Gapless topological Fulde-Ferrell superfluidity in spin-orbit coupled Fermi gases

Topological superfluids usually refer to a superfluid state which is gapped in the bulk but metallic at the boundary. Here we report that a gapless, topologically non-trivial superfluid with inhomogeneous Fulde-Ferrell pairing order parameter can emerge in a two-dimensional spin-orbit coupled Fermi gas, in the presence of both in-plane and out-of-plane Zeeman fields. The Fulde-Ferrell pairing - induced by the spin-orbit coupling and in-plane Zeeman field - is responsible for this gapless feature. This exotic superfluid has a significant Berezinskii-Kosterlitz-Thouless (BKT) transition temperature and has robust Majorana edge modes against disorder owing to its topological nature.Comment: 5 pages, 5 figures; add the results on the critical BKT temperature and superfluid density, as well as the discussion on the robustness of the chiral edge states against disorde

A \gamma-ray emitting NLS1 galaxy SDSS J095909.51+460014.3 identified by multiwavelength contemporaneous brightening

We report on an identification of a new gamma-ray emitting narrow-line Seyfert 1 galaxy (gamma-NLS1), SDSS J095909.51+460014.3 (z = 0.399), by establishing an association with a gamma-ray source 4FGL 0959.6+4606, although its low-energy counterpart was suggested to be a radio galaxy 2MASX J09591976+4603515. WISE long-term light curves of these two sources reveal diverse infrared variability patterns. Brightenings of 2.5 mag are detected for the former source, while flux decays of 0.5 mag are found for the other one. More importantly, the time that the infrared flux of the NLS1 rises, is coincident with the time of flux increase of 4FGL 0959.6+4606. At the same time, no infrared activity of the radio galaxy has been observed. A specific analysis of 15-month Fermi-LAT data, aiming at the high gamma-ray flux state, yields a significant source (TS =43). The corresponding gamma-ray localization analysis suggests that only the NLS1 falls into the uncertainty area, further supporting the updated association relationship. A broadband spectral energy distribution of SDSS J095909.51+460014.3 has been drawn and well described by the classic single-zone homogeneous leptonic jet model. Its jet properties are investigated and found to be comparable with the known gamma-NLS1s.Comment: 8 pages, 7 figures,2 tables, A&A in pres

结合轻量级麦穗检测模型和离线Android软件开发的田间小麦测产

The number of spikes per unit area is a key yield component for cereal crops such as wheat, which is popularly used in wheat research for crop improvement. With the fast maturity of smartphone imaging hardware and recent advances in image processing and lightweight deep learning techniques, it is possible to acquire high-resolution images using a smartphone camera, followed by the analysis of wheat spikes per unit area through pre-trained artificial intelligence algorithms. Then, by combining detected spike number with variety-based spikelet number and grain weight, it is feasible to carry out a near real-time estimation of yield potential for a given wheat variety in the field. This AI-driven approach becomes more powerful when a range of varieties are included in the training datasets, enabling an effective and valuable approach for yield-related studies in breeding, cultivation, and agricultural production. In this study, we present a novel smartphone-based software application that combines smartphone imaging, lightweight and embedded deep learning, with yield prediction algorithms and applied the software to wheat cultivation experiments. This open-source Android application is called YieldQuant-Mobile (YQ-M), which was developed to measure a key yield trait (i.e. spikes per unit area) and then estimate yield based on the trait. Through YQ-M and smartphones, we standardized the in-field imaging of wheat plots, streamlined the detection of spikes per unit area and the prediction of yield, without a prerequisite of in-field WiFi or mobile network. In this article, we introduce the YQ-M in detail, including: 1) the data acquisition designed to standardize the collection of wheat images from an overhead perspective using Android smartphones; 2) the data pre-processing of the acquired image to reduce the computational time for image analysis; 3) the extraction of wheat spike features through deep learning (i.e. YOLOV4) and transfer learning; 4) the application of TensorFlow.lite to transform the trained model into a lightweight MobileNetV2-YOLOV4 model, so that wheat spike detection can be operated on an Android smartphone; 5) finally, the establishment of a mobile phone database to incorporate historic datasets of key yield components collected from different wheat varieties into YQ-M using Android SDK and SQLite. Additionally, to ensure that our work could reach the broader research community, we developed a Graphical User Interface (GUI) for YQ-M, which contains: 1) the spike detection module that identifies the number of wheat spikes from a smartphone image; 2) the yield prediction module that invokes near real-time yield prediction using detected spike numbers and related parameters such as wheat varieties, place of production, accumulated temperature, and unit area. During our research, we have tested YQ-M with 80 representative varieties (240 one-square-meter plots, three replicates) selected from the main wheat producing areas in China. The computed accuracy, recall, average accuracy, and F1-score for the learning model are 84.43%, 91.05%, 91.96%, and 0.88, respectively. The coefficient of determination between YQ-M predicted yield values and post-harvest manual yield measurement is 0.839 (n=80 varieties, P<0.05; Root Mean Square Error=17.641 g/m2). The results suggest that YQ-M presented here has a high accuracy in the detection of wheat spikes per unit area and can produce a consistent yield prediction for the selected wheat varieties under complex field conditions. Furthermore, YQ-M can be easily accessed and expanded to incorporate new varieties and crop species, indicating the usability and extendibility of the software application. Hence, we believe that YQ-M is likely to provide a step change in our abilities to analyze yield-related components for different wheat varieties, a low-cost, accessible, and reliable approach that can contribute to smart breeding, cultivation and, potentially, agricultural production

An Updated Search of Steady TeV γ−\gamma-Ray Point Sources in Northern Hemisphere Using the Tibet Air Shower Array

Using the data taken from Tibet II High Density (HD) Array (1997 February-1999 September) and Tibet-III array (1999 November-2005 November), our previous northern sky survey for TeV $\gamma-$ray point sources has now been updated by a factor of 2.8 improved statistics. From $0.0^{\circ}$ to $60.0^{\circ}$ in declination (Dec) range, no new TeV $\gamma-$ray point sources with sufficiently high significance were identified while the well-known Crab Nebula and Mrk421 remain to be the brightest TeV $\gamma-$ray sources within the field of view of the Tibet air shower array. Based on the currently available data and at the 90% confidence level (C.L.), the flux upper limits for different power law index assumption are re-derived, which are approximately improved by 1.7 times as compared with our previous reported limits.Comment: This paper has been accepted by hepn

Anti–SARS-CoV Immunoglobulin G in Healthcare Workers, Guangzhou, China

Low level of immunity for SARS-CoV among well healthcare workers reinforces the need for infection control measures in hospitals to prevent epidemics

Salmonella paratyphi C: Genetic Divergence from Salmonella choleraesuis and Pathogenic Convergence with Salmonella typhi

BACKGROUND: Although over 1400 Salmonella serovars cause usually self-limited gastroenteritis in humans, a few, e.g., Salmonella typhi and S. paratyphi C, cause typhoid, a potentially fatal systemic infection. It is not known whether the typhoid agents have evolved from a common ancestor (by divergent processes) or acquired similar pathogenic traits independently (by convergent processes). Comparison of different typhoid agents with non-typhoidal Salmonella lineages will provide excellent models for studies on how similar pathogens might have evolved. METHODOLOGIES/PRINCIPAL FINDINGS: We sequenced a strain of S. paratyphi C, RKS4594, and compared it with previously sequenced Salmonella strains. RKS4594 contains a chromosome of 4,833,080 bp and a plasmid of 55,414 bp. We predicted 4,640 intact coding sequences (4,578 in the chromosome and 62 in the plasmid) and 152 pseudogenes (149 in the chromosome and 3 in the plasmid). RKS4594 shares as many as 4346 of the 4,640 genes with a strain of S. choleraesuis, which is primarily a swine pathogen, but only 4008 genes with another human-adapted typhoid agent, S. typhi. Comparison of 3691 genes shared by all six sequenced Salmonella strains placed S. paratyphi C and S. choleraesuis together at one end, and S. typhi at the opposite end, of the phylogenetic tree, demonstrating separate ancestries of the human-adapted typhoid agents. S. paratyphi C seemed to have suffered enormous selection pressures during its adaptation to man as suggested by the differential nucleotide substitutions and different sets of pseudogenes, between S. paratyphi C and S. choleraesuis. CONCLUSIONS: S. paratyphi C does not share a common ancestor with other human-adapted typhoid agents, supporting the convergent evolution model of the typhoid agents. S. paratyphi C has diverged from a common ancestor with S. choleraesuis by accumulating genomic novelty during adaptation to man

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

A Panel of Serum MicroRNAs as Specific Biomarkers for Diagnosis of Compound- and Herb-Induced Liver Injury in Rats

Drug-induced liver injury (DILI) has been a public, economic and pharmaceutical issue for many years. Enormous effort has been made for discovering and developing novel biomarkers for diagnosing and monitoring both clinical and preclinical DILI at an early stage, though progress has been relatively slow. Additionally, herb-induced liver injury is an emerging cause of liver disease because herbal medicines are increasingly being used worldwide. Recently, circulating microRNAs (miRNAs) have shown potential to serve as novel, minimally invasive biomarkers to diagnose and monitor human cancers and other diseases at early stages.In order to identify candidate miRNAs as diagnostic biomarkers for DILI, miRNA expression profiles of serum and liver tissue from two parallel liver injury Sprague-Dawley rat models induced by a compound (acetaminophen, APAP) or an herb (Dioscorea bulbifera, DB) were screened in this study. The initial screens were performed on serum using a MicroRNA TaqMan low-density qPCR array and on liver tissue using a miRCURY LNA hybridization array and were followed by a TaqMan probe-based quantitative reverse transcription-PCR (qRT-PCR) assay to validate comparison with serum biochemical parameters and histopathological examination. Two sets of dysregulated miRNA candidates in serum and liver tissue were selected in the screening phase. After qRT-PCR validation, a panel of compound- and herb- related serum miRNAs was identified.We have demonstrated that this panel of serum miRNAs provides potential biomarkers for diagnosis of DILI with high sensitivity and specificity