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Summary: Intimate partner violence (IPV) is a significant public health concern whose neurological/behavioral sequelae remain to be mechanistically explained. Using a mouse model recapitulating an IPV scenario, we evaluated the female brain neuroendocrine alterations produced by a reiterated male-to-female violent interaction (RMFVI). RMFVI prompted anxiety-like behavior in female mice whose hippocampus displayed a marked neuronal loss and hampered neurogenesis, namely reduced BrdU-DCX-positive nuclei and diminished dendritic arborization in the dentate gyrus (DG): effects paralleled by a substantial downregulation of the estrogen receptor β (ERβ). After RMFVI, the DG harbored reduced brain-derived neurotrophic factor (BDNF) pools and tyrosine kinase receptor B (TrkB) phosphorylation. Accordingly, ERβ knockout (KO) mice had heightened anxiety and curtailed BDNF levels at baseline while dying prematurely during the RMFVI procedure. Strikingly, injecting an ERβ antagonist or agonist into the wild-type (WT) female hippocampus enhanced or reduced anxiety, respectively. Thus, reiterated male-to-female violence jeopardizes hippocampal homeostasis, perturbing the ERβ/BDNF axis and ultimately instigating anxiety and chronic stress.

The Chain of Custody (CoC) standard tracks the recycled content (RC) of products, in most cases using the Mass Balance model. This model freely allows the selection of allocation methods and timeframes for the RC evaluation. Our work opens a discussion on the potential effects of this freedom in the RC evaluation. Firstly, we defined the general model representing the viable allocation methods and timeframe, and secondly, we applied the model to a case study. The mass balance model simplifies the monitoring of RC and encourages companies to use recycled materials. However, we outline the need for actions on stricter RC calculation and reporting, for instance, by reducing the timeframe of mass balance calculation or promoting the controlled blending model, which guarantees the physical presence of RC in the product. The results provide a basis for policymakers to set requirements for RC evaluation.

“Beta beams” produce collimated pure electron (anti)neutrino beams by accelerating beta active ions to high energies and having them decay in a racetrack shaped storage ring of 7 km circumference, the decay ring. EUROnu beta beams are based on CERN infrastructures and existing machines. Using existing machines may be an advantage for the cost evaluation, but will also constrain the physics performance. The isotope pair of choice for the beta beam is ^{6}He and ^{18}Ne. However, before the EUROnu studies one of the required isotopes, ^{18}Ne, could not be produced in rates that satisfy the needs for physics of the beta beam. Therefore, studies of alternative beta emitters, ^{8}Li and ^{8}B, with properties interesting for a beta beam have been proposed and have been studied within EUROnu. These alternative isotopes could be produced by using a small storage ring, in which the beam traverses a target, creating the ^{8}Li and ^{8}B isotopes. This production ring, the injection linac and the target system have been evaluated. Measurements of the cross section of the reactions to produce the beta beam isotopes show interesting results. A device to collect the produced isotopes from the target has been developed and tested. However, the yields of ^{8}Li and ^{8}B, using the production ring for production of ^{8}Li and ^{8}B, is not yet, according to simulations, giving the rates of isotopes that would be needed. Therefore, a new method of producing the ^{18}Ne isotope has been developed and tested giving good production rates. A 60 GHz ECRIS prototype, the first in the world, was developed and tested for ion production with contributions from EUROnu. The decay ring lattices for the ^{8}Li and ^{8}B have been developed and the lattice for ^{6}He and ^{18}Ne has been optimized to ensure the high intensity ion beam stability.

The EUROnu project has studied three possible options for future, high intensity neutrino oscillation facilities in Europe. The first is a Super Beam, in which the neutrinos come from the decay of pions created by bombarding targets with a 4 MW proton beam from the CERN High Power Superconducting Proton Linac. The far detector for this facility is the 500 kt MEMPHYS water Cherenkov, located in the Fréjus tunnel. The second facility is the Neutrino Factory, in which the neutrinos come from the decay of μ^{+} and μ^{-} beams in a storage ring. The far detector in this case is a 100 kt magnetized iron neutrino detector at a baseline of 2000 km. The third option is a Beta Beam, in which the neutrinos come from the decay of beta emitting isotopes, in particular ^{6}He and ^{18}Ne, also stored in a ring. The far detector is also the MEMPHYS detector in the Fréjus tunnel. EUROnu has undertaken conceptual designs of these facilities and studied the performance of the detectors. Based on this, it has determined the physics reach of each facility, in particular for the measurement of CP violation in the lepton sector, and estimated the cost of construction. These have demonstrated that the best facility to build is the Neutrino Factory. However, if a powerful proton driver is constructed for another purpose or if the MEMPHYS detector is built for astroparticle physics, the Super Beam also becomes very attractive.

Abstract The present article objective is to determine the net mass balance of the glacier Znosko for periods 2018-2019 and 2019-2020. It is situated on King George Island which belongs to the groups Shetland of the South, Antarctic Peninsula region. For this objective, during February 2018 a net of 19 stakes (which were controlled once during February 2019 and 2020) were installed on the glacier ablation zone, drilling in the accumulation zone and about flights using unmanned aerial vehicles (UAV) to control the glacier zone and geomorphological changes. For the year 2020, it was determined a glacier area of 1.71 ± 0.02 km2, moreover, using five different methods of interpolation, it was obtained on average, as a result, a specific net balance of -590.7 ± 46.6 mm w.e (in water equivalent) for 2018-2019 and -686.7 ± 28.2 mm w.e for 2019-2020, being the ELA altitude 146.5 ± 18.2 m and 144.2 ± 8.3 m respectively. The two consecutive years represent negative net mass balances which are in accordance with other similar studies on this region, also glacier data were obtained on a zone that is characterized by its difficult access.

Não existem dúvidas quanto à importância dos linfonodos axilares no prognóstico e tratamento do câncer da mama. Em 151 mastectomias realizadas, empregando-se as técnicas de Halsted-Meyer (104 casos) e de Patey-Dyson (47 casos), encontrou-se um total de 5.015 linfonodos axilares, com a média de 33,2 por axila. Não houve diferença significativa entre o número médio de linfonodos encontrados na axila, tanto com uma quanto com outra técnica de mastectomia. Os linfonodos foram divididos de acordo com os níveis de drenagem axilar e de acordo com os seus maiores diâmetros. Observou-se uma estreita correlação entre o diâmetro de cada um deles e o seu comprometimento histológico, de onde se conclui que esse parâmetro pode ser considerado como outro indicador prognóstico para o câncer da mama. Os pequenos linfonodos (0,5cm ou menos) estavam presentes em todos os níveis, ao contrário do que ocorreu com aqueles maiores que 2cm de diâmetro, encontrados quase exclusivamente no primeiro nível de drenagem axilar. Por outro lado, estes últimos apresentaram uma taxa de comprometimento histológico mais elevada que os demais. Observou-se uma taxa de falso negativo de 22,5%, a qual foi mais elevada entre os casos mais iniciais de estadiamento da moléstia. O comportamento histológico da axila só poderá ser comprovado se todos os linfonodos encontrados forem examinados, o que será possível após um bom esvaziamento axilar, incluindo a retirada do músculo peitoral menor. Dessa maneira, cirurgias mais conservadoras, com preservação dos músculos peitorais, apenas levarão a uma linfadenectomia axilar incompleta. Foi muito raro o comprometimento de linfonodos situados em níveis superiores sem que o primeiro nível estivesse comprometido. Assim como foi raro encontrar linfonodos pequenos comprometidos sem que os maiores estivessem histologicamente positivos. Foi descrito um grupo de linfonodos, considerado uma das cadeias mais altas da axila, denominado GRUPO PARACEFÁLICO, encontrado em 34 mastectomias (22,5%). Os linfonodos menores que 0,5cm de diâmetro, clinicamente não detectáveis, mas histologicamente com prometidos, foram denominados METASTASES AXILARES MINIMAS DA MAMA.

The wide application of beneficial microorganisms in agriculture as inoculants to combat pests and diseases and/or to improve soil fertility and the nutrients availability for plants has been considered as an effective and more sustainable alternative than chemical fertilizers and agricultural defensives. However, it is necessary to examine all processing steps of these bio-based products under a more integrated view including the type of microorganisms and the whole production process in order to reduce dependence on synthetic chemical inputs. Here, recent developments on the production and formulation technologies of microbial inoculants and the main types of inoculants currently applied in agriculture are addressed. The different types of microbial formulations are compared with emphasis on the encapsulation technology. Moreover, the application of biofertilizers in seed coating and a new approach to apply biocomposites as fertilizers are discussed, presenting the main challenges and future perspectives to promote more sustainable agriculture practices.

Abstract Two-photon fluorescence calcium imaging allows recording the activity of large neural populations with subcellular spatial resolution, but it is typically characterized by low signal-to-noise ratio (SNR) and poor accuracy in detecting single or few action potentials when large number of neurons are imaged. We recently showed that implementing a smart line scanning approach using trajectories that optimally sample the regions of interest increases both the SNR fluorescence signals and the accuracy of single spike detection in population imaging in vivo. However, smart line scanning requires highly specialised software to design recording trajectories, interface with acquisition hardware, and efficiently process acquired data. Furthermore, smart line scanning needs optimized strategies to cope with movement artefacts and neuropil contamination. Here, we develop and validate SmaRT2P, an open-source, user-friendly and easy-to-interface Matlab-based software environment to perform optimized smart line scanning in two-photon calcium imaging experiments. SmaRT2P is designed to interface with popular acquisition software (e.g., ScanImage) and implements novel strategies to detect motion artefacts, estimate neuropil contamination, and minimize their impact on functional signals extracted from neuronal population imaging. SmaRT2P is structured in a modular way to allow flexibility in the processing pipeline, requiring minimal user intervention in parameter setting. The use of SmaRT2P for smart line scanning has the potential to facilitate the functional investigation of large neuronal populations with increased SNR and accuracy in detecting the discharge of single and few action potentials.

Introduction: Cocaine use disorder is a significant public health issue without a current specific approved treatment. Among different approaches to this disorder, it is possible to highlight a promising immunologic strategy in which an immunogenic agent may reduce the reinforcing effects of the drug if they are able to yield sufficient specific antibodies capable to bind cocaine and/or its psychoactive metabolites before entering into the brain. Several carriers have been investigated in the anti-cocaine vaccine development; however, they generally present a very complex chemical structure, which potentially hampers the proper assessment of the coupling efficiency between the hapten units and the protein structure. Objectives: The present study reports the design, synthesis and preclinical evaluation of two novel calix[n]arene-based anti-cocaine immunogens (herein named as V4N2 and V8N2) by the tethering of the hydrolysis-tolerant hapten GNE (15) on calix[4]arene and calix[8]arene moieties. Methods: The preclinical assessment corresponded to the immunogenicity and dose–response evaluation of V4N2 and V8N2. The potential of the produced antibodies to reduce the passage of cocaine analogue through the blood–brain-barrier (BBB), modifying its biodistribution was also investigated. Results: Both calix[n]arene-based immunogens elicited high titers of cocaine antibodies that modified the biodistribution of a cocaine radiolabeled analogue (99mTc-TRODAT-1) and decreased cocaine-induced behavior, according to an animal model. Conclusion: The present results demonstrate the potential of V4N2 and V8N2 as immunogens for the treatment of cocaine use disorder.

Conventional Life Cycle Assessment (LCA) that relies on static coefficients is usually based on yearly averages. However, the impacts of electricity supply vary remarkably on an hourly basis. Thus, a company production plan is reassessed to reduce selected LCA impacts due to electricity consumption. To achieve this, the company will need a forecast of hourly LCA impacts due to electricity consumption, which can be directly forecast with the Direct Forecasting (DF) approach. Alternatively, the Electricity Technological Mix Forecasting (ETMF) forecasts the electricity production of the technologies in the mix and subsequently linearly combines it with unitary LCA impact indicators. Here, we assessed different machine learning models to forecast two LCA impact indicators for the consumption of electricity in the Italy-North control zone. The feed-forward neural network (NN) with the ETMF approach was the best perfomer among the assessed forecasting models. In our dataset, recurrent neural networks (RNNs) performed worse than feed-forward neural networks. Due to its better forecasting performance, the ETMF approach was preferred over the DF approach. This was due to its flexibility and scalability with easy updates or expansion of the selected forecast indicators, and due to its ability to assess technology-specific errors in the forecasting. Finally, we propose to adopt the correlation of LCA impact indicators within the dataset to select indicators while avoiding unconscious burden-shifting.

Aftershocks of earthquakes can destroy many urban infrastructures and exacerbate the damage already inflicted upon weak structures. Therefore, it is important to have a method to forecast the probability of occurrence of stronger earthquakes in order to mitigate their effects. In this work, we applied the NESTORE machine learning approach to Greek seismicity from 1995 to 2022 to forecast the probability of a strong aftershock. Depending on the magnitude difference between the mainshock and the strongest aftershock, NESTORE classifies clusters into two types, Type A and Type B. Type A clusters are the most dangerous clusters, characterized by a smaller difference. The algorithm requires region-dependent training as input and evaluates performance on an independent test set. In our tests, we obtained the best results 6 h after the mainshock, as we correctly forecasted 92% of clusters corresponding to 100% of Type A clusters and more than 90% of Type B clusters. These results were also obtained thanks to an accurate analysis of cluster detection in a large part of Greece. The successful overall results show that the algorithm can be applied in this area. The approach is particularly attractive for seismic risk mitigation due to the short time required for forecasting.

Ammonium carbonates are a group of fertilizer materials that include ammonium bicarbonate, ammonium carbonate hydrate, and ammonium carbamate. They can be synthesized from diverse nutrient-bearing liquid waste streams but are unstable in a moist environment. While extensively utilized several decades ago, their use gradually decreased in favor of large-scale, facility-synthesized urea fertilizers. The emergence of sustainable agriculture, however, necessitates the recovery and reuse of nutrients using conventional feedstocks, such as natural gas and air-derived nitrogen, and nutrient-containing biogenic waste streams. To this extent, anaerobic digestion liquid presents a convenient source of solid nitrogen and carbon to produce solid fertilizers, since no significant chemical transformations are needed as nitrogen is already present as an ammonium ion. This review describes detailed examples of such feedstocks and the methods required to concentrate and crystallize solid ammonium carbonates. The technologies currently proposed or utilized to stabilize ammonium carbonate materials in the environment are described in detail. Finally, the agricultural efficiency of these materials as nitrogen and carbon source is also described.

This Crossing Boundary hosts contributions accounting for experiences and theoretical perspectives which may look distant for how they address the socio-technical transition in the energy field but, we believe, when put in conversation, help common questions and tentative answers come to the fore. Giorgio Osti, Paul Upham, Paula Maria Bögel and Paula Castro have been engaged in reflecting on their respective disciplines in relation to socio-technical transitions. Recalling and valorising the STS basis of MLP and SPT in connection with other disciplinary approaches may contribute to enrich on one side STS debates and on the other empirical research on socio-technical transition in a historical juncture where such an endeavour looks definitely urgent.

Bioconversion of the complex carbohydrates present in lignocellulosic biomass into simple sugars, in order to obtain biofuels and bio-based products, is still limited by the low performance of the enzymatic saccharification reaction and the high cost of cellulolytic enzymes. Low-cost additives such as soybean protein can reduce the unproductive adsorption of cellulases onto lignin, increasing conversion rates and reducing enzyme losses. Here, investigation was made of the effects of different soybean protein fractions, extracted in-house, on the enzymatic saccharification of hydrothermally pretreated sugarcane bagasse. The glucose released during biomass saccharification increased by up to 76% in the presence of the in-house extracted soybean protein, compared to the control (without additive). A remarkable finding was that the technique allowed the enzyme loading to be decreased four-fold. The results suggested that the alkali-extracted proteins presented high surface hydrophobicity, which enhanced their interaction with lignin and reduced the unproductive binding of cellulases. Among the main soybean protein fractions, glycinin had the best effect in improving saccharification, which could have been due to its higher hydrophobicity. Hence, in-house extracted soybean proteins seem to be interesting alternative additives capable of increasing the lignocellulosic biomass conversion efficiency in future biorefineries.

The potential of furan-based epoxy thermosets as a greener alternative to diglycidyl ether of Bisphenol A (DGEBA)-based resins has been demonstrated in recent literature. Therefore, a deep investigation of the curing behaviour of these systems may allow their use for industrial applications. In this work, the curing mechanism of 2,5-bis[(oxiran-2-ylmethoxy)methyl]furan (BOMF) with methyl nadic anhydride (MNA) in the presence of 2-methylimidazole as a catalyst is analyzed. In particular, three systems characterized by different epoxy/anhydride molar ratios are investigated. The curing kinetics are studied through differential scanning calorimetry, both in isothermal and non-isothermal modes. The total heat of reaction of the epoxy resin as well as its activation energy are estimated by the non-isothermal measurements, while the fitting of isothermal data with Kamal’s autocatalytic model provides the kinetic parameters. The results are discussed as a function of the resin composition. The global activation energy for the curing process of BOMF/MNA resins is in the range 72–79 kJ/mol, depending on both the model used and the sample composition; higher values are experienced by the system with balanced stoichiometry. By the fitting of the isothermal analysis, it emerged that the order of reaction is not only dependent on the temperature, but also on the composition, even though the values range between 0.31 and 1.24.

This review aims to show the significance of the use of secondary carbon bio-carriers for iron and steel production. The term ‘secondary carbon bio-carriers’ in this review paper refers to biomass, torrefied biomass, biochar, charcoal, or biocoke. The main focus is on torrefied biomass, which can act as a carbon source for partial or complete replacement of fossil fuel in various metallurgical processes. The material requirements for the use of secondary carbon bio-carriers in different metallurgical processes are systematized, and pathways for the use of secondary carbon bio-carriers in four main routes of steel production are described; namely, blast furnace/basic oxygen furnace (BF/BOF), melting of scrap in electric arc furnace (scrap/EAF), direct reduced iron/electric arc furnace (DRI/EAF), and smelting reduction/basic oxygen furnace (SR/BOF). In addition, there is also a focus on the use of secondary carbon bio-carriers in a submerged arc furnace (SAF) for ferroalloy production. The issue of using secondary carbon bio-carriers is specific and individual, depending on the chosen process. However, the most promising ways to use secondary carbon bio-carriers are determined in scrap/EAF, DRI/EAF, SR/BOF, and SAF. Finally, the main priority of future research is the establishment of optimal parameters, material quantities, and qualities for using secondary carbon bio-carriers in metallurgical processes.

The Moderate Electric Field (MEF) processing of foods consists in the application of an electric potential gradient (????/??) ranging from 1 to 1000 V/cm on a food item (homogeneous or heterogeneous) placed between two electrodes, its main effect being the food heating due to the dissipation of a part of the electric energy into heat within the food item. The heating performances of such a system depend on several process and system parameters, including the applied ????/??, the food electrical conductivity, and its thermo-physical properties. In this study, the effects due to the salt composition and to the applied ????/?? to a heterogeneous food (constituted by a basil-based sauce, mainly fibers dispersed in a slightly salted water-oil emulsion) treated in a custom MEF system on the food heating rate are investigated. The samples were prepared at different salinities (3.25, 1.63, 0.86 and 0.43% w/w respectively). In the explored range of compositions, the heating rate increased linearly with the square power of applied ????/??. A slight linearity deviation above 55°C was observed for the basil-based sauce at 1.63% and 5.20 V/cm, associated with bubble formation within the ohmic system and the electrolytic reactions occurring at the electrode-solution interface during the MEF heating process. The salt content as well as the ratio between water and oil in the sample formulation played a crucial role in determining the thermo-electrical behavior of the basil-based sauce samples. Samples with salinity of 1.63%, compared to samples at 3.25%, exhibited a higher electrical conductivity, being due to a minor concentration of the non-conductive phase (namely the oil phase as well as the dispersed vegetable fibers into the solution) that exerts a major degree of electrical insulation. As the salinity decreases from 1.63% to 0.43%, samples were characterized by lower electrical conductivities, being due to a reduced ionic mobility when the salt contained into the sample is drastically reduced.

Successful ways of fully exploiting the excellent structural and multifunctional performance of graphene and related materials are of great scientific and technological interest. New opportunities are provided by the fabrication of a novel class of nanocomposites with a nanolaminate architecture. In this work, by using the iterative lift-off/float-on process combined with wet depositions, we incorporated cm-size graphene monolayers produced via Chemical Vapour Deposition into a poly (methyl methacrylate) (PMMA) matrix with a controlled, alternate-layered structure. The produced nanolaminate shows a significant improvement in mechanical properties, with enhanced stiffness, strength and toughness, with the addition of only 0.06 vol% of graphene. Furthermore, oxygen and carbon dioxide permeability measurements performed at different relative humidity levels, reveal that the addition of graphene leads to significant reduction of permeability, compared to neat PMMA. Overall, we demonstrate that the produced graphene–PMMA nanolaminate surpasses, in terms of gas barrier properties, the traditional discontinuous graphene–particle composites with a similar filler content. Moreover, we found that the gas permeability through the nanocomposites departs from a monotonic decrease as a function of relative humidity, which is instead evident in the case of the pure PMMA nanolaminate. This work suggests the possible use of Chemical Vapour Deposition graphene–polymer nanolaminates as a flexible gas barrier, thus enlarging the spectrum of applications for this novel material.

Investigating the neuronal dynamics supporting brain functions and understanding how the alterations in these mechanisms result in pathological conditions represents a fundamental challenge. Preclinical research on model organisms allows for a multiscale and multiparametric analysis in vivo of the neuronal mechanisms and holds the potential for better linking the symptoms of a neurological disorder to the underlying cellular and circuit alterations, eventually leading to the identification of therapeutic/rescue strategies. In recent years, brain research in model organisms has taken advantage, along with other techniques, of the development and continuous refinement of methods that use light and optical approaches to reconstruct the activity of brain circuits at the cellular and system levels, and to probe the impact of the different neuronal components in the observed dynamics. These tools, combining low-invasiveness of optical approaches with the power of genetic engineering, are currently revolutionizing the way, the scale and the perspective of investigating brain diseases. The aim of this review is to describe how brain functions can be investigated with optical approaches currently available and to illustrate how these techniques have been adopted to study pathological alterations of brain physiology.

The diffusion of Battery Electric Vehicles (BEVs) is projected to influence the electricity grid operation, potentially offering opportunities for load-shifting policies aimed at higher integration of renewable energy technologies in the electricity system. Moreover, the examined literature emphasizes electricity as a relevant driver of BEVs Life Cycle Assessment (LCA) results. To evaluate LCA impacts associated to future BEVs diffusion scenarios in Italy, we adopt the Consequential Life Cycle Assessment (CLCA) methodology. LCA conventionally assumes a proportional relation between environmental impact indicators and the functional unit. However, such relation may not be representative if the electricity system is significantly affected by the large-scale diffusion of BEVs. Our study couples the conventional CLCA methodology with the EnergyPLAN model through three different approaches, which progressively include BEV-specific dynamics, to capture correlations between additional BEVs fleets and the electricity grid operation, that affectthe mix of electricity consumed in the use phase by BEVs, in Italy in 2030. Here we show that if renewables capacity is not additionally installed in response to additional BEVs electricity demand, the marginal Climate change total indicator of BEVs may increase up to ~40%, with respect to a business-as-usual scenario. Moreover, we quantitatively support the literature indications on how to properly estimate BEVs LCA impacts. Indeed, we weight electricity LCA impacts on hourly BEV charge profiles, finding that this approach best captures BEVs interdependence with the electricity system. At low BEVs diffusion, this approach clearly shows the potential BEVs capability to increase exploitation of renewable energy, whereas at high BEVs diffusion, it fully highlights potential responses of fossil fuel power plants to additional electricity demand. Due to these dynamics, we find that linearly scaling the business-as-usual scenario results would lead to an underestimation of 12.45 Mton CO2-eq of the total impacts of an additional BEVs fleet, under a 100% BEV diffusion scenario. Our methodology could be replicated with different energy system models, or at various geographical scales. Our framework could be coupled with comprehensive assessments of transport systems, to further provide robustness to policymakers by including non-linearities in the mix of electricity consumed during the use phase of BEVs.