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Objective: To investigate the relationship between the quality of YouTube videos on penile enlargement surgery and the scholarly profiles of surgeons. Materials and Methods: A YouTube search was conducted using the keyword “penile enlargement surgery”. Of the first 200 videos from the search results, 66 that met the study criteria were included in the analyses. Two urologists scored each video using the DISCERN score, the Journal of the American Medical Association (JAMA) score, and the global quality scale (GQS) in a double-blind manner. After the video quality scores were determined, the number of publications and h-indexes of surgeons were obtained from Google Scholar. Results: Of the videos, 31 (46.9%) were uploaded by plastic surgeons and 35 (53.1%) by urologists. The median duration of the videos was 4.1 min (interquartile range: 1-8.5) minutes. Eighteen (27.2%) videos had low quality, 9 (13.6%) had medium quality, and 39 (59.1%) had high quality. A statistically significant correlation was found between the h-index of surgeons and video quality scores (DISCERN, r=0.678; JAMA, r=0.646; GQS, r=0.689; p

Objective: To investigate the relationship between the quality of YouTube videos on penile enlargement surgery and the scholarly profiles of surgeons. Materials and Methods: A YouTube search was conducted using the keyword "penile enlargement surgery". Of the first 200 videos from the search results, 66 that met the study criteria were included in the analyses. Two urologists scored each video using the DISCERN score, the Journal of the American Medical Association (JAMA) score, and the global quality scale (GQS) in a double-blind manner. After the video quality scores were determined, the number of publications and h-indexes of surgeons were obtained from Google Scholar. Results: Of the videos, 31 (46.9%) were uploaded by plastic surgeons and 35 (53.1%) by urologists. The median duration of the videos was 4.1 min (interquartile range: 1-8.5) minutes. Eighteen (27.2%) videos had low quality, 9 (13.6%) had medium quality, and 39 (59.1%) had high quality. A statistically significant correlation was found between the h-index of surgeons and video quality scores (DISCERN, r=0.678; JAMA, r=0.646; GQS, r=0.689; p<0.0001). There was also a statistically significant correlation between the total publication counts of surgeons and the video quality scores (DISCERN, r=0.614; JAMA, r=0.569; GQS, r=0.607; p<0.0001). Lastly, a weak, statistically significant correlation was detected between the DISCERN scores of the videos and the number of likes (r=0.278, p=0.029). Conclusion: This study revealed a significant correlation between the quality of YouTube videos on penile enlargement surgery and the h-indexes and total publication counts of surgeons. This study was the first to analyze the relationship between the quality of YouTube videos on penile enlargement surgery and the scholarly profiles of surgeons. [ABSTRACT FROM AUTHOR]

Hospital medical records are valuable and cost-effective documents for assessing the quality of healthcare provided to patients by a healthcare facility during hospitalization. However, there is a lack of internationally validated tools that measure the quality of the whole hospital medical record in terms of both form and content. In this study, we developed and validated a tool, named MeReQ (medical record quality) tool, which quantifies the quality of the hospital medical record and enables statistical modeling using the data obtained. The tool was applied to evaluate a sample of hospital individual patient medical records from a secondary referral hospital and to identify the departments that require quality improvement interventions and the effects of improvement actions already implemented. [ABSTRACT FROM AUTHOR]

Soil rating is a comparative characteristic of the quality of arable land and other areas, assessed in points. It is carried out on the basis of special soil surveys, taking into account the supply of nutrients and other factors that determine the level of natural fertility. Soil rating is a necessary starting point for the economic assessment of land, determining the standard price and creating a land cadastre. The article presents the results of a comprehensive study aimed at determining the qualitative characteristics of the soil cover of the Republic of Adygea. As a result of determining the quality score, it is possible to predict how much agricultural land will be harvested in the future, as well as to determine the sowing plan for this area for the year. Determining the soil quality score is also important when resolving issues such as regulation of land relations, timely adoption of measures to improve land plots that are unsatisfactory in terms of indicators. Establishing a quality score allows for high-quality implementation of agrotechnical, hydraulic and chemical measures in those areas that are considered the worst as a result of determining soil quality. The groups of soils established during rating, which are quantitatively characterized by certain natural characteristics, form the information basis for the economic assessment of land, the data of which are important for the rational use of selected agricultural production groups, taking into account their natural characteristics. Based on the conducted research, recommendations are offered for using the obtained assessment results for the cultivation of various agricultural crops. The importance of using the results of soil assessment to improve the efficiency of land use, improve agricultural production and ensure sustainable development of the region has been emphasized.

The mission of blind image quality evaluation is currently a challenging computer vision problem. Due to the shortage of reference images, it is hard for blind image quality evaluation methods to achieve the same performance as full reference image quality evaluation methods. In addition, current quality evaluation methods are difficult to effectively forecast the quality scores of synthesized distorted images as well as real distorted images. To address such issues, this study proposed a cyclic generative adversarial network composed of a quality perception network and a quality regression network on the grounds of generative adversarial networks. For further enhancing the predictive performance of quality aware networks, this study proposed using attention blocks for adaptively fusing high-level semantic features and low-level semantic features. It extracted content and distortion information from images through an image quality evaluation method on the grounds of content perception and distortion inference. And according to the different properties of the extracted features, adaptive fusion blocks were used for adaptively fusing content features and distortion features. Experiments showcased that the Spearman order correlation coefficient and Pearson linear correlation coefficient obtained by the proposed method on multiple datasets were higher than other similar methods. At the same time, the proposed method has achieved good prediction results on various types of distorted images, and has surpassed other methods. The prediction accuracy of the proposed method on five types of distortion was 0.971, 0.963, 0.984, 0.971, and 0.926, respectively. The proposed method achieved the highest predictive accuracy on all distortion types in the LIVE dataset, with predicted accuracy values of 0.973, 0.965, 0.984, 0.963, and 0.944, respectively. In summary, the proposed method not only achieved good prediction accuracy, but also had strong generalization performance in cross dataset testing. This provides a scientific and effective research direction for blind image quality evaluation.

ABSTRACT: Many studies over the last 30 years have shown the effects of farming practices on milk compounds. Combinations of practices may have antagonistic or synergistic effects on milk compounds, but these combination effects remain underinvestigated. Research needs to focus on overall intrinsic milk quality (including sensory, technological, health, and nutritional dimensions) and identify the combinations that can optimize it. The aim of this study was to identify which combinations of farming practices achieved the best scores for sensory, technological, health, and nutritional dimensions and for overall intrinsic milk quality. Ninety-nine private farms were visited once each to sample their bulk tank milk and survey their farming practices. The surveyed practices concerned herd characteristics, feeding management, housing conditions, and milking and milk storage conditions on the day of test. Analyses of bulk tank milk were designed to evaluate the overall intrinsic quality of the milk for 2 target products: raw milk cheese and semi-skimmed UHT milk. Regression trees were then used to identify the combinations of farming practices that achieved the best scores on each dimension and on overall intrinsic quality of the milk. Breed and diet (type of forage) were the most influential factors for sensory and health dimensions and for technological and nutritional dimension scores, respectively, in the cheese assessment. Overall cheese quality was highly positively correlated with these 4 dimension scores. Therefore, breed and diet emerged as the most influential practices in the regression tree for overall cheese quality. However, the combinations of practices that resulted in the best quality scores differed according to dimension studied and product targeted. This suggests that advice on farming practices to improve intrinsic milk quality needs to be adapted according to the end-purpose of the collected milk. This innovative approach combining on-farm data and regression trees provides farm managers with a valuable and practical tool to prioritize practices in terms of their role in shaping milk quality, and to identify the combinations of practices that promote good milk quality and practice thresholds or modalities needed to achieve it.

Genetic sequencing, or DNA sequencing, using the Sanger technique has become widely used in the veterinary diagnostic community. This technology plays a role in verification of PCR results and is used to provide the genetic sequence data needed for phylogenetic analysis, epidemiologic studies, and forensic investigations. The Laboratory Technology Committee of the American Association of Veterinary Laboratory Diagnosticians has prepared guidelines for sample preparation, submission to sequencing facilities or instrumentation, quality assessment of nucleic acid sequence data performed, and for generating basic sequencing data and phylogenetic analysis for diagnostic applications. This guidance is aimed at assisting laboratories in providing consistent, high-quality, and reliable sequence data when using Sanger-based genetic sequencing as a component of their laboratory services.

A next-generation sequencing (NGS) study identified a very high viral load of Torquetenovirus (TTV) in KD patients. We aimed to evaluate the feasibility of a newly developed quantitative species-specific TTV-PCR (ssTTV-PCR) method to identify the etiology of KD. We applied ssTTV-PCR to samples collected from 11 KD patients and 22 matched control subjects who participated in our previous prospective study. We used the NGS dataset from the previous study to validate ssTTV-PCR. The TTV loads in whole blood and nasopharyngeal aspirates correlated highly (Spearman's R = 0.8931, p < 0.0001, n = 33), supporting the validity of ssTTV-PCR. The ssTTV-PCR and NGS results were largely consistent. However, inconsistencies occurred when ssTTV-PCR was more sensitive than NGS, when the PCR primer sequences mismatched the viral sequences in the participants, and when the NGS quality score was low. Interpretation of NGS requires complex procedures. ssTTV-PCR is more sensitive than NGS but may fail to detect a fast-evolving TTV species. It would be prudent to update primer sets using NGS data. With this precaution, ssTTV-PCR can be used reliably in a future large-scale etiological study for KD. [ABSTRACT FROM AUTHOR]

The purpose of the no-reference image quality assessment (NR-IQA) is to measure perceived image quality based on subjective judgments; however, due to the lack of a clean reference image, this is a complicated and unresolved challenge. Massive new IQA datasets have facilitated the creation of deep learning-based image quality measurements. We present a unique model to handle the NR-IQA challenge in this research by employing a hybrid strategy that leverages from pre-trained CNN model and the unified learning mechanism that extracts both local and non-local characteristics from the input patch. The deep analysis of the proposed framework shows that the model uses features and a mechanism that improves the monotonicity relationship between objective and subjective ratings. The intermediary goal was mapped to a quality score using a regression architecture. To extract various feature maps, a deep architecture with an adaptive receptive field was used. Analyses of this biggest NR-IQA benchmark datasets demonstrate that the suggested technique outperforms current state-of-the-art NR-IQA measures. [ABSTRACT FROM AUTHOR]

Many studies over the last 30 years have shown the effects of farming practices on milk compounds. Combinations of practices may have antagonistic or synergistic effects on milk compounds, but these combination effects remain underinvestigated. Research needs to focus on overall intrinsic milk quality (including sensory, technological, health, and nutritional dimensions) and identify the combinations that can optimize it. The aim of this study was to identify which combinations of farming practices achieved the best scores for sensory, technological, health, and nutritional dimensions and for overall intrinsic milk quality. Ninety-nine private farms were visited once each to sample their bulk tank milk and survey their farming practices. The surveyed practices concerned herd characteristics, feeding management, housing conditions, and milking and milk storage conditions on the day of test. Analyses of bulk tank milk were designed to evaluate the overall intrinsic quality of the milk for 2 target products: raw milk cheese and semi-skimmed UHT milk. Regression trees were then used to identify the combinations of farming practices that achieved the best scores on each dimension and on overall intrinsic quality of the milk. Breed and diet (type of forage) were the most influential factors for sensory and health dimensions and for technological and nutritional dimension scores, respectively, in the cheese assessment. Overall cheese quality was highly positively correlated with these 4 dimension scores. Therefore, breed and diet emerged as the most influential practices in the regression tree for overall cheese quality. However, the combinations of practices that resulted in the best quality scores differed according to dimension studied and product targeted. This suggests that advice on farming practices to improve intrinsic milk quality needs to be adapted according to the end-purpose of the collected milk. This innovative approach combining on-farm data and regression trees provides farm managers with a valuable and practical tool to prioritize practices in terms of their role in shaping milk quality, and to identify the combinations of practices that promote good milk quality and practice thresholds or modalities needed to achieve it. [ABSTRACT FROM AUTHOR]

Video salient object detection has attracted growing interest in recent years. However, some existing video saliency models often suffer from the inappropriate utilization of spatial and temporal cues and the insufficient aggregation of different level features, leading to remarkable performance degradation. Therefore, we propose a quality-driven dual-branch feature integration network majoring in the adaptive fusion of multi-modal cues and sufficient aggregation of multi-level spatiotemporal features. Firstly, we employ the quality-driven multi-modal feature fusion (QMFF) module to combine the spatial and temporal features. Particularly, the quality scores estimated from each level's spatial and temporal cues are not only used to weigh the two modal features but also to adaptively integrate the coarse spatial and temporal saliency predictions into the guidance map, which further enhances the two modal features. Secondly, we deploy the dual-branch-based multi-level feature aggregation (DMFA) module to integrate multi-level spatiotemporal features, where the two branches including the progressive decoder branch and the direct concatenation branch sufficiently explore the cooperation of multi-level spatiotemporal features. In particular, in order to provide an adaptive fusion for the outputs of the two branches, we design the dual-branch fusion (DF) unit, where the channel weight of each output can be learned jointly from the two outputs. The experiments conducted on four video datasets clearly demonstrate the effectiveness and superiority of our model against the state-of-the-art video saliency models. [ABSTRACT FROM AUTHOR]

Hospital medical records are valuable and cost-effective documents for assessing the quality of healthcare provided to patients by a healthcare facility during hospitalization. However, there is a lack of internationally validated tools that measure the quality of the whole hospital medical record in terms of both form and content. In this study, we developed and validated a tool, named MeReQ (medical record quality) tool, which quantifies the quality of the hospital medical record and enables statistical modeling using the data obtained. The tool was applied to evaluate a sample of hospital individual patient medical records from a secondary referral hospital and to identify the departments that require quality improvement interventions and the effects of improvement actions already implemented.

Abstract Background Over the past few decades, the emergence and maturation of new technologies have substantially reduced the cost of genome sequencing. As a result, the amount of genomic data that needs to be stored and transmitted has grown exponentially. For the standard sequencing data format, FASTQ, compression of the quality score is a key and difficult aspect of FASTQ file compression. Throughout the literature, we found that the majority of the current quality score compression methods do not support random access. Based on the above consideration, it is reasonable to investigate a lossless quality score compressor with a high compression rate, a fast compression and decompression speed, and support for random access. Results In this paper, we propose CMIC, an adaptive and random access supported compressor for lossless compression of quality score sequences. CMIC is an acronym of the four steps (classification, mapping, indexing and compression) in the paper. Its framework consists of the following four parts: classification, mapping, indexing, and compression. The experimental results show that our compressor has good performance in terms of compression rates on all the tested datasets. The file sizes are reduced by up to 21.91% when compared with LCQS. In terms of compression speed, CMIC is better than all other compressors on most of the tested cases. In terms of random access speed, the CMIC is faster than the LCQS, which provides a random access function for compressed quality scores. Conclusions CMIC is a compressor that is especially designed for quality score sequences, which has good performance in terms of compression rate, compression speed, decompression speed, and random access speed. The CMIC can be obtained in the following way: https://github.com/Humonex/Cmic .

Abstract Background Advances in sequencing technology have drastically reduced sequencing costs. As a result, the amount of sequencing data increases explosively. Since FASTQ files (standard sequencing data formats) are huge, there is a need for efficient compression of FASTQ files, especially quality scores. Several quality scores compression algorithms are recently proposed, mainly focused on lossy compression to boost the compression rate further. However, for clinical applications and archiving purposes, lossy compression cannot replace lossless compression. One of the main challenges for lossless compression is time complexity, where it takes thousands of seconds to compress a 1 GB file. Also, there are desired features for compression algorithms, such as random access. Therefore, there is a need for a fast lossless compressor with a reasonable compression rate and random access functionality. Results This paper proposes a Fast and Concurrent Lossless Quality scores Compressor (FCLQC) that supports random access and achieves a lower running time based on concurrent programming. Experimental results reveal that FCLQC is significantly faster than the baseline compressors on compression and decompression at the expense of compression ratio. Compared to LCQS (baseline quality score compression algorithm), FCLQC shows at least 31x compression speed improvement in all settings, where a performance degradation in compression ratio is up to 13.58% (8.26% on average). Compared to general-purpose compressors (such as 7-zip), FCLQC shows 3x faster compression speed while having better compression ratios, at least 2.08% (4.69% on average). Moreover, the speed of random access decompression also outperforms the others. The concurrency of FCLQC is implemented using Rust; the performance gain increases near-linearly with the number of threads. Conclusion The superiority of compression and decompression speed makes FCLQC a practical lossless quality score compressor candidate for speed-sensitive applications of DNA sequencing data. FCLQC is available at https://github.com/Minhyeok01/FCLQC and is freely available for non-commercial usage.

A next-generation sequencing (NGS) study identified a very high viral load of Torquetenovirus (TTV) in KD patients. We aimed to evaluate the feasibility of a newly developed quantitative species-specific TTV-PCR (ssTTV-PCR) method to identify the etiology of KD. We applied ssTTV-PCR to samples collected from 11 KD patients and 22 matched control subjects who participated in our previous prospective study. We used the NGS dataset from the previous study to validate ssTTV-PCR. The TTV loads in whole blood and nasopharyngeal aspirates correlated highly (Spearman’s R = 0.8931, p < 0.0001, n = 33), supporting the validity of ssTTV-PCR. The ssTTV-PCR and NGS results were largely consistent. However, inconsistencies occurred when ssTTV-PCR was more sensitive than NGS, when the PCR primer sequences mismatched the viral sequences in the participants, and when the NGS quality score was low. Interpretation of NGS requires complex procedures. ssTTV-PCR is more sensitive than NGS but may fail to detect a fast-evolving TTV species. It would be prudent to update primer sets using NGS data. With this precaution, ssTTV-PCR can be used reliably in a future large-scale etiological study for KD.

Background: Over the past few decades, the emergence and maturation of new technologies have substantially reduced the cost of genome sequencing. As a result, the amount of genomic data that needs to be stored and transmitted has grown exponentially. For the standard sequencing data format, FASTQ, compression of the quality score is a key and difficult aspect of FASTQ file compression. Throughout the literature, we found that the majority of the current quality score compression methods do not support random access. Based on the above consideration, it is reasonable to investigate a lossless quality score compressor with a high compression rate, a fast compression and decompression speed, and support for random access. Results: In this paper, we propose CMIC, an adaptive and random access supported compressor for lossless compression of quality score sequences. CMIC is an acronym of the four steps (classification, mapping, indexing and compression) in the paper. Its framework consists of the following four parts: classification, mapping, indexing, and compression. The experimental results show that our compressor has good performance in terms of compression rates on all the tested datasets. The file sizes are reduced by up to 21.91% when compared with LCQS. In terms of compression speed, CMIC is better than all other compressors on most of the tested cases. In terms of random access speed, the CMIC is faster than the LCQS, which provides a random access function for compressed quality scores. Conclusions: CMIC is a compressor that is especially designed for quality score sequences, which has good performance in terms of compression rate, compression speed, decompression speed, and random access speed. The CMIC can be obtained in the following way: https://github.com/Humonex/Cmic. [ABSTRACT FROM AUTHOR]

Introduction: Head and neck cancer (HNC) is one of the most prevalent cancers of upper aerodigestive tract, with squamous cell carcinomas accounting for the majority of cases. Vitamin B such as folate has been associated with carcinogenesis. Folate is essential for one carbon metabolism, which involves the transfer of one carbon units for Deoxyribonucleic Acid (DNA) and Ribonucleic Acid (RNA) production, amino acid metabolism and methylation. Aim: To evaluate the association of folate mediated One carbon metabolism with Head and Neck Squamous Cell Carcinoma (HNSCC). Materials and Methods: For this systematic review, Electronic bibliographic databases search of PubMed, Google Scholar and Scopus was done. The electronic search was performed between November 15th to November 30th, 2020 by two researchers independently. All original research, observational studies, full text articles, in which blood samples or questionnaires or both, focused on the assessment of folate mediated one carbon metabolism in HNSCC, published upto November 2020 were reviewed. Four studies published from 2005 to 2019 were included in which three studies were case-control and one study was a comparative cross-sectional study. This systematic review was carried out by two reviewers, using Preferred Reporting Items for Systematic reviews and Meta Analyses (PRISMA) checklist and the New Castle Ottawa Scale (NOS) for quality assessment. Results: In this systematic review, total of four studies included, had 1504 HNSCC patients and 2970 Controls. One study was reported from Nigeria, one from the European countries, one from Japan, and one from the United States of America (USA). One study had a quality score of 8 whereas 3 studies had 7, considering all the four studies included are of good quality. Conclusion: Significant low levels of serum folate was present in HNSCC when compared to controls. Serum folate levels can differ due to tumor growth and subsequent metabolic changes, or they may precede and accelerate tumor progression. [ABSTRACT FROM AUTHOR]

Magnetic resonance elastography (MRE) has been used to successfully characterize the mechanical behavior of healthy and diseased muscles, but no study has been performed to investigate the reliability of MRE on lumbar muscles. The objective of this work was to determine the reliability of MRE techniques on lumbar muscles in both ex vivo phantom and in vivo human studies. In this study, fresh porcine leg muscles were used in the phantom study, and 80 healthy adults (38.6 ± 11.2 years, 40 women) were recruited in the human study. Five repeated stiffness maps were obtained from both the phantom and human muscles by using a gradient-echo MRE sequence with a pneumatic vibration on a 1.5 T MR scanner. The technical failure rate, coefficient of variation (CV), and quality score were assessed to evaluate the reliability of MRE, respectively. Analysis of variance was performed to compare the stiffness between different lumbar muscles, and the difference was significant if p < 0.05 after Bonferroni correction. The results showed that the MRE achieved a zero technical failure rate and a low CV of stiffness (6.24 ± 1.41%) in the phantom muscles. However, in the human study, the MRE exhibited high CVs of stiffness (21.57%–25.24%) in the lumbar muscles, and the technical failure rate was higher in psoas muscles (60.0–66.3% in) than in paraspinal muscles (0.0–2.5%). Further, higher quality scores were noticed in paraspinal muscles (7.31–7.71) than those in psoas muscles (1.83–2.06). In conclusion, the MRE was a reliable technique to investigate the mechanical property of lumbar muscles, but it was less reliable to assess stiffness in psoas muscles than paraspinal muscles. [ABSTRACT FROM AUTHOR]

Introduction: Head and neck cancer (HNC) is one of the most prevalent cancers of upper aerodigestive tract, with squamous cell carcinomas accounting for the majority of cases. Vitamin B such as folate has been associated with carcinogenesis. Folate is essential for one carbon metabolism, which involves the transfer of one carbon units for Deoxyribonucleic Acid (DNA) and Ribonucleic Acid (RNA) production, amino acid metabolism and methylation. Aim: To evaluate the association of folate mediated One carbon metabolism with Head and Neck Squamous Cell Carcinoma (HNSCC). Materials and Methods: For this systematic review, electronic bibliographic databases search of PubMed, Google Scholar and Scopus was done. The electronic search was performed between 15-30 November 2020 by two researchers independently. All original research, observational studies, full text articles, in which blood samples or questionnaires or both, focused on the assessment of folate mediated one carbon metabolism in HNSCC, published upto November 2020 were reviewed. Four studies published from 2005-2019 were included in which three studies were case-control and one study was a comparative cross-sectional study. This systematic review was carried out by two reviewers, using Preferred Reporting Items for Systematic reviews and Meta Analyses (PRISMA) checklist and the New Castle Ottawa Scale (NOS) for quality assessment. Results: In this systematic review, total of four studies included, had 1504 HNSCC patients and 2970 controls. One study was reported from Nigeria, one from the European countries, one from Japan, and one from the United States of America (USA). One study had a quality score of 8 whereas three studies had quality score of 7, considering all the four studies included are of good quality. Conclusion: Significant low levels of serum folate was present in HNSCC when compared to controls. Serum folate levels can differ due to tumour growth and subsequent metabolic changes, or they may precede and accelerate tumour progression.

The purpose of the no-reference image quality assessment (NR-IQA) is to measure perceived image quality based on subjective judgments; however, due to the lack of a clean reference image, this is a complicated and unresolved challenge. Massive new IQA datasets have facilitated the creation of deep learning-based image quality measurements. We present a unique model to handle the NR-IQA challenge in this research by employing a hybrid strategy that leverages from pre-trained CNN model and the unified learning mechanism that extracts both local and non-local characteristics from the input patch. The deep analysis of the proposed framework shows that the model uses features and a mechanism that improves the monotonicity relationship between objective and subjective ratings. The intermediary goal was mapped to a quality score using a regression architecture. To extract various feature maps, a deep architecture with an adaptive receptive field was used. Analyses of this biggest NR-IQA benchmark datasets demonstrate that the suggested technique outperforms current state-of-the-art NR-IQA measures.

Background: Advances in sequencing technology have drastically reduced sequencing costs. As a result, the amount of sequencing data increases explosively. Since FASTQ files (standard sequencing data formats) are huge, there is a need for efficient compression of FASTQ files, especially quality scores. Several quality scores compression algorithms are recently proposed, mainly focused on lossy compression to boost the compression rate further. However, for clinical applications and archiving purposes, lossy compression cannot replace lossless compression. One of the main challenges for lossless compression is time complexity, where it takes thousands of seconds to compress a 1 GB file. Also, there are desired features for compression algorithms, such as random access. Therefore, there is a need for a fast lossless compressor with a reasonable compression rate and random access functionality. Results: This paper proposes a Fast and Concurrent Lossless Quality scores Compressor (FCLQC) that supports random access and achieves a lower running time based on concurrent programming. Experimental results reveal that FCLQC is significantly faster than the baseline compressors on compression and decompression at the expense of compression ratio. Compared to LCQS (baseline quality score compression algorithm), FCLQC shows at least 31x compression speed improvement in all settings, where a performance degradation in compression ratio is up to 13.58% (8.26% on average). Compared to general-purpose compressors (such as 7-zip), FCLQC shows 3x faster compression speed while having better compression ratios, at least 2.08% (4.69% on average). Moreover, the speed of random access decompression also outperforms the others. The concurrency of FCLQC is implemented using Rust; the performance gain increases near-linearly with the number of threads. Conclusion: The superiority of compression and decompression speed makes FCLQC a practical lossless quality score compressor candidate for speed-sensitive applications of DNA sequencing data. FCLQC is available at https://github.com/Minhyeok01/FCLQC and is freely available for non-commercial usage. [ABSTRACT FROM AUTHOR]

Abstract Background Advanced sequencing machines dramatically speed up the generation of genomic data, which makes the demand of efficient compression of sequencing data extremely urgent and significant. As the most difficult part of the standard sequencing data format FASTQ, compression of the quality score has become a conundrum in the development of FASTQ compression. Existing lossless compressors of quality scores mainly utilize specific patterns generated by specific sequencer and complex context modeling techniques to solve the problem of low compression ratio. However, the main drawbacks of these compressors are the problem of weak robustness which means unstable or even unavailable results of sequencing files and the problem of slow compression speed. Meanwhile, some compressors attempt to construct a fine-grained index structure to solve the problem of slow random access decompression speed. However, they solve the problem at the sacrifice of compression speed and at the expense of large index files, which makes them inefficient and impractical. Therefore, an efficient lossless compressor of quality scores with strong robustness, high compression ratio, fast compression and random access decompression speed is urgently needed and of great significance. Results In this paper, based on the idea of maximizing the use of hardware resources, LCQS, a lossless compression tool specialized for quality scores, was proposed. It consists of four sequential processing steps: partitioning, indexing, packing and parallelizing. Experimental results reveal that LCQS outperforms all the other state-of-the-art compressors on all criteria except for the compression speed on the dataset SRR1284073. Furthermore, LCQS presents strong robustness on all the test datasets, with its acceleration ratios of compression speed increasing by up to 29.1x, its file size reducing by up to 28.78%, and its random access decompression speed increasing by up to 2.1x. Additionally, LCQS also exhibits strong scalability. That is, the compression speed increases almost linearly as the size of input dataset increases. Conclusion The ability to handle all different kinds of quality scores and superiority in compression ratio and compression speed make LCQS a high-efficient and advanced lossless quality score compressor, along with its strength of fast random access decompression. Our tool LCQS can be downloaded from https://github.com/SCUT-CCNL/LCQSand freely available for non-commercial usage.

Next-generation sequencing (NGS) enables massively parallel acquisition of large-scale omics data; however, objective data quality filtering parameters are lacking. Although a useful metric, evidence reveals that platform-generated Phred values overestimate per-base quality scores. We have developed novel and empirically based algorithms that streamline NGS data quality filtering. The pipeline leverages known sequence motifs to enable empirical estimation of error rates, detection of erroneous base calls and removal of contaminating adapter sequence. The performance of motif-based error detection and quality filtering were further validated with read compression rates as an unbiased metric. Elevated error rates at read ends, where known motifs lie, tracked with propagation of erroneous base calls. Barcode swapping, an inherent problem with pooled libraries, was also effectively mitigated. The ngsComposer pipeline is suitable for various NGS protocols and platforms due to the universal concepts on which the algorithms are based. [ABSTRACT FROM AUTHOR]

Frauke Pedersen,1,2 Ulrich M Zissler,3 Henrik Watz,2 Klaus F Rabe,1 Jens M Hohlfeld,4,5 Olaf Holz41LungenClinic Grosshansdorf, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Grosshansdorf, Germany; 2Pulmonary Research Institute at LungenClinic Grosshansdorf, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Grosshansdorf, Germany; 3Center of Allergy & Environment (ZAUM), Technical University of Munich and Helmholtz Center Munich, German Research Center for Environmental Health, Germany, Member of the German Center for Lung Research (DZL), Munich, Germany; 4Fraunhofer ITEM, Clinical Airway Research – Biomedical Research in End-stage and Obstructive Lung Disease Hannover (BREATH), German Center for Lung Research (DZL), Hannover, Germany; 5Department of Respiratory Medicine, Hannover Medical School (MHH), Biomedical Research in End-stage and Obstructive Lung Disease Hannover (BREATH), Hannover, GermanyBackgroundInduced sputum is a method to assess airway inflammation in clinical trials for asthma and COPD.1–3 Sputum is a heterogeneous, viscous material containing inflammatory cell plugs, cellular debris, mucus, and saliva with squamous cells.The quality of sputum cell preparations for differential cell count analysis depends on multiple factors and can be highly variable.4,5 Percentage of squamous cell contamination (SQ%) is often used to assess the quality of sputum cell preparations.6Here, we evaluated a comprehensive quality score,7 which also includes an assessment of the inflammatory cell morphology and amount of cellular debris.

Magnetic resonance elastography (MRE) has been used to successfully characterize the mechanical behavior of healthy and diseased muscles, but no study has been performed to investigate the reliability of MRE on lumbar muscles. The objective of this work was to determine the reliability of MRE techniques on lumbar muscles in both ex vivo phantom and in vivo human studies. In this study, fresh porcine leg muscles were used in the phantom study, and 80 healthy adults (38.6 ± 11.2 years, 40 women) were recruited in the human study. Five repeated stiffness maps were obtained from both the phantom and human muscles by using a gradient-echo MRE sequence with a pneumatic vibration on a 1.5 T MR scanner. The technical failure rate, coefficient of variation (CV), and quality score were assessed to evaluate the reliability of MRE, respectively. Analysis of variance was performed to compare the stiffness between different lumbar muscles, and the difference was significant if p < 0.05 after Bonferroni correction. The results showed that the MRE achieved a zero technical failure rate and a low CV of stiffness (6.24 ± 1.41%) in the phantom muscles. However, in the human study, the MRE exhibited high CVs of stiffness (21.57%–25.24%) in the lumbar muscles, and the technical failure rate was higher in psoas muscles (60.0–66.3% in) than in paraspinal muscles (0.0–2.5%). Further, higher quality scores were noticed in paraspinal muscles (7.31–7.71) than those in psoas muscles (1.83–2.06). In conclusion, the MRE was a reliable technique to investigate the mechanical property of lumbar muscles, but it was less reliable to assess stiffness in psoas muscles than paraspinal muscles.

Depth-image-based rendering (DIBR), as the most popular view synthesis method, is commonly used in the application of multi-view and free-viewpoint videos. However, the quality evaluation of DIBR-synthesised videos remains largely unexplored, which may hinder the development of more advanced view synthesis technology. With this motivation, this Letter presents a new quality metric for DIBR-synthesised videos. Specifically, the disoccluded regions are first detected based on an adaptive threshold to quantify geometric distortions. An energy-based sequence mapping strategy is proposed to portray spatiotemporal inconsistency by calculating first-order and second-order similarities in the gradient magnitude domain and the Laplace-of-Gaussian domain, respectively. Finally, the overall quality score is generated by pooling the scores of geometric distortion and spatiotemporal inconsistency. Experimental results demonstrate that the proposed metric outperforms the state-of-the-art metrics dedicated to DIBR-synthesised images and videos. [ABSTRACT FROM AUTHOR]

Background: Advanced sequencing machines dramatically speed up the generation of genomic data, which makes the demand of efficient compression of sequencing data extremely urgent and significant. As the most difficult part of the standard sequencing data format FASTQ, compression of the quality score has become a conundrum in the development of FASTQ compression. Existing lossless compressors of quality scores mainly utilize specific patterns generated by specific sequencer and complex context modeling techniques to solve the problem of low compression ratio. However, the main drawbacks of these compressors are the problem of weak robustness which means unstable or even unavailable results of sequencing files and the problem of slow compression speed. Meanwhile, some compressors attempt to construct a fine-grained index structure to solve the problem of slow random access decompression speed. However, they solve the problem at the sacrifice of compression speed and at the expense of large index files, which makes them inefficient and impractical. Therefore, an efficient lossless compressor of quality scores with strong robustness, high compression ratio, fast compression and random access decompression speed is urgently needed and of great significance. Results: In this paper, based on the idea of maximizing the use of hardware resources, LCQS, a lossless compression tool specialized for quality scores, was proposed. It consists of four sequential processing steps: partitioning, indexing, packing and parallelizing. Experimental results reveal that LCQS outperforms all the other state-of-the-art compressors on all criteria except for the compression speed on the dataset SRR1284073. Furthermore, LCQS presents strong robustness on all the test datasets, with its acceleration ratios of compression speed increasing by up to 29.1x, its file size reducing by up to 28.78%, and its random access decompression speed increasing by up to 2.1x. Additionally, LCQS also exhibits strong scalability. That is, the compression speed increases almost linearly as the size of input dataset increases. Conclusion: The ability to handle all different kinds of quality scores and superiority in compression ratio and compression speed make LCQS a high-efficient and advanced lossless quality score compressor, along with its strength of fast random access decompression. Our tool LCQS can be downloaded from https://github.com/SCUT-CCNL/LCQSand freely available for non-commercial usage. [ABSTRACT FROM AUTHOR]

Abstract Background Phred quality scores are essential for downstream DNA analysis such as SNP detection and DNA assembly. Thus a valid model to define them is indispensable for any base-calling software. Recently, we developed the base-caller 3Dec for Illumina sequencing platforms, which reduces base-calling errors by 44-69% compared to the existing ones. However, the model to predict its quality scores has not been fully investigated yet. Results In this study, we used logistic regression models to evaluate quality scores from predictive features, which include different aspects of the sequencing signals as well as local DNA contents. Sparse models were further obtained by three methods: the backward deletion with either AIC or BIC and the L 1 regularization learning method. The L 1-regularized one was then compared with the Illumina scoring method. Conclusions The L 1-regularized logistic regression improves the empirical discrimination power by as large as 14 and 25% respectively for two kinds of preprocessed sequencing signals, compared to the Illumina scoring method. Namely, the L 1 method identifies more base calls of high fidelity. Computationally, the L 1 method can handle large dataset and is efficient enough for daily sequencing. Meanwhile, the logistic model resulted from BIC is more interpretable. The modeling suggested that the most prominent quenching pattern in the current chemistry of Illumina occurred at the dinucleotide “GT”. Besides, nucleotides were more likely to be miscalled as the previous bases if the preceding ones were not “G”. It suggested that the phasing effect of bases after “G” was somewhat different from those after other nucleotide types.

The iris segmentation plays a major role in an iris recognition system to increase the performance of the system. This paper proposes a novel method for segmentation of iris images to extract the iris part of long range captured eye image and an approach to select best iris frame from the iris polar image sequences by analyzing the quality of iris polar images. The quality of iris image is determined by the frequency components present in the iris polar images. The experiments are carried out on CASIA-long range captured iris image sequences. The proposed segmentation method is compared with Hough transform based segmentation and it has been determined that the proposed method gives higher accuracy for segmentation than Hough transform.

Abstract This paper reports a novel electrocardiogram (ECG) data compression algorithm which employs DCT based discrete orthogonal Stockwell transform. Dead-zone quantization is utilized to apply quantization as well as a threshold condition to transform coefficients. Further, integer conversion of coefficients is performed. It improves compression at the cost of very less reconstruction error. All integer coefficients are encoded using run-length coding. It exploits the repetition of data instances. Run-length coding helps to achieve higher compression without any relevant information loss. Performance of the proposed compression algorithm is evaluated using 48 single channel ECG records which are taken from the MIT-BIH arrhythmia database. A competitive compression performance is observed in comparison with other ECG compression methods. [ABSTRACT FROM AUTHOR]

Inspired by the facts that visual saliency captures more attention and spectral residual (SR) can indicate the saliency of the image, a novel reduced‐reference image quality assessment metric is proposed based on the regularity of the SR. The orientation and frequency components of an image are first extracted in wavelet domain. Then SR is obtained to represent the saliency of the component. Next fractal dimension is adopted to encode SR and concatenated as the image features. Finally, the feature differences between reference image and distorted one are pooled as the quality score. The proposed metric is evaluated on four largest image databases (TID2013, TID2008, CSIQ, and LIVE databases), and experimental results confirm that the proposed metric has a good performance. [ABSTRACT FROM AUTHOR]