Cytochrome c signalosome in mitochondria

Cytochrome c delicately tilts the balance between cell life (respiration) and cell death (apoptosis). Whereas cell life is governed by transient electron transfer interactions of cytochrome c inside the mitochondria, the cytoplasmic adducts of cytochrome c that lead to cell death are amazingly stable. Interestingly, the contacts of cytochrome c with its counterparts shift from the area surrounding the heme crevice for the redox complexes to the opposite molecule side when the electron flow is not necessary. The cytochrome c signalosome shows a higher level of regulation by post-translational modifications—nitration and phosphorylation—of the hemeprotein. Understanding protein interfaces, as well as protein modifications, would puzzle the mitochondrial cytochrome c-controlled pathways out and enable the design of novel drugs to silence the action of pro-survival and pro-apoptotic partners of cytochrome c.Spanish Ministry of Science and Innovation BFU2009-07190Andalusian Government BIO198 P08-CVI-387

Non-Query-Based Pattern Mining and Sentiment Analysis for Massive Microblogging Online Texts

Pattern mining has been widely studied in the last decade given its great interest for research and its numerous applications in the real world. In this paper the definition of query and non-query based systems is proposed, highlighting the needs of non-query based systems in the era of Big Data. For this, we propose a new approach of a non-query based system that combines association rules, generalized rules and sentiment analysis in order to catalogue and discover opinion patterns in the social network Twitter. Association rules have been previously applied for sentiment analysis, but in most cases, they are used once the process of sentiment analysis is finished to see which tokens appear commonly related to a certain sentiment. On the other hand, they have also been used to discover patterns between sentiments. Our work differs from these in that it proposes a non-query based system which combines both techniques, in a mixed proposal of sentiment analysis and association rules to discover patterns and sentiment patterns in microblogging texts. The obtained rules generalize and summarize the sentiments obtained from a group of tweets about any character, brand or product mentioned in them. To study the performance of the proposed system, an initial set of 1.7 million tweets have been employed to analyse the most salient sentiments during the American pre-election campaign. The analysis of the obtained results supports the capability of the system of obtaining association rules and patterns with great descriptive value in this use case. Parallelisms can be established in these patterns that match perfectly with real life events.COPKIT Project, through the European Union's Horizon 2020 Research and Innovation Programme 786687Spanish Ministry for Economy and Competitiveness TIN2015-64776-C3-1-RAndalusian Government, through Data Analysis in Medicine: from Medical Records to Big Data Project P18-RT-2947Spanish Ministry of Education, Culture, and Sport FPU18/00150University of Granad

NOFACE: A new framework for irrelevant content filtering in social media according to credibility and expertise

Social networks have taken an irreplaceable role in our lives. They are used daily by millions of people to communicate and inform themselves. This success has also led to a lot of irrelevant content and even misinformation on social media. In this paper, we propose a user-centred framework to reduce the amount of irrelevant content in social networks to support further stages of data mining processes. The system also helps in the reduction of misinformation in social networks, since it selects credible and reputable users. The system is based on the belief that if a user is credible then their content will be credible. Our proposal uses word embeddings in a first stage, to create a set of interesting users according to their expertise. After that, in a later stage, it employs social network metrics to further narrow down the relevant users according to their credibility in the network. To validate the framework, it has been tested with two real Big Data problems on Twitter. One related to COVID-19 tweets and the other to last United States elections on 3rd November. Both are problems in which finding relevant content may be difficult due to the large amount of data published during the last years. The proposed framework, called NOFACE, reduces the number of irrelevant users posting about the topic, taking only those that have a higher credibility, and thus giving interesting information about the selected topic. This entails a reduction of irrelevant information, mitigating therefore the presence of misinformation on a posterior data mining method application, improving the obtained results, as it is illustrated in the mentioned two topics using clustering, association rules and LDA techniques.European Commission 786687Andalusian government FEDER operative program P18-RT-2947 B-TIC-145-UGR18University of Granada's internal plan PPJIB2021-04Spanish Government FPU18/0015

Development of an Integrated Virtual Engine Model to Simulate New Standard Testing Cycles

[EN] The combination of more strict regulation for pollutant and CO2 emissions and the new testing cycles, covering a wider range of transient conditions, makes very interesting the development of predictive tools for engine design and pre-calibration. This paper describes a new integrated Virtual Engine Model (VEMOD) that has been developed as a standalone tool to simulate new standard testing cycles. The VEMOD is based on a wave-action model that carries out the thermo-and fluid dynamics calculation of the gas in each part of the engine. In the model, the engine is represented by means of 1D ducts, while the volumes, such as cylinders and reservoirs, are considered as 0D elements. Different sub-models are included in the VEMOD to take into account all the relevant phenomena. Thus, the combustion process is calculated by the Apparent Combustion Time (ACT) 1D model, responsible for the prediction of the rate of heat release and NOx formation. Experimental correlations are used to determine the rest of pollutants. In order to predict tailpipe pollutant emissions to the ambient, different sub-models have been developed to reproduce the behavior of the aftertreatment devices (DOC and DPF) placed in the exhaust system. Dedicated friction and auxiliaries sub-models allow obtaining the brake power. The turbocharger consists of 0D compressor and turbine sub-models capable of extrapolating the available maps of both devices. The VEMOD includes coolant and lubricant circuits linked, on the one hand, with the engine block and the turbocharger through heat transfer lumped models; and on the other hand with the engine heat exchangers. A control system emulating the ECU along with vehicle and driver sub-models allow completing the engine simulation. The Virtual Engine Model has been validated with experimental tests in a 1.6 L Diesel engine using steady and transient tests in both hot and cold conditions. Engine torque was predicted with a mean error of 3 Nm and an error below 14 Nm for 90 % of the cycle duration. CO2 presented a mean error of 0.04 g/s, while during 80 % of the cycle, error was below 0.44 g/s.This research has been partially funded by the European Union’s Horizon 2020 Framework Programme for research, technological development and demonstration under grant agreement 723976 (“DiePeR”) and by the Spanish government under the grant agreement TRA2017-89894-R. The authors wish to thank Renault SAS, especially P. Mallet and E. Gaïffas, for supporting this research.Martín Díaz, J.; Arnau Martínez, FJ.; Piqueras, P.; Auñón-García, Á. (2018). Development of an Integrated Virtual Engine Model to Simulate New Standard Testing Cycles. SAE Technical Papers. https://doi.org/10.4271/2018-01-1413

Li-Po Battery Charger Based on the Constant Current/Voltage Parallel Resonant Converter Operating in ZVS

Battery requirements for electrical vehicles are continuously becoming more demanding in terms of energy density and reliability. Nowadays, batteries for drones must be able to supply 100 A for 15 min, not to mention the specifications required for batteries in electrical vehicles. These specifications result in more stringent specifications for battery chargers. They are required to be more efficient, flexible, and, as with any another power equipment, to have reduced size and weight. Since the parallel resonant converter can operate as a current source and as a voltage source, this paper presents a battery charger power stage for lithium ion polymer batteries, based on the above topology, operating in zero voltage switching mode, and implementing frequency and duty cycle control

Study of the Rotary Bending Fatigue Resistance of 30MnB5, 41CrS4 and 30MnVS6 Steels

[Abstract] In this study, a comparative analysis of the fatigue behavior of four types of steels, three of quenching and tempering (30MnB5 subjected to two different heat treatments and 41CrS4) and one microalloyed (30MnVS6), was carried out. The objective of the study is to determine if it is feasible to replace the quenching and tempering steel traditionally used in the manufacture of commercial vehicle axles (30MnB5) with alternative ones with the same composition but with modifications in their microstructure that improve their mechanical properties; a quenched and tempered chromium steel (41CrS4) and one that is microalloyed (30MnVS6). For this, rotary-bending fatigue tests have been carried out on the four types of steels with different stress levels. The fatigue resistance of quenched and tempered steels and microalloyed steel was evaluated using the fit of Basquin’s experimental data. Where possible, the fatigue limit was determined using the maximum likelihood method. It was concluded that, in general, the fatigue resistance of chromium-alloyed steel is higher than that of the reference steel, while the rest have lower fatigue resistance. On the other hand, it was determined that the fatigue limit of microalloyed steel is higher than the reference one and that of the reference steel is higher than that of the other two steels

Efficient class-E power amplifier for variable load operation

In this paper, a GaN HEMT class-E power amplifier (PA) has been designed for efficiently operating under variable load resistance at the 750 MHz frequency band. The desired zero voltage switching (ZVS) of the device can be approximated for a wide range of resistive loads, by means of a simple inductive impedance inverter, derived from [1]. The loadpull contours, obtained from simulations, allowed the drain terminating network to be properly adjusted in order to maximize the output power control while at the same time minimizing losses. Once the amplifier was implemented, an efficiency over 76% has been measured at 9.6 dB power back-off, with a peak of 85% at 50 Ω. In addition, the efficiency stays as high as 75% for a 150 MHz frequency range.This work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO) through the TEC2014-58341-C4-1-R project with FEDER co-funding. David Vegas also thanks the support provided by the predoctoral grant BES-2015-072203

Pyrosequencing Analysis Reveals Changes in Intestinal Microbiota of Healthy Adults Who Received a Daily Dose of Immunomodulatory Probiotic Strains

The colon microbiota plays a crucial role in human gastrointestinal health. Current attempts to manipulate the colon microbiota composition are aimed at finding remedies for various diseases. We have recently described the immunomodulatory effects of three probiotic strains (Lactobacillus rhamnosus CNCM I-4036, Lactobacillus paracasei CNCM I-4034, and Bifidobacterium breve CNCM I-4035). The goal of the present study was to analyze the compositions of the fecal microbiota of healthy adults who received one of these strains using high-throughput 16S ribosomal RNA gene sequencing. Bacteroides was the most abundant genus in the groups that received L. rhamnosus CNCM I-4036 or L. paracasei CNCM I-4034. The Shannon indices were significantly increased in these two groups. Our results also revealed a significant increase in the Lactobacillus genus after the intervention with L. rhamnosus CNCM I-4036. The initially different colon microbiota became homogeneous in the subjects who received L. rhamnosus CNCM I-4036. While some orders that were initially present disappeared after the administration of L. rhamnosus CNCM I-4036, other orders, such as Sphingobacteriales, Nitrospirales, Desulfobacterales, Thiotrichales, and Synergistetes, were detected after the intervention. In summary, our results show that the intake of these three bacterial strains induced changes in the colon microbiota.This study utilized fecal samples from the clinical trial NCT01479543 that was supported by Hero Spain S. A. through contract #3582 with the Fundacion General Empresa Universidad de Granada. Carolina Gomez-Llorente is the recipient of a postdoctoral fellowship of the University of Granada

Spatio-temporal error growth in the multi-scale Lorenz’96 model

The influence of multiple spatio-temporal scales on the error growth and predictability of atmospheric flows is analyzed throughout the paper. To this aim, we consider the two-scale Lorenz’96 model and study the interplay of the slow and fast variables on the error growth dynamics. It is shown that when the coupling between slow and fast variables is weak the slow variables dominate the evolution of fluctuations whereas in the case of strong coupling the fast variables impose a non-trivial complex error growth pattern on the slow variables with two different regimes, before and after saturation of fast variables. This complex behavior is analyzed using the recently introduced Mean-Variance Logarithmic (MVL) diagram