Characterization of phage resistance and phages capable of intestinal decolonization of carbapenem-resistant Klebsiella pneumoniae in mice

Carbapenem-resistant Klebsiella pneumoniae (CRKP) has emerged as a severe global health challenge. We isolate and characterize two previously unidentified lytic phages, P24 and P39, with large burst sizes active against ST11 KL64, a major CRKP lineage. P24 and P39 represent species of the genera Przondovirus (Studiervirinae subfamily) and Webervirus (Drexlerviridae family), respectively. P24 and P39 together restrain CRKP growth to nearly 8 h. Phage-resistant mutants exhibit reduced capsule production and decreased virulence. Modifications in mshA and wcaJ encoding capsule polysaccharide synthesis mediate P24 resistance whilst mutations in epsJ encoding exopolysaccharide synthesis cause P39 resistance. We test P24 alone and together with P39 for decolonizing CRKP using mouse intestinal colonization models. Bacterial load shed decrease significantly in mice treated with P24 and P39. In conclusion, we report the characterization of two previously unidentified lytic phages against CRKP, revealing phage resistance mechanisms and demonstrating the potential of lytic phages for intestinal decolonization.

SARS-CoV-2 viral dynamics in non-human primates

Non-human primates infected with SARS-CoV-2 exhibit mild clinical signs. Here we used a mathematical model to characterize in detail the viral dynamics in 31 cynomolgus macaques for which nasopharyngeal and tracheal viral load were frequently assessed. We identified that infected cells had a large burst size (>104 virus) and a within-host reproductive basic number of approximately 6 and 4 in nasopharyngeal and tracheal compartment, respectively. After peak viral load, infected cells were rapidly lost with a half-life of 9 hours, with no significant association between cytokine elevation and clearance, leading to a median time to viral clearance of 10 days, consistent with observations in mild human infections. Given these parameter estimates, we predict that a prophylactic treatment blocking 90% of viral production or viral infection could prevent viral growth. In conclusion, our results provide estimates of SARS-CoV-2 viral kinetic parameters in an experimental model of mild infection and they provide means to assess the efficacy of future antiviral treatments.

A machine learning approach to identify the universality of solitary perturbations accompanying boundary bursts in magnetized toroidal plasmas

Experimental observations assisted by 2-D imaging diagnostics on the KSTAR tokamak show that a solitary perturbation (SP) emerges prior to a boundary burst of magnetized toroidal plasmas, which puts forward SP as a potential candidate for the burst trigger. We have constructed a machine learning (ML) model based on a convolutional deep neural network architecture for a statistical study to identify the SP as a boundary burst trigger. The ML model takes sequential signals detected from 19 toroidal Mirnov coils as input and predicts whether each temporal frame corresponds to an SP. We trained the network in a supervised manner on a training set consisting of real signals with manually annotated SP locations and synthetic burst signals. The trained model achieves high performances in various metrics on a test data set. We also demonstrated the reliability of the model by visualizing the discriminative parts of the input signals that the model recognizes. Finally, we applied the trained model to new data from KSTAR experiments, which were never seen during training, and confirmed that the large burst at the plasma boundary that can fatally damage the fusion device always involves the emergence of SP. This result suggests that the SP is a key to understanding and controlling of the boundary burst in magnetized toroidal plasmas.

The influence of system settings on positioning accuracy in acoustic telemetry, using the YAPS algorithm.

Background Acoustic positioning telemetry allows to collect large amounts of data on the movement of aquatic animals by use of autonomous receiver stations. Essential in this process is the conversion from raw signal detections to reliable positions. A new advancement in the domain is YAPS (Yet Another Positioning Solver), which combines the detection data on the receivers with a model of animal movement. This transparent, flexible and on-line available positioning algorithm overcomes problems related to traditional point-by-point positioning and filtering techniques. However, its performance has only been tested on data from one telemetry system, providing transmitters with stable burst interval. To investigate the performance of YAPS on different system parameters and settings, we conducted a simulation study. Results This paper discusses the effect of varying burst types, burst intervals, number of observations, reflectivity levels of the environment, levels of out-of-array positioning and temporal receiver resolution on positioning accuracy. We found that a receiver resolution better than 1~ms is required for accurate fine-scale positioning. The positioning accuracy of YAPS increases with decreasing burst intervals, especially when the number of observations is low, when reflectivity is high or when information out-of-array is used. However, when the burst interval is stable, large burst intervals (in the order of 1 to 2 minutes) can be chosen without strongly hampering the accuracy (although this results in information loss). With random burst intervals, the accuracy can be much improved if the random sequence is known. Conclusions As it turns out, the key to accurate positioning is the burst type. If a stable burst interval is not possible, the availability of the random sequence improves the positioning of random burst interval data significantly.

The influence of system settings on positioning accuracy in acoustic telemetry, using the YAPS algorithm.

Background: Acoustic positioning telemetry allows to collect large amounts of data on the movement of aquatic animals by use of autonomous receiver stations. Essential in this process is the conversion from raw signal detections to reliable positions. A new advancement in the domain is YAPS (Yet Another Positioning Solver), which combines the detection data on the receivers with a model of animal movement. This transparent, flexible and on-line available positioning algorithm overcomes problems related to traditional point-by-point positioning and filtering techniques. However, its performance has only been tested on data from one telemetry system, providing transmitters with stable burst interval. To investigate the performance of YAPS on different system parameters and settings, we conducted a simulation study. Results: This paper discusses the effect of varying burst types, burst intervals, number of observations, reflectivity levels of the environment, levels of out-of-array positioning and temporal receiver resolution on positioning accuracy. We found that a receiver resolution better than 1 ms is required for accurate fine-scale positioning. The positioning accuracy of YAPS increases with decreasing burst intervals, especially when the number of observations is low, when reflectivity is high or when information out-of-array is used. However, when the burst interval is stable, large burst intervals (in the order of 1 to 2 minutes) can be chosen without strongly hampering the accuracy (although this results in information loss). With random burst intervals, the accuracy can be much improved if the random sequence is known. Conclusions: As it turns out, the key to accurate positioning is the burst type. If a stable burst interval is not possible, the availability of the random sequence improves the positioning of random burst interval data significantly.

The influence of system settings on positioning accuracy in acoustic telemetry, using the YAPS algorithm.

Background: Acoustic positioning telemetry allows to collect large amounts of data on the movement of aquatic animals by use of autonomous receiver stations. Essential in this process is the conversion from raw signal detections to reliable positions. A new advancement in the domain is YAPS (Yet Another Positioning Solver), which combines the detection data on the hydrophones with a model of animal movement. This transparent, flexible and on-line available positioning algorithm overcomes problems related to traditional point-by-point positioning and filtering techniques. However, its performance has only been tested on data from one telemetry system, providing transmitters with stable burst interval. To investigate the performance of YAPS on different system parameters and settings, we conducted a simulation study.Results: This paper discusses the effect of varying burst types, burst intervals, number of observations, reflectivity levels of the environment, levels of out-of-array positioning and temporal hydrophone resolution on positioning accuracy. We found that a hydrophone resolution better than 1~ms is required for accurate fine-scale positioning. The positioning accuracy of YAPS increases with decreasing burst intervals, especially when the number of observations is low, when reflectivity is high or when information out-of-array is used. However, when the burst interval is stable, large burst intervals (in the order of 1 to 2 minutes) can be chosen without strongly hampering the accuracy (although this results in information loss). With random burst intervals, the accuracy can be much improved if the random sequence is known.Conclusions: As it turns out, the key to accurate positioning is the burst type. If a stable burst interval is not possible, the availability of the random sequence improves the positioning of random burst interval data significantly.

Lookup Table Algorithm for Error Correction in Color Images

Error correction and error detection techniques are often used in wireless transmission systems. A color image of type BMP is considered as an application of developed lookup table algorithms to detect and correct errors in these images. Decimal Matrix Code (DMC) and Hamming code (HC) techniques were integrated to compose Hybrid Matrix Code (HMC) to maximize the error detection and correction. The results obtained from HMC still have some error not corrected because the redundant bits added by Hamming codes to the data are considered inadequate, and it is suitable when the error rate is low for detection and correction processes. Besides, a Hamming code could not detect large burst error period, in addition, the have same values sometimes which lead to not detect the error and consequently increase the error ratio. The proposed algorithm LUT_CORR is presented to detect and correct errors in color images over noisy channels, the proposed algorithm depends on the parallel Cyclic Redundancy Code (CRC) method that's based on two algorithms: Sarwate and slicing By N algorithms. The LUT-CORR and the aforementioned algorithms were merged to correct errors in color images, the output results correct the corrupted images with a 100 % ratio almost. The above high correction ratio due to some unique values that the LUT-CORR algorithm have. The HMC and the proposed algorithm applied to different BMP images, the obtained results from LUT-CORR are compared to HMC for both Mean Square Error (MSE) and correction ratio. The outcome from the proposed algorithm shows a good performance and has a high correction ratio to retrieve the source BMP image.