Study and Analysis of Advanced 3D Multi-Gate Junctionless Transistors

Silicon - As the IC technology is evolving very rapidly, the feature size of the device has been migrating to sub-nanometre regime for achieving the high packing density. To continue with further...     

Addressing image and Poisson noise deconvolution problem using deep learning approaches

Digital images are more important in numerous contemporary applications, and the need for images in the technical field is also increasing drastically. It is used to recognize signatures and faces in many industries and is applicable for intelligent departments. The images are usually associated with the noise content; this may happen due to the instrument imperfections, troubleshooting while collecting data from the acquisition process, and another natural phenomenon. Poisson noise, also known as photon noise, is caused in the images due to the statistical essence of electromagnetic waves. X-ray, visible light, and gamma rays are electromagnetic waves. The enhancement of the convolution model in addressing images is challenging due to the various constituents such as optical aberrations, noise level, and optical setup. The modeling configuration of the image is attained using the point spread function (PSF), which is responsible for the system's impulse response. The quality image is retrieved by denoising and super-resolution (SR) methods; these methods simultaneously eliminate the noise content from the images. A Richardson–Lucy and alternating direction method of multipliers type of non-blind iterative algorithmic approaches associated with the PSF performance in addressing image is comparatively analyzed. The deep learning approach, convolutional neural networks (CNNs), is also employed to understand the nonlinear mapping relationship between the observed data and ground reality. The performance of the various network approaches is compared in this article. The result obtained shows that the deep learning CNNs achieved higher accuracy in producing denoising images. The goal of the proposed system model is to remove the interference noise in images. The high-resolution images are obtained by implementing a SR-based CNN model.     

Angiojet™ mechanical thrombectomy-induced hemolysis triggering blood-leak alarms

Blood leak alarms are increasingly rare and may be triggered by rupture of the dialyzer membrane, or intravascular hemolysis. We report two patients who developed hemolysis following Angiojet™ thrombolysis and thrombectomy of thrombosed arteriovenous fistulas, triggering blood leak alarms on hemodialysis, the occurrence of which has not been reported before. AngioJet™-induced hemolysis should be considered in the differential diagnosis for blood leak alarms occurring soon after an intervention on an arterio-venous (AV) fistula.     

ChemoGraph: Interactive Visual Exploration of the Chemical Space

Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.     

Gate Stacked (GS) Junctionless Nanotube MOSFET: Design and Analysis

Silicon - This paper presents Gate Stacked junctionless nanotube gate all around MOSFET (GS JL NT GAA MOSFET) and its investigation for low power circuit applications. In GS architecture, high-k...     

Strategies to Reduce the On-Target Platelet Toxicity of Bcl-xL Inhibitors: PROTACs,SNIPERs and Prodrug-Based Approaches

Apoptosis is a highly regulated cellular process. Aberration in apoptosis is a common characteristic of various disorders. Therefore, proteins involved in apoptosis are prime targets in multiple therapies. Bcl-x_L is an antiapoptotic protein. Compared to other antiapoptotic proteins, the expression of Bcl-x_L is common in solid tumors and, to an extent, in some leukemias and lymphomas. The overexpression of Bcl-x_L is also linked to survival and chemoresistance in cancer and senescent cells. Therefore, Bcl-x_L is a promising anticancer and senolytic target. Various nanomolar range Bcl-x_L inhibitors have been developed. ABT-263 was successfully identified as a Bcl-x_L/Bcl-2 dual inhibitor. But it failed in the clinical trial (phase-II) because of its on-target platelet toxicity, which also implies an essential role of Bcl-x_L protein in the survival of human platelets. Classical Bcl-x_L inhibitor designs utilize occupancy-driven pharmacology with typical shortcomings (such as dose-dependent off-target and on-target platelet toxicities). Hence, event-driven pharmacology-based approaches, such as proteolysis targeting chimeras (PROTACs) and SNIPERs (specific non-genetic IAP-based protein erasers) have been developed. The development of Bcl-x_L based PROTACs was expected, as 600 E3-ligases are available in humans, while some (such as cereblon (CRBN), von Hippel-Lindau (VHL)) are relatively less expressed in platelets. Therefore, E3 ligase ligand-based Bcl-x_L PROTACs (CRBN: XZ424, XZ739; VHL: DT2216, PZ703b, 753b) showed a significant improvement in platelet therapeutic index than their parent molecules ( ABT-263 : DT2216, PZ703b, 753b, XZ739, PZ15227; A1155463 : XZ424). Other than their distinctive pharmacology, PROTACs are molecularly large, which limits their cell permeability and plays a role in improving their cell selectivity. We also discuss prodrug-based approaches, such as antibody-drug conjugates ( ABBV-155 ), phosphate prodrugs ( APG-1252 ), dendrimer conjugate ( AZD0466 ), and glycosylated conjugates ( Nav-Gal ). Studies of in-vitro, in-vivo, structure-activity relationships, biophysical characterization, and status of preclinical/clinical inhibitors derived from these strategies are also discussed in the review.     

Exploring the Potential of Wire Fed Direct Energy Deposition of Aluminum–Boron Nitride Nanotube Composite: Microstructural Evolution and Mechanical Properties

Recent advancements have shown great promise in utilizing wire-fed direct energy deposition (DED) for building aluminum alloy structures. However, utilizing the wire-fed DED approach for fabricating metal matrix composite structures remains a significant challenge. Herein, a wire-based additive manufacturing process is used to successfully produce a 1D boron nitride nanotube (BNNT)-reinforced aluminum composite with high strength. Al-BNNT electrode is developed in house. The microstructural changes that occur during layer-by-layer deposition are investigated. The grain morphology changes from equiaxed grains in the bottom layer to columnar grains in the top layer. BNNTs act as nuclei to promote the formation of equiaxed grains and interfacial compounds (AlN and AlB₂) during solidification. This results in improved strength, with Al-BNNT composite exhibiting a tensile strength of 47 MPa, 2.3 times higher than its pure Al. Higher strength is attributed to the retention and uniform distribution of BNNT reinforcement in the melt pool, leading to effective load transfer. This study demonstrates the potential of additive manufacturing for producing high-performance metal matrix composites with novel 1D reinforcements and improved multifunctional properties.     

Effect of Temperature on Performance of 5-nm Node Silicon Nanosheet Transistors for Analog Applications

Silicon - This work investigates the effects of temperature on the performance of a 5-nm node N-channel Nanosheet Transistor (NST) for analog applications. A fully calibrated commercial TCAD...