85 research outputs found
Time-varying Learning and Content Analytics via Sparse Factor Analysis
We propose SPARFA-Trace, a new machine learning-based framework for
time-varying learning and content analytics for education applications. We
develop a novel message passing-based, blind, approximate Kalman filter for
sparse factor analysis (SPARFA), that jointly (i) traces learner concept
knowledge over time, (ii) analyzes learner concept knowledge state transitions
(induced by interacting with learning resources, such as textbook sections,
lecture videos, etc, or the forgetting effect), and (iii) estimates the content
organization and intrinsic difficulty of the assessment questions. These
quantities are estimated solely from binary-valued (correct/incorrect) graded
learner response data and a summary of the specific actions each learner
performs (e.g., answering a question or studying a learning resource) at each
time instance. Experimental results on two online course datasets demonstrate
that SPARFA-Trace is capable of tracing each learner's concept knowledge
evolution over time, as well as analyzing the quality and content organization
of learning resources, the question-concept associations, and the question
intrinsic difficulties. Moreover, we show that SPARFA-Trace achieves comparable
or better performance in predicting unobserved learner responses than existing
collaborative filtering and knowledge tracing approaches for personalized
education
Excitability scores of goats administered ascorbic acid and transported during hot-dry conditions
In this study, we investigated the effect of ascorbic acid (AA) administration on goat excitability due to transportation. Ten goats administered AA (p.o.) at 100 mg/kg of body weight before transportation served as the experimental group, and seven goats administered only 10ml/kg of sterile water (p.o.) served as controls. Excitability scores were recorded for each goat; when weighed, before, immediately after, and 3 h after 8 h of transportation. A score of one to four was allocated to each goat; higher scores represent greater excitability. Immediately after transportation, excitability scores decreased significantly, especially those of control goats (p < 0.001). At 3 h post-transportation, the excitability scores of animals in the experimental group were not significantly (p>0.05) different from their pre-transportation normal values, whereas those of control goats were significantly lower (p < 0.01). The correlation i.e. the relationship between excitability score values and percent excitability (percentage of goat with particular excitability score) for different excitability score group 3 h post-transportation was positive and highly significant (p < 0.001), in both experimental and control goats. Our results indicate that road transportation induces considerable stress (depression) in goats as evidenced by a lower excitability score post-transportation. Moreover, the administration of AA pre-transportation facilitated the transition from a state of depression to excitation. In conclusion, AA administration to animals prior to transportation may ameliorate the depression often encountered after road transportation
Distinguishing Asthma Phenotypes Using Machine Learning Approaches.
Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as asthma endotypes. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies
Effects of increasing acylation and enzymatic hydrolysis on functional properties of bambara bean ( Vigna subterranea
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints
We study the semantic foundation of expressive probabilistic programming languages, that support higher-order functions, continuous distributions, and soft constraints (such as Anglican, Church, and Venture). We define a metalanguage (an idealised version of Anglican) for probabilistic computation with the above features, develop both operational and denotational semantics, and prove soundness, adequacy, and termination. They involve measure theory, stochastic labelled transition systems, and functor categories, but admit intuitive computational readings, one of which views sampled random variables as dynamically allocated read-only variables. We apply our semantics to validate nontrivial equations underlying the correctness of certain compiler optimisations and inference algorithms such as sequential Monte Carlo simulation. The language enables defining probability distributions on higher-order functions, and we study their properties
CD83 Modulates B Cell Function In Vitro: Increased IL-10 and Reduced Ig Secretion by CD83Tg B Cells
The murine transmembrane glycoprotein CD83 is an important regulator for both thymic T cell maturation and peripheral T cell responses. Recently, we reported that CD83 also has a function on B cells: Ubiquitous transgenic (Tg) expression of CD83 interfered with the immunoglobulin (Ig) response to infectious agents and to T cell dependent as well as T cell independent model antigen immunization. Here we compare the function of CD83Tg B cells that overexpress CD83 and CD83 mutant (CD83mu) B cells that display a drastically reduced CD83 expression. Correlating with CD83 expression, the basic as well as the lipopolysaccharide (LPS) induced expression of the activation markers CD86 and MHC-II are significantly increased in CD83Tg B cells and reciprocally decreased in CD83mu B cells. Wild-type B cells rapidly upregulate CD83 within three hours post BCR or TLR engagement by de novo protein synthesis. The forced premature overexpression of CD83 on the CD83Tg B cells results in reduced calcium signaling, reduced Ig secretion and a reciprocally increased IL-10 production upon in vitro activation. This altered phenotype is mediated by CD83 expressed on the B cells themselves, since it is observed in the absence of accessory cells. In line with this finding, purified CD83mu B cells displayed a reduced IL-10 production and slightly increased Ig secretion upon LPS stimulation in vitro. Taken together, our data strongly suggest that CD83 is expressed by B cells upon activation and contributes to the regulation of B cell function
Probabilistic machine learning and artificial intelligence.
How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.The author acknowledges an EPSRC grant EP/I036575/1, the DARPA PPAML programme, a Google Focused Research Award for the Automatic Statistician and support from Microsoft Research.This is the author accepted manuscript. The final version is available from NPG at http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html#abstract
A bispecific antibody approach for the potential prophylactic treatment of inherited bleeding disorders
Inherited bleeding disorders such as Glanzmann thrombasthenia (GT) lack prophylactic treatment options. As a result, serious bleeding episodes are treated acutely with blood product transfusions or frequent, repeated intravenous administration of recombinant activated coagulation factor VII (rFVIIa). Here we describe HMB-001, a bispecific antibody designed to bind and accumulate endogenous FVIIa and deliver it to sites of vascular injury by targeting it to the TREM (triggering receptor expressed on myeloid cells)-like transcript-1 (TLT-1) receptor that is selectively expressed on activated platelets. In healthy nonhuman primates, HMB-001 prolonged the half-life of endogenous FVIIa, resulting in its accumulation. Mouse bleeding studies confirmed antibody-mediated potentiation of FVIIa hemostatic activity by TLT-1 targeting. In ex vivo models of GT, HMB-001 localized FVIIa on activated platelets and potentiated fibrin-dependent platelet aggregation. Taken together, these results indicate that HMB-001 has the potential to offer subcutaneous prophylactic treatment to prevent bleeds in people with GT and other inherited bleeding disorders, with a low-frequency dosing regimen
Generative Embedding for Model-Based Classification of fMRI Data
Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups
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