Non-essential Ones Research Articles

Biochemical composition of biological substances extracted from red deer antlers

We aim to study biologically active agents in biological substances obtained from different parts of red deer antlers. Frozen red deer antlers were separated into skin and antler base, crushed, and subjected to ultrasonic enzymatic hydrolysis. The press cake from the extraction of antler base was dried in a vacuum unit. The content of protein, fat, ash, and moisture in the obtained biological substances was studied. In addition, the amino acid, fatty acid, and mineral compositions were analyzed, as well as the molecular weight distribution of peptide fractions. The concentrates from the skin and base of red deer antlers were found to contain the maximum of proteins (73.68–74.41%). The mineral biological substances comprise the maximum of macroand microelements (84.17%), in particular, a high level of 136,000 mg /kg of phosphorus, 29,230 mg /kg of sodium, and 5,140 mg /kg of calcium. All biological substances are characterized by a low-fat content in the range from 0.3 to 1.76%. Similar to the protein content, the total amount of amino acids prevails in concentrates from the skin and base of red deer antlers, equaling 40.90 and 43.66 g /100 g, respectively. Moreover, in both samples, the amount of essential amino acids is higher than that of non-essential ones. The amount of vitamins is low, being represented by B1, B2, B6 water-soluble vitamins. The fatty acid composition is most balanced in the concentrate from the red deer antler base, containing omega-3 and omega-6. The ratio of unsaturated to saturated fatty acids is equal to 0.96. In the concentrate from antler skin, the maximum content (54.7%) of peptides with a low molecular weight of less than 2.9 kDa and up to 6.4 kDa was found using the molecular-weight distribution of peptide fractions.

Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?

Despite implementing hundreds of strategies, cancer drug development suffers from a 95% failure rate over 30 years, with only 30% of approved cancer drugs extending patient survival beyond 2.5 months. Adding more criteria without eliminating nonessential ones is impractical and may fall into the "survivorship bias" trap. Machine learning (ML) models may enhance efficiency by saving time and cost. Yet, they may not improve success rate without identifying the root causes of failure. We propose a "STAR-guided ML system" (structure-tissue/cell selectivity-activity relationship) to enhance success rate and efficiency by addressing three overlooked interdependent factors: potency/specificity to the on/off-targets determining efficacy in tumors at clinical doses, on/off-target-driven tissue/cell selectivity influencing adverse effects in the normal organs at clinical doses, and optimal clinical doses balancing efficacy/safety as determined by potency/specificity and tissue/cell selectivity. STAR-guided ML models can directly predict clinical dose/efficacy/safety from five features to design/select the best drugs, enhancing success and efficiency of cancer drug development.

Covid‐19 and its effect on formal employment in Mexico: Are there signs of recovery?

AbstractThe conditions of the Mexican labor market during the Covid‐19 pandemic went through different stages. The government first declared a quarantine that lasted for several months, the effects of which continue to be quantified. In that sense, this article answers two questions, (1) what are the changes in labor formality rates before and 2 years after the Covid‐19 pandemic began in Mexico?; and (2) are there differences in economic sectors and between genders? We use the methodology proposed by Chacaltana and McMillan to measure the variation in the labor formality rate in its two components, (1) intrasectoral; and (2) intersectoral, through 9 quarterly editions of the National Occupation and Employment Survey. The results show that in the first 2 years of the pandemic and especially during the time of confinement, labor informality was not a means to maintain household income, as had happened in other economic crises. Likewise, important sectoral effects were found, with the most affected industries being nonessential ones. Finally, gender gaps widened, with women being the most affected in the formal and informal labor market due to pre‐existing conditions that were aggravated by the pandemic.

The Extracellular Metallometabolome: Metallophores, Metal Ionophores, and Other Chelating Agents as Natural Products

Natural products include inorganic as well as organic compounds. Living organisms face constant challenges in acquiring essential metal ions and getting rid of non-essential ones with toxic actions. They employ an extracellular biochemistry in these tasks and use it to engage in a chemical warfare against invaders and competitors by either increasing or decreasing the availability of metal ions for maintaining their welfare in aquatic or terrestrial ecological niches. To control mutualistic, cambialistic or parasitic symbiosis with other organisms they use a remarkably rich suite of secreted bioactive molecules with ligand donor atoms for metal binding. This overview discusses the interactions of these extracellular natural products with a multitude of metal ions in the periodic system of the elements. It focuses mainly on metallophores and metal ionophores secreted from bacteria, fungi, and plants, but metal-carrying cofactors and other chelating agents will also be mentioned in the context of related functions and with an intent to categorize. The intracellular fate of the metal ions and the controlled pathways for the biosynthesis, secretion, uptake, biodegradation or recycling of the secreted natural products that interact with metal ions will not be covered. Metallophores make extracellular metal ions available via delivery to specific transporters and unavailable to competing organisms, especially pathogens, though some invaders have developed ways to compete efficiently for metal ions. The classic concept of siderophores, carriers of iron(III) ions, is extended here to specific and broad-band metallophores for metal ions such as copper (chalkophores), zinc (zincophores), and yet others. Metal ionophores, in contrast, transport metal ions through biological membranes. There is a wide variety of chemical structures for either metallophores or metal ionophores. Together with physicochemical investigations of metal complexation und conditions mimicking the natural environment, “omics” mining and mapping the diversity of chemotypes is an on-going effort with analytic, genetic, and bioinformatic tools and comes together in defining the metallometabolome, which combines the metabolome and the metallome. Investigations are highly multidisciplinary, include an important, but academically infrequently crossed bridge between the biosciences (biochemistry) and the earth sciences (geochemistry), define significant applications in the pharmaceutical/medical sciences regarding immune modulation and the control of virulence at the host-pathogen interface, and have implications for the nutritional/toxicological and environmental/ecological sciences.

The biochemical composition of wild berries and fruits growing in the Southwestern Taimyr

This article presents research findings on the chemical composition of fruits and berries found in the Southwestern Taimyr region, including bilberry (Vaccinium myrtíllus), northern redcurrant (Ribes triste), blueberry (Vaccinium uliginosum), prickly wild rose (Rosa acicularis), lingonberry (Vaccinium vitis-idaea), almost-holarctic crowberry (Empetrum subholarcticum), Siberian rowan (Sorbus sibirica), and cloudberry (Rubus chamaemorus). We revealed that rowan, blueberries, and prickly wild rose are particularly rich in crude fat. Additionally, prickly wild rose, and red currants were found to be high in crude fiber content, while bilberries, red currants, and cloudberries were dominant in crude protein. Potassium and calcium content were highest in prickly wild rose and rowan, while bilberries, red currants, and prickly wild rose had high sodium content. Moreover, crowberries and rowan berries were rich in phosphorus, and lingonberries, rowan berries, bilberries, and cloudberries were abundant in magnesium. Red currants, bilberries, and cloudberries had the highest total amino acid content, with essential amino acids being more prevalent than non-essential ones in redcurrant, bilberry, crowberry, and cloudberry samples. The content included high levels of leucine, isoleucine, valine, and threonine. Overall, the fruits and berries in the Southwestern Taimyr region have significant biological value and could be effectively used in creating functional biologically active substances.

Trace Metals in Phytoplankton: Requirements, Function, and Composition in Harmful Algal Blooms

In a constantly changing environment brought about by the climate crisis and escalated anthropogenic perturbations driven by the growing population, harmful algal bloom dynamics and their impacts are expected to shift, necessitating adaptive management strategies and comprehensive research efforts. Similar to primary productivity, HABs have been thought to be driven primarily by major nutrients such as N, P, and Si. However, recent investigations on the role and importance of micronutrients as limiting factors in aquatic environments have been highlighted. This paper provides a review of metal and phytoplankton interactions, with a specific emphasis on pertinent information on the influence of trace nutrients on growth, toxin production, and other underlying mechanisms related to the dynamics of HABs. Low to near-depleted levels of essential nutrients, including Fe, Cu, Zn, Se, Mn, Co, and Mo, negatively impact cell growth and proliferation of various marine and freshwater HAB species. However, evidence shows that at elevated levels, these trace elements, along with other non-essential ones, could still cause toxic effects to certain HAB species manifested by decreased photosynthetic activities, oxidative stress, ultrastructure damage, and cyst formation. Interestingly, while elevated levels of these metals mostly result in increased toxin production, Co (i.e., yessotoxins, gymnodimine, and palytoxins) and Mn (i.e., isodomoic acid, okadaic and diol esters) enrichments revealed otherwise. In addition to toxin production, releasing dissolved organic matter (DOM), including dissolved organic carbon (DOC) and humic substances, was observed as an adaptation strategy, since these organic compounds have been proven to chelate metals in the water column, thereby reducing metal-induced toxicity. Whilst current research centers on free metal toxicity of specific essential elements such as Cu and Zn, a comprehensive account of how trace metals contribute to the growth, toxin production, and other metabolic processes under conditions reflective of in situ scenarios of HAB-prone areas would yield new perspectives on the roles of trace metals in HABs. With the growing demands of the global population for food security and sustainability, substantial pressure is exerted on the agriculture and aquaculture sector, highlighting the need for effective communication of information regarding the interactions of macro- and micronutrients with HABs to improve existing policies and practices.

Machine learning identifies key metabolic reactions in bacterial growth on different carbon sources

Carbon source-dependent control of bacterial growth is fundamental to bacterial physiology and survival. However, pinpointing the metabolic steps important for cell growth is challenging due to the complexity of cellular networks. Here, the elastic net model and multilayer perception model that integrated genome-wide gene-deletion data and simulated flux distributions were constructed to identify metabolic reactions beneficial or detrimental to Escherichia coli grown on 30 different carbon sources. Both models outperformed traditional in silico methods by identifying not just essential reactions but also nonessential ones that promote growth. They successfully predicted metabolic reactions beneficial to cell growth, with high convergence between the models. The models revealed that biosynthetic pathways generally promote growth across various carbon sources, whereas the impact of energy-generating pathways varies with the carbon source. Intriguing predictions were experimentally validated for findings beyond experimental training data and the impact of various carbon sources on the glyoxylate shunt, pyruvate dehydrogenase reaction, and redundant purine biosynthesis reactions. These highlight the practical significance and predictive power of the models for understanding and engineering microbial metabolism.

Association between Mediterranean diet and metal mixtures concentrations in pregnant people from the New Hampshire Birth Cohort Study

Diet is a primary source of nutrients but also toxic metal exposure. In pregnancy, balancing essential metal exposure while reducing non-essential ones is vital for fetal and maternal health. However, the effect of metal mixtures from diets like the Mediterranean, known for health benefits, remains unclear. This study aimed to explore the association between Mediterranean diet adherence and metals exposure, both individually and as mixtures. The study involved 907 pregnant participants from the New Hampshire Birth Cohort Study. We calculated the relative Mediterranean diet score (rMED) through a validated food frequency questionnaire, which includes 8 traditional Mediterranean dietary components. Also, at ~24–28 weeks of gestation, we used ICP-MS to measure speciation of Al, Cd, Co, Cu, Fe, Hg, Mo, Ni, Sb, Se, Sn, Zn, and As in urine, as well as Pb, Hg, As, Ni, and Se in toenails. We used multiple linear regression and Weighted Quantile Sum regression to analyze the association between rMED and metal mixtures. The models were adjusted for age, pre-pregnancy BMI, smoking during pregnancy, and educational level. High adherence to the Mediterranean diet was associated with increased urinary Al (® = 0.26 (95 % confidence interval (CI) = 0.05; 0.46)), Cd (β = 0.12 (95%CI = 0.00; 0.24)), Mo (β = 0.10 (95%CI = 0.00; 0.20)), and AsB (β = 0.88 (95%CI = 0.49; 1.27)) as well as toenail Hg (β = 0.44 (95%CI = 0.22; 0.65)), Ni (β = 0.37 (95%CI = 0.06; 0.67)), and Pb (β = 0.22 (95%CI = 0.03; 0.40)) compared to those with low adherence. The intake of fruits and nuts, fish and seafood, legumes, cereals, meat, and olive oil were found to be related to the metal biomarkers within the rMED. In conclusion, the Mediterranean diet enhances essential metal intake but may also increase exposure to harmful ones.

Effect of Diets on Plasma and Aorta Lipidome: A Study in the apoE Knockout Mouse Model.

Specific lipid molecules circulating in plasma at low concentrations have emerged as biomarkers of atherosclerotic risk. The aim of the present study is that of evaluating, in an athero-prone mouse model, how different diets can affect plasma and aorta lipidome. Thirty-six apoE knockout mice are divided in three groups and feed 12 weeks with diets differing for cholesterol and fatty acid content. Atherosclerosis is measured at the aortic sinus and aorta. Lipids are quantified in plasma and aorta with mass spectrometry. The cholesterol content of the diets is the main driver of lipid accumulation in plasma and aorta. The fatty acid composition of the diets affects plasma levels both of essential (linoleic acid) and nonessential (myristic and arachidonic acid) ones. Lipidomics show a comparable distribution, in plasma and aorta, of the main lipid components of oxidized LDL, including cholesteryl esters and lysophosphatidylcholines. Interestingly, lactosylceramide, glucosyl/galactosylceramide, and individual ceramide species are found to accumulate in diseased aortic segments. Both the cholesterol and fatty acid content of the diets profoundly affect plasma lipidome. Aorta lipidome is likewise affected with the accumulation of specific lipids known as markers of atherosclerosis.

GEGE: Çizge Gömülümleriyle Gen Esaslılığını Tahmin Etme

A gene is considered essential if its function is indispensable for the viability or reproductive success of a cell or an organism. Distinguishing essential genes from non-essential ones is a fundamental question in genetics, and it is key to understanding the minimal set of functional requirements of an organism. Knowledge of the set of essential genes is also crucial in drug discovery. Several reports in the literature show that the gene location in a protein-protein interaction network is correlated with the target gene’s essentiality. Here, we ask whether the node embeddings of a protein-protein interaction (PPI) network can help predict gene essentiality. Our results on predicting human gene essentiality show that node embeddings alone can achieve up to 88% AUC score, which is better than using topological features to characterize gene properties and other previous work’s results. We also show that, when combined with homology information across species, this performance reaches 89% AUC. Our work shows that node embeddings of a protein in the PPI network capture the network connectivity patterns of the proteins and improve the gene essentiality predictions.

The quintessence of metallomics: a harbinger of a different life science based on the periodic table of the bioelements.

This year marks the 20th anniversary of the field of metallomics. As a landmark in time, it is an occasion to reflect on the past, present, and future of this integrated field of biometal sciences. A fundamental bias is one reason for having metallomics as a scientific discipline. The focus of biochemistry on the six non-metal chemical elements, collectively known with the acronym SPONCH (sulphur, phosphorus, oxygen, nitrogen, carbon, hydrogen), glosses over the fact that the lower quantities of many other elements have qualities that made them instrumental in the evolution of life and pivotal in numerous life processes. The metallome, alongside the genome, proteome, lipidome, and glycome, should be regarded as a fifth pillar of elemental—vis-à-vis molecular—building blocks in biochemistry. Metallomics as ‘global approaches to metals in the biosciences’ considers the biological significance of most chemical elements in the periodic table, not only the ones essential for life, but also the non-essential ones that are present in living matter—some at higher concentrations than the essential ones. The non-essential elements are bioactive with either positive or negative effects. Integrating the significance of many more chemical elements into the life sciences requires a transformation in learning and teaching with a focus on elemental biology in addition to molecular biology. It should include the dynamic interactions between the biosphere and the geosphere and how the human footprint is changing the ecology globally and exposing us to many additional chemical elements that become new bioelements.

Innovative Artificial-Intelligence- Based Approach for the Biodegradation of Feather Keratin by Bacillus paramycoides, and Cytotoxicity of the Resulting Amino Acids.

The current study reported a new keratinolytic bacterium, which was characterized as Bacillus paramycoides and identified by 16S rRNA, and the sequence was then deposited in the GenBank (MW876249). The bacterium was able to degrade the insoluble chicken feather keratin (CFK) into amino acids (AA) through the keratinase system. The statistical optimization of the biodegradation process into AA was performed based on the Plackett–Burman design and rotatable central composite design (RCCD) on a simple solid-state fermentation medium. The optimum conditions were temperature, 37°C, 0.547 mg KH2PO4, 1.438 mg NH4Cl, and 11.61 days of incubation. Innovatively, the degradation of the CFK process was modeled using the artificial neural network (ANN), which was better than RCCD in modeling the biodegradation process. Differentiation of the AA by high-performance liquid chromatography (HPLC) revealed the presence of 14 AA including essential and non-essential ones; proline and aspartic acids were the most dominant. The toxicity test of AA on the HepG2 cell line did not show any negative effect either on the cell line or on the morphological alteration. B. paramycoides ZW-5 is a new eco-friendly tool for CFK degradation that could be optimized by ANN. However, additional nutritional trials are encouraged on animal models.

Design and Performance Investigation of a Pilot Micro-Grid in the Mediterranean: MCAST Case Study

This paper discusses the simulation framework developed for an in-campus pilot micro-grid at MCAST, Malta, to enhance its efficiency and reliability. One year of real-time metered data were used to arrive at the load curves, categorize the loads as essential and non-essential ones, and decide the micro-grid domain within MCAST. The potential scenarios were modeled to observe the behavior of the present status of the micro-grid, with an increased photovoltaic (PV) generation capacity, by using an optimum battery storage system with a diesel generator of suitable capacity and finally integrating electric vehicles (EVs) to discuss the potential of vehicle to grid (V2G) operation modes. The existing building management system (BMS) of MCAST was interfaced within the micro-grid to introduce the geographic information system (GIS) and Building Information Modeling (BIM) for developing an intelligent 3D model of the micro-grid. The results of the simulation framework for various potential case scenarios were obtained in a MATLAB/Simulink environment to assess the performance of the micro-grid. Previously formulated key performance indices (KPIs) that describe the financial aspects of micro-grid operation and ecological benefits of the investigated micro-grid were evaluated. A sensitivity analysis of these KPIs shows encouraging results with the potential of cost-competitiveness.

Versorgungsrelevante Arbeitszeiten? Arbeitszeitmuster bei Beschäftigten in kritischen Infrastrukturen

Essential working hours? Working time patterns among employees in critical infrastructures Objectives: Particular attention was given to employees in critical infrastructures or essential occupations at the beginning of the COVID-19 pandemic. These occupations are of particular importance in maintaining supplies and public order. Many employees in essential occupations were already faced with heavy demands on their time before the pandemic. The aim of this article is to identify the working time profiles of these employees. Methods: The working time conditions of employees in essential occupations before the pandemic are analysed on the basis of the BAuA Working Time Survey 2019. In order to identify the occupational groups, reference is made to an established categorisation of essential occupations (cf. Koebe et al. 2020). The two groups are then contrasted in terms of differences in the organisation of working time. Results: There is greater heterogeneity in the length of working hours in essential occupations. This group is characterised in particular by shortened rest periods and working time schedules. Atypical working hours (e.g. shift work and night work) and work at weekends (especially on Sundays and public holidays) are more likely to indicate that occupations are essential. In addition, there is a significantly higher incidence of on-call duties. At the same time, employees in essential occupations have less working time autonomy and work less often from home. Conclusions: Overall, a more negative stress profile in terms of working hours emerges for employees in essential occupations compared to nonessential ones, as the services provided must be available 24/7. In order to increase resilience for future times of crisis and to ensure the health, performance and safety of employees in essential occupations, working time conditions should be improved. This could also counteract the low attractiveness of these occupations in the long term, especially against the background of the shortage of skilled workers (cf. KOFA 2020). Keywords: essential occupations – working time – working time duration, schedule and flexibility

Efficient and decision boundary aware instance selection for support vector machines

Support vector machines (SVMs) are powerful classifiers that have high computational complexity in the training phase, which can limit their applicability to large datasets. An effective approach to address this limitation is to select a small subset of the most representative training samples such that desirable results can be obtained. In this study, a novel instance selection method called border point extraction based on locality-sensitive hashing (BPLSH) is designed. BPLSH preserves instances that are near the decision boundaries and eliminates nonessential ones. The performance of BPLSH is benchmarked against four approaches on different classification problems. The experimental results indicate that BPLSH outperforms the other methods in terms of classification accuracy, preservation rate, and execution time. The source code of BPLSH can be found in https://github.com/mohaslani/BPLSH.

Impact of phenolic compounds on free amino acid contents and electrophoretic patterns of the Nile Tilapia, Oreochromis niloticus

The present research was designed to determine the free amino acids (FAAs) composition and electrophoretic protein patterns by using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) in Oreochromis niloticus exposed to 1/10 LC50-96 hrs of phenol and nonylphenol ethoxylate 9 (NPEO 9) under laboratory condition for 4 weeks, in an attempt to develop sensitive bio-indicators for evaluation of pollution effects. Variations of FAAs were apparent in both treated groups. Significant increases were recorded in all individual amino acids including essential and non-essential ones, except for proline for both treated groups and leucine for phenol exposed fish group where they decreased significantly. The electrophoretic analysis of protein of the skeletal muscle homogenate using SDS-PAGE indicated that the control group yielded 10 fractions of molecular masses between 166 and 23 kDa. A total number of 9 bands were separated from fish of phenol group; they were varied from 168 to 23 kDa. However, 6 bands only were separated from fish of the NPEO 9 group; they were varied from 157 to 28 kDa. The muscle protein extract from fish of the phenol group showed five unique bands (168, 96, 68, 52, and 39 kDa). Moreover, fish that were exposed to NPEO 9 group showed three unique bands (157, 92, and 72 kDa). The highest similarity index S value observed between phenol and control groups was 0.37, followed by 0.33 between NPEO 9 group and the control one. This could be an indicator of changes in protein metabolism due to phenol and NPEO 9 pollution.

Omics approaches for understanding heavy metal responses and tolerance in plants

Recent years have witnessed a gradual increase in the bioavailability and groundwater leaching of toxic heavy metals (HMs) in the environment, driven mainly by anthropogenic invasions. HMs, especially ranked as toxic/carcinogenic non-essential ones including Cadmium (Cd), Arsenic (As), Aluminium (Al), Mercury (Hg) and Lead (Pb) have emerged as the major soil, air and water contaminants affecting food production, quality and security worldwide. HMs affect plant growth and crop yield. Plants have developed intricate defense mechanisms for safeguarding themselves against the toxic effects imposed by HMs, including compartmentalization and sequestration in cell-organelles, inactivation by complex formation with the organic ligands and their exclusion using transporters, ion channels, transcription factors and signaling molecules, beside others. Omics approaches have generated significant resources and updates on the plant genome, transcriptome and metabolome plasticity against HM-induced stress stimuli. Omics technologies are pragmatic and seen as feasible approaches for characterizing the roles of genomes (genomics), coding (transcriptomics) and non-coding (miRNAomics) RNA transcripts, and metabolites (metabolomics) including metals (metallomics), which can ultimately be used for improving stress tolerance or generating resilience plant systems. This review aims to summarize the current understandings on the mechanistic insights of selected toxic (Cd, As, Al, Hg and Pb) HM-plant interactions, including their uptake, transport, toxicity and chelation/sequestration in cellular components, besides how plants respond and adapt to these stress factors. State-of-the-art in Omics approaches including genomics, transcriptomics, metabolomics, miRNAomics and metallomics for plant HM research have been presented. Present status, challenges and future prospects are also discussed.

Examining ASEAN’s effectiveness in managing South China Sea disputes

For the Association of Southeast Asian Nations (ASEAN), the South China Sea (SCS) disputes between China and Southeast Asian claimant states (Vietnam, the Philippines, Malaysia, and Brunei) are a major concern. However, the dominant sceptics are pessimistic about ASEAN’s effectiveness in managing these disputes. This article contributes to the field by providing a systematic analysis on this topic, which is missing within the literature. Also, many of the arguments presented in this article challenge the dominant views and thus provide an alternative understanding of ASEAN’s effectiveness in managing the disputes. The arguments are as follows: First, ASEAN members do have shared interests and a common policy regarding managing the disputes. Second, China’s influence on individual ASEAN members is not as strong as the sceptics often argue. Third, the degree of ASEAN’s goal attainment is medium because it achieved essential goals while conceding important but non-essential ones. Fourth, compared with a legalistic and adversarial posturing approach, ASEAN’s approach is more effective in managing these disputes.

Building surrogate temporal network data from observed backbones.

Many systems of socioeconomic interests find a convenient representation in the form of temporal networks, i.e., sets of nodes and interactions occurring at specified times. In the corresponding data sets, however, crucial elements coexist with nonessential ones and noise. Several methods have thus been proposed to extract a "network backbone," i.e., the set of most important links in a network data set. The outcome of such methods can be seen as compressed versions of the original data. However, the question of how to practically use such reduced views of the data has not been tackled: for instance, using them directly in numerical simulations of processes on networks might lead to important biases. Overall, such reduced views of the data might not be actionable without an adequate decompression method. Here, we address this issue by putting forward and exploring several systematic procedures to build surrogate data from various kinds of temporal network backbones. In particular, we explore how much information about the original data needs to be retained alongside the backbone so that the surrogate data can be used in data-driven numerical simulations of spreading processes on a wide range of spreading parameters. We illustrate our results using empirical temporal networks with a broad variety of structures and properties. Our results give hints on how to best summarize complex data sets so that they remain actionable. Moreover, they show how ensembles of surrogate data with similar properties can be obtained from an original single data set, without any modeling assumptions.

The study of amino acid composition and oxidation processes in meat of Aberdeen-Angus bull-calves in chilled and supercooled state

Improving the safety and quality of meat raw materials from animals of special-purpose beef cattle helps to meet the demand for foods at high nutritional value. In the Russian Federation, the Aberdeen-Angus breed is one of the leading beef cattle, from which high quality marbled beef is obtained. Investigations have been carried out to study the qualitative indices of beef from this breed at storage under temperature conditions of cooling medium of 1 ± 0.5 ° C and minus 2.5 ± 0.5 ° C in cold chambers during 12 and 24 days. A decrease in the content of free amino acids in meat during storage was noted. It was found that after 24 days of storage, the biological value of the product (ratio of essential amino acids to nonessential ones) at minus 2.5±0.5 ° C was 1.14 times higher as compared with storage at 1.0±0.5 ° C temperature. After 12 days of storage at 1.0±0.5 ° C temperature, the values of acid and peroxide numbers increased about 3 times in comparison with the initial values, while at minus 2.5±0.5 ° C two times only. After 24 days of storage, the increase of acid number in chilled meat was 42% higher than in super-cooled meat; the values of the peroxide number in meat at 1.0±0.5 ° C was 1.3 times higher than in meat at minus 2.5±0.5 ° C. The researches resulted in recommendations of the temperature conditions providing the storage of super-cooled vacuum-packed meat without phase transition of water into ice as well as ensuring better preservation of biologically relevant components and nutritional value of meat.