Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduced Growth Factor Responses in Vascular Smooth Muscle Cells Derived from 12/15‐Lipoxygenase–Deficient Mice

Abstract— Biochemical and genetic evidence support the involvement of leukocyte‐type 12/15‐lipoxygenase enzyme and its products in the atherogenic process. We recently showed that products of the 12/15‐lipoxygenase pathway play an important role in mediating hypertrophy, matrix protein production, and inflammatory gene expression in vascular smooth muscle cells (VSMC) through activation of mitogen activated protein kinases and key transcription factors. The current study is aimed at establishing the in vivo role of 12/15‐lipoxygenase in VSMC by comparing growth factor–induced responses in VSMC derived from 12/15‐lipoxygenase knockout mice versus genetic control wild‐type mice. In the lipoxygenase knockout cells, 12/15‐lipoxygenase protein was not expressed, and levels of its product, 12(S)‐hydroxyeicosatetraenoic acid, were reduced (51% of wild type). Knockout cells exhibited significantly lower rates of growth factor–induced migration, fibronectin production, and incorporation of 3H‐thymidine and 3H‐leucine (54%, 55%, 61%, and 57% of wild type, respectively). Growth factor–induced superoxide production and p38 mitogen–activated protein kinase activation were also reduced in knockout cells. Serum‐stimulated AP‐1 transcription factor activation was markedly reduced (50% of wild type), whereas cAMP response element binding protein activation was abrogated in knockout cells. Furthermore, growth factor–induced mRNA expression of immediate early genes and fibronectin were also greatly reduced. These results suggest that the modulation of specific signaling pathways and growth‐responsive genes may be responsible for the altered growth factor responses in the lipoxygenase knockout cells. They also demonstrate the important in vivo role of vascular 12/15‐lipoxygenase in VSMC growth, migration, and matrix responses associated with hypertension, atherosclerosis, and restenosis.
AFFECTIVE STATE ESTIMATION FROM FACIAL IMAGES USING NEURAL NETWORKS AND FUZZY LOGIC

Many software systems would significantly improve performance if they could adapt to the emotional state of the user, for example if intelligent tutoring systems, information/ help kiosks, ATM’s, automatic ticketing machines could recognise when users were confused, frustrated or angry they could guide the user back to remedial help systems so improving the service. Current software systems are not able to estimate the affective state of the users and so are not able to offer these additional capabilities. This chapter discusses the implementation of an intelligent affective state estimator that uses facial images from web cams as input. This system consists of two neural network classifiers and a fuzzy logic facial analysis system. This system has been successfully prototyped for use in an intelligent tutoring system that adapts to the affective state of the user. Facial expressions are the most important means of detecting emotions. However, other bio-signals such as heartbeat, skin resistance and voice tone can also be used for detecting human emotions. This chapter also explores the future directions of research in this area and the potential applications.
Pulmonary Hypertension in Cardiac Surgery

Pulmonary hypertension is an important prognostic factor in cardiac surgery associated with increased morbidity and mortality. With the aging population and the associated increase severity of illness, the prevalence of pulmonary hypertension in cardiac surgical patients will increase. In this review, the definition of pulmonary hypertension, the mechanisms and its relationship to right ventricular dysfunction will be presented. Finally, pharmacological and non-pharmacological therapeutic and preventive approaches will be presented.
The Hidden-Zero Effect

Splurge on a vacation, or save for retirement? Sleep in late, or get up to exercise? The ability to resist a tempting, immediately available reward in order to obtain a larger delayed reward is the hallmark of self-control, and predicts important life outcomes such as academic achievement (Duckworth & Seligman, 2005) and social adjustment (Mischel & Ayduk, 2004). Unfortunately, immediate rewards are often difficult to resist. One key factor that makes them so appealing is temporal discounting – the tendency to consider events as less important if they occur in the distant future, rather than in the near future (Ainslie, 2001). This tendency tempts us to a choose small immediate reward over a larger delayed reward, simply because the first is immediately available – a choice that can greatly sabotage the attainment of our long-term goals. To study temporal discounting, researchers (e.g. Estle, Green, Myerson, & Holt, 2007) typically have participants choose between smaller-sooner rewards and larger-later rewards (e.g., “Would you prefer [A] $5 today, OR [B] $6.20 in 26 days? ”). Decision-makers commonly view such situations as a choice between a good alternative soon and a better alternative later. However, what is not mentioned explicitly in such situations is the fact that choosing to receive one alternative also means choosing not to receive the other. In other words, decision-makers are implicitly encouraged to choose between one-shot events, rather than between sequences of events, in which choosing an immediate reward also means choosing to receive nothing later, and vice versa (e.g., “Would you prefer [A] $5 today and $0 in 26 days, OR [B] $0 today and $6.20 in 26 days? ”). People’s choices are heavily influenced by ways in which the alternatives are framed, even when the different frames are logically equivalent (Tversky & Kahneman, 1981). Given that people prefer sequences that improve over time (Ariely & Zauberman, 2003; Frederick, Loewenstein, & O'Donoghue, 2002), we hypothesized that representing each alternative as a sequence of outcomes, by explicitly referring to the hidden zero in each alternative, would increase participants’ willingness to choose larger delayed rewards over smaller immediate rewards, as they would prefer to choose sequences that appear to improve over time.
A methodology for ® tting and validating metamodels in simulation 1

This paper proposes a methodology that replaces the usual ad hoc approach to metamodeling. This methodology considers validation of a metamodel with respect to both the underlying simulation model and the problem entity. It distinguishes between ®tting and validating a metamodel, and covers four types of goal: (i) understanding, (ii) prediction, (iii) optimization, and (iv) veri®cation and validation. The methodology consists of a metamodeling process with 10 steps. This process includes classic design of experiments (DOE) and measuring ®t through standard measures such as R-square and cross-validation statistics. The paper extends this DOE to stagewise DOE, and discusses several validation criteria, measures, and estimators. The methodology covers metamodels in general (including neural networks); it also gives a speci®c procedure for developing linear regression (including polynomial) metamodels for random simulation. Ó 2000 Elsevier Science B.V. All rights reserved.
Multiple concentric annuli for characterizingspatially nonuniform backgroundsJames

A method is presented for estimating the background at a given location on a sky map by interpolating the estimated background from a set of concentric annuli which surround this location. If the background is nonuniform but smoothly varying, this method provides a more accurate (though less precise) estimate than can be obtained with a single annulus. Several applications of multi-annulus background estimation are discussed, including direct testing for point sources in the presence of a nonuniform background, the generation of \surrogate maps" for characterizing false alarm rates, and precise testing of the null hypothesis that the background is uniform.
Don't Lose Sight of the Forest: Why the Big Picture of Social Intelligence is Essential

This paper argues that "social intelligence," a hot topic in this community in recent years, is still not properly understood, and that this has important implications for multi-agent systems. The term shows up repeatedly in the intelligent agents and distributed artificial intelligence literature over many years, but in most cases the authors refer to just limited sub-components of social intelligence, such as norms, or trust. It is argued here that these (and other) facets of social intelligence are inextricably linked, and that we should only attempt to model them in isolation if we know what the implications of ignoring the other facets will be on model outcomes. We do not necessarily need to implement a "big picture" of social intelligence for every agent system, but having a conceptual model of this big picture will help us to understand the implications of what we do choose to model. This paper sketches a possible conceptual model of social intelligence, and begins to untangle the implications for different facets of social intelligence that are commonly scrutinised by the multi-agent systems community. The community is challenged to go further in developing this model and understanding of how its various facets make up the whole.
On the use of weighted syslog time series for anomaly detection

Finding unusual (anomalous) events in a network is a crucial task for network operation. The collection and analysis of the log messages from the network devices (i.e., router and switch) are a common method. However, the detection of the anomalous events is painful because of a huge amount and many types of log messages; the important event messages are sometimes hidden in a large number of relatively less important and usual event messages. Many researches have been devoted to find out anomalies accurately and quickly by using time series of log message. To construct the log time series for anomaly detection in order to highlight such hidden anomalies, this paper focuses on the effectiveness of using a global weight that is based on a global appearance of a message type in the all data set. We introduce two types of the global weight called residual inverse document frequency (RIDF) and entropy that are well-known method in information retrieval field. Then, we evaluate the performance improvement due to the global weights with a wavelet-based abrupt change detection algorithm through 1-year long collection of core router log messages taken from a Japanese R&E network. Our main findings are (1) the global weights assign a low weight to less important and frequently appeared event messages, (2) they highlight unusual events by removing temporal correlation (i.e., periodic trend), and (3) the wavelet-based detection algorithm more accurately alarms the anomalous time period with the weighted time series.
The L-Cysteine Desulfurase NFS1 Is Localized in the Cytosol where it Provides the Sulfur for Molybdenum Cofactor Biosynthesis in Humans

In humans, the L-cysteine desulfurase NFS1 plays a crucial role in the mitochondrial iron-sulfur cluster biosynthesis and in the thiomodification of mitochondrial and cytosolic tRNAs. We have previously demonstrated that purified NFS1 is able to transfer sulfur to the C-terminal domain of MOCS3, a cytosolic protein involved in molybdenum cofactor biosynthesis and tRNA thiolation. However, no direct evidence existed so far for the interaction of NFS1 and MOCS3 in the cytosol of human cells. Here, we present direct data to show the interaction of NFS1 and MOCS3 in the cytosol of human cells using Förster resonance energy transfer and a split-EGFP system. The colocalization of NFS1 and MOCS3 in the cytosol was confirmed by immunodetection of fractionated cells and localization studies using confocal fluorescence microscopy. Purified NFS1 was used to reconstitute the lacking molybdoenzyme activity of the Neurospora crassa nit-1 mutant, giving additional evidence that NFS1 is the sulfur donor for Moco biosynthesis in eukaryotes in general.
Exploiting Big Data from Mobile Device Sensor-Based Apps: Challenges and Benefits

Sensor data gathered from mobile devices generates big data, which can help organizations continuously monitor a wide range of processes in ways that prompt actions and generate value. But generating reliable data from such devices is challenging and not straightforward. The full article describes the challenges and provides guidelines based on a case study of " Street Bump, " a mobile device app for iPhones that the City of Boston uses to facilitate management of potholes in over 800 miles of roads. Developed in 2012, Street Bump can be downloaded and left running on a driver's iPhone. When the driver runs over a pothole, the phone captures the location of the pothole and then sends that data to a cloud-based computer, which records the reported pothole information with the location of other potholes so that they can be scheduled for timely repair. The app uses the iPhones' accelerometer to detect potential potholes, and the phone's global positioning system (GPS) capabilities to record the location of the pothole (accurate to within 18 feet). Under user control, the data is uploaded to a server and submitted to the city department responsible for highway maintenance. However, not all bumps recorded are potholes. Many are speed bumps or damaged manhole covers or bridge joints. The app uses different algorithms to filter out these alternative sources on movement and bumps. Experience has also shown that the location of the phone in the car and the type of car can result in different data being generated for the same road anomaly. Direct Planned Benefits. The cost of gathering data from mobile apps in real time is potentially low. Once gathered, it can be analyzed and integrated with business processes to solve problems. Street Bump means that the task of finding potholes can be carried out by mobile phone owners, rather than city employees, thus reducing personnel, equipment and other costs. Indirect Benefits. As data is continuously gathered, managers gain real-time insights into the processes being monitored, facilitating transparency and eliminating information asymmetries. They can align their actions, strategies, processes and organization with the information and knowledge generated from the processes being monitored. Potential Future Benefits. Street Bump could be integrated with a pothole management system that allows rapid response and resource allocation. Such a system could provide an improved capability to facilitate management of city resources and help guide resource allocation decisions and infrastructure investment.
A face recognition method based on a combination of integrated neural network and KICA algorithm

In order to raise the efficiency of face recognition, a method based on back-propagation (BP) neural network and probabilistic neural network (PNN) integration was introduced. The method uses the kernel independent component analysis (KICA) to extract facial features, puts eigenvectors into the BP neural networks and PNN to learn, and outputs the two classification and recognition results by relative voting method. This method effectively solves the interferences of illumination, facial expression, etc., and as a result improves the classification of the human face recognition ability.
Study on Methodology for Detecting the Status of General Excavator without Renovation

Excavation and earthmoving equipment mostly operate in accident-prone areas. These areas normally have rollover, confinement, and fall or may be considered harmful work areas due to demolitions and sewage removals. It is inevitable that excavation and earthmoving systems should be developed to secure the safety of operators through unmanned and remote control systems. Therefore, this research proposes a system that enables excavators to be unmanned and remote controlled without any renovation, remodeling, change, or transformation by applying attachable and separable mechanism and modules. Moreover, this research proposes the sensor module to evaluate the dynamic status without processing remodeling or transformation the existing hydraulic drive excavation system by adopting the easy attachable and separable mechanism. The study verifies this proposal based on observed possibility and feasibility through the experiments.
Chemical Composition of Two H II Regions in NGC 6822 Based on VLT Spectroscopy

We present long-slit spectrophotometry of regions V and X of the Local Group irregular galaxy NGC 6822. The data consist of VLT FORS observations in the 3450-7500 Å range. We have obtained electron temperatures and densities using different line intensity ratios. We have derived the He, C, and O abundances relative to H based on recombination lines; the abundance ratios among these elements are almost independent of the temperature structure of the nebulae. We have also determined the N, O, Ne, S, Cl, and Ar abundances based on collisionally excited lines; the ratios of these abundances relative to that of H depend strongly on the temperature structure of the nebulae. The chemical composition of NGC 6822 V is compared with those of the Sun, the Orion Nebula, NGC 346 in the SMC, and 30 Doradus in the LMC. The value of O/H derived from recombination lines is in good agreement with that derived by Venn and coworkers from two A-type supergiants in NGC 6822.
BSIM6 -- Benchmarking the Next-Generation MOSFET Model for RF Applications

BSIM6 is the new Compact Model Coalition (CMC) standard bulk MOSFET model. It offers excellent fitting capability, accurate RF/analog simulations, and similar implementation features of the small dimension effects as included in the popular BSIM4 model. These features make BSIM6 a very desirable candidate for future nano-MOSFET simulations. In this work we evaluate the capabilities of the model and perform benchmark tests to assess the strengths of the model. Bulk 90nm technology MOSFET data is used to extract BSIM6 for analog/RF specific applications. Industry standard benchmark tests have been performed and results shown against the hardware data (where applicable). The model is found to correlate very well with hardware data and passes all important model quality benchmark tests for analog/RF applications.
Autonomic Factors of Successful Foreign Language Acquisition by Students and Schoolchildren

The aim of the study was to identify autonomic factors of successful acquisition of a foreign language by students and schoolchildren and find the optimal modes of autonomic regulation of the learning processes. Materials and Methods. 16 children in grade 4, 37 pupils in grades 5–11 studying at school with in-depth English language learning, and 10 second-year linguistics university students participated in the study. The data on the functional state dynamics of the university students and schoolchildren during English lessons were collected using event-related telemetry technology, which continuously recorded the heart rate. A specially designed protocol and a standard 5-point grading scale were used to assess the language progress. Results. The analysis of heart rate variability data in different contexts revealed a similarity in autonomic regulation modes for the schoolchildren and university students who studied English successfully. Consequently, similar functional states are typical for performing different language-related tasks. For all age groups, high activity of the autonomic nervous system was optimal for English learning. The difference was in the sympathetic and parasympathetic activity ratio: the contribution of the sympathetic circuit is more important for the university students, whereas the parasympathetic one — at school stage. Conclusion. The study of a foreign language acquisition by the Russian students and schoolchildren showed that from the standpoint of psychophysiology, the main factor of success for the students was a high autonomic nervous system activity with a prominent dominance of the sympathetic activation. For the schoolchildren, a coordinated activation of both sympathetic and parasympathetic regulation modes was the most optimal state for language learning.
A Particle-Based Unified Model for Non-Newtonian Fluid Simulation

Through adding stress tensors into the Navier-Stokes equations, in this paper a non-Newtonian fluid simulation model is presented, it profits from the essential method of smoothed particle hydrodynamics, and can exhibit concurrently viscosity, elasticity and plasticity of non-Newtonian fluid, thereby a unified model for simulating non-Newtonian fluid is established. The proposed model are tested under various elastic and plastic parameter values, the experimental results show that non-Newtonian fluid with various elastic and plastic characteristic can be simulated realistically by using this model.
Representing Data Quality in Sensor Data Streaming Environments

Sensors in smart-item environments capture data about product conditions and usage to support business decisions as well as production automation processes. A challenging issue in this application area is the restricted quality of sensor data due to limited sensor precision and sensor failures. Moreover, data stream processing to meet resource constraints in streaming environments introduces additional noise and decreases the data quality. In order to avoid wrong business decisions due to dirty data, quality characteristics have to be captured, processed, and provided to the respective business task. However, the issue of how to efficiently provide applications with information about data quality is still an open research problem. In this article, we address this problem by presenting a flexible model for the propagation and processing of data quality. The comprehensive analysis of common data stream processing operators and their impact on data quality allows a fruitful data evaluation and diminishes incorrect business decisions. Further, we propose the data quality model control to adapt the data quality granularity to the data stream interestingness.
Performance Evaluation of the Barrier Synchronization Solutions on the AP 1000

The aim of our research on AP1000 is to measure the overhead of some barrier algorithms and analyze how to take advantage of the AP100 architectural properties in order to obtain the best performance. We have measured the performance of various barrier algorithms on the AP1000. Our results were obtained by embedding barrier episodes inside a loop and averaging over a large number of operations. We have implemented five algorithms, two of which are centralized. Another one is a tree algorithm called U-mesh[1] that exploit the properties of the switching technologies found in machines li ke the AP1000. We also implemented the Dissemination barrier[2] and an algorithm with some characteristics of the U-mesh and Dissemination. The best performance was found in the last two algorithms. The barrier time was three times smaller than the barrier time of the MPI_Barrier collective routine implemented on AP1000[3].