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The massive deployment of smart meters in most Western countries in recent decades has allowed the creation and development of a significant variety of applications, mainly related to efficient energy management. The information provided about energy consumption has also been dedicated to the areas of social work and health. In this context, smart meters are considered single-point non-intrusive sensors that might be used to monitor the behaviour and activity patterns of people living in a household. This work describes the design of a short-term behavioural alarm generator based on the processing of energy consumption data coming from a commercial smart meter. The device captured data from a household for a period of six months, thus providing the consumption disaggregated per appliance at an interval of one hour. These data were used to train different intelligent systems, capable of estimating the predicted consumption for the next one-hour interval. Four different approaches have been considered and compared when designing the prediction system: a recurrent neural network, a convolutional neural network, a random forest, and a decision tree. By statistically analysing these predictions and the actual final energy consumption measurements, anomalies can be detected in the undertaking of three different daily activities: sleeping, breakfast, and lunch. The recurrent neural network achieves an F1-score of 0.8 in the detection of these anomalies for the household under analysis, outperforming other approaches. The proposal might be applied to the generation of a short-term alarm, which can be involved in future deployments and developments in the field of ambient assisted living.

Licochalcone A (Lico-A) is a flavonoid compound derived from the root of the Glycyrrhiza species, a plant commonly used in traditional Chinese medicine. While the Glycyrrhiza species has shown promise in treating various diseases such as cancer, obesity, and skin diseases due to its active compounds, the investigation of Licochalcone A's effects on the central nervous system and its potential application in Alzheimer's disease (AD) treatment have garnered significant interest. Studies have reported the neuroprotective effects of Lico-A, suggesting its potential as a multitarget compound. Lico-A acts as a PTP1B inhibitor, enhancing cognitive activity through the BDNF-TrkB pathway and exhibiting inhibitory effects on microglia activation, which enables mitigation of neuroinflammation. Moreover, Lico-A inhibits c-Jun N-terminal kinase 1, a key enzyme involved in tau phosphorylation, and modulates the brain insulin receptor, which plays a role in cognitive processes. Lico-A also acts as an acetylcholinesterase inhibitor, leading to increased levels of the neurotransmitter acetylcholine (Ach) in the brain. This mechanism enhances cognitive capacity in individuals with AD. Finally, Lico-A has shown the ability to reduce amyloid plaques, a hallmark of AD, and exhibits antioxidant properties by activating the nuclear factor erythroid 2-related factor 2 (Nrf2), a key regulator of antioxidant defense mechanisms. In the present review, we discuss the available findings analyzing the potential of Lico-A as a neuroprotective agent. Continued research on Lico-A holds promise for the development of novel treatments for cognitive disorders and neurodegenerative diseases, including AD. Further investigations into its multitarget action and elucidation of underlying mechanisms will contribute to our understanding of its therapeutic potential.

Depressive symptoms are common among people living with HIV (PLWH). The aim of this study was to identify the determinants of depressive symptoms in PLWH in Spain. A total of 1060 PLWH participated in this cross-sectional study and completed the Patient Health Questionnaire-9. The odds ratios for the presence of depressive symptoms were analyzed in a multivariable logistic regression model, including sociodemographic data, comorbidities, health-related behaviors, and social-environment-related variables. We found an overall prevalence of depressive symptoms of 21.42%; by subgroup, namely men, women, and transgender persons, prevalence was 18.13%, 32.81%, and 37.14%, respectively. Moreover, social isolation (OR = 1.05 [CI, 1.02-1.08]) and poor physical and mental quality of life (OR = 1.06 [CI, 1.02-1.09] and OR = 1.13 [CI, 1.09-1.17], respectively) were associated with depressive symptoms. As protective factors, we identified serodisclosure to more people (vs. none; OR = 0.39 [CI, 0.17-0.87]), satisfaction with social roles (OR = 0.86 [CI, 0.79-0.94]), better cognitive function (OR = 0.92 [CI, 0.89-0.95]), and sexualized drug use once in a lifetime (OR = 0.52 [CI, 0.29-0.93]). This study showed a high prevalence of depressive symptoms in PLWH, especially among women and transgender people. The association between psychosocial variables and depressive symptoms highlights the multidimensionality of the problem and identifies areas for intervention. This study found that the management of mental health issues is an area that needs to be improved and tailored to specific groups, with the aim of enhancing the well-being of PLWH.

Ultrasonic local positioning systems (ULPS) have been brought to the attention of researchers as one of the possibilities that can be used for indoor localization. Acoustic systems combine a suitable trade-off between precision, ease of development, and cost. This work proposes a method for measuring the time of arrival of encoded emissions from a set of ultrasonic beacons, which are used to implement an accurate ULPS. This method uses the generalized cross-correlation technique with PHAT filter and weighting factor β (GCC-PHAT- β ). To improve the performance of the GCC-PHAT- β in encoded emission detection, the employment is proposed of mixed-medium multiple-access techniques, based on code division and time division multiplexing of beacon emissions (CDMA and TDMA respectively), and to dynamically adjust the PHAT filter weighting factor. The receiver position is obtained by hyperbolic multilateration from the time differences of arrival (TDoA) between a reference beacon and the rest, thus avoiding the need for receiver synchronization. The results show how the dynamic adaptation of the weighting factor significantly reduces positioning errors from 20 cm to 2 cm in 80% of measurements. The simulated and real experiments prove that the proposed algorithms improve the performance of the ULPS in situations with lower signal-to-noise ratios (SNR) than 0 dB and in environments where the multipath effect makes it difficult to correctly detect the encoded ultrasonic emissions.

Despite the significant differences in pathological background of neurodegenerative diseases, epileptic seizures are a comorbidity in many disorders such as Huntington disease (HD), Alzheimer's disease (AD), and multiple sclerosis (MS). Regarding the last one, specifically, it has been shown that the risk of developing epilepsy is three to six times higher in patients with MS compared to the general population. In this context, understanding the pathological processes underlying this connection will allow for the targeting of the common and shared pathological pathways involved in both conditions, which may provide a new avenue in the management of neurological disorders. This review provides an outlook of what is known so far about the bidirectional association between epilepsy and MS.

The research interest on location-based services has increased during the last years ever since 3D centimetre accuracy inside intelligent environments could be confronted with. This work proposes an indoor local positioning system based on LED lighting, transmitted from a set of beacons to a receiver. The receiver is based on a quadrant photodiode angular diversity aperture (QADA) plus an aperture placed over it. This configuration can be modelled as a perspective camera, where the image position of the transmitters can be used to recover the receiver's 3D pose. This process is known as the perspective-n-point (PnP) problem, which is well known in computer vision and photogrammetry. This work investigates the use of different state-of-the-art PnP algorithms to localize the receiver in a large space of 2 × 2 m2 based on four co-planar transmitters and with a distance from transmitters to receiver up to 3.4 m. Encoding techniques are used to permit the simultaneous emission of all the transmitted signals and their processing in the receiver. In addition, correlation techniques (match filtering) are used to determine the image points projected from each emitter on the QADA. This work uses Monte Carlo simulations to characterize the absolute errors for a grid of test points under noisy measurements, as well as the robustness of the system when varying the 3D location of one transmitter. The IPPE algorithm obtained the best performance in this configuration. The proposal has also been experimentally evaluated in a real setup. The estimation of the receiver's position at three particular points for roll angles of the receiver of γ={0°, 120°, 210° and 300°} using the IPPE algorithm achieves average absolute errors and standard deviations of 4.33 cm, 3.51 cm and 28.90 cm; and 1.84 cm, 1.17 cm and 19.80 cm in the coordinates x , y and z , respectively. These positioning results are in line with those obtained in previous work using triangulation techniques but with the addition that the complete pose of the receiver ( x , y , z , α, β, γ) is obtained in this proposal.

(1) Background: The c-Jun-NH2-terminal protein kinase (JNK) is a mitogen-activated protein kinase involved in regulating physiological processes in the central nervous system. However, the dual genetic deletion of Mkk4 and Mkk7 (upstream activators of JNK) in adult mice is not reported. The aim of this study was to induce the genetic deletion of Mkk4/Mkk7 in adult mice and analyze their effect in hippocampal neurogenesis. (2) Methods: To achieve this goal, Actin - Cre ERT2 ( Cre +/ - ), Mkk4 flox/flox , Mkk7 flox/flox mice were created. The administration of tamoxifen in these 2-month-old mice induced the gene deletion ( Actin - Cre ERT2 ( Cre +/- ), Mkk4 ∆/∆ , Mkk7 ∆/∆ genotype), which was verified by PCR, Western blot, and immunohistochemistry techniques. (3) Results: The levels of MKK4/MKK7 at 7 and 14 days after tamoxifen administration were not eliminated totally in CNS, unlike what happens in the liver and heart. These data could be correlated with the high levels of these proteins in CNS. In the hippocampus, the deletion of Mkk4/Mkk7 induced a misalignment position of immature hippocampal neurons together with alterations in their dendritic architecture pattern and maturation process jointly to the diminution of JNK phosphorylation. (4) Conclusion: All these data supported that the MKK4/MKK7-JNK pathway has a role in adult neurogenic activity.

Human intestinal microbiota comprise of a dynamic population of bacterial species and other microorganisms with the capacity to interact with the rest of the organism and strongly influence the host during homeostasis and disease. Commensal and pathogenic bacteria coexist in homeostasis with the intestinal epithelium and the gastrointestinal tract's immune system, or GALT (gut-associated lymphoid tissue), of the host. However, a disruption to this homeostasis or dysbiosis by different factors (e.g., stress, diet, use of antibiotics, age, inflammatory processes) can cause brain dysfunction given the communication between the gut and brain. Recently, extracellular vesicles (EVs) derived from bacteria have emerged as possible carriers in gut-brain communication through the interaction of their vesicle components with immune receptors, which lead to neuroinflammatory immune response activation. This review discusses the critical role of bacterial EVs from the gut in the neuropathology of brain dysfunctions by modulating the immune response. These vesicles, which contain harmful bacterial EV contents such as lipopolysaccharide (LPS), peptidoglycans, toxins and nucleic acids, are capable of crossing tissue barriers including the blood-brain barrier and interacting with the immune receptors of glial cells (e.g., Toll-like receptors) to lead to the production of cytokines and inflammatory mediators, which can cause brain impairment and behavioral dysfunctions.

Indoor positioning remains a challenge and, despite much research and development carried out in the last decade, there is still no standard as with the Global Navigation Satellite Systems (GNSS) outdoors. This paper presents an indoor positioning system called LOCATE-US with adjustable granularity for use with commercial mobile devices, such as smartphones or tablets. LOCATE-US is privacy-oriented and allows every device to compute its own position by fusing ultrasonic, inertial sensor measurements and map information. Ultrasonic Local Positioning Systems (U-LPS) based on encoded signals are placed in critical zones that require an accuracy below a few decimeters to correct the accumulated drift errors of the inertial measurements. These systems are well suited to work at room level as walls confine acoustic waves inside. To avoid audible artifacts, the U-LPS emission is set at 41.67 kHz, and an ultrasonic acquisition module with reduced dimensions is attached to the mobile device through the USB port to capture signals. Processing in the mobile device involves an improved Time Differences of Arrival (TDOA) estimation that is fused with the measurements from an external inertial sensor to obtain real-time location and trajectory display at a 10 Hz rate. Graph-matching has also been included, considering available prior knowledge about the navigation scenario. This kind of device is an adequate platform for Location-Based Services (LBS), enabling applications such as augmented reality, guiding applications, or people monitoring and assistance. The system architecture can easily incorporate new sensors in the future, such as UWB, RFiD or others.

Indoor location and positioning systems (ILPS) are used to locate and track people, as well as mobile and/or connected targets, such as robots or smartphones, not only inside buildings with a lack of global navigation satellite systems (GNSS) signals but also in constrained outdoor situations with reduced coverage. Indoor positioning applications and their interest are growing in certain environments, such as commercial centers, airports, hospitals or factories. Several sensory technologies have already been applied to indoor positioning systems, where ultrasounds are a common solution due to its low cost and simplicity. This work proposes a 3D ultrasonic local positioning system (ULPS), based on a set of three asynchronous ultrasonic beacon units, capable of transmitting coded signals independently, and on a 3D mobile receiver prototype. The proposal is based on the aforementioned beacon unit, which consists of five ultrasonic transmitters oriented towards the same coverage area and has already been proven in 2D positioning by applying hyperbolic trilateration. Since there are three beacon units available, the final position is obtained by merging the partial results from each unit, implementing a minimum likelihood estimation (MLE) fusion algorithm. The approach has been characterized, and experimentally verified, trying to maximize the coverage zone, at least for typical sizes in most common public rooms and halls. The proposal has achieved a positioning accuracy below decimeters for 90% of the cases in the zone where the three ultrasonic beacon units are available, whereas these accuracies can degrade above decimeters according to whether the coverage from one or more beacon units is missing. The experimental workspace covers a large volume, where tests have been carried out at points placed in two different horizontal planes., Competing Interests: The authors declare no conflict of interest

This paper proposes an algorithm for calibrating the position of beacons which are placed on the ceiling of an indoor environment. In this context, the term calibration is used to estimate the position coordinates of a beacon related to a known reference system in a map. The positions of a set of beacons are used for indoor positioning purposes. The operation of the beacons can be based on different technologies such as radiofrequency (RF), infrared (IR) or ultrasound (US), among others. In this case we are interested in the positions of several beacons that compose an Ultrasonic Local Positioning System (ULPS) placed on different strategic points of the building. The calibration proposal uses several distances from a beacon to the neighbor walls measured by a laser meter. These measured distances, the map of the building in a vector format and other heuristic data (such as the region in which the beacon is located, the approximate orientation of the distance measurements to the walls and the equations in the map coordinate system of the line defining these walls) are the inputs of the proposed algorithm. The output is the best estimation of the position of the beacon. The process is repeated for all the beacons. To find the best estimation of the position of the beacons we have implemented a numerical minimization based on the use of a Genetic Algorithm (GA) and a Harmony Search (HS) methods. The proposal has been validated with simulations and real experiments, obtaining the positions of the beacons and an estimation of the error associated that depends on which walls (and the angle of incidence of the laser) are selected to make the distance measurements., Competing Interests: The authors declare no conflict of interest.

The ageing of the population, and their increasing wish of living independently, are motivating the development of welfare and healthcare models. Existing approaches based on the direct heath-monitoring using body sensor networks (BSN) are precise and accurate. Nonetheless, their intrusiveness causes non-acceptance. New approaches seek the indirect monitoring through monitoring activities of daily living (ADLs), which proves to be a suitable solution. ADL monitoring systems use many heterogeneous sensors, are less intrusive, and are less expensive than BSN, however, the deployment and maintenance of wireless sensor networks (WSN) prevent them from a widespread acceptance. In this work, a novel technique to monitor the human activity, based on non-intrusive load monitoring (NILM), is presented. The proposal uses only smart meter data, which leads to minimum intrusiveness and a potential massive deployment at minimal cost. This could be the key to develop sustainable healthcare models for smart homes, capable of complying with the elderly people' demands. This study also uses the Dempster-Shafer theory to provide a daily score of normality with regard to the regular behavior. This approach has been evaluated using real datasets and, additionally, a benchmarking against a Gaussian mixture model approach is presented.

This paper presents a flexible deployment of ultrasonic position sensors and a novel positioning algorithm suitable for the navigation of mobile robots (MRs) in extensive indoor environments. Our proposal uses several independently-referenced local positioning systems (LPS), which means that each one of them operates within its own local reference system. In a typical layout, an indoor extensive area can be covered using just a reduced set of globally-referenced LPS (GRLPS), whose beacon positions are known to the global reference system, while the rest of the space can be covered using locally-referenced LPSs (LRLPS) that can be distributed arbitrarily. The number of LRLPS and their position can be also changed at any moment. The algorithm is composed of several Bayesian filters running in parallel, so that when an MR is under the GRLPS coverage area, its position is updated by a global filter, whereas when the MR is inside the LRLPS area, its position is updated using position increments within a local filter. The navigation algorithm has been tested by simulation and with actual data obtained using a real set of ultrasonic LPSs.

One of the challenges of phased array (PA) ultrasonic imaging systems is their limited capability to deal with real-time applications, such as echocardiography and obstetrics. In its most basic outline, these systems require emitting and receiving with the entire array for each image line to be acquired; therefore, with many image lines, a higher acquisition time and a lower frame rate. This constraint requires one to find alternatives to reduce the total number of emissions needed to obtain the whole image. In this work, we propose a new PA scheme based on the Code Division Multiple Access (CDMA) technique, where a different code is assigned to each steering direction, allowing the array to emit in several directions simultaneously. However, the use of encoding techniques produces a reduction of the image contrast because of the interferences between codes. To solve this, a new scheme based on merging several images is proposed, allowing the system to get close to the theoretical maximum frame rate, as well as to limit the loss of contrast, intrinsic to the technique.

Accurate coordinates for active beacons placed in the environment are required in local positioning systems (LPS). These coordinates and the distances (or differences of distances) measured between the beacons and the mobile node to be localized are inputs to most trilateration algorithms. As a first approximation, such coordinates are obtained by means of manual measurements (a time-consuming and non-flexible method), or by using a calibration algorithm (i.e., automatic determination of beacon coordinates from ad hoc measurements). This paper presents a method to calibrate the beacons' positions in a LPS using a mobile receiver. The method has been developed for both, spherical and hyperbolic trilateration. The location of only three test points must be known a priori, while the position of the other test points can be unknown. Furthermore, the paper describes a procedure to estimate the optimal positions, or approximate areas in the coverage zone, where the test-points necessary to calibrate the ultrasonic LPS should be placed. Simulation and experimental results show the improvement achieved when these optimal test-points are used instead of randomly selected ones.

In this article, a time-of-flight detection technique in the frequency domain is described for an ultrasonic local positioning system (LPS) based on encoded beacons. Beacon transmissions have been synchronized and become simultaneous by means of the DS-CDMA (direct-sequence code Division multiple access) technique. Every beacon has been associated to a 255-bit Kasami code. The detection of signal arrival instant at the receiver, from which the distance to each beacon can be obtained, is based on the application of the generalized cross-correlation (GCC), by using the cross-spectral density between the received signal and the sequence to be detected. Prior filtering to enhance the frequency components around the carrier frequency (40 kHz) has improved estimations when obtaining the correlation function maximum, which implies an improvement in distance measurement precision. Positioning has been achieved by using hyperbolic trilateration, based on the time differences of arrival (TDOA) between a reference beacon and the others.

The great variability usually found in underwater media makes modeling a challenging task, but helpful for better understanding or predicting the performance of future deployed systems. In this work, an underwater acoustic propagation model is presented. This model obtains the multipath structure by means of the ray tracing technique. Using this model, the behavior of a relative positioning system is presented. One of the main advantages of relative positioning systems is that only the distances between all the buoys are needed to obtain their positions. In order to obtain the distances, the propagation times of acoustic signals coded by Complementary Set of Sequences (CSS) are used. In this case, the arrival instants are obtained by means of correlation processes. The distances are then used to obtain the position of the buoys by means of the Multidimensional Scaling Technique (MDS). As an early example of an application using this relative positioning system, a tracking of the position of the buoys at different times is performed. With this tracking, the surface current of a particular region could be studied. The performance of the system is evaluated in terms of the distance from the real position to the estimated one.

In this work, an acoustic sensor network for a relative localization system is analyzed by reporting the accuracy achieved in the position estimation. The proposed system has been designed for those applications where objects are not restricted to a particular environment and thus one cannot depend on any external infrastructure to compute their positions. The objects are capable of computing spatial relations among themselves using only acoustic emissions as a ranging mechanism. The object positions are computed by a multidimensional scaling (MDS) technique and, afterwards, a least-square algorithm, based on the Levenberg-Marquardt algorithm (LMA), is applied to refine results. Regarding the position estimation, all the parameters involved in the computation of the temporary relations with the proposed ranging mechanism have been considered. The obtained results show that a fine-grained localization can be achieved considering a Gaussian distribution error in the proposed ranging mechanism. Furthermore, since acoustic sensors require a line-of-sight to properly work, the system has been tested by modeling the lost of this line-of-sight as a non-Gaussian error. A suitable position estimation has been achieved even if it is considered a bias of up to 25 of the line-of-sight measurements among a set of nodes.