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Abstract In the process of natural gas extraction, the phenomenon of liquid loading will affect the efficiency of gas well extraction and reduce the life of the well. Compared with conventional drainage gas extraction technology, the jet pump can not only reduce the bottom back pressure and ensure the stable production of gas reservoirs but also promote the final recovery rate. Since the jet pump relies on the interaction between fluid particles to transfer energy, the energy loss is large and the efficiency is low. To maximize the advantages of the gas‐driven jet pump, this study innovatively combines a static mixer with an annular jet pump. Utilizing the cyclonic effect produced by the static mixer, the original gas‐liquid axial motion is transformed into a stronger vortex motion, and the liquid droplets are changed into a liquid film that is easier to carry, which significantly improves the discharge efficiency of the jet pump. This study uses a combination of numerical simulation and experimental analysis to compare the associated effects of the new annular jet pump (NAJP) and the conventional annular jet pump (CAJP) on the liquid‐carrying performance of gas wells in terms of cyclonic effect, droplet breakage ratio, and pump efficiency. The results show that, compared with CAJP, NAJP increases the mass flow rate of the sucked fluid. The droplet breakage ratio increases by 15.4% year‐on‐year, while the critical liquid‐carrying flow rate is reduced by about 10.7%, resulting in a maximum pumping efficiency of 37%, an increase of about 30.7% year‐on‐year. At the same time, the reduction of the energy coefficient means lower energy consumption. In summary, NAJP is better than CAJP in terms of liquid‐carrying effect and efficiency.

As one of the most critical features on the earth's surface, coastal zone mandates high-quality extraction of its representative feature, the coastline. Prior methodologies primarily emphasize on edge and small-scale information. However, during large-scale image processing, misclassification might occur due to the difficulty in determining whether a local area belongs to the land or sea. To address this, we propose a deep learning-based multiscale coastline extraction algorithm in this study. It comprises a multiscale coastal zone dataset built upon a tile map service structure and a scene classification-based multiscale coastal zone classifier, employing quadtree decomposition to identify coastal zones from low to high levels. Contrasting with conventional semantic segmentation, the scene classification network, owing to its larger receptive field, can accurately discern land and sea. This accuracy is further enhanced by using quadtree decomposition to process images with lower resolution and larger coverage. The results suggest that our proposed method effectively eliminates confusing features, with the overall experimental classification accuracy attesting to the effectiveness of our approach, yielding a 6% improvement. Moreover, the screening process in this study significantly reduces the number of input samples for the segmentation network, thus boosting computational speed.

Objective:Diabetic retinopathy (DR) is a severe diabetic complication that leads to severe visual impairment or blindness. He-Ying-Qing-Re formula (HF), a traditional Chinese medicinal concoction, has been identified as an efficient therapy for DR with retinal vascular dysfunction for decades and has been experimentally reported to ameliorate retinal conditions in diabetic mice. This study endeavors to explore the therapeutic potential of HF with key ingredients in DR and its underlying novel mechanisms.Methods:Co-expression gene modules and hub genes were calculated by weighted gene co-expression network analysis (WGCNA) based on transcriptome sequencing data from high-glucose-treated adult retinal pigment epithelial cell line-19 (ARPE-19). The chromatographic fingerprint of HF was established by ultra-performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-Q-TOF-MS). The molecular affinity of the herbal compound was measured by molecular docking. Reactive oxygen species (ROS) was measured by a DCFDA/H2DCFDA assay. Apoptosis was detected using the TUNEL Assay Kit, while ELISA, Western blot, and real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) were used for detecting the cytokine, protein, and mRNA expressions, respectively.Results:Key compounds in HF were identified as luteolin, paeoniflorin, and nobiletin. For WGCNA, ME-salmon (“protein deacetylation”) was negatively correlated with ME-purple (“oxidative impairment”) in high-glucose-treated ARPE-19. Luteolin has a high affinity for SIRT1 and P53, as indicated by molecular docking. Luteolin has a hypoglycemic effect on type I diabetic mice. Moreover, HF and luteolin suppress oxidative stress production (ROS and MDA), inflammatory factor expression (IL-6, TNF-α, IL1-β, and MCP-1), and apoptosis, as shown in the in vivo and in vitro experiments. Concurrently, treatment with HF and luteolin led to an upregulation of SIRT1 and a corresponding downregulation of P53.Conclusion:Using HF and its active compound luteolin as therapeutic agents offers a promising approach to diabetic retinopathy treatment. It primarily suppressed protein acetylation and oxidative stress via the SIRT1/P53 pathway in retinal pigment epithelial cells.

Osteoporosis (OP) is a systemic bone disease in which bone density and quality decrease and bone fragility increases due to a variety of causes, making it prone to fractures. The development of OP is closely related to oxidative stress. Uric acid (UA) is the end product of purine metabolism in the human body. Extracellular UA has antioxidant properties and is thought to have a protective effect on bone metabolism. However, the process of UA degradation can lead to intracellular oxidative stress, which together with UA-induced inflammatory factors, leads to increased bone destruction. In addition, UA can inhibit vitamin D production, resulting in secondary hyperparathyroidism and further exacerbating UA-associated bone loss. This review summarizes the relationship between serum UA levels and bone mineral density, bone turnover markers, and so on, in the hope of providing new insights into the pathogenesis and treatment of OP.

Abstract Composite insulators have excellent performance and are more and more widely used in power grid. The performance of composite insulators with different service duration will decline in varying degrees, which could pose a threat to the safe operation of power grid. In order to investigate the influence of service duration and electric field strength on insulator shed performance, the sheds at different positions of insulators with different service duration are sampled. The hydrophobicity, material and mechanical properties of the samples are tested, and then the micro material properties tests are performed in terms of SEM, FTIR and XPS tests. Based on the above test results, the aging law and its mechanism of silicone rubber sheds are analyzed. The results reveal that the performance of insulator shed gradually decline with the increase of service life. The hydrophobicity and hardness of high-voltage end insulator are similar to that of middle section insulator, while other parameters are obviously different, indicating that the electric field can aggravate the aging. FTIR results show that the main chain and hydrophobic side chain of silicone rubber are destroyed, and the oxygen-containing groups increased, indicating that thermal oxygen aging occurred during operation. XPS and SEM results show that the crosslinking degree of silicone rubber increases and the porosity increases. The above changes in the microstructure of silicone rubber lead to the decline of insulator performance.

The “The Use of Lactobacillus in Forage Storage and Processing” Special Issue (SI) in Fermentation has brought together groundbreaking research and innovative methodologies in the realm of forage preservation [...]

The comment by Yin (2024) referred to the systematic morphology and identification of the newly recovered in situ ammonite assemblage Tiltoniceras sp. and Hildoceratidae sp. from the Bilong Co section in the Qiangtang Basin. Yin (2024) suggests that Tiltoniceras sp. should be assigned to Oxycerites aspidoides. Upon re-examination of our paper and the reply to the released comment, it is demonstrated that the morphologic characteristics of the recovered ammonites are more consistent with Tiltoniceras rather than Oxycerites aspidoides. These ammonites, along with in situ calcite U–Pb age (ca. 181 Ma), carbon isotope stratigraphy, and age-diagnostic palynomorphs, constrain the age of the Bilong Co oil shale to the Lower Jurassic (Toarcian) with plausible credentials.

Coastal areas face escalating storm surge disasters due to rising sea levels and urban growth, posing greater risks to lives and property. Comprehensive storm surge risk assessment and sensitivity analysis in coastal areas are crucial for effective disaster prevention and mitigation. This research focuses on Huizhou, China, conducting a comparative study of storm surge risk assessment and sensitivity analysis based on an integrated approach, which includes the storm surge inundation numerical model (FVCOM-SWAN), Geographic Information System (GIS) and Remote Sensing (RS) techniques, and Multiple Criteria Decision-Making (MCDM) methods. Ten flood-related risk indicators are selected from the hazard, exposure and vulnerability, the weights of which are evaluated through a comprehensive comparison among Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (FAHP), Entropy Weight (EW), AHP-EW, and FAHP-EW methods. High-precision risk level maps are generated subsequently utilizing GIS and RS techniques. Sensitivities of the indicators are analyzed using One-At-A-Time (OAT) and Fourier Amplitude Sensitivity Test (FAST) methods. The proposed storm surge risk assessment framework, the MCDM comparative study and the sensitivity analysis can offer insights for better understanding and management of storm surge risks, and contribute to the standardization and application of storm surge risk assessment.

The phytoplankton size structure exerts a significant influence on ecological processes and biogeochemical cycles. In this study, the interannual variations in remotely sensed phytoplankton size structure in the southern Tropical Indian Ocean (TIO) and the underlying physical mechanisms were investigated. Significant interannual fluctuations in phytoplankton size structure occur in the southeastern TIO and central southern TIO and are very sensitive to Indian Ocean Dipole (IOD) events. During positive IOD events, the southeast wind anomalies reinforce coastal upwelling off of Java and Sumatra, leading to a shift toward a larger phytoplankton structure in the southeastern TIO. The anomalous anticyclonic circulation deepened the thermocline and triggered the oceanic downwelling Rossby waves, resulting in a smaller phytoplankton structure in the southwestern TIO. During the decay phase of the strong positive IOD events, the sustained warming in the southwestern TIO induced basin-wide warming, thereby maintaining such an anomalous phytoplankton size structure into the following spring. The response of phytoplankton size structure and ocean dynamics displayed inverse patterns during the negative IOD events, with an anomalous larger phytoplankton structure in the central southern TIO. These findings enhance our understanding of phytoplankton responses to climate events, with serious implications for ecosystem changes in a warming climate.

Abstract The objective was to determine effects of cellulase, xylanase, and commercial fibrolytic enzymes on fermentation quality, aerobic stability, bacterial community, and in vitro degradation of mixed silages. Mixtures of alfalfa, wheat bran, and rice straw [80:15:5 on a fresh matter (FM) basis] were ensiled for 1, 3, 5, 7, 15, 30, and 45 d after treatment with: distilled water (control, C); cellulase (E); xylanase (X); or commercial fibrolytic enzymes (EX), with all enzyme preparations applied at 100 U/g FM. The 45-day silages were subjected to an in vitro degradation test. Each of the three enzyme-treated groups enriched relative abundance (RA) of Lactobacillus, Weissella, and Stenotrophomonas maltophilia, increased water soluble carbohydrate (WSC) concentrations, and extended aerobic stability over 384 h, but concurrently inhibited growth of undesirable microbes (i.e., Acinetobacter sp, Lelliottia amnigena, and Sphingomonas sp), reducing pH and concentrations of ammonia nitrogen (AN), butyric acid (BA) and propionic acid (PA). Compared to C, adding X or EX increased the RA of L. paralimentarius and L. parabrevis, enhanced accumulation of acetic acid (AA) and crude protein (CP), and reduced hemicellulose content. Furthermore, E group silage had the highest abundance of W. cibaria. In addition, EX enriched RA of Bacillus velezensis, reduced AN concentration, increased DM degradability, total VFA production, and gas production during in vitro incubation. In conclusion, addition of X or EX enhanced ensiling by enhancing concentrations of AA; however, EX was the most promising enzyme, based on reducing AN concentration and increasing DM content and DM degradability. Graphical Abstract

Abstract Background Alzheimer’s disease (AD) is a major global health crisis in need of more effective therapies. However, difficult choices to optimize value-based care will need to be made. While identifying preferred therapeutic attributes of new AD therapies is necessary, few studies have explored how preferences may vary between the stakeholders. In this study, the trade-offs among key attributes of amyloid plaque-lowering therapies for AD were assessed using a discrete choice experiment (DCE) and compared between caregivers and neurologists. Methods An initial pilot study was conducted to identify the potentially relevant features of a new therapy. The DCE evaluated seven drug attributes: clinical effects in terms of delay in AD progression over the standard of care (SOC), variation in clinical effects, biomarker response (achieving amyloid plaque clearance on PET scan), amyloid-related imaging abnormalities-edema (ARIA-E), duration of therapy, need for treatment titration as well as route, and frequency of drug administration. Respondents were then randomly presented with 12 choice sets of treatment options and asked to select their preferred option in each choice set. Hierarchical Bayesian regression modeling was used to estimate weighted preference attributes, which were presented as mean partial utility scores (pUS), with higher scores suggesting an increased preference. Results Both caregivers (n = 137) and neurologists (n = 161) considered clinical effects (mean pUS = 0.47 and 0.82) and a 5% incremental in ARIA-E (mean pUS = − 0.26 and − 0.52) to be highly impactful determinants of therapeutic choice. In contrast, variation in clinical effects (mean pUS = 0.12 and 0.14) and treatment duration (mean pUS = − 0.02 and − 0.13) were the least important characteristics of any new treatment. Neurologists’ also indicated that subcutaneous drug delivery (mean pUS = 0.42 vs. 0.07) and administration every 4 weeks (mean pUS = 1.0 vs. 0.20) are highly desirable therapeutic features. Respondents were willing to accept up to a 9% increment in ARIA-E for one additional year of delayed progression. Conclusions Caregivers and neurologists considered incremental clinical benefit over SOC and safety to be highly desirable qualities for a new drug that could clear amyloid plaques and delay clinical progression and indicated a willingness to accept incremental ARIA-E to achieve additional clinical benefits.

With the increasing global reliance on maritime oil transportation, oil spills pose significant environmental hazards to coastal ecosystems. This study presents a comprehensive quantitative framework for assessing oil spill risks along the Jiaozhou Bay coastline in China. The research begins with an analysis of historical spill data to construct representative oil spill simulation scenarios. The advanced MEDSLIK-II oil spill prediction model is then employed to simulate oil spill trajectories under these scenarios, focusing on key parameters such as oil thickness and mass to evaluate the hazard levels associated with each scenario. Subsequently, the Environmental Sensitivity Index (ESI) is utilized to assess the vulnerability of coastal zones, while Geographic Information System (GIS) techniques are employed for a spatial analysis and visualization of the results. The case study, covering a 26.87 km stretch of the Jiaozhou Bay coastline, reveals 10 high-risk zones with a total length of 8561.2 m. These areas are predominantly characterized by saltwater marshes, brackish water marshes, and inundated low-lying areas, with ESI rankings of 9 and 10, accounting for 24% of the 339 analyzed segments. The modeling results indicate that in the simulated scenarios, oil spills originating from the Huangdao Oil Port and Qianwan Port pose the greatest risks, with potential impacts extending up to 12 km and 15 km along the coastline, respectively. The study highlights the importance of considering multiple factors, including oil spill trajectories, coastal geomorphology, and ecological sensitivity, in comprehensive risk assessments. The proposed framework demonstrates potential for adaptation and application to other coastal regions facing similar oil spill risks, contributing to the advancement of coastal management practices worldwide.

To estimate the changes in the annual mean sea level (MSL) and extreme sea levels (ESLs), the largest collection of tide gauge records from 10 tidal stations along the northern coast of the South China Sea (SCS) were analyzed in this study. Here, all the tide gauge records had been homogenized by a two-step process involving the detection of inhomogeneities, that is, breakpoints caused by non-climatic changes and the application of the adjustment. The study’s conclusions, based on the homogenized tide gauge records, can be summarized as follows: The instrument change and station relocation are the main causes for the identified inhomogeneities. From 1989 to 2018, the sea level along the SCS was at an average rate of 4.0 mm per year, as measured by the homogenized tide gauges. The ESLs from the nine tidal stations rose notably with interannual fluctuations, except for the XSA station. Additionally, the ESLs exhibited substantial decadal variations. The ESLs rose along the northern coast of the SCS and were accelerated at most stations throughout the whole study period, especially after the 1980s. There were significant positive correlations between the ESL and the annual MSL at most tide gauges. The MSL’s changes, especially long-term changes, play an important role in the change in ESLs.

Annually, hundreds of individuals tragically lose their lives at sea due to shipwrecks or aircraft accidents. For search and rescue personnel, the task of locating the debris of a downed aircraft in the vastness of the ocean presents a formidable challenge. A primary task these teams face is determining the search area, which is a critical step in the rescue operation. The movement of aircraft wreckage on the ocean surface is extremely complex, influenced by the combined effects of surface winds, waves, and currents. Establishing an appropriate drift motion prediction model is instrumental in accurately determining the search area for the wreckage. This article initially conducts maritime drift observation experiments on wreckage, and based on the results of these experiments, analyzes the drift characteristics and patterns of the debris. Subsequently, employing a wealth of observational experimental data, three types of drift prediction models for the wreckage are established using the least squares method. These models include the AP98 model, the dynamics model, and an improved model. In conclusion, the effectiveness and accuracy of the three models is evaluated and analyzed using Monte Carlo techniques. The results indicate that the probability of positive crosswind leeway (CWL) is 47.4%, while the probability of negative crosswind leeway (CWL) is 52.6%. The jibing frequency is 7.7% per hour, and the maximum leeway divergence angle observed is 40.4 degrees. Among the three drift prediction models, the refined AP98 drift model demonstrates the highest forecasting precision. The findings of this study offer a more accurate drift prediction model for the search of an aircraft lost at sea. These results hold significant guiding importance for maritime search and rescue operations in the South China Sea.

The processes of ocean dynamics are complex near the Pearl River Estuary and are not clear due to a lack of abundant observations. The spatial characteristics of the spring sea surface currents in the adjacent waters of the Pearl River Estuary were analyzed using the current data observed by a three-station high-frequency surface wave radar system (HFSWRS). Compared with the two-station HFSWRS, the deviation of current velocity and direction observed by the three-station HFSWRS from the underway measurements decreased by 42.86% and 38.30%, respectively. The analyzed results show that the M2 tidal current is the dominant current among all the tidal constituents, followed by K1, with angles of inclination ranging from 130° to 150°. The tidal flow is dominated by northwest–southeast back-and-forth flow. In the southern part of the observed area, which is far from the coastline, the tidal current ellipses exhibit a circular pattern. The prevalent tidal current type in this region is irregularly semi-diurnal, and the shallow water constituents also have a significant effect. The tidal energy in the adjacent waters of the Pearl River Estuary is affected by potential energy flux and kinetic energy flux. As the water depth and currents velocity increase in the southeast direction, the tidal energy flux increases. In the nearshore zone, the direction of tidal energy flux varies along the coastline. The changes in the residual current within the observed area are correlated with the sea surface wind field. Based on the high-precision sea surface current observed by the three-station HFSWRS, the characteristics of the ocean dynamic processes near the Pearl River Estuary are analyzed comprehensively, which provides important reference and confidence for the application of the developing new radar observing network with about 10 radar stations near the Pearl River Estuary.

Marine oil spill simulations typically employ the oil particle method to calculate particle trajectories, considering various factors such as wind, current, and turbulence. The wind drift factor (WDF), a random element determining the proportion of wind’s effect on oil particles, is often empirically set as a constant in traditional oil spill models, introducing limitations. This study proposes a support vector regression-based parameterization modeling (SVR-PM) for the WDF. Using extensive buoy data and ocean hydrodynamic reanalysis data, we trained an SVR model to compute the WDF in real-time based on real-time wind speed. The SVR-PM was integrated into an oil spill model to enhance the computation of the wind-induced velocity term. We validated the model using satellite images of two significant oil spills, resulting in an excellent average agreement. The SVR-PM’s advantage lies in enhancing the accuracy of wind-induced velocity term in oil spill simulations and demonstrating strong adaptability and generalizability over time and space. This advancement holds significant implications for maritime departments and emergency disaster response units.

Abstract Sestrin2 is identified as a stress-induced protein and could functionate in many aspects. In our study, we investigated the latent impact of Sestrin2 on podocyte injury and its molecular mechanism in vivo and in vitro in diabetic kidney disease (DKD). Sestrin2 was low-expressed in renal biopsies from individuals with DKD, the glomeruli from diabetic mice, and mouse podocytes exposed to high glucose (HG). Sestrin2 overexpression ameliorated HG-induced phenotypic alterations, apoptosis, and oxidative stress in conditionally immortalized mouse podocytes and modulated the activity of Thrombospondin-1 (TSP-1)/transforming growth factor (TGF-β1)/Smad3 pathway in podocytes. Moreover, TSP-1 inhibitor LSKL or TGF-β blocker Pirfenidone arrested podocyte injury induced by HG. Streptozotocin (STZ) was employed to render equivalent diabetes in B6-TgN (CMV-Sestrin2) (TgN) and wild-type (WT) control mice. Sestrin2 alleviated increased levels of 24‐h urinary protein, blood urea nitrogen, serum creatinine and triglyceride, and urine 8-OHdG in diabetic mice. Podocyte phenotypic alterations, increased expression of apoptosis-associated proteins and podocyte loss were observed in WT but not in diabetic TgN mice, as well as oxidative stress. Additionally, TSP-1/TGF-β1/Smad3 signaling pathway was also suppressed in glomeruli of diabetic TgN mice. Thus, Sestrin2 mitigates podocyte injury in DKD via orchestrating TSP-1/TGF-β1/Smad3 pathway, underlining Sestrin2 as a promising therapeutic target for DKD.

Although there are lots of researches about the application of FRP composites on the external bonding and reinforcement of timber structures, they are mostly aimed at Carbon FRP, which has a large difference in mechanical properties and inconsistent deformation with timber. Meanwhile, research about the interface between timber and Flax Fiber Reinforced Polymer (FFRP) with similar properties with timber is limited; hence, relevant models have not been established yet. Similar to linear elastic FRP confined timber structure, the failure of FFRP-timber structure interface is a process in which the interfacial stress is gradually transferred from the loaded end to the free end, and the interfacial stress transfer area, that is an effective bond length of the FFRP-timber interface. In order to study the interfacial stress transfer characteristics between FFRP and timber structure, based on the existing CFRP-timber structure interface bonding theory and mechanism, a single lap shear test is used to conduct a systematic study on bonding length and width are studied to optimized the application of FFRP. The bond-slip behavior is studied and fitted to provide theory support for the application of FFRP in the restoration of ancient timber structures.

BackgroundThe study aimed to identify core biomarkers related to diagnosis and immune microenvironment regulation and explore the immune molecular mechanism of diabetic nephropathy (DN) through bioinformatics analysis.MethodsGSE30529, GSE99325, and GSE104954 were merged with removing batch effects, and different expression genes (DEGs) were screened at a criterion |log2FC| >0.5 and adjusted P

Extremes in sea surface temperature (SST) have the potential to threaten marine biodiversity, ecosystem functions, and regional services. Using the OISST v2 data, we analyzed the spatial and temporal variability of marine hot and cold events in the China Seas during 1982–2020. Results showed that in majority of the China Seas, frequency of Hot Days (HDs) and Marine heatwaves (MHWs) has significantly increased; severe MHWs (SMHWs) have become more intense. Frequency of Cold Days (CDs) and Marine cold-spells (MCSs) has fallen dramatically; severe MCSs (SMCSs) have become weaker. All of the trend distributions are heterogeneous, with the largest magnitudes along the coast. Long-term temporal analysis revealed that cold extremes (CDs, MCSs, SMCSs) decreased significantly, while hot extremes (HDs, MHWs, SMHWs) increased at a higher confidence level and at a faster rate. Asymmetry characterizes the tendencies of hot and cold extremes. Further study demonstrated that mean SST warming, not SST variability, was the primary driver of trends in both MHW and MCS metrics. MHWs/MCSs related to the anthropogenic or natural forcing increased very rapidly in the post-1998 era.

Objective. In this study, we aimed to estimate the impact of sleep duration on left ventricular hypertrophy (LVH) in type 2 diabetes mellitus (T2DM). Methods. Consecutive patients with T2DM undergoing transthoracic echocardiography (TTE) in our center from October 2017 to February 2021 were analyzed. The association of the risk of LVH in T2DM patients was evaluated using univariable and multivariable logistic regression analyses. Results. This study finally included 2689 adult patients (mean age 51.8 ± 12.5 years, 56.2% men, mean sleep duration 7.6 ± 1.4 hours per day). Of all patients, 655 (24.4%) patients were diagnosed with LVH and 2034 did not have LVH. All patients were adults and were diagnosed with T2DM. In the univariate and multivariate regression analyses, gender, sleep duration, body mass index (BMI), waist, hemoglobin (Hb), blood creatinine (Cr), and high-density lipoprotein cholesterol (HDL-c) were associated with LVH. In the restricted cubic spline (RCS) model, the cut-off points of sleep duration given refer to the group of patients with T2DM and LVH were 8 hours per day. With the cut-off points, the multivariable analysis demonstrated that, for diabetic patients, LVH was significantly correlated with a sleep duration of 8 hours per day, hemoglobin, blood urea nitrogen (BUN), and HDL-c. Conclusion. For patients with T2DM, long sleep duration (>8 hours per day), hemoglobin, BUN, and HDL-c were independently associated with LVH. This trial is registered with NCT03811470.

Freak wave occurs unexpectedly in the ocean with an extreme wave crest and focused wave energy, resulting in many marine accidents. The interaction between a container ship and freak waves in beam sea is studied in this paper to better understand the influence of freak waves on ships. A three-dimensional in-house solver is developed and validated for the freak wave generation and the wave-ship interaction. Characteristics of the interaction process, motion responses of the ship and the green water loadings induced by the freak waves are obtained and analyzed. Comparisons are carried out to reveal the influences of the freak wave crest and sequence on the roll, heave and impact pressures. Relations between motion responses of the ship and the green water event are discussed. Influences of ship speeds on the wave-ship interaction are addressed.

In order to protect structures along the offshore and off the coast, breakwaters are commonly applied to reduce the influence of waves. Flat plate breakwater is studied frequently due to its great performance near the water surface, however, traditional passive methods such as fixed and floating flat plate breakwaters usually fail to give full play of effective wave dissipation when encountering variable unknown incoming waves. Therefore, this paper develops an interdisciplinary model coupling computational fluid dynamics (CFD) and deep reinforcement learning (DRL) to study the wave dissipation of a submerged movable flat plate breakwater against regular waves. An in-house numerical wave tank (NWT) is built to simulate the fluid-structure interaction between the plate and regular waves. The fluid domain in NWT is regarded as environment while the flat plate breakwater is agent. In addition, the wave dissipation strategy is learned by the artificial neural network (ANN) through the continuous wave-plate interaction. It is excited to find that the coupling model is able to learn the control strategies automatically and exhibits good adaptability to the changes of environment.

Over the past few decades, floods have severely damaged production and daily life, causing enormous economic losses. Streamflow forecasts prepare us to fight floods ahead of time and mitigate the disasters arising from them. Streamflow forecasting demands a high-capacity model that can make precise long-term predictions. Traditional physics-based hydrological models can only make short-term predictions for streamflow, while current machine learning methods can only obtain acceptable results in normal years without floods. Previous studies have demonstrated a close relation between El Niño-Southern Oscillation (ENSO) and the streamflow of the Yangtze River. However, traditional models, holding the encoder–decoder architecture, only have one encoder block that can not support bivariate time series forecasting. In this study, a transformer-based double-encoder-enabled model was proposed, called the double-encoder Transformer, with a distinctive characteristic: “cross-attention” mechanism that can capture the relation between two time series sequences. Using river flow observation collected by the Yangtze River Water Resources Commission and El Niño-Southern Oscillation (ENSO) observation collected by the National Oceanic and Atmospheric Administration, the model can achieve better performance. By using variational mode decomposition (VMD) technique for preprocessing, the model can make precise long-term predictions for the river flow of the Yangtze River. A monthly prediction of 21 years (from January 1998 to December 2018) was made, and the results indicate that the double-encoder Transformer outperforms mainstream time series models.

Tropical cyclones (TCs) pose a significant threat in terms of wind-induced damage and storm risk to the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) of China. In this research, annual power dissipation index (PDI) and storm surge and wave destructive potential (SDP) index from June to November were used to estimate the damage caused by the TC events in the buffer zone of the GBA. The regime shifts in 1993 and 2013 were identified through the Bayesian changepoint detection in six TC datasets. The TC-induced damage during 1994–2012 (the low-damage period) was weaker than that in 1977–1993 and 2013–2020 (the high-damage periods). The intensity and size of stronger TCs are the dominant factors responsible for the interdecadal changes in the TC damage. This may be explained by the influences of sea surface temperature (SST), surface latent heat flux and mid-level relative humidity. During high-damage periods, TCs can extract more energy from the ocean, leading to increased TC intensity and larger size. Conversely, during low-damage periods, TCs undergo a decrease in energy intake, resulting in reduced TC power and smaller size. The variations in the SST are relative to the Luzon Strait transport. In addition, the reduction in TC translation speed is unfavorable for the development of TCs in low-damage periods. Further research suggested that mid-level steering flow affects the TC movement velocity. The results offer valuable insights into the variations in the TC-induced damage in the GBA.

Nearshore bathymetry plays an essential role in various applications, and satellite-derived bathymetry (SDB) presents a promising approach due to its extensive coverage and comprehensive bathymetric map production capabilities. Nevertheless, existing retrieval techniques, encompassing physics-based and pixel-based statistical methodologies such as support vector regression (SVR), band ratio, and Kriging regression, exhibit limitations stemming from the intricate water reflectance process and the under-exploitation of the spatial component inherent in SDB. To surmount these obstacles, we introduce employment of deep convolutional networks (DCNs) for SDB in this study. We assembled multiple scenes utilizing networks with varying scale emphasis and an assortment of satellite datasets characterized by distinct spatial and spectral resolutions. Our findings reveal that these deep learning models yield high-caliber bathymetry outcomes, with nonlinear normalization further mitigating residuals in shallow water regions and substantially enhancing retrieval performance. A comparative analysis with the prevalent SVR technique substantiates the efficacy of the proposed methodology.

Non-alcoholic fatty liver disease (NAFLD) is a group of metabolic liver illnesses that lead to accumulation of liver fat mainly due to excessive nutrition. It is closely related to insulin resistance, obesity, type 2 diabetes, and cardiovascular disease, and has become one of the main causes of chronic liver disease worldwide. At present, there is no specific drug for the treatment of NAFLD; lifestyle interventions including dietary control and exercise are recommended as routine treatments. As a drug for the treatment of type 2 diabetes, sodium-glucose co-transporter type 2 (SGLT-2) inhibitors may also play a beneficial role in the treatment of NAFLD. This article reviews the mechanism of SGLT-2 inhibitors in the treatment of NAFLD.

Marine Science and technology are the basis for human beings to understand, develop and protect the ocean [...]

Abstract Objective To clarify the frequency and outcome of patients with systemic lupus erythematosus (SLE) who achieved the clinical state as serologically active clinically quiescent (SACQ) and to identify factors associated with the flare of disease. Methods Clinical data of patients diagnosed as SLE and followed in Peking University First Hospital from 2009 to 2015 were retrospectively reviewed. Six hundred eighty-two patients who were followed up for more than 6 months were analyzed. SACQ was defined as an at least a 6-month period with persistent serologic activity and without clinical activity and daily dose of prednisone or equivalent were less than 7.5 mg. Serologically quiescent clinically quiescent (SQCQ) patients served as control groups. Data including demographics, initial symptoms, duration to SACQ, treatments before and after SACQ, and characteristics of the patients suffered from flare were analyzed. Results Among the 682 patients, 170 patients were SACQ (24.9%) and 187 patients were SQCQ. SQCQ patients (38.61 ± 15.08 years old) were older at baseline than SACQ patients (38.61 ± 15.08 years vs. 32.09 ± 14.35 years, p < 0.001). Of 170 SACQ patients, 32.9% experienced flare that was significantly higher than 15.5% of SQCQ patients (29/187). Corticosteroids (OR 1.323, 95% CI 1.129 to 1.550; p = 0.001) was an independent risk factor for flare, while antimalarials (OR 0.045, 95% CI 0.004 to 0.474; p = 0.010) and immunosuppressants (OR 0.332, 95% CI 0.156 to 0.706; p = 0.004) were protective factors in SACQ patients; however, only antimalarials was protective factors in SQCQ patients (OR 0.028, 95% CI 0.001 to 0.743; p = 0.033). Conclusion About one third of SLE patients with SACQ experience flare, significantly more frequent than that of patients with SQCQ. Thus, approach to prevent flare in SACQ patient is required. Maintenance therapy of hydroxychloroquine and immunosuppressant agents may be protective and beneficial treatment strategy in these patients.

The purpose of this study was to explore platinum resistance-related biomarkers and mechanisms in lung adenocarcinoma. Through the analysis of gene expression data of lung adenocarcinoma patients and normal patients from The Cancer Genome Atlas, Gene Expression Omnibus database, and A database of genes related to platinum resistance, platinum resistance genes in lung adenocarcinoma and platinum resistance-related differentially expressed genes were obtained. After screening by a statistical significance threshold, a total of 252 genes were defined as platinum resistance genes with significant differential expression, of which 161 were up-regulated and 91 were down-regulated. The enrichment results of up-regulated gene Gene Ontology (GO) showed that TOP3 entries related to biological processes (BP) were double-strand break repair, DNA recombination, DNA replication, the down-regulated gene GO enriches the TOP3 items about biological processes (BP) as a response to lipopolysaccharide, muscle cell proliferation, response to molecule of bacterial origin. Gene Set Enrichment Analysis showed that the top three were e2f targets, g2m checkpoint, and rgf beta signaling. A prognostic model based on non-negative matrix factorization classification showed the characteristics of high- and low-risk groups. The prognostic model established by least absolute shrinkage and selection operator regression and risk factor analysis showed that genes such as HOXB7, NT5E, and KRT18 were positively correlated with risk score. By analyzing the differences in m6A regulatory factors between high- and low-risk groups, it was found that FTO, GPM6A, METTL3, and YTHDC2 were higher in the low-risk group, while HNRNPA2B1, HNRNPC, TGF2BP1, IGF2BP2, IGF2BP3, and RBM15B were higher in the high-risk group. Immune infiltration and drug sensitivity analysis also showed the gene characteristics of the platinum-resistant population in lung adenocarcinoma. ceRNA analysis showed that has-miR-374a-5p and RP6-24A23.7 were lower in the tumor expression group, and that the survival of the low expression group was worse than that of the high expression group. In conclusion, the results of this study show that platinum resistance-related differentially expressed genes in lung adenocarcinoma are mainly concentrated in biological processes such as DNA recombination and response to lipopolysaccharide. The validation set proved that the high-risk group of our prognostic model had poor survival. M6A regulatory factor analysis, immune infiltration, and drug sensitivity analysis all showed differences between high and low-risk groups. ceRNA analysis showed that has-miR-374a-5p and RP6-24A23.7 could be protective factors. Further exploration of the potential impact of these genes on the risk and prognosis of drug-resistant patients with lung adenocarcinoma would provide theoretical support for future research.

Lifeboat is one of the most important life-saving equipment for escaping at sea when a ship is abandoned in an extreme emergency. An accurate drift model can help rescuers find the drift position of lifeboat in the shortest time, thus improving the efficiency of marine search and rescue (SAR) at sea and ensuring the safety of wrecked people. The purpose of this paper is to investigate the drift characteristics and to develop an accurate drift prediction model for the open lifeboat. First, large-scale drift experiments were conducted to analyze the drift characteristics with three 6.5-meter-long real-size open lifeboats in the South China Sea. Next, three drift prediction models of the lifeboats were developed using the least squares method based on the drift experimental data. Finally, the drift prediction models of the lifeboats were compared and evaluated using the Lagrangian method and Monte Carlo technique, respectively. Results indicate that the probability of positive crosswind leeway (CWL) of the open lifeboat is 47.5%. The jibing frequency is 6% per hour, and the maximum leeway divergence angle is 45°. These drift characteristics are very important for the prediction of the open lifeboat drift trajectory. The comparison results of three drift models show that the improved drift model is more accurate than the other two drift models for predicting drift trajectories of the open lifeboat, which can be directly applied to maritime search and rescue operations in the South China Sea.

Proprotein convertase subtilisin kexin type 9 (PCSK9) inhibits the clearance of low-density lipoprotein (LDL) cholesterol (LDL-C) from plasma by directly binding with the LDL receptor (LDLR) and sending the receptor for lysosomal degradation. As the interaction promotes elevated plasma LDL-C levels, and therefore a predisposition to cardiovascular disease, PCSK9 has attracted intense interest as a therapeutic target. Despite this interest, an orally bioavailable small-molecule inhibitor of PCSK9 with extensive lipid-lowering activity is yet to enter the clinic. We report herein the discovery of NYX-PCSK9i, an orally bioavailable small-molecule inhibitor of PCSK9 with significant cholesterol-lowering activity in hyperlipidemic APOE∗3-Leiden.CETP mice. NYX-PCSK9i emerged from a medicinal chemistry campaign demonstrating potent disruption of the PCSK9-LDLR interaction in vitro and functional protection of the LDLR of human lymphocytes from PCSK9-directed degradation ex vivo. APOE∗3-Leiden.CETP mice orally treated with NYX-PCSK9i demonstrated a dose-dependent decrease in plasma total cholesterol of up to 57%, while its combination with atorvastatin additively suppressed plasma total cholesterol levels. Importantly, the majority of cholesterol lowering by NYX-PCSK9i was in non-HDL fractions. A concomitant increase in total plasma PCSK9 levels and significant increase in hepatic LDLR protein expression strongly indicated on-target function by NYX-PCSK9i. Determinations of hepatic lipid and fecal cholesterol content demonstrated depletion of liver cholesteryl esters and promotion of fecal cholesterol elimination with NYX-PCSK9i treatment. All measured in vivo biomarkers of health indicate that NYX-PCSK9i has a good safety profile. NYX-PCSK9i is a potential new therapy for hypercholesterolemia with the capacity to further enhance the lipid-lowering activities of statins.

Quantitative analyses of storm surge risk, which are mostly focused on physical vulnerability, have been widely used to help coastal communities mitigate impacts and damage. Such assessments, however, overlook the social aspect of vulnerability in storm surge risk. By considering both the community’s social vulnerability and buildings’ physical vulnerability, this study proposed a methodology that incorporates social vulnerability into the framework for making quantitative risk assessments of storm surge using a coupled hydrodynamic and wave model, Geographical Information System (GIS) techniques, and the Principal Component Analysis (PCA) method. The coastal area of Huizhou was chosen as the case study due to its high concentration of oil and gas infrastructure in China’s southern Guangdong Province. By combining hazard, exposure, physical vulnerability, and social vulnerability, it was possible to explore the effect of social vulnerability on the physical vulnerability-based risk assessment of storm surge and determine the overall risk level. First, the Gumbel distribution was utilized to establish five representative and plausible hypothetical typhoon events with different return periods (10, 20, 50, 100, and 1000 years) for the study area. Then, using the well-validated fully-coupled model, the Simulating Waves Nearshore (SWAN) model and the ADvanced CIRCulation (ADCIRC) model, storm surge simulations for defined return periods were run, and the geographical distribution of the maximum surge elevations displayed on a GIS platform was used to assess hazard levels. In terms of the physical aspect, the depth-damage functions for buildings were established to estimate direct economic losses and assign risk levels accordingly. For the social vulnerability of a community, a composite score was computed using the PCA method by combining and aggregating indicators representing various characteristics of the social group. The results show that the overall risk level, taking into account both social vulnerability and physical vulnerability, has decreased on average. It suggests that social vulnerability-based risk assessment may account for a significant portion of the overall risk assessment, which is frequently overlooked in traditional storm surge risk assessment. Additionally, the comprehensive and precise risk maps can assist local policymakers in identifying areas at different risk levels and developing evacuation plans, thereby minimizing potential losses, especially in high-risk areas.

In recent decades, freak waves, characterized by their unusual high amplitude, sharp crest, and concentrated energy, have attracted researchers’ attention due to their potential threat to marine structures. Green-water loads caused by freak waves can be significant and may lead to local damage to the ship structures. Therefore, this paper focuses on the study of green-water loads and examines the structural responses of ship bow structures under the influence of the green-water loads caused by freak waves. Firstly, a three-dimensional numerical wave tank is established in which the superposition model is used to generate freak waves. Validations on the freak-wave generation, ship motion response and the wave loading are carried out to verify the present solvers. The simulation on the interaction between the freak wave and the ship are conducted to obtain the interaction process and green-water loads. Secondly, a finite element (FEM) model of the ship bow is built, on which the green-water loads are applied to calculate the structural responses. Finally, the displacement and stress of the deck and breakwater structures are analyzed. It is found that green water events caused by freak waves can generate enormous impact forces on the bow deck and breakwater, resulting in severe structural responses and even possible damage to the structures. The local strength of structures under freak waves needs to be considered in practical engineering applications.

Abstract Accurate long-term streamflow and flood forecasting have always been an important research direction in hydrology research. Nowadays, climate change, floods, and other anomalies occurring more and more frequently and bringing great losses to society. The prediction of streamflow, especially flood prediction, is important for disaster prevention. Current hydrological models based on physical mechanisms can give accurate predictions of streamflow, but the effective prediction period is only about 1 month in advance, which is too short for decision making. The artificial neural network (ANN) has great potential for predicting runoff and is not only good at handling non-linear data but can also make long-period forecasts. However, most of ANN models are unstable in their predictions when faced with raw flow data, and have excessive errors in predicting extreme flows. Previous studies have shown a link between the El Niño–Southern Oscillation (ENSO) and the streamflow of the Yangtze River. In this paper, we use ENSO and the monthly streamflow data of the Yangtze River from 1952 to 2018 to predict the monthly streamflow of the Yangtze River in two extreme flood years and a small flood year by using deep neural networks. In this paper, three deep neural network frameworks are used: stacked long short-term memory, Conv long short-term memory encoder–decoder long short-term memory and Conv long short-term memory encoder–decoder gate recurrent unit. The results show that the use of ConvLSTM improves the stability of the model and increases the accuracy of the flood prediction. Besides, the introduction of ENSO to the experimental data resulted in a more accurate prediction of the time of the occurrence of flood peaks and flood flows. Furthermore, the best results were obtained on the convolutional long short-term memory + encoder–decoder gate recurrent unit model.

The objective of this study was to determine the effect of pre-fermented juice, Lactobacillus plantarum, and L. buchneri on chemical composition, fermentation, aerobic stability, dynamics of microbial community, and metabolic pathway of a mixture of lucerne, wheat bran (WB), and rice straw (RS). All mixtures were ensiled for 1, 3, 5, 7, 15, 30, and 45 days after treatment with uninoculated (control, C); L. plantarum [LP, 1 × 106 cfu/g of fresh weight (FW)]; L. buchneri (LB, 1 × 106 cfu/g of FW); LP + LB (LPB, 1 × 106 cfu/g of FW of each inoculant); and pre-fermented juice (J; 2 × 106 cfu/g of FW). Four lactic acid bacteria (LAB) species from three genera were cultured from the pre-fermented juice, with W. cibaria being dominant. The inoculants increased lactic acid (LA), decreased pH and ammonia nitrogen (AN) compared to C silage at earlier stages of ensiling, and high dry matter (DM) and water-soluble carbohydrate (WSC) content in inoculated silages. Adding LPB increased the abundance of L. plantarum, L. paralimentarius, and L. nodensis, resulting in the lowest pH. Pre-fermented juice enriched W. cibaria, L. sakei, L. parabrevis, Pseudomonas putida, and Stenotrophomonas maltophilia, mainly enhanced accumulation of acetic acid (AA) and LA, and decreased pH, crude protein losses, AN, and hemicellulose contents. L. buchneri and L. brevis had a high abundance in LB-treated and J silages, respectively, inhibited undesirable bacteria, and improved aerobic stability with more than 16 days. In addition, the metabolic pathways changed with time and L. buchneri inoculants promoted global metabolism. In conclusion, inoculations altered bacterial succession and metabolic pathways in silage; LB and pre-fermented juice enhanced ensiling by promoting pH reductions, enhancing concentrations of LA and AA, and extending aerobic stability more than 16 days.

Abstract Background Diabetic nephropathy (DN) is a primary complication of diabetes mellitus (DM). The pathology of DN is still vague, and diagnostic accuracy is not enough. This study was performed to identify miRNAs and genes that have possibilities of being used as therapeutic targets for DN in type 2 DM. Methods Human miRNA data GSE51674 and gene data GSE111154 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and miRNAs (DEmiRNAs) in the kidney between control and DN patients were screened out. The competing endogenous RNA (ceRNA) network was constructed, and key lncRNA-miRNA-mRNA pairs were selected accordingly. Potential drugs targeting DEGs were screened out and validated using PCR analysis. Results Totally, 83 DEmiRNAs and 293 DEGs were identified in GSE51674 and GSE111154, respectively. Thirteen of the top 20 DEmiRNAs (10 up and 10 down) targeted to 47 DEGs. In the ceRNA network, RP11-363E7.4/TTN-AS1/HOTAIRM1-hsa-miR-106b-5p-PTGER3 and LINC00960-hsa-miR-1237-3p-MMP-2 interaction pairs were identified as the key ceRNA network. Interestingly, PTGER3 and hsa-miR-1237-3p were downregulated, and MMP-2 and hsa-miR-106b-5p were upregulated in the kidney of patients with DN compared with normal controls, respectively. PTGER3 and MMP-2 were targeted by drugs including iloprost, treprostinil, or captopril, and the deregulation of the two genes was confirmed in the plasma samples from patients with DN as compared with controls. Conclusions We speculated that the RP11-363E7.4/TTN-AS1/HOTAIRM1-hsa-miR-106b-5p-PTGER3 and LINC00960-hsa-miR-1237-3p-MMP-2 networks were associated with diabetic renal injury.

In this study, we aim to provide a deep convolutional network based femoral neck fracture detection system on radiographs for emergency patients. We retrospectively collected 1,491 frontal pelvic radiographs from three institutions and assigned them to the following data sets: primary dataset (710 radiographs, to fine-tune and validate the initial model called the Digital Radiography Fracture Detection System [DR-FDS]), internal test set 1 (189 radiographs) and 2 (235 radiographs), and external test set 1 (189 radiographs) and 2 (168 radiographs). Per-bounding box recall and precision and per-image sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were computed. We randomly extracted 300 radiographs from the above test sets and compared their effect on the diagnostic accuracy and efficiency of fine-tuned model-assisted and unassisted clinicians. The fine-tuned DR-FDS showed a better overall performance in detecting femoral neck fractures than did the initial DR-FDS. The fine-tuned DR-FDS achieved AUC values of 0.9526 (95%CI, 0.9048–0.9767) and 0.9633(95%CI, 0.9346-0.9797) in internal test sets 1 and 2. In external test sets 1 and 2, this model also achieved promising results with AUC values of 0.9231 (95%CI, 0.8779–0.9520), and 0.9937 (95%CI 0.9739–0.9985), respectively. The clinicians showed a statistically significant increase in specificity, sensitivity, and accuracy for the identification of minimal/undisplaced fracture and a decrease in the average reading time. The object detection model that is fine-tuned has high sensitivity and specificity and the universal ability to detect and locate femoral neck fractures on pelvic radiographs.

Currently, the world is in a period of urbanization that will accelerate the processes of land-use cover and ecological change. Thus, establishing a land-use and land-cover change (LUCC) prediction and simulation model is of great significance for understanding the process of urban change and assessing its ecological impact. In previous studies, LUCC prediction models have been mainly based on cellular automata structures that calculate a future state pixel by pixel through transition rules. Because these transition rules are usually based on the global state and each pixel is calculated according to these fixed rules, the results of these methods have room for improvement in terms of generating details and heterogeneity. In this article, a generative adversarial network (GAN)-based LUCC prediction model using multiscale local spatial information is proposed. The model is based on a pix2pix GAN and an attention structure that predicts future land use through multiscale local spatial information. To validate our model, Shenzhen, a region that is experiencing rapid urbanization, was chosen as the source of the experimental data. The results indicate that the proposed method achieved the highest accuracy in both short-time interval and long-time interval scenarios. In addition, the results of the proposed method were also closest to the ground truth from the perspective of the landscape pattern.

Objective: To investigate the correlation between high-risk human papillomavirus (HR HPV)-negative cervical lesions and cervical microenvironment in Inner Mongolia, China, and to find the pathogenic factors of HR HPV-negative cervical lesions. Materials and methods: 74 cases of HR HPV-negative healthy women and 80 cases of patients with cervical lesions (28 cases of LSIL, 49 cases of HSIL and 3 cases of CSCC) were selected as the study group; 26 cases of HPV-positive women and 352 cases of patients with cervical lesions (108 cases of LSIL, 214 cases of HSIL and 30 cases of CSCC) were control group. Questionnaires were collected from the study group and the control group and specimens were collected. Gram staining, hematoxylin and eosin staining microscopy, and substrate colorimetry method were used to detect vaginal micro-ecological indicators; ELISA was used to detect the concentration of SIgA, IgG, IL-2 and IL-10 in vaginal lavage fluid. Genetic testing was used to detect HPV, mycoplasma, and chlamydia infection. The changes of vaginal micro-ecology evaluation index and local immune factor concentration in healthy women and cervical lesions of all grades in the study group and the control group were compared. Results: Patients with cervical lesions, compared with healthy women, had a decrease in dominant lactobacilli and dysbacteriosis (P

Purpose. This study aimed to measure the concentrations of ferroptosis-related biomarkers, namely, iron (Fe), lipid peroxide (LPO), reactive oxygen species (ROS), glutathione peroxidase-4 (GPX4), and glutathione (GSH) in DR in the attempt to evaluate the diagnostic performance of these biomarkers. Methods. This study included 30 NPDR patients, 30 PDR patients, and 30 healthy subjects matched in age and sex. The concentrations of Fe, LPO, ROS, GPX4, and GSH in serum of the subjects were measured. Results. Compared with the normal group, GPX4 and GSH concentrations were significantly lower, and LPO, Fe, and ROS concentrations were significantly higher in DR patients. Compared with the PDR group, the NPDR group had higher concentrations of LPO, Fe, and ROS and lower concentrations of GPX4 and GSH, but there was no statistical difference in Fe, GPX4, and GSH. ROC curve shows that ferroptosis-related biomarkers have accumulated accuracy in NPDR and PDR. Conclusion. This study shows that ferroptosis-related biomarkers may be involved in the pathological process of DR and can be used as one of the biomarkers of DR.

Diabetic nephropathy (DN) has become the most common secondary kidney disease causing end-stage renal disease (ESRD). Nevertheless, the underlying mechanisms responsible for DN remain largely unknown. Regulated in development and DNA damage response 1 (REDD1) is a prooxidative molecule known to contribute to diabetes mellitus and its complications. However, it has not been previously examined whether and how REDD1 can further drive renal tubular epithelial cell (RTEC) apoptosis and epithelial-to-mesenchymal transition in DN. The expression of REDD1 was elevated in the kidneys of DN patients and diabetic mice in this study. By generating the DN model in REDD1 knockout mice, we demonstrated that REDD1 deficiency significantly improved apoptosis and EMT in diabetic mice. In vitro experiments showed that REDD1 generation was induced by high glucose (HG) in HK-2 cells. Similarly, the transfection of REDD1 siRNA plasmid also suppressed HG-induced apoptosis and EMT. Furthermore, we discovered that the inhibition of REDD1 suppressed the expression of Nox4-induced HG and reactive oxygen species (ROS) synthesis in HK-2 cells. In addition, HG could induce endogenous REDD1 and TXNIP to form a powerful complex. In summary, our findings demonstrate that blocking the REDD1/TXNIP complex can prevent HG-induced apoptosis and EMT by inhibiting ROS production, highlighting REDD1 as a valuable therapeutic priority site for DN.

Room temperature vulcanized (RTV) silicone rubber is widely used to prevent pollution flashover with its excellent hydrophobicity and hydrophobicity transfer. However, RTV coatings are at the risk of deterioration and failure in heavily polluted operating environment. In this paper, RTV coated insulators with different suspension heights operating in coal ash polluted areaswere sampled. Pollution degree, pollution composition and aging degree of coatings were tested. The result shows that the insoluble pollution contains Al(OH)3 filler precipitated from RTV coating, which indicates the aging of the RTV coating. The top surface coating is more affected by ultraviolet and rainwater than the bottom surface resulting in more serious degradation. As the pollution degree of the lower phase insulator is heavier than that of the upper phase insulator, the erosion effect of pollution on the RTV coating is more intense. The fillers and rubber molecules of RTV continuously precipitate into the pollution layer, leading to further aging. Therefore, the overall aging degree of the lower insulator coating is more serious than that of the upper insulator coating.

Due to the irregularity of colorectal tumor contours, it is a challenging task for clinicians to segment colorectal tumors manually in CT scans. To solve this problem, a novel algorithm based on a convolutional neural network with an attention mechanism is proposed to automatically achieve tumor segmentation. The proposed network consists of three major modules: an encoder module, which is fed CT scans to attain the feature map; a dual attention module, which includes a channel attention module and a position attention module to obtain more contextual information in the deep layer of the network; and a decoder module, which restores the feature map to the original size of the input images. We used 1131 CT slices of colorectal tumors to train and test the proposed network. Compared with U-Net and CE-Net, the Dice coefficient increased by 1.46% and 0.66% respectively, for our model. The comprehensive results show that the proposed network performs more effectively in colorectal tumor segmentation than the other methods.

The most important motivation for streamflow forecasts is flood prediction and longtime continuous prediction in hydrological research. As for many traditional statistical models, forecasting flood peak discharge is nearly impossible. They can only get acceptable results in normal year. On the other hand, the numerical methods including physics mechanisms and rainfall-atmospherics could provide a better performance when floods coming, but the minima prediction period of them is about one month ahead, which is too short to be used in hydrological application. In this study, a deep neural network was employed to predict the streamflow of the Hankou Hydrological Station on the Yangtze River. This method combined the Empirical Mode Decomposition (EMD) algorithm and Encoder Decoder Long Short-Term Memory (En-De-LSTM) architecture. Owing to the hydrological series prediction problem usually contains several different frequency components, which will affect the precision of the longtime prediction. The EMD technique could read and decomposes the original data into several different frequency components. It will help the model to make longtime predictions more efficiently. The LSTM based En-De-LSTM neural network could make the forecasting closer to the observed in peak flow value through reading, training, remembering the valuable information and forgetting the useless data. Monthly streamflow data (from January 1952 to December 2008) from Hankou Hydrological Station on the Yangtze River was selected to train the model, and predictions were made in two years with catastrophic flood events and ten years rolling forecast. Furthermore, the Root Mean Square Error (RMSE), Coefficient of Determination (R2), Willmott's Index of agreement (WI) and the Legates-McCabe's Index (LMI) were used to evaluate the goodness-of-fit and performance of this model. The results showed the reliability of this method in catastrophic flood years and longtime continuous rolling forecasting.

The 2019 novel coronavirus (2019-nCoV) outbreak has been treated as a Public Health Emergency of International Concern by the World Health Organization. This work made an early prediction of the 2019-nCoV outbreak in China based on a simple mathematical model and limited epidemiological data. Combing characteristics of the historical epidemic, we found part of the released data is unreasonable. Through ruling out the unreasonable data, the model predictions exhibit that the number of the cumulative 2019-nCoV cases may reach 76,000 to 230,000, with a peak of the unrecovered infectives (22,000-74,000) occurring in late February to early March. After that, the infected cases will rapidly monotonically decrease until early May to late June, when the 2019-nCoV outbreak will fade out. Strong anti-epidemic may reduce the cumulative infected cases by 40%-49%. The improvement of medical care can also lead to about one-half transmission decrease and effectively shorten the duration of the 2019-nCoV.

Spaceborne altimeters are an important data source for obtaining global sea surface wind speeds (U10). Although many altimeter U10 algorithms have been proposed and they perform well, there is still room for improvement. In this study, the data from ten altimeters were collocated with buoys to investigate the error of the altimeter U10 retrievals. The U10 residuals were found to be significantly dependent on many oceanic and atmospheric parameters. Because these oceanic and atmospheric parameters are intercorrelated, an asymptotic strategy was used to isolate the impact of different parameters and establish a neural-network-based correction model of altimeter U10. The results indicated that significant wave heights and mean wave periods are effective in correcting U10 retrievals, probably due to the tilting modulation of long-waves on the sea surface. After the wave correction, the root-mean-square error between the U10 from altimeters and buoys was reduced from 1.45 to 1.24 m/s and the impacts of thermodynamic parameters, such as sea surface (air) temperate, became negligible. The U10 residuals after correction showed that the atmospheric instability can lead to errors on extrapolated buoy U10. The buoy measurements with large air-sea temperature differences need to be excluded in the Cal/Val of remotely sensed U10.