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Abstract Sunspots play a crucial role in both weather forecasting and the monitoring of solar storms. In this work, we propose a novel combined model for sunspot prediction using improved gated recurrent units (GRU) guided by pinball loss for probabilistic forecasts. Specifically, we optimize the GRU parameters using the slime mould algorithm and employ a seasonal-trend decomposition procedure based on loess to tackle challenges related to sequence prediction, such as self-correlations and non-stationarity. To address prediction uncertainty, we replace the traditional $$l_2$$ l 2 -norm loss with pinball loss. This modification extends the conventional GRU-based point forecasting to a probabilistic framework expressed as quantiles. We apply our proposed model to analyze a well-established historical sunspot dataset for both single- and multi-step ahead forecasting. The results demonstrate the effectiveness of our combined model in predicting sunspot values, surpassing the performance of other existing methods.

Abstract Internal solitary wave (ISW), as a typical marine dynamic process in the deep sea, widely exists in oceans and marginal seas worldwide. The interaction between ISW and the seafloor mainly occurs in the bottom boundary layer. For the seabed boundary layer of the deep sea, ISW is the most important dynamic process. This study analyzed the current status, hotspots, and frontiers of research on the interaction between ISW and the seafloor by CiteSpace. Focusing on the action of ISW on the seabed, such as transformation and reaction, a large amount of research work and results were systematically analyzed and summarized. On this basis, this study analyzed the wave–wave interaction and interaction between ISW and the bedform or slope of the seabed, which provided a new perspective for an in‐depth understanding of the interaction between ISW and the seafloor. Finally, the latest research results of the bottom boundary layer and marine engineering stability by ISW were introduced, and the unresolved problems in the current research work were summarized. This study provides a valuable reference for further research on the hazards of ISW to marine engineering geology.

ObjectiveThe aim of this study was to explore the risk factors for different metabolic status in patients with schizophrenia.MethodsA total of 968 hospitalized patients with schizophrenia were recruited. Fasting blood glucose (GLU) and lipid profile, including total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) were measured. Schizophrenia patients were divided into four groups: normal metabolism and weight (NMNW), abnormal metabolism and normal weight (AMNW), normal metabolism and overweight/obesity (NMO), and abnormal metabolism and overweight/obesity (AMO).ResultsOur results showed that NMNW, AMNW, NMO, and AMO accounted for 25.3%, 12.7%, 25.4%, and 36.6%, respectively. There were significant differences in age, disease duration, body mass index (BMI), waist circumference, chronic disease, systolic blood pressure (SBP), diastolic blood pressure (DBP), GLU, TG, TC, HDL-C, and LDL-C among these four groups (all p < 0.05). With the NMNW group as the reference, the disordered multiple classification regression analysis showed that chronic disease was a significant risk factor for AMNW (OR = 5.271, 95% CI = 3.165 to 8.780, p < 0.001) and AMO (OR = 3.245, 95% CI = 2.004 to 5.254, p < 0.001), age was an important protective factor for NMO (OR = 0.968, 95% CI = 0.943 to 0.994, p = 0.015) and AMO (OR = 0.973, 95% CI = 0.948 to 0.999, p < 0.042), waist circumference was a significant risk factor for NMO (OR = 1.218, 95% CI = 1.180 to 1.257, p < 0.001) and AMO (OR = 1.252, 95% CI = 1.212 to 1.291, p < 0.001), and college education was an obvious protective factor for AMO (OR = 0.343, 95% CI = 0.123 to 0.953, p < 0.040) among patients with schizophrenia.ConclusionThe findings of our study underscored the importance of factors such as age, education level, chronic disease, and waist circumference when exploring the influencing factors and biological mechanisms of obesity-related metabolic problems in schizophrenia patients.

Abstract China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framework. In addition to historical pollutant concentrations and meteorological factors, we incorporate social and spatio-temporal influences in the framework. In particular, spatial autocorrelation (SAC), which combines temporal autocorrelation with spatial correlation, is adopted to reflect the influence of neighbouring cities and historical data. Our deep learning analysis obtained the estimates of the lockdown effects as − 25.88 in Wuhan and − 20.47 in Shanghai. The corresponding prediction errors are reduced by about 47% for Wuhan and by 67% for Shanghai, which enables much more reliable AQI forecasts for both cities.

Accurately measuring the critical shear stress is crucial for numerous applications, such as sediment transport modeling, erosion prediction, and the design of sustainable coastal engineering structures. However, developing reliable and precise in-situ measurement devices faces significant challenges due to the harsh and dynamic nature of aquatic environments. Factors like turbulence and waves introduce complexities that must be considered when designing and calibrating these devices. The newly developed Openable Underwater Carousel In-situ Flume (OUC-IF) was used to determine the critical shear stress (τc) and quantify erosion rates. Acoustic Doppler Velocimeter (ADV) was employed to measure 3D near-bottom velocities, which were then used to estimate and pre-calibrate bed shear stress (τ) applied on the seabed in the annular flume. Three computation methods of shear stress were evaluated: turbulent kinetic energy (TKE), direct covariance (COV), and log profile (LP). In-situ erosion experiments were conducted for the first time at two sites in the tidal flat of the Yellow River Delta (site 1 with a water depth of 1.32 m and site 2 with a water depth of 0.75 m). The critical shear stress was found to be 0.10 Pa at site 1 and 0.19 Pa at site 2, and the erosion rates of the sediments were successfully measured. The effect of wave-seabed interactions on erosion resistance was explored by theoretically estimating the wave-induced pore pressure of the seabed based on the observed data. The max liquefaction degree of the seabed at site 1 and site 2 was 0.035 and 0.057, respectively, and the average erosion coefficient Me was 2.63E-05 kg m-2s-1 at site 1 and 3.48E-05 kg m-2s-1 at site 2.

The deep-sea lander is an important equipment for in-situ detection and monitoring. It is of great significance to understand the benthic boundary layer’s physical, chemical, and ecological environment. A 6000-meter double decelerating lander was created to meet the deployment requirements of underwater detection and monitoring, allowing for long-term in-situ monitoring of several benthic boundary layer components. Protection of the installed ocean bottom seismometer (OBS) is required due to the lander’s and OBS’s different impact resistances. The double decelerating unit enables the OBS to avoid colliding with the seabed when the lander lands and then collides with the seabed at a slow speed rather than the speed at which the lander falls, which is intended to safeguard OBS from damage. To ensure a safe deployment, the lander’s static analysis and simulation were performed using ANSYS, and the motion characteristics of the application process were derived. Numerous data have been obtained after the lander’s successful application in the South China Sea. The lander provides an investigation approach for marine science and geochemistry, complementing a technical approach to marine environmental investigations.

Seawalls are vital for protecting coastal areas. However, the seabed in front of seawalls may undergo severe scouring. This can result in destabilization of the seawall structure, the underlying mechanisms of which remains unclear. Therefore, an integrated observation system consisting of acoustic and optical instruments was deployed in areas with severe seabed scouring. This observation system was used to observe sediment dynamics elements such as waves, currents, tides, turbulence and suspended sediment concentration (SSC) for 31 days in winter. Using advanced time–frequency analysis techniques including wavelet transform and spectrum analysis, we examined the dynamic factors associated with sediment resuspension and transport. In calm weather conditions, a notable increase in SSC was not observed indicating that the tidal dynamics were not sufficient for sediment suspension. During high winds, the SSC increased sharply to 12,222 mg/L, and the sediment vertical diffusion flux induced by turbulence was coupled to SSC, indicating that the increased SSC was predominantly attributed to local resuspension. Consistent temporal distribution of turbulence-induced sediment vertical diffusion flux and momentum flux in high wavelet power spectra highlights the important role of turbulence in sediment dynamics. Enhanced longshore currents during high wave conditions intensified sediment transportation. Horizontal net sediment fluxes notably increased to 769 t/m2 per day during winter gales, which had a significant effect on seabed erosion. This study reveals the key processes associated with seabed scouring in front of seawalls during gale events.

Accurate air quality index (AQI) forecasting makes a difference to public health, local economic development, and ecological environment. As a typical geographical datum, the spatial autocorrelation (SAC) of the AQI is often ignored, which may violate the assumptions of some models, such as machine learning which requires variables to be independent and identically distributed. Considering the strong SAC of the AQI, this study proposes a novel statistical learning framework integrating SAC variables, feature selection, and support vector regression (SVR) for AQI prediction in which correlation analysis and time series analysis are used to extract the spatial-temporal features. In addition, the historical AQI series of the target site is adjusted by using trigonometric regression to eliminate the non-stationarity. To further improve prediction accuracy, a feature selection method combining reinforcement learning with a heuristic algorithm is adopted. To demonstrate the effectiveness of our proposed framework, we select the AQI data of 34 cities from the Yangtze River Delta, which is one of the most polluted areas in eastern China, and focus on the three largest cities, Nanjing, Hangzhou, and Shanghai. We compared the proposed framework with several baselines, and the experiment illustrates that the forecasting accuracy of the proposed framework is significantly better than the baselines at all selected key sites that can provide accurate predictions for air quality.

Chemo-radiotherapy (CRT) remains the main treatment modality for non-small-cell lung cancer (NSCLC). However, its clinical efficacy is largely limited by individual variations in radio-sensitivity and radiotherapy-associated toxicity. There is an urgent need to identify genetic determinants that can explain patients’ likelihood to develop recurrence and radiotherapy-associated toxicity following CRT. In this study, we performed comprehensive genomic profiling, using a 474-cancer- and radiotherapy-related gene panel, on pretreatment biopsy samples from patients with unresectable stage III NSCLCs who underwent definitive CRT. Patients’ baseline clinical characteristics and genomic features, including tumor genetic, genomic and molecular pathway alterations, as well as single nucleotide polymorphisms (SNPs), were correlated with progression-free survival (PFS), overall survival (OS), and radiotherapy-associated pneumonitis and/or esophagitis development after CRT. A total of 122 patients were enrolled between 2014 and 2019, with 84 (69%) squamous cell carcinomas and 38 (31%) adenocarcinomas. Genetic analysis confirmed the association between the KEAP1-NRF2 pathway gene alterations and unfavorable survival outcome, and revealed alterations in FGFR family genes, MET, PTEN, and NOTCH2 as potential novel and independent risk factors of poor post-CRT survival. Combined analysis of such alterations led to improved stratification of the risk populations. In addition, patients with EGFR activating mutations or any oncogenic driver mutations exhibited improved OS. On the other hand, we also identified genetic markers in relation to radiotherapy-associated thoracic toxicity. SNPs in the DNA repair-associated XRCC5 (rs3835) and XRCC1 (rs25487) were associated with an increased risk of high-grade esophagitis and pneumonitis respectively. MTHFR (rs1801133) and NQO1 (rs1800566) were additional risk alleles related to higher susceptibility to pneumonitis and esophagitis overall. Moreover, through their roles in genome integrity and replicative fidelity, somatic alterations in ZNF217 and POLD1 might also serve as risk predictors of high-grade pneumonitis and esophagitis. Taken together, leveraging targeted next-generating sequencing, we identified a set of novel clinically applicable biomarkers that might enable prediction of survival outcomes and risk of radiotherapy-associated thoracic toxicities. Our findings highlight the value of pre-treatment genetic testing to better inform CRT outcomes and clinical actions in stage III unresectable NSCLCs.

As one of the critical indicators of the lake ecosystem, the lake surface water temperature is an important indicator for measuring lake ecological environment. However, there is a complex nonlinear relationship between lake surface water temperature and climate variables, making it difficult to accurately predict. Fortunately, satellite remote sensing provides a wealth of data to support further improvements in prediction accuracy. In this paper, we construct a new deep learning model for mining the nonlinear dynamics from climate variables to obtain more accurate prediction of lake surface water temperature. The proposed model consists of the variable correlation information module and the temporal correlation information module. The variable correlation information module based on the Self-Attention mechanism extracts key variable features that affect lake surface water temperature. Then, the features are input into the temporal correlation information module based on the Gated Recurrent Unit (GRU) model to learn the temporal variation patterns. The proposed model, called Attention-GRU, is then applied to lake surface water temperature prediction in Qinghai Lake, the largest inland lake located in the Tibetan Plateau region in China. Compared with the seven baseline models, the Attention-GRU model achieved the most accurate prediction results; notably, it significantly outperformed the Air2water model which is the classic model for lake surface water temperature prediction based on the volume-integrated heat balance equation. Finally, we analyzed the factors influencing the surface water temperature of Qinghai Lake. There are different degrees of direct and indirect effects of climatic variables, among which air temperature is the dominant factor.

PurposeThe objective of this study was to explore the prognostic significance of pretreatment hematologic parameters in predicting disease-free survival (DFS) of breast cancer patients.Materials and MethodsThe medical records of 440 breast cancer patients in Shandong Cancer Hospital and Institute from 2003 to 2013 were analyzed retrospectively. Through the results of blood routine before treatment, the absolute lymphocyte count (ALC), absolute neutrophil count (ANC), absolute monocyte count (AMC), and absolute platelet count (APC) in peripheral blood were collected. The lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-monocyte ratio (NMR) were calculated. Cox proportional hazard model was used for univariate and multivariate analysis. The DFS was compared using Kaplan–Meier method. The prognostic nomogram of patients with breast cancer was developed.ResultsThe median DFS for all patients was 64.10 months. Univariate analysis showed that the DFS was associated with surgical approach, TNM stage, molecular subtype, neoadjuvant chemotherapy, radiotherapy, and LMR (p < 0.05). TNM stage, molecular subtype, and LMR were independent prognostic factors of breast cancer in multivariate analysis (p < 0.05). According to the Kaplan–Meier survival curve analysis, patients with higher LMR (≥4.85) were associated with longer median DFS (median DFS, 85.83 vs. 60.90, p < 0.001). The proposed nomogram that incorporated LMR, TNM stage, and molecular subtype got a concordance index (c-index) of 0.69 in predicting 5-year DFS.ConclusionIn breast cancer patients, higher LMR was associated with longer median DFS and the nomogram including LMR, TNM stage, and molecular subtype could accurately predict the prolonged 5-year DFS of breast cancer patients.

Marine sediment transport is closely related to seafloor topography, material transport, marine engineering safety, etc. With a developed time-series vector observation device, the sediment capture and transport process can be observed. The structure of the capture tube and the internal filter screen can significantly affect the flow field during the actual observation, further influencing the sediment transport observation and particle capture process. This paper presents a numerical model for investigating the effect of device structure on seawater flow to study the processes of marine sediment transport observation and sediment particle capture. The model is based on the solution of both porous media and the Realizable k-ε turbulence in Fluent software. The flow velocity distribution inside and outside the capture tube with different screen pore sizes (0.300, 0.150, and 0.075 mm) is analyzed. To enhance the reliability of the numerical simulation, the simulation calculation results are compared with the test results and have good coincidence. Finally, by analyzing the motion law of sediment in the capture tube, the accurate capture of sediment particles is achieved, and the optimal capture efficiency of the sediment trap is obtained.

Free-spanning submarine pipelines are usually affected by vortex-induced vibration (VIV). Such vibration could influence the liquefaction of the supporting soil at both ends of the free spans and could have catastrophic consequences, including the failure of the local seabed and the displacing, sinking, or floating of pipelines. The influence of pipeline vibration on soil liquefaction has not been studied sufficiently. Therefore, we explored the influence of vortex-induced pipeline vibration on the excess pore pressure of silty soil around a pipeline using flume experiments. Our results showed that pipeline vibration could induce the buildup of excess pore-water pressure, even without wave loading. A fully liquefied zone was found close to the pipeline, where excess pore pressure reached the soil liquefaction criterion, which was surrounded by a partially liquefied zone. The extent of liquefaction depended on the vibration conditions and the weight and burial depth of the pipeline. The pipeline vibration amplitude increased after soil liquefaction. Unlike wave-induced liquefaction, pipeline-induced vibration liquefaction occurred at a critical value smaller than the initial mean normal effective stress. Considering the possibility of pipeline-vibration-induced seabed liquefaction, conventional approaches could underestimate the potential risks to pipeline stability and result in unsafe maintenance practices.

The shelly chenier is a dike-like accumulation formed by waves, currents, and other hydrodynamic forces pushing the shells and their debris in the intertidal zone to the high tide line. It is a special type of coastal dike. The shelly chenier located in Wangzi Island of northern Shandong Province is the last well-preserved chenier of the Bohai Bay with natural attributes exposed on the surface. It has unique and irreplaceable attributes among the shell beaches of China. Based on remote sensing interpretation, field investigation, and GPS-RTK (Real-Time Kinematic) measurement, this paper investigates the coastal erosion and progradation evolution of the Binzhou Shelly Chenier in China usingcomparisons between normal and typhoon hydrodynamics. The results show that: (1) There are two kinds of shelly chenier in the study area, the first is “the tidal channel shelly chenier” which is significantly affected by the tidal current, and the second is “the open shelly chenier”, which is significantly affected by direct scouring and silting of waves. The open shelly chenier is continuously eroding under the normal hydrodynamics and the shell beach is supplied by a large number of shells and their debris under the typhoon hydrodynamics, while the tidal channel shelly chenier is gradually developed due to the action of alongshore currents and in–out flows. (2) The energy of waves and currents under normal hydrodynamics is insufficient to transport the shells and their debris in the intertidal zone to the shell-beach. On the contrary, the continuous action of waves makes the shells and their debris on the open-shell beach finer and transports the shells and their debris to the sea causing erosion and retreat of the shelly chenier. (3) The action of typhoons and other strong winds and waves results in the original accumulation on the open shell-beach being further transported to the land and provides a large amount of shells and debris from the intertidal zone to the shelly chenier. Based on GPS-RTK monitoring data from 2020–2021, it was found that the transport volume of shells and debris caused by a typhoon storm surge is equivalent to the annual transport volume under normal ocean dynamics.

The transport processes of marine suspended sediments are important to the material cycle and the shaping of seafloor topography. Existing sediment monitoring methods are limited in their use under high concentration conditions, and are not effective in monitoring and capturing sediment in 3D directions, and there is an inability to accurately explain sediment transport processes. To infer the transport process of suspended sediments, this study proposed a time-series vector in situ observation device. An accompanying time-series analytic method was developed for sediment transport fluxes. The correlation between the internal and external flow velocities of the capture tube was established through indoor tests, and then the applicability of the device was verified by the correlation between the theoretical capture quality and the actual capture quality, and the analytic formula of the flux was refined. The proposed observation technique can be used for in situ long-term observation and sampling of marine suspended sediments under conventional and even extreme sea conditions, achieving accurate time-series suspended sediment capture and high-resolution transport flux analysis. The technique thus provides a more effective means for scientific research into the dynamics of seafloor sedimentation, the mechanisms of ocean carbon sinks, and the processes of the carbon cycle.

Seabed liquefaction and sediment resuspension under wave loading are key issues in marine engineering, but are usually regarded as independent processes (instead of coexisting and interacting processes). Here, we analyzed random wave-induced seabed liquefaction and its impact on sediment resuspension using flume experiments. Results show that in a nonliquefaction scenario, excess pore pressure in the seabed oscillates with wave fluctuations, but pressure accumulation is low, while a consistent upward pressure gradient promotes sediment suspension. Wave-induced shear stress was the key driver of sediment resuspension in a nonliquefaction scenario. In the liquefied state, waves with different amplitudes differently responded to excess pore pressure; small-amplitude waves accumulated pressure, while large-amplitude waves dissipated it. Liquefied soil formed mud waves, creating elliptical motion along with random waves. Seabed liquefaction accelerated sediment resuspension in the following ways: reducing soil critical shear stress; forming seepage channels inside the seabed; forming mud waves, resulting in increased turbulent kinetic energy; dissipating excess pore pressure and releasing porewater, expelling fine-grained sediment from the liquefied soil. Our study reveals the variation in excess pore pressure in silty seabed under random waves and its effect on sediment resuspension, which is significant for understanding soil liquefaction and sediment movement of silt.

Peritonitis caused by LPS is a severe clinical challenge, which causes organ damage and death. However, the mechanism of LPS-induced peritonitis has not been fully revealed yet. Here, we investigated the transcriptome profile of the peritoneal tissue of LPS-induced peritonitis in mice. A model of LPS-induced peritonitis in mice was established (LPS 10 mg/kg, i.p.), and the influence of TAK 242 (TLR4 inhibitor) on the level of inflammatory cytokines in mouse peritoneal lavage fluid was investigated by using an ELISA test. Next, the peritoneal tissues of the three groups of mice (Control, LPS, and LPS+TAK 242) (n = 6) were isolated and subjected to RNA-seq, followed by a series of bioinformatics analyses, including differentially expressed genes (DEGs), enrichment pathway, protein-protein interaction, and transcription factor pathway. Then, qPCR verified-hub genes that may interact with TAK 242 were obtained. Subsequently, the three-dimensional structure of hub proteins was obtained by using homology modeling and molecular dynamics optimization (300 ns). Finally, the virtual docking between TAK 242 and hub proteins was analyzed. Our results showed that TAK 242 significantly inhibited the production of inflammatory cytokines in the peritoneal lavage fluid of mice with peritonitis, including IL-6, IFN-γ, IL-1β, NO, and TNF-α. Compared with the Control group, LPS treatment induced 4201 DEGs (2442 down-regulated DEGs and 1759 up-regulated DEGs). Compared with the LPS group, 30 DEGs were affected by TAK 242 (8 down-regulated DEGs and 22 up-regulated DEGs). A total of 10 TAK 242-triggered hub genes were obtained, and the possible docking modes between TAK 242 and hub proteins were acquired. Overall, our data demonstrated that a large number of DEGs were affected in LPS-triggered peritonitis mice. Moreover, the TLR4 inhibitor TAK 242 is capable of suppressing the inflammatory response of LPS-induced peritonitis. Our work provides clues for understanding the pathogenesis of LPS-induced peritonitis in mice.

The silty seabed in the Yellow River Delta (YRD) is exposed to deposition, liquefaction, and reconsolidation repeatedly, during which seepage flows are crucial to the seabed strength. In extreme cases, seepage flows could cause seepage failure (SF) in the seabed, endangering the offshore structures. A critical condition exists for the occurrence of SF, i.e., the critical hydraulic gradient (icr). Compared with cohesionless sands, the icr of cohesive sediments is more complex, and no universal evaluation theory is available yet. The present work first improved a self-designed annular flume to avoid SF along the sidewall, then simulated the SF process of the seabed with different consolidation times in order to explore the icr of newly deposited silty seabed in the YRD. It is found that the theoretical formula for icr of cohesionless soil grossly underestimated the icr of cohesive soil. The icr range of silty seabed in the YRD was 8–16, which was significantly affected by the cohesion and was inversely proportional to the seabed fluidization degree. SF could “pump” the sediments vertically from the interior of the seabed with a contribution to sediment resuspension of up to 93.2–96.8%. The higher the consolidation degree, the smaller the contribution will be.

The propagation of dam-break waves on different rough beds was observed to be quasi-steady in the range 11.3 < x/hdam < 18.8, where x is measured from the dam position. These quasi-steady propagation speeds converge with the steady ideal fluids model of Stoker (Water Waves, 1957, Interscience) when the tailwater depth h2 becomes greater than ~0.5ks, in the range 0.001 < ks/hdam < 0.2, where ks is the roughness and hdam the depth behind the dam. Hence, this convergence encourages the use of Stoker's steady, ideal fluid solution to develop more general models, including friction effects due to bed roughness and/or viscosity. The new experimental data support a MacLaurin series for the celerity c, in analogy with the series in terms of 4 √h2/hdam, derived for Stoker's model, h2 being the tailwater depth. Compared with the retarding effect of the tailwater, 1mm of roughness is found to be equivalent to μ13mm of tailwater, and 1μm of viscous length (ν/√ ghdam, where ν is the kinematic viscosity and g the acceleration due to gravity) is equivalent to ~1700μm of tailwater. While the MacLaurin series quantifies the similar effects of small roughness and small tailwater depths acting separately, the new data illustrate for the first time the complex interplay between tailwater and roughness on 'wet beds' with many details yet to be investigated. In particular, it was shown that a small amount of tailwater on a rough bed acts as a lubricant, so that c(h2, hdam, ks) is an increasing function of h2 for h2 < 0.5ks. [ABSTRACT FROM AUTHOR]