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Since choice is no more than the voluntary substitution of one particular response to another, experimental procedures and data on choice behavior are important for analyzing the distribution of behavior. The analysis of choice behavior data in behavior analysis has been performed by three main types of methods. They are logit analysis, probit analysis and double logarithmic analysis. These analyses are often discussed in comparison with the predictions of the generalized matching law [@baum1974; -@baum1979].In this study, nine types of data were artificially generated that follow the generalized matching law, and three types of logistic regression analyses (logit transform model, probit transform model, and double log transform model) were performed. A comparison of the results of these analyses with AIC weight revealed that there are appropriate variable transformations depending on the statistical characteristics of the data being analyzed and that a particular transformation model does not always make good predictions.

A superfluid helium insert was developed for cryogenic microscopy of millimeter-sized specimens. An optical-interferometric position sensor, cryogenic objective mirror, and piezo-driven cryogenic stage were fixed to an insert holder that was immersed in superfluid helium. The single-component design stabilized the three-dimensional position of the sample, with root-mean-square deviations of ( x, lateral) 0.33 nm, ( y, lateral) 0.29 nm, and ( z, axial) 0.25 nm. Because of the millimeter working range of the optical sensor, the working range of the sample under the active stabilization was ( x, y) 5 mm and ( z) 3 mm in superfluid helium at 1.8 K. The insert was used to obtain the millimeter-sized fluorescence image of cell nuclei at 1.8 K without a sample exchange.

The supraspinatus tendon is the most frequently torn tendon in the rotator cuff. Rotator cuff reconstruction is more likely to result in retear if the muscle has atrophy or fatty degeneration. Thus, atrophy and fatty degeneration of the supraspinatus muscle are predictors of the postoperative course, and volume analysis using three-dimensional segmentation of the supraspinatus muscle is necessary. The supraspinatus muscle is attached to the scapula, making it possible to estimate the region of the muscle based on the position of the scapula. In this paper, we propose a supraspinatus muscle segmentation method based on the scapula position in torso computed tomography (CT) images. Our proposed method consists of supraspinatus muscle localization using a scapula segmentation result and supraspinatus muscle segmentation based on the localization result. U-Net is used for scapula and supraspinatus muscle segmentation. In this experiment, we used torso CT images and pseudo-chest CT images which were generated from the scans of the same patient. The mean Dice values of the segmentation results obtained by applying the proposed method to the torso and pseudo-chest CT images were both 0.881. When localization was not used, the mean Dice values of the segmentation results in the torso and pseudo-chest CT images were 0.000 and 0.850, respectively. The experimental results demonstrate the effectiveness of bone-based localization in supraspinatus muscle segmentation using U-Net.

PURPOSE Automated analysis of skeletal muscle in whole-body computed tomography (CT) images uses bone information, but bone segmentation including the epiphysis is not achieved. The purpose of this research was the semantic segmentation of eight regions of upper and lower limb bones including the epiphysis in whole-body CT images. Our targets were left and right upper arms, forearms, thighs, and lower legs. METHOD We connected two 3D U-Nets in cascade for segmentation of eight upper and lower limb bones in whole-body CT images. The first 3D U-Net was used for skeleton segmentation in whole-body CT images, and the second 3D U-Net was used for eight upper and lower limb bones' segmentation in skeleton segmentation results. Thirty cases of whole-body CT images were used in the experiment, and the segmentation results were evaluated using Dice coefficient with 3-fold cross-validation. RESULT The mean Dice coefficient was 93% in the left and right upper arms, 89% in the left and right forearms, 95% in the left and right thighs, and 94% in the left and right lower legs. CONCLUSION Although the accuracy of the segmentation results of relatively small bones remains a challenge, the semantic segmentation of eight regions of upper and lower limb bones including the epiphysis in whole-body CT images has been achieved.

Nondestructive analysis of historical materials, such as paper, is vital in the elucidation of history and culture. This is because it is possible to elucidate the background of a book’s origin using information obtained from other sources, such as paper making methods and paper’s fiber composition. Herein, we propose a patch classification method for Japanese paper kozo using EfficientNet-B0. We integrate the results classified using patch images to estimate the origin of the kozo fiber in the entire paper. Consequently, the average success rate of our method was 93.1 % for two classifications and 87.5 % for four classifications of the production areas of kozo. In addition, the classification accuracy for the entire paper was evaluated from the patch image obtained after classification using the reconstructed image in which the position was rearranged on the original image. As a result, the classification of four production areas of kozo, from which the entire macro image was made, could be classified in all macro images, although the number of experiments was limited. In the future, we will investigate a multi-scale technique to consider the fiber continuity between patches in macro images reconstructed from patch images.

We visualized the flow patterns in an alveolated duct model with breathing-like expanding and contracting wall motions using particle image velocimetry, and then, we investigated the effect of acinar deformation on the flow patterns. We reconstructed a compliant, scaled-up model of an alveolated duct from synchrotron microcomputed tomography images of a mammalian lung. The alveolated duct did not include any bifurcation, and its entire surface was covered with alveoli. We embedded the alveolated duct in a sealed container that was filled with fluid. We oscillated the fluid in the duct and container simultaneously and independently to control the flow and duct volume. We examined the flow patterns in alveoli, with the Reynolds number (Re) at 0.03 or 0.22 and the acinar volume change at 0%, 20%, or 80%. At the same Re, the heterogeneous deformation induced different inspiration and expiration flow patterns, and the recirculating regions in alveoli changed during respiratory cycle. During a larger acinar deformation at Re = 0.03, the flow patterns tended to change from recirculating flow to radial flow during inspiration and vice versa during expiration. Additionally, the alveolar geometric characteristics, particularly the angle between the alveolar duct and mouth, affected these differences in flow patterns. At Re = 0.22, recirculating flow patterns tended to form during inspiration and expiration, regardless of the magnitude of the acinar deformation. Our in vitro experiments suggest that the alveolated flows with nonself-similar and heterogeneous wall motions may promote particle mixing and deposition.

Advancements in musculoskeletal analysis have been achieved by adopting deep learning technology in image recognition and analysis. Unlike musculoskeletal modeling based on computational anatomy, deep learning-based methods can obtain muscle information automatically. Through analysis of image features, both approaches can obtain muscle characteristics such as shape, volume, and area, and derive additional information by analyzing other image textures. In this chapter, we first discuss the necessity of musculoskeletal analysis and the required image processing technology. Then, the limitations of skeletal muscle recognition based on conventional handcrafted features are discussed, and developments in skeletal muscle recognition using machine learning and deep learning technology are described. Next, a technique for analyzing musculoskeletal systems using whole-body computed tomography (CT) images is shown. This study aims to achieve automatic recognition of skeletal muscles throughout the body and automatic classification of atrophic muscular disease using only image features, to demonstrate an application of whole-body musculoskeletal analysis driven by deep learning. Finally, we discuss future development of musculoskeletal analysis that effectively combines deep learning with handcrafted feature-based modeling techniques.

In recent years, the use of automation has been promoted in various systems such as automobiles, and the interaction between the system and user tends to decrease and be monotonous. Consequently, there is a concern that the arousal level of the user may decrease during work. In a semi-automatic system that requires user intervention in an emergency, performance degradation or accidents may occur because of the driver falling asleep. Therefore, an effective method of maintaining the arousal level must be established. Currently, the methods of stimulating the driver such as the presentation of siren sounds and injection of the scent of mint are being studied as the methods of maintaining the arousal level for automobile drivers. However, these methods of maintaining the arousal level via external stimulation have also suffered with challenges such as discomfort with the stimulation. In our previously conducted study, we proposed a method of maintaining the arousal level by presenting information that induced intrinsic motivation for maintaining the driving behavior in the driver; such a presentation might help maintain the arousal level. We aim to make such a method more applicable by optimizing the timing of the induction of intrinsic motivation.

Advancements in musculoskeletal analysis have been achieved by adopting deep learning technology in image recognition and analysis. Unlike musculoskeletal modeling based on computational anatomy, deep learning-based methods can obtain muscle information automatically. Through analysis of image features, both approaches can obtain muscle characteristics such as shape, volume, and area, and derive additional information by analyzing other image textures. In this chapter, we first discuss the necessity of musculoskeletal analysis and the required image processing technology. Then, the limitations of skeletal muscle recognition based on conventional handcrafted features are discussed, and developments in skeletal muscle recognition using machine learning and deep learning technology are described. Next, a technique for analyzing musculoskeletal systems using whole-body computed tomography (CT) images is shown. This study aims to achieve automatic recognition of skeletal muscles throughout the body and automatic classification of atrophic muscular disease using only image features, to demonstrate an application of whole-body musculoskeletal analysis driven by deep learning. Finally, we discuss future development of musculoskeletal analysis that effectively combines deep learning with handcrafted feature-based modeling techniques.

The Elucidation of the spread of paper is an important research that reveals historical culture. In particular, clarifying the paper fibers contained in paper is one of the important issues involved in elucidating the propagation of paper. The target of our research is an image of the Samarkand paper acquired from historical cultural properties. In this research, images of the paper were acquired by macro photography with a commercially available digital camera that can obtain samples nondestructively. By using the VGG-16, which is a deep convolutional neural network (DCNN), we easily determined whether the paper from which the macro image was obtained has a hemp or cotton fiber. In addition, as the piece of paper to be photographed is a cultural property, data acquisition through simple photography is required. However, one macro shot with a digital camera causes blurring at the periphery of the image. Therefore, the DCNN-based classification performance was compared using two datasets of the input image and the image obtained by trimming the periphery. As a result of trimming of the image periphery prone to blur in macro photography of digital images, classification results of up to 95.8% were obtained.

A thermophilic filamentous anoxygenic photosynthetic bacterium, Chloroflexus aggregans, is widely distributed in neutral to slightly alkaline hot springs. Sulfide has been suggested as an electron donor for autotrophic growth in microbial mats dominated with C. aggregans, but remarkable photoautotrophic growth of isolated C. aggregans has not been observed with sulfide as the sole electron source. From the idea that sulfide is oxidized to elemental sulfur by C. aggregans and the accumulation of elemental sulfur may have an inhibitory effect for the growth, the effects of an elemental sulfur-disproportionating bacterium that consumes elemental sulfur was examined on the autotrophic growth of C. aggregans, strain NA9-6, isolated from Nakabusa hot spring. A sulfur-disproportionating bacterium, Caldimicrobium thiodismutans strain TF1, also isolated from Nakabusa hot spring was co-cultured with C. aggregans. C. aggregans and C. thiodismutans were successfully co-cultured in a medium containing thiosulfate as the sole electron source and bicarbonate as the sole carbon source. Quantitative conversion of thiosulfate to sulfate and a small transient accumulation of sulfide was observed in the co-culture. Then the electron source of the established co-culture was changed from thiosulfate to sulfide, and the growth of C. aggregans and C. thiodismutans was successfully observed with sulfide as the sole electron donor for the autotrophic growth of the co-culture. During the cultivation in the light, simultaneous consumption and accumulation of sulfide and sulfate, respectively, were observed, accompanied with the increase of cellular DNAs of both species. C. thiodismutans likely works as an elemental sulfur scavenger for C. aggregans, and C. aggregans seems to work as a sulfide scavenger for C. thiodismutans. These results suggest that C. aggregans grows autotrophically with sulfide as the electron donor in the co-culture with C. thiodismutans, and the consumption of elemental sulfur by C. thiodismutans enabled the continuous growth of the C. aggregans in the symbiotic system. This study shows a novel symbiotic relationship between a sulfide-oxidizing photoautotroph and an elemental sulfur-disproportionating chemolithoautotroph via cooperative dissimilatory sulfide oxidation to sulfate.

The skeletal muscle exists in the whole body and can be observed in many cross sections in various tomographic images. Skeletal muscle atrophy is due to aging and disease, and the abnormality is difficult to distinguish visually. In addition, although skeletal muscle analysis requires a technique for accurate site-specific measurement of skeletal muscle, it is only realized in a limited region. We realized automatic site-specific recognition of skeletal muscle from whole-body CT images using model-based methods. Three-dimensional texture analysis revealed imaging features with statistically significant differences between amyotrophic lateral sclerosis (ALS) and other muscular diseases accompanied by atrophy. In recent years, deep learning technique is also used in the field of computer-aided diagnosis. Therefore, in this initial study, we performed automatic classification of amyotrophic diseases using deep learning for the upper extremity and lower limb regions. The classification accuracy was highest in the right forearm, which was 0.960 at the maximum (0.903 on average). In the future, methods for differentiating more kinds of muscular atrophy and clinical application of ALS detection by analyzing muscular regions must be considered.

This paper proposes force sensorless force control using a notch-type dual disturbance observer. The force control is structured by acceleration and speed controllers. The reaction force estimation system reduces the effect of the static friction using a dither signal. The proposed notch-type dual disturbance observer combines notch- type zero-order friction-free disturbance observer (FF - DOB) and notch-type first-order FFDOB. The proposed disturbance observer has the disturbance suppression characteristics of a notch-type zero-order FFDOB on the high-frequency side and a notch-type first-order FFDOB on the low-frequency side. It has a wide estimation band and allows the improvement of the suppression characteristics against low frequency disturbances. The validity of the proposed notch-type dual disturbance observer is verified through experimental results using an actual industrial robot.

Erector spinae muscle (ESM) is an important muscle in the torso region. Changes of sizes, shapes and densities in the cross section of the spinal column muscles have been found in chronic low back pain, degenerative lumbar sclerosis and chronic obstructive pulmonary disease. However, the image features of the ESM are measured manually by the physician. Therefore, automatic recognition in three dimensions (3D) not only for the limited two-dimensional (2D) section but also for the whole ESM is required. In this study, we realize automatic recognition of the ESMs and its attachment region on the skeleton using a 2D deep convolutional neural network. Each cross section of the 3D computed tomography (CT) image is input as a 2D image to the fully convolutional network. Then, the obtained result is reconstructed into a 3D image to obtain the recognition result of the ESM and its attachment region on the skeleton. ESM and attached area are extracted manually from the CT images of 11 cases and used for evaluation. In the experiments, automatic recognition was performed for each case using the leave-one-out method. The mean recognition accuracy of ESM and attached area was \(89.9\%\) and \(65.5\%\), respectively for the Dice coefficient. In this study, although there is over-extraction in the recognition of the attachment region, the initial region has been acquired successfully and it is the first study to simultaneously recognize the ESMs and its attachment region on the skeleton.

This study aimed to develop and validate an automated segmentation method for surface muscles using a three-dimensional (3D) U-Net based on selective voxel patches from whole-body computed tomography (CT) images. Our method defined a voxel patch (VP) as the input images, which consisted of 56 slices selected at equal intervals from the whole slices. In training, one VP was used for each case. In the test, multiple VPs were created according to the number of slices in the test case. Segmentation was then performed for each VP and the results of each VP merged. The proposed method achieved a segmentation accuracy mean dice coefficient of 0.900 for 8 cases. Although challenges remain in muscles adjacent to visceral organs and in small muscle areas, VP is useful for surface muscle segmentation using whole-body CT images with limited annotation data. The limitation of our study is that it is limited to cases of muscular disease with atrophy. Future studies should address whether the proposed method is effective for other modalities or using data with different imaging ranges.