Proton-Driven Dynamic Behavior of Nanoconfined Water in Hydrophilic MXene Sheets.

Liquid water under nanoscale confinement has attracted intensive attention due to its pivotal role in understanding various phenomena across many scientific fields. MXenes serve an ideal paradigm for investigating the dynamic behaviors of nanoconfined water in a hydrophilic environment. Combining deep neural networks and an active learning scheme, here we elucidate the proton-driven dynamics of water molecules confined between V2CTx sheets using molecular dynamics simulation. Firstly, we have found that the Eigen and Zundel cations can inhibit water-induced oxidation by adjusting the orientation of water molecules, thus proposing a general antioxidant strategy. Besides, we also identified a hexagonal ice phase with abnormal bonding rules at room temperature, rather than only at ultralow temperatures as other studies reported, and further captured the proton-induced water phase transition. This highlighted the importance of protons in the maintaining stable crystal phase and phase transition of water. Furthermore, we discussed the conversions of different water structures and water diffusivity with changing proton concentrations in detail. The results provide useful guidance in practical applications of MXenes including developing antioxidant strategies, identifying novel 2D water phases and optimizing energy storage and conversion.

Rupatadine-inhibited OTUD3 promotes DLBCL progression and immune evasion through deubiquitinating MYL12A and PD-L1.

The obstacle to effectively treating Diffuse Large B-cell Lymphoma (DLBCL) lies in the resistance observed toward standard therapies. Identifying therapeutic targets that prove effective for relapsed or refractory patients poses a significant challenge. OTUD3, a deubiquitinase enzyme, is overexpressed in DLBCL tissues. However, its role in DLBCL has not been investigated. Our study has brought to light the multifaceted impact of OTUD3 in DLBCL. Not only does it enhance cell survival through the deubiquitination of MYL12A, but it also induces CD8+ T cell exhaustion within the local environment by deubiquitinating PD-L1. Our findings indicate that the OTUD3 inhibitor, Rupatadine, exerts its influence through competitive binding with OTUD3. This operation diminishes the deubiquitination of both MYL12A and PD-L1 by OTUD3. This research unveils the central and oncogenic role of OTUD3 in DLBCL and highlights the potential clinical application value of the OTUD3 inhibitor, Rupatadine. These findings contribute valuable insights into addressing the challenges of resistant DLBCL cases and offer a promising avenue for further clinical exploration.

Early warning and proactive control strategies for power blackouts caused by gas network malfunctions.

There is growing consensus that gas-fired generators will play a crucial role during the transition to net-zero energy systems, both as an alternative to coal-fired generators and as a flexibility service provider for power systems. However, malfunctions of gas networks have caused several large-scale power blackouts. The transition from coal and oil to gas fuels significantly increases the interdependence between gas networks and electric power systems, raising the risks of more frequent and widespread power blackouts due to the malfunction of gas networks. In a coupled gas-electricity system, the identification and transmission of gas network malfunction information, followed by the redispatch of electric power generation, occur notably faster than the propagation and escalation of the malfunction itself, e.g., significantly diminished pressure. On this basis, we propose a gas-electric early warning system that can reduce the negative impacts of gas network malfunctions on the power system. A proactive control strategy of the power system is also formulated based on the early warning indicators. The effectiveness of this method is demonstrated via case studies of a real coupled gas-electricity system in China.

Multi-resource dynamic coordinated planning of flexible distribution network.

The flexible distribution network presents a promising architecture to accommodate highly integrated distributed generators and increasing loads in an efficient and cost-effective way. The distribution network is characterised by flexible interconnections and expansions based on soft open points, which enables it to dispatch power flow over the entire system with enhanced controllability and compatibility. Herein, we propose a multi-resource dynamic coordinated planning method of flexible distribution network that allows allocation strategies to be determined over a long-term planning period. Additionally, we establish a probabilistic framework to address source-load uncertainties, which mitigates the security risks of voltage violations and line overloads. A practical distribution network is adopted for flexible upgrading based on soft open points, and its cost benefits are evaluated and compared with that of traditional planning approaches. By adjusting the acceptable violation probability in chance constraints, a trade-off between investment efficiency and operational security can be realised.

Rolapitant treats lung cancer by targeting deubiquitinase OTUD3.

BACKGROUND: Lung cancer is cancer with the highest morbidity and mortality in the world and poses a serious threat to human health. Therefore, discovering new treatments is urgently needed to improve lung cancer prognosis. Small molecule inhibitors targeting the ubiquitin-proteasome system have achieved great success, in which deubiquitinase inhibitors have broad clinical applications. The deubiquitylase OTUD3 was reported to promote lung tumorigenesis by stabilizing oncoprotein GRP78, implying that inhibition of OTUD3 may be a therapeutic strategy for lung cancer. RESULTS: In this study, we identified a small molecule inhibitor of OTUD3, Rolapitant, by computer-aided virtual screening and biological experimental verification from FDA-approved drugs library. Rolapitant inhibited the proliferation of lung cancer cells by inhibiting deubiquitinating activity of OTUD3. Quantitative proteomic profiling indicated that Rolapitant significantly upregulated the expression of death receptor 5 (DR5). Rolapitant also promoted lung cancer cell apoptosis through upregulating cell surface expression of DR5 and enhanced TRAIL-induced apoptosis. Mechanistically, Rolapitant directly targeted the OTUD3-GRP78 axis to trigger endoplasmic reticulum (ER) stress-C/EBP homologous protein (CHOP)-DR5 signaling, sensitizing lung cancer cells to TRAIL-induced apoptosis. In the vivo assays, Rolapitant suppressed the growth of lung cancer xenografts in immunocompromised mice at suitable dosages without apparent toxicity. CONCLUSION: In summary, the present study identifies Rolapitant as a novel inhibitor of deubiquitinase OTUD3 and establishes that the OTUD3-GRP78 axis is a potential therapeutic target for lung cancer.

A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks.

Gas separation is crucial for industrial production and environmental protection, with metal-organic frameworks (MOFs) offering a promising solution due to their tunable structural properties and chemical compositions. Traditional simulation approaches, such as molecular dynamics, are complex and computationally demanding. Although feature engineering-based machine learning methods perform better, they are susceptible to overfitting because of limited labeled data. Furthermore, these methods are typically designed for single tasks, such as predicting gas adsorption capacity under specific conditions, which restricts the utilization of comprehensive datasets including all adsorption capacities. To address these challenges, we propose Uni-MOF, an innovative framework for large-scale, three-dimensional MOF representation learning, designed for multi-purpose gas prediction. Specifically, Uni-MOF serves as a versatile gas adsorption estimator for MOF materials, employing pure three-dimensional representations learned from over 631,000 collected MOF and COF structures. Our experimental results show that Uni-MOF can automatically extract structural representations and predict adsorption capacities under various operating conditions using a single model. For simulated data, Uni-MOF exhibits remarkably high predictive accuracy across all datasets. Additionally, the values predicted by Uni-MOF correspond with the outcomes of adsorption experiments. Furthermore, Uni-MOF demonstrates considerable potential for broad applicability in predicting a wide array of other properties.

Genome Sequencing and Analysis of Nigrospora oryzae, a Rice Leaf Disease Fungus.

Nigrospora oryzae is one of several fungal pathogens known to cause brown streaks, leaf spots, and latent infections in rice. In this study, the entire 42.09-Mb genome of N. oryzae was sequenced at a depth of 169× using the Oxford Nanopore Technologies platform. The draft genome sequence was comprised of 26 scaffolds, possessed an average GC content of 58.83%, and contained a total of 10,688 protein-coding genes. Analysis of the complete genome sequence revealed that CAZyme-encoding genes account for 6.11% of all identified genes and that numerous transcription factors (TFs) associated with diverse biological processes belong predominantly to Zn-clus (22.20%) and C2H2 (10.59%) fungal TF classes. In addition, genes encoding 126 transport proteins and 3307 pathogen-host interaction proteins were identified. Comparative analysis of the previously reported N. oryzae reference strain GZL1 genome and the genome of a representative strain ZQ1 obtained here revealed 9722 colinear genes. Collectively, these findings provide valuable insights into N. oryzae genetic mechanisms and phenotypic characteristics.

Unveiling the Role of Electrostatic Forces on Attraction between Opposing Polyelectrolyte Brushes.

Electrostatic interaction and molecular excluded-volume effects are responsible for a plethora of nonintuitive phenomena in soft-matter systems, including local charge inversion and attraction between similar charges. In the current work, we study the surface forces and swelling behavior of opposing polyelectrolyte brushes using a classical density functional theory that accounts for electrostatic and excluded-volume correlations. We observe that the detachment pressure between similarly charged brushes is sensitive to salt concentration in both the osmotic and salted regimes and can be negative in the presence of multivalent counterions. A comparison of the theoretical results with the mean-field predictions unravels the role of correlation effects in determining the surface forces and brush structure. For systems containing multivalent counterions, the detachment pressure attains negative values at an intermediate brush-brush separation, and the attractive region in the pressure vs distance plot is magnified in terms of both the depth and width of attraction with increasing counterion valency. However, the interbrush attraction vanishes when the size-induced correlations are switched off. We also investigated the role of counterion size and polymer chain length on the detachment pressure. It is found that smaller counterions are more effective in neutralizing the polymer charge than bigger counterions, leading to a reduced interbrush repulsion and, in some cases, attraction between like-charged brushes at intermediate distances. Meanwhile, varying the chain length of the grafted polymers only shifts the location of the attraction basin, with little influence on the interaction strength. The theoretical predictions show qualitative agreement with experimental observations and offer valuable insights into the interaction between similarly charged polymer brushes in the presence of multivalent ions.

Superstructural ordering in self-sorting coacervate-based protocell networks.

Bottom-up assembly of higher-order cytomimetic systems capable of coordinated physical behaviours, collective chemical signalling and spatially integrated processing is a key challenge in the study of artificial multicellularity. Here we develop an interactive binary population of coacervate microdroplets that spontaneously self-sort into chain-like protocell networks with an alternating sequence of structurally and compositionally dissimilar microdomains with hemispherical contact points. The protocell superstructures exhibit macromolecular self-sorting, spatially localized enzyme/ribozyme biocatalysis and interdroplet molecular translocation. They are capable of topographical reconfiguration using chemical or light-mediated stimuli and can be used as a micro-extraction system for macroscale biomolecular sorting. Our methodology opens a pathway towards the self-assembly of multicomponent protocell networks based on selective processes of coacervate droplet-droplet adhesion and fusion, and provides a step towards the spontaneous orchestration of protocell models into artificial tissues and colonies with ordered architectures and collective functions.

Single-chain simulation of Ising density functional theory for weak polyelectrolytes.

Conventional theories of weak polyelectrolytes are either computationally prohibitive to account for the multidimensional inhomogeneity of polymer ionization in a liquid environment or oversimplistic in describing the coupling effects of ion-explicit electrostatic interactions and long-range intrachain correlations. To bridge this gap, we implement the Ising density functional theory (iDFT) for ionizable polymer systems using the single-chain-in-mean-field algorithm. The single-chain-in-iDFT (sc-iDFT) shows significant improvements over conventional mean-field methods in describing segment-level dissociation equilibrium, specific ion effects, and long-range intrachain correlations. With an explicit consideration of the fluctuations of polymer configurations and the position-dependent ionization of individual polymer segments, sc-iDFT provides a faithful description of the structure and thermodynamic properties of inhomogeneous weak polyelectrolyte systems across multiple length scales.

Designing artificial ion channels with strict K+/Na+ selectivity toward next-generation electric-eel-mimetic ionic power generation.

A biological potassium channel is >1000 times more permeable to K+ than to Na+ and exhibits a giant permeation rate of ∼108 ions/s. It is a great challenge to construct artificial potassium channels with such high selectivity and ion conduction rate. Herein, we unveil a long-overlooked structural feature that underpins the ultra-high K+/Na+ selectivity. By carrying out massive molecular dynamics simulation for ion transport through carbonyl-oxygen-modified bi-layer graphene nanopores, we find that the twisted carbonyl rings enable strict potassium selectivity with a dynamic K+/Na+ selectivity ratio of 1295 and a K+ conduction rate of 3.5 × 107 ions/s, approaching those of the biological counterparts. Intriguingly, atomic trajectories of K+ permeation events suggest a dual-ion transport mode, i.e. two like-charged potassium ions are successively captured by the nanopores in the graphene bi-layer and are interconnected by sharing one or two interlayer water molecules. The dual-ion behavior allows rapid release of the exiting potassium ion via a soft knock-on mechanism, which has previously been found only in biological ion channels. As a proof-of-concept utilization of this discovery, we propose a novel way for ionic power generation by mixing KCl and NaCl solutions through the bi-layer graphene nanopores, termed potassium-permselectivity enabled osmotic power generation (PoPee-OPG). Theoretically, the biomimetic device achieves a very high power density of >1000 W/m2 with graphene sheets of <1% porosity. This study provides a blueprint for artificial potassium channels and thus paves the way toward next-generation electric-eel-mimetic ionic power generation.

Perfecting Liquid-State Theories with Machine Intelligence.

Recent years have seen a significant increase in the use of machine intelligence for predicting the electronic structure, molecular force fields, and physicochemical properties of various condensed systems. However, substantial challenges remain in developing a comprehensive framework capable of handling a wide range of atomic compositions and thermodynamic conditions. This perspective discusses potential future developments in liquid-state theories leveraging recent advancements in functional machine learning. By harnessing the strengths of theoretical analysis and machine learning techniques including surrogate models, dimension reduction, and uncertainty quantification, we envision that liquid-state theories will gain significant improvements in accuracy, scalability, and computational efficiency, enabling their broader applications across diverse materials and chemical systems.

Serum VEGF levels as a predictor of recurrence in patients with advanced­stage esophageal squamous cell carcinoma following curative esophagectomy followed by chemotherapy or concurrent radiotherapy.

The present study evaluated serum levels of vascular endothelial growth factor (VEGF) as a predictor of recurrence in patients with advanced-stage esophageal squamous cell carcinoma (ESCC) following curative esophagectomy followed by chemotherapy or concurrent radiotherapy. Patients with locally advanced resectable ESCC underwent R0 esophagectomy followed by chemotherapy or concurrent radiotherapy as an adjuvant. Serum VEGF levels in 173 patients, including 57 patients with recurrent disease, and 183 healthy controls were determined using a Luminex assay. The results demonstrated that the serum VEGF levels were significantly higher in 57 patients with locally advanced resectable ESCC at recurrence compared with the levels at pre-treatment (P<0.001). The patients with recurrence exhibited significantly higher serum VEGF levels during chemotherapy or concurrent radiotherapy than patients with no recurrence (P<0.05). Patients with low serum VEGF levels had a significantly longer survival time than those with high serum VEGF levels prior to treatment (P<0.01). The median survival times were 70 and 25 months in patients with locally advanced resectable ESCC with serum VEGF levels <161.75 and ≥161.75 pg/ml following treatment, respectively (P<0.01). Compared with patients with VEGF levels <147 pg/ml following treatment, patients with locally advanced resectable ESCC with VEGF levels ≥147 pg/ml had a significantly higher risk of recurrence (P<0.01). Patients with low serum VEGF levels (<147 pg/ml) had significantly higher recurrence-free survival rates than those with high serum VEGF levels (≥147 pg/ml) following treatment (P<0.01). The findings of the present study demonstrate that serum VEGF levels are a potential predictor of recurrence and of the treatment outcomes of chemotherapy or concurrent radiotherapy in patients with locally advanced resectable ESCC.

CLASRP oncogene as a novel target for colorectal cancer.

Clk4-associated serine/arginine-rich protein (CLASRP), an alternative splicing regulator, may be involved in the development and progression of cancer by regulating the activity of the CDC-like kinase (Clk) family. This study explored the biological function of CLASRP in colorectal cancer (CRC). The expression of CLASRP, which is associated with clinicopathological features, was analysed in CRC tissues and paired noncancer tissues by RT-PCR. The roles of CLASRP were investigated in CRC cells transfected with plasmids or shRNA through proliferation, migration and invasion assays in vitro and a xenograft model in vivo. Apoptosis was analysed using CLASRP-overexpressing CRC cells by western blotting. Clk inhibitors were used to perform functional research on CLASRP in CLASRP-overexpressing CRC cells. CLASRP was significantly upregulated in CRC cell lines, while high CLASRP expression was correlated with metastasis in CRC patients. Functionally, overexpression of CLASRP significantly promoted the proliferation, migration and invasion of CRC cells in vitro and tumour growth in vivo. Mechanistically, the proliferation, migration and invasion of CLASRP-overexpressing CRC cells were inhibited by Clk inhibitors, accompanied by low expression of CLASRP at the gene and protein levels. Clk inhibitors induced apoptosis of CLASRP-overexpressing CRC cells, resulting in direct blockade of cell growth. The expression levels of cleaved caspase 3 and cleaved caspase 8 were increased in CLASRP-overexpressing CRC cells treated with Clk inhibitors. CLASRP might serve as a promotional oncogene in CRC cells and be suppressed by Clk inhibitors through activation of caspase pathways.

CRNDE mediated hnRNPA2B1 stability facilitates nuclear export and translation of KRAS in colorectal cancer.

Development of colorectal cancer (CRC) involves activation of Kirsten rat sarcoma viral oncogene homolog (KRAS) signaling. However, the post-transcriptional regulation of KRAS has yet to be fully characterized. Here, we found that the colorectal neoplasia differentially expressed (CRNDE)/heterogeneous nuclear ribonucleoprotein A2/B1 (hnRNPA2B1) axis was notably elevated in CRC and was strongly associated with poor prognosis of patients, while also significantly promoting CRC cell proliferation and metastasis both in vitro and in vivo. Furthermore, CRNDE maintained the stability of hnRNPA2B1 protein by inhibiting E3 ubiquitin ligase TRIM21 mediated K63 ubiquitination-dependent protein degradation. CRNDE/hnRNPA2B1 axis facilitated the nuclear export and translation of KRAS mRNA, which specifically activated the MAPK signaling pathway, eventually accelerating the malignant progression of CRC. Our findings provided insight into the regulatory network for stable hnRNPA2B1 protein expression, and the molecular mechanisms by which the CRNDE/hnRNPA2B1 axis mediated KRAS nucleocytoplasmic transport and translation, deeply underscoring the bright future of hnRNPA2B1 as a promising biomarker and therapeutic target for CRC. By hindering hnRNPA2B1 from binding to the E3 ubiquitin ligase TRIM21, whose mediated ubiquitin-dependent degradation was thereby inhibited, CRNDE protected the stability of hnRNPA2B1's high protein expression in CRC. Supported by the high level of the oncogenic molecule CRNDE, hnRNPA2B1 bound to KRAS mRNA and promoted KRAS mRNA nucleus export to enter the ribosomal translation program, subsequently activating the MAPK signaling pathway and ultimately accelerating the malignant progression of CRC.

A net-zero emissions strategy for China's power sector using carbon-capture utilization and storage.

Decarbonized power systems are critical to mitigate climate change, yet methods to achieve a reliable and resilient near-zero power system are still under exploration. This study develops an hourly power system simulation model considering high-resolution geological constraints for carbon-capture-utilization-and-storage to explore the optimal solution for a reliable and resilient near-zero power system. This is applied to 31 provinces in China by simulating 10,450 scenarios combining different electricity storage durations and interprovincial transmission capacities, with various shares of abated fossil power with carbon-capture-utilization-and-storage. Here, we show that allowing up to 20% abated fossil fuel power generation in the power system could reduce the national total power shortage rate by up to 9.0 percentages in 2050 compared with a zero fossil fuel system. A lowest-cost scenario with 16% abated fossil fuel power generation in the system even causes 2.5% lower investment costs in the network (or $16.8 billion), and also increases system resilience by reducing power shortage during extreme climatic events.

Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors.

Porous carbons are the active materials of choice for supercapacitor applications because of their power capability, long-term cycle stability, and wide operating temperatures. However, the development of carbon active materials with improved physicochemical and electrochemical properties is generally carried out via time-consuming and cost-ineffective experimental processes. In this regard, machine-learning technology provides a data-driven approach to examine previously reported research works to find the critical features for developing ideal carbon materials for supercapacitors. Here, we report the design of a machine-learning-derived activation strategy that uses sodium amide and cross-linked polymer precursors to synthesize highly porous carbons (i.e., with specific surface areas > 4000 m2/g). Tuning the pore size and oxygen content of the carbonaceous materials, we report a highly porous carbon-base electrode with 0.7 mg/cm2 of electrode mass loading that exhibits a high specific capacitance of 610 F/g in 1 M H2SO4. This result approaches the specific capacitance of a porous carbon electrode predicted by the machine learning approach. We also investigate the charge storage mechanism and electrolyte transport properties via step potential electrochemical spectroscopy and quasielastic neutron scattering measurements.

Aldolase A Promotes Colorectal Cancer Progression through Targeting COPS6 and Regulating MAPK Signaling Pathway.

Colorectal cancer (CRC) is a serious threat to human health, and its underlying mechanisms remain to be further explored. Aldolase A (ALDOA) has received increasing attention for its reported association with multiple cancers, but the role and mechanisms of ALDOA in CRC are still unclear. In the current study, high expression levels and enzymatic activity of ALDOA were detected in CRC tissues and cell lines, indicating the clinical significance of ALDOA in human CRC. In addition, silencing ALDOA significantly impaired the proliferation and metastasis of CRC cells in vitro and in vivo. Mechanistically, immunoprecipitation assays and mass spectrometry analysis identified the binding protein COPS6 of ALDOA. Furthermore, the promoting effects of upregulated ALDOA on CRC cell proliferation and metastasis were inhibited by COPS6 depletion, demonstrating COPS6 was required for ALDOA in mediating CRC progress. Moreover, the epithelial-mesenchymal transition (EMT) program and MAPK signaling pathway were found to be activated by ALDOA overexpression as well. In summary, our findings suggested that ALDOA facilitated the proliferation and metastasis of CRC by binding and regulating COPS6, inducing EMT, and activating the mitogen-activated protein kinase (MAPK) signaling pathway. The present study provided evidence for ALDOA as a promising potential biomarker for CRC.

Transferability evaluation of the deep potential model for simulating water-graphene confined system.

Machine learning potentials (MLPs) are poised to combine the accuracy of ab initio predictions with the computational efficiency of classical molecular dynamics (MD) simulation. While great progress has been made over the last two decades in developing MLPs, there is still much to be done to evaluate their model transferability and facilitate their development. In this work, we construct two deep potential (DP) models for liquid water near graphene surfaces, Model S and Model F, with the latter having more training data. A concurrent learning algorithm (DP-GEN) is adopted to explore the configurational space beyond the scope of conventional ab initio MD simulation. By examining the performance of Model S, we find that an accurate prediction of atomic force does not imply an accurate prediction of system energy. The deviation from the relative atomic force alone is insufficient to assess the accuracy of the DP models. Based on the performance of Model F, we propose that the relative magnitude of the model deviation and the corresponding root-mean-square error of the original test dataset, including energy and atomic force, can serve as an indicator for evaluating the accuracy of the model prediction for a given structure, which is particularly applicable for large systems where density functional theory calculations are infeasible. In addition to the prediction accuracy of the model described above, we also briefly discuss simulation stability and its relationship to the former. Both are important aspects in assessing the transferability of the MLP model.