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We present nowcasts of sudden heavy rains on meso-γ-scales (2–20 km) using the high spatio-temporal resolution of a Multi-Parameter Phased-Array Weather Radar (MP-PAWR) sensitive to rain droplets. The onset of typical storms is successfully predicted with 10-minute lead time, i.e., the current predictability limit of rainfall caused by individual convective cores. A supervised recurrent neural network based on Long Short-Term Memory with 3D spatial convolutions (RN3D) is used to account for the horizontal and vertical changes of the convective cells with a time resolution of 30 sec. The model uses radar reflectivity at horizontal polarization (ZH) and the differential reflectivity. The input parameters are defined in a volume of 64×64×8 km3 with the lowest level at 1.9 km and a resolution of 0.4×0.4×0.25 km3. The prediction is a 10-minute sequence of ZH at the lowest grid level. The model is trained with a large number of observations of summer 2020 and an adversarial technique. RN3D is tested with different types of rapidly evolving localized heavy rainfalls of summers 2018 and 2019. The model performance is compared to that of an advection model for 3D extrapolation of PAWR echoes (A3DM). RN3D better predicts the formation and dissipation of precipitation. However, RN3D tends to underestimate heavy rainfall especially when the storm is well developed. In this phase of the storm, A3DM nowcast scores are found slightly higher. The high skill of RN3D to predict the onset of sudden localized rainfall is illustrated with an example for which RN3D outperforms the operational precipitation nowcasting system of Japan Meteorological Agency (JMA).

Brain damage in acute sepsis may be associated with poor long-term outcomes that impair reintegration into society. We aimed to clarify whether brain volume reduction occurs during the acute phase of sepsis in patients with acute brain damage. In this prospective, noninterventional observational study, brain volume reduction was evaluated by comparing head computed tomography findings at admission with those obtained during hospitalization. We examined the association between brain volume reduction and performance of the activities of daily living in 85 consecutive patients (mean age, 77 ± 12.7 years) with sepsis or septic shock. The bicaudate ratio increased in 38/58 (65.5%) patients, Evans index increased in 35/58 (60.3%) patients, and brain volume by volumetry decreased in 46/58 (79.3%) patients from the first to the second measurement, with significant increases in the bicaudate ratio (P < 0.0001) and Evans index (P = 0.0005) and a significant decrease in the brain volume by volumetry (P < 0.0001). The change rate for brain volume by volumetry was significantly correlated with the Katz index (ρ = −0.3790, P = 0.0094). In the acute phase of sepsis in this sample of older patients, 60–79% of patients showed decreased brain volumes. This was associated with a decreased capacity for performing activities of daily living.

Tsukasa Kuwana, Kosaku Kinoshita, Shingo Ihara, Nami Sawada, Toru Hosokawa, Tomokazu Mutoh, Umefumi Iguchi, Katsuhiro Nakagawa, Junko Yamaguchi Division of Emergency and Critical Care Medicine, Department of Acute Medicine, Nihon University School of Medicine, Tokyo, JapanCorrespondence: Kosaku Kinoshita, Division of Emergency and Critical Care Medicine, Department of Acute Medicine, Nihon University School of Medicine, 30-1 Oyaguchi Kamichou Itabashi-ku, Tokyo, Japan, Tel +81-3-3972-8111, Fax +81-3-5995-1069, Email kinoshita.kosaku@nihon-u.ac.jpPurpose: The characteristics of patients with severe COVID-19 pneumonia who underwent direct hemoperfusion using polymyxin B-immobilized fiber column (PMX-DHP), in addition to steroids and immunomodulators, remain unclear.Patients and Methods: We conducted a retrospective observational study on 31 patients with severe COVID-19 pneumonia treated with PMX-DHP in an intensive care unit (ICU) from December 2020 to September 2021.Results: Outcomes 28 days after admission to the ICU were 20 in the survival group and 11 in the death group. Parameters significantly different between the survival and death group before PMX-DHP were percentage of invasive mechanical ventilation (25% vs 72.7%, P = 0.0209), PaO2/FIO2 (P/F) ratio (104.5 vs 75, P = 0.0317), and sequential organ failure assessment (SOFA) score (2 vs 3, P = 0.0356). Invasive mechanical ventilation avoidance rate was significantly different between the survival (100%) and death group (0%) (P = 0.0012). P/F ratio, respiratory ratio (RR), and lymphocyte counts improved significantly after PMX-DHP for all patients. The lymphocyte counts changed significantly in the survival (P < 0.0001), but not the death group (P = 0.7927).Conclusion: PMX-DHP, in addition to steroids and immunomodulators, may improve oxygenation and alleviate tachypnea by modulating the lymphocyte numbers and levels of various mediator against severe COVID-19 pneumonia. It may be better to perform PMX-DHP before multi organ dysfunction and lung injury has progressed. Furthermore, the early increase in lymphocyte counts after PMX-DHP might be an indicate a positive outcome.Keywords: IL-6, lymphocyte, P/F ratio, respiratory rate, SOFA

The first X-band dual polarized phased array weather radar (DP-PAWR), which simultaneously transmits pulses of horizontal and vertical polarized radiation, was developed and installed at Saitama University, Japan, in December 2017. The DP-PAWR uses mechanical and electronic scanning at azimuth and elevation angles, respectively. It provides polarimetric precipitation measurements via 3-D volume scanning with an update rate between 10 and 60 s, for a range of up to 80 km. Here, we describe the initial DP-PAWR observation results. To evaluate the DP-PAWR observation accuracy, we compared the observational data with radar variables derived from Parsivel disdrometer data. In comparison with the disdrometer, the relative observation accuracy for the DP-PAWR radar reflectively factor had a standard deviation of 1.1 dB and mean value of 0.4 dB. We also conducted detailed observations of a developing thunderstorm using a specific differential phase ( $K_{\mathrm {dp}}$ ) column, focusing on the $K_{\mathrm {dp}}$ core during the storm. The $K_{\mathrm {dp}}$ core movements provided useful information about the convection flow during the storm.

Under the Japanese Cross Strategic Innovation Promotion Program (SIP), studies are conducted to perform very short-term predictions of local torrential rains based on a new multi-parameter phased-array weather radar (MP-PAWR) and deep neural networks (DNNs). The association of the two methods is expected to overcome the limitations of the conventional rains observation systems and numerical models that are not well suited to handle the rapid non-linear processes inherent in heavy convective rains. The unique spatio-temporal resolution of the observations allows us to train supervised DNNs to extrapolate the fast evolution of 3D convective cells. We compared two DNNs (CLM3D and CGRU3D) designed to fully exploit the information in the vertical dimension. Both methods use new techniques involving spatial convolutions in temporal recurrent iterations such as Long Short-Term Memory (LSTM) or Gated Recurrent Units (GRU) ones. The core of CLM3D is a stack of convLTSM2D layers, each of which is applied to a single altitude. CGRU3D uses a multilayer encoderdecoder with convGRU3D layers, each layer is associated with a size of 3D spatial features. Forecasts with a lead-time of 10 min at an altitude of 600 m with a horizontal resolution of about 500 m are compared. The models are tested with different types of heavy precipitation: localized short-lived rains on July 24, 2018 and wide-spread ones on the 29 of the same month. The models are evaluated with respect to a 3D linear advection nowcast model (OF3D) and a persistent one. We found that the DNN and OF3D models perform better on July 24 with similar scores that are significantly higher than those of the persistent model. Considering all rain events, critical success indexes (CSI) of 0.62, 0.53, 0.55 are found for CGRU3D, CLM3D and OF3D, respectively, and 0.43 for the persistent model. Regarding only heavy precipitation, the CSIs show a great variability between 0 and 0.4 on the predictions made that day. These results clearly illustrate the great challenge of nowcasting heavy precipitation. On July 29, none of the models have significantly higher scores than those obtained with the persistent nowcast. The interesting result of this study is that the two DNNs show similar nowcasting skills whatever the intensity and the type of rain, and this despite their architectures and training strategies being different. This may indicate that optimizing the tunning of the hyperpameters and the training dataset could not bring significant improvements and, the key, could be by feeding the models with more comprehensive information on the atmospheric state.

Background To the best of our knowledge, splenic rupture caused by hit by a pitch (HBP) has not been previously reported. We present a patient who underwent emergency laparotomy for splenic rupture after being HBP during a baseball game. Case presentation A 41-year-old male was HBP in the left abdomen during his first at-bat during a baseball game. During the operation, vascular injury of the splenic hilum and a deeply extending parenchymal injury were observed, and splenectomy was performed. Histologic findings were consistent with splenic rupture. Conclusions The patient’s postoperative course was uneventful. Although extremely rare, the possibility of intra-abdominal organ injury should be considered in batters who are hit in the abdomen by a pitched baseball, as illustrated by our patient.

This study is about the development of a deep neural network to make very short-term predictions of torrential rains at the urban scale (meso-γ). The new polarimetric Phased Array Weather Radar (MP-PAWR) operating at Saitama (Japan) since 2018 is used. Thanks to the unique spatio-temporal resolution of the measurements, the precursors of torrential rains are detected aloft more than 20 minutes before the rain occurs. With this information, we aim at the prediction of surface precipitation with a lead time of 20 min, a horizontal resolution better than 500 m within a radius of 25 km around the instrument. Two supervised neural networks are considered to extrapolate radar reflectivity (ZH) at the altitude of 600 m. The first model (model-1) is based on a technique developed for mesoscale predictions from observations at a single altitude. It uses horizontal (2D) convolutions in gated recurrent time iterations and a multilayer encoder-decoder (EC/DC) architecture. The technique is adapted to consider 3 radar parameters and 11 altitudes up to 10 km, in the same way as RBG channels in video analysis. The second model (model-2) uses similar architecture but with 3D spatial convolutions to properly describe the vertical motions between adjacent layers. The input to the models consist in 20 min long time series of ZH, Doppler velocity and differential reflectivity observations (30 sec sampling). The models are trained using all the rain events observed between August and October 2018, and are assessed using local heavy rains observed over a period of 1-hour on July, 24, 2018. The beginning of the rain is first predicted with a lead time of about 5 min, and its evolution is fairly well reproduced to lead times up to about 10 min. Results quickly degrades for longer lead times. We found that a deeper network with 4 layers EC/DC gives better 20 min predictions than a model with 3 layers, but final results were not yet obtained at the time of writing. Regarding lead-times of 10 min, model-2 gives critical success indexes (CSIs) of 0.60 and 0.40 for pixels with ZH> 10 dBZ and 37 dBZ, which is comparable or better than results presented in other studies. For lead-times of 20 min, CSIs dropped to 0.28 and 0.10, respectively, and no other studies was found for comparison. Model-1 clearly shows poorer performance, especially for high ZH. However, this approach demands much less calculations and the training lasts only 2 weeks long, namely half of the time spent for model-2. Therefore, it is worth further studying both approaches and potential improvements are discussed.

For the spaceborne precipitation radars, since the surface echoes are very strong, the clutter echoes caused by the antenna sidelobe mask the echoes of light precipitation even if the low sidelobe antenna is designed. This study develops a method of the clutter removal for the spaceborne precipitation radars to mitigate the misjudgment of the light precipitation detection. The radar data of the Dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement mission's (GPM 's) core satellite and the Precipitation Radar (PR) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite are analyzed to construct those clutter databases. Once the databases are created, the magnitude of the clutter echoes is estimated and its contribution is subtracted from the observed echo power. When the clutter removal developed in this study are applied, the residual clutter signals are mitigated compared with the clutter removal adopted in version 06 processing.

Seven phthalate esters (di-isobutyl phthalate (DIBP), di-n-butyl phthalate (DBP), benzylbutyl phthalate (BBP), di-(2-ethylhexyl) phthalate (DEHP), di-n-octyl phthalate (DNOP), di-isononyl phthalate (DINP) and di-isodecyl phthalate (DIDP)) were analyzed by pyrolyzer/thermal desorption-gas chromatography/mass spectrometry (Py/TD-GC/MS), the retention index and relative response factor (RRF) relative to DEHP was calculated for each compound and used to construct a quantitative database (qDB). This qDB enables normalization of the retention time and response factor of each phthalate ester between any laboratory simply by analyzing an n-alkane solution and DEHP standard material. This allows for easy calculation of the phthalate ester content of samples without preparation of calibration curves. The efficacy of this qDB method was verified by performing a quantitative analysis of phthalate esters at 4 different laboratories that showed actual retention times were within ±0.012 min of the estimated retention times for all compounds at all laboratories. Similarly, the mean recovery rate (n = 6) at each laboratory was within 79-113%. Quantitative analysis was also performed on 30 real samples using both the qDB method and the Py/TD-GC/MS method set forth in IEC62321-8, which involves the preparation of 1-point calibrations to perform quantitative analysis. The difference in quantitative results between the methods was approximately within ±200 mg/kg for compounds in the concentration region of2000 mg/kg.

This paper is an overview of a project concerned with “Early warning for torrential rainfall/tornado” under “Enhancement of Societal Resiliency against Natural Disasters,” which is one of eleven themes of the SIP (Cross-ministerial Strategic Innovation Promotion Program under the Council for Science, Technology and Innovation, the Cabinet Office, Government of Japan). The characteristics of the project are the development of a multi-parameter phased array weather radar (MP-PAWR) that enables the accomplishment of an accurate three-dimensional model of precipitation over 30 s within a 60 km radius. Various products developed ducts using MP-PAWR and other observations, and numerical predictions, are also discussed, with a demonstration experiment to provide early warnings for torrential rainfall and related disasters. For end users such as local governments and general citizens, the final goal of this project is the social implementation of these products.

Two products from the Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) algorithms, a flag of intense solid precipitation above the –10°C height (“flagHeavyIcePrecip”), and a classification of precipitation type (“typePrecip”) were validated quantitatively from the viewpoint of microphysics using ground-based in-situ hydrometeor measurements and X-band multi-parameter (X-MP) radar observations of snow clouds that occurred on 4 February 2018. The distribution of the “flagHeavyIcePrecip” footprints was in good agreement with that of the graupel-dominant pixels classified by the X-MP radar hydrometeor classification. In addition, the vertical profiles of X-MP radar reflectivity exhibited significant differences between footprints flagged and unflagged by “flagHeavyPrecip”. We confirmed the effectiveness of “flagHeavyIcePrecip”, which is built into “typePrecip” algorithm, for detecting intense ice precipitation and concluded that "flagHeavyIcePrecip" is appropriate to useful for determining convective clouds.It is well known that the lightning activity is closely related to the convection. We examined the lightning activity using GPM DPR product flagHeavyIcePrecip that indicates the existence of graupel in the upper cloud. On 20 June 2016, we experienced heavy rain with active lightning during Baiu monsoon rainy season, while the GPM DPR passed over Kyushu region in Japan. The distribution of “flagHeavyIcePrecip” obtained from the GPM DPR well corresponded to the CG/IC lightning concentration. On 4 September 2019, isolated thunder clouds observed by the GPM DPR was also similar to the “flagHeavyIcePrecip” distribution. However, partially there was IC lightning without “flagHeavyIcePrecip”, which was positive lightning. It was suggested to have been produced in the upper clouds in which positive ice crystals were dominant.

An estimation method for specific attenuation and equivalent radar reflectivity in the melting layer using a dual Ka-band radar system was studied. The system consists of two identically designed Ka-radars. When a precipitation system comes between the two radars, the radars observe the system from opposite directions. The radar applies the frequency-modulated continuous-wave type and is designed for the measurement of the scattering characteristics of precipitation particles. The precipitation echoes suffer from rain attenuation. The reduction due to rain attenuation symmetrically appears in both radar echoes. By differentiating averaged measured radar reflectivity with range, the specific attenuation can be estimated. After obtaining the specific attenuation, equivalent radar reflectivity is estimated. In the melting layer, specific attenuation and the equivalent radar reflectivity vary largely along the radio path, and the estimated specific attenuation is very sensitive to the setup configuration of the experiment. The accuracy of the estimated specific attenuation was found to depend on the curvature, that is, the doubly differentiated value of the equivalent radar reflectivity with respect to range and the distance for the differentiation. The measurement system and actual procedure for the data analysis are also described.

Schlafen-8 (Slfn8) is a member of the Schlafen family of proteins, which harbor helicase domains and are induced by LPS and interferons. It has been reported that the Schlafen family are involved in various cellular functions, including proliferation, differentiation and regulation of virus replication. Slfn8 has been implicated in T-cell differentiation in the thymus. However, the roles of Slfn8 in the immune system remains unclear. In this study, we generated Slfn8 knockout mice (Slfn8−/−) and investigated the immunological role of Slfn8 using the T-cell-mediated autoimmune model experimental autoimmune encephalomyelitis (EAE). We found that the clinical score was reduced in Slfn8−/− mice. IL-6 and IL-17A cytokine production, which are associated with EAE onset and progression, were decreased in the lymph nodes of Slfn8−/− mice. Immune cell populations in Slfn8−/− mice, including macrophages, neutrophils, T cells and B cells, did not reveal significant differences compared with wild-type mice. In vitro activation of Slfn8−/− T cells in response to TCR stimulation also did not reveal significant differences. To confirm the involvement of non-hematopoietic cells, we isolated CD45− CD31+ endothelial cells and CD45−CD31− gp38+ fibroblastic reticular cells by FACS sorting. We showed that the levels of IL-6 and Slfn8 mRNA in CD45− CD31+ endothelial cells were increased after EAE induction. In contrast, the level of IL-6 mRNA after EAE induction was markedly decreased in CD31+ endothelial cells from Slfn8−/− mice. These results indicate that Slfn8 may play a role in EAE by regulating inflammation in endothelial cells.

Monocytes and macrophages comprise a variety of subsets with diverse functions. It is thought that these cells play a crucial role in homeostasis of peripheral organs, key immunological processes and development of various diseases. Among these diseases, fibrosis is a life-threatening disease of unknown aetiology. Its pathogenesis is poorly understood, and there are few effective therapies. The development of fibrosis is associated with activation of monocytes and macrophages. However, the specific subtypes of monocytes and macrophages that are involved in fibrosis have not yet been identified. Here we show that Ceacam1+Msr1+Ly6C-F4/80-Mac1+ monocytes, which we term segregated-nucleus-containing atypical monocytes (SatM), share granulocyte characteristics, are regulated by CCAAT/enhancer binding protein β (C/EBPβ), and are critical for fibrosis. Cebpb deficiency results in a complete lack of SatM. Furthermore, the development of bleomycin-induced fibrosis, but not inflammation, was prevented in chimaeric mice with Cebpb-/- haematopoietic cells. Adoptive transfer of SatM into Cebpb-/- mice resulted in fibrosis. Notably, SatM are derived from Ly6C-FceRI+ granulocyte/macrophage progenitors, and a newly identified SatM progenitor downstream of Ly6C-FceRI+ granulocyte/macrophage progenitors, but not from macrophage/dendritic-cell progenitors. Our results show that SatM are critical for fibrosis and that C/EBPβ licenses differentiation of SatM from their committed progenitor.

Global three-dimensional wind measurement is important for weather prediction’ climate studies’ and various meteorological studies. Current space-based passive sensors can make wind measurements with a large coverage area and high temporal and horizontal resolutions but has a low vertical resolution. The World Meteorological Organization (WMO) wants to develop space-based wind profiling systems. A space-based Doppler Wind Lidar (DWL) is one of useful and promising remote sensing techniques to fill the gap of current observations. The National Institute of Information and Communications Technology (NICT) is developing a single-frequency high-energy Tm,Ho:YLF laser, 1-μm key technology and instrument for a future space-based coherent DWL. In the paper’ we will describe recent progress and results of development of 1-μm key technology.

Rainfall velocity, raindrop size distribution (DSD), and vertical wind velocity were simultaneously observed with 2.05- and 1.54-μm coherent Doppler lidars during convective and stratiform rain events. A retrieval method is based on identifying two separate spectra from the convolution of the aerosol and precipitation Doppler lidar spectra. The vertical wind velocity was retrieved from the aerosol spectrum peak and then the terminal rainfall velocity corrected by the vertical air motion from the precipitation spectrum peak was obtained. The DSD was derived from the precipitation spectrum using the relationship between the raindrop size and the terminal rainfall velocity. A comparison of the 1-min-averaged rainfall velocity from Doppler lidar measurements at a minimum range and that from a collocated ground-based optical disdrometer revealed high correlation coefficients of over 0.89 for both convective and stratiform rain events. The 1-min-averaged DSDs retrieved from the Doppler lidar spectrum using parametric and nonparametric methods are also in good agreement with those measured with the optical disdrometer with a correlation coefficient of over 0.80 for all rain events. To retrieve the DSD, the parametric method assumes a mathematical function for the DSD and the nonparametric method computes the direct deconvolution of the measured Doppler lidar spectrum without assuming a DSD function. It is confirmed that the Doppler lidar can retrieve the rainfall velocity and DSD during relatively heavy rain, whereas the ratio of valid data significantly decreases in light rain events because it is extremely difficult to separate the overlapping rain and aerosol peaks in the Doppler spectrum.

Special experiments using the Precipitation Radar (PR) onboard the Tropical Rainfall Measuring Mission satellite during descent after the orbital maintenance fuel was depleted are reported in this paper. In these special observation experiments, a 90 deg yaw maneuver experiment, a wide swath width experiment, and the dense sampling experiments were carried out. The key operation of these experiments was a large change in the phase shifter settings of the transmitter and receiver, which was not tested before or after the launch. In addition, the 90 deg yaw experiment required the special operation of the satellite, whereas the other two experiments were implemented by changing only the PR operation. During the experiments, the PR was operated, and the expected data were obtained. The preliminary results suggest the possibility of future radar observation from space, and the results of these experiments will be utilized as basic data to improve the algorithm of the dual-frequency PR onboard the Global Precipitation Measurement mission core satellite and the design of future spaceborne PRs., 形態: カラー図版あり, Physical characteristics: Original contains color illustrations, 資料番号: PA1610016000