Comparison of nitrification inhibitors to restrict nitrate leaching in a maize crop irrigated under mediterranean conditions

The aim of this paper was to compare dicyandiamide (DCD) and 3,4 dimethylpyrazole phosphate (DMPP) as inhibitors of ammonium oxidation and nitrate leaching after applying fertilizer to a maize (Zea mays L.) crop grown under Mediterranean conditions. The effects of nitrification inhibitors were also compared to those of N fertilization without inhibitors and with split N application. In plots fertilized with ammonium sulphate nitrate (ASN), either DCD or DMPP lengthened ammonium presence in soil and produced lower soil NO3- concentrations (30% lower than in plots with no inhibitor). The use of DCD or DMPP achieved significant reductions in nitrate leaching. DCD showed excellent properties for controlling nitrate leaching, taking into account the fact that grain yield and N accumulated by plant were similar for the ASN-DCD and ASN treatments applied at the same N doses. The split N treatment did not offer any advantages in terms of leached nitrate, either with the use of single ammonium sulphate nitrate (ASN) or with single application of nitrification inhibitors. The nitrification inhibitors did not increase the yield but did not reduce it either. The drainage rate was the most important component of nitrate leaching. The low drainage values of the first year resulted in a sharp decline of nitrate leaching. However, the experiment of the second year, showed clear differences in nitrate leaching between treatments due to the greater drainage

HBF4 Catalysed Nucleophilic Substitutions of Propargylic Alcohols

The activity of HBF4 (aqueous solution) as a catalyst in propargylation reactions is presented. Diverse types of nucleophiles were employed in order to form new C–O, C–N and C–C bonds in technical acetone and in air. Good to excellent yields and good chemoselectivities were obtained using low acid loading (typically 1 mol-%) under simple reaction conditions

Horizontal target size perturbations during grasping movements are described by subsequent size perception and saccade amplitude

: Perception and action are essential in our day-to-day interactions with the environment. Despite the dual-stream theory of action and perception, it is now accepted that action and perception processes interact with each other. However, little is known about the impact of unpredicted changes of target size during grasping actions on perception. We assessed whether size perception and saccade amplitude were affected before and after grasping a target that changed its horizontal size during the action execution under the presence or absence of tactile feedback. We have tested twenty-one participants in 4 blocks of 30 trials. Blocks were divided into two experimental tactile feedback paradigms: tactile and non-tactile. Trials consisted of 3 sequential phases: pre-grasping size perception, grasping, and post-grasping size perception. During pre- and post-phases, participants executed a saccade towards a horizontal bar and performed a manual size estimation of the bar size. During grasping phase, participants were asked to execute a saccade towards the bar and to make a grasping action towards the screen. While grasping, 3 horizontal size perturbation conditions were applied: non-perturbation, shortening, and lengthening. 30% of the trials presented perturbation, meaning a symmetrically shortened or lengthened by 33% of the original size. Participants' hand and eye positions were assessed by a motion capture system and a mobile eye-tracker, respectively. After grasping, in both tactile and non-tactile feedback paradigms, size estimation was significantly reduced in lengthening (p = 0.002) and non-perturbation (p<0.001), whereas shortening did not induce significant adjustments (p = 0.86). After grasping, saccade amplitude became significantly longer in shortening (p<0.001) and significantly shorter in lengthening (p<0.001). Non-perturbation condition did not display adjustments (p = 0.95). Tactile feedback did not generate changes in the collected perceptual responses, but horizontal size perturbations did so, suggesting that all relevant target information used in the movement can be extracted from the post-action target perception

Quantile regression forests-based modeling and environmental indicators for decision support in broiler farming

An efficient and sustainable animal production requires fine-tuning and control of all the parameters involved. But this is not a simple task. Animal farming is a complex biological system in which environmental parameters and management practices interact in a dynamic way. In addition, the typical non-linear response of biological processes implies that relationships across parameters that are critical to assure animal welfare and performance are difficult to determine. In this paper a novel decision support system based on environmental indicators and on weights, leg problems and mortality rates is proposed to address this issue. The data-driven modeling process is performed by a quantile regression forests approach that allows estimating growth, welfare and mortality parameters on the basis of environmental deviations from optimal farm conditions. Resulting models also provide confidence intervals able to deal with uncertainty. They are deployed in farm, offering an accessible tool for farmers, veterinarians and technical personnel. Experimental results involving 20 flocks of broiler meat chickens from different farms show the validity of the system, obtaining robust prediction intervals and high accuracy, namely over 81% for every model. The in-field use of the proposed approach will facilitate an efficient and animal welfare-friendly production management.This project was funded by the Spanish Ministry of Economy and Competitivity, General Directorate for Science and Technology, National Research Program ’Retos de la Sociedad’ Project #AGL2013-49173-C2-1-R P.I. Inma Estevez and #AGL2013-49173-C2-2-R. The authors wish to thank to AN and the farmers for facilitating access to their farms for data collection

Biases in the spectral amplitude distribution of a natural scene modulate horizontal size perception

Introduction: Visual perception is a complex process that involves the analysis of different spatial and temporal features of the visual environment. One critical aspect of this process is adaptation, which allows the visual system to adjust its sensitivity to specific features based on the context of the environment. Numerous theories highlight the significance of the visual scene and its spectral properties in perceptual and adaptation mechanisms. For example, size perception is known to be influenced by the spatial frequency content of the visual scene. Nonetheless, several inquiries still exist, including how specific spectral properties of the scene play a role in size perception and adaptation mechanisms. Methods: In this study, we explore aftereffects on size perception following adaptation to a natural scene with a biased spectral amplitude distribution. Twenty participants had to manually estimate the horizontal size of a projected rectangle after adaptation to three visually biased conditions: vertical-biased, non-biased, and horizontal-biased. Size adaptation aftereffects were quantified by comparing the perceptual responses from the non-biased condition with the vertical- and horizontal-biased conditions. Results: We found size perception shifts which were contingent upon the specific orientation and spatial frequency distribution inherent in the amplitude spectra of the adaptation stimuli. Particularly, adaptation to vertical-biased produced a horizontal enlargement, while adaptation to horizontal-biased generated a decrease in the horizontal size perception of the rectangle. On average, size perception was modulated by 5–6%. Discussion: These findings provide supporting evidence for the hypothesis that the neural mechanisms responsible for processing spatial frequency channels are involved in the encoding and perception of size information. The implications for neural mechanisms underlying spatial frequency and size information encoding are discussed

Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration

[EN] Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%, p < 0.005) to the magnitude of the baseline error level (13.61%). The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are Mets (Metabolic Equivalent of Tasks) from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only Mets is also viable for a more immediate implementation of this correction into market devices.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) (Grant Number DPI2016-78831-C2-1-R), the European Union (FEDER funds), and the Vicerectorate of Research, Innovation and Technology Transference from the Universitat Politecnica de Valencia (Grant Number PAID-06-18). We would like to acknowledge the work performed by Josep Vehí, Lyvia Biagi, Ignacio Conget and Carmen Quirós. We thank all the participants and clinical staff who participated in clinical acquisition and pre-formatting of data for study, without whom none of this work would have been possible.Laguna Sanz, AJ.; Diez, J.; Giménez, M.; Bondía Company, J. (2019). Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration. Sensors. 19(17):1-14. https://doi.org/10.3390/s19173757114191

Information flow between resting state networks

The resting brain dynamics self-organizes into a finite number of correlated patterns known as resting state networks (RSNs). It is well known that techniques like independent component analysis can separate the brain activity at rest to provide such RSNs, but the specific pattern of interaction between RSNs is not yet fully understood. To this aim, we propose here a novel method to compute the information flow (IF) between different RSNs from resting state magnetic resonance imaging. After haemodynamic response function blind deconvolution of all voxel signals, and under the hypothesis that RSNs define regions of interest, our method first uses principal component analysis to reduce dimensionality in each RSN to next compute IF (estimated here in terms of Transfer Entropy) between the different RSNs by systematically increasing k (the number of principal components used in the calculation). When k = 1, this method is equivalent to computing IF using the average of all voxel activities in each RSN. For k greater than one our method calculates the k-multivariate IF between the different RSNs. We find that the average IF among RSNs is dimension-dependent, increasing from k =1 (i.e., the average voxels activity) up to a maximum occurring at k =5 to finally decay to zero for k greater than 10. This suggests that a small number of components (close to 5) is sufficient to describe the IF pattern between RSNs. Our method - addressing differences in IF between RSNs for any generic data - can be used for group comparison in health or disease. To illustrate this, we have calculated the interRSNs IF in a dataset of Alzheimer's Disease (AD) to find that the most significant differences between AD and controls occurred for k =2, in addition to AD showing increased IF w.r.t. controls.Comment: 47 pages, 5 figures, 4 tables, 3 supplementary figures. Accepted for publication in Brain Connectivity in its current for

Single-molecule electrical contacts on silicon electrodes under ambient conditions

The ultimate goal in molecular electronics is to use individual molecules as the active electronic component of a real-world sturdy device. For this concept to become reality, it will require the field of single-molecule electronics to shift towards the semiconducting platform of the current microelectronics industry. Here, we report silicon-based single-molecule contacts that are mechanically and electrically stable under ambient conditions. The single-molecule contacts are prepared on silicon electrodes using the scanning tunnelling microscopy break-junction approach using a top metallic probe. The molecular wires show remarkable current–voltage reproducibility, as compared to an open silicon/nano-gap/metal junction, with current rectification ratios exceeding 4,000 when a low-doped silicon is used. The extension of the single-molecule junction approach to a silicon substrate contributes to the next level of miniaturization of electronic components and it is anticipated it will pave the way to a new class of robust single-molecule circuits

A Comprehensive Review of Continuous Glucose Monitoring Accuracy during Exercise Periods

[EN] Continuous Glucose Monitoring (CGM) has been a springboard of new diabetes management technologies such as integrated sensor-pump systems, the artificial pancreas, and more recently, smart pens. It also allows patients to make better informed decisions compared to a few measurements per day from a glucometer. However, CGM accuracy is reportedly affected during exercise periods, which can impact the effectiveness of CGM-based treatments. In this review, several studies that used CGM during exercise periods are scrutinized. An extensive literature review of clinical trials including exercise and CGM in type 1 diabetes was conducted. The gathered data were critically analysed, especially the Mean Absolute Relative Difference (MARD), as the main metric of glucose accuracy. Most papers did not provide accuracy metrics that differentiated between exercise and rest (non-exercise) periods, which hindered comparative data analysis. Nevertheless, the statistic results confirmed that CGM during exercise periods is less accurate.This work was supported by the Ministerio de Economía, Industria y Competitividad (MINECO), Grant Number DPI2016-78831-C2-1-R, the Agencia Estatal de Investigación PID2019-107722RB-C21 / AEI /10.13039/501100011033, the European Union (FEDER funds), and the Vicerectorate of Research, Innovation and Technology Transference from the Universitat Politècnica de València (Grant Number PAID-06-18).Muñoz Fabra, E.; Diez, J.; Bondía Company, J.; Laguna Sanz, AJ. (2021). A Comprehensive Review of Continuous Glucose Monitoring Accuracy during Exercise Periods. Sensors. 21(2). https://doi.org/10.3390/s2102047921

Artificial Pancreas System With Unannounced Meals Based on a Disturbance Observer and Feedforward Compensation

© 2021 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] This brief is focused on the closed-loop control of postprandial glucose levels of patients with type 1 diabetes mellitus after unannounced meals, which is still a major challenge toward a fully autonomous artificial pancreas. The main limitations are the delays introduced by the subcutaneous insulin pharmacokinetics and the glucose sensor, which typically leads to insulin overdelivery. Current solutions reported in the literature typically resort to meal announcement, which requires patient intervention. In this brief, a disturbance observer (DOB) is used to estimate the effect of unannounced meals, and the insulin pharmacokinetics is taken into account by means of a feedforward compensator. The proposed strategy is validated in silico with the UVa/Padova metabolic simulator. It is demonstrated how the DOB successfully estimates and counteracts not only the effect of meals but also the sudden drops in the glucose levels that may lead to hypoglycemia. For unannounced meals, results show a median time-in-range of 80% in a 30-day scenario with high carbohydrate content and large intrasubject variability. Optionally, users may decide to announce meals. In this case, considering severe bolus mismatch due to carbohydrate counting errors, the median time-in-range is increased up to 88%. In every case, hypoglycemia is avoided.This work was supported in part by the Ministerio de Economia y Competitividad under Grant DPI2016-78831-C2-1-R and in part by the European Union through FEDER Funds.Sanz Diaz, R.; García Gil, PJ.; Diez, J.; Bondía Company, J. (2021). Artificial Pancreas System With Unannounced Meals Based on a Disturbance Observer and Feedforward Compensation. IEEE Transactions on Control Systems Technology. 29(1):454-460. https://doi.org/10.1109/TCST.2020.2975147S45446029