Miracle-dz/UniFAR-FTUs huggingface dataset:
1 NOTCH SIGNALLING MODULATES HYPOXIA-INDUCED NEUROENDOCRINE DIFFERENTIATION OF HUMAN PROSTATE CANCER CELLS

NOTCH SIGNALLING MODULATES HYPOXIA-INDUCED NEUROENDOCRINE DIFFERENTIATION OF HUMAN PROSTATE CANCER CELLS Giovanna Danza, Claudia Di Serio, Fabiana Rosati, Giuseppe Lonetto, Niccolò Sturli, Doreen Kacer, Antonio Pennella, Giuseppina Ventimiglia, Riccardo Barucci, Annamaria Piscazzi, Igor Prudovsky, Matteo Landriscina, Niccolò Marchionni, Francesca Tarantini Endocrine Unit, Department of Clinical Physiopathology, University of Florence (Italy); Geriatric Medicine Unit, Department of Critical Care Medicine and Surgery, University of Florence (Italy); Maine Medical Center Research Institute (MMCRI), Scarborough, ME (USA); Department of Surgical Sciences, Section of Anatomic Pathology and Cytopathology, University of Foggia; Clinical Oncology Unit, Department of Medical Sciences, University of Foggia (Italy); IRCCS CROB Rionero in Vulture (Italy). *The Authors participated equally to the work. Running title: Notch and neuroendocrine differentiation of prostate cancer
Inconsistency of the slope and the volume intercept of the end-systolic pressure-volume relationship as individual indexes of inotropic state in conscious dogs: presentation of an index combining both variables.

We tested the ability of the slope (Emax) and the volume intercept (Vo) of the end-systolic pressure-volume relationship (ESPVR) to indicate contractility changes in conscious dogs instrumented with sonomicrometers measuring left ventricular diameter in three orthogonal axes and a left ventricular pressure microtransducer. ESPVRs were generated by inferior vena caval occlusion under control conditions (C1 and C2) and during enhanced (I+) and depressed (I-) inotropic states achieved by infusion of dobutamine and injection of propranolol, respectively. No significant difference between the first control (C1) and I+ or between the second control (C2) and I- were found for either Emax (C1, 5.31 +/- 1.68 mm Hg/ml, mean +/- SD; I+, 5.37 +/- 1.44; C2, 5.20 +/- 1.62; I-, 4.18 +/- 1.32) or Vo (C1, 10.3 +/- 9.6 ml; I+, 7.3 +/- 9.1; C2, 9.9 +/- 9.0; I-, 12.7 +/- 12.5), despite significant changes in other indexes of contractility. Comparison of changes in Emax in individual animals in response to I+ and I- revealed that 63% were nonsignificant, 28% were significant and expected, and 9% were significant and paradoxical. Within defined volume limits and irrespective of individual changes in Emax and Vo, in all animals I+ shifted the ESPVR above and to the left of C1 and I- shifted the ESPVR below and to the right of C2. We thus integrated the changes in Emax and Vo by measuring the area beneath each ESPVR between defined limits of end-systolic volume. The values for area were: C1, 612 +/- 150 mm Hg.ml; I+, 745 +/- 191 (p less than .001); C2, 520 +/- 198; I-, 420 +/- 139 (p less than .001). We conclude that (1) neither Emax nor Vo are individually reliable indexes of changed contractility, and (2) the area beneath the ESPVR between defined end-systolic volume limits is a consistent indicator of variations in inotropic state.
Human eye movement behavior in action observation

In human joint action, visual attention is required to monitor the actions of others, but also for controlling and planning one's own actions. In a ‘cup-clinking’ scenario we investigated how people divide their attention between both tasks. Our participants first watched an actor and than performed their own task in the same way as the actor or in a pre-defined different (complementary) way. Using an eye-tracker, we found that people closely follow the action of the actor until they are given a signal to start their own movement. Interestingly, in the complementary condition our participants inspected the cup of the actor at the position they were going to grab their own cup. This result may merely reflect movement preparation. We go one step further and propose that visual information is extracted and transformed into parameters for the upcoming movement. Thus, attention may facilitate movement preparation while simultaneously the joint action partner is monitored.
Characteristic scale detection in remote-sensing data

Texture models are widely in use for image content description. In remote-sensing images textures occur at very different scales, requiring the application of multiple texture models. We present an algorithm based on a multi-scale random field model to detect the characteristic scales at which textures are present, so that texture models can be applied to a few selected scales only. The algorithm is compared to two other models based on Haralick and wavelet features. Such a scale selection scheme reduces computation time and minimizes the index size. Both are critical parameters in the design of large remote-sensing databases with content-based retrieval services.
RNAfbinv: an interactive Java application for fragment-based design of RNA sequences

SUMMARY In RNA design problems, it is plausible to assume that the user would be interested in preserving a particular RNA secondary structure motif, or fragment, for biological reasons. The preservation could be in structure or sequence, or both. Thus, the inverse RNA folding problem could benefit from considering fragment constraints. We have developed a new interactive Java application called RNA fragment-based inverse that allows users to insert an RNA secondary structure in dot-bracket notation. It then performs sequence design that conforms to the shape of the input secondary structure, the specified thermodynamic stability, the specified mutational robustness and the user-selected fragment after shape decomposition. In this shape-based design approach, specific RNA structural motifs with known biological functions are strictly enforced, while others can possess more flexibility in their structure in favor of preserving physical attributes and additional constraints. AVAILABILITY RNAfbinv is freely available for download on the web at http://www.cs. bgu.ac. il/~RNAexinv/RNAfbinv. The site contains a help file with an explanation regarding the exact use.
Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier

The simple Bayesian classi er (SBC) is commonly thought to assume that attributes are independent given the class, but this is apparently contradicted by the surprisingly good performance it exhibits in many domains that contain clear attribute dependences. No explanation for this has been proposed so far. In this paper we show that the SBC does not in fact assume attribute independence, and can be optimal even when this assumption is violated by a wide margin. The key to this nding lies in the distinction between classi cation and probability estimation: correct classi cation can be achieved even when the probability estimates used contain large errors. We show that the previously-assumed region of optimality of the SBC is a second-order in nitesimal fraction of the actual one. This is followed by the derivation of several necessary and several su cient conditions for the optimality of the SBC. For example, the SBC is optimal for learning arbitrary conjunctions and disjunctions, even though they violate the independence assumption. The paper also reports empirical evidence of the SBC's competitive performance in domains containing substantial degrees of attribute dependence. 1 THE SIMPLE BAYESIAN
Characterization of 19 microsatellite loci in the clonal monkshood Aconitum kusnezoffii (Ranunculaceae)

Premise of the study: Microsatellite loci were isolated and characterized from Aconitum kusnezoffii (Ranunculaceae) to estimate male and female reproductive success and evaluate the effects of clonal growth on sexual reproduction. Methods and Results: A genomic enrichment approach was used to develop microsatellite markers. In three investigated A. kusnezoffii populations, a total of 19 microsatellite loci were successfully amplified, and 13 of these loci were polymorphic. Most of the primer pairs designed for the identified loci also amplified corresponding microsatellite loci in A. barbatum var. puberulum and A. alboviolaceum. Conclusions: The identified microsatellite loci will be useful for quantifying male and female fitness in A. kusnezoffii and evaluating the effects of clonal growth on sexual reproduction.
Clinical Electroencephalography and Topographic Brajn Mapping

In evaluating a textbook it is necessary to measure what is accomplished versus the needs of the target audience. This text by Duffy, Iyer, and Sunvillo seems to have never made up its mind about the identity of its prospective audience. Chapter 1 is a useful overview of EEG history and technology. Chapters 2 through 9 comprise a rather detailed review of EEG technology that might be most appropriate for EEG fellows and EEG technologists. This material is probably dealt with in too much depth for the advanced medical student/rotator/ beginning neurology resident at whom the remainder of the book seems to be aimed. Chapter 10 is a thoughtful synopsis of the basic neurophysiology pertinent to clinical neurophysiology. Chapters 11 and 12 deal with some basic information useful in the interpretation of EEGs. Chapters 13 through 16 comprise a quick overview of some of the features of classical clinical EEG. Chapters 17 through 20 deal with some of the newer technologies in clinical neurophysiology. Fairly regularly, for further specific information the reader is referred to the literature, but specific citations are scarce or missing, and the reader seeking additional information is left without much of a guide. Finally, there is a chapter which attempts to relate EEG to other neurodiagnostic tests in a variety of conditions. Use of the International Classifications of Seizures and the Epilepsies/Epileptic Syndromes. or mention of why the authors feel those classifications are not appropriate, would be desirable in this chapter. The authors have produced a book which is long on the philosophy of neurophysiological testing but short on facts, other than those regarding technology. Perhaps this can be partially explained by the authors’ stated intent of concentrating upon basic principles rather than upon details. The aspect of this text that is most intriguing to the experienced reader is the inclusion in the list of authors of one of the developers of topographic EEG analysis. The guidelines for topographic EEG studies reflect the crucial role played by the technologist in obtaining such studies. This is obviously a well thought out and common-sense approach which could very well serve as a basis for the development of formal guidelines for this area. The reader, however, would reasonably expect a thorough and in-depth treatment of all aspects oftopographic EEG. In this respect the book is disappointing, providing at best a superficial view of many of the problems associated with topographic EEG interpretations. This book is a workmanlike job. Some areas are covered better than in other texts and others less well. Aside from the inclusion of some brief considerations of up-to-date topics, there unfortunately is little to distinguish this introductory text from other competent texts in EEG and clinical neurop hysiology .
Classification of Vessels in Single-Pol COSMO-SkyMed Images Based on Statistical and Structural Features

Vessel monitoring is one of the most important maritime applications of Synthetic Aperture Radar (SAR) data. Because of the dihedral reflections between the vessel hull and sea surface and the trihedral reflections among superstructures, vessels usually have strong backscattering in SAR images. Furthermore, in high-resolution SAR images, detailed information on vessel structures can be observed, allowing for vessel classification in high-resolution SAR images. This paper focuses on the feature analysis of merchant vessels, including bulk carriers, container ships and oil tankers, in 3 m resolution COSMO-SkyMed stripmap HIMAGE mode images and proposes a method for vessel classification. After preprocessing, a feature vector is estimated by calculating the average value of the kernel density estimation, three structural features and the mean backscattering coefficient. Support vector machine (SVM) classifier is used for the vessel classification, and the results are compared with traditional methods, such as the K-nearest neighbor algorithm (K-NN) and minimum distance classifier (MDC). In situ investigations are conducted during the SAR data acquisition. Corresponding Automatic Identification System (AIS) reports are also obtained as ground truth to evaluate the effectiveness of the classifier. The preliminary results show that the combination of the average value of the kernel density estimation and mean backscattering coefficient has good ability for classifying the three types of vessels. When adding the three structural features, the results slightly improve. The result of the SVM classifier is better than that of K-NN and MDC. However, the SVM requires more time, when the parameters of the kernel are estimated.
Deriving the full-reducing Krivine machine from the small-step operational semantics of normal order

We derive by program transformation Pierre Crégut's full-reducing Krivine machine KN from the structural operational semantics of the normal order reduction strategy in a closure-converted pure lambda calculus. We thus establish the correspondence between the strategy and the machine, and showcase our technique for deriving full-reducing abstract machines. Actually, the machine we obtain is a slightly optimised version that can work with open terms and may be used in implementations of proof assistants.
On multi-class classification through the minimization of the confusion matrix norm

In imbalanced multi-class classification problems, the misclassification rate as an error measure may not be a relevant choice. Several methods have been developed where the performance measure retained richer information than the mere misclassification rate: misclassification costs, ROC-based information, etc. Following this idea of dealing with alternate measures of performance, we propose to address imbalanced classification problems by using a new measure to be optimized: the norm of the confusion matrix. Indeed, recent results show that using the norm of the confusion matrix as an error measure can be quite interesting due to the fine-grain informations contained in the matrix, especially in the case of imbalanced classes. Our first contribution then consists in showing that optimizing criterion based on the confusion matrix gives rise to a common background for cost-sensitive methods aimed at dealing with imbalanced classes learning problems. As our second contribution, we propose an extension of a recent multi-class boosting method — namely AdaBoost.MM — to the imbalanced class problem, by greedily minimizing the empirical norm of the confusion matrix. A theoretical analysis of the properties of the proposed method is presented, while experimental results illustrate the behavior of the algorithm and show the relevancy of the approach compared to other methods.
An Integrated Support Vector Machineand Quantum Behaved Particle Swarm Optimization Algorithm for Groundwater Level Forecasting

Groundwater level prediction in a water basin plays a significant role in the management of groundwater resources. Aground water level forecasting system is developed in this study using Support vector Machines (SVM). Further Quantum behaved Particle Swarm Optimization (QPSO) function is employed in this study to determine the SVM parameters. Later, the proposed SVM-QPSO model is used in determining the groundwater level of Visakhapatnam region of Andhra Pradesh in India. The performance of the SVM-QPSO model is then compared with the ANN (Artificial Neural Networks). The results indicate that SVM-QPSO is a far better technique for predicting groundwater levels as it provides a high degree of accuracy and reliability.
Trust within interfirm cooperation: a conceptualization

Trust is seen as an important factor enabling interfirm cooperation. However, questions as to the concept and determinants of trust still remain to be answered. Therefore, the main purpose of this paper is to conceptualise trust within B-2-B relationships by differentiating between its objective and subjective dimensions and by identifying its determinants. In its conclusion, the paper presents a model that hypothesises the effects of the trustor’s and trustee’s characteristics on trust-development within cooperation, allowing companies to exert influence specifically on the establishment and maintenance of cooperative B-2-B relationships.
An actor critic algorithm based on Grassmanian search

We propose the first online actor-critic scheme with adaptive basis to find a local optimal control policy for a Markov Decision Process (MDP) under the weighted discounted cost objective. We parameterize both the policy in the actor and the value function in the critic. The actor performs gradient search in the space of policy parameters using simultaneous perturbation stochastic approximation (SPSA) gradient estimates. This gradient computation requires estimates of value function that are provided by the critic by minimizing a mean square Bellman error objective. In order to obtain good estimates of the value function, the critic adaptively tunes the basis functions (or the features) to obtain the best representation of the value function using gradient search in the Grassmanian of features. Our control algorithm makes use of multi-timescale stochastic approximation. The actor updates its parameters along the slowest time scale. The critic uses two time scales to estimate the value function. For any given feature value, our algorithm performs gradient search in the parameter space via a residual gradient scheme on the faster timescale and, on a medium timescale, performs gradient search in the Grassman manifold of features. We provide an outline of the proof of convergence of our control algorithm to a locally optimum policy. We show empirical results using our algorithm as well as a similar algorithm that uses temporal difference (TD) learning in place of the residual gradient scheme for the faster timescale updates.
Adenovirus vector-based purging of multiple myeloma cells.

Adenoviruses are efficient gene delivery agents for a variety of neoplasms. In the present study, we have investigated the use of adenoviruses for the delivery of the thymidine kinase (tk) gene into multiple myeloma (MM) cells. We first demonstrated that MM cell lines and MM patient cells express both adenovirus receptors as well as the DF3/MUC1 protein, thus providing a rationale for using adenoviruses to selectively deliver genes under the control of the DF3 promoter. By using an adenoviral construct containing beta-galactosidase (beta-gal) gene driven by the DF3 promoter (Ad. DF3-betagal), we demonstrate greater than 80% transduction efficiency in OCI-My5 and RPMI 8226 MM cell lines at a multiplicity of infection of 1 to 100. Importantly, transduction with the tk gene driven by the DF3 promoter (Ad.DF3-tk) followed by treatment with 50 micromol/L ganciclovir (GCV) purged >/=6 log of contaminating OCI-My5 and RPMI 8226 MM cells within bone marrow mononuclear cells. In contrast, normal human hematopoietic progenitor cell number was unaffected under these conditions. Selectivity of DF3/MUC1 promoter was further confirmed, because Ad.DF3-betagal or Ad.DF3-tk did not transduce MUC1-negative HeLa cervical carcinoma cells. In addition, GCV treatment of Ad.DF3-tk-transduced RPMI 8226 MM cells did not induce a significant bystander effect. These findings demonstrate that transduction with Ad vectors using a tumor-selective promoter provides a highly efficient and selective approach for the ex vivo purging of MM cells.
Stimulation of Human EBV- and CMV-Specific Cytolytic Effector Function Using Allogeneic HLA Molecules

Viral infection is a major cause of morbidity and mortality, and there are few therapeutic options available to augment a virus-specific T cell response. Although allo-HLA cross-reactivity from virus-specific memory T cells is common, it is unclear whether priming with specific allogeneic cells could conversely elicit a viral peptide/self-HLA restricted cytotoxic T cell response in humans. First, we used the previously described allo-HLA-B*44:02 cross-reactivity of EBV peptide/HLA-B8 restricted T cells, to determine whether allogeneic HLA stimulation can elicit a cytolytic immune response against EBV. HLA-B8+ HLA-B44− EBV-seropositive PBMCs were stimulated with either HLA-B*44:02+ or HLA-B*44:03+ mismatched irradiated PBMCs in a 7–10 d MLR. The allo-HLA stimulated responder cells were then evaluated for cytotoxicity using EBV peptide loaded autologous target cells and unloaded HLA-B8+ EBV LCL target cells. PBMCs from EBV-seropositive donors gained EBV-specific cytolytic effector function following specific allo-HLA stimulation. Finally, we also elicited cytolytic CMV-specific responses using specific allogeneic cell stimulation, to confirm that this technique can be used to elicit viral peptide/self-HLA restricted responses even from nonpublic TCR responses. Allogeneic cell stimulation used as a cell therapy may be a potential tool to augment an antiviral T cell response in patients with EBV or CMV infection.
The Impact of Implicit Self-Theory on Rated Creativity in Third- and Sixth-Graders

A total of 114 3rd- and 6th-graders from a suburban elementary school participated in a study examining the influence of implicit self-theories on rated creativity in the domains of art and literature in a quasi-experiment. Creativity was measured via the Consensual Assessment Technique. Participants were divided into two groups and received different sets of instructions emphasizing either an incremental or entity-implicit theory of creativity, before completing a drawing task and a writing task. Implicit theories of creativity of participants were measured before and after the instructions were given. The results indicated that participants in the incremental group showed increased incremental views following the manipulation; participants in the entity group showed no significant change. The writing (but not the artwork) from the incremental group was rated as more creative than those from the entity group. This effect was stronger for third graders than for sixth graders, suggesting that implicit theory interventions are more effective with younger children. Implications are discussed.
Foreground separation using a flexible maximum-entropy algorithm: an application to COBE data

A flexible maximum-entropy component separation algorithm is presented that accommodates anisotropic noise, incomplete sky-coverage and uncertainties in the spectral parameters of foregrounds. The capabilities of the method are determined by first applying it to simulated spherical microwave data sets emulating the COBE-DMR, COBE-DIRBE and Haslam surveys. Using these simulations we find that is very difficult to determine unambiguously the spectral parameters of the galactic components for this data set due to their high level of noise. Nevertheless, we show that is possible to find a robust cosmic microwave background (CMB) reconstruction, especially at high galactic latitude. The method is then applied to these real data sets to obtain reconstructions of the CMB component and galactic foreground emission over the whole sky. The best reconstructions are found for values of the spectral parameters: T d = 19 K, α d = 2, β ff = -0.19 and β syn = -0.8. The CMB map has been recovered with an estimated statistical error of ∼22 μK on an angular scale of 7° outside the galactic cut, whereas the low galactic latitude region presents contamination from the foreground emissions.