Exposome Signatures of Gastrointestinal Disorders Among Active Duty Service Members.

INTRODUCTION: We evaluated the risk factors associated with gastrointestinal disorders (GD) among the soldiers of the Army and Marine Aviation community (AMAC) using an exposomic approach. Specifically, we aimed to determine the medical and operational factors associated with reported GD in the Military Health System. METHODS: Longitudinal data were obtained from the Medical Assessment and Readiness System (MARS) housed at Womack Army Medical Center, Fort Liberty, NC, for a retrospective cohort study that included 79,249 active duty United States AMAC active duty service members (ADSMs) from October 2015 to December 2019. Multivariable mixed-effects logistic regression was used to assess the relationship between the incidence occurrence of GD and variables including rank, service time, deployment, Armed Forces Qualification Test score, education, tobacco use, alcohol use, age, gender, race, ethnicity, body mass index (BMI), and marital status. RESULTS: The incidence of GD included 22,813 person-years of observations with a rate of 2.2 per 100 person-years and a period prevalence of 3.2%. GD was independently associated with rank, service time, and deployment (all P < 0.05). CONCLUSION: The high-risk categories associated with GD in the AMAC included age, BMI, race (not ethnicity), marital status, and service time. Our data also show that deployment time, not geographical location, was associated with a higher risk of GD. Together, these analyses suggest that White ADSMs older than 33 years of age who have experienced marriage, longer service time, and deployments appear to have a higher risk of GDs. Our assessment shows the utility of using an exposomic approach to create a member-specific, big data-informed personalized clinical algorithm of health outcomes.

From the Non-Living to the Death

This paper posits that life and death are two sides of one and the same process. Rather than considering states, we must adopt a process-oriented perspective. This article presents three speculative arguments. The first hypothesis suggests that existence is the strategy by which the universe manifests itself, and that the first tactic for activating this strategy is the simultaneity between life and non-life. The second hypothesis posits that consciousness is a stance of the living experience. The third hypothesis asserts that we are simultaneously alive and dead, but that we remain more time alive than dead. This is why we have the impression that we are alive. The paper is hence written in the first person. Speculation is a valid heuristic when there is no sufficient information available or when thinking ventures in a domain where the state-of-the-art is scarce.

Precision symptom phenotyping identifies early clinical and proteomic predictors of distinct COVID-19 sequelae.

BACKGROUND: Post-COVID conditions (PCC) are difficult to characterize, diagnose, predict, and treat due to overlapping symptoms and poorly understood pathology. Identifying inflammatory profiles may improve clinical prognostication and trial endpoints. METHODS: 1,988 SARS-CoV-2 positive U.S. Military Health System beneficiaries with quantitative post-COVID symptom scores were included in this analysis. Among participants who reported moderate-to-severe symptoms on surveys collected 6-months post-SARS-CoV-2 infection, principal component analysis (PCA) followed by K-means clustering identified distinct clusters of symptoms. RESULTS: Three symptom-based clusters were identified: a sensory cluster (loss of smell and/or taste), a fatigue/difficulty thinking cluster, and a difficulty breathing/exercise intolerance cluster. Individuals within the sensory cluster were all outpatients during their initial COVID-19 presentation. The difficulty breathing cluster had a higher likelihood of obesity and COVID-19 hospitalization compared to those with no/mild symptoms at 6-months post-infection. Multinomial regression linked early post-infection D-dimer and IL-1RA elevation to fatigue/difficulty thinking, and elevated ICAM-1 concentrations to sensory symptoms. CONCLUSIONS: We identified three distinct symptom-based PCC phenotypes with specific clinical risk factors and early post-infection inflammatory predictors. With further validation and characterization, this framework may allow more precise classification of PCC cases and potentially improve the diagnosis, prognostication, and treatment of PCC.

SARS-CoV-2 infection is associated with self-reported post-acute neuropsychological symptoms within six months of follow-up.

BACKGROUND: Chronic neuropsychological sequelae following SARS-CoV-2 infection, including depression, anxiety, fatigue, and general cognitive difficulties, are a major public health concern. Given the potential impact of long-term neuropsychological impairment, it is important to characterize the frequency and predictors of this post-infection phenotype. METHODS: The Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential (EPICC) study is a longitudinal study assessing the impact of SARS-CoV-2 infection in U.S. Military Healthcare System (MHS) beneficiaries, i.e. those eligible for care in the MHS including active duty servicemembers, dependents, and retirees. Four broad areas of neuropsychological symptoms were assessed cross-sectionally among subjects 1-6 months post-infection/enrollment, including: depression (Patient Health Questionnaire-9), anxiety (General Anxiety Disorder-7), fatigue (PROMIS® Fatigue 7a), and cognitive function (PROMIS® Cognitive Function 8a and PROMIS® Cognitive Function abilities 8a). Multivariable Poisson regression models compared participants with and without SARS-CoV-2 infection history on these measures, adjusting for sex, ethnicity, active-duty status, age, and months post-first positive or enrollment of questionnaire completion (MPFP/E); models for fatigue and cognitive function were also adjusted for depression and anxiety scores. RESULTS: The study population included 2383 participants who completed all five instruments within six MPFP/E, of whom 687 (28.8%) had at least one positive SARS-CoV-2 test. Compared to those who had never tested positive for SARS-CoV-2, the positive group was more likely to meet instrument-based criteria for depression (15.4% vs 10.3%, p<0.001), fatigue (20.1% vs 8.0%, p<0.001), impaired cognitive function (15.7% vs 8.6%, p<0.001), and impaired cognitive function abilities (24.3% vs 16.3%, p<0.001). In multivariable models, SARS-CoV-2 positive participants, assessed at an average of 2.7 months after infection, had increased risk of moderate to severe depression (RR: 1.44, 95% CI 1.12-1.84), fatigue (RR: 2.07, 95% CI 1.62-2.65), impaired cognitive function (RR: 1.64, 95% CI 1.27-2.11), and impaired cognitive function abilities (RR: 1.41, 95% CI 1.15-1.71); MPFP/E was not significant. CONCLUSIONS: Participants with a history of SARS-CoV-2 infection were up to twice as likely to report cognitive impairment and fatigue as the group without prior SARS-CoV-2 infection. These findings underscore the continued importance of preventing SARS-CoV-2 infection and while time since infection/enrollment was not significant through 6 months of follow-up, this highlights the need for additional research into the long-term impacts of COVID-19 to mitigate and reverse these neuropsychological outcomes.

Changes in the Seroprevalence of Tick-Borne Rickettsia and Ehrlichia Among Soldiers-Fort Liberty, North Carolina, 1991-2019.

We obtained samples from the Department of Defense Serum Repository from soldiers who were stationed at Fort Liberty, North Carolina, between 1991 and 2019 to assess temporal trends in tick-borne rickettsiosis and ehrlichiosis. Serological evidence of infection was common, with nearly 1 in 5 (18.9%) demonstrating antibodies. We observed significant decreases in Rickettsia seroprevalence (adjusted odds ratio [aOR], 0.42 [95% CI, .27-.65], P = .0001) while over the same period Ehrlichia seroprevalence, albeit less common, nearly doubled (aOR, 3.61 [95% CI, 1.10-13.99], P = .048). The increase in Ehrlichia seroprevalence likely reflects increased transmission resulting from the expanding geographic range of the lone star tick.

Neurodevelopmental assessment of normocephalic children born to Zika virus exposed and unexposed pregnant people.

BACKGROUND: Studies examining the association between in utero Zika virus (ZIKV) exposure and child neurodevelopmental outcomes have produced varied results. METHODS: We aimed to assess neurodevelopmental outcomes among normocephalic children born from pregnant people enrolled in the Zika in Pregnancy in Honduras (ZIPH) cohort study, July-December 2016. Enrollment occurred during the first prenatal visit. Exposure was defined as prenatal ZIKV IgM and/or ZIKV RNA result at enrollment. Normocephalic children, >6 months old, were selected for longitudinal follow-up using the Bayley Scales of Infant and Toddler Development (BSID-III) and the Ages & Stages Questionnaires: Social-Emotional (ASQ:SE-2). RESULTS: One hundred fifty-two children were assessed; after exclusion, 60 were exposed and 72 were unexposed to ZIKV during pregnancy. Twenty children in the exposed group and 21 children in the unexposed group had a composite score <85 in any of the BSID-III domains. Although exposed children had lower cognitive and language scores, differences were not statistically significant. For ASQ:SE-2 assessment, there were not statistically significant differences between groups. CONCLUSIONS: This study found no statistically significant differences in the neurodevelopment of normocephalic children between in utero ZIKV exposed and unexposed. Nevertheless, long-term monitoring of children with in utero ZIKV exposure is warranted. IMPACT: This study found no statistically significant differences in the neurodevelopment in normocephalic children with in utero Zika virus exposure compared to unexposed children, although the exposed group showed lower cognitive and language scores that persisted after adjustment by maternal age and education and after excluding children born preterm and low birth weight from the analysis. Children with prenatal Zika virus exposure, including those normocephalic and have no evidence of abnormalities at birth, should be monitored for neurodevelopmental delays. Follow-up is important to be able to detect developmental abnormalities that might not be detected earlier in life.

Homicidios de niños, niñas y adolescentes en Colombia durante 2010-2020

Introducción: Los derechos de los niños, niñas y adolescentes prevalecen sobre los derechos de los demás. Sin embargo, países de ingreso medio como Colombia tienen una alta tasa de homicidios para este grupo poblacional, esto deja en evidencia que la sociedad no es capaz de proteger efectivamente su derecho fundamental a la vida. Objetivo: Describir la tendencia de los homicidios de los niños, niñas y adolescentes en Colombia para el periodo 2010-2020. Materiales y métodos: Se utilizaron los microdatos anonimizados de las defunciones no fetales de las estadísticas vitales del DANE y calcularon las tasas de homicidios para los menores de 19 años. Resultados: Durante el periodo analizado, los homicidios de los niñas, niñas y adolescentes en Colombia se redujeron, al pasar de 2485 en el año 2010 a 1360 en el 2020. Discusión: Los resultados de este trabajo muestran que los homicidios tienen diferencias significativas por grupo de edad, sexo y departamento, las cuales tienen determinantes que las explican, pero que es necesario abordar a profundidad en posteriores investigaciones. Conclusiones: Es indispensable seguir con esta tendencia decreciente para lograr garantizar el derecho a la vida de este grupo poblacional.

Introduction: The rights of children and adolescents prevail over others. However, middle-income countries such as Colombia have a high homicide rate for this population group, showing that society is not able to effectively protect their fundamental right to life. Objective: To describe the trend of homicides of children and adolescents in Colombia for the period 2010-2020. Materials and methods: We used anonymized microdata of non-fetal deaths from DANE vital statistics and calculated homicide rates for children under 19 years of age. Outcomes: During the period analyzed, homicides of children and adolescents in Colombia decreased from 2485 in 2010 to 1360 in 2020. Discussion: The results of this study show homicides have significant differences by age group, sex, and department, which have determinants that describe them but need to be addressed in depth in further research. Conclusions: It is essential to continue with this decreasing trend to guarantee the right to life for this population group.

Decreased Self-reported Physical Fitness Following SARS-CoV-2 Infection and the Impact of Vaccine Boosters in a Cohort Study.

Background: The long-term effects of coronavirus disease 2019 (COVID-19) on physical fitness are unclear, and the impact of vaccination on that relationship is uncertain. Methods: We compared survey responses in a 1-year study of US military service members with (n = 1923) and without (n = 1591) a history of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We fit Poisson regression models to estimate the association between history of SARS-CoV-2 infection and fitness impairment, adjusting for time since infection, demographics, and baseline health. Results: The participants in this analysis were primarily young adults aged 18-39 years (75%), and 71.5% were male. Participants with a history of SARS-CoV-2 infection were more likely to report difficulty exercising (38.7% vs 18.4%; P < .01), difficulty performing daily activities (30.4% vs 12.7%; P < .01), and decreased fitness test (FT) scores (42.7% vs 26.2%; P < .01) than those without a history of infection. SARS-CoV-2-infected participants were at higher risk of these outcomes after adjusting for other factors (unvaccinated: exercising: adjusted risk ratio [aRR], 3.99; 95% CI, 3.36-4.73; activities: aRR, 5.02; 95% CI, 4.09-6.16; FT affected: aRR, 2.55; 95% CI, 2.19-2.98). Among SARS-CoV-2-positive participants, full vaccination before infection was associated with a lower risk of post-COVID-19 fitness impairment (fully vaccinated: exercise: aRR, 0.81; 95% CI, 0.70-0.95; activities: aRR, 0.76; 95% CI, 0.64-0.91; FT: aRR, 0.87; 95% CI, 0.76-1.00; boosted: exercise: aRR, 0.62; 95% CI, 0.51-0.74; activities: aRR, 0.52; 95% CI, 0.41-0.65; FT: aRR, 0.59; 95% CI, 0.49-0.70). Conclusions: In this study of generally young, healthy military service members, SARS-CoV-2 infection was associated with lower self-reported fitness and exercise capacity; vaccination and boosting were associated with lower risk of self-reported fitness loss.

Exposomic Signatures of Tinnitus and/or Hearing Loss.

INTRODUCTION: We evaluated the risk factors associated with tinnitus and/or hearing loss (THL) among active duty (AD) members of the U.S. Army and Marine Aviation Community (AMAC) using an exposomic approach. Specifically, we aimed to determine the factors associated with the reported THL in the Military Health System. METHODS: Longitudinal data were obtained from the Medical Assessment and Readiness System housed at Womack Army Medical Center, Fort Bragg, NC, for a retrospective cohort study that included 78,546 AD AMAC members from October 2015 to December 2019. Multivariable mixed-effects logistic regression was used to assess the relationship between THL and numerous variables to include rank, service time, deployment, tobacco use, alcohol use, age, gender, race, ethnicity, and body mass index. RESULTS: Our analysis included a total of 220,044 person-years of observations. The THL incidence rate was 6.7 per 100 person-years, with an 8.1% period prevalence. THL was associated with age, gender, body mass index, race, deployment, service time, marital status, and tobacco use (all P < .05). Service time greater than 16 years had the greatest odds ratio of THL (4.46, 95% CI: 3.58-5.55, P < .001). CONCLUSIONS: Our assessment shows the utility of using an exposomic approach to create member-specific personalized clinical algorithms for health outcomes. We examined individuals with THL diagnoses and identified a combination of risk factors from biomedical, lifestyle, environmental, and stochastic sources. Taken together, the risk factors identified across the four exposomic domains could help understand the etiology of THL. Our exposomic methodology could be the foundation for generating predictive models. Finally, a specific evaluation of occupational risk factors may provide insight into aspects not readily available from civilian literature. In upcoming years, as the Medical Assessment and Readiness System matures, we will expand our analyses to include prospective, untargeted metabolites and biomarker data.

Exposomic Signatures of Cervical Pain.

INTRODUCTION: We evaluated risk factors associated with cervical pain (CP) among officers and enlisted members of the U.S. Army and Marine Aviation community using an exposomic approach. Specifically, we aimed to determine the factors associated with reported CP. MATERIALS AND METHODS: This is a retrospective cohort study that utilized the Medical Assessment and Readiness System housed at Womack Army Medical Center to evaluate the longitudinal data taken from medical and workforce resources. This study included 77,864 active duty AMAC members during October 2015-December 2019. Multivariable mixed-effects logistic regression was used to assess the relationship between the independent variables of rank, service time, deployment, Armed Forces Qualification Test score, tobacco use, alcohol use, age, gender, race, ethnicity, body mass index, marital status, and education level and the dependent variable, incidence occurrence of CP. RESULTS: The total analysis included 77,864 individuals with 218,180 person-years of observations. The incidence rate of CP was 18.8 per 100 person-years, with a 12% period prevalence. Cervical pain was independently associated with rank, service time, Armed Forces Qualification Test score, and alcohol use (all P < .05). CONCLUSIONS: Our longitudinal exposomic signatures-based approach aims to complement the outcomes of data science and analytics from Medical Assessment and Readiness System with validations of objective biochemical indicator species observed in Army and Marine Aviation community members suffering from CP. This initial approach using parallel track complementarity has the potential of substantiating the underlying mechanisms foundational to design prospective personalized algorithms that can be used as a predictive model. Finally, a specific evaluation of occupational risk factors may provide insight into factors not readily ascertained from the civilian literature.

Genome sequence of Hemileia vastatrix Berk. and Br. (Race I), the causal agent of coffee leaf rust, isolate from Risaralda, Colombia.

Coffee leaf rust, caused by the fungus Hemileia vastatrix (Basidiomycota; Pucciniomycota), is a devastating disease spread worldwide. To improve the available genomes, we use PacBio HiFi sequencing enhanced by Dovetail Omni-C chromatin conformation capture to assemble a highly contiguous 747.98 Mb genome of an isolate collected from Coffea arabica.

Oryza glumaepatula: A wild relative to improve drought tolerance in cultivated rice.

Developing drought-resistant rice (Oryza sativa, L.) is essential for improving field productivity, especially in rain-fed areas affected by climate change. Wild relatives of rice are potential sources for drought-resistant traits. Therefore, we compared root growth and drought response among 22 wild Oryza species, from which Oryza glumaepatula was selected as a promising source for further exploration. A geographically diverse panel of 69 O. glumaepatula accessions was then screened for drought stress-related traits, and 6 of these accessions showed lower shoot dry weight (SDW) reduction, greater percentage of deep roots, and lower stomatal density (STO) under drought than the drought tolerant O. sativa variety, Sahbhagi dhan. Based on whole-genome resequencing of all 69 O. glumaepatula accessions and variant calling to a high-quality O. glumaepatula reference genome, we detected multiple genomic loci colocating for SDW, root dry weight at 30 to 45 cm depth, and STO in consecutive drought trials. Geo-referencing indicated that the potential drought donors originated in flood-prone locations, corroborating previous hypotheses about the coexistence of flood and drought tolerance within individual Oryza genomes. These findings present potential donor accessions, traits, and genomic loci from an AA genome wild relative of rice that, together with the recently developed reference genome, may be useful for further introgression of drought tolerance into the O. sativa backgrounds.

Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach.

Maize (Zea mays L.), the third most widely cultivated cereal crop in the world, plays a critical role in global food security. To improve the efficiency of selecting superior genotypes in breeding programs, researchers have aimed to identify key genomic regions that impact agronomic traits. In this study, the performance of multi-trait, multi-environment deep learning models was compared to that of Bayesian models (Markov Chain Monte Carlo generalized linear mixed models (MCMCglmm), Bayesian Genomic Genotype-Environment Interaction (BGGE), and Bayesian Multi-Trait and Multi-Environment (BMTME)) in terms of the prediction accuracy of flowering-related traits (Anthesis-Silking Interval: ASI, Female Flowering: FF, and Male Flowering: MF). A tropical maize panel of 258 inbred lines from Brazil was evaluated in three sites (Cambira-2018, Sabaudia-2018, and Iguatemi-2020 and 2021) using approximately 290,000 single nucleotide polymorphisms (SNPs). The results demonstrated a 14.4% increase in prediction accuracy when employing multi-trait models compared to the use of a single trait in a single environment approach. The accuracy of predictions also improved by 6.4% when using a single trait in a multi-environment scheme compared to using multi-trait analysis. Additionally, deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches. A complementary genome-wide association study identified associations with 26 candidate genes related to flowering time traits, and 31 marker-trait associations were identified, accounting for 37%, 37%, and 22% of the phenotypic variation of ASI, FF and MF, respectively. In conclusion, our findings suggest that deep learning models have the potential to significantly improve the accuracy of predictions, regardless of the approach used and provide support for the efficacy of this method in genomic selection for flowering-related traits in tropical maize.

A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms.

BACKGROUND: Accurate COVID-19 prognosis is a critical aspect of acute and long-term clinical management. We identified discrete clusters of early stage-symptoms which may delineate groups with distinct disease severity phenotypes, including risk of developing long-term symptoms and associated inflammatory profiles. METHODS: 1,273 SARS-CoV-2 positive U.S. Military Health System beneficiaries with quantitative symptom scores (FLU-PRO Plus) were included in this analysis. We employed machine-learning approaches to identify symptom clusters and compared risk of hospitalization, long-term symptoms, as well as peak CRP and IL-6 concentrations. RESULTS: We identified three distinct clusters of participants based on their FLU-PRO Plus symptoms: cluster 1 ("Nasal cluster") is highly correlated with reporting runny/stuffy nose and sneezing, cluster 2 ("Sensory cluster") is highly correlated with loss of smell or taste, and cluster 3 ("Respiratory/Systemic cluster") is highly correlated with the respiratory (cough, trouble breathing, among others) and systemic (body aches, chills, among others) domain symptoms. Participants in the Respiratory/Systemic cluster were twice as likely as those in the Nasal cluster to have been hospitalized, and 1.5 times as likely to report that they had not returned-to-activities, which remained significant after controlling for confounding covariates (P < 0.01). Respiratory/Systemic and Sensory clusters were more likely to have symptoms at six-months post-symptom-onset (P = 0.03). We observed higher peak CRP and IL-6 in the Respiratory/Systemic cluster (P < 0.01). CONCLUSIONS: We identified early symptom profiles potentially associated with hospitalization, return-to-activities, long-term symptoms, and inflammatory profiles. These findings may assist in patient prognosis, including prediction of long COVID risk.

Spectral-Based Classification of Genetically Differentiated Groups in Spring Wheat Grown under Contrasting Environments.

The global concern about the gap between food production and consumption has intensified the research on the genetics, ecophysiology, and breeding of cereal crops. In this sense, several genetic studies have been conducted to assess the effectiveness and sustainability of collections of germplasm accessions of major crops. In this study, a spectral-based classification approach for the assignment of wheat cultivars to genetically differentiated subpopulations (genetic structure) was carried out using a panel of 316 spring bread cultivars grown in two environments with different water regimes (rainfed and fully irrigated). For that, different machine-learning models were trained with foliar spectral and genetic information to assign the wheat cultivars to subpopulations. The results revealed that, in general, the hyperparameters ReLU (as the activation function), adam (as the optimizer), and a size batch of 10 give neural network models better accuracy. Genetically differentiated groups showed smaller differences in mean wavelengths under rainfed than under full irrigation, which coincided with a reduction in clustering accuracy in neural network models. The comparison of models indicated that the Convolutional Neural Network (CNN) was significantly more accurate in classifying individuals into their respective subpopulations, with 92 and 93% of correct individual assignments in water-limited and fully irrigated environments, respectively, whereas 92% (full irrigation) and 78% (rainfed) of cultivars were correctly assigned to their respective classes by the multilayer perceptron method and partial least squares discriminant analysis, respectively. Notably, CNN did not show significant differences between both environments, which indicates stability in the prediction independent of the different water regimes. It is concluded that foliar spectral variation can be used to accurately infer the belonging of a cultivar to its respective genetically differentiated group, even considering radically different environments, which is highly desirable in the context of crop genetic resources management.

Rootstocks Comparison in Grafted Watermelon under Water Deficit: Effects on the Fruit Quality and Yield.

Drought is widely recognized as one of the most significant agricultural constraints worldwide. A strategy to avoid the adverse effects of drought on crops is to cultivate high-yielding varieties by grafting them onto drought-tolerant rootstocks with a differentiated root system. Thus, the objective of this study was to evaluate fruit yield and quality, root system architecture, and water productivity of watermelon grafted onto Lagenaria siceraria rootstocks. To do so, a commercial watermelon cultivar "Santa Amelia" [Citrullus lanatus (Thunb.)] was grafted onto five L. siceraria rootstocks: 'Illapel', 'Osorno', 'BG-48', 'GC', and 'Philippines', which were grown under three irrigation treatments (100%, 75%, and 50% of evapotranspiration). The comparison of the L. siceraria rootstocks in the irrigation treatments demonstrated no significant effect on watermelon fruit quality parameters. The rootstocks 'Illapel', 'Osorno', and 'GC' significantly improved the fruit number and yield (total fruit weight) under water deficit. Similarly, 'Illapel', 'Osorno', and 'GC' consistently showed statistical differences for root system architecture traits compared to 'BG-48' and 'Philippines'. Based on these results, we concluded that the used L. siceraria rootstocks did not affect the fruit yield and quality of grafted watermelon under water deficit. This study may help adjust the amount of applied water for watermelon production where L. siceraria rootstocks are utilized.

Understanding "Hybrid Immunity": Comparison and Predictors of Humoral Immune Responses to Severe Acute Respiratory Syndrome Coronavirus 2 Infection (SARS-CoV-2) and Coronavirus Disease 2019 (COVID-19) Vaccines.

BACKGROUND: Comparison of humoral responses in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccinees, those with SARS-CoV-2 infection, or combinations of vaccine/ infection ("hybrid immunity") may clarify predictors of vaccine immunogenicity. METHODS: We studied 2660 US Military Health System beneficiaries with a history of SARS-CoV-2 infection-alone (n = 705), vaccination-alone (n = 932), vaccine-after-infection (n = 869), and vaccine-breakthrough-infection (n = 154). Peak anti-spike-immunoglobulin G (IgG) responses through 183 days were compared, with adjustment for vaccine product, demography, and comorbidities. We excluded those with evidence of clinical or subclinical SARS-CoV-2 reinfection from all groups. RESULTS: Multivariable regression results indicated that vaccine-after-infection anti-spike-IgG responses were higher than infection-alone (P < .01), regardless of prior infection severity. An increased time between infection and vaccination was associated with greater post-vaccination IgG response (P < .01). Vaccination-alone elicited a greater IgG response but more rapid waning of IgG (P < .01) compared with infection-alone (P < .01). BNT162b2 and mRNA-1273 vaccine-receipt was associated with greater IgG responses compared with JNJ-78436735 vaccine-receipt (P < .01), regardless of infection history. Those with vaccine-after-infection or vaccine-breakthrough-infection had a more durable anti-spike-IgG response compared to infection-alone (P < .01). CONCLUSIONS: Vaccine-receipt elicited higher anti-spike-IgG responses than infection-alone, although IgG levels waned faster in those vaccinated (compared to infection-alone). Vaccine-after-infection elicits a greater humoral response compared with vaccine or infection alone; and the timing, but not disease severity, of prior infection predicted these post-vaccination IgG responses. While differences between groups were small in magnitude, these results offer insights into vaccine immunogenicity variations that may help inform vaccination timing strategies.

Draft Genome Sequence of Plesiomonas shigelloides MD22D9, Isolated from the Digestive Tract of Macrobdella decora.

Here, we present a draft genome sequence of Plesiomonas shigelloides MD22D9, isolated from the digestive tract of the North American medicinal leech Macrobdella decora. The gut microbiome of the medicinal leech is hypothesized to be critical for maintaining host fitness. This genome can provide insights into this uncharacterized microbe-host relationship.

Performance Evaluation of Biomedical Time Series Transformation Methods for Classification Tasks / Evaluación del Rendimiento de Métodos de Transformación de Series Temporales Biomédicas para Tareas de Clasificación

Abstract The extraction of time series features is essential across various fields, yet it remains a challenging endeavor. Therefore, it's crucial to identify appropriate methods capable of extracting pertinent information that can significantly enhance classification performance. Among these methods are those that translate time series into different domains. This study investigates three distinct time series transformation approaches for addressing time series classification challenges within biomedical data. The first method involves a response vector transformation, while the other two employ image transformation techniques: RandOm Convolutional KErnel Transform (ROCKET), Gramian Angular Fields, and Markov Transition Fields. These transformation methods were applied to five biomedical datasets, exploring various format configurations to ascertain the optimal representation technique and configuration for input, which in turn improves classification performance. Evaluations were conducted on the effectiveness of these methods in conjunction with two classification algorithms. The outcomes underscore the significance of these time series transformation techniques as facilitators for enhanced classification algorithms documented in current literature.

Resumen La extracción de características de series temporales es esencial en diversos campos, pero sigue siendo un desafío. Por lo tanto, es crucial identificar métodos apropiados capaces de extraer información pertinente que pueda mejorar significativamente el rendimiento de clasificación. Entre estos métodos se encuentran aquellos que traducen las series temporales a diferentes dominios. Este estudio investiga tres enfoques distintos de transformación de series temporales para abordar los desafíos de clasificación de series temporales en datos biomédicos. El primer método implica una transformación de vector de respuesta, mientras que los otros dos emplean técnicas de transformación de imagen: RandOm Convolutional KErnel Transform (ROCKET), Gramian Angular Fields y Markov Transition Fields. Estos métodos de transformación se aplicaron a cinco conjuntos de datos biomédicos, explorando diversas configuraciones de formato para determinar la técnica y configuración de representación óptima para la entrada, lo que a su vez mejora el rendimiento de clasificación. Se realizaron evaluaciones sobre la efectividad de estos métodos en conjunción con dos algoritmos de clasificación. Los resultados subrayan la importancia de estas técnicas de transformación de series temporales como facilitadoras para mejorar los algoritmos de clasificación documentados en la literatura actual.

Effect of nano-silicon on the regulation of ascorbate-glutathione contents, antioxidant defense system and growth of copper stressed wheat (Triticum aestivum L.) seedlings.

Copper (Cu2+) toxicity can inhibit plant growth and development. It has been shown that silicon (Si) can relieve Cu2+ stress. However, it is unclear how Si-nanoparticles (SiNPs) relieve Cu2+ stress in wheat seedlings. Therefore, the current study was conducted by setting up four treatments: CK, SiNP: (2.5 mM), Cu2+: (500 µM), and SiNP+Cu2+: (2.5 mM SiNP+500 µM Cu2+) to explore whether SiNPs can alleviate Cu2+ toxicity in wheat seedlings. The results showed that Cu2+ stress hampered root and shoot growth and accumulated high Cu2+ concentrations in roots (45.35 mg/kg) and shoots (25.70 mg/kg) of wheat as compared to control treatment. Moreover, Cu2+ treatment inhibited photosynthetic traits and chlorophyll contents as well as disturbed the antioxidant defense system by accumulating malondialdehyde (MDA) and hydrogen peroxidase (H2O2) contents. However, SiNPs treatment increased root length and shoot height by 15.1% and 22%, respectively, under Cu2+ toxicity. Moreover, SiNPs application decreased MDA and H2O2 contents by 31.25% and 19.25%, respectively. SiNPs increased non-enzymatic compounds such as ascorbic acid-glutathione (AsA-GSH) and enhanced superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbic peroxidase (APX) activities by 77.5%, 141.7%, 68%, and 80%, respectively. Furthermore, SiNPs decreased Cu2+ concentrations in shoots by 26.2%, as compared to Cu2+ treatment alone. The results concluded that SiNPs could alleviate Cu2+ stress in wheat seedlings. The present investigation may help to increase wheat production in Cu2+ contaminated soils.