Functional antibodies exhibit light chain coherence.

The vertebrate adaptive immune system modifies the genome of individual B cells to encode antibodies that bind particular antigens1. In most mammals, antibodies are composed of heavy and light chains that are generated sequentially by recombination of V, D (for heavy chains), J and C gene segments. Each chain contains three complementarity-determining regions (CDR1-CDR3), which contribute to antigen specificity. Certain heavy and light chains are preferred for particular antigens2-22. Here we consider pairs of B cells that share the same heavy chain V gene and CDRH3 amino acid sequence and were isolated from different donors, also known as public clonotypes23,24. We show that for naive antibodies (those not yet adapted to antigens), the probability that they use the same light chain V gene is around 10%, whereas for memory (functional) antibodies, it is around 80%, even if only one cell per clonotype is used. This property of functional antibodies is a phenomenon that we call light chain coherence. We also observe this phenomenon when similar heavy chains recur within a donor. Thus, although naive antibodies seem to recur by chance, the recurrence of functional antibodies reveals surprising constraint and determinism in the processes of V(D)J recombination and immune selection. For most functional antibodies, the heavy chain determines the light chain.

HyPhy 2.5-A Customizable Platform for Evolutionary Hypothesis Testing Using Phylogenies.

HYpothesis testing using PHYlogenies (HyPhy) is a scriptable, open-source package for fitting a broad range of evolutionary models to multiple sequence alignments, and for conducting subsequent parameter estimation and hypothesis testing, primarily in the maximum likelihood statistical framework. It has become a popular choice for characterizing various aspects of the evolutionary process: natural selection, evolutionary rates, recombination, and coevolution. The 2.5 release (available from www.hyphy.org) includes a completely re-engineered computational core and analysis library that introduces new classes of evolutionary models and statistical tests, delivers substantial performance and stability enhancements, improves usability, streamlines end-to-end analysis workflows, makes it easier to develop custom analyses, and is mostly backward compatible with previous HyPhy releases.

Pacific Biosciences Sequencing and IMGT/HighV-QUEST Analysis of Full-Length Single Chain Fragment Variable from an In Vivo Selected Phage-Display Combinatorial Library.

Phage-display selection of immunoglobulin (IG) or antibody single chain Fragment variable (scFv) from combinatorial libraries is widely used for identifying new antibodies for novel targets. Next-generation sequencing (NGS) has recently emerged as a new method for the high throughput characterization of IG and T cell receptor (TR) immune repertoires both in vivo and in vitro. However, challenges remain for the NGS sequencing of scFv from combinatorial libraries owing to the scFv length (>800 bp) and the presence of two variable domains [variable heavy (VH) and variable light (VL) for IG] associated by a peptide linker in a single chain. Here, we show that single-molecule real-time (SMRT) sequencing with the Pacific Biosciences RS II platform allows for the generation of full-length scFv reads obtained from an in vivo selection of scFv-phages in an animal model of atherosclerosis. We first amplified the DNA of the phagemid inserts from scFv-phages eluted from an aortic section at the third round of the in vivo selection. From this amplified DNA, 450,558 reads were obtained from 15 SMRT cells. Highly accurate circular consensus sequences from these reads were generated, filtered by quality and then analyzed by IMGT/HighV-QUEST with the functionality for scFv. Full-length scFv were identified and characterized in 348,659 reads. Full-length scFv sequencing is an absolute requirement for analyzing the associated VH and VL domains enriched during the in vivo panning rounds. In order to further validate the ability of SMRT sequencing to provide high quality, full-length scFv sequences, we tracked the reads of an scFv-phage clone P3 previously identified by biological assays and Sanger sequencing. Sixty P3 reads showed 100% identity with the full-length scFv of 767 bp, 53 of them covering the whole insert of 977 bp, which encompassed the primer sequences. The remaining seven reads were identical over a shortened length of 939 bp that excludes the vicinity of primers at both ends. Interestingly these reads were obtained from each of the 15 SMRT cells. Thus, the SMRT sequencing method and the IMGT/HighV-QUEST functionality for scFv provides a straightforward protocol for characterization of full-length scFv from combinatorial phage libraries.

IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform.

Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals--a cure and a vaccine--remain elusive. One of the fundamental challenges in accomplishing these goals is the tremendous genetic variability of the virus, with some genes differing at as many as 40% of nucleotide positions among circulating strains. Because of this, the genetic bases of many viral phenotypes, most notably the susceptibility to neutralization by a particular antibody, are difficult to identify computationally. Drawing upon open-source general-purpose machine learning algorithms and libraries, we have developed a software package IDEPI (IDentify EPItopes) for learning genotype-to-phenotype predictive models from sequences with known phenotypes. IDEPI can apply learned models to classify sequences of unknown phenotypes, and also identify specific sequence features which contribute to a particular phenotype. We demonstrate that IDEPI achieves performance similar to or better than that of previously published approaches on four well-studied problems: finding the epitopes of broadly neutralizing antibodies (bNab), determining coreceptor tropism of the virus, identifying compartment-specific genetic signatures of the virus, and deducing drug-resistance associated mutations. The cross-platform Python source code (released under the GPL 3.0 license), documentation, issue tracking, and a pre-configured virtual machine for IDEPI can be found at https://github.com/veg/idepi.

The global transmission network of HIV-1.

Human immunodeficiency virus type 1 (HIV-1) is pandemic, but its contemporary global transmission network has not been characterized. A better understanding of the properties and dynamics of this network is essential for surveillance, prevention, and eventual eradication of HIV. Here, we apply a simple and computationally efficient network-based approach to all publicly available HIV polymerase sequences in the global database, revealing a contemporary picture of the spread of HIV-1 within and between countries. This approach automatically recovered well-characterized transmission clusters and extended other clusters thought to be contained within a single country across international borders. In addition, previously undescribed transmission clusters were discovered. Together, these clusters represent all known modes of HIV transmission. The extent of international linkage revealed by our comprehensive approach demonstrates the need to consider the global diversity of HIV, even when describing local epidemics. Finally, the speed of this method allows for near-real-time surveillance of the pandemic's progression.

Dynamics of viral evolution and neutralizing antibody response after HIV-1 superinfection.

Investigating the incidence and prevalence of HIV-1 superinfection is challenging due to the complex dynamics of two infecting strains. The superinfecting strain can replace the initial strain, be transiently expressed, or persist along with the initial strain in distinct or in recombined forms. Various selective pressures influence these alternative scenarios in different HIV-1 coding regions. We hypothesized that the potency of the neutralizing antibody (NAb) response to autologous viruses would modulate viral dynamics in env following superinfection in a limited set of superinfection cases. HIV-1 env pyrosequencing data were generated from blood plasma collected from 7 individuals with evidence of superinfection. Viral variants within each patient were screened for recombination, and viral dynamics were evaluated using nucleotide diversity. NAb responses to autologous viruses were evaluated before and after superinfection. In 4 individuals, the superinfecting strain replaced the original strain. In 2 individuals, both initial and superinfecting strains continued to cocirculate. In the final individual, the surviving lineage was the product of interstrain recombination. NAb responses to autologous viruses that were detected within the first 2 years of HIV-1 infection were weak or absent for 6 of the 7 recently infected individuals at the time of and shortly following superinfection. These 6 individuals had detectable on-going viral replication of distinct superinfecting virus in the env coding region. In the remaining case, there was an early and strong autologous NAb response, which was associated with extensive recombination in env between initial and superinfecting strains. This extensive recombination made superinfection more difficult to identify and may explain why the detection of superinfection has typically been associated with low autologous NAb titers.

Prevention validation and accounting platform: a framework for establishing accountability and performance measures of substance abuse prevention programs.

The field of substance abuse prevention has neither an overarching conceptual framework nor a set of shared terminologies for establishing the accountability and performance outcome measures of substance abuse prevention services rendered. Hence, there is a wide gap between what we currently have as data on one hand and information that are required to meet the performance goals and accountability measures set by the Government Performance and Results Act of 1993 on the other. The task before us is: How can we establish the accountability and performance measures of substance abuse prevention programs and transform the field of prevention into prevention science? The intent of this volume is to serve that purpose and accelerate the processes of this transformation by identifying the requisite components of the transformation (i.e., theory, methodology, convention on terms, and data) and by introducing an open forum called, Prevention Validation and Accounting (PREVA) Platform. The entire PREVA Platform (for short, the Platform) is designed as an analytic framework, which is formulated by a collectivity of common concepts, terminologies, accounting units, protocols for counting the units, data elements, and operationalizations of various constructs, and other summary measures intended to bring about an efficient and effective measurement of process input, program capacity, process output, performance outcome, and societal impact of substance abuse prevention programs. The measurement units and summary data elements are designed to be measured across time and across jurisdictions, i.e., from local to regional to state to national levels. In the Platform, the process input is captured by two dimensions of time and capital. Time is conceptualized in terms of service delivery time and time spent for research and development. Capital is measured by the monies expended for the delivery of program activities during a fiscal or reporting period. Program capacity is captured by fourteen measurement units, tapping into the dimensions of staff resources and community assets. Staff resources are, in turn, operationalized in terms of staff size, staff certification status, staff turnover rate, and the accreditation status of a provider agency. Community assets are operationalized by the number of community centers accessible to the funded agency, number of formalized teams or antidrug coalitions active in the catchment area, and other social/human services providers with whom the prevention agency has formalized networks. The totality of process output from all sources of program activities is reduced to eighteen classes of measures. These are operationalized by thirty-three summary measures. Some of these include: total count of events facilitated; total number of clients served; average number of clients served per event; clients served by single and multiple program sessions; classification of target population in terms of the severity of risk as defined by the Institute of Medicine; age groups and race/ethnicity of clients served; number of program participants retained by recurring programs; number of clients who have completed the program; penetration rates to the target population; client attrition rates; average referral rates per provider per time interval; referral success rates; and so on. All process output measures specified in the Platform are derived from two broad classes of events classified as either products or services. The collectivity of these measures is expected to present a cost-effective, parsimonious, yet comprehensive picture of the entire spectrum of the process output, i.e., "what came out of the program as program activities". For the measurement of performance outcomes, two types of data are incorporated into the Platform: outcome data from individuals and the behavior (or performance) of social indicators from aggregated data bases. Individual data are used to evaluate the outcome of substance abuse programs

Algorithms for resource allocation of substance abuse prevention funds based on composite risk-factor index score: a case study on state of Florida--Part 2.

The purpose of Part 2 is to develop a model for resource allocation of state prevention funds to be distributed to its substate jurisdictions based on the relative need for prevention services measured in terms of composite risk-factor index (COMRISK) scores computed for each county. The risk factors are extracted from an extensive review of risk and protective factors addressed in the prevention literature. Based on twenty-two risk and protective factors identified, we were able to explain 71.3 percent of the total variation in student drug using behavior observed at the individual level. By aggregating individual COMRISK scores to the county level, we were able to determine aggregated COMRISK index scores at the county level. By determining the proportion of each county's share of the total statewide COMRISK and by weighting the latter proportion by the population size of each county, we have devised Prevention Needs Index (PNI) score based on the risks for each county. Finally, the county's share of PNI score as a proportion of the total statewide PNI score is computed. The latter proportion is then multiplied by the total amount of prevention resources available at the state. In this way, we were able to develop an alternative resource allocation model solely based on risk and protective factors for determining prevention needs of each county, independent of composite index score of drug use (COMDRUG) presented in Part 1. A comparison of three models for resource allocation has shown a significant amount of similarity of the total funds computed for each county. Accordingly, no preference is made among the resource allocation models suggested, although it is emphasized that the final decision concerning the level of funding must be made on the selection of the resource allocation algorithms rather than the suggested amount of funding computed for each county.

Algorithms for resource allocation of substance abuse prevention funds based on the estimated need: a case study on state of Florida--Part 1.

The purposes of Parts 1-3 of this article is to develop a framework for county-based prevention resource allocation algorithms based on the aggregated need for substance abuse prevention services estimated at the county level. The development of these algorithms is founded upon two databases: statewide student drug survey and a set of social indicators routinely collected and published by various agencies of the state of Florida. The resource allocation models are devised by developing several indices of prevention needs that are conceptualized in terms of: 1) county-based composite drug use index (COMDRUG), 2) the definitions of prevention target populations as envisioned by the Institute of Medicine (IOM), 3) composite risk-factor index score, and 4) a set of social indicators that are empirically related to COMDRUG observed at the county level. The first three models are based on the prevention needs estimated from the statewide student survey on substance abuse. The social indicator model, however, is presented as an alternative resource allocation model which may be used in lieu of or in the absence of statewide survey. The resource allocation algorithms found on these four conceptualizations are thought to be more equitable and appropriate to the prevention needs of various communities than may be contrived otherwise. Due to a significant amount of information leading to the development of these models, Part 1 of this series is devoted to the following three topics: 1) sampling method used, 2) poststratification weighting methods used to estimate county-based COMDRUG, and 3) the development of resource allocation models based on COMDRUG and the IOM definitions of prevention target populations.

Toward a new paradigm in substance abuse and other problem behavior prevention for youth: youth development and empowerment approach.

The purpose of this article is to 1) address a paradigm shift taking place in the field of substance abuse prevention directed for youth and 2) to introduce an innovative approach to substance abuse and other problem behavior prevention that reflects this shift in prevention paradigm. The new path introduced is youth development and empowerment (YD&E) approach. In order to establish a conceptual foundation for this approach, this article 3) reviews the theoretical advances made in the field of substance abuse prevention during the last three decades. This is followed by a conceptualization of the processes of implementing the YD&E program by 4) specifying the mechanism used for the empowering processes and by 5) identifying the structural components of the youth empowerment model that serve the empowering processes. It is hoped that this article serves as a conduit for an improved approach to adolescent substance abuse prevention and youth development that goes beyond, rather than against, the traditional risk-factor approach. In this new approach, youths are viewed as assets and resources to our community rather than social problems or community liabilities. The organizing concept of this new paradigm is: social, economic, and public opportunity denied to youth is equal to social problems imposed on youth by adults.

Algorithms for resource allocation of substance abuse prevention funds based on social indicators: a case study on state of Florida--Part 3.

The purpose of Part 3 is to develop an algorithm for an equitable distribution of state prevention funds to its substate jurisdictions based on the need for prevention services. In this series, the need for prevention services is measured in terms of the existing social indicators observed at the county level. In order to establish a conceptual link as well as the empirical relevance of the selected social indicators as proxy measurements of the estimated need for prevention at the county level, we have employed both concurrent and construct validity tests using the following three constructs as the criterion variables in a multiple regressing setting: 1) county-based composite drug use index score (COMDRUG) measured via the statewide drug survey; 2) county-based proportions of prevention target populations using the conceptual definition advanced by the Institute of Medicine (IOM); and 3) the composite risk factor score (COMRISK) assembled from a list of twenty-two risk and protective factors observed for each county. These constructs were identified previously in Parts 1 and 2. While employing eight social indicators to estimate the overall prevention needs observed at the county level, the social indicators thus selected were able to explain 69 percent of the variations in COMDRUG, 68 percent of the variation in the proportions of students in need of prevention services using IOM definition, and 60 percent of the variation in COMRISK. Following successful validations of the social indicators as viable media with which to estimate county-based prevention needs, the ensuing multiple regression equation is, then, used to build a resource allocation model by determining the proportion of each county's share of the total statewide COMDRUG-predicted from the social indicators and, then, by weighting the latter proportion by the population size of each county under age eighteen. In this way, we have devised county-based Prevention Needs Index (PNI) scores based solely on social indicators. Finally, the county's share of PNI score is computed as a proportion of to the total statewide PNI score. Following this line of algorithm for resource allocation, we were able to develop yet another resource allocation model solely based on social indicators without the benefits of survey data. Comparing the funding results originating from four resource allocation models (i.e., COMDRUG, IOM Definition, COMRISK, and Social Indicators), it has been learned that there is a remarkable similarity from one funding level to another. Since all four schedules of county-based prevention funding levels have shown very high intercorrelations with a range from .9862 to .9993, it has been determined that these schedules are measuring essentially either the same domain or latent domains that are functionally equivalent to one another. Accordingly, no preference is made among the resource allocation models suggested, although it is suggested that the final decision on the level of funding must be based on the selection of the schedule for resource allocation rather than the suggested amount or level of funding computed for each county.

Benefit-cost analysis of drug abuse prevention programs: a macroscopic approach.

To date, benefit-cost analysis has rarely been used to justify the drug abuse prevention field. However, there is an increasing demand for this type of analysis as the field of substance abuse prevention enters a new phase--a phase characterized by a competitive marketplace, an increased demand for accountability, and the desire to measure return on the money invested in prevention. In response, an effort is made to stimulate discussion and further research on the topic. This article first determines the overall strategy for initiating benefit-cost analysis (BCA), followed by definitions of BCA and cost-effectiveness analysis (CEA). This is followed by the determination of some of the major variables used in BCA along with the algorithm for determining the benefit-cost efficiency ratio (R) as it applies to the macro level analysis. In estimating a value for the R, a decision has been made to incorporate uncertainty into the BCA. In a macroscopic approach to BCA, four independent variables are identified for computing R. These independent and dependent variables are assumed to be random variables with normal distributions. The population means and standard deviations pertaining to these independent variables are estimated from the existing literature. In order to incorporate uncertainty into the computation of R, ten measurements have been randomly selected for each of the four independent variables. Following this procedure, fifteen benefit-cost efficiency ratios are calculated by selecting one of the ten values at random per variable used in the R equation. In this way, we were able to determine the most likely population benefit-cost efficiency ratio of 15:1, indicating that there is a $15 savings on every dollar spent on drug abuse prevention. The 95 percent confidence level pertaining to the R has an interval from $13.7 to $16.1. This indicates that the population R resides within the range 95 percent of the time.

The orientation and direction selectivity of cells in macaque visual cortex.

Quantitative data are presented on the orientation and direction specificity of the responses of cells in macaque monkey striate cortex. There is a bimodal distribution of direction-specific and nondirection-specific cells, with similar orientation tuning in each class. Cells range in orientation bandwidth at half amplitude from 6 degrees to 360 degrees (i.e. no orientation tuning), with a median near 40 degrees. Foveal-parafoveal and simple-complex subsamples show similar ranges of orientation bandwidths as well as similar medians (the bandwidths being somewhat broader than those found in cat cortex). The foveal subsample and a high-spatial-frequency subsample have more horizontal and vertical optimal orientations than oblique ones. Most cells show inhibition to some orientations, as well as excitation to others. Minimum-response orientations are generally less than 90 degrees from the optimal orientation--indicating maximum inhibition adjacent to the excitatory orientations. Three simple receptive field models are shown to differ in their abilities to account for these results.

Responses of macaque lateral geniculate cells to luminance and color figures.

The spatial tuning of macaque lateral geniculate neurones was compared for luminance-based and color-based lines. Lines of various widths were flashed on and centered on the cell's receptive field, and the size of the increase or decrease in firing was noted. Luminance-based lines consisted of 0.7 log unit increments or decrements. Color-based lines consisted of shifts in wavelength with no change in luminance, e.g., from a red field to a green line on a red field. The cell fired most to intermediate widths of luminance-based lines, but to the widest pure-color lines.

Brightness contrast effects in monkey lateral geniculate nucleus.

Brightness contrast effects shown by single cells in the macaque's lateral geniculate nucleus were studied with black and white lines of various widths, consisting of either: (1) "simultaneous contrast" stimuli in which the line was produced by luminance changes in the flanking areas or (2) "successive contrast" stimuli in which the line itself changed in luminance. Line widths that gave optimal responses and response magnitudes themselves were similar for the two types of stimulus, except for the widest lines used (2 degrees). Thus, simultaneous brightness contrast is a primary determinant of the response of primate LGN cells but only within 2 degrees of the center of the receptive field. Neural processing up to this level cannot therefore explain the long distance effects of simultaneous brightness contrast in human perception.

Color: a motion-contingent aftereffect.

After human observers alternately view green stripes moving up and red stripes moving down for periods of 1/2 to 4 hours, they see a pink aftereffect when white stripes move up and a green aftereffect when white stripes move down. Longer exposures produce aftereffects which are visible 20 hours after stimulation. Thus, experience which pairs simple attributes (color and motion) of visual stimulation can result in a lasting modification of perception.