Three dimensional photonic Dirac points in metamaterials

Topological semimetals, representing a new topological phase that lacks a full bandgap in bulk states and exhibiting nontrivial topological orders, recently have been extended to photonic systems, predominantly in photonic crystals and to a lesser extent, metamaterials. Photonic crystal realizations of Dirac degeneracies are protected by various space symmetries, where Bloch modes span the spin and orbital subspaces. Here, we theoretically show that Dirac points can also be realized in effective media through the intrinsic degrees of freedom in electromagnetism under electromagnetic duality. A pair of spin polarized Fermi arc like surface states is observed at the interface between air and the Dirac metamaterials. These surface states show linear k-space dispersion relation, resulting in nearly diffraction-less propagation. Furthermore, eigen reflection fields show the decomposition from a Dirac point to two Weyl points. We also find the topological correlation between a Dirac point and vortex/vector beams in classic photonics. The theoretical proposal of photonic Dirac point lays foundation for unveiling the connection between intrinsic physics and global topology in electromagnetism.Comment: 15 pages, 5 figure

Zfp36l2 is required for self-renewal of early erythroid BFU-E progenitors

Stem cells and progenitors in many lineages undergo self- renewing divisions, but the extracellular and intracellular proteins that regulate this process are largely unknown. Glucocorticoids stimulate red cell formation by promoting self-renewal of early erythroid burst forming unit-erythrocyte (BFU-E) progenitors1-4. Here we show that the RNA binding protein Zfp36l2 is a transcriptional target of the glucocorticoid receptor (GR) in BFU-Es and is required for BFU-E self-renewal. Zfp36l2 is normally downregulated during erythroid differentiation from the BFU-E stage but its expression is maintained by all tested GR agonists that stimulate BFU-E self-renewal, and the GR binds to several potential enhancer regions of Zfp36l2. Knockdown of Zfp36l2 in cultured BFU-E cells did not affect the rate of cell division but disrupted glucocorticoid-induced BFU-E self-renewal, and knockdown of Zfp36l2 in transplanted erythroid progenitors prevented expansion of erythroid lineage progenitors normally seen following induction of anemia by phenylhydrazine treatment. Zfp36l2 preferentially binds to mRNAs that are induced or maintained at high expression levels during terminal erythroid differentiation and negatively regulates their expression levels. Thus Zfp36l2 functions as part of molecular switch promoting BFU-E self-renewal and thus a subsequent increase in the total numbers of CFU-E progenitors and erythroid cells that are generated

A Critical Review of Recent Advances in Maize Stress Molecular Biology

With the intensification of global climate change and environmental stress, research on abiotic and biotic stress resistance in maize is particularly important. High temperatures and drought, low temperatures, heavy metals, salinization, and diseases are widespread stress factors that can reduce maize yields and are a focus of maize-breeding research. Molecular biology provides new opportunities for the study of maize and other plants. This article reviews the physiological and biochemical responses of maize to high temperatures and drought, low temperatures, heavy metals, salinization, and diseases, as well as the molecular mechanisms associated with them. Special attention is given to key transcription factors in signal transduction pathways and their roles in regulating maize stress adaptability. In addition, the application of transcriptomics, genome-wide association studies (GWAS), and QTL technology provides new strategies for the identification of molecular markers and genes for maize-stress-resistance traits. Crop genetic improvements through gene editing technologies such as the CRISPR/Cas system provide a new avenue for the development of new stress-resistant varieties. These studies not only help to understand the molecular basis of maize stress responses but also provide important scientific evidence for improving crop tolerance through molecular biological methods.publishedVersio

MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing

Text-guided image editing is widely needed in daily life, ranging from personal use to professional applications such as Photoshop. However, existing methods are either zero-shot or trained on an automatically synthesized dataset, which contains a high volume of noise. Thus, they still require lots of manual tuning to produce desirable outcomes in practice. To address this issue, we introduce MagicBrush (https://osu-nlp-group.github.io/MagicBrush/), the first large-scale, manually annotated dataset for instruction-guided real image editing that covers diverse scenarios: single-turn, multi-turn, mask-provided, and mask-free editing. MagicBrush comprises over 10K manually annotated triplets (source image, instruction, target image), which supports trainining large-scale text-guided image editing models. We fine-tune InstructPix2Pix on MagicBrush and show that the new model can produce much better images according to human evaluation. We further conduct extensive experiments to evaluate current image editing baselines from multiple dimensions including quantitative, qualitative, and human evaluations. The results reveal the challenging nature of our dataset and the gap between current baselines and real-world editing needs.Comment: NeurIPS 2023; Website: https://osu-nlp-group.github.io/MagicBrush

Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance Segmentation

Semi-supervised segmentation methods have demonstrated promising results in natural scenarios, providing a solution to reduce dependency on manual annotation. However, these methods face significant challenges when directly applied to pathological images due to the subtle color differences between nuclei and tissues, as well as the significant morphological variations among nuclei. Consequently, the generated pseudo-labels often contain much noise, especially at the nuclei boundaries. To address the above problem, this paper proposes a boundary-aware contrastive learning network to denoise the boundary noise in a semi-supervised nuclei segmentation task. The model has two key designs: a low-resolution denoising (LRD) module and a cross-RoI contrastive learning (CRC) module. The LRD improves the smoothness of the nuclei boundary by pseudo-labels denoising, and the CRC enhances the discrimination between foreground and background by boundary feature contrastive learning. We conduct extensive experiments to demonstrate the superiority of our proposed method over existing semi-supervised instance segmentation methods.Comment: 12 pages, 3 figures, 6 table

Urban Ergonomics: A design science on spatial experience quality

Urban Ergonomics is a design science driven by modern ergonomic data. It reacts to the challenges to architecture posed by changes, emergencies and advancements of the contemporary world, signifying a return to architectural humanism powered by new sciences. The key methodology of Urban Ergonomics is the quantitative descriptive model through which ergonomic data inform design with precision. The rise of Urban Ergonomics is aligned with the new human-centred urbanisation movement in China while offering positive potential in the country's transformation towards carbon net zero. This paper is a general introduction to Urban Ergonomics, putting basic discussions on its "why". "what" and "how" in three sections: 1 emergence, 2 ranges, and 3 tools. Urban Ergonomics opens up a whole new dimension for the revival and redevelopment of design science in the 21st-century discourse.Combined with the emerging technologies, Urban Ergonomics supports a more precise design decision-making process based on ergonomic analysis. For the project to be built, the ergonomic analysis generally takes the parameter of enumeration of virtual scenario as the independent variable, and takes ergonomic data as the dependent variable. with the help of descriptive models for a precise selection among different solutions; for the built spaces, real-life scenarios are analysed and assessed through the descriptive models and ergonomic data to support the renovation and regeneration; for design purposes, ergonomic analysis adds new design targets based on new quantitative spatial experience to the traditional targets. All of the processes, results and quantitative design targets above can lead to replicable application scenarios and bring new knowledge to design science.According to the four-level system of the spatial experience of survival, efficiency, reception and expression_ Urban Ergonomics transforms relatively vague urban space quality issues into clear design targets, and divides these spatial experience issues into five scales: Macro. far. medium_ near and micro. Initial research progress has been made in the fields of urban design, architecture, lighting design_ ventilation and heating design, and so on. It shows that human factors techniques can be applied in the quantification of spatial experience and that their quantified results can lead to more precise design intervention.Urban Ergonomics demonstrates its great potential in the core area of urban-architecture design intervention - forming in which it fills the gap between the ergonomic data and the spatial form. With the help of tools such as Ergonomic Notation Systems. Urban Ergonomics studies the objective and regular parts of the traditional aesthetic experience and helps both post-occupancy evaluations (POE) as well as design decision-making, which can be widely applied in the design process in the future: In the early stage of design, Ergonomic Notation System can help determine spatial requirements; in the late stage of design, when the ergonomic notation of the design is decided, the ergonomic data collected in a VR scene can help accurately search for the optimal solution to some extent; when the environment has been built, ergonomic data can also be collected in real-life scenarios to help evaluate the spatial experience quality and to summarise and redefine the knowledge of design methodology, spatial typology, spatial cognition_ and so on. It is expected to provide a new pathway for the redevelopment of design science supported by the interdisciplinary in the 21st century

Developments in Fatty Acid-Derived Insect Pheromone Production Using Engineered Yeasts

The use of traditional chemical insecticides for pest control often leads to environmental pollution and a decrease in biodiversity. Recently, insect sex pheromones were applied for sustainable biocontrol of pests in fields, due to their limited adverse impacts on biodiversity and food safety compared to that of other conventional insecticides. However, the structures of insect pheromones are complex, and their chemical synthesis is not commercially feasible. As yeasts have been widely used for fatty acid-derived pheromone production in the past few years, using engineered yeasts may be promising and sustainable for the low-cost production of fatty acid-derived pheromones. The primary fatty acids produced by Saccharomyces cerevisiae and other yeasts are C16 and C18, and it is also possible to rewire/reprogram the metabolic flux for other fatty acids or fatty acid derivatives. This review summarizes the fatty acid biosynthetic pathway in S. cerevisiae and recent progress in yeast engineering in terms of metabolic engineering and synthetic biology strategies to produce insect pheromones. In the future, insect pheromones produced by yeasts might provide an eco-friendly pest control method in agricultural fields