総会抄録

<p>Antibody responses and protection of offspring when mothers were immunized via the IN route and their offspring via the IN route<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157041#t004fn001" target="_blank">^a</a>.</p

Implementation and Reconfiguration of Robot Operating System on Human Follower Transporter Robot

Robotic Operation System (ROS) is an im- portant platform to develop robot applications. One area of applications is for development of a Human Follower Transporter Robot (HFTR), which can be considered as a custom mobile robot utilizing differential driver steering method and equipped with Kinect sensor. This study discusses the development of the robot navigation system by implementing Simultaneous Localization and Mapping (SLAM)

Implementation of machine-learning classification in remote sensing: an applied review

<p>Machine learning offers the potential for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle data of high dimensionality and to map classes with very complex characteristics. Nevertheless, implementing a machine-learning classification is not straightforward, and the literature provides conflicting advice regarding many key issues. This article therefore provides an overview of machine learning from an applied perspective. We focus on the relatively mature methods of support vector machines, single decision trees (DTs), Random Forests, boosted DTs, artificial neural networks, and <i>k</i>-nearest neighbours (<i>k</i>-NN). Issues considered include the choice of algorithm, training data requirements, user-defined parameter selection and optimization, feature space impacts and reduction, and computational costs. We illustrate these issues through applying machine-learning classification to two publically available remotely sensed data sets.</p

Sequential sampling of visual objects during sustained attention

<div><p>In a crowded visual scene, attention must be distributed efficiently and flexibly over time and space to accommodate different contexts. It is well established that selective attention enhances the corresponding neural responses, presumably implying that attention would persistently dwell on the task-relevant item. Meanwhile, recent studies, mostly in divided attentional contexts, suggest that attention does not remain stationary but samples objects alternately over time, suggesting a rhythmic view of attention. However, it remains unknown whether the dynamic mechanism essentially mediates attentional processes at a general level. Importantly, there is also a complete lack of direct neural evidence reflecting whether and how the brain rhythmically samples multiple visual objects during stimulus processing. To address these issues, in this study, we employed electroencephalography (EEG) and a temporal response function (TRF) approach, which can dissociate responses that exclusively represent a single object from the overall neuronal activity, to examine the spatiotemporal characteristics of attention in various attentional contexts. First, attention, which is characterized by inhibitory alpha-band (approximately 10 Hz) activity in TRFs, switches between attended and unattended objects every approximately 200 ms, suggesting a sequential sampling even when attention is required to mostly stay on the attended object. Second, the attentional spatiotemporal pattern is modulated by the task context, such that alpha-mediated switching becomes increasingly prominent as the task requires a more uniform distribution of attention. Finally, the switching pattern correlates with attentional behavioral performance. Our work provides direct neural evidence supporting a generally central role of temporal organization mechanism in attention, such that multiple objects are sequentially sorted according to their priority in attentional contexts. The results suggest that selective attention, in addition to the classically posited attentional “focus,” involves a dynamic mechanism for monitoring all objects outside of the focus. Our findings also suggest that attention implements a space (object)-to-time transformation by acting as a series of concatenating attentional chunks that operate on 1 object at a time.</p></div

Additional file 1 of Tumor Area Positivity (TAP) score of programmed death-ligand 1 (PD-L1): a novel visual estimation method for combined tumor cell and immune cell scoring

Additional file 1: Supplementary Fig. 1. Neutrophils with weak cytoplasmic staining

Model Averaging for Prediction With Fragmentary Data

<p>One main challenge for statistical prediction with data from multiple sources is that not all the associated covariate data are available for many sampled subjects. Consequently, we need new statistical methodology to handle this type of “fragmentary data” that has become more and more popular in recent years. In this article, we propose a novel method based on the frequentist model averaging that fits some candidate models using all available covariate data. The weights in model averaging are selected by delete-one cross-validation based on the data from complete cases. The optimality of the selected weights is rigorously proved under some conditions. The finite sample performance of the proposed method is confirmed by simulation studies. An example for personal income prediction based on real data from a leading e-community of wealth management in China is also presented for illustration.</p

Experiment 3 (50% cue validity) and Experiment 4 (100% cue validity, multiple object tracking [MOT]).

<p>(A) In Experiment 3 (50% cue validity), subjects fixated on a central point and covertly attended to 2 discs presented in the left and right visual fields for target detection. Subjects were instructed to simultaneously pay attention to both discs and were informed that the target would be equally likely to appear within the discs and that the initial cue would not predict the target location. After a noninformative red circle cue (cue validity: 50%) appeared around 1 of the 2 discs, the luminance of the 2 discs was independently and randomly modulated for 5 s (top: cued visual sequence; bottom: uncued visual sequence), during which time subjects were instructed to monitor a randomly occurring target. (B) Grand average (<i>N</i> = 16) time–frequency plots for cued–uncued TRF power difference in Experiment 3 (cue validity: 50%). Note the prolonged alpha-band switching (blue–red pattern), suggesting that attentional shifting is enhanced when attention is evenly distributed across the 2 spatial locations (50% cue validity). (C) In Experiment 4 (MOT experiment), a red circle cue at the beginning of each trial indicated which disc the subjects should covertly attend to for subsequent target detection. The 2 disks were then moved randomly and smoothly across the screen for 5 s, during which time the subjects were instructed to detect the appearance of a target within the cued disc. Here, the cue validity was 100%, which means that the target only appeared in the cued disk, similar to Experiment 1. (D) Experiment 4 results. Top: Grand average (<i>N</i> = 11) time–frequency plots for att–unatt TRF power difference. Bottom: grand average (blue lines, <i>N</i> = 11, mean ± SEM) time course for att–unatt TRF power within the alpha band (8–12 Hz). Red horizontal lines at the bottom indicate points showing significant power differences in the alpha band between att and unatt (<i>p</i> < 0.05, one-tailed, false discovery rate [FDR] corrected). Note the initial alpha inhibition followed by an alpha rebound trend, similar to Experiment 1 (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001903#pbio.2001903.g002" target="_blank">Fig 2C</a>). The data are provided in the Supporting Information (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001903#pbio.2001903.s008" target="_blank">S3 Data</a>).</p

Alpha inhibition-rebound effects relate to attentional behavior.

<p>Correlations between behavioral (behavioral index, BI) and alpha switching (neuronal index, NI) measures across participants (Experiment 2: <i>n</i> = 20, disc; Experiment 3: <i>n</i> = 16, circle). BI: Contrast_unatt−Contrast_att; NI: alpha_reb−alpha_inh. The negative correlations indicate that, as the unattended object obtained more attentional behavioral benefit (smaller BI), the alpha rebound effects became stronger (larger NI). The data are provided in the Supporting Information (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001903#pbio.2001903.s009" target="_blank">S4 Data</a>).</p

Experimental paradigm for Experiments 1 and 2 and illustration of the temporal response function (TRF) approach.

<p>(A) A central arrow cue appeared at the beginning of each trial to indicate which side (left or right) the subject should covertly attend to for subsequent target detection. Two discs were then presented simultaneously in the left and right visual fields for 5 seconds, during which time subjects were instructed to detect the appearance of a target square within the discs by pressing 1 of 2 response keys at the end of each trial. The target occurred at a random time so that subjects had to maintain their attention on the discs. Across trials, the contrast of the target square relative to the momentary disc luminance was adjusted to maintain 80% detection performance. For 100% cue validity (Experiment 1), the target only appeared in the cued disc; for 75% cue validity (Experiment 2), the target appeared in the cued disc 75% of the time and in the uncued disc 25% of the time. (B) The luminance of the 2 discs was independently and randomly modulated throughout the trial, resulting in 2 independent 5 s random temporal sequences (example sequences are shown; top: attended visual stimulus luminance sequence, bottom: unattended visual stimulus luminance sequence). At the same time, electroencephalography (EEG) responses were recorded. (C) The TRF approach was used to calculate the impulse brain response for the attended (top, att) and unattended (bottom, unatt) visual sequences. TRF characterizes the brain response to a unit increase in luminance in a stimulus sequence, with the time axis representing the latency after each transient unit. Note that the att TRF and unatt TRF were derived from the same EEG responses but were separated based on the corresponding stimulus luminance sequence (see panel B).</p

Results for Experiment 2 (75% cue validity) and summary of alpha inhibition and alpha rebound.

<p>(A) Temporal response function (TRF) results for Experiment 2 (75% cue validity). Left: grand average (<i>N</i> = 20) time–frequency plots for att–unatt TRF power difference. Right: grand average (blue lines, <i>N</i> = 20, mean ± SEM) time course for att–unatt TRF power within the alpha band (8–12 Hz). Red horizontal lines at the bottom indicate points showing a significant power difference in the alpha band between att and unatt (<i>p</i> < 0.05, two-tailed, false discovery rate [FDR] corrected). Note the emergence of alpha-band rebound right after the alpha-band inhibition, suggesting attentional switching. (B) Grand average att–unatt TRF alpha-band power averaged over an early (alpha inhibition, 0.07–0.11 s, red dotted box in Fig 3A) and a subsequent late (alpha rebound, 0.24–0.28 s, black dotted box in Fig 3A) time range for each experiment. Experiment 1: <i>N</i> = 18; Experiment 2: <i>N</i> = 20; Experiment 3: <i>N</i> = 16; Experiment 4: <i>N</i> = 11. * <i>p</i> < 0.05, ** <i>p</i> < 0.01, <i>t</i> test. MOT: multiple object tracking. The data are provided in the Supporting Information (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001903#pbio.2001903.s007" target="_blank">S2 Data</a>).</p