Santiagoklint0106

Our method sxPDH boosts prediction performance using S-ISOMAP and XGBoost. The AUC of the model is 0.773, and the F1 score is 0.713. Experimental results on benchmark dataset indicate that sxPDH can achieve generally better performance in predicting hot spots compared to the state-of-the-art methods.

Our method sxPDH boosts prediction performance using S-ISOMAP and XGBoost. The AUC of the model is 0.773, and the F1 score is 0.713. Experimental results on benchmark dataset indicate that sxPDH can achieve generally better performance in predicting hot spots compared to the state-of-the-art methods.

Introducing deep learning approach to medical images has rendered a large amount of un-decoded information into usage in clinical research. But mostly, it has been focusing on the performance of the prediction modeling for disease-related entity, but not on the clinical implication of the feature itself. Here we analyzed liver imaging features of abdominal CT images collected from 2019 patients with stage I - III colorectal cancer (CRC) using convolutional neural network (CNN) to elucidate its clinical implication in oncological perspectives.

CNN generated imaging features from the liver parenchyma. Dimension reduction was done for the features by principal component analysis. We designed multiple prediction models for 5-year metachronous liver metastasis (5YLM) using combinations of clinical variables (age, sex, T stage, N stage) and top principal components (PCs), with logistic regression classification. The model using "1

PC (PC1) + clinical information" had the highest performance (mean AUC = 0.747)predictive for metachronous liver metastasis.

The imaging features combined with clinical information improved the performance compared to the standardized prediction model using only clinical information. The liver imaging features generated by CNN may have the potential to predict liver metastasis. These results suggest that even though there were no liver metastasis during the primary colectomy, the features of liver imaging can impose characteristics that could be predictive for metachronous liver metastasis.

The potential of genetic testing for rapid and accurate diagnosis of drug-resistant Mycobacterium tuberculosis strains is vital for efficient treatment and reduction in dissemination. MTBDR plus assays rapidly detect mutations related to drug resistance and wild type sequences allied with susceptibility. Although these methods are promising, the examination of molecular level performance is essential for improved assay result interpretation and continued diagnostic development. Therefore this study aimed to determine novel mutations that were inhibiting wild type probe hybridization in the Line probe assay by DNA sequencing. Using data collected from Line Probe assay (GenoType MTBDRplus assay) the contribution of absent wild type probe hybridization to the detection of rifampicin resistance was assessed via comparison to a reference standard method i.e. DNA sequencing.

Sequence analysis of the rpoB gene of 47 MTB resistant strains from clinical specimens showed that 37 had a single mutation, 9 had double mutations and one had triple mutations in the ropB gene.

The absence of wild type probe hybridization without mutation probe hybridization was mainly the result of the failure of mutation probe hybridization and the result of the novel or rare mutations. Additional probes are necessary to be included in the Line probe assay to improve the detection of rifampicin-resistant Mycobacterium tuberculosis strains.

The absence of wild type probe hybridization without mutation probe hybridization was mainly the result of the failure of mutation probe hybridization and the result of the novel or rare mutations. Additional probes are necessary to be included in the Line probe assay to improve the detection of rifampicin-resistant Mycobacterium tuberculosis strains.

In Overlap-Layout-Consensus (OLC) based de novo assembly, all reads must be compared with every other read to find overlaps. This makes the process rather slow and limits the practicality of using de novo assembly methods at a large scale in the field. Darwin is a fast and accurate read overlapper that can be used for de novo assembly of state-of-the-art third generation long DNA reads. Darwin is designed to be hardware-friendly and can be accelerated on specialized computer system hardware to achieve higher performance.

This work accelerates Darwin on GPUs. Using real Pacbio data, our GPU implementation on Tesla K40 has shown a speedup of 109x vs 8 CPU threads of an Intel Xeon machine and 24x vs 64 threads of IBM Power8 machine. The GPU implementation supports both linear and affine gap, scoring model. The results show that the GPU implementation can achieve the same high speedup for different scoring schemes.

The GPU implementation proposed in this work shows significant improvement in performance compared to the CPU version, thereby making it accessible for utilization as a practical read overlapper in a DNA assembly pipeline. Ziftomenib in vivo Furthermore, our GPU acceleration can also be used for performing fast Smith-Waterman alignment between long DNA reads. GPU hardware has become commonly available in the field today, making the proposed acceleration accessible to a larger public. The implementation is available at https//github.com/Tongdongq/darwin-gpu .

The GPU implementation proposed in this work shows significant improvement in performance compared to the CPU version, thereby making it accessible for utilization as a practical read overlapper in a DNA assembly pipeline. Furthermore, our GPU acceleration can also be used for performing fast Smith-Waterman alignment between long DNA reads. GPU hardware has become commonly available in the field today, making the proposed acceleration accessible to a larger public. The implementation is available at https//github.com/Tongdongq/darwin-gpu .

Advances in mobile sequencing devices and laptop performance make metagenomic sequencing and analysis in the field a technologically feasible prospect. However, metagenomic analysis pipelines are usually designed to run on servers and in the cloud.

MAIRA is a new standalone program for interactive taxonomic and functional analysis of long read metagenomic sequencing data on a laptop, without requiring external resources. The program performs fast, online, genus-level analysis, and on-demand, detailed taxonomic and functional analysis. It uses two levels of frame-shift-aware alignment of DNA reads against protein reference sequences, and then performs detailed analysis using a protein synteny graph.

We envision this software being used by researchers in the field, when access to servers or cloud facilities is difficult, or by individuals that do not routinely access such facilities, such as medical researchers, crop scientists, or teachers.

We envision this software being used by researchers in the field, when access to servers or cloud facilities is difficult, or by individuals that do not routinely access such facilities, such as medical researchers, crop scientists, or teachers.