High-Entropy Prussian Blue Analogues Enable Lattice Respiration for Ultra-Stable Aqueous Aluminum-Ion Batteries.

Aqueous aluminum ion batteries (AAIBs) hold significant potential for grid-scale energy storage owing to their intrinsic safety, high theoretical capacity, and abundance of aluminum. However, the strong electrostatic interactions and delayed charge compensation between high-charge-density aluminum ions and the fixed lattice in conventional cathodes impede the development of high-performance AAIBs. To address this issue, we introduce, for the first time, high-entropy Prussian blue analogs (HEPBAs) as cathodes in AAIBs with unique lattice tolerance and efficient multipath electron transfer. Benefiting from the intrinsic long-range disorder and robust lattice strain field, HEPBAs enable the manifestation of the lattice respiration effect and minimize lattice volume changes, thereby achieving one of the best long-term stabilities (91.2% capacity retention after 10,000 cycles at 5.0 A g-1) in AAIBs. Additionally, the interaction between the diverse metal atoms generates a broadened d-band and reduced degeneracy compared with conventional PBAs, which enhances the electron transfer efficiency with one of the best rate performance (79.2 mAh g-1 at 5.0 A g-1) in AAIBs. Furthermore, exceptional element selectivity in HEPBAs with unique cocktail effect could facile tune electrochemical behavior. Overall, the newly developed HEPBAs with a high-entropy effect exhibit promising solutions for advancing AAIBs and multivalent-ion batteries. This article is protected by copyright. All rights reserved.

Clinical implications of CSF-ctDNA positivity in newly diagnosed diffuse large B cell lymphoma.

The clinical implications of CSF-ctDNA positivity in newly diagnosed diffuse large B cell lymphoma (ND-DLBCL) remains largely unexplored. One hundred ND-DLBCL patients were consecutively enrolled as training cohort and another 26 ND-DLBCL patients were prospectively enrolled in validation cohort. CSF-ctDNA positivity (CSF(+)) was identified in 25 patients (25.0%) in the training cohort and 7 patients (26.9%) in the validation cohort, extremely higher than CNS involvement rate detected by conventional methods. Patients with mutations of CARD11, JAK2, ID3, and PLCG2 were more predominant with CSF(+) while FAT4 mutations were negatively correlated with CSF(+). The downregulation of PI3K-AKT signaling, focal adhesion, actin cytoskeleton, and tight junction pathways were enriched in CSF(+) ND-DLBCL. Furthermore, pretreatment CSF(+) was significantly associated with poor outcomes. Three risk factors, including high CSF protein level, high plasma ctDNA burden, and involvement of high-risk sites were used to predict the risk of CSF(+) in ND-DLBCL. The sensitivity and specificity of pretreatment CSF-ctDNA to predict CNS relapse were 100% and 77.3%. Taken together, we firstly present the prevalence and the genomic and transcriptomic landscape for CSF-ctDNA(+) DLBCL and highlight the importance of CSF-ctDNA as a noninvasive biomarker in detecting and monitoring of CSF infiltration and predicting CNS relapse in DLBCL.

Light-induced electronic polarization in antiferromagnetic Cr2O3.

In a solid, the electronic subsystem can exhibit incipient order with lower point group symmetry than the crystal lattice. Ultrafast external fields that couple exclusively to electronic order parameters have rarely been investigated, however, despite their potential importance in inducing exotic effects. Here we show that when inversion symmetry is broken by the antiferromagnetic order in Cr2O3, transmitting a linearly polarized light pulse through the crystal gives rise to an in-plane rotational symmetry-breaking (from C3 to C1) via optical rectification. Using interferometric time-resolved second harmonic generation, we show that the ultrafast timescale of the symmetry reduction is indicative of a purely electronic response; the underlying spin and crystal structures remain unaffected. The symmetry-broken state exhibits a dipole moment, and its polar axis can be controlled with the incident light. Our results establish a coherent nonlinear optical protocol by which to break electronic symmetries and produce unconventional electronic effects in solids.

PRMT5 activates lipid metabolic reprogramming via MYC contributing to the growth and survival of mantle cell lymphoma.

Mantle cell lymphoma (MCL) is an incurable and aggressive subtype of non-Hodgkin B-cell lymphoma. Increased lipid uptake, storage, and lipogenesis occur in a variety of cancers and contribute to rapid tumor growth. However, no data has been explored for the roles of lipid metabolism reprogramming in MCL. Here, we identified aberrant lipid metabolism reprogramming and PRMT5 as a key regulator of cholesterol and fatty acid metabolism reprogramming in MCL patients. High PRMT5 expression predicts adverse outcome prognosis in 105 patients with MCL and GEO database (GSE93291). PRMT5 deficiency resulted in proliferation defects and cell death by CRISPR/Cas9 editing. Moreover, PRMT5 inhibitors including SH3765 and EPZ015666 worked through blocking SREBP1/2 and FASN expression in MCL. Furthermore, PRMT5 was significantly associated with MYC expression in 105 MCL samples and the GEO database (GSE93291). CRISPR MYC knockout indicated PRMT5 can promote MCL outgrowth by inducing SREBP1/2 and FASN expression through the MYC pathway.

Integration of cognitive tasks into artificial general intelligence test for large models.

During the evolution of large models, performance evaluation is necessary for assessing their capabilities. However, current model evaluations mainly rely on specific tasks and datasets, lacking a united framework for assessing the multidimensional intelligence of large models. In this perspective, we advocate for a comprehensive framework of cognitive science-inspired artificial general intelligence (AGI) tests, including crystallized, fluid, social, and embodied intelligence. The AGI tests consist of well-designed cognitive tests adopted from human intelligence tests, and then naturally encapsulates into an immersive virtual community. We propose increasing the complexity of AGI testing tasks commensurate with advancements in large models and emphasizing the necessity for the interpretation of test results to avoid false negatives and false positives. We believe that cognitive science-inspired AGI tests will effectively guide the targeted improvement of large models in specific dimensions of intelligence and accelerate the integration of large models into human society.

Prognostic value of POD18 combined with improved IELSG in primary central nervous system lymphoma

Purpose The International Extranodal Lymphoma Study Group (IELSG) score is widely used in clinical practice to stratify the risk of primary central nervous system lymphoma (PCNSL) patients. Our study aims to confirm and improve the IELSG score in PCNSL patients based on Chinese populations. Materials and methods A total of 79 PCNSL patients were retrospectively analyzed. All patients treated with high-dose methotrexate (HD-MTX)-based therapy collected clinical data. The receiver-operating characteristic (ROC) curve was used to determine the optimal cut-off values for the factors in IELSG score. Progression of disease (POD) at the most landmark time point was determine by Epanechnikov kernel and the area under the ROC curve (AUROC). Kaplan–Meier and multivariable regression methods were used to analyze survival data. Nomogram was generated for calculating the weight of each selected factor. Results The traditional IELSG score had no significant difference on OS and PFS except ECOG ≥ 2 and could not stratify the risk groups in PCNSL. The improved IELSG scoring system was established, which incorporated age ≥ 54 years, ECOG ≥ 2, deep brain structure, elevated CSF protein, and LDH/ULN > 0.75. On the other hand, POD18 was identified as a new powerful prognostic factor for PCNSL. In multivariate analysis, POD18 and the improved IELSG scoring system were independent prognostic factors for OS. Nomogram including the two significant variables showed the best performance (C-index = 0.828). Conclusions In this study, the IELSG score was improved and a new prognostic indicator POD18 was incorporated to construct a nomogram prognostic model, thereby further improving the predictive ability of the model (AU)

Giant X-Ray Circular Dichroism in a Time-Reversal Invariant Antiferromagnet.

X-ray circular dichroism, arising from the contrast in X-ray absorption between opposite photon helicities, serves as a spectroscopic tool to measure the magnetization of ferromagnetic materials and identify the handedness of chiral crystals. Antiferromagnets with crystallographic chirality typically lack X-ray magnetic circular dichroism because of time-reversal symmetry, yet exhibit weak X-ray natural circular dichroism. Here, the observation of giant natural circular dichroism in the Ni L3-edge X-ray absorption of Ni3TeO6 is reported, a polar and chiral antiferromagnet with effective time-reversal symmetry. To unravel this intriguing phenomenon, a phenomenological model is proposed that classifies the movement of photons in a chiral crystal within the same symmetry class as that of a magnetic field. The coupling of X-ray polarization with the induced magnetization yields giant X-ray natural circular dichroism, revealing typical ferromagnetic behaviors allowed by the symmetry in an antiferromagnet, i.e., the altermagnetism of Ni3TeO6. The findings provide evidence for the interplay between magnetism and crystal chirality in natural optical activity. Additionally, the first example of a new class of magnetic materials exhibiting circular dichroism is established with time-reversal symmetry.

Synergistic π-Conjugation Organic Cathode for Ultra-Stable Aqueous Aluminum Batteries.

Rechargeable aqueous aluminum batteries (AABs) are promising energy storage technologies owing to their high safety and ultra-high energy-to-price ratio. However, either the strong electrostatic forces between high-charge-density Al3+ and host lattice, or sluggish large carrier-ion diffusion toward the conventional inorganic cathodes generates inferior cycling stability and low rate-capacity. To overcome these inherent confinements, a series of promising redox-active organic materials (ROMs) are investigated and a π-conjugated structure ROMs with synergistic C═O and C═N groups is optimized as the new cathode in AABs. Benefiting from the joint utilization of multi-redox centers and rich π-π intermolecular interactions, the optimized ROMs with unique ion coordination storage mechanism facilely accommodate complex active ions with mitigated coulombic repulsion and robust lattice structure, which is further validated via theoretical simulations. Thus, the cathode achieves enhanced rate performance (153.9 mAh g-1 at 2.0 A g-1 ) and one of the best long-term stabilities (125.7 mAh g-1 after 4,000 cycles at 1.0 A g-1 ) in AABs. Via molecular exploitation, this work paves the new direction toward high-performance cathode materials in aqueous multivalent-ion battery systems.

CD30 protects EBV-positive diffuse large B-cell lymphoma cells against mitochondrial dysfunction through BNIP3-mediated mitophagy.

Epstein-Barr virus (EBV) positive diffuse large B-cell lymphoma (EBV+ DLBCL) predicts poor prognosis and CD30 expression aggravates the worse consequences. Here, we reported that CD30 positivity was an independent prognostic indicator in EBV+ DLBCL patients in a retrospective cohort study. We harnessed CRISPR/Cas9 editing to engineer the first loss-of-function models of CD30 deficiency to identify that CD30 was critical for EBV+ DLBCL growth and survival. We established a pathway that EBV infection mediated CD30 expression through EBV-encoded latent membrane protein 1 (LMP1), which involved NF-κB signaling. CRISPR CD30 knockout significantly repressed BCL2 interacting protein 3 (BNIP3) expression and co-IP assay indicated a binding between CD30 and BNIP3. Moreover, silencing of CD30 induced mitochondrial dysfunction and suppressed mitophagy, resulting in the accumulation of damaged mitochondria by depressing BNIP3 expression. Additionally, CRISPR BNIP3 knockout caused proliferation defects and increased sensitivity to apoptosis. All the findings reveal a strong relationship between mitophagy and adverse prognosis of EBV+ DLBCL and discover a new regulatory mechanism of BNIP3-mediated mitophagy, which may help develop effective treatment regimens with anti-CD30 antibody brentuximab vedotin to improve the prognosis of CD30+ EBV+ DLBCL patients.

Prognostic value of POD18 combined with improved IELSG in primary central nervous system lymphoma.

PURPOSE: The International Extranodal Lymphoma Study Group (IELSG) score is widely used in clinical practice to stratify the risk of primary central nervous system lymphoma (PCNSL) patients. Our study aims to confirm and improve the IELSG score in PCNSL patients based on Chinese populations. MATERIALS AND METHODS: A total of 79 PCNSL patients were retrospectively analyzed. All patients treated with high-dose methotrexate (HD-MTX)-based therapy collected clinical data. The receiver-operating characteristic (ROC) curve was used to determine the optimal cut-off values for the factors in IELSG score. Progression of disease (POD) at the most landmark time point was determine by Epanechnikov kernel and the area under the ROC curve (AUROC). Kaplan-Meier and multivariable regression methods were used to analyze survival data. Nomogram was generated for calculating the weight of each selected factor. RESULTS: The traditional IELSG score had no significant difference on OS and PFS except ECOG ≥ 2 and could not stratify the risk groups in PCNSL. The improved IELSG scoring system was established, which incorporated age ≥ 54 years, ECOG ≥ 2, deep brain structure, elevated CSF protein, and LDH/ULN > 0.75. On the other hand, POD18 was identified as a new powerful prognostic factor for PCNSL. In multivariate analysis, POD18 and the improved IELSG scoring system were independent prognostic factors for OS. Nomogram including the two significant variables showed the best performance (C-index = 0.828). CONCLUSIONS: In this study, the IELSG score was improved and a new prognostic indicator POD18 was incorporated to construct a nomogram prognostic model, thereby further improving the predictive ability of the model.

Ferrorotational Selectivity in Ilmenites.

Unlike what happens in conventional ferroics, the ferrorotational (FR) domain manipulation and visualization in FR materials are nontrivial as they are invariant under both space-inversion and time-reversal operations. FR domains have recently been observed by using the linear electrogyration (EG) effect and X-ray diffraction (XRD) diffraction mapping. However, ferrorotational selectivity, such as the selective processing of the FR domains and direct visualization of the FR domains, e.g., under an optical microscope, would be the next step to study the FR domains and their possible applications in technology. Unexpectedly, we discovered that the microscopic FR structural distortions in ilmenite crystals can be directly coupled with macroscopic mechanical rotations in such a way that FR domains can be visualized under an optical microscope after innovative rotational polishing, a combined ion milling with a specific rotational polishing, or a twisting-induced fracturing process. Thus, the FR domains could be a unique medium to register the memory of a rotational mechanical process due to a novel selective coupling between its microscopic structural rotations and an external macroscopic rotation. Analogous to the important enantioselectivity in modern chemistry and the pharmaceutical industry, this newly discovered ferrorotational selectivity opens up opportunities for FR manipulation and new FR functionality-based applications.

Rapid Detection of SARS-CoV-2 in Clinical and Environmental Samples via a Resonant Cavity SERS Platform within 20 min.

The coronavirus disease 2019 (COVID-19) epidemic has given a warning that it is important to explore the rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in clinical specimens or environmental samples for public health strategies and future variants. The surface-enhanced Raman spectroscopy (SERS) technique was demonstrated to achieve this goal. However, the consistency of signals originating from the poor compatibility of virions with SERS hotspots remains a key scientific challenge for the practical applications of SERS. Herein, we develop a SERS platform for the ultrasensitive and rapid detection of SARS-CoV-2 antigen within 20 min by the combination of a highly consistent SERS substrate and a supervised deep learning algorithm. A V-shaped resonant cavity array (VRC) substrate was fabricated to trap SARS-CoV-2 virions in the periodic V cavity array and stimulate the integral SERS signal of the virus via a resonance coupling effect. Benefiting from the unique architecture of the VRC substrate, we were able to directly detect the SARS-CoV-2 virus with high sensitivity and high consistency. These excellent performances enabled us to identify five different kinds of SARS-CoV-2 variants and detect SARS-CoV-2 from clinical and environmental samples with high accuracies.

Causality of occupational exposure on rheumatoid arthritis and ankylosing spondylitis: a two-sample mendelian randomization study.

Objective: This study aimed to explore the potential causal link between three specific types of occupational exposure on rheumatoid arthritis (RA) and ankylosing spondylitis (AS). Method: A Two-sample Mendelian randomization (TSMR) analysis, comprising univariate MR (UVMR) and multivariate MR (MVMR) analyses, was performed to investigate the potential causal association between three types of occupational exposures, jobs involving mainly walking or standing (JWS), jobs involving heavy manual or physical work (JMP), and jobs involving shift work(JSW) on RA and AS. Genetic variants for genome-wide association studies (GWAS) of occupational exposure and AS were obtained from the UK Biobank. GWAS summary data for RA were obtained from FinnGen Biobank analysis. For UVMR, six methods of Inverse Variance Weighted (IVW), MR-Egger, Weighted Mode, Weighted Median, Simple Mode, MR pleiotropy residual sum, and outlier (MR-PRESSO) were used for the analysis. The MVMR was analyzed using the IVW model as well as the MR-Egger model. Results: The UVMR suggested no causal relationship between the three occupational exposure and RA [IVW: P=0.59,0.21,0.63] or AS [IVW: P=0.43,0.57,0.04], as did the bidirectional MR [IVW: P=0.73,0.70,0.16], [IVW: P=0.65,0.68,0.74]. Although unadjusted MVMR suggested a causal relationship between JMP and AS [IVW: OR = 1.01, 95% CI = 1.00- 1.02, p = 0.02], the adjusted MVMR denied this relationship and concluded that there was no causal relationship between the other occupational exposure and either RA or AS. Conclusion: Our MR analysis did not establish a direct causal relationship between certain occupational exposures and either RA or AS.

Modeling of Eyld2000-2d Anisotropic Yield Criterion Considering Strength Differential Effect and Analysis of Optimal Calibration Strategy.

Sheet metals usually experience various loading paths such as uniaxial tension, uniaxial compression, biaxial tension, and simple shear during the forming process. However, the existing constitutive models cannot always accurately describe blanks' anisotropic yield and plastic flow behavior of blanks under all typical stress states. Given this, this paper improves the Eyld2000-2d yield criterion by introducing hydrostatic pressure to the A-Eyld2000-2d yield criterion that can describe the strength differential effect of materials. Meanwhile, to control the curvature of the yield surface more effectively, the near-plane strain yield stresses were added in the parameter identification process to calibrate the exponent m, so that the exponent is no longer considered as a constant value. Taking the widely used AA6016-T4, AA5754-O, DP980, and QP980 blanks in the automotive stamping industry as an example, the effectiveness of the new model and different parameter identification methods was verified by predicting experimental data under various simple and complex loading paths. Subsequently, the new model employing the optimal parameter identification strategy was compared with four widely used asymmetric yield criteria under associated and non-associated flow rules, including CPB06, LHY2013, S-Y2004, and Hu & Yoon2021, to further verify the accuracy of the proposed constitutive model. The results indicate that parameter identification strategy with variable exponent can significantly improve the flexibility of the yield criterion in describing the plastic anisotropy of blanks. Compared to the other yield criteria examined in this work, the new model provides the best prediction accuracy for the yield stresses and plastic flows of all blanks, especially in the near-plane strain and simple shear stress states. Modeling under the concept of anisotropic hardening can more accurately capture the evolving plastic behavior of blanks than isotropic hardening.

Skill-Learning Method of Dual Peg-in-Hole Compliance Assembly for Micro-Device.

For the dual peg-in-hole compliance assembly task of upper and lower double-hole structural micro-devices, a skill-learning method is proposed. This method combines offline training in a simulation space and online training in a realistic space. In this paper, a dual peg-in-hole model is built according to the results of a force analysis, and contact-point searching methods are provided for calculating the contact force. Then, a skill-learning framework is built based on deep reinforcement learning. Both expert action and incremental action are used in training, and a reward system considers both efficiency and safety; additionally, a dynamic exploration method is provided to improve the training efficiency. In addition, based on experimental data, an online training method is used to optimize the skill-learning model continuously so that the error caused by the deviation in the offline training data from reality can be reduced. The final experiments demonstrate that the method can effectively reduce the contact force while assembling, improve the efficiency and reduce the impact of the change in position and orientation.

High Entropy Oxides Modulate Atomic-Level Interactions for High-Performance Aqueous Zinc-Ion Batteries.

The strong electrostatic interaction between high-charge-density zinc ions (112 C mm-3 ) and the fixed crystallinity of traditional oxide cathodes with delayed charge compensation hinders the development of high-performance aqueous zinc-ion batteries (AZIBs). Herein, to intrinsically promote electron transfer efficiency and improve lattice tolerance, a revolutionary family of high-entropy oxides (HEOs) materials with multipath electron transfer and remarkable structural stability as cathodes for AZIBs is proposed. Benefiting from the unique "cock-tail" effect, the interaction of diverse type metal-atoms in HEOs achieves essentially broadened d-band and lower degeneracy than monometallic oxides, which contribute to convenient electron transfer and one of the best rate-performances (136.2 mAh g-1 at 10.0 A g-1 ) in AZIBs. In addition, the intense lattice strain field of HEOs is highly tolerant to the electrostatic repulsion of high-charge-density Zn2+ , leading to the outstanding cycling stability in AZIBs. Moreover, the super selectability of elements in HEOs exhibits significant potential for AZIBs.

A GPU-based computational framework that bridges neuron simulation and artificial intelligence.

Biophysically detailed multi-compartment models are powerful tools to explore computational principles of the brain and also serve as a theoretical framework to generate algorithms for artificial intelligence (AI) systems. However, the expensive computational cost severely limits the applications in both the neuroscience and AI fields. The major bottleneck during simulating detailed compartment models is the ability of a simulator to solve large systems of linear equations. Here, we present a novel Dendritic Hierarchical Scheduling (DHS) method to markedly accelerate such a process. We theoretically prove that the DHS implementation is computationally optimal and accurate. This GPU-based method performs with 2-3 orders of magnitude higher speed than that of the classic serial Hines method in the conventional CPU platform. We build a DeepDendrite framework, which integrates the DHS method and the GPU computing engine of the NEURON simulator and demonstrate applications of DeepDendrite in neuroscience tasks. We investigate how spatial patterns of spine inputs affect neuronal excitability in a detailed human pyramidal neuron model with 25,000 spines. Furthermore, we provide a brief discussion on the potential of DeepDendrite for AI, specifically highlighting its ability to enable the efficient training of biophysically detailed models in typical image classification tasks.

Short-term effects of air pollution and weather changes on the occurrence of acute aortic dissection in a cold region.

Background: Air pollution and severe weather conditions can adversely affect cardiovascular disease emergencies. Nevertheless, it remains unclear whether air pollutants and low ambient temperature can trigger the occurrence of acute aortic dissection (AAD) in cold regions. Methods: We applied a retrospective analysis to assess the short-term effects of air pollution and ambient temperature on the occurrence of AAD in Harbin, China. A total of 564 AAD patients were enrolled from a major hospital in Harbin between January 1, 2017, and February 5, 2021. Weather condition data and air pollutant concentrations, including fine particulate matter smaller than 10 µm (PM10) and 2.5 µm in diameter (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3), were collected every day. Conditional logistic regressions and correlation analysis were applied to analyze the relationship of environmental and atmospheric parameters with AAD occurrence at lags of 0 to 7 days. Specifically, we appraised the air quality index, CO, NO2, SO2, O3, PM10, PM2.5, temperature, dew point temperature, atmospheric pressure, and cloud amount. Results: A total of 1,496 days at risk were assessed, of which 564 patients developed AAD. Specifically, AAD did not occur on 1,043 (69.72%) days, while 1 or more cases occurred on 453 (30.28%) days. Several pollution and weather predictors for AAD were confirmed by multilevel modeling. The air quality index (p = 0.0012), cloud amount (p = 0.0001), and concentrations of PM2.5 (p = 0.0004), PM10 (p = 0.0013), NO2 (p = 0.0007) and O3 (p = 0.0001) predicted AAD as early as 7 days before the incident (lag of 7 days) in the study period. However, only concentrations of the air pollutants NO2 (p = 0.0468) and O3 (p = 0.011) predicted the occurrence of AAD after the COVID-19 outbreak. Similar predictive effects were observed for temperature, dew point temperature, and atmospheric pressure (all p < 0.05) on all days. Conclusion: The risk of AAD is closely related to air pollution and weather characteristics in Harbin. While causation was not determined, the impact of air pollutants on the risk of AAD was reduced after the COVID-19 outbreak.