Notes

Biosecurity check regarding conjugated nanoparticles involving chitosan-protoporphyrin IX-vitamin B9 for his or her used in photodynamic treatments.
47% in identifying the shearing task.Nanopore-based approaches for the sequencing of DNA and RNA molecules are promising technologies with potential applications in clinical genomics. These approaches have generated large numbers of time series objects over the years, however, it remains a challenge to accurately decipher the underlying nucleotide sequence corresponding to a given signal. By using a combination of consensus signal averaging and stream monitoring of variable-length motifs, we outline an online pattern matching framework that can efficiently locate consensus sequences in real world Nanopore datasets. We demonstrate the applicability of our proposed framework across two use-cases demultiplexing of DNA barcodes and multiple motif site identification in RNA transcripts.Temporal enhanced ultrasound (TeUS) is a tissue characterization approach based on analysis of a temporal series of US data. Previously we demonstrated that intrinsic or external micro-motions of scatterers in the tissue contribute towards the tissue classification properties of TeUS. This property is beneficial to detect early stage cancer, for example, where changes in nuclei configuration (scatteres) dominate tissue properties. In this study, we propose an analytical derivation and experiments to acquire TeUS through manipulation of US imaging parameters, which may be simpler to translate to clinical applications. click here The feasibility of the proposed method is demonstrated on tissue-mimicking phantoms. Using an autoencoder classifier, we are able to classify phantoms of varying elasticities and scattering sizes.Cardiovascular disease is one of the leading factors for death cause of human beings. link2 In the past decade, heart sound classification has been increasingly studied for its feasibility to develop a non-invasive approach to monitor a subject's health status. Particularly, relevant studies have benefited from the fast development of wearable devices and machine learning techniques. Nevertheless, finding and designing efficient acoustic properties from heart sounds is an expensive and time-consuming task. It is known that transfer learning methods can help extract higher representations automatically from the heart sounds without any human domain knowledge. However, most existing studies are based on models pre-trained on images, which may not fully represent the characteristics inherited from audio. To this end, we propose a novel transfer learning model pre-trained on large scale audio data for a heart sound classification task. In this study, the PhysioNet CinC Challenge Dataset is used for evaluation. Experimental results demonstrate that, our proposed pre-trained audio models can outperform other popular models pre-trained by images by achieving the highest unweighted average recall at 89.7 %.Early prediction of sepsis is essential to give the patient timely treatment since each hour of delayed treatment has been associated with an increase in mortality. Current sepsis detection systems rely on empirical Clinical Decision Rules(CDR)s, which are based on vital signs that can be collected from the bedside. The main disadvantages of CDRs include questions of generalizability and performance variance when applied to the populations different from the groups used for derivation and often take years to develop and validate. This paper proposes a deep learning model using Bi-Directional Gated Recurrent Units(GRU), which uses a wide range of parameters that are associated with vitals, laboratory, and demographics of patients. The proposed model has an area under the receiver operating characteristic (AUROC) of 0.97, outperforming all the existing systems in the current literature. The model can handle the missing data, and irregular sampling intervals frequently present in medical records.Clinical relevance-The proposed model can be used to predict the onset of sepsis 6 hours ahead of time by the use of a machine learning algorithm. This proposed method outperforms the sepsis prediction machine learning models found in the current literature.This study analyzed the selective attention processing related to cognitive load on simultaneous interpretation (SI). We tested simultaneous interpreter's brain function using EEG signals and calculated inter-trial coherence (ITC) extracted by the 40-Hz auditory steady-state response (ASSR). link3 In this experiment, we set two conditions as Japanese-English translation and Japanese shadowing cognition. We also compared two subject groups S rank with more than 15 years of SI experience (n=7) and C rank with less than one year experience (n=15). As a result, the ITCs for S rank in interpreting conditions were more significantly increased than C rank in the shadowing conditions (ITC p less then 0.001). Our results demonstrate that 40-Hz ASSR might be a good indicator of selective attention and cognitive load during SI in ecologically valid environmental conditions. It can also be used to detect attention and cognitive control dysfunction in ADHD or schizophrenia.Affective personality traits have been associated with a risk of developing mental and cognitive disorders and can be informative for early detection and management of such disorders. However, conventional personality trait detection is often biased and unreliable, as it depends on the honesty of the subjects when filling out the lengthy questionnaires. In this paper, we propose a method for objective detection of personality traits using physiological signals. Subjects are shown affective images and videos to evoke a range of emotions. The electrical activity of the brain is captured using EEG during this process and the multi-channel EEG data is processed to compute the inter-hemispheric asynchrony of the brainwaves. The most discriminative features are selected and then used to build a machine learning classifier, which is trained to predict 16 personality traits. Our results show high predictive accuracy for both image and video stimuli individually, and an improvement when the two stimuli are combined, achieving a 95.49% accuracy. Most of the selected discriminative features were found to be extracted from the alpha frequency band. Our work shows that personality traits can be accurately detected with EEG data, suggesting possible use in practical applications for early detection of mental and cognitive disorders.Sleep spindles are associated with normal brain development, memory consolidation and infant sleep-dependent brain plasticity and can be used by clinicians in the assessment of brain development in infants. Sleep spindles can be detected in EEG, however, identifying sleep spindles in EEG recordings manually is very time-consuming and typically requires highly trained experts. Research on the automatic detection of sleep spindles in infant EEGs has been limited to-date. In this study, we present a novel supervised machine learning-based algorithm to detect sleep spindles in infant EEG recordings. EEGs collected from 141 ex-term born infants and 6 ex-preterm born infants, recorded at 4 months of age (adjusted), were used to train and test the algorithm. Sleep spindles were annotated by experienced clinical physiologists as the gold standard. The dataset was split into training (81 ex-term), validation (30 ex-term), and testing (30 ex-term + 6 ex-preterm) set. 15 features were selected for input into a random forest algorithm. Sleep spindles were detected in the ex-term infant EEG test set with 92.1% sensitivity and 95.2% specificity. For ex-preterm born infants, the sensitivity and specificity were 80.3% and 91.8% respectively. The proposed algorithm has the potential to assist researchers and clinicians in the automated analysis of sleep spindles in infant EEG.While machine learning algorithms are able to detect subtle patterns of interest in data, expert knowledge may contain crucial information that is not easily extracted from a given dataset, especially when the latter is small or noisy. In this paper we investigate the suitability of Gaussian Process Classification (GPC) as an effective model to implement the domain knowledge in an algorithm's training phase. Building on their Bayesian nature, we proceed by injecting problem- specific domain knowledge in the form of an a-priori distribution on the GPC latent function. We do this by extracting handcrafted features from the input data, and correlating them to the logits of the classification problem through fitting a prior function informed by the physiology of the problem. The physiologically-informed prior of the GPC is then updated through the Bayes formula using the available dataset. We apply the methods discussed here to a two-class classification problem associated to a dataset comprising Heart Rate Variability (HRV) and Electrodermal Activity (EDA) signals collected from 26 subjects who were exposed to a physical stressor aimed at altering their autonomic nervous systems dynamics. We provide comparative computational experiments on the selection of appropriate physiologically-inspired GPC prior functions. We find that the recognition of the presence of the physical stressor is significantly enhanced when the physiologically-inspired prior knowledge is injected into the GPC model.Transient electrophysiological anomalies in the human brain have been associated with neurological disorders such as epilepsy, may signal impending adverse events (e.g, seizurse), or may reflect the effects of a stressor, such as insufficient sleep. These, typically brief, high-frequency and heterogeneous signal anomalies remain poorly understood, particularly at long time scales, and their morphology and variability have not been systematically characterized. In continuous neural recordings, their inherent sparsity, short duration and low amplitude makes their detection and classification difficult. In turn, this limits their evaluation as potential biomarkers of abnormal neurodynamic processes (e.g., ictogenesis) and predictors of impending adverse events. A novel algorithm is presented that leverages the inherent sparsity of high-frequency abnormalities in neural signals recorded at the scalp and uses spectral clustering to classify them in very high-dimensional signals spanning several days. It is shown that estimated clusters vary dynamically with time and their distribution changes substantially both as a function of time and space.Vagal Nerve Stimulation (VNS) is an option in the treatment of drug-resistant epilepsy. However, approximately a quarter of VNS subjects does not respond to the therapy. In this retrospective study, we introduce heart-rate features to distinguish VNS responders and non-responders. Standard pre-implantation measurements of 66 patients were segmented in relation to specific stimuli (open/close eyes, photic stimulation, hyperventilation, and rests between). Median interbeat intervals were found for each segment and normalized (NMRR). Five NMRRs were significant; the strongest feature achieved significance with p=0.013 and AUC=0.66. Low mutual correlation and independence on EEG signals mean that presented features could be considered as an addition for models predicting VNS response using EEG.The study of working memory (WM) is a hot topic in recent years and accumulating literatures underlying the achievement and neural mechanism of WM. However, the effect of WM training on cognitive functions were rarely studied. In this study, nineteen healthy young subjects participated in a longitudinal design with one week N-back training (N=1,2,3,4). Experimental results demonstrated that training procedure could help the subjects master more complex psychological tasks when comparing the pre-training performance with those post-training. More specifically, the behavior accuracy increased from 68.14±9.34%, 45.09±14.90%, 39.12±12.71%, and 32.11±10.98% for 1-back, 2-back, 3-back and 4-back respectively to 73.52±4.01%, 69.14±5.28%, 69.09±6.41% and 64.41±5.12% after training. Furthermore, we applied electroencephalogram (EEG) power and functional connectivity to reveal the neural mechanisms of this beneficial effect and found that the EEG power of δ, θ and α band located in the left temporal and occipital lobe increased significantly.
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