bulkAnalyseR: An accessible, interactive pipeline for analysing and sharing bulk sequencing results

Motivation: Bulk sequencing experiments are essential for exploring a wide range of biological questions. To bring data analysis closer to its interpretation, and facilitate both interactive, exploratory tasks and the sharing of easily accessible information, we present bulkAnalyseR, an R package that offers a seamless, customisable solution for most bulk RNAseq datasets. Results: In bulkAnalyseR, we integrate state-of-the-art approaches, without relying on extensive computational support. We replace static summary images with interactive panels to further strengthen the usability and interpretability of data. The package enables standard analyses on bulk sequencing output, using an expression matrix as the starting point (with the added flexibility of choosing subsets of samples). In an interactive web-based interface, steps such as quality checking, noise detection, inference of differential expression and expression patterns, and biological interpretation (enrichment analyses and identification of regulatory interactions), can be customised, easing the exploration and testing of hypotheses. Availability: bulkAnalyseR is available on GitHub, along with extensive documentation and usage examples (https://github.com/Core-Bioinformatics/bulkAnalyseR).

Expressive Cognitive Architecture for a Curious Social Robot

Artificial curiosity, based on developmental psychology concepts wherein an agent attempts to maximize its learning progress, has gained much attention in recent years. Similarly, social robots are slowly integrating into our daily lives, in schools, factories, and in our homes. In this contribution, we integrate recent advances in artificial curiosity and social robots into a single expressive cognitive architecture. It is composed of artificial curiosity and social expressivity modules and their unique link, i.e., the robot verbally and non-verbally communicates its internally estimated learning progress, or learnability, to its human companion. We implemented this architecture in an interaction where a fully autonomous robot took turns with a child trying to select and solve tangram puzzles on a tablet. During the curious robot’s turn, it selected its estimated most learnable tangram to play, communicated its selection to the child, and then attempted at solving it. We validated the implemented architecture and showed that the robot learned, estimated its learnability, and improved when its selection was based on its learnability estimation. Moreover, we ran a comparison study between curious and non-curious robots, and showed that the robot’s curiosity-based behavior influenced the child’s selections. Based on the artificial curiosity module of the robot, we have formulated an equation that estimates each child’s moment-by-moment curiosity based on their selections. This analysis revealed an overall significant decrease in estimated curiosity during the interaction. However, this drop in estimated curiosity was significantly larger with the non-curious robot, compared to the curious one. These results suggest that the new architecture is a promising new approach to integrate state-of-the-art curiosity-based algorithms to the growing field of social robots.

Countrywide survey on utilization of medical devices by GPs in Hungary: advantages of the cluster-practice model

Background: Expectations towards general practitioners (GPs) are continuously increasing to provide a more systematic preventive- and definitive-based care, a wider range of multidisciplinary team-based services and to integrate state-of-the-art digital solutions into daily practice. Aided by development programmes, Hungarian primary care is facing the challenge to fulfil its role as the provider of comprehensive, high quality, patient-centred, preventive care, answering the challenges caused by non-communicable diseases (NCDs). Aim: The article aims to provide an insight into the utilization of simple, digital, medical devices. We show the relationship between the primary health care (PHC) practice models and the used types of devices. We point at further development directions of GP practices regarding the utilization of evidence-based medical technologies and how such devices support the screening and chronic care of patients with NCDs in everyday practice. Methods: Data were collected using an online self-assessment questionnaire from 1800 Hungarian GPs registered in Hungary. Descriptive statistics, Wilcoxon’s test and χ2 test were applied to analyze the ownership and utilization of 32 types of medical devices, characteristics of the GP practices and to highlight the differences between traditional and cluster-based operating model. Findings: Based on the responses from 27.7% of all Hungarian GPs, the medical device infrastructure was found to be limited especially in single GP-practices. Those involved in development projects of GP’s clusters in the last decade reported a wider range and significantly more intensive utilization of evidence-based technologies (average number of devices: 5.42 versus 7.56, P<.001), but even these GPs are not using some of their devices (e.g., various point of care testing devices) due to the lack of financing. In addition, GPs involved in GPs-cluster development model programmes showed significantly greater willingness for sharing relatively expensive, extra workforce-demanding technologies (χ2 = 24.5, P<.001).

DESIGN OF MULTIFUNCTION SIMULATOR FOR ENGINE ROOM PERSONNEL TRAINING

International requirements for improving energy efficiency and environmental protection and the necessary goals for their implementation in the marine industry are an actual problem. To integrate state-of-the-industry technologies and marine specialists education, the training complex is proposed. It is based on the platform of a hardware-software complex with the ability to integrate training equipment, simulators and software. That makes such a training complex multitask, universal, and flexible in achieving a variety of tasks and goals. The complex also implements high-quality education and training of marine specialists, conducting research after processing working out the results of engineering modelling of structural, thermal power, hydraulic, electrical, electronic, multi-physical and other solutions. The need to use the training complex allows us to form the necessary competence of the engine team personnel, develop methods and criteria for assessing competence, evaluate and demonstrate practical skills.

Designs and methods for implementation research: Advancing the mission of the CTSA program

Introduction:The US National Institutes of Health (NIH) established the Clinical and Translational Science Award (CTSA) program in response to the challenges of translating biomedical and behavioral interventions from discovery to real-world use. To address the challenge of translating evidence-based interventions (EBIs) into practice, the field of implementation science has emerged as a distinct discipline. With the distinction between EBI effectiveness research and implementation research comes differences in study design and methodology, shifting focus from clinical outcomes to the systems that support adoption and delivery of EBIs with fidelity.Methods:Implementation research designs share many of the foundational elements and assumptions of efficacy/effectiveness research. Designs and methods that are currently applied in implementation research include experimental, quasi-experimental, observational, hybrid effectiveness–implementation, simulation modeling, and configurational comparative methods.Results:Examples of specific research designs and methods illustrate their use in implementation science. We propose that the CTSA program takes advantage of the momentum of the field's capacity building in three ways: 1) integrate state-of-the-science implementation methods and designs into its existing body of research; 2) position itself at the forefront of advancing the science of implementation science by collaborating with other NIH institutes that share the goal of advancing implementation science; and 3) provide adequate training in implementation science.Conclusions:As implementation methodologies mature, both implementation science and the CTSA program would greatly benefit from cross-fertilizing expertise and shared infrastructures that aim to advance healthcare in the USA and around the world.

Parallel tracking and detection for long-term object tracking

High tracking frame rates have been achieved based on traditional tracking methods which however would fail due to drifts of the object template or model, especially when the object disappears from the camera’s field of view. To deal with it, tracking-and-detection-combination has become more and more popular for long-term unknown object tracking, whose detector almost does not drift and can regain the disappeared object when it comes back. However, for online machine learning and multiscale object detection, expensive computing resources and time are required. So it is not a good idea to combine tracking and detection sequentially like Tracking-Learning-Detection algorithm. Inspired from parallel tracking and mapping, this article proposes a framework of parallel tracking and detection for unknown object tracking. The object tracking algorithm is split into two separate tasks—tracking and detection which can be processed in two different threads, respectively. One thread is used to deal with the tracking between consecutive frames with a high processing speed. The other thread runs online learning algorithms to construct a discriminative model for object detection. Using our proposed framework, high tracking frame rates and the ability of correcting and recovering the failed tracker can be combined effectively. Furthermore, our framework provides open interfaces to integrate state-of-the-art object tracking and detection algorithms. We carry out an evaluation of several popular tracking and detection algorithms using the proposed framework. The experimental results show that different tracking and detection algorithms can be integrated and compared effectively by our proposed framework, and robust and fast long-term object tracking can be realized.

Image Fusion Based on Kernel Estimation and Data Envelopment Analysis

This paper reports the improvement of the image quality during the fusion of remote sensing images by minimizing a novel energy function. First, by introducing a gradient constraint term in the energy function, the spatial information of the panchromatic image is transferred to the fused results. Second, the spectral information of the multispectral image is preserved by importing a kernel function to the data fitting term in the energy function. Finally, an objective parameter selection method based on data envelopment analysis (DEA) is proposed to integrate state-of-the-art image quality metrics. Visual perception measurement and selected fusion metrics are employed to evaluate the fusion performance. Experimental results show that the proposed method outperforms other established image fusion techniques.

The Model Penal Code and the War on Crime

Part III of Dual Penal State uses dual penal state analysis to generate a comparative-historical account of American penality. With comparative glimpses at Germany and, to a lesser extent, England, it distinguishes between two responses to the shared challenge of legitimating state penal power in a modern liberal democratic state: (1) the failure to appreciate the legitimatory challenge of modern state penal power in particular (United States) and of modern state power in general (England); and (2) the failure to address the legitimatory challenge of modern state penal power as an ongoing existential threat to the legitimacy of the state (Germany). Chapter 7 brings the narrative of modern American penality up-to-date, following on the heels of the discussion of Jefferson’s Virginia criminal law bill of 1779 in Chapter 6. Chapter 7 focuses on the Model Penal Code of 1962, which was far superior to Jefferson’s draft in every respect but one: it, too, failed to integrate state punishment into the American legal-political project, leaving the penal paradox unaddressed and unresolved to this day.

Plant-wide modelling for assessment and optimization of upgraded full-scale wastewater treatment plant performance

This paper presents the design of a plant-wide CNP (carbon-nitrogen-phosphorus) simulation model of a full-scale wastewater treatment plant, which will be upgraded for tertiary treatment to achieve compliance with effluent total nitrogen (TN) and total phosphorus (TP) limit values. The plant-wide model of the existing plant was first designed and extensively validated under long-term dynamic operation. The most crucial step was a precise characterization of input wastewater that was performed by extending the plant performance indicators both to a water line and sludge line and systematically estimating identifiable wastewater characterization parameters from plant-wide performance indicators, i.e. effluent concentrations, biogas and sludge production, and sludge composition. The thus constructed simulation model with standard activated sludge model (ASM2d) and anaerobic digestion model (MantisAD) overpredicted ortho-P and ammonia-N on the sludge line, indicating a need to integrate state-of-the-art physico-chemical minerals precipitation models to simulate plant-wide interactions more precisely. The upgraded plant with multimode anaerobic/anoxic/oxic configuration shows limited denitrification potential. Therefore, additional reject water treatment was evaluated to improve effluent TN and TP performance.

Paediatric lung imaging: the times they are a-changin'

Until recently, functional tests were the most important tools for the diagnosis and monitoring of lung diseases in the paediatric population. Chest imaging has gained considerable importance for paediatric pulmonology as a diagnostic and monitoring tool to evaluate lung structure over the past decade. Since January 2016, a large number of papers have been published on innovations in chest computed tomography (CT) and/or magnetic resonance imaging (MRI) technology, acquisition techniques, image analysis strategies and their application in different disease areas. Together, these papers underline the importance and potential of chest imaging and image analysis for today's paediatric pulmonology practice. The focus of this review is chest CT and MRI, as these are, and will be, the modalities that will be increasingly used by most practices. Special attention is given to standardisation of image acquisition, image analysis and novel applications in chest MRI. The publications discussed underline the need for the paediatric pulmonology community to implement and integrate state-of-the-art imaging and image analysis modalities into their structure–function laboratory for the benefit of their patients.