Revisit assignments of the new excited Ωc\Omega_c states with QCD sum rules

In this article, we distinguish the contributions of the positive parity and negative parity $\Omega_c$ states, study the masses and pole residues of the 1S, 1P, 2S and 2P $\Omega_c$ states with the spin $J=\frac{1}{2}$ and $\frac{3}{2}$ using the QCD sum rules in a consistent way, and revisit the assignments of the new narrow excited $\Omega_c^0$ states. The predictions support assigning the $\Omega_c(3000)$ to be the 1P $\Omega_c$ state with $J^P={\frac{1}{2}}^-$, assigning the $\Omega_c(3090)$ to be the 1P $\Omega_c$ state with $J^P={\frac{3}{2}}^-$ or the 2S $\Omega_c$ state with $J^P={\frac{1}{2}}^+$, and assigning $\Omega_c(3119)$ to be the 2S $\Omega_c$ state with $J^P={\frac{3}{2}}^+$.Comment: 19 pages, 22 figures. arXiv admin note: text overlap with arXiv:1705.0774

DMSC: A Dynamic Multi-Seeds Method for Clustering 16S rRNA Sequences Into OTUs

Next-generation sequencing (NGS)-based 16S rRNA sequencing by jointly using the PCR amplification and NGS technology is a cost-effective technique, which has been successfully used to study the phylogeny and taxonomy of samples from complex microbiomes or environments. Clustering 16S rRNA sequences into operational taxonomic units (OTUs) is often the first step for many downstream analyses. Heuristic clustering is one of the most widely employed approaches for generating OTUs. However, most heuristic OTUs clustering methods just select one single seed sequence to represent each cluster, resulting in their outcomes suffer from either overestimation of OTUs number or sensitivity to sequencing errors. In this paper, we present a novel dynamic multi-seeds clustering method (namely DMSC) to pick OTUs. DMSC first heuristically generates clusters according to the distance threshold. When the size of a cluster reaches the pre-defined minimum size, then DMSC selects the multi-core sequences (MCS) as the seeds that are defined as the n-core sequences (n ≥ 3), in which the distance between any two sequences is less than the distance threshold. A new sequence is assigned to the corresponding cluster depending on the average distance to MCS and the distance standard deviation within the MCS. If a new sequence is added to the cluster, dynamically update the MCS until no sequence is merged into the cluster. The new method DMSC was tested on several simulated and real-life sequence datasets and also compared with the traditional heuristic methods such as CD-HIT, UCLUST, and DBH. Experimental results in terms of the inferred OTUs number, normalized mutual information (NMI) and Matthew correlation coefficient (MCC) metrics demonstrate that DMSC can produce higher quality clusters with low memory usage and reduce OTU overestimation. Additionally, DMSC is also robust to the sequencing errors. The DMSC software can be freely downloaded from https://github.com/NWPU-903PR/DMSC

Optimization of the structure of water axial piston pump and cavitation of plunger cavity based on the Kriging model

The cavitation flow of axial piston pump was simulated by the FLUENT software. Simulation results show that 1) Plunger cavity cavitation degree increase nearly one time when the piston pump rotation rate increase from 1500 r/min to 3000 r/min; 2) The axial piston pump L shape throttling groove is more conductive to inhibiting cavitation of plunger cavity than the V shape; 3) The variation law which shows the influence of the thickness of cylinder kidney shape port on the cavitation of plunger cavity. This paper put forward the two-way inclined type cylinder barrel kidney shape port, which was beneficial to improve the self-sucking of the plunger cavity under high speed rotation and could inhibit the cavitation of plunger cavity. The Kriging agent model of has been established by taking the configuration parameters of one-way inclined cylinder kidney shape port as independent variables and the mean value of the gas volume fraction of plunger cavity as target function, based on the Kriging interpolation principle. The optimized structure of the one-way inclined type cylinder barrel kidney shape port is obtained through the Kriging agent model which is optimized by using improved genetic algorithm. The structure of the cylinder kidney shape port and the valve plate throttling grooves are obtained, which mostly inhibit the cavitation of plunger cavity with above analysis. The structure has a strong inhibitory on the plunger cavity cavitation through the simulation analysis and verification

Characterization of a Superconducting Microstrip Single-Photon Detector Shunted with an External Resistor

A superconducting microstrip single-photon detector (SMSPD) generally requires a shunt resistor to avoid latching, caused by its high current-carrying capacity and low kinetic inductance. Here, the effect of the shunt resistor on the behaviors of microbridge SMSPDs was investigated. We analyzed the change in equivalent switching current at different shunt resistances in two ways and determined the operating current range using intrinsic dark count rate (iDCR) curves. We observed that the reduction in shunt resistance can increase the operating current range, which helps to improve the internal detection efficiency (IDE) and reduce the iDCR. However, the reduction in the shunt resistance can reduce the pulse amplitude and increase the pulse decay time, which can degrade the timing jitter and count rate performance of the SMSPD. The trends of the experimental results can be qualitatively reproduced using a circuit model for an SMSPD with a shunt resistor, which provides useful information for the selection of shunt resistors. Furthermore, we report the improved detection performance of a helium-ion-irradiated SMSPD shunted with a small resistance of 5.2 {\Omega}. We observed a weak IDE saturation with a bias current at a wavelength up to 2000 nm and a nonlinear relation between detection current and photon energy.Comment: 13 pages, 8 figures, 1 tabl

Experimental Generation of Spin-Photon Entanglement in Silicon Carbide

A solid-state approach for quantum networks is advantages, as it allows the integration of nanophotonics to enhance the photon emission and the utilization of weakly coupled nuclear spins for long-lived storage. Silicon carbide, specifically point defects within it, shows great promise in this regard due to the easy of availability and well-established nanofabrication techniques. Despite of remarkable progresses made, achieving spin-photon entanglement remains a crucial aspect to be realized. In this paper, we experimentally generate entanglement between a silicon vacancy defect in silicon carbide and a scattered single photon in the zero-phonon line. The spin state is measured by detecting photons scattered in the phonon sideband. The photonic qubit is encoded in the time-bin degree-of-freedom and measured using an unbalanced Mach-Zehnder interferometer. Photonic correlations not only reveal the quality of the entanglement but also verify the deterministic nature of the entanglement creation process. By harnessing two pairs of such spin-photon entanglement, it becomes straightforward to entangle remote quantum nodes at long distance.Comment: 8 pages in total, 4 figures in the main text, 1 figure in the supplemental materia

Genetic map of Triticum turgidum based on a hexaploid wheat population without genetic recombination for D genome

BACKGROUND: A synthetic doubled-haploid hexaploid wheat population, SynDH1, derived from the spontaneous chromosome doubling of triploid F(1) hybrid plants obtained from the cross of hybrids Triticum turgidum ssp. durum line Langdon (LDN) and ssp. turgidum line AS313, with Aegilops tauschii ssp. tauschii accession AS60, was previously constructed. SynDH1 is a tetraploidization-hexaploid doubled haploid (DH) population because it contains recombinant A and B chromosomes from two different T. turgidum genotypes, while all the D chromosomes from Ae. tauschii are homogenous across the whole population. This paper reports the construction of a genetic map using this population. RESULTS: Of the 606 markers used to assemble the genetic map, 588 (97%) were assigned to linkage groups. These included 513 Diversity Arrays Technology (DArT) markers, 72 simple sequence repeat (SSR), one insertion site-based polymorphism (ISBP), and two high-molecular-weight glutenin subunit (HMW-GS) markers. These markers were assigned to the 14 chromosomes, covering 2048.79 cM, with a mean distance of 3.48 cM between adjacent markers. This map showed good coverage of the A and B genome chromosomes, apart from 3A, 5A, 6A, and 4B. Compared with previously reported maps, most shared markers showed highly consistent orders. This map was successfully used to identify five quantitative trait loci (QTL), including two for spikelet number on chromosomes 7A and 5B, two for spike length on 7A and 3B, and one for 1000-grain weight on 4B. However, differences in crossability QTL between the two T. turgidum parents may explain the segregation distortion regions on chromosomes 1A, 3B, and 6B. CONCLUSIONS: A genetic map of T. turgidum including 588 markers was constructed using a synthetic doubled haploid (SynDH) hexaploid wheat population. Five QTLs for three agronomic traits were identified from this population. However, more markers are needed to increase the density and resolution of this map in the future study

A simulation study on the measurement of D0-D0bar mixing parameter y at BES-III

We established a method on measuring the \dzdzb mixing parameter $y$ for BESIII experiment at the BEPCII $e^+e^-$ collider. In this method, the doubly tagged $\psi(3770) \to D^0 \overline{D^0}$ events, with one $D$ decays to CP-eigenstates and the other $D$ decays semileptonically, are used to reconstruct the signals. Since this analysis requires good $e/\pi$ separation, a likelihood approach, which combines the $dE/dx$, time of flight and the electromagnetic shower detectors information, is used for particle identification. We estimate the sensitivity of the measurement of $y$ to be 0.007 based on a $20fb^{-1}$ fully simulated MC sample.Comment: 6 pages, 7 figure

A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems

The differentiation of pluripotent stem cells (PSCs) into diverse functional cell types provides a promising solution to support drug discovery, disease modeling, and regenerative medicine. However, functional cell differentiation is currently limited by the substantial line-to-line and batch-to-batch variabilities, which severely impede the progress of scientific research and the manufacturing of cell products. For instance, PSC-to-cardiomyocyte (CM) differentiation is vulnerable to inappropriate doses of CHIR99021 (CHIR) that are applied in the initial stage of mesoderm differentiation. Here, by harnessing live-cell bright-field imaging and machine learning (ML), we realize real-time cell recognition in the entire differentiation process, e.g., CMs, cardiac progenitor cells (CPCs), PSC clones, and even misdifferentiated cells. This enables non-invasive prediction of differentiation efficiency, purification of ML-recognized CMs and CPCs for reducing cell contamination, early assessment of the CHIR dose for correcting the misdifferentiation trajectory, and evaluation of initial PSC colonies for controlling the start point of differentiation, all of which provide a more invulnerable differentiation method with resistance to variability. Moreover, with the established ML models as a readout for the chemical screen, we identify a CDK8 inhibitor that can further improve the cell resistance to the overdose of CHIR. Together, this study indicates that artificial intelligence is able to guide and iteratively optimize PSC differentiation to achieve consistently high efficiency across cell lines and batches, providing a better understanding and rational modulation of the differentiation process for functional cell manufacturing in biomedical applications