Distribución espacial de posturas de controladores biológicos crisópidos Neuroptera, en cuatro cultivares de olivo en La Rioja.

Para determinar la presencia de los Neuroptera: Chrysopidae en el cultivo de olivo, se realizó una prospección de posturas de “crisópidos” el día 9 de abril de 2011, en la etapa fenológica de precosecha, en el banco de germoplasma de olivo ex situ de la Universidad Nacional de La Rioja. Se escogieron tres árboles de cada una de los cultivares “Arauco”, “Arbequina”, “Frantoio” y “Manzanilla”. En cada árbol se observó ramas por 5 minutos, en cada una de las cuatro orientaciones (N, S, E y O). En los 12 árboles estudiados, se encontró un total de 54 huevos colectados, 23 estaban en el haz de la hoja, 30 en el envés de la hoja y un huevo en el fruto. El cultivar “Frantoio” presentó el mayor número (n = 23) de huevos. Los otros cultivares de olivo presentaron un menor número (50%), y no mostrando diferencias entre ellos ("Arauco” = 10, “Arbequina” = 11, “Manzanilla”= 10). La ubicación de las posturas en relación a la orientación en el árbol, mostró una tendencia por la orientación Norte (n = 17), Oeste (n = 16) y Este (n = 14), mientras que la orientación Sur tuvo el menor número de posturas (n = 7). Estos resultados contribuyen a definir estrategias de control biológico aumentativo en el cultivo.Spatial distribution of eggs of beneficial lacewings Insecta: Neuroptera in four cultivars of olive trees in La Rioja.AbstractFor determined the presence of the Neuroptera: Chrysopidae in olive crops, conducted a survey of eggs of lacewings on April 9, 2011, at the time of pre-harvest, in the germoplasm collections of olive ex situ of the National University of La Rioja. They chose three trees of the “Arauco”, "Arbequina", "Frantoio" and "Manzanilla" cultivars. Each tree found branches for 5 minutes, in each of the four orientations (N, S, E and W). The 54 collected eggs, 23 were on the upper side of the road, 30 on the underside of the leaf and an egg in the fruit. The cultivar "Frantoio" presented the greatest number (n = 23) eggs. Other cultivars of olive tree presented a lower number (50%), and not showing differences between them (“Arauco” = 10, "Arbequina" = 11, "Manzanilla" = 10). The location of the eggs in relation to the guidance in the tree, showed a trend for the North direction (n = 17), West (n = 16) and East (n = 14), while the South direction had the lowest number of eggs (n = 7). These results help define strategies of augmentative biological control in crops.Key words: Eggs distribution; Chrysopidae; Biological control; Oliv

Diversidad específica de controladores biológicos crisópidos (Neuroptera: Chrysopidae) en el germoplasma olivícola en la Plaza Solar, La Rioja, Argentina.

Between the months of March toAugust of 2011, it was made prospection of lacewings adults and eggs of in germplasm olive trees of the Solar Square of the National University of La Rioja. The adults were collected by means of entomological net in the tree, during the hours of light in the day, and with plastic bottle of 500ml in the hours at night.The eggs were obtained in the leaves of the tree. The eggs entered in the laboratory of the CENIIT, until the obtaining of the adults. Its were prepared in boxes entomology and determined by the Dr. Enrique González Olazo in the Fundación Miguel Lillo.In the six months of sampling (autumn-winter) a total of six species was determined: Ceraeochrysa claveri Navás Chrysoperla asoralis (Banks), C argentina González Olazo y Reguilón, C externa (Hagen), Ungla argentina(Navás) y U binaria (Navás).They are new records for La Rioja and olive crops: C. asoralis, C. claveri, U argentina and U binaria.The most abundant species (n=9) C asoralis was . Present data on the biology and ecology of the species and a key for the determination of the genus and the six species of Chrysopidae.Entre los meses de marzo y agosto de 2011, se realizó prospección de adultos y posturas de crisópidos en el germoplasma olivícola de la Plaza Solar de la Universidad Nacional de La Rioja. Los adultos fueron colectados mediante red entomológica en el árbol, durante las horas de luz, y con botella plástica de 500ml en las horas de oscuridad.Los huevos fueron obtenidos en las hojas del árbol. Las posturas ingresaron a la cría en el laboratorio del CENIIT, hasta la obtención de los adultos, los cuales fueron acondicionados en cajas entomológicas y determinados por el Dr. Enrique González Olazo en la Fundación Miguel Lillo.En los seis meses de muestreo (otoño-invierno) se determinó un total de seis especies: Ceraeochrysa claveri Navás, Chrysoperla asoralis (Banks), C. argentina González Olazo & Reguilón, C. externa (Hagen), Ungla argentina (Navás) y U. binaria (Navás) Son nuevas citas para La Rioja y el cultivo del olivo: C. asoralis, C. claveri, U. argentina y U. binaria. La especie más abundante (n=9) fue C asolaris. Se presentan datos de la biología y ecología de las especies. Se elaboró una clave para la determinación de los géneros y las seis especies de Chrysopidae

Axonal Odorant Receptors Mediate Axon Targeting

In mammals, odorant receptors not only detect odors but also define the target in the olfactory bulb, where sensory neurons project to give rise to the sensory map. The odorant receptor is expressed at the cilia, where it binds odorants, and at the axon terminal. The mechanism of activation and function of the odorant receptor at the axon terminal is, however, still unknown. Here, we identify phosphatidylethanolamine- binding protein 1 as a putative ligand that activates the odorant receptor at the axon terminal and affects the turning behavior of sensory axons.Genetic ablation of phosphatidylethanolamine-binding protein 1 in mice results in a strongly disturbed olfactory sensory map. Our data suggest that the odorant receptor at the axon terminal of olfactory neurons acts as an axon guidance cue that responds to molecules originating in the olfactory bulb. The dual function of the odorant receptor links specificity of odor perception and axon targeting

MRI data quality assessment for the RIN - Neuroimaging Network using the ACR phantoms

PURPOSE: Generating big-data is becoming imperative with the advent of machine learning. RIN-Neuroimaging Network addresses this need by developing harmonized protocols for multisite studies to identify quantitative MRI (qMRI) biomarkers for neurological diseases. In this context, image quality control (QC) is essential. Here, we present methods and results of how the RIN performs intra- and inter-site reproducibility of geometrical and image contrast parameters, demonstrating the relevance of such QC practice. METHODS: American College of Radiology (ACR) large and small phantoms were selected. Eighteen sites were equipped with a 3T scanner that differed by vendor, hardware/software versions, and receiver coils. The standard ACR protocol was optimized (in-plane voxel, post-processing filters, receiver bandwidth) and repeated monthly. Uniformity, ghosting, geometric accuracy, ellipse’s ratio, slice thickness, and high-contrast detectability tests were performed using an automatic QC script. RESULTS: Measures were mostly within the ACR tolerance ranges for both T1- and T2-weighted acquisitions, for all scanners, regardless of vendor, coil, and signal transmission chain type. All measurements showed good reproducibility over time. Uniformity and slice thickness failed at some sites. Scanners that upgraded the signal transmission chain showed a decrease in geometric distortion along the slice encoding direction. Inter-vendor differences were observed in uniformity and geometric measurements along the slice encoding direction (i.e. ellipse’s ratio). CONCLUSIONS: Use of the ACR phantoms highlighted issues that triggered interventions to correct performance at some sites and to improve the longitudinal stability of the scanners. This is relevant for establishing precision levels for future multisite studies of qMRI biomarkers

Normative values of the topological metrics of the structural connectome: A multi-site reproducibility study across the Italian Neuroscience network

Purpose: The use of topological metrics to derive quantitative descriptors from structural connectomes is receiving increasing attention but deserves specific studies to investigate their reproducibility and variability in the clinical context. This work exploits the harmonization of diffusion-weighted acquisition for neuroimaging data performed by the Italian Neuroscience and Neurorehabilitation Network initiative to obtain normative values of topological metrics and to investigate their reproducibility and variability across centers. / Methods: Different topological metrics, at global and local level, were calculated on multishell diffusion-weighted data acquired at high-field (e.g. 3 T) Magnetic Resonance Imaging scanners in 13 different centers, following the harmonization of the acquisition protocol, on young and healthy adults. A “traveling brains” dataset acquired on a subgroup of subjects at 3 different centers was also analyzed as reference data. All data were processed following a common processing pipeline that includes data pre-processing, tractography, generation of structural connectomes and calculation of graph-based metrics. The results were evaluated both with statistical analysis of variability and consistency among sites with the traveling brains range. In addition, inter-site reproducibility was assessed in terms of intra-class correlation variability. / Results: The results show an inter-center and inter-subject variability of <10%, except for “clustering coefficient” (variability of 30%). Statistical analysis identifies significant differences among sites, as expected given the wide range of scanners’ hardware. / Conclusions: The results show low variability of connectivity topological metrics across sites running a harmonised protocol

Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN–Neuroimaging Network

Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

The role of leptin in the respiratory system: an overview

Since its cloning in 1994, leptin has emerged in the literature as a pleiotropic hormone whose actions extend from immune system homeostasis to reproduction and angiogenesis. Recent investigations have identified the lung as a leptin responsive and producing organ, while extensive research has been published concerning the role of leptin in the respiratory system. Animal studies have provided evidence indicating that leptin is a stimulant of ventilation, whereas researchers have proposed an important role for leptin in lung maturation and development. Studies further suggest a significant impact of leptin on specific respiratory diseases, including obstructive sleep apnoea-hypopnoea syndrome, asthma, COPD and lung cancer. However, as new investigations are under way, the picture is becoming more complex. The scope of this review is to decode the existing data concerning the actions of leptin in the lung and provide a detailed description of leptin's involvement in the most common disorders of the respiratory system

MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study

BACKGROUND: With the shift of research focus towards the pre-dementia stage of Alzheimer's disease (AD), there is an urgent need for reliable, non-invasive biomarkers to predict amyloid pathology. The aim of this study was to assess whether easily obtainable measures from structural MRI, combined with demographic data, cognitive data and apolipoprotein E (APOE) ε4 genotype, can be used to predict amyloid pathology using machine-learning classification. METHODS: We examined 810 subjects with structural MRI data and amyloid markers from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery study, including subjects with normal cognition (CN, n = 337, age 66.5 ± 7.2, 50% female, 27% amyloid positive), mild cognitive impairment (MCI, n = 375, age 69.1 ± 7.5, 53% female, 63% amyloid positive) and AD dementia (n = 98, age 67.0 ± 7.7, 48% female, 97% amyloid positive). Structural MRI scans were visually assessed and Freesurfer was used to obtain subcortical volumes, cortical thickness and surface area measures. We first assessed univariate associations between MRI measures and amyloid pathology using mixed models. Next, we developed and tested an automated classifier using demographic, cognitive, MRI and APOE ε4 information to predict amyloid pathology. A support vector machine (SVM) with nested 10-fold cross-validation was applied to identify a set of markers best discriminating between amyloid positive and amyloid negative subjects. RESULTS: In univariate associations, amyloid pathology was associated with lower subcortical volumes and thinner cortex in AD-signature regions in CN and MCI. The multi-variable SVM classifier provided an area under the curve (AUC) of 0.81 ± 0.07 in MCI and an AUC of 0.74 ± 0.08 in CN. In CN, selected features for the classifier included APOE ε4, age, memory scores and several MRI measures such as hippocampus, amygdala and accumbens volumes and cortical thickness in temporal and parahippocampal regions. In MCI, the classifier including demographic and APOE ε4 information did not improve after additionally adding imaging measures. CONCLUSIONS: Amyloid pathology is associated with changes in structural MRI measures in CN and MCI. An automated classifier based on clinical, imaging and APOE ε4 data can identify the presence of amyloid pathology with a moderate level of accuracy. These results could be used in clinical trials to pre-screen subjects for anti-amyloid therapies