Miracle-dz/UniFAR-FTUs huggingface dataset:
The Communication, Awareness, Relationships and Empowerment (C.A.R.E.) Model: An Effective Tool for Engaging Urban Communities in Community-Based Participatory Research

Little is known about recruitment methods for racial/ethnic minority populations from resource-limited areas for community-based health and needs assessments, particularly assessments that incorporate mobile health (mHealth) technology for characterizing physical activity and dietary intake. We examined whether the Communication, Awareness, Relationships and Empowerment (C.A.R.E.) model could reduce challenges recruiting and retaining participants from faith-based organizations in predominantly African American Washington, D.C. communities for a community-based assessment. Employing C.A. R.E. model elements, our diverse research team developed partnerships with churches, health organizations, academic institutions and governmental agencies. Through these partnerships, we cultivated a visible presence at community events, provided cardiovascular health education and remained accessible throughout the research process. Additionally, these relationships led to the creation of a community advisory board (CAB), which influenced the study’s design, implementation, and dissemination. Over thirteen months, 159 individuals were recruited for the study, 99 completed the initial assessment, and 81 used mHealth technology to self-monitor physical activity over 30 days. The culturally and historically sensitive C.A. R.E. model strategically engaged CAB members and study participants. It was essential for success in recruitment and retention of an at-risk, African American population and may be an effective model for researchers hoping to engage racial/ethnic minority populations living in urban communities.
STIM‐1 and ORAI‐1 channel mediate angiotensin‐II‐induced expression of Egr‐1 in vascular smooth muscle cells

An upregulation of Egr‐1 expression has been reported in models of atherosclerosis and intimal hyperplasia and, various vasoactive peptides and growth promoting stimuli have been shown to induce the expression of Egr‐1 in vascular smooth muscle cells (VSMC). Angiotensin‐II (Ang‐II) is a key vasoactive peptide that has been implicated in the pathogenesis of vascular diseases. Ang‐II elevates intracellular Ca2+ through activation of the store‐operated calcium entry (SOCE) involving an inositol‐3‐phosphate receptor (IP3R)‐coupled depletion of endoplasmic reticular Ca2+ and a subsequent activation of the stromal interaction molecule 1 (STIM‐1)/Orai‐1 complex. However, the involvement of IP3R/STIM‐1/Orai‐1‐Ca2+‐dependent signaling in Egr‐1 expression in VSMC remains unexplored. Therefore, in the present studies, we have examined the role of Ca2+ signaling in Ang‐II‐induced Egr‐1 expression in VSMC and investigated the contribution of STIM‐1 or Orai‐1 in mediating this response. 2‐aminoethoxydiphenyl borate (2‐APB), a dual non‐competitive antagonist of IP3R and inhibitor of SOCE, decreased Ang‐II‐induced Ca2+ release and attenuated Ang‐II‐induced enhanced expression of Egr‐1 protein and mRNA levels. Egr‐1 upregulation was also suppressed following blockade of calmodulin and CaMKII. Furthermore, RNA interference‐mediated depletion of STIM‐1 or Orai‐1 attenuated Ang‐II‐induced Egr‐1 expression as well as Ang‐II‐induced phosphorylation of ERK1/2 and CREB. In addition, siRNA‐induced silencing of CREB resulted in a reduction in the expression of Egr‐1 stimulated by Ang‐II. In summary, our data demonstrate that Ang‐II‐induced Egr‐1 expression is mediated by STIM‐1/Orai‐1/Ca2+‐dependent signaling pathways in A‐10 VSMC.
Mining tweets of Moroccan users using the framework Hadoop, NLP, K-means and basemap

The information revolution and exactly the explosion of Web 2.0 platforms such as discussion forums, blogs, and social networks allow users to share ideas and opinions, express their feelings and much more. This revolution leads to an accumulation of an enormous amount of data that may contain a lot of valuable information. Much work has focused on analyzing these data, in particular those provided from social networks platforms like Twitter. In this paper, our objective is to propose an approach for analyzing the data generated by Moroccan users in the social network Twitter, in order to discover the subjects that interest Moroccan society and then locate on Moroccan map the areas from where come the tweets related to these topics. Analyzing the tweets of Moroccan users is a real challenge for two main reasons. Firstly, Moroccan users utilize for their communication in Twitter a variety of languages and dialects, such as Standard Arabic, Moroccan Arabic “Darija”, Moroccan Amazigh dialect “Tamazight”, French, Spanish, and English. Secondly, the Moroccan tweets contain a lot of URLs, #hashtags, spelling mistakes, reduced syntactic structures, and many abbreviations. In this paper, we propose an approach for detecting the relevant subjects related to Moroccan users by extracting the data automatically, and storing it in a distributed file system using HDFS (Hadoop Distributed File System) of Framework Apache Hadoop. Then we preprocess this raw data and analyze it by developing a distributed program using three tools, MapReduce of Framework Apache Hadoop, Python language, and Natural Language Processing (NLP) techniques. Afterward, we convert the corpus generated by the previous step into numeric features, and apply the k-means algorithm to cluster all words into general topics. Finally, we plot tweets on our Moroccan map by using the coordinates extracted from them, in order to have an idea about the geolocation of these subjects.
A fault detection approach for aero-engines based on PCA

Traditional fault detection approaches for aeroengines based on PCA cannot effectively detect faults when the data does not follow the normal distribution. Meanwhile, there are few effective methods for the elimination of outliers during the modeling thus the model precision cannot be guaranteed. Aiming at a solution of the problems above, a new fault detection approach for aero-engines based on PCA is proposed, including the PCA-KDE fault detection approach and the R-PCA outliers' elimination approach. The instance analysis on a turbofan engine shows that this approach is able to detect potential faults accurately and can provide the maintenance staff with evidences for early elimination.
The leukocidin pore: Evidence for an octamer with four LukF subunits and four LukS subunits alternating around a central axis

The staphylococcal α‐hemolysin (αHL) and leukocidin (Luk) polypeptides are members of a family of related β‐barrel pore‐forming toxins. Upon binding to susceptible cells, αHL forms water‐filled homoheptameric transmembrane pores. By contrast, Luk pores are formed by two classes of subunit, F and S, rendering a heptameric structure displeasing on symmetry grounds at least. Both the subunit stoichiometry and arrangement within the Luk pore have been contentious issues. Here we use chemical and genetic approaches to show that (1) the predominant, or perhaps the only, form of the Luk pore is an octamer; (2) the subunit stoichiometry is 1:1; and (3) the subunits are arranged in an alternating fashion about a central axis of symmetry, at least when a fused LukS‐LukF construct is used. The experimental approaches we have used also open up new avenues for engineering the arrangement of the subunits of β‐barrel pore‐forming toxins.
Exploiting implicit beliefs to resolve sparse usage problem in usage-based specification mining

Frameworks and libraries provide application programming interfaces (APIs) that serve as building blocks in modern software development. As APIs present the opportunity of increased productivity, it also calls for correct use to avoid buggy code. The usage-based specification mining technique has shown great promise in solving this problem through a data-driven approach. These techniques leverage the use of the API in large corpora to understand the recurring usages of the APIs and infer behavioral specifications (preconditions and postconditions) from such usages. A challenge for such technique is thus inference in the presence of insufficient usages, in terms of both frequency and richness. We refer to this as a "sparse usage problem. " This paper presents the first technique to solve the sparse usage problem in usage-based precondition mining. Our key insight is to leverage implicit beliefs to overcome sparse usage. An implicit belief (IB) is the knowledge implicitly derived from the fact about the code. An IB about a program is known implicitly to a programmer via the language's constructs and semantics, and thus not explicitly written or specified in the code. The technical underpinnings of our new precondition mining approach include a technique to analyze the data and control flow in the program leading to API calls to infer preconditions that are implicitly present in the code corpus, a catalog of 35 code elements in total that can be used to derive implicit beliefs from a program, and empirical evaluation of all of these ideas. We have analyzed over 350 millions lines of code and 7 libraries that suffer from the sparse usage problem. Our approach realizes 6 implicit beliefs and we have observed that adding single-level context sensitivity can further improve the result of usage based precondition mining. The result shows that we achieve overall 60% in precision and 69% in recall and the accuracy is relatively improved by 32% in precision and 78% in recall compared to base usage-based mining approach for these libraries.
Computer quantification of short‐term variability as an adjunct to fetal electrocardiographic monitoring

Sir, The paper by Westerhuis et al.1 illustrates three cases of intrapartum fetal metabolic acidosis that were missed or alerted to very late by STAN analysis (Neoventa Medical, Göteborg, Sweden). The authors note the marked reduction in variability occurring in the final segments of each tracing. Wewere interested to quantify these changes, sowe obtained the original fetal heart rate files from the authors. With their permission, we have quantified changes in variability over time in each of these cases, using the Omniview-SisPorto 3.5 program for computerised analysis of cardiotocograms (CTGs) (Speculum, Lisbon, Portugal). This program analyses shortterm variability (STV) with no signal averaging or reduction and quantifies it as the percentage of signals displaying abnormal STV. A signal is considered to display abnormal STV if the difference in value to the preceding one is less than 0.80 beats DOI: 10.1111/j.1471-0528.2007. 01513.x www.blackwellpublishing. com/bjog Correspondence
Finding paths in tree graphs with a quantum walk

In this paper, we analyze the potential for new types of searches using the formalism of scattering random walks on Quantum Computers. Given a particular type of graph consisting of nodes and connections, a "Tree Maze", we would like to find a selected final node as quickly as possible, faster than any classical search algorithm. We show that this can be done using a quantum random walk, both exactly through numerical calculations as well as analytically using eigenvectors and eigenvalues of the quantum system.
An accurate RF 0°/180° phase splitter

In most receivers a balanced mixer is used, while the output of the LNA is single ended. The presented phase splitter generates an accurately balanced signal out of a single-ended source. The wide-band circuit consists of two RF transistors and a DC control loop. The circuit operates up to, say, ƒT/4. In the article the basic circuit is discussed and an IC implementation is given.
In Situ Activity of Enzymes on Polyacrylamide Gels of a Deoxyribonucleic Acid-Membrane Fraction Extracted from Pneumococci

A deoxyribonucleic acid (DNA)-membrane fraction extracted from Diplococcus pneumoniae was subjected to polyacrylamide gel electrophoresis after treatment with 0.16% sodium dodecyl sulfate. At least two DNA polymerase activities were detected by in situ assays with appropriate substrates, templates, and inhibitors, including a co-polymer of deoxyadenylic and thymidylic acid and N-ethylmaleimide. This activity coincided with a fraction in the gel containing 7.5, 9.4, and 24%, respectively of the DNA, phospholipid, and protein present in the DNA-membrane fraction before electrophoresis and sodium dodecyl sulfate treatment. Assays with minced gels showed that several nuclease activities, deoxyribonucleotide kinase activity, and DNA ligase activity also coincided with this fraction. However, ribonucleoside diphosphate reductase activity did not. These results demonstrate that a complex of enzymes involved in DNA replication is firmly bound to the DNA-membrane fraction in pneumococci.
From research to practice: factors affecting implementation of prospective targeted injury-detection systems

Aim This paper describes key factors that shaped implementation of prospective targeted injury-detection systems (TIDS) for adverse drug events (ADEs) and nosocomial pressure ulcers (PrU). Methods Using case-study methodology, the authors conducted semistructured interviews with implementation champions and TIDS users at five hospitals. Interviews focused on implementation experiences, assessment of TIDS' effectiveness and utility, and plans for sustainability. The authors used content analysis techniques to compare implementation experiences within and across organisations and triangulated data for explanation and confirmation of common themes. Findings Participating hospitals were more successful in implementing the low-complexity PrU-TIDS, as compared with high-complexity ADE-TIDS. This pattern reflected the greater complexity of ADE-TIDS, its higher costs and poorer alignment with existing workflows. Complexity affected the innovations' perceived usability, the time needed to learn and install the trigger systems, and their costs. Local factors affecting implementation and sustainability of both innovations included turnover affecting champions and other staff, shifting organisational priorities, changing information infrastructures, and institutional constraints on adapting existing IT to the electronic TIDS. Conclusions To facilitate implementation of complex healthcare innovations such as ADE-TIDS, staff in adopting organisations should give high priority to innovation implementation; allocate sufficient resources; effectively communicate with and involve local champions and users; and align innovations with workflows and information systems. In addition, they should monitor local factors, such as changes in organisational priorities and IT, availability of implementation staff and champions, and external regulations and constraints that may pose barriers to innovation implementation and sustainability.
Estimation and equalization of fading channels with random coefficients

The time-varying tap coefficients of frequency selective fading channels are typically modeled as random processes with low-pass power spectra. However, traditional adaptive techniques usually make no assumption on the channel's time variations and hence do not exploit this information. Kalman filtering methods are derived to track the channel by employing a multichannel autoregressive description of the time-varying taps in a decision-feedback equalization framework. Fitting a model to the variations of the channel's taps is a challenging task because the tap coefficients are not observed directly. A novel linear method is proposed in order to estimate the model parameters from input/output data. The consistency of the proposed method is shown, and some illustrative simulations are presented.
Learning robust representations using recurrent neural networks for skeleton based action classification and detection

Recently, skeleton based action recognition gains more popularity due to affordable depth sensors and real-time skeleton estimation algorithms. Previous Recurrent Neural Networks (RNN) based approaches focus on modeling spatial configuration of skeletons and temporal evolution of body joints. There are certain intrinsic characteristics of the skeleton based actions. For example, the starting point may be varied, an action can be observed at arbitrary viewpoints and the skeletons are noisy. To this end, we present a novel end-to-end architecture based on RNN to learn robust representations from raw skeletons. The architecture includes three new layers, i.e., starting point transformation layer, viewpoint transformation layer and spatial dropout layer, which address the corresponding three problems, respectively. We apply the proposed method to two different tasks: action classification and detection. Experiments on two large-scale datasets (NTU RGB+D and PKU-MMD) show the superiority of our model. Specially, for action detection, our results are more than 33.4% higher the previous results.
Design Support for Advanced Network Programming Applications

Building new advanced multimedia telecommunications services in a distributed and heterogeneous environment is very difficult, unless there is a methodology to support the entire service development process in a structured and systematic manner, and assist and constrain service designers and developers by setting out goals and providing specific means to achieve these goals. Therefore, in this paper, after a brief presentation of a proposed service creation methodology, its service design phase is examined in detail focusing on the essential activities and artifacts. In this process, the exploitation of important service engineering techniques and UML modelling principles is especially considered. Finally, alternative and complementary approaches for service design are highlighted and a validation attempt is briefly outlined.
Activation of the prefrontal cortex in a nonspatial working memory task with functional MRI

Functional magnetic resonance imaging (fMRI) was used to examine the pattern of activity of the prefrontal cortex during performance of subjects in a nonspatial working memory task. Subjects observed sequences of letters and responded whenever a letter repeated with exactly one nonidentical letter intervening. In a comparison task, subjects monitored similar sequences of letters for any occurrence of a single, prespecified target letter. Functional scanning was performed using a newly developed spiral scan image acquisition technique that provides high‐resolution, multislice scanning at approximately five times the rate usually possible on conventional equipment (an average of one image per second). Using these methods, activation of the middle and inferior frontal gyri was reliably observed within individual subjects during performance of the working memory task relative to the comparison task. Effect sizes (2–4%) closely approximated those that have been observed within primary sensory and motor cortices using similar fMRI techniques. Furthermore, activation increased and decreased with a time course that was highly consistent with the task manipulations. These findings corroborate the results of positron emission tomography studies, which suggest that the prefrontal cortex is engaged by tasks that rely on working memory. Furthermore, they demonstrate the applicability of newly developed fMRI techniques using conventional scanners to study the associative cortex in individual subjects. © 1994 Wiley‐Liss, Inc.
A Stepwise Auto-Proﬁling Method for Performance Optimization of Streaming Applications

Data stream management systems (DSMSs) are scalable, highly available, and fault-tolerant systems that aggregate and analyze real-time data in motion. To continuously perform analytics on the ﬂy within the stream, state-of-the-art DSMSs host streaming applications as a set of inter-connected operators, with each operator encapsulating the semantic of a speciﬁc operation. For parallel execution on a particular platform, these operators need to be appropriately replicated in multiple instances that split and process the workload simultaneously. Because the way operators are partitioned affects the resulting performance of streaming applications, it is essential for DSMSs to have a method to compare different operators and make holistic replication decisions to avoid performance bottlenecks and resource wastage. To this end, we propose a stepwise proﬁling approach to optimize application performance on a given execution platform. It automatically scales distributed computations over streams based on application features and processing power of provisioned resources, and builds the relationship between provisioned resources and application performance metrics to evaluate the efﬁciency of the resulting conﬁguration. Experimental results conﬁrm that the proposed approach successfully fulﬁls its goals with minimal proﬁling overhead.
Technological embeddedness and household computer adoption

– This study seeks to develop the concept of technological embeddedness by extending the social embeddedness theory of economic actions to household computer adoption. It also aims to propose a research framework in which technological embeddedness is a key factor that influences household computer adoption. , – The US 1989‐2003 Computer and Internet Use Supplements to the Current Population Surveys are used to validate the proposed research framework. , – The results show that technological embeddedness positively affects household computer adoption. In addition, the impact of technological embeddedness is positively moderated by household income, and this impact is particularly stronger on first‐time buyers than on repeat buyers. , – The results provide important policy and managerial implications for encouraging household computer adoption and bridging the digital divide. , – The paper proposes a new concept and develops a research framework for analyzing household computer adoption and technology adoption in general.
Contact allergy to textile dyes in southern Sweden

Contact allergy to disperse dyes in textiles is documented in prevalence studies from southern Europe. To evaluate the prevalence of allergic patch test reactions to different textile dyes in southern Sweden, and to look at the sites of dermatitis in individuals hypersensitive to textile dyes, we retrospectively investigated 3325 consecutively patch‐tested patients. They had all been patch tested with the standard test series supplemented with a textile dye mix (TDM) consisting of 8 disperse dyes, i.e. Disperse (D) Blue 35, 106 and 124, D Yellow 3, D Orange 1 and 3 and D Red 1 and 17. All but 3 of the TDM‐positive patients were additionally tested with the separate dyes included in the mix. The frequency of contact allergy to TDM was 1.5%, which is comparable with studies from southern Europe. The most common dye allergen was D Orange 1. The high prevalence of allergic reactions to D Orange 1 was unexpected, whereas test reactions to D Blue 106 and 124 were lower than expected from other studies. Compared to all tested patients, the TDM‐positive patients more often had dermatitis on their arms, face, neck and axillary folds, and women also had a higher frequency of hand dermatitis.