Enabling additive manufacturing part inspection of digital twins via collaborative virtual reality.

Digital twins (DTs) are an emerging capability in additive manufacturing (AM), set to revolutionize design optimization, inspection, in situ monitoring, and root cause analysis. AM DTs typically incorporate multimodal data streams, ranging from machine toolpaths and in-process imaging to X-ray CT scans and performance metrics. Despite the evolution of DT platforms, challenges remain in effectively inspecting them for actionable insights, either individually or in a multidisciplinary, geographically distributed team setting.

Generating Protein Structures for Pathway Discovery Using Deep Learning.

Resolving the intricate details of biological phenomena at the molecular level is fundamentally limited by both length- and time scales that can be probed experimentally. Molecular dynamics (MD) simulations at various scales are powerful tools frequently employed to offer valuable biological insights beyond experimental resolution. However, while it is relatively simple to observe long-lived, stable configurations of, for example, proteins, at the required spatial resolution, simulating the more interesting rare transitions between such states often takes orders of magnitude longer than what is feasible even on the largest supercomputers available today.

Impact of COVID-19 on Chinese urban consumers' food safety knowledge and behavior - A comparative study between pre and post pandemic eras.

The COVID-19 pandemic caused notable changes to the food-related habits of consumers worldwide due to their concerns about the risk of infection and the requirement to follow government mandates. To investigate the impact of the pandemic on Chinese consumers' food safety knowledge, food safety behaviors, and their most recent food poisoning experiences, we compared the results from an online survey (n = 583, Dec 2019) conducted before the pandemic was officially announced with an identical survey (n = 599, Aug 2023) conducted seven months after the Chinese government downgraded restrictions related to COVID-19. Post-pandemic there was a significant decrease in consumers' food safety knowledge and self-reported food safety behaviors and a significant increase in their self-reported experiences of food poisoning.

Impact of Different Carbon Sources on Volatile Organic Compounds (VOCs) Produced during Fermentation by WLP672 Measured Using Proton Transfer Reaction Time-of-Flight Mass Spectrometry (PTR-ToF-MS).

Bacterial fermentation is considered to be a cost-effective means of generating desired flavour compounds from plant-based substrates. However, the wide range of substrates present in plants makes it challenging to understand how individual components impact on flavour volatile organic compound (VOC) production. To simplify this, a defined medium can be used to better understand VOCs production with regard to individual compounds.

Bimodal Visualization of Industrial X-Ray and Neutron Computed Tomography Data.

Advanced manufacturing creates increasingly complex objects with material compositions that are often difficult to characterize by a single modality. Our collaborating domain scientists are going beyond traditional methods by employing both X-ray and neutron computed tomography to obtain complementary representations expected to better resolve material boundaries. However, the use of two modalities creates its own challenges for visualization, requiring either complex adjustments of bimodal transfer functions or the need for multiple views.

MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline.

Large-scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that requires many software packages with complex dependencies and high computational costs. We developed MaPPeRTrac, an edge-centric tractography pipeline that simplifies and accelerates this process in a wide range of high-performance computing (HPC) environments.

Volatile Organic Compounds (VOCs) Produced by WLP672 Fermentation in Defined Media Supplemented with Different Amino Acids.

Fermentation by lactic acid bacteria (LAB) is a promising approach to meet the increasing demand for meat or dairy plant-based analogues with realistic flavours. However, a detailed understanding of the impact of the substrate, fermentation conditions, and bacterial strains on the volatile organic compounds (VOCs) produced during fermentation is lacking. As a first step, the current study used a defined medium (DM) supplemented with the amino acids L-leucine (Leu), L-isoleucine (Ile), L-phenylalanine (Phe), L-threonine (Thr), L-methionine (Met), or L-glutamic acid (Glu) separately or combined to determine their impact on the VOCs produced by WLP672 (LB672).

The Physical and Structural Effects of 1-MCP on Four Different Apple Cultivars during Storage.

The impact of the ethylene inhibitor, 1-methylcyclopropene (1-MCP), on four apple cultivars (Braeburn, Fuji, Jazz and Golden Delicious) over 150 days of storage at 2 °C was assessed. Proton transfer reaction quadrupole mass spectrometry (PTR-QUAD-MS) was used to monitor changes in VOC composition, while texture analysis and X-ray microcomputer tomography (µ-CT) scanning were used to study microstructural changes. The application of 1-MCP on apples reduced VOC emissions, concurrently maintaining a firmer texture compared to the untreated apples at each time point.

The Application of Biometric Approaches in Agri-Food Marketing: A Systematic Literature Review.

A challenge in social marketing studies is the cognitive biases in consumers' conscious and self-reported responses. To help address this concern, biometric techniques have been developed to obtain data from consumers' implicit and non-verbal responses. A systematic literature review was conducted to explore biometric applications' role in agri-food marketing to provide an integrated overview of this topic.

SMetaS: A Sample Metadata Standardizer for Metabolomics.

Metabolomics has advanced to an extent where it is desired to standardize and compare data across individual studies. While past work in standardization has focused on data acquisition, data processing, and data storage aspects, metabolomics databases are useless without ontology-based descriptions of biological samples and study designs. We introduce here a user-centric tool to automatically standardize sample metadata.

Online monitoring of higher alcohols and esters throughout beer fermentation by commercial Saccharomyces cerevisiae and Saccharomyces pastorianus yeast.

Higher alcohols and esters are among the predominant classes of volatile organic compounds (VOCs) that influence the quality of beer. The concentrations of these compounds are determined through a specific yeast strain selection and fermentation conditions. The effect of yeast strains on the formation of higher alcohols and esters throughout fermentations (at 20°C) was investigated.

The BinDiscover database: a biology-focused meta-analysis tool for 156,000 GC-TOF MS metabolome samples.

Metabolomics by gas chromatography/mass spectrometry (GC/MS) provides a standardized and reliable platform for understanding small molecule biology. Since 2005, the West Coast Metabolomics Center at the University of California at Davis has collated GC/MS metabolomics data from over 156,000 samples and 2000 studies into the standardized BinBase database. We believe that the observations from these samples will provide meaningful insight to biologists and that our data treatment and webtool will provide insight to others who seek to standardize disparate metabolomics studies.

Food Technology Neophobia Scales in cross-national context: Consumers' acceptance of food technologies between Chinese and New Zealand.

An increasing number of novel food technologies have been developed to meet consumers' growing desire for safe and high-quality foods. However, consumers can be cautious of novel food technologies, and their acceptance cannot be guaranteed. Food Technology Neophobia Scales (FTNS) have been proven to be an effective tool to predict consumers' behavior toward novel food technologies in a range of individual countries, but not for cross-national contexts.

Flavour Volatiles of Fermented Vegetable and Fruit Substrates: A Review.

Health, environmental and ethical concerns have resulted in a dramatic increase in demand for plant-based dairy analogues. While the volatile organic compounds (VOCs) responsible for the characteristic flavours of dairy-based products have been extensively studied, little is known about how to reproduce such flavours using only plant-based substrates. As a first step in their development, this review provides an overview of the VOCs associated with fermented (bacteria and/or fungi/yeast) vegetable and fruit substrates.

A Visual Comparison of Silent Error Propagation.

High-performance computing (HPC) systems play a critical role in facilitating scientific discoveries. Their scale and complexity (e.g.

The confluence of machine learning and multiscale simulations.

Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials.

Yeast Strain Influences the Hop-Derived Sensory Properties and Volatile Composition of Beer.

The perception of hop-derived flavour in beer is not well understood, particularly regarding the effect that different yeast strains and fermentation parameters have on perceived hop aroma and the mechanisms responsible for these changes. To evaluate the influence of yeast strain on the sensory properties and volatile composition of beer, a standard wort, late-hopped with New Zealand Motueka hops (5 g·L), was fermented with one of twelve yeast strains under constant conditions (temperature and yeast inoculation rate). The bottled beers were evaluated using a free sorting sensory methodology, and their volatile organic compounds (VOC) were assessed using gas chromatography mass spectrometry (GC/MS) with headspace solid-phase microextraction (SPME) sampling.

Understanding the effect of meat electrical conductivity on Pulsed Electric Field (PEF) process parameters and the ability of PEF to enhance the quality and shorten sous vide processing for beef short ribs.

The aim of this research was to investigate how the electrical conductivity of short ribs affected Pulsed Electric Field (PEF) process parameters and the ability of PEF to enhance their quality and reduce sous vide (SV) processing time. Short ribs with different range of electrical conductivity (3-6, 6-9, and 9-12 mS/cm) values were treated using input voltage of 10 kV, pulse width of 20 µs, pulse frequency of 50 Hz and pulse number, of either 1600 (low intensity PEF/LPEF) or 5200 (high intensity PEF/HPEF), followed by SV processing at 60 °C for either 24 or 36 h. The quality parameters assessed were cooking loss (%), Texture Profile Analysis (TPA) parameters, and Commission Internationale d'Eclairage (CIE) L*a*b* colour parameters.

A Comprehensive Review of Variability in the Thermal Resistance (D-Values) of Food-Borne Pathogens-A Challenge for Thermal Validation Trials.

The thermal processing of food relies heavily on determining the right time and temperature regime required to inactivate bacterial contaminants to an acceptable limit. To design a thermal processing regime with an accurate time and temperature combination, the D-values of targeted microorganisms are either referred to or estimated. The D-value is the time required at a given temperature to reduce the bacterial population by 90%.

Upcycled foods: A nudge toward nutrition.

One of the aims of the United Nations Sustainable Development Goals (SDG) is to end hunger and ensure access by all people to safe, nutritious, and sufficient food all year round. An obvious synergy exists between the second SDG "Zero Hunger" and SDG target 12.3 which focuses on halving food waste and reducing food losses.

Beyond the Ground State: Predicting Electron Ionization Mass Spectra Using Excited-State Molecular Dynamics.

Here, we provide an algorithm that introduces excited states into the molecular dynamics prediction of the 70 eV electron ionization mass spectra. To decide the contributions of different electronic states, the ionization cross section associated with relevant molecular orbitals was calculated by the binary-encounter-Bethe (BEB) model. We used a fast orthogonalization model/single and double state configuration interaction (OM2/CISD) method to implement excited states calculations and combined this with the GFN1-xTB semiempirical model.

How Well Can We Predict Mass Spectra from Structures? Benchmarking Competitive Fragmentation Modeling for Metabolite Identification on Untrained Tandem Mass Spectra.

Competitive Fragmentation Modeling for Metabolite Identification (CFM-ID) is a machine learning tool to predict in silico tandem mass spectra (MS/MS) for known or suspected metabolites for which chemical reference standards are not available. As a machine learning tool, it relies on both an underlying statistical model and an explicit training set that encompasses experimental mass spectra for specific compounds. Such mass spectra depend on specific parameters such as collision energies, instrument types, and adducts which are accumulated in libraries.