An Efficient CNN-Based Method for Intracranial Hemorrhage Segmentation from Computerized Tomography Imaging.

Intracranial hemorrhage (ICH) resulting from traumatic brain injury is a serious issue, often leading to death or long-term disability if not promptly diagnosed. Currently, doctors primarily use Computerized Tomography (CT) scans to detect and precisely locate a hemorrhage, typically interpreted by radiologists. However, this diagnostic process heavily relies on the expertise of medical professionals. To address potential errors, computer-aided diagnosis systems have been developed. In this study, we propose a new method that enhances the localization and segmentation of ICH lesions in CT scans by using multiple images created through different data augmentation techniques. We integrate residual connections into a U-Net-based segmentation network to improve the training efficiency. Our experiments, based on 82 CT scans from traumatic brain injury patients, validate the effectiveness of our approach, achieving an IOU score of 0.807 ± 0.03 for ICH segmentation using 10-fold cross-validation.

Novel transcriptomic signatures associated with premature kidney allograft failure.

BACKGROUND: The power to predict kidney allograft outcomes based on non-invasive assays is limited. Assessment of operational tolerance (OT) patients allows us to identify transcriptomic signatures of true non-responders for construction of predictive models. METHODS: In this observational retrospective study, RNA sequencing of peripheral blood was used in a derivation cohort to identify a protective set of transcripts by comparing 15 OT patients (40% females), from the TOMOGRAM Study (NCT05124444), 14 chronic active antibody-mediated rejection (CABMR) and 23 stable graft function patients ≥15 years (STA). The selected differentially expressed transcripts between OT and CABMR were used in a validation cohort (n = 396) to predict 3-year kidney allograft loss at 3 time-points using RT-qPCR. FINDINGS: Archetypal analysis and classifier performance of RNA sequencing data showed that OT is clearly distinguishable from CABMR, but similar to STA. Based on significant transcripts from the validation cohort in univariable analysis, 2 multivariable Cox models were created. A 3-transcript (ADGRG3, ATG2A, and GNLY) model from POD 7 predicted graft loss with C-statistics (C) 0.727 (95% CI, 0.638-0.820). Another 3-transcript (IGHM, CD5, GNLY) model from M3 predicted graft loss with C 0.786 (95% CI, 0.785-0.865). Combining 3-transcripts models with eGFR at POD 7 and M3 improved C-statistics to 0.860 (95% CI, 0.778-0.944) and 0.868 (95% CI, 0.790-0.944), respectively. INTERPRETATION: Identification of transcripts distinguishing OT from CABMR allowed us to construct models predicting premature graft loss. Identified transcripts reflect mechanisms of injury/repair and alloimmune response when assessed at day 7 or with a loss of protective phenotype when assessed at month 3. FUNDING: Supported by the Ministry of Health of the Czech Republic under grant NV19-06-00031.

Improved recovery and annotation of genes in metagenomes through the prediction of fungal introns.

Metagenomics provides a tool to assess the functional potential of environmental and host-associated microbiomes based on the analysis of environmental DNA: assembly, gene prediction and annotation. While gene prediction is straightforward for most bacterial and archaeal taxa, it has limited applicability in the majority of eukaryotic organisms, including fungi that contain introns in gene coding sequences. As a consequence, eukaryotic genes are underrepresented in metagenomics datasets and our understanding of the contribution of fungi and other eukaryotes to microbiome functioning is limited. Here, we developed a machine intelligence-based algorithm that predicts fungal introns in environmental DNA with reasonable precision and used it to improve the annotation of environmental metagenomes. Intron removal increased the number of predicted genes by up to 9.1% and improved the annotation of several others. The proportion of newly predicted genes increased with the share of eukaryotic genes in the metagenome and-within fungal taxa-increased with the number of introns per gene. Our approach provides a tool named SVMmycointron for improved metagenome annotation, especially of microbiomes with a high proportion of eukaryotes. The scripts described in the paper are made publicly available and can be readily utilized by microbiome researchers analysing metagenomics data.

Epidemiology of Methicillin-Resistant Staphylococcus aureus in Slovakia, 2020 - Emergence of an Epidemic USA300 Clone in Community and Hospitals.

Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of health care-associated infections. Additionally, over the decades, the spread of community-associated (CA-MRSA) clones has become a serious problem. The aim of this study was to gain data on the current epidemiology of MRSA in Slovakia. Between January 2020 and March 2020, single-patient MRSA isolates (invasive and/or colonizing) were collected in Slovakia from hospitalized inpatients (16 hospitals) or outpatients (77 cities). Isolates were characterized via antimicrobial susceptibility testing, spa typing, SCCmec typing, the detection of mecA/mecC, genes coding for Panton-Valentine leukocidin (PVL), and the arcA gene (part of the arginine catabolic mobile element [ACME]). Out of 412 isolates, 167 and 245 originated from hospitalized patients and outpatients, respectively. Inpatients were most likely older (P < 0.001) and carried a strain exhibiting multiple resistance (P = 0.015). Isolates were frequently resistant to erythromycin (n = 320), clindamycin (n = 268), and ciprofloxacin/norfloxacin (n = 261). 55 isolates were resistant to oxacillin/cefoxitin only. By clonal structure, CC5-MRSA-II (n = 106; spa types t003, t014), CC22-MRSA-IV (n = 75; t032), and CC8-MRSA-IV (n = 65; t008) were the most frequent. We identified PVL in 72 isolates (17.48%; 17/412), with the majority belonging to CC8-MRSA-IV (n = 55; arcA+; t008, t622; the USA300 CA-MRSA clone) and CC5-MRSA-IV (n = 13; t311, t323). To the best of our knowledge, this is the first study on the epidemiology of MRSA in Slovakia. The presence of the epidemic HA-MRSA clones CC5-MRSA-II and CC22-MRSA-IV was found, as was, importantly, the emergence of the global epidemic USA300 CA-MRSA clone. The extensive spread of USA300 among inpatients and outpatients across the Slovakian regions warrants further investigation. IMPORTANCE The epidemiology of MRSA is characterized by the rise and fall of epidemic clones. Understanding the spread, as well as the evolution of successful MRSA clones, depends on the knowledge of global MRSA epidemiology. However, basic knowledge about MRSA epidemiology is still fragmented or completely missing in some parts of the world. This is the first study of MRSA epidemiology in Slovakia to identify the presence of the epidemic HA-MRSA clones CC5-MRSA-II and CC22-MRSA-IV and, importantly and unexpectedly, the emergence of the global epidemic USA300 CA-MRSA clone in the Slovakian community and hospitals. So far, USA300 has failed to spread in Europe, and this study documents an extensive spread of this epidemic clone in a European country for the first time.

Solvents govern rheology and jamming of polymeric bead suspensions.

Despite their apparent simplicity, suspensions of hard spheres in a Newtonian fluid show complex non-Newtonian behaviors and remain poorly understood. Recent works have pointed out the crucial role of interparticle contact forces in these behaviors. Here, we show that the same (polystyrene) particles, when immersed in different Newtonian solvents, show different behaviors at both the microscopic and macroscopic scales. Thanks to interparticle force measurements in each solvent together with rheological measurements, we show how the fine details of the pairwise particle interactions impact the macroscopic behavior. The rheological properties (shear thinning, shear thickening, jamming solid fraction value) of the suspensions, made up of same particles, are shown to depend on the nature of the solvent. Here, we highlight several mechanisms at the particle scale: the swelling of polymeric particles in an organic solvent, the role of colloidal repulsive forces and inertia for particles in a water solution, and the variation of the friction coefficient as a function of the load for particles immersed in silicone oils. Our study provides new quantitative data to test micromechanical models and simulations. It questions the interpretation of previous experimental works. Finally, it shows the need to systematically characterize the interparticle normal and tangential forces when studying a given suspension of hard spheres in a Newtonian fluid.

Optimal selective floor cleaning using deep learning algorithms and reconfigurable robot hTetro.

Floor cleaning robots are widely used in public places like food courts, hospitals, and malls to perform frequent cleaning tasks. However, frequent cleaning tasks adversely impact the robot's performance and utilize more cleaning accessories (such as brush, scrubber, and mopping pad). This work proposes a novel selective area cleaning/spot cleaning framework for indoor floor cleaning robots using RGB-D vision sensor-based Closed Circuit Television (CCTV) network, deep learning algorithms, and an optimal complete waypoints path planning method. In this scheme, the robot will clean only dirty areas instead of the whole region. The selective area cleaning/spot cleaning region is identified based on the combination of two strategies: tracing the human traffic patterns and detecting stains and trash on the floor. Here, a deep Simple Online and Real-time Tracking (SORT) human tracking algorithm was used to trace the high human traffic region and Single Shot Detector (SSD) MobileNet object detection framework for detecting the dirty region. Further, optimal shortest waypoint coverage path planning using evolutionary-based optimization was incorporated to traverse the robot efficiently to the designated selective area cleaning/spot cleaning regions. The experimental results show that the SSD MobileNet algorithm scored 90% accuracy for stain and trash detection on the floor. Further, compared to conventional methods, the evolutionary-based optimization path planning scheme reduces 15% percent of navigation time and 10% percent of energy consumption.

Noncoding RNAs and Their Response Predictive Value in Azacitidine-treated Patients With Myelodysplastic Syndrome and Acute Myeloid Leukemia With Myelodysplasia-related Changes.

BACKGROUND/AIM: Prediction of response to azacitidine (AZA) treatment is an important challenge in hematooncology. In addition to protein coding genes (PCGs), AZA efficiency is influenced by various noncoding RNAs (ncRNAs), including long ncRNAs (lncRNAs), circular RNAs (circRNAs), and transposable elements (TEs). MATERIALS AND METHODS: RNA sequencing was performed in patients with myelodysplastic syndromes or acute myeloid leukemia before AZA treatment to assess contribution of ncRNAs to AZA mechanisms and propose novel disease prediction biomarkers. RESULTS: Our analyses showed that lncRNAs had the strongest predictive potential. The combined set of the best predictors included 14 lncRNAs, and only four PCGs, one circRNA, and no TEs. Epigenetic regulation and recombinational repair were suggested as crucial for AZA response, and network modeling defined three deregulated lncRNAs (CTC-482H14.5, RP11-419K12.2, and RP11-736I24.4) associated with these processes. CONCLUSION: The expression of various ncRNAs can influence the effect of AZA and new ncRNA-based predictive biomarkers can be defined.

A Study of Physical Layer Security in SWIPT-Based Decode-and-Forward Relay Networks with Dynamic Power Splitting.

In this paper, we study the physical layer security for simultaneous wireless information and power transfer (SWIPT)-based half-duplex (HD) decode-and-forward relaying system. We consider a system model including one transmitter that tries to transmit information to one receiver under the help of multiple relay users and in the presence of one eavesdropper that attempts to overhear the confidential information. More specifically, to investigate the secrecy performance, we derive closed-form expressions of outage probability (OP) and secrecy outage probability for dynamic power splitting-based relaying (DPSBR) and static power splitting-based relaying (SPSBR) schemes. Moreover, the lower bound of secrecy outage probability is obtained when the source's transmit power goes to infinity. The Monte Carlo simulations are given to corroborate the correctness of our mathematical analysis. It is observed from simulation results that the proposed DPSBR scheme outperforms the SPSBR-based schemes in terms of OP and SOP under the impact of different parameters on system performance.

Adaptive Floor Cleaning Strategy by Human Density Surveillance Mapping with a Reconfigurable Multi-Purpose Service Robot.

Professional cleaning and safe social distance monitoring are often considered as demanding, time-consuming, repetitive, and labor-intensive tasks with the risk of getting exposed to the virus. Safe social distance monitoring and cleaning are emerging problems solved through robotics solutions. This research aims to develop a safe social distance surveillance system on an intra-reconfigurable robot with a multi-robot cleaning system for large population environments, like office buildings, hospitals, or shopping malls. We propose an adaptive multi-robot cleaning strategy based on zig-zag-based coverage path planning that works in synergy with the human interaction heat map generated by safe social distance monitoring systems. We further validate the proposed adaptive velocity model's efficiency for the multi-robot cleaning systems regarding time consumption and energy saved. The proposed method using sigmoid-based non-linear function has shown superior performance with 14.1 percent faster and energy consumption of 11.8 percent less than conventional cleaning methods.

Coverage Path Planning Using Reinforcement Learning-Based TSP for hTetran-A Polyabolo-Inspired Self-Reconfigurable Tiling Robot.

One of the critical challenges in deploying the cleaning robots is the completion of covering the entire area. Current tiling robots for area coverage have fixed forms and are limited to cleaning only certain areas. The reconfigurable system is the creative answer to such an optimal coverage problem. The tiling robot's goal enables the complete coverage of the entire area by reconfiguring to different shapes according to the area's needs. In the particular sequencing of navigation, it is essential to have a structure that allows the robot to extend the coverage range while saving energy usage during navigation. This implies that the robot is able to cover larger areas entirely with the least required actions. This paper presents a complete path planning (CPP) for hTetran, a polyabolo tiled robot, based on a TSP-based reinforcement learning optimization. This structure simultaneously produces robot shapes and sequential trajectories whilst maximizing the reward of the trained reinforcement learning (RL) model within the predefined polyabolo-based tileset. To this end, a reinforcement learning-based travel sales problem (TSP) with proximal policy optimization (PPO) algorithm was trained using the complementary learning computation of the TSP sequencing. The reconstructive results of the proposed RL-TSP-based CPP for hTetran were compared in terms of energy and time spent with the conventional tiled hypothetical models that incorporate TSP solved through an evolutionary based ant colony optimization (ACO) approach. The CPP demonstrates an ability to generate an ideal Pareto optima trajectory that enhances the robot's navigation inside the real environment with the least energy and time spent in the company of conventional techniques.

Locomotion with Pedestrian Aware from Perception Sensor by Pavement Sweeping Reconfigurable Robot.

Regular washing of public pavements is necessary to ensure that the public environment is sanitary for social activities. This is a challenge for autonomous cleaning robots, as they must adapt to the environment with varying pavement widths while avoiding pedestrians. A self-reconfigurable pavement sweeping robot, named Panthera, has the mechanisms to perform reconfiguration in width to enable smooth cleaning operations, and it changes its behavior based on environment dynamics of moving pedestrians and changing pavement widths. Reconfiguration in the robot's width is possible, due to the scissor mechanism at the core of the robot's body, which is driven by a lead screw motor. Panthera will perform locomotion and reconfiguration based on perception sensors feedback control proposed while using an Red Green Blue-D (RGB-D) camera. The proposed control scheme involves publishing robot kinematic parameters for reconfiguration during locomotion. Experiments were conducted in outdoor pavements to demonstrate the autonomous reconfiguration during locomotion to avoid pedestrians while complying with varying pavements widths in a real-world scenario.

Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot.

One of the essential attributes of a cleaning robot is to achieve complete area coverage. Current commercial indoor cleaning robots have fixed morphology and are restricted to clean only specific areas in a house. The results of maximum area coverage are sub-optimal in this case. Tiling robots are innovative solutions for such a coverage problem. These new kinds of robots can be deployed in the cases of cleaning, painting, maintenance, and inspection, which require complete area coverage. Tiling robots' objective is to cover the entire area by reconfiguring to different shapes as per the area requirements. In this context, it is vital to have a framework that enables the robot to maximize the area coverage while minimizing energy consumption. That means it is necessary for the robot to cover the maximum area with the least number of shape reconfigurations possible. The current paper proposes a complete area coverage planning module for the modified hTrihex, a honeycomb-shaped tiling robot, based on the deep reinforcement learning technique. This framework simultaneously generates the tiling shapes and the trajectory with minimum overall cost. In this regard, a convolutional neural network (CNN) with long short term memory (LSTM) layer was trained using the actor-critic experience replay (ACER) reinforcement learning algorithm. The simulation results obtained from the current implementation were compared against the results that were generated through traditional tiling theory models that included zigzag, spiral, and greedy search schemes. The model presented in the current paper was also compared against other methods where this problem was considered as a traveling salesman problem (TSP) solved through genetic algorithm (GA) and ant colony optimization (ACO) approaches. Our proposed scheme generates a path with a minimized cost at a lesser time.

Optimization Complete Area Coverage by Reconfigurable hTrihex Tiling Robot.

Completed area coverage planning (CACP) plays an essential role in various fields of robotics, such as area exploration, search, rescue, security, cleaning, and maintenance. Tiling robots with the ability to change their shape is a feasible solution to enhance the ability to cover predefined map areas with flexible sizes and to access the narrow space constraints. By dividing the map into sub-areas with the same size as the changeable robot shapes, the robot can plan the optimal movement to predetermined locations, transform its morphologies to cover the specific area, and ensure that the map is completely covered. The optimal navigation planning problem, including the least changing shape, shortest travel distance, and the lowest travel time while ensuring complete coverage of the map area, are solved in this paper. To this end, we propose the CACP framework for a tiling robot called hTrihex with three honeycomb shape modules. The robot can shift its shape into three different morphologies ensuring coverage of the map with a predetermined size. However, the ability to change shape also raises the complexity issues of the moving mechanisms. Therefore, the process of optimizing trajectories of the complete coverage is modeled according to the Traveling Salesman Problem (TSP) problem and solved by evolutionary approaches Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Hence, the costweight to clear a pair of waypoints in the TSP is defined as the required energy shift the robot between the two locations. This energy corresponds to the three operating processes of the hTrihex robot: transformation, translation, and orientation correction. The CACP framework is verified both in the simulation environment and in the real environment. From the experimental results, proposed CACP capable of generating the Pareto-optimal outcome that navigates the robot from the goal to destination in various workspaces, and the algorithm could be adopted to other tiling robot platforms with multiple configurations.

A Self-Organizing Fuzzy Logic Classifier for Benchmarking Robot-Aided Blasting of Ship Hulls.

Regular dry dock maintenance work on ship hulls is essential for maintaining the efficiency and sustainability of the shipping industry. Hydro blasting is one of the major processes of dry dock maintenance work, where human labor is extensively used. The conventional methods of maintenance work suffer from many shortcomings, and hence robotized solutions have been developed. This paper proposes a novel robotic system that can synthesize a benchmarking map for a previously blasted ship hull. A Self-Organizing Fuzzy logic (SOF) classifier has been developed to benchmark the blasting quality of a ship hull similar to blasting quality categorization done by human experts. Hornbill, a multipurpose inspection and maintenance robot intended for hydro blasting, benchmarking, and painting, has been developed by integrating the proposed SOF classifier. Moreover, an integrated system solution has been developed to improve dry dock maintenance of ship hulls. The proposed SOF classifier can achieve a mean accuracy of 0.9942 with an execution time of 8.42 µ s. Realtime experimenting with the proposed robotic system has been conducted on a ship hull. This experiment confirms the ability of the proposed robotic system in synthesizing a benchmarking map that reveals the benchmarking quality of different areas of a previously blasted ship hull. This sort of a benchmarking map would be useful for ensuring the blasting quality as well as performing efficient spot wise reblasting before the painting. Therefore, the proposed robotic system could be utilized for improving the efficiency and quality of hydro blasting work on the ship hull maintenance industry.

Table Cleaning Task by Human Support Robot Using Deep Learning Technique.

This work presents a table cleaning and inspection method using a Human Support Robot (HSR) which can operate in a typical food court setting. The HSR is able to perform a cleanliness inspection and also clean the food litter on the table by implementing a deep learning technique and planner framework. A lightweight Deep Convolutional Neural Network (DCNN) has been proposed to recognize the food litter on top of the table. In addition, the planner framework was proposed to HSR for accomplishing the table cleaning task which generates the cleaning path according to the detection of food litter and then the cleaning action is carried out. The effectiveness of the food litter detection module is verified with the cleanliness inspection task using Toyota HSR, and its detection results are verified with standard quality metrics. The experimental results show that the food litter detection module achieves an average of 96 % detection accuracy, which is more suitable for deploying the HSR robots for performing the cleanliness inspection and also helps to select the different cleaning modes. Further, the planner part has been tested through the table cleaning tasks. The experimental results show that the planner generated the cleaning path in real time and its generated path is optimal which reduces the cleaning time by grouping based cleaning action for removing the food litters from the table.

Evolutionary Algorithm-Based Complete Coverage Path Planning for Tetriamond Tiling Robots.

Tiling robots with fixed morphology face major challenges in terms of covering the cleaning area and generating the optimal trajectory during navigation. Developing a self-reconfigurable autonomous robot is a probable solution to these issues, as it adapts various forms and accesses narrow spaces during navigation. The total navigation energy includes the energy expenditure during locomotion and the shape-shifting of the platform. Thus, during motion planning, the optimal navigation sequence of a self-reconfigurable robot must include the components of the navigation energy and the area coverage. This paper addresses the framework to generate an optimal navigation path for reconfigurable cleaning robots made of tetriamonds. During formulation, the cleaning environment is filled with various tiling patterns of the tetriamond-based robot, and each tiling pattern is addressed by a waypoint. The objective is to minimize the amount of shape-shifting needed to fill the workspace. The energy cost function is formulated based on the travel distance between waypoints, which considers the platform locomotion inside the workspace. The objective function is optimized based on evolutionary algorithms such as the genetic algorithm (GA) and ant colony optimization (ACO) of the traveling salesman problem (TSP) and estimates the shortest path that connects all waypoints. The proposed path planning technique can be extended to other polyamond-based reconfigurable robots.

Modified A-Star Algorithm for Efficient Coverage Path Planning in Tetris Inspired Self-Reconfigurable Robot with Integrated Laser Sensor.

Advancing an efficient coverage path planning in robots set up for application such as cleaning, painting and mining are becoming more crucial. Such drive in the coverage path planning field proposes numerous techniques over the past few decades. However, the proposed approaches were only applied and tested with a fixed morphological robot in which the coverage performance was significantly degraded in a complex environment. To this end, an A-star based zigzag global planner for a novel self-reconfigurable Tetris inspired cleaning robot (hTetro) presented in this paper. Unlike the traditional A-star algorithm, the presented approach can generate waypoints in order to cover the narrow spaces while assuming appropriate morphology of the hTtero robot with the objective of maximizing the coverage area. We validated the efficiency of the proposed planning approach in the Robot Operation System (ROS) Based simulated environment and tested with the hTetro robot in real-time under the controlled scenarios. Our experiments demonstrate the efficiency of the proposed coverage path planning approach resulting in superior area coverage performance in all considered experimental scenarios.

Nonuniform video size reduction for moving objects.

Moving objects of interest (MOOIs) in surveillance videos are detected and encapsulated by bounding boxes. Since moving objects are defined by temporal activities through the consecutive video frames, it is necessary to examine a group of frames (GoF) to detect the moving objects. To do that, the traces of moving objects in the GoF are quantified by forming a spatiotemporal gradient map (STGM) through the GoF. Each pixel value in the STGM corresponds to the maximum temporal gradient of the spatial gradients at the same pixel location for all frames in the GoF. Therefore, the STGM highlights boundaries of the MOOI in the GoF and the optimal bounding box encapsulating the MOOI can be determined as the local areas with the peak average STGM energy. Once an MOOI and its bounding box are identified, the inside and outside of it can be treated differently for object-aware size reduction. Our optimal encapsulation method for the MOOI in the surveillance videos makes it possible to recognize the moving objects even after the low bitrate video compressions.

Directional joint bilateral filter for depth images.

Depth maps taken by the low cost Kinect sensor are often noisy and incomplete. Thus, post-processing for obtaining reliable depth maps is necessary for advanced image and video applications such as object recognition and multi-view rendering. In this paper, we propose adaptive directional filters that fill the holes and suppress the noise in depth maps. Specifically, novel filters whose window shapes are adaptively adjusted based on the edge direction of the color image are presented. Experimental results show that our method yields higher quality filtered depth maps than other existing methods, especially at the edge boundaries.