ABSTRACT Electroencephalogram (EEG)-based mobile system are getting mainstream although the true potential of EEG signals are yet to be discovered. With the help of a specific control paradigm, the success rate of a mobile system to reach to a certain location could be increased. To further extend the existed control paradigm of EEG-Based mobile robotic system, this paper demonstrated the real time target selection of a wireless mobile robot using only human mind. A unique protocol was developed to mimic a scanning process while at the same time allowing subject to make a selection. Our system utilized only single EEG channel with no subject training. We statistically verify that it is feasible to select a target by manipulating only alpha content of EEG. We also show that it is hard to achieve a stable high performance of synchronous EEG-Based BCI application in one trial with a single frequency band. However, we found that the BCI’s performance in term of sensitivity is getting more stable with increase in trial.
We conducted a series of experiments to determine whether negative priming is used in the process of target selection for a saccadic eye movement. The key questions addressed the circumstances in which the negative priming of an object takes place, and the distinction between spatial and object-based effects. Experiment 1 revealed that after fixating a target (cricket ball) amongst an array of semantically-related distracters, saccadic eye movements in a subsequent display were faster to the target than to distracters or new objects, irrespective of location. The main finding was that of the facilitation of a recent target, and not the inhibition of a recent distracter or location. Experiment 2 replicated this finding by using silhouettes of objects for selection that based on feature shape. Error rates were associated with distracters with high target-shape similarity therefore Experiment 3 presented silhouettes of animals using a
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The transition from target selection, in which the porpoise is scanning between targets, to continuous ensonification of a single object at the onset of the buzz is marked by a significant shortening of both the ICI and the lag time (Fig.4). Contrary to what was found in the selection phase, the ICI in the buzz appeared to follow the TWTT to the target with a fairly constant lag of 2–4ms (Fig.4B). However, given the proximity of the target during buzzes, the TWTT is almost an order of magnitude less than the lag time and so the constant lag may simply be a consequence of fairly invariant ICIs in the buzz (e.g. compare Fig.4A and 4B). Another possibility is that the ICI tracks the later-arriving surface bounce of the target echo during buzzes, which has an almost constant TWTT. This would ensure that both the target echo and the consistently strong surface bounce arrive before the following click is issued. The consistent geometry of the animal and the target in the study make it difficult to choose among these possibilities, but free-ranging Blainville’s beaked whales echolocating in deep water (i.e. free of surface bounces) have been found to adjust ICI during buzzes to track target range (Johnson et al., 2008), suggesting that this might be occurring in the present study also. In any case, the variable lag times during regular clicking and the relatively constant lag times and ICIs during buzzes suggest that a different type of echo processing may be occurring during these two phases. The ICIs are consistently longer than the TWTT (whether to the target or a surface bounce) during both phases, enabling, in principle, processing of each click–echo pair individually. Lag times of 20–50ms during regular clicking may be compatible with such individual echo processing, but the 2–4ms lags during buzzes are at least an order of magnitude too short for cognitive processing and motor responses (Ridgway, 2011), making this kind of processing unlikely.
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Eye movements can be made reflexively or voluntarily, and because they can be made voluntarily, the study of eye movements offers a window into the mechanisms of many high-level brain functions. With the development of awake-behaving neurophysiology techniques, the field of neuroscience became equipped to probe the working brain. Mainly in the last half-century, neurophysiologists have detailed the brain structures involved in eye movement control. At this stage it remains a challenge to understand how the different parts complement each together, and how they interface with other functional circuits in the brain, to support an organism in successfully completing the variety of tasks involved in normal behavior. In this dissertation we characterize some of the properties of two cortical nodes of the eye movement network, the supplementary eye fields (SEF) and the lateral intraparietal area (LIP), as well as a brain area that is just adjacent to SEF, the supplementary motor area (SMA), as they interface with (and participate in) other functional networks, particularly those supporting reward processing, the temporal organization of behavior, target selection, and object perception.
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In this study, subjects looked for residential design features that conformed to their description of what constituted a ‘good’ residential target. Therefore, the attractiveness of any given potential residential burglary target was shown to depend on the immediate characteristics of the target in question as perceived by the subject. A number of such specified characteristics were found to attract subjects to potential targets. Some of the characteristics or features that attract burglars to particular potential residential targets are the aspects over which the victim, that is, the owner or occupant of the residence in question, has little control. For example, the ‘location’ of the target, and the ‘escape routes’ away from it, are characteristics which were considered important by the subjects in this study, but both of these features would be difficult to change unless the residence were somehow
The question how observers are able to find visual target objects that are presented at an unpredictable location together with numerous task-irrelevant distractor objects has remained one of the central issues in visual attention. In the past three decades, Glyn Humphreys and his many collaborators have made numerous important empirical and conceptual contributions to this field. Together with John Duncan, Glyn proposed a theoretical account of the factors that determine search performance that remains among the most influential models of visual search. According to their Attentional Engagement Theory (Duncan & Humphreys, 1989, 1992), search efficiency is determined by the similarity between targets and distractors and by the similarity of individual distractor objects to each other. Search is easy when targets are clearly different from distractors and all distractors are similar, and hard under conditions where target-distractor similarity is high and distractor- distractor similarity low. Because these two types of similarity vary gradually, search efficiency across tasks varies on a continuum, rather than reflecting a dichotomy between two qualitatively distinct parallel versus serial mechanisms of target selection, as assumed in other accounts of visual search (e.g., Treisman & Gelade, 1980). To explain how the similarity relationships between targets and distractors impacts upon search performance, Duncan and Humphreys (1992) pointed to the central role of attentional templates in the control of visual search. These templates are internal representations of the expected features of an upcoming target object that are activated prior to search, and bias the competition for spatial attention towards objects with template-matching features (see also Desimone & Duncan, 1995; Eimer, 2014, 2015; for computational models of this type of template-based guidance of visual search, see Wolfe, 1994, 2007).
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search. This specific assumption is based on experiments demonstrating that search for targets defined by a conjunction of two colours or two orientations is much less efficient than search for colour/shape conjunctions (e.g., Wolfe et al., 1990; see also Wing & Allport, 1972). However, other studies have suggested that colour/colour conjunction search can be relatively efficient (e.g., Carrasco, Ponte, Rechea, & Sampedro, 1998), and that pairs of colour/colour conjunction targets can be processed in parallel (Linnell & Humphreys, 2001). For example, Carrasco et al. (1998) showed that when all distractor objects share the same non-target colour, practice led to highly efficient colour/colour conjunction search, presumably due to the colour-based perceptual grouping of distractors and the subsequent rapid rejection of these grouped non-target objects (see also Linnell & Humphreys, 2001; 2002, for further evidence of within-dimension grouping effects in this type of search task). The assumption that colour/colour conjunction search cannot be guided by both target- defining features has also been challenged by behavioural and electrophysiological results from spatial cueing experiments (Irons, Folk, & Remington, 2012; Grubert & Eimer, in press) which suggest that search templates for multiple colours can be activated simultaneously. Thus, the question whether qualitatively different attentional guidance processes are activated when target-defining features come from the same or from different dimensions remains unresolved.
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In this scenario, CAR-T cells could be used as thera- peutic tools to activate the antitumor activity of en- dogenous immune system. Many clinical cases have confirmed that lymphocyte infiltration in solid tumors would increase after CAR-T treatment. In addition to CAR-T cells themselves, the infiltration of endogenous dendritic cells (DCs), macrophages, and endogenous T cells could also be increased. In the activation loop (Fig. 4), the neoantigens released after CAR-T cells attacking could activate the more specific endogenous tumor-specific immune response if they are uptaken and presented by antigen-presenting cells. In addition, the CAR-T cells could be modified to release pro- inflammatory factors and form a favorable microenvir- onment for inflammatory response in the local area of tumors, which would further boost the endogenous tumor immune response. Under this conception, the choice of target in solid tumors CAR-T treatment does not necessarily follow the principles discussed previ- ously. For example, the coverage does not need to be
Figure 4. Stellar mass and Tonry redshift (adjusted to the Tonry et al. 2000 flow model) distribution defining the selection of SAMI galaxies from the GAMA-I catalogue. Black points are the GAMA catalogue from which SAMI targets were selected. In the final selection, the highest priority targets lie above the red line and within the redshift range z = 0.004–0.095 (pink region), while the yellow and cyan boxes represent lower priority targets to be used as fillers in pointings where 12 high-priority targets cannot be optimally tiled within the 1 ◦ diameter field. The blue and green lines are not the selection boundaries; they just indicate a flat stellar mass distribution for comparison. The blue line marks bin centres for where a continuous selection would lie in order to give 150 galaxies in each 0.25 dex stellar mass bin, while the green line marks the limits of 1 dex stellar mass bins that have 540 galaxies each.
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In summary, modern drug discovery and new drug development process is the product of a series of events which may takes an average 10-15 years and an approximate cost of $800 million-$1 billion. These events include pre-discovery phase for identification of a therapeutic need; discovery phase for target identification, target selection, lead finding and lead optimization; pre-clinical develop- ment for safety prior to be tested in humans; IND application; clinical development for testing in humans including clinical trials FDA; approval and an ongoing marketing monitoring.
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may be substantial differences in the ability of different visual features to guide attention (e.g., Wolfe & Horowitz, 2004), it is generally assumed that target selection processes operate more rapidly and more efficiently when search targets are defined by one or more visual-perceptual attributes than under conditions where these targets are defined at a more abstract level in terms of their category membership. In fact, Wolfe & Horowitz (2004) have argued that information about the category membership of target objects (e.g., their alphanumerical or semantic category) is unlikely to guide the deployment of spatial attention in visual search tasks. Many studies have demonstrated that search for specific visual target features is much more efficient than search for category-defined targets (e.g., Malcolm & Henderson, 2009; Yang & Zelinsky, 2009). When targets are defined by visual features, their selection can be based on a direct match between a stored feature template and the physical attributes of particular objects. During category-based search, objects within the current target category will often differ substantially with respect to their physical features, ruling out the possibility of a feature-based match with a particular target template as the mechanism of target selection. The important role of visual representations of target-defining properties for fast attentional selection has been demonstrated by behavioural and ERP visual search studies which have shown that search targets are detected more rapidly when they are specified by visual as compared to verbal descriptions (Wolfe, Horowitz, Kenner, Hyle, & Vasan, 2004; Nako, Smith, & Eimer, 2015).
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Phrase2Vec: We train a Phrase2Vec model (Mikolov et al., 2013) using English Wikipedia and the AQUAINT corpus of English news text (Graff, 2002). Mikolov et al. (2013) pointed out that it is possible to extend the word based embed- dings model to phrase-based model using a data- driven approach where each phrase or multi-word expressions are considered as individual tokens during the training process. We have used a to- tal of 79,349 multiword expression and phrase re- sources as given in Yimam et al. (2016). We train the Phrase2Vec embeddings with 200 dimensions using skip-gram training and a window size of 5. We have retrieved the top 10 similar words to the target units as candidate suggestions.
In the bacterial genome of free-living Escherichia coli , the chromosomal DNA sequences are considered free from any bound proteins and exist essentially as a naked DNA. The search kinetics of dCas/single-stranded RNA was studied in living E. coli by combining single-molecule fluorescence microscopy and bulk restriction-protection assay . Binding of Cas9 is more time-consuming than simple binding of transcription factor for correct base recognition, because Cas9 binds and also unwinds the DNA double-helix to test for correct base pairing to the guide RNA. The dCas molecules were visualized by fusing with fluorescent protein YPet . DNA-bound dCas/YPet molecules are detectable as individual diffraction-limited spots after five second image acquisition time while non-bound molecules are seen as the diffuse fluorescent background . Under these conditions dCas was demonstrated to take six hours to find the correct target sequence suggesting that each potential target is bound for less than 30 milliseconds that is 20 times faster than 750 milliseconds in eukaryotic mouse cells . To achieve fast targeting, both dCas9 and its guide RNA molecules have to be present at extremely high saturating concentrations.
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the 12 populations. We sampled and resequenced genomes of 115 individuals from populations at generation 200 or 300 and at gen- eration 500. For populations S2 and M8, we also sampled the bulk population at multiple time points to resolve the change in the frequencies of mutant alleles throughout the 500 generations. We found a repeatable pattern of evolution in the high-salt popula- tions with a broad mutational target in the PMA1 gene. In all, we found 11 unique mutations of PMA1 in the six high-salt popula- tions. In a low-glucose environment, mutations in the RAS1 and RAS2 sensing/signaling genes were common, one mutation in each of four low-glucose populations. The MDS3/MKT1 program of adaptation in population M8 was apparently unique to popu- lation M8 presumably due to the exceptionally small mutational target in MKT1—just one nucleotide site.
Our second zebrafish dataset (“Shkumatava” in Additional file 14 and Additional file 10: Table S5) is a set of 103 guide sequences from 11 different loci in zebrafish. Guides were transcribed in vitro with the T7 RNA polymerase kit. No guide was selected based on efficiency scores. Guide RNA (10 pg) and 150–200 pg of Cas9 mRNA were injected into wild-type AB zebra- fish at the one-cell stage. Cleavage efficiency was mea- sured by extracting genomic DNA from around 20 embryos, PCR of the target regions, cloning the result into a TOPO-vector, and shipping for Sanger sequen- cing a number of colonies in the range 10–20. The re- sult of the assay is the number of sequences with mutations over all sequences. Guides were manually assigned to a locus if they were located closer together than 3 kbp. The P value in the text is the probability to obtain at least nine successes, where a success is defined as finding at least one guide with a modifica- tion frequency among the top two values in a locus when selecting two guides randomly from each locus. The sampling was repeated 100,000 times.
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In a recent N2pc study (Eimer & Grubert, 2014), we adopted this logic to demonstrate that focal attention can be allocated concurrently and independently to two sequentially presented target-colour objects. Two stimulus displays that each contained a colour-defined target item and a distractor item in a different nontarget colour on opposite sides were presented in rapid succession. All items were letters or digits. Participants’ task was to identify the two target-colour items in the two consecutive displays, and to report whether their alphanumerical category was the same (both letters, both digits) or not (one letter and one digit). The target/nontarget pair in one display always appeared on the horizontal meridian (to the left and right of fixation), and the stimulus pair in the other display was presented on the vertical meridian (above and below fixation; Figure 1, top panel). Trials where the horizontal display preceded the vertical display (horizontal target first: H1 targets) and trials where this order was reversed (horizontal target second: H2 targets) were randomly intermixed. Given these stimulation parameters, N2pc components reflected the attentional selection of the horizontal target on any given trial, irrespective of a second attentional selection process for the vertical target in the other display on the same trial. When the two search displays were separated by a stimulus asynchrony (SOA) of 10 ms, N2pc components of similar size were elicited on trials with H1 versus H2 targets, and these components overlapped in time (Figure 1, bottom panel). The N2pc to H1 targets emerged 10 ms earlier than the N2pc to H2 targets, and this onset latency difference matched the objective SOA between the two displays precisely.
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N2pc components observed in Experiment 1 was very similar to the pattern found in previous experiments with feature-defined targets. On 2T trials where both displays contained a target object, lateral targets in the first and second display (H1 and H2 targets) both triggered clear N2pc components that overlapped in time and did not differ significantly in terms of their amplitudes, suggesting that attention was allocated concurrently to both target objects, in spite of the fact that targets and nontargets could only be distinguished on the basis of their category. Importantly, the temporal pattern of N2pc components observed in Experiment 1 during category-based search was similar to the pattern found in the exemplar-based search task of Experiment 2. Here, H1 and H2 targets again elicited temporally overlapping N2pc components, indicating that they were selected concurrently. This confirms earlier observations in tasks with simple geometrical stimuli and colour- or shape-defined targets, and shows that parallel feature-guided target selection processes also operate in contexts where targets and distractors are more complex real-world objects. Overall, these results show that there are no fundamental qualitative differences in the guidance of attention towards feature-defined and category- defined target objects. When these objects appear in rapid succession at different locations, they can be selected in parallel, regardless of whether these selection processes are controlled by feature or by category templates.
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A recent study on free-ranging striped dolphins tested the expression stability of ten commonly used control genes in skin biopsies and found that the genes coding for the tyrosine 3-monoxygenase ( YWHAZ ) and glyceraldehyde-3P -dehydrogenase ( GAPDH ) were the most reliable ones followed by those coding for the ribosomal proteins S18 ( RSP18 ) and the ribosomal proteins L4 ( RPL4 ) . How- ever, a second study that evaluated gene expression changes associated with organochlorine exposure in striped dolphin fibroblast cultures showed that the most reliable gene was the one encoding succinate dehydrogenase complex sub- unit A ( SDHA ) . These studies illustrate how, depend- ing on the purpose of the study and the target tissue to be analysed, different genes might be better suited as controls. Here, as part of a larger ongoing study on the effects of ex- posure to UV on whales , we examined expression levels and assessed stability of selected reference genes in skin bi- opsies collected from three species of large whales and used two of the most suitable genes to study expression patterns of key genes involved in genotoxic stress pathways.
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with pscan and patmatmotifs from EMBOSS. For protein- protein interactions, data from high-throughput yeast- two hybrid experiments  were annotated to the malaria sequences. Proteins previously predicted to be exported out of the red blood cell [31,32] were annotated. For the identification of candidate proteins for homology modelling, sequence similarity with a protein in the PDB was required. Contrastingly, candidate selection for X-ray crystallization required that proteins did not have sequence similarity with proteins in the PDB. Protein sequences with more than 30% predicted coiled-coils, dis- order, transmembrane regions and signal peptides were eliminated. The SMID-BLAST predictions were used to identify proteins to which small molecules bind and which might be suitable for in silico docking studies. In addition, these proteins had to have a crystal structure or good sequence similarity in the PDB.
Thus, as becomes apparent from the above considerations, there are two conceptually different notions of salience. The construct of physical feature contrast, which corresponds to motion coherence in the random dot kinematogram, is represent- ed as sensory data by the activity of salience detectors in the brain (analogous to the activity of motion detectors representing motion coherence). This momentary neural representation is distributed around its mean, that is, it is a noisy signal. Because the expected salience value, that is the mean of the neural salience represen- tation, is not linearly related to physical feature contrast [28,29], it needs to be estimated. This estimation is the intent of current salience models [22–24]. However, relevant for selection on a given trial is the accumulated signal of the neural representation, which is the decision variable. For clarity, in the remainder of the article, we refer to the concept of expected salience value as stimulus salience and the actual or accumulated estimate as selection salience, because the latter is the basis for attentional selection on a given trial. Stimulus salience is related to physical stimulus properties: for instance, a horizontal bar among vertical bars has a higher stimulus salience than a bar tilted by 30u. Solely based on the value of stimulus salience, focal-attentional selection would have to favor the horizontal bar. However, owing to noise in the computation process, the resulting estimates (i.e. selection salience) are distributed around the expected value of stimulus salience. Hence, if the distributions of selection salience for horizontal and 30u orientation contrasts overlap, first selection of the 30u bar is possible in principle: the selection salience of the 30 u bar can be higher on a given trial than that of the horizontal bar. Stimulus and selection salience do not usually have to be differentiated in standard visual search (detection) tasks with only one salient target being present – because, despite noise, the stimulus salience distributions of target and non-targets virtually never overlap and the selection salience of a non-target can never be higher than that of the target. However, this differentiation becomes important when two conspicuous stimuli are presented, but only one is task- relevant: if selection salience is higher for the irrelevant (distractor) stimulus, even though its stimulus salience is lower than that of the relevant (target) item, it will nevertheless be attentionally selected first.
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