An Optical Method for Measuring the Diameter of Polyester Filament in Real Time

An optical method has been proposed and its system has been developed for measuring the change in diameter during the production of polyester filament in a manufacturing plant. The principle of the method is based on the application of scattered light on the filament. The sensor head of the system consisted of a semiconductor laser, two silicon-photodiodes as a light receiver, and a cylindrical lens. It was found from a preliminary experiment that (1) the scattered light intensity was not proportional to the diameter but to the cross section of the filament in the range between a few μm and a few tens μm. (2) the measurement error for the diameter of the polyester filament was 8 % for a scattered light intensity measurement error of 15 %, and (3) the system could be used in practical applications.othe

A Neural Network Model for K(λ) Retrieval and Application to Global Kpar Monitoring

Accurate estimation of diffuse attenuation coefficients in the visible wavelengths Kd(λ) from remotely sensed data is particularly challenging in global oceanic and coastal waters. The objectives of the present study are to evaluate the applicability of a semi-analytical Kd(λ) retrieval model (SAKM) and Jamet’s neural network model (JNNM), and then develop a new neural network Kd(λ) retrieval model (NNKM). Based on the comparison of Kd(λ) predicted by these models with in situ measurements taken from the global oceanic and coastal waters, all of the NNKM, SAKM, and JNNM models work well in Kd(λ) retrievals, but the NNKM model works more stable and accurate than both SAKM and JNNM models. The near-infrared band-based and shortwave infrared band-based combined model is used to remove the atmospheric effects on MODIS data. The Kd(λ) data was determined from the atmospheric corrected MODIS data using the NNKM, JNNM, and SAKM models. The results show that the NNKM model produces <30% uncertainty in deriving Kd(λ) from global oceanic and coastal waters, which is 4.88-17.18% more accurate than SAKM and JNNM models. Furthermore, we employ an empirical approach to calculate Kpar from the NNKM model-derived diffuse attenuation coefficient at visible bands (443, 488, 555, and 667 nm). The results show that our model presents a satisfactory performance in deriving Kpar from the global oceanic and coastal waters with 20.2% uncertainty. The Kpar are quantified from MODIS data atmospheric correction using our model. Comparing with field measurements, our model produces ~31.0% uncertainty in deriving Kpar from Bohai Sea. Finally, the applicability of our model for general oceanographic studies is briefly illuminated by applying it to climatological monthly mean remote sensing reflectance for time ranging from July, 2002- July 2014 at the global scale. The results indicate that the high Kd(λ) and Kpar values are usually found around the coastal zones in the high latitude regions, while low Kd(λ) and Kpar values are usually found in the open oceans around the low-latitude regions. These results could improve our knowledge about the light field under waters at either the global or basin scales, and be potentially used into general circulation models to estimate the heat flux between atmosphere and ocean.journal articl

A Neural Network Model for K(λ) Retrieval and Application to Global Kpar Monitoring.

Accurate estimation of diffuse attenuation coefficients in the visible wavelengths Kd(λ) from remotely sensed data is particularly challenging in global oceanic and coastal waters. The objectives of the present study are to evaluate the applicability of a semi-analytical Kd(λ) retrieval model (SAKM) and Jamet's neural network model (JNNM), and then develop a new neural network Kd(λ) retrieval model (NNKM). Based on the comparison of Kd(λ) predicted by these models with in situ measurements taken from the global oceanic and coastal waters, all of the NNKM, SAKM, and JNNM models work well in Kd(λ) retrievals, but the NNKM model works more stable and accurate than both SAKM and JNNM models. The near-infrared band-based and shortwave infrared band-based combined model is used to remove the atmospheric effects on MODIS data. The Kd(λ) data was determined from the atmospheric corrected MODIS data using the NNKM, JNNM, and SAKM models. The results show that the NNKM model produces <30% uncertainty in deriving Kd(λ) from global oceanic and coastal waters, which is 4.88-17.18% more accurate than SAKM and JNNM models. Furthermore, we employ an empirical approach to calculate Kpar from the NNKM model-derived diffuse attenuation coefficient at visible bands (443, 488, 555, and 667 nm). The results show that our model presents a satisfactory performance in deriving Kpar from the global oceanic and coastal waters with 20.2% uncertainty. The Kpar are quantified from MODIS data atmospheric correction using our model. Comparing with field measurements, our model produces ~31.0% uncertainty in deriving Kpar from Bohai Sea. Finally, the applicability of our model for general oceanographic studies is briefly illuminated by applying it to climatological monthly mean remote sensing reflectance for time ranging from July, 2002- July 2014 at the global scale. The results indicate that the high Kd(λ) and Kpar values are usually found around the coastal zones in the high latitude regions, while low Kd(λ) and Kpar values are usually found in the open oceans around the low-latitude regions. These results could improve our knowledge about the light field under waters at either the global or basin scales, and be potentially used into general circulation models to estimate the heat flux between atmosphere and ocean

Primer validation (qPCR).

Liver sinusoidal endothelial cells (LSECs) are fenestrated endothelial cells with a unique, high endocytic clearance capacity for blood-borne waste macromolecules and colloids. This LSEC scavenger function has been insufficiently characterized in liver disease. The Glmpgt/gt mouse lacks expression of a subunit of the MFSD1/GLMP lysosomal membrane protein transporter complex, is born normal, but soon develops chronic, mild hepatocyte injury, leading to slowly progressing periportal liver fibrosis, and splenomegaly. This study examined how LSEC scavenger function and morphology are affected in the Glmpgt/gt model. FITC-labelled formaldehyde-treated serum albumin (FITC-FSA), a model ligand for LSEC scavenger receptors was administered intravenously into Glmpgt/gt mice, aged 4 months (peak of liver inflammation), 9–10 month, and age-matched Glmpwt/wt mice. Organs were harvested for light and electron microscopy, quantitative image analysis of ligand uptake, collagen accumulation, LSEC ultrastructure, and endocytosis receptor expression (also examined by qPCR and western blot). In both age groups, the Glmpgt/gt mice showed multifocal liver injury and fibrosis. The uptake of FITC-FSA in LSECs was significantly reduced in Glmpgt/gt compared to wild-type mice. Expression of LSEC receptors stabilin-1 (Stab1), and mannose receptor (Mcr1) was almost similar in liver of Glmpgt/gt mice and age-matched controls. At the same time, immunostaining revealed differences in the stabilin-1 expression pattern in sinusoids and accumulation of stabilin-1-positive macrophages in Glmpgt/gt liver. FcγRIIb (Fcgr2b), which mediates LSEC endocytosis of soluble immune complexes was widely and significantly downregulated in Glmpgt/gt liver. Despite increased collagen in space of Disse, LSECs of Glmpgt/gt mice showed well-preserved fenestrae organized in sieve plates but the frequency of holes >400 nm in diameter was increased, especially in areas with hepatocyte damage. In both genotypes, FITC-FSA also distributed to endothelial cells of spleen and bone marrow sinusoids, suggesting that these locations may function as possible compensatory sites of clearance of blood-borne scavenger receptor ligands in liver fibrosis.</div

Stabilin-1 and stabilin-2 expression in liver from <i>Glmp</i>^<i>gt/gt</i> and WT mice.

A-B) Quantitative PCR analysis of Stab1 (A) and Stab2 (B) expression in liver tissue from 4 months (“young”) and 9 months (“old”) WT and Glmpgt/gt male mice (young: n = 4 per group; old: n = 3 per group). Error bars represent standard deviation. Results were not significantly different with Mann Whitney U test or the Kruskal-Wallis test. C) Quantitative image analysis of stabilin-1-stained liver sections from WT and Glmpgt/gt mice, presented as % positively stained tissue area. Groups: Young WT, 3–6 months (n = 5); young Glmpgt/gt, 4 months (n = 5); old WT, 9–10 months (n = 4); and old Glmpgt/gt, 9–10 months (n = 4). Each dot represents one animal, the median value for each group is presented as a horizontal line, and the upper and lower lines represent the interquartile range. Statistical analysis showed no significant differences between groups (One-way non-parametric ANOVA on ranks/Kruskal-Wallis test). D-E) Distribution pattern of stabilin-1 (red fluorescence) in liver of D) 4 months old, and E) 9–10 months old Glmpgt/gt and WT mice injected intravenously with FITC-FSA (2 μg/g body weight, 10 min monitoring time). Scale bars: 20 μμm. In the WT mice (both age groups) stabilin-1 was widely distributed in the sinusoids, highly colocalizing with FITC-FSA (arrows in D-E). At the same time, a few stabilin-1 positive, FITC-negative cells were observed in the portal tract (arrowhead in D, WT row). In the Glmpgt/gt mice, stabilin-1 staining was seen in FITC-positive cells in the liver sinusoids (arrows in D-E) and FITC-negative inflammatory cell aggregates (arrowheads in D-E). F) Co-localisation of stabilin-1 (red fluorescence) with the macrophage marker VSIG4 [51] (light blue fluorescence, arrowheads) in Glmpgt/gt liver. Arrows point to positive stabilin-1 staining of sinusoidal endothelial cells, which are VSIG4 negative [20]. Scale bar: 20 μm.</p

Z-stack video of distribution of FITC-FSA and VSIG4 in WT mouse liver.

Z-stack of confocal laser scanning micrographs of the typical distribution pattern of FITC-FSA (green fluorescence dots) in mouse liver sinusoids 10 min after intravenous administration of ligand. Liver macrophages were stained with an antibody to VSIG4 (red fluorescence). Microscope: Zeiss LSM800 equipped with a 40x water objective (NA 1.2). (AVI)</p

Scanning EM of liver samples from 4 months old WT and <i>Glmp</i>^<i>gt/gt</i> mice.

A) Scanning EM image of a liver sample from WT mice. A1 and A2 show high magnification images of two typical sinusoids, labelled 1 and 2 in the overview image in A. B-D) Scanning EM of liver samples from Glmpgt/gt mice, including overview images and high magnification images from the areas indicated with numbered arrows in the overviews. The LSECs of Glmpgt/gt were generally well fenestrated, with fenestrae arranged in sieve plates (circles in A1, B1, C1, D2). Large gaps (Ga) were observed in LSECs close to or within areas with hepatocyte damage and infiltration of immune cells (C1, D1-2). D3 shows a non-fenestrated capillary; these were only observed in areas with hepatocyte destruction and infiltration of leukocytes (dashed ellipse in the overview image in D. Abbreviations in A-D: LSEC, liver sinusoidal endothelial cell; HC, hepatocyte; SD, space of Disse; Ga, gap in LSEC; Coll, collagen; Cap, non-fenestrated capillary; L, leukocyte.</p

Test of goat anti-human MMR/CD206 antibody for use in mouse.

Figure in A: Western blot of mannose receptor expression in protein lysates of mouse liver sinusoidal endothelial cells (LSEC; mouse strain: C57Bl/6JRj), reduced and non-reduced samples, 30 μg protein loaded per lane. The blot was stained with goat anti-human MMR/CD206 antibody (R&D Systems, Cat. No AF2534) at 1 μg/ml, following the protocol in Methods–Western Blots. Secondary antibody was donkey anti-goat IgG (H+L) cross-absorbed, HRP (Invitrogen, Cat. No A16005, diluted 1:10.000). A strong positive band was observed at approximately 180–200 kDa. The reported size of the mannose receptor in pig and rat LSEC is 180 kDa [25]. Two lower bands in the reduced lane are likely to represent proteolytic cleavage products. B) Confocal laser scanning microscopy images of immune labelled paraffin sections of liver from WT and mannose receptor knock-out mice (MR-KO; C57BL/6 background). The MR-KO mouse model is described in [73], and liver samples were collected from in-house bred mice for the study in [45]. Sections were stained with the goat anti-human MMR/CD206 antibody following the immunohistochemistry protocol in Methods. Positive staining for the mannose receptor is seen as red fluorescence along the liver sinusoids in the WT liver, while no positive staining was observed in the MR-KO liver. (TIF)</p

Human NCU-G1 can function as a transcription factor and as a nuclear receptor co-activator-1

<p><b>Copyright information:</b></p><p>Taken from "Human NCU-G1 can function as a transcription factor and as a nuclear receptor co-activator"</p><p>http://www.biomedcentral.com/1471-2199/8/106</p><p>BMC Molecular Biology 2007;8():106-106.</p><p>Published online 16 Nov 2007</p><p>PMCID:PMC2233640.</p><p></p>truncated version pAc-hNCU-G1(Δ exon 6). Whole cell extracts (20 μg/lane) were analyzed by Western blotting using preimmune serum (lanes 1 to 3) or an antiserum raised against a C-terminal 15 amino acid peptide (lanes 5 to 7). Lane 1 and 5: control, untreated S2 cells, lane 2 and 6: S2 cells expressing hNCU-G1, lane 3 and 7: S2 cells expressing hNCU-G1(Δ exon6). Panel B: Cytoplasmic and nuclear extracts (20 μg/lane) from JEG3, RPE and 293 cells were size-fractionated on 10% SDS-PAGE gels and analyzed by Western blotting using NCU-G1 antiserum. Lane 1: JEG3 cytosol, lane 2: JEG3 nuclear extract, lane 3: RPE cytosol, lane 4: RPE nuclear extract, lane 5: 293 cytosol, lane 6: 293 nuclear extract. Panel C: The membranes used in panel B were stripped and analyzed with an antibody against Sp1

Scanning EM of liver samples from 10 months old <i>Glmp</i>^<i>gt/gt</i> mice, and frequency of fenestrae and gaps in LSECs of <i>Glmp</i>^<i>gt/gt</i> and WT mice in situ.

A-C) Scanning EM images of representative sinusoids in the liver of 10 months old Glmpgt/gt mice. D) Insert in image C, showing part of a highly fenestrated LSEC overlaying a collagen bundle. White circles in A-B show fenestrae arranged in sieve plates, while arrows in D point to single fenestrae in sieve plates. Abbreviations in A-D: LSEC, liver sinusoidal endothelial cell; HC, hepatocyte; RBC, red blood cell; Ga, gap in LSEC; Coll, collagen. E) Frequency of fenestrae (i.e. number of open holes 30–400 nm in diameter, per μm2), and F) frequency of gaps (open holes > 400 nm in diameter, per μμm2) in LSECs were measured on scanning EM images of liver samples from 4 young WT mice (age: 4–6 months), 4 young Glmpgt/gt mice (age: 4 months), and 4 old Glmpgt/gt mice (age: 9–10 months). The images were captured at 20.000 x magnification, and 206 images (11–29 images/liver) were analysed as described in Methods. Each dot represents the average value for one mouse, i.e. the value included in the statistical analysis (Kruskal-Wallis test), the median value for each group is presented as a horizontal line, and the upper and lower lines represent the interquartile range. *p-value < 0.05.</p