An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine Learning.

Benign prostatic hyperplasia (BPH) is the main cause of lower urinary tract symptoms (LUTS) in aging males. Transurethral resection of the prostate (TURP) surgery is performed by a cystoscope passing through the urethra and scraping off the prostrate piece by piece through a cutting loop. Although TURP is a minimally invasive procedure, bleeding is still the most common complication. Therefore, the evaluation, monitoring, and prevention of interop bleeding during TURP are very important issues. The main idea of this study is to rank bleeding levels during TURP surgery from videos. Generally, to judge bleeding level by human eyes from surgery videos is a difficult task, which requires sufficient experienced urologists. In this study, machine learning-based ranking algorithms are proposed to efficiently evaluate the ranking of blood levels. Based on the visual clarity of the surgical field, the four ranking of blood levels, including score 0: excellent; score 1: acceptable; score 2: slightly bad; and 3: bad, were identified by urologists who have sufficient experience in TURP surgery. The results of extensive experiments show that the revised accuracy can achieve 90, 89, 90, and 91%, respectively. Particularly, the results reveal that the proposed methods were capable of classifying the ranking of bleeding level accurately and efficiently reducing the burden of urologists.

Clover: a clustering-oriented de novo assembler for Illumina sequences.

BACKGROUND: Next-generation sequencing technologies revolutionized genomics by producing high-throughput reads at low cost, and this progress has prompted the recent development of de novo assemblers. Multiple assembly methods based on de Bruijn graph have been shown to be efficient for Illumina reads. However, the sequencing errors generated by the sequencer complicate analysis of de novo assembly and influence the quality of downstream genomic researches. RESULTS: In this paper, we develop a de Bruijn assembler, called Clover (clustering-oriented de novo assembler), that utilizes a novel k-mer clustering approach from the overlap-layout-consensus concept to deal with the sequencing errors generated by the Illumina platform. We further evaluate Clover's performance against several de Bruijn graph assemblers (ABySS, SOAPdenovo, SPAdes and Velvet), overlap-layout-consensus assemblers (Bambus2, CABOG and MSR-CA) and string graph assembler (SGA) on three datasets (Staphylococcus aureus, Rhodobacter sphaeroides and human chromosome 14). The results show that Clover achieves a superior assembly quality in terms of corrected N50 and E-size while remaining a significantly competitive in run time except SOAPdenovo. In addition, Clover was involved in the sequencing projects of bacterial genomes Acinetobacter baumannii TYTH-1 and Morganella morganii KT. CONCLUSIONS: The marvel clustering-based approach of Clover that integrates the flexibility of the overlap-layout-consensus approach and the efficiency of the de Bruijn graph method has high potential on de novo assembly. Now, Clover is freely available as open source software from https://oz.nthu.edu.tw/~d9562563/src.html .

Association between Statin Use and Sepsis Risk in Patients with Dementia: A Retrospective Cohort Study.

This study investigated the association of statin use with sepsis risk in patients with dementia. This retrospective cohort study was conducted in Taiwan by using data from the National Health Insurance Research Database. We identified and enrolled 308 patients with newly diagnosed dementia who used statin after dementia diagnosis. These patients were individually propensity score matched (1:1) according to age, sex, hypertension, hyperlipidemia, diabetes, cerebrovascular disease, renal disease, liver disease, asthma, malignancy, parkinsonism, and dementia drugs used (donepezil, rivastigmine, galantamine, and memantine) with 251 controls (statin non-users). A Cox proportional hazard model was used to estimate the adjusted hazard ratio for sepsis in statin users and non-users. After adjustment for other confounding factors, the incidence of sepsis in statin users was 1.42-fold higher than that in non-users (95% confidence interval = 0.81-2.5). In conclusion, our analysis showed no positive association of sepsis with statin use in patients with dementia.

MetaSMC: a coalescent-based shotgun sequence simulator for evolving microbial populations.

MOTIVATION: High-throughput sequencing technology has revolutionized the study of metagenomics and cancer evolution. In a relatively simple environment, a metagenomics sequencing data is dominated by a few species. By analyzing the alignment of reads from microbial species, single nucleotide polymorphisms can be discovered and the evolutionary history of the populations can be reconstructed. The ever-increasing read length will allow more detailed analysis about the evolutionary history of microbial or tumor cell population. A simulator of shotgun sequences from such populations will be helpful in the development or evaluation of analysis algorithms. RESULTS: Here, we described an efficient algorithm, MetaSMC, which simulates reads from evolving microbial populations. Based on the coalescent theory, our simulator supports all evolutionary scenarios supported by other coalescent simulators. In addition, the simulator supports various substitution models, including Jukes-Cantor, HKY85 and generalized time-reversible models. The simulator also supports mutator phenotypes by allowing different mutation rates and substitution models in different subpopulations. Our algorithm ignores unnecessary chromosomal segments and thus is more efficient than standard coalescent when recombination is frequent. We showed that the process behind our algorithm is equivalent to Sequentially Markov Coalescent with an incomplete sample. The accuracy of our algorithm was evaluated by summary statistics and likelihood curves derived from Monte Carlo integration over large number of random genealogies. AVAILABILITY AND IMPLEMENTATION: MetaSMC is written in C. The source code is available at https://github.com/tarjxvf/metasmc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Composition Analysis and Feature Selection of the Oral Microbiota Associated with Periodontal Disease.

Periodontitis is an inflammatory disease involving complex interactions between oral microorganisms and the host immune response. Understanding the structure of the microbiota community associated with periodontitis is essential for improving classifications and diagnoses of various types of periodontal diseases and will facilitate clinical decision-making. In this study, we used a 16S rRNA metagenomics approach to investigate and compare the compositions of the microbiota communities from 76 subgingival plagues samples, including 26 from healthy individuals and 50 from patients with periodontitis. Furthermore, we propose a novel feature selection algorithm for selecting features with more information from many variables with a combination of these features and machine learning methods were used to construct prediction models for predicting the health status of patients with periodontal disease. We identified a total of 12 phyla, 124 genera, and 355 species and observed differences between health- and periodontitis-associated bacterial communities at all phylogenetic levels. We discovered that the genera Porphyromonas, Treponema, Tannerella, Filifactor, and Aggregatibacter were more abundant in patients with periodontal disease, whereas Streptococcus, Haemophilus, Capnocytophaga, Gemella, Campylobacter, and Granulicatella were found at higher levels in healthy controls. Using our feature selection algorithm, random forests performed better in terms of predictive power than other methods and consumed the least amount of computational time.

Hadoop-MCC: Efficient Multiple Compound Comparison Algorithm Using Hadoop.

AIM AND OBJECTIVE: In the past decade, the drug design technologies have been improved enormously. The computer-aided drug design (CADD) has played an important role in analysis and prediction in drug development, which makes the procedure more economical and efficient. However, computation with big data, such as ZINC containing more than 60 million compounds data and GDB-13 with more than 930 million small molecules, is a noticeable issue of time-consuming problem. Therefore, we propose a novel heterogeneous high performance computing method, named as Hadoop-MCC, integrating Hadoop and GPU, to copy with big chemical structure data efficiently. MATERIALS AND METHODS: Hadoop-MCC gains the high availability and fault tolerance from Hadoop, as Hadoop is used to scatter input data to GPU devices and gather the results from GPU devices. Hadoop framework adopts mapper/reducer computation model. In the proposed method, mappers response for fetching SMILES data segments and perform LINGO method on GPU, then reducers collect all comparison results produced by mappers. Due to the high availability of Hadoop, all of LINGO computational jobs on mappers can be completed, even if some of the mappers encounter problems. RESULTS: A comparison of LINGO is performed on each the GPU device in parallel. According to the experimental results, the proposed method on multiple GPU devices can achieve better computational performance than the CUDA-MCC on a single GPU device. CONCLUSION: Hadoop-MCC is able to achieve scalability, high availability, and fault tolerance granted by Hadoop, and high performance as well by integrating computational power of both of Hadoop and GPU. It has been shown that using the heterogeneous architecture as Hadoop-MCC effectively can enhance better computational performance than on a single GPU device.

Subgingival microbiota in individuals with severe chronic periodontitis.

BACKGROUND/PURPOSE: Subgingival microorganisms are potentially associated with periodontal diseases. However, the correlation between the variance in the periodontal microbiome and the prevalence and severity of periodontitis remains unclear. The aim of this study was to determine the subgingival microbiota in Taiwanese individuals with severe chronic periodontitis (SP). METHODS: The composition of the subgingival microbiota in healthy and diseased individuals was compared using a 16S rRNA metagenomic approach and quantitative polymerase chain reaction (qPCR). A total of 20 samples, including 10 from healthy individuals and 10 from SP patients, were analyzed. RESULTS: We found high microbial diversity, with an average of 774 classified phylotypes per sample and a total of six bacterial phyla across all samples. Cluster analysis by principal component analysis and heat map showed that the bacterial communities were different in the two groups. Streptococcus dominated across all the healthy samples, whereas Prevotella, Porphyromonas, and Treponema were highly abundant across all diseased samples. At least 13 bacterial genera were conserved among all the samples. Only eight genera, including Lautropia, Parvimonas, Actinomyces, Capnocytophaga, Paludibacter, Streptococcus, Haemophilus, and Corynebacterium, were significantly enriched in the healthy group, and six genera, including Porphyromonas, Treponema, Tannerella, Aggregatibacter, Peptostreptococcus, and Filifactor, were significantly enriched in the diseased group. Furthermore, a trend of abundance of bacteria at the species level measured by qPCR in all samples was consistent with the 16S rRNA metagenomics results. CONCLUSION: This study is the first in Taiwan to provide a picture of the microbiome in SP via 16S rRNA metagenomics.

Prevalence and comparative analysis of the type IV secretion system in Aggregatibacter actinomycetemcomitan.

BACKGROUD/PURPOSE: Aggregatibacter actinomycetemcomitans has emerged as one of the aetiological agents in periodontal disease. Although Type IV secretion systems (T4SSs) are widely distributed in many bacteria, the genetic features and distribution of T4SSs in A. actinomycetemcomitans remain unclear. In this study, we investigated the prevalence of A. actinomycetemcomitans serotypes and their T4SSs in a Taiwanese population. METHODS: A comparative analysis of 20 A. actinomycetemcomitans genomes and their T4SSs deposited in GenBank was performed. One hundred subjects, including 20 periodontitis and 80 normal subjects, were enrolled and PCR identification of A. actinomycetemcomitans serotypes and T4SS genes were performed. RESULTS: Of 100 subjects, serotypes C (22%) and E (11%) were most common. In addition, T4SSs were distributed in all of the serotypes. The prevalence of T4SSs and their location in plasmids in periodontitis subjects were 1.28-2 fold higher but not significantly different compared to normal subjects. Of 20 A. actinomycetemcomitans genomes, only ten with complete T4SS modules could be detected, which was highly correlated with localized aggressive periodontitis (p < 0.1). Nine of ten T4SS modules were from periodontitis subjects. Phylogenetic analysis of 10 T4SSs in A. actinomycetemcomitans showed that they were clustered into two groups, T4SSAaI and T4SSAaII, with only T4SSAaI appearing in the Taiwanese subjects. CONCLUSION: A. actinomycetemcomitans strains with different serotypes carrying T4SSAaI are widely distributed in a Taiwanese population. This is the first report to show the distribution and detailed comparative genomics of T4SSs in A. actinomycetemcomitans.

MGUPGMA: A Fast UPGMA Algorithm With Multiple Graphics Processing Units Using NCCL.

A phylogenetic tree is a visual diagram of the relationship between a set of biological species. The scientists usually use it to analyze many characteristics of the species. The distance-matrix methods, such as Unweighted Pair Group Method with Arithmetic Mean and Neighbor Joining, construct a phylogenetic tree by calculating pairwise genetic distances between taxa. These methods have the computational performance issue. Although several new methods with high-performance hardware and frameworks have been proposed, the issue still exists. In this work, a novel parallel Unweighted Pair Group Method with Arithmetic Mean approach on multiple Graphics Processing Units is proposed to construct a phylogenetic tree from extremely large set of sequences. The experimental results present that the proposed approach on a DGX-1 server with 8 NVIDIA P100 graphic cards achieves approximately 3-fold to 7-fold speedup over the implementation of Unweighted Pair Group Method with Arithmetic Mean on a modern CPU and a single GPU, respectively.

Bacterial diversity among four healthcare-associated institutes in Taiwan.

Indoor microbial communities have important implications for human health, especially in health-care institutes (HCIs). The factors that determine the diversity and composition of microbiomes in a built environment remain unclear. Herein, we used 16S rRNA amplicon sequencing to investigate the relationships between building attributes and surface bacterial communities among four HCIs located in three buildings. We examined the surface bacterial communities and environmental parameters in the buildings supplied with different ventilation types and compared the results using a Dirichlet multinomial mixture (DMM)-based approach. A total of 203 samples from the four HCIs were analyzed. Four bacterial communities were grouped using the DMM-based approach, which were highly similar to those in the 4 HCIs. The α-diversity and ß-diversity in the naturally ventilated building were different from the conditioner-ventilated building. The bacterial source composition varied across each building. Nine genera were found as the core microbiota shared by all the areas, of which Acinetobacter, Enterobacter, Pseudomonas, and Staphylococcus are regarded as healthcare-associated pathogens (HAPs). The observed relationship between environmental parameters such as core microbiota and surface bacterial diversity suggests that we might manage indoor environments by creating new sanitation protocols, adjusting the ventilation design, and further understanding the transmission routes of HAPs.

Isolation and Identification of Post-Transcriptional Gene Silencing-Related Micro-RNAs by Functionalized Silicon Nanowire Field-effect Transistor.

Many transcribed RNAs are non-coding RNAs, including microRNAs (miRNAs), which bind to complementary sequences on messenger RNAs to regulate the translation efficacy. Therefore, identifying the miRNAs expressed in cells/organisms aids in understanding genetic control in cells/organisms. In this report, we determined the binding of oligonucleotides to a receptor-modified silicon nanowire field-effect transistor (SiNW-FET) by monitoring the changes in conductance of the SiNW-FET. We first modified a SiNW-FET with a DNA probe to directly and selectively detect the complementary miRNA in cell lysates. This SiNW-FET device has 7-fold higher sensitivity than reverse transcription-quantitative polymerase chain reaction in detecting the corresponding miRNA. Next, we anchored viral p19 proteins, which bind the double-strand small RNAs (ds-sRNAs), on the SiNW-FET. By perfusing the device with synthesized ds-sRNAs of different pairing statuses, the dissociation constants revealed that the nucleotides at the 3'-overhangs and pairings at the terminus are important for the interactions. After perfusing the total RNA mixture extracted from Nicotiana benthamiana across the device, this device could enrich the ds-sRNAs for sequence analysis. Finally, this bionanoelectronic SiNW-FET, which is able to isolate and identify the interacting protein-RNA, adds an additional tool in genomic technology for the future study of direct biomolecular interactions.

Application of 16S rRNA metagenomics to analyze bacterial communities at a respiratory care centre in Taiwan.

In this study, we applied a 16S ribosomal RNA (rRNA) metagenomics approach to survey inanimate hospital environments (IHEs) in a respiratory care center (RCC). A total of 16 samples, including 9 from medical devices and 7 from workstations, were analyzed. Besides, clinical isolates were retrospectively analyzed during the sampling period in the RCC. A high amount of microbial diversity was detected, with an average of 1,836 phylotypes per sample. In addition to Acinetobacter, more than 60 % of the bacterial communities present among the top 25 abundant genera were dominated by skin-associated bacteria. Differences in bacterial profiles were restricted to individual samples. Furthermore, compliance with hand hygiene guidelines may be unsatisfactory among hospital staff according to a principal coordinate analysis that indicated clustering of bacterial communities between devices and workstations for most of the sampling sites. Compared to the high incidence of clinical isolates in the RCC, only Staphylococcus and Acinetobacter were highly abundant in the IHEs. Despite Acinetobacter was the most abundant genus present in IHEs of the RCC, potential pathogens, e.g., Acinetobacter baumannii, might remain susceptible to carbapenem. This study is the first in Taiwan to demonstrate a high diversity of human-associated bacteria in the RCC via 16S rRNA metagenomics, which allows for new assessment of potential health risks in RCCs, aids in the evaluation of existing sanitation protocols, and furthers our understanding of the development of healthcare-associated infections.

Transcriptome profiling in imipenem-selected Acinetobacter baumannii.

BACKGROUND: Carbapenem-resistance in Acinetobacter baumannii has gradually become a global challenge. To identify the genes involved in carbapenem resistance in A. baumannii, the transcriptomic responses of the completely sequenced strain ATCC 17978 selected with 0.5 mg/L (IPM-2 m) and 2 mg/L (IPM-8 m) imipenem were investigated using RNA-sequencing to identify differences in the gene expression patterns. RESULTS: A total of 88 and 68 genes were differentially expressed in response to IPM-2 m and IPM-8 m selection, respectively. Among the expressed genes, 50 genes were highly expressed in IPM-2 m, 30 genes were highly expressed in IPM-8 m, and 38 genes were expressed common in both strains. Six groups of genes were simultaneously expressed in IPM-2 m and IPM-8 m mutants. The three gene groups involved in DNA recombination were up-regulated, including recombinase, transposase and DNA repair, and beta-lactamase OXA-95 and homologous recombination. The remaining gene groups involved in biofilm formation were down-regulated, including quorum sensing, secretion systems, and the csu operon. The antibiotic resistance determinants, including RND efflux transporters and multidrug resistance pumps, were over-expressed in response to IPM-2 m selection, followed by a decrease in response to IPM-8 m selection. Among the genes over-expressed in both strains, blaOXA-95, previously clustered with the blaOXA-51-like family, showed 14-fold (IPM-2 m) to 330-fold (IPM-8 m) over-expression. The expression of blaOXA-95 in IPM-2 m and IPM-8 m cells was positively correlated with the rate of imipenem hydrolysis, as demonstrated through Liquid Chromatography-Mass Spectrometry/Mass Spectrometry, suggesting that blaOXA-95 plays a critical role in conferring carbapenem resistance. In addition, A. baumannii shows an inverse relationship between carbapenem resistance and biofilm production. CONCLUSION: Gene recombination and blaOXA-95 play critical roles in carbapenem resistance in A. baumannii. Taken together, the results of the present study provide a foundation for future studies of the network systems associated with carbapenem resistance.

Development of 3D-QSAR combination approach for discovering and analysing neuraminidase inhibitors in silico.

Zanamivir and Oseltamivir are both sialic acid analog inhibitors of Neuraminidase (NA), which is an important target in influenza A virus treatment. Quantitative Structure-Activity Relationships (QSAR) is a common computational method for correlating the structural properties of compounds (or inhibitors) with their biological activities. The pharmcophore model easily and quickly recognises related inhibitors and also fits the binding site interaction features of a protein structure. The Comparative Molecular Similarity Index Analysis (CoMSIA) model easily optimises molecular structures and describes the limit range of molecule weights. This study proposes a combination approach that integrates these two models based on the same training set inhibitors in order to screen and optimize NA inhibitor candidates during drug design.

Prevalence and mapping of a plasmid encoding a type IV secretion system in Acinetobacter baumannii.

We investigated the prevalence of a type IV secretion system (T4SS)-bearing plasmid among clinical isolates of carbapenem-resistant Acinetobacter baumannii (CRAB) using plasmid replicon typing. The complete sequence of a T4SS-bearing plasmid, pAB_CC, isolated from A. baumannii TYTH-1 was determined, and a comparative analysis of the T4SS gene modules was performed. Of the 129 isolates studied, GR6 (repAci6) was the most common (45 of 96 isolates) and was strongly linked with the T4SS. A comparative analysis of the T4SS locus in seven plasmid genomes, including pAB_CC, pACICU2, pABKp1, pABTJ1, p1BJAB0714, p2BJAB0868, and p2ABTCDC0715, indicated that fourteen genes on these plasmids were highly conserved compared to those of the F plasmid. Additionally, the chromosomes in the seven representative isolates may be evolutionarily distinct from their intrinsic T4SS-bearing plasmids, suggesting that the two T4SS lineages emerged long before the appearance of EC II. These two lineages are now widespread in A. baumannii strains.

HaplotypeCN: copy number haplotype inference with Hidden Markov Model and localized haplotype clustering.

Copy number variation (CNV) has been reported to be associated with disease and various cancers. Hence, identifying the accurate position and the type of CNV is currently a critical issue. There are many tools targeting on detecting CNV regions, constructing haplotype phases on CNV regions, or estimating the numerical copy numbers. However, none of them can do all of the three tasks at the same time. This paper presents a method based on Hidden Markov Model to detect parent specific copy number change on both chromosomes with signals from SNP arrays. A haplotype tree is constructed with dynamic branch merging to model the transition of the copy number status of the two alleles assessed at each SNP locus. The emission models are constructed for the genotypes formed with the two haplotypes. The proposed method can provide the segmentation points of the CNV regions as well as the haplotype phasing for the allelic status on each chromosome. The estimated copy numbers are provided as fractional numbers, which can accommodate the somatic mutation in cancer specimens that usually consist of heterogeneous cell populations. The algorithm is evaluated on simulated data and the previously published regions of CNV of the 270 HapMap individuals. The results were compared with five popular methods: PennCNV, genoCN, COKGEN, QuantiSNP and cnvHap. The application on oral cancer samples demonstrates how the proposed method can facilitate clinical association studies. The proposed algorithm exhibits comparable sensitivity of the CNV regions to the best algorithm in our genome-wide study and demonstrates the highest detection rate in SNP dense regions. In addition, we provide better haplotype phasing accuracy than similar approaches. The clinical association carried out with our fractional estimate of copy numbers in the cancer samples provides better detection power than that with integer copy number states.

Insertion sequence transposition determines imipenem resistance in Acinetobacter baumannii.

This study employed genomewide analysis to investigate potential resistance mechanisms in Acinetobacter baumannii following imipenem exposure. Imipenem-selected mutants were generated from the imipenem-susceptible strain ATCC 17978 by multistep selection resistance. Antibiotic susceptibilities were examined, and the selected mutants originated from the ATCC 17978 strain were confirmed by pulsed-field gel electrophoresis. The genomic sequence of a resistant mutant was analyzed using a next-generation sequencing platform, and genetic recombination was further confirmed by PCR. The result showed that phenotypic resistance was observed with carbapenem upon exposure to various concentrations of imipenem. Genomewide analysis showed that ISAba1 transposition was initiated by imipenem exposure at concentrations up to 0.5 mg/L. Transposition of ISAba1 upstream of blaOXA-95 was detected in all the selected mutants. The expression of blaOXA-95 was further analyzed by quantitative PCR, and the results demonstrated that a 200-fold increase in gene expression was required for resistance to imipenem. This study concluded that imipenem exposure at a concentration of 0.5 mg/L mediated the transposition of ISAba1 upstream of the blaOXA-95 gene and resulted in the overexpression of blaOXA-95 gene, which may play a major role in the resistance to imipenem in A. baumannii.