Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus with high nucleotide identity to SARS-CoV and to SARS-related coronaviruses that have been detected in horseshoe ...bats, has spread across the world and had a global effect on healthcare systems and economies
. A suitable small animal model is needed to support the development of vaccines and therapies. Here we report the pathogenesis and transmissibility of SARS-CoV-2 in golden (Syrian) hamsters (Mesocricetus auratus). Immunohistochemistry assay demonstrated the presence of viral antigens in nasal mucosa, bronchial epithelial cells and areas of lung consolidation on days 2 and 5 after inoculation with SARS-CoV-2, followed by rapid viral clearance and pneumocyte hyperplasia at 7 days after inoculation. We also found viral antigens in epithelial cells of the duodenum, and detected viral RNA in faeces. Notably, SARS-CoV-2 was transmitted efficiently from inoculated hamsters to naive hamsters by direct contact and via aerosols. Transmission via fomites in soiled cages was not as efficient. Although viral RNA was continuously detected in the nasal washes of inoculated hamsters for 14 days, the communicable period was short and correlated with the detection of infectious virus but not viral RNA. Inoculated and naturally infected hamsters showed apparent weight loss on days 6-7 post-inoculation or post-contact; all hamsters returned to their original weight within 14 days and developed neutralizing antibodies. Our results suggest that features associated with SARS-CoV-2 infection in golden hamsters resemble those found in humans with mild SARS-CoV-2 infections.
The emergence of SARS-CoV-2 variants of concern with progressively increased transmissibility between humans is a threat to global public health. The Omicron variant of SARS-CoV-2 also evades ...immunity from natural infection or vaccines
, but it is unclear whether its exceptional transmissibility is due to immune evasion or intrinsic virological properties. Here we compared the replication competence and cellular tropism of the wild-type virus and the D614G, Alpha (B.1.1.7), Beta (B.1.351), Delta (B.1.617.2) and Omicron (B.1.1.529) variants in ex vivo explant cultures of human bronchi and lungs. We also evaluated the dependence on TMPRSS2 and cathepsins for infection. We show that Omicron replicates faster than all other SARS-CoV-2 variants studied in the bronchi but less efficiently in the lung parenchyma. All variants of concern have similar cellular tropism compared to the wild type. Omicron is more dependent on cathepsins than the other variants of concern tested, suggesting that the Omicron variant enters cells through a different route compared with the other variants. The lower replication competence of Omicron in the human lungs may explain the reduced severity of Omicron that is now being reported in epidemiological studies, although determinants of severity are multifactorial. These findings provide important biological correlates to previous epidemiological observations.
We study amorphous systems with completely random sites and find that, through constructing and exploring a concrete model Hamiltonian, such a system can host an exotic phase of topological amorphous ...metal in three dimensions. In contrast to the traditional Weyl semimetals, topological amorphous metals break translational symmetry, and thus they cannot be characterized by the first Chern number defined based on the momentum space band structures. Instead, their topological properties will manifest in the Bott index and the Hall conductivity as well as the surface states. By studying the energy band and quantum transport properties, we find that topological amorphous metals exhibit a diffusive metal behavior. We further introduce a practical experimental proposal with electric circuits where the predicted phenomena can be observed using state-of-the-art technologies. Our results open the door to exploring topological gapless phenomena in amorphous systems.
Tensor networks are efficient representations of high-dimensional tensors with widespread applications in quantum many-body physics. Recently, they have been adapted to the field of machine learning, ...giving rise to an emergent research frontier that has attracted considerable attention. Here, we study the trainability of tensor-network based machine learning models by exploring the landscapes of different loss functions, with a focus on the matrix product states (also called tensor trains) architecture. In particular, we rigorously prove that barren plateaus (i.e., exponentially vanishing gradients) prevail in the training process of the machine learning algorithms with global loss functions. Whereas, for local loss functions the gradients with respect to variational parameters near the local observables do not vanish as the system size increases. Therefore, the barren plateaus are absent in this case and the corresponding models could be efficiently trainable. Our results reveal a crucial aspect of tensor-network based machine learning in a rigorous fashion, which provide a valuable guide for both practical applications and theoretical studies in the future.
Non-Hermitian topological phases exhibit a number of exotic features that have no Hermitian counterparts, including the skin effect and breakdown of the conventional bulk-boundary correspondence. ...Here, we implement the non-Hermitian Su-Schrieffer-Heeger Hamiltonian, which is a prototypical model for studying non-Hermitian topological phases, with a solid-state quantum simulator consisting of an electron spin and a 13C nuclear spin in a nitrogen-vacancy center in a diamond. By employing a dilation method, we realize the desired nonunitary dynamics for the electron spin and map out its spin texture in the momentum space, from which the corresponding topological invariant can be obtained directly. From the measured spin textures with varying parameters, we observe both integer and fractional winding numbers. The non-Hermitian topological phase with fractional winding number cannot be continuously deformed to any Hermitian topological phase and is intrinsic to non-Hermitian systems. Our result paves the way for further exploiting and understanding the intriguing properties of non-Hermitian topological phases with solid-state spins or other quantum simulation platforms.
Characterization of qubit couplings in many-body quantum systems is essential for benchmarking quantum computation and simulation. We propose a tomographic measurement scheme to determine all the ...coupling terms in a general many-body Hamiltonian with arbitrary long-range interactions, provided the energy density of the Hamiltonian remains finite. Different from quantum process tomography, our scheme is fully scalable with the number of qubits as the required rounds of measurements increase only linearly with the number of coupling terms in the Hamiltonian. The scheme makes use of synchronized dynamical decoupling pulses to simplify the many-body dynamics so that the unknown parameters in the Hamiltonian can be retrieved one by one. We simulate the performance of the scheme under the influence of various pulse errors and show that it is robust to typical noise and experimental imperfections.
Transmission of SARS-CoV-2 from humans to other mammals, including pet animals, has been reported. However, with the exception of farmed mink, there is no previous evidence that these infected ...animals can infect humans, resulting in sustained human-to-human transmission. Following a confirmed SARS-CoV-2 infection of a pet shop worker, animals in the shop and the warehouse supplying it were tested for evidence of SARS-CoV-2 infection.
In this case study, viral swabs and blood samples were collected from animals in a pet shop and its corresponding warehouse in Hong Kong. Nasal swab or saliva samples from human COVID-19 patients epidemiologically linked to the pet shop and from subsequent local cases confirmed to be infected by SARS-CoV-2 delta variant were collected. Oral swabs were tested by quantitative RT-PCR (RT-qPCR) for SARS-CoV-2 and blood samples were serologically tested by a surrogate virus neutralisation test and plaque reduction neutralisation test. The SARS-CoV-2 RT-qPCR positive samples were sequenced by next generation viral full genome sequencing using the ISeq sequencing platform (Illumina), and the viral genomes were phylogenetically analysed.
Eight (50%) of 16 individually tested Syrian hamsters in the pet shop and seven (58%) of 12 Syrian hamsters in the corresponding warehouse were positive for SARS-CoV-2 infection in RT-qPCR or serological tests. None of the dwarf hamsters (n=75), rabbits (n=246), guinea pigs (n=66), chinchillas (n=116), and mice (n=2) were confirmed positive for SARS-CoV-2 in RT-qPCR tests. SARS-CoV-2 viral genomes deduced from human and hamster cases in this incident all belong to the delta variant of concern (AY.127) that had not been circulating locally before this outbreak. The viral genomes obtained from hamsters were phylogenetically related with some sequence heterogeneity. Phylogenetic dating suggests infection in these hamsters occurred around Oct 14, 2021 (95% CI Sept 15 to Nov 9, 2021). Multiple zoonotic transmission events to humans were detected, leading to onward human-to-human transmission.
Pet hamsters can be naturally infected with SARS-CoV-2. The virus can circulate among hamsters and lead to human infections. Both genetic and epidemiological results strongly suggest that there was more than one hamster-to-human transmission event in this study. This incident also led to onward human transmission. Importation of SARS-CoV-2-infected hamsters was a likely source of this outbreak.
US National Institutes of Health, Research Grants Council of Hong Kong, Food and Health Bureau, and InnoHK.
Abstract
Classification and identification of different phases and the transitions between them is a central task in condensed matter physics. Machine learning, which has achieved dramatic success in ...a wide range of applications, holds the promise to bring unprecedented perspectives for this challenging task. However, despite the exciting progress made along this direction, the reliability of machine-learning approaches in experimental settings demands further investigation. Here, with the nitrogen-vacancy center platform, we report a proof-of-principle experimental demonstration of adversarial examples in learning topological phases. We show that the experimental noises are more likely to act as adversarial perturbations when a larger percentage of the input data are dropped or unavailable for the neural network-based classifiers. We experimentally implement adversarial examples which can deceive the phase classifier with a high confidence, while keeping the topological properties of the simulated Hopf insulators unchanged. Our results explicitly showcase the crucial vulnerability aspect of applying machine learning techniques in experiments to classify phases of matter, which can benefit future studies in this interdisciplinary field.
A systematic literature review and meta-analysis of the burden of healthcare-associated infections (HAIs) in Southeast Asia was performed on 41 studies out of the initially identified 14 089 records. ...The pooled prevalence of overall HAIs was 9.0% (95% confidence interval CI, 7.2%–10.8%), whereas the pooled incidence density of HAI was 20 cases per 1000 intensive care unit–days. The pooled incidence density of ventilator-associated pneumonia, central line–associated bloodstream infection, and catheter-associated urinary tract infection was 14.7 per 1000 ventilator-days (95% CI, 11.7–17.7), 4.7 per 1000 catheter-days (95% CI, 2.9–6.5), and 8.9 per 1000 catheter-days (95% CI, 6.2–11.7), respectively. The pooled incidence of surgical site infection was 7.8% (95% CI, 6.3%–9.3%). The attributed mortality and excess length of stay in hospitals of infected patients ranged from 7% to 46% and 5 to 21 days, respectively.
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