Discriminative Tandem Features for HMM-based EEG Classification

Abstract—We investigate the use of discriminative feature extractors in tandem configuration with generative EEG classification system. Existing studies on dynamic EEG classification typically use hidden Markov models (HMMs) which lack discriminative capability. In this paper, a linear and a non-linear classifier are discriminatively trained to produce complementary input features to the conventional HMM system. Two sets of tandem features are derived from linear discriminant analysis (LDA) projection output and multilayer perceptron (MLP) class-posterior probability, before appended to the standard autoregressive (AR) features. Evaluation on a two-class motor-imagery classification task shows that both the proposed tandem features yield consistent gains over the AR baseline, resulting in significant relative improvement of 6.2% and 11.2 % for the LDA and MLP features respectively. We also explore portability of these features across different subjects. Index Terms- Artificial neural network-hidden Markov models, EEG classification, brain-computer-interface (BCI)

Developing a logical model of yeast metabolism

With the completion of the sequencing of genomes of increasing numbers of organisms, the focus of biology is moving to determining the role of these genes (functional genomics). To this end it is useful to view the cell as a biochemical machine: it consumes simple molecules to manufacture more complex ones by chaining together biochemical reactions into long sequences referred to as em metabolic pathways. Such metabolic pathways are not linear but often interesect to form complex networks. Genes play a fundamental role in these networks by providing the information to synthesise the enzymes that catalyse biochemical reactions. Although developing a complete model of metabolism is of fundamental importance to biology and medicine, the size and complexity of the network has proven beyond the capacity of human reasoning. This paper presents the first results of the Robot Scientist research programme that aims to automatically discover the function of genes in the metabolism of the yeast em Saccharomyces cerevisiae. Results include: (1) the first logical model of metabolism;(2) a method to predict phenotype by deductive inference; and (3) a method to infer reactions and gene function by aductive inference. We describe the em in vivo experimental set-up which will allow these em in silico predictions to be automatically tested by a laboratory robot

Group studio cycling; an effective intervention to improve cardio-metabolic health in overweight physically inactive individuals

Introduction: Supervised, laboratory based studies of high intensity interval training (HIIT) is effective at improving health markers in groups at risk of cardiovascular and metabolic disease. Studio cycling, incorporating aerobic and high intensity exercise, may offer a platform for the implementation of HIIT within the wider community. Methods: Eight, overweight, physically inactive (<1.5 hr·wk-1) but otherwise healthy volunteers completed eight weeks of supervised studio cycling lasting 20-50 minutes 3 times per week. Participants underwent assessment for maximal oxygen uptake (VO2max) body composition, blood lipids, glucose tolerance and insulin resistance before and after the intervention. Results: Adherence to training was >95%. Mean and peak intensity were equivalent to 83% and 97% of HRmax·VO2max increased from 27.1 ± 4.7 mL·kg·min-1 to 30.3 ± 4.3 mL·kg·min-1 (p < 0.0001). Body fat percentage was reduced by 13.6% from 31.8 ± 2.4% to 27.5 ± 4.5% (p < 0.05). Total cholesterol (4.8 ± 1.1 mmol·L-1 to 4.2 ± 1.2 mmol·L-1) and low-density lipoprotein cholesterol (2.6 ± 0.9 mmol·L-1 to 2.0 ± 1.2 mmol·L-1) were reduced (both p < 0.05). There were no significant differences to glucose tolerance or insulin resistance. Discussion: Group exercise is effective at improving the cardio-metabolic health in previously physically inactive overweight individuals. Coupled with the high adherence rate, studio cycling offers an effective intervention improving cardiovascular health in physically inactive cohorts. Conclusions: Studio cycling can be implemented as a highly effective high intensity interval training intervention for improving health in overweight, inactive individuals and may promote improved exercise adherence

Combining inductive logic programming, active learning and robotics to discover the function of genes

The paper is addressed to AI workers with an interest in biomolecular genetics and also to biomolecular geneticists interested in what AI tools may do for them. The authors are engaged in a collaborative enterprise aimed at partially automating some aspects of scientific work. These aspects include the processes of forming hypotheses, devising trials to discriminate between these competing hypotheses, physically performing these trials and then using the results of these trials to converge upon an accurate hypothesis. As a potential component of the reasoning carried out by an "artificial scientist" this paper describes ASE-Progol, an Active Learning system which uses Inductive Logic Programming to construct hypothesised first-order theories and uses a CART-like algorithm to select trials for eliminating ILP derived hypotheses. In simulated yeast growth tests ASE-Progol was used to rediscover how genes participate in the aromatic amino acid pathway of Saccharomyces cerevisiae. The cost of the chemicals consumed in converging upon a hypothesis with an accuracy of around 88% was reduced by five orders of magnitude when trials were selected by ASE-Progol rather than being sampled at random. While the naive strategy of always choosing the cheapest trial from the set of candidate trials led to lower cumulative costs than ASE-Progol, both the naive strategy and the random strategy took significantly longer to converge upon a final hypothesis than ASE-Progol. For example to reach an accuracy of 80%, ASE-Progol required 4 days while random sampling required 6 days and the naive strategy required 10 days

New results on group classification of nonlinear diffusion-convection equations

Using a new method and additional (conditional and partial) equivalence transformations, we performed group classification in a class of variable coefficient $(1+1)$-dimensional nonlinear diffusion-convection equations of the general form $f(x)u_t=(D(u)u_x)_x+K(u)u_x.$ We obtain new interesting cases of such equations with the density $f$ localized in space, which have large invariance algebra. Exact solutions of these equations are constructed. We also consider the problem of investigation of the possible local trasformations for an arbitrary pair of equations from the class under consideration, i.e. of describing all the possible partial equivalence transformations in this class.Comment: LaTeX2e, 19 page

Investigation of Non-Stable Processes in Close Binary Ry Scuti

We present results of reanalysis of old electrophotometric data of early type close binary system RY Scuti obtained at the Abastumani Astrophysical Observatory, Georgia, during 1972-1990 years and at the Maidanak Observatory, Uzbekistan, during 1979-1991 years. It is revealed non-stable processes in RY Sct from period to period, from month to month and from year to year. This variation consists from the hundredths up to the tenths of a magnitude. Furthermore, periodical changes in the system's light are displayed near the first maximum on timescales of a few years. That is of great interest with regard to some similar variations seen in luminous blue variable (LBV) stars. This also could be closely related to the question of why RY Sct ejected its nebula.Comment: 11 pages, 6 figures, 2 table

Global parameter search reveals design principles of the mammalian circadian clock

Background: Virtually all living organisms have evolved a circadian (~24 hour) clock that controls physiological and behavioural processes with exquisite precision throughout the day/night cycle. The suprachiasmatic nucleus (SCN), which generates these ~24 h rhythms in mammals, consists of several thousand neurons. Each neuron contains a gene-regulatory network generating molecular oscillations, and the individual neuron oscillations are synchronised by intercellular coupling, presumably via neurotransmitters. Although this basic mechanism is currently accepted and has been recapitulated in mathematical models, several fundamental questions about the design principles of the SCN remain little understood. For example, a remarkable property of the SCN is that the phase of the SCN rhythm resets rapidly after a 'jet lag' type experiment, i.e. when the light/ dark (LD) cycle is abruptly advanced or delayed by several hours. Results: Here, we describe an extensive parameter optimization of a previously constructed simplified model of the SCN in order to further understand its design principles. By examining the top 50 solutions from the parameter optimization, we show that the neurotransmitters' role in generating the molecular circadian rhythms is extremely important. In addition, we show that when a neurotransmitter drives the rhythm of a system of coupled damped oscillators, it exhibits very robust synchronization and is much more easily entrained to light/dark cycles. We were also able to recreate in our simulations the fast rhythm resetting seen after a 'jet lag' type experiment. Conclusion: Our work shows that a careful exploration of parameter space for even an extremely simplified model of the mammalian clock can reveal unexpected behaviours and non-trivial predictions. Our results suggest that the neurotransmitter feedback loop plays a crucial role in the robustness and phase resetting properties of the mammalian clock, even at the single neuron level

T-Branes and Monodromy

We introduce T-branes, or "triangular branes," which are novel non-abelian bound states of branes characterized by the condition that on some loci, their matrix of normal deformations, or Higgs field, is upper triangular. These configurations refine the notion of monodromic branes which have recently played a key role in F-theory phenomenology. We show how localized matter living on complex codimension one subspaces emerge, and explain how to compute their Yukawa couplings, which are localized in complex codimension two. Not only do T-branes clarify what is meant by brane monodromy, they also open up a vast array of new possibilities both for phenomenological constructions and for purely theoretical applications. We show that for a general T-brane, the eigenvalues of the Higgs field can fail to capture the spectrum of localized modes. In particular, this provides a method for evading some constraints on F-theory GUTs which have assumed that the spectral equation for the Higgs field completely determines a local model.Comment: 110 pages, 5 figure

Similarity solutions for unsteady shear-stress-driven flow of Newtonian and power-law fluids : slender rivulets and dry patches

Unsteady flow of a thin film of a Newtonian fluid or a non-Newtonian power-law fluid with power-law index N driven by a constant shear stress applied at the free surface, on a plane inclined at an angle α to the horizontal, is considered. Unsteady similarity solutions representing flow of slender rivulets and flow around slender dry patches are obtained. Specifically, solutions are obtained for converging sessile rivulets (0 < α < π/2) and converging dry patches in a pendent film (π/2 < α < π), as well as for diverging pendent rivulets and diverging dry patches in a sessile film. These solutions predict that at any time t, the rivulet and dry patch widen or narrow according to |x|3/2, and the film thickens or thins according to |x|, where x denotes distance down the plane, and that at any station x, the rivulet and dry patch widen or narrow like |t|−1, and the film thickens or thins like |t|−1, independent of N

HUWE1 cooperates with RAS activation to control leukemia cell proliferation and human hematopoietic stem cells differentiation fate

Acute myeloid leukemia (AML) is a poor prognosis hematopoietic malignance characterized by abnormal proliferation and differentiation of hematopoietic stem cells (HSCs). Although advances in treatment have greatly improved survival rates in young patients, in the elderly population, ~70% of patients present poor prognosis. A pan-cancer analysis on the TCGA cohort showed that AML has the second higher HUWE1 expression in tumor samples among all cancer types. In addition, pathway enrichment analysis pointed to RAS signaling cascade as one of the most important pathways associated to HUWE1 expression in this particular AML cohort. In silico analysis for biological processes enrichment also revealed that HUWE1 expression is correlated with 13 genes involved in myeloid differentiation. Therefore, to understand the role of HUWE1 in human hematopoietic stem and progenitor cells (HSPC) we constitutively expressed KRASG12V oncogene concomitantly to HUWE1 knockdown in stromal co-cultures. The results showed that, in the context of KRASG12V, HUWE1 significantly reduces cell cumulative growth and changes myeloid differentiation profile of HSPCs. Overall, these observations suggest that HUWE1 might contribute to leukemic cell proliferation and impact myeloid differentiation of human HSCs, thus providing new venues for RAS-driven leukemia targeted therapy approach