A 2.91-billion base pair (bp) consensus sequence of the euchromatic portion of the human genome was generated by the whole-genome shotgun sequencing method. The 14.8-billion bp DNA sequence was generated over 9 months from 27,271,853 high-quality sequence reads (5.11-fold coverage of the genome) from both ends of plasmid clones made from the DNA of five individuals. Two assembly strategies—a whole-genome assembly and a regional chromosome assembly—were used, each combining sequence data from Celera and the publicly funded genome effort. The public data were shredded into 550-bp segments to create a 2.9-fold coverage of those genome regions that had been sequenced, without including biases inherent in the cloning and assembly procedure used by the publicly funded group. This brought the effective coverage in the assemblies to eightfold, reducing the number and size of gaps in the final assembly over what would be obtained with 5.11-fold coverage. The two assembly strategies yielded very similar results that largely agree with independent mapping data. The assemblies effectively cover the euchromatic regions of the human chromosomes. More than 90% of the genome is in scaffold assemblies of 100,000 bp or more, and 25% of the genome is in scaffolds of 10 million bp or larger. Analysis of the genome sequence revealed 26,588 protein-encoding transcripts for which there was strong corroborating evidence and an additional ∼12,000 computationally derived genes with mouse matches or other weak supporting evidence. Although gene-dense clusters are obvious, almost half the genes are dispersed in low G+C sequence separated by large tracts of apparently noncoding sequence. Only 1.1% of the genome is spanned by exons, whereas 24% is in introns, with 75% of the genome being intergenic DNA. Duplications of segmental blocks, ranging in size up to chromosomal lengths, are abundant throughout the genome and reveal a complex evolutionary history. Comparative genomic analysis indicates vertebrate expansions of genes associated with neuronal function, with tissue-specific developmental regulation, and with the hemostasis and immune systems. DNA sequence comparisons between the consensus sequence and publicly funded genome data provided locations of 2.1 million single-nucleotide polymorphisms (SNPs). A random pair of human haploid genomes differed at a rate of 1 bp per 1250 on average, but there was marked heterogeneity in the level of polymorphism across the genome. Less than 1% of all SNPs resulted in variation in proteins, but the task of determining which SNPs have functional consequences remains an open challenge.

Section/TopicNo. Item TITLE or ABSTRACT 1 Identification as a study of AI methodology, specifying the category of technology used (eg, deep learning) ABSTRACT 2 Structured summary of study design, methods, results, and conclusions

We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images). Our network achieves an AUC of 0.895 in predicting whether there is a cancer in the breast, when tested on the screening population. We attribute the high accuracy of our model to a two-stage training procedure, which allows us to use a very high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels. To validate our model, we conducted a reader study with 14 readers, each reading 720 screening mammogram exams, and find our model to be as accurate as experienced radiologists when presented with the same data. Finally, we show that a hybrid model, averaging probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately. To better understand our results, we conduct a thorough analysis of our network's performance on different subpopulations of the screening population, model design, training procedure, errors, and properties of its internal representations.deep learning | deep convolutional neural networks | breast cancer screening | mammography B reast cancer is the second leading cancer-related cause of death among women in the US. In 2014, over 39 million screening and diagnostic mammography exams were performed in the US. It is estimated that in 2015 232,000 women were diagnosed with breast cancer and approximately 40,000 died from it (1). Although mammography is the only imaging test that has reduced breast cancer mortality (2-4), there has been discussion regarding the potential harms of screening, including false positive recalls and associated false positive biopsies. The vast majority of the 10-15% of women asked to return following an inconclusive screening mammogram undergo another mammogram and/or ultrasound for clarification. After the additional imaging exams, many of these findings are determined as benign and only 10-20% are recommended to undergo a needle biopsy for further work-up. Among these, only 20-40% yield a diagnosis of cancer (5). Evidently, there is an unmet need to shift the balance of routine breast cancer screening towards more benefit and less harm.Traditional computer-aided detection (CAD) in mammography is routinely used by radiologists to assist with image interpretation, despite multicenter studies showing these CAD programs do not improve their diagnostic performance (6).These CAD programs typically use handcrafted features to mark sites on a mammogram that appear distinct from normal tissue structures. The radiologist decides whether to recall these findings, determining clinical significance and actionability. Recent developments in deep learning (7)-in particular, deep convolutional neural networks (CNNs) (8-12)-open possibilities for creating a new generation of CAD-like tools.This paper makes several contributions. Primarily, we train and evaluate a set of stro...

Early detection decreases breast cancer mortality. The ACR recommends annual mammographic screening beginning at age 40 for women of average risk. Higher-risk women should start mammographic screening earlier and may benefit from supplemental screening modalities. For women with genetics-based increased risk (and their untested first-degree relatives), with a calculated lifetime risk of 20% or more or a history of chest or mantle radiation therapy at a young age, supplemental screening with contrast-enhanced breast MRI is recommended. Breast MRI is also recommended for women with personal histories of breast cancer and dense tissue, or those diagnosed by age 50. Others with histories of breast cancer and those with atypia at biopsy should consider additional surveillance with MRI, especially if other risk factors are present. Ultrasound can be considered for those who qualify for but cannot undergo MRI. All women, especially black women and those of Ashkenazi Jewish descent, should be evaluated for breast cancer risk no later than age 30, so that those at higher risk can be identified and can benefit from supplemental screening.

Plants have evolved adaptation mechanisms to sense oxygen deficiency in their environments and make coordinated physiological and structural adjustments to enhance their hypoxic tolerance. To gain insight into how plants respond to lowoxygen stress, gene expression profiling using whole-genome DNA amplicon microarrays was carried out at seven time points over 24 h, in wild-type and transgenic P SAG12 :ipt Arabidopsis (Arabidopsis thaliana) plants under normoxic and hypoxic conditions. Transcript levels of genes involved in glycolysis and fermentation pathways, ethylene synthesis and perception, calcium signaling, nitrogen utilization, trehalose metabolism, and alkaloid synthesis were significantly altered in response to oxygen limitation. Analysis based on gene ontology assignments suggested a significant down-regulation of genes whose functions are associated with cell walls, nucleosome structures, water channels, and ion transporters and a significant upregulation of genes involved in transcriptional regulation, protein kinase activity, and auxin responses under conditions of oxygen shortage. Promoter analysis on a cluster of up-regulated genes revealed a significant overrepresentation of the AtMYB2-binding motif (GT motif), a sugar response element-like motif, and a G-box-related sequence, and also identified several putative anaerobic response elements. Finally, quantitative real-time polymerase chain reactions using 29 selected genes independently verified the microarray results. This study represents one of the most comprehensive analyses conducted to date investigating hypoxia-responsive transcriptional networks in plants.

reast MRI is an indispensable modality, along with mammography and US. Its main indications are staging of known cancer, screening for breast cancer in women at increased risk, and evaluation of response to neoadjuvant chemotherapy (1-3). As opposed to mammography and US, MRI is a functional technique. Heywang et al (4) and Kaiser and Zeitler (5) independently introduced this technique in the 1980s. Contrast material-enhanced MRI evaluates the permeability of blood vessels by using an intravenous contrast agent (gadolinium chelate) that shortens the local T1 time, leading to a higher signal on T1-weighted images (6). The underlying principle is that neoangiogenesis leads to formation of leaky vessels that allow for faster extravasation of contrast agents (7), thus leading to rapid local enhancement. Despite improvements in the technique of breast MRI, this principle is still the basis of all clinical MRI protocols. However, most MRI protocols nowadays are multiparametric (8,9). This review describes the current state of the art in breast MRI, with a focus on the major indications and the potential indication-based adaptations to the imaging protocol to maximize its value. Requirements for Breast MRI Breast MRI studies should be interpreted by radiologists with expertise in breast imaging, including mammographic and US studies, as these examinations are often complementary. Although empirical data on the learning curve for breast MRI are lacking, some studies showed improved performance of radiologists over time (10), and reporting breast MRI studies requires sufficient exposure to the technique. It is best practice to use a field strength of at least 1.5 T to acquire images at a sufficiently high spatial resolution (1-3). Utilization of a dedicated breast coil is mandatory to obtain images of diagnostic quality. Women lie in the prone position with the breasts hanging free in the recesses of the coil. This design allows the breast tissue to spread, which facilitates detection of abnormalities and prevents motion artifacts induced by respiration (11,12). A breast coil should have at least four channels, but modern designs

Diffusion-weighted imaging (DWI) plays important roles in cancer diagnosis, monitoring, and treatment. While most applications measure restricted diffusion by tumor cellularity, DWI is also sensitive to vascularity through the intravoxel incoherent motion (IVIM) effect. Hypervascularity can confound apparent diffusion coefficient (ADC) measurements in breast cancer. We acquired multiple b-value DWI at 3 T in a cohort of breast cancer patients and performed biexponential IVIM analysis to extract tissue diffusivity (Dt), perfusion fraction (fp), and pseudodiffusivity (Dp). Results indicated significant differences between normal fibroglandular tissue and malignant lesions in ADC mean (± standard deviation) values (2.44± 0.30 vs. 1.34 ± 0.39 μm2/ms, p < 0.01) and Dt (2.36± 0.38 vs. 1.15 ± 0.35 μm2/ms, p < 0.01). Lesion DWI signals demonstrated biexponential character in comparison to monoexponential normal tissue. There is some differentiation of lesion subtypes (invasive ductal carcinoma (IDC) vs. other (OT) malignant lesions) with fp (10.5 ± 5.0% vs. 6.9 ± 2.9%, p = 0.06), but less so with Dt (1.14±0.32 μm2/ms vs. 1.18 ± 0.52 μm2/ms, p = 0.88), and Dp (14.9 ± 11.4 μm2/ms vs. 16.1 ± 5.7 μm2/ms, p = 0.75). Comparison of IVIM biomarkers with contrast-enhancement suggests moderate correlations. These results suggest the potential of IVIM vascular and cellular biomarkers for initial grading, progression monitoring, or treatment assessment of breast tumors.

MRI appears to provide the best correlation with pathology-better than physical examination, mammography, and sonography-in patients undergoing neoadjuvant chemotherapy. However, MRI may overestimate (6%) or underestimate (23%) residual disease in approximately 29% of the patients (95% confidence interval, 14-48%).