Community standards for open cell migration data

Cell migration research has become a high-content field. However, the quantitative information encapsulated in these complex and high-dimensional datasets is not fully exploited owing to the diversity of experimental protocols and non-standardized output formats. In addition, typically the datasets are not open for reuse. Making the data open and Findable, Accessible, Interoperable, and Reusable (FAIR) will enable meta-analysis, data integration, and data mining. Standardized data formats and controlled vocabularies are essential for building a suitable infrastructure for that purpose but are not available in the cell migration domain. We here present standardization efforts by the Cell Migration Standardisation Organisation (CMSO), an open community-driven organization to facilitate the development of standards for cell migration data. This work will foster the development of improved algorithms and tools and enable secondary analysis of public datasets, ultimately unlocking new knowledge of the complex biological process of cell migration

Algorithms for Data Migration

This thesis is concerned with the problem related to data storage and management. A large storage server consists of several hundreds of disks. To balance the load across disks, the system computes data layouts that are typically adjusted according to the workload. As workloads change over time, the system recomputes the data layout, and rearranges the data items according to the new layout. We identify the problem of computing an efficient data migration plan that converts an initial layout to a target layout. We define the data migration problem as follows: for each item, there are a set of disks that have the item (sources) and a set of disks that want to receive the item (destinations). We want to migrate the data items from the sources to destinations. The crucial constraint is that each disk can participate in only one transfer at a time. The most common objective has been to minimize the makespan, which is the time when we finish all the migrations. The problem is NP-hard, and we develop polynomial time algorithms with constant factor approximation guarantees and several other heuristic algorithms. We present the performance evaluation of the different methods through an experimental study. We also consider the data migration problem to minimize the sum of completion times over all migration jobs or storage devices. Minimizing the sum of completion times of jobs is one of the most common objectives in scheduling literature. On the other hand, since a storage device may run inefficiently while the device is involved in migrations, another interesting objective is to minimize the sum of completion times over all storage devices. We present hardness results and constant factor approximation algorithms for these objectives. In addition, we consider the case when we have a heterogeneous collection of machines. We assume that heterogeneity is modeled by a non-uniform speed of the sending machine. For the basic problem of multicasting and broadcasting in the model, we show that Fastest Node First scheme gives a approximation ratio of 1.5 for minimizing the makespan. We also prove that there is a polynomial time approximation scheme

An optimal control approach to cell tracking

Cell tracking is of vital importance in many biological studies, hence robust cell tracking algorithms are needed for inference of dynamic features from (static) in vivo and in vitro experimental imaging data of cells migrating. In recent years much attention has been focused on the modelling of cell motility from physical principles and the development of state-of-the art numerical methods for the simulation of the model equations. Despite this, the vast majority of cell tracking algorithms proposed to date focus solely on the imaging data itself and do not attempt to incorporate any physical knowledge on cell migration into the tracking procedure. In this study, we present a mathematical approach for cell tracking, in which we formulate the cell tracking problem as an inverse problem for fitting a mathematical model for cell motility to experimental imaging data. The novelty of this approach is that the physics underlying the model for cell migration is encoded in the tracking algorithm. To illustrate this we focus on an example of Zebrafish (Danio rerio's larvae) Neutrophil migration and contrast an ad-hoc approach to cell tracking based on interpolation with the model fitting approach we propose in this study

A multi-method approach to delineate and validate migratory corridors

Context: Managers are faced with numerous methods for delineating wildlife movement corridors, and often must make decisions with limited data. Delineated corridors should be robust to different data and models. Objectives: We present a multi-method approach for delineating and validating wildlife corridors using multiple data sources, which can be used conserve landscape connectivity. We used this approach to delineate and validate migration corridors for wildebeest (Connochaetes taurinus) in the Tarangire Ecosystem of northern Tanzania. Methods: We used two types of locational data (distance sampling detections and GPS collar locations), and three modeling methods (negative binomial regression, logistic regression, and Maxent), to generate resource selection functions (RSFs) and define resistance surfaces. We compared two corridor detection algorithms (cost-distance and circuit theory), to delineate corridors. We validated corridors by comparing random and wildebeest locations that fell within corridors, and cross-validated by data type. Results: Both data types produced similar RSFs. Wildebeest consistently selected migration habitat in flatter terrain farther from human settlements. Validation indicated three of the combinations of data type, modeling, and corridor detection algorithms (detection data with Maxent modeling, GPS collar data with logistic regression modeling, and GPS collar data with Maxent modeling, all using cost-distance) far outperformed the other seven. We merged the predictive corridors from these three data-method combinations to reveal habitat with highest probability of use. Conclusions: The use of multiple methods ensures that planning is able to prioritize conservation of migration corridors based on all available information

A Multiple Migration and Stacking Algorithm Designed for Land Mine Detection

This paper describes a modification to a standard migration algorithm for land mine detection with a ground-penetrating radar (GPR) system. High directivity from the antenna requires a significantly large aperture in relation to the operating wavelength, but at the frequencies of operation of GPR, this would result in a large and impractical antenna. For operator convenience, most GPR antennas are small and exhibit low directivity and a wide beamwidth. This causes the GPR image to bear little resemblance to the actual target scattering centers. Migration algorithms attempt to reduce this effect by focusing the scattered energy from the source reflector and consequentially improve the target detection rate. However, problems occur due to the varying operational conditions, which result in the migration algorithm requiring vastly different calibration parameters. In order to combat this effect, this migration scheme stacks multiple versions of the same migrated data with different velocity values, whereas some other migration schemes only use a single velocity value