Miracle-dz/UniFAR-FTUs huggingface dataset:
MAYACQUIRE CARBON FROM STRESSED HOSTS

Hemiparasitic plants obtain water and solutes from their hosts, but much remains to be learned about these transfers. We used a forest girdling experiment to investigate how leaf gas exchange, carbon and nitrogen cycling in the root hemiparasite Melampyrum lineare Desr. responded to disturbance and changes in physiology of potential host trees. By preventing belowground C allocation by 35% of the canopy, girdling decreased the starch and soluble sugar contents of bulk forest floor fine roots. Photosynthetic rates of M. lineare were statistically significantly lower in the girdled plot, but their hypothesized drivers (foliar N, stomatal conductance and transpiration) had no statistically significant differences between girdled and non-girdled plots. However, M. lineare in the girdled plot had higher foliar C concentrations and ΔC than in the control plot, suggesting possible photosynthetic down-regulation in the girdled plot due to influx of older (e.g., host-derived) C into the leaves of M. lineare. Within the girdled plot (but not the control plot), M. lineare foliar C concentrations were positively correlated with foliar ΔC and δN, suggesting that M. lineare may respond to changes in both C and N biogeochemistry during the decline of dominant canopy species.
Optimal exercise of an executive stock option by an insider

We consider an optimal stopping problem arising in connection with the exercise of an executive stock option by an agent with inside information. The agent is assumed to have noisy information on the terminal value of the stock, does not trade the stock or outside securities, and maximises the expected discounted payoff over all stopping times with regard to an enlarged filtration which includes the inside information. This leads to a stopping problem governed by a time-inhomogeneous diffusion and a call-type reward. We establish conditions under which the option value exhibits time decay, and derive the smooth fit condition for the solution to the free boundary problem governing the maximum expected reward, and derive the early exercise decomposition of the value function. The resulting integral equation for the unknown exercise boundary is solved numerically and this shows that the insider may exercise the option before maturity, in situations when an agent without the privileged information may not. Hence we show that early exercise may arise due to the agent having inside information on the future stock price.
Snacking between main meals is associated with a higher risk of metabolic syndrome in a Mediterranean cohort: the SUN Project (Seguimiento Universidad de Navarra)

Abstract Objective To evaluate the association of snacking between main meals with the risk of developing metabolic syndrome. Design A dynamic prospective cohort study (the SUN Project; Seguimiento Universidad de Navarra). Snack consumption was evaluated using the question: ‘Do you have the habit of snacking between main meals? ’ Metabolic syndrome was defined according to the updated harmonizing criteria. We estimated multivariable-adjusted relative risks (RR) of metabolic syndrome and their 95 % confidence intervals using Poisson regression models. An exploratory factor analysis was also used to identify patterns of snacking. Setting University of Navarra, Spain. Subjects The study included 6851 university graduates, initially free of metabolic syndrome, and followed-up them for a median of 8·3 years. Results Among our participants, 34·6 % reported usual snacking between main meals. The cumulative incidence of metabolic syndrome was 5·1 % (9·5 % among men and 2·8 % among women). Snacking between main meals was significantly associated with higher risk for developing metabolic syndrome after multivariable adjustment (RR=1·44; 95 %CI 1·18, 1·77). Higher adherence to an ‘unhealthy snacking pattern’ was also independently associated with increased incidence of metabolic syndrome (fourth quartile of adherence compared with non-snacking: RR=1·68; 95 % CI 1·23, 2·29; P for trend <0·001). Conclusions Our findings suggest that avoidance of snacking between main meals can be included among the preventive approaches to reduce the risk of metabolic syndrome development, especially when snacks contain foods of poor nutritional quality.
A Method for Quantifying, Visualising, and Analysing Gastropod Shell Form

Quantitative analysis of organismal form is an important component for almost every branch of biology. Although generally considered an easily-measurable structure, the quantification of gastropod shell form is still a challenge because many shells lack homologous structures and have a spiral form that is difficult to capture with linear measurements. In view of this, we adopt the idea of theoretical modelling of shell form, in which the shell form is the product of aperture ontogeny profiles in terms of aperture growth trajectory that is quantified as curvature and torsion, and of aperture form that is represented by size and shape. We develop a workflow for the analysis of shell forms based on the aperture ontogeny profile, starting from the procedure of data preparation (retopologising the shell model), via data acquisition (calculation of aperture growth trajectory, aperture form and ontogeny axis), and data presentation (qualitative comparison between shell forms) and ending with data analysis (quantitative comparison between shell forms). We evaluate our methods on representative shells of the genera Opisthostoma and Plectostoma, which exhibit great variability in shell form. The outcome suggests that our method is a robust, reproducible, and versatile approach for the analysis of shell form. Finally, we propose several potential applications of our methods in functional morphology, theoretical modelling, taxonomy, and evolutionary biology.
Supervised OCR Error Detection and Correction Using Statistical and Neural Machine Translation Methods

For indexing the content of digitized historical texts, optical character recognition (OCR) errors are a hampering problem. To explore the effectivity of new strategies for OCR post-correction, this article focuses on methods of character-based machine translation, specifically neural machine translation and statistical machine translation. Using the ICDAR 2017 data set on OCR post-correction for English and French, we experiment with different strategies for error detection and error correction. We analyze how OCR post-correction with NMT can profit from using additional information and show that SMT and NMT can benefit from each other for these tasks. An ensemble of our models reached best performance in ICDAR’s 2017 error correction subtask and performed competitively in error detection. However, our experimental results also suggest that tuning supervised learning for OCR post-correction of texts from different sources, text types (periodicals and monographs), time periods and languages is a difficult task: the data on which the MT systems are trained have a large influence on which methods and features work best. Conclusive and generally applicable insights are hard to achieve.
Surgical Ventricular Restoration Effective : August 1 , 2018

PLEASE NOTE: Contracts exclude from coverage, among other things, services or procedures that are considered investigational or cosmetic. Providers may bill members for services or procedures that are considered investigational or cosmetic. Providers are encouraged to inform members before rendering such services that the members are likely to be financially responsible for the cost of these services.
Effects of Buprenorphine on Balance of Oxidant/Antioxidant System in the Different Ages of Male Rat Liver

Our knowledge about a link between buprenorphine and hepatotoxicity is controversial. This study evaluated the effects of buprenorphine on the liver of young, adult, and aged rats. For this reason, young, adult, and aged rats received intraperitoneally 0.25, 0.5, and 1 mg/kg buprenorphine for 30 days. The present results revealed that the normal aging was associated with a significant decrease in the activities of antioxidant enzymes, and an increase in the liver lipid peroxidation, serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), and lactate dehydrogenase (LDH) activities in the aged rats. This study also demonstrated that buprenorphine led to a significant increase in the serum activities of ALT, AST, and LDH as well as liver lipid peroxidation content with a decrease in the antioxidant enzymes in the liver of buprenorphine‐treated aged rat versus the aged matched control animals. In conclusion, the present results demonstrate that buprenorphine deteriorated oxidative damage in the aged livers.
An Indoor Localization System for Telehomecare Applications

In this paper, we present a novel probabilistic technique, based on the Bayes filter, able to estimate the user location, even with unreliable sensor data coming only from fixed sensors in the monitored environment. Our approach has been extensively tested in a home-like environment, as well as in a real home, and achieves very good results. We present results on two datasets, representative of real life conditions, collected during the testing phase. We detect the patient location with subroom accuracy, an improvement over the state of the art for localization using only environmental sensors. The main drawback is that it is only suitable for applications where a single person is present in the environment, like as with other approaches that do not use any mobile device. For this reason, we introduced the “telehomecare” term, therefore differentiating from generic telemedicine applications, where many people can be in the same environment at the same time.
Myeloid Cell-Derived Hypoxia-Inducible Factor Attenuates Inflammation in Unilateral Ureteral Obstruction-Induced Kidney Injury

Renal fibrosis and inflammation are associated with hypoxia, and tissue pO2 plays a central role in modulating the progression of chronic kidney disease. Key mediators of cellular adaptation to hypoxia are hypoxia-inducible factor (HIF)-1 and -2. In the kidney, they are expressed in a cell type-specific manner; to what degree activation of each homolog modulates renal fibrogenesis and inflammation has not been established. To address this issue, we used Cre-loxP recombination to activate or to delete both Hif-1 and Hif-2 either globally or cell type specifically in myeloid cells. Global activation of Hif suppressed inflammation and fibrogenesis in mice subjected to unilateral ureteral obstruction, whereas activation of Hif in myeloid cells suppressed inflammation only. Suppression of inflammatory cell infiltration was associated with downregulation of CC chemokine receptors in renal macrophages. Conversely, global deletion or myeloid-specific inactivation of Hif promoted inflammation. Furthermore, prolonged hypoxia suppressed the expression of multiple inflammatory molecules in noninjured kidneys. Collectively, we provide experimental evidence that hypoxia and/or myeloid cell-specific HIF activation attenuates renal inflammation associated with chronic kidney injury.
A CONTEXTUAL STUDY OF LINKS BETWEEN EMPLOYEE SATISFACTION , EMPLOYEE TURNOVER , CUSTOMER SATISFACTION AND FINANCIAL PERFORMANCE

An essential aspect of the balanced scorecard is the articulation of the links between nonfinancial measures such as employee satisfaction and customer satisfaction and financial measures of performance. However, there is little evidence documenting the link between employee satisfaction and customer satisfaction, and their impact on financial performance. Using cross-sectional data on a sample of more than 500 department stores, this study provides empirical evidence on the effects of employee satisfaction and turnover on customer satisfaction and, in turn, on revenue and cost measures. A contextual analysis of this evidence provides further insights into the sensitivity of these associations to urban or rural location of a store.
The Effect of Corporate Social Responsibility Initiatives on Internal Employee Motivation and Organizational Identification

The concept of corporate social responsibility has become increasingly important to businesses today and to those organizations who seek to influence industries and society at large. Based on stakeholder theory, successful companies need CSR to shape their public images and reputations to their customers, communities and other external beneficiaries. The more employees know the corporation contributes to society, the more employees match their values with the company's organizational visions, mission and goals. Despite the increased interest into CSR initiatives, employee attribution toward an internal motivation caused by the company's CSR activity is seldom discussed (Skudeiene & Auruskeviciene, 2012). In addition to consequences of SCR, the study aims to explore the mediating role of employee's internal motivation in the model. Based on the researches of Skudeiene & Auruskeviciene (2012), this study provides an integrated model that explores the effect of CSR initiatives on employee's internal motivation and organizational identification. In this study, employees' samples will be collected from restaurant industry in Taiwan.
Diagnostic Test Pattern Generation and Fault Simulation for Stuck-at and Transition Faults

In VLSI testing we need Automatic Test Pattern Generator (ATPG) to get input test vectors for Circuit Under Test (CUT). Generated test sets are usually compacted to save test time which is not good for failure diagnosis. Physical defects in circuits are modeled by different Fault Models to facilitate test generation. Stuck-at and transition fault models are widely used because of their practicality. In this work a Diagnostic Automatic Test Pattern Generation (DATPG) system is constructed by adding new algorithmic capabilities to conventional ATPG and fault simulation programs. The DATPG aims to generate tests to distinguish stuck-at fault pairs, i.e., two faults must have different output responses. This will help diagnosis to pin point the failure by narrowing down the fault candidates which may be the reason for this particular failure. Given a fault pair, by modifying circuit netlist a new single fault is modeled. Then we use a conventional ATPG to target that fault. If a test is generated, it is guaranteed to distinguish the fault pair in the original circuit. A fast diagnostic fault simulation algorithm is implemented to find undistinguished fault pairs from a fault list for a given test vector set. To determine the quality of the test vector set generated by DATPG, we use a proposed Diagnostic Coverage (DC) metric, which is similar to Fault Coverage (FC). The diagnostic ATPG system starts by first generating conventional fault coverage vectors. Those vectors are then simulated to determine the DC, followed by repeated applications of diagnostic test generation and simulation. We observe improved DC in all ISCAS’85 benchmark circuits. To distinguish between a pair of transition faults, we need to find a test vector pair (LOC or LOS type) that produces different output responses for the two faults. By adding a few logic gates and one modeling flip-flop to the circuit under test (CUT), we extend the ability of the previous DATPG system to target transition faults. Given a transition fault
Simplification in the Medical Domain

Health literacy, i.e. the ability to read and understandmedical text, is a relevant component of public health. Unfortunately, manymedical texts are hard to grasp by the general population as they are targeted at highly-skilled professionals and use complex language and domain-specific terms. Here, automatic text simplification making text commonly understandable would be very beneficial. However, research and development into medical text simplification is hindered by the lack of openly available training and test corporawhich contain complex medical sentences and their aligned simplified versions. In this paper, we introduce such a dataset to aid medical text simplification research. The dataset is created by filtering aligned health sentences using expert knowledge from an existing aligned corpus and a novel simple, language independent monolingual text alignment method. Furthermore, we use the dataset to train a state-of-the-art neural machine translation model, and compare it to a model trained on a general simplification dataset using an automatic evaluation, and an extensive human-expert evaluation.
DEEPG: Dual Heap Overlay Resource Discovery Protocol for Mobile Grid

Inherent characteristics of mobile devices make resource discovery in mobile grid challenging. Adding to the complexity is heterogeneity and hence multi-attribute management of these devices. Decentralized resource discovery using DHTs incur maintenance overheads when changes in attributes are continuous and rapid. The paper proposes a novel dual heap non-DHT overlay (DEEPG) for query resolution dealing with inherent mobile characteristics along with a mathematical model to estimate the problem size. Simulation results show that DEEPG reduces search bound on query resolution and results in limited maintenance overheads. The results also indicate that higher the query sum reduced is the number of lookups for exact match query resolution.
Self-Correcting Pattern Recognition System of Surface EMG Signals for Upper Limb Prosthesis Control

Pattern recognition methods for classifying user motion intent based on surface electromyography developed by research groups in well-controlled laboratory conditions are not yet clinically viable for upper limb prosthesis control, due to their limited robustness in users' real-life situations. To address this problem, a novel postprocessing algorithm, aiming to detect and remove misclassifications of a pattern recognition system of forearm and hand motions, is proposed. Using the maximum likelihood calculated by a classifier and the mean global muscle activity of the forearm, an artificial neural network was trained to detect potentially erroneous classification decisions. This system was compared to four previously proposed classification postprocessing methods, in both able-bodied and amputee subjects. Various nonstationarities were included in the experimental protocol to account for challenges posed in real-life settings, such as different contraction levels, static and dynamic motion phases, and effects induced by day-to-day transfers, such as electrode shifts, impedance changes, and psychometric user variability. The improvement in classification accuracy with respect to the unprocessed classifier ranged from 4.8% to 31.6%, depending on the scenarios investigated. The system significantly reduced misclassifications to wrong active classes and is thus a promising approach for improving the robustness of hand prosthesis controllability.
ReComment: towards critiquing-based recommendation with speech interaction

In contrast to search-based approaches, critiquing-based recommender systems provide a navigation-based interface where users are enabled to critique displayed recommendations as a means of preference elicitation. In this paper we present Recomment, our approach to natural language based unit critiquing. We discuss the developed prototype and present the corresponding user interface. In order to show the applicability of our concepts, we present the results of a user study. This study shows that speech interfaces have the potential to improve the perceived ease of use as well as the overall quality of recommendations.
Rapid Usability Assessment of an Enterprise Application in an Agile Environment with CogTool

Lab based user testing with participants quickly becomes a bottleneck for UX teams in the industry that exist in an Agile software development environment characterized by frequent release cycles and continually changing requirements. For such teams to reduce this testing time and to quickly glean usability insights we leverage human performance modeling via CogTool. Our work compares CogTool's expert user model's task time with actual user time from lab sessions in two user studies. In these two studies CogTool's task time estimates were statistically significantly lower when compared to lab based user times but the two task times were positively correlated. We leverage this correlation between CogTool task times and lab user times to build a predictive user model. Next, we apply this user model to rapidly evaluate two new designs without lab based user testing. Based on these results we provide recommendations for Agile UX teams to harness CogTool for enhancing user research efforts and thereby reduce the bottleneck of lab based user testing.
Anthropogenic increase in carbon dioxide compromises plant defense against invasive insects

Elevated levels of atmospheric carbon dioxide (CO2), a consequence of anthropogenic global change, can profoundly affect the interactions between crop plants and insect pests and may promote yet another form of global change: the rapid establishment of invasive species. Elevated CO2 increased the susceptibility of soybean plants grown under field conditions to the invasive Japanese beetle (Popillia japonica) and to a variant of western corn rootworm (Diabrotica virgifera virgifera) resistant to crop rotation by down-regulating gene expression related to defense signaling [lipoxygenase 7 (lox7), lipoxygenase 8 (lox8), and 1-aminocyclopropane-1-carboxylate synthase (acc-s)]. The down-regulation of these genes, in turn, reduced the production of cysteine proteinase inhibitors (CystPIs), which are specific deterrents to coleopteran herbivores. Beetle herbivory increased CystPI activity to a greater degree in plants grown under ambient than under elevated CO2. Gut cysteine proteinase activity was higher in beetles consuming foliage of soybeans grown under elevated CO2 than in beetles consuming soybeans grown in ambient CO2, consistent with enhanced growth and development of these beetles on plants grown in elevated CO2. These findings suggest that predicted increases in soybean productivity under projected elevated CO2 levels may be reduced by increased susceptibility to invasive crop pests.
Balanced hierarchical face clustering algorithm on triangle meshes

In this paper we propose a new algorithm of face clustering on triangle meshes, we call it balanced hierarchical face clustering (BHFC for short). Given a triangle mesh model, the algorithm iteratively generates a binary tree of face clusters on it. Each cluster consists of a set of connected faces. At every iteration, two connected clusters are chosen according to these rules we define, and they are merged into a new cluster. For each iteration there are two steps. In the first step, a set of cluster pair candidates is determined. In the second step, a single pair in the candidate set is chosen to merge. After the iteration, a hierarchical structure of clusters is constructed. The hierarchical structure is balanced, which avoids extreme size clusters appearing in the clustered result.