Miracle-dz/UniFAR-FTUs huggingface dataset:
Prospective Study of Breakfast Eating and Incident Coronary Heart Disease in a Cohort of Male US Health Professionals

Background— Among adults, skipping meals is associated with excess body weight, hypertension, insulin resistance, and elevated fasting lipid concentrations. However, it remains unknown whether specific eating habits regardless of dietary composition influence coronary heart disease (CHD) risk. The objective of this study was to prospectively examine eating habits and risk of CHD. Methods and Results— Eating habits, including breakfast eating, were assessed in 1992 in 26 902 American men 45 to 82 years of age from the Health Professionals Follow-up Study who were free of cardiovascular disease and cancer. During 16 years of follow-up, 1527 incident CHD cases were diagnosed. Cox proportional hazards models were used to estimate relative risks and 95% confidence intervals for CHD, adjusted for demographic, diet, lifestyle, and other CHD risk factors. Men who skipped breakfast had a 27% higher risk of CHD compared with men who did not (relative risk, 1.27; 95% confidence interval, 1.06–1.53). Compared with men who did not eat late at night, those who ate late at night had a 55% higher CHD risk (relative risk, 1.55; 95% confidence interval, 1.05–2.29). These associations were mediated by body mass index, hypertension, hypercholesterolemia, and diabetes mellitus. No association was observed between eating frequency (times per day) and risk of CHD. Conclusions— Eating breakfast was associated with significantly lower CHD risk in this cohort of male health professionals.
Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network

Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.
Neuroblastoma patient outcomes, tumor differentiation, and ERK activation are correlated with expression levels of the ubiquitin ligase UBE4B

Background UBE4B is an E3/E4 ubiquitin ligase whose gene is located in chromosome 1p36.22. We analyzed the associations of UBE4B gene and protein expression with neuroblastoma patient outcomes and with tumor prognostic features and histology. Methods We evaluated the association of UBE4B gene expression with neuroblastoma patient outcomes using the R2 Platform. We screened neuroblastoma tumor samples for UBE4B protein expression using immunohistochemistry. FISH for UBE4B and 1p36 deletion was performed on tumor samples. We then evaluated UBE4B expression for associations with prognostic factors and with levels of phosphorylated ERK in neuroblastoma tumors and cell lines. Results Low UBE4B gene expression is associated with poor outcomes in patients with neuroblastoma and with worse outcomes in all patient subgroups. UBE4B protein expression was associated with neuroblastoma tumor differentiation, and decreased UBE4B protein levels were associated with high-risk features. UBE4B protein levels were also associated with levels of phosphorylated ERK. Conclusions We have demonstrated associations between UBE4B gene expression and neuroblastoma patient outcomes and prognostic features. Reduced UBE4B protein expression in neuroblastoma tumors was associated with high-risk features, a lack of differentiation, and with ERK activation. These results suggest UBE4B may contribute to the poor prognosis of neuroblastoma tumors with 1p36 deletions and that UBE4B expression may mediate neuroblastoma differentiation.
Real Projects with Informal Models

Informal models help to generate a shared understanding about the software to be developed as early as possible. In this paper we describe how we use informal models to react to different initial situations for project teams in our capstone course. We explain how we handle three cases ranging from projects without requirements to projects with detailed specifications. Project participants are convinced of the advantages of informal models and agree that they improve the outcome of their projects.
Commercial Science: A New Arena for Gender Differences in Scientific Careers?

This paper examines gender differences in the participation of university life science faculty in commercial science. In part based on interviews, we develop hypotheses regarding how scientists’ career achievements—their productivity, co-authorship networks, and institutional affiliations—have different effects on whether male and female faculty will become “academic entrepreneurs”. We then statistically examine this framework in a case cohort dataset containing the career histories of 6,000 life scientists. We find that participation in for-profit ventures, which we measure as the hazard of joining the scientific advisory board (SAB) of a biotechnology firm, is a new arena in which gender differences are sharp: compared to men, women life scientists are far less likely to receive compensated advisory roles at for-profit biotechnology companies. Moreover, the gap in participation rates persists after conditioning on numerous measures of human and social capital. We also find that this gender difference is contoured by a number of factors, such as co-authorship network structure and the level of institutional support for commercial science. Surprisingly, we find that the (conditional) gender gap is largest among scientists employed at high prestige institutions.
Accurate and E cient Filtering using Anistropic Filter Decomposition

E cient ltering remains an important challenge in computer graphics, particularly when lters are spatially-varying, have large extent, and/or exhibit complex anisotropic pro les. We present an e cient ltering approach for these di cult cases based on an isotropic lter decomposition (IFD). By decomposing complex lters into linear combinations of b simpler, displaced isotropic kernels, and precomputing a compact pre ltered dataset, we are able to interactively apply any number of potentially transformed lters to a signal. Our performance scales linearly with the size of the decomposition s n (i.e.O(s) time), not the size n nor the dimensionality of the lter, and our pre ltered data requires O(bn) storage, comparing favorably to the state-of-the-art. We apply IFD to interesting problems in image processing and realistic rendering. Key-words: Filtering, Compressive Sensing, Rendering ∗ Footnote for rst author † Shared foot note ‡ Footnote for second author § etc ha l-0 08 54 19 3, v er si on 1 26 A ug 2 01 3 Filtrage E cace par Decomposition Isotrope Résumé : Les opérations de ltrage en synthèse/analyse d'images sont coûteuses à e ectuer lorsque les ltres varient spatialement, sont très étendus et/ou très anisotropes. Nous présentons dans ce cas précis une méthode pour rendre le ltrage e cace, basée sur une décomposition du ltre en une combinaison linéaire de ltres isotropes, en translation. Le coût de notre méthode est linéaire par rapport au nombre de ltres utilisés dans la décomposition, et ne dépend pas de la taille des données ltrées. Nous en présentons di érentes applications, en analyses d'images et en rendu. Mots-clés : Filtrage, Compressive Sensing, Rendu ha l-0 08 54 19 3, v er si on 1 26 A ug 2 01 3 Isotropic Filter Decomposition 3 Figure 1: Illustrating the IFD for 2D images (Section 4.1). We decompose an input lter into a sum of displaced kernels Gl that are invariant under transforms S (here, 2D rotations). This allows us interactive lter the input signal with any transformed (by Sx ∈ S) copy of the lter, all with only a small number of lookups into pre ltered data. We similarly apply our theory to spherical ltering (Section 4.2)
Interaction Through Non-interaction: Context Awareness and Distributed Applications

Spoken interaction with machines is not yet commonplace, despite extensive research and some commercial successes. Systems that allow unlimited conversation with arbitrary speakers are still to be seen. In addition to computational difficulties, environmental noise, social issues, multiple languages, and the general ambiguity of human dialogue, one fundamental aspect behind the problem is the generic difficulty of determining the space of discourse. To limit this semantic search space, we hope to get a little help from the contextual info available in ubiquitous computing environments. We discuss some developments in the areas of user profiling, context awareness, sensor networks, social clues, and semantic networks, together with some expectations of how the gained additional information can enhance spoken interaction. We then look at some applications made possible by these developments and suggest topics for further research.
The GrabCut Segmentation Technique as Used in the Study of Tree Image Extraction

Tree image segmentation is an important algorithm and technique to separate an individual tree from the surrounding landscape in a photographic image. In recent years, this problem is a hot issue for scientific workers in forestry research field. In order to get a better processing result to the problem that a large number of holes and transparent phenomenon appear in the internal area of tree image, this paper makes use of GrabCut algorithm to extract the object tree. GrabCut is an innovative segmentation technique that uses both region and boundary information in order to perform segmentation. This approach based on optimization by graph-cut has been developed which combines both texture and edge information. The user interaction of this algorithm is friendly and the alpha-matte around an object tree boundary and colors of foreground pixels estimating is scientific. Therefore, it is suited for tree image extraction. The experimental results show that it is very effective when the background is not so complex. But when there are a lot of green plants in the background, the extraction result is not ideal. And because the delay due to the Graph Cut step is still noticeable, so the segmentation needs much computational time. Methods for incrementally updating the segmentation speed and accuracy could be explored in the future research.
Crystallization-induced stress in reactively sputter-deposited molybdenum nitride thin films

Thin films of molybdenum nitride have been produced by reactive dc magnetron sputtering of Mo in an Ar-N 2 gas mixture. The residual stress of a film having nearly the stoichiometric composition of Mo 2 N was measured as a function of annealing temperature by a wafer-curvature-based technique. When the initially amorphous film was heated to about 500 ° C, crystallization occurred and the stress of the film drastically changed from -0.75 to 1.65 GPa. Using a combination of X-ray diffraction, energy-filtered electron diffraction, electron-energy-loss spectrometry and atomic force microscopy, the stress development mechanism during crystallization was found to be a volumetric shrinkage of the film. The stress increase can also be explained in terms of an increase in the average grain size of the crystallized film and its densification.
PHD3-mediated prolyl hydroxylation of nonmuscle actin impairs polymerization and cell motility

Actin filament formation plays an essential role in cell movement, and posttranslational modifications regulate actin filament assembly. Prolyl hydroxylase 3 (PHD3) interacts with nonmuscle actins in HeLa cells and catalyzes prolyl hydroxylation of actin. Inhibition of PHD3 activity or expression increases F-actin formation and cell motility.
Two Oxidosqualene Cyclases Responsible for Biosynthesis of Tomato Fruit Cuticular Triterpenoids1[C][W][OA]

The first committed step in triterpenoid biosynthesis is the cyclization of epoxysqualene into various triterpene alcohol isomers, a reaction catalyzed by oxidosqualene cyclases (OSCs). The different OSCs have characteristic product specificities, which are mainly due to differences in the numbers of high-energy intermediates the enzymes can stabilize. The goal of this investigation was to clone and characterize OSCs from tomato (Solanum lycopersicum), a species known to accumulate δ-amyrin in its fruit cuticular wax, in order to gain insights into the enzymatic formation of this particular triterpenoid. We used a homology-based approach to isolate two tomato OSCs and tested their biochemical properties by heterologous expression in yeast as well as overexpression in tomato. One of the enzymes was found to be a product-specific β-amyrin synthase, while the other one was a multifunctional OSC synthesizing 48% δ-amyrin and six other products. The product spectra of both OSCs together account for both the range and the relative amounts of the triterpenoids found in the fruit cuticle. Both enzymes were expressed exclusively in the epidermis of the tomato fruit, indicating that their major function is to form the cuticular triterpenoids. The relative expression levels of both OSC genes, determined by quantitative reverse transcription-polymerase chain reaction, were consistent with product profiles in fruit and leaves of the tomato cultivar MicroTom. However, the transcript ratios were only partially consistent with the differences in amounts of product triterpenoids between the tomato cultivars MicroTom, M82, and Ailsa Craig; thus, transcriptional control of the two OSCs alone cannot explain the fruit triterpenoid profiles of the cultivars.
The Open and Closed Styles of Talent Acquisition and Management : Which takes an edge where

The phenomenal success of Free/Open Source Software during the recent years has led researchers in all related fields to re-think and re-evaluate the studies and concepts, especially those which the open source violates. Talent management and talent acquisition is no exception to this. The sudden rise in open source projects and their success is a cause of motivation for all of us to evaluate their philosophy and implement the same. Proper talent management and acquisition of talent can play a key role in strengthening the industry. Traditional closed management with their classic way of functioning may still look at the open source model as a big mystery. Open source has violated many of the theories of Software Engineering like limited team size, decentralized project management, etc. The concepts of self-motivated individuals, adaptive division of labor, informal or indirect leadership, etc. are the contributions of the open source to the world. In this paper we discuss these concepts and state their differences with the way of working of closed talent management. Even though open source has various benefits in working, but requirement of a good coordination process, redundancy of development, versioning problems, etc. pose a limitation. We present an analogy of the talent management in the two concepts with that of the natural ecosystem. Talent in open source works in a manner similar to the ant colonies. Here the process is more community oriented and there is no single dictator power. The proprietary, on the other hand, works on talent as the lions ruling the den. There is a hierarchy and that needs to be respected. Only the leader has the right to make final decision, and that is binding. We also take a look into the present conditions of meltdown and show how the open source way is better suited for this scenario. Even though the present firms may be seeing a setback at this time of recession, the impact on firms with an open source way of working is much lighter or none at all. We also discuss the interactions between the two styles of talent management and present a model that can be adopted by the proprietary companies to benefit from open model. Our arguments of the paper not only deal with software, but also on content that comes under the domain of Creative Commons. We have taken the examples of Wikipedia, Sourceforge.net, Linux etc. for the various issues we discuss.
Granger Causality Analysis of Steady-State Electroencephalographic Signals during Propofol-Induced Anaesthesia

Changes in conscious level have been associated with changes in dynamical integration and segregation among distributed brain regions. Recent theoretical developments emphasize changes in directed functional (i.e., causal) connectivity as reflected in quantities such as ‘integrated information’ and ‘causal density’. Here we develop and illustrate a rigorous methodology for assessing causal connectivity from electroencephalographic (EEG) signals using Granger causality (GC). Our method addresses the challenges of non-stationarity and bias by dividing data into short segments and applying permutation analysis. We apply the method to EEG data obtained from subjects undergoing propofol-induced anaesthesia, with signals source-localized to the anterior and posterior cingulate cortices. We found significant increases in bidirectional GC in most subjects during loss-of-consciousness, especially in the beta and gamma frequency ranges. Corroborating a previous analysis we also found increases in synchrony in these ranges; importantly, the Granger causality analysis showed higher inter-subject consistency than the synchrony analysis. Finally, we validate our method using simulated data generated from a model for which GC values can be analytically derived. In summary, our findings advance the methodology of Granger causality analysis of EEG data and carry implications for integrated information and causal density theories of consciousness.
UV stepwise cured fabrication of glass fiber/acrylate composites: Effects of exposure dose on curing uniformity and interlaminar shear strength

To determine the quality of composites cured with UV light ply by ply, the effects of exposure dose on the cure uniformity and interlaminar shear strength of composite laminates were investigated. This was done by using UV power meter, differential scanning calorimetry, and the fiber optic strain sensors. According to differential photocalorimety results, the accumulative curing effect is remarkable in the stepwise UV-curing process, which results in gradual improvement of the degree of cure of the underlying ply. The cure uniformity increased with increasing exposure dose, but higher exposure dose led to a decrease of the interlaminar shear strength. This was due to increased shrinking strain and residual stress according to the results from the fiber optic strain sensors.
A semantic concept for the mapping of low-level analysis data to high-level scene descriptions

Along with the growing need for security, an increasing amount of surveillance content is created. It is indispensable to index the content in advance in order to enable quick and reliable searches on the output of hundreds or thousands of surveillance sensors installed at a single facility. For this purpose, the concept of Smart Indexing and Retrieval (SIR) enables cost efficient searches by generating high-level meta data. The generation of this meta data has to be done automatically, based on the low-level features extracted by content analysis algorithms. Creating it manually becomes more and more difficult to handle in reasonable time at reasonable costs. Whereas formerly proposed approaches have been strongly application dependent, in this thesis, a generic concept for mapping the results of the lowlevel content analysis data to semantic event descriptions and its application is presented. The constituting elements of this approach and their underlying concepts as well as an introduction to their application are shown. The main contribution of the approach are the generality and the early stage at which the step from low-level to high-level representation is taken. This reasoning in the meta data domain is performed on small time frames while the reasoning on complexer scenes is done in the semantic space. Even an unsupervised self-assessment is possible using the semantic approach.
Thermal Cycling Effects in an Aged Ni-rich Ti–Ni Shape Memory Alloy

Etude experimentale par calorimetrie differentielle a balayage et par microscopie electronique en transmission dans l'alliage Ti-51% at Ni soumis a differents traitements de vieillissement et a differents cycles thermiques. Deux types de cycles sont adoptes: le cycle complet provoquant la transformation matrice B 2 ⇇phase intermediaire rhomboedrique R⇄martensite M, le cycle incomplet conduit seulement a la transformation B 2 ⇄R. On observe des variations significatives de la temperature de transformation et de la microstructure en fonction du cycle thermique. Discussion des resultats
Skeleton based action recognition with convolutional neural network

Temporal dynamics of postures over time is crucial for sequence-based action recognition. Human actions can be represented by the corresponding motions of articulated skeleton. Most of the existing approaches for skeleton based action recognition model the spatial-temporal evolution of actions based on hand-crafted features. As a kind of hierarchically adaptive filter banks, Convolutional Neural Network (CNN) performs well in representation learning. In this paper, we propose an end-to-end hierarchical architecture for skeleton based action recognition with CNN. Firstly, we represent a skeleton sequence as a matrix by concatenating the joint coordinates in each instant and arranging those vector representations in a chronological order. Then the matrix is quantified into an image and normalized to handle the variable-length problem. The final image is fed into a CNN model for feature extraction and recognition. For the specific structure of such images, the simple max-pooling plays an important role on spatial feature selection as well as temporal frequency adjustment, which can obtain more discriminative joint information for different actions and meanwhile address the variable-frequency problem. Experimental results demonstrate that our method achieves the state-of-art performance with high computational efficiency, especially surpassing the existing result by more than 15 percentage on the challenging ChaLearn gesture recognition dataset.
xMAP Technology: Applications in Detection of Pathogens

xMAP technology is applicable for high-throughput, multiplex and simultaneous detection of different analytes within a single complex sample. xMAP multiplex assays are currently available in various nucleic acid and immunoassay formats, enabling simultaneous detection and typing of pathogenic viruses, bacteria, parasites and fungi and also antigen or antibody interception. As an open architecture platform, the xMAP technology is beneficial to end users and therefore it is used in various pharmaceutical, clinical and research laboratories. The main aim of this review is to summarize the latest findings and applications in the field of pathogen detection using microsphere-based multiplex assays.