Jet: Multilevel Graph Partitioning on GPUs

The multilevel heuristic is the dominant strategy for high-quality sequential and parallel graph partitioning. Partition refinement is a key step of multilevel graph partitioning. In this work, we present Jet, a new parallel algorithm for partition refinement specifically designed for Graphics Processing Units (GPUs). We combine Jet with GPU-aware coarsening to develop a $k$-way graph partitioner. The new partitioner achieves superior quality when compared to state-of-the-art shared memory graph partitioners on a large collection of test graphs.Comment: Submitted as a non-archival track paper for SIAM ACDA 202

Adipose Derived Stem Cells Reduce Fibrosis and Promote Nerve Regeneration in Rats.

Peripheral nerve regeneration is critical and challenging in the adult humans. High level of collagen infiltration (i.e., scar tissue), in the niche of injury, impedes axonal regeneration and path finding. Unfortunately, studies focusing on the modulation of scar tissue in the nerves are scarce. To address part of this problem, we have evaluated the differentiated adipose derived stem cells (dASCs) for their antifibrotic and regenerative effects in a 10 mm nerve gap model in rats. Three different animal groups (N = 5) were treated with fibrin nerve conduits (empty), or seeded with dASCs (F + dASCs) and autograft, respectively. Histological analysis of regenerated nerves, at 12 weeks postoperatively, reveled the high levels of collagen infiltration (i.e., 21.5% ± 6.1% and 24.1% ± 2.9%) in the middle and distal segment of empty conduit groups in comparison with stem cells treated (16.6% ± 2.1% and 12.1% ± 2.9%) and autograft (15.0% ± 1.7% and 12.8% ± 1.0%) animals. Thus, the dASCs treatment resulted in significant reduction of fibrotic tissue formation. Consequently, enhanced axonal regeneration and remyelination was found in the animals treated with dASCs. Interestingly, these effects of dASCs appeared to be equivalent to that of autograft treatment. Thus, the dASCs hold great potential for preventing the scar tissue formation and for promoting nerve regeneration in the adult organisms. Future experiments will focus on the validation of these findings in a critical nerve injury model. Anat Rec, 301:1714-1721, 2018. © 2018 Wiley Periodicals, Inc

A scalable dc microgrid architecture for rural electrification in emerging regions

Abstract—We present the design and experimental validation of a scalable dc microgrid architecture for rural electrification. The microgrid design has been driven by field data collected from Kenya and India. The salient features of the microgrid are distributed voltage control and distributed storage, which enable developed world grid cost parity. In this paper, we calculate that the levelized cost of electricity (LCOE) for the proposed dc microgrid system will be less than $0.40 per kW-hr. We also present experimental results from a locally installed dc microgrid prototype that demonstrate the steady state behavior, the perturbation response, and the overall efficiency of the system. The experimental results demonstrate the suitability of the presented dc microgrid architecture as a technically advantageous and cost effective method for electrifying emerging regions. I

Perioperative Management of Patients Undergoing Total Pancreatectomy with/without Islet Cell Autotransplantation: A Single Center Experience

Total pancreatectomy (TP) and islet cell autotransplantation (IAT) are complex operations that require intensive postoperative monitoring with standardized protocols. There are few studies detailing immediate perioperative management. The purpose of this study was to describe the perioperative management of post-pancreatectomy patients in the first week following surgery to guide clinicians in addressing salient points from different organ systems. This is a retrospective cohort review of prospectively collected data from September 2017 to September 2022 at a single institution, including patients 16 years and older who underwent TP or TPIAT for chronic pancreatitis. Patients were maintained on a heparin drip (TPIAT), insulin drip, and ketamine infusion. Primary outcomes were complications in the first 5 days following surgery and ICU length of stay (LOS). Secondary outcomes included overall LOS and mortality. Of 31 patients, 26 underwent TPIAT, and 5 underwent TP. Median ICU LOS was five days (IQR 4–6). The most common immediate postoperative complications were reintubation [n = 5 (16%)] and bleeding [n = 2 (6%)]. Median insulin drip use was 70 h (IQR 20–124). There was no mortality. Patients were extubated quickly and progressed well on the protocol. Immediate postoperative complications were generally minor and without long-term effects

Cell-Envelope Remodeling as a Determinant of Phenotypic Antibacterial Tolerance in Mycobacterium tuberculosis

[Image: see text] The mechanisms that lead to phenotypic antibacterial tolerance in bacteria remain poorly understood. We investigate whether changes in NaCl concentration toward physiologically higher values affect antibacterial efficacy against Mycobacterium tuberculosis (Mtb), the causal agent of human tuberculosis. Indeed, multiclass phenotypic antibacterial tolerance is observed during Mtb growth in physiologic saline. This includes changes in sensitivity to ethionamide, ethambutol, d-cycloserine, several aminoglycosides, and quinolones. By employing organism-wide metabolomic and lipidomic approaches combined with phenotypic tests, we identified a time-dependent biphasic adaptive response after exposure of Mtb to physiological levels of NaCl. A first rapid, extensive, and reversible phase was associated with changes in core and amino acid metabolism. In a second phase, Mtb responded with a substantial remodelling of plasma membrane and outer lipid membrane composition. We demonstrate that phenotypic tolerance at physiological concentrations of NaCl is the result of changes in plasma and outer membrane lipid remodeling and not changes in core metabolism. Altogether, these results indicate that physiologic saline-induced antibacterial tolerance is kinetically coupled to cell envelope changes and demonstrate that metabolic changes and growth arrest are not the cause of phenotypic tolerance observed in Mtb exposed to physiologic concentrations of NaCl. Importantly, this work uncovers a role for bacterial cell envelope remodeling in antibacterial tolerance, alongside well-documented allterations in respiration, metabolism, and growth rate

Fast network centrality analysis using GPUs

<p>Abstract</p> <p>Background</p> <p>With the exploding volume of data generated by continuously evolving high-throughput technologies, biological network analysis problems are growing larger in scale and craving for more computational power. General Purpose computation on Graphics Processing Units (GPGPU) provides a cost-effective technology for the study of large-scale biological networks. Designing algorithms that maximize data parallelism is the key in leveraging the power of GPUs.</p> <p>Results</p> <p>We proposed an efficient data parallel formulation of the All-Pairs Shortest Path problem, which is the key component for shortest path-based centrality computation. A betweenness centrality algorithm built upon this formulation was developed and benchmarked against the most recent GPU-based algorithm. Speedup between 11 to 19% was observed in various simulated scale-free networks. We further designed three algorithms based on this core component to compute closeness centrality, eccentricity centrality and stress centrality. To make all these algorithms available to the research community, we developed a software package <it>gpu</it>-<it>fan </it>(GPU-based Fast Analysis of Networks) for CUDA enabled GPUs. Speedup of 10-50× compared with CPU implementations was observed for simulated scale-free networks and real world biological networks.</p> <p>Conclusions</p> <p><it>gpu</it>-<it>fan </it>provides a significant performance improvement for centrality computation in large-scale networks. Source code is available under the GNU Public License (GPL) at <url>http://bioinfo.vanderbilt.edu/gpu-fan/</url>.</p

Fast Computing Betweenness Centrality with Virtual Nodes on Large Sparse Networks

Betweenness centrality is an essential index for analysis of complex networks. However, the calculation of betweenness centrality is quite time-consuming and the fastest known algorithm uses time and space for weighted networks, where and are the number of nodes and edges in the network, respectively. By inserting virtual nodes into the weighted edges and transforming the shortest path problem into a breadth-first search (BFS) problem, we propose an algorithm that can compute the betweenness centrality in time for integer-weighted networks, where is the average weight of edges and is the average degree in the network. Considerable time can be saved with the proposed algorithm when , indicating that it is suitable for lightly weighted large sparse networks. A similar concept of virtual node transformation can be used to calculate other shortest path based indices such as closeness centrality, graph centrality, stress centrality, and so on. Numerical simulations on various randomly generated networks reveal that it is feasible to use the proposed algorithm in large network analysis

Spider Silk Constructs Enhance Axonal Regeneration and Remyelination in Long Nerve Defects in Sheep

BACKGROUND: Surgical reapposition of peripheral nerve results in some axonal regeneration and functional recovery, but the clinical outcome in long distance nerve defects is disappointing and research continues to utilize further interventional approaches to optimize functional recovery. We describe the use of nerve constructs consisting of decellularized vein grafts filled with spider silk fibers as a guiding material to bridge a 6.0 cm tibial nerve defect in adult sheep. METHODOLOGY/PRINCIPAL FINDINGS: The nerve constructs were compared to autologous nerve grafts. Regeneration was evaluated for clinical, electrophysiological and histological outcome. Electrophysiological recordings were obtained at 6 months and 10 months post surgery in each group. Ten months later, the nerves were removed and prepared for immunostaining, electrophysiological and electron microscopy. Immunostaining for sodium channel (NaV 1.6) was used to define nodes of Ranvier on regenerated axons in combination with anti-S100 and neurofilament. Anti-S100 was used to identify Schwann cells. Axons regenerated through the constructs and were myelinated indicating migration of Schwann cells into the constructs. Nodes of Ranvier between myelin segments were observed and identified by intense sodium channel (NaV 1.6) staining on the regenerated axons. There was no significant difference in electrophysiological results between control autologous experimental and construct implantation indicating that our construct are an effective alternative to autologous nerve transplantation. CONCLUSIONS/SIGNIFICANCE: This study demonstrates that spider silk enhances Schwann cell migration, axonal regrowth and remyelination including electrophysiological recovery in a long-distance peripheral nerve gap model resulting in functional recovery. This improvement in nerve regeneration could have significant clinical implications for reconstructive nerve surgery

Exploring the Design Space of Static and Incremental Graph Connectivity Algorithms on GPUs

Connected components and spanning forest are fundamental graph algorithms due to their use in many important applications, such as graph clustering and image segmentation. GPUs are an ideal platform for graph algorithms due to their high peak performance and memory bandwidth. While there exist several GPU connectivity algorithms in the literature, many design choices have not yet been explored. In this paper, we explore various design choices in GPU connectivity algorithms, including sampling, linking, and tree compression, for both the static as well as the incremental setting. Our various design choices lead to over 300 new GPU implementations of connectivity, many of which outperform state-of-the-art. We present an experimental evaluation, and show that we achieve an average speedup of 2.47x speedup over existing static algorithms. In the incremental setting, we achieve a throughput of up to 48.23 billion edges per second. Compared to state-of-the-art CPU implementations on a 72-core machine, we achieve a speedup of 8.26--14.51x for static connectivity and 1.85--13.36x for incremental connectivity using a Tesla V100 GPU