Sharing activities that people do in everyday life, such as physical activity, health management, or hobbies, help people receive benefits like social support and positive self-presentation. Short-form videos present new opportunities for activity-sharing, which has traditionally been studied in static contexts like text- and image-sharing. We therefore aim to understand what information people incorporate into short-form activity videos, and how. We qualitatively analyzed 420 short-form activity videos on TikTok across three domains: running, studying, and sketching. We found people often present information before, during, and after activities, developing strategies for qualitatively and quantitatively incorporating activity-relevant information in each. We also uncover practices for aligning the sharing of activity-relevant information with the nature of short-form videos, such as modifying broader-scale goals into video-scale goals. We further discuss design opportunities and challenges for designers to create tools that support the practice, such as closer integration with tracking tools and encouraging narrative structure.

People often leverage multiple platforms to share activities they undertake in their lives, from music listening to eating. Broad-purpose platforms, which people use to share a wide variety of activities with a diverse audience, and dedicated platforms, which often focus on tracking and sharing a specific activity with connections with similar interests, both help individuals seeking social benefits from sharing their activity. Researchers designing systems for activity sharing have often reflected on whether to support sharing on dedicated or broad-purpose platforms, suggesting a need to better understand their relative utility. We collected and compared the responses received between 700,000 pairs of activity-sharing posts on four sets of broad-purpose and dedicated platforms across two domains: physical activity (Strava, MapMyRun) and creativity (Dribbble, Behance). Results showed that dedicated platforms were more likely to receive responses (likes and comments), and comments were more likely to be encouraging and refer to specific qualities of the activities being shared. We reflect on the tradeoff between sheer audience volume and likelihood of response, and discuss how to design prompts and templates into sharing features which better align with the norms of respective platforms.

The CSCW community has a history of designing, implementing, and evaluating novel social interactions in technology, but the process requires significant technical effort for uncertain value. We discuss the opportunities and applications of "piggyback prototyping", building and evaluating new ideas for social computing on top of existing ones, expanding on its potential to contribute design recommendations. Drawing on about 50 papers which use the method, we critically examine the intellectual and technical benefits it provides, such as ecological validity and leveraging well-tested features, as well as research-product and ethical tensions it imposes, such as limits to customization and violation of participant privacy. We discuss considerations for future researchers deciding whether to use piggyback prototyping and point to new research agendas which can reduce the burden of implementing the method.

Sharing personal informatics data can support accountability, connectedness, and self-expression, but people often find their data too trivial to share on social media. Ephemeral social platforms like Snapchat and Story features have emerged as spaces for sharing more trivial life events, presenting an opportunity to incorporate self-tracked data into sharing. Past work suggests that including data-driven stickers on these platforms can help add additional context to what people share, but little is known about the benefits and challenges of people's everyday experiences with this concept. To understand people's everyday use of data-driven stickers, we designed and developed SnapPI, an app for flexibly incorporating data into stickers for Snapchat. We deployed SnapPI to 21 participants for two weeks, finding that participants value aligning data sharing with Snapchat's communication and stylistic norms. Perceiving Snapchat as a playful platform, participants connected data stickers with various visual components of their Snaps. Stickers were used to incorporate personal informatics data into their existing conversations, and were edited to align with different audience needs or to be expressive. We discuss recommendations for personal data sharing, suggesting supporting flexibility in presentation and aligning with the norms of existing platforms.

Cancer treatments can be highly toxic and frequently only a subset of the patient population will benefit from a given treatment. Tumour genetic makeup plays an important role in cancer drug sensitivity. We suspect that gene expression markers could be used as a decision aid for treatment selection or dosage tuning. Using in vitro cancer cell line dose-response and gene expression data from the Genomics of Drug Sensitivity in Cancer (GDSC) project, we build a dose-varying regression model. Unlike existing approaches, this allows us to estimate dosage-dependent associations with gene expression. We include the transcriptomic profiles as dose-invariant covariates into the regression model and assume that their effect varies smoothly over the dosage levels. A two-stage variable selection algorithm (variable screening followed by penalized regression) is used to identify genetic factors that are associated with drug response over the varying dosages. We evaluate the effectiveness of our method using simulation studies focusing on the choice of tuning parameters and cross-validation for predictive accuracy assessment. We further apply the model to data from five BRAF targeted compounds applied to different cancer cell lines under different dosage levels. We highlight the dosage-dependent dynamics of the associations between the selected genes and drug response, and we perform pathway enrichment analysis to show that the selected genes play an important role in pathways related to tumorigenesis and DNA damage response.

Digital gifting in live streaming, in which viewers buy digital gifts to reward the streamers, was worth over $200 million in 2018 in China and its growth has been accelerating. This paper explores what motivates people to tip and how it impacts interactions between viewers and streamers. Through a survey, we identified the main categories of viewers' tipping motivations. We found that viewers were motivated by the reciprocal acts of streamers, who would engage in various types of social interactions with tippers during the live streams. The styles of interactions and contents of live stream based on the tipping are differently influenced by the motivations of viewers and streamers. For example, viewers often tip large to attract attentions from the crowd or promote preferred live-streaming content. These findings provide more knowledge on the social interaction in live streaming platforms.

Live streaming is an emergent social medium that allows remote interaction between the streamer and an audience of any size. Major live-streaming platforms in some Asian countries have a digital gift-giving feature that allows viewers to directly reward streamers during live sessions. Streamers can later exchange the digital gifts they have received for money, and this monetary incentive appears likely to influence how they interact with their viewers and generate live-streaming content. However, the precise nature and mechanisms of such impact have not previously been explored. Therefore, this qualitative study with 13 streamer participants examines how digital gifting influences streamers' motivations and the nature of both the content that they generate and their social interactions with their audiences. It reports that the digital-gifting function serves as a major incentive for active streaming, but may also disincentivize some streamers from continuing to contribute, for reasons that will be explored. Moreover, the participants devised strategies for both content generation and social interaction with the specific objective of earning gifts from viewers: practices that, in some cases, appeared to limit the quality of their live-streaming content. It was also noted that the streamers tended to have constrained social relationships with their viewers, in part because such relationships were seen as unequal or one-sided due to gift-giving behavior. The paper concludes with a discussion of design considerations for the incorporation of gift-giving features into live-streaming platforms, and additional recommendations for future research and the design of such platforms.

MOTIVATION: Many drug combinations are routinely assessed to identify synergistic interactions in the attempt to develop novel treatment strategies. Appropriate software is required to analyze the results of these studies. RESULTS: We present Combenefit, new free software tool that enables the visualization, analysis and quantification of drug combination effects in terms of synergy and/or antagonism. Data from combinations assays can be processed using classical Synergy models (Loewe, Bliss, HSA), as single experiments or in batch for High Throughput Screens. This user-friendly tool provides laboratory scientists with an easy and systematic way to analyze their data. The companion package provides bioinformaticians with critical implementations of routines enabling the processing of combination data. AVAILABILITY AND IMPLEMENTATION: Combenefit is provided as a Matlab package but also as standalone software for Windows (http://sourceforge.net/projects/combenefit/). CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: ChemDB is a chemical database containing nearly 5M commercially available small molecules, important for use as synthetic building blocks, probes in systems biology and as leads for the discovery of drugs and other useful compounds. The data is publicly available over the web for download and for targeted searches using a variety of powerful methods. The chemical data includes predicted or experimentally determined physicochemical properties, such as 3D structure, melting temperature and solubility. Recent developments include optimization of chemical structure (and substructure) retrieval algorithms, enabling full database searches in less than a second. A text-based search engine allows efficient searching of compounds based on over 65M annotations from over 150 vendors. When searching for chemicals by name, fuzzy text matching capabilities yield productive results even when the correct spelling of a chemical name is unknown, taking advantage of both systematic and common names. Finally, built in reaction models enable searches through virtual chemical space, consisting of hypothetical products readily synthesizable from the building blocks in ChemDB. AVAILABILITY: ChemDB and Supplementary Materials are available at http://cdb.ics.uci.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.