Asymmetry to symmetry transition of Fano line-shape: Analytical derivation

An analytical derivation of Fano line-shape asymmetry ratio has been presented here for a general case. It is shown that Fano line-shape becomes less asymmetric as \q is increased and finally becomes completely symmetric in the limiting condition of q equal to infinity. Asymmetry ratios of Fano line-shapes have been calculated and are found to be in good consonance with the reported expressions for asymmetry ratio as a function of Fano parameter. Application of this derivation is also mentioned for explanation of asymmetry to symmetry transition of Fano line-shape in quantum confined silicon nanostructures.Comment: 3 figures, Latex files, Theoretica

Advanced deep learning methodology for accurate, real-time segmentation of high-resolution intravascular ultrasound images

AIMS: The aim of this study is to develop and validate a deep learning (DL) methodology capable of automated and accurate segmentation of intravascular ultrasound (IVUS) image sequences in real-time. METHODS AND RESULTS: IVUS segmentation was performed by two experts who manually annotated the external elastic membrane (EEM) and lumen borders in the end-diastolic frames of 197 IVUS sequences portraying the native coronary arteries of 65 patients. The IVUS sequences of 177 randomly-selected vessels were used to train and optimise a novel DL model for the segmentation of IVUS images. Validation of the developed methodology was performed in 20 vessels using the estimations of two expert analysts as the reference standard. The mean difference for the EEM, lumen and plaque area between the DL-methodology and the analysts was ≤0.23mm2 (standard deviation ≤0.85mm2), while the Hausdorff and mean distance differences for the EEM and lumen borders was ≤0.19 mm (standard deviation≤0.17 mm). The agreement between DL and experts was similar to experts' agreement (Williams Index ranges: 0.754-1.061) with similar results in frames portraying calcific plaques or side branches. CONCLUSIONS: The developed DL-methodology appears accurate and capable of segmenting high-resolution real-world IVUS datasets. These features are expected to facilitate its broad adoption and enhance the applications of IVUS in clinical practice and research

A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.

Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to acquire gated or to retrospectively gate IVUS images have failed to dominate in research. We developed a novel deep learning (DL)-methodology for end-diastolic frame detection in IVUS and compared its efficacy against expert analysts and a previously established methodology using electrocardiographic (ECG)-estimations as reference standard. Near-infrared spectroscopy-IVUS (NIRS-IVUS) data were prospectively acquired from 20 coronary arteries and co-registered with the concurrent ECG-signal to identify end-diastolic frames. A DL-methodology which takes advantage of changes in intensity of corresponding pixels in consecutive NIRS-IVUS frames and consists of a network model designed in a bidirectional gated-recurrent-unit (Bi-GRU) structure was trained to detect end-diastolic frames. The efficacy of the DL-methodology in identifying end-diastolic frames was compared with two expert analysts and a conventional image-based (CIB)-methodology that relies on detecting vessel movement to estimate phases of the cardiac cycle. A window of ± 100 ms from the ECG estimations was used to define accurate end-diastolic frames detection. The ECG-signal identified 3,167 end-diastolic frames. The mean difference between DL and ECG estimations was 3 ± 112 ms while the mean differences between the 1st-analyst and ECG, 2nd-analyst and ECG and CIB-methodology and ECG were 86 ± 192 ms, 78 ± 183 ms and 59 ± 207 ms, respectively. The DL-methodology was able to accurately detect 80.4%, while the two analysts and the CIB-methodology detected 39.0%, 43.4% and 42.8% of end-diastolic frames, respectively (P < 0.05). The DL-methodology can identify NIRS-IVUS end-diastolic frames accurately and should be preferred over expert analysts and CIB-methodologies, which have limited efficacy

Novel near-infrared spectroscopy-intravascular ultrasound-based deep-learning methodology for accurate coronary computed tomography plaque quantification and characterization.

AIMS: Coronary computed tomography angiography (CCTA) is inferior to intravascular imaging in detecting plaque morphology and quantifying plaque burden. We aim to, for the first time, train a deep-learning (DL) methodology for accurate plaque quantification and characterization in CCTA using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS). METHODS AND RESULTS: Seventy patients were prospectively recruited who underwent CCTA and NIRS-IVUS imaging. Corresponding cross sections were matched using an in-house developed software, and the estimations of NIRS-IVUS for the lumen, vessel wall borders, and plaque composition were used to train a convolutional neural network in 138 vessels. The performance was evaluated in 48 vessels and compared against the estimations of NIRS-IVUS and the conventional CCTA expert analysis. Sixty-four patients (186 vessels, 22 012 matched cross sections) were included. Deep-learning methodology provided estimations that were closer to NIRS-IVUS compared with the conventional approach for the total atheroma volume (ΔDL-NIRS-IVUS: -37.8 ± 89.0 vs. ΔConv-NIRS-IVUS: 243.3 ± 183.7 mm3, variance ratio: 4.262, P < 0.001) and percentage atheroma volume (-3.34 ± 5.77 vs. 17.20 ± 7.20%, variance ratio: 1.578, P < 0.001). The DL methodology detected lesions more accurately than the conventional approach (Area under the curve (AUC): 0.77 vs. 0.67, P < 0.001) and quantified minimum lumen area (ΔDL-NIRS-IVUS: -0.35 ± 1.81 vs. ΔConv-NIRS-IVUS: 1.37 ± 2.32 mm2, variance ratio: 1.634, P < 0.001), maximum plaque burden (4.33 ± 11.83% vs. 5.77 ± 16.58%, variance ratio: 2.071, P = 0.004), and calcific burden (-51.2 ± 115.1 vs. -54.3 ± 144.4, variance ratio: 2.308, P < 0.001) more accurately than conventional approach. The DL methodology was able to segment a vessel on CCTA in 0.3 s. CONCLUSIONS: The DL methodology developed for CCTA analysis from co-registered NIRS-IVUS and CCTA data enables rapid and accurate assessment of lesion morphology and is superior to expert analysts (Clinicaltrials.gov: NCT03556644)

Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of Merit, Gap Statistics and Model Explorer

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

The Action Mechanism of the Myc Inhibitor Termed Omomyc May Give Clues on How to Target Myc for Cancer Therapy

Recent evidence points to Myc – a multifaceted bHLHZip transcription factor deregulated in the majority of human cancers – as a priority target for therapy. How to target Myc is less clear, given its involvement in a variety of key functions in healthy cells. Here we report on the action mechanism of the Myc interfering molecule termed Omomyc, which demonstrated astounding therapeutic efficacy in transgenic mouse cancer models in vivo. Omomyc action is different from the one that can be obtained by gene knockout or RNA interference, approaches designed to block all functions of a gene product. This molecule – instead – appears to cause an edge-specific perturbation that destroys some protein interactions of the Myc node and keeps others intact, with the result of reshaping the Myc transcriptome. Omomyc selectively targets Myc protein interactions: it binds c- and N-Myc, Max and Miz-1, but does not bind Mad or select HLH proteins. Specifically, it prevents Myc binding to promoter E-boxes and transactivation of target genes while retaining Miz-1 dependent binding to promoters and transrepression. This is accompanied by broad epigenetic changes such as decreased acetylation and increased methylation at H3 lysine 9. In the presence of Omomyc, the Myc interactome is channeled to repression and its activity appears to switch from a pro-oncogenic to a tumor suppressive one. Given the extraordinary therapeutic impact of Omomyc in animal models, these data suggest that successfully targeting Myc for cancer therapy might require a similar twofold action, in order to prevent Myc/Max binding to E-boxes and, at the same time, keep repressing genes that would be repressed by Myc

A Comparison of the Epidemiology and Clinical Presentation of Seasonal Influenza A and 2009 Pandemic Influenza A (H1N1) in Guatemala

A new influenza A (H1N1) virus was first found in April 2009 and proceeded to cause a global pandemic. We compare the epidemiology and clinical presentation of seasonal influenza A (H1N1 and H3N2) and 2009 pandemic influenza A (H1N1) (pH1N1) using a prospective surveillance system for acute respiratory disease in Guatemala.Patients admitted to two public hospitals in Guatemala in 2008-2009 who met a pneumonia case definition, and ambulatory patients with influenza-like illness (ILI) at 10 ambulatory clinics were invited to participate. Data were collected through patient interview, chart abstraction and standardized physical and radiological exams. Nasopharyngeal swabs were taken from all enrolled patients for laboratory diagnosis of influenza A virus infection with real-time reverse transcription polymerase chain reaction. We identified 1,744 eligible, hospitalized pneumonia patients, enrolled 1,666 (96%) and tested samples from 1,601 (96%); 138 (9%) had influenza A virus infection. Surveillance for ILI found 899 eligible patients, enrolled 801 (89%) and tested samples from 793 (99%); influenza A virus infection was identified in 246 (31%). The age distribution of hospitalized pneumonia patients was similar between seasonal H1N1 and pH1N1 (P = 0.21); the proportion of pneumonia patients <1 year old with seasonal H1N1 (39%) and pH1N1 (37%) were similar (P = 0.42). The clinical presentation of pH1N1 and seasonal influenza A was similar for both hospitalized pneumonia and ILI patients. Although signs of severity (admission to an intensive care unit, mechanical ventilation and death) were higher among cases of pH1N1 than seasonal H1N1, none of the differences was statistically significant.Small sample sizes may limit the power of this study to find significant differences between seasonal influenza A and pH1N1. In Guatemala, influenza, whether seasonal or pH1N1, appears to cause severe disease mainly in infants; targeted vaccination of children should be considered

Multiplex PCR technique could be an alternative approach for early detection of leprosy among close contacts - a pilot study from India

<p>Abstract</p> <p>Background</p> <p>Implementation of Multi drug Therapy (MDT) regimen has resulted in the decline of the total number of leprosy cases in the world. Though the prevalence rate has been declining, the incidence rate remains more or less constant and high in South East Asian countries particularly in India, Nepal, Bangladesh, Pakistan and Srilanka. Leprosy, particularly that of multibacillary type spreads silently before it is clinically detected. An early detection and treatment would help to prevent transmission in the community. Multiplex PCR (M-PCR) technique appears to be promising towards early detection among contacts of leprosy cases.</p> <p>Methods</p> <p>A total of 234 paucibacillary (PB) and 205 multibacillary (MB) leprosy cases were studied in a community of an endemic area of Bankura district of West Bengal (Eastern India). They were assessed by smear examination for acid-fast bacilli (AFB) and M-PCR technique. These patients were treated with Multidrug Therapy (MDT) as prescribed by WHO following detection. A total of 110 MB and 72 PB contacts were studied by performing M-PCR in their nasal swab samples.</p> <p>Results</p> <p>83.4% of MB patients were observed to be positive by smear examination for AFB and 89.2% by M-PCR. While 22.2% of PB patients were found to be positive by smear examination for AFB, 80.3% of these patients were positive by M-PCR. Among leprosy contacts (using M-PCR), 10.9% were found to be positive among MB contacts and 1.3% among PB contacts. Interestingly, two contacts of M-PCR positive MB cases developed leprosy during the period of two years follow up.</p> <p>Conclusion</p> <p>The M-PCR technique appears to be an efficient tool for early detection of leprosy cases in community based contact tracing amongst close associates of PB and MB cases. Early contact tracing using a molecular biology tool can be of great help in curbing the incidence of leprosy further.</p

X-ray emission from the Sombrero galaxy: discrete sources

We present a study of discrete X-ray sources in and around the bulge-dominated, massive Sa galaxy, Sombrero (M104), based on new and archival Chandra observations with a total exposure of ~200 ks. With a detection limit of L_X = 1E37 erg/s and a field of view covering a galactocentric radius of ~30 kpc (11.5 arcminute), 383 sources are detected. Cross-correlation with Spitler et al.'s catalogue of Sombrero globular clusters (GCs) identified from HST/ACS observations reveals 41 X-rays sources in GCs, presumably low-mass X-ray binaries (LMXBs). We quantify the differential luminosity functions (LFs) for both the detected GC and field LMXBs, whose power-low indices (~1.1 for the GC-LF and ~1.6 for field-LF) are consistent with previous studies for elliptical galaxies. With precise sky positions of the GCs without a detected X-ray source, we further quantify, through a fluctuation analysis, the GC LF at fainter luminosities down to 1E35 erg/s. The derived index rules out a faint-end slope flatter than 1.1 at a 2 sigma significance, contrary to recent findings in several elliptical galaxies and the bulge of M31. On the other hand, the 2-6 keV unresolved emission places a tight constraint on the field LF, implying a flattened index of ~1.0 below 1E37 erg/s. We also detect 101 sources in the halo of Sombrero. The presence of these sources cannot be interpreted as galactic LMXBs whose spatial distribution empirically follows the starlight. Their number is also higher than the expected number of cosmic AGNs (52+/-11 [1 sigma]) whose surface density is constrained by deep X-ray surveys. We suggest that either the cosmic X-ray background is unusually high in the direction of Sombrero, or a distinct population of X-ray sources is present in the halo of Sombrero.Comment: 11 figures, 5 tables, ApJ in pres