Hypotheses of pathophysiology in obsessive-compulsive disorder (OCD) have developed in parallel with advances in neuroimaging. Based on findings from early PET and SPECT studies evaluating cerebral blood flow and glucose metabolism, one theory proposed involvement of the orbitofrontal-striatum-thalamus loop (the loop), which is relevant to the enforced learning and maintenance of OC symptoms. This OCD loop hypothesis has been revised in accordance with advances in neuroimaging techniques and the accumulation of findings. PET and SPECT molecular neuroimaging studies have provided the biological evidence to support the serotonin and dopamine hypotheses that were based on psychopharmacological findings. A symptom dimension hypothesis, based on symptomatology, has also been proposed. Neuroimaging has revealed the distinct neural basis of each symptom dimension. Neuroimaging has contributed to understanding the pathophysiology of OCD, and is expected to contribute to the development of treatment.
Inflammatory/immunological process and glial contribution are suggested in the pathophysiology of schizophrenia. We investigated peripheral benzodiazepine receptors in brains of patients with chronic schizophrenia, which were reported to be located on mitochondria of glial cells, using [11C]DAA1106 with positron emission tomography. Fourteen patients and 14 age- and sex-matched normal controls participated in this study. PET data were analysed by two-tissue compartment model with metabolite-corrected plasma input. Clinical symptoms were assessed using the Positive and Negative Syndrome Scale. There was no significant difference between [11C]DAA1106 binding of the cortical regions of normal controls and patients with schizophrenia, whereas the patients showed a positive correlation between cortical [11C]DAA1106 binding and positive symptom scores. There was also a positive correlation between [11C]DAA1106 binding and duration of illness. Although the correlations need to be interpreted very cautiously, involvement of glial reaction process in the pathophysiology of positive symptoms or progressive change of schizophrenia might be suggested.
Genus Dendrobium (Orchidaceae) contains numerous species. Phylogenetic analyses based on morphological characteristics and DNA sequences indicated that this genus is divided into two major groups: Asian and Australasian clades. On the other hand, little is known about the phytochemical differences and similarities among the species in each clade. In this study, we selected 18 Dendrobium species (11 from the Asian clade and 7 from the Australasian clade) and constructed HPLC profiles, arrays composed of relative intensity of the chromatographic peaks. Next, orthogonal partial least square discriminant analysis (OPLS-DA) was applied to the profile matrix to classify Dendrobium species into the Asian and Australasian clades in order to identify the peaks that significantly contribute to the class separation. In the end, two phenanthrenes, 4,9-dimethoxyphenanthrene-2,5-diol 1 and 1,5-dimethoxyphenanthrene-2,7-diol 2, which contributed to the class separation, were isolated from the HPLC peaks. The existence of 2 was limited to the genetically related Australasian species.
Abstract The complete automation of materials manufacturing with high productivity is a key problem in some materials processing. In floating zone (FZ) crystal growth, which is a manufacturing process for semiconductor wafers such as silicon, an operator adaptively controls the input parameters in accordance with the state of the crystal growth process. Since the operation dynamics of FZ crystal growth are complicated, automation is often difficult, and usually the process is manually controlled. Here we demonstrate automated control of FZ crystal growth by reinforcement learning using the dynamics predicted by Gaussian mixture modeling (GMM) from small numbers of trajectories. Our proposed method of constructing the control model is completely data-driven. Using an emulator program for FZ crystal growth, we show that the control model constructed by our proposed model can more accurately follow the ideal growth trajectory than demonstration trajectories created by human operation. Furthermore, we reveal that policy optimization near the demonstration trajectories realizes accurate control following the ideal trajectory.
Demand for nursing care facilities such as senior daycares has increased in Japan because of an aging population. In these facilities, multiple staff members offer nursing care services to the elderly such as physical therapy and exercise using machines, according to a staff schedule planned manually. These staff members face some issues regarding heavy workloads, limited human resources, etc. Therefore, it is necessary to plan the staff schedules by distributing the workloads among all the staff members. Additionally, it is observed that staff members are stressed because of physical as well as mental stress when providing services since they also need to vigilant to prevent user accidents. A previous study proposed a scheduling model considering the feeling of physical and mental workloads separately. Thus, the differences for either of the workloads may become very large in planned schedules. This study proposes a scheduling model considering both physical and mental workloads, and produces a balancing schedule considering both types of workloads simultaneously. Additionally, the impact of a movement constraint that is added to the model to reduce the inefficient movement of staff is also discussed. Lastly, to analyze actual cases of the surveyed facility, we conducted numerical experiments with practical scenarios such as increasing the number of staff members, and changing the staff role according to actual staff shifts in the facility.
