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Background To adapt seasonal climate changes under natural environments, Polygonatum sibiricum seeds have a long period of epicotyl morphophysiological dormancy, which limits their wide-utilization in the large-scale plant progeny propagation. It has been proven that the controlled consecutive warm and cold temperature treatments can effectively break and shorten this seed dormancy status to promote its successful underdeveloped embryo growth, radicle emergence and shoot emergence. To uncover the molecular basis of seed dormancy release and seedling establishment, a SMRT full-length sequencing analysis and an Illumina sequencing-based comparison of P. sibiricum seed transcriptomes were combined to investigate transcriptional changes during warm and cold stratifications. Results A total of 87,251 unigenes, including 46,255 complete sequences, were obtained and 77,148 unigenes (88.42%) were annotated. Gene expression analyses at four stratification stages identified a total of 27,059 DEGs in six pairwise comparisons and revealed that more differentially expressed genes were altered at the Corm stage than at the other stages, especially Str_S and Eme. The expression of 475 hormone metabolism genes and 510 hormone signaling genes was modulated during P. sibiricum seed dormancy release and seedling emergence. One thousand eighteen transcription factors and five hundred nineteen transcription regulators were detected differentially expressed during stratification and germination especially at Corm and Str_S stages. Of 1246 seed dormancy/germination known DEGs, 378, 790, and 199 DEGs were associated with P. sibiricum MD release (Corm vs Seed), epicotyl dormancy release (Str_S vs Corm), and the seedling establishment after the MPD release (Eme vs Str_S). Conclusions A comparison with dormancy- and germination-related genes in Arabidopsis thaliana seeds revealed that genes related to multiple plant hormones, chromatin modifiers and remodelers, DNA methylation, mRNA degradation, endosperm weakening, and cell wall structures coordinately mediate P. sibiricum seed germination, epicotyl dormancy release, and seedling establishment. These results provided the first insights into molecular regulation of P. sibiricum seed epicotyl morphophysiological dormancy release and seedling emergence. They may form the foundation of future studies regarding gene interaction and the specific roles of individual tissues (endosperm, newly-formed corm) in P. sibiricum bulk seed dormancy.

Epimedium folium is the major medicinally-used organ of Epimedium species and its metabolic changes during the leaf growth have not been studied at the metabolomic level. E. pubescens is one of five recorded species in the Pharmacopoeia of the People’s Republic of China and widely grows in China. A UPLC-ESI-MS/MS-based targeted metabolomic analysis was implemented to explore the metabolite composition in E. pubescens leaves under the cultivation condition and further to investigate their temporal variations among four representative growth stages. A total of 403 metabolites, including 32 hitherto known in Epimedium species, were identified in E. pubescens leaf, of which 302 metabolites showed the growth/development-dependent alterations. Flavonoid-type compounds were the major composition of the metabolites identified in this study. Most flavonoids, together with tannin-type and lignans and coumarin-type compounds, were up-regulated with E. pubescens leaf growth and maturation after the full flowering stage. Our results not only greatly enriched the existing Epimedium phytochemical composition database and also, for the first time, provided the metabolomics-wide information on metabolic changes during E. pubescens leaf growth and development, which would facilitate in the choice of an optimum harvest time to balance a higher biomass yield of Epimedium folium with its better medicinal quality.

An active site is normally located inside enzymes, hence substrates should go through a tunnel to access the active site. Tunnel engineering is a powerful strategy for refining the catalytic properties of enzymes. Here, P450BsβHI (Q85H/V170I) derived from hydroxylase P450Bsβ from Bacillus subtilis was chosen as the study model, which is reported as a potential decarboxylase. However, this enzyme showed low decarboxylase activity towards long-chain fatty acids. Here, a tunnel engineering campaign was performed for modulating the substrate preference and improving the decarboxylation activity of P450BsβHI. The finally obtained BsβHI-F79A variant had a 15.2-fold improved conversion for palmitic acid; BsβHI-F173V variant had a 3.9-fold improved conversion for pentadecanoic acid. The study demonstrates how the substrate preference can be modulated by tunnel engineering strategy.

Innovations in nanotechnology have had an immense impact on medicine, such as in drug delivery, tissue engineering, and medical devices that combat different pathogens. The pathogens that may cause biofilm-associated nosocomial diseases are multidrug-resistant (MDR) bacteria, such as Escherichia coli (E. coli), Pseudomonas aeruginosa (P. aeruginosa), Staphylococcus aureus (S. aureus), including both Gram-positive and Gram-negative bacterial species. About 65–80% of infections are caused by biofilm-associated pathogens creating a move in the international community toward developing antimicrobial therapies to eliminate such pathogenic infections. Several nanomaterials (NMs) have been discovered and significantly employed in various antipathogenic therapies. These NMs have unique properties of singlet oxygen production, high absorption of near-infrared irradiation, and reasonable conversion of light to heat. In this review, functionalized NPs that combat different pathogenic infections are introduced. This review highlights NMs that combat infections caused by multidrug-resistant (MDR) and other pathogenic microorganisms. It also highlights the biomedical application of NPs with regard to antipathogenic activities.

We establish a characterization of when a matrix algebra is a Frobenius extension of its centralizer subalgebra.

Named entity recognition (NER), a core task in natural language processing (NLP), remains constrained by heavy reliance on annotated data, limited cross domain generalization, and difficulty in recognizing name entities out of vocabulary entities. In specialized domains such as analysis of Global Navigation Satellite System (GNSS) countermeasures, including anti-jamming and anti-spoofing, where datasets are small and domain knowledge is scarce, existing models exhibit marked performance degradation. To address these challenges, we propose HybridNER, a framework that integrates locally trained span-based models with large language models (LLMs). The approach employs a span prediction metasystem that first fuses outputs from multiple base learners by computing span to label compatibility scores and assigns an uncertainty estimate to each candidate entity. Entities with uncertainty above a preset threshold are then routed to an LLM for a second stage classification, and the final decision integrates both sources to realize complementary strengths. Experiments on multiple general purpose and domain specific datasets show that HybridNER achieves higher precision, recall, and F1 than traditional ensemble methods such as majority voting and weighted voting, with especially pronounced gains in specialized domains, thereby improving the robustness and generalization of NER.

This Special Issue of the Journal of Risk and Financial Management brings together 17 original research articles that offer timely and impactful insights into the evolving landscape of finance and risk management [...]

This work is an overview of our preliminary experience in developing a high-performance iterative linear solver accelerated by GPU coprocessors. Our goal is to illustrate the advantages and difficulties encountered when deploying GPU technology to perform sparse linear algebra computations. Techniques for speeding up sparse matrix-vector product (SpMV) kernels and finding suitable preconditioning methods are discussed. Our experiments with an NVIDIA TESLA M2070 show that for unstructured matrices SpMV kernels can be up to 8 times faster on the GPU than the Intel MKL on the host Intel Xeon X5675 Processor. Overall performance of the GPU-accelerated Incomplete Cholesky (IC) factorization preconditioned CG method can outperform its CPU counterpart by a smaller factor, up to 3, and GPU-accelerated The incomplete LU (ILU) factorization preconditioned GMRES method can achieve a speed-up nearing 4. However, with better suited preconditioning techniques for GPUs, this performance can be further improved.

Bioinspired multi-compartment architectures are desired in synthetic biology and metabolic engineering, as credited by their cell-like structures and intrinsic ability of assembling catalytic species for spatiotemporal control over cascade reactions like in living systems. Herein, we describe a general Pickering double emulsion-directed interfacial synthesis method for the fabrication of multicompartmental MOF microreactors. This approach employs multiple liquid–liquid interfaces as a controllable platform for the self-completing growth of dense MOF layers, enabling the microreactor with tailor-made inner architectures and selective permeability. Importantly, simultaneous encapsulation of incompatible functionalities, including hydrophilic enzyme and hydrophobic molecular catalyst, can be realized in a single MOF microreactor for operating chemo-enzymatic cascade reactions. As exemplified by the Grubb’ catalyst/CALB lipase driven olefin metathesis/ transesterification cascade reaction and glucose oxidase (GOx)/Fe-porphyrin catalyzed oxidation reaction, the multicompartmental microreactor exhibits 2.24–5.81 folds enhancement in cascade reaction efficiency in comparison to the homogeneous counterparts or physical mixture of individual analogues, due to the restrained mutual inactivation and substrate channelling effects. Our study prompts further design of multicompartment systems and the development of artificial cells capable of complex cellular transformations. The cell-like structures and the ability of assembling catalytic species are interesting features of bioinspired multicompartment architectures but it remains a challenge to build them. Here, the authors describe a Pickering double emulsion-directed interfacial synthesis to fabricate multi-compartmented metal-organic framework microreactors. The cell-like structures and the ability of assembling catalytic species are interesting features of bioinspired multicompartment architectures but it remains a challenge to build them. Here, the authors describe a Pickering double emulsion-directed interfacial synthesis to fabricate multi-compartmented metal-organic framework microreactors.

The behavior of microplastics (MPs) in aquatic environments can vary significantly according to their composition, shape, and physical and chemical properties. To predict the settling trajectory of MPs in aquatic environments, this study investigates the settlement law of MPs under static and dynamic conditions. Four types of materials were analyzed, namely polystyrene, polyamide, polyethylene terephthalate, and polyvinyl chloride. Approximately 1270 MP particles with irregular shapes (near-sphere, polygonal ellipsoid, and fragment) were selected for the settling experiments. The experimental results show that the main factors affecting the settling velocity of MPs were shape irregularity, density, and particle size. The settling velocity of irregular MPs was significantly lower than that of perfectly spherical MPs. We proposed a model that predicts the correlation between the settling velocity of MPs and their shape, density, particle size, and water density.