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This study aimed to investigate the dietary n-6:n-3 PUFA (polyunsaturated fatty acids) intake of pregnant Chinese women in different trimesters. We conducted a cross-sectional study for 300 singleton pregnant women in Hefei city, China. The dietary intake of pregnant women were measured by a 3-d food record. Energy and nutrient intake for the 3 d were calculated according to the Chinese food composition table (Standard Version). The ANOVA and Kruskal-Wallis test were performed to analyze the dietary fatty acids intake of pregnant women. In the first, second and third trimester, the intake of n-6:n-3 PUFA were 5.87±2.37, 6.03±2.89, 6.14±2.26, respectively, without significant difference (p0.05). But it was all slightly higher than the recommendation for general population (4-6) of Chinese Nutrition Society. An adequate and balanced intake of n-6 and n-3 fatty acids, from a well-balanced diet, should be recommended for pregnant women.

The purpose of this study was to explore the relationship between the characteristics of gut microbiome and the effect of medical nutrition therapy (MNT) on glycemic control in pregnant women with gestational diabetes mellitus (GDM). Seventy-four pregnant women newly diagnosed with GDM received MNT for one-week. The effect of glycemic control was evaluated by fasting and 2-hour postprandial blood glucose; and stool samples of pregnant women were collected to detect the gut microbiome before and after MNT. We used a nested case-control study design, with pregnant women with GDM who did not meet glycemic standards after MNT as the ineffective group and those with an age difference of ≤5 years, matched for pre-pregnancy body mass index (BMI) 1:1, and meeting glycemic control criteria as the effective group. Comparison of the gut microbiome characteristics before MNT showed that the ineffective group was enriched inDesulfovibrio,Aeromonadales,Leuconostocaceae,Weissella,Prevotella,Bacillales_Incertae Sedis XI,GemellaandBacillales, while the effective group was enriched inRoseburia,Clostridium,Bifidobacterium,Bifidobacteriales,Bifidobacteriaceae,HoldemaniaandProteus. After treatment, the effective group was enriched inBifidobacteriumandActinomycete, while the ineffective group was enriched inHoldemania,Proteus,CarnobacteriaceaeandGranulicatella. In conclusion, the decrease in the abundance of characteristic gut microbiome positively correlated with blood glucose may be a factor influencing the poor hypoglycemic effect of MNT in pregnant women with GDM. Abundance of more characteristic gut microbiome negatively correlated with blood glucose could help control blood glucose in pregnant women with GDM.

Maternal lipopolysaccharide (LPS) exposure during pregnancy induces metabolic abnormalities in male offspring, but the underlying mechanisms remain unclear. The purpose of this study was to investigate the effects of maternal LPS exposure during pregnancy on metabolic profiling of maternal serum and male fetal liver using Liquid Chromatograph Mass Spectrometer techniques. From day 15 to day 17 of gestation, pregnant mice were administered intraperitoneal LPS (experimental group) (50 μg/kg/d) or saline (control group). On day 18 of gestation, maternal serum and male fetal liver were collected. After LPS exposure, levels of 38 and 75 metabolites, mainly glycerophospholipid and fatty acid metabolites, were altered in maternal serum and male fetal liver, respectively. It was found that in maternal serum and male fetal livers, the glycerophospholipids containing saturated fatty acids (SFAs) and the SFAs were upregulated, while the glycerophospholipids containing polyunsaturated fatty acids (PUFAs) and the PUFAs were downregulated. This concordance between maternal and fetal alterations in glycerophospholipid and fatty acid metabolites may be a metabolomic signature of the early intrauterine period and may provide insight into the mechanisms by which maternal LPS exposure induces disorders of glucose metabolism in male offspring.

For the past few years, nanotechnology has provided a lot of new treatment opportunities for prostate cancer patients, and brilliant achievements have been acquired indeed. It not only prolonged circulation time in vivo but also increased bio-availability of drugs. Among them, nanoparticles with specificity ligand can be better targeted at prostate cancer, which improves the curative effect and reduces side effects. What's more, in terms of combined administration, the synergistic effect of chemotherapeutic drugs and hormones, or co-delivery two or more different drugs into the same delivery system, has achieved good therapeutic progress as well. In this paper, a comprehensive overview of nano-technology and the combination therapy for prostate cancer by pharmaceutical and clinical pharmaceutical strategies have been proposed to further appreciate and recommend the design and development of prostate cancer treatment.

The purpose of this research is to develop a liposomal drug delivery system, which can selectively target hepatocellular carcinoma (HCC) to deliver the antitumor agent N-14NCTDA, a C14 alkyl chain norcantharimide derivative of norcantharidin. N-14NCTDA-loaded liposomes were successfully prepared by lipid membrane hydration and extrusion methods. SP94, a targeting peptide for HCC cells, was attached to the liposomes loaded with N-14NCTDA by the post-insertion method to obtain SP94 modified liposomes (SP94-LPs). SP94-LPs had a significant cytotoxicity against Hep G2 cells with the IC50 of 15.395 ± 0.89 μg/mL, which is lower than that of NCTD-S (IC50 = 20.863 ± 0.56 μg/mL) and GAL-LPs (IC50 = 24.589 ± 1.02 μg/mL). Compared with conventional liposomes (Con-LPs), SP94-LPs showed greater cellular uptake in Hep G2 cells. Likewise, significant tumor suppression was achieved in H22 tumor-bearing mice which were treated with SP94-LPs. The tumor inhibition rate (IRw) of SP94-LPs was 82 ± 0.98%, obviously higher than that of GAL-LPs (69 ± 1.39%), Con-LPs (60 ± 2.78%), and NCTD-S (51 ± 3.67%). SP94-LPs exhibited a significant hepatocellular carcinoma-targeting activity in vitro and in vivo, which will provide a new alternative for hepatocellular carcinoma treatment in future.

To mask the bitterness of drug is profoundly important especially in children’s medication. This study designed and investigated a quaternary enteric solid dispersion (QESD) by secondary hot-melt-extrusion. Erythromycin (EM) was chosen as a model drug. The optimal QESD contained enteric polymer HPMCP-55, plasticizer and water-soluble polymer copovidone VA64. Raman and Atomic force microscope has exploited that majority EM was distributed in VA64 matrix, nanometer-sized EM-VA64 system was entrapped within enteric continuous phase to form a solid emulsion-like structure. For the prepared QESD, EM released concentration was far less than bitterness threshold (7 μg/mL to 20 μg/mL) in artificial saliva within the first 30 s. And dissolution rate was increased by 10% in article intestine fluid, which dominated by water-soluble VA64. Stress testing after two months at high-humidity (75% RH) and high-temperature (60 °C) revealed, compared with traditional enteric SDs, the chemical degradation of EM was slowed down in QESD. Furthermore, hydrogen and salt bonds were respectively formed between EM and VA64 and between leaking EM and HPMCP-55, which increasing the system stability and taste-masking. The effect of masking bitter taste can be satisfied as well as enhance drug dissolution rate in the intestine, and formulation physicochemical stability during storage.

The short half-life and bitter taste of carbinoxamine maleate2 (CAM) lead to poor compliance by pediatric patients who are being treated for allergic rhinitis. To address these issues, carbinoxamine-resin complexes3 (CRCs) were prepared by ion exchange and then coated with Kollicoat SR 30D. The resultant microencapsulated carbinoxamine-resin complexes4 (MCRCs) were dispersed into the medium to obtain the final suspensions. The drug loading kinetics and thermodynamics of CRCs, anti-swelling mechanism of the impregnant in MCRCs, in vitro release, and in vivo pharmacokinetics of the suspensions were systematically evaluated. The drug loading process was found to obey a first-order kinetic process that was spontaneous, entropy-reduced and exothermic, and the diffusion of CAM into the resin was the rate-limiting step. During microencapsulation, the impregnant could create a certain buffer space to control the swelling of CRCs and maintain the coating film intact. The homemade preparations had release behaviors similar to that of the reference in vitro and achieved sustained release in vivo. The low drug loading preparation had a higher relative bioavailability of 109% owing to its faster release and better dispersibility. Therefore, the suspensions based on MCRCs could be successfully applied to treating allergic rhinitis in children.

This study uses 2010–2011 Landsat Thematic Mapper (TM) imagery to estimate total forested area in Haiti. The thematic map was generated using radiometric normalization of digital numbers by a modified normalization method utilizing pseudo-invariant polygons (PIPs), followed by supervised classification of the mosaicked image using the Food and Agriculture Organization (FAO) of the United Nations Land Cover Classification System. Classification results were compared to other sources of land-cover data produced for similar years, with an emphasis on the statistics presented by the FAO. Three global land cover datasets (GLC2000, Globcover, 2009 , and MODIS MCD12Q1), and a national-scale dataset (a land cover analysis by Haitian National Centre for Geospatial Information (CNIGS)) were reclassified and compared. According to our classification, approximately 32.3% of Haiti's total land area was tree covered in 2010–2011. This result was confirmed using an error-adjusted area estimator, which predicted a tree covered area of 32.4%. Standardization to the FAO's forest cover class definition reduces the amount of tree cover of our supervised classification to 29.4%. This result was greater than the reported FAO value of 4% and the value for the recoded GLC2000 dataset of 7.0%, but is comparable to values for three other recoded datasets: MCD12Q1 (21.1%), Globcover (2009) (26.9%), and CNIGS (19.5%). We propose that at coarse resolutions, the segmented and patchy nature of Haiti's forests resulted in a systematic underestimation of the extent of forest cover. It appears the best explanation for the significant difference between our results, FAO statistics, and compared datasets is the accuracy of the data sources and the resolution of the imagery used for land cover analyses. Analysis of recoded global datasets and results from this study suggest a strong linear relationship (R2 = 0.996 for tree cover) between spatial resolution and land cover estimates.

This article first examines three existing methods of delineating open water features, i.e. the normalized difference water index NDWI, the modified normalized difference water index MNDWI and a method combining the near-infrared NIR band and the maximum likelihood classification. We then propose two new methods for the fast extraction of water features in remotely sensed imagery. Our first method is a pixel-based procedure that utilizes indices and band values. Based on their characteristic spectral reflectance curves, waterbodies are grouped into three types – clear, green and turbid. We found that the MNDWI is best suited for identifying clear water. Green water has its maximum reflectance in Landsat Thematic Mapper TM band 4 NIR band, whereas turbid water has its maximum reflectance in TM band 5 mid-infrared band. Our second method integrates our pixel-based classification with object-based image segmentation. Two Landsat scenes in Shaanxi Province, China, were used as the primary data source. Digital elevation models DEMs and their derived slope maps were used as ancillary information. To evaluate the performance of the proposed methods, extraction results of the three existing methods and our two new methods were compared and assessed. A manual interpretation was made and used as reference data. Results suggest that our methods, which consider the diversity of waterbodies, achieved better accuracy. Our pixel-based method achieved a producer's accuracy of 92%, user's accuracy of 90% and kappa statistics of 0.91. Our integrated method produced a higher producer's accuracy 95%, but a lower user's accuracy 72% and kappa statistics 0.72, compared with the pixel-based method. The advantages and limitations of the proposed methods are discussed.

The complex nature of coastal ecosystems and their protection require a deeper understanding of land cover change and dynamics. Although a number of ecological studies have been conducted to realise this important objective, little information is available regarding the quantification of this land cover change. The role of mangroves as living barriers was under appreciated prior to the 2004 tsunami event. In this paper, we investigate the buffering functions of mangroves in the 2004 tsunami by employing the methodology developed in our companion paper. We focus more on mangrove distribution patterns in different buffer zones before and after the 2004 tsunami. The presence of mangroves before and after the event was statistically significant for the North (χ 2 =154.08, p≤ 0.001) and Upper South (χ 2 =62.25, p≤ 0.001). We observed positive linear relationships suggesting a loss of mangrove resulted into a gain of barren and sand land cover as a result of a devastating impact from the 2004 tsunami e...

Few researchers have developed large-scale habitat models for sympatric carnivore species. We created habitat models for red foxes ( Vulpes vulpes ), coyotes ( Canis latrans ) and bobcats ( Lynx rufus ) in southern Illinois, USA, using the Penrose distance statistic, remotely sensed landscape data, and sighting location data within a GIS. Our objectives were to quantify and spatially model potential habitat differences among species. Habitat variables were quantified for 1-km 2 buffered areas around mesocarnivore sighting locations. Following variable reduction procedures, five habitat variables (percentage of grassland patches, interspersion–juxtaposition of forest patches, mean fractal dimension of wetland patches and the landscape, and road density) were used for analysis. Only one variable differed ( P

While tsunami characteristics and effects are not fully understood in the countries around the Indian Ocean, there are reports suggesting that mangroves, acting as a barrier, significantly reduce the devastation caused by the waves. This study proposes a creative approach to investigating the impact of the 2004 tsunami on mangrove vegetation. The approach involves a combination of Geographic Information System (GIS) proximity analyses and change detection methods in remote sensing to delineate multiple buffer distances from the coastline into four homogeneous subregions. The changes in land cover are then assessed in these subregions before and after the tsunami event. The proposed approach provides a more reliable and accurate means than conventional methods to evaluate spatial patterns of damaged areas through different land characteristics along the coastline. There are major damages to land cover, representing an average of 26.87% change, in those geographic locations with low mangrove coverage that are in close proximity to the coastline in all four subregions, whereas less damage is apparent in locations with high mangrove coverage, representing an average of only 2.77% change. The optimum distance between 1000 and 1500 m of mangrove buffer would be favourable and most effective for reducing the damage by potential tsunami waves. The findings support the need for mangrove replantation and management in the future and may serve as a prototype for studying impacts of tsunamis in other affected countries.

Reliable information about the geographic distribution and abundance of major plant functional types (PFTs) around the world is increasingly needed for global change research. Using remote sensing techniques to map PFTs is a relatively recent field of research. This paper presents a method to map PFTs from the Moderate Resolution Imaging Spectroradiometer (MODIS) data using a multisource evidential reasoning (ER) algorithm. The method first utilizes a suite of improved and standard MODIS products to generate evidence measures for each PFT class. The multiple lines of evidence computed from input data are then combined using Dempster's Rule of combination. Finally, a decision rule based on maximum support is used to make classification decisions. The proposed method was tested over the states of Illinois, Indiana, Iowa, and North Dakota, USA where crops dominate. The Cropland Data Layer (CDL) data provided by the United States Department of Agriculture were employed to validate our new PFT maps and the current MODIS PFT product. Our preliminary results suggest that multisource data fusion is a promising approach to improve the mapping of PFTs. For several major PFT classes such as crop, trees, and grass and shrub, the PFT maps generated with the ER method provide greater spatial details compared to the MODIS PFT. The overall accuracies increased for all the four states, with the biggest improvement occurring in Iowa from 51% (MODIS) to 64% (ER). The overall kappa statistic also increased for all the four states, with the biggest improvement occurring in Iowa from 0.03 (MODIS) to 0.38 (ER). The paper concludes with a discussion of several methodological issues pertaining to the further improvement of the ER approach.

Mandelbrot's fractal geometry has sparked considerable interest in the remote sensing community since the publication of his highly influential book in 1977. Fractal models have been used in several image processing and pattern recognition applications such as texture analysis and classification. Applications of fractal geometry in remote sensing rely heavily on estimation of the fractal dimension. The fractal dimension (D) is a central construct developed in fractal geometry to describe the geometric complexity of natural phenomena as well as other complex forms. This paper provides a survey of several commonly used methods for estimating the fractal dimension and their applications to remote sensing problems. Methodological issues related to the use of these methods are summarized. Results from empirical studies applying fractal techniques are collected and discussed. Factors affecting the estimation of fractal dimension are outlined. Important issues for future research are also identified and discussed.

Based on Clarke’s (1986) triangular prism concept, this paper proposes three new methods to compute the fractal dimension (D) of remote sensing images. Our first method involves searching a pixel on each edge of a square whose digital numbers (DN) value has the largest deviation from the central pixel. Our second method uses a pixel on each edge of a square whose DN deviation from the central pixel is closest to the mean DN deviation from the central pixel to all pixels on the same edge. In our third method, eight pixels on the four edges of a square are used. Furthermore, common to the three proposed methods is the use of actual DN of the central pixel. The proposed computation methods have been tested using both simulated fractal surfaces and real images. Results show that the proposed methods appear to generally perform better than Clarke’s 1986 method for synthetic images with complex textures.

The presence of anisotropy (direction-dependency) in image texture may result in significant bias in estimated fractal dimension (D) values obtained using existing computation methods, which may affect the effectiveness of fractal techniques in the characterization and classification of image textures. We propose two computation methods to detect the presence of anisotropy in remote sensing imagery. The proposed methods are based on the well accepted walking-dividers and triangular prism concepts and they allow the user to compute D in the 0°, 45°, 90°, and 135 directions. The proposed methods have been tested on real images with different textural appearance. Our results show that the proposed methods appear generally effective in detecting directional bias in estimated D values. The implications of our findings for remote sensing applications of fractal techniques are also discussed.

A stratified method of separating dead tree crowns from the forest mosaic, especially bare soil, is proposed. Both spectral and spatial information extracted from 1 m high-resolution digital airborne imagery was used. First, spectrally homogeneous objects were recognized based on a Normalized Difference Vegetation Index (NDVI) image. From this image, large patches of bare soil and small patches of noise were excluded by object-size thresholding. Next, an incomplete bare soil image was generated from thresholding the red band using a histogram-based method. A region-based subtraction algorithm was developed and performed on the homogeneous object image and the bare soil image to remove most small patches of bare soil. Finally, medium-sized patches of bare soil were deleted using the assumption that bare soil areas are spatially much closer to larger, pure bare soil areas. The method proved to be effective. The classification accuracy of dead tree crowns was increased from 21% to 75%. Index Terms-Sudden Oak Death, hardwood forest, remote sensing, NDVI, object size, homogeneous object, region-based subtraction, high-resolution imagery.

We propose an information fusion method for the extraction of land-use information based on both the panchromatic and multispectral Indian Remote Sensing Satellite 1C (IRS-1C) satellite imagery. It integrates spectral, spatial and structural information existing in the image. A thematic map was first produced with a maximum-likelihood classification (MLC) applied to the multispectral imagery. Probabilistic relaxation (PR) was then performed on the thematic map to refine the classification with neighborhood information. Furthermore, we incorporated edges extracted from the higher resolution panchromatic imagery in the classification. An edge map was generated using operations such as edge detection, edge thresholding and edge thinning. Finally, a modified region-growing approach was used to improve image classification. The procedure proved to be more effective in land-use classification than conventional methods based only on multispectral data. The improved land-use map is characterized with sharp interregional boundaries, reduced number of mixed pixels and more homogeneous regions. The overall kappa statistics increased considerably from 0.52 before the fusion to 0.75 after.