Miracle-dz/UniFAR-FTUs huggingface dataset:
KnowledgePearls: Provenance-Based Visualization Retrieval

Storing analytical provenance generates a knowledge base with a large potential for recalling previous results and guiding users in future analyses. However, without extensive manual creation of meta information and annotations by the users, search and retrieval of analysis states can become tedious. We present KnowledgePearls, a solution for efficient retrieval of analysis states that are structured as provenance graphs containing automatically recorded user interactions and visualizations. As a core component, we describe a visual interface for querying and exploring analysis states based on their similarity to a partial definition of a requested analysis state. Depending on the use case, this definition may be provided explicitly by the user by formulating a search query or inferred from given reference states. We explain our approach using the example of efficient retrieval of demographic analyses by Hans Rosling and discuss our implementation for a fast look-up of previous states. Our approach is independent of the underlying visualization framework. We discuss the applicability for visualizations which are based on the declarative grammar Vega and we use a Vega-based implementation of Gapminder as guiding example. We additionally present a biomedical case study to illustrate how KnowledgePearls facilitates the exploration process by recalling states from earlier analyses.
Activation of the apoptotic pathway during prolonged prometaphase blocks daughter cell proliferation

When untransformed human cells spend >1.5 h in prometaphase under standard culture conditions, all daughters arrest in G1 despite normal division of their mothers. We investigate what happens during prolonged prometaphase that leads to daughter cell arrest in the absence of DNA damage. We find that progressive loss of anti-apoptotic MCL-1 activity and oxidative stress act in concert to partially activate the apoptosis pathway, resulting in the delayed death of some daughters and senescence for the rest. At physiological oxygen levels, longer prometaphase durations are needed for all daughters to arrest. Partial activation of apoptosis during prolonged prometaphase leads to persistent caspase activity, which activates the kinase cascade mediating the post–mitotic activation of p38. This in turn activates p53, and the consequent expression of p21stops the cell cycle. This mechanism can prevent cells suffering intractable mitotic defects, which modestly prolong mitosis but allow its completion without DNA damage, from producing future cell generations that are susceptible to the evolution of a transformed phenotype.
Sixth graders’ evaluation strategies when reading Internet search results: an eye-tracking study

ABSTRACT Eye-tracking technology was used to examine Internet search result evaluation strategies adopted by sixth-grade students (N = 36) during ten experimental information search tasks. The relevancy of the search result’s title, URL, and snippet components was manipulated and selection of search results as well as looking into probabilities on the search result components was analysed. The results revealed that during first-pass inspection, students read the search engine page by first looking at the title of a search result. If the title was relevant, the probability of looking at the snippet of the search result increased. During second-pass inspection, there was a high probability of students focusing on the most promising search result by inspecting all of its components before making their selection. A cluster analysis revealed three viewing strategies: half of the students looked mainly at the titles and snippets; one-third with high probability examined all components; and one-sixth mainly focused on titles, leading to more frequent errors in search result selection. The results indicate that students generally made a flexible use of both eliminative and confirmatory evaluation strategies when reading Internet search results, while some seemed to not pay attention to snippet and URL components of the search results.
Hypoxia‐induced Pulmonary Hypertension in Different Mouse Strains: Relation to Transcriptome

Abstract Patients with pulmonary arterial hypertension (PAH) can harbor mutations in several genes, most commonly in BMPR2. However, disease penetrance in patients with BMPR2 mutations is low. In addition, most patients do not carry known PAH gene mutations, suggesting that other factors determine susceptibility to PAH. To begin to identify additional genomic factors contributing to PAH pathogenesis, we exposed 32 mouse strains to chronic hypoxia. We found that the PL/J strain has extremely high right ventricular systolic pressure (RVSP; 86.58 mm Hg) but minimal lung remodeling. To identify potential genomic factors contributing to the high RVSP, RNAseq analysis of PL/J lung mRNAs and microRNAs (miRNAs) after hypoxia was performed, and it demonstrated that 4 of 43 upregulated miRNAs in the Dlk1‐Dio3 imprinting region are predicted to target T cell marker mRNAs. These target mRNAs, as well as the numbers of T cells were downregulated. In addition, C5a and its receptor, C5AR1, were increased. Analysis of Rho‐associated protein kinase (Rock) 2 mRNA expression, in the RhoA/Rock pathway, demonstrated a significant increase in PL/J. Inhibition of Rock2 ameliorated a portion of the elevated RVSP. In addition, we identified miR‐150‐5p as a potential regulator of Rock2 expression. In conclusion, we identified two possible pathways contributing to the hypoxia pulmonary hypertension phenotype of extreme RVSP elevation: aberrant T cell expression driven by hypoxia‐induced miRNAs and increased expression of C5a and C5AR1. We suggest that the PL/J mouse will be a good model for seeking mechanism(s) of RVSP elevation in hypoxia‐induced PAH.
API Method Recommendation without Worrying about the Task-API Knowledge Gap

Developers often need to search for appropriate APIs for their programming tasks. Although most libraries have API reference documentation, it is not easy to find appropriate APIs due to the lexical gap and knowledge gap between the natural language description of the programming task and the API description in API documentation. Here, the lexical gap refers to the fact that the same semantic meaning can be expressed by different words, and the knowledge gap refers to the fact that API documentation mainly describes API functionality and structure but lacks other types of information like concepts and purposes, which are usually the key information in the task description. In this paper, we propose an API recommendation approach named BIKER (Bi-Information source based KnowledgE Recommendation) to tackle these two gaps. To bridge the lexical gap, BIKER uses word embedding technique to calculate the similarity score between two text descriptions. Inspired by our survey findings that developers incorporate Stack Overflow posts and API documentation for bridging the knowledge gap, BIKER leverages Stack Overflow posts to extract candidate APIs for a program task, and ranks candidate APIs by considering the query's similarity with both Stack Overflow posts and API documentation. It also summarizes supplementary information (e.g., API description, code examples in Stack Overflow posts) for each API to help developers select the APIs that are most relevant to their tasks. Our evaluation with 413 API-related questions confirms the effectiveness of BIKER for both class- and method-level API recommendation, compared with state-of-the-art baselines. Our user study with 28 Java developers further demonstrates the practicality of BIKER for API search.
Recurrent laryngeal nerve monitoring with a modified laryngeal surface electrode in a 19‐month‐old child

lenges to understanding and managing dead space in small infants. As Templeton and colleagues point out, the 9.9 mL of dead space in an Air-Q 1.0 SGA represent over a third of a typical tidal volume in the spontaneously breathing 5 kg infant. However, the dead space added by a SGA does not actually cause this amount of dead space increase relative to a natural airway due to the obligatory dead space of the infant natural airway. As we discuss in our review, the total anatomic dead space in the young infant is approximately 3.3 mL/kg, with 2.3 mL/kg as the extrathoracic component. In the hypothetical 5 kg infant, the extrathoracic component would therefore be 11.5 mL. The majority of the 11.5 mL is found in the nasopharynx and oral cavity and is bypassed by the SGA, so the net increase in dead space resulting from the SGA relative to the natural airway is less than 9.9 mL. While the device likely contributes to hypercarbia in anesthetized infants, other factors also contribute, such as the apparatus used to connect the SGA to the breathing circuit or the work of breathing if spontaneous ventilation is used. Templeton et al’s findings certainly suggest that efforts should be made to minimize apparatus dead space in small infants in concert with our recommendations. Given the unknown gradient between ETCO2 and arterial CO2, we cannot be certain about the impact of the SGA dead space on CO2 elimination without blood gas data as a confirmation. It is worthwhile emphasizing that the relationship between the dead space to tidal volume ratio and the minute ventilation required to maintain normocarbia is exponential, so that as dead space increases relative to tidal volume it becomes increasingly difficult to control CO2. 4,5
Model Interpretation: A Unified Derivative-based Framework for Nonparametric Regression and Supervised Machine Learning

Interpreting a nonparametric regression model with many predictors is known to be a challenging problem. There has been renewed interest in this topic due to the extensive use of machine learning algorithms and the difficulty in understanding and explaining their input-output relationships. This paper develops a unified framework using a derivative-based approach for existing tools in the literature, including the partial-dependence plots, marginal plots and accumulated effects plots. It proposes a new interpretation technique called the accumulated total derivative effects plot and demonstrates how its components can be used to develop extensive insights in complex regression models with correlated predictors. The techniques are illustrated through simulation results.
Correlation between nursing managers' leadership styles and nurses' job stress and anticipated turnover.

AIM To investigate the relationship between leadership style with nurse job stress and anticipated turnover. BACKGROUND An appropriate leadership style should promote the efficacy of nurses. Different leadership styles can affect nurse's job stress and the numbers of nurses leaving their workplace or their profession. METHODS In this cross-sectional correlational study, 1,617 nurses were selected from the governmental hospitals in Iran 2016-2017. Data were analyzed using descriptive and inferential statistics in SPSS20 . RESULTS Nursing Manager's leadership style was transactional leadership. Both transformational and transactional leadership style have a significant relationship with job stress and anticipate staff turnover. A positive relationship was found between a laissez-faire leadership style with job stress and anticipated turnover. CONCLUSION The transformational and transactional leadership styles can reduce nurse's job stress and intention to leave, so nurse leaders can use combination of transformational and transactional leadership for improving job satisfaction and quality of nursing services. IMPLICATIONS FOR NURSING MANAGEMENT Leaders should emphasize a clear expression of values, objectives, and mission of the organisation; and try to raise confidence in employees, respecting and caring for them, supporting their views and suggestions, and being optimistic about the future.
Job satisfaction among registered nurses in two Scandinavian acute care hospitals

AIM To explore job satisfaction among registered nurses working in acute care hospitals, and to examine which background factors influence registered nurses' total job satisfaction. BACKGROUND Job satisfaction among registered nurses is important when considering the well-being and retention of registered nurses. Research has shown that registered nurses in different countries experience various levels of job satisfaction. METHOD In 2015, a total of 406 Finnish and Norwegian registered nurses answered a survey based on the Kuopio University Hospital Job Satisfaction Scale. The collected data were analysed with nonparametric tests and multiple linear analyses. RESULTS The registered nurses rated motivating factors of work and work welfare highest, while the lowest degrees of job satisfaction were related to requiring factors of work and participation in decision-making. CONCLUSIONS Registered nurses appreciate their work in both countries, however attention should be paid to work demands and the extent to which registered nurses are involved in decision-making to enhance their job satisfaction. IMPLICATIONS FOR NURSING MANAGEMENT Nurse leaders should be aware of registered nurses' diverse backgrounds and use this knowledge to recognize registered nurses in need of support. The paths through which registered nurses participate in decision-making should be streamlined and workloads should be regularly assessed so that work is distributed evenly.
A fractional motion diffusion model for a twice‐refocused spin‐echo pulse sequence

The purpose of this study was to develop an analytical expression for a fractional motion (FM) diffusion model to characterize diffusion‐induced signal attenuation in a twice‐refocused spin‐echo (TRSE) sequence that is resilient to eddy currents, and to demonstrate its applicability to human brain imaging in vivo. Based on the FM theory, which provides a unified statistical description for Langevin motions, the diffusion‐weighted (DW) MR signal was measured with a TRSE sequence that balances the concomitant gradients. The analytical expression was fitted to a set of DW images acquired with 14 b‐values (0–4000 s/mm2) from a total of 10 healthy human subjects at 3 T, yielding three FM parameter maps based on anomalous diffusion coefficient Dφ, ψ, diffusion increment variance φ, and diffusion correlation ψ, respectively. These parameters were used to characterize different brain regions in gray matter (GM), white matter (WM), and cerebrospinal fluid. The analytical expression for the TRSE‐based FM model accurately described diffusion signal attenuation in healthy brain tissues at high b‐values. TRSE's robustness against eddy currents was illustrated by comparing results from an expression for a conventional Stejskal‐Tanner sequence. The TRSE‐based FM model also produced consistent GM‐WM contrast (p < 0.01) across all brain regions studied, whereas the consistency was not observed with the Stejskal‐Tanner‐based FM model. This new analytical expression is expected to enable further investigations to probe tissue structures by exploiting anomalous diffusion properties without being hindered by eddy‐current perturbations at high b‐values.
Um Estudo Comparativo entre Algoritmos de Proteção da Privacidade Aplicado à Bases de Dados na Área de Saúde

O crescente aumento no volume de dados coletados, armazenados e compartilhados por instituições da área da saúde gera benefícios para o processo de tomada de decisão com base no conhecimento adquirido a partir da aplicação de técnicas de análise e mineração de dados na extração de informações úteis. A despeito dos benefícios propiciados, o compartilhamento desses dados em seu formato original pode por em risco a privacidade dos pacientes. Na tentativa de validar soluções para este problema, este artigo compara algumas técnicas de anonimização e perturbação de dados, avaliando a eficácia dessas técnicas na garantia da privacidade e segurança de dados compartilhados, em particular, quando aplicadas a bases de dados na área da saúde.
Long‐term results of the risk‐stratified treatment of childhood acute lymphoblastic leukemia in China

Long‐term follow‐up data for childhood acute lymphoblastic leukemia (ALL) are scarce in China because of lacking population‐based and hospitalized registry system. This retrospective study, conducted at Shanghai's Children's Medical Center in China (SCMC), aimed to investigate the long‐term results of childhood ALL and to identify prognostic factors. The Pediatric Oncology Network Database, designed by St. Jude Children's Research Hospital, USA, were used to collect data for the enrolled patients starting in 2005. From 2005 to 2014, 1085 evaluable patients with ALL aged 1 to 18 years old were enrolled and treated using SCMC‐ALL‐2005 risk‐stratified protocol. Complete remission was achieved in 95.6% of patients. At 5 and 10 years, the event‐free survival rate was 68.3 ± 1.4% and 64.6 ± 1.6%, and the overall survival rate was 80.0 ± 1.2% and 76.3 ± 1.6%, respectively. The 5‐year event‐free survival rates were 81.8 ± 2.0%, 67.0 ± 1.9%, and 14.3 ± 4.0% for patients in low‐risk, intermediate‐risk, and high‐risk groups, respectively. The cumulative risk of relapse was 24.5% at 10 years. Induction failure conferred worse prognosis. Patients younger than 1 year of age at diagnosis, intermediate‐risk/high‐risk group, male gender, and positive minimal residual disease (MRD) results at day 55, both in the univariate and multivariate analysis, were associated with significantly worse prognosis (P < .05). Patients with positive MRD at both day 35 and day 55 were related to a significantly poor outcome (P < .0001), but not for patients with negitive MRD at day 35. The overall outcomes for ALL patients treated with protocol SCMC‐ALL‐2005 in SCMC are lower than in developed countries. Factors including age, gender, risk group and MRD results at day 55 were associated with treatment outcomes in childhood ALL.
CPA-SymExec: Efficient Symbolic Execution in CPAchecker

We present CPA-SymExec, a tool for symbolic execution that is implemented in the open-source, configurable verification framework CPAchecker. Our implementation automatically detects which symbolic facts to track, in order to obtain a small set of constraints that are necessary to decide reachability of a program area of interest. CPA-SymExec is based on abstraction and counterexample-guided abstraction refinement (CEGAR), and uses a constraint-interpolation approach to detect symbolic facts. We show that our implementation can better mitigate the path-explosion problem than symbolic execution without abstraction, by comparing the performance to the state-of-the-art Klee-based symbolic-execution engine Symbiotic and to Klee itself. For the experiments we use two kinds of analysis tasks: one for finding an executable path to a specific location of interest (e.g., if a test vector is desired to show that a certain behavior occurs), and one for confirming that no executable path to a specific location exists (e.g., if it is desired to show that a certain behavior never occurs). CPA-SymExec is released under the Apache 2 license and available (inclusive source code) at https://cpachecker.sosy-lab.org. A demonstration video is available at https://youtu.be/qoBHtvPKtnw.
k-meansNet: When k-means Meets Differentiable Programming

In this paper, we study two challenging problems. The first one is how to implement \textit{k}-means in the neural network, which enjoys efficient training based on the stochastic algorithm. The second one is how to enhance the interpretability of network design for clustering. To solve the problems, we propose a neural network which is a novel formulation of the vanilla $k$-means objective. Our contribution is in twofold. From the view of neural networks, the proposed \textit{k}-meansNet is with explicit interpretability in neural processing. We could understand not only why the network structure is presented like itself but also why it could perform data clustering. Such an interpretable neural network remarkably differs from the existing works that usually employ visualization technique to explain the result of the neural network. From the view of \textit{k}-means, three highly desired properties are achieved, i.e. robustness to initialization, the capability of handling new coming data, and provable convergence. Extensive experimental studies show that our method achieves promising performance comparing with 12 clustering methods on some challenging datasets.
Sewer Discharge Estimation by Stereoscopic Imaging and Synchronized Frame Processing

A system for fully automatic contact‐less image‐based measurement of volumetric flow rate in urban drainage structures is presented. The hardware includes two original equipment manufacturer cameras and a single‐board computer on which our custom image processing software is running. The value of water discharge depends on the surface velocity, water level and channel's geometry. The level of the flow is estimated as the difference between distances from the camera to the water surface and from the camera to the channel's bottom. Camera‐to‐water distance is recovered automatically using large‐scale stereo‐matching, whereas the distance to the channel's bottom is measured upon installation. Surface velocity is calculated using cross‐correlation template matching: individual natural particles in the flow are detected and tracked throughout the sequence of images recorded over a fixed time interval. The relative discharge computation error is lower than 1.34% of the theoretical maximal discharge for a given location, which makes our system competitive to commercial components such as ultrasonic flow meters, while using cheaper technologies.
Does AI make PD obsolete?: exploring challenges from artificial intelligence to participatory design

In this paper1, we explore if and how Artificial Intelligence (AI) challenges Participatory Design (PD). We base our analysis on the basic characteristics of AI and its subfield Machine Learning and discuss how and what kinds of design decisions users are able to participate in when technology that includes AI is designed. We conclude that AI challenges PD but that classic PD methods can be useful for parts of the design process. However, AI poses new challenges to PD all originating in the fact that AI technologies change unpredictably over time.
Verifying Temporal Trust Logic using CTL Model Checking

Several formal trust frameworks have been introduced in the area of MultiAgent Systems (MASs). However, the problem of model checking trust logics is still a challenging research topic that has not been sufficiently investigated yet. In this paper, we address this challenge by proposing a formal and fully automatic model checking technique for a temporal logic of trust. From the logical perspective, the starting point of our proposal is TCTL, a Computation Tree Logic of preconditional Trust that has been recently proposed. We extend this logic by introducing a new modality for conditional trust and describe the logical relationship between preconditional and conditional trust. From the formal verification perspective, we develop transformation-based algorithms fully implemented in a Java toolkit that automatically interacts with the NuSMV model checker. Our verification approach automatically transforms the problem of model checking TCTL into the problem of model checking CTL. We also develop a model checking algorithm for the conditional trust. We provide proofs of the soundness and completeness of our transformation algorithms. Finally, experiments conducted on a standard industrial case study of auto-insurance claim processing demonstrate the efficiency and scalability of our approach in verifying TCTL and conditional trust formulae.