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Permutation entropy and its associated frameworks are remarkable examples of physics-inspired techniques adept at processing complex and extensive datasets. Despite substantial progress in developing and applying these tools, their use has been predominantly limited to structured datasets such as time series or images. Here, we introduce the k-nearest neighbor permutation entropy, an innovative extension of the permutation entropy tailored for unstructured data, irrespective of their spatial or temporal configuration and dimensionality. Our approach builds upon nearest neighbor graphs to establish neighborhood relations and uses random walks to extract ordinal patterns and their distribution, thereby defining the k-nearest neighbor permutation entropy. This tool not only adeptly identifies variations in patterns of unstructured data but also does so with a precision that significantly surpasses conventional measures such as spatial autocorrelation. Additionally, it provides a natural approach for incorporating amplitude information and time gaps when analyzing time series or images, thus significantly enhancing its noise resilience and predictive capabilities compared to the usual permutation entropy. Our research substantially expands the applicability of ordinal methods to more general data types, opening promising research avenues for extending the permutation entropy toolkit for unstructured data.

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We present a multiscale stochastic analysis of foreign exchange rates using the H-theory formalism, which provides a hierarchical intermittency model for the information cascade in the currency market. We examine the distributions of returns and volatilities for the three most traded currency pairs: euro-U.S. dollar, U.S. dollar-Japanese yen, and British pound-U.S. dollar. We find that these markets have a hierarchy of timescales, with larger markets exhibiting more hierarchy levels. We provide a theoretical framework for understanding why the number of levels in the information cascade increases with market size, in analogy with similar behavior for the energy cascade in turbulence as a function of Reynolds number. We briefly argue that using turbulence-like models for financial markets can also provide valuable insights for developing efficient algorithmic trading strategies.

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BACKGROUND: This article describes a new procedure to estimate the mean and variance of greenhouse gases (GHG) emission factors based on different, possibly conflicting, estimates for these emission factors. The procedure uses common information such as mean and standard deviation usually reported in IPCC (Intergovernmental Panel on Climate Change) database and other references in the literature that estimate emission factors. Essentially, it is a procedure in the class of meta-analysis, based on the computation of [Formula: see text], a new estimator for the variance of the emission factor. RESULTS: We discuss the quality of this estimator in terms of its probability distribution and show that it is unbiased. The resulting confidence interval for the mean emission factor is tighter than those that would have resulted from using other estimators such as pooled variance and thus, the new procedure improves the accuracy in estimating GHG emissions. The application of the procedure is illustrated in a case study involving the estimation of methane emissions from rice cultivation. CONCLUSIONS: The estimation of emission factors using [Formula: see text] was demonstrated to be more accurate because it is not biased and more precise than alternative methods.

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Microorganisms that inhabit the cold Antarctic environment can produce ligninolytic enzymes potentially useful in bioremediation. Our study focused on characterizing Antarctic bacteria and fungi from marine sediment samples of King George and Deception Islands, maritime Antarctica, potentially affected by hydrocarbon influence, able to produce enzymes for use in bioremediation processes in environments impacted with petroleum derivatives. A total of 168 microorganism isolates were obtained: 56 from sediments of King George Island and 112 from Deception Island. Among them, five bacterial isolates were tolerant to cell growth in the presence of diesel oil and gasoline and seven fungal were able to discolor RBBR dye. In addition, 16 isolates (15 bacterial and one fungal) displayed enzymatic emulsifying activities. Two isolates were characterized taxonomically by showing better biotechnological results. Psychrobacter sp. BAD17 and Cladosporium sp. FAR18 showed pyrene tolerance (cell growth of 0.03 g mL-1 and 0.2 g mL-1) and laccase enzymatic activity (0.006 UL-1 and 0.10 UL-1), respectively. Our results indicate that bacteria and fungi living in sediments under potential effect of hydrocarbon pollution may represent a promising alternative to bioremediate cold environments contaminated with polluting compounds derived from petroleum such as polycyclic aromatic hydrocarbons and dyes.

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BACKGROUND: In 2012, the Brazilian government launched a radiotherapy (RT) expansion plan (PER-SUS) to install 100 linear accelerators. This study assesses the development of this program after eight years. METHODS: Official reports from the Ministry of Health (MoH) were reviewed. RT centres projects status, timeframes, and cost data (all converted to US dollars) were extracted. The time analysis was divided into seven phases, and for cost evaluation, there were five stages. The initial predicted project time (IPPT) and costs (estimated by the MoH) for each phase were compared between the 18 operational RT centres (able to treat patients) and 30 non-operational RT centres using t-tests, ANOVA, and the Mann-Whitney U. A p-value < 0.05 indicates statistical significance. RESULTS: A significant delay was observed when comparing the IPPT with the overall time to conclude each 48 RT centres project (p < 0.001), with considerable delays in the first five phases (p < 0.001 for all). Moreover, the median time to conclude the first 18 operational RT centres (77.4 months) was shorter compared with the 30 non-operational RT centres (94.0 months), p < 0.001. The total cost of 48 RT services was USD 82,84 millions (mi) with a significant difference in the per project median total cost between 18 operational RT centres, USD1,34 mi and 30 non-operational RT centres USD2,11 mi, p < 0.001. All phases had a higher cost when comparing 30 non-operational RT centres to 18 operational RT centres, p < 0.001. The median total cost for expanding existing RT centres was USD1,30 mi versus USD2,18 mi for new RT services, p < 0.0001. CONCLUSION: After eight years, the PER-SUS programs showed a substantial delay in most projects and their phases, with increased costs over time. POLICY SUMMARY: Our findings indicate a need to act to increase the success of this plan. This study may provide a benchmark for other developing countries trying to expand RT capacity.

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OBJECTIVE: The development of microsurgical skills is crucial for neurosurgical education. The human placenta is a promising model for practicing vascular anastomosis due to its similarities with brain vessels. We propose a 2-stage model for training in extracranial-to-intracranial anastomosis using the placenta. METHODS: Initially, we propose practicing anastomosis in 2 adjacent placentas. Once successful, the procedure advances to a more challenging configuration that employs a 3-dimensionally printed skull with a window simulating a pterional craniotomy. It is positioned an intracranial placenta and an extracranial one, and the latter has a prominent vessel exposed toward the side of the craniotomy. Both placentas have one artery and vein cannulated in the umbilical cord, and we present an artificial placental circulation system for microvascular training that regulates pulsation and hydrodynamic pressure while keeping veins engorged with a pressurized bag. To verify anastomosis patency, we utilize sodium fluorescein and iodine contrast. RESULTS: The 2-stage model simulated several aspects of microvascular anastomosis. Our perfusion system allowed for intraoperative adjustments of hydrodynamic pressure and pulsation. Using iodine contrast and fluorescein enabled proper evaluation of anastomosis patency and hydrodynamic features. CONCLUSIONS: Training in the laboratory is essential for developing microsurgical skills. We have presented a model for microvascular anastomosis with artificial circulation and postoperative imaging evaluation, which is highly beneficial for enhancing the learning curve in microvascular procedures.

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While extensive literature exists on the COVID-19 pandemic at regional and national levels, understanding its dynamics and consequences at the city level remains limited. This study investigates the pandemic in Maringá, a medium-sized city in Brazil's South Region, using data obtained by actively monitoring the disease from March 2020 to June 2022. Despite prompt and robust interventions, COVID-19 cases increased exponentially during the early spread of COVID-19, with a reproduction number lower than that observed during the initial outbreak in Wuhan. Our research demonstrates the remarkable impact of non-pharmaceutical interventions on both mobility and pandemic indicators, particularly during the onset and the most severe phases of the emergency. However, our results suggest that the city's measures were primarily reactive rather than proactive. Maringá faced six waves of cases, with the third and fourth waves being the deadliest, responsible for over two-thirds of all deaths and overwhelming the local healthcare system. Excess mortality during this period exceeded deaths attributed to COVID-19, indicating that the burdened healthcare system may have contributed to increased mortality from other causes. By the end of the fourth wave, nearly three-quarters of the city's population had received two vaccine doses, significantly decreasing deaths despite the surge caused by the Omicron variant. Finally, we compare these findings with the national context and other similarly sized cities, highlighting substantial heterogeneities in the spread and impact of the disease.

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Stress exposure is associated with psychiatric conditions, such as depression, anxiety, and post-traumatic stress disorder (PTSD). It is also a vulnerability factor to developing or reinstating substance use disorder. Stress causes several changes in the neuro-immune-endocrine axis, potentially resulting in prolonged dysfunction and diseases. Changes in several transmitters, including serotonin, dopamine, glutamate, gamma-aminobutyric acid (GABA), glucocorticoids, and cytokines, are associated with psychiatric disorders or behavioral alterations in preclinical studies. Complex and interacting mechanisms make it very difficult to understand the physiopathology of psychiatry conditions; therefore, studying regulatory mechanisms that impact these alterations is a good approach. In the last decades, the impact of stress on biology through epigenetic markers, which directly impact gene expression, is under intense investigation; these mechanisms are associated with behavioral alterations in animal models after stress or drug exposure, for example. The endocannabinoid (eCB) system modulates stress response, reward circuits, and other physiological functions, including hypothalamus-pituitary-adrenal axis activation and immune response. eCBs, for example, act retrogradely at presynaptic neurons, limiting the release of neurotransmitters, a mechanism implicated in the antidepressant and anxiolytic effects after stress. Epigenetic mechanisms can impact the expression of eCB system molecules, which in turn can regulate epigenetic mechanisms. This review will present evidence of how the eCB system and epigenetic mechanisms interact and the consequences of this interaction in modulating behavioral changes after stress exposure in preclinical studies or psychiatric conditions. Moreover, evidence that correlates the involvement of the eCB system and epigenetic mechanisms in drug abuse contexts will be discussed.

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We present the software ModInterv as an informatics tool to monitor, in an automated and user-friendly manner, the evolution and trend of COVID-19 epidemic curves, both for cases and deaths. The ModInterv software uses parametric generalized growth models, together with LOWESS regression analysis, to fit epidemic curves with multiple waves of infections for countries around the world as well as for states and cities in Brazil and the USA. The software automatically accesses publicly available COVID-19 databases maintained by the Johns Hopkins University (for countries as well as states and cities in the USA) and the Federal University of Viçosa (for states and cities in Brazil). The richness of the implemented models lies in the possibility of quantitatively and reliably detecting the distinct acceleration regimes of the disease. We describe the backend structure of software as well as its practical use. The software helps the user not only to understand the current stage of the epidemic in a chosen location but also to make short term predictions as to how the curves may evolve. The app is freely available on the internet (http://fisica.ufpr.br/modinterv), thus making a sophisticated mathematical analysis of epidemic data readily accessible to any interested user.

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Cryptocurrencies are considered the latest innovation in finance with considerable impact across social, technological, and economic dimensions. This new class of financial assets has also motivated a myriad of scientific investigations focused on understanding their statistical properties, such as the distribution of price returns. However, research so far has only considered Bitcoin or at most a few cryptocurrencies, whilst ignoring that price returns might depend on cryptocurrency age or be influenced by market capitalization. Here, we therefore present a comprehensive investigation of large price variations for more than seven thousand digital currencies and explore whether price returns change with the coming-of-age and growth of the cryptocurrency market. We find that tail distributions of price returns follow power-law functions over the entire history of the considered cryptocurrency portfolio, with typical exponents implying the absence of characteristic scales for price variations in about half of them. Moreover, these tail distributions are asymmetric as positive returns more often display smaller exponents, indicating that large positive price variations are more likely than negative ones. Our results further reveal that changes in the tail exponents are very often simultaneously related to cryptocurrency age and market capitalization or only to age, with only a minority of cryptoassets being affected just by market capitalization or neither of the two quantities. Lastly, we find that the trends in power-law exponents usually point to mixed directions, and that large price variations are likely to become less frequent only in about 28% of the cryptocurrencies as they age and grow in market capitalization.

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2,4-D or dicamba can cause injuries and other deleterious effects on non-tolerant soybeans. Thus, the objective was to evaluate the potential for injury of subdoses of 2,4-D or dicamba, in drift simulation, for application in non-tolerant soybeans. Two experiments were carried out, one with 2,4-D and the other with dicamba. The treatments consisted of the application, in post-emergence of non-tolerant soybean, of subdoses 0; 1.35; 2.68; 5.37; 10.72; 21.45 and 42.9 g acid equivalent (ae) ha-1 2,4-D choline salt or dicamba diglycolamine (DGA) salt. Injury symptoms in plants, plant height and yield were evaluated, and the results were subjected to regression analysis. Polynomial fit was possible for the doses of both herbicides, with deleterious effects on soybean, with reductions in height and yield. The application of 2,4-D ≥ 10.72 g ae ha-1 was enough to cause injuries greater than 10% in plants, in simulated drift. The application of dicamba ≥1.35 g ae ha-1 was enough to cause injuries greater than 30% in plants, in simulated drift. For both herbicides, greater potential for injury and reductions in soybean yield were observed for the application of the highest doses (21.45 and 42.9 g ae ha-1).

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The COVID-19 pandemic has proven the importance of mathematical tools to understand the evolution of epidemic outbreaks and provide reliable information to the general public and health authorities. In this perspective, we have developed ModInterv, an online software that applies growth models to monitor the evolution of the COVID-19 epidemic in locations chosen by the user among countries worldwide or states and cities in the USA or Brazil. This paper describes the software capabilities and its use both in recent research works and by technical committees assisting government authorities. Possible applications to other epidemics are also briefly discussed.

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Stress exposure can result in several proinflammatory alterations in the brain, including overexpression of the inducible isoform of nitric oxide synthase (iNOS) in the medial prefrontal cortex (mPFC). These changes may be involved in the development of many psychiatric conditions. However, it is unknown if iNOS in mPFC plays a significant role in stress-induced behavioral changes. The endocannabinoid (ECB) system is also influenced by stress. Its activation seems to be a counter regulatory mechanism to prevent or decrease the stress-mediated neuroinflammatory consequences. However, it is unclear if the ECB system and iNOS interact to influence stress consequences. This study aimed to test the hypothesis that the anti-stress effect of iNOS inhibition in mPFC involves the local ECB system, particularly the CB1 cannabinoid receptors. Male Wistar rats with guide cannula aimed at the mPFC were submitted to acute restraint stress (RS) for 2 h. In the following morning, rats received bilateral microinjections of vehicle, AM251 (CB1 antagonist; 100 pmol), and/or 1400W (iNOS selective inhibitor; 10-4, 10-3, or 10-2 nmol) into the prelimbic area of mPFC (PL-mPFC) before being tested in the elevated plus-maze (EPM). iNOS inhibition by 1400W prevented the anxiogenic-like effect observed in animals submitted to RS. The drug did not promote behavior changes in naive animals, demonstrating a stress-dependent effect. The 1400W-anti-stress effect was prevented by local pretreatment with AM251. Our data suggest that iNOS inhibition may facilitate the local endocannabinoid signaling, attenuating stress effects.

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Since the start of the COVID-19 pandemic many studies investigated the correlation between climate variables such as air quality, humidity and temperature and the lethality of COVID-19 around the world. In this work we investigate the use of climate variables, as additional features to train a data-driven multivariate forecast model to predict the short-term expected number of COVID-19 deaths in Brazilian states and major cities. The main idea is that by adding these climate features as inputs to the training of data-driven models, the predictive performance improves when compared to equivalent single input models. We use a Stacked LSTM as the network architecture for both the multivariate and univariate model. We compare both approaches by training forecast models for the COVID-19 deaths time series of the city of São Paulo. In addition, we present a previous analysis based on grouping K-means on AQI curves. The results produced will allow achieving the application of transfer learning, once a locality is eventually added to the task, regressing out using a model based on the cluster of similarities in the AQI curve. The experiments show that the best multivariate model is more skilled than the best standard data-driven univariate model that we could find, using as evaluation metrics the average fitting error, average forecast error, and the profile of the accumulated deaths for the forecast. These results show that by adding more useful features as input to a multivariate approach could further improve the quality of the prediction models.

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BACKGROUND: Cognition heavily relies on social determinants and genetic background. Latin America comprises approximately 8% of the global population and faces unique challenges, many derived from specific demographic and socioeconomic variables, such as violence and inequality. While such factors have been described to influence mental health outcomes, no large-scale studies with Latin American population have been carried out. Therefore, we aim to describe the cognitive performance of a representative sample of Latin American individuals with schizophrenia and its relationship to clinical factors. Additionally, we aim to investigate how socioeconomic status (SES) relates to cognitive performance in patients and controls. METHODS: We included 1175 participants from five Latin American countries (Argentina, Brazil, Chile, Colombia, and Mexico): 864 individuals with schizophrenia and 311 unaffected subjects. All participants were part of projects that included cognitive evaluation with MATRICS Consensus Cognitive Battery and clinical assessments. RESULTS: Patients showed worse cognitive performance than controls across all domains. Age and diagnosis were independent predictors, indicating similar trajectories of cognitive aging for both patients and controls. The SES factors of education, parental education, and income were more related to cognition in patients than in controls. Cognition was also influenced by symptomatology. CONCLUSIONS: Patients did not show evidence of accelerated cognitive aging; however, they were most impacted by a lower SES suggestive of deprived environment than controls. These findings highlight the vulnerability of cognitive capacity in individuals with psychosis in face of demographic and socioeconomic factors in low- and middle-income countries.

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A identidade de Psalidodon eigenmanniorum (Cope, 1894) e a possibilidade de se constituir em mais de uma espécie é testada através de análises da morfometria (19 medidas), dos caracteres merísticos (14 contagens) e do padrão de colorido de 705 exemplares provenientes dos sistemas dos rios Tramandaí/Mampituba, da laguna dos Patos e drenagem do baixo rio Uruguai. Foram diafanizados e corados 40 exemplares. Os dados morfométricos foram utilizados na Análise de Componentes Principais, Análise Discriminante, Morfometria Geométrica e Função Discriminante. As análises foram feitas considerando os sexos em separado dentro de cada sistema hidrográfico, bem como comparando as populações entre os sistemas hidrográficos e finalmente no conjunto de sistemas representando a área de ocorrência da espécie. A partir dos dados analisados não foram encontradas diferenças entre os sexos. Os resultados mostraram variação morfológica que não sustenta o reconhecimento de possíveis espécies crípticas. A variação encontrada nos dados merísticos, morfométricos e no padrão de colorido justifica a redescrição da espécie. Os resultados das comparações entre as populações indicaram variações nesses caracteres indicando que a espécie possui considerável plasticidade fenotípica.(AU)

The identity of Psalidodon eigenmanniorum (Cope, 1894) and the possibility of constituting more than one species is tested through analyzes of morphometry (19 measurements), meristic characters (14 counts) and the color pattern of 705 specimens from the Tramandaí/Mampituba, from the Patos lagoon and from the lower Uruguay River drainage. Forty specimens were cleared and stained. Morphometric data were used in Principal Component Analysis, Discriminant Analysis, Geometric Morphometry and Discriminant Function. The analysis was carried out considering the sexes separately within each hydrographic system, as well as comparing the populations between the hydrographic systems and finally in the set of systems representing the area of occurrence of the species. No differences were found between the sexes in the analyzed data. The results showed morphological variation that does not support the recognition of possible cryptic species. The variation found in meristic, morphometric and color pattern data justifies the redescription of the species. The species is described to the aforementioned drainages, and the results demonstrate its phenotypic plasticity.(AU)

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Since Bandt and Pompe's seminal work, permutation entropy has been used in several applications and is now an essential tool for time series analysis. Beyond becoming a popular and successful technique, permutation entropy inspired a framework for mapping time series into symbolic sequences that triggered the development of many other tools, including an approach for creating networks from time series known as ordinal networks. Despite increasing popularity, the computational development of these methods is fragmented, and there were still no efforts focusing on creating a unified software package. Here, we present ordpy (http://github.com/arthurpessa/ordpy), a simple and open-source Python module that implements permutation entropy and several of the principal methods related to Bandt and Pompe's framework to analyze time series and two-dimensional data. In particular, ordpy implements permutation entropy, Tsallis and Rényi permutation entropies, complexity-entropy plane, complexity-entropy curves, missing ordinal patterns, ordinal networks, and missing ordinal transitions for one-dimensional (time series) and two-dimensional (images) data as well as their multiscale generalizations. We review some theoretical aspects of these tools and illustrate the use of ordpy by replicating several literature results.

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On December 23rd, 2020 in the Marajó Bay, Amazon estuary, we found a specimen of Plagioscion squamosissimus floating dead and choked and suffocated by a plastic bag. Here, we presented images of record and discuss the rarity and factors that may be involved in this to date unreported plastic-animal interaction for bony fishes.

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