 2014 John Wiley & Sons, Ltd.

Target recognition in high-resolution synthetic aperture radar (SAR) images is a challenging task, because SAR images have higher ambiguity for different target, which will reduce the correct recognition rate. This paper presents an improved SAR recognition algorithm based on fuzzy neural network (FNN), which deals with the ambiguity SAR target recognition very well. This improved FNN system improves learning algorithm and structure which has fuzzy multi-input and fuzzy multi-output. This paper takes the MSTAR data as the test data in simulation to show that this improved fuzzy neural network obtains a higher correct recognition rate.

Most practical target tracking are usually maneuvering, while most target tracking algorithm are linear filter. More estimation error is introduced from linear filter. Nowadays more and more researchers pay their attention in Maneuvering Target Tracking algorithm. Particle filter has been developed for estimation of nonlinear system states. This paper presents an improved particle filter, which can apply the maneuvering target tracking problem. In practice, the particle filter would take abundant computation for estimate the maneuvering target tracking. The ChinaGrid system use the agile and distributed federations to reduce the computing time, which achieve to fast resolution for particle filter computation of target tracing application. Lastly the simulation proves it.

For the first time, LiFePO4 was successfully prepared from a distilled water-assisted solid-state method, i.e., a mixture containing α-Fe2O3, (NH4)2HPO4, LiOH, glucose and oxalic acid was dissolved into hot distilled water leading to a bright yellow-green solution, and then this solution was evaporated at 120°C, directly generating to a brown and fluffy powder, lastly, the well-ground powder was heated in a carbon-coated crucible at 700°C, presenting the product of LiFePO4. The obtained samples were featured by X-ray diffraction (XRD), scanning electron microscope (SEM), and its specific capacity, cycle property and rate capabilities were also measured. Encouragingly, compared to the previously reported value, satisfactory results were acquired. This method, without inert gas flow, is a very simple and useful tool to synthesize LiFePO4, especially for the industrial application.

Neural stem/progenitor cells transit from fibroblast growth factor-responsive to epidermal growth factor (EGF)-responsive stem cells in the subventricular zone (SVZ). Here, we provide evidences that Pax6 plays a crucial role in the regulation of the EGF-responsive stem cell pool of the SVZ. Using Pax6 homozygous mutant mice (E18.5d), we found that the neurospheres were formed less than that from the wild-type mice, and the expression of EGF receptor in these neurospheres is downregulated very much. The amount of EGF-responsive cells in the mutant dorsal cortex SVZ (E18.5d) was also decreased from 16.8 (wild) to 4.5% (mutant), by flow cytometry method. Immunostaining of the cortex showed a downregulation of EGF receptor expression. All these results suggest that Pax6 regulate the EGF-responsive stem cells in the SVZ.

Palladium (Pd) nanoparticle catalysts were electrodeposited onto a graphite electrode by cyclic voltammetry (CV) technique from a 0.5 M HCl solution containing 0.2 M PdCl2, and then a film of polypyrrole (PPy) was immobilized onto the as-prepared Pd-coated graphite electrode, leading to a PPy/Pd-coated electrode. Pd particles were repeatedly electrodeposited on the PPy/Pd-coated electrode to generate a Pd/PPy/Pd sandwich structured composite coated electrode. This developed composite electrode showed a satisfactory electrocatalysis toward the formic acid oxidation (FAO). The catalysis mechanism of the sandwich structured Pd/PPy/Pd composite catalyst toward FAO was also discussed on the basis of the obtained cyclic voltammograms and FT-IR spectra.

A gas chromatography-mass spectrometry (GC-MS) method was developed for the determination of 46 plasticizers in food contact polyvinyl chloride (PVC) packaging materials and their migration into food simulants, i. e. water, 3% acetic acid, 10% ethanol and olive oil. Plasticizers in the PVC packaging materials, aqueous food simulants and olive oil food simulants were extracted by the dissolution-precipitation, liquid-liquid extraction and gel permeation chromatography (GPC) approaches, respectively. The extracts were analyzed by GC-MS in selective ion monitoring (SIM) mode and quantified using the external standard method. The cal-ibration curves were linear in the ranges of 0.1-2.0 mg/L with the correlation coefficients of 0.9910-0. 999 9. The limits of detection were from 0. 005 mg/kg to 0. 05 mg/kg ( S/N = 5 ). The recoveries at 3 spiked levels were 69.51%-107. 21% and the relative standard deviations (RSDs n = 6) ranged from 3.53% to 18.95%. These results show that this method is fast, sensitive and accurate for the qualitative and quantitative determination of plasticizers in food contact plastic products and 4 types of food simulants.

Phenylketonuria (PKU) is an inherited autosomal recessive disorder of phenylalanine metabolism, mainly caused by a deficiency of phenylalanine hydroxylase (PAH). The incidence of various PAH mutations differs among race and ethnicity. Here we report a spectrum of PAH mutations complied from 796 PKU patients from mainland China. The all 13 exons and adjacent intronic regions of the PAH gene were determined by next-generation sequencing. We identified 194 different mutations, of which 41 are not reported before. Several mutations reoccurred with high frequency including p.R243Q, p.EX6-96A > G, p.V399V, p.R241C, p.R111*, p.Y356*, p.R413P and IVS4-1G > A. 76.33% of mutations were localized in exons 3, 6, 7, 11, 12. We further compared the frequency of each mutation between populations in northern and southern China and found significant differences in 19 mutations. Furthermore, we identified 101 mutations that are not reported before in Chinese population, our study thus broadens the mutational spectrum of Chinese PKU patients. Additionally, 41 novel mutations will expand and improve PAH mutation database. Finally, our study offers proof that NGS is effective, reduces screening times and costs and facilitates the provision of appropriate genetic counseling for PKU patients.

Background Alternative splicing (AS) is an important regulatory mechanism that greatly contributes to eukaryotic transcriptome diversity. A substantial amount of evidence has demonstrated that AS complexity is relevant to eukaryotic evolution, development, adaptation, and complexity. In this study, six teosinte and ten maize transcriptomes were sequenced to analyze AS changes and signatures of selection in maize domestication and improvement. Results In maize and teosinte, 13,593 highly conserved genes, including 12,030 multiexonic genes, were detected. By identifying AS isoforms from mutliexonic genes, we found that AS types were not significantly different between maize and teosinte. In addition, the two main AS types (intron retention and alternative acceptor) contributed to more than 60% of the AS events in the two species, but the average unique AS events per each alternatively spliced gene in maize (4.12) was higher than that in teosinte (2.26). Moreover, 94 genes generating 98 retained introns with transposable element (TE) sequences were detected in maize, which is far more than 9 retained introns with TEs detected in teosinte. This indicates that TE insertion might be an important mechanism for intron retention in maize. Additionally, the AS levels of 3864 genes were significantly different between maize and teosinte. Of these, 151 AS level-altered genes that are involved in transcriptional regulation and in stress responses are located in regions that have been targets of selection during maize improvement. These genes were inferred to be putatively improved genes. Conclusions We suggest that both maize and teosinte share similar AS mechanisms, but more genes have increased AS complexity during domestication from teosinte to maize. Importantly, a subset of AS level-increased genes that encode transcription factors and stress-responsive proteins may have been selected during maize improvement. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1582-5) contains supplementary material, which is available to authorized users.

The stealth coat on the surface can help target escaping from being detected by enemy radar, making the stealth target an import role on the modern war. But the coat will be damaged during the transformation, training and other situations, which can reduce the hiding peculiarity of the target, using Hilbert Huang Transform we can get the identity of the damaged target echo, realizing the stealth target coating damage detection, after improving Hilbert Huang Transform, this paper obtain a new Hilbert Huang Transform method which can be applied to detect the stealth target coating damage and obtain an effective result.

Classifying large-scale face images is a very hot topic in the field of prevention. Our fast face recognition algorithm include two parts: (1) parallel facial feature extraction algorithm based on MapReduce, (2) DSVM that distributed SVM based on MapReuce. Firstly, combine some face images to blocks, and parallel extract HOG feature on every block. DSVM algorithm will be used to train face classification. Inadequate sampling and sufficient sampling will be used to address the problem of uneven samples. We adopt Chain Mapper/Chain Reducer model to improve the efficiency of DSVM. Finally, results show that parallel facial feature extraction algorithm and DSVM both have high processing efficiency, and DSVM can achieve good recognition accuracy.

Modern science is increasingly data-driven and collaborative in nature. Comparing to ordinary data processing, big data processing that is mixed with great missing date must be processed rapidly. The Rough Set was generated to deal with the large data. The QuickReduct is a popular attribute algorithm as the attribute reduction of big database. But less effort has been put on fuzziness and vagueness data. Considering this requirement this paper proposes an improved attribute reduction based on condition entropy of fuzzy rough sets (FRCE) which can deal with the continuous and fuzzy data. This algorithm rewrites the expression of condition entropy by using the information theory. Last this paper takes the UCI database to simulate the efficiency of this algorithm. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4795

Unveiling the genetic architecture of grain yield and yield-related traits is useful for guiding the genetic improvement of crop plants. Kernel row number (KRN) per ear is an important yield component, which directly affects the grain yield of maize. In this study, we constructed a set of 130 chromosome segment substitution lines (CSSLs), using Nongxi531 as the donor parent and H21 as recipient parent, by continuous backcrossing and selfing. In total, 11 quantitative trait loci (QTL) were detected for KRN by stepwise regression under 3 environmental settings, with 9.87-19.44% phenotypic variation being explained by a single QTL. All 11 QTL were also detected by single-factor ANOVA across the 3 environments tested. Of these 11 QTL, 4 were identified across more than 2 environments, indicating that they are authentically expressed under different environments to control the formation and development of KRN in female maize inflorescences. The CSSLs harbored a greater number of favorable alleles for KRN compared to the H21 line, and could be employed as improved H21 lines in maize breeding programs.

Argonaute (AGO) proteins play a key role in regulation of gene expression through small RNA-directed RNA cleavage and translational repression, and are essential for multiple developmental processes. In the present study, 17 AGO genes of maize (Zea mays L., ZmAGOs) were identified using a Hidden Markov Model and validated by rapid amplification of cDNA ends assay. Subsequently, quantitative PCR revealed that expressions of these genes were higher in reproductive than in vegetative tissues. AGOs presented five temporal and spatial expression patterns, which were likely modulated by DNA methylation, 5'-untranslated exons and microRNA-mediated feedback loops. Intriguingly, ZmAGO18b was highly expressed in tassels during meiosis. Furthermore, in situ hybridization and immunofluorescence showed that ZmAGO18b was enriched in the tapetum and germ cells in meiotic anthers. We hypothesized that ZmAGOs are highly expressed in reproductive tissues, and that ZmAGO18b is a tapetum and germ cell-specific member of the AGO family in maize.

Modern science is increasingly data-driven and collaborative in nature. Comparing to ordinary data processing, big data processing that is mixed with great missing date must be processed rapidly. The Rough Set was generated to deal with the large data.In this paper, we proposed animproved algorithm for dynamic Cognitive extractionwhich deals with adaptive fuzzy attribute values and the fuzzy attribute reduction aiming at uncertainty datasuch asdata with diversity or missing character faced by the big data with using Fuzzy Rough Set Theory.At the aspect of information decision, according to the Real-time input information, it deep analyzes the dynamic information entropy of the data itself and chooses the biggest prediction information entropy direction for the cognitive rules to achieve rapid recognitive of data, complete information of quick decision.Because the algorithm is adopted to predict the best direction of information entropy, so the recognitive effect is also improved. At the end of the paper, we have analyzed superiority of the dynamic cognitive algorithm by using breast cancer data as the foundation.

At present, application of dense targets is one of the important means of penetration. In many observation conditions, the echoes from the dense targets mixed with many aliasing signals, and conventional radar signal processing algorithms do not take the aliasing signals into account. Therefore it is difficult for conventional algorithms to recognize multi-targets. In this paper, an improved ESPRIT algorithm is proposed which can recognize the multi-targets from the aliasing echoes and greatly reduce the computational complexity without changing the algorithm accuracy, especially can obtain a better estimation in the case of low SNR environment. The proposed algorithm can firstly quickly realize the estimate of scattering center parameters of target echoes, and then based on the estimation, the aliasing targets can be recognized. The Simulation also verifies the improved ESPRIT algorithm has a better identification and recognition capability of aliasing targets in low SNR condition. Moreover because of reduction of the computational complexity, the performance of proposed algorithm is faster than conventional methods, especially in the case of multiple aliasing scattering centers.

In the study field of using HRRP for radar multi-target recognition, besides the azimuth angle, target species, numbers, degree of aliasing are influencing the change of HRRP, which makes HRRP templates number increase exponentially. Accordingly, it is very difficult to classify the sample by using conventional matching method in a short time. This article introduces the genetic algorithm to solve this puzzle. By taking advantages of the characteristics of self-evolution of the genetic algorithm, we can get a convergence result. At the same time, we also propose a constraint-based radar azimuth information search algorithm which will further improve the efficiency of radar multi-target recognition.

Nowadays various unmanned aerial vehicles (UAV) have been popularly used to perform intelligence, surveillance and reconnaissance mission. In the practical application the search planning is kernel problem for UAV. This paper presents a UAV search planning algorithm based on perceptual cue, which uses the predicting information to plan the UAV search route. The predicting information shows the probability of the situation and can be used to find the optimization strategy to complete the UAV's mission. The Monte Carlo simulation proves that this algorithm can promote the UAVsearch efficiency.