X-ray study in the diagnosis of traumatic mediastinitis and its complications

The study was based on the clinical and X-ray analysis of 68 patients treated at the Thoracoabdominal Surgery, N.V. Sklifosovsky Research Institute of Emergency Care, in 1992-1997. The paper gives pre- and postoperative X-ray findings of patients with mediastinitis. It shows it possible to define the precise location of a mediastinal inflammatory process, its severity and possible complications. The X-ray symptomatology of mediastinitis is presented according to the location and developmental stage of a mediastinal process. Jointly with surgeons, the authors have developed a procedure for X-ray examination of patients in the postoperative period, which involves the study of the course of the disease and the evaluation of the adequacy of performed treatment. The paper analyzes the most specific and severe complications of traumatic mediastinitis. Special emphasis is laid on the fact that during control X-ray studies it is important to keep the procedure that permits timely diagnosis of complications at different developmental stages of mediastinitis.     

Density modification for macromolecular phase improvement

Density modification provides a simple and largely automatic tool for improving phase estimates for observed structure factors. The phase information arises from a combination of the known structure factor magnitudes, the current phase estimates, and stereochemical information. The magnitudes, the current phase estimates, and stereochemical information. The addition of these phase information derived from theoretical sources renders new structures amenable to solution, and reduces the effort required to solve other structures. A diverse array of techniques which have been applied to the phase improvement problem are reviewed.     

Antibodies to granulocytic ehrlichiae in white-footed and cotton mice in eastern United States

Serum samples, collected from Peromyscus leucopus (white-footed mouse) or Peromyscus gossypinus (cotton mouse) during 1987 through 1990 in Florida, Georgia, Maryland, Mississippi, and North Carolina (USA), and in 1997 in southern Connecticut were analyzed by indirect fluorescent antibody (IFA) staining methods or Western blot procedures for antibodies to granulocytic ehrlichiae. Of the 82 sera from white-footed mice in Connecticut tested by IFA methods with either the BDS or NCH-1 strain of the human granulocytic ehrlichiosis (HGE) agent, 45 (55%) and 42 (51%) of the samples contained antibodies to these strains, respectively, at concentrations ranging from 1:80 to 1:2560. One (2%) of 43 sera from P. leucopus captured in Assateague Island (Maryland) had a titer of 1:80, while three (20%) of 15 sera from P. gossypinus, captured in Sapelo Island (Georgia) and four (40%) of 10 sera from cotton mice caught in Amelia Island (Florida) had antibodies to the NCH-1 strain at titers of 1:160 to 1:1,280. Fifty-five sera from P. leucopus in Cape Hatteras (North Carolina) and 30 sera from P. gossypinus in Mississippi were negative. Western blot analyses confirmed seropositivity for 19 (95%) of 20 mouse sera positive by IFA staining methods, including samples from both mouse species captured in Connecticut, Maryland, or Florida. There were key banding patterns to proteins having molecular masses of about 44, 80, 105, 110, or 120 kDa. Both serologic assays can be used to determine if mice have been exposed to granulocytic ehrlichiae. These rodents also may be useful in surveillance programs to identify endemic sites for HGE and in performing laboratory studies on immune responses to the etiologic agent.     

Disseminated granulomas associated with nematode larvae in a shortfin mako shark

A shortfin mako shark (Isurus oxyrinchus) caught in 1996 by sportfishermen in Long Island (New York, USA) had many granulomas containing larval nematodes. Granulomas were present in the myocardium, spleen, pancreas, stomach, spiral intestine, hematopoietic tissue within the anterior kidney, and in the branchial septum and primary lamellae of the gills. Epicardial hyperplasia and granulomatous myocarditis were associated with the larvae. Although identification of the larvae was impossible due to lack of distinct morphological features, they resembled dracunculoid larvae previously reported from sharks.     

Growing of a Fuzzy Recurrent Artificial Neural Network (FRANN) for pattern classification

This paper describes a method for growing a recurrent neural network of fuzzy threshold units for the classification of feature vectors. Fuzzy networks seem natural for performing classification, since classification is concerned with set membership and objects generally belonging to sets of various degrees. A fuzzy unit in the architecture proposed here determines the degree to which the input vector lies in the fuzzy set associated with the fuzzy unit. This is in contrast to perceptrons that determine the correlation between input vector and a weighting vector. The resulting membership value, in the case of the fuzzy unit, is compared with a threshold, which is interpreted as a membership value. Training of a fuzzy unit is based on an algorithm for linear inequalities similar to Ho-Kashyap recording. These fuzzy threshold units are fully connected in a recurrent network. The network grows as it is trained. The advantages of the network and its training method are: (1) Allowing the network to grow to the required size which is generally much smaller than the size of the network which would be obtained otherwise, implying better generalization, smaller storage requirements and fewer calculations during classification; (2) The training time is extremely short; (3) Recurrent networks such as this one are generally readily implemented in hardware; (4) Classification accuracy obtained on several standard data sets is better than that obtained by the majority of other standard methods; and (5) The use of fuzzy logic is very intuitive since class membership is generally fuzzy.     

Short communication. Biomass and production of cyanobacteria in a coastal water of Sagami Bay, Japan

Cyanobacteria were relatively small contributors to carbon biomass (097-18%) in the euphotic zone. However, a higher contribution to production obtained at the surface water (16-45%) implies that they contribute more to carbon cycling than is expected from their biomass      

Joint surface modeling with thin-plate splines.

Mathematical joint surface models based on experimentally determined data points can be used to investigate joint characteristics such as curvature, congruency, cartilage thickness, joint contact areas, as well as to provide geometric information well suited for finite element analysis. Commonly, surface modeling methods are based on B-splines, which involve tensor products. These methods have had success; however, they are limited due to the complex organizational aspect of working with surface patches, and modeling unordered, scattered experimental data points. An alternative method for mathematical joint surface modeling is presented based on the thin-plate spline (TPS). It has the advantage that it does not involve surface patches, and can model scattered data points without experimental data preparation. An analytical surface was developed and modeled with the TPS to quantify its interpolating and smoothing characteristics. Some limitations of the TPS include discontinuity of curvature at exactly the experimental surface data points, and numerical problems dealing with data sets in excess of 2000 points. However, suggestions for overcoming these limitations are presented. Testing the TPS with real experimental data, the patellofemoral joint of a cat was measured with multistation digital photogrammetry and modeled using the TPS to determine cartilage thicknesses and surface curvature. The cartilage thickness distribution ranged between 100 to 550 microns on the patella, and 100 to 300 microns on the femur. It was found that the TPS was an effective tool for modeling joint surfaces because no preparation of the experimental data points was necessary, and the resulting unique function representing the entire surface does not involve surface patches. A detailed algorithm is presented for implementation of the TPS.