Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing

This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL) for image processing. After the introduction of the deep Q-network, deep RL has been achieving great success. However, the applications of deep RL for image processing are still limited. Therefore, we extend deep RL to pixelRL for various image processing applications. In pixelRL, each pixel has an agent, and the agent changes the pixel value by taking an action. We also propose an effective learning method for pixelRL that significantly improves the performance by considering not only the future states of the own pixel but also those of the neighbor pixels. The proposed method can be applied to some image processing tasks that require pixel-wise manipulations, where deep RL has never been applied. We apply the proposed method to three image processing tasks: image denoising, image restoration, and local color enhancement. Our experimental results demonstrate that the proposed method achieves comparable or better performance, compared with the state-of-the-art methods based on supervised learning.Comment: Accepted to AAAI 201

Semi- and Weakly-Supervised Domain Generalization for Object Detection

Object detectors do not work well when domains largely differ between training and testing data. To solve this problem, domain generalization approaches, which require training data with ground-truth labels from multiple domains, have been proposed. However, it is time-consuming and labor-intensive to collect those data for object detection because not only class labels but also bounding boxes must be annotated. To overcome the problem of domain gap in object detection without requiring expensive annotations, we propose to consider two new problem settings: semi-supervised domain generalizable object detection (SS-DGOD) and weakly-supervised DGOD (WS-DGOD). In contrast to the conventional domain generalization for object detection that requires labeled data from multiple domains, SS-DGOD and WS-DGOD require labeled data only from one domain and unlabeled or weakly-labeled data from multiple domains for training. We show that object detectors can be effectively trained on the proposed settings with the same student-teacher learning framework, where a student network is trained with pseudo labels output from a teacher on the unlabeled or weakly-labeled data. The experimental results demonstrate that the object detectors trained on the proposed settings significantly outperform baseline detectors trained on one labeled domain data and perform comparably to or better than those trained on unsupervised domain adaptation (UDA) settings, while ours do not use target domain data for training in contrast to UDA

Elimination of TDP-43 inclusions linked to amyotrophic lateral sclerosis by a misfolding-specific intrabody with dual proteolytic signals.

Aggregation of TAR DNA-binding protein of 43 kDa (TDP-43) is implicated in the pathogenesis of sporadic and certain familial forms of amyotrophic lateral sclerosis (ALS), suggesting elimination of TDP-43 aggregates as a possible therapeutic strategy. Here we generated and investigated a single-chain variable fragment (scFv) derived from the 3B12A monoclonal antibody (MAb) that recognises D247 of the TDP-43 nuclear export signal, an epitope masked in the physiological state. In transfected HEK293A cells, 3B12A scFv recapitulated the affinity of the full-length MAb to mislocalised TDP-43 with a defective nuclear localising signal and to a TDP-43 inclusion mimic with cysteine-to-serine substitution at RRM1. Moreover, 3B12A scFv accelerated proteasome-mediated degradation of aggregated TDP-43, likely due to an endogenous PEST-like proteolytic signal sequence in the VH domain CDR2 region. Addition of the chaperone-mediated autophagy (CMA)-related signal to 3B12A scFv induced HSP70 transcription, further enhancing TDP-43 aggregate clearance and cell viability. The 3B12A scFv also reduced TDP-43 aggregates in embryonic mouse brain following in utero electroporation while causing no overt postnatal brain pathology or developmental anomalies. These results suggest that a misfolding-specific intrabody prone to synergistic proteolysis by proteasomal and autophagic pathways is a promising strategy for mitigation of TDP-43 proteinopathy in ALS.筋萎縮性側索硬化症の異常凝集体を除去する治療抗体の開発に成功-ALS の根治治療への道を開く -滋賀医科大学プレスリリース. 2018-05-3

Pathological Endogenous α-Synuclein Accumulation in Oligodendrocyte Precursor Cells Potentially Induces Inclusions in Multiple System Atrophy.

Glial cytoplasmic inclusions (GCIs), commonly observed as α-synuclein (α-syn)-positive aggregates within oligodendrocytes, are the pathological hallmark of multiple system atrophy. The origin of α-syn in GCIs is uncertain; there is little evidence of endogenousα-syn expression in oligodendrocyte lineage cells, oligodendrocyte precursor cells (OPCs),and mature oligodendrocytes (OLGs). Here, based on in vitro analysis using primary rat cell cultures, we elucidated that preformed fibrils (PFFs) generated from recombinant human α-syn trigger multimerization and an upsurge of endogenous α-syn in OPCs, which is attributable to insufficient autophagic proteolysis. RNA-seq analysis of OPCs revealed that α-syn PFFs interfered with the expression of proteins associated with neuromodulation and myelination. Furthermore, we detected cytoplasmic α-syn inclusions in OLGs through differentiation of OPCs pre-incubated with PFFs. Overall, our findings suggest the possibility of endogenous α-syn accumulation in OPCs that contributes to GCI formation and perturbation of neuronal/glial support in multiple system atrophy brains

Effects of Neutron-Induced Well Potential Perturbation for Multiple Cell Upset of Flip-Flops in 65 nm

We measure and investigate the relationship between well potential perturbation and multiple cell upsets (MCUs) by neutron irradiation. Area-efficient cell-based perturbation detectors are placed adjacent to FFs (Flip-Flops). They can measure duration time of perturbation with 5 μm spatial resolution at two voltage levels. The measurement results by neutron irradiation on a 65-nm bulk CMOS show that 95% of MCUs occur simultaneously with well-potential perturbation, while there is very weak relationship between single event upsets (SEUs) and the perturbation

Modeling And Simulation Of A Service System In A Disaster To Assess Its Resilience

This paper presents a model and simulation of a service system during a disaster with the aim of developing a method to assess the resilience of the system. The dialysis treatment service is adopted as the case study and modeled using a multi-agent model to represent the service activities and multi-layered simple networks for the critical lifeline infrastructures. A resilience triangle of the service system is drawn from the simulation under the optimized infrastructure recovery obtained by genetic algorithms. The results present the simple outcome that it is necessary to consider both the service activities and restoration of the infrastructure to enhance the resilience of a service system. The results also suggest that it is possible to improve service resilience not only by reinforcing critical infrastructures, but also by redesigning service activities

Effects of Transanal Irrigation on Gut Microbiota in Pediatric Patients with Spina Bifida

Recent studies using 16S rRNA-based microbiota profiling have demonstrated dysbiosis of gut microbiota in constipated patients. The aim of this study was to investigate the changes in gut microbiota after transanal irrigation (TAI) in patients with spina bifida (SB). A questionnaire on neurogenic bowel disfunction (NBD), Bristol scale, and gut microbiota using 16S rRNA sequencing were completed in 16 SB patients and 10 healthy controls aged 6–17 years. Then, 11 of 16 SB patients with moderate to severe NBD scores received TAI for 3 months. Changes in urine cultures were also examined before and after the TAI treatments. In addition, correlation of gut microbiota and Bristol scale was analyzed. Significantly decreased abundance in Faecalibacterium, Blautia and Roseburia, and significantly increased abundance in Bacteroides and Roseburia were observed in the SB patients compared with controls and after TAI, respectively. The abundance of Roseburia was significantly correlated positively with Bristol scale. Urinary tract infection tended to decrease from 82% to 55% after TAI (p = 0.082) despite persistent fecal incontinence. Butyrate-producing bacteria such as Roseburia play a regulatory role in the intestinal motility and host immune system, suggesting the effects of TAI on gut microbiota

Effects of Transanal Irrigation on Gut Microbiota in Pediatric Patients with Spina Bifida

Recent studies using 16S rRNA-based microbiota profiling have demonstrated dysbiosis of gut microbiota in constipated patients. The aim of this study was to investigate the changes in gut microbiota after transanal irrigation (TAI) in patients with spina bifida (SB). A questionnaire on neurogenic bowel disfunction (NBD), Bristol scale, and gut microbiota using 16S rRNA sequencing were completed in 16 SB patients and 10 healthy controls aged 6–17 years. Then, 11 of 16 SB patients with moderate to severe NBD scores received TAI for 3 months. Changes in urine cultures were also examined before and after the TAI treatments. In addition, correlation of gut microbiota and Bristol scale was analyzed. Significantly decreased abundance in Faecalibacterium, Blautia and Roseburia, and significantly increased abundance in Bacteroides and Roseburia were observed in the SB patients compared with controls and after TAI, respectively. The abundance of Roseburia was significantly correlated positively with Bristol scale. Urinary tract infection tended to decrease from 82% to 55% after TAI (p = 0.082) despite persistent fecal incontinence. Butyrate-producing bacteria such as Roseburia play a regulatory role in the intestinal motility and host immune system, suggesting the effects of TAI on gut microbiota