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Social determining factors such as the adverse influence of globalization, supermarket growth, fast unplanned urbanization, sedentary lifestyle, economy, and social position slowly develop behavioral risk factors in humans. Behavioral risk factors such as unhealthy habits, improper diet, and physical inactivity lead to physiological risks, and “obesity/overweight” is one of the consequences. “Obesity and overweight” are one of the major lifestyle diseases that leads to other health conditions, such as cardiovascular diseases (CVDs), chronic obstructive pulmonary disease (COPD), cancer, diabetes type II, hypertension, and depression. It is not restricted within the age and socio-economic background of human beings. The “World Health Organization” (WHO) has anticipated that 30% of global death will be caused by lifestyle diseases by 2030 and it can be prevented with the appropriate identification of associated risk factors and behavioral intervention plans. Health behavior change should be given priority to avoid life-threatening damages. The primary purpose of this study is not to present a risk prediction model but to provide a review of various machine learning (ML) methods and their execution using available sample health data in a public repository related to lifestyle diseases, such as obesity, CVDs, and diabetes type II. In this study, we targeted people, both male and female, in the age group of >20 and <60, excluding pregnancy and genetic factors. This paper qualifies as a tutorial article on how to use different ML methods to identify potential risk factors of obesity/overweight. Although institutions such as “Center for Disease Control and Prevention (CDC)” and “National Institute for Clinical Excellence (NICE)” guidelines work to understand the cause and consequences of overweight/obesity, we aimed to utilize the potential of data science to assess the correlated risk factors of obesity/overweight after analyzing the existing datasets available in “Kaggle” and “University of California, Irvine (UCI) database”, and to check how the potential risk factors are changing with the change in body-energy imbalance with data-visualization techniques and regression analysis. Analyzing existing obesity/overweight related data using machine learning algorithms did not produce any brand-new risk factors, but it helped us to understand: (a) how are identified risk factors related to weight change and how do we visualize it? (b) what will be the nature of the data (potential monitorable risk factors) to be collected over time to develop our intended eCoach system for the promotion of a healthy lifestyle targeting “obesity and overweight” as a study case in the future? (c) why have we used the existing “Kaggle” and “UCI” datasets for our preliminary study? (d) which classification and regression models are performing better with a corresponding limited volume of the dataset following performance metrics?

“Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)”, the novel coronavirus, is responsible for the ongoing worldwide pandemic. “World Health Organization (WHO)” assigned an “International Classification of Diseases (ICD)” code—“COVID-19”-as the name of the new disease. Coronaviruses are generally transferred by people and many diverse species of animals, including birds and mammals such as cattle, camels, cats, and bats. Infrequently, the coronavirus can be transferred from animals to humans, and then propagate among people, such as with “Middle East Respiratory Syndrome (MERS-CoV)”, “Severe Acute Respiratory Syndrome (SARS-CoV)”, and now with this new virus, namely “SARS-CoV-2”, or human coronavirus. Its rapid spreading has sent billions of people into lockdown as health services struggle to cope up. The COVID-19 outbreak comes along with an exponential growth of new infections, as well as a growing death count. A major goal to limit the further exponential spreading is to slow down the transmission rate, which is denoted by a “spread factor (f)”, and we proposed an algorithm in this study for analyzing the same. This paper addresses the potential of data science to assess the risk factors correlated with COVID-19, after analyzing existing datasets available in “ourworldindata.org (Oxford University database)”, and newly simulated datasets, following the analysis of different univariate “Long Short Term Memory (LSTM)” models for forecasting new cases and resulting deaths. The result shows that vanilla, stacked, and bidirectional LSTM models outperformed multilayer LSTM models. Besides, we discuss the findings related to the statistical analysis on simulated datasets. For correlation analysis, we included features, such as external temperature, rainfall, sunshine, population, infected cases, death, country, population, area, and population density of the past three months—January, February, and March in 2020. For univariate timeseries forecasting using LSTM, we used datasets from 1 January 2020, to 22 April 2020.

The widespread use of devices like mobile phones and wearables allows for automatic monitoring of human daily activities, generating vast datasets that offer insights into long-term human behavior. A structured and controlled data collection process is essential to unlock the full potential of this information. While wearable sensors for physical activity monitoring have gained significant traction in healthcare, sports science, and fitness applications, securing diverse and comprehensive datasets for research and algorithm development poses a notable challenge. In this proof-of-concept study, we underscore the significance of semantic representation in enhancing data interoperability and facilitating advanced analytics for physical activity sensor observations. Our approach focuses on enhancing the usability of physical activity datasets by employing a medical-grade (CE certified) sensor to generate synthetic datasets. Additionally, we provide insights into ethical considerations related to synthetic datasets. The study conducts a comparative analysis between real and synthetic activity datasets, assessing their effectiveness in mitigating model bias and promoting fairness in predictive analysis. We have created an ontology for semantically representing observations from physical activity sensors and conducted predictive analysis on data collected using MOX2-5 activity sensors. Until now, there has been a lack of publicly available datasets for physical activity collected with MOX2-5 activity monitoring medical grade (CE certified) device. The MOX2-5 captures and transmits high-resolution data, including activity intensity, weight-bearing, sedentary, standing, low, moderate, and vigorous physical activity, as well as steps per minute. Our dataset consists of physical activity data collected from 16 adults (Male: 12; Female: 4) over a period of 30–45 days (approximately 1.5 months), yielding a relatively small volume of 539 records. To address this limitation, we employ various synthetic data generation methods, such as Gaussian Capula (GC), Conditional Tabular General Adversarial Network (CTGAN), and Tabular General Adversarial Network (TABGAN), to augment the dataset with synthetic data. For both the authentic and synthetic datasets, we have developed a Multilayer Perceptron (MLP) classification model for accurately classifying daily physical activity levels. The findings underscore the effectiveness of semantic ontology in semantic search, knowledge representation, data integration, reasoning, and capturing meaningful relationships between data. The analysis supports the hypothesis that the efficiency of predictive models improves as the volume of additional synthetic training data increases. Ontology and Generative AI hold the potential to expedite advancements in behavioral monitoring research. The data presented, encompassing both real MOX2-5 and its synthetic counterpart, serves as a valuable resource for developing robust methods in activity type classification. Furthermore, it opens avenues for exploration into research directions related to synthetic data, including model efficiency, detection of generated data, and considerations regarding data privacy.

El mixe de Tamazulápam es una lengua de verbo final (Santiago 2015). La oración se organiza por cuatro posiciones preverbales y uno posverbal (top-x-arg-y-v-z). El tópico presenta su propio contorno entonacional, X o foco es ocupada por un solo elemento, le siguen los argumentos centrales y Y es ocupada por adverbios, pronombres indefinidos, predicados secundarios o un preverbo relacional. La posición posverbal, Z o elemento extrapuesto es ocupada por algunos adverbios de la posición Y, por argumentos centrales o por una oración compleja. El presente artículo se centra en el estudio de los argumentos centrales de sujeto para intransitivo, agente y objeto primario para transitivo en la posición Z. Justificaré que el argumento extrapuesto es sistemáticamente información dada en el discurso, no presenta una pausa entre el predicado y el elemento dislocado, y se encuentra extrapuesto. Los argumentos centrales en posición Z, al no correferir con un pronombre en posición preverbal, los analizo como argumentos intraclausales; en cambio, cuando una frase nominal o un pronombre en posición Z correfiere con un pronombre en posición preverbal, automáticamente el argumento posverbal se asume como un antitópico, es decir, se analizará como un argumento que se encuentra fuera de la oración simple.

This chapter covers the history of integrated pest management (IPM); organizational structure of IPM; policy, research and extension in IPM; and successful IPM case studies (management of fruit flies, in crucifers, and and in tomato by biological, chemical and integrated control methods) in Mexico.

Latin America continues to be severely underrepresented in genomics research, and fine-scale genetic histories and complex trait architectures remain hidden owing to insufficient data 1 . To fill this gap, the Mexican Biobank project genotyped 6,057 individuals from 898 rural and urban localities across all 32 states in Mexico at a resolution of 1.8 million genome-wide markers with linked complex trait and disease information creating a valuable nationwide genotype–phenotype database. Here, using ancestry deconvolution and inference of identity-by-descent segments, we inferred ancestral population sizes across Mesoamerican regions over time, unravelling Indigenous, colonial and postcolonial demographic dynamics 2 – 6 . We observed variation in runs of homozygosity among genomic regions with different ancestries reflecting distinct demographic histories and, in turn, different distributions of rare deleterious variants. We conducted genome-wide association studies (GWAS) for 22 complex traits and found that several traits are better predicted using the Mexican Biobank GWAS compared to the UK Biobank GWAS 7 , 8 . We identified genetic and environmental factors associating with trait variation, such as the length of the genome in runs of homozygosity as a predictor for body mass index, triglycerides, glucose and height. This study provides insights into the genetic histories of individuals in Mexico and dissects their complex trait architectures, both crucial for making precision and preventive medicine initiatives accessible worldwide. Nationwide genomic biobank in Mexico unravels demographic history and complex trait architecture from 6,057 individuals.

This study combines neutrality tests and environmental correlations to identify nonneutral patterns of evolution in candidate genes related to drought stress in two closely related Mediterranean conifers, Pinus pinaster Ait. and P. halepensis Mill. Based on previous studies, we selected twelve amplicons covering six candidate genes that were sequenced in a large sample spanning the full range of these two species. Neutrality tests relatively robust to demography (DHEW compound test and maximum likelihood multilocus Hudson–Kreitman–Aguadé test) were used to detect selection events at different temporal scales. Environmental associations between variation at candidate genes and climatic variables were also examined. These combined approaches detected distinct genes that may be targeted by selection, most of them specific to only one of the two conifers, despite their recent divergence (<10 Ma). An exception was 4-coumarate: CoA ligase, a gene involved in the production of various important secondary products that appeared to play a role in local adaptation processes of both pines. Another remarkable result was that all significant environmental correlations involved temperature indices, highlighting the importance of this climatic factor as a selective driver on Mediterranean pines. The ability to detect natural selection at the DNA sequence level depends on the nature and the strength of the selection events, on the timescale at which they occurred, and on the sensitivity of the methods to other evolutionary forces that can mimic selection (e.g., demography and population structure). Using complementary approaches can help to capture different aspects of the evolutionary processes that govern molecular variation at both intra- and interspecific levels.

The 17-subunit RNA polymerase III (RNAP III) synthesizes essential untranslated RNAs such as tRNAs and 5S rRNA. In yeast and vertebrates, subunit C82 forms a stable subcomplex with C34 and C31 that is necessary for promoter-specific transcription initiation. Little is known about RNAP III transcription in trypanosomatid parasites. To narrow this knowledge gap, we characterized the C82 subunit in Trypanosoma brucei and Leishmania major . Bioinformatic analyses showed that the 4 distinctive extended winged-helix (eWH) domains and the coiled-coil motif are present in C82 in these microorganisms. Nevertheless, C82 in trypanosomatids presents certain unique traits, including an exclusive loop within the eWH1 domain. We found that C82 localizes to the nucleus and binds to RNAP III-dependent genes in the insect stages of both parasites. Knock-down of C82 by RNA interference significantly reduced the levels of tRNAs and 5S rRNA and led to the death of procyclic forms of T. brucei . Tandem affinity purifications with both parasites allowed the identification of several C82-interacting partners, including C34 and some genus-specific putative regulators of transcription. However, the orthologue of C31 was not found in trypanosomatids. Interestingly, our data suggest a strong association of C82 with TFIIIC subunits in T. brucei , but not in L. major .

Wildfire is a major ecological driver of plant evolution. Understanding the genetic basis of plant adaptation to wildfire is crucial, because impending climate change will involve fire regime changes worldwide. We studied the molecular genetic basis of serotiny, a fire-related trait, in Mediterranean maritime pine using association genetics. A single nucleotide polymorphism (SNP) set was used to identify genotype : phenotype associations in situ in an unstructured natural population of maritime pine (eastern Iberian Peninsula) under a mixed-effects model framework. RR-BLUP was used to build predictive models for serotiny in this region. Model prediction power outside the focal region was tested using independent range-wide serotiny data. Seventeen SNPs were potentially associated with serotiny, explaining approximately 29% of the trait phenotypic variation in the eastern Iberian Peninsula. Similar prediction power was found for nearby geographical regions from the same maternal lineage, but not for other genetic lineages. Association genetics for ecologically relevant traits evaluated in situ is an attractive approach for forest trees provided that traits are under strong genetic control and populations are unstructured, with large phenotypic variability. This will help to extend the research focus to ecological keystone non-model species in their natural environments, where polymorphisms acquired their adaptive value.

Background/Objectives: Current urate-lowering therapies may cause serious side effects in patients. Thus, alternative treatments are needed to regulate uric acid (UA) levels in patients with hyperuricemia associated with kidney injury, and natural antioxidant sources have demonstrated utility in this field. For the first time, our study evaluated the effects of an extract of Dactylopius opuntiae insects on the levels of xanthine oxidase (XO) enzymes and synthetic free radicals in vitro and in vivo. Methods: Insects were bred and collected, and two different extracts (D1 and D2) were obtained. For both extracts, XO inhibition and radical scavenging assays were performed. Subsequently, serum purine levels and renal markers were quantified in male BALB/c mice who received a hyperuricemia induction using potassium oxonate, hypoxanthine, and gentamicin. Results: The D2 extract contained 18,037.7 µg/mL of carminic acid, inhibited 53.2% of XO activity at one concentration, and showed IC50 values of 18,207.8 and 5729.6 µg/mL against ABTS and DPPH radicals, respectively. D2 administration reduced serum UA and creatinine levels and prevented an increase in kidney weight and reduction in renal antioxidant capacity caused by hyperuricemia induction and allopurinol use in mice. Despite the satisfactory antioxidant results obtained in vitro, the D1 extract killed the animal models due to its citric acid content. Conclusions: The D2 insect extract can be used as an effective urate-lowering therapy when the increased level of serum uric acid is due to kidney damage.