Comparison Of Ensemble Kalman Filtering And Particle Filtering On Short-Term Streamflow Forecasting Using A Distributed Hydrologic Model

Floods are the most common and widespread disasters in the world and are responsible for a greater number of damaging events than any other type of natural event. However, due to various uncertainties that originate from simulation models, observations, and forcing data, it is still insufficient to obtain accurate flood forecasting results with the required lead times. Recently, ensemble forecasting techniques based on data assimilation (DA) have become increasingly popular, due to their potential ability to explicitly handle the various sources of uncertainty in operational hydrological models. Difficulty lies in DA for flood forecasting because nonlinearity increases sharply during flood events and the probability of streamflow cannot be characterized by the Gaussian assumption. Particle filtering (PF), also known as sequential Monte Carlo (SMC) methods, is a Bayesian learning process in which the propagation of all uncertainties is conducted by a suitable selection of randomly generated particles without any assumptions about the nature of the distributions. In this paper, the performance of ensemble Kalman filtering (EnKF) and PF is assessed for short-term streamflow forecasting with a distributed hydrologic model, namely, the water and energy transfer processes (WEP) model. To mitigate the drawbacks of conventional filters, the ensemble square root filter (EnSRF) and the regularized particle filter (RPF) are implemented. For both the EnSRF and the RPF, sequential data assimilation is performed within a lag-time window to consider the response times of internal hydrologic processes. The proposed methods are applied to several catchments in Korea and Japan to assess their performance. The discussion will be focused on how non-Gaussian and non-linear property of floods affects updating results by two DA methods

Flood Estimation and Prediction Using Particle Filters

Data assimilation methods have received increased attention to accomplish uncertainty assessment and enhancement of forecasting capability in various areas. Despite their potential, applicable software frameworks for probabilistic approaches and data assimilation are still limited because most hydrologic modeling software are based on a deterministic approach. In this study, we developed a hydrologic modeling framework for data assimilation, namely MPI-OHyMoS. While adapting object-oriented features of the original OHyMoS, MPI-OHyMoS allows user to build a probabilistic hydrologic model with data assimilation. In this software framework, sequential data assimilation based on particle filtering is available for any hydrologic models considering various sources of uncertainty originating from input forcing, parameters, and observations. Ensemble simulations are parallelized by a message passing interface (MPI), which can take advantage of high-performance computing (HPC) systems. Structure and implementation processes of data assimilation via MPI-OHyMoS are illustrated using a simple lumped model. We apply this software framework for uncertainty assessment of a distributed hydrologic model in synthetic and real experiment cases. In the synthetic experiment, dual state-parameter updating results in a reasonable estimation of parameters to cover synthetic true within their posterior distributions. In the real experiment, dual updating with identifiable parameters results in a reasonable agreement to the observed hydrograph with reduced uncertainty of parameters

Probabilistic Integrated Urban Inundation Modeling Using Sequential Data Assimilation

Urban inundation due to climate change and heavy rainfall is one of the most common natural disasters worldwide. However, it is still insufficient to obtain accurate urban inundation predictions due to various uncertainties coming from input forcing data, model parameters, and observations. Despite of numerous sophisticated data assimilation algorithms proposed to increase the certainty of predictions, there have been few attempts to combine data assimilation with integrated inundation models due to expensive computations and computational instability such as breach of conservation and momentum equations in the updating procedure. In this study, we propose a probabilistic integrated urban inundation modeling scheme using sequential data assimilation. The original integrated urban inundation model consists of a 2D inundation model on the ground surface and a 1D network model of sewer pipes, which are combined by a sub-model to exchange storm water between the ground surface and the sewerage system. In our method, uncertainties of modeling conditions are explicitly expressed by ensembles having different rainfall input, initial conditions, and model parameters. Then, particle filtering(PF), one of sequential data assimilation techniques for non-linear and non-Gaussian models, is applied to sequentially update model states and parameters when new observations are arrived from monitoring systems. Several synthetic experiments are implemented to demonstrate applicability of the proposed method in an urbanized area located in Osaka, Japan. The discussion will be focused on noise specification and updating methods in PF and comparison of accuracy between deterministic and probabilistic inundation modeling methods

Performance Analysis On A Variable Capacity Swash Plate Compressor

A numerical study on the performance of a variable capacity swash plate compressor for an automotive air-conditioning system was carried out. The compressor under investigation had six cylinders and capacity regulation was made by changing the swash plate inclination angle. A numerical simulation program was made based on mathematical modelings on the swash plate dynamics, refrigerant states in various control volumes such as cylinders and crank room, and flows in the opening passages of electric control valve for crank room pressure control. The simulation results such as mass flow rate, compressor power consumption, cooling capacity and COP were compared with measurements within ±5% deviation over various operating conditions except at low operating speed such as idling condition. By using the simulation program, the effect of the crank room pressure on the swash plate inclination angle and the determination of the crank room pressure level by the electric control valve openings could be investigated

Understanding NK cell biology for harnessing NK cell therapies: targeting cancer and beyond

Gene-engineered immune cell therapies have partially transformed cancer treatment, as exemplified by the use of chimeric antigen receptor (CAR)-T cells in certain hematologic malignancies. However, there are several limitations that need to be addressed to target more cancer types. Natural killer (NK) cells are a type of innate immune cells that represent a unique biology in cancer immune surveillance. In particular, NK cells obtained from heathy donors can serve as a source for genetically engineered immune cell therapies. Therefore, NK-based therapies, including NK cells, CAR-NK cells, and antibodies that induce antibody-dependent cellular cytotoxicity of NK cells, have emerged. With recent advances in genetic engineering and cell biology techniques, NK cell-based therapies have become promising approaches for a wide range of cancers, viral infections, and senescence. This review provides a brief overview of NK cell characteristics and summarizes diseases that could benefit from NK-based therapies. In addition, we discuss recent preclinical and clinical investigations on the use of adoptive NK cell transfer and agents that can modulate NK cell activity

Clinical implications of gut microbiota and cytokine responses in coronavirus disease prognosis

ObjectivesSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects gut luminal cells through the angiotensin-converting enzyme-2 receptor and disrupts the gut microbiome. We investigated whether the gut microbiome in the early stage of SARS-CoV-2 infection was associated with the prognosis of coronavirus disease (COVID-19).MethodsThirty COVID-19 patients and 16 healthy controls were prospectively enrolled. Blood and stool samples and clinical details were collected on days 0 (enrollment), 7, 14, and 28. Participants were categorized into four groups by their clinical course.ResultsGut microbiota composition varied during the clinical course of COVID-19 and was closely associated with cytokine levels (p=0.003). A high abundance of the genus Dialister (linear discriminant analysis [LDA] effect size: 3.97856, p=0.004), species Peptoniphilus lacrimalis (LDA effect size: 4.00551, p=0.020), and Anaerococcus prevotii (LDA effect size: 4.00885, p=0.007) was associated with a good prognosis. Starch, sucrose, and galactose metabolism was highly activated in the gut microbiota of the poor prognosis group. Glucose-lowering diets, including whole grains, were positively correlated with a good prognosis.ConclusionGut microbiota may mediate the prognosis of COVID-19 by regulating cytokine responses and controlling glucose metabolism, which is implicated in the host immune response to SARS-CoV-2

Arthroscopic Treatment of Septic Arthritis of Acromioclavicular Joint

Septic arthritis requires an early diagnosis and proper treatment to prevent the destruction of articular cartilage and joint contracture. This paper presents a rare case of septic arthritis of the acromioclavicular joint that was treated with arthroscopic debridement and resection of the distal clavicle

Long-term humoral and cellular immunity against vaccine strains and Omicron subvariants (BQ.1.1, BN.1, XBB.1, and EG.5) after bivalent COVID-19 vaccination

BackgroundThe assessment of long-term humoral and cellular immunity post-vaccination is crucial for establishing an optimal vaccination strategy.MethodsThis prospective cohort study evaluated adults (≥18 years) who received a BA.4/5 bivalent vaccine. We measured the anti-receptor binding domain immunoglobulin G antibody and neutralizing antibodies (NAb) against wild-type and Omicron subvariants (BA.5, BQ.1.1, BN.1, XBB.1 and EG.5) up to 9 months post-vaccination. T-cell immune responses were measured before and 4 weeks after vaccination.ResultsA total of 108 (28 SARS-CoV-2-naïve and 80 previously infected) participants were enrolled. Anti-receptor binding domain immunoglobulin G (U/mL) levels were higher at 9 months post-vaccination than baseline in SAR-CoV-2-naïve individuals (8,339 vs. 1,834, p<0.001). NAb titers against BQ.1.1, BN.1, and XBB.1 were significantly higher at 9 months post-vaccination than baseline in both groups, whereas NAb against EG.5 was negligible at all time points. The T-cell immune response (median spot forming unit/106 cells) was highly cross-reactive at both baseline (wild-type/BA.5/XBB.1.5, 38.3/52.5/45.0 in SARS-CoV-2-naïve individuals; 51.6/54.9/54.9 in SARS-CoV-2-infected individuals) and 4 weeks post-vaccination, with insignificant boosting post-vaccination.ConclusionRemarkable cross-reactive neutralization was observed against BQ.1.1, BN.1, and XBB.1 up to 9 months after BA.4/5 bivalent vaccination, but not against EG.5. The T-cell immune response was highly cross-reactive

Inhibitory Effect of Inflexinol on Nitric Oxide Generation and iNOS Expression via Inhibition of NF-κB Activation

Inflexinol, an ent-kaurane diterpenoid, was isolated from the leaves of Isodon excisus. Many diterpenoids isolated from the genus Isodon (Labiatae) have antitumor and antiinflammatory activities. We investigated the antiinflammatory effect of inflexinol in RAW 264.7 cells and astrocytes. As a result, we found that inflexinol (1, 5, 10 μM) suppressed the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) as well as the production of nitric oxide (NO) in LPS-stimulated RAW 264.7 cells and astrocytes. Consistent with the inhibitory effect on iNOS and COX-2 expression, inflexinol also inhibited transcriptional and DNA binding activity of NF-κB via inhibition of IκB degradation as well as p50 and p65 translocation into nucleus. These results suggest that inflexinol inhibits iNOS and COX-2 expression through inhibition of NF-κB activation, thereby inhibits generation of inflammatory mediators in RAW 264.7 cells and astrocytes, and may be useful for treatment of inflammatory diseases