Comparison of the start and stop codons between phytophagous bugs and predatory bugs.

(A) Use of initiation codons in phytophagous bugs. (B) Use of initiation codons in predatory bugs. (C) Stop codon usage in phytophagous bugs. (D) Stop codon usage in predatory bugs. (TIF)</p

Gene arrangement in <i>Priassus spiniger</i>.

Aelia fieberi Scott, 1874 is a pest of crops. The mitogenome of A. fieberi (OL631608) was decoded by next-generation sequencing. The mitogenome, with 41.89% A, 31.70% T, 15.44% C and 10.97% G, is 15,471 bp in size. The phylogenetic tree showed that Asopinae and Phyllocephalinae were monophyletic; however, Pentatominae and Podopinae were not monophyletic, suggesting that the phylogenetic relationships of Pentatomoidae are complex and need revaluation and revision. Phytophagous bugs had a ~20-nucleotide longer in nad2 than predatory bugs. There were differences in amino acid sequence at six sites between phytophagous bugs and predatory bugs. The codon usage analysis indicated that frequently used codons used either A or T at the third position of the codon. The analysis of amino acid usage showed that leucine, isoleucine, serine, methionine, and phenylalanine were the most abundant in 53 species of Pentatomoidae. Thirteen protein-coding genes were evolving under purifying selection, cox1, and atp8 had the strongest and weakest purifying selection stress, respectively. Phytophagous bugs and predatory bugs had different evolutionary rates for eight genes. The mitogenomic information of A. fieberi could fill the knowledge gap for this important crop pest. The differences between phytophagous bugs and predatory bugs deepen our understanding of the effect of feeding habit on mitogenome.</div

Mitogenome map of <i>Aelia fieberi</i>.

Protein-coding genes (PCGs, CDS) and ribosomal genes (rRNAs) are presented with standard abbreviations. Genes coding for transfer RNAs (tRNAs) are presented with one letter abbreviation. S1 = AGN, S2 = UCN, L1 = CUN, L2 = UUR. Wathet blue, orange, pink and grey represent PCGs, tRNAs, rRNAs and D-loop (noncoding control region), respectively. The colors black, green, and purple represent the GC content, positive GC skew (GC skew+) and negative GC skew (GC skew-), respectively. The orientation of the gene is indicated by arrows.</p

Codon usage in the mitogenome of <i>Aelia fieberi</i>.

Aelia fieberi Scott, 1874 is a pest of crops. The mitogenome of A. fieberi (OL631608) was decoded by next-generation sequencing. The mitogenome, with 41.89% A, 31.70% T, 15.44% C and 10.97% G, is 15,471 bp in size. The phylogenetic tree showed that Asopinae and Phyllocephalinae were monophyletic; however, Pentatominae and Podopinae were not monophyletic, suggesting that the phylogenetic relationships of Pentatomoidae are complex and need revaluation and revision. Phytophagous bugs had a ~20-nucleotide longer in nad2 than predatory bugs. There were differences in amino acid sequence at six sites between phytophagous bugs and predatory bugs. The codon usage analysis indicated that frequently used codons used either A or T at the third position of the codon. The analysis of amino acid usage showed that leucine, isoleucine, serine, methionine, and phenylalanine were the most abundant in 53 species of Pentatomoidae. Thirteen protein-coding genes were evolving under purifying selection, cox1, and atp8 had the strongest and weakest purifying selection stress, respectively. Phytophagous bugs and predatory bugs had different evolutionary rates for eight genes. The mitogenomic information of A. fieberi could fill the knowledge gap for this important crop pest. The differences between phytophagous bugs and predatory bugs deepen our understanding of the effect of feeding habit on mitogenome.</div

Predicted secondary structure of 22 tRNA of <i>A</i>. <i>fieberi</i>.

Predicted secondary structure of 22 tRNA of A. fieberi.</p

Comparison of the nucleotide sequence of <i>nad2</i> between phytophagous bugs and predatory bugs.

Comparison of the nucleotide sequence of nad2 between phytophagous bugs and predatory bugs.</p

Combining Amplitude Spectrum Area with Previous Shock Information Using Neural Networks Improves Prediction Performance of Defibrillation Outcome for Subsequent Shocks in Out-Of-Hospital Cardiac Arrest Patients

<div><p>Objective</p><p>Quantitative ventricular fibrillation (VF) waveform analysis is a potentially powerful tool to optimize defibrillation. However, whether combining VF features with additional attributes that related to the previous shock could enhance the prediction performance for subsequent shocks is still uncertain.</p><p>Methods</p><p>A total of 528 defibrillation shocks from 199 patients experienced out-of-hospital cardiac arrest were analyzed in this study. VF waveform was quantified using amplitude spectrum area (AMSA) from defibrillator's ECG recordings prior to each shock. Combinations of AMSA with previous shock index (PSI) or/and change of AMSA (ΔAMSA) between successive shocks were exercised through a training dataset including 255shocks from 99patientswith neural networks. Performance of the combination methods were compared with AMSA based single feature prediction by area under receiver operating characteristic curve(AUC), sensitivity, positive predictive value (PPV), negative predictive value (NPV) and prediction accuracy (PA) through a validation dataset that was consisted of 273 shocks from 100patients.</p><p>Results</p><p>A total of61 (61.0%) patients required subsequent shocks (N = 173) in the validation dataset. Combining AMSA with PSI and ΔAMSA obtained highest AUC (0.904 vs. 0.819, <i>p</i><0.001) among different combination approaches for subsequent shocks. Sensitivity (76.5% vs. 35.3%, <i>p</i><0.001), NPV (90.2% vs. 76.9%, <i>p</i> = 0.007) and PA (86.1% vs. 74.0%, <i>p</i> = 0.005)were greatly improved compared with AMSA based single feature prediction with a threshold of 90% specificity.</p><p>Conclusion</p><p>In this retrospective study, combining AMSA with previous shock information using neural networks greatly improves prediction performance of defibrillation outcome for subsequent shocks.</p></div

Annotation of the <i>Aelia fieberi</i> mitogenome.

Aelia fieberi Scott, 1874 is a pest of crops. The mitogenome of A. fieberi (OL631608) was decoded by next-generation sequencing. The mitogenome, with 41.89% A, 31.70% T, 15.44% C and 10.97% G, is 15,471 bp in size. The phylogenetic tree showed that Asopinae and Phyllocephalinae were monophyletic; however, Pentatominae and Podopinae were not monophyletic, suggesting that the phylogenetic relationships of Pentatomoidae are complex and need revaluation and revision. Phytophagous bugs had a ~20-nucleotide longer in nad2 than predatory bugs. There were differences in amino acid sequence at six sites between phytophagous bugs and predatory bugs. The codon usage analysis indicated that frequently used codons used either A or T at the third position of the codon. The analysis of amino acid usage showed that leucine, isoleucine, serine, methionine, and phenylalanine were the most abundant in 53 species of Pentatomoidae. Thirteen protein-coding genes were evolving under purifying selection, cox1, and atp8 had the strongest and weakest purifying selection stress, respectively. Phytophagous bugs and predatory bugs had different evolutionary rates for eight genes. The mitogenomic information of A. fieberi could fill the knowledge gap for this important crop pest. The differences between phytophagous bugs and predatory bugs deepen our understanding of the effect of feeding habit on mitogenome.</div

Comparison of amino acid sequence at six sites between phytophagous bugs and predatory bugs.

Comparison of amino acid sequence at six sites between phytophagous bugs and predatory bugs.</p

Maximum likelihood phylogenetic tree of 53 species of Pentatomidae.

Eurygaster testudinaria (Hemiptera: Scutelleridae) was selected as representative of the outgroup. The bootstrap values were labeled at each node. GenBank accession numbers of sequences were listed after the species name. (TIF)</p