Genetic Comparison of Stemness of Human Umbilical Cord and Dental Pulp

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An integrated model of care for neurological infections: the first six years of referrals to a specialist service at a university teaching hospital in Northwest England

Background A specialist neurological infectious disease service has been run jointly by the departments of infectious disease and neurology at the Royal Liverpool University Hospital since 2005. We sought to describe the referral case mix and outcomes of the first six years of referrals to the service. Methods Retrospective service review. Results Of 242 adults referred to the service, 231 (95 %) were inpatients. Neurological infections were confirmed in 155 (64 %), indicating a high degree of selection before referral. Viral meningitis (35 cases), bacterial meningitis (33) and encephalitis (22) accounted for 38 % of referrals and 61 % of confirmed neurological infections. Although an infrequent diagnosis (n = 19), neurological TB caused the longest admission (median 23, range 5 – 119 days). A proven or probable microbiological diagnosis was found in 100/155 cases (64.5 %). For the whole cohort, altered sensorium, older age and longer hospital stay were associated with poor outcome (death or neurological disability); viral meningitis was associated with good outcome. In multivariate analysis altered sensorium remained significantly associated with poor outcome, adjusted odds ratio 3.04 (95 % confidence interval 1.28 – 7.22, p = 0.01). Conclusions A service of this type provides important specialist care and a focus for training and clinical research on complex neurological infections

Topic-driven toxicity: Exploring the relationship between online toxicity and news topics

Hateful commenting, also known as 'toxicity', frequently takes place within news stories in social media. Yet, the relationship between toxicity and news topics is poorly understood. To analyze how news topics relate to the toxicity of user comments, we classify topics of 63,886 online news videos of a large news channel using a neural network and topical tags used by journalists to label content. We score 320,246 user comments from those videos for toxicity and compare how the average toxicity of comments varies by topic. Findings show that topics like Racism, Israel-Palestine, and War & Conflict have more toxicity in the comments, and topics such as Science & Technology, Environment & Weather, and Arts & Culture have less toxic commenting. Qualitative analysis reveals five themes: Graphic videos, Humanistic stories, History and historical facts, Media as a manipulator, and Religion. We also observe cases where a typically more toxic topic becomes non-toxic and where a typically less toxic topic becomes "toxicified" when it involves sensitive elements, such as politics and religion. Findings suggest that news comment toxicity can be characterized as topic-driven toxicity that targets topics rather than as vindictive toxicity that targets users or groups. Practical implications suggest that humanistic framing of the news story (i.e., reporting stories through real everyday people) can reduce toxicity in the comments of an otherwise toxic topic

Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data

We propose a novel approach for isolating customer segments using online customer data for products that are distributed via online social media platforms. We use non-negative matrix factorization to first identify behavioral customer segments and then to identify demographic customer segments. We employ a methodology for linking the two segments to present integrated and holistic customer segments, also known as personas. Behavioral segments are generated from customer interactions with online content. Demographic segments are generated using the gender, age, and location of these customers. In addition to evaluating our approach, we demonstrate its practicality via a system leveraging these customer segments to automatically generate personas, which are fictional but accurate representations of each integrated behavioral and demographic segment. Results show that this approach can accurately identify both behavioral and demographical customer segments using actual online customer data from which we can generate personas representing real groups of people

Test database of the mechanical properties of mild, high-tensile and stainless steel and aluminium alloy associated with cold temperatures and strain rates

In structural analysis and design, it is essential to define the material properties associated with the targeted structural systems. When harsh environmental or operational conditions are of primary concern, the mechanical properties of materials must be quantified by considering the effects of the conditions. The aim of this study is to develop a new test database on the mechanical properties of materials for marine applications, such as mild steel, high-tensile steel, aluminium alloy 5083-O and stainless steel 304L, focusing on the effects of cold temperatures and strain rates. Discussion of the new test database refers to extant test databases where available. Moreover, test coefficients that may be useful in the existing constitutive equations for the materials are suggested and the details of the test database are documented

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology

We predicted residual fluid intelligence scores from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions across the cortex. Individual structural covariance networks (SCN) were abstracted into graph-theory metrics averaged over nodes across the brain and in data-driven communities/modules. Metrics included degree, path length, clustering coefficient, centrality, rich club coefficient, and small-worldness. These features derived from the training set were used to build various regression models for predicting residual fluid intelligence scores, with performance evaluated both using cross-validation within the training set and using the held-out validation set. Our predictions on the test set were generated with a support vector regression model trained on the training set. We found minimal improvement over predicting a zero residual fluid intelligence score across the sample population, implying that structural covariance networks calculated from T1-weighted MR imaging data provide little information about residual fluid intelligence.Comment: 8 pages plus references, 3 figures, 2 tables. Submission to the ABCD Neurocognitive Prediction Challenge at MICCAI 201

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression

We applied several regression and deep learning methods to predict fluid intelligence scores from T1-weighted MRI scans as part of the ABCD Neurocognitive Prediction Challenge (ABCD-NP-Challenge) 2019. We used voxel intensities and probabilistic tissue-type labels derived from these as features to train the models. The best predictive performance (lowest mean-squared error) came from Kernel Ridge Regression (KRR; $\lambda=10$), which produced a mean-squared error of 69.7204 on the validation set and 92.1298 on the test set. This placed our group in the fifth position on the validation leader board and first place on the final (test) leader board.Comment: Winning entry in the ABCD Neurocognitive Prediction Challenge at MICCAI 2019. 7 pages plus references, 3 figures, 1 tabl

Predicting fracture outcomes from clinical registry data using artificial intelligence supplemented models for evidence-informed treatment (PRAISE) study protocol

BackgroundDistal radius (wrist) fractures are the second most common fracture admitted to hospital. The anatomical pattern of these types of injuries is diverse, with variation in clinical management, guidelines for management remain inconclusive, and the uptake of findings from clinical trials into routine practice limited. Robust predictive modelling, which considers both the characteristics of the fracture and patient, provides the best opportunity to reduce variation in care and improve patient outcomes. This type of data is housed in unstructured data sources with no particular format or schema. The “Predicting fracture outcomes from clinical Registry data using Artificial Intelligence (AI) Supplemented models for Evidence-informed treatment (PRAISE)” study aims to use AI methods on unstructured data to describe the fracture characteristics and test if using this information improves identification of key fracture characteristics and prediction of patient-reported outcome measures and clinical outcomes following wrist fractures compared to prediction models based on standard registry data.Methods and designAdult (16+ years) patients presenting to the emergency department, treated in a short stay unit, or admitted to hospital for >24h for management of a wrist fracture in four Victorian hospitals will be included in this study. The study will use routine registry data from the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), and electronic medical record (EMR) information (e.g. X-rays, surgical reports, radiology reports, images). A multimodal deep learning fracture reasoning system (DLFRS) will be developed that reasons on EMR information. Machine learning prediction models will test the performance with/without output from the DLFRS.DiscussionThe PRAISE study will establish the use of AI techniques to provide enhanced information about fracture characteristics in people with wrist fractures. Prediction models using AI derived characteristics are expected to provide better prediction of clinical and patient-reported outcomes following distal radius fracture

Alpha correlates of practice during mental preparation for motor imagery

IEEE In this study we quantified performance variations of motor imagery (MI)-based brain-computer interface (BCI) systems induced by practice. Two experimental sessions were recorded from ten healthy subjects while playing a BCI-oriented videogame for two weeks. The analysis focused on the exploration of electroencephalographic changes during mental preparation between novice and practiced subjects. EEG changes were quantified using global field power (GFP), dynamic time warping (TW) and mutual information (MutInf): GFP represents the strength of the electric field, TW measures signal similarities and MutInf signals inter-dependency. Each metric was selected to relate insights extracted from mental preparation to the three experimental hypotheses associating practice with BCI performance. Significant results were identified in lower alpha for GFP and upper alpha for TW and MutInf. GFP in lower alpha during mental preparation assessed not only novice vs practiced variations but also “intra-session” differences. Findings suggest that EEG changes during mental preparation provide a quantitative measure of practice level. These metrics extracted before motor intention could be applied to BCI models targeting MI to monitor a user’s degree of training

Export of functional Streptomyces coelicolor alditol oxidase to the periplasm or cell surface of Escherichia coli and its application in whole-cell biocatalysis

Streptomyces coelicolor A3(2) alditol oxidase (AldO) is a soluble monomeric flavoprotein in which the flavin cofactor is covalently linked to the polypeptide chain. AldO displays high reactivity towards different polyols such as xylitol and sorbitol. These characteristics make AldO industrially relevant, but full biotechnological exploitation of this enzyme is at present restricted by laborious and costly purification steps. To eliminate the need for enzyme purification, this study describes a whole-cell AldO biocatalyst system. To this end, we have directed AldO to the periplasm or cell surface of Escherichia coli. For periplasmic export, AldO was fused to endogenous E. coli signal sequences known to direct their passenger proteins into the SecB, signal recognition particle (SRP), or Twin-arginine translocation (Tat) pathway. In addition, AldO was fused to an ice nucleation protein (INP)-based anchoring motif for surface display. The results show that Tat-exported AldO and INP-surface-displayed AldO are active. The Tat-based system was successfully employed in converting xylitol by whole cells, whereas the use of the INP-based system was most likely restricted by lipopolysaccharide LPS in wild-type cells. It is anticipated that these whole-cell systems will be a valuable tool for further biological and industrial exploitation of AldO and other cofactor-containing enzymes.