Miracle-dz/UniFAR-FTUs huggingface dataset:
Economic Holobiont: Influence of Parasites, Microbiota and Chemosignals on Economic Behavior

The article is a perspective on utilization of microorganisms and chemosignals in studying human economic behavior. Research in biological roots of economic development has already confirmed that parasitic pressure influenced the creation and development of cultural norms and institutions. However, other effects of microorganisms on human groups and individual decision-making and behavior are heavily understudied. The perspective discusses how parasitic infections, sexually transmitted organisms and microbiota (i.e., “human holobiont”) could causally influence risk-seeking behavior, impulsivity, social dominance, empathy, political views and gender differences. As a case study, the parasite Toxoplasma gondii and its influence on economic preferences, personal characteristics and human appearance are examined. I also briefly review how chemosignals influence decision-making, particularly in the social preferences domain. I mention some predictions that arise from the paradigm of economic holobiont for the economic science. The conclusion summarizes limitations of the discussed findings and the stated speculations.
A salience-based approach for the modeling of landscape descriptions

While conventional GIS maps have long been a privileged way for the integration and diffusion of geographical information, novel forms of representation and description of urban and natural environments are nowadays emerging. In particular, verbal and textual descriptions of landscapes are progressively considered as alternative modeling resources for GIS. The research presented in this paper introduces a salience-based approach whose objective is to identify the noticeable entities of a natural landscape description. The model is based on a structural analysis of a given description, where salient entities are identified at the linguistic and structural levels. Salient entities are also spatially qualified according to their relative location with respect to a given observer.
A framework for integrating usability evaluations methods: The Mawhiba web portal case study

Different usability evaluation methods (UEMs) are often combined to assess the compliance of interactive systems with usability standards and guidelines. In practice, combinations of UEMs have been applied in a rather ad hoc manner resulting in inadequate analyses of rich usability data sets. While the potential offered by recent advances in technologies such as eye tracking for usability is recognized, and the challenges of evaluating complex web systems such as portals which involve different interaction styles are highlighted, the issue of effectively integrating multiple UEMs to make sense of observations has emerged. This paper presents a framework developed for effectively integrating UEMs in usability evaluations by matching the methods' capabilities and limitations with a classification of usability problems. The framework is applied to evaluating the usability of the Mawhiba web portal with target user populations which demonstrates how better coverage of usability issues can be achieved.
Optimized Backhaul Compression for Uplink Cloud Radio Access Network

This paper studies the uplink of a cloud radio access network (C-RAN) where the cell sites are connected to a cloud-computing-based central processor (CP) with noiseless backhaul links with finite capacities. We employ a simple compress-and-forward scheme in which the base stations (BSs) quantize the received signals and send the quantized signals to the CP using either distributed Wyner-Ziv coding or single-user compression. The CP first decodes the quantization codewords and then decodes the user messages as if the remote users and the cloud center form a virtual multiple-access channel (VMAC). This paper formulates the problem of optimizing the quantization noise levels for weighted sum rate maximization under a sum backhaul capacity constraint. We propose an alternating convex optimization approach to find a local optimum solution to the problem efficiently, and more importantly, to establish that setting the quantization noise levels to be proportional to the background noise levels is near optimal for sum-rate maximization when the signal-to-quantization-noise-ratio (SQNR) is high. In addition, with Wyner-Ziv coding, the approximate quantization noise level is shown to achieve the sum-capacity of the uplink C-RAN model to within a constant gap. With single-user compression, a similar constant-gap result is obtained under a diagonal dominant channel condition. These results lead to an efficient algorithm for allocating the backhaul capacities in C-RAN. The performance of the proposed scheme is evaluated for practical multicell and heterogeneous networks. It is shown that multicell processing with optimized quantization noise levels across the BSs can significantly improve the performance of wireless cellular networks.
Safety and ef fi cacy analysis by histology of weekly nab-paclitaxel in combination with carboplatin as fi rst-line therapy in patients with advanced non-small-cell lung cancer

M. A. Socinski1, I. Okamoto2, J. K. Hon3, V. Hirsh4, S. R. Dakhil5, R. D. Page6, J. Orsini7, N. Yamamoto8, H. Zhang9 & M. F. Renschler9 Division of Hematology/Oncology, University of Pittsburgh Cancer Institute, Pittsburgh, USA; Department of Medical Oncology, Kinki University School of Medicine, Osaka-Sayama, Japan; Stem Cell Transplant Program, Clearview Cancer Institute, Huntsville, USA; Department of Oncology, McGill University, Montreal, Canada; Cancer Center of Kansas, Wichita; The Center for Cancer and Blood Disorders, Fort Worth; Essex Oncology of New Jersey, Belleville, USA; Shizuoka Cancer Center, Shizuoka, Japan; Medical Affairs, Celgene, Summit, USA
NMF-based source separation utilizing prior knowledge on encoding vector

Non-negative matrix factorization (NMF) is an unsupervised technique to represents a nonnegative data matrix with a product of nonnegative basis and encoding matrices. The encoding matrix for the training phase contains information on the pattern of how each basis vector is utilized. The histogram for each row of this matrix corresponding to a specific basis turned out to be sparse, while the level of sparsity varied significantly in each basis. In this paper, the distribution of each component of an encoding vector is modeled as an independent exponential or gamma distribution, and a new objective function with the log-likelihood of the current encoding vector is proposed. Experimental results on audio source separation demonstrate that the utilization of the prior knowledge on the encoding matrix based on sparse statistical models can enhance the source separation performance.
Asymptotic Compatibility between LOCC Conversion and Recovery

Recently, entanglement concentration was explicitly shown to be irreversible. However, it is still not clear what kind of states can be reversibly converted in the asymptotic setting by LOCC when neither the initial nor the target state is maximally entangled. We derive the necessary and sufficient condition for the reversibility of LOCC conversions between two bipartite pure entangled states in the asymptotic setting. In addition, we show that conversion can be achieved perfectly with only local unitary operation under such condition except for special cases. Interestingly, our result implies that an error-free reversible conversion is asymptotically possible even between states whose copies can never be locally unitarily equivalent with any finite numbers of copies, although such a conversion is impossible in the finite setting. In fact, we show such an example. Moreover, we establish how to overcome the irreversibility of LOCC conversion in two ways. As for the first method, we evaluate how many copies of the initial state is to be lost to overcome the irreversibility of LOCC conversion. The second method is to add a supplementary state appropriately, which also works for LU conversion unlike the first method. Especially, for the qubit system, any non-maximally pure entangled state can be a universal resource for the asymptotic reversibility when copies of the state is sufficiently many. More interestingly, our analysis implies that far-from-maximally entangled states can be better than nearly maximally entangled states as this type of resource. This fact brings new insight to the resource theory of state conversion.
Using Regulatory Instructions for Information Extraction

In this paper, we describe a novel approach for learning to extract content from the text segments of regulatory filings for the purpose of competitive analysis and regulatory audit. Existing strategies that rely upon an explicit schema or a training set of representative documents are less suited for managing thousands of idiosyncratic submissions by independent filers. We introduce a technique that learns from regulatory instructions. Knowledge about document structure is drawn from the policy documents to initialize a set of extraction patterns. Patterns are relaxed to account for single insertion, deletion, and substitution errors within individual filings. Preliminary results are reported on various sets of filings submitted to the SEC in 2004 and 05.
Embedded system for acquisition and enhancement of audio signals

Enhancing speech from audio signals recorded using several microphones is of interest to many audio applications such as human-computer interfaces and teleconference systems. With an increasing availability of low-cost micro-electro-mechanical systems (MEMS) and powerful processing units, embedded systems are capable of recording and processing several audio signals in realtime. In this paper, an overview of the entire signal processing chain from audio signal acquisition with an array of MEMS microphones to the signal enhancement using directional filtering is provided. The presented results indicate that good noise and interference suppression can be achieved in realtime using an embedded system equipped with low-cost microphones.
Random social networks, unemployment and wage inequality

Empirical studies of labor markets show that social contacts are an important source of job-related information. At the same time, wage differences among workers may be explained only in part by differences in individual background characteristics. Such findings motivate our model in which differences in "social connectedness" among otherwise identical workers result in wage inequality and differences in unemployment rates. The model of this paper allows for heterogeneity in the number of connections among workers within the Pissarides model of labor market turnover. The paper derives conditions for which a unique labor market equilibrium exists. It also shows that such heterogeneity has important consequences. Workers with more connections both receive a higher wage and face a lower rate of unemployment at equilibrium. For the specific cases in which connections follow Poisson and negative binomial distributions our numerical results show that variability in connections can produce substantial variation in labor market outcomes. One lesson from the computational analysis is that (changes in) the social structure sometimes affect labor market outcomes in nontrivial ways. For example, when society becomes more connected, the average unemployment level falls but the unemployment rate of workers with few connections rises.
A Comparison of Two Motion Sensors for the Assessment of Free-Living Physical Activity of Adolescents

This study assessed and compared the daily step counts recorded by two different motion sensors in order to estimate the free-living physical activity of 135 adolescent girls. Each girl concurrently wore a Yamax pedometer and an ActiGraph accelerometer (criterion measure) every day for seven consecutive days. The convergent validity of the pedometer can be considered intermediate when used to measure the step counts in free-living physical activity; but should be considered with caution when used to classify participants’ step counts into corresponding physical activity categories because of a likelihood of ‘erroneous’ classification in comparison with the accelerometer.
Sex, antisocial behaviour, and mating: mate selection and early childbearing

Thus far we have reported evidence that as they enter adulthood females, relative to males, commit fewer crimes, are less likely to be involved in serious or violent crime, and their antisocial disorders are less likely to persist beyond adolescence into adulthood (see chapter 13). The exception to this pattern is that young women are as likely as young men to be involved in violence within their intimate relationships (see chapter 5). This suggests the need to examine in greater detail the types of men with whom antisocial females form intimate relationships. In addition, it has been remarked that although women who have conduct problems are seldom serious and violent offenders, they are the mothers who produce the next generation of serious and violent males (Henry et al. , 1993; Tremblay, 1991). An examination of assortative mating may help us to understand how this might occur. By age 21, many of the Dunedin Study members were involved in committed intimate relationships with partners and, through de facto unions and marriages, some Study members already moved from their families of origin into new families of ‘destination’. In this chapter, we examine who pairs off with whom and test whether antisocial females, as well as antisocial males, are likely to form unions with other antisocial persons. We also examine the consequences of assortative mating for the persistence of antisocial behaviour into adulthood, as well as the implications of this assortative mating for reproduction.
No Evidence That Short-Term Cognitive or Physical Training Programs or Lifestyles Are Related to Changes in White Matter Integrity in Older Adults at Risk of Dementia

Cognitive and physical activities can benefit cognition. However, knowledge about the neurobiological mechanisms underlying these activity-induced cognitive benefits is still limited, especially with regard to the role of white matter integrity (WMI), which is affected in cognitive aging and Alzheimer’s disease. To address this knowledge gap, we investigated the immediate and long-term effects of cognitive or physical training on WMI, as well as the association between cognitive and physical lifestyles and changes in WMI over a 6-month period. Additionally, we explored whether changes in WMI underlie activity-related cognitive changes, and estimated the potential of both trainings to improve WMI by correlating training outcomes with WMI. In an observational and interventional pretest, posttest, 3-month follow-up design, we assigned 47 community-dwelling older adults at risk of dementia to 50 sessions of auditory processing and working memory training (n = 13), 50 sessions of cardiovascular, strength, coordination, balance and flexibility exercises (n = 14), or a control group (n = 20). We measured lifestyles trough self-reports, cognitive training skills through training performance, functional physical fitness through the Senior Fitness Test, and global cognition through a cognitive test battery. WMI was assessed via a composite score of diffusion tensor imaging-based fractional anisotropy (FA) of three regions of interest shown to be affected in aging and Alzheimer’s disease: the genu of corpus callosum, the fornix, and the hippocampal cingulum. Effects for training interventions on FA outcomes, as well as associations between lifestyles and changes in FA outcomes were not significant. Additional analyses did show associations between cognitive lifestyle and global cognitive changes at the posttest and the 3-month follow-up (β ≥ 0.40, p ≤ 0.02) and accounting for changes in WMI did not affect these relationships. The targeted training outcomes were related to FA scores at baseline (cognitive training skills and FA composite score, rs = 0.68, p = 0.05; functional physical fitness and fornix FA, r = 0.35, p = 0.03). Overall, we found no evidence of a link between short-term physical or cognitive activities and WMI changes, despite activity-related cognitive changes in older adults at risk of dementia. However, we found positive associations between the two targeted training outcomes and WMI, hinting at a potential of long-term activities to affect WMI.
KI-Zeitschrift-Auszug als Leseprobe

Online platforms are virtual places where people interact for different purposes, thus forming online communities. So far several mining methods have been developed to detect these community structures as densely connected subgraphs in networks. The evolution of these networks is typically analyzed by observing changes in the interaction behavior of single members only. However, we observe that communities are highly dynamic social networks, whose members change over time. People leave gradually while new ones join but the community is perceived as still active. We therefore consider methods for modeling and discovering evolving communities and present our approach CoDyM for the analysis of community dynamics. Furthermore, we report on our first series of experiments on real data from an online community.
Automatic generation of programming exercises for learning programming language

Not only knowledge such as the grammar but also practical programming experiences are important to the learning of the programming language. In this paper, we classify programming exercises into the following two types: 1) syntax practices for understanding the grammar and 2) semantics practices for understanding the flow of a program, and then we develop a web-base application that automatically generates programming exercises by using templates and PHP language.We also confirm the effectiveness of the system by our preliminary experiments.
Assessing the Amount of Quadruplex Structures Present within G2-Tract Synthetic Random-Sequence DNA Libraries

The process of in vitro selection has led to the discovery of many aptamers with potential to be developed into inhibitors and biosensors, but problems in isolating aptamers against certain targets with desired affinity and specificity still remain. One possible improvement is to use libraries enhanced for motifs repeatedly isolated in aptamer molecules. One such frequently observed motif is the two-tiered guanine quadruplex. In this study we investigated whether DNA libraries could be designed to contain a large fraction of molecules capable of folding into two-tiered guanine quadruplexes. Using comprehensive circular dichroism analysis, we found that DNA libraries could be designed to contain a large proportion of sequences that adopt guanine quadruplex structures. Analysis of individual sequences from a small library revealed a mixture of quadruplexes of different topologies providing the diversity desired for an in vitro selection. We also found that primer-binding sites are detrimental to quadruplex formation and devised a method for post-selection amplification of primer-less quadruplex libraries. With the development of guanine quadruplex enriched DNA libraries, it should be possible to improve the chances of isolating aptamers that utilize a quadruplex scaffold and enhance the success of in vitro selection experiments.
Experimental and Clinical Treatment of Chagas Disease: A Review

Abstract. Chagas disease (CD) is caused by the protozoan parasite Trypanosoma cruzi that infects a broad range of triatomines and mammalian species, including man. It afflicts 8 million people in Latin America, and its incidence is increasing in nonendemic countries owing to rising international immigration and nonvectorial transmission routes such as blood donation. Since the 1960s, the only drugs available for the clinical treatment of this infection have been benznidazole (BZ) and nifurtimox (NFX). Treatment with these trypanocidal drugs is recommended in both the acute and chronic phases of CD. These drugs have low cure rates mainly during the chronic phase, in addition both drugs present side effects that may result in the interruption of the treatment. Thus, more efficient and better-tolerated new drugs or pharmaceutical formulations containing BZ or NFX are urgently needed. Here, we review the drugs currently used for CD chemotherapy, ongoing clinical assays, and most-promising new experimental drugs. In addition, the mechanism of action of the commercially available drugs, NFX and BZ, the biodistribution of the latter, and the potential for novel formulations of BZ based on nanotechnology are discussed. Taken together, the literature emphasizes the urgent need for new therapies for acute and chronic CD.
Statistical aspects of storage systems modelling in energy networks

Future energy networks face the significant challenge of integrating inherently intermittent and variable renewable power generation while maintaining a high degree of security of supply. The increasing penetration of renewables in electric power generation aims to lessen dependence on fossil fuels and reduce carbon dioxide emissions. In this paper we formulate and study a stochastic model for large scale fast response storage and slow-to-moderate ramping generators with high wind penetration. We define a strategy for operating the storage facility and investigate the system-wide long-term effects of fast response energy storage in reducing the amount of conventional power used. In particular, we study the trade-offs between various system performance quantities, including wind spill and the loss of load probability.
Sphingomyelin in High-Density Lipoproteins: Structural Role and Biological Function

High-density lipoprotein (HDL) levels are an inverse risk factor for cardiovascular diseases, and sphingomyelin (SM) is the second most abundant phospholipid component and the major sphingolipid in HDL. Considering the marked presence of SM, the present review has focused on the current knowledge about this phospholipid by addressing its variable distribution among HDL lipoparticles, how they acquire this phospholipid, and the important role that SM plays in regulating their fluidity and cholesterol efflux from different cells. In addition, plasma enzymes involved in HDL metabolism such as lecithin–cholesterol acyltransferase or phospholipid transfer protein are inhibited by HDL SM content. Likewise, HDL SM levels are influenced by dietary maneuvers (source of protein or fat), drugs (statins or diuretics) and modified in diseases such as diabetes, renal failure or Niemann–Pick disease. Furthermore, increased levels of HDL SM have been shown to be an inverse risk factor for coronary heart disease. The complexity of SM species, described using new lipidomic methodologies, and their distribution in different HDL particles under many experimental conditions are promising avenues for further research in the future.