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Abstract Humid heat extreme (HHE) is a type of compound extreme weather event that poses severe risks to human health. Skillful forecasts of HHE months in advance are crucial for developing strategies to enhance community resilience to extreme events1,2. This study demonstrates that the frequency of summertime HHE in the southeastern United States (SEUS) can be skillfully predicted 0–1 months in advance using the SPEAR (Seamless system for Prediction and EArth system Research) seasonal forecast system. Sea surface temperatures (SSTs) in the tropical North Atlantic (TNA) basin are identified as the primary driver of this prediction skill. The responses of large-scale atmospheric circulation and winds to anomalous warm SSTs in the TNA favor the transport of heat and moisture from the Gulf of Mexico to the SEUS. This research underscores the role of slowly varying sea surface conditions in modifying large-scale environments, thereby contributing to the skillful prediction of HHE in the SEUS. The results of this study have potential applications in the development of early warning systems for HHE.

Abstract To investigate the safety of Indocalamu Iatifolius McClur leaves sold in the market, a study was conducted using Indocalamu Iatifolius McClur leaves randomly collected from an online store and a large supermarket. Acute toxicity experiments were performed on mice, and their body weight was monitored for 14 days after administration. After the observation period, blood samples were collected for biochemical analysis, and organ pathology was examined. Then, the content of copper (Cu), lead (Pb), cadmium (Cd), mercury (Hg), arsenic (As), and the residues of nine organochlorine pesticides in Indocalamu Iatifolius McClur leaves were measured according to the National Food Safety Standard (GB/T5009-2003) and the pesticide residue determination methods in the 2020 edition of the Chinese Pharmacopoeia. The results showed that the mice in the Indocalamu Iatifolius McClur leaves (online store) group experienced mortality and severe liver and lung damage. The levels of lead, cadmium, mercury, arsenic, and the nine organochlorine pesticides met the relevant standards and regulations. However, the copper content in the Indocalamu Iatifolius McClur leaves (online store) group was nearly 80 times higher than that in the supermarket group. Mice in the Indocalamu Iatifolius McClur leaves (supermarket) group remained healthy without any abnormalities, and the levels of harmful metals and organochlorine pesticides complied with the standards and regulations. The study suggests the need for regulatory policies and safety standards for the sale of Indocalamu Iatifolius McClur leaves.

Abstract A key challenge with the wind energy utilization is that winds, and thus wind power, are highly variable on seasonal to interannual timescales because of atmospheric variability. There is a growing need of skillful seasonal wind energy prediction for energy system planning and operation. Here we demonstrate model’s capability in producing skillful seasonal wind energy prediction over the U.S. Great Plains during peak energy seasons (winter and spring), using seasonal prediction products from a climate model. The dominant source of that skillful prediction mainly comes from year-to-year variations of El Niño-Southern Oscillation in the tropical Pacific, which alters large-scale wind and storm track patterns over the United States. In the Southern Great Plains, the model can predict strong year-to-year wind energy changes with high skill multiple months in advance. Thus, this seasonal wind energy prediction capability offers potential benefits for optimizing wind energy utilization during peak energy production seasons.

Abstract The rate of sea level rise (SLR) along the Southeast Coast of the U.S. increased significantly after 2010. While anthropogenic radiative forcing causes an acceleration of global mean SLR, regional changes in the rate of SLR are strongly influenced by internal variability. Here we use observations and climate models to show that the rapid increase in the rate of SLR along the U.S. Southeast Coast after 2010 is due in part to multidecadal buoyancy-driven Atlantic meridional overturning circulation (AMOC) variations, along with heat transport convergence from wind-driven ocean circulation changes. We show that an initialized decadal prediction system can provide skillful regional SLR predictions induced by AMOC variations 5 years in advance, while wind-driven sea level variations are predictable 2 years in advance. Our results suggest that the rate of coastal SLR and its associated flooding risk along the U.S. southeastern seaboard are potentially predictable on multiyear timescales.

Evaluating underground gas storage (UGS) sealing capacity is essential for its safe construction and operational efficiency. This involves evaluating both the static sealing capacity of traps during hydrocarbon accumulation and the dynamic sealing capacity of UGS under intensive gas injection and withdrawal, and alternating loads. This study detailed the methodology developed by Sinopec. The approach merges disciplines like geology, geomechanics, and hydrodynamics, employing both dynamic-static and qualitative-quantitative analyses. Sinopec's evaluation methods, grounded in the in situ stress analysis, include mechanistic studies, laboratory tests, geological surveys, stress analysis, and fluid-solid interactions. Through tests on the static and dynamic sealing capacity of UGS, alongside investigations into sealing mechanisms and the geological and geomechanical properties of cap rocks and faults, A geomechanics - rock damage - seepage mechanics dynamic coupling analysis method has been developed to predict in situ stress variations relative to pore pressure changes during UGS operations and evaluate fault sealing capacity and cap rock integrity, thereby setting the maximum operational pressures. Utilizing this evaluation technique, Sinopec has defined performance metrics and criteria for evaluating the sealing capacity of depleted gas reservoirs, enabling preliminary sealing capacity evaluations at UGS sites. These evaluations have significantly informed the design of UGS construction schemes and the evaluation of fault sealing capacity and cap rock integrity during UGS operations.

Abstract As the first point of contact for patients, General Practitioners (GPs) play a crucial role in the National Health Service (NHS). An accurate primary diagnosis from the GP can alleviate the burden on specialists and reduce the time needed to re-confirm the patient’s condition, allowing for more efficient further examinations. However, GPs have broad but less specialized knowledge, which limits the accuracy of their diagnosis. Therefore, it is imperative to introduce an intelligent system to assist GPs in making decisions. This paper introduces two data augmentation methods, the Complaint Symptoms Integration Method and Symptom Dot Separating Method, to integrate essential information into the Integration dataset. Additionally, it proposes a hybrid architecture that fuses the features of words from different representation spaces. Experiments demonstrate that, compared to commonly used pre-trained attention-based models, our hybrid architecture delivers the best classification performance for four common neurological diseases on the enhanced Integration dataset. For example, the classification accuracy of the BERT+CNN hybrid architecture is 0.897, which is a 5.1% improvement over both BERT and CNN with 0.846. Finally, this paper develops an AI diagnosis assistant web application that leverages the superior performance of this architecture to help GPs complete primary diagnosis efficiently and accurately.

Celastrol, the primary constituent of Tripterygium wilfordii, has demonstrated neuroprotective properties in rats with dementia by reducing inflammation. A high-fat diet and streptozotocin injection were utilized to establish a diabetic rat model, which was then employed to investigate the possible protective effect of celastrol against the development of diabetes-induced learning and memory deficits. Afterwards, the experimental animals received a dose of celastrol by gavage (4 mg/kg/d). An animal study showed that celastrol enhanced insulin sensitivity and glucose tolerance in diabetic rats. In the Morris water maze test, rats with diabetes performed poorly in terms of spatial learning and memory; treatment with celastrol improved these outcomes. Additionally, administration of celastrol downregulated the expression of inflammatory-related proteins (NF-κB, IKKα, TNF-α, IL-1β, and IL-6) and greatly reduced the generation of Aβ in the diabetic hippocampus tissue. Moreover, the insulin signaling pathway-related proteins PI3K, AKT, and GSK-3β were significantly upregulated in diabetic rats after celastrol was administered. Also, celastrol prevented damage to the brain structures and increased the synthesis of synaptic proteins like PSD-95 and SYT1. In conclusion, celastrol exerts a neuroprotective effect by modulating the insulin signaling system and reducing inflammatory responses, which helps to ameliorate the cognitive impairment associated with diabetes.

Biochar can serve as a soil amendment to immobilize soil nitrogen (N) and reduce N leaching from cropland without negative effect on crop yield. However, the interaction effect of biochar application and irrigation regimes on soil N status (N retention and N loss) and crop yield is rarely reported in the open perennial vegetable field. A two-years field trial (transplanting in first year and consecutive growth in second year) was conducted in citron daylily vegetable cropping system on a sandy brown alluvial soil. Two biochar application rates (0 and 30 t ha−1) and three irrigation regimes (CDI, conventional drip irrigation; WSDI, water-saving drip irrigation with 80% of full irrigation quota; APRDI, alternate partial root-zone drip irrigation with 80% of full irrigation quota) were included. The response of crop yield and soil N status to both biochar application and irrigation regimes varied across planting years for perennial citron daylily. After the first planting year's harvest, APRDI enhanced flower bud yield by 18–28% compared to CDI and WSDI, likely due to improved nitrate use efficiency evidenced by lower soil nitrate retention in the surface soil (0–20 cm) post-harvest. However, biochar application resulted in a reduction of yield by 27% under APRDI. Additionally, WSDI with reduced yields, decreased soil TN in the sub-surface layer (20–50 cm) with 9–19% by comparison with other two irrigation regimes, resulting in higher TN concentration in the soil solution (14–28%) in and thus an increased risk for N leaching. However, after the second harvest year, there were no variations in crop yield induced by biochar application and irrigation regimes. Irrigation regimes exhibited limited influence on soil N status, while biochar application mitigated soil nitrogen decline in the 0–50 cm layer by enhancing organic nitrogen retention capacity for nearly 16–85%. Furthermore, the lowest TN concentration in the soil solution in the sub-surface layer (20–50 cm) with biochar application under APRDI suggested a reduced risk for N leaching. We conclude that combining biochar application with the APRDI regime could help retain soil N, decrease the risk of N leaching, and enhance crop yield in total for two consecutive planting years. Therefore, this approach is recommended for sustainable N management in long-term planting of perennial crops.

Abstract Long-term sea-level rise and multiyear to decadal sea level variations pose substantial risks for flooding and erosion in coastal communities. Here we use observations and climate model predictions to show that sea level variations along the U.S. East Coast are skillfully predictable 3 to 10 years in advance. The most predictable component of sea level is a basin scale upward trend, predictable a decade in advance and primarily a response to increasing greenhouse gases. Significant additional predictability comes from multidecadal variations of the Atlantic Meridional Overturning Circulation (AMOC). While perfect model simulations show AMOC-related sea level predictability of 5-7 years, model biases and initialization uncertainties reduce the realized predictive skill to 3-5 years, depending on location. Overall, greenhouse gas warming and predictable AMOC variations lead to multiyear to decadal prediction skill for sea level along the U.S. East Coast. Such skill could have significant societal benefit for planning and adaptation.

Purpose. Type 2 diabetes mellitus is considered as one of the risk factors for cognitive impairment. DPP4 inhibitors are effective drugs for the treatment of type 2 diabetes mellitus. However, the relationship between DPP4 inhibitors and cognitive dysfunction remains unclear. Therefore, we used a meta-analysis to determine the association between DPP4 inhibitors and cognitive impairment in type 2 diabetes mellitus. Methods. We systematically searched PubMed, CNKI, and the Cochrane Library at the time of establishment, 2022, and then made inclusion criteria and screened strategies to identify studies with more precise correlations. Results. We included 10 studies with 5,583 participants. The data showed that DPP4 inhibitors significantly reduced the incidence rate of cognitive impairment in type 2 diabetes mellitus (SMD: 0.99; 95% CI [0.59, 1.38]). Furthermore, there was a linear correlation found between cognitive impairment in type 2 diabetes mellitus and fasting blood glucose, 2-hour postprandial blood glucose, and glycosylated hemoglobin. DPP4 inhibitors decreased fasting blood glucose (FPG) (SMD: 0.52; 95% CI [−0.68, −0.37]), blood glucose (2hPPG) at 2 hours after the meal (SMD: 0.82; 95% CI, [−1.2, −0.43]), and HbA1c (SMD: 0.34; 95% CI [−0.48, −0.21]). All data were statistically significant (P

Abstract The Kuroshio-Oyashio Extension (KOE) is the North Pacific oceanic frontal zone where air-sea heat and moisture exchanges allow strong communication between the ocean and atmosphere. Using satellite observations and reanalysis datasets, we show that the KOE surface heat flux variations are very closely linked to Kuroshio Extension (KE) sea surface height (SSH) variability on both seasonal and decadal time scales. We investigate seasonal oceanic and atmospheric anomalies associated with anomalous KE upper ocean temperature, as reflected in SSH anomalies (SSHa). We show that the ocean-induced seasonal changes in air-sea coupled processes, which are accompanied by KE upper-ocean temperature anomalies, lead to significant ocean-to-atmosphere heat transfer during November-December-January (i.e., NDJ). This anomalous NDJ KOE upward heat transfer has recently grown stronger in the observational record, which also appears to be associated with the enhanced KE decadal variability. Highlighting the role of KOE heat fluxes as a communicator between the upper-ocean and the overlying atmosphere, our findings suggest that NDJ KOE heat flux variations could be a useful North Pacific climate indicator.

Evidence from clinical research and animal studies indicates that inflammation is an important factor in the occurrence and development of cardiovascular disease (CVD). Emerging evidence shows that nucleic acids serve as crucial pathogen-associated molecular patterns (PAMPs) or non-infectious damage-associated molecular patterns (DAMPs), are released and then recognized by pattern recognition receptors (PRRs), which activates immunological signaling pathways for host defense. Mechanistically, the released nucleic acids activate cyclic GMP-AMP synthase (cGAS) and its downstream receptor stimulator of interferon genes (STING) to promote type I interferons (IFNs) production, which play an important regulatory function during the initiation of an innate immune response to various diseases, including CVD. This pathway represents an essential defense regulatory mechanism in an organism’s innate immune system. In this review, we outline the overall profile of cGAS-STING signaling, summarize the latest findings on nucleic acid release and trafficking, and discuss their potential role in CVD. This review also sheds light on potential directions for future investigations on CVD.

Due to large northward heat transport, the Atlantic meridional overturning circulation (AMOC) strongly affects the climate of various regions. Its internal variability has been shown to be predictable decades ahead within climate models, providing the hope that synchronizing ocean circulation with observations can improve decadal predictions, notably of the North Atlantic subpolar gyre (SPG). Climate predictions require a starting point which is a reconstruction of the past climate. This is usually performed with data assimilation methods that blend available observations and climate model states together. There is no unique method to derive the initial conditions. Moreover, this can be performed using full-field observations or their anomalies superimposed on the model's climatology to avoid strong drifts in predictions. How critical ocean circulation drifts are for prediction skill has not been assessed yet. We analyze this possible connection using the dataset of 12 decadal prediction systems from the World Meteorological Organization Lead Centre for Annual-to-Decadal Climate Prediction. We find a variety of initial AMOC errors within the predictions related to a dynamically imbalanced ocean states leading to strongly displaced or multiple maxima in the overturning structures. This likely results in a blend of what is known as model drift and initial shock. We identify that the AMOC initialization influences the quality of the SPG predictions. When predictions show a large initial error in their AMOC, they usually have low skill for predicting internal variability of the SPG for a time horizon of 6-10 years. Full-field initialized predictions with low AMOC drift show better SPG skill than those with a large AMOC drift. Nevertheless, while the anomaly-initialized predictions do not experience large drifts, they show low SPG skill when skill also present in historical runs is removed using a residual correlation metric. Thus, reducing initial shock and model biases for the ocean circulation in prediction systems might help to improve their prediction for the SPG beyond 5 years. Climate predictions could also benefit from quality-check procedure for assimilation/initialization because currently the research groups only reveal the problems in initialization once the set of predictions has been completed, which is an expensive effort.

Cognitive impairment frequently coexists with diabetes. Trelagliptin is a once-weekly taking selective dipeptidyl peptidase-4 (DPP-4) inhibitor and a long-term effective hypoglycemic medicine; nonetheless, its effects for the treatment of diabetes-related cognitive impairment have only sometimes been explored. In this study, a DM model was built using streptozotocin (STZ) and a high-fat diet (HFD). The morris water maze test on DM rats revealed a considerably reduced capacity for spatial learning and memory, but trelagliptin was able to restore function. Trelagliptin could lower the mRNA expression of inflammatory factors such IL-1β, TNF-α, and IL-6 in DM rats. It could also reduce the ratio of p-IKKα/IKKα, and the immunofluorescence result of NF-κB also demonstrated a drop. Trelagliptin partially restored dendritic spines and prevented the loss or shrinkage of neurons, respectively, according to the results of Nissl's staining and golgi staining. Furthermore, PI3K/Akt/GSK-3β has been activated, and synaptic plasticity has been modified during this process. In conclusion, trelagliptin improved the cognitive lesion in DM rats by suppressing the activation of the inflammatory route and by activating the PI3K/Akt/GSK-3β pathway at the same time, as well as interacting with the pathways that protect neurons, which still need further research.

Abstract Background Sulfur (S) has been widely used to alleviate cadmium (Cd) toxicity and control Cd accumulation in rice under waterlogging conditions. However, the results are contradictory, and the reasons remain unclear. This could be because most studies rarely simultaneously monitor the processes of S-induced soil Cd bioavailability and Cd accumulation in rice throughout its growth period. A pot experiment was conducted to investigate the influence of two sulfur levels (0, and 30 mg S kg−1) on Cd concentration and translocation in rice at three growth stages (booting, filling and maturity) under waterlogging conditions. Paddy soil deficient in S but contaminated with Cd (10.16 mg Cd kg−1) was used for the pot experiment. Results S application increased concentrations of Cd in grain at the filling stage partially because S induced the promotion of Cd transfer from roots to stems, leaves, and grains, and S induced the accumulation and fixation of Cd in iron plaques at the filling stage. However, the application of S significantly reduced Cd concentrations in brown rice at the maturity stage, which could be attributed to three aspects, as described below. First, S supply reduced the availability of Cd in soil and iron plaque on the root surface by reducing dissolved Cd in soil pore water and transferring Cd from iron plaque on the root surface to roots. Second, S supply inhibited the transfer of Cd in other tissues to brown rice based on Cd transfer factors from roots, stems, leaves, and husks to brown rice, which were obviously lower with S supply than without S supply at the maturity stage. Third, S induced the dilution of Cd in brown rice because the application of S significantly increased brown rice biomass by 215%. Conclusions A S-induced decline in Cd accumulation in brown rice was related to S-regulated Cd transfer among rice plants, S-induced promotion of rice growth and a decrease in Cd bioavailability in S-deficient but Cd-contaminated paddy soil under waterlogging conditions. This study provides valuable information for growing rice in low-S and Cd-contaminated paddy soil and reducing the risk of Cd in rice to humans. Graphical abstract

For video style transfer, naively applying still image techniques to process a video frame-by-frame independently often causes flickering artefacts. Some works adopt optical flow into the design of temporal constraint loss to secure temporal consistency. However, these works still suffer from incoherence (including ghosting artefacts) where large motions or occlusions occur, as optical flow fails to detect the boundaries of objects accurately. To address this problem, we propose a novel framework which consists of the following two stages: (1) creating new initialization images from proposed mask techniques, which are able to significantly reduce the flow errors; (2) process these initialized images iteratively with proposed losses to obtain stylized videos which are free of artefacts, which also increases the speed from over 3 min per frame to less than 2 s per frame for the gradient-based optimization methods. To be specific, we propose a multi-scale mask fusion scheme to reduce untraceable flow errors, and obtain an incremental mask to reduce ghosting artefacts. In addition, a multi-frame mask fusion scheme is designed to reduce traceable flow errors. In our proposed losses, the Sharpness Losses are used to deal with the potential image blurriness artefacts over long-range frames, and the Coherent Losses are performed to restrict the temporal consistency at both the multi-frame RGB level and Feature level. Overall, our approach produces stable video stylization outputs even in large motion or occlusion scenarios. The experiments demonstrate that the proposed method outperforms the state-of-the-art video style transfer methods qualitatively and quantitatively on the MPI Sintel dataset.

Background: One of the typical symptoms of diabetes mellitus patients was memory impairment, which was followed by gradual cognitive deterioration and for which there is no efficient treatment. The anti-diabetic incretin hormones glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) were demonstrated to have highly neuroprotective benefits in animal models of AD. We wanted to find out how the GLP-1/GIP dual agonist tirzepatide affected diabetes’s impairment of spatial learning memory.Methods: High fat diet and streptozotocin injection-induced diabetic rats were injected intraperitoneally with Tirzepatide (1.35 mg/kg) once a week. The protective effects were assessed using the Morris water maze test, immunofluorescence, and Western blot analysis. Golgi staining was adopted for quantified dendritic spines.Results: Tirzepatide significantly improved impaired glucose tolerance, fasting blood glucose level, and insulin level in diabetic rats. Then, tirzepatide dramatically alleviated spatial learning and memory impairment, inhibited Aβ accumulation, prevented structural damage, boosted the synthesis of synaptic proteins and increased dendritic spines formation in diabetic hippocampus. Furthermore, some aberrant changes in signal molecules concerning inflammation signaling pathways were normalized after tirzepatide treatment in diabetic rats. Finally, PI3K/Akt/GSK3β signaling pathway was restored by tirzepatide.Conclusion: Tirzepatide obviously exerts a protective effect against spatial learning and memory impairment, potentially through regulating abnormal insulin resistance and inflammatory responses.

Warming of the subsurface Southern Ocean played only a minor role in shrinking Antarctic Sea ice extent in 2016, but was critical for the persistence of negative anomalies between 2016 and 2021, according to coupled climate model simulations partially constrained by observations.

Abstract Background Polycyclic aromatic hydrocarbons (PAHs) pose a potential risk to ecological safety and human health. They have a range of effects on plant growth and there have been few reports on the health risks associated with ingestion of vegetable crops at different growth stages. Methodology In this study, a pot experiment in which Chinese cabbage (Brassica campestris L.) were grown in a greenhouse for 75 days was used to investigate the dose–effect relationship of pyrene with plant growth and also the exposure risk for adults of ingestion of Chinese cabbage at different growth stages. Results The results showed that low doses of pyrene (5–45 mg kg−1) promoted plant growth (20–220% and 55–97% higher than control treatment for the root biomass and shoot biomass, respectively), but significant inhibition was observed at a high dose (405 mg kg−1) (41–66% and 43–91% lower than control treatment for the root biomass and shoot biomass, respectively). High doses of pyrene reduced soil bacterial abundance and diversity during the growth of Chinese cabbage, and increased malondialdehyde (MDA) levels in the plant. The effects of pyrene on plant biomass were mainly attributed to changes in root activity induced by pyrene, as the relationship between soil pyrene concentration and biomass was similar to that between soil pyrene concentration and root activity. Furthermore, structural equation modeling analysis showed that pyrene altered growth of the vegetable by directly affecting root activity. The incremental lifetime cancer risk for adults is highest for ingestion of Chinese cabbage at the seedling stage, followed in decreasing order by the rosette stages and heading stages. Conclusions The health risk of consumers who have the possibility to ingest the Chinese cabbage planted in pyrene-contaminated soil would be decreased with the increasing growth periods. However, further studies are required to confirm the dose–effect relationship between pyrene concentration and Chinese cabbage growth on a field scale. Graphical Abstract

Abstract Midlatitude baroclinic waves drive extratropical weather and climate variations, but their predictability beyond 2 weeks has been deemed low. Here we analyze a large ensemble of climate simulations forced by observed sea surface temperatures (SSTs) and demonstrate that seasonal variations of baroclinic wave activity (BWA) are potentially predictable. This potential seasonal predictability is denoted by robust BWA responses to SST forcings. To probe regional sources of the potential predictability, a regression analysis is applied to the SST-forced large ensemble simulations. By filtering out variability internal to the atmosphere and land, this analysis identifies both well-known and unfamiliar BWA responses to SST forcings across latitudes. Finally, we confirm the model-indicated predictability by showing that an operational seasonal prediction system can leverage some of the identified SST-BWA relationships to achieve skillful predictions of BWA. Our findings help to extend long-range predictions of the statistics of extratropical weather events and their impacts.

Rhamnolipid biosurfactants are multifunctional compounds that can play an indispensable role in biotechnological, biomedical, and environmental bioremediation-related fields, and have attracted significant attention in recent years. Herein, a novel strain Pseudomonas sp. S1WB was isolated from an oil-contaminated water sample. The biosurfactants produced by this strain have capabilities to reduce surface tension (SFT) at 32.75 ± 1.63 mN/m and emulsified 50.2 ± 1.13 % in liquid media containing 1 % used engine oil (UEO) as the sole carbon source. However, the lowest SFT reduction (28.25 ± 0.21), highest emulsification index (60.15 ± 0.07), and the maximum yields (900 mg/L) were achieved under optimized conditions; where, the glucose/urea and glycerol/urea combinations were found efficient carbon and nitrogen substrates for improved biosurfactants production. Biosurfactants product was characterized using ultra-high performance liquid chromatography-mass spectrometry (UHPLC- MS) and detected various di- rhamnolipids congeners. In addition, the di-rhamnolipids produced by S1WB strain was found highly stable in terms of surface activity and EI indices at different environmental factors i.e. temperature, pH and various NaCl concentrations, where, emulsifying property was found high stable till 30 days of incubation. Moreover, the stain was capable to degrade hydrocarbon at 42.2 ± 0.04 %, and the Gas chromatography- mass spectrometry (GC-MS) profile showed the majority of peak intensities of hydrocarbons have been completely degraded compared to control.

The rapid day-to-day temperature swings associated with extratropical storm tracks can cause cascading infrastructure failure and impact human outdoor activities, thus research on seasonal prediction and predictability of extreme temperature swings is of huge societal importance. To measure the extreme surface air temperature (SAT) variations associated with the winter extratropical storm tracks, a Temperature Swing Index (TSI) is formulated as the standard deviation of 24-h-difference-filtered data of the 6-hourly SAT. The dominant term governing the TSI variability is shown to be proportional to the product of eddy heat flux and mean temperature gradient. The seasonal prediction skill of the winter TSI over North America was assessed using Geophysical Fluid Dynamics Laboratory's new seasonal prediction system. The locations with skillful TSI prediction show a geographic pattern that is distinct from the pattern of skillful seasonal mean SAT prediction. The prediction of TSI provides additional predictable climate information beyond the traditional seasonal mean temperature prediction. The source of the seasonal TSI prediction can be attributed to year-to-year variations of the El Niño-Southern Oscillation (ENSO), North Pacific Oscillation (NPO), and Pacific/North American (PNA) teleconnection. Over the central United States, the correlation skill of TSI prediction reaches 0.75 with strong links to observed ENSO, NPO, and PNA, while the skill of seasonal SAT prediction is relatively low with a correlation of 0.36. As a first attempt of diagnosing the combined predictability of the first-order (the seasonal mean) and second-order (TSI) statistics for SAT, this study highlights the importance of the eddy-mean flow interaction perspective for understanding the seasonal climate predictability in the extra tropics. These results point toward providing skillful prediction of higher-order statistical information related to winter temperature extremes, thus enriching the seasonal forecast products for the research community and decision makers.

There has emerged substantial research in addressing single-view 3D reconstruction and the majority of the state-of-the-art implicit methods employ CNNs as the backbone network. On the other hand, transformers have shown remarkable performance in many vision tasks. However, it is still unknown whether transformers are suitable for single-view implicit 3D reconstruction. In this paper, we propose the first end-to-end single-view 3D reconstruction network based on the Pyramid Vision Transformer (PVT), called ED2IF2-Net, which disentangles the reconstruction of an implicit field into the reconstruction of topological structures and the recovery of surface details to achieve high-fidelity shape reconstruction. ED2IF2-Net uses a Pyramid Vision Transformer encoder to extract multi-scale hierarchical local features and a global vector of the input single image, which are fed into three separate decoders. A coarse shape decoder reconstructs a coarse implicit field based on the global vector, a deformation decoder iteratively refines the coarse implicit field using the pixel-aligned local features to obtain a deformed implicit field through multiple implicit field deformation blocks (IFDBs), and a surface detail decoder predicts an enhanced displacement field using the local features with hybrid attention modules (HAMs). The final output is a fusion of the deformed implicit field and the enhanced displacement field, with four loss terms applied to reconstruct the coarse implicit field, structure details through a novel deformation loss, overall shape after fusion, and surface details via a Laplacian loss. The quantitative results obtained from the ShapeNet dataset validate the exceptional performance of ED2IF2-Net. Notably, ED2IF2-Net-L stands out as the top-performing variant, exhibiting the highest mean IoU, CD, EMD, ECD-3D, and ECD-2D scores, reaching impressive values of 61.1, 7.26, 2.51, 6.08, and 1.84, respectively. The extensive experimental evaluations consistently demonstrate the state-of-the-art capabilities of ED2IF2-Net in terms of reconstructing topological structures and recovering surface details, all while maintaining competitive inference time.

The Model-Analogs technique is used in the present study to assess the decadal sea surface temperature (SST) prediction skill over the Southern Ocean (SO). The Model-Analogs here is based on reanalysis products and model control simulations that have ∼1° ocean/ice (refined to 0.5° at high latitudes) components and 100 km atmosphere/land components. It is found that the model analog hindcasts show comparable skills with the initialized retrospective decadal hindcasts south of 50°S, with even higher skills over the Weddell Sea at longer lead years. The high SST skills primarily arise from the successful capture of SO deep convection states. This deep ocean memory and the associated decadal predictability are also clearly seen when we assess the Model-Analogs technique in a perfect model context. Within 30°S–50°S latitudinal band, the model analog hindcasts show low skills. When we include the externally forced signals estimated from the large ensemble simulations, the model analog hindcasts and initialized decadal hindcasts show identical skills. The Model-Analogs method therefore provides a great baseline for developing future decadal forecast systems. It is unclear whether such analog techniques would also be successful with models that explicitly resolve ocean mesoscale eddies or other small-scale processes. This area of research needs to be explored further.

The signature composed of immune-related long noncoding ribonucleic acids (irlncRNAs) with no requirement of specific expression level seems to be valuable in predicting the survival of patients with hepatocellular carcinoma (HCC). Here, we retrieved raw transcriptome data from The Cancer Genome Atlas (TCGA), identified irlncRNAs by co-expression analysis, and recognized differently expressed irlncRNA (DEirlncRNA) pairs using univariate analysis. In addition, we modified Lasso penalized regression. Then, we compared the areas under curve, counted the Akaike information criterion (AIC) values of 5-year receiver operating characteristic curve, and identified the cut-off point to set up an optimal model for distinguishing the high- or low-disease-risk groups among patients with HCC. We then reevaluated them from the viewpoints of survival, clinic-pathological characteristics, tumor-infiltrating immune cells, chemotherapeutics efficacy, and immunosuppressed biomarkers. 36 DEirlncRNA pairs were identified, 12 of which were included in a Cox regression model. After regrouping the patients by the cut-off point, we could more effectively differentiate between them based on unfavorable survival outcome, aggressive clinic-pathological characteristics, specific tumor immune infiltration status, low chemotherapeutics sensitivity, and highly expressed immunosuppressed biomarkers. The signature established by paring irlncRNA regardless of expression levels showed a promising clinical prediction value.

We discovered a unique expression pattern of two histone methyltransferases Suv39h1 and Suv39h2 during 3T3-L1 adipogenesis, both of which preferentially catalyse the formation of H3K9 dimethylation (H3K9me2) and further H3K9 trimethylation (H3K9me3), a transcriptional repressive mark. The expression of Suv39h1 and Suv39h2 displayed a sharp increase at the early stage of 3T3-L1 differentiation, which peaked after differentiation induction, and then declined towards later stage of differentiation, suggesting a key role for these two histone methyltransferases in adipogenesis. Indeed, inactivating Suv39h1 or Suv39h2 via lentiviral shRNA knockdown inhibited adipogenesis, while overexpressing Suv39h1 promoted adipogenesis. Notably, overexpressing or knocking down Suv39h1 in 3T3-L1 cells was associated with reciprocal changes in the expression of Wnt10a, an anti-adipogenic regulator. Further, Wnt10a knockdown largely prevented the inhibitory effect of Suv39h1 on adipogenesis, indicating Wnt10a as a downstream target mediating Suv39h1’s action in adipogenesis. Mechanistically, our comprehensive approaches involving ChIP, co-immunoprecipitation and pyrosequencing analysis demonstrated that Suv39h1 may regulate Wnt10a expression via H3K9 methylation and interaction with DNA methyltransferase 1 (DNMT1) at the Wnt10a promoter, resulting in altered DNA methylation at the promoter. We conclude that Suv39h promotes adipogenesis by epigenetically down-regulating Wnt10a expression via H3K9me3 and DNA methylation at the Wnt10a promoter.Abbreviated title: Suv39h and 3T3-L1 Adipogenesis

We aimed to investigate how sulfur (S) application prior to oilseed rape cultivation influences the uptake of cadmium (Cd) by rice grown in low- and high-Cd soils. A pot experiment involving four S levels (0, 30, 60, 120 mg S kg−1) combined with two Cd rates (low and high—0.35 and 10.35 mg Cd kg−1, respectively) was conducted. Soil pore water during rice growth and plant tissues at maturity were analyzed. The soil pore water results indicated that S application decreased Cd solubility under submergence due to the S-induced increase of soil pH and the enhancement of sulfide formation in soil micropores. When S was applied at rates of 30, 60 and 120 mg S kg−1, brown rice Cd concentrations decreased by 18%, 18%, and 55% (p

Abstract Using GFDL's new coupled model SPEAR, we have developed a decadal coupled reanalysis/initialization system (DCIS) that does not use subsurface ocean observations. In DCIS, the winds and temperature in the atmosphere, along with sea surface temperature (SST), are restored to observations. Under this approach the ocean component of the coupled model experiences a sequence of surface heat and momentum fluxes that are similar to observations. DCIS offers two initialization approaches, called A1 and A2, which differ only in the atmospheric forcing from observations. In A1, the atmospheric winds/temperature are restored toward the JRA reanalysis; in A2, surface pressure observations are assimilated in the model. Two sets of coupled reanalyses have been completed during 1961–2019 using A1 and A2, and they show very similar multi‐decadal variations of the Atlantic Meridional Overturning Circulation (AMOC). Two sets of retrospective decadal forecasts were then conducted using initial conditions from the A1 and A2 reanalyses. In comparison with previous prediction system CM2.1, SPEAR‐A1/A2 shows comparable skill of predicting the North Atlantic subpolar gyre SST, which is highly correlated with initial values of AMOC at all lead years. SPEAR‐A1 significantly outperforms CM2.1 in predicting multi‐decadal SST trends in the Southern Ocean (SO). Both A1 and A2 have skillful prediction of Sahel precipitation and the associated ITCZ shift. The prediction skill of SST is generally lower in A2 than A1 especially over SO presumably due to the sparse surface pressure observations.

Abstract Most dust forecast models focus on short, subseasonal lead times, that is, 3 to 6 days, and the skill of seasonal prediction is not clear. In this study we examine the potential of seasonal dust prediction in the United States using an observation‐constrained regression model and key variables predicted by a seasonal prediction model developed at National Oceanic and Atmospheric Administration Geophysical Fluid Dynamics Laboratory, the Forecast‐Oriented Low Ocean Resolution (FLOR) model. Our method shows skillful predictions of spring dustiness 3 to 6 months in advance. It is found that the regression model explains about 71% of the variances of dust event frequency over the Great Plains and 63% over the southwestern United States in March‐May from 2004 to 2016 using predictors from FLOR initialized on 1 December. Variations in springtime dustiness are dominated by springtime climatic factors rather than wintertime factors. Findings here will help development of a seasonal dust prediction system and hazard prevention.

Abstract The next‐generation seasonal prediction system is built as part of the Seamless System for Prediction and EArth System Research (SPEAR) at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). SPEAR is an effort to develop a seamless system for prediction and research across time scales. The ensemble‐based ocean data assimilation (ODA) system is updated for Modular Ocean Model Version 6 (MOM6), the ocean component of SPEAR. Ocean initial conditions for seasonal predictions, as well as an ocean state estimation, are produced by the MOM6 ODA system in coupled SPEAR models. Initial conditions of the atmosphere, land, and sea ice components for seasonal predictions are constructed through additional nudging experiments in the same coupled SPEAR models. A bias correction scheme called ocean tendency adjustment (OTA) is applied to coupled model seasonal predictions to reduce model drift. OTA applies the climatological temperature and salinity increments obtained from ODA as three‐dimensional tendency terms to the MOM6 ocean component of the coupled SPEAR models. Based on preliminary retrospective seasonal forecasts, we demonstrate that OTA reduces model drift—especially sea surface temperature (SST) forecast drift—in coupled model predictions and improves seasonal prediction skill for applications such as El Niño–Southern Oscillation (ENSO).

The extension of seasonal to interannual prediction of the physical climate system to include the marine ecosystem has a great potential to inform marine resource management strategies. Along the east coast of Africa, recent findings suggest that skillful Earth system model (ESM)-based chlorophyll predictions may enable anticipation of fisheries fluctuations. The mechanisms underlying skillful chlorophyll predictions, however, were not identified, eroding confidence in potential adaptive management steps. This study demonstrates that skillful chlorophyll predictions up to two years in advance arise from the successful simulation of westward-propagating off-equatorial Rossby waves in the Indian ocean. Upwelling associated with these waves supplies nutrients to the surface layer for the large coastal areas by generating north- and southward propagating waves at the east African coast. Further analysis shows that the off-equatorial Rossby wave is initially excited by wind stress forcing caused by El Niño/Southern Oscillation-Indian Ocean teleconnections.

Abstract We document the development and simulation characteristics of the next generation modeling system for seasonal to decadal prediction and projection at the Geophysical Fluid Dynamics Laboratory (GFDL). SPEAR (Seamless System for Prediction and EArth System Research) is built from component models recently developed at GFDL—the AM4 atmosphere model, MOM6 ocean code, LM4 land model, and SIS2 sea ice model. The SPEAR models are specifically designed with attributes needed for a prediction model for seasonal to decadal time scales, including the ability to run large ensembles of simulations with available computational resources. For computational speed SPEAR uses a coarse ocean resolution of approximately 1.0° (with tropical refinement). SPEAR can use differing atmospheric horizontal resolutions ranging from 1° to 0.25°. The higher atmospheric resolution facilitates improved simulation of regional climate and extremes. SPEAR is built from the same components as the GFDL CM4 and ESM4 models but with design choices geared toward seasonal to multidecadal physical climate prediction and projection. We document simulation characteristics for the time mean climate, aspects of internal variability, and the response to both idealized and realistic radiative forcing change. We describe in greater detail one focus of the model development process that was motivated by the importance of the Southern Ocean to the global climate system. We present sensitivity tests that document the influence of the Antarctic surface heat budget on Southern Ocean ventilation and deep global ocean circulation. These findings were also useful in the development processes for the GFDL CM4 and ESM4 models.

Due to the complexity of a train crash, it is a challenging process to describe and estimate mathematically. Although different mathematical models have been developed, it is still difficult to balance the complexity of models and the accuracy of estimation. This paper proposes a nonlinear spring-mass-damper model of train frontal crash, which achieves high accuracy and maintains low complexity. The Convolutional Long-short-term-memory Generation Adversarial Network (CLGAN) model is applied to study the nonlinear parameters dynamic variation of the key components of a rail vehicle (e.g., the head car, anticlimbing energy absorber, and the coupler buffer devices). Firstly, the nonlinear lumped model of train frontal crash is built, and then the physical parameters are deduced in twenty different cases using D’Alembert’s principle. Secondly, the input/output relationship of the CLGAN model is determined, where the inputs are the nonlinear physical parameters in twenty initial conditions, and the output is the nonlinear relationship between the train crash nonlinear parameters under other initial cases. Finally, the train crash dynamic characteristics are accurately estimated during the train crash processes through the training of the CLGAN model, and then the crash processes under different given conditions can be described effectively. The estimation results exhibit good agreement with finite element (FE) simulations and experimental results. Furthermore, the CLGAN model shows great potential in nonlinear estimation, and CLGAN can better describe the variation of nonlinear spring damping compared with the traditional model. The nonlinear spring-mass-damper modeling is involved in improving the speed and accuracy of the train crash estimation, as well as being able to offer guidance for structure optimization in the early design stage.

Crustal melting may play a fundamental role in orogenic processes, but quantifying crustal melt remains difficult. Here, the authors combine pressure-temperature paths, electrical conductivity and geophysical data to elucidate the melting conditions in Tibet since the Miocene.

The existence of homoclinic orbits or heteroclinic cycle plays a crucial role in chaos research. This paper investigates the existence of the homoclinic orbits to a saddle-focus equilibrium point in several classes of three-dimensional piecewise affine systems with two switching planes regardless of the symmetry. An analytic proof is provided using the concrete expression forms of the analytic solution, stable manifold, and unstable manifold. Meanwhile, a sufficient condition for the existence of two homoclinic orbits is also obtained. Furthermore, two concrete piecewise affine asymmetric systems with two homoclinic orbits have been constructed successfully, demonstrating the method’s effectiveness.

Abstract Background There are three minimally invasive methods for the management of large upper impacted ureteral stones: mini-percutaneous nephrolithotomy (MPCNL), transurethral ureteroscope lithotripsy (URSL), and retroperitoneal laparoscopic ureterolithotomy (RPLU). This study aimed to compare MPCNL, URSL, and RPLU, and to evaluate which one is the best choice for large upper impacted ureteral stones. Methods Between January 2012 and December 2015, at the Department of Urology, Huai’an First People’s Hospital, 150 consecutively enrolled patients with a large upper impacted ureteral stone (>15 mm) were included. The patients were randomly divided (1:1:1) into the MPCNL, URSL, and RPLU groups. The primary endpoint was success of stone removal measured 1 month postoperatively and the secondary endpoints were intraoperative and postoperative parameters and complications. Results Fifteen patients needed auxiliary ESWL after URSL, and 3 patients after MPCNL, but none after RPLU. The stone clearance rate was 96% (48/50) in the MPCNL group and 72% (33/46) in the URSL group. In the RPLU group the stones were completely removed and the stone clearance rate was 100% (48/48) (P = 0.021 vs. URSL; P = 0.083 vs. MPCNL). Operation-related complications were similar among the three groups (all P > 0.05). Hospital stay was shorter in the URSL group compared with MPCNL (P = 0.003). Operation time was the shortest with URSL and the longest with MPCNL (all P 15 mm. URSL could be considered if the patient is not suitable for general anesthesia, or the patient requests transurethral uretroscopic surgery. Trial registration This study was registered with the Chinese Clinical Trial Registry (Registration number: ChiCTR-INR-17011507 ; Registration date: 2017–5-22).

Abstract We present a novel approach for automatically detecting and tracking facial landmarks across poses and expressions from in-the-wild monocular video data, e.g., YouTube videos and smartphone recordings. Our method does not require any calibration or manual adjustment for new individual input videos or actors. Firstly, we propose a method of robust 2D facial landmark detection across poses, by combining shape-face canonical-correlation analysis with a global supervised descent method. Since 2D regression-based methods are sensitive to unstable initialization, and the temporal and spatial coherence of videos is ignored, we utilize a coarse-todense 3D facial expression reconstruction method to refine the 2D landmarks. On one side, we employ an in-the-wild method to extract the coarse reconstruction result and its corresponding texture using the detected sparse facial landmarks, followed by robust pose, expression, and identity estimation. On the other side, to obtain dense reconstruction results, we give a face tracking flow method that corrects coarse reconstruction results and tracks weakly textured areas; this is used to iteratively update the coarse face model. Finally, a dense reconstruction result is estimated after it converges. Extensive experiments on a variety of video sequences recorded by ourselves or downloaded from YouTube show the results of facial landmark detection and tracking under various lighting conditions, for various head poses and facial expressions. The overall performance and a comparison with state-of-art methods demonstrate the robustness and effectiveness of our method.

Compared with the tailless flapping wing micro air vehicle (FMAV), the tailed FMAV has a simpler structure and is easier to control. However, although biplane FMAVs with tails have been used for flight control in practice for a long time, a theoretical model of the tailed FMAV has not previously been established. In this paper, we report modeling of the longitudinal dynamics of a tailed biplane FMAV using the Newton‐Euler equations. In this study, the vehicle was trimmed and linearized near its hovering equilibrium, assuming small disturbances. Then the stability of the hovering FMAV was analyzed with a modal analysis method. A state feedback controller was synthesized to stabilize the disturbance. Finally, we investigated the flight control of the tailed biplane FMAV with different control signals. Our results show that the natural‐motion mode determines the oscillation divergence characteristics of the tailed FMAV, a mode that can be suppressed with the state feedback controller by real‐time modulation of the tail. The tail can also be used to achieve different flight modes with different control‐signal functions. The tailed FMAV cruises in a line when the tail is controlled with a step function and spirals in an elliptical trajectory in the longitudinal plane when the tail is controlled by a sinusoidal function. Our longitudinal‐ dynamics model provides an analytical basis for further dynamic analyses of the tailed FMAV, as well as the corresponding controller synthesis. Moreover, the proposed attitude stabilization and flight control schemes for the vehicle near hovering provide a basis for developing practical uses of the tailed FMAV.

Spatial variability and source apportionment of river pollution flowing into the Bohai Sea are of great significance to the pollution liability and development of control strategies to reduce the terrestrial discharge of pollution in the ocean. In this study, ten water quality variables from 14 monitoring sites in rivers flowing into Laizhou Bay were obtained to investigate the spatial variation and pollution sources in Dongying District from 2018–2019. The survey area was divided into a low pollution (LP) zone and a high pollution (HP) zone by cluster analysis based on ten indicators. Principle component analysis/factor analysis with a geographic information system was performed to identify the four main pollution sources in the survey area. Compared with the positive matrix factorization model, the absolute principal component score-multiple linear regression (APCS-MLR) model was more appropriate for the source apportionment of pollution in the surface water of Dongying District. The point source pollution of domestic sewage (23.6%) was the most crucial pollution source of water in the LP zone, followed by non-point pollution from agricultural activity (16.4%). The contribution rate in the HP zone analyzed by the APCS-MLR model followed a decreasing order: point source pollution from domestic sewage (28.5%) > non-point pollution source of overland runoff (14.8%) > point source pollution of hybrid wastewater (12.4%) > point source pollution from industries sewage (10.6%). Therefore, the spatial distribution and sources of pollution in the investigated area should be considered while developing control measures to reduce the discharge of pollution to Laizhou Bay.

Vetiver grass [Vetiveria zizanioides (L.) Nash] without seeds, suitable for growing on coastal saline land, has attracted attention because of oil extraction from its roots and industrial and agricultural use. In this study, a pot experiment with different NaCl contents was used to investigate the influence of water salinity levels on vetiver, salt tolerance, and the feasibility of transferring it to coastal saline regions. The results indicated that the fresh weight of roots and shoots increased initially and then gradually decreased with an increase in NaCl content, and the maximum was attributed to a water salinity of 0.3%. The vetiver can tolerate a maximum saline content of up to 2%. The promotion of vetiver growth under water salinity could be attributed to the acceleration of nutrient uptake-induced saline, including K, N, and Cl. The growth of vetiver was insignificantly inhibited with 0.5% water salinity (mild stress), significantly inhibited with 1.0% water salinity (moderate stress: biomass decrease), and severe inhibited with >1.5% water salinity (intense stress: biomass decrease). The salt tolerance of vetiver was due to osmotic regulation by reducing sugars under mild stress and of proline under intense stress, and Na+ sequestration in roots and the transformation of Cl− away from sensitive roots. The vetiver could be cultivated in slightly coastal saline soil (0.1–0.2% soil salinity) and even moderately saline coastal soil (0.2–0.4% soil salinity) under irrigation with low salt water during transplanting.

Extreme weather and climate events, including hurricanes [...]

Given knowledge at the time, the recent 2015–2016 zika virus (ZIKV) epidemic probably could not have been predicted. Without the prior knowledge of ZIKV being already present in South America, and given the lack of understanding of key epidemiologic processes and long-term records of ZIKV cases in the continent, the best related prediction could be carried out for the potential risk of a generic Aedes-borne disease epidemic. Here we use a recently published two-vector basic reproduction number model to assess the predictability of the conditions conducive to epidemics of diseases like zika, chikungunya, or dengue, transmitted by the independent or concurrent presence of Aedes aegypti and Aedes albopictus. We compare the potential risk of transmission forcing the model with the observed climate and with state-of-the-art operational forecasts from the North American Multi Model Ensemble (NMME), finding that the predictive skill of this new seasonal forecast system is highest for multiple countries in Latin America and the Caribbean during the December-February and March-May seasons, and slightly lower—but still of potential use to decision-makers—for the rest of the year. In particular, we find that above-normal suitable conditions for the occurrence of the zika epidemic at the beginning of 2015 could have been successfully predicted at least 1 month in advance for several zika hotspots, and in particular for Northeast Brazil: the heart of the epidemic. Nonetheless, the initiation and spread of an epidemic depends on the effect of multiple factors beyond climate conditions, and thus this type of approach must be considered as a guide and not as a formal predictive tool of vector-borne epidemics.

Decisions made by fishers and fisheries managers are informed by climate and fisheries observations that now often span more than 50 years. Multi-annual climate forecasts could further inform such decisions if they were skillful in predicting future conditions relative to the 50-year scope of past variability. We demonstrate that an existing multi-annual prediction system skillfully forecasts the probability of next year, the next 1–3 years, and the next 1–10 years being warmer or cooler than the 50-year average at the surface in coastal ecosystems. Probabilistic forecasts of upper and lower seas surface temperature (SST) terciles over the next 3 or 10 years from the GFDL CM 2.1 10-member ensemble global prediction system showed significant improvements in skill over the use of a 50-year climatology for most Large Marine Ecosystems (LMEs) in the North Atlantic, the western Pacific, and Indian oceans. Through a comparison of the forecast skill of initialized and uninitialized hindcasts, we demonstrate that this skill is largely due to the predictable signature of radiative forcing changes over the 50-year timescale rather than prediction of evolving modes of climate variability. North Atlantic LMEs stood out as the only coastal regions where initialization significantly contributed to SST prediction skill at the 1 to 10 year scale.

The original PDF version of this Article contained an error in which Fig. 3 and its legend were omitted and Equations 5 and 6 contained errors.This has been corrected in the PDF version of the Article. The HTML version was correct from the time of publication.

In recent years, as natural and social sciences are rapidly evolving, classical chaos theoryand modern complex networks studies are gradually interacting each other with a great joineddevelopment [...]

A simple memristive circuit model is revisited and the stability analysis is to be given. Furthermore, we resort to Poincaré section and Poincaré map technique and present rigorous computer-assisted verification of horseshoe chaos by virtue of topological horseshoe theory.