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PubMed1983

Study of diarrhea in critically ill patients.

There has been an impression that diarrhea occurs commonly in seriously ill patients treated in ICUs. In view of the sparsity of published work on the problem, we embarked on a prospective study of all patients admitted to the ICU for more than 48 h over a 12-month period. Three factors were examined in detail: nasogastric feeding, cimetidine administration, and antibiotic treatment. Other factors also were considered, notably the nature of the underlying illness and the spread of a possible infective agent by cross-infection. There was a 41% incidence of diarrhea. A significant increase in the incidence of diarrhea occurred in patients on nasogastric feeding (p less than 0.01) and in those receiving cimetidine (p less than 0.05); there was no increased incidence in those receiving antibiotic therapy. The cytotoxin of Clostridium difficile was specifically looked for in all patients with diarrhea, but was not detected.

Anti-Bacterial Agents↗
PubMedDate not supplied

CT or US-guided fine needle aspiration biopsy in gastric neoplasms.

Gastric carcinoma of the linitis plastica type is occasionally difficult to diagnose endoscopically because of the large inflammatory response and the sparsity of tumor cells. Five patients who presented with signs and symptoms of gastric carcinoma underwent upper gastrointestinal endoscopy to confirm the diagnosis of carcinoma. In each case the gross appearance of the stomach was felt to represent gastric carcinoma but the biopsy and/or brushing specimens were unable to make the diagnosis. Ultrasound or CT in each of these five patients demonstrated thickening of the gastric wall and, in one instance, evidence of extensive metastatic disease. Fine needle aspiration biopsy was performed and a diagnosis of adenocarcinoma was made cytologically. Four were primary gastric adenocarcinoma of the linitis plastica type and one was metastatic adenocarcinoma from the breast. It is suggested that guided aspiration biopsy be performed when the diagnosis of gastric carcinoma cannot be confirmed endoscopically.

Adenocarcinoma↗
PubMed1994

Symptomatic lumbar spondylolysis. Neuroimmunologic studies.

OBJECTIVES: This study characterized the defect using neuroimmunologic and inflammatory cell analysis. SUMMARY OF BACKGROUND DATA: Spondylolysis/spondylolisthesis is thought to be caused by a congenital weakness and mechanical stress causing a fracture associated with defective healing. Most of the spondylolysis patients are asymptomatic and the mechanisms of pain in symptomatic patients are unknown. METHODS: Tissue from the spondylolysis defect was collected from seven patients undergoing posterolateral fusion operations. RESULTS: Histologic examination disclosed delayed union/pseudoarthrosis with fibroblasts and macrophages in a pseudosynovial lining membrane and occasional perivascular infiltrates containing mainly CD2 lymphocytes and CD11b monocytes/macrophages. In a vascularized connective tissue stroma PGP 9.5, synaptophysin and neurofilament staining disclosed perivascular nerves, which did not extend to the synovial lining layer and which mainly represented postganglionic sympathetic nerve fibers but also calcitonin gene-related peptide and substance P containing sensory fibers. CONCLUSIONS: Pain in spondylolysis/spondylolisthesis might derive from the spondylolytic defect itself, probably from stretching of the local neural elements rather than from their sensitization/stimulation by local inflammatory mediators. The resemblance of neuroimmunohistochemical changes compared with those reported in the nonunion of long bones and the sparsity of stromal innervation, indicate that the characteristic defective healing is in part due to lack of neurogenic influences.

Adolescent↗
PubMed1994

A method for simultaneous measurement of cocaine and nicotine in neonatal hair.

Maternal exposure to drugs and chemicals is increasingly being recognized as adversely affecting the developing fetus. To date, a very large number of fetuses have been exposed in utero to cocaine and cigarette smoke. Neonatal hair as a biological marker for fetal exposure in cocaine and nicotine has emerged as an excellent tool in correlating in utero exposure and outcome. However, a limiting factor is the sparsity of neonatal hair. We report on the first method that allows determination of both cocaine and nicotine as well as their metabolites in the same hair samples.

Cocaine↗
PubMed1995

Non-metric multidimensional scaling in the analysis of neuroanatomical connection data and the organization of the primate cortical visual system.

Neuroanatomists have established that the various gross structures of the brain are divided into a large number of different processing regions and have catalogued a large number of connections between these regions. The connectional data derived from neuroanatomical studies are complex, and reliable conclusions about the organization of brain systems cannot be drawn from considering them without some supporting analysis. Recognition of this problem has recently led to the application of a variety of techniques to the analysis of connection data. One of the techniques that we previously employed, non-metric multidimensional scaling (NMDS), appears to have revealed important aspects of the organization of the central nervous system, such as the gross organization of the whole cortical network in two species. We present here a detailed treatment of methodological aspects of the application of NMDS to connection data. We first examine in detail the particular properties of neuroanatomical connection data. Second, we consider the details of NMDS and discuss the propriety of different possible NMDS approaches. Third, we present results of the analyses of connection data from the primate visual system, and discuss their interpretation. Fourth, we study independent analyses of the organization of the visual system, and examine the relation between the results of these analyses and those from NMDS. Fifth, we investigate quantitatively the performance of a number of data transformation and conditioning procedures, as well as tied and untied NMDS analysis of untransformed low-level data, to determine how well NMDS can recover known metric parameters from artificial data. We then re-analyse real connectivity data with the most successful methods at removing the effects of sparsity, to ensure that this aspect of data structure does not obscure others. Finally, we summarize the evidence on the connectional organization of the primate visual system, and discuss the reliability of NMDS analyses of neuroanatomical connection data.

Animals↗
PubMed2026

Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework.

Multimodal single-cell assays profile complementary layers of cell state, but integration is complicated by modality mismatch, sparsity, and uneven cohort coverage. Here, we present Unified Variational Inference (UniVI), a scalable mixture-of-experts β-variational autoencoder that learns a shared latent space while preserving modality-specific structure. UniVI couples modality-specific encoders/decoders with a shared latent prior and a symmetric cross-modal alignment objective, enabling consistent integration of paired measurements without curated feature-link graphs or preannotated reference atlases; optional supervised heads can be added when labels are available. Across paired RNA-protein (CITE-seq) and RNA-chromatin (10x Genomics Multiome, SHARE-seq) data spanning human PBMCs and mouse back skin-a nonhematopoietic tissue with continuous differentiation hierarchies-UniVI produces coherent embeddings, improves label transfer, and enables cross-modal reconstruction and denoising. Extending to trimodal measurements, UniVI maintains robust three-way alignment among RNA, chromatin accessibility, and surface proteins (TEA-seq), and accommodates DNA methylation in a paired scNMT-seq mouse gastrulation proof-of-concept under beta-binomial likelihoods. Performance degrades gracefully under severe cell type imbalance and in the presence of modality-exclusive populations. In an acute myeloid leukemia mosaic design, a paired RNA-protein bridge anchors independent RNA-only and protein+genotype cohorts, revealing genotype-associated neighborhoods that sharpen with mutation-aware fine-tuning. UniVI thus provides a flexible, interpretable framework for multimodal integration across paired, trimodal, and mosaic study designs and supports practical reference-to-query projection in partially observed studies.

Journal Article↗
PubMed2001

Discreteness and quasiresonances in weak turbulence of capillary waves.

A numerical study is presented which deals with the kinematics of quasiresonant energy transfer in a system of capillary waves with a discrete wave number space in a periodic box. For a given set of initially excited modes and a given level of resonance broadening, the modes of the system are partitioned into two classes, one active, the other forbidden. For very weak nonlinearity the active modes are very sparse. It is possible that this sparsity explains discrepancies between the values of the Kolmogorov constant measured in numerical simulations of weakly turbulent cascades and the theoretical values obtained from the continuum theory. There is a critical level of nonlinearity below which the set of active modes has finite radius in wave number space. In this regime, an energy cascade to dissipative scales may not be possible and the usual Kolmogorov spectrum predicted by the continuum theory not realized.

Journal Article↗
PubMed2001

Linear-scaling quantum Monte Carlo calculations.

A method is presented for using truncated, maximally localized Wannier functions to introduce sparsity into the Slater determinant part of the trial wave function in quantum Monte Carlo calculations. When combined with an efficient numerical evaluation of these localized orbitals, the dominant cost in the calculation, namely, the evaluation of the Slater determinant, scales linearly with system size. This technique is applied to accurate total energy calculation of hydrogenated silicon clusters and carbon fullerenes containing 20-1000 valence electrons.

Journal Article↗
PubMed1977

Photosynthetic Pod Wall of Pea (Pisum sativum L.): Distribution of Carbon Dioxide-fixing Enzymes in Relation to Pod Structure.

The pod wall of pea (Pisum sativum L.) was shown to contain two distinct photosynthetic layers. The outer, comprising chlorenchyma of the mesocarp, captured CO(2) from the outside atmosphere; the inner, a chloroplast-containing epidermis lining the pod gas cavity, was involved in photoassimilation of the CO(2) released from respiring seeds.Structural features of the pod included the thick cuticle and stomata of the outer epidermis, the inward projecting veinlets of the vascular network in the mesocarp, the sparsity of air spaces, the fiber and parenchyma layers of the endocarp, and the abundant chloroplasts, thin cuticle, and rounded outer contours of cells of the inner epidermis.The inner epidermis showed high specific activities of ribulose 1,5-diphosphate (RuDP) carboxylase (EC 4.1.1.39) and phosphoenolpyruvate (PEP) carboxylase (EC 4.1.1.31), contained up to 20% of the pod's chlorophyll, and was capable of fixing 66% of the CO(2) released during the photoperiod to the pod gas space by the seeds of a fully grown fruit.The in vitro carboxylation capacity of the pod exceeded the estimated gross photosynthesis of the fruit for all but the last few days of development. Chlorophyll content and carboxylation activity declined more markedly in the outer photosynthetic layers than in the inner epidermis.The ratio of activities of RuDP carboxylase to PEP carboxylase in pod extracts varied from 2.4:1 to 12:1 as against 48:1 to 156:1 in extracts of leaves.Structural and physiological properties of the pod were related to its capacity to conserve respired CO(2) and provide photosynthate to developing seeds.

Journal Article↗
PubMed2000

Prospects for kinematical least-squares refinement in polymer electron crystallography.

Least-squares refinement is unusual in the context of electron crystallography because of the sparsity of the measured intensity data set and the problems of systematic errors due to multiple dynamical scattering. With 120 unique hkl electron diffraction intensities measured from polymorphic form III of isotactic poly(1-butene), conditions for improving an existing structural model derived from initial direct structure analysis have been evaluated. The polymer crystallizes in space group P2(1)2(1)2(1) with a = 12.38, b = 8.88, c = 7.56 A and there are 8 unique atoms in the asymmetric unit. Starting with atomic positions resulting from Fourier refinement, four cycles of least-squares refinement, where the positional shifts of atomic positions were constrained, produced better bonding parameters than found before while lowering the conventional crystallographic residual, based on absolute value(F), from an overall value of R = 0.26 to R = 0.185 for the 58 most intense reflections where magnitude of absolute value(Fh(obs)) > or = 4sigma (Fh(obs)) or 0.216 for the complete data set of 120 reflections. The weighted residuals based on magnitude of absolute value(F)2 fell from 0.50 to 0.41 for the complete data set. This refinement was not improved however when attempts were made to fill in very weak intensities by default values. Also, effects of multiple-scattering perturbations were found in the irregularity of the final isotropic thermal parameters.

Journal Article↗
PubMed1993

Finite element computations of specific absorption rates in anatomically conforming full-body models for hyperthermia treatment analysis.

Finite element techniques for three-dimensional specific absorption rate (SAR) computation in anatomically based human models are presented. The formulations center on Helmholtz weak forms which have been shown to be numerically robust and to afford additional sparsity in the resulting system of algebraic equations. Practical solution of these equations depends critically on the realization of an effective sparse matrix solver. Experience with several conjugate gradient-type methods is reported. The findings show that convergence rate (and even convergence in some cases) degrades significantly with increasing matrix rank and decreasing electrical loss for mesh spacings which adequately resolve the physical wavelengths of the electromagnetic wave propagation. However, with proper choice of algorithm and preconditioning, reliable convergence has been achieved for matrix ranks exceeding 2 x 10(5) on domains having sizeable volumes of electrically lossless regions. An automatic grid generation scheme for constructing meshes which consist of variable element sizes that conform to a predefined set of boundaries is discussed. Example meshes of homogeneous and heterogeneous human anatomies, the boundaries of which have been derived from CT-scan information, are shown. These results highlight the fact that 3D finite element mesh generation remains a difficult problem, but usable meshes with this level of complexity can be generated. Integration of the finite element formulation, the sparse matrix solver, and the mesh generation scheme is shown to lead to algorithms that can be implemented on inexpensive reduced instruction set computer (RISC) workstations with run times on the order of hours. An example of hyperthermia device simulation is presented which suggests that the finite element method is a practical alternative that rivals the impressive finite-difference time-domain (FDTD) computations that have appeared.

Algorithms↗
PubMed2001

BICAV: a block-iterative parallel algorithm for sparse systems with pixel-related weighting.

Component averaging (CAV) was recently introduced by Censor, Gordon, and Gordon as a new iterative parallel technique suitable for large and sparse unstructured systems of linear equations. Based on earlier work of Byrne and Censor, it uses diagonal weighting matrices, with pixel-related weights determined by the sparsity of the system matrix. CAV is inherently parallel (similar to the very slowly converging Cimmino method) but its practical convergence on problems of image reconstruction from projections is similar to that of the algebraic reconstruction technique (ART). Parallel techniques are becoming more important for practical image reconstruction since they are relevant not only for supercomputers but also for the increasingly prevalent multiprocessor workstations. This paper reports on experimental results with a block-iterative version of component averraging (BICAV). When BICAV is optimized for block size and relaxation parameters, its very first iterates are far superior to those of and more or less on a par with ART. Similar to CAV, BICAV is also inherently parallel. The fast convergence is demonstrated on problems of image reconstruction from projections, using the SNARK93 image reconstruction software package. Detailed plots of various measures of convergence, and reconstructed images are presented.

Algorithms↗
PubMed2005

A meshless method for solving the EEG forward problem.

We present a numerical method to solve the quasistatic Maxwell equations and compute the electroencephalography (EEG) forward problem solution. More generally, we develop a computationally efficient method to obtain the electric potential distribution generated by a source of electric activity inside a three-dimensional body of arbitrary shape and layers of different electric conductivities. The method needs only a set of nodes on the surface and inside the head, but not a mesh connecting the nodes. This represents an advantage over traditional methods like boundary elements or finite elements since the generation of the mesh is typically computationally intensive. The performance of the proposed method is compared with the boundary element method (BEM) by numerically solving some EEG forward problems examples. For a large number of nodes and the same precision, our method has lower computational load than BEM due to a faster convergence rate and to the sparsity of the linear system to be solved.

Algorithms↗
PubMed2006

Bayesian wavelet-based image deconvolution: a GEM algorithm exploiting a class of heavy-tailed priors.

Image deconvolution is formulated in the wavelet domain under the Bayesian framework. The well-known sparsity of the wavelet coefficients of real-world images is modeled by heavy-tailed priors belonging to the Gaussian scale mixture (GSM) class; i.e., priors given by a linear (finite of infinite) combination of Gaussian densities. This class includes, among others, the generalized Gaussian, the Jeffreys, and the Gaussian mixture priors. Necessary and sufficient conditions are stated under which the prior induced by a thresholding/shrinking denoising rule is a GSM. This result is then used to show that the prior induced by the "nonnegative garrote" thresholding/shrinking rule, herein termed the garrote prior, is a GSM. To compute the maximum a posteriori estimate, we propose a new generalized expectation maximization (GEM) algorithm, where the missing variables are the scale factors of the GSM densities. The maximization step of the underlying expectation maximization algorithm is replaced with a linear stationary second-order iterative method. The result is a GEM algorithm of O(N log N) computational complexity. In a series of benchmark tests, the proposed approach outperforms or performs similarly to state-of-the art methods, demanding comparable (in some cases, much less) computational complexity.

Algorithms↗
PubMed2006

Image denoising via sparse and redundant representations over learned dictionaries.

We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itself, or training on a corpus of high-quality image database. Since the K-SVD is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm. This leads to a state-of-the-art denoising performance, equivalent and sometimes surpassing recently published leading alternative denoising methods.

Algorithms↗
PubMed2006

Efficient Huber-Markov edge-preserving image restoration.

The regularization of the least-squares criterion is an effective approach in image restoration to reduce noise amplification. To avoid the smoothing of edges, edge-preserving regularization using a Gaussian Markov random field (GMRF) model is often used to allow realistic edge modeling and provide stable maximum a posteriori (MAP) solutions. However, this approach is computationally demanding because the introduction of a non-Gaussian image prior makes the restoration problem shift-variant. In this case, a direct solution using fast Fourier transforms (FFTs) is not possible, even when the blurring is shift-invariant. We consider a class of edge-preserving GMRF functions that are convex and have nonquadratic regions that impose less smoothing on edges. We propose a decomposition-enabled edge-preserving image restoration algorithm for maximizing the likelihood function. By decomposing the problem into two subproblems, with one shift-invariant and the other shift-variant, our algorithm exploits the sparsity of edges to define an FFT-based iteration that requires few iterations and is guaranteed to converge to the MAP estimate.

Algorithms↗
PubMed2004

Nonnegative matrix factorization for rapid recovery of constituent spectra in magnetic resonance chemical shift imaging of the brain.

We present an algorithm for blindly recovering constituent source spectra from magnetic resonance (MR) chemical shift imaging (CSI) of the human brain. The algorithm, which we call constrained nonnegative matrix factorization (cNMF), does not enforce independence or sparsity, instead only requiring the source and mixing matrices to be nonnegative. It is based on the nonnegative matrix factorization (NMF) algorithm, extending it to include a constraint on the positivity of the amplitudes of the recovered spectra. This constraint enables recovery of physically meaningful spectra even in the presence of noise that causes a significant number of the observation amplitudes to be negative. We demonstrate and characterize the algorithm's performance using 31P volumetric brain data, comparing the results with two different blind source separation methods: Bayesian spectral decomposition (BSD) and nonnegative sparse coding (NNSC). We then incorporate the cNMF algorithm into a hierarchical decomposition framework, showing that it can be used to recover tissue-specific spectra given a processing hierarchy that proceeds coarse-to-fine. We demonstrate the hierarchical procedure on 1H brain data and conclude that the computational efficiency of the algorithm makes it well-suited for use in diagnostic work-up.

Algorithms↗
PubMed2006

Nonlinear multiscale wavelet diffusion for speckle suppression and edge enhancement in ultrasound images.

This paper introduces a novel nonlinear multiscale wavelet diffusion method for ultrasound speckle suppression and edge enhancement. This method is designed to utilize the favorable denoising properties of two frequently used techniques: the sparsity and multiresolution properties of the wavelet, and the iterative edge enhancement feature of nonlinear diffusion. With fully exploited knowledge of speckle image models, the edges of images are detected using normalized wavelet modulus. Relying on this feature, both the envelope-detected speckle image and the log-compressed ultrasonic image can be directly processed by the algorithm without need for additional preprocessing. Speckle is suppressed by employing the iterative multiscale diffusion on the wavelet coefficients. With a tuning diffusion threshold strategy, the proposed method can improve the image quality for both visualization and auto-segmentation applications. We validate our method using synthetic speckle images and real ultrasonic images. Performance improvement over other despeckling filters is quantified in terms of noise suppression and edge preservation indices.

Algorithms↗