ORIENTATE: automated machine learning classifiers for oral health prediction and research

© The Author(s) 2023. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/. This document is the Published version of a Published Work that appeared in final form in BMC Oral Health. To access the final edited and published work see https://doi.org/10.1186/s12903-023-03112-wBackground The application of data-driven methods is expected to play an increasingly important role in healthcare. However, a lack of personnel with the necessary skills to develop these models and interpret its output is preventing a wider adoption of these methods. To address this gap, we introduce and describe ORIENTATE, a software for automated application of machine learning classification algorithms by clinical practitioners lacking specific technical skills. ORIENTATE allows the selection of features and the target variable, then automatically generates a number of classification models and cross-validates them, finding the best model and evaluating it. It also implements a custom feature selection algorithm for systematic searches of the best combination of predictors for a given target variable. Finally, it outputs a comprehensive report with graphs that facilitates the explanation of the classification model results, using global interpretation methods, and an interface for the prediction of new input samples. Feature relevance and interaction plots provided by ORIENTATE allow to use it for statistical inference, which can replace and/or complement classical statistical studies. Results Its application to a dataset with healthy and special health care needs (SHCN) children, treated under deep sedation, was discussed as case study. On the example dataset, despite its small size, the feature selection algorithm found a set of features able to predict the need for a second sedation with a f1 score of 0.83 and a ROC (AUC) of 0.92. Eight predictive factors for both populations were found and ordered by the relevance assigned to them by the model. A discussion of how to derive inferences from the relevance and interaction plots and a comparison with a classical study is also provided. Conclusions ORIENTATE automatically finds suitable features and generates accurate classifiers which can be used in preventive tasks. In addition, researchers without specific skills on data methods can use it for the application of machine learning classification and as a complement to classical studies for inferential analysis of features. In the case study, a high prediction accuracy for a second sedation in SHCN children was achieved. The analysis of the relevance of the features showed that the number of teeth with pulpar treatments at the first sedation is a predictive factor for a second sedation

Simultaneous data rate and transmission power adaptation in V2V communications: A deep reinforcement learning approach

In Vehicle-to-Vehicle (V2V) communications, channel load is key to ensuring the appropriate operation of safety applications as well as driver-assistance systems. As the number of vehicles increases, so do their communication messages. Therefore, channel congestion may arise, negatively impacting channel performance. Through suitable adjustment of the data rate, this problem would be mitigated. However, this usually involves using different modulation schemes, which can jeopardize the robustness of the solution due to unfavorable channel conditions. To date, little effort has been made to adjust the data rate, alone or together with other parameters, and its effects on the aforementioned sensitive safety applications remain to be investigated. In this paper, we employ an analytical model which balances the data rate and transmission power in a non-cooperative scheme. In particular, we train a Deep Neural Network (DNN) to precisely optimize both parameters for each vehicle without using additional information from neighbors, and without requiring any additional infrastructure to be deployed on the road. The results obtained reveal that our approach, called NNDP, not only alleviates congestion, leaving a certain fraction of the channel available for emergency-related messages, but also provides enough transmission power to fulfill the application layer requirements at a given coverage distance. Finally, NNDP is thoroughly tested and evaluated in three realistic scenarios and under different channel conditions, demonstrating its robustness and excellent performance in comparison with other solutions found in the scientific literature.This work was supported in part by the AEI/FEDER/UE [Agencia Estatal de Investigación (AEI), Fondo Europeo de Desarrollo Regional (FEDER), and Unión Europea (UE)] under Grant PID2020-116329GB-C22 [ARISE2: Future IoT Networks and Nano-networks (FINe)] and Grant PID2020-112675RB-C41 (ONOFRE-3), in part by the Fundación Séneca, Región de Murcia, under Grant 20889/PI/18 (ATENTO), and in part by the LIFE project (Fondo SUPERA COVID-19 through the Agencia Estatal Consejo Superior de Investigaciones Científicas CSIC, Universidades Españolas, and Banco Santander). The work of Juan Aznar-Poveda was supported by the Spanish Ministerio de Educación, Cultura y Deporte (MECD) through the Formación de Personal Investigador (FPI) Predoctoral Scholarship under Grant BES-2017-08106

MDPRP: A Q-learning approach for the joint control of beaconing rate and transmission power in VANETs

Vehicular ad-hoc communications rely on periodic broadcast beacons as the basis for most of their safety applications, allowing vehicles to be aware of their surroundings. However, an excessive beaconing load might compromise the proper operation of these crucial applications, especially regarding the exchange of emergency messages. Therefore, congestion control can play an important role. In this article, we propose joint beaconing rate and transmission power control based on policy evaluation. To this end, a Markov Decision Process (MDP) is modeled by making a set of reasonable simplifying assumptions which are resolved using Q-learning techniques. This MDP characterization, denoted as MDPRP (indicating Rate and Power), leverages the trade-off between beaconing rate and transmission power allocation. Moreover, MDPRP operates in a non-cooperative and distributed fashion, without requiring additional information from neighbors, which makes it suitable for use in infrastructureless (ad-hoc) networks. The results obtained reveal that MDPRP not only balances the channel load successfully but also provides positive outcomes in terms of packet delivery ratio. Finally, the robustness of the solution is shown since the algorithm works well even in those cases where none of the assumptions made to derive the MDP model apply.This work was supported in part by the AIM Project [Agencia Estatal de Investigación (AEI)/Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea (UE)] under Grant TEC2016-76465-C2-1-R, in part by the Fundación Séneca, Región de Murcia, through the ATENTO Project, under Grant 20889/PI/18, and in part by the LIFE (Fondo SUPERA Covid-19 funded by the Agencia Estatal Consejo Superior de Investigaciones Científicas CSIC, Universidades Españolas, and Banco Santander). The work of Juan Aznar-Poveda was supported by the Spanish Ministerio de Educación, Cultura y Deporte (MECD) for the FPI Grant BES-2017-081061

Approximate reinforcement learning to control beaconing congestion in distributed networks

In vehicular communications, the increase of the channel load caused by excessive periodical messages (beacons) is an important aspect which must be controlled to ensure the appropriate operation of safety applications and driver-assistance systems. To date, the majority of congestion control solutions involve including additional information in the payload of the messages transmitted, which may jeopardize the appropriate operation of these control solutions when channel conditions are unfavorable, provoking packet losses. This study exploits the advantages of non-cooperative, distributed beaconing allocation, in which vehicles operate independently without requiring any costly road infrastructure. In particular, we formulate the beaconing rate control problem as a Markov Decision Process and solve it using approximate reinforcement learning to carry out optimal actions. Results obtained were compared with other traditional solutions, revealing that our approach, called SSFA, is able to keep a certain fraction of the channel capacity available, which guarantees the delivery of emergency-related notifications with faster convergence than other proposals. Moreover, good performance was obtained in terms of packet delivery and collision ratios.This research has been supported by the projects AIM, ref. TEC2016-76465-C2-1-R, ARISE2 “Future IoT Networks and Nano-networks (FINe)” ref. PID2020-116329GB-C22, ONOFRE-3, ref. PID2020-112675RB-C41 [Agencia Estatal de Investigación (AEI), European Regional Development Fund (FEDER), European Union (EU)], ATENTO, ref. 20889/PI/18 (Fundación Séneca, Región de Murcia), and LIFE [Fondo SUPERA Covid-19, funded by Agencia Estatal Consejo Superior de Investigaciones Científicas (CSIC), Universidades Españolas and Banco Santander]. J.A.P. thanks the Spanish MECD for an FPI grant ref. BES-2017-081061. Finally, the authors acknowledge Laura Wettersten for her contribution in reviewing the grammar and spell of the manuscript

Hydroponics as a valid tool to assess arsenic availability in mine soils

The low solubility of As in mine soils limits its phytoavailability. This makes the extrapolation of data obtained under hydroponic conditions unrealistic because the concentration in nutrient solution frequently overexposes plants to this metalloid. This work evaluates whether As supply in hydroponics resembles, to some extent, the As phytoavailable fraction in soils and the implications for phytoremediation. Phytotoxicity of As, in terms of biomass production, chlorophyll levels, and As concentrations in plants, was estimated and compared in both soils and hydroponics. In order for hydroponic conditions to be compared to soil conditions, plant exposure levels were measured in both cultures. Hydroponic As concentration ranging from 2-8 μM equated to the same plant organ concentrations from soils with 700-3000 mg kg-1. Total and extractable As fractions exceeded those values, but As concentrations in pore water were bellow them. According to our results (i) hydroponics should include doses in the range 0-10 μM As to allow the extrapolation of the results to As-polluted soils, and (ii) phytoextraction of As in mining sites will be limited by low As phytoavailabilityThis study was supported by the Spanish Ministry of Education and Science, project CTM 2007-66401-CO2/TECNO, and by Comunidad de Madrid, project S-0505/AMB/029

Urban crowdsensing by personal mobility vehicles to manage air pollution

The digitalization of cities and the development of smart, green, and integrated transport are societal challenges to transform cities into places with good quality of life now and in the future. The Internet of Things (IoT) comes with new advances to connect a multitude of sensing devices and even actuators, and they are presenting the cornerstone of Smart City deployments worldwide. So far, these advances have focused on static sensors in scenarios such as gardens, smart lighting, climate monitoring, or traffic management. However, moving sensors could rise the monitoring capabilities of smart cities to the next level, helping to better reflect the status of large areas without replicating fixed stations. This work proposes taking advantage of urban vehicles and, especially, personal mobility vehicles (PMVs), to implement such a perspective. Hence, a low-cost and energy-aware on-board unit (OBU) is designed to gather environmental data and support sustainable mobility applications. This on-board platform is provided with Low-Power Wide Area Network (LPWAN) communication technologies, enabling an Internet connection following an IoT scheme. The unit is equipped with sensors to measure air pollution in terms of NO2, CO, SO2, O3 and PMx, noise, and weather parameters. While moving across the city, PMVs mounting this device can collect data in a crowdsensing scheme. This data feed is complemented by a set of wireless traffic sensors, and they are subject to intelligent processing to monitor pollution and mobility parameters. For this, a back-end software module is powered with temporal series analysis to generate predictions based on tendencies detected in both pollution and mobility values. A front-end Web application has been implemented to show all past, current, and predicted data, offering functionalities to monitor urban mobility, minimize travel times, detect pollution areas, and recommend healthy routes across streets with low contamination levels.This work was supported by the grants PID2020-112675RBC41 (ONOFRE-3), funded by MCIN/AEI/10.13039/501100011033; RYC-2017-23823, funded by MCIN/AEI /10.13039/501100011033 and by “ESF Investing in your future”; PGE-MOVES-SING-2019-000104 (MECANO), funded by the Spanish MITECO; and H2020 957258 (ASSIST-IoT), funded by the European Commission

On the Optimal Identification of Tag Sets in Time-Constrained RFID Configurations

In Radio Frequency Identification facilities the identification delay of a set of tags is mainly caused by the random access nature of the reading protocol, yielding a random identification time of the set of tags. In this paper, the cumulative distribution function of the identification time is evaluated using a discrete time Markov chain for single-set time-constrained passive RFID systems, namely those ones where a single group of tags is assumed to be in the reading area and only for a bounded time (sojourn time) before leaving. In these scenarios some tags in a set may leave the reader coverage area unidentified. The probability of this event is obtained from the cumulative distribution function of the identification time as a function of the sojourn time. This result provides a suitable criterion to minimize the probability of losing tags. Besides, an identification strategy based on splitting the set of tags in smaller subsets is also considered. Results demonstrate that there are optimal splitting configurations that reduce the overall identification time while keeping the same probability of losing tags

Cyclin D1—Cdk4 regulates neuronal activity through phosphorylation of GABAA receptors

Nuclear Cyclin D1 (Ccnd1) is a main regulator of cell cycle progression and cell proliferation. Interestingly, Ccnd1 moves to the cytoplasm at the onset of differentiation in neuronal precursors. However, cytoplasmic functions and targets of Ccnd1 in post-mitotic neurons are unknown. Here we identify the α4 subunit of gamma-aminobutyric acid (GABA) type A receptors (GABARs) as an interactor and target of Ccnd1–Cdk4. Ccnd1 binds to an intracellular loop in α4 and, together with Cdk4, phosphorylates the α4 subunit at threonine 423 and serine 431. These modifications upregulate α4 surface levels, increasing the response of α4-containing GABARs, measured in whole-cell patch-clamp recordings. In agreement with this role of Ccnd1–Cdk4 in neuronal signalling, inhibition of Cdk4 or expression of the non-phosphorylatable α4 decreases synaptic and extra-synaptic currents in the hippocampus of newborn rats. Moreover, according to α4 functions in synaptic pruning, CCND1 knockout mice display an altered pattern of dendritic spines that is rescued by the phosphomimetic α4. Overall, our findings molecularly link Ccnd1–Cdk4 to GABARs activity in the central nervous system and highlight a novel role for this G cyclin in neuronal signalling.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was funded by the Spanish Ministry of Innovation and Science MICINN (PID2019-104859GBI00 to E.G. and PID2020-117651RB to J.A.E.) and by Generalitat de Catalunya (2017-SGR-569). M. Ventura Monserrat was supported by a predoctoral fellowship of University of Lleida

Procedimiento y sistema de identificación de elementos identificadores móviles y lector y elemento identificador móvil utilizados en dicho procedimiento

Número de publicación: 2 291 097 Número de solicitud: 200503124Procedimiento para identificar elementos identificadores móviles (504, 506) por un lector (500) que emite una señal de radio maestra dentro de un canal, comprendiendo el procedimiento las siguientes etapas: (a) el lector (500) interroga a los elementos identificadores (506) que pasan por el área de cobertura (502) durante un tiempo superior a un tiempo mínimo de paso, para que respondan durante un tiempo acotado de identificación, y (b) cada elemento identificador (506) envía su señal de identificación dentro de un intervalo de identificación del tiempo acotado de identificación. Se caracteriza por el hecho de que cada elemento identificador (506) sale de manera independiente de un estado dónde no observa el canal, cuya duración es inferior al tiempo mínimo de paso, para observar el canal.Universidad Politécnica de Cartagen

Epidemiological trends of HIV/HCV coinfection in Spain, 2015-2019

Altres ajuts: Spanish AIDS Research Network; European Funding for Regional Development (FEDER).Objectives: We assessed the prevalence of anti-hepatitis C virus (HCV) antibodies and active HCV infection (HCV-RNA-positive) in people living with HIV (PLWH) in Spain in 2019 and compared the results with those of four similar studies performed during 2015-2018. Methods: The study was performed in 41 centres. Sample size was estimated for an accuracy of 1%. Patients were selected by random sampling with proportional allocation. Results: The reference population comprised 41 973 PLWH, and the sample size was 1325. HCV serostatus was known in 1316 PLWH (99.3%), of whom 376 (28.6%) were HCV antibody (Ab)-positive (78.7% were prior injection drug users); 29 were HCV-RNA-positive (2.2%). Of the 29 HCV-RNA-positive PLWH, infection was chronic in 24, it was acute/recent in one, and it was of unknown duration in four. Cirrhosis was present in 71 (5.4%) PLWH overall, three (10.3%) HCV-RNA-positive patients and 68 (23.4%) of those who cleared HCV after anti-HCV therapy (p = 0.04). The prevalence of anti-HCV antibodies decreased steadily from 37.7% in 2015 to 28.6% in 2019 (p < 0.001); the prevalence of active HCV infection decreased from 22.1% in 2015 to 2.2% in 2019 (p < 0.001). Uptake of anti-HCV treatment increased from 53.9% in 2015 to 95.0% in 2019 (p < 0.001). Conclusions: In Spain, the prevalence of active HCV infection among PLWH at the end of 2019 was 2.2%, i.e. 90.0% lower than in 2015. Increased exposure to DAAs was probably the main reason for this sharp reduction. Despite the high coverage of treatment with direct-acting antiviral agents, HCV-related cirrhosis remains significant in this population