Social distancing cut down the prevalence of acute otitis media in children

ObjectivesTo evaluate the additional, unintended benefits of social distancing in cutting down the prevalence of acute otitis media (AOM) in children, especially during coronavirus disease 2019 (COVID-19) periods.MethodsThe daily outpatient attendance of AOM for childhood (from 6 months to 12 years) was compared in the tertiary hospital in Shanghai during pre-COVID-19 and COVID-19 year.ResultsA total of 24,543 AOM cases were included from 2015 to 2020. When age was taken into account, children in kindergarten (aged 4–6) constitute 66.2% (16,236/24,543) of all case, followed by primary school students (6,441/24,543, 26.2%) and preschoolers <3 years old (1,866/24,543, 7.6%). There was an estimated 63.6% (54.32–70.36%) reduction in the daily outpatient attendance of AOM associated with the introduction of social distancing in 2020 (COVID-19 year). The epidemic trend of AOM in 2015–2019 was characterized by seasonal fluctuations, with highest incidence in December (18.8 ± 0.5%) and lower in February (4.5 ± 0.2%), June (3.7 ± 0.7%) and August (3.5 ± 0.5%). And distribution characteristics of different ages in COVID-19 period broadly in line with that in non-pandemic period.ConclusionSeasonal fluctuation in the prevalence of AOM was observed in pre-COVID-19 period (2015–2019), with a peak in winter and a nadir in summer. The >50% drop of outpatient attendance of AOM in 2020 (COVID-19 year) suggest that social distancing, mask effects and good hand hygiene can significantly reduce the incidence of AOM, which provides a preventive and therapeutic point of view for AOM

Two-dimensional Massless Dirac Fermions in Antiferromagnetic AFe2As2 (A = Ba, Sr)

We report infrared studies of AFe$_{2}$As$_{2}$ (A = Ba, Sr), two representative parent compounds of iron-arsenide superconductors, at magnetic fields (B) up to 17.5 T. Optical transitions between Landau levels (LLs) were observed in the antiferromagnetic states of these two parent compounds. Our observation of a $\sqrt{B}$ dependence of the LL transition energies, the zero-energy intercepts at B = 0 T under the linear extrapolations of the transition energies and the energy ratio ($\sim$ 2.4) between the observed LL transitions, combined with the linear band dispersions in two-dimensional (2D) momentum space obtained by theoretical calculations, demonstrates the existence of massless Dirac fermions in antiferromagnetic BaFe$_{2}$As$_{2}$. More importantly, the observed dominance of the zeroth-LL-related absorption features and the calculated bands with extremely weak dispersions along the momentum direction $k_{z}$ indicate that massless Dirac fermions in BaFe$_{2}$As$_{2}$ are 2D. Furthermore, we find that the total substitution of the barium atoms in BaFe$_{2}$As$_{2}$ by strontium atoms not only maintains 2D massless Dirac fermions in this system, but also enhances their Fermi velocity, which supports that the Dirac points in iron-arsenide parent compounds are topologically protected.Comment: Magneto-infrared study, Landau level spectroscopy, DFT+DMFT calculation

Disrupting the Interaction between Retinoblastoma Protein and Raf-1 Leads to Defects in Progenitor Cell Proliferation and Survival during Early Inner Ear Development

The retinoblastoma protein (pRb) is required for cell-cycle exit of embryonic mammalian hair cells but is not required for hair cell fate determination and early differentiation, and this provides a strategy for hair cell regeneration by manipulating the pRb pathway. To reveal the mechanism of pRb functional modification in the inner ear, we compared the effects of attenuated pRb phosphorylation by an inhibitor of the Mitogen-Activated Protein (MAP) kinase pathway and an inhibitor of the Rb–Raf-1 interaction on cultured chicken otocysts. We demonstrated that the activity of pRb is correlated with its phosphorylation state, which is regulated by a newly established cell cycle-independent pathway mediated by the physical interaction between Raf-1 and pRb. The phosphorylation of pRb plays an important role during the early stage of inner ear development, and attenuated phosphorylation in progenitor cells leads to cell cycle arrest and increased apoptosis along with a global down-regulation of the genes involved in cell cycle progression. Our study provides novel routes to modulate pRb function for hair cell regeneration

Impact of Uniaxial Pressure on Structural and Magnetic Phase Transitions in Electron-Doped Iron Pnictides

We use neutron resonance spin echo and Larmor diffraction to study the effect of uniaxial pressure on the tetragonal-to-orthorhombic structural ($T_s$) and antiferromagnetic (AF) phase transitions in iron pnictides BaFe$_{2-x}$Ni$_{x}$As$_{2}$ ($x=0,0.03,0.12$), SrFe$_{1.97}$Ni$_{0.03}$As$_2$, and BaFe$_2$(As$_{0.7}$P$_{0.3}$)$_2$. In antiferromagnetically ordered BaFe$_{2-x}$Ni$_{x}$As$_{2}$ and SrFe$_{1.97}$Ni$_{0.03}$As$_2$ with $T_N$ and $T_s$ ($T_N\leq T_s$), a uniaxial pressure necessary to detwin the sample also increases $T_N$, smears out the structural transition, and induces an orthorhombic lattice distortion at all temperatures. By comparing temperature and doping dependence of the pressure induced lattice parameter changes with the elastoresistance and nematic susceptibility obtained from transport and ultrasonic measurements, we conclude that the in-plane resistivity anisotropy found in the paramagnetic state of electron underdoped iron pnictides depends sensitively on the nature of the magnetic phase transition and a strong coupling between the uniaxial pressure induced lattice distortion and electronic nematic susceptibility.Comment: 18 pages, 15 figure

Endoscopic image classification algorithm based on Poolformer

Image desmoking is a significant aspect of endoscopic image processing, effectively mitigating visual field obstructions without the need for additional surgical interventions. However, current smoke removal techniques tend to apply comprehensive video enhancement to all frames, encompassing both smoke-free and smoke-affected images, which not only escalates computational costs but also introduces potential noise during the enhancement of smoke-free images. In response to this challenge, this paper introduces an approach for classifying images that contain surgical smoke within endoscopic scenes. This classification method provides crucial target frame information for enhancing surgical smoke removal, improving the scientific robustness, and enhancing the real-time processing capabilities of image-based smoke removal method. The proposed endoscopic smoke image classification algorithm based on the improved Poolformer model, augments the model’s capacity for endoscopic image feature extraction. This enhancement is achieved by transforming the Token Mixer within the encoder into a multi-branch structure akin to ConvNeXt, a pure convolutional neural network. Moreover, the conversion to a single-path topology during the prediction phase elevates processing speed. Experiments use the endoscopic dataset sourced from the Hamlyn Centre Laparoscopic/Endoscopic Video Dataset, augmented by Blender software rendering. The dataset comprises 3,800 training images and 1,200 test images, distributed in a 4:1 ratio of smoke-free to smoke-containing images. The outcomes affirm the superior performance of this paper’s approach across multiple parameters. Comparative assessments against existing models, such as mobilenet_v3, efficientnet_b7, and ViT-B/16, substantiate that the proposed method excels in accuracy, sensitivity, and inference speed. Notably, when contrasted with the Poolformer_s12 network, the proposed method achieves a 2.3% enhancement in accuracy, an 8.2% boost in sensitivity, while incurring a mere 6.4 frames per second reduction in processing speed, maintaining 87 frames per second. The results authenticate the improved performance of the refined Poolformer model in endoscopic smoke image classification tasks. This advancement presents a lightweight yet effective solution for the automatic detection of smoke-containing images in endoscopy. This approach strikes a balance between the accuracy and real-time processing requirements of endoscopic image analysis, offering valuable insights for targeted desmoking process

An image caption model based on attention mechanism and deep reinforcement learning

Image caption technology aims to convert visual features of images, extracted by computers, into meaningful semantic information. Therefore, the computers can generate text descriptions that resemble human perception, enabling tasks such as image classification, retrieval, and analysis. In recent years, the performance of image caption has been significantly enhanced with the introduction of encoder-decoder architecture in machine translation and the utilization of deep neural networks. However, several challenges still persist in this domain. Therefore, this paper proposes a novel method to address the issue of visual information loss and non-dynamic adjustment of input images during decoding. We introduce a guided decoding network that establishes a connection between the encoding and decoding parts. Through this connection, encoding information can provide guidance to the decoding process, facilitating automatic adjustment of the decoding information. In addition, Dense Convolutional Network (DenseNet) and Multiple Instance Learning (MIL) are adopted in the image encoder, and Nested Long Short-Term Memory (NLSTM) is utilized as the decoder to enhance the extraction and parsing capability of image information during the encoding and decoding process. In order to further improve the performance of our image caption model, this study incorporates an attention mechanism to focus details and constructs a double-layer decoding structure, which facilitates the enhancement of the model in terms of providing more detailed descriptions and enriched semantic information. Furthermore, the Deep Reinforcement Learning (DRL) method is employed to train the model by directly optimizing the identical set of evaluation indexes, which solves the problem of inconsistent training and evaluation standards. Finally, the model is trained and tested on MS COCO and Flickr 30 k datasets, and the results show that the model has improved compared with commonly used models in the evaluation indicators such as BLEU, METEOR and CIDEr