Overview of Impacts of Feral and Introduced Ungulates on the Environment in the Eastern United States and Caribbean

Non-native wild and feral ungulates have been introduced throughout the world for many centuries. Often the reasons for introductions were narrow in scope and benefits or the ungulates escaped or were released. Justifications for some introductions have included providing hunting opportunity, meeting cultural and dietary needs of people, fund raising, and aesthetics. Evaluations about the impacts to the environment, native wildlife, livestock, and people were most likely looked at in a narrow prism or not fully evaluated. Ungulates commonly introduced in the Eastern United States and Caribbean islands over the last 150 years included white-tailed deer, sika deer, hogs, horses, goats, and donkeys. Introductions have resulted in harm to endemic vegetation, competition with native herbivores for food, safety hazards to humans, disease threats to farm livestock and native wildlife, crop damage, and predation on eggs and young of native species. Some introductions provide significant positive economic benefits to local communities and present a unique set of resource and social challenges for the resource manager. Social and economic considerations may preclude removal as a management option. Once problems are recognized, management options can be assessed. However, the cost and effort of eradicating or suppressing non-native and feral ungulate populations can be daunting. Scarcity of funding can limit the scope and ability to remove enough of the animals to result in long-term benefits. Also, once substantial population reduction of undesirable animals is achieved, support for continued management may decline as recognition of the problem fades. An integration of control methods and some support from the local people are required to achieve removal of non-native and feral ungulates. Various challenges need to be addressed for control or eradication methods and strategies to be successful

Checklist of the Vascular Flora of Lyon and Sioux Counties, Iowa

The combined vascular flora of Lyon and Sioux counties, Iowa, based upon field and herbarium study, is composed of 612 species, of which 454 species (74%) occur in both counties. The Lyon County vascular flora consists of 561 species, including 13 state endangered, 9 state threatened species, and 102 non-native species. The Sioux County vascular flora consists of 506 species, including 2 state threatened species and 106 non-native species. The floras are most notable for the presence of plants with floristic affinities to the Great Plains to the west of Iowa. They also have a very high percentage (18%-20%) of their floras comprised of non-native species, reflecting the intensity of human activities on the landscape

Early Prediction of Chronic Kidney Disease Using Machine Learning Supported by Predictive Analytics

© 2018 IEEE. Chronic Kidney Disease is a serious lifelong condition that induced by either kidney pathology or reduced kidney functions. Early prediction and proper treatments can possibly stop, or slow the progression of this chronic disease to end-stage, where dialysis or kidney transplantation is the only way to save patient's life. In this study, we examine the ability of several machine-learning methods for early prediction of Chronic Kidney Disease. This matter has been studied widely; however, we are supporting our methodology by the use of predictive analytics, in which we examine the relationship in between data parameters as well as with the target class attribute. Predictive analytics enables us to introduce the optimal subset of parameters to feed machine learning to build a set of predictive models. This study starts with 24 parameters in addition to the class attribute, and ends up by 30 % of them as ideal sub set to predict Chronic Kidney Disease. A total of 4 machine learning based classifiers have been evaluated within a supervised learning setting, achieving highest performance outcomes of AUC 0.995, sensitivity 0.9897, and specificity 1. The experimental procedure concludes that advances in machine learning, with assist of predictive analytics, represent a promising setting by which to recognize intelligent solutions, which in turn prove the ability of predication in the kidney disease domain and beyond

A Checklist of the Vascular Flora of Lee County, Iowa

The vascular flora of Lee County, Iowa, based upon field and herbarium study, is composed of 876 taxa (species plus hybrids), including 25 endangered and 5 threatened Iowa species. Naturalized species totaled 154. The study added 318 species to the county flora, a 57% increase to the flora of what had been called one of lowa\u27s best collected counties. Two species are reported as additions to the state flora: Habenaria lacera (Michx.) Lodd. and Vitis baileyana Munson. The flora is most notable for the presence of many species which do not occur much farther northward in Iowa, being plants with floristic affinity to the Ozark Plateau in Missouri

Early Prediction of Chronic Kidney Disease Using Machine Learning Supported by Predictive Analytics

© 2018 IEEE. Chronic Kidney Disease is a serious lifelong condition that induced by either kidney pathology or reduced kidney functions. Early prediction and proper treatments can possibly stop, or slow the progression of this chronic disease to end-stage, where dialysis or kidney transplantation is the only way to save patient's life. In this study, we examine the ability of several machine-learning methods for early prediction of Chronic Kidney Disease. This matter has been studied widely; however, we are supporting our methodology by the use of predictive analytics, in which we examine the relationship in between data parameters as well as with the target class attribute. Predictive analytics enables us to introduce the optimal subset of parameters to feed machine learning to build a set of predictive models. This study starts with 24 parameters in addition to the class attribute, and ends up by 30 % of them as ideal sub set to predict Chronic Kidney Disease. A total of 4 machine learning based classifiers have been evaluated within a supervised learning setting, achieving highest performance outcomes of AUC 0.995, sensitivity 0.9897, and specificity 1. The experimental procedure concludes that advances in machine learning, with assist of predictive analytics, represent a promising setting by which to recognize intelligent solutions, which in turn prove the ability of predication in the kidney disease domain and beyond