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This article describes how the transcriptional alterations of the innate immune system divide dysplasias into aggressive forms that, despite the treatment, relapse quickly and more easily, and others where the progression is slow and more treatable. It elaborates on how the immune system can change the extracellular matrix, favoring neoplastic progression, and how infections can enhance disease progression by increasing epithelial damage due to the loss of surface immunoglobulin and amplifying the inflammatory response. We investigated whether these dysregulated genes were linked to disease progression, delay, or recovery. These transcriptional alterations were observed using the RNA-based next-generation sequencing (NGS) panel Oncomine Immune Response Research Assay (OIRRA) to measure the expression of genes associated with lymphocyte regulation, cytokine signaling, lymphocyte markers, and checkpoint pathways. During the analysis, it became apparent that certain alterations divide dysplasia into two categories: progressive or not. In the future, these biological alterations are the first step to provide new treatment modalities with different classes of drugs currently in use in a systemic or local approach, including classical chemotherapy drugs such as cisplatin and fluorouracile, older drugs like fenretinide, and new checkpoint inhibitor drugs such as nivolumab and pembrolizumab, as well as newer options like T cell therapy (CAR-T). Following these observed alterations, it is possible to differentiate which dysplasias progress or not or relapse quickly. This information could, in the future, be the basis for determining a close follow-up, minimizing surgical interventions, planning a correct and personalized treatment protocol for each patient and, after specific clinical trials, tailoring new drug treatments.

Imaging is increasingly used to capture information on the marine environment thanks to the improvements in imaging equipment, devices for carrying cameras and data storage in recent years. In that context, biologists, geologists, computer specialists and end-users must gather to discuss the methods and procedures for optimising the quality and quantity of data collected from images. The 4 Marine Imaging Workshop was organised from 3-6 October 2022 in Brest (France) in a hybrid mode. More than a hundred participants were welcomed in person and about 80 people attended the online sessions. The workshop was organised in a single plenary session of presentations followed by discussion sessions. These were based on dynamic polls and open questions that allowed recording of the imaging community’s current and future ideas. In addition, a whole day was dedicated to practical sessions on image analysis, data standardisation and communication tools. The format of this edition allowed the participation of a wider community, including lower income countries, early career scientists, all working on laboratory, benthic and pelagic imaging. This article summarises the topics addressed during the workshop, particularly the outcomes of the discussion sessions for future reference and to make the workshop results available to the open public.

Il contributo analizza il modello organizzativo e la filosofia di funzionamento dell'esperienza di teatro sociale e di comunità di SCT Centre a Torino, a venti anni dalla sua costituzione. Rispetto ai consueti modelli organizzativi emerge una impronta maggiormente di natura olografica che caratterizza SCT Centre come gruppo creativo, con una forte valorizzazione del principio di auto organizzazione e con una tendenza verso l'ambidestrismo, ovvero saper condurre sia l’attività di exploration come capacità di accedere e praticare in nuovi ambiti ed esplorare nuove opportunità e idee, sia quella di exploitation quindi saper sfruttare le competenze caratteristiche di cui si dispone e i processi maturati, all'interno della stretta interrelazione tra creazione e produzione artistica, persone, comunità e territori

Current research on riverine macrolitter does not yet provide a theoretic framework on the dynamics behind its accumulation and distribution along riverbanks. In an attempt to better understand these dynamics a detailed field survey of three months was conducted in which location of macrolitter items within a single groyne field along the Waal riverbanks was tracked. The data provided insight into the daily changing patterns of spatial item distribution with respect to the waterline. Furthermore, the rates of item uptake and deposition were monitored and related to hydrologic fluctuations. Uptake was initiated by rising water levels and was generally higher when the water level increased faster. Deposition occurred continuously, despite hydrologic fluctuations. This caused the riverbank macrolitter budget to be positive during stable or dropping water levels and negative during rising water levels. Although the results show clear patterns an extended monitoring duration is required to fully understand the fate of plastic objects., Sanitary Engineering, Water Resources

Imaging is increasingly used to capture information on the marine environment thanks to the improvements in imaging equipment, devices for carrying cameras and data storage in recent years. In that context, biologists, geologists, computer specialists and end-users must gather to discuss the methods and procedures for optimising the quality and quantity of data collected from images. The 4 th Marine Imaging Workshop was organised from 3-6 October 2022 in Brest (France) in a hybrid mode. More than a hundred participants were welcomed in person and about 80 people attended the online sessions. The workshop was organised in a single plenary session of presentations followed by discussion sessions. These were based on dynamic polls and open questions that allowed recording of the imaging community’s current and future ideas. In addition, a whole day was dedicated to practical sessions on image analysis, data standardisation and communication tools. The format of this edition allowed the participation of a wider community, including lower-income countries, early career scientists, all working on laboratory, benthic and pelagic imaging. This article summarises the topics addressed during the workshop, particularly the outcomes of the discussion sessions for future reference and to make the workshop results available to the open public.

Imaging is increasingly used to capture information on the marine environment thanks to the improvements in imaging equipment, devices for carrying cameras and data storage in recent years. In that context, biologists, geologists, computer specialists and end-users must gather to discuss the methods and procedures for optimising the quality and quantity of data collected from images. The 4th Marine Imaging Workshop was organised from 3-6 October 2022 in Brest (France) in a hybrid mode. More than a hundred participants were welcomed in person and about 80 people attended the online sessions. The workshop was organised in a single plenary session of presentations followed by discussion sessions. These were based on dynamic polls and open questions that allowed recording of the imaging community’s current and future ideas. In addition, a whole day was dedicated to practical sessions on image analysis, data standardisation and communication tools. The format of this edition allowed the participation of a wider community, including lower-income countries, early career scientists, all working on laboratory, benthic and pelagic imaging. This article summarises the topics addressed during the workshop, particularly the outcomes of the discussion sessions for future reference and to make the workshop results available to the open public.

We present the results from a complex study of an eclipsing O-type binary (Aa+Ab) with the orbital period $P_{A}=3.2254367$ days, that forms part of a higher-order multiple system in a configuration (A+B)+C. We derived masses of the Aa+Ab binary $M_{1}= 19.02 \pm 0.12 \,M_\odot$, $M_{2}= 17.50 \pm 0.13 \,M_\odot$, radii $R_{1}= 7.70 \pm 0.05 \,R_\odot$, $R_{2}= 6.64 \pm 0.06 \,R_\odot$, and temperatures $T_1 = 34250 \pm 500 $ K, $T_2 = 33750 \pm 500 $ K. From the analysis of radial velocities, we found a spectroscopic orbit of A in the outer A+B system with $P_{A+B}=195.8$ days ($P_{A+B}/P_{A}\approx 61$). In the O-C analysis, we confirmed this orbit and found another component orbiting the A+B system with $P_{AB+C}=2550$ days ($P_{AB+C}\,/P_{A+B}\approx 13$). From the total mass of the inner binary and its outer orbit, we estimated the mass of the third object, $M_B \gtrsim 10.7 M_\odot$. From the light-travel time effect fit to the O-C data, we obtained the limit for the mass of the fourth component, $M_C \gtrsim 7.3 M_\odot$. These extra components contribute to about 20% to 30% (increasing with wavelength) of the total system light. From the comparison of model spectra with the multiband photometry, we derived a distance modulus of 18.59 $\pm$ 0.06 mag, a reddening of 0.16 $\pm$ 0.02 mag, and an $R_V$ of $3.2$. This work is part of our ongoing project, which aims to calibrate the surface brightness-color relation for early-type stars., Comment: 15 pages, 14 figures, 3 tables, accepted for publication in The Astrophysical Journal

Binary Cepheids with giant companions are crucial for studying the physical properties of Cepheid variables, providing the best means to measure their masses. Systems composed of two Cepheids are even more important but to date, only one such system in the Large Magellanic Cloud (LMC) was known. Our current aim is to increase the number of these systems tenfold and provide their basic characteristics. The final goal is to obtain the physical properties of the component Cepheids, including their masses and radii, and to learn about their evolution in the multiple systems, also revealing their origin. We started a spectroscopic monitoring of nine unresolved pairs of Cepheids from the OGLE catalog, to check if they are gravitationally bound. Two of these so-called double Cepheids are located in the LMC, five in the Small Magellanic Cloud (SMC), and two in the Milky Way (MW). We report the spectroscopic detection of binarity of all 9 of these double Cepheids with orbital periods from 2 to 18 years. This increases the number of known binary double (BIND) Cepheids from 1 to 10 and triples the number of all confirmed double-lined binary (SB2) Cepheids. For five BIND Cepheids disentangled pulsational light curves of the components show anti-correlated phase shifts due to orbital motion. We show the first empirical evidence that typical period-luminosity relations (PLRs) are rather binary Cepheid PLRs that include the companion's light. The statistics of pulsation period ratios of BIND Cepheids do not agree with those expected for pairs of the same-age Cepheids. These ratios together with the mass ratios far from unity suggest merger-origin of at least one component for about half of the systems. The SMC and MW objects are the first found in SB2 systems composed of giants in their host galaxies. The Milky Way BIND Cepheids are also the closest such systems, being located at about 11 and 26 kpc., Comment: 13 pages, 7 figures, 4 tables, accepted for publication in Astronomy & Astrophysics

Imaging is increasingly used to capture information on the marine environment thanks to the improvements in imaging equipment, devices for carrying cameras and data storage in recent years. In that context, biologists, geologists, computer specialists and end-users must gather to discuss the methods and procedures for optimising the quality and quantity of data collected from images. The 4 th Marine Imaging Workshop was organised from 3-6 October 2022 in Brest (France) in a hybrid mode. More than a hundred participants were welcomed in person and about 80 people attended the online sessions. The workshop was organised in a single plenary session of presentations followed by discussion sessions. These were based on dynamic polls and open questions that allowed recording of the imaging community’s current and future ideas. In addition, a whole day was dedicated to practical sessions on image analysis, data standardisation and communication tools. The format of this edition allowed the participation of a wider community, including lower-income countries, early career scientists, all working on laboratory, benthic and pelagic imaging. This article summarises the topics addressed during the workshop, particularly the outcomes of the discussion sessions for future reference and to make the workshop results available to the open public.

Field experiences are an important element of paleontological knowledge building. However, there is little information about the effects field experiences have on science stewardship and personal growth in high school learners. This pilot study analyzed the reflections of a group of female and male 9th grade U.S. high school students (N = 72) on their first field paleontology experience at Rainbow Basin Natural Area, California. We investigate the attitudinal impact of this experience within a human dimensions research framework using mixed methods. Likert-style surveys indicate relative consistency in pre- and post-treatment total group responses student-to-student. Qualitative responses highlight themes such as the power of science stewardship and a personal sense of connection to public lands. An increase in reflections of stewardship across coded segments occurred, from 18.4% to 29.4% pretreatment versus post-treatment. Qualitative responses and the human dimensions research framework are highly recommended as tools for paleontology education researchers to more effectively document attitudinal changes during field experiences and to better understand field paleontology narratives. In combination, these methods can highlight the intersection of science stewardship, personal growth through experiential education, and the importance of field paleontology. Future studies can make use of human dimensions research to illuminate the impact of field paleontology on pre-college students, and document the influence of field science on future generations of policy-makers, educators, and scientists.

Numerical simulation models are a fundamental tool for planning and managing smart water networks—an evolution of water distribution systems in which physical assets are monitored and controlled by information and communication technologies. While simulation models allow us to understand the interactions between physical processes and abstract control strategies, they ignore key implementation aspects of distributed control systems, such as the required communication over digital links. As a result, the effects of anomalies and faults in the communication on the process control cannot be investigated with existing tools. In this work, we fill this gap by introducing DHALSIM (Digital HydrAuLic SIMulator), a numerical modelling platform combining EPANET-based process simulation with a network and host emulation environment, offering a high-fidelity representation of the processes occurring in the cyber domain. We illustrate DHALSIM’s key functionalities by implementing it on a benchmark water distribution system, present case studies of simulated network traffic, and demonstrate how anomalies in the behavior of the communication network affect the process data received by the supervisory control and data acquisition (SCADA) server. In a companion paper, we further illustrate how DHALSIM enables research opportunities in the domain of cyber-physical security. The easily customizable and open source DHALSIM provides a “workbench” for studying smart water networks, developing digital twins, and designing a broad spectrum of engineering solutions., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Sanitary Engineering

Metamodels accurately reproduce the output of physics-based hydraulic models with a significant reduction in simulation times. They are widely employed in water distribution system (WDS) analysis since they enable computationally expensive applications in the design, control, and optimisation of water networks. Recent machine-learning-based metamodels grant improved fidelity and speed; however, they are only applicable to the water network they were trained on. To address this issue, we investigate graph neural networks (GNNs) as metamodels for WDSs. GNNs leverage the networked structure of WDS by learning shared coefficients and thus offering the potential of transferability. This work evaluates the suitability of GNNs as metamodels for estimating nodal pressures in steady-state EPANET simulations. We first compare the effectiveness of GNN metamodels against multi-layer perceptrons (MLPs) on several benchmark WDSs. Then, we explore the transferability of GNNs by training them concurrently on multiple WDSs. For each configuration, we calculate model accuracy and speedups with respect to the original numerical model. GNNs perform similarly to MLPs in terms of accuracy and take longer to execute but may still provide substantial speedup. Our preliminary results indicate that GNNs can learn shared representations across networks, although assessing the feasibility of truly general metamodels requires further work., Sanitary Engineering, Multimedia Computing

Supervised Deep Learning (DL) methods have shown promise in monitoring the floating litter in rivers and urban canals but further advancements are hard to obtain due to the limited availability of relevant labeled data. To address this challenge, researchers often utilize techniques such as transfer learning (TL) and data augmentation (DA). However, there is no study currently reporting a rigorous evaluation of the effectiveness of these approaches for floating litter detection and their effects on the models' generalization capability. To overcome the problem of limited data availability, this work introduces the “TU Delft—Green Village” dataset, a novel labeled dataset of 9,473 camera and phone images of floating macroplastic litter and other litter items, captured using experiments in a drainage canal of TU Delft. We use the new dataset to conduct a thorough evaluation of the detection performance of five DL architectures for multi-class image classification. We focus the analysis on a systematic evaluation of the benefits of TL and DA on model performances. Moreover, we evaluate the generalization capability of these models for unseen litter items and new device settings, such as increasing the cameras' height and tilting them to 45°. The results obtained show that, for the specific problem of floating litter detection, fine-tuning all layers is more effective than the common approach of fine-tuning the classifier alone. Among the tested DA techniques, we find that simple image flipping boosts model accuracy the most, while other methods have little impact on the performance. The SqueezeNet and DenseNet121 architectures perform the best, achieving an overall accuracy of 89.6 and 91.7%, respectively. We also observe that both models retain good generalization capability which drops significantly only for the most complex scenario tested, but the overall accuracy raises significantly to around 75% when adding a limited amount of images to training data, combined w, Sanitary Engineering

Numerical modelling is a reliable tool for flood simulations, but accurate solutions are computationally expensive. In recent years, researchers have explored data-driven methodologies based on neural networks to overcome this limitation. However, most models are only used for a specific case study and disregard the dynamic evolution of the flood wave. This limits their generalizability to topographies that the model was not trained on and in time-dependent applications. In this paper, we introduce shallow water equation–graph neural network (SWE–GNN), a hydraulics-inspired surrogate model based on GNNs that can be used for rapid spatio-temporal flood modelling. The model exploits the analogy between finite-volume methods used to solve SWEs and GNNs. For a computational mesh, we create a graph by considering finite-volume cells as nodes and adjacent cells as being connected by edges. The inputs are determined by the topographical properties of the domain and the initial hydraulic conditions. The GNN then determines how fluxes are exchanged between cells via a learned local function. We overcome the time-step constraints by stacking multiple GNN layers, which expand the considered space instead of increasing the time resolution. We also propose a multi-step-ahead loss function along with a curriculum learning strategy to improve the stability and performance. We validate this approach using a dataset of two-dimensional dike breach flood simulations in randomly generated digital elevation models generated with a high-fidelity numerical solver. The SWE–GNN model predicts the spatio-temporal evolution of the flood for unseen topographies with mean average errors in time of 0.04 m for water depths and 0.004 m2 s−1 for unit discharges. Moreover, it generalizes well to unseen breach locations, bigger domains, and longer periods of time compared to those of the training set, outperforming other deep-learning models. On top of this, SWE–GNN has a computational speed-up of up, Sanitary Engineering, Multimedia Computing, Hydraulic Structures and Flood Risk

Researchers and engineers employ machine learning (ML) tools to detect pipe bursts and prevent significant non-revenue water losses in water distribution systems (WDS). Nonetheless, many approaches developed so far consider a fixed number of sensors, which requires the ML model redevelopment and collection of sufficient data with the new sensor configuration for training. To overcome these issues, this study presents a novel approach based on Long Short-Term Memory neural networks (NNs) that leverages transfer learning to manage a varying number of sensors and retain good detection performance with limited training data. The proposed detection model first learns to reproduce the normal behavior of the system on a dataset obtained in burst-free conditions. The training process involves predicting flow and pressure one-time step ahead using historical data and time-related features as inputs. During testing, a post-prediction step flags potential bursts based on the comparison between the observations and model predictions using a time-varied error threshold. When adding new sensors, we implement transfer learning by replicating the weights of existing channels and then fine-tune the augmented NN. We evaluate the robustness of the methodology on simulated fire hydrant bursts and real-bursts in 10 district metered areas (DMAs) of the UK. For real bursts, we perform a sensitivity analysis to understand the impact of data resolution and error threshold on burst detection performance. The results obtained demonstrate that this ML-based methodology can achieve Precision of up to 98.1% in real-life settings and can identify bursts, even in data scarce conditions., Sanitary Engineering

West African food systems and rural socio-economics are based on rainfed agriculture, which makes society highly vulnerable to rainfall uncertainty and frequent floods and droughts. Reliable rainfall information is currently missing. There is a sparse and uneven rain gauge distribution and, despite continuous efforts, rainfall satellite products continue to show weak correlations with ground measurements. This paper aims to investigate whether water vapor (WV) observations together with temporal information can complement thermal infrared (TIR) data for satellite rainfall retrieval in a Deep Learning (DL) framework. This is motivated by the fact that water vapor plays a key role in the highly seasonal West African rainfall dynamics. We present a DL model for satellite rainfall detection based on WV and TIR channels of Meteosat Second Generation and temporal information. Results show that the WV inhibition of low-level features enables the depiction of strong convective motions usually related to heavy rainfall. This is especially relevant in areas where convective rainfall is dominant, such as the tropics. Additionally, WV data allow us to detect dry air masses over our study area, that are advected from the Sahara Desert and create discontinuities in precipitation events. The developed DL model shows strong performance in rainfall binary classification, with less false alarms and lower rainfall overdetection (FBias <2.0) than the state-of-the-art Integrated MultisatellitE Retrievals for GPM (IMERG) Final Run., Water Resources, Sanitary Engineering

Streamflow predictions remain a challenge for poorly gauged and ungauged catchments. Recent research has shown that deep learning methods based on Long Short-Term Memory (LSTM) cells outperform process-based hydrological models for rainfall-runoff modeling, opening new possibilities for prediction in ungauged basins (PUB). These studies usually feature local datasets for model development, while predictions in ungauged basins at a global scale require training on global datasets. In this study, we develop LSTM models for over 500 catchments from the CAMELS-US data base using global ERA5 meteorological forcing and global catchment characteristics retrieved with the HydroMT tool. Comparison against an LSTM trained with local datasets shows that, while the latter generally yields superior performances due to the higher spatial resolution meteorological forcing (overall median daily NSE 0.54 vs. 0.71), training with ERA5 results in higher NSE in most catchments of Western and North-Western US (median daily NSE of 0.83 vs. 0.78). No significant changes in performance occur when substituting local with global data sources for deriving the catchment characteristics. These results encourage further research to develop LSTM models for worldwide predictions of streamflow in ungauged basins using available global datasets. Promising directions include training the models with streamflow data from different regions of the world and with higher quality meteorological forcing., Water Resources, Sanitary Engineering

The book consists of a number of short articles that present achievements of the Araucaria members, collaborators, and friends, in various aspects of distance determinations and related topics. It celebrates the 20-year anniversary of the Araucaria Project, acknowledges the people who worked for its success, and popularises our methods and results among broader readership. This book is a part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 695099., Comment: 114 pages, book published in 2021 on behalf of the Nicolaus Copernicus Astronomical Center of the Polish Academy of Sciences, to celebrate 20 years of the Arauria Project

We present new period-luminosity and period-luminosity-metallicity relations for Galactic RR Lyrae stars based on a sample of 28 pulsators located at distances up to $1.5$ kpc from the Sun. Near-infrared photometry was obtained at the Cerro Armazones Observatory and parallaxes were taken from the Gaia Early Data Release 3. Relations were determined for the 2MASS $JHK_s$ bands and the $W_{JK}$ Wesenheit index. We compare our results with other calibrations available in the literature and obtain very good agreement with the photometry of RR Lyraes from the Large Magellanic Cloud anchored using the distance to the Cloud, which based on detached eclipsing binaries. We find that the dependence of absolute magnitudes on metallicity of $0.070\pm 0.042$ mag/dex ($J-$ band) to $0.087 \pm 0.031$ mag/dex ($W_{JK}$ index) for the population of fundamental pulsators (RRab) that is in agreement with previously published phenomenological works. We perform a refined determination of distance to the LMC based on our new calibration and photometry from Szewczyk et al. (2008). We study the dependence of the fitted parameters of fiducial relations and the LMC distance on the systematic parallax offset., Comment: 32 pages, 9 figures, 9 tables. Accepted for publication in ApJ

The Baade-Wesselink (BW) method of distance determination of Cepheids is used to calibrate the distance scale. Various versions of this method are mainly based on interferometry and/or the surface-brightness color relation (SBCR). We quantify the impact of the SBCR, its slope, and its zeropoint on the projection factor. This quantity is used to convert the pulsation velocity into the radial velocity in the BW method. We also study the impact of extinction and of a potential circumstellar environment on the projection factor. We analyzed HARPS-N spectra of eta Aql to derive its radial velocity curve using different methods. We then applied the inverse BW method using various SBCRs in the literature in order to derive the BW projection factor. We find that the choice of the SBCR is critical: a scatter of about 8% is found in the projection factor for different SBCRs in the literature. The uncertainty on the coefficients of the SBCR affects the statistical precision of the projection factor only little (1-2\%). Confirming previous studies, we find that the method with which the radial velocity curve is derived is also critical, with a potential difference on the projection factor of 9%. An increase of 0.1 in E(B-V) translates into a decrease in the projection factor of 3%. A 0.1 magnitude effect of a circumstellar envelope (CSE) in the visible domain is rather small on the projection factor, about 1.5%. However, we find that a 0.1 mag infrared excess in the K band due to a CSE can increase the projection factor by about 6%. The impact of the surface-brightness color relation on the BW projection factor is found to be critical. Efforts should be devoted in the future to improve the SBCR of Cepheids empirically, but also theoretically, taking their CSE into account as well., Comment: Accepted for publication in Astronomy & Astrophysics

Two-dimensional hydrodynamic models are computationally expensive. This drawback can limit their application to solving problems requiring real-time predictions or several simulation runs. Although the literature presented improvements in using Deep Learning as an alternative to hydrodynamic models, Artificial Neural Networks applications for flood prediction cannot satisfactorily predict floods for areas outside the training datasets with different boundary conditions. In this paper, we used a conditional generative adversarial network (cGAN) aiming to generalize flood predictions in catchments not included in the training process. The proposed method, called cGAN-Flood, uses two cGAN models to solve a rain-on-grid problem by first identifying wet cells and then estimating the water depths. The cGANs were trained using HEC-RAS outputs as ground truth. cGAN-Flood distributes a target flood volume (vt) in a given catchment, which can be calculated via water balance from hydrological simulations. Our approach was trained on ten and tested on five urban catchments with distinct characteristics. The cGAN-Flood was compared to HEC-RAS for different rainfall magnitudes and surface roughness. We also compared our approach to the Weighted Cellular Automata 2D (WCA2D), a rapid flood model (RFM) used for rain-on-grid simulations. Our method successfully predicted water depths in the testing areas, showing that cGAN-Flood could generalize to different locations. However, cGAN-Flood tended to underestimate depths in channels in some areas for events with a small peak of precipitation intensity. cGAN-Flood was 50 and 250 times faster than WCA2D and HEC-RAS, respectively. Due to its computational efficiency and accuracy, we suggest that cGAN-Flood can be applied when fast simulations are necessary, and it can be a viable modeling solution for flood forecasts in large-scale watersheds., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Sanitary Engineering

A fundamental problem in the realm of cyber-physical security of smart water networks is attack detection, a key step towards designing adequate countermeasures. This task is typically carried out by algorithms that analyze time series of process data. However, the nature of the data available to develop these algorithms limits their capabilities: by relying on process data only, one cannot distinguish a cyber-attack from the failure of a system’s component or identify the root cause of an attack. Here, we show that these limitations can be addressed through the joint analysis of process and network data—with the latter representing the information exchanged between the components constituting the Industrial Control System, such as sensors and Programmable Logic Controllers (PLCs). For this purpose, we utilize a dataset generated by digital hydraulic simulator (DHALSIM)—a numerical modelling platform built on a two-way interaction between EPANET version 2.2 and a network emulation tool—which is extended here to include a framework for launching cyber-physical attacks. This paper presents a dataset with realistic network information of a smart water network under cyber-physical attacks and presents an analysis of how that information can enable the development of better intrusion detection systems that can localize and identify attacks. Through this analysis, the dataset provided here, and the open-source availability of DHALSIM, our work paves the way to a novel class of analytics for actionable detection., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Sanitary Engineering

Food and economic security in West Africa rely heavily on rainfed agriculture and are threatened by climate change and demographic growth. Accurate rainfall information is therefore crucial to tackling these challenges. Particularly, information about the occurrence and length of droughts as well as the onset date of the rainy season is essential for agricultural planning. However, existing rainfall models fail to accurately represent the highly variable and sparsely monitored West African rainfall patterns. In this paper, we show the potential of deep learning (DL) to model rainfall in the region and propose a methodology to develop DL models in data-scarce areas. We built two DL models for satellite rainfall (rain/no-rain) detection over northern Ghana from Meteosat TIR data based on standard DL architectures: Convolutional neural networks (CNNs) and convolutional long short-term memory neural networks (ConvLSTM). The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) and Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System (PERSIANN-CCS) products are used as benchmarks. We use rain gauge data from the Trans-African Hydro-Meteorological Observatory (TAHMO) for model development and performance evaluation. We show that our models compare well against existing products despite being considerably simpler, developed with a small training dataset—i.e., 8 stations covering 2.5 years with 20.4% of the data missing—and using TIR data alone. Concretely, our models consistently outperform PERSIANN-CCS for rain/no-rain detection at a sub-daily timescale. While IMERG is the overall best performer, the DL models perform better in the second half of the rainy season despite their simplicity (i.e., up to 120 k parameters). Our results suggest that DL-based regional models are a promising alternative to state-of-the-art global products for providing regional rainfall, Water Resources, Sanitary Engineering

Plastic pollution in water bodies is an unresolved environmental issue that damages all aquatic environments, and causes economic and health problems. Accurate detection of macroplastic litter (plastic items >5 mm) in water is essential to estimate the quantities, compositions and sources, identify emerging trends, and design preventive measures or mitigation strategies. In recent years, researchers have demonstrated the potential of computer vision (CV) techniques based on deep learning (DL) for automated detection of macroplastic litter in water bodies. However, a systematic review to describe the state-of-the-art of the field is lacking. Here we provide such a review, and we highlight current knowledge gaps and suggest promising future research directions. The review compares 34 papers with respect to their application and modeling related criteria. The results show that the researchers have employed a variety of DL architectures implementing different CV techniques to detect macroplastic litter in various aquatic environments. However, key knowledge gaps must be addressed to overcome the lack of: (i) DL-based macroplastic litter detection models with sufficient generalization capability, (ii) DL-based quantification of macroplastic (mass) fluxes and hotspots and (iii) scalable macroplastic litter monitoring strategies based on robust DL-based quantification. We advocate for the exploration of data-centric artificial intelligence approaches and semi-supervised learning to develop models with improved generalization capabilities. These models can boost the development of new methods for the quantification of macroplastic (mass) fluxes and hotspots, and allow for structural monitoring strategies that leverage robust DL-based quantification. While the identified gaps concern all bodies of water, we recommend increased efforts with respect to riverine ecosystems, considering their major role in transport and storage of litter., Sanitary Engineering

This study focuses on exploring the potential of using Long Short-Term Memory networks (LSTMs) for low-flow forecasting for the Rhine River at Lobith on a daily scale with lead times up to 46 days ahead. A novel LSTM-based model architecture is designed to leverage both historical observation and forecasted meteorological data to carry out multi-step discharge time series forecasting. The feature and target selection for this deep learning (DL) model involves evaluating the use of different spatial resolutions for meteorological forcing (basin-averaged or subbasin-averaged), the impact of incorporating past discharge observations, and the use of different target variables (discharge Q or time-differenced discharge dQ). Then, the model is trained using the ERA5 dataset as meteorological forcing, and employed for operational forecast with ECMWF seasonal forecast (SEAS5) data. The forecast results are compared to a benchmark process-based model, wflow_sbm. This study also explores the flexibility of the DL model by fine-tuning the pretrained model with limited SEAS5 dataset. Key findings from feature and target selection include: (1) opting for subbasin-averaged meteorological variables significantly improves model performance compared to a basin-averaged approach. (2) Utilizing dQ as the target variable greatly boosts short-term forecast accuracy compared to using Q, with a mean absolute error (MAE) of 25 m3 s−1 and mean absolute percentage error (MAPE) of 0.02 for the first lead time, ensuring reliability and accuracy at the onset of the forecast horizon. (3) While incorporating historical discharge improves the forecasting of Q, its impact on predicting dQ is less pronounced for short lead times. In the operational forecast with SEAS5, compared to the wflow_sbm model, the DL model exhibits skill in forecasting low flows as evidenced by Continuous Ranked Probability Skill Score (CRPSS) median values of all lead times above zero, and better accuracy in forecasting dro

Il contributo affronta le criticità relative al mercato del lavoro culturale, evidenziandone le contraddizioni, le evoluzioni, i vuoti normativi, le peculiarità e prospettando alcuni possibili spazi d'intervento relativamente alle politiche culturali e alle strategie istituzionali.

Surrogate models replace computationally expensive simulations of physically-based models to obtain accurate results at a fraction of the time. These surrogate models, also known as metamodels, have been employed for analysis, control, and optimization of water distribution and urban drainage systems. With the advent of machine learning (ML), water engineers have increasingly resorted to these data-driven techniques to develop metamodels of urban water networks (UWNs). In this article, we review 31 recent articles on ML-based metamodeling of UWNs to outline the state-of-the-art of the field, identify outstanding gaps, and propose future research directions. For each article, we critically examined the purpose of the metamodel, the metamodel characteristics, and the applied case study. The review shows that current metamodels suffer several drawbacks, including (a) the curse of dimensionality, hindering implementation for large case studies; (b) black-box deterministic nature, limiting explainability and applicability; and (c) rigid architecture, preventing generalization across multiple case studies. We argue that researchers should tackle these issues by resorting to recent advancements in ML concerning inductive biases, robustness, and transferability. Recently developed neural network architectures, which extend deep learning methods to graph data structures, are preferred candidates for advancing surrogate modeling in UWNs. Furthermore, we foresee increasing efforts for complex applications where metamodels may play a fundamental role, such as uncertainty analysis and multi-objective optimization. Lastly, the development and comparison of ML-based metamodels can benefit from the availability of new benchmark datasets for urban drainage systems and realistic complex networks., Sanitary Engineering

A key challenge in designing algorithms for leakage detection and isolation in drinking water distribution systems is the performance evaluation and comparison between methodologies using benchmarks. For this purpose, the Battle of the Leakage Detection and Isolation Methods (BattLeDIM) competition was organized in 2020 with the aim to objectively compare the performance of methods for the detection and localization of leakage events, relying on supervisory control and data acquisition (SCADA) measurements of flow and pressure sensors installed within a virtual water distribution system. Several teams from academia and the industry submitted their solutions using various techniques including time series analysis, statistical methods, machine learning, mathematical programming, met-heuristics, and engineering judgment, and were evaluated using realistic economic criteria. This paper summarizes the results of the competition and conducts an analysis of the different leakage detection and isolation methods used by the teams. The competition results highlight the need for further development of methods for leakage detection and isolation, and also the need to develop additional open benchmark problems for this purpose., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Sanitary Engineering

Pancreatic intraductal tubulopapillary neoplasm (ITPN) is a recently recognized intraductal neoplasm. This study aimed to clarify the clinicopathologic and molecular features of this entity, based on a multi-institutional cohort of 16 pancreatic ITPNs and associated adenocarcinomas. The genomic profiles were analyzed using histology-driven multi-regional sequencing to provide insight on tumor heterogeneity and evolution. Furthermore, an exploratory transcriptomic characterization was performed on eight invasive adenocarcinomas. The clinicopathologic parameters and molecular alterations were further analyzed based on survival indices. The main findings were as follows: 1) the concomitant adenocarcinomas, present in 75% of cases, were always molecularly associated with the intraductal components. These data definitively establish ITPN as origin of invasive pancreatic adenocarcinoma; 2) alterations restricted to infiltrative components included mutations in chromatin remodeling genes ARID2, ASXL1, and PBRM1, and ERBB2-P3H4 fusion; 3) pancreatic ITPN can arise in the context of genetic syndromes, such as BRCA-germline and Peutz-Jeghers syndrome; 4) mutational profile: mutations in the classical PDAC drivers are present, but less frequently, in pancreatic ITPN; 5) novel genomic alterations were observed, including amplification of the Cyclin and NOTCH family genes and ERBB2, fusions involving RET and ERBB2, and RB1 disruptive variation; 6) chromosomal alterations: the most common was 1q gain (75% of cases); 7) by transcriptome analysis, ITPN-associated adenocarcinomas clustered into three subtypes that correlate with the activation of signaling mechanism pathways and tumor microenvironment, displaying squamous features in their majority; and 8) TP53 mutational status is a marker for adverse prognosis. ITPNs are precursor lesions of pancreatic cancer with a high malignant transformation risk. A personalized approach for patients with ITPN should recognize that such neopla, This study is supported by Associazione Italiana Ricerca sul Cancro (AIRC IG n. 26343); Fondazione Cariverona: Oncology Biobank Project “Antonio Schiavi” (prot. 203885/2017); Fondazione Italiana Malattie Pancreas (FIMP-Ministero Salute J38D19000690001); Italian Ministry of Health (RF CO-2019-12369662: CUP: B39C21000370001).

The ITER Collective Thomson scattering (CTS) diagnostic will measure the dynamics of fusion-born alpha particles in the burning ITER plasma by scattering a 1 MW 60 GHz gyrotron beam off fast-ion induced fluctuations in the plasma. The diagnostic will have seven measurement volumes across the ITER cross section and will resolve the alpha particle energies in the range from 300 keV to 3.5 MeV; importantly, the CTS diagnostic is the only diagnostic capable of measuring confined alpha particles for energies below ∼1.7 MeV and will also be sensitive to the other fast-ion populations. The temporal resolution is 100 ms, allowing the capture of dynamics on that timescale, and the typical spatial resolution is 10-50 cm. The development and design of the in-vessel and primary parts of the CTS diagnostic has been completed. This marks the beginning of a new phase of preparation to maximize the scientific benefit of the diagnostic, e.g., by investigating the capability to contribute to the determination of the fuel-ion ratio and the bulk ion temperature as well as integrating data analysis with other fast-ion and bulk-ion diagnostics.

We report the discovery of a surprising binary configuration of the double-mode Cepheid OGLE-LMC-CEP-1347 pulsating in the first (P_1=0.690d) and second overtone (P_2=0.556d) modes. The orbital period (P_orb=59d) of the system is five times shorter than the shortest known to date (310d) for a binary Cepheid. The Cepheid itself is also the shortest-period one ever found in a binary system and the first double-mode Cepheid in a spectroscopically double-lined binary. OGLE-LMC-CEP-1347 is most probably on its first crossing through the instability strip, as inferred from both its short period and fast period increase, consistent with evolutionary models, and from the short orbital period (not expected for binary Cepheids whose components have passed through the red giant phase). Our evolutionary analysis yielded a first-crossing Cepheid with a mass in a range of 2.9-3.4 Msun (lower than any measured Cepheid mass), consistent with observations. The companion is a stable star, at least two times fainter and less massive than the Cepheid (preliminary mass ratio q=0.55), while also redder and thus at the subgiant or more advanced evolutionary stage. To match these characteristics, the Cepheid has to be a product of binary interaction, most likely a merger of two less massive stars, which makes it the second known classical Cepheid of binary origin. Moreover, further evolution of the components may lead to another binary interaction., Comment: 6 pages, 4 figures, 1 table, published in The Astrophysical Journal Letters

Our goal is to determine, with high accuracy, the physical and orbital parameters of two double-lined eclipsing binary systems, where the components are two giant stars. We also aim to study the evolutionary status of the binaries, to derive the distances towards them by using a surface brightness-colour relation, and to compare these measurements with the measurements presented by the Gaia mission. In order to measure the physical and orbital parameters of the systems, we analysed the light curves and radial-velocity curves with the Wilson-Devinney code. We used V band and I-band photometry from the OGLE catalogue and near-infrared photometry obtained with the New Technology Telescope (NTT) equipped with the SOFI instrument. The spectroscopic data were collected with the HARPS spectrograph mounted at the ESO 3.6m telescope and the MIKE spectrograph mounted at the 6.5m Clay telescope. We present the first analysis of this kind for two evolved eclipsing binary systems from the OGLE catalogue: OGLE-BLG-ECL-305487 and OGLE-BLG-ECL-116218. The masses of the components of OGLE-BLG-ECL-305487 are $M_1$ = 1.059 $\pm$ 0.019 and $M_2$ = 0.991 $\pm$ 0.018 $M_\odot$, and the radii are $R_1$ = 19.27 $\pm$ 0.28 and $R_2$ = 29.99 $\pm$ 0.24 R$_\odot$. For OGLE-BLG-ECL-116218, the masses are $M_1$= 0.969 $\pm$ 0.012 and $M_2$= 0.983 $\pm$ 0.012 $M_\odot$, while the radii are $R_1$= 16.73 $\pm$ 0.28 and $R_2$= 22.06 $\pm$ 0.26 $R_\odot$. The evolutionary status of the systems is discussed based on the PARSEC and MIST isochrones. The ages of the systems were established to be between 7.3-10.9 Gyr for OGLE-BLG-ECL-305487 and around 10 Gyr for OGLE-BLG-ECL-116218. We also measured the distances to the binaries. For OGLE-BLG-ECL-305487, $d$= 7.80 $\pm$ 0.18 (stat.) $\pm$ 0.19 (syst.) kpc and for OGLE-BLG-ECL-116218, $d$= 7.57 $\pm$ 0.28 (stat.) $\pm$ 0.19 (syst.) kpc., Comment: 12 pages, 5 figures, accepted in A&A

Achernar, the closest and brightest classical Be star, presents rotational flattening, gravity darkening, occasional emission lines due to a gaseous disk, and an extended polar wind. It is also a member of a close binary system with an early A-type dwarf companion. We aim to determine the orbital parameters of the Achernar system and to estimate the physical properties of the components. We monitored the relative position of Achernar B using a broad range of high angular resolution instruments of the VLT/VLTI (VISIR, NACO, SPHERE, AMBER, PIONIER, GRAVITY, and MATISSE) over a period of 13 years (2006-2019). These astrometric observations are complemented with a series of more than 700 optical spectra for the period from 2003 to 2016. We determine that Achernar B orbits the Be star on a seven-year period, eccentric orbit (e = 0.7255 +/- 0.0014) which brings the two stars within 2 au at periastron. The mass of the Be star is found to be mA = 6.0 +/- 0.6 Msun for a secondary mass of mB = 2.0 +/- 0.1 Msun. We find a good agreement of the parameters of Achernar A with the evolutionary model of a critically rotating star of 6.4 Msun at an age of 63 million years. We also identify a resolved comoving low-mass star, which leads us to propose that Achernar is a member of the Tucana-Horologium moving group. Achernar A is presently in a short-lived phase of its evolution following the turn-off, during which its geometrical flattening ratio is the most extreme. Considering the orbital parameters, no significant interaction occurred between the two components, demonstrating that Be stars may form through a direct, single-star evolution path without mass transfer. Since component A will enter the instability strip in a few hundred thousand years, Achernar appears to be a promising progenitor of the Cepheid binary systems., Comment: 27 pages, Astronomy & Astrophysics, in press. The full set of continuum normalized high resolution spectra of Achernar is available at https://doi.org/10.5281/zenodo.6977303

Surface brightness -- colour relations (SBCRs) are very useful tools for predicting the angular diameters of stars. They offer the possibility to calculate very precise spectrophotometric distances by the eclipsing binary method or the Baade-Wesselink method. Double-lined Detached Eclipsing Binary stars (SB2 DEBs) with precisely known trigonometric parallaxes allow for a calibration of SBCRs with unprecedented precision. In order to improve such calibrations, it is important to enlarge the calibration sample of suitable eclipsing binaries with very precisely determined physical parameters. We carefully chose a sample of ten SB2 DEBs in the solar neighbourhood which contain inactive main-sequence components. The components have spectral types from early A to early K. All systems have high-precision parallaxes from the Gaia mission. We analysed high precision ground- and space-based photometry simultaneously with the radial velocity curves derived from HARPS spectra. We used spectral disentangling to obtain the individual spectra of the components and used these to derive precise atmospheric parameters and chemical abundances. For almost all components, we derived precise surface temperatures and metallicities. We derived absolute dimensions for 20 stars with an average precision of 0.2% and 0.5% for masses and radii, respectively. Three systems show slow apsidal motion. One system, HD 32129, is most likely a triple system with a much fainter K6V companion. Also three systems contain metallic-line components and show strong enhancements of barium and ittrium. The components of all systems compare well to the SBCR derived before from the detached eclipsing binary stars. With a possible exception of HD 32129, they can be used to calibrate SBCRs with a precision better than 1% with available Gaia DR3 parallaxes., Comment: Accepted in Astronomy & Astrophysics, 18 pages

We study 147 star clusters in the Large Magellanic Cloud (LMC) in order to determine their mean metallicities and ages, as well as the mean metallicities of 80 surrounding fields. We construct an age-metallicity relation (AMR) for the clusters in the LMC. For this purpose, we used Str\"omgren photometry obtained with the SOI camera on the 4.1 m SOAR telescope. We found the mean metallicity and age for 110 star clusters. For the remaining 37, we provide an age estimation only. To the best of our knowledge, for 29 clusters from our sample, we provide both the metallicity and age for the first time, whereas for 66 clusters, we provide a first determination of the metallicity, and for 43 clusters, the first estimation of the age. We also calculated the mean metallicities for stars from 80 fields around the clusters. The old, metal-poor star clusters occur both in and out of the LMC bar region, while intermediate-age clusters are located mostly outside of the bar. The majority of star clusters younger than 1 Gyr are located in the bar region. We find a good agreement between our AMR and theoretical models of the LMC chemical enrichment, as well as with AMRs for clusters from the literature. Next, we took advantage of 26 stellar clusters from our sample which host Cepheid variables and used them as an independent check of the correctness of our age determination procedure. We used period-age relations for Cepheids to calculate the mean age of a given cluster and compared it with the age obtained from isochrone fitting. We find good agreement between these ages, especially for models taking into account additional physical processes (e.g., rotation). We also compared the AMR of the LMC and Small Magellanic Cloud (SMC) derived in a uniform way and we note that they indicate possible former interaction between these two galaxies., Comment: 40 pages, 17 figures, 6 tables, Published in A&A; The 8th (last) column in Table A.1 has been corrected compared to the published version

Deep learning techniques have been increasingly used in flood management to overcome the limitations of accurate, yet slow, numerical models and to improve the results of traditional methods for flood mapping. In this paper, we review 58 recent publications to outline the state of the art of the field, identify knowledge gaps, and propose future research directions. The review focuses on the type of deep learning models used for various flood mapping applications, the flood types considered, the spatial scale of the studied events, and the data used for model development. The results show that models based on convolutional layers are usually more accurate, as they leverage inductive biases to better process the spatial characteristics of the flooding events. Models based on fully connected layers, instead, provide accurate results when coupled with other statistical models. Deep learning models showed increased accuracy when compared to traditional approaches and increased speed when compared to numerical methods. While there exist several applications in flood susceptibility, inundation, and hazard mapping, more work is needed to understand how deep learning can assist in real-time flood warning during an emergency and how it can be employed to estimate flood risk. A major challenge lies in developing deep learning models that can generalize to unseen case studies. Furthermore, all reviewed models and their outputs are deterministic, with limited considerations for uncertainties in outcomes and probabilistic predictions. The authors argue that these identified gaps can be addressed by exploiting recent fundamental advancements in deep learning or by taking inspiration from developments in other applied areas. Models based on graph neural networks and neural operators can work with arbitrarily structured data and thus should be capable of generalizing across different case studies and could account for complex interactions with the natural and built environment. Phy, Sanitary Engineering, Multimedia Computing, Hydraulic Structures and Flood Risk

Surrogate models replace computationally expensive simulations of physically-based models to obtain accurate results at a fraction of the time. These surrogate models, also known as metamodels, have been employed for analysis, control, and optimization of water distribution and urban drainage systems. With the advent of machine learning (ML), water engineers have increasingly resorted to these data-driven techniques to develop metamodels of urban water networks (UWNs). In this article, we review 31 recent articles on ML-based metamodeling of UWNs to outline the state-of-the-art of the field, identify outstanding gaps, and propose future research directions. For each article, we critically examined the purpose of the metamodel, the metamodel characteristics, and the applied case study. The review shows that current metamodels suffer several drawbacks, including (a) the curse of dimensionality, hindering implementation for large case studies; (b) black-box deterministic nature, limiting explainability and applicability; and (c) rigid architecture, preventing generalization across multiple case studies. We argue that researchers should tackle these issues by resorting to recent advancements in ML concerning inductive biases, robustness, and transferability. Recently developed neural network architectures, which extend deep learning methods to graph data structures, are preferred candidates for advancing surrogate modeling in UWNs. Furthermore, we foresee increasing efforts for complex applications where metamodels may play a fundamental role, such as uncertainty analysis and multi-objective optimization. Lastly, the development and comparison of ML-based metamodels can benefit from the availability of new benchmark datasets for urban drainage systems and realistic complex networks., Sanitary Engineering

Contains fulltext : 238941.pdf (Publisher’s version ) (Open Access), Tumor mutational burden (TMB) is a numeric index that expresses the number of mutations per megabase (muts/Mb) harbored by tumor cells in a neoplasm. TMB can be determined using different approaches based on next-generation sequencing. In the case of high values, it indicates a potential response to immunotherapy. In this systematic review, we assessed the potential predictive role of high-TMB in pancreatic ductal adenocarcinoma (PDAC), as well as the histo-molecular features of high-TMB PDAC. High-TMB appeared as a rare but not-negligible molecular feature in PDAC, being present in about 1.1% of cases. This genetic condition was closely associated with mucinous/colloid and medullary histology (p < 0.01). PDAC with high-TMB frequently harbored other actionable alterations, with microsatellite instability/defective mismatch repair as the most common. Immunotherapy has shown promising results in high-TMB PDAC, but the sample size of high-TMB PDAC treated so far is quite small. This study highlights interesting peculiarities of PDAC harboring high-TMB and may represent a reliable starting point for the assessment of TMB in the clinical management of patients affected by pancreatic cancer.

We present time-series photometry of 21 nearby Type II Cepheids in the near-infrared J, H and Ks passbands. We use this photometry, together with the Third Gaia Early Data Release parallaxes, to determine for the first time period-luminosity relations (PLRs) for Type II Cepheids from field representatives of these old pulsating stars in the near-infrared regime. We found PLRs to be very narrow for BL Herculis stars, which makes them candidates for precision distance indicators. We then use archival photometry and the most accurate distance obtained from eclipsing binaries to recalibrate PLRs for Type II Cepheids in the Large Magellanic Cloud (LMC). Slopes of our PLRs in the Milky Way and in the LMC differ by slightly more than 2{\sigma} and are in a good agreement with previous studies of the LMC, Galactic Bulge and Galactic Globular Clusters Type II Cepheids samples. We use PLRs of Milky Way Type II Cepheids to measure the distance to the LMC and we obtain a distance modulus of 18.540$\pm$0.026(stat.)$\pm$0.034(syst.)mag in the WJKs Wesenheit index. We also investigate the metallicity effect within our Milky Way sample and we find rather significant value of about -0.2mag/dex in each band meaning that more metal-rich Type II Cepheids are intrinsically brighter than their more metal-poor counterparts, in agreement with the value obtained from Type II Cepheids in Galactic Globular Clusters. The main source of systematic error on our Milky Way PLRs calibration and the LMC distance is the current uncertainty of the Gaia parallax zero point., Comment: Accepted for ApJ

Our work presents an independent calibration of the J-region Asymptotic Giant Branch (JAGB) method using Infrared Survey Facility (IRSF) photometric data and a custom luminosity function profile to determine JAGB mean magnitudes for nine galaxies. We determine a mean absolute magnitude of carbon stars of $M_{LMC}=-6.212 \pm 0.010$ (stat.) $\pm 0.030$ (syst.) mag. We then use near-infrared photometry of a number of nearby galaxies, originally obtained by our group to determine their distances from Cepheids using the Leavitt law, in order to independently determine their distances with the JAGB method. We compare the JAGB distances obtained in this work with the Cepheid distances resulting from the same photometry and find very good agreement between the results from the two methods. The mean difference is 0.01 mag with an rms scatter of 0.06 mag after taking into account seven out of the eight analyzed galaxies that had their distances determined using Cepheids. The very accurate distance to the Small Magellanic Cloud (SMC) based on detached eclipsing binaries (Graczyk et al. 2020) is also in very good agreement with the distance obtained from carbon stars., Comment: accepted for ApJ

Surface brightness-color relations (SBCRs) are used for estimating angular diameters and deriving stellar properties. They are critical to derive extragalactic distances of early-type and late-type eclipsing binaries or, potentially, for extracting planetary parameters of late-type stars hosting planets. Various SBCRs have been implemented so far, but strong discrepancies in terms of precision and accuracy still exist in the literature. We aim to develop a precise SBCR for early-type B and A stars using selection criteria, based on stellar characteristics, and combined with homogeneous interferometric angular diameter measurements. We also improve SBCRs for late-type stars, in particular in the Gaia photometric band. We observed 18 early-type stars with the VEGA interferometric instrument, installed on the CHARA array. We then applied additional criteria on the photometric measurements, together with stellar characteristics diagnostics in order to build the SBCRs. We calibrated a SBCR for subgiant and dwarf early-type stars. The RMS of the relation is $\sigma_{F_{V_{0}}} = 0.0051\,$mag, leading to an average precision of 2.3% on the estimation of angular diameters, with 3.1% for $V-K < -0.2\,$mag and 1.8% for $V-K > -0.2\,$mag. We found that the conversion between Johnson-$K$ and 2MASS-$K_s$ photometries is a key issue for early-type stars. Following this result, we have revisited our previous SBCRs for late-type stars by calibrating them with either converted Johnson-$K$ or 2MASS-$K_s$ photometries. We also improve the calibration of these SBCRs based on the Gaia photometry. The expected precision on the angular diameter using our SBCRs for late-type stars ranges from 1.0% to 2.7%. By reaching a precision of 2.3% on the estimation of angular diameters for early-type stars, significant progress has been made to determine extragalactic distances using early-type eclipsing binaries., Comment: 18 pages Journal ref: AA/2021/40763

Sanitary Engineering

Contains fulltext : 238941.pdf (Publisher’s version ) (Open Access), Tumor mutational burden (TMB) is a numeric index that expresses the number of mutations per megabase (muts/Mb) harbored by tumor cells in a neoplasm. TMB can be determined using different approaches based on next-generation sequencing. In the case of high values, it indicates a potential response to immunotherapy. In this systematic review, we assessed the potential predictive role of high-TMB in pancreatic ductal adenocarcinoma (PDAC), as well as the histo-molecular features of high-TMB PDAC. High-TMB appeared as a rare but not-negligible molecular feature in PDAC, being present in about 1.1% of cases. This genetic condition was closely associated with mucinous/colloid and medullary histology (p < 0.01). PDAC with high-TMB frequently harbored other actionable alterations, with microsatellite instability/defective mismatch repair as the most common. Immunotherapy has shown promising results in high-TMB PDAC, but the sample size of high-TMB PDAC treated so far is quite small. This study highlights interesting peculiarities of PDAC harboring high-TMB and may represent a reliable starting point for the assessment of TMB in the clinical management of patients affected by pancreatic cancer.

This paper aims to give an overview on COVID-19 impact on cultural and creative sectors. The strongest repercussions mainly concerned enterprises and performing arts workers. New challenges await us, in particular those of employment and economic sustainability. The support measures implemented in our country were largely addressed to the immediate crisis needs of the sector. But this crises can represent a turning point that could change, as it is already changing, the way we live, communicate and work.

Contains fulltext : 238941.pdf (Publisher’s version ) (Open Access), Tumor mutational burden (TMB) is a numeric index that expresses the number of mutations per megabase (muts/Mb) harbored by tumor cells in a neoplasm. TMB can be determined using different approaches based on next-generation sequencing. In the case of high values, it indicates a potential response to immunotherapy. In this systematic review, we assessed the potential predictive role of high-TMB in pancreatic ductal adenocarcinoma (PDAC), as well as the histo-molecular features of high-TMB PDAC. High-TMB appeared as a rare but not-negligible molecular feature in PDAC, being present in about 1.1% of cases. This genetic condition was closely associated with mucinous/colloid and medullary histology (p < 0.01). PDAC with high-TMB frequently harbored other actionable alterations, with microsatellite instability/defective mismatch repair as the most common. Immunotherapy has shown promising results in high-TMB PDAC, but the sample size of high-TMB PDAC treated so far is quite small. This study highlights interesting peculiarities of PDAC harboring high-TMB and may represent a reliable starting point for the assessment of TMB in the clinical management of patients affected by pancreatic cancer.

We present time-series photometry of 21 nearby Type II Cepheids in the near-infrared J, H and Ks passbands. We use this photometry, together with the Third Gaia Early Data Release parallaxes, to determine for the first time period-luminosity relations (PLRs) for Type II Cepheids from field representatives of these old pulsating stars in the near-infrared regime. We found PLRs to be very narrow for BL Herculis stars, which makes them candidates for precision distance indicators. We then use archival photometry and the most accurate distance obtained from eclipsing binaries to recalibrate PLRs for Type II Cepheids in the Large Magellanic Cloud (LMC). Slopes of our PLRs in the Milky Way and in the LMC differ by slightly more than 2{\sigma} and are in a good agreement with previous studies of the LMC, Galactic Bulge and Galactic Globular Clusters Type II Cepheids samples. We use PLRs of Milky Way Type II Cepheids to measure the distance to the LMC and we obtain a distance modulus of 18.540$\pm$0.026(stat.)$\pm$0.034(syst.)mag in the WJKs Wesenheit index. We also investigate the metallicity effect within our Milky Way sample and we find rather significant value of about -0.2mag/dex in each band meaning that more metal-rich Type II Cepheids are intrinsically brighter than their more metal-poor counterparts, in agreement with the value obtained from Type II Cepheids in Galactic Globular Clusters. The main source of systematic error on our Milky Way PLRs calibration and the LMC distance is the current uncertainty of the Gaia parallax zero point., Comment: Accepted for ApJ

Surface brightness-color relations (SBCRs) are used for estimating angular diameters and deriving stellar properties. They are critical to derive extragalactic distances of early-type and late-type eclipsing binaries or, potentially, for extracting planetary parameters of late-type stars hosting planets. Various SBCRs have been implemented so far, but strong discrepancies in terms of precision and accuracy still exist in the literature. We aim to develop a precise SBCR for early-type B and A stars using selection criteria, based on stellar characteristics, and combined with homogeneous interferometric angular diameter measurements. We also improve SBCRs for late-type stars, in particular in the Gaia photometric band. We observed 18 early-type stars with the VEGA interferometric instrument, installed on the CHARA array. We then applied additional criteria on the photometric measurements, together with stellar characteristics diagnostics in order to build the SBCRs. We calibrated a SBCR for subgiant and dwarf early-type stars. The RMS of the relation is $\sigma_{F_{V_{0}}} = 0.0051\,$mag, leading to an average precision of 2.3% on the estimation of angular diameters, with 3.1% for $V-K < -0.2\,$mag and 1.8% for $V-K > -0.2\,$mag. We found that the conversion between Johnson-$K$ and 2MASS-$K_s$ photometries is a key issue for early-type stars. Following this result, we have revisited our previous SBCRs for late-type stars by calibrating them with either converted Johnson-$K$ or 2MASS-$K_s$ photometries. We also improve the calibration of these SBCRs based on the Gaia photometry. The expected precision on the angular diameter using our SBCRs for late-type stars ranges from 1.0% to 2.7%. By reaching a precision of 2.3% on the estimation of angular diameters for early-type stars, significant progress has been made to determine extragalactic distances using early-type eclipsing binaries., Comment: 18 pages Journal ref: AA/2021/40763

Our work presents an independent calibration of the J-region Asymptotic Giant Branch (JAGB) method using Infrared Survey Facility (IRSF) photometric data and a custom luminosity function profile to determine JAGB mean magnitudes for nine galaxies. We determine a mean absolute magnitude of carbon stars of $M_{LMC}=-6.212 \pm 0.010$ (stat.) $\pm 0.030$ (syst.) mag. We then use near-infrared photometry of a number of nearby galaxies, originally obtained by our group to determine their distances from Cepheids using the Leavitt law, in order to independently determine their distances with the JAGB method. We compare the JAGB distances obtained in this work with the Cepheid distances resulting from the same photometry and find very good agreement between the results from the two methods. The mean difference is 0.01 mag with an rms scatter of 0.06 mag after taking into account seven out of the eight analyzed galaxies that had their distances determined using Cepheids. The very accurate distance to the Small Magellanic Cloud (SMC) based on detached eclipsing binaries (Graczyk et al. 2020) is also in very good agreement with the distance obtained from carbon stars., Comment: accepted for ApJ

Masses of classical Cepheids of 3 to 11 M$\odot$ are predicted by theory but those measured, clump between 3.6 and 5 M$\odot$. As a result, their mass-luminosity relation is poorly constrained, impeding our understanding of basic stellar physics and the Leavitt Law. All Cepheid masses come from the analysis of 11 binary systems, including only 5 double-lined and well-suited for accurate dynamical mass determination. We present a project to analyze a new, numerous group of Cepheids in double-lined binary (SB2) systems to provide mass determinations in a wide mass interval and study their evolution. We analyze a sample of 41 candidate binary LMC Cepheids spread along the P-L relation, that are likely accompanied by luminous red giants, and present indirect and direct indicators of their binarity. In a spectroscopic study of a subsample of 18 brightest candidates, for 16 we detected lines of two components in the spectra, already quadrupling the number of Cepheids in SB2 systems. Observations of the whole sample may thus lead to quadrupling all the Cepheid mass estimates available now. For the majority of our candidates, erratic intrinsic period changes dominate over the light travel-time effect due to binarity. However, the latter may explain the periodic phase modulation for 4 Cepheids. Our project paves the way for future accurate dynamical mass determinations of Cepheids in the LMC, Milky Way, and other galaxies, which will potentially increase the number of known Cepheid masses even 10-fold, hugely improving our knowledge about these important stars., Comment: 12 pages, 7 figures, 3 tables, accepted for publication in ApJ