Anti-leptospiral agglutinins in marmosets (Saguinus oedipus and Saguinus leucopus) from illegal trade

ABSTRACT Objective. Determine the infection status with pathogenic Leptospira of one Saguinus oedipus and nine Saguinus leucopus at the Cali Zoo that had been confiscated in Colombia from illegal trade. Materials and methods. A full physical examination, blood work, urinalysis were conducted in all individuals during the reception health check-up, in addition to running the microagglutination test with a pool of 19 serovars, with a starting dilution of 1:50. Results. A high positive titer (≥1:3200) to Leptospira alexanderi serovar manhao in an asymptomatic S. oedipus was detected. All S. leucopus tested negative or less than 1:50. Conclusions. Captive locations have been documented to artificially enhance opportunities to come into contact with contaminated bodily fluids from peridomestic rodents. However, infectious diseases acquired during the illegal transport of wildlife to major metropolitan centers are rarely considered a wildlife conservation or public health threat. Infection with zoonotic pathogens should also be considered an additional threat to endangered wild primates involved in illegal trade, which could hamper reintroduction efforts or other population management procedures for primate species with restricted and fragmented distributions

Network-based brain computer interfaces: principles and applications

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user s mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability

Towards the use of spatial network metrics for characterizing brain mental states

International audienc

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Développement de propriétés des réseaux complexes pour les Interfaces Cerveau-Machine

A Brain-Computer Interface (BCI) is a system that can translate brain activity patterns into messages or commands for an interactive application. It enables a subject to send commands to a device only by means of brain activity, without requiring any peripherical muscular activity. These systems are increasingly explored for control and communication, as well as for treatment of neurological disorders, especially via the ability of subjects to voluntarily modulate their brain activity through mental imagery (MI). To control a BCI, the user must produce different brain signal patterns that the system will identify and translate into commands. Even though this technique has been widely used, subjects performance, measured as the correct classification of the user’s intent, still shows low scores. Much of the efforts to solve this problem have focused on the BCI classification block. While, the research of alternative features has been poorly explored. In most implemented systems, pattern recognition relies on power spectrum density (PSD) of a reduced number of sources, focusing on features that characterize a single brain region. However, the brain is not a collection of isolated pieces working independently. It rather consists of a distributed complex network that integrates information across differently specialized regions. It turns out that examining signals from one specific region, while neglecting its interactions with others, oversimplifies the phenomenon. It would be preferable to have an understanding of the system’s collective behaviour to fully capture the brain functioning. Thus, we hypothesize that functional connectivity (FC) features could be more representative of the complexity of neurophysiological processes, since they measure interactions between different brain areas, reflecting the information exchange that is essential to decode brain organization. Then, these interactions can be quantified using network theoretic approaches, extracting few summary properties of the entire complex brain network. Thus, network analysis may also be more efficient by reducing the problem dimension and optimizing the computational cost. Nevertheless, extracting topological properties of the network, while disregarding the intrinsic spatial nature of the brain, could overlook crucial information for understanding brain functioning. Recent neuroimaging studies demonstrated that brain connectivity reveals hemisphere lateralization during motor MI-related tasks. Covering these two concepts, we explored the dual contribution of brain network topology and space in modelling motor-related mental states through the concept of functional lateralization. Specifically, we introduced new metrics to quantify segregation and integration within and between the hemispheres, and we showed that they are highly relevant features for decoding a motor imagery mental task. These network properties not only give competitive classification accuracy but also have the advantage of being neurophysiologically interpretable, compared to state-of-the-art approaches that are instead blind to the underlying mechanism.Une interface cerveau-machine (ICM) est un système capable de traduire les modèles d’activité cérébrale en messages pour une application. Il permet à un sujet d’envoyer des commandes à un appareil à travers l’activité cérébrale, sans nécessiter d’activité musculaire périphérique. Ces systèmes sont de plus en plus explorés pour le contrôle et la communication, ainsi que pour le traitement des troubles neurologiques, notamment via la capacité des sujets à moduler volontairement leur activité cérébrale grâce à l’imagerie mentale (IM). Pour contrôler une ICM, l’utilisateur doit produire différents types de signaux cérébraux que le système identifiera et traduira en commandes. Même si cette technique a été largement utilisée, la performance des sujets, mesurée comme la correcte classification de l’intention de l’utilisateur, affiche toujours de faibles scores. Une grande partie des efforts pour résoudre ce problème s’est concentrée sur la classification. Alors que la recherche de features alternatives a été peu explorée. Dans la plupart des systèmes mis en œuvre, la reconnaissance des états mentaux repose sur la puissance spectrale d’un nombre réduit de sources, en se concentrant sur les caractérisation d’une seule région du cerveau. Cependant, le cerveau n’est pas un ensemble de pièces isolées travaillant de manière indépendante. Il s’agit plutôt d’un réseau complexe qui intègre des informations dans des régions différemment spécialisées. Il s’avère que l’examen des signaux d’une région spécifique, tout en négligeant ses interactions avec les autres, simplifie à l’extrême le phénomène. Il serait préférable de comprendre le comportement collectif du système pour bien saisir le fonctionnement cérébral. Ainsi, nous pensons que l’étude à travers la connectivité fonctionnelle pourraient être plus représentatives de la complexité des processus neurophysiologiques, puisqu’elles mesurent les interactions entre différentes aires cérébrales, reflétant l’échange d’informations qui est essentiel pour décoder l’organisation cérébrale. Ensuite, ces interactions peuvent être synthétisées à l’aide d’estimateurs des réseaux complexes, modélisant le cerveau humain comme un réseau. Certes, l’analyse de réseau peut présenter une performance plus précise car elle optimise le coût de calcul et la dimensionnalité. Néanmoins, la simple extraction des propriétés topologiques du réseau, sans tenir compte de la nature spatiale intrinsèque du cerveau, pourrait manquer des informations cruciales pour comprendre le fonctionnement du cerveau. Des études récentes ont démontré que la connectivité cérébrale révèle la latéralisation des hémisphères lors de tâches liées à l’IM moteur. Couvrant ces deux concepts, nous avons exploré la double contribution de la topologie et de l’espace dans la modélisation des états mentaux moteurs par la latéralisation fonctionnelle. Plus précisément, nous avons introduit de nouvelles métriques pour quantifier la ségrégation et l’intégration au sein et entre les hémisphères, et nous avons montré qu’il s’agit de caractéristiques très pertinentes pour décoder une tâche mentale d’imagerie motrice. Ces propriétés de réseau donnent non seulement des précisions de classification compétitives, mais ont également l’avantage d’être interprétables sur le plan neurophysiologique, par rapport aux approches de pointe qui sont plutôt aveugles au mécanisme sous-jacent

Développement de propriétés des réseaux complexes pour les Interfaces Cerveau-Machine

A Brain-Computer Interface (BCI) is a system that can translate brain activity patterns into messages or commands for an interactive application. It enables a subject to send commands to a device only by means of brain activity, without requiring any peripherical muscular activity. These systems are increasingly explored for control and communication, as well as for treatment of neurological disorders, especially via the ability of subjects to voluntarily modulate their brain activity through mental imagery (MI). To control a BCI, the user must produce different brain signal patterns that the system will identify and translate into commands. Even though this technique has been widely used, subjects performance, measured as the correct classification of the user’s intent, still shows low scores. Much of the efforts to solve this problem have focused on the BCI classification block. While, the research of alternative features has been poorly explored. In most implemented systems, pattern recognition relies on power spectrum density (PSD) of a reduced number of sources, focusing on features that characterize a single brain region. However, the brain is not a collection of isolated pieces working independently. It rather consists of a distributed complex network that integrates information across differently specialized regions. It turns out that examining signals from one specific region, while neglecting its interactions with others, oversimplifies the phenomenon. It would be preferable to have an understanding of the system’s collective behaviour to fully capture the brain functioning. Thus, we hypothesize that functional connectivity (FC) features could be more representative of the complexity of neurophysiological processes, since they measure interactions between different brain areas, reflecting the information exchange that is essential to decode brain organization. Then, these interactions can be quantified using network theoretic approaches, extracting few summary properties of the entire complex brain network. Thus, network analysis may also be more efficient by reducing the problem dimension and optimizing the computational cost. Nevertheless, extracting topological properties of the network, while disregarding the intrinsic spatial nature of the brain, could overlook crucial information for understanding brain functioning. Recent neuroimaging studies demonstrated that brain connectivity reveals hemisphere lateralization during motor MI-related tasks. Covering these two concepts, we explored the dual contribution of brain network topology and space in modelling motor-related mental states through the concept of functional lateralization. Specifically, we introduced new metrics to quantify segregation and integration within and between the hemispheres, and we showed that they are highly relevant features for decoding a motor imagery mental task. These network properties not only give competitive classification accuracy but also have the advantage of being neurophysiologically interpretable, compared to state-of-the-art approaches that are instead blind to the underlying mechanism.Une interface cerveau-machine (ICM) est un système capable de traduire les modèles d’activité cérébrale en messages pour une application. Il permet à un sujet d’envoyer des commandes à un appareil à travers l’activité cérébrale, sans nécessiter d’activité musculaire périphérique. Ces systèmes sont de plus en plus explorés pour le contrôle et la communication, ainsi que pour le traitement des troubles neurologiques, notamment via la capacité des sujets à moduler volontairement leur activité cérébrale grâce à l’imagerie mentale (IM). Pour contrôler une ICM, l’utilisateur doit produire différents types de signaux cérébraux que le système identifiera et traduira en commandes. Même si cette technique a été largement utilisée, la performance des sujets, mesurée comme la correcte classification de l’intention de l’utilisateur, affiche toujours de faibles scores. Une grande partie des efforts pour résoudre ce problème s’est concentrée sur la classification. Alors que la recherche de features alternatives a été peu explorée. Dans la plupart des systèmes mis en œuvre, la reconnaissance des états mentaux repose sur la puissance spectrale d’un nombre réduit de sources, en se concentrant sur les caractérisation d’une seule région du cerveau. Cependant, le cerveau n’est pas un ensemble de pièces isolées travaillant de manière indépendante. Il s’agit plutôt d’un réseau complexe qui intègre des informations dans des régions différemment spécialisées. Il s’avère que l’examen des signaux d’une région spécifique, tout en négligeant ses interactions avec les autres, simplifie à l’extrême le phénomène. Il serait préférable de comprendre le comportement collectif du système pour bien saisir le fonctionnement cérébral. Ainsi, nous pensons que l’étude à travers la connectivité fonctionnelle pourraient être plus représentatives de la complexité des processus neurophysiologiques, puisqu’elles mesurent les interactions entre différentes aires cérébrales, reflétant l’échange d’informations qui est essentiel pour décoder l’organisation cérébrale. Ensuite, ces interactions peuvent être synthétisées à l’aide d’estimateurs des réseaux complexes, modélisant le cerveau humain comme un réseau. Certes, l’analyse de réseau peut présenter une performance plus précise car elle optimise le coût de calcul et la dimensionnalité. Néanmoins, la simple extraction des propriétés topologiques du réseau, sans tenir compte de la nature spatiale intrinsèque du cerveau, pourrait manquer des informations cruciales pour comprendre le fonctionnement du cerveau. Des études récentes ont démontré que la connectivité cérébrale révèle la latéralisation des hémisphères lors de tâches liées à l’IM moteur. Couvrant ces deux concepts, nous avons exploré la double contribution de la topologie et de l’espace dans la modélisation des états mentaux moteurs par la latéralisation fonctionnelle. Plus précisément, nous avons introduit de nouvelles métriques pour quantifier la ségrégation et l’intégration au sein et entre les hémisphères, et nous avons montré qu’il s’agit de caractéristiques très pertinentes pour décoder une tâche mentale d’imagerie motrice. Ces propriétés de réseau donnent non seulement des précisions de classification compétitives, mais ont également l’avantage d’être interprétables sur le plan neurophysiologique, par rapport aux approches de pointe qui sont plutôt aveugles au mécanisme sous-jacent

Anti-leptospiral agglutinins in marmosets (Saguinus oedipus and Saguinus leucopus) from illegal trade

Objective. Determine the infection status with pathogenic Leptospira of one Saguinus oedipus and nine Saguinus leucopus at the Cali Zoo that had been confiscated in Colombia from illegal trade. Materials and methods. A full physical examination, blood work, urinalysis were conducted in all individuals during the reception health check-up, in addition to running the microagglutination test with a pool of 19 serovars, with a starting dilution of 1:50. Results. A high positive titer (≥1:3200) to Leptospira alexanderi serovar manhao in an asymptomatic S. oedipus was detected. All S. leucopus tested negative or less than 1:50. Conclusions. Captive locations have been documented to artificially enhance opportunities to come into contact with contaminated bodily fluids from peridomestic rodents. However, infectious diseases acquired during the illegal transport of wildlife to major metropolitan centers are rarely considered a wildlife conservation or public health threat. Infection with zoonotic pathogens should also be considered an additional threat to endangered wild primates involved in illegal trade, which could hamper reintroduction efforts or other population management procedures for primate species with restricted and fragmented distributions

On the classification of mental states by means of network-based features

International audienc