Poor posture during desk-based learning activities can lead to many health issues, such as spinal problems, musculoskeletal discomfort, and myopia. While traditional posture correction systems use immediate feedback to notify users of their wrong posture, they often disrupt users’ concentration. This study explores an ambient projection notification system for non-intrusive posture correction notifications during desk-based learning scenarios. Through a notification elicitation study and an expert co-design workshop, we investigated users’ perception towards basic elements of projection notification and derived a design space for desk-based projection notification. We then implemented and evaluated PosProjector, an ambient projection notification system, by applying two notification strategies that embody the most representative dimensions of the design space. Results showed that PosProjector can improve users’ posture with little task interference and support various media, including paper and tablets. We further discussed the implications of how to design the least intrusive projection notification system for posture correction.

Technologies designed to support reminiscence, defined as the practice of engaging with one's personal past, have become a significant area of inquiry within HCI. Although this has generated a diverse range of creative systems, the field still lacks a systematic account of the design principles that guide them. In this paper, we review 60 studies to examine both the psychosocial functions these technologies target and the mechanisms through which they operate. Our analysis suggests a predominant emphasis on positive identity construction and social connection, with comparatively less focus on functions related to everyday problem solving. To synthesize the mechanisms identified, we propose a cue-centered framework that treats mnemonic cues (e.g., photographs) as the basic unit of design. The framework organizes design mechanisms into a four-stage lifecycle: cue generation, augmentation, interaction, and sharing. It provides a conceptual vocabulary for analyzing reminiscence technologies and highlights underexplored opportunities for future research and design.

While search is a common need in Virtual Reality (VR) applications, current approaches are cumbersome, often requiring users to type on a mid-air keyboard using controllers in VR or remove VR equipment to search on a computer. We first conducted a literature review and a formative study, identifying six common search needs: knowing about one object, knowing about the object’s partial details, knowing objects with environmental context, knowing about interactions with objects, and finding objects within field of view (FOV) and out of FOV in the VR scene. Informed by these needs, we designed technology probes that leveraged recent advances in Vision Large Language Models and conducted a probe-based study with users to elicit feedback. Based on the findings, we derived design principles for VR designers and developers to consider when designing a user-friendly search interface in VR. While prior work about VR search tended to address specific aspects of search, our work contributes design considerations aimed at enhancing the ease of search in VR and potential future directions.

Recent advances in high-throughput biotechnologies have led to the rapid growing research interest in reverse engineering of biomolecular systems (REBMS). 'Data-driven' approaches, i.e. data mining, can be used to extract patterns from large volumes of biochemical data at molecular-level resolution while 'design-driven' approaches, i.e. systems modeling, can be used to simulate emergent system properties. Consequently, both data- and design-driven approaches applied to -omic data may lead to novel insights in reverse engineering biological systems that could not be expected before using low-throughput platforms. However, there exist several challenges in this fast growing field of reverse engineering biomolecular systems: (i) to integrate heterogeneous biochemical data for data mining, (ii) to combine top-down and bottom-up approaches for systems modeling and (iii) to validate system models experimentally. In addition to reviewing progress made by the community and opportunities encountered in addressing these challenges, we explore the emerging field of synthetic biology, which is an exciting approach to validate and analyze theoretical system models directly through experimental synthesis, i.e. analysis-by-synthesis. The ultimate goal is to address the present and future challenges in reverse engineering biomolecular systems (REBMS) using integrated workflow of data mining, systems modeling and synthetic biology.

UNLABELLED: High-throughput deep-sequencing technology has generated an unprecedented number of expressed short sequence reads, presenting not only an opportunity but also a challenge for prediction of novel microRNAs. To verify the existence of candidate microRNAs, we have to show that these short sequences can be processed from candidate pre-microRNAs. However, it is laborious and time consuming to verify these using existing experimental techniques. Therefore, here, we describe a new method, miRD, which is constructed using two feature selection strategies based on support vector machines (SVMs) and boosting method. It is a high-efficiency tool for novel pre-microRNA prediction with accuracy up to 94.0% among different species. AVAILABILITY: miRD is implemented in PHP/PERL+MySQL+R and can be freely accessed at http://mcg.ustc.edu.cn/rpg/mird/mird.php.