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Fig. 5 in A mosaic of conserved and novel modes of gene expression and morphogenesis in mesoderm and muscle formation of a larval bivalve

Fig. 5 Expression of myosin II heavy chain (Dro-mhc_c1) and myogenesis in Dreissena rostriformis veliger larvae. Lateral view in all images, anterior faces upwards and dorsal to the left except in c which is a dorso-anterior view, e and f which are anterior views (dorsal is up), and i which is a posterior view (dorsal is up). Arrowheads indicate the stomodaeum. Scale bar equals 20 µm. Brightfield images of the gene expression (a and e) and confocal images (b–d and f–i) with F-actin (yellow–red), cilia (green), and cell nuclei staining (cyan). a Expression of Dro-mhc_c1 is in the central and dorsal mesoderm. Velum (ve). b First distinct muscle bundles are the dorsal velum retractor (dv), the ventral velum retractor (vv), and the larval retractor (lr). First appearance of the velum muscle ring (vr), the (pal-

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Fig. 3 in A mosaic of conserved and novel modes of gene expression and morphogenesis in mesoderm and muscle formation of a larval bivalve

Fig. 3 Expression of myosin II heavy chain (Dro-mhc_c1) and immunofluorescence staining in Dreissena rostriformis trochophore larvae. Anterior is up. Arrowheads indicate the stomodaeum, sf marks the shell field, dotted line outlines the region of the prototroch (pt). Scale bar equals 20 µm. Brightfield images (a, b) of the gene expression and confocal images (c, d) with F-actin (red), cilia (green; pt: prototroch; tt: telotroch), and cell nuclei staining (cyan). a Dro-mhc_c1 expression is first present in the anterior mesoderm. b Anterior mesodermal expression in dorsal view. c First F-actinpositive domain in the mesoderm below the shell field in the dorso-median region. d Slightly further developed trochophore larva showing two developing myofilaments in the median region. A, anterior; D, dorsal; P, posterior; V, ventral

Figure 4. (a) Therapy player software screen, where a) is the stimuli time, b) is the total therapy time, c) is the file path, d) displays the numeric values of each sequence of the therapy, e) shows the current value, and f) shows the current lag angle for zenith and azimuth values; (b) USB mechanism for conversion, where a) USB-UART converter, and b) USB-Zigbee converter.-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy

Where tt time expended by the servomotors to point the laser to a given position and execute a laser beam sequence; tspin is the time that a servomotor needs to spin one degree; ttol is a given the tolerance time; θservo is the addition of degrees that both servos in a laser driver need to spin point the laser in a given position; tstimuli is the time expended in execute a laser beam, between 250 and 605 ms (Weiskrantz et al., 1991); T is the total time of all repetitions in a therapy, suggested between 20 and 60 minutes and N is the number of repetitions in a therapy.

Figure 3. (Top): Illustration of the therapy selection main menu. This enables the user to select one of three options for the therapy. Stimuli sequence selectors; (Bottom): (a) Short distance – complete visual field; (b) Short distance – macular; (c) Middle-long distance.-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy

distance therapies for the prescribed time suggested by the ophthalmologist. Note that the complete visual field therapy stimulates different parts in the entire visual field whereas macular therapy stimulate only a small part of the visual field, only the first 10° of vision range. In contrast, middle-long distance therapies are not developed inside the device; instead the patient must sit watching a wall, where the stimuli will be presented. Figure 3 (Bottom) shows the sequence selectors for the three different cases. The therapist will choose a desired number of sequences according to the results of the examination to each patient; hence it is completely patient dependent. Once the therapist finishes the particular design of the stimuli sequence, the software automatically displays a window where he can save the customized patient-specific details for future use as a text file.

Figure 2. (a) Representation of laser servo-driver for inverse kinematics analysis; (b) Representation of the lag angle, B, of the internal servomechanism (magnified version of the chin-rest).-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy

As the servo-driver will be attached in the chin-rest in a non-central area with respect to the semispherical structure shown in Figure 1(a), it is necessary to calculate a lag angle, according to the measurements from the chin-rest, see Figure 2(b). This was done using a hybrid formula based on the law of cosines,

Figure 1. (a) Part of the acrylic structure where the patient is enclosed to avoid external stimulus; (b) Chin rest, corresponding proportions and measurements.-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy

The device consists mainly of an acrylic semi-spherical structure (Figure 1(a)) where visual stimuli will be shown, according to a pre-designed therapy. Four servomotors will drive the lasers, two inside the structure (short distances drive the lasers, two inside the structure (short distance therapies) and two outside (middle-long distance therapies). A chin-rest must be used to have a better line of sight fixation. A webcam with infrared light will catch the Purkinje-Sanson images to identify the sight line (Borah, 2006; Halswanter, 2011; Pambakian et al., 2000). LabVIEW software is used to control the device, including an audio stimulus along with an image-processing pipeline. Finally a microcontroller is used to control the servo movements, laser beams and buzzers.

Figure 11. Cognitive architecture of the process of social signals perception-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

A possible cognitive architecture and formalization of the process of learning via multisensory integration is presented in figure 11. The formal description of the proposed cognitive architecture, capable of interpreting social-communication signals, signs and symbols, is based on multisensory integration at the level of perception, parallel processing at the level of interpretation and decision making followed by verbalization, as well as performing an action (eye contact, gesture, mimicking) at the level of behaviour.

Figure 9. The impossible figure (right) is not noticeable as such at first glance-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

In the lexical domain a similar effect of holistic word processing is described in (Anstis, 2005b). The viewers were presented with pairs of three-letter words in quick succession and asked to report if the upper halves of the successively presented words were identical. Surprisingly, even when the upper halves of the words were orthographically identical, the error rate was reliably higher than expected and in comparison with matching identical successive words. As the author of the study Stuart Anstis points out: “students were processing the words not as separable parts, but holistically as perceptual units that could not be perceptually split apart. These results show that in normal circumstances, the visual system cannot, or does not, divide words into upper and lower halves” (Anstis, 2005b, p. 239).The author relates the results of his study to studies of visual perception of faces as evidence that the mechanism of holistic processing in the visual and the lexical domains is essentially the same.

Figure 8. Machine faces, perceived as more figure-like(left) and less figure-like (right)-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

In an experimental study on visual perception, addressing directly Gestalt phenomena, a new Gestalt cue for figure-ground assignment was introduced (Vechera et al., 2002). The foreground versus the background organization is a strong determinant for decisions on objects seen among image elements. A well-known set of perceptual cues that are often called Gestalt cues are the size or area, the symmetry and the convexity vs. concavity judgments. It is generally assumed that figures are ‘small, symmetrical and convex’. The authors asked the question whether these cues are all that are necessary for a region of the image to be judged as a figure. The main result of this study is that regions in the lower portion of a stimulus array appear more figure-like than regions in the upper portion of the display.

Figure 6. Noticeable subjective response to the distorted face to the right-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

Quite surprisingly, if the distortion is viewed in the normal upward position, it evokes strong emotional response to the distorted face to the right in figure 6.

Figure 5. The distortion is barely noticeable if the faces are viewed in the reversed position-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

According to the feature-based processing theories of human faces the main elements, noticed and remembered in a face, are the eyes, the nose and the mouth (Thompson, 1980; Anstis, 2005a). If, however, we distort some of the elements of a face, these should influence perception,regardless of the position of the image – upright or reversed – from the observer viewpoint. Figure 5 presents the reversed image of the face on the left and the reversed distorted face on the right. The distortion was achieved by rotating the eyes of the image in the vertical direction. Figure

Figure 4. Main elements of a face, according to the feature-based processing theories-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

In 1980 Peter Thompson proposed a new experimental paradigm for investigation of perception, called “face thatcherization” (also named “Thomson illusion”) (Thompson, 1980). Imagine that the following face, depicted in figure 4, is a photo of the then UK Prime Minister Margaret Thatcher.

Figure 3. Robotic faces, similar to smiley emoticons-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

The smiley Gestalt is the result from a dynamic (evolved in time) cognitive process, it is immediately given in cognition, memorable, emotionally rich and socially relevant and reflects the special kind of Gestalt complexity as defined by Edwin Rausch (1988). Conventional representations of holistic entities like smileys or novel robotic faces come to life because they capture essential Gestalt qualities of the perceived image. For example, in figure 3 the robotic faces resemble the smileys in terms of the evoked internal/emotional reactions.

Figure 2. Taxonomy of the educational technologies for children with ASC-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

learner (Perlin, 1997). MOSOCO is an emergent technology implemented in a smartphone called “Mobile Social Compass” (Escobedo et al., 2012). Six basic social skills are being encouraged by prompting the user to initiate social contact. The menu displays symbols for the basic social skills – eye contact, space and proximity, start an interaction, asking questions, sharing interests and finish an interaction. The MOSOCO application has turned out to be an extremely useful tool as an online prompt in starting, maintaining and finishing social interaction for both typical and autistic students, as well as to anyone that feels need for improving their social competence.

Figure 1. Necker cube depth illusion (Adapted from [http://en.wikipedia.org/wiki/Necker_cube])-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

Often the term Gestalt is used interchangeably with the term “emergent whole” (Johansson, 1998). The emergence of a cognitive Gestalt structure adds dynamical and psychophysical forces, which are different from the static notion of the “emergent whole”. An eminent example for the dynamic nature of the emergent process is the Necker cube, which cannot be perceived as static, but rotates in front of our eyes to the complete exhaustion of the eye gazing process (figure 1).

Figure 7. Kanizsa square makes us see a non-existing figure – white square (Adapted from [http://en.wikipedia.org/wiki/Optical_illusion]-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

A special case of Gestalt processing is the perceiving of illusions. Illusions make us see things or processes that are not there – for example the Kanizsa square like the one depicted in figure 7.

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 10. Detailed accuracy separated by classes and a confusion matrix which belongs to the dataset of Coiflet 1 applied by our main method (ANNSVM)

With regard to accuracy values of each class as presented in Figure 10, we observed that the accuracy of the two-dimensional chart class was the lowest (i.e., 0.875), while others were over 0.9. Results here suggested that both the bar and pie classes have their own unique characteristics, as opposed to the 2Dchart class. For example, the graph images that contained some rectangles were individually categorized in the bar graph class. A similar phenomenon occurred for circles in the pie chart class. In contrast, the 2Dchart class contained mixed types of graphs; hence, the graph characteristics belonging to the 2Dchart class varied.

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 8. Simulation of Coiflet 1 (PyWavelets discussion group, 2008), analyzing as one-dimensional images

Using only the wavelet coefficients was inadequate for classification. For example, for the pie chart, we obtained large wavelet coefficients located in the low-frequency domain; however, if we changed a circle in the pie chart to other shapes, such as a radar chart, the wavelet transformation gave results that were similar to those of the original pie chart. The Hough transformation can solve this problem since it detects the shapes of objects

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 7b. Results from SVM, ANN, SVMANN, and ANNSVM that used WLHT

From the results of ANNSVM_WL and ANNSVM_HT, we found that wavelet coefficients had a larger impact on classification than the Hough transformation data because the results from our proposed method applied to WL were more accurate than those of HT. The wavelet coefficients can capture the dominant characteristics from the graphs better than the Hough transformation. The one-dimensional image represented in the frequency domain had oscillations with different amplitudes depending on the graph types. For example, a dominant part of a pie chart should be in the low-frequency domain, because there is a large island of concatenated pixels in a onedimensional image, and it has only a few changes. Conversely, since the scatter plot contains many widely spread points, its dominant part should be located in the high-frequency domain. Performing the wavelet transformation, if a mother wavelet and a part of the wavelet function have a close match, the wavelet coefficient will be large. Assuming we use a suitable wavelet family with the example pie chart case, the wavelet coefficients in the low-frequency domain should be large as compared to other parts of the domain.

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 7c. Results from SVM, ANN, SVMANN, and ANNSVM that used WLHT