Constitutional ring chromosomes are rare orphan chromosomal disorders. Ring chromosome syndrome featuring growth retardation and mild to intermediate intellectual disability is likely caused by the ...dynamic behavior of ring chromosome through cell cycles. Chromosomal and regional specific phenotypes likely result from segmental losses and gains during the ring formation. Although recent applications of genomic copy number and sequencing analyses revealed various ring chromosome structures from an increasing number of case studies, there was no organized effort for compilating and curating cytogenomic and clinical finding for ring chromosomes.
A web-based interactive 'Human Ring Chromosome Registry' using Microsoft Access based relational database was developed to present genetic and phenotypic findings of ring chromosome cases. Chinese ring chromosome cases reported in the literature was reviewed and compiled as a testing data set to validate this registry.
A total of 113 cases of ring chromosomes were retrieved in all chromosomes except for chromosomes 16, 17 and 19. The most frequently seen ring chromosomes by a decreasing order of relative frequencies were ring 13 (14%), X (12%), 22 (10%), 15 (9%), 14 (7%), and 18 (7%). Genomic imbalances were detected in 18 out of 19 cases analyzed by microarray or sequencing. Variable clinical manifestations of developmental delay, dysmorphic facial features, intellectual disability, microcephaly, and hypotonia were noted in most autosomal rings. Chromosomal specific syndromic phenotypes included Wolf-Hirschhorn syndrome in a ring chromosome 4, cri-du-chat syndrome in a ring chromosome 5, epilepsy in ring chromosomes 14 and 20, Turner syndrome in ring chromosome X, and infertility in ring chromosomes 13, 21, 22 and Y. Effective growth hormone supplemental treatment for growth retardation in a ring chromosome 18 was noted.
Based on findings from these Chinese ring chromosome cases, guidelines for cytogenomic diagnosis and criteria for case registration were proposed. Further research to define underlying mechanisms of ring chromosome formation and dynamic mosaicism, to delineate the genotype-phenotype correlations, and to develop chromosome therapy for ring chromosomes were discussed.
Human ring chromosomes (RCs) are rare diseases with an estimated newborn incidence of 1/50,000 and an annual occurrence of 2,800 patients globally. Over the past 60 years, banding cytogenetics, ...fluorescence
in situ
hybridization (FISH), chromosome microarray analysis (CMA), and whole-genome sequencing (WGS) has been used to detect an RC and further characterize its genomic alterations. Ring syndrome featuring sever growth retardation and variable intellectual disability has been considered as general clinical presentations for all RCs due to the cellular losses from the dynamic mosaicism of RC instability through mitosis. Cytogenomic heterogeneity ranging from simple complete RCs to complex rearranged RCs and variable RC intolerance with different relative frequencies have been observed. Clinical heterogeneity, including chromosome-specific deletion and duplication syndromes, gene-related organ and tissue defects, cancer predisposition to different types of tumors, and reproductive failure, has been reported in the literature. However, the patients with RCs reported in the literature accounted for less than 1% of its occurrence. Current diagnostic practice lacks laboratory standards for analyzing cellular behavior and genomic imbalances of RCs to evaluate the compound effects on patients. Under-representation of clinical cases and lack of comprehensive diagnostic analysis make it a challenge for evidence-based interpretation of clinico-cytogenomic correlations and recommendation of follow-up clinical management. Given recent advancements in genomic technologies and organized efforts by international collaborations and patient advocacy organizations, the prospective of standardized cytogenomic diagnosis and evidence-based clinical management for all patients with RCs could be achieved at an unprecedented global scale.
Significant advances in the genetic diagnosis and clinical management for constitutional ring chromosomes have been achieved in the past 60 years. Li et al. performed a systematic review on the progress of diagnostic technologies for analyzing ring chromosomes and the current understanding of clinical and cytogenomic heterogeneity of ring chromosomes. An international consortium for human ring chromosomes is working toward standardized comprehensive genetic diagnosis and evidence-based genetic counseling and clinical management for patients with this rare and unique chromosomal rearrangement.
Oligonucleotide array comparative genomic hybridization (aCGH) analysis has been used for detecting somatic copy number alterations (CNAs) in various types of tumors. This study aimed to assess the ...clinical utility of aCGH for cases of hepatocellular carcinoma (HCC) and to evaluate the correlation between CNAs and clinicopathologic findings.
aCGH was performed on 75 HCC cases with paired DNA samples from tumor and adjacent nontumor tissues. Survival outcomes from these cases were analyzed based on Barcelona-Clinic Liver Cancer Stage (BCLC), Edmondson-Steiner grade (E-S), and recurrence status. Correlation of CNAs with clinicopathologic findings was analyzed by Wilcoxon rank test and clustering vs. K means.
The survival outcomes indicated that BCLC stages and recurrence status could be predictors and E-S grades could be a modifier for HCC. The most common CNAs involved gains of 1q and 8q and a loss of 16q (50%), losses of 4q and 17p and a gain of 5p (40%), and losses of 8p and 13q (30%). Analyses of genomic profiles and clusters identified that losses of 4q13.2q35.2 and 10q22.3q26.13 seen in cases of stage A, grade III and nonrecurrence were likely correlated with good survival, while loss of 1p36.31p22.1 and gains of 2q11.2q21.2 and 20p13p11.1 seen in cases of stage C, grade III and recurrence were possibly correlated with worst prognosis.
These results indicated that aCGH analysis could be used to detect recurrent CNAs and involved key genes and pathways in patients with HCC. Further analysis on a large case series to validate the correlation of CNAs with clinicopathologic findings of HCC could provide information to interpret CNAs and predict prognosis.
Background:
The outbreak of COVID-19 in 2019 has rapidly swept the world, causing irreparable loss to human beings. The pandemic has shown that there is still a delay in the early response to disease ...outbreaks and needs a method for unknown disease outbreak detection. The study's objective is to establish a new medical knowledge representation and reasoning model, and use the model to explore the feasibility of unknown disease outbreak detection.
Methods:
The study defined abnormal values with diagnostic significances from clinical data as the Features, and defined the Features as the antecedents of inference rules to match with knowledge bases, achieved in detecting known or emerging infectious disease outbreaks. Meanwhile, the study built a syndromic surveillance base to capture the target cases' Features to improve the reliability and fault-tolerant ability of the system.
Results:
The study combined the method with Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and early COVID-19 outbreaks as empirical studies. The results showed that with suitable surveillance guidelines, the method proposed in this study was capable to detect outbreaks of SARS, MERS, and early COVID-19 pandemics. The quick matching accuracies of confirmed infection cases were 89.1, 26.3–98%, and 82%, and the syndromic surveillance base would capture the Features of the remaining cases to ensure the overall detection accuracies. Based on the early COVID-19 data in Wuhan, this study estimated that the median time of the early COVID-19 cases from illness onset to local authorities' responses could be reduced to 7.0–10.0 days.
Conclusions:
This study offers a new solution to transfer traditional medical knowledge into structured data and form diagnosis rules, enables the representation of doctors' logistic thinking and the knowledge transmission among different users. The results of empirical studies demonstrate that by constantly inputting medical knowledge into the system, the proposed method will be capable to detect unknown diseases from existing ones and perform an early response to the initial outbreaks.
This paper presents a model-predictive control-based scheduling strategy called ThermoRing to reduce cooling costs in data centers. ThermoRing makes use of an online feedback control mechanism to ...improve thermal management of energy-efficient clusters in a data center. ThermoRing aims at keeping the maximum inlet temperatures of the nodes under a redline temperature limit with little stability errors. Importantly, the ThermoRing approach is capable of dealing with emergency conditions (e.g., node fan shutdown and unexpected rising task arrival rates) by dynamically balancing load among the nodes. ThermoRing incorporates a heat distribution matrix to model the thermal characteristics of a data center housing cluster. ThermoRing is conducive to thermal management in data centers with high-scheduling performance and stability. Using a real-world online bookstore trace, we conduct extensive experiments to compare the performance of ThermoRing with three existing solutions (i.e., C-Oracle, Ad-hoc, and MinHR). The experimental results show that ThermoRing improved the system throughput by more than 10% under regular load conditions and by 40% in emergency cases. ThermoRing also significantly improves the energy efficiency of MinHR, which is a thermal-aware scheduler.
With the development of technology, fingerprint identification has become an effective means of personal identification, and has been widely used in the fields of public security, custom, banking, ...network security and other areas requiring identification. Nowadays, many effective methods have been proposed for fingerprint identification, but these methods are not effective in identifying damaged fingerprints, and the correct recognition rate is low. In order to effectively solve the problem of identification and classification of damaged fingerprints, this paper proposes a method for classification of broken fingerprints based on deep learning fuzzy theory. Firstly, after pre-processing the fingerprint, using the bifurcation point and the endpoint in the broken fingerprint image as the minutiae, the feature extraction ability of the deep convolutional neural network is utilized to extract the feature of the damaged fingerprint minutiae. Secondly, the fuzzy rough set is used to reduce the feature. Finally, using the reduced feature uses the Softmax classifier to classify the damaged fingerprint image. The simulation results show that, after preprocessing the damaged fingerprint image, using OPTA algorithm to refine the damaged fingerprint image, the features of the fingerprint image can be extracted effectively by deep convolutional neural network, and then the classification accuracy can be improved by using Softmax classifier to reduce the features.
With the development of society, health has attracted more and more attention. Heart disease is a common and frequently occurring disease, and it is fatal. Rapid and timely diagnosis and treatment of ...heart disease is very important. Electrocardiogram (ECG) reflects human heart health and is widely used in heart disease examination. Existing methods depending on doctors’ personal experience and diagnostic level are time-consuming and inefficient. Therefore, a classification method that can automatically analyze ECG is required. Aiming at the classification of 12-lead ECG, based on the good performance of convolution neural network, this paper proposes a method of ECG classification based on lead convolution neural network, which can effectively and accurately detect, recognize and classify ECG. First, the image features are extracted after the ECG is preprocessed, and then using the fuzzy set reduces the extracted ECG image features. Then, residual learning is used to optimize the convolutional neural network, and in order to ensure that the network is easy to train and fast convergence, a random parameter initialization method is introduced to achieve better classification results. The simulation results show that the proposed multi-lead filtering algorithm reduces the loss of useful information while eliminating noise; at the same time, the convolution neural network can effectively and accurately classify ECG images; and the introduction of residual network can improve the classification effect.
The particle swarm optimizer (PSO) is a swarm intelligence based on heuristic optimization technique that can be applied to a wide range of problems. After analyzing the dynamics of traditional PSO, ...this paper presents a new PSO variant on the basis of local stochastic search strategy (LSSPSO) for performance improvement. This is encouraged by a social phenomenon that everyone wants to first exceed the nearest superior and then all superior. Specifically, LSSPSO employs a local stochastic search to adjust inertia weight in terms of keeping a balance between the diversity and the convergence speed, aiming to improve the performance of traditional PSO. Experiments conducted on unimodal and multimodal test functions demonstrate the effectiveness of LSSPSO in solving multiple benchmark problems as compared to several other PSO variants.
Hepatocellular carcinoma (HCC) is one of the deadliest cancers worldwide. Metallothioneins (MTs) are metal-binding proteins involved in multiple biological processes such as metal homeostasis and ...detoxification, as well as in oncogenesis. Copy number variation (CNV) plays a vital role in pathogenesis and carcinogenesis. Nevertheless, there is no study on the role of MT1 CNV in HCC.
Array-based Comparative Genomic Hybridization (aCGH) analysis was performed to obtain the CNV data of 79 Guangxi HCC patients. The prognostic effect of MT1-deletion was analyzed by univariate and multivariate Cox regression analysis. The differentially expressed genes (DEGs) were screened based on The Gene Expression Omnibus database (GEO) and the Liver Hepatocellular Carcinoma of The Cancer Genome Atlas (TCGA-LIHC). Then function and pathway enrichment analysis, protein-protein interaction (PPI) and hub gene selection were applied on the DEGs. Lastly, the hub genes were validated by immunohistochemistry, tissue expression and prognostic analysis.
The MT1-deletion was demonstrated to affect the prognosis of HCC and can act as an independent prognostic factor. 147 common DEGs were screened. The most significant cluster of DEGs identified by Molecular Complex Detection (MCODE) indicated that the expression of four MT1s were down-regulated. MT1X and other five hub genes (TTK, BUB1, CYP3A4, NR1I2, CYP8B1) were associated with the prognosis of HCC. TTK, could affect the prognosis of HCC with MT1-deletion and non-deletion. NR1I2, CYP8B1, and BUB1 were associated with the prognosis of HCC with MT1-deletion.
In the current study, we demonstrated that MT1-deletion can be an independent prognostic factor in HCC. We identified TTK, BUB1, NR1I2, CYP8B1 by processing microarray data, for the first time revealed the underlying function of MT1 deletion in HCC, MT1-deletion may influence the gene expression in HCC, which may be the potential biomarkers for HCC with MT1 deletion.
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