TEICOPLANIN RESISTANCE IN GRAM-POSITIVE BACTERIAL ISOLATE: AN EMERGING THREAT

Objectives: Development of antimicrobial resistance in microorganism isolated from blood stream infection constitutes a major concern about their treatment. Teicoplanin is a glycopeptide antibiotic used in the treatment of infection caused by Gram-positive bacteria. This study was planned to determine Teicoplanin resistance in the Central India and recommend policy changes for prevention of the future resistance to the higher antibiotics. Methods: A total of 1855 septicemia suspected blood samples were studied. The blood culture samples were processed and identified in the microbiology laboratory according to the Clinical and Laboratory Standards Institute guidelines. Antibiotic susceptibility test was done using Kirby B disk diffusion method. Results: About 39.5% of blood culture samples showed positive growth for organism. We observed high teicoplanin resistance (29.5%) among Gram-positive isolates, predominantly (53%) in the Enterococcus species. Conclusion: Teicoplanin resistance has emerged tremendously in the present study. Hence, attention is required about this serious issue otherwise very limited choice of antibiotics will be available for treating infections in the future

Learning Neuro-symbolic Programs for Language Guided Robot Manipulation

Given a natural language instruction and an input scene, our goal is to train a model to output a manipulation program that can be executed by the robot. Prior approaches for this task possess one of the following limitations: (i) rely on hand-coded symbols for concepts limiting generalization beyond those seen during training [1] (ii) infer action sequences from instructions but require dense sub-goal supervision [2] or (iii) lack semantics required for deeper object-centric reasoning inherent in interpreting complex instructions [3]. In contrast, our approach can handle linguistic as well as perceptual variations, end-to-end trainable and requires no intermediate supervision. The proposed model uses symbolic reasoning constructs that operate on a latent neural object-centric representation, allowing for deeper reasoning over the input scene. Central to our approach is a modular structure consisting of a hierarchical instruction parser and an action simulator to learn disentangled action representations. Our experiments on a simulated environment with a 7-DOF manipulator, consisting of instructions with varying number of steps and scenes with different number of objects, demonstrate that our model is robust to such variations and significantly outperforms baselines, particularly in the generalization settings. The code, dataset and experiment videos are available at https://nsrmp.github.ioComment: International Conference on Robotics and Automation (ICRA), 202

Impact Factor: 3.145

ABSTRACT Adaptive filtering has become a spacious area of researcher since last few decades in the field of communication. Adaptive noise cancellation is an approach used for noise reduction in speech signal. The speech signal easily gets contaminated with background noise. Channel noise addition makes this speech signal even poorer. Speech signal and noise signal both change continuously with time, then to separate them only adaptive filtering is desirable. This paper deals with cancellation of noise on speech signal using two old (LMS and NLMS) and one new UNANR algorithm. The UNANR (Unbiased and Normalized Adaptive Noise Rejection) model does not contain any bias unit, and the coefficients are adaptively updated by using the steepest-descent algorithm. Two modulation techniques, AM and FM are applied separately in combination with two communication channels i.e. AWGN and Rician. Signal quality parameter PSNR and RMSE measured and compared with respect to SNR. The results show that the performance of the UNANR based algorithm is superior to that of the LMS algorithm in noise reduction