Mouse sperm membrane potential hyperpolarization is necessary and sufficient to prepare sperm for the acrosome reaction

Mammalian sperm are unable to fertilize the egg immediately after ejaculation; they acquire this capacity during migration in the female reproductive tract. This maturational process is called capacitation and in mouse sperm it involves a plasma membrane reorganization, extensive changes in the state of protein phosphorylation, increases in intracellular pH (pHi) and Ca2+ ([Ca2+]i), and the appearance of hyperactivated motility. In addition, mouse sperm capacitation is associated with the hyperpolarization of the cell membrane potential. However, the functional role of this process is not known. In this work, to dissect the role of this membrane potential change, hyperpolarization was induced in noncapacitated sperm using either the ENaC inhibitor amiloride, the CFTR agonist genistein or the K+ ionophore valinomycin. In this experimental setting, other capacitation-associated processes such as activation of a cAMP-dependent pathway and the consequent increase in protein tyrosine phosphorylation were not observed. However, hyperpolarization was sufficient to prepare sperm for the acrosome reaction induced either by depolarization with high K+ or by addition of solubilized zona pellucida (sZP). Moreover, K+ and sZP were also able to increase [Ca2+]i in non-capacitated sperm treated with these hyperpolarizing agents but not in untreated cells. On the other hand, in conditions that support capacitation-associated processes blocking hyperpolarization by adding valinomycin and increasing K+ concentrations inhibited the agonist-induced acrosome reaction as well as the increase in [Ca2+]i. Altogether, these results suggest that sperm hyperpolarization by itself is key to enabling mice sperm to undergo the acrosome reaction.Fil: de La Vega Beltrán, José Luis. Universidad Nacional Autónoma de México. Instituto de Biotecnología; MéxicoFil: Sánchez Cárdenas, Claudia. Universidad Nacional Autónoma de México. Instituto de Biotecnología; MéxicoFil: Krapf, Dario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; ArgentinaFil: Hernández González, Enrique. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados; MéxicoFil: Wertheimer Hermitte, Eva Victoria. University of Massachussets; Estados UnidosFil: Trevinio, Claudia L.. Universidad Nacional Autónoma de México. Instituto de Biotecnología; MéxicoFil: Visconti, Pablo E.. University of Massachussets; Estados UnidosFil: Darszon, Alberto. Universidad Nacional Autónoma de México. Instituto de Biotecnología; Méxic

Compartmentalization of distinct cAMP signaling pathways in mammalian sperm.

Fertilization competence is acquired in the female tract in a process known as capacitation. Capacitation is needed for the activation of motility (e.g. hyperactivation) and to prepare the sperm for an exocytotic process known as acrosome reaction. While the HCO3--dependent soluble adenylyl cyclase Adcy10 plays a role in motility, less is known about the source of cAMP in the sperm head. Transmembrane adenylyl cyclases (tmACs) are another possible source of cAMP. These enzymes are regulated by stimulatory heterotrimeric Gs proteins; however, the presence of Gs or tmACs in mammalian sperm has been controversial. In this manuscript, we used Western blotting and cholera toxin-dependent ADP ribosylation to show Gs presence in the sperm head. Also, we showed that forskolin, a tmAC specific activator, induces cAMP accumulation in sperm from both WT and Adcy10 null mice. This increase is blocked by the tmAC inhibitor SQ-22536 but not by the Adcy10 inhibitor KH7. While Gs immunoreactivity and tmAC activity are detected in the sperm head, PKA is only found in the tail, where Adcy10 was previously shown to reside. Consistent with an acrosomal localization, Gs reactivity is lost in acrosome reacted sperm, and forskolin is able to increase intracellular Ca2+ and induce the acrosome reaction. Altogether, these data suggest that cAMP pathways are compartmentalized in sperm, with Gs and tmAC in the head and Adcy10 and PKA in the flagellum.Fil: Wertheimer Hermitte, Eva Victoria. University Of Massachussets; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Centro de Estudios Farmacológicos y Botánicos; ArgentinaFil: Krapf, Dario. Universidad Nacional de Rosario; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Instituto de Biología Molecular y Celular de Rosario; ArgentinaFil: de la Vega Beltran, José L.. Universidad Nacional Autónoma de México; MéxicoFil: Sánchez Cárdenas, Claudia. Universidad Nacional Autónoma de México; MéxicoFil: Navarrete, Felipe. University Of Massachussets; Estados UnidosFil: Haddad, Douglas. University Of Massachussets; Estados UnidosFil: Escoffier, Jessica. University Of Massachussets; Estados UnidosFil: Salicioni, Ana M.. University Of Massachussets; Estados UnidosFil: Levin, Lonny R.. Cornell University; Estados UnidosFil: Buck, Jochen. Cornell University; Estados UnidosFil: Mager, Jesse. University Of Massachussets; Estados UnidosFil: Darszon, Alberto. Universidad Nacional Autónoma de México; MéxicoFil: Visconti, Pablo E.. University Of Massachussets; Estados Unido

Accident causation and pre-accidental driving situations: Part 1. Overview and general statistics

WP2 of the European Project TRACE is concerned with “Types of Situations” to analyse the causation of road traffic accidents from the pre-accidental driving situation point of view. Four complementary situations were defined: stabilized situations, intersection, specific manoeuvre and degradation scenario. To reach this objective, the analysis is based on a common methodology composed on 3 steps: the “descriptive analysis” which from general statistics will allow to identify among the studied situations those them relevant and to give their characteristics, the “in-depth analysis” allowing to obtain accident causes from the generic description of the problems identified in the previous step and the risk analysis identifying the risk of being involved in an accident taking into account the results obtained from the ‘in–depth’ level. This report is dedicated to the descriptive analysis with the identification of the most relevant scenario regarding the situation in which the driver is involved just prior the accident. The results are based on the literature review, general statistics and the analysis of the national databases available in TRACE via WP8. Because the information level differ from databases to another, the available scenario presented here for the 4 predefined types of situations are generics and some specific situations could not have be distinguished. For each situation some key indicators are given, such as prevalence, severity, KSI (killed x severely injured), etc. When it is possible, these indicators are estimated at the EU27 level

Accident causation and pre-accidental driving situations. Part 3. Summary report

This report aims to present the final results of the descriptive statistical, in-depth and risk analysis performed within TRACE Work Package ‘WP2-Type of situations’, in order to identify the main problems and the magnitude of these problems related to accident causation and risk factors for the following four types of situations: the stabilized situations, the specific manoeuvres, the intersection situations and the degraded situations. The different analysis (descriptive, in-depth and risk) of each of these five tasks has been performed using the available European accident databases within TRACE (national, in-depth and exposure databases). The objectives achieved in this WP are: • Identify and quantify accident causation factors associated to particular types of driving and pre-accidental situations, at a statistical level, by analyzing various available databases in Europe. • Obtain a focused understanding of accident causation issues related to these types of situations at an in-depth level by analyzing data from available in-depth databases. • Identify the level of risk associated to these selected types of situation in causing accidents

Accident causation and pre-accidental driving situations. Part 2. In-depth accident causation analysis

WP2 of the European Project TRACE is concerned with “Types of Situations” to analyse the causation of road traffic accidents from the pre-accidental driving situation point of view. Four complementary situations were defined: stabilized situations, intersection, specific manoeuvre and degradation scenario. To reach this objective, the analysis is based on a common methodology composed on 3 steps: the “descriptive analysis” which from general statistics will allow to identify among the studied situations those them relevant and to give their characteristics, the “in-depth analysis” allowing to obtain accident causes from the generic description of the problems identified in the previous step and the risk analysis identifying the risk of being involved in an accident taking into account the results obtained from the ‘in–depth’ level. This report is dedicated to the identification of the accident causes analysed for the pre-accidental driving situation point of view, i.e. the circumstances in which the driver is involved just prior the accident. This analysis has been conducted from the scenarios identified for each type of situation during the descriptive analysis realized in a first part (Report D2.1: Accident causation and pre-accidental driving situations. Part 1. Overview and general statistics). These results are based on the study of disaggregated data (in-depth accidents collection databases) available via WP8 in TRACE. With the identification of the main causes and contributing factor, the aspect related to the human functional failure has been taken into account. This innovative concept studied in TRACE WP5, has been used here in order to have a more complete overview of the problems in working on each road users involved in the accident and not only on the whole accident

Evaluation of the realism of an MRI simulator for stroke lesion prediction using convolutional neural network

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Identification of vehicle related risk factors, deliverable 6.1 of the H2020 project SafetyCube

The present Deliverable (D6.1) describes the identification and evaluation of vehicle related risk factors. It outlines the results of Task 6.1 of Work Package 6 (WP6) of SafetyCube, which aimed to identify and evaluate vehicle related risk factors and related road safety problems by (i) presenting a taxonomy of vehicle related risks, (ii) identifying “hot topics” of concern for relevant stakeholders and (iii) evaluating the relative importance for road safety outcomes (crash risk, crash frequency and severity etc.) within the scientific literature for each identified risk factor. To reach this objective, Task 6.1 has initially exploited current knowledge (e.g. existing studies) and existing accident data (macroscopic and in-depth) in order to quantify scenarios (defined in Work Package 8) related to the vehicle element. This information will help further on in WP6 to identify countermeasures for addressing these risk factors and finally to undertake an assessment of the effects of these countermeasures (...continues)

Description of data-sources used in SafetyCube. Deliverable 3.1 of the H2020 project SafetyCube

Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported Horizon 2020 project with the objective of developing an innovative road safety Decision Support System (DSS) that will enable policy-makers and stakeholders to select and implement the most appropriate strategies, measures and cost-effective approaches to reduce casualties of all road user types and all severities. This deliverable describes the available data in the form of an inventory of databases that can be used for analyses within the project. Two general types of data are available: one describing the involvement of different components for the road safety (vehicles, infrastructure, and the road user) and one describing the injury outcomes of a crash. These two database categories are available to the partners of SafetyCube and gathered in two excel tables. One table contains traffic databases (accident and naturalistic driving studies) and the second table contains injury databases. The tables contain information on 58 and 35 variables, respectively. The key information describing the databases that was needed for the inventory were items such as: Type of data collected (crashes, injuries, etc.) Documentation of the variables Sampling criteria for the data collected SafetyCube partners with access to the data Extent of data access (raw data vs. summary tables) The tables contain 36 traffic accident databases, five naturalistic driving studies or field-tests and 22 injury databases where of four were coded in both sheets