Understanding “influence”: An exploratory study of academics’ process of knowledge construction through iterative and interactive information seeking

The motivation for this study is to better understand the searching and sensemaking processes undertaken to solve exploratory tasks for which people lack pre-existing frames. To investigate people’s strategies for that type of task, we focused on “influence” tasks because, although they appear to be unfamiliar, they arise in much academic discourse, at least tacitly. This qualitative study reports the process undertaken by academics of different levels of seniority to complete exploratory search tasks that involved identifying influential members of their academic community and “rising stars, ” and to identify similar roles in an unfamiliar academic community. 11 think-aloud sessions followed by semi-structured interviews were conducted to investigate the role of specific and general domain expertise in the process of information seeking and knowledge construction. Academics defined and completed the task through an iterative and interactive process of seeking and sensemaking, during which they constructed an understanding of their communities and determined qualities of “being influential”. Elements of the Data/Frame Theory of Sensemaking (Klein et al., 2007) were used as sensitising theoretical constructs. The study shows that both external and internal knowledge resources are essential to define a starting point or frame, make and support decisions, and experience satisfaction. Ill-defined or non-existent initial frames may cause unsubstantial or arbitrary decisions, and feelings of uncertainty and lack of confidence

Using machine learning to infer reasoning provenance from user interaction log data: based on the data/frame theory of sensemaking

The reconstruction of analysts’ reasoning processes (reasoning provenance) during complex sensemaking tasks can support reflection and decision making. One potential approach to such reconstruction is to automatically infer reasoning from low-level user interaction logs. We explore a novel method for doing this using machine learning. Two user studies were conducted in which participants performed similar intelligence analysis tasks. In one study, participants used a standard web browser and word processor; in the other, they used a system called INVISQUE (Interactive Visual Search and Query Environment). Interaction logs were manually coded for cognitive actions based on captured think-aloud protocol and posttask interviews based on Klein, Phillips, Rall, and Pelusos’s data/frame model of sensemaking as a conceptual framework. This analysis was then used to train an interaction frame mapper, which employed multiple machine learning models to learn relationships between the interaction logs and the codings. Our results show that, for one study at least, classification accuracy was significantly better than chance and compared reasonably to a reported manual provenance reconstruction method. We discuss our results in terms of variations in feature sets from the two studies and what this means for the development of the method for provenance capture and the evaluation of sensemaking systems

Keeping up to date: An academic researcher's information journey

Keeping up to date with research developments is a central activity of academic researchers, but researchers face difficulties in managing the rapid growth of available scientific information. This study examined how researchers stay up to date, using the information journey model as a framework for analysis and investigating which dimensions influence information behaviors. We designed a 2-round study involving semistructured interviews and prototype testing with 61 researchers with 3 levels of seniority (PhD student to professor). Data were analyzed following a semistructured qualitative approach. Five key dimensions that influence information behaviors were identified: level of seniority, information sources, state of the project, level of familiarity, and how well defined the relevant community is. These dimensions are interrelated and their values determine the flow of the information journey. Across all levels of professional expertise, researchers used similar hard (formal) sources to access content, while soft (interpersonal) sources were used to filter information. An important “pain point” that future information tools should address is helping researchers filter information at the point of need

Understanding “influence”: An empirical test of the Data-Frame theory of Sensemaking

This paper reports findings from a study designed to gain broader understanding of sensemaking activities using the Data/Frame Theory (Klein et al., 2007) as the analytical framework. Although this theory is one of the dominant models of sensemaking, it has not been extensively tested with a range of sensemaking tasks. The tasks discussed here focused on making sense of structures rather than processes or narratives. Eleven researchers were asked to construct understanding of how a scientific community in a particular domain is organized (e.g. people, relationships, contributions, factors) by exploring the concept of “influence” in academia. This topic was chosen as, although researchers frequently handle this type of task, it is unlikely that they have explicitly sought this type of information. We conducted a think-aloud study and semi-structured interviews with junior and senior researchers from the Human Computer Interaction domain, asking them to identify current leaders and rising stars, in both HCI and chemistry. Data were coded and analyzed using the Data/Frame Model to both test and extend the model. Three themes emerged from the analysis: novices and experts’ sensemaking activity chains, constructing frames through indicators, and characteristics of structure tasks. We propose extensions to the Data/Frame Model to accommodate structure sensemaking

Using Machine Learning to Infer Reasoning Provenance from User Interaction Log Data

The reconstruction of analysts’ reasoning processes (reasoning provenance) during complex sensemaking tasks can support reflection and decision making. One potential approach to such reconstruction is to automatically infer reasoning from low-level user interaction logs. We explore a novel method for doing this using machine learning. Two user studies were conducted in which participants performed similar intelligence analysis tasks. In one study, participants used a standard web browser and word processor; in the other, they used a system called INVISQUE (Interactive Visual Search and Query Environment). Interaction logs were manually coded for cognitive actions based on captured think-aloud protocol and posttask interviews based on Klein, Phillips, Rall, and Pelusos’s data/frame model of sensemaking as a conceptual framework. This analysis was then used to train an interaction frame mapper, which employed multiple machine learning models to learn relationships between the interaction logs and the codings. Our results show that, for one study at least, classification accuracy was significantly better than chance and compared reasonably to a reported manual provenance reconstruction method. We discuss our results in terms of variations in feature sets from the two studies and what this means for the development of the method for provenance capture and the evaluation of sensemaking systems

Researchers' attitudes towards the use of social networking sites

Purpose: The purpose of this paper is to better understand why many researchers do not have a profile on social networking sites (SNS), and whether this is the result of conscious decisions. / Design/methodology/approach: Thematic analysis was conducted on a large qualitative data set from researchers across three levels of seniority, four countries and four disciplines to explore their attitudes toward and experiences with SNS. / Findings: The study found much greater scepticism toward adopting SNS than previously reported. Reasons behind researchers’ scepticism range from SNS being unimportant for their work to not belonging to their culture or habits. Some even felt that a profile presented people negatively and might harm their career. These concerns were mostly expressed by junior and midlevel researchers, showing that the largest opponents to SNS may unexpectedly be younger researchers. / Research limitations/implications: A limitation of this study was that the authors did not conduct the interviews, and therefore reframing or adding questions to specifically unpack comments related to attitudes, feelings or the use of SNS in academia was not possible. / Originality/value: By studying implicit attitudes and experiences, this study shows that instead of being ignorant of SNS profiles, some researchers actively opt for a non-use of profiles on SNS

Gender specific decrease of a set of circulating Nacylphosphatidyl ethanolamines (NAPEs) in the plasma of Parkinson’s disease patients

Introduction: Current markers of Parkinson's disease (PD) fail to detect the early progression of disease state. Conversely, current omics techniques allow the investigation of hundreds of molecules potentially altered by disease conditions. Based on evidence previously collected by our group in a mouse model of PD, we speculated that a particular set of circulating lipids might be significantly altered by the pathology. Objectives: The aim of current study was to evaluate the potential of a particular set of N-acyl-phosphatidylethanolamines (NAPEs) as potential non-invasive plasma markers of ongoing neurodegeneration from Parkinson's disease in human subjects. Methods: A panel of seven NAPEs were quantified by LC-MS/MS in the plasma of 587 individuals (healthy controls, n = 319; Parkinson's disease, n = 268); Random Forest classification and statistical modeling was applied to compare Parkinson's disease versus controls. All p-values obtained in different tests were corrected for multiplicity by controlling the false discovery rate (FDR). Results: The results indicate that this panel of NAPEs is able to distinguish female PD patients from the corresponding healthy controls. Further to this, the observed downregulation of these NAPEs is in line with the results in plasma of a mouse model of Parkinson's (6-OHDA). Conclusions: In the current study we have shown the downregulation of NAPEs in plasma of PD patients and we thus speculate that these lipids might serve as candidate biomarkers for PD. We also suggest a molecular mechanism, explaining our findings, which involves gut microbiota

CHESTNUT: Improve serendipity in movie recommendation by an Information Theory-based collaborative filtering approach

The term serendipity has been understood narrowly in the Recommender System. Applying a user-centered approach, user-friendly serendipitous recommender systems are expected to be developed based on a good understanding of serendipity. In this paper, we introduce CHESTNUT , a memory-based movie collaborative filtering system to improve serendipity performance. Relying on a proposed Information Theory-based algorithm and previous study, we demonstrate a method of successfully injecting insight, unexpectedness and usefulness, which are key metrics for a more comprehensive understanding of serendipity, into a practical serendipitous runtime system. With lightweight experiments, we have revealed a few runtime issues and further optimized the same. We have evaluated CHESTNUT in both practicability and effectiveness , and the results show that it is fast, scalable and improves serendip-ity performance significantly, compared with mainstream memory-based collaborative filtering. The source codes of CHESTNUT are online at https://github.com/unnc-idl-ucc/CHESTNUT/

Novel activity-based probes for N-acylethanolamine acid amidase

Bio-organic SynthesisMolecular Physiolog

Acetonic Extract of Buxus sempervirens Induces Cell Cycle Arrest, Apoptosis and Autophagy in Breast Cancer Cells

Plants are an invaluable source of potential new anti-cancer drugs. Here, we investigated the cytotoxic activity of the acetonic extract of Buxus sempervirens on five breast cancer cell lines, MCF7, MCF10CA1a and T47D, three aggressive triple positive breast cancer cell lines, and BT-20 and MDA-MB-435, which are triple negative breast cancer cell lines. As a control, MCF10A, a spontaneously immortalized but non-tumoral cell line has been used. The acetonic extract of Buxus sempervirens showed cytotoxic activity towards all the five studied breast cancer cell lines with an IC50 ranging from 7.74 µg/ml to 12.5 µg/ml. Most importantly, the plant extract was less toxic towards MCF10A with an IC50 of 19.24 µg/ml. Fluorescence-activated cell sorting (FACS) analysis showed that the plant extract induced cell death and cell cycle arrest in G0/G1 phase in MCF7, T47D, MCF10CA1a and BT-20 cell lines, concomitant to cyclin D1 downregulation. Application of MCF7 and MCF10CA1a respective IC50 did not show such effects on the control cell line MCF10A. Propidium iodide/Annexin V double staining revealed a pre-apoptotic cell population with extract-treated MCF10CA1a, T47D and BT-20 cells. Transmission electron microscopy analyses indicated the occurrence of autophagy in MCF7 and MCF10CA1a cell lines. Immunofluorescence and Western blot assays confirmed the processing of microtubule-associated protein LC3 in the treated cancer cells. Moreover, we have demonstrated the upregulation of Beclin-1 in these cell lines and downregulation of Survivin and p21. Also, Caspase-3 detection in treated BT-20 and T47D confirmed the occurrence of apoptosis in these cells. Our findings indicate that Buxus sempervirens extract exhibit promising anti-cancer activity by triggering both autophagic cell death and apoptosis, suggesting that this plant may contain potential anti-cancer agents for single or combinatory cancer therapy against breast cancer