Miracle-dz/UniFAR-FTUs huggingface dataset:
Second-homeowners' intention to move: an integrated ordered logit model with latent variable

Second-homes represent a very peculiar reality for the tourism market, particularly in Switzerland in which this segment has a long tradition and represents an important share in the accommodation sector. Very few studies took into account the intention of second-home owners to permanently move to the place (typically representing the destination of leisure trips) where they own their dwelling. The purpose of the research is to investigate how the intention to relocate is determined by a set of factors; among these, we include socio-economic covariates characterizing the second-home owners and the extent and habits of usage of the second-home. We apply an ordered logit model in which the dependent variable is the self-assessed probability to relocate; we extend the classical framework of ordered logit models including a latent variable, described by a series of indicators such as the attraction of the owners towards the region of relocation, the desire to spend time at destination and ownersâ€™ relationship with neighbors. The rationale behind the use of unobservable factors is the idea that the decision to permanently relocate in the second-home destination is not only affected by observable variables but also by different attitudinal and psychological aspects which are not directly observable. Data used to pursue the research objectives refer to a structured survey submitted to individuals owning a second-home in the Lake Maggiore region in Canton Ticino (Switzerland) and the subsample of Swiss respondents was analyzed. About one fifth of the eligible sample declared a very high probability to relocate in a permanent way in the vacation home while more or less 40% declared a very low likelihood. Results indicate that the attitudinal and psychological traits expressed by the second-home owners represent a fundamental source of explanation of their intention to permanently relocate.
PKC-Dependent Phosphorylation of eNOS at T495 Regulates eNOS Coupling and Endothelial Barrier Function in Response to G+ -Toxins

Gram positive (G+) infections make up ∼50% of all acute lung injury cases which are characterized by extensive permeability edema secondary to disruption of endothelial cell (EC) barrier integrity. A primary cause of increased permeability are cholesterol-dependent cytolysins (CDCs) of G+-bacteria, such as pneumolysin (PLY) and listeriolysin-O (LLO) which create plasma membrane pores, promoting Ca2+-influx and activation of PKCα. In human lung microvascular endothelial cells (HLMVEC), pretreatment with the nitric oxide synthase (NOS) inhibitor, ETU reduced the ability of LLO to increase microvascular cell permeability suggesting an endothelial nitric oxide synthase (eNOS)-dependent mechanism. LLO stimulated superoxide production from HLMVEC and this was prevented by silencing PKCα or NOS inhibition suggesting a link between these pathways. Both LLO and PLY stimulated eNOS T495 phosphorylation in a PKC-dependent manner. Expression of a phosphomimetic T495D eNOS (human isoform) resulted in increased superoxide and diminished nitric oxide (NO) production. Transduction of HLMVEC with an active form of PKCα resulted in the robust phosphorylation of T495 and increased peroxynitrite production, indicative of eNOS uncoupling. To determine the mechanisms underlying eNOS uncoupling, HLMVEC were stimulated with LLO and the amount of hsp90 and caveolin-1 bound to eNOS determined. LLO stimulated the dissociation of hsp90, and in particular, caveolin-1 from eNOS. Both hsp90 and caveolin-1 have been shown to influence eNOS uncoupling and a peptide mimicking the scaffolding domain of caveolin-1 blocked the ability of PKCα to stimulate eNOS-derived superoxide. Collectively, these results suggest that the G+ pore-forming toxins promote increased EC permeability via activation of PKCα, phosphorylation of eNOS-T495, loss of hsp90 and caveolin-1 binding which collectively promote eNOS uncoupling and the production of barrier disruptive superoxide.
The widespread occurrence of coupled carbonate dissolution/reprecipitation in surface sediments on the Bahamas Bank

Using two complimentary approaches (pore water advection/diffusion/ reaction modeling and stable isotope mass balance calculations) we show that carbonate dissolution/reprecipitation occurs on early diagenetic time scales across a broad range of sediments on the Great Bahamas Bank. The input of oxygen into the sediments, which strongly controls sediment carbonate dissolution, has two major sources—belowground input by seagrasses (that is, seagrass O2 pumping), and permeability-driven advective pore water exchange. The relative importance of these O2 delivery mechanisms depends on both seagrass density, and on how bottom water flow interacts with the seagrass canopy and leads to this advective exchange. Dissolution appears to involve the preferential dissolution of high-Mg calcite, and the rates of dissolution increase linearly with increasing seagrass density. Isotopic evidence of dissolution/reprecipitation is consistent with the occurrence of Ostwald ripening as the mechanism of reprecipitation, in which smaller crystals dissolve and then reprecipitate as larger crystals, with little or no change in mineralogy. Estimates of the aerially-integrated dissolution flux on the Bahamas Bank suggest that carbonate dissolution is an important loss term in the budget of shallow water carbonate sediments, and that on-bank carbonate dissolution, rather than offshore transport, may represent an important sink for gross shallow water carbonate production. Dissolution in carbonate bank and bay sediments may also be a significant alkalinity source to the surface ocean, and should be considered in global alkalinity/carbonate budget. Finally, coupled dissolution/reprecipitation may have a major impact on the stable isotope composition of carbonate sediments that are ultimately preserved in the rock record. These processes may therefore need to be considered, for example, when using carbon isotope records to obtain information on the operation of the global carbon cycle during the Phanerozoic.
Global Dietary Surveillance: Data Gaps and Challenges

Background: Detailed information on global individual-level consumption patterns is imperative for informed policy making. However, such data are dispersed and incomplete. Objective: To review and discuss the methodologies, observed data availability, challenges, and opportunities pertaining to global dietary surveillance. Methods: This investigation provides an extensive review of global dietary assessment methodologies and challenges, including at the survey level, the dietary collection and assessment level, and the dietary data processing and analysis level. The focus is on nationally representative individual-level data, and additional types of dietary data, such as dietary biomarkers, household assessment, and food availability, are reviewed as alternatives. Practical guidance is provided to inform key decisions when designing dietary surveys and collecting, analyzing, and using dietary data. This article further identifies and describes existing global and regional dietary initiatives/data sets. Results: Harmonized and standardized primary individual-level dietary data collection, processing, and analysis worldwide are currently not available. Evaluation and decision-making should be based on best available data, that is, secondary nonharmonized yet to the extent possible, standardized individual-level dietary data. Existing initiatives differ substantially in methodologies, including survey design/representativeness, coverage, diet assessment, and dietary metric standardization and processing. Data gaps have been identified that were more profound for certain countries, certain dietary indicators across countries, population subgroups, representativeness, or time periods. Conclusions: Optimizing worldwide dietary habits to improve population health requires systematically identified and evaluated data on a continuing basis. Leveraging existing available dietary data and efforts is an indispensable prerequisite for informed priority setting targeting the intersections between diet and disease.
Frequency characteristics of human muscle and cortical responses evoked by noisy Achilles tendon vibration.

Noisy stimuli, along with linear systems analysis, have proven to be effective for mapping functional neural connections. We explored the use of noisy (10-115 Hz) Achilles tendon vibration to examine somatosensory reflexes in the triceps surae muscles in standing healthy young adults (n = 8). We also examined the association between noisy vibration and electrical activity recorded over the sensorimotor cortex using electroencephalography. We applied 2 min of vibration and recorded ongoing muscle activity of the soleus and gastrocnemii using surface electromyography (EMG). Vibration amplitude was varied to characterize reflex scaling and to examine how different stimulus levels affected postural sway. Muscle activity from the soleus and gastrocnemii was significantly correlated with the tendon vibration across a broad frequency range (~10-80 Hz), with a peak located at ~40 Hz. Vibration-EMG coherence positively scaled with stimulus amplitude in all three muscles, with soleus displaying the strongest coupling and steepest scaling. EMG responses lagged the vibration by ~38 ms, a delay that paralleled observed response latencies to tendon taps. Vibration-evoked cortical oscillations were observed at frequencies ~40-70 Hz (peak ~54 Hz) in most subjects, a finding in line with previous reports of sensory-evoked γ-band oscillations. Further examination of the method revealed 1) accurate reflex estimates could be obtained with <60 s of low-level (root mean square = 10 m/s2) vibration; 2) responses did not habituate over 2 min of exposure; and importantly, 3) noisy vibration had a minimal influence on standing balance. Our findings suggest noisy tendon vibration is an effective novel approach to characterize somatosensory reflexes during standing.NEW & NOTEWORTHY We applied noisy (10-115 Hz) vibration to the Achilles tendon to examine the frequency characteristics of lower limb somatosensory reflexes during standing. Ongoing muscle activity was coherent with the noisy vibration (peak coherence ~40 Hz), and coherence positively scaled with increases in stimulus amplitude. Our findings suggest that noisy tendon vibration, along with linear systems analysis, is an effective novel approach to study somatosensory reflex actions in active muscles.
Using background knowledge to speed reinforcement learning in physical agents

This paper describes Icarus, an agent architecture that embeds a hierarchical reinforcement learning algorithm within a language for specifying agent behavior. An Icarus program expresses an approximately correct theory about how to behave with options at varying levels of detail, while the Icarus agent determines the best options by learning from experience. We describe Icarus and its learning algorithm, then report on two experiments in a vehicle control domain. The first examines the benefit of new distinctions about state, whereas the second explores the impact of added plan structure. We show that background knowledge increases learning rate and asymptotic performance, and decreases plan size by three orders of magnitude, relative to the typical formulation of the learning problem in our test domain.
A Two-Tier Classification Model for Financial Fraud Detection

Financial fraud has become a daunting challenge for the business companies and baking organizations worldwide. The development of new technologies has provided further and more complicated ways in which criminals commit fraud that result in disastrous consequences. In this paper, we propose a Linear Discriminant Analysis-based novel financial fraud detection model which performs a two-tier classification based on three separate linear discriminant functions. Each function performs its own classification based on the training data and derives its own decision boundary for classification. Then, our two-tier model takes the final classification decision by utilizing the individual decisions of these discriminant functions. We evaluate the performance of our model using reallife datasets in terms of several standard metrics. Besides, we compare the performance of our model with that of several other models found in the literature. Our experimental results suggest that our model achieve reasonably improved classification performance compared to the state-of-the-art ones.
Sulfated Alginates as Heparin Analogues: A Review of Chemical and Functional Properties

Heparin is widely recognized for its potent anticoagulating effects, but has an additional wide range of biological properties due to its high negative charge and heterogeneous molecular structure. This heterogeneity has been one of the factors in motivating the exploration of functional analogues with a more predictable modification pattern and monosaccharide sequence, that can aid in elucidating structure-function relationships and further be structurally customized to fine-tune physical and biological properties toward novel therapeutic applications and biomaterials. Alginates have been of great interest in biomedicine due to their inherent biocompatibility, gentle gelling conditions, and structural versatility from chemo-enzymatic engineering, but display limited interactions with cells and biomolecules that are characteristic of heparin and the other glycosaminoglycans (GAGs) of the extracellular environment. Here, we review the chemistry and physical and biological properties of sulfated alginates as structural and functional heparin analogues, and discuss how they may be utilized in applications where the use of heparin and other sulfated GAGs is challenging and limited.
Learning causal Bayes networks using interventional path queries in polynomial time and sample complexity

Causal discovery from empirical data is a fundamental problem in many scientific domains. Observational data allows for identifiability only up to Markov equivalence class. In this paper, we propose a polynomial time algorithm for learning the exact correctly-oriented structure of the transitive reduction of any causal Bayesian networks with high probability, by using interventional path queries. Each path query takes as input an origin node and a target node, and answers whether there is a directed path from the origin to the target. This is done by intervening the origin node and observing samples from the target node. We theoretically show the logarithmic sample complexity for the size of interventional data per path query, for continuous and discrete networks. We extent our work by presenting how to learn the transitive edges using logarithmic sample complexity (albeit in time exponential in the maximum number of parents for discrete networks) and by providing an analysis of imperfect interventions.
Enhanced Mucosal Immune Responses to HIV Virus-Like Particles Containing a Membrane-Anchored Adjuvant

ABSTRACT Previously, a modified HIV Env protein with a heterologous membrane anchor was found to be incorporated into HIV virus-like particles (VLPs) at 10-fold-higher levels than those of unmodified Env. To further improve the immunogenicity of such VLPs, membrane-anchored forms of bacterial flagellin (FliC) or a flagellin with a truncated variable region (tFliC) were constructed to be incorporated into the VLPs as adjuvants. HIV-specific immune responses induced by the resulting VLPs were determined in a guinea pig model. The VLPs induce enhanced systemic antibody responses by either systemic or mucosal vaccination and enhanced mucosal immunity by a mucosal immunization route, as demonstrated by high levels of HIV-specific serum IgG and mucosal IgG and IgA. The quality of the antibody responses was also improved, as shown by enhanced neutralization capacity. VLPs incorporating FliC were more effective in inducing systemic responses, while VLPs containing tFliC were more effective in inducing mucosal IgA responses. The IgG titers in sera were found to last for at least 5 months without a significant drop. These results indicate that HIV VLPs incorporating high levels of Env and a molecular adjuvant have excellent potential for further development as a prophylactic HIV vaccine. IMPORTANCE A prophylactic vaccine is urgently needed to control the spread of HIV/AIDS. Antigens inducing strong systemic and mucosal immune responses are promising as vaccines for this mucosally transmitted disease. We found that novel HIV virus-like particles (VLPs) presenting a high level of Env in its native membrane-bound form and coincorporating an innate immune-signaling adjuvant in the same particles were effective in inducing enhanced systemic and mucosal immunity. As new HIV vaccine candidates, these VLPs bridge the gaps of the innate and adaptive, as well as systemic and mucosal, immune responses, providing a new approach for HIV vaccine development. A prophylactic vaccine is urgently needed to control the spread of HIV/AIDS. Antigens inducing strong systemic and mucosal immune responses are promising as vaccines for this mucosally transmitted disease. We found that novel HIV virus-like particles (VLPs) presenting a high level of Env in its native membrane-bound form and coincorporating an innate immune-signaling adjuvant in the same particles were effective in inducing enhanced systemic and mucosal immunity. As new HIV vaccine candidates, these VLPs bridge the gaps of the innate and adaptive, as well as systemic and mucosal, immune responses, providing a new approach for HIV vaccine development.
Frictional Asset Reallocation under Adverse Selection

The aim of this paper is to study asset reallocation in financial markets subject to search, bargaining, and information frictions, and to analyze the impact of monetary policy on equilibrium outcomes. The main results show that private information regarding the quality of an asset impairs its liquidity and reduces both trading volume and consumption. As a consequence, a positive liquidity differential between money and real assets emerges, resulting in an increased demand for fiat money, as observed since the eruption of the global financial crisis. A policy intervention replacing information sensitive assets with government bonds or fiat money, as done in the asset-purchase program implemented by the Federal Reserve Bank, improves welfare.
Bias and variance reduction in nonparametric estimation of time‐dependent accuracy measures

A new nonparametric approach is developed to estimate the time‐dependent accuracy measure curves, which are defined on the cumulative cases and dynamic controls, for censored survival data. Based on an estimable survival process, the main intention of this study is to reduce the finite‐sample biases of nearest neighbor estimators. The asymptotic variances of some retrospective accuracy measure estimators are further reduced by applying a smoothing technique to the underlying process of a marker. Meanwhile, practically feasible and theoretically valid procedures are proposed for bandwidth selection in the presented estimators. In addition, the proposed methodology can be reasonably extended to accommodate stratified survival data and survival data with multiple markers. As shown in the simulations, our new estimators outperform the nearest neighbor and inverse censoring weighted estimators. Data from the AIDS Clinical Trials Group study 175 and an angiographic coronary artery disease study are also used to illustrate the proposed methodology. Copyright © 2016 John Wiley & Sons, Ltd.
Learning autonomy in two or three steps: linking open-ended development, authority, and agency to motivation

In this paper we connect open-ended development, authority, agency, and motivation through (1) an analysis of the demands of existing in a complex world and (2) environmental appraisal in terms of affordance content and the complexity to select appropriate behavior. We do this by identifying a coherent core from a wide range of contributing fields. Open-ended development is a structured three-step process in which the agent first learns to master the body and then aims to make the mind into a reliable tool. Preconditioned on success in step two, step three aims to effectively co-create an optimal living environment. We argue that these steps correspond to right-left-right hemispheric dominance, where the left hemisphere specializes in control and the right hemisphere in exploration. Control (e.g., problem solving) requires a closed and stable world that must be maintained by external authorities or, in step three, by the right hemisphere acting as internal authority. The three-step progression therefore corresponds to increasing autonomy and agency. Depending on how we appraise the environment, we formulate four qualitatively different motivational states: submission, control, exploration, and consolidation. Each of these four motivational states has associated reward signals of which the last three—successful control, discovery of novelty, and establishing new relations—form an open-ended development loop that, the more it is executed, helps the agent to become progressively more agentic and more able to co-create a pleasant-to-live-in world. We conclude that for autonomy to arise, the agent must exist in a (broad) transition region between order and disorder in which both danger and opportunity (and with that open-ended development and motivation) are defined. We conclude that a research agenda for artificial cognitive system research should include open-ended development through intrinsic motivations and ascribing more prominence to right hemispheric strengths.
Design and Implementation of a Toolkit for the Aspect-Based Sentiment Analysis of Tweets

The microblogging service Twitter is a lively social media channel where people report on almost everything imaginable. Using tweets to analyze consumers' opinions about individual products and services is a valuable source of information used by brands and companies to learn about their public perception. We present an aspect-based sentiment analysis approach for extracting positive, negative, and neutral aspects from tweets by leveraging part-of-speech tagging and dependency parsing from natural language processing. The approach manifests in the design of a software toolkit that facilitates the sentiment analysis, starting from the extraction of tweets, filtering, over the automated analysis, up to the interactive display of the results. By applying dependency patterns between nouns, adjectives, adverbs, and negotiations to the individual sentences, the toolkit identifies aspects and sentiment expressions, for which the polarities are determined subsequently. We demonstrate the developed solution by looking at the case of Airbnb, where people can rent out their homes to others over a Web platform. It is important to note that Airbnb only provides the platform and has little influence on the actual lodging service. Thus, looking at word-of-mouth regarding individual service experiences yields evidence of praise and criticism, which can help to seize improvement potential.