Prediction of Recovery From Severe Hemorrhagic Shock Using Logistic Regression.

This paper implements logistic regression models (LRMs) and feature selection for creating a predictive model for recovery form hemorrhagic shock (HS) with resuscitation using blood in the multiple experimental rat animal protocols. A total of 61 animals were studied across multiple HS experiments, which encompassed two different HS protocols and two resuscitation protocols using blood stored for short periods using five different techniques. Twenty-seven different systemic hemodynamics, cardiac function, and blood gas parameters were measured in each experiment, of which feature selection deemed only 25% of the them as relevant. The reduced feature set was used to train a final logistic regression model. A final test set accuracy is 84% compared to 74% for a baseline classifier using only MAP and HR measurements. Receiver operating characteristics (ROC) curve analysis and Cohens kappa statistics were also used as measures of performance, with the final reduced model outperforming the model, including all parameters. Our results suggest that LRMs trained with a combination of systemic hemodynamics, cardiac function, and blood gas parameters measured at multiple timepoints during HS can successfully classify HS recovery groups. Our results show the predictive ability of traditional and novel hemodynamic and cardiac function features and their combinations, many of which had not previously been taken into consideration, for monitoring HS. Furthermore, we have devised an effective methodology for feature selection and shown ways in which the performance of such predictive models should be assessed in future studies

An adaptive weighted least square support vector regression for hysteresis in piezoelectric actuators

© 2017 Elsevier B.V. To overcome the low positioning accuracy of piezoelectric actuators (PZAs) caused by the hysteresis nonlinearity, this paper proposes an adaptive weighted least squares support vector regression (AWLSSVR) to model the rate-dependent hysteresis of PZA. Firstly, the AWLSSVR hyperparameters are optimized by using particle swarm optimization. Then an adaptive weighting strategy is proposed to eliminate the effects of noises in the training dataset and reduce the sample size at the same time. Finally, the proposed approach is applied to predict the hysteresis of PZA. The results show that the proposed method is more accurate than other versions of least squares support vector regression for training samples with noises, and meanwhile reduces the sample size and speeds up calculation

Effects of Missing Data Imputation Methods on Univariate Time Series Forecasting with Arima and LSTM

Missing data are common in real-life studies and missing observations within the univariate time series cause analytical problems in the flow of the analysis. Imputation of missing values is an inevitable step in the analysis of every incomplete univariate time series data. The reviewed literature has shown that the focus of existing studies is on comparing the distribution of imputed data. There is a gap of knowledge on how different imputation methods for univariate time series data affect the fit and prediction performance of time series models. In this work, we evaluated the predictive performance of autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models on imputed time-series data using Kalman smoothing on ARIMA, Kalman smoothing on structural time series model, mean imputation, exponentially weighted moving average, simple moving average, linear, cubic spline, stine, and KNN interpolation techniques under missing completely at random (MCAR) mechanism. Missing values were generated at 10%, 15%, 25%, and 35% rates using complete data of 24-hour ambulatory diastolic blood pressure readings. The performance of models was compared on imputed and original data using mean absolute percentage error (MAPE) and root mean square error (RMSE). Kalman smoothing on structural time series, exponentially weighted moving average, and Kalman smoothing on ARIMA were the best missing data replacement techniques as the gap of the missingness increased. The performance of mean imputation, cubic spline, KNN, and the other simple interpolation methods reduced significantly as the gap of missingness increased. The LSTM gave better predictions on the original training data, but the ARIMA predictions on imputed data gave consistent results across the four scenarios

Aerospace Medicine and Biology: A cumulative index to the 1982 issues

This publication is a cumulative index to the abstracts contained in the Supplements 229 through 240 of Aerospace Medicine and Biology: A continuing Bibliography. It includes three indexes: subject, personal author, and corporate source

Preclinical Animal Modeling in Medicine

The results of preclinical animal research have been successfully implemented in various medical and biological practices. The use of animals in medicine is based on significant anatomical, physiological, and molecular similarities between humans and animals. Particularly, mammals that have vast biological commonalities with humans represent not only a valuable model to explore the mechanisms of varied human diseases, but also to define new diagnostic and treatment strategies. This book covers broad but important aspects of animal modeling for scientific medicine as well as for translational systems and biological sciences. Alternative methods such as cell culture and in vitro experiments that do not require the sacrifice of an animal are encouraged for scientific and medical studies

Augmentation of Brain Function: Facts, Fiction and Controversy. Volume III: From Clinical Applications to Ethical Issues and Futuristic Ideas

The final volume in this tripartite series on Brain Augmentation is entitled “From Clinical Applications to Ethical Issues and Futuristic Ideas”. Many of the articles within this volume deal with translational efforts taking the results of experiments on laboratory animals and applying them to humans. In many cases, these interventions are intended to help people with disabilities in such a way so as to either restore or extend brain function. Traditionally, therapies in brain augmentation have included electrical and pharmacological techniques. In contrast, some of the techniques discussed in this volume add specificity by targeting select neural populations. This approach opens the door to where and how to promote the best interventions. Along the way, results have empowered the medical profession by expanding their understanding of brain function. Articles in this volume relate novel clinical solutions for a host of neurological and psychiatric conditions such as stroke, Parkinson’s disease, Huntington’s disease, epilepsy, dementia, Alzheimer’s disease, autism spectrum disorders (ASD), traumatic brain injury, and disorders of consciousness. In disease, symptoms and signs denote a departure from normal function. Brain augmentation has now been used to target both the core symptoms that provide specificity in the diagnosis of a disease, as well as other constitutional symptoms that may greatly handicap the individual. The volume provides a report on the use of repetitive transcranial magnetic stimulation (rTMS) in ASD with reported improvements of core deficits (i.e., executive functions). TMS in this regard departs from the present-day trend towards symptomatic treatment that leaves unaltered the root cause of the condition. In diseases, such as schizophrenia, brain augmentation approaches hold promise to avoid lengthy pharmacological interventions that are usually riddled with side effects or those with limiting returns as in the case of Parkinson’s disease. Brain stimulation can also be used to treat auditory verbal hallucination, visuospatial (hemispatial) neglect, and pain in patients suffering from multiple sclerosis. The brain acts as a telecommunication transceiver wherein different bandwidth of frequencies (brainwave oscillations) transmit information. Their baseline levels correlate with certain behavioral states. The proper integration of brain oscillations provides for the phenomenon of binding and central coherence. Brain augmentation may foster the normalization of brain oscillations in nervous system disorders. These techniques hold the promise of being applied remotely (under the supervision of medical personnel), thus overcoming the obstacle of travel in order to obtain healthcare. At present, traditional thinking would argue the possibility of synergism among different modalities of brain augmentation as a way of increasing their overall effectiveness and improving therapeutic selectivity. Thinking outside of the box would also provide for the implementation of brain-to-brain interfaces where techniques, proper to artificial intelligence, could allow us to surpass the limits of natural selection or enable communications between several individual brains sharing memories, or even a global brain capable of self-organization. Not all brains are created equal. Brain stimulation studies suggest large individual variability in response that may affect overall recovery/treatment, or modify desired effects of a given intervention. The subject’s age, gender, hormonal levels may affect an individual’s cortical excitability. In addition, this volume discusses the role of social interactions in the operations of augmenting technologies. Finally, augmenting methods could be applied to modulate consciousness, even though its neural mechanisms are poorly understood. Finally, this volume should be taken as a debate on social, moral and ethical issues on neurotechnologies. Brain enhancement may transform the individual into someone or something else. These techniques bypass the usual routes of accommodation to environmental exigencies that exalted our personal fortitude: learning, exercising, and diet. This will allow humans to preselect desired characteristics and realize consequent rewards without having to overcome adversity through more laborious means. The concern is that humans may be playing God, and the possibility of an expanding gap in social equity where brain enhancements may be selectively available to the wealthier individuals. These issues are discussed by a number of articles in this volume. Also discussed are the relationship between the diminishment and enhancement following the application of brain-augmenting technologies, the problem of “mind control” with BMI technologies, free will the duty to use cognitive enhancers in high-responsibility professions, determining the population of people in need of brain enhancement, informed public policy, cognitive biases, and the hype caused by the development of brain- augmenting approaches

Simulations in statistical physics and biology: some applications

One of the most active areas of physics in the last decades has been that of critical phenomena, and Monte Carlo simulations have played an important role as a guide for the validation and prediction of system properties close to the critical points. The kind of phase transitions occurring for the Betts lattice (lattice constructed removing 1/7 of the sites from the triangular lattice) have been studied before with the Potts model for the values q=3, ferromagnetic and antiferromagnetic regime. Here, we add up to this research line the ferromagnetic case for q=4 and 5. In the first case, the critical exponents are estimated for the second order transition, whereas for the latter case the histogram method is applied for the occurring first order transition. Additionally, Domany's Monte Carlo based clustering technique mainly used to group genes similar in their expression levels is reviewed. Finally, a control theory tool --an adaptive observer-- is applied to estimate the exponent parameter involved in the well-known Gompertz curve. By treating all these subjects our aim is to stress the importance of cooperation between distinct disciplines in addressing the complex problems arising in biology. Contents: Chapter 1 - Monte Carlo simulations in stat. physics; Chapter 2: MC simulations in biology; Chapter 3: Gompertz equationComment: 82 pages, 33 figures, 4 tables, somewhat reduced version of the M.Sc. thesis defended in Jan. 2006 at IPICyT, San Luis Potosi, Mx. (Supervisers: Drs. R. Lopez-Sandoval and H.C. Rosu). Last sections 3.3 and 3.4 can be found at http://lanl.arxiv.org/abs/physics/041108

Activation of the pro-resolving receptor Fpr2 attenuates inflammatory microglial activation

Poster number: P-T099 Theme: Neurodegenerative disorders & ageing Activation of the pro-resolving receptor Fpr2 reverses inflammatory microglial activation Authors: Edward S Wickstead - Life Science & Technology University of Westminster/Queen Mary University of London Inflammation is a major contributor to many neurodegenerative disease (Heneka et al. 2015). Microglia, as the resident immune cells of the brain and spinal cord, provide the first line of immunological defence, but can become deleterious when chronically activated, triggering extensive neuronal damage (Cunningham, 2013). Dampening or even reversing this activation may provide neuronal protection against chronic inflammatory damage. The aim of this study was to determine whether lipopolysaccharide (LPS)-induced inflammation could be abrogated through activation of the receptor Fpr2, known to play an important role in peripheral inflammatory resolution. Immortalised murine microglia (BV2 cell line) were stimulated with LPS (50ng/ml) for 1 hour prior to the treatment with one of two Fpr2 ligands, either Cpd43 or Quin-C1 (both 100nM), and production of nitric oxide (NO), tumour necrosis factor alpha (TNFα) and interleukin-10 (IL-10) were monitored after 24h and 48h. Treatment with either Fpr2 ligand significantly suppressed LPS-induced production of NO or TNFα after both 24h and 48h exposure, moreover Fpr2 ligand treatment significantly enhanced production of IL-10 48h post-LPS treatment. As we have previously shown Fpr2 to be coupled to a number of intracellular signaling pathways (Cooray et al. 2013), we investigated potential signaling responses. Western blot analysis revealed no activation of ERK1/2, but identified a rapid and potent activation of p38 MAP kinase in BV2 microglia following stimulation with Fpr2 ligands. Together, these data indicate the possibility of exploiting immunomodulatory strategies for the treatment of neurological diseases, and highlight in particular the important potential of resolution mechanisms as novel therapeutic targets in neuroinflammation. References Cooray SN et al. (2013). Proc Natl Acad Sci U S A 110: 18232-7. Cunningham C (2013). Glia 61: 71-90. Heneka MT et al. (2015). Lancet Neurol 14: 388-40