Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease Treatment.

Deep brain stimulation (DBS) has shown great promise toward treating motor symptoms caused by Parkinson's disease (PD), by delivering electrical pulses to the Basal Ganglia (BG) region of the brain. However, DBS devices approved by the U.S. Food and Drug Administration (FDA) can only deliver continuous DBS (cDBS) stimuli at a fixed amplitude; this energy inefficient operation reduces battery lifetime of the device, cannot adapt treatment dynamically for activity, and may cause significant side-effects (e.g., gait impairment). In this work, we introduce an offline reinforcement learning (RL) framework, allowing the use of past clinical data to train an RL policy to adjust the stimulation amplitude in real time, with the goal of reducing energy use while maintaining the same level of treatment (i.e., control) efficacy as cDBS. Moreover, clinical protocols require the safety and performance of such RL controllers to be demonstrated ahead of deployments in patients. Thus, we also introduce an offline policy evaluation (OPE) method to estimate the performance of RL policies using historical data, before deploying them on patients. We evaluated our framework on four PD patients equipped with the RC+S DBS system, employing the RL controllers during monthly clinical visits, with the overall control efficacy evaluated by severity of symptoms (i.e., bradykinesia and tremor), changes in PD biomakers (i.e., local field potentials), and patient ratings. The results from clinical experiments show that our RL-based controller maintains the same level of control efficacy as cDBS, but with significantly reduced stimulation energy. Further, the OPE method is shown effective in accurately estimating and ranking the expected returns of RL controllers.

A histological study of atherosclerotic characteristics in age-related macular degeneration.

This study investigated the pathogenesis of age-related macular degeneration (AMD) using histological methods that are commonly used for atherosclerotic vascular disease (ASVD). 1 normal, 3 early dry AMD, and 1 late dry AMD eyes were obtained from the Lions Eye Bank of Oregon and systematically dissected. They were stained with hematoxylin and eosin, Oil red O, Masson, Elastica van Gieson, Alizarin red, and Prussian blue. Additionally, the normal and late dry AMD eyes were immunostained for a-smooth muscle actin, CD45, and CD68 with Nile red and DAPI. Correlations were found between severity of AMD and lipid accumulation in the deep sclera (+), numbers of drusen between the Bruch's membrane and retinal pigment epithelium (RPE) (+), amount of collagen in the deep sclera (+), and amount of elastin in the deep sclera (-) (P < 0.1). Geographic atrophy, RPE detachment, and abnormal capillary shape and distribution in the choriocapillaris were observed in the fovea of late AMD. There were no stenosis, plaque, hemorrhage, and calcification. Additionally, late AMD tended to have higher smooth muscle thicknesses of the choroidal vascular walls, lower numbers of T lymphocytes in the choroid, and higher numbers of macrophages near the RPE and in the choroid relative to normal (P < 0.1). Macrophages-derived foam cells were detected near the Bruch's membrane in late AMD. Therefore, the present study showed many histological characteristics of ASVD in AMD, which suggests an association between them; however, there were also some histological characteristics of ASVD that were not found in AMD, which indicates that there exist pathogenic differences between them. The results generally support the vascular model of AMD, but some details still need clarification.

[Characterization of ibeB gene of meningitic Escherichia coli strains in calves from Xinjiang].

Objective: To understand the molecular biology information of ibeB gene of meningitic Escherichia coli isolates in calves. Methods: The strain used was isolated from the brain and liver tissue of calves died from Meningitis. It was identified to be an O161-K99-STa pathogenic Escherichia coli strain and named as bovine-EN and bovine-EG. Based on the sequence of ibeB gene of meningitic Escherichia coli K1 RS218 strain in GenBank, a pair of primers was designed and the ibeB gene was cloned from isolates by PCR. Part molecular biology information of ibeB among different strains was compared. Results: The sequence length of isolates ibeB gene was 1500 bp, containing a 1371 bp open reading frame (ORF) encoding 457 amino acids. Bioinformatics analysis showed that the nucleotide and amino acid homology of ibeB gene of bovine-EN strain shared 90.5% and 96.9% identity with Escherichia coli K1 RS218 ibeB gene, respectively, while bovine-EG strain shared 99.4% and 100.0% identity with Escherichia coli K12 respectively. The ibeB gene of bovine-E strains encoded water-soluble protein whose molecular weight was 50.26 kDa and isoelectric point was 6.05. This protein contained a signal peptide A but no transmembrane domain. Subcellular localization of ibeB belonged to the secreted protein, which secretory signal path site (SP) proportion was 0.939. Conclusion: The ibeB gene was cloned from meningitic E. coli isolates and had higher homology and similar biological characteristics with meningitis E. coli K1 RS218ibeB, which belongs to extraintestinal pathogenic Escherichia coli.

Error tolerant NMR backbone resonance assignment and automated structure generation.

Error tolerant backbone resonance assignment is the cornerstone of the NMR structure determination process. Although a variety of assignment approaches have been developed, none works sufficiently well on noisy fully automatically picked peaks to enable the subsequent automatic structure determination steps. We have designed an integer linear programming (ILP) based assignment system (IPASS) that has enabled fully automatic protein structure determination for four test proteins. IPASS employs probabilistic spin system typing based on chemical shifts and secondary structure predictions. Furthermore, IPASS extracts connectivity information from the inter-residue information and the (automatically picked) (15)N-edited NOESY peaks which are then used to fix reliable fragments. When applied to automatically picked peaks for real proteins, IPASS achieves an average precision and recall of 82% and 63%, respectively. In contrast, the next best method, MARS, achieves an average precision and recall of 77% and 36%, respectively. The assignments generated by IPASS are then fed into our protein structure calculation system, FALCON-NMR, to determine the 3D structures without human intervention. The final models have backbone RMSDs of 1.25Å, 0.88Å, 1.49Å, and 0.67Å to the reference native structures for proteins TM1112, CASKIN, VRAR, and HACS1, respectively. The web server is publicly available at http://monod.uwaterloo.ca/nmr/ipass.