Miracle-dz/UniFAR-FTUs huggingface dataset:
Guinea‐pig sympathetic neurons express varying proportions of two distinct P2X receptors

1 Characterization of P2X receptors on neurons of guinea‐pig superior cervical ganglion (SCG) has been carried out using a whole‐cell voltage‐clamp technique. 2 Application of ATP and α,β‐methylene ATP (αβ‐MeATP) produced fast activating inward currents, which desensitized slowly. The maximum response to αβ‐MeATP was 36 ± 23 % (range 0·1‐100 %) of that evoked by ATP in the same cell. 3 Co‐application of αβ‐MeATP (300 μM) with ATP (300 μM) produced a response that was 97 ± 1 % of that given by ATP alone. Following desensitization with αβ‐MeATP, the decrease in response to ATP was equal to the absolute reduction in response to αβ‐MeATP in the same cell. 4 The concentration‐response curve for αβ‐MeATP had an EC50 of 42 μM and a Hill coefficient of 1·17. For cells where the ratio of αβ‐MeATP/ATP currents at 100 μM was < 0·1, the ATP concentration‐response curve had an EC50 of 56 μM and a Hill coefficient of 1·95. However, in cells where the ratio was > 0·7, the curve had an EC50 of 60 μM and a Hill coefficient of 0·97. 5 The response to 100 μM αβ‐MeATP was inhibited by 2′ (or 3′)‐O‐trinitrophenyl‐ATP (TNP‐ATP) with an IC50 of 70 nM. However, on cells where the ratio of αβ‐MeATP/ATP currents was < 0·1, ATP was inhibited by TNP‐ATP with an IC50 of 522 nM. 6 Immunohistochemical staining with antibodies raised against rat P2X2 and P2X3 epitopes suggested that both subunits were expressed by guinea‐pig SCG neurons. 7 We conclude that varying proportions of two distinct P2X receptors coexist on the cell bodies of individual guinea‐pig SCG neurons, which may correspond to homomeric P2X2 and heteromeric P2X2/3 receptors.
Efficacy of cognitive behavioral therapy in reducing psychiatric symptoms in patients with implantable cardioverter defibrillator: an integrative review

This article is a systematic review of the available literature on the benefits that cognitive behavioral therapy (CBT) offers patients with implanted cardioverter defibrillators (ICDs) and confirms its effectiveness. After receiving the device, some patients fear that it will malfunction, or they remain in a constant state of tension due to sudden electrical discharges and develop symptoms of anxiety and depression. A search with the key words “anxiety”, “depression”, “implantable cardioverter”, “cognitive behavioral therapy” and “psychotherapy” was carried out. The search was conducted in early January 2013. Sources for the search were ISI Web of Knowledge, PubMed, and PsycINFO. A total of 224 articles were retrieved: 155 from PubMed, 69 from ISI Web of Knowledge. Of these, 16 were written in a foreign language and 47 were duplicates, leaving 161 references for analysis of the abstracts. A total of 19 articles were eliminated after analysis of the abstracts, 13 were eliminated after full-text reading, and 11 articles were selected for the review. The collection of articles for literature review covered studies conducted over a period of 13 years (1998-2011), and, according to methodological design, there were 1 cross-sectional study, 1 prospective observational study, 2 clinical trials, 4 case-control studies, and 3 case studies. The criterion used for selection of the 11 articles was the effectiveness of the intervention of CBT to decrease anxiety and depression in patients with ICD, expressed as a ratio. The research indicated that CBT has been effective in the treatment of ICD patients with depressive and anxiety symptoms. Research also showed that young women represented a risk group, for which further study is needed. Because the number of references on this theme was small, further studies should be carried out.
Synergistic Effect of CTLA-4 Blockade and Cancer Chemotherapy in the Induction of Anti-Tumor Immunity

Several chemotherapeutics exert immunomodulatory effects. One of these is the nucleoside analogue gemcitabine, which is widely used in patients with lung cancer, ovarian cancer, breast cancer, mesothelioma and several other types of cancer, but with limited efficacy. We hypothesized that the immunopotentiating effects of this drug are partly restrained by the inhibitory T cell molecule CTLA-4 and thus could be augmented by combining it with a blocking antibody against CTLA-4, which on its own has recently shown beneficial clinical effects in the treatment of patients with metastatic melanoma. Here we show, using two non-immunogenic murine tumor models, that treatment with gemcitabine chemotherapy in combination with CTLA-4 blockade results in the induction of a potent anti-tumor immune response. Depletion experiments demonstrated that both CD4(+) and CD8(+) T cells are required for optimal therapeutic effect. Mice treated with the combination exhibited tumor regression and long-term protective immunity. In addition, we show that the efficacy of the combination is moderated by the timing of administration of the two agents. Our results show that immune checkpoint blockade and cytotoxic chemotherapy can have a synergistic effect in the treatment of cancer. These results provide a basis to pursue combination therapies with anti-CTLA-4 and immunopotentiating chemotherapy and have important implications for future studies in cancer patients. Since both drugs are approved for use in patients our data can be immediately translated into clinical trials.
The Random Nature of Genome Architecture: Predicting Open Reading Frame Distributions

Background A better understanding of the size and abundance of open reading frames (ORFS) in whole genomes may shed light on the factors that control genome complexity. Here we examine the statistical distributions of open reading frames (i.e. distribution of start and stop codons) in the fully sequenced genomes of 297 prokaryotes, and 14 eukaryotes. Methodology/Principal Findings By fitting mixture models to data from whole genome sequences we show that the size-frequency distributions for ORFS are strikingly similar across prokaryotic and eukaryotic genomes. Moreover, we show that i) a large fraction (60–80%) of ORF size-frequency distributions can be predicted a priori with a stochastic assembly model based on GC content, and that (ii) size-frequency distributions of the remaining “non-random” ORFs are well-fitted by log-normal or gamma distributions, and similar to the size distributions of annotated proteins. Conclusions/Significance Our findings suggest stochastic processes have played a primary role in the evolution of genome complexity, and that common processes govern the conservation and loss of functional genomics units in both prokaryotes and eukaryotes.
Scallop : An open peer-to-peer framework for distributed sensor networks

Distributed smart cameras and sensors have been an active area of research in recent years. Most of the research has focused on either the machine vision algorithms or on a specific implementation. There has been less activity on building generic frameworks which allow different algorithms, sensors and distribution methods to be used. This paper presents an open and extendable framework for development of distributed sensor networks with an emphasis on peer-to-peer networking. The user is provided with easy access to sensors and communication channels between distributed nodes, allowing the effort to be focused on the development of machine vision algorithms and their use in distributed environments. The framework was used while implementing a simple demonstration system to test the sensor and network functionality. The system contains processing nodes which receive sensor data from cameras and communicate with each other through a peer-to-peer mesh.
Autophagy-dependent EIF2AK3 activation compromises ursolic acid-induced apoptosis through upregulation of MCL1 in MCF-7 human breast cancer cells

Ursolic acid (UA) is a pentacyclic triterpenoid with promising cancer chemopreventive properties. A better understanding of the mechanisms underlying anticancer activity of UA is needed for further development as a clinically useful chemopreventive agent. Here, we found that both endoplasmic reticulum (ER) stress and autophagy were induced by UA in MCF-7 human breast cancer cells. Surprisingly, ER stress was identified as an effect rather than a cause of UA-induced autophagy. Autophagy-dependent ER stress protected the cells from UA-induced apoptosis through EIF2AK3-mediated upregulation of MCL1. Activation of MAPK1/3 but not inhibition of MTOR pathway contributed to UA-induced cytoprotective autophagy in MCF-7 cells. Our findings uncovered a novel cellular mechanism involved in the anticancer activity of UA, and also provided a useful model to study biological significance and mechanisms of autophagy-mediated ER stress.
A Probabilistic Perspective on Gaussian Filtering and Smoothing

We present a general probabilistic perspective on Gaussian ltering and smoothing. We showthat di erent approaches to Gaussian ltering/smoothing can be distinguished solely by theirmethods of computing means and covariances of joint probabilities. Novel lters and smootherscan therefore be derived easily by providing methods for computing these moments. We proposea ltering and smoothing algorithm based on Gibbs sampling as an example.Inference in latent variable models is about extracting information about a not directly observable quantity,the latent variable, from noisy observations. Both recursive and batch methods are of interest and referredto as ltering respective smoothing. Filtering and smoothing in latent variable time series models, includinghidden Markov models and dynamic systems, have been playing an important role in signal processing,control, and machine learning for decades [9, 11, 2]. In the context of dynamic systems, ltering is widely used in control and robotics for online Bayesianstate estimation [16], while smoothing is commonly used in machine learning algorithms for learning [2]. Forcomputational eciency reasons, many lters and smoothers approximate all distributions by Gaussians.This is why they are referred to as Gaussian lters/smoothers, which we will be focusing on in this paper.Here, we discuss Gaussian ltering and smoothing from a general probabilistic perspective without focusingon particular implementations. We identify the high-level concepts and the components required for lteringand smoothing, while avoiding getting lost in the implementation and computational details of particularalgorithms (see e.g., standard derivations of the Kalman lter [1, 16]). In the following, we show that for Gaussian lters/smoothers in (non)linear systems, common algorithms(e.g., the EKF and the UKF) can be distinguished by di erent methods to determine the joint probabilitydistributions p(x
iOptimize: A software capability for analyzing and optimizing connection-oriented data networks in real time

This paper describes a service called iOptimize that analyzes and optimizes service providers' connection-oriented data networks. In these networks, online connection routing is used to set up connections quickly, but the simple path selection scheme and the limited information available for online routing can cause network capacity to be used inefficiently. This may result in the loss or disruption of user data transmissions, leading to a degraded quality of service (QoS) in a network capable of supporting much higher throughput. The objective of iOptimize is to detect and analyze such inefficiencies and to offset them by the occasional rerouting of a selected set of connections, while causing little or no disruption of the existing data services and network operations. By helping service providers support data transport services at the QoS levels desired, iOptimize lets them make the most of their existing network resources and helps them defer capital expenditure. Moreover, by tuning their networks to perform at optimal efficiency, it improves end users' experiences, resulting in lower churn/turnover and reduced maintenance costs.
Performance Analysis of Scheduling in Multiuser MIMO Systems with Zero-Forcing Receivers

Despite its low-complexity, the zero-forcing receiver is known to suffer from noise enhancement to restore the spatially multiplexed data in a single-user MI MO system. Nevertheless, in the multiuser system, the poor-channel avoidance property of the scheduling technique provides a natural way to overcome the drawback of noise enhancement (Heath et al., 2001). In this paper, we present an analytical framework to evaluate the performance of the zero-forcing receiver operating in the multiuser MIMO system with user scheduling. Using the order statistics technique, we derive closed-form expressions for the sum-rate capacity of the multiuser MIMO system that employs the simple spatial multiplexing at the transmitter and zero-forcing processing at the receiver with a number of scheduling algorithms. These closed-form expressions hold for an arbitrary finite number of users and facilitate efficient numerical evaluations for cases of practical interest. In addition, the tractable analysis provides insight into how the scheduling technique affects the performance of the multiuser MIMO system under scalar feedback and vector feedback. The results are also extended to the case of heterogonous users with unequal average SNR.
AN INVESTIGATION OF THE RELATIONSHIPS BETWEEN HOSPITALITY EMPLOYEES' WORK-FAMILY CONFLICT AND RELAXATION/SOCIAL INTENTIONS

A field survey was conducted to investigate the effects of hospitality employees’ work-family conflict on relaxation and social types of leisure intentions. Hypothesis testing followed the two-step approach of Structural Equation Modeling (SEM). Based on 271 valid responses in four full-service hotels, the study found that work-family conflict had both direct effects on hospitality employees’ leisure intentions and indirect effects mediated by job tension. Practically, hospitality organizations can utilize the results in applying appropriate leisure activities to help employees effectively cope with work and family role conflicts.
Automated regression testing of BPMN 2.0 processes: a capture and replay framework for continuous delivery

Regression testing is a form of software quality assurance (QA) that involves comparing the behavior of a newer version of a software artifact to its earlier correct behavior, and signaling the QA engineer when deviations are detected. Given the large potential in automated generation and execution of regression test cases for business process models in the context of running systems, powerful tools are required to make this practically feasible, more specifically to limit the potential impact on production systems, and to reduce the manual effort required from QA engineers. In this paper, we present a regression testing automation framework that implements the capture & replay paradigm in the context of BPMN 2.0, a domain-specific language for modeling and executing business processes. The framework employs parallelization techniques and efficient communication patterns to reduce the performance overhead of capturing. Based on inputs from the QA engineer, it manipulates the BPMN2 model before executing tests for isolating the latter from external dependencies (e.g. human actors or expensive web services) and for avoiding undesired side-effects. Finally, it performs a regression detection algorithm and reports the results to the QA engineer. We have implemented our framework on top of a BPMN2-compliant execution engine, namely jBPM, and performed functional validations and evaluations of its performance and fault-tolerance. The results, indicating 3.9% average capturing performance overhead, demonstrate that the implemented framework can be the foundation of a practical regression testing tool for BPMN 2.0, and a key enabler for continuous delivery of business process-driven applications and services.
A 0.35–0.5-V 18–152 MHz Digitally Controlled Relaxation Oscillator With Adaptive Threshold Calibration in 65-nm CMOS

This brief describes a pseudodigital oscillator design for low-voltage applications. To overcome the start-up problem of the digitally controlled ring oscillator with an ultralow supply voltage, a digitally controlled relaxation oscillator (DCRXO) that utilizes an inverter-based comparator is proposed. The pseudocomparator (PC), which is a variable threshold inverter, followed by an inverter, is introduced and a digital calibration circuit is used to automatically set the threshold of the PC for process-insensitive operation in the initial state. The DCRXO implemented in 65-nm CMOS consumes 17.8 μW from a 0.35-V supply at 26-MHz output and achieves a tuning range of 18.2-29.2 MHz with a sufficient overlap between tuning curves. The phase noise of -104 dBc/Hz at 1 MHz offset is measured at 26-MHz output frequency. When the supply voltage of 0.5 V is used, the DCRXO consumes 59 μW at 100-MHz output with the tuning range of 43.5-152 MHz. The core area of the DCRXO is 0.0362 mm2.
General general game AI

Arguably the grand goal of artificial intelligence research is to produce machines with general intelligence: the capacity to solve multiple problems, not just one. Artificial intelligence (AI) has investigated the general intelligence capacity of machines within the domain of games more than any other domain given the ideal properties of games for that purpose: controlled yet interesting and computationally hard problems. This line of research, however, has so far focused solely on one specific way of which intelligence can be applied to games: playing them. In this paper, we build on the general game-playing paradigm and expand it to cater for all core AI tasks within a game design process. That includes general player experience and behavior modeling, general non-player character behavior, general AI-assisted tools, general level generation and complete game generation. The new scope for general general game AI beyond game-playing broadens the applicability and capacity of AI algorithms and our understanding of intelligence as tested in a creative domain that interweaves problem solving, art, and engineering.
Generic Object Recognition with Probabilistic Models and Weak Supervision

Real world images of objects belonging to a particular class typically show large variability in shape, appearance, scale, degree of occlusion, etc. Thus, a major challenge for generic object recognition is to develop object models that are flexible enough to accommodate these large intra-class variabilities. Such powerful models, in turn, require large amounts of training data to be effective and it becomes imperative to reduce the degree of human supervision required to a minimum. In this project, parameterized probabilistic models were used to explicitly model different object attributes and these parameters were estimated by maximizing the likelihood of training data. The training data used was mostly from the Caltech dataset with labels for categories. This project is inspired by the work of Fergus et al. (2003).
Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number

Both learning and inference tasks on Bayesian networks are NP-hard in general. Bounded tree-width Bayesian networks have recently received a lot of attention as a way to circumvent this complexity issue; however, while inference on bounded tree-width networks is tractable, the learning problem remains NP-hard even for tree-width 2. In this paper, we propose bounded vertex cover number Bayesian networks as an alternative to bounded tree-width networks. In particular, we show that both inference and learning can be done in polynomial time for any fixed vertex cover number bound k, in contrast to the general and bounded tree-width cases; on the other hand, we also show that learning problem is W[1]-hard in parameter k. Furthermore, we give an alternative way to learn bounded vertex cover number Bayesian networks using integer linear programming (ILP), and show this is feasible in practice.
Improved Direct Power Control of Grid-Connected DC/AC Converters

This paper proposes new direct power control (DPC) strategies for three-phase DC/AC converters with improved dynamic response and steady-state performance. As with an electrical machine, source and converter flux, which equal the integration of the respective source and converter voltage, are used to define active and reactive power flow. Optimization of the lookup table used in conventional DPC is outlined first so as to improve power control and reduce current distortion. Then, constant switching frequency DPC is developed where the required converter voltage vector within a fixed half switching period is calculated directly from the active and reactive power errors. Detailed angle compensation due to the finite sampling frequency and the use of an integral controller to further improve the power control accuracy are described. Both simulation and experimental results are used to compare conventional DPC and vector control, and to demonstrate the effectiveness and robustness of the proposed control strategies during active and reactive power steps, and line inductance variations.
Variance Reduction Three Approaches to Control Variates

In option price simulations, simulation-time is of great importance. Control variates is a variance reduction technique that can reduce simulation-time. Three approaches to the use of control variates in Monte Carlo option pricing are presented and evaluated. Employed methods include ordinary control variate implementation, a replicating delta hedge and re-simulation. Ordinary control variates uses a highly correlated random variable with known mean to reduce variance. The delta hedge tries to replicate the option and is constructed with an approximative delta formula, which is new to stock markets. The third method evaluated, called re-simulation, is a new method which use an earlier simulated option price as control variate. Applying an earlier option price as control variate results in a more generic method, since earlier simulated prices often exists. The three models are evaluated on Asian and Cliquet options, either in the standard Black and Scholes model or in Merton’s jump diffusion model. Presented results show that the re-simulation method almost always yield a more efficient simulation procedure compared with the other methods. For some Cliquet options the simulation speed up over crude Monte Carlo is remarkable. ∗Postal address: Mathematical Statistics, Stockholm University, SE-106 91, Sweden. E-mail: thomas.lidebrandt@gmail.com. Supervisor: Ola Hammarlid. Acknowledgments I am grateful to my supervisor Ola Hammarlid and Swedbank Markets for giving me the opportunity to perform my master thesis in collaboration with them. Ola Hammarlid has also provided me with the main ideas for this thesis. Finally, I also would like to thank Carl Rehn at Swedbank Markets for reviewing the report.