BackgroundMethicillin-resistant coagulase-negative staphylococci (MR-CoNS) are opportunistic pathogens and serve as a large reservoir of staphylococcal cassette chromosome mec (SCCmec). Characterization of SCCmec in MR-CoNS can generate useful information on the mobilization and evolution of this element.Methodology/Principal FindingsNon-repetitive MR-CoNS clinical isolates (n = 84; 39 S. epidermidis, 19 S. haemolyticus, 9 S. hominis, 6 S. capitis, 4 S. warneri, 2 S. cohnii, 2 S. saprophyticus, 1 S. kloosii, 1 S. simulans and 1 S. massiliensis) were collected. All isolates could grow on plates with 4 mg/L cefoxitin and all had mecA as detected by PCR. Strain typing using RAPD and ERIC-PCR revealed that almost all isolates were of different strains. SCCmec typing was performed using multiplex PCR published previously. For isolates in which SCCmec could not be typed, the mec complex classes were determined by additional PCR and the ccr genes were amplified with published or newly-designed primers and then sequenced. SCCmec types were assigned for 63 isolates by multiplex PCR and were assigned for 14 other isolates by PCR targeting mec and ccr. Among 77 isolates with determined SCCmec types, 54 had a single type, including type III (n = 19), IV (n = 14), V (n = 10), II (n = 2), I (n = 1), VIII (n = 1) and five unnamed types (n = 7), while 23 isolates had two types, III+V (n = 12), II+V (n = 8), II+IV (n = 2) or IV+V (n = 1). The five unnamed types were assigned UT1 (class A mec, ccrA1/ccrB4), UT2 (class C1 mec, ccrA4/ccrB4), UT3 (class A mec, ccrA5/ccrB3), UT4 (class C2 mec, ccrA2/ccrB2 plus ccrC1) and UT5 (class A mec, ccrA1/ccrB1 plus ccrC1).Conclusions/SignificanceSCCmec types III, IV and V were prevalent in MR-CoNS and many isolates could harbor more than one type. Several new types of SCCmec were identified, highlighting the great genetic diversity and the need of developing classification schemes for SCCmec in MR-CoNS.

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A data-driven model is proposed for the prediction of the velocity field around a cylinder by fusion convolutional neural networks (CNNs) using measurements of the pressure field on the cylinder. The model is based on the close relationship between the Reynolds stresses in the wake, the wake formation length, and the base pressure. Numerical simulations of flow around a cylinder at various Reynolds numbers are carried out to establish a dataset capturing the effect of the Reynolds number on various flow properties. The time series of pressure fluctuations on the cylinder is converted into a grid-like spatial-temporal topology to be handled as the input of a CNN. A CNN architecture composed of a fusion of paths with and without a pooling layer is designed. This architecture can capture both accurate spatial-temporal information and the features that are invariant of small translations in the temporal dimension of pressure fluctuations on the cylinder. The CNN is trained using the computational fluid dynamics (CFD) dataset to establish the mapping relationship between the pressure fluctuations on the cylinder and the velocity field around the cylinder. Adam (adaptive moment estimation), an efficient method for processing large-scale and high-dimensional machine learning problems, is employed to implement the optimization algorithm. The trained model is then tested over various Reynolds numbers. The predictions of this model are found to agree well with the CFD results, and the data-driven model successfully learns the underlying flow regimes, i.e., the relationship between wake structure and pressure experienced on the surface of a cylinder is well established.

Women with aggressive triple-negative breast cancer (TNBC) are at high risk of brain metastasis, which has no effective therapeutic option partially due to the poor penetration of drugs across the blood−brain barrier. Trifluoperazine (TFP) is an approved antipsychotic drug with good bioavailability in brain and had shown anticancer effect in several types of cancer. It drives us to investigate its activities to suppress TNBC, especially the brain metastasis. In this study, we chose three TNBC cell lines MDA-MB-468, MDA-MB-231, and 4T1 to assess its anticancer activities along with the possible mechanisms. In vitro, it induced G0/G1 cell cycle arrest via decreasing the expression of both cyclinD1/CDK4 and cyclinE/CDK2, and stimulated mitochondria-mediated apoptosis. In vivo, TFP suppressed the growth of subcutaneous xenograft tumor and brain metastasis without causing detectable side effects. Importantly, it prolonged the survival of mice bearing brain metastasis. Immunohistochemical analysis of Ki67 and cleaved caspase-3 indicated TFP could suppress the growth and induce apoptosis of cancer cells in vivo. Taken together, TFP might be a potential available drug for treating TNBC with brain metastasis, which urgently needs novel treatment options.