Electronic structure of heavy fermion system CePt2In7 from angle-resolved photoemission spectroscopy

We have carried out high-resolution angle-resolved photoemission measurements on the Cebased heavy fermion compound CePt2In7 that exhibits stronger two-dimensional character than the prototypical heavy fermion system CeCoIn5. Multiple Fermi surface sheets and a complex band structure are clearly resolved. We have also performed detailed band structure calculations on CePt2In7. The good agreement found between our measurements and the calculations suggests that the band renormalization effect is rather weak in CePt2In7. A comparison of the common features of the electronic structure of CePt2In7 and CeCoIn5 indicates that CeCoIn5 shows a much stronger band renormalization effect than CePt2In7. These results provide new information for understanding the heavy fermion behaviors and unconventional superconductivity in Ce-based heavy fermion systems.Comment: 24 pages, 10 figure

Efficient Enumeration of Large Maximal k-Plexes

Finding cohesive subgraphs in a large graph has many important applications, such as community detection and biological network analysis. Clique is often a too strict cohesive structure since communities or biological modules rarely form as cliques for various reasons such as data noise. Therefore, $k$-plex is introduced as a popular clique relaxation, which is a graph where every vertex is adjacent to all but at most $k$ vertices. In this paper, we propose a fast branch-and-bound algorithm as well as its task-based parallel version to enumerate all maximal $k$-plexes with at least $q$ vertices. Our algorithm adopts an effective search space partitioning approach that provides a lower time complexity, a new pivot vertex selection method that reduces candidate vertex size, an effective upper-bounding technique to prune useless branches, and three novel pruning techniques by vertex pairs. Our parallel algorithm uses a timeout mechanism to eliminate straggler tasks, and maximizes cache locality while ensuring load balancing. Extensive experiments show that compared with the state-of-the-art algorithms, our sequential and parallel algorithms enumerate large maximal $k$-plexes with up to $5 \times$ and $18.9 \times$ speedup, respectively. Ablation results also demonstrate that our pruning techniques bring up to $7 \times$ speedup compared with our basic algorithm.Comment: Accepted by EDBT2025. Camera-ready versio

Polyploidy levels of Chinese large-flower chrysanthemum determined by flow cytometry

Flow cytometry was used to determine the ploidy level of 405 Chinese large-flower chrysanthemum (Chrysanthemum morifolium Ramat.) cultivars. Sixty-three cultivars are triploid, 175 cultivars tetraploid, 32 cultivars pentaploid, 46 cultivars hexaploid and 1 cultivar heptaploid. Forty-eight cultivars were then randomly selected for confirmation by chromosome-counting; the results are in agreement with the classification of ploidy level by flow cytometry. Most cultivars are aneuploid. The high percentage of tetraploid and triploid, instead of hexaploid in previous studies, represents the first evidence of low ploidy in large-flower chrysanthemum, which indicated a wider range of ploidy variation in this population. The results also offer further insights to the possible evolution and the regulation of flower size of this large-flower population. Additionally, the combination of flow cytometry and chromosome-counting is proved to be efficient and necessary for large-scale ploidy screening of chrysanthemum.Keywords: Chrysanthemum, ploidy level, flow cytometr

Platelet Membrane-Coated Nanocarriers Targeting Plaques to Deliver Anti-CD47 Antibody for Atherosclerotic Therapy

Atherosclerosis, the principle cause of cardiovascular disease (CVD) worldwide, is mainly characterized by the pathological accumulation of diseased vascular cells and apoptotic cellular debris. Atherogenesis is associated with the upregulation of CD47, a key antiphagocytic molecule that is known to render malignant cells resistant to programmed cell removal, or "efferocytosis." Here, we have developed platelet membrane-coated mesoporous silicon nanoparticles (PMSN) as a drug delivery system to target atherosclerotic plaques with the delivery of an anti-CD47 antibody. Briefly, the cell membrane coat prolonged the circulation of the particles by evading the immune recognition and provided an affinity to plaques and atherosclerotic sites. The anti-CD47 antibody then normalized the clearance of diseased vascular tissue and further ameliorated atherosclerosis by blocking CD47. In an atherosclerosis model established in ApoE-/- mice, PMSN encapsulating anti-CD47 antibody delivery significantly promoted the efferocytosis of necrotic cells in plaques. Clearing the necrotic cells greatly reduced the atherosclerotic plaque area and stabilized the plaques reducing the risk of plaque rupture and advanced thrombosis. Overall, this study demonstrated the therapeutic advantages of PMSN encapsulating anti-CD47 antibodies for atherosclerosis therapy, which holds considerable promise as a new targeted drug delivery platform for efficient therapy of atherosclerosis

Learning biological neuronal networks with artificial neural networks: neural oscillations

First-principles-based modelings have been extremely successful in providing crucial insights and predictions for complex biological functions and phenomena. However, they can be hard to build and expensive to simulate for complex living systems. On the other hand, modern data-driven methods thrive at modeling many types of high-dimensional and noisy data. Still, the training and interpretation of these data-driven models remain challenging. Here, we combine the two types of methods to model stochastic neuronal network oscillations. Specifically, we develop a class of first-principles-based artificial neural networks to provide faithful surrogates to the high-dimensional, nonlinear oscillatory dynamics produced by neural circuits in the brain. Furthermore, when the training data set is enlarged within a range of parameter choices, the artificial neural networks become generalizable to these parameters, covering cases in distinctly different dynamical regimes. In all, our work opens a new avenue for modeling complex neuronal network dynamics with artificial neural networks.Comment: 18 pages, 8 figure

A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense Retrieval

Dense retrieval methods have been mostly focused on unstructured text and less attention has been drawn to structured data with various aspects, e.g., products with aspects such as category and brand. Recent work has proposed two approaches to incorporate the aspect information into item representations for effective retrieval by predicting the values associated with the item aspects. Despite their efficacy, they treat the values as isolated classes (e.g., "Smart Homes", "Home, Garden & Tools", and "Beauty & Health") and ignore their fine-grained semantic relation. Furthermore, they either enforce the learning of aspects into the CLS token, which could confuse it from its designated use for representing the entire content semantics, or learn extra aspect embeddings only with the value prediction objective, which could be insufficient especially when there are no annotated values for an item aspect. Aware of these limitations, we propose a MUlti-granulaRity-aware Aspect Learning model (MURAL) for multi-aspect dense retrieval. It leverages aspect information across various granularities to capture both coarse and fine-grained semantic relations between values. Moreover, MURAL incorporates separate aspect embeddings as input to transformer encoders so that the masked language model objective can assist implicit aspect learning even without aspect-value annotations. Extensive experiments on two real-world datasets of products and mini-programs show that MURAL outperforms state-of-the-art baselines significantly.Comment: Accepted by WSDM2024, updat

Over expression of Zmda1-1 gene increases seed mass of corn

Genetic engineering of seed size and increasing biomass in crop plants has an important significant contribution to the world. Arabidopsis DA1 is one of the key factors that negatively control seed and organ size by restricting the period of cell proliferation, and the mutant of Arabidopsis DA1, da1-1 (DA1R358K) can dramatically increase the size of seed. However, it is not clear whether overexpression of Zmda1-1, the mutant of ZmDA1 which is homology of DA1 in Arabidopsis, has the same biological effect as da1-1 in Arabidopsis. Therefore, in this study, the plant expression vector harboring both Zmda1-1 driven by the corn ubiquitin promoter and a PAT selectable marker gene driven by 35S CAMV promoter was constructed and introduced into maize inbred line ‘ji444’ using pollen-tube-pathway method. Screened with herbicide phosphinothricin (PPT), 22 seedlings of 2563 transformed samples survived, and 21 independence lines of which were positive in polymerase chain reaction (PCR) analysis, and the transformation rate of T0 generation was about 0.82%. Further PCR-southern blotting results proved that the Zmda1-1 had integrated into maize genome, and the Zmda1-1 had expression in low level by reverse transcription-polymerase chain reaction (RT-PCR) analysis. The seed mass of transgenic maize increased at an average of 33.6% of empty vector control lines, and the harvest yield was increased by 23.6 to 114.1% in different lines than empty vector control lines. The result suggests that Zmda1-1 can be used to engineer higher harvest yield in crops plant, thus providing the first successful example of increasing the harvest yield of maize by transgenic technology.Key words: Transgenic maize, pollen-tube pathway, Zmda1-1, seed mass

Dual-Modal Attention-Enhanced Text-Video Retrieval with Triplet Partial Margin Contrastive Learning

In recent years, the explosion of web videos makes text-video retrieval increasingly essential and popular for video filtering, recommendation, and search. Text-video retrieval aims to rank relevant text/video higher than irrelevant ones. The core of this task is to precisely measure the cross-modal similarity between texts and videos. Recently, contrastive learning methods have shown promising results for text-video retrieval, most of which focus on the construction of positive and negative pairs to learn text and video representations. Nevertheless, they do not pay enough attention to hard negative pairs and lack the ability to model different levels of semantic similarity. To address these two issues, this paper improves contrastive learning using two novel techniques. First, to exploit hard examples for robust discriminative power, we propose a novel Dual-Modal Attention-Enhanced Module (DMAE) to mine hard negative pairs from textual and visual clues. By further introducing a Negative-aware InfoNCE (NegNCE) loss, we are able to adaptively identify all these hard negatives and explicitly highlight their impacts in the training loss. Second, our work argues that triplet samples can better model fine-grained semantic similarity compared to pairwise samples. We thereby present a new Triplet Partial Margin Contrastive Learning (TPM-CL) module to construct partial order triplet samples by automatically generating fine-grained hard negatives for matched text-video pairs. The proposed TPM-CL designs an adaptive token masking strategy with cross-modal interaction to model subtle semantic differences. Extensive experiments demonstrate that the proposed approach outperforms existing methods on four widely-used text-video retrieval datasets, including MSR-VTT, MSVD, DiDeMo and ActivityNet.Comment: Accepted by ACM MM 202

RSAVS superconductors: materials with a superconducting state that is robust against large volume shrinkage

TThe transition temperature (TC) between normal and superconducting states usually exhibits a dramatic increase or decrease with increasing applied pressure. Here we present, in contrast, a new kind of superconductor that exhibits the exotic feature that TC is robust against large volume shrinkages induced by applied pressure (here naming them as "RSAVS superconductors"). Extraordinarily, the TC in these materials stays almost constant over a large pressure range, e.g. over 136 GPa in the (TaNb)0.67(HfZrTi)0.33 high entropy alloy and 141 GPa in the NbTi commercial alloy. We show that the RSAVS behavior also exists in another high entropy alloy (ScZrNbTa)0.6(RhPd)0.4, and in superconducting elemental Ta and Nb, indicating that this behavior, which has never previously been identified or predicted by theory, occurs universally in some conventional superconductors. Our electronic structure calculations indicate that although the electronic density of state (DOS) at the Fermi level in the RSAVS state is dominated by the electrons from the degenerate dxy, dxz and dyz orbitals, these electrons decrease in influence with increasing pressure. In contrast, however, the contribution of the degenerate dx2-y2 and dz2 orbital electrons remains almost unchanged at the Fermi level, suggesting that these are the electrons that may play a crucial role in stabilizing the TC in the RSAVS state.Comment: 12 pages, 4 figure

Identification of glycolysis-related gene signatures for prognosis and therapeutic targeting in idiopathic pulmonary fibrosis

BackgroundGlycolysis plays a crucial role in fibrosis, but the specific genes involved in glycolysis in idiopathic pulmonary fibrosis (IPF) are not well understood.MethodsThree IPF gene expression datasets were obtained from the Gene Expression Omnibus (GEO), while glycolysis-related genes were retrieved from the Molecular Signatures Database (MsigDB). Differentially expressed glycolysis-related genes (DEGRGs) were identified using the “limma” R package. Diagnostic glycolysis-related genes (GRGs) were selected through least absolute shrinkage and selection operator (LASSO) regression regression and support vector machine-recursive feature elimination (SVM-RFE). A prognostic signature was developed using LASSO regression, and time-dependent receiver operating characteristic (ROC) curves were generated to evaluate predictive performance. Single-cell RNA sequencing (scRNA-seq) data were analyzed to examine GRG expression across various cell types. Immune infiltration analysis, Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were performed to elucidate potential molecular mechanisms. A bleomycin (BLM)-induced pulmonary fibrosis mouse model was used for experimental validation via reverse transcription-quantitative polymerase chain reaction (RT-qPCR).Results14 GRGs (VCAN, MERTK, FBP2, TPBG, SDC1, AURKA, ARTN, PGP, PLOD2, PKLR, PFKM, DEPDC1, AGRN, CXCR4) were identified as diagnostic markers for IPF, with seven (ARTN, AURKA, DEPDC1, FBP2, MERTK, PFKM, SDC1) forming a prognostic model demonstrating predictive power (AUC: 0.831–0.793). scRNA-seq revealed cell-type-specific GRG expression, particularly in macrophages and fibroblasts. Immune infiltration analysis linked GRGs to imbalanced immune responses. Experimental validation in a bleomycin-induced fibrosis model confirmed the upregulation of GRGs (such as AURKA, CXCR4). Drug prediction identified inhibitors (such as Tozasertib for AURKA, Plerixafor for CXCR4) as potential therapeutic agents.ConclusionThis study identifies GRGs as potential prognostic biomarkers for IPF and highlights their role in modulating immune responses within the fibrotic lung microenvironment. Notably, AURKA, MERTK, and CXCR4 were associated with pathways linked to fibrosis progression and represent potential therapeutic targets. Our findings provide insights into metabolic reprogramming in IPF and suggest that targeting glycolysis-related pathways may offer novel pharmacological strategies for antifibrotic therapy