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The availability of high-resolution data and accurate ground truth is essential to evaluate and compare methods and algorithms properly. Moreover, it is often difficult to acquire real data for a given application domain that is sufficiently representative and heterogeneous in terms of scene representation, acquisition conditions, point of view, etc. To overcome the limitations of available datasets, this paper presents a new synthetic, multi-purpose dataset called ENRICH for testing photogrammetric and computer vision algorithms. Compared to existing datasets, ENRICH offers higher resolution images rendered with different lighting conditions, camera orientations, scales, and fields of view. Specifically, ENRICH is composed of three sub-datasets: ENRICH-Aerial, ENRICH-Square, and ENRICH-Statue, each exhibiting different characteristics. We show the usefulness of the proposed dataset on several examples of photogrammetry and computer vision-related tasks such as: evaluation of hand-crafted and deep learning-based local features, effects of ground control points (GCPs) configuration on the 3D accuracy, and monocular depth estimation. We make ENRICH publicly available at: https://github.com/davidemarelli/ENRICH.

Cellulose nanocrystal and gold nanoparticles are assembled, in a unique way, to yield a novel modular glyconanomaterial whose surface is then easily engineered with one or two different headgroups, by exploiting a robust click chemistry route. We demonstrate the potential of this approach by conjugating monosaccharide headgroups to the glyconanomaterial and show that the sugars retain their binding capability to C-type lectin receptors, as also directly visualized by cryo-TEM.

Nonarbitrary phenomena in language, such as systematic association in the form-meaning interface, have been widely reported in the literature. Exploiting such systematic associations previous studies have demonstrated that pseudowords can be indicative of meaning. However, whether semantic activation from words and pseudowords is supported by the very same processes, activating a common semantic memory system, is currently not known. Here, we take advantage of recent progresses from computational linguistics models allowing to induce meaning representations for out-of-vocabulary strings of letters via domain-general associative-learning mechanisms applied to natural language. We combined these models with data from priming tasks, in which participants are showed two strings of letters presented sequentially one after the other and are then asked to indicate if the latter is a word or a pseudoword. In Experiment 1 we reanalyzed the data of the largest behavioral database on semantic priming, while in Experiment 2 we ran an independent replication on a new language, Italian, controlling for a series of possible confounds. Results were consistent across the two experiments and showed that the prime-word meaning interferes with the semantic pattern elicited by the target pseudoword (i.e., at increasing estimated semantic relatedness between prime word and target pseudoword, participants' reaction times increased and accuracy decreased). These findings indicate that the same associative mechanisms governing word meaning also subserve the processing of pseudowords, suggesting in turn that human semantic memory can be conceived as a distributional system that builds upon a general-purpose capacity of extracting knowledge from complex statistical patterns. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Zusammenfassung. Patienten mit psychischen Einschränkungen geraten am Arbeitsplatz häufig in Schwierigkeiten, die in letzter Konsequenz den Arbeitsplatz gefährden können. In diesen Situationen kommt einer niedrigschwelligen Kommunikation zwischen Hausarzt und Arbeitgeber eine besondere Bedeutung zu, um Eskalationen und Verlust des Arbeitsplatzes zu vermeiden. Der Artikel beschreibt die einzelnen Schritte der psychosozialen Abklärung durch den Hausarzt und seine Interaktion mit Arbeitgebern und Vorgesetzten. Bei ihren Empfehlungen greifen die Autoren auf umfangreiche eigene Forschung und ihre langjährigen Erfahrungen mit der Schulung von Verantwortlichen für die berufliche Wiedereingliederung zurück.

La presentazione e comunicazione delle caratteristiche di prodotti, così come di raccolte di oggetti o servizi, sono di fondamentale importanza in diversi campi. Il caso d’uso più evidente è dato dalla necessità di produttori e negozi di pubblicizzare le proprie offerte ai potenziali clienti: a tal scopo vengono frequentemente impiegati cataloghi cartacei o digitali. Inoltre, i cataloghi possono avere obiettivi diversi dalla pubblicizzazione di prodotti fisici, possono anche presentare della conoscenza (ad esempio in una biblioteca o in un museo) oppure dei servizi forniti da una azienda o da un individuo. Con la crescente diffusione di smartphone, tablet ed head-mounted display si aprono nuove possibilità per i cataloghi, che possono essere presentati sfruttando i concetti della Mixed Reality (MR), i quali consentono l’inserimento di elementi virtuali all'interno del mondo reale. A tal fine, questa tesi propone un framework computazionale che supporta la creazione di cataloghi MR attraverso le fasi di progettazione, sviluppo e fruizione del catalogo stesso. La creazione di cataloghi MR coinvolge diversi aspetti; in questa ricerca vengono indagati quelli chiave, inclusi la ricostruzione tridimensionale della geometria, l'acquisizione dell'aspetto del materiale, la presentazione e fruizione del catalogo MR. I ricercatori hanno ampiamente studiato questi campi separatamente; questa tesi si propone quindi di affrontare problemi specifici del loro utilizzo nella creazione di un catalogo MR. La ricostruzione 3D della geometria trova impiego nella creazione di modelli tridimensionali virtuali di elementi reali da visualizzare nei cataloghi MR: viene quindi affrontato il problema della selezione del metodo di ricostruzione 3D più adatto. Oltre alla geometria, una corretta riproduzione dell’aspetto degli elementi virtuali è fondamentale per farli apparire realistici. Questa tesi propone la progettazione e lo sviluppo di un dispositivo portatile ed economico per l'acquisizione dell'aspetto dei materiali. Infine, la presentazione del catalogo MR prevede il rendering degli elementi virtuali e l'interazione con l'utente. A tal fine, viene definito un framework generico per la creazione di cataloghi MR. L’impiego del framework proposto viene investigato in possibili scenari d’uso. Presentation and communication of characteristics about products and, in general, collections of objects or services are of crucial relevance in several fields. The most prominent application is given by the necessity of manufacturers and stores to advertise their offers to potential customers. To this purpose, paper or digital catalogs are often employed. Furthermore, catalogs may have goals that differ from the advertisement of physical products. They may also build on the presentation of knowledge (e.g. in a library or museum) or refer to services provided by some company or individual. With the growing diffusion of smartphones, tablets, and head-mounted displays, new possibilities arise for the catalogs to be presented by taking advantage of Mixed Reality (MR) concepts that allow the blending of virtual elements within the real world. To this end, this thesis proposes a computational framework that supports the creation of MR catalogs throughout the stages of design, development, and fruition of the catalog itself. The creation of MR catalogs involves several aspects. The key ones among them are investigated in this research. These include 3D geometry reconstruction, material appearance acquisition, and presentation of the MR catalog. Researchers have largely investigated these fields separately. This thesis aims to tackle specific problems of their usage in the creation of an MR catalog. 3D geometry reconstruction finds use in the creation of virtual 3D models of real elements for display in MR catalogs. The problem of selecting the most suitable 3D reconstruction method is addressed. In addition to 3D geometry, the look and feel of virtual elements are crucial to make them appear realistic. This thesis proposes the design and development of a low cost portable device for material appearance acquisition. Finally, the presentation of the MR catalog involves the rendering of the virtual elements and interaction with the user. In order to accomplish that, a generic framework for the creation of MR catalogs is defined. Possible use case scenarios of the proposed framework in the development of MR catalog applications are investigated.

People with schizophrenia spectrum disorders (SSD) show anomalies in language processing with respect to “who is doing what” in an action. This linguistic behavior is suggestive of an atypical representation of the formal concepts of “Agent” in the lexical representation of a verb, i.e., its thematic grid. To test this hypothesis, we administered a silent-reading task with sentences including a semantic violation of the animacy trait of the grammatical subject to 30 people with SSD and 30 healthy control participants (HCs). When the anomalous grammatical subject was the Agent of the event, a significant increase of Gaze Duration was observed in HCs, but not in SSDs. Conversely, when the anomalous subject was a Theme, SSDs displayed an increased probability of go-back movements, unlike HCs. These results are suggestive of a higher tolerability for anomalous Agents in SSD compared to the normal population. The fact that SSD participants did not show a similar tolerability for anomalous Themes rules out the issue of an attention deficit. We suggest that general communication abilities in SSD might benefit from explicit training on deep linguistic structures.

Massively multilingual models such as mBERT and XLM-R are increasingly valued in Natural Language Processing research and applications, due to their ability to tackle the uneven distribution of resources available for different languages. The models’ ability to process multiple languages relying on a shared set of parameters raises the question of whether the grammatical knowledge they extracted during pre-training can be considered as a data-driven cross-lingual grammar. The present work studies the inner workings of mBERT and XLM-R in order to test the cross-lingual consistency of the individual neural units that respond to a precise syntactic phenomenon, that is, number agreement, in five languages (English, German, French, Hebrew, Russian). We found that there is a significant overlap in the latent dimensions that encode agreement across the languages we considered. This overlap is larger (a) for long- vis-à-vis short-distance agreement and (b) when considering XLM-R as compared to mBERT, and peaks in the intermediate layers of the network. We further show that a small set of syntax-sensitive neurons can capture agreement violations across languages; however, their contribution is not decisive in agreement processing.

An active and stable hydrogen evolution electrocatalyst composed of MoO2 surface nanorod arrays was prepared using high-temperature reductive annealing. Electrodes with an area of 78.5 cm2 were evaluated in a three-cell AEM electrolyser stack.

The chemistry of (P,C)-cyclometalated complexes derived from naphthyl phosphines [Np(P,C)M] is presented and analysed in this review.

Whether adjuvant therapy with aromatase inhibitors (AIs) causes sleep disturbances or not in postmenopausal women with early breast cancer (EBC) is still a controversial issue.Between March 2014 and November 2017, validated questionnaires for assessing insomnia, anxiety, depression, quality of life (QoL) and restless legs syndrome (RLS) were administered to 160 EBC patients at baseline and after 3, 6, 12, and 24 months of AI therapy.AI therapy significantly decreased the patients' QoL, but did not influence insomnia, anxiety or depression. However, it significantly increased the frequency and severity of RLS. Patients with RLS at baseline (19%) or who developed RLS during AI therapy (26.3%) reported statistically lower quality of sleep, higher anxiety and depression, and worse QoL compared to patients who never reported RLS (54.7%).Although AI therapy does not affect sleep quality, it may increase RLS frequency. The presence of RLS could identify a group of EBC patients who may benefit from psychological support.

This paper is concerned with near-optimal source search problem using a multiagent system in cluttered indoor environments. The goal of the problem is to maximize the detection probability within the minimum search time. We propose a two-stage strategy to achieve this goal. In the first stage, a greedy approach is used to define a set of grid cells with the aim of maximizing the detection probability. In the second stage, an iterative branch-and-bound procedure is used to design the search paths of all agents so that all grid cells are visited by one agent and the largest search path among all agents is minimized. Simulation results show that the proposed search algorithm has better performance in terms of exploration time compared to other existing methods.

Aims Hypertensive disorders of pregnancy (HDP) occur in 10% of pregnancies in the general population, pre-eclampsia specifically in 3–5%. Hypertensive disorders of pregnancy may have a high prevalence in, and be poorly tolerated by, women with heart disease. Methods and results The prevalence and outcomes of HDP (chronic hypertension, gestational hypertension or pre-eclampsia) were assessed in the ESC EORP ROPAC (n = 5739), a worldwide prospective registry of pregnancies in women with heart disease. The overall prevalence of HDP was 10.3%, made up of chronic hypertension (5.9%), gestational hypertension (1.3%), and pre-eclampsia (3%), with significant differences between the types of underlying heart disease (P Conclusion Hypertensive disorders of pregnancy and pre-eclampsia rates were higher in women with CMP, IHD, and PAH than in the general population. Adverse outcomes were increased in women with HDP, and maternal mortality was strikingly high in women with pre-eclampsia. The combination of HDP and heart disease should prompt close surveillance in a multidisciplinary context and the diagnosis of pre-eclampsia requires hospital admission and continued monitoring during the post-partum period.

The distributed optimization problem is set up in a collection of nodes interconnected via a communication network. The goal is to find the minimizer of a global objective function formed by the addition of partial functions locally known at each node. A number of methods are available for addressing this problem, having different advantages. The goal of this work is to achieve the maximum possible convergence rate. As the first step towards this end, we propose a new method which we show converges faster than other available options. As with most distributed optimization methods, convergence rate depends on a step size parameter. As the second step towards our goal we complement the proposed method with a fully distributed method for estimating the optimal step size that maximizes convergence speed. We provide theoretical guarantees for the convergence of the resulting method in a neighborhood of the solution. Also, for the case in which the global objective function has a single local minimum, we provide a different step size selection criterion together with theoretical guarantees for convergence. We present numerical experiments showing that, when using the same step size, our method converges significantly faster than its rivals. Experiments also show that the distributed step size estimation method achieves an asymptotic convergence rate very close to the theoretical maximum.

One of the crucial aspects in design of a landfill capping is the interface behavior between the different layers of the cover system, from levelling layer above waste up to the topsoil. Design guidelines and international codes require a geotechnical stability analysis to be performed along every interface. The critical interface is the one which gives the minimum shear resistance, in terms of friction angle and adhesion. Evaluation of the correct values to be used is then essential. Shear resistance at the interface between different geosynthetics or between a geosynthetic and a soil can be measured through laboratory tests. Testing methods are EN ISO 12957-1 and ASTM D5321 (for direct shear test) and EN ISO 12957-2 (for inclined plane). The paper briefly describes direct shear and inclined plane testing methods and enhances pros and cons. In the last 25 years the authors have coordinated a great number of the above tests with different types of geosynthetics and soils. The main results of these tests are reported in the paper, summarizing the values obtained with contact interface between different products belonging to the same families. The purpose of this work is to validate the already big database of interface strength measured with direct shear tests and to evaluate the differences with the results obtained for the different types of tests. This can give to designers the chance to have a critical approach toward the most suitable testing method to be used according to the specific needs of a project.

Dilated cardiomyopathy (DCM) is typically defined by left ventricular dilation and systolic dysfunction in the absence of a clear precipitant. Idiopathic disease is common; up to 50% of patients with DCM have no cause found despite imaging, genetic and biopsy assessments. Treatment remains focused on managing symptoms, reducing the risk of sudden cardiac death and ameliorating the structural and electrical complications of disease progression. In the absence of aetiology-specific treatments, the condition remains associated with a poor prognosis; mortality is approximately 40% at 10 years. The role of immune-mediated inflammatory injury in the development and progression of DCM was first proposed over 30 years ago. Despite the subsequent failures of three large clinical trials of immunosuppressive treatment (ATTACH, RENEWAL and the Myocarditis Treatment Trial), evidence for an abnormal adaptive immune response in DCM remains significant. In this review, we summarise and discuss available evidence supporting immune dysfunction in DCM, with a specific focus on cellular immunity. We also highlight current clinical and experimental treatments. We propose that the success of future immunosuppressive treatment trials in DCM will be dependent on the deep immunophenotyping of patients, to identify those with active inflammation and/or an abnormal immune response who are most likely to respond to therapy.

Optimizing the Diels-Alder (DA) reaction for aqueous coupling has resulted in practical methods to link molecules such as drugs and diagnostic agents to proteins. Both normal electron demand (NED) and inverse electron demand (IED) DA coupling schemes have been employed, but neither mechanism entails a common multipurpose reactive group. This report focuses on expanding the bioconjugation toolbox for cyclopentadiene through the identification of reactive groups that couple through NED or IED mechanisms in aqueous solution. Dienophiles and tetrazine derivatives were screened for reactivity and selectivity toward antibodies bearing cyclopentadiene amino acids to yield bioconjugates. Twelve NED dienophiles and four tetrazine-based IED substrates were identified as capable of practical biocoupling. Furthermore, tetrazine ligation to cyclopentadiene occurred at a rate of 3.3 ± 0.5 Msup-1/supssup-1/supand was capable of bioorthogonal transformations, as evidenced by the selective protein labeling in serum. Finally, an antibody-drug conjugate (ADC)-bearing monomethyl auristatin E was prepared via tetrazine conjugation to cyclopentadiene. The resulting ADC was stable and demonstrated potent activityiin vitro/i. These findings expand the utility of cyclopentadiene as a tool to couple entities to proteins via dual DA addition mechanisms.

Introdução: Por constituir um contexto social e sanitário distinto, a abordagem das influências da pandemia da COVID-19 sobre o aleitamento materno torna-se fundamental para a compreensão do comportamento humano mediante a essa prática de extrema importância para o desenvolvimento do lactente. Objetivo: Verificar o conhecimento e a prática da amamentação de nutrizes no contexto da COVID-19. Métodos: Trata-se de um estudo qualitativo, do tipo descritivo, realizado com nutrizes usuárias do Banco de Leite Humano, iniciada após prévia aprovação do Comitê de Ética em Pesquisa e consentimento das mulheres entrevistadas. Resultados: A pandemia trouxe fragilidades na assistência direta ao binômio mãe/filho e isoladas vivenciaram muitas dificuldades no processo de amamentação. Apesar da insuficiência de conhecimento sobre o conceito de AME, as nutrizes foram convincentes quanto às vantagens da amamentação para saúde do lactente e buscaram novas estratégias para auxílio assistencial, tais como teleconsultas nos Bancos de Leite Humano, consultoras de amamentação domiciliar e lives profissionais. Conclusão: As limitações assistenciais dificultaram o acesso direto aos serviços de saúde, potencializando novas estratégias de informação e auxílio no manejo clínico da amamentação. A busca pelo conhecimento e apoio profissional pelas nutrizes foi valiosa para superar dificuldades e garantir as taxas de aleitamento materno exclusivo e oportuno no contexto da pandemia COVID-19.

Modelling the dynamic response of civil structures is vital for many applications, including structural health monitoring, reliability analysis and design optimization. These systems often feature responses governed by highly uncertain exogenous excitations, for example, ground motions, wind, or wave loads. To quantify the effects of this uncertainty, many evaluations of the underlying numerical models are usually required. Therefore, in the presence of expensive simulations, surrogate models become necessary as a fast-to-evaluate proxy. However, for many time-dependent systems it is difficult to build surrogates that provide precise and stable predictions. Additional non-linearity is introduced by controllers that actively change the system properties depending on multiple state variables. One approach to surrogate such systems is to use non-linear auto-regressive with exogenous input (NARX) models, which exploit the temporal coherence of the system response and its strong dependence on the exogenous excitations. Constructing a stable and accurate NARX model, however, is often unattainable when the system response is highly non-linear, the dimensionality of the exogenous input is high, or when data is scarce. In this work, we present a novel approach called manifold NARX (mNARX) to tackle this class of problems. mNARX takes advantage of prior knowledge about the physics of the system to incrementally build an input manifold suitable for efficiently surrogating the dynamic system response. We showcase its efficiency on an aero-servo-elastic (ASE) wind turbine simulation benchmark. ASE simulators take turbulent wind as input, and model selected time-dependent quantities of interest (QoIs), such as power output or blade and tower loads. The exogenous input wind field is a spatio-temporal random field with high spatial dimensionality. Furthermore, most QoIs are affected by both the control system and rotor orientation, making the mapping from the exogenous inputs to the QoIs even more complex. Within the mNARX framework, we first predict physically meaningful auxiliary quantities, such as control and state variables, based on a NARX model built on a truncated set of time-dependent spectral coefficients of the wind input. We then use these predictions in conjunction with the spectral coefficients to form an exogenous input manifold onto which the final QoI NARX model is constructed. We demonstrate that this sequential approach leads to accurate predictions over a long time horizon, even in the presence of complex localized spectral features. Finally, we show that the training of this chain of surrogate models and its evaluation are computationally inexpensive, up to multiple orders of magnitude cheaper than its original ASE simulator counterpart.

In this study, we propose a novel surrogate modelling approach to efficiently and accurately approximate the response of complex dynamical systems driven by time-varying exogenous excitations over extended time periods. Our approach, that we name \emph{manifold nonlinear autoregressive modelling with exogenous input} (mNARX), involves constructing a problem-specific exogenous input manifold that is optimal for constructing autoregressive surrogates. The manifold, which forms the core of mNARX, is constructed incrementally by incorporating the physics of the system, as well as prior expert- and domain- knowledge. Because mNARX decomposes the full problem into a series of smaller sub-problems, each with a lower complexity than the original, it scales well with the complexity of the problem, both in terms of training and evaluation costs of the final surrogate. Furthermore, mNARX synergizes well with traditional dimensionality reduction techniques, making it highly suitable for modelling dynamical systems with high-dimensional exogenous inputs, a class of problems that is typically challenging to solve.Since domain knowledge is particularly abundant in physical systems, such as those found in engineering applications, mNARX is well suited for these applications. We demonstrate that mNARX outperforms traditional autoregressive surrogates in predicting the response of a classical coupled spring-mass system excited by a one-dimensional random excitation. Additionally, we show that mNARX is well suited for emulating very high-dimensional time- and state-dependent systems, even when affected by active controllers, by surrogating the dynamics of a realistic aero-servo-elastic onshore wind turbine simulator. In general, our results demonstrate that mNARX offers promising prospects for modelling complex dynamical systems, in terms of accuracy and efficiency.

Surrogate models have become a standard tool in uncertainty quantification to emulate the response of real-world systems at a low computational cost. Within the HIPERWIND EU project, we recently proposed a class of surrogate models that can accurately model the time-dependent response of dynamical state- and control system-dependent structures excited by high-dimensional exogenous inputs, such as wind turbines (Dimitrov et al. (2022)). We achieved this by combining spectral compression strategies to handle the high spatial dimensionality of the inputs, with non-linear auto-regressive with exogenous input (NARX) models to capitalize on the temporal coherence of both inputs and outputs. In this novel approach called manifold NARX (mNARX) we build an input manifold consisting of the compressed exogenous input and several intermediate NARX models of problem-specific and modelling-relevant quantities. This manifold is then used as an exogenous input to a final NARX surrogate that is able to predict the target quantity of interest. This approach can emulate highly non-linear systems with simple, fast-to-train and fast-to-evaluate polynomial NARX base models. As a drawback of using multiple NARX models and dimensionality reduction steps, the space of tunable parameters in this surrogate modelling chain grows large. Consequently, despite the fast polynomial models, the process of model structure selection and parameter estimation can become laborious. In this contribution, we extend and partially automate the mNARX emulator design and training by first assembling a large set of potentially important features. We then capitalize on techniques from the machine learning literature to perform an automated feature selection and dimensionality reduction step. These techniques are applied with the goal of finding a suitable manifold that minimizes the error of the final NARX surrogate. Algorithmically, we adopt the DRSM (dimensionality reduction for surrogate modelling) framework originally developed in Lataniotis et al. (2020). DRSM is a state-of-the-art supervised algorithm that can jointly identify and train both non-linear dimensionality reduction techniques and surrogate models, with the goal to maximize surrogate modelling accuracy. Because DRSM is a data-driven framework that can be easily automated, it is ideal to circumvent the disadvantages of mNARX. We demonstrate that this extended mNARX can yield comparably or more accurate surrogates on a set of complex time-dependent benchmark systems, than the originally hand-tuned mNARX. Exemplary results of a wind turbine case study are shown in the figure. Further, we show that the extended mNARX remains fast despite the computationally intensive DRSM algorithm. By providing a more data-driven, less elaborate surrogate modelling procedure, we also push the application of mNARX toward other areas of research and industry. [1] Dimitrov, N., S. Marelli, and S. Schär (2022). Novel surrogate modelling approaches for wind turbine reliability assessment. H2020 Project HIPERWIND. Deliverable D4.1. [2] Lataniotis, C., S. Marelli, and B. Sudret (2020). Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approach. International Journal for Uncertainty Quantification 10 (1), 55–82.

DNA-based data storage emerged in this decade as a promising solution for long data durability, low power consumption, and high density. However, such technology has not yet reached a good maturity level, requiring many investigations to improve the information encoding and decoding processes. Simulations can be key to overcoming the time and the cost burdens of the many experiments imposed by thorough design space explorations. In response to this, we have developed a DNA storage simulator (DNAssim) that allows simulating the different steps in the DNA storage pipeline using a proprietary software infrastructure written in Python/C language. Among the many operations performed by the tool, the edit distance calculation used during clustering operations has been identified as the most computationally intensive task in software, thus calling for hardware acceleration. In this work, we demonstrate the integration in the DNAssim framework of a dedicated FPGA hardware accelerator based on the Xilinx VC707 evaluation kit to boost edit distance calculations by up to 11 times with respect to a pure software approach. This materializes in a clustering simulation latency reduction of up to 5.5 times and paves the way for future scale-out DNA storage simulation platforms.

Calls for solidarity have been an ubiquitous feature in the response to the COVID-19 pandemic. However, we know little about how people have thought of and practised solidarity in their everyday lives since the beginning of the pandemic. What role does solidarity play in people’s lives, how does it relate to COVID-19 public health measures and how has it changed in different phases of the pandemic? Situated within the medical humanities at the intersection of philosophy, bioethics, social sciences and policy studies, this article explores how the practice-based understanding of solidarity formulated by Prainsack and Buyx helps shed light on these questions. Drawing on 643 qualitative interviews carried out in two phases (April–May 2020 and October 2020) in nine European countries (Austria, Belgium, France, Germany, Ireland, Italy, The Netherlands, German-speaking Switzerland and the UK), the data show that interpersonal acts of solidarity are important, but that they are not sustainable without consistent support at the institutional level. As the pandemic progressed, respondents expressed a longing for more institutionalised forms of solidarity. We argue that the medical humanities have much to gain from directing their attention to individual health issues, and to collective experiences of health or illness. The analysis of experiences through a collective lens such as solidarity offers unique insights to understandings of the individual and the collective. We propose three essential advances for research in the medical humanities that can help uncover collective experiences of disease and health crises: (1) an empirical and practice-oriented approach alongside more normative approaches; (2) the confidence to make recommendations for practice and policymaking and (3) the pursuit of cross-national and multidisciplinary research collaborations.

The mechanical properties of 3D printed materials produced with additive manufacturing depend on the printing process, which is controlled by several tuning parameters. This paper focuses on Binder Jet technology and studies the influence of printing resolution, activator percentage, droplet mass, and printing speed on the compressive and flexural strength, as well as on the Young's modulus of the bulk printed material. As the number of tests required using a one factor at a time approach is not time efficient, a Design of Experiments approach was applied and optimal points in the 4-dimensional parameter space were selected. Then Sobol' sensitivity indices were calculated for each mechanical property through polynomial chaos expansion. We found that the mechanical properties are primarily controlled by the binder content of the bulk material, namely printing resolution and droplet mass. A smaller dependence on the activator percentage was also found. The printing speed does not affect the mechanical properties studied. In parallel, curing of the specimens at 80-115 degrees C for 30-120 min increases their strength., Progress in Additive Manufacturing, ISSN:2363-9512

Recent evidence has indicated that spatial representations, such as large-scale geographical maps, can be retrieved from natural language alone through cognitively plausible distributional-semantic models based on non-spatial associative-learning mechanisms. Here, we demonstrate that analogous spatial maps can be extracted from purely linguistic data even at the medium-scale level. Our results indeed show that it is possible to retrieve the underground maps of five European cities from linguistic data, suggesting in turn that the ability to reconstruct spatial maps from language does not strictly depend on the scale being mapped. Furthermore, we show that different spatial representations (i.e., with information encoded either in terms of relative spatial distances or absolute locations defined by coordinate axes) can be retrieved from natural language. These findings contribute to a growing body of research that challenges the traditional view of cognitive maps as exclusively relying on specialized spatial computations and highlight the importance of non-spatial associative-learning mechanisms within the linguistic environment in the setting of spatial representations.

International audience; The coordination of secondary phosphine oxides (SPO) was shown to efficiently promote the activation of C(sp2)−I bonds by gold, as long as a base is added (NEt3, K2CO3). These transformations stand as a new type of chelation-assisted oxidative addition to gold. The role of the base and the influence of the electronic properties of the P-ligand were analyzed computationally. Accordingly, the oxidative addition was found to be dominated by Au→(Ar−I) backdonation. In this case, gold behaves similarly to palladium, suggesting that the inverse electron flow reported previously (with prevailing (Ar−I)→Au donation, resulting in faster reactions of electron-enriched substrates) is a specific feature of electron-deficient cationic gold(I) complexes. The reaction gives straightforward access to (P=O,C)-cyclometallated Au(III) complexes. The possibility to chemically derivatize the SPO moiety at Au(III) was substantiated by protonation and silylation reactions.

The Athena X-ray Integral Unit (X-IFU) is the high resolution X-ray spectrometer, studied since 2015 for flying in the mid-30s on the Athena space X-ray Observatory, a versatile observatory designed to address the Hot and Energetic Universe science theme, selected in November 2013 by the Survey Science Committee. Based on a large format array of Transition Edge Sensors (TES), it aims to provide spatially resolved X-ray spectroscopy, with a spectral resolution of 2.5 eV (up to 7 keV) over an hexagonal field of view of 5 arc minutes (equivalent diameter). The X-IFU entered its System Requirement Review (SRR) in June 2022, at about the same time when ESA called for an overall X-IFU redesign (including the X-IFU cryostat and the cooling chain), due to an unanticipated cost overrun of Athena. In this paper, after illustrating the breakthrough capabilities of the X-IFU, we describe the instrument as presented at its SRR, browsing through all the subsystems and associated requirements. We then show the instrument budgets, with a particular emphasis on the anticipated budgets of some of its key performance parameters. Finally we briefly discuss on the ongoing key technology demonstration activities, the calibration and the activities foreseen in the X-IFU Instrument Science Center, and touch on communication and outreach activities, the consortium organisation, and finally on the life cycle assessment of X-IFU aiming at minimising the environmental footprint, associated with the development of the instrument. Thanks to the studies conducted so far on X-IFU, it is expected that along the design-to-cost exercise requested by ESA, the X-IFU will maintain flagship capabilities in spatially resolved high resolution X-ray spectroscopy, enabling most of the original X-IFU related scientific objectives of the Athena mission to be retained. (abridged)., Comment: 48 pages, 29 figures, Accepted for publication in Experimental Astronomy with minor editing

Despite its widespread use to measure functional lateralization of language in healthy subjects, the neurocognitive bases of the visual field effect in lateralized reading are still debated. Crucially, the lack of knowledge on the nature of the visual field effect is accompanied by a lack of knowledge on the relative impact of psycholinguistic factors on its measurement, thus potentially casting doubts on its validity as a functional laterality measure. In this study, an eye-tracking-controlled tachistoscopic lateralized lexical decision task (Experiment 1) was administered to 60 right-handed and 60 left-handed volunteers and word length, orthographic neighborhood, word frequency, and imageability were manipulated. The magnitude of visual field effect was bigger in right-handed than in left-handed participants. Across the whole sample, a visual field-by-frequency interaction was observed, whereby a comparatively smaller effect of word frequency was detected in the left visual field/right hemisphere (LVF/RH) than in the right visual field/left hemisphere (RVF/LH). In a subsequent computational study (Experiment 2), efficient (LH) and inefficient (RH) activation of lexical orthographic nodes was modelled by means of the Naïve Discriminative Learning approach. Computational data simulated the effect of visual field and its interaction with frequency observed in the Experiment 1. Data suggest that the visual field effect can be biased by word frequency. Less distinctive connections between orthographic cues and lexical/semantic output units in the RH than in the LH can account for the emergence of the visual field effect and its interaction with word frequency. Remarkably, the size of the interaction between the visual field effect and word frequency did not differ in the two hand preference groups, suggesting that psycholinguistic factors don't hamper the measurement of interindividual differences in the functional lateralization of language.

Background: Autoimmunity is increasingly recognized as a key contributing factor in heart muscle diseases. The functional features of cardiac autoimmunity in humans remain undefined because of the challenge of studying immune responses in situ. We previously described a subset of c-mesenchymal epithelial transition factor (c-Met)–expressing (c-Met + ) memory T lymphocytes that preferentially migrate to cardiac tissue in mice and humans. Methods: In-depth phenotyping of peripheral blood T cells, including c-Met + T cells, was undertaken in groups of patients with inflammatory and noninflammatory cardiomyopathies, patients with noncardiac autoimmunity, and healthy controls. Validation studies were carried out using human cardiac tissue and in an experimental model of cardiac inflammation. Results: We show that c-Met + T cells are selectively increased in the circulation and in the myocardium of patients with inflammatory cardiomyopathies. The phenotype and function of c-Met + T cells are distinct from those of c-Met–negative (c-Met − ) T cells, including preferential proliferation to cardiac myosin and coproduction of multiple cytokines (interleukin-4, interleukin-17, and interleukin-22). Furthermore, circulating c-Met + T cell subpopulations in different heart muscle diseases identify distinct and overlapping mechanisms of heart inflammation. In experimental autoimmune myocarditis, elevations in autoantigen-specific c-Met + T cells in peripheral blood mark the loss of immune tolerance to the heart. Disease development can be halted by pharmacologic c-Met inhibition, indicating a causative role for c-Met + T cells. Conclusions: Our study demonstrates that the detection of circulating c-Met + T cells may have use in the diagnosis and monitoring of adaptive cardiac inflammation and definition of new targets for therapeutic intervention when cardiac autoimmunity causes or contributes to progressive cardiac injury.

A key issue in language processing is how we recognize and understand words in sentences. Research on sentence reading indicates that the time we need to read a word depends on how (un)expected it is. Research on single word recognition shows that each word also has its own recognition dynamics based on the relation between its orthographic form and its meaning. It is not clear, however, how these sentence-level and word-level dynamics interact. In the present study, we examine the joint impact of these sources of information during sentence reading. We analyze existing eye-tracking and self-paced reading data (Frank et al., 2013, Behavior Research Methods, 45[4], 1182–1190) to investigate the interplay of sentence-level prediction (operationalized as Surprisal) and word Orthography-Semantics Consistency in activating word meaning in sentence processing. Results indicate that both Surprisal and Orthography-Semantics Consistency exert an influence on several reading measures. The shape of the observed interaction differs, but the results give compelling indication for a general trade-off between expectations based on sentence context and cues to meaning from word orthography.

A partire dal dibattito sul concetto di spatial turn, questo articolo tenterà di interrogare il concetto da un punto di vista empirico, ovvero allontanandosi dalle formulazioni che lo identificano unicamente come una pura svolta teorica, per pensarlo come categoria analitica, capace di fornire un quadro di analisi utile a leggere quanto emerso in un campo di ricerca etnografico all'interno di una periferia napoletana. Un'esplorazione del concetto a partire dalla ricerca sul campo permette, infatti, di pensare alla nozione al di là del suo solo statuto teorico, cercando di coglierne le manifestazioni nella quotidianità di un luogo. Si cercherà, dunque, di considerare lo spazio in quanto variabile esplicativa fondamentale, cercando di comprendere chi pone al centro del proprio agire lo spazio (chi fa il turn?) e le ragioni di questa centralità dello spazio (perché lo fa? verso quale/i spatial?).

La inmunoterapia pasiva como tratamiento contra el virus SARS-CoV-2 (del inglés, severe acute respiratory syndrome coronavirus-2) requiere de la cuantificación de la capacidad neutralizante de los anticuerpos para el desarrollo de terapias efectivas. Para ello, es necesario el uso de partículas virales pseudotipadas no replicativas. Sin embargo, los reactivos de transfección necesarios para su producción son costosos y, en consecuencia, resulta una limitante al momento de escalar la producción. Una manera de eludir esta limitante es el uso de la polietilenimina (PEI), un polímero catiónico de bajo costo. El objetivo del trabajo consistió en optimizar un ensayo de transfección transitoria de bajo costo para producir pseudopartículas virales que expresen la proteína Spike del virus SARS-CoV-2. Se realizaron ensayos de transfección en células HEK-293T evaluando diferentes concentraciones de PEI y tiempos de incubación. La transfección se evaluó mediante microscopía de epifluorescencia y citometría de flujo, mostrando que la condición de transfección que tiende a arrojar mejores resultados fue luego de 48 horas de incubación utilizando una relación 1/10 (ADNp/PEI). En conclusión, se ha logrado adaptar un protocolo para producir partículas lentivirales que expresan Spike utilizando un reactivo de transfección de bajo costo. Esto permitiría escalar la producción para, posteriormente, diseñar una plataforma para evaluar terapias para COVID-19.