Browse
Submit Data
Databases
API
Help

Dataset Information

19 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Experimental Evidence for the Optimal Design of a High-Performing Cloth Mask.

ABSTRACT: Cloth masks can be an alternative to medical masks during pandemics. Recent studies have examined the performance of fabrics under various conditions; however, the performance against violent respiratory events such as human sneezes is yet to be explored. Accordingly, we present a comprehensive experimental study using sneezes by a healthy adult and a tailored image-based flow measurement diagnostic system evaluating all dimensions of protection of commonly available fabrics and their layered combinations: the respiratory droplet blocking efficiency, water resistance, and breathing resistance. Our results reveal that a well-designed cloth mask can outperform a three-layered surgical mask for such violent respiratory events. Specifically, increasing the number of layers significantly increases the droplet blocking efficiency, on average by ∼20 times per additional fabric layer. A minimum of three layers is necessary to resemble the droplet blocking performance of surgical masks, and a combination of cotton/linen (hydrophilic inner layer)-blends (middle layer)-polyester/nylon (hydrophobic outer layer) exhibited the best performance among overall indicators tested. In an optimum three-layered design, the average thread count should be greater than 200, and the porosity should be less than 2%. Furthermore, machine washing at 60 °C did not significantly impact the performance of cloth masks. These findings inform the design of high-performing homemade cloth masks.

SUBMITTER: Bhattacharjee S

PROVIDER: S-EPMC8171220 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Accelerated knowledge discovery from omics data by optimal experimental design

Project description:How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. Here, we present OPEX, an optimal experimental design method to identify informative omics experiments for both experimental space exploration and model training. OPEX-guided exploration of  Escherichia coli's cross-behavior potential, when exposed to novel biocide and antibiotic combinations, led to accelerated knowledge discovery with predictive models that are more accurate while needing 44% fewer data to train. Selecting experiments favoring broader exploration followed by fine-tuning emerged as the optimal strategy. This led to the discovery of 29 cross-protection and 4 cross-vulnerability conditions, with further validation revealing the central role of chaperones, stress response proteins and transport pumps in cross-stress exposure. This work demonstrates how active learning can be used to automate omics data collection for training accurate predictive models, evidence-driven decision making and accelerated knowledge discovery in life sciences.

2020-07-09 | GSE144604 | GEO

Bayesian Optimal Interval Design: A Simple and Well-Performing Design for Phase I Oncology Trials.

Project description:Despite more than two decades of publications that offer more innovative model-based designs, the classical 3 + 3 design remains the most dominant phase I trial design in practice. In this article, we introduce a new trial design, the Bayesian optimal interval (BOIN) design. The BOIN design is easy to implement in a way similar to the 3 + 3 design, but is more flexible for choosing the target toxicity rate and cohort size and yields a substantially better performance that is comparable with that of more complex model-based designs. The BOIN design contains the 3 + 3 design and the accelerated titration design as special cases, thus linking it to established phase I approaches. A numerical study shows that the BOIN design generally outperforms the 3 + 3 design and the modified toxicity probability interval (mTPI) design. The BOIN design is more likely than the 3 + 3 design to correctly select the MTD and allocate more patients to the MTD. Compared with the mTPI design, the BOIN design has a substantially lower risk of overdosing patients and generally a higher probability of correctly selecting the MTD. User-friendly software is freely available to facilitate the application of the BOIN design. Clin Cancer Res; 22(17); 4291-301. ©2016 AACR.

| S-EPMC5047439 | biostudies-literature

Optimal experimental design for mathematical models of haematopoiesis.

Project description:The haematopoietic system has a highly regulated and complex structure in which cells are organized to successfully create and maintain new blood cells. It is known that feedback regulation is crucial to tightly control this system, but the specific mechanisms by which control is exerted are not completely understood. In this work, we aim to uncover the underlying mechanisms in haematopoiesis by conducting perturbation experiments, where animal subjects are exposed to an external agent in order to observe the system response and evolution. We have developed a novel Bayesian hierarchical framework for optimal design of perturbation experiments and proper analysis of the data collected. We use a deterministic model that accounts for feedback and feedforward regulation on cell division rates and self-renewal probabilities. A significant obstacle is that the experimental data are not longitudinal, rather each data point corresponds to a different animal. We overcome this difficulty by modelling the unobserved cellular levels as latent variables. We then use principles of Bayesian experimental design to optimally distribute time points at which the haematopoietic cells are quantified. We evaluate our approach using synthetic and real experimental data and show that an optimal design can lead to better estimates of model parameters.

| S-EPMC7879761 | biostudies-literature

In situ synthesis of peptide microarrays - shadow mask design

Project description:This SuperSeries is composed of the SubSeries listed below.

2014-01-16 | GSE50045 | GEO

Nanometer-Thick Superhydrophobic Coating Renders Cloth Mask Potentially Effective against Aerosol-Driven Infections.

Project description:The advent of COVID-19 pandemic has made it necessary to wear masks across populations. While the N95 mask offers great performance against airborne infections, its multilayered sealed design makes it difficult to breathe for a longer duration of use. The option of using highly breathable cloth or silk masks especially for a large populace is fraught with the danger of infection. As a normal cloth or silk mask absorbs airborne liquid, it can be a source of plausible infection. We demonstrate the chemical modification of one such mask, Eri silk, to make it hydrophobic (contact angle of water is 143.7°), which reduces the liquid absorption capacity without reducing the breathability of the mask significantly. The breathability reduces only 22% for hydrophobic Eri silk compared to the pristine Eri silk, whereas N95 shows a 59% reduction of breathability. The modified hydrophobic silk can repel the incoming aqueous liquid droplets without wetting the surface. The results indicate that a multilayered modified silk mask to make it hydrophobic can be an affordable and breathable alternative to the N95 mask.

| S-EPMC8525343 | biostudies-literature

In situ synthesis of peptide microarrays - shadow mask design [2]

Project description:An experiment was designed to use a computer program to create lithography masks using a pseudo-random pattern generator. The data in this file are results from immunosignaturing 8 different monoclonals using a 10,000 peptide random-sequence microarray. Peptides were synthesized by Sigma Aldrich, and printed onto glass slides and used to test several different parameters.

2014-01-16 | GSE50044 | GEO

In situ synthesis of peptide microarrays - shadow mask design [1]

Project description:A computer program was used to create random amino acid sequences based on and restricted by physical shadow masks which will be used for lithography-based synthesis of peptides. The output from this algorithm was used to create peptides that were synthesized by Sigma Aldrich, and printed onto glass slides. The arrays contained 384 peptides printed in duplicate for each of 4 different mask designs. 52 different monoclonal antibodies were incubated on these microarrays and analyzed for their propensity to bind the peptides created from each mask set. The diversity of binding served as a proxy for the 'randomness' of these peptides, and provided information about how many masks are needed to truly generate random sequence peptides.

2014-01-16 | GSE49217 | GEO

In situ synthesis of peptide microarrays - shadow mask design [3]

Project description:In this paper several computer programs were used to simulate in situ synthesis of peptides using shadow masks and BOC synthesis. The peptides were designed to be random, or pseudo-random, but fulfill requirements of immunosignaturing. This file contains data from actual 330,000 peptide arrays that used the first iteration of the peptide generation algorithm. Monoclonal antibodies were bound to the microarrays and the total number of peptides that distinguished each monoclonal was measured. This provides a baseline against which to compare purely random sequences.

2014-01-16 | GSE50571 | GEO

In situ synthesis of peptide microarrays - shadow mask design

Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

2014-01-16 | E-GEOD-50045 | biostudies-arrayexpress

Near-optimal experimental design for model selection in systems biology.

Project description:MotivationBiological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data. Motivated by biological applications, the method enables the design of crucial experiments: it determines a highly informative selection of measurement readouts and time points.ResultsWe demonstrate formal guarantees of design efficiency on the basis of previous results. By reducing our task to the setting of graphical models, we prove that the method finds a near-optimal design selection with a polynomial number of evaluations. Moreover, the method exhibits the best polynomial-complexity constant approximation factor, unless P = NP. We measure the performance of the method in comparison with established alternatives, such as ensemble non-centrality, on example models of different complexity. Efficient design accelerates the loop between modeling and experimentation: it enables the inference of complex mechanisms, such as those controlling central metabolic operation.AvailabilityToolbox 'NearOED' available with source code under GPL on the Machine Learning Open Source Software Web site (mloss.org).

| S-EPMC3789540 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data