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Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.

In solar forecasting, it is common practice for solar data (be it irradiance or photovoltaic power) to be converted into a stationary index (e.g., clear-sky or clearness index) before being used as inputs for solar-forecasting models. However, its actual impact is rarely quantified. Thus, this paper aims to study the impact of including this processing step in the modeling workflow within the scope of very-short-term spatio-temporal forecasting. Several forecasting models are considered, and the observed impact is shown to be model-dependent. Persistence does not benefit from this for such short timescales; however, the statistical models achieve an additional 0.5 to 2.5 percentual points (PPs) in terms of the forecasting skill. Machine-learning (ML) models achieve 0.9 to 1.9 more PPs compared to a linear regression, indicating that stationarization reveals non-linear patterns in the data. The exception is Random Forest, which underperforms in comparison with the other models. Lastly, the inclusion of solar elevation and azimuth angles as inputs is tested since these are easy to compute and can inform the model on time-dependent patterns. Only the cases where the input is not made stationary, or the underperforming Random Forest model, seem to benefit from this. This indicates that the apparent Sun position data can compensate for the lack of stationarization in the solar inputs and can help the models to differentiate the daily and seasonal variability from the shorter-term, weather-driven variability.

Abstract The EU Copernicus Climate Change Service (C3S) has produced an operational climate service, called C3S Energy, designed to enable the energy industry and policymakers to assess the impacts of climate variability and climate change on the energy sector in Europe. The C3S Energy service covers different time horizons, for the past 40 years and the future. It provides time series of electricity demand and supply from wind, solar photovoltaic and hydropower, and can be used for recent trends analysis, seasonal outlooks or the assessment of climate change impacts on energy mixes in the long term. This article introduces this service and the resulting dataset, with a focus on the design and validation of the energy conversion models, based on ENTSO‐E energy data and the ERA5 climate reanalysis. Flexibility and coherence across all countries have been preferred upon models' accuracy. However, the comparison with ENTSO‐E data shows that the models provide plausible energy indicators and, in particular, allow comparing climate variability effects on power demand and generation in a harmonized manner all over Europe.

AIMS:Quantitative estimation of cortical neurone loss in cases with chorea-acanthocytosis (ChAc) and its impact on laminar composition. METHODS:We used unbiased stereological tools to estimate the degree of cortical pathology in serial gallocyanin-stained brain sections through the complete hemispheres of three subjects with genetically verified ChAc and a range of disease durations. We compared these results with our previous data of five Huntington's disease (HD) and five control cases. Pathoarchitectonic changes were exemplarily documented in TE1 of a 61-year-old female HD-, a 60-year-old female control case, and ChAc3. RESULTS:Macroscopically, the cortical volume of our ChAc cases (ChAc1-3) remained close to normal. However, the average number of neurones was reduced by 46% in ChAc and by 33% in HD (P = 0.03 for ChAc & HD vs. controls; P = 0.64 for ChAc vs. HD). Terminal HD cases featured selective laminar neurone loss with pallor of layers III, V and VIa, a high density of small, pale, closely packed radial fibres in deep cortical layers VI and V, shrinkage, and chromophilia of subcortical white matter. In ChAc, pronounced diffuse astrogliosis blurred the laminar borders, thus masking the complete and partial loss of pyramidal cells in layer IIIc and of neurones in layers III, V and VI. CONCLUSION:ChAc is a neurodegenerative disease with distinct cortical neurodegeneration. The hypertrophy of the peripheral neuropil space of minicolumns with coarse vertical striation was characteristic of ChAc. The role of astroglia in the pathogenesis of this disorder remains to be elucidated.

We develop a new way of retrieving the cloud index from a large variety of satellite instruments sensitive to reflected solar radiation, embedded on geostationary and non-geostationary platforms. The cloud index is a widely used proxy for the effective cloud transmissivity, also called the “clear-sky index”. This study is in the framework of the development of the Heliosat-V method for estimating downwelling solar irradiance at the surface of the Earth (DSSI) from satellite imagery. To reach its versatility, the method uses simulations from a fast radiative transfer model to estimate overcast (cloudy) and clear-sky (cloud-free) satellite scenes of the Earth’s reflectances. Simulations consider the anisotropy of the reflectances caused by both surface and atmosphere and are adapted to the spectral sensitivity of the sensor. The anisotropy of ground reflectances is described by a bidirectional reflectance distribution function model and external satellite-derived data. An implementation of the method is applied to the visible imagery from a Meteosat Second Generation satellite, for 11 locations where high-quality in situ measurements of DSSI are available from the Baseline Surface Radiation Network. For 15 min means of DSSI, results from our preliminary implementation of Heliosat-V and ground-based measurements show a bias of 20 W m−2, a root-mean-square difference of 93 W m−2, and a correlation coefficient of 0.948. The statistics, except for the bias, are similar to operational and corrected satellite-based data products HelioClim3 version 5 and the CAMS Radiation Service.

The pedunculopontine nucleus (PPN) has been proposed as target for deep brain stimulation (DBS) in patients with postural instability and gait disorders due to its involvement in muscle tonus adjustments and control of locomotion. However, it is a deep-seated brainstem nucleus without clear imaging or electrophysiological markers. Some studies suggested that diffusion tensor imaging (DTI) may help guiding electrode placement in the PPN by showing the surrounding fiber bundles, but none have provided a direct histological correlation. We investigated DTI fractional anisotropy (FA) maps from in vivo and in situ post-mortem magnetic resonance images (MRI) compared to histological evaluations for improving PPN targeting in humans. A post-mortem brain was scanned in a clinical 3T MR system in situ. Thereafter, the brain was processed with a special method ideally suited for cytoarchitectonic analyses. Also, nine volunteers had in vivo brain scanning using the same MRI protocol. Images from volunteers were compared to those obtained in the post-mortem study. FA values of the volunteers were obtained from PPN, inferior colliculus, cerebellar crossing fibers and medial lemniscus using histological data and atlas information. FA values in the PPN were significantly lower than in the surrounding white matter region and higher than in areas with predominantly gray matter. In Nissl-stained histologic sections, the PPN extended for more than 10 mm in the rostro-caudal axis being closely attached to the lateral parabrachial nucleus. Our DTI analyses and the spatial correlation with histological findings proposed a location for PPN that matched the position assigned to this nucleus in the literature. Coregistration of neuroimaging and cytoarchitectonic features can add value to help establishing functional architectonics of the PPN and facilitate neurosurgical targeting of this extended nucleus.

The CYGNO project for the development of a high precision optical readout gaseous TPC for directional Dark Matter search and solar neutrino spectroscopy will be presented. It is to be hosted at Laboratori Nazionali del Gran Sasso. CYGNO peculiar features are the use of sCMOS cameras and PMTs coupled to GEMs amplification of a helium-based gas mixture at atmospheric pressure, in order to achieve 3D tracking with head tail capability and background rejection down to O(keV) energy, to boost sensitivity to low WIMP masses. The latest R&D results within the CYGNO project will be discussed along with the underground installation and operation of a 50 l prototype, soon to be followed by a O(1) m3 experiment demonstrator in 2024-2026. The latest results on the negative ion drift operation at atmospheric pressure within CYGNO optical readout approach will be illustrated, which is the aim of the ERC Consolidator Grant project INITIUM.

Abstract This study aims to illustrate the landscape of passing opportunities of a football team across a set of competitive matches. To do so positional data of 5 competitive matches was used to create polygons of pass availability. Passes were divided into three types depending on the hypothetical threat they may pose to the opposing defense (penetrative, support, and backwards passes). These categories were used to create three heatmaps per match. Moreover, the mean time of passing opportunities was calculated and compared across matches and for the three categories of passes. Due to the specificity of player’s interactive behavior, results showed heatmaps with a variety of patterns. Specifically the fifth match was very dissimilar to the other four. However, characterizing a football match in terms of passing opportunities with a single heatmap dismisses the variety of dynamics that occur throughout a match. Therefore, three temporal heatmaps over windows of 10 min were presented highlighting on-going dynamical changes in pass availability. Results also display that penetrative passes were available over shorter periods of time than backward passes that were available shorter than support passes. The results highlight the sensibility of the model to different task constrains that emerge within football matches.

In solar forecasting, it is common practice for solar data (be it irradiance or photovoltaic power) to be converted into a stationary index (e.g., clear-sky or clearness index) before being used as inputs for solar-forecasting models. However, its actual impact is rarely quantified. Thus, this paper aims to study the impact of including this processing step in the modeling workflow within the scope of very-short-term spatio-temporal forecasting. Several forecasting models are considered, and the observed impact is shown to be model-dependent. Persistence does not benefit from this for such short timescales; however, the statistical models achieve an additional 0.5 to 2.5 percentual points (PPs) in terms of the forecasting skill. Machine-learning (ML) models achieve 0.9 to 1.9 more PPs compared to a linear regression, indicating that stationarization reveals non-linear patterns in the data. The exception is Random Forest, which underperforms in comparison with the other models. Lastly, the inclusion of solar elevation and azimuth angles as inputs is tested since these are easy to compute and can inform the model on time-dependent patterns. Only the cases where the input is not made stationary, or the underperforming Random Forest model, seem to benefit from this. This indicates that the apparent Sun position data can compensate for the lack of stationarization in the solar inputs and can help the models to differentiate the daily and seasonal variability from the shorter-term, weather-driven variability. [ABSTRACT FROM AUTHOR]

To better forecast solar variability, spatio-temporal methods exploit spatially distributed solar time series, seeking to improve forecasting accuracy by including neighboring solar information. This review work is, to the authors’ understanding, the first to offer a compendium of references published since 2011 on such approaches for global horizontal irradiance and photovoltaic generation. The identified bibliography was categorized according to different parameters (method, data sources, baselines, performance metrics, forecasting horizon), and associated statistics were explored. Lastly, general findings are outlined, and suggestions for future research are provided based on the identification of less explored methods and data sources.

ABSTRACT Shipworms play critical roles in recycling wood in the sea. Symbiotic bacteria supply enzymes that the organisms need for nutrition and wood degradation. Some of these bacteria have been grown in pure culture and have the capacity to make many secondary metabolites. However, little is known about whether such secondary metabolite pathways are represented in the symbiont communities within their hosts. In addition, little has been reported about the patterns of host-symbiont co-occurrence. Here, we collected shipworms from the United States, the Philippines, and Brazil and cultivated symbiotic bacteria from their gills. We analyzed sequences from 22 shipworm gill metagenomes from seven shipworm species and from 23 cultivated symbiont isolates. Using (meta)genome sequencing, we demonstrate that the cultivated isolates represent all the major bacterial symbiont species and strains in shipworm gills. We show that the bacterial symbionts are distributed among shipworm hosts in consistent, predictable patterns. The symbiotic bacteria harbor many gene cluster families (GCFs) for biosynthesis of bioactive secondary metabolites, only

Here we report the complete genome sequence of Teredinibacter turnerae T7901. T. turnerae is a marine gamma proteobacterium that occurs as an intracellular endosymbiont in the gills of wood-boring marine bivalves of the family Teredinidae (shipworms). This species is the sole cultivated member of an endosymbiotic consortium thought to provide the host with enzymes, including cellulases and nitrogenase, critical for digestion of wood and supplementation of the host's nitrogen-deficient diet. T. turnerae is closely related to the free-living marine polysaccharide degrading bacterium Saccharophagus degradans str. 2-40 and to as yet uncultivated endosymbionts with which it coexists in shipworm cells. Like S. degradans, the T. turnerae genome encodes a large number of enzymes predicted to be involved in complex polysaccharide degradation (>100). However, unlike S. degradans, which degrades a broad spectrum (>10 classes) of complex plant, fungal and algal polysaccharides, T. turnerae primarily encodes enzymes associated with deconstruction of terrestrial woody plant material. Also unlike S. degradans and many other eubacteria, T. turnerae dedicates a large proportion of its genome to genes predicted to function in secondary metabolism. Despite its intracellular niche, the T. turnerae genome lacks many features associated with obligate intracellular existence (e.g. reduced genome size, reduced %G+C, loss of genes of core metabolism) and displays evidence of adaptations common to free-living bacteria (e.g. defense against bacteriophage infection). These results suggest that T. turnerae is likely a facultative intracellular ensosymbiont whose niche presently includes, or recently included, free-living existence. As such, the T. turnerae genome provides insights into the range of genomic adaptations associated with intracellular endosymbiosis as well as enzymatic mechanisms relevant to the recycling of plant materials in marine environments and the production of cellulose-derived biofuels.

Studies in microbiology have long been mostly restricted to small spatial scales. However, recent technological advances, such as new sequencing methodologies, have ushered an era of large-scale sequencing of environmental DNA data from multiple biomes worldwide. These global datasets can now be used to explore long standing questions of microbial ecology. New methodological approaches and concepts are being developed to study such large-scale patterns in microbial communities, resulting in new perspectives that represent a significant advances for both microbiology and macroecology. Here, we identify and review important conceptual, computational, and methodological challenges and opportunities in microbial macroecology. Specifically, we discuss the challenges of handling and analyzing large amounts of microbiome data to understand taxa distribution and co-occurrence patterns. We also discuss approaches for modeling microbial communities based on environmental data, including information on biological interactions to make full use of available Big Data. Finally, we summarize the methods presented in a general approach aimed to aid microbiologists in addressing fundamental questions in microbial macroecology, including classical propositions (such as “everything is everywhere, but the environment selects”) as well as applied ecological problems, such as those posed by human induced global environmental changes.

Probabilistic solar forecasting is an issue of growing relevance for the integration of photovoltaic (PV) energy. However, for short-term applications, estimating the forecast uncertainty is challenging and usually delegated to statistical models. To address this limitation, the present work proposes an approach which combines physical and statistical foundations and leverages on satellite-derived clear-sky index (kc) and cloud motion vectors (CMV), both traditionally used for deterministic forecasting. The forecast uncertainty is estimated by using the CMV in a different way than the one generally used by standard CMV-based forecasting approach and by implementing an ensemble approach based on a Gaussian noise-adding step to both the kc and the CMV estimations. Using 15-min average ground-measured Global Horizontal Irradiance (GHI) data for two locations in France as reference, the proposed model shows to largely surpass the baseline probabilistic forecast Complete History Persistence Ensemble (CH-PeEn), reducing the Continuous Ranked Probability Score (CRPS) between 37% and 62%, depending on the forecast horizon. Results also show that this is mainly driven by improving the model’s sharpness, which was measured using the Prediction Interval Normalized Average Width (PINAW) metric.

Solar‐powered vehicles have the potential to reduce CO2 emissions, operational costs and charging frequency needs of electric vehicles. This potential will depend on the local solar irradiation but also shadowing conditions, a relevant issue for urban contexts. The potential of solar‐powered vehicles in the urban context is modelled for 100 cities across the world showing that the median solar extended driving range is 18 and 8 km/day/kWp for driving and parked vehicles, respectively. The most favourable geographies include Africa, the Middle East and Southeast Asia; nonetheless, solar‐powered mobility has relevant potential across the full sample, including China, Europe, North America and Australia. Highlights: Urban VIPV potential is assessed for 100 cities across the world.Solar extended driving range varies between 11 and 29 km/day/kWp.Charging frequency ratio ranges from 0% to 80%, with a median of 57%.Urban shadowing reduces driving range by about 25% for driving vehicles. [ABSTRACT FROM AUTHOR]

The recently approved regulation on Energy Communities in Europe is paving the way for new collective forms of energy consumption and production, mainly based on photovoltaics. However, energy modeling approaches that can adequately evaluate the impact of these new regulations on energy community configurations are still lacking, particularly with regards to the grid tariffs imposed on collective systems. Thus, the present work models three different energy community configurations sustained on collective photovoltaics self-consumption for a small city in southern Portugal. This energy community, which integrates the city consumers and a local winery, was modeled using the Python-based Calliope framework. Using real electricity demand data from power transformers and an actual winery, the techno-economic feasibility of each configuration was assessed. Results show that all collective arrangements can promote a higher penetration of photovoltaic capacity (up to 23%) and a modest reduction in the overall cost of electricity (up to 8%). However, there are clear trade-offs between the different pathways: more centralized configurations have 53% lower installation costs but are more sensitive to grid use costs (which can represent up to 74% of the total system costs). Moreover, key actor’s individual self-consumption rate may decrease by 10% in order to benefit the energy community as a whole.

Plug-in electric vehicles (PEVs) are expected to play a role as power grid ancillary service providers through vehicle-to-grid (V2G) chargers, enabling higher levels of renewable electricity penetration. However, to fully exploit the storage capacity of PEVs and fast responsiveness, it is crucial to understand their operational characteristics. This work proposes a characterization procedure for V2G systems providing grid services. It extends the existing literature on response time, AC/DC conversion and reactive power assessment. Illustrative results were obtained by implementing the procedure using a Nissan Leaf battery electric vehicle (BEV) connected to a remotely operated commercial V2G CHAdeMO charger. The V2G system was characterized as having a relative inaccuracy and variability of response inferior to 3% and 0.4%, respectively. Its average communication and ramping times are 2.37 s and 0.26 s/kW, respectively. Its conversion efficiency and power factor both showed degradation in the power values below 50% of the charger’s nominal power. Moreover, the proposed visualizations revealed that: i) the V2G system implements power requests for the DC power flow; ii) the power factor control algorithm was nonoperational; and iii) the acquired data can leverage statistical models that describe the operation of V2G systems (which is of extreme value for researchers and operators).

Teredinidae are a family of highly adapted wood-feeding and wood-boring bivalves, commonly known as shipworms, whose evolution is linked to the acquisition of cellulolytic gammaproteobacterial symbionts harbored in bacteriocytes within the gills. In the present work we applied metagenomics to characterize microbiomes of the gills and digestive tract of Neoteredo reynei, a mangrove-adapted shipworm species found over a large range of the Brazilian coast. Comparative metagenomics grouped the gill symbiont community of different N. reynei specimens, indicating closely related bacterial types are shared. Similarly, the intestine and digestive gland communities were related, yet were more diverse than and showed no overlap with the gill community. Annotation of assembled metagenomic contigs revealed that the gill symbiotic community of N. reynei encodes a plethora of plant cell wall polysaccharides degrading glycoside hydrolase encoding genes, and Biosynthetic Gene Clusters (BGCs). In contrast, the digestive tract microbiomes seem to play little role in wood digestion and secondary metabolites biosynthesis. Metagenome binning recovered the nearly complete genome sequences of two symbiotic Teredinibacter strains from the gills, a representative of Teredinibacter turnerae "clade I" strain, and a yet to be cultivated Teredinibacter sp. type. These Teredinibacter genomes, as well as un-binned gill-derived gammaproteobacteria contigs, also include an endo-β-1,4-xylanase/acetylxylan esterase multi-catalytic carbohydrate-active enzyme, and a trans-acyltransferase polyketide synthase (trans-AT PKS) gene cluster with the gene cassette for generating β-branching on complex polyketides. Finally, we use multivariate analyses to show that the secondary metabolome from the genomes of Teredinibacter representatives, including genomes binned from N. reynei gills' metagenomes presented herein, stands out within the Cellvibrionaceae family by size, and enrichments for polyketide, nonribosomal peptide and hybrid BGCs. Results presented here add to the growing characterization of shipworm symbiotic microbiomes and indicate that the N. reynei gill gammaproteobacterial community is a prolific source of biotechnologically relevant enzymes for wood-digestion and bioactive compounds production.

André C Silva1, Tiago R Oliveira1,2, Javier B Mamani1, Suzana MF Malheiros3,4, Luciana Malavolta1, Lorena F Pavon1, Tatiana T Sibov1, Edson Amaro Jr1,5, Alberto Tannús6, Edson LG Vidoto6, Mateus J Martins6, Ricardo S Santos6, Lionel F Gamarra11Instituto Israelita de Ensino e Pesquisa Albert Einstein, IIEPAE, São Paulo, Brazil; 2Instituto de Física, Universidade de São Paulo, São Paulo, Brazil; 3Departament of Neurology and Neurosurgery, Universidade Federal de São Paulo, São Paulo, Brazil; 4Neuro-Oncology Program of Hospital Israelita Albert Einstein, São Paulo, Brazil; 5Instituto de Radiologia, Faculdade de Medicina; 6CIERMag-Instituto de Física de São Carlos, Universidade de São Paulo, São Paulo, BrazilAbstract: Gliomas are a group of heterogeneous primary central nervous system (CNS) tumors arising from the glial cells. Malignant gliomas account for a majority of malignant primary CNS tumors and are associated with high morbidity and mortality. Glioblastoma is the most frequent and malignant glioma, and despite the recent advances in diagnosis and new treatment options, its prognosis remains dismal. New opportunities for the development of effective therapies for malignant gliomas are urgently needed. Magnetic hyperthermia (MHT), which consists of heat generation in the region of the tumor through the application of magnetic nanoparticles subjected to an alternating magnetic field (AMF), has shown positive results in both preclinical and clinical assays. The aim of this review is to assess the relevance of hyperthermia induced by magnetic nanoparticles in the treatment of gliomas and to note the possible variations of the technique and its implication on the effectiveness of the treatment. We performed an electronic search in the literature from January 1990 to October 2010, in various databases, and after application of the inclusion criteria we obtained a total of 15 articles. In vitro studies and studies using animal models showed that MHT was effective in the promotion of tumor cell death and reduction of tumor mass or increase in survival. Two clinical studies showed that MHT could be applied safely and with few side effects. Some studies suggested that mechanisms of cell death, such as apoptosis, necrosis, and antitumor immune response were triggered by MHT. Based on these data, we could conclude that MHT proved to be efficient in most of the experiments, and that the improvement of the nanocomposites as well as the AMF equipment might contribute toward establishing MHT as a promising tool in the treatment of malignant gliomas.Keywords: brain tumor, magnetic hyperthermia, magnetic nanoparticle

Nutrition in the Teredinidae family of wood-boring mollusks is sustained by cellulolytic/nitrogen fixing symbiotic bacteria of the Teredinibacter clade. The mangrove Teredinidae Neoteredo reynei is popularly used in the treatment of infectious diseases in the north of Brazil. In the present work, the symbionts of N. reynei, which are strictly confined to the host's gills, were conclusively identified as Teredinibacter turnerae. Symbiont variants obtained in vitro were able to grow using casein as the sole carbon/nitrogen source and under reduced concentrations of NaCl. Furthermore, cellulose consumption in T. turnerae was clearly reduced under low salt concentrations. As a point of interest, we hereby report first hand that T. turnerae in fact exerts antibiotic activity. Furthermore, this activity was also affected by NaCl concentration. Finally, T. turnerae was able to inhibit the growth of Gram-negative and Gram-positive bacteria, this including strains of Sphingomonas sp., Stenotrophomonas maltophilia, Bacillus cereus and Staphylococcus sciuri. Our findings introduce new points of view on the ecology of T. turnerae, and suggest new biotechnological applications for this marine bacterium.