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

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

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

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

Coordination between electron transfer and molecule diffusion through a bioinspired amorphous titania nanoshell for photocatalytic nicotinamide cofactor regeneration

In-depth understanding and rational manipulation of the electron transfer process and molecule diffusion process are critical to promote the overall photocatalytic efficiency. In our study, core@shell photocatalysts that embody graphitic carbon nitride (GCN) core and amorphous titania (a-TiO 2) nanoshell are prepared to elucidate and coordinate the electron transfer and molecule diffusion for the regeneration of nicotinamide adenine dinucleotide (NADH) with [Cp*Rh(bpy)H 2O] 2+ as the redox mediator. The GCN core absorbs visible light to generate electron-hole pairs, whereas the a-TiO 2 nanoshell facilitates the transfer of photogenerated electrons from GCN to the a-TiO 2 surface for NADH regeneration, which also enables the diffusion of electron donor molecules (triethanolamine, TEOA) from the a-TiO 2 surface to GCN for consuming the holes left on GCN. The transfer of photogenerated electrons and the diffusion of electron donor molecules are coordinated by finely tuning the thickness of the a-TiO 2 nanoshell. Under the optimized nanoshell thickness of â¼2.1 nm, the GCN@a-TiO 2 photocatalyst exhibits the highest NADH regeneration yield of 82.1% after a 10 min reaction under LED light (405 nm), over 200% higher than that of the GCN photocatalyst. Combined with the highly controllable and mild features of the bioinspired mineralization method, our study may offer a facile and generic strategy to design high performance photocatalysts through rational coordination of different substances/species transport processes

Pollen tube emergence is mediated by ovary-expressed ALCATRAZ in cucumber

Pollen tube guidance within female tissues of flowering plants can be divided into preovular guidance, ovular guidance and a connecting stage called pollen tube emergence. As yet, no female factor has been identified to positively regulate this transition process. In this study, we show that an ovary-expressed bHLH transcription factor Cucumis sativus ALCATRAZ (CsALC) functions in pollen tube emergence in cucumber. CsALC knockout mutants showed diminished pollen tube emergence, extremely reduced entry into ovules, and a 95% reduction in female fertility. Further examination showed two rapid alkalinization factors CsRALF4 and CsRALF19 were less expressed in Csalc ovaries compared to WT. Besides the loss of male fertility derived from precocious pollen tube rupture as in Arabidopsis, Csralf4 Csralf19 double mutants exhibited a 60% decrease in female fertility due to reduced pollen tube distribution and decreased ovule targeting efficiency. In brief, CsALC regulates female fertility and promotes CsRALF4/19 expression in the ovary during pollen tube guidance in cucumber. Pollen tube growth is guided towards ovules. Here the authors show that a bHLH transcriptional factor CsALC functions in pollen tube emergence towards ovules to regulate female fertility in cucumber and promotes the expression of two rapid alkalinization factors CsRALF4/19 in the ovary