Search

Online continuing education (CE) holds promise as an effective method for rapid dissemination of emerging evidence-based practices in health care. Yet, the field of CE continues to develop and delivery is predominately face-to-face programs. Practice-oriented online educational methods and e-learning platforms are not fully utilized. Educational theorists suggest an experiential approach to CE consistent with adult learning theory. A compelling question remains: Can online asynchronous CE programming prepare health care providers in delivering higher-level practice competencies?. To address this question, the authors have identified seven composite "sticky" factors that have been critical to the engagement of learners and the creation and delivery of practice-oriented online educational programs (Zaghab et al, 2015). The sticky factors are based in knowledge management (Nonaka, 1994; Szulanski, 2002) and adult education or andragogy (Knowles, 1970; 1984). In this paper, sticky factors are mapped to Moore and colleagues' (2009) higher level learning outcomes in health care CE. Data are presented on learner reported practice-related outcomes in a selection of online CE courses on the CIPS Knowledge Enterprise™ portal with the University of Maryland School of Pharmacy's Center for Innovative Pharmacy Solutions (CIPS). A dynamic, adaptive e-learning environment built by technology partner, Connect for Education, Inc., provides the innovative platform and the Acclaim! interactive learning technology. This technology-instructional partnership is dedicated to an iterative continuous improvement process called the Learner Stewardship Cycle (Zaghab et al, 2015). The cycle improves stickiness and learner engagement in order to achieve highe-level learning outcomes in CE. Findings suggest that of the 769 learners successfully completing an online course with two or more sticky design segments, the majority report reaching level 4, 5 and 6 learning competencies. Learners from the professions of pharmacy, nursing, medicine, and other health decision makers also found the courses relevant, easy to use and evidence-based.

Eucalyptus globulus Labill., is a recognized multipurpose tree, which stands out not only for the valuable qualities of its wood but also for the medicinal applications of the essential oil extracted from its leaves. In this study, we implemented an integrated strategy comprising genomic and phenomic approaches to predict foliar essential oil content, stem quality, and growthrelated traits within a 9-year-old breeding population of E. globulus. The strategy involved evaluating Uni/Multi-trait deep learning (DL) models by incorporating genomic data related to single nucleotide polymorphisms (SNPs) and haplotypes, as well as the phenomic data from leaf near-infrared (NIR) spectroscopy. Our results showed that essential oil content (oil yield) ranged from 0.01 to 1.69% v/fw and had no significant correlation with any growth-related traits. This suggests that selection solely based on growth-related traits did n The emphases (colored text) from revisions were removed throughout the article. Confirm that this change is fine. ot influence the essential oil content. Genomic heritability estimates ranged from 0.25 (diameter at breast height (DBH) and oil yield) to 0.71 (DBH and stem straightness (ST)), while pedigree-based heritability exhibited a broader range, from 0.05 to 0.88. Notably, oil yield was found to be moderate to highly heritable, with genomic values ranging from 0.25 to 0.60, alongside a pedigree-based estimate of 0.48. The DL prediction models consistently achieved higher prediction accuracy (PA) values with a Multi-trait approach for most traits analyzed, including oil yield (0.699), tree height (0.772), DBH (0.745), slenderness coefficient (0.616), stem volume (0.757), and ST (0.764). The Uni-trait approach achieved superior PA values solely for branching quality (0.861). NIR spectral absorbance was the best omics data for CNN or MLP models with a Multi-trait approach. These results highlight considerable genetic variation within the Eucalyptus progeny trial, particularly regarding oil production. Our results contribute significantly to understanding omics-assisted deep learning models as a breeding strategy to improve growth-related traits and optimize essential oil production in this species. [ABSTRACT FROM AUTHOR]

Water quality is essential for a healthy life, so it is necessary to look for technologies to measure its parameters in real time and automatically. The purpose of this study was to implement and determine the reliability of an automated system to evaluate the organoleptic water quality intended for human consumption, in the urban distribution network of the district of Daniel Hernandez (Peru), using a programmable logic controller (PLC) and Simulink. The study was carried out from January to March 2024, corresponding to the rainfall season. In the process of the research, a data acquisition and processing algorithm was implemented in a Simatic S7 1500 PLC with analog input module; using the national sanitation foundation water quality index (NSF-WQI) methodology. The work focused on five key water parameters: potential hydrogen (pH), electrical conductivity (EC), turbidity, free chlorine (FCL) and temperature. The methodology included programming in contact language (KOP) of the algorithm for calculating subscripts for each parameter, according to the functions established by NSF-WQI. Measurements were performed with 4-wire sensing devices with 4–20 mA current signals, ensuring data accuracy. The interface to visualize the parameters and the water quality index was implemented in Simulink, communicating via OPC UA with the PLC server, facilitating the graphical representation of the organoleptic water quality index. The overall equipment efficiency (OEE) or automated system implemented was 90.56%, indicating its acceptable reliability for evaluating water quality. By performing the measurements, with the sensors of the five parameters immersed in tap water, at each of the three established sampling points (Dwelling_1, Dwelling_2 and Dwelling_3) along the water distribution network, the system facilitated the automated and real-time evaluation of the quality, resulting in an average NSF-WQI of 83.08%, classifying the water as good for human consumption. This information is important for water quality management and can guide future treatments to achieve better quality. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Previous research on remediation has examined course placement practices, but little is known about first responders and how their beliefs about proper course placements help maintain patterns in course access. This study examined how taken-for-granted racial beliefs were used as legitimate knowledge by community college counselors. Haney López's (2000) race as commonsense theoretical perspective was used to analyze interview data from 34 counselors in 2 southern California community colleges. Data showed that counselors recreated race categories and racial hierarchy, and they did so by reinforcing beliefs about white students as intelligent and deserving higher placements and Latina/o students as comparatively lower in ability and deserving remediation. The results suggest that counselors begin institutionalizing racial sorting immediately as first-time students begin to enroll. The findings highlight the importance of racial mindfulness in policy, practice, and research.

Cardiovascular disease (CVD) remains the leading cause of mortality globally, and the greatest contributor to disability. Likewise, CVD are one of the largest causes of premature death and represent exuberant costs for public health. Given this high mortality in recent decades, a need has risen to find risk factors for the early identification of the populations which may be affected. At present, novel markers have been proporsed, such as the mean platelet volume (MPV). In fact, the MPV has been closely correlated to the development of ischemic coronary events and greater mortality after and infarction. Nevertheless, it is unknown if this relationship is causal, as platelets play an important pathophysiological role in ischemic events. Thus, the applicability of MPV as a predictive factor remains under debate for its use in clinical practice. The objective of this review is to assess the evidence on the MPV as a positive predictive factor for CVD, as evaluate the participation of platelets in coronary events. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: The carnivorous Yellowtail Snapper Ocyurus chrysurus has the potential for production in aquaculture. However, there is a lack of studies investigating the digestibility of ingredients needed to formulate balanced diets for this species. Methods: The in vitro protein digestibility of fish meal (FM), poultry by‐product meal (PBM), poultry meal (PM), protiblend, canola meal (CM), soybean meal, and wheat gluten was evaluated using the pH‐stat technique. The in vivo digestibility was determined by formulating test diets with a 70:30 ratio of the reference diet and the test ingredients. Result: In terms of in vitro digestibility, FM, PBM, and PM exhibited the highest degree of hydrolysis (DH). Conversely, PBM displayed the lowest apparent digestibility coefficient of protein (ADCprotein) in the in vivo digestibility test. Meanwhile, FM, PM, and CM demonstrated higher values of protein digestibility. Beta regression between ADCprotein and the DH and ash content of the ingredients resulted in a high correlation (R2 = 0.94). Conclusion: The results of the present study indicate that Yellowtail Snapper efficiently digested and absorbed FM, PM, and CM ingredients, which contain a lower content of ash. Impact statementSeveral fish species are overexploited in the production of fish meal, the primary ingredient in fish feed. Therefore, the comprehensive evaluation of potential ingredient substitutes for fish meal is imperative to foster the sustainable development of aquaculture. [ABSTRACT FROM AUTHOR]

Gold nanoparticles in their colloidal state have different colors, and the equipment for their characterization, such as UV-Vis spectrophotometers, scanning electron microscopy (SEM), and transmission electron microscopy (TEM), has high costs. The research aimed to characterize metallic gold nanoparticles by artificial vision based on the color of the samples in the colloidal state. The sensor used for the sampling was a 50 MP triple-lens camera with the optical image stabilization (OIS) of a smartphone. The Vision Acquisition and Vision Assistant blocks in the NI LabVIEW platform were used to implement an artificial vision device. The camera interface was used to identify the color of each of the 10 samples of colloidal gold nanoparticles produced by the YAG laser and chemical reduction in 15 ml of deionized water. The characterization consisted of the determination of the size and concentration of the gold nanoparticles based on their color, which ranged from pink to red wine. As a result, the artificial vision device adequately identified the color of the metallic gold nanoparticles in a colloidal state with a certainty of more than 95%, allowing the nanoparticles to be adequately characterized. Therefore, it is concluded that artificial vision adequately characterized gold nanoparticles’ wavelength, absorbance, diameter, and concentration. [ABSTRACT FROM AUTHOR]

Phenotypic characterization of the variability present within bottle gourd has been limited to morpho-agronomic traits, and this evaluation is a prerequisite for a bottle gourd breeding program. Despite playing an important role in the phenotypic variation in plants, the root system has limited use in studies of morphological diversity. Thus, the objective of this study was to characterize the morphological diversity present in bottle gourd accessions of different countries based on roots and morpho-agronomic traits. The magnitude of morphological diversity and divergence among nineteen bottle gourd accessions that represent nine different countries of origin were evaluated with analysis of variance, principal component analysis, and an agglomerative hierarchical clustering (AHC) analysis. ANOVA for morpho-agronomic and root traits revealed significant morphological effects among the accessions, suggesting substantial differences among the bottle gourd accessions. The nineteen accessions were grouped into three clusters, and while these were not grouped according to the country of origin, clear differences among the roots and flowering traits were observed between the L. siceraria var. hispida with L. siceraria var. siceraria accessions, which were confirmed with the AHC analysis, revealing the divergence between these varieties and the opportunities for rootstock breeding programs. [ABSTRACT FROM AUTHOR]

The research sought to evaluate the normalized difference vegetation index (NDVI) of the study area located in the province of Tayacaja, which includes the districts of Acraquia, Ahuaycha, Pampas and Daniel Hernández, which is part of the central Andes of Peru. The data were collected in low water seasons with a longitudinal cut of 30 years with one sample per year, starting in 1993 until 2022; these samples corresponded to the month of August of each year. The images were extracted from maps from Landsat satellite databases, which were filtered for low cloud cover to avoid interference with the images. Maps from 1993 to 2012 were obtained from Landsat 5 satellite, while from 2013 to 2022 data were obtained from Landsat 8 satellite. The normalized difference vegetation index was determined using Quantum GIS based on the red and near infrared maps; being the minimum NDVI value obtained -0.18, which corresponds to the aquatic body of the Upamayu River that crosses from west to east the study area; while the highest NDVI obtained was 0.79 indicating a greater vegetation cover constituted by mainly eucalyptus plants. The mean NDVI of the 30 years is close to 0.21; this is an indicator that the vegetation is scarce and that it is decreasing mainly due to population growth. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Phthorimaea operculella Zeller moth is a pest that affects potato crops and is found mainly in the inter-Andean valleys of the highlands of Peru (Huancavelica). The objective of the research was to use the phenomenon of light generated by light-emitting diodes (LEDs) to attract the potato moth in storage. Every day at seven o'clock in the morning, the number of moths falling dead in the light trap on the water contained in the container was counted. Five LED lights with different light densities per square centimeter were tested for five nights with different frequencies: 3.125 Hz, 12.5 Hz, 50 Hz, 200 Hz and 400 Hz. Every five nights the operating current of the LEDs was changed: 5 mA, 10 mA and 15 mA. Every fifteen nights the color of the LEDs was changed, according to the following sequence: white, blue, green, yellow and red; the color that attracted the most was white light, while the one with the least attraction was red light. The white light trap using the 64 LED array attracted 38.73% of moths and consumed a total of 43 W of energy in seven days, so it is considered low energy consumption. [ABSTRACT FROM AUTHOR]

We study the evolution and production of axion dark matter in a universe model with two scale factors corresponding to different patches of the universe. The interaction between patches is described through a deformed Poisson bracket structure. The first part of the present paper is devoted to a review of the results reported in previous works concerning the study of dark matter as WIMPs and FIMPs. The new results concerning axionic dark matter in this bi-metric scenario show that different values of the deformation parameter κ allow values of masses and misalignment angles forbidden in standard cosmology. The present model can also be considered a different type of nonstandard cosmology consistent with previously reported results. [ABSTRACT FROM AUTHOR]

This paper is a short essay that claims that a systemic problem cannot be rightly understood and even less solved on the same level, in the same framework or with the same tools that entail the very same problem. This is a radical variation of Einstein's idea set out in the context of the Copenhaguen debate according to which a problem cannot be solved if we do not change the conditions in which the problem arose originally. More exactly, a systemic problem cannot be tackled and solved with tools, approaches and rods of the very same kind, level, nature of framework that define the problem. To truly tackle and solve any problem, the approach, tools, concepts, models, and the like need have a higher or heavier calibre so to speak than the problem. Otherwise, we do not solve a problem at all. At its best, we displace it. This essay argues that to solve any systemic problem a complexity endeavour is needed. Reasons for the insufficiency of systems science and about the rationale of complexity theory are provided. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We study the possibility to describe dark matter in a model of the universe with two-scale factors and a nonstandard Poisson bracket structure characterized by the deformation parameter κ. The dark matter evolution is analyzed in the early stages of the universe, and its relic density is obtained via the Freeze-In and Freeze-Out mechanism. We show that by fixing κ and the initial ratio of energy densities present in the different sectors of the universe, the space of thermal average annihilation cross-sections and dark matter masses compatible with the standard cosmology prior to Big Bang Nucleosynthesis (BBN), is enlarged. This feature of the model is compatible with nonstandard cosmology. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The main organic input for the elaboration of growing media is peat (Sphagnum spp.), due to its physical and chemical characteristics. However, the mining of this material creates a considerable impact in the local ecosystems from which this is obtained, along with a global impact because of the emission of greenhouse gasses. Thus, sustainable materials that can replace, or reduce the use of peat, while maintaining or improving attributes in the growing media and plant growth, are greatly needed. Therefore, this work aims to evaluate the effects of the use of different proportions of compost and biochar on the biological characteristics of growing media and (Lactuca sativa L.) seedling growth prior to transplanting. Out of the biological variables evaluated, the β-glucosidase activity showed the greatest results in growing media based on 80% peat and based on 70% peat, 5% compost and 5% biochar. Moreover, growing media based on the combination of compost, biochar and peat maintained most of the Lactuca sativa L. (Oak Leaf variety) seedling traits obtained in the growing media based on only peat. These findings emphasize the need to further investigate further biological conditions for alternative materials to peat, and the need to pay attention to feedstock initial characteristics and processing in order to obtain high quality organic inputs for optimum growing media. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]