Generation of realistic nonwoven and foam filter geometry and mesh for filtration simulation using open-source tools

Recovery of liquid aerosols (mists) from industrial processes is typically accomplished through coalescence filtration, employing highly porous nonwoven (fibrous), knitted or foam media which are regarded to potentially provide high collection efficiencies. Highly resolved pore-scale computational fluid dynamics (CFD) analysis of mist filtration processes is increasingly becoming an important tool for design and optimization of such filter media. A key to efficient application-specific optimization of filter media is the ability to generate CFD-suitable virtual filter geometries with controllable geometric parameters including solidity, fibre diameters, morphology, etc. - yet, a review of the literature suggests that the current designs are heavily reliant on computed tomography (CT) scans of available filter media for accurate representation of the pore-scale structures in a computational simulation. In the present study, a novel methodology is presented for generating realistic virtual nonwoven (fibrous) and foam filter geometries with parametric customizability, using open-source tools including Python, OpenFOAM libraries, Gmsh and Blender. Further, a methodology for the generation of a computational mesh suitable for multiphase CFD at the pore-scale is delineated for the two types of filter media generated using the present technique, viz: nonwoven and foam, using open-source tools available within the OpenFOAM framework. The proposed methodology for the generation of virtual filter media and computational mesh is validated by qualitative comparison against with images from electron-microscopy (SEM) scans of real filters as well as comparison of the single-phase pressure drops predicted from CFD simulations using the generated fibrous and foam media with different solidities, fibre (or strand of foam) diameters, filter thicknesses, against the literature. The excellent agreement between the predicted pressure drops and the literature and its consistency over the several different geometric conditions considered for comparison reaffirms the validity of the proposed methodology for efficient virtual filter media development, which can eventually lead to enhanced parametric optimization capabilities and reduced design costs and lead times

Influence of filter domain size on the simulation of gas-liquid filtration in nonwoven and foam media

The process of coalescence mist filtration in liquid- or gas-liquid systems is strongly controlled by the dynamics of the multi-component fluid transport at the pore- or fibrescales and its interactions with the filter media. However, current designs of mist filters are largely based on empirical data or on single-fibre filtration theory, primarily because of the complexity and difficulty in making accurate measurements at such (small) length scales. Current advancements in high performance computing provide a unique possibility to understand the dynamics of such flows using highly resolved droplet and interface capturing computational fluid dynamics (CFD) simulations - which can provide vital data for application-specific optimization of filter media. However it is important that the spatio-temporal resolutions required to accurately numerical model the fluid dynamics of micro or nano-fibre filtration processes at full size of the filters may typically demand simulations to be run with several hundred million (to over a billion) computational cells and long run-times. Hence, for reduced design lead times as well as computational cost, it is desirable to keep the size of the filter domain to a minimum, while ensuring that the largest fluid structures and scales are captured in the simulations. A review of the (limited) literature on CFD simulations of the mist-filtration process reveals that the size of the filtration domain have been predominantly chosen rather arbitrarily based on the a set multiple of the Brinkman screening length or by the computing power available - and no reported studies are yet available that address conditions of high levels of fluid saturation that involve large fluid structures. In the present study, a series of systematic computational simulations using successively larger domain sizes are carried out to identify the relationship between the characteristics of the two phase flow (such as saturation, thickness of liquid layer, pressure drop, etc.) and the size of the filter domains considered. Two vastly different types of filter media, nonwoven and foam, with similar properties such as packing density, fibre (or element of foam) diameters are chosen to additionally infer the influence of filter structures at the pore-scales to the domain size. The transient simulations are carried out using the interface capturing volume-of-fluid (VOF) solver available within the open-source CFD framework OpenFOAM

Simulating transport in and entrainment form nonwoven fibrous, knitted, and opne-cell foam filters

The movement and re-entrainment of liquid droplets from three different filter media, namely fibrous, knitted, and open-cell foam was investigated numerically using computational fluid dynamics (CFD). A range of face velocities were considered which resulted in a range of oil transform rates and steady state saturation levels. It will be shown that liquid volume fraction in filters depends on velocity and time. The minimum velocity required for detachment of droplets was also identified. The main purpose of this research is to investigate the behaviour of pre saturated oil-mist filters in different geometric configuration and also different air flow conditions. In this study, all the produced filter geometries have a packing density (solidity) of 2 % with fiber/element diameter of 9 μm and overall dimensions of: 2mm (z), 0.5 mm (x), and 0.5 mm (y). In order to capture the gas-liquid interface, the Volume of Fluid (VOF) method will be applied. To perform the simulations, the open source computational fluid dynamics (CFD) toolbox, OpenFOAM is used. To verify the accuracy of computations, the calculation of clean pressure drop is compared against well-established pressure approximation in the literature. This work has examined the movement and re-entrainment of droplets in fibrous, knitted, and open-cell foam media with a range of different face velocities. It was found that by increasing in velocity and time, liquid volume fraction in the filters reduced though re-entrainment once a threshold of 2 m/s in all three cases. Furthermore, it has been shown that knitted media produced largest re-entrainment and the fibrous media the least. It is worth mentioning that other factors such as saturation, initial droplet position, temperature may play an important role in re-entrainment form filter which are not investigated in this study. It is important to note however that these results would need to be validated in real media. The large drops entrained from knitted media would be advantageous in many cases as they would readily settle under gravity

Bone marrow-derived endothelial progenitor cells are a major determinant of nascent tumor neovascularization

Tumors build vessels by cooption of pre-existing vasculature and de novo recruitment of bone marrow (BM)-derived endothelial progenitor cells (EPCs). However, the contribution and the functional role of EPCs in tumor neoangiogenesis are controversial. Therefore, by using genetically marked BM progenitor cells, we demonstrate the precise spatial and temporal contribution of EPCs to the neovascularization of three transplanted and one spontaneous breast tumor in vivo using high-resolution microscopy and flow cytometry. We show that early tumors recruit BM-derived EPCs that differentiate into mature BM-derived endothelial cells (ECs) and luminally incorporate into a subset of sprouting tumor neovessels. Notably, in later tumors, these BM-derived vessels are diluted with non-BM-derived vessels from the periphery, which accounts for purported differences in previously published reports. Furthermore, we show that specific ablation of BM-derived EPCs with alpha-particle-emitting anti-VE-cadherin antibody markedly impaired tumor growth associated with reduced vascularization. Our results demonstrate that BM-derived EPCs are critical components of the earliest phases of tumor neoangiogenesis

New mechanism for Notch signaling to endothelium at a distance by Delta-like 4 incorporation into exosomes.

Notch signaling is an evolutionary conserved pathway that is mediated by cell-cell contact. It is involved in a variety of developmental processes and has an essential role in vascular development and angiogenesis. Delta-like 4 (Dll4) is a Notch ligand that is up-regulated during angiogenesis. It is expressed in endothelial cells and regulates the differentiation between tip cells and stalk cells of neovasculature. Here, we present evidence that Dll4 is incorporated into endothelial exosomes. It can also be incorporated into the exosomes of tumor cells that overexpress Dll4. These exosomes can transfer the Dll4 protein to other endothelial cells and incorporate it into their cell membrane, which results in an inhibition of Notch signaling and a loss of Notch receptor. Transfer of Dll4 was also shown in vivo from tumor cells to host endothelium. Addition of Dll4 exosomes confers a tip cell phenotype on the endothelial cell, which results in a high Dll4/Notch-receptor ratio, low Notch signaling, and filopodia formation. This was further evidenced by increased branching in a tube-formation assay and in vivo. This reversal in phenotype appears to enhance vessel formation and is a new form of signaling for Notch ligands that expands their signaling potential beyond cell-cell contact

Antiviral activity of the mineralocorticoid receptor NR3C2 against Herpes simplex virus Type 1 (HSV-1) infection

Abstract Analysis of a genome-scale RNA interference screen of host factors affecting herpes simplex virus type 1 (HSV-1) revealed that the mineralocorticoid receptor (MR) inhibits HSV-1 replication. As a ligand-activated transcription factor the MR regulates sodium transport and blood pressure in the kidney in response to aldosterone, but roles have recently been elucidated for the MR in other cellular processes. Here, we show that the MR and other members of the mineralocorticoid signalling pathway including HSP90 and FKBP4, possess anti-viral activity against HSV-1 independent of their effect on sodium transport, as shown by sodium channel inhibitors. Expression of the MR is upregulated upon infection in an interferon (IFN) and viral transcriptional activator VP16-dependent fashion. Furthermore, the MR and VP16, together with the cellular co-activator Oct-1, transactivate the hormone response element (HRE) present in the MR promoter and those of its transcriptional targets. As the MR induces IFN expression, our data suggests the MR is involved in a positive feedback loop that controls HSV-1 infection

ANN multiscale model of anti-HIV Drugs activity vs AIDS prevalence in the US at county level based on information indices of molecular graphs and social networks

[Abstract] This work is aimed at describing the workflow for a methodology that combines chemoinformatics and pharmacoepidemiology methods and at reporting the first predictive model developed with this methodology. The new model is able to predict complex networks of AIDS prevalence in the US counties, taking into consideration the social determinants and activity/structure of anti-HIV drugs in preclinical assays. We trained different Artificial Neural Networks (ANNs) using as input information indices of social networks and molecular graphs. We used a Shannon information index based on the Gini coefficient to quantify the effect of income inequality in the social network. We obtained the data on AIDS prevalence and the Gini coefficient from the AIDSVu database of Emory University. We also used the Balaban information indices to quantify changes in the chemical structure of anti-HIV drugs. We obtained the data on anti-HIV drug activity and structure (SMILE codes) from the ChEMBL database. Last, we used Box-Jenkins moving average operators to quantify information about the deviations of drugs with respect to data subsets of reference (targets, organisms, experimental parameters, protocols). The best model found was a Linear Neural Network (LNN) with values of Accuracy, Specificity, and Sensitivity above 0.76 and AUROC > 0.80 in training and external validation series. This model generates a complex network of AIDS prevalence in the US at county level with respect to the preclinical activity of anti-HIV drugs in preclinical assays. To train/validate the model and predict the complex network we needed to analyze 43,249 data points including values of AIDS prevalence in 2,310 counties in the US vs ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4,856 protocols, and 10 possible experimental measures.Ministerio de Educación, Cultura y Deportes; AGL2011-30563-C03-0

A prospective, randomized, single-blinded, crossover trial to investigate the effect of a wearable device in addition to a daily symptom diary for the remote early detection of SARS-CoV-2 infections (COVID-RED): a structured summary of a study protocol for a randomized controlled trial

Abstract Objectives It is currently thought that most—but not all—individuals infected with SARS-CoV-2 develop symptoms, but that the infectious period starts on average two days before the first overt symptoms appear. It is estimated that pre- and asymptomatic individuals are responsible for more than half of all transmissions. By detecting infected individuals before they have overt symptoms, wearable devices could potentially and significantly reduce the proportion of transmissions by pre-symptomatic individuals. Using laboratory-confirmed SARS-CoV-2 infections (detected via serology tests [to determine if there are antibodies against the SARS-CoV-2 in the blood] or SARS-CoV-2 infection tests such as polymerase chain reaction [PCR] or antigen tests) as the gold standard, we will determine the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for the following two algorithms to detect first time SARS-CoV-2 infection including early or asymptomatic infection: the algorithm using Ava bracelet data when coupled with self-reported Daily Symptom Diary data (Wearable + Symptom Data Algo; experimental condition) the algorithm using self-reported Daily Symptom Diary data alone (Symptom Only Algo; control condition) In addition, we will determine which of the two algorithms has superior performance characteristics for detecting SARS-CoV-2 infection including early or asymptomatic infection as confirmed by SARS-CoV-2 virus testing. Trial design The trial is a randomized, single-blinded, two-period, two-sequence crossover trial. All subjects will participate in an initial Learning Phase (varying from 2 weeks to 3 months depending on enrolment date), followed by two contiguous 3-month test phases, Period 1 and Period 2. Each subject will undergo the experimental condition (the Wearable + Symptom Data Algo) in one of these periods and the control condition (Symptom Only Algo) in the other period. The order will be randomly assigned, resulting in subjects being allocated 1:1 to either Sequence 1 (experimental condition first) or Sequence 2 (control condition first). Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence. Participants The trial will be conducted in the Netherlands. A target of 20,000 subjects will be enrolled. Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence. This results in approximately 6,500 normal-risk individuals and 3,500 high-risk individuals per sequence. Subjects will be recruited from previously studied cohorts as well as via public campaigns and social media. All data for this study will be collected remotely through the Ava COVID-RED app, the Ava bracelet, surveys in the COVID-RED web portal, and self-sampling serology and PCR kits. During recruitment, subjects will be invited to visit the COVID-RED web portal ( www.covid-red.eu ). After successfully completing the enrolment questionnaire, meeting eligibility criteria and indicating interest in joining the study, subjects will receive the subject information sheet and informed consent form. Subjects can enrol in COVID-RED if they comply with the following inclusion and exclusion criteria. Inclusion criteria: Resident of the Netherlands At least 18 years old Informed consent provided (electronic) Willing to adhere to the study procedures described in the protocol Must have a smartphone that runs at least Android 8.0 or iOS 13.0 operating systems and is active for the duration of the study (in the case of a change of mobile number, study team should be notified) Be able to read, understand and write Dutch Exclusion criteria: Previous positive SARS-CoV-2 test result (confirmed either through PCR/antigen or antibody tests; self-reported) Previously received a vaccine developed specifically for COVID-19 or in possession of an appointment for vaccination in the near future (self-reported) Current suspected (e.g., waiting for test result) COVID-19 infection or symptoms of a COVID-19 infection (self-reported) Participating in any other COVID-19 clinical drug, vaccine, or medical device trial (self-reported) Electronic implanted device (such as a pacemaker; self-reported) Pregnant at time of informed consent (self-reported) Suffering from cholinergic urticaria (per the Ava bracelet’s User Manual; self-reported) Staff involved in the management or conduct of this study Intervention and comparator All subjects will be instructed to complete the Daily Symptom Diary in the Ava COVID-RED app daily, wear their Ava bracelet each night and synchronise it with the app each day for the entire period of study participation. Provided with wearable sensor and/or self-reported symptom data within the last 24 hours, the Ava COVID-RED app’s underlying algorithms will provide subjects with a real-time indicator of their overall health and well-being. Subjects will see one of three messages, notifying them that: no seeming deviations in symptoms and/or physiological parameters have been detected; some changes in symptoms and/or physiological parameters have been detected and they should self-isolate; or alerting them that deviations in their symptoms and/or physiological parameters could be suggestive of a potential COVID-19 infection and to seek additional testing. We will assess intraperson performance of the algorithms in the experimental condition (Wearable + Symptom Data Algo) and control conditions (Symptom Only Algo). Main outcomes The trial will evaluate the use and performance of the Ava COVID-RED app and Ava bracelet, which uses sensors to measure breathing rate, pulse rate, skin temperature, and heart rate variability for the purpose of early and asymptomatic detection and monitoring of SARS-CoV-2 in general and high-risk populations. Using laboratory-confirmed SARS-CoV-2 infections (detected via serology tests, PCR tests and/or antigen tests) as the gold standard, we will determine the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for each of the following two algorithms to detect first-time SARS-CoV-2 infection including early or asymptomatic infection: the algorithm using Ava Bracelet data when coupled with the self-reported Daily Symptom Diary data, and the algorithm using self-reported Daily Symptom Diary data alone. In addition, we will determine which of the two algorithms has superior performance characteristics for detecting SARS-CoV-2 infection including early or asymptomatic infection as confirmed by SARS-CoV-2 virus testing. The protocol contains an additional seventeen secondary outcomes which address infection incidence rates, health resource utilization, symptoms reported by SARS-CoV-2 infected participants, and the rate of breakthrough and asymptomatic SARS-CoV-2 infections among individuals vaccinated against COVID-19. PCR or antigen testing will occur when the subject receives a notification from the algorithm to seek additional testing. Subjects will be advised to get tested via the national testing programme, and report the testing result in the Ava COVID-RED app and a survey. If they cannot obtain a test via the national testing programme, they will receive a nasal swab self-sampling kit at home, and the sample will be tested by PCR in a trial-affiliated laboratory. In addition, all subjects will be asked to take a capillary blood sample at home at baseline (Month 0), and at the end of the Learning Phase (Month 3), Period 1 (Month 6) and Period 2 (Month 9). These samples will be used for SARS-CoV-2-specific antibody testing in a trial-affiliated laboratory, differentiating between antibodies resulting from a natural infection and antibodies resulting from COVID-19 vaccination (as vaccination will gradually be rolled out during the trial period). Baseline samples will only be analysed if the sample collected at the end of the Learning Phase is positive, and samples collected at the end of Period 1 will only be analysed if the sample collected at the end of Period 2 is positive. When subjects obtain a positive PCR/antigen or serology test result during the study, they will continue to be in the study but will be moved into a so-called “COVID-positive” mode in the Ava COVID-RED app. This means that they will no longer receive recommendations from the algorithms but can still contribute and track symptom and bracelet data. The primary analysis of the main objective will be executed using data collected in Period 2 (Month 6 through 9). Within this period, serology tests (before and after Period 2) and PCR/antigen tests (taken based on recommendations by the algorithms) will be used to determine if a subject was infected with SARS-CoV-2 or not. Within this same time period, it will be determined if the algorithms gave any recommendations for testing. The agreement between these quantities will be used to evaluate the performance of the algorithms and how these compare between the study conditions. Randomisation All eligible subjects will be randomized using a stratified block randomization approach with an allocation ratio of 1:1 to one of two sequences (experimental condition followed by control condition or control condition followed by experimental condition). Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence, resulting in equal numbers of high-risk and normal-risk individuals between the sequences. Blinding (masking) In this study, subjects will be blinded as to study condition and randomization sequence. Relevant study staff and the device manufacturer will be aware of the assigned sequence. The subject will wear the Ava bracelet and complete the Daily Symptom Diary in the Ava COVID-RED app for the full duration of the study, and they will not know if the feedback they receive about their potential infection status will only be based on data they entered in the Daily Symptom Diary within the Ava COVID-RED app or based on both the data from the Daily Symptom Diary and the Ava bracelet. Numbers to be randomised (sample size) 20,000 subjects will be recruited and randomized 1:1 to either Sequence 1 (experimental condition followed by control condition) or Sequence 2 (control condition followed by experimental condition), taking into account their risk level. This results in approximately 6,500 normal-risk and 3,500 high-risk individuals per sequence. Trial Status Protocol version: 1.2, dated January 22nd, 2021 Start of recruitment: February 22nd, 2021 End of recruitment (estimated): April 2021 End of follow-up (estimated): December 2021 Trial registration The trial has been registered at the Netherlands Trial Register on the 18th of February, 2021 with number NL9320 ( https://www.trialregister.nl/trial/9320 ) Full protocol The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol

The Return of the Mid-infrared to the VLT: News from the VISIR Upgrade

The VLT mid-infrared imager and spectrometer VISIR returns to science operations following an extended upgrade period. Among the most important modifications are: the imaging and spectroscopic detectors have been replaced with larger AQUARIUS (1024 by 1024 pixel) detector arrays; the N-band low-resolution grating has been exchanged; and support is now provided for precipitable water vapour monitoring, in order to select the best observing conditions. The AQUARIUS detectors stem from a development for very low background applications which result in excess noise under ground-based conditions. A series of interventions was needed to find a scheme that effectively exploits these detectors for ground-based use, involving the implementation of faster chopping. VISIR has been returned to service at the VLT with enhanced capabilities