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BACKGROUND & AIMS: Current hepatocellular carcinoma (HCC) risk scores do not reflect changes in HCC risk resulting from liver disease progression/regression over time. We aimed to develop and validate two novel prediction models using multivariate longitudinal data, with or without cell-free DNA (cfDNA) signatures. METHODS: A total of 13,728 patients from two nationwide multicenter prospective observational cohorts, the majority of whom had chronic hepatitis B, were enrolled. aMAP score, as one of the most promising HCC prediction models, was evaluated for each patient. Low-pass whole-genome sequencing was used to derive multi-modal cfDNA fragmentomics features. A longitudinal discriminant analysis algorithm was used to model longitudinal profiles of patient biomarkers and estimate the risk of HCC development. RESULTS: We developed and externally validated two novel HCC prediction models with a greater accuracy, termed aMAP-2 and aMAP-2 Plus scores. The aMAP-2 score, calculated with longitudinal data on the aMAP score and alpha-fetoprotein values during an up to 8-year follow-up, performed superbly in the training and external validation cohorts (AUC 0.83-0.84). The aMAP-2 score showed further improvement and accurately divided aMAP-defined high-risk patients into two groups with 5-year cumulative HCC incidences of 23.4% and 4.1%, respectively (p = 0.0065). The aMAP-2 Plus score, which incorporates cfDNA signatures (nucleosome, fragment and motif scores), optimized the prediction of HCC development, especially for patients with cirrhosis (AUC 0.85-0.89). Importantly, the stepwise approach (aMAP -> aMAP-2 -> aMAP-2 Plus) stratified patients with cirrhosis into two groups, comprising 90% and 10% of the cohort, with an annual HCC incidence of 0.8% and 12.5%, respectively (p <0.0001). CONCLUSIONS: aMAP-2 and aMAP-2 Plus scores are highly accurate in predicting HCC. The stepwise application of aMAP scores provides an improved enrichment strategy, identifying patients at a high risk of HCC, which could effectively guide individualized HCC surveillance. IMPACT AND IMPLICATIONS: In this multicenter nationwide cohort study, we developed and externally validated two novel hepatocellular carcinoma (HCC) risk prediction models (called aMAP-2 and aMAP-2 Plus scores), using longitudinal discriminant analysis algorithm and longitudinal data (i.e., aMAP and alpha-fetoprotein) with or without the addition of cell-free DNA signatures, based on 13,728 patients from 61 centers across mainland China. Our findings demonstrated that the performance of aMAP-2 and aMAP-2 Plus scores was markedly better than the original aMAP score, and any other existing HCC risk scores across all subsets, especially for patients with cirrhosis. More importantly, the stepwise application of aMAP scores (aMAP -> aMAP-2 -> aMAP-2 Plus) provides an improved enrichment strategy, identifying patients at high risk of HCC, which could effectively guide individualized HCC surveillance.

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This study investigates the role and functionality of special nucleotide sequences ("DNA signatures") to detect the presence of an organism and to distinguish it from all others. After highlighting vulnerabilities of the prevalent DNA signature paradigm for the identification of agricultural genetically modified (GM) organisms it will be argued that these so-called signatures really are no signatures at all - when compared to the notion of traditional (handwritten) signatures and their generalizations in the modern (digital) world. It is suggested that a recent contamination event of an unauthorized GM Bacillus subtilis strain (Paracchini et al., 2017) in Europe could have been-or the same way could be - the consequence of exploiting gaps of prevailing DNA signatures. Moreover, a recent study (Mueller, 2019) proposes that such DNA signatures may intentionally be exploited to support the counterfeiting or even weaponization of GM organisms (GMOs). These concerns mandate a re-conceptualization of how DNA signatures need to be realized. After identifying central issues of the new vulnerabilities and overlying them with practical challenges that bio-cyber hackers would be facing, recommendations are made how DNA signatures may be enhanced. To overcome the core problem of signature transferability in bioengineered mediums, it is necessary that the identifier needs to remain secret during the entire verification process. On the other hand, however, the goal of DNA signatures is to enable public verifiability, leading to a paradoxical dilemma. It is shown that this can be addressed with ideas that underlie special cryptographic signatures, in particular those of "zero-knowledge" and "invisibility." This means more than mere signature hiding, but relies on a knowledge-based proof and differentiation of a secret (here, as assigned to specific clones) which can be realized without explicit demonstration of that secret. A re-conceptualization of these principles can be used in form of a combined (digital and physical) method to establish confidentiality and prevent un-impersonation of the manufacturer. As a result, this helps mitigate the circulation of possibly hazardous GMO counterfeits and also addresses the situation whereby attackers try to blame producers for deliberately implanting illicit adulterations hidden within authorized GMOs.

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A total of 21 thermophilic bacteria were isolated and identified using 16S rRNA gene sequencing method. Sequences were submitted to NCBI website. Short DNA sequences JN392966-JN392972; KC120909-KC120919; KM998072-KM998074 and KP053645 strains were downloaded from NCBI BioSample database. ENDMEMO GC calculating tool was used for calculation of maximum, minimum and average GC percentage and graphical representation of GC content. Data generated indicate 20 short DNA sequences have maximum GC content ranged from 60% to 100% with an average GC content 52.5-59.8%. It is recorded that Bacillus sp. W7, Escherichia coli strain NW1 and Geobacillus thermoleovorans strain rekadwadsis strains showed GC content maximum up to 70%; Actinobacterium EF_NAK1-7 up to 85.7%, while Bacillus megaterium and E. coli strain NW2 showed GC content maximum to 100%. Digital data on thermophilic bacteria isolated from Unkeshwar hot springs would be useful for interpretation of presence of biodiversity in addition to phenotypic, physiological characteristics and data generated through 16S rRNA gene sequencing technology.

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16S rRNA sequences of morphologically and biochemically identified 21 thermophilic bacteria isolated from Unkeshwar hot springs (19°85'N and 78°25'E), Dist. Nanded (India) has been deposited in NCBI repository. The 16S rRNA gene sequences were used to generate QR codes for sequences (FASTA format and full Gene Bank information). Diversity among the isolates is compared with known isolates and evaluated using CGR, FCGR and PCA i.e. visual comparison and evaluation respectively. Considerable biodiversity was observed among the identified bacteria isolated from Unkeshwar hot springs. The hyperlinked QR codes, CGR, FCGR and PCA of all the isolates are made available to the users on a portal https://sites.google.com/site/bhagwanrekadwad/.

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Although most of the pertinent data on the sequence-directed processes leading to genome rearrangements (GRs) have come from studies on somatic tissues, little is known about GRs in the germ line of patients with hereditary disorders. This study aims at identifying DNA motifs and higher order structures of genome architecture, which can result in losses and gains of genetic material in the germ line. We first identified candidate motifs by studying 112 pathogenic germ-line GRs in hereditary colorectal cancer patients, and subsequently created an algorithm, termed recombination type ratio, which correctly predicts the propensity of rearrangements with respect to homologous versus nonhomologous recombination events.

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