[This corrects the article DOI: 10.1371/journal.pone.0317412.].

The 2030 Agenda for Sustainable Development of the United Nations outlines 17 goals for countries of the world to address global challenges in their development. However, the progress of countries towards these goal has been slower than expected and, consequently, there is a need to investigate the reasons behind this fact. In this study, we have used a novel data-driven methodology to analyze time-series data for over 20 years (2000-2022) from 107 countries using unsupervised machine learning (ML) techniques. Our analysis reveals strong positive and negative correlations between certain SDGs (Sustainable Development Goals). Our findings show that progress toward the SDGs is heavily influenced by geographical, cultural and socioeconomic factors, with no country on track to achieve all the goals by 2030. This highlights the need for a region-specific, systemic approach to sustainable development that acknowledges the complex interdependencies between the goals and the variable capacities of countries to reach them. For this our machine learning based approach provides a robust framework for developing efficient and data-informed strategies to promote cooperative and targeted initiatives for sustainable progress.

IntroductionIn veterinary medicine, cancer is the leading cause of death in companion animals, and mammary gland tumors represent the most common neoplasm in female dogs. Several epidemiological risk factors, such as age, breed, hormones, diet, and obesity have been reported to be relevant for canine mammary tumors. Nowadays, the gold standard for diagnosis of canine mammary tumors is the pathological examination of the suspected tissue. However, tumor grade can only be assessed after surgical removal or biopsy of the altered tissue. Therefore, in cases of tumors that could be surgically removed, it would be very helpful to be able to predict the biological behavior of the tumor, before performing any surgery. Since, inflammation constitutes part of the tumor microenvironment and it influences each step of tumorigenesis, cellular and biochemical blood markers of systemic inflammation, such as the neutrophil to lymphocyte ratio (NLR) and the albumin to globulin ratio (AGR) have been proposed as prognostic factors for human cancer development. The NLR and the AGR have not been explored enough as prognostic factors for cancer development in veterinary medicine.MethodsTo determine the prognostic value of NLR in canine mammary tumors, clinical records including biochemistry and hematological studies of female dogs with mammary tumors and of control healthy dogs, were used to determine the pre-treatment NLR and AGR. Other clinical data included age, breed, tumor size, histological tumor grade, and survival time after surgery.Results and discussionIt was found that a higher pre-treatment NLR value (NLR > 5) associates with less survival rate. In contrast, the AGR did not show any predictive value on the malignancy of the tumor. However, by combining the NLR with AGR, age of the dog, and tumor size in a principal component analysis (PCA), the grade of the tumor and survival after surgery could be appropriately predicted. These data strongly suggest that pre-treatment NLR values have a prognostic value for the survival rate after surgery of dogs with mammary tumors.

Although friendship networks have been extensively studied, few models and studies are available to understand the reciprocity of friendship and foes. Here a model is presented to explain the directed friendship and foes network formation observed in experiments of Mexican and Hungarian schools. Within the presented model, each agent has a private opinion and a public one that shares to the group. There are two kinds of interactions between agents. The first kind represent interactions with the neighbors while the other represents the attitude of an agent to the overall public available information. Links between agents evolve as a combination of the public and private information available. Friendship is defined using a fitness function according to the strength of the agent's bonds, clustering coefficient, betweenness centrality and degree. Enmity is defined as very negative links. The model allows us to reproduce the distribution of mentions for friends and foes observed in the experiments, as well as the topology of the directed networks.

This paper analyzes the research performance evolution of a scientific institute, from its genesis through various stages of development. The main aim is to obtain, and visually represent, bibliometric evidence of the correlation of organizational changes on the development of its scientific performance; particularly, structural and leadership changes. The study involves six bibliometric indicators to multidimensionally assess the evolution of the institution’s performance profile. For a case study, we selected the Renewable Energy Institute at the National Autonomous University of Mexico, created 35 years ago as a small laboratory, then it evolved to a research center and finally to a formal institute, which over the last 8 years changed from the traditional departmental structure to a network-based structure. The evolution of the multidimensional performance profiles is analyzed, and graphically represented, using a novel artificial intelligence-based approach. We analyzed the performance profiles evolution yearly, using Principal Components Analysis, and a self-organizing neural network mapping technique. This approach, combining bibliometric and machine learning techniques, proved to be effective for the assessment of the institution’s evolution process. The results were represented with a series of graphs and maps that clearly reveal the magnitude and nature of the performance profile evolution, as well as its correlation with each of the structural and leadership transitions. These exploratory results have provided us data and insights into the probable effects of these transitions on academic performance, that have been useful to create a dynamical model.

A global disaster, such as the recent Covid-19 pandemic, affects every aspect of our lives and there is a need to investigate these highly complex phenomena if one aims to diminish their impact in the health of the population, as well as their socio-economic stability. In this paper we present an attempt to understand the role of the governmental authorities and the response of the rest of the population facing such emergencies. We present a mathematical model that takes into account the epidemiological features of the pandemic and also the actions of people responding to it, focusing only on three aspects of the system, namely, the fear of catching this serious disease, the impact on the economic activities and the compliance of the people to the mitigating measures adopted by the authorities. We apply the model to the specific case of Spain, since there are accurate data available about these three features. We focused on tourism as an example of the economic activity, since this sector of economy is one of the most likely to be affected by the restrictions imposed by the authorities, and because it represents an important part of Spanish economy. The results of numerical calculations agree with the empirical data in such a way that we can acquire a better insight of the different processes at play in such a complex situation, and also in other different circumstances.

In this article, we will describe a model to examine the influence of differently organized institutions on their scientific productivity. We take two extreme cases, on one hand, an institution divided in departments with no collaboration between people in different departments. These could be disciplines or merely projects. On the other hand, we consider an institution that allows interactions between all individuals, without a departmental structure to a department. We compare the results with data from the Institute of Renewable Energy (IER) at UNAM, which has changed its organization and policies during the last 30 years, and we could quantitatively predict the changes observed in productivity and multidisciplinarity. This model can be applied to a broader set of institutions and processes.

Many COVID-19 vaccines are proving to be highly effective to prevent severe disease and to diminish infections. Their uneven geographical distribution favors the appearance of new variants of concern, as the highly transmissible Delta variant, affecting particularly non-vaccinated people. It is important to device reliable models to analyze the spread of the different variants. A key factor is to consider the effects of vaccination as well as other measures used to contain the pandemic like social behaviour. The stochastic geographical model presented here, fulfills these requirements. It is based on an extended compartmental model that includes various strains and vaccination strategies, allowing to study the emergence and dynamics of the new COVID-19 variants. The model conveniently separates the parameters related to the disease from the ones related to social behavior and mobility restrictions. We applied the model to the United Kingdom by using available data to fit the recurrence of the currently prevalent variants. Our computer simulations allow to describe the appearance of periodic waves and the features that determine the prevalence of certain variants. They also provide useful predictions to help planning future vaccination boosters. We stress that the model could be applied to any other country of interest.

In a world being hit by waves of COVID-19, vaccination is a light on the horizon. However, the roll-out of vaccination strategies and their influence on the pandemic are still open questions. In order to compare the effect of various strategies proposed by the World Health Organization and other authorities, a previously developed SEIRS stochastic model of geographical spreading of the virus is extended by adding a compartment for vaccinated people. The parameters of the model were fitted to describe the pandemic evolution in Argentina, Mexico and Spain to analyze the effect of the proposed vaccination strategies. The mobility parameters allow to simulate different social behaviors (e.g. lock-down interventions). Schemes in which vaccines are applied homogeneously in all the country, or limited to the most densely-populated areas, are simulated and compared. The second strategy is found to be more effective. Moreover, under the current global shortage of vaccines, it should be remarked that immunization is enhanced when mobility is reduced. Additionally, repetition of vaccination campaigns should be timed considering the immunity lapse of the vaccinated (and recovered) people. Finally, the model is extended to include the effect of isolation of detected positive cases, shown to be important to reduce infections.

Abstract We have studied the dynamic evolution of the Covid-19 pandemic in Argentina. The marked heterogeneity in population density and the very extensive geography of the country becomes a challenge itself. Standard compartment models fail when they are implemented in the Argentina case. We extended a previous successful model to describe the geographical spread of the AH1N1 influenza epidemic of 2009 in two essential ways: we added a stochastic local mobility mechanism, and we introduced a new compartment in order to take into account the isolation of infected asymptomatic detected people. Two fundamental parameters drive the dynamics: the time elapsed between contagious and isolation of infected individuals ( $$\alpha$$ α ) and the ratio of people isolated over the total infected ones (p). The evolution is more sensitive to the $$p-$$ p - parameter. The model not only reproduces the real data but also predicts the second wave before the former vanishes. This effect is intrinsic of extensive countries with heterogeneous population density and interconnection.The model presented has proven to be a reliable predictor of the effects of public policies as, for instance, the unavoidable vaccination campaigns starting at present in the world an particularly in Argentina.

Twitter is a popular social medium for sharing opinions and engaging in topical debates, yet presents a wide spread of misinformation, especially in political debates, from bots and adversarial attacks. The current state-of-the-art methods for detecting humans and bots in Twitter often lack generalizability beyond English. Here, a language-agnostic method to detect real users and their interactions by leveraging network topology from retweets is presented. To that end, the chosen topic is COVID-19 policies in Mexico, which has been considered by users as polemic. Two kinds of network are built: a directed network of retweets; and the co-event network, where a non-directed link between two users exists if they have retweeted the same post in a given time window (projection of a bipartite network). Then, single node properties of these networks, such as the clustering coefficient and the degree, are studied. Three kinds of users are observed: some with a high clustering coefficient but a very small degree, a second group with zero clustering coefficient and a variable degree, and a third group in which the clustering coefficient as a function of the degree decays as a power law. This third group represents ∼2% of the users and is characteristic of dynamical networks with feedback. The latter seems to represent strongly interacting followers/followed in a real social network as confirmed by an inspection of such nodes. A percolation analysis of the resulting co-retweet and co-hashtag network reveals the relevance of such weak links, typical of real social human networks. The presented methods are simple to implement in other social media platforms and can be used to mitigate misinformation and conflicts.

ABSTRACT Most COVID-19 vaccines have proved to be effective to combat the pandemic and to prevent severe disease but their distribution proceeds in a context of global vaccine shortage Their uneven distribution favors the appearance of new variants of concern, as the highly transmissible Delta variant, affecting especially non-vaccinated people. We consider that devising reliable models to analyse the spread of the different variants is crucial. These models should include the effects of vaccination as well as non-pharmaceutical measures used to contain the pandemic by modifying social behaviour. In this work, we present a stochastic geographical model that fulfills these requirements. It consists of an extended compartmental model that includes various strains and vaccination strategies, allowing to study the emergence and dynamics of the new COVID-19 variants. The models conveniently separates the parameters related to the disease from the ones related to social behavior and mobility restrictions. The geographical spread of the virus is modeled taking into account the actual population distribution in any given country of interest. Here we choose the UK as model system, taking advantage of the reliable available data, in order to fit the recurrence of the currently prevalent variants. Our computer simulations allow to describe some global features observed in the daily number of cases, as the appearance of periodic waves and the features that determine the prevalence of certain variants. They also provide useful predictions aiming to help planning future vaccination boosters. We stress that the model could be applied to any other country of interest.

A study of the dynamical formation of networks of friends and enemies in social media, in this case Twitter, is presented. We characterise the single node properties of such networks, as the clustering coefficient and the degree, to investigate the structure of links. The results indicate that the network is made from three kinds of nodes: one with high clustering coefficient but very small degree, a second group has zero clustering coefficient with variable degree, and finally, a third group in which the clustering coefficient as a function of the degree decays as a power law. This third group represents $\sim2\%$ of the nodes and is characteristic of dynamical networks with feedback. This part of the lattice seemingly represents strongly interacting friends in a real social network.

The shocking severity of the Covid-19 pandemic has woken up an unprecedented interest and accelerated effort of the scientific community to model and forecast epidemic spreading to find ways to control it regionally and between regions. Here we present a model that in addition to describing the dynamics of epidemic spreading with the traditional compartmental approach takes into account the social behaviour of the population distributed over a geographical region. The region to be modelled is defined as a two-dimensional grid of cells, in which each cell is weighted with the population density. In each cell a compartmental SEIRS system of delay difference equations is used to simulate the local dynamics of the disease. The infections between cells are modelled by a network of connections, which could be terrestrial, between neighbouring cells, or long range, between cities by air, road or train traffic. In addition, since people make trips without apparent reason, noise is considered to account for them to carry contagion between two randomly chosen distant cells. Hence, there is a clear separation of the parameters related to the biological characteristics of the disease from the ones that represent the spatial spread of infections due to social behaviour. We demonstrate that these parameters provide sufficient information to trace the evolution of the pandemic in different situations. In order to show the predictive power of this kind of approach we have chosen three, in a number of ways different countries, Mexico, Finland and Iceland, in which the pandemics have followed different dynamic paths. Furthermore we find that our model seems quite capable of reproducing the path of the pandemic for months with few initial data. Unlike similar models, our model shows the emergence of multiple waves in the case when the disease becomes endemic.

The Cytochrome P450 (CYP) enzyme family comprises a wide array of monooxygenases involved in the oxidation of endobiotic and xenobiotic molecules. The active site of a CYP enzyme contains an iron protoporphyrin center coordinated to a cysteine thiolate, and then, molecular oxygen is associated with the iron to be converted into dioxygen complex plus substrate. Reduction by CYP reductase expedites hydroxylation of the compound. In this oxidation reaction, insufficient oxygen molecules would affect enzyme catalysis. Nevertheless, biochemical data about CYP kinetics at low oxygen concentrations are not available. In this work, we present the results on the variation in rat liver microsomal CYP Vmax app and Km app under normal and hypoxic conditions. Using alkoxyresorufin molecules as substrates, the Vmax/Km ratios for resorufin production decreased from 426 to 393 for CYP1A1 and from 343 to 202 for CYP2B1 at a low oxygen concentration (4.1 ppm) compared to the ratios observed at a normal oxygen concentration (6.5 ppm). Additionally, the bacterial mutagenicity of 2-aminoanthracene and cyclophosphamide, decreased by 32% and 42%, respectively, at low oxygen concentrations. These results support the hypothesis that low oxygen availability is implicated in the low efficiency of substrate oxidation by CYP.

BACKGROUND:Repetitive DNA elements such as direct and inverted repeat sequences are present in every genome, playing numerous biological roles. In amphibians, the functions and effects of the repeat sequences have not been extensively explored. We consider that the data of mitochondrial genomes in the NCBI database are a valuable alternative to generate a better understanding of the molecular dynamic of the repeat sequences in the amphibians.RESULTS:This work presents the development of a strategy to identify and quantify the total amount of repeat sequences with lengths from 5 to 30 base pairs in the amphibian mitogenomes. The results show differences in the abundance of repeat sequences among amphibians and bias to specific genomic regions that are not easily associated with the classical amphibian ancestry.CONCLUSIONS:Derived from these analyses, we show that great variability of the repeat sequences exists among amphibians, demonstrating that the mitogenomes of these organisms are dynamic.

Electrophysiological time series were taken simultaneously in two locations in the hippocampus of a rat brain previously described as receiving innervation from the osmosensitive vasopressinergic neurons of the hypothalamus. A hyperosmotic saline solution injection was administered during the time of the experiment. We analyze the recorded time series using different methods. We detect a modification of the delta and theta oscillations just after the perturbation caused by the injection. We compare the quality and information that each one of the methods exhibit and we analyze the characteristics of the perturbation based on a hypothesis that the strength of the functional connections between the vasopressinergic hypothalamic magnocellular neurons and their target in the hippocampus is modified by the perturbation. We built a model of the hypothetic neural connections and numerically calculate the time series produced by the system when simulating the perturbation caused by the saline injection. The theoretical results resemble the experimental findings concerning the frequency and amplitude alterations of the delta and theta bands.

Fish skin colouration has been widely studied because it involves a variety of processes that are important to the broad field of the developmental biology. Mathematical modelling of fish skin patterning first predicted the existence of morphogens and helped to elucidate the mechanisms of pattern formation. The catfishes of the genus Pseudoplatystoma offer a good biological study model, since its species exhibit the most spectacular and amazing variations of colour patterns on the skin. They present labyrinths, closed loops (or cells), alternate spots and stripes, only spots and combinations of these. We have extended a well known mathematical model to study the skin of Pseudoplatystoma. The basic model is a two component, non-linear reaction diffusion system that presents a richness of bifurcations. The extended model assumes that there are two interacting cell/tissue layers in which morphogens diffuse and interact giving rise to the skin colouration pattern. We have found that by varying only two parameters we are able to accurately reproduce the distinct patterns found in all species of Pseudoplatystoma. The histological analysis of skin samples of two species of this genus, with different patterns, revealed differences on the disposition of the colouration cells that are consistent with our theoretical predictions.

Over the past two decades there has been a number of global outbreaks of viral diseases. This has accelerated the efforts to model and forecast the disease spreading, in order to find ways to confine the spreading regionally and between regions. Towards this we have devised a model of geographical spreading of viral infections due to human spatial mobility and adapted it to the latest COVID-19 pandemic. In this the region to be modelled is overlaid with a two-dimensional grid weighted with the population density defined cells, in each of which a compartmental SEIRS system of delay difference equations simulate the local dynamics (microdynamics) of the disease. The infections between cells are stochastic and allow for the geographical spreading of the virus over the two-dimensional space (macrodynamics). This approach allows to separate the parameters related to the biological aspects of the disease from the ones that represent the spatial contagious behaviour through different kinds of mobility of people acting as virus carriers. These provide sufficient information to trace the evolution of the pandemic in different situations. In particular we have applied this approach to three in many ways different countries, Mexico, Finland and Iceland and found that the model is capable of reproducing and predicting the stochastic global path of the pandemic. This study sheds light on how the diverse cultural and socioeconomic aspects of a country influence the evolution of the epidemics and also the efficacy of social distancing and other confinement measures.

Cytochrome P4501A1 (CYP1A1) is involved in the metabolism of several genotoxic/carcinogenic environmental xenobiotics including polycyclic aromatic hydrocarbons (PAHs) like benzo[a]pyrene. Several authors had proposed CYP1A inhibition as a plausible strategy for cancer chemoprevention. Using ethoxyresorufin O-deethylase activity (EROD), we tested the inhibitory properties of nine flavonoids: quercetin, miricetin, luteolin, fisetin, morin, kaempferol, 5-hydroxyflavone (5-HF), 3-hydroxyflavone (3-HF), and flavone (F) against human recombinant CYP1A1. The last three compounds exerted the highest inhibitory effect with IC50 values of 0.07, 0.10 and 0.08 μM respectively; the more hydroxyl-groups were present, the lower the potency of inhibition was. Biochemical characterization leads to the conclusion that flavone and its hydroxy derivatives are mixed-type inhibitors. In silico studies have shown that, Phe224 and other aromatic residues in the human CYP1A1 active site play an important role in flavonoid-CYP interaction, through a π/π stacking between the aminoacid and the flavonoid C-ring. Outside the active site, the three flavonoids bind preferentially between A and K helices of the enzyme. Results from the Ames test using human S9 fraction revealed that none of the three compounds was mutagenic. We can consider 5-HF, 3-HF, and F as potential chemopreventive agents against genotoxic damage caused by metabolites resulting from CYP1A1 activity.