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We address the problem of multi-modal object tracking in video and explore various options available for fusing the complementary information conveyed by the visible (RGB) and thermal infrared (TIR) modalities, including pixel-level, feature-level and decision-level fusion. Specifically, in contrast to the existing approaches, we propose and develop the paradigm for combining multi-modal information for image fusion at pixel level. At the feature level, two different kinds of fusion strategies are investigated for completeness, i.e. , the attention-based online fusion strategy and the offline-trained fusion block. At the decision level, a novel fusion strategy is put forward, inspired by the success of the simple averaging configuration which has shown so much promise. The effectiveness of the proposed decision-level fusion strategy owes to a number of innovative contributions, including a dynamic weighting of the RGB and TIR contributions and a linear template update operation. A variant of the proposed decision fusion method produced the winning tracker at the Visual Object Tracking Challenge 2020 (VOT-RGBT2020). A comprehensive comparison of the innovative pixel and feature-level fusion strategies with the proposed decision-level fusion method highlights the advantages fusing multimodal information at the decision score level. Extensive experimental results on five challenging datasets, i.e. , GTOT, VOT-RGBT2019, RGBT234, LasHeR and VOT-RGBT2020, demonstrate the effectiveness and robustness of the proposed method, compared to the state-of-the-art approaches. The Code is available at https://github.com/Zhangyong-Tang/DFAT. • Multiple fusion strategies. • A decision-level fusoin method. • Winner of VOT-RGBT2020. [ABSTRACT FROM AUTHOR]

Spectral line enhancement and transient interference suppression of underwater objects are critical issues for passive sonar systems. Conventional approaches for processing spectral lines have focused on either time-domain or frequency-domain methods. In this study, the constrained non-negative matrix factorization is proposed to process the underwater spectral lines in the joint time-frequency domain. Based on the sparsity of spectral lines in the frequency domain, the sparseness criterion is utilized to constrain the basis matrix that represents the frequency-mode of the signal. The correlation between sparsity and frequency estimation accuracy is examined with weight coefficients, and an effective weight coefficient interval for the sparseness term is determined to optimize the detection of the spectral line. To address the issue of abrupt changes in signal energy caused by transient interference, the temporal continuity criterion is applied to constrain the coefficient matrix representing the temporal gain mode of the signal. An analysis is conducted to determine the impact of weight coefficient on the continuity of the coefficient matrix, and the optimal weight coefficient of the temporal continuity term is established to suppress local transient strong interference. Experimental results demonstrate that the algorithm significantly enhances the capacity for the detection and extraction of spectral lines. • Constrained non-negative matrix factorization is proposed to process underwater spectral lines in the time-frequency domain. • Sparseness criterion is constrained on the basis matrix to improve the enhancement of the underwater spectral line. • Temporal continuity criterion is constrained on the coefficient matrix to suppress the local transient strong interference. [ABSTRACT FROM AUTHOR]

To ensure that honey belongs to a very appreciated botanical class, the classical methodology is melissopalynology analysis to identify and count pollen grains. However, this method is time-consuming and laborious. In this work, four stable isotopes (δ13C h , δ13C p , δ18O and δ2H) and twelve elemental contents (Na, Mg, Ca, K, Fe, Cr, Mn, Co, Cu, Sr, Se, Mo) were used to build the dataset, and the Random Forest (RF) algorithm, Support Vector Machines (SVM), Classification and Regression Trees (CART) and Linear Discriminant Analysis (LDA) were investigated to classify six varieties of Chinese honey (linden, sunflower, vetch, rape, acacia, and jujube). The results showed the RF algorithm exhibits the highest training accuracy (99.4%) and testing accuracy (96.5%) of the four algorithms. Hence, the RF algorithm was selected to rank the 16 attributes according to their contribution, and δ2H, Sr, δ18O, Mn, Ca, and K were considered the most important factors for identifying six varieties of honey. Furthermore, the results of the RF algorithm were verified by the parallel coordinates plot. This suggests that the RF algorithm provides an effective and accurate approach for classifying Chinese honey according to stable isotopic and elemental composition, which theoretically can be used to classify more types of honey. • A simple and rapid random forest algorithm classification method was developed. • Six varieties of Chinese honey samples were analyzed. • δ2H, Sr, δ18O, Mn, Ca and K establish valuable contributions for classification. • The RF algorithm exhibits a higher classification accuracy (96.5%). [ABSTRACT FROM AUTHOR]

With the development of neural networking techniques, several architectures for symmetric positive definite (SPD) matrix learning have recently been put forward in the computer vision and pattern recognition (CV&PR) community for mining fine-grained geometric features. However, the degradation of structural information during multi-stage feature transformation limits their capacity. To cope with this issue, this paper develops a U-shaped neural network on the SPD manifolds (U-SPDNet) for visual classification. The designed U-SPDNet contains two subsystems, one of which is a shrinking path (encoder) making up of a prevailing SPD manifold neural network (SPDNet (Huang and Van Gool, 2017)) for capturing compact representations from the input data. Another is a constructed symmetric expanding path (decoder) to upsample the encoded features, trained by a reconstruction error term. With this design, the degradation problem will be gradually alleviated during training. To enhance the representational capacity of U-SPDNet, we also append skip connections from encoder to decoder, realized by manifold-valued geometric operations, namely Riemannian barycenter and Riemannian optimization. On the MDSD, Virus, FPHA, and UAV-Human datasets, the accuracy achieved by our method is respectively 6.92%, 8.67%, 1.57%, and 1.08% higher than SPDNet, certifying its effectiveness. • This paper designs a U-shaped neural network in the context of SPD manifolds (USPDNet) to cope with the problem of statistical information degradation. • The geometric computation-based skip connections are added from the encoder to the decoder to improve the representational capacity of the proposed SPD network. • Extensive experiments show that our method can achieve improved accuracy, even with limited data. [ABSTRACT FROM AUTHOR]

Existing image fusion algorithms mostly concentrate on the design of network architecture and loss functions, and using unified feature extraction strategies while neglecting the division of redundant and effective information. However, for complementary information, unified feature extractor may not appropriate. Thus, this paper presents a unified image fusion algorithm based on Bézier curves image augmentation and hierarchical decomposition, and a self-supervised learning task is constructed to learn the meaningful information. Where Bézier curves aim to simulate different image features and constructed special self-supervised learning samples, so our method does not require task specific data and can be easily trained on public natural image datasets. Meanwhile, our dual decomposition self-supervised training method can bring redundant information filtering capability to the model. During the decomposition stage, we classify and extract different features of the images and only utilize the extracted effective information in the fusion stage, and the decomposition ability of images provides a foundation for advanced visual tasks, such as image segmentation and object detection. Finally, more detailed and comprehensive fusion images are generated, and the existence of redundant information is effectively reduced. The validity of the proposed method is verified through qualitative and quantitative analysis of multiple image fusion tasks, and our algorithm gets the state-of-the-art results on multiple datasets of different image fusion tasks. The code of our fusion method is available at https://github.com/Yukarizz/DDBFusion. • A novel self-supervised learning method is proposed to decompose the image. • Interpretability of the fusion process. • An unified image fusion method. • Good performance on high-level vision tasks. [ABSTRACT FROM AUTHOR]

Background: The study aimed to explore whether Miya (MY), a kind of Clostridium butyricum, regulated osteoarthritis (OA) progression through adenosine 5'-monophosphate-activated protein kinase (AMPK) pathway. Methods: The OA rats were orally given MY daily for 4 weeks and were intramuscularly injected with AMPK inhibitor once a week for 4 weeks. Hematoxylin eosin (HE) staining was used to observe the histological morphology of the knee joint. The levels of succinate dehydrogenase (SDH) and muscle glycogen (MG) in the tibia muscle of rats were detected by the corresponding kits, as well as the expression of related genes/proteins were assessed by real-time quantitative PCR (RT-qPCR) and western blot. Results: HE staining suggested that MY suppressed the symptoms of OA, which was abolished by AMPK inhibitor. Furthermore, the SDH and MG contents in the OA + MY + AMPK inhibitor group were lower than in the OA + MY group. At last, the levels of AMPK, PI3K, AKT1, Ldh, Myod, Chrna1, and Chrnd were notably decreased after AMPK inhibitor treatment, while the levels of Lcad and Mcad were up-regulated by AMPK inhibitor. Furthermore, their protein expression levels detected by western blot were consistent with those from RT-qPCR. Conclusion: MY may partially regulate skeletal muscle changes and prevente OA development through the AMPK pathway. [ABSTRACT FROM AUTHOR]

• A new target-cognisant siamese-based anchor-free tracker. • The proposed method computes cross-spatial attention for refining the measurement of spatial similarity. • Two tracking mechanisms are used to promote the precision of bounding box prediction. • A max filtering module is proposed to filter out similar distractors. • Our method achieves competitive performance on several tracking datasets. Siamese trackers have become the mainstream framework for visual object tracking in recent years. However, the extraction of the template and search space features is disjoint for a Siamese tracker, resulting in a limited interaction between its classification and regression branches. This degrades the model capacity accurately to estimate the target, especially when it exhibits severe appearance variations. To address this problem, this paper presents a target-cognisant Siamese network for robust visual tracking. First, we introduce a new target-cognisant attention block that computes spatial cross-attention between the template and search branches to convey the relevant appearance information before correlation. Second, we advocate two mechanisms to promote the precision of obtained bounding boxes under complex tracking scenarios. Last, we propose a max filtering module to utilise the guidance of the regression branch to filter out potential interfering predictions in the classification map. The experimental results obtained on challenging benchmarks demonstrate the competitive performance of the proposed method. [ABSTRACT FROM AUTHOR]

Multi-view clustering has attracted widespread attention because of its capability to identify the common semantics shared by the data captured from different views of data, objects or phenomena. This is a challenging problem but with the emergence of deep auto-encoder networks, the performance of multi-view clustering methods has considerably improved. However, it is notable that most existing methods merely utilize the features outputted by the last encoder layer to carry out the clustering task. Such approach neglects potentially useful information conveyed by the features of the previous layers. To address the this problem, we propose a novel m ulti-layer m ulti-level comprehensive learning framework for deep m ulti-view c lustering (3MC). 3MC firstly conducts a contrastive learning involving different views based on deep features in each encoder layer separately, so as to achieve multi-view feature consistency. The next step is to construct layer-specific label MLPs to transform the features in each layer to high-level semantic labels. Finally, 3MC conducts an inter-layer contrastive learning using the high-level semantic labels in order to obtain multi-layer consistent clustering assignments. We demonstrate that the proposed comprehensive learning strategy, commencing from layer specific inter-view feature comparison to inter-layer high-level label comparison extracts and utilizes the underlying multi-view complementary information very successfully and achieves more accurate clustering. An extensive experimental comparison with the state-of-the-art methods demonstrates the effectiveness of the proposed framework. The code of this paper is available at https://github.com/chenzhe207/3MC. • A novel multi-layer feature learning method is designed to solve deep MvC problem. • A double contrastive learning strategy is proposed to realize multi-layer learning. • Detailed ablation study demonstrates the effectiveness of multi-layer learning. [ABSTRACT FROM AUTHOR]

In the majority of existing multi-view clustering methods, the prerequisite is that the data have the correct cross-view correspondence. However, this strong assumption may not always hold in real-world applications, giving rise to the so-called View-shuffled Problem (VsP). To address this challenge, we propose a novel multi-view clustering method, namely View-shuffled Clustering via the Modified Hungarian Algorithm (VsC-mH). Specifically, we first establish the cross-view correspondence of the shuffled data utilizing strategies of the global alignment and modified Hungarian algorithm (mH) based intra-category alignment. Subsequently, we generate the partition of the aligned data employing matrix factorization. The fusion of these two processes facilitates the interaction of information, resulting in improved quality of both data alignment and partition. VsC-mH is capable of handling the data with alignment ratios ranging from 0 to 100%. Both experimental and theoretical evidence guarantees the convergence of the proposed optimization algorithm. Extensive experimental results obtained on six practical datasets demonstrate the effectiveness and merits of the proposed method. • We propose a clustering solution for the view-shuffled problem with an alignment ratio ranging from 0 to 1. • The data alignment and partition are linked seamlessly, leading to improved performance. • The convergence of the optimization problem is guaranteed theoretically and experimentally. • The effectiveness and merits of the proposed method are demonstrated on six real datasets. [ABSTRACT FROM AUTHOR]

Conducting reliable feature interaction plays a critical role in the visual tracking community, especially in recent dominated Siamese-based tracking paradigm. In general, there are two primary approaches for fusing representations from template and search area in the Siamese setting, i.e. , cross-correlation and transformer modeling. The former provides a straightforward interaction solution, which may have limitations in handling complex scenarios, such as appearance variations and occlusion. While the latter offers an effective interaction mechanism, albeit with higher computation complexity and model cost. In contrast to traditional Siamese-based trackers which rely on two mentioned feature cross-correlation operators, this paper proposes a novel Correlation-Refine network to address the issue of lacking semantic information caused by local linear matching in correlation, from both spatial and channel perspectives. Correlation-Refine network (named CR) is solely built on top of fully convolutional layers, without employing intricate transformer mechanisms or complex methods to fuse features from multiple scales. Moreover, we present a concise yet effective convolutional tracking framework based on the correlation-refine network. CR network can increase the discriminative ability of semantic information in a coarse-to-fine manner: it gradually learns the semantic features of the target to be tracked and suppresses interference from similar objects by stacking multiple CR layers. Extensive experiments and comparisons with recent competitive trackers in challenging large-scale benchmarks demonstrate that, our tracker outperforms all previous convolutional trackers and has competitive results with transformer-based method. The code will be made available. • We put forward Correlation-Refine network to address the issue of lacking semantic information. • We construct an efficient tracking framework, gradually eliminating similarity interference from semantic level. • Our tracker outperforms all previous convolutional trackers on four authoritative benchmarks. [ABSTRACT FROM AUTHOR]

Systemic immune-inflammatory index (SII) has been recognized as a novel inflammatory indicator in numerous diseases. It remains unknown how SII affects all-cause mortality among patients with osteoarthritis (OA). In this prospective cohort study, we intended to examine the relationship of SII with all-cause mortality among OA populations and assess the interaction between depression and SII. Data was collected from National Health and Nutrition Examination Survey (NHANES) in 2005–2018. The National Death Index (NDI) provided vital status records. Multivariable Cox regression analyses with cubic spines were applied to estimate the association between SII and all-cause and CVD mortality. Stratified analysis and interaction tests assessed the interaction of SII and depression on all-cause mortality. In total 3174 OA adults were included. The lowest quartile Q1 (HR:1.44, 95%CI:1.02–2.04) and highest quartile Q4 (HR:1.44, 95%CI:1.02–2.04) of SII presented a higher risk of death compared with those in second quartile Q2 (Ref.) and third quartile Q3 (HR:1.23, 95%CI:0.89–1.68. Restricted cubic splines analysis revealed a U-shaped association of SII with all-cause mortality, the inflection points were 412.93 × 109/L. The interaction test observed a more significant relationship of SII with all-cause mortality in depression patients than in non-depression patients, indicating that depression can modify this association. First, the observational study design failed to make causal inferences. Second, the baseline SII cannot reflect the long-term level of inflammation. Finally, there may be potential bias. SII was U-shaped associated with all-cause mortality in OA patients, and this association was significantly heightened by depression. • Systemic immune-inflammation index (SII) predicts prognosis of osteoarthritis patients. • SII is U-shaped associated with all-cause mortality in osteoarthritis patients. • Depression heightens the association of SII with all-cause mortality. [ABSTRACT FROM AUTHOR]

Appropriate structural design engineering is an effective method to achieve high adsorption performance, and optimizing the architecture to meet the desired properties remains challenging for adsorbents. Herein, a novel porous layered K 2 Ti 8 O 17 (PLKTO) formed by interweaved nanowires has been successfully synthesized using MAX phase Ti 2 AlN and g-C 3 N 4 as the precursors. The K 2 Ti 8 O 17 nanowires consisted of numerous interconnected pores in a network-like distribution configuration, with a diameter of approximately 20 nm and a length exceeding one μm. Attractively, the PLKTO exhibited fast kinetics, large adsorption capacity, and excellent reusability toward methylene blue (MB) and Cu2+ adsorption. The maximum equilibrium adsorption capacity of MB and Cu2+ was 95.00 mg/g and 330.75 mg/g, with equilibrium times of ∼18 min and ∼25 min, respectively. The adsorption mechanism unveiled that the elimination of MB and Cu2+ was dominated by the pore-filling effect, electrostatic interaction, hydrogen bond, and surface complexation. Additionally, the PLKTO enjoyed highly selective for Cu2+ while investigating competitive adsorption behaviors in binary systems and real-world applications in various water matrices. These results indicated that the PLKTO revealed appealing application prospects for treating wastewater and pollutants and provided methodological guidance for design engineering in fabricating porous and layered structures. [ABSTRACT FROM AUTHOR]

Purpose: Arthroscopic superior capsule reconstruction (SCR) with the long head of the biceps (LHBT) was performed to restore structural stability, force couple balance, and shoulder joint function. This study aimed to evaluate the functional outcomes of SCR using the LHBT over at least 24 months of follow-up. Method: This retrospective study included 89 patients with massive rotator cuff tears who underwent SCR using the LHBT, met the inclusion criteria and underwent follow up for at least 24 months. The preoperative and postoperative shoulder range of motion (forward flexion, external rotation, and abduction), acromiohumeral interval (AHI), visual analog scale (VAS) score, American Shoulder and Elbow Surgeons (ASES) score and Constant–Murley score were obtained, and the tear size, and Goutallier and Hamada grades were also investigated. Results: Compared with those measured preoperatively, the range of motion, AHI, and VAS, Constant–Murley, and ASES scores were significantly improved immediately postoperatively (P < 0.001) and at the 6-month, 12-month, and final follow-ups (P < 0.001). At the last follow-up, the postoperative ASES score and Constant-Murley score increased from 42.8 ± 7.6 to 87.4 ± 6.1, and 42.3 ± 8.9 to 84.9 ± 10.7, respectively; with improvements of 51 ± 21.7 in forward flexion, 21.0 ± 8.1 in external rotation, and 58.5 ± 22.5 in abduction. The AHI increased 2.1 ± 0.8 mm and the VAS score significantly changed from 6.0 (5.0, 7.0) to 1.0 (0.0, 1.0), at the final follow-up. Eleven of the 89 patients experienced retears, and one patient needed reoperation. Conclusion: In this study with at least 24-months of follow-up, SCR using the LHBT for massive rotator cuff tears could effectively relieve shoulder pain, restore shoulder function and increase shoulder mobility to some extent. Level of evidence: IV. [ABSTRACT FROM AUTHOR]

Spatial boundary effect can significantly reduce the performance of a learned discriminative correlation filter (DCF) model. A commonly used method to relieve this effect is to extract appearance features from a wider region of a target. However, this way would introduce unexpected features from background pixels and noises, which will lead to a decrease of the filter's discrimination power. To address this shortcoming, this paper proposes an innovative method called enhanced robust spatial feature selection and correlation filter Learning (EFSCF), which performs jointly sparse feature learning to handle boundary effects effectively while suppressing the influence of background pixels and noises. Unlike the ℓ 2 -norm-based tracking approaches that are prone to non-Gaussian noises, the proposed method imposes the ℓ 2 , 1 -norm on the loss term to enhance the robustness against the training outliers. To enhance the discrimination further, a jointly sparse feature selection scheme based on the ℓ 2 , 1 -norm is designed to regularize the filter in rows and columns simultaneously. To the best of the authors' knowledge, this has been the first work exploring the structural sparsity in rows and columns of a learned filter simultaneously. The proposed model can be efficiently solved by an alternating direction multiplier method. The proposed EFSCF is verified by experiments on four challenging unmanned aerial vehicle datasets under severe noise and appearance changes, and the results show that the proposed method can achieve better tracking performance than the state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

Performing pattern analysis over the symmetric positive definite (SPD) manifold requires specific mathematical computations, characterizing the non-Euclidian property of the involved data points and learning tasks, such as the image set classification problem. Accompanied with the advanced neural networking techniques, several architectures for processing the SPD matrices have recently been studied to obtain fine-grained structured representations. However, existing approaches are challenged by the diversely changing appearance of the data points, begging the question of how to learn invariant representations for improved performance with supportive theories. Therefore, this paper designs two Riemannian operation modules for SPD manifold neural network. Specifically, a Riemannian batch regularization (RBR) layer is firstly proposed for the purpose of training a discriminative manifold-to-manifold transforming network with a novelly-designed metric learning regularization term. The second module realizes the Riemannian pooling operation with geometric computations on the Riemannian manifolds, notably the Riemannian barycenter, metric learning, and Riemannian optimization. Extensive experiments on five benchmarking datasets show the efficacy of the proposed approach. • This work designs a Riemannian batch regularization module for SPD neural network. • A Riemannian pooling module is designed for SPD neural network in this article. • Extensive experiments certify the efficacy of the two Riemannian operation modules. [ABSTRACT FROM AUTHOR]

Non-alcoholic fatty liver disease (NAFLD) is one of the most serious global public health concerns. However, there are currently no effective drugs for treatment of this disease. Icariin (ICA), a small-molecule natural product extracted from Epimedium brevicornu Maxim, offers various pharmacological activities. In the present work, we wondered whether ICA can attenuate NAFLD in db/db mice treated with ICA for 8 weeks and how ICA exerts an influence on NAFLD. In db/db mice, ICA treatment had a robust effect on inhibition of lipogenesis associated with NAFLD amelioration by decreasing liver lipid deposition, together with ameliorating insulin sensitivity, glucose tolerance, and fasting serum glucose. Of note, ICA-treated rats showed a much higher concentration of icaritin (ICT) in plasma, a major metabolite of ICA, about 2000 times higher than that of ICA by liquid chromatography mass spectrometry (LC-MS). Interestingly, ICT, rather than ICA, can dramatically decrease hepatic lipogenesis-related markers in oleate acid/palmitate acid (OA/PA)-induced steatosis in primary hepatocytes (PH) and HepG2 cells, and hepatic lipid accumulation in db/db mice, demonstrating the inhibitory effect of ICT on lipogenesis. Mechanistically, we found that anti-lipogenic activities of ICT were related to reducing endoplasmic reticulum (ER) stress as evidenced by Western blot, qPCR, and other assays in thapsigargin (THP) induced-ER stress models. To our knowledge, this is the first report showing the unexpected and key role for ICT on the prevention of NAFLD in db/db mice through an ER stress mechanism. [ABSTRACT FROM AUTHOR]

Inadequate immune response remains a critical cause of immunotherapy failure in various tumor treatments. Herein, we offer a new approach to achieve a cross-talk between innate and adaptive immune responses based on a new nanoplatform for photothermal therapeutics. The nanoplatform was formed by linking titanium carbide MXene with Mn2+-contained ovalbumin (OVA), where it can trigger efficient mt-DNA presentation and the release of OVA and Mn2+ upon the irradiation of near-infrared laser. More importantly, the released mt-DNA and Mn2+ synergistically activate innate immunity via the cGAS-stimulator of the interferon genes signaling pathway, and the OVA and protein antigens from tumor cells enhance adaptive immunity. Furthermore, in an osteosarcoma model, we observed that the proposed nanoplatform leads to the effective presentation of tumor antigens, which boost the maturation of dendritic cells (DCs) to the hilt and thus improve the infiltration of cytotoxic T lymphocyte in primary and distant tumors. Collectively, our work not only demonstrates a method for constructing a new nanoplatform for photothermal therapeutics but also provides a general strategy for synchronously activating innate and adaptive immunities to promote the maturation of DCs for antimetastasis tumor therapy. Upon irradiation, tumor-derived antigen and mt-DNA release from dying tumors which recruits the DCs in situ. And TPOM NPs decompose into Mn2+ and OVA under NIR. Immature DCs (iDCs) would uptake the OVA and antigen which promote DCs maturation through the adaptive immunity. MtDNA and Mn2+ could stimulate innate immune response in iDCs by upregulating STING pathway to get activation. Eventually iDC cells transformed into mature DCs to activate T lymphocytes for anti-tumor and anti-mestastic immunotherapy. [Display omitted] [ABSTRACT FROM AUTHOR]

The regulation of artificial interphase for advanced Zn anode is an effective solution to achieve superior electrochemical performance for aqueous batteries. However, the deployment of atomically precise architectures and ligand engineering to achieve functionalization-oriented regulatory screening is lacking, which is hindered by higher requirements for synthetic chemistry and structural chemistry. Herein, we have first performed ligand engineering which selected zinc ion trapping ligands (-CH 3 ) based on the coordination effect, and zinc substrate binding ligands (-N=N-C 6 H 5 ) based on the electrostatic interaction. Correspondingly, octa nuclear Zn(II)-Siloxane-PhPz/BiPhPz/TriPhPz Cluster (OZSPC/OZSBPC/OZSTPC) are accurately synthesized, and OZSBPC is verified to serve as the most suitable artificial interphase via balancing the interactions with both Zn 2+ and Zn substrate ("Zn-Zn effect"). Consequently, at -30 °C, the assembled OZSBPC-Zn symmetric cells run for 3000 h and the assembled full cells with OZSBPC-Zn anode could be stable for 10,000 cycles. The pouch cells using OZSBPC-Zn anode deliver a reversible capacity of ~1.2 Ah and the energy density of 41 Wh kg total -1 with excellent cycling performance. The successful structural design of OZSBPC explores a novel well-defined structural design concept as one criterion for artificial interface as well as motivates batteries systems to meet the requirements of industrialization., (© 2025 Wiley-VCH GmbH.)

Background: Osteosarcoma (OS), the commonest primary malignant bone tumor, is mainly seen in children and teenagers. LINC00960, a newly discovered long intergenic non-protein coding RNA, has been shown to be important in certain cancers. The objective of this study was to assess LINC00960's prognostic and therapeutic value and analyze its mechanism of action in osteosarcoma., Methods: With the transcriptome information of 85 osteosarcomas from the TARGET database, the Cox regression analyses, K-M curve, and ROC curve, were conducted for survival and prognostic analysis. The functional analysis was conducted using GO, KEGG, GSEA, and GSVA. The ESTIMATE, ssGSEA, MCP-counter, ImmuCellAI algorithms, and immune checkpoint correlation analysis were performed for immune-related analysis. The single-cell RNA sequencing data of 6 osteosarcoma patients was obtained from the Gene Expression Omnibus database. The Tumor Immune Dysfunction and Exclusion algorithm and the "pRRophetic" R package were performed to predict the response to immunotherapy and chemotherapy., Results: LINC00960 overexpression is associated with osteosarcoma metastasis and poor prognosis. Based on the LINC00960 expression, the nomogram prediction model was created, which showed good accuracy and precision to predict the overall survival of osteosarcoma. Single-cell and immune-related analysis showed that LINC00960 is mainly highly expressed in the tumor-exhausted CD8 T cells in osteosarcoma. In osteosarcoma, the expression of LIC00960 was favorably connected with immune checkpoint-related genes and negatively correlated with immune infiltration. TIDE analysis indicated that low LINC00960 expression patients might have a better response to immunotherapy. Drug sensitivity analysis showed that high LINC00960 expression patients might have better responses to Bleomycin and Doxorubicin., Conclusion: LINC00960 has the potential to be a novel biomarker for predicting overall survival in osteosarcoma patients and to guide more individualized treatment and clinical decision-making., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Symmetric Positive Definite (SPD) matrices have received wide attention in machine learning due to their intrinsic capacity to encode underlying structural correlation in data. Many successful Riemannian metrics have been proposed to reflect the non-Euclidean geometry of SPD manifolds. However, most existing metric tensors are fixed, which might lead to sub-optimal performance for SPD matrix learning, especially for deep SPD neural networks. To remedy this limitation, we leverage the commonly encountered pullback techniques and propose Adaptive Log-Euclidean Metrics (ALEMs), which extend the widely used Log-Euclidean Metric (LEM). Compared with the previous Riemannian metrics, our metrics contain learnable parameters, which can better adapt to the complex dynamics of Riemannian neural networks with minor extra computations. We also present a complete theoretical analysis to support our ALEMs, including algebraic and Riemannian properties. The experimental and theoretical results demonstrate the merit of the proposed metrics in improving the performance of SPD neural networks. The efficacy of our metrics is further showcased on a set of recently developed Riemannian building blocks, including Riemannian batch normalization, Riemannian Residual blocks, and Riemannian classifiers.

Spatial transcriptomic tools and platforms help researchers to inspect tissues and cells with fine details of how they differentiate in expressions and how they orient themselves. With the higher resolution we get and higher throughput of expression targets, spatial analysis can truly become the core player for cell clustering, migration study, and, eventually, the novel model for pathological study. We present the demonstration of HiFi-slide, a whole transcriptomic sequencing technique that recycles used sequenced-by-synthesis flow cell surfaces to a high-resolution spatial mapping tool that can be directly applied to tissue cell gradient analysis, gene expression analysis, cell proximity analysis, and other cellular-level spatial studies.

Toward the well-explored lithium-sulfur (Li-S) catalytic chemistry, the slow adsorption-migration-conversion kinetics of lithium polysulfides on catalytic materials and Li 2 S deposition-induced passivation of active sites limit the rapid and complete conversion of sulfur. Conceptively, molecular architectures can provide atom-precise models to understand the underlying active sites responsible for selective adsorption and conversion of LiPSs and Li 2 S 2 /Li 2 S species. Here, an octanuclear Zn(II) (Zn 8 ) cluster is presented, which features a metallacalix[8]arene with double cavities up and down the Zn 8 ring. The central Zn 8 ring and the specific double cavities with organic ligands of different electronegativity and bonding environments render active sites with variable steric hindrance and interaction toward the sulfur-borne species. An intramolecular tandem transformation mechanism is realized exclusively by Zn 8 cluster, which promotes the self-cleaning of active sites and continuous electrochemical reaction. Notably, the external azo groups and internal Zn/O sites of Zn 8 cluster in sequence stimulate the adsorption and conversion of long chain Li 2 S x (x ≥ 4) and short chain Li 2 S/Li 2 S 2 , contributing to remarkable rate performance and cycling stability. This work pioneers the application of metallacalix[n]arene clusters with atom-precise structure in Li-S batteries, and the proposed mechanism advances the molecule-level understanding of Li-S catalytic chemistry., (© 2022 Wiley-VCH GmbH.)

Identifying the p K a values of aspartic acid (Asp) and glutamic acid (Glu) in active sites is essential for understanding enzyme reaction mechanisms. In this study, we investigated the correlation between the C═O stretching vibrational frequency (ν C═O ) of protonated carboxylic acids and the p K a values using density functional theory calculations. In unsaturated carboxylic acids (e.g., benzoic acid analogues), ν C═O decreases as the p K a increases (the negative correlation), whereas in saturated carboxylic acids (e.g., acetic acid analogues, Asp, and Glu), ν C═O increases as the p K a increases (the positive correlation) as long as the structure of the H-bond network around the acid is identical. The negative/positive correlation between ν C═O and p K a can be rationalized by the presence or absence of the C═C double bond. The p K a shift was estimated from the ν C═O shift of Asp and Glu in proteins on the basis of the negative correlation derived from benzoic acids. The previous estimations should be revisited by using the positive correlation derived in this study, as demonstrated by quantum mechanical/molecular mechanical calculations of ν C═O and electrostatic calculations of p K a on a key Asp85 in the proton-transfer pathway of bacteriorhodopsin.