Miracle-dz/UniFAR-FTUs huggingface dataset:
Protein Kinase C Activation Attenuates N‐Methyl‐d‐Aspartate‐Induced Increases in Intracellular Calcium in Cerebellar Granule Cells

Abstract: Activation of the N‐methyl‐d‐aspartate (NMDA) subtype of glutamate receptor increases levels of intracellular calcium and can lead to stimulation of protein kinase C activity. Several reports have demonstrated that stimulation of protein kinase C can, in turn, increase electrophysiological responses to NMDA in certain cells or in oocytes expressing certain NMDA receptor subunits. In the present study, the effects of protein kinase C activation on NMDA receptor‐mediated increases in intracellular Ca2+ levels were investigated in primary cultures of rat cerebellar granule cells using fura‐2 fluorescence spectroscopy. Pretreatment of the cells with the protein kinase C activator phorbol 12‐myristate 13‐acetate (PMA), but not the inactive analogue 4α‐phorbol 12‐myristate 13‐acetate, inhibited NMDA‐induced increases in intracellular Ca2+ levels. Coincubation of cells with PMA and the kinase inhibitor staurosporine or calphostin C blocked the PMA effect. The potency of NMDA was reduced twofold, and the potency of the NMDA receptor coagonist, glycine, to enhance the response to NMDA was decreased fourfold by pretreatment of cells with PMA. The effect on glycine was mimicked by pretreatment with okadaic acid, a protein phosphatase inhibitor. PMA treatment did not significantly alter Mg2+ inhibition of the NMDA response but decreased the potency of the competitive antagonist CGS‐19755. These data suggest that, in cerebellar granule cells, the function of the NMDA receptor may be subject to feedback inhibition by protein kinase C stimulation. Under physiological conditions, this inhibition may result from a decreased effectiveness of the endogenous coagonists, glutamate and glycine.
Expression of mucin-like carcinoma-associated antigen in the cyst fluid differentiates mucinous from nonmucinous pancreatic cysts.

OBJECTIVES Differential diagnosis of pancreatic cystic lesions may be difficult: the main problem is to distinguish mucinous neoplasms from nonmucinous cysts. We evaluated the usefulness of the mucin-like carcinoma-associated antigen (MCA) in the fluid of pancreatic cysts for detecting mucinous neoplasms. Results were compared with those of CA 15-3, carcinoembryonic antigen (CEA), and CA 72-4 fluid content, and cytology. METHODS Twenty-four pancreatic cyst fluids were collected from 10 pseudocysts, eight mucinous cystic tumors, and six serous cystadenomas. RESULTS MCA was elevated in seven of eight mucinous tumors (sensitivity 87.5%, specificity 100%). A significant difference was found between MCA levels in mucinous neoplasms versus pseudocysts (p = 0.0003) and serous cystadenomas (p = 0.001). Mean MCA levels were higher (133.7 U/ml) in mucinous cystadenocarcinomas than in cystadenomas (37.5 U/ml). The sensitivity of CA 15-3, CEA, and CA 72-4 in detecting mucinous neoplasms was 50, 87.5, and 87.5%, respectively, with a specificity of 94%, 44%, and 94%, respectively. Cytology showed mucinous epithelial cells in only four of eight mucinous neoplasms, with a specificity of 100%. CONCLUSIONS These data suggest that MCA determination in the cyst fluid is a promising new tumor marker for the preoperative diagnosis of mucinous cystic neoplasms of the pancreas.
A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd

The study of argumentation and the development of argument mining tools depends on the availability of annotated data, which is challenging to obtain in sufficient quantity and quality. We present a method that breaks down a popular but relatively complex discourse-level argument annotation scheme into a simpler, iterative procedure that can be applied even by untrained annotators. We apply this method in a crowdsourcing setup and report on the reliability of the annotations obtained. The source code for a tool implementing our annotation method, as well as the sample data we obtained (4909 gold-standard annotations across 982 documents), are freely released to the research community. These are intended to serve the needs of qualitative research into argumentation, as well as of data-driven approaches to argument mining.
Formal Analysis of Energy Consumption in IoT Systems

In this paper, we apply model-checking approach to formally analyze energy consumption of the radio interface in order to guarantee network lifetime in the context of the Internet of Things. We propose a joint MAC-physical model of the IEEE 802.15. 4 standard to capture and represent key operations that consume energy resources of the nodes at the radio interface component. We argue that the combination of the radio interface ON/OFF state switching mechanism with CSMA/CA medium access method leads to better modeling of the energy consumption and help understanding the interaction between MAC and physical layers. Our model provides accurate representation of simulation models at the first two layers of node’s protocol stack compliant with IEEE 802.15. 4 standard.
A Fog-Cloud Computing Infrastructure for Condition Monitoring and Distributing Industry 4.0 Services

Data-driven Industry 4.0 applications require low latency data processing and reliable communication models to enable efficient operation of production lines, fast response to failures and quickly adapt the manufacturing process to changing environmental conditions. Data processing in the Cloud has been widely accepted and in combination with Fog technologies, it can also satisfy these requirements. This paper investigates the placement of service containers and wheater they should be carried out in the Cloud or at a Fog node. It shows how to provide an uniform well-monitored execution environment to automatically distribute services concerning their application-specific requirements. An infrastructure is presented, that utilizes measurement probes to observe the node and environmental conditions, derive and evaluate appropriate distribution algorithms and finally deploy the application services to the node that meets the requirements.
The human volatilome: volatile organic compounds (VOCs) in exhaled breath, skin emanations, urine, feces and saliva

Breath analysis is a young field of research with its roots in antiquity. Antoine Lavoisier discovered carbon dioxide in exhaled breath during the period 1777–1783, Wilhelm (Vilém) Petters discovered acetone in breath in 1857 and Johannes Müller reported the first quantitative measurements of acetone in 1898. A recent review reported 1765 volatile compounds appearing in exhaled breath, skin emanations, urine, saliva, human breast milk, blood and feces. For a large number of compounds, real-time analysis of exhaled breath or skin emanations has been performed, e.g., during exertion of effort on a stationary bicycle or during sleep. Volatile compounds in exhaled breath, which record historical exposure, are called the ‘exposome’. Changes in biogenic volatile organic compound concentrations can be used to mirror metabolic or (patho)physiological processes in the whole body or blood concentrations of drugs (e.g. propofol) in clinical settings—even during artificial ventilation or during surgery. Also compounds released by bacterial strains like Pseudomonas aeruginosa or Streptococcus pneumonia could be very interesting. Methyl methacrylate (CAS 80–62–6), for example, was observed in the headspace of Streptococcus pneumonia in concentrations up to 1420 ppb. Fecal volatiles have been implicated in differentiating certain infectious bowel diseases such as Clostridium difficile, Campylobacter, Salmonella and Cholera. They have also been used to differentiate other non-infectious conditions such as irritable bowel syndrome and inflammatory bowel disease. In addition, alterations in urine volatiles have been used to detect urinary tract infections, bladder, prostate and other cancers. Peroxidation of lipids and other biomolecules by reactive oxygen species produce volatile compounds like ethane and 1-pentane. Noninvasive detection and therapeutic monitoring of oxidative stress would be highly desirable in autoimmunological, neurological, inflammatory diseases and cancer, but also during surgery and in intensive care units. The investigation of cell cultures opens up new possibilities for elucidation of the biochemical background of volatile compounds. In future studies, combined investigations of a particular compound with regard to human matrices such as breath, urine, saliva and cell culture investigations will lead to novel scientific progress in the field.
Distributed, Egocentric Representations of Graphs for Detecting Critical Structures

We study the problem of detecting critical structures using a graph embedding model. Existing graph embedding models lack the ability to precisely detect critical structures that are specific to a task at the global scale. In this paper, we propose a novel graph embedding model, called the Ego-CNNs, that employs the ego-convolutions convolutions at each layer and stacks up layers using an ego-centric way to detects precise critical structures efficiently. An Ego-CNN can be jointly trained with a task model and help explain/discover knowledge for the task. We conduct extensive experiments and the results show that Ego-CNNs (1) can lead to comparable task performance as the state-of-the-art graph embedding models, (2) works nicely with CNN visualization techniques to illustrate the detected structures, and (3) is efficient and can incorporate with scale-free priors, which commonly occurs in social network datasets, to further improve the training efficiency.
Influence of Drone Altitude, Image Overlap, and Optical Sensor Resolution on Multi-View Reconstruction of Forest Images

Recent technical advances in drones make them increasingly relevant and important tools for forest measurements. However, information on how to optimally set flight parameters and choose sensor resolution is lagging behind the technical developments. Our study aims to address this gap, exploring the effects of drone flight parameters (altitude, image overlap, and sensor resolution) on image reconstruction and successful 3D point extraction. This study was conducted using video footage obtained from flights at several altitudes, sampled for images at varying frequencies to obtain forward overlap ratios ranging between 91 and 99%. Artificial reduction of image resolution was used to simulate sensor resolutions between 0.3 and 8.3 Megapixels (Mpx). The resulting data matrix was analysed using commercial multi-view reconstruction (MVG) software to understand the effects of drone variables on (1) reconstruction detail and precision, (2) flight times of the drone, and (3) reconstruction times during data processing. The correlations between variables were statistically analysed with a multivariate generalised additive model (GAM), based on a tensor spline smoother to construct response surfaces. Flight time was linearly related to altitude, while processing time was mainly influenced by altitude and forward overlap, which in turn changed the number of images processed. Low flight altitudes yielded the highest reconstruction details and best precision, particularly in combination with high image overlaps. Interestingly, this effect was nonlinear and not directly related to increased sensor resolution at higher altitudes. We suggest that image geometry and high image frequency enable the MVG algorithm to identify more points on the silhouettes of tree crowns. Our results are some of the first estimates of reasonable value ranges for flight parameter selection for forestry applications.
IAGREE: Infrastructure-agnostic Resilience Benchmark Tool for Cloud Native Platforms

The cloud native approach is getting more and more popular with the proposal of decomposing application into small components called microservices, which are designed to minimize the costs with upgrades and maintenance and increase the resources usage efficiency. However, microservices architecture brings some challenges such as preserving the manageability of the platforms, since the greater the number of microservices the applications have, the greater the complexity of ensuring that everything is working as expected. In this context, one of the concerns is to evaluate the resilience of platforms. Current resilience benchmark tools are designed for running in specific infrastructures. Thus, in this paper we present IAGREE, a benchmark tool designed for measuring multiple resilience metrics in cloud native platforms based on Cloud Foundry and running upon any infrastructure.
A Framework for Bayesian Optimization in Embedded Subspaces

We present a theoretically founded approach for high-dimensional Bayesian optimization based on low-dimensional subspace embeddings. We prove that the error in the Gaussian process model is bounded tightly when going from the original high-dimensional search domain to the lowdimensional embedding. This implies that the optimization process in the low-dimensional embedding proceeds essentially as if it were run directly on an unknown active subspace of low dimensionality. The argument applies to a large class of algorithms and GP models, including non-stationary kernels. Moreover, we provide an efficient implementation based on hashing and demonstrate empirically that this subspace embedding achieves considerably better results than the previously proposed methods for highdimensional BO based on Gaussian matrix projections and structure-learning.
Joint Detection and Location of English Puns

A pun is a form of wordplay for an intended humorous or rhetorical effect, where a word suggests two or more meanings by exploiting polysemy (homographic pun) or phonological similarity to another word (heterographic pun). This paper presents an approach that addresses pun detection and pun location jointly from a sequence labeling perspective. We employ a new tagging scheme such that the model is capable of performing such a joint task, where useful structural information can be properly captured. We show that our proposed model is effective in handling both homographic and heterographic puns. Empirical results on the benchmark datasets demonstrate that our approach can achieve new state-of-the-art results.
Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data

Argument compatibility is a linguistic condition that is frequently incorporated into modern event coreference resolution systems. If two event mentions have incompatible arguments in any of the argument roles, they cannot be coreferent. On the other hand, if these mentions have compatible arguments, then this may be used as information towards deciding their coreferent status. One of the key challenges in leveraging argument compatibility lies in the paucity of labeled data. In this work, we propose a transfer learning framework for event coreference resolution that utilizes a large amount of unlabeled data to learn argument compatibility of event mentions. In addition, we adopt an interactive inference network based model to better capture the compatible and incompatible relations between the context words of event mentions. Our experiments on the KBP 2017 English dataset confirm the effectiveness of our model in learning argument compatibility, which in turn improves the performance of the overall event coreference model.
Suggesting Software Measurement Plans with Unsupervised Learning Data Analysis

Software measurement processes require to consider more and more data, measures and metrics. Measurement plans become complex, time and resource consuming, considering diverse kinds of software project phases. Experts in charge of defining the measurement plans have to deal with management and performance constraints to select the relevant metrics. They need to take into account a huge number of data though distributed processes. Formal models and standards have been standardized to facilitate some of these aspects. However, the maintainability of the measurements activities is still constituted of complex activities. In this paper, we aim at improving our previous work, which aims at reducing the number of needed software metrics when executing measurement process and reducing the expertise charge. Based on unsupervised learning algorithm, our objective is to suggest software measurement plans at runtime and to apply them iteratively. For that purpose, we propose to generate automatically analysis models using unsupervised learning approach in order to efficiently manage the efforts, time and resources of the experts. An implementation has been done and integrated on an industrial platform. Experiments are processed to show the scalability and effectiveness of our approach. Discussions about the results have been provided. Furthermore, we demonstrate that the measurement process performance could be optimized while being effective , more accurate and faster with reduced expert intervention
FreebaseQA: A New Factoid QA Data Set Matching Trivia-Style Question-Answer Pairs with Freebase

In this paper, we present a new data set, named FreebaseQA, for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase. The data set is generated by matching trivia-type question-answer pairs with subject-predicate-object triples in Freebase. For each collected question-answer pair, we first tag all entities in each question and search for relevant predicates that bridge a tagged entity with the answer in Freebase. Finally, human annotation is used to remove any false positive in these matched triples. Using this method, we are able to efficiently generate over 54K matches from about 28K unique questions with minimal cost. Our analysis shows that this data set is suitable for model training in factoid QA tasks beyond simpler questions since FreebaseQA provides more linguistically sophisticated questions than other existing data sets.
SELFIE: Refurbishing Unclean Samples for Robust Deep Learning

Owing to the extremely high expressive power of deep neural networks, their side effect is to totally memorize training data even when the labels are extremely noisy. To overcome overﬁtting on the noisy labels, we propose a novel robust training method called SELFIE . Our key idea is to selectively refurbish and exploit unclean samples that can be corrected with high precision, thereby gradually increasing the number of available training samples. Taking advantage of this design, SELFIE effectively prevents the risk of noise accumulation from the false correction and fully exploits the training data. To validate the superiority of SELFIE , we conducted extensive experimentation using four real-world or synthetic data sets. The result showed that SELFIE remarkably improved absolute test error compared with two state-of-the-art methods.
Sublinear Time Nearest Neighbor Search over Generalized Weighted Space

Nearest Neighbor Search (NNS) over generalized weighted space is a fundamental problem which has many applications in various ﬁelds. However, to the best of our knowledge, there is no sublinear time solution to this problem. Based on the idea of Asymmetric Locality-Sensitive Hashing (ALSH), we introduce a novel spherical asymmetric transformation and propose the ﬁrst two novel weight-oblivious hashing schemes SL-ALSH and S2-ALSH accordingly. We further show that both schemes enjoy a quality guarantee and can answer the NNS queries in sublinear time. Evaluations over three real datasets demonstrate the superior performance of the two proposed schemes.
GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver

There are two types of ordinary differential equations (ODEs): initial value problems (IVPs) and boundary value problems (BVPs). While many probabilistic numerical methods for the solution of IVPs have been presented to-date, there exists no efficient probabilistic general-purpose solver for nonlinear BVPs. Our method based on iterated Gaussian process (GP) regression returns a GP posterior over the solution of nonlinear ODEs, which provides a meaningful error estimation via its predictive posterior standard deviation. Our solver is fast (typically of quadratic convergence rate) and the theory of convergence can be transferred from prior non-probabilistic work. Our method performs on par with standard codes for an established benchmark of test problems.
Mutating UML State Machine Behavior with Semantic Mutation Operators

In Model-Driven Software Development (MDSD), an application can be built using classes and their state machines as source models. The final application can be tested as any source code. In this paper, we discuss a specific approach to mutation testing in which modifications relate to different variants of behavioural features modelled by UML state machines, while testing deals with standard executions of the final application against its test cases. We have proposed several mutation operators aimed at mutating behaviour of UML state machines. The operators take into account event processing, time management, behaviour of complex states with orthogonal regions, and usage of history pseudostates. Different possible semantic interpretations are associated with each operator. The operators have been implemented in the Framework for eXecutable UML (FXU). The framework, that supports code generation from UML classes and state machines and building target C# applications, has been extended to realize mutation testing with use of multiple libraries. The semantic mutation operators have been verified in some MDSD experiments.
Interactive Visualization of Differences between Software Model Versions

During the development of software systems, various software models are created in the software design phase. The models are being further modified not only by customers’ changes in system requirements, but as well by design decisions based on problems being solved. These changes are sometimes not visible without previous familiarity with original versions of software models, which can cause loss of efficiency not only for software analysts, but also for developers, who must reflect these modifications into source code. The solution to these problems can be found in the proposed method for visualization of modifications in software models, which visualizes modifications directly in diagrams and supports interaction with the modifications. As the basis for the difference detection, we propose an extendible modelling architecture. We evaluate this architecture and the proposed method via working prototype which compares two model versions given in XMI form and visualizes their differences. The evaluation proves that our method significantly shortens time necessary to detect differences by developers and also decreases a number of unidentified differences.
Communication-Constrained Inference and the Role of Shared Randomness

A central server needs to perform statistical inference based on samples that are distributed over multiple users who can each send a message of limited length to the center. We study problems of distribution learning and identity testing in this distributed inference setting and examine the role of shared randomness as a resource. We propose a general purpose simulate-and-infer strategy that uses only private-coin communication protocols and is sample-optimal for distribution learning. This general strategy turns out to be sample-optimal even for distribution testing among private-coin protocols. Interestingly, we propose a public-coin protocol that outperforms simulate-and-infer for distribution testing and is, in fact, sample-optimal. Underlying our public-coin protocol is a random hash that when applied to the samples minimally contracts the chi-squared distance of their distribution from the uniform distribution.