Characterization and Genome Analysis of Staphylococcus aureus Podovirus CSA13 and Its Anti-Biofilm Capacity

Staphylococcus aureus is one of the notable human pathogens that can be easily encountered in both dietary and clinical surroundings. Among various countermeasures, bacteriophage therapy is recognized as an alternative method for resolving the issue of antibiotic resistance. In the current study, bacteriophage CSA13 was isolated from a chicken, and subsequently, its morphology, physiology, and genomics were characterized. This Podoviridae phage displayed an extended host inhibition effect of up to 23 hours of persistence. Its broad host spectrum included methicillin susceptible S. aureus (MSSA), methicillin resistant S. aureus (MRSA), local S. aureus isolates, as well as non-aureus staphylococci strains. Moreover, phage CSA13 could successfully remove over 78% and 93% of MSSA and MRSA biofilms in an experimental setting, respectively. Genomic analysis revealed a 17,034 bp chromosome containing 18 predicted open reading frames (ORFs) without tRNAs, representing a typical chromosomal structure of the staphylococcal Podoviridae family. The results presented here suggest that phage CSA13 can be applicable as an effective biocontrol agent against S. aureus

An Urban Autodriving Algorithm Based on a Sensor-Weighted Integration Field with Deep Learning

This paper proposes two algorithms for adaptive driving in urban environments: The first uses vision deep learning, which is named the sparse spatial convolutional neural network (SSCNN); and the second uses a sensor integration algorithm, named the sensor-weighted integration field (SWIF). These algorithms utilize three kinds of sensors, namely vision, Light Detection and Range (LiDAR), and GPS sensors, and decide critical motions for autonomous vehicle, such as steering angles and vehicle speed. SSCNN, which is used for lane recognition, has 2.7 times faster processing speed than the existing spatial CNN method. Additionally, the dataset for SSCNN was constructed by considering both normal and abnormal driving in 7 classes. Thus, lanes can be recognized by extending lanes for special characteristics in urban settings, in which the lanes can be obscured or erased, or the vehicle can drive in any direction. SWIF generates a two-dimensional matrix, in which elements are weighted by integrating both the object data from LiDAR and waypoints from GPS based on detected lanes. These weights are the integers, indicating the degree of safety. Based on the field formed by SWIF, the safe trajectories for two vehicles’ motions, steering angles, and vehicle speed are generated by applying the cost field. Additionally, to flexibly follow the desired steering angle and vehicle speed, the Proportional-Integral-Differential (PID) control is moderated by an integral anti-windup scheme. Consequently, as the dataset considers characteristics of the urban environment, SSCNN is able to be adopted for lane recognition on urban roads. The SWIF algorithm is also useful for flexible driving owing to the high efficiency of its sensor integration, including having a resolution of 2 cm per pixel and speed of 24 fps. Thus, a vehicle can be successfully maneuvered with minimized steering angle change, without lane or route departure, and without obstacle collision in the presence of diverse disturbances in urban road conditions. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.TRU

An Urban Autodriving Algorithm Based on a Sensor-Weighted Integration Field with Deep Learning

This paper proposes two algorithms for adaptive driving in urban environments: the first uses vision deep learning, which is named the sparse spatial convolutional neural network (SSCNN); and the second uses a sensor integration algorithm, named the sensor-weighted integration field (SWIF). These algorithms utilize three kinds of sensors, namely vision, Light Detection and Range (LiDAR), and GPS sensors, and decide critical motions for autonomous vehicle, such as steering angles and vehicle speed. SSCNN, which is used for lane recognition, has 2.7 times faster processing speed than the existing spatial CNN method. Additionally, the dataset for SSCNN was constructed by considering both normal and abnormal driving in 7 classes. Thus, lanes can be recognized by extending lanes for special characteristics in urban settings, in which the lanes can be obscured or erased, or the vehicle can drive in any direction. SWIF generates a two-dimensional matrix, in which elements are weighted by integrating both the object data from LiDAR and waypoints from GPS based on detected lanes. These weights are the integers, indicating the degree of safety. Based on the field formed by SWIF, the safe trajectories for two vehicles’ motions, steering angles, and vehicle speed are generated by applying the cost field. Additionally, to flexibly follow the desired steering angle and vehicle speed, the Proportional-Integral-Differential (PID) control is moderated by an integral anti-windup scheme. Consequently, as the dataset considers characteristics of the urban environment, SSCNN is able to be adopted for lane recognition on urban roads. The SWIF algorithm is also useful for flexible driving owing to the high efficiency of its sensor integration, including having a resolution of 2 cm per pixel and speed of 24 fps. Thus, a vehicle can be successfully maneuvered with minimized steering angle change, without lane or route departure, and without obstacle collision in the presence of diverse disturbances in urban road conditions

A novel ensemble learning for post-processing of NWP Model's next-day maximum air temperature forecast in summer using deep learning and statistical approaches

A reliable and accurate extreme air temperature in summer is necessary to prepare for and respond to thermal disasters such as heatstroke and power outages. The numerical weather prediction (NWP) model is commonly used to forecast air temperature using dynamic mechanisms. Because of its high uncertainty from coarse spatial resolution and unstable parameterization, however, it requires post-processing. Recent studies have proposed advanced post-processing methods using machine learning and deep learning techniques. This study compared various individual post-processing models-multi-linear regression (MLR), support vector regression (SVR), gated recurrent units (GRU), and convolutional neural network (CNN). It also proposed a novel multi-model ensemble (MMESS) that aggregates individual post-processing models based on the skill score (SS) for the Local Data Assimilation and Prediction System (LDAPS, a local NWP model over Korea) model's next-day maximum air temperature (Tmax) forecast data in two different domains: South Korea and Seoul. The pressure and surface data of the present-day analysis and next-day forecast fields of LDAPS were used as input variables. As a result of hindcast validation, CNN showed good overall performance (root mean square error (RMSE) of 1.41 (?)degrees C in South Korea and 1.50 C in Seoul) among individual models. We found that CNN demonstrated lower RMSE (1.17-1.58 ?degrees C) than other post-processing models (1.43-2.17 C) at stations where the bias of LDAPS changes, using surrounding spatial information. The proposed MMESS exhibited more reliable, robust results than the individual models did. A further comparison to the simple average ensemble and the constrained linear squares-based MMEsupported the proposed MMESS as a more suitable ensemble method for next-day Tmax forecast, considering the relative significance of the individual models