TASK-SPECIFIC VIRTUAL TRAINING FOR IMPROVED PATTERN RECOGNITION-BASED PROSTHESES CONTROL

The emergence of dexterous prostheses presents the potential to significantly improve amputees’ quality of life. The use of intuitive pattern recognition algorithm is among the most promising control strategy for dexterous prostheses, with the demonstration of near perfect classification accuracies in laboratory settings. However, recent literatures show a weak correlation between classification accuracy and usability of the prostheses. External factors such as varying limb positions affect electromyography signals and consequently deteriorate usability of the prostheses; therefore, task-specific user training is proposed to enhance usability of the pattern recognition-based prostheses. Eight able-bodied subjects and one transradial amputee subject participated in the study to validate the efficacy of task-specific virtual training and examine the relationship between the virtual reality and real-world environment performance of prostheses use. Subjects were evaluated in 2 functional tests, Modified Box and Block Test and Reach-Grasp-Release Test, in both virtual and real-world environment, and received five sessions of one-on-one virtual training that lasted for one hour. Subjects were evaluated once again after completing five virtual training sessions and showed a significant improvement in functional tests. The amputee subject, despite the fact that he had been a pattern recognition- based prosthesis wearer for 5 months, also showed improvement upon virtual training, especially in the test that enforced him to use his prosthesis in postures that are outside of his usual range. In addition, no statistically significant difference was observed between the performance in virtual reality and real-world environment, indicating the potential for virtual reality evaluation to be a diagnostic tool to determine individual’s usability of pattern recognition-based myoelectric prostheses. It was shown that high classification accuracy alone does not guarantee proficiency in prostheses control; rather, it only represented the capacity of one’s prostheses control. To effectively prepare amputees for pattern recognition-based myoelectric prostheses control in activities of daily living, task-specific virtual training should be administered prior to prosthesis fitting. For future study, the integration of accurate, stable motion tracking system with head-mounted display is suggested for more immersive experience that enables users to practice proper positioning of the terminal device, an essential skill for object interaction with prostheses

Kidney and Kidney Tumor Segmentation Using Two- stage Convolutional Neural Network

Kidney tumor is typically diagnosed using computed tomography (CT) imaging by investigating geometric features of kidney tumor. For a reliable diagnosis and treatment planning, kidney tumor quantification is necessary. However, manual segmentation by human requires time and expertise. In addition, inter/intra variability of segmentation results can lead to suboptimal decision. In this study, we propose the two-stage segmentation method using 2.5D and 3D convolutional neural network for kidney and kidney tumor delineation. The two stage model was trained with multi-task loss for pixel-wise cross-entropy loss function for segmentation task and mean square error function for regression task. Experimental results confirm that the proposed method effectively segments kidney and kidney tumor

Moving Through Adolescence: Developmental Trajectories of African American and European American Youth. II: Method

<p>Heat map of TLR4-dependent gene expression changes induced by cotreatment with palmitate and mmLDL (A). J774 cells were stimulated with palmitate for 16h and incubated with or without mmLDL and LPS. Profiles of mRNA were determined by RNA-seq analysis (B-top). To validate RNA-seq results, independent real time PCRs were used to assess the expression of <i>Ccr5</i>, <i>Il-6</i>, <i>Csf-3</i>, <i>Il-1β</i>, and β–actin (B-bottom). The combination of palmitate and mmLDL caused an increase of mRNA expression of <i>Il-6</i>, <i>Csf-3</i>, and <i>Il-1β</i> genes (normalized to that of <i>Actb</i>). The real time PCR was conducted with technical duplicates, and the data shown represent three independent replicate experiments. *p <0.05 and **p <0.01 compared to LPS treatment without palmitate or mmLDL.</p

Development and Application of Gene-Specific Markers for Tomato Yellow Leaf Curl Virus Resistance in Both Field and Artificial Infections

Tomato yellow leaf curl virus (TYLCV) is a disease that is damaging to tomato production worldwide. Resistance to TYLCV has been intensively investigated, and single resistance genes such as Ty-1 have been widely deployed in breeding programs. However, resistance-breaking incidences are frequently reported, and achieving durable resistance against TYLCV in the field is important. In this study, gene-specific markers for Ty-2 and ty-5, and closely-linked markers for Ty-4 were developed and applied to distinguish TYLCV resistance in various tomato genotypes. Quantitative infectivity assays using both natural infection in the field and artificial inoculation utilizing infectious TYLCV clones in a growth chamber were optimized and performed to investigate the individual and cumulative levels of resistance. We confirmed that Ty-2 could also be an effective source of resistance for TYLCV control, together with Ty-1. Improvement of resistance as a result of gene-pyramiding was speculated, and breeding lines including both Ty-1 and Ty-2 showed the strongest resistance in both field and artificial infections

Improving Adversarial Robustness via Distillation-Based Purification

Despite the impressive performance of deep neural networks on many different vision tasks, they have been known to be vulnerable to intentionally added noise to input images. To combat these adversarial examples (AEs), improving the adversarial robustness of models has emerged as an important research topic, and research has been conducted in various directions including adversarial training, image denoising, and adversarial purification. Among them, this paper focuses on adversarial purification, which is a kind of pre-processing that removes noise before AEs enter a classification model. The advantage of adversarial purification is that it can improve robustness without affecting the model’s nature, while another defense techniques like adversarial training suffer from a decrease in model accuracy. Our proposed purification framework utilizes a Convolutional Autoencoder as a base model to capture the features of images and their spatial structure.We further aim to improve the adversarial robustness of our purification model by distilling the knowledge from teacher models. To this end, we train two Convolutional Autoencoders (teachers), one with adversarial training and the other with normal training. Then, through ensemble knowledge distillation, we transfer the ability of denoising and restoring of original images to the student model (purification model). Our extensive experiments confirm that our student model achieves high purification performance(i.e., how accurately a pre-trained classification model classifies purified images). The ablation study confirms the positive effect of our idea of ensemble knowledge distillation from two teachers on performance.TRU

Precise Filtration of Chronic Myeloid Leukemia Cells by an Ultrathin Microporous Membrane with Backflushing to Minimize Fouling

A cell filtration platform that affords accurate size separation and minimizes fouling was developed. The platform features an ultra-thin porous membrane (UTM) filter, a pumping head filtration with backflush (PHF), and cell size measurement (CSM) software. The UTM chip is an ultrathin free-standing membrane with a large window area of 0.68 mm2, a pore diameter of 5 to 9 μm, and a thickness of less than 0.9 μm. The PHF prevents filter fouling. The CSM software analyzes the size distributions of the supernatants and subnatants of isolated cells and presents the data visually. The D99 particle size of cells of the chronic myeloid leukemia (CML) line K562 decreased from 22.2 to 17.5 μm after passage through a 5-μm filter. K562 cells could be separated by careful selection of the pore size; the recovery rate attained 91.3%. The method was compared to conventional blocking models by evaluating the mean square errors (MSEs) between the measured and calculated filtering volumes. The filtering rate was fitted by a linear regression model with a significance that exceeded 0.99 based on the R2 value. The platform can be used to separate various soft biomaterials and afford excellent stability during filtration

Cooptimization of Adhesion and Power Conversion Efficiency of Organic Solar Cells by Controlling Surface Energy of Buffer Layers

Here, we demonstrate the cooptimization of the interfacial fracture energy and power conversion efficiency (PCE) of poly­[<i>N</i>-9′-heptadecanyl-2,7-carbazole-<i>alt</i>-5,5-(4′,7′-di-2-thienyl-2′,1′,3′-benzothiadiazole)] (PCDTBT)-based organic solar cells (OSCs) by surface treatments of the buffer layer. The investigated surface treatments of the buffer layer simultaneously changed the crack path and interfacial fracture energy of OSCs under mechanical stress and the work function of the buffer layer. To investigate the effects of surface treatments, the work of adhesion values were calculated and matched with the experimental results based on the Owens–Wendt model. Subsequently, we fabricated OSCs on surface-treated buffer layers. In particular, ZnO layers treated with poly­[(9,9-bis­(3′-(<i>N</i>,<i>N</i>-dimethyl­amino)­propyl)-2,7-fluorene)-<i>alt</i>-2,7-(9,9-dioctylfluorene)] (PFN) simultaneously satisfied the high mechanical reliability and PCE of OSCs by achieving high work of adhesion and optimized work function

Large area multi-stacked lithium-ion batteries for flexible and rollable applications

The demand for lithium ion batteries (LIBs) in various flexible mobile electronic devices is continuously increasing. With this in mind, a vast number of smart approaches, such as implementation of conductive nanomaterials onto paper and textiles, have been recently demonstrated. Most of them were, however, limited to the single-cell level. In the present study, large area flexible battery modules were developed in an attempt to expand the knowledge and design accumulated from the single-cell level approaches to larger-scale applications. A multi-stacked configuration was adopted to produce a high areal energy density in each single-cell. Meanwhile textile-based electrodes on both sides grant mechanical stability, even on the module level, by efficiently releasing the stress generated during aggressive folding and rolling motions. Moreover, the connection between and stacking of the single-cells allow the wide tuning of the overall voltage and capacity of the module. This battery design should be immediately applicable to a broad range of outdoor, building, and military items.

In vitro protocol for validating interface pressure sensors for therapeutic compression garments: Importance of sphygmomanometer placement and initial cuff diameter

An optimal protocol is needed to validate the performance of future interface pressure sensors for compression garments when using a sphygmomanometer. PicoPress® was used on a rigid plastic cylinder (r=4 cm). An FDA-cleared aneroid sphygmomanometer was used to apply pressures from 10-60 mmHg with a diameter of 8 cm or 12 cm placed either beneath the sphygmomanometer’s airbag or fabric cuff. A two-tail t-test was performed (P<0.05 for significance) for all applied pressures. PicoPress® outputs vary with sensor placement (airbag vs fabric cuff) and the initial cuff diameter. Sensor placement overlying the sphygmomanometer’s fabric cuff compared to the airbag led to significantly higher pressures (37%-135%) depending on the cuff diameter size. These differences were nearly all statistically significant (P<0.05). Validation of new interface pressure sensors deploying a sphygmomanometer for calibration should specify the location of sensor placement location and initial diameter with a preference for placement under the airbag