Unknown Aberrations Research Articles

Narrative Review: Update on the Molecular Diagnosis of Fragile X Syndrome.

The diagnosis and management of fragile X syndrome (FXS) have significantly improved in the last three decades, although the current diagnostic techniques are not yet able to precisely identify the number of repeats, methylation status, level of mosaicism, and/or the presence of AGG interruptions. A high number of repeats (>200) in the fragile X messenger ribonucleoprotein 1 gene (FMR1) results in hypermethylation of promoter and gene silencing. The actual molecular diagnosis is performed using a Southern blot, TP-PCR (Triplet-Repeat PCR), MS-PCR (Methylation-Specific PCR), and MS-MLPA (Methylation-Specific MLPA) with some limitations, with multiple assays being necessary to completely characterise a patient with FXS. The actual gold standard diagnosis uses Southern blot; however, it cannot accurately characterise all cases. Optical genome mapping is a new technology that has also been developed to approach the diagnosis of fragile X syndrome. Long-range sequencing represented by PacBio and Oxford Nanopore has the potential to replace the actual diagnosis and offers a complete characterization of molecular profiles in a single test. The new technologies have improved the diagnosis of fragile X syndrome and revealed unknown aberrations, but they are a long way from being used routinely in clinical practice.

Generalization of learned Fourier-based phase-diversity wavefront sensing.

Proper initialization of the nonlinear optimization is important to avoid local minima in phase diversity wavefront sensing (PDWS). An effective neural network based on low-frequency coefficients in the Fourier domain has proved effective to determine a better estimate of the unknown aberrations. However, the network relies significantly on the training settings, such as imaging object and optical system parameters, resulting in a weak generalization ability. Here we propose a generalized Fourier-based PDWS method by combining an object-independent network with a system-independent image processing procedure. We demonstrate that a network trained with a specific setting can be applied to any image regardless of the actual settings. Experimental results show that a network trained with one setting can be applied to images with four other settings. For 1000 aberrations with RMS wavefront errors bounded within [0.2 λ, 0.4 λ], the mean RMS residual errors are 0.032 λ, 0.039 λ, 0.035 λ, and 0.037 λ, respectively, and 98.9% of the RMS residual errors are less than 0.05 λ.

Phase-diversity wavefront sensing enhanced by a Fourier-based neural network.

Phase diversity wavefront sensing (PDWS) has been a successful approach to quantifying wavefront aberrations with only a few intensity measurements and nonlinear optimization. However, the inherent non-convexity of the inverse problem may lead to stagnation at a local minimum far from the true solution. Proper initialization of the nonlinear optimization is important to avoid local minima and improve wavefront retrieval accuracy. In this paper, we propose an effective neural network based on low-frequency coefficients in the Fourier domain to determine a better estimate of the unknown aberrations. By virtue of the proposed network, only a small amount of simulation data suffice for a robust training, two orders of magnitude less than those in existing work. Experimental results show that, when compared with some existing methods, our method achieves the highest accuracy while drastically reducing the training time to 1.4 min. The minimum, maximum, and mean values of the root mean square (RMS) residual errors for 800 aberrations are 0.017λ, 0.056λ, and 0.039λ, respectively, and 95% of the RMS residual errors are less than 0.05λ.

Genomic characterization of patients (pts) with de-novo high-volume metastatic castration-sensitive prostate cancer (dn-hv-mCSPC) compared to those without dn-hv-mCSPC.

186 Background: Dn-hv-mCSPC have the poorest survival outcomes among various subsets pts with mCSPC (Fizazi, Lancet Oncol 2019). However, the molecular underpinnings behind the poor prognosis is not entirely understood. In this study, we aimed to assess the tumoral genomic characteristics in pts with dn-hv-mCSPC. Methods: In this IRB-approved study, patient-level data were collected retrospectively. Eligibility: patients with mCSPC with availability of tumor comprehensive genomic profiling (CGP) from a CLIA-certified lab before start of ADT. Clinically relevant genes previously reported to be associated with prognosis in metastatic prostate cancer were included: TP53, PTEN, RB1, BRCA2, CDK12, MYC, PIK3CA, SPOP, APC, CTNNB1, and, ATM. Variants of unknown significance and genomic aberrations present in < 5% pts were excluded. High-volume disease was defined per CHAARTED criteria. Genomic aberration prevalence was compared using a Chi-square test and was adjusted for false discovery (Benjamini-Hochberg). Results: 304 pts were eligible: dn-hv-mCSPC (N = 100) vs non-dn-hv-mCSPC (N = 204); median age 67 vs 63 years, median PSA at baseline 35 vs 14 ng/mL, median Gleason score 9 vs 9, visceral metastases 15% vs 3%. 41% and 27% of pts had ≥2 mutations in the dn-hv-mcSPC and non-dn-hv-mCSPC respectively (P = 0.01). Overall, most commonly mutated genes were TP53, TMPRSS2, and PTEN. Pts with dn-hv-mCSPC had higher frequency of TP53 (P = 0.016) and BRCA2 (P = 0.03) mutations. Conclusions: In this real-world patient population, dn-hv-mCSPC pts were shown to have a greater number of clinically relevant genomic aberrations. Detailed genomic landscape of these pts will be presented in the meeting. These hypotheses-generating data need external validation.

Learned hardware-in-the-loop phase retrieval for holographic near-eye displays

Holography is arguably the most promising technology to provide wide field-of-view compact eyeglasses-style near-eye displays for augmented and virtual reality. However, the image quality of existing holographic displays is far from that of current generation conventional displays, effectively making today's holographic display systems impractical. This gap stems predominantly from the severe deviations in the idealized approximations of the "unknown" light transport model in a real holographic display, used for computing holograms. In this work, we depart from such approximate "ideal" coherent light transport models for computing holograms. Instead, we learn the deviations of the real display from the ideal light transport from the images measured using a display-camera hardware system. After this unknown light propagation is learned, we use it to compensate for severe aberrations in real holographic imagery. The proposed hardware-in-the-loop approach is robust to spatial, temporal and hardware deviations, and improves the image quality of existing methods qualitatively and quantitatively in SNR and perceptual quality. We validate our approach on a holographic display prototype and show that the method can fully compensate unknown aberrations and erroneous and non-linear SLM phase delays, without explicitly modeling them. As a result, the proposed method significantly outperforms existing state-of-the-art methods in simulation and experimentation - just by observing captured holographic images.

Large Aberration Correction by Magnetic Fluid Deformable Mirror with Model-Based Wavefront Sensorless Control Algorithm.

Magnetic fluid is a stable colloidal suspension of nano-sized, single-domain ferri/ferromagnetic particles dispersed in a liquid carrier. The liquid can be magnetized by the ferromagnetic particles aligned with the external magnetic field, which can be used as a wavefront corrector to correct the large aberrations up to more than 100 µm in adaptive optics (AO) systems. Since the measuring range of the wavefront sensor is normally small, the application of the magnetic fluid deformable mirror (MFDM) is limited with the WFS based AO system. In this paper, based on the MFDM model and the relationship between the second moment (SM) of the aberration gradients and the far-field intensity distribution, a model-based wavefront sensorless (WFSless) control algorithm is proposed for the MFDM. The correction performance of MFDM using the model-based control algorithm is evaluated in a WFSless AO system setup with a prototype MFDM, where a laser beam with unknown aberrations is supposed to produce a focused spot on the CCD. Experimental results show that the MFDM can be used to effectively compensate for unknown aberrations in the imaging system with the proposed model-based control algorithm.

Abstract 671: Evaluation of machine learning tools for cancer drug response prediction with genomic profile data and drug response data from PDX model studies

Abstract Cancer are diseases caused by genomic aberrations which lead to abnormal cell proliferation and metastasis. Cancer treatment based on cancer mutation profiles are the focus of research and development of cancer precision medicine. Currently only a small percentage of cancer patients’ genomic information are associated with known drug responses, while others lack known cancer drivers or have cancer drivers that are not druggable. Machine learning (ML) tools have been developed recently for cancer biomarker discovery and cancer drug response prediction. However, due to a lack of sufficient amount of high quality drug response data, especially data for new drugs, the application of ML tools in this area is still in its early stage. In this study, we compared the performance of about a dozen ML methods, such as random forest classifiers, logistic regression classifiers, and support vector machines (SVM), trained on data of about 2300 treatment responses to average 12 different cancer drugs in 98 NSCLC, 55 gastric and 38 HCC PDX models with WES, CNV and RNA-seq information. Data preparation including genomic profile data selection, dimensionality feature reduction and processing was performed with considerations of known cancer gene functions, signal pathways, and pharmacodynamics information. Each dataset was randomly divided into training dataset and test dataset for ML algorithm training and evaluation. Performance of ML algorithms in testing was measured by their drug response prediction accuracy. Different regularization methods, such as LASSO and Elastic Net, were also evaluated in order to improve classification accuracy and identify relevant biomarker features. From comparing results from different algorithms or from the same algorithms but with differently prepared datasets, it seems that selection of functionally unknown gene aberrations for data preparation plays a significant role in prediction accuracy. Increasing dataset number or different data selection in different cancer types can improve prediction accuracy. Citation Format: Henry Gu, Jingjing Jiang. Evaluation of machine learning tools for cancer drug response prediction with genomic profile data and drug response data from PDX model studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 671.

Clinical impact of comprehensive versus targeted genomic analysis for precision oncology.

e13033 Background: Panel sequencing (PS) has become a standard-of-care in cancer diagnostics. More comprehensive analyses such as whole-exome (WES) or RNA sequencing (RNAseq) allow for the detection of rare and unknown genetic aberrations that are not covered by predefined assays. The clinical impact of targeted versus comprehensive genomic assays were analyzed in patients presented at the Charité Molecular Tumor Board (MTB). Methods: Patients (pts) with advanced and/or metastatic cancer for whom no standard therapy was available were discussed in the MTB to allocate diagnostic profiling and guide biomarker-based treatment (BBT). Pts had to be < 50 years of age or diagnosed with a rare tumor entity to undergo WES/RNAseq, performed on fresh tissue. If ineligible, standard PS was performed on archival tissue. BBT recommendations, ranked by pre-specified evidence levels, were made by the MTB and pts were followed up. Results: 228 patients (median age 49 years, 108 female and 120 male) were discussed in the MTB between January 2016 and February 2019. We assigned 73 and 155 pts to PS and WES/RNAseq and results were obtained for 78.1% (n = 57/73) and 54.8% (n = 85/155) pts, respectively. Sequencing failed for 11 (PS; 15.1%) and 62 (WES/RNAseq; 40%) pts, most commonly due to insufficient tissue (n = 29). Sequencing was ongoing in 5 (PS) and 8 (WES/RNAseq) pts at the time of analysis. A median of 2 BBTs were recommended for 75.4% (43/57) of PS (range r: 1-3) and 90.6% (77/85) of WES/RNAseq pts (r: 1-6) each. 22% (n = 17/77) of WES/RNAseq pts had ≥4 BBTs made by the MTB. Treatment was initiated in 30.2% (n = 13/43) of PS and 40.2% (n = 31/77) of WES/RNAseq pts. Clinical benefit rates (CBRs) were 23.1% (2 PR, 1 SD) for PS and 45.2% (2 CR, 3 PR, 9 SD) for WES/RNAseq pts. Overall survival data was immature at the time of analysis. Conclusions: Utilizing WES/RNAseq is a feasible approach to perform tumor profiling in a heterogeneous cohort. We here show a higher rate of pts receiving confident evidence-based treatment recommendations in the WES/RNAseq group and a higher rate of treatment initiation. The CBR nearly doubled in the WES/RNAseq cohort when compared to standard PS pts, thus emphasizing the need for larger comparative analyses to guide diagnostic decision-making.

Optical Transfer Function Reconstruction in Incoherent Fourier Ptychography**Supported by the National Natural Science Foundation of China under Grant No 61205144, the Research Project of National University of Defense Technology under Grant No JC13-07-01, and the Key Laboratory of High Power Laser and Physics of Chinese Academy of Sciences.

An optical transfer function (OTF) reconstruction model is first embedded into incoherent Fourier ptychography (IFP). The leading result is a proposed algorithm that can recover both the super-resolution image and the OTF of an imaging system with unknown aberrations simultaneously. This model overcomes the difficult problem of OTF estimation that the previous IFP faces. The effectiveness of this algorithm is demonstrated by numerical simulations, and the superior reconstruction is presented. We believe that the reported algorithm can extend the original IFP for more complex conditions and may provide a solution by using structured light for characterization of optical systems' aberrations.

Statistical framework for the utilization of simultaneous pupil plane and focal plane telemetry for exoplanet imaging. I. Accounting for aberrations in multiple planes.

A new generation of telescopes with mirror diameters of 20 m or more, called extremely large telescopes (ELTs), has the potential to provide unprecedented imaging and spectroscopy of exoplanetary systems, if the difficulties in achieving the extremely high dynamic range required to differentiate the planetary signal from the star can be overcome to a sufficient degree. Fully utilizing the potential of ELTs for exoplanet imaging will likely require simultaneous and self-consistent determination of both the planetary image and the unknown aberrations in multiple planes of the optical system, using statistical inference based on the wavefront sensor and science camera data streams. This approach promises to overcome the most important systematic errors inherent in the various schemes based on differential imaging, such as angular differential imaging and spectral differential imaging. This paper is the first in a series on this subject, in which a formalism is established for the exoplanet imaging problem, setting the stage for the statistical inference methods to follow in the future. Every effort has been made to be rigorous and complete, so that validity of approximations to be made later can be assessed. Here, the polarimetric image is expressed in terms of aberrations in the various planes of a polarizing telescope with an adaptive optics system. Further, it is shown that current methods that utilize focal plane sensing to correct the speckle field, e.g., electric field conjugation, rely on the tacit assumption that aberrations on multiple optical surfaces can be represented as aberration on a single optical surface, ultimately limiting their potential effectiveness for ground-based astronomy.

Molecular pathology of soft tissue tumors: Contribution to diagnosis and therapy prediction

Soft tissue tumors are often challenging for pathologists on the basis of morphology alone; therefore, tumor-specific chromosomal aberrations, such as translocations and fusions, amplifications or deletions can be diagnostically useful. Fluorescence in situ hybridization is widely used for the detection of most aberrations in routine diagnostics. Furthermore, reverse transcriptase PCR, sequencing and specific immunohistochemical assays are also applied. Next generation sequencing has already contributed to the identification of hitherto unknown aberrations. Molecular pathology is mainly used in sarcomas to discriminate between different tumor entities. In terms of personalized therapy and targeted treatment, molecular pathology can be utilized to detect predictive markers.

Clinical genealogical and molecular genetic study of patients with mental retardation

The results of clinical, genealogical, cytogenetic and molecular genetic studies of 113 patients from 96 families with different forms of mental retardation from Ukraine are presented. This study was held as part of the CHERISH project of the 7-th Framework Program. The aim of the project is to improve diagnostics of mental retardation in children in Eastern Europe and Central Asia through detailed analysis of known chromosomal and gene's aberrations and to find the new gene-candidates that cause mental retardation. All patients have normal chromosome number (46XY or 46XX). The cases with fragile-X syndrome were eliminated using molecular genetic methods. Genome rearrangements were found among 28 patients using cytogenetic analysis, multiplex ligation-dependent probe amplification (MLPA analysis) ofsubtelomeric regions and array-based comparative genomic hybridisation (array CGH screening). In 10 cases known pathogenic CNV's were identified, 11 cases are unknown aberrations; their pathogenicity is being determined. The rest cases are known nonpathogenic gene rearrangements. Obtained results show the strong genetic heterogeneity of hereditary forms of mental retardation. The further studies will allow to identificate genes candidates and certain mutations in these genes that may be associated with this pathology.

Blind phase retrieval for aberrated linear shift-invariant imaging systems

We develop a means to reconstruct an input complex coherent scalar wavefield, given a through focal series (TFS) of three intensity images output from a two-dimensional (2D) linear shift-invariant optical imaging system with unknown aberrations. This blind phase retrieval technique unites two methods, namely (i) TFS phase retrieval and (ii) iterative blind deconvolution. The efficacy of our blind phase retrieval procedure has been demonstrated using simulated data, for a variety of Poisson noise levels.

KIT mutations confer a distinct gene expression signature in core binding factor leukaemia

Core binding factor (CBF) leukaemias, characterized by either inv(16)(p13.1q22) or t(8;21)(q22;q22), constitute acute myeloid leukaemia (AML) subgroups with favourable prognosis. However, 40-50% of patients relapse, emphasizing the need for risk-adapted treatment approaches. In this regard, studying secondary genetic aberrations, such as mutations of the KIT gene, is of great interest, particularly as they can be targeted by receptor tyrosine kinase inhibitors (TKI). However, so far little is known about the biology underlying KIT-mutated CBF leukaemias. We analysed gene expression profiles of 83 CBF AML cases with known KIT mutation status in order to gain novel insights in KIT-mutated CBF pathogenesis. KIT-mutated cases were characterized by deregulation of genes belonging to the NFkB signalling complex suggesting impaired control of apoptosis. Notably, a subgroup of KIT wildtype cases was also characterized by the KIT mutation signature due to yet unknown aberrations. Our data suggest that this CBF leukaemia subgroup might profit from TKI therapy, however, the relevance of the KIT mutation-associated signature remains to be validated prior to clinical implementation. Nevertheless, the existence of such a signature supports the notion of relevant biological differences in CBF leukaemia and might serve as diagnostic tool in the future.

Impact of Gene Expression Profiling on Diagnosis and Prognostication in Cytogenetically Normal AML.

Cytogenetically normal acute myeloid leukemia (CN-AML) comprises a biologically and clinically heterogeneous group of AML. In the past years, molecular markers like FLT3, CEBPA and NPM1 gene mutations have been identified in CN-AML, and the presence of such mutations carries important prognostic information. Furthermore, DNA microarray-based gene expression profiling (GEP) has been shown to capture the molecular heterogeneity of cancers, and has been applied to build classifiers and clinical outcome predictors in AML.While prior studies have defined gene expression patterns associated with NPM1, CEBPA, and FLT3, we assessed the clinical relevance of gene signatures. We profiled a large set of clinically well annotated CN-AML specimens (n=296 entered on two multicenter trials for patients <60 years (AMLSG HD98A and AMLSG 07-04). The 142 cases from the AMLSG HD98A trial were analyzed using a 40k cDNA microarray platform and the 154 cases from trial AMLSG 07-04 using Affymetrix microarrays (Human Genome U133 Plus 2.0 Arrays). In this data set we applied supervised analyses (LASSO penalized logistic regression) to define gene expression patterns characterizing FLT3 internal tandem duplication (ITD), CEPBA and NPM1 mutations as well as outcome signatures.We were able to define distinct signatures associated with NPM1, CEBPA, and FLT3 consisting of 39, 27, and 47 genes, respectively. The NPM1 signature revealed a high prediction accuracy of >95% in leave-one-out cross validated classification. Prediction of FLT3-ITD or CEBPA mutation performed less well with accuracies of 80% and 73%, respectively. However, for both CEBPA and FLT3-ITD the predicted mutation class labels performed slightly better than the marker itself with regard to the prognostic impact on overall survival (CEPBA: p=0.006 vs. p=0.007, FLT3-ITD p=9.57e-06 vs. p=5.11e-05; logrank test). In addition, using LASSO we also could define a signature associated with event free survival (EFS) in the cases from the AMLSG 07-04 trial. Adjusted for age, NPM1, and FLT3-ITD mutational status this signature was significantly associated with EFS (p=0.005; Wald test), and validation in our independent cDNA data set also provided significant prognostic information (p=0.02; Wald test).Thus, GEP-based classification of CN-AML might help to identify alternative genetic changes that either phenocopy or block the effects of common molecular aberrations. Furthermore, gene expression patterns of yet unknown aberrations are reflected in prognostic signatures. Therefore, signature genes also provide a starting point to dissect “mutations” pathways, and our findings underscore the potential clinical utility of a gene expression based measure in CN-AML.

Studies of the correction for phase diverse speckle

Phase diverse speckle is a novel kind of imaging technique and can be used to overcome image degradation from unknown phase aberrations, such as atmospheric turbulence. The wave-front phase expanded on the Zernike polynomials is estimated from a pair of images (in the focal and out of focus planes). In this paper the principle of PDS is analyzed, and genetic algorithm is used as the iterative algorithm to simulate some characteristics, such as the influence of Zernike polynomials’ mode, amplitude of turbulence on the phase estimation. Thus, a new method for recovery of images is explored.

Comprehensive cytogenetic characterization of an esthesioneuroblastoma

Esthesioneuroblastoma is a malignant neuroectodermal tumor originating from olfactory epithelial cells in the nasal vault. Due to the rarity of this tumor entity, cytogenetic data are very limited. Therefore, we performed comprehensive cytogenetic analyses of an esthesioneuroblastoma, Hyam's grade III–IV, using trypsin-Giemsa staining (GTG banding), multicolor fluorescence in situ hybridization (M-FISH), and locus-specific FISH complemented by molecular karyotyping using high-density single nucleotide polymorphism arrays. GTG banding of 25 metaphases revealed 54 structural intrachromosomal aberrations, predominantly located on 2q, 6q, 21q, and 22q, which were confirmed by FISH analysis. Interestingly, we found two novel, so far not described deletions, del(2)(q37) and del(21)(q22). Using GTG banding, locus-specific FISH, and M-FISH, we detected numeric changes of chromosomes 5, 17, 19, and 22, as well as trisomy 8 at low frequency. Applying SNP array karyotyping, we confirmed the chromosomal aberrations del(2)(q37.3), del(3)(q27.2), del(10)(q26.11), chromosomal imbalance on 17q, del(21)(q22), and revealed a number of so far unknown aberrations (gain of 2q14.3, 13q33.3, and 13q34). While the cytogenetically revealed low frequency mosaic del(6)(q22q24) was not visible using SNP array karyotyping, some of the smaller imbalances (SNP array data) could not have been detected by classic cytogenetic analysis. Therefore, our study supports the usefulness of applying complementary methods for cytogenetic analysis.

Case study: diagnosis and treatment of pyoderma gangrenosum

Pyoderma gangrenosum, is an ulcerative, inflammatory disorder often associated in the field of stoma care with inflammatory bowel disease (IBD) (Lyon and Smith, 2001). Pyoderma gangrenosum is distinctive in that it normally has a purple rolled edge and is extremely painful. The pathogenesis is unknown but immunologic aberrations of neutrophil granulocytes seem to be important (Petering et al, 2001). This paper demonstrates the rapid development and effective multi-disciplinary team management of an extensive case of Pyoderma gangrenosum. It also explores the possible causes of this particular manifestation.

Estimating mode coupling effects on shallow-water propagation uncertainty

The introduction of unknown aberrations to the background sound speed of the ocean waveguide and sediments, as well as unknown free-surface excursions and bottom bathymetry, causes uncertainty in the characteristics of acoustic time series. Not all of this uncertainty can be adequately explained by simply assuming that energy in various paths is perturbed only in phase and travel time. Data and simulations both clearly show that significant mode coupling is introduced by even small aberrations introduced over significant ranges. This talk estimates the effects of mode coupling introduced by statistically characterized sound-speed defects on the statistical properties of time series in shallow water. In this approach EOF decompositions of the sound-speed field are used, along with the differential form of the coupled -modeequations and perturbation expressions for mode-coupling coefficients. Results are compared to previously derived expressions for the statistical properties of time series obtained under the adiabatic approximation [Work supported by ONR.]

Digital fringe projection system for large-volume 360-deg shape measurement

We present a system for 3-D shape measurement in large volumes based on combined digital-fringe-Gray-code projection. With the help of a new calibration procedure, the system provides accurate results despite its crossed-axis configuration and unknown aberrations of the digital light projector and CCD camera. Also, the separate clouds of points captured from different directions are automatically merged into the main cloud. The system delivers results in the form of (x,y,z) coordinates of the object points with additional (R,G,B) color information about their texture. Applicability of the system is proven by presenting sample results of measurements performed on complex objects. The uncertainty of the system was estimated at 10 4 of the measurement volume.