Miracle-dz/UniFAR-FTUs huggingface dataset:
OLIGOCENE ANGIOSPERM WOODS FROM NORTHWESTERN BOHEMIA, CZECH REPUBLIC

Two species of fossil angiosperm wood are described from the Oligocene of northwestern Bohemia in the Czech Republic. One specimen from Kadan–Zadni vrch Hill is identified as Cercidiphylloxylon kadanense Prakash et al. Because of its superior preservation, the specimen is designated as an epitype to the original holotype specimen of the species and genus. Cercidiphylloxylon kadanense is known only from the locality of Kadan–Zadni vrch Hill, and it represents the oldest fossil wood of true Cercidiphyllum Sieb. & Zucc. Three other wood specimens from Zichov are attributed to Liquidambaroxylon speciosum Felix. Modern wood of some species of Cercidiphyllum Sieb. & Zucc. , Liquidambar L. , Altingia Noronha, Corylopsis Sieb. & Zucc. , Distylium Sieb. & Zucc. , and Hamamelis L. was examined to determine how to distinguish the wood of Cercidiphyllum (Cercidiphyllaceae) from similar woods of Hamamelidaceae. The number of bars in the scalariform perforation plates of the vessels is about 40 in Cercidiphyllum, and about 20 in the Hamamelidaceae. Rays are variable, even at intra-specific level, and are not suitable for distinguishing these woods. These criteria were found to be useful in evaluating affinities of the fossil woods.
On a general class of conditional tests for family‐based association studies in genetics: the asymptotic distribution, the conditional power, and optimality considerations

Family‐based association tests (FBATs) provide simple and powerful tests to detect association between a genetic marker and a disease‐susceptibility locus, manifest in subjects by a phenotype or disease trait. Here we propose a new class of conditional tests for family‐based association studies that includes most of the established tests and their generalizations. The class of tests is very general; it can be applied to longitudinal and multivariate traits or phenotypes, multiple genetic markers, and many other situations not yet discussed in the literature. For any test in this class, we derive the asymptotic distribution under the null hypothesis, the conditional power under any alternative hypothesis, and the optimal offset for single degree of freedom tests. The proposed methodology is illustrated with a genetic study of asthma. Genet. Epidemiol. 23:165–180, 2002. © 2002 Wiley‐Liss, Inc.
Return on investment of different combinations of bupropion SR dose and behavioral treatment for smoking cessation in a health care setting: an employer's perspective.

UNLABELLED The net benefit (i.e., benefits minus costs) of sustained-release (SR) bupropion for smoking cessation from an employer's perspective has previously been evaluated in clinical trials including frequent, in-person behavioral counseling and manufacturer recommended dosing but not in actual practice settings and lower dosing. OBJECTIVES The objective of this research was to determine the return on investment (ROI) and internal rate of return (IRR) from an employer's perspective of two dosing schedules of bupropion SR in combination with behavioral interventions of minimal intensity (tailored mailings, TM) or moderate intensity (proactive telephone calls, PTC) in an actual practice setting. METHODS An open-label, randomized trial with 1-year follow-up was conducted in a large health system (Group Health Cooperative) based in Seattle, WA. Participants included 1524 adult smokers interested in quitting smoking. Participants were randomly assigned to receive 150 mg of bupropion SR daily and PTC (n=382), 150 mg of bupropion SR daily and TM (n=381), 300 mg of bupropion SR daily and PTC (n=383) or 300 mg of bupropion SR daily and TM (n=378). Sufficient medications for 8 weeks of dosing were provided to patients. The primary outcome measure of the field trial was self-reported point-prevalence 7-day nonsmoking status at 12 months, and the primary outcome measures of the economic analysis were employer net benefit, employer ROI, and the ROI-associated IRR using 2002 dollars. RESULTS Using net benefit, the 300-mg/PTC and the 150-mg/PTC treatments were approximately equally preferred. Using ROI or IRR, both the 150-mg/TM and 150-mg/PTC treatments were about equally preferred, with IRR values of 31.7% and 31.4%, respectively. Under a pessimistic scenario regarding effectiveness and costs, 150 mg/PTC became more cost-effective than 150 mg/TM, and employer IRR for 150 mg/PTC was 13%. Under an optimistic scenario IRR exceeded 45% for all treatments. CONCLUSIONS These results suggest that employers can receive competitive returns on investment from sponsoring smoking cessation programs, that 150 mg of bupropion doses yield better returns than 300-mg doses, and that PTC treatments should be preferred to TM if smoking cessation rates in the targeted employee population are lower than those in the study population.
Monoclonal Antibodies to Plant Cell Wall Xylans and Arabinoxylans

Two rat monoclonal antibodies have been generated to plant cell wall (1→4)-β-D-xylans using a penta-1,4-xylanoside-containing neoglycoprotein as an immunogen. The monoclonal antibodies, designated LM10 and LM11, have different specificities to xylans in relation to the substitution of the xylan backbone as indicated by immunodot assays and competitive-inhibition ELISAs. LM10 is specific to unsubstituted or low-substituted xylans, whereas LM11 binds to wheat arabinoxylan in addition to unsubstituted xylans. Immunocytochemical analyses indicated the presence of both epitopes in secondary cell walls of xylem but differences in occurrence in other cell types.
MICROLENSING BINARIES WITH CANDIDATE BROWN DWARF COMPANIONS

Brown dwarfs are important objects because they may provide a missing link between stars and planets, two populations that have dramatically different formation histories. In this paper, we present the candidate binaries with brown dwarf companions that are found by analyzing binary microlensing events discovered during the 2004–2011 observation seasons. Based on the low mass ratio criterion of q < 0.2, we found seven candidate events: OGLE-2004-BLG-035, OGLE-2004-BLG-039, OGLE-2007-BLG-006, OGLE-2007-BLG-399/MOA-2007-BLG-334, MOA-2011-BLG-104/OGLE-2011-BLG-0172, MOA-2011-BLG-149, and MOA-201-BLG-278/OGLE-2011-BLG-012N. Among them, we are able to confirm that the companions of the lenses of MOA-2011-BLG-104/OGLE-2011-BLG-0172 and MOA-2011-BLG-149 are brown dwarfs by determining the mass of the lens based on the simultaneous measurement of the Einstein radius and the lens parallax. The measured masses of the brown dwarf companions are 0.02 ± 0.01 M☉ and 0.019 ± 0.002 M☉ for MOA-2011-BLG-104/OGLE-2011-BLG-0172 and MOA-2011-BLG-149, respectively, and both companions are orbiting low-mass M dwarf host stars. More microlensing brown dwarfs are expected to be detected as the number of lensing events with well-covered light curves increases with new-generation searches.
The relationship between histamine secretion and 45calcium‐uptake by mast cells

1. Unstimulated mast cells from the peritoneal cavity of the rat take up 45Ca: the initial phase of rapid uptake being complete after 1 min incubation of the cells with the isotope. Stimulation of the mast cells with an antigen‐antibody reaction, dextran or concanavalin A induces an increase in the uptake of 45Ca which is accompanied by a release of granular material: this increase in 45Ca uptake is also complete in 1 min. The majority of the stimulated 45Ca uptake cannot be explained in terms of binding of Ca to released granular material, or to an enlargement in either the extracellular compartment or the cell surface area.
Robust Stability of Switched Cohen–Grossberg Neural Networks With Mixed Time-Varying Delays

By combining Cohen-Grossberg neural networks with an arbitrary switching rule, the mathematical model of a class of switched Cohen-Grossberg neural networks with mixed time-varying delays is established. Moreover, robust stability for such switched Cohen-Grossberg neural networks is analyzed based on a Lyapunov approach and linear matrix inequality (LMI) technique. Simple sufficient conditions are given to guarantee the switched Cohen-Grossberg neural networks to be globally asymptotically stable for all admissible parametric uncertainties. The proposed LMI-based results are computationally efficient as they can be solved numerically using standard commercial software. An example is given to illustrate the usefulness of the results
Anisotropic diffusion pyramids for image segmentation

We introduce the Anisotropic Diffusion Pyramid (ADP), a structure for multiresolution image processing. We also develop the ADP for use in region-based segmentation. The pyramid is constructed using the anisotropic diffusion equations, creating an efficient scale-space representation. Segmentation is accomplished using pyramid node linking. Since anisotropic diffusion preserves edge localization as the scale is increased, the region boundaries in the coarse-to-fine ADP segmentation are accurately delineated. An application to segmentation of remotely sensed data is provided. The results of ADP segmentation are compared to Gaussian-based pyramidal segmentation. The examples show that the ADP has a superior ability to subdivide the image into integral groupings, minimizing the error in boundary localization and in pixel intensity. <<ETX>>
Bagging Ensembles for the Diagnosis and Prognostication of Alzheimer's Disease

Alzheimer's disease (AD) is a chronic neurodegenerative disease, which involves the degeneration of various brain functions, resulting in memory loss, cognitive disorder and death. Large amounts of multivariate heterogeneous medical test data are available for the analysis of brain deterioration. How to measure the deterioration remains a challenging problem. In this study, we first investigate how different regions of the human brain change as the patient develops AD. Correlation analysis and feature ranking are performed based on the feature vectors from different stages of the pathologic process in Alzheimer disease. Then, an automatic diagnosis system is presented, which is based on a hybrid manifold learning for feature embedding and the bootstrap aggregating (Bagging) algorithm for classification.We investigate two different tasks, i.e. diagnosis and progression prediction. Extensive comparison is made against Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT) and Random Subspace (RS) methods. Experimental results show that our proposed algorithm yields superior results when compared to the other methods, suggesting promising robustness for possible clinical applications.
Porous Monoliths : The Newest Generation of Stationary Phases for HPLC and Related Methods

2 Introduction A monolith can be described as a single large “particle” without interparticular voids. Consequently, all the mobile phase flows through the stationary phase greatly accelerating the rate of mass transfer. In contrast to diffusion, which is the typical driving force for mass transfer within the pores of particulate stationary phases, this convective flow through the pores enables a substantial increase in the separation speed of large molecules such as proteins. A thorough theoretical treatment of mass transfer within monolithic materials has recently been developed by Liapis.1 The first attempts to make “single-piece” stationary phases from highly swollen polymer gel2 and open-pore polyurethane foams3,4 during the late 1960s and the early 1970s were not particularly successful. Interest in these formats was revived in the late 1980s as novel approaches towards both separation media with significantly decreased interparticular void volumes (stacked membranes and rolled woven matrices) and true monoliths (compressed hydrophilic gels, macroporous polymer discs, columns, tubes and silica rods, in which the void volume is completely eliminated) were developed. Some of these stationary phases are now commercialized and their features are summarized in Table 1. A comparison of morphologies characteristic of packed and monolithic columns is presented in this issue by Rozing5 and a very detailed account of these materials can be found in a monograph published recently.6
THE FORMATION OF CATACLYSMIC VARIABLES: THE INFLUENCE OF NOVA ERUPTIONS

The theoretical and observed populations of pre-cataclysmic variables are dominated by systems with low-mass white dwarfs (WDs), while the WD masses in cataclysmic variables (CVs) are typically high. In addition, the space density of CVs is found to be significantly lower than in the theoretical models. We investigate the influence of nova outbursts on the formation and initial evolution of CVs. In particular, we calculate the stability of the mass transfer in the case where all of the material accreted on the WD is lost in classical novae and part of the energy to eject the material comes from a common-envelope-like interaction with the companion. In addition, we study the effect of an asymmetry in the mass ejection that may lead to small eccentricities in the orbit. We find that a common-envelope-like ejection significantly decreases the stability of the mass transfer, particularly for low-mass WDs. Similarly, the influence of asymmetric mass loss can be important for short-period systems and even more so for low-mass WDs; however, this influence likely disappears long before the next nova outburst due to orbital circularization. In both cases the mass-transfer rates increase, which may lead to observable (and perhaps already observed) consequences for systems that do survive to become CVs. However, a more detailed investigation of the interaction between nova ejecta and the companion and the evolution of slightly eccentric CVs is needed before definite conclusions can be drawn.
Low-order control design for chatter suppression in high-speed milling

Chatter is an instability phenomenon in machining processes which must be avoided at all times. The occurrence of chatter can be predicted using stability lobes diagrams. In this paper a low-order active chatter control design is proposed, which enables dedicated shaping of the chatter stability boundary such that working points of higher machining productivity become feasible while avoiding chatter. The control design problem is cast into a nonsmooth optimization problem, which is solved using bundle methods. A static output feedback controller is designed for an illustrative example, which illustrates the power of the proposed methodology.
Response of radiochemotherapy-associated cerebral edema to a phytotherapeutic agent, H15

Twelve patients with brain tumors and progressive edema caused by tumor progression or radiochemotherapy-related leukoencephalopathy were treated with H15, a phytotherapeutic anti-inflammatory agent. Edema was reduced in two of seven patients with glioblastoma with tumor progression and in three of five patients with treatment-related leukoencephalopathy. All patients with leukoencephalopathy improved clinically for several months.
TEMPO AND PROSODY IN TURKISH TAKSIM IMPROVISATION

Instrumental improvisation in Turkish makam music, the taksim, is considered to be free-rhythm, that is its rhythm develops without the underlying template of a meter or continuous organized pulsation. In this paper, we want to examine how in this setting, rhythmic idioms are formed and maintained throughout a performance. For this, we will apply a simple signal processing approach. We show differences that can be observed between performers, and raise the question if a tempo could be evoked by certain regularities in the occurring rhythmic elaborations.
[Screening of protein markers on the plasma of obese young men].

OBJECTIVE To screen obesity-related protein biomarkers of young men using differential proteomic method and OffGel electrophoresis. METHODS Ten male obese volunteers with the age of 18 - 44 years were selected. The control group was matched with the ratio of 1:1 considering the factors of age and gender etc. Two milliliter venous blood was collected after 8 hours fasting. Albumin and IgG were removed from the plasma samples with highly specific immune-affinity method. Then the peptide-mixed samples were separated by pI with OffGel electrophoresis after solution digestion. Further separation and identification were performed by Nano HPLC-Chip-MS/MS system. The different proteins between the two groups were compared. RESULTS Overall, 332 and 301 proteins were identified in the obesity and control groups, respectively. There were 43 proteins with significant differences between the two groups, 17 of which were in the control group and 26 in the obesity group. Protein function annotation results showed that the level of adiponectin was lower while the level of C-reaction protein and three other phosphatases were higher in the obesity group compared with the control group. CONCLUSION Adiponectin, C-reaction protein and three phosphatases were closely related to obesity of young men.
Single-Level and Multilevel Mediation Analysis

Mediation analysis is a statistical approach used to examine how the effect of an independent variable on an outcome is transmitted through an intervening variable (mediator). In this article, we provide a gentle introduction to single-level and multilevel mediation analyses. Using single-level data, we demonstrate an application of structural equation modeling (SEM) in estimating mediation models with multiple mediators and multiple outcomes. We also describe the estimation and interpretation of a 2→1→1 multilevel mediation model in which the first, second, and third numbers correspond to the measurement levels of the independent, mediator, and outcome variables, respectively. We present two numerical examples that use simulated data based on published studies in adolescence research to demonstrate how to specify, estimate, and interpret the results of single-level and multilevel mediation analyses to answer key research questions. We briefly discuss underlying assumptions required to make a valid causal interpretation of a mediation analysis.
Rapid separation of uranium and plutonium by extraction chromatography for determination by thermal ionisation mass spectrometry

A rapid method based upon extraction chromatography was developed for the separation of U and Pu from solutions of spent nuclear fuel for thermal ionization mass spectrometry. The method involves retention of Pu(IV) and U(VI) on a UTEVA column, thereby accomplishing a separation of these actinides from fission products. A separation of Pu from U was then achieved by selective elution of Pu. Two approaches for accomplishing this selective elution were investigated: (a) competitive complexation using oxalic acid and (b) reduction of Pu(IV) to Pu(III) using ascorbic acid. The latter method yielded a purer isolate as the former method yielded a Pu isolate that was contaminated with an unacceptable amount of U. The UTEVA separation was 3–5 times more rapid than the existing separation and was considered to be more suitable for automation.
Scalable and Distributed Control Laws for Network Flow Optimization

We investigate the design of scalable and distributed control laws for flow control in a large network – equivalently an optimization problem. We identify the implications of the desired “plug-and-play” property of such protocols and propose design principles upon which our algorithm should be built: the class of our algorithms must achieve little extra communication cost and global asympto tic stability. General structural properties of such algorithms are presented and we finally provide an algorithm which satisfies our design principles for a network with heterogeneous delays.
Efficient hemodynamic states stimulation using fNIRS data with the extended Kalman filter and bifurcation analysis of balloon model

This paper introduces a stochastic hemodynamic system to describe the brain neural activity based on the balloon model. A continuous-discrete extended Kalman filter is used to estimate the nonlinear model states. The stability, controllability and observability of the proposed model are described based on the simulation and measurement data analysis. The observability and controllability characteristics are in- troduced as significant factors to validate the preference of different hemodynamic factors to be considered for diagnosis and monitoring in clinical applications. This model also can be efficiently applied in any monitoring and control platform include brain and for study of hemodynamics in brain imaging modalities such as pulse oximetry and functional near infrared spectroscopy. The work is on progress to extend the proposed model to cover more hemodynamic and neural brain signals for real-time in-vivo application.
AN OVERVIEW OF EFFICIENT NONLINEAR FILTERING - FROM KALMAN FILTER TO PARTICLE FILTERS TO EIS

Generally, in most applied fields, the dynamic state space models are of nonlinearity with non-Gaussian noise. However, as a famous and simple algorithmic filter, Kalman filter can only estimate linear system with Gaussian noise state space models. The Extend Kalman filter and the Unscented Kalman filter still have limitations and therefore are not accurate enough for nonlinear estimation. The Bayesian filtering approach which is based on sequential Monte Carlo sampling is called particle filters. Particle filters were developed and widely applied in various areas because of the ability to process observations represented by nonlinear state-space models where the noise of the models can be non-Gaussian. However, particle filters suffer from two long-standing problems that are referred as sample degeneracy and impoverishment. To fight these problems, resampling step is necessary. In this review work, a variety of resampling of particle filter methods as well as their characteristics and algorithms are introduced and discussed, such as Sampling-Importance resampling, Auxiliary particle filter, Optimal resampling and so on to combat against the sample degeneracy and impoverishment. Finally, efficient importance sampling, as a more accurate method, capable of estimating high-dimensional integration and carrying out global optimization, will be introduced and compared to particle filters.
Size and distortion invariant object recognition by hierarchical graph matching

A neural system is presented for invariant object recognition. Its flexibility is demonstrated with freely taken camera images of human faces. The system is an application of the dynamic link architecture, which owes its strength to an enhancement of tradition neural networks by a new kind of variable to express the hierarchical grouping of neurons. This capability is used to group primitive local feature detectors (Gabor-based wavelets) into composite feature detectors (jets) and to preserve neighborhood relationships between jets when they lose position information on the way from the image domain to the object domain. Due to the potential for grouping, objects can be represented as attributed graphs, with jets serving as attributes. Recognition is formulated as graph matching and is implemented as a topologically constrained diffusion of image-object links. A hierarchical sequence of matches, from low-frequency components of jets to high-frequency components, is used. Size invariance is achieved by interposing diffusion steps in magnification space. The system is implemented on a network of transputers