Exploration of GPU acceleration for pair-HMM algorithm and its application in the DNA alignment problem

The hidden Markov model, known as HMM, is an important type of statistical model with extensive application in estimating hidden parameters and decoding observed Markov chains. On top of the HMM, the Pair-HMM Algorithm with Halotype-Caller is developed as a popular solution for the DNA alignment problem. For two aligned sequences of DNA observations, one named as reference, and the other one named as read, there are only three possible hidden states, i.e. match (A , A), insertion (- , A), and deletion (A , -). However, what we could observe by DNA sequencing in real-life is the summation of the possibilities for match, insertion, and deletion as macrostates. In order to determine the alignment with maximum probability, we need to score each possible pairwise alignment and which leads to a computationally intensive problem that usually contributes to the most latency in a variant calling with the GATK HaplotypeCaller. In the CPU implementation of a proper Pair-HMM forward algorithm, there are 7 multiply-accumulate operations for each ( i , j ) location on the read-reference matrix. Moreover, since transitions and emission matrices are fixed throughout a single alignment process, a CUDA implementation with single-precision floating-point is proposed to accelerate the Pair-HMM forward algorithm. CUDA implementation with minibatch and states-parallelization, along with the use of float32, gives us an around 22.6x speedup compared to the CPU implementation. While it comes with a price, using single-precision instead of double-precision floating-point introduces a more serious under flow problem at the beginning of the alignment scoring process. A normalization technique is used to help fix this problem.Ope

Quantifying Spatiotemporal Dynamics of Solar Radiation over the Northeast China Based on ACO-BPNN Model and Intensity Analysis

Reliable information on the spatiotemporal dynamics of solar radiation plays a crucial role in studies relating to global climate change. In this study, a new backpropagation neural network (BPNN) model optimized with an Ant Colony Optimization (ACO) algorithm was developed to generate the ACO-BPNN model, which had demonstrated superior performance for simulating solar radiation compared to traditional BPNN modelling, for Northeast China. On this basis, we applied an intensity analysis to investigate the spatiotemporal variation of solar radiation from 1982 to 2010 over the study region at three levels: interval, category, and conversion. Research findings revealed that (1) the solar radiation resource in the study region increased from the 1980s to the 2000s and the average annual rate of variation from the 1980s to the 1990s was lower than that from the 1990s to the 2000s and (2) the gains and losses of solar radiation at each level were in different conditions. The poor, normal, and comparatively abundant levels were transferred to higher levels, whereas the abundant level was transferred to lower levels. We believe our findings contribute to implementing ad hoc energy management strategies to optimize the use of solar radiation resources and provide scientific suggestions for policy planning

Robotic approach together with an enhanced recovery programme improve the perioperative outcomes for complex hepatectomy

ObjectiveRobotic surgery has more advantages than traditional surgical approaches to complex liver resection; however, the robotic approach is invariably associated with increased cost. Enhanced recovery after surgery (ERAS) protocols are beneficial in conventional surgeries.MethodsThe present study investigated the effects of robotic surgery combined with an ERAS protocol on perioperative outcomes and hospitalization costs of patients undergoing complex hepatectomy. Clinical data from consecutive robotic and open liver resections (RLR and OLR, respectively) performed in our unit in the pre-ERAS (January 2019–June 2020) and ERAS (July 2020–December 2021) periods were collected. Multivariate logistic regression analysis was performed to determine the impact of ERAS and surgical approaches—alone or in combination—on LOS and costs.ResultsA total of 171 consecutive complex liver resections were analyzed. ERAS patients had a shorter median LOS and decreased total hospitalization cost, without a significant difference in the complication rate compared with the pre-ERAS cohort. RLR patients had a shorter median LOS and decreased major complications, but with increased total hospitalization cost, compared with OLR patients. Comparing the four combinations of perioperative management and surgical approaches, ERAS + RLR had the shortest LOS and the fewest major complications, whereas pre-ERAS + RLR had the highest hospitalization costs. Multivariate analysis found that the robotic approach was protective against prolonged LOS, whereas the ERAS pathway was protective against high costs.ConclusionsThe ERAS + RLR approach optimized postoperative complex liver resection outcomes and hospitalization costs compared with other combinations. The robotic approach combined with ERAS synergistically optimized outcome and overall cost compared with other strategies, and may be the best combination for optimizing perioperative outcomes for complex RLR

Improved GPU implementations of the Pair-HMM forward algorithm for DNA sequence alignment

With the rise of Next-Generation Sequencing (NGS), clinical sequencing services have become more accessible but also facing new challenges. As we discovered the closed connection between key DeoxyriboNucleic Acid (DNA) mutation spots and major diseases or conditions, the need for computational genomics has increased significantly. The surging demand motivates developments of more efficient algorithms for genome assembly, error correction, k-mer counting etc. In this thesis, we focus on DNA sequencing analysis, one of the fastest-growing markets in NGS, and its related alignment problems. In recent years, many new hardware technologies and algorithms have been researched for their potential applications in massive parallel sequencing. The emerging hardware includes GPU, FPGA and other ASICs providing parallel processing resources. In this thesis, we choose GPU as our computation platform for its massive parallel processing capabilities. The Forward Algorithm (FA) still remains one of the most commonly used methods in solving sequences alignment problems modeled as Pair-Hidden Markov Model (HMM). The Pair-HMM Forward Algorithm (FA) is not only a computation but data intensive algorithm. Multiple previous works have been done in efforts to accelerate the computation of the FA by applying massive parallelization on the workload, and in this thesis, we bring more optimizations not only by improving the computation concurrency of both initialization process and Pair-HMM FA but also by tackling the communications overhead between the host and devices. We will discuss the general principles of optimizing the Forward Algorithm on GPU and present an improved implementation of the Pair-HMM FA with native CUDA C++. Our design has shown a speedup of 25.10x over the C++ baseline on the GATK HaplotypeCaller Pair-HMM workload with a portion of the real dataset from human genome database, NA12878. This is a major improvement that beats the state-of-the-art implementation with a margin of 60%.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

Design of Small MEMS Microphone Array Systems for Direction Finding of Outdoors Moving Vehicles

In this paper, a MEMS microphone array system scheme is proposed which implements real-time direction of arrival (DOA) estimation for moving vehicles. Wind noise is the primary source of unwanted noise on microphones outdoors. A multiple signal classification (MUSIC) algorithm is used in this paper for direction finding associated with spatial coherence to discriminate between the wind noise and the acoustic signals of a vehicle. The method is implemented in a SHARC DSP processor and the real-time estimated DOA is uploaded through Bluetooth or a UART module. Experimental results in different places show the validity of the system and the deviation is no bigger than 6° in the presence of wind noise

Enabling High-Quality Machine Learning Model Trading on Blockchain-Based Marketplace

Machine learning model sharing markets have emerged as a popular platform for individuals and companies to share and access machine learning models. These markets enable more people to benefit from the field of artificial intelligence and to leverage its advantages on a broader scale. However, these markets face challenges in designing effective incentives for model owners to share their models, and for model users to provide honest feedback on model quality. This paper proposes a novel game theoretic framework for machine learning model sharing markets that addresses these challenges. Our framework includes two main components: a mechanism for incentivizing model owners to share their models, and a mechanism for encouraging the honest evaluation of model quality by the model users. To evaluate the effectiveness of our framework, we conducted experiments and the results demonstrate that our mechanism for incentivizing model owners is effective at encouraging high-quality model sharing, and our reputation system encourages the honest evaluation of model quality

P53 together with ferroptosis: a promising strategy leaving cancer cells without escape

TP53, functioning as the keeper of the genome, assumes a pivotal function in the inhibition of tumorigenesis. Recent studies have revealed that p53 regulates ferroptosis pathways within tumor cells and is closely related to tumorigenesis. Therefore, we summarize the pathways and mechanisms by which p53 regulates ferroptosis and identify a series of upstream and downstream molecules involved in this process. Furthermore, we construct a p53-ferroptosis network centered on p53. Finally, we present the progress of drugs to prevent wild-type p53 (wtp53) degeneration and restore wtp53, highlighting the deficiencies of drug development and the prospects for p53 in cancer treatment. These findings provide novel strategies and directions for future cancer therapy

Leakage performance of labyrinth seal for oil sealing of aero-engine

Experimental investigation has been done to evaluate the leakage performance of labyrinth seal for oil sealing on high-speed sealing test rig at different working and geometric parameters. Typical values of pressure ratio ranging from 1.0 to 2.0 were used and the rotating speed varied from 0 to 30,000 rpm. Dimensionless Taylor number was invited to response the effect of rotation. Oil was injected at the rate from 1.2 L/min to 2.8 L/min to check the sealing capacity. Leakage was measured at different seal configurations including sealing clearance, tooth tip thickness, pitch, teeth number, front inclined angle and oil-throwing angle. Different from gas sealing, the application of oil-throwing tooth in oil sealing attracted much interest as an obvious alternative to the conventional labyrinth seal. A blocking ring was captured during testing, which establishes understanding of underlying flow mechanisms in the clearance and plays an important role in oil sealing. There is a critical Taylor number at which the leakage coefficient drops drastically. After the critical Taylor number, a parabola rule appears. An optimal composition of tooth tip thickness, teeth number, oil-throwing angle and front inclined angle exists where the leakage performance behaves better. Keywords: Labyrinth seal, Leakage, Rotating, Oil sealing, Aero-engin

Study on the Constitutive Equation and Mechanical Properties of Natural Snow under Step Loading

Snow, as an important component of the cryosphere, holds a crucial role in the construction of polar infrastructure. However, the current research on the mechanical properties of snow is not comprehensive. To contribute to our understanding of the mechanical behaviors of snow in cold regions, uniaxial compression tests under step loading were performed on the snow. With the Maxwell model as the basis, different temperatures, densities, and loading rates were set to establish constitutive equations of snow. The changes in the elastic modulus and viscosity coefficient of snow with respect to three variables were investigated. The results show that the loading rate has no obvious effect on the elastic modulus and viscosity coefficient of snow. Both the elastic modulus and viscosity coefficient of snow follow an exponential function with respect to density, with an increase in density, resulting in a higher value. As temperature decreases, the elastic modulus and viscosity coefficient initially decrease and then increase, whereas no specific functional relationship between them was observed. Additionally, a new constitutive equation considering snow density is derived based on the Maxwell model