Clinical Distribution and Drug Resistance of 224 Strains of Pseudomonas Aeruginosa

Objective: To provide evidence for a rational and effective prevention and treatment of Pseudomonas aeruginosa, the clinical characteristics and the resistance to various antibiotics of were investigated.Methods：A retrospective analysis of 224 strains of Pseudomonas aeruginosa isolated from various specimens from various clinical departments of our hospital (April 1, 2018 to June 31, 2019) were conducted. Identification and drug susceptibility test of isolated strains was performed using a fully automatic bacterial identification analyzer (MicroScan WalkAway-96 plus), and data analysis was performed using WH0NET5.6 software. Result：Among all the bacteria isolated in our hospital during the above period, Pseudomonas aeruginosa accounted for 10.09% of them all and 12.57% of Gram-negative bacilli, respectively. These isolates were mainly derived from sputum specimens (68.75%), mainly from male patients (70.54%), and mostly 61-70 (27.23%) or 51-60 (22.77%) years old. Pseudomonas aeruginosa isolates are mainly from Rehabilitation Ward, ICU, and Liver Transplantation Unit, accounted for 29.91%, 12.95% and 10.27% of all isolates, respectively. The sensitivity of Pseudomonas aeruginosa to various antibacterial drugs, in the order of high to low were carbapenems, aztreonam, quinolones, cephalosporins, piperacillin/ tazobactam, aminoglycoside, with a lowest resistance rate (2.4%) to amikacin and a highest resistance rate to imipenem (33.0%). Conclusion：The isolation rate of Pseudomonas aeruginosa was relatively stable during the study period, and among all the P. aeruginosa detected, most of them were from the respiratory secretions of elderly male patients. The resistance rate of Pseudomonas aeruginosa isolates to various antibiotics is mainly within 30%. Clinical units such as Rehabilitation Ward, ICU, and Liver Transplantation Unit have a high detection rate, therefore, these departments should be monitored in a focused manner. Our research provide a scientific basis for the rational use of antibiotics and a better control of Pseudomonas aeruginosa infection

Combined Detection of Mean Platelet Volume and Immunoglobins as a Strategy for the Diagnosis of Systemic Lupus Erythematosus

Objective:To explore the possibility of diagnosing and monitoring patients with systemic lupus erythematosus (SLE) using the combination of mean platelet volume (MPV) and routine immunoglobulin test. Method:116 patients with SLE were divided into 3 groups according to their clinical characteristics, including 29 patients with renal impairment, 44 cases of active stage and 43 cases of inactive patients. 40 healthy subjects were randomly selected as controls. Subjects were tested for routine blood test and plasma Immunoglobins, such as IgG, IgA, IgM, C3, C4, CH50, CRP. The results were analyzed and the characteristics of each group of subjects were determined, the correlation between test results and diagnosis were studied. Result: In comparison to the control group, the serum level of MPV, C3 and C4 were decreased (P<0.05), and C reactive protein level was elevated (P<0.001) in the three groups of SLE patients. The IgG level in active and inactive SLE patients was increased (P<0.0001), CH50 level was decreased in patients with inactive SLE (P<0.05), IgA level of active SLE subjects was found to be elevated (P<0.05), IgM in patients with renal impairment was decreased (P<0.05). Other than that, no other significant characteristic were found. Conclusion: The pathogenesis of SLE is a complex process involving multiple factors. The changes of MPV, IgG, IgA, IgM, C3, C4, CH50 and CRP in SLE patients are characteristic parameters. The combination of the above indicators can help to determine the diagnosis and staging of SLE. The timely diagnosis and treatment of SLE patients has important clinical significance in protecting the organ function of SLE patients and improving the prognosis

In Vitro Antibacterial Activity of Galla Chinensis Combined with Different Antibacterial Drugs against Carbapenem-Resistant E.Coli

Objective: To evaluate the antibacterial effects of meropenem and levofloxacin respectively combined with Galla chinensis on carbapenem-resistant Escherichia coli in vitro. Methods： The protocol was designed with checkerboard method and the carbapenem-resistant E.coli was isolated in our hospital. The minimum inhibitory concentrations(MICs) of G. chinensis alone and combined with 2 antimicrobial agents against carbapenem-resistant E.coli were determined by broth dilution method and the fractional inhibitory concentration index (FICI) was calculated according to MICs results. Result： the combined use of G. chinensis and meropenem (or levofloxacin) significantly decreased both MIC50 and MIC90; After the combination of G. chinensis and meropenem, the synergistic effect was 86.7%, and the additive effect was 13.3%, no irrelevant and antagonistic effects. After combined use of G. chinensis and levofloxacin, the synergistic effect was 66.7%, and the additive effect was 33.3%. No irrelevant and antagonistic effects. Conclusion： Galla chinensis combined with meropenem or levofloxacin has synergistic and additive antibacterial effect, with certain combined antibacterial activity

Evaluation of Ecosystem Services in Strategic Environmental Assessment for River Basin Planning

Water Resources Planning and Managemen

CEIL: Generalized Contextual Imitation Learning

In this paper, we present \textbf{C}ont\textbf{E}xtual \textbf{I}mitation \textbf{L}earning~(CEIL), a general and broadly applicable algorithm for imitation learning (IL). Inspired by the formulation of hindsight information matching, we derive CEIL by explicitly learning a hindsight embedding function together with a contextual policy using the hindsight embeddings. To achieve the expert matching objective for IL, we advocate for optimizing a contextual variable such that it biases the contextual policy towards mimicking expert behaviors. Beyond the typical learning from demonstrations (LfD) setting, CEIL is a generalist that can be effectively applied to multiple settings including: 1)~learning from observations (LfO), 2)~offline IL, 3)~cross-domain IL (mismatched experts), and 4) one-shot IL settings. Empirically, we evaluate CEIL on the popular MuJoCo tasks (online) and the D4RL dataset (offline). Compared to prior state-of-the-art baselines, we show that CEIL is more sample-efficient in most online IL tasks and achieves better or competitive performances in offline tasks.Comment: NeurIPS 202

On Sparse Modern Hopfield Model

We introduce the sparse modern Hopfield model as a sparse extension of the modern Hopfield model. Like its dense counterpart, the sparse modern Hopfield model equips a memory-retrieval dynamics whose one-step approximation corresponds to the sparse attention mechanism. Theoretically, our key contribution is a principled derivation of a closed-form sparse Hopfield energy using the convex conjugate of the sparse entropic regularizer. Building upon this, we derive the sparse memory retrieval dynamics from the sparse energy function and show its one-step approximation is equivalent to the sparse-structured attention. Importantly, we provide a sparsity-dependent memory retrieval error bound which is provably tighter than its dense analog. The conditions for the benefits of sparsity to arise are therefore identified and discussed. In addition, we show that the sparse modern Hopfield model maintains the robust theoretical properties of its dense counterpart, including rapid fixed point convergence and exponential memory capacity. Empirically, we use both synthetic and real-world datasets to demonstrate that the sparse Hopfield model outperforms its dense counterpart in many situations.Comment: 37 pages, accepted to NeurIPS 202

Semiparametric inference for data with a continuous outcome from a two-phase probability-dependent sampling scheme

Multi-phased designs and biased sampling designs are two of the well recognized approaches to enhance study efficiency. In this paper, we propose a new and cost-effective sampling design, the two-phase probability dependent sampling design (PDS), for studies with a continuous outcome. This design will enable investigators to make efficient use of resources by targeting more informative subjects for sampling. We develop a new semiparametric empirical likelihood inference method to take advantage of data obtained through a PDS design. Simulation study results indicate that the proposed sampling scheme, coupled with the proposed estimator, is more efficient and more powerful than the existing outcome dependent sampling design and the simple random sampling design with the same sample size. We illustrate the proposed method with a real data set from an environmental epidemiologic study