Changes in Food Consumption Trends among American Adults since the COVID-19 Pandemic.

The quality and quantity of food consumption have changed due to the COVID-19 pandemic. In this study, we examined how the COVID-19 pandemic has changed the consumption of different food groups in order to close the research gap by providing current evidence that reflects a later stage of the pandemic compared to other circulating research conducted at earlier stages. Data collection for this cross-sectional study was performed via an online Qualtrics survey from 10,050 adults aged 40-100 years. Nutritional status was measured using the 24-item short-form Dietary Screening Tool (DST) twice: before and since the COVID-19 pandemic. The DST questions were categorized based on MyPlate items, along with fat, sugar, and sweet items, as well as nutritional supplement intake. In addition, the total DST score was calculated for each participant, which categorized them into one of three groups: "at risk", "possible risk", and "not at risk". The results revealed that the consumption of grains, fruit, lean protein, and dairy decreased significantly, while the consumption of fat, sugar, and sweet items increased significantly due to COVID-19. The biggest decreases in consumption of food subcategories were related to whole grain bread and cereal, followed by fruit as a snack, in comparison with other types of grain and fruit. No changes in the consumption of vegetables, processed meat, or supplement intake were seen. The total DST score showed that, before and since COVID-19, the overall nutrition status of adult Americans has been at risk. In addition, of those participants who were not at risk before COVID-19, 28.5% were either at risk or at possible risk since COVID-19; moreover, of those participants who were at possible risk before COVID-19, 21% were at risk since COVID-19. As a good nutritional status can reduce the risk of severe illness or even mortality rate in times of crisis, the findings of this study can help policymakers and health educators to develop heath-protecting behavior sessions against future pandemics to manage crises.

Return connectedness and multiscale spillovers across clean energy indices and grain commodity markets around COVID-19 crisis.

The aim of this paper is to examine the return connectedness and multiscale spillovers between the Clean Energy Index and the grain commodity market around COVID-19. Using daily data from January 4, 2017 to July 1, 2022, a time-varying parametric vector autoregressive (TVP-VAR) connectedness approach is first used to reveal connectedness patterns before and during COVID-19. We further used Baruník and Krehlík (2018)'s frequency domain spillover method to assess connectedness in different domain horizons. Our results show spillover effects over time and frequency, with COVID-19 significantly affecting the connectedness of the whole system. Dynamic connectedness peaks significantly after sudden bursts of COVID-19, validating the reported uncertainty. We also documented higher spillover levels in the short term than in the medium and long term. In addition, We find that (i) most clean energy indices are positively affected by the COVID-19 outbreak; (ii) the NASDAQ OMX Bio/Clean Fuels Index and NASDAQ OMX Geothermal Index send spillovers to all grain commodities throughout the sample period, while the WilderHill Clean Energy Index and NASDAQ OMX Wind Energy Index indices are the largest recipients of spillovers from other markets regardless of time horizon; (iii) the OMX Bio/Clean Fuels and OMX Geothermal Energy indices dominate spillover shocks to grain commodity markets. This is the first study to analyse the connectedness and time-frequency dynamics of returns in the green energy index and the grain commodity market. These results provide valuable insights to investors and key policymakers, especially at a time of more significant uncertainty.

Construction of rice supply chain supervision model driven by blockchain smart contract.

The outbreak of the COVID-19 and the Russia Ukraine war has had a great impact on the rice supply chain. Compared with other grain supply chains, rice supply chain has more complex structure and data. Using digital means to realize the dynamic supervision of rice supply chain is helpful to ensure the quality and safety of rice. This study aimed to build a dynamic supervision model suited to the circulation characteristics of the rice supply chain and implement contractualization, analysis, and verification. First, based on an analysis of key information in the supervision of the rice supply chain, we built a dynamic supervision model framework based on blockchain and smart contracts. Second, under the logical framework of a regulatory model, we custom designed three types of smart contracts: initialization smart contract, model-verification smart contract, and credit-evaluation smart contract. To implement the model, we combined an asymmetric encryption algorithm, virtual regret minimization algorithm, and multisource heterogeneous fusion algorithm. We then analyzed the feasibility of the algorithm and the model operation process. Finally, based on the dynamic supervision model and smart contract, a prototype system is designed for example verification. The results showed that the dynamic supervision model and prototype system could achieve the real-time management of the rice supply chain in terms of business information, hazard information, and personnel information. It could also achieve dynamic and credible supervision of the rice supply chain's entire life cycle at the information level. This new research is to apply information technology to the digital management of grain supply chain. It can strengthen the digital supervision of the agricultural product industry.

Towards an optimal monoclonal antibody with higher binding affinity to the receptor-binding domain of SARS-CoV-2 spike proteins from different variants.

A highly efficient and robust multiple scales in silico protocol, consisting of atomistic Molecular Dynamics (MD), coarse-grain (CG) MD, and constant-pH CG Monte Carlo (MC), has been developed and used to study the binding affinities of selected antigen-binding fragments of the monoclonal antibody (mAbs) CR3022 and several of its here optimized versions against 11 SARS-CoV-2 variants including the wild type. Totally 235,000 mAbs structures were initially generated using the RosettaAntibodyDesign software, resulting in top 10 scored CR3022-like-RBD complexes with critical mutations and compared to the native one, all having the potential to block virus-host cell interaction. Of these 10 finalists, two candidates were further identified in the CG simulations to be the best against all SARS-CoV-2 variants. Surprisingly, all 10 candidates and the native CR3022 exhibited a higher affinity for the Omicron variant despite its highest number of mutations. The multiscale protocol gives us a powerful rational tool to design efficient mAbs. The electrostatic interactions play a crucial role and appear to be controlling the affinity and complex building. Studied mAbs carrying a more negative total net charge show a higher affinity. Structural determinants could be identified in atomistic simulations and their roles are discussed in detail to further hint at a strategy for designing the best RBD binder. Although the SARS-CoV-2 was specifically targeted in this work, our approach is generally suitable for many diseases and viral and bacterial pathogens, leukemia, cancer, multiple sclerosis, rheumatoid, arthritis, lupus, and more.

Rapid Detection of Aspergillus flavus and Quantitative Determination of Aflatoxin B1 in Grain Crops Using a Portable Raman Spectrometer Combined with Colloidal Au Nanoparticles.

Aspergillus flavus and Aflatoxins in grain crops give rise to a serious threat to food security and cause huge economic losses. In particular, aflatoxin B1 has been identified as a Class I carcinogen to humans by the International Agency for Research on Cancer (IARC). Compared with conventional methods, Surface-Enhanced Raman Scattering (SERS) has paved the way for the detection of Aspergillus flavus and Aflatoxins in grain crops as it is a rapid, nondestructive, and sensitive analytical method. In this work, the rapid detection of Aspergillus flavus and quantification of Aflatoxin B1 in grain crops were performed by using a portable Raman spectrometer combined with colloidal Au nanoparticles (AuNPs). With the increase of the concentration of Aspergillus flavus spore suspension in the range of 102-108 CFU/mL, the better the combination of Aspergillus flavus spores and AuNPs, the better the enhancement effect of AuNPs solution on the Aspergillus flavus. A series of different concentrations of aflatoxin B1 methanol solution combined with AuNPs were determined based on SERS and their spectra were similar to that of solid powder. Moreover, the characteristic peak increased gradually with the increase of concentration in the range of 0.0005-0.01 mg/L and the determination limit was 0.0005 mg/L, which was verified by HPLC in ppM concentration. This rapid detection method can greatly shorten the detection time from several hours or even tens of hours to a few minutes, which can help to take effective measures to avoid causing large economic losses.

Food Security: 3D Dynamic Display and Early Warning Platform Construction and Security Strategy.

Since it affects a nation's economy and people's wellbeing, food security is a crucial national security requirement. In order to realize multi-angle grain data presentation and analysis and achieve the goal of deep mining, we propose a 3D dynamic visualization analysis method of multidimensional agricultural spatial-temporal data based on the self-organizing map. This method realizes the multi-angle display and analysis of grain data and achieves the purpose of deep mining. With the outbreak of COVID-19, the global food security situation is not optimistic, so it is necessary to use the food security early warning system to solve the food security issue. Machine learning has emerged widely in recent years and has been applied in various fields. Therefore, it is an excellent way to solve food security to apply the model in machine learning to construct a food security early warning system. Afterward, a food security early warning platform is developed with a support vector regression (SVR) model to ensure food security. Finally, we analyze China's medium and long-term food security policy in line with modernization objectives. The experimental results show that the food security early warning platform based on the SVR model from 2007 to 2016 is effective compared with the actual situation every year. Through analyses, we should improve the stability, reliability, and sustainability of food supply, firmly hold the food security initiative, and construct a national food security guarantee system matching the goal of modernization.

Ensuring Global Food Security by Improving Protein Content in Major Grain Legumes Using Breeding and 'Omics' Tools.

Grain legumes are a rich source of dietary protein for millions of people globally and thus a key driver for securing global food security. Legume plant-based 'dietary protein' biofortification is an economic strategy for alleviating the menace of rising malnutrition-related problems and hidden hunger. Malnutrition from protein deficiency is predominant in human populations with an insufficient daily intake of animal protein/dietary protein due to economic limitations, especially in developing countries. Therefore, enhancing grain legume protein content will help eradicate protein-related malnutrition problems in low-income and underprivileged countries. Here, we review the exploitable genetic variability for grain protein content in various major grain legumes for improving the protein content of high-yielding, low-protein genotypes. We highlight classical genetics-based inheritance of protein content in various legumes and discuss advances in molecular marker technology that have enabled us to underpin various quantitative trait loci controlling seed protein content (SPC) in biparental-based mapping populations and genome-wide association studies. We also review the progress of functional genomics in deciphering the underlying candidate gene(s) controlling SPC in various grain legumes and the role of proteomics and metabolomics in shedding light on the accumulation of various novel proteins and metabolites in high-protein legume genotypes. Lastly, we detail the scope of genomic selection, high-throughput phenotyping, emerging genome editing tools, and speed breeding protocols for enhancing SPC in grain legumes to achieve legume-based dietary protein security and thus reduce the global hunger risk.

COVID-19 epidemic lockdown-induced changes of cereals and animal protein foods consumption of Iran population: the first nationwide survey.

BACKGROUND: The COVID-19 epidemic has affected diverse issues of life including economy, health and nutrition. This nationwide study was conducted to evaluate the effects of the epidemic lockdown-induced socio-economic changes on animal source proteins and cereals intakes of Iran population for the first time. METHODS: This was a cross-sectional descriptive-analytical study using a web-based electronic self-administered questionnaire. A questionnaire was designed with the aim of detecting any changes in the dietary pattern of the Iranian household following coronavirus epidemic. RESULTS: A total of 21,290 households participated in the study. Approximately 33%, 24%, 14.2% and 7% of the households had decreased the weekly consumption of red meat, white meat, eggs and rice/bread, respectively, following COVID-19 epidemic. The results of ordered logistic regression showed that the female-headed households, as compared with male-headed ones, were 30% more likely to decrease their weekly consumption of white meat (OR = 1.3, 95% CI: 1.1-1.5). About 8.6% of the households had reduced all three major dietary sources of animal protein. Close to half of the households (46.9%) who decreased their intake of the animal protein sources had increased their intake of rice or bread, too. The major reasons for these changes were decrease of income and job loss. CONCLUSION: In conclusion, animal protein food consumption decreased during epidemic lockdown but the amounts of rice and bread, as the major sources of dietary energy, have increased. These changes, if persist long enough, can seriously affect micronutrient status of the whole population. Early nutritional interventions for needy families are warranted.

Connected and extracted: Understanding how centrality in the global wheat supply chain affects global hunger using a network approach.

While global food trade has allowed countries to buffer against domestic food production shortfalls and gain access to larger markets, engaging in trade has also opened economies up to shocks and increased extraction of food resources. With this research, we analyze how the global grain network influences country-level nourishment, while controlling for per capita land and food production. First, we model the trade network structure of the global wheat supply chain to measure the centrality or positionality of countries. We use spatial regression analysis to assess the impact of trade networks, volume, purchasing power, production capacity and geography on undernourishment. We find that the six countries most central to the global grain trade by betweenness and eigenvector centralities account for more than half of all wheat exports globally by volume. The centrality of these countries as opposed to volume of wheat produced or traded, determines their influence in the wheat supply chain network. The parametric component of our analysis confirms that trade, and centrality have significant implications for national levels of nourishment. Our findings suggest that for countries with low purchasing power, increasing centrality allows improvements in nourishment levels but for countries with very high purchasing power, increasing centrality can increase hunger outcomes. To counteract perturbations and shortfalls such as those being experienced currently in the globalized food system, local and regional governments may consider refocusing on regional and local based food systems.

Chickpeas from a Chilean Region Affected by a Climate-Related Catastrophe: Effects of Water Stress on Grain Yield and Flavonoid Composition.

The Valparaiso region in Chile was decreed a zone affected by catastrophe in 2019 as a consequence of one of the driest seasons of the last 50 years. In this study, three varieties ('Alfa-INIA', 'California-INIA', and one landrace, 'Local Navidad') of kabuli-type chickpea seeds produced in 2018 (control) and 2019 (climate-related catastrophe, hereafter named water stress) were evaluated for their grain yield. Furthermore, the flavonoid profile of both free and esterified phenolic extracts was determined using liquid chromatography-mass spectrometry, and the concentration of the main flavonoid, biochanin A, was determined using liquid chromatography with diode array detection. The grain yield was decreased by up to 25 times in 2019. The concentration of biochanin A was up to 3.2 times higher in samples from the second season (water stress). This study demonstrates that water stress induces biosynthesis of biochanin A. However, positive changes in the biochanin A concentration are overshadowed by negative changes in the grain yield. Therefore, water stress, which may be worsened by climate change in the upcoming years, may jeopardize both the production of chickpeas and the supply of biochanin A, a bioactive compound that can be used to produce dietary supplements and/or nutraceuticals.

Increased brain volume from higher cereal and lower coffee intake: shared genetic determinants and impacts on cognition and metabolism.

It is unclear how different diets may affect human brain development and if genetic and environmental factors play a part. We investigated diet effects in the UK Biobank data from 18,879 healthy adults and discovered anticorrelated brain-wide gray matter volume (GMV)-association patterns between coffee and cereal intake, coincidence with their anticorrelated genetic constructs. The Mendelian randomization approach further indicated a causal effect of higher coffee intake on reduced total GMV, which is likely through regulating the expression of genes responsible for synaptic development in the brain. The identified genetic factors may further affect people's lifestyle habits and body/blood fat levels through the mediation of cereal/coffee intake, and the brain-wide expression pattern of gene CPLX3, a dedicated marker of subplate neurons that regulate cortical development and plasticity, may underlie the shared GMV-association patterns among the coffee/cereal intake and cognitive functions. All the main findings were successfully replicated. Our findings thus revealed that high-cereal and low-coffee diets shared similar brain and genetic constructs, leading to long-term beneficial associations regarding cognitive, body mass index (BMI), and other metabolic measures. This study has important implications for public health, especially during the pandemic, given the poorer outcomes of COVID-19 patients with greater BMIs.

Large-scale integration of meta-QTL and genome-wide association study discovers the genomic regions and candidate genes for yield and yield-related traits in bread wheat.

KEY MESSAGE: Based on the large-scale integration of meta-QTL and Genome-Wide  Association Study, 76 high-confidence MQTL regions and 237 candidate genes that affected wheat yield and yield-related traits were discovered. Improving yield and yield-related traits are key goals in wheat breeding program. The integration of accumulated wheat genetic resources provides an opportunity to uncover important genomic regions and candidate genes that affect wheat yield. Here, a comprehensive meta-QTL analysis was conducted on 2230 QTL of yield-related traits obtained from 119 QTL studies. These QTL were refined into 145 meta-QTL (MQTL), and 89 MQTL were verified by GWAS with different natural populations. The average confidence interval (CI) of these MQTL was 2.92 times less than that of the initial QTL. Furthermore, 76 core MQTL regions with a physical distance less than 25 Mb were detected. Based on the homology analysis and expression patterns, 237 candidate genes in the MQTL involved in photoperiod response, grain development, multiple plant growth regulator pathways, carbon and nitrogen metabolism and spike and flower organ development were determined. A novel candidate gene TaKAO-4A was confirmed to be significantly associated with grain size, and a CAPS marker was developed based on its dominant haplotype. In summary, this study clarified a method based on the integration of meta-QTL, GWAS and homology comparison to reveal the genomic regions and candidate genes that affect important yield-related traits in wheat. This work will help to lay a foundation for the identification, transfer and aggregation of these important QTL or candidate genes in wheat high-yield breeding.

The Potential of Selected Agri-Food Loss and Waste to Contribute to a Circular Economy: Applications in the Food, Cosmetic and Pharmaceutical Industries.

The food sector includes several large industries such as canned food, pasta, flour, frozen products, and beverages. Those industries transform agricultural raw materials into added-value products. The fruit and vegetable industry is the largest and fastest-growing segment of the world agricultural production market, which commercialize various products such as juices, jams, and dehydrated products, followed by the cereal industry products such as chocolate, beer, and vegetable oils are produced. Similarly, the root and tuber industry produces flours and starches essential for the daily diet due to their high carbohydrate content. However, the processing of these foods generates a large amount of waste several times improperly disposed of in landfills. Due to the increase in the world's population, the indiscriminate use of natural resources generates waste and food supply limitations due to the scarcity of resources, increasing hunger worldwide. The circular economy offers various tools for raising awareness for the recovery of waste, one of the best alternatives to mitigate the excessive consumption of raw materials and reduce waste. The loss and waste of food as a raw material offers bioactive compounds, enzymes, and nutrients that add value to the food cosmetic and pharmaceutical industries. This paper systematically reviewed literature with different food loss and waste by-products as animal feed, cosmetic, and pharmaceutical products that strongly contribute to the paradigm shift to a circular economy. Additionally, this review compiles studies related to the integral recovery of by-products from the processing of fruits, vegetables, tubers, cereals, and legumes from the food industry, with the potential in SARS-CoV-2 disease and bacterial diseases treatment.