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We report a dataset of known and published occurrence records of Italian taxa from species (and subspecies) to family rank of the phylum Rotifera; we considered only Bdelloidea, Monogononta, and Seisonacea, and did not include Acanthocephala. The dataset in-cludes 15,525 records (12,015 of which with georeferenced coordinates) of 584 valid species and subspecies names and other taxa at family level, gathered from 332 published papers. The published literature spans the period from 1838 to 2022, with the lowest number of papers published during the first half of the twentieth century, followed by an increasing number of papers, from 20 to more than 60 in each decade. The Italian regions with the highest number of records and species are Emilia-Romagna, Lombardy, and Piedmont, whereas no records are known for Molise. The number of species known from each region mostly mirrors sampling efforts, measured as the number of publications per region. The dataset is available through the Open Science Framework (OSF), and all the georeferenced occurrence data have been uploaded to the Global Biodiversity Information Facility (GBIF). ial use only.

Este artículo contiene 5 páginas, 3 figuras., Small fish species such as minnows (Phoxinus sp.) are introduced into mountain lakes by anglers that use them as live bait for fishing salmonids that were previously introduced in these naturally fishless habitats. Introduced fish severely impact native biota but minnows are nevertheless released believing to provide forage for game fish, i.e., salmonids. However, our results indicate that minnows negatively interact with game fish, reducing their relative densities. On some occasions, minnows can remain the only present fish following trout extinction. We believe that this information should be urgently disseminated among anglers as a pragmatic argument to also convince anglers less sensitive to conservation issues that carrying minnows to the mountains is not a good idea whatever the main interest is, e.g., nature conservation or outdoor recreation., Funding was provided by the EU Projects LIFE BIOAQUAE (LIFE11 BIO/IT/000020) and LIFE LIMNOPIRINEUS (LIFE13 NAT/ES/001210), LIFE RESQUE ALPYR (LIFE20 NAT/ES/000369), MONOCLE (H2020-CLIMATE/ 0357), and Biodiversa FISHME (BiodivRestor-280), and by the Spanish Government projects FUNBIO (RTI2018- 096217-B-I00) and BIOOCULT (2413/2017).

While there is growing awareness of the issue of food losses at the political level, official post-harvest loss data for informing policymaking and reporting on SDG Indicator 12.3.1. (a) Food Loss Index is scarce. Representative sample-based surveys are necessary to obtain information on on-farm losses at the country level, but due to the issue’s complexity, a loss module covering several key questions is needed. One main strategy proposed by the 50x2030 Initiative for optimizing data collection is sub-sampling for some of the survey modules. This paper examines whether modelling approaches can be combined with sub-sampling to improve harvest and post-harvest loss estimates and allow for further sample and cost reduction. The paper first presents the loss models generated on four selected surveys conducted in Malawi, Zimbabwe, and Nigeria, which were built using the Classification and Regression Tree (CART) method. The performance of each model is assessed for different sizes of sub-samples to improve the sample-based estimates, either by model-based estimates or by model-based imputation. The research concludes that the model-based estimates improve the loss estimates of the sub-samples due to post-stratification implied in the CART method, whereby they can constitute a cost-effective complement to sub-sampling strategies, while model-based imputations should only be used on a reduced number of missing observations. The models perform best when the survey invests in obtaining more detailed on-farm loss data and considers some key variables identified as relevant for on-farm loss models. Sub-sampling allows for investment in more detailed questionnaires and some considerations are derived for its design.

Despite evolving evidence that Africa is experiencing urbanisation in a different way, empirical evaluations of the welfare implications of urban-development programs in Africa remain scant. We investigate the welfare implications of recent urbanisation processes in Ethiopia using household-level longitudinal data and satellite-based nightlight intensity. We also examine the impact of urban growth on the composition of household consumption and welfare. We employ temporal and spatial variations in nightlight intensity to capture urban expansion and growth. Controlling for time-invariant unobserved heterogeneity across individuals and localities, we find that urbanisation, as measured by nightlight intensity, is associated with significant welfare improvement. We find that tripling existing average nightlight intensity in a village is associated with a 42–46% improvement in household welfare. Urbanisation is also associated with a significant increase in the share of non-food consumption, which is a good measure of overall welfare and poverty. In addition, we find significant heterogeneity in urban expansion across major towns and small towns. Urban expansion in rural areas and small towns appears more impactful than similar expansion in major cities. Finally, quantile regression results suggest that better-off households are likely to benefit more from urban expansion, which may translate into higher inequality across households or communities. Our results can inform public policy debates on the consequences and implications of urban expansion in Africa.

The undersea portion of the Northern Apennines is characterized by blind thrust faults running parallel to the Adriatic Sea coastline in northeastern peninsular Italy. These thrusts are buried below a thick cover of syntectonic Quaternary deposits. Their elusive geological signature at shallow depths and the low seismicity associated with them gave rise to diverging interpretations and views concerning the current activity of these thrusts and their earthquake potential.On 9 November 2022, a seismic sequence started with an Mw 5.5 earthquake in the Pesaro Offshore. Hypocentral depth, focal mechanism, and aftershocks location all suggest that the earthquake was generated by one of the outermost thrusts of the Northern Apennines front that was already mapped as a potential seismogenic source in the DISS database (https://diss.ingv.it/diss330/sources.php?ITCS106).We present a 3D reconstruction of the thrust system that caused the Pesaro Offshore seismic sequence obtained through the reinterpretation of publicly available seismic reflection profiles and well logs. The 3D geometry and size of the thrust activated during the seismic sequence suggest that it can also host larger earthquakes. We also present the application of a well-established workflow for calculating the slip rates of this buried thrust already tested in nearby structures. The outcomes of this study represent a step forward for earthquake and tsunami hazard models, the study of the seismic source, the enhancement of earthquake location by mix and match of seismological and geological independent data, and the expected kinematics of future potential earthquake ruptures.These results are particularly relevant in offshore areas, where neither surface co-seismic ruptures nor GPS/InSAR deformation data are available in the aftermath of a significant earthquake. In these cases, multichannel seismic reflection profiles represent the only tool to appraise the subsurface structural setting.

Aims To evaluate the relationship between SARS-CoV-2 infection and autoimmunity in type 1 diabetes (T1D) and SARS-CoV-2 antibodies frequency at diagnosis of T1D during pandemic. Methods The presence of T1D-specific autoimmunity was evaluated in a cohort of 99 children and adolescents without diabetes that contracted SARS-CoV-2 infection. Moreover, the frequency of IgM- and IgG-SARS-CoV-2 antibodies was evaluated in 41 newly diagnosed T1D patients not yet vaccinated against SARS-CoV-2 disease, collected during the pandemic, compared to healthy subjects (CTRL). Results None of the 99 patients that contracted SARS-CoV-2 infection during the pandemic period was found positive for T1D autoantibodies. The frequency of SARS-CoV-2 antibodies was not significantly different in patients newly diagnosed with T1D (12.2%), compared with CTRL (8.4%). Among SARS-CoV-2 antibody positive T1D patients, 80% were target of diabetes autoantibodies and 60% had another concomitant autoimmune disease. Among the CTRL subjects positive for SARS-CoV-2Abs (n = 10), none was found positive for T1D autoantibodies. Conclusions The results of the present study do not confirm, at least in the short term, a role of COVID-19 as a potential trigger of T1D autoimmunity and do not provide evidence of an increased frequency of SARS-CoV-2 antibodies in newly diagnosed T1D patients in comparison with healthy population.

Structural bioinformatics and molecular modeling of proteins strongly depend on the protein structure selected for investigation. The choice of protein structure relies on direct application from the Protein Data Bank (PDB), homology- or de-novo modeling. Recent de-novo models, such as AlphaFold2, require little preprocessing and omit the need to navigate the many parameters of choosing an experimentally determined model. Yet, the experimentally determined structure still has much to offer, why it should be of interest to the community to ease the choice of experimentally determined models. We provide an open-source software package, PDBminer, to mine both the AlphaFold Database (AlphaFoldDB) and the PDB based on search criteria set by the user. This tool provides an up-to-date, quality-ranked table of structures applicable for further research. PDBminer provides an overview of the available protein structures to one or more input proteins, parallelizing the runs if multiple cores are specified. The output table reports the coverage of the protein structures aligned to the UniProt sequence, overcoming numbering differences in PDB structures, and providing information regarding model quality, protein complexes, ligands, and nucleotide binding. The PDBminer2coverage and PDBminer2network tools assist in visualizing the results. We suggest that PDBminer can be applied to overcome the tedious task of choosing a PDB structure without losing the wealth of additional information available in the PDB. As developers, we will guarantee the introduction of new functionalities, assistance, training of new contributors, and package maintenance. The package is available athttp://github.com/ELELAB/PDBminer.

PurposeThis paper assesses the impact of this program on the rural women's employment opportunities using data from the 2015 round of the household's living standard survey (HLSS) of Côte d'Ivoire.Design/methodology/approachIn 2013, in order to improve the living conditions of the rural population, the Ivorian government launched the National Program for rural electrification (PRONER) to electrify all localities with more than 500 inhabitants.FindingsThe results show that PRONER, while reducing the time allocated to performing household chores, increases women's employment through the reallocation of time to full-time paid work in the agricultural and non-agricultural sectors. The authors also find that the allocation of men's time is not affected by this programme. A possible mechanism that would explain such a pro-women effect is the labour-saving technology introduced to home production as an effect of the reform.Research limitations/implicationsAs a limitation, it is important to note that these results were obtained in the specific context of PRONER in Côte d’Ivoire and are not necessarily applicable to rural electrification programmes in other contexts. Furthermore, the choice of other indicators to measure women's empowerment is limited by the quality of the data available. It would be interesting for future research to extend this analysis to include other aspects of women's empowerment and household welfare.Originality/valueThis paper is the first to the author’s knowledge to apply a robust econometric method by combining an inverse probability weighted regression adjustment model with Heckman sample selection method to access a robust causal effect of the PRONER in Côte d'Ivoire.

Type 1 diabetes (T1D) represents a risk factor for bone loss and impaired bone quality.We conducted an exploratory retrospective cross-sectional study involving youths with new-onset T1D, to investigate the relationship between lumbar spine dual-energy X-ray absorptiometry (DXA) and phalangeal quantitative ultrasound (QUS) measurements, along with their correlation with markers of bone turnover, glucose homeostasis, and residual β-cell function.17 children and adolescents (8 females) with recent-onset T1D were enrolled into this study. Lumbar spine areal bone mineral density (aBMD) and age-adjusted amplitude-dependent speed of sound (AD-SoS) Z-scores were indicative of low BMD status (≤ -2.0 SD) in 11.7% and 17.6% of participants, respectively. Spearman's correlation analysis revealed significant inverse correlations between AD-SoS values and circulating levels of β-CrossLaps, alkaline phosphatase, and osteocalcin, along with a significant positive correlation between bone transmission time (BTT) values and fasting plasma C-peptide (FCP) levels. There was no statistically significant correlation between DXA-QUS parameters, fasting plasma glucose (FPG), and glycated haemoglobin (HbA1c). Finally, there was a significant positive correlation between lumbar spine aBMD and BTT values.Our study suggests that DXA and/or QUS parameters may be altered in a small proportion of T1D children and adolescents at the disease onset. Additionally, residual β-cell function may represent a protective factor against T1D-related detrimental skeletal changes. Large and long-term prospective studies are needed to confirm these preliminary findings since the present study is limited by the retrospective cross-sectional design and by its small sample size.

Background Strongyloidiasis is a neglected tropical disease affecting an estimated 600 million people, particularly in resource-limited settings. The infection can persist lifelong due to unusual auto-infective cycle of Strongyloides stercoralis. The lack of a diagnostic gold standard and limited knowledge of the mechanisms underpinning this chronic infection are key issues in disease management. To date, only a few proteomics studies have been conducted to elucidate the molecular mechanisms associated with Strongyloides parasitism or to highlight novel immunological markers, with the result that our knowledge of S. stercoralis proteome remains limited. This study aims at expanding the characterization of S. stercoralis infective larvae (iL3) in order to further explore the mechanisms of parasitism and to highlight possible novel targets for serodiagnosis. Methods iL3 obtained from an infected subject were analysed by high-throughput tandem mass spectrometry. To achieve a more comprehensive characterization of the iL3 proteome we analysed the experimental dataset using an automatic search strategy combined with manual annotation, which included gene ontology (GO) analysis, InterPro annotation, assessment of the homology with Homo sapiens and other pathogens of clinical importance and B-cell epitope prediction. Results Our pipeline identified 430 S. stercoralis proteins, 187 (43%) of which were uncharacterized. Oxidoreductases and peptidases were amongst the most represented protein categories, as highlighted by molecular function GO analyses, while membrane and mitochondrial proteins were the most represented cellular component GO categories. A high proportion of proteins bearing the CAP, SCP or thioredoxin domain or belonging to cysteine-rich secretory, transthyretin-like or peptidase protein families were also identified. Additionally, we highlighted nine proteins displaying low homology with H. sapiens or other related pathogens and bearing amino acid sequences with immunogenic properties. Conclusions Our comprehensive description and annotation of the S. stercoralis iL3 proteome contribute to expanding the ‘omics characterization of this parasite and provide experimental evidence on the most represented proteins associated with S. stercoralis parasitism, as inferred from genomic and transcriptomic data. Moreover, novel candidate immunogenic proteins to be evaluated as novel serological diagnostic markers are highlighted. Graphical Abstract

All-atom molecular dynamics (MD) simulations are a powerful approach to study the structure and dynamics of proteins related to health and disease. Advances in the MD field allow modeling proteins with high accuracy. However, modeling metal ions and their interactions with proteins is still challenging for MD simulations. Over one-third of known protein structures bind metal ions and have various cellular functions, such as structural stability, catalysis, and regulation. NPL4 is a zinc-binding protein and works as a cofactor for p97, and together they regulate protein homeostasis. NPL4 is also of biomedical importance and has been proposed as the target of Antabuse, a drug recently repurposed for cancer treatment. Recent experimental studies have proposed that the Antabuse metabolites, bis- (diethyldithiocarbamate)-copper (CuET) and cupric ions released from CuET, induce NPL4 misfolding and consequent aggregation. However, the molecular details of the mechanisms of interactions of Antabuse metabolites with NPL4 and the consequent structural effects are still elusive. In this context, biomolecular simulations can help to shed light on the related structural details. To apply MD simulations to NPL4 and its interaction with copper or Antabuse metabolites the first important step is identifying a suitable force field to describe the protein in its zinc-bound states. We first examined different sets of non-bonded parameters, because we want to study the misfolding mechanism and cannot rule out that the zinc ion may detach from the protein structure during the process and copper replaces it in the metal binding site. We investigated the force-field ability to model the coordination geometry of the metal ions by comparing the results from MD simulations with optimized geometries from quantum mechanics (QM) calculations using model systems of the zinc coordination site for NPL4. Furthermore, we investigated the performance of a MD force field including bonded parameters to treat copper ions and metal-coordinating atoms in NPL4 that we obtained based on QM calculations.

Lake Maggiore is a site of the Long-Term Ecosystem Research (LTER) network, belonging to the deep subalpine Lake District in Northern Italy. Studies on the physical, chemical, and biological features of the lake have been performed continuously since the 1980s. The lake recovered from eutrophication reaching the present oligotrophic condition. In the last decade, climate change represents the main driving factor for the long-term evolution of the lake, affecting its hydrodynamics, nutrient status, and biological communities. In 2020 a high-frequency monitoring (HFM) system was deployed, with the aim to integrate long-term monitoring based on discrete sampling and analysis. The system consists of a buoy equipped with sensors for limnological variables and algal pigments. The high-frequency monitoring program is part of a cross-border project between Italy and Switzerland focusing on lake quality monitoring as a critical input for successful lake management. In this paper we focus on Chlorophyll-a data, with the aim to test whether in-situ fluorescence measurements may provide a reliable estimate of lake phytoplankton biovolume and its seasonal dynamic. Sensor’s performance was regularly tested comparing chlorophyll-a data taken by the in-situ fluorescent sensors (Cyclops7, Turner Design), data from laboratory fluorescence analysis (FluoroProbe, BBE Moldaenke), values obtained from chlorophyll-a analysis by UV-VIS spectrophotometry and data from phytoplankton microscopy analysis. We found a general good agreement between the Chlorophyll-a data obtained with the different methods, confirming the use of in-situ sensors as a reliable approach to measure algal pigments, especially to assess their variability in the short-term, but also to describe the seasonal pattern of phytoplankton biovolume. However, phytoplankton community composition played a substantial role in the performance of the different methods and in the reliability of in-situ data as a tool to assess algal biovolume. This study demonstrates that high-frequency monitoring (HFM), used in conjunction with discrete chemical and biological monitoring, represents an important advance and support in the long-term monitoring of freshwaters and is a useful tool to detect ecological changes. Regular checking and validation of the sensor readings through laboratory analyses are important to get trustworthy data.

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein–protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.

S-nitrosylation is a post-translational modification in which nitric oxide (NO) binds to the thiol group of cysteine, generating an S-nitrosothiol (SNO) adduct. S-nitrosylation has different physiological roles, and its alteration has also been linked to a growing list of pathologies, including cancer. SNO can affect the function and stability of different proteins, such as the mitochondrial chaperone TRAP1. Interestingly, the SNO site (C501) of TRAP1 is in the proximity of another cysteine (C527). This feature suggests that the S-nitrosylated C501 could engage in a disulfide bridge with C527 in TRAP1, resembling the well-known ability of S-nitrosylated cysteines to resolve in disulfide bridge with vicinal cysteines. We used enhanced sampling simulations and in-vitro biochemical assays to address the structural mechanisms induced by TRAP1 S-nitrosylation. We showed that the SNO site induces conformational changes in the proximal cysteine and favors conformations suitable for disulfide bridge formation. We explored 4172 known S-nitrosylated proteins using high-throughput structural analyses. Furthermore, we used a coarse-grained model for 44 protein targets to account for protein flexibility. This resulted in the identification of up to 1248 proximal cysteines, which could sense the redox state of the SNO site, opening new perspectives on the biological effects of redox switches. In addition, we devised two bioinformatic workflows (https://github.com/ELELAB/SNO_investigation_pipelines) to identify proximal or vicinal cysteines for a SNO site with accompanying structural annotations. Finally, we analyzed mutations in tumor suppressors or oncogenes in connection with the conformational switch induced by S-nitrosylation. We classified the variants as neutral, stabilizing, or destabilizing for the propensity to be S-nitrosylated and undergo the population-shift mechanism. The methods applied here provide a comprehensive toolkit for future high-throughput studies of new protein candidates, variant classification, and a rich data source for the research community in the NO field. S-nitrosylation is a post-translational modification in which nitric oxide (NO) binds to the thiol group of cysteine, generating an S-nitrosothiol (SNO) adduct. S-nitrosylation has different physiological roles, and its alteration has also been linked to a growing list of pathologies, including cancer. SNO can affect the function and stability of different proteins, such as the mitochondrial chaperone TRAP1. Interestingly, the SNO site (C501) of TRAP1 is in the proximity of another cysteine (C527). This feature suggests that the S-nitrosylated C501 could engage in a disulfide bridge with C527 in TRAP1, resembling the well-known ability of S-nitrosylated cysteines to resolve in disulfide bridge with vicinal cysteines. We used enhanced sampling simulations and in-vitro biochemical assays to address the structural mechanisms induced by TRAP1 S-nitrosylation. We showed that the SNO site induces conformational changes in the proximal cysteine and favors conformations suitable for disulfide bridge formation. We explored 4172 known S-nitrosylated proteins using high-throughput structural analyses. Furthermore, we used a coarse-grained model for 44 protein targets to account for protein flexibility. This resulted in the identification of up to 1248 proximal cysteines, which could sense the redox state of the SNO site, opening new perspectives on the biological effects of redox switches. In addition, we devised two bioinformatic workflows ( https://github.com/ELELAB/SNO_investigation_pipelines ) to identify proximal or vicinal cysteines for a SNO site with accompanying structural annotations. Finally, we analyzed mutations in tumor suppressors or oncogenes in connection with the conformational switch induced by S-nitrosylation. We classified the variants as neutral, stabilizing, or destabilizing for the propensity to be S-nitrosylated and undergo the population-shift mechanism. The methods applied here provide a comprehensive toolkit for future high-throughput studies of new protein candidates, variant classification, and a rich data source for the research community in the NO field.

The vast amount of sequencing data presently available allow the scientific community to explore a range of genetic variables that may drive and progress cancer. A myriad of predictive tools has been proposed, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tool. However, such limitations forces researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases every day, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartment, mutational burden, or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive tools for discovery of cancer drivers. We introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving in the direction of integrative frameworks., Comment: 35 pages, 7 figures

Data for the case studies reported in the publication: The data should be use together with the code deposited in GitHub including the workflows: https://github.com/ELELAB/SNO_investigation_pipelines - the data are discussed in the publication: https://www.biorxiv.org/content/10.1101/2022.12.11.519943v1

Adding steel fibers to concrete improves the post-cracking tensile strength of the composite material due to fibers bridging the cracks. The residual performance of the material is influenced by fiber type, content and orientation with respect to the crack plane. The latter is a main issue in fiber-reinforced concrete elements, since it significantly influences the structural behavior. The aim of this research is to develop a tailor-made composite material and casting method to orient fibers in order to optimize the performance of the material for structural applications. To this aim, a mechanized concreting device that induces such preferred fiber orientation is designed and fabricated. It uses vibration and a series of narrow channels to guide and orient fibers. A composite with oriented fibers is produced using a hybrid system of macro and micro fibers and high-performance concrete. From the same concrete batch, specimens are cast both with and without the fiber orientation device, obtaining different levels of fiber orientation. Three-point bending tests are performed to measure and compare the residual tensile strength capacities with standard specimens cast according to EN 14651. Elements with favorable fiber orientation show a significant increase in residual tensile strength with respect to the standard beams. Finally, computed tomography and an electromagnetic induction method are employed to better assess the orientation and distribution of fibers in the beams. Their results are in good agreement and enable to link the residual tensile strength parameters with fiber orientation.

This paper describes the distribution and spread of the non-indigenous red alga Lophocladia lallemandii (Montagne) F. Schmitz along the coast of the Island of Ischia (Tyrrhenian Sea, Italy). Lophocladia lallemandii was monitored through surveys from July 2019 to January 2020 at the Capo Sant’Angelo (Ischia), where L. lallemandii was observed, but not reported, in the years preceding the invasion of the upper rocky infralittoral shore reported here. It is noteworthy that a large portion of the study area is included within one of the two “B no-take” zones of the Marine Protected Area of the “Regno di Nettuno” (“Neptune’s Realm”). During the surveys, the alga was first observed in the middle of July 2019 and totally disappeared by the middle of January 2020. Algal cover showed two peaks in August (55%) and November (58.5%). Fertile thalli (tetrasporophytes) of L. lallemandii were observed in all of the analysed samples. Thalli were not always strongly attached to the substrate or other algae and could often be easily detached by strong hydrodynamic conditions. These detached thalli were found laying on the bottom in dense turfs or floating or stranding on the beach. Noteworthy were the macroflora and fauna, the latter essentially composed by mollusks and amphipods, living among the branches of the alga, and various fishes hiding within the thick algal turf. These observations indicate that this alga may be a source of food and refuge for the native animal community of the upper rocky infralittoral zone.

Notwithstanding several research efforts in the past years, robust and replicable molecular signatures for autism spectrum disorders from peripheral blood remain elusive. The available literature on blood transcriptome in ASD suggests that through accurate experimental design it is possible to extract important information on the disease pathophysiology at the peripheral level. Here we exploit the availability of a resource for molecular biomarkers in ASD, the Italian Autism Network (ITAN) collection, for the investigation of transcriptomic signatures in ASD based on a discordant sibling pair design. Whole blood samples from 75 discordant sibling pairs selected from the ITAN network where submitted to RNASeq analysis and data analyzed by complementary approaches. Overall, differences in gene expression between affected and unaffected siblings were small. In order to assess the contribution of differences in the relative proportion of blood cells between discordant siblings, we have applied two different cell deconvolution algorithms, showing that the observed molecular signatures mainly reflect changes in peripheral blood immune cell composition, in particular NK cells. The results obtained by the cell deconvolution approach are supported by the analysis performed by WGCNA. Our report describes the largest differential gene expression profiling in peripheral blood of ASD subjects and controls conducted by RNASeq. The observed signatures are consistent with the hypothesis of immune alterations in autism and an increased risk of developing autism in subjects exposed to prenatal infections or stress. Our study also points to a potential role of NMUR1, HMGB3, and PTPRN2 in ASD.

Longe de ser um campo fechado em si mesmo, a Arquitetura sempre procurou estabelecer pontes com outros campos disciplinares – notadamente, com a Filosofia. Entre as correntes de pensamento que fomentaram e que continuam a instigar o pensamento arquitetônico está a fenomenologia. Tal corrente filosófica, fundada por Edmund Husserl no início do século XX, teve um papel fundamental nas discussões sobre a experiência, a relação corpo-espaço em projetos de Arquitetura, contribuindo para a formação e o desenvolvimento do pensamento arquitetônico Pós-Moderno – uma questão ainda pouco estudada e fonte de debates. A fenomenologia representa uma alternativa crítica ao pensamento positivista, que, no campo arquitetônico, identifica-se, sobretudo, com o Movimento Moderno. É possível observar a influência da fenomenologia tanto nas questões de Teoria e História da Arquitetura, quanto nas discussões projetuais – em ambos, o problema do “empobrecimento” da experiência comparece de diversos modos. O presente artigo procura estabelecer diálogos entre o pensamento de três arquitetos e autores que têm a fenomenologia como base de suas discussões: o espanhol Jorge Otero-Pailos (1971-), o norueguês Christian Norberg-Schulz (1926-2000) e o finlandês Juhani Pallasmaa (1936-). Com a escolha destes autores, nota-se a persistência das discussões sobre a fenomenologia no interior do campo arquitetônico ao longo das décadas de 1970, 1990 e 2010, especialmente, nos contextos estadunidense e europeu. Além disso, observa-se que a fenomenologia arquitetônica não é um pensamento unívoco: ocorreram desenvolvimentos e modificações ao longo dos mais de 50 anos de discussões no meio acadêmico e no campo da prática profissional.

Data for the case studies reported in the publication: https://www.biorxiv.org/content/10.1101/2022.11.18.517066v1. The data should be use together with the code deposited in GitHub to reproduce the case study: https://github.com/ELELAB/Moonlight2_DMA_basal_like and using Moonlight2 https://github.com/ELELAB/Moonlight2R

Prediction of tumor suppressors and oncogenes, also called driver genes, is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed changes in gene expression to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene or mutations in the regulator itself. In this work, we developed new functionalities and release Moonlight2, to provide the user with the mutation-based mechanistic indicator to streamline the analyses of this second layer of evidence. The function analyzes mutation information in a cancer cohort to classify them into driver and passenger mutations. Moreover, the function estimates the potential effect of a mutation on the transcriptional, translational, or protein structure/function level. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 and the newly developed function to a case study on Basal-like breast cancer subtype using data from The Cancer Genome Atlas. We found six oncogenes (SF3B4, EBNA1BP2, KRTCAP2, ZBTB8OS, RUNX2, andPOLR2J) and ten tumor suppressor genes (KIF26B, NR5A2, ARHGAP25, EMCN, ARL15, PCOLCE, TPK1, TEK, KIR2DL4, andGMFG) containing a driver mutation in their promoter region, possibly explaining their deregulation. The Moonlight2R source code is available athttps://github.com/ELELAB/Moonlight2R.

Data and code for the case studies reported in our publication: https://doi.org/10.1101/2022.09.02.506350. The data are divided in subfolder for each case study presented in the publication. They are provided as tar.gz files and each of them contains its own readme file with the command lines used to reproduce the analyses

NK cells represent key players capable of driving antitumor immune responses. However, the potent immunosuppressive activity of the tumor microenvironment (TME) may impair their effector function. Here, we strengthen the importance of metabolic interactions between NK cells and TME and propose metabolic dysfunction as one of the major mechanisms behind NK failure in cancer treatment. In particular, we described that TME has a direct negative impact on NK cell function by disrupting their mitochondrial integrity and function in pediatric and adult patients with primary and metastatic cancer. Our results will help to design new strategies aimed at increasing the NK cell antitumor efficacy by their metabolic reprogramming. In this regard, we reveal an unprecedented role of IL15 in the metabolic reprogramming of NK cells enhancing their antitumor functions. IL15 prevents the inhibitory effect of soluble factors present in TME and restores both the metabolic characteristics and the effector function of NK cells inhibited by exposure to malignant pleural fluid. Thus, we propose here that IL15 may be exploited as a new strategy to metabolically reprogram NK cells with the aim of increasing the efficacy of NK-based immunotherapy in a wide range of currently refractory adult and pediatric solid tumors.

CD8+T cells are a major prognostic determinant in solid tumors, including colorectal cancer (CRC). However, understanding how the interplay between different immune cells impacts on clinical outcome is still in its infancy. Here, we describe that the interaction of tumor infiltrating neutrophils expressing high levels of CD15 with CD8+T effector memory cells (TEM) correlates with tumor progression. Mechanistically, stromal cell-derived factor-1 (CXCL12/SDF-1) promotes the retention of neutrophils within tumors, increasing the crosstalk with CD8+T cells. As a consequence of the contact-mediated interaction with neutrophils, CD8+T cells are skewed to produce high levels of GZMK, which in turn decreases E-cadherin on the intestinal epithelium and favors tumor progression. Overall, our results highlight the emergence of GZMKhighCD8+TEMin non-metastatic CRC tumors as a hallmark driven by the interaction with neutrophils, which could implement current patient stratification and be targeted by novel therapeutics.

Cancer is a complex group of diseases due to the accumulation of mutations in tumor suppressors or oncogenes in the genome. Cancer alterations can be very heterogeneous, even in tumors from the same tissue, affecting cancer predisposition, response to treatment, and risks of relapse in different patients. The role of genomics variants in this context continues to be realized. Thanks to advances in sequencing techniques and their introduction in the clinics, the number of genomic variants discovered is growing exponentially. Many of these variants are classified as Variants of Uncertain Significance (VUS), while other variants have been reported with conflicting evidence or with a ‘likely’ effect. Applications of bioinformatic-based approaches to characterize protein variants demonstrated their full potential thanks to advances in machine learning, comparisons between predicted effects and cellular readouts, and progresses in the field of structural biology and biomolecular simulations. We here introduce a modular structure-based framework for the annotations and classification of the impact of variants affecting proteins or their interactions and impacting on the corresponding protein product (MAVISp,Multi-layeredAssessment ofVarIants byStructure forproteins) together with a Streamlit-based web application (https://github.com/ELELAB/MAVISp) where the variants and the data generated by the assessment are made available to the community for consultation or further studies. Currently, MAVISp includes information for 127 different proteins and approximately 42000 variants. New protein targets are routinely analyzed in batches by biocurators through standardized Python-based workflows and high-throughput free energy and biomolecular simulations. We also illustrate the potential of the approach for each protein included in the database. New variants will be deposited on a regular base or in connection with future publications where the approach will be applied. We also welcome new contributors who are interested in participating to the collection in relation to their research.

Reliable prediction of free energy changes upon amino acidic substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein-protein interaction. Moreover, advances in experimental mutational scans allow high-throughput studies thanks to sophisticated multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput calculations of ΔΔGs. In this context, the Rosetta modeling suite implements effective approaches to predict the change in the folding free energy in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. Their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. RosettaDDGPrediction assists with checking whether the runs are completed successfully aggregates raw data for multiple variants, and generates publication-ready graphics. We showed the potential of the tool in selected case studies, including variants of unknown significance found in children who developed cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and a disordered functional motif, and phospho-mimetic variants. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, athttps://github.com/ELELAB/RosettaDDGPrediction.

The NEAM Tsunami Hazard Model 2018 (NEAMTHM18) is a probabilistic hazard model for tsunamis generated by earthquakes. It covers the coastlines of the North-eastern Atlantic, the Mediterranean, and connected seas (NEAM). NEAMTHM18 was designed as a three-phase project. The first two phases were dedicated to the model development and hazard calculations, following a formalized decision-making process based on a multiple-expert protocol. The third phase was dedicated to documentation and dissemination. The hazard assessment workflow was structured in Steps and Levels. There are four Steps: Step-1) probabilistic earthquake model; Step-2) tsunami generation and modeling in deep water; Step-3) shoaling and inundation; Step-4) hazard aggregation and uncertainty quantification. Each Step includes a different number of Levels. Level-0 always describes the input data; the other Levels describe the intermediate results needed to proceed from one Step to another. Alternative datasets and models were considered in the implementation. The epistemic hazard uncertainty was quantified through an ensemble modeling technique accounting for alternative models’ weights and yielding a distribution of hazard curves represented by the mean and various percentiles. Hazard curves were calculated at 2,343 Points of Interest (POI) distributed at an average spacing of ∼20 km. Precalculated probability maps for five maximum inundation heights (MIH) and hazard intensity maps for five average return periods (ARP) were produced from hazard curves. In the entire NEAM Region, MIHs of several meters are rare but not impossible. Considering a 2% probability of exceedance in 50 years (ARP≈2,475 years), the POIs with MIH >5 m are fewer than 1% and are all in the Mediterranean on Libya, Egypt, Cyprus, and Greece coasts. In the North-East Atlantic, POIs with MIH >3 m are on the coasts of Mauritania and Gulf of Cadiz. Overall, 30% of the POIs have MIH >1 m. NEAMTHM18 results and documentation are available through the TSUMAPS-NEAM project website (http://www.tsumaps-neam.eu/), featuring an interactive web mapper. Although the NEAMTHM18 cannot substitute in-depth analyses at local scales, it represents the first action to start local and more detailed hazard and risk assessments and contributes to designing evacuation maps for tsunami early warning.

This chapter is devoted to the presentation of the most popular approaches employed to design mitigation measures. Initially, the mitigation measures necessary for a safe excavation and a permanent cavity stabilization are considered. Subsequently, the observational method, a design procedure aimed at optimizing the management of the unavoidable geological and geotechnical uncertainties affecting both tunnel design and construction, is presented. Finally, the long-term tunnel performance under accidental loads is discussed.

During autophagy, the coordinated actions of autophagosomes and lysosomes result in the controlled removal of damaged intracellular organelles and superfluous substrates. The evolutionary conservation of this process and its requirement for maintaining cellular homeostasis emphasizes the need to better dissect the pathways governing its molecular regulation. In our previously performed high-content screen, we assessed the effect of 1530 RNA-binding proteins on autophagy. Among the top regulators, we identified the eukaryotic translation initiation factor 4A-3 (eIF4A3). Here we show that depletion of eIF4A3 leads to a potent increase in autophagosome and lysosome biogenesis and an enhanced autophagic flux. This is mediated by the key autophagy transcription factor, TFEB, which becomes dephosphorylated and translocates from the cytoplasm to the nucleus where it elicits an integrated transcriptional response. We further identified an exon-skipping event in the transcript encoding for the direct TFEB kinase, GSK3B, which leads to a reduction in GSK3B expression and activity. Through analysis of TCGA data, we found a significant upregulation of eIF4A3 expression across several cancer types and confirmed the potential relevance of this newly identified signaling axis in human tumors. Hence, our data suggest a previously unrecognized role for eIF4A3 as a gatekeeper of autophagy through the control of TFEB activation, revealing a new mechanism for autophagy regulation.

This paper uses a large database of surveys of household incomes to characterize income underreporting in household surveys in low- and middle-income countries. The objective is to document (a) the extent of this underreporting, and (b) whether and how it varies systematically with respondent, household, income, and survey design features. Drawing on rural household data from 20 developing and transition countries, and using consumption expenditure as a benchmark, results indicate that the observed income/consumption ratios are very small, being on average around 0.76. Results suggest that income underreporting is systematically associated with household and survey characteristics. In particular, the degree of underreporting is strongly associated with the income source, with agricultural income being the component suffering more than any other components from underreporting. The analysis also provides evidence supporting the well-established proposition that underreporting tends to increase with household welfare: richer households appear to underreport income more. Implications for survey design and for future research are drawn.

Molecular dynamics (MD) simulations are a powerful approach to studying the structure and dynamics of proteins related to health and disease. Advances in the MD field allow modeling proteins with high accuracy. However, modeling metal ions and their interactions with proteins is still challenging. NPL4 is a zinc-binding protein and works as a cofactor for p97 to regulate protein homeostasis. NPL4 is of biomedical importance and has been proposed as the target of disulfiram, a drug recently repurposed for cancer treatment. Experimental studies proposed that the disulfiram metabolites, bis-(diethyldithiocarbamate)‑copper and cupric ions, induce NPL4 misfolding and aggregation. However, the molecular details of their interactions with NPL4 and consequent structural effects are still elusive. Here, biomolecular simulations can help to shed light on the related structural details. To apply MD simulations to NPL4 and its interaction with copper the first important step is identifying a suitable force field to describe the protein in its zinc-bound states. We examined different sets of non-bonded parameters because we want to study the misfolding mechanism and cannot rule out that the zinc may detach from the protein during the process and copper replaces it. We investigated the force-field ability to model the coordination geometry of the metal ions by comparing the results from MD simulations with optimized geometries from quantum mechanics (QM) calculations using model systems of NPL4. Furthermore, we investigated the performance of a force field including bonded parameters to treat copper ions in NPL4 that we obtained based on QM calculations.

Stocking game fish into originally fishless mountain systems is a major threat for high-altitude lakes and the inhabiting native species. The adoption of stocking and fishing bans and the implement...

1. Invasive alien species are among the most important threats to biodiversity. Plans for their eradication have been implemented worldwide but estimating residual population size and eradication probability to assess removal success is complicated by the imperfect detection of residual individuals. 2. Most methods to assess residual abundance and eradication probability rely on the often unrealistic assumption that a population is closed to mortality and recruitment processes during the implementation of removal actions. We extended existing removal models and developed a novel analytical approach to estimate residual population size and derive eradication probability in open populations, while accounting for multiple removal methods. 3. We apply this approach to 20 eradication projects in Europe and the United States that used mechanical methods to return high mountain lakes to their original fishless condition. 4. The new removal model incorporates i) a mechanistic description of the ecological process underlying survival and recruitment probabilities during the eradication period, and ii) the use of multiple, concomitant removal methods (i.e., electrofishing, gillnetting, fyke-netting) at multiple sites simultaneously. 5. We used a subset of “control” lakes where eradication success was confirmed by more than 5 years of post-removal surveys to validate the model. For these lakes, eradication success, evaluated by whether the 95% Bayesian credible interval for estimated residual population size encompassed values of less than 2 individuals, was confirmed in 13 out of 15 lake-by-species case studies. In addition, the model correctly assigned an eradication probabilities equal to 0 for the eradications that are still in progress. 6. Synthesis and applications. Our study provides insights into the dynamics of fish populations subject to eradication in high mountain lakes of different countries. In addition, the analytical approach proposed accounts for demographic processes and multiple removal methods in multiple sites and seasons. It can represent an informative tool to estimate residual population size and eradication probability of alien species to optimize eradication efforts and efficacy of conservation actions. The extension to open populations makes the approach useful to evaluate long-term eradication plans.

Collection of simulations and free energy calculations used in the publication Bcl-xL dynamics under the lens of protein structure networks. Valentina Sora, Dionisio Sanchez, Elena Papaleo*,J Phys Chem B, 2021, 125(17):4308-4320. doi: 10.1021/acs.jpcb.0c11562. Other data in https://github.com/ELELAB/BCLxL-allo

Collection of simulations and free energy calculations used in the publication "The conformational and mutational landscape of the ubiquitin-like marker for the autophagosome formation in cancer". The script and input files can be found in the associated Github repository of our group: https://github.com/ELELAB/lc3b_cancer_paper Reference: Fas et al., Autophagy,2021, DOI: 10.1080/15548627.2020.1847443

Urinary extracellular vesicles (UEV) mainly derive from cells of the urogenital tract and their cargo (proteins, nucleic acids, lipids, etc.) reflects their cells of origin. Na chloride cotransporter (NCC) is expressed at the kidney level in the distal convoluted tubule, is involved in salt reabsorption, and is the target of the diuretic thiazides. NCC protein has been recognized and quantified in UEV in previous studies; however, UEV NCC mRNA has never been studied. This study aimed to identify and analyze NCC mRNA levels in primary aldosteronism (PA). The rationale for this investigation stems from previous observations regarding NCC (protein) as a possible biomarker for the diagnosis of PA. To evaluate modulations in the expression of NCC, we analyzed NCC mRNA levels in UEV in PA and essential hypertensive (EH) patients under different conditions, that is, before and after saline infusion, anti-aldosterone pharmacological treatment, and adrenal surgery. NCC mRNA was measured by RT-qPCR in all the samples and was regulated by volume expansion. Its response to mineralocorticoid receptor antagonist was correlated with renin, and it was increased in PA patients after adrenalectomy. NCC mRNA is evaluable in UEV and it can provide insights into the pathophysiology of distal convolute tubule in different clinical conditions including PA.

Early diagnosis is crucial for Autism spectrum disorder (ASD) and is achieved through a screening of developmental indicators to recognise children who are at risk of autism. One of the most widely used instruments in clinical practice for assessing child development is the Griffiths Mental Development Scale (GMDS). We sought (a) to assess longitudinally whether children diagnosed with ASD, with a mean age of 33.50 months (SD 7.69 months), show a developmental delay of abilities measured by the GMDS over time and (b) to analyse which skills of the GMDS could be associate to the symptomatologic severity of ASD. Our results showed lower scores of General Quotient and all sub-quotients of GMDS from first (T0) to second assessment (T1), except for the Performance sub-quotient. Three sub-quotients (Personal-Social, Hearing and Language and Practical Reasoning) also associate symptom severity at the time when the diagnosis of ASD is made.