SJPedPanel: A pan-cancer gene panel for childhood malignancies.

Background: Large scale genomics projects have identified driver alterations for most childhood cancers that provide reliable biomarkers for clinical diagnosis and disease monitoring using targeted sequencing. However, there is lack of a comprehensive panel that matches the list of known driver genes. Here we fill this gap by developing SJPedPanel for childhood cancers. Results: SJPedPanel covers 5,275 coding exons of 357 driver genes, 297 introns frequently involved in rearrangements that generate fusion oncoproteins, commonly amplified/deleted regions (e.g., MYCN for neuroblastoma, CDKN2A and PAX5 for B-/T-ALL, and SMARCB1 for AT/RT), and 7,590 polymorphism sites for interrogating tumors with aneuploidy, such as hyperdiploid and hypodiploid B-ALL or 17q gain neuroblastoma. We used driver alterations reported from an established real-time clinical genomics cohort (n=253) to validate this gene panel. Among the 485 pathogenic variants reported, our panel covered 417 variants (86%). For 90 rearrangements responsible for oncogenic fusions, our panel covered 74 events (82%). We re-sequenced 113 previously characterized clinical specimens at an average depth of 2,500X using SJPedPanel and recovered 354 (91%) of the 389 reported pathogenic variants. We then investigated the power of this panel in detecting mutations from specimens with low tumor purity (as low as 0.1%) using cell line-based dilution experiments and discovered that this gene panel enabled us to detect ∼80% variants with allele fraction of 0.2%, while the detection rate decreases to ∼50% when the allele fraction is 0.1%. We finally demonstrate its utility in disease monitoring on clinical specimens collected from AML patients in morphologic remission. Conclusions: SJPedPanel enables the detection of clinically relevant genetic alterations including rearrangements responsible for subtype-defining fusions for childhood cancers by targeted sequencing of ∼0.15% of human genome. It will enhance the analysis of specimens with low tumor burdens for cancer monitoring and early detection.

Characteristics and Sintering Mechanisms of Iron Ores with a High Proportion of High-Al2O3 Limonite.

In this study, a sintering test of high-alumina limonite from Indonesia, matched with an appropriate magnetite concentration, is performed. The sintering yield and quality index are effectively improved by optimizing the ore matching and regulating the basicity. For the optimal coke dosage of 5.8% and basicity of 1.8, the tumbling index of the ore blend is found to be 61.5% and the productivity is 1.2 t/(h·m2). The main liquid phase in the sinter is the silico-ferrite of calcium and aluminum (SFCA), followed by a mutual solution, both of which maintain the sintering strength. However, when the basicity is increased from 1.8 to 2.0, the production of SFCA is found to increase gradually, whereas the mutual solution content decreases dramatically. A metallurgical performance test of the optimal sinter sample demonstrates that the sinter can meet the requirements of small- and medium-sized blast furnace smelting, even for high-alumina limonite ratios of 60.0-65.0%, thereby greatly reducing the sintering production costs. The results of this study are expected to provide theoretical guidance for the practical high-proportion sintering of high-alumina limonite.

Study and Classification of Porosity Stress Sensitivity in Shale Gas Reservoirs Based on Experiments and Optimized Support Vector Machine Algorithm for the Silurian Longmaxi Shale in the Southern Sichuan Basin, China.

To understand the characteristics of variation in porosity and permeability, the physical properties of the shale reservoir under different stress conditions play an important role in guiding shale gas production. With the shale of the Wufeng-Longmaxi Formation in the south of the Sichuan Basin as the research object, stress-dependent porosity and permeability test, high-pressure mercury injection, and scanning electron microscope test were performed in this study to thoroughly analyze the variation in physical properties of different shale lithofacies with effective stress. Besides, the stress sensitivity of different lithofacies reservoirs was evaluated by using parameters such as pore compressibility coefficient (PCC) and porosity sensitivity exponent (PSE), while the optimized support vector machine (SVM) algorithm was adopted to predict the coefficient of reservoir porosity sensitivity. According to the research results, the porosity and permeability of shale reservoirs decline as a negative exponential function. When the effective stress falls below 15 MPa, the damage rate of permeability/porosity increases rapidly with the rise of effective stress. By contrast, the permeability curvature of the shale reservoirs plunges with the rise of effective stress. It was discovered that a higher siliceous content results in a higher permeability curvature of shale, indicating the greater stress sensitivity of the reservoir. The ratio of matrix porosity to microfracture porosity determines the PSE, which is relatively low, and low aspect ratio pores contribute to high porosity compressibility and stress sensitivity. Young's modulus shows a negative correlation with pore compressibility and a positive correlation with Poisson's ratio. High clay minerals have a large number of low aspect ratio pores and a low elastic modulus, which leads to both high PCC and low PSE. Based on the principal component analysis, a multiclassification SVM model was established to predict the PSE, revealing that the accuracy of the sigmoid, radial basis function (RBF), and linear kernel function is consistently above 70%. According to error analysis, the accuracy can exceed 80% with the RBF kernel function and appropriate penalty factor. The research results serve to advance the research on the parameters related to overburden pressure, porosity, and permeability. Moreover, the optimized SVM algorithm is applied to make a classification prediction, which provides a reference for shale reservoir exploration and development both in theory and practice.

Feasibility study of fast intensity-modulated proton therapy dose prediction method using deep neural networks for prostate cancer.

PURPOSE: Compared to the pencil-beam algorithm, the Monte-Carlo (MC) algorithm is more accurate for dose calculation but time-consuming in proton therapy. To solve this problem, this study uses deep learning to provide fast 3D dose prediction for prostate cancer patients treated with intensity-modulated proton therapy (IMPT). METHODS: A novel recurrent U-net (RU-net) architecture was trained to predict the 3D dose distribution. Doses, CT images, and beam spot information from IMPT plans were used to train the RU-net with a five-fold cross-validation. However, predicting the complicated dose properties of the IMPT plan is difficult for neural networks. Instead of the peak-monitor unit (MU) model, this work develops the multi-MU model that adopted more comprehensive inputs and was trained with a combinational loss function. The dose difference between the prediction dose and Monte Carlo (MC) dose was evaluated with gamma analysis, dice similarity coefficient (DSC), and dose-volume histogram (DVH) metrics. The MC dropout was also added to the network to quantify the uncertainty of the model. RESULTS: Compared to the peak-MU model, the multi-MU model led to smaller mean absolute errors (3.03% vs. 2.05%, p = 0.005), higher gamma-passing rate (2 mm, 3%: 97.42% vs. 93.69%, p = 0.005), higher dice similarity coefficient, and smaller relative DVH metrics error (clinical target volume (CTV) D98% : 3.03% vs. 6.08%, p = 0.017; in Bladder V30: 3.08% vs. 5.28%, p = 0.028; and in Bladder V20: 3.02% vs. 4.42%, p = 0.017). Considering more prior knowledge, the multi-MU model had better-predicted accuracy with a prediction time of less than half a second for each fold. The mean uncertainty value of the multi-MU model is 0.46%, with a dropout rate of 10%. CONCLUSION: This method was a nearly real-time IMPT dose prediction algorithm with accuracy comparable to the pencil beam (PB) analytical algorithms used in prostate cancer. This RU-net might be used in plan robustness optimization and robustness evaluation in the future.

Design of a light and fast energy degrader for a compact superconducting gantry with large momentum acceptance.

PURPOSE: Proton therapy is a precise radiation cancer treatment with low side effects. To reduce the cost and footprint of the facility, the superconducting gantry with large momentum acceptance becomes a potential solution. Benefit from this feature, beam delivery time depends largely on the energy-switching process and short time is helpful for increasing the number of volume repaintings. METHODS: This note introduces an energy degrader with lightweight moving parts and a new hybrid structure (wedge-block-block). The total energies are separated into three stages and are degraded at fixed rates in two boron carbide blocks. As only one pair of graphite wedges is used for energy modulation, the energy switching at each step reaches a 10 ms level. RESULTS: The transport process in the degrader was simulated in TOPAS. After the degradation, the maximum energy spread (1σ) was approximately 5.5%, and the distance between successive energy layers can be increased for treating non-sensitive tissues. Six configurations of the hybrid degrader achieved distinctly higher transmission efficiencies than the usual graphite multi-wedge degrader. Finally, the configuration that maximized the beam transmission in the lower-energy range (namely, the W-B1-B2 configuration) was chosen as the degrader. CONCLUSIONS: This new degrader not only improved the transmission efficiency, but also reduced the energy-switching time by virtue of its light and compact structure.

Functional Divergence between Subgenomes and Gene Pairs after Whole Genome Duplications.

Gene loss following whole genome duplication (WGD) is often biased, with one subgenome retaining more ancestral genes and the other sustaining more gene deletions. While bias toward the greater expression of gene copies on one subgenome can explain bias in gene loss, this raises the question to what drives differences in gene expression levels between subgenomes. Differences in chromatin modifications and epigenetic markers between subgenomes in several model species are now being identified, providing an explanation for bias in gene expression between subgenomes. WGDs can be classified into duplications with higher, biased gene loss and bias in gene expression between subgenomes versus those with lower, unbiased rates of gene loss and an absence of detectable bias between subgenomes; however, the originally proposed link between these two classes and whether WGD results from an allo- or autopolyploid event is inconsistent with recent data from the allopolyploid Capsella bursa-pastoris. The gene balance hypothesis can explain bias in the functional categories of genes retained following WGD, the difference in gene loss rates between unbiased and biased WGDs, and how plant genomes have avoided being overrun with genes encoding dose-sensitive subunits of multiprotein complexes. Comparisons of gene expression patterns between retained transcription factor pairs in maize suggest the high degree of retention for WGD-derived pairs of transcription factors may instead be explained by the older duplication-degeneration-complementation model.

STAG-CNS: An Order-Aware Conserved Noncoding Sequences Discovery Tool for Arbitrary Numbers of Species.

One method for identifying noncoding regulatory regions of a genome is to quantify rates of divergence between related species, as functional sequence will generally diverge more slowly. Most approaches to identifying these conserved noncoding sequences (CNSs) based on alignment have had relatively large minimum sequence lengths (≥15 bp) compared with the average length of known transcription factor binding sites. To circumvent this constraint, STAG-CNS that can simultaneously integrate the data from the promoters of conserved orthologous genes in three or more species was developed. Using the data from up to six grass species made it possible to identify conserved sequences as short as 9 bp with false discovery rate ≤0.05. These CNSs exhibit greater overlap with open chromatin regions identified using DNase I hypersensitivity assays, and are enriched in the promoters of genes involved in transcriptional regulation. STAG-CNS was further employed to characterize loss of conserved noncoding sequences associated with retained duplicate genes from the ancient maize polyploidy. Genes with fewer retained CNSs show lower overall expression, although this bias is more apparent in samples of complex organ systems containing many cell types, suggesting that CNS loss may correspond to a reduced number of expression contexts rather than lower expression levels across the entire ancestral expression domain.