4DRoot: Root phenotyping software for temporal 3D scans by X-ray computed tomography.

Currently, plant phenomics is considered the key to reducing the genotype-to-phenotype knowledge gap in plant breeding. In this context, breakthrough imaging technologies have demonstrated high accuracy and reliability. The X-ray computed tomography (CT) technology can noninvasively scan roots in 3D; however, it is urgently required to implement high-throughput phenotyping procedures and analyses to increase the amount of data to measure more complex root phenotypic traits. We have developed a spatial-temporal root architectural modeling software tool based on 4D data from temporal X-ray CT scans. Through a cylinder fitting, we automatically extract significant root architectural traits, distribution, and hierarchy. The open-source software tool is named 4DRoot and implemented in MATLAB. The source code is freely available at https://github.com/TIDOP-USAL/4DRoot. In this research, 3D root scans from the black walnut tree were analyzed, a punctual scan for the spatial study and a weekly time-slot series for the temporal one. 4DRoot provides breeders and root biologists an objective and useful tool to quantify carbon sequestration throw trait extraction. In addition, 4DRoot could help plant breeders to improve plants to meet the food, fuel, and fiber demands in the future, in order to increase crop yield while reducing farming inputs.

Review of Active Extracorporeal Medical Devices to Counteract Freezing of Gait in Patients with Parkinson Disease.

Parkinson Disease (PD) primarily affects older adults. It is the second-most common neurodegenerative disease after Alzheimer's disease. Currently, more than 10 million people suffer from PD, and this number is expected to grow, considering the increasing global longevity. Freezing of Gait (FoG) is a symptom present in approximately 80% of advanced-stage PD's patients. FoG episodes alter the continuity of gait, and may be the cause of falls that can lead to injuries and even death. The recent advances in the development of hardware and software systems for the monitoring, stimulus, or rehabilitation of patients with FoG has been of great interest to researchers because detection and minimization of the duration of FoG events is an important factor in improving the quality of life. This article presents a review of the research on non-invasive medical devices for FoG, focusing on the acquisition, processing, and stimulation approaches used.

4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography.

BACKGROUND: Breakthrough imaging technologies may challenge the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. In this context, X-Ray CT (computed tomography) technology can accurately obtain the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial-temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting to spatially quantify several traits. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA). RESULTS: Roots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, several traits were computed. Two of them were accurately validated using the root digital twin as a ground truth against the cylindrical model: number of branches (RRMSE better than 9%) and volume, reaching a coefficient of determination (R2) of 0.84 and a P < 0.001. CONCLUSIONS: The experimental results support the viability of the developed methodology, being able to provide scalability to a comprehensive analysis in order to perform high throughput root phenotyping.

Yield prediction by machine learning from UAS-based mulit-sensor data fusion in soybean.

BACKGROUND: Nowadays, automated phenotyping of plants is essential for precise and cost-effective improvement in the efficiency of crop genetics. In recent years, machine learning (ML) techniques have shown great success in the classification and modelling of crop parameters. In this research, we consider the capability of ML to perform grain yield prediction in soybeans by combining data from different optical sensors via RF (Random Forest) and XGBoost (eXtreme Gradient Boosting). During the 2018 growing season, a panel of 382 soybean recombinant inbred lines were evaluated in a yield trial at the Agronomy Center for Research and Education (ACRE) in West Lafayette (Indiana, USA). Images were acquired by the Parrot Sequoia Multispectral Sensor and the S.O.D.A. compact digital camera on board a senseFly eBee UAS (Unnamed Aircraft System) solution at R4 and early R5 growth stages. Next, a standard photogrammetric pipeline was carried out by SfM (Structure from Motion). Multispectral imagery serves to analyse the spectral response of the soybean end-member in 2D. In addition, RGB images were used to reconstruct the study area in 3D, evaluating the physiological growth dynamics per plot via height variations and crop volume estimations. As ground truth, destructive grain yield measurements were taken at the end of the growing season. RESULTS: Algorithms and feature extraction techniques were combined to develop a regression model to predict final yield from imagery, achieving an accuracy of over 90.72% by RF and 91.36% by XGBoost. CONCLUSIONS: Results provide practical information for the selection of phenotypes for breeding coming from UAS data as a decision support tool, affording constant operational improvement and proactive management for high spatial precision.

Canopy Roughness: A New Phenotypic Trait to Estimate Aboveground Biomass from Unmanned Aerial System.

Cost-effective phenotyping methods are urgently needed to advance crop genetics in order to meet the food, fuel, and fiber demands of the coming decades. Concretely, characterizing plot level traits in fields is of particular interest. Recent developments in high-resolution imaging sensors for UAS (unmanned aerial systems) focused on collecting detailed phenotypic measurements are a potential solution. We introduce canopy roughness as a new plant plot-level trait. We tested its usability with soybean by optical data collected from UAS to estimate biomass. We validate canopy roughness on a panel of 108 soybean [Glycine max (L.) Merr.] recombinant inbred lines in a multienvironment trial during the R2 growth stage. A senseFly eBee UAS platform obtained aerial images with a senseFly S.O.D.A. compact digital camera. Using a structure from motion (SfM) technique, we reconstructed 3D point clouds of the soybean experiment. A novel pipeline for feature extraction was developed to compute canopy roughness from point clouds. We used regression analysis to correlate canopy roughness with field-measured aboveground biomass (AGB) with a leave-one-out cross-validation. Overall, our models achieved a coefficient of determination (R 2) greater than 0.5 in all trials. Moreover, we found that canopy roughness has the ability to discern AGB variations among different genotypes. Our test trials demonstrate the potential of canopy roughness as a reliable trait for high-throughput phenotyping to estimate AGB. As such, canopy roughness provides practical information to breeders in order to select phenotypes on the basis of UAS data.

Multiparametric Monitoring in Equatorian Tomato Greenhouses (I): Wireless Sensor Network Benchmarking.

In recent years, attention has been paid to wireless sensor networks (WSNs) applied to precision agriculture. However, few studies have compared the technologies of different communication standards in terms of topology and energy efficiency. This paper presents the design and implementation of the hardware and software of three WSNs with different technologies and topologies of wireless communication for tomato greenhouses in the Andean region of Ecuador, as well as the comparative study of the performance of each of them. Two companion papers describe the study of the dynamics of the energy consumption and of the monitored variables. Three WSNs were deployed, two of them with the IEEE 802.15.4 standard with star and mesh topologies (ZigBee and DigiMesh, respectively), and a third with the IEEE 802.11 standard with access point topology (WiFi). The measured variables were selected after investigation of the climatic conditions required for efficient tomato growth. The measurements for each variable could be displayed in real time using either a laboratory virtual instrument engineering workbench (LabVIEWTM) interface or an Android mobile application. The comparative study of the three networks made evident that the configuration of the DigiMesh network is the most complex for adding new nodes, due to its mesh topology. However, DigiMesh maintains the bit rate and prevents data loss by the location of the nodes as a function of crop height. It has been also shown that the WiFi network has better stability with larger precision in its measurements.

Multiparametric Monitoring in Equatorian Tomato Greenhouses (II): Energy Consumption Dynamics.

Tomato greenhouses are a crucial element in the Equadorian economy. Wireless sensor networks (WSNs) have received much attention in recent years in specialized applications such as precision farming. The energy consumption in WSNs is relevant nowadays for their adequate operation, and attention is being paid to analyzing the affecting factors, energy optimization techniques working on the network hardware or software, and characterizing the consumption in the nodes (especially in the ZigBee standard). However, limited information exists on the analysis of the consumption dynamics in each node, across different network technologies and communication topologies, or on the incidence of data transmission speed. The present study aims to provide a detailed analysis of the dynamics of the energy consumption for tomato greenhouse monitoring in Ecuador, in three types of WSNs, namely, ZigBee with star topology, ZigBee with mesh topology (referred to here as DigiMesh), and WiFi with access point topology. The networks were installed and maintained in operation with a line of sight between nodes and a 2-m length, whereas the energy consumption measurements of each node were acquired and stored in the laboratory. Each experiment was repeated ten times, and consumption measurements were taken every ten milliseconds at a rate of fifty thousand samples for each realization. The dynamics were scrutinized by analyzing the recorded time series using stochastic-process analysis methods, including amplitude probability functions and temporal autocorrelation, as well as bootstrap resampling techniques and representations of various embodiments with the so-called M-mode plots. Our results show that the energy consumption of each network strongly depends on the type of sensors installed in the nodes and on the network topology. Specifically, the CO2 sensor has the highest power consumption because its chemical composition requires preheating to start logging measurements. The ZigBee network is more efficient in energy saving independently of the transmission rate, since the communication modules have lower average consumption in data transmission, in contrast to the DigiMesh network, whose consumption is high due to its topology. Results also show that the average energy consumption in WiFi networks is the highest, given that the coordinator node is a Meshlium™ router with larger energy demand. The transmission duration in the ZigBee network is lower than in the other two networks. In conclusion, the ZigBee network with star topology is the most energy-suitable one when designing wireless monitoring systems in greenhouses. The proposed methodology for consumption dynamics analysis in tomato greenhouse WSNs can be applied to other scenarios where the practical choice of an energy-efficient network is necessary due to energy constrains in the sensor and coordinator nodes.

Multiparametric Monitoring in Equatorian Tomato Greenhouses (III): Environmental Measurement Dynamics.

World population growth currently brings unequal access to food, whereas crop yields are not increasing at a similar rate, so that future food demand could be unmet. Many recent research works address the use of optimization techniques and technological resources on precision agriculture, especially in large demand crops, including climatic variables monitoring using wireless sensor networks (WSNs). However, few studies have focused on analyzing the dynamics of the environmental measurement properties in greenhouses. In the two companion papers, we describe the design and implementation of three WSNs with different technologies and topologies further scrutinizing their comparative performance, and a detailed analysis of their energy consumption dynamics is also presented, both considering tomato greenhouses in the Andean region of Ecuador. The three WSNs use ZigBee with star topology, ZigBee with mesh topology (referred to here as DigiMesh), and WiFi with access point topology. The present study provides a systematic and detailed analysis of the environmental measurement dynamics from multiparametric monitoring in Ecuadorian tomato greenhouses. A set of monitored variables (including CO2, air temperature, and wind direction, among others) are first analyzed in terms of their intrinsic variability and their short-term (circadian) rhythmometric behavior. Then, their cross-information is scrutinized in terms of scatter representations and mutual information analysis. Based on Bland⁻Altman diagrams, good quality rhythmometric models were obtained at high-rate sampling signals during four days when using moderate regularization and preprocessing filtering with 100-coefficient order. Accordingly, and especially for the adjustment of fast transition variables, it is appropriate to use high sampling rates and then to filter the signal to discriminate against false peaks and noise. In addition, for variables with similar behavior, a longer period of data acquisition is required for the adequate processing, which makes more precise the long-term modeling of the environmental signals.

Force Trends and Pulsatility for Catheter Contact Identification in Intracardiac Electrograms during Arrhythmia Ablation.

The intracardiac electrical activation maps are commonly used as a guide in the ablation of cardiac arrhythmias. The use of catheters with force sensors has been proposed in order to know if the electrode is in contact with the tissue during the registration of intracardiac electrograms (EGM). Although threshold criteria on force signals are often used to determine the catheter contact, this may be a limited criterion due to the complexity of the heart dynamics and cardiac vorticity. The present paper is devoted to determining the criteria and force signal profiles that guarantee the contact of the electrode with the tissue. In this study, we analyzed 1391 force signals and their associated EGM recorded during 2 and 8 s, respectively, in 17 patients (82 ± 60 points per patient). We aimed to establish a contact pattern by first visually examining and classifying the signals, according to their likely-contact joint profile and following the suggestions from experts in the doubtful cases. First, we used Principal Component Analysis to scrutinize the force signal dynamics by analyzing the main eigen-directions, first globally and then grouped according to the certainty of their tissue-catheter contact. Second, we used two different linear classifiers (Fisher discriminant and support vector machines) to identify the most relevant components of the previous signal models. We obtained three main types of eigenvectors, namely, pulsatile relevant, non-pulsatile relevant, and irrelevant components. The classifiers reached a moderate to sufficient discrimination capacity (areas under the curve between 0.84 and 0.95 depending on the contact certainty and on the classifier), which allowed us to analyze the relevant properties in the force signals. We conclude that the catheter-tissue contact profiles in force recordings are complex and do not depend only on the signal intensity being above a threshold at a single time instant, but also on time pulsatility and trends. These findings pave the way towards a subsystem which can be included in current intracardiac navigation systems assisted by force contact sensors, and it can provide the clinician with an estimate of the reliability on the tissue-catheter contact in the point-by-point EGM acquisition procedure.

Automatic tree parameter extraction by a Mobile LiDAR System in an urban context.

In an urban context, tree data are used in city planning, in locating hazardous trees and in environmental monitoring. This study focuses on developing an innovative methodology to automatically estimate the most relevant individual structural parameters of urban trees sampled by a Mobile LiDAR System at city level. These parameters include the Diameter at Breast Height (DBH), which was estimated by circle fitting of the points belonging to different height bins using RANSAC. In the case of non-circular trees, DBH is calculated by the maximum distance between extreme points. Tree sizes were extracted through a connectivity analysis. Crown Base Height, defined as the length until the bottom of the live crown, was calculated by voxelization techniques. For estimating Canopy Volume, procedures of mesh generation and α-shape methods were implemented. Also, tree location coordinates were obtained by means of Principal Component Analysis. The workflow has been validated on 29 trees of different species sampling a stretch of road 750 m long in Delft (The Netherlands) and tested on a larger dataset containing 58 individual trees. The validation was done against field measurements. DBH parameter had a correlation R2 value of 0.92 for the height bin of 20 cm which provided the best results. Moreover, the influence of the number of points used for DBH estimation, considering different height bins, was investigated. The assessment of the other inventory parameters yield correlation coefficients higher than 0.91. The quality of the results confirms the feasibility of the proposed methodology, providing scalability to a comprehensive analysis of urban trees.

Leaf Movements of Indoor Plants Monitored by Terrestrial LiDAR.

Plant leaf movement is induced by some combination of different external and internal stimuli. Detailed geometric characterization of such movement is expected to improve understanding of these mechanisms. A metric high-quality, non-invasive and innovative sensor system to analyze plant movement is Terrestrial LiDAR (TLiDAR). This technique has an active sensor and is, therefore, independent of light conditions, able to obtain accurate high spatial and temporal resolution point clouds. In this study, a movement parameterization approach of leaf plants based on TLiDAR is introduced. For this purpose, two Calathea roseopicta plants were scanned in an indoor environment during 2 full-days, 1 day in natural light conditions and the other in darkness. The methodology to estimate leaf movement is based on segmenting individual leaves using an octree-based 3D-grid and monitoring the changes in their orientation by Principal Component Analysis. Additionally, canopy variations of the plant as a whole were characterized by a convex-hull approach. As a result, 9 leaves in plant 1 and 11 leaves in plant 2 were automatically detected with a global accuracy of 93.57 and 87.34%, respectively, compared to a manual detection. Regarding plant 1, in natural light conditions, the displacement average of the leaves between 7.00 a.m. and 12.30 p.m. was 3.67 cm as estimated using so-called deviation maps. The maximum displacement was 7.92 cm. In addition, the orientation changes of each leaf within a day were analyzed. The maximum variation in the vertical angle was 69.6° from 12.30 to 6.00 p.m. In darkness, the displacements were smaller and showed a different orientation pattern. The canopy volume of plant 1 changed more in the morning (4.42 dm3) than in the afternoon (2.57 dm3). The results of plant 2 largely confirmed the results of the first plant and were added to check the robustness of the methodology. The results show how to quantify leaf orientation variation and leaf movements along a day at mm accuracy in different light conditions. This confirms the feasibility of the proposed methodology to robustly analyse leaf movements.

Computational modeling of peripheral pain: a commentary.

This commentary is intended to find possible explanations for the low impact of computational modeling on pain research. We discuss the main strategies that have been used in building computational models for the study of pain. The analysis suggests that traditional models lack biological plausibility at some levels, they do not provide clinically relevant results, and they cannot capture the stochastic character of neural dynamics. On this basis, we provide some suggestions that may be useful in building computational models of pain with a wider range of applications.

Lamina specific loss of inhibition may lead to distinct neuropathic manifestations: a computational modeling approach

Introduction It has been reported that inhibitory control at the superficial dorsal horn (SDH) can act in a regionally distinct manner, which suggests that regionally specific subpopulations of SDH inhibitory neurons may prevent one specific neuropathic condition. Methods In an attempt to address this issue, we provide an alternative approach by integrating neuroanatomical information provided by different studies to construct a network-model of the SDH. We use Neuroids to simulate each neuron included in that model by adapting available experimental evidence. Results Simulations suggest that the maintenance of the proper level of pain sensitivity may be attributed to lamina II inhibitory neurons and, therefore, hyperalgesia may be elicited by suppression of the inhibitory tone at that lamina. In contrast, lamina III inhibitory neurons are more likely to be responsible for keeping the nociceptive pathway from the mechanoreceptive pathway, so loss of inhibitory control in that region may result in allodynia. The SDH network-model is also able to replicate non-linearities associated to pain processing, such as Aβ-fiber mediated analgesia and frequency-dependent increase of the neural response. Discussion By incorporating biophysical accuracy and newer experimental evidence, the SDH network-model may become a valuable tool for assessing the contribution of specific SDH connectivity patterns to noxious transmission in both physiological and pathological conditions.

A review of automatic patient identification options for public health care centers with restricted budgets.

A comparative review is presented of available technologies suitable for automatic reading of patient identification bracelet tags. Existing technologies' backgrounds, characteristics, advantages and disadvantages, are described in relation to their possible use by public health care centers with budgetary limitations. A comparative assessment is presented of suitable automatic identification systems based on graphic codes, both one- (1D) and two-dimensional (2D), printed on labels, as well as those based on radio frequency identification (RFID) tags. The analysis looks at the tradeoffs of these technologies to provide guidance to hospital administrator looking to deploy patient identification technology. The results suggest that affordable automatic patient identification systems can be easily and inexpensively implemented using 2D code printed on low cost bracelet labels, which can then be read and automatically decoded by ordinary mobile smart phones. Because of mobile smart phones' present versatility and ubiquity, the implantation and operation of 2D code, and especially Quick Response® (QR) Code, technology emerges as a very attractive alternative to automate the patients' identification processes in low-budget situations.

[Transoperative tamponade due to perforation with a catheter for central blood pressure: report and case analysis]. / Tamponade transoperatorio debido a perforación con catéter venoso central. Informe y análisis de un caso.

Pericardial tamponade due to myocardial perforation from central venous catheter is rather unusual, especially if it happens during a trans-operative period. When it comes up, it has a high mortality risk if the pericardial fluid is not drained at the proper time. At the Oncology Hospital of the National Medical Center, IMSS, Mexico City (Hospital de Oncología del Centro Médico Nacional, Siglo XXI), at the Anesthesia Service, a case of tamponade in transoperative period came up, after a surgical procedure that lasted more than 6 hours, and that manifested itself with signs of cardiogenic shock that did not improve with treatment. The etiology was not diagnosed until a postmortem study. If it had been diagnosed on time, its clinical evolution would have been different with an opportune and correct treatment.

Photoinduced metalation of nonactivated C-Cl bonds with samarium diiodide: synthesis of alkenes with high (Z)-selectivity through beta-elimination reactions.

[reaction: see text] The photoinduced metalation of nonactivated C-Cl bonds of O-acetyl chlorohydrins is promoted by samarium diiodide. As a result of this, beta-elimination of O-acetyl chlorohydrins is achieved, affording the corresponding (Z)-alkenes with total or high stereoselectivity.

Highly selective reaction of alpha-halo-alpha,beta-unsaturated esters with ketones or aldehydes promoted by SmI2: an efficient alternative access to Baylis-Hillman adducts.

A samarium diiodide promoted addition of aromatic or aliphatic beta-substituted-alpha-halo-alpha,beta-unsaturated esters 1 or 3 to both ketones (in THF) and aldehydes (in acetonitrile) led to (Z)-2-(1-hydroxyalkyl)-2,3-alkenoates 2 and 4 in good yields and very high stereoselectivity. This method constitutes an efficient and valuable alternative to the synthesis of Baylis-Hillman adducts. A mechanism is proposed to explain this transformation.

[Analgesic alternatives for the control of postoperatory pain in radical mastectomy]. / Opciones analgésicas para el control del dolor posterior a mastectomía radical.

INTRODUCTION: The purpose of preventive analgesia based on regional blocks with local anesthetics is to avoid sharp and hard-to-control pain in the anterior side of thorax and axilla secondary to radical mastectomy. OBJECTIVE: Our aim was to compare the analgesic effectiveness of direct trans-operational block applied to nerves vs conventional analgesia using analgesic-antipiretic and antiinflammatory (AINES) drugs. MATERIAL AND METHODS: We designed a 1-year case-control study at the Hospital de Oncología of the Instituto Mexicano del Seguro Social's (IMSS's) Centro Medico Nacional Siglo XXI (CMN-SXXI) in Mexico City. A total of 112 patients were mastectomized under general anesthesia: Thirty one patients received preventive analgesia (cases) by direct transoperational infiltration of brachial and intercostal nerves III-VI; bupivacaine 1 mg/kg and buprenorphine 0.005 mg/kg in 15 ml were distributed among appropriate nerves accompanied by a single dose of AINES in emersion stage; during anesthetic emersion, 81 patients received conventional analgesia (control), including endovenous AINES alone or combined, and buprenorphine 0.3 mg or nalbuphine 10 mg. Three evaluations employing analog visual scale were conducted: recovery (immediate post surgery); floor (mediate), and following day. RESULTS: We studied 110 women and two men. Demographic, laboratory, anesthetic, and surgical variables were similar in both groups (p = not significant [ns]). In no case was post-surgical pain reported. Among controls, slight pain was reported by 13 patients, moderate pain by 66, and severe pain was reported by two patients, whereas in patients returned to their rooms on the floor, slight pain was reported by 38, moderate by 42, and severe by one. Finally, on the following day all patients reported slight pain. Types of pain included 15 patients with somatic pain, 10 with neuropathic, and 56 patients with mixed pain (p <0.0001). CONCLUSIONS: Preventive analgesia by direct transoperational block on nerves turned out to be an effective alternative with low level of systemic morbidity, which must be considered to prevent moderate and severe pain syndromes that are difficult to control in thoracic oncological surgery.

Addition reaction of vinylic reagents, derived from alpha-chloroenones, to carbonyl compounds promoted by samarium diiodide.

A new samarium diiodide-promoted addition reaction of vinylsamarium reagents, derived from (Z)-alpha-chloro-alpha,beta-unsaturated phenones 1, to both ketones (in THF) and aldehydes (in acetonitrile) led to (Z)-2-(1-hydroxyalkyl)-2,3-unsaturated ketones in good yield. These transformations took place with total or very high inversion of the stereochemistry of the C-C double bond of the starting chloroenone, producing the Z diastereoisomer. A new methodology to prepare SmI(2) in acetonitrile by sonic treatment of 1,2-diiodoethane with Sm powder is also described. A mechanism to explain this transformation is proposed.