Chutes and Ladders: Dual Tracks and the Motherhood Dip

Using rich panel data recently available from Spanish Social Security records, we find that a negative motherhood earnings differential of 2.3 log points remains even after controlling for both individual- and firm-level unobserved heterogeneity. The analysis of the mothers and childless women's earnings trajectories over time reveals that "mothers to be" experience important earnings increases (of up to 6 log points) several years prior to giving birth to their first child. However, this earnings' advantage gets seriously hit right after birth, and it is not until nine years later that mothers' earnings return to their pre-birth (relative) levels. The study finds that heterogeneity matters as most of the motherhood penalty and earnings' dip is driven by mothers working in the primary labor market (with permanent contracts). For these women, much of the earnings losses occur because mothers change employers to work part-time, or (if they stay with their former employer) they take leave of absence. An instrumental variable approach is used to address concerns of selection into type of contract. We exploit variation in the amount, timing and profiling of subsidies offered to firms when hiring permanent workers, a policy that started to be implemented in Spain in 1997.earnings trajectories, individual- and firm-level fixed-effects estimator, permanent and fixed-term contracts, underlying channels

A bivalent live-attenuated influenza vaccine for the control and prevention of H3N8 and H3N2 canine influenza viruses

Canine influenza viruses (CIVs) cause a contagious respiratory disease in dogs. CIV subtypes include H3N8, which originated from the transfer of H3N8 equine influenza virus (EIV) to dogs; and the H3N2, which is an avian-origin virus adapted to infect dogs. Only inactivated influenza vaccines (IIVs) are currently available against the different CIV subtypes. However, the efficacy of these CIV IIVs is not optimal and improved vaccines are necessary for the efficient prevention of disease caused by CIVs in dogs. Since live-attenuated influenza vaccines (LAIVs) induce better immunogenicity and protection efficacy than IIVs, we have combined our previously described H3N8 and H3N2 CIV LAIVs to create a bivalent vaccine against both CIV subtypes. Our findings show that, in a mouse model of infection, the bivalent CIV LAIV is safe and able to induce, upon a single intranasal immunization, better protection than that induced by a bivalent CIV IIV against subsequent challenge with H3N8 or H3N2 CIVs. These protection results also correlated with the ability of the bivalent CIV LAIV to induce better humoral immune responses. This is the first description of a bivalent LAIV for the control and prevention of H3N8 and H3N2 CIV infections in dogs

Development of a novel equine influenza virus live-attenuated vaccine

H3N8 equine influenza virus (EIV) is an important and significant respiratory pathogen of horses. EIV is enzootic in Europe and North America, mainly due to the suboptimal efficacy of current vaccines. We describe, for the first time, the generation of a temperature sensitive (ts) H3N8 EIV live-attenuated influenza vaccine (LAIV) using reverse-genetics approaches. Our EIV LAIV was attenuated (att) in vivo and able to induce, upon a single intranasal administration, protection against H3N8 EIV wild-type (WT) challenge in both a mouse model and the natural host, the horse. Notably, since our EIV LAIV was generated using reverse genetics, the vaccine can be easily updated against drifting or emerging strains of EIV using the safety backbone of our EIV LAIV as master donor virus (MDV). These results demonstrate the feasibility of implementing a novel EIV LAIV approach for the prevention and control of currently circulating H3N8 EIVs in horse populations

Hybrid classical-quantum computing: are we forgetting the classical part in the binomial?

The expectations arising from the latest achievements in the quantum computing field are causing that researchers coming from classical artificial intelligence to be fascinated by this new paradigm. In turn, quantum computing, on the road towards usability, needs classical procedures. Hybridization is, in these circumstances, an indispensable step but can also be seen as a promising new avenue to get the most from both computational worlds. Nonetheless, hybrid approaches have now and will have in the future many challenges to face, which, if ignored, will threaten the viability or attractiveness of quantum computing for real-world applications. To identify them and pose pertinent questions, a proper characterization of the hybrid quantum computing field, and especially hybrid solvers, is compulsory. With this motivation in mind, the main purpose of this work is to propose a preliminary taxonomy for classifying hybrid schemes, and bring to the fore some questions to stir up researchers minds about the real challenges regarding the application of quantum computing.Comment: 2 pages, 1 figure, paper accepted for being presented in the upcoming IEEE International Conference on Quantum Computing and Engineering - IEEE QCE 202

A temperature sensitive live-attenuated canine influenza virus H3N8 vaccine

Canine influenza is a respiratory disease of dogs caused by canine influenza virus (CIV). CIV subtypes responsible for influenza in dogs include H3N8, which originated from the transfer of H3N8 equine influenza virus to dogs; and the H3N2 CIV, which is an avian-origin virus that adapted to infect dogs. Influenza infections are most effectively prevented through vaccination to reduce transmission and future infection. Currently, only inactivated influenza vaccines (IIVs) are available for the prevention of CIV in dogs. However, the efficacy of IIVs is suboptimal, and novel approaches are necessary for the prevention of disease caused by this canine respiratory pathogen. Using reverse genetics techniques, we have developed a live-attenuated CIV vaccine (LACIV) for the prevention of H3N8 CIV. The H3N8 LACIV replicates efficiently in canine cells at 33°C but is impaired at temperatures of 37 to 39°C and was attenuated compared to wild-type H3N8 CIV in vivo and ex vivo. The LACIV was able to induce protection against H3N8 CIV challenge with a single intranasal inoculation in mice. Immunogenicity and protection efficacy were better than that observed with a commercial CIV H3N8 IIV but provided limited cross-reactive immunity and heterologous protection against H3N2 CIV. These results demonstrate the feasibility of implementing a LAIV approach for the prevention and control of H3N8 CIV in dogs and suggest the need for a new LAIV for the control of H3N2 CIV. Importance: Two influenza A virus subtypes has been reported in dogs in the last 16 years: the canine influenza viruses (CIV) H3N8 and H3N2 of equine and avian origins, respectively. To date, only inactivated influenza vaccines (IIVs) are available to prevent CIV infections. Here, we report the generation of a recombinant, temperature-sensitive H3N8 CIV as a live-attenuated influenza vaccine (LAIV), which was attenuated in mice and dog tracheal, explants compared to CIV H3N8 wild type. A single dose of H3N8 LACIV showed immunogenicity and protection against a homologous challenge that was better than that conferred with an H3N8 IIV, demonstrating the feasibility of implementing a LAIV approach for the improved control of H3N8 CIV infections in dogs

Assessment and training in home-based telerehabilitation of arm mobility impairment

In an era where rehabilitation services are diminishing under the weight of the growing demands and fewer therapists, home-based telerehabilitation offers a way of increasing duration and intensity of post-stroke training. Novel systems that guide the therapist and patient in planning, executing, and assessing the training can reduce the burden on the healthcare system while maintaining or improving the quality of care. To achieve this effectively, a unified approach is needed that can address the diverse needs of the users and adequately assess the level of mobility deficits remotely. This document presents a methodology and prototype system for assessment and training adaptation within a telerehabilitation framework targeting home-based rehabilitation of the upper limbs after stroke. The framework uses 4 games for assessment of motor performance based on measures of range and control of movement. Assessment games include range of motion, range of force, control of motion, and control of force. The initial assessment games are used to tune the deficit-specific parameters in each successive game for assessment and training. Games are administered over the web-based TeleREHA platform through a novel arm rehabilitation device called the ArmAssist. An overview of the developments in each project is presented including the basic assessment parameters and a methodology for making patient-specific adaptation to game levels. Preliminary feedback from an ongoing usability evaluation is also presented and discussed

Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case

Disease diagnosis based on the detection of early symptoms is a usual threshold taken into account for integrated pest management strategies. Early phytosanitary treatment minimizes yield losses and increases the efficacy and efficiency of the treatments. However, the appearance of new diseases associated to new resistant crop variants complicates their early identification delaying the application of the appropriate corrective actions. The use of image based automated identification systems can leverage early detection of diseases among farmers and technicians but they perform poorly under real field conditions using mobile devices. A novel image processing algorithm based on candidate hot-spot detection in combination with statistical inference methods is proposed to tackle disease identification in wild conditions. This work analyses the performance of early identification of three European endemic wheat diseases – septoria, rust and tan spot. The analysis was done using 7 mobile devices and more than 3500 images captured in two pilot sites in Spain and Germany during 2014, 2015 and 2016. Obtained results reveal AuC (Area under the Receiver Operating Characteristic –ROC– Curve) metrics higher than 0.80 for all the analyzed diseases on the pilot tests under real conditions

How effectively (or not) can science and research be turned into adopted solutions and policies?

How to create an impact on policies, operations, and society across the interdisciplinary sectors in which we - as researchers - are involved? Managing the Water-Energy-Food-Ecosystem (WEFE) nexus and pursuing climate resilience is the core task of several European (EU) projects and is in the highest interests of our society. The European Commission’s research funding programs attempt to address a large range of topics and offer unique opportunities for scientists to create a tangible impact on the environment and society. We are currently involved in different EU projects, including AWESOME (PRIMA), which aims at managing the WEFE nexus across sectors and scales in the South Mediterranean exploring innovative technologies such as soilless agriculture in the Nile Delta; CLINT (H2020), which is developing Machine Learning (ML) techniques to improve climate science in the detection, causation, and attribution of extreme events to advance climate services; IMPETUS (H2020), whose efforts are dedicated on the elaboration of climate data space enhanced with ML algorithms to support the elaboration of climate policies; REACT4MED (PRIMA), which focuses on combating land degradation and desertification by improving sustainable land and water management through the identification of local good restoration practices and their potential upscaling; Gaza H2.0: Innovation and water efficiency (EuropeAid), which aims at promoting efficient and sustainable water supply and demand as well as knowledge transfer to enhance resilience against water scarcity in Gaza; GoNEXUS (H2020), which is developing an evaluation framework to design and assess innovative solutions for an efficient and sustainable coordinated governance of the WEFE nexus; NexusNet (COST), which creates the network and the community of WEF nexus advocates for a low-carbon economy in Europe and beyond; NEXOGENESIS (H2020), which focuses on streamlining water-related policies with artificial intelligence and reinforcement learning; MAGO (PRIMA), which builds web applications for water and agriculture in the Mediterranean; BIONEXT (HEU), which is interlinked with the Intergovernmental Panel on Biodiversity and Ecosystem Services and aims at creating transformative change through nexus analysis. Despite the efforts of the scientific community, there is still a gap between research and practice. Researchers face difficulties in engaging stakeholders and decision-makers to jointly explore and shape the developed solutions, as well as to truly adopt them. The large-scale implementation of suitable technological solutions might require time and financial resources beyond the project’s lifetime and capacity. The lack of follow-ups and collaboration among projects with similar aims can be some of the reasons lying behind. Also, the complexity of finding open data in data-scarce regions makes results less trustable in the eyes of international agencies, while the pressure of publishing often turns research tasks into pure academic exercises. To what extent does the European strategy work? Is it only gaining scientific advances or also leading to local policy changes? Engaging important local actors (e.g., ministries), small-medium enterprises and societal members in the project consortia, empowering scientists by ensuring feedback loops with local governmental agencies, including the human dimension into modelling, and running effective capacity-building campaigns can be some food for thoughts to shape new strategies

SAVASA project @ TRECVID 2012: interactive surveillance event detection

In this paper we describe our participation in the interactive surveillance event detection task at TRECVid 2012. The system we developed was comprised of individual classifiers brought together behind a simple video search interface that enabled users to select relevant segments based on down~sampled animated gifs. Two types of user -- `experts' and `end users' -- performed the evaluations. Due to time constraints we focussed on three events -- ObjectPut, PersonRuns and Pointing -- and two of the five available cameras (1 and 3). Results from the interactive runs as well as discussion of the performance of the underlying retrospective classifiers are presented