Prevalence of allergic rhinitis among adults in urban and rural areas of China : a population-based cross-sectional survey

Purpose: The aim of the present study was to compare the prevalence of self-reported and confirmable allergic rhinitis (AR) with positive skin prick test (SPT) results among adults living in urban and rural areas of China. Methods: Adults from a community in Beijing and a village in Baoding were selected as representative urban and rural dwellers, respectively. All eligible residents were enrolled from the population register and received a face-to-face interview using modified validated questionnaires. Equal sets of randomly selected self-reporting AR-positive and AR-negative participants who responded to the questionnaires were also investigated using skin prick tests. Results: A total of 803 participants in the rural area and a total of 1,499 participants in the urban area completed the questionnaires, with response rates being 75.9% and 81.5% respectively. The prevalence of self-reported AR of the rural area (19.1%) was significantly higher than that of the urban area (13.5%). The elementary school of educational level increased the risk of having AR (adjusted OR=2.198, 95% CI=1.072-2.236). The positive SET rates among subjects with self-reported AR in the rural and urban areas were 32.5% and 53.3%, respectively; the confirmable AR prevalence of 6.2% and 7.2% among the rural and urban adults, respectively. Conclusions: The prevalence of confirmable AR is similar between rural and urban areas in China, although there is a higher prevalence of self-reported AR in the former

Making Multimodal Generation Easier: When Diffusion Models Meet LLMs

We present EasyGen, an efficient model designed to enhance multimodal understanding and generation by harnessing the capabilities of diffusion models and large language models (LLMs). Unlike existing multimodal models that predominately depend on encoders like CLIP or ImageBind and need ample amounts of training data to bridge the gap between modalities, EasyGen is built upon a bidirectional conditional diffusion model named BiDiffuser, which promotes more efficient interactions between modalities. EasyGen handles image-to-text generation by integrating BiDiffuser and an LLM via a simple projection layer. Unlike most existing multimodal models that are limited to generating text responses, EasyGen can also facilitate text-to-image generation by leveraging the LLM to create textual descriptions, which can be interpreted by BiDiffuser to generate appropriate visual responses. Extensive quantitative and qualitative experiments demonstrate the effectiveness of EasyGen, whose training can be easily achieved in a lab setting. The source code is available at https://github.com/zxy556677/EasyGen

Shedding New Light on R(D(s)(∗)){\cal R} (D_{(s)}^{(\ast)} ) and ∣Vcb∣|V_{cb}| from Semileptonic Bˉ(s)→D(s)(∗)ℓνˉℓ\bar B_{(s)} \to D_{(s)}^{(\ast)} \ell \bar {\nu}_{\ell} Decays

We compute for the first time the next-to-leading-order QCD corrections to the $\bar B_{(s)} \to D_{(s)}^{(\ast)}$ form factors at large hadronic recoil. Both the charm-quark-mass and the strange-quark-mass dependent pieces can generate the leading-power contributions to these form factors. Including further various power-suppressed contributions, we perform the combined fits of the considered form factors to both our large-recoil theory predictions and the lattice QCD results, thus improving upon the previous determinations of the lepton-flavour-universality ratios ${\cal R} (D^{(\ast)})$ significantly.Comment: 6 pages plus Supplemental Material, 3 figures, 1 table, 2 ancillary file