Search

Abstract: Spectral clustering consists of two distinct stages: (a) construct an affinity graph from the dataset and (b) cluster the data points through finding an optimal partition of the affinity graph. The focus of the paper is the first step. Existing spectral clustering algorithms adopt Gaussian function to define the affinity graph since it is easy to implement. However, Gaussian function is hard to depict the intrinsic structure of the data, and it has to specify a scaling parameter whose selection is still an open issue in spectral clustering. Therefore, we propose a new definition of affinity graph for spectral clustering from the graph partition perspective. In particular, we propose two consistencies: smooth consistency and constraint consistency, for affinity graph to hold, and then define the affinity graph respecting these consistencies in a regularization framework of ranking on manifolds. Meanwhile the proposed definition of affinity graph is applicable to both unsupervised and semi-supervised spectral clustering. Encouraging experimental results on synthetic and real world data demonstrate the effectiveness of the proposed approach. [Copyright &y& Elsevier]

Abstract: Recently, a new bivariate simplex spline scheme based on Delaunay configuration has been introduced into the geometric computing community, and it defines a complete spline space that retains many attractive theoretic and computational properties. In this paper, we develop a novel shape modeling framework to reconstruct a closed surface of arbitrary topology based on this new spline scheme. Our framework takes a triangulated set of points, and by solving a linear least-square problem and iteratively refining parameter domains with newly added knots, we can finally obtain a continuous spline surface satisfying the requirement of a user-specified error tolerance. Unlike existing surface reconstruction methods based on triangular B-splines (or DMS splines), in which auxiliary knots must be explicitly added in advance to form a knot sequence for construction of each basis function, our new algorithm completely avoids this less-intuitive and labor-intensive knot generating procedure. We demonstrate the efficacy and effectiveness of our algorithm on real-world, scattered datasets for shape representation and computing. [Copyright &y& Elsevier]

Abstract: Topic models have been successfully used in information classification and retrieval. These models can capture word correlations in a collection of textual documents with a low-dimensional set of multinomial distribution, called “topics”. However, it is important but difficult to select the appropriate number of topics for a specific dataset. In this paper, we study the inherent connection between the best topic structure and the distances among topics in Latent Dirichlet allocation (LDA), and propose a method of adaptively selecting the best LDA model based on density. Experiments show that the proposed method can achieve performance matching the best of LDA without manually tuning the number of topics. [Copyright &y& Elsevier]

Abstract: The Class A Bézier curves presented in Farin (2006) were constructed by so-called Class A matrix, which are special matrices satisfying two appropriate conditions. The speciality of the Class A matrix causes the Class A Bézier to possess two properties, which are sufficient conditions for the proof of the curvature and torsion monotonicity. In this paper, we discover that, in Farin (2006), the conditions Class A matrix satisfied cannot guarantee one of the two properties of the Class A Bézier curves, then the proof of the curvature and torsion monotonicity becomes incomplete. Furthermore, we modified the conditions for the Class A matrices to complete the proof. [Copyright &y& Elsevier]

In this paper, a set of quasi-Bernstein polynomials of degree n with one parameter is presented, which is an extension of the Bernstein polynomials over the triangular domain. Using the presented polynomials as basis functions, we construct a class of shape adjusting surfaces defined over the triangular domain with a shape parameter, namely, quasi-B-B parametric surfaces. These surfaces share many properties with the B-B parametric surfaces. In particular, when shape parameters equal 1, they degenerate to be the B-B parametric surfaces. By changing the value of the shape parameter, we can get different surfaces under the fixed control net. [ABSTRACT FROM AUTHOR]

• Two polysaccharide fractions (WXA-1 and AXA-1) are obtained from wheat bran. • The compositions and structural characterizations of WXA-1 and AXA-1 are different. • AXA-1 exhibits stronger α-amylase and α-glucosidase inhibitory activities compared with WXA-1. In this study, two polysaccharide fractions were isolated from wheat bran by sequential extraction with water and alkaline solution, DEAE Cellulose-52 chromatography and Sephacryl S-400 gel permeation chromatography, they were named as WXA-1 and AXA-1, respectively. Structural analyses indicated that both polysaccharide fractions were heteropolysaccharides, their average molecular weights were 193 kDa and 107 kDa, respectively. The backbone of WXA-1 was → 4)-β- d -Xyl p -(1→, which was substituted at O -3 positions by arabinose, glucose and galactose residues, while the backbone of AXA-1 was → 4)-β- d -Xyl p -(1→, which was mainly substituted at O -3 positions by arabinose. AXA-1 exerted a stronger inhibitory effect on the activities of α-amylase and α-glucosidase compared with WXA-1. Moreover, AXA-1 exhibited a competitive inhibition of α-amylase and a mixed-type noncompetitive inhibition of α-glucosidase. These results suggest that AXA-1 can be used as α-amylase and α-glucosidase inhibitors. [ABSTRACT FROM AUTHOR]

• Adapting three machine/deep learning models to predict rice yield at county-level • LSTM performs better than RF and LASSO models • Combining EVI and SIF together improves yield prediction than using EVI or SIF • Develop a scalable, simple and inexpensive framework for timely predicting rice yield • The yield prediction framework use publicly available multi-source data Timely and reliable yield prediction at a large scale is imperative and prerequisite to prevent climate risk and ensure food security, especially with climate change and increasing extreme climate events. In this study, integrating the publicly available data (i.e., satellite vegetation indexes, meteorological indexes, and soil properties) within the Google Earth Engine (GEE) platform, we developed one Least Absolute Shrinkage and Selection Operator (LASSO) regression, one machine learning (Random Forest, RF), and one deep learning (Long Short-Term Memory Networks, LSTM) model to predict rice yield at county-level across China. For satellite data, we compared the contiguous solar-induced chlorophyll fluorescence (SIF), a newly emerging satellite retrieval, with a traditional vegetation index (enhanced vegetation index, EVI). The results showed that LSTM (with R2 ranging from 0.77 to 0.87, RMSE from 298.11 to 724kg/ha) and RF (with R2 ranging from 0.76 to 0.82, RMSE from 366 to 723.3 kg/ha) models outperformed LASSO (with R2 ranging from 0.33 to 0.42, RMSE from 633.46 kg/ha to 1231.39 kg/ha) in yield prediction; and LSTM was better than RF. Besides, ESI (combining EVI and SIF together) could slightly improve the model performance compared with only using EVI or SIF as the single input, primarily due to the ability of satellite-based SIF in capturing extra information on drought and heat stress. Furthermore, we also explored the potential for timely rice yield prediction, and concluded that the optimal prediction could be achieved with approximately two/one-month leading-time before single/double rice maturity. Our findings demonstrated a scalable, simple and inexpensive methods for timely predicting rice yield over a large area with publicly available multi-source data, which can potentially be applied to areas with sparsely observed data and worldwide for estimating crop yields. [ABSTRACT FROM AUTHOR]

• Develop a scalable and simple framework to timely estimate yields. • Adapt deep learning and machine learning models to predict yields over large areas. • Spatial characteristics parameters are important for predicting wheat yields. • Machine learning sometimes performs better than deep learning. To meet the challenges of climate change, increasing population and food demand, a timely, accurate and reliable estimation of crop yield at a large scale is more imperative than ever for crop management, food security evaluation, food trade and policy-making. In this study, taking the major winter wheat production regions of China as an example, we compared a traditional machine learning method (random forest, RF) and three deep learning (DL) models, including DNN (deep neural networks), 1D-CNN (1D convolutional neural networks), and LSTM (long short-term memory networks) to predict crop yields by integrating publicly available data within the GEE (Google Earth Engine) platform, including climate, satellite, soil properties, and spatial information data. The results showed that all four models could capture winter wheat yield variations in all the county-years, with R2 of recorded and simulated yields ranging from 0.83 to 0.90 and RMSE ranging from 561.18 to 959.62 kg/ha. They all performed well for winter wheat yield prediction at a county level from 2011 to 2015, with mean R2≥0.85 and RMSE ≤ 768 kg/ha. At a field level, the spatial pattern of estimated winter wheat yield could capture the spatial heterogeneity and yield differences between individual fields across a county fairly well. However, only the DNN and RF models had relatively good performance at the field level, with mean R2 values of 0.71, 0.66 and RMSE values of 1127 kg/ha and 956 kg/ha, respectively. The model comparisons showed that the performance of RF was not always worse than DL at both the county and field levels. Our findings demonstrated a scalable, simple and inexpensive framework for estimating crop yields at various scales in a timely manner and with reliable accuracy, which has important implications for crop yield forecasting, agricultural disaster monitoring, food trade policy, and food security warning. [ABSTRACT FROM AUTHOR]

Dyslipidemia is one of major risk factors for cardiovascular disease. The early detection and treatment of dyslipidemia can reduce cardiovascular disease risk. A cross-sectional study was carried out in Ningxia, China to determine the prevalence of dyslipidemia and its association with body mass index (BMI) and pubertal stage. A total of 1783 students were selected from middle schools and high schools in September 2014 using stratified random cluster sampling. Serum triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured by using MOL-300 automatic biochemical analyzer with associated kits. The prevalence of adolescents with one abnormal serum lipid component was 43.2% and was significantly different across three pubertal stages (p < 0.0001). The abnormal rates of HDL-C and TG increased as the students maturated through the early, middle, and late stages of puberty (all p < 0.0001). Similar results were obtained when separate analyses were performed for boys and girls. In linear regression analysis, BMI was positively associated with serum levels of TC, LDL-C, and TG, but inversely associated with serum levels of HDL-C after the adjustment for age, sex, and race. In multivariable logistic regression analysis, obesity was associated with an increased risk of developing high TC, while pubertal maturation was associated with an elevated risk of experiencing low HDL-C and high TG (all p < 0.05). In conclusions, dyslipidemia is common in an adolescent population of Northwest China and its prevalence rates substantially vary with weight status and pubertal stage. [ABSTRACT FROM AUTHOR]

• We propose an improved dataset for research on image privacy. • We build a knowledge graph that represents the relevance between the objects and image privacy. • Based on the graph, we deal with the traditional image classification problem with the novel graph-based neural network. • The introduction of the knowledge graph not only makes the model more explainable but also makes better use of the information of objects provided by the images. • Our method relies on the intrinsic relevance in the dataset rather than extra knowledge, thus is applicable for other tasks. With the development of social media and smartphones, people share their daily lives via a large number of images, but the convince also raises a problem of privacy leakage. Therefore, effective methods are needed to infer the privacy risk of images and identify images that may disclose privacy. Several works have tried to solve this problem with deep learning models. However, we know little about how the models infer the privacy label of an image, thus it is not easy to understand why the image may disclose privacy. Inspired by recent research on graph neural networks, we introduce prior knowledge to the deep models to make the inference more explainable. We propose the Graph-based neural networks for Image Privacy (GIP) to infer the privacy risk of images. The GIP mainly focuses on objects in an image, and the knowledge graph is extracted from the objects in the dataset without reliance on extra knowledge. Experimental results show that the GIP achieves higher performance compared with the object-based methods and comparable performance even compared with the multi-modal fusion method. The results show that the introduction of the knowledge graph not only makes the deep model more explainable but also makes better use of the information of objects provided by the images. Combing the knowledge graph with deep learning is a promising way to help protect image privacy that is worth exploring. [ABSTRACT FROM AUTHOR]

The grain size of sediment delivered to a river by hillslope processes is an important factor which could impact fluvial erosion, sediment transport, and associated geomorphic changes. Grain size distribution (GSD) is increasingly recognised as an important factor for the impact of landslides on sediment pulses and long-term erosion rates. Therefore a better comprehension of grain size control on landslide-generated sediment transport and dynamics will help with understanding post-seismic fluvial processes and landscape evolution. In this study, we modelled the recovery of the Hongxi River basin affected by the Wenchuan earthquake under different landslide GSD scenarios. The CAESAR-Lisflood (CL) model was first modified to enable the spatial input of sediment GSD, which allows the model to distinguish the grain size composition of landslide sediment and the background sediment sourced from other areas. Using the modified CL model, we simulated three different landslide GSD scenarios (Original, Coarser, Finer) by altering the original landslide sediment GSD observed from a post-earthquake basin. In particular, we analysed the fate of landslide-generated sediment using a new sediment tracing function embedded in CL. This enabled us to evaluate the role of landslide GSD variation on the spatio-temporal heterogeneity of sediment transport. Our results show that the GSD of landslide material exerts a clear control on both sediment yield and channel deposition, the sediment transport of landslides disconnected to the fluvial system is more sensitive to grain size variation, while the GSD of landslides connected to the fluvial system has less impact on sediment dynamics. The duration of landslide disturbances on basin sediment dynamics can largely be dependent on the period of time (e.g., 20 years in this study basin) taken for grain coarsening (the grain size is increasing upward), which is dominated by the evacuation of fine sediments. The modelling results and findings highlight the importance of grain size distribution of landslide material and provide information to support basin sediment management and environmental risk mitigation. [ABSTRACT FROM AUTHOR]

• Circ-0060428 high expressed in osteosarcoma cells. • Circ-0060428 promoted proliferation and reduced apoptosis of osteosarcoma cells. • Circ-0060428 target sponges miR-375 to upregulate RBPJ. Osteosarcoma (OS) is the most common primary bone tumor, and mainly occurs in children and early adults. The overall pathogenesis of osteosarcoma remains unclear. Circular RNAs (circRNAs) have been demonstrated to be one of the regulators in tumors as non-coding RNAs. In present study, we identified an elevated expressed circ-0060428 in osteosarcoma cells. The results of CCK-8 and flow cytometry assay showed that circ-0060428 promoted cell proliferation and reduced cell apoptosis of osteosarcoma cells. Bioinformatics analyses predicted the binding site between miR-375 to RPBJ and circ-0060428, and dual luciferase reporter assay verified this prediction. Furthermore, miRNA inhibitor of miR-375 could recover the influence of cell proliferation and apoptosis by knocking down the expression of circ-0060428. Meanwhile, knockdown of both circ-0060428 and RBPJ resulted in a lower apoptosis rate than circ-0060428 alone. Western blot analyses indicated that circ-0060428 regulated the expression of apoptosis related proteins (Bax, Bcl-2, and cleaved-Caspase-3) by upregulating RPBJ expression in osteosarcoma cells. Altogether, we confirmed the up-regulated circ-0060428 could improve the proliferation and survival of osteosarcoma cells by sponging miR-375 to upregulate RBPJ expression. This finding supported a novel clinically biomarker and treatment target for osteosarcoma therapy. [ABSTRACT FROM AUTHOR]

Wheat is a leading cereal grain throughout the world. Timely and reliable wheat yield prediction at a large scale is essential for the agricultural supply chain and global food security, especially in China as an important wheat producing and consuming country. The conventional approach using either climate or satellite data or both to build empirical and crop models has prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, socio-economic (SC) factors may also improve crop yield prediction, but their contributions need in-depth investigation, especially in regions with good irrigation conditions, sufficient fertilization, and pesticide application. Here, we performed the first attempt to predict wheat yield across China from 2001 to 2015 at the county-level by integrating multi-source data, including monthly climate data, satellite data (i.e., Vegetation indices (VIs)), and SC factors. The results show that incorporating all the datasets by using three machine learning methods (Ridge Regression (RR), Random Forest (RF), and Light Gradient Boosting (LightGBM)) can achieve the best performance in yield prediction (R2: 0.68~0.75), with the most individual contributions from climate (~0.53), followed by VIs (~0.45), and SC factors (~0.30). In addition, the combinations of VIs and climate data can capture inter-annual yield variability more effectively than other combinations (e.g., combinations of climate and SC, and combinations of VIs and SC), while combining SC with climate data can better capture spatial yield variability than others. Climate data can provide extra and unique information across the entire growing season, while the peak stage of VIs (Mar.~Apr.) do so. Furthermore, incorporating spatial information and soil proprieties into the benchmark models can improve wheat yield prediction by 0.06 and 0.12, respectively. The optimal wheat prediction can be achieved with approximately a two-month leading time before maturity. Our study develops timely and robust methods for winter wheat yield prediction at a large scale in China, which can be applied to other crops and regions. [ABSTRACT FROM AUTHOR]

Recently, triangle configuration based bivariate simplex splines (referred to as TCB-spline) have been introduced to the geometric computing community. TCB-splines retain many attractive theoretic properties of classical B-splines, such as partition of unity, local support, polynomial reproduction and automatic inbuilt high-order smoothness. In this paper, we propose a computational framework for isogeometric analysis using TCB-splines. The centroidal Voronoi tessellation method is used to generate a set of knots that are distributed evenly over the domain. Then, knot subsets are carefully selected by a so-called link triangulation procedure (LTP), on which shape functions are defined in a recursive manner. To achieve high-precision numerical integration, triangle faces served as background integration cells are obtained by triangulating the entire domain restricted to all knot lines, i.e., line segments defined by any two knots in a knot subset. Various numerical examples are carried out to demonstrate the efficiency, flexibility and optimal convergence rates of the proposed method. • A TCB-splines based method is proposed for FEA on general domains. • Local refinement is flexible without introducing undesirable propagation. • Spline functions are globally C1-continuous. • Optimal convergence rates are numerically demonstrated. [ABSTRACT FROM AUTHOR]

The traditional Chinese medicine formula Danggui-Shaoyao-San (DSS) has been reported to have estrogen-like effects and therapeutic effects on the symptoms of Alzheimer's disease (AD). To explore whether the central oxytocin and neuroendocrine system is involved in the modulating effects of DSS on the cognition and neuropsychiatric hebaviors in female AD rats, and to investigate the pharmacokinetics of paeoniflorin and ferulic acid in female AD rats with DSS treatment. DSS (1.2, 3.2, 8.6 g/kg/day) was orally administered to ovariectomized (OVX) rats, and saline was orally administered to sham operation rats as control group. The Morris water maze test, novel object recognition test, and passive avoidance test were conducted for evaluation of learning and memory abilities, while elevated plus maze test and forced swim test were performed to assess anxiety- and depressive-like behaviors. ELISA kits were used to detect the levels of estrogen (E), estrogen receptor α (ERα), oxytocin (OT), oxytocin receptor (OTR), acetylcholine (Ach), acetylcholin esterase (AchE), and choline acetyl transferase (ChAT) in the cortex. The concentrations of Ach, glutamate (Glu), γ-aminobutyric acid (GABA), 5-hydroxytryptamine (5-HT), norepinephrine (NE) and dopamine (DA) in the hippocampus were assessed by HPLC-MS. The changes of neuronal morphology in the hippocampus were observed by Nissl staining. The pharmacokinetics of paeoniflorin and ferulic acid in OVX rats with DSS treatment were studied by HPLC. In the Morris water maze test, novel object recognition test, and passive avoidance test, OVX rats showed cognitive impairment. In the elevated plus maze test and forced swim test, the anxiety- and depressive-like behaviors of OVX rats were significant as compared to the control group. Treatment of DSS significantly imporved the cognitive deficits, and ameliorated anxiety- and depressive-like behaviors of OVX rats. The expression of E, ERα, OT, OTR, AchE and ChAT in the cortex of model group were significantly decreased, and DSS significantly reversed these changes. The concentrations of Ach, Glu, GABA, 5-HT and NE in the hippocampus of OVX rats were significantly decreased, whereas DSS significantly increased the levels of Ach, Glu, GABA, 5-HT and NE. There was no significant difference in the concentration of DA in the hippocampus among groups. Degenerating neurons in the hippocampal CA3 region were observed in OVX rats, and the number of neurons was decreased. DSS treatment reduced the degenerating neurons, and incresed the number of neurons. The MRT (0 - ∞), AUC (0 - ∞), Cmax and t1/2z values of paeoniflorin, and the AUC 0-∞ and C max value of ferulic acid were higher in DSS-treated OVX rats than those in the DSS-treated control rats. DSS improves the learning and memory ability, and attenuates anxiety- and depressive-like behaviors of OVX rats. The mechanism may be through increasing estrogen, reducing cholinergic damage, and modulating neurotransmitters. The increase in absorption and elimination time of paeoniflorin and ferulic acid in OVX rats may enhance the efficacy of DSS. [Display omitted] • DSS can improve the learning and memory ability and pathological changes of OVX rats, which may be related to the increase of estrogen level, inhibition of AChE and up-regulation of ChAT expression. • DSS can improve the neurotransmitters related to anxiety and depression in OVX rats. • DSS in OVX rats were increased, and the time of drug effect in vivo was prolonged, thus the efficacy of DSS was enhanced. [ABSTRACT FROM AUTHOR]

We aimed to investigate whether geniposide, a main component extracted from Gardenia jasminoides Ellis fruit, could exert neuroprotective functions against traumatic brain injury (TBI). Enzyme-linked immunosorbent assay (ELISA) was used for detection of plasma cytokines. Real-time polymerase chain reaction (RT-PCR) was employed for measurements of mRNA levels of cytokines. Neurological outcomes were evaluated by modified neurological severity score (mNSS) and Rota-Rod. Blood–brain barrier (BBB) integrity and brain edema were assessed. Protein expression was tested by Western blot. The plasma levels of interleukin (IL)-1β, IL-6, IL-8 and IL-10 were all elevated in patients with TBI compared to those of healthy controls. TBI rats treated with geniposide showed lower mNSS and longer fall latency time than untreated TBI rats. BBB integrity was maintained and brain edema was reduced by geniposide treatment in TBI rats. Plasma levels of IL-1β, IL-6 and IL-8 were significantly repressed by geniposide treatment in TBI rats, whereas IL-10 level was upregulated. mRNA expression levels of these cytokines in the brain tissues of TBI rats exhibited the same trends of changes. By testing p38 mitogen-activated protein kinase and NF-κB p65 activities, it was observed that phosphorylated (p)-p38 and p-p65 were dramatically inhibited by geniposide. In conclusion, geniposide exerts neuroprotective functions against TBI by inhibiting p-p38 and p-p65. [ABSTRACT FROM AUTHOR]

Background: Non-small cell lung cancer (NSCLC) is a common and fatal cancer worldwide with a very low 5-year overall survival rate. Ribonucleotide reductase M2 subunit (RRM2), a small subunit of the ribonucleotide reductase complex, has been found to be an oncogenic role in a variety of tumors including NSCLC. However, the regulatory mechanism of RRM2 in NSCLC is not clear. Increasing evidence suggests that non-coding RNAs (ncRNAs) including miRNAs and lincRNAs may promote or inhibit tumor initiation and development through regulating the expression of oncogenic genes. It is interesting to find ncRNAs which play important role in regulating RRM2 expression. Materials and methods: The expression levels of RRM2, LINC0066 and miR-143-3p in NSCLC tumor tissues and cell lines were detected using qRT-PCR. The regulatory relationships among RRM2, LINC0066 and miR-143-3p were predicted using database analysis and verified by luciferase reporter assay and RIP analysis. The proliferation ability of NSCLC cells was assessed using CCK8 and colony formation assays. The expression of related proteins was determined by Western blot. In vivo effect of RRM2, LINC0066 and miR-143-3p to NSCLC were detected through xenograft experiments. Results: In this study, we found RRM2 was upregulated in NSCLC tumor and cell lines, and the aberrant upregulation predicted a poor prognosis. Then, we predicted and confirmed that RRM2 was negatively regulated by miR-143-3p. Further study implied that LINC00667 acted as a ceRNA by sponging miR-143-3p and regulated RRM2 expression indirectly. Moreover, we found that the growth of NSCLC was regulated by LINC00667/miR-143-3p/RRM2 signal pathway both in vitro and in vivo. LINC00667 and RRM2 promoted the tumor growth while miR-143-3p inhibited it. Conclusion: Our study revealed a LINC00667/miR-143-3p/RRM2 signal pathway that played an important role in the progress of NSCLC, which might be potential therapeutic targets for NSCLC. [ABSTRACT FROM AUTHOR]

Abstract Polynomial Pythagorean hodograph (PH) curves have a polynomial arc length function and rational offsets, which distinguish PH curves from general polynomial parametric curves. However, these algebraic properties can hardly be used directly for the identification of PH curves. In order to determine whether or not a given septic planar polynomial curve is a PH curve, Zheng et al. (2016) studied a class of septic curves' geometric properties and proposed an efficient algorithm. In this paper, we further complete their work on Bézier control polygons of septic PH curves. We point out that there are three classes of septic PH curves according to different factorizations of their derivatives. Except the first class which has been studied, geometric characteristics of the other two classes are proposed. By introducing auxiliary points, the results are in terms of angles and lengths of legs of their Bézier control polygons. Moreover, we give feasible methods for the construction of auxiliary points, including various degenerate cases. [ABSTRACT FROM AUTHOR]

Smoke inhalation injury (SII) is an independent risk factor for morbidity and mortality in patients with severe burns, however, the underlying mechanisms of SII are still not fully understood. In our study, we established an advanced rat model of SII based on the previous work, and explored the dynamic changes of pathophysiology and inflammatory factors during 28days post SII. We also measured the different expressions of miRNAs in bronchoalveolar lavage fluid (BALF) between SII and normal control rats by miRNA microarray. At 1day after smoke inhalation, the histopathological results exhibited inflammatory exudates in the lung tissue with significant edema. As time went on, the lung injuries gradually appeared at alveolar septum thickening and alveolar collapse, which suggested that it further induced damage to lung parenchyma by smoke inhalation. Particularly, the collagen deposition indicating pulmonary fibrosis happened at 28days post-injury. Plasma IL-6 and TNF-a were significantly increased after 1day of smoke inhalation. Plasma IL-10, BALF TNF-α and IL-10 were significantly increased after 2days of smoke inhalation. By extending the observation time, the levels of plasma IL-6, BALF TNF-a and IL-10 appeared a second peak again after 14days of injury. Compared with the normal control group, there were 23 upregulated miRNAs and 2 downregulated miRNAs in BALF of SII group at 1day post-injury. RT-qPCR validation assay confirmed that the changes of miR-34c-5p, miR-92b-3p, miR-205, miR-34b-3p, miR-92a-3p, let-7b-5p, let-7c-5p in BALF were consistent with the conclusion of the miRNA microarray. In summary, we showed the dynamic changes of pathologic changes and inflammatory factors in rats with SII, and a subset of seven miRNAs changed in BALF after SII which may be used for diagnosis and potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Abstract Although vitamin D is essential for bone health, little is known about prevalence of vitamin D deficiency and low bone mineral density (BMD) among children, especially those in developing countries. It also remains unclear whether serum 25-hydroxyvitamin D [25(OH)D] is associated with BMD among children. We investigated these questions among children and adolescents in Yinchuan (latitude: 38° N), Ningxia, an economically underdeveloped province in Northwest China. A total of 1582 children (756 boys and 826 girls), aged 6–18 years, were recruited from schools using the stratified random sampling method in fall 2015. Serum 25(OH)D concentrations were measured by enzyme-linked immunosorbent assay, and BMD was quantified by dual-energy X-ray absorptiometry. Vitamin D deficiency (defined as serum 25(OH)D ≤ 37.5 nmol/L) was present in 35.5% of study subjects. There were no clear patterns of differences in serum 25(OH)D concentrations across the four age groups compared (6–9 years, 10–13 years, 14–16 years, and 17–18 years). The prevalence of low total body less head (TBLH) BMD (defined as a Z -score of ≤ −2.0 standard deviations away from the mean BMD values of the Chinese pediatric reference population) among children examined was 1.8% and was not significantly different among the four age groups considered. Linear regression analysis revealed that age, weight, and height were significantly and positively associated with TBLH BMD and that the strongest determinant of TBLH BMD was age in boys and weight in girls. There were no significant correlations between serum 25(OH)D concentrations and BMD obtained for total body and at various skeletal sites (r ranged from −0.005 to 0.014) regardless of whether children evaluated were sufficient, insufficient, or deficient in vitamin D. In conclusion, more than one-third of children and adolescents in a Northwest Chinese city were deficient in vitamin D but only <2% of them developed low BMD. Highlights • Vitamin D deficiency (serum 25(OH)D ≤37.5 nmol/L) existed in 35.5% of 1582 school children in a Northwest Chinese city. • The prevalence of low total body less head (TBLH) bone mineral density (BMD) among school children examined was 1.8%. • Age, weight, and height were positively associated with TBLH BMD among this pediatric Chinese population in both sexes. • The strongest determinant of TBLH BMD was age in boys and weight in girls. • Serum 25(OH)D was overall not associated with BMD (total body and various skeletal sites) regardless of vitamin D status. [ABSTRACT FROM AUTHOR]

This paper presents a novel technique for resampling point clouds of a smooth surface. The key contribution of this paper is the generalization of centroidal Voronoi tessellation (CVT) to point cloud datasets to make point resampling practical and efficient. In particular, the CVT on a point cloud is efficiently computed by restricting the Voronoi cells to the underlying surface, which is locally approximated by a set of best-fitting planes. We also develop an efficient method to progressively improve the resampling quality by interleaving optimization of resampling points and update of the fitting planes. Our versatile framework is capable of generating high-quality resampling results with isotropic or anisotropic distributions from a given point cloud. We conduct extensive experiments to demonstrate the efficacy and robustness of our resampling method. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel method for generating cell complexes with anisotropy conforming to the Hessian of an arbitrary given function. This is done by variationally optimizing the discontinuous piecewise linear approximation of the given functions over power diagrams. The resulting cell complexes corresponding to the approximations are referred to as Optimal Power Diagram (OPD). A hybrid optimization technique, coupling a modified Monte Carlo method with a local search strategy, is tailored for effectively solving the specific optimization task. In contrast to the Optimal Voronoi Tessellation (OVT) method (Budninskiy et al., 2016), our OPD method does not restrict the target functions to be convex, providing more diverse classes of tessellations of the domain. Furthermore, our OPD method generally yields smaller approximation errors than the OVT method, which uses underlaid approximants. We conduct several experiments to demonstrate the efficacy of our optimization algorithm in finding good local minima and generating high-quality anisotropic polytopal meshes. [ABSTRACT FROM AUTHOR]

Objective: This study addressed whether preoperative chemotherapy (PECT) plus surgery prolongs overall survival (OS) compared with surgery plus postoperative chemotherapy (POCT) among gastric cancer (GC) patients in Northwest China. Materials and Methods: The authors included 157 GC patients confirmed histologically or by gastroscopic pathological examination treated at the General Hospital of Ningxia Medical University of China between 2012 and 2018. All patients were followed up by telephone in January 2019 within a 2-week period. The endpoint was death due to GC or its complications. Results: Thirty-eight patients received PECT, 41 patients received POCT, 40 patients received surgery alone, and 38 patients received chemotherapy alone. Surgery was performed with R0 resection and subsequent extended lymph node dissection. Chemotherapy was performed with the S-1, oxaliplatin capecitabine regimen. Patients who received PECT had longer OS than those with POCT treatment (hazard ratio = 2.409, p = 0.037). The 5-year OS rate was 32.7% higher in the PECT group than in the POCT group. Conclusions: PECT was associated with better OS in GC patients and should be considered by clinicians in GC treatment, although prospective studies are needed for confirmation. [ABSTRACT FROM AUTHOR]

Rice is the most important staple food in Asia. However, high-spatiotemporal-resolution rice yield datasets are limited over this large region. The lack of such products greatly hinders studies that are aimed at accurately assessing the impacts of climate change and simulating agricultural production. Based on annual rice maps in Asia, we incorporated multisource predictors into three machine learning (ML) models to generate a high-spatial-resolution (4 km) seasonal rice yield dataset (AsiaRiceYield4km) for the 1995–2015 period. Predictors were divided into four categories that considered the most comprehensive rice growth conditions, and the optimal ML model was determined based on an inverse probability weighting method. The results showed that AsiaRiceYield4km achieves good accuracy for seasonal rice yield estimation (single rice: R2=0.88 , RMSE = 920 kg ha -1 ; double rice: R2=0.91 , RMSE = 554 kg ha -1 ; and triple rice: R2=0.93 , RMSE = 588 kg ha -1). Compared with single rice from the Spatial Production Allocation Model (SPAM), the R2 of AsiaRiceYield4km was improved by 0.20, and the RMSE was reduced by 618 kg ha -1 on average. In particular, constant environmental conditions, including longitude, latitude, elevation and soil properties, contributed the most (∼ 45 %) to rice yield estimation. For different rice growth periods, we found that the predictors of the reproductive period had greater impacts on rice yield prediction than those of the vegetative period and the whole growing period. AsiaRiceYield4km is a novel long-term gridded rice yield dataset that can fill the unavailability of high-spatial-resolution seasonal yield products across major rice production areas and promote more relevant studies on agricultural sustainability worldwide. AsiaRiceYield4km can be downloaded from the following open-access data repository: https://doi.org/10.5281/zenodo.6901968 (Wu et al., 2022). [ABSTRACT FROM AUTHOR]

Novel porous ternary nanocomposite systems containing reduced graphene oxide (RGO)/polyaniline (PANI)/cuprous oxide (CuO) were prepared via one-step in situ redox method. The RGO/PANI/CuO nanocomposites present a flower-like structure with an average size of 2.0 μm in diameter. The morphologies and properties of the products can be controlled by adjusting the molar ratios of aniline to Cu. When the molar ratio of aniline to Cu is 1:1, the product exhibits excellent microwave absorption property in the frequency range of 2-18 GHz. It can be seen that the maximum reflection loss (RL) of the ternary composite is up to −52.8 dB at 2.7 GHz with a thickness of only 2 mm, and the absorption bandwidth corresponding to −10 dB (90% of EM wave absorption) is 13.2 GHz. The microwave absorption property of ternary RGO/PANI/CuO composite is significantly improved due to its special flower-like porous structure, dielectric loss property and well impedance matching characteristics, which is 8.12 times than that of pure RGO and 5.28 times than that of pure PANI. Therefore, our study paves a new way to prepare the promising lightweight and high-performance composite materials combined with the characteristics of three components for electromagnetic absorption. [ABSTRACT FROM AUTHOR]

In this paper, we analyze planar parametric sextic curves to determine conditions for Pythagorean hodograph (PH) curves. By expressing the curves to be analyzed in the complex form, the analysis is conducted in algebraic form. Since sextic PH curves can be classified into two classes according to the degrees of their derivatives' factors, we introduce auxiliary control points to reconstruct the internal algebraic structure for both classes. We prove that a sextic curve is completely characterized by the lengths of legs and angles formed by the legs of their Bézier control polygons. As such conditions are invariant under rotations and translations, we call them the geometric characteristics of sextic PH curves. We demonstrate that the geometric characteristics form the basis for an easy and intuitive method for identifying sextic PH curves. Benefiting from our results, the computations of the parameters of cusps and/or inflection points can also be simplified. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a key variable of the regional hydrological cycle and has important applications for water resource and agricultural drought management. Various global soil moisture products have been mostly retrieved from microwave remote sensing data. However, currently there is rarely spatially explicit and time-continuous soil moisture information with a high resolution at the national scale. In this study, we generated a 1 km soil moisture dataset for dryland wheat and maize in China (ChinaCropSM1 km) over 1993–2018 through a random forest (RF) algorithm based on numerous in situ daily observations of soil moisture. We independently used in situ observations (181 327 samples) from the agricultural meteorological stations (AMSs) across China for training (164 202 samples) and others for testing (17 125 samples). An irrigation module was first developed according to crop type (i.e., wheat, maize), soil depth (0–10, 10–20 cm) and phenology. We produced four daily datasets separately by crop type and soil depth, and their accuracies were all satisfactory (wheat r 0.93, ubRMSE 0.033 m 3 m -3 ; maize r 0.93, ubRMSE 0.035 m 3 m -3). The spatiotemporal resolutions and accuracy of ChinaCropSM1 km were significantly better than those of global soil moisture products (e.g., r increased by 116 %, ubRMSE decreased by 64 %), including the global remote-sensing-based surface soil moisture dataset (RSSSM) and the European Space Agency (ESA) Climate Change Initiative (CCI) SM. The approach developed in our study could be applied to other regions and crops in the world, and our improved datasets are very valuable for many studies and field management, such as agricultural drought monitoring and crop yield forecasting. The data are published in Zenodo at https://doi.org/10.5281/zenodo.6834530 (wheat 0–10) (Cheng et al., 2022a), https://doi.org/10.5281/zenodo.6822591 (wheat 10–20) (Cheng et al., 2022b), https://doi/org/10.5281/zenodo.6822581 (maize 0–10) (Cheng et al., 2022c) and https://doi.org/10.5281/zenodo.6820166 (maize 10–20) (Cheng et al., 2022d). [ABSTRACT FROM AUTHOR]

Atmospheric carbonyls are important precursors of PM 2.5 and ground-level ozone, and some carbonyls are toxic and harmful; thus, it is crucial to obtain accurate information on the ambient levels of carbonyls. However, the detection of carbonyls is difficult due to their relatively higher reactivities and chemical instabilities; therefore, accurate determination of atmospheric carbonyls is important. In this study, an analytical method for atmospheric carbonyls with high concentration or reactivity was developed, the precursor ion of each carbonyl compound was selected, and the declustering potential (DP) and entrance potential (EP) for each precursor ion were optimized. A 2,4-dinitrophenylhydrazine cartridge derivatization-high performance liquid chromatography/atmospheric pressure chemical ionization-mass spectrometry (DNPH-HPLC/APCI-MS) method for the determination of 30 carbonyls was established. The results showed that the linear range of 24 carbonyls was 1.2–600 ng/mL, while other 6 carbonyls was 1.2–300 ng/mL, and the detection limits of 30 carbonyls ranged from 0.092 to 0.947 ng/mL (0.005–0.049 μg/m3 with an ambient air sampling volume of 96 L). The intra-day and inter-day repeatability ranges were 0.55–4.20% and 1.40–12.48%, respectively. A preliminary application of the method was carried out in the urban area of Beijing in spring and summer of 2021, and it was found that the mean total mass concentration of 30 carbonyls was 35.894 μg/m3. This study provided additional concentration information for 14 atmospheric carbonyls, including mono-, di-, oxygen-containing and heterocyclic carbonyls, which accounted for 38% and 35% of the total mass concentration and OH radical reactivities of 30 carbonyls, respectively. This is the first investigation of simultaneous quantitative analysis of multiple atmospheric carbonyls based on commercial standard derivatives. The optimized method could provide more comprehensive information for atmospheric carbonyls and further support research concerning the role of chemical reaction processes and health effects than traditional measuring techniques. [Display omitted] • A comprehensive method for detecting 30 carbonyls using HPLC/MS was investigated. • Simultaneous detection can be applied with simple pre-treatment. • 30 atmospheric carbonyls were quantified in Beijing by this optimized method. • The top three species in Beijing urban area were acetone, formaldehyde and nonanal. • This study highlights the necessity and advancement of carbonyl determination. [ABSTRACT FROM AUTHOR]

As a reliable indicator of regional climate change, the growth and decline of lake ice thickness affect the regional intra–annual heat and energy balance. In this study, a ground-penetrating radar (GPR) ice monitoring system, located approximately 1.7 km west of Bird Island in Qinghai Lake, in the territory of Qinghai Province and located in northwest China, was designed to carry out continuous fixed–point observations of local ice thickness and meteorological elements from 7 to 24 March 2021. The characteristics of continuous daily changes in ice thickness during the ablation period of Qinghai Lake and their relationship with meteorological elements were analyzed. The results showed that the average daily ice thickness of Qinghai Lake increased and then decreased during the observation period, with an average ice thickness of 42.83 cm, an average daily ice thickness range of 39.35~46.15 cm, and a growth rate of 0.54 cm/day during 8–13 March 2021, with an ice melting rate of −0.61 cm/day during 14–24 March 2021. The daily ice thickness variations were divided into two phases, which were relatively stable before dawn and followed a decreasing, increasing, and then decreasing trend during 8–13 March 2021 and a decreasing, increasing (for several hours), and then decreasing trend during 14–24 March 2021. There was a significant positive correlation (R = 0.745, p < 0.01) between near-surface air temperature and ice surface temperature during the observation period, but a significant negative correlation (R = −0.93, p < 0.05) between the average daily ice thickness and cumulative temperature of the ice surface. Temperature was the dominant factor affecting lake ice thickness, as compared to near-surface air humidity, wind speed, and illuminance. However, a sudden increase in wind speed have also played an important role at certain periods. A large number of cracks appeared on the ice surface on 26 March 2021, which, combined with the forces of wind speed, wind direction, and temperature, contributed to the rapid melt of the lake ice. This study filled the gap in situ measurement data on the continuous ice thickness variability during the ablation period in Qinghai Lake. It provided scientific support for the further study of lake ice on the Qinghai–Tibet Plateau (QTP). [ABSTRACT FROM AUTHOR]

In this article, we propose a unified geolocation framework to automatically determine where on the earth a web video was shot. We analyze different social, visual, and textual relationships from a real-world dataset and find four relationships with apparent geography clues that can be used for web video geolocation. Then, the geolocation process is formulated as an optimization problem that simultaneously takes the social, visual, and textual relationships into consideration. The optimization problem is solved by an iterative procedure, which can be interpreted as a propagation of the geography information among the web video social network. Extensive experiments on a real-world dataset clearly demonstrate the effectiveness of our proposed framework, with the geolocation accuracy higher than state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Precise and timely information on crop spatial distribution over large areas is paramount to agricultural monitoring, food security, and policy development. Currently, automatically classifying crop types at a large scale is challenging due to the scarcity of ground data. Although previous studies have indicated that transductive transfer learning (TTL) is a promising method to address this problem, it performs poorly within regions where crop compositions and phenology differ largely. Here we transferred random forest classifiers trained in limited regions with diversified growing conditions and land covers to the rest of the study area where ground data are scarce, with more than 130,000 Sentinel-2 images processed using the Google Earth Engine (GEE) platform. We established the 10 m crop maps for four major crops (i.e., maize, rapeseed, winter, and spring Triticeae crops) across 10 European Union (EU) countries from 2018 to 2019. The final crop maps had a high accuracy with overall accuracy generally greater than 0.89, with user's accuracy and producer's accuracy ranging from 0.72 to 0.98. Moreover, the resulting maps were consistent with the NUTS-2 level official statistics, with R2 consistently greater than 0.9. We further analyzed the crop rotation patterns and found that the rotation intervals across these EU countries were generally at least one year. Maize was dominantly rotated with winter Triticeae crops or converted to other land covers in the following year. Rapeseed was generally grown in rotation with winter Triticeae crops, whereas the rotation patterns of winter and spring Triticeae crops were more diversified. Red Edge Position (REP) and Normalized Difference Yellow Index (NDYI) played significant roles in crop classification across the EU. This study highlights the potential of the developed TTL method for crop classification over large spatial extents where labeled data are limited and the differences in crop compositions and phenology are relatively large. [ABSTRACT FROM AUTHOR]

To investigate the effect of low-level occupational benzene exposure on human peripheral blood leukocyte counts of the workers, domestic and foreign published research data on the change of human peripheral blood leukocyte counts under low-level occupational benzene exposure from January 1990 to December 2020 were collected and analyzed. According to the literature inclusion and exclusion criteria, 18 independent studies from 12 publications were selected for meta-analysis to explore the effect of low-level occupational benzene exposure on human peripheral blood leukocyte counts. The results showed that the peripheral blood leukocyte counts abnormal rates of low-level occupational benzene exposure group were higher than those of the control group, and the difference was statistically significant. Low-level occupational benzene exposure could result in a relatively higher abnormal rate of peripheral blood leukocyte counts in the exposed population, indicating that low-level occupational benzene exposure at workplaces specified by the current benzene occupational exposure limit in China would affect the peripheral blood leukocyte counts of the workers, thus benzene with concentrations under the limit in the ambient air of workplace could be still harmful to the health of the exposed workers. The results of this study could provide a scientific basis for future revision of the benzene occupational exposure limit in China, and could also be a reference for the formulation of environmental standard concerning benzene in China in the future. [Display omitted] [ABSTRACT FROM AUTHOR]

A high-sensitivity fiber sensor integrated by two cascaded balloon-shaped bent single-mode fiber (BBSMF) Mach-Zehnder interferometers is proposed and demonstrated. A high-order mode can be greatly stimulated due to embedding a seven-core fiber (SCF) between the two BBSMFs. The experimental results show that the maximum temperature sensitivity of the proposed sensor is − 3.35 nm/°C between 13 °C and 71 °C, which is currently the highest temperature sensitivity, in the BBSMF structure as far as we have ever known. Furthermore, the temperature sensitivity can be optimized by changing the length of the SCF and the diameter of the BBSMF. The proposed sensor has the advantages of easy production, low cost, and high sensitivity, which makes it well-suited for precise temperature measurement applications. [Display omitted] • A fiber sensor integrated by two cascaded balloon-shaped bent single-mode fiber (BBSMF) Mach-Zehnder interferometers is proposed. • The maximum intensity temperature sensitivity is − 3.35 nm/°C in the range of 13–71 °C. • The sensor provides a novel way to fabricate a high-sensitivity sensor by combining different types of optical fibers. [ABSTRACT FROM AUTHOR]

Dear Editor, Light chain deposition disease (LCDD) is a monoclonal gammopathy-related disease, which can cause damage to many organs, including the liver, heart, lung, and kidney. However, some LCDD patients do not have myeloma or any other malignant hematological disease. Abnormal serum-free kappa/lambda ratios have been previously reported in LCDD patients [[1], [6]]. [Extracted from the article]

• We proposed a hybrid framework for the crop yield forecasting at regional level. • This framework integrates data assimilation, crop model, and machine learning. • The framework was successfully applied with a higher accuracy than previous studies. • This framework is helpful for developing regional food security early warning system. Timely and reliable regional crop yield forecasting before harvest is critical for managing climate risk, adjusting agronomic management, and making food trade policy. Although various methods exist for crop yield forecasting, including process-based crop models and machine learning techniques, the potential of integrating these methods for early-season yield forecasts has not been well investigated. In this study, we proposed a hybrid framework for crop yield forecasting that firstly assimilated leaf area index and soil moisture into a crop model and then combined the data-assimilated crop model with machine learning techniques to improve the prediction skill further. The proposed framework was applied to winter wheat yield forecasting in the North China Plain during 2009–2015. We found that the assimilation significantly enhances wheat yield estimates, achieving additional ACC = 0.27, MAPE = 6.12 %. Incorporating weather forecasts enabled reliable winter wheat yield forecasts up to 1–3 months in advance, achieving ACC = 0.69, MAPE = 7.79 %. Furthermore, integrating the assimilated crop model with machine learning techniques improved the forecasting further, achieving ACC = 0.97 and MAPE = 1.74 %. The proposed framework for crop yield forecasting can be adapted to other crops and regions and has great potential in developing food security early warning system at a regional scale. [ABSTRACT FROM AUTHOR]

Abstract: New bounds on the magnitudes of the first- and second-order partial derivatives of rational triangular Bézier surfaces are presented. Theoretical analysis shows that the proposed bounds are tighter than the existing ones. The superiority of the proposed new bounds is also illustrated by numerical tests. [Copyright &y& Elsevier]

We prove the H-boundedness of the pullback attractor for a two-dimensional non-autonomous non-Newtonian fluid in bounded domains. [ABSTRACT FROM AUTHOR]

Mobile station (MS) localization in a cellular network is appealing to both industrial community and academia, due to the wide applications of location-based services. The main challenge is the unknown one-bound (OB) and multiple-bound (MB) scattering environment in dense multipath environment. Moreover, multiple base stations (BSs) are required to be involved in the localization process, and the precise time synchronization between MS and BSs is assumed. In order to address these problems, hybrid time of arrival (TOA), angle of departure (AOD), and angle of arrival (AOA) measurement model from the serving BS with the synchronization error is investigated in this paper. In OB scattering environment, four linear least square (LLS), one quadratic programming and data fusion-based localization algorithms are proposed to eliminate the effect of the synchronization error. In addition, the Cramer-Rao lower bound (CRLB) of our localization model on the root mean-square error (RMSE) is derived. In hybrid OB and MB scattering environment, a novel double identification algorithm (DIA) is proposed to identify the MB path. Simulation results demonstrate that the proposed algorithms are capable to deal with the synchronization error, and LLS-based localization algorithms show better localization accuracy. Furthermore, the DIA can correctly identify the MB path, and the RMSE comparison of different algorithms further prove the effectiveness of the DIA. [ABSTRACT FROM AUTHOR]

Acquiring spatiotemporal patterns of phenological information and its drivers is essential for understanding the response of crops to climate change and implementing adaptation measures. However, current approaches to obtain phenology and analyse its drivers have deficiencies such as sparse observations, excessive dependence of remote sensing inversion on sensors, and inevitable difficulties in upscaling site-based crop models into larger regions. Based on the Wang-Engel temperature response function, we improved the Crop Estimation through Resource and Environment Synthesis-Wheat (CERES-Wheat) model. First, we calibrated the model at the regional scale and evaluated its performance. Furthermore, the spatiotemporal changes in winter wheat phenology in China from 2000 to 2015 were analysed. The results showed that the improved model significantly enhanced the simulation accuracy of the anthesis and maturity dates by averages of 13% and 12% in most planting areas, especially in the Yunnan-Guizhou Plateau (YG) with improvements of 26% and 28%. The simulated phenology of winter wheat grown in a colder environment (e.g., the average temperatures during the vegetative growth period range from 0 to 5°C and from 15 to 20°C, and the reproductive growth period ranges from 10 to 15°C) also notably improved. These results confirmed that the original temperature response function indeed had limitations. Further analyses revealed that the key phenological dates and growth periods over the past 16 years were dominantly advanced and shortened. Specifically, the anthesis date, vegetative growth period (VGP), and reproductive growth period (RGP) indicated obviously spatial characteristics. For example, the anthesis date and VGP in the North China Plain (NCP) and the Middle-Lower Yangtze Plain (YZ) and the RGP in northwestern China (NW) showed opposite trends of delay and prolongation as comparing with the dominant patterns. Sensitivity analysis indicated that the key phenological dates and growth periods were advanced and shortened as the minimum (Tmin) and maximum temperatures (Tmax) rose, while they were postponed and prolonged with the increased precipitation. However, their responses to solar radiation did not show spatial consistency. Additionally, we found that the sensitivity of phenology to climatic factors differed across subregions. In particular, phenology in southwestern China and YG was more sensitive to Tmin, Tmax, and solar radiation than in the NCP and NW. Moreover, the sensitivity to precipitation in NW was higher than that in YZ. Totally, the improved crop model could provide more refined spatial characteristics of phenology at a large scale and benefit to explore its drivers more objectively. Furthermore, our results highlight that different planting areas should adopt suitable adaptation measures to cope with climate change impacts. Ultimately, the improved model is promising to enhance the accuracy of yield prediction and provide powerful tools for assessing regional climate change impact and adaptability. [ABSTRACT FROM AUTHOR]

3.3 Comparison of the composite paddy rice map and IRRI dataset The composite paddy rice map is a mosaic of rice planting areas in 3 years (2017-2019) where rice has been detected in 1 or more years. MAP: Figure 10 Estimated distribution of paddy rice in 2017 in mountainous regions in South Korea: (a) flooding signal based on MODIS indices, a (b) paddy rice map generated by our method, and (c) mountainous landscapes acquired from © Google Earth.. The example data for b can be found at the following link: (Han et al., 2021b; see example 04). To summarize, we provided more accurate paddy rice maps at continental scales using the improved method for paddy rice mapping. [Extracted from the article]

Dear Editor, In China, the "pull technique" is increasingly used for catheter removal in peritoneal dialysis (PD) patients. He used this technique in 46 patients of PD catheter removal, and only one patient exhibited retained cuff-related infection. In addition, 8 PD patients who discontinued PD treatment owing to refractory peritonitis did not have retained cuff-related infection after PD catheter removal by the "pull technique" at our PD center, suggesting that retained cuff-related infection is not related to the presence of peritonitis during PD catheter removal. Here, we report the cases of three patients with retained cuff-related infection after catheter removal, who were cured by surgical resection and anti-infection therapy. [Extracted from the article]

Abstract: This paper studies the asymptotic behavior of solutions for the discrete coupled nonlinear Schrödinger–Boussinesq equations. The authors first prove the existence of a global attractor for the generated semigroup and then obtain an upper bound of the Kolmogorov ε-entropy for the obtained global attractor. Finally, they establish the upper semicontinuity of the global attractor when the infinite lattice systems are approximated by finite lattice systems. [Copyright &y& Elsevier]

A previously constructed introgression lines (ILs) population including 239 lines was used to evaluate the cold-tolerant ability. The ILs with strong cold tolerance which were identified in the present study were used for further cytological and microsatellite (SSR) marker analyses. The results showed that the IL5243 and IL5335 had strong cold-tolerant ability. Cytological analysis showed that the rate of pollen mother cells (PMCs) with normal meiotic behavior in the IL5243 and IL5335 was to 89.93 and 90.22 %, respectively, and finally formed normal mature pollen. And meanwhile, the low frequency of abnormal chromosome behavior was observed in the IL5243 and IL5335, such as univalent, 8-shape bivalent, multivalent at diakinesis. At anaphase I, one or two lagging chromosomes were observed in some PMCs (3.95-5.15 %). The results of SSR marker analysis further confirmed that partial alien DNA of common wild rice has been transferred into the IL5243 and IL5335. These results implied that the IL5243 and IL5335 might be excellent bridging germplasm for exploring and utilizing the cold-tolerant gene of common wild rice. In addition, IL5243 and IL5335 would provide a better experimental system for understanding some epigenetic phenomenon induced by alien gene introgression. [ABSTRACT FROM AUTHOR]

Large-scale, high-resolution maps of rapeseed (Brassica napus L.), a major oilseed crop, are critical for predicting annual production and ensuring global energy security, but such maps are still not freely available for many areas. In this study, we developed a new pixel- and phenology-based algorithm and produced a new data product for rapeseed planting areas (2017–2019) in 33 countries at 10 m spatial resolution based on multiple data. Our product is strongly consistent at the national level with official statistics of the Food and Agricultural Organization of the United Nations. Our rapeseed maps achieved F1 spatial consistency scores of at least 0.81 when compared with the Cropland Data Layer in the United States, the Annual Crop Inventory in Canada, the Crop Map of England, and the Land Cover Map of France. Moreover, F1 scores based on independent validation samples ranged from 0.84 to 0.91, implying a good consistency with ground truth. In almost all countries covered in this study, the rapeseed crop rotation interval was at least 2 years. Our derived maps suggest, with reasonable accuracy, the robustness of the algorithm in identifying rapeseed over large regions with various climates and landscapes. Scientists and local growers can use the freely downloadable derived rapeseed planting areas to help predict rapeseed production and optimize planting structures. The product is publicly available at 10.17632/ydf3m7pd4j.3 (Han et al., 2021). [ABSTRACT FROM AUTHOR]

The envelope tracking (ET) power supply can output a variable voltage which tracks the envelope of the radio frequency input signal to the power amplifier (PA), and the system efficiency can be significantly improved. To achieve a high tracking bandwidth, the switching frequency of the ET power supply is usually too high. The pulse edge independent distribution (PEID) method has been proposed to reduce the switching frequency to 1/n (n ∊ N) of the tracking bandwidth, but a large number of isolated voltage sources are needed, leading to increased power stage and system complexity. In this letter, a switched capacitor based high bandwidth ET power supply is proposed, which employs optimized charging topologies and mechanisms to stabilize the voltages across the switched capacitors, and thus they can fully replace the isolated voltage sources. Since the proposed configuration is derived from the PEID method, all the features of PEID method are retained. A prototype with 6−26-V output voltage and 20-W peak output power is fabricated and tested with a 1-MHz sine wave and a 5-MHz bandwidth communication envelope. The experimental results validate the proposed configuration. [ABSTRACT FROM AUTHOR]

• The pH-responsive hydrogel has a good adhesion performance to skin tissue. • The adhesive hydrogel did not cause damage to tissues during the second operation. • Double Schiff base bonds promoted rapid release of AgNPs and DFO at the wound sites. • Dual-mechanisms to promote wound healing included antibacterial and angiogenesis. • The hydrogel was biodegradable and had great biocompatibility. The healing of chronic diabetic wounds remains a key challenge due to its susceptibility to bacterial infection, the inflammatory wound microenvironment, and difficulty in angiogenesis. Herein, a pH-responsive, mussel-inspired, double-crosslinking injectable, and adhesive smart hydrogel formula was exploited to overcome these obstacles simultaneously. The multifunctional double-crosslinking hydrogel was developed through the formation of catechol-catechol adducts and a Schiff-based reaction between amino groups (−NH 2) in chitosan quaternary ammonium salt (HTCC) and aldehyde groups (–CHO) in oxidized dextran-dopamine (OD-DA). The double-crosslinking mechanism gave the hydrogels great mechanical properties. Most importantly, via the effective encapsulation of silver nanoparticles (AgNPs) and pro-angiogenic drug deferoxamine (DFO), the hydrogel was given with antibacterial and angiogenic features respectively. At the infected diabetic wound area, the double Schiff base bonds (DA and OD; HTCC and OD-DA) in the hydrogels could quickly achieve pH-response and accomplish a sustained and controlled release of drugs to accelerate wound healing. Through the combination of AgNPs and HTCC, the hydrogel exhibited antimicrobial capacities to gram-positive bacteria (S. aureus) and gram-negative bacteria (E. coli), it did not cause toxic side effects and avoided drug resistance. The DFO released at the infected diabetic wound area promoted angiogenesis by enhancing the expression of hypoxia-inducible factor-1 alpha (HIF-1α) and vascular endothelial growth factor (VEGF). We elucidated the mechanisms by which the designed hydrogels accelerated the healing of bacterial infected diabetic wounds in vitro and in vivo , and our hydrogels represented a general strategy for the healing of a wide range of tissue injuries. [ABSTRACT FROM AUTHOR]

Background: Germline RET mutations and variants are involved in development of multiple endocrine neoplasia type 2 (MEN2). The present study investigated a spectrum of RET variants, analyzed genotype-phenotype relationships, and evaluated their effect on the MEN2 phenotype in Han Chinese patients.Methods: Targeted sequencing detected germline RET variants in 697 individuals, including 245 MEN2, 120 sporadic medullary thyroid cancer (MTC), and 15 pheochromocytoma (PHEO) patients and their 493 relatives. In silico analyses and classifications following ACMG-2015 were performed. Demographic, clinical variant types, and endocrine neoplasia molecular diagnosis records were also analyzed.Results: Nineteen different RET mutations (18 point and 1 del/ins mutations) in 214 patients with MEN2A (97.7%) or MEN2B (2.3%) were found, of which exon 11/10 mutations accounted for 79% (169/214). Nineteen compound mutations were found in 31 patients with MEN2A. Twenty-three variants (18 single and 5 double base substitution/compound variants) non-classification were also found. Of these, 17 (3 of pathogenic, 10 of uncertain significance, 2 of likely benign and 2 as benign) were found in 31 patients with MTC/PHEO. The remaining 6 variants (4 of uncertain significance and 2 of likely benign) found in 8 carriers had no evidence of MEN2. The entire cohort showed MEN2A-related PHEO, all occurring in exons 11/10, particularly at C634. Kaplan-Meier curves showed age-dependent penetration rates of MTC and PHEO, and occurrence rates of PHEO in patients with exon 11 mutations were all higher than those within exon 10; these bilateral PHEO were always associated with exon 11 mutations (all P < 0.05). While patient offspring had PHEO, parents with MEN2A had none, the frequency was approximately 10%. Interestingly, at least 6.8% of families were adoptive. Also, 3 non-hotspot RET variants (R114H, T278N, and D489N) appeared with high frequency. Conversely, polymorphism S836S was absent.Conclusions: These data are largely consistent with current evidence-based recommendations in the clinical practice guidelines. Diversity of RET variants or carriers may involve a different natural disease course. Further large-scale targeted sequencing studies will serve as an accurate and cost-effective approach to investigating MEN2 genotype-phenotype correlations for discovery of rare or unknown variants of RET. [ABSTRACT FROM AUTHOR]

Purpose. Damage to corneal nerve fibers has been demonstrated in people with type 2 diabetes mellitus (T2DM) that further progresses with increasing severity of diabetic peripheral neuropathy. However, the role of C-peptide in corneal nerve damage has not been reported in T2DM. The present study investigated the relationship of fasting C-peptide levels with corneal neuropathy evaluated by corneal confocal microscopy (CCM) in patients with T2DM. Methods. 160 T2DM patients (72 females) aged 34-78 with duration ranging from 0 to 40 years underwent CCM to measure corneal nerve fiber length (CNFL), corneal nerve fiber density (CNFD), and corneal nerve branch density (CNBD). Pearson correlation analysis and multiple linear regression analysis were used to explore the association of fasting C-peptide levels with corneal nerve parameters. Partial correlation analysis (adjusted for age and gender) was also conducted to analyze the correlation of metabolic indexes with these three corneal nerve parameters. The relationship between fasting C-peptide levels and duration of diabetes was also explored by Pearson correlation analysis. Results. With an increase in fasting C-peptide levels, the values of CNFL, CNFD, and CNBD also showed a corresponding trend for an increase. Partial correlation analysis revealed that fasting C-peptide levels were positively associated with CNFL (r = 0.245 , P = 0.002), CNFD (r = 0.180 , P = 0.024), and CNBD (r = 0.214 , P = 0.008) after adjusting for age and gender. Using multiple linear regression analysis, fasting C-peptide levels were also closely associated with CNFL (P = 0.047) and CNBD (P = 0.038) after multiple adjustments. However, this association disappeared after further adjusting for duration of diabetes. Further analysis indicated that fasting C-peptide levels declined with duration of diabetes (r = − 0.267 , P = 0.001). Conclusions. C-peptide was closely associated with corneal neuropathy and disease duration in T2DM. C-peptide levels might be both an indicator of beta-cell function and a marker of disease severity (such as diabetic corneal neuropathy) and duration. [ABSTRACT FROM AUTHOR]

Identifying the rapeseed (Brassica napus L.) flowering dates are important for planting area estimation, growth monitoring, and yield estimation. However, there is currently a lack of data on rapeseed flowering dates at the parcel scale. In this study, a new spectral index (Normalized Rapeseed Flowering Index, NRFI) is proposed to detect rapeseed flowering dates from time series data generated from Landsat 8 OLI and Sentinel-2 sensors. This study also analyzed the feasibility of using the backscattering coefficients (VV, VH, and VV/VH) of Sentinel-1 to detect the flowering dates of rapeseed at the parcel scale. Based on the spectral and polarization characteristics of 718 rapeseed parcels collected in 2018, we developed a method to automatically identify peak flowering dates by the local maximum of NRFI series and the local minimum of VH and VV, along with the maximum of VV/VH. The results show that most of the peak flowering dates derived from Sentinel-1 and Sentinel-2 can be confirmed by the in-situ phenological observations at the Deutscher Wetterdienst (DWD) stations in Germany. The NRFI outperforms the Normalized Difference Yellow Index (NDYI) in identifying the peak flowering dates from Landsat 8. The derived medians of peak flowering dates by NRFI, NDYI (Sentinel-2), and VH are similar, while a systematic delay is observed by NDYI (Landsat 8). The method with the spectrum and backscattering coefficients will be a potential tool to identify crop flowering dynamics and map crop planting area. [ABSTRACT FROM AUTHOR]