Colorectal cancer is the third most diagnosed cancer in the world, but it has a higher mortality rate in men compared to women. However, we are not close to understanding how and why sex influences the outcome of the ...
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ISBN:
(数字)9781665468190
ISBN:
(纸本)9781665468206
Colorectal cancer is the third most diagnosed cancer in the world, but it has a higher mortality rate in men compared to women. However, we are not close to understanding how and why sex influences the outcome of the disease. This study focuses on mRNA expression profiles of colon cancer patients to look for molecular differences in the development of colon cancer between men and women. We used paired expression data (i.e., data collected in pairs of normal and cancer cells, by taking samples from the same individual), we identified differentially expressed genes (cancer vs normal) and computed co-expression and differential co-expression gene networks (men vs women). Doing so, we inferred the main changes and alterations happening in cancer tissues, and specifically how these changes were different among men and women. We found that the co-expression networks of women and men affected by colon cancer are quite different and we reported the genes that show the most differences in this comparison, checking if they could also be associated to sexual dimorphism or sexual hormones. Among these genes we found a interesting presence of genes associated to the Wnt signaling pathway which has been found to be regulated by estrogen and whose activation is strongly linked with colon cancer.
This paper presents a GPU-based massively parallel implementation of the Best- Worst-Play (BWP) metaphor-less optimization algorithm, which results from the combination of two other simple and quite efficient populati...
This paper presents a GPU-based massively parallel implementation of the Best- Worst-Play (BWP) metaphor-less optimization algorithm, which results from the combination of two other simple and quite efficient population-based algorithms, Jaya and Rao-l, that have been used to solve a variety of prob-lems. The proposed parallel GPU version of the algorithm is here used for solving large nonlinear equation systems, which have enormous importance in different areas of science, engineering, and economics and are usually considered the most difficult class of problems to solve by traditional numerical methods. The proposed parallelization of the BWP algorithm was implemented using the Julia programming language on a GeForce RTX 3090 GPU with 10496 CUDA cores and 24 GB of VRAM and tested on a set of challenging scalable systems of nonlinear equations with dimensions between 500 and 2000. Depending on the tested problem and dimension, the GPU-based implementation of BWP exhibited a speedup up to $283.17\times$ , with an average of $161.21\times$ , which shows the efficiency of the proposed G PU - based parallel version of the BWP algorithm.
Cancer disparities are adverse differences in cancer measures that exist among certain population groups. Given that the role they play not only in the disease prognosis but also in therapy response, there is an urgen...
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ISBN:
(数字)9798350371499
ISBN:
(纸本)9798350371505
Cancer disparities are adverse differences in cancer measures that exist among certain population groups. Given that the role they play not only in the disease prognosis but also in therapy response, there is an urgent need to understand what causes them. Most studies investigate these disparities by analyzing transcriptomic data and in particular miRNAs for their regulatory role, but only focusing on expression levels. To face this challenge we propose MIRROR, a new method which analyzes a differential co-expression network of miRNAs between patients’ cohorts, to study the role they play at the target genes’ level. Doing so, we can study the altered molecular mechanism that are linked to cancer disparities. The application of MIRROR to two different cases of cancer disparities has demonstrated its efficacy in identifying molecular players involved in the considered disparity, presenting itself as a viable option to approach this challenge.
The primary objective of this research is to employ artificial intelligence, machine learning, and neural networks in order to construct a network traffic prediction model. The analysis of network traffic data obtaine...
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ISBN:
(数字)9798350379433
ISBN:
(纸本)9798350379440
The primary objective of this research is to employ artificial intelligence, machine learning, and neural networks in order to construct a network traffic prediction model. The analysis of network traffic data obtained from a digital media and entertainment provider operating in Turkey is conducted through the application of multivariate time-series analysis techniques in order to get insights into the temporal patterns and trends. In model development, Vector Autoregression (VAR), Vector Error Correction Model (VECM), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) algorithms have been utilized. LSTM and GRU models have performed better with low Mean Absolute Percentage Error (MAPE) and high R-squared Score (R
2
). LSTM model has reached 0.98 R2 and 8.95% MAPE. These results indicate that the models can be utilized in network management optimization as resource allocation, congestion detection, anomaly detection, and quality of service.
Super-resolution algorithms aim to produce magnified high-resolution versions from low-resolution images. Some methods, however, are prone to generate blur during the process. Simple sharpening filters are adopted to ...
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Speech brain-computer interfaces aim to decipher what a person is trying to say from neural activity alone, restoring communication to people with paralysis who have lost the ability to speak intelligibly. The Brain-t...
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Objectives:The difficulties in the early detection consequent to the lack of sensitive biomarkers render patients with cholangiocarcinoma(CCA)to have poor ***,sensitive and specific volatile organic compounds(VOCs)wer...
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Objectives:The difficulties in the early detection consequent to the lack of sensitive biomarkers render patients with cholangiocarcinoma(CCA)to have poor ***,sensitive and specific volatile organic compounds(VOCs)were identified in several ***,the VOC profiles in CCA are not ***,we investigated the VOC profiles in exhaled breath of CCA patients and ***:We prospectively collected exhaled breath samples from 30 consecutive patients newly diagnosed with CCA and 30 controls who did not have CCA(seven had benign biliary strictures and 23 had other medical conditions).Exhaled VOCs were identified using gas chromatography mass spectrometry Triple Quadrupoles *** of the significant differences in VOCs between cases and controls was conducted using supervised multivariate regression *** validation was performed for these VOCs in another cohort of 18 CCA patients and 22 ***:Levels of six compounds were significantly different between CCA patients and controls,namely,acetone,isopropyl alcohol,dimethyl sulfide,1,4-pentadiene,allyl methyl sulfide,and N,*** and dimethyl sulfide were independently associated with CCA as demonstrated in the multivariate *** the cut-off value of 8.59107 arbitrary unit(AU),acetone had a sensitivity and specificity of 82.1%and 75.8%,respectively,with an area under the receiving operator curve(AUROC)of 0.85 for the CCA *** level was also significantly different between cases and controls in the validation *** the same cut-off value,the sensitivity,specificity,and AUROC was 59.1%,66.7%,and 0.85,***:Breath analysis may potentially be useful for CCA diagnosis.A cohort of patients with earlystage CCA in further studies is needed to confirm the ability of exhaled VOCs for the early detection of CCA.
Recent studies of genotype-phenotype maps have reported universally enhanced phenotypic robustness to genotype mutations, a feature essential to evolution. Virtually all of these studies make a simplifying assumption ...
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Recent studies of genotype-phenotype maps have reported universally enhanced phenotypic robustness to genotype mutations, a feature essential to evolution. Virtually all of these studies make a simplifying assumption that each genotype—represented as a sequence—maps deterministically to a single phenotype, such as a discrete structure. Here we introduce probabilistic genotype-phenotype (PrGP) maps, where each genotype maps to a vector of phenotype probabilities, as a more realistic and universal language for investigating robustness in a variety of physical, biological, and computational systems. We study three model systems to show that PrGP maps offer a generalized framework which can handle uncertainty emerging from various physical sources: (1) thermal fluctuation in RNA folding, (2) external field disorder in the spin-glass ground state search problem, and (3) superposition and entanglement in quantum circuits, which are realized experimentally on IBM quantum computers. In all three cases, we observe a biphasic robustness scaling which is enhanced relative to random expectation for more frequent phenotypes and approaches random expectation for less frequent phenotypes. We derive an analytical theory for the behavior of PrGP robustness, and we demonstrate that the theory is highly predictive of empirical robustness.
Deep learning is revolutionizing the field of medical image segmentation. The U-shaped (Unet) model, with its encoder-decoder architecture and skip connections, has become the dominant architecture for this task. Howe...
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Simulation on directed graphs is an important method for understanding the dynamics in the systems where connectivity graphs contain cycles. Discrete Stochastic Heterogeneous Simulator (DiSH) is one of the simulation ...
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