Project description:Heartbeat monitoring may play an essential role in the early detection of cardiovascular disease. When using a traditional monitoring system, an abnormal heartbeat may not appear during a recording in a healthcare facility due to the limited time. Thus, continuous and long-term monitoring is needed. Moreover, the conventional equipment may not be portable and cannot be used at arbitrary times and locations. A wearable sensor device such as Polar H10 offers the same capability as an alternative. It has gold-standard heartbeat recording and communication ability but still lacks analytical processing of the recorded data. An automatic heartbeat classification system can play as an analyzer and is still an open problem in the development stage. This paper proposes a heartbeat classifier based on RR interval data for real-time and continuous heartbeat monitoring using the Polar H10 wearable device. Several machine learning and deep learning methods were used to train the classifier. In the training process, we also compare intra-patient and inter-patient paradigms on the original and oversampling datasets to achieve higher classification accuracy and the fastest computation speed. As a result, with a constrain in RR interval data as the feature, the random forest-based classifier implemented in the system achieved up to 99.67% for accuracy, precision, recall, and F1-score. We are also conducting experiments involving healthy people to evaluate the classifier in a real-time monitoring system.
Project description:Cardiogenic shock (CS) is associated with high short-term mortality and a precise CS risk stratification could guide interventions to improve patient outcome. We used mass spectrometry based proteomics to first to a discovery approach to select some biomarker canduidates and then verify them by targeted mass spectrometry (PRM). A 4-protein risk score (CS4P) was derived and validated for 90-day risk of mortality. The CS4P risk score which comprises proteins Liver-fatty acid-binding protein (L-FABP), Beta-2-microglobulin (B2MG), Fructose-bisphosphate aldolase B (ALDOB), and SerpinG1 (IC1), identified short-term mortality risk with a C-statistic of 0.83 (95% CI 0.74–0.89).
Project description:BackgroundMemory retrieval is driven by similarity between a present situation and some prior experience, but not all similarity is created equal. Analogical retrieval, rooted in the similarity between two situations in their underlying structural relations, is often responsible for new insights and innovative solutions to problems. However, superficial similarity is instead more likely to drive spontaneous retrieval. How can we make analogical retrieval more likely? Inducing a relational mindset via an analogical reasoning task has previously been shown to boost subsequent relational thinking. In this paper, we examined whether inducing a relational mindset could also boost analogical retrieval.ResultsWe find that a relational mindset can increase analogical retrieval if induced before information is encoded in the first place, amplifying the effect of a clearly labelled relational structure. On the other hand, inducing a relational mindset at the time of retrieval did not increase analogical retrieval.ConclusionThis work further demonstrates the central importance of high-quality relational encoding for subsequent relation-based analogical retrieval, and that inducing a relational mindset can improve those encodings.
Project description:Most statistical problems encountered throughout life require the ability to quantify probabilities based on proportions. Recent findings on the early ontogeny of this ability have been mixed: For example, when presented with jars containing preferred and less preferred items, 12-month-olds, but not 3- and 4-years-olds, seem to rely on the proportions of objects in the jars to predict the content of samples randomly drawn out of them. Given these contrasting findings, it remains unclear what the probabilistic reasoning abilities of young children are and how they develop. In our study, we addressed this question and tested, with identical methods across age groups and similar methods to previous studies, whether 12-month-olds and 3- and 4-years-olds rely on proportions of objects to estimate probabilities of random sampling events. Results revealed that neither infants nor preschoolers do. While preschoolers' performance is in line with previous findings, infants' performance is difficult to interpret given their failure in a control condition in which the outcomes happened with certainty rather than a graded probability. More systematic studies are needed to explain why infants succeeded in a previous study but failed in our study.
Project description:Time series classification (TSC) is a pervasive and transversal problem in various fields ranging from disease diagnosis to anomaly detection in finance. Unfortunately, the most effective models used by Artificial Intelligence (AI) systems for TSC are not interpretable and hide the logic of the decision process, making them unusable in sensitive domains. Recent research is focusing on explanation methods to pair with the obscure classifier to recover this weakness. However, a TSC approach that is transparent by design and is simultaneously efficient and effective is even more preferable. To this aim, we propose an interpretable TSC method based on the patterns, which is possible to extract from the Matrix Profile (MP) of the time series in the training set. A smart design of the classification procedure allows obtaining an efficient and effective transparent classifier modeled as a decision tree that expresses the reasons for the classification as the presence of discriminative subsequences. Quantitative and qualitative experimentation shows that the proposed method overcomes the state-of-the-art interpretable approaches.
Project description:Categorization is an essential cognitive process useful for transferring knowledge from previous experience to novel situations. The mechanisms by which trained categorization behavior extends to novel stimuli, especially in animals, are insufficiently understood. To understand how pigeons learn and transfer category membership, seven pigeons were trained to classify controlled, bi-dimensional stimuli in a two-alternative forced-choice task. Following either dimensional, rule-based (RB) or information integration (II) training, tests were conducted focusing on the "analogical" extension of the learned discrimination to novel regions of the stimulus space (Casale, Roeder, & Ashby, 2012). The pigeons' results mirrored those from human and non-human primates evaluated using the same analogical task structure, training and testing: the pigeons transferred their discriminative behavior to the new extended values following RB training, but not after II training. Further experiments evaluating rule-based models and association-based models suggested the pigeons use dimensions and associations to learn the task and mediate transfer to stimuli within the novel region of the parametric stimulus space.
Project description:Analogical reasoning, or the ability to find correspondences between entities based on shared relationships, supports knowledge acquisition. As such, the development of this ability during childhood is thought to promote learning. Here, we sought to better understand the mechanisms by which analogical reasoning about semantic relations improves over childhood and adolescence (e.g. chalk is to chalkboard as pen is to…?). We hypothesized that age-related differences would manifest as differences in the brain regions associated with one or more of the following cognitive functions: (1) controlled semantic retrieval, or the ability to retrieve task-relevant semantic associations; (2) response control, or the ability to override the tendency to respond to a salient distractor; and/or (3) relational integration, or the ability to consider jointly two mental relations. In order to test these hypotheses, we analyzed patterns of fMRI activation during performance of a pictorial propositional analogy task across 95 typically developing children between the ages of 6 and 18 years old. Despite large age-related differences in task performance, particularly over ages 6-10 but through to around age 14, participants across the whole age range recruited a common network of frontal, parietal and temporal regions. However, activation in a brain region that has been implicated in controlled semantic retrieval - left anterior prefrontal cortex (BA 47/45) - was positively correlated with age, and also with performance after controlling for age. This finding indicates that improved performance over middle childhood and early adolescence on this analogical reasoning task is driven largely by improvements in the ability to selectively retrieve task-relevant semantic relationships.
Project description:Human reasoning is grounded in an ability to identify highly abstract commonalities governing superficially dissimilar visual inputs. Recent efforts to develop algorithms with this capacity have largely focused on approaches that require extensive direct training on visual reasoning tasks, and yield limited generalization to problems with novel content. In contrast, a long tradition of research in cognitive science has focused on elucidating the computational principles underlying human analogical reasoning; however, this work has generally relied on manually constructed representations. Here we present visiPAM (visual Probabilistic Analogical Mapping), a model of visual reasoning that synthesizes these two approaches. VisiPAM employs learned representations derived directly from naturalistic visual inputs, coupled with a similarity-based mapping operation derived from cognitive theories of human reasoning. We show that without any direct training, visiPAM outperforms a state-of-the-art deep learning model on an analogical mapping task. In addition, visiPAM closely matches the pattern of human performance on a novel task involving mapping of 3D objects across disparate categories.
Project description:Non-coding RNA (ncRNA) are small non-coding sequences involved in gene expression regulation of many biological processes and diseases. The recent discovery of a large set of different ncRNAs with biologically relevant roles has opened the way to develop methods able to discriminate between the different ncRNA classes. Moreover, the lack of knowledge about the complete mechanisms in regulative processes, together with the development of high-throughput technologies, has required the help of bioinformatics tools in addressing biologists and clinicians with a deeper comprehension of the functional roles of ncRNAs. In this work, we introduce a new ncRNA classification tool, nRC (non-coding RNA Classifier). Our approach is based on features extraction from the ncRNA secondary structure together with a supervised classification algorithm implementing a deep learning architecture based on convolutional neural networks.We tested our approach for the classification of 13 different ncRNA classes. We obtained classification scores, using the most common statistical measures. In particular, we reach an accuracy and sensitivity score of about 74%.The proposed method outperforms other similar classification methods based on secondary structure features and machine learning algorithms, including the RNAcon tool that, to date, is the reference classifier. nRC tool is freely available as a docker image at https://hub.docker.com/r/tblab/nrc/. The source code of nRC tool is also available at https://github.com/IcarPA-TBlab/nrc.
Project description:The placenta mediates adverse pregnancy outcomes, including preeclampsia, characterized by gestational hypertension and proteinuria. Placental cell type heterogeneity in preeclampsia is not well-understood and limits mechanistic interpretation of bulk gene expression measures. We generated single-cell RNA-sequencing samples for integration with existing data to create the largest deconvolution reference of 19 fetal and 8 maternal cell types from placental villous tissue at term. We deconvoluted eight published microarray case-control studies of preeclampsia. Our findings indicate substantial placental cellular heterogeneity in preeclampsia that predict previously observed bulk gene expression differences. Our deconvolution reference lays the groundwork for cellular heterogeneity-aware investigation into placental dysfunction and adverse birth outcomes.