Miracle-dz/UniFAR-FTUs huggingface dataset:
Biomarker Selection and Classification of “-Omics” Data Using a Two-Step Bayes Classification Framework

Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel two-step machine-learning framework is presented to address this need. First, a Naïve Bayes estimator is used to rank features from which the top-ranked will most likely contain the most informative features for prediction of the underlying biological classes. The top-ranked features are then used in a Hidden Naïve Bayes classifier to construct a classification prediction model from these filtered attributes. In order to obtain the minimum set of the most informative biomarkers, the bottom-ranked features are successively removed from the Naïve Bayes-filtered feature list one at a time, and the classification accuracy of the Hidden Naïve Bayes classifier is checked for each pruned feature set. The performance of the proposed two-step Bayes classification framework was tested on different types of -omics datasets including gene expression microarray, single nucleotide polymorphism microarray (SNParray), and surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) proteomic data. The proposed two-step Bayes classification framework was equal to and, in some cases, outperformed other classification methods in terms of prediction accuracy, minimum number of classification markers, and computational time.
Improved Entropic Threshold based on GLSC Histogram with Varying Similarity Measure

Image segmentation is a necessary task in computer vision and digital image processing applications, where foreground objects are to be separated from background. Many thresholding techniques are found in literature with their own limitations. The Gray level Spatial Correlation (GLSC) Histogram is used in entropic techniques to decide the threshold. In this paper we propose an improved GLSC Histogram, computed with varying similarity measure ( ) by considering local and global characteristics, because Yang Xiao et. al. used a constant 4 as the similarity measure by considering the image local properties only, which does not suits for all types of images and probability error is minimized by redistributing the missing probability amount in floating precisions. For low contrast images contrast enhancement is assumed. Experimental results demonstrate a quantitative improvement against existing techniques by calculating the parameter efficiency η based on the misclassification error and variations in various yielding towards ground truth threshold on two dimensional histogram of image. General Terms Image processing, entropy
Uncertainty Representation in Visualizations of Learning Analytics for Learners: Current Approaches and Opportunities

Adding uncertainty information to visualizations is becoming increasingly common across domains since its addition helps ensure that informed decisions are made. This work has shown the difficulty that is inherent to representing uncertainty. Moreover, the representation of uncertainty has yet to be thoroughly explored in educational domains even though visualizations are often used in educational reporting. We analyzed 50 uncertainty-augmented visualizations from various disciplines to map out how uncertainty has been represented. We then analyzed 106 visualizations from educational reporting systems where the learner can see the visualization; these visualizations provide learners with information about several factors including their knowledge, performance, and abilities. This analysis mapped the design space that has been employed to communicate a learner's abilities, knowledge, and interests. It also revealed several opportunities for the inclusion of uncertainty information within visualizations of educational data. We describe how uncertainty information can be added to visualizations of educational data and illustrate these opportunities by augmenting several of the types of visualizations that are found in existing learning analytics reports. The definition of this design space, based on a survey of the literature, will enable the systematic exploration of how different design decisions affect learner trust, understanding, and decision making.
A G-protein activation cascade from Arl13B to Arl3 and implications for ciliary targeting of lipidated proteins

Small G-proteins of the ADP-ribosylation-factor-like (Arl) subfamily have been shown to be crucial to ciliogenesis and cilia maintenance. Active Arl3 is involved in targeting and releasing lipidated cargo proteins from their carriers PDE6δ and UNC119a/b to the cilium. However, the guanine nucleotide exchange factor (GEF) which activates Arl3 is unknown. Here we show that the ciliary G-protein Arl13B mutated in Joubert syndrome is the GEF for Arl3, and its function is conserved in evolution. The GEF activity of Arl13B is mediated by the G-domain plus an additional C-terminal helix. The switch regions of Arl13B are involved in the interaction with Arl3. Overexpression of Arl13B in mammalian cell lines leads to an increased Arl3·GTP level, whereas Arl13B Joubert-Syndrome patient mutations impair GEF activity and thus Arl3 activation. We anticipate that through Arl13B’s exclusive ciliary localization, Arl3 activation is spatially restricted and thereby an Arl3·GTP compartment generated where ciliary cargo is specifically released. DOI: http://dx.doi. org/10.7554/eLife.11859. 001
Managerial Incentive Horizons and the Quality of Firms ’ Information Environments

We examine empirically the relation between managerial incentive horizons and the quality of firms‘ information environments. When a firm‘s managerial incentive horizon is short, it is more likely to report income-increasing discretionary accruals and to ―walk down‖ analysts‘ earnings forecasts; analyst forecast dispersion and absolute forecast errors are greater; and share turnover is greater. These results are consistent with the hypothesis that short incentive horizons induce managers to adopt strategies that reduce the quality of their firms‘ information environments. We also find evidence suggesting that investors understand these managerial incentives because they attach less credibility to the information in earnings surprises when managers face short incentive horizons.
Implicit detection of event interdependencies, and a PDP model of the process

We report on an experiment in which subjects were asked to predict the location of a stimulus based on observation of a series of five events. Unbeknownst to subjects, the location of the sixth event was determined by a double contingency between the second and fourth events in the sequence. This material is therefore highly complex, since the relevant events are embedded in a large number of irrelevant contexts. The results indicated that subjects improved their prediction performance over 10 sessions encompassing over 2400 trials of training, despite the fact that they remained completely unaware of the existence of the rule, and unable to verbalize their knowledge of the contingencies in the material. We propose a model of performance in this task, in the form of a PDP model of sequence processing. The model successfully accounts for performance and illustrates how knowledge about the temporal context may develop in a way that does not necessarily yield decomposable representations. Interestingly, the model also predicts that performance would be worse if subjects were required to predict successive events rather than simply observe them.
The Role of Bradykinin in Mediating Ischemic Brain Edema in Rats

Background and Purpose: We investigated the hypothesis that bradykinin generation may induce ischemic brain edema in spontaneously hypertensive rats. Methods: Cerebral ischemia lasting 3 hours was produced by bilateral common carotid artery occlusion in 67 rats. After the ischemic period, the rats were reperfused. Cerebral water content and energy metabolites (adenosine triphosphate, lactate, and pyruvate), as well as plasma and tissue bradykinin, were measured. Additionally, using the same experimental paradigm, bradykinin synthesis inhibitors (aprotinin [n =7] and soybean trypsin inhibitor [n =7]) were administered immediately after ischemia induction to determine the relation of bradykinin generation to the progression of ischemic brain edema. Results: Cerebral water content increased during the 3‐hour ischemic period, peaked at 30 minutes of reperfusion, and declined thereafter. Bradykinin levels in plasma and tissue rose markedly 30 minutes after reperfusion and fell thereafter. The progressive loss of adenosine triphosphate was mirrored by the rise in lactate. In the treated groups, aprotinin and soybean trypsin inhibitor administration significantly attenuated cerebral edema (p < 0.01 and p <0.05, respectively). The treated groups also showed less lactate accumulation and more adenosine triphosphate preservation than did the controls. Conclusions: These results demonstrate that bradykinin levels in plasma and tissue corresponded to cerebral edema progression and that bradykinin suppression decreased edema formation. These novel findings indicate that bradykinin activation augments the progression of ischemic brain edema. (Stroke 1993;24:571‐576)
Case‐Control study of dopamine transporter‐1, monoamine oxidase‐B, and catechol‐O‐methyl transferase polymorphisms in Parkinson's disease

We investigated the association of Parkinson's disease (PD) with dopamine transporter‐1 (DAT1), monoamine oxidase‐B (MAO‐B), and catechol‐O‐methyltransferase (COMT) gene polymorphisms. Overall, we observed no significant association of PD with the DAT1‐3′–variable numbers of tandem repeats, the MAO‐B‐(GT)n, and the COMT‐Val108Met gene polymorphisms in a sample of 319 unrelated PD cases and 196 control subjects. Analyses stratified by sex, age at examination, family history of PD, and ethnic origin also yielded negative findings, with three exceptions. We found statistically significant associations of PD with MAO‐B polymorphisms in older patients and with a COMT polymorphism in younger subjects and in women. These significant differences at the two‐tailed alpha level of 0.05 and restricted to subgroup analyses may have a biological basis or may be chance findings. © 2002 Movement Disorder Society
Multiphysic optimal design of an inverter fed PMSM and choice of switching frequency

The aim of this paper is to perform an optimal design of a surface mounted PMSM, dedicated to an embedded application, based on a multiphysic modelling and using a genetic algorithm. Finally, a quantitative criterion, regarding the torque ripple, is introduced to choose the switching frequency of the power electronic supply.
Older adults’ experiences of community integration following traumatic brain injury

Literature suggests community integration should be the primary rehabilitation goal for older people following a traumatic brain injury (TBI). However, little is known about older people’s lived experience of community integration following TBI. This mixed method study explores community integration from the perspective of four older adults following mild TBI, and compares findings with results from two community integration outcome measures: the Community Integration Questionnaire (CIQ) and the Community Integration Measure (CIM). Findings showed that TBI caused major disruption in life planning, with participants discussing a battle to maintain their pre-TBI independence and having to deal with the consequences of losing some independence following their injury. Setting up or maintaining good support networks were identified as vital to reintegration into the community following TBI, including with physiotherapists whose ability to engage and listen to their patients’ experiences can play an important role in their recovery. Physiotherapists must take care when using the CIQ or CIM to measure the experiences of older adults’ community integration following TBI as this study suggests that the measures do not wholly reflect people’s lived experiences. Findings from this study may be referred to by the providers of community-based services for older adults with TBI to develop strategies for supporting older adults’ community integration following TBI. Kent J, Wright-St Clair VA, Kersten P (2014) Older adults’ experiences of community integration following traumatic brain injury New Zealand Journal of Physiotherapy 42(2): 148-153.
Museum activities in dementia care: Using visual analog scales to measure subjective wellbeing

Introduction Previous research has shown that people with dementia and caregivers derive wellbeing-related benefits from viewing art in a group, and that facilitated museum object handling is effective in increasing subjective wellbeing for people with a range of health conditions. The present study quantitatively compared the impact of two museum-based activities and a social activity on the subjective wellbeing of people with dementia and their caregivers. Methods A quasi-experimental crossover design was used. People with early to middle stage dementia and caregivers (N = 66) participated in museum object handling, a refreshment break, and art viewing in small groups. Visual analog scales were used to rate subjective wellbeing pre and post each activity. Results Mixed-design analysis of variances indicated wellbeing significantly increased during the session, irrespective of the order in which the activities were presented. Wellbeing significantly increased from object handling and art viewing for those with dementia and caregivers across pooled orders, but did not in the social activity of a refreshment break. An end-of-intervention questionnaire indicated that experiences of the session were positive. Conclusion Results provide a rationale for considering museum activities as part of a broader psychosocial, relational approach to dementia care and support the use of easy to administer visual analog scales as a quantitative outcome measure. Further partnership working is also supported between museums and healthcare professionals in the development of nonclinical, community-based programs for this population.
Crawling Experience Predicts Avoidance of Real Cliffs and Water Cliffs: Insights from a New Paradigm

This study examined the influence of age, crawling-onset age, and crawling experience on infants’ adaptive behavior in situations that can lead to dangerous falls. Thirty-one infants with variable amounts of crawling experience were tested in a new paradigm that used a real cliff and a water cliff, with a harness system to guarantee safety. Consistent with previous findings obtained in studies with real and visual cliffs, infants with more crawling experience were more likely to avoid the real cliff. More importantly, greater crawling experience was associated with a greater likelihood of avoiding the water cliff. The new paradigm offers exciting opportunities to study the development of adaptive behavior in risky environments.
Selective Duty cycle Algorithm for beacon-enabled Low Rate Wireless Personal Area Networks

Today in various fields, Low-Rate Wireless Personal Area Network (LR-WPAN) with IEEE 802.15. 4 can be utilized for various applications such as Home Automation, Factory Automation, Search & Rescue, Environmental Control, Health Monitoring, etc. In some of the applications, there is no issue of energy consumption; however, there are so many in which energy consumption is a major issue. In fact, performance of the standard should be optimized according to the traffic condition and application. In this paper, performance of IEEE 802.15. 4 or Low-Rate WPAN is analyzed with the variation of Superframe Order (SO) or Duty Cycle. On the basis of performance analysis, a Selective Duty cycle Algorithm (SDA) is proposed which can be employed to optimize the performance of the standard according to the packet rate. Through simulations, it is shown that SDA is performing well as compared to other duty cycles.
Analysis of block matrix preconditioners for elliptic optimal control problems

In this paper, we describe and analyse several block matrix iterative algorithms for solving a saddle point linear system arising from the discretization of a linear‐quadratic elliptic control problem with Neumann boundary conditions. To ensure that the problem is well posed, a regularization term with a parameter α is included. The first algorithm reduces the saddle point system to a symmetric positive definite Schur complement system for the control variable and employs conjugate gradient (CG) acceleration, however, double iteration is required (except in special cases). A preconditioner yielding a rate of convergence independent of the mesh size h is described for Ω ⊂ R2 or R3, and a preconditioner independent of h and α when Ω ⊂ R2. Next, two algorithms avoiding double iteration are described using an augmented Lagrangian formulation. One of these algorithms solves the augmented saddle point system employing MINRES acceleration, while the other solves a symmetric positive definite reformulation of the augmented saddle point system employing CG acceleration. For both algorithms, a symmetric positive definite preconditioner is described yielding a rate of convergence independent of h. In addition to the above algorithms, two heuristic algorithms are described, one a projected CG algorithm, and the other an indefinite block matrix preconditioner employing GMRES acceleration. Rigorous convergence results, however, are not known for the heuristic algorithms. Copyright © 2007 John Wiley & Sons, Ltd.
Gender classification based on facial surface normals

In this paper, we perform gender classification based on 2.5D facial surface normals (facial needle-maps), and present two novel principal geodesic analysis (PGA) methods, weighted PGA and supervised PGA, to parameterize the facial needle-maps, and compare their performances with PGA for gender classification. Experimental results demonstrate the feasibility of gender classification based on facial needle-maps, and show that incorporating weights or pairwise relationships of labeled data into PGA improves the gender discriminating powers in the leading eigenvectors and the gender classification accuracy.
The task-dependent effect of tags and ratings on social media access

Recently, online social networks have emerged that allow people to share their multimedia files, retrieve interesting content, and discover like-minded people. These systems often provide the possibility to annotate the content with tags and ratings. Using a random walk through the social annotation graph, we have combined these annotations into a retrieval model that effectively balances the personal preferences and opinions of like-minded users into a single relevance ranking for either content, tags, or people. We use this model to identify the influence of different annotation methods and system design aspects on common ranking tasks in social content systems. Our results show that a combination of rating and tagging information can improve tasks like search and recommendation. The optimal influence of both sources on the ranking is highly dependent on the retrieval task and system design. Results on content search and tag suggestion indicate that the profile created by a user's annotations can be used effectively to adapt the ranking to personal preferences. The random walk reduces sparsity problems by smoothly integrating indirectly related concepts in the relevance ranking, which is especially valuable for cold-start users or individual tagging systems like YouTube and Flickr.
Simplified motion modeling for snake robots

We present a general method of estimating a snake robot's motion over flat ground using only knowledge of the robot's shape changes over time. Estimating world motion of snake robots is often difficult because of the complex way a robot's cyclic shape changes (gaits) interact with the surrounding environment. By using the virtual chassis to separate the robot's internal shape changes from its external motions through the world, we are able to construct a motion model based on the differential motion of the robot's modules between time steps. In this way, we effectively treat the snake robot like a wheeled robot where the bottom-most modules propel the robot in much the way the bottom of the wheels would propel the chassis of a car. Experimental results using a 16-DOF snake robot are presented to demonstrate the effectiveness of this method for a variety of gaits that have been designed to traverse flat ground.
Wideband Fully-Programmable Dual-Mode CMOS Analogue Front-End for Electrical Impedance Spectroscopy

This paper presents a multi-channel dual-mode CMOS analogue front-end (AFE) for electrochemical and bioimpedance analysis. Current-mode and voltage-mode readouts, integrated on the same chip, can provide an adaptable platform to correlate single-cell biosensor studies with large-scale tissue or organ analysis for real-time cancer detection, imaging and characterization. The chip, implemented in a 180-nm CMOS technology, combines two current-readout (CR) channels and four voltage-readout (VR) channels suitable for both bipolar and tetrapolar electrical impedance spectroscopy (EIS) analysis. Each VR channel occupies an area of 0.48 mm 2 , is capable of an operational bandwidth of 8 MHz and a linear gain in the range between −6 dB and 42 dB. The gain of the CR channel can be set to 10 kΩ, 50 kΩ or 100 kΩ and is capable of 80-dB dynamic range, with a very linear response for input currents between 10 nA and 100 μ A. Each CR channel occupies an area of 0.21 mm 2 . The chip consumes between 530 μ A and 690 μ A per channel and operates from a 1.8-V supply. The chip was used to measure the impedance of capacitive interdigitated electrodes in saline solution. Measurements show close matching with results obtained using a commercial impedance analyser. The chip will be part of a fully flexible and configurable fully-integrated dual-mode EIS system for impedance sensors and bioimpedance analysis.
A longitudinal study of Parkinsonian speech characteristics

A LONGITUDINAL STUDY OF PARKINSONIAN SPEECH CHARACTERISTICS By Juliane Leigh Brinkman It is well documented that changes in speech occur with age and can be augmented by the presence of neuropathology (Little et. al, 2009; Ramig & Ringel, 1983; Sadagopan & Smith, 2013). Parkinson’s disease (PD) is a progressive, neurodegenerative disorder that can have a profound effect on speech and its subsystems. Advances in technology and the use of acoustic applications have made it possible to objectively measure PD speech, and the current literature reports success in accurately differentiating PD speakers from healthy speakers. The present study was conducted with two goals in mind. First, to determine whether acoustic variables can be used to differentiate PD speakers from healthy speakers during times before the clinical diagnosis of PD. Second, to determine the usefulness of acoustic variables for monitoring the disease progression of PD. To achieve this, recordings spanning two to seven decades were obtained for 10 PD speakers and 9 control speakers. Recordings were edited for target speaker and assigned to a speaker group: PD Pre-Dx, PD Post-Dx, or Control. Values for voice and articulation measures were obtained from the recordings, and ANOVA and ANCOVA analyses were completed using MATLAB. A second ANOVA and ANCOVA analysis was performed on a combined PD speaker group and the Control group. Several acoustic measures demonstrated PD Pre-Dx group mean values significantly less than Control group mean values, making them a potential tool for early PD detection. Other acoustic measures demonstrated trends that may show potential in future research for use in PD progression monitoring. Limitations of study and suggestions for future research are discussed. Copyright by Juliane Leigh Brinkman 2014