59,927 research outputs found

    Biochemical prevention and treatment of viral infections – A new paradigm in medicine for infectious diseases

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    For two centuries, vaccination has been the dominating approach to develop prophylaxis against viral infections through immunological prevention. However, vaccines are not always possible to make, are ineffective for many viral infections, and also carry certain risk for a small, yet significant portion of the population. In the recent years, FDA's approval and subsequent market acceptance of Synagis, a monoclonal antibody indicated for prevention and treatment of respiratory syncytial virus (RSV) has heralded a new era for viral infection prevention and treatment. This emerging paradigm, herein designated "Biochemical Prevention and Treatment", currently involves two aspects: (1) preventing viral entry via passive transfer of specific protein-based anti-viral molecules or host cell receptor blockers; (2) inhibiting viral amplification by targeting the viral mRNA with anti-sense DNA, ribozyme, or RNA interference (RNAi). This article summarizes the current status of this field

    Unsupervised Domain Adaptation on Reading Comprehension

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    Reading comprehension (RC) has been studied in a variety of datasets with the boosted performance brought by deep neural networks. However, the generalization capability of these models across different domains remains unclear. To alleviate this issue, we are going to investigate unsupervised domain adaptation on RC, wherein a model is trained on labeled source domain and to be applied to the target domain with only unlabeled samples. We first show that even with the powerful BERT contextual representation, the performance is still unsatisfactory when the model trained on one dataset is directly applied to another target dataset. To solve this, we provide a novel conditional adversarial self-training method (CASe). Specifically, our approach leverages a BERT model fine-tuned on the source dataset along with the confidence filtering to generate reliable pseudo-labeled samples in the target domain for self-training. On the other hand, it further reduces domain distribution discrepancy through conditional adversarial learning across domains. Extensive experiments show our approach achieves comparable accuracy to supervised models on multiple large-scale benchmark datasets.Comment: 8 pages, 6 figures, 5 tables, Accepted by AAAI 202

    Analysis of the impact of the inlet boundary conditions in FDS results for air curtain flows in the near-field region

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    CFD results are discussed for planar jet flows, resembling configurations in use for air curtain flows in the context of smoke and heat control in buildings in case of fire. The CFD package FDS (Fire Dynamics Simulator), Version 6.0.1, is used. Special focus is given to the impact of the inlet boundary condition, in combination with the mesh size, on the flow field in the near-field region. Investigation of different slot configurations, including calculations inside a straight rectangular duct ahead of the air slot, reveals a small vena contracta effect when the slot is flush with a solid boundary, leading to an acceleration of the flow in the symmetry plane in the near-field region. More important is the effect of the duct length: starting from a top hat velocity profile, a duct length of about 15 hydraulic diameters is required for the flow to become fully developed at the slot. The vena contracta effect disappears if the co-flow at the nozzle exit is aligned with the jet. The FDS results capture the self-similarity in the far-field jet region, regardless of the inlet configuration
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