A multi-level hypoglycemia early alarm system based on sequence pattern mining

Background Early alarm of hypoglycemia, detection of asymptomatic hypoglycemia, and effective control of blood glucose fluctuation make a great contribution to diabetic treatment. In this study, we designed a multi-level hypoglycemia early alarm system to mine potential information in Continuous Glucose Monitoring (CGM) time series and improve the overall alarm performance for different clinical situations. Methods Through symbolizing the historical CGM records, the Prefix Span was adopted to obtain the early alarm/non-alarm frequent sequence libraries of hypoglycemia events. The longest common subsequence was used to remove the common frequent sequence for achieving the hypoglycemia early alarm in different clinical situations. Then, the frequent sequence pattern libraries with different risk thresholds were designed as the core module of the proposed multi-level hypoglycemia early alarm system. Results The model was able to predict hypoglycemia events in the clinical dataset of level-I (sensitivity 85.90%, false-positive 23.86%, miss alarm rate 14.10%, average early alarm time 20.61 min), level-II (sensitivity 80.36%, false-positive 17.37%, miss alarm rate 19.63%, average early alarm time 27.66 min), and level-III (sensitivity 78.07%, false-positive 13.59%, miss alarm rate 21.93%, average early alarm time 33.80 min), respectively. Conclusions The proposed approach could effectively predict hypoglycemia events based on different risk thresholds to meet different prevention and treatment requirements. Moreover, the experimental results confirm the practicality and prospects of the proposed early alarm system, which reflects further significance in personalized medicine for hypoglycemia prevention.

THE PATTERN OF EGG LAYING IN DOMESTIC FOWLS KEPT IN WARM HUMID TROPICS

Oviposition times, sequence of laying, lag time, laying intensity, egg weights and specific gravity of two commercial hybrids, Hypeco Gold Line (HGL) and Shavers Black (SB), were studied with 192 individually caged birds (96 of each) over a forty six day period. The most frequent sequence size was three for HGL and two for SB, the longest egg sequence for HGL was 43 while that of SB was 22. The interval between successive eggs ranged from 23.00 to 27.67 hours for HGL and from 23.56 to 27.80 hours for SB. The laying intensity was 72.10 and 63.54 per cent for HGL and SB respectively. The mean egg weight was 62.83 ± 0.38 g for HGL and 68.81 ± 0.39 g for SB; the egg weight tended to decrease from the first to the last egg in a sequence. The mean specific gravity of the eggs were 1.057 ± 0.003 and 1.056 ± 0.005 for HGL and SB respectively. The peak of lay was between 0900 to 1000 hours in both crosses with 99 per cent of all eggs being laid between 0600 and 1800 hours. Over 70 per cent of the eggs were laid before 12 noon in both Cases.

A Multi-level Hypoglycemia Early Alarm System Based on Sequence Pattern Mining

Background: Early alarm of hypoglycemia, detection of asymptomatic hypoglycemia, and effective control of blood glucose fluctuation make a great contribution to the treatment for diabetic patients. In this study, we designed a multi-level hypoglycemia early alarm system to improve the overall performance of hypoglycemia early alarm. Methods: Through symbolizing the historical CGM data, the Prefix Span was adopted to obtain the early alarm and non-alarm frequent sequence libraries of hypoglycemia. The longest common subsequence was used to remove the common frequent sequence for achieving the hypoglycemia early alarm on the clinical dataset. Frequent sequence pattern libraries with different risk levels were designed by choosing different thresholds and a multi-level hypoglycemia early alarm system for different clinical situations were established. Results: The model was able to predict hypoglycemia events in the clinical dataset of level-I (sensitivity 85.90%, false-positive 23.86%, miss alarm rate 14.10%, average early alarm time 20.61min), level-II (sensitivity 80.36%, false-positive 17.37%, miss alarm rate 19.63%, average early alarm time 27.66min), and level-III (sensitivity 78.07%, false-positive 13.59%, miss alarm rate 21.93%, average early alarm time 33.80min), respectively.Conclusions: The proposed approach could effectively predict hypoglycemia events on the basis of different thresholds to meet different prevention and treatment requirements in clinical situations. Moreover, the experimental results confirm the practicality and prospects of the proposed early alarm system, which reflects further significance in personalized medicine for hypoglycemia prevention.

CFSP: a collaborative frequent sequence pattern discovery algorithm for nucleic acid sequence classification

Background Conserved nucleic acid sequences play an essential role in transcriptional regulation. The motifs/templates derived from nucleic acid sequence datasets are usually used as biomarkers to predict biochemical properties such as protein binding sites or to identify specific non-coding RNAs. In many cases, template-based nucleic acid sequence classification performs better than some feature extraction methods, such as N-gram and k-spaced pairs classification. The availability of large-scale experimental data provides an unprecedented opportunity to improve motif extraction methods. The process for pattern extraction from large-scale data is crucial for the creation of predictive models. Methods In this article, a Teiresias-like feature extraction algorithm to discover frequent sub-sequences (CFSP) is proposed. Although gaps are allowed in some motif discovery algorithms, the distance and number of gaps are limited. The proposed algorithm can find frequent sequence pairs with a larger gap. The combinations of frequent sub-sequences in given protracted sequences capture the long-distance correlation, which implies a specific molecular biological property. Hence, the proposed algorithm intends to discover the combinations. A set of frequent sub-sequences derived from nucleic acid sequences with order is used as a base frequent sub-sequence array. The mutation information is attached to each sub-sequence array to implement fuzzy matching. Thus, a mutate records a single nucleotide variant or nucleotides insertion/deletion (indel) to encode a slight difference between frequent sequences and a matched subsequence of a sequence under investigation. Conclusions The proposed algorithm has been validated with several nucleic acid sequence prediction case studies. These data demonstrate better results than the recently available feature descriptors based methods based on experimental data sets such as miRNA, piRNA, and Sigma 54 promoters. CFSP is implemented in C++ and shell script; the source code and related data are available at https://github.com/HePeng2016/CFSP.