Understanding the role of elementary and secondary schools in SARS-CoV-2 transmission is essential for designing effective public health policies. We developed a stochastic age-stratified transmission model, fitted to epidemiological and contact survey data, to evaluate how alternative school closure and reopening strategies might have shaped the course of the pandemic in the Netherlands from early 2020 through late 2021. Using counterfactual simulations and time-varying elasticity analysis, we found that school closures could mitigate transmission and reduce healthcare burden. However, their effectiveness was highly context-dependent, influenced by school type, contact patterns, policy timing, and population immunity. Age groups driving transmission shifted over time, with adults dominating early in the pandemic, adolescents in late 2020 and early 2021, and children in late 2021. Accordingly, secondary schools had a greater impact on national hospitalizations in 2020, while elementary schools became more important in 2021, partly due to lower infection-induced and negligible vaccine-induced immunity among children compared to adolescents. Together, these results underscore the importance of accounting for age-specific transmission dynamics and the evolving epidemiological landscape when evaluating school closure and reopening strategies. Our study supports a more targeted and adaptive approach to school policies in future pandemics.

For several decades, experimental and computational studies have been used to investigate the potential functional role of knots in protein structures. A property that has attracted considerable attention is thermal stability, i.e., the extent to which a protein retains its native conformation and biological activity at high temperatures, without undergoing denaturation or aggregation. Thermal stability is quantified by the melting temperature Tm, an equilibrium property that corresponds to the peak of heat capacity in differential scanning calorimetry (DSC) experiments. Experimental and computational studies report conflicting effects of knotting on protein thermal stability [M. A. Soler and P. F. N. Faisca, PLoS One 8, e74755 (2013);Y.-C. Chuang, I.-C. Hu, P.-C. Lyu, and S.-T. D. Hsu, J. Mol. Biol. 431, 857 (2019)]. Here, we use extensive Monte Carlo simulations of a simple C-alpha model of protein YibK, with energetics modeled by the Go potential, to show that Tm does not depend on the topological state of the protein. Our simulations further support the view that the discrepancy between the experimental and computational results stems from a pronounced separation of timescales for unknotting and unfolding that is inherent to deeply knotted proteins like YibK. In particular, the timescale separation implies that the complete unfolding-untying transition may not be accessible within the duration of a DSC experiment, whose apparent Tm measurements likely reflect a nonequilibrium distribution lacking unfolded states that are also unknotted.

Rationale 2,4,6‐Tris(4‐formylphenyl)boroxine (TFPB) is a substituted boroxine containing a benzaldehyde molecule bonded to each boron atom. Boroxine cages are an emerging class of functional nanostructures used in host–guest chemistry, and benzaldehyde is a potential radiosensitizer. Reactions initiated by low‐energy electrons with such complexes may dictate and bring new fundamental knowledge for biomedical and pharmaceutical applications. Methods The electron ionization properties of TFPB are investigated using a gas‐phase electron–molecule crossed beam apparatus coupled with a reflectron time‐of‐flight mass spectrometer in an orthogonal geometry. Ionization and threshold energies are experimentally determined by mass spectra acquisition as a function of the electron energy. Results The abundance of the molecular precursor cation in the mass spectrum at 70 eV is significantly lower than that of the most abundant fragment C7H5O+. Twenty‐nine cationic fragments with relative intensities >2% are detected and identified. The appearance energies of six fragment cations are reported, and the experimental first ionization potential is found at 9.46±0.11 eV. Moreover, eight double cations are identified. The present results are supported by quantum chemical calculations based on bound state techniques, electron ionization models and thermodynamic thresholds. Conclusions According to these results, the TPFB properties may combine the potential radiosensitizer effect of benzaldehyde with the stability of the boroxine ring.

IntroductionAccurate prevalence estimates for Autism Spectrum Disorder (ASD) are fundamental to adequately program medical and educational resources for children. However, estimates vary globally and across Europe, and it is therefore wise to conduct epidemiological studies in defined geo-cultural contexts.MethodsWe used a population screening approach to estimate the prevalence of ASD in the Centro region of Portugal, using a harmonized protocol as part of the Autism Spectrum Disorders in the European Union (ASDEU) project.ResultsThe overall prevalence was estimated at 0.5% (95% CI 0.3–0.7), higher in schools with Autism Units (3.3%, 95%CI 2.7–3.9) than in regular schools (0.3%, 95% CI 0.1–0.5) or schools with Multiple Disability Units (0.3%, 95% CI 0.04–0.6).DiscussionThe results indicate that the diagnosis of ASD is followed by the most effective educational policies in Centro Region. The variability in prevalence estimates across the different regions from the ASDEU project, and globally, is discussed.

Early-life exposure to air pollutants, including ozone (O3), particulate matter (PM2.5 or PM10, depending on diameter of particles), nitrogen dioxide (NO2) and sulfur dioxide (SO2) has been suggested to contribute to the etiology of Autism Spectrum Disorder (ASD). In this study, we used air quality monitoring data to examine whether mothers of children with ASD were exposed to high levels of air pollutants during critical periods of pregnancy, and if higher exposure levels may lead to a higher clinical severity in their offspring. We used public data from the Portuguese Environment Agency to estimate exposure to these pollutants during the first, second and third trimesters of pregnancy, full pregnancy and first year of life of the child, for 217 subjects with ASD born between 2003 and 2016. These subjects were stratified in two subgroups according to clinical severity, as defined by the Autism Diagnostic Observational Schedule (ADOS). For all time periods, the average levels of PM2.5, PM10 and NO2 to which the subjects were exposed were within the admissible levels defined by the European Union. However, a fraction of these subjects showed exposure to levels of PM2.5 and PM10 above the admissible threshold. A higher clinical severity was associated with higher exposure to PM2.5 (p = 0.001), NO2 (p = 0.011) and PM10 (p = 0.041) during the first trimester of pregnancy, when compared with milder clinical severity. After logistic regression, associations with higher clinical severity were identified for PM2.5 exposure during the first trimester (p = 0.002; OR = 1.14, 95%CI: 1.05-1.23) and full pregnancy (p = 0.04; OR = 1.07, 95%CI: 1.00-1.15) and for PM10 (p = 0.02; OR = 1.07, 95%CI: 1.01-1.14) exposure during the third trimester. Exposure to PM is known to elicit neuropathological mechanisms associated with ASD, including neuroinflammation, mitochon-drial disruptions, oxidative stress and epigenetic changes. These results offer new insights on the impact of early -life exposure to PM in ASD clinical severity.

Heritability estimates support the contribution of genetics and the environment to the etiology of Autism Spectrum Disorder (ASD), but a role for gene-environment interactions is insufficiently explored. Genes involved in detoxification pathways and physiological permeability barriers (e.g., blood-brain barrier, placenta and respiratory airways), which regulate the effects of exposure to xenobiotics during early stages of neurodevelopment when the immature brain is extremely vulnerable, may be particularly relevant in this context. Our objective was to identify genes involved in the regulation of xenobiotic detoxification or the function of physiological barriers (the XenoReg genes) presenting predicted damaging variants in subjects with ASD, and to understand their interaction patterns with ubiquitous xenobiotics previously implicated in this disorder. We defined a panel of 519 XenoReg genes through literature review and database queries. Large ASD datasets were inspected for in silico predicted damaging Single Nucleotide Variants (SNVs) (N = 2,674 subjects) or Copy Number Variants (CNVs) (N = 3,570 subjects) in XenoReg genes. We queried the Comparative Toxicogenomics Database (CTD) to identify interaction pairs between XenoReg genes and xenobiotics. The interrogation of ASD datasets for variants in the XenoReg gene panel identified 77 genes with high evidence for a role in ASD, according to pre-specified prioritization criteria. These include 47 genes encoding detoxification enzymes and 30 genes encoding proteins involved in physiological barrier function, among which 15 are previous reported candidates for ASD. The CTD query revealed 397 gene-environment interaction pairs between these XenoReg genes and 80% (48/60) of the analyzed xenobiotics. The top interacting genes and xenobiotics were, respectively, CYP1A2, ABCB1, ABCG2, GSTM1, and CYP2D6 and benzo-(a)-pyrene, valproic acid, bisphenol A, particulate matter, methylmercury, and perfluorinated compounds. Individuals carrying predicted damaging variants in high evidence XenoReg genes are likely to have less efficient detoxification systems or impaired physiological barriers. They can therefore be particularly susceptible to early life exposure to ubiquitous xenobiotics, which elicit neuropathological mechanisms in the immature brain, such as epigenetic changes, oxidative stress, neuroinflammation, hypoxic damage, and endocrine disruption. As exposure to environmental factors may be mitigated for individuals with risk variants, this work provides new perspectives to personalized prevention and health management policies for ASD.

There is a consensus that mass vaccination against SARS-CoV-2 will ultimately end the COVID-19 pandemic. However, it is not clear when and which control measures can be relaxed during the rollout of vaccination programmes. We investigate relaxation scenarios using an age-structured transmission model that has been fitted to age-specific seroprevalence data, hospital admissions, and projected vaccination coverage for Portugal. Our analyses suggest that the pressing need to restart socioeconomic activities could lead to new pandemic waves, and that substantial control efforts prove necessary throughout 2021. Using knowledge on control measures introduced in 2020, we anticipate that relaxing measures completely or to the extent as in autumn 2020 could launch a wave starting in April 2021. Additional waves could be prevented altogether if measures are relaxed as in summer 2020 or in a step-wise manner throughout 2021. We discuss at which point the control of COVID-19 would be achieved for each scenario. Despite the consensus that mass vaccination against SARS-CoV-2 will ultimately end the pandemic, it is not clear when and which control measures can be relaxed during the rollout of vaccination programmes. Here, the authors investigate relaxation scenarios using an age-structured transmission model that has been fitted to data for Portugal.

Protein beta-2-microglobulin (β2m) is classically considered the causative agent of dialysis related amyloidosis, a conformational disorder that affects patients undergoing long-term hemodialysis. The wild type (WT) form, the ΔN6 structural variant, and the D76N mutant have been extensively used as model systems ofβ2m aggregation. In all of them, the native structure is stabilized by a disulfide bridge between the sulphur atoms of the cysteine residues 25 (at B strand) and 80 (at F strand), which has been considered fundamental inβ2m fibrillogenesis. Here, we use extensive discrete molecular dynamics simulations of a full atomistic structure-based model to explore the role of this disulfide bridge as a modulator of the folding space ofβ2m. In particular, by considering different models for the disulfide bridge, we explore the thermodynamics of the folding transition, and the formation of intermediate states that may have the potential to trigger the aggregation cascade. Our results show that the dissulfide bridge affects folding transition and folding thermodynamics of the considered model systems, although to different extents. In particular, when the interaction between the sulphur atoms is stabilized relative to the other intramolecular interactions, or even locked (i.e. permanently established), the WT form populates an intermediate state featuring a well preserved core and two unstructured termini, which was previously detected only for the D76N mutant. The formation of this intermediate state may have important implications in our understanding ofβ2m fibrillogenesis.

MSc thesis project proposal For the last twelve months, data on the pandemic have been all over the news, and the results of mathematical models became part of our everyday life. At the same time, much debate has been going on about the efficacy of containment measures, and the scientific literature devoted to their assessment exploded. We have developed the first study of this type devoted to Portugal. The study is still undergoing peer-review [1], but it already received much attention from the media [2]. The matrix that represents the frequency of personal contacts according to age class is a key ingredient of all mathematical models of COVID-19 spread. However, this matrix for Portugal available in the most recent literature [3] is outdated. The goal of this thesis is then to build a synthetic population for Portugal, including the features that are more relevant for COVID-19, and to assess the importance of introducing this level of detail in transmission models of COVID-19. The student will learn how to model the viral spread in the population using differential equations and how to fit the model to various data sources using Hamiltonian Monte Carlo method [4]. We anticipate that this work will have a significant impact in our capacity to control the spread of COVID-19 in Portugal in the post-pandemic stage. This project will be developed in collaboration with Professor Manuel Gomes who is one of the key advisors on the COVID-19 pandemic for the Ministry of Health and with researchers from the Utrecht University, The Netherlands. Bibliography: [1] Viana J, van Dorp CH, Nunes A, et al. Controlling the pandemic during the SARS-CoV-2 vaccination rollout: a modeling study, 24 March 2021, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-358417/v1] [2] https://www.rtp.pt/noticias/pais/portugal-pode-enfrentar-quarta-vaga-de-covid-19-ja-noproximo-mes_v1310404 [3] Mistry D, Litvinova M, Pastore y Piontti A, Chinazzi M, Fumanelli L, Gomes MFC, et al. Inferring high-resolution human mixing patterns for disease modeling. Nature Communications. 2021;12(1):323. doi:10.1038/s41467-020-20544-y. [4] Rozhnova G, van Dorp CH, Bruijning-Verhagen P, Bootsma MCJ, van de Wijgert JHHM, Bonten MJM, et al. Model-based evaluation of schooland non-school-related measures to control the COVID-19 pandemic. Nature Communications. 2021;12(1):1614.

A small fraction of all protein structures characterized so far are entangled. The challenge of understanding the properties of these knotted proteins, and the why and the how of their natural folding process, has been taken up in the past decade with different approaches, such as structural characterization, in vitro experiments, and simulations of protein models with varying levels of complexity. The simplest among these are the lattice G\=o models, which belong to the class of structure-based models, i.e., models that are biased to the native structure by explicitly including structural data. In this review we highlight the contributions to the field made in the scope of lattice G\=o models, putting them into perspective in the context of the main experimental and theoretical results and of other, more realistic, computational approaches.

In this work we consider a cosmological model in which dark energy is portrayed by a canonical scalar field which is allowed to couple to the other species by means of a disformal transformation of the metric. We revisit the current literature by assuming that the disformal function in the metric transformation can depend both on the scalar field itself and on its derivatives, encapsulating a wide variety of scalar-tensor theories. This generalization also leads to new and richer phenomenology, explaining some of the features found in previously studied models. We present the background equations and perform a detailed dynamical analysis, from where new disformal fixed points emerge, that translate into novel cosmological features. These include early scaling regimes between the coupled species and broader stability parameter regions. However, viable cosmological models seem to have suppressed disformal late-time contributions.

O projeto ASDEU foi financiado pelo Directorate-General of Health and Consumers of the European Union (DG-SANCO), ref.a: SANCO/2014/C2/035.

Demographic theory and data have emphasized that nonheritable variation in individual frailty enables selection within cohorts, affecting the dynamics of a population while being invisible to its evolution. Here, we include the component of individual variation in longevity or viability which is nonheritable in simple bacterial growth models and explore its ecological and evolutionary impacts. First, we find that this variation produces consistent trends in longevity differences between bacterial genotypes when measured across stress gradients. Given that direct measurements of longevity are inevitably biased due to the presence of this variation and ongoing selection, we propose the use of the trend itself for obtaining more exact inferences of genotypic fitness. Second, we show how species or strain coexistence can be enabled by nonheritable variation in longevity or viability. These general conclusions are likely to extend beyond bacterial systems.

We present and study a conformally coupled dark energy model, characterised by an interaction between a tachyon field $\phi$, with an inverse square potential $V (\phi)$, and the matter sector. A detailed analysis of the cosmological outcome reveals different possibilities, in contrast with the previously studied uncoupled model, for which there exists only one stable critical point that gives late time acceleration of the Universe. The introduction of the coupling translates into an energy exchange between the fluids. We find the interesting possibility of the emergence of a new fixed point, which is a scaling solution and an attractor of the system. In this case, it is possible to describe an everlasting Universe with $\Omega_{\phi} \simeq 0.7$, therefore alleviating the cosmic coincidence problem. However we find that, in order for the evolution to be cosmologically viable, there is the need of introducing one uncoupled matter species, dominant at early times.

There is growing support for the idea that the in vivo folding process of knotted proteins is assisted by chaperonins, but the mechanism of chaperonin assisted folding remains elusive. Here, we conduct extensive Monte Carlo simulations of lattice and off-lattice models to explore the effects of confinement and hydrophobic intermolecular interactions with the chaperonin cage in the folding and knotting processes. We find that moderate to high protein-cavity interactions (which are likely to be established in the beginning of the chaperonin working cycle) cause an energetic destabilization of the protein that overcomes the entropic stabilization driven by excluded volume, and leads to a decrease of the melting temperature relative to bulk conditions. Moreover, mild-to-moderate hydrophobic interactions with the cavity (which would be established later in the cycle) lead to a significant enhancement of knotting probability in relation to bulk conditions while simultaneously moderating the effect of steric confinement in the enhancement of thermal stability. Our results thus indicate that the chaperonin may be able to assist knotting without simultaneously thermally stabilizing potential misfolded states to a point that would hamper productive folding thus compromising its functional role.

Introduction Autism Spectrum Disorder (ASD) is a clinically heterogeneous neurodevelopmental disorder defined by deficits in social communication and interaction and repetitive and stereotyped interests and behaviors. ASD heritability estimates of 50-83% support a strong role of genetics in its onset, with large sequencing studies reporting a high burden of rare potentially pathogenic copy number variants (CNVs) and single nucleotide variants (SNVs) in affected subjects. Recent data strongly suggests that prenatal to postnatal exposure to ubiquitous environmental factors (e.g. environmental toxins, medications and nutritional factors) contribute to ASD risk. Detoxification processes and physiological permeability barriers (i.e. blood-brain barrier, placenta and respiratory cilia) are crucial in regulating exposure and response to external agents during early development. Thus, the objectives of this study were: 1) to find genes involved in detoxification and regulation of barriers permeability with a high load of relevant CNVs and SNVs in ASD subjects; 2) to explore interactions between the identified genes and environmental factors relevant for the disorder. Material and Methods Through literature and databases review we searched for genes involved in detoxification and regulation of barriers permeability processes. Genetic data collected from large datasets of subjects with ASD (Autism Genome Project (AGP), Simmons Simplex Collection (SSC), and Autism Sequencing Consortium (ASC)) was used to identify potentially pathogenic variants targeting detoxification and barrier genes. Data from control subjects without neuropsychiatric disorder history was used for comparison purposes. The Comparative Toxicogenomics Database (CTD) was interrogated to identify putatively relevant gene-environment interactions reported in humans throughout the literature. Results We compiled a list of 519 genes involved in detoxification and regulation of permeability barriers. The analysis of AGP and SSC data resulted in the identification of 7 genes more-frequently targeted by CNVs in ASD-subjects from both datasets, after Bonferroni correction for multiple testing (AGP: P<3.5211×10 −4 ; SSC: P< 4.587×10 −4 ). Moreover, 8 genes were exclusively targeted by CNVs from ASD subjects. Regarding SNVs analyses using the ASC dataset, we found 40 genes targeted by potentially pathogenic loss-of-function and/or missense SNVs exclusive to 6 or more cases. The CTD was interrogated for interactions between 55 identified genes and 54 terms for unique chemicals associated with the disorder. A total of 212 gene-environment interaction pairs, between 51/55 (92.7%) genes and 38/54 (70.4%) chemicals, putatively relevant for ASD, were discovered. ABCB1, ABCG2, CYP2C19, GSTM1, CYP2D6 , and SLC3A2 were the genes that interacted with more chemicals, while valproic acid, benzo(a)pyrene (b(a)p), bisphenol A, particulate matter and perfluorooctane sulfonic acid (PFOS) were the top chemicals. Discussion The identified genes code for functionally diverse proteins, ranging from enzymes that increase the degradability of xenobiotics (CYP450s, UGTs and GSTs), to transporters (ABCs and SLCs), proteins that regulate the correct function of barriers (claudins and dyneins) and placental hormones. The identified gene-environment interactions may reflect the fact that some genes and chemicals are understudied and that the potential neurotoxicity of many substances is unreported. We suggest that environmental factors can have pathogenic effects when individuals carry variants targeting these genes and discuss the potential mechanisms by which these genes can influence ASD risk. Conclusion We reinforce the hypothesis that gene-environment interactions are relevant, at least, for a subset of ASD cases. Given that no treatment exists for the pathology, the identification of relevant modifiable exposures can contribute to the development of preventive strategies for health management policies in ASD.