Abstract: Methods of assaying a breath sample from a subject for the presence of one or more toxins or toxin associated compounds are provided. In some embodiments, the one or more toxins or toxin associated compounds includes trichloroethylene (TCE) and/or one or more metabolites thereof. Embodiments of the methods further include providing a toxin exposure assessment that can be used to determine if the subject has been exposed to a toxin and to monitor changes in toxin exposure over time in the subject. In certain embodiments, a machine learning model may be used to determine if a subject has been exposed to one or more toxins.

Abstract: The present invention is a horse training device that is secured to and removably worn on a cannon bone CB of each front leg of a horse to train and prevent the horse from pawing includes an elongated planar base, a hook and loop fastener to removably secure the device to the horse, a pair of stimulus prongs that is disposed on the elongated planar base that are in communication with a pendulum switch disposed within the elongated planar base that deliver an electric shock to the horse and a remote control that includes a power indicator light, a power activation button and a pair of power settings that include a first sliding bar, a second sliding bar and a plurality of predetermined measuring indicia that are relative measurement indicia or quantitative measurement indicia.

Abstract: Given a log of previous web-surfer behavior listing the order in which each surfer downloaded specific items at the web site, and given a meaningful classification of those same items, future surfer behavior is predicted by the present invention. The algorithm utilizes a quantitative model relating items downloaded prior to some specified event to items downloaded after that same event. When the model is applied to a new surfer's session prior to an analogous event, the present invention predicts the likely behavior of the surfer subsequent to that event. The predicted behavior is then further analyzed to derive a quantitative value for the utility of the expected behavior. By randomly selecting sample sessions from a web log, multiple models of surfer behavior can be generated. The multiple models can then be applied to a new surfer's session to produce a predicted behavior/utility distribution and thus a confidence interval for the predicted behavior/utility.