Disturbances in groundwater chemical parameters related to seismic and volcanic activity in Kamchatka (Russia)

International audienceStarting from 1992 geochemical data are being collected with a mean sampling frequency of three days in the form of the pH value and of the most common ions and gases in the groundwater in one deep well located in Petropavlovsk, the capital city of Kamchatka (Russia). On 1 January 1996 a strong eruption started from the Karymsky volcano, that is located about 100km far from the well, in the north-northeastern direction. At the same time, a large earthquake (M=6.9) occurred in the Karymsky area. On 5 December 1997 a very large earthquake (M=7.7) occurred offshore, at a distance of 350km from the well and towards the same direction. The analysis of the geochemical data shows clear variations in the raw temporal trends on both cases. For the first event, a clear premonitory phase appeared; for the second one, some pre-seismic variations could be revealed but permanent modifications of the chemistry of the water subsequent to the earthquake are very clear. In both cases the feature of the geochemical variations is consistent with an afflux of new water in the aquifer connected with the well and with an escape of the Carbon dioxide gas from the ground in different directions. A schematic model able to justify such a phenomenology and the connections of the geochemical variations with the previous tectonic activities is proposed

Retrospective analysis for detecting seismic precursors in groundwater argon content

We examined the groundwater Argon content data sampled from 1988 to 2001 at two wells in Kamchatka (Russia) and anomalous increases appeared clearly during June-July&nbsp;1996. On 21&nbsp;June, a shallow (1km) earthquake with <i>M</i>=7.1 occurred at a distance less than 250km from the wells and so the previous increases could be related to this earthquake and, in particular, could be considered premonitory anomalies. In order to support this raw interpretation, we analysed the data collected in details. At first we smoothed out the high frequency fluctuations arising from the errors in a single measurement. Next we considered the known external effects on the water of a well that are the slow tectonic re-adjustment processes, the meteorology and the gravity tides and we separated these effects applying band-pass filters to the Argon content raw trends. Then we identified the largest fluctuations in these trends applying the 3 σ criterion and we found three anomalies in a case and two anomalies in other case. Comparing the time occurrence of the anomalies at the two wells we found out that a coincidence exists only in the case of the premonitory anomalies we are studying. The simultaneous appearance of well definite anomalies in the residual trends of the same parameter at two different sites supports their meaning and the possibility that they are related to some large scale effect, as the occurrence of a strong earthquake. But, other earthquakes similar to the June&nbsp;1996 event took place during the Argon content measurements time and no anomaly appeared in this content. In the past, some of the authors of this paper studied the Helium content data collected in three natural springs of the Caucasus during seven years. A very similar result, that is the simultaneous appearance of clear premonitory anomalies only on the occasion of a strong (<i>M</i>=7.0) but shallow (2–4km) earthquake, was obtained. The correspondence with the case of the Caucasus validates the interpretation of the Kamchatkian anomalies as precursors

A Comparison of PCA-LDA and PLS-DA Techniques for Classification of Vibrational Spectra

Vibrational spectroscopies provide information about the biochemical and structural environment of molecular functional groups inside samples. Over the past few decades, Raman and infrared-absorption-based techniques have been extensively used to investigate biological materials under different pathological conditions. Interesting results have been obtained, so these techniques have been proposed for use in a clinical setting for diagnostic purposes, as complementary tools to conventional cytological and histological techniques. In most cases, the differences between vibrational spectra measured for healthy and diseased samples are small, even if these small differences could contain useful information to be used in the diagnostic field. Therefore, the interpretation of the results requires the use of analysis techniques able to highlight the minimal spectral variations that characterize a dataset of measurements acquired on healthy samples from a dataset of measurements relating to samples in which a pathology occurs. Multivariate analysis techniques, which can handle large datasets and explore spectral information simultaneously, are suitable for this purpose. In the present study, two multivariate statistical techniques, principal component analysis-linear discriminate analysis (PCA-LDA) and partial least square-discriminant analysis (PLS-DA) were used to analyse three different datasets of vibrational spectra, each one including spectra of two different classes: (i) a simulated dataset comprising control-like and exposed-like spectra, (ii) a dataset of Raman spectra measured for control and proton beam-exposed MCF10A breast cells and (iii) a dataset of FTIR spectra measured for malignant non-metastatic MCF7 and metastatic MDA-MB-231 breast cancer cells. Both PCA-LDA and PLS-DA techniques were first used to build a discrimination model by using calibration sets of spectra extracted from the three datasets. Then, the classification performance was established by using test sets of unknown spectra. The achieved results point out that the built classification models were able to distinguish the different spectra types with accuracy between 93% and 100%, sensitivity between 86% and 100% and specificity between 90% and 100%. The present study confirms that vibrational spectroscopy combined with multivariate analysis techniques has considerable potential for establishing reliable diagnostic models

A possible preseismic anomaly in the ground wave of a radio broadcasting (216 kHz) during July-August 1998 (Italy)

International audienceOn February 1996, a receiver able to measure the electric field strength of LF radio broadcastings, with a sampling frequency of ten minutes, was put into operation in a site (AS) located in central Italy. One of the broadcasting stations selected is MCO (f=216 kHz), located in southeast France, 518 km far from the receiver. The MCO data collected since February 1996 up to September 2004 were examined and, at first, the night time data and the day time data (in winter and summer) were separated. Then, the wavelet analysis on the night and day time data was applied. The main result of the analysis was the appearance of a very clear anomaly during summer (July?August) 1998, at day time and at night time. The anomaly is a strong exaltation of the signal components with period in the 25?40 days range. Theoretical calculations of electric field strength were made and the only way to justify this anomaly seems to be the occurrence of an increase of the ground wave propagation mode of the radio signal. Such an increase could have been produced by an increase of the ground conductivity and by modifications of some parameter of the troposphere, mainly the refractive index. On 15 August 1998 a seismic sequence started with 17 earthquakes (M=2.2?4.6) on the Reatini mountains, a seismogenic zone located 30 km far from the AS receiver along the path MCO-AS. In this paper, the possibility that the previous radio anomaly can be a precursor of this seismic sequence is proposed. </p

Effectiveness of dolutegravir-based regimens as either first-line or switch antiretroviral therapy: data from the Icona cohort

Introduction: Concerns about dolutegravir (DTG) tolerability in the real-life setting have recently arisen. We aimed to estimate the risk of treatment discontinuation and virological failure of DTG-based regimens from a large cohort of HIV-infected individuals. Methods: We performed a multicentre, observational study including all antiretroviral therapy (ART)-naïve and virologically suppressed treatment-experienced (TE) patients from the Icona (Italian Cohort Naïve Antiretrovirals) cohort who started, for the first time, a DTG-based regimen from January 2015 to December 2017. We estimated the cumulative risk of DTG discontinuation regardless of the reason and for toxicity, and of virological failure using Kaplan–Meier curves. We used Cox regression model to investigate predictors of DTG discontinuation. Results: About 1679 individuals (932 ART-naïve, 747 TE) were included. The one- and two-year probabilities (95% CI) of DTG discontinuation were 6.7% (4.9 to 8.4) and 11.5% (8.7 to 14.3) for ART-naïve and 6.6% (4.6 to 8.6) and 7.6% (5.4 to 9.8) for TE subjects. In both ART-naïve and TE patients, discontinuations of DTG were mainly driven by toxicity with an estimated risk (95% CI) of 4.0% (2.6 to 5.4) and 2.5% (1.3 to 3.6) by one year and 5.6% (3.8 to 7.5) and 4.0% (2.4 to 5.6) by two years respectively. Neuropsychiatric events were the main reason for stopping DTG in both ART-naïve (2.1%) and TE (1.7%) patients. In ART-naïve, a concomitant AIDS diagnosis predicted the risk of discontinuing DTG for any reason (adjusted relative hazard (aRH)&nbsp;=&nbsp;3.38, p&nbsp;=&nbsp;0.001), whereas starting DTG in combination with abacavir (ABC) was associated with a higher risk of discontinuing because of toxicity (aRH&nbsp;=&nbsp;3.30, p&nbsp;=&nbsp;0.009). TE patients starting a DTG-based dual therapy compared to a triple therapy had a lower risk of discontinuation for any reason (adjusted hazard ratio (aHR)&nbsp;=&nbsp;2.50, p&nbsp;=&nbsp;0.037 for ABC-based triple-therapies, aHR&nbsp;=&nbsp;3.56, p&nbsp;=&nbsp;0.012 for tenofovir-based) and for toxicity (aHR&nbsp;=&nbsp;5.26, p&nbsp;=&nbsp;0.030 for ABC-based, aHR&nbsp;=&nbsp;6.60, p&nbsp;=&nbsp;0.024 for tenofovir-based). The one- and two-year probabilities (95% CI) of virological failure were 1.2% (0.3 to 2.0) and 4.6% (2.7 to 6.5) in the ART naïve group and 2.2% (1.0 to 3.3) and 2.9% (1.5 to 4.3) in the TE group. Conclusions: In this large cohort, DTG showed excellent efficacy and optimal tolerability both as first-line and switching ART. The low risk of treatment-limiting toxicities in ART-naïve as well as in treated individuals reassures on the use of DTG in everyday clinical practice

Photometric redshifts and clustering of emission line galaxies selected jointly by DES and eBOSS

We present the results of the first test plates of the extended Baryon Oscillation Spectroscopic Survey. This paper focuses on the emission line galaxies (ELG) population targetted from the Dark Energy Survey (DES) photometry. We analyse the success rate, efficiency, redshift distribution, and clustering properties of the targets. From the 9000 spectroscopic redshifts targetted, 4600 have been selected from the DES photometry. The total success rate for redshifts between 0.6 and 1.2 is 71\% and 68\% respectively for a bright and faint, on average more distant, samples including redshifts measured from a single strong emission line. We find a mean redshift of 0.8 and 0.87, with 15 and 13\% of unknown redshifts respectively for the bright and faint samples. In the redshift range 0.6<z<1.2, for the most secure spectroscopic redshifts, the mean redshift for the bright and faint sample is 0.85 and 0.9 respectively. Star contamination is lower than 2\%. We measure a galaxy bias averaged on scales of 1 and 10~Mpc/h of 1.72 \pm 0.1 for the bright sample and of 1.78 \pm 0.12 for the faint sample. The error on the galaxy bias have been obtained propagating the errors in the correlation function to the fitted parameters. This redshift evolution for the galaxy bias is in agreement with theoretical expectations for a galaxy population with MB-5\log h < -21.0. We note that biasing is derived from the galaxy clustering relative to a model for the mass fluctuations. We investigate the quality of the DES photometric redshifts and find that the outlier fraction can be reduced using a comparison between template fitting and neural network, or using a random forest algorithm

From Microbial Ecology to Innovative Applications in Food Quality Improvements: the Case of Sourdough as a Model Matrix.

Since millennia, humankind has exploited microbial diversity associated to give foodmatrices in order to obtain fermented foods and beverages, resulting in products with improvedquality and extended shelf life. This topic has received deserved and continuous interest in thescientific community, for the reason of its significance as a driver of innovation in the food and beveragesector. In this review paper, using sourdough as a model matrix, we provide some insights into thefield, testifying the relevance as a transdisciplinary subject. Firstly, we encompassed the prokaryoticand eukaryotic microbial diversity associated with the sourdough ecosystems. The importance ofthis micro-biodiversity in the light of flour-related chemical diversity was examined. Finally, wehighlighted the increasing interest in microbial-based applications oriented toward biocontrol solutionin the field of sourdough-based products (i.e., bread)