Analyzing heterogeneity and complexity of white matter using deep learning

In this work, we identify the heterogeneity of regions of white matter across individuals and the complexity of different regions of white matter using diffusion MRI data. We analyze the heterogeneity of a region across individuals by computing the pairwise difference between voxels. We review various complexity measures like entropy and Kolmogorov complexity. Using autoencoders, we analyze the inherent dimensionality of regions of white matter structure as a means for measuring complexity. The intrinsic complexity of a region can be determined by how effectively it can be predicted given its neighborhood. We pose this as the problem of inpainting a three dimensional region of the brain given its context with deep generative modeling using Generative Adversarial Networks (GANs) conditioned on the neighborhood. The discriminator is trained on differentiating between real and generated patches while the generator is simultaneously trained to adversarially fool the discriminator and minimize the voxel-wise reconstruction loss between the actual and generated patches. We study the comparative performance of a well-trained GAN across regions of the brain in multiple subjects and identify particularly challenging or complex regions of brain wiring. Finally, we analyze the correlation between these measures of heterogeneity and complexity

Analyzing heterogeneity and complexity of white matter using deep learning

In this work, we identify the heterogeneity of regions of white matter across individuals and the complexity of different regions of white matter using diffusion MRI data. We analyze the heterogeneity of a region across individuals by computing the pairwise difference between voxels. We review various complexity measures like entropy and Kolmogorov complexity. Using autoencoders, we analyze the inherent dimensionality of regions of white matter structure as a means for measuring complexity. The intrinsic complexity of a region can be determined by how effectively it can be predicted given its neighborhood. We pose this as the problem of inpainting a three dimensional region of the brain given its context with deep generative modeling using Generative Adversarial Networks (GANs) conditioned on the neighborhood. The discriminator is trained on differentiating between real and generated patches while the generator is simultaneously trained to adversarially fool the discriminator and minimize the voxel-wise reconstruction loss between the actual and generated patches. We study the comparative performance of a well-trained GAN across regions of the brain in multiple subjects and identify particularly challenging or complex regions of brain wiring. Finally, we analyze the correlation between these measures of heterogeneity and complexity

Original Article Section: Pathology Histopathological Pattern of Soft Tissue Tumours in 200 Cases

were the second commonest (18%) followed by peripheral nerve sheath tumors (10.5%). The benign tumors were seen in younger age as compared to malignant tumors. Conclusion: Benign tumors were more common than malignant. The most common benign tumors were lipoma followed by hemangioma (14.0%) and schwannoma (5.5%).There is wide range of their distribution according to age, sex and site. The benign tumors were seen in younger age as compared to malignant tumors

ASPEK HUKUM TANGGUNG JAWAB SOSIAL DAN LINGKUNGAN USAHA MIKRO KECIL DAN MENENGAH

Penelitian ini bertujuan untuk melihat bagaimana penerapan dan bentuk tanggung jawab sosial yang dilaksanakan oleh Usaha Mikro Kecil dan Menengah (UMKM) dalam menjaga hubungan sosial dan dengan lingkungan sekitar. Tanggung Jawab Sosial dan Lingkungan adalah kegiatan yang dilaksanakan perusahaan untuk menjaga hubungan dengan masyarakat dan mengurangi pengaruh negatif pada lingkungan dan merupakan kewajiban bersama, perusahaan besar maupun perusahaan yang masih dikategorikan Perusahaan UMKM. Pertanyaannya adalah bagaimanakah pelaksanaan Tanggung Jawab Sosial dan Lingkungan di Perusahaan UMKM. Metode penelitian yang digunakan pendekatan yuridis normatif. Temuan penelitian menunjukkan, dalam pelaksanaan tanggung jawab sosial, di Perusahaan UMKM lebih termotivasi sebab jiwa sosial yang dimiliki dan hubungan baik yang terjalin antara pelaku usaha dengan lingkungan sosial sekitar secara alami terbentuk tanpa adanya sifat paksaan. Selain itu, tanggung jawab sosial dan lingkungan yang dilaksanakan oleh Perusahaan UMKM adalah pengolahan limbah, sumbangan kegiatan masyarakat sekitar, dan menyediakan lapangan pekerjaan untuk mengurangi pengangguran

Electrochemical and computational investigations of the Thysanolaena latifolia leaves extract: An eco-benign solution for the corrosion mitigation of mild steel

The development of eco-benign corrosion inhibitors is gaining increased attention due to the non-biodegradability and toxic effects of conventional inhibitors. In this study, the anti-corrosive efficacy of Thysanolaena latifolia leaves extract (TLLE) has been evaluated on mild steel in an acidic solution (0.5 M HCl) by employing various approaches including gravimetric assessment, electrochemical measurements, scanning electron microscopy and energy dispersive X-ray spectroscopy. The outcomes demonstrated that with the increased concentration of TLLE from 50 ppm to 1000 ppm, the inhibition efficiency (IE%) elevated up to 93.90 % and the corrosion rate decreased significantly from 2.513 mm/yr to 0.203 mm/yr at 298 K. Moreover, it was revealed that at a higher concentration (1000 ppm), the Rp values increased from 30.784 Ω to 157.83 Ω with an Rct in value of 159.627 Ω cm2 suggesting the defensive film formation by TLLE molecules over the metallic surface. The quantum chemical analytics further underpinned the empirical findings, showing a high ΔE(eV) value of 0.239 and interaction and binding energies of −995.45 and 995.45 for TLLE molecules upon Fe (110) substrate. The findings indicate that TLLE has a promising prospect as an eco-benign and efficient inhibitor for preventing the corrosion of mild steel in corrosive environments

Coco Monoethanolamide Surfactant as a Sustainable Corrosion Inhibitor for Mild Steel: Theoretical and Experimental Investigations

Recent studies indicate that surfactants are a relatively new and effective class of corrosion inhibitors that almost entirely meet the criteria for a chemical to be used as an aqueous phase corrosion inhibitor. They possess the ideal hydrophilicity to hydrophobicity ratio, which is crucial for effective interfacial interactions. In this study, a coconut-based non-ionic surfactant, namely, coco monoethanolamide (CMEA), was investigated for corrosion inhibition behaviour against mild steel (MS) in 1 M HCl employing the experimental and computational techniques. The surface morphology was studied employing the scanning electron microscope (SEM), atomic force microscope (AFM), and contact measurements. The critical micelle concentration (CMC) was evaluated to be 0.556 mM and the surface tension corresponding to the CMC was 65.28 mN/m. CMEA manifests the best inhibition efficiency (η%) of 99.01% at 0.6163 mM (at 60 °C). CMEA performs as a mixed-type inhibitor and its adsorption at the MS/1 M HCl interface followed the Langmuir isotherm. The theoretical findings from density functional theory (DFT), Monte Carlo (MC), and molecular dynamics (MD) simulations accorded with the experimental findings. The MC simulation’s assessment of CMEA’s high adsorption energy (−185 Kcal/mol) proved that the CMEA efficiently and spontaneously adsorbs at the interface

Coco Monoethanolamide Surfactant as a Sustainable Corrosion Inhibitor for Mild Steel: Theoretical and Experimental Investigations

Recent studies indicate that surfactants are a relatively new and effective class of corrosion inhibitors that almost entirely meet the criteria for a chemical to be used as an aqueous phase corrosion inhibitor. They possess the ideal hydrophilicity to hydrophobicity ratio, which is crucial for effective interfacial interactions. In this study, a coconut-based non-ionic surfactant, namely, coco monoethanolamide (CMEA), was investigated for corrosion inhibition behaviour against mild steel (MS) in 1 M HCl employing the experimental and computational techniques. The surface morphology was studied employing the scanning electron microscope (SEM), atomic force microscope (AFM), and contact measurements. The critical micelle concentration (CMC) was evaluated to be 0.556 mM and the surface tension corresponding to the CMC was 65.28 mN/m. CMEA manifests the best inhibition efficiency (η%) of 99.01% at 0.6163 mM (at 60 °C). CMEA performs as a mixed-type inhibitor and its adsorption at the MS/1 M HCl interface followed the Langmuir isotherm. The theoretical findings from density functional theory (DFT), Monte Carlo (MC), and molecular dynamics (MD) simulations accorded with the experimental findings. The MC simulation’s assessment of CMEA’s high adsorption energy (−185 Kcal/mol) proved that the CMEA efficiently and spontaneously adsorbs at the interface