Impaired Sacculocollic Reflex in Lateral Medullary Infarction

Objective: The aim of this study was to determine saccular dysfunction by measuring cervical vestibular-evoked myogenic potentials (cVEMP) and to correlate abnormality of cVEMP with results of other vestibular function tests in lateral medullary infarction (LMI). Methods: We recorded cVEMP in 21 patients with LMI documented on MRI. cVEMP was induced by a short tone burst and was recorded in contracting sternocleidomastoid muscle while patients turned their heads forcefully to the contralateral side against resistance. Patients also underwent video-oculographic recording of spontaneous, gaze-evoked and head shaking nystagmus (HSN), evaluation of ocular tilt reaction (OTR), measurement of the subjective visual vertical (SVV) tilt, bithermal caloric tests, and audiometry. Results: Nine patients (43%) showed abnormal cVEMP, unilateral in seven and bilateral in two. The cVEMP abnormalities included decreased p13–n23 amplitude in four, delayed p13/n23 responses in five, and both decreased and delayed responses in two. The abnormal cVEMP was ipsilesional in five, contralesional in two, and bilateral in two. The prevalence of OTR/SVV tilt, spontaneous nystagmus, and HSN did not differ between the patients with normal and abnormal cVEMP. Conclusion: cVEMP was abnormal in approximately half of the patients with LMI. The abnormal cVEMP indicates damage to the descending sacculocollic reflex pathway or disruption of commissural modulation between the vestibular nuclei

A zinc finger protein array for the visual detection of specific DNA sequences for diagnostic applications.

The visual detection of specific double-stranded DNA sequences possesses great potential for the development of diagnostics. Zinc finger domains provide a powerful scaffold for creating custom DNA-binding proteins that recognize specific DNA sequences. We previously demonstrated sequence-enabled reassembly of TEM-1 β-lactamase (SEER-LAC), a system consisting of two inactive fragments of β-lactamase each linked to engineered zinc finger proteins (ZFPs). Here the SEER-LAC system was applied to develop ZFP arrays that function as simple devices to identify bacterial double-stranded DNA sequences. The ZFP arrays provided a quantitative assay with a detection limit of 50 fmol of target DNA. The method could distinguish target DNA from non-target DNA within 5 min. The ZFP arrays provided sufficient sensitivity and high specificity to recognize specific DNA sequences. These results suggest that ZFP arrays have the potential to be developed into a simple and rapid point-of-care (POC) diagnostic for the multiplexed detection of pathogens

Application of Depth-Averaged 2D Numerical Model for Evaluation of the Vegetal Resistance in a Natural River

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Accelerating Large-Scale Graph-based Nearest Neighbor Search on a Computational Storage Platform

K-nearest neighbor search is one of the fundamental tasks in various applications and the hierarchical navigable small world (HNSW) has recently drawn attention in large-scale cloud services, as it easily scales up the database while offering fast search. On the other hand, a computational storage device (CSD) that combines programmable logic and storage modules on a single board becomes popular to address the data bandwidth bottleneck of modern computing systems. In this paper, we propose a computational storage platform that can accelerate a large-scale graph-based nearest neighbor search algorithm based on SmartSSD CSD. To this end, we modify the algorithm more amenable on the hardware and implement two types of accelerators using HLS- and RTL-based methodology with various optimization methods. In addition, we scale up the proposed platform to have 4 SmartSSDs and apply graph parallelism to boost the system performance further. As a result, the proposed computational storage platform achieves 75.59 query per second throughput for the SIFT1B dataset at 258.66W power dissipation, which is 12.83x and 17.91x faster and 10.43x and 24.33x more energy efficient than the conventional CPU-based and GPU-based server platform, respectively. With multi-terabyte storage and custom acceleration capability, we believe that the proposed computational storage platform is a promising solution for cost-sensitive cloud datacenters.Comment: Extension of FCCM 20201 and Accepted in Transaction on Computer