Modeling circadian clock-cell cycle interaction effects on cell population growth rates

Accepted for publication in Journal of Theoretical Biology, post-print versionInternational audienceThe circadian clock and the cell cycle are two tightly coupled oscillators. Recent analytical studies have shown counter-intuitive effects of circadian gating of the cell cycle on growth rates of proliferating cells which cannot be explained by a molecular model or a population model alone. In this work, we present a combined molecular-population model that studies how coupling the circadian clock to the cell cycle, through the protein WEE1, affects a proliferating cell population. We show that the cell cycle can entrain to the circadian clock with different rational period ratios and characterize multiple domains of entrainment. We show that coupling increases the growth rate for autonomous periods of the cell cycle around 24 h and above 48 h. We study the effect of mutation of circadian genes on the growth rate of cells and show that disruption of the circadian clock can lead to abnormal proliferation. Particularly, we show that $\mathit{Cry1}$, $\mathit{Cry2}$ mutations decrease the growth rate of cells, $\mathit{Per2}$ mutation enhances it and $\mathit{Bmal1}$ knockout increases it for autonomous periods of the cell cycle less than 21 h and decreases it elsewhere. Combining a molecular model to a population model offers new insight on the influence of the circadian clock on the growth of a cell population. This can help chronotherapy which takes benefits of physiological rhythms to improve anti-cancer efficacy and tolerance to drugs by administering treatments at a specific time of the day

Investigation of Layer Based Thermal Behavior in Fused Deposition Modeling Process by Infrared Thermography

There are numerous research efforts that address the monitoring and control of additive manufacturing (AM) processes to improve part quality. Much less research exists on process monitoring and control of Fused Deposition Modeling (FDM). FDM is inherently a thermal process and thus, lends itself to being study by thermography. In this regard, there are various process parameters or process signatures such as built-bed temperature, temperature mapping of parts during deposition of layers, and the nozzle extrusion temperature that may monitor to optimize the quality of fabricated parts. In this work, we applied image based thermography layer by layer with the usage of an infrared camera to investigate the thermal behavior and thermal evolution of the FDM process for the standard samples printed by ABS filament. The combination of the layer based temperature profile plot and the temporal plot has been utilized to understand the temperature distribution and average temperature through the layers under fabrication. This information provides insights for potential modification of the scan strategy and optimization of process parameters in future research, based on the thermal evolution. Accordingly, this can reduce some frequent defects which have roots in thermal characteristics of the deposited layers and also, improve the surface quality and/or mechanical properties of the fabricated parts. In addition, this approach for monitoring the process will allow manufacturers to build, qualify, and certify parts with greater throughput and accelerate the proliferation of products into high-quality applications

Nota Editorial

Volume 53(1) of the journal Publicaciones (University of Granada) is the first regular issue corresponding to the year 2023. This journal increases year after year its impact factor in the prestigious databases JCR (Journal Citation Report) of the Web of Science and SJR (SCImago Journal Rank) of Scopus, which is possible thanks to the great work carried out by the large team of people linked to Publicaciones, the institutional and financial support of the institutions that sponsor its regular publication (Department of Education, Culture, Festivities and Equality of the Autonomous City of Melilla, Vice-Rectorate for Research and Transfer of the University of Granada and the Faculty of Education and Sport Sciences of Melilla) and the trust of the authors to publish in this journal.El volumen 53(1) de la revista Publicaciones (Universidad de Granada) supone el primer número ordinario correspondiente al año 2023. Esta revista incrementa año tras año su factor de impacto en las prestigiosas bases de datos JCR (Journal Citation Report) de la Web of Science y SJR (SCImago Journal Rank) de Scopus, lo que es posible gracias al gran trabajo que realiza el amplio equipo de personas vinculado a Publicaciones, al apoyo institucional y económico de las instituciones que patrocinan su publicación regular (Consejería de Educación, Cultura, Festejos e Igualdad de la Ciudad Autónoma de Melilla, Vicerrectorado de Investigación y Transferencia de la Universidad de Granada y Facultad de Ciencias de la Educación y del Deporte de Melilla) y a la confianza de los autores para publicar en esta revista

From the Internet of Things to the web of things-enabling by sensing as-A service

© 2016 IEEE. Sensing as a Service (SenaaS) is emerging as a prominent element in the middleware linking together the Internet of Things (IoT) and the Web of Things (WoT) layers of future ubiquitous systems. An architecture framework is discussed in this paper whereby things are abstracted into services via embedded sensors which expose a thing as a service. The architecture acts as a blueprint to guide software architects realizing WoT applications. Web-enabled things are eventually appended into Web platforms such as Social Web platforms to drive data and services that are exposed by these things to interact with both other things and people, in order to materialize further the future social Web of Things. Research directions are discussed to illustrate the integration of SenaaS into the proposed WoT architectural framework

Federated Stochastic Gradient Langevin Dynamics

Publisher Copyright: © 2021 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021. All Rights Reserved.Stochastic gradient MCMC methods, such as stochastic gradient Langevin dynamics (SGLD), employ fast but noisy gradient estimates to enable large-scale posterior sampling. Although we can easily extend SGLD to distributed settings, it suffers from two issues when applied to federated non-IID data. First, the variance of these estimates increases significantly. Second, delaying communication causes the Markov chains to diverge from the true posterior even for very simple models. To alleviate both these problems, we propose conducive gradients, a simple mechanism that combines local likelihood approximations to correct gradient updates. Notably, conducive gradients are easy to compute, and since we only calculate the approximations once, they incur negligible overhead. We apply conducive gradients to distributed stochastic gradient Langevin dynamics (DSGLD) and call the resulting method federated stochastic gradient Langevin dynamics (FSGLD). We demonstrate that our approach can handle delayed communication rounds, converging to the target posterior in cases where DSGLD fails. We also show that FSGLD outperforms DSGLD for non-IID federated data with experiments on metric learning and neural networks.Peer reviewe

CrowdPower: A Novel Crowdsensing-as-a-Service Platform for Real-Time Incident Reporting

Crowdsensing using mobile phones is a novel addition to the Internet of Things applications suite. However, there are many challenges related to crowdsensing, including (1) the ability to manage a large number of mobile users with varying devices’ capabilities; (2) recruiting reliable users available in the location of interest at the right time; (3) handling various sensory data collected with different requirements and at different frequencies and scales; (4) brokering the relationship between data collectors and consumers in an efficient and scalable manner; and (5) automatically generating intelligence reports after processing the collected sensory data. No comprehensive end-to-end crowdsensing platform has been proposed despite a few attempts to address these challenges. In this work, we aim at filling this gap by proposing and describing the practical implementation of an end-to-end crowdsensing-as-a-service system dubbed CrowdPower. Our platform offers a standard interface for the management and brokerage of sensory data, enabling the transformation of raw sensory data into valuable smart city intelligence. Our solution includes a model for selecting participants for sensing campaigns based on the reliability and quality of sensors on users’ devices, then subsequently analysing the quality of the data provided using a clustering approach to predict user reputation and identify outliers. The platform also has an elaborate administration web portal developed to manage and visualize sensing activities. In addition to the architecture, design, and implementation of the backend platform capabilities, we also explain the creation of CrowdPower’s sensing mobile application that enables data collectors and consumers to participate in various sensing activities

A Novel Quality and Reliability-Based Approach for Participants\u27 Selection in Mobile Crowdsensing

© 2013 IEEE. With the advent of mobile crowdsensing, we now have the possibility of tapping into the sensing capabilities of smartphones carried by citizens every day for the collection of information and intelligence about cities and events. Finding the best group of crowdsensing participants that can satisfy a sensing task in terms of data types required, while satisfying the quality, time, and budget constraints is a complex problem. Indeed, the time-constrained and location-based nature of crowdsensing tasks, combined with participants\u27 mobility, render the task of participants\u27 selection, a difficult task. In this paper, we propose a comprehensive and practical mobile crowdsensing recruitment model that offers reliability and quality-based approach for selecting the most reliable group of participants able to provide the best quality possible for the required sensory data. In our model, we adopt a group-based approach for the selection, in which a group of participants (gathered into sites) collaborate to achieve the sensing task using the combined capabilities of their smartphones. Our model was implemented using MATLAB and configured using realistic inputs such as benchmarked sensors\u27 quality scores, most widely used phone brands in different countries, and sensory data types associated with various events. Extensive testing was conducted to study the impact of various parameters on participants\u27 selection and gain an understanding of the compromises involved when deploying such process in practical MCS environments. The results obtained are very promising and provide important insights into the different aspects impacting the quality and reliability of the process of mobile crowdsensing participants\u27 selection