Targeting Borrelia burgdorferi HtpG with a berserker molecule, a strategy for anti-microbial development.

Conventional antimicrobial discovery relies on targeting essential enzymes in pathogenic organisms, contributing to a paucity of new antibiotics to address resistant strains. Here, by targeting a non-essential enzyme, Borrelia burgdorferi HtpG, to deliver lethal payloads, we expand what can be considered druggable within any pathogen. We synthesized HS-291, an HtpG inhibitor tethered to the photoactive toxin verteporfin.

Cyphenothrin Flea and Tick Squeeze-On for Dogs: Evaluation of Potential Health Risks Based on the Results of Observational Biological Monitoring.

An observational biomonitoring study was conducted involving adults and children in households that purchased and applied a cyphenothrin-containing spot-on product for dogs as part of their normal pet care practices. The 3- to 6-yr-old children had greater exposure than the adult applicators in the same house, 3.8 and 0.

Sky island diversification meets the multispecies coalescent - divergence in the spruce-fir moss spider (Microhexura montivaga, Araneae, Mygalomorphae) on the highest peaks of southern Appalachia.

Microhexura montivaga is a miniature tarantula-like spider endemic to the highest peaks of the southern Appalachian mountains and is known only from six allopatric, highly disjunct montane populations. Because of severe declines in spruce-fir forest in the late 20th century, M. montivaga was formally listed as a US federally endangered species in 1995.

A flexible algorithm framework for closed-loop neuromodulation research systems.

Modulation of neural activity through electrical stimulation of tissue is an effective therapy for neurological diseases such as Parkinson's disease and essential tremor. Researchers are exploring improving therapy through adjustment of stimulation parameters based upon sensed data. This requires classifiers to extract features and estimate patient state.

Design and validation of a fully implantable, chronic, closed-loop neuromodulation device with concurrent sensing and stimulation.

Chronically implantable, closed-loop neuromodulation devices with concurrent sensing and stimulation hold promise for better understanding the nervous system and improving therapies for neurological disease. Concurrent sensing and stimulation are needed to maximize usable neural data, minimize time delays for closed-loop actuation, and investigate the instantaneous response to stimulation. Current systems lack concurrent sensing and stimulation primarily because of stimulation interference to neural signals of interest.

Emerging technology for advancing the treatment of epilepsy using a dynamic control framework.

We briefly describe a dynamic control system framework for neuromodulation for epilepsy, with an emphasis on its practical challenges and the preliminary validation of key prototype technologies in a chronic animal model. The current state of neuromodulation can be viewed as a classical dynamic control framework such that the nervous system is the classical "plant", the neural stimulator is the controller/actuator, clinical observation, patient diaries and/or measured bio-markers are the sensor, and clinical judgment applied to these sensor inputs forms the state estimator. Technology can potentially address two main factors contributing to the performance limitations of existing systems: "observability," the ability to observe the state of the system from output measurements, and "controllability," the ability to drive the system to a desired state.

An implantable bi-directional brain-machine interface system for chronic neuroprosthesis research.

An implantable bi-directional brain-machine interface (BMI) prototype is presented. With sensing, algorithm, wireless telemetry, and stimulation therapy capabilities, the system is designed for chronic studies exploring closed-loop and diagnostic opportunities for neuroprosthetics. In particular, we hope to enable fundamental chronic research into the physiology of neurological disorders, define key electrical biomarkers related to disease, and apply this learning to patient-specific algorithms for therapeutic stimulation and diagnostics.

A micropower support vector machine based seizure detection architecture for embedded medical devices.

Implantable neurostimulators for the treatment of epilepsy that are capable of sensing seizures can enable novel therapeutic applications. However, detecting seizures is challenging due to significant intracranial EEG signal variability across patients. In this paper, we illustrate how a machine-learning based, patient-specific seizure detector provides better performance and lower power consumption than a patient non-specific detector using the same seizure library.