Mutant Lef1 controls Gata6 in sebaceous gland development and cancer

Mutations in Lef1 occur in human and mouse sebaceous gland (SG) tumors, but their contribution to carcinogenesis remains unclear. Since Gata6 controls lineage identity in SG, we investigated the link between these two transcription factors. Here, we show that Gata6 is a β-catenin-independent transcriptional target of mutant Lef1. During epidermal development, Gata6 is expressed in a subset of Sox9-positive Lef1-negative hair follicle progenitors that give rise to the upper SG Overexpression of Gata6 by in utero lentiviral injection is sufficient to induce ectopic sebaceous gland elements. In mice overexpressing mutant Lef1, Gata6 ablation increases the total number of skin tumors yet decreases the proportion of SG tumors. The increased tumor burden correlates with impaired DNA mismatch repair and decreased expression of Mlh1 and Msh2 genes, defects frequently observed in human sebaceous neoplasia. Gata6 specifically marks human SG tumors and also defines tumors with elements of sebaceous differentiation, including a subset of basal cell carcinomas. Our findings reveal that Gata6 controls sebaceous gland development and cancer

Immunomodulatory role of Keratin 76 in oral and gastric cancer

Keratin 76 (Krt76) is an epithelial differentiation marker that is downregulated in oral squamous cell carcinomas, correlating with poor prognosis. Here the authors show that genetic ablation of Krt76 in a mouse model results in increased susceptibility to carcinogenesis via enhanced accumulation of Tregs

Visible and Near-Infrared Reflectance Spectroscopy for Determining Physicochemical Properties of Rice

To assess rice grain quality, various types of analyses of the physicochemical properties of rice must be conducted. Although several methods exist, different properties require specific instruments and chemicals, and most of them are time-consuming. The authors have therefore developed calibration models for determining multiple physicochemical properties of rice using a single instrument, a visible and near-infrared (VIS/NIR) reflectance spectrometer. These models were used to measure the physicochemical properties of 61 samples of Japanese short-grain, non-waxy rice. The results of partial least squares (PLS) regression modeling with full cross-validation methods indicated that reasonably accurate models (correlation coefficient [r] of the validation greater than 0.8) could be obtained for moisture content, sound whole kernel, and appearance (whiteness, translucency, and color) of brown rice, and for moisture content, appearance, and amylogram characteristics (maximum viscosity and breakdown) of milled rice. The level of accuracy was sufficient for classifying rice samples into qualitative groups. Thus, the results demonstrate that VIS/NIR spectroscopy can be used to determine the physicochemical properties of rice and assess its quality

Determination of undried rough rice constituent content using near-infrared transmission spectroscopy

Near-infrared transmission (NIRT) spectroscopy was used in an attempt to predict moisture content, protein content and amylose content from undried whole-grain rough rice spectra. One hundred-fifty undried rough rice samples were collected. Using partial least squares calibration models obtained from undried whole-grain rough rice spectra, the coefficient of determination (r 2) and the standard error of prediction (SEP) of the validation set were r 2 = 0.96 and SEP = 0.70 for rough rice moisture content, r 2 = 0.70 and SEP = 0.24 for brown rice protein content, r 2 = 0.76 and SEP = 0.22 for milled rice protein content, and r 2 = 0.00 and SEP = 0.27 for milled rice amylose content. The results of the validation indicated that NIRT could be used to determine moisture content and protein content. Thus, NIRT technology may be used to classify undried rough rice into qualitative groups such as high protein content rice and low protein content rice upon arrival at a rice-drying facility after harvesting

Visible and near-infrared reflectance spectroscopy for rice taste evaluation

Visible and near-infrared (VIS/NIR) spectroscopy calibration models for rice taste evaluation were developed using 61 short-grain rice samples. The best performance calibration model was obtained from original spectra of whole grain milled rice using multiple linear regression (MLR) analysis. The correlation coefficient (r) and the standard error of prediction (SEP) of the validation set was 0.62 and 0.27, respectively. However, this was not adequate to justify replacing sensory tests with the calibration model for evaluating rice taste. The results indicated that VIS/NIR technology could be used for classifying rice samples into qualitative groups, such as poor taste, better taste and the best taste

Near-infrared Spectroscopy for On-line Real-time Monitoring of Milk Quality : Spectrum Analysis by Principal Component Analysis

We have constructed a near-infrared (NIR) spectroscopic sensing system on an experimental basis. The NIR system can be used for on-line real-time monitoring of milk quality items such as fat, protein, lactose, somatic cell count, and milk urea nitrogen during milking with sufficient precision and accuracy. However, when the calibration models developed from a dataset were validated using a different data set, the performance of the calibration models was poor except for fat content. It seemed that various factors such as cow individuality, lactation stage and calving time caused the poor performance. However, it was not know which factor affected milk spectra and which wavelength range of the spectra was affected by the factors. We therefore analyzed milk spectra by principal component analysis in order to determine the reasons for poor performance. It was found that milk spectra were greatly affected by fat content and calving time. Calving time had a particularly great effect on the spectra in the wavelength range of 860 to 880 nm. In summary, using principal component analysis, we found the factor most affecting milk spectra and the wavelength of the spectra affected by the factor.Written for presentation at the 2006 ASABE Annual International Meeting Sponsored by ASAB

Online Real-time Monitoring of Milk Quality during Milking by Near-infrared Spectroscopy

Recently, there has been a need by dairy farmers for a method to assess milk quality in real time during milking. We have constructed an on-line near-infrared (NIR) spectroscopic sensing system on an experimental basis. The NIR system can be used for real-time monitoring of milk quality items such as fat, protein, lactose, somatic cell count, and milk urea nitrogen during milking with sufficient precision and accuracy. We tried to improve the robustness of calibration (CAL) models for measurement of milk quality items using NIR spectrum data obtained from two dairy herds. When CAL models developed from data obtained from one herd were used for validation of data obtained from the same herd, the milk quality items could be measured with high levels of accuracy. On the other hand, when the CAL models were used for validation of data obtained from the other herd, the levels of accuracy in measurements of all milk quality items except fat were low. The low levels of accuracy may be caused by factors such as differences in cow individuality, lactation stage, calving times, feeding stage and experimental period (temperatures in the dairy barn). To develop robust calibration models for measurement of milk quality items, therefore, data acquisition from various milk spectra caused by these factors is necessary.Written for presentation at the 2005 ASAE Annual International Meeting Sponsored by ASA