Takagi–Sugeno Fuzzy Modeling of Skin Permeability

The skin is a major exposure route for many potentially toxic chemicals. It is, therefore, important to be able to predict the permeability of compounds through skin under a variety of conditions. Available skin permeability databases are often limited in scope and not conducive to developing effective models. This sparseness and ambiguity of available data prompted the use of fuzzy set theory to model and predict skin permeability. Using a previously published database containing 140 compounds, a rule-based Takagi–Sugeno fuzzy model is shown to predict skin permeability of compounds using octanol-water partition coefficient, molecular weight, and temperature as inputs. Model performance was estimated using a cross-validation approach. In addition, 10 data points were removed prior to model development for additional testing with new data. The fuzzy model is compared to a regression model for the same inputs using both R2 and root mean square error measures. The quality of the fuzzy model is also compared with previously published models. The statistical analysis demonstrates that the fuzzy model performs better than the regression model with identical data and validation protocols. The prediction quality for this model is similar to others that were published. The fuzzy model provides insights on the relationships between lipophilicity, molecular weight, and temperature on percutaneous penetration. This model can be used as a tool for rapid determination of initial estimates of skin permeability

Takagi–Sugeno Fuzzy Modeling of Skin Permeability

The skin is a major exposure route for many potentially toxic chemicals. It is, therefore, important to be able to predict the permeability of compounds through skin under a variety of conditions. Available skin permeability databases are often limited in scope and not conducive to developing effective models. This sparseness and ambiguity of available data prompted the use of fuzzy set theory to model and predict skin permeability. Using a previously published database containing 140 compounds, a rule-based Takagi–Sugeno fuzzy model is shown to predict skin permeability of compounds using octanol-water partition coefficient, molecular weight, and temperature as inputs. Model performance was estimated using a cross-validation approach. In addition, 10 data points were removed prior to model development for additional testing with new data. The fuzzy model is compared to a regression model for the same inputs using both R2 and root mean square error measures. The quality of the fuzzy model is also compared with previously published models. The statistical analysis demonstrates that the fuzzy model performs better than the regression model with identical data and validation protocols. The prediction quality for this model is similar to others that were published. The fuzzy model provides insights on the relationships between lipophilicity, molecular weight, and temperature on percutaneous penetration. This model can be used as a tool for rapid determination of initial estimates of skin permeability

Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks

This paper presents a method for automatic segmentation, localization, and identification of vertebrae in arbitrary 3D CT images. Many previous works do not perform the three tasks simultaneously even though requiring a priori knowledge of which part of the anatomy is visible in the 3D CT images. Our method tackles all these tasks in a single multi-stage framework without any assumptions. In the first stage, we train a 3D Fully Convolutional Networks to find the bounding boxes of the cervical, thoracic, and lumbar vertebrae. In the second stage, we train an iterative 3D Fully Convolutional Networks to segment individual vertebrae in the bounding box. The input to the second networks have an auxiliary channel in addition to the 3D CT images. Given the segmented vertebra regions in the auxiliary channel, the networks output the next vertebra. The proposed method is evaluated in terms of segmentation, localization, and identification accuracy with two public datasets of 15 3D CT images from the MICCAI CSI 2014 workshop challenge and 302 3D CT images with various pathologies introduced in [1]. Our method achieved a mean Dice score of 96%, a mean localization error of 8.3 mm, and a mean identification rate of 84%. In summary, our method achieved better performance than all existing works in all the three metrics

Indoor Air Pollutant Standards and Potential Impact on Human Health due to Poor Indoor Air Quality in Indian Context

Human being spent their substantial part of life living indoor. Thus the quality of indoor environment becomes significant to consider in terms of purity of air available for breathing and other parameters i.e. building design, Heating, ventilation, and air Conditioning systems (HVAC), rugs & carpets, paints & polishing, household cleaning appliances, aerosols, insecticides, pesticides, and personal care products & devices, etc. which generates potentially harmful by-products and contributes to indoor environment deterioration directly. Better indoor environment is subject to the quality of thermal comfort i.e. Relative Humidity and Temperature, and the air quality (concentrations of pollutants) inside the buildings. Indoor air quality pertains to the purity of inside air within the building that is essential requirement and equivalent for human comfort. In this article, focus on “required indoor air quality” (RIAQ) as per human comfort, and Indoor Air Quality (IAQ) standards for various indoor air pollutants along with impacts of major indoor pollutants like CO2, Formaldehyde, Radon, and volatile organic compounds, etc in indoor air and on human health have been reported. From literature survey it has been observed that the indoor air quality standards are not available for most indoor air pollutants. Thus, from different research outcomes, and government directories the indoor air pollutant standards are tabulated and presented for a quick understanding & its availability for their permissible limits

Improving biomass production and saccharification in Brachypodium distachyon through overexpression of a sucrose-phosphate synthase from sugarcane

The substitution of fossil by renewable energy sources is a major strategy in reducing CO2 emission and mitigating climate change. In the transport sector, which is still mainly dependent on liquid fuels, the production of second generation ethanol from lignocellulosic feedstock is a promising strategy to substitute fossil fuels. The main prerequisites on designated crops for increased biomass production are high biomass yield and optimized saccharification for subsequent use in fermentation processes. We tried to address these traits by the overexpression of a sucrose-phosphate synthase gene (SoSPS) from sugarcane (Saccharum officinarum) in the model grass Brachypodium distachyon. The resulting transgenic B. distachyon lines not only revealed increased plant height at early growth stages but also higher biomass yield from fully senesced plants, which was increased up to 52 % compared to wild-type. Additionally, we determined higher sucrose content in senesced leaf biomass from the transgenic lines, which correlated with improved biomass saccharification after conventional thermo-chemical pretreatment and enzymatic hydrolysis. Combining increased biomass production and saccharification efficiency in the generated B. distachyon SoSPS overexpression lines, we obtained a maximum of 74 % increase in glucose release per plant compared to wild-type. Therefore, we consider SoSPS overexpression as a promising approach in molecular breeding of energy crops for optimizing yields of biomass and its utilization in second generation biofuel production

Selection of Conditions for Cellulase and Xylanase Extraction from Switchgrass Colonized by Acidothermus cellulolyticus

Solid-state fermentation has been widely used for enzyme production. However, secreted enzymes often bind to the solid substrate preventing their detection and recovery. A series of screening studies was performed to examine the role of extraction buffer composition including NaCl, ethylene glycol, sodium acetate buffer, and Tween 80, on xylanase and cellulase recovery from switchgrass. Our results indicated that the selection of an extraction buffer is highly dependent on the nature and source of the enzyme being extracted. While a buffer containing 50 mM sodium acetate at pH 5 was found to have a positive effect on the recovery of commercial fungal-derived cellulase and xylanase amended to switchgrass, the same buffer had a significant negative effect on enzyme extraction from solid fermentation samples colonized by the bacterium Acidothermus cellulolyticus. Xylanase activity was more affected by components in the extraction buffers compared to cellulase. This study demonstrated that extraction followed by diafiltration is important for assessing enzyme recovery from solid fermentation samples. Reduction in activity due to compounds present in the switchgrass extracts is reversible when the compounds are removed via diafiltration

Downregulation of Cinnamyl-Alcohol Dehydrogenase in Switchgrass by RNA Silencing Results in Enhanced Glucose Release after Cellulase Treatment

Cinnamyl alcohol dehydrogenase (CAD) catalyzes the last step in monolignol biosynthesis and genetic evidence indicates CAD deficiency in grasses both decreases overall lignin, alters lignin structure and increases enzymatic recovery of sugars. To ascertain the effect of CAD downregulation in switchgrass, RNA mediated silencing of CAD was induced through Agrobacterium mediated transformation of cv. “Alamo” with an inverted repeat construct containing a fragment derived from the coding sequence of PviCAD2. The resulting primary transformants accumulated less CAD RNA transcript and protein than control transformants and were demonstrated to be stably transformed with between 1 and 5 copies of the T-DNA. CAD activity against coniferaldehyde, and sinapaldehyde in stems of silenced lines was significantly reduced as was overall lignin and cutin. Glucose release from ground samples pretreated with ammonium hydroxide and digested with cellulases was greater than in control transformants. When stained with the lignin and cutin specific stain phloroglucinol-HCl the staining intensity of one line indicated greater incorporation of hydroxycinnamyl aldehydes in the lignin

Phosphoregulation of Ire1 RNase splicing activity.

Abstract Ire1 is activated in response to accumulation of misfolded proteins within the endoplasmic reticulum as part of the unfolded protein response (UPR). It is a unique enzyme, possessing both kinase and RNase activity that is required for specific splicing of Xbp1 mRNA leading to UPR activation. How phosphorylation impacts on the Ire1 splicing activity is unclear. In this study, we isolate distinct phosphorylated species of Ire1 and assess their effects on RNase splicing both in vitro and in vivo. We find that phosphorylation within the kinase activation loop significantly increases RNase splicing in vitro. Correspondingly, mutants of Ire1 that cannot be phosphorylated on the activation loop show decreased specific Xbp1 and promiscuous RNase splicing activity relative to wild-type Ire1 in cells. These data couple the kinase phosphorylation reaction to the activation state of the RNase, suggesting that phosphorylation of the activation loop is an important step in Ire1-mediated UPR activation.</jats:p