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Objective: Accurate prognosis prediction is critical for individualized-therapy making of gastric cancer patients. We aimed to develop and test 6-month, 1-, 2-, 3-, 5-, and 10-year overall survival (OS) and cancer-specific survival (CSS) prediction models for gastric cancer patients following gastrectomy. Methods: We derived and tested Survival Quilts, a machine learning-based model, to develop 6-month, 1-, 2-, 3-, 5-, and 10-year OS and CSS prediction models. Gastrectomy patients in the development set (n = 20,583) and the internal validation set (n = 5,106) were recruited from the Surveillance, Epidemiology, and End Results (SEER) database, while those in the external validation set (n = 6,352) were recruited from the China National Cancer Center Gastric Cancer (NCCGC) database. Furthermore, we selected gastrectomy patients without neoadjuvant therapy as a subgroup to train and test the prognostic models in order to keep the accuracy of tumor-node-metastasis (TNM) stage. Prognostic performances of these OS and CSS models were assessed using the Concordance Index (C-index) and area under the curve (AUC) values. Results: The machine learning model had a consistently high accuracy in predicting 6-month, 1-, 2-, 3-, 5-, and 10-year OS in the SEER development set (C-index = 0.861, 0.832, 0.789, 0.766, 0.740, and 0.709; AUC = 0.784, 0.828, 0.840, 0.849, 0.869, and 0.902, respectively), SEER validation set (C-index = 0.782, 0.739, 0.712, 0.698, 0.681, and 0.660; AUC = 0.751, 0.772, 0.767, 0.762, 0.766, and 0.787, respectively), and NCCGC set (C-index = 0.691, 0.756, 0.751, 0.737, 0.722, and 0.701; AUC = 0.769, 0.788, 0.790, 0.790, 0.787, and 0.788, respectively). The model was able to predict 6-month, 1-, 2-, 3-, 5-, and 10-year CSS in the SEER development set (C-index = 0.879, 0.858, 0.820, 0.802, 0.784, and 0.774; AUC = 0.756, 0.827, 0.852, 0.863, 0.874, and 0.884, respectively) and SEER validation set (C-index = 0.790, 0.763, 0.741, 0.729, 0.718, and 0.708; AUC = 0.706, 0.758, 0.767, 0.766, 0.766, and 0.764, respectively). In multivariate analysis, the high-risk group with risk score output by 5-year OS model was proved to be a strong survival predictor both in the SEER development set (hazard ratio [HR] = 14.59, 95% confidence interval [CI]: 1.872–2.774, P < 0.001), SEER validation set (HR = 2.28, 95% CI: 13.089–16.293, P < 0.001), and NCCGC set (HR = 1.98, 95% CI: 1.617–2.437, P < 0.001). We further explored the prognostic value of risk score resulted 5-year CSS model of gastrectomy patients, and found that high-risk group remained as an independent CSS factor in the SEER development set (HR = 12.81, 95% CI: 11.568–14.194, P < 0.001) and SEER validation set (HR = 1.61, 95% CI: 1.338–1.935, P < 0.001). Conclusion: Survival Quilts could allow accurate prediction of 6-month, 1-, 2-, 3-, 5-, and 10-year OS and CSS in gastric cancer patients following gastrectomy.

Abstract Pseudo threshold pressure gradient (PTPG) exists in the propped fractured reservoir, but its nonlinear flow law remains unclear. The effects of the mineral composition of shale and microstructure of fracturing fluid on PTPG were analyzed by X-ray diffraction and liquid nitrogen quick-freezing method. The results demonstrate that a proppant with a large particle size is more likely to form an effective flow channel and reduce liquid flow resistance, thus decreasing PTPG and increasing conductivity. The polymer fracturing fluid with rectangular microstructures significantly increased the PTPG supporting the fractured core. Experimental results show that the PTPG of the resin-coated sand-supported core in the fracturing fluid with a concentration of 1.2% is 245 times higher than that in the fracturing fluid with a concentration of 0.1% when the confining pressure is 5 MPa. Wetting hysteresis and the Jamin effect are responsible for the rise of PTPG in two-phase flow. The equivalent fracture width shows a good power function relationship with the PTPG. Thus, this study further explains the nonlinear flow behavior of reservoirs with fully propped fractures.

Abstract Background Recent studies have shown that there exists a significant correlation between oral microbiome and the occurrence of malignancies. However, the prognostic significance of oral microbiome for cancer patients remains unclear. The purpose of this meta-analysis is to evaluate the impact of oral microbiome on the survival of patients with malignant neoplasms. Methods We conducted a thorough literature search of PubMed, Embase, and Cochrane Library databases until September 2022. The hazard ratio (HR) with a corresponding 95% confidence interval (CI) was analyzed using Review Manager 5.4 software for survival outcomes, including overall survival (OS), disease-specific survival (DSS), progression-free survival (PFS), and disease-free survival (DFS). Results A total of 15 studies, covering 5191 samples with various types of cancers, were selected based on specified inclusion and exclusion criteria. In both univariate and multivariate analysis, patients with low diversity of the oral microbiome, or those with Fusobacterium-high/positive, or P. gingivalis positive in cancer tissue displayed poorer OS (univariate HR = 1.74; 95% CI 1.15–2.62; P = 0.009; multivariate HR = 1.56; 95% CI 1.07–2.27; P = 0.02), DSS (univariate HR = 2.06; 95% CI 1.50–2.84; P

Abstract Introduction The current National Comprehensive Cancer Network (NCCN) guidelines recommend that at least 16 lymph nodes should be examined for gastric cancer patients to reduce staging migration. However, there is still debate regarding the optimal management of examined lymph nodes (ELNs) for gastric cancer patients. In this study, we aimed to develop and test the minimum number of ELNs that should be retrieved during gastrectomy for optimal survival in patients with gastric cancer. Methods We used the restricted cubic spline (RCS) to identify the optimal threshold of ELNs that should be retrieved during gastrectomy based on the China National Cancer Center Gastric Cancer (NCCGC) database. Northwest cohort, which sourced from the highest gastric cancer incidence areas in China, was used to verify the optimal cutoff value. Survival analysis was performed via Kaplan-Meier estimates and Cox proportional hazards models. Results In this study, 12,670 gastrectomy patients were included in the NCCGC cohort and 4941 patients in the Northwest cohort. During 1999–2019, the average number of ELNs increased from 17.88 to 34.45 nodes in the NCCGC cohort, while the number of positive lymph nodes remained stable (5–6 nodes). The RCS model showed a U-curved association between ELNs and the risk of all-cause mortality, and the optimal threshold of ELNs was 24 [Hazard ratio (HR) = 1.00]. The ELN ≥ 24 group had a better overall survival (OS) than the ELN

Abstract Human Epidermal Growth Factor Receptor-2 (HER2)-negative breast cancers (BCs) contain HER2-low and HER2-zero ones. HER2-low breast cancer has been receiving wide-spread concerns as the marvelous effect of novel anti-HER2 antibody-drug conjugates, however, the characteristic remains unknown. Our aim was to explore the differences of clinicopathological indicators and survival outcomes between HER2-low and HER2-0 breast cancers. We retrospectively analyzed 501 invasive breast cancer patients with complete data on HER2 status from 2017 to 2021 in our single center, of whom 415 HER2 negative patients were included for subsequent analysis. Each cohort was further divided into hormone receptor (HR) positive and HR negative subgroup. Clinicopathological factors and survival outcomes were collected and compared between HER2-low BCs and HER2-0 BCs. HER2-low BCs was obviously higher in HR positive BCs, with 277 (90.5%) HER2-low HR positive patients, 29 (9.5%) HER2-low HR negative patients, 68 (62.4%) HER2-0 HR positive patients and 41 (37.6%) HER2-0 HR negative patients (P

BackgroundBreast cancer (BC) is one of the females’ most common malignant tumors there are large individual differences in its prognosis. We intended to uncover novel useful genetic biomarkers and a risk signature for BC to aid determining clinical strategies.MethodsA combined significance (pcombined) was calculated for each gene by Fisher’s method based on the RNA-seq, CNV, and DNA methylation data from TCGA-BRCA. Genes with a pcombined< 0.01 were subjected to univariate cox and Lasso regression, whereby an RS signature was established. The predicted performance of the RS signature would be assessed in GSE7390 and GSE20685, and emphatically analyzed in triple-negative breast cancer (TNBC) patients, while the expression of immune checkpoints and drug sensitivity were also examined. GSE176078, a single-cell dataset, was used to validate the differences in cellular composition in tumors between TNBC patients with different RS.ResultsThe RS signature consisted of C15orf52, C1orf228, CEL, FUZ, PAK6, and SIRPG showed good performance. It could distinguish the prognosis of patients well, even stratified by disease stages or subtypes and also showed a stronger predictive ability than traditional clinical indicators. The down-regulated expressions of many immune checkpoints, while the decreased sensitivity of many antitumor drugs was observed in TNBC patients with higher RS. The overall cells and lymphocytes composition differed between patients with different RS, which could facilitate a more personalized treatment.ConclusionThe six genes RS signature established based on multi-omics data exhibited well performance in predicting the prognosis of BC patients, regardless of disease stages or subtypes. Contributing to a more personalized treatment, our signature might benefit the outcome of BC patients.

Abstract In this study, a new lightning data assimilation (LDA) scheme using Geostationary Lightning Mapper (GLM) flash extent density (FED) is developed and implemented in the National Severe Storms Laboratory Warn‐on‐Forecast System (WoFS). The new LDA scheme first assigns a pseudo relative humidity between the cloud base and a specific layer based on the FED value. Then at each model layer, the pseudo relative humidity is converted to pseudo dewpoint temperature according to the corresponding air temperature. Some sensitivity experiments are performed to investigate how to assign and use GLM/FED in an optimum way. The impact of assimilating this pseudo dewpoint temperature on a short‐term severe weather forecast is preliminarily assessed in this proof‐of‐concept study. A high‐impact weather event in Kansas on 24 May 2021 is used to evaluate the performance of the new scheme on analyses and subsequent short‐term forecasts. The results show that the assimilation of additional FED‐based dewpoint temperature observations along with radar, satellite radiance, and cloud water can improve short‐term (3‐hr) forecast skill in terms of quantitative and qualitative verifications against the observations. The improvement is primarily due to the direct and indirect adjustment of dynamic and thermodynamic conditions through the LDA process. More specifically, the assimilation of FED‐based dewpoint temperature, in addition to the other observations currently used in WoFS, tends to enhance the ingredients required for thunderstorm formation, namely moisture, instability, and lifting mechanism.

By sampling perturbed state vectors from each ensemble forecast at additional time levels shifted by ±τ (where τ is a selected time interval) from the analysis time, time-expanded sampling (TES) can not only sample timing errors (or phase errors) but also triple the analysis ensemble size for covariance construction without increasing the forecast ensemble size. In this study, TES was applied to the convection-allowing ensemble-based warn-on-forecast system (WoFS), for four severe storm events, to reduce the computational costs that constrain real-time applications in the assimilation of remote-sensing data from radars and the geostationary satellite GOES-16. For each event, TES was implemented against a 36-member control experiment (E36) by reducing the forecast ensemble size to 12 but tripling the analysis ensemble size to 12 × 3 = 36 with τ = 2.5 min, 5 min and 7.5 min in three TES experiments, named E12×3τ2.5, E12×3τ5 and E12×3τ7.5, respectively. A 0–6-h forecast was created hourly after the second hour during the assimilation in each experiment. The assimilation statistics were evaluated for each experiment applied to each event and were found to be little affected by the TES, while reducing the computational cost. The forecasts produced in each experiment were verified against multi-sensor observed/estimated rainfall, reported tornadoes and damaging winds for each event. The verifications indicated that the forecasts produced in the three TES experiments had about the same capability and quality as that in the E36 for predicting hourly rainfall and the probabilities of tornadoes and damaging winds; in addition, the predictive capability and quality were not sensitive to τ, although they were slightly enhanced by selecting τ = 7.5 min. These results suggest that TES is attractive and useful for cost-saving real-time applications of WoFS in the assimilation of remote-sensing data and the generation of short-term severe-weather forecasts.

Objective: To develop a nomogram for predicting the possibility of four or more positive nodes in breast cancer patients with 1–3 positive sentinel lymph nodes (SLN). Materials and methods: Retrospective analysis of data of patients from two institutions was conducted. The inclusion criteria were: invasive breast cancer; clinically node negative; received lumpectomy or mastectomy plus SLN biopsy followed by axillary lymph node dissection (ALND); and pathologically confirmed T1-2 tumor, with 1–3 positive SLNs. Patients from one institution formed the training group and patients from the other the validation group. Univariate and multivariate analyses were performed to identify the predictors of four or more positive nodes. These predictors were used to build the nomogram. The area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of the model. Results: Of the 1480 patients (966 patients in the training group, 514 in the validation group), 306 (20.7%) had four or more positive nodes. Multivariate stepwise logistic regression showed number of positive (p

BackgroundImplant-based breast reconstruction is increasingly becoming the most common method of postmastectomy breast reconstruction in use today. As the traditional autologous reconstruction technique, latissimus dorsi flap (LDF) is employed by surgeons for reconstruction after breast cancer surgery, including partial mastectomy, modified radical mastectomy, and others. The authors aim to compare patient-reported outcomes (PROs) and complications between the SIS matrix-assisted direct-to-implant (DTI) breast reconstruction and the autologous LDF breast reconstruction.MethodsPatients undergoing the SIS matrix-assisted DTI reconstruction or mastectomy with LDF reconstruction or partial mastectomy with mini latissimus dorsi flap (MLDF) reconstruction were enrolled in a single institution from August 2010 to April 2019. Patients were included for analysis and divided into three groups: those who underwent LDF reconstruction, those who underwent MLDF reconstruction, and patients who underwent SIS matrix-assisted DTI breast reconstruction. PROs (using the BREAST-Q version 2.0 questionnaire) and complications were evaluated.ResultsA total of 135 patients met the inclusion criteria: 79 patients (58.5%) underwent SIS matrix-assisted DTI, 29 patients (21.5%) underwent LDF breast reconstruction, and 27 patients (20%) underwent MLDF breast reconstruction. PROs and complication rates between LDF reconstruction group and MLDF reconstruction group showed no statistically significant differences. Furthermore, BREAST-Q responses found that patients in the whole autologous LDF reconstruction group had better psychosocial well-being, showing a mean score of 84.31 ± 17.28 compared with SIS matrix-assisted DTI reconstruction, with a mean score of 73.52 ± 19.96 (p = 0.005), and expressed higher sexual well-being (69.65 ± 24.64 vs. 50.95 ± 26.47; p = 0.016). But there were no statistically significant differences between the two groups for postoperative complications.ConclusionThis retrospective study showed no statistically significant differences between LDF breast reconstruction and MLDF breast reconstruction. However, patients in the whole autologous LDF reconstruction group yielded superior PROs than patients in the SIS matrix-assisted DTI reconstruction group in the psychosocial well-being and sexual well-being domains.

Spontaneous imbibition has been considered as a significant method to enhanced oil recovery (EOR) in unconventional reservoirs. The main impediment to comprehending the variation characteristics of spontaneous imbibition at different pore scales was the reservoir’s opacity. To this end, a series of spontaneous imbibition experiments of visualized oil-wet microtubes with six different diameters (10 μm~60 μm) were performed, as were the corresponding macroscopic core imbibition tests with six different permeabilities under the same conditions. The results showed that formation brine mostly advanced along the wall surface of a capillary during the imbibition process, and some were even isolated within a capillary. The imbibition recovery in the capillary with a diameter of 10 μm was the highest (27.81%), which was more than three times that of the capillary with a diameter of 60 μm (8.3%). There was a good power function decline relationship between capillary diameter and imbibition recovery, and 30 μm appeared to be a critical inflection point in both capillary tube and macroscopic core imbibition tests. In addition, the majority of the detained residual oil clusters not only cut off the continuity of formation brine but also increased the imbibition flow resistance, accelerating the imbibition to balance. This research provides a new perspective for comprehending the imbibition characteristics at different pore scales.

Abstract This research investigates to what extent the high‐spatiotemporal‐resolution Atmospheric Motion Vector (AMV) product derived from the new‐generation Geostationary Operational Environmental Satellites‐Series R can benefit convective‐scale data assimilation (DA) and forecasts of potential high impact weather events. In the first part of this two‐part study, the impact of AMV DA on convective‐scale numerical weather prediction (NWP) is evaluated with an idealized supercell storm. The simulated AMV observations are synthesized from the idealized supercell storm generated by the Weather Research and Forecasting model and then the data are assimilated with a three‐dimensional variational analysis and forecast system. A baseline DA experiment demonstrates that the wind errors within and around the storms are remarkably reduced by assimilating the AMV data, consequently enhancing the storm top‐level divergence and low‐level convergence signatures associated with two strong splitting supercells. Three sets of sensitivity experiments are then performed to test the impact of observation resolution, DA cycling frequency and horizontal correlation length scale respectively. Generally, assimilating higher‐spatial‐resolution AMVs at higher cycling frequency is able to produce reasonable divergence analysis as well as the subsequent 90 min forecasts. However, too frequent DA cycling tends to produce a warm bias and overestimate nonprecipitating hydrometeor and storm‐relative helicity features, resulting in more spurious cells in the short‐term forecasts. It is also found that the correlation length scale of 20 km produces the best results when assimilating the AMV data.

Abstract Building on the results from the observing system simulation experiments in Part I, this study investigates the impact of assimilating Geostationary Operational Environmental Satellite‐16 (GOES‐16) derived atmospheric motion vector (AMV) data on the convective scale numerical weather prediction (NWP) by using the National Severe Storms Laboratory (NSSL) three‐dimensional variational (3DVAR) data assimilation (DA) system. The benefit of the AMV DA for short‐term severe weather forecast is assessed with three high‐impact weather events that occurred in spring 2018 and 2019 over the Great Plains of the United States. The results show that the wind and equivalent potential temperature fields associated with the storm environment and the nearby ongoing convection are improved by the AMV DA, which yields better simulation of the boundaries and the subsequent forecasts of storm evolution. For the quasi‐linear or mesoscale convective system, the assimilation of AMVs has a positive impact on the 0–3 h forecasts of composite reflectivity and accumulated precipitation in terms of the shape, location, and magnitude. However, the AMV DA has difficulty in capturing the sharp moisture gradient associated with the dryline and mostly underpredicts the associated scattered storms.

In order to obtain the potential of the speed ratio parameter optimization of a CVT vehicle on the direct improvement of fuel economy， the first step is to obtain the distribution characteristic of engine operating condition from test data in the NEDC cycle， most of the operating conditions are located in the urban cycle are below the optimal operation curve（OOC）. Then，the area that can be optimized is evaluated according to the speed ratio control method， there is 7% of the engine operating condition in the urban cycle can be optimized，in some working conditions in the suburban cycle（when the vehicle speed is up to 70 km/h and 100 km/h）， it cannot be optimized and adjusted due to the speed ratio limited value of the CVT. According to the simulation results， only the speed ratio parameters of CVT transmission are adjusted， including adjusting some operation conditions to the optimal fuel economy curve， or optimizing in all area， or enlarging the speed ratio range of CVT transmission， and the direct contribution of the maximum fuel saving potential reaches 3.12%.

The vertical distribution of water vapor affects the intensity of the updraft, downdraft and cold pool in convection, so how to adjust lightning proxy-humidity in the vertical direction is very important for convective scale numerical weather prediction (NWP). In this study, a data assimilation approach is presented that uses information from FengYun4 (FY4) lightning data with cloud top height (CTH) data. Specifically, the FY4 CTH is used to locate the upper boundary of the relative humidity adjustment. This method can effectively determine the vertical distribution of water vapor and obtain accurate pseudo-observations. Two severe convection events with different characteristics were studied to evaluate the data assimilation approach for short-term precipitation forecast. For comparison, two other relative humidity adjustment schemes with different vertical ranges were performed. One scheme adjusted the relative humidity between two isothermal layers and introduced the smallest water vapor increments compared with the other two data assimilation experiments and showed a slight improvement on precipitation forecast. The other scheme adjusted the relative humidity between the lifting condensation level (LCL) and a fixed height and introduced the maximum water vapor increments and exhibited better precipitation forecast based on Equitable Threat Scores (ETSs). The adjustment between LCL and CTH introduced appropriate amounts of water vapor and was adaptable for various convection developments and effectively avoided producing spurious convection in the particular case. Assimilation of FY4 lightning and CTH data improves the short-term precipitation forecast and provides the best forecast skill in a particular forecast period.

A dual-resolution, hybrid, three-dimensional ensemble-variational (3DEnVAR) data assimilation method combining static and ensemble background error covariances is used to assimilate radar data, and pseudo-water vapor observations to improve short-term severe weather forecasts with the Weather Research and Forecast (WRF) model. The higher-resolution deterministic forecast and the lower-resolution ensemble members have 3 and 9 km horizontal resolution, respectively. The water vapor pseudo-observations are derived from the combined use of total lightning data and cloud top height from the Fengyun-4A(FY-4A) geostationary satellite. First, a set of single-analysis experiments are conducted to provide a preliminary performance evaluation of the effectiveness of the hybrid method for assimilating multisource observations; second, a set of cycling analysis experiments are used to evaluate the forecast performance in convective-scale high-frequency analysis; finally, different hybrid coefficients are tested in both the single and cycling experiments. The single-analysis results show that the combined assimilation of radar data and water vapor pseudo-observations derived from the lightning data is able to generate reasonable vertical velocity, water vapor and hydrometeor adjustments, which help to trigger convection earlier in the forecast/analysis and reduce the spin-up time. The dual-resolution hybrid 3DEnVAR method is able to adjust the wind fields and hydrometeor variables with the assimilation of lightning data, which helps maintain the triggered convection longer and partially suppress spurious cells in the forecast compared with the three-dimensional variational (3DVAR) method. A cycling analysis that introduced a large number of observations with more frequent small adjustments is able to better resolve the observed convective events than a single-analysis approach. Different hybrid coefficients can affect the forecast results, either in the single deterministic or cycling analysis experiments. Overall, we found that a static coefficient of 0.4 and an ensemble coefficient of 0.6 yields the best forecast skill for this event.

Polarimetric radar data (PRD) have potential to be used in numerical weather prediction (NWP) models to improve convective-scale weather forecasts. However, thus far only a few studies have been undertaken in this research direction. To assimilate PRD in NWP models, a forward operator, also called a PRD simulator, is needed to establish the relation between model physics parameters and polarimetric radar variables. Such a forward operator needs to be accurate enough to make quantitative comparisons between radar observations and model output feasible, and to be computationally efficient so that these observations can be easily incorporated into a data assimilation (DA) scheme. To address this concern, a set of parameterized PRD simulators for the horizontal reflectivity, differential reflectivity, specific differential phase, and cross-correlation coefficient were developed. In this study, we have tested the performance of these new operators in a variational DA system. Firstly, the tangent linear and adjoint (TL/AD) models for these PRD simulators have been developed and checked for the validity. Then, both the forward operator and its adjoint model have been built into the three-dimensional variational (3DVAR) system. Finally, some preliminary DA experiments have been performed with an idealized supercell storm. It is found that the assimilation of PRD, including differential reflectivity and specific differential phase, in addition to radar radial velocity and horizontal reflectivity, can enhance the accuracy of both initial conditions for model hydrometer state variables and ensuing model forecasts. The usefulness of the cross-correlation coefficient is very limited in terms of improving convective-scale data analysis and NWP.

In this paper, two important issues are raised for multistep variational data assimilation in which broadly distributed coarse-resolution observations are analysed in the first step, and then locally distributed high-resolution observations are analysed in the second step (and subsequent steps if any). The first one concerns how to objectively estimate or efficiently compute the analysis error covariance for the analysed field obtained in the first step and used to update the background field in the next step. To attack this issue, spectral formulations are derived for efficiently calculating the analysis error covariance functions. The calculated analysis error covariance functions are verified against their respective benchmarks for one- and two-dimensional cases and shown to be very (or fairly) good approximations for uniformly (or non-uniformly) distributed coarse-resolution observations. The second issue concerns whether and under what conditions the above calculated analysis error covariance can make the two-step analysis more accurate than the conventional single-step analysis. To answer this question, idealised numerical experiments are performed to compare the two-step analyses with their respective counterpart single-step analyses while the background error covariance is assumed to be exactly known in the first step but the number of iterations performed by the minimisation algorithm is limited (to mimic the computationally constrained situations in operational data assimilation). The results show that the two-step analysis is significantly more accurate than the single-step analysis until the iteration number becomes so large that the single-step analysis can reach the final convergence or nearly so. The two-step analysis converges much faster and thus is more efficient than the single-step analysis to reach the same accuracy. Its computational efficiency can be further enhanced by properly coarsening the grid resolution in the first step with the high-resolution grid used only over the nested domain in the second step.

An improved approach to derive pseudo water vapor mass mixing ratio and in- cloud potential temperature was developed in this paper to better initialize numerical weather prediction (NWP) and build convective-scale predictions of severe weather events. The process included several steps. The first was to identify areas of deep moist convection, utilizing Vertically Integrated Liquid water (VIL) derived from a mosaicked 3D radar reflectivity field. Then, pseudo- water vapor and pseudo- in- cloud potential temperature observations were derived based on the VIL. For potential temperature, the latent heat initialization for stratiform cloud and moist adiabatic initialization for deep moist convection were used based on a cloud analysis method. The third step was to assimilate the derived pseudo- water vapor and potential temperature observations, together with radar radial velocity and reflectivity into a convective-scale NWP model during data assimilation cycles spanning several hours. Finally, 3-h forecasts were launched each hour during the data assimilation period. The effects of radar data and pseudo- observation assimilation on the prediction of rainfall associated with convective systems surrounding the Meiyu front in 2018 were explored using two real cases. Two sets of experiments, each including several experiments in each real case, were designed to compare the effects of assimilation radar and pseudo- observations on the ensuing forecasts. Relative to the control experiment without data assimilation and radar experiment, the analyses and forecasts of convections were found to be improved for the two Meiyu front cases after pseudo- water vapor and potential temperature information was assimilated.

To combine the data from previously conducted studies about the associations between miR-608 rs4919510 polymorphism (C>G) and breast cancer risks.According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a systematic review of the related literatures searched from PubMed, Embase, Cochrane Library, Web of Science, and China National Knowledge Internet (CNKI) (time: ~ December 2016). Using DerSimonian-Laird random-effects models [Pooling Model: Mantel Haenszel (MH)], odd ratios (ORs) with 95% confidence intervals (95% CIs) were estimated in the allele model, homozygote model, heterozygote model, dominant model and recessive model. Heterogeneity was analyzed using Labbr plots and I2 statistic. Publication bias was analyzed using contour-enhanced funnel plots.We included 5 eligible studies with 7948 patients. The ORs and their 95% CIs in the 5 genetic models mentioned above were 1.009 (95% CI: 0.922, 1.104; p = 0.847), 1.098 (95% CI: 0.954, 1.264; p = 0.194), 1.076 (95% CI: 0.956, 1.211; p = 0.227), 1.043 (95% CI: 0.880, 1.236; p = 0.628), 1.007 (95% CI: 0.906, 1.118; p = 0.899), respectively.In the present meta-analysis, no relationships between miR-608 rs4919510 polymorphism (C>G) and the risk of breast cancer were found. More studies are warranted to further validate the conclusion.

A hybrid 3DVAR-EnKF data assimilation algorithm is developed based on 3DVAR and ensemble Kalman filter (EnKF) programs within the Advanced Regional Prediction System (ARPS). The hybrid algorithm uses the extended alpha control variable approach to combine the static and ensemble-derived flow-dependent forecast error covariances. The hybrid variational analysis is performed using an equal weighting of static and flow-dependent error covariance as derived from ensemble forecasts. The method is first applied to the assimilation of simulated radar data for a supercell storm. Results obtained using 3DVAR (with static covariance entirely), hybrid 3DVAR-EnKF, and the EnKF are compared. When data from a single radar are used, the EnKF method provides the best results for the model dynamic variables, while the hybrid method provides the best results for hydrometeor related variables in term of rms errors. Although storm structures can be established reasonably well using 3DVAR, the rms errors are generally worse than seen from the other two methods. With two radars, the results from 3DVAR are closer to those from EnKF. Our tests indicate that the hybrid scheme can reduce the storm spin-up time because it fits the observations, especially the reflectivity observations, better than the EnKF and the 3DVAR at the beginning of the assimilation cycles.

A diagnostic pressure equation constraint has been incorporated into a storm-scale three-dimensional variational (3DVAR) data assimilation system. This diagnostic pressure equation constraint (DPEC) is aimed to improve dynamic consistency among different model variables so as to produce better data assimilation results and improve the subsequent forecasts. Ge et al. (2012) described the development of DPEC and testing of it with idealized experiments. DPEC was also applied to a real supercell case, but only radial velocity was assimilated. In this paper, DPEC is further applied to two real tornadic supercell thunderstorm cases, where both radial velocity and radar reflectivity data are assimilated. The impact of DPEC on radar data assimilation is examined mainly based on the storm forecasts. It is found that the experiments using DPEC generally predict higher low-level vertical vorticity than the experiments not using DPEC near the time of observed tornadoes. Therefore, it is concluded that the use of DPEC improves the forecast of mesocyclone rotation within supercell thunderstorms. The experiments using different weighting coefficients generate similar results. This suggests that DPEC is not very sensitive to the weighting coefficients.

A three-dimensional variational (3DVAR) assimilation technique developed for a convective-scale NWP model—advanced regional prediction system (ARPS)—is used to analyze the 8 May 2003, Moore/Midwest City, Oklahoma tornadic supercell thunderstorm. Previous studies on this case used only one or two radars that are very close to this storm. However, three other radars observed the upper-level part of the storm. Because these three radars are located far away from the targeted storm, they were overlooked by previous studies. High-frequency intermittent 3DVAR analyses are performed using the data from five radars that together provide a more complete picture of this storm. The analyses capture a well-defined mesocyclone in the midlevels and the wind circulation associated with a hook-shaped echo. The analyses produced through this technique are used as initial conditions for a 40-minute storm-scale forecast. The impact of multiple radars on a short-term NWP forecast is most evident when compared to forecasts using data from only one and two radars. The use of all radars provides the best forecast in which a strong low-level mesocyclone develops and tracks in close proximity to the actual tornado damage path.

For the first time ever, convection-resolving forecasts at 1 km grid spacing were produced in realtime in spring 2009 by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. The forecasts assimilated both radial velocity and reflectivity data from all operational WSR-88D radars within a domain covering most of the continental United States. In preparation for the realtime forecasts, 1 km forecast tests were carried out using a case from spring 2008 and the forecasts with and without assimilating radar data are compared with corresponding 4 km forecasts produced in realtime. Significant positive impact of radar data assimilation is found to last at least 24 hours. The 1 km grid produced a more accurate forecast of organized convection, especially in structure and intensity details. It successfully predicted an isolated severe-weather-producing storm nearly 24 hours into the forecast, which all ten members of the 4 km real time ensemble forecasts failed to predict. This case, together with all available forecasts from 2009 CAPS realtime forecasts, provides evidence of the value of both convection-resolving 1 km grid and radar data assimilation for severe weather prediction for up to 24 hours.

Numerical experiments over the past years indicate that incorporating environmental variability is crucial for successful very short-range convective-scale forecasts. To explore the impact of model physics on the creation of environmental variability and its uncertainty, combined mesoscale-convective scale data assimilation experiments are conducted for a tornadic supercell storm. Two 36-member WRF-ARW model-based mesoscale EAKF experiments are conducted to provide background environments using either fixed or multiple physics schemes across the ensemble members. Two 36-member convective-scale ensembles are initialized using background fields from either fixed physics or multiple physics mesoscale ensemble analyses. Radar observations from four operational WSR-88Ds are assimilated into convective-scale ensembles using ARPS model-based 3DVAR system and ensemble forecasts are launched. Results show that the ensemble with background fields from multiple physics ensemble provides more realistic forecasts of significant tornado parameter, dryline structure, and near surface variables than ensemble from fixed physics background fields. The probabilities of strong low-level updraft helicity from multiple physics ensemble correlate better with observed tornado and rotation tracks than probabilities from fixed physics ensemble. This suggests that incorporating physics diversity across the ensemble can be important to successful probabilistic convective-scale forecast of supercell thunderstorms, which is the main goal of NOAA’s Warn-on-Forecast initiative.

Raindrop size distributions (DSD) and rain rate have been estimated from polarimetric radar data using different approaches with the accuracy depending on the errors both in the radar measurements and the estimation methods. Herein, a deep neural network (DNN) technique was utilized to improve the estimation of the DSD and rain rate by mitigating these errors. The performance of this approach was evaluated using measurements from a two-dimensional video disdrometer (2DVD) at the Kessler Atmospheric and Ecological Field Station in Oklahoma as ground truth with the results compared against conventional estimation methods for the period 2006-17. Physical parameters (mass-/volume-weighted diameter and liquid water content), rain rate, and polarimetric radar variables (including radar reflectivity and differential reflectivity) were obtained from the DSD data. Three methods--physics-based inversion, empirical formula, and DNN--were applied to two different temporal domains (instantaneous and rain-event average) with three diverse error assumptions (fitting, measurement, and model errors). The DSD retrievals and rain estimates from 18 cases were evaluated by calculating the bias and root-mean-squared error (RMSE). DNN produced the best performance for most cases, with up to a 5% reduction in RMSE when model errors existed. DSD and rain estimated from a nearby polarimetric radar using the empirical and DNN methods were well correlated with the disdrometer observations; the rain-rate estimate bias of the DNN was significantly reduced (3.3%in DNN vs 50.1%in empirical). These results suggest that DNN has advantages over the physics-based and empirical methods in retrieving rain microphysics from radar observations. [ABSTRACT FROM AUTHOR]