Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network

Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. Experiments show our proposed AL framework is able to achieve state of the art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods

Anomalous in-plane magneto-optical anisotropy of self-assembled quantum dots

We report on a complex nontrivial behavior of the optical anisotropy of quantum dots that is induced by a magnetic field in the plane of the sample. We find that the optical axis either rotates in the opposite direction to that of the magnetic field or remains fixed to a given crystalline direction. A theoretical analysis based on the exciton pseudospin Hamiltonian unambiguously demonstrates that these effects are induced by isotropic and anisotropic contributions to the heavy-hole Zeeman term, respectively. The latter is shown to be compensated by a built-in uniaxial anisotropy in a magnetic field B_c = 0.4 T, resulting in an optical response typical for symmetric quantum dots.Comment: 5 pages, 3 figure

Effect of moisture content on thermal properties of cowpea flours

The effects of moisture content on thermal properties of cowpea flour were investigated on a range of 3.81% to 28.31% wet basis at 5% intervals, totaling six moisture levels, using a KD2 Pro Thermal Properties Analyzer.  The considered thermal properties were thermal conductivity, thermal diffusivity, and specific heat.  As the moisture content increased from 3.81% to 28.31 %, the thermal conductivity, thermal diffusivity, and specific heat increased from 0.109 to 0.213 W m-1 K-1, 0.099 to 0.136 mm2 s-1, and 1.092 to 1.573 MJ m-3 K-1, respectively.  The data are necessary for design of equipment for handling, transportation, processing, and storage of cowpea flour.   Keywords: cowpea, Vigna unguiculata, flour, thermal properties, KD2 Pro, moisture conten

Progress on the hybrid gun project at UCLA

UCLA/INFN-LNF/Univ. Rome has been developing the hybrid gun which has an RF gun and a short linac for velocity bunching in one structure. After the cavity was manufactured at INFN-LNF in 2012, tests of the gun was carried out at UCLA. The field in the standing wave part was 20 % smaller than the simulation but the phase advance was fine. The cavity was commissioned successfully up to 13 MW. The beam test was performed at 11.5 MW and demonstrated the bunch compression

Quantum-dot-based optical polarization conversion

We report circular-to-linear and linear-to-circular conversion of optical polarization by semiconductor quantum dots. The polarization conversion occurs under continuous wave excitation in absence of any magnetic field. The effect originates from quantum interference of linearly and circularly polarized photon states, induced by the natural anisotropic shape of the self assembled dots. The behavior can be qualitatively explained in terms of a pseudospin formalism.Comment: 5 pages, 3 figures; a reference adde

Evaluation of an IUL Flash & Go Automated Colony Counter

An IUL Flash & Go automated colony counter was used to enumerate E. coli (ATCC 700728) colonies and its performance was compared with manual counting on spiral plates. A total of 85 plates were analyzed. Linear regression analysis and the log differences between the manual and automated counts were determined. The results were analyzed to evaluate the reliability and accuracy of the colony counter.  A correlation coefficient of 0.969, a slope of 0.932 and intercept of 0.25 all indicate a strong, linear relationship. The mean log value difference between the manual and Flash & Go count methods was -0.035. Of the 85 plates counted, 95% of the plates were within 0.15 log10 difference between the manual and Flash & Go automated counts. These results demonstrate that the Flash & Go automated colony counter is an effective, accurate and time saving alternative to the standard method of manual counting.      

Experimental determination of the electrical resistivity of beef

 A. K. Mahapatra, B. L. Jones, C. N. Nguyen, G. Kannan(Agricultural Research Station, Fort Valley State University, 1005 State University Drive, Fort Valley, GA 31030, USA) Abstract: Electrical resistivity properties of beef were investigated.  The resistivity behavior under three frequencies of 1, 10 and 100-kHz, different temperatures (5, 10, 15, and 20℃), different length and cross-sectional areas (width: 7 cm, two depths:  3 and 5 cm, and four lengths: 7, 11, 15, and 19 cm) were determined.  The electrical series circuit was found to be adequate to measure the resistivity properties of beef.  Samples with warmer temperatures offered much less resistance and the resistivity values obtained at temperatures 5℃ and below were not consistent.  Increasing temperature had a significant effect on the resistivity values of beef (p<0.05).  Increase in frequency did not have any significant effect on the resistivity properties of beef (p>0.05).  It was observed that resistivity was higher across the myofiber axes than along the myofiber axes.  However, there was no significant difference between the fiber directions in terms of resistivity (p>0.05).  The mean resistivity of beef at 20℃ for across the myofiber and along the myofiber directions was found to be 365.42 Ohms.cm and 346.67 Ohms.cm, respectively.Keywords: electrical resistivity, beef, anisotropy Citation: Mahapatra A. K., B. L. Jones, C. N. Nguyen, and G. Kannan.  Experimental determination of the electrical resistivity of beef.  Agric Eng Int: CIGR Journal, 2010, 12(3): 124-128. &nbsp

Clinical response to primary letrozole therapy in elderly patients with early breast cancer : possible role for p53 as a biomarker

Primary tamoxifen therapy has been widely used to treat elderly women with ER-positive breast cancer in the past. Aromatase inhibitors may be more beneficial than tamoxifen when used as primary endocrine therapy in elderly patients. We aimed to retrospectively evaluate a series of elderly women with ER-positive breast cancer treated with primary letrozole therapy as sole therapy with a minimum of 5 years follow up. To identify possible predictive biomarkers a pilot immunohistochemical analysis was performed to assess the expression of PR, HER2, EGFR, BCL2 and p53. A total of 45 women, aged more than 70 years with a diagnosis of ER-positive breast cancer that was treated with primary letrozole therapy were identified. A case note review was undertaken to obtain clinical information. Formalin fixed paraffin embedded tumour tissue from diagnostic core biopsies was available for all patients. Immunohistochemical analysis was performed to establish the protein expression status of p53, PR, HER2, EGFR and BCL2. The mean age of the 45 patients was 87 years (range 70–101). Clinical benefit was seen in 60% of the patients. Median progression free survival was 53 months (95% CI – 34–72) and the median time to progression was 43 months (95% CI – 22–64). BCL2 was expressed in 45/45 (100%); PR in 38/45 (84%); EGFR in 13/45 (28%); HER2 in 9/45 (20%) and p53 in 5/45 (11%) of tissue samples. Positive expression of p53 was associated with poor progression free survival (p = 0.03) in this pilot study. This study demonstrates that letrozole as sole treatment appears to be a suitable treatment option for elderly patients with ER-positive breast cancer who are not fit for, or decline, surgery. The analysis of p53 in a larger study is warranted in order to assess its role as a biomarker in this patient group

Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network

Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. Experiments show our proposed AL framework is able to achieve state of the art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods

Structure Preserving Stain Normalization of Histopathology Images Using Self Supervised Semantic Guidance

© 2020, Springer Nature Switzerland AG. Although generative adversarial network (GAN) based style transfer is state of the art in histopathology color-stain normalization, they do not explicitly integrate structural information of tissues. We propose a self-supervised approach to incorporate semantic guidance into a GAN based stain normalization framework and preserve detailed structural information. Our method does not require manual segmentation maps which is a significant advantage over existing methods. We integrate semantic information at different layers between a pre-trained semantic network and the stain color normalization network. The proposed scheme outperforms other color normalization methods leading to better classification and segmentation performance