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Background The joint committee of the Japanese Society of Intensive Care Medicine/Japanese Respiratory Society/Japanese Society of Respiratory Care Medicine on ARDS Clinical Practice Guideline has created and released the ARDS Clinical Practice Guideline 2021. Methods The 2016 edition of the Clinical Practice Guideline covered clinical questions (CQs) that targeted only adults, but the present guideline includes 15 CQs for children in addition to 46 CQs for adults. As with the previous edition, we used a systematic review method with the Grading of Recommendations Assessment Development and Evaluation (GRADE) system as well as a degree of recommendation determination method. We also conducted systematic reviews that used meta-analyses of diagnostic accuracy and network meta-analyses as a new method. Results Recommendations for adult patients with ARDS are described: we suggest against using serum C-reactive protein and procalcitonin levels to identify bacterial pneumonia as the underlying disease (GRADE 2D); we recommend limiting tidal volume to 4–8 mL/kg for mechanical ventilation (GRADE 1D); we recommend against managements targeting an excessively low SpO2 (PaO2) (GRADE 2D); we suggest against using transpulmonary pressure as a routine basis in positive end-expiratory pressure settings (GRADE 2B); we suggest implementing extracorporeal membrane oxygenation for those with severe ARDS (GRADE 2B); we suggest against using high-dose steroids (GRADE 2C); and we recommend using low-dose steroids (GRADE 1B). The recommendations for pediatric patients with ARDS are as follows: we suggest against using non-invasive respiratory support (non-invasive positive pressure ventilation/high-flow nasal cannula oxygen therapy) (GRADE 2D), we suggest placing pediatric patients with moderate ARDS in the prone position (GRADE 2D), we suggest against routinely implementing NO inhalation therapy (GRADE 2C), and we suggest against implementing daily sedation interruption for pediatric patients with respiratory failure (GRADE 2D). Conclusions This article is a translated summary of the full version of the ARDS Clinical Practice Guideline 2021 published in Japanese (URL: https://www.jsicm.org/publication/guideline.html). The original text, which was written for Japanese healthcare professionals, may include different perspectives from healthcare professionals of other countries.

Background: Left atrial myxoma is the most common benign tumor. Its growth rate is still unknown because it does not present specific symptoms until before the tumor grows to a certain size; early surgical management is performed in most of cases once physicians detect it. Despite the common perception of cardiac myxomas as slow-growing tumors, some authors have reported rapid enlargement of myxomas. Case presentation: A 64-year-old woman was referred to our hospital with a diagnosis of left atrial tumor. The pointed tumor changed morphologically in a few hours and her respiratory condition, which had been normal at admission, suddenly deteriorated. Emergent surgery was performed, and the diagnosis was myxoma with multiple intratumor massive hematomas. The patient recovered uneventfully and was discharged on postoperative day 12 without any complications. Conclusions: We report an extremely rare case of left atrial myxoma collapsed by acute multiple hemorrhages within itself. Massive internal hemorrhage alters the size, shape, and fragility of the tumor. We should recognize the potential risk of internal hemorrhage, which leads to acute deterioration in myxomas, that are so called ‘slow-growing benign’ tumors.

In this paper, we present an end-to-end unsupervised anomaly detection framework for 3D point clouds. To the best of our knowledge, this is the first work to tackle the anomaly detection task on a general object represented by a 3D point cloud. We propose a deep variational autoencoder-based unsupervised anomaly detection network adapted to the 3D point cloud and an anomaly score specifically for 3D point clouds. To verify the effectiveness of the model, we conducted extensive experiments on the ShapeNet dataset. Through quantitative and qualitative evaluation, we demonstrate that the proposed method outperforms the baseline method. Our code is available at https://github.com/llien30/point_cloud_anomaly_detection., ICIP2021

This paper describes the total synthesis of (+)-coriamyrtin, a picrotoxane-type sesquiterpene. The natural product is widely known as a neurotoxin of the Coriariaceae family and bears a highly functionalized cis-hydrindane skeleton. Despite being biologically and synthetically attractive molecule, only two examples of the total synthesis are reported to date. Our synthetic strategy involves the highly stereoselective construction of the cis-hydrindane skeleton via the desymmetric strategy of a 1,3-cyclopentanedione moiety using an intramolecular aldol reaction and elaborate functionalization of the cyclopentane ring in the bicyclic structure for the formation of the 1,3-diepoxide moiety of coriamyrtin. Our method could be applied to synthesize various natural products with similar bicyclic skeletons and to expand neurobiological studies using synthesized products.

A nearly saturated 2,2,2-trifluoroethyl acetate (FEA)-based electrolyte solution has a low viscosity and delivers superior charge/discharge performance for LiNi0.8Co0.1Mn0.1O2 (NCM811) positive electrodes for lithium ion batteries. However, FEA has a low flash point, and hence needs to be mixed with an appropriate co-solvent to improve the safety of electrolyte solutions. In this study, two kinds of co-solvent were introduced into LiBF4/FEA electrolyte solutions and the LiBF4 concentration was increased. The mixture ratio of FEA and a co-solvent had a major effect on the charge/discharge performance of NCM811. The resultant nearly saturated electrolyte solution further improved the charge/discharge properties in terms of both cycle- and high rate performance. A very thin protective layer formed on NCM811 to suppress continual decomposition of the electrolyte solution, and did not interfere with interfacial Li+ transfer between the NCM811 electrode and electrolyte solution. In addition, 2,2-difluoroethyl acetate, which has a higher flash point than FEA, was used as a main solvent to study the highly concentrated electrolyte solutions.

Background and Objectives: Silver nanoparticles (AgNPs) have a dual effect showing both inflammatory and anti-inflammatory effects; however, the molecular mechanism of their anti-inflammatory effect is not clearly understood. In this study, we investigated the effect of AgNPs on the inflammatory response. Methods: We induced an inflammatory response in a lung epithelial cell line using tumor necrosis factor-α (TNFα) as an in vitro inflammatory model. Then the effect of AgNPs on the TNFα-induced inflammatory response was observed. Results: The mRNA expression of pro-inflammatory cytokines (IL-1β and IL-18) showed upregulation of IL-1β by AgNPs alone. However, AgNPs reduced the TNFα-induced upregulation of IL-1β and IL-18. AgNPs reduced the TNFα-induced NF-KB response, reactive oxygen species (ROS) generation, Nod Like Receptor Family-Pyrin domain containing 3 (NLRP3) gene expression, and caspase-1 activation, indicating that the anti-inflammatory effect of AgNPs was by inhibition of both NF-KB transcriptional and inflammasome pathways. Conversely, AgNPs alone induced the activation of both NF-KB transcriptional and inflammasome pathways, suggesting their involvement in the molecular mechanism of the inflammatory effect of AgNPs. Conclusion: Altogether, these findings show that two different pathways are involved in the molecular mechanism of both the dose-dependent inflammatory effect of AgNPs alone and the anti-inflammatory effect of AgNPs against the TNFα-induced inflammatory response. Understanding this mechanism will help to improve the medical applications of AgNPs and suggest their potential as a TNFα inhibitor to treat TNFα-induced inflammatory diseases.

Trajectory prediction has gained great attention and significant progress has been made in recent years. However, most works rely on a key assumption that each video is successfully preprocessed by detection and tracking algorithms and the complete observed trajectory is always available. However, in complex real-world environments, we often encounter miss-detection of target agents (e.g., pedestrian, vehicles) caused by the bad image conditions, such as the occlusion by other agents. In this paper, we address the problem of trajectory prediction from incomplete observed trajectory due to miss-detection, where the observed trajectory includes several missing data points. We introduce a two-block RNN model that approximates the inference steps of the Bayesian filtering framework and seeks the optimal estimation of the hidden state when miss-detection occurs. The model uses two RNNs depending on the detection result. One RNN approximates the inference step of the Bayesian filter with the new measurement when the detection succeeds, while the other does the approximation when the detection fails. Our experiments show that the proposed model improves the prediction accuracy compared to the three baseline imputation methods on publicly available datasets: ETH and UCY ($9\%$ and $7\%$ improvement on the ADE and FDE metrics). We also show that our proposed method can achieve better prediction compared to the baselines when there is no miss-detection., Accepted by IEEE Access

Non-flammable and highly concentrated electrolyte solutions were designed using tris(2,2,2-trifluoroethyl) phosphate (TFEP) as a main solvent toward a radical improvement in the safety and energy density of lithium-ion batteries. Unlike conventional carbonate ester-based solutions, simple TFEP-based electrolyte solutions were not intrinsically compatible with 5 V-class LiNi0.5 Mn1.5 O4 positive electrodes, even at high concentrations. Based on the degradation mechanism that was analyzed by Raman spectroscopy, scanning electron microscopy/energy dispersive X-ray spectroscopy, and X-ray photoelectron spectroscopy, a fluorinated diluent of methyl 3,3,3-trifluoropropionate (FMP) was introduced to suppress the decomposition of LiBF4 and TFEP at high potentials. A nearly saturated LiBF4 /TFEP+FMP electrolyte solution with a specific composition improved the charge and discharge performance of a LiNi0.5 Mn1.5 O4 electrode, and the solution structure was studied by pulsed-field-gradient NMR spectroscopy.

Single-cell level analysis is powerful tool to assess the heterogeneity of cellular components in tumor microenvironments (TME). In this study, we investigated immune-profiles using the single-cell analyses of endoscopically- or surgically-resected tumors, and peripheral blood mononuclear cells from gastric cancer patients. Furthermore, we technically characterized two distinct platforms of the single-cell analysis; RNA-seq-based analysis (scRNA-seq), and mass cytometry-based analysis (CyTOF), both of which are broadly embraced technologies. Our study revealed that the scRNA-seq analysis could cover a broader range of immune cells of TME in the biopsy-resected small samples of tumors, detecting even small subgroups of B cells or Treg cells in the tumors, although CyTOF could distinguish the specific populations in more depth. These findings demonstrate that scRNA-seq analysis is a highly-feasible platform for elucidating the complexity of TME in small biopsy tumors, which would provide a novel strategies to overcome a therapeutic difficulties against cancer heterogeneity in TME.

Assistive technology is increasingly important as the senior population grows. The purpose of this study is to develop a means of preventing fatal injury by monitoring the movements of the elderly and sounding an alarm if an accident occurs. We present a method of detecting an anomaly in a first-person’s gait from an egocentric video. Followed by the conventional anomaly detection methods, we train the model in an unsupervised manner. We use optical flow images to capture ego-motion information in the first person. To verify the effectiveness of our model, we introduced and conducted experiments with a novel first-person video anomaly detection dataset and showed that our model outperformed the baseline method.

Neural Machine Translation (NMT) has shown drastic improvement in its quality when translating clean input, such as text from the news domain. However, existing studies suggest that NMT still struggles with certain kinds of input with considerable noise, such as User-Generated Contents (UGC) on the Internet. To make better use of NMT for cross-cultural communication, one of the most promising directions is to develop a model that correctly handles these expressions. Though its importance has been recognized, it is still not clear as to what creates the great gap in performance between the translation of clean input and that of UGC. To answer the question, we present a new dataset, PheMT, for evaluating the robustness of MT systems against specific linguistic phenomena in Japanese-English translation. Our experiments with the created dataset revealed that not only our in-house models but even widely used off-the-shelf systems are greatly disturbed by the presence of certain phenomena., 15 pages, 4 figures, accepted at COLING 2020