Politicizing Impartiality: Redefining the Role of the Senate in Federal Judicial Selection

The judicial selection process is heavily backlogged, resulting in excessive vacant judgeships, many in geographical areas with extremely high caseloads. Thus, the federal courts are falling further behind every year in settling disputes. The Senate’s action with President Obama’s nominee to the Supreme Court after the death of Justice Antonin Scalia has only escalated the dysfunction of the judicial selection process. Coupled with the fallout surrounding the death of Justice Ginsburg and Senator McConnell’s complete refusal to honor the precedent set by him in 2016, it has become glaringly apparent that the confirmation process conducted by the Senate needs to be standardized and streamlined. In this article, we perform a Constitutional analysis of the actual textual role of the Senate in the Confirmation process and apply the historical interpretations of the Senate’s role by the drafters of the Constitution, explaining how the judicial selection process can be updated and standardized regarding both the Supreme Court and the lower federal courts without requiring a Constitutional amendment to expedite the selection and approval of nominees and improve the efficiency of the process

A Study of Learning Modules and the Traditional Lecture Discussion Method for Teaching Weed Control Practices to Small Vegetable Farmers.

This study was conducted to develop and evaluate various uses of learning modules and to compare them with the traditional method of teaching weed control practices to small vegetable farmers. One hundred forty eight small farmers were selected for the study from St. Landry Parish in Louisiana and from Copiah, Simpson and Jefferson Davis Counties in Mississippi. They were divided into four treatment groups at random. Treatment I consisted of 63 farmers who were exposed to the modules in a group under professional supervision. Treatment II consisted to 48 farmers who participated in the same weed control program under the supervision of a professional using the traditional lecture-discussion method. Treatment III consisted of 20 farmers exposed to the learning modules on weed control under the supervision of a para-professional. Treatment IV involved 17 farmers who were exposed individually to the modules. The learning modules consisted of a series of slides synchronized with a tape recorded narrative. The programs for all farmers required two hours and ten minutes to complete. The modules were administered in two sixty-five minute sessions separated by a fifteen minute break. Farmers in the traditional or lecture-discussion group were exposed to the same subject matter for the same period of time as the module treatments. Data for the study was collected using pre-test and post-test questionnaires. The pre-test was administered just before farmers were exposed to the weed control program. Seven days after completing the program a post-test was administered. Both pre and post-tests were graded and coded for analysis. The following findings were observed: the comparison of the learning module and traditional treatments resulted in a highly significant difference in favor of the learning module treatments. A comparison of other module treatments indicated that farmers exposed to the modules in groups under professional supervision made significantly higher gains than farmers exposed to the modules in groups under para-professional supervision. It was also discovered that farmers exposed to modules in a group under para-professional supervision made significantly higher gains than farmers in the traditional treatment. When comparing mean gain of farmers exposed to the modules individually under para-professional supervision, with farmers exposed to the modules in groups under para-professional supervision, it was found that individual exposure under para-professional supervision was superior to the other treatments

Farm Security Administration rehabilitation loan experience in five Missouri counties (June 1942)

Cover title.Includes bibliographical references

The Contributions of Skeletal Muscle PKC Theta to Diet-Induced Obesity

Protein Kinase C- Theta (PKCθ) is a gene predominantly expressed in hematopoietic cells and skeletal muscle. In skeletal muscle, PKCθ regulates fat metabolism and insulin sensitivity. PKCθ activity increases in response to high levels of diacylglycerol in the cell, a common outcome of chronic high fat diet consumption and obesity. PKCθ is associated with skeletal muscle metabolic dysfunction, which may exacerbate weight gain and metabolic disease. The purpose of this study was to test the hypothesis that the selective deletion of PKCθ from skeletal muscle protects against diet-induced obesity. Mice lacking PKCθ in skeletal muscle were created using Cre-Lox recombination. At weaning, control (PKCθSkM+/+) and knockout (PKCθSkM-/-) mice were randomly assigned to regular or high fat diet (RD or HFD, respectively) groups. Mouse weights were taken weekly for 15 weeks. During the 15-week diet intervention, male PKCθSkM+/+ mice on a HFD became obese. Male PKCθSkM-/- mice consuming a HFD showed attenuated weight gain, which was similar to mice on a RD. This trend was not present for female mice, in which weight changed to a similar magnitude independent of diet and genotype. In conclusion, PKC-θ in the skeletal muscle may contribute to the regulation of diet-induced obesity. It is unclear whether these affects are sex specific

A Comparison of Unsupervised Methods for DNA Microarray Leukemia Data

Advancements in DNA microarray data sequencing have created the need for sophisticated machine learning algorithms and feature selection methods. Probabilistic graphical models, in particular, have been used to identify whether microarrays or genes cluster together in groups of individuals having a similar diagnosis. These clusters of genes are informative, but can be misleading when every gene is used in the calculation. First feature reduction techniques are explored, however the size and nature of the data prevents traditional techniques from working efficiently. Our method is to use the partial correlations between the features to create a precision matrix and predict which associations between genes are most important to predicting Leukemia diagnosis. This technique reduces the number of genes to a fraction of the original. In this approach, partial correlations are then extended into a spectral clustering approach. In particular, a variety of different Laplacian matrices are generated from the network of connections between features, and each implies a graphical network model of gene interconnectivity. Various edge and vertex weighted Laplacians are considered and compared against each other in a probabilistic graphical modeling approach. The resulting multivariate Gaussian distributed clusters are subsequently analyzed to determine which genes are activated in a patient with Leukemia. Finally, the results of this are compared against other feature engineering approaches to assess its accuracy on the Leukemia data set. The initial results show the partial correlation approach of feature selection predicts the diagnosis of a Leukemia patient with almost the same accuracy as using a machine learning algorithm on the full set of genes. More calculations of the precision matrix are needed to ensure the set of most important genes is correct. Additionally more machine learning algorithms will be implemented using the full and reduced data sets to further validate the current prediction accuracy of the partial correlation method