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IMPORTANCE: The prevalence, pathophysiology, and long-term outcomes of COVID-19 (post-acute sequelae of SARS-CoV-2 [PASC] or Long COVID) in children and young adults remain unknown. Studies must address the urgent need to define PASC, its mechanisms, and potential treatment targets in children and young adults. OBSERVATIONS: We describe the protocol for the Pediatric Observational Cohort Study of the NIHs REsearching COVID to Enhance Recovery (RECOVER) Initiative. RECOVER-Pediatrics is an observational meta-cohort study of caregiver-child pairs (birth through 17 years) and young adults (18 through 25 years), recruited from more than 100 sites across the US. This report focuses on two of four cohorts that comprise RECOVER-Pediatrics: 1) a de novo RECOVER prospective cohort of children and young adults with and without previous or current infection; and 2) an extant cohort derived from the Adolescent Brain Cognitive Development (ABCD) study (n = 10,000). The de novo cohort incorporates three tiers of data collection: 1) remote baseline assessments (Tier 1, n = 6000); 2) longitudinal follow-up for up to 4 years (Tier 2, n = 6000); and 3) a subset of participants, primarily the most severely affected by PASC, who will undergo deep phenotyping to explore PASC pathophysiology (Tier 3, n = 600). Youth enrolled in the ABCD study participate in Tier 1. The pediatric protocol was developed as a collaborative partnership of investigators, patients, researchers, clinicians, community partners, and federal partners, intentionally promoting inclusivity and diversity. The protocol is adaptive to facilitate responses to emerging science. CONCLUSIONS AND RELEVANCE: RECOVER-Pediatrics seeks to characterize the clinical course, underlying mechanisms, and long-term effects of PASC from birth through 25 years old. RECOVER-Pediatrics is designed to elucidate the epidemiology, four-year clinical course, and sociodemographic correlates of pediatric PASC. The data and biosamples will allow examination of mechanistic hypotheses and biomarkers, thus providing insights into potential therapeutic interventions. CLINICAL TRIALS.GOV IDENTIFIER: Clinical Trial Registration: http://www.clinicaltrials.gov. Unique identifier: NCT05172011.

ImportanceSARS-CoV-2 infection can result in ongoing, relapsing, or new symptoms or other health effects after the acute phase of infection; termed post-acute sequelae of SARS-CoV-2 infection (PASC), or long COVID. The characteristics, prevalence, trajectory and mechanisms of PASC are ill-defined. The objectives of the Researching COVID to Enhance Recovery (RECOVER) Multi-site Observational Study of PASC in Adults (RECOVER-Adult) are to: (1) characterize PASC prevalence; (2) characterize the symptoms, organ dysfunction, natural history, and distinct phenotypes of PASC; (3) identify demographic, social and clinical risk factors for PASC onset and recovery; and (4) define the biological mechanisms underlying PASC pathogenesis.MethodsRECOVER-Adult is a combined prospective/retrospective cohort currently planned to enroll 14,880 adults aged ≥18 years. Eligible participants either must meet WHO criteria for suspected, probable, or confirmed infection; or must have evidence of no prior infection. Recruitment occurs at 86 sites in 33 U.S. states, Washington, DC and Puerto Rico, via facility- and community-based outreach. Participants complete quarterly questionnaires about symptoms, social determinants, vaccination status, and interim SARS-CoV-2 infections. In addition, participants contribute biospecimens and undergo physical and laboratory examinations at approximately 0, 90 and 180 days from infection or negative test date, and yearly thereafter. Some participants undergo additional testing based on specific criteria or random sampling. Patient representatives provide input on all study processes. The primary study outcome is onset of PASC, measured by signs and symptoms. A paradigm for identifying PASC cases will be defined and updated using supervised and unsupervised learning approaches with cross-validation. Logistic regression and proportional hazards regression will be conducted to investigate associations between risk factors, onset, and resolution of PASC symptoms.DiscussionRECOVER-Adult is the first national, prospective, longitudinal cohort of PASC among US adults. Results of this study are intended to inform public health, spur clinical trials, and expand treatment options.RegistrationNCT05172024.

ImportanceSARS-CoV-2 infection can result in ongoing, relapsing, or new symptoms or other health effects after the acute phase of infection; termed post-acute sequelae of SARS-CoV-2 infection (PASC), or long COVID. The characteristics, prevalence, trajectory and mechanisms of PASC are ill-defined. The objectives of the Researching COVID to Enhance Recovery (RECOVER) Multi-site Observational Study of PASC in Adults (RECOVER-Adult) are to: (1) characterize PASC prevalence; (2) characterize the symptoms, organ dysfunction, natural history, and distinct phenotypes of PASC; (3) identify demographic, social and clinical risk factors for PASC onset and recovery; and (4) define the biological mechanisms underlying PASC pathogenesis.MethodsRECOVER-Adult is a combined prospective/retrospective cohort currently planned to enroll 14,880 adults aged ≥18 years. Eligible participants either must meet WHO criteria for suspected, probable, or confirmed infection; or must have evidence of no prior infection. Recruitment occurs at 86 sites in 33 U.S. states, Washington, DC and Puerto Rico, via facility- and community-based outreach. Participants complete quarterly questionnaires about symptoms, social determinants, vaccination status, and interim SARS-CoV-2 infections. In addition, participants contribute biospecimens and undergo physical and laboratory examinations at approximately 0, 90 and 180 days from infection or negative test date, and yearly thereafter. Some participants undergo additional testing based on specific criteria or random sampling. Patient representatives provide input on all study processes. The primary study outcome is onset of PASC, measured by signs and symptoms. A paradigm for identifying PASC cases will be defined and updated using supervised and unsupervised learning approaches with cross-validation. Logistic regression and proportional hazards regression will be conducted to investigate associations between risk factors, onset, and resolution of PASC symptoms.DiscussionRECOVER-Adult is the first national, prospective, longitudinal cohort of PASC among US adults. Results of this study are intended to inform public health, spur clinical trials, and expand treatment options.RegistrationNCT05172024.

ObjectiveTo optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings.MethodsIn a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume).ResultsThe EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R(2) = 0.38±0.05, and that between EHR-derived and true BPF has a mean R(2) = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10(-12)).ConclusionIncorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.

Inducibility of defences in response to biotic stimuli is considered an important trait in plant resistance. In conifers, previous research has mostly focused on the inducibility of the volatile fraction of the oleoresin (mono- and sesquiterpenes), leaving the inducibility of the non-volatile resin acids largely unexplored, particularly in response to real herbivory. Here we investigated the differences in the inducibility of resin acids in two pine species, one native from Europe (Pinus pinaster Ait.) and another from North America (Pinus radiata D. Don), in response to wounding by two European insects: a bark chewer, the pine weevil (Hylobius abietis L.), and a defoliator, the pine processionary caterpillar (Thaumetopoea pityocampa Schiff.). We quantified the constitutive (control) and induced concentrations of resin acids in the stem and needles of both pine species by gas chromatography techniques. Both pine species strongly increased the concentration of resin acids in the stem after pine weevil feeding, although the response was greater in P. pinaster than in P. radiata. However, systemic defensive responses in the needles were negligible in both pine species after pine weevil feeding in the stem. On the other hand, P. radiata locally reduced the resin acid concentration in the needles after pine caterpillar feeding, whereas in P. pinaster resin acid concentration was apparently unaffected. Nevertheless, systemic induction of resin acids was only observed in the stem of P. pinaster in response to pine caterpillar feeding. In summary, pine induced responses were found highly compartmentalized, and specific to herbivore identity. Particularly, plant defence suppression mechanisms by the pine caterpillar, and ontogenetic factors might be potentially affecting the induced response of resin acids in both pine species.

Accumulating evidence shows that increased biodiversity has a positive effect on ecosystem functioning, but the mechanisms that underpin this positive relationship are contentious. Complete extinctions of regional species pools are comparatively rare whereas compositional changes and reductions in abundance and biomass are common, although seldom the focus of biodiversity-ecosystem functioning studies. We use natural, small-scale patchiness in the density of two species of large bivalves with contrasting feeding modes (the suspension-feeding Austrovenus stutchburyi and deposit-feeding Macomona liliana) to examine their influence on the uptake of nitrogen from macroalgae detritus (i.e. measure of ecosystem function and food web efficiency) by other infauna in a 10-d laboratory isotope-tracer experiment. We predicted that densities of these key bivalve species and functional group diversity (calculated as Shannons H, a density-independent measure of community composition) of the intact infaunal community will be critical factors explaining variance in macroalgal per capita uptake rates by the community members and hence determine total uptake by the community. Results show that only two species, M. liliana and a large orbiniid polychaete (Scoloplos cylindrifer) dominated macroalgal nitrogen taken up by the whole community due to their large biomass. However, their densities were mostly not important or negatively influenced per capita uptake by other species. Instead, the density of a head-down deposit-feeder (the capitellid Heteromastus filiformis), scavengers (mainly nemertines and nereids) and species and functional group diversity, best explained per capita uptake rates in community members. Our results demonstrate the importance of species identity, density and large body size for ecosystem functioning and highlight the complex interactions underlying loss of ecological functions with declining biodiversity and compositional changes.

BACKGROUND:Typically, algorithms to classify phenotypes using electronic medical record (EMR) data were developed to perform well in a specific patient population. There is increasing interest in analyses which can allow study of a specific outcome across different diseases. Such a study in the EMR would require an algorithm that can be applied across different patient populations. Our objectives were: (1) to develop an algorithm that would enable the study of coronary artery disease (CAD) across diverse patient populations; (2) to study the impact of adding narrative data extracted using natural language processing (NLP) in the algorithm. Additionally, we demonstrate how to implement CAD algorithm to compare risk across 3 chronic diseases in a preliminary study. METHODS AND RESULTS:We studied 3 established EMR based patient cohorts: diabetes mellitus (DM, n = 65,099), inflammatory bowel disease (IBD, n = 10,974), and rheumatoid arthritis (RA, n = 4,453) from two large academic centers. We developed a CAD algorithm using NLP in addition to structured data (e.g. ICD9 codes) in the RA cohort and validated it in the DM and IBD cohorts. The CAD algorithm using NLP in addition to structured data achieved specificity >95% with a positive predictive value (PPV) 90% in the training (RA) and validation sets (IBD and DM). The addition of NLP data improved the sensitivity for all cohorts, classifying an additional 17% of CAD subjects in IBD and 10% in DM while maintaining PPV of 90%. The algorithm classified 16,488 DM (26.1%), 457 IBD (4.2%), and 245 RA (5.0%) with CAD. In a cross-sectional analysis, CAD risk was 63% lower in RA and 68% lower in IBD compared to DM (p

Despite the success of genome-wide association studies (GWAS) in detecting a large number of loci for complex phenotypes such as rheumatoid arthritis (RA) susceptibility, the lack of information on the causal genes leaves important challenges to interpret GWAS results in the context of the disease biology. Here, we genetically fine-map the RA risk locus at 19p13 to define causal variants, and explore the pleiotropic effects of these same variants in other complex traits. First, we combined Immunochip dense genotyping (n = 23,092 case/control samples), Exomechip genotyping (n = 18,409 case/control samples) and targeted exon-sequencing (n = 2,236 case/controls samples) to demonstrate that three protein-coding variants in TYK2 (tyrosine kinase 2) independently protect against RA: P1104A (rs34536443, OR = 0.66, P = 2.3 x 10(-21)), A928V (rs35018800, OR = 0.53, P = 1.2 x 10(-9)), and I684S (rs12720356, OR = 0.86, P = 4.6 x 10(-7)). Second, we show that the same three TYK2 variants protect against systemic lupus erythematosus (SLE, Pomnibus = 6 x 10(-18)), and provide suggestive evidence that two of the TYK2 variants (P1104A and A928V) may also protect against inflammatory bowel disease (IBD; P(omnibus) = 0.005). Finally, in a phenome-wide association study (PheWAS) assessing >500 phenotypes using electronic medical records (EMR) in >29,000 subjects, we found no convincing evidence for association of P1104A and A928V with complex phenotypes other than autoimmune diseases such as RA, SLE and IBD. Together, our results demonstrate the role of TYK2 in the pathogenesis of RA, SLE and IBD, and provide supporting evidence for TYK2 as a promising drug target for the treatment of autoimmune diseases.

Benthic communities below the photic zone depend for food on allochthonous organic matter derived from seasonal phytoplankton blooms. In the Baltic Sea, the spring diatom bloom is considered the most important input of organic matter, whereas the contribution of the summer bloom dominated by diazotrophic cyanobacteria is less understood. The possible increase in cyanobacteria blooms as a consequence of eutrophication and climate change calls for evaluation of cyanobacteria effects on benthic community functioning and productivity. Here, we examine utilization of cyanobacterial nitrogen by deposit-feeding benthic macrofauna following a cyanobacteria bloom at three stations during two consecutive years and link these changes to isotopic niche and variations in body condition (assayed as C:N ratio) of the animals. Since nitrogen-fixing cyanobacteria have δ(15)N close to -2‰, we expected the δ(15)N in the deposit-feeders to decrease after the bloom if their assimilation of cyanobacteria-derived nitrogen was substantial. We also expected the settled cyanobacteria with their associated microheterotrophic community and relatively high nitrogen content to increase the isotopic niche area, trophic diversity and dietary divergence between individuals (estimated as the nearest neighbour distance) in the benthic fauna after the bloom. The three surface-feeding species (Monoporeia affinis, Macoma balthica and Marenzelleria arctia) showed significantly lower δ(15)N values after the bloom, while the sub-surface feeder Pontoporeia femorata did not. The effect of the bloom on isotopic niche varied greatly between stations; populations which increased niche area after the bloom had better body condition than populations with reduced niche, regardless of species. Thus, cyanobacterial nitrogen is efficiently integrated into the benthic food webs in the Baltic, with likely consequences for their functioning, secondary production, transfer efficiency, trophic interactions, and intra- and interspecific competition.

Genome wide association studies (GWAS) have identified several SNPs associated with colorectal cancer (CRC) susceptibility. Vitamin D is also inversely associated with CRC risk.We examined main and joint effects of previously GWAS identified genetic markers of CRC and plasma 25-hydroxyvitamin D (25(OH)D) on CRC risk in three prospective cohorts: the Nurses' Health Study (NHS), the Health Professionals Follow-up Study (HPFS), and the Physicians' Health Study (PHS). We included 1895 CRC cases and 2806 controls with genomic DNA. We calculated odds ratios and 95% confidence intervals for CRC associated with additive genetic risk scores (GRSs) comprised of all CRC SNPs and subsets of these SNPs based on proximity to regions of increased vitamin D receptor binding to vitamin D response elements (VDREs), based on published ChiP-seq data. Among a subset of subjects with additional prediagnostic 25(OH)D we tested multiplicative interactions between plasma 25(OH)D and GRS's. We used fixed effects models to meta-analyze the three cohorts.The per allele multivariate OR was 1.12 (95% CI, 1.06-1.19) for GRS-proximalVDRE; and 1.10 (95% CI, 1.06-1.14) for GRS-nonproxVDRE. The lowest quartile of plasma 25(OH)D compared with the highest, had a multivariate OR of 0.63 (95% CI, 0.48-0.82) for CRC. We did not observe any significant interactions between any GRSs and plasma 25(OH)D.We did not observe evidence for the modification of genetic susceptibility for CRC according to vitamin D status, or evidence that the effect of common CRC risk alleles differed according to their proximity to putative VDR binding sites.

We aimed to mine the data in the Electronic Medical Record to automatically discover patients' Rheumatoid Arthritis disease activity at discrete rheumatology clinic visits. We cast the problem as a document classification task where the feature space includes concepts from the clinical narrative and lab values as stored in the Electronic Medical Record.The Training Set consisted of 2792 clinical notes and associated lab values. Test Set 1 included 1749 clinical notes and associated lab values. Test Set 2 included 344 clinical notes for which there were no associated lab values. The Apache clinical Text Analysis and Knowledge Extraction System was used to analyze the text and transform it into informative features to be combined with relevant lab values.Experiments over a range of machine learning algorithms and features were conducted. The best performing combination was linear kernel Support Vector Machines with Unified Medical Language System Concept Unique Identifier features with feature selection and lab values. The Area Under the Receiver Operating Characteristic Curve (AUC) is 0.831 (σ = 0.0317), statistically significant as compared to two baselines (AUC = 0.758, σ = 0.0291). Algorithms demonstrated superior performance on cases clinically defined as extreme categories of disease activity (Remission and High) compared to those defined as intermediate categories (Moderate and Low) and included laboratory data on inflammatory markers.Automatic Rheumatoid Arthritis disease activity discovery from Electronic Medical Record data is a learnable task approximating human performance. As a result, this approach might have several research applications, such as the identification of patients for genome-wide pharmacogenetic studies that require large sample sizes with precise definitions of disease activity and response to therapies.

To optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings.In a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume).The EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R(2) = 0.38±0.05, and that between EHR-derived and true BPF has a mean R(2) = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10(-12)).Incorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.

Storm clouds frequently form in the summer period in temperate climate zones. Studies on these inaccessible and short-lived atmospheric habitats have been scarce. We report here on the first comprehensive biogeochemical investigation of a storm cloud using hailstones as a natural stochastic sampling tool. A detailed molecular analysis of the dissolved organic matter in individual hailstones via ultra-high resolution mass spectrometry revealed the molecular formulae of almost 3000 different compounds. Only a small fraction of these compounds were rapidly biodegradable carbohydrates and lipids, suitable for microbial consumption during the lifetime of cloud droplets. However, as the cloud environment was characterized by a low bacterial density (Me = 1973 cells/ml) as well as high concentrations of both dissolved organic carbon (Me = 179 µM) and total dissolved nitrogen (Me = 30 µM), already trace amounts of easily degradable organic compounds suffice to support bacterial growth. The molecular fingerprints revealed a mainly soil origin of dissolved organic matter and a minor contribution of plant-surface compounds. In contrast, both the total and the cultivable bacterial community were skewed by bacterial groups (γ-Proteobacteria, Sphingobacteriales and Methylobacterium) that indicated the dominance of plant-surface bacteria. The enrichment of plant-associated bacterial groups points at a selection process of microbial genera in the course of cloud formation, which could affect the long-distance transport and spatial distribution of bacteria on Earth. Based on our results we hypothesize that plant-associated bacteria were more likely than soil bacteria (i) to survive the airborne state due to adaptations to life in the phyllosphere, which in many respects matches the demands encountered in the atmosphere and (ii) to grow on the suitable fraction of dissolved organic matter in clouds due to their ecological strategy. We conclude that storm clouds are among the most extreme habitats on Earth, where microbial life exists.

It has previously been shown that nano-meter sized vesicles (30-100 nm), exosomes, secreted by antigen presenting cells can induce T cell responses thus showing the potential of exosomes to be used as immunological tools. Additionally, activated CD3⁺ T cells can secrete exosomes that have the ability to modulate different immunological responses. Here, we investigated what effects exosomes originating from activated CD3⁺ T cells have on resting CD3⁺ T cells by studying T cell proliferation, cytokine production and by performing T cell and exosome phenotype characterization. Human exosomes were generated in vitro following CD3⁺ T cell stimulation with anti-CD28, anti-CD3 and IL-2. Our results show that exosomes purified from stimulated CD3⁺ T cells together with IL-2 were able to generate proliferation in autologous resting CD3⁺ T cells. The CD3⁺ T cells stimulated with exosomes together with IL-2 had a higher proportion of CD8⁺ T cells and had a different cytokine profile compared to controls. These results indicate that activated CD3⁺ T cells communicate with resting autologous T cells via exosomes.

Plants produce a number of antimicrobial substances and the roots of the shrub Salvadora persica have been demonstrated to possess antimicrobial activity. Sticks from the roots of S. persica, Miswak sticks, have been used for centuries as a traditional method of cleaning teeth. Diverging reports on the chemical nature and antimicrobial repertoire of the chewing sticks from S. persica led us to explore its antibacterial properties against a panel of pathogenic or commensal bacteria and to identify the antibacterial component/s by methodical chemical characterization. S. persica root essential oil was prepared by steam distillation and solid-phase microextraction was used to sample volatiles released from fresh root. The active compound was identified by gas chromatography-mass spectrometry and antibacterial assays. The antibacterial compound was isolated using medium-pressure liquid chromatography. Transmission electron microscopy was used to visualize the effect on bacterial cells. The main antibacterial component of both S. persica root extracts and volatiles was benzyl isothiocyanate. Root extracts as well as commercial synthetic benzyl isothiocyanate exhibited rapid and strong bactericidal effect against oral pathogens involved in periodontal disease as well as against other Gram-negative bacteria, while Gram-positive bacteria mainly displayed growth inhibition or remained unaffected. The short exposure needed to obtain bactericidal effect implies that the chewing sticks and the essential oil may have a specific role in treatment of periodontal disease in reducing Gram-negative periodontal pathogens. Our results indicate the need for further investigation into the mechanism of the specific killing of Gram-negative bacteria by S. persica root stick extracts and its active component benzyl isothiocyanate.

Tree-killing bark beetles (Coleoptera, Scolytinae) are among the most economically and ecologically important forest pests in the northern hemisphere. Induction of terpenoid-based oleoresin has long been considered important in conifer defense against bark beetles, but it has been difficult to demonstrate a direct correlation between terpene levels and resistance to bark beetle colonization.To test for inhibitory effects of induced terpenes on colonization by the spruce bark beetle Ips typographus (L.) we inoculated 20 mature Norway spruce Picea abies (L.) Karsten trees with a virulent fungus associated with the beetle, Ceratocystis polonica (Siem.) C. Moreau, and investigated induced terpene levels and beetle colonization in the bark.Fungal inoculation induced very strong and highly variable terpene accumulation 35 days after inoculation. Trees with high induced terpene levels (n = 7) had only 4.9% as many beetle attacks (5.1 vs. 103.5 attacks m(-2)) and 2.6% as much gallery length (0.029 m m(-2) vs. 1.11 m m(-2)) as trees with low terpene levels (n = 6). There was a highly significant rank correlation between terpene levels at day 35 and beetle colonization in individual trees. The relationship between induced terpene levels and beetle colonization was not linear but thresholded: above a low threshold concentration of ∼100 mg terpene g(-1) dry phloem trees suffered only moderate beetle colonization, and above a high threshold of ∼200 mg terpene g(-1) dry phloem trees were virtually unattacked.This is the first study demonstrating a dose-dependent relationship between induced terpenes and tree resistance to bark beetle colonization under field conditions, indicating that terpene induction may be instrumental in tree resistance. This knowledge could be useful for developing management strategies that decrease the impact of tree-killing bark beetles.

Cumulative genetic profiles can help identify individuals at high-risk for developing RA. We examined the impact of 39 validated genetic risk alleles on the risk of RA phenotypes characterized by serologic and erosive status.We evaluated single nucleotide polymorphisms at 31 validated RA risk loci and 8 Human Leukocyte Antigen alleles among 542 Caucasian RA cases and 551 Caucasian controls from Nurses' Health Study and Nurses' Health Study II. We created a weighted genetic risk score (GRS) and evaluated it as 7 ordinal groups using logistic regression (adjusting for age and smoking) to assess the relationship between GRS group and odds of developing seronegative (RF- and CCP-), seropositive (RF+ or CCP+), erosive, and seropositive, erosive RA phenotypes. In separate case only analyses, we assessed the relationships between GRS and age of symptom onset. In 542 RA cases, 317 (58%) were seropositive, 163 (30%) had erosions and 105 (19%) were seropositive with erosions. Comparing the highest GRS risk group to the median group, we found an OR of 1.2 (95% CI = 0.8-2.1) for seronegative RA, 3.0 (95% CI = 1.9-4.7) for seropositive RA, 3.2 (95% CI = 1.8-5.6) for erosive RA, and 7.6 (95% CI = 3.6-16.3) for seropositive, erosive RA. No significant relationship was seen between GRS and age of onset.Results suggest that seronegative and seropositive/erosive RA have different genetic architecture and support the importance of considering RA phenotypes in RA genetic studies.