Clinical characteristics and surgical outcomes of vertebral lesions associated with tumor-induced osteomalacia: report of 16 patients and review of the literature.

Vertebral tumors in patients with tumor-induced osteomalacia (TIO) have a low diagnostic rate and poor postoperative outcomes. The application of 68 Ga-DOTATATE-PET/CT significantly increased the detection rate. Compared with tumor curettage, segmental resection was recommended as the preferred surgical type due to its high recovery rate. PURPOSE: Tumor-induced osteomalacia (TIO) is an acquired hypophosphatemic osteomalacia, and surgery is the first-line therapy. Most TIO tumors are found in the bones of the appendicular skeleton, cranium, and paranasal sinuses but rarely in the vertebrae. Tumor curettage and segmental resection are the two main surgical options for vertebral TIO patients. However, research on the clinical characteristics and surgical prognosis of vertebral TIO patients is rare. In the present study, for the first time, we investigated the clinical characteristics of 16 vertebral TIO patients and compared the surgical outcomes of patients who underwent surgery via two different surgical methods. METHODS: This was a retrospective cohort study. In this study, we included 16 adult TIO patients with lesions in vertebrae from Peking Union Medical College Hospital (PUMCH), all of whom underwent surgery. Baseline laboratory data were collected through medical records review. Technetium-99 m octreotide scintigraphy (99Tcm-OCT) and 68gallium-DOTA-TATE-positron emission tomography/computed tomography (68 Ga-DOTATATE-PET/CT) were conducted at the Department of Nuclear Medicine of PUMCH. The tumor histopathology was confirmed by a senior pathologist at our center. RESULTS: Vertebral TIO patients had lower serum phosphorus and TmP/GFR and higher serum alkaline phosphatase (ALP), serum parathyroid hormone (PTH), and serum C-terminal cross-linked telopeptide of type I collagen (ß-CTX) levels than the normal range. The sensitivity of 68 Ga‒DOTATATE PET/CT was 100%, significantly greater than that of 99Tcm-OCT (40%). After comparing the outcomes between the two surgical methods, we found that the recovery rate after segmental resection (62.5%) was greater than that after tumor curettage (12.5%). In the thoracic and sacral vertebrae, segmental resection surgery had a good prognosis. CONCLUSION: 68 Ga-DOTATATE PET/CT could serve as the first diagnostic tool in patients with vertebral TIO, and segmental resection could be used as the preferred surgery. This study would raise awareness of the clinical features and management of these rare vertebral TIO patients.

Study on the key parameters of ice particle air jet ejector structure.

Existing ice particle jet surface treatment technology is prone to ice particle adhesion during application, significantly affecting surface treatment efficiency. Based on the basic structure of the jet pump, the ice particle air jet surface treatment technology is proposed for the instant preparation and utilization of ice particles, solving the problem of ice particle adhesion and clogging. To achieve efficient utilization of ice particles and high-speed jetting, an integrated jet structure for ice particle ejection and acceleration was developed. The influence of the working nozzle position (Ld), expansion ratio (n), and acceleration nozzle diameter ratio (Dn) length-to-diameter ratio (Ln) on the ice particle ejection and acceleration was systematically studied. The structural parameters of the ejector were determined using the impact kinetic energy of ice particles as the comprehensive evaluation index, and the surface treatment test was conducted to verify the results. The study shows that under 2 MPa air pressure, the ejector nozzle parameters of n = 1.5, Dn = 4.0, Ld = 4, and Ln = 0 mm can effectively eject and accelerate the ice particles. The aluminum alloy plate depainting test obtained a larger paint removal radius and resulted in a smoother aluminum alloy plate surface, reducing the surface roughness from 3.194 ± 0.489 µm to 1.156 ± 0.136 µm. The immediate preparation and utilization of ice particles solved the problems of adhesion and storage in the engineering application of ice particle air jet technology, providing a feasible technical method in the field of material surface treatment.

Health welfare in the digital era: Exploring the impact of digital trade on residents' health.

As a product combining information and communication technology, digital technology, and traditional trade, digital trade represents a new form of international trade development in the context of economic globalization. As its scale continues to expand, digital trade not only profoundly impacts consumer health behaviors and environmental pollution control but also enhances opportunities for residents to access healthcare products and services. This could potentially have a significant promoting effect on residents' health levels. However, the extent and mechanisms through which digital trade affects residents' health remain unclear. Accordingly, this study fills in a gap in the research by calculating the provincial-level digital trade index for China from 2012-2020 and matching it with data from the China Family Panel Studies. The goal is to find the micro-causal mechanisms of digital trade on residents' health from green consumption and environmental improvement perspectives. The results show that digital trade reduces residents' medical expenses and improves their health. We use a quasi-natural experiment by treating the cross-border e-commerce comprehensive pilot zone as a digital trade treatment group and conducting a difference-in-differences estimation, finding that the health effects of digital trade remain significant. Heterogeneity indicates that the health effects of digital trade are powerful for middle- and high-income households and rural residents. In the east and center, in regions with well-developed transportation infrastructure and digital financial inclusion, the development of digital trade is more conducive to residents' health. Additionally, we demonstrate that digital trade can affect residents' health by promoting green consumption, eliminating energy poverty (i.e., improving indoor air pollution), and enhancing environmental quality (i.e., improving outdoor environmental pollution). This study provides solid scientific empirical evidence for enhancing human sustainable development through global digital trade.

Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior Enhancement.

The reconstruction of indoor scenes from multi-view RGB images is challenging due to the coexistence of flat and texture-less regions alongside delicate and fine-grained regions. Recent methods leverage neural radiance fields aided by predicted surface normal priors to recover the scene geometry. These methods excel in producing complete and smooth results for floor and wall areas. However, they struggle to capture complex surfaces with high-frequency structures due to the inadequate neural representation and the inaccurately predicted normal priors. This work aims to reconstruct high-fidelity surfaces with fine-grained details by addressing the above limitations. To improve the capacity of the implicit representation, we propose a hybrid architecture to represent low-frequency and high-frequency regions separately. To enhance the normal priors, we introduce a simple yet effective image sharpening and denoising technique, coupled with a network that estimates the pixel-wise uncertainty of the predicted surface normal vectors. Identifying such uncertainty can prevent our model from being misled by unreliable surface normal supervisions that hinder the accurate reconstruction of intricate geometries. Experiments on the benchmark datasets show that our method outperforms existing methods in terms of reconstruction quality. Furthermore, the proposed method also generalizes well to real-world indoor scenarios captured by our hand-held mobile phones. Our code is publicly available at: https://github.com/yec22/Fine-Grained-Indoor-Recon.

Knocking down EGR1 inhibits nucleus pulposus cell senescence and mitochondrial damage through activation of PINK1-Parkin dependent mitophagy, thereby delaying intervertebral disc degeneration.

Mitophagy plays a crucial role in maintaining the homeostasis of intervertebral disc (IVD). Early Growth Response 1 (EGR1), a conservative transcription factor, is commonly upregulated under oxidative stress conditions and participates in regulating cellular senescence, apoptosis, and inflammatory responses. However, the specific role of EGR1 in nucleus pulposus (NP) cell senescence and mitophagy remains unclear. In this study, through bioinformatics analysis and validation using human tissue specimens, we found that EGR1 is significantly upregulated in IVD degeneration (IDD). Further experimental results demonstrate that knockdown of EGR1 inhibits TBHP-induced NP cell senescence and mitochondrial dysfunction while promoting the activation of mitophagy. The protective effect of EGR1 knockdown on NP cell senescence and mitochondrion disappears upon inhibition of mitophagy with mdivi1. Mechanistic studies reveal that EGR1 suppresses NP cell senescence and mitochondrial dysfunction by modulating the PINK1-Parkin dependent mitophagy pathway. Additionally, EGR1 knockdown delays acupuncture-induced IDD in rats. In conclusion, our study demonstrates that under TBHP-induced oxidative stress, EGR1 knockdown mitigates NP cell senescence and mitochondrial dysfunction through the PINK1-Parkin dependent mitophagy pathway, thereby alleviating IDD.

Aspirin attenuates the detrimental effects of TNF-α on BMMSC stemness by modulating the YAP-SMAD7 axis.

BACKGROUND: Bone marrow mesenchymal stem cells (BMMSCs) are commonly used for cell transplantation to treat refractory diseases. However, the presence of inflammatory factors, such as tumour necrosis factor-alpha (TNF-α), at the transplantation site severely compromises the stemness of BMMSCs, thereby reducing the therapeutic effect of cell transplantation. Aspirin (AS) is a drug that has been in use for over a century and has a wide range of effects, including the regulation of cell proliferation, multidirectional differentiation, and immunomodulatory properties of stem cells. However, it is still unclear whether AS can delay the damaging effects of TNF-α on BMMSC stemness. METHODS: This study investigated the effects of AS and TNF-α on BMMSC stemness and the molecular mechanisms using colony formation assay, western blot, qRT-PCR, and overexpression or knockdown of YAP and SMAD7. RESULTS: The results demonstrated that TNF-α inhibited cell proliferation, the expression of stemness, osteogenic and chondrogenic differentiation markers of BMMSCs. Treatment with AS was shown to mitigate the TNF-α-induced damage to BMMSC stemness. Mechanistic studies revealed that AS may reverse the damage caused by TNF-α on BMMSC stemness by upregulating YAP and inhibiting the expression of SMAD7. CONCLUSION: AS can attenuate the damaging effects of TNF-α on BMMSC stemness by regulating the YAP-SMAD7 axis. These findings are expected to promote the application of AS to improve the efficacy of stem cell therapy.

Molecular epidemiology and phylogenetic analysis of influenza viruses A (H3N2) and B/Victoria during the COVID-19 pandemic in Guangdong, China.

BACKGROUND: Non-pharmaceutical measures and travel restrictions have halted the spread of coronavirus disease 2019 (COVID-19) and influenza. Nonetheless, with COVID-19 restrictions lifted, an unanticipated outbreak of the influenza B/Victoria virus in late 2021 and another influenza H3N2 outbreak in mid-2022 occurred in Guangdong, southern China. The mechanism underlying this phenomenon remains unknown. To better prepare for potential influenza outbreaks during COVID-19 pandemic, we studied the molecular epidemiology and phylogenetics of influenza A(H3N2) and B/Victoria that circulated during the COVID-19 pandemic in this region. METHODS: From January 1, 2018 to December 31, 2022, we collected throat swabs from 173,401 patients in Guangdong who had acute respiratory tract infections. Influenza viruses in the samples were tested using reverse transcription-polymerase chain reaction, followed by subtype identification and sequencing of hemagglutinin (HA) and neuraminidase (NA) genes. Phylogenetic and genetic diversity analyses were performed on both genes from 403 samples. A rigorous molecular clock was aligned with the phylogenetic tree to measure the rate of viral evolution and the root-to-tip distance within strains in different years was assessed using regression curve models to determine the correlation. RESULTS: During the early period of COVID-19 control, various influenza viruses were nearly undetectable in respiratory specimens. When control measures were relaxed in January 2020, the influenza infection rate peaked at 4.94% (39/789) in December 2021, with the influenza B/Victoria accounting for 87.18% (34/39) of the total influenza cases. Six months later, the influenza infection rate again increased and peaked at 11.34% (255/2248) in June 2022; influenza A/H3N2 accounted for 94.51% (241/255) of the total influenza cases in autumn 2022. The diverse geographic distribution of HA genes of B/Victoria and A/H3N2 had drastically reduced, and most strains originated from China. The rate of B/Victoria HA evolution (3.11 × 10-3, P < 0.05) was 1.7 times faster than before the COVID-19 outbreak (1.80 × 10-3, P < 0.05). Likewise, the H3N2 HA gene's evolution rate was 7.96 × 10-3 (P < 0.05), which is 2.1 times faster than the strains' pre-COVID-19 evolution rate (3.81 × 10-3, P < 0.05). CONCLUSIONS: Despite the extraordinarily low detection rate of influenza infection, concealed influenza transmission may occur between individuals during strict COVID-19 control. This ultimately leads to the accumulation of viral mutations and accelerated evolution of H3N2 and B/Victoria viruses. Monitoring the evolution of influenza may provide insights and alerts regarding potential epidemics in the future.

MTF2 facilitates the advancement of osteosarcoma through mediating EZH2/SFRP1/Wnt signaling.

BACKGROUND: Osteosarcoma is a soft tissue neoplasm with elevated recurrence risk and highly metastatic potential. Metal response element binding transcriptional factor 2 (MTF2) has been revealed to exert multiple activities in human tissues. The present research was conducted to explore the functions and related response mechanism of MTF2 in osteosarcoma which have not been introduced yet. METHODS: Bioinformatics tools identified the differential MTF2 expression in osteosarcoma tissues. MTF2 expression in osteosarcoma cells was examined with Western blot. Cell Counting Kit-8 (CCK-8) assay, 5-Ethynyl-2'-deoxyuridine (EDU) staining, wound healing as well as transwell assays measured cell proliferation, migration and invasion, respectively. Flow cytometry assay detected the cellular apoptotic level. Western blot also measured the expressions of proteins associated with epithelial mesenchymal transition (EMT), apoptosis and enhancer of zeste homolog 2 (EZH2)/secreted frizzled-related protein 1 (SFRP1)/Wnt signaling. Co-immunoprecipitation (Co-IP) assay confirmed MTF2-EZH2 interaction. RESULTS: MTF2 expression was increased in osteosarcoma tissues and cells. MTF2 interference effectively inhibited the proliferation, migration and invasion of osteosarcoma cells and promoted the cellular apoptotic rate. MTF2 directly bound to EZH2 and MTF2 silence reduced EZH2 expression, activated SFRP1 expression and blocked Wnt signaling in osteosarcoma cells. EZH2 upregulation or SFRP1 antagonist WAY-316606 partly counteracted the impacts of MTF2 down-regulation on the SFRP1/Wnt signaling and the biological phenotypes of osteosarcoma cells. CONCLUSIONS: MTF2 might down-regulate SFRP1 to activate Wnt signaling and drive the progression of osteosarcoma via interaction with EZH2 protein.

Museomics allows comparative analyses of mitochondrial genomes in the family Gryllidae (Insecta, Orthoptera) and confirms its phylogenetic relationships.

Background: Next-generation sequencing technology can now be used to sequence historical specimens from natural history collections, an approach referred to as museomics. The museomics allows obtaining molecular data from old museum-preserved specimens, a resource of biomolecules largely underexploited despite the fact that these specimens are often unique samples of nomenclatural types that can be crucial for resolving scientific questions. Despite recent technical progress, cricket mitogenomes are still scarce in the databases, with only a handful of new ones generated each year from freshly collected material. Methods: In this study, we used the genome skimming method to sequence and assemble three new complete mitogenomes representing two tribes of the cricket subfamily Eneopterinae: two were obtained from old, historical type material of Xenogryllus lamottei (68 years old) and X. maniema (80 years old), the third one from a freshly collected specimen of Nisitrus vittatus. We compared their genome organization and base composition, and reconstructed the molecular phylogeny of the family Gryllidae. Results: Our study not only confirmed that the genome skimming method used by next generation sequencing allows us to efficiently obtain the whole mitogenome from dry-pinned historical specimens, but we also confirmed how promising it is for large-scale comparative studies of mitogenomes using resources from natural history collections. Used in a phylogenetic context the new mitogenomes attest that the mitogenomic data contain valuable information and also strongly support phylogenetic relationships at multiple time scales.

Application of machine learning approaches to predict ammonium nitrogen transport in different soil types and evaluate the contribution of control factors.

The loss of nitrogen in soil damages the environment. Clarifying the mechanism of ammonium nitrogen (NH4+-N) transport in soil and increasing the fixation of NH4+-N after N application are effective methods for improving N use efficiency. However, the main factors are not easily identified because of the complicated transport and retardation factors in different soils. This study employed machine learning (ML) to identify the main influencing factors that contribute to the retardation factor (Rf) of NH4+-N in soil. First, NH4+-N transport in the soil was investigated using column experiments and a transport model. The Rf (1.29 - 17.42) was calculated and used as a proxy for the efficacy of NH4+-N transport. Second, the physicochemical parameters of the soil were determined and screened using lasso and ridge regressions as inputs for the ML model. Third, six machine learning models were evaluated: Adaptive Boosting, Extreme Gradient Boosting (XGB), Random Forest, Gradient Boosting Regression, Multilayer Perceptron, and Support Vector Regression. The optimal ML model of the XGB model with a low mean absolute error (0.81), mean squared error (0.50), and high test r2 (0.97) was obtained by random sampling and five-fold cross-validation. Finally, SHapely Additive exPlanations, entropy-based feature importance, and permutation characteristic importance were used for global interpretation. The cation exchange capacity (CEC), total organic carbon (TOC), and Kaolin had the greatest effects on NH4+-N transport in the soil. The accumulated local effect offered a fundamental insight: When CEC > 6 cmol+ kg-1, and TOC > 40 g kg-1, the maximum resistance to NH4+-N transport within the soil was observed. This study provides a novel approach for predicting the impact of the soil environment on NH4+-N transport and guiding the establishment of an early-warning system of nutrient loss.

Comparative Analysis of Vision Transformers and Conventional Convolutional Neural Networks in Detecting Referable Diabetic Retinopathy.

Objective: Vision transformers (ViTs) have shown promising performance in various classification tasks previously dominated by convolutional neural networks (CNNs). However, the performance of ViTs in referable diabetic retinopathy (DR) detection is relatively underexplored. In this study, using retinal photographs, we evaluated the comparative performances of ViTs and CNNs on detection of referable DR. Design: Retrospective study. Participants: A total of 48 269 retinal images from the open-source Kaggle DR detection dataset, the Messidor-1 dataset and the Singapore Epidemiology of Eye Diseases (SEED) study were included. Methods: Using 41 614 retinal photographs from the Kaggle dataset, we developed 5 CNN (Visual Geometry Group 19, ResNet50, InceptionV3, DenseNet201, and EfficientNetV2S) and 4 ViTs models (VAN_small, CrossViT_small, ViT_small, and Hierarchical Vision transformer using Shifted Windows [SWIN]_tiny) for the detection of referable DR. We defined the presence of referable DR as eyes with moderate or worse DR. The comparative performance of all 9 models was evaluated in the Kaggle internal test dataset (with 1045 study eyes), and in 2 external test sets, the SEED study (5455 study eyes) and the Messidor-1 (1200 study eyes). Main Outcome Measures: Area under operating characteristics curve (AUC), specificity, and sensitivity. Results: Among all models, the SWIN transformer displayed the highest AUC of 95.7% on the internal test set, significantly outperforming the CNN models (all P < 0.001). The same observation was confirmed in the external test sets, with the SWIN transformer achieving AUC of 97.3% in SEED and 96.3% in Messidor-1. When specificity level was fixed at 80% for the internal test, the SWIN transformer achieved the highest sensitivity of 94.4%, significantly better than all the CNN models (sensitivity levels ranging between 76.3% and 83.8%; all P < 0.001). This trend was also consistently observed in both external test sets. Conclusions: Our findings demonstrate that ViTs provide superior performance over CNNs in detecting referable DR from retinal photographs. These results point to the potential of utilizing ViT models to improve and optimize retinal photo-based deep learning for referable DR detection. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Ms gene and Mr gene: Microbial-mediated spatiotemporal communication between plants.

Within dynamic agroecosystems, microbes can act as key intermediaries, facilitating spatiotemporal communication among plants. Future research could categorize key plant genes involved in plant-microbe interactions into microbiome-shaping genes (Ms genes) and microbiome-responsive genes (Mr genes), potentially leading to the construction of spatiotemporal molecular networks with microbes as intermediaries.

Contributions of Surface Oxidizing Species and Cu+ to the Antibacterial Activities of Cu2O with Different Crystalline Structures.

Although precise regulation of the crystalline structures of metal oxides is an effective method to improve their antibacterial activities, the corresponding mechanisms involved in this process are still unclear. In this study, three kinds of cuprous oxide (Cu2O) samples with different structures of cubes, octahedra, and rhombic dodecahedra (c-Cu2O, o-Cu2O, and r-Cu2O) have been successfully synthesized and their antibacterial activities are compared. The antibacterial activities follow the order of r-Cu2O > o-Cu2O > c-Cu2O, revealing the significant dependence of the antibacterial activities on the crystalline structures of Cu2O. Quenching experiments, as well as the NBT and DPD experiments indicate that ≡CuII─OO• superoxo and ≡CuII─OOH peroxo, instead of •OH, O2•-, and H2O2, are the primary oxidizing species in the oxidative damage to E. coli. Raman analysis further confirms the presence of both ≡CuII─OO• superoxo and ≡CuII─OOH peroxo on the surface of r-Cu2O. On the other hand, the NCP experiment reveals that Cu+, instead of Cu2+, also contributes to the antibacterial process. This study provides new insight into the antibacterial mechanisms of Cu2O.

Tailoring the electrode material and structure of rocking-chair capacitive deionization for high-performance desalination.

With the gradually increasing requirement for freshwater, capacitive deionization (CDI) as a burgeoning desalination technique has gained wide attention owing to its merits of easy operation, high desalination efficiency, and environmental friendliness. To enhance the desalination performance of CDI, different CDI architectures are designed, such as membrane CDI, hybrid CDI, and flow-electrode CDI. However, these CDI systems have their own drawbacks, such as the high cost of membranes, capacity limitation of carbon materials and slurry blockage, which severely limit their practical application. Notably, rocking-chair CDI (RCDI) composed of symmetric electrode materials delivers excellent desalination performance because of its special dual chamber structure, which can not only break through the capacity limitations of carbon materials, but also deliver a continuous desalination process. Although RCDI showcases high promise for efficient desalination, few works systematically summarize the advantages and applications of RCDI in the desalination field. This review offers a thorough analysis of RCDI, focusing on its electrode materials, structure designs and desalination applications. Furthermore, the desalination performances of RCDI and other CDI architectures are compared to demonstrate the advantages of RCDI and the prospect of RCDI is elucidated.

Distilling mathematical reasoning capabilities into Small Language Models.

This work addresses the challenge of democratizing advanced Large Language Models (LLMs) by compressing their mathematical reasoning capabilities into sub-billion parameter Small Language Models (SLMs) without compromising performance. We introduce Equation-of-Thought Distillation (EoTD), a novel technique that encapsulates the reasoning process into equation-based representations to construct an EoTD dataset for fine-tuning SLMs. Additionally, we propose the Ensemble Thoughts Distillation (ETD) framework to enhance the reasoning performance of SLMs. This involves creating a reasoning dataset with multiple thought processes, including Chain-of-Thought (CoT), Program-of-Thought (PoT), and Equation-of-Thought (EoT), and using it for fine-tuning. Our experimental performance demonstrates that EoTD significantly boosts the reasoning abilities of SLMs, while ETD enables these models to achieve state-of-the-art reasoning performance.

Improved physicochemical and functional properties of dietary fiber from matcha fermented by Trichoderma viride.

The low-grade matcha is rich in insoluble dietary fiber. Trichoderma viride was used to increase the soluble dietary fiber to improve its functional properties. The soluble dietary fiber content increased from 6.74% to 15.24%. Pectin, hemicellulose, maltose, d-xylose, and glucose contents increased by 63.35% and 11.54%, 2.18, 0.11, and 7.04 mg/g, respectively. Trichoderma viride fermentation disrupted the dense structure of insoluble dietary fiber, resulting in a honeycomb structure and improving crystallinity by 22.75%. These structural changes led to an improved cation exchange capacity from 1.69 to 4.22 mmol/g, an increase in the inhibitory effect of α-amylase from 47.38% to 72.04%, and a 2.13-fold in the ferrous ion scavenging ability, and the IC50 values of superoxide anion was reduced from 7.00 to 1.54 mg/mL, respectively. Therefore, Trichoderma viride fermentation is an excellent method for improving the quality of dietary fiber in matcha processing by-products.

A Multimodal Dataset for Mixed Emotion Recognition.

Mixed emotions have attracted increasing interest recently, but existing datasets rarely focus on mixed emotion recognition from multimodal signals, hindering the affective computing of mixed emotions. On this basis, we present a multimodal dataset with four kinds of signals recorded while watching mixed and non-mixed emotion videos. To ensure effective emotion induction, we first implemented a rule-based video filtering step to select the videos that could elicit stronger positive, negative, and mixed emotions. Then, an experiment with 80 participants was conducted, in which the data of EEG, GSR, PPG, and frontal face videos were recorded while they watched the selected video clips. We also recorded the subjective emotional rating on PANAS, VAD, and amusement-disgust dimensions. In total, the dataset consists of multimodal signal data and self-assessment data from 73 participants. We also present technical validations for emotion induction and mixed emotion classification from physiological signals and face videos. The average accuracy of the 3-class classification (i.e., positive, negative, and mixed) can reach 80.96% when using SVM and features from all modalities, which indicates the possibility of identifying mixed emotional states.

Constructing Three-Dimensional Architectures to Design Advanced Copper-Based Current Collector Materials for Alkali Metal Batteries: From Nanoscale to Microscale.

Alkali metals (Li, Na, and K) are deemed as the ideal anode materials for next-generation high-energy-density batteries because of their high theoretical specific capacity and low redox potentials. However, alkali metal anodes (AMAs) still face some challenges hindering their further applications, including uncontrollable dendrite growth and unstable solid electrolyte interphase during cycling, resulting in low Coulombic efficiency and inferior cycling performance. In this regard, designing 3D current collectors as hosts for AMAs is one of the most effective ways to address the above-mentioned problems, because their sufficient space could accommodate AMAs' volume expansion, and their high specific surface area could lower the local current density, leading to the uniform deposition of alkali metals. Herein, we review recent progress on the application of 3D Cu-based current collectors in stable and dendrite-free AMAs. The most widely used modification methods of 3D Cu-based current collectors are summarized. Furthermore, the relationships among methods of modification, structure and composition, and the electrochemical properties of AMAs using Cu-based current collectors, are systematically discussed. Finally, the challenges and prospects for future study and applications of Cu-based current collectors in high-performance alkali metal batteries are proposed.

Strigolactones Negatively Regulate Tobacco Mosaic Virus Resistance in Nicotiana benthamiana.

Strigolactones (SLs) are plant hormones that regulate diverse developmental processes and environmental responses in plants. It has been discovered that SLs play an important role in regulating plant immune resistance to pathogens but there are currently no reports on their role in the interaction between Nicotiana benthamiana and the tobacco mosaic virus (TMV). In this study, the exogenous application of SLs weakened the resistance of N. benthamiana to TMV, promoting TMV infection, whereas the exogenous application of Tis108, a SL inhibitor, resulted in the opposite effect. Virus-induced gene silencing (VIGS) inhibition of two key SL synthesis enzyme genes, NtCCD7 and NtCCD8, enhanced the resistance of N. benthamiana to TMV. Additionally, we conducted a screening of N. benthamiana related to TMV infection. TMV-infected plants treated with SLs were compared to the control by using RNA-seq. The KEGG enrichment analysis and weighted gene co-expression network analysis (WGCNA) of differentially expressed genes (DEGs) suggested that plant hormone signaling transduction may play a significant role in the SL-TMV-N. benthamiana interactions. This study reveals new functions of SLs in regulating plant immunity and provides a reference for controlling TMV diseases in production.