Plant-microbial interactions facilitate grassland species coexistence at the community level

Interspecific competition and plant-soil feedbacks are powerful drivers of plant community structure. However, across a range of edaphic conditions the interactive effects of these drivers on complex plant communities remain unclear. For example, plant-soil feedback studies focus on soil trained by a single plant species. We developed a method to assess effects of plant-microbial interactions (PMI) on a complex plant community. We established mesocosms with 13 grassland species, grown individually or together, in overgrazed or restored soil, with or without soil microbial inoculum collected from a productive and diverse native grassland. We assessed biomass production as influenced by edaphic conditions, interspecific competition and PMI. Furthermore, we assessed potential influences of interspecific competition and edaphic conditions on strength and direction of PMI. Our results indicate PMI drives negative growth responses for graminoids while forbs experience positive growth responses. Generally, interspecific competition did not alter the magnitude or direction of PMI-mediated growth responses. Edaphic conditions altered the influence of soil microbial communities on individual plant growth while PMI facilitated plant evenness. In plant community mesocosms, PMI-associated benefits were observed in overgrazed soil. However, interspecific competition overwhelmed plant growth benefits associated with soil microbial communities when plant communities were grown in restored soil. In mesocosms containing dominant grass species, interspecific competition had negative effects on species coexistence, but both positive and negative PMI partially counterbalanced this influence on plant species evenness. Understanding these mechanisms may improve our capacity to manage diverse and productive grasslands by enabling prediction of plant community composition following disturbance and subsequent restoration

Baseline Characteristics of Sars-Cov-2 Vaccine Non-Responders in a Large Population-Based Sample

INTRODUCTION: Studies indicate that individuals with chronic conditions and specific baseline characteristics may not mount a robust humoral antibody response to SARS-CoV-2 vaccines. In this paper, we used data from the Texas Coronavirus Antibody REsponse Survey (Texas CARES), a longitudinal state-wide seroprevalence program that has enrolled more than 90,000 participants, to evaluate the role of chronic diseases as the potential risk factors of non-response to SARS-CoV-2 vaccines in a large epidemiologic cohort. METHODS: A participant needed to complete an online survey and a blood draw to test for SARS-CoV-2 circulating plasma antibodies at four-time points spaced at least three months apart. Chronic disease predictors of vaccine non-response are evaluated using logistic regression with non-response as the outcome and each chronic disease + age as the predictors. RESULTS: As of April 24, 2023, 18,240 participants met the inclusion criteria; 0.58% (N = 105) of these are non-responders. Adjusting for age, our results show that participants with self-reported immunocompromised status, kidney disease, cancer, and other non-specified comorbidity were 15.43, 5.11, 2.59, and 3.13 times more likely to fail to mount a complete response to a vaccine, respectively. Furthermore, having two or more chronic diseases doubled the prevalence of non-response. CONCLUSION: Consistent with smaller targeted studies, a large epidemiologic cohort bears the same conclusion and demonstrates immunocompromised, cancer, kidney disease, and the number of diseases are associated with vaccine non-response. This study suggests that those individuals, with chronic diseases with the potential to affect their immune system response, may need increased doses or repeated doses of COVID-19 vaccines to develop a protective antibody level

Methodology to Estimate Natural- and Vaccine-induced antibodies to Sars-Cov-2 in a Large Geographic Region

Accurate estimates of natural and/or vaccine-induced antibodies to SARS-CoV-2 are difficult to obtain. Although model-based estimates of seroprevalence have been proposed, they require inputting unknown parameters including viral reproduction number, longevity of immune response, and other dynamic factors. In contrast to a model-based approach, the current study presents a data-driven detailed statistical procedure for estimating total seroprevalence (defined as antibodies from natural infection or from full vaccination) in a region using prospectively collected serological data and state-level vaccination data. Specifically, we conducted a longitudinal statewide serological survey with 88,605 participants 5 years or older with 3 prospective blood draws beginning September 30, 2020. Along with state vaccination data, as of October 31, 2021, the estimated percentage of those 5 years or older with naturally occurring antibodies to SARS-CoV-2 in Texas is 35.0% (95% CI = (33.1%, 36.9%)). This is 3× higher than, state-confirmed COVID-19 cases (11.83%) for all ages. The percentage with naturally occurring or vaccine-induced antibodies (total seroprevalence) is 77.42%. This methodology is integral to pandemic preparedness as accurate estimates of seroprevalence can inform policy-making decisions relevant to SARS-CoV-2

Antibody Duration after infection From Sars-Cov-2 in the Texas Coronavirus antibody Response Survey

Understanding the duration of antibodies to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that causes COVID-19 is important to controlling the current pandemic. Participants from the Texas Coronavirus Antibody Response Survey (Texas CARES) with at least 1 nucleocapsid protein antibody test were selected for a longitudinal analysis of antibody duration. A linear mixed model was fit to data from participants (n = 4553) with 1 to 3 antibody tests over 11 months (1 October 2020 to 16 September 2021), and models fit showed that expected antibody response after COVID-19 infection robustly increases for 100 days postinfection, and predicts individuals may remain antibody positive from natural infection beyond 500 days depending on age, body mass index, smoking or vaping use, and disease severity (hospitalized or not; symptomatic or not)

Scalable Skyline Computation Using Object-based Space Partitioning

The skyline operator returns from a set of multi-dimensional objects a subset of superior objects that are not dominated by others. This operation is considered very important in multi-objective analysis of large datasets. Although a large number of skyline methods have been proposed, the majority of them focuses on minimizing the I/O cost. However, in high dimensional spaces, the problem can easily become CPU-bound due to the large number of computations required for comparing objects with current skyline points while scanning the database. Based on this observation, we propose a dynamic indexing technique for skyline points that can be integrated into state-of-the-art sort-based skyline algorithms to boost their computational performance. The new indexing and dominance checking approach is supported by a theoretical analysis, while our experiments show that it scales well with the input size and dimensionality not only because unnecessary dominance checks are avoided but also because it allows efficient dominance checking with the help of bitwise operations