Honeybees exposure to natural feed additives: How is the gut microbiota affected?

The role of a balanced gut microbiota to maintain health and prevent diseases is largely established in humans and livestock. Conversely, in honeybees, studies on gut microbiota perturbations by external factors have started only recently. Natural methods alternative to chemical products to preserve honeybee health have been proposed, but their effect on the gut microbiota has not been examined in detail. This study aims to investigate the effect of the administration of a bacterial mixture of bifidobacteria and Lactobacillaceae and a commercial product HiveAlive™ on honeybee gut microbiota. The study was developed in 18 hives of about 2500 bees, with six replicates for each experimental condition for a total of three experimental groups. The absolute abundance of main microbial taxa was studied using qPCR and NGS. The results showed that the majority of the administered strains were detected in the gut. On the whole, great perturbations upon the administration of the bacterial mixture and the plant-based commercial product were not observed in the gut microbiota. Significant variations with respect to the untreated control were only observed for Snodgrassella sp. for the bacterial mixture, Bartonella sp. in HiveAlive™ and Bombilactobacillus sp. for both. Therefore, the studied approaches are respectful of the honeybee microbiota composition, conceivably without compromising the bee nutritional, social and ecological functions

Reconstruction of reflectivity vertical profiles and data quality control for C-band radar rainfall estimation

International audienceMicrowave Doppler radars are considered a fairly established technique to retrieve rain rate fields from measured reflectivity volumes. However, in a complex orographic environment radar observations are affected by several impairments which should be carefully evaluated. Together with the enhancement of ground-clutter effects, the major limitation is represented by partial or total beam blocking caused by natural obstructions which very often impose to scan at high-elevation angles. These range-related limitations tend to reduce the potential role of operational weather radars in monitoring precipitation amount at ground within mountainous areas since, if either the nature or intensity of rainfall varies with height (e.g., melting effects during stratiform rain), radar returns at higher altitudes may be not representative of surface rain rate. Therefore, before to use the radar data, it is necessary to reduce, as much as possible, this evaluation errors and to estimate the reliability of the processed data. Near to the quality control, are needed quality indexes, taking into account each correction and elaboration step, that could be useful to retrieve a final quality value. In this work, we analyse the main factors that could be affect the efficiency of a reconstruction methodology of near-surface reflectivity fields from high-elevation reflectivity bins, in presence of complex orography. A climatologic schema is applied to infer near-surface reflectivity at a given range interval. The technique is developed in polar coordinates partially taking into account the antenna beam width degradation at longer ranges and overall computational efficiency for operational purposes. Thereafter, it is applied on a rainfall event observed by a C-band Doppler radar operating in S. Pietro Capofiume (Bologna, Italy) and the relation between the reconstruction error and possible quality indicators is analysed and discussed

Clutter and rainfall discrimination by means of doppler-polarimetric measurements and vertical reflectivity profile analysis

International audienceThe estimation of rainfall rate and other parameters from radar scattering volume is heavily affected by the presence of intense sea and ground clutter and echoes which appears in anomalous propagation condition. To deal with these non meteorological echoes we present a new clutter removal algorithm which combines the results of previous works. The algorithm fully exploits both the Doppler and polarimetric capabilities of the radar used and the analysis of vertical reflectivity profile in order to achieve the better identification of the meteorological and non-meteorological targets. The algorithm has been applied to the C-band radar of Monte Settepani (Savona, Italy), which runs in a high-topography environment. Preliminary results are presented

Hydrometeor classification from dual-polarized weather radar: extending fuzzy logic from S-band to C-band data

International audienceA model-based fuzzy classification method for C-band polarimetric radar data, named Fuzzy Radar Algorithm for Hydrometeor Classification at C-band (FRAHCC), is presented. Membership functions are designed for best fitting simulation data at C-band, and they are derived for ten different hydrometeor classes by means of a scattering model, based on T-Matrix numerical method. The fuzzy logic classification technique uses a reduced set of polarimetric observables, i.e. copolar reflectivity and differential reflectivity, and it is finally applied to data coming from radar sites located in Gattatico and S. Pietro Capofiume in North Italy. The final purpose is to show qualitative accuracy improvements with respect to the use of a set of ten bidimensional MBFs, previously adopted and well suited to S-band data but not to C-band data

Clutter and rainfall discrimination by means of doppler-polarimetric measurements and vertical reflectivity profile analysis

The estimation of rainfall rate and other parameters from radar scattering volume is heavily affected by the presence of intense sea and ground clutter and echoes which appears in anomalous propagation condition. To deal with these non meteorological echoes we present a new clutter removal algorithm which combines the results of previous works. The algorithm fully exploits both the Doppler and polarimetric capabilities of the radar used and the analysis of vertical reflectivity profile in order to achieve the better identification of the meteorological and non-meteorological targets. The algorithm has been applied to the C-band radar of Monte Settepani (Savona, Italy), which runs in a high-topography environment. Preliminary results are presented

Reflectivity and velocity radar data assimilation for two flash flood events in central Italy: A comparison between 3D and 4D variational methods

The aim of this study is to provide an evaluation of the impact of two largely used data assimilation techniques, namely three- and four-dimensional variational data assimilation systems (3D-Var and 4D-Var), on the forecasting of heavy precipitation events using the Weather Research and Forecasting (WRF) model. For this purpose, two flash flood events in central Italy are analysed. The first occurred on September 14, 2012 during an Intensive Observation Period of the Hydrological cycle in the Mediterranean experiment (HyMeX) campaign, while the other occurred on May 3, 2018. Radial velocity and reflectivity acquired by C-band weather radars at Mt. Midia (central Italy) and San Pietro Capofiume (northern Italy), as well as conventional observations (SYNOP and TEMP), are assimilated into the WRF model to simulate these damaging flash flood events. In order to evaluate the impact of the 3D-Var and 4D-Var assimilation systems on the estimation of short-term quantitative precipitation forecasts, several experiments are carried out using conventional observations with and without radar data. Rainfall evaluation is performed by means of point-by-point and filtering methodologies. The results point to a positive impact of the 4D-Var technique compared to results without assimilation and with 3D-Var experiments. More specifically, the 4D-Var system produces an increase of up to 22% in terms of the Fractions Skill Score compared to 3D-Var for the first flash flood event, while an increase of about 5% is achieved for the second event. The use of a warm start initialization results in a considerable reduction in the spin-up time and a significant improvement in the rainfall forecast, suggesting that the initial precipitation spin-up problem still occurs when using 4D-Var

The ground beetle Pseudoophonus rufipes gut microbiome is influenced by the farm management system

: Intensive conventional farm management, characterized by high agrochemicals input, could alter the composition of microbial communities with potential negative effects on both functional traits and the ecosystem services provided. In this study, we investigated the gut microbial composition of a high ecological relevance carabid Pseudoophonus rufipes, sampled in two fields subjected to conventional and organic management practices. Carabids' gut microbiota was analyzed via qPCR and NGS. Profound differences between the microbial composition of organic and conventional samples were detected: the abundance of Tenericutes and Proteobacteria was significant higher in organic and conventional samples, respectively. Spiroplasmataceae and Bifidobacteriaceae families were significantly more abundant in samples from organic management, while Enterococcaceae, Morganellaceae and Yersiniaceae were more abundant in samples from conventional management. The diverse gut microbial composition of insects between the two management systems is related to the pressure of environmental stressors and it may representing an important bioindication of ecological functions and services provided by a carabid species

Technology-enhanced multi-domain at home continuum of care program with respect to usual care for people with cognitive impairment: the Ability-TelerehABILITation study protocol for a randomized controlled trial

Background: According to the World Alzheimer Report (Prince, The Global Impact of Dementia: an Analysis of Prevalence, Incidence, Cost and Trends, 2015), 46.8 million people worldwide are nowadays living with dementia. And this number is estimated to approximate 131.5 million by 2050, with an increasing burden on society and families. The lack of medical treatments able to stop or slow down the course of the disease has moved the focus of interest toward the nonpharmacological approach and psychosocial therapies for people with/at risk of dementia, as in the Mild Cognitive Impairment (MCI) condition. The purpose of the present study is to test an individualized home-based multidimensional program aimed at enhancing the continuum of care for MCI and outpatients with dementia in early stage using technology. Methods: The proposed study is a single blind randomized controlled trial (RCT) involving 30 subjects with MCI and Alzheimer's disease (AD) randomly assigned to the intervention group (Ability group), who will receive the "Ability Program", or to the active control group (ACG), who will receive "Treatment As Usual" (TAU). The protocol provides for three steps of assessment: at the baseline (T_0), after treatment, (T_1) and at follow-up (T_2) with a multidimensional evaluation battery including cognitive functioning, behavioral, functional, and quality of life measures. The Ability Program lasts 6 weeks, comprises tablet-delivered cognitive (5 days/week) and physical activities (7 days/week) combined with a set of devices for the measurement and monitoring from remote of vital and physical health parameters. The TAU equally lasts 6 weeks and includes paper and pencil cognitive activities (5 days/week), with clinician's prescription to perform physical exercise every day and to monitor selected vital parameters. Discussion: Results of this study will inform on the efficacy of a technology-enhanced home care service to preserve cognitive and motor levels of functioning in MCI and AD, in order to slow down their loss of autonomy in daily life. The expected outcome is to ensure the continuity of care from clinical practice to the patient's home, enabling also cost effectiveness and the empowerment of patient and caregiver in the care process, positively impacting on their quality of lif