OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics.

Organoids have immense potential as ex vivo disease models for drug discovery and personalized drug screening. Dynamic changes in individual organoid morphology, number, and size can indicate important drug responses. However, these metrics are difficult and labor-intensive to obtain for high-throughput image datasets. Here, we present OrganoID, a robust image analysis platform that automatically recognizes, labels, and tracks single organoids, pixel-by-pixel, in brightfield and phase-contrast microscopy experiments. The platform was trained on images of pancreatic cancer organoids and validated on separate images of pancreatic, lung, colon, and adenoid cystic carcinoma organoids, which showed excellent agreement with manual measurements of organoid count (95%) and size (97%) without any parameter adjustments. Single-organoid tracking accuracy remained above 89% over a four-day time-lapse microscopy study. Automated single-organoid morphology analysis of a chemotherapy dose-response experiment identified strong dose effect sizes on organoid circularity, solidity, and eccentricity. OrganoID enables straightforward, detailed, and accurate image analysis to accelerate the use of organoids in high-throughput, data-intensive biomedical applications.

Semi-occluded Nasal Tract Exercises (SONTEs): Nasal Tube in Water Exercises Using Nasal Consonants.

OBJECTIVE: This study analyzes the effects of the vocal exercises called semi-occluded nasal tract exercises (SONTEs), which were carried out with a new appliance that extends the nasal cavity as a part of the vocal tract. The acoustic, aerodynamic and electroglottographic (EGG) measurements were compared with those of the traditional semi-occluded vocal tract exercises (SOVTEs) of phonation in water. METHODS: In this study, 34 women were randomly asked to perform phonation in water for 5 min through the nasal and oral routes with the sounds /m/ and /ɔ/, respectively, using a tube with a submersion depth of 5 cm. The acoustic, aerodynamic and EGG measurements before and after the exercises were analyzed using the appropriate statistical methods. RESULTS: No significant difference was found in the time and frequency domain parameters before and after the exercises, except for the amplitude perturbation quotient (APQ) values, which decreased after both exercises. In addition, there was no significant difference in any aerodynamic parameters before and after the exercises, but the mean SPL values significantly increased after both exercises. The oral and nasal peak inspiratory flow rates increased after both exercises, but the increase peaked after the SONTEs implementation. As expected, the EGG-jitter and EGG-periodicity values had a reciprocal interaction with each other, while differences were observed between the values of the vocal fold movements measured in both exercises. CONCLUSIONS: SONTEs may be as effective as the conventional SOVTEs because it made tube phonation into water possible through artificial extension of the nasal cavity and increased the resonant effect by using the positive effects based on the principles of SOVTEs.

Computer vision reveals hidden variables underlying NF-κB activation in single cells.

Individual cells are heterogeneous when responding to environmental cues. Under an external signal, certain cells activate gene regulatory pathways, while others completely ignore that signal. Mechanisms underlying cellular heterogeneity are often inaccessible because experiments needed to study molecular states destroy the very states that we need to examine. Here, we developed an image-based support vector machine learning model to uncover variables controlling activation of the immune pathway nuclear factor κB (NF-κB). Computer vision analysis predicts the identity of cells that will respond to cytokine stimulation and shows that activation is predetermined by minute amounts of "leaky" NF-κB (p65:p50) localization to the nucleus. Mechanistic modeling revealed that the ratio of NF-κB to inhibitor of NF-κB predetermines leakiness and activation probability of cells. While cells transition between molecular states, they maintain their overall probabilities for NF-κB activation. Our results demonstrate how computer vision can find mechanisms behind heterogeneous single-cell activation under proinflammatory stimuli.

Interactive entity resolution in relational data: a visual analytic tool and its evaluation.

Databases often contain uncertain and imprecise references to real-world entities. Entity resolution, the process of reconciling multiple references to underlying real-world entities, is an important data cleaning process required before accurate visualization or analysis of the data is possible. In many cases, in addition to noisy data describing entities, there is data describing the relationships among the entities. This relational data is important during the entity resolution process; it is useful both for the algorithms which determine likely database references to be resolved and for visual analytic tools which support the entity resolution process. In this paper, we introduce a novel user interface, D-Dupe, for interactive entity resolution in relational data. D-Dupe effectively combines relational entity resolution algorithms with a novel network visualization that enables users to make use of an entity's relational context for making resolution decisions. Since resolution decisions often are interdependent, D-Dupe facilitates understanding this complex process through animations which highlight combined inferences and a history mechanism which allows users to inspect chains of resolution decisions. An empirical study with 12 users confirmed the benefits of the relational context visualization on the performance of entity resolution tasks in relational data in terms of time as well as users' confidence and satisfaction.

Dynamic processing allocation in video.

Large stores of digital video pose severe computational challenges to existing video analysis algorithms. In applying these algorithms, users must often trade off processing speed for accuracy, as many sophisticated and effective algorithms require large computational resources that make it impractical to apply them throughout long videos. One can save considerable effort by applying these expensive algorithms sparingly, directing their application using the results of more limited processing. We show how to do this for retrospective video analysis by modeling a video using a chain graphical model and performing inference both to analyze the video and to direct processing. We apply our method to problems in background subtraction and face detection, and show in experiments that this leads to significant improvements over baseline algorithms.