Estimation of Cell Cycle States of Human Melanoma Cells with Quantitative Phase Imaging and Deep Learning

Visualization and classification of cell cycle stages in live cells requires the introduction of transient or stably expressing fluorescent markers. This is not feasible for all cell types, and can be time consuming to implement. Labelling of living cells also has the potential to perturb normal cellular function. Here we describe a computational strategy to estimate core cell cycle stages without markers by taking advantage of features extracted from information-rich ptychographic time-lapse movies. We show that a deep-learning approach can estimate the cell cycle trajectories of individual human melanoma cells from short 3-frame (~23 minute) snapshots, and can identify cell cycle arrest induced by chemotherapeutic agents targeting melanoma driver mutations

Tunable Landau-Zener transitions in a spin-orbit-coupled Bose-Einstein condensate

The Landau-Zener (LZ) transition is one of the most fundamental phenomena in quantum dynamics. It describes nonadiabatic transitions between quantum states near an avoided crossing that can occur in diverse physical systems. Here we report experimental measurements and tuning of LZ transitions between the dressed eigenlevels of a Bose-Einstein condensate (BEC) that is synthetically spin-orbit (SO) coupled. We measure the transition probability as the BEC is accelerated through the SO avoided crossing and study its dependence on the coupling between the diabatic (bare) states, eigenlevel slope, and eigenstate velocity-the three parameters of the LZ model that are independently controlled in our experiments. Furthermore, we performed time-resolved measurements to demonstrate the breaking down of the spin-momentum locking of the spin-orbit-coupled BEC in the nonadiabatic regime, and we determined the diabatic switching time of the LZ transitions. Our observations show quantitative agreement with the LZ model and numerical simulations of the quantum dynamics in the quasimomentum space. The tunable LZ transition may be exploited to enable a spin-dependent atomtronic transistor

Crowdsourcing step-by-step information extraction to enhance existing how-to videos

Millions of learners today use how-to videos to master new skills in a variety of domains. But browsing such videos is often tedious and inefficient because video player interfaces are not optimized for the unique step-by-step structure of such videos. This research aims to improve the learning experience of existing how-to videos with step-by-step annotations. We first performed a formative study to verify that annotations are actually useful to learners. We created ToolScape, an interactive video player that displays step descriptions and intermediate result thumbnails in the video timeline. Learners in our study performed better and gained more self-efficacy using ToolScape versus a traditional video player. To add the needed step annotations to existing how-to videos at scale, we introduce a novel crowdsourcing workflow. It extracts step-by-step structure from an existing video, including step times, descriptions, and before and after images. We introduce the Find-Verify-Expand design pattern for temporal and visual annotation, which applies clustering, text processing, and visual analysis algorithms to merge crowd output. The workflow does not rely on domain-specific customization, works on top of existing videos, and recruits untrained crowd workers. We evaluated the workflow with Mechanical Turk, using 75 cooking, makeup, and Photoshop videos on YouTube. Results show that our workflow can extract steps with a quality comparable to that of trained annotators across all three domains with 77% precision and 81% recall

Understanding in-video dropouts and interaction peaks in online lecture videos

With thousands of learners watching the same online lecture videos, analyzing video watching patterns provides a unique opportunity to understand how students learn with videos. This paper reports a large-scale analysis of in-video dropout and peaks in viewership and student activity, using second-by-second user interaction data from 862 videos in four Massive Open Online Courses (MOOCs) on edX. We find higher dropout rates in longer videos, re-watching sessions (vs first-time), and tutorials (vs lectures). Peaks in re-watching sessions and play events indicate points of interest and confusion. Results show that tutorials (vs lectures) and re-watching sessions (vs first-time) lead to more frequent and sharper peaks. In attempting to reason why peaks occur by sampling 80 videos, we observe that 61% of the peaks accompany visual transitions in the video, e.g., a slide view to a classroom view. Based on this observation, we identify five student activity patterns that can explain peaks: starting from the beginning of a new material, returning to missed content, following a tutorial step, replaying a brief segment, and repeating a non-visual explanation. Our analysis has design implications for video authoring, editing, and interface design, providing a richer understanding of video learning on MOOCs

FGFR2-activating mutations disrupt cell polarity to potentiate migration and invasion in endometrial cancer cell models

Fibroblast growth factor receptors (FGFRs) are a family of receptor tyrosine kinases that control a diverse range of biological processes during development and in adult tissues. We recently reported that somatic FGFR2 mutations are associated with shorter survival in endometrial cancer. However, little is known about how these FGFR2 mutations contribute to endometrial cancer metastasis. Here, we report that expression of the activating mutations FGFR2N550K and FGFR2Y376C in an endometrial cancer cell model induce Golgi fragmentation, and loss of polarity and directional migration. In mutant FGFR2-expressing cells, this was associated with an inability to polarise intracellular pools of FGFR2 towards the front of migrating cells. Such polarization defects were exacerbated in three-dimensional culture, where FGFR2 mutant cells were unable to form well-organised acini, instead undergoing exogenous ligand-independent invasion. Our findings uncover collective cell polarity and invasion as common targets of disease-associated FGFR2 mutations that lead to poor outcome in endometrial cancer patients

High prevalence of <i>Rickettsia africae</i> variants in <i>Amblyomma variegatum</i> ticks from domestic mammals in rural western Kenya: implications for human health

Tick-borne spotted fever group (SFG) rickettsioses are emerging human diseases caused by obligate intracellular Gram-negative bacteria of the genus Rickettsia. Despite being important causes of systemic febrile illnesses in travelers returning from sub-Saharan Africa, little is known about the reservoir hosts of these pathogens. We conducted surveys for rickettsiae in domestic animals and ticks in a rural setting in western Kenya. Of the 100 serum specimens tested from each species of domestic ruminant 43% of goats, 23% of sheep, and 1% of cattle had immunoglobulin G (IgG) antibodies to the SFG rickettsiae. None of these sera were positive for IgG against typhus group rickettsiae. We detected Rickettsia africae–genotype DNA in 92.6% of adult Amblyomma variegatum ticks collected from domestic ruminants, but found no evidence of the pathogen in blood specimens from cattle, goats, or sheep. Sequencing of a subset of 21 rickettsia-positive ticks revealed R. africae variants in 95.2% (20/21) of ticks tested. Our findings show a high prevalence of R. africae variants in A. variegatum ticks in western Kenya, which may represent a low disease risk for humans. This may provide a possible explanation for the lack of African tick-bite fever cases among febrile patients in Kenya

The Joinpoint-Jump and Joinpoint-Comparability Ratio Model for Trend Analysis with Applications to Coding Changes in Health Statistics

Analysis of trends in health data collected over time can be affected by instantaneous changes in coding that cause sudden increases/decreases, or “jumps,” in data. Despite these sudden changes, the underlying continuous trends can present valuable information related to the changing risk profile of the population, the introduction of screening, new diagnostic technologies, or other causes. The joinpoint model is a well-established methodology for modeling trends over time using connected linear segments, usually on a logarithmic scale. Joinpoint models that ignore data jumps due to coding changes may produce biased estimates of trends. In this article, we introduce methods to incorporate a sudden discontinuous jump in an otherwise continuous joinpoint model. The size of the jump is either estimated directly (the Joinpoint-Jump model) or estimated using supplementary data (the Joinpoint-Comparability Ratio model). Examples using ICD-9/ICD-10 cause of death coding changes, and coding changes in the staging of cancer illustrate the use of these models

The Joinpoint-Jump and Joinpoint-Comparability Ratio Model for Trend Analysis with Applications to Coding Changes in Health Statistics

Analysis of trends in health data collected over time can be affected by instantaneous changes in coding that cause sudden increases/decreases, or “jumps,” in data. Despite these sudden changes, the underlying continuous trends can present valuable information related to the changing risk profile of the population, the introduction of screening, new diagnostic technologies, or other causes. The joinpoint model is a well-established methodology for modeling trends over time using connected linear segments, usually on a logarithmic scale. Joinpoint models that ignore data jumps due to coding changes may produce biased estimates of trends. In this article, we introduce methods to incorporate a sudden discontinuous jump in an otherwise continuous joinpoint model. The size of the jump is either estimated directly (the Joinpoint-Jump model) or estimated using supplementary data (the Joinpoint-Comparability Ratio model). Examples using ICD-9/ICD-10 cause of death coding changes, and coding changes in the staging of cancer illustrate the use of these models

The Role of Melanoma Cell-Stroma Interaction in Cell Motility, Invasion, and Metastasis

The importance of studying cancer cell invasion is highlighted by the fact that 90% of all cancer-related mortalities are due to metastatic disease. Melanoma metastasis is driven fundamentally by aberrant cell motility within three-dimensional or confined environments. Within this realm of cell motility, cytokines, growth factors, and their receptors are crucial for engaging signaling pathways, which both mediate crosstalk between cancer, stromal, and immune cells in addition to interactions with the surrounding microenvironment. Recently, the study of the mechanical biology of tumor cells, stromal cells and the mechanics of the microenvironment have emerged as important themes in driving invasion and metastasis. While current anti-melanoma therapies target either the MAPK signaling pathway or immune checkpoints, there are no drugs available that specifically inhibit motility and thus invasion and dissemination of melanoma cells during metastasis. One of the reasons for the lack of so-called “migrastatics” is that, despite decades of research, the precise biology of metastatic disease is still not fully understood. Metastatic disease has been traditionally lumped into a single classification, however what is now emergent is that the biology of melanoma metastasis is highly diverse, heterogeneous and exceedingly dynamic—suggesting that not all cases are created equal. The following mini-review discusses melanoma heterogeneity in the context of the emergent theme of mechanobiology and how it influences the tumor-stroma crosstalk during metastasis. Thus, highlighting future therapeutic options for migrastatics and mechanomedicines in the prevention and treatment of metastatic melanoma