A Program for Monitoring Biological Diversity in the Amazon: An Alternative Perspective to Threat-based Monitoring

Ferraz et al . (2008) indicated the need for planning in biological monitoring in the Amazon, and reviewed what they considered recent progress. The problems and solutions they discuss are well known, and it is unlikely that many biologists would disagree with most of them. However, the authors do not indicate that most of these issues are already being addressed in practice in the Amazon, especially by the Programa de Pesquisas em Biodiversidade (PPBio) of the Brazilian Ministry of Science and Technology. The only major issue about which we do not agree with Ferraz et al . (2008) relates to the design of monitoring programs. The authors recommended tailoring experimental designs to specific threats, which will result in a multitude of idiosyncratic designs and incompatible data sets. We suggest that generic broad-scale designs will allow us to answer more questions with greater confidence, because the investment in each location provides information about regional and global questions. Although specific designs may be more effective in some cases, there are insufficient resources available to install different research infrastructure for every possible threat.     

BPDA - A Bayesian peptide detection algorithm for mass spectrometry

Background  

Mass spectrometry (MS) is an essential analytical tool in proteomics. Many existing algorithms for peptide detection are based on isotope template matching and usually work at different charge states separately, making them ineffective to detect overlapping peptides and low abundance peptides.     

Expression of γ-H2AX and patient prognosis in breast cancer cohort

H2AX phosphorylation is a novel marker of DNA double-stranded breaks. In the present study, we assessed the γ-H2AX expression, its association with other clinicopathologic characteristics, and the prognosis in a cohort of 97 patients with breast cancer. Ninety-seven specimens of tumor tissue and 77 adjacent normal tissues from patients with breast cancer were examined. All patients underwent modified radical mastectomy or local tumor resection without lymph node dissection. γ-H2AX expression was assessed by standard immunohistochemistry. Patients were followed after surgery for a mean duration of 70.1 ± 18.7 months (range, 6-93 months). The γ-H2AX staining was positive in 27 (27.8%) patients. The positive rates of H2AX were 26.0% and 2.6% in tumor tissue and adjacent normal tissues, respectively. γ-H2AX positive status was negatively associated with TNM staging, with 24 positive cases (32.4%) in TNM staging I-II, while no positive cases in TNM staging III-IV (P = 0.026). Sixteen patients (16.5%) died during the follow-up. No significant association between γ-H2AX expression and patient survival was detected. The unadjusted HR (hazard ratio) for γ-H2AX positive was 0.84 (95% CI: 0.27, 2.60). In TNM staging subgroup analysis, death only occurred in γ-H2AX negative patients. Our study is the first study to demonstrate that expression of γ-H2AX is associated with TNM staging. Due to the small sample and limited follow-up time, we did not observe a significant association between γ-H2AX and patient survival. γ-H2AX expression could be a potential biomarker for cancer diagnosis and prediction, and further studies are in need.     

Alternative Complement Pathway Deregulation Is Correlated with Dengue Severity

Background

The complement system, a key component that links the innate and adaptive immune responses, has three pathways: the classical, lectin, and alternative pathways. In the present study, we have analyzed the levels of various complement components in blood samples from dengue fever (DF) and dengue hemorrhagic fever (DHF) patients and found that the level of complement activation is associated with disease severity.

Methods and Results

Patients with DHF had lower levels of complement factor 3 (C3; p = 0.002) and increased levels of C3a, C4a and C5a (p<0.0001) when compared to those with the less severe form, DF. There were no significant differences between DF and DHF patients in the levels of C1q, immunocomplexes (CIC-CIq) and CRP. However, small but statistically significant differences were detected in the levels of MBL. In contrast, the levels of two regulatory proteins of the alternative pathway varied widely between DF and DHF patients: DHF patients had higher levels of factor D (p = 0.01), which cleaves factor B to yield the active (C3bBb) C3 convertase, and lower levels of factor H (p = 0.03), which inactivates the (C3bBb) C3 convertase, than did DF patients. When we considered the levels of factors D and H together as an indicator of (C3bBb) C3 convertase regulation, we found that the plasma levels of these regulatory proteins in DHF patients favored the formation of the (C3bBb) C3 convertase, whereas its formation was inhibited in DF patients (p<0.0001).

Conclusion

The data suggest that an imbalance in the levels of regulatory factors D and H is associated with an abnormal regulation of complement activity in DHF patients.     

Is cross-validation valid for small-sample microarray classification?

MOTIVATION: Microarray classification typically possesses two striking attributes: (1) classifier design and error estimation are based on remarkably small samples and (2) cross-validation error estimation is employed in the majority of the papers. Thus, it is necessary to have a quantifiable understanding of the behavior of cross-validation in the context of very small samples. RESULTS: An extensive simulation study has been performed comparing cross-validation, resubstitution and bootstrap estimation for three popular classification rules-linear discriminant analysis, 3-nearest-neighbor and decision trees (CART)-using both synthetic and real breast-cancer patient data. Comparison is via the distribution of differences between the estimated and true errors. Various statistics for the deviation distribution have been computed: mean (for estimator bias), variance (for estimator precision), root-mean square error (for composition of bias and variance) and quartile ranges, including outlier behavior. In general, while cross-validation error estimation is much less biased than resubstitution, it displays excessive variance, which makes individual estimates unreliable for small samples. Bootstrap methods provide improved performance relative to variance, but at a high computational cost and often with increased bias (albeit, much less than with resubstitution).     

Is cross-validation better than resubstitution for ranking genes?

MOTIVATION: Ranking gene feature sets is a key issue for both phenotype classification, for instance, tumor classification in a DNA microarray experiment, and prediction in the context of genetic regulatory networks. Two broad methods are available to estimate the error (misclassification rate) of a classifier. Resubstitution fits a single classifier to the data, and applies this classifier in turn to each data observation. Cross-validation (in leave-one-out form) removes each observation in turn, constructs the classifier, and then computes whether this leave-one-out classifier correctly classifies the deleted observation. Resubstitution typically underestimates classifier error, severely so in many cases. Cross-validation has the advantage of producing an effectively unbiased error estimate, but the estimate is highly variable. In many applications it is not the misclassification rate per se that is of interest, but rather the construction of gene sets that have the potential to classify or predict. Hence, one needs to rank feature sets based on their performance. RESULTS: A model-based approach is used to compare the ranking performances of resubstitution and cross-validation for classification based on real-valued feature sets and for prediction in the context of probabilistic Boolean networks (PBNs). For classification, a Gaussian model is considered, along with classification via linear discriminant analysis and the 3-nearest-neighbor classification rule. Prediction is examined in the steady-distribution of a PBN. Three metrics are proposed to compare feature-set ranking based on error estimation with ranking based on the true error, which is known owing to the model-based approach. In all cases, resubstitution is competitive with cross-validation relative to ranking accuracy. This is in addition to the enormous savings in computation time afforded by resubstitution.     

Posters

Introduction  Fine needle aspiration (FNA) cytology of the thyroid is a well-established test in the clinical work-up of patients with solitary nodules of the thyroid. Thyroid FNA does however have limitations and audit of diagnostic performance is important.
Methods  The histopathology archives of the Royal Victoria Hospital were searched for all thyroid resections and the histopathological diagnosis was correlated with the pre-operative cytological diagnosis, where available. Special emphasis was placed on the accuracy of tumour diagnosis.
Results  A total of 173 cases were identified during the 2-year period, of these 93 had available pre-operative FNA. A total of 57 tumours were identified. A small number (six of 57) of significant discrepancies were identified. These included a malignant lymphoma diagnosed as Hashimoto's thyroiditis, a metastasis which the FNA had suggested was a medullary carcinoma and an insular carcinoma diagnosed as medullary carcinoma on FNA. False positives included a colloid cyst diagnosed as suspicious of malignancy and a cytological diagnosis of papillary carcinoma not confirmed on histology.
Discussion  At present, the majority of thyroid FNAs in our clinics are performed by surgeons and material is not routinely available for immunocytochemistry. In spite of these limitations, there were few major discrepancies. These might be reduced if pathologist aspirators were able to perform FNAs and collect material for further studies, where necessary. This would allow identification of medullary carcinomas and malignant lymphomas.
Conclusion  FNA of thyroid lesions is a useful investigation in our clinical setting, however, some areas of potential for improvement have been identified.     

From functional genomics to functional immunomics: new challenges, old problems, big rewards

The development of DNA microarray technology a decade ago led to the establishment of functional genomics as one of the most active and successful scientific disciplines today. With the ongoing development of immunomic microarray technology—a spatially addressable, large-scale technology for measurement of specific immunological response—the new challenge of functional immunomics is emerging, which bears similarities to but is also significantly different from functional genomics. Immunonic data has been successfully used to identify biological markers involved in autoimmune diseases, allergies, viral infections such as human immunodeficiency virus (HIV), influenza, diabetes, and responses to cancer vaccines. This review intends to provide a coherent vision of this nascent scientific field, and speculate on future research directions. We discuss at some length issues such as epitope prediction, immunomic microarray technology and its applications, and computation and statistical challenges related to functional immunomics. Based on the recent discovery of regulation mechanisms in T cell responses, we envision the use of immunomic microarrays as a tool for advances in systems biology of cellular immune responses, by means of immunomic regulatory network models.