Search

Background: Fumonisin B1 (FB1) is one of the most common mycotoxins contaminating feed and food. Although regulatory limits about fumonisins have been established in some countries, it is still very important to conduct research on lower doses of FB1 to determine the tolerance limits. The aim of this study was to investigate the effects of different concentrations of FB1, provide further evidence about the toxic doses- and exposure time-associated influence of FB1 on mice, especially low levels of FB1 for long-term exposure. Methods: Female BALB/c mice were treated intragastrically (i.g.) with fumonisin B1 (FB1) solutions (0 mg/kg body weight (BW), 0.018 mg/kg BW, 0.054 mg/kg BW, 0.162 mg/kg BW, 0.486 mg/kg BW, 1.458 mg/kg BW and 4.374 mg/kg BW) once a day for 8 weeks to obtain dose- and time-dependent effects on body and organ weights, hematology, blood chemical parameters and liver and kidney histopathology. Results: After the long-term administration of FB1, the body weights of the mice tended to decrease. Over time, FB1 first increased the relative spleen weight, then increased the relative kidney weight, and finally increased the relative liver weight. The mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), hemoglobin (HGB), white blood cells (WBC), platelets (PLT), and mean platelet volume (MPV) were significantly elevated after treatment with FB1 for 8 weeks. Moreover, exposure time-dependent responses were found for aspartate aminotransferase (AST), alanine aminotransferase (ALT) and alkaline phosphatase (ALP) level, which were coupled with hepatic histopathological findings, necroinflammation and vacuolar degeneration and detrital necrosis. Linear dose response was also found for liver histopathology, in which, even the minimum dose of FB1 exposure also caused changes. Renal alterations were moderate compared to hepatic alterations. Conclusion: In conclusion, we demonstrated the systemic toxic effects of different doses of FB1 in female BALB/c mice at different times. Our data indicated that the effects observed in this study at the lowest dose tested are discussed in relation to the currently established provisional maximum tolerable daily intake (PMTDI) for fumonisins. This study suggested that recommendations for the concentration of FB1 in animals and humans are not sufficiently protective and that regulatory doses should be modified to better protect animal and human health. The toxicity of FB1 needs more attention.

We consider the secure computation problem in a minimal model, where Alice and Bob each holds an input and wish to securely compute a function of their inputs at Carol without revealing any additional information about the inputs. For this minimal secure computation problem, we propose a novel coding scheme built from two steps. First, the function to be computed is expanded such that it can be recovered while additional information might be leaked. Second, a randomization step is applied to the expanded function such that the leaked information is protected. We implement this expand-and-randomize coding scheme with two algebraic structures—the finite field and the modulo ring of integers, where the expansion step is realized with the addition operation and the randomization step is realized with the multiplication operation over the respective algebraic structures.

The objective of this study is to evaluate the effects of organic acids on piglet growth performance and health status. A total of 360 weanling pigs (5.3 ± 0.6 kg) were randomly allotted to 3 treatment groups with 12 replicates of 10 pigs/pen. Piglets were fed the same basal diet and given either water (control) or water plus 2.0 L/Ton organic acid (OA) blends, such as OA1 or OA2, respectively, for 7 weeks. Compared to the control, OA1 and OA2 improved growth performance and/or reduced the piglets’ diarrhea rate during the various periods and improved small intestinal morphology at days 14 and/or 49. OA1 and OA2 also increased serum CAT and SOD activities and/or T-AOC and, as expected, decreased MDA concentration. Moreover, at day 14 and/or day 49, OA1 and OA2 increased the jejunal mRNA levels of host defense peptides (PBD1, PBD2, NPG1, and NPG3) and tight junction genes (claudin-1) and decreased that of cytokines (IL-1β and IL-2). Additionally, the two acidifiers regulated the abundance of several cecum bacterial genera, including Blautia, Bulleidia, Coprococcus, Dorea, Eubacterium, Subdoligranulum, and YRC2. In conclusion, both of the organic acid blends improved piglet growth performance and health status, potentially by regulating intestinal redox homeostasis, immunity, and microflora.

This study has determined whether hydroxy-selenomethionine (OH-SeMet) exerts a better protective action on broilers against environmental stress than sodium selenite (SS) or seleno-yeast (SY). Day-old male Cobb 500 broilers (12 cages/diet, 9 broilers/cage) were fed a selenium (Se)-deficient diet (0.047 mg/kg) supplemented with SS, SY or OH-SeMet at 0.3 mg Se/kg under a high stocking density and heat stress condition for six weeks. OH-SeMet improved the FCR and Se concentration in the tissues than SS and SY. SY and OH-SeMet both reduced the serum cortisol, T3, IL-6, IgA, IgM and LPS, more than SS, while only OH-SeMet further increased IL-10 and IgG. SY and OH-SeMet improved the intestinal morphology and increased the T-AOC, TXRND, SELENON and OCCLUDIN activities but decreased CLAUDIN2 in the jejunum than SS, while OH-SeMet further improved these values than SY. SY and OH-SeMet both increased SELENOS and TXNRD2 in the muscles than SS, and OH-SeMet further raised T-AOC, GPX4, SELENOP, SELENOW and TXNRD1, and reduced malondialdehyde and protein carbonyl in the muscles than SS and SY. OH-SeMet showed a better ability to maintain the performance and the redox and immune status of broilers under a high stocking density and heat stress challenge than SS and SY.

Fumonisins are a kind of mycotoxin that has harmful influence on the health of humans and animals. Although some research studies associated with fumonisins have been reported, the regulatory limits of fumonisins are imperfect, and the effects of fumonisins on fecal bacterial flora of mice have not been suggested. In this study, in order to investigate the effects of fumonisin B1 (FB1) on fecal bacterial flora, BALB/c mice were randomly divided into seven groups, which were fed intragastrically with 0 mg/kg, 0.018 mg/kg, 0.054 mg/kg, 0.162 mg/kg, 0.486 mg/kg, 1.458 mg/kg and 4.374 mg/kg of FB1 solutions, once a day for 8 weeks. Subsequently, feces were collected for analysis of microflora. The V3-V4 16S rRNA of fecal bacterial flora was sequenced using the Illumina MiSeq platform. The results revealed that fecal bacterial flora of mice treated with FB1 presented high diversity. Additionally, the composition of fecal bacterial flora of FB1 exposure groups showed marked differences from that of the control group, especially for the genus types including Alloprevotella, Prevotellaceae_NK3B31_group, Rikenellaceae_RC9_gut_group, Parabacteroides and phylum types including Cyanobacteria. In conclusion, our data indicate that FB1 alters the diversity and composition of fecal microbiota in mice. Moreover, the minimum dose of FB1 exposure also causes changes in fecal microbiota to some extent. This study is the first to focus on the dose-related effect of FB1 exposure on fecal microbiota in rodent animals and gives references to the regulatory doses of fumonisins for better protection of human and animal health.

Urban forest is an important component of terrestrial ecosystems and is highly related to global climate change. However, because of complex city landscapes, deriving the spatial distribution of urban forest carbon density and conducting accuracy assessments are difficult. This study proposes a novel spatial simulation method, optimized geographically weighted logarithm regression (OGWLR), using Landsat 8 data acquired by the Google Earth Engine (GEE) and field survey data to map the forest carbon density of Shenzhen city in southern China. To verify the effectiveness of the novel method, multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF) and geographically weighted regression (GWR) models were established for comparison. The results showed that OGWLR achieved the highest coefficient of determination (R2 = 0.54) and the lowest root mean square error (RMSE = 13.28 Mg/ha) among all estimation models. In addition, OGWLR achieved a more consistent spatial distribution of carbon density with the actual situation. The carbon density of the forests in the study area was large in the central and western regions and coastal areas and small in the building and road areas. Therefore, this method can provide a new reference for urban forest carbon density estimation and mapping.

The objective of this study was to evaluate the efficacy of supplementing a corn-wheat-soybean meal-based diet with a multicarbohydrase and phytase complex (MCPC) on growth performance, apparent total tract digestibility (ATTD) of nutrients, carcass traits, and meat quality in growing-finishing pigs. A total of 300 pigs (Duroc × Large White × Landrace; body weight = 25.3 ± 0.7 kg) were randomly allotted to three groups with 10 replicates of 10 pigs each. Pigs from three groups were fed positive control (PC) or negative control (NC), without or with MCPC diets, respectively. The MCPC supplied at least 1800, 1244, 6600, and 1000 units of xylanase, β-glucanase, α-arabinofuranosidase, and phytase per kilogram of diet, respectively. The NC diet was the PC diet but reduced in net energy (NE), digestible amino acids (dig. AA), digestible P (dig. P), and Ca by 74 kcal/kg, 7.0%, 0.134, and 0.119 percentage points, respectively. The diets were fed in 4 growth phases based on body weight (BW): phase 1: 25–50 kg, phase 2: 50–75 kg, phase 3: 75–100 kg, and phase 4: 100–135 kg. Compared to the PC, the NC diet decreased (p < 0.05) body weight gain, feed intake, and(or) feed to gain ratio during the growing/finishing phases 1, 2, 3, and 4. It also reduced (p < 0.05) the ATTD of crude protein, crude fat, P, and Ca of pigs. MCPC supplementation improved (p < 0.05) the body weight gain, feed intake, and(or) feed to gain ratio in phases 2, 3, and 4 and the ATTD of crude protein, crude fat, ash, P, and Ca for the NC diet. Additionally, dietary treatment had no effects on carcass traits and meat quality with the exception that the loin eye area in the NC plus MCPC diet was higher (p < 0.05) than the NC diet. In conclusion, the addition of MCPC to a corn-soybean meal-wheat-based diet reduced in energy and nutrients improved the growth performance and nutrient digestibility but had little effect on carcass traits and meat quality in growing-finishing pigs.

The forest canopy height (FCH) plays a critical role in forest quality evaluation and resource management. The accurate and rapid estimation and mapping of the regional forest canopy height is crucial for understanding vegetation growth processes and the internal structure of the ecosystem. A stacking algorithm consisting of multiple linear regression (MLR), support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF) was used in this paper and demonstrated optimal performance in predicting the forest canopy height by synergizing Sentinel-2 images acquired from the cloud-based computation platform Google Earth Engine (GEE) with data from ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). This research was conducted to achieve continuous mapping of the canopy height of plantations in Saihanba Mechanical Forest Plantation, which is located in Chengde City, northern Hebei province, China. The results show that stacking achieved the best prediction accuracy for the forest canopy height, with an R2 of 0.77 and a root mean square error (RMSE) of 1.96 m. Compared with MLR, SVM, kNN, and RF, the RMSE obtained by stacking was reduced by 25.2%, 24.9%, 22.8%, and 18.7%, respectively. Since Sentinel-2 images and ICESat-2 data are publicly available, this opens the door for the accurate mapping of the continuous distribution of the forest canopy height globally in the future.

Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction.

To establish an animal model of acute-on-chronic liver failure (ACLF) that would replicate the pathological process of ACLF in humans, rats were administered porcine serum (PS) for 11 weeks. Liver fibrosis was determined by pathological and biochemical assessments. The animals then were injected with d-galactosamine (d-gal) and lipopolysaccharide (LPS). The survival times of animals with cirrhosis and ACLF were determined over 48 h. Other animals were killed at 0, 4, 8 and 12 h after administration of d-gal/LPS. Liver injury was assessed by histopathological analysis and biochemical indices, and apoptosis was detected by Western blot and TUNEL analysis. After PS administration for 11 weeks the serum levels of hyaluronic acid and N-procollagen type III peptide increased significantly, and serious fibrosis and cirrhosis was observed at weeks 10 and 11. Cirrhotic rats were injected with d-gal/LPS to induced ACLF; the rate of mortality over 48 h was 80%. ALT and AST levels increased markedly at 4 h, but decreased significantly at 8 and 12 h post-treatment. The total bilirubin, direct bilirubin, and total bile acids levels increased markedly at 8 and 12 h. Clotting times, TNF-α and IL-6 levels increased significantly, except for 12 h post-treatment. Apoptosis, inflammation and necrosis were elevated as determined by hematoxylin-eosin staining and TUNEL assays. BCL-2 levels decreased significantly, While BAX levels increased significantly. Cytochrome c expression peaked at 8 h post-d-gal/LPS treatment. In conclusion, an ACLF model induced by PS and d-gal/LPS was established and the underlying mechanisms of ACLF development were explored.

Bacillus amyloliquefaciens NCPSJ7 could secrete extracellular antimicrobial substances, showing potent antifungal activities. An active peptide AFP3 was isolated from the fermentation supernatant. After chromatography, the purified peptide was tested for the fungicidal activity, molecular mass, and stability. The results indicated that the peptide with a molecular mass of around 3.3 kDa, showed discernible inhibition of the pathogen Fusarium oxysporum f. sp. niveum with the minimum fungicidal concentration of 31 µg/ml. It also exhibited excellent inhibition of some representative pathogenic fungi at a low concentration. Moreover, the peptide remained active at a wide range of temperatures and pH. Ion Na+ may even increase the antifungal activities. At the same time, the peptide could well tolerate the treatment with trypsin. Electron microscopy was used to investigate the effect of the peptide on the pathogens. The peptide inhibited the growth of pathogens by disrupting the integrity of the hyphal membranes, resulting in their lysis. The potent fungicidal activities and stability made the peptide be a candidate for a biopreservative.

Background: There are ON- and OFF-pathways in the normal vertebrate retina. Short- and long-flash electroretinogram (ERG) are suitable methods to observe the function of ON- and OFF-pathways in vivo, respectively. It is clear that high intraocular pressure (IOP) might cause dysfunction of cone-dominated photopic negative response (PhNR) in monkeys with high IOP in ON-pathway. However, whether cone-dominated OFF-responses are also affected is less known. The aim of this study was to observe photopic OFF-responses of ERG in monkeys with high IOP. Methods: Nine monkeys were involved in the experiment from January 2006 to December 2016. High IOP was induced in the right eye by laser coagulation of the mid-trabecular meshwork in five monkeys. Six years after the laser coagulation, both short- and long-flash of the photopic ERG were recorded. Stimulus light was red flashes superimposed on a blue background. Four normal monkeys were examined under the same ERG protocols as controls. Paired t- test was used to compare the difference of each ERG parameter between the lasered eye and the fellow eye. Analysis of variance (ANOVA) with Tukey adjustment was adopted to calculate the differences among the lasered eye, the fellow eye, and the eyes of normal monkeys. Results: The mean amplitude of a-wave (11.73 ± 2.05) and PhNR (8.67 ± 2.44) in lasered eyes was significantly lower than that of a-wave (21.47 ± 3.15) and PhNR (22.05 ± 3.42) in fellow eyes (P = 0.03 and P = 0.01, respectively) in response to short flash. The mean amplitude of d-wave (1.60 ± 0.59) and i-wave (3.13 ± 0.64) was significantly reduced in the lasered eyes than that of d-wave (4.01 ± 0.56) and i-wave (8.79 ± 1.75) in the fellow eyes (P = 0.02 and P = 0.02, respectively) in response to long flash. Conclusions: Reduced OFF-responses are recorded in monkeys with high IOP when dysfunction of photoreceptor is involved. The reduced OFF-responses to long-flash stimulus show evidence of anomalous retinal circuitry in glaucomatous retinopathy.

Background: Rupture of the medial canthal ligament can be caused by many events. It remains a challenge to rebuild the drainage system and restore the function. The aim of this study was to evaluate the clinical efficacy of medial canthoplasty combined with conjunctivodacryocystorhinostomy (CDCR) in patients with medial telecanthal deformities and lacrimal drainage system damage. Methods: Twenty-two patients (22 eyes) treated with medial canthoplasty and CDCR during June 2012 to June 2014 were included in this retrospective study. For all patients, a self-tapping, titanium, low-profile head microscrew was drilled into the solid bone on the posterior aspect of the anterior lacrimal crest at the attachment position of the medial canthal ligament. Medpor-coated tear drainage tubes were applied. Distance of patient's lateral displacement before and after operation was recorded and compared. The complications of CDCR were described. Results: Before the surgery, distance of patient's canthal displacement was 4–6 mm. The canthal distance between two eyes of patients with surgery was 1 mm or less. Among patients with CDCR, four patients had proximal obstruction and two patients had distal obstruction. Five patients had tube malposition, for example, tube extrusion 1–3 months after surgery. Conclusions: Medial canthoplasty combined with CDCR is an effective surgical method for treatment of patients with medial telecanthal deformity and lacrimal drainage system obstruction. The study indicates that medial canthoplasty combined with CDCR surgery rebuilds normal appearance of eyelid and contour of the medial canthus and successfully repairs the function of the lacrimal drainage system.

Cinobufotalin is one of the major anti-tumor components isolated from toad venom and has been used in the clinical therapy of hepatocellular carcinoma (HCC), known as Cinobufacini injection. However, the pharmacokinetic (PK) behaviors of cinobufotalin in vivo with HCC are still unknown. Hence, we have established a HCC model in Sprague Dawley (SD) rats induced by diethylnitrosamine (DEN), named as DEN-injured rats. Then, we developed and validated a sensitive and rapid ultra-performance liquid chromatography-tandem mass spectrometric (UPLC-MS/MS) method to quantify cinobufotalin in rat plasma. This UPLC-MS/MS method was successfully used to characterize the PK behaviors of cinobufotalin in normal and DEN-injured rats after intravenous (i.v.) injection at a dosage of 2.5 mg/kg. Cinobufotalin pharmacokinetics was well described by the two-compartment pharmacokinetic model and the PK parameters were calculated using WinNonlin 3.3 software. The transfer rate constant of cinobufotalin from the central compartment to the peripheral compartment (k12) in DEN-injured rats was significantly greater than that in normal rats (p < 0.01), accompanied by the shorter half-life for the distribution phase (t1/2α). Additionally, the elimination rate constant (K10) and clearance (CL) values in DEN-injured rats were significantly higher than that in normal rats (p < 0.05 for K10 and p < 0.001 for CL, respectively). Therefore, the values of areas under concentration – time curve (AUC) and the liver concentration of cinobufotalin in DEN-injured rats was obviously lower than that in normal rats (p < 0.001 and p < 0.01, respectively). This indicated that the PK behaviors of cinobufotalin will be altered in rats with HCC. In addition, P-glycoprotein (P-gp) has shown higher expression in live tissues of DEN-injured rats. Furthermore, cinobufotalin was identified as the substrate of P-gp using MDCK II and MDCK-MDR1 cell models for the first time. Consequently, P-gp will play an important role in the disposition of cinobufotalin in vivo, which provided a new combination therapy for the clinical treatment of HCC.

In this paper, we demonstrate that the performance of the industrial multicrystalline silicon solar cells can be improved by embedding the silver nanoparticles (Ag-NPs) into the SiNx layer. On the one hand, the cells have a certain optical loss in short wavelengths near the plasmonic resonance frequency of Ag-NPs, but their open circuit voltages and filling factors are increased due to depressed surface recombination as those short wavelength photons are mainly absorbed by Ag-NPs instead of the surface; on the other hand, the cells show strong absorption in long wavelengths, which can be attributed to the forward-scattering effect of Ag-NPs. Taking together, UV-absorbing Ag-NPs may act as a “sunscreen” to shield the UV damage, while improve the cell efficiency from 18.05 % to 18.25 % by embedding proper Ag-NPs. The techniques presented in this work can be easily incorporated into the current mc-Si solar cell production line, thus have great potential for the mass practical application.

Our previous study found that desmethylxanthohumol (1) inhibited α-glucosidase in vitro. Recently, further investigations revealed that dehydrocyclodesmethylxanthohumol (2) and its dimer analogue rottlerone (3) exhibited more potent α-glucosidase inhibitory activity than 1. The aim of this study was to synthesize a series of rottlerone analogues and evaluate their α-glucosidase and DPP-4 dual inhibitory activity. The results showed that compounds 4d and 5d irreversibly and potently inhibited α-glucosidase (IC50 = 0.22 and 0.12 μM) and moderately inhibited DPP-4 (IC50 = 23.59 and 26.19 μM), respectively. In addition, compounds 4d and 5d significantly promoted glucose consumption, with the activity of 5d at 0.2 μM being comparable to that of metformin at a concentration of 1 mM.

PI3K pathway activation is frequently observed in triple negative breast cancer (TNBC). However, single agent PI3K inhibitors have shown limited anti-tumor activity. To investigate biomarkers of response and resistance mechanisms, we tested 17 TNBC patient-derived xenograft (PDX) models representing diverse genomic backgrounds and varying degrees of PI3K pathway signaling activities for their tumor growth response to the pan-PI3K inhibitor, BKM120. Baseline and post-treatment PDX tumors were subjected to reverse phase protein array (RPPA) to identify protein markers associated with tumor growth response. While BKM120 consistently reduced PI3K pathway activity, as demonstrated by reduced levels of phosphorylated AKT, percentage tumor growth inhibition (%TGI) ranged from 35% in the least sensitive to 84% in the most sensitive model. Several biomarkers showed significant association with resistance, including elevated baseline levels of growth factor receptors (EGFR, pHER3 Y1197), PI3Kp85 regulatory subunit, anti-apoptotic protein BclXL, EMT (Vimentin, MMP9, IntegrinaV), NFKB pathway (IkappaB, RANKL), and intracellular signaling molecules including Caveolin, CBP, and KLF4, as well as treatment-induced increases in the levels of phosphorylated forms of Aurora kinases. Interestingly, increased AKT phosphorylation or PTEN loss at baseline were not significantly correlated to %TGI. These results provide important insights into biomarker development for PI3K inhibitors in TNBC.

Studies on the assessment of green infrastructure (GI) practice implementation effect and cost efficiency on an urban watershed scale helps the GI practice selection and investment decisions for sponge city construction in China. However, few studies have been conducted for these topics at present. In this study, the Long-Term Hydrologic Impact Assessment—Low Impact Development (L-THIA-LID) 2.1 model was applied to assess the effectiveness and cost efficiency of GI practices on surface runoff volume reduction in an urban watershed—the Hexi watershed, Nanjing City, China. Grassed swales, bioretentions, green roofs, rain cisterns, permeable pavements, wet ponds, dry ponds, and wetlands were chosen as potential GI practices for sponge city construction based on feasibility analysis. Results showed that grassed swales were the most cost-effective practice (0.7 CNY/m3/yr), but the total implementation effect of grassed swales was not obvious due to the small area of suitable locations. Permeable pavements performed best on runoff reduction, but the cost efficiency was much lower. Correspondingly, bioretentions were compromise practices. Green roofs were the least cost-effective practices, with the cost efficiency at 122.3 CNY/m3/yr, but it was much lower for rain cisterns, which were 3.2 CNY/m3/yr. Wet ponds, dry ponds, and wetlands were potential practices implemented in development areas, of which dry ponds were the most cost-effective (2.7 CNY/m3/yr), followed by wet ponds (10.9 CNY/m3/yr). The annual runoff volume of the total area could be reduced by up to 47.01% by implementing GI practices in buildup areas. Rain cisterns (RC) and permeable pavements (PP) were the best combination for this area, and bioretentions (BR) and green roofs (GR) followed. Grassed swales (GS1), dry ponds (DP), wet ponds (WP), and wetlands (WL) were not wise choices due to the small suitable location areas. This study also demonstrated the feasibility of the L-THIA-LID 2.1 model for the evaluation of GI practice implementation effects and cost efficiency on urban runoff in sponge city construction in China.

Coded Caching, proposed by Maddah-Ali and Niesen (MAN), has the potential to reduce network traffic by pre-storing content in the users’ local memories when the network is underutilized and transmitting coded multicast messages that simultaneously benefit many users at once during peak-hour times. This paper considers the linear function retrieval version of the original coded caching setting, where users are interested in retrieving a number of linear combinations of the data points stored at the server, as opposed to a single file. This extends the scope of the authors’ past work that only considered the class of linear functions that operate element-wise over the files. On observing that the existing cache-aided scalar linear function retrieval scheme does not work in the proposed setting, this paper designs a novel coded caching scheme that outperforms uncoded caching schemes that either use unicast transmissions or let each user recover all files in the library.

Forest growing stem volume (GSV) reflects the richness of forest resources as well as the quality of forest ecosystems. Remote sensing technology enables robust and efficient GSV estimation as it greatly reduces the survey time and cost while facilitating periodic monitoring. Given its red edge bands and a short revisit time period, Sentinel-2 images were selected for the GSV estimation in Wangyedian forest farm, Inner Mongolia, China. The variable combination was shown to significantly affect the accuracy of the estimation model. After extracting spectral variables, texture features, and topographic factors, a stepwise random forest (SRF) method was proposed to select variable combinations and establish random forest regressions (RFR) for GSV estimation. The linear stepwise regression (LSR), Boruta, Variable Selection Using Random Forests (VSURF), and random forest (RF) methods were then used as references for comparison with the proposed SRF for selection of predictors and GSV estimation. Combined with the observed GSV data and the Sentinel-2 images, the distributions of GSV were generated by the RFR models with the variable combinations determined by the LSR, RF, Boruta, VSURF, and SRF. The results show that the texture features of Sentinel-2’s red edge bands can significantly improve the accuracy of GSV estimation. The SRF method can effectively select the optimal variable combination, and the SRF-based model results in the highest estimation accuracy with the decreases of relative root mean square error by 16.4%, 14.4%, 16.3%, and 10.6% compared with those from the LSR-, RF-, Boruta-, and VSURF-based models, respectively. The GSV distribution generated by the SRF-based model matched that of the field observations well. The results of this study are expected to provide a reference for GSV estimation of coniferous plantations.

The objective of this study was to evaluate the effects of partially substituting for conventional forage, Chinese wildrye (CW), with mulberry leaves (ML) on the growth, digestion, ruminal fermentation, blood metabolites, and meat quality of sheep in a 65-day feedlot study. Thirty-two four-month-old male small-tailed Han sheep (25.15 ± 1.03 kg) were randomly assigned to one of four treatments. The dietary treatments consisted of four proportions of ML (0, 8, 24, and 32%) as a substitute for CW (designated as ML0, ML8, ML24, and ML32, respectively). Rumen digesta and blood samples were collected at day 63 of the trial. Carcass traits were assessed after slaughter at the end of performance period. The results from this study revealed no differences in average daily bodyweight gain (ADG), feed conversion ratio (FCR), and final body weight (FBW) among treatments. The apparent digestibility of dry matter (DM), organic matter (OM), and acid detergent fiber (ADF) was higher in the sheep fed with ML than in those fed CW. The ML24 treatment had a higher digestibility of crude protein (CP) and ether extract (EE). There were no differences (p = 0.13) in ruminal pH values among the treatments. However, there was more microbial protein (p < 0.01) in ML24 and ML32 treatments than the ML0 treatment. Ruminal concentrations of acetate and butyrate were significantly different among treatments, although no difference in concentrations of total volatile fatty acid were found. Additionally, no differences were detected for serum parameters except blood urea nitrogen (BUN). No differences were observed for carcass weight (p = 0.62), dressing percentage (p = 0.31) or longissimus dorsi muscle (LM) area (p = 0.94) among treatments. However, intramuscular fat was higher in the ML24 treatment than in the ML0 treatment. (p < 0.01). There were higher pH values of the 24-h longissimus dorsi in the ML24 treatment than in the ML0 treatment. In addition, the saturated fatty acid (SFA) content was lower (p < 0.01) and the monounsaturated fatty acid (MUFA) content higher (p < 0.01) in the ML24 treatment than in the ML0 treatment. In conclusion, the partially substitution of mulberry leaves for Chinese wildrye in the diet of sheep had a beneficial influence on the growth performance, blood metabolites and carcass characteristics. The inclusion of 24% (air dry basis) mulberry leaf hay in the ration of sheep is recommended based on these findings.

Colour-shift keying (CSK) constitutes an important modulation scheme conceived for the visible light communications (VLC). The signal constellation of CSK relies on three different-color light sources invoked for information transmission. The CSK constellation has been optimized for minimizing the bit error rate, but no effort has been invested in investigating the feasibility of CSK aided unequal error protection (UEP) schemes conceived for video sources. Hence, in this treatise, we conceive a hierarchical CSK (HCSK) modulation scheme based on the traditional CSK, which is capable of generating interdependent layers of signals having different error probability, which can be readily reconfigured by changing its parameters. Furthermore, we conceived an HCSK design example for transmitting scalable video sources with the aid of a recursive systematic convolutional (RSC) code. An optimization method is conceived for enhancing the UEP and for improving the quality of the received video. Our simulation results show that the proposed optimized-UEP 16-HCSK-RSC system outperforms the traditional equal error protection scheme by ~ 1.7 dB of optical SNR at a peak signal-to-noise ratio of 37 dB, while optical SNR savings of up to 6.5 dB are attained at a lower PSNR of 36 dB.

Accurately mapping urban vegetation carbon density is challenging because of complex landscapes and mixed pixels. In this study, a novel methodology was proposed that combines a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) and k-Nearest Neighbors (kNN), to map the forest carbon density of Shenzhen City of China, using Landsat 8 imagery and sample plot data collected in 2014. The independent variables that contributed to statistically significantly improving the fit of a model to data and reducing the sum of squared errors were first selected from a total of 284 spectral variables derived from the image bands. The vegetation fraction from LSUA was then added as an independent variable. The results obtained using cross-validation showed that: (1) Compared to the methods without the vegetation information, adding the vegetation fraction increased the accuracy of mapping carbon density by 1%–9.3%; (2) As the observed values increased, the LSR and kNN residuals showed overestimates and underestimates for the smaller and larger observations, respectively, while LMSR improved the systematical over and underestimations; (3) LSR resulted in illogically negative and unreasonably large estimates, while KNN produced the greatest values of root mean square error (RMSE). The results indicate that combining the spatial modeling method LMSR and the spectral unmixing analysis LUSA, coupled with Landsat imagery, is most promising for increasing the accuracy of urban forest carbon density maps. In addition, this method has considerable potential for accurate, rapid and nondestructive prediction of urban and peri-urban forest carbon stocks with an acceptable level of error and low cost.

Increasing the area of planted forests is rather important for compensation the loss of natural forests and slowing down the global warming. Forest growing stem volume (GSV) is a key indicator for monitoring and evaluating the quality of planted forest. To improve the accuracy of planted forest GSV located in south China, four L-band ALOS PALSAR-2 quad-polarimetric synthetic aperture radar (SAR) images were acquired from June to September with short intervals. Polarimetric characteristics (un-fused and fused) derived by the Yamaguchi decomposition from time series SAR images with different intervals were considered as independent variables for the GSV estimation. Then, the general linear model (GLM) obeyed the exponential distribution were proposed to retrieve the stand-level GSV in plantation. The results show that the un-fused power of double bounce scatters and four fused variables derived from single SAR image is highly sensitive to the GSV, and these polarimeric characteristics derived from the time series images more significantly contribute to improved estimation of GSV. Moreover, compared with the estimated GSV using the semi-exponential model, the employed GLM model with less limitations and simple algorithm has a higher saturation level (nearly to 300 m3/ha) and higher sensitivity to high forest GSV values than the semi-exponential model. Furthermore, by reducing the external disturbance with the help of time average, the accuracy of estimated GSV is improved using fused polarimeric characteristics, and the estimation accuracy of forest GSV was improved as the images increase. Using the fused polarimetric characteristics (Dbl×Vol/Odd) and the GLM, the minimum RRMSE was reduced from 33.87% from single SAR image to 24.42% from the time series SAR images. It is implied that the GLM is more suitable for polarimetric characteristics derived from the time series SAR images and has more potential to improve the planted forest GSV.

As an important vegetation canopy parameter, the leaf area index (LAI) plays a critical role in forest growth modeling and vegetation health assessment. Estimating LAI is helpful for understanding vegetation growth and global ecological processes. Machine learning methods such as k-nearest neighbors (kNN) and random forest (RF) with remote sensing images have been widely used for mapping LAI. However, the accuracy of mapping LAI in arid and semi-arid areas using these methods is limited due to remote and large areas, the high cost of collecting field data, and the great spatial variability of the vegetation canopy. Here, a novel and modified kNN method was presented for mapping LAI in arid and semi-arid areas of China using Sentinel-2 and Landsat 8 images with field data collected in Ganzhou and Kangbao of China. The modified kNN was developed by integrating the traditional kNN estimation and RF classification. The results were compared with those from kNN and RF regression alone using three sets of input predictors: (i) spectral reflectance bands (input 1); (ii) vegetation indices (input 2); and (iii) a combination of spectral reflectance bands and vegetation indices (input 3). Our analysis showed that in Ganzhou, the red-edge bands of the Sentinel-2 image had a high correlation with LAI. Using the red-edge band-derived vegetation indices increased the accuracy of mapping LAI compared with using other spectral variables. Among the three sets of input predictors, input 3 resulted in the highest prediction accuracy. Based on the combination, the values of RMSE obtained by the traditional kNN, RF, and modified kNN were 0.526, 0.523, and 0.372, respectively, and the modified kNN significantly improved the accuracy of LAI prediction by 29.3% and 28.9% compared with the kNN and RF alone, respectively. A similar improvement was achieved for input 1 and input 2. In Kangbao, the improvement of the prediction accuracy obtained by the modified kNN was 31.4% compared with both the kNN and RF. Therefore, this study implied that the modified kNN provided the potential to improve the accuracy of mapping LAI in arid and semi-arid regions using the images.

Accurately estimating growing stem volume (GSV) is very important for forest resource management. The GSV estimation is affected by remote sensing images, variable selection methods, and estimation algorithms. Optical images have been widely used for modeling key attributes of forest stands, including GSV and aboveground biomass (AGB), because of their easy availability, large coverage and related mature data processing and analysis technologies. However, the low data saturation level and the difficulty of selecting feature variables from optical images often impede the improvement of estimation accuracy. In this research, two GaoFen-2 (GF-2) images, a Landsat 8 image, and fused images created by integrating GF-2 bands with the Landsat multispectral image using the Gram–Schmidt method were first used to derive various feature variables and obtain various datasets or data scenarios. A DC-FSCK approach that integrates feature variable screening and a combination optimization procedure based on the distance correlation coefficient and k-nearest neighbors (kNN) algorithm was proposed and compared with the stepwise regression analysis (SRA) and random forest (RF) for feature variable selection. The DC-FSCK considers the self-correlation and combination effect among feature variables so that the selected variables can improve the accuracy and saturation level of GSV estimation. To validate the proposed approach, six estimation algorithms were examined and compared, including Multiple Linear Regression (MLR), kNN, Support Vector Regression (SVR), RF, eXtreme Gradient Boosting (XGBoost) and Stacking. The results showed that compared with GF-2 and Landsat 8 images, overall, the fused image (Red_Landsat) of GF-2 red band with Landsat 8 multispectral image improved the GSV estimation accuracy of Chinese pine and larch plantations. The Red_Landsat image also performed better than other fused images (Pan_Landsat, Blue_Landsat, Green_Landsat and Nir_Landsat). For most of the combinations of the datasets and estimation models, the proposed variable selection method DC-FSCK led to more accurate GSV estimates compared with SRA and RF. In addition, in most of the combinations obtained by the datasets and variable selection methods, the Stacking algorithm performed better than other estimation models. More importantly, the combination of the fused image Red_Landsat with the DC-FSCK and Stacking algorithm led to the best performance of GSV estimation with the greatest adjusted coefficients of determination, 0.8127 and 0.6047, and the smallest relative root mean square errors of 17.1% and 20.7% for Chinese pine and larch, respectively. This study provided new insights on how to choose suitable optical images, variable selection methods and optimal modeling algorithms for the GSV estimation of Chinese pine and larch plantations.

Rapidly advancing airborne laser scanning technology has become greatly useful to estimate tree- and stand-level variables at a large scale using high spatial resolution data. Compared with that of ground measurements, the accuracy of the inferred information of diameter at breast height (DBH) from a remotely sensed database and the models developed with traditional regression approaches (e.g., ordinary least square regression) may not be sufficient. Thus, this regression approach is no longer appropriate to develop accurate models and predict DBH from remotely sensed-related variables because DBH is subject to the random effects of forest stands. This study developed a generalized nonlinear mixed-effects DBH estimation model from remotely sensed imagery data. The light detection and ranging (LiDAR)-derived stand canopy density, crown projection area, and tree height were used as predictors in the DBH estimation model. These variables can be more readily measured over an extensive forest area with higher accuracy compared to the conventional field-based methods. The airborne LiDAR data for a total of 402 Picea crassifolia Kom trees on a sample plot that were divided into 16 sub-sample plots and located in the most important distribution region of western China were used. The leave-one sub-sample plot-out cross-validation method was applied to evaluate the model’s prediction accuracy. The results indicated that the random effects of the sub-sample plot on the prediction of DBH were large and their inclusion into the DBH model significantly improved the prediction accuracy. The prediction accuracy of the proposed model at the mean (M) response was also substantially improved relative to the accuracy obtained from the base model. Among several tree selection alternatives evaluated, a sample size of the two largest trees per sub-sample plot used in estimating the random effects showed a significantly higher accuracy compared to other sampling alternatives. This sample size would balance both the measurement cost and potential prediction errors. The nonlinear mixed-effects DBH estimation model at the M response can also be applied if obtaining the estimates of individual tree DBH with a relatively lower cost, and a lower prediction accuracy was the purpose of the study.

Selective laser melting (SLM) is known to generate large and anisotropic residual stresses in the samples. Accurate measurement of residual stresses on SLM-produced samples is essential for understanding the residual stress build-up mechanism during SLM, while a dramatic fluctuation can be observed in the residual stress values reported in the literature. On the basis of studying the influence of surface roughness on residual stress measured using X-ray diffraction (XRD), we propose a procedure coupling XRD technique with pretreatment consisting of mechanical polishing and chemical etching. The results highlight that residual stresses measured using XRD on as-built SLM-produced samples with high surface roughness are significantly lower than those measured on samples with finished surface, which is due to the stress relaxation on the spiked surface of as-built samples. Surface distribution of residual stresses and the effect of scanning strategy were systematically investigated for SLM-produced AlSi10Mg samples. Microstructural morphology was observed at the interface between sample and building platform and was linked to the surface distribution of residual stresses. This procedure can help us accurately measure the residual stresses in SLM-produced samples and thus better understand its build-up mechanism during the SLM process.

Hydroelasticity is a phenomenon which occurs during the interaction between water and a deformable structure. Predicting the structural deformation and stress during water entry is a highly challenging endeavor and suitable computer-aided design tools are currently being developed and validated. This article discusses a hydrodynamic problem of the water impact on the stiffened side of elastic stiffened plates; the problem is addressed via an experimental approach and by explicit finite element method with a Multi-Material Arbitrary Lagrangian–Eulerian solver. A series of water entry tests were conducted to measure the impact pressure and effective stress on the stiffened panel at different drop heights. Convergence studies were conducted to run the numerical analysis; the results are consistent with the experimental data. Influences of drop height, stiffeners, and air cushion on pressure and effective stresses are discussed. Impact pressure and effective stress increase linearly with drop height. Stiffeners with different dimensions and directions appear to exert various effects on the maximum value, duration, and starting time of pressure and stress. The air cushion primarily increases the pressure duration and reduces its maximum value, and its property is discussed in detail.

Hierarchical modulation (HM), which is also known as layered modulation, has been widely adopted across the telecommunication industry. Its strict backward compatibility with single-layer modems and its low complexity facilitate the seamless upgrading of wireless communication services. The specific features of HM may be conveniently exploited for improving the throughput/information-rate of the system without requiring any extra bandwidth, while its complexity may even be lower than that of the equivalent system relying on conventional modulation schemes. As a recent research trend, the potential employment of HM in the context of cooperative communications has also attracted substantial research interests. Motivated by the lower complexity and higher flexibility of HM, we provide a comprehensive survey and conclude with a range of promising future research directions. Our contribution is the conception of a new cooperative communication paradigm relying on turbo trellis-coded modulation-aided twin-layer HM-16QAM and the analytical performance investigation of a four-node cooperative communication network employing a novel opportunistic routing algorithm. The specific performance characteristics evaluated include the distribution of delay, the outage probability, the transmit power of each node, the average packet power consumption, and the system throughput. The simulation results have demonstrated that when transmitting the packets formed by layered modulated symbol streams, our opportunistic routing algorithm is capable of reducing the transmit power required for each node in the network compared with that of the system using the traditional opportunistic routing algorithm. We have also illustrated that the minimum packet power consumption of our system using our opportunistic routing algorithm is also lower than that of the system using the traditional opportunistic routing algorithm.

Mutations in Inverted Formin 2 (INF2), a diaphanous formin family protein that regulates actin cytoskeleton dynamics, cause focal segmental glomerulosclerosis (FSGS) and Charcot–Marie–Tooth Disease (CMT) in humans. In addition to directly remodeling actin filaments in vitro, we have shown that INF2 regulates intracellular actin dynamics and actin dependent cellular behavior by opposing Rhoa/Dia signaling. As a step towards a better understanding of the human kidney disease, we wanted to explore the relevance of these findings to the in vivo situation. We used dose dependent knockdown of INF2 to first define an in vivo model and establish an overt glomerular phenotype in zebrafish. This simple assay was validated by rescue with wild type INF2 confirming the specificity of the findings. The edema, podocyte dysfunction, and an altered glomerular filtration barrier observed in the zebrafish pronephros correlate with mistrafficking of glomerular slit diaphragm proteins, defective slit-diaphragm signaling, and disinhibited diaphanous formin (mDia) activity. In contrast to wild-type human INF2, INF2 mutants associated with kidney disease fail to rescue the zINF2 morphant phenotype. Of particular interest, this INF2 knockdown phenotype is also rescued by loss of either RhoA or Dia2. This simple assay allows the demonstration that INF2 functions, at least in part, to modulate Dia-mediated Rho signaling, and that disease causing mutations specifically impair this regulatory function. These data support a model in which disease-associated diaphanous inhibitory domain (DID) mutants in INF2 interfere with its binding to and inhibition of Dia, leading to uncontrolled Rho/Dia signaling and perturbed actin dynamics. Methods to fine tune Rho signaling in the glomerulus may lead to new approaches to therapy in humans.

For the planning and sustainable management of forest resources, well-managed plantations are of great significance to mitigate the decrease of forested areas. Monitoring these planted forests is essential for forest resource inventories. In this study, two ALOS PALSAR-2 quad-polarimetric synthetic aperture radar (SAR) images and ground measurements were employed to estimate growing stem volume (GSV) of Chinese fir plantations located in a hilly area of southern China. To investigate the relationships between forest GSV and polarization characteristics, single and fused variables were derived by the Yamaguchi decomposition and the saturation value of GSV was estimated using a semi-exponential empirical model as a base model. Based on the estimated saturation values and relative root mean square error (RRMSE), the single and fused characteristics and corresponding models were selected and integrated, which led to a novel saturation-based multivariate method used to improve the GSV estimation and mapping of Chinese fir plantations. The new findings included: (1) All the original polarimetric characteristics, statistically, were not significantly correlated with the forest GSV, and their logarithm and ratio transformation fused variables greatly improved the correlations, thus the estimation accuracy of the forest GSV. (2) The logarithm transformation of surface scattering resulted in the greatest saturation, value but the logarithm transformation of double-bounce scattering resulted in the smallest RRMSE of the GSV estimates. (3) Compared with the single transformations, the fused variables led to more reasonable saturation values and obviously reduced the values of RRMSE. (4) The saturation-based multivariate method led to more accurate estimates of the forest GSV than the univariate method, with the smallest value (29.64%) of RRMSE achieved using the set of six variables. This implied that the novel saturation-based multivariate method provided greater potential to improve the estimation and mapping of the forest GSV.

The capacity of private information retrieval (PIR) from databases coded using maximum distance separable (MDS) codes was previously characterized by Banawan and Ulukus, where it was assumed that the messages are encoded and stored separably in the databases. This assumption was also usually made in other related works in the literature, and this capacity is usually referred to as the MDS-PIR capacity colloquially. In this work, we considered the question of if and when this capacity barrier can be broken through joint encoding and storing of the messages. Our main results are two classes of novel code constructions, which allow joint encoding, as well as the corresponding PIR protocols, which indeed outperformed the separate MDS-coded systems. Moreover, we show that a simple, but novel expansion technique allows us to generalize these two classes of codes, resulting in a wider range of the cases where this capacity barrier can be broken.

We have been made aware that the experimental data, methodological framework, and description of the corresponding sections in this article are similar to those of another publication (in Chinese with an English abstract) by the first author himself, Zhang and Zeng [...]

The authors wish to make the following corrections to this paper [...]

The objective of this study was to evaluate the ability of a modified hydrated sodium calcium aluminosilicate (HSCAS) adsorbent to reduce the toxicity of T-2 toxin in broilers. Ninety-six one-day-old male broilers were randomly allocated into four experimental groups with four replicates of six birds each. The four groups, 1–4, received a basal diet (BD), a BD plus 6.0 mg/kg T-2 toxin, a BD plus 6.0 mg/kg T-2 toxin with 0.05% modified HSCAS adsorbent, and a BD plus 0.05% modified HSCAS adsorbent, respectively, for two weeks. Growth performance, nutrient digestibility, serum biochemistry, and small intestinal histopathology were analyzed. Compared to the control group, dietary supplementation of T-2 toxin decreased (p < 0.05) body weight gain, feed intake, and the feed conversion ratio by 11.4–31.8% during the whole experiment. It also decreased (p < 0.05) the apparent metabolic rates of crude protein, calcium, and total phosphorus by 14.9–16.1%. The alterations induced by T-2 toxin were mitigated (p < 0.05) by the supplementation of the modified HSCAS adsorbent. Meanwhile, dietary modified HSCAS adsorbent supplementation prevented (p < 0.05) increased serum aspartate aminotransferase by T-2 toxin at d 14. It also prevented (p < 0.05) T-2 toxin-induced morphological changes and damage in the duodenum, jejunum, and ileum of broilers. However, dietary supplementation of the modified HSCAS adsorbent alone did not affect (p > 0.05) any of these variables. In conclusion, these findings indicate that the modified HSCAS adsorbent could be used against T-2 toxin-induced toxicity in growth performance, nutrient digestibility, and hepatic and small intestinal injuries in chicks.

In this paper, glass fiber-reinforced polyamide-6 (PA-6) composites with up to 70 wt% fiber contents were successfully manufactured using a pultrusion process, utilizing the anionic polymerization of caprolactam (a monomer of PA-6). A novel thermoplastic reaction injection pultrusion test line was developed with a specifically designed injection chamber to achieve complete impregnation of fiber bundles and high speed pultrusion. Process parameters like temperature of injection chamber, temperature of pultrusion die, and pultrusion speed were studied and optimized. The effects of die temperature on the crystallinity, melting point, and mechanical properties of the pultruded composites were also evaluated. The pultruded composites exhibited the highest flexural strength and flexural modulus, reaching 1061 MPa and 38,384 MPa, respectively. Then, effects of fiber contents on the density, heat distortion temperature, and mechanical properties of the composites were analyzed. The scanning electron microscope analysis showed the great interfacial adhesion between fibers and matrix at 180 °C, which greatly improved the mechanical properties of the composites. The thermoplastic reaction injection pultrusion in this paper provided an alternative for the preparation of thermoplastic composites with high fiber content.

Estimating the net primary production (NPP) of vegetation is essential for eco-environment conservation and carbon cycle research. Remote sensing techniques, combined with algorithm models, have been proven to be promising methods for NPP estimation. High-precision and real-time NPP monitoring in heterogeneous areas requires high spatio-temporal resolution remote sensing data, which are not easy to acquire by single remote sensors, especially in cloudy weather. This study proposes to fuse images of different sensors to provide high spatio-temporal resolution data for NPP estimation in cloud-prone areas. Firstly, the time series Normalized Difference Vegetation Index (NDVI) with a spatial resolution of 30 m and a temporal resolution of 16 days, are obtained by the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Then, the time series NDVI data, combined with meteorological data are input into an improved Carnegie–Ames–Stanford Approach (CASA) model for NPP estimation. This method is validated by a case study of a heavily urbanized area, in the middle reaches of the Yangtze River in China. The results indicate that the NPP estimated by the fused NDVI data has more detailed spatial information than by using the MODIS data. The results show a strong correlation between the actual Landsat8 NDVI and the fused NDVI images, which means that the accuracy of synthetic NDVI images (a 16 day interval and a 30 m resolution) is reliable, and it can provide superior inputs for accurate estimations of a NPP time series. The correlation coefficient (R) and root mean square error between the NPP, based on the fused NDVI and the measured NPP, are 0.66 and 14.280 g C/(m2·yr), respectively, indicating a good consistency. The small discrepancy is caused by the uncertainties of fused NDVI, measurement errors, conversion errors, and other factors in the CASA model. In this study, we achieved NPP with high spatial and temporal resolutions, which can provide higher accuracies of NPP data for analyzing the carbon cycling heavily urbanized areas, compared with similar studies using mono-temporal NPP data. The spatio-temporal fusion technique is an effective way of generating high spatio-temporal resolution images from different sensors, thereby providing enough data for NPP monitoring in urbanized areas.

Rice is one of the world’s major staple foods, especially in China. Highly accurate monitoring on rice-producing land is, therefore, crucial for assessing food supplies and productivity. Recently, the deep-learning convolutional neural network (CNN) has achieved considerable success in remote-sensing data analysis. A CNN-based paddy-rice mapping method using the multitemporal Landsat 8, phenology data, and land-surface temperature (LST) was developed during this study. First, the spatial⁻temporal adaptive reflectance fusion model (STARFM) was used to blend the moderate-resolution imaging spectroradiometer (MODIS) and Landsat data for obtaining multitemporal Landsat-like data. Subsequently, the threshold method is applied to derive the phenological variables from the Landsat-like (Normalized difference vegetation index) NDVI time series. Then, a generalized single-channel algorithm was employed to derive LST from the Landsat 8. Finally, multitemporal Landsat 8 spectral images, combined with phenology and LST data, were employed to extract paddy-rice information using a patch-based deep-learning CNN algorithm. The results show that the proposed method achieved an overall accuracy of 97.06% and a Kappa coefficient of 0.91, which are 6.43% and 0.07 higher than that of the support vector machine method, and 7.68% and 0.09 higher than that of the random forest method, respectively. Moreover, the Landsat-derived rice area is strongly correlated (R2 = 0.9945) with government statistical data, demonstrating that the proposed method has potential in large-scale paddy-rice mapping using moderate spatial resolution images.

This paper explores the use of social media or Web 2.0 services for an international collaborative project. Participants in this collaboration used free and inexpensive social media tools to communicate and work together. This case study presents a model for using inexpensive social media tools to forge new partnerships among academic libraries. Academic libraries can now tap the expertise of fellow librarians in other countries and explore new cultures to improve and extend their services without the huge financial cost once attributed to international collaboration.

Secure aggregation is an essential component of modern distributed applications and data mining platforms. Aggregated statistical results are typically adopted in constructing a data cube for data analysis at multiple abstraction levels in data warehouse platforms. Generating different types of statistical results efficiently at the same time (or referred to as enabling multi-functional support) is a fundamental requirement in practice. However, most of the existing schemes support a very limited number of statistics. Securely obtaining typical statistical results simultaneously in the distribution system, without recovering the original data, is still an open problem. In this paper, we present SEDAR, which is a SEcure Data Aggregation scheme under the Range segmentation model. Range segmentation model is proposed to reduce the communication cost by capturing the data characteristics, and different range uses different aggregation strategy. For raw data in the dominant range, SEDAR encodes them into well defined vectors to provide value-preservation and order-preservation, and thus provides the basis for multi-functional aggregation. A homomorphic encryption scheme is used to achieve data privacy. We also present two enhanced versions. The first one is a Random based SEDAR (REDAR), and the second is a Compression based SEDAR (CEDAR). Both of them can significantly reduce communication cost with the trade-off lower security and lower accuracy, respectively. Experimental evaluations, based on six different scenes of real data, show that all of them have an excellent performance on cost and accuracy.

Land degradation and desertification in arid and semi-arid areas is of great concern. Accurately mapping percentage vegetation cover (PVC) of the areas is critical but challenging because the areas are often remote, sparsely vegetated, and rarely populated, and it is difficult to collect field observations of PVC. Traditional methods such as regression modeling cannot provide accurate predictions of PVC in the areas. Nonparametric constant k-nearest neighbors (Cons_kNN) has been widely used in estimation of forest parameters and is a good alternative because of its flexibility. However, using a globally constant k value in Cons_kNN limits its ability of increasing prediction accuracy because the spatial variability of PVC in the areas leads to spatially variable k values. In this study, a novel method that spatially optimizes determining the spatially variable k values of Cons_kNN, denoted with Opt_kNN, was proposed to map the PVC in both Duolun and Kangbao County located in Inner Mongolia and Hebei Province of China, respectively, using Landsat 8 images and sample plot data. The Opt_kNN was compared with Cons_kNN, a linear stepwise regression (LSR), a geographically weighted regression (GWR), and random forests (RF) to improve the mapping for the study areas. The results showed that (1) most of the red and near infrared band relevant vegetation indices derived from the Landsat 8 images had significant contributions to improving the mapping accuracy; (2) compared with LSR, GWR, RF and Cons_kNN, Opt_kNN resulted in consistently higher prediction accuracies of PVC and decreased relative root mean square errors by 5%, 11%, 5%, and 3%, respectively, for Duolun, and 12%, 1%, 23%, and 9%, respectively, for Kangbao. The Opt_kNN also led to spatially variable and locally optimal k values, which made it possible to automatically and locally optimize k values; and (3) the RF that has become very popular in recent years did not perform the predictions better than the Opt_kNN for the both areas. Thus, the proposed method is very promising to improve mapping the PVC in the arid and semi-arid areas.

A series of low density, highly porous clay/poly(vinyl alcohol) composite aerogels, incorporating ammonium alginate, were fabricated via a convenient and eco-friendly freeze drying method. It is significant to understand rheological properties of precursor gels because they directly affect the form of aerogels and their processing behaviors. The introduction of ammonium alginate impacted the rheological properties of colloidal gels and improved the mechanical performance of the subject aerogels. The specific compositions and processing conditions applied to those colloidal gel systems brought about different aerogel morphologies, which in turn translated into the observed mechanical properties. The bridge between gel rheologies and aerogel structures are established in the present work.