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Cells can position multiple copies of components like carboxysomes, nucleoids, and nuclei at regular intervals. By controlling positions, cells, for example, ensure equal partitioning of organelles over daughter cells and, in the case of nuclei, control cell sizes during cellularization. Mechanisms that generate regular patterns are as yet poorly understood. We used fission yeast cell cycle mutants to investigate the dispersion of multiple nuclei by microtubule-generated forces in single cells. After removing internuclear attractive forces by microtubule-based molecular motors, we observed the establishment of regular patterns of nuclei. Based on live-cell imaging, we hypothesized that microtubule growth within internuclear spaces pushes neighbouring nuclei apart. In the proposed mechanism, which was validated by stochastic simulations, the repulsive force weakens with increasing separation because stochastic shortening events limit the extent over which microtubules generate forces. Our results, therefore, demonstrate how cells can exploit the dynamics of microtubule growth for the equidistant positioning of organelles.

Nonlinear mixed effects (NLME) modeling is a powerful tool to analyze time-series data from several individual entities in an experiment. In this paper, we give a brief overview of a package for NLME modeling in Wolfram Mathematica entitled NLMEModeling, implementing the first-order conditional estimation method with sensitivity equation-based gradients for parameter estimation. NLMEModeling supports mixed effects modeling of dynamical systems where the underlying dynamics are described by either ordinary or stochastic differential equations combined with observation equations with flexible observation error models. Moreover, NLMEModeling is a user-friendly package with functionality for parameter estimation, model diagnostics (such as goodness-of-fit analysis and visual predictive checks), and model simulation. The package is freely available and provides an extensible add-on to Wolfram Mathematica.

Structures for guiding fish around migration barriers are frequently used for maintaining connectivity in regulated riverine systems. However, for non-physical barriers, experimental studies providing direct and detailed observations of fish-barrier interactions in rivers are largely lacking. In this study, we quantify the efficiency of bubble barriers (alone or in combination with light stimuli, and in both daylight and darkness) for diverting downstream migrating Atlantic salmon (Salmo salar). Both a laboratory-based migration experiment and a largescale field experiment in a regulated river were used to evaluate efficiency of bubble barriers. In the latter, we used acoustic telemetry to provide in situ measurements of how downstream migrating Atlantic salmon smolts interact with bubble barriers. We show that bubbles divert smolts with high efficiency in both a laboratory flume (95%) and in natural settings (90%). This latter efficiency is higher compared to an already present physical barrier (46%) covering the upper two meters of the water column in the large river. The bubble barrier did not affect flume migration in darkness, suggesting that visual cues are crucial for the observed repelling effect of bubbles. We conclude that bubble barriers can be effective, largely maintenance free and low-cost alternatives to physical structures currently used to divert salmon away from high-mortality passages.

Augmented Reality (AR) applications provide major opportunities for healthcare. Mental health rehabilitation in particular is an area that stands to gain from innovations in this field. Debilitating medical conditions related to traumatic brain injuries, aging, and drug abuse, can take advantage from the benefits that these applications provide. Coupled with IoMT (Internet of Medical Things) sensors and machine learning, effective system implementations that predict mental rehabilitation outcomes can be built. This can help in administering better treatment protocols. The collective use of these technologies for therapeutic approaches to alter patient behaviour at a physiological level falls in the emerging field of Digital Therapeutics (DT). In this paper, the current state of AR for mental health rehabilitation is described and used to motivate the implementation of a proof of concept mental DT rehabilitation system developed by us. Similar AR-IoMT systems can reduce inpatient counts in smart cities where an increase in aging populations is already a concern, and existing healthcare resources may be clogged up due to minor inpatient health checkups. In addition, the paper discusses the deployment challenges of the implemented system, which include the lack and need thereof of a standardized protocol for IoMT data collection and analysis for cross interoperability across different healthcare providers and vendors.

Reproduction and replication of prior work is the corner stone of scientific research. It allows to test whether the conclusions drawn by a group of researchers also generalize to other context conditions (different researchers, different assumptions, different data, subjects, etc.). The most basic form is reproduction where the experimental or empirical evaluation that has been documented in the original study is repeated by a different (group of) researchers. Repeating the evaluation by applying the same method and techniques on the same data as in the original study allows to test whether results and interpretation described in the original study also hold outside of the original context. In this document, we report on 16 reproductions of the experimental evaluations of 10 different studies in the area of process mining, predictive process monitoring, and text mining. The report contains all 16 reproduction reports to which consent for publication has been given., {"references":["1.\tRaichelson, L., Soffer, P.: Merging event logs with many to many relationships. In: Fournier, F., Mendling, J. (eds.) BPM 2014. LNBIP, vol. 202, pp. 330–341. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15895-2_28","2.\tTax, Niek, Benjamin Dalmas, Natalia Sidorova, Wil M. P. van der Aalst and Sylvie Norre. \"Interest-driven discovery of local process models.\" Inf. Syst. 77 (2018): 105-117. http://doi.org/10.1016/j.is.2018.04.006","3.\tEck, Maikel L. van, Natalia Sidorova and Wil M. P. van der Aalst. \"Discovering and Exploring State-Based Models for Multi-perspective Processes.\" BPM (2016). http://doi.org/10.1007/978-3-319-45348-4_9","4.\tVadim Denisov, Dirk Fahland, Wil M. P. van der Aalst: Unbiased, Fine-Grained Description of Processes Performance from Event Data. BPM 2018: 139-157. https://doi.org/10.1007/978-3-319-98648-7_9","5.\tTax, Niek, Ilya Verenich, Marcello La Rosa and Marlon Dumas. \"Predictive Business Process Monitoring with LSTM Neural Networks.\" CAiSE (2017). http://doi.org/10.1007/978-3-319-59536-8_30","6.\tDongen, Boudewijn F. van, Josep Carmona and Thomas Chatain. \"A Unified Approach for Measuring Precision and Generalization Based on Anti-alignments.\" BPM (2016). http://doi.org/10.1007/978-3-319-45348-4_3","7.\tXixi Lu, Dirk Fahland, Robert Andrews, Suriadi Suriadi, Moe Thandar Wynn, Arthur H. M. ter Hofstede and Wil M. P. van der Aalst. \"Semi-supervised Log Pattern Detection and Exploration Using Event Concurrence and Contextual Information.\" OTM Conferences (2017). http://doi.org/10.1007/978-3-319-69462-7_11","8.\tM. L. van Eck, N. Sidorova and W. M. P. van der Aalst, \"Enabling process mining on sensor data from smart products,\" 2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS), Grenoble, 2016, pp. 1-12. https://10.1109/RCIS.2016.7549355","9.\tCampos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., Jatowt A. (2018) A Text Feature Based Automatic Keyword Extraction Method for Single Documents. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science, vol 10772. Springer, Cham https://doi.org/10.1007/978-3-319-76941-7_63","10.\tGarcia-Banuelos, Luciano, Nick R. T. P. van Beest, Marlon Dumas, Marcello La Rosa and Willem Mertens. \"Complete and Interpretable Conformance Checking of Business Processes.\" IEEE Transactions on Software Engineering 44 (2018): 262-290. http://doi.org/10.1109/TSE.2017.2668418"]}

Navies have been operating with unmanned underwater vehicles (UUVs) for decades. Recently, these platforms are assigned more and more complicated tasks. Compared to other domains, the underwater domain offers substantial additional challenges. One of these challenges is accurate navigation of these UUVs. In this thesis, the use of directional acoustic modems integrated onto autonomous platforms is evaluated, specifically in the role they can play in improving underwater navigation accuracy of submerged assets. The main research effort within this thesis was the evaluation of data from sea trials in the shallow parts of Monterey Bay in September 2017 and April 2018. Two directional acoustic modems were installed in tow bodies that were tethered to two unmanned surface vehicles (USVs). These USVs were then directed on multiple missions in an area surrounding the deployment of a bottom-moored acoustic echo repeater, which acted as a surrogate underwater platform. Data collected during the sea trials is then compared between measured and best-estimated ranges and bearings. Using the Bellhop ray-tracing model and beam pattern analysis, the actual accuracy of these systems was extensively evaluated. Results indicate that, when operating properly, these modems can provide accurate data to assist in underwater navigation. http://archive.org/details/evaluatingeffect1094559609 Outstanding Thesis Lieutenant, Netherlands Navy Approved for public release; distribution is unlimited.

Nonlinear mixed effects (NLME) modeling based on stochastic differential equations (SDEs) have evolved into a promising approach for analysis of PK/PD data. SDE-NLME models go beyond the realm of standard population modeling as they consider stochastic dynamics, thereby introducing a probabilistic perspective on the state variables. This article presents a summary of the main contributions to SDE-NLME models found in the literature. The aims of this work were to develop an exact gradient version of the first-order conditional estimation (FOCE) method for SDE-NLME models and to investigate whether it enabled faster estimation and better gradient precision/accuracy compared to the use of gradients approximated by finite differences. A simulation-estimation study was set up whereby finite difference approximations of the gradients of each level were interchanged with the exact gradients. Following previous work, the uncertainty of the state variables was accounted for using the extended Kalman filter (EKF). The exact gradient FOCE method was implemented in Mathematica 11 and evaluated on SDE versions of three common PK/PD models. When finite difference gradients were replaced by exact gradients at both FOCE levels, relative runtimes improved between 6- and 32-fold, depending on model complexity. Additionally, gradient precision/accuracy was significantly better in the exact gradient case. We conclude that parameter estimation using FOCE with exact gradients can successfully be applied to SDE-NLME models.