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Abstract JEM-EUSO is an international program for the development of space-based Ultra-High Energy Cosmic Ray observatories. The program consists of a series of missions which are either under development or in the data analysis phase. All instruments are based on a wide-field-of-view telescope, which operates in the near-UV range, designed to detect the fluorescence light emitted by extensive air showers in the atmosphere. We describe the simulation software ESAF in the framework of the JEM-EUSO program and explain the physical assumptions used. We present here the implementation of the JEM-EUSO, POEMMA, K-EUSO, TUS, Mini-EUSO, EUSO-SPB1 and EUSO-TA configurations in ESAF. For the first time ESAF simulation outputs are compared with experimental data.

The data presented in this article are related to the research paper entitled “Observation of night-time emissions of the Earth in the near UV range from the International Space Station with the Mini-EUSO detector” (Remote Sensing of Environment, Volume 284, January 2023, 113336, https://doi.org/10.1016/j.rse.2022.113336).The data have been acquired with the Mini-EUSO detector, an UV telescope operating in the range 290-430 nm and located inside the International Space Station. The detector was launched in August 2019, and it has started operations from the nadir-facing UV-transparent window in the Russian Zvezda module in October 2019. The data presented here refer to 32 sessions acquired between 2019-11-19 and 2021-05-06. The instrument consists of a Fresnel-lens optical system and a focal surface composed of 36 multi-anode photomultiplier tubes, each with 64 channels, for a total of 2304 channels with single photon counting sensitivity. The telescope, with a square field-of-view of 44°, has a spatial resolution on the Earth surface of 6.3 km and saves triggered transient phenomena with a temporal resolution of 2.5 µs and 320 µs. The telescope also operates in continuous acquisition at a 40.96 ms scale.In this article, large-area night-time UV maps obtained processing the 40.96 ms data, taking averages over regions of some specific geographical areas (e.g., Europe, North America) and over the entire globe, are presented. Data are binned into 0.1° × 0.1° or 0.05° × 0.05° cells (depending on the scale of the map) over the Earth's surface. Raw data are made available in the form of tables (latitude, longitude, counts) and .kmz files (containing the .png images). These are – to the best of our knowledge – the highest sensitivity data in this wavelength range and can be of use to various disciplines.

We show, for the first time, radio measurements of the depth of shower maximum (X_{max}) of air showers induced by cosmic rays that are compared to measurements of the established fluorescence method at the same location. Using measurements at the Pierre Auger Observatory we show full compatibility between our radio and the previously published fluorescence dataset, and between a subset of air showers observed simultaneously with both radio and fluorescence techniques, a measurement setup unique to the Pierre Auger Observatory. Furthermore, we show radio X_{max} resolution as a function of energy and demonstrate the ability to make competitive high-resolution X_{max} measurements with even a sparse radio array. With this, we show that the radio technique is capable of cosmic-ray mass composition studies, both at Auger and at other experiments.

The heat shock protein 90 (Hsp90) is a molecular chaperone that controls the folding and activation of client proteins using the free energy of ATP hydrolysis. The Hsp90 active site is in its N-terminal domain (NTD). Our goal is to characterize the dynamics of NTD using an autoencoder-learned collective variable (CV) in conjunction with adaptive biasing force Langevin dynamics. Using dihedral analysis, we cluster all available experimental Hsp90 NTD structures into distinct native states. We then perform unbiased molecular dynamics (MD) simulations to construct a dataset that represents each state and use this dataset to train an autoencoder. Two autoencoder architectures are considered, with one and two hidden layers, respectively, and bottlenecks of dimension k ranging from 1 to 10. We demonstrate that the addition of an extra hidden layer does not significantly improve the performance, while it leads to complicated CVs that increase the computational cost of biased MD calculations. In addition, a two-dimensional (2D) bottleneck can provide enough information of the different states, while the optimal bottleneck dimension is five. For the 2D bottleneck, the 2D CV is directly used in biased MD simulations. For the five-dimensional (5D) bottleneck, we perform an analysis of the latent CV space and identify the pair of CV coordinates that best separates the states of Hsp90. Interestingly, selecting a 2D CV out of the 5D CV space leads to better results than directly learning a 2D CV and allows observation of transitions between native states when running free energy biased dynamics., (© 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).)

Molecular generative artificial intelligence is drawing significant attention in the drug design community, with several experimentally validated proof of concepts already published. Nevertheless, generative models are known for sometimes generating unrealistic, unstable, unsynthesizable, or uninteresting structures. This calls for methods to constrain those algorithms to generate structures in drug-like portions of the chemical space. While the concept of applicability domains for predictive models is well studied, its counterpart for generative models is not yet well-defined. In this work, we empirically examine various possibilities and propose applicability domains suited for generative models. Using both public and internal data sets, we use generative methods to generate novel structures that are predicted to be actives by a corresponding quantitative structure-activity relationships model while constraining the generative model to stay within a given applicability domain. Our work looks at several applicability domain definitions, combining various criteria, such as structural similarity to the training set, similarity of physicochemical properties, unwanted substructures, and quantitative estimate of drug-likeness. We assess the structures generated from both qualitative and quantitative points of view and find that the applicability domain definitions have a strong influence on the drug-likeness of generated molecules. An extensive analysis of our results allows us to identify applicability domain definitions that are best suited for generating drug-like molecules with generative models. We anticipate that this work will help foster the adoption of generative models in an industrial context., Competing Interests: The authors declare the following competing financial interest(s): All authors are or have been employed by Sanofi and may hold shares and/or stock options in the company., (© 2023 The Authors. Published by American Chemical Society.)

The data presented in this article are related to the research paper entitled " Observation of night-time emissions of the Earth in the near UV range from the International Space Station with the Mini-EUSO detector " (Remote Sensing of Environment, Volume 284, January 2023, 113336, https://doi.org/10.1016/j.rse.2022.113336). The data have been acquired with the Mini-EUSO detector, an UV telescope operating in the range 290-430 nm and located inside the International Space Station. The detector was launched in August 2019, and it has started operations from the nadir-facing UV-transparent window in the Russian Zvezda module in October 2019. The data presented here refer to 32 sessions acquired between 2019-11-19 and 2021-05-06. The instrument consists of a Fresnel-lens optical system and a focal surface composed of 36 multi-anode photomultiplier tubes, each with 64 channels, for a total of 2304 channels with single photon counting sensitivity. The telescope, with a square field-of-view of 44°, has a spatial resolution on the Earth surface of 6.3 km and saves triggered transient phenomena with a temporal resolution of 2.5 µs and 320 µs. The telescope also operates in continuous acquisition at a 40.96 ms scale. In this article, large-area night-time UV maps obtained processing the 40.96 ms data, taking averages over regions of some specific geographical areas (e.g., Europe, North America) and over the entire globe, are presented. Data are binned into 0.1 °  × 0.1 ° or 0.05 °  × 0.05 ° cells (depending on the scale of the map) over the Earth's surface. Raw data are made available in the form of tables (latitude, longitude, counts) and .kmz files (containing the .png images). These are - to the best of our knowledge - the highest sensitivity data in this wavelength range and can be of use to various disciplines., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

Despite growing interest and success in automated in-silico molecular design, questions remain regarding the ability of goal-directed generation algorithms to perform unbiased exploration of novel chemical spaces. A specific phenomenon has recently been highlighted: goal-directed generation guided with machine learning models produce molecules with high scores according to the optimization model, but low scores according to control models, even when trained on the same data distribution and the same target. In this work, we show that this worrisome behavior is actually due to issues with the predictive models and not the goal-directed generation algorithms. We show that with appropriate predictive models, this issue can be resolved, and molecules generated have high scores according to both the optimization and the control models., (© 2022. The Author(s).)

The binding kinetic properties of potential drugs may significantly influence their subsequent clinical efficacy. Predictions of these properties based on computer simulations provide a useful alternative to their expensive and time-consuming experimental counterparts, even at an early drug discovery stage. Herein, we perform scaled molecular dynamics (ScaledMD) simulations on a set of 27 ligands of HSP90 belonging to more than seven chemical series to estimate their relative residence times. We introduce two new techniques for the analysis and the classification of the simulated unbinding trajectories. The first technique, which helps in estimating the limits of the free energy well around the bound state, and the second one, based on a new contact map fingerprint, allow the description and the comparison of the paths that lead to unbinding. Using these analyses, we find that ScaledMD's relative residence time generally enables the identification of the slowest unbinders. We propose an explanation for the underestimation of the residence times of a subset of compounds, and we investigate how the biasing in ScaledMD can affect the mechanistic insights that can be gained from the simulations.

Flexible regions in proteins, such as loops, cannot be represented by a single conformation. Instead, conformational ensembles are needed to provide a more global picture. In this context, identifying statistically meaningful conformations within an ensemble generated by loop sampling techniques remains an open problem. The difficulty is primarily related to the lack of structural data about these flexible regions. With the majority of structural data coming from x-ray crystallography and ignoring plasticity, the conception and evaluation of loop scoring methods is challenging. In this work, we compare the performance of various scoring methods on a set of eight protein loops that are known to be flexible. The ability of each method to identify and select all of the known conformations is assessed, and the underlying energy landscapes are produced and projected to visualize the qualitative differences obtained when using the methods. Statistical potentials are found to provide considerable reliability despite their being designed to tradeoff accuracy for lower computational cost. On a large pool of loop models, they are capable of filtering out statistically improbable states while retaining those that resemble known (and thus likely) conformations. However, computationally expensive methods are still required for more precise assessment and structural refinement. The results also highlight the importance of employing several scaffolds for the protein, due to the high influence of small structural rearrangements in the rest of the protein over the modeled energy landscape for the loop., (© 2020 Wiley Periodicals LLC.)

One of the major applications of generative models for drug discovery targets the lead-optimization phase. During the optimization of a lead series, it is common to have scaffold constraints imposed on the structure of the molecules designed. Without enforcing such constraints, the probability of generating molecules with the required scaffold is extremely low and hinders the practicality of generative models for de novo drug design. To tackle this issue, we introduce a new algorithm, named SAMOA (Scaffold Constrained Molecular Generation), to perform scaffold-constrained in silico molecular design. We build on the well-known SMILES-based Recurrent Neural Network (RNN) generative model, with a modified sampling procedure to achieve scaffold-constrained generation. We directly benefit from the associated reinforcement learning methods, allowing to design molecules optimized for different properties while exploring only the relevant chemical space. We showcase the method's ability to perform scaffold-constrained generation on various tasks: designing novel molecules around scaffolds extracted from SureChEMBL chemical series, generating novel active molecules on the Dopamine Receptor D2 (DRD2) target, and finally, designing predicted actives on the MMP-12 series, an industrial lead-optimization project.

Background: The existence of conformational changes in antibodies upon binding has been previously established. However, existing analyses focus on individual cases and no quantitative study provides a more global view of potential moves and repacking, especially on recent data. The present study focuses on analyzing the conformational changes in various antibodies upon binding, providing quantitative observations to be exploited for antibody-related modeling., Methods: Cartesian and dihedral Root-Mean-Squared Deviations were calculated for different subparts of 27 different antibodies, for which X-ray structures in the bound and unbound states are available. Elbow angle variations were also calculated. Previously reported results of four docking algorithms were condensed into one score giving overall docking success for each of 16 antibody-antigen cases., Results: Very diverse movements are observed upon binding. While many loops stay very rigid, several others display side-chain repacking or backbone rearrangements, or both, at many different levels. Large conformational changes restricted to one or more antibody hypervariable loops were found to be a better indicator of docking difficulty than overall conformational variation at the antibody-antigen interface. However, the failure of docking algorithms on some almost-rigid cases shows that scoring is still a major bottleneck in docking pose prediction., Conclusions: This study is aimed to help scientists working on antibody analysis and design by giving insights into the nature and the extent of conformational changes at different levels upon antigen binding., (Copyright © 2018 European Federation of Immunological Societies. Published by Elsevier B.V. All rights reserved.)

Drug-target residence time (τ), one of the main determinants of drug efficacy, remains highly challenging to predict computationally and, therefore, is usually not considered in the early stages of drug design. Here, we present an efficient computational method, τ-random acceleration molecular dynamics (τRAMD), for the ranking of drug candidates by their residence time and obtaining insights into ligand-target dissociation mechanisms. We assessed τRAMD on a data set of 70 diverse drug-like ligands of the N-terminal domain of HSP90α, a pharmaceutically important target with a highly flexible binding site, obtaining computed relative residence times with an accuracy of about 2.3τ for 78% of the compounds and less than 2.0τ within congeneric series. Analysis of dissociation trajectories reveals features that affect ligand unbinding rates, including transient polar interactions and steric hindrance. These results suggest that τRAMD will be widely applicable as a computationally efficient aid to improving drug residence times during lead optimization.

Background: Henoch-Schönlein purpura (HSP) nephritis and primary IgA nephropathy (pIgAN) present with glomerular IgA deposits, but differ with regard to clinical features. The suspected involvement of different immune system pathways is largely unknown., Methods: This study was aimed at investigating some of the immunological features including Toll-like receptors (TLR), proteasome (PS)/immunoproteasome (iPS) switch, and the regulatory T cell system (Treg/Th17 cells) in 63 children with HSP with/without renal involvement and in 25 with pIgAN. Real-time PRC (Taqman) was used to quantify mRNA levels in peripheral blood mononuclear cells (PBMC)., Results: The expression of mRNAs encoding for TLR4 in both HSP and pIgAN was higher than in controls (HC) and in both diseases FoxP3mRNA and TGF-β1mRNA expression was significantly lower than in HC. A switch from PS to iPS (LMP2/β1) was detected only in PBMC of HSP and it correlated with the level of TLR2mRNA, which was selectively increased only in children with HSP., Conclusion: Children with HSP and pIgAN present with similar signs of engagement of the innate immunity and regulatory T cell depression. The increased immunoproteasome switch, which correlated with TLR2 activation, may suggest an innate immunity pathway peculiar to HSP vasculitic presentation. This research area also deserves further investigation for possible therapeutic applications.

Receptor tyrosine kinases (RTK) are targets for anticancer drug development. To date, only RTK inhibitors that block orthosteric binding of ligands and substrates have been developed. Here, we report the pharmacologic characterization of the chemical SSR128129E (SSR), which inhibits fibroblast growth factor receptor (FGFR) signaling by binding to the extracellular FGFR domain without affecting orthosteric FGF binding. SSR exhibits allosteric properties, including probe dependence, signaling bias, and ceiling effects. Inhibition by SSR is highly conserved throughout the animal kingdom. Oral delivery of SSR inhibits arthritis and tumors that are relatively refractory to anti-vascular endothelial growth factor receptor-2 antibodies. Thus, orally-active extracellularly acting small-molecule modulators of RTKs with allosteric properties can be developed and may offer opportunities to improve anticancer treatment., (Copyright © 2013 Elsevier Inc. All rights reserved.)

The fibroblast growth factor (FGF)/fibroblast growth factor receptor (FGFR) signaling network plays an important role in cell growth, survival, differentiation, and angiogenesis. Deregulation of FGFR signaling can lead to cancer development. Here, we report an FGFR inhibitor, SSR128129E (SSR), that binds to the extracellular part of the receptor. SSR does not compete with FGF for binding to FGFR but inhibits FGF-induced signaling linked to FGFR internalization in an allosteric manner, as shown by crystallography studies, nuclear magnetic resonance, Fourier transform infrared spectroscopy, molecular dynamics simulations, free energy calculations, structure-activity relationship analysis, and FGFR mutagenesis. Overall, SSR is a small molecule allosteric inhibitor of FGF/FGFR signaling, acting via binding to the extracellular part of the FGFR., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Objective: To evaluate the risk of upper gastrointestinal complications (UGIC) associated with drug use in the paediatric population., Methods: This study is part of a large Italian prospective multicentre study. The study population included children hospitalised for acute conditions through the emergency departments of eight clinical centres. Patients admitted for UGIC (defined as endoscopically confirmed gastroduodenal lesions or clinically defined haematemesis or melena) comprised the case series; children hospitalised for neurological disorders formed the control group. Information on drug and vaccine exposure was collected through parental interview during the children's hospitalisation. Logistic regression was used to estimate ORs for the occurrence of UGIC associated with drug use adjusted for age, clinical centre and concomitant use of any drug., Results: 486 children hospitalised for UGIC and 1930 for neurological disorders were enrolled between November 1999 and November 2010. Drug use was higher in cases than in controls (73% vs 54%; p<0.001). UGICs were associated with the use of non-steroidal anti-inflammatory drugs (NSAIDs) (adjusted OR 2.9, 95% CI 2.1 to 4.0), oral steroids (adjusted OR 2.9, 95% CI 1.7 to 4.8) and antibiotics (adjusted OR 2.3, 95% CI 1.8 to 3.1). The duration of use of these drug categories was short (range 1-8 days). Paracetamol showed a lower risk (adjusted OR 2.0, 95% CI 1.5 to 2.6) compared to ibuprofen (adjusted OR 3.7, 95% CI 2.3 to 5.9), although with partially overlapping CIs., Conclusions: NSAIDs, oral steroids and antibiotics, even when administered for a short period, were associated with an increased risk of UGIC.

Introduction. Invasive bacterial diseases continue to represent a significant burden in paediatric age, and the emergence of previously secondary bacteria and antibiotic-resistant strains requires a continuous surveillance. Materials and methods. A one-year prospective survey on laboratory confirmed community-acquired bloodstream infections (CA-LBSIs) cases admitted to an Italian tertiary specialistic paediatric Hospital. Results. Twelve cases were documented, with an incidence rate of 0.39/1,000 admissions to the Emergency Department, and of 2.9/1,000 hospitalizations to general and specialized wards. Mean age at diagnosis was 5.2 +/- 5.9 years, with 58.3% of episodes regarding children younger than years. Six episodes were caused by Gram positive and six by Gram negative bacteria, with potential vaccine-preventable pathogens responsible of 50% of CA-LBSIs. Empiric antibiotic therapy prescribed at admission was found appropriate to microbiological results in the totality of cases and administered for a mean time of 17.7 +/- 10.1 days. No resistant strains were found. All patients had a good outcome. Conclusions. Prompt collection of samples for microbiological tests together with the rapid institution of empiric antibiotic therapy are essential for the correct management of CA-LBSIs in paediatric patients. Implementation of vaccinations against the major responsible pathogens remains the most important prevention strategy.

The accurate yet efficient evaluation of the free energy profiles of ligand-target association is a long sought goal in rational drug design. Methods that calculate the free energy along realistic association pathways, such as metadynamics, have been shown to provide reliable profiles, while accounting properly for solvation and target flexibility. However, these approaches usually require prohibitive computational resources and expert human intervention. Here, we show how multiple walkers metadynamics, when performed with optimal path collective variables (PCV), provides in a predetermined amount of computer time an accurate set of free energy profiles for a series of p38 inhibitors. The chosen test set, spanning a wide range of activity, is a challenging benchmark, both for computational methods and for human intuition, as the correct order for the binding affinity cannot be easily guessed. An excellent ranking of the ligands was obtained with minimal human assistance, an important step toward a fully automated pharmaceutical work-flow.

Fibroblast growth factor (FGF) signaling regulates mammalian development and metabolism, and its dysregulation is implicated in many inherited and acquired diseases, including cancer. Heparan sulfate glycosaminoglycans (HSGAGs) are essential for FGF signaling as they promote FGF.FGF receptor (FGFR) binding and dimerization. Using novel organic synthesis protocols to prepare homogeneously sulfated heparin mimetics (HM), including hexasaccharide (HM(6)), octasaccharide (HM(8)), and decasaccharide (HM(10)), we tested the ability of these HM to support FGF1 and FGF2 signaling through FGFR4. Biological assays show that both HM(8) and HM(10) are significantly more potent than HM(6) in promoting FGF2-mediated FGFR4 signaling. In contrast, all three HM have comparable activity in promoting FGF1.FGFR4 signaling. To understand the molecular basis for these differential activities in FGF1/2.FGFR4 signaling, we used NMR spectroscopy, isothermal titration calorimetry, and size-exclusion chromatography to characterize binding interactions of FGF1/2 with the isolated Ig-domain 2 (D2) of FGFR4 in the presence of HM, and binary interactions of FGFs and D2 with HM. Our data confirm the existence of both a secondary FGF1.FGFR4 interaction site and a direct FGFR4.FGFR4 interaction site thus supporting the formation of the symmetric mode of FGF.FGFR dimerization in solution. Moreover, our results show that the observed higher activity of HM(8) relative to HM(6) in stimulating FGF2.FGFR4 signaling correlates with the higher affinity of HM(8) to bind and dimerize FGF2. Notably FGF2.HM(8) exhibits pronounced positive binding cooperativity. Based on our findings we propose a refined symmetric FGF.FGFR dimerization model, which incorporates the differential ability of HM to dimerize FGFs.

Free-energy pathway methods show great promise in computing the mode of action and the free energy profile associated with the binding of small molecules with proteins, but are generally very computationally demanding. Here we apply a novel approach based on metadynamics and path collective variables. We show that this combination is able to find an optimal reaction coordinate and the free energy profile of binding with explicit solvent and full flexibility, while minimizing human intervention and computational costs. We apply it to predict the binding affinity of a congeneric series of 5 CDK2 inhibitors. The predicted binding free energy profiles are in accordance with experiment.

The dissociative hydrolysis reaction of the methyl phosphate monoanion has been studied for the reactant species CH(3)OPO(3)H(-) (1) and CH(3)OPO(3)H(-) x H(2)O (1a) in the gas and aqueous phases by density functional theory (B3LYP) calculations. Nonspecific solvation effects were taken into account with the polarizable continuum model PCM either by solvating the gas-phase reaction paths or by performing geometry searches directly in the presence of the solvation correction. In agreement with previous theoretical studies, our gas-phase calculations indicate that proton transfer to the methoxy group of 1 is concerted with P-O bond cleavage. In contrast, optimizations performed with the PCM solvation model establish the existence of the tautomeric form CH(3)O(+)(H)PO(3)(2-) (2) as an intermediate, indicating that proton transfer and P-O bond cleavage become uncoupled in aqueous solution. The dissociative pathway of 1a is energetically favored over the dissociative pathway of 1 only when the added water molecule plays an active catalytic role in the prototropic rearrangement 1 <--> 2. In that case, it is found that the collapse (via P-O bond cleavage) of the hydrated zwitterionic form CH(3)O(+)(H)PO(3)(2-) x H(2)O (2a) is rate-determining. This collapse may occur by a stepwise mechanism through a very short-lived metaphosphate intermediate (PO(3)(-)), or by a concerted S(N)2-like displacement through a loose metaphosphate-like transition state. The present calculations do not allow a distinction to be made between these two alternatives, which are both in excellent agreement with experiment. The present study also reveals that PO(3)(-) reacts selectively with CH(3)OH and H(2)O nucleophiles in aqueous solution. However, the observed selectivity of metaphosphate is governed by solvation effects, not nucleophilicity (water being much more effective than methanol in capturing PO(3)(-)). This arises from a better solvation of the addition product H(2)O(+)PO(3)(2-) as compared to CH(3)O(+)(H)PO(3)(2-).

The experiment which was performed in different animal special confirmed that Streptococcus mutans is the main etiological agent implicated in dental caries. Nonetheless, since the use of anti-Streptococcal vaccines might lead to severe side effects, research is now focused on obtaining purified vaccines that do not create cruciate cardiac cross-reactivity.

Following the fortuitous finding of a bilateral bifid mandibular canal, suspected on the basis of orthopanoramic X-ray examination, CT of the mandibular region was carried out. As a result of this investigation, a bifid conformation of the mandibular canal was unequivocably evidenced.

328 cases of chronic tooth lesions (apical granulomas and bone cysts) were examined in the Dental Surgery of the Martini Hospital in Turin and many received a later check-up. The complete series was statistically analysed in terms of lesion frequency in the different areas of the dental arch, lesion diameters and the chances of successful surgical intervention.