Long stroke pump

A very high pressure pump apparatus which minimizes wear on the seals thereof and on valves connected thereto, by utilizing a very long stroke piston rod whose opposite ends are received in long cylinders. An electric motor which drives the rod, includes a rotor with a threaded aperture that receives a long threaded middle portion of the rod, so that as the rotor turns it advances the rod

Modeling hydrodynamic self-propulsion with Stokesian Dynamics. Or teaching Stokesian Dynamics to swim

We develop a general framework for modeling the hydrodynamic self-propulsion (i.e., swimming) of bodies (e.g., microorganisms) at low Reynolds number via Stokesian Dynamics simulations. The swimming body is composed of many spherical particles constrained to form an assembly that deforms via relative motion of its constituent particles. The resistance tensor describing the hydrodynamic interactions among the individual particles maps directly onto that for the assembly. Specifying a particular swimming gait and imposing the condition that the swimming body is force- and torque-free determine the propulsive speed. The body’s translational and rotational velocities computed via this methodology are identical in form to that from the classical theory for the swimming of arbitrary bodies at low Reynolds number. We illustrate the generality of the method through simulations of a wide array of swimming bodies: pushers and pullers, spinners, the Taylor=Purcell swimming toroid, Taylor’s helical swimmer, Purcell’s three-link swimmer, and an amoeba-like body undergoing large-scale deformation. An open source code is a part of the supplementary material and can be used to simulate the swimming of a body with arbitrary geometry and swimming gait

Graph Neural Networks and Applied Linear Algebra

Sparse matrix computations are ubiquitous in scientific computing. With the recent interest in scientific machine learning, it is natural to ask how sparse matrix computations can leverage neural networks (NN). Unfortunately, multi-layer perceptron (MLP) neural networks are typically not natural for either graph or sparse matrix computations. The issue lies with the fact that MLPs require fixed-sized inputs while scientific applications generally generate sparse matrices with arbitrary dimensions and a wide range of nonzero patterns (or matrix graph vertex interconnections). While convolutional NNs could possibly address matrix graphs where all vertices have the same number of nearest neighbors, a more general approach is needed for arbitrary sparse matrices, e.g. arising from discretized partial differential equations on unstructured meshes. Graph neural networks (GNNs) are one approach suitable to sparse matrices. GNNs define aggregation functions (e.g., summations) that operate on variable size input data to produce data of a fixed output size so that MLPs can be applied. The goal of this paper is to provide an introduction to GNNs for a numerical linear algebra audience. Concrete examples are provided to illustrate how many common linear algebra tasks can be accomplished using GNNs. We focus on iterative methods that employ computational kernels such as matrix-vector products, interpolation, relaxation methods, and strength-of-connection measures. Our GNN examples include cases where parameters are determined a-priori as well as cases where parameters must be learned. The intent with this article is to help computational scientists understand how GNNs can be used to adapt machine learning concepts to computational tasks associated with sparse matrices. It is hoped that this understanding will stimulate data-driven extensions of classical sparse linear algebra tasks

Postprandial plasma free amino acid profile and hepatic gene expression in juvenile barramundi (Lates calcarifer) is more responsive to feed consumption than to dietary methionine inclusion

The effects of dietary methionine (Met) supply on the postprandial pattern of plasma free amino acids and the differential expression of several genes associated with a number of sulfur amino acid and protein turnover pathways in the liver of juvenile barramundi (Lates calcarifer) was investigated. At the conclusion of a 49-day growth trial assessing the requirement for dietary Met, three treatments were selected (with deficient (DEF; 8.6 g kg−1), adequate (ADQ; 14.9 g kg−1) and excessive (EXC; 21.4 g kg−1)) levels of dietary Met, based on their respective growth responses. A peak occurred in plasma free Met at 2 h post-feeding in fish fed the DEF and ADQ diets and at 4 h post-feeding in fish in the EXC treatment. Liver samples collected at these timepoints, as well as those taken as a pre-feeding control, were analyzed for expression of genes involved in Met turnover (CGL, MAT-1, MAT-2a) and taurine biosynthetic pathways (CSAD, ADO, CDO), target of rapamycin inhibition (Redd-1), the somatotropic axis (GHR-II, IGFI, IGF-II) and protein turnover pathways (MUL-1, ZFAND-5). Markers of sulfur amino acid turnover were more significantly affected by time after feeding than by dietary Met level, suggesting production of these enzymes may be primarily regulated by the consumption of feed or protein, rather than by the dietary composition. Further, metabolised Met appeared likely to have been directed through S-Adenosylmethionine (SAM) dependent pathways, rather than converted to Cys, which may have contributed to the observed growth response. Both genes influencing the conversion of Met to SAM appear to be active at this lifestage in barramundi. Previously described markers of proteolytic pathways appear to be conserved in this species and we have confirmed that ZFAND-5 is a reliable biomarker of this process in barramundi. A number of important genes were investigated for the first time in this species and shown to be nutritionally regulated

Peak picking as a pre-processing technique for imaging time of flight secondary ion mass spectrometry

High surface sensitivity and lateral resolution imaging make time-of-flight secondary ion mass spectrometry (ToF-SIMS) a unique and powerful tool for biological analysis. However, with the leaps forward made in the capabilities of the ToF-SIMS instrumentation, the data being recorded from these instruments has dramatically increased. Unfortunately, with these large, often complex, datasets, a bottleneck appears in their processing and interpretation. Here, an application of peak picking is described and applied to ToF-SIMS images allowing for large compression of data, noise removal and improved contrast, while retaining a high percentage of the original signal. Peak picking is performed to locate peaks within ToF-SIMS data. By using this information, signal arising from the same distribution can be summed and overlapping signals separated. As a result, the data size and complexity can be dramatically reduced. This method also acts as an effective noise filter, discarding unwanted noise from the data set. Peak picking and separation are evaluated against the conventional methods of mass binning and manually selecting regions of a peak to image on a model data set

Oncolog, Volume 37, Issue 02, April-June 1992

The primacy of patient welfare Potential doubling time of tumors may be the key to accurate prognosis, appropriate treatment Cognitive deficits in survivors of childhood cancershttps://openworks.mdanderson.org/oncolog/1038/thumbnail.jp

Differential expression of type X collagen in a mechanically active 3-D chondrocyte culture system: a quantitative study

OBJECTIVE: Mechanical loading of cartilage influences chondrocyte metabolism and gene expression. The gene encoding type X collagen is expressed specifically by hypertrophic chondrocytes and up regulated during osteoarthritis. In this study we tested the hypothesis that the mechanical microenvironment resulting from higher levels of local strain in a three dimensional cell culture construct would lead to an increase in the expression of type X collagen mRNA by chondrocytes in those areas. METHODS: Hypertrophic chondrocytes were isolated from embryonic chick sterna and seeded onto rectangular Gelfoam sponges. Seeded sponges were subjected to various levels of cyclic uniaxial tensile strains at 1 Hz with the computer-controlled Bio-Stretch system. Strain distribution across the sponge was quantified by digital image analysis. After mechanical loading, sponges were cut and the end and center regions were separated according to construct strain distribution. Total RNA was extracted from the cells harvested from these regions, and real-time quantitative RT-PCR was performed to quantify mRNA levels for type X collagen and a housing-keeping gene 18S RNA. RESULTS: Chondrocytes distributed in high (9%) local strain areas produced more than two times type X collagen mRNA compared to the those under no load conditions, while chondrocytes located in low (2.5%) local strain areas had no appreciable difference in type X collagen mRNA production in comparison to non-loaded samples. Increasing local strains above 2.5%, either in the center or end regions of the sponge, resulted in increased expression of Col X mRNA by chondrocytes in that region. CONCLUSION: These findings suggest that the threshold of chondrocyte sensitivity to inducing type X collagen mRNA production is more than 2.5% local strain, and that increased local strains above the threshold results in an increase of Col X mRNA expression. Such quantitative analysis has important implications for our understanding of mechanosensitivity of cartilage and mechanical regulation of chondrocyte gene expression

miR-21-mediated regulation of 15-hydroxyprostaglandin dehydrogenase in colon cancer

This work is licensed under a Creative Commons Attribution 4.0 International License.Elevated prostaglandin E2 (PGE2) levels are observed in colorectal cancer (CRC) patients, and this increase is associated with poor prognosis. Increased synthesis of PGE2 in CRC has been shown to occur through COX-2-dependent mechanisms; however, loss of the PGE2-catabolizing enzyme, 15-hydroxyprostaglandin dehydrogenase (15-PGDH, HPGD), in colonic tumors contributes to increased prostaglandin levels and poor patient survival. While loss of 15-PGDH can occur through transcriptional mechanisms, we demonstrate that 15-PGDH can be additionally regulated by a miRNA-mediated mechanism. We show that 15-PGDH and miR-21 are inversely correlated in CRC patients, with increased miR-21 levels associating with low 15-PGDH expression. 15-PGDH can be directly regulated by miR-21 through distinct sites in its 3′ untranslated region (3′UTR), and miR-21 expression in CRC cells attenuates 15-PGDH and promotes increased PGE2 levels. Additionally, epithelial growth factor (EGF) signaling suppresses 15-PGDH expression while simultaneously enhancing miR-21 levels. miR-21 inhibition represses CRC cell proliferation, which is enhanced with EGF receptor (EGFR) inhibition. These findings present a novel regulatory mechanism of 15-PGDH by miR-21, and how dysregulated expression of miR-21 may contribute to loss of 15-PGDH expression and promote CRC progression via increased accumulation of PGE2.NIH R01 CA134609NIH R01 AR069044NIH/NCI Cancer Center Support Grant (P30 CA168524)New Jersey Commission on Cancer ResearchAmerican Heart Association (15GRNT23240019