Subsurface flow from hillslopes is widely recognized as an important contributor to streamflow generation; however, processes that control how and when hillslopes connect to streams remain unclear. ...We investigated stream and hillslope runoff dynamics through a wet‐up period in watershed 10 of the H. J. Andrews Experimental Forest in the western Cascades of Oregon where the riparian zone has been removed by debris flows. We examined the controls on hillslope‐stream connectivity on the basis of observations of hydrometric, stable isotope, and applied tracer responses and computed transit times for multiple runoff components for a series of storms during the wet‐up phase of the 2002–2003 winter rainy season. Hillslope discharge was distinctly threshold‐like with a near linear response and average quick flow ratio of 0.58 when antecedent rainfall was greater than 20 mm. Hillslope and stream stormflow varied temporally and showed strong hysteretic relationships. Event water mean transit times (8–34 h) and rapid breakthrough from applied hillslope tracer additions demonstrated that subsurface contributing areas extend far upslope during events. Despite rapid hillslope transport processes during events, soil water and runoff mean transit times during nonstorm conditions were greater than the time scale of storm events. Soil water mean transit times ranged between 10 and 25 days. Hillslope seepage and catchment base flow mean transit times were between 1 and 2 years. We describe a conceptual model that captures variable physical flow pathways, their synchronicity, threshold activation, hysteresis, and transit times through changing antecedent wetness conditions that illustrate the different stages of hillslope and stream connectivity.
B cell receptor (BCR) sequencing is a powerful tool for interrogating immune responses to infection and vaccination, but it provides limited information about the antigen specificity of the sequenced ...BCRs. Here, we present LIBRA-seq (linking B cell receptor to antigen specificity through sequencing), a technology for high-throughput mapping of paired heavy- and light-chain BCR sequences to their cognate antigen specificities. B cells are mixed with a panel of DNA-barcoded antigens so that both the antigen barcode(s) and BCR sequence are recovered via single-cell next-generation sequencing. Using LIBRA-seq, we mapped the antigen specificity of thousands of B cells from two HIV-infected subjects. The predicted specificities were confirmed for a number of HIV- and influenza-specific antibodies, including known and novel broadly neutralizing antibodies. LIBRA-seq will be an integral tool for antibody discovery and vaccine development efforts against a wide range of antigen targets.
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•LIBRA-seq: high-throughput mapping of BCR sequence to antigen specificity•Identified HIV- and influenza-specific B cells in two HIV-infected subjects•Predicted antigen reactivity for thousands of single B cells•Identified a previously unknown broadly neutralizing HIV antibody
LIBRA-seq enables high-throughput mapping of B cell receptor sequence to antigen specificity at the single-cell level.
Transit time is a fundamental catchment descriptor that reveals information about storage, flow pathways and source of water in a single characteristic. Given the importance of transit time, little ...guidance exists for the application of transit time modeling in complex catchment systems. This paper presents an evaluation and review of the transit time literature in the context of catchments and water transit time estimation. It is motivated by new and emerging interests in transit time estimation in catchment hydrology and the need to distinguish approaches and assumptions in groundwater applications from catchment applications. The review is focused on lumped parameter transit time modeling for water draining catchments and provides a critical analysis of unresolved issues when applied at the catchment-scale. These issues include: (1) input characterization, (2) recharge estimation, (3) data record length problems, (4) stream sampling issues, (5) selection of transit time distributions, and (6) model evaluation. The intent is to promote new advances in catchment hydrology by clarifying and formalizing the assumptions, limitations, and methodologies in applying transit time models to catchments.
Recognized clinical risk factors for progression (emergency presentation, poorly differentiated tumor, depth of tumor invasion, and adjacent organ involvement) are insufficient to identify those ...patients with stage II CRC at higher risk of disease progression 4,5. ...a meta-analysis aimed to assess the predictive ability of these signatures revealed that although gene expression signatures may be associated with prognosis, their ability to accurately predict patients’ risk of progression was limited, probably due to the molecular heterogeneity of tumors 7. ...the identification of new biomarkers to inform clinical decision-making for adjuvant chemotherapy is needed 8. Methods Patients and samples The discovery dataset (named ICO/CLX) included a previously described set of 100 patients with colon cancer diagnosed at stage II and MSS paired normal-tumor samples (Colonomics study, “CLX”: www.colonomics.org; NCBI BioProject PRJNA188510). Raw sequence data were filtered based on the TCRβ V, D, and J gene definitions provided by the international ImMunoGeneTics information system (IMGT) database and binned using a modified nearest-neighbor algorithm to merge closely related sequences and remove both PCR and sequencing errors.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily ...engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.
Time variant catchment transit time distributions are fundamental descriptors of catchment function but yet not fully understood, characterized, and modeled. Here we present a new approach for use ...with standard runoff and tracer data sets that is based on tracking of tracer and age information and time variant catchment mixing. Our new approach is able to deal with nonstationarity of flow paths and catchment mixing, and an irregular shape of the transit time distribution. The approach extracts information on catchment mixing from the stable isotope time series instead of prior assumptions of mixing or the shape of transit time distribution. We first demonstrate proof of concept of the approach with artificial data; the Nash‐Sutcliffe efficiencies in tracer and instantaneous transit times were >0.9. The model provides very accurate estimates of time variant transit times when the boundary conditions and fluxes are fully known. We then tested the model with real rainfall‐runoff flow and isotope tracer time series from the H.J. Andrews Watershed 10 (WS10) in Oregon. Model efficiencies were 0.37 for the 18O modeling for a 2 year time series; the efficiencies increased to 0.86 for the second year underlying the need of long time tracer time series with a long overlap of tracer input and output. The approach was able to determine time variant transit time of WS10 with field data and showed how it follows the storage dynamics and related changes in flow paths where wet periods with high flows resulted in clearly shorter transit times compared to dry low flow periods.
Key Points:
Approach for time variant catchment transit time
Modeling irregular shape of transit time distributions by time variant mixing
Modeling catchment transit time in WS10 of HJA Forest
Water transit time is now a standard measure in catchment hydrological and ecohydrological research. The last comprehensive review of transit time modeling approaches was published 15+ years ago. But ...since then the field has largely expanded with new data, theory and applications. Here, we review these new developments with focus on water‐age‐balance approaches and data‐based approaches. We discuss and compare methods including StorAge‐Selection functions, well/partially mixed compartments, water age tracking through spatially distributed models, direct transit time estimates from controlled experiments, young water fractions, and ensemble hydrograph separation. We unify some of the heterogeneity in the literature that has crept in with these many new approaches, in an attempt to clarify the key differences and similarities among them. Finally, we point to open questions in transit time research, including what we still need from theory, models, field work, and community practice.
Key Points
A synthesis of 15‐years of transit time research shows progression in how lumped approaches are used at catchment scales
That synthesis reveals open questions and emerging challenges in transit time research that are summarized here
Reduced diversity at Human Leukocyte Antigen (HLA) loci may adversely affect the host's ability to recognize tumor neoantigens and subsequently increase disease burden. We hypothesized that increased ...heterozygosity at HLA loci is associated with a reduced risk of developing colorectal cancer (CRC).
We imputed HLA class I and II four-digit alleles using genotype data from a population-based study of 5,406 cases and 4,635 controls from the Molecular Epidemiology of Colorectal Cancer Study (MECC). Heterozygosity at each HLA locus and the number of heterozygous genotypes at HLA class -I (
,
, and
) and HLA class -II loci (
,
, and
) were quantified. Logistic regression analysis was used to estimate the risk of CRC associated with HLA heterozygosity. Individuals with homozygous genotypes for all loci served as the reference category, and the analyses were adjusted for sex, age, genotyping platform, and ancestry. Further, we investigated associations between HLA diversity and tumor-associated T cell repertoire features, as measured by tumor infiltrating lymphocytes (TILs; N=2,839) and immunosequencing (N=2,357).
Individuals with all heterozygous genotypes at all three class I genes had a reduced odds of CRC (OR: 0.74; 95% CI: 0.56-0.97,
= 0.031). A similar association was observed for class II loci, with an OR of 0.75 (95% CI: 0.60-0.95,
= 0.016). For class-I and class-II combined, individuals with all heterozygous genotypes had significantly lower odds of developing CRC (OR: 0.66, 95% CI: 0.49-0.87,
= 0.004) than those with 0 or one heterozygous genotype. HLA class I and/or II diversity was associated with higher T cell receptor (TCR) abundance and lower TCR clonality, but results were not statistically significant.
Our findings support a heterozygote advantage for the HLA class-I and -II loci, indicating an important role for HLA genetic variability in the etiology of CRC.
To evaluate the contribution of germline genetics to regulating the briskness and diversity of T cell responses in CRC, we conducted a genome-wide association study to examine the associations ...between germline genetic variation and quantitative measures of T cell landscapes in 2,876 colorectal tumors from participants in the Molecular Epidemiology of Colorectal Cancer Study (MECC).
Germline DNA samples were genotyped and imputed using genome-wide arrays. Tumor DNA samples were extracted from paraffin blocks, and T cell receptor clonality and abundance were quantified by immunoSEQ (Adaptive Biotechnologies, Seattle, WA). Tumor infiltrating lymphocytes per high powered field (TILs/hpf) were scored by a gastrointestinal pathologist. Regression models were used to evaluate the associations between each variant and the three T-cell features, adjusting for sex, age, genotyping platform, and global ancestry. Three independent datasets were used for replication.
We identified a SNP (rs4918567) near RBM20 associated with clonality at a genome-wide significant threshold of 5 × 10
, with a consistent direction of association in both discovery and replication datasets. Expression quantitative trait (eQTL) analyses and in silico functional annotation for these loci provided insights into potential functional roles, including a statistically significant eQTL between the T allele at rs4918567 and higher expression of ADRA2A (P = 0.012) in healthy colon mucosa.
Our study suggests that germline genetic variation is associated with the quantity and diversity of adaptive immune responses in CRC. Further studies are warranted to replicate these findings in additional samples and to investigate functional genomic mechanisms.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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