Digital Surveillance: A Novel Approach to Monitoring the Illegal Wildlife Trade

A dearth of information obscures the true scale of the global illegal trade in wildlife. Herein, we introduce an automated web crawling surveillance system developed to monitor reports on illegally traded wildlife. A resource for enforcement officials as well as the general public, the freely available website, http://www.healthmap.org/wildlifetrade, provides a customizable visualization of worldwide reports on interceptions of illegally traded wildlife and wildlife products. From August 1, 2010 to July 31, 2011, publicly available English language illegal wildlife trade reports from official and unofficial sources were collected and categorized by location and species involved. During this interval, 858 illegal wildlife trade reports were collected from 89 countries. Countries with the highest number of reports included India (n = 146, 15.6%), the United States (n = 143, 15.3%), South Africa (n = 75, 8.0%), China (n = 41, 4.4%), and Vietnam (n = 37, 4.0%). Species reported as traded or poached included elephants (n = 107, 12.5%), rhinoceros (n = 103, 12.0%), tigers (n = 68, 7.9%), leopards (n = 54, 6.3%), and pangolins (n = 45, 5.2%). The use of unofficial data sources, such as online news sites and social networks, to collect information on international wildlife trade augments traditional approaches drawing on official reporting and presents a novel source of intelligence with which to monitor and collect news in support of enforcement against this threat to wildlife conservation worldwide.     

The Human Ecology of World Systems in East Africa: The Impact of the Ivory Trade

The impact on human ecology of the ivory trade entailed direct and indirect effects. First, the reduction or extermination of elephant populations had direct effects on the vegetation patterns over large areas. Second, the economic activities connected with hunting, transport, and trading affected regional systems of exchange and thereby, indirectly through the political economy, settlements, patterns of resource utilization, population parameters, and specialization of production. Ethnohistorical information from the 1800s suggests how coastal goods interacted with regional systems of exchange and environmental exploitation. Although such information cannot be directly projected onto the more distant past, it can be used to establish some possible pathways through which the hunting of elephants and transportation and trade of ivory could have affected the ecology of human resource use.     

Dissecting membrane protein architecture: An annotation of structural complexity

α‐Helical membrane proteins exist in an anisotropic environment which strongly influences their folding, stability, and architecture, which is far more complex than a simple bundle of transmembrane helices, notably due to helix deformations, prosthetic groups and extramembrane structures. However, the role and the distribution of such heterogeneity in the supra molecular organization of membrane proteins remains poorly investigated. Using a nonredundant subset of α‐helical membrane proteins, we have annotated and analyze the statistics of several types of new elements such as incomplete helices, intramembrane loops, helical extensions of helical transmembrane domains, extracellular loops, and helices lying parallel to the membrane surface. The relevance of the annotation scheme was studied using residue composition, statistics, physical chemistry, and symmetry of their distribution in relation to the immediate membrane environment. Calculation of hydrophobicity using different scales show that different structural elements appear to have affinities coherent with their position in the membrane. Examination of the annotation scheme suggests that there is considerable information content in the amino acid compositions of the different elements suggesting that it might be useful for structural prediction. More importantly, the proposed annotation will help to decipher the complex hierarchy of interactions involved in membrane protein architecture. © 2009 Wiley Periodicals, Inc. Biopolymers 91: 815–829, 2009. This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com     

An examination of the phenol-croton oil peel: Part I. Dissecting the formula

This article investigates which ingredients are the active ones in the most popular peel formula. The benefits of the "phenol" peel have been attributed to the effects of phenol on the dermis. Baker published a simple peel formula in 1962 that became a classic that has been used since by almost all plastic surgeons and dermatologists. Brown et al., in 1960, passed along a set of dogmas: (1) phenol is the active ingredient; (2) phenol peels more deeply in lower concentrations; and (3) adding a surface tension-lowering agent increases the peel. This article seeks to dissect the Baker formula by removing the croton oil. A patient was peeled serially with 18% phenol, 35% phenol, and 50% phenol solutions containing Septisol (surface tension-lowering agent) but no croton oil. This showed that increasing concentrations of phenol caused more clinical tissue reaction as evidenced by edema and erythema, but no significant dermal injury was seen. USP 88% phenol without Septisol did cause injury to the dermis. To test the effect of croton oil in the formula, the patient's face was peeled with two variations: the perioral area was peeled with 50% phenol to which croton oil was added to a strength of 2.1% and the remainder with 50% phenol without croton oil. The perioral area showed vesiculation, slough, and dermal exposure characteristic of a deep peel requiring 11 days to heal. The remainder of the face treated with 50% phenol without croton oil showed only edema and erythema without significant dermal injury. This experiment shows that the main postulates of Brown et al.--that phenol in lesser concentrations peels more than in higher concentrations and that phenol is the sole agent--are not true. In a fourth peel, a 0.7% concentration of croton oil in 50% phenol was applied to the parts of the face not peeled with croton oil in the third peel. The areas peeled with 50% phenol with 0.7% croton oil healed in 7 days, whereas the treatment with 50% phenol with 2.1% croton oil required 11 days. Deconstructing the Baker formula reveals fallacies in the four-decade-long belief system regarding these peels. The serial peels performed in this study show that increasing concentrations of phenol without croton oil cause increasing skin reaction but insignificant peeling effect. The addition of croton oil to 50% phenol, however, causes a marked increase in the depth of peeling into the dermis. Lowering the concentration of croton oil caused a lesser burn, as evidenced by fewer days to heal. The depth of the peel, therefore, seems to be more dependent on the concentration of croton oil than phenol. This will be further explored in Parts II, III, and IV.     

Peptide-Centric Proteome Analysis: An Alternative Strategy for the Analysis of Tandem Mass Spectrometry Data

In mass spectrometry-based bottom-up proteomics, data-independent acquisition is an emerging technique because of its comprehensive and unbiased sampling of precursor ions. However, current data-independent acquisition methods use wide precursor isolation windows, resulting in cofragmentation and complex mixture spectra. Thus, conventional database searching tools that identify peptides by interpreting individual tandem MS spectra are inherently limited in analyzing data-independent acquisition data. Here we discuss an alternative approach, peptide-centric analysis, which tests directly for the presence and absence of query peptides. We discuss how peptide-centric analysis resolves some limitations of traditional spectrum-centric analysis, and we outline the unique characteristics of peptide-centric analysis in general.Tandem mass spectrometry has become the technology of choice for proteome characterization. In a typical bottom-up proteomic experiment, a mixture of proteins is proteolytically digested into peptides, separated by liquid chromatography, and analyzed using tandem mass spectrometry. The ultimate goal is to identify and quantify proteins by detecting and quantifying individual peptides, thereby shedding light on the underlying cellular mechanisms or phenotype. Several modes of data acquisition have been developed for bottom-up proteomics. The most commonly applied mode uses data-dependent acquisition (DDA)¹, in which tandem MS (MS/MS) spectra are acquired from the dissociation of precursor ions selected from an MS survey spectrum. Constrained by the speed of instrumentation, DDA can sample only a subset of precursor ions for MS/MS characterization, generally targeting the top-N most abundant ions detected in the most recent survey spectrum. In addition, DDA is typically coupled with a method referred to as “dynamic exclusion” (1) that attempts to prevent reselection of the same m/z for some specified period of time. These acquisition strategies greatly increase proteome coverage and extend the dynamic range of detection for shotgun proteomics. The resulting MS/MS spectra are typically analyzed using sequence database searching software such as SEQUEST, Mascot, X!Tandem, MaxQuant, Comet, MS-GF+, or OMSSA (2–8). Because these algorithms identify peptides by first associating each individual spectrum with a matching peptide sequence and then aggregating the thus matched spectra into a list of identified peptides, we refer to them as “spectrum-centric analyses.” In spectrum-centric analysis, spectra are most commonly interpreted using database searching, but can also be interpreted using de novo sequencing (9–11), or by searching against a spectrum library (12–14). For the past two decades, spectrum-centric analysis has been an essential driving force for the development of large-scale shotgun proteomics using DDA.DDA is a powerful and well-established technique for LC-MS/MS data acquisition. By targeting precursor ions observed in MS survey scans with highly selective MS/MS scans, DDA generates a large set of high quality MS/MS spectra, which can be automatically interpreted by database searching to identify thousands of proteins in a complex sample. When DDA was introduced, instrumentation was not fast enough to sample every observed precursor in the survey scan; thus, high-intensity precursors were preferentially targeted because they tend to generate higher quality MS/MS spectra that lead to peptide identification. Although this stochastic sampling approach results in a large amount of peptide identification in a single sample run, it comes at the cost of reproducibility of MS/MS acquisition between sample analyses and an inherent bias against low abundance analytes that are less likely to be sampled (15). On modern instrumentation, the speed of MS/MS acquisition has dramatically improved to the point where the majority of MS precursors that are not already in the dynamic exclusion list can be sampled for MS/MS analysis. However, even if every precursor observed in each survey MS scan is sampled, DDA is still biased against low abundance analytes that fall below the limit of detection in the MS analysis and will never be sampled. This bias is a practical limitation in the analysis of a complex mixture with high dynamic range in which many analytes will be below the limit of detection in MS analysis, but remain detectable by the more selective and more sensitive MS/MS analysis (16).DDA remains a powerful method for identifying a large number of proteins in a sample. However, because of the incomplete sampling, when a peptide is not identified in a conventional shotgun experiment using DDA, it is incorrect to conclude that the peptide is missing from the sample, or even below the limit of detection of MS/MS, because the peptide ions may have never been sampled for MS/MS analysis. To overcome such limitations, targeted acquisition approaches such as selected reaction monitoring (SRM, also commonly referred to as multiple reaction monitoring) are often the methods of choice. In targeted acquisition approaches, a set of predetermined precursor ions are systematically subjected to MS/MS characterization throughout the LC time domain. The collision energy for each targeted ion can be optimized for fragmentation efficiency. The resulting data are typically analyzed using “targeted analysis” (17–19), in which the software looks for the co-eluting patterns from a group of predetermined pairs of precursor–product ions (called transitions). With systematic MS/MS sampling and the combined specificity of chromatographic retention time, precursor ion mass, and the distribution of product ions, targeted acquisition allows highly sensitive and reproducible detection of the targeted analytes within a complex mixture.Modern targeted acquisition approaches are the gold standard for sensitively and reproducibly measuring hundreds of peptides in a single LC-MS/MS run (20–22). However, data acquired in this manner is only informative for the set of peptides targeted for analysis. Because of this narrow focus, iterative testing of different hypotheses (i.e. a different set of target peptides) also requires iterative acquisition of additional data. Moreover, assay development often requires retention time scheduling and/or refinement steps to find the optimal peptides and transitions for testing a particular hypothesis.With the existence of two complementary but distinct approaches—DDA for broad sample characterization and targeted acquisition for interrogation of a specific hypothesis—the natural question is if the benefits of both techniques may be combined in a single technique. A potential solution is an alternative mode of bottom-up proteomics referred to as data-independent acquisition (DIA) that has been described and realized with various implementations (16, 23–32). In DIA, the instrument acquires MS/MS spectra systematically and independently from the content of MS survey spectra. These DIA approaches differ from DDA methods, targeted acquisition methods, and from each other in MS/MS isolation window width, total range of precursor m/z covered, duration of completing one cycle of isolation scheme (called the cycle time), single or multiple isolation windows per MS/MS analysis, and instrument platform. Because of the benefits of systematic sampling of the precursor m/z range by MS/MS, data from a single DIA experiment can be useful for both peptide detection and quantification in a complex mixture. Similar to DDA approaches, DIA data is broadly informative because the MS/MS characterization is not specific to a predefined set of peptide targets. Similar to targeted approaches, MS/MS information of peptides across the entire LC time domain can be extracted from DIA data to test a particular hypothesis. As the acquisition speed of modern instrumentation continues to increase, DIA has become more popular because of its comprehensive and unbiased sampling.Although DIA resolves the problem of biased or incomplete MS/MS sampling, current DIA methods come with compromises (33), where the most common compromise is precursor selectivity. Constrained by the speed and accuracy of instrumentation, DIA methods typically use five- to 10-fold wider isolation windows compared with DDA to achieve the same breadth and depth of a single LC-MS/MS run. Because of this reduction in precursor selectivity, MS/MS spectra from DIA are noisier than DDA spectra. In particular, DIA by design generates mixture spectra, each containing product ions from multiple analytes with various abundance and different charge states. Fragmenting multiple analytes together also precludes DIA from tailoring collision energy for every analyte, a standard optimization in DDA and targeted acquisition.The low precursor selectivity and resulting complexity of DIA spectra severely challenges the performance of traditional spectrum-centric analysis, which generally assumes that the detected product ions were derived from a single, isolated precursor. The major challenges in interpreting mixture spectra lie in allowing for multiple contributing precursor ions, assessing the dynamic range of mixture peptides, distributing intensities of product ions shared by contributing peptides, and adjusting statistical confidence estimates. Because almost every spectrum is mixed in DIA data, it is poorly suited for analysis by classic spectrum-centric approaches initially designed for DDA data. Some sophisticated spectrum-centric approaches (34–39) address these challenges by deconvolving mixture spectra into pseudo spectra or by matching mixture spectra to combinations of product ions from multiple candidate peptides. However, identification of low abundance analytes by interpreting mixture spectra is inherently difficult because the MS/MS signals from low abundance analytes are naturally overwhelmed by the signals from high abundance ones.Recently, Gillet et al. demonstrated an alternative approach that analyzes DIA data in a targeted fashion (24), opening a new door for the investigation of tandem mass spectrometry data. Much like targeted analysis of transitions used in targeted acquisition methods, Gillet et al. use extracted ion chromatograms to detect and quantify query peptides. Similarly, Weisbrod et al. identify peptides by searching peptide fragmentation patterns against DIA data (25). Instead of interpreting individual spectra in a spectrum-centric fashion, these alternative approaches take each peptide of interest and ask: “Is this peptide detected in the data?” We refer to this approach as “peptide-centric analysis” in contrast with “spectrum-centric analysis.” In peptide-centric analysis, each peptide is detected by searching the MS and MS/MS data for signals selective for the query peptide. Peptide-centric analysis covers all methods that use peptides as an independent query unit, including but not limited to the targeted analysis. Peptide-centric analysis is intrinsically very different from spectrum-centric analysis (Fig. 1) and better suited for addressing many biological problems. This perspective discusses the analytical advantages of peptide-centric analysis and how they could translate to improvements in protein inference, and the analysis of DIA data.

Table I

Analytical comparison of spectrum-centric analysis versus peptide-centric analysis

An Analysis of Variance of Nominal Data

This paper considers a common problem in analysis of variance where the responses to a set of treatments are nominal (i.e. are recorded in frequencies) with no underlying metric. Reasoning by analogy from standard analysis of variance of a two-way classification we develop chi-square tests for significance of treatments and interactions. Two tests are proposed for interaction and their asymptotic properties are studied.     

An Approximate Analysis of Incomplete Ranked Data

An approximation for the probability of the rank order of k independent random variables is used to analyze incomplete ranked data, consisting of rankings of objects by judges. The approximation allows linear models to be fitted to the data so that differences amongst groups of judges can be investigated, and factorial models applied to investigate differences amongst the objects.     

Dissecting autophagosome formation: the missing pieces

The formation of autophagosomes is the central part of the macroautophagy pathway. Little is known, however, about how the participants in this process affect the membrane dynamics at the phagophore assembly site (PAS). Recently, we demonstrated that Atg8, a lipid-conjugated ubiquitin-like protein, controls the expansion of the phagophore. In addition, we showed that the autophagosome formation process can be traced and dissected by time-lapse fluorescence microscopy observation of GFP-Atg8. These findings constitute one step further in our understanding of autophagosome formation. Key questions remain open, however, on how the actions of other proteins at the PAS are coordinated with that of Atg8 and on the precise role of Atg8.     

Using Ari Kara Osteological Data To Evaluate An Assumpti on oF Fur Trade Archaeology

Abstract

Osteological data from the Arikara-affiliated Mobridge site in South Dakota are used to test an hypothesis frequently expressed in archaeology that the relative quantity of recovered Euro-American goods can be used to infer a site? s relative age. Crania from the Mobridge site are examined for intra-site differences and thecompared with crania from four other sites. Our analysis suggests that the dating assumption may have validity for some archaeological sites, particularly those associated with the Arikara, and may even be valid on the intra-site level as demonstrated by our analysis of the Mobridge data     

Patterns of Information-Seeking for Cancer on the Internet: An Analysis of Real World Data

Although traditionally the primary information sources for cancer patients have been the treating medical team, patients and their relatives increasingly turn to the Internet, though this source may be misleading and confusing. We assess Internet searching patterns to understand the information needs of cancer patients and their acquaintances, as well as to discern their underlying psychological states. We screened 232,681 anonymous users who initiated cancer-specific queries on the Yahoo Web search engine over three months, and selected for study users with high levels of interest in this topic. Searches were partitioned by expected survival for the disease being searched. We compared the search patterns of anonymous users and their contacts. Users seeking information on aggressive malignancies exhibited shorter search periods, focusing on disease- and treatment-related information. Users seeking knowledge regarding more indolent tumors searched for longer periods, alternated between different subjects, and demonstrated a high interest in topics such as support groups. Acquaintances searched for longer periods than the proband user when seeking information on aggressive (compared to indolent) cancers. Information needs can be modeled as transitioning between five discrete states, each with a unique signature representing the type of information of interest to the user. Thus, early phases of information-seeking for cancer follow a specific dynamic pattern. Areas of interest are disease dependent and vary between probands and their contacts. These patterns can be used by physicians and medical Web site authors to tailor information to the needs of patients and family members.