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The Polyakov loop is included in the S U(2)L × S U(2)R chiral quark-meson model by considering the propagation of the constituent quarks, coupled to the (σ, π) meson multiplet, on the homogeneous background of a temporal gauge field, diagonal in color space. The model is solved at finite temperature and quark baryon chemical potential both in the chiral limit and for the physical value of the pion mass by using an expansion in the number of flavors Nf. Keeping the fermion propagator at its tree-level, a resummation on the pion propagator is constructed which resums infinitely many orders in 1/Nf, where O(1/Nf) represents the order at which the fermions start to contribute in the pion propagator. The influence of the Polyakov loop on the tricritical or the critical point in the µq – T phase diagram is studied for various forms of the Polyakov loop potential.

Abstract Glycans, consisting of covalently linked sugar units, are a major class of biopolymers essential to all known living organisms. To better understand their biological functions and further applications in fields from biomedicine to materials science, detailed knowledge of their structure is essential. However, due to the extraordinary complexity and conformational flexibility of glycans, state-of-the-art glycan analysis methods often fail to provide structural information with atomic precision. Here, we combine electrospray deposition in ultra-high vacuum with non-contact atomic force microscopy and theoretical calculations to unravel the structure of β-cyclodextrin, a cyclic glucose oligomer, with atomic-scale detail. Our results, established on the single-molecule level, reveal the different adsorption geometries and conformations of β-cyclodextrin. The position of individual hydroxy groups and the location of the stabilizing intramolecular H-bonds are deduced from atomically resolved images, enabling the unambiguous assignment of the molecular structure and demonstrating the potential of the method for glycan analysis.

Abstract Interactions between adipose tissue, liver and immune system are at the center of metabolic dysfunction-associated steatotic liver disease and type 2 diabetes. To address the need for an accurate in vitro model, we establish an interconnected microphysiological system (MPS) containing white adipocytes, hepatocytes and proinflammatory macrophages derived from isogenic human induced pluripotent stem cells. Using this MPS, we find that increasing the adipocyte-to-hepatocyte ratio moderately affects hepatocyte function, whereas macrophage-induced adipocyte inflammation causes lipid accumulation in hepatocytes and MPS-wide insulin resistance, corresponding to initiation of metabolic dysfunction-associated steatotic liver disease. We also use our MPS to identify and characterize pharmacological intervention strategies for hepatic steatosis and systemic insulin resistance and find that the glucagon-like peptide-1 receptor agonist semaglutide improves hepatocyte function by acting specifically on adipocytes. These results establish our MPS modeling the adipose tissue-liver axis as an alternative to animal models for mechanistic studies or drug discovery in metabolic diseases.

Tourism destinations represent a central element of research in tourism, given that the majority of the tourist activities take place in them. Accordingly, this paper is aimed at highlighting the importance of adequate strategic management of tourism destinations, as well as the key role of strategic marketing management of tourism destinations. This paper presents the review of literature devoted to the stages of the tourism destination marketing planning process, as well as the literature devoted to the differences between traditional and digital marketing planning process in all sectors, without directly focusing on tourism. Accordingly, the contribution of this paper lies in connecting the two subjects and proposing the framework for tourism destinations digital marketing planning process.

IntroductionFootball is a physically demanding sport that requires effective recovery strategies to maintain performance level and prevent injuries. This study investigated if a single 1-h hyperbaric oxygen therapy (HBOT) session affects recovery and performance after a football match in elite youth players.MethodsTwenty elite youth football players (age 17.3 ± 0.5 years) were randomly assigned to a HBOT group or a control group (CON). They played a 90-min football game and underwent either a 60-min HBOT or placebo intervention. Before (T1), at the end of the match (T2), 1 h after HBOT or CON session (T3), and 12 h after HBOT session (T4), subjects underwent biochemical (serum samples (myoglobin (MB), creatine kinase (CK), lactate dehydrogenase (LDH), alanine aminotransferase (ALT), and aspartate aminotransferase (AST)) and performance measurements (linear speed at 5 m, 10 m and 20 m, squat jump (SJ), countermovement jump (CMJ) and countermovement jump with arm swing (CMJa)). The Hooper Index (HI) was collected and heart rate was measured during the game.ResultsThe football match induced significant increases in all biochemical markers, but no significant differences were found between the HBOT and control group in biochemical or performance parameters at any time point. However, there was a significant interaction effect between time and group for HI (p = 0.012, η2 = 0.124), with the HBOT group showing significantly lower HI values (8.6 ± 2.41) than the control group (11.0 ± 3.23) at 1 h post-HBOT.DiscussionA single 1-h session of HBOT did not significantly affect recovery or performance parameters in elite youth football players, though it did show a moderate positive affect on the HI at 1 h post-HBOT. Further studies should explore the impact of either longer or sequential HBOT sessions on recovery.

Abstract This study presents a single‐site microkinetic model for methanol synthesis by CO2 hydrogenation over intermetallic Pd2Ga/SiO2. A reaction path analysis (RPA) combining theoretical results and realistic catalyst surface reaction data is established to elucidate the reaction mechanism and kinetic models of CO2 hydrogenation to methanol and CO. The RPA leads to the derivation of rate expressions for both reactions without presumptions about the most abundant reactive intermediate (MARI) and rate‐determining step (rds). The formation of H2COOH* is found to be the rds (step 19) for methanol synthesis via the formate pathway, with CO2 and H‐atoms adsorbed on intermetallic sites as the MARIs. The derived kinetic model is corroborated with experimental data acquired under different reaction conditions, using a lab‐scale fixed‐bed reactor and Pd2Ga/SiO2 nanoparticles prepared by incipient wetness impregnation. The excellent agreement between the experimental data and the kinetic model (R2 = 0.99) substantiates the proposed mechanism with an activation energy of 61.52 kJ mol‐1 for methanol synthesis. The reported catalyst exhibits high selectivity to methanol (96%) at 1 bar, 150 °C, and H2/CO2 ratio of 3:1. These findings provide critical insights to optimize catalysts and processes targeting CO2 hydrogenation at atmospheric pressure and low temperatures for on‐demand energy production.

More than eight million premature deaths annually can be attributed to air pollution, with 99% of the world’s population residing in areas below recommended air quality standards. Hence, the present study aimed to examine the association between primary DNA damage and air pollution data among 123 participants enrolled between 2011 and 2015 in Zagreb, Croatia. While most measured air pollutants adhered to regulatory limits, benzo[a]pyrene concentrations bound to PM10 exceeded them. Factorial analysis narrowed down air pollution data to four exposure factors (particulate matter, two metal factors, and other pollutants). Despite the absence of significant positive associations between modeled air pollution exposure factors and comet assay descriptors (tail length, tail intensity, tail moment, and highly damaged nuclei), the critical health implications of air pollution warrant further investigations, particularly with biomarkers of exposure and different biomarkers of effect in populations facing air pollution exposure.

Abstract Background For centuries, wild mushrooms have been a forest resource of significant cultural value in several ethnic groups worldwide. In Mexico, extensive traditional knowledge on the use of fungal resources has been developed and deeply rooted. Mexico is the second country in the world in which the most species of wild mushroom are consumed, and it is considered a pioneer in ethnomycology. Nonetheless, there are still many indigenous groups in this country that have not been studied from an ethnomycological approach. The present study aimed to record the traditional knowledge on wild mushrooms in three indigenous groups of the state of Oaxaca, Mexico, and assess the variation in this knowledge within and across the studied groups. Methods The data were recorded from April to October 2022 within three communities belonging to the indigenous groups Chatino, Chontal, and Chinanteco. Through 84 interviews, information related to their knowledge of wild mushrooms was obtained. The cultural significance index of wild edible mushrooms was calculated for each community. Regression analyses, analysis of variance and covariance, t test, and non-metric multidimensional scaling analysis were performed to assess the distribution of traditional knowledge in the communities. Results A total of 32 culturally important mushroom species were recorded for the three indigenous groups (30 edible, 2 medicinal); 23 used by Chatinos, 16 by Chontales, and 6 by Chinantecos. Only Chatinos and Chinantecos use wild mushrooms in medicine. The cultural significance of wild edible mushrooms differed among groups. Traditional knowledge about wild mushrooms declines when the level of schooling increases and age decreases, especially in the Chatino group. This knowledge distributes more homogeneously in the Chontal and Chinanteco groups. Their age determines the difference in knowledge between men and women. Conclusion Documenting how traditional knowledge differs among ethnic groups is relevant for preserving cultural and biological diversity. Factors such as level of schooling and age can affect traditional knowledge of wild mushrooms, but the effects of these factors vary within and across communities. Conducting studies encompassing a broader range of variables is of interest for a better understanding of the human–mushroom relationship.

Catalysis has optimized and improved production rates in many industrial processes. Conventional catalysis plays a key role in the mass-production of otherwise difficult to obtain substances. Plasma catalysis, plasma incorporation to appropriate catalysts, has been shown in literature to further outperform the typical conventional methods, and has shown potential to become a key production method in deep space exploration and survival. However, it faces a few more challenges that hinder it from being used industrially. In this review, we discuss known mechanisms in literature and the instrumentation and diagnostics that were utilized to be able to determine and explain these mechanisms in detail, and thus have led to the development of plasma catalysts with up to 80 % conversion rates for CO2 conversion processes. We also discuss diagnostics that may be employed in the near future to reveal the last few unconventional mechanisms that must be explained in order to address the current instability and short life of catalysts due to the harsh conditions of plasma. In successful implementations of diagnostics in literature, they have proven to be the key to unlocking the knowledge required to develop appropriate catalysts optimized for converting CO2 in a plasma environment.

Modern text generation metrics use semantic representations of words to assess the quality of a text generation model without considering the fluency of the generated text. This paper proposes a novel text generation metric that combines adequacy and fluency to measure the quality of the generated text. When computing the final score using optimal transport, the metric considers semantic meaning and word order. We evaluate the metric on text translation data sets consisting of 20 language pairs from various language families and scripts. Using a novel statistic for measuring word order sensitivity, we analyze its adequacy-based performance using Pearson’s r and Kendall’s $\tau $ correlation coefficients and their sensitivity to fluency-related modifications. Results show that the proposed metric is the most sensitive to fluency-related changes among all top-performing embedding-based metrics, which were found to be relatively invariant to variations in word order. The proposed metric’s overall adequacy-based performance is lower than the best embedding-based metric but higher than the n-gram matching metrics. Our code is publicly available on GitHub (https://github.com/eriknovak/metric-OPWScore) under the BSD-2-Clause license.

This paper introduces an innovative methodology for conducting demand analysis within various domains across multiple countries, presenting insights derived from a comprehensive analysis conducted in seven distinct nations. The proposed methodology provides a systematic process for gathering, processing, and analyzing textual data to discern global talent demand within specified domains. Three fundamental steps characterize the methodology: identification of areas of interest, calculation of relative demand, and execution of analysis and forecasting. Areas of interest within the job market are pinpointed through a combination of manual curation and automated techniques, ensuring the inclusion of only pertinent job postings. Relative demand for jobs within each specified domain is then computed for each country, furnishing a standardized metric for cross-country comparisons. Subsequently, analysis and forecasting unveil trends and patterns in job demand, enabling stakeholders to anticipate shifts in the job market landscape. Application of this methodology to scrutinize demand analysis across seven countries - the US, Canada, the UK, France, India, Singapore, and Australia - reveals substantial variations in demand across diverse regions, along with correlations and instances of skill shortages. These findings offer invaluable insights for policymakers, businesses, and researchers, facilitating informed decision-making and fostering growth and innovation within the specified domains.

Low-income regions do not have the economic strength to use conventional isolators; therefore, low-cost alternatives are needed. The recent series of earthquakes in Turkey has once again demonstrated the destructive power of this natural disaster and highlighted the need for high-quality earthquake-resistant structures. In this context, a comprehensive experimental parametric study on the use of natural stone pebbles (ASL-1) and stone pebbles in combination with a geogrid layer (ASL-2) as suitable materials for a GSI system is conducted and the main results are presented. The seismic behavior of five different models was tested using four different acceleration diagrams with different peak ground accelerations (PGAs). Characteristic displacements, accelerations, and strains were measured. The results and conclusions presented are based on the integration and synthesis of several previously conducted studies.

Abstract Background Back pain is the leading cause of disability worldwide. Despite guidelines discouraging opioids as first-line treatment, opioids remain the most prescribed drugs for back pain. There is renewed interest in exploring the potential medical applications of cannabis, and with the recent changes in national legislation there is a unique opportunity to investigate the analgesic properties of cannabis. Methods This was a multi-center survey-based study examining patient perceptions regarding cannabis for spine pain. We included patients presenting with back or neck pain to one of three Orthopedic clinics in Ontario. Our primary outcome was perceived effect of cannabis on back pain, while secondary outcomes were perceptions regarding potential applications and barriers to cannabis use. Results 259 patients participated in this study, 35.3% (90/255) stating they used cannabis medically. Average pain severity was 6.5/10 ± 0.3 (95% CI 6.2–6.8). Nearly three-quarters were prescribed opioids (73.6%, 148/201), with oxycodone/oxycontin (45.9% 68/148) being the most common, and almost half of (49.3%, 73/148) had used an opioid in the last week. Patients estimated cannabis could treat 54.3% ± 4.0 (95% CI 50.3–58.3%) of their spine pain and replace 46.2% ± 6. 6 (95% CI 39.6–52.8%) of their current analgesics. Age (β = − 0.3, CI − 0.6–0.0), higher pain severity (β = 0.4, CI 0.1–0.6) and previous cannabis use (β = 14.7, CI 5.1–24.4) were associated with a higher perceived effect of cannabis. Patients thought cannabis would be beneficial to treat pain (129/146, 88.4%), and reduce (116/146, 79.5%) or eliminate opioids (102/146, 69.9%). Not considering using cannabis for medical purposes (65/150, 43.3%) was the number one reported barrier. Conclusions Patients estimated medical cannabis could treat more than half of their spine pain, with one in three patients already using medical cannabis. 79% of patients also believe cannabis could reduce opioid usage. This data will help support more research into cannabis for musculoskeletal pain.

The current advancements in the field of machine learning can have an important application in agriculture and global food security. Machine learning has considerable potential in establishing knowledge-based farming systems. One of the main challenges of data-driven agriculture is to minimize food waste and establish more sustainable farming systems. The prediction of the right harvest time is one of the ways to obtain the mentioned goals. This paper describes multiple machine learning algorithms that are used to predict peach firmness. By accurately predicting peach firmness based on various peach measurement data, a more precise harvest time can be obtained. The evaluation of nature-inspired metaheuristic optimization algorithms in enhancing machine learning model accuracy is the primary objective of this paper. The possibility of improving the peach firmness prediction accuracy of regression tree models using various metaheuristic optimization techniques implemented in GA and metaheuristicOpt R packages is studied. The RMSE on test data of the default regression tree model is 1.722285, while the regression tree model optimized using the gray wolf optimization algorithm scored the lowest RMSE of 1.570924. The obtained results show that it is possible to improve the peach firmness prediction accuracy of the regression tree model by 8.8% using the described method.

When individual oscillators age and become inactive, the collective dynamics of coupled oscillators is often affected as well. Depending on the fraction of inactive oscillators or cascading failures that percolate from crucial information exchange points, the critical shift toward macroscopic inactivity in coupled oscillator networks is known as the aging transition. Here, we study this phenomenon in two overlayed square lattices that together constitute a multilayer network, whereby one layer is populated with slow Poincaré oscillators and the other with fast Rulkov neurons. Moreover, in this multimodal setup, the excitability of fast oscillators is influenced by the phase of slow oscillators that are gradually inactivated toward the aging transition in the fast layer. Through extensive numerical simulations, we find that the progressive inactivation of oscillators in the slow layer nontrivially affects the collective oscillatory activity and the aging transitions in the fast layer. Most counterintuitively, we show that it is possible for the intensity of oscillatory activity in the fast layer to progressively increase to up to 100%, even when up to 60% of units in the slow oscillatory layer are inactivated. We explain our results with a numerical analysis of collective behavior in individual layers, and we discuss their implications for biological systems.

Within the islets of Langerhans, beta cells orchestrate synchronized insulin secretion, a pivotal aspect of metabolic homeostasis. Despite the inherent heterogeneity and multimodal activity of individual cells, intercellular coupling acts as a homogenizing force, enabling coordinated responses through the propagation of intercellular waves. Disruptions in this coordination are implicated in irregular insulin secretion, a hallmark of diabetes. Recently, innovative approaches, such as integrating multicellular calcium imaging with network analysis, have emerged for a quantitative assessment of the cellular activity in islets. However, different groups use distinct experimental preparations, microscopic techniques, apply different methods to process the measured signals and use various methods to derive functional connectivity patterns. This makes comparisons between findings and their integration into a bigger picture difficult and has led to disputes in functional connectivity interpretations. To address these issues, we present here a systematic analysis of how different approaches influence the network representation of islet activity. Our findings show that the choice of methods used to construct networks is not crucial, although care is needed when combining data from different islets. Conversely, the conclusions drawn from network analysis can be heavily affected by the pre-processing of the time series, the type of the oscillatory component in the signals, and by the experimental preparation. Our tutorial-like investigation aims to resolve interpretational issues, reconcile conflicting views, advance functional implications, and encourage researchers to adopt connectivity analysis. As we conclude, we outline challenges for future research, emphasizing the broader applicability of our conclusions to other tissues exhibiting complex multicellular dynamics.

Abstract Objective The liver acts as an innate immunity-dominant organ and natural killer (NK) cells, are the main lymphocyte population in the human liver. NK cells are in close interaction with other immune cells, acting as the first line of defense against pathogens, infections, and injury. A previously developed, three-dimensional, perfused liver-on-a-chip comprised of human cells was used to integrate NK cells, representing pivotal immune cells during liver injury and regeneration. The objective of this study was to integrate functional NK cells in an in vitro model of the human liver and assess utilization of the model for NK cell-dependent studies of liver inflammation. Results NK cells from human blood and liver specimen were isolated by Percoll separation with subsequent magnetic cell separation (MACS), yielding highly purified blood and liver derived NK cells. After stimulation with toll-like-receptor (TLR) agonists (lipopolysaccharides, Pam3CSK4), isolated NK cells showed increased interferon (IFN)-gamma secretion. To study the role of NK cells in a complex hepatic environment, these cells were integrated in the vascular compartment of a microfluidically supported liver-on-a-chip model in close interaction with endothelial and resident macrophages. Successful, functional integration of NK cells was verified by immunofluorescence staining (NKp46), flow cytometry analysis and TLR agonist-dependent secretion of interleukin (IL)-6 and tumor necrosis factor (TNF)-alpha. Lastly, we observed that inflammatory activation of NK cells in the liver-on-a-chip led to a loss of vascular barrier integrity. Overall, our data shows the first successful, functional integration of NK cells in a liver-on-a-chip model that can be utilized to investigate NK cell-dependent effects on liver inflammation in vitro.

We examine the experiences of LGBT+ people in education, which international research shows is characterised by a lack of sensitivity to gender and sexual minorities. We conducted a qualitative study involving 27 individual interviews with LGBT+ persons who attend or have attended primary, secondary and/or tertiary education in Slovenia. The analysis revealed that the typical experience entails a combination of hate and invisibility. The data show the all-encompassing influence of prejudice, which people mention in various situations. Less common are descriptions of protective factors, which are generally limited to peer support and the commitment of individual staff members. The lack of systemic solutions is a challenge that must be addressed in the future by developing opportunities for inclusive formal and hidden curricula and staff training.

The gadolinium vanadate doped with samarium (GdVO4:Sm3+) nanopowder was prepared by the solution combustion synthesis (SCS) method. After synthesis, in order to achieve full crystallinity, the material was annealed in air atmosphere at 900 °C. Phase identification in the post-annealed powder samples was performed by X-ray diffraction, and morphology was investigated by high-resolution scanning electron microscope (SEM) and transmission electron microscope (TEM). Photoluminescence characterization of emission spectrum and time resolved analysis was performed using tunable laser optical parametric oscillator excitation and streak camera. In addition to samarium emission bands, a weak broad luminescence emission band of host VO43− was also observed by the detection system. In our earlier work, we analyzed the possibility of using the host luminescence for two-color temperature sensing, improving the method by introducing the temporal dependence in line intensity ratio measurements. Here, we showed that further improvements are possible by using the machine learning approach. To facilitate the initial data assessment, we incorporated Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) clustering of GdVO4:Sm3+ spectra at various temperatures. Good predictions of temperature were obtained using deep neural networks. Performance of the deep learning network was enhanced by data augmentation technique.

In addition to the general forms of stress characteristic of all students, LGBT+ people experience minority stress. The minority stress model represents general stress processes and minority stress processes. It is the minority stress processes that shape the lives of LGBT+ people, as they are based on gender and sexual orientation, that are characterized in this model as distal processes of minority stress. From these, proximal processes develop in response to distal stressors. With the aim of gaining a deeper understanding of this topic, presenting relevant foreign literature on the minority stress of LGBT+ persons in education, and creating the possibility of laying the foundations for empirical research in this area in Slovenia, we identified situations that represent a source of minority stress for LGBT+ persons in the context of the school environment. 57 materials from Scopus, Web of Science and COBISS+ databases were included in the integrative literature review, and 21 studies are presented in this paper, while the rest are mentioned in a separate paper due to the consideration of the related topic of resilience. The selected research addresses the topic of minority stress among LGBT+ individuals and includes young LGBT+ individuals and/or educationally relevant topics. All materials were reviewed with the Nvivo program and processed with qualitative content analysis. Some findings suggest that school spaces can represent a central source of distal (violence, discrimination, microaggressions, prejudice, stereotypes) and proximal (internalized stigma, anticipated rejection, stigma management) minority stressors for LGBT+ individuals. These have a number of negative effects on the lives and health of LGBT+ students, indicating the need for change to reduce minority stress levels and provide LGBT+ individuals with a safe and respectful educational experience.

Abstract Hepatic insulin resistance is recognized as a driver of type 2 diabetes and fatty liver disease but specific therapies are lacking. Here we explore the potential of human induced pluripotent stem cells (iPSCs) for modeling hepatic insulin resistance in vitro, with a focus on resolving the controversy about the impact of inflammation in the absence of steatosis. For this, we establish the complex insulin signaling cascade and the multiple inter-dependent functions constituting hepatic glucose metabolism in iPSC-derived hepatocytes (iPSC-Heps). Co-culture of these insulin-sensitive iPSC-Heps with isogenic iPSC-derived pro-inflammatory macrophages induces glucose output by preventing insulin from inhibiting gluconeogenesis and glycogenolysis and activating glycolysis. Screening identifies TNFα and IL1β as the mediators of insulin resistance in iPSC-Heps. Neutralizing these cytokines together restores insulin sensitivity in iPSC-Heps more effectively than individual inhibition, reflecting specific effects on insulin signaling and glucose metabolism mediated by NF-κB or JNK. These results show that inflammation is sufficient to induce hepatic insulin resistance and establish a human iPSC-based in vitro model to mechanistically dissect and therapeutically target this metabolic disease driver.

Despite all the measures already taken and those still underway, pollution remains a major global problem, as the transport sector is the one where emissions are expected to increase in the coming years. Companies and policy makers are under increasing pressure to reduce the impact of their logistics activities in order to make transportation more environmentally friendly. One of the solutions to reduce emissions from intermodal transport is to choose the “right” mode of transport for each step in the transport chain. Such a measure increases the complexity of the transport chain and places an additional burden on transport companies in planning and organising transport for the entire transport chain. Additional difficulties arise from the fragmentation of information on emissions emitted for a single transport link and the lack of a unified approach to measuring and estimating transport chain emissions. As a result, this work finds that there is a lack of knowledge among users about the environmental impacts of transportation, despite the desire to contribute to greener transportation by paying more for a product or transportation service. The current research fills the gap in stakeholders’ understanding of the negative environmental impacts for individual transportation and for the entire transport chain. In addition, the study reveals a need for a systematically regulated and adapted way of informing users of intermodal transport chains due to the lack of transparency and comparison between different intermodal transport chains. To successfully address the challenges, the study proposes a 2-pillar approach. The 1st pillar approach focuses on designing a set of necessary measures (combination of top-down and bottom-up approach) for the transition to a low-carbon transport chain, while the 2nd pillar mainly focuses on mapping the level of green transport for easy comparison of similar products or services. The results of the research study show that the combination of numerical data with symbolic data is best suited to provide information on the level of green transport.

Tight control of beta cell stimulus-secretion coupling is crucial for maintaining homeostasis of energy-rich nutrients. While glucose serves as a primary regulator of this process, incretins augment beta cell function, partly by enhancing cytosolic [Ca2+] dynamics. However, the details of how precisely they affect beta cell recruitment during activation, their active time, and functional connectivity during plateau activity, and how they influence beta cell deactivation remain to be described. Performing functional multicellular Ca2+ imaging in acute mouse pancreas tissue slices enabled us to systematically assess the effects of the GLP-1 receptor agonist exendin-4 (Ex-4) simultaneously in many coupled beta cells with high resolution. In otherwise substimulatory glucose, Ex-4 was able to recruit approximately a quarter of beta cells into an active state. Costimulation with Ex-4 and stimulatory glucose shortened the activation delays and accelerated beta cell activation dynamics. More specifically, active time increased faster, and the time required to reach half-maximal activation was effectively halved in the presence of Ex-4. Moreover, the active time and regularity of [Ca2+]IC oscillations increased, especially during the first part of beta cell response. In contrast, subsequent addition of Ex-4 to already active cells did not significantly enhance beta cell activity. Network analyses further confirmed increased connectivity during activation and activity in the presence of Ex-4, with hub cell roles remaining rather stable in both control experiments and experiments with Ex-4. Interestingly, Ex-4 demonstrated a biphasic effect on deactivation, slightly prolonging beta cell activity at physiological concentrations and shortening deactivation delays at supraphysiological concentrations. In sum, costimulation by Ex-4 and glucose increases [Ca2+]IC during beta cell activation and activity, indicating that the effect of incretins may, to an important extent, be explained by enhanced [Ca2+]IC signals. During deactivation, previous incretin stimulation does not critically prolong cellular activity, which corroborates their low risk of hypoglycemia.

Observing the world on a global scale can help us understand better the context of problems that engage us all. In this paper, we propose a data-driven global observatory methodology that puts together the different perspectives of media, science, statistics and sensing over heterogeneous data sources and text mining algorithms. We also discuss the implementation of this global observatory in the context of epidemic intelligence, monitoring the impact of the COVID-19 pandemic, and in the context of climate change, with a specific focus on water resource management. Moreover, we discuss the value of this global solution in local contexts and priorities, based on the exchange with stakeholders in municipalities, utilities and governmental institutions.

In the original publication [...]