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The development of a CAD system by an object-oriented approach is considered. The configuration of the integrated environment is described. The organization of a uniform information space is discussed, and the organization of interactions between the CAD components is outlined.
Methods of grinding and shaping the rear surface of hard-alloy polyhedral inserts are considered. Machining of the tool’s rear surface is simulated, and several kinematic configurations for margin removal are proposed. An algorithm for kinematic shaping of the rear surface of a hard-alloy polyhedral insert in CAD systems is outlined.
The dependence of the pressure angle on the angle of cam rotation is considered. Analytical methods are outlined for determining the minimum size of cam mechanisms with sliding and rocking shafts.
A market analysis of additive technologies is presented. Three-dimensional printing is discussed, and Russian and non-Russian manufacturers of such equipment are noted.
Experience in optimal synthesis of individual vehicle components and mechanisms on the basis of additive technology is analyzed. A list of basic additive technologies is presented. The benefits of additive technology in combination with three-dimensional computer simulation are noted. The possibility of visualizing CAE calculations by means of additive technology is considered. In addition, the role of additive technology in the manufacture of individual vehicle components and mechanisms is discussed.
A technology is developed for 3D printing of the large-diameter gas-discharge chambers of electric rocket engines. The material employed is a ceramic composite based on silicon nitride.
A cooled transceiver housing of an active phased array antenna is produced by 3D printing from aluminum, on the basis of selective laser melting (SLM). The housing walls contain cooling channels, whose geometry ensures effective heat transfer from the active electronic components of the transceiver and also permits the manufacture of high-quality samples on existing 3D printing systems and facilitates cleaning of the channels after printing. The housing is made of AlSi10Mg aluminum, with an acceptable content of closed pores (no more than 1%). Its physicomechanical properties correspond to those of regular aluminum alloys used in the traditional manufacturing technology for such components. This housing may be used in advanced radar systems. The research permits recommendations for further development of the technology.
Prior to additive manufacturing, the geometric compatibility of the electronic model with the capabilities of the equipment must be verified. The significance of the technological constraints in terms of potential production problems is analyzed.
This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein–protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.
We propose a new framework, PlasmodiumVF-Net, to analyze thick smear microscopy images for a malaria diagnosis on both image and patient-level. Our framework detects whether a patient is infected, and in case of a malarial infection, reports whether the patient is infected by Plasmodium falciparum or Plasmodium vivax. PlasmodiumVF-Net first detects candidates for Plasmodium parasites using a Mask Regional-Convolutional Neural Network (Mask R-CNN), filters out false positives using a ResNet50 classifier, and then follows a new approach to recognize parasite species based on a score obtained from the number of detected patches and their aggregated probabilities for all of the patient images. Reporting a patient-level decision is highly challenging, and therefore reported less often in the literature, due to the small size of detected parasites, the similarity to staining artifacts, the similarity of species in different development stages, and illumination or color variations on patient-level. We use a manually annotated dataset consisting of 350 patients, with about 6000 images, which we make publicly available together with this manuscript. Our framework achieves an overall accuracy above 90% on image and patient-level.
In this study, we presented an AISID method extending AlphaFold-Multimer’s success in structure prediction towards identifying specific protein interactions with an optimized AISIDscore. The method was tested to identify the binding proteins in 18 human TNFSF (Tumor Necrosis Factor superfamily) members for each of 27 human TNFRSF (TNF receptor superfamily) members. For each TNFRSF member, we ranked the AISIDscore among the 18 TNFSF members. The correct pairing resulted in the highest AISIDscore for 13 out of 24 TNFRSF members which have known interactions with TNFSF members. Out of the 33 correct pairing between TNFSF and TNFRSF members, 28 pairs could be found in the top five (including 25 pairs in the top three) seats in the AISIDscore ranking. Surprisingly, the specific interactions between TNFSF10 (TNF-related apoptosis-inducing ligand, TRAIL) and its decoy receptors DcR1 and DcR2 gave the highest AISIDscore in the list, while the structures of DcR1 and DcR2 are unknown. The data strongly suggests that AlphaFold-Multimer might be a useful computational screening tool to find novel specific protein bindings. This AISID method may have broad applications in protein biochemistry, extending the application of AlphaFold far beyond structure predictions.
Its high-specific strength and stiffness with lower cost make discontinuous fiber-reinforced thermoplastic (FRT) materials an ideal choice for lightweight applications in the automotive industry. Compression molding is one of the preferred manufacturing processes for such materials as it offers the opportunity to maintain a longer fiber length and higher volume production. In the past, we have demonstrated that compression molding of FRT in bulk form can be simulated by treating melt flow as a continuum using the conservation of mass and momentum equations. However, the compression molding of such materials in sheet form using a similar approach does not work well. The assumption of melt flow as a continuum does not hold for such deformation processes. To address this challenge, we have developed a novel simulation approach. First, the draping of the sheet was simulated as a structural deformation using the explicit finite element approach. Next, the draped shape was compressed using fluid mechanics equations. The proposed method was verified by building a physical part and comparing the predicted fiber orientation and warpage measurements performed on the physical parts. The developed method and tools are expected to help in expediting the development of FRT parts, which will help achieve lightweight targets in the automotive industry.
The domains of DNA and RNA nanotechnology are steadily gaining in popularity while proving their value with various successful results, including biosensing robots and drug delivery cages. Nowadays, the nanotechnology design pipeline usually relies on computer-based design (CAD) approaches to design and simulate the desired structure before the wet lab assembly. To aid with these tasks, various software tools exist and are often used in conjunction. However, their interoperability is hindered by a lack of a common file format that is fully descriptive of the many design paradigms. Therefore, in this paper, we propose a Unified Nanotechnology Format (UNF) designed specifically for the biomimetic nanotechnology field. UNF allows storage of both design and simulation data in a single file, including free-form and lattice-based DNA structures. By defining a logical and versatile format, we hope it will become a widely accepted and used file format for the nucleic acid nanotechnology community, facilitating the future work of researchers and software developers. Together with the format description and publicly available documentation, we provide a set of converters from existing file formats to simplify the transition. Finally, we present several use cases visualizing example structures stored in UNF, showcasing the various types of data UNF can handle.
Histone deacetylases (HDACs) are important cancer drug targets. Existing FDA-approved drugs target the catalytic pocket of HDACs, which is conserved across subfamilies (classes) of HDAC. However, engineering specificity is an important goal. Herein, we use molecular modeling approaches to identify and target potential novel pockets specific to Class IIA HDAC-HDAC4 at the interface between HDAC4 and the transcriptional corepressor component protein NCoR. These pockets were screened using an ensemble docking approach combined with consensus scoring to identify compounds with a different binding mechanism than the currently known HDAC modulators. Binding was compared in experimental assays between HDAC4 and HDAC3, which belong to a different family of HDACs. HDAC4 was significantly inhibited by compound 88402 but not HDAC3. Two other compounds (67436 and 134199) had IC50 values in the low micromolar range for both HDACs, which is comparable to the known inhibitor of HDAC4, SAHA (Vorinostat). However, both of these compounds were significantly weaker inhibitors of HDAC3 than SAHA and thus more selective, albeit to a limited extent. Five compounds exhibited activity on human breast carcinoma and/or urothelial carcinoma cell lines. The present result suggests potential mechanistic and chemical approaches for developing selective HDAC4 modulators.
In order for humans and electronics to fully and safely operate in the space environment, codes like HZETRN (High Charge and Energy Transport) must be included in any designer's toolbox for design evaluation with respect to radiation damage. Currently, spacecraft designers do not have easy access to accurate radiation codes like HZETRN to evaluate their design for radiation effects on humans and electronics. Today, the World Wide Web is sophisticated enough to support the entire HZETRN code and all of the associated pre and post processing tools. This package is called SIREST (Space Ionizing Radiation Effects and Shielding Tools). There are many advantages to SIREST. The most important advantage is the instant update capability of the web. Another major advantage is the modularity that the web imposes on the code. Right now, the major disadvantage of SIREST will be its modularity inside the designer's system. This mostly comes from the fact that a consistent interface between the designer and the computer system to evaluate the design is incomplete. This, however, is to be solved in the Intelligent Synthesis Environment (ISE) program currently being funded by NASA.
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Plan recognition and agent modeling techniques are potentially useful for intelligent tutoring, but are difficult to employ in practice.
This paper describes the implementation of a software reliability measurement tool, CASRE, that incorporates the methematical modeling capabilities of the public domain tool SMERFS, and is being implemented in a Microsoft Windows environment.