Extragenital Blaschkoid lichen sclerosus et atrophicus in a child

Lichen sclerosus et atrophicus is a chronic inflammatory skin disease that typically affects prepubertal girls and peri- or post-menopausal women in genital and perineal areas. In some cases, it can also manifest as extragenital lesions. Extragenital Blaschkoid lesions have infrequently been reported. Here, we report a case of extragenital Blaschkoid lichen sclerosus et atrophicus in a child

Spent mushroom compost as a soil amendment for vegetables

Spent mushroom compost (SMC) was obtained from Ralston Purina Mushroom Plant in Loudon, Tennessee to determine its effects as a soil amendment for growing vegetables under field conditions. Four rates (0, 2, 10, and 20 kg/m2) of SMC were applied to a Sequatchie fine sandy loam in 1981 and 1982. Yields of cabbage, cucumber, mustard, onion, radish, snap bean, spinach, and tomato were evaluated. Spent mushroom compost application decreased the bulk density, increased the percentage of small pore space, pH, and electrical conductivity. Within the year of 1981 or 1982, the difference in the response of vegetable yield to SMC application was probably due to the difference in ability of each vegetable species to tolerate salinity. Difference in the yield response of the same vegetable species to SMC application between the two years of test period was perhaps because of the combined effects of salinity, climatic conditions, and the residual effect from the first year\u27s application of SMC. All vegetables tested in both years had significantly increased potassium concentration, but decreased magnesium concentration in leaf tissue. Under greenhouse conditions with adequate watering and leaching, snap bean, cucumber, radish, spinach, and tomato were grown from seed in soils amended with 0, 10, 20, 30 and 50% of SMC. The percentage of seedling emergence was not affected while the rate of seedling emergence was delayed by the addition of SMC. Optimum seedling growth was observed at 30% or 50% of SMC. The elemental content in seedling tissue indicated a potassium and calcium and/or magnesium antagonism in ion uptake. Under greenhouse conditions with a non-leached system, vegetables grown at 50% of SMC exhibited some stunting. The increased plant growth and yield were obtained with addition of 20% to 30% of SMC. In general, using SMC as a soil amendment was beneficial to some vegetables, but its high soluble salt content could make excessive rates or long term use harmful to plants that are sensitive to salinity

Developing and improving risk models using machine-learning based algorithms

The objective of this study is to develop a good risk model for classifying business delinquency by simultaneously exploring several machine learning-based methods including regularization, hyperparameter optimization, and model ensembling algorithms. The rationale under the analyses is firstly to obtain good base binary classifiers (include Logistic Regression (LR), K-Nearest Neighbors (KNN ), Decision Tree (DT), and Artificial Neural Networks (ANN )) via regularization and appropriate settings of hyper-parameters. Then two model ensembling algorithms including bagging and boosting are performed on the good base classifiers for further model improvement. The models are evaluated using accuracy, Area Under the Receiver Operating Characteristic Curve (AUC of ROC), recall, and F1 score via repeating 10-fold cross-validation 10 times. The results show the optimal base classifiers along with the hyper-parameter settings are LR without regularization, K N N by using 9 nearest neighbors, DT by setting the maximum level of the tree to be 7, and ANN with three hidden layers. Bagging on K N N with K valued 9 is the optimal model we can get for risk classification as it reaches the average accuracy, AUC, recall, and F1 score valued 0.90, 0.93, 0.82, and 0.89, respectively

Predicting class-imbalanced business risk using resampling, regularization, and model ensembling algorithms

We aim at developing and improving the imbalanced business risk modeling via jointly using proper evaluation criteria, resampling, cross-validation, classifier regularization, and ensembling techniques. Area Under the Receiver Operating Characteristic Curve (AUC of ROC) is used for model comparison based on 10-fold cross-validation. Two undersampling strategies including random undersampling (RUS) and cluster centroid undersampling (CCUS), as well as two oversampling methods including random oversampling (ROS) and Synthetic Minority Oversampling Technique (SMOTE), are applied. Three highly interpretable classifiers, including logistic regression without regularization (LR), L1-regularized LR (L1LR), and decision tree (DT) are implemented. Two ensembling techniques, including Bagging and Boosting, are applied to the DT classifier for further model improvement. The results show that Boosting on DT by using the oversampled data containing 50% positives via SMOTE is the optimal model and it can achieve AUC, recall, and F1 score valued 0.8633, 0.9260, and 0.8907, respectively

Electrospinning of biphasic biopolymer and polymer : a feasibility and characterization study

Electrospinning of polymeric materials have been experimented to achieve nanoscaled diameters. The approaches to the current study are to combine a natural material such as collagen with synthetic materials and determine if the interactions between the materials can be used in the electrospinning process. Collagen is a material of choice due to its biocompatibility. When collagen is combined with polymer, Polyethylene Oxide (PEG), the fiber diameters ranged from 150nm to one micron. The fibers produced have shown drastic phase separation. The next study involved using a duel solvent system to electrospin fibers. The polymer of choice is Poly (1-lactic acid) (PLLA) combining with collagen. This approach has given fibers with diameter ranging from 650nm to over a micron. This method of electrospinning is the least successful due to the poor solvent miscibility and evaporation. The third approach to electrospin PLLA with collagen is through the use of a single solvent Trifluoroacetic acid (TFA). This is a common solvent for collagen and PLLA and it is very volatile. The diameter of fibers produced through this process is around 350nm to 500nm. This method showed the most promise in producing excellent fiber mats. When thermal analyses are performed the results indicated rapid densification and reorientation of PLLA. This is determined to be interactions that are occurring between collagen and PLLA resulting in rapid enthalpic recovery of PLLA

An Automatic Interaction Detection Hybrid Model for Bankcard Response Classification

Data mining techniques have numerous applications in bankcard response modeling. Logistic regression has been used as the standard modeling tool in the financial industry because of its almost always desirable performance and its interpretability. In this paper, we propose a hybrid bankcard response model, which integrates decision tree-based chi-square automatic interaction detection (CHAID) into logistic regression. In the first stage of the hybrid model, CHAID analysis is used to detect the possible potential variable interactions. Then in the second stage, these potential interactions are served as the additional input variables in logistic regression. The motivation of the proposed hybrid model is that adding variable interactions may improve the performance of logistic regression. Theoretically, all possible interactions could be added in logistic regression and significant interactions could be identified by feature selection procedures. However, even the stepwise selection is very time-consuming when the number of independent variables is large and tends to cause the p \u3e\u3e n problem. On the other hand, using CHAID analysis for the detection of variable interactions has the potential to overcome the above-mentioned drawbacks. To demonstrate the effectiveness of the proposed hybrid model, it is evaluated on a real credit customer response data set. As the results reveal, by identifying potential interactions among independent variables, the proposed hybrid approach outperforms the logistic regression without searching for interactions in terms of classification accuracy, the area under the receiver operating characteristic curve (ROC), and Kolmogorov-Smirnov (KS) statistics. Furthermore, CHAID analysis for interaction detection is much more computationally efficient than the stepwise search mentioned above and some identified interactions are shown to have statistically significant predictive power on the target variable. Last but not least, the customer profile created based on the CHAID tree provides a reasonable interpretation of the interactions, which is required by regulations of the credit industry. Hence, this study provides an alternative for handling bankcard classification tasks