Filariasis in axillary lymph node.

A case of adult filarial worms detected in an axillary lymph node of an asymptomatic patient. A 64 year-old Indian female underwent a mammogram and was incidentally found to have punctate microcalcifications in the upper outer quadrant of the left breast with left axillary lymphadenopathy. She has underlying hypertension and diabetes mellitus on oral medications. She has no family history of breast malignancy. Fine needle aspiration of the left axillary lymph node was suggestive of reactive lympha-denitis. Histopathological examination of excisional biopsy of left breast lump showed fibrocystic disease; no evidence of malignancy was detected whereas excisional biopsy of left axillary lymph node showed reactive lymphoid hyperplasia, featuring variably sized lymphoid follicles with intact mantle zone. No expansion of marginal zone was noted. Occasional pigment-laden macrophages were seen. One of the lymph node showed presence of calcified serpinginous tubular bodies, in keeping with non viable parasite organisms with intact outlines of the structures. There were no eosinophilic infiltrates. The possibility of filarial infestation was suspected. Histopathological sample was sent for further identification and confirmed the presence of adult filarial worm.

Identification and in vitro antimicrobial susceptibility of Brucella species isolated from human brucellosis.

Brucellosis is a world-wide zoonotic disease with a major impact on the public health. Due to the high risk of laboratory acquired infection, limited laboratory investigations were performed on this organism, including detailed identification and susceptibility study. Brucella melitensis is the commonest aetiological agent for human brucellosis in this region. The in vitro susceptibility pattern against selected antimicrobial agents was assessed using E-test. All isolates were noted to be sensitive to all the antimicrobial agents tested except for rifampicin where elevated MIC > 1 µg/mL was noted in 30 out of 41 isolates tested.

Photocatalytic degradation of di(2-ethylhexyl)phthalate adsorbed by chitin A.

Di(2-ethylhexyl)phthalate (DEHP) is a ubiquitous environmental contaminant due to its extensive use as a plasticiser and its persistence. Currently, there is no cost-effective treatment method for its removal from industrial wastewater. In a previous study, DEHP was effectively adsorbed from aqueous solution by biosorption onto chitinous materials. Biosorption can pre-concentrate DEHP from the aqueous phase for further treatment. As biosorption cannot degrade DEHP, in this study the degradation (and detoxification) of DEHP adsorbed onto chitinous material by photocatalytic oxidation (PCO) is attempted. PCO relies on hydroxyl radical (.OH), which is a strong oxidising agent, for the oxidative degradation of pollutants. It is a non-selective process which can degrade DEHP adsorbed onto chitinous material. The first part of this study is the optimisation of the degradation of adsorbed DEHP by PCO. Adsorption was carried out in the physicochemical conditions optimised in the previous study, with 500 mg/L chitin A and 40 mg/L DEHP at initial pH 2, 22+/-2 degrees C and 150 rpm agitation for 5 min. After optimisation of PCO, a 61% removal efficiency of 10 mg/L of DEHP was achieved within 45 min under 0.65 mW/cm2 of UV-A with 100 mg/L TiO2, and 10 mM of H2O2 at initial pH 12. The optimisation study showed that UV-A and TiO(2) are essential for the degradation of DEHP by PCO. The degradation intermediates/products were identified by GC-MS analysis. GC-MS results showed that the di(2-ethylhexyl) side chain was first degraded, producing phthalates with shorter side chains. Further reaction produced phathalic anhydride and aliphatic compounds such as alkanol and ester. The toxicities of parental and degradation intermediates in the solution phase and on chitinous materials were followed by the Microtox test. Results indicated that toxicity can be removed after 4 h treatment by PCO. Thus the decontamination of DEHP by integrating biosorption and PCO is feasible.

Nanoadhesion between rough surfaces.

A model is developed to describe the adhesion between deformable fractal surfaces over the mesoscopic realm that covers the familiar range of interest in nanotechnology from atomic dimensions to microns. This model helps us gain a quantitative understanding of the variation of adhesion with surface energy, with microstructure of rough surfaces, and with bulk deformability. The present analysis goes beyond the Gaussian distribution of asperity heights by investigating the influence of the microstructure of self-affine fractal surfaces. Our calculation reveals that orders of magnitude increase in adhesion are possible as the roughness exponent decreases.

Feedforward networks training speed enhancement by optimal initialization of the synaptic coefficients.

This letter aims at determining the optimal bias and magnitude of initial weight vectors based on multidimensional geometry. This method ensures the outputs of neurons are in the active region and the range of the activation function is fully utilized. In this letter, very thorough simulations and comparative study were performed to validate the performance of the proposed method. The obtained results on five well-known benchmark problems demonstrate that the proposed method deliver consistent good results compared with other weight initialization methods.

Neural computation approach for developing a 3D shape reconstruction model.

The shape from shading problem refers to the well-known fact that most real images usually contain specular components and are affected by unknown reflectivity. In this paper, these limitations are addressed and a new neural-based 3D shape reconstruction model is proposed. The idea behind this approach is to optimize a proper reflectance model by learning the parameters of the proposed neural reflectance model. In order to do this, new neural-based reflectance models are presented. The feedforward neural network (FNN) model is able to generalize the diffuse term, while the RBF model is able to generalize the specular term. A hybrid structure of FNN-based and RBF-based models is also presented because most real surfaces are usually neither Lambertian models nor ideally specular models. Experimental results, including synthetic and real images, are presented to demonstrate the performance of our approach given different specular effects, unknown illuminate conditions, and different noise environments.

Learning parametric specular reflectance model by radial basis function network.

For the shape from shading problem, it is known that most real images usually contain specular components and are affected by unknown reflectivity. In this paper, these limitations are addressed and a new neural-based specular reflectance model is proposed. The idea of this method is to optimize a proper specular model by learning the parameters of a radial basis function network and to recover the object shape by the variational approach with this resulting model. The obtained results are very encouraging and the performance is demonstrated by using the synthetic and real images in the case of different specular effects and noisy environments.

A neural-based crowd estimation by hybrid global learning algorithm.

A neural-based crowd estimation system for surveillance in complex scenes at underground station platform is presented. Estimation is carried out by extracting a set of significant features from sequences of images. Those feature indexes are modeled by a neural network to estimate the crowd density. The learning phase is based on our proposed hybrid of the least-squares and global search algorithms which are capable of providing the global search characteristic and fast convergence speed. Promising experimental results are obtained in terms of accuracy and real-time response capability to alert operators automatically.

Blind equalization of a noisy channel by linear neural network.

In this paper, a new neural approach is introduced for the problem of blind equalization in digital communications. Necessary and sufficient conditions for blind equalization are proposed, which can be implemented by a two-layer linear neural network. In the hidden layer, the received signals are whitened, while the network outputs provide directly an estimation of the source symbols. We consider a stochastic approximate learning algorithm for each layer according to the property of the correlation matrices of the transmitted symbols. The proposed class of networks yield good results in simulation examples for the blind equalization of a three-ray multipath channel.

Shape recovery from shading by a new neural-based reflectance model.

In this paper, we present a neural-based reflectance model of which the physical parameters of the reflectivity under different lighting conditions are interpreted by the network weights. The idea of our method is to optimize a proper reflectance model by an effective learning algorithm and to recover the object surface by a simple shape from shading recursive algorithm with this resulting model. Experimental results, including synthetic and real images, were performed to demonstrate the performance of the proposed method for practical applications.

Unusual foreign body causing quadriparesis: case report.

OBJECTIVE AND IMPORTANCE: An unusual foreign body traversing the spinal canal at the foramen magnum level is described. Interesting radiological findings and a review of nonmissile penetrating injuries are presented. This case demonstrates the importance of a thorough physical examination and the use of neurodiagnostic imaging in an inebriated, uncooperative patient with neurological dysfunction. CLINICAL PRESENTATION: The patient presented with quadriparesis confounded by cocaine intoxication. A physical examination revealed only a small punctate lesion in the posterior occipital region. INTERVENTION: After detection of the foreign body, the patient underwent immediate surgical exploration and removal of the object. The dura was repaired primarily, and the patient was maintained on intravenous antibiotics for 7 days. CONCLUSION: With physical therapy, the patient was walking with assistance at 2 weeks postsurgery. Upper extremity strength, especially intrinsic hand movement, was most severely affected. At 10 months' follow-up, the patient's only deficits were mild intrinsic hand weakness and incoordination with fine finger movements. Immediate surgical exploration is indicated for patients with retained fragments and progressive neurological dysfunction.

Extended least squares based algorithm for training feedforward networks.

An extended least squares-based algorithm for feedforward networks is proposed. The weights connecting the last hidden and output layers are first evaluated by least squares algorithm. The weights between input and hidden layers are then evaluated using the modified gradient descent algorithms. This arrangement eliminates the stalling problem experienced by the pure least squares type algorithms; however, still maintains the characteristic of fast convergence. In the investigated problems, the total number of FLOPS required for the networks to converge using the proposed training algorithm are only 0.221%-16.0% of that using the Levenberg-Marquardt algorithm. The number of floating point operations per iteration of the proposed algorithm are only 1.517-3.521 times of that of the standard backpropagation algorithm.

The surgical treatment of metastatic spinal tumors within the intradural extramedullary compartment.

The authors retrospectively reviewed the surgical outcomes in 10 cases of symptomatic intradural extramedullary spinal metastases of nonneurogenic origin because the collective experience in treating this rare manifestation of systemic cancer is limited. Pain and weakness were the presenting complaints in 70% of the patients and sensory changes were found in all cases. Cytological tests on one specimen of cerebrospinal fluid (CSF) from each of seven patients showed malignant cells in two cases. Gadolinium contrast-enhanced biplanar magnetic resonance (MR) imaging was effective in localizing the lesion and showed evidence of leptomeningeal carcinomatosis in two cases; myelography showed leptomeningeal carcinomatosis in one case and erroneously identified the lesion as intramedullary in the other. Eight of 10 cases had antecedent intracranial metastatic foci with the interval from presentation of the intracranial lesion to appearance of the spinal disease ranging from 3 to 51 months. The majority of the spinal lesions occurred in the thoracolumbar area. The most frequent histological type was adenocarcinoma and the most frequent source was the lung. In all cases laminectomies, intradural exploration, and biopsy or subtotal excision aided by microscopy and ultrasonography were performed. Results of surgical decompression were poor with only 30% of the patients showing improvement, at a 20% risk of perioperative mortality and a 60% risk of morbidity. Plans for surgical intervention in patients with intradural extramedullary metastases from a distant noneurogenic source should be weighed against the high association with intracranial lesions, overall poor prognosis, and modest symptomatic results of decompression. Comprehensive evaluation including multiple specimens of CSF for cytology and contrast-enhanced MR imaging should be undertaken to exclude patients with diffuse leptomeningeal involvement, who should be treated by means other than surgery.