Gas concentration mapping and source localization for environmental monitoring through unmanned aerial systems using model-free reinforcement learning agents.

There are three primary objectives of this work; first: to establish a gas concentration map; second: to estimate the point of emission of the gas; and third: to generate a path from any location to the point of emission for UAVs or UGVs. A mountable array of MOX sensors was developed so that the angles and distances among the sensors, alongside sensors data, were utilized to identify the influx of gas plumes. Gas dispersion experiments under indoor conditions were conducted to train machine learning algorithms to collect data at numerous locations and angles. Taguchi's orthogonal arrays for experiment design were used to identify the gas dispersion locations. For the second objective, the data collected after pre-processing was used to train an off-policy, model-free reinforcement learning agent with a Q-learning policy. After finishing the training from the training data set, Q-learning produces a table called the Q-table. The Q-table contains state-action pairs that generate an autonomous path from any point to the source from the testing dataset. The entire process is carried out in an obstacle-free environment, and the whole scheme is designed to be conducted in three modes: search, track, and localize. The hyperparameter combinations of the RL agent were evaluated through trial-and-error technique and it was found that ε = 0.9, γ = 0.9 and α = 0.9 was the fastest path generating combination that took 1258.88 seconds for training and 6.2 milliseconds for path generation. Out of 31 unseen scenarios, the trained RL agent generated successful paths for all the 31 scenarios, however, the UAV was able to reach successfully on the gas source in 23 scenarios, producing a success rate of 74.19%. The results paved the way for using reinforcement learning techniques to be used as autonomous path generation of unmanned systems alongside the need to explore and improve the accuracy of the reported results as future works.

The monitoring of oil production process by deep learning based on morphology in oleaginous yeasts.

BACKGROUND: Monitoring jar fermenter-cultured microorganisms in real time is important for controlling productivity of bioproducts in large-scale cultivation settings. Morphological data is used to understand the growth and fermentation states of these microorganisms during monitoring. Oleaginous yeasts are used for their high productivity of single-cell oils but the relationship between lipid productivity and morphology has not been elucidated in these organisms. RESULTS: In this study, we investigated the relationship between the morphology of oleaginous yeasts (Lipomyces starkeyi and Rhodosporidium toruloides were used) and their cultivation state in a large-scale cultivation setting using a real-time monitoring system. We combined this with deep learning by feeding a large amount of high-definition cell images obtained from the monitoring system to a deep learning algorithm. Our results showed that the cell images could be grouped into 7 distinct groups and that a strong correlation existed between each group and its biochemical activity (growth and oil-productivity). CONCLUSIONS: This is the first report describing the morphological variations of oleaginous yeasts in a large-scale cultivation, and describes a promising new avenue for improving productivity of microorganisms in large-scale cultivation through the use of a real-time monitoring system combined with deep learning. KEY POINTS: • A real-time monitoring system followed the morphological change of oleaginous yeasts. • Deep learning grouped them into 7 distinct groups based on their morphology. • A correlation between the cultivation state and the shape of the yeast was observed.

Trend clustering from COVID-19 tweets using graphical lasso-guided iterative principal component analysis.

This article presents a method for trend clustering from tweets about coronavirus disease (COVID-19) to help us objectively review the past and make decisions about future countermeasures. We aim to avoid detecting usual trends based on seasonal events while detecting essential trends caused by the influence of COVID-19. To this aim, we regard daily changes in the frequencies of each word in tweets as time series signals and define time series signals with single peaks as target trends. To successfully cluster the target trends, we propose graphical lasso-guided iterative principal component analysis (GLIPCA). GLIPCA enables us to remove trends with indirect correlations generated by other essential trends. Moreover, GLIPCA overcomes the difficulty in the quantitative evaluation of the accuracy of trend clustering. Thus, GLIPCA's parameters are easier to determine than those of other clustering methods. We conducted experiments using Japanese tweets about COVID-19 from March 8, 2020, to May 7, 2020. The results show that GLIPCA successfully distinguished trends before and after the declaration of a state of emergency on April 7, 2020. In addition, the results reveal the international argument about whether the Tokyo 2020 Summer Olympics should be held. The results suggest the tremendous social impact of the words and actions of Japanese celebrities. Furthermore, the results suggest that people's attention moved from worry and fear of an unknown novel pneumonia to the need for medical care and a new lifestyle as well as the scientific characteristics of COVID-19.

Ranking of Importance Measures of Tweet Communities: Application to Keyword Extraction From COVID-19 Tweets in Japan.

This article presents a method that detects tweet communities with similar topics and ranks the communities by importance measures. By identifying the tweet communities that have high importance measures, it is possible for users to easily find important information about the coronavirus disease (COVID-19). Specifically, we first construct a community network, whose nodes are tweet communities obtained by applying a community detection method to a tweet network. The community network is constructed based on textual similarities between tweet communities and sizes of tweet communities. Second, we apply algorithms for calculating centrality to the community network. Because the obtained centrality is based on tweet community sizes as well, we call it the importance measure in distinction to conventional centrality. The importance measure can simultaneously evaluate the importance of topics in the entire data set and occupancy (or dominance) of tweet communities in the network structure. We conducted experiments by collecting Japanese tweets about COVID-19 from March 1, 2020 to May 15, 2020. The results show that the proposed method is able to extract keywords that have a high correlation with the number of people infected with COVID-19 in Japan. Because users can browse the keywords from a small number of central tweet communities, quick and easy understanding of important information becomes feasible.

River state classification combining patch-based processing and CNN.

This paper proposes a method for classifying the river state (a flood risk exists or not) from river surveillance camera images by combining patch-based processing and a convolutional neural network (CNN). Although CNN needs much training data, the number of river surveillance camera images is limited because flood does not frequently occur. Also, river surveillance camera images include objects that are irrelevant to the flood risk. Therefore, the direct use of CNN may not work well for the river state classification. To overcome this limitation, this paper develops patch-based processing for adjusting CNN to the river state classification. By increasing training data via the patch segmentation of an image and selecting patches that are relevant to the river state, the adjustment of general CNNs to the river state classification becomes feasible. The proposed patch-based processing and CNN are developed independently. This yields the practical merits that any CNN can be used according to each user's purposes, and the maintenance and improvement of each component of the whole system can be easily performed. In the experiment, river state classification is defined as the following problems using two datasets, to verify the effectiveness of the proposed method. First, river images from the public dataset called Places are classified to images with Muddy labels and images with Clear labels. Second, images from the river surveillance camera in Nagaoka City, Japan are classified to images captured when the government announced heavy rain or flood warning and the other images.

Long-Term Results of Crossover Bypass for Iliac Atherosclerotic Lesions in the Era of Endovascular Treatment: The Re-ACTION Study (Retrospective Assessment of Crossover Bypass as a Treatment for Iliac LesiONs).

Objective: The aim of this study was to elucidate the long-term results of crossover bypass (CB) for iliac atherosclerotic lesions in the era of endovascular treatment (EVT). Methods: A retrospective multicenter cohort study was performed. CB was performed in 242 patients between 2003 and 2014 by vascular surgeons at multiple medical centers in Japan. Results: Perioperative mortality was 1.7%. Primary patency rates were 86% at 5 years and 82% at 8 years. Univariate analysis showed that critical limb ischemia (Rutherford class 4-6), vein graft, and superficial femoral artery occlusion were significantly associated with low primary patency. In multivariate analysis, only critical limb ischemia influenced primary patency. The secondary patency rate was 87% at both 5 and 8 years. The limb salvage rate was 98% at both 5 and 8 years. The overall survival rates were 71% at 5 years and 49% at 8 years. Conclusion: The long-term results of CB were good in our study, compared with previous reports. Our results suggest that CB remains an option for the arterial reconstruction in unilateral iliac occlusive disease after EVT failed.

Asymmetricity Between Sister Cells of Pluripotent Stem Cells at the Onset of Differentiation.

Various somatic stem cells divide asymmetrically; however, it is not known whether embryonic stem cells (ESCs) divide symmetrically or asymmetrically, not only while maintaining an undifferentiated state but also at the onset of differentiation. In this study, we observed single ESCs using time-lapse imaging and compared sister cell pairs derived from the same mother cell in either the maintenance or differentiation medium. Mouse ESCs were cultured on E-cadherin-coated glass-based dishes, which allowed us to trace single cells. The undifferentiated cell state was detected by green fluorescent protein (GFP) expression driven by the Nanog promoter, which is active only in undifferentiated cells. Cell population analysis using flow cytometry showed that the peak width indicating distribution of GFP expression broadened when cells were transferred to the differentiation medium compared to when they were in the maintenance medium. This finding suggested that the population of ESCs became more heterogeneous at the onset of differentiation. Using single-cell analysis by time-lapse imaging, we found that although the total survival ratio decreased by changing to differentiation medium, the one-live-one-dead ratio of sister cell pairs was smaller compared with randomly chosen non-sister cell pairs, defined as an unsynchronized cell pair control, in both media. This result suggested that sister cell pairs were more positively synchronized with each other compared to non-sister cell pairs. The differences in interdivision time (the time interval between mother cell division and the subsequent cell division) between sister cells was smaller than that between non-sister cell pairs in both media, suggesting that sister cells divided synchronously. Although the difference in Nanog-GFP intensity between sister cells was smaller than that between non-sister cells in the maintenance medium, it was the same in differentiation medium, suggesting asymmetrical Nanog-GFP intensity. These data suggested that ESCs may divide asymmetrically at the onset of differentiation resulting in heterogeneity.

Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal. We now propose a novel approach for identifying artifactual components separated by wavelet-ICA using a pretrained support vector machine (SVM). Our method presents a robust and extendable system that enables fully automated identification and removal of artifacts from EEG signals, without applying any arbitrary thresholding. Using test data contaminated by eye blink artifacts, we show that our method performed better in identifying artifactual components than did existing thresholding methods. Furthermore, wavelet-ICA in conjunction with SVM successfully removed target artifacts, while largely retaining the EEG source signals of interest. We propose a set of features including kurtosis, variance, Shannon's entropy, and range of amplitude as training and test data of SVM to identify eye blink artifacts in EEG signals. This combinatorial method is also extendable to accommodate multiple types of artifacts present in multichannel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.

Evaluation method for in situ electric field in standardized human brain for different transcranial magnetic stimulation coils.

This study proposes a method to evaluate the electric field induced in the brain by transcranial magnetic stimulation (TMS) to realize focal stimulation in the target area considering the inter-subject difference of the brain anatomy. The TMS is a non-invasive technique used for treatment/diagnosis, and it works by inducing an electric field in a specific area of the brain via a coil-induced magnetic field. Recent studies that report on the electric field distribution in the brain induced by TMS coils have been limited to simplified human brain models or a small number of detailed human brain models. Until now, no method has been developed that appropriately evaluates the coil performance for a group of subjects. In this study, we first compare the magnetic field and the magnetic vector potential distributions to determine if they can be used as predictors of the TMS focality derived from the electric field distribution. Next, the hotspots of the electric field on the brain surface of ten subjects using six coils are compared. Further, decisive physical factors affecting the focality of the induced electric field by different coils are discussed by registering the computed electric field in a standard brain space for the first time, so as to evaluate coil characteristics for a large population of subjects. The computational results suggest that the induced electric field in the target area cannot be generalized without considering the morphological variability of the human brain. Moreover, there was no remarkable difference between the various coils, although focality could be improved to a certain extent by modifying the coil design (e.g., coil radius). Finally, the focality estimated by the electric field was more correlated with the magnetic vector potential than the magnetic field in a homogeneous sphere.

Rituximab in combination with CHOP chemotherapy for the treatment of diffuse large B cell lymphoma in Japan: a retrospective analysis of 1,057 cases from Kyushu Lymphoma Study Group.

We performed a retrospective analysis of patients with diffuse large B cell lymphoma treated with rituximab plus CHOP (cyclophosphamide, adriamycin, vincristine and prednisone) as a first-line therapy at 22 hospitals of the Kyushu Lymphoma Study Group. During the period 1996-2005, 1,057 patients (aged 22-90 years) were analyzed. Of these, 678 were treated with CHOP, and 379 were treated with rituximab plus CHOP (R-CHOP). The complete response rate was 59.9% in the CHOP group and 67.0% in the R-CHOP group (P < 0.001). Three-year progression-free survival (PFS) and overall survival (OS) rates were significantly higher in the R-CHOP group than in the CHOP group (61.3 vs. 45.6% for PFS, P < 0.001; 68.3 vs. 54.5% for OS, P < 0.001). The International Prognostic Index was a good prognostic marker for both groups; a survival benefit of rituximab addition was found for each risk subgroup and also for both age groups (60 years). Among 345 patients who received localized radiation therapy, the adding rituximab to CHOP attenuated the survival difference between CHOP and R-CHOP groups (P = 0.104), compared with no radiation group (P < 0.001). Results of this large-scale, multicenter study confirm that rituximab plus CHOP provided a greater survival benefit than CHOP alone.

Prognostic impact of immunohistochemical biomarkers in diffuse large B-cell lymphoma in the rituximab era.

We evaluated the usefulness of prognostic markers in patients with diffuse large B-cell lymphoma (DLBCL) treated with cyclophosphamide, vincristine, doxorubicin, and prednisolone (CHOP) +/- rituximab (R-CHOP) in Japan. We studied 730 patients with DLBCL; 451 received CHOP and 279 R-CHOP. We analyzed biopsy samples immunohistochemically for markers of germinal center B cells (CD10, Bcl-6), postgerminal center B cells (Multiple myeloma-1), and apoptosis (Bcl-2). The median follow-up period for surviving patients was 56.4 months for the CHOP group and 25.2 months for the R-CHOP group. DLBCL were categorized as germinal center B (GCB) subtype (352/730; 48.2%) or non-GCB subtype (378/730; 51.8%). In the CHOP group, the high expression of CD10 (P = 0.022) or Bcl-6 (P = 0.021), or GCB subtype (P = 0.05) was associated with better overall survival, whereas the high expression of Bcl-2 (P = 0.001) or MUM1 (P = 0.011), or non-GCB subtype (P = 0.05) was associated with worse overall survival. In the R-CHOP group, however, these biomarkers except Bcl-6 were not significant prognostic factors. The patients with non-GCB subtype showed improved survival in the R-CHOP group (P = 0.756). The International Prognostic Index was a useful clinical marker of survival in the CHOP group (P < 0.001) and also in the R-CHOP group (P < 0.001). Results of improved survival with rituximab addition indicate that the relevance of previously recognized prognostic factors should be re-evaluated.

Apicoaortic valved conduit is a good alternative for aortic stenosis with porcelain aorta after off-pump coronary artery bypass grafting.

We report a case of aortic stenosis with porcelain aorta using an apicoaortic valved conduit after 'aortic no touch' off-pump coronary artery bypass grafting. Apicoaortic valved conduit is a good surgical option for this patient to avoid re-entry through the sternum, injury to patent graft and manipulation of the porcelain ascending aorta.

Successful treatment of mycotic thoracic aortic aneurysm by in situ graft replacement with omental wrapping.

Mycotic thoracic aortic aneurysm is a fatal disease. We report a case of a 67-year-old man presenting with contained rupture of a mycotic thoracic aortic aneurysm. Urgent in situ graft replacement was successfully performed with omental wrapping to prevent postoperative graft infection.

Carotid stenosis and peripheral artery disease in Japanese patients with coronary artery disease undergoing coronary artery bypass grafting.

The combination of a change in lifestyle toward Western habits and an aging society, has led to a steady increase in the incidence of atherosclerotic diseases in the Japanese population. Coronary artery disease (CAD), carotid stenosis (CS), and peripheral artery disease (PAD) are major manifestations of generalized atherosclerosis and increase the risk of cardiovascular events. However, the incidence of CS and PAD in Japanese patients with CAD is not well known, so the present study investigated this in 380 consecutive patients with CAD undergoing elective coronary aorta bypass grafting (CABG) at Kishiwada Tokushukai Hospital between October 1999 and October 2001. The coexistence of CS and PAD in all patients was preoperatively evaluated by duplex ultrasonography and the ankle - brachial index (ABI). The average age of the study population was 66.09.1 years (range, 42-87). The number of male patients was 293 (77.1%). The incidence of CS was 13.7% and 15.3% for PAD. Multivariate logistic regression analysis demonstrated that no particular traditional atherosclerotic risk factor, such as hypertension, hyperlipidemia, diabetes mellitus, and smoking, was able to predict either CS or PAD, but CS and PAD were independent predictors of each other. The results of the study suggest that CS and PAD were not only highly prevalent but also strongly associated with each other in this cohort of CAD patients. Accordingly, extracoronary atherosclerotic disease should be assessed in Japanese CAD patients.