Implementation of a Real-Time Brain-to-Brain Synchrony Estimation Algorithm for Neuroeducation Applications.

This study centers on creating a real-time algorithm to estimate brain-to-brain synchronization during social interactions, specifically in collaborative and competitive scenarios. This type of algorithm can provide useful information in the educational context, for instance, during teacher-student or student-student interactions. Positioned within the context of neuroeducation and hyperscanning, this research addresses the need for biomarkers as metrics for feedback, a missing element in current teaching methods. Implementing the bispectrum technique with multiprocessing functions in Python, the algorithm effectively processes electroencephalography signals and estimates brain-to-brain synchronization between pairs of subjects during (competitive and collaborative) activities that imply specific cognitive processes. Noteworthy differences, such as higher bispectrum values in collaborative tasks compared to competitive ones, emerge with reliability, showing a total of 33.75% of significant results validated through a statistical test. While acknowledging progress, this study identifies areas of opportunity, including embedded operations, wider testing, and improved result visualization. Beyond academia, the algorithm's utility extends to classrooms, industries, and any setting involving human interactions. Moreover, the presented algorithm is shared openly, to facilitate implementations by other researchers, and is easily adjustable to other electroencephalography devices. This research not only bridges a technological gap but also contributes insights into the importance of interactions in educational contexts.

Identifying leverage points using material flow analysis to circularise resources from urban wastewater and organic waste.

Anthropogenic systems are synonymous with linear economies that cause widespread resource waste and environmental degradation. Urban areas are hotspots for this behaviour due to their high population density and resource consumption. Changing this situation is limited by the lack of a holistic but sufficiently detailed understanding of system units where resource waste occurs. The objectives of this study were: (1) to develop and apply a model of the material and substance (nitrogen, phosphorus, and carbon) flows of organic waste and wastewater systems at a local scale, taking Christchurch, New Zealand, as a study case, and (2) to identify leverage points within the system to achieve resource circularisation. Results show that groundwater, infiltrated water, and industrial wastewater are the predominant material flows into the system. Nitrogen and phosphorus inputs predominantly come from food products, detergents, green waste, and industrial wastewater. The Christchurch wastewater system is a prime example of a linear economy, where ∼66 % of the nitrogen and ∼63 % of the phosphorus entering the wastewater system is discharged to the ocean. Leakage from the water supply system reduces water resource efficiency, while water infiltration into the wastewater network inflates the quantity of wastewater treated at the centralised treatment plant, limiting nutrient recovery. In the compost facility, 86 % of the waste is composted, with 33% of the nitrogen and all the phosphorus exiting as compost, while ∼66 % of the nitrogen treated exits through volatilisation. The remaining 14 % of the organic waste entering the treatment plant is deemed unsuitable for composting and is landfilled. The material and substance flow analysis allowed the identification of flows with leverage points in the system where there are opportunities to reduce, reuse, or recover materials and substances to encourage circularisation. These flows include food products, detergents, unsuitable materials for composting, domestic water supply leakages, wastewater network infiltration, and wastewater treatment plant's nutrient recovery.

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors.

This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver's health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle's central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver's heart condition and vehicular traffic have been found in this analysis.

Insights into the Molecular Basis of Huanglongbing Tolerance in Persian Lime (Citrus latifolia Tan.) through a Transcriptomic Approach.

Huanglongbing (HLB) is a vascular disease of Citrus caused by three species of the α-proteobacteria "Candidatus Liberibacter", with "Candidatus Liberibacter asiaticus" (CLas) being the most widespread and the one causing significant economic losses in citrus-producing regions worldwide. However, Persian lime (Citrus latifolia Tanaka) has shown tolerance to the disease. To understand the molecular mechanisms of this tolerance, transcriptomic analysis of HLB was performed using asymptomatic and symptomatic leaves. RNA-Seq analysis revealed 652 differentially expressed genes (DEGs) in response to CLas infection, of which 457 were upregulated and 195 were downregulated. KEGG analysis revealed that after CLas infection, some DEGs were present in the plant-pathogen interaction and in the starch and sucrose metabolism pathways. DEGs present in the plant-pathogen interaction pathway suggests that tolerance against HLB in Persian lime could be mediated, at least partly, by the ClRSP2 and ClHSP90 genes. Previous reports documented that RSP2 and HSP90 showed low expression in susceptible citrus genotypes. Regarding the starch and sucrose metabolism pathways, some genes were identified as being related to the imbalance of starch accumulation. On the other hand, eight biotic stress-related genes were selected for further RT-qPCR analysis to validate our results. RT-qPCR results confirmed that symptomatic HLB leaves had high relative expression levels of the ClPR1, ClNFP, ClDR27, and ClSRK genes, whereas the ClHSL1, ClRPP13, ClPDR1, and ClNAC genes were expressed at lower levels than those from HLB asymptomatic leaves. Taken together, the present transcriptomic analysis contributes to the understanding of the CLas-Persian lime interaction in its natural environment and may set the basis for developing strategies for the integrated management of this important Citrus disease through the identification of blanks for genetic improvement.

Prognosis patients with COVID-19 using deep learning.

BACKGROUND: The coronavirus (COVID-19) is a novel pandemic and recently we do not have enough knowledge about the virus behaviour and key performance indicators (KPIs) to assess the mortality risk forecast. However, using a lot of complex and expensive biomarkers could be impossible for many low budget hospitals. Timely identification of the risk of mortality of COVID-19 patients (RMCPs) is essential to improve hospitals' management systems and resource allocation standards. METHODS: For the mortality risk prediction, this research work proposes a COVID-19 mortality risk calculator based on a deep learning (DL) model and based on a dataset provided by the HM Hospitals Madrid, Spain. A pre-processing strategy for unbalanced classes and feature selection is proposed. To evaluate the proposed methods, an over-sampling Synthetic Minority TEchnique (SMOTE) and data imputation approaches are introduced which is based on the K-nearest neighbour. RESULTS: A total of 1,503 seriously ill COVID-19 patients having a median age of 70 years old are comprised in the research work, with 927 (61.7%) males and 576 (38.3%) females. A total of 48 features are considered to evaluate the proposed method, and the following results are achieved. It includes the following values i.e., area under the curve (AUC) 0.93, F2 score 0.93, recall 1.00, accuracy, 0.95, precision 0.91, specificity 0.9279 and maximum probability of correct decision (MPCD) 0.93. CONCLUSION: The results show that the proposed method is significantly best for the mortality risk prediction of patients with COVID-19 infection. The MPCD score shows that the proposed DL outperforms on every dataset when evaluating even with an over-sampling technique. The benefits of the data imputation algorithm for unavailable biomarker data are also evaluated. Based on the results, the proposed scheme could be an appropriate tool for critically ill Covid-19 patients to assess the risk of mortality and prognosis.

Weeds Harbor Fusarium Species that Cause Malformation Disease of Economically Important Trees in Western Mexico.

Mango malformation disease (MMD) caused by Fusarium spp. is an important limiting factor in most production areas worldwide. Fusarium mexicanum and F. pseudocircinatum have been reported as causing MMD in Mexico. These two pathogens also cause a similar disease in Swietenia macrophylla (big-leaf mahogany malformation disease) in central western Mexico, and F. pseudocircinatum was recently reported as causing malformation disease in Tabebuia rosea (rosy trumpet) in the same region. These studies suggest that additional plant species, including weeds, might be hosts of these pathogens. The role that weed hosts might have in the disease cycle is unknown. The objectives of this work were to recover Fusarium isolates from understory vegetation in mango orchards with MMD, identify the Fusarium isolates through DNA sequence data, and determine whether F. mexicanum is capable of inducing disease in the weedy legume Senna uniflora (oneleaf senna). Additional objectives in this work were to compare Fusarium isolates recovered from weeds and mango trees in the same orchards by characterizing their phylogenetic relationships, assessing in vitro production of mycotoxins, and identifying their mating type idiomorph. A total of 59 Fusarium isolates from five species complexes were recovered from apical and lateral buds from four weed species. Two of the species within the F. fujikuroi species complex are known to cause MMD in Mexico. Trichothecene production was detected in five isolates, including F. sulawense and F. irregulare in the F. incarnatum-equiseti species complex and F. boothii in the F. sambucinum species complex. Both mating types were present among mango and weed isolates. This is the first report of herbaceous hosts harboring Fusarium species that cause mango malformation in Mexico. The information provided should prove valuable for further study of the epidemiological role of weeds in MMD and help manage the disease.

Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning.

Analyzing data related to the conditions of city streets and avenues could help to make better decisions about public spending on mobility. Generally, streets and avenues are fixed as soon as they have a citizen report or when a major incident occurs. However, it is uncommon for cities to have real-time reactive systems that detect the different problems they have to fix on the pavement. This work proposes a solution to detect anomalies in streets through state analysis using sensors within the vehicles that travel daily and connecting them to a fog-computing architecture on a V2I network. The system detects and classifies the main road problems or abnormal conditions in streets and avenues using Machine Learning Algorithms (MLA), comparing roughness against a flat reference. An instrumented vehicle obtained the reference through accelerometry sensors and then sent the data through a mid-range communication system. With these data, the system compared an Artificial Neural Network (supervised MLA) and a K-Nearest Neighbor (Supervised MLA) to select the best option to handle the acquired data. This system makes it desirable to visualize the streets' quality and map the areas with the most significant anomalies.

Malformation Disease in Tabebuia rosea (Rosy Trumpet) Caused by Fusarium pseudocircinatum in Mexico.

Tabebuia rosea (rosy trumpet) is an economically important neotropical tree in Mexico that is highly valued for the quality of its wood, which is used for furniture, crafts, and packing, and for its use as an ornamental and shade tree in parks and gardens. During surveys conducted in the lower Balsas River Basin region in the states of Guerrero and Michoacán, symptoms of floral malformation were detected in T. rosea trees. The main objectives of this study were to describe this new disease, to determine its causal agent, and to identify it using DNA sequence data. A second set of objectives was to analyze the phylogenetic relationship of the causal agent to Fusarium spp. associated with Swietenia macrophylla trees with malformation surveyed in the same region and to compare mycotoxin production and the mating type idiomorphs of fusaria recovered from T. rosea and S. macrophylla. Tabebuia rosea showed malformed inflorescences with multiple tightly curled shoots and shortened internodes. A total of 31 Fusarium isolates recovered from symptomatic T. rosea (n = 20) and S. macrophylla (n = 11) trees were identified by molecular analysis as Fusarium pseudocircinatum. Pathogenicity tests showed that isolates of F. pseudocircinatum recovered from T. rosea induced malformation in inoculated T. rosea seedlings. Eighteen F. pseudocircinatum isolates were tested for their ability to produce mycotoxins and other secondary metabolites. Moniliformin, fusaric acid, bikaverin, beauvericin, aurofusarin. and 8-O-methylbostrycoidin were produced by at least one strain of the 18 isolates tested. A multiplex PCR assay for mating type idiomorph revealed that 22 F. pseudocircinatum isolates were MAT1-1 and that 9 were MAT1-2. Here, we report a new disease of T. rosea in Mexico caused by F. pseudocircinatum.

Enhanced Photocatalytic Activity of TiO2 Modified with GaI toward Environmental Application.

Gallium (Ga) ion-doped TiO2 (Ga-TiO2) nanocomposite with small particle size (9-10 nm) and high surface area (104 m2/g) has been easily synthesized via sol-gel method at low temperature by using low-valent GaI as a doping precursor. The structural and morphological characterization of Ga-TiO2 was carried out with standard analytical and spectroscopic techniques. Ga doping into the TiO2 matrix inhibited a phase transformation from anatase to rutile (photocatalytically inactive) form, even at a higher temperature of 750 °C. Finally, Ga-TiO2 nanocomposite showed high photocatalytic activity and exhibited 97% degradation of acid violet 63 dye within 60 min. The dye degradation rate constant was calculated as 1.6 × 10-1 and 1.4 × 10-1 min-1 under UV and white light irradiation, respectively, which is higher, as compared to the previously reported Ga-TiO2 composites to date.

Genetic diversity of Fusarium pseudocircinatum in the central western region of Mexico: the case of big-leaf mahogany malformation disease.

Fusarium pseudocircinatum is the main causal agent of big-leaf mahogany malformation disease (BLMMD) of mahogany (Swietenia macrophylla) in Mexico. Although, BLMMD is the most important disease for this high-value timber species, there is a lack of information on the genetic variation present in geographically diverse isolates of F. pseudocircinatum. The objective of this study was to determine the genetic diversity of populations of F. pseudocircinatum causing BLMMD in the central western region of Mexico. A total of 611 big-leaf mahogany trees were inspected at eight sites in four states (Colima, Guerrero, Jalisco and Michoacán); of these, 42.7% showed malformation symptoms similar to those of BLMMD. Of 374 Fusarium isolates that were recovered, 277 were identified as F. pseudocircinatum, 56 were F. mexicanum, and 41 were Fusarium spp. An ISSR analysis of the F. pseudocircinatum isolates generated 51 bands of which 38 were polymorphic (76.8%) with a mean of 17 bands per primer. A total of 87 multilocus genotypes (MLGs) were identified. Nei's genetic diversity analysis showed that the isolates had a high genetic diversity average (0.147), with values ranging from 0.070 to 0.365 depending of the geographical location. An analysis of molecular variance revealed that the variation within the populations was low (27.36%), while the variation within MLGs was significant (72.64%), indicating genetic flow. Overall, the genetic variability of F. pseudocircinatum populations was high and the MLGs from Colima (Colima) and Gabriel Zamora (Michoacán) were placed centrally, which possibly is evidence of ancestry and indicates its dispersion routes in the central western region of Mexico.

Genome Sequence Data of Six Isolates of Phytophthora capsici from Mexico.

Phytophthora capsici is an oomycete plant pathogen with a wide host range. Worldwide, P. capsici is known for causing the principal disease of chili pepper crops. Our goal was to expand the available genome resources for this diverse pathogen by generating whole-genome sequences for six isolates of P. capsici from Mexico.

Genetic diversity of Fusarium mexicanum, causal agent of mango and big-leaf mahogany malformation in Mexico.

In Mexico, Fusarium mexicanum has been reported causing mango malformation disease and big-leaf mahogany malformation disease. Our objective was to determine the genetic diversity of F. mexicanum isolates obtained from malformed big-leaf mahogany and mango trees, using an internal simple sequence repeat (ISSR) analysis. A total of 61 isolates of F. mexicanum, 32 from mango and 29 from big-leaf mahogany, were initially genotyped using fourteen ISSR primers. Data from five primers that produced the highest number of polymorphic bands were selected for further analysis. The primers generated 49 polymorphic bands (85.96%) from a total of 57 fragments ranging in size from 250 to 2800 bp, with an average of 11.4 bands per primer. An analysis of molecular variance (AMOVA) indicated that the variation within populations, isolates grouped by host and geographic origin, was significant (43%), followed by the variation between the big-leaf mahogany versus mango isolates (34%), while among populations the variation was the lowest (22%). The genetic fingerprints suggested that genetic variability of F. mexicanum populations are structured by the host of origin rather than the geographic region.

Dynamic complexity measures and entropy paths for modelling and comparison of evolution of patients with drug resistant epileptic encephalopathy syndromes (DREES).

Epileptic encephalopathies (EE) is a term coined by the International League Against Epilepsy (ILAE) to refer to a group of epilepsies in which the ictal and interictal abnormalities may contribute to progressive cerebral dysfunction. Among them, two affect mainly children and are very difficult to deal with, Doose and Lennox-Gastaut syndromes, (DS and LGS, respectively). So far (Zavala-Yoe et al., J Integr Neurosci 15(2):205-223, 2015a and works of ours there), quantitative analysis of single case studies of EE have been performed. All of them are manifestations of drug resistant epileptic encephalopathies (DREES) and as known, such disorders require a lot of EEG studies through all patient's life. As a consequence, dozens of EEG records are stored by parents and neurologists as time goes by. However, taking into account all this massive information, our research questions (keeping colloquial wording by parents) arise: a) Which zone of the brain has been the most affected so far? b) On which year was the child better? c) How bad is our child with respect to others? We must reflect that despite clinical assessment of the EEG has undergone standardization by establishment of guidelines such as the recently published guidelines of the American Clinical Neurophysiology Society (Tsuchida et al., J Clin Neurophysiol 4(33):301-302, 2016), qualitative EEG will never be as objective as quantitative EEG, since it depends largely on the education and experience of the conducting neurophysiologist (Grant et al., Epilepsy Behav 2014(32):102-107, 2014, Rating, Z Epileptologie, Springer Med 27(2):139-142, 2014). We already answered quantitatively the above mentioned questions in the references of ours given above where we provided entropy curves and an entropy index which encompasses the complexity of bunches of EEG making possible to deal with massive data and to make objective comparisons among some patients simultaneously. However, we have refined that index here and we also offer another two measures which are spatial and dynamic. Moreover, from those indices we also provide what we call a temporal dynamic complexity path which shows in a standard 10-20 system head diagram the evolution of the lowest complexity per brain zone with respect to the EEG period. These results make it possible to compare quantitatively/graphically the progress of several patients at the same time, answering the questions posed above. The results obtained showed that we can associate low spatio-temporal entropy indices to multiple seizures events in several patients at the same time as well as tracking seizure progress in space and time with our entropy path, coinciding with neurophysiologists observations.

Electroencephalographic evaluation of acoustic therapies for the treatment of chronic and refractory tinnitus.

BACKGROUND: To date, a large number of acoustic therapies have been applied to treat tinnitus. The effect that produces those auditory stimuli is, however, not well understood yet. Furthermore, the conventional clinical protocol is based on a trial-error procedure, and there is not a formal and adequate treatment follow-up. At present, the only way to evaluate acoustic therapies is by means of subjective methods such as analog visual scale and ad-hoc questionnaires. METHODS: This protocol seeks to establish an objective methodology to treat tinnitus with acoustic therapies based on electroencephalographic (EEG) activity evaluation. On the hypothesis that acoustic therapies should produce perceptual and cognitive changes at a cortical level, it is proposed to examine neural electrical activity of patients suffering from refractory and chronic tinnitus in four different stages: at the beginning of the experiment, at one week of treatment, at five weeks of treatment, and at eight weeks of treatment. Four of the most efficient acoustic therapies found at the moment are considered: retraining, auditory discrimination, enriched acoustic environment, and binaural. DISCUSSION: EEG has become a standard brain imaging tool to quantify and qualify neural oscillations, which are basically spatial, temporal, and spectral patterns associated with particular perceptual, cognitive, motor and emotional processes. Neural oscillations have been traditionally studied on the basis of event-related experiments, where time-locked and phase-locked responses (i.e., event-related potentials) along with time-locked but not necessary phase-locked responses (i.e., event-related (de) synchronization) have been essentially estimated. Both potentials and levels of synchronization related to auditory stimuli are herein proposed to assess the effect of acoustic therapies. TRIAL REGISTRATION: Registration Number: ISRCTN14553550. ISRCTN Registry: BioMed Central. Date of Registration: October 31st, 2017.

Sodium thiosulphate attenuates brain inflammation induced by systemic lipopolysaccharide administration in C57BL/6J mice.

It has been demonstrated that peripheral infections accompanied by neuroinflammation may modify brain development or affect normal brain aging and represent major risk factors for the development of neurological disorders. A wide range of synthetic and natural compounds with anti-inflammatory properties have been evaluated in animal models of neuroinflammation and neurodegeneration as an adjuvant therapeutic strategy. In the present study we have demonstrated for the first time that sodium thiosulphate (STS), a known antidote approved for treatment of certain medical conditions, is capable of reducing brain inflammation caused by systemic LPS administration. STS reduced brain levels of pro-inflammatory cytokine interleukin-1ß (IL-1ß), cyclooxygenase-2 (COX-2), ionized calcium binding adaptor molecule 1 (Iba-1) and 18 kDa translocator protein (TSPO) in an animal model of systemic LPS-induced neuroinflammation. In addition, we demonstrated for the first time elevated TSPO expression in retinal ganglion cells layer after peripheral LPS challenge and inhibition of ocular TSPO expression after treatment with STS. We think that STS may be used as an adjuvant anti-inflammatory therapy for many pathological conditions associated with inflammation in the brain.

Entropy measures to study and model long term simultaneous evolution of children in Doose and Lennox-Gastaut syndromes.

Doose and Lennox-Gastaut (syndromes) are rare generalized electroclinical affections of early infancy of variable prognosis which manifest with very diverse kinds of seizures. Very frequently, these types of epilepsy become drug resistant and finding reliable treatment results is very difficult. As a result of this, fighting against these syndromes becomes a long term (or endless) event for the little patient, the neurologist and the parents. A lot of Electroencephalographic (EEG) records are so accumulated during the child's life in order to monitor evolution and correlate it with medications. So, given a bunch of EEG, three questions arise: (a) On which year was the child healthier (less affected by seizures)? (b) Which area of the brain has been the most affected? (c) What is the status of the child with respect to others (which also have a bunch of EEG, each)? Answering these interrogations by traditional scrutinizing of the whole database becomes subjective, if not impossible. We propose to answer these questions objectively by means of time series entropies. We start with our modified version of the Multiscale Entropy (MSE) in order to generalize it as a Bivariate MSE (BMSE) and from them, we compute two indices. All were tested in a series of patients and coincide with medical conclusions. As far as we are concerned, our contribution is new.

[Agroindustrial wastes methanization and bacterial composition in anaerobic digestion]. / Residuos agroindustriales con potencial para la producción de metano mediante la digestión anaerobia.

The tons of organic waste that are annually generated by agro-industry, can be used as raw material for methane production. For this reason, it is important to previously perform biodegradability tests to organic wastes for their full scale methanization. This paper addresses biodegradability, methane production and the behavior of populations of eubacteria and archaeabacteria during anaerobic digestion of banana, mango and papaya agroindustrial wastes. Mango and banana wastes had higher organic matter content than papaya in terms of their volatile solids and total solid rate (94 and 75% respectively). After 63 days of treatment, the highest methane production was observed in banana waste anaerobic digestion: 63.89ml CH4/per gram of chemical oxygen demand of the waste. In the PCR-DGGE molecular analysis, different genomic footprints with oligonucleotides for eubacteria and archeobacteria were found. Biochemical methane potential results proved that banana wastes have the best potential to be used as raw material for methane production. The result of a PCR- DGGE analysis using specific oligonucleotides enabled to identify the behavior of populations of eubacteria and archaeabacteria present during the anaerobic digestion of agroindustrial wastes throughout the process.

Occurrence of glyphosate in water bodies derived from intensive agriculture in a tropical region of southern Mexico.

Glyphosate is an agrochemical widely used to control weeds. However, glyphosate spreads to water bodies by spray-drift, run-off and leaching, potentially causing detrimental effects on non-target biota. There is no information on the occurrence of this herbicide in water bodies near crop fields in Mexico, although it is the most commonly used pesticide in this country. To fill this gap, we quantified glyphosate in water bodies from twenty-three locations, including natural protected areas and agricultural areas in southern Mexico, during the dry and the rainy seasons. We expected (1) higher concentrations during the dry season due to reduced dilution by precipitation and, (2) absence of glyphosate in the protected areas. In agreement with our expectation, concentration of glyphosate was higher during the dry season (up to 36.7 µg/L). Nonetheless, glyphosate was detected in all samples-including natural protected areas. These results emphasize the need for an evaluation of the impact of glyphosate on native species as well as regulate its use.

Specialized diving traits in the generalist morphology of Fulica (Aves, Rallidae).

Foot-propelled diving comprises the primary locomotion-based feeding strategy for many birds, including families such as Phalacrocoracidae, Anhingidae, Podicipedidae, Gaviidae, and the diving ducks within Anatidae. While the morphology of specialized divers is well known, the corresponding morphology is less known for birds not as specialized but capable of diving, such as the coots (Rallidae, Fulica spp.). To compare the osteology of Fulica with other (non-diving) Rallidae, and with foot-propelled diving birds that are distantly related, we considered osteological characters, as well as the proportion of the hind limb bones and the femoral splay angle to construct a phylomorphospace, and to perform a comparative disparity analysis considering ecomorphologically relevant characters related to swimming and diving. Coots resulted to be significantly disparate from other Rallidae showing many traits of specialized foot-propelled divers, but only noticeable when compared with other rallids, as the degree of development of these traits is markedly less than in loons, grebes, or cormorants. This may correspond to a stabilizing selection of characteristics associated with a generalist morphology in Fulica. Studying adaptation in generalist taxa broadens our understanding of ecomorphologically significant features, thereby enabling us to generalize their evolutionary patterns.