Machine learning in prenatal MRI predicts postnatal ventricular abnormalities in fetuses with isolated ventriculomegaly.

OBJECTIVES: To evaluate the intracranial structures and brain parenchyma radiomics surrounding the occipital horn of the lateral ventricle in normal fetuses (NFs) and fetuses with ventriculomegaly (FVs), as well as to predict postnatally enlarged lateral ventricle alterations in FVs. METHODS: Between January 2014 and August 2023, 141 NFs and 101 FVs underwent 1.5 T balanced steady-state free precession (BSSFP), including 68 FVs with resolved lateral ventricles (FVM-resolved) and 33 FVs with stable lateral ventricles (FVM-stable). Demographic data and intracranial structures were analyzed. To predict the enlarged ventricle alterations of FVs postnatally, logistic regression models with 5-fold cross-validation were developed based on lateral ventricle morphology, blended-cortical or/and subcortical radiomics characteristics. Validation of the models' performance was conducted using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). RESULTS: Significant alterations in cerebral structures were observed between NFs and FVs (p < 0.05), excluding the maximum frontal horn diameter (FD). However, there was no notable distinction between the FVM-resolved and FVM-stable groups (all p > 0.05). Based on subcortical-radiomics on the aberrant sides of FVs, this approach exhibited high efficacy in distinguishing NFs from FVs in the training/validation set, yielding an impressive AUC of 1/0.992. With an AUC value of 0.822/0.743 in the training/validation set, the Subcortical-radiomics model demonstrated its ability to predict lateral ventricle alterations in FVs, which had the greatest predictive advantages indicated by DCA. CONCLUSIONS: Microstructural alterations in subcortical parenchyma associated with ventriculomegaly can serve as predictive indicators for postnatal lateral ventricle variations in FVs. CLINICAL RELEVANCE STATEMENT: It is critical to gain pertinent information from a solitary fetal MRI to anticipate postnatal lateral ventricle alterations in fetuses with ventriculomegaly. This approach holds the potential to diminish the necessity for recurrent prenatal ultrasound or MRI examinations. KEY POINTS: Fetal ventriculomegaly is a dynamic condition that affects postnatal neurodevelopment. Machine learning and subcortical-radiomics can predict postnatal alterations in the lateral ventricle. Machine learning, applied to single-fetal MRI, might reduce required antenatal testing.

Exploring the mechanism of cytisine in treating respiratory depression following venomous snake bites based on network pharmacology and molecular docking.

OBJECTIVE: To investigate the antivenom mechanism of cytisine through network pharmacology and molecular docking (MD) techniques, with the intention of exploring its clinical applications. METHODS: The cytisine target and the snakebite respiratory inhibition target were obtained using the Swiss Target Prediction platform and the Gene Cards database. The two target sets were overlapped to form a protein interaction network. Additionally, pathway enrichment analysis was conducted on cross targets, and the related pathways for the treatment of snake venom-induced respiratory failure were obtained. Verification of the MD between cytisine and its related targets was performed using the Autodock 1.5.7 software. The respiratory depression model of rats bitten by venomous snakes was established, and the expression of key target genes in the rat model was verified by western blot (WB). RESULT: A total of 16 targets of cytisine and 9 potential targets of cytisine in treating snake venom-induced respiratory depression were obtained. Core targets including CHRNA7, CHRNG, CHRNB1, CHRND, CHRNA1 and DRD2 were obtained. These targets are mainly enriched in neuroactive ligand-receptor interaction pathway and cholinergic synaptic pathway. The MD results demonstrated favorable docking activity of cytisine with its related targets. WB experiments showed that snake venom reduced the levels of CHRNA7 and CHRNG. Treatment with serum and cytisine could slow down this decline. CONCLUSION: Cytisine may synergistically target CHRNA7, CHRNG, CHRNB1, CHRND, CHRNA1, DRD2 and other proteins, modulating cholinergic and neuroactive pathways to alleviate neuromuscular block and protect acetylcholine receptors.

Searching for Two-Neutrino and Neutrinoless Double Beta Decay of

^{134}Xe is a candidate isotope for neutrinoless double beta decay (0νßß) search. In addition, the two-neutrino case (2νßß) allowed by the standard model of particle physics has not yet been observed. With the 656-kg natural xenon in the fiducial volume of the PandaX-4T detector, which contains 10.4% of ^{134}Xe, and its initial 94.9-day exposure, we have established the most stringent constraints on 2νßß and 0νßß of ^{134}Xe half-lives, with limits of 2.8×10^{22} yr and 3.0×10^{23} yr at 90% confidence level, respectively. The 2νßß (0νßß) limit surpasses the previously reported best result by a factor of 32 (2.7), highlighting the potential of large monolithic natural xenon detectors for double beta decay searches.

A real-world evaluation of tislelizumab in patients with head and neck cancer.

Background: Various studies support the use of programmed cell death protein 1 (PD-1) blockades, also known as immune checkpoint inhibitors (ICIs), to treat head and neck cancer (HNC). Tislelizumab is a humanised immunoglobulin G4 (IgG4) monoclonal antibody with a high affinity and specificity for PD-1. However, the "real-world" clinical evidence of tislelizumab for HNC is limited. Methods: In this study, the medical records of 39 patients with head and neck squamous cell carcinoma (HNSCC) or nasopharyngeal carcinoma (NPC) who received tislelizumab between January 2021 and March 2022 were reviewed retrospectively. Tislelizumab was administered to 15 patients during neoadjuvant therapy (Group 1), five patients during adjuvant therapy (Group 2), 14 patients during consolidation therapy (Group 3), and five patients during salvage therapy (Group 4). The Kaplan-Meier method was used to calculate progression-free survival (PFS) and overall survival (OS). Results: The median age of enrolled patients was 55 (range, 28-83) years. The median follow-up time was 27.1, 26.1, 28.6, and 20.9 months for Groups 1, 2, 3, and 4, respectively. The mean PFS and OS of Groups 1, 2, 3, and 4 were 21.5 and 22.8; 24.1 and 24.2; 26.9 and 28.1; and 13.9 and 17.1 months, respectively. In Groups 1 and 4, the objective response rate (ORR) was 86.7% and 60%, respectively. Meanwhile, except for one (2.6%) patient with grade 4 enteritis, the other observed non-haematological adverse events (AEs) were ≤ grade 2. Conclusions: Tislelizumab demonstrated promising efficacy and tolerability in patients with HNSCC or NPC in a real-world setting, consistent with previous reports.

Development and Validation of Multimodal Models to Predict the 30-Day Mortality of ICU Patients Based on Clinical Parameters and Chest X-Rays.

We aimed to develop and validate multimodal ICU patient prognosis models that combine clinical parameters data and chest X-ray (CXR) images. A total of 3798 subjects with clinical parameters and CXR images were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and an external hospital (the test set). The primary outcome was 30-day mortality after ICU admission. Automated machine learning (AutoML) and convolutional neural networks (CNNs) were used to construct single-modal models based on clinical parameters and CXR separately. An early fusion approach was used to integrate both modalities (clinical parameters and CXR) into a multimodal model named PrismICU. Compared to the single-modal models, i.e., the clinical parameter model (AUC = 0.80, F1-score = 0.43) and the CXR model (AUC = 0.76, F1-score = 0.45) and the scoring system APACHE II (AUC = 0.83, F1-score = 0.77), PrismICU (AUC = 0.95, F1 score = 0.95) showed improved performance in predicting the 30-day mortality in the validation set. In the test set, PrismICU (AUC = 0.82, F1-score = 0.61) was also better than the clinical parameters model (AUC = 0.72, F1-score = 0.50), CXR model (AUC = 0.71, F1-score = 0.36), and APACHE II (AUC = 0.62, F1-score = 0.50). PrismICU, which integrated clinical parameters data and CXR images, performed better than single-modal models and the existing scoring system. It supports the potential of multimodal models based on structured data and imaging in clinical management.

Adult head and neck rhabdomyosarcoma: radiotherapy- based treatment, outcomes, and predictors of survival.

BACKGROUND: Adult head and neck rhabdomyosarcoma (HNRMS) is an exceptionally rare malignancy, and there is a paucity of data and research dedicated to understanding its characteristics and management in adult populations. This study aimed to assess the outcomes and identify survival predictors in adult HNRMS. METHODS: We retrospectively evaluated 42 adult patients (> 16 years) with HNRMS who received radiotherapy (RT)-based treatment at our institute between 2008 and 2022. We analysed the clinical characteristics and prognosis of these patients, including the locoregional recurrence-free survival (LRFS), progression-free survival (PFS), and overall survival (OS), using the Kaplan-Meier method. The chi-square and Fisher's exact tests were used to analyse differences between groups for dichotomous and categorical variables, respectively. Survival rates were calculated using the Kaplan-Meier method. Prognostic variables were assessed through univariate Cox analyses. RESULTS: The median patient age was 28 years (range, 16-82 years). Alveolar RMS was the most common histological type, observed in 21 patients (50.0%), followed by embryonal in 16 patients (38.1%). The anatomic sites of origin were orbital in one (2.4%), parameningeal in 26 (61.9%), and non-orbital/non-parameningeal in 15 (35.7%) patients. Nineteen patients (45.2%) had regional lymph node metastasis, and five patients (11.9%) presented with distant metastatic disease. Distant metastasis (n = 17) was the primary cause of treatment failure. At a median follow-up of 47.0 months, the 5-year LRFS, PFS, and OS rates were 69.0%, 39.7%, and 41.0%, respectively. Univariate analysis revealed that tumour size, lymph node involvement, and the local treatment pattern (surgery and RT vs. RT alone) were significant predictors of survival. CONCLUSIONS: The main failure pattern in patients with HNRMS receiving RT-based treatment was distant metastasis. Tumour size > 5 cm and lymph node involvement were predictors of worse LRFS. Multimodality local treatment, combining surgery and RT, is effective and provides survival benefits.

A novel derivative synchronous fluorescence method for the rapid, non-destructive and intuitive differentiation of denitrifying bacteria.

It is challenging to differentiate bacteria residing in the same habitat by direct observation. This difficulty impedes the harvest, application and manipulation of functional bacteria in environmental engineering. In this study, we developed a novel method for rapid differentiation of living denitrifying bacteria based on derivative synchronous fluorescence spectroscopy, as exemplified by three heterotrophic nitrification-aerobic denitrification bacteria having the maximum nitrogen removal efficiencies greater than 90%. The intact bacteria and their living surroundings can be analyzed as an integrated target, which eliminates the need for the complex pre-processing of samples. Under the optimal synchronous scanning parameter (Δλ = 40 nm), each bacterium possesses a unique fluorescence spectral structure and the derivative synchronous fluorescence technique can significantly improve the spectral resolution compared to other conventional fluorescence methods, which enables the rapid differentiation of different bacteria through derivative synchronous fluorescence spectra as fast as 2 min per spectrum. Additionally, the derivative synchronous fluorescence technique can extract the spectral signals contributed by bacterial extracellular substances produced in the biological nitrogen removal process. Moreover, the results obtained from our method can reflect the real-time denitrification properties of bacteria in the biological nitrogen removal process of wastewater. All these merits highlight derivative synchronous fluorescence spectroscopy as a promising analytic method in the environmental field.

Boron nanosheets as a phosphatase mimicking nanozyme with ultrahigh catalytic activity for prodrug-based cancer therapy.

Boron nanosheets (BNSs) are reported as a new phosphatase mimicking nanozyme. Surprisingly, the catalytic rate of BNSs is up to 17 times those of known phosphatase mimicking nanozymes. By adding polyols and Lewis bases, the catalytic activity of BNSs was attributed to the Lewis acidity of the B centers of the BNSs. Theoretical investigation shows that the B centers are responsible for the catalytic hydrolysis of phosphoesters. Moreover, the biomimetic activity of the BNSs was further explored for enhancing anticancer therapy through nanozyme-catalyzed prodrug conversion.

Schisandrin A alleviates renal fibrosis by inhibiting PKCß and oxidative stress.

BACKGROUND: Renal fibrosis is a common pathway that drives the advancement of numerous kidney maladies towards end-stage kidney disease (ESKD). Suppressing renal fibrosis holds paramount clinical importance in forestalling or retarding the transition of chronic kidney diseases (CKD) to renal failure. Schisandrin A (Sch A) possesses renoprotective effect in acute kidney injury (AKI), but its effects on renal fibrosis and underlying mechanism(s) have not been studied. STUDY DESIGN: Serum biochemical analysis, histological staining, and expression levels of related proteins were used to assess the effect of PKCß knockdown on renal fibrosis progression. Untargeted metabolomics was used to assess the effect of PKCß knockdown on serum metabolites. Unilateral Ureteral Obstruction (UUO) model and TGF-ß induced HK-2 cells and NIH-3T3 cells were used to evaluate the effect of Schisandrin A (Sch A) on renal fibrosis. PKCß overexpressed NIH-3T3 cells were used to verify the possible mechanism of Sch A. RESULTS: PKCß was upregulated in the UUO model. Knockdown of PKCß mitigated the progression of renal fibrosis by ameliorating perturbations in serum metabolites and curbing oxidative stress. Sch A alleviated renal fibrosis by downregulating the expression of PKCß in kidney. Treatment with Sch A significantly attenuated the upregulated proteins levels of FN, COL-I, PKCß, Vimentin and α-SMA in UUO mice. Moreover, Sch A exhibited a beneficial impact on markers associated with oxidative stress, including MDA, SOD, and GSH-Px. Overexpression of PKCß was found to counteract the renoprotective efficacy of Sch A in vitro. CONCLUSION: Sch A alleviates renal fibrosis by inhibiting PKCß and attenuating oxidative stress.

Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning.

OBJECTIVE: Aim to establish a multimodal model for predicting severe acute pancreatitis (SAP) using machine learning (ML) and deep learning (DL). METHODS: In this multicentre retrospective study, patients diagnosed with acute pancreatitis at admission were enrolled from January 2017 to December 2021. Clinical information within 24 h and CT scans within 72 h of admission were collected. First, we trained Model α based on clinical features selected by least absolute shrinkage and selection operator analysis. Second, radiomics features were extracted from 3D-CT scans and Model ß was developed on the features after dimensionality reduction using principal component analysis. Third, Model γ was trained on 2D-CT images. Lastly, a multimodal model, namely PrismSAP, was constructed based on aforementioned features in the training set. The predictive accuracy of PrismSAP was verified in the validation and internal test sets and further validated in the external test set. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, recall, precision and F1-score. RESULTS: A total of 1,221 eligible patients were randomly split into a training set (n = 864), a validation set (n = 209) and an internal test set (n = 148). Data of 266 patients were for external testing. In the external test set, PrismSAP performed best with the highest AUC of 0.916 (0.873-0.960) among all models [Model α: 0.709 (0.618-0.800); Model ß: 0.749 (0.675-0.824); Model γ: 0.687 (0.592-0.782); MCTSI: 0.778 (0.698-0.857); RANSON: 0.642 (0.559-0.725); BISAP: 0.751 (0.668-0.833); SABP: 0.710 (0.621-0.798)]. CONCLUSION: The proposed multimodal model outperformed any single-modality models and traditional scoring systems.

The role of nitrogen sources and hydrogen adsorption on the dynamic stability of Fe-N-C catalysts in oxygen reduction reaction.

Fe-N-C catalysts are promising alternatives to Pt-based electrocatalysts for the oxygen reduction reaction (ORR) in various electrochemical applications. However, their practical implementation is impeded by their instability during prolonged operation. Various degradation mechanisms have been proposed, yet the real origin of the intrinsic instability of Fe-N-C structures under ORR operations is still disputed. Herein, we observed a new type of protonation mechanism based on advanced first-principles simulations and experimental characterizations. The results revealed strong evidence of pyrrolic-N protonation in pyrrolic-type FeN4, which plays a vital role for the low kinetic barrier of Fe leaching. Conversely, the pyridinic-type FeN4 prefers protonation at the Fe site, contributing to the higher barrier of Fe leaching and relatively higher stability. The facile pyrrolic-N protonation is verified by various spectroscopy characterizations in the Nafion-treated FePc molecule. Crucially, the presence of oxygen-containing intermediates at the Fe site can further work synergistically with N protonation to promote conversion of iron atoms (Fe-N4) into ferric oxide under working potentials, and the more positive the electrode potential, the lower the kinetic barrier of Fe leaching. These findings serve as a foundation for future research endeavors on the stability issues of Fe-N-C catalysts and advancing their application in sustainable energy conversion technologies.

Attractant and repellent induce opposing changes in the four-helix bundle ligand-binding domain of a bacterial chemoreceptor.

Motile bacteria navigate toward favorable conditions and away from unfavorable environments using chemotaxis. Mechanisms of sensing attractants are well understood; however, molecular aspects of how bacteria sense repellents have not been established. Here, we identified malate as a repellent recognized by the MCP2201 chemoreceptor in a bacterium Comamonas testosteroni and showed that it binds to the same site as an attractant citrate. Binding determinants for a repellent and an attractant had only minor differences, and a single amino acid substitution in the binding site inverted the response to malate from a repellent to an attractant. We found that malate and citrate affect the oligomerization state of the ligand-binding domain in opposing way. We also observed opposing effects of repellent and attractant binding on the orientation of an alpha helix connecting the sensory domain to the transmembrane helix. We propose a model to illustrate how positive and negative signals might be generated.

Search for Dark-Matter-Nucleon Interactions with a Dark Mediator in PandaX-4T.

We report results of a search for dark-matter-nucleon interactions via a dark mediator using optimized low-energy data from the PandaX-4T liquid xenon experiment. With the ionization-signal-only data and utilizing the Migdal effect, we set the most stringent limits on the cross section for dark matter masses ranging from 30 MeV/c^{2} to 2 GeV/c^{2}. Under the assumption that the dark mediator is a dark photon that decays into scalar dark matter pairs in the early Universe, we rule out significant parameter space of such thermal relic dark-matter model.

Regulation of ferroptosis-related genes in CD8+ NKT cells and classical monocytes may affect the immunotherapy response after combined treatment in triple negative breast cancer.

BACKGROUND: Drug resistance has led to the failure of immunotherapy in triple negative breast cancer patients. Here we aimed to explore the mechanisms of drug resistance in patients in order to enhance their response to immunotherapy. METHODS: We downloaded publicly available single-cell RNA-sequencing data of peripheral blood mononuclear cells from patients after treatment to investigate the possible mechanisms of drug resistance. The publicly available TCGA transcriptomic data and somatic mutation data were used for further validation. In this study, a series of bioinformatics and machine learning methods were employed. RESULTS: We identified the vital roles of CD8+ NKT cells and classical monocytes in the immunotherapy response of triple-negative breast cancer patients. The proportion of these cell types was significantly increased in group partial response. We also found that downregulation of ferroptosis-related genes regulates the immune pathway. The analysis of scRNA data and TCGA transcriptomic data presented that DUSP1 may play a crucial role in immunotherapy resistance. CONCLUSION: Overall, the composition of the tumor microenvironment affects the immunotherapy response of patients, and DUSP1 may be a potential target for overcoming drug resistance.

Assessment of neurotransmitter imbalances within the anterior cingulate cortex in women with primary dysmenorrhea: An initial proton magnetic resonance spectroscopy study.

PURPOSE: The neural pathophysiology underlying primary dysmenorrhea (PDM), which leads to poor mode and changes in central pain modulatory systems, remains largely unknown. The objective of this study was to investigate the changes in glutamate/glutamine (Glx) and gamma-aminobutyric acid (GABA+) levels within anterior cingulate cortex (ACC), and their associations with clinical indicators in PDM women. METHODS: Using 3 T proton magnetic resonance spectroscopy (1H-MRS), we acquired and compared ACC-Glx and ACC-GABA+ levels in PDMs (N = 41) and age- and education-matched healthy controls (HCs) (N = 39) during both the menstrual and periovulatory phases, and between menstrual and periovulatory phases within each group. Total creatine (Cr referencing) level was used as an endogenous reference. The correlations of ACC-neurotransmitter levels with clinical characteristics and the correlations of ACC-Glx with ACC-GABA+ levels in the two groups were analyzed. RESULTS: Compared to HCs or the periovulatory phase, PDMs exhibited significantly increased ACC-Glx levels (p < 0.05) during the menstrual phase. Positive correlations between GABA+ and Glx levels (r = 0.385, p = 0.025) were found in PDMs during the menstrual phase. ACC-GABA+ levels were associated with self-rating distress scale (SDS) scores (GABA+/Cr: r = 0.369, p = 0.045) and pain catastrophizing scale (PCS) scores (GABA+/Cr: r = 0.373, p = 0.042) in PDM group in only the menstrual phase. CONCLUSION: Our study represents the first report of ACC-GABA+/Glx imbalances in PDMs during the menstrual phase, which may underlie the mechanisms mediating depression and painful catastrophic symptoms.

A clinical radiomics nomogram preoperatively to predict ductal carcinoma in situ with microinvasion in women with biopsy-confirmed ductal carcinoma in situ: a preliminary study.

PURPOSE: To predict ductal carcinoma in situ with microinvasion (DCISMI) based on clinicopathologic, conventional breast magnetic resonance imaging (MRI), and dynamic contrast enhanced MRI (DCE-MRI) radiomics signatures in women with biopsy-confirmed ductal carcinoma in situ (DCIS). METHODS: Eighty-six women with eighty-seven biopsy-proven DCIS who underwent preoperative MRI and underwent surgery were retrospectively identified. Clinicopathologic, conventional MRI, DCE-MRI radiomics, combine (based on conventional MRI and DCE-MRI radiomics), traditional (based on clinicopathologic and conventional MRI) and mixed (based on clinicopathologic, conventional MRI and DCE-MRI radiomics) models were constructed by logistic regression (LR) with a 3-fold cross-validation, all evaluated using receiver operating characteristic (ROC) curve analysis. A clinical radiomics nomogram was then built by incorporating the Radiomics score, significant clinicopathologic and conventional MRI features of mixed model. RESULTS: The area under the curves (AUCs) of clinicopathologic, conventional MRI, DCE-MRI radiomics, traditional, combine, and mixed model were 0.76 (95% confidence interval [CI] 0.59-0.94), 0.77 (95%CI 0.59-0.95), 0.74 (95%CI 0.55-0.93), 0.87 (95%CI 0.73-1), 0.8 (95%CI 0.63-0.96), and 0.93 (95%CI 0.84-1) in the validation cohort, respectively. The clinical radiomics nomogram based on mixed model showed higher AUCs than both clinicopathologic and DCE-MRI radiomics models in training/test (all P < 0.05) set and showed the greatest overall net benefit for upstaging according to decision curve analysis (DCA). CONCLUSION: A nomogram constructed by combining clinicopathologic, conventional MRI features and DCE-MRI radiomics signatures may be useful in predicting DCISMI from DICS preoperatively.

Dihydroartemisinin attenuates ischemia/reperfusion-induced renal tubular senescence by activating autophagy.

Acute kidney injury (AKI) is an important factor for the occurrence and development of CKD. The protective effect of dihydroartemisinin on AKI and and reported mechanism have not been reported. In this study, we used two animal models including ischemia-reperfusion and UUO, as well as a high-glucose-stimulated HK-2 cell model, to evaluate the protective effect of dihydroartemisinin on premature senescence of renal tubular epithelial cells in vitro and in vivo. We demonstrated that dihydroartemisinin improved renal aging and renal injury by activating autophagy. In addition, we found that co-treatment with chloroquine, an autophagy inhibitor, abolished the anti-renal aging effect of dihydroartemisinin in vitro. These findings suggested that activation of autophagy/elimination of senescent cell might be a useful strategy to prevent AKI/UUO induced renal tubular senescence and fibrosis.

Search for Light Dark Matter from the Atmosphere in PandaX-4T.

We report a search for light dark matter produced through the cascading decay of η mesons, which are created as a result of inelastic collisions between cosmic rays and Earth's atmosphere. We introduce a new and general framework, publicly accessible, designed to address boosted dark matter specifically, with which a full and dedicated simulation including both elastic and quasielastic processes of Earth attenuation effect on the dark matter particles arriving at the detector is performed. In the PandaX-4T commissioning data of 0.63 tonne·year exposure, no significant excess over background is observed. The first constraints on the interaction between light dark matter generated in the atmosphere and nucleus through a light scalar mediator are obtained. The lowest excluded cross section is set at 5.9×10^{-37} cm^{2} for a dark matter mass of 0.1 MeV/c^{2} and mediator mass of 300 MeV/c^{2}. The lowest upper limit of η to the dark matter decay branching ratio is 1.6×10^{-7}.

Matching end-of-life household vehicle generation and recycling capacity in Chinese cities: A spatio-temporal analysis for 2022-2050.

End-of-life vehicles (ELVs) present both opportunities and challenges for the environment and the economy, where effective recycling management plays a decisive role. Recently, the primary focus of recycling management has shifted from simply meeting demand to refining and optimizing processes at the city-scale. However, the mismatch in recycling capacity has become a significant obstacle to maximizing environmental and economic benefits. To reveal this issue and propose improvements in the context of China, this study simulates end-of-life internal combustion engine vehicles (ICEVs) and new energy vehicles (NEVs) at the city-scale from 2021 to 2050, and analyzes their spatio-temporal pattern and recycling capacity matching. The results indicate that the number of ELVs in China will continue to increase, peaking between 3.5 and 3.7 million. This growth will be mainly driven by third- to fifth-tier cities, as well as central and southwestern cities. Regarding recycling capacity matching, most cities possess excess dismantling capacity, while first-tier cities face coordination problems in battery collection. Spatial coordination across cities or provinces is a viable approach for dismantling enterprises and should be prioritized over indiscriminate deregistration or establishing new facilities. The absence of initiative within the recycling system results in uncoordinated battery collection. Implementing a recycling-sharing mechanism and establishing a reuse market can effectively tackle this problem by leveraging market incentives. These analyses provide practical suggestions to maximize the environmental and economic benefits of resource recycling, thereby contributing to the UN's 2030 Sustainable Development Goals (SDGs).

Search for Light Dark Matter with Ionization Signals in the PandaX-4T Experiment.

We report the search results of light dark matter through its interactions with shell electrons and nuclei, using the commissioning data from the PandaX-4T liquid xenon detector. Low energy events are selected to have an ionization-only signal between 60 to 200 photoelectrons, corresponding to a mean nuclear recoil energy from 0.77 to 2.54 keV and electronic recoil energy from 0.07 to 0.23 keV. With an effective exposure of 0.55 tonne·year, we set the most stringent limits within a mass range from 40 MeV/c^{2} to 10 GeV/c^{2} for pointlike dark matter-electron interaction, 100 MeV/c^{2} to 10 GeV/c^{2} for dark matter-electron interaction via a light mediator, and 3.2 to 4 GeV/c^{2} for dark matter-nucleon spin-independent interaction. For DM interaction with electrons, our limits are closing in on the parameter space predicted by the freeze-in and freeze-out mechanisms in the early Universe.