Miracle-dz/UniFAR-FTUs huggingface dataset:
Nerve Growth Factor Signaling through p75 Induces Apoptosis in Schwann Cells via a Bcl-2-Independent Pathway

Apoptosis is involved in the regulation of Schwann cell numbers during normal development and after axonal damage, but the molecular regulation of Schwann cell death remains unknown. We have used stably transfected rat Schwann cell lines to study the potential roles of nerve growth factor (NGF), the antiapoptotic protein Bcl-2 and the cytokine response modifier A (CrmA) in modulating Schwann cell death in vitro. Bcl-2 inhibited Schwann cell apoptosis induced by survival factor withdrawal, whereas CrmA did not. In contrast, Bcl-2-transfected Schwann cells were susceptible to apoptosis in response to exogenous NGF, whereas CrmA-expressing cell lines were resistant. Demonstration of high levels of the low-affinity neurotrophin receptor p75 but not the high-affinity TrkA receptor on the Bcl-2-transfected cell lines suggested that the NGF-induced killing was mediated by p75. This was confirmed by resistance of Schwann cells isolated from p75 knockout mice to the NGF-induced cell death. Nerve growth factor also promoted the death of wild-type mouse and rat Schwann cells in the absence of survival factor withdrawal. Endogenous Bcl-2 mRNA was expressed by wild-type Schwann cells in all conditions that promoted survival but was downregulated to undetectable levels after survival factor withdrawal. In conclusion, our results demonstrate the existence of two separate pathways that expedite apoptosis in Schwann cells: a Bcl-2-blockable pathway initiated on loss of trophic support, and a Bcl-2-independent, CrmA-blockable pathway mediated via the p75 receptor.
The Mondrian Process for Machine Learning

This report is concerned with the Mondrian process and its applications in machine learning. The Mondrian process is a guillotine-partition-valued stochastic process that possesses an elegant self-consistency property. The first part of the report uses simple concepts from applied probability to define the Mondrian process and explore its properties. The Mondrian process has been used as the main building block of a clever online random forest classification algorithm that turns out to be equivalent to its batch counterpart. We outline a slight adaptation of this algorithm to regression, as the remainder of the report uses regression as a case study of how Mondrian processes can be utilized in machine learning. In particular, the Mondrian process will be used to construct a fast approximation to the computationally expensive kernel ridge regression problem with a Laplace kernel. The complexity of random guillotine partitions generated by a Mondrian process and hence the complexity of the resulting regression models is controlled by a lifetime hyperparameter. It turns out that these models can be efficiently trained and evaluated for all lifetimes in a given range at once, without needing to retrain them from scratch for each lifetime value. This leads to an efficient procedure for determining the right model complexity for a dataset at hand. The limitation of having a single lifetime hyperparameter will motivate the final Mondrian grid model, in which each input dimension is endowed with its own lifetime parameter. In this model we preserve the property that its hyperparameters can be tweaked without needing to retrain the modified model from scratch.
Type of Speech Material Affects Acceptable Noise Level Test Outcome

The acceptable noise level (ANL) test, in which individuals indicate what level of noise they are willing to put up with while following speech, has been used to guide hearing aid fitting decisions and has been found to relate to prospective hearing aid use. Unlike objective measures of speech perception ability, ANL outcome is not related to individual hearing loss or age, but rather reflects an individual’s inherent acceptance of competing noise while listening to speech. As such, the measure may predict aspects of hearing aid success. Crucially, however, recent studies have questioned its repeatability (test–retest reliability). The first question for this study was whether the inconsistent results regarding the repeatability of the ANL test may be due to differences in speech material types used in previous studies. Second, it is unclear whether meaningfulness and semantic coherence of the speech modify ANL outcome. To investigate these questions, we compared ANLs obtained with three types of materials: the International Speech Test Signal (ISTS), which is non-meaningful and semantically non-coherent by definition, passages consisting of concatenated meaningful standard audiology sentences, and longer fragments taken from conversational speech. We included conversational speech as this type of speech material is most representative of everyday listening. Additionally, we investigated whether ANL outcomes, obtained with these three different speech materials, were associated with self-reported limitations due to hearing problems and listening effort in everyday life, as assessed by a questionnaire. ANL data were collected for 57 relatively good-hearing adult participants with an age range representative for hearing aid users. Results showed that meaningfulness, but not semantic coherence of the speech material affected ANL. Less noise was accepted for the non-meaningful ISTS signal than for the meaningful speech materials. ANL repeatability was comparable across the speech materials. Furthermore, ANL was found to be associated with the outcome of a hearing-related questionnaire. This suggests that ANL may predict activity limitations for listening to speech-in-noise in everyday situations. In conclusion, more natural speech materials can be used in a clinical setting as their repeatability is not reduced compared to more standard materials.
The Maize Floury1 Gene Encodes a Novel Endoplasmic Reticulum Protein Involved in Zein Protein Body Formation[W]

The maize (Zea mays) floury1 (fl1) mutant was first reported almost 100 years ago, but its molecular identity has remained unknown. We report the cloning of Fl1, which encodes a novel zein protein body membrane protein with three predicted transmembrane domains and a C-terminal plant-specific domain of unknown function (DUF593). In wild-type endosperm, the FL1 protein accumulates at a high level during the period of zein synthesis and protein body development and declines to a low level at kernel maturity. Immunogold labeling showed that FL1 resides in the endoplasmic reticulum surrounding the protein body. Zein protein bodies in fl1 mutants are of normal size, shape, and abundance. However, mutant protein bodies ectopically accumulate 22-kD α-zeins in the γ-zein–rich periphery and center of the core, rather than their normal discrete location in a ring at outer edge of the core. The 19-kD α-zein is uniformly distributed throughout the core in wild-type protein bodies, and this distribution is unaffected in fl1 mutants. Pairwise yeast two-hybrid experiments showed that FL1 DUF593 interacts with the 22-kD α-zein. Results of these studies suggest that FL1 participates in protein body formation by facilitating the localization of 22-kD α-zein and that this is essential for the formation of vitreous endosperm.
“Because I’m Smooth”: Material Intra-actions and Text Productions among Young Latino Picture Book Makers

As theorization of multimodal text processes and productions continues to outpace classroom practices, research that contributes understandings of how composers are living out multimodal processes is needed. In response, we turn to thinking with theory (Jackson & Mazzei, 2012) as both a methodological and an analytic approach to understand how multimodal composing processes and products come to be. We provide strategic sketches (Leander & Boldt, 2013) focused on third graders “Efrain” and “Trinidad”, not aiming to display the data in a traditional sense, but instead to ask of ourselves, the data, and theory: What material intra-actions emerge among two young picture book makers? What social, cultural, and material worlds are performed in their final picture book productions? Thinking with theory and data was an effort to experience some of the moment-to-moment nuances of young children’s multimodal processes, to appreciate the lived social, cultural, and material realities animated in their picture books, and to develop sensitivities to the possibilities of the material turn in post-humanist studies for literacy research. The analytic questions produced point to the saliency of diverse literature as aesthetic inspirations for multimodal texts, and of improvisations with varied art tools and media as openings for multimodal processes. This paper advances previous related scholarship through strategic sketches that invite readers to experience the complexity and the cultural significance of the multimodal processes and products that emerge when classroom expectations of a proficient writer include the ability to improvise and become with diverse materials and meanings, not just to command “standardized written English. ”
Grid based navigation for autonomous robots-an algorithm based on the integration of vision and sonar data

In this article, visual data obtained by a binocular active vision system is integrated, together with ultrasonic range measurements, in the development of an obstacle detection and avoidance system based on a connectionist grid. This grid based framework allows the integration of two different sensing modalities (vision and sonar) in a way that incoming sensorial data can be mutually enhanced and validated. Each grid node maps a configuration in a discrete subset of the robot's configuration space. Detected obstacles result in sets of restricted configurations. Using the grid data, a potential field is interactively computed for each mapped configuration. This computation is done in real-time, during the robot's motion, so the potential field changes as new data is integrated into the grid. The authors also propose the implementation of some innovative sensing and control strategies, using the integrated sensorial information.
The Programmable Logic-in-Memory (PLiM) computer

Realization of logic and storage operations in memristive circuits have opened up a promising research direction of in-memory computing. Elementary digital circuits, e.g., Boolean arithmetic circuits, can be economically realized within memristive circuits with a limited performance overhead as compared to the standard computation paradigms. This paper takes a major step along this direction by proposing a fully-programmable in-memory computing system. In particular, we address, for the first time, the question of controlling the in-memory computation, by proposing a lightweight unit managing the operations performed on a memristive array. Assembly-level programming abstraction is achieved by a natively-implemented majority and complement operator. This platform enables diverse sets of applications to be ported with little effort. As a case study, we present a standardized symmetric-key cipher for lightweight security applications. The detailed system design flow and simulation results with accurate device models are reported validating the approach.
Design and simulation of a novel classification framework for separating sentiment from assorted game related tweets

Twitter has been exceedingly a ubiquitous phenomenon with a thriving appearance in almost every country in the world. In this modern epoch, game is one of the most popular genres to tweet. Furthermore, sentiment analysis is used to analyze the patent opinion of a person by mining text units i.e. tweets. In this paper, strategy based categories of virtual games were identified; top most countries according to users were detected; foremost usable language of the users was ascertained; and advertisement from assorted game related tweets were filtered to retrieve sentiment tweets exclusively through simulation. In light of these findings, a new gestalt of sentiment analysis of virtual game related tweets is accomplished.
Effect of the Nutrient Microenvironment of an Ovarian Follicle on Gap Junction Activity in Cumulus-Oocyte Complexes in 
Post-Partum Cows

For mammalian oocyte maturation it is important for the oocyte to communicate with the surrounding somatic cells in a bidirectional manner. In part, this is achieved through gap junction connections between cumulus cells and the oocyte. These gap junctions permit the transfer of essential metabolites, nucleotides, amino acids and ions from the cumulus cells to the oocyte in order for growth and development to occur. It is hypothesized that one reason for post-partum infertility in dairy cows is poor oocyte quality due to an inappropriate nutrient microenvironment in the developing antral follicle. In turn, this might compromise the nutrient and/or energy delivery by cumulus cells to the oocyte and thus affect oocyte quality. The objectives of this study were to: (1) -develop a reliable in vitro measure of gap junction activity for bovine cumulus–oocyte-complexes (COC) using a fluorescent (calcein) dye technique, and (2)- assess the effects of different amino acid compositions, cholesterol and fatty acid concentrations on gap junction activity in COC. The development and validation of the bovine COC gap junction assay was undertaken using a standard commercial medium (M199). Thereafter, media were prepared representing the amino acid concentrations in follicular fluid of both non-lactating and lactating dairy cows as measured in a NZ pastoral situation. These media formed the basis for subsequent measures of COC gap junction activity with differing concentrations of cholesterol and the essential fatty acids- oleic acid, stearic acid and palmitic acid. The major findings from this study were that the different amino acid concentrations typical of that in the follicular fluid of the non-lactating and lactating dairy cow and M199 do not significantly affect the gap junction activity in COC. Given that the amino acid compositions in lactating and non-lactating cows were approximately 50% of those in M199; this indicates that the amino acid concentrations within the ovarian follicle are not a critical factor affecting oocyte quality. However, high concentrations of cholesterol or of the essential fatty acids significantly compromised gap junction activity in bovine COC. High concentrations of the fatty acids were identified in a number of lactating dairy cows during the post-partum interval. In times of negative energy balance, which is common in pasture fed NZ dairy cows during the early post-partum interval and lactation, a significant mobilisation of fatty acids occurs. In turn, this is known to result in an increase in lipid concentrations within the ovarian follicle. The results from this study support the hypothesis that the nutrient environment of the ovarian follicle during early lactation is detrimental to the quality and developmental competence of the oocyte and thus may account for post-partum infertility in some animals.
The effect of superstitious thinking on psychosocial stress responses and perceived task performance

Previous research on superstitious belief, a subset of paranormal belief (Irwin,), has suggested that people tend to invoke luck-related superstitions in stressful situations as an attempt to gain an illusion of control over outcomes. Based on this, the current study examined whether luck-related superstition, in the form of a “lucky” pen, could influence the psychological response to a psychosocial stressor. Participants (N = 114), aged between 17 and 59 years (M = 22.98, SD = 4.57) from James Cook University Singapore, were randomly assigned to one of four conditions: (a) no-stress with no lucky pen; (b) no-stress with a lucky pen; (c) stress with no lucky pen or; (d) stress with a lucky pen. The results revealed that participants provided with a lucky pen experienced lower state anxiety when exposed to the stressor. Further, participants provided with a lucky pen perceived their performance to be better than those without it. However, superstitious belief did not significantly change following exposure to stress. Taken together, the present findings add some support to the suggestion that belief in transferable luck may facilitate coping with a stressor. However, further research is needed to fully understand the mechanisms behind the potential benefits of superstitious belief.
Document image dewarping using robust estimation of curled text lines

Digital cameras have become almost ubiquitous and their use for fast and casual capturing of natural images is unchallenged. For making images of documents, however, they have not caught up to flatbed scanners yet, mainly because camera images tend to suffer from distortion due to the perspective and are therefore limited in their further use for archival or OCR. For images of non-planar paper surfaces like books, page curl causes additional distortion, which poses an even greater problem due to its nonlinearity. This paper presents a new algorithm for removing both perspective and page curl distortion. It requires only a single camera image as input and relies on a priori layout information instead of additional hardware. Therefore, it is much more user friendly than most previous approaches, and allows for flexible ad hoc document capture. Results are presented showing that the algorithm produces visually pleasing output and increases OCR accuracy, thus having the potential to become a general purpose preprocessing tool for camera based document capture.
The spectral action for Moyal planes

Extending a result of Vassilevich, we obtain the asymptotic expansion for the trace of a spatially regularized heat operator LΘ(f)e−tΔΘ, where ΔΘ is a generalized Laplacian defined with Moyal products and LΘ(f) is Moyal left multiplication. The Moyal planes corresponding to any skewsymmetric matrix Θ being spectral triples, the spectral action introduced in noncommutative geometry by Chamseddine and Connes is computed. This result generalizes the Connes–Lott action previously computed by Gayral for symplectic Θ.
Latent force models for earth observation time series prediction

We introduce latent force models for Earth observation time series analysis. The model uses Gaussian processes and differential equations to combine data driven modelling with a physical model of the system. The LFM presented here performs multi-output structured regression, adapts to the signal characteristics, it can cope with missing data in the time series, and provides explicit latent functions that allow system analysis and evaluation. We successfully illustrate the performance in challenging scenarios of crop monitoring from space, providing time-resolved time series predictions.
Pedestrian detection for automotive night vision using thermal camera

The sudden appearance of pedestrian is a major worry for the driver especially during night time since it may lead to an accident due to poor human eyesight at night. To overcome the problem, a pedestrian detection for automotive night vision is introduced. The analysis is done based on the distance, size and clothing. There are four condition has been taken into consideration that are with average weight of 40 to 50 kg with wearing hijab and without hijab, and to the average weight of 60 to 70 kg wearing hijab and without hijab. The first and second condition have 0% error for emitting the first and second warning while the third condition has 9.3% error during first warning and 0% error for second warning. The fourth condition has 23.5% error for the first warning and 0% error for the second warning.
Meta-Analysis of Melanin-Concentrating Hormone Signaling-Deficient Mice on Behavioral and Metabolic Phenotypes

The demand for meta-analyses in basic biomedical research has been increasing because the phenotyping of genetically modified mice does not always produce consistent results. Melanin-concentrating hormone (MCH) has been reported to be involved in a variety of behaviors that include feeding, body-weight regulation, anxiety, sleep, and reward behavior. However, the reported behavioral and metabolic characteristics of MCH signaling-deficient mice, such as MCH-deficient mice and MCH receptor 1 (MCHR1)-deficient mice, are not consistent with each other. In the present study, we performed a meta-analysis of the published data related to MCH-deficient and MCHR1-deficient mice to obtain robust conclusions about the role of MCH signaling. Overall, the meta-analysis revealed that the deletion of MCH signaling enhanced wakefulness, locomotor activity, aggression, and male sexual behavior and that MCH signaling deficiency suppressed non-REM sleep, anxiety, responses to novelty, startle responses, and conditioned place preferences. In contrast to the acute orexigenic effect of MCH, MCH signaling deficiency significantly increased food intake. Overall, the meta-analysis also revealed that the deletion of MCH signaling suppressed the body weight, fat mass, and plasma leptin, while MCH signaling deficiency increased the body temperature, oxygen consumption, heart rate, and mean arterial pressure. The lean phenotype of the MCH signaling-deficient mice was also confirmed in separate meta-analyses that were specific to sex and background strain (i.e., C57BL/6 and 129Sv). MCH signaling deficiency caused a weak anxiolytic effect as assessed with the elevated plus maze and the open field test but also caused a weak anxiogenic effect as assessed with the emergence test. MCH signaling-deficient mice also exhibited increased plasma corticosterone under non-stressed conditions, which suggests enhanced activity of the hypothalamic-pituitary-adrenal axis. To the best of our knowledge, the present work is the first study to systematically compare the effects of MCH signaling on behavioral and metabolic phenotypes.
Variable Selection in Classification Trees Based on Imprecise Probabilities

Classification trees are a popular statistical tool with multiple applications. Recent advancements of traditional classification trees, such as the approach of classification trees based on imprecise probabilities by Abellán and Moral (2004), effectively address their tendency to overfitting. However, another flaw inherent in traditional classification trees is not eliminated by the imprecise probability approach: Due to a systematic finite sample-bias in the estimator of the entropy criterion employed in variable selection, categorical predictor variables with low information content are preferred if they have a high number of categories. Mechanisms involved in variable selection in classification trees based on imprecise probabilities are outlined theoretically as well as by means of simulation studies. Corrected estimators are proposed, which prove to be capable of reducing estimation bias as a source of variable selection bias.
A simple graphical method for the analysis of the selective population transfer in many-level quantum systems

Controlled manipulation of the quantum systems is becoming an important technological tool. Rabi oscillations are one of the simplest, yet quite useful mechanisms for achieving such manipulation. However, the validity of simple Rabi theory is limited and its upgrades are usually both technically involved as well as limited to simple two-level systems. The aim of the paper is to demonstrate a simple graphical method for the analysis of the applicability of simple Rabi solutions to the controlled population transfer in complex multi-level quantum systems. Preprint submitted to Elsevier Science 11th March 2008
Low power image processing: analog versus digital comparison

In this paper, a programmable analog retina is presented and compared with state of the art MPU for embedded imaging applications. The comparison is based on the energy requirement to implement the same image processing task. Results showed that analog processing requires lower power consumption than digital processing. In addition, the execution time is shorter since the size of the retina is reasonably large.