Drug Delivery: Enabling Technology for Drug Discovery and Development. iPRECIO® Micro Infusion Pump: Programmable, Refillable, and Implantable

Successful drug delivery using implantable pumps may be found in over 12,500 published articles. Their versatility in delivering continuous infusion, intermittent or complex infusion protocols acutely or chronically has made them ubiquitous in drug discovery and basic research. The recent availability of iPRECIO®, a programmable, refillable, and implantable infusion pump has made it possible to carry out quantitative pharmacology (PKPD) in single animals. When combined with specialized catheters, specific administration sites have been selected. When combined with radiotelemetry, the physiologic gold standard, more sensitive and powerful means of detecting drug induced therapeutic, and/or adverse effects has been possible. Numerous application examples are cited from iPRECIO® use in Japan, United States, and Europe with iPRECIO® as an enabling drug delivery device where the refillable and programmability functionality were key benefits. The ability to start/stop drug delivery and to have control periods prior dosing made it possible to have equivalent effects at a much lower dose than previously studied. Five different iPRECIO® applications are described in detail with references to the original work where the implantable, refillable, and programmable benefits are demonstrated with their different end-points

Deep convolutional neural network classifier for travel patterns using binary sensors

The early detection of dementia is crucial in independent life style of elderly people. Main intention of this study is to propose device-free non-privacy invasive Deep Convolutional Neural Network classifier (DCNN) for Martino-Saltzman's (MS) travel patterns of elderly people living alone using open dataset collected by binary (passive infrared) sensors. Travel patterns are classified as direct, pacing, lapping, or random according to MS model. MS travel pattern is highly related with person's cognitive state, thus can be used to detect early stage of dementia. The dataset was collected by monitoring a cognitively normal elderly resident by wireless passive infrared sensors for 21 months. First, over 70000 travel episodes are extracted from the dataset and classified by MS travel pattern classifier algorithm for the ground truth. Later, 12000 episodes (3000 for each pattern) were randomly selected from the total episodes to compose training and testing dataset. Finally, DCNN performance was compared with three other classical machine-learning classifiers. The Random Forest and DCNN yielded the best classification accuracies of 94.48% and 97.84%, respectively. Thus, the proposed DCNN classifier can be used to infer dementia through travel pattern matching

Odorant Inhibition in Mosquito Olfaction.

How chemical signals are integrated at the peripheral sensory system of insects is still an enigma. Here we show that when coexpressed with Orco in Xenopus oocytes, an odorant receptor from the southern house mosquito, CquiOR32, generated inward (regular) currents when challenged with cyclohexanone and methyl salicylate, whereas eucalyptol and fenchone elicited inhibitory (upward) currents. Responses of CquiOR32-CquiOrco-expressing oocytes to odorants were reduced in a dose-dependent fashion by coapplication of inhibitors. This intrareceptor inhibition was also manifested in vivo in fruit flies expressing the mosquito receptor CquiOR32, as well in neurons on the antennae of the southern house mosquito. Likewise, an orthologue from the yellow fever mosquito, AaegOR71, showed intrareceptor inhibition in the Xenopus oocyte recording system and corresponding inhibition in antennal neurons. Inhibition was also manifested in mosquito behavior. Blood-seeking females were repelled by methyl salicylate, but repellence was significantly reduced when methyl salicylate was coapplied with eucalyptol

2D Barcode and Augmented Reality Supported English Learning System

This study aims to construct a 2D barcode and handheld augmented reality supported learning system called HELLO (Handheld English Language Learning Organization), to improve students ’ English level. The HELLO integrates the 2D barcodes, the Internet, augmented reality, mobile computing and database technologies. The proposed system consists of two subsystems: an English learning management system and a mobile learning tools system. A four-week pilot study and questionnaire survey were conducted in college to evaluate effects of proposed learning system and student learning attitudes. Furthermore, the evaluation results indicate that 2D barcodes and augmented reality technology are useful for English learning. 1

Detection of genetic and epigenetic DNA markers in urine for the early detection of primary and recurrent hepatocellular carcinoma

Poster presented at American Association of the Study of Liver Diseases (AASLD) meeting in San Francisco California. Objective: Develop a urine test using a panel of select genetic and epigenetic markers for the early detection of primary and recurrent HCC. Introduction: Hepatocellular carcinoma (HCC) or liver cancer is an aggressive disease and one of the fastest growing cancers by incidence in the United States. Early detection is the key for effective treatment of HCC as the 5-year survival rate is 26% in early stage HCC as compared to only 2% when found after spreading to distant organs. The current marker, alpha-feto protein (AFP) and its fucosylated glycoform, L3, are of limited value with only 40-60% sensitivity.https://jdc.jefferson.edu/gastrohepposters/1000/thumbnail.jp

MODELING OF A DIFFUSION CONTROLLING OXIDATION PROCESS WITH SCALE REMOVAL IN OXYGEN-CONTAINING LIQUID FLOW

ABSTRACT A diffusion controlling oxidation model, considering scale removal, is developed in an oxygen-containing liquid flow environment. Scale removal is implemented and the effect of the scale removal rate on the formation mechanism of the duplex oxide layer structure is analyzed in the model. The volume expansion effect caused by density difference is coupled with the weight gain during oxidation. A coordinate transform technique is employed to obtain the diffusion equations with advection term. The governing equations are non-dimensionalized and analogized with the Stefan problem and solved numerically by the finite difference method. The non-dimensional parameters are studied and the model is extended to an oxide growth model with duplex layer structure and noble elements. The model is benchmarked with previous results, and good agreement is obtained

Analysis of Resistance to Clarithromycin and Virulence Markers in Helicobacter pylori Clinical Isolates from Eastern Taiwan

AbstractObjectiveLittle information is available concerning the relationships between clarithromycin resistance and virulence marker genes (iceA, cagA and vacA) in Helicobacter pylori isolated in Taiwan. The aim of this study was to evaluate the possible association between clarithromycin resistance and genotypes of the virulence markers on clarithromycin-resistant H. pylori isolates obtained in eastern TaiwanMaterials and MethodsThe genotypes of the virulence marker genes (iceA, cagA and vacA) were analyzed by PCR, and the 23S rDNA region from 18 clarithromycin-resistant clinical isolates of H. pylori was amplified by PCR and sequenced.ResultsPoint mutations were found to occur in all isolates. Two isolates had A2143G, six had T2182C, one had C2227T, six had A2143G plus T2182C, and three had heterozygous alleles. The latter included a wild-type allele (A2143) plus (i) an A2143G, (ii) an A2143G plus an A2223G, and (iii) an A2143G plus a T2182C. The prevalence of the marker genes cagA, iceA1, iceA2, and both iceA1 and iceA2, in the isolates was 95.5%, 66.9%, 7.5%, and 25.6%, respectively. The vacAs1 allele was detected in all isolates, whereas the m1 and m2 alleles were found in 44.4% and 55.6% of the isolates, respectivelyConclusionThere were no significant associations between clarithromycin resistance and the presence of the cagA gene, vacA allele mosaicism, and the iceA genotypes. The most notable finding of our study was that the C2227T single mutation in 23S rDNA could also be related to the high clarithromycin minimal inhibitory concentrations in clinical isolates from eastern Taiwan

Traffic-Aware Multi-Camera Tracking of Vehicles Based on ReID and Camera Link Model

Multi-target multi-camera tracking (MTMCT), i.e., tracking multiple targets across multiple cameras, is a crucial technique for smart city applications. In this paper, we propose an effective and reliable MTMCT framework for vehicles, which consists of a traffic-aware single camera tracking (TSCT) algorithm, a trajectory-based camera link model (CLM) for vehicle re-identification (ReID), and a hierarchical clustering algorithm to obtain the cross camera vehicle trajectories. First, the TSCT, which jointly considers vehicle appearance, geometric features, and some common traffic scenarios, is proposed to track the vehicles in each camera separately. Second, the trajectory-based CLM is adopted to facilitate the relationship between each pair of adjacently connected cameras and add spatio-temporal constraints for the subsequent vehicle ReID with temporal attention. Third, the hierarchical clustering algorithm is used to merge the vehicle trajectories among all the cameras to obtain the final MTMCT results. Our proposed MTMCT is evaluated on the CityFlow dataset and achieves a new state-of-the-art performance with IDF1 of 74.93%.Comment: Accepted by ACM International Conference on Multimedia 202