The amorphous drug structure represents an important feature to be reached in the pharmaceutical field due to its possibility of increasing drug solubility, considering that at least 40% of commercially available crystalline drugs are poorly soluble in water. However, it is known that the amorphous local structure can vary depending on the amorphization technique used. Therefore, recognizing such variations related to a specific amorphization technique through the pair distribution function (PDF) method, for example, is an important tool for drug characterization concerns. This work presents a method to classify amorphous drugs according to their amorphization techniques and related to the local structure variations using machine learning. We used experimental PDF patterns obtained from low-energy X-rays scattering data to extract information and expanded the data through the Monte Carlo method to create a synthetic dataset. Then, we proposed the evaluation of such a technique using a Deep Neural Network. Based on the results obtained, it is suggested that the proposed technique is suitable for the amorphization technique and local structure recognition task.

We investigate Eigen's model for the evolution of the genetic code of microorganisms using a novel method based on population dynamics analysis. This model, for a given number of offspring, determines long-term survival as a function of the "genetic" information length and copy error probability. There exists a maximum threshold for the quantity of information that can be consistently preserved through the process of evolution within a population of perfectly replicating sequences, meaning no errors are allowed. With our formula, we expand upon the traditional error threshold formula of Eigen's theory and introduce a new expression for general cases where the self-reproduction process allows up to any integer number of copying errors per digit per replication step.

A simplified representation or interpretation of reality using conceptual and mathematical models plays a role in understanding important phenomena, especially biological ones. Based on Medawar's mutation accumulation theory, which predicts that the strength of natural selection decreases with age, favouring the accumulation of deleterious genes to be expressed at advanced ages, an individual-based model (IBM) published in 1995 was designed to demonstrate the biological ageing mechanism, with each individual being represented by a bitstring in a chronological manner. This model is called Penna Model (Penna, 1995) in honour of the creator, the physicist Thadeu Josino Pereira Penna. For this first time, this article carries out a systematic review of the use of Penna Model to assess its applicability in other phenomena and biological mechanisms. Our results indicate that the model was extensively explored and improved by researchers from different parts of the world, through small changes in the structure and base parameters of the model aimed at the biological phenomena of interest including Ecology, Evolutionary Ecology and Genetics issues. These changes have allowed the use of the model in other biological phenomena besides aging, with studies in systems of predation, competition, fragmentation, natural selection, speciation, genetics, immunology, epidemiology and others. The model is highlighted as a fairly flexible tool for applied biological dynamics of simple use, with the possibility of implementing different scales - temporal or spatial; and different languages - computational, logical or numerical; in addition to the possibility of inserting parameters that modify the individual and/or population that allow consistent results for decision-making.

One hundred years after the flu pandemic of 1918, the world faces an outbreak of a new severe acute respiratory syndrome, caused by a novel coronavirus. With a high transmissibility, the pandemic has spread worldwide, creating a scenario of devastation in many countries. By the middle of 2021, about 3% of the world population had been infected and more than 4 million people had died. Different from the H1N1 pandemic, which had a deadly wave and ceased, the new disease is maintained by successive waves, mainly produced by new virus variants and the small number of vaccinated people. In the present work, we create a version of the SIR model using the spatial localization of persons, their movements, and considering social isolation probabilities. We discuss the effects of virus variants, and the role of vaccination rate in the pandemic dynamics. We show that, unless a global vaccination is implemented, we will have continuous waves of infections.

A second deadlier wave of COVID-19 and the causes of the recent public health collapse of Manaus are compared with the Spanish flu events in that city, and Brazil. Historic sanitarian problems, and its hub position in the Brazilian airway network are combined drivers of deadly events related to COVID-19. These drivers were amplified by misleading governance, highly transmissible variants, and relaxation of social distancing. Several of these same factors may also have contributed to the dramatically severe outbreak of H1N1 in 1918, which caused the death of 10% of the population in seven months. We modelled Manaus parameters for the present pandemic and confirmed that lack of a proper social distancing might select the most transmissible variants. We succeeded to reproduce a first severe wave followed by a second stronger wave. The model also predicted that outbreaks may last for up to five and half years, slowing down gradually before the disease disappear. We validated the model by adjusting it to the Spanish Flu data for the city, and confirmed the pattern experienced by that time, of a first stronger wave in October-November 1918, followed by a second less intense wave in February-March 1919.

AbstractRecently, a wide array of epidemiological models have been developed to guide public health actors in containing the rapid dissemination of the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), cause of COVID-19. Despite their usefulness, many epidemiological models recently developed to understand the spread of SARS-CoV-2 and infection rates of COVID-19 fall short as they ignore human mobility, limiting our understanding of the spread of the disease, together with the vulnerability of population centers in a broad scale. We developed a new temporally-explicit model and simulated several social distancing scenarios to predict the vulnerability to COVID-19 of 50 Mexican cities that are interconnected by their air transportation network. Additionally, we assessed the sufficiency of the public health infrastructure in the focal cities to face the pandemic over time. Based on our model, we show that the most important cities within the Mexican air transportation network are the most vulnerable to COVID-19, with all assessed public health infrastructure being insufficient to face the modeled scenario for the pandemic after 100 days. Despite these alarming findings, our results show that social distancing could dramatically decrease the total number of infected people (77% drop-off for the 45% distancing scenario when contrasted with no distancing), flattening the growth of infection rate. Thus, we consider that this study provides useful information that may help decision-makers to timely implement health policies to anticipate and lessen the impact of the current pandemic in Mexico.Significance StatementWe used a new temporally-explicit model focused on air transportation networks to predict the vulnerability of 50 focal Mexican cities to COVID-19. We found that most vulnerable cities lack of the required public health infrastructure (i.e., number of inpatient and intensive care unit beds) to face this new pandemic, overloading in all cases after 100 days. However, our results show that a 45% social distancing scenario can reduce the number of infected people by up to 78.7%, flattening the growth rate of people with COVID-19 before infection rates soar exponentially countrywide.

Background We investigated a likely scenario of COVID-19 spreading in Brazil through the complex airport network of the country, for the 90 days after the first national occurrence of the disease. After the confirmation of the first imported cases, the lack of a proper airport entrance control resulted in the infection spreading in a manner directly proportional to the amount of flights reaching each city, following the first occurrence of the virus coming from abroad. Methodology We developed a Susceptible-Infected-Recovered model divided in a metapopulation structure, where cities with airports were demes connected by the number of flights. Subsequently, we further explored the role of the Manaus airport for a rapid entrance of the pandemic into indigenous territories situated in remote places of the Amazon region. Results The expansion of the SARS-CoV-2 virus between cities was fast, directly proportional to the city closeness centrality within the Brazilian air transportation network. There was a clear pattern in the expansion of the pandemic, with a stiff exponential expansion of cases for all the cities. The more a city showed closeness centrality, the greater was its vulnerability to SARS-CoV-2. Conclusions We discussed the weak pandemic control performance of Brazil in comparison with other tropical, developing countries, namely India and Nigeria. Finally, we proposed measures for containing virus spreading taking into consideration the scenario of high poverty.

The spread of SARS-CoV-2 and the distribution of cases worldwide followed no clear biogeographic, climatic, or cultural trend. Conversely, the internationally busiest cities in all countries tended to be the hardest hit, suggesting a basic, mathematically neutral pattern of the new coronavirus early dissemination. We tested whether the number of flight passengers per time and the number of international frontiers could explain the number of cases of COVID-19 worldwide by a stepwise regression. Analysis were taken by 22 May 2020, a period when one would claim that early patterns of the pandemic establishment were still detectable, despite of community transmission in various places. The number of passengers arriving in a country and the number of international borders explained significantly 49% of the variance in the distribution of the number of cases of COVID-19, and number of passengers explained significantly 14.2% of data variance for cases per million inhabitants. Ecological neutral theory may explain a considerable part of the early distribution of SARS-CoV-2 and should be taken into consideration to define preventive international actions before a next pandemic.

Vamos discutir o chamado modelo de Eigen utilizado para modelar a evolução do código genético de micro-organismo. Este modelo determina, em termos do tamanho da cadeia de informação e de sua probabilidade de erro ao se multiplicar, quais configurações genéticas sobrevivem a longo prazo. Mostramos algumas simulações deste modelo e discutiremos como utilizar este tema no estuda da astrobiologia.

In this paper, we show how to build a basic computer program using the Monte Carlo method to display the hydrogen atomic orbitals. For this, in a heuristic way, we applied a von Neumann acceptance-rejection method in simple problems of potential wells, and we end with the hydrogen orbitals representation. In this technique, we spread points uniformly on the 1D and 2D charts of probability density distributions, then we filtered points under these “curves or surfaces”, and we extended this logic to 3D cases. Throughout the work, we also made some comments to help beginner students better understand the term “wave function” present in the Schrödinger equation. Also, we made all source code available at a third-party platform, for any purpose under the MIT license.

Among the stages a fertilized egg undergoes until reaching its final shape, gastrulation represents the first step in breaking its initial symmetry. This process is of enormous importance in development of the embryos sagittal symmetry plane or dorso-ventral axis. Gastrulation also results in the appearance of three regions, the germ layers, from which all of the organs and systems of the organism originate. In this paper, following the hypothesis which affirms that pattern formation at some stages of organism development are due to morphogens gradient, we introduce a model which mimics the early stages of gastrulation of many multicellular organisms. In this model, the cause of symmetry breaking is given by the intrinsic dynamics of the system.