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Bioaugmentation strategies were tested to improve energetic valorization of shrimp processing waste (SPW) by anaerobic digestion (AD). A fermenting bacteria pool (F210) obtained from coastal lake sediments and two strains of anaerobic fungi (AF), Orpynomyces sp. and Neocallimastix sp., commonly found as components of microbial community of AD plants, were used with the aim of improving the fermentative and hydrolytic phases of AD, respectively. The experiment was carried out by testing single bioaugmentation at an SPW concentration of 6.5 gVS L−1 and combined bioaugmentation at three SPW concentrations (6.5, 9.7 and 13.0 gVS L−1, respectively), in batch mode and mesophilic conditions. Cumulative CH4 productions were higher in the combined bioaugmentation tests and increased in line with SPW concentration. The F210 played a key role in enhancing CH4 production while no effect was attributable to the addition of AFs. The CH4 content (%) in the biogas increased with substrate concentrations, with average values of 67, 70, and 73%, respectively. Microbial community abundance increased in line with the SPW concentration and the acetoclastic Methanosarcina predominated within the methanogen Archaea guild in the combined bioaugmentation test (in all cases > 65%).

Landslides pose a significant risk to human life. The Twisting Theory (TWT) and Crown Clustering Algorithm (CCA) are innovative adaptive algorithms that can determine the shape of a landslide and predict its future evolution based on the movement of position sensors located in the affected area. In the first part of this study, the TWT and CCA will be thoroughly explained from a mathematical and theoretical perspective. In the second part, these algorithms will be applied to real-life cases, the Assisi landslide (1995–2008) and the Corvara landslide (2000–2008). A correlation of 0.9997 was attained between the model estimates and the expert’s posterior measurements at both examined sites. The results of these applications reveal that the TWT can accurately identify the overall shape of the landslides and predict their progression, while the CCA identifies complex cause-and-effect relationships among the sensors and represents them in a clear, weighted graph. To apply this model to a wider area and secure regions at risk of landslides, it is important to emphasize its operational feasibility as it only requires the installation of GNSS sensors in a predetermined grid in the target area.

In the present work, an anaerobic bioremediation treatment was investigated for reclamation of polycyclic aromatic hydrocarbons (PAHs) contaminated soil. The PAHs contaminated soil was artificially prepared and seven different contamination conditions were tested. In particular, four soils were contaminated solely by naphthalene (A), anthracene (B), pyrene (C) and benzo[a]pyrene (D), respectively, whereas, three soils were contaminated by benzo[a]pyrene coupled with one of the other investigated PAHs (i.e. A+D, B+D, and C+D tests). Such conditions were tested in order to study the possible degradation kinetic for the single involved PAH (with aromatic rings ranging from 2 to 5) as well as for PAHs mixed with a 5-aromatic rings contaminant (i.e. benzo[a]pyrene). The investigated treatment was carried out in bioaugmented condition through two microbial inocula obtained from anaerobic digestion tests on lignocellulosic substrate. In more detail, the two inocula were differently enriched through experiments characterized by sequential re-inoculation on new substrate, for its subsequent treatment, every 24 h and 96 h, respectively. The present study focused on the PAHs degradation efficiency and pathways, and microbiological abundance characterization, thus providing a comprehensive and interdisciplinary view to assess the feasibility of the suggested treatment in the field of PAHs contaminated soil remediation.

Autoimmune diseases account for a cumulative overall prevalence of about 3-5% worlwide. Among them, autoimmune thyroid diseases (ATDs) are the most common and comprise two main entities: Hashimoto's thyroiditis (HT) and Graves-Basedow disease (GD). The pathogenesis of ATDs remains not fully elucidated, however the role of microbioma has been proposed. Gut microbiota exert an important influence on the intestinal barrier, nutrient metabolism and immune system development and functions.In this narrative review, we describe on the main features of ATDs in pediatrics, focusing on the reciprocal influence between gut microbiota, thyroid hormone metabolism and thyroid autoimmunity and consider the role of probiotics and other microbiotatargeted therapies in thyroid diseases with a perspective on pediatric endocrinology.Microbiome affects both endogenous and exogenous thyroid hormone metabolism and influences the absorption of minerals important to the thyroid function, which are iodine, selenium, zinc and iron. The alteration of the gut microbiota, with the consequent modifications in the barrier function and the increased gut permeability, seems involved in the development of autoimmune and chronic inflammatory diseases, including ATDs. The supplementation with probiotics showed beneficial effects on the thyroid hormone and thyroid function because this strategy could restore the intestinal eubiosis and the good strain microorganism proliferation.Eventhough the evidence about the interaction between microbiota and ATDs in pediatric patients is limited, the promising results obtained in the adult population, and in other autoimmune disorders affecting children, highlight the need of for further research in the pediatric field.

The degradation and bioaccumulation of selected antibiotics such as the sulfonamide sulfamethoxazole (SMX) and the fluoroquinolones enrofloxacin (ENR) and ciprofloxacin (CIP) were investigated in soil microcosm experiments where Lactuca sativa was grown with manure or digestate (1%) and spiked with a mixture of the three antibiotics (7.5 mg/kg each). The soil, rhizosphere and leaf phyllosphere were sampled (at 0 and 46 days) from each microcosm to analyze the antibiotic concentrations, main resistance genes (sul1, sul2, qnrS, aac-(6')-Ib-crand qepA), the intI1and tnpA mobile genetic elements and the microbial community structure.Overall results showed that SMX and CIP decreased (70-85% and 55-79%, respectively), and ENR was quite persistent during the 46-day experiment. In plant presence, CIP and ENR were partially up-taken from soil to plant. In fact the bioaccumulation factors were1, with higher values in manure than digestate amended soils. The most abundant gene in soil was sul2 in digestate- and aac-(6')-Ib-cr in the manure-amended microcosms. In soil, neither sulfamethoxazole-resistance (sul1 and sul2), nor fluoroquinolone-resistance (aac-(6')-Ib-cr, qepA and qnrS) gene abundances were correlated with any antibiotic concentration. On the contrary, in lettuce leaves, the aac-(6')-Ib-cr gene was the most abundant, in accordance with the fluoroquinolone bioaccumulation. Finally, digestate stimulated a higher soil microbial biodiversity, introducing and promoting more bacterial genera associated with antibiotic degradation and involved in soil fertility and decreased fluoroquinolone bioaccumulation.

Puberty is a critical phase of life associated with physiological changes related to sexual maturation, and represents a complex process regulated by multiple endocrine and genetic controls. Puberty is driven by hormones, and it can impact the gut microbiome (GM). GM differences between sex emerge at puberty onset, confirming a relationship between microbiota and sex hormones. In this narrative review, we present an overview of precocious pubertal development and the changes in the GM in precocious puberty (PP) in order to consider the role of the sex hormone-gut microbiome axis from the perspective of pediatric endocrinology. Bidirectional interactions between the GM and sex hormones have been proposed in different studies. Although the evidence on the interaction between microbiota and sex hormones remains limited in pediatric patients, the evidence that GM alterations may occur in girls with central precocious puberty (CPP) represents an interesting finding for the prediction and prevention of PP. Deepening the understanding of the connection between the sex hormones and the role of microbiota changes can lead to the implementation of microbiota-targeted therapies in pubertal disorders by offering a pediatric endocrinology perspective.

This study is part of a project on early hearing dysfunction induced by combined exposure to volatile organic compounds (VOCs) and noise in occupational settings. In a previous study, 56 microRNAs were found differentially expressed in exposed workers compared to controls. Here, we analyze the statistical association of microRNA expression with audiometric hearing level (HL) and distortion product otoacoustic emission (DPOAE) level in that subset of differentially expressed microRNAs. The highest negative correlations were found; for HL, with miR-195-5p and miR-122-5p, and, for DPOAEs, with miR-92b-5p and miR-206. The homozygous (mut) and heterozygous (het) variants of the gene hOGG1 were found disadvantaged with respect to the wild-type (wt), as regards the risk of hearing impairment due to exposure to VOCs. An unsupervised artificial neural network (auto contractive map) was also used to detect and show, using graph analysis, the hidden connections between the explored variables. These findings may contribute to the formulation of mechanistic hypotheses about hearing damage due to co-exposure to noise and ototoxic solvents.

Aim To examine whether in Europe perceptions of ‘alcoholism’ differ in a discrete manner according to geographical area. Method Secondary analysis of a data set from a European project carried out in 2013–2014 among 1767 patients treated in alcohol addiction units of nine countries/regions across Europe. The experience of all 11 DSM-4 criteria used for diagnosing ‘alcohol dependence’ and ‘alcohol abuse’ were assessed in patient interviews. The analysis was performed through Multiple Correspondence Analysis. Results The symptoms of ‘alcohol dependence’ and ‘alcohol abuse’, posited by DSM-IV, were distributed according to three discrete geographical patterns: a macro-area mainly centered on drinking beer and spirit, a culture traditionally oriented toward wine and a mixed intermediate alcoholic beverage situation. Conclusion These patterns of perception seem to parallel the diverse drinking cultures of Europe.

In this study, we investigated thermophilic (55 &deg, C) anaerobic digestion (AD) performance and microbial community structure, before and after hydrogen addition, in a novel hybrid gas-stirred tank reactor (GSTR) implemented with a partial immobilization of the microbial community and fed with second cheese whey (SCW). The results showed that H2 addition led to a 25% increase in the methane production rate and to a decrease of 13% in the CH4 concentration as compared with the control. The recovery of methane content (56%) was reached by decreasing the H2 flow rate. The microbial community investigations were performed on effluent (EF) and on interstitial matrix (IM) inside the immobilized area. Before H2 addition, the Anaerobaculaceae (42%) and Lachnospiraceae (27%) families dominated among bacteria in the effluent, and the Thermodesulfobiaceae (32%) and Lachnospiraceae (30%) familiesdominated in the interstitial matrix. After H2 addition, microbial abundance showed an increase inthe bacteria and archaea communities in the interstitial matrix. The Thermodesulfobiaceae family (29%)remained dominant in the interstitial matrix, suggesting its crucial role in the immobilized community and the SHA-31 family was enriched in both the effluent (36%) and the interstitial matrix (15%). The predominance of archaea Methanothermobacter thermoautrophicus indicated that CH4 was produced almost exclusively by the hydrogenotrophic pathway.

Little is known about the role of Lp(a) in the assessment of cardiovascular risk in the paediatric population. Trying to clarify the clinical relevance of Lp(a) in risk stratification, the aim of the study is to evaluate the association between Lp(a) plasma levels in children with familial hypercholesterolaemia (FH) and positive family history for premature cardiovascular disease (pCVD) in first- and second-degree relatives.653 Caucasian children and adolescents (334 females and 319 males), aged 2-17 years, with diagnosis of FH from a paediatric cohort included in the LIPIGEN Network, were selected. We compared family history of pCVD, lipid and genetic profile in two groups based on Lp(a) levels below or above 30 mg/dL. To determine the independent predictors of pCVD, a multivariate logistic regression was used, with all clinical characteristics and blood measurements as predictors.Subjects with Lp(a) 30 mg/dl more frequently reported positive family history of pCVD compared to subjects with Lp(a)≤30 mg/dl (69.90% vs 36.66%, p 0.0001), while did not show differences in terms of median [interquartile range] LDL-cholesterol level (153.00 [88.00 vs 164.50 [90.25] mg/dL, p = 0.3105). In the regression analysis, Lp(a) 30 mg/dl was an independent predictor of family history of pCVD. Comparing subjects with or without family history of pCVD, we reported significant differences for Lp(a) 30 mg/dl (46.25% vs 17.65%, p 0.0001), FH genetic mutation (50.48% vs 40.75%, p = 0,0157), as well as for LDL-cholesterol (p = 0.0013) and total cholesterol (p = 0.0101).Children/adolescents with FH and Lp(a) 30 mg/dl where more likely to have a positive family history of pCVD. Lp(a) screening in children and adolescents with FH may enhance risk assessment and help identify those subjects, children and relatives, at increased pCVD risk.

Background and Purpose: The Active Connection Matrixes (ACMs) are unsupervised artificial adaptive systems able to extract from digital images features of interest (edges, tissue differentiation, etc.) unnoticeable with conventional systems. In this proof-of-concept study, we assessed the potentiality of ACMs to increase measurement precision of morphological structures (e.g., stenosis and lumen diameter) and to grasp morphological features (arterial walls) from quantitative coronary angiography (QCA), unnoticeable on the original images.Methods: Archive images of QCA and intravascular ultrasound (IVUS) of 10 patients (8 men, age 69.1 ± 9.7 years) who underwent both procedures for clinical reasons were retrospectively analyzed. Arterial features derived from “IVUS images,” “conventional QCA images,” and “ACM-reprocessed QCA images” were measured in 21 coronary segments. Portions of 1-mm length (263 for lumen and 526 for arterial walls) were head-to-head compared to assess quali-quantitative between-methods agreement.Results: When stenosis was calculated on “ACM-reprocessed QCA images,” the bias vs. IVUS (gold standard) did not improve, but the correlation coefficient of the QCA–IVUS relationship increased from 0.47 to 0.83. When IVUS-derived lumen diameters were compared with diameters obtained on ACM-reprocessed QCA images, the bias (−0.25 mm) was significantly smaller (p < 0.01) than that observed with original QCA images (0.58 mm). ACMs were also able to extract arterial wall features from QCA. The bias between the measures of arterial walls obtained with IVUS and ACMs, although significant (p < 0.01), was small [0.09 mm, 95% CI (0.03, 0.14)] and the correlation was fairly good (r = 0.63; p < 0.0001).Conclusions: This study provides proof of concept that ACMs increase the measurement precision of coronary lumen diameter and allow extracting from QCA images hidden features that mirror well the arterial walls derived by IVUS.

Childhood obesity is characterized by an increased risk of several metabolic derangements including insulin resistance (IR). The strongest recommendations to prevent obesity and related complications are a balanced and adequate diet and practicing physical activity from early childhood. In this review, we propose to present the effects of healthy lifestyle strategies, including physical exercise and dietary approaches, on the management of IR and related metabolic derangements. All types of exercise (aerobic, resistance and combined training) effectively reduce IR in pediatric patients with obesity; it seems that aerobic and combined training stimulate greater improvements in IR compared to resistance training. Balanced normocaloric or hypocaloric dietary approaches are also valid strategies to address IR; it is not possible to assess the long-term impact of varying macronutrients on cardiometabolic risk. The glycemic index/load evaluation is a useful dietary approach to glucose metabolism control. Similarly, they should adopt the principle of the Mediterranean diet. Randomized studies with longer monitoring are needed to define the benefits of nutritional supplementation on IR. Considering that healthy style acquisition could track to later ages, programs of healthy lifestyle starting with children offer a better preventive strategy to preserve metabolic control and children’s health.

Some livestock farms rely on anaerobic digestion (AD) technology for manure disposal, thus obtaining energy (biogas) and fertilizer (digestate). Mixtures of antibiotics used for animal health often occur in organic waste and their possible synergistic/antagonistic effects on microorganisms involved in AD are still poorly studied. This work focuses on the effects of adding ciprofloxacin, alone (5 mg L−1) and in combination with sulfamethoxazole (2.5–5–10 mg L−1), on AD efficiency and microbial community structure. The experiment consisted of 90-day cattle manure batch tests and antibiotic removal percentages were assessed. Adding antibiotics always promoted CH4 and H2 production compared to untreated controls; however, CH4 production was lowered with the highest ciprofloxacin (CIP) concentrations. The overall results show antibiotic degradation caused by acidogenic Bacteria, and CH4 was mainly produced through the hydrogenotrophic-pathway by methanogenic Archaea. Shifts in microbial community abundance (DAPI counts) and composition (Illumina-MiSeq and FISH analyses) were observed.

Anaerobic digestion is one of the best ways to re-use animal manure and agricultural residues, through the production of combustible biogas and digestate. However, the use of antibiotics for preventing and treating animal diseases and, consequently, their residual concentrations in manure, could introduce them into anaerobic digesters. If the digestate is applied as a soil fertilizer, antibiotic residues and/or their corresponding antibiotic resistance genes (ARGs) could reach soil ecosystems. This work investigated three common soil emerging contaminants, i.e., sulfamethoxazole (SMX), ciprofloxacin (CIP), enrofloxacin (ENR), their ARGs sul1, sul2, qnrS, qepA, aac-(6′)-Ib-cr and the mobile genetic element intI1, for one year in a full scale anaerobic plant. Six samplings were performed in line with the 45-day hydraulic retention time (HRT) of the anaerobic plant, by collecting input and output samples. The overall results show both antibiotics and ARGs decreased during the anaerobic digestion process. In particular, SMX was degraded by up to 100%, ENR up to 84% and CIP up to 92%, depending on the sampling time. In a similar way, all ARGs declined significantly (up to 80%) in the digestate samples. This work shows how anaerobic digestion can be a promising practice for lowering antibiotic residues and ARGs in soil.

The cardiovascular prevention represents a hot topic in particular when referred to children or adolescents. Indeed it is demonstrated that atherosclerosis starts early in childhood in the presence of cardiovascular risks factors such as dyslipidemia. It arises precociously when inherited lipoprotein disorders occur as in familial hypercholesterolemia, but not only, or in the presence of a cluster of metabolic changes leading to metabolic syndrome. In these conditions it is mandatory to apply all measures aimed to improve the lipid profile as recommended by international guidelines. Lifestyle is recognized the main basic approach while hypolipidemic drugs should be limited to very high risk children since 8–10 years of age. To comply with the expected LDL-Cholesterol (LDL-C) goal some limitations occur at times then nutraceuticals have been considered in children, as demonstrated beneficial in reducing LDL-C in most of adults. The experience in children is so far very poor since limited randomized controlled trials have been performed including a limited number of subjects. Studies mainly concern Fibre and Phytosterols/stanols and the majority improved lipid profile resulting safe and well tolerated. In this review we synthetize these results including other nutraceuticals as Red Yeast Rice, Omega-3/−6 PUFAs, Probiotic, Soy derivative and Nut.

Anaerobic digestion (AD) treatment of cattle manure and slurry makes it possible to produce biogas, a renewable and storable biofuel, as well as digestate, a residual organic matter that can be used to replace chemical fertilizers. On the other hand, the intense use of antibiotics (e.g., sulfamethoxazole) in animal husbandry practices is showing increasing negative impacts resulting from the release of still metabolically active molecules into agroecosystems. In the present study, cattle manure collected from an AD plant-feeding tank was used as feedstock for AD experiments in which some batches were spiked with 5 mg L-1 of sulfamethoxazole (SMX). Adding the antibiotic affected the microbial community dynamic; in particular, the efficiency of the acidogenic and acetogenic phases of the process corresponded to higher CH4 and H2 production than in the control. SMX was also degraded, and at the end of the experiment (69 days), just 20% of its initial concentration was found. The relative abundance (ARG/16S) of resistance genes sul1, sul2, and the proxy intI1 initially found in the ingestate decreased during the AD in both the spiked and control batches, suggesting that this process lowers the likelihood of antibiotic resistance genes spreading.

In this article we want to show the potential of an evolutionary algorithm called Topological Weighted Centroid (TWC). This algorithm can obtain new and relevant information from extremely limited and poor datasets. In a world dominated by the concept of big (fat?) data we want to show that it is possible, by necessity or choice, to work profitably even on small data. This peculiarity of the algorithm means that even in the early stages of an epidemic process, when the data are too few to have sufficient statistics, it is possible to obtain important information. To prove our theory, we addressed one of the most central issues at the moment: the COVID-19 epidemic. In particular, the cases recorded in Italy have been selected. Italy seems to have a central role in this epidemic because of the high number of measured infections. Through this innovative artificial intelligence algorithm, we have tried to analyze the evolution of the phenomenon and to predict its future steps using a dataset that contained only geospatial coordinates (longitude and latitude) of the first recorded cases. Once the coordinates of the places where at least one case of contagion had been officially diagnosed until February 26th, 2020 had been collected, research and analysis was carried out on: outbreak point and related heat map (TWC alpha); probability distribution of the contagion on February 26th (TWC beta); possible spread of the phenomenon in the immediate future and then in the future of the future (TWC gamma and TWC theta); how this passage occurred in terms of paths and mutual influence (Theta paths and Markov Machine). Finally, a heat map of the possible situation towards the end of the epidemic in terms of infectiousness of the areas was drawn up. The analyses with TWC confirm the assumptions made at the beginning., Highlights • In this article the potential of an evolutionary algorithm called TWC is shown. • TWC can obtain new and relevant information from extremely limited and poor data. • TWC has been applied to the COVID-19 epidemic data. • TWC correctly identifies the location of the official Italian outbreak. • TWC correctly identifies the trend in the geographical expansion of the epidemic.

The translation of precision medicine in clinical practice will depend mostly on the possibility to make statistical inference at the individual level, exactly positioning a new case in the taxonomy space (diagnosis) or in the time space (prognosis). As a matter of the fact, clinical epidemiology and medical statistics have not been suited to answer specific questions at the individual level. They focus on groups of individuals and not on single individuals. Classical statistics by definition needs samples to work, and samples by definition are always greater than one. This explains why for traditional statistics the single individual is a sort of moving and vague target to intercept. The objective of this chapter is to show the feasibility of the use of potent machine learning system developed at the Semeion Institute in approaching the problem of single individual statistics in a consistent and sound way. Three case studies relevant to different unsupervised machine learning systems are reviewed: (a) the use of self-organizing maps (SOMs) to determine the confidence interval of a quality of life scale total score in seven new individual subjects having a group of 1000 individuals as reference data set; (b) the use of the evolutionary algorithm “pick and squash tracking” (PST) to cluster and discriminate patients affected by Barrett disease from those affected by simple gastroesophageal reflux disease, (c) the use of the auto-contractive map (Auto-CM) system, a fourth-generation artificial neural network, to map individual patients with and without acute myocardial infarction (AMI) on the basis of genetic, clinical traits, and classical risk factors. A further case study is described relying on the use of supervised machine learning systems, based on the concept of Fermi mathematics. The three unsupervised methods proved to be reliable and easily applicable to real-world examples in terms of readability, accuracy, and reproducibility. The confidence interval related to the seven new cases in the first case study allowed the clinician to identify easily the outlier. The accuracy of the map projection with PST algorithm in the second case study allowed an immediate visual evidence of the degree of membership of each individual subject to the two diagnostic classes. In the third case study, the overall accuracy of clustering obtained by the Auto-CM system resulted is found to be 93%.The conceptual advantages obtainable are explained. The fourth method shows that it is possible by using several independent classification models on the same individual to establish a degree of confidence of the prediction and therefore to overcome the dogma that it is not possible to make a statistical inference when a sample is composed by just one subject.

Huge amounts of antibiotics are currently being used in both human and veterinary medicine. Moreover, most are recalcitrant to biodegradation and can persist in the environment. In fact these compounds have been increasingly found as micro-contaminants in natural ecosystems and cause particular concern because of the development of multi-resistant bacteria, posing serious risks for human and animal health. In particular, the antibiotics used in livestock farms can persist in manure, causing environmental contamination if used as a biofertilizer. However, farm manure can also be a feed for biogas reactors and the digested effluent (digestate) used as fertilizer or soil improver. The fact that the latter can still contain antibiotic residues and possibly antibiotic resistance genes still remains to be clarified.

Background: Diet is considered the cornerstone of lipid management in hyperlipidemic children but evidence to demonstrate the effects of nutrient benefits on the lipid profile is limited. Aim: The aim of this study is to evaluate the impact of the Mediterranean diet on low-density lipoprotein (LDL-C) and non-high density lipoprotein (HDL-C) decrease in primary hyperlipidemia affected children and in the achievement of therapeutical target levels. Methods: A retrospective cohort study was used, recruiting n = 223 children (10.05 ± 3.26 mean age years) with familial hypercholesterolemia (FH) (n = 61, 27%) and polygenic hypercholesterolemia (PH) (n =162, 73%). Secondary hyperlipidemias were excluded. Based on LDL-C and non-HDL-C decrease, participants were divided into two groups, named the Responder Group and Non-Responder Group. Participants and their families underwent dietary education by an expert nutritionist and were asked to fill in a weekly diary to be delivered at visits. Dietary indications were in line with daily caloric requirement, daily food quality and quantity intakes typical of the Mediterranean diet. These include carbohydrates, extra virgin olive oil, yoghurt and milk derivatives, fish and vegetable proteins, fresh seasonal vegetables and fresh fruits. Nuts or almonds were also recommended. The advice to limit intakes of meat, in particular red meat, and caution against junk food and sugar added food and beverages was provided. At medical visits, carried out at baseline (T0) and 6 months later (T1), children underwent anthropometric measurements and blood collection. Standard kits and methods were applied for lipid analysis. Statistical methods were performed by SAS version 9.4 (SAS Institute, Cary, NC, USA). Signed informed consent was given by parents according to the Declaration of Helsinki and the study was approved by the Local Committee. Results: The Responder Group (n = 156/223, 70%) included 45 FH and 111 PH children, while the Non-Responder Group (n = 67/223, 30%) included 16 FH and 51 PH children. The Responder Group showed total cholesterol (TC), LDL-C and non-HDL-C median percentage decreases of 9.45, 13.51 and 10.90, respectively. These statistically significant changes (p ≤ 0.0001) were similar in the FH and PH subgroups but just PH subjects reached the LDL-C and non-HDL-C target, which fell below 130 mg/dL and 145 mg/dL, respectively. Saturated fatty acids (SFAs) were the main dietary parameter that distinguished between the Responder Group and the Non-Responder Group (p = 0.014). Positive correlations were found at T1 between dietary total lipids, SFAs and cholesterol with serum LDL-C, non-HDL-C and TC variations. These latter serum parameters had an inverse correlation with dietary carbohydrate at T1. Among macronutrients, SFAs were finally demonstrated to be the predictor of serum lipids variation at T1. Conclusions: The dietary intervention with a Mediterranean diet in children with primary hyperlipidemia significantly improves the lipid profile both in FH and PH subgroups and allows target levels of LDL-C and non-HDL-C in PH subjects to be reached. Responsiveness benefits should be primarily attributed to the reduction in SFAs, but changes in dietary lipids, cholesterol and carbohydrate intake may also play a role. In contrast, the Non-Responder Group showed a worsening of lipid profile regarding the unchanged diet.

In recent decades, the innovative practice of management and valorization of agrozootechnical waste as energy through anaerobic digestion (AD) has been rapidly growing. However, whether applying digestate to soil as biofertilizer can be a source of antibiotics (ABs) and antibiotic resistance genes (ARGs) has not been fully investigated so far. In this work the ARGs responsible for sulfamethoxazole (SMX) resistance (sul1, sul2), ciprofloxacin (CIP) resistance (qnrS, qepA, aac-(6′)-Ib-cr) and the mobile genetic element intl1, together with the concentrations of the antibiotics SMX and CIP, were measured in several anaerobic digesters located in Central Italy. Based on these results, the concentrations of antibiotics and ARGs which can potentially reach soil through amendment with digestate were also estimated. The highest CIP and SMX concentrations were found during winter and spring in anaerobic digesters. The highest ARG abundances were found for the aac-(6’)-Ib-cr and sul2 genes. The overall results showed that application of digestate to soil does not exclude AB contamination and spread of ARGs in agroecosystems, especially in the case of ciprofloxacin, owing to its high intrinsic persistence.

In this paper, we introduce a new approach to the analysis and prediction of the spatial pattern of terrorist attacks. Our analysis makes use of a new methodology that is based upon a topological rather than geometric approach. It exploits the space grammar of the observed events to derive complex inferences which relate to the deep structural layers of the data generating process, and to the organizational architecture of its spatial distribution. We test our methodology on two of the currently most important and virulent theaters of terrorist activity, Libya and Syria, and we find that our methodology provides a rich array of insights which cannot be expected to be generated by more traditional geographical profiling methods.

The first case of COVID-19 in USA was reported on January 20, 2020. The number of COVID-19 confirmed cases and death has increased since the first reported case and the outbreak has appeared in all states. This paper analyzes disease outbreak using Topological Weighted Centroid (TWC), which is a data driven intelligent geographical dynamical system that models disease spread in space and time. In this analysis the COVID-19 cases in USA on March 26, 2020 as provided by Johns Hopkins University is used. The COVID-19 outbreak is mapped by the TWC method. We were able to predict and capture some features of the pandemic spread using the early data. Although we have used the geographical distance from the latitude and longitude coordinates, our results indicate that one of the main paths of diseases spread are arguably airline routes. In this analysis, we used a large set of data. A modified version of TWC, is named TWC-Windowing to elaborate the effect of data from all places.

This paper proposes an innovative bioaugmentation approach for the remediation of polycyclic aromatic hydrocarbon (PAH) contaminated soils, based on a novel habitat-based strategy. This approach was tested using two inocula (i-24 and i-96) previously enriched through an anaerobic digestion process on wheat straw. It relies on the application of allochthonous microorganisms characterized by specific functional roles obtained by mimicking a natural hydrolytic environment such as the rumen. The inocula efficiency was tested in presence of naphthalene alone, benzo[a]pyrene alone, and a mix of both of them. In single-contamination tests, i-24 inoculum showed the highest biodegradation rates (84.7% for naphthalene and 51.7% for benzo[a]pyrene). These values were almost 1.2 times higher than those obtained for both contaminants with i-96 inoculum and in the control test in presence of naphthalene alone, while they were 3 times higher compared to the control test in presence of benzo[a]pyrene alone. In mixed-contamination tests, i-96 inoculum showed final biodegradation efficiencies for naphthalene and benzo[a]pyrene between 1.1 and 1.5 higher than i-24 inoculum or autochthonous biomass. Total microbial abundances increased in the bioaugmented tests in line with the PAH degradation. The microbial community structure showed the highest diversity at the end of the experiment in almost all cases. Values of the Firmicutes active fraction up to 7 times lower were observed in the i-24 bioaugmented tests compared to i-96 and control tests. This study highlights a successful bioaugmentation strategy with biological components that can be reused in multiple processes supporting an integrated and environmentally sustainable bioremediation system.

We investigated the relationship between the temporal monitoring series routinely recorded at Mt. Etna and the flank eruptions that occurred between January 2001 and April 2005 by the K-contractive map (K-CM) method pattern classifier with supervised learning. The reference dataset includes 28 variables and 1580 records collected over 52 months for a total of 301 eruptive days. A two-step analysis was performed. In the first step analysis, we used the 28 parameters of each day to recognize anomalies heralding a flank eruption. K-CM estimated a sensitivity higher than 95% and a specificity close to 100%. In the second step analysis, we considered each record comprising the 28 variables for 6 days as an input (for a total of 180 inputs) and the outcomes of the seventh day as an output to predict eruption or rest. In this case, K-CM showed sensitivity and specificity close to 98% and 100%, respectively. Results highlight the reliability of the K-CM method to build up a prediction algorithm able to alert the volcano experts a day before the occurrence of a potential flank eruption. The robustness of the two analyses was investigated by the behavior of the receiver operating characteristic curve. The relative area under the curve showed values close to 1, thus providing a valid measure of the performance of the classifier. Finally, a complete overview of the performance levels of the method used was explored analyzing the retrieved Molchan error diagram, in both cases, trajectories very close to the theoretical minimum.

Mathematical model applications for the bioaugmented anaerobic digestion (BAD) process seem to be lacking in the scientific literature, even more so when related to microbiological data. The present study suggests a simplified mathematical model to investigate and simulate the process kinetics of bioaugmented anaerobic digestion (BAD) aimed at improving biogas production from wheat straw (WS). Bioaugmented conditions were obtained through a mixed inoculum of anaerobic ruminal fungi (ARF) and hydrogen-producing fermenting bacteria (F210) added to a methanogenic inoculum. The investigation focused on two process configurations characterized by a mono (I-BAD) and two-stage (II-BAD) process and a conventional anaerobic digestion (AD) control test. Each configuration was used on two operating scales (i.e., 120 ml and 12,000 ml reactor volume) to provide different data sets for the calibration and validation of the mathematical model proposed. The model calibration step was used to determine the optimal values of selected parameters displaying higher significance for experimental result predictability. The model calibration results highlighted a similar behavior for both BAD tests, which was further strengthened by a statistical analysis supporting the observed correlation regardless of the BAD configuration involved. The BAD configuration always enhanced the CH4 production (>70%) with a faster kinetic in the II-BAD test. The microbiological results support the superior performance of the II-BAD test, displaying higher Archaea fractions (up to 14.5% on day 23) with values more than double compared to I-BAD and triple compared to the AD test. Furthermore, the presence of Methanosarcina inside the Archaea guild (6.4% and 4.5% at days 11 and 61, respectively) ensures a greater diversification of the metabolic pathways and supports the strength of the process performance. Cell density values are strongly in line with these results.

Inocula enrichment was performed using an innovative habitat-based selection approach to improve wheat straw (WS) anaerobic digestion (AD) efficiency. The procedure was carried out by sequentially re-inoculating the primary microbial community seven times in subsequent anaerobic reactors containing untreated WS. Re-inocula were performed at different re-inoculum times (24, 48, and 96 h) by moving a porous support mimicking a rumen structure from one batch to the next (S-tests) or re-inoculating only the culture medium (C-tests). Highest H2 production yields were observed after four and five re-inocula (0.08 ± 0.02 NmL h−1 gVS−1 and 0.09 ± 0.02 NmL h−1 gVS−1) for S-24 and S-48, respectively. For S-96, higher CH4 yields were observed after the start-up test and sixth re-inoculum (0.05 ± 0.003 NmL h−1 gVS−1 and 0.04 ± 0.005 NmL h−1 gVS−1, respectively). Accordingly, S-96 showed the highest active Archaea component (7%). C-test microbial communities were dominated by fermenting, hydrogen-producing bacteria and showed lower microbial community diversity than S-tests.

Background and objective Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice, having been recognized as a true cardiovascular epidemic. In this paper, a new methodology for Computer Aided Diagnosis of AF based on a special kind of artificial adaptive systems has been developed. Methods Following the extraction of data from the PhysioNet repository, a new dataset composed of the R/R distances of 73 patients was created. To avoid redundancy, the training set was created by randomly selecting 50% of the subjects from the entire sample, thus making a choice by patient and not by record. The remaining 50% of subjects were randomly split by records in testing and prediction sets. The original ECG data has been transformed according to the following four orders of abstraction: a) sequence of R/R intervals; b) composition of ECG data into a moving window; c) training of different machine learning systems to abstract the function governing the AF; d) fuzzy transformation of Machine learning estimations. In this paper, in parallel with the classic method of windowing, we propose a variant based on a system of progressive moving averages. Results The best performing machine learning, Supervised Contractive Map (SVCm), reached an overall mean accuracy of 95%. SVCm is a new deep neural network based on a different principle than the usual descending gradient. The minimization of the error occurs by means of decomposition into contracted sine functions. Conclusions In this research, atrial fibrillation is considered from a completely different point of view than classical methods. It is seen as the stable process, i.e. the function, that manages the irregularity of the irregularities of the R/R intervals. The idea, therefore, is to abstract from mere physiology to investigate fibrillation as a mathematical object that handles irregularities. The attained results seem to open new perspectives for the use of potent artificial adaptive systems for the automatic detection of atrial fibrillation, with accuracy rates extremely promising for real world applications.

The encoding of the spatial-temporal archeological, historical and anthropological records can be considered an ideal-typical representation of the human reasoning and thus also an artificial membrane interposed between the researcher and the past. These membranes are here considered artificial networks and can undergo interrogation processes through the most advanced analytical tools for learning and modeling complex configurations. The aim of this paper is to synthesize recent advances in Artificial Intelligence and Computer Science and – at the same time – to support the connectionists and symbolic computational paradigms as a new epistemic frontier in the automatic annotation of tangible and intangible heritage as well in the contemporary theories and methods of the archeological thought.

Background Familial hypercholesterolemia (FH) is a genetic disorder caused by mutations in genes involved in low-density lipoprotein (LDL) uptake (LDLR, APOB and PCSK9). Genetic diagnosis is particularly useful in asymptomatic children allowing for the detection of definite FH patients. Furthermore, defining their genetic status may be of considerable importance as the compound heterozygous status is much more severe than the heterozygous one. Our study aims at depicting the genetic background of an Italian pediatric population with FH focusing on the correlation between lipid profile and genetic status. Methods Out of 196 patients with clinically suspected FH (LDL-cholesterol [LDL-C] levels above 3.37 mmol/L, cholesterol level above 6.46 mmol/L in a first-degree relative or the presence of premature cardiovascular acute disease in a first/second-degree relative), we screened 164 index cases for mutations in the LDLR, APOB and PCSK9 genes. Results Patients with mutations (129/164) showed increased levels of LDL-C, 95th percentile-adjusted LDL-C and LDL/high-density lipoprotein (HDL) ratio and decreased levels of HDL-C, adjusted HDL-C. The association of the LDL/HDL ratio with the presence of mutations was assessed independently of age, (body mass index) BMI, parental hypercholesterolemia, premature coronary artery disease (CAD), triglycerides by multivariate logistic regression (odds ratio [OR]=1.701 [1.103–2.621], p=0.016). The LDL/HDL ratio gradually increased from patients without mutations to patients with missense mutations, null mutations and compound heterozygotes. Conclusions In conclusion, the LDL/HDL ratio proved to be a better parameter than LDL-C for discriminating patients with from patients without mutations across different genetic statuses.

In this paper, we introduce an innovative approach to the fusion between datasets in terms of attributes and observations, even when they are not related at all. With our technique, starting from datasets representing independent worlds, it is possible to analyze a single global dataset, and transferring each dataset onto the others is always possible. This procedure allows a deeper perspective in the study of a problem, by offering the chance of looking into it from other, independent points of view. Even unrelated datasets create a metaphoric representation of the problem, useful in terms of speed of convergence and predictive results, preserving the fundamental relationships in the data. In order to extract such knowledge, we propose a new learning rule named double backpropagation, by which an auto-encoder concurrently codifies all the different worlds. We test our methodology on different datasets and different issues, to underline the power and flexibility of the Theory of Impossible Worlds.

Children affected by primary hyperlipidemia have a high risk of developing cardiovascular diseases (CVDs) during adulthood. Several studies have reported a positive association between the intake of polyunsaturated fatty acids (PUFAs) and improvements in lipid markers and CVD risk. Dietary supplements may represent a potential strategy in the management of hyperlipidemia. In this context, the effectiveness of hempseed oil (HSO) rich in PUFAs (particularly linoleic acid (LA) and α-linolenic acid (ALA)) in the modulation of hyperlipidemia has been poorly investigated. The present pilot study aimed to explore the impact of HSO supplementation on the serum lipid profile and fatty acid (FA) composition of red blood cells (RBCs) in children and adolescents with primary hyperlipidemia. A randomized, 8 week long, parallel dietary intervention study was performed. Thirty-six hyperlipidemic probands (6-16 years) on diet therapy were randomized into two groups: the HSO group, receiving 3 g of HSO providing 1.4 g of LA and 0.7 g/day of ALA, and the control group. Both groups received specific dietary guidelines. Before and after the intervention, blood samples were collected and the serum lipid profile, FA composition of RBCs and omega-3-index were analyzed. Eight weeks of supplementation with HSO significantly (p .01) reduced the RBC content of total saturated and monounsaturated FAs (-5.02 ± 7.94% and - 2.12 ± 2.23%, respectively), increased the levels of total n-3 and n-6 PUFAs (+1.57 ± 1.96% and + 5.39 ± 7.18%, respectively) and the omega-3 index (+1.18 ± 1.42%), but failed to affect the serum lipid profile compared to the control group. In conclusion, our findings seem to support the contribution of HSO supplementation in improving the RBC phospholipid composition and omega-3 index, while no effect was observed regarding modulation of the lipid profile. Further controlled studies are necessary to achieve a complete understanding of the effects of HSO in the modulation of hyperlipidemia and CVD risk in this and other target groups.

Bioaugmentation with anaerobic ruminal fungi and a pool of hydrogen-producing fermenting bacteria was tested on wheat straw (WS) and mushroom spent straw (MSS) with the aim of improving anaerobic digestion performance. Batch tests were set up to simulate a Bioaugmentation Anaerobic Digestion (BAD) treatment comparing single- (I-BAD) and two-stage (II-BAD) process configurations, at two reactor scales, 120 and 1200 ml (×10). In both cases, higher CH4 cumulative production was obtained in the II-BAD configuration on WS (65.1 ± 8.9 Nml and 922 ± 73.8 Nml respectively). The II-BADx10 tests allowed increasing CH4 production (≃290% and ≃330% on WS and MSS, respectively) when compared to the unaugmented condition. Final results highlighted the achievable advantages of the two stage configuration in terms of CH4 production enhancement. Microbial community investigations confirmed the efficiency of the bioaugmentation treatment and revealed that such a result was mainly related to the Methanosarcinales increase, mostly composed by Methanosaeta. © 2018 Elsevier Ltd

One of the most powerful aspects of our approach to neural networks is not only the development of the Auto-CM neural network but the visualization of its results. In this chapter we look at two visualization approaches—the Minimal Spanning Tree (MST) and the Maximal Regular Graph (MRG). The resultant from Auto-CM is a matrix of weights. This weight matrix naturally fits into a graph theoretic framework since the weights connecting the nodes will be viewed as edges and the weights as the weights on these edges.

Normalized root mean square error (NRMSE) results from calibration and validation phases confirmed the model robustness and stability; kd calibrated values were consistent with the initial faster process kinetic observed in the anaerobic digestion (AD) configuration; α values from model calibration step further confirmed the beneficial effects of F210 and ARF on the hydrolysis step enhancement of wheat straw (WS).

We compare Auto-CM with various other methods that extract patterns from data. The way that we measure the results of comparisons uses MST.

Artificial Adaptive Systems include Artificial Neural Networks (ANNs or simply neural networks as they are commonly known). The philosophy of neural networks is to extract from data the underlying model that relates this data as an input/output (domain/range) pair. This is quite different from the way most mathematical modeling processes operate. Most mathematical modeling processes normally impose on the given data a model from which the input to output relationship is obtained. For example, a linear model that is a “best fit” in some sense, that relates the input to the output is such a model. What is imposed on the data by artificial neural networks is an a priori architecture rather than an a priori model. From the architecture, a model is extracted. It is clear, from any process that seeks to relate input to output (domain to range), requires a representation of the relationships among data. The advantage of imposing an architecture rather than a data model, is that it allows for the model to adapt. Fundamentally, a neural network is represented by its architecture. Thus, we look at the architecture first followed by a brief introduction of the two types of approaches for implementing the architecture—supervised and unsupervised neural networks. Recall that Auto-CM, which we discuss in Chap. 3, is an unsupervised ANN while K-CM, discussed in Chap. 6, is a supervised version of Auto-CM. However, in this chapter, we show that, in fact, supervised and unsupervised neural networks can be viewed within one framework in the case of the linear perceptron. The chapter ends with a brief look at some theoretical considerations.

This chapter focuses on Auto-Contractive Maps, which is a particularly useful ANN. Moreover, the relationship between Auto-Contractive Map (Auto-CM), which is the main topic of this monograph, its relationship to other ANNs and some illustrative example applications are presented.