Application Engineers Research Articles

A Novel Hyperactive Variant of the Sleeping Beauty Transposase Facilitates Non-Viral Genome Engineering

The Sleeping Beauty (SB) transposon system is a useful tool for genetic applications, including gene therapy. We discovered a hyperactive variant of the SB100X transposase, called SB200X. This mutant, resulting from a specific amino acid replacement (Q124C), showed a ∼2-fold increase in transposition activity in various human and murine cells. Other amino acid replacements in position 124 also led to a hyperactive phenotype. Position 124 is located at the very edge of the linker region that connects the DNA-binding and catalytic domains of the transposase. Consistent with a role of the linker in an autoregulatory mechanism called overproduction inhibition (OPI) in the monophyletic group of mariner transposases, we show that the hyperactivity of Q124C manifests at high concentrations of the transposase, suggesting a partial resistance of SB200X to OPI. We demonstrate that the hyperactive phenotype of Q124C can be combined with features of other useful mutations in the SB transposase. Namely, Q124C improves the transposition efficiency of the previously described K248R variant, while maintaining or even slightly improving its safer genome-wide integration profile. SB200X transposase could enhance the utility of SB transposon-mediated genome engineering in preclinical and clinical applications.

Reverse engineering in medical application: literature review, proof of concept and future perspectives

Reverse engineering, a process of extracting information or knowledge from existing objects or systems, has gained significant attention in various fields, including medicine. This article presents a comprehensive literature review and a proof of concept on the application of reverse engineering in the medical field. The review particularly focuses on the reverse engineering process, available technologies, and their specific relevance to the medical domain. Various imaging techniques, such as computed tomography and magnetic resonance imaging, are discussed in respect of their integration with reverse engineering methodologies. Furthermore, the article explores the wide range of medical applications facilitated by reverse engineering, including prosthetics, implants, tissue engineering, and surgical planning. The potential of reverse engineering to enhance personalized medicine and patient-specific treatments is highlighted. A detailed proof of concept focusing on femur reconstruction is a significant component of the article. The proof of concept showcases the practical implementation of reverse engineering techniques to assist in designing and manufacturing precise custom-made implants and bone reconstruction. It emphasizes the integration of patient-specific anatomical data obtained through imaging technologies and the subsequent utilization of reverse engineering processes for anatomical reconstruction (solid modeling). Overall, this article provides an extensive overview of reverse engineering in medical applications, incorporating a literature review and a case study. The findings highlight reverse engineering’s potential to advance medical practices, improve patient outcomes, and foster personalized treatments. The review emphasizes the reverse engineering process, available technologies, and their specific relevance to the medical field, as well as their potential and effectiveness in advancing medical practices.

Optimizing a CRISPR-Cas13d Gene Circuit for Tunable Target RNA Downregulation with Minimal Collateral RNA Cutting.

The invention of RNA-guided DNA cutting systems has revolutionized biotechnology. More recently, RNA-guided RNA cutting by Cas13d entered the scene as a highly promising alternative to RNA interference to engineer cellular transcriptomes for biotechnological and therapeutic purposes. Unfortunately, "collateral damage" by indiscriminate off-target cutting tampered enthusiasm for these systems. Yet, how collateral activity, or even RNA target reduction depends on Cas13d and guide RNA abundance has remained unclear due to the lack of expression-tuning studies to address this question. Here we use precise expression-tuning gene circuits to show that both nonspecific and specific, on-target RNA reduction depend on Cas13d and guide RNA levels, and that nonspecific RNA cutting from trans cleavage might contribute to on-target RNA reduction. Using RNA-level control techniques, we develop new Multi-Level Optimized Negative-Autoregulated Cas13d and crRNA Hybrid (MONARCH) gene circuits that achieve a high dynamic range with low basal on-target RNA reduction while minimizing collateral activity in human kidney cells and green monkey cells most frequently used in human virology. MONARCH should bring RNA-guided RNA cutting systems to the forefront, as easily applicable, programmable tools for transcriptome engineering in biotechnological and medical applications.

Total capacity and LOS estimation of innovative and conventional roundabouts through macroscopic fundamental diagrams

Traffic congestion remains a significant problem, especially in urban areas. To avoid congestion phenomena, several measures can be adopted, including the construction of roundabouts and the control of the measures of effectiveness (MOEs) at intersections. This article compares different types of roundabouts by estimating the deterministic fundamental diagram (MFD) and using microscopic traffic simulations. The deduction of MFD for different roundabout types (single and double-lane, flower and turbo-roundabout) is based on reasonably simplified traffic and boundary conditions. MFDs are estimated through several OD traffic matrices, considering as outputs the values of the macroscopic traffic variable deduced in time intervals of 5 min and 1 h. The findings from this research, in terms of total capacity, travel times, and level of service (LOS) estimation, may be useful for traffic and highway engineers in many realistic applications to partially face congestion phenomena in urban areas. MFDs allow us to determine the best roundabout type based on the traffic demand and represent a parsimonious method for monitoring and controlling traditional and unconventional or smart roundabouts, even in the presence of connected and automated vehicles (CAVs).

ShuffleAnalyzer: A Comprehensive Tool to Visualize DNA Shuffling.

DNA shuffling is a powerful technique for generating synthetic DNA via recombination of homologous parental sequences. Resulting chimeras are often incorporated into complex libraries for functionality screenings that identify novel variants with improved characteristics. To survey shuffling efficiency, subsequences of chimeras can be computationally assigned to their corresponding parental counterpart, yielding insight into frequency of recombination events, diversity of shuffling libraries and actual composition of final variants. Whereas tools for parental assignment exist, they do not provide direct visualization of the results, making the analysis time-consuming and cumbersome. Here we present ShuffleAnalyzer, a comprehensive, user-friendly, Python-based analysis tool that directly generates graphical outputs of parental assignments and is freely available under a BSD-3 license (https://github.com/joerg-swg/ShuffleAnalyzer/releases). Besides DNA shuffling, peptide insertions can be simultaneously analyzed and visualized, which makes ShuffleAnalyzer a highly valuable tool for integrated approaches often used in synthetic biology, such as AAV capsid engineering in gene therapy applications.

The Effects of Liquid Density and Impeller Size with Volute Clearance on the Performance of Radial Blade Centrifugal Pumps: An Experimental Approach

This study was carried out to experimentally investigate the effect of impeller size with volute clearance and liquid density on the performance of radial blade centrifugal pumps. Three impeller sizes of 121.54mm, 109.38mm and 97.23mm with respective volute clearance of 6mm, 12mm and 18mm were considered, and five different liquid blend which comprise of water, two liquids that are denser than water and two other liquids that are less-dense than water (with respective liquid densities of: 1197 kg/m3 , 1097 kg/m3 , 1000kg/m3 , 898.2 kg/m3 and 798.4 kg/m3 ). In the experimental set-up, the pump flow capacity were varied from 12m3 /hr to 62m3 /hr at interval of 5m3 /hr and the corresponding pump power consumption, attained pump head were recorded and pump efficiency were determined. Microsoft excel was used to evaluate the trend, performance trend was used to develop the pump performance model showing the relationship between the various parameters. The results from the investigation revealed that with the various impeller sizes of 121.54mm, 109.38mm and 97.23mm of respective volute clearance of 6mm, 12mm and 18mm, the attained optimum efficiency were 74.42%, 54.78 and 33.54% respectively at a correspondence optimum pump head of 23.66m, 20.60m and 18.87m. The results also showed that there is a direct relationship between pump power consumption and process liquid density, while showing an inverse linear relationship between the pump instantaneous start up power and impeller diameter. It was therefore concluded among others that liquid with higher density will usually require higher power to initiate and maintain flow at constant flow rate and impeller size. It is then recommended among others that pump designers, application engineers and users of centrifugal pumps should consider possible increase in pump power consumption when working with a process application that has higher tendency for dynamic increase on the process fluid density.

Wide-Temperature Characteristics of Additively PBF-LB/M Processed Material Ti6Al4V

Titanium-based alloys are a widely applicable engineering material with high strength, low weight, non-magnetic, and corrosion resistance. At the same time, resistance to low temperatures is declared, which offers the material’s applicability for, e.g., aircraft or ship technology. Additive technologies are part of the industrial spectrum of material processing, especially the Laser Powder Bed Fusion of Metals method for metal alloys, which creates a layered structure of the resulting body. The topology of the internal structure, in relation to the temperature history of the functional environment, influences thermal expansion and the associated functional characteristics. Knowledge of the thermal expansion of printed strength and non-strength functional components and accessories is essential for future applications, especially in environments with high repeatable temperature changes, such as the aerospace industry. This paper presents the results of testing the expansion, mechanical, microstructural, and mineralogical characteristics of Ti6Al4V over the temperature range of −70 to 60 °C using a combination of instrumental techniques such as X-ray diffraction and nanoindentation. It was found that the topological orientations of the printed samples directly influenced the tested properties, e.g., the coefficient of thermal expansion in the direction perpendicular to the printed layers showed approximately 12% lower value compared to the other directions. Due to the progression of the application of the manufacturing method and its applicability within selected industries, the research provides results in a new area, which is supported by the relevant research.

Experimental Investigation of Stress Concentration and Fatigue Behavior in 9% Ni Steel Welded Joints under Cryogenic Conditions

This experimental study delves into the intricate mechanics of stress concentration and fatigue behavior exhibited by 9% Ni steel welded joints under cryogenic conditions. The study specifically examines butt-welded, fillet longitudinal, and fillet transverse specimens, comparing their fatigue properties under room and cryogenic temperatures. Notably, determining hot-spot stress presents a challenge, as it cannot be directly obtained through traditional means. To overcome this limitation, a method for predicting hot-spot stress is introduced, which considers the effects of misalignments and weld bead characteristics. The study also highlights the impact of grip-clamping-induced specimen deformation and the reduced middle section on stress concentration resulting from misalignments. Furthermore, it proposes separate consideration of the effects of the weld bead on the axial nominal stress and on the bending stress of the specimen. The accuracy of strain gauge measurements in cryogenic environments is addressed by suggesting a method to correct the output of 2-wire strain gauges using a fixed ratio derived from 2-wire and 3-wire strain gauges. By comparing predicted hot-spot stress with actual measurements, the study validates the reliability of the proposed predictive method. These findings contribute to a deeper understanding of the behavior of 9% Ni steel welded joints under cryogenic conditions and provide valuable insights for design and engineering in similar applications.

Boosting plant oil yields: the role of genetic engineering in industrial applications

Boosting plant oil yields: the role of genetic engineering in industrial applications

Three-dimensional analysis for optimizing thermo-hydrodynamic performance of heat exchangers with perforated semi-circular inserts

This study focuses on the current energy crisis, highlighting the challenges and opportunities it presents, particularly in the electricity, coal, and natural gas sectors. In this context, a compact and efficient heat exchanger has been developed to recover waste heat from power plants and industries, thus promoting energy savings. The aim of this study is to enhance the thermal performance of this heat exchanger by combining two methods. By analyzing various parameters such as the Nusselt number and thermal performance factor across a range of Reynolds numbers and using different fluids, the study reveals the positive impact of semi-circular inserts on thermal performance. Results show a significant improvement in thermal performance factor with the addition of inserts, particularly in case (4) where it reaches 1.86 at a Reynolds number of 140,000. Additionally, the study compares five perforated models, demonstrating that model (2) stands out as the most thermally efficient, with a maximum value of 1.8787 at a Reynolds number of 140,000. Perforations in the inserts promote better fluid flow uniformity and reduce pressure losses. In conclusion, the optimal configuration, specifically model (2) with perforations, exhibits superior thermal and hydrodynamic performance, providing valuable insights for thermal engineering and heat exchanger optimization in various industrial and technological applications. The findings of this study offer tangible prospects for thermal engineering, providing important guidelines for heat exchanger design and optimization, with significant implications for a wide range of industrial and technological applications, and offering a solid foundation for future research in the field of heat transfer and fluid thermodynamics.

One-parametric class of smoothing functions for (WSOCCP) and its Jacobian consistency

In this paper, we are interested in the study of weighted second-order cone complementarity problem (WSOCCP) and the smoothing function associated with it. A weighted second-order cone complementarity problem is to find a pair of vectors ( x , y ) that belong to the intersection of a manifold and a second-order cone such that the product of the vectors in a certain algebra, x ∘ y equals a given weight vector w. It is obvious to see that if w is zero, we get a second-order cone complementarity problem (SOCCP). Thus, (WSOCCP) provides the essential framework for a broader class of problems arising from real applications (science and engineering) more conveniently. This paper demonstrates the Jacobian consistency of the one-parametric class of smoothing functions. Furthermore, we provide an estimation of the distance between the subgradient of the one-parametric class of the weighted SOC complementarity functions and the gradient of its associated smoothing function. This estimation facilitates to adjust an appropriate parameter in smoothing methods.

Ruthenium–Nickel Nanoparticles with Unconventional Face‐Centered Cubic Crystal Phase for Highly Active Electrocatalytic Hydrogen Evolution

AbstractRu‐based nanomaterials are promising alternatives to Pt electrocatalysts for green hydrogen energy generation, and crystal phase engineering holds the key to unlocking their catalytic potential to new heights. However, controllable phase regulation on Ru‐alloys remains a formidable challenge, and unraveling the crystal phase‐related electrocatalytic performance is of great significance. Herein, RuNi nanoparticles are successfully synthesized with an unconventional face‐centered cubic (fcc) phase via a facile route. Comprehensive transmission electron microscopy confirmed the structural characteristics of the fcc‐RuNi and electrochemical analyses indicate the impressive electrocatalytic hydrogen evolution reaction of fcc‐RuNi. Notably, the anomalous fcc‐RuNi nanoparticles possess an extraordinarily low overpotential of 16 mV in hydrogen evolution. Density functional theory calculations indicate that these nanoparticles are energetically conducive to intermediate desorption and H2O dissociation. This study enhances comprehension of phase regulation in Ru‐based nanostructures and will propel phase engineering in diverse catalytic applications.

Building Trustworthy Generative Artificial Intelligence for Diabetes Care and Limb Preservation: A Medical Knowledge Extraction Case.

Large language models (LLMs) offer significant potential in medical information extraction but carry risks of generating incorrect information. This study aims to develop and validate a retriever-augmented generation (RAG) model that provides accurate medical knowledge about diabetes and diabetic foot care to laypersons with an eighth-grade literacy level. Improving health literacy through patient education is paramount to addressing the problem of limb loss in the diabetic population. In addition to affecting patient well-being through improved outcomes, improved physician well-being is an important outcome of a self-management model for patient health education. We used an RAG architecture and built a question-and-answer artificial intelligence (AI) model to extract knowledge in response to questions pertaining to diabetes and diabetic foot care. We utilized GPT-4 by OpenAI, with Pinecone as a vector database. The NIH National Standards for Diabetes Self-Management Education served as the basis for our knowledge base. The model's outputs were validated through expert review against established guidelines and literature. Fifty-eight keywords were used to select 295 articles and the model was tested against 175 questions across topics. The study demonstrated that with appropriate content volume and few-shot learning prompts, the RAG model achieved 98% accuracy, confirming its capability to offer user-friendly and comprehensible medical information. The RAG model represents a promising tool for delivering reliable medical knowledge to the public which can be used for self-education and self-management for diabetes, highlighting the importance of content validation and innovative prompt engineering in AI applications.

Hyperbolic Bloch points in ferrimagnetic exchange spring

Bloch points in magnetic materials are attractive entities in view of magnetic information transport. Here, Bloch point configuration has been investigated and experimentally determined in a magnetic trilayer (Gd12Co88/Nd17Co83/Gd24Co76) with carefully adjusted composition within the ferrimagnetic GdxCo1-x alloys in order to engineer saturation magnetization, exchange length, and interlayer couplings (ferromagnetic vs antiferromagnetic). X-ray vector magnetic tomography has allowed us to determine experimentally Bloch point polarity (related to topological charge) and Bloch point helicity γ (determined by magnetostatic energy). At the bottom layer (close to the ferromagnetic interface), Bloch points adopt a standard circulating configuration with helicity γ close to π/2. Within the top layer (with much lower saturation magnetization), Bloch points nucleate within a Neel-like exchange spring domain wall created by the antiferromagnetic coupling and adopt an uncommon hyperbolic configuration, characterized by much larger helicity angles. Our results indicate a path for Bloch point engineering in future applications adjusting material parameters and domain wall characteristics.

Matrix‐phase material selection for shape memory polymer composites: A comparative analysis of multi‐criteria decision‐making

AbstractIn the present study, the selection of suitable shape memory polymers (SMPs) to be used as the matrix‐phase material with various (In)organic filler materials to achieve the required optimum multi‐stimuli response in shape memory polymer composites (SMPC) systems is analyzed. The selection of these materials is based on their mechanical and physical properties as well as other underlying factors such as cost, availability, shape recovery rate, and aesthetic characteristics. In this study, the entropy method was applied to estimate the weightages of the various criteria while the gray relation analysis (GRA) and the technique for order preference by similarity to ideal solution (TOPSIS). Multi‐criteria decision‐making (MCDM) approaches have been used to rank the suitable matrix‐phase polymer materials for manufacturing shape memory polymer composites (SMPC) system. A total of eight alternative SMP matrix‐phase materials based on a set of nine criteria were analyzed and ranked. The proposed methodology and the result obtained thereof have been illustrated in detail. The results obtained from TOPSIS and GRA methods have been compared to conclude the effects of the material properties on the ranking and the selection of the SMP materials. Among all the eight alternatives considered, thermoplastic polyurethane (TPU) was found to be the best material in both the MCDM methods. The material cost, resistivity, % elongation, and hydrophobicity present the most influencing properties on the SMP material selection, whereas density presents no effect on the SMP matrix material selection. The robustness of the results for the comparative analysis was verified using TOPSIS methodology to validate its reliability. It was revealed that the TPU, polycarbonate, polypropylene, and epoxy‐resin/poly(lactic acid) are the most dominant matrix‐phase SMP material alternatives when a deviation in the entropy weights of the primary evaluation criteria is applied. The novelty of this study is the exploration and application of statistical MCDM methods of engineering material selection problems based on a set of decision criteria, which can be time‐consuming and costly while using experimental analysis methods.Highlights SMPCs are applicable engineering materials thus making their research viable. SMPC systems can undergo various shape changes under external stimuli. Selection of matrix‐phase polymer is critical in achieving desired objectives. GRA and TOPSIS MCDM approaches have been applied in the selection process. TPU was found as the best material in both methods.

Feature Engineering Versus Deep Learning for Scene Recognition: A Brief Survey

Scene recognition is an important computer vision task that has evolved from the study of the biological visual system. Its applications range from video surveillance, autopilot systems, to robotics. The early works were based on feature engineering that involved the computation and aggregation of global and local image descriptors. Several popular image features such as SIFT, SURF, HOG, ORB, LBP, KAZE, etc. have been proposed and applied to the task with successful results. Features can be either computed from the entire image on a global scale, or extracted from local sub-regions and aggregated across the image. Suitable classifier models are deployed that learn to classify these features. This review paper analyzes several of these handcrafted features that have been applied to the scene recognition task over the past decades, and tracks the transition from the traditional feature engineering to deep learning which forms the current state of the art in computer vision. Deep learning is now deemed to have overtaken feature engineering in several computer vision applications. Deep convolutional neural networks and vision transformers are the current state of the art for object recognition. However, scenes from urban landscapes are bound to contain similar objects posing a challenge to deep learning solutions for scene recognition. In our study, a critical analysis of feature engineering and deep learning methodologies for scene recognition is provided, and results on benchmark scene datasets are presented, concluding with a discussion on challenges and possible solutions that may facilitate more accurate scene recognition.

Application of thermotropic ferroelectric liquid crystals in electrical vehicle

This intermediate phase between solid and liquid enables liquid crystal engineers for various interesting yet fascinating thermo-optical applications. One such application of exploited for electric vehicle safety is optical shutter realized using thermotropic liquid crystals. Optical shutters are essential components in various optical systems, in providing control over the transmission of visible light. These shutters are categorized into various types of mechanical shutters, electro-optical shutters, liquid crystal optical shutters (LCOS) etc. liquid crystal cells and polarizing elements to quickly change the transmittance of light, making them ultimate for applications such as imaging and precision illumination. In this work, we explored the experimental techniques, key specifications, features, and applications of an optical shutter developed using liquid crystalline material LCOS.

Effects of blending poly (polyol sebacate) and poly (polyol adipate) bioelastomers for soft tissue engineering in biomedical applications

AbstractIn this article, the synthesis of four novel bioelastomers known as poly (xylitol sebacate) (PXS), poly (xylitol adipate) (PXA), poly (sorbitol sebacate) (PSS), and poly (sorbitol adipate) (PSA) was carried out through bulk polymerization, followed by their blending using the solution method. Six blends were prepared namely PXS25/PSS75, PXS50/PSS50, PXS75/PSS25, PXS50/PXA50, PSA50/PXA50, and PSS50/PSA50. After blending, a thermal curing process was applied to the samples. The intrinsic viscosity, density, Tg, hydrophilicity, degradation, wound healing, and mechanical properties of polymers and blends were measured to investigate the effects of blending. Analyzing hardness, modulus, stress at breakpoint, degradation, and hydrophilicity data showed that the mechanical properties of bioelastomers are adjustable by blending for tissue engineering. Furthermore, the scratch assay proved the biocompatibility of pure polymers and indicated that blending does not have a negative impact on this matter.

The advent of ChatGPT: Job Made Easy or Job Loss to Data Analysts

Artificial Intelligence (AI) has proven valuable in almost every field of endeavour, including education, sciences, engineering, technology, medical sciences, and numerous other areas of application. Despite its widespread usefulness, concerns have arisen about AI potentially displacing jobs due to its highly advanced capabilities, commonly called the "god-man effect." One remarkable AI product is ChatGPT, a Chatbot developed by an open AI company in the USA that is capable of engaging in conversations that resemble human interactions. This study explores the strengths and limitations of ChatGPT for data analysis, with the primary objective of assessing whether ChatGPT poses a threat to the job of data analysts. An econometric dataset with a sample size of thirty (30), which consists of one dependent variable and three independent variables, was simulated. The dataset was intentionally generated with issues like multicollinearity, outliers, and heteroscedasticity. Subsequently, multiple tests were conducted on the datasets to confirm the presence of these problems. The ChatGPT 3.5 and 4.0 versions were then used to analyse the data to examine this chatbot's prowess in performing data analysis. ChatGPT 3.5 and 4.0 accurately predicted the suitable statistical tool for analyzing the simulated datasets. Both versions of ChatGPT emphasized that the expertise of a professional data analyst would be necessary. While they could offer guidance on data analysis, they cannot perform the analysis themselves as they are solely AI models. ChatGPT can help with what to do next when a data analyst gets stuck. However, they should not be recognized as an authority in making statistical decisions. Therefore, ChatGPT may not replace data analysts but could make their job easier by serving as a helpful resource to turn to when they encounter challenges.

Unlocking the Therapeutic Potential of Marine Collagen: A Scientific Exploration for Delaying Skin Aging.

Aging is closely associated with collagen degradation, impacting the structure and strength of the muscles, joints, bones, and skin. The continuous aging of the skin is a natural process that is influenced by extrinsic factors such as UV exposure, dietary patterns, smoking habits, and cosmetic supplements. Supplements that contain collagen can act as remedies that help restore vitality and youth to the skin, helping combat aging. Notably, collagen supplements enriched with essential amino acids such as proline and glycine, along with marine fish collagen, have become popular for their safety and effectiveness in mitigating the aging process. To compile the relevant literature on the anti-aging applications of marine collagen, a search and analysis of peer-reviewed papers was conducted using PubMed, Cochrane Library, Web of Science, and Embase, covering publications from 1991 to 2024. From in vitro to in vivo experiments, the reviewed studies elucidate the anti-aging benefits of marine collagen, emphasizing its role in combating skin aging by minimizing oxidative stress, photodamage, and the appearance of wrinkles. Various bioactive marine peptides exhibit diverse anti-aging properties, including free radical scavenging, apoptosis inhibition, lifespan extension in various organisms, and protective effects in aging humans. Furthermore, the topical application of hyaluronic acid is discussed as a mechanism to increase collagen production and skin moisture, contributing to the anti-aging effects of collagen supplementation. The integration of bio-tissue engineering in marine collagen applications is also explored, highlighting its proven utility in skin healing and bone regeneration applications. However, limitations to the scope of its application exist. Thus, by delving into these nuanced considerations, this review contributes to a comprehensive understanding of the potential and challenges associated with marine collagen in the realm of anti-aging applications.