Explore the vast range of titles available.

T and B cell receptor loci undergo combinatorial rearrangement, generating a diverse immune receptor repertoire, which is vital for recognition of potential antigens. Here we use a multiplex PCR with a mixture of primers targeting the rearranged variable and joining segments to capture receptor diversity. Differential hybridization kinetics can introduce significant amplification biases that alter the composition of sequence libraries prepared by multiplex PCR. Using a synthetic immune receptor repertoire, we identify and minimize such biases and computationally remove residual bias after sequencing. We apply this method to a multiplex T cell receptor gamma sequencing assay. To demonstrate accuracy in a biological setting, we apply the method to monitor minimal residual disease in acute lymphoblastic leukaemia patients. A similar methodology can be extended to any adaptive immune locus. Immunosequencing enables cost-effective sequencing of repertoires of immune cells, but it often suffers from amplification biases when attempting cell quantification. Here, the authors present a powerful multiplex PCR assay that allows for quantitative and unbiased analysis of frequency of different T cell receptors.

Background The clonoSEQ® Assay (Adaptive Biotechnologies Corporation, Seattle, USA) identifies and tracks unique disease-associated immunoglobulin (Ig) sequences by next-generation sequencing of IgH, IgK, and IgL rearrangements and IgH-BCL1/2 translocations in malignant B cells. Here, we describe studies to validate the analytical performance of the assay using patient samples and cell lines. Methods Sensitivity and specificity were established by defining the limit of detection (LoD), limit of quantitation (LoQ) and limit of blank (LoB) in genomic DNA (gDNA) from 66 patients with multiple myeloma (MM), acute lymphoblastic leukemia (ALL), or chronic lymphocytic leukemia (CLL), and three cell lines. Healthy donor gDNA was used as a diluent to contrive samples with specific DNA masses and malignant-cell frequencies. Precision was validated using a range of samples contrived from patient gDNA, healthy donor gDNA, and 9 cell lines to generate measurable residual disease (MRD) frequencies spanning clinically relevant thresholds. Linearity was determined using samples contrived from cell line gDNA spiked into healthy gDNA to generate 11 MRD frequencies for each DNA input, then confirmed using clinical samples. Quantitation accuracy was assessed by (1) comparing clonoSEQ and multiparametric flow cytometry (mpFC) measurements of ALL and MM cell lines diluted in healthy mononuclear cells, and (2) analyzing precision study data for bias between clonoSEQ MRD results in diluted gDNA and those expected from mpFC based on original, undiluted samples. Repeatability of nucleotide base calls was assessed via the assay’s ability to recover malignant clonotype sequences across several replicates, process features, and MRD levels. Results LoD and LoQ were estimated at 1.903 cells and 2.390 malignant cells, respectively. LoB was zero in healthy donor gDNA. Precision ranged from 18% CV (coefficient of variation) at higher DNA inputs to 68% CV near the LoD. Variance component analysis showed MRD results were robust, with expected laboratory process variations contributing ≤3% CV. Linearity and accuracy were demonstrated for each disease across orders of magnitude of clonal frequencies. Nucleotide sequence error rates were extremely low. Conclusions These studies validate the analytical performance of the clonoSEQ Assay and demonstrate its potential as a highly sensitive diagnostic tool for selected lymphoid malignancies.

Background: The clonoSEQ® Assay (Adaptive Biotechnologies Corporation, Seattle, USA) identifies and tracks unique disease-associated immunoglobulin (Ig) sequences by next-generation sequencing of IgH, IgK, and IgL rearrangements and IgH-BCL1/2 translocations in malignant B cells. Here, we describe studies to validate the analytical performance of the assay using patient samples and cell lines. Methods: Sensitivity and specificity were established by defining the limit of detection (LoD), limit of quantitation (LoQ) and limit of blank (LoB) in genomic DNA (gDNA) from 66 patients with multiple myeloma (MM), acute lymphoblastic leukemia (ALL), or chronic lymphocytic leukemia (CLL), and three cell lines. Healthy donor gDNA was used as a diluent to contrive test samples with specific DNA masses and malignant-cell frequencies. Precision was validated using a range of samples contrived from patient gDNA, healthy donor gDNA, and 9 cell lines to generate numerous measurable residual disease (MRD) frequencies spanning clinically relevant thresholds. Linearity was determined using samples contrived from cell line gDNA spiked into healthy gDNA to generate 11 MRD frequencies for each DNA input, then confirmed using clinical samples. Quantitation accuracy was assessed by (1) comparing clonoSEQ and multiparametric flow cytometry (mpFC) measures of ALL and MM cell lines diluted in healthy mononuclear cells, and (2) analyzing precision study data for quantitation bias between MRD results from clonoSEQ measurements of diluted gDNA and those expected from mpFC of original, undiluted samples. Repeatability of nucleotide base calls was assessed via the assay’s ability to recover malignant clonotype sequences across several replicates, process features, and MRD levels. Results: LoD and LoQ were estimated at 1.903 cells and 2.390 malignant cells, respectively. LoB was zero in healthy donor gDNA. Precision ranged from 18% CV at higher DNA inputs to 68% CV near the LoD. Variance component analysis showed MRD results were robust, with expected laboratory process variations contributing ≤3% CV. Linearity and accuracy were demonstrated for each disease across orders of magnitude of clonal frequencies. Nucleotide sequence error rates were extremely low. Conclusions: These studies validate the analytical performance of the clonoSEQ Assay, and demonstrate its potential as a highly sensitive diagnostic tool for selected lymphoid malignancies.

Wildfire risk in temperate forests has become a nearly intractable problem that can be characterized as a socioecological “pathology”: that is, a set of complex and problematic interactions among social and ecological systems across multiple spatial and temporal scales. Assessments of wildfire risk could benefit from recognizing and accounting for these interactions in terms of socioecological systems, also known as coupled natural and human systems (CNHS). We characterize the primary social and ecological dimensions of the wildfire risk pathology, paying particular attention to the governance system around wildfire risk, and suggest strategies to mitigate the pathology through innovative planning approaches, analytical tools, and policies. We caution that even with a clear understanding of the problem and possible solutions, the system by which human actors govern fire‐prone forests may evolve incrementally in imperfect ways and can be expected to resist change even as we learn better ways to manage CNHS.

General intelligence is an important human quantitative trait that accounts for much of the variation in diverse cognitive abilities. Individual differences in intelligence are strongly associated with many important life outcomes, including educational and occupational attainments, income, health and lifespan. Data from twin and family studies are consistent with a high heritability of intelligence, but this inference has been controversial. We conducted a genome-wide analysis of 3511 unrelated adults with data on 549 692 single nucleotide polymorphisms (SNPs) and detailed phenotypes on cognitive traits. We estimate that 40% of the variation in crystallized-type intelligence and 51% of the variation in fluid-type intelligence between individuals is accounted for by linkage disequilibrium between genotyped common SNP markers and unknown causal variants. These estimates provide lower bounds for the narrow-sense heritability of the traits. We partitioned genetic variation on individual chromosomes and found that, on average, longer chromosomes explain more variation. Finally, using just SNP data we predicted ∼1% of the variance of crystallized and fluid cognitive phenotypes in an independent sample ( P =0.009 and 0.028, respectively). Our results unequivocally confirm that a substantial proportion of individual differences in human intelligence is due to genetic variation, and are consistent with many genes of small effects underlying the additive genetic influences on intelligence.

Accurate species delimitation and description are necessary to guide effective conservation of imperiled species, and this synergy is maximized when multiple data sources are used to delimit species. We illustrate this point by examining Drymarchon couperi (Eastern Indigo Snake), a large, federally-protected species in North America that was recently divided into two species based on gene sequence data from three loci and heuristic morphological assessment. Here, we re-evaluate the two-species hypothesis for D. couperi by evaluating both population genetic and gene sequence data. Our analyses of 14 microsatellite markers revealed 6-8 genetic population clusters with significant admixture, particularly across the contact zone between the two hypothesized species. Phylogenetic analyses of gene sequence data with maximum-likelihood methods suggested discordance between mitochondrial and nuclear markers and provided phylogenetic support for one species rather than two. For these reasons, we place Drymarchon kolpobasileus into synonymy with D. couperi. We suggest inconsistent patterns between mitochondrial and nuclear DNA are driven by high dispersal of males relative to females. We advocate for species delimitation exercises that evaluate admixture and gene flow in addition to phylogenetic analyses, particularly when the latter reveal monophyletic lineages. This is particularly important for taxa, such as squamates, that exhibit strong sex-biased dispersal. Problems associated with over-delimitation of species richness can become particularly acute for threatened and endangered species, because of high costs to conservation when taxonomy demands protection of more individual species than are supported by accumulating data.

Hemophagocytic lymphohistiocytosis (HLH) is a hyperinflammatory disorder characterized by the inability to properly terminate an immune response. Familial HLH (FHLH) and related immune dysregulation syndromes are associated with mutations in the genes PRF1, UNC13D, STX11, STXBP2, LYST, AP3B1 , and RAB27A , all of which are required for the assembly, exocytosis, and function of cytotoxic granules within CD8+ T cells and natural killer (NK) cells. Loss-of-function mutations in these genes render the cytotoxicity pathway ineffective, thereby failing to eradicate immune stimuli, such as infectious pathogens or malignant cells. The resulting persistent immune system stimulation drives hypercytokinemia, ultimately leading to severe tissue inflammation and end-organ damage. Traditionally, a diagnosis of FHLH requires the identification of biallelic loss-of-function mutations in one of these degranulation pathway genes. However, this narrow definition fails to encompass patients with other genetic mechanisms underlying degranulation pathway dysfunction. In particular, mounting clinical evidence supports a potential digenic mode of inheritance of FHLH in which single loss-of-function mutations in two different degranulation pathway genes cooperate to impair pathway activity. Here, we review the functions of the FHLH-associated genes within the degranulation pathway and summarize clinical evidence supporting a model in which cumulative defects along this mechanistic pathway may underlie HLH.

Fire-prone landscapes are not well studied as coupled human and natural systems (CHANS) and present many challenges for understanding and promoting adaptive behaviors and institutions. Here, we explore how heterogeneity, feedbacks, and external drivers in this type of natural hazard system can lead to complexity and can limit the development of more adaptive approaches to policy and management. Institutions and social networks can counter these limitations and promote adaptation. We also develop a conceptual model that includes a robust characterization of social subsystems for a fire-prone landscape in Oregon and describe how we are building an agent-based model to promote understanding of this social-ecological system. Our agent-based model, which incorporates existing ecological models of vegetation and fire and is based on empirical studies of landowner decision-making, will be used to explore alternative management and fire scenarios with land managers and various public entities. We expect that the development of CHANS frameworks and the application of a simulation model in a collaborative setting will facilitate the development of more effective policies and practices for fire-prone landscapes.

We examine the influence of wildfire institutions on management and forest resilience over time, drawing on research from a multiownership, frequent-fire, coupled human and natural system (CHANS) in the eastern Cascades of Oregon, USA. We constructed social-ecological histories of the study area’s three main landowner groups (national forest, private corporate, and tribal) using a historical framework (1905–2010). Our findings highlight two infrequently recognized linkages of multiownership, frequentfire CHANS: (1) informal institutions (e.g., cultural norms, knowledge system and fire paradigm) and institutional history often influence wildfire management adaptation (changes in forest fuel treatment, harvest fuel treatment, and wildfire incident response) through interactions with formal institutions (e.g., policy, law) and consequent effects on managers’ decision-making flexibility; (2) institutional interactions over time can influence forest resilience, thereby contributing to forest structural variation in multiownership landscapes. Consequently, the factors that contribute to maladaptive wildfire management are heterogeneously distributed across ownerships and the landscape. The timing of institutional dynamics also matters: manager flexibility to respond adaptively to wildfire hazard change seems to depend on synchronicity in evolution between informal and formal institutions, whereas asynchronous evolution (e.g., policy change, coupled with delayed shift in cultural norms or fire paradigms) may generate a time lag between unanticipated ecological feedbacks and management response. Thus, interventions that promote informal institutional evolution in tandem with developments in policy and law may shorten time lags, accelerating adaptation. A historical perspective can facilitate broad-scale, adaptive responses to wildfire-related ecological feedbacks in several ways: by providing insight into how informal institutions and institutional history interact with formal institutions to influence wildfire management behavior; by providing a historical baseline and system stages that contextualize current management behavior, ecological conditions, and policy options; and by illuminating historical sources of variation among ownerships and how they might be addressed.

Multiple studies across a variety of scientific disciplines have shown that the number of times that a paper is shared on Twitter (now called X) is correlated with the number of citations that paper receives. However, these studies were not designed to answer whether tweeting about scientific papers causes an increase in citations, or whether they were simply highlighting that some papers have higher relevance, importance or quality and are therefore both tweeted about more and cited more. The authors of this study are leading science communicators on Twitter from several life science disciplines, with substantially higher follower counts than the average scientist, making us uniquely placed to address this question. We conducted a three-year-long controlled experiment, randomly selecting five articles published in the same month and journal, and randomly tweeting one while retaining the others as controls. This process was repeated for 10 articles from each of 11 journals, recording Altmetric scores, number of tweets, and citation counts before and after tweeting. Randomization tests revealed that tweeted articles were downloaded 2.6–3.9 times more often than controls immediately after tweeting, and retained significantly higher Altmetric scores (+81%) and number of tweets (+105%) three years after tweeting. However, while some tweeted papers were cited more than their respective control papers published in the same journal and month, the overall increase in citation counts after three years (+7% for Web of Science and +12% for Google Scholar) was not statistically significant ( p > 0.15). Therefore while discussing science on social media has many professional and societal benefits (and has been a lot of fun), increasing the citation rate of a scientist’s papers is likely not among them.