Intradermal and subcutaneous leiomyosarcoma: a clinicopathological and immunohistochemical study of 41 cases

Superficial leiomyosarcomas are rare tumours. The lesions confined to the dermis, contrary to those involving the subcutis, have been reported to carry a favourable prognosis. A retrospective study of 41 consecutive cases of surgically treated intradermal and subcutaneous leiomyosarcomas was undertaken in order to determine the prognostic factors that may influence the survival of these patients. Seven tumours were predominantly intradermal and 34 involved the subcutaneous tissue. Fifty-four percent of the tumours were located in the lower extremities. All cases stained positively for smooth muscle antigen and 66% for desmin. The tumours were classified with regard to tumour grade I (low grade, 3%), II (intermediate, 12%), IIIA (high grade, 46%) and IIIB (high grade, 39%). In all patients, follow-up information was available. Mean follow-up time was 5 years. The patients with intradermal tumours were all alive without signs of recurrence, whereas 14 of those with leiomyosarcomas involving the subcutis have died with pulmonary metastases. Our study confirms that "pure" intradermal leiomyosarcomas independent of tumour grade behave in a benign fashion, probably due to small tumour size. Tumour size > or = 5 cm, deep localization with fascia involvement, and high malignancy grade (IIIB) were found to deteriorate survival based on a univariate analysis. However, in a multivariate analysis only tumour size was found to be an independent prognostic factor.     

Attribute selection with fuzzy decision reducts

Rough set theory provides a methodology for data analysis based on the approximation of concepts in information systems. It revolves around the notion of discernibility: the ability to distinguish between objects, based on their attribute values. It allows to infer data dependencies that are useful in the fields of feature selection and decision model construction. In many cases, however, it is more natural, and more effective, to consider a gradual notion of discernibility. Therefore, within the context of fuzzy rough set theory, we present a generalization of the classical rough set framework for data-based attribute selection and reduction using fuzzy tolerance relations. The paper unifies existing work in this direction, and introduces the concept of fuzzy decision reducts, dependent on an increasing attribute subset measure. Experimental results demonstrate the potential of fuzzy decision reducts to discover shorter attribute subsets, leading to decision models with a better coverage and with comparable, or even higher accuracy.     

Miniaturization and integration of photoacoustic detection with amicrofabricated chemical reactor system

Photoacoustic spectroscopy is a useful technique for monitoring chemical composition in mesoscale analysis systems because the detection limit scales favorably with miniaturization. The key element of a photoacoustic spectrometry system is the detector. This work focuses on the miniaturization of photoacoustic detection. In particular, we are using 3.4 μm light to detect propane in a carbon dioxide background-a system that is useful for monitoring combustion reactions. Two systems have been developed. In the first, a miniature photoacoustic cell has been machined into the mounting block of a microfabricated chemical reactor, demonstrating the integration of a photoacoustic detector with a microsystem. The cell used a hearing aid microphone and an infrared diode that was modulated at the first acoustic resonance of the cell. As the gas composition of the cell changed from carbon dioxide to propane the resonance peak was observed to shift and increase, as was expected from theory. This work also presents the first demonstration of a microfabricated photoacoustic detection cell. The cell used an optical microphone and laser excitation brought into the cell via an optical fiber. The light was modulated at a frequency far below the first acoustic resonance, and a signal of 0.05 Pa was observed in the presence of propane     

Multiple Comparisons in Induction Algorithms

A single mechanism is responsible for three pathologies of induction algorithms: attribute selection errors, overfitting, and oversearching. In each pathology, induction algorithms compare multiple items based on scores from an evaluation function and select the item with the maximum score. We call this a multiple comparison procedure (MCP). We analyze the statistical properties of MCPs and show how failure to adjust for these properties leads to the pathologies. We also discuss approaches that can control pathological behavior, including Bonferroni adjustment, randomization testing, and cross-validation.     

Effects of training to implement new tools and working methods to reduce knee load in floor layers

The purpose was to measure the effect of a participatory ergonomics implementation strategy consisting of information, education, and facilitation on the use of new tools and working methods in the floor laying trade. Floor layers (n=292) were trained in using new working methods. The effects were evaluated by using questionnaires, interviews, and assessments of quality and productivity. Following the training, 43% had used the new working methods weekly/daily compared to 11% before. There was a reduction in the degree of self-reported pain in the knees among the floor layers using the new working methods weekly or daily compared to those using them never or occasionally. The musculoskeletal complaints did not increase from any other region and the quality and the productivity of the work were not decreased. This indicates that within a 3-months perspective the implementation strategy succeeded.     

Simulation of cardiovascular system diseases by including the autonomic nervous system into a minimal model

Diagnosing cardiovascular system (CVS) diseases from clinically measured data is difficult, due to the complexity of the hemodynamic and autonomic nervous system (ANS) interactions. Physiological models could describe these interactions to enable simulation of a variety of diseases, and could be combined with parameter estimation algorithms to help clinicians diagnose CVS dysfunctions. This paper presents modifications to an existing CVS model to include a minimal physiological model of ANS activation. A minimal model is used so as to minimise the number of parameters required to specify ANS activation, enabling the effects of each parameter on hemodynamics to be easily understood. The combined CVS and ANS model is verified by simulating a variety of CVS diseases, and comparing simulation results with common physiological understanding of ANS function and the characteristic hemodynamics seen in these diseases. The model of ANS activation is required to simulate hemodynamic effects such as increased cardiac output in septic shock, elevated pulmonary artery pressure in left ventricular infarction, and elevated filling pressures in pericardial tamponade. This is the first known example of a minimal CVS model that includes a generic model of ANS activation and is shown to simulate diseases from throughout the CVS.     

Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma

The degree of malignancy in brain glioma is assessed based on magnetic resonance imaging (MRI) findings and clinical data before operation. These data contain irrelevant features, while uncertainties and missing values also exist. Rough set theory can deal with vagueness and uncertainty in data analysis, and can efficiently remove redundant information. In this paper, a rough set method is applied to predict the degree of malignancy. As feature selection can improve the classification accuracy effectively, rough set feature selection algorithms are employed to select features. The selected feature subsets are used to generate decision rules for the classification task. A rough set attribute reduction algorithm that employs a search method based on particle swarm optimization (PSO) is proposed in this paper and compared with other rough set reduction algorithms. Experimental results show that reducts found by the proposed algorithm are more efficient and can generate decision rules with better classification performance. The rough set rule-based method can achieve higher classification accuracy than other intelligent analysis methods such as neural networks, decision trees and a fuzzy rule extraction algorithm based on Fuzzy Min-Max Neural Networks (FRE-FMMNN). Moreover, the decision rules induced by rough set rule induction algorithm can reveal regular and interpretable patterns of the relations between glioma MRI features and the degree of malignancy, which are helpful for medical experts.     

Autogenous shrinkage in high-performance cement paste: An evaluation of basic mechanisms

In this paper, various mechanisms suggested to cause autogenous shrinkage are presented. The mechanisms are evaluated from the point of view of their soundness and applicability to quantitative modeling of autogenous shrinkage. The capillary tension approach is advantageous, because it has a sound mechanical and thermodynamical basis. Furthermore, this mechanism is easily applicable in a numerical model when dealing with a continuously changing microstructure. In order to test the numerical model, autogenous deformation and internal relative humidity (RH) of a Portland cement paste were measured during the first week of hardening. The isothermal heat evolution was also recorded to monitor the progress of hydration and the elastic modulus in compression was measured. RH change, degree of hydration and elastic modulus were used as input data for the calculation of autogenous deformation based on the capillary tension approach. Because a part of the RH drop in the cement paste is due to dissolved salts in the pore solution, a method is suggested to separate this effect from self-desiccation and to calculate the actual stress in the pore fluid associated with menisci formation.     

Chromosome studies in "preleukemia". III. Myelofibrosis

Cytogenetic studies were done on 18 patients with myelofibrosis or the closely related syndrome, undifferentiated myeloproliferative disorder (MPD). Clones of cells with chromosome abnormalities were demonstrated in the blood of eight individuals, including two with a history of radiation therapy and two with "acute myelofibrosis". Trisomy 8 was present in the latter two patients, but otherwise, there was no consistent cytogenetic pattern or correlation with specific hematologic findings. Sixteen of these patients have been followed for more than 1 year or until death; none has progressed to leukemia. The results indicate that chromosome abnormalities are relatively common in this disorder, but as with polycythemia vera, and unlike some other "preleukemic" states, the aberrant clones in myelofibrosis do not appear to indicate that clinical leukemia is imminent.     

Structural optimization of non-linear systems under stochastic excitation

This work concentrates on the structural optimization of a class of non-linear systems with deterministic structural parameters subject to stochastic excitation. The optimization problem is formulated as the minimization of an objective function subject to constraints on the response level. The stochastic response is characterized by its first two statistical moments, which are computed by a statistical equivalent linearization technique. The implicit structural optimization problem is replaced by a sequence of explicit sub-optimization problems. The sub-problems are constructed by using a conservative first-order approximation of the objective and constraint functions. The applicability of the proposed design process is demonstrated in three numerical examples where the methodology is applied to systems with nonlinearity of hardening and hysteretic type. The effects of the nonlinearity on the general performance of the final designs are discussed. At the same time, some engineering implications of the results obtained in this work are addressed.