Computational methods for investigating cell motility with applications to neutrophil cell migration

Cell motility is closely linked to many important physiological and pathological events such as the immune response, wound healing, tissue differentiation, embryogenesis, in ammation, tumour invasion and metastasis. Understanding the ability of cells to alter their shape, deform and migrate is of vital importance in many biological studies. The rapid development in microscopy and imaging techniques has generated a huge amount of discrete data on migrating cells in vivo and in vitro. A key challenge is the use of discrete experimental observations to develop novel methods and algorithms that track cells and construct continuous trajectories of their motion as well as characterising key geometric quantities associated with cell migration. Therefore, in this work using robust numerical tools we focus on proposing and implementing mathematical methodologies for cell movement and apply them to model neutrophil cell migration. We derive and implement a computational framework that encompasses modelling of cell motility and cell tracking based on phase field and optimal control theory. The cell membrane is represented by an evolving curve and approximated by a diffuse interface; while the motion of the cell is driven by a force balance acting normal on the cell membrane. This approach allows us to characterise the locus of the centroid cell-surface position. In addition, we describe a surface partial differential equation framework that can be coupled with the phase-field framework, thereby offering a wholistic approach for modelling biochemical processes and biomechanics properties associated with cell migration

Understanding Electricity-Theft Behavior via Multi-Source Data

Electricity theft, the behavior that involves users conducting illegal operations on electrical meters to avoid individual electricity bills, is a common phenomenon in the developing countries. Considering its harmfulness to both power grids and the public, several mechanized methods have been developed to automatically recognize electricity-theft behaviors. However, these methods, which mainly assess users' electricity usage records, can be insufficient due to the diversity of theft tactics and the irregularity of user behaviors. In this paper, we propose to recognize electricity-theft behavior via multi-source data. In addition to users' electricity usage records, we analyze user behaviors by means of regional factors (non-technical loss) and climatic factors (temperature) in the corresponding transformer area. By conducting analytical experiments, we unearth several interesting patterns: for instance, electricity thieves are likely to consume much more electrical power than normal users, especially under extremely high or low temperatures. Motivated by these empirical observations, we further design a novel hierarchical framework for identifying electricity thieves. Experimental results based on a real-world dataset demonstrate that our proposed model can achieve the best performance in electricity-theft detection (e.g., at least +3.0% in terms of F0.5) compared with several baselines. Last but not least, our work has been applied by the State Grid of China and used to successfully catch electricity thieves in Hangzhou with a precision of 15% (an improvement form 0% attained by several other models the company employed) during monthly on-site investigation.Comment: 11 pages, 8 figures, WWW'20 full pape

Suppression of a Prolyl 4 Hydroxylase Results in Delayed Abscission of Overripe Tomato Fruits

The tomato pedicel abscission zone (AZ) is considered a model system for flower and fruit abscission development, activation, and progression. O-glycosylated proteins such as the Arabidopsis IDA (INFLORESCENCE DEFICIENT IN ABSCISSION) peptide and Arabinogalactan proteins (AGPs) which undergo proline hydroxylation were demonstrated to participate in abscission regulation. Considering that the frequency of occurrence of proline hydroxylation might determine the structure as well the function of such proteins, the expression of a tomato prolyl 4 hydroxylase, SlP4H3 (Solanum lycopersicum Prolyl 4 Hydroxylase 3) was suppressed in order to investigate the physiological significance of this post-translational modification in tomato abscission. Silencing of SlP4H3 resulted in the delay of abscission progression in overripe tomato fruits 90 days after the breaker stage. The cause of this delay was attributed to the downregulation of the expression of cell wall hydrolases such as SlTAPGs (tomato abscission polygalacturonases) and cellulases as well as expansins. In addition, minor changes were observed in the mRNA levels of two SlAGPs and one extensin. Moreover, structural changes were observed in the silenced SlP4H3AZs. The fracture plane of the AZ was curved and not along a line as in wild type and there was a lack of lignin deposition in the AZs of overripe fruits 30 days after breaker. These results suggest that proline hydroxylation might play a role in the regulation of tomato pedicel abscission

Acción : diario de Teruel y su provincia: Año III Número 633 - (11/12/34)

New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.</p

One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques

In recent years, demand for electric energy has steadily increased; therefore, the integration of renewable energy sources (RES) at a large scale into power systems is a major concern. Wind and solar energy are among the most widely used alternative sources of energy. However, there is intense variability both in solar irradiation and even more in windspeed, which causes solar and wind power generation to fluctuate highly. As a result, the penetration of RES technologies into electricity networks is a difficult task. Therefore, more accurate solar irradiation and windspeed one-day-ahead forecasting is crucial for safe and reliable operation of electrical systems, the management of RES power plants, and the supply of high-quality electric power at the lowest possible cost. Clouds’ influence on solar irradiation forecasting, data categorization per month for successive years due to the similarity of patterns of solar irradiation per month during the year, and relative seasonal similarity of windspeed patterns have not been taken into consideration in previous work. In this study, three deep learning techniques, i.e., multi-head CNN, multi-channel CNN, and encoder–decoder LSTM, were adopted for medium-term windspeed and solar irradiance forecasting based on a real-time measurement dataset and were compared with two well-known conventional methods, i.e., RegARMA and NARX. Utilization of a walk-forward validation forecast strategy was combined, firstly with a recursive multistep forecast strategy and secondly with a multiple-output forecast strategy, using a specific cloud index introduced for the first time. Moreover, the similarity of patterns of solar irradiation per month during the year and the relative seasonal similarity of windspeed patterns in a timeseries measurements dataset for several successive years demonstrates that they contribute to very high one-day-ahead windspeed and solar irradiation forecasting performance

Modeling Vehicles to Grid as a Source of Distributed Frequency Regulation in Isolated Grids with Significant RES Penetration

The rapid development of technology used in electric vehicles, and in particular their penetration in electricity networks, is a major challenge for the area of electric power systems. The utilization of battery capacity of the interconnected vehicles can bring significant benefits to the network via the Vehicle to Grid (V2G) operation. The V2G operation is a process that can provide primary frequency regulation services in the electric network by exploiting the total capacity of a fleet of electric vehicles. In this paper, the impact of the plug-in hybrid electric vehicles (PHEVs) in the primary frequency regulation is studied and the effects PHEVs cause in non-interconnected isolated power systems with significant renewable energy sources (RES) penetration. Also it is taken into consideration the requirements of users for charging their vehicles. The V2G operation can be performed either with fluctuations in charging power of vehicles, or by charging or discharging the battery. So an electric vehicle user can participate in V2G operation either during the loading of the vehicle to the charging station, or by connecting the vehicle in the charging station without any further demands to charge its battery. In this paper, the response of PHEVs with respect to the frequency fluctuations of the network is modeled and simulated. Additionally, by using the PowerWorld Simulator software, simulations of the isolated power system of Cyprus Island, including the current RES penetration are performed in order to demonstrate the effectiveness of V2G operation in its primary frequency regulation