Miracle-dz/UniFAR-FTUs huggingface dataset:
Analgesic Effect of Ethanolic Extract of Bougainvillea X Buttiana ( Var . Rose ) Holttum & Standl

Background: Bougainvillea sp is a medicinal plant traditionally used in Mexico to alleviate several diseases including painful disorders. The present study was carried out to evaluate the analgesic activity of ethanolic extracts of Bougainvillea x buttiana (var. Rose) Holttum & Standl. Methods: Bougainvillea x buttiana bracts were extracted with ethanol. The extract was submitted to phytochemical qualitative analysis. The extract was orally administered at dose of 0.04, 0.4, 4 and 40 mg/kg. For analgesic evaluation were included methods for a peripheral mechanism pain such as the acetic-acid induced writhing method, and a central mechanism of pain as tail flick and formalin tests were used. Results: The results obtained from the phytochemical qualitative analysis showed the presence of alkaloids, carbohydrates, fatty acids, lipids, saponins and tannins. Our results also clearly indicate that the oral administration of the Bougainvillea x buttiana extract caused a significant (p < 0.05) dose-dependent reduction in the number of abdominal writhes. The extract also produced a remarkable increase in tail immersion latency time and paw licking time. Conclusion: The results obtained demonstrated that Bougainvillea x buttiana extract showed analgesic potential in all models of nociception implying that both peripheral and central pathways of analgesia are involved. This might be due to the presence of various classes of phytochemicals in the plan extract.
Zeolitic imidazolate framework-coupled resonators for enhanced gas detection

This work presents for the first time a zeolitic imidazolate framework (ZIF)-coupled resonant gas sensor whose sensitivity shows an improvement up to 78 times over bare silicon resonant sensors with identical dimensions. ZIFs are among the highest surface area material used for resonant-based sensing. We utilize high surface area-to-volume ratios of ZIFs to demonstrate how a microresonator coupled with ZIF crystals can provide high sensitivity to chemical vapors.
Experiencing AUML in the GAIA Methodology

In the last few years a great number of AOSE methodologies have been proposed, some of which centered on organizational aspects to better capture the behavior of agents societies. Those methodologies may be considered very useful for modeling open systems composed of a great number of interacting autonomous agents. Gaia exploits organizational abstractions to provide clear guidelines for the analysis and design of complex and open Multi-Agent Systems (MAS). However, the notation of the Gaia methodology is probably less powerful (and perhaps less acceptable for industry solutions) than others (like AUML). In this perspective, this aims at performing a preliminary exploration towards the potential application of the AUML notation into the Gaia methodology: it explores the above issues using an application example and pays specific attention to the problem of modeling the complexity of open MAS and emergent behaviors.
Genetic engineered color silk: fabrication of a photonics material through a bioassisted technology

Silk produced by the silkworm Bombyx mori is an attractive material because of its luster, smooth and soft texture, conspicuous mechanical strength, good biocompatibility, slow biodegradation, and carbon neutral synthesis. Silkworms have been domesticated and bred for production of better quality and quantity of silk, resulting in the development of sericulture and the textile industry. Silk is generally white, so dyeing is required to obtain colored fiber. However, the dyeing process involves harsh conditions and generates a large volume of waste water, which have environmentally and economically negative impacts. Although some strains produce cocoons that contain pigments derived from the mulberry leaves that they eat, the pigments are distributed in the sericin layer and are lost during gumming. In trials for production of colored silk by feeding silkworms on diets containing dyes, only limited species of dye molecules were incorporated into the silk threads. A method for the generation of transgenic silkworm was established in conjunction with the discovery of green fluorescent protein (GFP), and silkworms carrying the GFP gene spun silk threads that formed cocoons that glowed bright green and still retained the original properties of silk. A wide range of color variation of silk threads has been obtained by replacing the GFP gene with the genes of other fluorescent proteins chosen from the fluorescent protein palette. The genetically modified silk with photonic properties can be processed to form various products including linear threads, 2D fabrics, and 3D materials. The transgenic colored silk could be economically advantageous due to addition of a new value to silk and reduction of cost for water waste, and environmentally preferable for saving water. Here, I review the literature regarding the production methods of fluorescent silk from transgenic silkworms and present examples of genetically modified color silk.
Diagnosing Cervical Neoplasia in Rural Brazil Using a Mobile Van Equipped with In Vivo Microscopy: A Cluster-Randomized Community Trial

Cervical cancer is a leading cause of death in underserved areas of Brazil. This prospective randomized trial involved 200 women in southern/central Brazil with abnormal Papanicolaou tests. Participants were randomized by geographic cluster and referred for diagnostic evaluation either at a mobile van upon its scheduled visit to their local community, or at a central hospital. Participants in both arms underwent colposcopy, in vivo microscopy, and cervical biopsies. We compared rates of diagnostic follow-up completion between study arms, and also evaluated the diagnostic performance of in vivo microscopy compared with colposcopy. There was a 23% absolute and 37% relative increase in diagnostic follow-up completion rates for patients referred to the mobile van (102/117, 87%) compared with the central hospital (53/83, 64%; P = 0.0001; risk ratio = 1.37, 95% CI, 1.14–1.63). In 229 cervical sites in 144 patients, colposcopic examination identified sites diagnosed as cervical intraepithelial neoplasia grade 2 or more severe (CIN2+; 85 sites) with a sensitivity of 94% (95% CI, 87%–98%) and specificity of 50% (95% CI, 42%–58%). In vivo microscopy with real-time automated image analysis identified CIN2+ with a sensitivity of 92% (95% CI, 84%–97%) and specificity of 48% (95% CI, 40%–56%). Women referred to the mobile van were more likely to complete their diagnostic follow-up compared with those referred to a central hospital, without compromise in clinical care. In vivo microscopy in a mobile van provides automated diagnostic imaging with sensitivity and specificity similar to colposcopy. Cancer Prev Res; 11(6); 359–70. ©2018 AACR.
Enabling Cost-Effective Flash based Caching with an Array of Commodity SSDs

SSD based cache solutions are being widely utilized to improve performance in network storage systems. With a goal of providing a cost-effective, high performing SSD cache solution, we propose a new caching solution called SRC (SSD RAID as a Cache) for an array of commodity SSDs. In designing SRC, we borrow both the well-known RAID technique and the log-structured approach and adopt them into the cache layer. In so doing, we explore a wide variety of design choices such as flush issue frequency, write units, forming stripes without parity, and garbage collection through copying rather than destaging that become possible as we make use of RAID and a log-structured approach at the cache level. Using an implementation in Linux under the Device Mapper framework, we quantitatively present and analyze results of the design space options that we considered in our design. Our experiments using realistic workload traces show that SRC performs at least 2 times better in terms of throughput than existing open source solutions. We also consider cost-effectiveness of SRC with a variety of SSD products. In particular, we compare SRC configured with MLC and TLC SATA SSDs and a single high-end NVMe SSD. We find that SRC configured as RAID-5 with low-cost MLC and TLC SATA SSDs generally outperforms that configured with a single high-end SSD in terms of both performance and lifetime per dollars spent.
Reconstruction of Graph Signals Through Percolation from Seeding Nodes

New schemes to recover signals defined in the nodes of a graph are proposed. Our focus is on reconstructing bandlimited graph signals, which are signals that admit a sparse representation in a frequency domain related to the structure of the graph. Most existing formulations focus on estimating an unknown graph signal by observing its value on a subset of nodes. By contrast, in this paper, we study the problem of inducing a known graph signal using as input a graph signal that is nonzero only for a small subset of nodes. The sparse signal is then percolated (interpolated) across the graph using a graph filter. Alternatively, one can interpret graph signals as network states and study graph-signal reconstruction as a network-control problem where the target class of states is represented by bandlimited signals. Three setups are investigated. In the first one, a single simultaneous injection takes place on several nodes in the graph. In the second one, successive value injections take place on a single node. The third one is a generalization where multiple nodes inject multiple signal values. For noiseless settings, conditions under which perfect reconstruction is feasible are given, and the corresponding schemes to recover the desired signal are specified. Scenarios leading to imperfect reconstruction, either due to insufficient or noisy signal value injections, are also analyzed. Moreover, connections with classical interpolation in the time domain are discussed. Specifically, for time-varying signals, where the ideal interpolator after uniform sampling is a (low-pass) filter, our proposed approach and the reconstruction of a sampled signal coincide. Nevertheless, for general graph signals, we show that these two approaches differ. The last part of the paper presents numerical experiments that illustrate the results developed through synthetic and real-world signal reconstruction problems.
MapNet: An Allocentric Spatial Memory for Mapping Environments

Autonomous agents need to reason about the world beyond their instantaneous sensory input. Integrating information over time, however, requires switching from an egocentric representation of a scene to an allocentric one, expressed in the world reference frame. It must also be possible to update the representation dynamically, which requires localizing and registering the sensor with respect to the world reference. In this paper, we develop a differentiable module that satisfies such requirements, while being robust, efficient, and suitable for integration in end-to-end deep networks. The module contains an allocentric spatial memory that can be accessed associatively by feeding to it the current sensory input, resulting in localization, and then updated using an LSTM or similar mechanism. We formulate efficient localization and registration of sensory information as a dual pair of convolution/deconvolution operators in memory space. The map itself is a 2.5D representation of an environment storing information that a deep neural network module learns to distill from RGBD input. The result is a map that contains multi-task information, different from classical approaches to mapping such as structure-from-motion. We present results using synthetic mazes, a dataset of hours of recorded gameplay of the classic game Doom, and the very recent Active Vision Dataset of real images captured from a robot.
Activation of stimulator of interferon genes (STING) induces ADAM17-mediated shedding of the immune semaphorin SEMA4D

Stimulator of interferon genes (STING) is an endoplasmic reticulum–resident membrane protein that mediates cytosolic pathogen DNA–induced innate immunity and inflammatory responses in host defenses. STING is activated by cyclic di-nucleotides and is then translocated to the Golgi apparatus, an event that triggers STING assembly with the downstream enzyme TANK-binding kinase 1 (TBK1). This assembly leads to the phosphorylation of the transcription factor interferon regulatory factor 3 (IRF3), which in turn induces expression of type-I interferon (IFN) and chemokine genes. STING also mediates inflammatory responses independently of IRF3, but these molecular pathways are largely unexplored. Here, we analyzed the RAW264.7 macrophage secretome to comprehensively identify proinflammatory factors released into the extracellular medium upon STING activation. In total, we identified 1299 proteins in macrophage culture supernatants, of which 23 were significantly increased after STING activation. These proteins included IRF3-dependent cytokines, as well as previously unknown targets of STING, such as the immune semaphorin SEMA4D/CD100, which possesses proinflammatory cytokine-like activities. Unlike for canonical cytokines, the expression of the SEMA4D gene was not up-regulated. Instead, upon STING activation, membrane-bound SEMA4D was cleaved into a soluble form, suggesting the presence of a post-translational shedding machinery. Importantly, the SEMA4D shedding was blocked by TMI-1, an inhibitor of the sheddase ADAM metallopeptidase domain 17 (ADAM17) but not by the TBK1 inhibitor BX795. These results suggest that STING activates ADAM17 and that this activation produces soluble proinflammatory SEMA4D independently of the TBK1/IRF3-mediated transcriptional pathway.
An Analysis of More Than 1, 400 Articles, 900 Scales, and 17 Years of Research: The State of Scales in Cyberpsychology, Behavior, and Social Networking

Cyberpsychology is a recently emergent field that examines the impact of technology upon human cognition and behavior. Given its infancy, authors have rapidly created new measures to gauge their constructs of interest. Unfortunately, few of these authors have had the opportunity to test their scales' psychometric properties and validity. This is concerning, as many theoretical assumptions may be founded upon scales with inadequate attributes. If this were found to be true, then previous findings in cyberpsychology studies would need to be retested, and future research would need to shift its focus to creating psychometrically sound and valid measures. To provide inferences on this concern, the current study examines the article reporting, scale creation, and scale reliabilities of every article published in Cyberpsychology, Behavior, and Social Networking from its inception to July 2014. The final data set encompassed the coding of 1,478 individual articles, including 921 scales, and spanning 17 years. The results demonstrate that the simple survey methodology has become more popular over time. Authors are gradually applying empirically tested scales. However, self-created measures are still the most popular, leading to concerns about the measures' validity. Also, the use of multi-item measures has increased over time, but many articles still fail to report adequate information to assess the reliability of the applied scales. Lastly, the average scale reliability is 0.81, which barely meets standard cutoffs. Overall, these results are not overly concerning, but suggestions are given on methods to improve the reporting of measures, the creation of scales, and the state of cyberpsychology.
Evaluation of Methods for the Extraction and Characterization of Waxes from Crude Oils

Wax precipitation is one of the most important flow assurance problems. The adequate characterization of the content and composition of the waxes present in the oil is one of the major obstacles in dealing with this issue. In this work, two different methods for the extraction of waxes from different crude oils are compared. The methods used differ significantly with regard to the separation of waxes from asphalthenes. Further purification of parafffins for characterization was carried out using sequential elution chromatography. The n-paraffin distribution determined by gas chromatography along with wax melting temperature and total n-paraffin content determined by differential scanning calorimetry show remarkable differences in the waxes depending upon the precipitation method used. The separation carried out by method 1 allows for heavier n-alkanes to be retained but with a higher content of impurities, as revealed by their lower melting points and n-paraffin content. Method 2 provides a better purific.
Classification of atrial fibrillation in short-term ECG recordings using a machine learning approach and hybrid QRS detection

Atrial fibrillation (AF) is one of the most common sustained cardiac arrhythmia, occurring in 1–2% of the general population. Significant mortality and morbidity is related to occurrence of AF arrhythmia due to high risk of hospitalization, stroke, heart failure and coronary artery disease, etc. In many cases AF may not produce any symptoms and may go unnoticed by a patient, which is why there is a high importance to develop methods of detecting this heart disorder. Creating an algorithm for AF and other arrhythmias classification of short-term single lead ECG signals was the aim of the PhysioNet Challenge 2017. The database was composed by over 8.5 thousand ECG recordings (between 10 sec and 60 sec length) measured by AliveCor device, provided by organizers. We prepared an alternative hybrid approach for QRS detection in order to obtain RR time intervals. It consists of two complementary methods in hierarchical order: one based on nonlinear transformation and first-order Gaussian differentiator as superior and another one proposed in sample entry as inferior. We introduce the machine learning algorithm in order to classify whether it is normal sinus rhythm, AF or an alternative heart rhythm using features considered regularity of RR time intervals and morphology of the ECG signal. The separate part of the algorithm based on beat averaging method is dedicated for preceding extraction of too noisy recordings from the input to the classifier. The best overall F1 score we achieved in the official phase of the PhysioNet Challenge 2017 was 0.77 (0.86 for normal, 0.78 for AF and 0.66 for other rhythms).
Syntax and sensibility: Using language models to detect and correct syntax errors

Syntax errors are made by novice and experienced programmers alike; however, novice programmers lack the years of experience that help them quickly resolve these frustrating errors. Standard LR parsers are of little help, typically resolving syntax errors and their precise location poorly. We propose a methodology that locates where syntax errors occur, and suggests possible changes to the token stream that can fix the error identified. This methodology finds syntax errors by using language models trained on correct source code to find tokens that seem out of place. Fixes are synthesized by consulting the language models to determine what tokens are more likely at the estimated error location. We compare n-gram and LSTM (long short-term memory) language models for this task, each trained on a large corpus of Java code collected from GitHub. Unlike prior work, our methodology does not rely that the problem source code comes from the same domain as the training data. We evaluated against a repository of real student mistakes. Our tools are able to find a syntactically-valid fix within its top-2 suggestions, often producing the exact fix that the student used to resolve the error. The results show that this tool and methodology can locate and suggest corrections for syntax errors. Our methodology is of practical use to all programmers, but will be especially useful to novices frustrated with incomprehensible syntax errors.
Learning Bayesian Networks with Largest Chain Graphs

This paper proposes a new approach for designing learning bayesian network algorithms that explore the structure equivalence classes space. Its main originality consists in the representation of equivalence classes by largest chain graphs, instead of essential graphs which are generally used in the similar task. We show that this approach drastically simplifies the algorithms formulation and has some beneficial aspects on their execution time.
Guiding optical flow estimation using superpixels

In this paper, we show how the segmentation of an image into superpixels may be used as preprocessing paradigm to improve the accuracy of the optical flow estimation in an image sequence. Superpixels play the role of accurate support masks for the integration of the optical flow equation. We employ a variation of a recently proposed optical flow algorithm relying on local image properties that are taken into account only if the involved pixels belong to the same image segment. Experimental results show that the proposed optical flow estimation scheme significantly improves the accuracy of the estimated motion field with respect to other standard methods.
Modeling and visualizing semantic and spatio-temporal evolution of topics in interpersonal communication on Twitter

ABSTRACT Interpersonal communication on online social networks has a significant impact on the society by not only diffusing information, but also forming social ties, norms, and behaviors. Knowing how the conversational discourse semantically and geographically vary over time can help uncover the changing dynamics of interpersonal ties and the digital traces of social events. This article introduces a framework for modeling and visualizing the semantic and spatio-temporal evolution of topics in a spatially embedded and time-stamped interpersonal communication network. The framework consists of (1) a topic modeling workflow for modeling topics and extracting the evolution of conversational discourse; (2) a geo-social network modeling and smoothing approach to projecting connection characteristics and semantics of communication onto geographic space and time; (3) a web-based geovisual analytics environment for exploring semantic and spatio-temporal evolution of topics in a spatially embedded and time-stamped interpersonal communication network. To demonstrate, geo-located and reciprocal user mention and reply tweets over the course of the 2016 primary and presidential elections in the United States from 1 August 2015 to 15 November 2016 were analyzed. The large portion of the topics extracted from mention tweets were related to daily life routines, human activities, and interests such as school, work, sports, dating, wearing, birthday celebration, music, food, and live-tweeting. Specific focus on the analysis of political conversations revealed that the content of conversational discourse was split between civil rights and election-related discussions of the political campaigns and candidates. These political topics exhibited major shifts in terms of content and the popularity in reaction to primaries, debates, and events throughout the study period. While civil rights discussions were more dominant and in higher intensity across the nation and throughout the whole time period, election-specific conversations resulted in temporally varying local hotspots that correlated with locations of primaries and events.