Diagnosing benign and malignant lesions in breast tissue sections by using IR-microspectroscopy

The collection of IR spectra through microscope optics and the visualization of the IR data by IR imaging represent a visualization approach, which uses infrared spectral features as a native intrinsic contrast mechanism. To illustrate the potential of this spectroscopic methodology in breast cancer research, we have acquired IR-microspectroscopic data from benign and malignant lesions in breast tissue sections by point microscopy with spot sizes of 30-40 μm. Four classes of distinct breast tissue spectra were defined and stored in the data base: fibroadenoma (a total of 1175 spectra from 14 patients), ductal carcinoma in situ (a total of 1349 spectra from 8 patients), connective tissue (a total of 464 spectra), and adipose tissue (a total of 146 spectra). Artifical neural network analysis, a supervised pattern recognition method, was used to develop an automated classifier to separate the four classes. After training the artifical neural network classifier, infrared spectra of independent external validation data sets (“unknown spectra”) were analyzed. In this way, all spectra (a total of 386) taken from micro areas inside the epithelium of fibroadenomas from 4 patients were correctly classified. Out of the 421 spectra taken from micro areas of the in situ component of invasive ductal carcinomas of 3 patients, 93% were correctly identified. Based on these results, the potential of the IR-microspectroscopic approach for diagnosing breast tissue lesions is discussed.     

Performance comparison of different interpretative algorithms utilized to derive sleep parameters from wrist actigraphy data

ABSTRACT

We compared performance of four popular interpretative algorithms (IAs), i.e., Cole–Kripke, Rescored Cole–Kripke, Sadeh, and UCSD, utilized to derive sleep parameters from wrist actigraphy data. We conducted in-home sleep study of 40 healthy adults (17 female/23 male; age 26.7 ± 12.1 years), assessing sleep variables both by Motionlogger® Micro Watch Actigraphy (MMWA) and Zmachine® Insight+ electroencephalography (EEG). Data of MMWA were separately scored per 30 sec epochs by each of the four popular IAs, and data of the Zmachine were also scored per 30 sec epochs by its proprietary IA. In reference to the EEG Zmachine method, all four of the MMWA algorithms showed high (~94 to 98%) sensitivity and moderate (~42 to 54%) specificity in detecting Sleep epochs. All of them significantly underestimated Sleep Onset Latency (SOL: ~9 to 20 min), and all of them, except the Sadeh IA, significantly underestimated Wake After Sleep Onset (WASO: ~22 to 25 min) and overestimated Total Sleep Time (TST: ~32 to 45 min) and Sleep Efficiency (SE: ~7 to 9%). The Sadeh IA showed significantly smaller bias than the other three IAs in deriving WASO, TST, and SE. Overall, application of ‘Rescoring Rules’ improved performance of the Cole–Kripke IA. The Sadeh and Rescored Cole–Kripke IAs exhibited highest agreement with the EEG Zmachine method (Cohen’s Kappa: ~51%), while the UCSD IA exhibited lowest agreement (Cohen’s kappa: ~47%). However, minimum detectable change across all sleep parameters was smallest with use of the UCSD IA and, except for SOL, largest with use of the Sadeh algorithm. Findings of this study indicate the Sadeh IA is most appropriate for deriving sleep parameters of healthy adults, while the UCSD IA is most appropriate for evaluating change in sleep parameters over time or in response to medical intervention.     

Attention, the presolution period, and choice accuracy in pigeons

Six pigeons were trained first on a color then on a form discrimination; four other pigeons were trained first on form and then on color. One of two colors or one of two forms (sample stimuli) appeared in the center of a touch sensitive monitor for 5 pigeons and in the center and in 16 other locations for 5 other pigeons. A peck anywhere within the region in which the sample stimuli appeared produced two white disks (comparison stimuli), one on the left and one on the lower right corners of the screen. Correct left-right choices provided food. Although of no consequence, the location of pecks in presence of the sample was predictive of the pigeon's subsequent choice. Accuracy, choice of the correct comparison stimulus, was greater when the sample stimuli appeared in the center as well as 16 other locations than when it appeared only in the center. The presolution period, the period of chance accuracy prior to evidence of discrimination learning, was decreased on each task following training on the other task. This evidence of facilitation following an extra-dimensional shift was attributed to continued relevance of the conditions under which the first task was learned. The duration of the presolution period was inversely related to asymptotic accuracy-data accounted for by Heinemann's (1983) theory of information processing during the presolution period.     

Does the cylinder model of gastric evacuation predict observed evacuation of mixed meals of prey of contrasting geometries in a piscivorous fish?

The simple surface abstraction of the cylinder model (each prey as well as the total stomach contents is considered a cylinder that is gradually reduced by successive peeling off its curved side) was challenged by data on evacuation of a meal composed of three sandeels Ammodytes tobianus and a dab Limanda limanda fed to Atlantic cod Gadus morhua. While the body shape of A. tobianus comes close to that of a cylinder, the flatfish L. limanda takes a discoid form. As opposed to a modified form of the cylinder model, where the contrasting geometries of the fish prey were implemented, the simple, original cylinder model held the potential to predict evacuation of the individual prey types as well as the total stomach contents. Thus, the present study adds significantly to the increasing evidence that points to the generic nature of the model and its implicit square root function. Also, the present study corroborated a basic assumption that the variability of evacuation data not accounted for by the cylinder model primarily can be ascribed to the intraspecific variation in gastric performance of the predator.     

Diagnosis of deep‐seated lymphomas by endoscopic ultrasound‐guided fine needle aspiration combined with flow cytometry

A. Stacchini, P. Carucci, D. Pacchioni, G. Accinelli, A. Demurtas, S. Aliberti, M. Bosco, M. Bruno, A. Balbo Mussetto, M. Rizzetto, G. Bussolati and C. De Angelis
Diagnosis of deep‐seated lymphomas by endoscopic ultrasound‐guided fine needle aspiration combined with flow cytometry Objective: Although endoscopic ultrasound combined with fine needle aspiration (EUS‐FNA) is rapidly becoming the preferred diagnostic approach for the sampling and diagnosis of gastrointestinal and mediastinal malignancies, there are limited data as to its use in the diagnosis of lymphoproliferative disorders. Therefore, we carried out a retrospective evaluation of the performance of EUS‐guided FNA combined with flow cytometry (FC) as a tool to improve overall sensitivity and specificity in the diagnosis of lymphoma. Methods: Of 1560 patients having EUS‐guided FNA during the period of the study, a total of 56 patients were evaluated by cytology with FC after EUS‐FNA. There was adequate material to perform FC analysis for all but one case. Results: EUS‐FNA‐FC gave a diagnosis of lymphoma in 11 cases and of reactive lymphadenopathy in 20. A specific histological type was defined by FC alone in eight cases. The remaining cases were diagnosed later by cytology and cell block sections: 13 carcinomas, nine granulomatous lymphadenopathies and one mediastinal extramedullary haematopoiesis. One case was considered only suspicious for lymphoma on cytology and FC but was not confirmed on molecular analysis and one had insufficient material for FC. Conclusions: Our results show that a combination of EUS‐FNA‐FC is a feasible and highly accurate method, which may be used for the diagnosis and subtyping of deep‐seated lymphoma, providing a significant improvement to cytomorphology alone both for diagnosis and treatment planning, as long as immunocytochemistry is available for non‐lymphoma cases.     

Assessing land cover change in Kenya's Mau Forest region using remotely sensed data

Kenya's Rift Valley has been undergoing rapid land cover change for the past two decades, which has resulted in ecological and hydrological changes. An effort is under way to quantify the timing and rate of these changes in and around the River Njoro watershed located near the towns of Njoro and Nakuru using remote sensing and geographic information system (GIS) methods. Three Landsat TM images, representing a 17‐year period from 1986 to 2003 in which the area underwent a significant land cover transition, were classified and compared with one another. Vegetation diversity and temporal variability, common to tropical and sub‐tropical areas, posed several challenges in disaggregating classified data into sub‐classes. An iterative approach for the resolving challenges is presented that incorporates unsupervised and supervised classification routines in coordination with knowledge‐based spatial analyses. Changes are analysed at three spatial scales ranging from the highly impacted and deforested uplands to the watershed and landscape scales. Land cover transitions primarily occurred after 1995, and included large forest losses coupled with increases in mixed small‐scale agriculture and managed pastures and degraded areas. These changes in cover type are highly spatially variable and are theorized to have significant impacts on ecological and hydrologic systems with implications for environmental sustainability.     

Assignment of Staphylococcus aureus isolates to clonal complexes based on microarray analysis and pattern recognition

A DNA microarray was designed for the rapid genotyping of Staphylococcus aureus. It covers 185 distinct genes and about 300 alleles thereof, including species-specific controls, accessory gene regulator (agr) alleles, genes encoding virulence factors, and microbial surface components recognizing adhesive matrix molecules, capsule type-specific genes, as well as resistance determinants. It was used to examine 100 clinical isolates and reference strains. Relationships of leukocidin and ssl/set (staphylococcal superantigen-like or exotoxin-like) genes were reviewed considering these experimental results as well as published sequences. A good correlation of overall hybridization pattern and multilocus sequence typing was found. Analysis of hybridization profiles thus allowed not only to assess virulence and drug resistance, but also to assign isolates to strains and to clonal complexes. Hybridization data were used to construct a split network tree and to analyse relationships between strains. Allelic variations of a number of genes indicate a division of S. aureus into three major branches that are not in accordance to agr group or capsule-type affiliations. Additionally, there are some isolated lineages, such as ST75, ST93, or ST152. These strains produce aberrant hybridization profiles, indicating that only a part of the gene pool of S. aureus has been investigated yet.     

Nonparametric and semiparametric group sequential methods for comparing accuracy of diagnostic tests

SUMMARY: Comparison of the accuracy of two diagnostic tests using the receiver operating characteristic (ROC) curves from two diagnostic tests has been typically conducted using fixed sample designs. On the other hand, the human experimentation inherent in a comparison of diagnostic modalities argues for periodic monitoring of the accruing data to address many issues related to the ethics and efficiency of the medical study. To date, very little research has been done on the use of sequential sampling plans for comparative ROC studies, even when these studies may use expensive and unsafe diagnostic procedures. In this article we propose a nonparametric group sequential design plan. The nonparametric sequential method adapts a nonparametric family of weighted area under the ROC curve statistics (Wieand et al., 1989, Biometrika 76, 585-592) and a group sequential sampling plan. We illustrate the implementation of this nonparametric approach for sequentially comparing ROC curves in the context of diagnostic screening for nonsmall-cell lung cancer. We also describe a semiparametric sequential method based on proportional hazard models. We compare the statistical properties of the nonparametric approach with alternative semiparametric and parametric analyses in simulation studies. The results show the nonparametric approach is robust to model misspecification and has excellent finite-sample performance.     

蛋白质二级结构预测的系统误差

目前蛋白质二级结构的预测准确率徘徊在75%左右,难以作进一步提高。本文通过统计学的方法,对蛋白质的冗余数据库进行了分析。并由此证明,目前影响预测准确率继续的真正原因是蛋白质数据库本身的系统误差,系统误差大约为25%。而该误差是由于实验条件的客观原因带来的。