Nb2AlC MAX Nanosheets Supported Ru Nanocrystals as Efficient Catalysts for Boosting pH-Universal Hydrogen Production.

MAX phase combines both ceramic and metallic properties, which exhibits widespread application prospects. 2D MAX nanosheets have more abundant surface-active sites, being anticipated to improve the performance of surface-related applications. Herein, for the first time, 2D Nb2AlC nanosheets (NSs) as novel supports anchored with Ru catalysts for overall water splitting are developed. The optimized catalyst of Ru@Nb2AlC NSs exhibit Pt-comparable kinetics and superior catalytic activity toward hydrogen evolution reaction (HER) (low overpotentials of 61 and 169 mV at 10 and 100 mA cm-2, respectively) with excellent durability (5000 cycles or 80 h) in alkaline media. In particular, Ru@Nb2AlC NSs achieve a mass activity of ≈4.8 times larger than the commercial Pt/C (20 wt.%) catalyst. The post-oxidation resultant catalyst of RuO2@Nb2AlC NSs also exhibit boosting HER and oxygen evolution reaction activities and ≈100% Faraday efficiency for overall water splitting with a cell voltage of 1.61 V to achieve 10 mA cm-2. Therefore, the novel category of 2D MAX supports anchored with Ru nanocrystals offers a novel strategy for designing a wide range of MAX-supported metal catalysts for the renewable energy field.

Modulation of Buried Interface by 1-(3-aminopropyl)-Imidazole for Efficient Inverted Formamidinium-Cesium Perovskite Solar Cells.

Considering the direct influence of substrate surface nature on perovskite (PVK) film growth, buried interfacial engineering is crucial to obtain ideal perovskite solar cells (PSCs). Herein, 1-(3-aminopropyl)-imidazole (API) is introduced at polytriarylamine (PTAA)/PVK interface to modulate the bottom property of PVK. First, the introduction of API improves the growth of PVK grains and reduces the Pb2+ defects and residual PbI2 present at the bottom of the film, contributing to the acquisition of high-quality PVK film. Besides, the presence of API can optimize the energy structure between PVK and PTAA, which facilitates the interfacial charge transfer. Density functional theory (DFT) reveals that the electron donor unit (R-C ═ N) of the API prefers to bind with Pb2+ traps at the PVK interface, while the formation of hydrogen bonds between the R-NH2 of API and I- strengthens the above binding ability. Consequently, the optimum API-treated inverted formamidinium-cesium (FA/Cs) PSCs yields a champion power conversion efficiency (PCE) of 22.02% and exhibited favorable stability.

Asiatic acid inhibits osteosarcoma cell migration and invasion via the AKT/Sp1/MMP1 axis.

Osteosarcoma is a malignant bone tumor affecting adolescents and children. No effective treatment is currently available. Asiatic acid (AA), a triterpenoid compound found in Centella asiatica, possesses anti-tumor, anti-inflammatory, and anti-oxidant properties in various types of tumor cells. This study aims to determine whether AA exerts antitumor effects in human osteosarcoma cells. Our results indicate that AA does not influence the viability, proliferative rate, or cell cycle phase of human osteosarcoma cells under non-toxic conditions. AA suppressed osteosarcoma cell migration and invasion by down-regulating matrix metalloproteinase 1 (MMP1) expression. Data in the TNMplot database suggested MMP1 expression was higher in osteosarcoma than in normal tissues, with associated clinical significance observed in osteosarcoma patients. Overexpression of MMP1 in osteosarcoma cells reversed the AA-induced suppression of cell migration and invasion. AA treatment decreased the expression of specificity protein 1 (Sp1), while Sp1 overexpression abolished the effect of AA on MMP1 expression and cell migration and invasion. AA inhibited AKT phosphorylation, and treatment with a PI3K inhibitor (wortmannin) increased the anti-invasive effect of AA on osteosarcoma cells via the p-AKT/Sp1/MMP1 axis. Thus, AA exhibits the potential for use as an anticancer drug against human osteosarcoma.

Licochalcone A induces endoplasmic reticulum stress-mediated apoptosis of endometrial cancer cells via upregulation of GRP78 expression.

Licochalcone A (LicA), a natural compound extracted from licorice root, has been shown to exert a variety of anticancer activities. Whether LicA has such effects on endometrial cancer (EMC) is unclear. This study aims to investigate the antitumor effects of LicA on EMC. Our results show that LicA significantly reduced the viability and induced apoptosis of EMC cells and EMC-7 cells from EMC patients. LicA was also found to induce endoplasmic reticulum (ER) stress, leading to increased expression of ER-related proteins (GRP78/PERK/IRE1α/CHOP) in EMC cell lines. Suppression of GRP78 expression in human EMC cells treated with LicA significantly attenuated the effects of LicA, resulting in reduced ER-stress mediated cell apoptosis and decreased expression of ER- and apoptosis-related proteins. Our findings demonstrate that LicA induces apoptosis in EMC cells through the GRP78-mediated ER-stress pathway, emphasizing the potential of LicA as an anticancer therapy for EMC.

An NMPC-Based Integrated Longitudinal and Lateral Vehicle Stability Control Based on the Double-Layer Torque Distribution.

With the ongoing promotion and adoption of electric vehicles, intelligent and connected technologies have been continuously advancing. Electrical control systems implemented in electric vehicles have emerged as a critical research direction. Various drive-by-wire chassis systems, including drive-by-wire driving and braking systems and steer-by-wire systems, are extensively employed in vehicles. Concurrently, unavoidable issues such as conflicting control system objectives and execution system interference emerge, positioning integrated chassis control as an effective solution to these challenges. This paper proposes a model predictive control-based longitudinal dynamics integrated chassis control system for pure electric commercial vehicles equipped with electro-mechanical brake (EMB) systems, centralized drive, and distributed braking. This system integrates acceleration slip regulation (ASR), a braking force distribution system, an anti-lock braking system (ABS), and a direct yaw moment control system (DYC). This paper first analyzes and models the key components of the vehicle. Then, based on model predictive control (MPC), it develops a controller model for integrated stability with double-layer torque distribution. The required driving and braking torque for each wheel are calculated according to the actual and desired motion states of the vehicle and applied to the corresponding actuators. Finally, the effectiveness of this strategy is verified through simulation results from Matlab/Simulink. The simulation shows that the braking deceleration of the braking condition is increased by 32% on average, and the braking distance is reduced by 15%. The driving condition can enter the smooth driving faster, and the time is reduced by 1.5 s~5 s. The lateral stability parameters are also very much improved compared with the uncontrolled vehicles.

A Vision/Inertial Navigation/Global Navigation Satellite Integrated System for Relative and Absolute Localization in Land Vehicles.

This paper presents an enhanced ground vehicle localization method designed to address the challenges associated with state estimation for autonomous vehicles operating in diverse environments. The focus is specifically on the precise localization of position and orientation in both local and global coordinate systems. The proposed approach integrates local estimates generated by existing visual-inertial odometry (VIO) methods into global position information obtained from the Global Navigation Satellite System (GNSS). This integration is achieved through optimizing fusion in a pose graph, ensuring precise local estimation and drift-free global position estimation. Considering the inherent complexities in autonomous driving scenarios, such as the potential failures of a visual-inertial navigation system (VINS) and restrictions on GNSS signals in urban canyons, leading to disruptions in localization outcomes, we introduce an adaptive fusion mechanism. This mechanism allows seamless switching between three modes: utilizing only VINS, using only GNSS, and normal fusion. The effectiveness of the proposed algorithm is demonstrated through rigorous testing in the Carla simulation environment and challenging UrbanNav scenarios. The evaluation includes both qualitative and quantitative analyses, revealing that the method exhibits robustness and accuracy.

Intelligent Vehicle Path Planning Based on Optimized A* Algorithm.

With the rapid development of the intelligent driving technology, achieving accurate path planning for unmanned vehicles has become increasingly crucial. However, path planning algorithms face challenges when dealing with complex and ever-changing road conditions. In this paper, aiming at improving the accuracy and robustness of the generated path, a global programming algorithm based on optimization is proposed, while maintaining the efficiency of the traditional A* algorithm. Firstly, turning penalty function and obstacle raster coefficient are integrated into the search cost function to increase the adaptability and directionality of the search path to the map. Secondly, an efficient search strategy is proposed to solve the problem that trajectories will pass through sparse obstacles while reducing spatial complexity. Thirdly, a redundant node elimination strategy based on discrete smoothing optimization effectively reduces the total length of control points and paths, and greatly reduces the difficulty of subsequent trajectory optimization. Finally, the simulation results, based on real map rasterization, highlight the advanced performance of the path planning and the comparison among the baselines and the proposed strategy showcases that the optimized A* algorithm significantly enhances the security and rationality of the planned path. Notably, it reduces the number of traversed nodes by 84%, the total turning angle by 39%, and shortens the overall path length to a certain extent.

RBS and ABS Coordinated Control Strategy Based on Explicit Model Predictive Control.

During the braking process of electric vehicles, both the regenerative braking system (RBS) and anti-lock braking system (ABS) modulate the hydraulic braking force, leading to control conflict that impacts the effectiveness and real-time capability of coordinated control. Aiming to enhance the coordinated control effectiveness of RBS and ABS within the electro-hydraulic composite braking system, this paper proposes a coordinated control strategy based on explicit model predictive control (eMPC-CCS). Initially, a comprehensive braking control framework is established, combining offline adaptive control law generation, online optimized control law application, and state compensation to effectively coordinate braking force through the electro-hydraulic system. During offline processing, eMPC generates a real-time-oriented state feedback control law based on real-world micro trip segments, improving the adaptiveness of the braking strategy across different driving conditions. In the online implementation, the developed three-dimensional eMPC control laws, corresponding to current driving conditions, are invoked, thereby enhancing the potential for real-time braking strategy implementation. Moreover, the state error compensator is integrated into eMPC-CCS, yielding a state gain matrix that optimizes the vehicle braking status and ensures robustness across diverse braking conditions. Lastly, simulation evaluation and hardware-in-the-loop (HIL) testing manifest that the proposed eMPC-CCS effectively coordinates the regenerative and hydraulic braking systems, outperforming other CCSs in terms of braking energy recovery and real-time capability.

Tunable Structural Coloration in Eccentric Water-in-Oil-in-Water Droplets.

Structural colors show diverse advantages such as fade resistance, eco-friendliness, iridescence, and high saturation in comparison with chemical pigments. In this paper, we show tunable structural coloration in colorless water-in-oil-in-water double emulsion droplets via total internal reflection and interference at the microscale concave interfaces. Through experimental work and simulations, we demonstrate that the shell thickness and the eccentricity of the core-shell structures are key to the successful formation of iridescent structural colors. Only eccentric thin-shell water-in-oil-in-water droplets show structural colors. Importantly, structural colors based on water-oil interfaces are readily responsive to a variety of environmental stimuli, such as osmotic pressure, temperature, magnetic fields, and light composition. This work highlights an alternative structural coloration that expands the applications of droplet-based structural colors to aqueous systems.

Controllable Fabrication of Highly Uniform Sub-10 nm Nanoparticles from Spontaneous Confined Nanoemulsification.

Sub-10 nm nanoparticles are known to exhibit extraordinary size-dependent properties for wide applications. Many approaches have been developed for synthesizing sub-10 nm inorganic nanoparticles, but the fabrication of sub-10 nm polymeric nanoparticles is still challenging. Here, a scalable, spontaneous confined nanoemulsification strategy that produces uniform sub-10 nm nanodroplets for template synthesis of sub-10 nm polymeric nanoparticles is proposed. This strategy introduces a high-concentration interfacial reaction to create overpopulated surfactants that are insoluble at the droplet surface. These overpopulated surfactants act as barriers, resulting in highly accumulated surfactants inside the droplet via a confined reaction. These surfactants exhibit significantly changed packing geometry, solubility, and interfacial activity to enhance the molecular-level impact on interfacial instability for creating sub-10 nm nanoemulsions via self-burst nanoemulsification. Using the nanodroplets as templates, the fabrication of uniform sub-10 nm polymeric nanoparticles, as small as 3.5 nm, made from biocompatible polymers and capable of efficient drug encapsulation is demonstrated. This work opens up brand-new opportunities to easily create sub-10 nm nanoemulsions and advanced ultrasmall functional nanoparticles.

Comparison of host cell protein removal by depth filters with diatomaceous earth and synthetic silica filter aids using model proteins.

A number of studies have demonstrated that depth filtration can provide significant adsorptive removal of host cell proteins (HCP), but there is still considerable uncertainty regarding the underlying factors controlling HCP binding. This study compared the binding characteristics of two fine grade depth filters, the X0SP (polyacrylic fiber with a synthetic silica filter aid) and X0HC (cellulose fibers with diatomaceous earth (DE) as a filter aid), using a series of model proteins with well-defined physical characteristics. Protein binding to the X0SP filter was dominated by electrostatic interactions with greatest capacity for positively-charged proteins. In contrast, the X0HC filter showed greater binding of more hydrophobic proteins although electrostatic interactions also played a role. In addition, ovotransferrin showed unusually high binding capacity to the X0HC, likely due to interactions with metals in the DE. Scanning Electron Microscopy with Energy Dispersive Spectroscopy was used to obtain additional understanding of the binding behavior. These results provide important insights into the physical phenomena governing HCP binding to both fully synthetic and natural (cellulose + DE) depth filters.

Adaptive Cruise System Based on Fuzzy MPC and Machine Learning State Observer.

Under the trend of vehicle intelligentization, many electrical control functions and control methods have been proposed to improve vehicle comfort and safety, among which the Adaptive Cruise Control (ACC) system is a typical example. However, the tracking performance, comfort and control robustness of the ACC system need more attention under uncertain environments and changing motion states. Therefore, this paper proposes a hierarchical control strategy, including a dynamic normal wheel load observer, a Fuzzy Model Predictive Controller and an integral-separate PID executive layer controller. Firstly, a deep learning-based dynamic normal wheel load observer is added to the perception layer of the conventional ACC system and the observer output is used as a prerequisite for brake torque allocation. Secondly, a Fuzzy Model Predictive Control (fuzzy-MPC) method is adopted in the ACC system controller design, which establishes performance indicators, including tracking performance and comfort, as objective functions, dynamically adjusts their weights and determines constraint conditions based on safety indicators to adapt to continuously changing driving scenarios. Finally, the executive controller adopts the integral-separate PID method to follow the vehicle's longitudinal motion commands, thus improving the system's response speed and execution accuracy. A rule-based ABS control method was also developed to further improve the driving safety of vehicles under different road conditions. The proposed strategy has been simulated and validated in different typical driving scenarios and the results show that the proposed method provides better tracking accuracy and stability than traditional techniques.

Intelligent Vehicle Decision-Making and Trajectory Planning Method Based on Deep Reinforcement Learning in the Frenet Space.

The complexity inherent in navigating intricate traffic environments poses substantial hurdles for intelligent driving technology. The continual progress in mapping and sensor technologies has equipped vehicles with the capability to intricately perceive their exact position and the intricate interplay among surrounding traffic elements. Building upon this foundation, this paper introduces a deep reinforcement learning method to solve the decision-making and trajectory planning problem of intelligent vehicles. The method employs a deep learning framework for feature extraction, utilizing a grid map generated from a blend of static environmental markers such as road centerlines and lane demarcations, in addition to dynamic environmental cues including vehicle positions across varied lanes, all harmonized within the Frenet coordinate system. The grid map serves as the input for the state space, and the input for the action space comprises a vector encompassing lane change timing, velocity, and vertical displacement at the lane change endpoint. To optimize the action strategy, a reinforcement learning approach is employed. The feasibility, stability, and efficiency of the proposed method are substantiated via experiments conducted in the CARLA simulator across diverse driving scenarios, and the proposed method can increase the average success rate of lane change by 6.8% and 13.1% compared with the traditional planning control algorithm and the simple reinforcement learning method.

A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs.

The mechanical coupling of multiple powertrain components makes the energy management of 4-wheel-drive (4WD) plug-in fuel cell electric vehicles (PFCEVs) relatively complex. Optimizing energy management strategies (EMSs) for this complex system is essential, aiming at improving the vehicle economy and the adaptability of operating conditions. Accordingly, a novel adaptive equivalent consumption minimization strategy (A-ECMS) based on the dragonfly algorithm (DA) is proposed to achieve coordinated control of the powertrain components, front and rear motors, as well as the fuel cell system and the battery. To begin with, the equivalent consumption minimization strategy (ECMS) with extraordinary instantaneous optimization ability is used to distribute the vehicle demand power into the front and rear motor power, considering the different motor characteristics. Subsequently, under the proposed novel hierarchical energy management framework, the well-designed A-ECMS based on DA empowers PFCEVs with significant energy-saving advantages and adaptability to operating conditions, which are achieved by precise power distribution considering the operating characteristics of the fuel cell system and battery. These provide state-of-the-art energy-saving abilities for the multi-degree-of-freedom systems of PFCEVs. Lastly, a series of detailed evaluations are performed through simulations to validate the improved performance of A-ECMS. The corresponding results highlight the optimal control performance in the energy-saving performance of A-ECMS.

H19 Promotes HCC Bone Metastasis Through Reducing Osteoprotegerin Expression in a Protein Phosphatase 1 Catalytic Subunit Alpha/p38 Mitogen-Activated Protein Kinase-Dependent Manner and Sponging microRNA 200b-3p.

BACKGROUND AND AIMS: Bone is the second most frequent site of metastasis for HCC, which leads to an extremely poor prognosis. HCC bone metastasis is typically osteolytic, involving the activation of osteoclasts. Long noncoding RNA H19 plays an important role in the pathogenesis of human cancers. Nonetheless, the mechanism underlying the participation of H19 in HCC bone metastasis remains unclear. APPROACH AND RESULTS: The current study established a mouse HCC bone metastasis model by using serial intracardiac injection and cell isolation to obtain cells with distinct bone metastasis ability. H19 was highly expressed in these cells and in clinical HCC bone metastasis specimens. Both osteoclastogenesis in vitro and HCC bone metastasis in vivo were promoted by H19 overexpression, whereas these processes were suppressed by H19 knockdown. H19 overexpression attenuated p38 phosphorylation and further down-regulated the expression of osteoprotegerin (OPG), also known as osteoclastogenesis inhibitory factor. However, up-regulated OPG expression as well as suppressed osteoclastogenesis caused by H19 knockdown were recovered by p38 interference, indicating that p38 mitogen-activated protein kinase (MAPK)-OPG contributed to H19-promoted HCC bone metastasis. Furthermore, we demonstrated that H19 inhibited the expression of OPG by binding with protein phosphatase 1 catalytic subunit alpha (PPP1CA), which dephosphorylates p38. SB-203580-mediated inactivation of p38MAPK reversed the down-regulation of HCC bone metastasis caused by H19 knockdown in vivo. Additionally, H19 enhanced cell migration and invasion by up-regulating zinc finger E-box binding homeobox 1 through the sequestration of microRNA (miR) 200b-3p. CONCLUSIONS: H19 plays a critical role in HCC bone metastasis by reducing OPG expression, which is mediated by the PPP1CA-induced inactivation of the p38MAPK pathway; and H19 also functions as a sponge for miR-200b-3p.

Exploration of the Adsorption Kinetics of Surfactants at the Water-Oil Interface via Grand-Canonical Molecular Dynamics Simulations.

It is well-known that surfactants tend to aggregate into clusters or micelles in aqueous solutions due to their special structures, and it is difficult for the surfactant molecules involved in the aggregation to move spontaneously to the oil-water interface. In this article, we developed a new grand-canonical molecular dynamics (GCMD) model to predict the saturated adsorption amount of surfactant with constant concentration of surfactant molecules in the bulk phase, which can prevent surfactants aggregating in the bulk phase and get the atomic details of the interfacial structural change with increase of the adsorption amount through a single GCMD run. The adsorption of anionic surfactant sodium dodecyl sulfate (SDS) at the heptane-water interface was studied to validate the model. The saturated adsorption amount obtained from the GCMD simulation is consistent with the experimental results. The adsorption kinetics of SDS molecules during the simulation can be divided into three stages: linear adsorption stage, transition adsorption stage, and dynamic equilibrium stage. We also carried out equilibrium molecular dynamics (EMD) simulations to compare with GCMD simulation. This GCMD model can effectively reduce the simulation time with correct prediction of the interfacial saturation adsorption. We believe the GCMD method could be especially helpful for the computational study of surfactant adsorption under complex environments or emulsion systems with the adsorption of multiple types of surfactants.

Pseudo Polyampholytes with Sensitively Ion-Responsive Conformational Transition Based on Positively Charged Host-Guest Complexes.

Biological polyampholytes are ubiquitous in living organisms with primary functions including serving as transporters for moving chemical molecular species across the cell membranes. Synthetic amphoteric macromolecules that can change their phase states depending on the environment to simulate some properties of natural polyampholytes are of great interest. Here, the implementation of synthetic pseudo polymeric ampholytes is explored with ion-recognition-triggered conformational change. The phase transition behaviors of the ion-recognition-creative polyampholytes that contain deprotonated carboxylic acid groups as negative charges and 18-crown-6 units for forming positively charged host-guest complexes are systematically investigated. The ion-recognition-triggered phase transition behaviors of pseudo polyampholytes significantly depend on cation species and concentrations. Only those specific ions such as K+ , Ba2+ , Sr2+ and Pb2+ ions that can form 1:1 host-guest complexes with 18-crown-6 units in polymers enable control over conformational change like that of traditional pH-dependent polyampholytes. By regulating the content of carboxylic acid groups to match the content of ion-recognized positive charges provided by the host-guest complexes, the pseudo polyampholytes are more sensitive to the recognizable cations. Such ion-recognition-triggered amphoteric characteristics make the pseudo polyampholytes act like biological proteins, nucleic acids, and enzymes as molecular transporters, genetic code storage, and biocatalysts in artificial systems.

Hsa-microRNA-27b-3p inhibits hepatocellular carcinoma progression by inactivating transforming growth factor-activated kinase-binding protein 3/nuclear factor kappa B signalling.

BACKGROUND: MicroRNAs (miRNAs) play crucial roles in the development of hepatocellular carcinoma (HCC). Hsa-microRNA-27b-3p (hsa-miR-27b) is involved in the formation and progression of various cancers, but its role and clinical value in HCC remain unclear. METHODS: The expression of hsa-miR-27b in HCC was examined by quantitative real-time PCR (qRT-PCR) and in situ hybridization (ISH) assays of clinical samples. Cell Counting Kit-8 assays (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU) incorporation assays, Transwell assays, filamentous actin (F-actin) staining and western blot analyses were used to determine the effects of hsa-miR-27b on HCC cells in vitro. Subcutaneous xenograft and lung metastatic animal experiments were conducted to verify the role of hsa-miR-27b in HCC in vivo. In silico prediction, qRT-PCR, western blot, anti-Argonaute 2 (AGO2) RNA immunoprecipitation (RIP) and dual luciferase reporter assays were applied to identify the target genes of hsa-miR-27b. To detect the impacts of hsa-miR-27b on nuclear factor kappa B (NF-кB) signalling cascades mediated by transforming growth factor-activated kinase-binding protein 3 (TAB3), we performed qRT-PCR, western blot assays, immunofluorescence staining, immunohistochemistry (IHC) and dual-luciferase reporter assays. Recombinant oncolytic adenovirus (OncoAd) overexpressing hsa-miR-27b was constructed to detect their therapeutic value in HCC. RESULTS: The expression of hsa-miR-27b was lower in HCC than in adjacent non-tumourous tissues (ANTs), and the reduced expression of hsa-miR-27b was associated with worse outcomes in patients with HCC. Hsa-miR-27b significantly inhibited the proliferation, migration, invasion, subcutaneous tumour growth and lung metastasis of HCC cells. The suppression of hsa-miR-27b promoted the nuclear translocation of NF-κB by upregulating TAB3 expression. TAB3 was highly expressed in HCC compared with ANTs and was negatively correlated with the expression of hsa-miR-27b. The impaired cell proliferation, migration and invasion by hsa-miR-27b overexpression were recovered by ectopic expression of TAB3. Recombinant OncoAd with overexpression of hsa-miR-27b induced anti-tumour activity compared with that induced by negative control (NC) OncoAd in vivo and in vitro. CONCLUSIONS: By targeting TAB3, hsa-miR-27b acted as a tumour suppressor by inactivating the NF-кB pathway in HCC in vitro and in vivo, indicating its therapeutic value against HCC.

DrLLPS: a data resource of liquid-liquid phase separation in eukaryotes.

Here, we presented an integrative database named DrLLPS (http://llps.biocuckoo.cn/) for proteins involved in liquid-liquid phase separation (LLPS), which is a ubiquitous and crucial mechanism for spatiotemporal organization of various biochemical reactions, by creating membraneless organelles (MLOs) in eukaryotic cells. From the literature, we manually collected 150 scaffold proteins that are drivers of LLPS, 987 regulators that contribute in modulating LLPS, and 8148 potential client proteins that might be dispensable for the formation of MLOs, which were then categorized into 40 biomolecular condensates. We searched potential orthologs of these known proteins, and in total DrLLPS contained 437 887 known and potential LLPS-associated proteins in 164 eukaryotes. Furthermore, we carefully annotated LLPS-associated proteins in eight model organisms, by using the knowledge integrated from 110 widely used resources that covered 16 aspects, including protein disordered regions, domain annotations, post-translational modifications (PTMs), genetic variations, cancer mutations, molecular interactions, disease-associated information, drug-target relations, physicochemical property, protein functional annotations, protein expressions/proteomics, protein 3D structures, subcellular localizations, mRNA expressions, DNA & RNA elements, and DNA methylations. We anticipate DrLLPS can serve as a helpful resource for further analysis of LLPS.

SGGformer: Shifted Graph Convolutional Graph-Transformer for Traffic Prediction.

Accurate traffic prediction is significant in intelligent cities' safe and stable development. However, due to the complex spatiotemporal correlation of traffic flow data, establishing an accurate traffic prediction model is still challenging. Aiming to meet the challenge, this paper proposes SGGformer, an advanced traffic grade prediction model which combines a shifted window operation, a multi-channel graph convolution network, and a graph Transformer network. Firstly, the shifted window operation is used for coarsening the time series data, thus, the computational complexity can be reduced. Then, a multi-channel graph convolutional network is adopted to capture and aggregate the spatial correlations of the roads in multiple dimensions. Finally, the improved graph Transformer based on the advanced Transformer model is proposed to extract the long-term temporal correlation of traffic data effectively. The prediction performance is evaluated by using actual traffic datasets, and the test results show that the SGGformer proposed exceeds the state-of-the-art baseline.