Search

Abstract Background Fargesia macclureana (Poaceae) is a woody bamboo species found on the Qinghai–Tibet Plateau (QTP) approximately 2000 ~ 3800 m above sea level. It rarely blossoms in the QTP, but it flowered 20 days after growing in our lab, which is in a low-altitude area outside the QTP. To date, little is known regarding the molecular mechanism of bamboo flowering, and no studies of flowering have been conducted on wild bamboo plants growing in extreme environments. Here, we report the first de novo transcriptome sequence for F. macclureana to investigate the putative mechanisms underlying the flowering time control used by F. macclureana to adapt to its environment. Results Illumina deep sequencing of the F. macclureana transcriptome generated 140.94 Gb of data, assembled into 99,056 unigenes. A comprehensive analysis of the broadly, specifically and differentially expressed unigenes (BEUs, SEUs and DEUs) indicated that they were mostly involved in metabolism and signal transduction, as well as DNA repair and plant-pathogen interactions, which may be of adaptive importance. In addition, comparison analysis between non-flowering and flowering tissues revealed that expressions of FmFT and FmHd3a, two putative F. macclureana orthologs, were differently regulated in NF- vs F- leaves, and carbohydrate metabolism and signal transduction were two major KEGG pathways that DEUs were enriched in. Finally, we detected 9296 simple sequence repeats (SSRs) that may be useful for further molecular marker-assisted breeding. Conclusions F. macclureana may have evolved specific reproductive strategies for flowering-related pathways in response to photoperiodic cues to ensure long vegetation growing period. Our findings will provide new insights to future investigations into the mechanisms of flowering time control and adaptive evolution in plants growing at high altitudes.

The ambient population has been regarded as an important indicator for analyzing or predicting thefts. However, the literature has taken it as a homogenous group and seldom explored the varied impacts of different kinds of ambient populations on thefts. To fill this gap, supported by mobile phone trajectory data, this research investigated the relationship between ambient populations of different social groups and theft in a major city in China. With the control variables of motivated offenders and guardianship, spatial-lag negative binominal models were built to explore the effects of the ambient populations of different social groups on the distribution of theft. The results found that the influences of ambient populations of different social groups on the spatial distribution of theft are different. Accounting for the difference in the “risk–benefit” characteristics among different activity groups to the offenders, individuals from the migrant population are the most likely to be potential victims, followed by suburban and middle-income groups, while college, affluent, and affordable housing populations are the least likely. The local elderly population had no significant impact. This research has further enriched the studies of time geography and deepened routine activity theory. It suggests that the focus of crime prevention and control strategies developed by police departments should shift from the residential space to the activity space.

Abstract Paleobotanical and sedimentological records indicate that marked climatic and environmental transitions had taken place in East China by the early Miocene. These transitions were driven by paleogeographic and global climatic shifts, but the respective roles of these shifts in the Asian climate remain unclear. Here, we use the low‐resolution Norwegian Earth System Model and Community Atmosphere Model version 4 to compare the effects of Tibetan Plateau (TP) growth, Paratethys Sea retreat and global cooling on the East Asian climate change that occurred by the early Miocene. The modeling results indicate that these three factors had clearly different climatic effects in East China. In particular, TP growth increased annual precipitation in East China, while Paratethys Sea retreat and global cooling generally decreased annual precipitation in East China. In addition, the three factors studied all drove aridification in inland Asia. Considering the increased precipitation, arid/semiarid‐to‐humid shift in vegetation and high plant diversity present in East China by the early Miocene, as shown in geological records, TP growth thus played a major role in these respects.

Nowadays, pitch motors play an important role in many manufacturing plants. To ensure the other components run normally, it is urgent to automatically monitor the running state of pitch motors and early warning faults to avoid huge losses at a later period. Based on the normal behavior modeling technique, this paper studies the status monitoring of the pitch motors. Based on the fact that the state of the motor varies with time, we propose to train an echo state network with the SCADA data to predict the temperature of the pitch motor. Subsequently, the EWMA (exponentially weighted moving average) technique is used to set the alarm limit lines of each parameter. By employing some real data collected in a wind farm in China to conduct experiments, the results show that in comparison with several other methods, the proposed method can more effectively identify and early warn the faults of the pitch motor.

The Collaborative Filtering (CF) recommendation algorithm, one of the most popular algorithms in Recommendation Systems (RS), mainly includes memory-based and model-based methods. When performing rating prediction using a memory-based method, the approach used to measure the similarity between users or items can significantly influence the recommendation performance. Traditional CFs suffer from data sparsity when making recommendations based on a rating matrix, and cannot effectively capture changes in user interest. In this paper, we propose an improved hybrid collaborative filtering algorithm based on tags and a time factor (TT-HybridCF), which fully utilizes tag information that characterizes users and items. This algorithm utilizes both tag and rating information to calculate the similarity between users or items. In addition, we introduce a time weighting factor to measure user interest, which changes over time. Our experimental results show that our method alleviates the sparsity problem and demonstrates promising prediction accuracy.

This paper empirically evaluates two kinds of features, which are extracted, respectively, with traditional statistical methods and convolutional neural networks (CNNs), in order to improve the performance of seismic patch image classification. In the latter case, feature vectors, named “CNN-features”, were extracted from one trained CNN model, and were then used to learn existing classifiers, such as support vector machines. In this case, to learn the CNN model, a technique of transfer learning using synthetic seismic patch data in the source domain, and real-world patch data in the target domain, was applied. The experimental results show that CNN-features lead to some improvements in the classification performance. By analyzing the data complexity measures, the CNN-features are found to have the strongest discriminant capabilities. Furthermore, the transfer learning technique alleviates the problems of long processing times and the lack of learning data.

Abstract VEGF-C is essential for lymphangiogenesis during development and tumor progression. VEGFR-3 is the well-known cognate receptor of VEGF-C to regulate lymphatic migration and proliferation, but the receptor of VEGF-C in regulating lymphatic sprouting, the initiating step of lymphangiogenesis, still remains elusive. Here we use both in vitro and in vivo methods to demonstrate CD146 as a receptor of VEGF-C to regulate lymphangiogenesis, especially at the sprouting step. Mechanistically, CD146 selectively activates the downstream p38 kinase, upon VEGF-C stimulation, to regulate lymphatic sprouting. Moreover, CD146 can also activate ERK to mediate VEGF-C regulation of the subsequent proliferation and migration of lymphatic endothelial cells. In zebrafish embryos, knockdown or dysfunction of CD146 results in similar developmental defects in lymphatic sprouting, capillary network, parachordal lymphangioblast (PL), and thoracic duct (TD) similar to down-regulation of VEGF-C. Altogether, our data reveals a critical role of CD146 to mediate VEGF-C signaling pathway in lymphangiogenesis.

OBJECTIVE:Cognitive impairment is common among hemodialysis patient, but still lack adequate screening in clinical settings. The Montreal Cognitive Assessment (MoCA) is reportedly to be a sensitive screening tool for cognitive impairment, but its clinical value in patients undergoing hemodialysis is not well established. We aimed to validate the utility of the Beijing version of the MoCA (MoCA-BJ) for detecting cognitive impairment in comparison to a detailed neuropsychological battery as the gold standard. METHODS:We assessed 613 patients undergoing hemodialysis using the MoCA-BJ, the Mini-Mental State Examination (MMSE), and a comprehensive neuropsychological battery. Cognitive dysfunction was defined by the fifth version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V). Spearman's correlation and linear regression were used to estimate the performance of the MoCA-BJ and MMSE in predicting cognitive impairment. A receiver operating characteristic (ROC) curve analysis was used to evaluate the utility of various cutoffs of the MoCA-BJ and MMSE for predicting cognitive impairment. RESULTS:Cognitive impairment was diagnosed in 80.91% (496/613), 75.69% (464/613), and 61.34% (376 /613) of the patients using the DSM-V, MoCA-BJ, and MMSE, respectively. Spearman's rank correlation analysis indicated that the MoCA-BJ was significantly correlated with the neuropsychological battery (rs = 0.639, p

OBJECTIVE:Atrial fibrillation (AF) is associated with adverse outcomes in the general population, but its impact on patients with chronic kidney disease (CKD) remains unclear. In this study, we assessed the association between AF and risks of all-cause mortality and stroke in Chinese adults with CKD. METHODS:We enrolled adults aged 45 years or older with CKD (defined as an estimated glomerular filtration rate

Background. Gestational diabetes mellitus (GDM) seriously affects the health of mothers and infants. The high-glucose-induced inhibition in trophoblast cell viability is an important event in GDM pathogenesis. This study evaluated the expression and clinical significance of miR-136 in GDM patients, and the biological function and related mechanisms of miR-136 in the regulation of trophoblast cell proliferation were explored. Methods. The expression of miR-136 in serum and placenta of GDM patients was measured using quantitative Real-Time PCR. Trophoblast cells were stimulated with high-glucose medium to mimic the pathological changes of GDM, and the effect of miR-136 was examined by CCK-8 assay. A luciferase reporter assay was used to confirm the target gene of miR-136, and the relationship of E2F transcription factor 1 (E2F1) with miR-136 in GDM was further analyzed. Results. miR-136 expression was significantly elevated in GDM serum and tissue samples. By high-glucose treatment, trophoblast cell proliferation was inhibited and miR-136 expression was promoted. The knockdown of miR-136 could promote the proliferation of trophoblast cells exposed to high glucose, whereas the overexpression of miR-136 could suppress it. In addition, E2F1 was identified as a target gene of miR-136, which could mediate the regulatory effect of miR-136 on trophoblast cell proliferation. Conclusion. Collectively, miR-136 expression is increased in both serum and placental tissues in GDM patients, and miR-136 mediates the inhibiting effect of high glucose on trophoblast cell viability by targeting E2F1.

We present the complete mitochondrial genome of Pomacea maculate in this study. The mitochondrial genome is 15,512 bp in length, containing 13 protein-coding genes, 2 rRNA genes, 22 tRNA genes. Overall nucleotide compositions of the light strand are 41.13% of A, 30.81% of T, 15.25% of C and 12.81% of G. Its gene arrangement and distribution are different from the typical vertebrates. The absence of D-loop is consistent with the Gastropoda, but at least one lengthy non-coding region is essential regulatory element for the initiation of transcription and replication. Phylogenetic tree is constructed by the maximum-likelihood method based on the complete mitochondrial genomes of 15 species of Caenogastropoda, using Helix aspersa as outgroup to assess their actual phylogenetic relationship and evolution. The result provides fundamental data for resolving phylogenetic and genetic problems related to effective management strategies.