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Abstract In this work, a machine translation framework is proposed to tackle the flight plan generation in the air transport field. Diverging from the traditional human expert‐based way, a novel sequence‐to‐sequence transfer transformer network to automatic flight plan generation with enhanced operational acceptability is presented. It allows the user to translate the departure and arrival airport pairs denoted as test sentences, into the flyable waypoint sequences denoted as the corresponding source sentences. The approach leverages deep neural networks to autonomously learn air transport specialized knowledge and human expert insights from industry legacy data. Moreover, a multi‐head attention mechanism is adopted to model the complex correlation between airport pairs. Besides, we introduce an innovative waypoint embedding layer to learn effective embeddings for waypoint sequences. Additionally, an extensive flight plan dataset is constructed utilizing real‐world data in China spanning from July to September 2019. Employing the proposed model, rigorous training and testing procedures are conducted on this dataset, yielding remarkably favourable outcomes based on automatic evaluation metrics that are BLEU and METEOR, which outperform other popular approaches. More importantly, the proposed approach achieves high performance in the operational validation and visualization, showing its application potential for real‐world air traffic operation.

Abstract Stretchable electronics have the unique capability of 3D (three dimensional) deformation, overcoming the brittleness of traditional inorganic electronics. However, during large deformations, different scale strains between the rigid and stretchable components lead to mismatch, causing interconnect failures. Therefore, the development of the rigidity‐programmable substrate with effective strain shielding capabilities has become a research hotspot. Furthermore, the exponential growth in electronic density presents challenges in the circuit design of stretchable electronics. The urgent need is to develop highly integrated stretchable electronic systems. In this study, a highly integrated stretchable pulse sensor with effective strain shielding capabilities using hybrid 3D printing technology is developed, which comprises electronic chips, a rigidity‐programmable substrate/encapsulation layer printed by using PSC (polydimethylsiloxane/silica‐nanoparticles composite)‐based ink, and LM (liquid metal)‐based 3D circuits. First, the PSC‐based ink is optimized to enhance the strain shielding effectiveness of the rigidity‐programmable substrate. Meanwhile, 3D printing parameters are optimized to achieve high printing precision with minimum line widths below 100 µm. The resulting stretchable pulse sensor demonstrated good mechanical and electrical stability under complex 3D deformations, including bending, twisting, and stretching. The PSC region strain of the sensor is only ≈2% when the global strain is up to ≈65%, which exhibited effective strain shielding capabilities.

In this study, a phase-field scheme that rigorously obeys conservation laws and irreversible thermodynamics is developed for modeling stress-corrosion coupled damage (SCCD). The coupling constitutive relationships of the deformation, phase-field damage, mass transfer, and electrostatic field are derived from the entropy inequality. The SCCD localization induced by secondary phases in Mg is numerically simulated using the implicit iterative algorithm of the self-defined finite elements. The quantitative evaluation of the SCCD of a C-ring is in good agreement with the experimental results. To capture the damage localization, a micro-galvanic corrosion domain is defined, and the buffering effect on charge migration is explored. Three cases are investigated to reveal the effect of localization on corrosion acceleration and provide guidance for the design for resistance to SCCD at the crystal scale.

This study explored the development of polyvinylpyrrolidone (PVP)–honey-gel (PHG) through 3D printing, aiming to develop a multifunctional bio-tape with adhesive and degradable properties. Based on the potential of honey to promote wound healing due to its antibacterial and anti-inflammatory properties, a PVP–honey-based 3D printing ink was developed by combining honey and PVP to create PHG through 3D printing technology that can form a film with strong adhesion and flexibility. First, the chemical–physical properties of PHG and its 3D printing performance were characterized. The line width achievable in 3D printing for the PHG line can extend to ∼100 µm. In addition, the adhesive properties of the PHG film were evaluated by using a 180° peeling test on various materials (glass, copper, wood, and pig skin), highlighting its potential for diverse applications. Finally, the application of the PHG film as a bio-tape was demonstrated through a successful animal experiment on a rabbit’s skin wound. Due to its adhesive and degradable properties, the bio-tape exhibited 3D conformability and ease of removal without residue.

Abstract Convectively coupled equatorial waves are a significant source of atmospheric variability in the tropics. Current numerical models continue to struggle in simulating the coupled diabatic heating fields that are responsible for the development and maintenance of these waves. This study investigates how the diabatic fields associated with Mixed Rossby–Gravity waves (MRGs) are represented in four reanalysis products by using a unique observational dataset from the TRMM‐KWAJEX (Tropical Rainfall Measuring Mission‐Kwajalein Experiment) field campaign. These reanalyses include ERA5, Japanese 55‐year Reanalysis (JRA‐55), Climate Forecast System Reanalysis (CFSR), and Modern‐Era Retrospective Analysis for Research and Applications (MERRA). We found that all four reanalyses captured the MRG structures in winds and temperature, and to a lesser degree in the humidity field except in the boundary layer. However, only the ERA5 and MERRA reanalyses captured the gradual rise and succession of the diabatic heating from boundary layer turbulence, shallow convection, cumulus congestus, and deep convection within the waves. ERA5 is the only product that also captured the gradual rise of the subgrid‐scale vertical transport of moist static energy. All reanalysis products underestimated the diabatic heating from cumulus congestus. Results provide observational basis on what aspects of MRG can be trusted and what cannot in the reanalysis products.

This study investigates the storage life of particle-filled polymer composites (PFPCs) under the influence of aging effects. High-temperature accelerated aging tests were conducted at 60 °C, 70 °C, and 80 °C for various days to analyze the impact of aging time and temperature on the mechanical behavior of the materials. A predictive model for crosslink density was established using the Arrhenius equation, and the relationship between crosslink density and relaxation modulus was determined based on polymer physics theory. On this basis, a viscoelastic constitutive model that incorporates aging effects was developed. Structural analyses of a PFPC column with a length of 2.3 m and outer diameter of 1.8 m were performed using the UMAT subroutine in ABAQUS. Subsequently, a safety margin assessment method based on dewetting strain was employed to predict the storage life of the PFPC column. The results indicate that the aging viscoelastic constitutive model effectively characterizes the hardening effects caused by aging in the composites during storage. The storage life for the PFPC column considering aging effects decreases from 22 years to 19 years compared to models that ignore such effects. This approach provides a reference for estimating the storage life of PFPC columns considering aging effects.

Carbon fiber resin-based composite materials are widely employed in the manufacturing of composite shells. During the curing process, the temperature gradients and cure degree gradients make it easy to generate thermal strains in both carbon fibers and resin, with the resin experiencing cure shrinkage strain due to the curing reaction, ultimately leading to residual stresses and strains. In this paper, a three-dimensional thermo–chemo–mechanical coupled curing model of the composite shell was established based on a resin test, and the changes of temperature, curing degree, residual stress, and strain during the solidification of the composite shell were investigated. First, the curing property parameters and elastic modulus of HCM-2184 resin were obtained through a curing dynamic test and a tensile test. Then, considering the heat release and shrinkage reaction of solidification, a coupled thermo–chemo–mechanical curing model was developed with the CHILE (α) elastic model, and the curing process of the composite shell was simulated numerically. The results show that the resin used in the test belongs to the autocatalytic reaction. For thin composite shells, the heat accumulation inside the shell during curing is not obvious. During the curing process, the curing shrinkage behavior of the resin is an important factor for the generation of residual stress and residual strain.

Abstract SARS-CoV-2 and its variants, with the Omicron subvariant XBB currently prevailing the global infections, continue to pose threats on public health worldwide. This non-segmented positive-stranded RNA virus encodes the multi-functional nucleocapsid protein (N) that plays key roles in viral infection, replication, genome packaging and budding. N protein consists of two structural domains, NTD and CTD, and three intrinsically disordered regions (IDRs) including the NIDR, the serine/arginine rich motif (SRIDR), and the CIDR. Previous studies revealed functions of N protein in RNA binding, oligomerization, and liquid–liquid phase separation (LLPS), however, characterizations of individual domains and their dissected contributions to N protein functions remain incomplete. In particular, little is known about N protein assembly that may play essential roles in viral replication and genome packing. Here, we present a modular approach to dissect functional roles of individual domains in SARS-CoV-2 N protein that reveals inhibitory or augmented modulations of protein assembly and LLPS in the presence of viral RNAs. Intriguingly, full-length N protein (NFL) assembles into ring-like architecture whereas the truncated SRIDR-CTD-CIDR (N182-419) promotes filamentous assembly. Moreover, LLPS droplets of NFL and N182-419 are significantly enlarged in the presence of viral RNAs, and we observed filamentous structures in the N182-419 droplets using correlative light and electron microscopy (CLEM), suggesting that the formation of LLPS droplets may promote higher-order assembly of N protein for transcription, replication and packaging. Together this study expands our understanding of the multiple functions of N protein in SARS-CoV-2.

A novel type of tubular structure with negative Poisson's ratio based on gyroid-type triply periodic minimal surfaces (TPMSs) is proposed in this study. This work is an attempt to design auxetic tubular structures based on TPMS. A series of auxetic tubular structures were designed and then fabricated using laser power bed fusion. Compressive behaviours of the fabricated auxetic tubular structures were investigated using experimental and numerical methods. To obtain optimal designs of tubular structures with controllable auxetic properties, the influence of several parameters were investigated comprehensively. Subsequently, several graded auxetic tubular structures were designed based on the parametric analysis and studied numerically. The mechanical properties of the tubular structures could be controlled effectively using the proposed approach. The proposed method can be used for guiding the design and optimisation of auxetic tubular structures, showing excellent potential for various applications such as biomedical devices, vehicle crashworthiness, and protective engineering.

Abstract Climate models exhibit significant rainfall bias in mountainous regions. One reason is the insufficient representation of orographic anisotropy in these models. In this study, we implement the orographic drag scheme with 3‐D orographic anisotropy (all flow directions (AFD)) into a general circulation model and investigate its impact on Nanling rainfall simulation where models have systematic dry bias in the summer. It is shown that the AFD alleviated the Nanling mountain rainfall bias by over 60%. This is through an anomalous “lower‐convergence‐higher‐divergence” deceleration pattern of the flow windward of the Nanling Mountains that enhanced vertical motion and low‐level moisture convergence. The results suggest the importance of explicit orographic anisotropy representation in rainfall simulation in mountainous regions.

Abstract Climate change is a critical issue of our time, and its causes, pathways, and forecasts remain a topic of broader discussion. In this paper, we present a novel data driven pathway analysis framework to identify the key processes behind mean global temperature and sea level rise, and to forecast the magnitude of their increase from the present to 2100. Based on historical data and dynamic statistical modeling alone, we have established the causal pathways that connect increasing greenhouse gas emissions to increasing global mean temperature and sea level, with its intermediate links encompassing humidity, sea ice coverage, and glacier mass, but not for sunspot numbers. Our results indicate that if no action is taken to curb anthropogenic greenhouse gas emissions, the global average temperature would rise to an estimated 3.28 °C (2.46–4.10 °C) above its pre-industrial level while the global sea level would be an estimated 573 mm (474–671 mm) above its 2021 mean by 2100. However, if countries adhere to the greenhouse gas emission regulations outlined in the 2021 United Nations Conference on Climate Change (COP26), the rise in global temperature would lessen to an average increase of 1.88 °C (1.43–2.33 °C) above its pre-industrial level, albeit still higher than the targeted 1.5 °C, while the sea level increase would reduce to 449 mm (389–509 mm) above its 2021 mean by 2100.

The double-hole rice vacuum seed meter is critical equipment for the planting precision of rice direct seeding. The effects of shaped holes and seed disturbance on the precision of rice bunch planting were investigated to improve the precision of bunch planting with the double-hole rice vacuum seed meter. A test bench with the rice vacuum seed meter was set up to analyze the trends in the quality of feed index, miss index, and multiple index of seed meters with different shaped holes at different speeds and vacuum pressures. Based on the optimal hole structure, different seed disturbance structures were designed to investigate the influence of the seed disturbance structure on the precision of bunch planting. A multiple linear regression model was established for the relationship between the disturbance structure, vacuum pressure, rotational speed, and the precision of bunch planting. Discrete element numerical simulation experiments were carried out to analyze the effect of disturbance structures on seeds. The planting precision of the seed meter with the shaped hole was significantly higher than that of the seed meter without the shaped hole while the shaped hole B was the optimum structure. Disturbance structure affects the quality of feed index, multiple index rate, and miss index. The planting precision of the seed disturbance structure II was better than the other structures. At a speed of 60 rpm and vacuum pressures of 2.0 kPa, 2.4 kPa, and 2.8 kPa, the qualities of feed index of seed disturbance structure II were 90%, 91.11%, and 89.17%, respectively, and the miss indexes were 2.96%, 1.94%, and 1.57%, respectively. At high rotational speeds, the precision of rice bunch planting with the seed disturbance structure is better than that without the seed disturbance structure. In the simulation test, the seed velocity and total force magnitude of the meter without disturbance structures were less than those with the disturbed structure. Simulation experiments showed that the seed disturbance structure breaks up the stacked state of seeds. Research has shown that the shaped hole holds the seed in a stable suction posture, which helps to increase the seed-filling rate. Seed disturbance improves seed mobility, thereby enhancing the precision of bunch planting.

This investigation considered the effects of both internal and external excitation vibrations on the efficacy of the seed dispenser in a rice precision hole seeder. Through comprehensive field tests, we analyzed vibrational characteristics during direct seeder operations and established a vibration seeding test bed for systematic examination of these effects. Time-domain analysis of the vibration data revealed a predominantly vertical vibration direction, with notably higher levels in sandy loam soil compared to clay loam. A correlation was observed between increased engine size and rotary ploughing speeds, as well as forward speed and elevated vibration amplitudes. Frequency domain analysis pinpointed the primary vibration frequency of the machinery within the 0–170 Hz range, remaining consistent across different operating conditions. Crucially, bench test results indicated that seeding accuracy and dispersion were significantly influenced by vibration frequencies, particularly within the 70–130 Hz range, where a decrease in accuracy and increase in dispersion were noted. A regression model suggested a complex, non-linear relationship between seeding performance and vibration frequency. These insights highlight the necessity for a robust mechanism to effectively address these vibrational impacts. This study paves the way for enhancing the operational efficiency of the rice precision hole seeder, aiming to achieve the design goals of minimized vibrations in the paddy power chassis.

Abstract Sulfate-reducing bacteria (SRB) corrosion of X80 steel welded joint under organic carbon source starvation was investigated in the paper. The results showed that the number of planktonic cells is much less than that of sessile cells after 720 h immersion. The number of sessile cells in the base metal (BM) in the medium with 1% carbon source is the largest. Starving SRB cells accelerated simultaneously the uniform corrosion and localized corrosion of the welded joints, and the acceleration effect on the latter was much higher than that of the former. In all mediums, the density and average depth of pits in the BM and weld zone (WZ) were much larger than those in the heat affected zone (HAZ). SRB corrosion of the welded joint was strongly selective, and organic carbon source starvation further enhanced the selectivity.

Underwater object detection plays a significant role in marine ecosystem research and marine species conservation. The improvement of related technologies holds practical significance. Although existing object-detection algorithms have achieved an excellent performance on land, they are not satisfactory in underwater scenarios due to two limitations: the underwater objects are often small, densely distributed, and prone to occlusion characteristics, and underwater embedded devices have limited storage and computational capabilities. In this paper, we propose a high-precision, lightweight underwater detector specifically optimizing for underwater scenarios based on the You Only Look Once Version 8 (YOLOv8) model. Firstly, we replace the Darknet-53 backbone of YOLOv8s with FasterNet-T0, reducing model parameters by 22.52%, FLOPS by 23.59%, and model size by 22.73%, achieving model lightweighting. Secondly, we add a Prediction Head for Small Objects, increase the number of channels for high-resolution feature map detection heads, and decrease the number of channels for low-resolution feature map detection heads. This results in a 1.2% improvement in small-object detection accuracy, while the remaining model parameters and memory consumption are nearly unchanged. Thirdly, we use Deformable ConvNets and Coordinate Attention in the neck part to enhance the accuracy in the detection of irregularly shaped and densely occluded small targets. This is achieved by learning convolution offsets from feature maps and emphasizing the regions of interest (RoIs). Our method achieves 52.12% AP on the underwater dataset UTDAC2020, with only 8.5 M parameters, 25.5 B FLOPS, and 17 MB model size. It surpasses the performance of large model YOLOv8l, at 51.69% AP, with 43.6 M parameters, 164.8 B FLOPS, and 84 MB model size. Furthermore, by increasing the input image resolution to 1280 × 1280 pixels, our model achieves 53.18% AP, making it the state-of-the-art (SOTA) model for the UTDAC2020 underwater dataset. Additionally, we achieve 84.4% mAP on the Pascal VOC dataset, with a substantial reduction in model parameters compared to previous, well-established detectors. The experimental results demonstrate that our proposed lightweight method retains effectiveness on underwater datasets and can be generalized to common datasets.

In this study, the aging performance of particle-filled polymer composites (PFPCs) under thermo-oxidative conditions was investigated on multiple scales. High-temperature-accelerated tests were conducted to analyze the effects of aging time and temperature. A representative volume element (RVE) model was established for the PFPCs using a random particle-filling algorithm. A predictive model for the crosslink density was conducted based on the closed-loop chain reaction of polymer oxidation. According to the theory of polymer physics, the relation between the crosslink density and matrix modulus was determined. The particle/matrix interface in the RVE model was represented by the cohesive zone model (CZM). The parameters of the CZM were determined by the inversion techniques. Then, a comprehensive multiscale RVE model was constructed, which was applied to predict the modulus and dewetting strain of the aged PFPCs. The predicted results show good agreement with the test results, which verifies the reliability of our model.

BackgroundAtrial fibrillation (AF) is the most common cardiac arrhythmia and significantly increases the risk of stroke and heart failure (HF), contributing to a higher mortality rate. Increasing age is a major risk factor for AF; however, the mechanisms of how aging contributes to the occurrence and progression of AF remain unclear. This study conducted weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes and determine their potential associations with aging-related AF.Materials and methodsWGCNA was performed using the AF dataset GSE2240 obtained from the Gene Expression Omnibus, which contained data from atrial myocardium in cardiac patients with permanent AF or sinus rhythm (SR). Hub genes were identified in clinical samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were also performed.ResultsGreen and pink were the most critical modules associated with AF, from which nine hub genes, PTGDS, COLQ, ASTN2, VASH1, RCAN1, AMIGO2, RBP1, MFAP4, and ALDH1A1, were hypothesized to play key roles in the AF pathophysiology in elderly and seven of them have high diagnostic value. Functional enrichment analysis demonstrated that the green module was associated with the calcium, cyclic adenosine monophosphate (cAMP), and peroxisome proliferator-activated receptors (PPAR) signaling pathways, and the pink module may be associated with the transforming growth factor beta (TGF-β) signaling pathway in myocardial fibrosis.ConclusionWe identified nine genes that may play crucial roles in the pathophysiological mechanism of aging-related AF, among which six genes were associated with AF for the first time. This study provided novel insights into the impact of aging on the occurrence and progression of AF, and identified biomarkers and potential therapeutic targets for AF.

To elucidate the vibrational characteristics of power chassis in paddy fields, we examined the Yanmar VPG6G rice transplanter across diverse terrains, including paddy fields, dry land, and concrete roads. Vibrational acceleration measurements, taken in longitudinal, transverse, and vertical orientations at key chassis locations, revealed noteworthy findings. The Mizuta power chassis exhibited its lowest root-mean-square (RMS) vibrational acceleration on concrete, while the highest was observed on paddy fields. The acceleration power spectra predominantly peaked between 1~14 Hz, with peak values amplifying as speed increased. Additionally, pendant orientation frequencies exceeded those of longitudinal and lateral directions. Both front and rear wheels mirrored the vibrational accelerations of the rear axle, but dynamic load coefficients for the front wheels consistently surpassed the rear, particularly at elevated speeds. This research not only enhances our understanding of terrain-induced vibrations and the intricate dynamics between terrain and tires but also lays the groundwork for designing optimized vibration-damping solutions tailored to prevalent road conditions.

ObjectiveThis study was performed to compare the clinical effect of arthroscopic debridement vs. arthroscopic microfracture in the treatment of osteochondral lesions of the talus.MethodsWe retrospectively reviewed patients with osteochondral lesion of talus who were admitted to our hospital from April 2020 to April 2021. The patients were divided into Group A (arthroscopic debridement group, n = 39) and Group B (arthroscopic microfracture group, n = 42), and the intraoperative details in the two groups were analyzed. The American Orthopaedic Foot and Ankle Society (AOFAS) score and visual analogue scale (VAS) score were compared between the two groups before surgery and at the last follow-up.ResultsThe postoperative AOFAS score (Group A, 40.9–82.26; Group B, 38.12–87.38), VAS score (Group A, 6.44–3.92; Group B, 6.38–2.05) significantly improved in both groups, but the improvement was significantly greater in Group B than in Group A (P

Deep placement of nitrogen fertilizer is a key strategy for improving nitrogen use efficiency. A two-year field experiment was conducted during the early rice growing seasons (March–July) of 2016 and 2017. The experimental treatments comprised two rice cultivars: Wufengyou 615 (WFY 615) and Yuxiangyouzhan (YXYZ), and three N treatments: mechanical deep placement of all fertilizers as basal dose at 10 cm soil depth (one-time deep-placement fertilization, namely OTDP fertilization); manual surface broadcast (the common farmer practice) of 40% N fertilizer at one day before sowing (basal fertilizer) followed by broadcast application of 30% each at tillering and panicle initiation stages; and no fertilizer application at any growth stage as a control. One-time deep-placement fertilization increased grain yield of both rice cultivars by 11.8%–19.6%, total nitrogen accumulation by 10.3%–13.1%, nitrogen grain production efficiency by 29.7%–31.5%, nitrogen harvest index by 27.8%–30.0%, nitrogen agronomic efficiency by 71.3%–77.2%, and nitrogen recovery efficiency by 42.4%–56.7% for both rice cultivars, compared with the multiple-broadcast treatment. One-time deep-placement fertilization reduced CH4-induced global warming potential (GWP) by 20.7%–25.3%, N2O-induced GWP by 7.2%–12.3%, and total GWP by 14.7%–22.9% for both rice cultivars relative to the multiple-broadcast treatment. The activities of glutamine synthetase and nitrate reductase were increased at both panicle-initiation and heading stages in both rice cultivars following one-time deep-placement fertilization treatment. Larger leaf area index at heading stage and more favorable root morphological traits expressed as larger total root length, mean root diameter, and total root volume per hill were also observed. One-time deep-placement fertilization could be an effective strategy for increasing grain yield and nitrogen use efficiency and lowering greenhouse-gas emissions under mechanical direct-seeded cropping systems.

Abstract Mesoscale convective systems (MCS) contribute about half of the world's precipitation and create flash flooding as well as other extreme weather events. Despite steady progress in research of these systems in the last several decades, better theoretical understanding is still needed to understand their dynamical organizations and to improve their forecasts in numerical models. By using the Moncrieff‐Green horizontal vorticity equation, this paper presents an analytical model of one type of MCS, the steady‐state squall lines. It describes the organization, propagation, and properties of mesoscale circulations of two‐dimensional steady‐state convective systems under sheared environment. Far‐side solutions are formulated to illustrate the underlying physical processes of squall line flows. Numerical procedures are described to solve the model under general environmental conditions. The model leads to the following prediction of squall line properties: Given the environmental profiles of wind and convective available potential energy (CAPE), the propagation speed, the depths and mass fluxes of the tilted ascending front‐to‐rear flow, the overturning updraft, and the descending rear inflow of the squall line can be all determined from the cold pool buoyancy or CAPE. The squall lines are therefore self‐organized dynamical systems that have limited degrees of freedom in their properties. The model advances the theoretical understanding of how mesoscale flow components interact to sustain organized convection. It provides a new tool to interpret squall line systems in high resolution models and to parameterize them in climate models.

Background: Coronary artery disease (CAD) is a common cardiovascular disease that has attracted attention worldwide due to its high morbidity and mortality. Recent studies have shown that abnormal microRNA (miRNA) expression is effective in CAD diagnoses and processes. However, the potential relationship between miRNAs and CAD remains unclear.Methods: Microarray datasets GSE105449 and GSE28858 were downloaded directly from the Gene Expression Omnibus (GEO) to identify miRNAs involved in CAD. Target gene prediction and enrichment analyses were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG).Results: There were nine differentially expressed miRNAs in CAD patients compared to the controls. A total of 352 genes were predicted and subjected to GO analysis, which showed that differentially expressed genes (DEGs) were mainly associated with axon guidance, neuron projection guidance, neuron-to-neuron synapses, and postsynaptic density. According to the KEGG pathway analysis, the most enriched pathways were those involved in transcriptional misregulation in cancer, growth hormone synthesis, secretion and action, endocrine resistance, axon guidance, and Cushing syndrome. Pathway analysis was mainly involved in the HIPPO and prion disease signaling pathways. Furthermore, a competing endogenous RNA (ceRNA) interaction network centered on miR-22-3p revealed eight related transcription factors in the cardiovascular system. The receiver operating characteristic (ROC) curve analysis suggested that miR-22-3p may be a better CAD predictor.Conclusion: The results indicate that miR-22-3p may function in pathophysiological CAD processes. Our study potentiates miR-22-3p as a specific biomarker for diagnosing CAD.

In this study, we developed a novel multi-scale model to predict the aging performance of particle-filled polymer composites (PFPCs) under thermo-oxidative aging conditions. To investigate the aging behavior, high-temperature accelerated aging tests were conducted in combination with macroscopic and microscopic characterization. At the microscopic level, the crosslinking density of the polymer matrix is calculated using the closed-loop chain reaction of polymer oxidation. In addition, the theory of polymer physics was used to determine the relationship between crosslinking density and elastic modulus. Relationships between elastic modulus and dewetting strain were analyzed at the macroscopic level. Based on the observations and analyses, a multi-scale model was developed to predict the aging performance of PFPCs. The predicted results show good agreement with the test results, which verifies the reliability of the model.

In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) have been widely employed and achieved promising performance. However, CNN-based methods face difficulties in achieving both accurate and efficient HSI classification due to their limited receptive fields and deep architectures. To alleviate these limitations, we propose an effective HSI classification network based on multi-head self-attention and spectral-coordinate attention (MSSCA). Specifically, we first reduce the redundant spectral information of HSI by using a point-wise convolution network (PCN) to enhance discriminability and robustness of the network. Then, we capture long-range dependencies among HSI pixels by introducing a modified multi-head self-attention (M-MHSA) model, which applies a down-sampling operation to alleviate the computing burden caused by the dot-product operation of MHSA. Furthermore, to enhance the performance of the proposed method, we introduce a lightweight spectral-coordinate attention fusion module. This module combines spectral attention (SA) and coordinate attention (CA) to enable the network to better weight the importance of useful bands and more accurately localize target objects. Importantly, our method achieves these improvements without increasing the complexity or computational cost of the network. To demonstrate the effectiveness of our proposed method, experiments were conducted on three classic HSI datasets: Indian Pines (IP), Pavia University (PU), and Salinas. The results show that our proposed method is highly competitive in terms of both efficiency and accuracy when compared to existing methods.

Precipitation responses to climate warming in East Asia differ between summer and winter as a result of steepening atmospheric moisture gradients, weakening westerlies and a higher hydrological amplitude in atmospheric eddies, according to climate model simulations.

NAC (NAM, ATAF, and CUC) is a ubiquitously expressed plant-specific transcription factor (TF) family which is involved in the regulation of various biological processes. However, a systematic characterization of NAC gene family is yet to be reported in lotus. Here, 82 NnNAC genes which included five predicted membrane-bound NAC proteins were identified in the lotus genome. Phylogenetic analysis revealed seven-subfamily clusters (I–VII) of NnNAC proteins, with homologous gene pairs displaying similar conserved motifs and gene structure characteristics. Transactivation assay of NnNAC proteins revealed an extensive transcriptional activation capacity which is mediated by the highly divergent C-terminal activation domain (AD). Expression analysis of NnNAC genes in lotus tissues showed high transcript levels in root, stamen, petal and seed coat. In addition, 30 and 29 differentially expressed NnNAC candidate genes putatively involved in lotus seed development and response to complete submergence stress, respectively, were identified. Overall, our study provides potentially useful candidate gene resources for future molecular breeding of lotus varieties with novel agronomic traits.

Lotus (Nelumbo nucifera) is a large aquatic plant that accumulates pharmacologically significant benzylisoquinoline alkaloids (BIAs). However, little is known about their biosynthesis and regulation. Here, we show that the two group III WRKY transcription factors (TFs), NnWRKY70a and NnWRKY70b, positively regulate the BIA biosynthesis in lotus. Both NnWRKY70s are jasmonic acid (JA) responsive, with their expression profiles highly correlated to the BIA concentration and BIA pathway gene expression. A dual-luciferase assay showed that NnWRKY70a could transactivate the NnTYDC promoter, whereas NnWRKY70b could activate promoters of the three BIA structural genes, including NnTYDC, NnCYP80G, and Nn7OMT. In addition, the transient overexpression of NnWRKY70a and NnWRKY70b in lotus petals significantly elevated the BIA alkaloid concentrations. Notably, NnWRKY70b seems to be a stronger BIA biosynthesis regulator, because it dramatically induced more BIA structural gene expressions and BIA accumulation than NnWRKY70a. A yeast two-hybrid assay further revealed that NnWRKY70b physically interacted with NnJAZ1 and two other group III WRKY TFs (NnWRKY53b and NnWRKY70a), suggesting that it may cooperate with the other group III WRKYs to adjust the lotus BIA biosynthesis via the JA-signaling pathway. To illustrate the mechanism underlying NnWRKY70b-mediated BIA regulation in the lotus, a simplified model is proposed. Our study provides useful insights into the regulatory roles of WRKY TFs in the biosynthesis of secondary metabolites.

Lotus plumule is a green tissue in the middle of seeds that predominantly accumulates bisbenzylisoquinoline alkaloids (bis-BIAs) and chlorophyll (Chl). However, the biosynthetic mechanisms of these two metabolites remain largely unknown in lotus. This study used physiological and RNA sequencing (RNA-Seq) approaches to characterize the development and molecular mechanisms of bis-BIAs and Chl biosynthesis in lotus plumule. Physiological analysis revealed that exponential plumule growth occurred between 9 and 15 days after pollination (DAP), which coincided with the onset of bis-BIAs biosynthesis and its subsequent rapid accumulation. Transcriptome analysis of lotus plumule identified a total of 8,725 differentially expressed genes (DEGs), representing ~27.7% of all transcripts in the lotus genome. Sixteen structural DEGs, potentially associated with bis-BIAs biosynthesis, were identified. Of these, 12 encoded O-methyltransferases (OMTs) are likely involved in the methylation and bis-BIAs diversity in lotus. In addition, functionally divergent paralogous and redundant homologous gene members of the BIAs biosynthesis pathway, as well as transcription factors co-expressed with bis-BIAs and Chl biosynthesis genes, were identified. Twenty-two genes encoding 16 conserved enzymes of the Chl biosynthesis pathway were identified, with the majority being significantly upregulated by Chl biosynthesis. Photosynthesis and Chl biosynthesis pathways were simultaneously activated during lotus plumule development. Moreover, our results showed that light-driven Pchlide reduction is essential for Chl biosynthesis in the lotus plumule. These results will be useful for enhancing our understanding of alkaloids and Chl biosynthesis in plants.

This study aimed to investigate the efficacy of balloon dilation in ureteral stricture and to analyze the risk factors for the failure of balloon dilation, which will hopefully provide some reference for clinicians to develop treatment plans. We retrospectively analyzed 196 patients who underwent balloon dilation between January 2012 and August 2022, 127 of whom had complete baseline and follow-up data. General clinical data, perioperative data, balloon parameters at the time of surgery, and follow-up results were collected from the patients. Univariate and multivariate logistic regression analyses were performed for the risk factors for surgical failure in patients undergoing balloon dilatation. The success rates of balloon dilatation (n = 30) and balloon dilatation combined with endoureterotomy (n = 37) for lower ureteral stricture at 3 months, 6 months, and 1 year were 81.08%, 78.38%, and 78.38% and 90%, 90%, and 86.67%, respectively. The success rates of balloon dilation at 3 months, 6 months, and 1 year in patients with recurrent upper ureteral stricture after pyeloplasty (n = 15) and primary treatment (n = 30) were 73.33%, 60%, and 53.33% and 80%, 80%, and 73.33%, respectively. The success rates of surgery at 3 months, 6 months, and 1 year for patients with recurrence of lower ureteral stricture after ureteral reimplantation or endoureterotomy (n = 4) and primary treatment with balloon dilatation (n = 34) were 75%, 75%, and 75% and 85.29%, 79.41%, and 79.41%, respectively. Multivariate analysis of the failure of balloon dilation showed that balloon circumference and multiple ureteral strictures were risk factors for balloon dilation failure (OR = 0.143, 95% CI: 0.023–0.895, p = 0.038; OR = 1.221, 95% CI: 1.002–1.491, p = 0.05). Balloon dilation combined with endoureterotomy in lower ureteral stricture had a higher success rate than balloon dilation alone. The success rate of balloon dilation in the primary treatment of the upper and lower ureter was higher than that of balloon dilation in the secondary treatment after failed repair surgery. Balloon circumference and multiple ureteral strictures are risk factors for balloon dilation failure.

Preservation of denatured dermis exerts promotive functions in wound healing and improves the appearance and function of skin. Angiogenesis is crucial for wound healing during burn injury. However, the potential molecular mechanism of angiogenesis in the recovery after burn injury remains to be elucidated. Herein, RNA chromatin immunoprecipitation (ChIP) sequencing analysis revealed upregulation of long intergenic non-coding RNA 00174 (linc00174) in the post-burn tissues. linc00174 overexpression promoted angiogenic activities of human umbilical vein endothelial cells (HUVECs) in the heat-denatured cell model, characterized by the promotion of cell proliferation, migration, and tube formation. Mechanistically, linc00174 directly bound to enhancer of zeste homolog 2 (EZH2), thus stimulating the protein level of trimethylation at lysine 27 of histone H3 (H3K27me3). Moreover, inhibition of EZH2 resulted in downregulation of ZNF24 and Runx1, as well as a decline of vascular endothelial growth factor A (VEGFA). Furthermore, EZH2 modulated epigenetic repression of ZNF24 and Runx1 through the promoter of H3K27me3. Additionally, ZNF24 and Runx1 both functioned as transcriptional inhibitors of VEGFA. Taken together, these findings uncover that linc00174 epigenetically inhibits ZNF24 and Runx1 expression through binding to EZH2, thus attenuating the suppression of VEGFA, contributing to the facilitation of angiogenesis during the recovery of heat-denatured endothelial cells.

Abstract Background This study was designed to evaluate the clinical outcomes of patients with diaphysis malignant tumors of femur and tibia treated with microwave ablation (MWA) in situ. Methods Retrospective study of 32 patients with diaphysis malignant bone tumors of femur or tibia have been treated by microwave ablation. Instead of en bloc resection, hyperthermia ablation in situ was carried out followed by strengthen procedure. The patients were followed up for a period ranging from 36 to 180 months. Results Twenty-five patients survived over 3 years and all of the patients alive have a satisfactory functional and cosmetic limb. The postoperative survival rate of MWA group was significantly higher than the amputation group in consecutive inclusions. Conclusions MWA is a feasible and effective surgical method for limb salvage operation and it might offer an innovative and distinctive therapeutic alternative for diaphysis malignant bone tumors, which avoiding osteotomy or prosthesis replacement. Level of evidence Level IV, clinical cohort study

Context Curcumin has antitumor, antioxidative, anti-inflammatory, and anti-proliferative properties. Objective To investigate the role of miR-22 during curcumin-induced changes in vascular smooth muscle cells (VSMC) and neointima formation in balloon-injured rat abdominal aorta. Materials and methods Sprague-Dawley rats were randomised to the sham-operated (n = 10), operated control (injured, n = 10), and curcumin treatment (n = 10) groups. miR-22 expression was determined by real-time PCR. SP1 was assessed by western blot and real-time PCR. Rat aortic smooth muscle A7r5 cells were used to determine VSMC proliferation and migration, which were measured by the MTS, EdU staining, Transwell, and wound healing assays. Results miR-22 levels declined following arterial balloon injury in vivo (48% at 3d, p

Effective Jan. 1, 2018, the California Department of Pesticide Regulation enacted a regulation regarding the use of pesticides near public K-12 schools and licensed child day care centers, including a provision that bans specific types of applications, including air-blast and air-assist, during weekday school hours (6 a.m. to 6 p.m.) to provide an additional safety margin for pesticide exposure beyond those provided by other regulations. We considered the financial effect on almond growers in nine counties, accounting for four-fifths of total almond production in 2014, if they had been unable to complete a standard spring disease management program on any buffer zone acreage. Results indicated that total annual losses for those counties if such a regulation had been in effect would have been $8.7 million, with per-acre losses ranging from 22% to over 50% of total operating costs, depending on the county. However, using a methodology that took into account historical weather and soil hydrologic group data, we estimated average annual losses in the nine counties among almond growers would have been under $0.2 million because the regulation would have affected the number of sprays completed for relatively few acres in relatively few years.

Conductive polymers and their composites have been widely applied in different applications, including sensing applications. Herein, we constructed a conductive composite of polypropylene, carbon black, and multi-walled carbon nanotubes (PP/CB/MWCNTs) to experimentally study its sensing behaviors in a humid thermal environment. The as-synthesized PP/CB/MWCNT composite polymer was immersed in simulated sweat in deionized water at 67 °C. Regarding their electrical and mechanical properties, different experimental parameters, such as cyclic loading and hygrothermal aging, were investigated by recording the mass changes, carrying out strain sensing experiments, and performing dynamic mechanical analyses before and after the immersion test. The results reveal that the filler content improved the rate of water absorption but decreased at higher concentrations of the solution. The sensitivity of the material decreased by up to 53% after the hygrothermal ageing and cyclic loading. Moreover, the sensitivity under cyclic compression loading decreased with an increasing immersion time, qualitatively illustrated by an effective quantum tunneling effect and conducting path model. Finally, hygrothermal aging reduced the composite’s glass transition temperature. This reduction was the most significant for specimens immersed in deionized water, ascribed to the moisture absorption, reducing the molecular chain activity.

The propagation of surface acoustic waves in two types of tunable magneto-electro-elastic phononic crystal structures is investigated, for which Terfenol-D rods are vertically deposited on the piezoelectric substrate or embedded in the piezoelectric substrate. The bandgaps of the phononic crystal structures are analyzed by using the finite element method considering the nonlinear physical characteristics of Terfenol-D under a magnetic field and compressive pre-stress. Furthermore, through a detailed discussion of the calculated results, it is shown that the bandgaps of surface acoustic waves can be enlarged and new bandgaps will appear under an appropriate magnitude of magnetic field and compressive stress. Therefore, the present numerical model may provide relevant guidance for adjusting the bandgaps of surface acoustic waves in a two-dimensional magneto-electro-elastic phononic crystal.

Abstract Deficiencies in convection trigger functions, used in deep convection parameterizations in General Circulation Models (GCMs), have critical impacts on climate simulations. A novel convection trigger function is developed using the machine learning (ML) classification model XGBoost. The large‐scale environmental information associated with convective events is obtained from the long‐term constrained variational analysis forcing data from the Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) and Manaus (MAO) sites representing, respectively, continental mid‐latitude and tropical convection. The ML trigger is separately trained and evaluated per site, and jointly trained and evaluated at both sites as a unified trigger. The performance of the ML trigger is compared with four convective trigger functions commonly used in GCMs: dilute convective available potential energy (CAPE), undilute CAPE, dilute dynamic CAPE (dCAPE), and undilute dCAPE. The ML trigger substantially outperforms the four CAPE‐based triggers in terms of the F1 score metric, widely used to estimate the performance of ML methods. The site‐specific ML trigger functions can achieve, respectively, 91% and 93% F1 scores at SGP and MAO. The unified trigger also has a 91% F1 score, with virtually no degradation from the site‐specific training, suggesting the potential of a global ML trigger function. The ML trigger alleviates a GCM deficiency regarding the overprediction of convection occurrence, offering a promising improvement to the simulation of the diurnal cycle of precipitation. Furthermore, to overcome the black box issue of the ML methods, insights derived from the ML model are discussed, which may be leveraged to improve traditional CAPE‐based triggers.

Abstract We revise the convective triggering function in Department of Energy's Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1) by introducing a dynamic constraint on the initiation of convection that emulates the collective dynamical effects to prevent convection from being triggered too frequently and allowing air parcels to launch above the boundary layer to capture nocturnal elevated convection. The former is referred to as the dynamic Convective Available Potential Energy (dCAPE) trigger and the latter as the Unrestricted Launch Level (ULL) trigger. Compared to the original trigger in EAMv1 that initiates convection whenever CAPE is larger than a threshold, the revised trigger substantially improves the simulated diurnal cycle of precipitation over both midlatitude and tropical lands. The nocturnal peak of precipitation and the eastward propagation of convection downstream of the Rockies and over the adjacent Great Plains are much better captured than those in the default model. The overall impact on mean precipitation is minor with some notable improvements over the Indo‐Western Pacific, subtropical Pacific and Atlantic, and South America. In general, the dCAPE trigger helps to better capture late afternoon rainfall peak, while ULL is key to capturing nocturnal elevated convection and the eastward propagation of convection. The dCAPE trigger also primarily contributes to the considerable reduction of convective precipitation over subtropical regions and the frequency of light‐to‐moderate precipitation occurrence. However, no clear improvement is seen in intense convection and the amplitude of diurnal precipitation.

Context: Caragana pruinosa Kom. (Fabaceae), a commonly used folk medicine, has been found to possess antitumor effects. However, the antiproliferative effect of 2,4-dihydroxy-3′-methoxy-4′-ethoxychalcone (DMEC) derived from C. pruinosa against multiple myeloma (MM) has never been investigated. Objective: This study systematically evaluates the antiproliferative effect of DMEC against MM cells. Materials and methods: The antiproliferative effect of DMEC (1, 2, 4, 8, 16, 32, and 64 μM) on MM cells lines, including RPMI8226, MM.1S, and U266, was examined using Cell counting kit-8 (CCK-8) assay after 24 h incubation. The proapoptotic effect of DMEC (20 μM) was determined using fluorescent microscope and flow cytometer, and its possible underlying mechanisms were further studied by using western blotting analysis. Results: The half maximal inhibitory concentrations (IC50) of DMEC on RPMI8226, MM.1S, and U266 cells were calculated as 25.97, 18.36, and 15.02 μM, respectively. The inhibitory effect of DMEC on MM cells was related to mitochondria-mediated apoptosis via upregulation of the cleaved-caspase-3 (C-3), cleaved-caspase-9 (C-9), Bad, and cytochrome C (Cyto C), but downregulation of the Bcl-2 and poly ADP-ribose polymerase (PARP). Furthermore, DMEC (5, 10, and 20 μM) reduced the expression of phosphatidylinositol-3-kinase (PI3K), phosphorylated (p)-protein kinase B (Akt), and p-mammalian target of rapamycin (p-mTOR), which were further evidenced by pretreatment with IGF-1, a PI3K activator. Conclusion: Collectively, our results indicate that the DMEC could be treated as a new candidate for treatment of multiple myeloma in the future. Also, an in vivo study is warranted in the future.

In atherosclerotic plaques, transformation of macrophages into foam cells is a key step in initiating the inflammatory response. Here Fan et al. show that casein kinase 2-interacting protein-1 (CKIP-1) limits foam cell formation and atherosclerosis by preventing expression of the scavenger receptor LOX-1 through REGγ-mediated degradation of Oct-1.

A sea ice model is an important component of an Earth system model, which is an essential tool for the study of sea ice, including its internal processes, interactions with other components, and projected future changes. This paper evaluates a simulation of sea ice by the Chinese Academy of Sciences Earth System Model version 2 (CAS-ESM 2.0), focusing on a historical simulation in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared with the observations, CAS-ESM 2.0 reproduces reasonable seasonal cycle features and the climatological spatial distribution of Arctic and Antarctic sea ice, including sea ice extent (SIE), sea ice concentration, and sea ice thickness and motion. However, the SIE in CAS-ESM 2.0 is too large in winter and too low in summer in both hemispheres, indicating higher seasonal variations of the model relative to observations. Further sea ice mass budget diagnostics show that basal growth contributes most to ice increase in both hemispheres, basal melt and top melt make a comparable contribution to Arctic ice decrease, and basal melt plays a dominant role in Antarctic ice loss. This, combined with surface air temperature (SAT) and sea surface temperature (SST) biases, suggests that the excess of sea ice simulated in wintertime in both hemispheres and the lower SIE simulated in the Antarctic summer are mainly attributable to the bias in SST, whereas the lower SIE simulated in the Arctic summer is probably due to the combined effects of both the SST and SAT biases.

A sector is a basic unit of airspace whose operation is managed by air traffic controllers. The operation complexity of a sector plays an important role in air traffic management system, such as airspace reconfiguration, air traffic flow management, and allocation of air traffic controller resources. Therefore, accurate evaluation of the sector operation complexity (SOC) is crucial. Considering there are numerous factors that can influence SOC, researchers have proposed several machine learning methods recently to evaluate SOC by mining the relationship between factors and complexity. However, existing studies rely on hand-crafted factors, which are computationally difficult, specialized background required, and may limit the evaluation performance of the model. To overcome these problems, this paper for the first time proposes an end-to-end SOC learning framework based on deep convolutional neural network (CNN) specifically for free of hand-crafted factors environment. A new data representation, i.e., multichannel traffic scenario image (MTSI), is proposed to represent the overall air traffic scenario. A MTSI is generated by splitting the airspace into a two-dimension grid map and filled with navigation information. Motivated by the applications of deep learning network, the specific CNN model is introduced to automatically extract high-level traffic features from MTSIs and learn the SOC pattern. Thus, the model input is determined by combining multiple image channels composed of air traffic information, which are used to describe the traffic scenario. The model output is SOC levels for the target sector. The experimental results using a real dataset from the Guangzhou airspace sector in China show that our model can effectively extract traffic complexity information from MTSIs and achieve promising performance than traditional machine learning methods. In practice, our work can be flexibly and conveniently applied to SOC evaluation without the additional calculation of hand-crafted factors.

Abstract The second version of Chinese Academy of Sciences Earth System Model (CAS‐ESM 2) is described with emphasis on the development process, strength and weakness, and climate sensitivities in simulations of the Coupled Model Intercomparison Project (CMIP6) DECK experiments. CAS‐ESM 2 was built as a numerical model to simulate both the physical climate system as well as atmospheric chemistry and carbon cycle. It is a newcomer in the international modeling community to provide sufficiently independent solutions of climate simulations from those of other models. Performances of the model in simulating the basic states of the radiation budget of the atmosphere and ocean, precipitation, circulations, variabilities, and the twentieth century warming are presented. Model biases and their possible causes are discussed. Strength includes horizontal heat transport in the atmosphere and oceans, vertical profile of the Atlantic Meridional Overturning Circulation; weakness includes the double intertropical convergence zone (ITCZ) and stronger amplitude of the El Niño–Southern Oscillation (ENSO) that are also common in many other models. The simulated the twentieth century warming shares a similar discrepancy with observations as in several other models—less warming in the 1920s and stronger cooling in the 1960s than in observation—at the time when there was a steep increase of anthropogenic aerosols. As a result, the twentieth century warming is about 60% of the observed warming despite that the model simulated a similar slope of warming trend after 1980 to observation. The model has an equilibrium climate sensitivity of 3.4 K with a positive cloud feedback from the shortwave radiation.

Lotus seed epicarp, a byproduct of lotus, is commonly discarded directly or burned in the cropland, resulting in waste of resources and environmental pollution. In this work, a green ultrasonic-assisted extraction method with ethyl lactate as the extraction solvent was established to extract alkaloids from lotus seed epicarp. The extraction conditions were optimized by response surface methodology. Under the optimal extraction conditions, the extraction of alkaloids from 1 g lotus seed epicarp was accomplished with only 10 mL of extraction solvent within 15 min. Combined with ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry with information-dependent acquisition mode, a total of 42 alkaloids were annotated in the lotus seed epicarp extracts. Among them, 39 alkaloids were reported in lotus seed epicarp for the first time. According to quantitative analysis, the distributions and trends of alkaloids in the lotus seed epicarp were found to be similar to those of lotus leaves. The five growth stages of lotus seed epicarp could be successfully distinguished based on the ten representative alkaloids. This study demonstrates that ultrasonic-assisted extraction with ethyl lactate as extractant solvent was efficient in the extraction of alkaloids from lotus seed epicarp, which is a potential renewable resource of bioactive ingredients.

Abstract Orographic drag is an essential process for numerical weather predictions in complex terrain regions, which depends on the inflow direction. In this study, we define the orographic asymmetry vector (OAV) for a coarse grid as the normalized vector between the grid's center point and its center of mass, and the orographic asymmetry in a flow direction—which describes the inclination direction and the extent of the subgrid terrain—is calculated as the projection of OAV on this direction. Calculations of the effective orographic length (OL) and the model grid length λ are extended to all flow directions. A new orographic drag scheme, which considers the effect of orographic anisotropy in all directions, is then developed based on the OAV projection and the extended OL and λ for any given direction. Sensitivity tests of the orographic drag under the new scheme are conducted using a 5 m/s vertically uniform wind along different directions for four coarse grid points in typical mountain regions. The new scheme is shown to provide a more continuous transition of the orographic parameters and the resulting stress as a function of flow direction than piecewise transition of schemes with only eight directions. The predicted momentum flux profile of the new scheme is compared with mountain‐wave simulations obtained from the integrated modeling system IAP‐WRF (Institute of Atmospheric Physics‐Weather Research and Forecasting Model) for the Rocky Mountain. The new scheme is shown to predict an overall narrower stress scatter about the reference simulation than the old scheme.

Summary: Protein drugs own a large share in the market and hold great prospects for the treatment of many diseases. However, the available protein drugs are limited to the extracellular target, owing to the inefficient transduction and activity modulation of proteins targeting intracellular environment. In this study, we constructed ATP-charged platforms to overcome the above-mentioned barriers for cancer theranostics. The phenylboronic acid-modified polycations (PCD) were synthesized to assemble with enzymes and shield its activity in the blood circulation. When the PCD nanoclusters reached tumor site, they effectively transported the enzymes into the cells, followed by recovering its catalytic activity after being charged with ATP. Importantly, the cascaded enzyme systems (GOx&HRPA) selectively induced starvation therapy as well as photoacoustic imaging of tumor. Our results revealed that the intelligent nanoclusters were broadly applicable for protein transduction and enzyme activity modulation, which could accelerate the clinical translation of protein drugs toward intracellular target. : Medical Imaging; Catalysis; Cancer; Nanostructure Subject Areas: Medical Imaging, Catalysis, Cancer, Nanostructure

A challenging and attractive task in computer vision is underwater object detection. Although object detection techniques have achieved good performance in general datasets, problems of low visibility and color bias in the complex underwater environment have led to generally poor image quality; besides this, problems with small targets and target aggregation have led to less extractable information, which makes it difficult to achieve satisfactory results. In past research of underwater object detection based on deep learning, most studies have mainly focused on improving detection accuracy by using large networks; the problem of marine underwater lightweight object detection has rarely gotten attention, which has resulted in a large model size and slow detection speed; as such the application of object detection technologies under marine environments needs better real-time and lightweight performance. In view of this, a lightweight underwater object detection method based on the MobileNet v2, You Only Look Once (YOLO) v4 algorithm and attentional feature fusion has been proposed to address this problem, to produce a harmonious balance between accuracy and speediness for target detection in marine environments. In our work, a combination of MobileNet v2 and depth-wise separable convolution is proposed to reduce the number of model parameters and the size of the model. The Modified Attentional Feature Fusion (AFFM) module aims to better fuse semantic and scale-inconsistent features and to improve accuracy. Experiments indicate that the proposed method obtained a mean average precision (mAP) of 81.67% and 92.65% on the PASCAL VOC dataset and the brackish dataset, respectively, and reached a processing speed of 44.22 frame per second (FPS) on the brackish dataset. Moreover, the number of model parameters and the model size were compressed to 16.76% and 19.53% of YOLO v4, respectively, which achieved a good tradeoff between time and accuracy for underwater object detection.

In hyperspectral image (HSI) classification, convolutional neural networks (CNN) have been attracting increasing attention because of their ability to represent spectral-spatial features. Nevertheless, the conventional CNN models perform convolution operation on regular-grid image regions with a fixed kernel size and as a result, they neglect the inherent relation between HSI data. In recent years, graph convolutional networks (GCN) used for data representation in a non-Euclidean space, have been successfully applied to HSI classification. However, conventional GCN methods suffer from a huge computational cost since they construct the adjacency matrix between all HSI pixels, and they ignore the local spatial context information of hyperspectral images. To alleviate these shortcomings, we propose a novel method termed spectral-spatial offset graph convolutional networks (SSOGCN). Different from the usually used GCN models that compute the adjacency matrix between all pixels, we construct an adjacency matrix only using pixels within a patch, which contains rich local spatial context information, while reducing the computation cost and memory consumption of the adjacency matrix. Moreover, to emphasize important local spatial information, an offset graph convolution module is proposed to extract more robust features and improve the classification performance. Comprehensive experiments are carried out on three representative benchmark data sets, and the experimental results effectively certify that the proposed SSOGCN method has more advantages than the recent state-of-the-art (SOTA) methods.

In order to reduce the viscosity of heavy oil, the performance of emulsifying viscosity reducers is limited. In this study, a new kind of amphiphilic low molecular weight viscosity reducer was prepared by emulsion copolymerization of acrylamide (AM), acrylic acid (AA), 2-acrylamido-2-methylpropanesulfonic acid (AMPS), and Butene benzene (PB). The synthesis feasibility and viscosity reduction mechanism of viscosity reducer in heavy oil were explored using Materials Studio software from the perspective of molecular dynamics. The results of the molecular dynamics simulation revealed that the addition of viscosity reducer into heavy oil varied the potential energy, non-potential energy, density and hydrogen bond distribution of heavy oil. Benefiting from its structure, the benzene ring in PB was well embedded in the interlayer structure of asphaltene, contributing to weaken the network structure of the heavy oil. Moreover, the two strong polar groups (COO− and SO3−) of AA and AMPS, which constituted the branched chains of the viscosity reducer’s molecular structure, gradually disassembled the network structure from the ‘inward’ to the ‘outward’ of the heavy oil network structure, thereby driving heavy oil viscosity reduction (as clarified by molecular dynamics). Owing to its temperature resistance, this kind of new amphiphilic low molecular copolymer could be an effective viscosity reducer for heavy oil cold recovery at elevated temperatures.

Climate models repeatedly show a warm and dry bias over the central United States, but the origin of this bias remains unclear. Here the authors associate this bias to precipitation deficits in models and after applying a correction, projected precipitation in this region shows no significant changes.