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Background The integration of empirical data in computational frameworks designed to model the spread of infectious diseases poses a number of challenges that are becoming more pressing with the increasing availability of high-resolution information on human mobility and contacts. This deluge of data has the potential to revolutionize the computational efforts aimed at simulating scenarios, designing containment strategies, and evaluating outcomes. However, the integration of highly detailed data sources yields models that are less transparent and general in their applicability. Hence, given a specific disease model, it is crucial to assess which representations of the raw data work best to inform the model, striking a balance between simplicity and detail. Methods We consider high-resolution data on the face-to-face interactions of individuals in a pediatric hospital ward, obtained by using wearable proximity sensors. We simulate the spread of a disease in this community by using an SEIR model on top of different mathematical representations of the empirical contact patterns. At the most detailed level, we take into account all contacts between individuals and their exact timing and order. Then, we build a hierarchy of coarse-grained representations of the contact patterns that preserve only partially the temporal and structural information available in the data. We compare the dynamics of the SEIR model across these representations. Results We show that a contact matrix that only contains average contact durations between role classes fails to reproduce the size of the epidemic obtained using the high-resolution contact data and also fails to identify the most at-risk classes. We introduce a contact matrix of probability distributions that takes into account the heterogeneity of contact durations between (and within) classes of individuals, and we show that, in the case study presented, this representation yields a good approximation of the epidemic spreading properties obtained by using the high-resolution data. Conclusions Our results mark a first step towards the definition of synopses of high-resolution dynamic contact networks, providing a compact representation of contact patterns that can correctly inform computational models designed to discover risk groups and evaluate containment policies. We show in a typical case of a structured population that this novel kind of representation can preserve in simulation quantitative features of the epidemics that are crucial for their study and management.

Understanding the complexities of public opinion is crucial for a green-energy transition. This present study examines the sentiment of public opinion towards various energy technologies on Twitter during the Dutch 2021 general elections. A dataset comprising 186,822 tweets and profile descriptions was analyzed using two automated text classifiers to explore how individuals with different self-proclaimed identities perceive green-energy technologies. The analysis involved the application of the sentiment and social identity classifier models, followed by a frequency and co-occurrence analysis. The findings revealed a negative overall sentiment towards green-energy technologies in the Twitter discourse. It further showed that perceptions may differ depending on a technology’s development stage, with emerging technologies generally receiving more favorable views compared to established ones. Furthermore, it was found that, although there is a general trend of negative sentiment based on political identity, and positive sentiment based on occupational identity, this trend did not consistently apply to specific energy technologies. This discrepancy can likely be attributed to varying implementation effects and contextual situations associated with the technologies. The findings suggest that personalized communication strategies for specific social groups may be beneficial for understanding and addressing public opinions, needs, and concerns within the energy transition. The complexity of understanding public opinion in the context of green-energy highlights the need for a nuanced approach in future research.

In recent years polynuclear transition metal molecules have been synthesized and proposed for example as magnetic storage units or qubits in quantum computers. They are known as molecular nanomagnets and belong in the class of mesoscopic systems, which are large enough to display many-body effects but small enough to be away from the finite-size scaling regime. It is a challenge for physicists to understand their magnetic properties, and for synthetic chemists to efficiently tailor them by assembling fundamental units. They are complementary to artificially engineered spin systems for surface deposition, as they support a wider variety of complex states in their low energy spectrum. Here a few characteristic examples of molecular nanomagnets showcasing unusual many-body effects are presented. Antiferromagnetic wheels and chains can be described in classical terms for small sizes and large spins to a great extent, even though their wavefunctions do not significantly overlap with semiclassical configurations. Hence, surprisingly, for them the transition from the classical to the quantum regime is blurred. A specific example is the Fe18 wheel, which displays quantum phase interference by allowing Néel vector tunneling in a magnetic field. Finally, the Co5Cl single-molecule magnet is shown to have an unusual anisotropic response to a magnetic field.

Spreading processes represent a very efficient tool to investigate the structural properties of networks and the relative importance of their constituents, and have been widely used to this aim in static networks. Here we consider simple disease spreading processes on empirical time-varying networks of contacts between individuals, and compare the effect of several immunization strategies on these processes. An immunization strategy is defined as the choice of a set of nodes (individuals) who cannot catch nor transmit the disease. This choice is performed according to a certain ranking of the nodes of the contact network. We consider various ranking strategies, focusing in particular on the role of the training window during which the nodes' properties are measured in the time-varying network: longer training windows correspond to a larger amount of information collected and could be expected to result in better performances of the immunization strategies. We find instead an unexpected saturation in the efficiency of strategies based on nodes' characteristics when the length of the training window is increased, showing that a limited amount of information on the contact patterns is sufficient to design efficient immunization strategies. This finding is balanced by the large variations of the contact patterns, which strongly alter the importance of nodes from one period to the next and therefore significantly limit the efficiency of any strategy based on an importance ranking of nodes. We also observe that the efficiency of strategies that include an element of randomness and are based on temporally local information do not perform as well but are largely independent on the amount of information available.

The integration of empirical data in computational frameworks to model the spread of infectious diseases poses challenges that are becoming pressing with the increasing availability of high-resolution information on human mobility and contacts. This deluge of data has the potential to revolutionize the computational efforts aimed at simulating scenarios and designing containment strategies. However, the integration of detailed data sources yields models that are less transparent and general. Hence, given a specific disease model, it is crucial to assess which representations of the raw data strike the best balance between simplicity and detail. We consider high-resolution data on the face-to-face interactions of individuals in a hospital ward, obtained by using wearable proximity sensors. We simulate the spread of a disease in this community by using an SEIR model on top of different mathematical representations of the contact patterns. We show that a contact matrix that only contains average contact durations fails to reproduce the size of the epidemic obtained with the high-resolution contact data and also to identify the most at-risk classes. We introduce a contact matrix of probability distributions that takes into account the heterogeneity of contact durations between (and within) classes of individuals, and we show that this representation yields a good approximation of the epidemic spreading properties obtained by using the high-resolution data. Our results mark a step towards the definition of synopses of high-resolution dynamic contact networks, providing a compact representation of contact patterns that can correctly inform computational models designed to discover risk groups and evaluate containment policies. We show that this novel kind of representation can preserve in simulation quantitative features of the epidemics that are crucial for their study and management.

Brain function relies on the coordination of activity across multiple, recurrently connected brain areas. For instance, sensory information encoded in early sensory areas is relayed to, and further processed by, higher cortical areas and then fed back. However, the way in which feedforward and feedback signaling interact with one another is incompletely understood. Here we investigate this question by leveraging simultaneous neuronal population recordings in early and midlevel visual areas (V1–V2 and V1–V4). Using a dimensionality reduction approach, we find that population interactions are feedforward-dominated shortly after stimulus onset and feedback-dominated during spontaneous activity. The population activity patterns most correlated across areas were distinct during feedforward- and feedback-dominated periods. These results suggest that feedforward and feedback signaling rely on separate “channels”, which allows feedback signals to not directly affect activity that is fed forward. How cortical areas interact via feedforward and feedback signaling remains unclear. Here, the authors recorded from V1 and V2/V4 in macaque visual cortex and found that feedforward and feedback interactions vary with stimulus drive and involve different neuronal population activity patterns.

Technological advances now allow us to record from large populations of neurons across multiple brain areas. These recordings may illuminate how communication between areas contributes to brain function, yet a substantial barrier remains: how do we disentangle the concurrent, bidirectional flow of signals between populations of neurons? We propose here a dimensionality reduction framework, delayed latents across groups (DLAG), that disentangles signals relayed in each direction, identifies how these signals are represented by each population and characterizes how they evolve within and across trials. We demonstrate that DLAG performs well on synthetic datasets similar in scale to current neurophysiological recordings. Then we study simultaneously recorded populations in primate visual areas V1 and V2, where DLAG reveals signatures of bidirectional yet selective communication. Our framework lays a foundation for dissecting the intricate flow of signals across populations of neurons, and how this signalling contributes to cortical computation.

Conceptually, premature initiation of post-wound angiogenesis could interfere with hemostasis, as it relies on fibrinolysis. The mechanisms facilitating orchestration of these events remain poorly understood, however, likely due to limitations in discerning the individual contribution of cells and extracellular matrix. Here, we designed an in vitro Hemostatic-Components-Model (HCM) to investigate the role of the fibrin matrix as protein factor-carrier, independent of its cell-scaffold function. After characterizing the proteomic profile of HCM-harvested matrix releasates, we demonstrate that the key pro-/anti-angiogenic factors, VEGF and PF4, are differentially bound by the matrix. Changing matrix fibrin mass consequently alters the balance of releasate factor concentrations, with differential effects on basic endothelial cell (EC) behaviors. While increasing mass, and releasate VEGF levels, promoted EC chemotactic migration, it progressively inhibited tube formation, a response that was dependent on PF4. These results indicate that the clot's matrix component initially serves as biochemical anti-angiogenic barrier, suggesting that post-hemostatic angiogenesis follows fibrinolysis-mediated angiogenic disinhibition. Beyond their significance towards understanding the spatiotemporal regulation of wound healing, our findings could inform the study of other pathophysiological processes in which coagulation and angiogenesis are prominent features, such as cardiovascular and malignant disease.

Background/Objectives: Plastic and reconstructive surgery (PRS) is characterized by its wide range of techniques and procedures, aiming to address the need for individualized treatment approaches. As PRS is predominantly performed in an elective and non-emergency setting, a thorough preoperative risk assessment through standardized diagnostics remains essential. Lab testing is often routinely performed, yet its overall role and how specific parameters are currently used to stratify risk in PRS is poorly understood. We herein aim to explore the role of preoperative lab value testing and characterize current practices, perceived importance, and variability in their use for risk stratification. Methods: We conducted an anonymous, web-based cross-sectional survey of international PRS surgeons. Survey items captured demographics, routine preoperative assessment methods, ordering frequency of laboratory tests, and their perceived importance. Group comparisons were stratified by work setting, years of experience, and PRS subspecialization. Results: A total of 140 PRS surgeons from 24 countries completed the survey. Clinical evaluation (97.9%) and laboratory testing (84.3%) were the most common risk assessment methods investigated in our study; 70.7% agreed that preoperative lab values are important for surgical risk stratification while 64.3% would adopt a standardized lab-based risk assessment tool. The most ordered lab tests were hemoglobin (80.0%), hematocrit (76.4%), platelets (69.3%), creatinine (68.6%), and white blood cell count (67.1%). Hospital-based PRS surgeons ordered creatinine, WBC, INR and albumin more often and rated albumin of greater importance compared with PRS surgeons based in private practice. Conclusions: Preoperative labs are widely used in PRS with emphasis on hematologic and coagulation parameters, in both hospitals and private practices. Broad consensus on the importance of preoperative lab values in surgical risk stratification and a willingness to adopt a standardized, lab-based risk assessment tool highlight the need to harmonize current practice and integrate specific labs into standardized procedure-specific risk stratification.