Hybrid Power Spectral and Wavelet Image Roughness Analysis

The Two-Dimensional Wavelet Transform Modulus Maxima (2D WTMM) sliding window methodology has proven to be a robust approach, in particular for the extraction of the Hurst (H) roughness exponent from grayscale mammograms. The power spectrum is a computational analysis based on the Fourier transform that can be used to estimate the roughness of a scale-invariant image or region via the calculation of H. We aim to examine how the calculation of H in fractional Brownian motion (fBm) images and mammograms can be improved. fBm images are generated for H ∈ [0.00,1.00] for testing through the previous 2D WTMM sliding window analysis using the Gaussian smoothing function, the second-order derivative of the Gaussian smoothing function, the Mexican hat, and the power spectrum analysis. The power spectrum is shown to provide a more accurate calculation of H for Htheo \u3c 0.45 (RMSE = 0.01), while the 2D WTMM analysis with the Mexican hat smoothing function provides this for H ≥ 0.45 (RMSE = 0.058) in fBm images. Through the previous implementation of the 2D WTMM sliding window analysis, we have categorized mammographic subregions into three categories: Fatty (H \u3c 0.45), risky dense (0.45 ≤ H ≤ 0.55), and healthy dense mammographic tissue (H \u3e 0.55). The power spectrum and the 2D WTMM analysis are further tested on the CompuMAINE Laboratory’s acquired de-identified Perm and Maine mammographic datasets. From this analysis, it can be concluded that the power spectrum analysis cannot accurately distinguish fatty from dense tissue in grayscale mammograms. The implementation of the Mexican hat smoothing function provides a decrease in the number of mammographic subregions rejected during our analysis. In addition, the Mexican hat smoothing function indicates a greater difference in risky dense mammographic tissue between cancerous and normal patients compared to the previously adapted 2D WTMM analysis with the Gaussian smoothing function. The presence of noise in the Perm mammographic dataset indicates a larger minimum size for the range of wavelet scales a (MinADelta = 3.0) should be used in the calculation of H using the Mexican hat smoothing function in the 2D WTMM sliding window analysis. Higher quality (16-bit) mammograms in the Maine mammographic dataset indicate a similar minimum range of wavelet scales used in previous studies (MinADelta = 1.0) should be used to calculate H with the Mexican hat smoothing function. Through extensive calibration and testing of the power spectrum and 2D WTMM methodologies, we conclude the implementation of the 2D WTMM methodology with the Mexican hat smoothing function provides the most accurate calculation of H ∈ [0.00,1.00] in fBm and mammographic images

Wavelet-Based Automatic Breast Segmentation for Mammograms

As part of a first of its kind analysis of longitudinal mammograms, there are thousands of mammograms that need to be analyzed computationally. As a pre- processing step, each mammogram needs to be converted into a binary (black or white) spatial representation in order to delineate breast tissue from the pectoral muscle and image background, which is called a mammographic mask. The current methodology for completing this task is for a lab member to manually trace the outline of the breast, which takes approximately three minutes per mammogram. Thus, reducing the time cost and human subjectivity when completing this task for all mammograms in a large dataset is extremely valuable. In this thesis, an automated breast segmentation algorithm was adapted from a multi-scale gradient-based edge detection approach called the 2D Wavelet Transform Modulus Maxima (WTMM) segmentation method. This automated masking algorithm incorporates the first-derivative Gaussian Wavelet Transform to identify potential edge detection contour lines called maxima chains. The candidate chains are then transformed into a binary mask, which is then compared with the original manual delineation through the use of the Sorenson-Dice Coefficient (DSC). The analysis of 556 grayscale mammograms with this developed methodology produced a median DSC of 0.988 and 0.973 for craniocaudal (CC) and mediolateral oblique (MLO) grayscale mammograms respectively. Based on these median DSCs, in which a perfect overlap score is 1, it can be concluded a wavelet-based automatic breast segmentation algorithm is able to quickly segment the pectoral muscle and produce accurate binary spatial representations of breast tissue in grayscale mammograms

DNA Polymerase Epsilon Deficiency Causes IMAGe Syndrome with Variable Immunodeficiency.

During genome replication, polymerase epsilon (Pol ε) acts as the major leading-strand DNA polymerase. Here we report the identification of biallelic mutations in POLE, encoding the Pol ε catalytic subunit POLE1, in 15 individuals from 12 families. Phenotypically, these individuals had clinical features closely resembling IMAGe syndrome (intrauterine growth restriction [IUGR], metaphyseal dysplasia, adrenal hypoplasia congenita, and genitourinary anomalies in males), a disorder previously associated with gain-of-function mutations in CDKN1C. POLE1-deficient individuals also exhibited distinctive facial features and variable immune dysfunction with evidence of lymphocyte deficiency. All subjects shared the same intronic variant (c.1686+32C>G) as part of a common haplotype, in combination with different loss-of-function variants in trans. The intronic variant alters splicing, and together the biallelic mutations lead to cellular deficiency of Pol ε and delayed S-phase progression. In summary, we establish POLE as a second gene in which mutations cause IMAGe syndrome. These findings add to a growing list of disorders due to mutations in DNA replication genes that manifest growth restriction alongside adrenal dysfunction and/or immunodeficiency, consolidating these as replisome phenotypes and highlighting a need for future studies to understand the tissue-specific development roles of the encoded proteins

Lung transplantation for pulmonary fibrosis in dyskeratosis congenita: Case Report and systematic literature review

<p>Abstract</p> <p>Background</p> <p>Dyskeratosis congenita (DC) is a progressive, multi-system, inherited disorder of telomere biology with high risks of morbidity and mortality from bone marrow failure, hematologic malignancy, solid tumors and pulmonary fibrosis. Hematopoietic stem cell transplantation (HSCT) can cure the bone marrow failure, but it does not eliminate the risks of other complications, for which life-long surveillance is required. Pulmonary fibrosis is a progressive and lethal complication of DC.</p> <p>Case presentation</p> <p>In this report, we describe a patient with DC who developed pulmonary fibrosis seven years after HSCT for severe aplastic anemia, and was successfully treated with bilateral lung transplantation. We also performed a systematic literature review to understand the burden of pulmonary disease in patients with DC who did or did not receive an HSCT. Including our patient, we identified 49 DC patients with pulmonary disease (12 after HSCT and 37 without HSCT), and 509 with no reported pulmonary complications.</p> <p>Conclusion</p> <p>Our current case and literature review indicate that pulmonary morbidity is one of the major contributors to poor quality of life and reduced long-term survival in DC. We suggest that lung transplantation be considered for patients with DC who develop pulmonary fibrosis with no concurrent evidence of multi-organ failure.</p

Epidemiological, clinical, and public health response characteristics of a large outbreak of diphtheria among the Rohingya population in Cox's Bazar, Bangladesh, 2017 to 2019: A retrospective study.

BACKGROUND: Unrest in Myanmar in August 2017 resulted in the movement of over 700,000 Rohingya refugees to overcrowded camps in Cox's Bazar, Bangladesh. A large outbreak of diphtheria subsequently began in this population. METHODS AND FINDINGS: Data were collected during mass vaccination campaigns (MVCs), contact tracing activities, and from 9 Diphtheria Treatment Centers (DTCs) operated by national and international organizations. These data were used to describe the epidemiological and clinical features and the control measures to prevent transmission, during the first 2 years of the outbreak. Between November 10, 2017 and November 9, 2019, 7,064 cases were reported: 285 (4.0%) laboratory-confirmed, 3,610 (51.1%) probable, and 3,169 (44.9%) suspected cases. The crude attack rate was 51.5 cases per 10,000 person-years, and epidemic doubling time was 4.4 days (95% confidence interval [CI] 4.2-4.7) during the exponential growth phase. The median age was 10 years (range 0-85), and 3,126 (44.3%) were male. The typical symptoms were sore throat (93.5%), fever (86.0%), pseudomembrane (34.7%), and gross cervical lymphadenopathy (GCL; 30.6%). Diphtheria antitoxin (DAT) was administered to 1,062 (89.0%) out of 1,193 eligible patients, with adverse reactions following among 229 (21.6%). There were 45 deaths (case fatality ratio [CFR] 0.6%). Household contacts for 5,702 (80.7%) of 7,064 cases were successfully traced. A total of 41,452 contacts were identified, of whom 40,364 (97.4%) consented to begin chemoprophylaxis; adherence was 55.0% (N = 22,218) at 3-day follow-up. Unvaccinated household contacts were vaccinated with 3 doses (with 4-week interval), while a booster dose was administered if the primary vaccination schedule had been completed. The proportion of contacts vaccinated was 64.7% overall. Three MVC rounds were conducted, with administrative coverage varying between 88.5% and 110.4%. Pentavalent vaccine was administered to those aged 6 weeks to 6 years, while tetanus and diphtheria (Td) vaccine was administered to those aged 7 years and older. Lack of adequate diagnostic capacity to confirm cases was the main limitation, with a majority of cases unconfirmed and the proportion of true diphtheria cases unknown. CONCLUSIONS: To our knowledge, this is the largest reported diphtheria outbreak in refugee settings. We observed that high population density, poor living conditions, and fast growth rate were associated with explosive expansion of the outbreak during the initial exponential growth phase. Three rounds of mass vaccinations targeting those aged 6 weeks to 14 years were associated with only modestly reduced transmission, and additional public health measures were necessary to end the outbreak. This outbreak has a long-lasting tail, with Rt oscillating at around 1 for an extended period. An adequate global DAT stockpile needs to be maintained. All populations must have access to health services and routine vaccination, and this access must be maintained during humanitarian crises

Genomic analyses in Cornelia de Lange Syndrome and related diagnoses: Novel candidate genes, <scp>genotype–phenotype</scp> correlations and common mechanisms

Cornelia de Lange Syndrome (CdLS) is a rare, dominantly inherited multisystem developmental disorder characterized by highly variable manifestations of growth and developmental delays, upper limb involvement, hypertrichosis, cardiac, gastrointestinal, craniofacial, and other systemic features. Pathogenic variants in genes encoding cohesin complex structural subunits and regulatory proteins (NIPBL, SMC1A, SMC3, HDAC8, and RAD21) are the major pathogenic contributors to CdLS. Heterozygous or hemizygous variants in the genes encoding these five proteins have been found to be contributory to CdLS, with variants in NIPBL accounting for the majority (&gt;60%) of cases, and the only gene identified to date that results in the severe or classic form of CdLS when mutated. Pathogenic variants in cohesin genes other than NIPBL tend to result in a less severe phenotype. Causative variants in additional genes, such as ANKRD11, EP300, AFF4, TAF1, and BRD4, can cause a CdLS‐like phenotype. The common role that these genes, and others, play as critical regulators of developmental transcriptional control has led to the conditions they cause being referred to as disorders of transcriptional regulation (or “DTRs”). Here, we report the results of a comprehensive molecular analysis in a cohort of 716 probands with typical and atypical CdLS in order to delineate the genetic contribution of causative variants in cohesin complex genes as well as novel candidate genes, genotype–phenotype correlations, and the utility of genome sequencing in understanding the mutational landscape in this population

The Credit Rating Industry: An Industrial Organization Analysis

to be presented at the World Bank