Xi’an Jiaotong-Liverpool University

Traumatic brain injury (TBI) is a global medical concern and has a lasting impact on brain activity with high risks of mortality. Current treatments are inadequate for repairing damaged brain cells or correcting cognitive and behavioral disabilities in TBI patients. Mounting evidence links TBI to the activation of the Integrated Stress Response (ISR) signaling in the brain. A novel small molecule, ISRIB, is an effective inhibitor of the ISR pathway, offering potential advantages for brain health. Here, we investigated how ISRIB affects brain transcriptome and behavior in zebrafish TBI model evoked by telencephalic brain injury. Overall, while TBI diminished memory and social behavior in zebrafish, administering ISRIB post-injury markedly reduced these behavioral deficits, and modulated brain gene expression, rescuing TBI-activated pathways related to inflammation and brain cell development. Collectively, this supports the role of brain ISR in TBI, and suggests potential utility of ISRIB for the treatment of TBI-related states.

This paper examines the impact of the carbon emissions trading scheme (ETS) on directed technological change in the context of Chinese pilot schemes. We focus on firms’ heterogeneity in driving innovation and explore policy variations across pilots. Using a matched difference-in-differences design with a zero-inflated Poisson model, we find that the low-carbon innovation is driven by firms at the intensive margin. On average, a firm files 0.16 additional low-carbon patents annually at the intensive margin. In addition, when looking across pilots, the effect on low-carbon innovation is significant in two pilots, Beijing and Shanghai. We further find that, when looking at firms with different productivity levels measured by output per worker, the pilot ETS encourages low-carbon innovation at the intensive margin but reduces entry into low-carbon innovation at the extensive margin for the more productive firms. Our results suggest that innovation inertia matters, and future policies should encourage smaller firms covered by ETS to start innovation in low-carbon technologies.

53BP1 plays an important role in DNA double-strand break (DSB) repair and this activity is negatively regulated by its interaction with Tudor interacting repair regulator (TIRR). However, how the TIRR-53BP1 repair axis is regulated in response to DNA damage remains elusive. Here, we demonstrate that TIRR is translocated to the cytoplasm and degraded upon DNA damage. Ubiquitination of TIRR at lysine 187 by DTX3L is a critical process that regulates NHEJ pathway activity and PARP inhibitor sensitivity by facilitating XPO1-mediated TIRR nuclear export and degradation after DNA damage. We show that DTX3L is overexpressed in prostate cancers in patients and that decreased expression of TIRR due to DTX3L overexpression impairs the negative regulatory effect of TIRR on 53BP1, which consequently induces HR deficiency and chromosomal instability and sensitizes prostate cancer cells to poly (ADP-ribose) polymerase (PARP) inhibitors. Our work reveals a dual action of DTX3L on TIRR degradation and nuclear exportation and identifies DTX3L as an upstream regulator of the TIRR-53BP1 axis that governs DNA repair pathway choice and PARP inhibitor sensitivity. These findings suggest that TIRR ubiquitination and DTX3L overexpression could be viable biomarkers predicting PARP inhibitor sensitivity in cancers.

Background Clozapine is widely regarded as a highly efficacious psychotropic drug that is largely underused worldwide. Recent disproportionality analyses and nationwide case-control studies suggested a potential association between clozapine use and hematological malignancy (HM). Nevertheless, the absolute rate difference is not well-established due to the absence of analytic cohort studies. The clinical significance of such a potential risk remains unclear. Methods and findings We extracted data from a territory-wide public healthcare database from January 2001 to August 2022 in Hong Kong to conduct a retrospective cohort study of anonymized patients aged 18⁺ years with a diagnosis of schizophrenia who used clozapine or olanzapine (drug comparator with highly similar chemical structure and pharmacological mechanisms) for 90⁺ days, with at least 2 prior other antipsychotic use records within both groups. Weighted by inverse probability of treatment (IPTW) based on propensity scores, Poisson regression was used to estimate the incidence rate ratio (IRR) of HM between clozapine and olanzapine users. The absolute rate difference was also estimated. In total, 9,965 patients with a median follow-up period of 6.99 years (25th to 75th percentile: 4.45 to 10.32 years) were included, among which 834 were clozapine users. After IPTW, the demographic and clinical characteristics of clozapine users were comparable to those of olanzapine users. Clozapine users had a significant weighted IRR of 2.22 (95% confidence interval (CI) [1.52, 3.34]; p < 0.001) for HM compared to olanzapine users. The absolute rate difference was estimated at 57.40 (95% CI [33.24, 81.55]) per 100,000 person-years. Findings were consistent across subgroups by age and sex. Sensitivity analyses all supported the robustness of the results and showed good specificity to HM but no other cancers. The main limitation of this observational study is the potential residual confounding effects that could have arisen from the lack of randomization in clozapine or olanzapine use. Conclusions Absolute rate difference in HM incidence associated with clozapine is small despite a 2-fold elevated rate. Given the rarity of HM and existing blood monitoring requirements, more restrictive indication for clozapine or special warnings may not be necessary.

Asymmetric collaboration is an important topic for the research of multiuser collaborative systems. Previous works have shown that by providing different abilities, devices or content to different users, users can take advantage of the unique features of each side and collaborate effectively with each other. However, there is limited work comparing the differences between asymmetric and symmetric Virtual Reality (VR) collaboration systems. How task complexity may affect symmetric and asymmetric VR collaboration is also unclear. In this paper, we present a comparative study that investigated how user experiences and task performance vary in symmetric and asymmetric VR collaboration. In addition, we also explored how task interdependence correlates with user experience and task performance. Participants were asked to collaboratively perform 3D object selection and manipulation tasks in pairs. A within-subjects study was conducted, where participants used PC and PC, VR and VR, and PC and VR, respectively in three conditions. Our results revealed that the asymmetric collaboration using both PC and VR showed the best results in closeness of relationship, social presence and task performance; the PC symmetric collaborative system showed the worst user experiences and task performance. Both user experience and task performance showed a positive correlation with task interdependence. We discussed the effects of the collaborativ mode and device on the user experience and task performance, and the implications for future symmetric and asymmetric VR collaboration systems. Graphical abstract

Piezoelectricity, a fundamental property of perovskite ferroelectrics, endows the materials at the heart of electromechanical systems spanning from macro to micro/nano scales. Defect engineering strategies, particularly involving heterovalent trace impurities and derived vacancies, hold great potential for adjusting piezoelectric performance. Despite the prevalent use of defect engineering for modification, a comprehensive understanding of the specific features that positively impact material properties is still lacking, this knowledge gap impedes the advancement of a universally applicable defect selection and design strategy. In this work, we select perovskite KTa1−xNbxO3 single crystals with orthorhombic phase as the matrix and introduce Fe and Mn elements, which are commonly used in “hard” ferroelectrics as dopants. We investigate how transition-metal doping modifies piezoelectric properties from the perspective of intrinsic polarization behaviors. Interestingly, despite both being doped into the B-site as an acceptor, Mn doping enhances the local structural heterogeneity, greatly bolstering the piezoelectric coefficient beyond 1000 pC/N, whereas Fe doping tends to stabilize the polarization, leading to a substantial improvement in the mechanical quality factor up to 700. This work deciphers the diverse impacts of transition metal impurities on regulating polarization structures and modifying piezoelectric properties, providing a good paradigm for strategically designing perovskite ferroelectrics.

In present study, we adopt molecular dynamics simulations to investigate the influences of typical planar defects, including twin boundaries (TBs), stacking faults (SFs) and grain boundaries (GBs), on the mechanical properties of fcc copper nanoparticles. Groups of nanoparticle samples, including defect-free single crystal and those with specific defects, are examined for elastic modulus, yield strength, and deformation mechanisms. Detailed results reveal that the elastic behavior of nanoparticles can be well described by a modified theoretical model regardless the type of defects. While the planar defects have negligible influence on the elastic modulus, they significantly enhance the yield strength of nanoparticles. Notably, nanoparticles containing fivefold TBs exhibit the highest yield stress, i.e. ∼17.0 GPa, even surpassing that of the defect-free counterparts, i.e. ∼10.0 GP. Analysis of atomic deformation unravels that the distinct yielding behaviors are attributed to the activation of different slip systems and the nucleation of dislocations at specific preferential sites. These findings highlight the potential of fabricating planar defects to tailor the mechanical properties of metallic nanoparticles for targeted applications in nanotechnology and materials science.

In large language models (LLMs), full-parameter fine-tuning is crucial for task-specific adaptation. Traditionally, this relies on deep learning training frameworks utilizing the back-propagation scheme. However, this scheme presents inherent issues, e.g. activation memory bottlenecks and backward locking, which limit the efficient computational resource usage. In this work, we propose the design and analysis of ZeROf-Offload, an innovative fine-tuning framework that adapts the forward-gradient scheme. This framework adopts a unique forward-gradient-oriented CPU offload strategy, enabling fine-tuning of billion-scale LLMs solely in the forward phase and enhancing computational efficiency. Empirical evaluations reveal the advantage of eliminating the backward phase in fine-tuning. ZeROf-Offload achieves134 TFlops/GPU for models with over 130 billion parameters on a single DGX-A100 node, outperforming DeepSpeed’s ZeRO-Offload, which achieves 102 TFlops/GPU for models with up to 53.7 billion parameters, the largest size manageable within GPU memory limitations. Furthermore, we have expanded ZeROf-Offload for multi-DGX-A100 environments with integrated 3D parallelism, achieving near-linear speedup across up to 128 GPUs and the token throughput by 1.4x and 1.5x, respectively. The experimental results demonstrate that the proposed ZeROf-Offload has achieved the highest throughput performance compared to all examined state-of-the-art frameworks.

Background Ovarian cancer is a type of gynecological cancer with extremely high fatality rate. Ferroptosis, an iron-dependent regulated cell death, inhibits the immune infiltration of tumor cells. Therefore, it is worthwhile to explore the effects of ferroptosis-related gene signatures and immune infiltration patterns on the clinical prognosis of ovarian cancer. Methods In this study, we used the mRNA expression matrix and related medical information of those who suffer from ovarian cancer in the TCGA database. After that, we established a ferroptosis-related gene signature based on LASSO Cox regression model, and employed several specific enrichment analyses to explore the bioinformatics functions of differentially expressed genes (DEGs). Additionally, we analyzed the link between ferroptosis and immune cells by single-sample gene set enrichment analysis (ssGSEA) to create a heatmap of gene-immune cell correlation. We then examined the expression of immune checkpoints and verified the gene expression in ovarian cancer tissues by qPCR assays. Finally, we induced ferroptosis in ovarian cancer cells using drugs and analyzed their migration, invasion and gene expression. Results According to LASSO Cox regression analysis, 9 prognostic DEGs were in association with overall survival (OS), which was utilized to construct a 9-gene signature for patients. Patients were divided into two groups, in which high-risk group’s OS was markedly shorter than that of low-risk group (Log−rank p<0.001). KEGG enrichment analysis showed that these DEGs were linked to human cytomegalovirus (HCMV) infection. The ssGSEA analysis revealed significant differences in immune cell type and expression between ALOX12 and GLRX5 groups (p<0.05). Heatmap showed high correlation of prognostic genes with various immune cells. qPCR assay confirmed the 9 gene expression signature in ovarian cancer tissues. The ovarian cancer cell invasion and migration were significantly inhibited after induction of ferroptosis. Conclusion We decoded the ferroptosis-related gene signatures and immune infiltration patterns that can be used to predict the prognosis of ovarian cancer patients.

Schizophrenia is a severe neuropsychiatric disease, but the initiation mechanisms are unclear. Although antipsychotics are effective against positive symptoms, therapeutic interventions for negative symptoms are limited due to the lack of pathophysiological mechanisms. Here we identify synaptotagmin-11 (Syt11) as a potential genetic risk factor and dopamine over-transmission as a mechanism in the development of schizophrenia. Syt11 expression is reduced in individuals with schizophrenia but restored following the treatment with antipsychotics. Syt11 deficiency in dopamine neurons in early adolescence, but not in adults, leads to persistent social deficits and other schizophrenia-like behaviors by mediating dopamine over-transmission in mice. Accordingly, dopamine neuron over-excitation before late adolescence induces persistent schizophrenia-associated behavioral deficits, along with the structural and functional alternations in the mPFC. Notably, local intervention of D2R with clinical drugs presynaptically or postsynaptically exhibits both acute and long-lasting therapeutic effects on social deficits in schizophrenia mice models. These findings not only define Syt11 as a risk factor and DA over-transmission as a potential risk factor initiating schizophrenia, but also propose two D2R-targeting strategies for the comprehensive and long-term recovery of schizophrenia-associated social withdrawal.

Photoacoustic imaging is a promising non-invasive functional imaging modality for fundamental research and clinical diagnosis. However, achieving capillary-level resolution, wide field-of-view, and high frame rates remains challenging. To address this, we propose a transparent ultrasonic transducer design using our developed transparent Pb(In1/2Nb1/2)O3-Pb(Mg1/3Nb2/3)O3-PbTiO3 crystals. Our fabrication technique incorporates quartz-glass-and-epoxy matching layers with low-resistance indium-tin-oxide electrodes through a brass-ring based structure, enabling a high frequency (28.5 MHz), wide bandwidth (78%), and enhanced pulse-echo sensitivity (2.5 V under 2-μJ pulse excitation). Our Pb(In1/2Nb1/2)O3-Pb(Mg1/3Nb2/3)O3-PbTiO3-based transparent ultrasonic transducer demonstrates a four-fold enhancement in photoacoustic detection sensitivity when compared to the LiNbO3-based counterpart, leading to a 13 dB improvement of signal-to-noise ratio in microvascular photoacoustic imaging. This enables dynamic monitoring of mouse cerebral cortex microvasculature during seizures at 0.8 Hz frame rates over a 1.5 × 1.5 mm² field-of-view. Our work paves the way for high-performance and compact photoacoustic imaging systems using advanced piezoelectric materials.

Tropical hydroclimate in monsoonal regions has been largely understood according to the orbital monsoon hypothesis, in which rainfall exhibits strong covariation with local summer insolation on precessional (~21,000 years) time scales, as exemplified in the Asian and South American monsoon stalagmite records. However, paleo-rainfall variations in some tropical regions are poorly explained by the orbital hypothesis, suggesting alternative forcing mechanisms of regional monsoon changes. Here, we show a 140,000-year record of Central American rainfall from oxygen-isotope (δ¹⁸O) time series of precisely dated stalagmites which reveals two dominant thermally-controlled monsoon regimes in which the Atlantic Ocean thermal state linked to the meridional overturning circulation (AMOC) is the primary driver, and local orbital summer insolation control is limited. Our reconstruction, supported by isotope-enabled climate model simulations, pinpoints the potential impacts of future AMOC weakening on the Central American and Caribbean climate.

Wearable strain sensor prepared with ionic conductive hydrogel holds great promises in a variety of engineering fields. In this work, we introduce sodium casein (SC) into a dual network hydrogel system made of polyvinyl alcohol (PVA) and polyacrylamide (PAM), to prepare an ionic hydrogel sensor. Compared to the PAM/PVA dual network hydrogel, the introduction of SC plays a significant synergistic role. Such dual network PAM/PVA/SC hydrogels exhibit excellent mechanical properties (a maximum strain of 719%, a maximum stress of 444.3 kPa), low hysteresis, and rapid recovery after uni-axial stretching. Since SC drives a large number of free ions, PAM/PVA/SC hydrogels present good conductivity while maintaining high physical stability, to enable an excellent sensitivity in a comparatively large strain range (Gauge factor, GF = 2.17 under 400% strain). The unique properties allow the generation of stable and accurate electrical signals transduced from different locations of the human body. As such, the PAM/PVA/SC hydrogel has the potential to be used as human–machine interface for continuous, real-time physiological monitoring.

Purpose Patient-reported outcome measures (PROMs) are being used more frequently in total knee arthroplasty (TKA). By utilizing high-quality scales, surgeons can achieve a more comprehensive and accurate evaluation of the effectiveness of TKA surgery. Currently, there is no widely accepted conceptual model for TKA PROMs. The objective of this study is to fill this gap by developing a conceptual model and preliminary content for a PROM that is specifically designed for TKA patients in mainland China. Methods The study design consisted of three stages: (1) a targeted literature review followed by the formation of a conceptual model pool; (2) qualitative data collection involving experts and patients, leading to the development of the preliminary Chinese TKA PROM (CTP); and (3) review of the CTP by experts using the Delphi method, along with cognitive debriefing interviews with patients. Results 64 patients and 28 experts took part in this study. The conceptual model focused on six key concepts: pain, symptom, function, quality of life, expectation, and satisfaction. To match the model, the authors developed a total of 35 items. Conclusion A conceptual model and preliminary content for CTP was developed with substantial participation from patients and a multidisciplinary group of experts. The integration of patient and clinical perspectives ensured a comprehensive representation of all relevant disease experiences and the focus of clinical practice. With further refinement through psychometric testing, the CTP is positioned to provide a standardized, comprehensive measure for research specific to Chinese TKA patients.

Unconventional information processing techniques leveraging advanced materials and structures have recently gained widespread attention. Bistable structures offer innovative methods for information processing due to their binary physical states, which directly correspond to electronic binary signals. However, conventional bistable structures typically require external driving elements, complicating the system. In this study, we fabricated intelligent bistable bits (IBBs) using 4D printing technology. Simulation and experimental results demonstrate that the proposed IBBs can sense environmental changes and autonomously achieve configuration transformation. Building on this, the tunable energy barriers and ‘time-sequence’ deformation properties of the IBBs are realised through simple stacking of structures and stimulation at different positions. Furthermore, this paper presents a design example of environment-responsive mechanical metamaterials based on IBBs, highlighting their potential in unconventional data storage. This work provides new insights into the design of IBBs adapted to complex environments and holds significant value for developing similar intelligent devices.