Concentrations and size distributions of fungal bioaerosols in a municipal landfill

The object of this research was to study the behavior of fungal bioaerosols during a sampling period of 12 months (April 2015–April 2016), in each treatment stages of a landfill located in Atlántico Department, Colombia. The fungi bioaerosol samples were collected using a Six-Stage Viable Andersen Cascade Impactor - Thermo Fisher Scientific, a vacuum pump with a flow rate of 28.3 L/min–1, and ammeter KESTREL 4500 for the weather conditions. With the large amount of data obtained, a database was made in excel and analyzed using Statgraphics Centurion XVI software. The processing of data mining was carried out applying to a generalized linear regression model and Multifactorial ANOVA. Golden Surfer 11 program was used to stablish the distribution of temporal and spational mold airborne. The Variables: sampling campaign, stage, taxa, temperature and relative humidity presented a statistically significant correlation with the concentration P-value = 0. The concentrations of fungal bioaerosols varied considerably over the whole sampling period with average concentrations from 73.02 ± 26, 75 CFUs/m3 to 1830.38 ± 971.28 CFUs/m3. The fungal bioaerosols presented in both the coarse and fine fraction; but the fraction of 2.1–3.3 μm (stage 4) was the fraction of the dominant size in terms of higher concentration. According to the taxa identification, there was a higher prevalence of Aspergillus: the highest concentration corresponds to A. fumigatus, associated to toxins that may be cytotoxic [1, 2]

Nuclear emulsion techniques for muography

Nuclear emulsions are currently being used in the field of muography, more specifically muon radiography of volcanic edifices and fault regions. The peculiar features of such detector for cosmic muons demand appropriate data processing and analysis techniques. The paper shows the current development status of readout devices and analysis techniques developed by some research groups that established a collaborative network in Italy and Japan. An overview is given of nuclear emulsion-based detectors, from the detection principles to detector operation and set-up techniques, in connection with the expectations in terms of geophysics information. Two systems for readout are presented, one developed in the first decade of the 21st century and one that is entering duty now. The evolution in terms of data quality and speed is discussed. Finally, the most relevant data processing steps that allow working out muon absorption maps from nuclear emulsion data are described

muography with nuclear emulsions stromboli and other projects

The muon radiography is a novel imaging technique to probe the volcanoes interior, using the capability of high energy cosmic ray muons to penetrate large thicknesses of rock. In this way it is possible to derive a 2D density map along the muon trajectory of volcanic edifices and deduce information on the variations in the rock density distribution, like those expected from dense lava conduits, or low density magma supply paths. This method is applicable also to study geological objects as glaciers, faults, oil underground reservoirs, engineering constructions, where a density contrast is present. Nuclear emulsions are well suited to be employed in this context for their excellent angular resolution; they are compact and robust detectors, able to work in harsh environments without need of power supply. On the other side, a long exposure time is required for a reasonable detector surface (~10 m 2 ) in order to collect a sufficient statistics of muons, and a quasi-real time analysis of the emulsion data is rather difficult due to the scanning time needed by the optical microscopes. Such drawback is on the way to be overcome thanks to a recent R&D program on ultra-fast scanning systems. Muon radiography technique, even if limited to the summit part of the volcano edifice, represents an important tool of investigation, at higher spatial resolution, complementary to the conventional geophysics techniques. The first successful result in this field was obtained by a Japanese group that observed in 2007 the conduit structure of Mt. Asama. Since 2010, other interesting volcanoes have been probed with the same method: Stromboli in 2011, Mt. Teide in 2012 and La Palma in 2014. Here we discuss the muon imaging technique reporting the nuclear emulsion detector design exposed at Stromboli and results of the data analysis

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Chemical sensing with Au and Ag nanoparticles

Noble metal nanoparticles (NPs) are ideal scaffolds for the fabrication of sensing devices because of their high surface-to-volume ratio combined with their unique optical and electrical properties which are extremely sensitive to changes in the environment. Such characteristics guarantee high sensitivity in sensing processes. Metal NPs can be decorated with ad hoc molecular building blocks which can act as receptors of specific analytes. By pursuing this strategy, and by taking full advantage of the specificity of supramolecular recognition events, highly selective sensing devices can be fabricated. Besides, noble metal NPs can also be a pivotal element for the fabrication of chemical nose/tongue sensors to target complex mixtures of analytes. This review highlights the most enlightening strategies developed during the last decade, towards the fabrication of chemical sensors with either optical or electrical readout combining high sensitivity and selectivity, along with fast response and full reversibility, with special attention to approaches that enable efficient environmental and health monitoring

Follow-up a lungo termine della linfangectasia multicistica peripielica renale (LMPR)

Renal peripelvic multicystic lymphangiectasia is a benign disease characterized by multiple cysts arising from the lymphatic vessels of the renal sinus. Cysts, almost always bilateral, surround the profiles of the calices; the biggest cysts can compress pelvis or iuxtapielic ureter and it is difficult to differentiate RPML from hydronephrosis at the Ultrasound. Cysts are asymptomatic, the profiles of calices appear irregular with thin membrane separating each other, the cortical of the kidney is preserved: these are principle elements to distinguish RPML to hydronephrosis at the Ultrasound. Usually it is necessary to confirm the diagnosis at IVP and CT scan evaluation. From 1995 to March 2002, 10 cases of RPML with a long-term follow-up have been studied. IVP in 8 patients and CT scan in 2 have shown compression of the collecting system by multiple cysts. We followed the cases with periodic lab tests of kidney function, ultrasound, IVP, CT scan. In order to valuate the presence of "true obstruction", we tested in over-night urine EGF and MCP-1 as markers of urinary tract obstruction and subsequent renal damage. A valuable data regarding how to differentiate RPML from hydronephrosis at Ultrasound show that RPML does not modify renal function and cyst volume has not changed in the time. In long-term follow-up the prognosis of the RPML is not clear. Particolarly this renal sinus disease has not neoplastic degeneration and the effect of cysts on kidney function is unknown. RPML is an uncommon disease and it can be enclosed in the group of renal sinus pathologies. The absence of symptoms, the hystologic diagnosis, the unchanged dimensions of the cysts confirm the benign prognosis of RPML which does not need a close functional and morphologic monitoring in the long-term follow-up

Fluorinated and Charged Hydrogenated Alkanethiolates Grafted on Gold: Expanding the Diversity of Mixed-Monolayer Nanoparticles for Biological Applications

Low intrinsic toxicity, high solubility, and stability are important and necessary features of gold nanoparticles to be used in the biomedical field. In this context, charged nanoparticles proved to be very versatile, and among them charged mixed-monolayer gold nanoparticles, displaying monolayers with well-defined morphologies, represent a paradigm. By using mixtures of hydrogenated and fluorinated thiols, the formation of monolayer domains may be brought to an extreme because of the immiscibility of fluorinated and hydrogenated chains. Following this rationale, mixed monolayer gold nanoparticles featuring ammonium, sulfonate, or carboxylic groups on their surface were prepared by using amphiphilic hydrogenated thiols and 1H,1H,2H,2H-perfluoro-alkanethiols. The toxicity of these systems was assessed in HeLa cells and was found to be, in general, low even for the cationic nanoparticles which usually show a high cytotoxicity and is comparable to that of homoligand gold nanoparticles displaying amphiphilic charge neutral hydrogenated or fluorinated thiolates in their monolayer. These properties make the mixed ligand monolayer gold nanoparticles an interesting new candidate for medical application

Distribution of superparamagnetic Au/Fe nanoparticles in an isolated guinea pig brain with an intact blood brain barrier

Diagnosis and treatment of brain disorders, such as epilepsy, neurodegenerative diseases and tumors, would benefit from innovative approaches to deliver therapeutic or diagnostic compounds into the brain parenchyma, with either a homogeneous or a targeted localized distribution pattern. To assess the mechanistic aspect of penetration of nanoparticles (NPs) into the brain parenchyma, a complex, yet controlled and facilitated environment was used: the isolated guinea pig brain maintained in vitro by arterial perfusion. In this unique preparation the blood-brain barrier and the interactions between vascular and neuronal compartments are morphologically and functionally preserved. In this study, superparamagnetic Au/Fe nanoparticles (MUS:OT Au/Fe NPs), recently studied as a promising magnetic resonance T2 contrast agent with high cellular penetration, were arterially perfused into the in vitro isolated brain and showed high and homogeneous penetration through transcytosis into the brain parenchyma. Ultramicroscopy investigation of the in vitro isolated brain sections by TEM analysis of the electron-dense core of the MUS:OT Au/Fe NPs was conducted to understand NPs' brain penetration through the BBB after in vitro arterial perfusion and their distribution in the parenchyma. Our data suggest that MUS:OT Au/Fe NPs enter the brain utilizing a physiological route and therefore can be exploited as brain penetrating nanomaterials with potential contrast agent and theranostics capabilities

Quantitative Analysis of Scanning Tunneling Microscopy Images of Mixed-Ligand-Functionalized Nanoparticles

Ligand-protected gold nanoparticles exhibit large local curvatures, features rapidly varying over small scales, and chemical heterogeneity. Their imaging by scanning tunneling microscopy (STM) can, in principle, provide direct information on the architecture of their ligand shell, yet STM images require laborious analysis and are challenging to interpret. Here, we report a straightforward, robust, and rigorous method for the quantitative analysis of the multiscale features contained in STM images of samples consisting of functionalized Au nanoparticles deposited onto Au/mica. The method relies on the analysis of the topographical power spectral density (PSD) and allows us to extract the characteristic length scales of the features exhibited by nanoparticles in STM images. For the mixed-ligand-protected Au nanoparticles analyzed here, the characteristic length scale is 1.2 ± 0.1 nm, whereas for the homoligand Au NPs this scale is 0.75 ± 0.05 nm. These length scales represent spatial correlations independent of scanning parameters, and hence the features in the PSD can be ascribed to a fingerprint of the STM contrast of ligand-protected nanoparticles. PSD spectra from images recorded at different laboratories using different microscopes and operators can be overlapped across most of the frequency range, proving that the features in the STM images of nanoparticles can be compared and reproduced.status: publishe