nils-herrmann/clean_scirepeval huggingface dataset:
Regulation of eNOS expression in brain endothelial cells by perinuclear EP(3) receptors.

We reported upregulation of endothelial nitric oxide synthase (eNOS) by PGE(2) in tissues and presence of perinuclear PGE(2) receptors (EP). We presently studied mechanisms by which PGE(2) induces eNOS expression in cerebral microvessel endothelial cells (ECs). 16,16-Dimethyl PGE(2) and selective EP(3) receptor agonist M&B28767 increased eNOS expression in ECs and the NO-dependent vasorelaxant responses induced by substance P on cerebral microvessels. These effects could be prevented by prostaglandin transporter blocker bromcresol green and actinomycin D. EP(3) immunoreactivity was confirmed on plasma and perinuclear membrane of ECs. M&B28767 increased eNOS RNA expression in EC nuclei, and this effect was augmented by overexpression of EP(3) receptors. M&B28767 also induced increased phosphorylation of Erk-1/2 and Akt, as well as changes in membrane potential revealed by the potentiometric fluorescent dye RH421, which were prevented by iberiotoxin; perinuclear K(Ca) channels were detected, and their functionality corroborated by NS1619-induced Ca(2+) signals and nuclear membrane potential changes. Moreover, pertussis toxin, Ca(2+) chelator, and channel blockers EGTA, BAPTA, and SK&F96365, as well as K(Ca) channel blocker iberiotoxin, protein-kinase inhibitors wortmannin and PD 98059, and NF-kappaB inhibitor pyrrolidine dithiocarbamate prevented M&B28767-induced increase in Ca(2+) transients and/or eNOS expression in EC nuclei. We describe for the first time that PGE(2) through its access into cell by prostaglandin transporters induces eNOS expression by activating perinuclear EP(3) receptors coupled to pertussis toxin-sensitive G proteins, a process that depends on nuclear envelope K(Ca) channels, protein kinases, and NF-kappaB; the roles for nuclear EP(3) receptors seem different from those on plasma membrane.
A Capacitive Intrabody Communication Channel from 100 kHz to 100 MHz

Intrabody communication (IBC) uses the human body as a signal transmission medium. In the capacitive coupling IBC approach, the signal is transmitted through the body, and the signal return path is closed through the environment. The received signal level is affected by the orientation of the transmitter with respect to the receiver, the number of ground electrodes connected to the body, the size of the receiver ground plane, and the surrounding environment. In this paper, we present a characterization of the capacitive IBC channel in the frequency range from 100 kHz to 100 MHz, obtained using a network analyzer and a pair of baluns. In order to better understand the transmission path in the frequency range of interest, we analyze the intrabody channel transmission characteristics using different electrode arrangements, test persons, environments, and body positions and movements. The transmission gain increases with frequency for 20 dB/dec and depends on the transmitter to the receiver distance, and the electrode arrangements. For a proper IBC configuration, the variations of the environment, test persons, body positions, and movements affect the transmission gain less than 2 dB.
Resonant PZT MEMS Scanner for High-Resolution Displays

A resonant piezoelectric scanner is developed for high-resolution laser-scanning displays. A novel actuation scheme combines the principle of mechanical amplification with lead zirconate titanate (PZT) thin-film actuation. Sinusoidal actuation with 24 V at the mechanical resonance frequency of 40 kHz provides an optical scan angle of 38.5° for the 1.4-mm-wide mirror. This scanner is a significant step toward achieving full-high-definition resolution (1920 × 1080 pixels) in mobile laser projectors without the use of vacuum packaging. The reported piezoscanner requires no bulky components and consumes <; 30-mW power at maximum deflection, thus providing significant power and size advantages, compared with reported electromagnetic and electrostatic scanners. Interferometry measurements show that the dynamic deformation is at acceptable levels for a large fraction of the mirror and can be improved further for diffraction-limited performance at full resolution. A design variation with a segmented electrode pair illustrated that reliable angle sensing can be achieved with PZT for closed-loop control of the scanner.
Comparing models of rule-based classification learning: a replication and extension of Shepard, Hovland, and Jenkins (1961).

We partially replicate and extend Shepard, Hovland, and Jenkins's (1961) classic study of task difficulty for learning six fundamental types of rule-based categorization problems. Our main results mirrored those of Shepard et al., with the ordering of task difficulty being the same as in the original study. A much richer data set was collected, however, which enabled the generation of block-by-block learning curves suitable for quantitative fitting. Four current computational models of classification learning were fitted to the learning data: ALCOVE (Kruschke, 1992), the rational model (Anderson, 1991), the configural-cue model (Gluck & Bower, 1988b), and an extended version of the configural-cue model with dimensionalized, adaptive learning rate mechanisms. Although all of the models captured important qualitative aspects of the learning data, ALCOVE provided the best overall quantitative fit. The results suggest the need to incorporate some form of selective attention to dimensions in category-learning models based on stimulus generalization and cue conditioning.
Common surgical procedures in pilonidal sinus disease: A meta-analysis, merged data analysis, and comprehensive study on recurrence

We systematically searched available databases. We reviewed 6,143 studies published from 1833 to 2017. Reports in English, French, German, Italian, and Spanish were considered, as were publications in other languages if definitive treatment and recurrence at specific follow-up times were described in an English abstract. We assessed data in the manner of a meta-analysis of RCTs; further we assessed non-RCTs in the manner of a merged data analysis. In the RCT analysis including 11,730 patients, Limberg & Dufourmentel operations were associated with low recurrence of 0.6% (95%CI 0.3–0.9%) 12 months and 1.8% (95%CI 1.1–2.4%) respectively 24 months postoperatively. Analysing 89,583 patients from RCTs and non-RCTs, the Karydakis & Bascom approaches were associated with recurrence of only 0.2% (95%CI 0.1–0.3%) 12 months and 0.6% (95%CI 0.5–0.8%) 24 months postoperatively. Primary midline closure exhibited long-term recurrence up to 67.9% (95%CI 53.3–82.4%) 240 months post-surgery. For most procedures, only a few RCTs without long term follow up data exist, but substitute data from numerous non-RCTs are available. Recurrence in PSD is highly dependent on surgical procedure and by follow-up time; both must be considered when drawing conclusions regarding the efficacy of a procedure.
Abstracting Interactions Based on Message Sets

ing Interactions Based on Message Sets Svend Frr 1 and Gul Agha 2. 1 Hewlett-Packard Laboratories, 1501 Page Mill Road, Palo Alto, CA 94303 2 University of Illinois, 1304 W. Springfield Avenue, Urbana, IL 61801 Abs t rac t . An important requirement of programming languages for distributed systems is to provide abstractions for coordination. A common type of coordination requires reactivity in response to arbitrary communication patterns. We have developed a communication model in which concurrent objects can be activated by sets of messages. Specifically, our model allows direct and abstract expression of common interaction patterns found in concurrent systems. For example, the model captures multiple clients that collectively invoke shared servers as a single activation. Furthermore, it supports definition of individual clients that concurrently invoke multiple servers and wait for subsets of the returned reply messages. Message sets are dynamically defined using conjunctive and disjunctive combinators that may depend o n the patterns of messages. The model subsumes existing models for multiRPC and multi-party synchronization within a single, uniform activation framework. 1 I n t r o d u c t i o n Distributed objects are often reactive, i.e. they carry out their actions in response to received response. Tradit ional object-oriented languages require one to one correspondence between response and a receive message: i.e. each response is caused by exactly one message. However, many coordination schemes involve object behaviors whose logical cause is a set of messages rather than a single message. For example, consider a transaction manager in a distributed database system. In order to commit a distributed transaction, the manager must coordinate the action taken at each site involved in the transaction. A two-phase commit protocol is a possible implementation of this coordination pattern. In carrying out a two-phase commit protocol, the manager first sends out a status inquiry to all the sites involved. In response to a status inquiry, each site sends a positive reply if it can commit the transaction; a site sends back a negative reply if it cannot commit the transaction. After sending out inquiries, the manager becomes a reactive object waiting for sites to reply. The logical structure of the manager is to react to a set of replies rather than a single reply: if a positive reply is received from all sites, the manager decides to commit the transaction; if a negative reply is received from any site, the manager must abort the transaction. In tradit ional object-oriented languages, the programmer must implement a response to a set of messages in terms of sequences of responses to single messages.  The reported work was carried out while the first author was affiliated with the University of Illinois. The current emaJl addresses are f rolund@hpl .hp. corn and agha@cs.uiue, edu
Scapular muscle activity from selected strengthening exercises performed at low and high intensities.

A balanced level of muscle strength between the different parts of the scapular muscles is important in optimizing performance and preventing injuries in athletes. Emerging evidence suggests that many athletes lack balanced strength in the scapular muscles. Evidence-based recommendations are important for proper exercise prescription. This study determines scapular muscle activity during strengthening exercises for scapular muscles performed at low and high intensities (Borg CR10 levels 3 and 8). Surface electromyography (EMG) from selected scapular muscles was recorded during 7 strengthening exercises and expressed as a percentage of the maximal EMG. Seventeen women (aged 24-55 years) without serious disorders participated. Several of the investigated exercises-press-up, prone flexion, one-arm row, and prone abduction at Borg 3 and press-up, push-up plus, and one-arm row at Borg 8-predominantly activated the lower trapezius over the upper trapezius (activation difference [Δ] 13-30%). Likewise, several of the exercises-push-up plus, shoulder press, and press-up at Borg 3 and 8-predominantly activated the serratus anterior over the upper trapezius (Δ18-45%). The middle trapezius was activated over the upper trapezius by one-arm row and prone abduction (Δ21-30%). Although shoulder press and push-up plus activated the serratus anterior over the lower trapezius (Δ22-33%), the opposite was true for prone flexion, one-arm row, and prone abduction (Δ16-54%). Only the press-up and push-up plus activated both the lower trapezius and the serratus anterior over the upper trapezius. In conclusion, several of the investigated exercises both at low and high intensities predominantly activated the serratus anterior and lower and middle trapezius, respectively, over the upper trapezius. These findings have important practical implications for exercise prescription for optimal shoulder function. For example, both workers with neck pain and athletes at risk of shoulder impingement (e.g., overhead sports) should perform push-up plus and press-ups to specifically strengthen the serratus anterior and lower trapezius.
Considering Community Psychology Competencies: A Love Letter to Budding Scholar-Activists Who Wonder if They Have What It Takes.

Recently, community psychologists have re-vamped a set of 18 competencies considered important for how we practice community psychology. Three competencies are: (1) ethical, reflexive practice, (2) community inclusion and partnership, and (3) community education, information dissemination, and building public awareness. This paper will outline lessons I-a white working class woman academic-learned about my competency development through my research collaborations, using the lens of affective politics. I describe three lessons, from school-based research sites (elementary schools serving working class students of color and one elite liberal arts school serving wealthy white students). The first lesson, from an elementary school, concerns ethical, reflective practice. I discuss understanding my affect as a barometer of my ability to conduct research from a place of solidarity. The second lesson, which centers community inclusion and partnership, illustrates how I learned about the importance of "before the beginning" conversations concerning social justice and conflict when working in elementary schools. The third lesson concerns community education, information dissemination, and building public awareness. This lesson, from a college, taught me that I could stand up and speak out against classism in the face of my career trajectory being threatened. With these lessons, I flesh out key aspects of community practice competencies.
Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata

This paper focuses on detecting anomalies in a digital video broadcasting (DVB) system from providers’ perspective. We learn a probabilistic deterministic real timed automaton profiling benign behavior of encryption control in the DVB control access system. This profile is used as a one-class classifier. Anomalous items in a testing sequence are detected when the sequence is not accepted by the learned model.
Design an architecture that allows the interoperability between information systems, software or medical device adopting the standard HL7 V2.x.

The implementation of information systems for the healthcare is one of the tasks as complex in the context of the software engineering and software development due to factors as the large amount of medical specialties, different viewpoints of health professionals, information diversity in each one of the health specialties, the integration with others information systems dedicated to a specific process, among others. The implementation of Service Oriented Architecture provides many benefits that enrich the information systems to gain agility, flexibility, efficiency and productivity in organizations. Use of the standard HL7 allows the exchange of medical information between the HIS and the medical devices or between hospitals and obtaining Universal communication interfaces. This paper presents a design architecture that offer high interoperability on internal and external information systems and medical devices and supported in the use of international standards, ensuring that the medical information be consistent and timely for make clinical and administrative decisions, increasing their effectiveness.
Bio-Inspired Applications of Connectionism

The space of bio-inspired hardware can be partitioned along three axes: phylogeny, ontogeny, and epigenesis. We refer to this as the POE model. Our Embryonics (for embryonic electronics) project is situated along the ontogenetic axis of the POE model and is inspired by the processes of molecular biology and by the embryonic development of living beings. We will describe the architecture of multicellular automata that are endowed with self-replication and self-repair properties. In the conclusion, we will present our major on-going project: a giant self-repairing electronic watch, the BioWatch, built on a new reconfigurable tissue, the electronic wall or e–wall.
The road to chromatin — nuclear entry of retroviruses

Human immunodeficiency virus 1 (HIV-1) and other retroviruses synthesize a DNA copy of their genome after entry into the host cell. Integration of this DNA into the host cell's genome is an essential step in the viral replication cycle. The viral DNA is synthesized in the cytoplasm and is associated with viral and cellular proteins in a large nucleoprotein complex. Before integration into the host genome can occur, this complex must be transported to the nucleus and must cross the nuclear envelope. This Review summarizes our current knowledge of how this journey is accomplished.
Deep Learning of Spatial and Temporal Features for Automotive Prediction

Vehicle behavior models and motion prediction are critical for advanced safety systems and safety system validation. This paper studies the effectiveness of convolutional recurrent neural networks in predicting action profiles for vehicles on highways. Instead of using hand-selected features, the neural network is given an image-like representation of the local scene. Convolutional neural networks and recurrence allow for the automatic identification of robust features based on spatial and temporal relations. Real driving data from the NGSIM dataset is used for the evaluation, and the resulting models are used to propagate simulated vehicle trajectories over ten-second horizons. Prediction models using Long Short Term Memory (LSTM) networks are shown to quantitatively and qualitatively outperform baseline methods in generating realistic vehicle trajectories. Predictions over driver actions are shown to depend heavily on previous action values. Efforts to improve performance through inclusion of information about the local scene proved unsuccessful, and will be the focus of further study.
Identifying Speakers and Addressees in Dialogues Extracted from Literary Fiction

This paper describes an approach to identifying speakers and addressees in dialogues extracted from literary fiction, along with a dataset annotated for speaker and addressee. The overall purpose of this is to provide annotation of dialogue interaction between characters in literary corpora in order to allow for enriched search facilities and construction of social networks from the corpora. To predict speakers and addressees in a dialogue, we use a sequence labeling approach applied to a given set of characters. We use features relating to the current dialogue, the preceding narrative, and the complete preceding context. The results indicate that even with a small amount of training data, it is possible to build a fairly accurate classifier for speaker and addressee identification across different authors, though the identification of addressees is the more difficult task.
Predicting Vulnerable Components via Text Mining or Software Metrics? An Effort-Aware Perspective

In order to identify vulnerable software components, developers can take software metrics as predictors or use text mining techniques to build vulnerability prediction models. A recent study reported that text mining based models have higher recall than software metrics based models. However, this conclusion was drawn without considering the sizes of individual components which affects the code inspection effort to determine whether a component is vulnerable. In this paper, we investigate the predictive power of these two kinds of prediction models in the context of effort-aware vulnerability prediction. To this end, we use the same data sets, containing 223 vulnerabilities found in three web applications, to build vulnerability prediction models. The experimental results show that: (1) in the context of effort-aware ranking scenario, text mining based models only slightly outperform software metrics based models, (2) in the context of effort-aware classification scenario, text mining based models perform similarly to software metrics based models in most cases, and (3) most of the effect sizes (i.e. the magnitude of the differences) between these two kinds of models are trivial. These results suggest that, from the viewpoint of practical application, software metrics based models are comparable to text mining based models. Therefore, for developers, software metrics based models are practical choices for vulnerability prediction, as the cost to build and apply these models is much lower.
The segmented and annotated IAPR TC-12 benchmark

Automatic image annotation (AIA), a highly popular topic in the field of information retrieval research, has experienced significant progress within the last decade. Yet, the lack of a standardized evaluation platform tailored to the needs of AIA, has hindered effective evaluation of its methods, especially for region-based AIA. Therefore in this paper, we introduce the segmented and annotated IAPR TC-12 benchmark; an extended resource for the evaluation of AIA methods as well as the analysis of their impact on multimedia information retrieval. We describe the methodology adopted for the manual segmentation and annotation of images, and present statistics for the extended collection. The extended collection is publicly available and can be used to evaluate a variety of tasks in addition to image annotation. We also propose a soft measure for the evaluation of annotation performance and identify future research areas in which this extended test collection is likely to make a contribution.
A fully on-line adaptive BCI

A viable fully on-line adaptive brain computer interface (BCI) is introduced. On-line experiments with nine naive and able-bodied subjects were carried out using a continuously adaptive BCI system. The data were analyzed and the viability of the system was studied. The BCI was based on motor imagery, the feature extraction was performed with an adaptive autoregressive model and the classifier used was an adaptive quadratic discriminant analysis. The classifier was on-line updated by an adaptive estimation of the information matrix (ADIM). The system was also able to provide continuous feedback to the subject. The success of the feedback was studied analyzing the error rate and mutual information of each session and this analysis showed a clear improvement of the subject's control of the BCI from session to session.
Improvement of Generalization Ability of Deep CNN via Implicit Regularization in Two-Stage Training Process

Optimization of deep learning is no longer an imminent problem, due to various gradient descent methods and the improvements of network structure, including activation functions, the connectivity style, and so on. Then the actual application depends on the generalization ability, which determines whether a network is effective. Regularization is an efficient way to improve the generalization ability of deep CNN, because it makes it possible to train more complex models while maintaining a lower overfitting. In this paper, we propose to optimize the feature boundary of deep CNN through a two-stage training method (pre-training process and implicit regularization training process) to reduce the overfitting problem. In the pre-training stage, we train a network model to extract the image representation for anomaly detection. In the implicit regularization training stage, we re-train the network based on the anomaly detection results to regularize the feature boundary and make it converge in the proper position. Experimental results on five image classification benchmarks show that the two-stage training method achieves a state-of-the-art performance and that it, in conjunction with more complicated anomaly detection algorithm, obtains better results. Finally, we use a variety of strategies to explore and analyze how implicit regularization plays a role in the two-stage training process. Furthermore, we explain how implicit regularization can be interpreted as data augmentation and model ensemble.