The effects of pelvic floor muscle therapy on symptoms, voiding, and pelvic floor muscle activity parameters in children with overactive bladder.

INTRODUCTION: Refractory overactive bladder (OAB) in children can be treated with second line modalities such as as biofeedback, transcutaneous electrical stimulation (TENS), and botulinum toxin. In this study, we aimed to investigate the efficacy of biofeedback-assisted pelvic floor muscle therapy (PFMT) on symptoms, bladder capacity, uroflowmetry, and pelvic floor muscle activity (PFMA) in children with resistant OAB or dysfunctional voiding (DV) with associated seconder bladder overactivity (DV/SBO). MATERIALS AND METHODS: A total of 24 children with resistant OAB were included in the study. Patients were divided into two groups as: group-1 pure OAB and group-2 DV/SBO. Children were evaluated with voiding diary, uroflowmetry-EMG, PFMA before and after treatment. All patients were treated with PFMT. RESULTS: Urgency cured or improved in 12 of 17 (71%) of children in group-1 and in six of seven (86%) children in group-2 (P < 0.0001 and 0.031, respectively). Other symptoms cured or improved with 64%-100% recovery rates in group-1 and 50%-80% in group-2. Maximum voided volume (maxVV) in voiding diary increased from 81.6 to 150.9 mL in group-1 and from 115.6 to 175.7 mL in group-2 (P < 0.0001 and 0.063, respectively). Mean work value of PFMA increased and mean rest value of PFMA decreased significantly (P < 0.0001, 0.018 and P = 0.002 and 0.018, respectively). CONCLUSION: The measurement of PFMA in children with refractory OAB or DV/SBO gives information on the strength and endurance of PFMs. In children with refractory OAB or DV/SBO, biofeedback-assisted PFMT provides symptomatic improvement and increases functional bladder capacity.

GRIM-Filter: Fast seed location filtering in DNA read mapping using processing-in-memory technologies.

BACKGROUND: Seed location filtering is critical in DNA read mapping, a process where billions of DNA fragments (reads) sampled from a donor are mapped onto a reference genome to identify genomic variants of the donor. State-of-the-art read mappers 1) quickly generate possible mapping locations for seeds (i.e., smaller segments) within each read, 2) extract reference sequences at each of the mapping locations, and 3) check similarity between each read and its associated reference sequences with a computationally-expensive algorithm (i.e., sequence alignment) to determine the origin of the read. A seed location filter comes into play before alignment, discarding seed locations that alignment would deem a poor match. The ideal seed location filter would discard all poor match locations prior to alignment such that there is no wasted computation on unnecessary alignments. RESULTS: We propose a novel seed location filtering algorithm, GRIM-Filter, optimized to exploit 3D-stacked memory systems that integrate computation within a logic layer stacked under memory layers, to perform processing-in-memory (PIM). GRIM-Filter quickly filters seed locations by 1) introducing a new representation of coarse-grained segments of the reference genome, and 2) using massively-parallel in-memory operations to identify read presence within each coarse-grained segment. Our evaluations show that for a sequence alignment error tolerance of 0.05, GRIM-Filter 1) reduces the false negative rate of filtering by 5.59x-6.41x, and 2) provides an end-to-end read mapper speedup of 1.81x-3.65x, compared to a state-of-the-art read mapper employing the best previous seed location filtering algorithm. CONCLUSION: GRIM-Filter exploits 3D-stacked memory, which enables the efficient use of processing-in-memory, to overcome the memory bandwidth bottleneck in seed location filtering. We show that GRIM-Filter significantly improves the performance of a state-of-the-art read mapper. GRIM-Filter is a universal seed location filter that can be applied to any read mapper. We hope that our results provide inspiration for new works to design other bioinformatics algorithms that take advantage of emerging technologies and new processing paradigms, such as processing-in-memory using 3D-stacked memory devices.

GateKeeper: a new hardware architecture for accelerating pre-alignment in DNA short read mapping.

MOTIVATION: High throughput DNA sequencing (HTS) technologies generate an excessive number of small DNA segments -called short reads- that cause significant computational burden. To analyze the entire genome, each of the billions of short reads must be mapped to a reference genome based on the similarity between a read and 'candidate' locations in that reference genome. The similarity measurement, called alignment, formulated as an approximate string matching problem, is the computational bottleneck because: (i) it is implemented using quadratic-time dynamic programming algorithms and (ii) the majority of candidate locations in the reference genome do not align with a given read due to high dissimilarity. Calculating the alignment of such incorrect candidate locations consumes an overwhelming majority of a modern read mapper's execution time. Therefore, it is crucial to develop a fast and effective filter that can detect incorrect candidate locations and eliminate them before invoking computationally costly alignment algorithms. RESULTS: We propose GateKeeper, a new hardware accelerator that functions as a pre-alignment step that quickly filters out most incorrect candidate locations. GateKeeper is the first design to accelerate pre-alignment using Field-Programmable Gate Arrays (FPGAs), which can perform pre-alignment much faster than software. When implemented on a single FPGA chip, GateKeeper maintains high accuracy (on average >96%) while providing, on average, 90-fold and 130-fold speedup over the state-of-the-art software pre-alignment techniques, Adjacency Filter and Shifted Hamming Distance (SHD), respectively. The addition of GateKeeper as a pre-alignment step can reduce the verification time of the mrFAST mapper by a factor of 10. AVAILABILITY AND IMPLEMENTATION: https://github.com/BilkentCompGen/GateKeeper. CONTACT: mohammedalser@bilkent.edu.tr or onur.mutlu@inf.ethz.ch or calkan@cs.bilkent.edu.tr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.