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Abstract Caloric Restriction (CR) has established anti-cancer effects, but its clinical relevance and molecular mechanism remain largely undefined. Here, we investigate CR’s impact on several mouse models of Acute Myeloid Leukemias, including Acute Promyelocytic Leukemia, a subtype strongly affected by obesity. After an initial marked anti-tumor effect, lethal disease invariably re-emerges. Initially, CR leads to cell-cycle restriction, apoptosis, and inhibition of TOR and insulin/IGF1 signaling. The relapse, instead, is associated with the non-genetic selection of Leukemia Initiating Cells and the downregulation of double-stranded RNA (dsRNA) sensing and Interferon (IFN) signaling genes. The CR-induced adaptive phenotype is highly sensitive to pharmacological or genetic ablation of LSD1, a lysine demethylase regulating both stem cells and dsRNA/ IFN signaling. CR + LSD1 inhibition leads to the re-activation of dsRNA/IFN signaling, massive RNASEL-dependent apoptosis, and complete leukemia eradication in ~90% of mice. Importantly, CR-LSD1 interaction can be modeled in vivo and in vitro by combining LSD1 ablation with pharmacological inhibitors of insulin/IGF1 or dual PI3K/MEK blockade. Mechanistically, insulin/IGF1 inhibition sensitizes blasts to LSD1-induced death by inhibiting the anti-apoptotic factor CFLAR. CR and LSD1 inhibition also synergize in patient-derived AML and triple-negative breast cancer xenografts. Our data provide a rationale for epi-metabolic pharmacologic combinations across multiple tumors.

Abstract Long-read sequencing allows analyses of single nucleic-acid molecules and produces sequences in the order of tens to hundreds kilobases. Its application to whole-genome analyses allows identification of complex genomic structural-variants (SVs) with unprecedented resolution. SV identification, however, requires complex computational methods, based on either read-depth or intra- and inter-alignment signatures approaches, which are limited by size or type of SVs. Moreover, most currently available tools only detect germline variants, thus requiring separate computation of sample pairs for comparative analyses. To overcome these limits, we developed a novel tool (Germline And SOmatic structuraL varIants detectioN and gEnotyping; GASOLINE) that groups SV signatures using a sophisticated clustering procedure based on a modified reciprocal overlap criterion, and is designed to identify germline SVs, from single samples, and somatic SVs from paired test and control samples. GASOLINE is a collection of Perl, R and Fortran codes, it analyzes aligned data in BAM format and produces VCF files with statistically significant somatic SVs. Germline or somatic analysis of 30 $$\times $$ × sequencing coverage experiments requires 4–5 h with 20 threads. GASOLINE outperformed currently available methods in the detection of both germline and somatic SVs in synthetic and real long-reads datasets. Notably, when applied on a pair of metastatic melanoma and matched-normal sample, GASOLINE identified five genuine somatic SVs that were missed using five different sequencing technologies and state-of-the art SV calling approaches. Thus, GASOLINE identifies germline and somatic SVs with unprecedented accuracy and resolution, outperforming currently available state-of-the-art WGS long-reads computational methods.

Abstract The cross-talk between T cells and astrocytes occurring under physiological and, even more, neuroinflammatory conditions may profoundly impact the generation of adaptive immune responses in the nervous tissue. In this study, we used a standardized in vitro co-culture assay to investigate the immunomodulatory properties of astrocytes differing for age, sex, and species. Mouse neonatal astrocytes enhanced T cell vitality but suppressed T lymphocyte proliferation in response to mitogenic stimuli or myelin antigens, regardless of the Th1, Th2 or Th17 T cell phenotype. Studies comparing glia cells from adult and neonatal animals showed that adult astrocytes were more efficient in inhibiting T lymphocyte activation than neonatal astrocytes, regardless of their sex. Differently from primary cultures, mouse and human astrocytes derived from reprogrammed fibroblasts did not interfere with T cell proliferation. Overall, we describe a standardized astrocyte-T cell interaction in vitro assay and demonstrate that primary astrocytes and iAstrocytes may differ in modulating T cell function.

Whether Clonal Hematopoiesis (CH) represents a risk factor for severity of the COVID-19 disease remains a controversial issue. We report the first high- sensitivity analysis of CH in COVID-19 patients (threshold of detection at 0.5% vs 1 or 2% in previous studies). We analyzed 24 patients admitted to ICU for COVID-19 (COV-ICU) and 19 controls, including healthy subjects and asymptomatic SARS-CoV2-positive individuals. Despite the significantly higher numbers of CH mutations identified (80% mutations with

Abstract Aberrant DNA methylation at CpG dinucleotides is a cancer hallmark that is associated with the emergence of resistance to anti cancer treatment, though molecular mechanisms and biological significance remain elusive. Genome scale methylation maps by currently used methods are based on chemical modification of DNA and are best suited for analyses of methylation at CpG rich regions (CpG islands). We report the first high coverage whole-genome map in cancer using the long read nanopore technology, which allows simultaneous DNA-sequence and -methylation analyses on native DNA. We analyzed clonal epigenomic/genomic evolution in Acute Myeloid Leukemias (AMLs) at diagnosis and relapse, after chemotherapy. Long read sequencing coupled to a novel computational method allowed definition of differential methylation at unprecedented resolution, and showed that the relapse methylome is characterized by hypermethylation at both CpG islands and sparse CpGs regions. Most differentially methylated genes, however, were not differentially expressed nor enriched for chemoresistance genes. A small fraction of under-expressed and hyper-methylated genes at sparse CpGs, in the gene body, was significantly enriched in transcription factors (TFs). Remarkably, these few TFs supported large gene-regulatory networks including 50% of all differentially expressed genes in the relapsed AMLs and highly-enriched in chemoresistance genes. Notably, hypermethylated regions at sparse CpGs were poorly conserved in the relapsed AMLs, under-represented at their genomic positions and showed higher methylation entropy, as compared to CpG islands. Analyses of available datasets confirmed TF binding to their target genes and conservation of the same gene-regulatory networks in large patient cohorts. Relapsed AMLs carried few patient specific structural variants and DNA mutations, apparently not involved in drug resistance. Thus, drug resistance in AMLs can be mainly ascribed to the selection of random epigenetic alterations at sparse CpGs of a few transcription factors, which then induce reprogramming of the relapsing phenotype, independently of clonal genomic evolution.

Access to reliable and sustainable electricity is still precluded for a large share of global population living in rural areas of developing countries, especially in sub-Saharan Africa. Hybrid microgrids are considered a suitable solution for providing affordable and reliable access to electricity to isolated communities. Properly planning and sizing such systems is although an aspect that can greatly influence the sustainability of the intervention, and the arrival to the market of the third generation minigrids poses new challenges to the process. Three main challenges are identified as pivotal for the proper sizing of new generation microgrids: arrival of the main grid, inappropriateness of Net Present Cost as only objective function in the strategy selection process, and necessity to operate on already existing minigrids. Such aspects are addressed in this work by proposing a methodological advancement to an existing open-source microgrid sizing model: a grid outage model alongside the definition of new constraints and variables for the optimization problem with grid-connected microgrids, a multi-objective optimization option, and a brown-field optimization option. The new version of the model is tested on real life case studies in rural Rwanda (greenfield) and Mozambique (brownfield), proving the profitability of grid-connected and grid-extension solutions for sufficiently low connection distances. Sensitivity analyses are performed to assess variations in system size, cost and CO2 emissions with respect to microgrid and grid connection parameters.

Energy access is a key need for socio-economic growth. Proven to be a key enabler of development and progress, access to electricity has been prioritized by governments using grid extension actions and off-grid solutions, namely microgrids and home systems technologies, fed by renewable sources. However, achieving universal access to energy remains a huge challenge given the lack of resources and the large population currently unserved. The lack of adequate socio-economic data at granular scale and of a good understanding of demand uptake led by economic growth is a barrier for efficient energy planning. Access to conjoint demand and socio-economic data at local level is crucial, yet hard to obtain: often such data are unavailable or very difficult to collect, and current data platforms often lack the ability to conjointly store variegated socio-economic and time series data. For these reasons, in this paper, we present a comprehensive methodology that, based on an extensive literature review, draws guidelines for developing data-sharing platforms in energy access, develops a proposed architecture to support the data collection of conjoint socio-economic and time-series data, and proposes a prototype of the final application. The methodology leverages on a novel extensive literature review to identify the major determinants of demand uptake and the corresponding consuming entities: villages, households, and appliances. The proposed architecture is able to capture numeric, categorical, and time series information for all consuming entities, based on state-of-the-art NoSQL databases. Finally, a prototype implementation with a web-based interface developed with Angular and Spring is proposed and discussed.

As a key material for manufacturing clean energy technologies, steel is crucial for energy transition, but its production causes 2.6 Gton of CO _2 emissions at global level each year. In 2020 the European Union (EU) set a net-zero emissions target by 2050, fostering innovation in the steel industry to reduce its environmental impact. However, a scenario-oriented and technologically comprehensive analysis assessing prospected environmental and market implications of steel decarbonisation strategies remains a gap, which is addressed in this paper. The analysis adopts a hybrid input-output-based life-cycle assessment model built in the MARIO framework, extending the Exiobase database to represent the supply chains of the most promising low-carbon steelmaking technologies in the EU, such as hydrogen- or charcoal-injected blast furnaces and natural gas- and hydrogen-based direct reduction routes. The penetration of these technologies is explored by formulating scenarios resembling European climate targets. The results show a reduction in the carbon footprint of steel across all scenarios, ranging up to −26% in 2030 and to −60% in 2050. However, the extent of footprint reduction is highly dependent on the share of clean electricity in the European supply mix, highlighting the relevance of holistic decarbonisation strategies. Economic implications affect steel prices, which rise up to 25% in 2030 and 56% in 2050, opening discussions on the need for suitable policies such as CBAM to avoid protectionism and encourage international technological progress.

Access to affordable, reliable, and modern energy remains a critical goal under the Agenda 2030 for Sustainable Development, especially in remote areas of developing countries. Based on traditional engineering approaches, many energy solution planning tools have been developed to identify the optimal solution in these areas to assess the competition across different technological options. Nevertheless, these approaches, based on an economic optimum, do not necessarily grant long-term sustainability of the solution in specific local contexts, since they are not able to capture the social implications within the Energy-Development nexus. Moreover, also in light of the 2030 Agenda, scientific and grey literature on energy access highlights how energy solutions planning methodologies developed in the last decades need to be complemented by a more comprehensive view, able to integrate evidence from various disciplines, especially engineering and social sciences. Based on the above considerations, this paper introduces a novel framework under the name of CESP, where three social sciences-based phases complement three engineering phases, each one characterized by specific tools, to offer an informed decision framework for the local planner. CESP encompasses a set of techno-economic and socio-technical actions to prevent potential failure as evidenced by a counterfactual analysis used to identify the reasons behind past project failures. The CESP framework presents a sequential and iterative structure that underlines the cyclic perspective of a holistic decision process where social sciences feed the engineering analysis and vice versa. Finally, CESP emerges as a practical and applicable framework for supporting energy access planning in critical areas.

MARIO (Multi-Regional Analysis of Regions through Input-Output) is a Python-based framework for building input-output models. It automates the parsing of well-known databases (e.g. EXIOBASE, EORA, Eurostat) and of customized tables. With respect to similar tools, like pymrio, it broadens the scope of application to supply-use tables and handles both monetary and physical units. Employing an intuitive Excel-based API, it facilitates advanced table manipulations and allows for modelling additional supply chains through a hybrid LCA approach. It provides built-in functions for footprinting and scenario analyses as well as for visualizations of model outcomes. Results are exportable into various formats, possibly supplemented by a metadata file tracking the full history of applied changes. MARIO comes with extensive documentation and is available on Zenodo, GitHub, or installable via PyPI.

Electricity is crucial for each country's economic and social development. More than half of the population in Sub-Saharan African (SSA) lack access to energy, which has dire consequences for the local population's living conditions, as well as affecting the local economy and increasing poverty. Renewable energy-based mini-grid (REBM) systems are the promising solution to mitigate the issue of energy access in rural areas, however, the expansion of renewable energy (RE) systems and the rate of energy access are both advancing at a slow pace. This study used PESTLE analysis (Political, Economic, Social, Technological, Legal, and Environmental) in the context of Mozambique, a SSA country, to identify the most important categories and factors to support decision-making processes by breaking down existing barriers and hurdles in the energy sector enabling them to plan and implement measures, subdivided in primary, secondary and tertiary for the development of REBM in remote settlements. Even though PESTLE analysis is rather often worldwide applied to the energy sector, its application to the Sub-Saharan context is rare, while its application to the Mozambique energy sector is absent in scientific literature. Besides, within our study, energy experts from different professional backgrounds and nationalities have been interviewed using an online questionnaire: 62 energy experts (48 Africans, and 14 Non-African) from 14 different countries assessed the categories and factors. The experts questioned identified the political category as the most crucial for policy-makers to prioritize, with the top three global weighting factors being, Clear Government policies (plan, regulation, priority, strategy) 5.79%, National financial scheme for RES investments 4.93%, and cost of investment 4.86%. The findings can support policy-makers breakdown barriers in the energy sector, planning and implementing measures for the REBM development, and contribute to achieving the SDG7 target.

Oligodendrocytes are the primary target of demyelinating disorders, and progressive neurodegenerative changes may evolve in the CNS. DNA damage and oxidative stress are considered key pathogenic events, but the underlying molecular mechanisms remain unclear. Moreover, animal models do not fully recapitulate human diseases, complicating the path to effective treatments. Here we report that mice with cell-autonomous deletion of the nuclear COP9 signalosome component CSN5 (JAB1) in oligodendrocytes develop DNA damage and defective DNA repair in myelinating glial cells. Interestingly, oligodendrocytes lacking JAB1 expression underwent a senescence-like phenotype that fostered chronic inflammation and oxidative stress. These mutants developed progressive CNS demyelination, microglia inflammation, and neurodegeneration, with severe motor deficits and premature death. Notably, blocking microglia inflammation did not prevent neurodegeneration, whereas the deletion of p21CIP1 but not p16INK4a pathway ameliorated the disease. We suggest that senescence is key to sustaining neurodegeneration in demyelinating disorders and may be considered a potential therapeutic target.

In light of a national policy aiming at satisfying a growing demand for electricity, while achieving a greater diversification of power generation technologies and full electrification by 2050, this research models and contrasts alternative electrification pathways for Tanzania in the time frame 2015–2040. The study relies on an improved model grounded on the OSeMOSYS framework. GIS data are used both to determine the electricity demand projections and to inform the decision about the optimal production technologies made by OSeMOSYS with a least-cost criterion. Findings indicate that the stated policy goals (New Policy scenario) are within reach, but they also imply an increase in installed capacity from less than 2 GW to at least 13.8 GW, corresponding to an investment of 25.3 billion USD, which is significantly above historical spending in the power sector. Also, only an additional environmental policy (450TZ scenario) would ensure that the carbon intensity of the power sector lowers from a current 440 gCO2/kWh to around 100 gCO2/kWh in 2040, with the additional benefit of a lower average cost of providing electricity (compared to the New Policy scenario). An Energy For All scenario where universal access is achieved two decades earlier (in 2030) is also feasible but implies more difficulties in lowering carbon intensity or the cost of providing electricity. Results for universal access are the object of a separate in-depth discussion and a sensitivity analysis looks at the effect of key assumption (e.g., on demand projections and discount rate) on the main results.

In state-of-the-art energy systems modelling, reservoir hydropower is represented as any other thermal power plant: energy production is constrained by the plant's installed capacity and a capacity factor calibrated on the energy produced in previous years. Natural water resource variability across different temporal scales and the subsequent filtering effect of water storage mass balances are not accounted for, leading to biased optimal power dispatch strategies. In this work, we aim at introducing a novelty in the field by advancing the representation of reservoir hydropower generation in energy systems modelling by explicitly including the most relevant hydrological constraints, such as time-dependent water availability, hydraulic head, evaporation losses, and cascade releases. This advanced characterization is implemented in an open-source energy modelling framework. The improved model is then demonstrated on the Zambezi River Basin in the South Africa Power Pool. The basin has an estimated hydropower potential of 20,000 megawatts (MW) of which about 5,000 MW has been already developed. Results show a better alignment of electricity production with observed data, with a reduction of estimated hydropower production up to 35% with respect to the baseline Calliope implementation. These improvements are useful to support hydropower management and planning capacity expansion in countries richly endowed with water resource or that are already strongly relying on hydropower for electricity production.

The coexistence of the need to improve economic conditions and the conscious use of environmental resources plays a central role in today’s sustainable development challenge. In this study, a novel integrated framework to evaluate the impact of new technological interventions is presented and an application to smallholder coffee farms and their supply chains in Kenya is proposed. This methodology is able to combine multiple information through the joint use of three approaches: supply chain analysis, input-output analysis, and energy system modeling. Application to the context of the Kenyan coffee sector enables framework validation: shading management measures, the introduction of eco-pulpers, and the exploitation of coffee waste biomass for power generation were compared within a holistic high-level perspective. The implementation of shading practices, carried out with fruit trees, shows the most relevant effects from the economic point of view, providing farmers with an additional source of income and generating $903 of work for every million of local currency (about $9k) invested in this solution. The same investment would save up to 1.46 M m3 of water per year with the eco-pulpers technology. Investing the same amount in coffee-biomass power plants would displace a small portion of production from heavy-duty oil and avoid importing a portion of fertilizer, saving up to 11 tons of CO2 and around $4k per year. The results suggest the optimal allocation of a $100m budget, which can be affected by adding additional constraints on minimum environmental or social targets in line with sustainable development goals.

As it emerges from the literature, electricity access in rural contexts is deeply intertwined with socioeconomic dynamics. However, the advent of a reliable and sufficient source of electricity is not the sole driver that might contribute to local development. Indeed, complementary activities might have a crucial role in sustaining the development of rural communities as well as the electricity access. The current research addresses the lack of counterfactual scenarios in which the impact of complementary activities on electrification projects can be investigated. The authors introduce the case study of Matembwe village, a rural community in the Njombe region of Tanzania. The data collection includes the electricity consumption, number of electricity connections, and number of income-generating activities in a timespan ranging from 1989 to 2015. The analysis is based on system dynamics. The study considers different scenarios representing the dynamics related to the following complementary actions: access to market measures, access to credit measures, and access to usable skills. On the one hand, the study reveals that the effectiveness of the considered complementary actions is limited except from the access to microcredit which fosters an increase in electricity connections by 17%. On the other hand, both access to microcredit and the starting up of a local cooperative by CEFA Onlus that reinvests its profits in the local market impact the socio-economic dimension by 69% and 22%, respectively.

Multiple sclerosis (MS) is an inflammatory neurodegenerative disease of the central nervous system (CNS) with heterogeneous pathophysiology. In its progressive course oligodendrocyte and neuroaxonal damage is sustained by compartmentalized inflammation due to glial dysregulation. Siponimod (BAF312), a modulator of two sphingosine-1-phosphate (S1P) receptors (S1P1 and S1P5) is the first oral treatment specifically approved for active secondary progressive MS. To address potential direct effects of BAF312 on glial function and glia-neuron interaction, we set up a series of in vitro functional assays with astrocytes generated from human fibroblasts. These cells displayed the typical morphology and markers of astroglia, and were susceptible to the action of inflammatory mediators and BAF312, because expressing receptors for IL1, IL17, and S1P (namely S1P1 and S1P3). Targeting of S1P signaling by BAF312 inhibited NFκB translocation evoked by inflammatory cytokines, indicating a direct anti-inflammatory activity of the drug on the human astrocyte. Further, while glia cells exposed to IL1 or IL17 downregulated protein expression of glutamate transporters, BAF312-treated astrocytes maintained high levels of GLAST and GLT1 regardless of the presence of inflammatory mediators. Interestingly, despite potential glial susceptibility to S1P signaling via S1P3, which is not targeted by BAF312, NFκB translocation and downregulation of glutamate transporters in response to S1P were inhibited at similar levels by BAF312 and FTY720, another S1P signaling modulator targeting also S1P3. Accordingly, specific inhibition of S1P1 via NIBR-0213 blocked S1P-evoked NFκB translocation, demonstrating that modulation of S1P1 is sufficient to dampen signaling via other S1P receptors. Considering that NFκB-dependent responses are regulated by Nrf2, we measured activation of this critical transcription factor for anti-oxidant reactions, and observed that BAF312 rapidly induced nuclear translocation of Nrf2, but this effect was attenuated in the presence of an inflammatory milieu. Finally, in vitro experiments with spinal neurons exposed to astrocyte-conditioned media showed that modulation of S1P or cytokine signaling in astrocytes via BAF312 prevented neurons from astrocyte-induced degeneration. Overall, these experiments on human astrocytes suggest that during neuroinflammation targeting of S1P1 via BAF312 may modulate key astrocyte functions and thereby attain neuroprotection indirectly.

Abstract The increasing number of displaced people in the world not only requires rapid humanitarian actions, but also attention to host communities and a holistic and long-term vision. Energy has not been really considered a major topic in people displacement, yet, resulting in negative impacts on several aspects, including food security. New solutions are required, in terms of energy planning, technology development, and adaptation, as well as decision making, sensitization, training, and support to humanitarian actors. The Sustainable Energy Technologies for food security (SET4food) project phase 1 (2014–2015) developed a number of tools to support identification, adaptation, and introduction of appropriate solutions, tested some pilot innovations in critical areas, and promoted the enhancement of humanitarian response capability in the energy sector via an extensive capacity building program. In addition, a second phase of the project (2015–2018) fostered networking and collaboration between the main actors by developing an e-sharing platform, called ENERGYCoP, including a global not-for-profit community of practices for humanitarian professionals working in the energy sector. The platform may enable the shift from traditional “technological transfer” to a more participative approach on co-design and technological cooperation activated by a knowledge sharing mechanism. This paper outlines the main challenges and the achieved results of SET4food, providing recommendations for researchers and practitioners on the way forward.

Globally about 800 million people live without electricity at home, over two thirds of which are in sub-Saharan Africa. Planning electricity access infrastructure and allocating resources efficiently requires a careful assessment of the diverse energy needs across space, time, and sectors. Because of data scarcity, most country or regional-scale electrification planning studies have however assumed a spatio-temporally homogeneous (top-down) potential electricity demand. Poorly representing the heterogeneity in the potential electricity demand across space, time, and energy sectors can lead to inappropriate energy planning, inaccurate energy system sizing, and misleading cost assessments. Here we introduce M-LED, a Multi-sectoral Latent Electricity Demand geospatial data processing platform to estimate electricity demand in communities that live in energy poverty. The platform shows how big data and bottom-up energy modelling can be leveraged together to represent the potential electricity demand with high spatio-temporal and sectoral granularity. We apply the methodology to Kenya as a country-study and devote specific attention to the implications for water-energy-agriculture-development interlinkages. A more detailed representation of the demand-side in large-scale electrification planning tools bears a potential for improving energy planning and policy.

Astrocytes greatly participate to inflammatory and neurotoxic reactions occurring in neurodegenerative diseases and are valuable pharmacological targets to support neuroprotection. Here we used human astrocytes generated from reprogrammed fibroblasts as a cellular model to study the effect of the compound Laquinimod and its active metabolite de-Laquinimod on astrocyte functions and the astrocyte–neuron interaction. We show that human iAstrocytes expressed the receptor for the inflammatory mediator IL1 and responded to it via nuclear translocation of NFκB, an event that did not occur if cells were treated with Laquinimod, indicating a direct anti-inflammatory activity of the drug on the human astrocyte. Similarly, while exposure to IL1 downregulated glial glutamate transporters GLAST and GLT1, treatment with Laquinimod supported maintenance of physiological levels of these proteins despite the inflammatory milieu. Laquinimod also induced nuclear translocation of the aryl hydrocarbon receptor (AHR), suggesting that drug action was mediated by activation of the AHR pathway. However, the drug was effective despite AHR inhibition via CH223191, indicating that AHR signaling in the astrocyte is dispensable for drug responses. Finally, in vitro experiments with rat spinal neurons showed that laquinimod did not exert neuroprotection directly on the neuron but dampened astrocyte-induced neurodegeneration. Our findings indicate that fibroblast-derived human astrocytes represent a suitable model to study astrocyte–neuron crosstalk and demonstrate indirect, partial neuroprotective efficacy for laquinimod.

Less than 15% of rural areas of Cameroon have access to grid electricity. Only 53% of the population has access to grid electricity. Notwithstanding, Cameroon has a huge hydropower potential which could be harnessed. Mini grids, powered by pico and micro hydropower plants, are a relatively new rural electrification strategy in Cameroon. Several of such mini grids have been realized in the mountain regions of the country. Some of these systems have been more successful than others. This paper aims to share the experiences of community-based pico and micro hydropower schemes for rural electrification in Cameroon. The paper provides insight to the challenges that three of such mini grid systems powered by pico and micro hydropower plants had encountered and it attempts to identify issues related to their performances. The study was based on personal experience, field visits, participant observations, interviews and focus group discussions with key members of the beneficiary communities and documentations from the local NGO which implemented the schemes. Key findings of this study relate to the description of the main aspects about: planning of a robust system design, organizational aspects, like social cohesion at all levels of scheme management, community leadership and ownership of the system and involvement of the beneficiaries at all stages of the project cycle. These aspects were particularly addressed within the context of rural communities in Cameroon.

Energy system models for off-grid systems usually tend to focus solely on the provision of electricity for powering simple appliances, thus neglecting more energy-intensive and critical needs, such as water heating. The adoption of a Multi-Energy System (MES) perspective would allow us not only to provide comprehensive solutions addressing all types of energy demand, but also to exploit synergies between the electric and thermal sectors. To this end, we expand an existing open-source micro-grid optimization model with a complementary thermal model. Results show how the latter achieves optimal solutions that are otherwise restricted, allowing for a reduction in the Levelized Cost of Energy (LCOE) of 59% compared to a conventional microgrid, and an increase of reliance on renewable sources of 70%.

The cytoplasmic accumulation of NPM1 (NPMc+) is found in acute myeloid leukemia (AML) with NPM1 mutation. NPM1 must shuttle between nucleus and cytoplasm to assure physiological protein synthesis and, therefore, the elimination of NPMc+ is not a suitable therapeutic option. We isolated, characterized, and produced a functional scFv intrabody fused to nuclear localization signal(s) (NLS) that does not recognize NPM1 but binds to the mutant-specific C-terminal NES (nuclear export signal) of NPMc+, responsible for its cytoplasmic accumulation. The scFv-NLS fusion accumulated in the nuclei of wild type cells and strongly bound to its antigen in the cytoplasm of NPMc+ expressing cells. However, it failed to relocate the majority of NPMc+ in the nucleus, even when fused to four NLS. Our results show the technical feasibility of producing recombinant intrabodies with defined sub-cellular targeting and nuclear accumulation but the lack of information concerning the features that confer variable strength to the signal peptides impairs the development of biomolecules able to counteract pathological sub-cellular distribution of shuttling proteins.

The transport sector is currently facing a significant transition, with strong drivers includingdecarbonization and digitalization trends, especially in urban passenger transport. The availability ofmonitoring data is at the basis of the development of optimization models supporting an enhancedurban mobility, with multiple benefits including lower pollutants and CO2 emissions, lower energyconsumption, better transport management and land space use. This paper presents two datasetsthat represent time series with a high temporal resolution (five-minute time step) both for vehiclesand bike sharing use in the city of Turin, located in Northern Italy. These high-resolution profileshave been obtained by the collection and elaboration of available online resources providing liveinformation on traffic monitoring and bike sharing docking stations. The data are provided for theentire year 2018, and they represent an interesting basis for the evaluation of seasonal and dailyvariability patterns in urban mobility. These data may be used for different applications, rangingfrom the chronological distribution of mobility demand, to the estimation of passenger transportflows for the development of transport models in urban contexts. Moreover, traffic profiles are at thebasis for the modeling of electric vehicles charging strategies and their interaction with the powergrid.

The growing complexity and the many challenges related to fast-changing and highly de-carbonised electricity systems require reliable and robust open source energy modelling frameworks. Their reliability may be tested on a series of well-posed benchmarks that can be used and shared by the modelling community. This paper describes and integrates stand-alone, independent modules to compute the costs and benefits of flexible generation options in the open source energy investment modelling framework OSeMOSYS. The modules are applied to a case study that may work as a benchmark. The whole documentation of the modules and the test case study are retrievable, reproducible, reusable, interoperable, and auditable. They create a case to help establish a FAIR-compliant, user-friendly, and low-threshold model and data standards in modelling practices. As is well known, one of the options for balancing high shares of variable renewables is flexible power generation by dispatchable units. The associated costs need to be considered for short-term operational analyses and for long-term investment plans. The added modules contribute to extending the modelling capacity by introducing (a) costs of ramping, (b) non-linear decrease of efficiency at partial load operation, and (c) refurbishment of existing units in the cost minimisation objective function of OSeMOSYS. From application to the test case study, two main insights are drawn: costs of ramping and decreased partial load efficiency may influence the competitiveness of generation technologies in the provision of reserve capacity; and refurbishment of existing units may represent attractive investment options for increasing flexibility. Both effects are also seen in the long-term and may impact infrastructure investment decisions to meet decarbonisation targets. These effects would not be captured without the introduction of the modules.

Currently, about 2.7 billion people across the world still lack access to clean cooking means. Humanitarian emergencies and post-emergencies are among the most critical situations: the utilization of traditional devices such as three-stone fires have a huge negative impact not only on food security but also on the socio-economic status of people, their health and the surrounding environment. Advanced Cooking Stoves may constitute better systems compared to actual ones, however, financial, logistic and time constraints have strongly limited the interventions in critical contexts until now. The innovative, low-cost Mewar Angithi insert for improving energy efficiency of three-stone fires may play a role in the transition to better cooking systems in such contexts. In this paper, we rely on the Water Boiling Test 4.2.3 to assess the performances of the Mewar Angithi insert respect to a traditional three-stone fire and we analyse the results through a robust statistical procedure. The potentiality and suitability of this novel solution is discussed for its use in critical contexts.

The latest developments in solar technologies demonstrated that the solar central receiver configuration is the most promising application among concentrated solar power (CSP) plants. In CSPs solar-heated air can be used as the working fluid in a Brayton thermal cycle and as the heat transfer fluid for a Rankine thermal cycle as an alternative to more traditional working fluids thereby reducing maintenance operations and providing the power section with a higher degree of flexibility To supply thermal needs when the solar source is unavailable, an auxiliary burner is requested. This configuration is adopted in the Julich CSP (J-CSP) plant, operating in Germany and characterized by a nominal power of 1.5 MW, the heat transfer fluid (HTF) is air which is heated in the solar tower and used to produce steam for the bottoming Rankine cycle. In this paper, the J-CSP plant with thermal energy storage has been compared with a hybrid CSP plant (H-CSP) using air as the working fluid. Thermodynamic and economic performances of all the simulated plants have been evaluated by applying both exergy analysis and thermoeconomic analysis (TA) to determine the yearly average operation at nominal conditions. The exergy destructions and structure as well as the exergoeconomic costs of products have been derived for all the components of the plants. Based on the obtained results, the thermoeconomic design evaluation and optimization of the plants has been performed, allowing for improvement of the thermodynamic and economic efficiency of the systems as well as decreasing the exergy and exergoeconomic cost of their products.

Aims. To investigate the postprandial changes in serum lipoproteins and blood glucose and to verify whether different nutrient composition of the meal elicits different response in patients with (MetS+) and without (MetS−) metabolic syndrome. Research Design and Methods. 50 MetS+ patients and 50 age- and sex-matched MetS− consumed a regular lunch chosen among those more similar to their usual diet. Blood was drawn in the morning after 12-hour fasting and 2 and 4:30 hours after the meal. Results. Serum triglycerides increased more in MetS+ (35%, 4:30 hours after the meal) than in MetS− (29%), HDL-cholesterol decreased 2 hours after the meal in both groups (−4% and −5%, resp.). Blood sugar similarly increased in both groups (19%, 2 hours after the meal in MetS+ and 17% in MetS−) and plasma insulin increased more and remained high longer in MetS+ (73.5 and 52.3 μU/mL, 2 and 4:30 hours after the meal) than in MetS− (46.7 and 21.6 μU/mL). Difference in nutrient composition of the meal (carbohydrate 57%, fat 28% versus carbohydrate 45%, fat 35%) was not associated with differences in postprandial levels of triglycerides, HDL-cholesterol, glucose, and insulin within each group. Conclusions. As compared with MetS−, MetS+ patients show a greater hypertriglyceridemic and hyperinsulinemic response to a regular lunch whatever the carbohydrate or fat content of the meal.