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The segmentation of retinal vasculature from eye fundus images is a fundamental task in retinal image analysis. Over recent years, increasingly complex approaches based on sophisticated Convolutional Neural Network architectures have been pushing performance on well‑established benchmark datasets. In this paper, we take a step back and analyze the real need of such complexity. We first compile and review the performance of 20 different techniques on some popular databases, and we demonstrate that a minimalistic version of a standard U‑Net with several orders of magnitude less parameters, carefully trained and rigorously evaluated, closely approximates the performance of current best techniques. We then show that a cascaded extension (W‑Net) reaches outstanding performance on several popular datasets, still using orders of magnitude less learnable weights than any previously published work. Furthermore, we provide the most comprehensive cross‑dataset performance analysis to date, involving up to 10 different databases. Our analysis demonstrates that the retinal vessel segmentation is far from solved when considering test images that differ substantially from the training data, and that this task represents an ideal scenario for the exploration of domain adaptation techniques. In this context, we experiment with a simple self‑labeling strategy that enables moderate enhancement of cross‑dataset performance, indicating that there is still much room for improvement in this area. Finally, we test our approach on Artery/Vein and vessel segmentation from OCTA imaging problems, where we again achieve results well‑aligned with the state‑of‑the‑art, at a fraction of the model complexity available in recent literature. Code to reproduce the results in this paper is released.

Previous studies have reported a decrease in air pollution levels following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference and did not assess the role of different policy interventions. This study contributes to knowledge by quantifying the association between specific lockdown measures and the decrease in NO2, O3, PM2.5, and PM10 levels across 47 European cities. It also estimated the number of avoided deaths during the period. This paper used new modelled data from the Copernicus Atmosphere Monitoring Service (CAMS) to define business-as-usual and lockdown scenarios of daily air pollution trends. This study applies a spatio-temporal Bayesian non-linear mixed effect model to quantify the changes in pollutant concentrations associated with the stringency indices of individual policy measures. The results indicated non-linear associations with a stronger decrease in NO2 compared to PM2.5 and PM10 concentrations at very strict policy levels. Differences across interventions were also identified, specifically the strong effects of actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements. Finally, the observed decrease in pollution potentially resulted in hundreds of avoided deaths across Europe., This research had free and open access to all data sources. The work described in this paper has received funding from European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf the European Union through commercial contract Ref. CAMS_95p. Several CAMS Regional Models of the CAMS_50 Service contributed to the present work (CHIMERE, LOTOS-EUROS, MINNI, MOCAGE, MONARCH, SILAM) under CAMS_71 coordination. CAMS_COP066 service provided the lockdown emissions information. O.J. and M.G. thankfully acknowledge the computer resources at Marenostrum and the technical support provided by Barcelona Supercomputing Center (RES-AECT-2020-1-0007). SILAM model runs was also funded by Finnish Academy GLORIA project (No310372). The study was supported by the European Union’s Horizon 2020 Project Exhaustion (Grant ID: 820655)., Peer Reviewed, "Article signat per 18 autors/es: Rochelle Schneider, Pierre Masselot, Ana M. Vicedo-Cabrera, Francesco Sera, Marta Blangiardo, Chiara Forlani, John Douros, Oriol Jorba, Mario Adani, Rostislav Kouznetsov, Florian Couvidat, Joaquim Arteta, Blandine Raux, Marc Guevara, Augustin Colette, Jérôme Barré, Vincent-Henri Peuch & Antonio Gasparrini ", Postprint (published version)

Clinical studies from WHO have demonstrated that only 50-70% of patients adhere properly to prescribed drug therapy. Such adherence failure can impact therapeutic efficacy for the patients in question and compromises data quality around the population-level efficacy of the drug for the indications targeted. In this study, we applied various ensemble learning and deep learning models to predict medication adherence among patients. Our contribution to this endeavour involves targeting the problem of adherence prediction for a particularly challenging class of patients who self-administer injectable medication at home. Our prediction pipeline, based on event history, comprises a connected sharps bin which aims to help patients better manage their condition and improve outcomes. In other words, the efficiency of interventions can be significantly improved by prioritizing the patients who are most likely to be non-adherent. The collected data comprising a rich event feature set may be exploited for the purposes of predicting the status of the next adherence state for individual patients. This paper reports on how this concept can be realized through an investigation using a wide range of ensemble learning and deep learning models on a real-world dataset collected from such a system. The dataset investigated comprises 342,174 historic injection disposal records collected over the course of more than 5 years. A comprehensive comparison of different models is given in this paper. Moreover, we demonstrate that the selected best performer, long short-term memory (LSTM), generalizes well by deploying it in a true future testing dataset. The proposed end-to-end pipeline is capable of predicting patient failure in adhering to their therapeutic regimen with 77.35% accuracy (Specificity: 78.28%, Sensitivity: 76.42%, Precision: 77.87%, F1 score: 0.7714, ROC AUC: 0.8390).

This paper discusses the use of nanomaterials for the improved performance of time-of-flight particle detectors based on secondary electron emission (SEE). The purpose of the research presented in this paper is to find a nanomaterial that has a higher SEE than gold. In this article, we present a measurement of the SEE properties from 1D (one-dimensional) nanostructures of ZnO and ZnO/GaN (ZnO with GaN coating) composed of a mostly regular pattern of nanotubes grown on a thin Si3N4 substrate. The study was performed with 4.77 meV/u Au beam. We observed an average increase of 2.5 in the SEE properties from the 1D ZnO nanotubes compared to gold.

This paper discusses the use of nanomaterials for the improved performance of time-of-flight particle detectors based on secondary electron emission (SEE). The purpose of the research presented in this paper is to find a nanomaterial that has a higher SEE than gold. In this article, we present a measurement of the SEE properties from 1D (one-dimensional) nanostructures of ZnO and ZnO/GaN (ZnO with GaN coating) composed of a mostly regular pattern of nanotubes grown on a thin Si3N4 substrate. The study was performed with 4.77 meV/u Au beam. We observed an average increase of 2.5 in the SEE properties from the 1D ZnO nanotubes compared to gold.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Clinical studies from WHO have demonstrated that only 50-70% of patients adhere properly to prescribed drug therapy. Such adherence failure can impact therapeutic efficacy for the patients in question and compromises data quality around the population-level efficacy of the drug for the indications targeted. In this study, we applied various ensemble learning and deep learning models to predict medication adherence among patients. Our contribution to this endeavour involves targeting the problem of adherence prediction for a particularly challenging class of patients who self-administer injectable medication at home. Our prediction pipeline, based on event history, comprises a connected sharps bin which aims to help patients better manage their condition and improve outcomes. In other words, the efficiency of interventions can be significantly improved by prioritizing the patients who are most likely to be non-adherent. The collected data comprising a rich event feature set may be exploited for the purposes of predicting the status of the next adherence state for individual patients. This paper reports on how this concept can be realized through an investigation using a wide range of ensemble learning and deep learning models on a real-world dataset collected from such a system. The dataset investigated comprises 342,174 historic injection disposal records collected over the course of more than 5 years. A comprehensive comparison of different models is given in this paper. Moreover, we demonstrate that the selected best performer, long short-term memory (LSTM), generalizes well by deploying it in a true future testing dataset. The proposed end-to-end pipeline is capable of predicting patient failure in adhering to their therapeutic regimen with 77.35% accuracy (Specificity: 78.28%, Sensitivity: 76.42%, Precision: 77.87%, F1 score: 0.7714, ROC AUC: 0.8390).

Tahitian pearls, artificially cultivated from the black-lipped pearl oyster Pinctada margaritifera, are renowned for their unique color and large size, making the pearl industry vital for the French Polynesian economy. Understanding the mechanisms of pearl formation is essential for enabling quality and sustainable production. In this paper, we explore the process of pearl formation by studying pearl rotation. Here we show, using a deep convolutional neural network, a direct link between the rotation of the pearl during its formation in the oyster and its final shape. We propose a new method for non-invasive pearl monitoring and a model for predicting the final shape of the pearl from rotation data with 81.9% accuracy. These novel resources provide a fresh perspective to study and enhance our comprehension of the overall mechanism of pearl formation, with potential long-term applications for improving pearl production and quality control in the industry.

Armed conflicts have, in addition to severe impacts on human lives and infrastructure, also impacts on the environment, which needs to be assessed and documented. On September the 26th 2022, unknown perpetrators deliberately ruptured the two gas pipelines Nord Stream 1 and 2 with four coordinated explosions near a major chemical munition dump site near the Danish island of Bornholm in the Baltic Sea. While the massive release of natural gas into atmosphere raised serious concerns concerning the contribution to climate change—this paper assesses the overlooked direct impact of the explosions on the marine ecosystem. Seals and porpoises within a radius of four km would be at high risk of being killed by the shockwave, while temporary impact on hearing would be expected up to 50 km away. As the Baltic Proper population of harbour porpoises ( Phocoena phocoena ) is critically endangered, the loss or serious injury of even a single individual is considered a significant impact on the population. The rupture moreover resulted in the resuspension of 250000 metric tons of heavily contaminated sediment from deep-sea sedimentary basin for over a week, resulting in unacceptable toxicological risks towards fish and other biota in 11 km 3 water in the area for more than a month.

High-efficiency organic solar cells are often achieved using toxic halogenated solvents and additives that are constrained in organic solar cells industry. Therefore, it is important to develop materials or processing methods that enabled highly efficient organic solar cells processed by halogen free solvents. In this paper, we report an innovative processing method named auxiliary sequential deposition that enables 19%-efficiency organic solar cells processed by halogen free solvents. Our auxiliary sequential deposition method is different from the conventional blend casting or sequential deposition methods in that it involves an additional casting of dithieno[3,2-b:2’,3’-d]thiophene between the sequential depositions of the donor (D18-Cl) and acceptor (L8-BO) layers. The auxiliary sequential deposition method enables dramatic performance enhancement from 15% to over 18% compared to the blend casting and sequential deposition methods. Furthermore, by incorporating a branched-chain-engineered acceptor called L8-BO-X, device performance can be boosted to over 19% due to increased intermolecular packing, representing top-tier values for green-solvent processed organic solar cells. Comprehensive morphological and time-resolved characterizations reveal that the superior blend morphology achieved through the auxiliary sequential deposition method promotes charge generation while simultaneously suppressing charge recombination. This research underscores the potential of the auxiliary sequential deposition method for fabricating highly efficient organic solar cells using environmentally friendly solvents. © 2023, The Author(s).

The Cauvery Delta, the ‘Rice Bowl’ of India follows a time-tested cultivation pattern over several irrigation zones. However, in this era of the Anthropocene, it is now well-established that short-duration, intense precipitation episodes will batter the flood plains year after year. The purpose of this first study is thus to quantify the impacts that such episodes may have on the floodplains of the Cauvery Delta and the concomitant threats to the historic Kallanai Dam. Precipitation events during the North-East monsoon period are driven not just by warm rain microphysics but also by large frozen hydrometeors falling from deep clouds causing undesirable flooding over the region to the extent of 66%. Additionally, from an assessment of the velocity heads and the floodwater depths, this study projects a heightened vulnerability. The total extent of submergence along riverbanks and other flow paths was estimated to be 145.98 km2 out of which 65.14% of the submerged area is agricultural land. The most important conceptual advance established in this paper is that sub-zones in major watersheds that are currently safe will get inundated in the RCP8.5 warming scenario in 2050.

Armed conflicts have, in addition to severe impacts on human lives and infrastructure, also impacts on the environment, which needs to be assessed and documented. On September the 26th 2022, unknown perpetrators deliberately ruptured the two gas pipelines Nord Stream 1 and 2 with four coordinated explosions near a major chemical munition dump site near the Danish island of Bornholm in the Baltic Sea. While the massive release of natural gas into atmosphere raised serious concerns concerning the contribution to climate change—this paper assesses the overlooked direct impact of the explosions on the marine ecosystem. Seals and porpoises within a radius of four km would be at high risk of being killed by the shockwave, while temporary impact on hearing would be expected up to 50 km away. As the Baltic Proper population of harbour porpoises ( Phocoena phocoena ) is critically endangered, the loss or serious injury of even a single individual is considered a significant impact on the population. The rupture moreover resulted in the resuspension of 250000 metric tons of heavily contaminated sediment from deep-sea sedimentary basin for over a week, resulting in unacceptable toxicological risks towards fish and other biota in 11 km 3 water in the area for more than a month.

The COVID-19 disease caused by coronavirus is constantly changing due to the emergence of different variants and thousands of people are dying every day worldwide. Early detection of this new form of pulmonary disease can reduce the mortality rate. In this paper, an automated method based on machine learning (ML) and deep learning (DL) has been developed to detect COVID-19 using computed tomography (CT) scan images extracted from three publicly available datasets (A total of 11,407 images; 7397 COVID-19 images and 4010 normal images). An unsupervised clustering approach that is a modified region-based clustering technique for segmenting COVID-19 CT scan image has been proposed. Furthermore, contourlet transform and convolution neural network (CNN) have been employed to extract features individually from the segmented CT scan images and to fuse them in one feature vector. Binary differential evolution (BDE) approach has been employed as a feature optimization technique to obtain comprehensible features from the fused feature vector. Finally, a ML/DL-based ensemble classifier considering bagging technique has been employed to detect COVID-19 from the CT images. A fivefold and generalization cross-validation techniques have been used for the validation purpose. Classification experiments have also been conducted with several pre-trained models (AlexNet, ResNet50, GoogleNet, VGG16, VGG19) and found that the ensemble classifier technique with fused feature has provided state-of-the-art performance with an accuracy of 99.98%.

Evaluation, prediction, and measurement of carbon dioxide ( CO2) solubility in different polymers are crucial for engineers in various chemical applications, such as extraction and generation of novel materials. In this paper, correlations based on gene expression programming (GEP) were generated to predict the value of carbon dioxide solubility in three polymers. Results showed that the generated correlations could represent an outstanding efficiency and provide predictions for carbon dioxide solubility with satisfactory average absolute relative errors of 9.71%, 5.87%, and 1.63% for polystyrene (PS), polybutylene succinate-co-adipate (PBSA), and polybutylene succinate (PBS), respectively. Trend analysis based on Henry’s law illustrated that increasing pressure and decreasing temperature lead to an increase in carbon dioxide solubility. Finally, outlier discovery was applied using the leverage approach to detect the suspected data points. The outlier detection demonstrated the statistical validity of the developed correlations. William’s plot of three generated correlations showed that all of the data points are located in the valid zone except one point for PBS polymer and three points for PS polymer.

The Global Parkinson’s Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia.

In a stochastic Stackelberg asymmetric security game, the strong Stackelberg equilibrium (SSE) strategy is a popular option for the defender to get the highest utility against an attacker with the best response (BR) strategy. However, the attacker may be a boundedly rational player, who adopts a combination of the BR strategy and a fixed stubborn one. In such a condition, the SSE strategy may not maintain the defensive performance due to the stubbornness. In this paper, we focus on how the defender can adopt the unilateral-control zero-determinate (ZD) strategy to confront the boundedly rational attacker. At first, we verify the existence of ZD strategies for the defender. We then investigate the performance of the defender’s ZD strategy against a boundedly rational attacker, with a comparison of the SSE strategy. Specifically, when the attacker’s strategy is close to the BR strategy, the ZD strategy admits a bounded loss for the defender compared with the SSE strategy. Conversely, when the attacker’s strategy is close to the stubborn strategy, the ZD strategy can bring higher defensive performance for the defender than the SSE strategy does., QC 20230818

Estimation of Remaining Useful Lifetime (RUL) of discrete power electronics is important to enable predictive maintenance and ensure system safety. Conventional data-driven approaches using neural networks have been applied to address this challenge. However, due to ignoring the physical properties of the target RUL function, neural networks can result in unreasonable RUL estimates such as going upwards and wrong endings. In the paper, we apply the fundamental principle of Physics-Informed Neural Network (PINN) to enhance Recurrent Neural Network (RNN) based RUL estimation methods. Through formulating proper constraints into the loss function of neural networks, we demonstrate in our experiments with the NASA IGBT dataset that PINN can make the neural networks trained more realistically and thus achieve performance improvements in estimation error and coefficient of determination. Compared to the baseline vanilla RNN, our physics-informed RNN can improve Mean Squared Error (MSE) of out-of-sample estimation on average by 24.7% in training and by 51.3% in testing; Compared to the baseline Long Short Term Memory (LSTM, a variant of RNN), our physics-informed LSTM can improve MSE of out-of-sample estimation on average by 15.3% in training and 13.9% in testing., QC 20230821

Advancements in plant space biology are required for the realization of human space exploration missions, where the re-supply of resources from Earth is not feasible. Until a few decades ago, space life science was focused on the impact of the space environment on the human body. More recently, the interest in plant space biology has increased because plants are key organisms in Bioregenerative Life Support Systems (BLSS) for the regeneration of resources and fresh food production. Moreover, plants play an important role in psychological support for astronauts. The definition of cultivation requirements for the design, realization, and successful operation of BLSS must consider the effects of space factors on plants. Altered gravitational fields and radiation exposure are the main space factors inducing changes in gene expression, cell proliferation and differentiation, signalling and physiological processes with possible consequences on tissue organization and organogenesis, thus on the whole plant functioning. Interestingly, the changes at the cellular and molecular levels do not always result in organismic or developmental changes. This apparent paradox is a current research challenge. In this paper, the main findings of gravity- and radiation-related research on higher plants are summarized, highlighting the knowledge gaps that are still necessary to fill. Existing experimental facilities to simulate the effect of space factors, as well as requirements for future facilities for possible experiments to achieve fundamental biology goals are considered. Finally, the need for making synergies among disciplines and for establishing global standard operating procedures for analyses and data collection in space experiments is highlighted.

Tahitian pearls, artificially cultivated from the black-lipped pearl oyster Pinctada margaritifera, are renowned for their unique color and large size, making the pearl industry vital for the French Polynesian economy. Understanding the mechanisms of pearl formation is essential for enabling quality and sustainable production. In this paper, we explore the process of pearl formation by studying pearl rotation. Here we show, using a deep convolutional neural network, a direct link between the rotation of the pearl during its formation in the oyster and its final shape. We propose a new method for non-invasive pearl monitoring and a model for predicting the final shape of the pearl from rotation data with 81.9% accuracy. These novel resources provide a fresh perspective to study and enhance our comprehension of the overall mechanism of pearl formation, with potential long-term applications for improving pearl production and quality control in the industry.

Geranylgeranylacetone (GGA) is used to treat patients suffering from peptic ulcers and gastritis. We examined the effect of GGA on Helicobacter pylori, which is a causative factor of gastrointestinal diseases. Previously, we have reported that GGA binds specifically to the molecular chaperone HSP70. In this paper, we report that GGA bounds to H. pylori HSP70 (product of the DnaK gene) with 26-times higher affinity than to human HSP70, and induced large conformational changes as observed from surface plasmon resonance and circular dichroism. Binding of GGA suppressed the activity of the H. pylori chaperone. GGA also altered several characteristics of H. pylori cells. GGA-treated cells elicited enhanced interleukin-8 production by gastric cancer cell lines and potentiated susceptibility to complement as compared to untreated cells. GGA also caused morphological alterations in H. pylori as reflected in fewer coccoid-like cells, suggesting that GGA converts H. pylori to an actively dividing, spiral state (vegetative form) from a non-growing, coccoid state. This morphological conversion by GGA resulted in accelerated growth of H. pylori. These results suggest a model in which GGA sensitizes H. pylori to antibiotic treatment by converting the cells to an actively growing state.

The mammalian molecular chaperone, HSP60, plays an essential role in protein homeostasis through mediating protein folding and assembly. The structure and ATP-dependent function of HSP60 has been well established in recent studies. After ATP, GTP is the major cellular nucleotide. In this paper, we have investigated the role of GTP in the activity of HSP60. It was found that HSP60 has different properties with respect to allostery, complex formation and protein folding activity depending on the nucleoside triphosphate present. The presence of GTP slightly affected the ATPase activity of HSP60 during protein folding. These results provide clues as to the functional mechanism of the HSP60-HSP10 complex.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Echeverri, K. The various routes to functional regeneration in the central nervous system. Communications Biology, 3(1), (2020): 47, doi:10.1038/s42003-020-0773-z., The axolotl is a type of Mexican salamander with astonishing regenerative capacity1. In our recent paper, we identified a signaling heterodimer that is formed directly after injury in the glial cells adjacent to the injury in axolotls. The c-Fos and JunB genes forming this heterodimer are not unique to animals with high regenerative capacity but they are present in humans too. In this paper I propose perspectives on molecular control of regeneration and future directions that need to be taken to advance our understanding of regeneration at a molecular level.

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Echeverri, K. The various routes to functional regeneration in the central nervous system. Communications Biology, 3(1), (2020): 47, doi:10.1038/s42003-020-0773-z., The axolotl is a type of Mexican salamander with astonishing regenerative capacity1. In our recent paper, we identified a signaling heterodimer that is formed directly after injury in the glial cells adjacent to the injury in axolotls. The c-Fos and JunB genes forming this heterodimer are not unique to animals with high regenerative capacity but they are present in humans too. In this paper I propose perspectives on molecular control of regeneration and future directions that need to be taken to advance our understanding of regeneration at a molecular level.

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Echeverri, K. The various routes to functional regeneration in the central nervous system. Communications Biology, 3(1), (2020): 47, doi:10.1038/s42003-020-0773-z., The axolotl is a type of Mexican salamander with astonishing regenerative capacity1. In our recent paper, we identified a signaling heterodimer that is formed directly after injury in the glial cells adjacent to the injury in axolotls. The c-Fos and JunB genes forming this heterodimer are not unique to animals with high regenerative capacity but they are present in humans too. In this paper I propose perspectives on molecular control of regeneration and future directions that need to be taken to advance our understanding of regeneration at a molecular level.

Climate change is shaping extreme heat and rain. To what degree human activity has increased the risk of high impact events is of high public concern and still heavily debated. Recent studies attributed single extreme events to climate change by comparing climate model experiments where the influence of an external driver can be included or artificially suppressed. Many of these results however did not properly account for model errors in simulating the probabilities of extreme event occurrences. Here we show, exploiting advanced correction techniques from the weather forecasting field, that correcting properly for model probabilities alters the attributable risk of extreme events to climate change. This study illustrates the need to correct for this type of model error in order to provide trustworthy assessments of climate change impacts., This work has been supported by the EUropean CLimate and weather Events: Interpretation and Attribution (EUCLEIA) project, funded by the European Union’s Seventh Framework Programme [FP7/2007–2013] under Grant agreement no. 607085 as well as the Horizon 2020 EUCP EUropean Climate Prediction system under Grant agreement no. 776613 and by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 grant reference RYC-2017-22964. We thank Antje Weisheimer and Tim Palmer from the University of Oxford for their support to conduct this research. We are further indepted to Fraser Lott, Jonas Bhend, Stefan Siegert, Karsten Haustein, and Myles Allen for their help on the analysis and interpretation. O. Bellprat is the corresponding author (omar.bellprat@env.ethz.ch) and carried out the core analysis and writing of the paper. V. Guemas, F. Doblas-Reyes, and M. Donat have all either contributed in the study design or the interpretation of the analysis as well as the writing of the paper, Peer Reviewed, Postprint (published version)

The present paper examines longitudinally how subjective perceptions about COVID-19, one’s community, and the government predict adherence to public health measures to reduce the spread of the virus. Using an international survey (N = 3040), we test how infection risk perception, trust in the governmental response and communications about COVID-19, conspiracy beliefs, social norms on distancing, tightness of culture, and community punishment predict various containment-related attitudes and behavior. Autoregressive analyses indicate that, at the personal level, personal hygiene behavior was predicted by personal infection risk perception. At social level, social distancing behaviors such as abstaining from face-to-face contact were predicted by perceived social norms. Support for behavioral mandates was predicted by confidence in the government and cultural tightness, whereas support for anti-lockdown protests was predicted by (lower) perceived clarity of communication about the virus. Results are discussed in light of policy implications and creating effective interventions. © 2022, The Author(s)., New York University Abu Dhabi

The future satellite platform and 5G communication systems place high demands on antennas, in which the antenna should offer low-cost, lightweight, electronically steerable features. In this paper, the design of a digital slot antenna element based on substrate integrated waveguide (SIW) is proposed. SIW guides the microwave inside the substrate confined with planar metallic covers and through-hole synthetized side-walls in conventional applications, and can also radiate the microwave towards free space in antenna applications through opening slots in its metallic covers. The slot antenna element is realized by implementing PIN diodes across the gaps on both sides of the pad in the center of the slot antenna, to provide the switching freedom of the slot antenna element between radiating and non-radiating states. Besides, radial decoupling stubs are introduced into the bias line so as to reduce the leakage of the energy in the SIW structure. Applying a series of on/off states to the diodes produces various radiation patterns, thus wide range scanning is possible supposing that enough array elements are equipped. Finally, a digital SIW-slot array composed of 8 by 4 elements with tunable field programmable gate array circuits are fabricated and measured. The measured results validate the reconfigurable characteristics for the radiation pattern of the proposed digital SIW-slot antenna array without heavy engineering of phase shifter in conventional antenna arrays. The antenna is consisted by 4 by 8 elements and its dimension, simulated gain and radiation efficiency are 145 mm × 127 mm × 1.524 mm, 15 dBi and 53.5%, respectively. Our designed SIW antenna has the advantage of both size and weight. Furthermore, its digitalized control of beamforming allows a programming-friendly interface for smart antenna development. © 2022, The Author(s).

Hydraulic equipment, as a typical mechanical product, has been wildly used in various fields. Accurate acquisition and secure transmission of assembly deviation data are the most critical issues for hydraulic equipment manufacturer in the PLM-oriented value chain collaboration. Existing deviation prediction methods are mainly used for assembly quality control, which concentrate in the product design and assembly stage. However, the actual assembly deviations generated in the service stage can be used to guide the equipment maintenance and tolerance design. In this paper, a high-fidelity prediction and privacy-preserving method is proposed based on the observable assembly deviations. A hierarchical graph attention network (HGAT) is established to predict the assembly feature deviations. The hierarchical generalized representation and differential privacy reconstruction techniques are also introduced to generate the graph attention network model for assembly deviation privacy-preserving. A derivation gradient matrix is established to calculate the defined modified necessary index of assembly parts. Two privacy-preserving strategies are designed to protect the assembly privacy of node representation and adjacent relationship. The effectiveness and superiority of the proposed method are demonstrated by a case study with a four-column hydraulic press. © 2022, The Author(s).

Despite the vast amount of studies based on magnetorheological elastomers (MREs), a very limited number of investigations have been initiated on their reprocessing. This paper presents a new type of recyclable MRE which is composed of thermoplastic polyurethane (TPU) and carbonyl iron particles (CI). The chosen TPU can be processed using injection moulding (IM), followed by several reprocessing cycles while preserving its properties. Numerous types of injection moulded and reprocessed MREs have been prepared for various particle concentrations. The effect of thermo-mechanical degradation on the recycled MREs has been investigated while simulating the reprocessing procedure. An apparent decrease in molecular weight was observed for all the examined matrices during the reprocessing cycles. These changes are attributed to the intermolecular bonding between the hydroxyl groups on the surface of the CI particles and the matrix which is studied in depth. The effect of reprocessing and the presence of magnetic particles is evaluated via tensile test, magnetorheology and piezoresistivity. These characterization techniques prove that the properties of our MREs are preserved at an acceptable level despite using 100% of recyclates while in real applications only up to 30% of recycled material is generally used. © 2022, The Author(s).

Marine sponges (phylum Porifera) are leading organisms for the discovery of bioactive compounds from nature. Their often rich and species-specific microbiota is hypothesised to be producing many of these compounds. Yet, environmental influences on the sponge-associated microbiota and bioactive compound production remain elusive. Here, we investigated the changes of microbiota and metabolomes in sponges along a depth range of 1232 m. Using 16S rRNA gene amplicon sequencing and untargeted metabolomics, we assessed prokaryotic and chemical diversities in three deep-sea sponge species: Geodia barretti, Stryphnus fortis, and Weberella bursa. Both prokaryotic communities and metabolome varied significantly with depth, which we hypothesized to be the effect of different water masses. Up to 35.5 % of microbial ASVs (amplicon sequence variants) showed significant changes with depth while phylum-level composition of host microbiome remained unchanged. The metabolome varied with depth, with relative quantities of known bioactive compounds increasing or decreasing strongly. Other metabolites varying with depth were compatible solutes regulating osmolarity of the cells. Correlations between prokaryotic community and the bioactive compounds in G. barretti suggested members of Acidobacteria, Proteobacteria, Chloroflexi, or an unclassified prokaryote as potential producers., Title in dissertation list of papers: Go with the flow: oceanographic setting influences the prokaryotic community and metabolome in deep-sea sponges De två första författarna delar förstaförfattarskapet.De två sista författarna delar sistaförfattarskapet.

The world has gone through unprecedented changes since the global pandemic hit. During the early phase of the pandemic, the absence of known drugs or pharmaceutical treatments forced governments to introduce different policies in order to help reduce contagion rates and manage the economic consequences of the pandemic. This paper analyses the causal impact on mobility and COVID19 incidence from policy makers in Cataluña, Spain. We use anonymized phone-based mobility data together with reported incidence and apply a series of causal impact models frequently used in econometrics and policy evaluation in order to measure the policies impact. We analyse the case of Cataluña and the public policy decision of closing all bars and restaurants down for a 5 week period between 2020-16-10 and 2020-23-11. We find that this decision led to a significant reduction in mobility. It not only led to reductions in mobility but from a behavioural economics standpoint, we highlight how people responded to the policy decision. Moreover, the policy of closing bars and restaurants slowed the incidence rate of COVID19 after a time lag has been taken into account. These findings are significant since governments worldwide want to restrict movements of people in order to slow down COVID19 incidence without infringing on their rights directly., Peer Reviewed, Postprint (published version)

Despite the vast amount of studies based on magnetorheological elastomers (MREs), a very limited number of investigations have been initiated on their reprocessing. This paper presents a new type of recyclable MRE which is composed of thermoplastic polyurethane (TPU) and carbonyl iron particles (CI). The chosen TPU can be processed using injection moulding (IM), followed by several reprocessing cycles while preserving its properties. Numerous types of injection moulded and reprocessed MREs have been prepared for various particle concentrations. The effect of thermo-mechanical degradation on the recycled MREs has been investigated while simulating the reprocessing procedure. An apparent decrease in molecular weight was observed for all the examined matrices during the reprocessing cycles. These changes are attributed to the intermolecular bonding between the hydroxyl groups on the surface of the CI particles and the matrix which is studied in depth. The effect of reprocessing and the presence of magnetic particles is evaluated via tensile test, magnetorheology and piezoresistivity. These characterization techniques prove that the properties of our MREs are preserved at an acceptable level despite using 100% of recyclates while in real applications only up to 30% of recycled material is generally used. © 2022, The Author(s).

Marine sponges (phylum Porifera) are leading organisms for the discovery of bioactive compounds from nature. Their often rich and species-specific microbiota is hypothesised to be producing many of these compounds. Yet, environmental influences on the sponge-associated microbiota and bioactive compound production remain elusive. Here, we investigated the changes of microbiota and metabolomes in sponges along a depth range of 1232 m. Using 16S rRNA gene amplicon sequencing and untargeted metabolomics, we assessed prokaryotic and chemical diversities in three deep-sea sponge species: Geodia barretti, Stryphnus fortis, and Weberella bursa. Both prokaryotic communities and metabolome varied significantly with depth, which we hypothesized to be the effect of different water masses. Up to 35.5 % of microbial ASVs (amplicon sequence variants) showed significant changes with depth while phylum-level composition of host microbiome remained unchanged. The metabolome varied with depth, with relative quantities of known bioactive compounds increasing or decreasing strongly. Other metabolites varying with depth were compatible solutes regulating osmolarity of the cells. Correlations between prokaryotic community and the bioactive compounds in G. barretti suggested members of Acidobacteria, Proteobacteria, Chloroflexi, or an unclassified prokaryote as potential producers., Title in dissertation list of papers: Go with the flow: oceanographic setting influences the prokaryotic community and metabolome in deep-sea sponges De två första författarna delar förstaförfattarskapet.De två sista författarna delar sistaförfattarskapet.

Marine sponges (phylum Porifera) are leading organisms for the discovery of bioactive compounds from nature. Their often rich and species-specific microbiota is hypothesised to be producing many of these compounds. Yet, environmental influences on the sponge-associated microbiota and bioactive compound production remain elusive. Here, we investigated the changes of microbiota and metabolomes in sponges along a depth range of 1232 m. Using 16S rRNA gene amplicon sequencing and untargeted metabolomics, we assessed prokaryotic and chemical diversities in three deep-sea sponge species: Geodia barretti, Stryphnus fortis, and Weberella bursa. Both prokaryotic communities and metabolome varied significantly with depth, which we hypothesized to be the effect of different water masses. Up to 35.5 % of microbial ASVs (amplicon sequence variants) showed significant changes with depth while phylum-level composition of host microbiome remained unchanged. The metabolome varied with depth, with relative quantities of known bioactive compounds increasing or decreasing strongly. Other metabolites varying with depth were compatible solutes regulating osmolarity of the cells. Correlations between prokaryotic community and the bioactive compounds in G. barretti suggested members of Acidobacteria, Proteobacteria, Chloroflexi, or an unclassified prokaryote as potential producers., Title in dissertation list of papers: Go with the flow: oceanographic setting influences the prokaryotic community and metabolome in deep-sea sponges De två första författarna delar förstaförfattarskapet.De två sista författarna delar sistaförfattarskapet.

Constructing a large biological model is a difficult, error-prone process. Small errors in writing a part of the model cascade to the system level and their sources are difficult to trace back. In this paper we extend a recent approach based on Event-B, a state-based formal method with refinement as its central ingredient, allowing us to validate for model consistency step-by-step in an automated way. We demonstrate this approach on a model of the heat shock response in eukaryotes and its scalability on a model of the ErbB signaling pathway. All consistency properties of the model were proved automatically with computer support.

Marine sponges (phylum Porifera) are leading organisms for the discovery of bioactive compounds from nature. Their often rich and species-specific microbiota is hypothesised to be producing many of these compounds. Yet, environmental influences on the sponge-associated microbiota and bioactive compound production remain elusive. Here, we investigated the changes of microbiota and metabolomes in sponges along a depth range of 1232 m. Using 16S rRNA gene amplicon sequencing and untargeted metabolomics, we assessed prokaryotic and chemical diversities in three deep-sea sponge species: Geodia barretti, Stryphnus fortis, and Weberella bursa. Both prokaryotic communities and metabolome varied significantly with depth, which we hypothesized to be the effect of different water masses. Up to 35.5 % of microbial ASVs (amplicon sequence variants) showed significant changes with depth while phylum-level composition of host microbiome remained unchanged. The metabolome varied with depth, with relative quantities of known bioactive compounds increasing or decreasing strongly. Other metabolites varying with depth were compatible solutes regulating osmolarity of the cells. Correlations between prokaryotic community and the bioactive compounds in G. barretti suggested members of Acidobacteria, Proteobacteria, Chloroflexi, or an unclassified prokaryote as potential producers., Title in dissertation list of papers: Go with the flow: oceanographic setting influences the prokaryotic community and metabolome in deep-sea sponges De två första författarna delar förstaförfattarskapet.De två sista författarna delar sistaförfattarskapet.

The present paper examines longitudinally how subjective perceptions about COVID-19, one’s community, and the government predict adherence to public health measures to reduce the spread of the virus. Using an international survey (N = 3040), we test how infection risk perception, trust in the governmental response and communications about COVID-19, conspiracy beliefs, social norms on distancing, tightness of culture, and community punishment predict various containment-related attitudes and behavior. Autoregressive analyses indicate that, at the personal level, personal hygiene behavior was predicted by personal infection risk perception. At social level, social distancing behaviors such as abstaining from face-to-face contact were predicted by perceived social norms. Support for behavioral mandates was predicted by confidence in the government and cultural tightness, whereas support for anti-lockdown protests was predicted by (lower) perceived clarity of communication about the virus. Results are discussed in light of policy implications and creating effective interventions. © 2022, The Author(s)., New York University Abu Dhabi

The present paper examines longitudinally how subjective perceptions about COVID-19, one’s community, and the government predict adherence to public health measures to reduce the spread of the virus. Using an international survey (N = 3040), we test how infection risk perception, trust in the governmental response and communications about COVID-19, conspiracy beliefs, social norms on distancing, tightness of culture, and community punishment predict various containment-related attitudes and behavior. Autoregressive analyses indicate that, at the personal level, personal hygiene behavior was predicted by personal infection risk perception. At social level, social distancing behaviors such as abstaining from face-to-face contact were predicted by perceived social norms. Support for behavioral mandates was predicted by confidence in the government and cultural tightness, whereas support for anti-lockdown protests was predicted by (lower) perceived clarity of communication about the virus. Results are discussed in light of policy implications and creating effective interventions. © 2022, The Author(s)., New York University Abu Dhabi

Marine sponges (phylum Porifera) are leading organisms for the discovery of bioactive compounds from nature. Their often rich and species-specific microbiota is hypothesised to be producing many of these compounds. Yet, environmental influences on the sponge-associated microbiota and bioactive compound production remain elusive. Here, we investigated the changes of microbiota and metabolomes in sponges along a depth range of 1232 m. Using 16S rRNA gene amplicon sequencing and untargeted metabolomics, we assessed prokaryotic and chemical diversities in three deep-sea sponge species: Geodia barretti, Stryphnus fortis, and Weberella bursa. Both prokaryotic communities and metabolome varied significantly with depth, which we hypothesized to be the effect of different water masses. Up to 35.5 % of microbial ASVs (amplicon sequence variants) showed significant changes with depth while phylum-level composition of host microbiome remained unchanged. The metabolome varied with depth, with relative quantities of known bioactive compounds increasing or decreasing strongly. Other metabolites varying with depth were compatible solutes regulating osmolarity of the cells. Correlations between prokaryotic community and the bioactive compounds in G. barretti suggested members of Acidobacteria, Proteobacteria, Chloroflexi, or an unclassified prokaryote as potential producers., Title in dissertation list of papers: Go with the flow: oceanographic setting influences the prokaryotic community and metabolome in deep-sea sponges De två första författarna delar förstaförfattarskapet.De två sista författarna delar sistaförfattarskapet.