This is, to our knowledge, the largest single report of diagnostic testing for germline p53 mutations, yielding practical mutation prevalence tables and suggesting clinical utility of classic LFS and Chompret criteria for identifying a subset of cancer-prone families with p53 germline mutations, with important implications for diagnosis and management.

Dideoxy fingerprinting (ddF) was used as a tool to search for a generic set of conditions with sufficient power to detect virtually all mutations. For each condition tested, a very large sample of mutation-containing, single-stranded segments (about 1500) were analyzed with ddF. Correlation coefficients identified pairs of conditions in which single-strand conformation polymorphism (SSCP) mobilities were poorly correlated. The data strongly suggest that tertiary structure (e.g., base-sugar and sugar-sugar interactions) rather than secondary structure is the predominant determinant of mobility shifts by SSCP. Five conditions were selected with sufficient redundancy to detect all the mutations. The sensitivity of detection of virtually all mutations-SSCP (DOVAM-S) was determined by blinded analyses on samples containing additional mutations scattered throughout the eight exons and splice junctions in the factor IX gene. The factor IX gene sequence (2.5 kb) was scanned in one lane by 15 PCR-amplified segments (125 kb of sequence scanned per gel). All of the 84 single-base substitutions were detected in the blinded analyses, the first consisting of 50 hemizygous mutant and wild-type (WT) samples and the second consisting of 50 heterozygous mutant and WT samples. DOVAM-S is estimated to be five times faster than fluorescent DNA sequencing for the detection of virtually all mutations when the five conditions are applied.

The analysis of partial discharge (PD) signals has been identified as a standard diagnostic tool for monitoring the condition of different electrical apparatuses. This study proposes an approach to detecting PD patterns in gas-insulated switchgear (GIS) using a long short-term memory (LSTM) recurrent neural network (RNN). The proposed method uses phase-resolved PD (PRPD) signals as input, extracts low-level features, and finally, classifies faults in GIS. In the proposed method, LSTM networks can learn temporal dependencies directly from PRPD signals. Most existing models use support vector machines (SVMs) and mainly focus on improving feature representation and extraction manually to analyze PRPD signals. However, the proposed model captures important temporal features with the help of its low-level feature extraction capability from raw inputs. It outperforms conventional SVMs and achieves 96.74% classification accuracy for PRPDs in GIS.

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