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The increasing public demand for fresh products has catalyzed the requirement for cold chain logistics distribution systems. However, challenges such as temperature control and delivery delays have led a significant product loss and increased costs. To improve the current situation, a novel approach to optimize cold chain logistics distribution for fresh products will be presented in the paper, utilizing a hybrid Tabu-Grey wolf optimizer (TGWO) algorithm. The proposed hybrid approach combines Tabu search (TS) and Grey wolf optimizer (GWO), employing TS for exploration and GWO for exploitation, aiming to minimize distribution costs in total and establish efficient vehicle scheduling schemes considering various constraints. The effectiveness of the TGWO algorithm is demonstrated through experiments and case studies compared to other heuristic algorithms. Comparative analysis against traditional optimization methods, including Particle swarm optimization (PSO), Whale optimization algorithm (WOA), and original GWO, highlights its superior efficiency and solution quality. This study contributes theories by demonstrating the efficacy of hybrid optimization techniques in complex supply chain networks and dynamic market environments. The practical implication lies in the implementation of TGWO to bolster distribution efficiency, cost reduction, and product quality maintenance throughout the logistics process, offering valuable insights for operational and strategic improvements by decision-makers. However, the study has limitations in generalizability and assumptions, suggesting future research areas including exploring new search operators, applying additional parameters, and using the algorithm in diverse real-life scenarios to improve its effectiveness and applicability.

An inexpensive and high-performing solid Coumarone resin was added to Styrene-butadiene-styrene (SBS) copolymer-modified asphalt to enhance its storage stability and road performance. To assess the effect of Coumarone resin dosage on the SBS-modified asphalt, a series of laboratory tests were conducted. The composite modified asphalt's segregation test was used to evaluate its storage stability, Dynamic Shear Rheometer (DSR) and Multiple Stress Creep Recovery (MSCR) tests were employed to investigate its high-temperature performance and permanent deformation resistance, and the Bending Beam Rheology (BBR) test was utilized to measure its low-temperature performance. Fluorescence microscopy was used to observe the composite modified asphalt's microstructure, and Fourier Transform Infrared Spectroscopy (FTIR) was conducted to study the changes in chemical structure during the modification process. The results showed that Coumarone resin can improve the compatibility of SBS and asphalt, improve the high-temperature performance and deformation resistance of SBS-modified asphalt, and adding an appropriate amount of Coumarone resin can help enhance the low-temperature cracking resistance of modified asphalt. The optimal dosage of Coumarone resin recommended for SBS-modified asphalt performance enhancement is 2% under the test conditions, as determined by comparing the test results of samples with various dosages.

Low-carbon cooperation among cloud manufacturing service providers is one way to achieve carbon peak and neutrality. Such cooperation is related to the benefits to service providers adopting low-carbon strategies and stochastic factors such as government low-carbon policies, providers' environmental awareness, and demanders' low-carbon preferences. Focusing on the evolutionary process of service providers' low-carbon strategy selection under uncertain factors, a stochastic evolutionary game model is constructed based on the Moran process, and the equilibrium conditions for low-carbon cooperation among providers are analyzed under benefit-dominated and stochastic factor-dominated situations. Through numerical simulation, the effects of the cloud platform's cost-sharing coefficient for low-carbon investment, matching growth rate, carbon trading price, and group size on providers' low-carbon strategy evolution are analyzed. The research results show that increasing the cloud platform's low-carbon cost-sharing, carbon trading price, and group size can promote low-carbon cooperation among service providers. With greater low-carbon investment costs and greater stochastic factor interference, the providers' enthusiasm for low-carbon cooperation decreases. This study fills the research gap in the low-carbon cooperation evolution of cloud manufacturing providers based on the stochastic evolutionary game and provides decision-making suggestions for governments and cloud platforms to encourage provider participation in low-carbon cooperation and for providers to adopt low-carbon strategies.

The extraction of roadways from remote sensing imagery constitutes a pivotal task, with far-reaching implications across diverse domains such as urban planning, management of transportation systems, emergency response initiatives, and environmental monitoring endeavors. Satellite images captured during daytime have customarily served as the primary resource for this extraction process. However, the emergence of Nighttime Light (NTL) remote sensing data introduces an innovative dimension to this arena. The exploration of NTL data for road extraction remains in its nascent stage, and this study seeks to bridge this gap. We present a refined U-Net model (CA U-Net) integrated with Cross-Attention Mechanisms, meticulously designed to extract roads from Yangwang-1 NTL images. This model incorporates several enhancements, thereby improving its proficiency in identifying and delineating road networks. Through extensive experimentation conducted in the urban landscape of Wenzhou City, the model delivers highly accurate results, achieving an F1 score of 84.46%. These outcomes significantly surpass the performance benchmarks set by Support Vector Machines (SVM) and the Optimal Threshold (OT) method. This promising development paves the way towards maximizing the utility of NTL data for comprehensive mapping and analysis of road networks. Furthermore, the findings underscore the potential of utilizing Yangwang-1 data as a reliable source for road extraction and reaffirm the viability of deploying deep learning frameworks for road extraction tasks utilizing NTL data.

ObjectiveThis study aimed to assess the correlation between the circulating cell-free mitochondria DNA and inflammation factors in noninfectious disease by meta-analysis of data from eligible studies.Materials and methodsThrough a comprehensive searching of pubmed, embase, web of science, cochrane from establishment of the database to October 31, 2022, studies were selected that investigated the association of circulating cell free mitochondria DNA with inflammatory factors in non-infectious diseases. Studies that met the inclusion criteria and were published in English or Chinese were included. Data of each correlation coefficients were extracted from the paper and 95% confidence intervals were calculated. Sensitivity and heterogeneity tests were carried out for each data.ResultsA total of 660 articles were retrieved and 22 were included in this meta-analysis, including 2600 patients. A fixed effects model was employed to examine ISS and IL-8, others were analyzed using random effects models. The correlation coefficient between mtDNA and ISS score was 0.37 (95%CI = [0.232;0.494]), pConslusionCirculating cell free mtDNA was moderate positively correlated with the expression of inflammatory factors and the degree of trauma.

Reverberation is the primary background interference of active sonar systems in shallow water environments, affecting target position detection accuracy. Reverberation suppression is a signal processing technique used to improve the clarity and accuracy of echo by eliminating the echoes, reverberations, and noise that occur during underwater propagation.This paper proposes an end-to-end network structure called the Reverberation Suppression Network (RS-U-Net) to suppress the reverberation of underwater echo signals. The proposed method effectively improves the signal-to-reverberation ratio (SRR) of the echo signal, outperforming existing methods in the literature. The RS-U-Net architecture uses sonar echo signal data as input, and a one-dimensional convolutional network (1D-CNN) is used in the network to train and extract signal features to learn the main features. The algorithm's effectiveness is verified by the pool experiment echo data, which shows that the filter can improve the detection of echo signals by about 10 dB. The weights of reverberation suppression tasks are initialized with an auto-encoder, which effectively uses the training time and improves performance. By comparing with the experimental pool data, it is found that the proposed method can improve the reverberation suppression by about 2 dB compared with other excellent methods.

Recently, many robust adaptive beamforming (RAB) algorithms have been proposed to improve beamforming performance when model mismatches occur. For a uniform linear array, a larger aperture array can obtain higher array gain for beamforming when the inter-sensor spacing is fixed. However, only the small aperture array can be used in the equipment limited by platform installation space, significantly weakening beamforming output performance. This paper proposes two beamforming methods for improving beamforming output in small aperture sensor arrays. The first method employs an integration algorithm that combines angular sector and gradient vector search to reconstruct the interference covariance matrix (ICM). Then, the interference-plus-noise covariance matrix (INCM) is reconstructed combined with the estimated noise power. The INCM and ICM are used to optimize the desired signal steering vector (SV) by solving a quadratically constrained quadratic programming (QCQP) problem. The second method proposes a beamforming algorithm based on a virtual extended array to increase the degree of freedom of the beamformer. First, the virtual conjugated array element is designed based on the structural characteristics of a uniform linear array, and received data at the virtual array element are obtained using a linear prediction method. Then, the extended INCM is reconstructed, and the desired signal SV is optimized using an algorithm similar to the actual array. The simulation results demonstrate the effectiveness of the proposed methods under different conditions.

Information asymmetry is widespread in the P2P online lending market, creating an imbalance in the position of lenders and borrowers. This paper aims to expand the process of information exchange between lenders and borrowers by analyzing the link between soft information such as borrowers' loan descriptions and lending outcomes. Based on the transaction data of the 'Renrendai' platform, this paper analyzed the linguistic features and extracted the content of loan descriptions using a latent Dirichlet allocation (LDA) theme model. To further explore the value of loan descriptions in predicting lending success, this paper conducts a prediction study based on a support vector machine model. It is found that: lenders focus on effective information in the loan descriptions, the linguistic complexity affects the transaction, with simple and direct statements being more favorable; the content for building a good personal image of the borrower will significantly contribute to the lending success. In the prediction study section, it is demonstrated that loan descriptions' language feature indicators can improve prediction accuracy. This paper uncovers the importance of loan descriptions in online lending transactions, which has implications in assisting lenders' investment judgments, as well as in platform information system improvements.

For the problem of direction-of-arrival (DOA) estimation using a coprime array, there are high spatial spectrum outputs of false alarms caused by the overlap of main and grating lobes from subarrays. In this paper, a DOA estimation method of more than two co-frequency sources for a coprime vector hydrophone array is proposed. The method is based on vector cross terms (VCTs), making full use of the directivity of channel combinations for vector hydrophones. Based on VCTs, the characteristic data point identification method is conducted and ensures that the bearing data with the characteristic can be preserved. For further interference rejection, the paper designs Queue Selection (QS) method based on inverse beamforming. The influence of grating lobes can be weakened with the QS, further improving the accuracy of direction extraction. The algorithm in this work does not require decoherence processing, and the simulation work shows that it achieves stable DOA estimation with a low signal-to-noise ratio (SNR).

The concise concept and good optimization performance are the advantages of particle swarm optimization algorithm (PSO), which makes it widely used in many fields. However, when solving complex multimodal optimization problems, it is easy to fall into early convergence. The rapid loss of population diversity is one of the important reasons why the PSO algorithm falls into early convergence. For this reason, this paper attempts to combine the PSO algorithm with wavelet theory and levy flight theory to propose a new hybrid algorithm called PSOLFWM. It applies the random wandering of levy flight and the mutation operation of wavelet theory to enhance the population diversity and seeking performance of the PSO to make it search more efficiently in the solution space to obtain higher quality solutions. A series of classical test functions and 19 optimization algorithms proposed in recent years are used to evaluate the optimization performance accuracy of the proposed method. The experimental results show that the proposed algorithm is superior to the comparison method in terms of convergence speed and convergence accuracy. The success of the high-dimensional function test and dynamic shift performance test further verifies that the proposed algorithm has higher search stability and anti-interference performance than the comparison algorithm. More importantly, both t-Test and Wilcoxon's rank sum test statistical analyses were carried out. The results show that there are significant differences between the proposed algorithm and other comparison algorithms at the significance level α = 0.05, and the performance is better than other comparison algorithms.

The cross-view 3D human pose estimation model has made significant progress, it better completed the task of human joint positioning and skeleton modeling in 3D through multi-view fusion method. The multi-view 2D pose estimation part of this model is very important, but its training cost is also very high. It uses some deep learning networks to generate heatmaps for each view. Therefore, in this article, we tested some new deep learning networks for pose estimation tasks. These deep networks include Mobilenetv2, Mobilenetv3, Efficientnetv2 and Resnet. Then, based on the performance and drawbacks of these networks, we built multiple deep learning networks with better performance. We call our network in this article LHPE-nets, which mainly includes Low-Span network and RDNS network. LHPE-nets uses a network structure with evenly distributed channels, inverted residuals, external residual blocks and a framework for processing small-resolution samples to achieve training saturation faster. And we also designed a static pose sample simplification method for 3D pose data. It implemented low-cost sample storage, and it was also convenient for models to read these samples. In the experiment, we used several recent models and two public estimation indicators. The experimental results show the superiority of this work in fast start-up and network lightweight, it is about 1-5 epochs faster than the Resnet-34 during training. And they also show the accuracy improvement of this work in estimating different joints, the estimated performance of approximately 60% of the joints is improved. Its performance in the overall human pose estimation exceeds other networks by more than 7mm. The experiment analyzes the network size, fast start-up and the performance in 2D and 3D pose estimation of the model in this paper in detail. Compared with other pose estimation models, its performance has also reached a higher level of application.

In the field of object detection, domain adaptation is one of popular solution to align the distribution between the real scene (target domain) and the training scene (source domain) by adversarial training. However, only global features are applied to the Domain Adaptive Faster R-CNN (DA Faster R-CNN) method. The lack of local features reduces the performance of domain adaptation. Therefore, a novel method for domain adaptive detection called Skip-Layer Network with Optimization (SLNO) method is proposed in this paper. Three improvements are presented in SLNO. Firstly, different level convolutional features are fused by a multi-level features fusion component for domain classifier. Secondly, a multi-layer domain adaptation component is developed to align the image-level and the instance-level distributions simultaneously. Among this component, domain classifiers are used in both image-level and instance-level distributions through the skip layer. Thirdly, the cuckoo search (CS) optimization method is applied to search for the best coefficient of SLNO. As a result, the capability of domain alignment is strengthened. The Cityscapes, Foggy Cityscapes, SIM10K, KITTI data sets are applied to test our proposed novel approach. Consequently, excellent results are achieved by our proposed methods against state-of-the-art object detection methods. The results demonstrate our improvements are effective on domain adaptation detection.

Among the three existing targeted gene editing technologies, zinc-finger nucleases, transcription activator-like effector nucleases, and clustered regularly interspaced short palindromic repeats-CRISPR-associated 9 (CRISPR-Cas9), the latter is widely used owing to its simplicity, efficiency, and low cost. Here, we routinely infected A172 and U251 cells with lentiviral vectors, in which aquaporin-8 (AQP8) was knocked out using CRISPR/Cas9. Our results indicated that cryopreservation did not significantly alter the viral infection efficiency, but influenced AQP8 expression in the infected cells at both protein and mRNA levels compared with the non-cryopreserved samples. Further, AQP8 expression at protein and mRNA levels in recovered cryopreserved infected cells did not significantly differ from those in the blank and negative controls, indicating that the lentivirus was still infectious at low temperatures. However, it failed to release the AQP8-targeting guide RNA in the infected cells, or the guide RNA was released, but underwent changes that caused it to malfunction in the cells with CRISPR/Cas9-mediated AQP8 knock-out. Our findings possibly provide some insights into the reliability of lentiviruses as CRISPR/Cas9 vectors.

Since the word2vec model was proposed, many researchers have vectorized the data in the research field based on it. In the field of social network, the Node2Vec model improved on the basis of word2vec can vectorize nodes and edges in social networks, so as to carry out relevant research on social networks, such as link prediction, and community division. However, social network is a network with homogeneous structure. When dealing with heterogeneous networks such as knowledge graph, Node2Vec will lead to inaccurate prediction and unreasonable vector quantization data. Specifically, in the Node2Vec model, the walk strategy for homogeneous networks is not suitable for heterogeneous networks, because the latter has distinguishing features for nodes and edges. In this paper, a Heterogeneous Network vector representation method is proposed based on random walks and Node2Vec, called KG2vec (Heterogeneous Network to Vector) that solves problems related to the inadequate consideration of the full-text semantics and the contextual relations that are encountered by the traditional vector representation of the knowledge graph. First, the knowledge graph is reconstructed and a new random walk strategy is applied. Then, two training models and optimizing strategies are proposed, so that the contextual environment between entities and relations is obtained, semantically providing a full vector representation of the Heterogeneous Network. The experimental results show that the KG2VEC model solves the problem of insufficient context consideration and unsatisfactory results of one-to-many relationship in the vectorization process of the traditional knowledge graph. Our experiments show that KG2vec achieves better performance with higher accuracy than traditional methods.

To alleviate the data sparsity and cold start problems for collaborative filtering in recommendation systems, side information is usually leveraged by researchers to improve the recommendation performance. The utility of knowledge graph regards the side information as part of the graph structure and gives an explanation for recommendation results. In this paper, we propose an enhanced multi-task neighborhood interaction (MNI) model for recommendation on knowledge graphs. MNI explores not only the user-item interaction but also the neighbor-neighbor interactions, capturing a more sophisticated local structure. Besides, the entities and relations are also semantically embedded. And with the cross&compress unit, items in the recommendation system and entities in the knowledge graph can share latent features, and thus high-order interactions can be investigated. Through extensive experiments on real-world datasets, we demonstrate that MNI outperforms some of the state-of-the-art baselines both for CTR prediction and top-N recommendation.

Accurate and reliable state estimation and mapping are the foundation of most autonomous driving systems. In recent years, researchers have focused on pose estimation through geometric feature matching. However, most of the works in the literature assume a static scenario. Moreover, a registration based on a geometric feature is vulnerable to the interference of a dynamic object, resulting in a decline of accuracy. With the development of a deep semantic segmentation network, we can conveniently obtain the semantic information from the point cloud in addition to geometric information. Semantic features can be used as an accessory to geometric features that can improve the performance of odometry and loop closure detection. In a more realistic environment, semantic information can filter out dynamic objects in the data, such as pedestrians and vehicles, which lead to information redundancy in generated map and map-based localization failure. In this paper, we propose a method called LiDAR inertial odometry (LIO) with loop closure combined with semantic information (LIO-CSI), which integrates semantic information to facilitate the front-end process as well as loop closure detection. First, we made a local optimization on the semantic labels provided by the Sparse Point-Voxel Neural Architecture Search (SPVNAS) network. The optimized semantic information is combined into the front-end process of tightly-coupled light detection and ranging (LiDAR) inertial odometry via smoothing and mapping (LIO-SAM), which allows us to filter dynamic objects and improve the accuracy of the point cloud registration. Then, we proposed a semantic assisted scan-context method to improve the accuracy and robustness of loop closure detection. The experiments were conducted on an extensively used dataset KITTI and a self-collected dataset on the Jilin University (JLU) campus. The experimental results demonstrate that our method is better than the purely geometric method, especially in dynamic scenarios, and it has a good generalization ability.

Introducing a knowledge graph into a recommender system as auxiliary information can effectively solve the sparse and cold start problems existing in traditional recommender systems. In recent years, many researchers have performed related work. A recommender system with knowledge graph embedding learning characteristics can be combined with a recommender system of the following three forms: one-by-one learning, joint learning, and alternating learning. For current knowledge graph embedding, a deep learning framework only has one embedding mode, which fails to excavate the potential information from the knowledge graph thoroughly. To solve this problem, this paper proposes the Ripp-MKR model, a multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet, which combines joint learning and alternating learning of knowledge graphs and recommender systems. Ripp-MKR is a deep end-to-end framework that utilizes a knowledge graph embedding task to assist recommendation tasks. Similar to the MKR model, in the Ripp-MKR model, two tasks are associated with cross and compress units, which automatically share latent features and learn the high-order interactions among items in recommender systems and entities in the knowledge graph. Additionally, the model borrows ideas from RippleNet and combines the knowledge graph with the historical interaction record of a user's historically clicked items to represent the user's characteristics. Through extensive experiments on real-world datasets, we demonstrate that Ripp-MKR achieves substantial gains over state-of-the-art baselines in movie, book, and music recommendations.

[This corrects the article DOI: 10.1371/journal.pone.0224537.].

[This corrects the article DOI: 10.1371/journal.pone.0103482.].

Gut microbiome plays an essential role in asthma development, and probiotic-based manipulation of the gut microbiome has been proposed to prevent asthma. Although the preventive effect of Lactobacillus supplementation against allergies has been reported, the precise Lactobacillus species beneficial for effective prevention of asthma remain unidentified and the underlying mechanisms remain unclear. Therefore, we aimed to investigate the efficacy of oral administration of six Lactobacillus species and the mechanism underlying asthma prevention via gut microbiome modulation. We investigated the effects of oral administration of L. rhamnosus, L. fermentum, L. casei, L. gasseri, L. salivarius, and L. reuteri (five strains of each species) on asthma and gut microbiome of house dust mite (HDM)-treated murine models of asthma. Of these, L. reuteri administration was the most effective: it alleviated airway inflammation, decreased total IgE and HDM-IgG1, and reduced Th2-associated pro-inflammatory cytokines. Moreover, modulation of specific microbial genera by L. reuteri was more effective in asthma prevention than the modulation of the overall microbiota composition. Lactobacillus and Enterococcus were enriched after L. reuteri supplementation and were closely associated with total IgE and IL-13 production. Furthermore, L. reuteri specifically altered the gut microbial function toward butyrate generation. Thus, L. reuteri may reduce the risk of asthma development by modulating specific gut microbiota to improve the lung immune environment. Our study suggests a novel option for gut microbiome manipulation via L. reuteri supplementation for suppression of asthma and other allergic diseases.

Bowel health is an important factor for duck rearing that has been linked to feed uptake and growth and death rates. Because the regulatory networks associated with acute stress-mediated injury in the duck gastrointestinal tract have not clearly elucidated, we aimed to explore potential miRNA-mRNA pairs and their regulatory roles in oxidative stress injury caused by transport stress. Here, 1-day-old mallard ducklings from the same breeder flock were collected and transported for 8 h, whereas the control group was not being transported. Various parameters reflecting oxidative stress and the tissue appearance of the intestine were assessed. The data showed that the plasma T-AOC and SOD concentrations were decreased in the transported ducklings. The intestine of the transported ducklings also displayed significant damage. High-throughput sequencing of the intestine revealed 44 differentially expressed miRNAs and 75 differentially expressed genes, which constituted 344 miRNA-mRNA pairs. KEGG pathway analysis revealed that the metabolic, FoxO signaling, influenza A and TGF-β signaling pathways were mainly involved in the mechanism underlying the induction of intestinal damage induced by simulated transport stress in ducks. A miRNA-mRNA pair, miR-217-5p/CHRDL1, was selected to validate the miRNA-mRNA negative relationship, and the results showed that miR-217-5p could influence CHRDL1 expression. This study provides new useful information for future research on the regulatory network associated with mucosal damage in the duck intestine.

In recent years, there has been growing concern among consumers about pesticide contamination in fruits. Therefore, rapid, reliable, and consistent detection methods for OPPs, especially dimethoate, are crucially needed. The existing quantitative methods for detecting dimethoate are not suitable for rapid measuring system such as the dimethoate samples from two channels. Hence this paper examines the utilization of a dual-channel system for utilize the absorption variations of the Localized Surface Plasmon Resonance (LSPR) bands of gold nanoparticles (AuNPs) were investigate for detection of dimethoate. Under optimized conditions, the relationship between concentrations of dimethoate and absorbance ratios (A(520)/A(640)) was linearly found in the concentration range of 10-100 nM. Result from the experiment shows that both channels exhibit a linear correlation coefficient as high as 0.97 and a limit of detection (LOD) as low as 5.5 nM. This LSPR detection system was characterized by testing the dimethoate in apple samples and the recovery rates were found to be in the range of 85.90% to 107.37%. The proposed dual-channel LSPR system for detecting dimethoate creating a new approach for detecting organophosphate insecticide in agricultural fields. It could lay the foundation for designing a high-throughput analysis of the insecticides using a wavelength division multiplexing switch (WDMS).

In this study, we ascertained the chest CT data of 60 patients admitted to 3 hospitals in Chongqing with confirmed COVID-19. We conducted anatomical and pathological analyses to elucidate the possible reasons for the distribution, morphology, and characteristics of COVID-19 in chest CT. We also shared a semiquantitative scoring of affected lung segments, which was recommended by our local medical association. This scoring system was applied to quantify the severity of the disease. The most frequent imaging findings of COVID-19 were subpleural ground glass opacities and consolidation; there was a significant difference in semiquantitative scores between the early, progressive, and severe stages of the disease. We conclude that the chest CT findings of COVID-19 showed certain characteristics because of the anatomical features of the human body and pathological changes caused by the virus. Therefore, chest CT is a valuable tool for facilitating the diagnosis of COVID-19 and semiquantitative scoring of affected lung segments may further elucidate diagnosis and assessment of disease severity. This will assist healthcare workers in diagnosing COVID-19 and assessing disease severity, facilitate the selection of appropriate treatment options, which is important for reducing the spread of the virus, saving lives, and controlling the pandemic.

It has been widely recognized that the early or delayed puberty appears to display harmful effects on adult health outcomes. During the timing of puberty, pituitaries responds to the hypothalamus and then introduce the following response of ovaries in hypothalamic-pituitary-gonadal axis. DNA methylation has been recently suggested to regulate the onset of puberty in female mammals. However, to date, the changes of DNA methylation in pituitaries have not been investigated during pubertal transition. In this study, using gilts as the pubertal model, the genome-scale DNA methylation of pituitaries was profiled and compared across Pre-, In- and Post-puberty by using the reduced representation bisulfite sequencing. We found that average methylation levels of each genomic feature in Post- were lower than Pre- and In-pubertal stage in CpG context, but they were higher in In- than that in Pre- and Post-pubertal stage in CpH (where H = A, T, or C) context. The methylation patterns of CpHs were more dynamic than that of CpGs at the location of high CpG content, low CpG content promoter genes, and differently genomic CGIs. Furthermore, the differently genomic CGIs were likely to show in a similar manner in CpG context but display in a stage-specific manner in the CpH context across the Pre-, In- and Post-pubertal stage. Among these pubertal stages, 5 kb upstream regions of the transcription start sites were protected from both CpG and CpH methylation changes. 12.65% of detected CpGs were identified as the differentially methylated CpGs, regarding 4301 genes which were involved in the fundamental functions of pituitaries. 0.35% of detected CpHs were identified as differentially methylated CpHs, regarding 3691 genes which were involved in the biological functions of releasing gonadotropin hormones. These observations and analyses would provide valuable insights into epigenetic mechanism of the initiation of puberty in pituitary level.

[This corrects the article DOI: 10.1371/journal.pone.0187519.].

Phosphate (Pi) transporters play critical roles in Pi acquisition and homeostasis. However, little is known about these transporters in oilseed rape. Therefore, the aim of the present study was to characterize the members of the PHT1 gene family in allotetraploid Brassica napus and to analyze their expression profiles in response to environmental stresses. In total, 49 PHT1 family members were identified in B. napus, including 27 genes in the A subgenome and 22 in the C subgenome. Most of the PHT1 proteins were predicted to localize to the plasma membrane. Phylogenetic analysis suggested that the members of the PHT1 gene family can be divided into seven clades, with the introns/exons and protein motifs conserved in each clade. Collinearity analysis revealed that most of the BnaPHT1 genes shared syntenic relationships with PHT1 members in Arabidopsis thaliana, B. rapa, and B. oleracea, and that whole-genome duplication (polyploidy) played a major driving force for BnaPHT1 evolution in addition to segmental duplication. Transcript abundance analysis showed that a broad range of expression patterns of individual BnaPHT1 genes occurred in response to phosphorus (P) deficiency. In addition, the expression levels of BnaPHT1 genes can be regulated by different nutrient stresses, including nitrogen (N), potassium (K), sulfur (S) and iron (Fe) stresses. Moveover, salt and drought stresses can regulate the transcript abundances of BnaPHT1s, as well as phytohormones including auxin and cytokinin. Gene coexpression analysis based on the RNA-seq data implied that BnaPHT1s might cooperate with each other as well as with other genes to regulate nutrient homeostasis in B. napus. Further analysis of the promoters revealed that GT-1, DRE and P1BS elements are widely distributed within the promoter regions of BnaPHT1 genes. Our results indicate that BnaPHT1s might be involved in cross-talk for sensing the external status of P, N, K, S and Fe, as well as salt and drought stresses. Moreover, these processes might be mediated by phytohormones. Our findings provide the first step in the complex genetic dissection of the Pi transport system in plants and implicate multiple transcriptional regulation, which probably refers to new roles of PHT1 genes in B. napus.

The Gram-negative pathogen, Acinetobacter baumannii has emerged as a global nosocomial health threat affecting the majority of hospitals in the U.S. and abroad. The redox protein thioredoxin has been shown to play several roles in modulation of cellular functions affecting various virulence factors in Gram-negative pathogens. This study aims to explore the role of thioredoxin-A protein (TrxA) in A. baumannii virulence. We determined that deletion of the TrxA gene did not significantly affect resistance to environmental stressors such as temperature, salt, and pH. However, TrxA was critical for survival in the presence of elevated levels of hydrogen peroxide. Lack of TrxA was associated with decreased expression of type IV pili related genes and an inability to undergo normal twitching motility. Interestingly, the TrxA-null mutant was able to form biofilms better than the wildtype (WT) and was observed to be significantly less virulent than the WT in a pulmonary infection model. These results are supportive of thioredoxin playing a key role in A. baumannii virulence.

During the atmospheric correction of remote sensing data in inland waters, the original Second Simulation of the Satellite Signal in the Solar Spectrum-Vector version (6SV) model does not eliminate the specular reflection of downward skylight radiance at the air-water interface. Thus, we propose a modified version of the 6SV model (M6SV) that does remove reflected skylight at the air-water interface. We apply the new model to the atmospheric correction of a Landsat 8 Operational Land Imager (OLI) image over Taihu Lake, China, where the aerosol optical depth is known. In situ reflectance measurements acquired concurrently with the L8/OLI image are used to validate the performance of the new M6SV algorithm. To further analyze the merits and demerits of M6SV, the model is compared with two short-wave infrared (SWIR)-based atmospheric correction models: the Sea-Viewing Wide Field-of-View Sensor Data Analysis System short-wave infrared (SD-SWIR) model and the Vanhellemont & Ruddick short-wave infrared with a per scene fixed aerosol type (VR-SWIR-F) model. Comparisons of results from all three L8/OLI image atmospheric corrections with the in situ remote sensing reflectance data show that M6SV produces reliable atmospheric corrections in the green and red spectral bands and is an effective alternative for Landsat 8 OLI atmospheric correction in inland waters.

The Tibetan horse is a species endemic to the Tibetan plateau, with considerable economic value in the region. However, we currently have little genetic evidence to verify whether the breed originated in Tibet or if it entered the area via an ancient migratory route. In the present study, we analyzed the hypervariable segment I sequences of mitochondrial DNA (mtDNA) in 2,050 horses, including 290 individuals from five Tibetan populations and 1,760 from other areas across Asia. Network analysis revealed multiple maternal lineages in the Tibetan horse. Component analysis of sub-lineage F3 indicated that it decreased in frequency from east to west, a trend reflected both southward and northward from Inner Mongolia. Analysis of population genetics showed that the Deqen horse of eastern Tibet was more closely related to the Ningqiang horse of northern China than to other Tibetan horses or the Yunnan horse. These results indicated that the Tibetan horse migrated first from Central Asia to Mongolia, moved south to eastern Tibet (near Deqen), then finally westward to other regions of Tibet. We also identified a novel lineage K that mainly comprises Tibetan and Yunnan horses, suggesting autochthonous domesticated origin for some Tibetan horse breeds from local wild horses. In conclusion, our study demonstrated that modern Tibetan horse breeds originated from the introgression of local wild horses with exotic domesticated populations outside China.

The white-rot fungus Cerrena unicolor BBP6 produced up to 243.4 U mL-1 laccase. A novel laccase isoform LacA was purified; LacA is a homodimer with an apparent molecular mass of 55 kDa and an isoelectric point of 4.7. Its optimal pH was 2.5, 4.0, and 5.5 when 2, 2'-Azinobis-(3-ethylbenzthiazoline-6-sulphonate) (ABTS), guaiacol, and 2, 6-dimethoxyphenol (2, 6-DMP) were used as the substrates, respectively. The optimal temperature was 60°C for ABTS and 80°C for both guaiacol and 2, 6-DMP. LacA retained 82-92% activity when pH was greater than 4 and 42%-92% activity at or below 50°C. LacA was completely inhibited by 0.1 mM L-cysteine, 1 mM Dithiothreitol, and 10 mM metal ions, Ca2+, Mg2+ and Co2+. LacA had good affinity for ABTS, with a Km of 49.1 μM and a kcat of 3078.9 s-1. It decolorized synthetic dyes at 32.3-87.1%. In the presence of 1-hydroxybenzotriazole (HBT), LacA decolorized recalcitrant dyes such as Safranine (97.1%), Methylene Blue (98.9%), Azure Blue (96.6%) and simulated textile effluent (84.6%). With supplemented manganese peroxidase (MnP), Mn2+ and HBT, the purified LacA and BBP6 fermentation broth showed great potential in denim bleaching, with an up to 5-fold increase in reflectance values.

Acupuncture has been used in the therapy of Alzheimer disease (AD); however, its neural mechanisms are still unclear. The aim of this study is to examine the effect of acupuncture on the functional connectivity in AD by using resting-state functional magnetic resonance imaging (rs-fMRI). Twenty-eight subjects (14 AD and 14 normal controls) participated in this study. The rs-fMRI data were acquired before and after acupuncture stimulation at the acupoints of Tai chong (Liv3) and Hegu (LI4). During the baseline resting state, by using the amplitude of low-frequency fluctuations (ALFF), we found a significantly decreased or increased ALFF in the AD patients relative to the controls. These regions were located in the right superior frontal gyrus (SFG), left postcentral gyrus, subgenual cingulate cortex (SCC), right middle cingulate cortex (MCC), right inferior frontal gyrus (IFG), right hippocampus and the right inferior temporal gyrus (ITG). Then, we selected these brain regions as seeds to investigate whether regional activity and functional connectivity could be modulated by acupuncture in the AD patients. When compared to the pre-acupuncture stage, several of the above regions showed an increased or decreased ALFF after acupuncture in the AD patients. In addition, the functional connectivity between the hippocampus and the precentral gyrus showed enhancement after acupuncture in the AD patients. Finally, there were close correlations between the functional activity, connectivity and clinical performance in the AD patients. The current study confirmed that acupuncture at Tai chong (Liv3) and He gu (LI4) can modulate functional activity and connectivity of specific cognition-related regions in AD patients.

Although mice are commonly used to study different aspects of fatty liver disease, currently there are no validated fully automated methods to assess steatosis in mice. Accurate detection of macro- and microsteatosis in murine models of fatty liver disease is important in studying disease pathogenesis and detecting potential hepatotoxic signature during drug development. Further, precise quantification of macrosteatosis is essential for quantifying effects of therapies. Here, we develop and validate the performance of automated classifiers built using image processing and machine learning methods for detection of macro- and microsteatosis in murine fatty liver disease and study the correlation of automated quantification of macrosteatosis with expert pathologist's semi-quantitative grades. The analysis is performed on digital images of 27 Hematoxylin & Eosin stained murine liver biopsy samples. An expert liver pathologist scored the amount of macrosteatosis and also annotated macro- and microsteatosis lesions on the biopsy images using a web-application. Using these annotations, supervised machine learning and image processing techniques, we created classifiers to detect macro- and microsteatosis. For macrosteatosis prediction, the model's precision, sensitivity and area under the receiver operator characteristic (AUROC) were 94.2%, 95%, 99.1% respectively. When correlated with pathologist's semi-quantitative grade of steatosis, the model fits with a coefficient of determination value of 0.905. For microsteatosis prediction, the model has precision, sensitivity and AUROC of 79.2%, 77%, 78.1% respectively. Validation by the expert pathologist of classifier's predictions made on unseen images of biopsy samples showed 100% and 63% accuracy for macro- and microsteatosis, respectively. This novel work demonstrates that fully automated assessment of steatosis is feasible in murine liver biopsies images. Our classifier has excellent sensitivity and accuracy for detection of macrosteatosis in murine fatty liver disease.

This paper explores the mechanism for the harmonic voltage measurement error (HVME) of a capacitor voltage transformer (CVT) in a comprehensive way and develops a practical error correction method to improve the harmonic measurement performance of a CVT. The harmonic equivalent circuit (HEC) of CVTs with different types of dampers are established and the parameter value calculation methods of the circuit elements are presented. Characteristics of the individual inherent resonance mode of the HEC, including the resonant frequency, the resonant type and the circuit elements involved, are analyzed systematically. Centered on resonance mode formulation, an universal error correction method for CVTs in measuring harmonic voltage is proposed based on the piecewise fitting of the transformation ratio. A physical experimental platform available for low-voltage CVTs is developed in laboratory, which utilizes a 10-kV harmonic generator and a step-up transformer to provide harmonic signals with high voltage level. Moreover, a novel experimental scheme for testing the HVME of high-voltage CVTs is proposed based on alternating current/direct current (AC/DC) filters. Experiments are conducted on these experimental platforms to test the HVME of a 35-kV CVT in laboratory and a 525-kV CVT in a back-to-back DC converter station. The experimental results verify the validity of the built HECs of CVTs, the resonant mode analysis results and the resonant frequency calculation method. Finally, the effectiveness of the proposed harmonic error correction method is proved by several examples.

In recent years, an increasing number of studies have reported the presence of plant miRNAs in human samples, which resulted in a hypothesis asserting the existence of plant-derived exogenous microRNA (xenomiR). However, this hypothesis is not widely accepted in the scientific community due to possible sample contamination and the small sample size with lack of rigorous statistical analysis. This study provides a systematic statistical test that can validate (or invalidate) the plant-derived xenomiR hypothesis by analyzing 388 small RNA sequencing data from human samples in 11 types of body fluids/tissues. A total of 166 types of plant miRNAs were found in at least one human sample, of which 14 plant miRNAs represented more than 80% of the total plant miRNAs abundance in human samples. Plant miRNA profiles were characterized to be tissue-specific in different human samples. Meanwhile, the plant miRNAs identified from microbiome have an insignificant abundance compared to those from humans, while plant miRNA profiles in human samples were significantly different from those in plants, suggesting that sample contamination is an unlikely reason for all the plant miRNAs detected in human samples. This study also provides a set of testable synthetic miRNAs with isotopes that can be detected in situ after being fed to animals.

[This corrects the article DOI: 10.1371/journal.pone.0184681.].

Using the phoA-fusion technology, the recombinant metallothionein (MT) from freshwater crab (Sinopotamon yangtsekiense) has been successfully produced in Escherichia coli. MT purified from the bacterial suspension showed one polypeptide with a molecular weight of 7 kDa by tricine-sodium dodecyl sulfate-polyacrylamide gel electrophoresis (Tricine-SDS-PAGE). Western-blotting confirmed the polypeptides had a specific reactivity with mouse polyclonal MT anti-serum. Based on the purified MT and MT anti-serum, the reaction parameters for an enzyme-linked immunosorbent assay (ELISA) were developed. The direct coating ELISA showed a higher linear relationship compared to antibody sandwich coating ELISA. The optimal dilution rates of purified MT anti-serum and coating period were shown to be 1:160,000 and 12 hours at 4°C. At 37°C, the appropriate reaction duration of the first antibody and the second antibody were 2 hours and 1 hour, respectively. According to these optimal parameters, the standard linear equation, y = 0.0032x + 0.1769 (R2 = 0.9779, x, y representing MT concentration and OD450 value), was established for the determination of MT concentration with a valid range of 3.9-500 ng/ml. In verification experiments, the mean coefficients of variation of the intra-assay and inter-assay were 3.260% and 3.736%, respectively. According to the result of MT recovery, ELISA with an approaching 100% MT recovery was more reliable and sensitive than the Cd saturation assay. In conclusion, the newly developed ELISA of this study was precise, stable and repeatable, and could be used as a biomarker tool to monitor pollution by heavy metals.

Phytolacca americana L. (pokeweed) has metal phytoremediation potential, but little is known about its metal accumulation-related genes. In this study, the de novo sequencing of total RNA produced 53.15 million reads covering 10.63 gigabases of transcriptome raw data in cadmium (Cd)-treated and untreated pokeweed. Of the 97,502 assembled unigenes, 42,197 had significant matches in a public database and were annotated accordingly. An expression level comparison between the samples revealed 1515 differentially expressed genes (DEGs), 923 down- and 592 up-regulated under Cd treatment. A KEGG pathway enrichment analysis of DEGs revealed that they were involved in 72 metabolism pathways, with photosynthesis, phenylalanine metabolism, ribosome, phenylpropanoid biosynthesis, flavonoid biosynthesis and carbon fixation in photosynthetic organisms containing 24, 18, 72, 14, 7 and 15 genes, respectively. Genes related to heavy metal tolerance, absorption, transport and accumulation were also identified, including 11 expansins, 8 nicotianamine synthases, 6 aquaporins, 4 ZRT/IRT-like proteins, 3 ABC transporters and 3 metallothioneins. The gene expression results of 12 randomly selected DEGs were validated using quantitative real-time PCR, and showed different response patterns to Cd in their roots, stems and leaves. These results may be helpful in increasing our understanding of heavy metal hyperaccumulators and in future phytoremediation applications.

[This corrects the article DOI: 10.1371/journal.pone.0141138.].

A biosensing system with optical fibers is proposed for the colorimetric detection of melamine in liquid milk samples by using the localized surface plasmon resonance (LSPR) of unmodified gold nanoparticles (AuNPs). The biosensing system consists of a broadband light source that covers the spectral range from 200 nm to 1700 nm, an optical attenuator, three types of 600 μm premium optical fibers with SMA905 connectors and a miniature spectrometer with a linear charge coupled device (CCD) array. The biosensing system with optical fibers is low-cost, simple and is well-proven for the detection of melamine. Its working principle is based on the color changes of AuNPs solution from wine-red to blue due to the inter-particle coupling effect that causes the shifts of wavelength and absorbance in LSPR band after the to-be-measured melamine samples were added. Under the optimized conditions, the detection response of the LSPR biosensing system was found to be linear in melamine detection in the concentration range from 0μM to 0.9 μM with a correlation coefficient (R2) 0.99 and a detection limit 33 nM. The experimental results obtained from the established LSPR biosensing system in the actual detection of melamine concentration in liquid milk samples show that this technique is highly specific and sensitive and would have a huge application prospects.

Eucalyptus species are widely planted for reforestation in subtropical China. However, the effects of Eucalyptus plantations on the regional water use remain poorly understood. In an age sequence of 2-, 4- and 6-year-old Eucalyptus plantations, the tree water use and soil evaporation were examined by linking model estimations and field observations. Results showed that annual evapotranspiration of each age sequence Eucalyptus plantations was 876.7, 944.1 and 1000.7 mm, respectively, accounting for 49.81%, 53.64% and 56.86% of the annual rainfall. In addition, annual soil evaporations of 2-, 4- and 6-year-old were 318.6, 336.1, and 248.7 mm of the respective Eucalyptus plantations. Our results demonstrated that Eucalyptus plantations would potentially reduce water availability due to high evapotranspiration in subtropical regions. Sustainable management strategies should be implemented to reduce water consumption in Eucalyptus plantations in the context of future climate change scenarios such as drought and warming.

Abscisic acid (ABA) plays an important role in abiotic stress response and physiological signal transduction resisting to the adverse environment. Therefore, it is very essential for the quantitative detection of abscisic acid (ABA) due to its indispensable role in plant physiological activities. Herein, a new detection method based on localized surface plasmon resonance (LSPR) using aptamer-functionalized gold nanoparticles (AuNPs) is developed without using expensive instrument and antibody. In the presence of ABA, ABA specifically bind with their aptamers to form the ABA-aptamer complexes with G-quadruplex-like structure and lose the ability to stabilize AuNPs against NaCl-induced aggregation. Meanwhile, the changes of the LSPR spectra of AuNP solution occur and therefore the detection of ABA achieved. Under optimized conditions, this method showed a good linear range covering from 5×10-7 M to 5×10-5 M with a detection limit of 0.33 μM. In practice, the usage of this novel method has been demonstrated by its application to detect ABA from fresh leaves of rice with the relative error of 6.59%-7.93% compared with ELISA bioassay. The experimental results confirmed that this LSPR-based biosensor is simple, selective and sensitive for the detection of ABA. The proposed LSPR method could offer a new analytical platform for the detection of other plant hormones by changing the corresponding aptamer.

The Yangtze River Delta White Goat is the only goat breed that produces high-quality brush hair, or type III hair, which is specialized for use in top-grade writing brushes. There has been little research, especially molecular research, on the traits that result in high-quality brush hair in the Yangtze River Delta White Goat. To explore the molecular mechanisms of the formation of high-quality brush hair, High-throughput RNA-Seq technology was used to compare skin samples from Yangtze River Delta White Goats that produce high-quality hair and non high-quality hair for identification of the important genes and related pathways that might influence the hair quality traits. The results showed that 295 genes were expressed differentially between the goats with higher and lower hair quality, respectively. Of those genes, 132 were up-regulated, 62 were down-regulated, and 101 were expressed exclusively in the goats with high-quality brush hair. Gene Ontology and Metabolic Pathway Significant Enrichment analyses of the differentially expressed genes indicated that the MAP3K1, DUSP1, DUSP6 and the MAPK signaling pathway might play important roles in the traits important for high-quality brush hair.

Our previous study found that Lactobacillus plantarum CCFM639 had the ability to alleviate acute aluminum (Al) toxicity when the strain was introduced simultaneously with Al exposure. This research was designed to elucidate the therapeutic effects of living and dead L. plantarum CCFM639 against chronic Al toxicity and to gain insight into the protection modes of this strain. Animals were assigned into control, Al only, Al + living CCFM639, and Al + dead CCFM639 groups. The Al exposure model was established by drinking water for the first 4 weeks. The strain was given after Al exposure by oral gavage at 109 colony-forming units once per day for 12 weeks. The results show that the Al binding ability of dead CCFM639 was similar to that of living CCFM639 in vitro. The ingestion of living or dead CCFM639 has similar effects on levels of Al and trace element in tissues, but living strains led to more significant amelioration of oxidative stress and improvement of memory deficits in Al-exposed mice. In conclusion, in addition to intestinal Al sequestration, CCFM639 treatment offers direct protection against chronic Al toxicity by alleviation of oxidative stress. Therefore, L. plantarum CCFM639 has a potential as dietary supplement ingredient that provides protection against Al-induced injury.

SecA is an essential protein in the major bacterial Sec-dependent translocation pathways. E. coli SecA has 901 aminoacyl residues which form multi-functional domains that interact with various ligands to impart function. In this study, we constructed and purified tethered C-terminal deletion fragments of SecA to determine the requirements for N-terminal domains interacting with lipids to provide ATPase activity, pore structure, ion channel activity, protein translocation and interactions with SecYEG-SecDF•YajC. We found that the N-terminal fragment SecAN493 (SecA1-493) has low, intrinsic ATPase activity. Larger fragments have greater activity, becoming highest around N619-N632. Lipids greatly stimulated the ATPase activities of the fragments N608-N798, reaching maximal activities around N619. Three helices in amino-acyl residues SecA619-831, which includes the "Helical Scaffold" Domain (SecA619-668) are critical for pore formation, ion channel activity, and for function with SecYEG-SecDF•YajC. In the presence of liposomes, N-terminal domain fragments of SecA form pore-ring structures at fragment-size N640, ion channel activity around N798, and protein translocation capability around N831. SecA domain fragments ranging in size between N643-N669 are critical for functional interactions with SecYEG-SecDF•YajC. In the presence of liposomes, inactive C-terminal fragments complement smaller non-functional N-terminal fragments to form SecA-only pore structures with ion channel activity and protein translocation ability. Thus, SecA domain fragment interactions with liposomes defined critical structures and functional aspects of SecA-only channels. These data provide the mechanistic basis for SecA to form primitive, low-efficiency, SecA-only protein-conducting channels, as well as the minimal parameters for SecA to interact functionally with SecYEG-SecDF•YajC to form high-efficiency channels.

We conducted a meta-analysis to explore the relationships between p14ARF gene methylation and clinicopathological features of colorectal cancer (CRC). Databases, including Pubmed, Embase and Cochrane Library, were searched and, finally, a total of 18 eligible researches encompassing 1988 CRC patients were selected. Combined odds ratios (ORs) with 95% confidence intervals (95% CIs) were evaluated under a fixed effects model for absence of heterogeneity. Significant associations were observed between p14ARF gene methylation and tumor location (OR = 2.35, 95% CI: 1.55-3.55, P = 0.001), microsatellite instability (MSI) status (OR = 3.28, 95% CI: 2.12-5.07, P

This study aimed to report our experiences with surgical repair in patients of all ages with persistent truncus arteriosus.From July 2004 to July 2014, 50 consecutive patients with persistent truncus arteriosus who underwent anatomical repair were included in the retrospective review. Median follow-up time was 3.4 years (range, 3 months to 10 years).Fifty patients underwent anatomical repair at a median age of 19.6 months (range, 20 days to 19.1 years). Thirty patients (60%) were older than one year. The preoperative pulmonary vascular resistance and mean pulmonary artery pressure were 4.1±2.1 (range, 0.1 to 8.9) units.m2 and 64.3±17.9 (range, 38 to 101) mmHg, respectively. Significant truncal valve regurgitation was presented in 14 (28%) patients. Hospital death occurred in 3 patients, two due to pulmonary hypertensive crisis and the other due to pneumonia. Three late deaths occurred at 3, 4 and 11 months after surgery. The actuarial survival rates were 87.7% and 87.7% at 1 year and 5 years, respectively. Multivariate analysis identified significant preoperative truncal valve regurgitation was a risk factor for overall mortality (odds ratio, 7.584; 95%CI: 1.335-43.092; p = 0.022). Two patients required reoperation of truncal valve replacement. One patient underwent reintervention for conduit replacement. Freedom from reoperation at 5 years was 92.9%. At latest examination, there was one patient with moderate-to-severe truncal valve regurgitation and four with moderate. Three patients had residual pulmonary artery hypertension. All survivors were in New York Heart Association class I-II.Complete repair of persistent truncus arteriosus can be achieved with a relatively low mortality and acceptable early- and mid-term results, even in cases with late presentation. Significant preoperative truncal valve regurgitation remains a risk factor for overall mortality. The long-term outcomes warrant further follow-up.

User anonymity is one of the key security features of an authenticated key agreement especially for communicating messages via an insecure network. Owing to the better properties and higher performance of chaotic theory, the chaotic maps have been introduced into the security schemes, and hence numerous key agreement schemes have been put forward under chaotic-maps. Recently, Xie et al. released an enhanced scheme under Farash et al.'s scheme and claimed their improvements could withstand the security loopholes pointed out in the scheme of Farash et al., i.e., resistance to the off-line password guessing and user impersonation attacks. Nevertheless, through our careful analysis, the improvements were released by Xie et al. still could not solve the problems troubled in Farash et al‥ Besides, Xie et al.'s improvements failed to achieve the user anonymity and the session key security. With the purpose of eliminating the security risks of the scheme of Xie et al., we design an anonymous password-based three-party authenticated key agreement under chaotic maps. Both the formal analysis and the formal security verification using AVISPA are presented. Also, BAN logic is used to show the correctness of the enhancements. Furthermore, we also demonstrate that the design thwarts most of the common attacks. We also make a comparison between the recent chaotic-maps based schemes and our enhancements in terms of performance.

This study determined if the variation in grain filling parameters between two different spikelet types of rice (Oryza sativa L.) is regulated by the hormonal levels in the grains. Two rice mutants, namely, a large-grain mutant (AZU-M) and a small-grain mutant (ZF802-M), and their respective wild types (AZU-WT and ZF802-WT) were grown in the field. The endosperm cell division rate, filling rate, and hormonal levels: zeatin + zeatin riboside (Z+ZR), indo-3-acetic acid (IAA), polyamines (PAs), and abscisic acid (ABA) were determined. The results showed that there was no significant difference between the filling and endosperm cell division rates. These rates were synchronous between the superior and inferior spikelets for both mutants. However, the abovementioned parameters were significantly different between the two spikelet types for the two wild types. The superior spikelets filled faster and their filling rate was higher compared to the inferior ones. Changes in the concentrations of plant hormones were consistent with the observed endosperm cell division rate and the filling rate for both types of spikelets of mutant and wild type plants. Regression analysis showed a significant positive correlation between cell division and filling rates with the concentrations of the investigated hormones. Exogenous chemical application verified the role of ABA, IAA, and PAs in grain filling. The results indicate that poor filling of inferior spikelets in rice occurs primarily due to the reduced hormone concentrations therein, leading to lower division rate of endosperm cells, fewer endosperm cells, slower filling rate, and smaller grain weight.

We aimed to investigate the associations between diabetes and quality of life (QOL) among breast cancer survivors.A cross-sectional survey was conducted at 34 Cancer Recovery Clubs across China from May 2014 to January 2015. Quality of life was measured by the Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30) and the Quality of Life Questionnaire-Breast Cancer Module 23 (QLQ-BR23, simplified Chinese version). Information on social-demography, diagnosis and treatment of tumors, and diabetes mellitus were collected by self-reported questionnaires. Univariate analyses of covariance (ANCOVA) was performed to assess the difference in QOL between patients with or without diabetes mellitus, and multiple linear regression models were used to examine the associations after controlling for confounders.Diabetes, both of type 1 diabetes (T1DM) and type 2 diabetes (T2DM) significantly reduced QOL. This effect of diabetes on QOL is independent of tumor size, regional lymph node metastasis, distant metastasis and tumor stage index (TNM). After adjusting for different social-demography, diagnosis and treatment of the tumor, the tumor's stage and other chronic comorbidities, breast cancer survivors with diabetes got significantly lower scores in functional dimensions (including physical, role, emotional and social functionings measured by EORTC QLQ-C30; body image (BRBI) and future perspective (BRFU) measured by QLQ-BR23, as well as economic difficulties than those without diabetes (Padjusted