An Integrated Multilevel Approach Unveils Complex Seed-Nanoparticle Interactions and Their Implications for Seed Priming.

Nanotechnology has the potential to revolutionize agriculture with the introduction of engineered nanomaterials. However, their use is hindered by high cost, marginal knowledge of their interactions with plants, and unpredictable effects related to massive use in crop cultivation. Nanopriming is an innovative seed priming technology able to match economic, agronomic, and environmental needs in agriculture. The present study was focused on unveiling, by a multilevel integrated approach, undisclosed aspects of seed priming mediated by iron oxide magnetic nanoparticles in pepper seeds (Capsicum annuum), one of the most economically important crops worldwide. Inductively coupled plasma atomic emission mass spectrometry and scanning electron microscopy were used to quantify the MNP uptake and assess seed surface changes. Magnetic resonance imaging mapped the distribution of MNPs prevalently in the seed coat. The application of MNPs significantly enhanced the root and vegetative growth of pepper plants, whereas seed priming with equivalent Fe concentrations supplied as FeCl3 did not yield these positive effects. Finally, global gene expression by RNA-sequencing identified more than 2,200 differentially expressed genes, most of them involved in plant developmental processes and defense mechanisms. Collectively, these data provide evidence on the link between structural seed changes and an extensive transcriptional reprogramming, which boosts the plant growth and primes the embryo to cope with environmental challenges that might occur during the subsequent developmental and growth stages.

A Simple and Versatile Strategy for Oriented Immobilization of His-Tagged Proteins on Magnetic Nanoparticles.

Oriented and covalent immobilization of proteins on magnetic nanoparticles (MNPs) is particularly challenging as it requires both the functionality of the protein and the colloidal stability of the MNPs to be preserved. Here, we describe a simple, straightforward, and efficient strategy for MNP functionalization with proteins using metal affinity binding. Our method involves a single-step process where MNPs are functionalized using a preformed, ready-to-use nitrilotriacetic acid-divalent metal cation (NTA-M2+) complex and polyethylene glycol (PEG) molecules. As a proof-of-concept, we demonstrate the oriented immobilization of a recombinant cadherin fragment engineered with a hexahistidine tag (6His-tag) onto the MNPs. Our developed methodology is simple and direct, enabling the oriented bioconjugation of His-tagged cadherins to MNPs while preserving protein functionality and the colloidal stability of the MNPs, and could be extended to other proteins expressing a polyhistidine tag. When compared to the traditional method where NTA is first conjugated to the MNPs and afterward free metal ions are added to form the complex, this novel strategy results in a higher functionalization efficiency while avoiding MNP aggregation. Additionally, our method allows for covalent bonding of the cadherin fragments to the MNP surface while preserving functionality, making it highly versatile. Finally, our strategy not only ensures the correct orientation of the protein fragments on the MNPs but also allows for the precise control of their density. This feature enables the selective targeting of E-cadherin-expressing cells only when MNPs are decorated with a high density of cadherin fragments.

Fuzzy Ontology-Based System for Driver Behavior Classification.

Intelligent transportation systems encompass a series of technologies and applications that exchange information to improve road traffic and avoid accidents. According to statistics, some studies argue that human mistakes cause most road accidents worldwide. For this reason, it is essential to model driver behavior to improve road safety. This paper presents a Fuzzy Rule-Based System for driver classification into different profiles considering their behavior. The system's knowledge base includes an ontology and a set of driving rules. The ontology models the main entities related to driver behavior and their relationships with the traffic environment. The driving rules help the inference system to make decisions in different situations according to traffic regulations. The classification system has been integrated on an intelligent transportation architecture. Considering the user's driving style, the driving assistance system sends them recommendations, such as adjusting speed or choosing alternative routes, allowing them to prevent or mitigate negative transportation events, such as road crashes or traffic jams. We carry out a set of experiments in order to test the expressiveness of the ontology along with the effectiveness of the overall classification system in different simulated traffic situations. The results of the experiments show that the ontology is expressive enough to model the knowledge of the proposed traffic scenarios, with an F1 score of 0.9. In addition, the system allows proper classification of the drivers' behavior, with an F1 score of 0.84, outperforming Random Forest and Naive Bayes classifiers. In the simulation experiments, we observe that most of the drivers who are recommended an alternative route experience an average time gain of 66.4%, showing the utility of the proposal.

Magnetogenetics: remote activation of cellular functions triggered by magnetic switches.

During the last decade, the possibility to remotely control intracellular pathways using physical tools has opened the way to novel and exciting applications, both in basic research and clinical applications. Indeed, the use of physical and non-invasive stimuli such as light, electricity or magnetic fields offers the possibility of manipulating biological processes with spatial and temporal resolution in a remote fashion. The use of magnetic fields is especially appealing for in vivo applications because they can penetrate deep into tissues, as opposed to light. In combination with magnetic actuators they are emerging as a new instrument to precisely manipulate biological functions. This approach, coined as magnetogenetics, provides an exclusive tool to study how cells transform mechanical stimuli into biochemical signalling and offers the possibility of activating intracellular pathways connected to temperature-sensitive proteins. In this review we provide a critical overview of the recent developments in the field of magnetogenetics. We discuss general topics regarding the three main components for magnetic field-based actuation: the magnetic fields, the magnetic actuators and the cellular targets. We first introduce the main approaches in which the magnetic field can be used to manipulate the magnetic actuators, together with the most commonly used magnetic field configurations and the physicochemical parameters that can critically influence the magnetic properties of the actuators. Thereafter, we discuss relevant examples of magneto-mechanical and magneto-thermal stimulation, used to control stem cell fate, to activate neuronal functions, or to stimulate apoptotic pathways, among others. Finally, although magnetogenetics has raised high expectations from the research community, to date there are still many obstacles to be overcome in order for it to become a real alternative to optogenetics for instance. We discuss some controversial aspects related to the insufficient elucidation of the mechanisms of action of some magnetogenetics constructs and approaches, providing our opinion on important challenges in the field and possible directions for the upcoming years.

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management.

One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO) traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS) Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network.

Ontology-Based Architecture for Intelligent Transportation Systems Using a Traffic Sensor Network.

Intelligent transportation systems are a set of technological solutions used to improve the performance and safety of road transportation. A crucial element for the success of these systems is the exchange of information, not only between vehicles, but also among other components in the road infrastructure through different applications. One of the most important information sources in this kind of systems is sensors. Sensors can be within vehicles or as part of the infrastructure, such as bridges, roads or traffic signs. Sensors can provide information related to weather conditions and traffic situation, which is useful to improve the driving process. To facilitate the exchange of information between the different applications that use sensor data, a common framework of knowledge is needed to allow interoperability. In this paper an ontology-driven architecture to improve the driving environment through a traffic sensor network is proposed. The system performs different tasks automatically to increase driver safety and comfort using the information provided by the sensors.

Ontology alignment architecture for semantic sensor Web integration.

Sensor networks are a concept that has become very popular in data acquisition and processing for multiple applications in different fields such as industrial, medicine, home automation, environmental detection, etc. Today, with the proliferation of small communication devices with sensors that collect environmental data, semantic Web technologies are becoming closely related with sensor networks. The linking of elements from Semantic Web technologies with sensor networks has been called Semantic Sensor Web and has among its main features the use of ontologies. One of the key challenges of using ontologies in sensor networks is to provide mechanisms to integrate and exchange knowledge from heterogeneous sources (that is, dealing with semantic heterogeneity). Ontology alignment is the process of bringing ontologies into mutual agreement by the automatic discovery of mappings between related concepts. This paper presents a system for ontology alignment in the Semantic Sensor Web which uses fuzzy logic techniques to combine similarity measures between entities of different ontologies. The proposed approach focuses on two key elements: the terminological similarity, which takes into account the linguistic and semantic information of the context of the entity's names, and the structural similarity, based on both the internal and relational structure of the concepts. This work has been validated using sensor network ontologies and the Ontology Alignment Evaluation Initiative (OAEI) tests. The results show that the proposed techniques outperform previous approaches in terms of precision and recall.

Caracterización de la cirugía mayor ambulatoria en el Centro Médico Diagnóstico Integral de Pampán, Venezuela / Characterization of ambulatory surgery at the Medical and diagnostic Center Pampán, Venezuela

Se realiza un estudio con el objetivo de caracterizar la cirugía mayor ambulatoria en el Centro de Diagnóstico Integral Rafael Antonio Pérez Ruedas del municipio Pampán, Venezuela, en el período comprendido entre febrero y octubre de 2011. Incluye los 115 pacientes intervenidos quirúrgicamente en este período de tiempo y que dieron su consentimiento a participar en el estudio. Se estudian las variables: edad, sexo, tipo de intervención quirúrgica, método anestésico, evolución, complicaciones y estadía hospitalaria. El 73.25 por ciento de los pacientes son intervenidos quirúrgicamente de forma ambulatoria con un mínimo de complicaciones y una gran aceptación por parte de los mismos, quedando demostrado el bienestar psicosocial que representa para el paciente y sus familiares este método y las ventajas del mismo (AU)

a study is done to characterize the ambulatory surgery in the Integral Diagnostic Center Rafael Antonio Perez Pampán, Venezuela, from February to October 2011. Includes the 115 patients operated on during this period of time and who consented to participate in the study. Variables were studied: age, sex, type of surgery, anesthetic method, evolution, complications and hospital stay. The 73.25por ciento of the patients are operated on an ambulatory way with minimal complications and a large acceptance therefore, psychosocial well-being to the patient and family were demonstrated in the research besides methods and the advantages (AU)