Stationary Light Pulses in Cold Atomic Media

Stationary light pulses (SLPs), i.e., light pulses without motion, are formed via the retrieval of stored probe pulses with two counter-propagating coupling fields. We show that there exist non-negligible hybrid Raman excitations in media of cold atoms that prohibit the SLP formation. We experimentally demonstrate a method to suppress these Raman excitations and realize SLPs in laser-cooled atoms. Our work opens the way to SLP studies in cold as well as in stationary atoms and provides a new avenue to low-light-level nonlinear optics.Comment: 4 pages, 4 figure

Towards Seamless Management of AI Models in High-Performance Computing

With the increasing prevalence of artificial intelligence (AI) in diverse science/engineering communities, AI models emerge on an unprecedented scale among various domains. However, given the complexity and diversity of the software and hardware environments, reusing AI artifacts (models and datasets) is extremely challenging, especially with AI-driven science applications. Building an ecosystem to run and reuse AI applications/datasets at scale efficiently becomes increasingly essential for diverse science and engineering and high-performance computing (HPC) communities. In this paper, we innovate over an HPC-AI ecosystem -- HPCFair, which enables the Findable, Accessible, Interoperable, and Reproducible (FAIR) principles. HPCFair enables the collection of AI models/datasets allowing users to download/upload AI artifacts with authentications. Most importantly, our proposed framework provides user-friendly APIs for users to easily run inference jobs and customize AI artifacts to their tasks as needed. Our results show that, with HPCFair API, users irrespective of technical expertise in AI, can easily leverage AI artifacts to their tasks with minimal effort.Comment: Accepted at the 2nd Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE

A Potential Antifungal Effect of Chitosan Against Candida albicans Is Mediated via the Inhibition of SAGA Complex Component Expression and the Subsequent Alteration of Cell Surface Integrity

Due to the high incidence of nosocomial Candida albicans infection, the first-line drugs for C. albicans infection have been heavily used, and the emergence of drug-resistant strains has gradually increased. Thus, a new antifungal drug or therapeutic method is needed. Chitosan, a product of chitin deacetylation, is considered to be potentially therapeutic for fungal infections because of its excellent biocompatibility, biodegradability and low toxicity. The biocidal action of chitosan against C. albicans shows great commercial potential, but the exact mechanisms underlying its antimicrobial activity are unclear. To reveal these mechanisms, mutant library screening was performed. ADA2 gene, which encodes a histone acetylation coactivator in the SAGA complex, was identified. Transmission electronic microscopy images showed that the surface of chitosan-treated ada2Δ cells was substantially disrupted and displayed an irregular morphology. Interestingly, the cell wall of ada2Δ cells was significantly thinner than that of wild-type cells, with a thickness similar to that seen in the chitosan-treated wild-type strain. Although ADA2 is required for chitosan tolerance, expression of ADA2 and several Ada2-mediated cell wall-related genes (ALS2, PGA45, and ACE2) and efflux transporter genes (MDR1 and CDR1) were significantly inhibited by chitosan. Furthermore, GCN5 encoding a SAGA complex catalytic subunit was inhibited by chitosan, and gcn5Δ cells exhibited phenotypes comparable to those of ada2Δ cells in response to chitosan and other cell surface-disrupting agents. This study demonstrated that a potential antifungal mechanism of chitosan against C. albicans operates by inhibiting SAGA complex gene expression, which decreases the protection of the cell surface against chitosan

Development and evaluation of a loop-mediated isothermal amplification method for rapid detection and differentiation of two genotypes of porcine circovirus type 2

BackgroundPorcine circovirus type 2 (PCV2) is one of the major swine viral diseases and has caused significant economic loss to pig producers. PCV2 has been divided into two major genotypes: PCV2a, PCV2b. A loop-mediated isothermal amplification (LAMP) method was developed for the detection and differentiation of PCV2a and PCV2b in clinical samples.MethodsLAMP-specific primer sets were designed based on six PCV2a and six PCV2b reference isolates. To determine the analytical specificity of LAMP, DNA samples extracted from 36 porcine virus isolates were tested by LAMP, including eight PCV2a, 11 PCV2b, four PCV type 1, two porcine parvovirus, three pseudorabies virus, and eight porcine reproductive and respiratory virus. To evaluate the analytical sensitivity of the assay, 10-fold serial dilutions of PCV2a and PCV2b recombinant plasmids were performed to prepare the dilutions at concentration from 106 to 1 copy(ies)/μL, and each dilution was tested by both LAMP and nested polymerase chain reaction (nested PCR). A total of 168 clinical samples were analyzed by both LAMP and nested PCR, and the relative sensitivity and specificity of LAMP compared to nested PCR were calculated.ResultsUsing different primer sets of LAMP, LAMP could be completed within 50 minutes. This method was found to be highly analytically specific for PCV2a and PCV2b; only the target gene was detected without cross-reaction. The analytical sensitivity of LAMP for PCV2a and PCV2b were 10 copies/μL, demonstrating analytical sensitivity comparable to that obtained using nested PCR. In addition, the sensitivity and specificity of LAMP relative to those of nested PCR were 97.7% and 100.0%, respectively. The percentage of observed agreement was 98.2%, and the κ statistic was 0.949.ConclusionLAMP is a rapid, specific, and sensitive diagnostic method for the detection and differentiation of PCV2a and PCV2b in clinical samples

Sensing User's Activity, Channel, and Location with Near-Field Extra-Large-Scale MIMO

This paper proposes a grant-free massive access scheme based on the millimeter wave (mmWave) extra-large-scale multiple-input multiple-output (XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency, high data rate, and high localization accuracy in the upcoming sixth-generation (6G) networks. The XL-MIMO consists of multiple antenna subarrays that are widely spaced over the service area to ensure line-of-sight (LoS) transmissions. First, we establish the XL-MIMO-based massive access model considering the near-field spatial non-stationary (SNS) property. Then, by exploiting the block sparsity of subarrays and the SNS property, we propose a structured block orthogonal matching pursuit algorithm for efficient active user detection (AUD) and channel estimation (CE). Furthermore, different sensing matrices are applied in different pilot subcarriers for exploiting the diversity gains. Additionally, a multi-subarray collaborative localization algorithm is designed for localization. In particular, the angle of arrival (AoA) and time difference of arrival (TDoA) of the LoS links between active users and related subarrays are extracted from the estimated XL-MIMO channels, and then the coordinates of active users are acquired by jointly utilizing the AoAs and TDoAs. Simulation results show that the proposed algorithms outperform existing algorithms in terms of AUD and CE performance and can achieve centimeter-level localization accuracy.Comment: Submitted to IEEE Transactions on Communications, Major revision. Codes will be open to all on https://gaozhen16.github.io/ soo