A Comparative Analysis of Transfer Learning-based Techniques for the Classification of Melanocytic Nevi

Skin cancer is a fatal manifestation of cancer. Unrepaired deoxyribo-nucleic acid (DNA) in skin cells, causes genetic defects in the skin and leads to skin cancer. To deal with lethal mortality rates coupled with skyrocketing costs of medical treatment, early diagnosis is mandatory. To tackle these challenges, researchers have developed a variety of rapid detection tools for skin cancer. Lesion-specific criteria are utilized to distinguish benign skin cancer from malignant melanoma. In this study, a comparative analysis has been performed on five Transfer Learning-based techniques that have the potential to be leveraged for the classification of melanocytic nevi. These techniques are based on deep convolutional neural networks (DCNNs) that have been pre-trained on thousands of open-source images and are used for day-to-day classification tasks in many instances.Comment: 12 pages, 5 figures, submitted to International Conference on Advances and Applications of Artificial Intelligence and Machine Learning (ICAAAIML) 2022, to be published in Springer's Lecture Notes in Electrical Engineerin

Classification of Melanocytic Nevus Images using BigTransfer (BiT)

Skin cancer is a fatal disease that takes a heavy toll over human lives annually. The colored skin images show a significant degree of resemblance between different skin lesions such as melanoma and nevus, making identification and diagnosis more challenging. Melanocytic nevi may mature to cause fatal melanoma. Therefore, the current management protocol involves the removal of those nevi that appear intimidating. However, this necessitates resilient classification paradigms for classifying benign and malignant melanocytic nevi. Early diagnosis necessitates a dependable automated system for melanocytic nevi classification to render diagnosis efficient, timely, and successful. An automated classification algorithm is proposed in the given research. A neural network previously-trained on a separate problem statement is leveraged in this technique for classifying melanocytic nevus images. The suggested method uses BigTransfer (BiT), a ResNet-based transfer learning approach for classifying melanocytic nevi as malignant or benign. The results obtained are compared to that of current techniques, and the new method's classification rate is proven to outperform that of existing methods.Comment: 5 pages, 3 figure

Comptonization by Reconnection Plasmoids in Black Hole Coronae III: Dependence on the Guide Field in Pair Plasma

We perform two-dimensional particle-in-cell simulations of magnetic reconnection for various strengths of the guide field (perpendicular to the reversing field), in magnetically-dominated electron-positron plasmas. Magnetic reconnection under such conditions could operate in accretion disk coronae around black holes. There, it has been suggested that the trans-relativistic bulk motions of reconnection plasmoids containing inverse-Compton-cooled electrons could Compton-upscatter soft photons to produce the observed non-thermal hard X-rays. Our simulations are performed for magnetizations $3 \leq \sigma \leq 40$ (defined as the ratio of enthalpy density of the reversing field to plasma enthalpy density) and guide field strengths $0 \leq B_{\rm g}/B_0 \leq 1$ (normalized to the reversing field strength $B_0$). We find that the mean bulk energy of the reconnected plasma depends only weakly on the flow magnetization but strongly on the guide field strength -- with $B_{\rm g}/B_0 = 1$ yielding a mean bulk energy twice smaller than $B_{\rm g}/B_0 = 0$. Similarly, the dispersion of bulk motions around the mean -- a signature of stochasticity in the plasmoid chain's motions -- is weakly dependent on magnetization (for $\sigma \gtrsim 10$) but strongly dependent on the guide field strength -- dropping by more than a factor of two from $B_{\rm g}/B_0 = 0$ to $B_{\rm g}/B_0 = 1$. In short, reconnection in strong guide fields ($B_{\rm g}/B_0 \sim 1$) leads to slower and more ordered plasmoid bulk motions than its weak guide field ($B_{\rm g}/B_0 \sim 0$) counterpart

CHARACTERIZATION OF LIBRARY CELLS FOR OPEN AND SHORT CIRCUIT DEFECT EXPOSURE: A SYSTEMATIC METHODOLOGY

With the advancement in technology scaling and voltage scaling, achieving a high defect coverage is a major challenge for the IC test industry. Traditional gate-level test methods using fault models such as stuck-at and transition faults have proven to be insufficient for detecting intra-cell open and short circuit defects. Cell-Aware test methodology has shown to improve the defect coverage for such defects with limited magnitudes (due to simulation overhead) using a single pattern and two pattern tests. Further, more than two pattern test can be required to detect subtle defects due to effects such as charge sharing. In this work, circuit simulations are used to expose defect locations and sizes that can escape the current test methods, and identify the patterns that can detect them. Additionally, an algorithmic methodology to characterize cell input stimuli to detect the defects faster than simulation-based defect detection.M.S

Mapping hierarchical and heterogeneous micromechanics of a transformative high entropy alloy by nanoindentation and machine learning augmented clustering

Conventional macromechanical tests provide limited insights into complex hierarchical deformation behavior of a transformative high entropy alloy (HEA). In this work, a high throughput microstructure-micromechanical correlative study is presented by combining high-resolution nanoindentation, site-specific microscopy, and Gaussian mixture model (GMM) clustering. The investigated HEA has a heterogenous microstructure consisting of austenite and martensite phases. Comparison of elastoplastic and microstructural maps illustrate dependency of phase, crystal orientation, and interfacial constraints on the micromechanical response. The disproportionately high hardness found in martensite-rich area is attributed to its higher lattice stability to shear, creation of coherent twin boundaries, and copious dislocation activities in the twin interfaces formed in martensite phase during nanoindentation. The hierarchy in twinning behavior depends on the relative direction of loading with the c-axis of h.c.p. martensitic phase. Deformation in f.c.c. austenitic grains is slip-dominated and demonstrates orientation dependency during incipient plasticity and phase transformation. GMM based classification of hardness to modulus ratio intuitively correlates work-hardening with phase distribution due to the distinctive deformation micromechanical responses of austenite and martensite phases

Size-Selective Detection of Picric Acid by Fluorescent Palladium Macrocycles

This work presents the synthesis and characterization of two palladium-based fluorescent macrocycles offering hydrogen-bonding cavities of contrasting dimensions. Both palladium macrocycles function as chemosensors for the detection of nitroaromatics, whereas the larger macrocycle not only illustrates nanomolar detection of picric acid but also transports its significant amount from an aqueous to an organic phase

Genetic manipulation of <i>Leishmania donovani</i> threonyl tRNA synthetase facilitates its exploration as a potential therapeutic target

<div><p>Background</p><p>Aminoacyl tRNA synthetases are central enzymes required for protein synthesis. These enzymes are the known drug targets in bacteria and fungi. Here, we for the first time report the functional characterization of threonyl tRNA synthetase (<i>Ld</i>ThrRS) of <i>Leishmania donovani</i>, a protozoan parasite, the primary causative agent of visceral leishmaniasis.</p><p>Methodology</p><p>Recombinant <i>Ld</i>ThrRS (r<i>Ld</i>ThrRS) was expressed in <i>E</i>. <i>coli</i> and purified. The kinetic parameters for r<i>Ld</i>ThrRS were determined. The subcellular localization of <i>Ld</i>ThrRS was done by immunofluorescence analysis. Heterozygous mutants of <i>LdThrRS</i> were generated in <i>Leishmania</i> promastigotes. These genetically manipulated parasites were checked for their proliferation, virulence, aminoacylation activity and sensitivity to the known ThrRS inhibitor, borrelidin. An <i>in silico</i> generated structural model of <i>L</i>. <i>donovani</i> ThrRS was compared to that of human.</p><p>Conclusions</p><p>Recombinant <i>Ld</i>ThrRS displayed aminoacylation activity, and the protein is possibly localized to both the cytosol and mitochondria. The comparison of the 3D-model of <i>Ld</i>ThrRS to human ThrRS displayed considerable similarity. Heterozygous parasites showed restrictive growth phenotype and had attenuated infectivity. These heterozygous parasites were more susceptible to inhibition by borrelidin. Several attempts to obtain ThrRS homozygous null mutants were not successful, indicating its essentiality for the <i>Leishmania</i> parasite. Borrelidin showed a strong affinity for <i>Ld</i>ThrRS (K_D: 0.04 μM) and was effective in inhibiting the aminoacylation activity of the r<i>Ld</i>ThrRS (IC₅₀: 0.06 μM). Borrelidin inhibited the promastigotes (IC₅₀: 21 μM) stage of parasites. Our data shows that <i>Ld</i>ThrRS is essential for <i>L</i>. <i>donovani</i> survival and is likely to bind with small drug-like molecules with strong affinity, thus making it a potential target for drug discovery efforts.</p></div

Effect of borrelidin on the growth of WT, <i>ThrRS/NEO</i> and <i>ThrRS/NEO[pThrRS</i>^<i>+</i><i>]</i> parasites.

<p>(A) and (B) Inhibition profile of borrelidin for the promastigote (A) and intracellular amastigote (B) growth of WT parasites. Percentage parasite survival was plotted against different concentrations of borrelidin. (C) and (D) The leishmanicidal effect of borrelidin was checked on promastigotes (C) and amastigotes (D) of WT, <i>ThrRS/NEO</i> and <i>ThrRS/NEO[pThrRS</i>^<i>+</i><i>]</i> parasites. The mean IC₅₀ values were calculated for borrelidin and plotted as bar graphs. (E) The effect of miltefosine on WT, <i>ThrRS/NEO</i> and <i>ThrRS/NEO[pThrRS</i>^<i>+</i><i>]</i> promastigotes. The bar graphs represent mean ± SD with <i>n</i> = 3. * <i>p</i> < 0.05.</p