The 2020 COVID-19 pandemic has greatly accelerated the adoption of online learning and teaching in many colleges and universities. Video, as a key integral part of online education, largely influences student learning experiences. Though many guidelines on designing educational videos have been reported, the quantitative data showing the impacts of video length on students’ academic performance in a credit-bearing course is limited, particularly for an online-flipped college engineering course. The forced pandemic lockdown enables a suitable environment to address this research gap. In this paper, we present the first step to examine the impact of short videos on students’ academic performance in such circumstances. Our results indicate that short videos can greatly improve student engagement by 24.7% in terms of video viewing time, and the final exam score by 9.0%, both compared to the long-video group. The quantitative Likert questionnaire also indicates students’ preference for short videos over long videos. We believe this study has important implications for course design for future online-flipped engineering courses.

Nanofibrillated cellulose paper (nanopaper) has gained growing interest as one promising substrate material for paper-based microfluidics, thanks to its ultrasmooth surface, high optical transparency, uniform nanofiber matrix with nanoscale porosity, and tunable chemical properties. Recently, research on nanopaper-based microfluidics has quickly advanced; however, the current technique of patterning microchannels on nanopaper (i.e., 3D printing, spray coating, or manual cutting and sticking), that is fundamental for application development, still has some limitations, such as ease-of-contamination, and more importantly, only enabling millimeter-scale channels. This paper reports a facile process that leverages the simple operations of microembossing with the convenient plastic micro-molds, for the first time, patterning nanopaper microchannels downing to 200 μm, which is 4 times better than the existing methods and is time-saving (<45 mins). We also optimized the patterning parameters and provided one quick look-up table as the guideline for application developments. As proof-of-concept, we first demonstrated two fundamental microfluidic devices on nanopaper, the laminar-mixer and droplet generator, and two functional nanopaper-based analytical devices (NanoPADs) for glucose and Rhodamine B (RhB) sensing based on optical colorimetry and surface-enhanced Raman spectroscopy, respectively. The two NanoPADs showed outstanding performance with low limits of detection (2 mM for glucose and 19fM for RhB), which are 1.25× and 500× fold improvement compared to the previously reported values. This can be attributed to our newly developed highly accurate microchannel patterning process that enables high integration and fine-tunability of the NanoPADs along with the superior optical properties of nanopaper.

Caenorhabditis elegans ( C. elegans ) has been a popular model organism for several decades since its first discovery of the huge research potential for modeling human diseases and genetics. Sorting is an important means of providing stage- or age-synchronized worm populations for many worm-based bioassays. However, conventional manual techniques for C. elegans sorting are tedious and inefficient, and commercial complex object parametric analyzer and sorter is too expensive and bulky for most laboratories. Recently, the development of lab-on-a-chip (microfluidics) technology has greatly facilitated C. elegans studies where large numbers of synchronized worm populations are required and advances of new designs, mechanisms, and automation algorithms. Most previous reviews have focused on the development of microfluidic devices but lacked the summaries and discussion of the biological research demands of C. elegans , and are hard to read for worm researchers. We aim to comprehensively review the up-to-date microfluidic-assisted C. elegans sorting developments from several angles to suit different background researchers, i.e., biologists and engineers. First, we highlighted the microfluidic C. elegans sorting devices' advantages and limitations compared to the conventional commercialized worm sorting tools. Second, to benefit the engineers, we reviewed the current devices from the perspectives of active or passive sorting, sorting strategies, target populations, and sorting criteria. Third, to benefit the biologists, we reviewed the contributions of sorting to biological research. We expect, by providing this comprehensive review, that each researcher from this multidisciplinary community can effectively find the needed information and, in turn, facilitate future research.

The Caenorhabditis elegans (C. elegans) is an ideal model organism for studying human diseases and genetics due to its transparency and suitability for optical imaging. However, manually sorting a large population of C. elegans for experiments is tedious and inefficient. The microfluidic-assisted C. elegans sorting chip is considered a promising platform to address this issue due to its automation and ease of operation. Nevertheless, automated C. elegans sorting with multiple parameters requires efficient identification technology due to the different research demands for worm phenotypes. To improve the efficiency and accuracy of multi-parameter sorting, we developed a deep learning model using You Only Look Once (YOLO)v7 to detect and recognize C. elegans automatically. We used a dataset of 3931 annotated worms in microfluidic chips from various studies. Our model showed higher precision in automated C. elegans identification than YOLOv5 and Faster R-CNN, achieving a mean average precision (mAP) at a 0.5 intersection over a union (mAP@0.5) threshold of 99.56%. Additionally, our model demonstrated good generalization ability, achieving an mAP@0.5 of 94.21% on an external validation set. Our model can efficiently and accurately identify and calculate multiple phenotypes of worms, including size, movement speed, and fluorescence. The multi-parameter identification model can improve sorting efficiency and potentially promote the development of automated and integrated microfluidic platforms.

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