Novel integrative elements and genomic plasticity in ocean ecosystems.

Horizontal gene transfer accelerates microbial evolution. The marine picocyanobacterium Prochlorococcus exhibits high genomic plasticity, yet the underlying mechanisms are elusive. Here, we report a novel family of DNA transposons-"tycheposons"-some of which are viral satellites while others carry cargo, such as nutrient-acquisition genes, which shape the genetic variability in this globally abundant genus. Tycheposons share distinctive mobile-lifecycle-linked hallmark genes, including a deep-branching site-specific tyrosine recombinase. Their excision and integration at tRNA genes appear to drive the remodeling of genomic islands-key reservoirs for flexible genes in bacteria. In a selection experiment, tycheposons harboring a nitrate assimilation cassette were dynamically gained and lost, thereby promoting chromosomal rearrangements and host adaptation. Vesicles and phage particles harvested from seawater are enriched in tycheposons, providing a means for their dispersal in the wild. Similar elements are found in microbes co-occurring with Prochlorococcus, suggesting a common mechanism for microbial diversification in the vast oligotrophic oceans.

An inquiline mosquito modulates microbial diversity and function in an aquatic microecosystem.

Understanding microbial roles in ecosystem function requires integrating microscopic processes into food webs. The carnivorous pitcher plant, Sarracenia purpurea, offers a tractable study system where diverse food webs of macroinvertebrates and microbes facilitate digestion of captured insect prey, releasing nutrients supporting the food web and host plant. However, how interactions between these macroinvertebrate and microbial communities contribute to ecosystem functions remains unclear. We examined the role of the pitcher plant mosquito, Wyeomyia smithii, in top-down control of the composition and function of pitcher plant microbial communities. Mosquito larval abundance was enriched or depleted across a natural population of S. purpurea pitchers over a 74-day field experiment. Bacterial community composition and microbial community function were characterized by 16S rRNA amplicon sequencing and profiling of carbon substrate use, bulk metabolic rate, hydrolytic enzyme activity, and macronutrient pools. Bacterial communities changed from pitcher opening to maturation, but larvae exerted minor effects on high-level taxonomic composition. Higher larval abundance was associated with lower diversity communities with distinct functions and elevated nitrogen availability. Treatment-independent clustering also supported roles for larvae in curating pitcher microbial communities through shifts in community diversity and function. These results demonstrate top-down control of microbial functions in an aquatic microecosystem.

Environmental and Taxonomic Drivers of Bacterial Extracellular Vesicle Production in Marine Ecosystems.

Extracellular vesicles are small (approximately 50 to 250 nm in diameter), membrane-bound structures that are released by cells into their surrounding environment. Heterogeneous populations of vesicles are abundant in the global oceans, and they likely play a number of ecological roles in these microbially dominated ecosystems. Here, we examine how vesicle production and size vary among different strains of cultivated marine microbes as well as explore the degree to which this is influenced by key environmental variables. We show that both vesicle production rates and vesicle sizes significantly differ among cultures of marine Proteobacteria, Cyanobacteria, and Bacteroidetes. Further, these properties vary within individual strains as a function of differences in environmental conditions, such as nutrients, temperature, and light irradiance. Thus, both community composition and the local abiotic environment are expected to modulate the production and standing stock of vesicles in the oceans. Examining samples from the oligotrophic North Pacific Gyre, we show depth-dependent changes in the abundance of vesicle-like particles in the upper water column in a manner that is broadly consistent with culture observations: the highest vesicle abundances are found near the surface, where the light irradiances and the temperatures are the greatest, and they then decrease with depth. This work represents the beginnings of a quantitative framework for describing extracellular vesicle dynamics in the oceans, which is essential as we begin to incorporate vesicles into our ecological and biogeochemical understanding of marine ecosystems. IMPORTANCE Bacteria release extracellular vesicles that contain a wide variety of cellular compounds, including lipids, proteins, nucleic acids, and small molecules, into their surrounding environment. These structures are found in diverse microbial habitats, including the oceans, where their distributions vary throughout the water column and likely affect their functional impacts within microbial ecosystems. Using a quantitative analysis of marine microbial cultures, we show that bacterial vesicle production in the oceans is shaped by a combination of biotic and abiotic factors. Different marine taxa release vesicles at rates that vary across an order of magnitude, and vesicle production changes dynamically as a function of environmental conditions. These findings represent a step forward in our understanding of bacterial extracellular vesicle production dynamics and provide a basis for the quantitative exploration of the factors that shape vesicle dynamics in natural ecosystems.

Learning Pathways and Students Performance: A Dynamic Complex System.

In this study, learning pathways are modelled by networks constructed from the log data of student-LMS interactions. These networks capture the sequence of reviewing the learning materials by the students enrolled in a given course. In previous research, the networks of successful students showed a fractal property; meanwhile, the networks of students who failed showed an exponential pattern. This research aims to provide empirical evidence that students' learning pathways have the properties of emergence and non-additivity from a macro level; meanwhile, equifinality (same end of learning process but different learning pathways) is presented at a micro level. Furthermore, the learning pathways of 422 students enrolled in a blended course are classified according to learning performance. These individual learning pathways are modelled by networks from which the relevant learning activities (nodes) are extracted in a sequence by a fractal-based method. The fractal method reduces the number of nodes to be considered relevant. A deep learning network classifies these sequences of each student into passed or failed. The results show that the accuracy of the prediction of the learning performance was 94%, the area under the receiver operating characteristic curve was 97%, and the Matthews correlation was 88%, showing that deep learning networks can model equifinality in complex systems.

Prochlorococcus extracellular vesicles: molecular composition and adsorption to diverse microbes.

Extracellular vesicles are small (~50-200 nm diameter) membrane-bound structures released by cells from all domains of life. While vesicles are abundant in the oceans, their functions, both for cells themselves and the emergent ecosystem, remain a mystery. To better characterize these particles - a prerequisite for determining function - we analysed the lipid, protein, and metabolite content of vesicles produced by the marine cyanobacterium Prochlorococcus. We show that Prochlorococcus exports a diverse array of cellular compounds into the surrounding seawater enclosed within discrete vesicles. Vesicles produced by two different strains contain some materials in common, but also display numerous strain-specific differences, reflecting functional complexity within vesicle populations. The vesicles contain active enzymes, indicating that they can mediate extracellular biogeochemical reactions in the ocean. We further demonstrate that vesicles from Prochlorococcus and other bacteria associate with diverse microbes including the most abundant marine bacterium, Pelagibacter. Together, our data point toward hypotheses concerning the functional roles of vesicles in marine ecosystems including, but not limited to, possibly mediating energy and nutrient transfers, catalysing extracellular biochemical reactions, and mitigating toxicity of reactive oxygen species.

An Entropy-Based Measure of Complexity: An Application in Lung-Damage.

The computed tomography (CT) chest is a tool for diagnostic tests and the early evaluation of lung infections, pulmonary interstitial damage, and complications caused by common pneumonia and COVID-19. Additionally, computer-aided diagnostic systems and methods based on entropy, fractality, and deep learning have been implemented to analyse lung CT images. This article aims to introduce an Entropy-based Measure of Complexity (EMC). In addition, derived from EMC, a Lung Damage Measure (LDM) is introduced to show a medical application. CT scans of 486 healthy subjects, 263 diagnosed with COVID-19, and 329 with pneumonia were analysed using the LDM. The statistical analysis shows a significant difference in LDM between healthy subjects and those suffering from COVID-19 and common pneumonia. The LDM of common pneumonia was the highest, followed by COVID-19 and healthy subjects. Furthermore, LDM increased as much as clinical classification and CO-RADS scores. Thus, LDM is a measure that could be used to determine or confirm the scored severity. On the other hand, the d-summable information model best fits the information obtained by the covering of the CT; thus, it can be the cornerstone for formulating a fractional LDM.

A Two-Parameter Fractional Tsallis Decision Tree.

Decision trees are decision support data mining tools that create, as the name suggests, a tree-like model. The classical C4.5 decision tree, based on the Shannon entropy, is a simple algorithm to calculate the gain ratio and then split the attributes based on this entropy measure. Tsallis and Renyi entropies (instead of Shannon) can be employed to generate a decision tree with better results. In practice, the entropic index parameter of these entropies is tuned to outperform the classical decision trees. However, this process is carried out by testing a range of values for a given database, which is time-consuming and unfeasible for massive data. This paper introduces a decision tree based on a two-parameter fractional Tsallis entropy. We propose a constructionist approach to the representation of databases as complex networks that enable us an efficient computation of the parameters of this entropy using the box-covering algorithm and renormalization of the complex network. The experimental results support the conclusion that the two-parameter fractional Tsallis entropy is a more sensitive measure than parametric Renyi, Tsallis, and Gini index precedents for a decision tree classifier.

Fractional Online Learning Rate: Influence of Psychological Factors on Learning Acquisition.

The quantification of learning acquisition in a blended and online course is still slightly explored from the complex systems lens. The fractional online learning rate (fOLR) using fractional integrals is introduced. The notion of fOLR is based on the nonlinearity of the individual students learning pathway network, built from Learning Management System log files. Several learning pathway networks from students that pass or fail the course were constructed. The Akaike information criterion shows that the minimum number of boxes to cover these networks follow a power-law model. Further analysis shows that the fOLR model and its parameters were significantly compared with the online learning rate model. Thus, the fOLR was computing power and delayed power models, inspired by the "law of practice." The results show that the fractional definition is a better model and has a nonlinear relationship with the overall grade. Also, engagement and disengagement mould the fOLR curve. It means that the student's performance is affected by the engagement, and it is necessary that they are encouraged to pay more effort and attention to the learning activities, and those activities need to be designed to be fun and pleasant to improve the learning achievements.

Coping with darkness: The adaptive response of marine picocyanobacteria to repeated light energy deprivation.

The picocyanobacteria Prochlorococcus and Synechococcus are found throughout the ocean's euphotic zone, where the daily light:dark cycle drives their physiology. Periodic deep mixing events can, however, move cells below this region, depriving them of light for extended periods of time. Here, we demonstrate that members of these genera can adapt to tolerate repeated periods of light energy deprivation. Strains kept in the dark for 3 d and then returned to the light initially required 18-26 d to resume growth, but after multiple rounds of dark exposure they began to regrow after only 1-2 d. This dark-tolerant phenotype was stable and heritable; some cultures retained the trait for over 132 generations even when grown in a standard 13:11 light:dark cycle. We found no genetic differences between the dark-tolerant and parental strains of Prochlorococcus NATL2A, indicating that an epigenetic change is likely responsible for the adaptation. To begin to explore this possibility, we asked whether DNA methylation-one potential mechanism mediating epigenetic inheritance in bacteria-occurs in Prochlorococcus. LC-MS/MS analysis showed that while DNA methylations, including 6 mA and 5 mC, are found in some other Prochlorococcus strains, there were no methylations detected in either the parental or dark-tolerant NATL2A strains. These findings suggest that Prochlorococcus utilizes a yet-to-be-determined epigenetic mechanism to adapt to the stress of extended light energy deprivation, and highlights phenotypic heterogeneity as an additional dimension of Prochlorococcus diversity.

Fractional information dimensions of complex networks.

In this article, new information dimensions of complex networks are introduced underpinned by fractional order entropies proposed in the literature. This fractional approach of the concept of information dimension is applied to several real and synthetic complex networks, and the achieved results are analyzed and compared with the corresponding ones obtained using classic information dimension based on the Shannon entropy. In addition, we have investigated an extensive classification of the treated complex networks in correspondence with the fractional information dimensions.

Classification of Literary Works: Fractality and Complexity of the Narrative, Essay, and Research Article.

A complex network as an abstraction of a language system has attracted much attention during the last decade. Linguistic typological research using quantitative measures is a current research topic based on the complex network approach. This research aims at showing the node degree, betweenness, shortest path length, clustering coefficient, and nearest neighbourhoods' degree, as well as more complex measures such as: the fractal dimension, the complexity of a given network, the Area Under Box-covering, and the Area Under the Robustness Curve. The literary works of Mexican writers were classify according to their genre. Precisely 87% of the full word co-occurrence networks were classified as a fractal. Also, empirical evidence is presented that supports the conjecture that lemmatisation of the original text is a renormalisation process of the networks that preserve their fractal property and reveal stylistic attributes by genre.

Gradual plasticity alters population dynamics in variable environments: thermal acclimation in the green alga Chlamydomonas reinhartdii.

Environmental variability is ubiquitous, but its effects on populations are not fully understood or predictable. Recent attention has focused on how rapid evolution can impact ecological dynamics via adaptive trait change. However, the impact of trait change arising from plastic responses has received less attention, and is often assumed to optimize performance and unfold on a separate, faster timescale than ecological dynamics. Challenging these assumptions, we propose that gradual plasticity is important for ecological dynamics, and present a study of the plastic responses of the freshwater green algae Chlamydomonas reinhardtii as it acclimates to temperature changes. First, we show that C. reinhardtii's gradual acclimation responses can both enhance and suppress its performance after a perturbation, depending on its prior thermal history. Second, we demonstrate that where conventional approaches fail to predict the population dynamics of C. reinhardtii exposed to temperature fluctuations, a new model of gradual acclimation succeeds. Finally, using high-resolution data, we show that phytoplankton in lake ecosystems can experience thermal variation sufficient to make acclimation relevant. These results challenge prevailing assumptions about plasticity's interactions with ecological dynamics. Amidst the current emphasis on rapid evolution, it is critical that we also develop predictive methods accounting for plasticity.

Beyond canonical models: why a broader understanding of Diptera-microbiota interactions is essential for vector-borne disease control.

Vector-borne diseases constitute a major global public health threat. The most significant arthropod disease vectors are predominantly comprised of members of the insect order Diptera (true flies), which have long been the focus of research into host-pathogen dynamics. Recent studies have revealed the underappreciated diversity and function of dipteran-associated gut microbial communities, with important implications for dipteran physiology, ecology, and pathogen transmission. However, the effective parameterization of these aspects into epidemiological models will require a comprehensive study of microbe-dipteran interactions across vectors and related species. Here, we synthesize recent research into microbial communities associated with major families of dipteran vectors and highlight the importance of development and expansion of experimentally tractable models across Diptera towards understanding the functional roles of the gut microbiota in modulating disease transmission. We then posit why further study of these and other dipteran insects is not only essential to a comprehensive understanding of how to integrate vector-microbiota interactions into existing epidemiological frameworks, but our understanding of the ecology and evolution of animal-microbe symbiosis more broadly.

Identification of Relevant Protein Interactions with Partial Knowledge: A Complex Network and Deep Learning Approach.

Protein-protein interactions (PPIs) are the basis for understanding most cellular events in biological systems. Several experimental methods, e.g., biochemical, molecular, and genetic methods, have been used to identify protein-protein associations. However, some of them, such as mass spectrometry, are time-consuming and expensive. Machine learning (ML) techniques have been widely used to characterize PPIs, increasing the number of proteins analyzed simultaneously and optimizing time and resources for identifying and predicting protein-protein functional linkages. Previous ML approaches have focused on well-known networks or specific targets but not on identifying relevant proteins with partial or null knowledge of the interaction networks. The proposed approach aims to generate a relevant protein sequence based on bidirectional Long-Short Term Memory (LSTM) with partial knowledge of interactions. The general framework comprises conducting a scale-free and fractal complex network analysis. The outcome of these analyses is then used to fine-tune the fractal method for the vital protein extraction of PPI networks. The results show that several PPI networks are self-similar or fractal, but that both features cannot coexist. The generated protein sequences (by the bidirectional LSTM) also contain an average of 39.5% of proteins in the original sequence. The average length of the generated sequences was 17% of the original one. Finally, 95% of the generated sequences were true.

Bacterial communities in carnivorous pitcher plants colonize and persist in inquiline mosquitoes.

BACKGROUND: The leaves of carnivorous pitcher plants harbor diverse communities of inquiline species, including bacteria and larvae of the pitcher plant mosquito (Wyeomyia smithii), which aid the plant by processing captured prey. Despite the growing appreciation for this microecosystem as a tractable model in which to study food web dynamics and the moniker of W. smithii as a 'keystone predator', very little is known about microbiota acquisition and assembly in W. smithii mosquitoes or the impacts of W. smithii-microbiota interactions on mosquito and/or plant fitness. RESULTS: In this study, we used high throughput sequencing of bacterial 16S rRNA gene amplicons to characterize and compare microbiota diversity in field- and laboratory-derived W. smithii larvae. We then conducted controlled experiments in the laboratory to better understand the factors shaping microbiota acquisition and persistence across the W. smithii life cycle. Methods were also developed to produce axenic (microbiota-free) W. smithii larvae that can be selectively recolonized with one or more known bacterial species in order to study microbiota function. Our results support a dominant role for the pitcher environment in shaping microbiota diversity in W. smithii larvae, while also indicating that pitcher-associated microbiota can persist in and be dispersed by adult W. smithii mosquitoes. We also demonstrate the successful generation of axenic W. smithii larvae and report variable fitness outcomes in gnotobiotic larvae monocolonized by individual bacterial isolates derived from naturally occurring pitchers in the field. CONCLUSIONS: This study provides the first information on microbiota acquisition and assembly in W. smithii mosquitoes. This study also provides the first evidence for successful microbiota manipulation in this species. Altogether, our results highlight the value of such methods for studying host-microbiota interactions and lay the foundation for future studies to understand how W. smithii-microbiota interactions shape the structure and stability of this important model ecosystem.

Frequency of mispackaging of Prochlorococcus DNA by cyanophage.

Prochlorococcus cells are the numerically dominant phototrophs in the open ocean. Cyanophages that infect them are a notable fraction of the total viral population in the euphotic zone, and, as vehicles of horizontal gene transfer, appear to drive their evolution. Here we examine the propensity of three cyanophages-a podovirus, a siphovirus, and a myovirus-to mispackage host DNA in their capsids while infecting Prochlorococcus, the first step in phage-mediated horizontal gene transfer. We find the mispackaging frequencies are distinctly different among the three phages. Myoviruses mispackage host DNA at low and seemingly fixed frequencies, while podo- and siphoviruses vary in their mispackaging frequencies by orders of magnitude depending on growth light intensity. We link this difference to the concentration of intracellular reactive oxygen species and protein synthesis rates, both parameters increasing in response to higher light intensity. Based on our findings, we propose a model of mispackaging frequency determined by the imbalance between the production of capsids and the number of phage genome copies during infection: when protein synthesis rate increase to levels that the phage cannot regulate, they lead to an accumulation of empty capsids, in turn triggering more frequent host DNA mispackaging errors.

Factores que cuantifican la percepción de calidad en el servicio al cliente en un restaurante mexicano / Factors that quantify the perception of quality in customer service in a Mexican restaurant

Resumen Una de las industrias más destacadas de la economía mexicana es la restaurantera. Su importancia, debido a su número de empresas, creación de empleos y emprendimientos, ha ocasionado que se genere un alto índice de competitividad. Esto provoca que se busquen estrategias para mejorar la calidad del servicio que ofrecen, con el propósito de retener y atraer clientes. El objetivo de este trabajo fue identificar los factores que conforman la percepción de la calidad en el servicio en un restaurante mexicano. Para ello, se utilizó el instrumento DINESERV, mediante un enfoque cuantitativo y un análisis factorial confirmatorio. Los resultados mostraron que el instrumento DINESERV es válido para restaurantes mexicanos. Asimismo, se detectaron los factores que integran el servicio al cliente, enfatizando los aspectos de tangibilidad, confiabilidad, respuesta y empatía. Características como personal competente y con experiencia, tener siempre presente los intereses del cliente y la apariencia de la vestimenta y limpieza del personal de servicio son elementos clave para que el restaurante genere mayor satisfacción en sus clientes.

Abstract One of the most prominent industries in the Mexican economy is the restaurant industry. Its importance, due to the number of companies, job creation and business ventures, has caused a high competitiveness index to be generated. This causes the search of strategies to be sought to improve the quality of the service they offer, in order to retain and attract customers. The objective of this work was to identify the factors that comprise service quality perception in a Mexican restaurant. For that purpose, we employed the DINESERV instrument, through a quantitative approach and a confirmatory factor analysis. Results showed that the DINESERV instrument is valid for Mexican restaurants. Likewise, the factors that make up customer service were identified, emphasizing the aspects such as tangibility, reliability, response and empathy. Factors such as competent and experienced staff, always keeping in mind the interests of the client, the appearance of the service personnel´s clothing and cleanliness are key elements for the restaurant to generate greater satisfaction in its customers.

Estudio comparativo de técnicas de analítica del aprendizaje para predecir el rendimiento académico de los estudiantes de educación superior / Comparative study of learning analytics techniques to predict student academic performance in higher education

Resumen La deserción escolar involucra diversos factores, entre ellos, el compromiso del estudiante, a través del cual se puede predecir su éxito en la escuela. Ese compromiso tiene varios componentes, tales como conductual, emocional y cognitivo. La motivación y el compromiso están fuertemente relacionadas, ya que la primera es un precursor del compromiso. El objetivo de este estudio fue comparar la eficacia de la regresión lineal contra dos técnicas de minería de datos para predecir el rendimiento académico de los estudiantes en la educación superior. Se hizo un estudio transversal explicativo en el que se encuestó a 222 estudiantes universitarios de una institución pública de la Ciudad de México. Se realizó un análisis de regresión lineal jerárquico (RL) y de técnicas de analítica del aprendizaje, como redes neuronales (RN) y máquinas de vector soporte (SVM). Para evaluar la exactitud de las técnicas de analítica del aprendizaje se realizó un análisis de varianza (ANOVA). Se compararon las técnicas de analítica del aprendizaje y de regresión lineal usando la validación cruzada. Los resultados mostraron que el compromiso conductual y la autoeficacia tuvieron efectos positivos en el desempeño del estudiante, mientras que la pasividad mostró un efecto negativo. Asimismo, las técnicas de RL y de SVM pronosticaron igualmente el desempeño académico de los estudiantes. La RL tuvo la ventaja de producir un modelo simple y de fácil interpretación. Por el contrario, la técnica de SVM generó un modelo más complejo, aunque, si el modelo tuviese como objetivo el pronóstico del desempeño, la técnica SVM sería la más adecuada, ya que no requiere la verificación de ningún supuesto estadístico.

Abstract The issue of school dropout involves factors such as students' engagement that can predict his or her success in school. It has been shown that student engagement has three components: behavioral, emotional and cognitive. Motivation and engagement are strongly related since the former is a precursor of engagement. The aim of this study was to compare the efficiency of linear regression against two data mining techniques to predict the students' academic performance in higher education. A descriptive cross-sectional study was carried out with 222 students from a public higher education institution in Mexico city. An analysis of hiererchical linear regression (LR) and learning analytics techniques such as neural networks (NN) and support vector machine (SVM) was conducted. To assess the accuracy of the learning analytics techniques, an analysis of variance (ANOVA) was carried out. The techniques were compared using cross validation. The results showed that behavioral engagement and self-efficacy had positive effects on student achievements, while passivity showed a negative effect. Likewise, the LR and SVM techniques had the same performance on predicting students' achievements. The LR has the advantage of producing a simple and easy model. On the contrary, the SVM technique generates a more complex model. Although, if the model were aimed to forecast the performance, the SVM technique would be the most appropriate, since it does not require to verify any statistical assumption.