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Accurate delineation of key waveforms in an ECG is a critical step in extracting relevant features to support the diagnosis and treatment of heart conditions. Although deep learning based methods using segmentation models to locate P, QRS, and T waves have shown promising results, their ability to handle arrhythmias has not been studied in any detail. In this paper we investigate the effect of arrhythmias on delineation quality and develop strategies to improve performance in such cases. We introduce a U-Net-like segmentation model for ECG delineation with a particular focus on diverse arrhythmias. This is followed by a post-processing algorithm which removes noise and automatically determines the boundaries of P, QRS, and T waves. Our model has been trained on a diverse dataset and evaluated against the LUDB and QTDB datasets to show strong performance, with F1-scores exceeding 99% for QRS and T waves, and over 97% for P waves in the LUDB dataset. Furthermore, we assess various models across a wide array of arrhythmias and observe that models with a strong performance on standard benchmarks may still perform poorly on arrhythmias that are underrepresented in these benchmarks, such as tachycardias. We propose solutions to address this discrepancy. [ABSTRACT FROM AUTHOR]

Malaria rapid diagnostic test (mRDT) kit is one of the techniques for diagnosing malaria. Due to its inherent advantages over the microscopy technique, several brands of the kit have flooded malaria endemic countries, without prior in-country evaluation. Two of such mRDT kits are Oscar (India) and Standard Q (Korea Republic). In this study, the performance of Oscar and Standard Q mRDT kits were compared to First Response (India) and CareStart (USA) mRDTs, which have been evaluated and deployed for use approved by the Ministry of Health (MOH). In this comparative study, whole blood samples were collected from patients suspected of malaria. Plasmodium falciparum was detected in each sample using nested polymerase chain reaction (nPCR), microscopy and the four mRDTs. The sensitivities, specificities, accuracies, positive and negative predictive values and accuracies of the mRDTs were determined using nPCR as a reference technique. Kappa statistic was used to determine the level of agreement among the techniques. Two hundred (200) blood samples were analyzed in this study. The overall detection rates of P. falciparum by microscopy, First Response, CareStart, Oscar-PfHRP2, Standard Q mRDT kits and nPCR were 31.5%, 34.5%, 33.5%, 32%, 31% and 43% (x2 = 6.1, p = 0.046), respectively. The accuracies of CareStart and First Response were comparable (90.5% vs. 89.5%). Further, comparing their sensitivities, Oscar-PfHRP2 was 74.4% (95% confidence interval (CI): 63.9–83.2) while that of Standard Q was 72.1% (95% CI: 61.4–81.2), with comparable accuracies (Oscar-PfHRP2–89% and Standard Q -88%). Apart from First Response that was 98.3% specific, the others were 100% specific. Kappa test revealed perfect diagnostic agreement (κ = 0.90–0.98) among the four mRDTs. That notwithstanding, Oscar-PfHRP2 agreed better with CareStart (κ = 0.94) and First Response (κ = 0.92) compared to the agreement between Standard Q and, CareStart (κ = 0.92) and First Response (κ = 0.90). Taken together, the diagnostic performance of the four mRDT kits were statistically similar. That notwithstanding, new mRDT kits should be evaluated prior to deployment for use. [ABSTRACT FROM AUTHOR]

We provide new algorithms for two tasks relating to heterogeneous tabular datasets: clustering, and synthetic data generation. Tabular datasets typically consist of heterogeneous data types (numerical, ordinal, categorical) in columns, but may also have hidden cluster structure in their rows: for example, they may be drawn from heterogeneous (geographical, socioeconomic, methodological) sources, such that the outcome variable they describe (such as the presence of a disease) may depend not only on the other variables but on the cluster context. Moreover, sharing of biomedical data is often hindered by patient confidentiality laws, and there is current interest in algorithms to generate synthetic tabular data from real data, for example via deep learning. We demonstrate a novel EM-based clustering algorithm, MMM ("Madras Mixture Model"), that outperforms standard algorithms in determining clusters in synthetic heterogeneous data, and recovers structure in real data. Based on this, we demonstrate a synthetic tabular data generation algorithm, MMMsynth, that pre-clusters the input data, and generates cluster-wise synthetic data assuming cluster-specific data distributions for the input columns. We benchmark this algorithm by testing the performance of standard ML algorithms when they are trained on synthetic data and tested on real published datasets. Our synthetic data generation algorithm outperforms other literature tabular-data generators, and approaches the performance of training purely with real data. [ABSTRACT FROM AUTHOR]

This study introduces the global-local least-squares support vector machine (GLocal-LS-SVM), a novel machine learning algorithm that combines the strengths of localised and global learning. GLocal-LS-SVM addresses the challenges associated with decentralised data sources, large datasets, and input-space-related issues. The algorithm is a double-layer learning approach that employs multiple local LS-SVM models in the first layer and one global LS-SVM model in the second layer. The key idea behind GLocal-LS-SVM is to extract the most informative data points, known as support vectors, from each local region in the input space. Local LS-SVM models are developed for each region to identify the most contributing data points with the highest support values. The local support vectors are then merged at the final layer to form a reduced training set used to train the global model. We evaluated the performance of GLocal-LS-SVM using both synthetic and real-world datasets. Our results demonstrate that GLocal-LS-SVM achieves comparable or superior classification performance compared to standard LS-SVM and state-of-the-art models. In addition, our experiments show that GLocal-LS-SVM outperforms standard LS-SVM in terms of computational efficiency. For instance, on a training dataset of 9, 000 instances, the average training time for GLocal-LS-SVM was only 2% of the time required to train the LS-SVM model while maintaining classification performance. In summary, the GLocal-LS-SVM algorithm offers a promising solution to address the challenges associated with decentralised data sources and large datasets while maintaining high classification performance. Furthermore, its computational efficiency makes it a valuable tool for practical applications in various domains. [ABSTRACT FROM AUTHOR]

Chronic Kidney disease (CKD) is an important yet under-recognized contributor to morbidity and mortality globally. Machine-learning (ML) based decision support tools have been developed across many aspects of CKD care. Notably, algorithms developed in the prediction and diagnosis of CKD development and progression may help to facilitate early disease prevention, assist with early planning of renal replacement therapy, and offer potential clinical and economic benefits to patients and health systems. Clinical implementation can be affected by the uncertainty surrounding the methodological rigor and performance of ML-based models. This systematic review aims to evaluate the application of prognostic and diagnostic ML tools in CKD development and progression. The protocol has been prepared using the Preferred Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) guidelines. The systematic review protocol for CKD prediction and diagnosis have been registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42022356704, CRD42022372378). A systematic search will be undertaken of PubMed, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), the Web of Science, and the IEEE Xplore digital library. Studies in which ML has been applied to predict and diagnose CKD development and progression will be included. The primary outcome will be the comparison of the performance of ML-based models with non-ML-based models. Secondary analysis will consist of model use cases, model construct, and model reporting quality. This systematic review will offer valuable insight into the performance and reporting quality of ML-based models in CKD diagnosis and prediction. This will inform clinicians and technical specialists of the current development of ML in CKD care, as well as direct future model development and standardization. [ABSTRACT FROM AUTHOR]

Rapidly developing technology for large scale neural recordings has allowed researchers to measure the activity of hundreds to thousands of neurons at single cell resolution in vivo. Neural decoding analyses are a widely used tool used for investigating what information is represented in this complex, high-dimensional neural population activity. Most population decoding methods assume that correlated activity between neurons has been estimated accurately. In practice, this requires large amounts of data, both across observations and across neurons. Unfortunately, most experiments are fundamentally constrained by practical variables that limit the number of times the neural population can be observed under a single stimulus and/or behavior condition. Therefore, new analytical tools are required to study neural population coding while taking into account these limitations. Here, we present a simple and interpretable method for dimensionality reduction that allows neural decoding metrics to be calculated reliably, even when experimental trial numbers are limited. We illustrate the method using simulations and compare its performance to standard approaches for dimensionality reduction and decoding by applying it to single-unit electrophysiological data collected from auditory cortex. [ABSTRACT FROM AUTHOR]

Introduction: Implementation of evidence-based care for heavy drinking and depression remains low in global health systems. We tested the impact of providing community support, training, and clinical packages of varied intensity on depression screening and management for heavy drinking patients in Latin American primary healthcare. Materials and methods: Quasi-experimental study involving 58 primary healthcare units in Colombia, Mexico and Peru randomized to receive: (1) usual care (control); (2) training using a brief clinical package; (3) community support plus training using a brief clinical package; (4) community support plus training using a standard clinical package. Outcomes were proportion of: (1) heavy drinking patients screened for depression; (2) screen-positive patients receiving appropriate support; (3) all consulting patients screened for depression, irrespective of drinking status. Results: 550/615 identified heavy drinkers were screened for depression (89.4%). 147/230 patients screening positive for depression received appropriate support (64%). Amongst identified heavy drinkers, adjusting for country, sex, age and provider profession, provision of community support and training had no impact on depression activity rates. Intensity of clinical package also did not affect delivery rates, with comparable performance for brief and standard versions. However, amongst all consulting patients, training providers resulted in significantly higher rates of alcohol measurement and in turn higher depression screening rates; 2.7 times higher compared to those not trained. Conclusions: Training using a brief clinical package increased depression screening rates in Latin American primary healthcare. It is not possible to determine the effectiveness of community support on depression activity rates due to the impact of COVID-19. [ABSTRACT FROM AUTHOR]

Introduction: Training Basic Life Support saves lives. However, current BLS training approaches are time-consuming and costly. Alternative cost-efficient and effective training methods are highly needed. The present study evaluated whether a video-feedback supported peer-guided Basic Life Support training approach achieves similar practical performance as a standard instructor-guided training in laypersons. Methods: In a randomized controlled non-inferiority trial, 288 first-year medical students were randomized to two study arms with different Basic Life Support training methods: 1) Standard Instructor Feedback (SIF) or 2) a Peer Video Feedback (PVF). Outcome parameters were objective data for Basic Life Support performance (compression depth and rate) from a resuscitation manikin with recording software as well as overall Basic Life Support performance and subjective confidence. Non-inferiority margins (Δ) for these outcome parameters and sample size calculation were based on previous studies with Standard Instructor Feedback. Two-sided 95% confidence intervals were employed to determine significance of non-inferiority. Results: Results confirmed non-inferiority of Peer Video Feedback to Standard Instructor Feedback for compression depth (proportion difference PVF–SIF = 2.9%; 95% CI: -8.2% to 14.1%; Δ = -19%), overall Basic Life Support performance (proportion difference PVF–SIF = 6.7%; 95% CI: 0.0% to 14.3%; Δ = -27%) and subjective confidence for CPR performance (proportion difference PVF–SIF = -0.01; 95% CI: -0.18–0.17; Δ = -0.5) and emergency situations (proportion difference PVF–SIF = -0.02; 95% CI: -0.21–0.18; Δ = -0.5). Results for compression rate were inconclusive. Discussion: Peer Video Feedback achieves comparable results as standard instructor-based training methods. It is an easy-to-apply and cost-efficient alternative to standard Basic Life Support training methods. To improve performance with respect to compression rate, additional implementation of a metronome is recommended. [ABSTRACT FROM AUTHOR]

A recent result obtained by means of an in vitro experiment with cancer cultured cells has configured the endoplasmic reticulum as the preferential site for the accumulation of 2-deoxy-2-[18F]fluoro-D-glucose (FDG). Such a result is coherent with cell biochemistry and is made more significant by the fact that the reticular accumulation rate of FDG is dependent upon extracellular glucose availability. The objective of the present paper is to confirm in vivo the result obtained in vitro concerning the crucial role played by the endoplasmic reticulum in FDG cancer metabolism. This study utilizes data acquired by means of a Positron Emission Tomography scanner for small animals in the case of CT26 models of cancer tissues. The recorded concentration images are interpreted within the framework of a three-compartment model for FDG kinetics, which explicitly assumes that the endoplasmic reticulum is the dephosphorylation site for FDG in cancer cells. The numerical reduction of the compartmental model is performed by means of a regularized Gauss-Newton algorithm for numerical optimization. This analysis shows that the proposed three-compartment model equals the performance of a standard Sokoloff's two-compartment system in fitting the data. However, it provides estimates of some of the parameters, such as the phosphorylation rate of FDG, more consistent with prior biochemical information. These results are made more solid from a computational viewpoint by proving the identifiability and by performing a sensitivity analysis of the proposed compartment model. [ABSTRACT FROM AUTHOR]

This study compared the results of data collected from a longitudinal query analysis of the MEDLINE database hosted on multiple platforms that include PubMed, EBSCOHost, Ovid, ProQuest, and Web of Science. The goal was to identify variations among the search results on the platforms after controlling for search query syntax. We devised twenty-nine cases of search queries comprised of five semantically equivalent queries per case to search against the five MEDLINE database platforms. We ran our queries monthly for a year and collected search result count data to observe changes. We found that search results varied considerably depending on MEDLINE platform. Reasons for variations were due to trends in scholarly publication such as publishing individual papers online first versus complete issues. Some other reasons were metadata differences in bibliographic records; differences in the levels of specificity of search fields provided by the platforms and large fluctuations in monthly search results based on the same query. Database integrity and currency issues were observed as each platform updated its MEDLINE data throughout the year. Specific biomedical bibliographic databases are used to inform clinical decision-making, create systematic reviews, and construct knowledge bases for clinical decision support systems. They serve as essential information retrieval and discovery tools to help identify and collect research data and are used in a broad range of fields and as the basis of multiple research designs. This study should help clinicians, researchers, librarians, informationists, and others understand how these platforms differ and inform future work in their standardization. [ABSTRACT FROM AUTHOR]

Twitter is among the most used online platforms for the political communications, due to the concision of its messages (which is particularly suitable for political slogans) and the quick diffusion of messages. Especially when the argument stimulate the emotionality of users, the content on Twitter is shared with extreme speed and thus studying the tweet sentiment if of utmost importance to predict the evolution of the discussions and the register of the relative narratives. In this article, we present a model able to reproduce the dynamics of the sentiments of tweets related to specific topics and periods and to provide a prediction of the sentiment of the future posts based on the observed past. The model is a recent variant of the Pólya urn, introduced and studied in Aletti and Crimaldi (2019, 2020), which is characterized by a "local" reinforcement, i.e. a reinforcement mechanism mainly based on the most recent observations, and by a random persistent fluctuation of the predictive mean. In particular, this latter feature is capable of capturing the trend fluctuations in the sentiment curve. While the proposed model is extremely general and may be also employed in other contexts, it has been tested on several Twitter data sets and demonstrated greater performances compared to the standard Pólya urn model. Moreover, the different performances on different data sets highlight different emotional sensitivities respect to a public event. [ABSTRACT FROM AUTHOR]

Background: Since COVID-19 pandemic is a global crisis, tests with high sensitivity and specificity are crucial for the identification and management of COVID-19 patients. There is an urgent need for low-cost rapid antigen COVID-19 test with a good diagnostic performance. Although various antigen rapid detection tests are widely available, strong evidence of their usefulness in clinical practice are still limited. Therefore, our aim was to evaluate clinical performance of STANDARD Q COVID-19 Ag Test (SD Biosensor, Gyeonggi-do, South Korea). Methods: The performance of the STANDARD Q COVID-19 Ag Test for the detection of SARS-CoV-2 antigen was evaluated in comparison to RT-qPCR results in 120 symptomatic patients (median age 49, IQR 36–70) who presented to health care facility in Novi Sad, Vojvodina, Serbia. Results: Twenty five out of 120 samples have been tested positive using STANDARD Q COVID-19 Ag Test, and all of them were also positive on RT-qPCR. Overall, the STANDARD Q COVID-19 Ag Test showed sensitivity of 58.1% (95% CI 42.1–73.0) but it was higher in the early days of disease, when the highest viral loads were detected. During the first five days after the symptom onset, the sensitivity ranged from 66.7% to 100% and the pooled accuracy and Kappa values were high (0.92 and 0.852). Conclusions: A strong agreement between performance of STANDARD Q COVID-19 Ag Test and RT-qPCR was observed during the first five days of illness, suggesting that this rapid antigenic test can be very useful for COVID-19 diagnosis in the early phase of disease. [ABSTRACT FROM AUTHOR]

Background: Fundamental Movement Skills (FMS) play a critical role in ontogenesis. Many children have insufficient FMS, highlighting the need for universal screening in schools. There are many observational FMS assessment tools, but their psychometric properties are not readily accessible. A systematic review was therefore undertaken to compile evidence of the validity and reliability of observational FMS assessments, to evaluate their suitability for screening. Methods: A pre-search of 'fundamental movement skills' OR 'fundamental motor skills' in seven online databases (PubMed, Ovid MEDLINE, Ovid Embase, EBSCO CINAHL, EBSCO SPORTDiscus, Ovid PsycINFO and Web of Science) identified 24 assessment tools for school-aged children that: (i) assess FMS; (ii) measure actual motor competence and (iii) evaluate performance on a standard battery of tasks. Studies were subsequently identified that: (a) used these tools; (b) quantified validity or reliability and (c) sampled school-aged children. Study quality was assessed using COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklists. Results: Ninety studies were included following the screening of 1863 articles. Twenty-one assessment tools had limited or no evidence to support their psychometric properties. The Test of Gross Motor Development (TGMD, n = 34) and the Movement Assessment Battery for Children (MABC, n = 37) were the most researched tools. Studies consistently reported good evidence for validity, reliability for the TGMD, whilst only 64% of studies reported similarly promising results for the MABC. Twelve studies found good evidence for the reliability and validity of the Bruininks-Oseretsky Test of Motor Proficiency but poor study quality appeared to inflate results. Considering all assessment tools, those with promising psychometric properties often measured limited aspects of validity/reliability, and/or had limited feasibility for large scale deployment in a school-setting. Conclusion: There is insufficient evidence to justify the use of any observational FMS assessment tools for universal screening in schools, in their current form. [ABSTRACT FROM AUTHOR]

What is the role of real-time control and learning in the formation of social conventions? To answer this question, we propose a computational model that matches human behavioral data in a social decision-making game that was analyzed both in discrete-time and continuous-time setups. Furthermore, unlike previous approaches, our model takes into account the role of sensorimotor control loops in embodied decision-making scenarios. For this purpose, we introduce the Control-based Reinforcement Learning (CRL) model. CRL is grounded in the Distributed Adaptive Control (DAC) theory of mind and brain, where low-level sensorimotor control is modulated through perceptual and behavioral learning in a layered structure. CRL follows these principles by implementing a feedback control loop handling the agent's reactive behaviors (pre-wired reflexes), along with an Adaptive Layer that uses reinforcement learning to maximize long-term reward. We test our model in a multi-agent game-theoretic task in which coordination must be achieved to find an optimal solution. We show that CRL is able to reach human-level performance on standard game-theoretic metrics such as efficiency in acquiring rewards and fairness in reward distribution. [ABSTRACT FROM AUTHOR]

Despite growing demand for practicable methods of research evaluation, the use of bibliometric indicators remains controversial. This paper examines performance assessment practice in Europe—first, identifying the most commonly used bibliometric methods and, second, identifying the actors who have defined wide-spread practices. The framework of this investigation is Abbott's theory of professions, and I argue that indicator-based research assessment constitutes a potential jurisdiction for both individual experts and expert organizations. This investigation was conducted using a search methodology that yielded 138 evaluation studies from 21 EU countries, covering the period 2005 to 2019. Structured content analysis revealed the following findings: (1) Bibliometric research assessment is most frequently performed in the Nordic countries, the Netherlands, Italy, and the United Kingdom. (2) The Web of Science (WoS) is the dominant database used for public research assessment in Europe. (3) Expert organizations invest in the improvement of WoS citation data, and set technical standards with regards to data quality. (4) Citation impact is most frequently assessed with reference to international scientific fields. (5) The WoS classification of science fields retained its function as a de facto reference standard for research performance assessment. A detailed comparison of assessment practices between five dedicated organizations and other individual bibliometric experts suggests that corporate ownership and limited access to the most widely used citation databases have had a restraining effect on the development and diffusion of professional bibliometric methods during this period. [ABSTRACT FROM AUTHOR]

Handling of laboratory mice is essential for experiments and husbandry, but handling can increase anxiety in mice, compromising their welfare and potentially reducing replicability between studies. The use of non-aversive handling (e.g., tunnel handling or cupping), rather than the standard method of picking mice up by the tail, has been shown to enhance interaction with a handler, reduce anxiety-like behaviours, and increase exploration and performance in standard behavioural tests. Despite this, some labs continue to use tail handling for routine husbandry, and the extent to which non-aversive methods are being used is currently unknown. Here we conducted an international online survey targeting individuals that work with and/or conduct research using laboratory mice. The survey aimed to identify the handling methods currently being used, and to determine common obstacles that may be preventing the wider uptake of non-aversive handling. We also surveyed opinions concerning the current data in support of non-aversive handling for mouse welfare and scientific outcomes. 390 complete responses were received and analysed quantitatively and thematically. We found that 35% report using tail handling only, and 43% use a combination of tail and non-aversive methods. 18% of respondents reported exclusively using non-aversive methods. The vast majority of participants were convinced that non-aversive handling improves animal welfare and scientific outcomes. However, the survey indicated that researchers were significantly less likely to have heard of non-aversive handling and more likely to use tail handling compared with animal care staff. Thematic analysis revealed there were concerns regarding the time required for non-aversive methods compared with tail handling, and that there was a perceived incompatibility of tunnel handling with restraint, health checks and other routine procedures. Respondents also highlighted a need for additional research into the impact of handling method that is representative of experimental protocols and physiological indicators used in the biomedical fields. This survey highlights where targeted research, outreach, training and funding may have the greatest impact on increasing uptake of non-aversive handling methods for laboratory mice. [ABSTRACT FROM AUTHOR]

A growing body of literature suggests that post-secondary students experience food insecurity (FI) at greater rates than the general population. However, these rates vary dramatically across institutions and studies. FI assessment methods commonly used in studies with college students have not been scrutinized for psychometric properties, and varying protocols may influence resulting FI prevalence estimates. The objective of this study was to assess the performance of standard food security assessment protocols and to evaluate their agreement as well as the relative accuracy of these protocols in identifying student FI. A randomized sample of 4,000 undergraduate students were invited to participate in an online survey (Qualtrics, LLC, Provo, Utah, USA) that evaluated sociodemographic characteristics and FI with the 2-item food sufficiency screener and the 10-item USDA Adult Food Security Survey Module (FSSM; containing the abbreviated 6-item module). Four hundred sixty-two eligible responses were included in the final sample. The psychometric analysis revealed inconsistencies in college student response patterns on the FSSM when compared to national evaluations. Agreement between FI protocols was generally high (>90%) but was lessened when compared with a protocol that incorporated the 2-item screener. The 10-item FSSM with the 2-item screener had the best model fit (McFadden's R2 = 0.15 and Bayesian Information Criterion = -2049.72) and emerged as the tool providing the greatest relative accuracy for identifying students with FI. Though the 10-item FSSM and 2-item screener yields the most accuracy in this sample, it is unknown why students respond to FSSM items differently than the general population. Further qualitative and quantitative evaluations are needed to determine which assessment protocol is the most valid and reliable for use in accurately identifying FI in post-secondary students across the U.S. [ABSTRACT FROM AUTHOR]

The trial objective was to compare the performance and animal health parameters of pigs raised according to one of 3 antibiotic (AB) protocols: standard AB medication consisting of mass treatment on days 4 and 21 and judicious AB therapy given therapeutically thereafter as group medication in water and feed or by individual injection (group T1, N = 702); modified AB medication identical to group T1 but with mass treatment only on day 4 and without subsequent therapeutic feed medication (group T2, N = 675); or an antibiotic-free (ABF) regimen (group T3, N = 702). All pigs were vaccinated with a modified-live porcine reproductive and respiratory syndrome virus (PRRSV) vaccine 3 days after weaning. Using a seeder pig model to mimic endemic field infection dynamics, pigs were contact-challenged with virulent PRRSV lineage 1 strain 174 four weeks after vaccination. At finishing, average daily gain (ADG) and mean feed conversion ratio (FCR) were significantly better (p ≤ 0.05) for the T1 and T2 groups compared to the T3 group. There were no significant differences in post-weaning ADG and FCR between the T1 and T2 groups. Mortality and removals significantly favored (p ≤0.05) the T1 and T2 groups (20.94% and 24.89%, respectively) versus the T3 group (57.98%). Net revenue per pig was $105.43, $98.79, and $33.81 for the T1, T2 and T3 groups, respectively. Under the conditions of this study, these results indicate that in a PRRSV-endemic setting involving bacterial co-infections, an ABF production strategy may leave pigs at considerable risk of exposure to severe clinical disease and that judicious use of antibiotics can significantly improve animal health. [ABSTRACT FROM AUTHOR]

The topic of unconscious influences on behaviour has long been explored as a means of understanding human performance and the neurobiological correlates of intention, motivation, and action. However, what is relatively unknown is whether subconsciously delivered priming stimuli, with or without rewards, can affect individuals’ maximum level of force produced with their best effort. We demonstrated using transcranial magnetic stimulation that barely visible priming of an action concept, when combined with a reward in the form of a consciously visible positive stimulus, could alter the state of the motor system. In accordance with this neurophysiological alteration, the prime-plus-reward stimuli significantly increased the hand-grip force level of maximum voluntary contraction with little conscious awareness. This is the first objective evidence that the barely conscious presence of a behavioral goal can influence the state of the motor system and arouse latent ability for human force exertion. [ABSTRACT FROM AUTHOR]

Background: A patient’s journey through the health care system is influenced by clinical and system processes across the continuum of care. Methods: To inform optimized access to care and patient flow for individuals with traumatic spinal cord injury (tSCI), we developed a simulation model that can examine the full impact of therapeutic or systems interventions across the care continuum for patients with traumatic spinal cord injuries. The objective of this paper is to describe the detailed development of this simulation model for a major trauma and a rehabilitation centre in British Columbia (BC), Canada, as part of the Access to Care and Timing (ACT) project and is referred to as the BC ACT Model V1.0. Findings: To demonstrate the utility of the simulation model in clinical and administrative decision-making we present three typical scenarios that illustrate how an investigator can track the indirect impact(s) of medical and administrative interventions, both upstream and downstream along the continuum of care. For example, the model was used to estimate the theoretical impact of a practice that reduced the incidence of pressure ulcers by 70%. This led to a decrease in acute and rehabilitation length of stay of 4 and 2 days, respectively and a decrease in bed utilization of 9% and 3% in acute and rehabilitation. Conclusion: The scenario analysis using the BC ACT Model V1.0 demonstrates the flexibility and value of the simulation model as a decision-making tool by providing estimates of the effects of different interventions and allowing them to be objectively compared. Future work will involve developing a generalizable national Canadian ACT Model to examine differences in care delivery and identify the ideal attributes of SCI care delivery. [ABSTRACT FROM AUTHOR]

Alzheimer's Disease irremediably alters the proficiency of word search and retrieval processes even at its early stages. Such disruption can sometimes be paradoxical in specific language tasks, for example semantic priming. Here we focus in the striking side-effect of hyperpriming in Alzheimer's Disease patients, which has been well-established in the literature for a long time. Previous studies have evidenced that modern network theory can become a powerful complementary tool to gain insight in cognitive phenomena. Here, we first show that network modeling is an appropriate approach to account for semantic priming in normal subjects. Then we turn to priming in degraded cognition: hyperpriming can be readily understood in the scope of a progressive degradation of the semantic network structure. We compare our simulation results with previous empirical observations in diseased patients finding a qualitative agreement. The network approach presented here can be used to accommodate current theories about impaired cognition, and towards a better understanding of lexical organization in healthy and diseased patients. [ABSTRACT FROM AUTHOR]

Damage to the parietal lobe can induce a condition known as spatial neglect, characterized by a lack of awareness of the personal and/or extrapersonal space opposite the damaged brain region. Spatial neglect is commonly assessed clinically using either the line bisection or the target cancellation task. However, it is unclear whether poor performance on each of these two tasks is associated with the same or different lesion locations. To date, methodological limitations and differences have prevented a definitive link between task performance and lesion location to be made. Here we report findings from a voxel-based lesion symptom mapping (VLSM) analysis of an unbiased selection of 44 patients with a recent unifocal stroke. Patients performed both the line bisection and target cancellation task. For each of the two tasks a continuous score was incorporated into the VLSM analysis. Both tasks correlated highly with each other (r = .76) and VLSM analyses indicated that the angular gyrus was the critical lesion site for both tasks. The results suggest that both tasks probe the same underlying cortical deficits and although the cancellation task was more sensitive than the line bisection task, both can be used in a clinical setting to test for spatial neglect. [ABSTRACT FROM AUTHOR]