Deep learning system for screening AIDS-related cytomegalovirus retinitis with ultra-wide-field fundus images.

Background: Ophthalmological screening for cytomegalovirus retinitis (CMVR) for HIV/AIDS patients is important to prevent lifelong blindness. Previous studies have shown good properties of automated CMVR screening using digital fundus images. However, the application of a deep learning (DL) system to CMVR with ultra-wide-field (UWF) fundus images has not been studied, and the feasibility and efficiency of this method are uncertain. Methods: In this study, we developed, internally validated, externally validated, and prospectively validated a DL system to detect AIDS-related from UWF fundus images from different clinical datasets. We independently used the InceptionResnetV2 network to develop and internally validate a DL system for identifying active CMVR, inactive CMVR, and non-CMVR in 6960 UWF fundus images from 862 AIDS patients and validated the system in a prospective and an external validation data set using the area under the curve (AUC), accuracy, sensitivity, and specificity. A heat map identified the most important area (lesions) used by the DL system for differentiating CMVR. Results: The DL system showed AUCs of 0.945 (95 % confidence interval [CI]: 0.929, 0.962), 0.964 (95 % CI: 0.870, 0.999) and 0.968 (95 % CI: 0.860, 1.000) for detecting active CMVR from non-CMVR and 0.923 (95 % CI: 0.908, 0.938), 0.902 (0.857, 0.948) and 0.884 (0.851, 0.917) for detecting active CMVR from non-CMVR in the internal cross-validation, external validation, and prospective validation, respectively. Deep learning performed promisingly in screening CMVR. It also showed the ability to differentiate active CMVR from non-CMVR and inactive CMVR as well as to identify active CMVR and inactive CMVR from non-CMVR (all AUCs in the three independent data sets >0.900). The heat maps successfully highlighted lesion locations. Conclusions: Our UWF fundus image-based DL system showed reliable performance for screening AIDS-related CMVR showing its potential for screening CMVR in HIV/AIDS patients, especially in the absence of ophthalmic resources.

Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis.

Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way of increasing the understanding of "black box" models and building trust. In this work, we applied transfer learning to develop a deep neural network to predict sex from electrocardiograms. Using the visual explanation method Grad-CAM, heat maps were generated from the model in order to understand how it makes predictions. To evaluate the usefulness of the heat maps and determine if the heat maps identified electrocardiogram features that could be recognized to discriminate sex, medical doctors provided feedback. Based on the feedback, we concluded that, in our setting, this mode of explainable artificial intelligence does not provide meaningful information to medical doctors and is not useful in the clinic. Our results indicate that improved explanation techniques that are tailored to medical data should be developed before deep neural networks can be applied in the clinic for diagnostic purposes.

Non-Invasive Mapping of Cerebral Autoregulation Using Near-Infrared Spectroscopy: A Study Protocol.

The ability of cerebral vessels to maintain a fairly constant cerebral blood flow is referred to as cerebral autoregulation (CA). Using near-infrared spectroscopy (NIRS) paired with arterial blood pressure (ABP) monitoring, continuous CA can be assessed non-invasively. Recent advances in NIRS technology can help improve the understanding of continuously assessed CA in humans with high spatial and temporal resolutions. We describe a study protocol for creating a new wearable and portable imaging system that derives CA maps of the entire brain with high sampling rates at each point. The first objective is to evaluate the CA mapping system's performance during various perturbations using a block-trial design in 50 healthy volunteers. The second objective is to explore the impact of age and sex on regional disparities in CA using static recording and perturbation testing in 200 healthy volunteers. Using entirely non-invasive NIRS and ABP systems, we hope to prove the feasibility of deriving CA maps of the entire brain with high spatial and temporal resolutions. The development of this imaging system could potentially revolutionize the way we monitor brain physiology in humans since it would allow for an entirely non-invasive continuous assessment of regional differences in CA and improve our understanding of the impact of the aging process on cerebral vessel function.

Some examples of privacy-preserving sharing of COVID-19 pandemic data with statistical utility evaluation.

BACKGROUND: A considerable amount of various types of data have been collected during the COVID-19 pandemic, the analysis and understanding of which have been indispensable for curbing the spread of the disease. As the pandemic moves to an endemic state, the data collected during the pandemic will continue to be rich sources for further studying and understanding the impacts of the pandemic on various aspects of our society. On the other hand, naïve release and sharing of the information can be associated with serious privacy concerns. METHODS: We use three common but distinct data types collected during the pandemic (case surveillance tabular data, case location data, and contact tracing networks) to illustrate the publication and sharing of granular information and individual-level pandemic data in a privacy-preserving manner. We leverage and build upon the concept of differential privacy to generate and release privacy-preserving data for each data type. We investigate the inferential utility of privacy-preserving information through simulation studies at different levels of privacy guarantees and demonstrate the approaches in real-life data. All the approaches employed in the study are straightforward to apply. RESULTS: The empirical studies in all three data cases suggest that privacy-preserving results based on the differentially privately sanitized data can be similar to the original results at a reasonably small privacy loss ([Formula: see text]). Statistical inferences based on sanitized data using the multiple synthesis technique also appear valid, with nominal coverage of 95% confidence intervals when there is no noticeable bias in point estimation. When [Formula: see text] and the sample size is not large enough, some privacy-preserving results are subject to bias, partially due to the bounding applied to sanitized data as a post-processing step to satisfy practical data constraints. CONCLUSIONS: Our study generates statistical evidence on the practical feasibility of sharing pandemic data with privacy guarantees and on how to balance the statistical utility of released information during this process.

Exploring data reduction strategies in the analysis of continuous pressure imaging technology.

BACKGROUND: Science is becoming increasingly data intensive as digital innovations bring new capacity for continuous data generation and storage. This progress also brings challenges, as many scientific initiatives are challenged by the shear volumes of data produced. Here we present a case study of a data intensive randomized clinical trial assessing the utility of continuous pressure imaging (CPI) for reducing pressure injuries. OBJECTIVE: To explore an approach to reducing the amount of CPI data required for analyses to a manageable size without loss of critical information using a nested subset of pressure data. METHODS: Data from four enrolled study participants excluded from the analytical phase of the study were used to develop an approach to data reduction. A two-step data strategy was used. First, raw data were sampled at different frequencies (5, 30, 60, 120, and 240 s) to identify optimal measurement frequency. Second, similarity between adjacent frames was evaluated using correlation coefficients to identify position changes of enrolled study participants. Data strategy performance was evaluated through visual inspection using heat maps and time series plots. RESULTS: A sampling frequency of every 60 s provided reasonable representation of changes in interface pressure over time. This approach translated to using only 1.7% of the collected data in analyses. In the second step it was found that 160 frames within 24 h represented the pressure states of study participants. In total, only 480 frames from the 72 h of collected data would be needed for analyses without loss of information. Only ~ 0.2% of the raw data collected would be required for assessment of the primary trial outcome. CONCLUSIONS: Data reduction is an important component of big data analytics. Our two-step strategy markedly reduced the amount of data required for analyses without loss of information. This data reduction strategy, if validated, could be used in other CPI and other settings where large amounts of both temporal and spatial data must be analysed.

High spatial and temporal resolution cerebrovascular reactivity for humans and large mammals: A technological description of integrated fNIRS and niABP mapping system.

Introduction: The process of cerebral vessels maintaining cerebral blood flow (CBF) fairly constant over a wide range of arterial blood pressure is referred to as cerebral autoregulation (CA). Cerebrovascular reactivity is the mechanism behind this process, which maintains CBF through constriction and dilation of cerebral vessels. Traditionally CA has been assessed statistically, limited by large, immobile, and costly neuroimaging platforms. However, with recent technology advancement, dynamic autoregulation assessment is able to provide more detailed information on the evolution of CA over long periods of time with continuous assessment. Yet, to date, such continuous assessments have been hampered by low temporal and spatial resolution systems, that are typically reliant on invasive point estimations of pulsatile CBF or cerebral blood volume using commercially available technology. Methods: Using a combination of multi-channel functional near-infrared spectroscopy and non-invasive arterial blood pressure devices, we were able to create a system that visualizes CA metrics by converting them to heat maps drawn on a template of human brain. Results: The custom Python heat map module works in "offline" mode to visually portray the CA index per channel with the use of colourmap. The module was tested on two different mapping grids, 8 channel and 24 channel, using data from two separate recordings and the Python heat map module was able read the CA indices file and represent the data visually at a preselected rate of 10 s. Conclusion: The generation of the heat maps are entirely non-invasive, with high temporal and spatial resolution by leveraging the recent advances in NIRS technology along with niABP. The CA mapping system is in its initial stage and development plans are ready to transform it from "offline" to real-time heat map generation.

The visualization of Orphadata neurology phenotypes.

Disease phenotypes are characterized by signs (what a physician observes during the examination of a patient) and symptoms (the complaints of a patient to a physician). Large repositories of disease phenotypes are accessible through the Online Mendelian Inheritance of Man, Human Phenotype Ontology, and Orphadata initiatives. Many of the diseases in these datasets are neurologic. For each repository, the phenotype of neurologic disease is represented as a list of concepts of variable length where the concepts are selected from a restricted ontology. Visualizations of these concept lists are not provided. We address this limitation by using subsumption to reduce the number of descriptive features from 2,946 classes into thirty superclasses. Phenotype feature lists of variable lengths were converted into fixed-length vectors. Phenotype vectors were aggregated into matrices and visualized as heat maps that allowed side-by-side disease comparisons. Individual diseases (representing a row in the matrix) were visualized as word clouds. We illustrate the utility of this approach by visualizing the neuro-phenotypes of 32 dystonic diseases from Orphadata. Subsumption can collapse phenotype features into superclasses, phenotype lists can be vectorized, and phenotypes vectors can be visualized as heat maps and word clouds.

Risk reduction via spatial and temporal visualization of road accidents: a way forward for emergency response optimization in developing countries.

To achieve an effective emergency response and road safety, this study aims to assist a semi-automated dynamic system to analyze and predict the spatial distribution and temporal pattern of road crashes. Kasur, an intermediate city of Pakistan, was selected and data including location, time and reasons of accidents for five years (2014-2018) was utilized. Radar charts, Getis-Ord Gi* statistic, Moran's I spatial auto-correlation, and time series indices were engaged to present temporal, spatial and spatial-temporal variation of accidents, using python-based tools and jupyter notebook. A dynamic user interface was created using Github and Tableau to visualize a real-time zoom-able spatiotemporal variation of accidents. The results explain that out of 12 months, October faces the peak while April sees the least of road accidents. 7am is the peak hour for accidents and the weekends record a significantly higher number of road accidents as compared to weekdays. The city core witnesses the major hotspot areas with huge cluster of accidents. The findings contribute towards a well-informed decision support system, the knowledge of spatial analytics and its application in road safety science, and the preparedness of the rescue agencies for rapid response to reduce the impacts of road accidents.

Mapping of Orthopaedic Fractures for Optimal Surgical Guidance.

Orthopaedic fractures may be difficult to treat surgically if accurate information on the fracture propagation and its exit points are not known. Even with two-dimensional (2D) radiographic images, it is difficult to be completely certain of the exact location of the fracture site, the fracture propagation pattern and the exit points of the fracture. Three-dimensional (3D) computerised tomographic models are better in providing surgeons with the extent of bone fractures, but they may still not be sufficient to allow surgeons to plan open reduction and internal fixation (ORIF) surgery.Fracture patterns and fracture maps are developed to be visual tools in 2D and 3D. These tools can be developed using fractured bones either before or after fracture reduction. Aside from being beneficial to surgeons during pre-surgical planning, these maps aid bioengineers who design fracture fixation plates and implants for these fractures, as well as represent fracture classifications.Fracture maps can be either created ex silico or in silico. Ex silico models are created using 3D printed bone models, onto which fracture patterns are marked. In silico fracture models are created by tracing the fracture lines from a fractured bone to a healthy bone template on a computer. The points of interest in both of these representations are the path of fracture propagation on the bone's surface and exit zones, which eventually determine the surgeon's choice of plate and fracture reduction. Both ex silico and in silico fracture maps are used for pre-surgical planning by the surgeons where they can plan the best way to reduce the fracture as well as template various implants in a low-risk environment before performing the surgery.Recently, fracture maps have been further digitised into heat maps. These heat maps provide visual representations of critical regions of fractures propagating through the bone and identify the weaker zones in the bone structure. These heat maps can allow engineers to develop optimal surgical plates to fix an array of fracture patterns propagating through the bone. Correlation of fractured regions with the mechanisms of injury, age, gender, etc. may improve fracture predictability in the future and optimise the intervention, along with making sure that surgeons do not miss fractures of the bone that may otherwise be hidden from plain sight.

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention.

Aiming at detecting COVID-19 effectively, a multiscale class residual attention (MCRA) network is proposed via chest X-ray (CXR) image classification. First, to overcome the data shortage and improve the robustness of our network, a pixel-level image mixing of local regions was introduced to achieve data augmentation and reduce noise. Secondly, multi-scale fusion strategy was adopted to extract global contextual information at different scales and enhance semantic representation. Last but not least, class residual attention was employed to generate spatial attention for each class, which can avoid inter-class interference and enhance related features to further improve the COVID-19 detection. Experimental results show that our network achieves superior diagnostic performance on COVIDx dataset, and its accuracy, PPV, sensitivity, specificity and F1-score are 97.71%, 96.76%, 96.56%, 98.96% and 96.64%, respectively; moreover, the heat maps can endow our deep model with somewhat interpretability.

Engineering Proteins by Combining Deep Mutational Scanning and Yeast Display.

Protein engineering using display platforms such as yeast display and phage display has allowed discovery of proteins with therapeutic and industrial applications. Antibodies and T cell receptors developed for therapeutic applications are often engineered by constructing libraries of mutations in loops of five to ten residues called complementarity determining regions that are in proximity to the antigen. In the past decade, deep mutational scanning has become a powerful tool in a protein engineer's toolbox, as it allows one to compare the impact of all 20 amino acids at each position, across the length of the protein. Thus, a single experiment can provide a sequence-activity landscape with information about hotspots or suboptimal binding sites in the original proteins. These residues or regions may be overlooked by engineering methods that are driven solely by structures or directed evolution of error-prone PCR libraries. Here, we describe experimental methods to engineer proteins by combining yeast display and deep mutational scanning mutagenesis, using T cell receptors as an example.

Decoding family-level features for modern and fossil leaves from computer-vision heat maps.

PREMISE: Angiosperm leaves present a classic identification problem due to their morphological complexity. Computer-vision algorithms can identify diagnostic regions in images, and heat map outputs illustrate those regions for identification, providing novel insights through visual feedback. We investigate the potential of analyzing leaf heat maps to reveal novel, human-friendly botanical information with applications for extant- and fossil-leaf identification. METHODS: We developed a manual scoring system for hotspot locations on published computer-vision heat maps of cleared leaves that showed diagnostic regions for family identification. Heat maps of 3114 cleared leaves of 930 genera in 14 angiosperm families were analyzed. The top-5 and top-1 hotspot regions of highest diagnostic value were scored for 21 leaf locations. The resulting data were viewed using box plots and analyzed using cluster and principal component analyses. We manually identified similar features in fossil leaves to informally demonstrate potential fossil applications. RESULTS: The method successfully mapped machine strategy using standard botanical language, and distinctive patterns emerged for each family. Hotspots were concentrated on secondary veins (Salicaceae, Myrtaceae, Anacardiaceae), tooth apices (Betulaceae, Rosaceae), and on the little-studied margins of untoothed leaves (Rubiaceae, Annonaceae, Ericaceae). Similar features drove the results from multivariate analyses. The results echo many traditional observations, while also showing that most diagnostic leaf features remain undescribed. CONCLUSIONS: Machine-derived heat maps that initially appear to be dominated by noise can be translated into human-interpretable knowledge, highlighting paths forward for botanists and paleobotanists to discover new diagnostic botanical characters.

The effect of COVID-19 epidemic on vital signs in hospitalized patients: a pre-post heat-map study from a large teaching hospital.

The Lombardy SARS-CoV-2 outbreak in February 2020 represented the beginning of COVID-19 epidemic in Italy. Hospitals were flooded by thousands of patients with bilateral pneumonia and severe respiratory, and vital sign derangements compared to the standard hospital population. We propose a new visual analysis technique using heat maps to describe the impact of COVID-19 epidemic on vital sign anomalies in hospitalized patients. We conducted an electronic health record study, including all confirmed COVID-19 patients hospitalized from February 21st, 2020 to April 21st, 2020 as cases, and all non-COVID-19 patients hospitalized in the same wards from January 1st, 2018 to December 31st, 2018. All data on temperature, peripheral oxygen saturation, respiratory rate, arterial blood pressure, and heart rate were retrieved. Derangement of vital signs was defined according to predefined thresholds. 470 COVID-19 patients and 9241 controls were included. Cases were older than controls, with a median age of 79 vs 76 years in non survivors (p = < 0.002). Gender was not associated with mortality. Overall mortality in COVID-19 hospitalized patients was 18%, ranging from 1.4% in patients below 65 years to about 30% in patients over 65 years. Heat maps analysis demonstrated that COVID-19 patients had an increased frequency in episodes of compromised respiratory rate, acute desaturation, and fever. COVID-19 epidemic profoundly affected the incidence of severe derangements in vital signs in a large academic hospital. We validated heat maps as a method to analyze the clinical stability of hospitalized patients. This method may help to improve resource allocation according to patient characteristics.

Two decades of advances in muscle imaging in children: from pattern recognition of muscle diseases to quantification and machine learning approaches.

Muscle imaging has progressively gained popularity in the neuromuscular field. Together with detailed clinical examination and muscle biopsy, it has become one of the main tools for deep phenotyping and orientation of etiological diagnosis. Even in the current era of powerful new generation sequencing, muscle MRI has arisen as a tool for prioritization of certain genetic entities, supporting the pathogenicity of variants of unknown significance and facilitating diagnosis in cases with an initially inconclusive genetic study. Although the utility of muscle imaging is increasingly clear, it has not reached its full potential in clinical practice. Pattern recognition is known for a number of diseases and will certainly be enhanced by the use of machine learning approaches. For instance, MRI heatmap representations might be confronted with molecular results by obtaining a probabilistic diagnosis based in each disease "MRI fingerprints". Muscle ultrasound as a screening tool and quantified techniques such as Dixon MRI seem still underdeveloped. In this paper, we aim to appraise the advances in recent years in pediatric muscle imaging and try to define areas of uncertainty and potential advances that might become standardized to be widely used in the future.

Creation of a novel simple heat mapping method for curriculum mapping, using pathology teaching as the exemplar.

BACKGROUND: The undergraduate five-year MBChB programme at the University of Glasgow has a high volume of pathology teaching integrated into the course. The ability to better understand what pathology is taught and when, so as to build a picture of the types and depth of pathology topics covered across the programme stages is crucial, especially in a spiral curriculum. A novel method of curriculum mapping, known as curriculum heat mapping, was developed as a way to visualise where and when topics are taught, in an easier to understand format. METHODS: This method involved comparing the Glasgow curriculum to a pre-determined standard of what should be taught. In this case, The Royal College of Pathologists' 'Pathology Undergraduate Curriculum' was used as a comparison of what a graduating doctor should know about pathology. RESULTS: Following the developed template, heat maps showcasing the range of pathology topics covered, and where they are covered, were developed for local use. These heat maps provided a clear visual representation of where and when topics are taught, and how they cluster. CONCLUSIONS: Heat mapping is a novel low-cost, high-input method of curriculum mapping. It requires a person to input the data which can take a long time for large curricula. There are no other upfront financial costs. It can be used in any area with a curriculum and an external or internal comparator. Examples of gold standard external comparators include validated national or international curricula. Heat mapping can help integrated, spiral curriculum programmes to identify where core topics are taught throughout their course. The heat maps themselves successfully demonstrate the required information and are easy to interpret. The process of mapping, as well as the final heat map, can yield important information. This includes information about trends within the curriculum, areas for potential improvement in sessional design and a clearer understanding of the depth to which each topic is covered in each lecture. Overall, it is a viable novel method, which has been successful locally and is easily transferable to other areas such as pharmacology.

Comparison of self-reported & device-based, measured physical activity among children in Germany.

BACKGROUND: As children show a more complex but less structured movement behavior than adults, assessment of their many spontaneous and impulsive movements is a challenge for physical activity (PA) assessment. Since neither questionnaires nor accelerometers enable optimal detection of all facets of PA, a multimodal, combined approach of self-reported and device-based methods is recommended. Based on the number of days on which the participants reached the physical activity (PA) values given in the WHO guideline, this study examines 1) the difference between self-reported and device-based, measured PA and 2) whether PA differences between age and gender groups obtained by two methods are comparable. METHODS: Participants aged 6-17 years were randomly chosen and data were collected representatively at 167 sample points throughout Germany within the Motorik-Modul Study. PA of n = 2694 participants (52.3% female) was measured using the ActiGraph accelerometer (ACC) and a physical activity questionnaire (PAQ). The sample was divided into three age groups (6-10 yrs. n = 788, 11-13 yrs. n = 823, 14-17 yrs. n = 1083). Numbers of days per week with at least 60 min moderate to vigorous PA (MVPA) were analyzed for both methods. RESULTS: Only every 25th respondent (4%) reaches the WHO standard of 60 min MVPA every day if measured with ACC. Self-reported PA was slightly higher (9%) (meanPAQ = 3.82 days; meanACC = 2.34 days; Fmethod = 915.85; p = <.001; fCohen = .64). The differences between the methods are significantly smaller in younger children than in the older age groups (Fage = 264.2, p < .001; fCohen = .48). The older the subjects are, the lower is the proportion of those who meet the WHO guideline on each day, with girls meeting the guideline less frequently than boys in all age groups. CONCLUSION: Children and adolescents living in Germany show a very low adherence to the WHO guideline on PA. While younger children are much more active with their free play, especially children over 10 years of age and especially girls should be the target of programs to increase PA.

Heat maps present the spatial distribution of human papillomavirus infection in Zhejiang Province, China.

Determining the spatial distribution of human papillomavirus (HPV) and performing accurate public health analyses helps to distinguish areas of healthcare that require further research, and enables therapeutic techniques and approaches in healthcare to be focused more accurately. A total of 4,560 women were enrolled in the present study. Flow-through hybridization and gene chip assays were used to detect the genotypes of HPV infection. Heat maps were then generated to present the spatial distribution of HPV infections in Zhejiang Province according to genotype. Of the exfoliated cervical cell samples from the 4,560 women, HPV was detected in 1,886 samples. HPV-16, -58, -52 and -18 were the most prevalently identified genotypes in the population included in the present study. HPV-16 and -58 infections were mainly distributed in the northern and central regions of Zhejiang Province, such as in Hangzhou and Shaoxing, where the prevalence was higher than that in the southern regions (P<0.05). HPV-18 infection was widespread throughout Zhejiang Province, but had a much lower infection rate in Ningbo and Huzhou (P<0.05). High infection rates of HPV-52 were mainly detected in Hangzhou and the eastern coastal areas of Wenzhou, with a relatively low rate of infection in the center of the province (P<0.05). In conclusion, HPV-16, -58, -52 and -18 were the four most prevalent HPV genotypes observed in Zhejiang Province. Heat maps were created to display the spatial distribution of HPV infection according to genotype, which varied by geographical regions. The results indicate that for individuals in Ningbo or Wenzhou, bivalent or quadrivalent vaccines may be suitable, but for those in Hangzhou and Shaoxing, nonavalent vaccines are strongly recommended.

Assessing need for extracorporeal cardiopulmonary resuscitation for out-of-hospital cardiac arrest using Power BI for data visualisation.

OBJECTIVE: To estimate the number of patients in refractory out-of-hospital cardiac arrest (OHCA) potentially suitable for transport to an extracorporeal cardiopulmonary resuscitation (ECPR)-capable hospital in Brisbane, Queensland, Australia, based on outcome predictors for ECPR, ambulance geolocation and patient data. METHODS: A retrospective cohort study was performed using data from all patients in OHCA attended by Queensland Ambulance Service between 1 January 2014 and 31 December 2018. The number of refractory arrest patients who could potentially be transferred to an ECPR-capable centre within 45 min of the time of arrest was modelled using theoretical on-scene treatment times. RESULTS: Of 25 518 ambulance-attended OHCA in Queensland during the study period, 540 (2%) patients met criteria of refractory arrest for study inclusion. Further age and arrest rhythm criteria for transport to an ECPR-capable hospital were met in 253 (47%) study patients, an average of 51 patients per year. In 2018, 72 patients met study criteria for transport to an ECPR-capable centre. Based on theoretical on-scene treatment times of 12 and 20 min, in 2018 only 14 (19%) and 11 (15%) patients respectively would potentially arrive at an ECPR-capable hospital within accepted timeframes for ECPR. CONCLUSIONS: Retrospective data collected from existing ambulance databases can be used to model patient suitability for ECPR. Relatively few patients with refractory OHCA in Queensland, Australia, could be attended and transported to an ECPR-capable centre within clinically acceptable timeframes. Further studies of the transport logistics and economic implications of providing ECPR services for OHCA are required to better inform decisions around this intervention.

Become Competent in Generating RNA-Seq Heat Maps in One Day for Novices Without Prior R Experience.

Heat map visualization of RNA-seq data is a commonplace task. However, most laboratories rely on bioinformaticians who are not always available. Biological scientists are afraid to prepare heat maps independently because R is a programming platform. Here, using RNA-seq data for 16 differentially expressed genes in WNT pathway between embryonic stem cells and fibroblasts, I share a tutorial for novices without any prior R experience to master the skills, in one day, required for preparation of heat maps using the pheatmap package. Procedures described include installation of R, RStudio, and the pheatmap package, as well as hands-on practices for some basic R commands, conversion of RNA-seq data frame to a numeric matrix suitable for generation of heat maps, and defining arguments for the pheatmap function to make a desired heat map. More than 20 template scripts are provided to generate heat maps and to control the dimensions and appearances of the heat maps.

Behaviour of Low Molecular Weight Compounds, Iron and Copper of Wine Spirit Aged with Chestnut Staves under Different Levels of Micro-Oxygenation.

Alternative technologies for a more sustainable wine spirits' ageing have been studied but a lack of knowledge on the effect of oxygenation level remains. This work examined the behaviour of low molecular weight compounds, iron and copper of a wine spirit aged in 50 L demijohns with chestnut wood staves combined with three levels of micro-oxygenation or nitrogen. Compounds and mineral elements were quantified by HPLC and FAAS, respectively, in samples collected at 8, 21, 60, 180, 270 and 365 days of ageing. Results showed that most of the compounds underwent significant changes in their content over time and behave differently depending on the wine spirit's oxygenation level: higher contents of gallic acid, syringic acid and vanillin were associated with lower micro-oxygenation level while higher contents of ellagic acid, syringaldehyde, coniferaldehyde and sinapaldehyde resulted from higher one; lowest contents of these compounds were found in the nitrogen modality. Weak correlation between copper and the studied compounds was evidenced whereas closer relationship between iron, vanillin, gallic, syringic and ellagic acids at end of ageing was observed. This study provides innovative information on the role of oxygen in wine spirit's ageing, and on chestnut wood effect on wine spirit's mineral composition.