Characters and Environments

The concepts of reaction norms and phenotypic plasticity help us better understand what a biological trait is

White-tailed Deer (\u3ci\u3eOdocoileus virginianus\u3c/i\u3e Zimmermann) Browsing Effects on Quality of Tallgrass Prairie Community Forbs

We examined the effect of white~tailed deer (Odocoiieus virginianus Zimmermann) browsing on community quality of tallgrass prairie forbs at a site in northeastern Illinois over a period of ten years (1992- 2001). Deer densities in the study area varied from 32- 50 km-2 (83- 130 deer mi-2) between 1992 and 1997 and declined to 7- 9 km-2 (18- 23 mi-2) following initiation of hunting. In a plot protected from deer browsing, abundances of browse-sensitive species increased and unpreferred and browse~tolerant species decreased. Community quality of forbs measured with a new index, Weighted Mean Fidelity, decreased on the unprotected plot until deer density was reduced. Several commonly used indices of floristic quality, mean C and floristic quality index, were unable to detect changes in community quality because the compliment of species on our site did not change over time. However, changes occurred in the relative abundances of species with different coefficients of conservatism, which was detected by Weighted Mean Fidelity. In contrast, on the protected plot community quality initially declined, followed by an increase, suggesting a lag time for recovery from browsing. Previous studies on our study site demonstrated that diversity of prairie forbs was maximized at an intermediate level of deer browsing, supporting the intermediate disturbance hypothesis, which posits that diversity is maximized at intermediate levels of disturbance. However, we found that community quality of forbs declined as duration of intense deer browsing (disturbance) increased, and was highest after eight years of protection from browsing, suggesting a potential trade-off between maximizing diversity and maintaining quality of forb communities that land managers should consider

Table_1_A deep learning approach for detecting liver cirrhosis from volatolomic analysis of exhaled breath.docx

Liver disease such as cirrhosis is known to cause changes in the composition of volatile organic compounds (VOC) present in patient breath samples. Previous studies have demonstrated the diagnosis of liver cirrhosis from these breath samples, but studies are limited to a handful of discrete, well-characterized compounds. We utilized VOC profiles from breath samples from 46 individuals, 35 with cirrhosis and 11 healthy controls. A deep-neural network was optimized to discriminate between healthy controls and individuals with cirrhosis. A 1D convolutional neural network (CNN) was accurate in predicting which patients had cirrhosis with an AUC of 0.90 (95% CI: 0.75, 0.99). Shapley Additive Explanations characterized the presence of discrete, observable peaks which were implicated in prediction, and the top peaks (based on the average SHAP profiles on the test dataset) were noted. CNNs demonstrate the ability to predict the presence of cirrhosis based on a full volatolomics profile of patient breath samples. SHAP values indicate the presence of discrete, detectable peaks in the VOC signal.</p

Image_1_A deep learning approach for detecting liver cirrhosis from volatolomic analysis of exhaled breath.jpg

Liver disease such as cirrhosis is known to cause changes in the composition of volatile organic compounds (VOC) present in patient breath samples. Previous studies have demonstrated the diagnosis of liver cirrhosis from these breath samples, but studies are limited to a handful of discrete, well-characterized compounds. We utilized VOC profiles from breath samples from 46 individuals, 35 with cirrhosis and 11 healthy controls. A deep-neural network was optimized to discriminate between healthy controls and individuals with cirrhosis. A 1D convolutional neural network (CNN) was accurate in predicting which patients had cirrhosis with an AUC of 0.90 (95% CI: 0.75, 0.99). Shapley Additive Explanations characterized the presence of discrete, observable peaks which were implicated in prediction, and the top peaks (based on the average SHAP profiles on the test dataset) were noted. CNNs demonstrate the ability to predict the presence of cirrhosis based on a full volatolomics profile of patient breath samples. SHAP values indicate the presence of discrete, detectable peaks in the VOC signal.</p

Table_2_A deep learning approach for detecting liver cirrhosis from volatolomic analysis of exhaled breath.docx

Liver disease such as cirrhosis is known to cause changes in the composition of volatile organic compounds (VOC) present in patient breath samples. Previous studies have demonstrated the diagnosis of liver cirrhosis from these breath samples, but studies are limited to a handful of discrete, well-characterized compounds. We utilized VOC profiles from breath samples from 46 individuals, 35 with cirrhosis and 11 healthy controls. A deep-neural network was optimized to discriminate between healthy controls and individuals with cirrhosis. A 1D convolutional neural network (CNN) was accurate in predicting which patients had cirrhosis with an AUC of 0.90 (95% CI: 0.75, 0.99). Shapley Additive Explanations characterized the presence of discrete, observable peaks which were implicated in prediction, and the top peaks (based on the average SHAP profiles on the test dataset) were noted. CNNs demonstrate the ability to predict the presence of cirrhosis based on a full volatolomics profile of patient breath samples. SHAP values indicate the presence of discrete, detectable peaks in the VOC signal.</p

Table_3_A deep learning approach for detecting liver cirrhosis from volatolomic analysis of exhaled breath.docx

Liver disease such as cirrhosis is known to cause changes in the composition of volatile organic compounds (VOC) present in patient breath samples. Previous studies have demonstrated the diagnosis of liver cirrhosis from these breath samples, but studies are limited to a handful of discrete, well-characterized compounds. We utilized VOC profiles from breath samples from 46 individuals, 35 with cirrhosis and 11 healthy controls. A deep-neural network was optimized to discriminate between healthy controls and individuals with cirrhosis. A 1D convolutional neural network (CNN) was accurate in predicting which patients had cirrhosis with an AUC of 0.90 (95% CI: 0.75, 0.99). Shapley Additive Explanations characterized the presence of discrete, observable peaks which were implicated in prediction, and the top peaks (based on the average SHAP profiles on the test dataset) were noted. CNNs demonstrate the ability to predict the presence of cirrhosis based on a full volatolomics profile of patient breath samples. SHAP values indicate the presence of discrete, detectable peaks in the VOC signal.</p