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The increasing scientific interest in antioxidants and naturally derived compounds as potential remedies for obesity and non-alcoholic fatty liver disease (NAFLD) has led to extensive research. The objective of this bibliometric analysis is to present an updated perspective on the topic of antioxidants, herbs, phytochemicals, and natural compounds, in the control of obesity and NAFLD, to identify new areas for future research. Publications from the years 2012–2022 were retrieved using the Scopus database. The research trends were analyzed using the Biblioshiny and VOSviewer tools. The field has seen a significant increase in research activity, as indicated by an annual growth rate of 10 % in the number of published manuscripts. China, Korea, and the USA emerged as the most prominent contributors in this specific field, supported by their notable volumes of publications and citations. The density analysis revealed that the most frequently occurring authors' keywords related to herbal species are, in rank order, Camelia sinensis, Momordica charantia, Curcuma longa, Ilex paraguariensis, Panax ginseng, Moringa oleifera, Garcinia cambogia, Garcinia mangostana, Zingiber officinale, and Cinnamomum verum. In the group of antioxidants, phytochemicals, and natural compounds, the top 10 were resveratrol, curcumin, quercetin, vitamin E, alpha-lipoic acid, vitamin C, chlorogenic acid, lycopene, fucoxanthin, and berberine. The co-occurrence analysis unveiled significant themes and potential trends, including a notable interest in the impact of herbal species, antioxidants, phytochemicals, and natural compounds on obesity and NAFLD through the modulation of the gut microbiome. Another recurring theme that arises, is the ongoing investigation of molecular targets that demonstrate anti-adipogenesis properties. The analysis presented in this study provides valuable insights for researchers investigating the efficacy of antioxidants, herbs, phytochemicals, and natural compounds in addressing obesity and NAFLD. Through the use of bibliometric methods, the study offers a comprehensive overview. Furthermore, the findings of this analysis can serve as a foundation for future research in this specific domain.

Abstract Scientific interest related to the role of gut microbiome dysbiosis in the pathogenesis of non-alcoholic fatty liver disease (NAFLD) has now been established and is constantly growing. Therefore, balancing dysbiosis, through probiotics, would be a potential therapy. In addition to scientific interest, on the other hand, it is important to evaluate the interest in these topics among the population. This study aimed to analyze, temporally and geographically, the public's interest in gut microbiome dysbiosis, NAFLD, and the use of gut probiotics. The most widely used free tool for analyzing online behavior is Google Trends. Using Google Trends data, we have analyzed worldwide volume searches for the terms “gut microbiome”, “dysbiosis”, “NAFLD” and “gut probiotic” for the period from 1, January 2007 to 31 December 2022. Google's relative search volume (RSV) was collected for all terms and analyzed temporally and geographically. The RSV for the term “gut microbiome” has a growth rate of more than 1400% followed, by “gut probiotics” (829%), NAFLD (795%), and “dysbiosis” (267%) from 2007 to 2012. In Australia and New Zealand, we found the highest RSV score for the term “dysbiosis” and “gut probiotics”. Moreover, we found the highest RSV score for the term “NAFLD” in the three countries: South Korea, Singapore, and the Philippines. Google Trends analysis showed that people all over the world are interested in and aware of gut microbiome dysbiosis, NAFLD, and the use of gut probiotics. These data change over time and have a geographical distribution that could reflect the epidemiological worldwide condition of NAFLD and the state of the probiotic market.

Background: Obesity is an escalating concern in our society. Bariatric surgery appears to be the only feasible alternative for severe obesity. This study aims to conduct an integrative analysis of the Italian context concerning bariatric surgery, with a specific focus on the surgical procedures of sleeve gastrectomy, mini-gastric bypass, and gastric bypass. Methods: The analysis is based on bibliometric data extracted from the Scopus database to find Italian publications in this field from 2012 to 2022. Along with scientific interest, it is essential to assess the level of public interest in these topics. Google Trends is the most extensively utilized free tool for the analysis of online behavior. Using Google Trends data, we analyzed the amount of internet volume searches (Relative Search Volume = RSV) for the Italian terms “sleeve gastrectomy”, “mini-gastric bypass” and “gastric bypass”, both temporally (from 2012 to 2022 years) and geographically. Furthermore, to illustrate the Italian landscape, we compared Google Trends data with data from the Italian registry “Società Italiana di Chirurgia dell’Obesità e delle malattie metaboliche” (SICOB), which includes information about bariatric surgery procedures performed in the country. Results: Over the past decade, there has been a significant increase in the scientific literature on sleeve gastrectomy, mini-gastric bypass, and gastric bypass surgeries in Italy. Google Trends data has shown an increase in online public interest in these surgical procedures, from 2012 to 2022. The examination of the Google Trends information combined with the SICOB data demonstrated a remarkable association between the two data sets. Conclusions: Bariatric surgery has increased quickly in Italy in terms of scientific research and surgical interventions. The analysis of Google Trends data in conjunction with data from SICOB has revealed an interesting correlation between the two datasets therefore, Google Trends can be useful for estimating the demand for bariatric surgical procedures.

SARS-CoV-2 RNA quantification in wastewater has emerged as a relevant additional means to monitor the COVID-19 pandemic. However, the concentration can be affected by black water dilution factors or movements of the sewer shed population, leading to misinterpretation of measurement results. The aim of this study was to evaluate the performance of different indicators to accurately interpret SARS-CoV-2 in wastewater. Weekly/bi-weekly measurements from three cities in France were analysed from February to September 2021. The concentrations of SARS-CoV-2 gene copies were normalised to the faecal-contributing population using simple sewage component indicators. To reduce the measurement error, a composite index was created to combine simultaneously the information carried by the simple indicators. The results showed that the regularity (mean absolute difference between observation and the smoothed curve) of the simple indicators substantially varied across sampling points. The composite index consistently showed better regularity compared to the other indicators and was associated to the lowest variation in correlation coefficient across sampling points. These findings suggest the recommendation for the use of a composite index in wastewater-based epidemiology to compensate for variability in measurement results. HIGHLIGHTS SARS-CoV-2 RNA quantification in wastewater shows high variability and need to be normalized.; The performance of various normalized indicators, including a composite index was assessed via two criteria: regularity and correlation with the incidence rate.; The composite index showed the best performance.; Except for the F-specific RNA bacteriophage normalisation index, correlation of all indicators with the incidence rate was substantial.;

Bleeding in spine surgery is a common occurrence but when bleeding is uncontrolled the consequences can be severe due to the potential for spinal cord compression and damage to the central nervous system. There are many factors that influence bleeding during spine surgery including patient factors and those related to the type of surgery and the surgical approach to bleeding. There are a range of methods that can be employed to both reduce the risk of bleeding and achieve hemostasis, one of which is the adjunct use of hemostatic agents. Hemostatic agents are available in a variety of forms and materials and with considerable variation in cost, but specific evidence to support their use in spine surgery is sparse. A literature review was conducted to identify the pre-, peri-, and postsurgical considerations around bleeding in spine surgery. The review generated a set of recommendations that were discussed and ratified by a wider expert group of spine surgeons. The results are intended to provide a practical guide to the selection of hemostats for specific bleeding situations that may be encountered in spine surgery.

Plant breeding is focused on the genotype and population levels while targeting effects at higher levels of biodiversity, from crop covers to agroecosystems. Making predictions across nested levels of biodiversity is therefore a major challenge for the development of intercropping practices. New prediction tools and concepts are required to design breeding strategies with desirable outcomes at the crop community level. We reviewed theoretical advances in the field of evolutionary ecology to identify potentially operational ways of predicting the effects of artificial selection on community-level performances. We identified three main types of approaches differing in the way they model interspecific indirect genetic effects (IIGEs) at the community level: (1) The community heritability approach estimates the variance for IIGE induced by a focal species at the community level; (2) the joint phenotype approach quantifies genetic constraints between direct genetic effects and IIGE for a set of interacting species; (3) the community-trait genetic gradient approach decomposes the IIGE for a focal species across a multivariate set of its functional traits. We discuss the potential operational capacities of these approaches and stress that each is a special case of a general multitrait and multispecies selection index. Choosing one therefore involves assumptions and goals regarding the breeding target and strategy. Obtaining reliable quantitative, community-level predictions at the genetic level is constrained by the size and complexity of the experimental designs usually required. Breeding strategies should instead be compared using theoretically informed qualitative predictions. The need to estimate genetic covariances between traits measured both within and among species (for IIGE) is another obstacle, as the two are not determined by the exact same biological processes. We suggest future research directions and strategies to overcome these limits. Our synthesis offers an integrative theoretical framework for breeders interested in the genetic improvement of crop communities but also for scientists interested in the genetic bases of plant community functioning.

Fermentation is a widely used process in the biotechnology industry, in which sugar-based substrates are transformed into a new product through chemical reactions carried out by microorganisms. Fermentation yields depend heavily on critical process parameter (CPP) values which need to be finely tuned throughout the process; this is usually performed by a biotech production expert relying on empirical rules and personal experience. Although developing a mathematical model to analytically describe how yields depend on CPP values is too challenging because the process involves living organisms, we demonstrate the benefits that can be reaped by using a black-box machine learning (ML) approach based on recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks to predict real time OD600nm values from fermentation CPP time series. We tested both networks on an E. coli fermentation process (upstream) optimized to obtain inclusion bodies whose purification (downstream) in a later stage will yield a targeted neurotrophin recombinant protein. We achieved root mean squared error (RMSE) and relative error on final yield (REFY) performances which demonstrate that RNN and LSTM are indeed promising approaches for real-time, in-line process yield estimation, paving the way for machine learning-based fermentation process control algorithms.

The natural genetic diversity of agricultural species is an essential genetic resource for breeding programs aiming to improve their ecosystem and production services. A large natural ecotype diversity is usually available for most grassland species. This could be used to recombine natural climatic adaptations and agronomic value to create improved populations of grassland species adapted to future regional climates. However describing natural genetic resources can be long and costly. Molecular markers may provide useful information to help this task. This opportunity was investigated for Lolium perenne L., using a set of 385 accessions from the natural diversity of this species collected right across Europe and provided by genebanks of several countries. For each of these populations, genotyping provided the allele frequencies of 189,781 SNP markers. GWAS were implemented for over 30 agronomic and/or putatively adaptive traits recorded in three climatically contrasted locations (France, Belgium, Germany). Significant associations were detected for hundreds of markers despite a strong confounding effect of the genetic background; most of them pertained to phenology traits. It is likely that genetic variability in these traits has had an important contribution to environmental adaptation and ecotype differentiation. Genomic prediction models calibrated using natural diversity were found to be highly effective to describe natural populations for almost all traits as well as commercial synthetic populations for some important traits such as disease resistance, spring growth or phenological traits. These results will certainly be valuable information to help the use of natural genetic resources of other species.

Abstract KM3NeT is a research infrastructure located in the Mediterranean Sea, that will consist of two deep-sea Cherenkov neutrino detectors. With one detector (ARCA), the KM3NeT Collaboration aims at identifying and studying TeV–PeV astrophysical neutrino sources. With the other detector (ORCA), the neutrino mass ordering will be determined by studying GeV-scale atmospheric neutrino oscillations. The first KM3NeT detection units were deployed at the Italian and French sites between 2015 and 2017. In this paper, a description of the detector is presented, together with a summary of the procedures used to calibrate the detector in-situ. Finally, the measurement of the atmospheric muon flux between 2232–3386 m seawater depth is obtained.

In a context of increasing environmental challenges, there is an emerging demand for plant cultivars that are adapted to cultivation in species mixture. It is thus pressing to look for the optimization of selection schemes to grow species mixtures, and especially recurrent selection schemes which are at the core of the improvement of many plant species. We considered the case of two populations from different species to be improved by recurrent selection for their performances in mixture. We set up an analytical model of performances in mixture. We expressed the expected responses of the performances in mixture to one cycle of selection in the case of a Reciprocal Mixture Ability selection scheme and of two parallel selection schemes aiming to improve General Mixture Abilities or performances in pure stands. We numerically compared these selection schemes when half-sib or topcross progeny families of selection candidates are tested in mixture. Selection in pure stands appeared efficient within a limited range of genetic correlations between pure stand performance and mixture model effects. The Reciprocal Mixture Ability selection scheme was expected to be less efficient than parallel selections for General Mixture Ability in some situations. The last option enables to control the ratio of expected responses of species contributions to the mixture performance without bias when using selection indices. When more than two species are be improved for their performances in mixture, the advantage of parallel selections for General Mixture Ability is even more marked, providing that compensation trends between species are not too prevalent.

Subtalar osteochondral lesions of the talus are uncommon but disabling conditions that require most of the time subtalar arthrodesis. Recent studies have shown promising results with the use of intraosseous injection of stem cell and platelet rich plasma (PRP) in osteonecrosis of the femoral head or talus in combination with bone decompression. We report a 26-year-old patient with a post-traumatic subtalar osteochondral lesion of the talus treated by bone decompression and intraosseous injection of PRP. At 12 months, the patient presented an almost complete resolution of his pain and a regression of the marrow edema around the lesions on the MRI. Intraosseous injection of PRP seems to be a promising treatment in the management of subtalar osteochondral lesion.Level of Clinical Evidence: 4Level of Evidence: level V, expert opinion.

The rate at which obesity is becoming an epidemic in many countries is alarming. Obese individuals have a high risk of developing elevated intraocular pressure and glaucoma. Additionally, glaucoma is a disease of epidemic proportions. It is characterized by neurodegeneration and neuroinflammation with optic neuropathy and the death of retinal ganglion cells (RGC). On the other hand, there is growing interest in microbiome dysbiosis, particularly in the gut, which has been widely acknowledged to play a prominent role in the etiology of metabolic illnesses such as obesity. Recently, studies have begun to highlight the fact that microbiome dysbiosis could play a critical role in the onset and progression of several neurodegenerative diseases, as well as in the development and progression of several ocular disorders. In obese individuals, gut microbiome dysbiosis can induce endotoxemia and systemic inflammation by causing intestinal barrier malfunction. As a result, bacteria and their metabolites could be delivered via the bloodstream or mesenteric lymphatic vessels to ocular regions at the level of the retina and optic nerve, causing tissue degeneration and neuroinflammation. Nowadays, there is preliminary evidence for the existence of brain and intraocular microbiomes. The altered microbiome of the gut could perturb the resident brain–ocular microbiome ecosystem which, in turn, could exacerbate the local inflammation. All these processes, finally, could lead to the death of RGC and neurodegeneration. The purpose of this literature review is to explore the recent evidence on the role of gut microbiome dysbiosis and related inflammation as common mechanisms underlying obesity and glaucoma.

Non-alcoholic fatty liver disease (NAFLD) is a prevalent, multifactorial, and poorly understood liver disease with an increasing incidence worldwide. NAFLD is typically asymptomatic and coupled with other symptoms of metabolic syndrome. The prevalence of NAFLD is rising in tandem with the prevalence of obesity. In the Western hemisphere, NAFLD is one of the most prevalent causes of liver disease and liver transplantation. Recent research suggests that gut microbiome dysbiosis may play a significant role in the pathogenesis of NAFLD by dysregulating the gut–liver axis. The so-called “gut–liver axis” refers to the communication and feedback loop between the digestive system and the liver. Several pathological mechanisms characterized the alteration of the gut–liver axis, such as the impairment of the gut barrier and the increase of the intestinal permeability which result in endotoxemia and inflammation, and changes in bile acid profiles and metabolite levels produced by the gut microbiome. This review will explore the role of gut–liver axis disruption, mediated by gut microbiome dysbiosis, on NAFLD development.

Coastal-water hypoxia is increasing globally due to global warming and urbanization, and the need to define management solutions to improve the water quality of coastal ecosystems has become important. The lower tidal Garonne River (TGR; southwestern France), characterized by the seasonal presence of a turbidity maximum zone (TMZ) and urban water discharge, is subject to episodic hypoxia events during low river flow periods in the summer. Future climatic conditions (higher temperature and summer droughts) and increasing urbanization could enhance hypoxia risks near the city of Bordeaux in the coming decades. A 3-D model of dissolved oxygen (DO) that couples hydrodynamics, sediment transport and biogeochemical processes was used to assess the efficiency of different management solutions for oxygenation of the TGR during summer low-discharge periods. We ran different scenarios of reductions in urban sewage overflows, displacement of urban discharges downstream from Bordeaux and/or temporary river flow support during the summer period. The model shows that each option mitigates hypoxia, but with variable efficiency over time and space. Sewage overflow reduction improves DO levels only locally near the city of Bordeaux. Downstream relocation of wastewater discharges allows for better oxygenation levels in the lower TGR. The support of low river flow limits the upstream TMZ propagation and dilutes the TGR water with well-oxygenated river water. Scenarios combining wastewater network management and low-water replenishment indicate an improvement in water quality over the entire TGR. These modelling outcomes constitute important tools for local water authorities to develop the most appropriate strategies to limit hypoxia in the TGR.Highlights. A 3-D model shows different efficiencies of management actions to limit hypoxia. Downstream relocation of wastewater discharge totally mitigates hypoxia. Sewage overflow reduction improves DO levels but only locally. Water replenishment improves DO in the upper estuary.

This paper proposes a novel approach for semi-supervised domain adaptation for holistic regression tasks, where a DNN predicts a continuous value y∈R given an input image x. The current literature generally lacks specific domain adaptation approaches for this task, as most of them mostly focus on classification. In the context of holistic regression, most of the real-world datasets not only exhibit a covariate (or domain) shift, but also a label gap—the target dataset may contain labels not included in the source dataset (and vice versa). We propose an approach tackling both covariate and label gap in a unified training framework. Specifically, a Generative Adversarial Network (GAN) is used to reduce covariate shift, and label gap is mitigated via label normalisation. To avoid overfitting, we propose a stopping criterion that simultaneously takes advantage of the Maximum Mean Discrepancy and the GAN Global Optimality condition. To restore the original label range—that was previously normalised—a handful of annotated images from the target domain are used. Our experimental results, run on 3 different datasets, demonstrate that our approach drastically outperforms the state-of-the-art across the board. Specifically, for the cell counting problem, the mean squared error (MSE) is reduced from 759 to 5.62; in the case of the pedestrian dataset, our approach lowered the MSE from 131 to 1.47. For the last experimental setup, we borrowed a task from plant biology, i.e., counting the number of leaves in a plant, and we ran two series of experiments, showing the MSE is reduced from 2.36 to 0.88 (intra-species), and from 1.48 to 0.6 (inter-species).

Abstract GeSbTe-based materials exhibit multiple crystalline phases, from disordered rocksalt, to rocksalt with ordered vacancy layers, and to the stable trigonal phase. In this paper we investigate the role of the interfaces on the structural and electrical properties of Ge2Sb2Te5. We find that the site of nucleation of the metastable rocksalt phase is crucial in determining the evolution towards vacancy ordering and the stable phase. By properly choosing the substrate and the capping layers, nucleation sites engineering can be obtained, thus promoting or preventing the vacancy ordering in the rocksalt structure or the conversion into the trigonal phase. The vacancy ordering occurs at lower annealing temperatures (170 °C) for films deposited in the amorphous phase on silicon (111), compared to the case of SiO2 substrate (200 °C), or in presence of a capping layer (330 °C). The mechanisms governing the nucleation have been explained in terms of interfacial energies. Resistance variations of about one order of magnitude have been measured upon transition from the disordered to the ordered rocksalt structure and then to the trigonal phase. The possibility to control the formation of the crystalline phases characterized by marked resistivity contrast is of fundamental relevance for the development of multilevel phase change data storage.

Selection and plasticity are two mechanisms that allow the adaptation of a population to a changing environment. Interaction between these nonexclusive mechanisms must be considered if we are to understand population survival. This review discusses the ways in which plasticity and selection can interact, based on a review of the literature on selection and phenotypic plasticity in the evolution of populations. The link between selection and phenotypic plasticity is analysed at the level of the individual. Plasticity can affect an individual’s response to selection and so may modify the end result of genetic diversity evolution at population level. Genetic diversity increases the ability of populations or communities to adapt to new environmental conditions. Adaptive plasticity increases individual fitness. However this effect must be viewed from the perspective of the costs of plasticity, although these are not easy to estimate. It is becoming necessary to engage in new experimental research to demonstrate the combined effects of selection and plasticity for adaptation and their consequences on the evolution of genetic diversity.

Silicon carbide (SiC) is a compound semiconductor, which is considered as a possible alternative to silicon for particles and photons detection. Its characteristics make it very promising for the next generation of nuclear and particle physics experiments at high beam luminosity. Silicon Carbide detectors for Intense Luminosity Investigations and Applications (SiCILIA) is a project starting as a collaboration between the Italian National Institute of Nuclear Physics (INFN) and IMM-CNR, aiming at the realization of innovative detection systems based on SiC. In this paper, we discuss the main features of silicon carbide as a material and its potential application in the field of particles and photons detectors, the project structure and the strategies used for the prototype realization, and the first results concerning prototype production and their performance.

This paper presents an in-depth numerical analysis on the hydrodynamics of a bubble column. As in previous works on the subject, the focus here is on three important parameters characterizing the flow: interfacial forces, turbulence and inlet superficial Gas Velocity (UG). The bubble size distribution is taken into account by the use of the Quadrature Method of Moments (QMOM) model in a two-phase Euler-Euler approach using the open-source Computational Fluid Dynamics (CFD) code OpenFOAM (Open Field Operation and Manipulation). The interfacial forces accounted for in all the simulations presented here are drag, lift and virtual mass. For the turbulence analysis in the water phase, three versions of the Reynolds Averaged Navier-Stokes (RANS) k-ε turbulence model are examined: namely, the standard, modified and mixture variants. The lift force proves to be of major importance for a trustworthy prediction of the gas volume fraction profiles for all the (superficial) gas velocities tested. Concerning the turbulence, the mixture k-ε model is seen to provide higher values of the turbulent kinetic energy dissipation rate in comparison to the other models, and this clearly affects the prediction of the gas volume fraction in the bulk region, and the bubble-size distribution. In general, the modified k-ε model proves to be a good compromise between modeling simplicity and accuracy in the study of bubble columns of the kind undertaken here.

In March 2013, the Nemo Phase-2 tower was successfully deployed at 80 km off-shore Capo Passero (Italy) at 3500 m depth. The tower operated continuously until August 2014. We present the results of the atmospheric muon analysis from the data collected in 411 days of live time. The zenith-angle distribution of atmospheric muons was measured and results compared with Monte Carlo simulations. The associated depth intensity relation was then measured and compared with previous measurements and theoretical predictions.

A low-cost array of modular sensors for online monitoring of radioactive waste was developed at INFN-LNS. We implemented a new kind of gamma counter, based on Silicon PhotoMultipliers and scintillating fibers, that behaves like a cheap scintillating Geiger-Muller counter. It can be placed in shape of a fine grid around each single waste drum in a repository. Front-end electronics and an FPGA-based counting system were developed to handle the field data, also implementing data transmission, a graphical user interface and a data storage system. A test of four sensors in a real radwaste storage site was performed with promising results. Following the tests an agreement was signed between INFN and Sogin for the joint development and installation of a prototype DMNR (Detector Mesh for Nuclear Repository) system inside the Garigliano radwaste repository in Sessa Aurunca (CE, Italy). Such a development is currently under way, with the installation foreseen within 2014.