Given a context passage, generate a question from the passage such that its answer is shortest continous span from the passage.

[EX Q]: Our objective was to evaluate the diagnostic value of computed tomography angiography (CTA) and ventilation perfusion (V/Q) scan in the assessment of pulmonary embolism (PE) by means of a Bayesian statistical model. Wells criteria defined pretest probability. Sensitivity and specificity of CTA and V/Q scan for PE were derived from pooled meta-analysis data. Likelihood ratios calculated for CTA and V/Q were inserted in the nomogram. Absolute (ADG) and relative diagnostic gains (RDG) were analyzed comparing post- and pretest probability. Comparative gain difference was calculated for CTA ADG over V/Q scan integrating ANOVA p value set at 0.05. The sensitivity for CT was 86.0% (95% CI: 80.2%, 92.1%) and specificity of 93.7% (95% CI: 91.1%, 96.3%). The V/Q scan yielded a sensitivity of 96% (95% CI: 95%, 97%) and a specificity of 97% (95% CI: 96%, 98%). Bayes nomogram results for CTA were low risk and yielded a posttest probability of 71.1%, an ADG of 56.1%, and an RDG of 374%, moderate-risk posttest probability was 85.1%, an ADG of 56.1%, and an RDG of 193.4%, and high-risk posttest probability was 95.2%, an ADG of 36.2%, and an RDG of 61.35%. The comparative gain difference for low-risk population was 46.1%; in moderate-risk 41.6%; and in high-risk a 22.1% superiority. ANOVA analysis for LR+ and LR- showed no significant difference (p=0.8745, p=0.9841 respectively). This Bayesian model demonstrated a superiority of CTA when compared to V/Q scan for the diagnosis of pulmonary embolism. Low-risk patients are recognized to have a superior overall comparative gain favoring CTA.
[EX A]: What can be predicted with the Wells criteria?

[EX Q]: One day, a slave owner entered a local store and threatened a young boy who was his slave. At first, the slave refused to move and ran for the door. The slave owner reached for a heavy weight. He threw it at his slave. Instead, it hit Harriet on the head. Harriet almost died. For the rest of her life, she carried the mark of a deep wound on her head. Miser Brodas felt he never needed Harriet. He decided to sell her. Harriet thought of a way to prevent this. Each time she was shown to someone who might buy her, she acted as if she were falling asleep. After a while, Mister Brodas gave up the hope of selling Harriet. He sent her back to the fields. At 24 she married a free black man, John Tubman. By then, Harriet was sure she should escape. In 1849, Mister Brodas died. His slaves probably would be sold to further South. John Tubman tried to make Harriet forget about running away. Harriet decided she must end her marriage and that she needed to tell her family that she was leaving. Harriet escaped for Pennsylvania where slavery was banned there. Once she hid under hay that had been cut from the fields. Another time, she wore men's clothing. Finally, she arrived in Pennsylvania. Now that Harriet was free, she didn't forget other slaves in Maryland. During the next ten years, she freed her parents and other family members. She traveled back and forth eighteen times, helping about 300 slaves escape. She became an expert at hiding from slave hunters. At one time, anyone finding Harriet was promised 40,000dollars for catching her--dead or alive. The people she helped called her Moses. She had rescued them from slavery just as the biblical Moses rescued the Jews. After the Civil War began, Harriet started to spy for the North. She also served as a nurse. After four years of bloody fighting, the North won the war. Abraham Lincoln freed the slaves in 1863. There was no longer any need for Harriet to be Moses.
[EX A]: We know from the passage that Harriet was Mister Brodas' .

[EX Q]: A common challenge in pathogen discovery by deep sequencing approaches is to recognize viral or subviral pathogens in samples of diseased tissue that share no significant homology with a known pathogen. Here we report a homology-independent approach for discovering viroids, a distinct class of free circular RNA subviral pathogens that encode no protein and are known to infect plants only. Our approach involves analyzing the sequences of the total small RNAs of the infected plants obtained by deep sequencing with a unique computational algorithm, progressive filtering of overlapping small RNAs (PFOR). Viroid infection triggers production of viroid-derived overlapping siRNAs that cover the entire genome with high densities. PFOR retains viroid-specific siRNAs for genome assembly by progressively eliminating nonoverlapping small RNAs and those that overlap but cannot be assembled into a direct repeat RNA, which is synthesized from circular or multimeric repeated-sequence templates during viroid replication. We show that viroids from the two known families are readily identified and their full-length sequences assembled by PFOR from small RNAs sequenced from infected plants. PFOR analysis of a grapevine library further identified a viroid-like circular RNA 375 nt long that shared no significant sequence homology with known molecules and encoded active hammerhead ribozymes in RNAs of both plus and minus polarities, which presumably self-cleave to release monomer from multimeric replicative intermediates. A potential application of the homology-independent approach for viroid discovery in plant and animal species where RNA replication triggers the biogenesis of siRNAs is discussed.
[EX A]:
Which are the smallest known subviral pathogens of plants?