Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore the prior knowledge of transcriptomes and of the probable structures of the data. Moreover, cell identification heavily relies on subjective and inaccurate human inspection afterwards. To address these analytical challenges, we developed the Semi-supervised Category Identification and Assignment (SCINA) algorithm, a semi-supervised model, for analyses of scRNA-Seq and flow cytometry/CyTOF data, and other data of similar format, by automatically exploiting previously established gene signatures using an expectation–maximization (EM) algorithm. We applied SCINA on a wide range of datasets, and showed its accuracy, stableness and efficiency exceeded most popular unsupervised approaches. SCINA discovered an intermediate stage of oligodendrocyte from mouse brain scRNA-Seq data. SCINA also detected immune cell population shifting in Stk4 knock-out -knockoutmouse cytometry data. Finally, SCINA identified a new kidney tumor clade with similarity to FH-deficient tumors from bulk tumor data. Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods.

The Augmented Reality (AR)‐based learning environment not only provides educators with novel ways to present learning materials but also give learners the opportunity to spontaneously interact with the material. Previous studies have shown that AR has many advantages in education; however, few focuses on the mechanisms behind promoting inquiry motivation, such as the effect of AR on learners’ self‐efficacy and conceptions of learning. This study developed an AR‐based wave‐particle duality learning application, “AROSE,” to explore the effect of AR technology on students’ self‐efficacy and conceptions of learning physics. A quasi‐experimental study method was used, and 98 high school students aged between 16 and 18 were randomly assigned to experimental and control group. After a 4‐week intervention, it was found that integrating AR technology into physics classrooms can (1) significantly enhance students’ physics learning self‐efficacy, as indicated by understanding of concepts, higher‐level cognitive skills, practice and communication; (2) guide students to be more inclined to higher‐level conceptions of learning physics rather than lower ones; and (3) stimulates students’ motivation to learn more deeply.

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Background: The Pull-Request (PR) model is a widespread approach adopted by open source software (OSS) projects to support collaborative software development. However, it is often challenging to continuously evaluate and revise PRs in several iterations of code reviewsinvolving technical and social aspects. Aim: Our objective is twofold: identifying best practices for effective collaboration in continuous PR improvement and uncovering problems that deserve special attention to improve collaboration efficiency and productivity. Method: We conducted a mixed-methods empirical study of repeatedly revised PRs (i.e. those that have undergone a high number of revisions). Historical trace data of five long-lived popular GitHub projects were used for manual investigation of practices for requesting changes to PRs and reasons for nonacceptance of repeatedly revised PRs. Surveys of OSS practitioners were conducted to evaluate the results of manual analysis and to provide additional insights into developers' willingness regarding PR revisions and factors causing avoidable revisions in practice. Results: The main results of our research were as follows: (1) We identified 15 code review practices for requesting changes to PRs, among which practices with respect to explaining the reasoning behind requested changes and tracking the progress of PR review and revision were undervalued by reviewers; (2) While submitters can in general undergo 1-5 rounds of revisions, they are willing to offer more revisions when they are in a friendly community and receive helpful feedback; (3) We revealed 11 factors causing avoidable revisions regarding to reviewers' feedback, code review policy, pre-submission issues, and implementation of new revisions; and (4) Nonacceptance of repeatedly revised PRs was due mainly to inactivity of submitters or reviewers and being superseded for better maintenance. Finally, based on these findings, we proposed recommendations and implications for OSS practitioners and tool designers to facilitate efficient collaboration in PR revisions.

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