Miracle-dz/UniFAR-FTUs huggingface dataset:
The response of fine root morphological and physiological traits to added nitrogen in Schrenk’s spruce (Picea schrenkiana) of the Tianshan mountains, China

Fine roots are essential for water and nutrient uptake in plants, but little is known about the variation in fine root traits and the underlying mechanisms that drive it. Understanding the responses of fine root function traits to changing environmental conditions and the role of fine root traits as drivers of forest ecosystem processes are critical for informing physiological and ecological theory as well as ecosystem management. We measured morphological and physiological traits of fine roots from six soil layers and three diameter classes in Schrenk’s spruce (Picea shrenkiana) forests of the Tianshan mountains, China. We found significant effects of nitrogen addition on these morphological and physiological traits, which varied by soil layer and root diameter. Specifically, specific root length (SRL) was higher in medium N addition group (N2) than in control group (N0). Specific root area (SRA) was higher in the control group (N0) than fertilized groups (N1, N2 and N3). Root tissue density (RTD) was higher in low N addition group (N1) than in the other group. Root dry matter content had no significant difference among four treatment groups. SRL, SRA, and RTD of fine roots in different diameter classes were all significantly different between high N addition (N3) and the control (N0) groups. The physiological characteristics of fine roots showed that soluble sugar (SS), fine root vitality (FRV), and tissue water content (TWC) in different soil layers were higher in the control group than in the fertilized groups. While soluble protein (SP), malondialdehyde (MDA) and free proline (FP) were lower in the control group (N0) than in the fertilized groups. In addition, SS, FRV, SP, TWC, FP, and MDA in all N addition treatments groups were significantly different from the control group. Fine root morphological traits were closely related to physiological traits, and added nitrogen inputs change these correlations. Our study confirms that nitrogen addition has specific effects on the morphological and physiological traits of fine roots of Schrenk’s spruce, and the effects of N addition vary according to the amount added.
Code Forking and Software Development Project Sustainability: Evidence from GitHub

Increasing numbers of software projects—proprietary and open source—are developed and maintained by heterogeneous communities of developers. As a result, the sustainability of software projects has become an important issue for members of those communities and their user bases. Building on the idea of forking as “an individual developer’s behavior of copying an existing project’s code base” and its signaling of active community involvement and participation, we ask the question: How does forking impact the sustainability of software development projects? Through the curation of digital trace data gathered from 749 software projects hosted on GitHub, we develop a longitudinal model to explain the contributions of various types of forking on software development project sustainability. Our findings contribute to research by moving beyond the conceptualization of forking as a monolithic concept and shows the benefits of certain types of forking for software projects’ sustainability.
The Impact of Students' Pre-Knowledge on Learning Computer Programming

Students taking courses of computer programming have different pre-knowledge on the topic, which not only includes their knowledge of a specific computer programming language but also their ability for algorithmic thinking and the understanding of the concept of computer programming. At the beginning of the course, they were asked to self-evaluate their knowledge of computer programming and take a pre-course test of their knowledge in JavaScript. In the scope of the research, we have compared the results of the pre-course test of students with technical and general High School education, as well as the results they achieved at the post-course test. Altogether, 55 students collaborated in both pre and post-course tests. The results indicate that students with technical and general High School backgrounds scored similarly on a pre-course test of computer programming and that students with general High School backgrounds acquired more knowledge in the course compared to the ones with a technical High School background. The results also indicate that the students with lower selfevaluation of computer programming have acquired more knowledge during the course compared to the ones with higher selfevaluation scores.
Mechanisms for Automatic Training Data Labeling for Machine Learning

One of the most pervasive challenges in adopting machine or deep learning is the scarcity of training data. This problem is amplified in IS research, where application domains usually require specialized knowledge. This study compares three systems to create a large dataset for training when only a small amount of human-labeled data is available: a high-precision LSTM classifier, a high-recall LSTM classifier, and manually created rule-based system. Based on fewer than 20,000 human-labeled training examples, we used automated labeling to add an additional 100,000 examples to the training data. We found that combining a small human-labeled dataset with a system-labeled dataset improves classification performance. In our evaluation, adding training data labeled by the high-recall LSTM to the human-labeled dataset achieved F1 of 0.578, and adding training data labeled by the rule-based system achieved F1 of 0.598, over 4% improvement compared to a baseline system that only uses human-labeled data.
Studying and Theorizing Knowledge Work in the Age of Intelligent Machines

Organizing knowledge work and workers has been a long-standing theme in information systems research (Bailey et al. 2010; Brown and Duguid 2000; Dougherty and Dunne 2012; Newell et al. 2009; Orlikowski 2002; Schultze 2000). Maintaining a focus on knowledge work is critical insofar as so-called “intelligent” machines, such as deep learning algorithms, are changing the ways knowledge is created and shared in organizations and epistemic communities more generally (e.g., professions/ occupations) (Faraj et al. 2018). Understanding the consequences of emerging knowledge worker-intelligent machine (KWIM) reconfigurations is of huge practical and theoretical significance (Rai et al. 2019). In this PDW, we are interested in exploring the potential opportunities and challenges for studying and theorizing knowledge work in the age of intelligent machines. We will jointly map the current terrain of knowledge work research; discuss what KWIM reconfigurations IS researchers may need to attend to; explore what alternative theoretical lenses might be useful; and reflect on how to tackle potential methodological challenges in studying KWIM reconfigurations.
AIS Electronic Library (AISeL) AIS Electronic Library (AISeL) Geo-Targeting, Privacy, and the Rise of Consumer Location Geo-Targeting, Privacy, and the Rise of Consumer Location Trajectories

Consumer location tracking is becoming omnipresent on mobile devices, producing vast volumes of behavior-rich location trajectory data. However, this comes at a cost – potential invasion of consumer privacy. Existing approaches to privacy preservation are largely unsuited for these new data, not personalized, and difficult to interpret the trade-off between consumer privacy and data utility. We propose a personalized and interpretable framework to enable location data collectors to optimize the privacy-utility trade-off. Validating on a sample of nearly one million location trajectories from over 40,000 individuals, we find that high privacy risks indeed prevail in the absence of data obfuscation. Outperforming multiple baselines, the proposed framework significantly reduces the privacy risk with minimal decrease in an advertiser’s utility. As novel and powerful consumer location trajectory data become increasingly leveraged, we demonstrate its value and propose an interpretable framework to mitigate its risk while maximizing its value.
Spotlight on Warsaw Breakage Syndrome

Abstract Warsaw breakage syndrome (WABS) is a very rare recessive hereditary disease caused by mutations in the gene coding for the DNA helicase DDX11, involved in genome stability maintenance and sister cohesion establishment. Typical clinical features observed in WABS patients include growth retardation, facial dysmorphia, microcephaly, hearing loss due to cochlear malformations and, at cytological level, sister chromatid cohesion defects. Molecular bases of WABS have not yet been elucidated, due to lack of disease animal model systems and limited knowledge of the DDX11 physiological functions. However, WABS is considered to belong to the group of cohesinopathies, genetic disorders due to mutations of subunits or regulators of cohesin, the protein complex responsible for tethering sister chromatids from the time of their synthesis till they separate in mitosis. Recent evidences suggest that cohesin and its regulators have additional key roles in chromatin organization by promoting the formation of chromatin loops. This “non-canonical” function of cohesin is expected to impact gene transcription during cell differentiation and embryonic development and its dis-regulation, caused by mutation/loss of genes encoding cohesin subunits or regulators, could originate the developmental defects observed in cohesinopathies. Ethiopathogenesis of WABS is discussed in line with these recent findings and evidence of a possible role of DDX11 as a cohesin regulator.
Rethinking Public Value Co-Creation in Smart City Ecosystems: A Meta-Analysis of Smart City Case Studies

Despite the supply-side smart city innovation stimuli and smart digital technology investments, extant smart city research is unclear about whether or not they produced better and smarter cities with realized benefits and values for the public. This paper drew on Moore’s framework for creating public value as the theoretical lens to undertake qualitative meta-analysis of 30 most highly cited smart city case study papers. The meta-analysis results found an overall lack of public value focus and realized benefits of the smart city initiatives. The lack of public value focus is more evident in ten of the case study papers which did not mention city names. The paper identified key enablers and inhibitors of public value co-creation in the contemporary smart city ecosystems environments. It then outlined the future research agenda, including a theoretical extension to the Moore’s framework by explicating two governance mechanisms underpinning the government’s public value co-creation process.
The impact of storage conditions on human stool 16S rRNA microbiome composition and diversity

Background Multiple factors can influence stool sample integrity upon sample collection. Preservation of faecal samples for microbiome studies is therefore an important step, particularly in tropical regions where resources are limited and high temperatures may significantly influence microbiota profiles. Freezing is the accepted standard to preserve faecal samples however, cold chain methods are often unfeasible in fieldwork scenarios particularly in low and middle-income countries and alternatives are required. This study therefore aimed to address the impact of different preservative methods, time-to-freezing at ambient tropical temperatures, and stool heterogeneity on stool microbiome diversity and composition under real-life physical environments found in resource-limited fieldwork conditions. Methods Inner and outer stool samples collected from one specimen obtained from three children were stored using different storage preservation methods (raw, ethanol and RNAlater) in a Ugandan field setting. Mixed stool was also stored using these techniques and frozen at different time-to-freezing intervals post-collection from 0–32 h. Metataxonomic profiling was used to profile samples, targeting the V1–V2 regions of 16S rRNA with samples run on a MiSeq platform. Reads were trimmed, combined and aligned to the Greengenes database. Microbial diversity and composition data were generated and analysed using Quantitative Insights Into Microbial Ecology and R software. Results Child donor was the greatest predictor of microbiome variation between the stool samples, with all samples remaining identifiable to their child of origin despite the stool being stored under a variety of conditions. However, significant differences were observed in composition and diversity between preservation techniques, but intra-preservation technique variation was minimal for all preservation methods, and across the time-to-freezing range (0–32 h) used. Stool heterogeneity yielded no apparent microbiome differences. Conclusions Stool collected in a fieldwork setting for comparative microbiome analyses should ideally be stored as consistently as possible using the same preservation method throughout.
The Impact of Probabilistic Classifiers on Appointment Scheduling With No-Shows

Appointment no-shows are common in outpatient clinics and increase clinic costs and patients’ dissatisfaction. We develop a framework to predict the no-show probabilities of a given set of patients, and to subsequently employ these predictions to find the optimal appointment schedule. Some existing work assumes that all patients have the same no-show probability (1-class approach); other work assumes that patients have either a low or a high no-show probability (2-class approach). In contrast, we utilize probabilistic classifiers to obtain the individual patients’ no-show probabilities (N-class approach). Our approach results in better-quality schedules, as measured by a weighted average of patient waiting time and provider overtime. We also find that a small increase in the prediction performance (measured by the Brier score) translates into a large decrease in the schedule cost. Our results are obtained through a large-scale computational study and validated on a real-world data set from an outpatient clinic.
Team Formation and Performance in Online Crowdsourcing Competitions: The Role of Homophily and Diversity in Solver Characteristics

Rapid advances in Information Technology (IT) have enabled organizations to venture beyond their workforce to seek solutions to vital business problems through online crowdsourcing platforms. Such platforms are characterized by geographically dispersed self-organizing teams that compete with one another to evolve the best solutions to challenging issues that confront organizations. Despite the growing popularity of crowdsourcing, there is a paucity of empirical research on: a) how participants on these platforms form teams; and b) how the composition of these teams affects their performance. In this paper, we investigated solvers’ teaming preferences and their impact on performance in an online crowdsourcing competition platform. Specifically, we explored demographics and acquired characteristics as potential predictors of the choice of a teammate. The findings of this study provide insights on the role of homophily and diversity of solver characteristics on team formation and performance in crowdsourcing competitions.
Trading on Cryptocurrency Markets: Analyzing the Behavior of Bitcoin Investors

Driven by innovative information technologies, the financial industry is facing a recent disruptive fintech revolution. One emerging technology within this field is cryptocurrency, aiming to change the future means of payment. In this paper, we study Bitcoin exchange trading and examine what factors influence the behavior of different cryptocurrency investor types. To answer this question, market bids are considered in form of investors' offers and orders as a proxy for their trading behavior. First, an unsupervised clustering technique is applied in order to group different types of investors based on similarities in trading behavior. Second, a supervised classification mechanism is used on social media news to measure the sentiment influencing trading decisions. Among other indicators this bullishness is integrated in an autoregressive distributed lag (ARDL) model to identify the factors influencing the trading behavior of investor types. Besides large investors, foreign traders and speculators, cryptocurrency-specific market participants are characterized in the form of miners. With identifying indicators driving investors' actions (i.e., macro-financial fundamentals, technical trading indicators, technological measures and market sentiment), this study contributes to recent research by explaining the trading behavior on cryptocurrency markets and its impact on exchange rates.
Recent Trends in Software Testing - A Case Study with Google Calendar

Testing is one of the most important and crucial parts in developing any kind of software [Ammann and Offutt 2016]. Approximately half budget spent on the development of the software projects is spent on software testing [Harman et al. 2015]. To create quality software (reliable, secure, etc.) means to test the software from different aspects and to create optimal combination of different coverage criteria because of the deadlines and system size [Rojas et al. 2015]. The purpose of the test team is to provide effectively and efficiently accurate and useful testing services and quality information about the project [Black 2016]. One of the common software testing categorization is: black box testing, white box testing and gray box (combination of black box and white box) [Jan et al. 2016]. If the software testers/developers have the software source code then they can use both black box and white box testing, but if they do not have the internal source code, then, only a black box testing is possible. Usually, black box testing, also called a functional testing, is performed by using the software documentation and design specification [Nidhra and Dondeti 2012]. From another point of view, the testing can be divided into automated and manual testing. In a case of manual testing, the software testers have the role of end users and they are responsible for checking the behavior of software by following a test plan and a set of test cases. Automated testing is performed by using a specific testing software framework that executes the tests and compares the actual outputs with the expected outputs [Garousi and Mäntylä 2016]. Software testing includes different level of testing such as unit, integration, system, and acceptance testing. Unit and integration testing focus on individual modules, while system and acceptance testing focus on overall behavior of the system [Dhir and Kumar 2019].
Optimal Design and Ownership Structures of Innovative Retail Payment Systems

In response to the Fintech trend, an ongoing debate in the banking industry is how to design the new-generation interbank retail payment and settlement system. We propose a two-stage analytical model that takes into account the value–risk tradeoff in the new payment system design, as well as banks’ participation incentives and adoption timing decisions. We find that, as the system base value increases, banks tend to synchronize their investment and adoption decisions. When the system base value is low and banks are heterogeneous, bank association ownership maximizes social welfare. When both the system base value and bank heterogeneity are moderate, government mandate leads to the socially optimal solution. When the system base value is high and banks are relatively homogenous, government ownership is socially optimal. We offer important policy implications regarding the optimal system design and the government regulator’s role in shaping the banking industry in future financial innovation.
Patient characteristics in German allergological practices – a nationwide survey

Introduction: In Western societies a significant incidence and prevalence of allergic asthma and other allergic diseases is observable. The present study investigated epidemiological patterns of allergic diseases and the utilization of health care resources by subjects who are already under specialized allergological treatment. Furthermore the study was performed to identify factors which had a significant impact on accessibility to specific immunotherapy (SIT). Methods: The study was based on a cross-sectional survey on patient characteristics, which was performed by participating physicians, who were specialized in the field of allergological disorders and SIT, in collaboration with their patients. The analysis of data was divided into descriptive analyses and an analytical part, in which influencing factors for accessibility to specific immunotherapy were investigated. Logistic regression models to identify several predictor variables were used. Results: 495 physicians documented the data of 19,990 patients. 18,177 patients were included in the analyses. Patients had a mean age of 31.5 ± 15.5 years and 53.2% were female. The most frequent and most severe allergic disorders observable in German allergological practices were conjunctivitis and rhinitis. The seasonal symptoms occurred mainly during March to August, while seasonal disease manifestation was 2.5 times more frequent than perennial forms. The most received anti-symptomatic medications are antihistamines and corticosteroids. Patients who receive SIT were mainly treated using subcutaneous immunotherapy (SCIT) – only in lower age groups, the likelihood of receiving sublingual immunotherapy (SLIT) was increased. Conclusion: In Germany, conjunctivitis and rhinitis are the most severe allergic disorders in allergological practices. Compared to the German general patient population, people who were already in allergological treatment had better access to SIT.
Tensions in Digital Platform Business Models: A Literature Review

Digital platform business models consist of conflicting elements that often create tensions. We integrate the tension and business model perspectives to explore what set of interrelated tensions platform owners face when continuously designing, adapting, and/or redesigning their digital platform business model. For this purpose, we systematically reviewed information systems literature resulting in 21 unique tensions that platform owners face when crafting business models. We provide a theory for analyzing tensions in digital platform business models by classifying tensions into categories of tensions (i