Optimization of Gaussian Random Fields

Many engineering systems are subject to spatially distributed uncertainty, i.e. uncertainty that can be modeled as a random field. Altering the mean or covariance of this uncertainty will in general change the statistical distribution of the system outputs. We present an approach for computing the sensitivity of the statistics of system outputs with respect to the parameters describing the mean and covariance of the distributed uncertainty. This sensitivity information is then incorporated into a gradient-based optimizer to optimize the structure of the distributed uncertainty to achieve desired output statistics. This framework is applied to perform variance optimization for a model problem and to optimize the manufacturing tolerances of a gas turbine compressor blade

Building Peapods

Eric Dow talks about how he came to build the type of boat known as a “peapod.”https://digitalcommons.library.umaine.edu/songstorysamplercollection/1009/thumbnail.jp

The Implications of Tolerance Optimization on Compressor Blade Design

Geometric variability increases performance variability and degrades the mean performance of turbomachinery compressor blades. These detrimental effects can be reduced by using robust optimization to design the blade geometry or by imposing stricter manufacturing tolerances. This paper presents a novel computational framework for optimizing compressor blade manufacturing tolerances, and incorporates this framework into existing robust geometry design frameworks. Optimizations of an exit guide vane geometry are conducted. The single-point optimal geometry is found to depend on the manufacturing tolerances due to a switch in the dominant loss mechanism. Multi-point geometry optimization avoids this switch so that the geometry and tolerance optimization problems are decoupled

Simultaneous Robust Design and Tolerancing of Compressor Blades

The manufacturing processes used to create compressor blades inevitably introduce geometric variability to the blade surface. In addition to increasing the performance variability, it has been observed that introducing geometric variability tends to reduce the mean performance of compressor blades. For example, the mean adiabatic efficiency observed in compressor blades with geometric variability is typically lower than the efficiency in the absence of variability. This “mean-shift” in performance leads to increased operating costs over the life of the compressor blade. These detrimental effects can be reduced by using robust optimization techniques to optimize the blade geometry. The impact of geometric variability can also be reduced by imposing stricter tolerances, thereby directly reducing the allowable level of variability. However, imposing stricter manufacturing tolerances increases the cost of manufacturing. Thus, the blade design and tolerances must be chosen with both performance and manufacturing cost in mind. This paper presents a computational framework for performing simultaneous robust design and tolerancing of compressor blades subject to manufacturing variability. The manufacturing variability is modelled as a Gaussian random field with non-stationary variance to simulate the effects of spatially varying manufacturing tolerances. The statistical performance of the compressor blade system is evaluated using the Monte Carlo method. A gradient based optimization scheme is used to determine the optimal blade geometry and distribution of manufacturing tolerances

Immune DNA signature of T-cell infiltration in breast tumor exomes.

Tumor infiltrating lymphocytes (TILs) have been associated with favorable prognosis in multiple tumor types. The Cancer Genome Atlas (TCGA) represents the largest collection of cancer molecular data, but lacks detailed information about the immune environment. Here, we show that exome reads mapping to the complementarity-determining-region 3 (CDR3) of mature T-cell receptor beta (TCRB) can be used as an immune DNA (iDNA) signature. Specifically, we propose a method to identify CDR3 reads in a breast tumor exome and validate it using deep TCRB sequencing. In 1,078 TCGA breast cancer exomes, the fraction of CDR3 reads was associated with TILs fraction, tumor purity, adaptive immunity gene expression signatures and improved survival in Her2+ patients. Only 2/839 TCRB clonotypes were shared between patients and none associated with a specific HLA allele or somatic driver mutations. The iDNA biomarker enriches the comprehensive dataset collected through TCGA, revealing associations with other molecular features and clinical outcomes

“They Have Already Thrown Away Their Chicken”: barriers affecting participation by HIV-infected women in care and treatment programs for their infants in Blantyre, Malawi

HIV-infected infants and young children are at high risk of serious illness and death. Morbidity and mortality can be greatly reduced through early infant diagnosis (EID) of HIV and timely initiation of antiretroviral therapy (ART). Despite global efforts to scale-up of EID and infant ART, uptake of these services in resource poor, high HIV burden countries remains low. We conducted a qualitative study of 59 HIV-infected women to identify and explore barriers women face in accessing HIV testing and care for their infants. To capture different perspectives, we included mothers whose infants were known positive (n=9) or known negative (n=14), mothers of infants with unknown HIV status (n=13), and pregnant HIV-infected women (n=20). Five important themes emerged: lack of knowledge regarding EID and infant ART, the perception of health care workers as authority figures, fear of disclosure of own and/or child’s HIV status, lack of psychosocial support, and intent to shorten the life of the child. A complex array of cultural, economic and psychosocial factors creates barriers for HIV-infected women to participate in early infant HIV testing and care programs. For optimal impact of EID and infant ART, reasons for poor uptake should be better understood and addressed in a culturally sensitive manner

Diffusionâ weighted imaging outside the brain: Consensus statement from an ISMRMâ sponsored workshop

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134160/1/jmri25196_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134160/2/jmri25196.pd

Development and Evaluation of Machine Learning in Whole-Body Magnetic Resonance Imaging for Detecting Metastases in Patients With Lung or Colon Cancer: A Diagnostic Test Accuracy Study

OBJECTIVES: Whole-body magnetic resonance imaging (WB-MRI) has been demonstrated to be efficient and cost-effective for cancer staging. The study aim was to develop a machine learning (ML) algorithm to improve radiologists' sensitivity and specificity for metastasis detection and reduce reading times. MATERIALS AND METHODS: A retrospective analysis of 438 prospectively collected WB-MRI scans from multicenter Streamline studies (February 2013-September 2016) was undertaken. Disease sites were manually labeled using Streamline reference standard. Whole-body MRI scans were randomly allocated to training and testing sets. A model for malignant lesion detection was developed based on convolutional neural networks and a 2-stage training strategy. The final algorithm generated lesion probability heat maps. Using a concurrent reader paradigm, 25 radiologists (18 experienced, 7 inexperienced in WB-/MRI) were randomly allocated WB-MRI scans with or without ML support to detect malignant lesions over 2 or 3 reading rounds. Reads were undertaken in the setting of a diagnostic radiology reading room between November 2019 and March 2020. Reading times were recorded by a scribe. Prespecified analysis included sensitivity, specificity, interobserver agreement, and reading time of radiology readers to detect metastases with or without ML support. Reader performance for detection of the primary tumor was also evaluated. RESULTS: Four hundred thirty-three evaluable WB-MRI scans were allocated to algorithm training (245) or radiology testing (50 patients with metastases, from primary 117 colon [n = 117] or lung [n = 71] cancer). Among a total 562 reads by experienced radiologists over 2 reading rounds, per-patient specificity was 86.2% (ML) and 87.7% (non-ML) (-1.5% difference; 95% confidence interval [CI], -6.4%, 3.5%; P = 0.39). Sensitivity was 66.0% (ML) and 70.0% (non-ML) (-4.0% difference; 95% CI, -13.5%, 5.5%; P = 0.344). Among 161 reads by inexperienced readers, per-patient specificity in both groups was 76.3% (0% difference; 95% CI, -15.0%, 15.0%; P = 0.613), with sensitivity of 73.3% (ML) and 60.0% (non-ML) (13.3% difference; 95% CI, -7.9%, 34.5%; P = 0.313). Per-site specificity was high (>90%) for all metastatic sites and experience levels. There was high sensitivity for the detection of primary tumors (lung cancer detection rate of 98.6% with and without ML [0.0% difference; 95% CI, -2.0%, 2.0%; P = 1.00], colon cancer detection rate of 89.0% with and 90.6% without ML [-1.7% difference; 95% CI, -5.6%, 2.2%; P = 0.65]). When combining all reads from rounds 1 and 2, reading times fell by 6.2% (95% CI, -22.8%, 10.0%) when using ML. Round 2 read-times fell by 32% (95% CI, 20.8%, 42.8%) compared with round 1. Within round 2, there was a significant decrease in read-time when using ML support, estimated as 286 seconds (or 11%) quicker (P = 0.0281), using regression analysis to account for reader experience, read round, and tumor type. Interobserver variance suggests moderate agreement, Cohen κ = 0.64; 95% CI, 0.47, 0.81 (with ML), and Cohen κ = 0.66; 95% CI, 0.47, 0.81 (without ML). CONCLUSIONS: There was no evidence of a significant difference in per-patient sensitivity and specificity for detecting metastases or the primary tumor using concurrent ML compared with standard WB-MRI. Radiology read-times with or without ML support fell for round 2 reads compared with round 1, suggesting that readers familiarized themselves with the study reading method. During the second reading round, there was a significant reduction in reading time when using ML support

Radical stereotactic radiosurgery with real-time tumor motion tracking in the treatment of small peripheral lung tumors

<p>Abstract</p> <p>Background</p> <p>Recent developments in radiotherapeutic technology have resulted in a new approach to treating patients with localized lung cancer. We report preliminary clinical outcomes using stereotactic radiosurgery with real-time tumor motion tracking to treat small peripheral lung tumors.</p> <p>Methods</p> <p>Eligible patients were treated over a 24-month period and followed for a minimum of 6 months. Fiducials (3–5) were placed in or near tumors under CT-guidance. Non-isocentric treatment plans with 5-mm margins were generated. Patients received 45–60 Gy in 3 equal fractions delivered in less than 2 weeks. CT imaging and routine pulmonary function tests were completed at 3, 6, 12, 18, 24 and 30 months.</p> <p>Results</p> <p>Twenty-four consecutive patients were treated, 15 with stage I lung cancer and 9 with single lung metastases. Pneumothorax was a complication of fiducial placement in 7 patients, requiring tube thoracostomy in 4. All patients completed radiation treatment with minimal discomfort, few acute side effects and no procedure-related mortalities. Following treatment transient chest wall discomfort, typically lasting several weeks, developed in 7 of 11 patients with lesions within 5 mm of the pleura. Grade III pneumonitis was seen in 2 patients, one with prior conventional thoracic irradiation and the other treated with concurrent Gefitinib. A small statistically significant decline in the mean % predicted DLCO was observed at 6 and 12 months. All tumors responded to treatment at 3 months and local failure was seen in only 2 single metastases. There have been no regional lymph node recurrences. At a median follow-up of 12 months, the crude survival rate is 83%, with 3 deaths due to co-morbidities and 1 secondary to metastatic disease.</p> <p>Conclusion</p> <p>Radical stereotactic radiosurgery with real-time tumor motion tracking is a promising well-tolerated treatment option for small peripheral lung tumors.</p