Highlights d IVF embryos could be profiled with polygenic scores for traits such as height or IQ d The top-scoring embryo is expected to be z2.5 cm or z2.5 IQ points above the average d The adult trait value of the top-scoring embryo would remain widely distributed d Multiple ethical and other factors impose practical limits on the actual gain

reast cancer is the second leading cause of cancer-related deaths and the most commonly diagnosed cancer in women across the world (1). Digital mammography (DM) is the primary imaging modality of breast cancer screening in women who are asymptomatic. In a diagnostic workup setting (2), DM has been shown to reduce breast cancer mortality (3). In standard clinical practice, a radiologist reads mammograms and classifies the findings according to the American College of Radiology (4) Breast Imaging Reporting and Data System (BI-RADS) lexicon. An abnormal finding depicted at DM typically requires a diagnostic workup, which may include additional mammographic views or possibly additional imaging modalities. If a lesion is suspicious for cancer, further evaluation with a biopsy is recommended. Analyzing these images is challenging because of the subtle differences between lesions and background fibroglandular tissue, different lesion types, the nonrigid nature of the breast, and the relatively small proportion of cancers in a screening population of women at average risk (2). This leads to substantial intraobserver and interobserver variability (5). The average performance measures for screening mammography by a radiologist was reported by Lehman et al (6) to be 86.9% sensitivity and 88.9% specificity. Breast cancer risk prediction models on the basis of clinical features can help physicians estimate the probability of an individual or population to develop breast cancer within certain time frames. As a result, they are often used to recommend an individual screening plan. In a systematic survey of risk prediction models, Meads et al (7) reported a limited performance when applied to general populations (area under the receiver operating characteristic curve [AUC], 0.67; 95% confidence interval [CI]: 0.65, 0.68), and showed improved results when applied to high-risk populations (AUC, 0.76; 95% CI: 0.70, 0.82).

Bacterial RNase III plays important roles in the processing and degradation of RNA transcripts. A major goal is to identify the cleavage targets of this endoribonuclease at a transcriptome-wide scale and delineate its in vivo cleavage rules. Here we applied to Escherichia coli grown to either exponential or stationary phase a tailored RNA-seq-based technology, which allows transcriptome-wide mapping of RNase III cleavage sites at a nucleotide resolution. Our analysis of the large-scale in vivo cleavage data substantiated the established cleavage pattern of a double cleavage in an intra-molecular stem structure, leaving 2-nt-long 3′ overhangs, and refined the base-pairing preferences in the cleavage site vicinity. Intriguingly, we observed that the two stem positions between the cleavage sites are highly base-paired, usually involving at least one G-C or C-G base pair. We present a clear distinction between intra-molecular stem structures that are RNase III substrates and intra-molecular stem structures randomly selected across the transcriptome, emphasizing the in vivo specificity of RNase III. Our study provides a comprehensive map of the cleavage sites in both intra-molecular and inter-molecular duplex substrates, providing novel insights into the involvement of RNase III in post-transcriptional regulation in the bacterial cell.