Miracle-dz/UniFAR-FTUs huggingface dataset:
Ghrelin and des-acyl ghrelin inhibit cell death in cardiomyocytes and endothelial cells through ERK1/2 and PI 3-kinase/AKT

Ghrelin is an acyl-peptide gastric hormone acting on the pituitary and hypothalamus to stimulate growth hormone (GH) release, adiposity, and appetite. Ghrelin endocrine activities are entirely dependent on its acylation and are mediated by GH secretagogue (GHS) receptor (GHSR)-1a, a G protein–coupled receptor mostly expressed in the pituitary and hypothalamus, previously identified as the receptor for a group of synthetic molecules featuring GH secretagogue (GHS) activity. Des-acyl ghrelin, which is far more abundant than ghrelin, does not bind GHSR-1a, is devoid of any endocrine activity, and its function is currently unknown. Ghrelin, which is expressed in heart, albeit at a much lower level than in the stomach, also exerts a cardio protective effect through an unknown mechanism, independent of GH release. Here we show that both ghrelin and des-acyl ghrelin inhibit apoptosis of primary adult and H9c2 cardiomyocytes and endothelial cells in vitro through activation of extracellular signal–regulated kinase-1/2 and Akt serine kinases. In addition, ghrelin and des-acyl ghrelin recognize common high affinity binding sites on H9c2 cardiomyocytes, which do not express GHSR-1a. Finally, both MK-0677 and hexarelin, a nonpeptidyl and a peptidyl synthetic GHS, respectively, recognize the common ghrelin and des-acyl ghrelin binding sites, inhibit cell death, and activate MAPK and Akt. These findings provide the first evidence that, independent of its acylation, ghrelin gene product may act as a survival factor directly on the cardiovascular system through binding to a novel, yet to be identified receptor, which is distinct from GHSR-1a.
The Retrograde Frequency Response of Passive Dendritic Trees Constrains the Nonlinear Firing Behaviour of a Reduced Neuron Model

Our goal was to investigate how the propagation of alternating signals (i.e. AC), like action potentials, into the dendrites influenced nonlinear firing behaviour of motor neurons using a systematically reduced neuron model. A recently developed reduced modeling approach using only steady-current (i.e. DC) signaling was analytically expanded to retain features of the frequency-response analysis carried out in multicompartment anatomically reconstructed models. Bifurcation analysis of the extended model showed that the typically overlooked parameter of AC amplitude attenuation was positively correlated with the current threshold for the activation of a plateau potential in the dendrite. Within the multiparameter space map of the reduced model the region demonstrating “fully-bistable” firing was bounded by directional DC attenuation values that were negatively correlated to AC attenuation. Based on these results we conclude that analytically derived reduced models of dendritic trees should be fit on DC and AC signaling, as both are important biophysical parameters governing the nonlinear firing behaviour of motor neurons.
Secondary infiltration of the central nervous system in patients with diffuse large B-cell lymphoma

Objective To investigate the incidence and risk factors of infiltration of the central nervous system after the initial treatment of diffuse large B-cell lymphoma in patients treated at Santa Casa de Misericórdia de São Paulo. Methods A total of 133 patients treated for diffuse large B-cell lymphoma from January 2001 to April 2008 were retrospectively analyzed in respect to the incidence and risk factors of secondary central nervous system involvement of lymphoma. Intrathecal prophylaxis was not a standard procedure for patients considered to be at risk. This analysis includes patients whether they received rituximab as first-line treatment or not. Results Nine of 133 (6.7%) patients developed central nervous system disease after a mean observation time of 29 months. The median time to relapse or progression was 7.9 months after diagnosis and all but one patient died despite the treatment administered. Twenty-six (19.5%) patients of this cohort received rituximab as first-line treatment and nine (7.1%) received intrathecal chemoprophylaxis. Of the nine patients that relapsed, seven (77.7%) had parenchymal central nervous system involvement; seven (77.7%) had stage III or IV disease; one (11.1%) had bone marrow involvement; two (22.2%) had received intrathecal chemoprophylaxis; and 3 (33.3%) had taken rituximab. In a multivariate analysis, the risk factors for this infiltration were being male, previous use of intrathecal chemotherapy and patients that were refractory to initial treatment. Conclusion Central nervous system infiltration in this cohort is similar to that of previous reports in the literature. As this was a small cohort with a rare event, only three risk factors were important for this infiltration
Explicit Loss Minimization in Quantification Applications (Preliminary Draft)

In recent years there has been a growing interest in quantification, a variant of classification in which the final goal is not accurately classifying each unlabelled document but accurately estimating the prevalence (or “relative frequency”) of each class c in the unlabelled set. Quantification has several applications in information retrieval, data mining, machine learning, and natural language processing, and is a dominant concern in fields such as market research, epidemiology, and the social sciences. This paper describes recent research in addressing quantification via explicit loss minimization, discussing works that have adopted this approach and some open questions that they raise.
The density of the image of the exponential function and spacious locally compact groups

Let G be a connected locally compact group. Following Hofmann and Mukherjea [3] we call G spacious if it admits a nonempty open subset U such that U ∩ U = Ø for every positive integer n ∈ N . We call G weakly exponential if the union of its one-parameter subgroups is dense in G . When G = SL(n,R) and U ⊆ G is the set of matrices with all entries strictly negative, then obviously U ∩ U = Ø for all n . SL(n,R) is thus an elementary example of a spacious connected locally compact group. On the other hand, when G = R , U ⊆ R is open, and x ∈ U , there will be a positive integer n such that y = nx/(n + 1) ∈ U . So nx = (n + 1)y ∈ U ∩ U , and therefore R is not spacious. Using this result one immediately concludes that a weakly exponential locally compact group is not spacious. Hofmann and Mukherjea conjectured that the converse is also true, i.e., G is spacious iff it fails to be weakly exponential [3, Conjecture 2.13]. The purpose of the present paper is to give a proof of this conjecture. We begin by quoting two lemmas from [3].
Evolutionary Histories of Transposable Elements in the Genome of the Largest Living Marsupial Carnivore, the Tasmanian Devil

The largest living carnivorous marsupial, the Tasmanian devil (Sarcophilus harrisii), is the sole survivor of a lineage originating about 12 Ma. We set out to investigate the spectrum of transposable elements found in the Tasmanian devil genome, the first high-coverage genome of an Australian marsupial. Marsupial genomes have been shown to have the highest amount of transposable elements among vertebrates. We analyzed the horizontally transmitted DNA transposons OC1 and hAT-1_MEu in the Tasmanian devil genome. OC1 is present in all carnivorous marsupials, while having a very limited distribution among the remaining Australian marsupial orders. In contrast, hAT-1_MEu is present in all Australian marsupial orders, and has so far only been identified in a few placental mammals. We screened 158 introns for phylogenetically informative retrotransposons in the order Dasyuromorphia, and found that the youngest SINE (Short INterspersed Element), WSINE1, is no longer active in the subfamily Dasyuridae. The lack of detectable WSINE1 activity in this group may be due to a retrotransposon inactivation event approximately 30 Ma. We found that the Tasmanian devil genome contains a relatively low number of continuous full-length LINE-1 (Long INterspersed Element 1, L1) retrotransposons compared with the opossum genome. Furthermore, all L1 elements in the Tasmanian devil appeared to be nonfunctional. Hidden Markov Model approaches suggested that other potential sources of functional reverse transcriptase are absent from the genome. We discuss the issues associated with assembling long, highly similar L1 copies from short read Illumina data and describe how assembly artifacts can potentially lead to erroneous conclusions.
A Cluster Based Delay Tolerant MAC Protocol for Underwater Wireless Sensor Network

the propagation speed of an acoustic signal is much slower than the speed of a terrestrial radio signal due to the physical characteristics of an underwater acoustic channel. This large delay can impact the throughput of the channel. There is also a very high delay variance which presents unique challenges to the designs of efficient protocols. The inconsistent delay renders many traditional communication protocols insufficient since they rely on accurate estimations of the round trip time (RTT) between two communication nodes. We presented a unique approach to develop a Cluster based delay-tolerant protocol (CBDTP) to address these problems by predicating a value for a sensor node if its data were not received at the sink node instead of having the sensor node retransmit its data. The CBDTP can reduce data traffic over the networks and uses the network resource more efficiently. In this paper we improved our CBDTP protocol by introduced a self-adoptive algorithm at MAC layer protocol to better address the inconsistent delay problems. It improves the performance of CBDTP protocol in a dynamic deployment environment.
Multilingual Sentiment Classification on Large Textual Data

At present, Big Data have been created lot of buzz in the technology world. Sentiment Analysis or opinion mining is one of the important applications of 'Big Data', where sentiment analysis is used for recognising voice or response of crowd for products, services. This concept describes the items in some detail and evaluate them as good/bad, preferred/not preferred. The results are very important for a company because customer feedback can yield extremely valuable insights about a company's customer. However, in a commercial website of product reviews, many customers can access to describe the items in some detail and evaluate them with different languages. Therefore, many companies will gather customer feedback in multiple languages. Definitely, feedback in multiple languages raises problems in analysing the material. As this, this paper proposes a solution to classify a product review dataset into two classes: positive and negative sentiments. The proposed methodology is called "Multilingual Sentiment Classification (MSC)". It consists of two main processing steps: lingual separation and sentiment classification. The first main processing step is to classify online product reviews into language classes. The second processing step is to classify each textual dataset into two classes: positive and negative sentiments. It is noted, we concentrate and experiment on bilingual texts (Thai and English).
1 Photoplethysmography ( PPG ) system

2 Basic implementation . 4 2.1 The optoelectronic parts . 4 2.2 The electronic circuit . 4 2.3 Arduino code: Analog “oscilloscope” mode . 8 2.4 Arduino code: Digital timing interval mode . 8 2.5 Postprocessing of interval data . 9
Big data academic and learning analytics

Purpose Although big data analytics (BDA) have great benefits for higher education institutions (HEIs), due to lack of sufficient evidence on how BDA investment can pay off, it is tough for HEIs practitioners to realize value from such adoption. The purpose of this paper is to propose a big data academic and learning analytics enabled business value model to explain BDA potential benefits and business value which can be obtained by developing such analytics capabilities in HEIs. Design/methodology/approach The study examined 47 case descriptions from 26 HEIs to investigate the causal association between the BDA current and potential benefits and business value creation path for big data academic and learning analytics success in HEIs. Findings The pressure of compliance with all legal and regulatory requirements and competition had pushed HEIs hard to adopt BDA tools. However, the study found out that application of risk and security and predictive analytics to higher education fields is still in its infancy. Using this theoretical model, the results provide new insights to higher education administrators on ways to create BDA capabilities for HEIs transformation and suggest an empirical foundation that can lead to more thorough analysis of BDA implementation. Originality/value A distinctive theoretical contribution of this study is its conceptualization of understanding business value from BDA in the typical setting of higher education. The study provides HEIs with an all-inclusive understanding of BDA and gives insights on how it helps to transform HEIs. The new perspectives associated with the big data academic and learning analytics enabled business value model will contribute to future research in this area.
QoE: A Micro and Macro Perspective

Quality of experience (QoE) takes a user-focused view of the Internet experience, derived from lower-layer metrics like quality of service, reliability and usability, measuring "the dierence between what a customer expected, and what they actually received. " (FY/c) QoE can be usefully divided into a micro and macro aspect, covering the individualized and often shortterm experience of an individual Internet user, and the aggregate QoE across a service territory, country or region. Just as macroeconomics builds on the aggregate behavior of households and rms, macro-QoE can usefully build on micro-QoE. We believe that QoE-related research must be both descriptive and actionable, that is, allow users to determine the source of QoE problems, identify the responsible part of the network and, where possible, take corrective action. Often, for both residential and enterprise users, the rst question to be answered is: \Is it just me? " At the macro level, the question turns to the diagnosis of wholesale infrastructure disruption, policy and economic failures or successes. We present two examples that address both the \micro" and \macro" aspects. Our DYSWIS (Do You See What I See) prototype allows end users to detect and isolate QoE impairment, while our YouSlow [1] project uses application behavior to diagnose local problems and to establish the correlation between low-level network metrics and user behavior.
In their humble opinion: How expressions of humility affect superiors’ assessments of leadership potential in the US Army

ABSTRACT While leader humility has been linked to a number of positive outcomes, existing research speculates that expressions of humility may not be viewed positively in organizations with a highly directive or masculine culture – such as the military. Research has also suggested that men and women may be perceived differently when behaving humbly. A combination of four studies revealed that military superiors do take a positive view of humble behaviors in their subordinates, but it is not clear that humble leaders are viewed as having more potential than those who exhibit other positive leadership behaviors. Further, it appears that gender and humility interact to affect perceptions of leadership potential in the military, with humble men receiving more benefit from acting humbly than their female counterparts.
Applying Multi-dimensional Scaling Analysis for Finding Similarity Knowledge in OLAP Reports

On Line Analysis Processing (OLAP) is a common solution that modern enterprises use to generate, monitor, share, and administrate their analysis reports. When daily, weekly, and/or monthly reports are generated or published by the OLAP operators, the report readers can only rely on their smart eyes to find out hidden rules, similar reports, or trend inside the potentially huge amount of reports. Data mining is a well-developed field for finding hidden rules inside the data itself. However, there is few techniques focus on finding hidden rules, similarity, or trend using OLAP reports as the unit of analysis. In this paper, we explore how to use Multi-Dimensional Scaling (MDS) on OLAP reports in order to automatically and effectively find the similarity knowledge of OLAP reports. We also address the appropriate presentation of this similarity knowledge to OLAP users.
Adaptive predistorter based oninverse saleh function and saleh model

This paper puts forward a adaptive inverse saleh function predistorter based on direct learning architecture according to the feature of saleh model to compensate for the nonlinearity of Power Amplifiers (PA). In this paper, we focus directly on a particular PA model and building a corresponding predistorter, After approximating the nonlinear static part by the four parameters function, Memoryless inverse saleh function model are adopted according to the saleh model. its parameters are estimated by using the intelligent algorithm. The identification technique of the predistortion is easy . It is proved to be correct through Matlab simulation.
Evolutionary Optimisation for Obstacle Detection and Avoidance in Mobile Robotics

This paper presents an artificial evolution-based method for stereo image analysis and its application to real-time obstacle detection and avoidance for a mobile robot. It uses the Parisian approach, which consists here in splitting the representation of the robot’s environment into a large number of simple primitives, the “flies”, which are evolved according to a biologically inspired scheme. Results obtained on real scene with different fitness functions are presented and discussed, and an exploitation for obstacle avoidance in mobile robotics is proposed.
Records and Record Types in Semantic Theory

This paper explores possibilities for formulating linguistic semantics in terms of records and record types of the kind used in recent developments of Martin-Lof type theory. We will suggest that this gives us tools to develop a single theory which includes aspects of Montague semantics, Discourse Representation Theory, situation semantics and Head-Driven Phrase Structure Grammar. We will also argue that formulating these theories in terms of record types may provide us not only with a unified approach but also with certain improvements over the individual theories.
A high coding-gain reduced-complexity serial concatenated error-control coding solution for wireless sensor networks

Error-Control Coding (ECC) now plays an important role in wireless transceivers because it helps to increase link reliability and lower required transmit power. One popular problem of ECC implementation is the trade-off between high coding-gain and decoder's complexity. In this paper, we propose a serial concatenated ECC solution which is a combination of truncated-iteration layered-decoding LDPC block code with low-constraint Viterbi decoder at low code rate. Simulation results show that very high coding-gain and reduced-complexity are achieved. This paper also gives a supposition about applying low code rate ECC for less-active wireless nodes in Wireless Sensor Networks (WSN) to reduce transmit power and expand sensor network topology.
The Social Psychology of False Confessions: Compliance, Internalization, and Confabulation

An experiment demonstrated that false incriminating evidence can lead people to accept guilt for a crime they did not commit Subjects in a fast- or slow-paced reaction time task were accused of damaging a computer by pressing the wrong key All were truly innocent and initially denied the charge A confederate then said she saw the subject hit the key or did not see the subject hit the key Compared with subjects in the slow-pacelno-witness group, those in the fast-pace/witness group were more likely to sign a confession, internalize guilt for the event, and confabulate details in memory consistent with that belief Both legal and conceptual implications are discussed
Protein network topology metric conservation: from yeast to human

One of the most surprising results of the human genome project and the current sequencing efforts of other organisms is the remarkable similarity of the number of genes between species: the number of genes in C. elegans is of the same order of magnitude as that of Homo sapiens. It has been conjectured that these surprising results can be explained by a layer of protein-protein interactions, responsible for the expected difference in functional richness between worms and humans. For example, as the number of proteins n increases, the number of potential interactions increases proportional to n. The quantity of interactions does appear to vary between organisms of different complexity. But, are there some constant parameters in these networks of interactions? As with the (Fibonacci) golden ratio ~1.618 (which appears in nature everywhere from flower pedals to shell patterns), perhaps there is a constant of nature with regard to biological network topology statistics. While it is not expected that such an absolutely invariant metric emerges, evolutionary pressures may have led to certain constraints on network parameters. By comparing yeast and human interaction data, we find that while the number of proteins and interactions are very different, topologic properties such as the average minimum distance between nodes, the power-law degree exponent, and the power-law proportionality constant appear to be relatively conserved.
Fundamental results on the performance of ATM multiplexers with applications to video teleconferencing

The main contributions of this paper are two-fold. First, we prove fundamental, similarly behaving lower and upper bounds, and give an approximation based on the bounds, which is effective for analyzing ATM multiplexers, even when the traffic has many, possibly heterogeneous, sources and their models are of high dimension. Second, we apply our analytic approximation to statistical models of video teleconference traffic, obtain the multiplexing system's capacity as determined by the number of admissible sources for given cell loss probability, buffer size and trunk bandwidth, and, finally, compare with results from simulations, which are driven by actual data from coders. The results are surprisingly close. Our bounds are based on Large Deviations theory. Our approximation has two easily calculated parameters, one is from Chernoff's theorem and the other is the system's dominant eigenvalue. A broad range of systems are analyzed and the time for analysis in each case is a fraction of a second.
An algorithm for finding maximal whitespace rectangles at arbitrary orientations for document layout analysis

The analysis of the background structure (whitespace) of page images has become an important technique for physical document layout analysis. Globally maximal whites-pace rectangles have been previously demonstrated to constitute a concise representation of the major layout features of documents. However, previous methods for computing maximal whitespace rectangles were limited to axis-aligned rectangles. This paper presents an algorithm that finds globally maximal whitespace rectangles on page images at arbitrary orientations. The new algorithm eliminates the need for page rotation correction prior to background analysis and can be applied to considerably more complex page layouts than previously possible. The algorithm is resolution independent and takes as input a list of foreground shapes (e.g., character or word bounding boxes or polygons) and a set of parameter ranges; it outputs the N largest non-overlapping maximal whitespace rectangles whose parameters (location, width, height, orientation) fall within the required parameter ranges. Examples of applications of the method to severely skewed documents, as well as the UW3 database, are presented.