Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of incubation with NADPH generator on progesterone bound to nuclei of the rat uterus.

The metabolism of progesterone bound to uterine nuclear progesterone receptors was accelerated in vitro by incubation at 25°C for 30min with NADPH generator. The characteristics of progesterone metabolites and receptors were examined by 3H-R5020 exchange assay. Progesterone bound nuclei were prepared from uteri of estradiol-primed progesteronetreated ovariectomized rats. The specific nuclear R5020 binding sites examined by exchange assay were identified as specific progesterone receptors by the binding specificity of R5020 to progesterone receptors, their high affinity for progesterone, and the increase in the number of specific binding sites in response to a progesterone injection.Major metabolites of progesterone specifically bound to nuclear receptors by incubation with the cofactor were 5α-dihydroprogesterone (5α-DHP) and 3α-hydroxy-5α-pregnan-20-one. The relative binding affinity of the progesterone receptor for 5α-DHP at 0°C was 27.5% of progesterone, but 3α-hydroxy-5α-pregnan-20-one had no binding affinity for the receptor. Incubation of the nuclei in the presence of NADPH generator decreased the number of R5020 binding sites from 143 fmol to 25 fmol per 100μg DNA, and increased the dissociation constant of the receptor with progesterone from 2.56nM to 7.78nM. Results suggest that after gene activation, the specific binding sites of progesterone to nuclear receptors may be denatured in the presence of NADPH, causing both progesterone and receptors to dissociate from the receptors and chromatins, respectively, and that the dissociated progesterone may be converted to 5α-reduced pregnanes which have lower affinity for the receptor.
Collaborative multi-strategical classification for object-oriented image analysis

This paper deals with the description of the use of a collaborative multi-strategy classification applied to image analysis. This system integrates different kinds of unsupervised classification methods and produce for each classifier a result built according to the results of all the other methods. Each method occurrence tries to make its result converge towards the results of the other method occurrences thank to different operators. In this paper we highlight how classifiers collaborate and we present results in the paradigm of object-oriented classification of a VHR remotely sensed image of an urban area.
How to use smartphones for less obtrusive ambulatory mood assessment and mood recognition

We present MoA2, a context-aware smartphone app for the ambulatory assessment of mood, tiredness and stress level. In principle, it has two features: (1) mood assessment and (2) mood recognition. The mood assessment system combines benefits of state of the art approaches. The mood recognition is concluded by smartphone-based wearable sensing. In a formative study, we evaluated the usability and unobtrusiveness of our mood assessment. A median SUS score of 90 shows a high usability. Subjects reported an easy, fast and intuitive use. The mood recognition was evaluated in terms of classification accuracy. First, we analyzed which features are best for the recognition. Spatio-temporal attributes, i.e. daytime, day of week and location, correlate most with the monitored mood. Based on the identified attributes, we trained personalized classifiers using Naïve Bayes and applied ten-fold-cross validation. The average recognition accuracy was 0.76 which is comparable to related work.
On N = 1 gauge models from geometric engineering in M-theory

We study geometric engineering of four-dimensional N = 1 gauge models from M-theory on a seven-dimensional manifold with G2 holonomy. The manifold is constructed as a K3 fibration over a three-dimensional base space with ADE geometry. The resulting gauge theory is discussed in the realm of (p, q) webs. We discuss how the anomaly cancellation condition translates into a condition on the associated affine ADE Lie algebras.
Recombinant Adenovirus-Mediated Intestinal Trefoil Factor Gene Therapy for Burn-Induced Intestinal Mucosal Injury

Intestinal trefoil factor (ITF) is a small polypeptide with potential medical values whose main pharmacological effects are to alleviate gastrointestinal mucosal injury caused by various injury factors and promote the repair of damaged mucosa. However, its low yield limits its application. The purpose of our study was to construct a recombinant adenoviral vector containing the hITF gene and observe the therapeutic effect of burn-induced intestinal mucosal injury using in vitro and in vivo analysis. First, a recombinant shuttle plasmid was constructed by ligating a pAdTrack-CMV vector with a full-length hITF gene containing a signal peptide and the mature peptide, followed by the recombinant Ad-hITF adenovirus vector after linearization and homologous recombination with the backbone plasmid in the competent BJ5183 strain. Second, the hITF expression level was detected using reverse transcription polymerase chain reaction and western blotting after Ad-hITF infection of colon cancer HT-29 cells. The recombinant adenovirus significantly promoted cell migration in an in vitro wounding model. Finally, we confirmed that the recombinant adenovirus could significantly expedite the healing of intestinal mucosal injury after establishing a mouse model in which severe burns were stimulated and the recombinant adenovirus was delivered by intragastric injection. In summary, we constructed a recombinant adenoviral vector containing the hITF gene and confirmed its role in promoting repair of the intestinal mucosa. Our study provides a novel way to treat burn-induced intestinal mucosal injury.
Interactive Exploration of Semantic Clusters In proceedings of the International Workshop on Visualizing Software for Understanding and Analysis (VISSOFT 2005)

Using visualization and exploration tools can be of great use for the understanding of a software system when only its source code is available. However, understanding a large software system by visualizing only its lower level artifacts (e.g., classes, methods) and the relations between them does not scale for industrial-size systems. To address the scalability issue, higher level hierarchical abstractions (e.g., package structure, clustered decompositions of the system) should be used together with relations between them that are usually aggregated from the lower level relations. In this paper, we present the concepts behind Softwarenaut, a tool aimed at exploring any kind of hierarchical decompositions of a system, and then we look at a specific exploration of a system. In the experiment, the hierarchical decomposition of the system is the result of applying a semantical clustering to group classes that use similar terms.
Comparison of MRI and endoscope ultrasound detection in preoperative T/N staging of gastric cancer

Gastric cancer is a common malignancy and cause of mortality. The aim of this study was to evaluate the accuracy of magnetic resonance imaging (MRI) and endoscopic ultrasound (EUS) detection in preoperative clinical T/N staging in gastric cancer. Thirty-eight patients diagnosed with gastric cancer by gastroscopy and pathological examination were included in the study. All 38 patients underwent MRI and EUS detection prior to surgery. The accuracy of MRI, EUS and MRI+EUS was evaluated according to postoperative pathological staging. Results identified the accuracy of EUS, MRI and EUS+MRI in T clinical staging to be 86.64, 73.68 and 89.47%, respectively (MRI vs. EUS+MRI, P=0.035), while the accuracy for N clinical staging was 65.78, 68.42 and 71.05%, respectively (P>0.05). The accuracy rate in EUS and EUS+MRI detection in N0 stage was markedly higher compared with that in MRI (100 vs. 86.67%, P=0.032), whereas the rate in EUS detection in N2 stage was lower compared with that in MRI and EUS+MRI (45.45 vs. 54.54%, P=0.021). Thus, both MRI and EUS had a higher accuracy in preoperative T/N staging. Additionally, the accuracy rate was improved significantly when the two procedures were combined.
Tractable Classes of Metric Temporal Problems with Domain Rules

In this paper, we will deal with some important kinds of metric temporal reasoning problems that arise in many real-life situations. In particular, events X0, X1 . XN are modeled as time points, and a constraint between the execution times of two events Xi and Xj is either simple temporal (of the form Xi - Xj ∈ [a, b]), or has a connected feasible region that can be expressed using a finite set of domain rules each in turn of the form Xi ∈ [a, b] → Xj ∈ [c, d] (and conversely Xj ∈ [e, f] → Xi ∈ [g, h]). We argue that such rules are useful in capturing important kinds of non-monotonic relationships between the execution times of events when they are governed by potentially complex (external) factors. Our polynomial-time (deterministic and randomized) algorithms for solving such problems therefore enable us to efficiently deal with very expressive representations of time.
The North European way of ransoming: explorations into an unknown dimension of the early modern welfare state

»Die nordeuropäische Art des Gefangenenfreikaufs: Eine unbekannte Dimension des frühmodernen Wohlfahrtsstaates«. This article is concerned with distinctly “confessional” characteristics in the organization of buying back captured sailors out of Northern Africa. The history and ways of slave redemption of Hamburg, Lubeck, the Kingdom of Denmark-Norway and the Netherlands are presented, analyzed and compared. As a result it is possible to distinguish the comparatively prominent role that centralized, bureaucratized and governmentally administered institutions played in the ransoming business of the Lutheran world.
SCATS Ramp Metering: Strategies, arterial integration and results

Ramp metering is a dynamic traffic management tool used to eliminate or reduce the duration of congestion on motorways. A ramp metering implementation can present some problems to the adjacent arterial network. One source of these problems is the treatment of motorways and arterial network as two separate areas of control. This paper presents three novel methods for ramp metering. The three methods are aimed at promoting a coordinated, efficient and integrated ramp metering approach. These methods are integrated in the Sydney Coordinated Adaptive Traffic System (SCATS) Ramp Metering System which currently controls the largest southern-hemisphere ramp metering network. The achievements of these methods are demonstrated by years of before and after field data from Auckland, New Zealand. An average 25% improvement in both congestion duration and traffic speed is achieved as well as an 8% increase in throughput. A total of 22% reduction in crashes is also demonstrated. Based on average results of 25 surveyed sites, the estimated annual benefits for a ramp metering site are a reduction of 26K hours of delay and a reduction of 6 crashes. In terms of economic value, this results in an estimated annual cost reduction of AU$2M per ramp metering site. These estimates were designed to exclude benefits from any civil works resulting in capacity increases to the motorway network.
Invariant sets for constrained nonlinear discrete-time systems with application to feasibility in model predictive control

An understanding of invariant set theory is essential in the design of controllers for constrained systems, since state and control constraints can be satisfied if and only if the initial state belongs to a positively invariant set for the closed-loop system. The paper briefly reviews some concepts in invariant set theory and shows that the various sets can be computed using a single recursive algorithm. The ideas presented in the first part of the paper are applied to the fundamental design goal of guaranteeing feasibility in predictive control. New necessary and sufficient conditions based on the control horizon, prediction horizon and terminal constraint set are given in order to guarantee that the predictive control problem will be feasible for all time, given any feasible initial state.
Research on Domain-independent Opinion Target Extraction

Opinion Target Extraction is one of the important tasks for text sentiment analysis, which has attracted much attention from many researchers. For this task, we proposed an M-Score algorithm utilized in the model which realized the domain-independent opinion target extraction function. This algorithm is derived from the Pointwise Mutual Information algorithm, but the difference is that it doesn’t need any manual seeds collection or any web searching engines, which reduces the manual participation and easy to be transplanted. This model starts with document preprocessing, effective opinion sentences extraction and candidate opinion target extraction by employing Conditional Random Fields Model with feature templates. Next, the M-Score algorithm is employed to extract seed set, and the bootstrapping approach is invoked to process the candidate opinion targets. Finally, the model uses word frequency and the Noun pruning algorithm to filter the opinion targets, and then obtains the final opinion targets for output. The experimental results show that the M-score method performs better than Pointwise Mutual Information algorithm in precision and recall.
Intra and Inter-Cell Resource Management in Full-Duplex Heterogeneous Cellular Networks

Full-duplex communication is drawing high attention as a means of enhancing wireless capacity considerably by enabling simultaneous transmission and reception on the same frequency spectrum. While it had been considered infeasible for a long time due to strong self-interference, recent researches have made substantial progress on addressing this challenge. This paper focuses on designing full-duplex cellular networks with co-channel femtocells. Due to relatively high transmit power, cancellation of self-interference by existing techniques could still be imperfect for cellular systems and thus residual interference may impact performance significantly. To overcome this in OFDMA systems, a new radio resource management scheme assigning downlink and uplink transmissions jointly while considering the gain of self-interference cancellation is developed. Three available transmission modes of a frequency resource block and crossover points between their achievable capacities are identified for the mode selection of each resource block. Users are then assigned resource blocks and transmit power levels are determined such that the total utility sum is maximized. To handle new femtocell interference scenarios, the transmit powers of femtocells and their connected users are adjusted by a coordination algorithm such that both data transmission and mode selection of an underlying macrocell are protected.
Using Object\'s Contour and Form to Embed Recognition Capability into Industrial Robots

A great effort has been made towards the integration of object recognition capability in robotics especially in humanoids, mobile robots and advanced industrial manipulators. Industrial robots today are not equipped with this capability in its standard version, but as an option. Robot vision systems can differentiate parts by pattern matching irrespective of part orientation and location and even some manufacturers offer 3D guidance using robust vision and laser systems so that a 3D programmed point can be repeated even if the part is moved varying its rotation and orientation within the working space. Despite these developments, current industrial robots are still unable to recognise objects in a robust manner; that is, to distinguish among equally shaped objects unless and alternative method is used, for instance taking into account not only the object’s contour but also its form, which is precisely the major contribution of this chapter. How objects are recognized by humans is still an open research field. There are researchers that favour the theory of object recognition via object-models like Geons (Biederman, 1987), but other researchers agree on two types of image-based models: viewpoint dependent or viewpoint invariant. But, in general there is an agreement that humans recognise objects as established by the similarity principle –among othersof the Gestalt theory of visual perception, which states that things which share visual characteristics such as shape, size, colour, texture, value or orientation will be seen as belonging together. This principle applies to human operators; for instance, when an operator is given the task to pick up a specific object from a set of similar objects; the first approaching action will probably be guided solely by visual information clues such as shape similarity. But, if further information is given (i.e. type of surface), then a finer clustering is accomplished to identify the target object. We believe that it is possible to integrate a robust invariant object recognition capability in industrial robots following the above assumptions by using image features from the object’s contour (boundary object information) and its form (i.e. type of curvature or topographical surface information). Both features can be concatenated in order to form an invariant vector descriptor which is the input to an Artificial Neural Network (ANN) for learning and recognition purposes. In previous work, it was demonstrated the feasibility of the approach 21
Enhancing In-Car Navigation Systems with Personal Experience

Computers are extremely powerful for data processing but less adept at handling problems that involve subjective reasoning. People, on the other hand, are good at such tasks. A framework is presented for adding subjective human experience to in-car navigation systems. People often rely on personal experience when planning trips, choosing the route that is fastest, prettiest, or recommended by a friend. A set of methods was developed to help people record personal driving history, add textual annotations describing subjective experiences, and share data with friends and family or even the broader community. Users can then learn from personal data or harness the multiplicity of individual experiences to enjoy new routes. This approach can be used in conjunction with traditional in-car navigation systems.
Do Incentives Destroy Voluntary Cooperation ? †

We investigate experimentally how explicit performance incentives in incomplete employment contracts interact with agents’ voluntary cooperation in one-shot and repeated gift-exchange experiments. If contracts are incentive compatible, agents choose their bestreply effort and there is no voluntary cooperation. By contrast, there is substantial voluntary cooperation if the contract is not incentive compatible. Experiencing incentive contracts reduces voluntary cooperation even after incentives are abolished. We then examine the robustness of these findings with regard to two variables: Experience with voluntary cooperation and the possibility of implicit incentives in repeated interactions. We find that incentive contracts have no lasting negative effects on voluntary cooperation if agents experience voluntary cooperation before being exposed to incentive contracts or if there are implicit incentives in repeated interactions. Implicit incentives increase voluntary cooperation strongly and make explicit incentives unnecessary or even counterproductive.
Research on identification of coal and waste rock based on GLCM and BP neural network

When exploring the parameters for the coal and waste rock, eight characteristic parameters were selected as the whole characters of an image according to their significant differences in gray scale and texture features. On the basis, the error back-propagation algorithm of neural network is applied for the nonlinear identification of samples. The identification network was trained successfully through learning samples. Then, the validity of eight characteristic parameters was verified through the tests of experimental images. Meanwhile, the goal of intelligent identification of coal and waste rock is achieved successfully.
Attention to 3-D Shape, 3-D Motion, and Texture in 3-D Structure from Motion Displays

We used fMRI to directly compare the neural substrates of three-dimensional (3-D) shape and motion processing for realistic textured objects rotating in depth. Subjects made judgments about several different attributes of these objects, including 3-D shape, the 3-D motion, and the scale of surface texture. For all of these tasks, we equated visual input, motor output, and task difficulty, and we controlled for differences in spatial attention. Judgments about 3-D shape from motion involve both parietal and occipito-temporal regions. The processing of 3-D shape is associated with the analysis of 3-D motion in parietal regions and the analysis of surface texture in occipito-temporal regions, which is consistent with the different behavioral roles that are typically attributed to the dorsal and ventral processing streams.
Numerical study of three-dimensional overturning waves in shallow water

Simulations in a three-dimensional numerical wave tank are performed to investigate the shoaling and breaking of solitary waves over a sloping ridge. The numerical model solves fully nonlinear potential flow equations with a high-order boundary-element method combined with an explicit time-integration method, expressed in a mixed Eulerian–Lagrangian formulation. Analyses of shoaling and breaking-wave profiles and kinematics (both on the free surface and within the flow) are carried out. It is observed that the transverse modulation of the ridge topography induces three-dimensional effects on the time evolution, shape and kinematics of breaking waves. Comparisons of two- and three-dimensional results in the middle cross-section of the ridge, however, show remarkable similarities, especially for the shape and dynamics of the plunging jet.
Preliminary Evidence That Yoga Practice Progressively Improves Mood and Decreases Stress in a Sample of UK Prisoners

Objectives. In the first randomized controlled trial of yoga on UK prisoners, we previously showed that yoga practice was associated with improved mental wellbeing and cognition. Here, we aimed to assess how class attendance, self-practice, and demographic factors were related to outcome amongst prisoners enrolled in the 10-week yoga intervention. Methods. The data of 55 participants (52 male, 3 female) who completed a 10-week yoga course were analysed. Changes in pre- and postyoga measures of affect, perceived stress, and psychological symptoms were entered into linear regression analyses with bias-corrected and accelerated bootstrap confidence intervals. Class attendance, self-practice, demographic variables, and baseline psychometric variables were included as regressors. Results. Participants who attended more yoga classes and those who engaged in frequent (5 times or more) self-practice reported significantly greater decreases in perceived stress. Decreases in negative affect were also significantly related to high frequency self-practice and greater class attendance at a near-significant level. Age was positively correlated with yoga class attendance, and higher levels of education were associated with greater decreases in negative affect. Conclusions. Our results suggest that there may be progressive beneficial effects of yoga within prison populations and point to subpopulations who may benefit the most from this practice.