Modelling of colour appearance

A colour may have a different appearance under different viewing conditions. This causes many problems in the colour reproduction industry. Thus the importance of prediction of colour appearance has arisen. In this study, a mathematical model to predict colour appearance was developed based on the investigation of the changes of colour appearance under a wide range of media and viewing conditions. The media studied included large cut-sheet transparency films, 35mm projected slides, reflection samples and monitor colours. The viewing conditions varied were light source, luminance level and viewing background. Colour appearance was studied using the magnitude estimation technique. In general, colours appeared more colourful, lighter and brighter with an increase in luminance level. Background and flare light had considerable influence on colour appearance for cut-sheet transparency media. Simultaneous contrast effects occurred when a monitor colour was displayed against a chromatic surround. The monitor colour appeared lighter with a darker induction field. When a coloured area was enlarged, lightness tended to increase while colourfulness tended to decrease. Colour appearance was also affected by the closest neighbouring colour. In this case, the hue of the colour largely shifted towards the direction of the opponent hue of the induction colour. The data obtained were applied to test three colour spaces and two colour appearance models. For reflection media, the Hunt91 model performed the best. However it was not satisfactory when applied to transmissive media. Based on these results, the Hunt93 model was developed by modification of the Hunt91 model. The new model widens the application range of the Hunt91 and is reversible

Dendritic Crystallization of Poly(l‑lactide)/poly(d‑lactide) Stereocomplexes in Ultrathin Films

Ultrathin films (12–125 nm) of poly­(l-lactide)/poly­(d-lactide) (PLLA/PDLA) blends of different compositions have been crystallized between 180 and 210 °C, i.e., above the melting point of each polymer crystallized separately. The overall crystal shape depends on the temperature, film thickness and ratio of the two polyenantiomers in the blends. In nonequimolar blends, lamellae show curvatures, and the sense of the curvature is determined by the chirality of the polyenantiomer in excess, blend ratio, film thickness, crystallization temperature and lamellar orientation (flat-on or edge-on). The curvature of the stereocomplex lamellae is ascribed to the unequal amount of PLLA and PDLA segments at the crystal growth front, creating an unbalanced mechanical stress at the chain folding surfaces which can be released by a curvature of the growth tip

Two Pathways for Dissociation of Highly Energized <i>syn</i>-CH₃CHOO to OH Plus Vinoxy

Ozonolysis of alkenes is an important nonphotolytic source of hydroxl radicals in the troposphere. The reaction proceeds through cycloaddition and subsequent decomposition to a carbonyl oxide, known as Criegee intermediates. Ozonolysis of alkene releases about 50 kcal/mol excess energy to form highly energized Criegee molecules, which can be stabilized and undergo further reaction or dissociate to OH+vinoxy products. The dissociation dynamics of partially stabilized Criegee (<i>syn</i>-CH₃CHOO) has been thoroughly studied recently, in which the molecules dissociate by first isomerizing to vinyl hydroperoxide (VHP). Here we examine the dissociation dynamics of highly energized <i>syn</i>-CH₃CHOO (42 kcal/mol), and a second, prompt dissociation path is discovered. The dissociation dynamics of these two paths are carefully examined through the animation of trajectories and the energy distributions of products. The new prompt path reveals a distinctly different translational energy and internal energy distributions of products compared to the known path through VHP

Additional file 1 of E2 level > 2950 pg/ml on hCG trigger day is an independent predictor for birthweight loss of full-term singletons born after fresh embryo transfers in non-PCOS patients

Additional file 1

Data_Sheet_2_A multifaceted evaluation of microgliosis and differential cellular dysregulation of mammalian target of rapamycin signaling in neuronopathic Gaucher disease.pdf

Neuronopathic Gaucher disease (nGD) is an inherited neurodegenerative disease caused by mutations in GBA1 gene and is associated with premature death. Neuroinflammation plays a critical role in disease pathogenesis which is characterized by microgliosis, reactive astrocytosis, and neuron loss, although molecular mechanisms leading to neuroinflammation are not well-understood. In this report, we developed a convenient tool to quantify microglia proliferation and activation independently and uncovered abnormal proliferation of microglia (∼2-fold) in an adult genetic nGD model. The nGD-associated pattern of inflammatory mediators pertinent to microglia phenotypes was determined, showing a unique signature favoring pro-inflammatory chemokines and cytokines. Moreover, highly polarized (up or down) dysregulations of mTORC1 signaling with varying lysosome dysfunctions (numbers and volume) were observed among three major cell types of nGD brain. Specifically, hyperactive mTORC1 signaling was detected in all disease-associated microglia (Iba1high) with concurrent increase in lysosome function. Conversely, the reduction of neurons presenting high mTORC1 activity was implicated (including Purkinje-like cells) which was accompanied by inconsistent changes of lysosome function in nGD mice. Undetectable levels of mTORC1 activity and low Lamp1 puncta were noticed in astrocytes of both diseased and normal mice, suggesting a minor involvement of mTORC1 pathway and lysosome function in disease-associated astrocytes. These findings highlight the differences and complexity of molecular mechanisms that are involved within various cell types of the brain. The quantifiable parameters established and nGD-associated pattern of neuroinflammatory mediators identified would facilitate the efficacy evaluation on microgliosis and further discovery of novel therapeutic target(s) in treating neuronopathic Gaucher disease.</p

Data_Sheet_1_A multifaceted evaluation of microgliosis and differential cellular dysregulation of mammalian target of rapamycin signaling in neuronopathic Gaucher disease.pdf

Neuronopathic Gaucher disease (nGD) is an inherited neurodegenerative disease caused by mutations in GBA1 gene and is associated with premature death. Neuroinflammation plays a critical role in disease pathogenesis which is characterized by microgliosis, reactive astrocytosis, and neuron loss, although molecular mechanisms leading to neuroinflammation are not well-understood. In this report, we developed a convenient tool to quantify microglia proliferation and activation independently and uncovered abnormal proliferation of microglia (∼2-fold) in an adult genetic nGD model. The nGD-associated pattern of inflammatory mediators pertinent to microglia phenotypes was determined, showing a unique signature favoring pro-inflammatory chemokines and cytokines. Moreover, highly polarized (up or down) dysregulations of mTORC1 signaling with varying lysosome dysfunctions (numbers and volume) were observed among three major cell types of nGD brain. Specifically, hyperactive mTORC1 signaling was detected in all disease-associated microglia (Iba1high) with concurrent increase in lysosome function. Conversely, the reduction of neurons presenting high mTORC1 activity was implicated (including Purkinje-like cells) which was accompanied by inconsistent changes of lysosome function in nGD mice. Undetectable levels of mTORC1 activity and low Lamp1 puncta were noticed in astrocytes of both diseased and normal mice, suggesting a minor involvement of mTORC1 pathway and lysosome function in disease-associated astrocytes. These findings highlight the differences and complexity of molecular mechanisms that are involved within various cell types of the brain. The quantifiable parameters established and nGD-associated pattern of neuroinflammatory mediators identified would facilitate the efficacy evaluation on microgliosis and further discovery of novel therapeutic target(s) in treating neuronopathic Gaucher disease.</p

Highly Stretchable Microsupercapacitor Arrays with Honeycomb Structures for Integrated Wearable Electronic Systems

The rapid development of portable and wearable electronics has greatly increased the demand for energy storage devices with similar physical properties and integration capability. This paper introduces a honeycomb polydimethylsiloxane substrate for stretchable microsupercapacitor (MSC) arrays, which enables facile integration with other electronics. The honeycomb structure can accommodate a large deformation without producing excessive strain in the MSCs and interconnects. The results of this study show that such stretchable MSC arrays with single-walled carbon nanotube electrodes demonstrate excellent rate capability and power performance as well as electrochemical stability up to 150% (zero prestrain) or 275% (−50% prestrain) stretching and under excessive bending or twisting. The present stretchable MSC arrays with honeycomb structures show high potential for integration with other electronics, such as energy harvesters, power management circuits, wireless charging circuits, and various sensors, encompassing a wide range of wearable, bioimplantable electronic systems

Image_1_Development and validation of nomograms for predicting overall survival and cancer specific survival in locally advanced breast cancer patients: A SEER population-based study.TIF

BackgroundFor patients with locally advanced breast cancer (LABC), conventional TNM staging is not accurate in predicting survival outcomes. The aim of this study was to develop two accurate survival prediction models to guide clinical decision making.MethodsA retrospective analysis of 22,842 LABC patients was performed from 2010 to 2015 using the Surveillance, Epidemiology and End Results (SEER) database. An additional cohort of 200 patients from the Binzhou Medical University Hospital (BMUH) was analyzed. The least absolute shrinkage and selection operator (LASSO) regression was used to screen for variables. The identified variables were used to build a survival prediction model. The performance of the nomogram models was assessed based on the concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).ResultsThe LASSO analysis identified 9 variables in patients with LABC, including age, marital status, Grade, histological type, T-stage, N-stage, surgery, radiotherapy, and chemotherapy. In the training cohort, the C-index of the nomogram in predicting the overall survival (OS) was 0.767 [95% confidence intervals (95% CI): 0.751–0.775], cancer specific survival (CSS) was 0.765 (95% CI: 0.756–0.774). In the external validation cohort, the C-index of the nomogram in predicting the OS was 0.858 (95% CI: 0.812–0.904), the CSS was 0.866 (95% CI: 0.817–0.915). In the training cohort, the area under the receiver operator characteristics curve (AUC) values of the nomogram in prediction of the 1, 3, and 5-year OS were 0.836 (95% CI: 0.821–0.851), 0.769 (95% CI: 0.759–0.780), and 0.750 (95% CI: 0.738–0.762), respectively. The AUC values for prediction of the 1, 3, and 5-year CSS were 0.829 (95% CI: 0.811–0.847), 0.769 (95% CI: 0.757–0.780), and 0.745 (95% CI: 0.732–0.758), respectively. Results of the C-index, ROC curve, and DCA demonstrated that the nomogram was more accurate in predicting the OS and CSS of patients compared with conventional TNM staging.ConclusionTwo prediction models were developed and validated in this study which provided more accurate prediction of the OS and CSS in LABC patients than the TNM staging. The constructed models can be used for predicting survival outcomes and guide treatment plans for LABC patients.</p

Table_3_Development and validation of nomograms for predicting overall survival and cancer specific survival in locally advanced breast cancer patients: A SEER population-based study.XLSX

BackgroundFor patients with locally advanced breast cancer (LABC), conventional TNM staging is not accurate in predicting survival outcomes. The aim of this study was to develop two accurate survival prediction models to guide clinical decision making.MethodsA retrospective analysis of 22,842 LABC patients was performed from 2010 to 2015 using the Surveillance, Epidemiology and End Results (SEER) database. An additional cohort of 200 patients from the Binzhou Medical University Hospital (BMUH) was analyzed. The least absolute shrinkage and selection operator (LASSO) regression was used to screen for variables. The identified variables were used to build a survival prediction model. The performance of the nomogram models was assessed based on the concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).ResultsThe LASSO analysis identified 9 variables in patients with LABC, including age, marital status, Grade, histological type, T-stage, N-stage, surgery, radiotherapy, and chemotherapy. In the training cohort, the C-index of the nomogram in predicting the overall survival (OS) was 0.767 [95% confidence intervals (95% CI): 0.751–0.775], cancer specific survival (CSS) was 0.765 (95% CI: 0.756–0.774). In the external validation cohort, the C-index of the nomogram in predicting the OS was 0.858 (95% CI: 0.812–0.904), the CSS was 0.866 (95% CI: 0.817–0.915). In the training cohort, the area under the receiver operator characteristics curve (AUC) values of the nomogram in prediction of the 1, 3, and 5-year OS were 0.836 (95% CI: 0.821–0.851), 0.769 (95% CI: 0.759–0.780), and 0.750 (95% CI: 0.738–0.762), respectively. The AUC values for prediction of the 1, 3, and 5-year CSS were 0.829 (95% CI: 0.811–0.847), 0.769 (95% CI: 0.757–0.780), and 0.745 (95% CI: 0.732–0.758), respectively. Results of the C-index, ROC curve, and DCA demonstrated that the nomogram was more accurate in predicting the OS and CSS of patients compared with conventional TNM staging.ConclusionTwo prediction models were developed and validated in this study which provided more accurate prediction of the OS and CSS in LABC patients than the TNM staging. The constructed models can be used for predicting survival outcomes and guide treatment plans for LABC patients.</p

Table_2_Development and validation of nomograms for predicting overall survival and cancer specific survival in locally advanced breast cancer patients: A SEER population-based study.XLSX

BackgroundFor patients with locally advanced breast cancer (LABC), conventional TNM staging is not accurate in predicting survival outcomes. The aim of this study was to develop two accurate survival prediction models to guide clinical decision making.MethodsA retrospective analysis of 22,842 LABC patients was performed from 2010 to 2015 using the Surveillance, Epidemiology and End Results (SEER) database. An additional cohort of 200 patients from the Binzhou Medical University Hospital (BMUH) was analyzed. The least absolute shrinkage and selection operator (LASSO) regression was used to screen for variables. The identified variables were used to build a survival prediction model. The performance of the nomogram models was assessed based on the concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).ResultsThe LASSO analysis identified 9 variables in patients with LABC, including age, marital status, Grade, histological type, T-stage, N-stage, surgery, radiotherapy, and chemotherapy. In the training cohort, the C-index of the nomogram in predicting the overall survival (OS) was 0.767 [95% confidence intervals (95% CI): 0.751–0.775], cancer specific survival (CSS) was 0.765 (95% CI: 0.756–0.774). In the external validation cohort, the C-index of the nomogram in predicting the OS was 0.858 (95% CI: 0.812–0.904), the CSS was 0.866 (95% CI: 0.817–0.915). In the training cohort, the area under the receiver operator characteristics curve (AUC) values of the nomogram in prediction of the 1, 3, and 5-year OS were 0.836 (95% CI: 0.821–0.851), 0.769 (95% CI: 0.759–0.780), and 0.750 (95% CI: 0.738–0.762), respectively. The AUC values for prediction of the 1, 3, and 5-year CSS were 0.829 (95% CI: 0.811–0.847), 0.769 (95% CI: 0.757–0.780), and 0.745 (95% CI: 0.732–0.758), respectively. Results of the C-index, ROC curve, and DCA demonstrated that the nomogram was more accurate in predicting the OS and CSS of patients compared with conventional TNM staging.ConclusionTwo prediction models were developed and validated in this study which provided more accurate prediction of the OS and CSS in LABC patients than the TNM staging. The constructed models can be used for predicting survival outcomes and guide treatment plans for LABC patients.</p