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This report shows evidence for nosocomial transmission of H7N9 influenza from a patient to two physicians who provided care. To the Editor: Avian influenza A (H7N9) virus emerged in eastern China in the spring of 2013, 1 with 698 cases and 281 deaths reported as of January 10, 2016. 2 Human H7N9 infections appear to be acquired through zoonotic transmission, although clusters of human-to-human household transmission have occurred. 3 , 4 We report here a hospital cluster of H7N9 infections that took place from January to February 2015. This study was approved by the ethics committee at Shantou University Medical College. A 28-year-old man (index patient), with repeated exposure to live poultry, presented with respiratory infection and was admitted to the respiratory department, . . .

Southern China experienced few cases of H7N9 during the first wave of human infections in the spring of 2013. The second and now the third waves of H7N9 infections have been localized mostly in Southern China with the Guangdong province an epicenter for the generation of novel H7N9 reassortants. Clusters of human infections show human-to-human transmission to be a rare but well-documented event. A recent cluster of infections involving hospital health care workers stresses the importance of care givers utilizing personal protective equipment in treating H7N9 infected or suspected patients.

Background Influenza H7N9 has become an endemic pathogen in China where circulating virus is found extensively in wild birds and domestic poultry. Two epidemic waves of Human H7N9 infections have taken place in Eastern and South Central China during the years of 2013 and 2014. In this study, we report on the first four human cases of influenza H7N9 in Shantou, Guangdong province, which occurred during the second H7N9 wave, and the subsequent analysis of the viral isolates. Methods Viral genomes were subjected to multisegment amplification and sequenced in an Illumina MiSeq. Later, phylogenetic analyses of influenza H7N9 viruses were performed to establish the evolutionary context of the disease in humans. Results The sequences of the isolates from Shantou have closer evolutionary proximity to the predominant Eastern H7N9 cluster (similar to A/Shanghai/1/2013 (H7N9)) than to the Southern H7N9 cluster (similar to A/Guangdong/1/2013 (H7N9)). Conclusions Two distinct phylogenetic groups of influenza H7N9 circulate currently in China and cause infections in humans as a consequence of cross-species spillover from the avian disease. The Eastern cluster, which includes the four isolates from Shantou, presents a wide geographic distribution and overlaps with the more restricted area of circulation of the Southern cluster. Continued monitoring of the avian disease is of critical importance to better understand and predict the epidemiological behaviour of the human cases.

As an insidious metabolic bone disease, osteoporosis plagues the world, with high incidence rates. Patients with osteoporosis are prone to falls and becoming disabled, and their cone fractures and hip fractures are very serious, so the diagnosis and treatment of osteoporosis is very urgent. Extracellular vesicles (EVs) are particles secreted from cells to the outside of the cell and they are wrapped in a bilayer of phospholipids. According to the size of the particles, they can be divided into three categories, namely exosomes, microvesicles, and apoptotic bodies. The diameter of exosomes is 30–150 nm, the diameter of microvesicles is 100–1000 nm, and the diameter of apoptotic bodies is about 50–5000 nm. EVs play an important role in various biological process and diseases including osteoporosis. In this review, the role of EVs in osteoporosis is systematically reviewed and some insights for the prevention and treatment of osteoporosis are provided.

The rapid and accurate prediction of crop yield and the construction of optimal yield prediction models are important for guiding field-scale agronomic management practices in precision agriculture. This study selected the leaf area index (LAI) of winter wheat (Triticum aestivum L.) at four different stages, and collected canopy spectral information and extracted vegetation indexes through unmanned aerial vehicle (UAV) multi-spectral sensors to establish the yield prediction model under the condition of slow-release nitrogen fertilizer and proposed optimal fertilization strategies for sustainable yield increase in wheat. The prediction results were evaluated using random forest (RF), support vector machine (SVM) and back propagation neural network (BPNN) methods to select the optimal spectral index and establish yield prediction models. The results showed that LAI has a significantly positive correlation with yield across four growth stages of winter wheat, and the correlation coefficient at the anthesis stage reached 0.96 in 2018–2019 and 0.83 in 2019–2020. Therefore, yield prediction for winter wheat could be achieved through a remote sensing estimation of LAI at the anthesis stage. Six vegetation indexes calculated from UAV-derived reflectance data were modeled against LAI, demonstrating that the red-edge vegetation index (CIred edge) achieved superior accuracy in estimating LAI for winter wheat yield prediction. RF, SVM and BPNN models were used to evaluate the accuracy and precision of CIred edge in predicting yield, respectively. It was found that RF outperformed both SVM and BPNN in predicting yield accuracy. The CIred edge of the anthesis stage was the best vegetation index and stage for estimating yield of winter wheat based on UAV remote sensing. Under different N application rates, both predicted and measured yields exhibited a consistent trend that followed the order of SRF (slow-release N fertilizer) > SRFU1 (mixed TU and SRF at a ratio of 2:8) > SRFU2 (mixed TU and SRF at a ratio of 3:7) > TU (traditional urea). The optimum N fertilizer rate and N fertilizer type for winter wheat in this study were 220 kg ha−1 and SRF, respectively. The results of this study will provide significant technical support for regional crop growth monitoring and yield prediction.

The pursuit of designing superconductors with high Tc has been a long‐standing endeavor. However, the widespread incorporation of doping in high Tc superconductors significantly impacts electronic structure, intricately influencing Tc. The complex interplay between the structural composition and material performance presents a formidable challenge in superconductor design. Based on a novel generative model, diffusion model, and doping adaptive representation: three‐channel matrix, we have designed a high Tc superconductors inverse design model called Supercon‐Diffusion. It has achieved remarkable success in accurately generating chemical formulas for doped high Tc superconductors. Supercon‐Diffusion is capable of generating superconductors that exhibit high Tc and excels at identifying the optimal doping ratios that yield the peak Tc. The doping effectiveness (55%) and electrical neutrality (55%) of the generated doped superconductors exceed those of traditional GAN models by more than tenfold. Density of state calculations on the structures further confirm the validity of the generated superconductors. Additionally, we have proposed 200 potential high Tc superconductors that have not been documented yet. This groundbreaking contribution effectively reduces the search space for high Tc superconductors. Moreover, it successfully establishes a bridge between the interrelated aspects of composition, structure, and property in superconductors, providing a novel solution for designing other doped materials. The pursuit of designing superconductors with high Tc has been a long‐standing endeavor. Based on a novel generative model, diffusion model, and doping adaptive representation, we have developed a high Tc superconductors inverse design model called Supercon‐Diffusion. This model has achieved remarkable success in accurately generating chemical formulas for doped high Tc superconductors.

Effective nitrogen (N) management practices are essential for achieving efficient and sustainable agricultural production. The purpose of this study was to improve N use efficiency (NUE) and minimize N loss by optimizing the rate and type of N fertilizer application while maintaining a high yield of maize. A two-year field experiment with U (urea), S (slow-release N fertilizer), and SU (blending of S and U) under four N application levels (N1: 90 kg ha−1, N2: 120 kg ha−1, N3: 180 kg ha−1, N4: 240 kg ha−1) was conducted to investigate their effects on ammonia (NH3) volatilization, residual soil nitrate N (NO3−-N), yield, NUE, apparent N losses of rainfed maize. NH3 volatilization in SU and S were 38.46% and 16.57% lower than that in U, respectively. SU and S were found to reduce the apparent N losses by 42.98% and 62.23%. SU decreased NO3−-N leaching in deep soils and increased NO3−-N content in topsoil. Compared with U and S, SU significantly increased yield, plant N accumulation, and NUE. SUN4 achieved the maximum maize yield and plant N accumulation, averaging 7968.36 kg ha−1 and 166.45 kg ha−1. In addition, the high yield and NUE were obtained when the mixing ratio of S and U was 53–58% and the N application rate was 150–220 kg ha−1. The findings highlight that SU effectively reduces N losses while ensuring high yield, which could be used as one of the optimal N fertilization strategies for rainfed maize in Northwest China.

Polyamine biosynthesis is an essential metabolic pathway for cell growth and differentiation in non-small-cell lung cancer (NSCLC). Fragile-site associated tumour suppressor (FATS) is a novel gene involved in cancer. The results of our previous study showed that FATS-mediated polyubiquitination of p53 promotes the activation of p53 in response to DNA damage; however, little is known about the role of FATS in metabolic reprogramming in NSCLC. In the present study, FATS was observed to be significantly downregulated in NSCLC tissues compared with paired adjacent normal tissues and was associated with the survival of NSCLC patients. We further showed that the presence of the tumour suppressor FATS in NSCLC cells led to apoptosis by inducing pro-death autophagy. In addition, FATS was shown to function as a suppressor of polyamine biosynthesis by inhibiting ornithine decarboxylase (ODC) at the protein and mRNA levels, which was partially dependent on oestrogen receptor (ER). Furthermore, FATS was observed to bind to ERβ and translocate to the cytosol, leading to ODC degradation. The findings of our study demonstrate that FATS plays important roles in polyamine metabolism in NSCLC and provides a new perspective for NSCLC progression.

The pursuit of designing superconductors with high T c has been a long‐standing endeavor. However, the widespread incorporation of doping in high T c superconductors significantly impacts electronic structure, intricately influencing T c . The complex interplay between the structural composition and material performance presents a formidable challenge in superconductor design. Based on a novel generative model, diffusion model, and doping adaptive representation: three‐channel matrix, we have designed a high T c superconductors inverse design model called Supercon‐Diffusion. It has achieved remarkable success in accurately generating chemical formulas for doped high T c superconductors. Supercon‐Diffusion is capable of generating superconductors that exhibit high T c and excels at identifying the optimal doping ratios that yield the peak T c . The doping effectiveness (55%) and electrical neutrality (55%) of the generated doped superconductors exceed those of traditional GAN models by more than tenfold. Density of state calculations on the structures further confirm the validity of the generated superconductors. Additionally, we have proposed 200 potential high T c superconductors that have not been documented yet. This groundbreaking contribution effectively reduces the search space for high T c superconductors. Moreover, it successfully establishes a bridge between the interrelated aspects of composition, structure, and property in superconductors, providing a novel solution for designing other doped materials. image

The image highlights our artificial intelligence framework, named Supercon‐Diffusion, for the inverse design of high‐temperature superconductors. image