Miracle-dz/UniFAR-FTUs huggingface dataset:
The Influence of Store Environment on Perceived Experiential Value and Behavior Intention

This study attempts to embed perceived experiential value into the integrated model of perception and behavior intention to exploit the relationships between environmental stimuli, perceived service quality, perceived merchandise quality, perceived price, emotion, and behavior intention. We employ LISREL to analyze the research framework and a modified model. We conclude that social cues would have little effect on the perceived price and emotion, while ambient cues have a small impact on the perceived merchandise quality. We consider that emotion not only mediates between design cues and the perceived price, but also directly or indirectly influences the perceived experiential value, which then contributes to behavior intention. Notwithstanding the importance of the perceived merchandise quality in prior studies, this study proposes that it is not so crucial when juxtaposing other cues in stores, thus providing another insight into store management.
Diacylglycerol kinase (DGKA) regulates the effect of the epilepsy and bipolar disorder treatment valproic acid in Dictyostelium discoideum

ABSTRACT Valproic acid (VPA) provides a common treatment for both epilepsy and bipolar disorder; however, common cellular mechanisms relating to both disorders have yet to be proposed. Here, we explore the possibility of a diacylglycerol kinase (DGK) playing a role in regulating the effect of VPA relating to the treatment of both disorders, using the biomedical model Dictyostelium discoideum. DGK enzymes provide the first step in the phosphoinositide recycling pathway, implicated in seizure activity. They also regulate levels of diacylglycerol (DAG), thereby regulating the protein kinase C (PKC) activity that is linked to bipolar disorder-related signalling. Here, we show that ablation of the single Dictyostelium dgkA gene results in reduced sensitivity to the acute effects of VPA on cell behaviour. Loss of dgkA also provides reduced sensitivity to VPA in extended exposure during development. To differentiate a potential role for this DGKA-dependent mechanism in epilepsy and bipolar disorder treatment, we further show that the dgkA null mutant is resistant to the developmental effects of a range of structurally distinct branched medium-chain fatty acids with seizure control activity and to the bipolar disorder treatment lithium. Finally, we show that VPA, lithium and novel epilepsy treatments function through DAG regulation, and the presence of DGKA is necessary for compound-specific increases in DAG levels following treatment. Thus, these experiments suggest that, in Dictyostelium, loss of DGKA attenuates a common cellular effect of VPA relating to both epilepsy and bipolar disorder treatments, and that a range of new compounds with this effect should be investigated as alternative therapeutic agents. This article has an associated First Person interview with the first author of the paper. Editor's choice: Here, using a tractable model system, Dictyostelium discoideum, we show that diacylglycerol kinase activity might contribute to the cellular mechanism of action of the epilepsy and bipolar disorder treatment, valproic acid.
Non-antibiotic antimicrobial triclosan induces multiple antibiotic resistance through genetic mutation

Antibiotic resistance poses a major threat to public health. Overuse and misuse of antibiotics are generally recognised as the key factors contributing to antibiotic resistance. However, whether non-antibiotic, anti-microbial (NAAM) chemicals can directly induce antibiotic resistance is unclear. We aim to investigate whether the exposure to a NAAM chemical triclosan (TCS) has an impact on inducing antibiotic resistance on Escherichia coli. Here, we report that at a concentration of 0.2 mg/L TCS induces multi-drug resistance in wild-type Escherichia coli after 30-day TCS exposure. The oxidative stress induced by TCS caused genetic mutations in genes such as fabI, frdD, marR, acrR and soxR, and subsequent up-regulation of the transcription of genes encoding beta-lactamase and multi-drug efflux pump, together with down-regulation of genes related to membrane permeability. The findings advance our understanding of the potential role of NAAM chemicals in the dissemination of antibiotic resistance in microbes, and highlights the need for controlling biocide applications.
Multivariable quantitative relation between cell viability and the exposure parameters of 9.33 GHz RF-EMP irradiation

ABSTRACT Qualitative analysis of the influence of a certain exposure parameter is commonly performed in bioelectromagnetic studies. However, since the exposure condition requires the control of multiple parameters, the diverse results caused by different combinations of these parameters requires further quantitative study of the multivariable (exposure parameters)-bioeffect relation to identify the rule describing bioelectromagnetic effects. The present work investigated the relation between cell viability and the three main exposure parameters (electric intensity (Es), pulse duration (τ) and pulse number (N)) of 9.33 GHz radiofrequency electromagnetic field (RF-EMP). Experiments showed that the inhibitory rate of cell viability (ρ) had a proportional relationship with Es and exponential relationship with N; the equation is proposed to quantitatively describe the relation between the cell viability and these three exposure parameters. This equation can be used to predict the significance of a 9.33 GHz RF-EMP-induced bioeffect under the conditions Es <106 kV/m, N < 100, and 300 < τ < 750 ns, under which nonthermal bioeffects dominate for 9.33GHz RF-EMP exposure.
Dual MAPK Inhibition Is an Effective Therapeutic Strategy for a Subset of Class II BRAF Mutant Melanomas

Purpose: Dual MAPK pathway inhibition (dMAPKi) with BRAF and MEK inhibitors improves survival in BRAF V600E/K mutant melanoma, but the efficacy of dMAPKi in non-V600 BRAF mutant tumors is poorly understood. We sought to characterize the responsiveness of class II (enhanced kinase activity, dimerization dependent) BRAF mutant melanoma to dMAPKi. Experimental Design: Tumors from patients with BRAF wild-type (WT), V600E (class I), and L597S (class II) metastatic melanoma were used to generate patient-derived xenografts (PDX). We assembled a panel of melanoma cell lines with class IIa (activation segment) or IIb (p-loop) mutations and compared these with WT or V600E/K BRAF mutant cells. Cell lines and PDXs were treated with BRAFi (vemurafenib, dabrafenib, encorafenib, and LY3009120), MEKi (cobimetinib, trametinib, and binimetinib), or the combination. We identified 2 patients with BRAF L597S metastatic melanoma who were treated with dMAPKi. Results: BRAFi impaired MAPK signaling and cell growth in class I and II BRAF mutant cells. dMAPKi was more effective than either single MAPKi at inhibiting cell growth in all class II BRAF mutant cells tested. dMAPKi caused tumor regression in two melanoma PDXs with class II BRAF mutations and prolonged survival of mice with class II BRAF mutant melanoma brain metastases. Two patients with BRAF L597S mutant melanoma clinically responded to dMAPKi. Conclusions: Class II BRAF mutant melanoma is growth inhibited by dMAPKi. Responses to dMAPKi have been observed in 2 patients with class II BRAF mutant melanoma. These data provide rationale for clinical investigation of dMAPKi in patients with class II BRAF mutant metastatic melanoma. See related commentary by Johnson and Dahlman, p. 6107.
Environmental impact of switching from the synthetic glucocorticoid prednisolone to the natural alkaloid berberine

Low amounts of human pharmaceuticals in the aquatic environment can affect bacteria, animals and ultimately humans. Here, the environmental consequences of a shift in prescription behavior from prednisolone to berberine was modeled using an environmental decision support system based on four consecutive steps: emission, fate, exposure and effect. This model estimates the relative aquatic and human health impacts of alternative pharmaceutical prescriptions throughout Europe. Since a Defined Daily Dose (DDD) of berberine has yet to be formulated, the environmental impacts of berberine and prednisolone were compared under the assumption of equal DDDs. Subsequently, the relative impact ratio indicates the extent to which the actual DDD of berberine might be higher to still be environmentally preferable over prednisolone. In fact, berberine can be administered at a six times higher dose throughout Europe before its impact on the aquatic environment exceeds that of one prescription of prednisolone. On average, the results for impacts on human health are similar, with the median impact ratio ranging between 5.87 and 22.8 depending on the level of drinking water purification. However, for some regions in Spain, Austria, Baltic States and Finland, berberine can only be considered an environmentally better alternative if it is administered at a lower dose than prednisolone. We conclude that for most regions in Europe it is, up until a certain dose of berberine, beneficial for the aquatic environment and therefore human health to prefer prescription of berberine over prednisolone.
Small-Variance Asymptotics for Nonparametric Bayesian Overlapping Stochastic Blockmodels

The latent feature relational model (LFRM) for graphs represents each node as having binary memberships in one or more communities. The community memberships can be represented in form of a binary vector and LFRM defines the link probability between any pair of nodes as a bilinear function of their community membership vectors. Moreover, using nonparametric Bayesian prior - Indian Buffet Process - on the community membership matrix enables learning the number of communities automatically from the data. However, despite its modeling flexibility, strong link predictive performance, and nice interpretability of binary embeddings, inference in LFRM remains a challenge and is typically done via MCMC or variational methods. These methods can be slow and may take a long time to converge. In this work, we apply the small variance asymptotics idea to the non-parametric Bayesian LFRM, utilizing the connection between exponential families and Bregman divergence. This leads to an overlapping k-means like objective function for the nonparametric Bayesian LFRM, which can be optimized using generic or specialized solvers. We also propose an iterative greedy algorithm to optimize the objective function and compare our approach with other inference methods on several benchmark datasets. Our results demonstrate that our inference algorithm is competitive to methods such as MCMC while being much faster.
Partnership with Persistence

In this paper we analyze a continuous-time model of partnership with persistence. In the model, agents exert private efforts affecting persistent internal capital, which drives the profitability of the partnership. We characterize the optimal equilibrium with a novel Hamilton-Jacobi-Bellmann equation. It describes the maximal incentives for the partners, as a function of continuation values net of the internal capital. We show that imperfect monitoring of the internal capital discontinuously helps the agents. Even a partnership with high level of internal capital may unravel as a consequence of a short spell of bad outcomes. Good profit outcomes increase effort when partnership is doing badly, but decrease effort when partnership is doing well.
Textile Strain Sensors Characterization- Sensitivity, Linearity, Stability and Hysteresis

In this paper 4 different textile based strain sensors for measuring different level of strains were presented. Sensor consist a conductive part formed by coating or weaving technique. Both elastic and inelastic textile substrates were selected to achieve the required stains in applications. Sensor configuration was characterized using a tensile tester and measuring the resistance parallel by microprocessor. A linear working range with transfer function of each sensor was found. Coated sensor gives a good stability, while woven sensor was relative less stable. Inelastic textile substrate reduces the hysteresis error caused by refraction and construction of materials. The sensitivities were between 2.5 to 9 vary with different sensors. This paper finished by a discussion of how to choose sensors with different applications, among which sensor function and processability are most important aspects to be considering.
Interview with the Robot: Question-Guided Collaboration in a Storytelling System

An automated storytelling system that presents its stories as text on a screen is limited in its engagement with readers. An embodied robotic agent can overcome these limitations by using gesture to physically enact its stories. This paper presents a robotic agent that en-acts its own novel stories, which it shapes from the feedback it obtains using probing personal questions. Our robotic writer/presenter has two alternate modes of story-generation: a straight “telling” mode and an interview-oriented back-and-forth that extracts personal experiences from the user as raw material for new stories. We explore the practical issues of implementing both modes on a NAO humanoid robot that integrates gestural capabilities in an existing story-telling system.
Use of asymmetric models to estimate the distribution of usual nutrient intakes

The issue of estimating usual nutrient intake distribution is a challenge for dietitian and statistics. The 24-hour recall is usually one method to collect data and the distributions of usual nutrient intakes are, in general, asymmetric. Thus, this study aims to use asymmetric models in order to estimate the distribution of usual nutrient intakes. Data were drawn from a Health Inquires Survey from São Paulo city, Brazil. It was a cross-sectional population-based study with 1662 individuals. Two 24-hours recalls were collected and it was used in the Nutrition Data System for Research (NDSR) to obtain micronutrient intake data. A random intercept model was used to fit the distribution of micronutrient intake data, which characterizes different measures at the same subject. Asymmetric distributions were proposed for response variable and, for the random effect, a normal distribution was used. The results based on asymmetric models were compared with the National Cancer Institute (NCI) method for amount. No important differences among methods were observed, but this new approach shows advantages: it does not require data transformation and results can be directly interpreted from the estimated parameter of the considered distribution.
Exposing Digital Forgery Detection by Illumination Using SVM classifier

Photographs often act as evidence for criminals cases in courts. It also plays a vital role in media such as newspapers, television, etc. But however, image forgery becomes easier nowadays as image editing software becomes available. Here we analyze the often utilized manipulation technique called splicing or image composition. Physics-based and statistical-based illuminate estimation technique is incorporated so that textureand edge-based features are extracted to identify image forgery. Machine-learning approach is also employed; hence requires minimal human interaction.
Monetary policy rules in practice: evidence from Turkey and Israel

Forward looking monetary policy rules are estimated for Israel and Turkey. When variable inflation targets are taken into consideration, as opposed to the fixed targets used in prior research that use data from developed countries, forward looking Taylor rules seem to provide reasonable description of Central Bank behaviour in both countries. In general, it can be said that monetary policy appears to be quite strong in these countries, and especially so in Turkey, when compared with developed countries.
Sparse identification of nonlinear dynamics for model predictive control in the low-data limit

Data-driven discovery of dynamics via machine learning is pushing the frontiers of modelling and control efforts, providing a tremendous opportunity to extend the reach of model predictive control (MPC). However, many leading methods in machine learning, such as neural networks (NN), require large volumes of training data, may not be interpretable, do not easily include known constraints and symmetries, and may not generalize beyond the attractor where models are trained. These factors limit their use for the online identification of a model in the low-data limit, for example following an abrupt change to the system dynamics. In this work, we extend the recent sparse identification of nonlinear dynamics (SINDY) modelling procedure to include the effects of actuation and demonstrate the ability of these models to enhance the performance of MPC, based on limited, noisy data. SINDY models are parsimonious, identifying the fewest terms in the model needed to explain the data, making them interpretable and generalizable. We show that the resulting SINDY-MPC framework has higher performance, requires significantly less data, and is more computationally efficient and robust to noise than NN models, making it viable for online training and execution in response to rapid system changes. SINDY-MPC also shows improved performance over linear data-driven models, although linear models may provide a stopgap until enough data is available for SINDY. SINDY-MPC is demonstrated on a variety of dynamical systems with different challenges, including the chaotic Lorenz system, a simple model for flight control of an F8 aircraft, and an HIV model incorporating drug treatment.
Nanofibers from electrically driven viscoelastic jets: modeling and experiments

Modeling and experiments of three electrospinning systems have been presented and they are i) axisymmetric instabilities in electrospinning of various polymeric solutions, ii) non-isothermal modeling of polymer melt electrospinning, and iii) control of nanoparticle distribution and location via confined self-assembly of block copolymers during electrospinning. It has been demonstrated that predicted simulations are in good agreement with corresponding electro spinning experiments, and theoretical analysis provides fundamental understanding of phenomena that take place during electrospinning of various polymeric liquids.
The Power of Projection for Powerless and Powerful People

Past social projection research has mainly focused on target characteristics as a moderator of projective effects. The current research considers the power of the perceiver and how it affects projection of competence and warmth. In three studies, participants first rated themselves on a list of traits/preferences, then performed a power manipulation task, and, finally, rated a target person on the same list. Studies 1 and 2 reveal that the effect of power on social projection is moderated by dimension of judgment: high-power/low-power participants project more on competence/warmth than low-power/high-power participants. A meta-analysis conducted on Studies 1, 2, 3, and two additional studies confirmed those results. Study 3 additionally shows that high power increases the salience of competence, whereas low power increases the salience of warmth. Implications for both the power and the social perception literatures are discussed.
Comparative Study on Efficacy of CowManager Technology

A comparative study of the CowManager tag versus visual observations was conducted to measure the efficacy of cow behavior measuring technologies. The study used 30 Angus-cross beef steers grouped into pens of 6. The steers were monitored once a week every other week for a total of 6 days. Each observation period was within 4 hours from the hours of 7am to 11am using the scanning method at an interval of 5 minutes. The behaviors that were monitored by the observer and CowManager technology included time spent ruminating, eating, and being active. Each behavior time was put on a minute by hour time scale and compared. After SAS analysis there was no statistical difference in the time spent ruminating when using observational methods versus those that the CowManager reported. However, there was a statistical difference in time spent eating when comparing observational and CowManager data. The CowManager data appeared to underreport the amount of time spent eating. These results indicate that CowManager tags can accurately measure rumination time but are unable to accurately measure time spent eating when used in beef feedlot production systems. Because the system was built for dairy systems some adaptation is likely needed to better suit the feedlot industry.
Networks of power and influence: the role of mycorrhizal mycelium in controlling plant communities and agroecosystem functioning

Extraradical mycelia of mycorrhizal fungi are normally the �hidden half� of the symbiosis, but they are powerful underground influences upon biogeochemical cycling, the composition of plant communities, and agroecosystem functioning. Mycorrhizal mycelial networks are the most dynamic and functionally diverse components of the symbiosis, and recent estimates suggest they are empowered by receiving as much as 10% or more of the net photosynthate of their host plants. They often constitute 20%�30% of total soil microbial biomass yet are undetected by standard measures of biomass used by soil scientists and agromomists. Mycorrhizal mycelia provide extensive pathways for carbon and nutrient fluxes through soil, often exceeding tens of metres per gram of soil. We consider the amounts of photosynthate �power� allocated to these mycelial networks and how this is used in fungal respiration, biomass, and growth and in influencing soil, plant, and ecosystem processes. The costs and functional �benefits� to plants linking to these networks are fungal specific and, because of variations in physiology and host specificity, are not shared equally; some plants even depend exclusively on these networks for carbon. We briefly assess the potential contribution of extraradical mycorrhizal mycelium to sustainable agriculture and maintenance of biodiversity and highlight technologies that promise new vistas and improved fine-scale resolution of the dynamic spatial and temporal functioning of these networks in soil.Key words: arbuscular mycorrhiza, ectomycorrhiza, extraradical mycelium, hyphal networks.
Automatic benign and malignant classification of pulmonary nodules in thoracic computed tomography based on RF algorithm

Classification of benign and malignant pulmonary nodules can provide useful indicators for estimating the risk of lung cancer. In this study, an improved random forest (RF) algorithm is proposed for classification of benign and malignant pulmonary nodules in thoracic computed tomography images. First, an improved random walk algorithm is proposed to automatically segment pulmonary nodules. Then, intensity, geometric and texture features based on the grey-level co-occurrence matrix, rotation invariant uniform local binary pattern and Gabor filter methods are combined to generate an effective and discriminative feature vector. Mutual information is employed to reduce the dimensionality. Finally, an improved RF classifier is trained to classify benign and malignant nodules. An appropriate feature subset is selected by the bootstrap method and an effective combination method is introduced to predict a class label. The proposed classification method on the lung images dataset consortium dataset achieves a sensitivity of 0.92 and the area under the receiver-operating-characteristic curve of 0.95. An additional evaluation is performed on another dataset coming from General Hospital of Guangzhou Military Command. A mean sensitivity and a mean specificity of the proposed method are 0.85 and 0.82, respectively. Experimental results demonstrate that the proposed method achieves the satisfactory classification performance.