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Vision and Robotic technologies are progressively becoming ubiquitous for automating and digitizing quality control in the food industry. This paper examines the crucial role of advanced automation technologies, including versatile or dedicated robotic systems, specialized end-effectors, machine vision, and efficient material handling systems, which collectively enhance food processing efficiency. This paper aspires to provide a high-level technical review on the crucial role of advanced automation technologies, including versatile or dedicated robotic systems, specialized end-effectors, machine vision, and efficient material handling systems, which collectively enhance food processing efficiency. While the manuscript aims to document the various automation sub-systems utilized generally in food processing, it places a particular emphasis on the primary processing phase of food production. Most food products in the primary processing phase exhibit a plethora of complex physical properties and manipulation conditions, making it difficult to reliably automate the various processes. This research aims to outline the contemporary advances and requirements for integrating various automation technologies, to enhance the efficiency and precision of primary food processing. Furthermore, it aspires to serve as a valuable, up-to-date survey and analysis of the latest advances in automation and vision technologies and their capability to automate a food processing line.

The Robot Operating System (ROS) has significantly gained popularity among robotic engineers and researchers over the past five years, primarily due to its powerful infrastructure for node communication, which enables developers to build modular and large robotic applications. However, ROS presents a steep learning curve and lacks the intuitive usability of vendor-specific robotic Graphical User Interfaces (GUIs). Moreover, its modular and distributed nature complicates the control and monitoring of extensive systems, even for advanced users. To address these challenges, this paper introduces a novel, highly adaptable and reconfigurable web-based GUI for intuitively controlling, monitoring, and configuring complex ROS-based robotic systems. The GUI leverages ROSBridge and roslibjs to ensure seamless communication with ROS systems via topics and services. Special attention has been paid to enable the easy reconfiguration of the GUI and to minimize the modifications required to integrate it with new robotic systems. Thus, it has been designed as a versatile platform, which allows for the selective incorporation of modular features to accommodate diverse robotic systems and applications. A predefined template has been defined to ensure the compatibility of the developed features with the GUI platform. Additionally, an initial set of commonly used features in robotic applications is presented. To demonstrate its reconfigurability, the GUI was customized and tested for four industrial use cases, receiving positive feedback. The project’s repository has been made publicly available to support the robotics community and lower the entry barrier for ROS in industrial applications.

Photo-realistic avatar is a modern term referring to the digital asset that represents a human in computer graphic advanced systems such as video games and simulation tools. These avatars utilize the advances in graphic technologies in both software and hardware aspects. While photo-realistic avatars are increasingly used in industrial simulations, representing human factors such as human workers’ psychophysiological states, remains a challenge. This article contributes to resolving this issue by introducing the novel concept of MetaStates which are the digitization and representation of the psychophysiological states of a human worker in the digital world. The MetaStates influence the physical representation and performance of a digital human worker while performing a task. To demonstrate this concept, this study presents the development of a photo-realistic avatar enhanced with multi-level graphical representations of psychophysiological states relevant to Industry 5.0. This approach represents a major step forward in the use of digital humans for industrial simulations, allowing companies to better leverage the benefits of the Industrial Metaverse in their daily operations and simulations while keeping human workers at the center of the system.

The purpose of this study is to explore the measurement of human factors in the workplace that can provide critical insights into workers’ well-being. Human factors refer to physical, cognitive, and psychological states that can impact the efficiency, effectiveness, and mental health of workers. The article identifies six human factors that are particularly crucial in today’s workplaces: physical fatigue, attention, mental workload, stress, trust, and emotional state. Each of these factors alters the human physiological response in a unique way, affecting the human brain, cardiovascular, electrodermal, muscular, respiratory, and ocular reactions. This paper provides an overview of these human factors and their specific influence on psycho-physiological responses, along with suitable technologies to measure them in working environments and the currently available commercial solutions to do so. By understanding the importance of these human factors, employers can make informed decisions to create a better work environment that leads to improved worker well-being and productivity.

Three-phase four-wire systems require a reference frame similar to that used in 3-phase 3-wire system configurations that allow them to be easily analysed, modelled and controlled. A new reference frame called Reduced Reference Frame (RRF) was presented by the authors in a previous work to satisfy these objectives. This is based on the locus of the voltage space vector which is used to perform a transformation applied to 3-phase 4-wire systems. This paper uses the proposed RRF to demonstrate the benefits of this transformation under different conditions of load or voltage imbalance. To this end, the different types of imbalances considered are studied by comparing the results in the classical $\alpha \beta \gamma $ axes, the so-called $mno$ -transform specifically developed for 3-phase 4-wire systems and the RRF coordinates. In addition, a theoretical identification of some types of imbalances is done comparing the simulation results and the fundamentals of the RRF.

Abstract Knowledge graphs (KGs) are useful data structures for the integration, retrieval, dissemination, and inference of information in various information domains. One of the main challenges in building KGs is the extraction of named entities (nodes) and their relations (edges), particularly when processing unstructured text as it has no semantic descriptions. Generating KGs from texts written in Spanish represents a research challenge as the existing structures, models, and strategies designed for other languages are not compatible in this scenario. This paper proposes a method to design and construct KGs from unstructured text in Spanish. We defined lexical patterns to extract named entities and (non) taxonomic, equivalence, and composition relations. Next, named entities are linked and enriched with DBpedia resources through a strategy based on SPARQL queries. Finally, OWL properties are defined from the predicate relations for creating resource description framework (RDF) triples. We evaluated the performance of the proposed method to determine the degree of elements extracted from the input text and to assess their quality through standard information retrieval measures. The evaluation revealed the feasibility of the proposed method to extract RDF triples from datasets in general and computer science domains. Competitive results were observed by comparing our method regarding an existing approach from the literature.

Here the authors show that a Newcastle disease virus based COVID-19 vaccine expressing a stabilized spike protein induces protective immunity in small animal models and reduces replication of variants of concerns. This vaccine candidate can be produced by influenza virus vaccine manufactures around the world.

The movement of organisms facilitated by anthropogenic activities is a serious threat to marine diversity, especially for endemic species that may be outcompeted from non-indigenous species (NIS). In this study, we have analyzed communities inhabiting the north of the Gulf of Aqaba, Red Sea, employing environmental DNA (eDNA) metabarcoding. That gulf is especially rich in species and population endemism. We have detected NIS representing 36% of the total number of species found from eDNA. Primary producers were more abundant in the NIS than in the native fraction of species, suggesting that functional diversity could be altered if NIS thrive there. We discuss maritime traffic as a factor that may enhance the introduction of non-natives in this region and emphasize the importance of the control of these species that may threaten the rich endemic biota of the Red Sea.

We present a method to compress the 2D transverse phase space projections from a hadron accelerator and use that information to predict the beam transmission. This method assumes that obtaining at least three projections of the 4D transverse phase space is possible and that an accurate simulation model is available for the beamline. Using a simulated model, we show that—a computer can train a convolutional autoencoder to reduce phase-space information which can later be used to predict the beam transmission. Finally, we argue that although using projections from a realistic nonlinear distribution produces less accurate results, the method still generalizes well.

Sensitivities are broadly appreciated because of its simplicity and good results in solving voltage problems and congestions in transmission networks. However, since sensitivity calculation is based on a linear approximation of the power flow equations, all decisions commit an intrinsic error that might lead into unexpected results, especially in distribution networks. This paper analyzes the error associated to sensitivities considering a wide range of control variables and proposes a novel formulation to extend the use of sensitivities to manage congestions in active distribution networks. First, a theoretical analysis is performed to study the origin and propagation of errors. Then, numerical results are presented for two types of distribution networks (20 kV and low voltage grids). Finally, a novel formulation is proposed to extend the use of sensitivities to manage congestions, which will allow to reuse previous sensitivity-based methodologies to solve both voltage problems and congestions in active distribution networks.

The peak demand or demand limit control is an important part of the actions that industries carry out to optimize their energy consumption and reduce the costs related to their electricity billing. Prioritized switching of multiple appliances is often needed in order to reduce demand and energy consumption during peak load periods. The present article describes a peak load limitation algorithm that estimates the optimal disconnection time for one or more electrical loads before the electric demand exceeds a preset limit. This algorithm uses parametric and variable load factors that vary dynamically depending on what loads are present at a given time. For its validation, a software-in-the-loop testbed was designed and developed, in which multiple electrical loads were simulated via LabVIEW software and connected to a PLC controller emulated through CODESYS software. In this environment, several test configurations were executed and evaluated to study the influence of variables such as the nominal power and the disconnection priority of loads in the algorithm output. The results showed that the control algorithm is effective for peak load limitation, the maximum demand value reached during simulations tests did not exceed the preset demand limit at any time interval. The performance of the algorithm could be improved when prioritizing the shutdown of loads with higher nominal power or when increasing the anticipation time used for the disconnection of the controllable loads.

BackgroundAcademic surgery has been a traditionally male-dominated field. Female contribution remains challenging. In Mexico, there is no published evidence regarding gender disparity in academic surgery. We aimed to analyze the female role in clinical research submitted to the Asociación Mexicana de Cirugía General (AMCG).MethodsRetrospective study evaluating abstracts submitted to AMCG annual meetings from 2013 to 2019. Categorical variables were compared using χ2 test. Univariate logistic regression was performed to calculate odds ratios (OR) followed by a log-binomial logistic regression model to obtain the adjusted relative risk (aRR) for acceptance as an oral presentation.ResultsOverall, 7,439 abstracts were analyzed of which 24.2% were submitted by females. Female-submitted abstracts increased from 22.5% to 25.3% during 2013–2019 (p = 0.15). The proportion of 47 abstracts submitted by females was higher in the resident group (27.7% vs. 18.8%; p

Abstract Background There is a clinical need to identify diagnostic parameters that objectively quantify and monitor the effective visual ability of patients with homonymous visual field defects (HVFDs). Visual processing speed (VPS) is an objective measure of visual ability. It is the reaction time (RT) needed to correctly search and/or reach for a visual stimulus. VPS depends on six main brain processing systems: auditory-cognitive, attentional, working memory, visuocognitive, visuomotor, and executive. We designed a new assessment methodology capable of activating these six systems and measuring RTs to determine the VPS of patients with HVFDs. Methods New software was designed for assessing subject visual stimulus search and reach times (S-RT and R-RT respectively), measured in seconds. Thirty-two different everyday visual stimuli were divided in four complexity groups that were presented along 8 radial visual field positions at three different eccentricities (10o, 20o, and 30o). Thus, for each HVFD and control subject, 96 S- and R-RT measures related to VPS were registered. Three additional variables were measured to gather objective data on the validity of the test: eye-hand coordination mistakes (ehcM), eye-hand coordination accuracy (ehcA), and degrees of head movement (dHM, measured by a head-tracker system). HVFD patients and healthy controls (30 each) matched by age and gender were included. Each subject was assessed in a single visit. VPS measurements for HFVD patients and control subjects were compared for the complete test, for each stimulus complexity group, and for each eccentricity. Results VPS was significantly slower (p

This paper proposes a new reference frame, namely Reduced Reference Frame (RRF), specially suited for unbalanced three–phase four–wire systems which improves the performance of the classical Fortescue, Clarke and Park transformations. The RRF transfomation allows to represent any unbalanced three–phase sinusoidal magnitude with just two components even if the zero–sequence component is present. For doing so, the RRF transformation takes into account that the abc space–vector trajectory of the transformed three–phase sinusoidal magnitude is always within a plane. The geometric properties of this trajectory are considered for outlining a general classification of the transformation depending on the input voltages and an adequate reference frame within the plane. Then, a step–by–step procedure for computing the transformation matrix is detailed. Once the voltages and currents are transformed into the RRF, it is proposed a power theory which allows to compute the instantaneous active and reactive powers. The paper includes two simulations and an experimental validation through a real–time application to highlight the benefits of the proposal. The paper closes with the main conclusions and some future research lines where this transformation can be applied.

Human–robot collaboration (HRC) is one of the key aspects of Industry 4.0 (I4.0) and requires intuitive modalities for humans to communicate seamlessly with robots, such as speech, touch, or bodily gestures. However, utilizing these modalities is usually not enough to ensure a good user experience and a consideration of the human factors. Therefore, this paper presents a software component, Multi-Modal Offline and Online Programming (M2O2P), which considers such characteristics and establishes a communication channel with a robot with predefined yet configurable hand gestures. The solution was evaluated within a smart factory use case in the Smart Human Oriented Platform for Connected Factories (SHOP4CF) EU project. The evaluation focused on the effects of the gesture personalization on the perceived workload of the users using NASA-TLX and the usability of the component. The results of the study showed that the personalization of the gestures reduced the physical and mental workload and was preferred by the participants, while overall the workload of the tasks did not significantly differ. Furthermore, the high system usability scale (SUS) score of the application, with a mean of 79.25, indicates the overall usability of the component. Additionally, the gesture recognition accuracy of M2O2P was measured as 99.05%, which is similar to the results of state-of-the-art applications.

Down syndrome (DS, trisomy 21) is characterized by intellectual impairment at birth and Alzheimer’s disease (AD) pathology in middle age. As individuals with DS age, their cognitive functions decline as they develop AD pathology. The susceptibility to degeneration of a subset of neurons, known as basal forebrain cholinergic neurons (BFCNs), in DS and AD is a critical link between cognitive impairment and neurodegeneration in both disorders. BFCNs are the primary source of cholinergic innervation to the cerebral cortex and hippocampus, as well as the amygdala. They play a critical role in the processing of information related to cognitive function and are directly engaged in regulating circuits of attention and memory throughout the lifespan. Given the importance of BFCNs in attention and memory, it is not surprising that these neurons contribute to dysfunctional neuronal circuitry in DS and are vulnerable in adults with DS and AD, where their degeneration leads to memory loss and disturbance in language. BFCNs are thus a relevant cell target for therapeutics for both DS and AD but, despite some success, efforts in this area have waned. There are gaps in our knowledge of BFCN vulnerability that preclude our ability to effectively design interventions. Here, we review the role of BFCN function and degeneration in AD and DS and identify under-studied aspects of BFCN biology. The current gaps in BFCN relevant imaging studies, therapeutics, and human models limit our insight into the mechanistic vulnerability of BFCNs in individuals with DS and AD.

The rapid emerging technologies in various fields permitted the creation of simulation tools. These tools are designed to replicate physical systems in order to provide faster, cheaper and more detailed illustrative analysis of the physical system. In this regard, the concept of digital twins has been introduced to generally define these simulation tools. In fact, and according to the creator of the digital twin term Micheal Grieves, a digital twin is defined as a physical system, a digital replica of the physical system and information flow between the former parts. This definition is simple and generic for describing digital twins and yet, holistic. This broad definition creates a challenge for developers who target the development of such applications. Therefore, this paper presents a paradigm for architecting digital twins for manufacturing processes. The approach is inspired by the definitions of the ISA95 standard and the onion concept of computer applications to create multi-layer and multi-level concepts. Furthermore, and to satisfy the different required features by industries, the approach considers a multi-perspective concept that allows the separation of the digital twin views based on functionality. This paradigm aims at providing a modular, scalable, reusable, interoperable and composable approach for developing digital twins. Then, an implementation of the approach has been introduced using an ontology-based system and the IEC61499 standard. This implementation has been demonstrated on a discrete manufacturing assembly line.

The vast adoption of machine learning techniques in developing smart solutions increases the need of training and testing data. This data can be either collected from physical systems or created using simulation tools. In this regard, this paper presents a set of data collected using a digital twin known as the FASTory Simulator. The data contains more than 100 K events which are collected during a simulated assembly process. The FASTory simulator is a replica of a real assembly line with web-based industrial controllers. The data have been collected using specific-developed orchestrator. During the simulated process, the orchestrator was able to record all the events that occurred in the system. The provided data contains raw JavaScript Object Notation (JSON) formatted data and filtered Comma Separated Values (CSV) formatted data. This data can be exploited in machine learning for modelling the behaviour of the production systems or as testing data for optimization solution for the production system. Finally, this data has been utilized in a research for comparing different data analysis approaches including Knowledge-based systems and data-based systems.

Niches are spaces for the biological units of selection, from cells to complex communities. In a broad sense, “species” are biological units of individuation. Niches do not exist without individual organisms, and every organism has a niche. We use “niche” in the Hutchinsonian sense as an abstraction of a multidimensional environmental space characterized by a variety of conditions, both biotic and abiotic, whose quantitative ranges determine the positive or negative growth rates of the microbial individual, typically a species, but also parts of the communities of species contained in this space. Microbial organisms (“species”) constantly diversify, and such diversification (radiation) depends on the possibility of opening up unexploited or insufficiently exploited niches. Niche exploitation frequently implies “niche construction,” as the colonized niche evolves with time, giving rise to new potential subniches, thereby influencing the selection of a series of new variants in the progeny. The evolution of niches and organisms is the result of reciprocal interacting processes that form a single unified process. Centrifugal microbial diversification expands the limits of the species’ niches while a centripetal or cohesive process occurs simultaneously, mediated by horizontal gene transfers and recombinatorial events, condensing all of the information recovered during the diversifying specialization into “novel organisms” (possible future species), thereby creating a more complex niche, where the selfishness of the new organism(s) establishes a “homeostatic power” limiting the niche’s variation. Once the niche’s full carrying capacity has been reached, reproductive isolation occurs, as no foreign organisms can outcompete the established population/community, thereby facilitating speciation. In the case of individualization-speciation of the microbiota, its contribution to the animal’ gut structure is a type of “niche construction,” the result of crosstalk between the niche (host) and microorganism(s). Lastly, there is a parallelism between the hierarchy of niches and that of microbial individuals. The increasing anthropogenic effects on the biosphere (such as globalization) might reduce the diversity of niches and bacterial individuals, with the potential emergence of highly transmissible multispecialists (which are eventually deleterious) resulting from the homogenization of the microbiosphere, a possibility that should be explored and prevented.

Loneliness and social isolation are subjective measures associated with the feeling of discomfort and distress. Various factors associated with the feeling of loneliness or social isolation are: the built environment, long-term illnesses, the presence of disabilities or health problems, etc. One of the most important aspect which could impact feelings of loneliness is mobility. In this paper, we present a machine-learning based approach to classify the user loneliness levels using their indoor and outdoor mobility patterns. User mobility data has been collected based on indoor and outdoor sensors carried on by volunteers frequenting an elderly nursing house in Tampere region, Finland. The data was collected using Pozyx sensor for indoor data and Pico minifinder sensor for outdoor data. Mobility patterns such as the distance traveled indoors and outdoors, indoor and outdoor estimated speed, and frequently visited clusters were the most relevant features for classifying the user’s perceived loneliness levels.Three types of data used for classification task were indoor data, outdoor data and combined indoor-outdoor data. Indoor data consisted of indoor mobility data and statistical features from accelerometer data, outdoor data consisted of outdoor mobility data and other parameters such as speed recorded from sensors and course of a person whereas combined indoor-outdoor data had common mobility features from both indoor and outdoor data. We found that the machine-learning model based on XGBoost algorithm achieved the highest performance with accuracy between 90% and 98% for indoor, outdoor, and combined indoor-outdoor data. We also found that Lubben-scale based labelling of perceived loneliness works better for both indoor and outdoor data, whereas UCLA scale-based labelling works better with combined indoor-outdoor data.

The enterprises to be competitive are constantly looking for continuous increase of productivity, quality and level of services. With the development of Industry 4.0 concept, manufacturers are more confident about new advantages of automation and systems integration. Lean management is well developed and empirically proven effective managerial approach. Combining Lean and Industry 4.0 practices seems to be necessary evolutionary step for further raise the level of operational excellence (exploitation of finance, workload, materials, machines/devices). There is an increasing number of Industry 4.0 solutions used to reduce waste (as known from Lean Management). Therefore, the main objective of this article aims at presenting the results of a literature review on the concept of ‘Lean Industry 4.0ʹ. Dynamic methodology called “Systematic Literature Network Analysis (SLNA)” was used. It combines the Systematic Literature Review approach with the quantitative analysis of bibliographic networks to detect emerging topics and the dynamic evolution of the topics. The paper is a comprehensive systematization and rationalization of knowledge about the integration of LM and I4.0 concepts, identifies the most important research trends and defines directions for future research. The article contains a framework that presents the current state of knowledge in the area of Lean Industry 4.0.

Indoor navigation systems incorporating augmented reality allow users to locate places within buildings and acquire more knowledge about their environment. However, although diverse works have been introduced with varied technologies, infrastructure, and functionalities, a standardization of the procedures for elaborating these systems has not been reached. Moreover, while systems usually handle contextual information of places in proprietary formats, a platform-independent model is desirable, which would encourage its access, updating, and management. This paper proposes a methodology for developing indoor navigation systems based on the integration of Augmented Reality and Semantic Web technologies to present navigation instructions and contextual information about the environment. It comprises four modules to define a spatial model, data management (supported by an ontology), positioning and navigation, and content visualization. A mobile application system was developed for testing the proposal in academic environments, modeling the structure, routes, and places of two buildings from independent institutions. The experiments cover distinct navigation tasks by participants in both scenarios, recording data such as navigation time, position tracking, system functionality, feedback (answering a survey), and a navigation comparison when the system is not used. The results demonstrate the system’s feasibility, where the participants show a positive interest in its functionalities.

The data produced by sensors of IoT devices are becoming keystones for organizations to conduct critical decision-making processes. However, delivering information to these processes in real-time represents two challenges for the organizations: the first one is achieving a constant dataflow from IoT to the cloud and the second one is enabling decision-making processes to retrieve data from dataflows in real-time. This paper presents a cloud-based Web of Things method for creating digital twins of IoT devices (named sentinels).The novelty of the proposed approach is that sentinels create an abstract window for decision-making processes to: (a) find data (e.g., properties, events, and data from sensors of IoT devices) or (b) invoke functions (e.g., actions and tasks) from physical devices (PD), as well as from virtual devices (VD). In this approach, the applications and services of decision-making processes deal with sentinels instead of managing complex details associated with the PDs, VDs, and cloud computing infrastructures. A prototype based on the proposed method was implemented to conduct a case study based on a blockchain system for verifying contract violation in sensors used in product transportation logistics. The evaluation showed the effectiveness of sentinels enabling organizations to attain data from IoT sensors and the dataflows used by decision-making processes to convert these data into useful information.

The utilization of robotic systems has been increasing in the last decade. This increase has been derived by the evolvement in the computational capabilities, communication systems, and the information systems of the manufacturing systems which is reflected in the concept of Industry 4.0. Furthermore, the robotics systems are continuously required to address new challenges in the industrial and manufacturing domain, like keeping humans in the loop, among other challenges. Briefly, the keeping humans in the loop concept focuses on closing the gap between humans and machines by introducing a safe and trustworthy environment for the human workers to work side by side with robots and machines. It aims at increasing the engagement of the human as the automation level increases rather than replacing the human, which can be nearly impossible in some applications. Consequently, the collaborative robots (Cobots) have been created to allow physical interaction with the human worker. However, these cobots still lack of recognizing the human emotional state. In this regard, this paper presents an approach for adapting cobot parameters to the emotional state of the human worker. The approach utilizes the Electroencephalography (EEG) technology for digitizing and understanding the human emotional state. Afterwards, the parameters of the cobot are instantly adjusted to keep the human emotional state in a desirable range which increases the confidence and the trust between the human and the cobot. In addition, the paper includes a review on technologies and methods for emotional sensing and recognition. Finally, this approach is tested on an ABB YuMi cobot with commercially available EEG headset.

Background Many fish species have been introduced in wild ecosystems around the world to provide food or leisure, deliberately or from farm escapes. Some of those introductions have had large ecological effects. The north American native rainbow trout (Oncorhynchus mykiss Walbaum, 1792) is one of the most widely farmed fish species in the world. It was first introduced in Spain in the late 19th century for sport fishing (Elvira 1995) and nowadays is used there for both fishing and aquaculture. On the other hand, the European native brown trout (Salmo trutta L.) is catalogued as vulnerable in Spain. Detecting native and invasive fish populations in ecosystem monitoring is crucial, but it may be difficult from conventional sampling methods such as electrofishing. These techniques encompass some mortality, thus are not adequate for some ecosystems as the case of protected areas. Environmental DNA (eDNA) analysis is a sensitive and non-invasive method that can be especially useful for rare and low-density species detection and inventory in water bodies. Methods In this study we employed two eDNA based methods (qPCR and nested PCR-RFLP) to detect salmonid species from mountain streams within a protected area, The Biosphere Reserve and Natural Park of Redes (Upper Nalón Basin, Asturias, Northern Spain), where brown trout is the only native salmonid. We also measured some habitat variables to see how appropriate for salmonids the area is. The sampling area is located upstream impassable dams and contains one rainbow trout fish farm. Results Employing qPCR methodology, brown trout eDNA was detected in all the nine sampling sites surveyed, while nested PCR-RFLP method failed to detect it in two sampling points. Rainbow trout eDNA was detected with both techniques at all sites in the Nalón River’ (n1, n2 and n3). Salmonid habitat units and water quality were high from the area studied. Discussion In this study, a high quantity of rainbow trout eDNA was found upstream and downstream of a fish farm located inside a Biosphere Reserve. Unreported escapes from the fish farm are a likely explanation of these results. Since salmonid habitat is abundant and the water quality high, the establishment of rainbow trout populations would be favored should escapes occur. Environmental DNA has here proved to be a valuable tool for species detection in freshwater environments, and the probe-based qPCR highly sensitive technique for detection of scarce species. We would recommend this method for routine monitoring and early detection of introduced species within natural reserves.

The development of antibiotic resistance is usually associated with genetic changes, either to the acquisition of resistance genes, or to mutations in elements relevant for the activity of the antibiotic. However, in some situations resistance can be achieved without any genetic alteration; this is called phenotypic resistance. Non-inherited resistance is associated to specific processes such as growth in biofilms, a stationary growth phase or persistence. These situations might occur during infection but they are not usually considered in classical susceptibility tests at the clinical microbiology laboratories. Recent work has also shown that the susceptibility to antibiotics is highly dependent on the bacterial metabolism and that global metabolic regulators can modulate this phenotype. This modulation includes situations in which bacteria can be more resistant or more susceptible to antibiotics. Understanding these processes will thus help in establishing novel therapeutic approaches based on the actual susceptibility shown by bacteria during infection, which might differ from that determined in the laboratory. In this review, we discuss different examples of phenotypic resistance and the mechanisms that regulate the crosstalk between bacterial metabolism and the susceptibility to antibiotics. Finally, information on strategies currently under development for diminishing the phenotypic resistance to antibiotics of bacterial pathogens is presented.

Some real functions on the plane can be expressed as a linear combination of powers of distances to certain points.