Library Design Research Articles

Lower the RISC: Designing optical-probing-attack-resistant cores

Recently, a new Side-Channel Analysis (SCA)-based attack, namely the Optical Probing (OP) attack, has been shown to bypass the implemented protection mechanisms on the chip, allowing unauthorized access to confidential information such as stored security keys or Intellectual Property (IP). Several countermeasures against the OP attack exist, which require changes in the chip’s fabrication process, i.e., chip fabrication using OP-resistant materials, resulting in increased fabrication costs. On the other hand, other countermeasures are implemented at the layout level. These countermeasures suffer from a significant drop in performance due to the utilization of custom logic cells. Additionally, available techniques against OP at the layout level require a layout design of the logic cell library from scratch which is a time-consuming process. In this work, we mitigate these limitations and propose a methodology to design high-performance OP-attack-resistant circuits. Using a two-folded methodology, we achieve an OP attack-resistant circuit. Firstly, we design a high-performance, and Low optical Leakage-Dual Rail Logic (LoL-DRL) cell library based on a standard CMOS logic cell library. Hence, no complete redesign of the layout is required. Secondly, we propose a streamlined synthesis technique to synthesize OP-attack-resistant circuits from the original circuit’s netlist. Thus, our method seamlessly integrates into the existing synthesis flow. On top of that, we analyzed the optical leakage information of several logic cells from both the standard logic cell library and our proposed LoL-DRL logic cell library against the OP attack. We used a metric called Optical Leakage Value (OLV) to report the robustness of a logic cell against the OP attack. Furthermore, as a case study, we applied our design methodology to an open-source RISC-V core to design the first OP-attack-resistant RISC-V core, named Lo-RISK. Our approach minimizes any adverse impact on performance yet incurs significant expenses in terms of both area and power consumption, which is acceptable for an OP-secure end product. On average, our proposed LoL-DRL logic cell library exhibits 2× less information leakage through OP compared to the standard CMOS logic cell library. Our approach to designing OP-resistant circuits result in 2× the area and a 1.36× power increase while operating at the same frequency in comparison to a circuit designed using a standard CMOS logic cell library.

Leveraging multiplexed metasurfaces for multi-task learning with all-optical diffractive processors

Abstract Diffractive Neural Networks (DNNs) leverage the power of light to enhance computational performance in machine learning, offering a pathway to high-speed, low-energy, and large-scale neural information processing. However, most existing DNN architectures are optimized for single tasks and thus lack the flexibility required for the simultaneous execution of multiple tasks within a unified artificial intelligence platform. In this work, we utilize the polarization and wavelength degrees of freedom of light to achieve optical multi-task identification using the MNIST, FMNIST, and KMNIST datasets. Employing bilayer cascaded metasurfaces, we construct dual-channel DNNs capable of simultaneously classifying two tasks, using polarization and wavelength multiplexing schemes through a meta-atom library. Numerical evaluations demonstrate performance accuracies comparable to those of individually trained single-channel, single-task DNNs. Extending this approach to three-task parallel recognition reveals an expected performance decline yet maintains satisfactory classification accuracies of greater than 80 % for all tasks. We further introduce a novel end-to-end joint optimization framework to redesign the three-task classifier, demonstrating substantial improvements over the meta-atom library design and offering the potential for future multi-channel DNN designs. Our study could pave the way for the development of ultrathin, high-speed, and high-throughput optical neural computing systems.

Two Step One-Pot Synthesis of 7-Azaindole Linked 1,2,3-Triazole Hybrids: In-Vitro and In-Silico Antimicrobial Evaluation.

Herein, we report design, synthesis and characterization of a new library of 7-azaindole N-ethyl linked 1,2,3-triazoles containing ethylene as a spacer unit, and evaluation of all the synthesized compounds for their antimicrobial properties. Antibacterial potential was checked against two Gram positive (B. subtilis and S. aureus) and two Gram negative (E. coli and P. aeruginosa) bacterial strains while antifungal potential was assayed against two fungal strains (C. albicans and A. niger). All the tested compounds showed satisfactory antibacterial potency in comparison to reference drug ciprofloxacin with MIC values ranging from 0.0108 to 0.0432 μmol/mL. Interestingly, except two, all the target compounds showed better antifungal property as compared to the reference drug fluconazole with MIC values less than 0.0408 μmol/mL. One of the compounds exhibited two-fold better antifungal potential in comparison to fluconazole. Furthermore, in-silico ADMET and DFT studies reported drug likeness behavior and chemical reactivity parameters, respectively. The cytotoxicity results on substrate azide 3 and most potent 1,2,3-triazoles (5 d and 5 l) were found to be non-toxic.

Workflow for predictive risk assessments of UVCBs: cheminformatics library design, QSAR, and read-across approaches applied to complex mixtures of metal naphthenates.

Substances of unknown or variable composition, complex reaction products, and biological materials (UVCBs) are commonly found in the environment. However, assessing their human toxicological risk is challenging due to their variable composition and many constituents. Metal naphthenate salts are one such category of UVCBs that are the reaction products of naphthenic acids with metals to form complex mixtures. Metal naphthenates are often found or used in household and industrial materials with potential for human exposure, but very few of these materials have been evaluated for causing human health hazards. Herein, we evaluate metal naphthenates using predictions derived from read-across and quantitative structure-activity/property relationship (QSAR/QSPR) models. Accordingly, we first built a computational chemistry library by enumerating the structures of naphthenic acids and derived 11,850 QSAR-acceptable structures; then, we used open and commercial in silico tools on these structures to predict a set of physicochemical properties and toxicity endpoints. We then compared the QSAR/QSPR predictions with available experimental data on naphthenic acids to provide a more complete picture of the contributions of the components to the toxicity profiles of metal naphthenate mixtures. The available systematic acute oral toxicity values (LD50) and QSAR LD50 predictions of all the naphthenic acid components indicated low concern for toxicity. The point of departure predictions for chronic repeated dose toxicity for the naphthenic acid components using QSAR models developed from studies on rats ranged from 25 to 50mg/kg/day. These values are in good agreement with findings from studies on copper and zinc naphthenates, which had no observed adverse effect levels of 30 and 118mg/kg/day, respectively. Hence, this study demonstrates how published in silico approaches can be used to identify the potential components of metal naphthenates for further testing, inform groupings of UVCBs such as naphthenates, as well as fill the data gaps using read-across and QSAR models to inform risk assessment.

Confirmation dialog box in the physical world: design strategies on tangible confirmation behavior in human-machine interaction

In tangible irreversible human-machine interactions (HMIs), erroneous inputs resulting from users’ unfamiliarity, inattention, or ingrained habits can lead to unintended consequences. Although some design measures like buttons with protective shells or safety switches before critical actions have been implemented to minimize human errors in both everyday products and industrial interfaces, a cohesive guiding strategy on when, where, and how to prevent unintended consequences is lacking. Inspired by the confirmation dialog boxes commonly utilized in graphical user interfaces, this study proposes design strategies for effective tangible confirmation behaviors in HMIs. Initially, a workshop was conducted to gather design cases and explore potential design opportunities in future scenarios. By analysing the outcomes, we propose an evaluation method for assessing the necessity and complexity requirements of confirmation design and introduce a design library considering complexity levels graded by users’ time, effort, and cognitive load expenditure. Subsequently, a user behavior experiment was conducted to validate the feasibility and efficacy of the proposed strategies. Our strategies offer valuable references for designers, highlighting the significance of confirmation design in preventing human errors. Following these strategies, designers and engineers can integrate more Kansai engineering principles into HMIs to ensure user safety, protect property, and enhance interaction efficiency.

Molecular Identification and Engineering a Salt-Tolerant GH11 Xylanase for Efficient Xylooligosaccharides Production.

This study identified a salt-tolerant GH11 xylanase, Xynst, which was isolated from a soil bacterium Bacillus sp. SC1 and can resist as high as 4 M NaCl. After rational design and high-throughput screening of site-directed mutant libraries, a double mutant W6F/Q7H with a 244% increase in catalytic activity and a 10 °C increment in optimal temperature was obtained. Both Xynst and W6F/Q7H xylanases were stimulated by high concentrations of salts. In particular, the activity of W6F/Q7H was more than eight times that of Xynst in the presence of 2 M NaCl at 65 °C. Kinetic parameters indicated they have the highest affinity for beechwood xylan (Km = 0.30 mg mL-1 for Xynst and 0.18 mg mL-1 for W6F/Q7H), and W6F/Q7H has very high catalytic efficiency (Kcat/Km = 15483.33 mL mg-1 s-1). Molecular dynamic simulation suggested that W6F/Q7H has a more compact overall structure, improved rigidity of the active pocket edge, and a flexible upper-end alpha helix. Hydrolysis of different xylans by W6F/Q7H released more xylooligosaccharides and yielded higher proportions of xylobiose and xylotriose than Xynst did. The conversion efficiencies of Xynst and W6F/Q7H on all tested xylans exceeded 20%, suggesting potential applications in the agricultural and food industries.

Comparative Study on the Interest in Non-Uniform Rational B-Splines Representation versus Polynomial Surface Description in a Freeform Three-Mirror Anastigmat

Novel freeform optical design methods can be classified in two categories, depending on whether they focus on the generation of a starting point or the development of new optimization tools. In this paper, we design a freeform three-mirror anastigmat (TMA) and compare different surface representations using either a differential ray tracer as a new optimization tool or a commercial ray tracer (ANSYS-ZEMAX OpticStudio). For differential ray tracing, we used FORMIDABLE (Freeform Optics Raytracer with Manufacturable Imaging Design cApaBiLitiEs), an optical design library with differential ray tracing and Non-Uniform Rational B-Splines (NURBS) optimization capabilities, available under the European Software Community License (ESCL). NURBS allow a freeform surface to be represented without needing any prior knowledge of the surface, such as the polynomial degree in polynomial descriptions. OpticStudio and other commercial optical design software are designed to optimize polynomial surfaces but are not well-suited to optimize NURBS surfaces, requiring a custom optical design library. In order to demonstrate the interest in using NURBS representation, we designed and independently optimized two freeform telescopes over different iteration cycles; with NURBS using FORMIDABLE or with XY polynomials using OpticStudio. We then compared the resulting systems using their root mean square field maps to assess the optimization quality of each surface representation. We also provided a full-system comparison, including mirror freeform departures. This study shows that NURBS can be a relevant alternative to XY polynomials for the freeform optimization of reflective three-mirror telescopes as it achieves more a uniform imaging quality in the field of view.

GGAssembler: Precise and economical design and synthesis of combinatorial mutation libraries.

Golden Gate assembly (GGA) can seamlessly generate full-length genes from DNA fragments. In principle, GGA could be used to design combinatorial mutation libraries for protein engineering, but creating accurate, complex, and cost-effective libraries has been challenging. We present GGAssembler, a graph-theoretical method for economical design of DNA fragments that assemble a combinatorial library that encodes any desired diversity. We used GGAssembler for one-pot in vitro assembly of camelid antibody libraries comprising >105 variants with DNA costs <0.007$ per variant and dropping significantly with increased library complexity. >93% of the desired variants were present in the assembly product and >99% were represented within the expected order of magnitude as verified by deep sequencing. The GGAssembler workflow is, therefore, an accurate approach for generating complex variant libraries that may drastically reduce costs and accelerate discovery and optimization of antibodies, enzymes and other proteins. The workflow is accessible through a Google Colab notebook at https://github.com/Fleishman-Lab/GGAssembler.

Enriching productive mutational paths accelerates enzyme evolution.

Darwinian evolution has given rise to all the enzymes that enable life on Earth. Mimicking natural selection, scientists have learned to tailor these biocatalysts through recursive cycles of mutation, selection and amplification, often relying on screening large protein libraries to productively modulate the complex interplay between protein structure, dynamics and function. Here we show that by removing destabilizing mutations at the library design stage and taking advantage of recent advances in gene synthesis, we can accelerate the evolution of a computationally designed enzyme. In only five rounds of evolution, we generated a Kemp eliminase-an enzymatic model system for proton transfer from carbon-that accelerates the proton abstraction step >108-fold over the uncatalyzed reaction. Recombining the resulting variant with a previously evolved Kemp eliminase HG3.17, which exhibits similar activity but differs by 29 substitutions, allowed us to chart the topography of the designer enzyme's fitness landscape, highlighting that a given protein scaffold can accommodate several, equally viable solutions to a specific catalytic problem.

DrugSynthMC: An Atom-Based Generation of Drug-like Molecules with Monte Carlo Search.

A growing number of deep learning (DL) methodologies have recently been developed to design novel compounds and expand the chemical space within virtual libraries. Most of these neural network approaches design molecules to specifically bind a target based on its structural information and/or knowledge of previously identified binders. Fewer attempts have been made to develop approaches for de novo design of virtual libraries, as synthesizability of generated molecules remains a challenge. In this work, we developed a new Monte Carlo Search (MCS) algorithm, DrugSynthMC (Drug Synthesis using Monte Carlo), in conjunction with DL and statistical-based priors to generate thousands of interpretable chemical structures and novel drug-like molecules per second. DrugSynthMC produces drug-like compounds using an atom-based search model that builds molecules as SMILES, character by character. Designed molecules follow Lipinski's "rule of 5″, show a high proportion of highly water-soluble nontoxic predicted-to-be synthesizable compounds, and efficiently expand the chemical space within the libraries, without reliance on training data sets, synthesizability metrics, or enforcing during SMILES generation. Our approach can function with or without an underlying neural network and is thus easily explainable and versatile. This ease in drug-like molecule generation allows for future integration of score functions aimed at different target- or job-oriented goals. Thus, DrugSynthMC is expected to enable the functional assessment of large compound libraries covering an extensive novel chemical space, overcoming the limitations of existing drug collections. The software is available at https://github.com/RoucairolMilo/DrugSynthMC.

Research on reconfigurable systems in discrete manufacturing workshops

Abstract With the development of smart manufacturing, the demand for personalized production has made reconfigurable systems in the manufacturing industry a focal point of research and application in high-end manufacturing. The paper analyzes the current status of the manufacturing industry and the challenges of reconfigurable technology. Furthermore, it designs and develops a reconfigurable system for discrete manufacturing of automotive components. The paper focuses on issues such as creating reconfigurable models, designing adaptive reconfiguration algorithms, accomplishing the creation of multi-layered models, the design of rule libraries, and algorithm implementation. The reconfiguration result scheme can be input into commercial software for performance simulation, and can directly integrate with production systems to complete production planning. This demonstrates the rationality of the reconfigurable models in the system, the correctness of the reconfiguration algorithms, and the effectiveness of the system, providing a theoretical basis for reconfigurable production lines.

Analysis of Library Building Design from the Concept of Green Building

Library is a symbol of urbanization in the present era. The external shape of the library is more elegant and solemn, and the interior also pays more attention to the convenience and comfort. In the design, we need to pay attention to the enjoyment of architectural art beauty, and at the same time, we need to make the layout of the library more scientific and applicable, and meet the needs of the public reading. As a public building with huge flow of people, the library needs to consume a lot of energy when it is open, which is not in line with the characteristics of green building advocated at present. Therefore, in order to implement the concept of environmental protection, it is necessary to integrate the design of library building with the concept of green building. At the same time, the library is also a cultural building, and it needs to reflect the scientific concept of humanistic care and people- oriented. Therefore, this paper mainly analyzes and discusses the library design under the concept of green building.

Evolving Landscape of Smart Libraries: A Diachronic Analysis of Themes and Trends

ABSTRACT This study uses Natural Language Processing (NLP) to analyze 110 articles from the Scopus database (2018–2023). Employing TextBlob for preprocessing and sentiment analysis, and Latent Dirichlet Allocation (LDA) for topic modeling, the research explores evolving themes in smart library abstracts. Key findings highlight a shift toward user-centricity, technological integration, and emerging concepts like Internet of Things (IoT) and Artificial Intelligence (AI). Sentiment analysis shows a generally positive outlook for this evolution. The study provides valuable insights for researchers and practitioners, guiding future innovation and service design in smart libraries.

Screening, Growing, and Validation by Catalog: Using Synthetic Intermediates from Natural Product Libraries to Discover Fragments for an Aspartic Protease Through Crystallography

Fragment screening directly on protein crystals has been applied using AnalytiCon’s collection of intermediates that have been utilized to generate libraries of larger synthetic natural product-like molecules. The fragments with well-balanced physicochemical properties show an impressively high hit rate for a screen using the aspartic protease endothiapepsin. The subsequent validation and expansion of the discovered fragment hits benefits from AnalytiCon’s comprehensive library design. Since the screened fragments are intermediates that share a common core with larger and closely related analogs with modulated substitution patterns, they allow for the retrieval of off-the-shelf follow-up compounds, which enable the development of design strategies for fragment optimization. A promising bicyclic core scaffold found in several fragment hits could be validated by selecting a set of enlarged follow-up compounds. Due to unexpected changes in binding mode and no significant improvement in ligand efficiency, this series was quickly deemed unsuitable and therefore discontinued. The structures of follow-up compounds of two other fragments helped to evaluate a putative fusion of two overlapping fragment hits. A design concept on how to fuse the two fragments could be proposed and helps to plan a suitable substitution pattern and promising central bridging element.

Phage Immunoprecipitation and Sequencing—a Versatile Technique for Mapping the Antibody Reactome

Characterizing the antibody reactome for circulating antibodies provide insight into pathogen exposure, allergies, and autoimmune diseases. This is important for biomarker discovery, clinical diagnosis, and prognosis of disease progression, as well as population-level insights into the immune system. The emerging technology phage display immunoprecipitation and sequencing (PhIP-seq) is a high-throughput method for identifying antigens/epitopes of the antibody reactome. In PhIP-seq, libraries with sequences of defined lengths and overlapping segments are bioinformatically designed using naturally occurring proteins and cloned into phage genomes to be displayed on the surface. These libraries are used in immunoprecipitation experiments of circulating antibodies. This can be done with parallel samples from multiple sources, and the DNA inserts from the bound phages are barcoded and subjected to next-generation sequencing for hit determination. PhIP-seq is a powerful technique for characterizing the antibody reactome that has undergone rapid advances in recent years. In this review, we comprehensively describe the history of PhIP-seq and discuss recent advances in library design and applications.

Library design, amenities, and services for enhancing the reading experience

This study explored user perspectives on library design elements, amenities, and services that can potentially enhance the reading experience. Data were collected through a self-administered questionnaire with 537 students. The majority of respondents (86.8%) reported enjoying reading in library environments. Regarding design aspects, having an integrated layout with shelves, chairs and tables cohesively arranged in the reading room was most appealing. Users also favored the presence of other readers and collaborative table designs. Group-study rooms were viewed positively, with 51.2% strongly liking the option to use them and perceiving benefits for learning. While opinions were divided on introducing food services, most supported adding new technologies like 3D printers, art galleries, and museum exhibits. Chi-square analysis revealed significant associations between enjoying library reading and preferences for shared reading spaces with other users (p = 0.024) and group-study rooms. In conclusion, fostering a sense of community, scholarly exchange, and integrated space conducive to focused reading emerged as key factors for enhancing the reading experience in libraries.

Solid-phase synthesis of amphiphilic cyclic peptides as a surfactin mimetic

The cyclic lipopeptide surfactin, an anionic biosurfactant, has received increased interest owing to its unique characteristics and promising applications. Although surfactin is known to exhibit distinctive surface activities and self-assembly ability, the role of the cyclic peptide moiety in its physico-chemical properties requires further investigation. Here, we report the solid-phase peptide synthesis of surfactin mimics such as cyclic heptapeptides with the same amino acid sequence as surfactin (CSF) and cyclic octapeptides with dodecyl chains (CSF12). The structure–property relationships of the cyclic peptides were then examined. The acidic forms of CSF and CSF12 were poorly soluble in water, but the addition of 1.5 or 2.0 equiv. of NaOH increased their solubility in aqueous solutions. The disodium salt of CSF (CSF-2Na) reduced the surface tension of water to 39.0 mN/m despite its absence of a long alkyl chain, indicating that the cyclic peptide moiety plays a crucial role in the surface activity of the surfactin mimic. In the case of the disodium salt of CSF12 (CSF12–2Na), the introduction of a dodecyl chain to the cyclic peptide moiety led to an increase in molecular orientation such that saturation at the air–water interface was observed. Thus, a long alkyl chain enhances the molecular assembly of the cyclic peptide at the air–water interface. The introduction of a long alkyl chain also enhanced the solubilisation of phospholipids. For instance, CSF12–1.5Na induced the solubilisation of L-α-dimyristoylphosphatidylcholine (DMPC) multilamellar vesicles in phosphate-buffered saline (PBS). These findings greatly contribute to the fundamental understanding of the broad spectrum of physico-chemical properties and biological activities of bio-derived cyclic peptides. Furthermore, these insights can also be helping design libraries for developing cyclic peptides for challenging therapeutics in skin disease or as cosmeceuticals to enhance skin appearance.

Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering

The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly those that are poorly characterized or unknown in biology. We introduce MODIFY, a machine learning (ML) algorithm that learns from natural protein sequences to infer evolutionarily plausible mutations and predict enzyme fitness. MODIFY co-optimizes predicted fitness and sequence diversity of starting libraries, prioritizing high-fitness variants while ensuring broad sequence coverage. In silico evaluation shows that MODIFY outperforms state-of-the-art unsupervised methods in zero-shot fitness prediction and enables ML-guided directed evolution with enhanced efficiency. Using MODIFY, we engineer generalist biocatalysts derived from a thermostable cytochrome c to achieve enantioselective C-B and C-Si bond formation via a new-to-nature carbene transfer mechanism, leading to biocatalysts six mutations away from previously developed enzymes while exhibiting superior or comparable activities. These results demonstrate MODIFY’s potential in solving challenging enzyme engineering problems beyond the reach of classic directed evolution.

AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery

Ionizable lipid nanoparticles (LNPs) are seeing widespread use in mRNA delivery, notably in SARS-CoV-2 mRNA vaccines. However, the expansion of mRNA therapies beyond COVID-19 is impeded by the absence of LNPs tailored for diverse cell types. In this study, we present the AI-Guided Ionizable Lipid Engineering (AGILE) platform, a synergistic combination of deep learning and combinatorial chemistry. AGILE streamlines ionizable lipid development with efficient library design, in silico lipid screening via deep neural networks, and adaptability to diverse cell lines. Using AGILE, we rapidly design, synthesize, and evaluate ionizable lipids for mRNA delivery, selecting from a vast library. Intriguingly, AGILE reveals cell-specific preferences for ionizable lipids, indicating tailoring for optimal delivery to varying cell types. These highlight AGILE’s potential in expediting the development of customized LNPs, addressing the complex needs of mRNA delivery in clinical practice, thereby broadening the scope and efficacy of mRNA therapies.

RAbDesFlow: a novel workflow for computational recombinant antibody design for healthcare engineering.

Recombinant antibodies (rAbs) have emerged as a promising solution to tackle antigen specificity, enhancement of immunogenic potential and versatile functionalization to treat human diseases. The development of single chain variable fragments has helped accelerate treatment in cancers and viral infections, due to their favorable pharmacokinetics and human compatibility. However, designing rAbs is traditionally viewed as a genetic engineering problem, with phage display and cell free systems playing a major role in sequence selection for gene synthesis. The process of antibody engineering involves complex and time-consuming laboratory techniques, which demand substantial resources and expertise. The success rate of obtaining desired antibody candidates through experimental approaches can be modest, necessitating iterative cycles of selection and optimization. With ongoing advancements in technology, in silico design of diverse antibody libraries, screening and identification of potential candidates for in vitro validation can be accelerated. To meet this need, we have developed rAbDesFlow, a unified computational workflow for recombinant antibody engineering with open-source programs and tools for ease of implementation. The workflow encompasses five computational modules to perform antigen selection, antibody library generation, antigen and antibody structure modeling, antigen-antibody interaction modeling, structure analysis, and consensus ranking of potential antibody sequences for synthesis and experimental validation. The proposed workflow has been demonstrated through design of rAbs for the ovarian cancer antigen Mucin-16 (CA-125). This approach can serve as a blueprint for designing similar engineered molecules targeting other biomarkers, allowing for a simplified adaptation to different cancer types or disease-specific antigens.