Transformants Research Articles

Differential requirement for RecFOR pathway components in Thermus thermophilus.

Recombinational repair is an important mechanism that allows DNA replication to overcome damaged templates, so the DNA is duplicated timely and correctly. The RecFOR pathway is one of the common ways to load RecA, while the RuvABC complex operates in the resolution of DNA intermediates. We have generated deletions of recO, recR and ruvB genes in Thermus thermophilus, while a recF null mutant could not be obtained. The recO deletion was in all cases accompanied by spontaneous loss of function mutations in addA or addB genes, which encode a helicase-exonuclease also key for recombination. The mutants were moderately affected in viability and chromosome segregation. When we generated these mutations in a Δppol/addAB strain, we observed that the transformation efficiency was maintained at the typical level of Δppol/addAB, which is 100-fold higher than that of the wild type. Most mutants showed increased filamentation phenotypes, especially ruvB, which also had DNA repair defects. These results suggest that in T. thermophilus (i) the components of the RecFOR pathway have differential roles, (ii) there is an epistatic relationship of the AddAB complex over the RecFOR pathway and (iii) that neither of the two pathways or their combination is strictly required for viability although they are necessary for normal DNA repair and chromosome segregation.

Evaluation of the Electrical Power Transformer Fins Design Technology: Numerical Analysis and Experimental Validation

The study includes numerical analysis and experimental verification on an electrical power distribution transformer (250 kVA, 11 kW, Oil Natural Air Natural). ANSYS Fluent R3 2019 software was used to develop the numerical simulation model. The validity of the numerical model was confirmed by comparing the results of the numerical model and experimental data. The study aims to improve the efficiency and performance of electrical power distribution transformers by proposing a design that reduces the temperature of the transformer while maintaining its traditional size. Numerically, the effect of fin geometry on the temperature and density of transformer oil was studied. Four different fin designs were proposed and compared with the traditional design. According to the results, all proposed designs contributed to improving the cooling performance of the transformer compared to the traditional design. Design A is similar to the traditional transformer design, with the only modification being manipulation of the fin length, and achieves an average oil temperature reduction of 4 K. Design B showed the smallest temperature drop of the four designs, with a 3 K drop. Designs C and D include ventilation channels that match the shape of the fin, providing distinct design differences. The difference between both designs relied on the fact that for design C, the orthogonally of fin plates was retained. On the other hand, in design D, skewing of fin plates was introduced. Design D proved to be the most effective in reducing the average oil temperature, being reduced by 10 K. On the other hand, Design C reduced the average oil temperature by 7 K.

Influences of synoptic circulations on regional transport, local accumulation and chemical transformation for PM2.5 heavy pollution over Twain-Hu Basin, central China

The Twain-Hu Basin (THB), located in Central China, serves as a key juncture where the northerly “polluted” airflows of the East Asian winter monsoon meet the southerly warm and humid airflows. Using the T-PCA (T- Principal Component Analysis) objective synoptic pattern classification, FLEXPART-WRF (Flexible Particle Dispersion Model), and Random Forest model, we investigate the influences of synoptic circulations on regional transport, local accumulation, and chemical transformation of PM2.5 during heavy air pollution over the THB in January of 2015–2022. The results show that the transport-type synoptic pattern accounts for 65.16% of heavy PM2.5 pollution, indicating that regional transport of PM2.5 dominates the THB's heavy air pollution. The PM2.5/CO ratio is higher in the transport-type pattern and positively correlated with PM2.5 concentrations, reflecting a higher efficiency of chemical transformation to secondary PM2.5 in transport-type pollution compared with the accumulation-type pollution. Transport-type heavy PM2.5 pollution is predominantly influenced by upstream anomalous northerly and easterly airflows at the bottom of the high-pressure system, converging with the southern wind in the receptor area over the THB. Accumulation-type heavy pollution exhibits weak wind anomalies in central and eastern China under the control of a uniform pressure field. Furthermore, thermally-induced vertical circulations with sinking airflows in the middle and lower troposphere suppress the vertical air pollutant dispersions. The relative contributions of atmospheric factors for transport-type PM2.5 heavy pollution events are 38.0% for dynamical driver, 26.8% for thermal driver, and 35.1% for chemical transformation, while in accumulation-type, the contribution rates are 33.9%, 36.3%, and 29.7%, respectively. This study elucidates the influences of synoptic patterns on regional transport, local accumulation, and chemical transformation of PM2.5 for heavy air pollution, with implications for understanding changes of air quality in the receptor region of regional transport.

Track fastener detection in special scenarios based on TSR-Net

The traditional method of detecting track fasteners can lead to erroneous results due to the presence of rail bolts, wires, and extraneous objects such as stones. Consequently, the study of fastener detection in specific environments is essential. To address these issues, the TSR-Net target detection model is introduced, which employs an efficient vision transformer as a backbone to increase the speed of feature extraction. The global image perception of the network is enhanced by integrating translation convolutions and inverted residual blocks within the neck network. A detection head with a self-supervised equivariant attention mechanism is designed to deal with the occlusion challenges of small fastener targets. The model is implemented on edge AI computing devices. Technical validation indicates that TSR-Net achieves 94.2% detection precision and operates at 47 frames per second, thereby enabling accurate, real-time detection of small-target occlusion fasteners.

LiteFormer: A Lightweight and Efficient Transformer for Rotating Machine Fault Diagnosis

LiteFormer: A Lightweight and Efficient Transformer for Rotating Machine Fault Diagnosis

IoT Based Transformer Monitoring System using ESP-32

Abstract: The integration of Internet of Things (IoT) technology with power transformer monitoring systems presents a significant advancement in electrical grid management. This paper explores the development of an IoT-based transformer monitoring system utilizing the ESP-32 microcontroller. The primary objective is to enhance the reliability and efficiency of transformers by providing real-time data on their operational status and health. The system leverages the ESP-32's capabilities in wireless communication and data processing to monitor critical parameters such as temperature, load current, voltage, and oil levels. Data is transmitted to a centralized cloud platform for analysis and visualization, enabling predictive maintenance and rapid response to potential failures. The proposed system promises to reduce downtime and maintenance costs while ensuring the longevity and optimal performance of transformers. This research highlights the design, implementation, and testing phases of the monitoring system, demonstrating its effectiveness through various case studies and field trials.

Advancing Nutritional Quality through Genomic Approaches for Biofortification in Cereal Crops: A Review

Hidden hunger, characterized by micronutrient deficiencies, remains a pervasive challenge in marginalized regions worldwide, where the main source of sustenance is cereal crops. This review study examines how improving the nutritional value of cereal crops through genetic "biofortification" could help fight hidden hunger. It focuses on the biochemical and genetic underpinnings of zinc, iron, and critical amino acid accumulation. While conventional breeding efforts have made significant contributions, the complex genetic nature of mineral content in grains presents challenges. In response, molecular techniques such as CRISPR/Cas genome editing offer promising solutions. The paper underscores the widespread prevalence of iron and zinc deficiencies, affecting over a large population. Children under five and pregnant and lactating women in developing countries are particularly vulnerable. Biofortification, defined as the breeding of staple food crops with elevated micronutrient levels, emerges as a cost-effective and sustainable strategy to improve health in resource-poor households. It discusses the transfer of genes and quantitative trait loci (QTLs) from wild and related species to cultivated wheat, emphasizing the need for marker-assisted selection and genomic selection to accelerate breeding progress. The emergence of CRISPR/Cas genome editing techniques in recent decades has revolutionized the field of plant breeding. The paper highlights the successful application of CRISPR/Cas9 in numerous cereal crops such as rice, wheat, maize and barley, to improve crop yields and nutritional content. It also explores the potential for precise base editing and gene expression modifications. However, challenges such as transformation efficiency, specific promoters, and ethical and regulatory concerns are also mentioned and discussed. In conclusion, genetic biofortification through CRISPR/Cas-mediated genome editing presents a promising avenue for alleviating hidden hunger in cereal-dependent regions.

The impact of circular economy strategies on municipal waste management: A system dynamics approach

The low levels of reuse and recycling of solid waste in cities in developing countries can be attributed to decision-making processes with sub investment that do not consider waste streams, recovery capacities, and potential for recycling. This is due to a lack of understanding of the systemic structure of urban waste management.This article presents the development of a model of an urban waste management system that integrates decisions based on strategies that close cycles, belonging to the concept of a circular economy. These strategies aim to increase the potential use of organic waste, plastics, and glass.The model was built using the Systems Dynamics methodology, which allows obtaining mathematical models for the study and observation of trends and future scenarios of complex systems. The model is made up of three modules: health, financial, and circular economy. The strategies evaluated and compared in the simulation environment were: Economic incentives at the price of the material used; Increase in waste sorting capacity; Increase in the capacity to use waste.Methodologically, novel mathematical representations were achieved for the estimation of the useful life of sanitary landfills. Likewise, a mathematical formulation was found for the automatic evaluation of the efficiency in the capacity to use different wastes, which allows to know the need for investment or increase of capacity to use the type of waste evaluated.Using novel mathematical representations, this study achieved a methodological breakthrough in estimating the useful life of sanitary landfills and waste transformation efficiency. These representations also enable the automatic evaluation of how effectively different types of waste can be utilized. This information can then be used to determine the need for investment in, or expansion of, the capacity to handle specific waste types or to increase the overall waste collection capacity of the city.

Transition-Metal-Catalyzed Transformations for the Synthesis of Marine Drugs.

Transition metal catalysis has contributed to the discovery of novel methodologies and the preparation of natural products, as well as new chances to increase the chemical space in drug discovery programs. In the case of marine drugs, this strategy has been used to achieve selective, sustainable and efficient transformations, which cannot be obtained otherwise. In this perspective, we aim to showcase how a variety of transition metals have provided fruitful couplings in a wide variety of marine drug-like scaffolds over the past few years, by accelerating the production of these valuable molecules.

Copper-Catalyzed Three-Component Synthesis of Highly Substituted Morpholines.

Unprotected, highly substituted morpholines were obtained through a copper-catalyzed three-component reaction utilizing amino alcohols, aldehydes, and diazomalonates. The transformation was effective for diversely substituted aldehydes and for a broad range of readily available vicinal amino alcohols, including those derived from glycine, α-substituted, and α,α-disubstituted amino acids. Epimerization of morpholines using light-mediated stereochemical editing was demonstrated, and the unprotected morpholine products were readily elaborated through efficient transformations.

The Influence of Homologous Arm Length on Homologous Recombination Gene Editing Efficiency Mediated by SSB/CRISPR-Cas9 in Escherichia coli.

The precise editing of genes mediated by CRISPR-Cas9 necessitates the application of donor DNA with appropriate lengths of homologous arms and fragment sizes. Our previous development, SSB/CRISPR-Cas9, has demonstrated high efficiency in homologous recombination and non-homologous end joining gene editing within bacteria. In this study, we optimized the lengths and sizes of homologous arms of the donor DNA within this system. Two sets of donor DNA constructs were generated: one set comprised donors with only 10-100 bp homologous arms, while the other set included donors with homologous arms ranging from 10-100 bp, between which was a tetracycline resistance expression cassette (1439 bp). These donor constructs were transformed into Escherichia coli MG1655 cells alongside pCas-SSB/pTargetF-lacZ. Notably, when the homologous arms ranged from 10 to 70 bp, the transformation efficiency of non-selectable donors was significantly higher than that of selectable donors. However, within the range of 10-100 bp homologous arm lengths, the homologous recombination rate of selectable donors was significantly higher than that of non-selectable donors, with the gap narrowing as the homologous arm length increased. For selectable donor DNA with homologous arm lengths of 10-60 bp, the homologous recombination rate increased linearly, reaching a plateau when the homologous arm length was between 60-100 bp. Conversely, for non-selectable donor DNA, the homologous recombination rate increased linearly with homologous arm lengths of 10-90 bp, plateauing at 90-100 bp. Editing two loci simultaneously with 100 bp homologous arms, whether selectable or non-selectable, showed no difference in transformation or homologous recombination rates. Editing three loci simultaneously with 100 bp non-selectable homologous arms resulted in a 45% homologous recombination rate. These results suggest that efficient homologous recombination gene editing mediated by SSB/CRISPR-Cas9 can be achieved using donor DNA with 90-100 bp non-selectable homologous arms or 60-100 bp selectable homologous arms.

Desymmetric Hydrogenation of meso-Dicarboxylic Acids.

Efficient transformation of platform chemicals into key intermediates has been increasingly important for the pharmaceutical industry. The development of the catalytic reduction of abundant carboxylic acids with molecular hydrogen has been of both practical and theoretical value. We herein report the homogeneous hydrogenation of dicarboxylic acids with the strategy of desymmetrization. Using a rhodium/bisphosphine catalyst, one carboxyl group of meso-diacids was selectively reduced to yield chiral lactones with satisfactory enantioselectivity. This method provides a straightforward approach to produce chiral lactone intermediates for the manufacture of biotin, telaprevir, and other antivirus drugs. Both experimental and computational investigations were carried out, revealing a novel neighboring group coordination mechanism in the catalytic cycle.

Improving Laryngoscopy Image Analysis Through Integration of Global Information and Local Features in VoFoCD Dataset.

The diagnosis and treatment of vocal fold disorders heavily rely on the use of laryngoscopy. A comprehensive vocal fold diagnosis requires accurate identification of crucial anatomical structures and potential lesions during laryngoscopy observation. However, existing approaches have yet to explore the joint optimization of the decision-making process, including object detection and image classification tasks simultaneously. In this study, we provide a new dataset, VoFoCD, with 1724 laryngology images designed explicitly for object detection and image classification in laryngoscopy images. Images in the VoFoCD dataset are categorized into four classes and comprise six glottic object types. Moreover, we propose a novel Multitask Efficient trAnsformer network for Laryngoscopy (MEAL) to classify vocal fold images and detect glottic landmarks and lesions. To further facilitate interpretability for clinicians, MEAL provides attention maps to visualize important learned regions for explainable artificial intelligence results toward supporting clinical decision-making. We also analyze our model's effectiveness in simulated clinical scenarios where shaking of the laryngoscopy process occurs. The proposed model demonstrates outstanding performance on our VoFoCD dataset. The accuracy for image classification and mean average precision at an intersection over a union threshold of 0.5 (mAP50) for object detection are 0.951 and 0.874, respectively. Our MEAL method integrates global knowledge, encompassing general laryngoscopy image classification, into local features, which refer to distinct anatomical regions of the vocal fold, particularly abnormal regions, including benign and malignant lesions. Our contribution can effectively aid laryngologists in identifying benign or malignant lesions of vocal folds and classifying images in the laryngeal endoscopy process visually.

Social and biological innovations are essential to deliver transformative forest biotechnologies.

Forests make immense contributions to societies in the form of ecological services and sustainable industrial products. However, they face major challenges to their viability and economic use due to climate change and growing biotic and economic threats, for which recombinant DNA (rDNA) technology can sometimes provide solutions. But the application of rDNA technologies to forest trees faces major social and biological obstacles that make its societal acceptance a 'wicked' problem without straightforward solutions. We discuss the nature of these problems, and the social and biological innovations that we consider essential for progress. As case studies of biological challenges, we focus on studies of modifications in wood chemistry and transformation efficiency. We call for major innovations in regulations, and the dissolution of method-based market barriers, that together could lead to greater research investments, enable wide use of field studies, and facilitate the integration of rDNA-modified trees into conventional breeding programs. Without near-term adoption of such innovations, rDNA-based solutions will be largely unavailable to help forests adapt to the growing stresses from climate change and the proliferation of forest pests, nor will they be available to provide economic and environmental benefits from expanded use of wood and related bioproducts as part of an expanding bioeconomy.

Development of a chloroplast expression system for Dunaliella salina

Dunaliella salina is an innovative expression system due to its distinct advantages such as high salt tolerance, low susceptibility to contamination, and the absence of the cell wall. While nuclear transformation has been extensively studied, research on D. salina chloroplast transformation remains in the preliminary stages. In this study, we established an efficient chloroplast expression system for D. salina using Golden Gate assembly. We developed a D. salina toolkit comprising essential components such as chloroplast-specific promoters, terminators, homologous fragments, and various vectors. We confirmed its functionality by expressing the EGFP protein. Moreover, we detailed the methodology of the entire construction process. This expression system enables the specific targeting of foreign genes through simple homologous recombination, resulting in stable expression in chloroplasts. The toolkit achieved a relatively high transformation efficiency within a shorter experimental cycle. Consequently, the construction and utilization of this toolkit have the potential to enhance the efficiency of transgenic engineering in D. salina and advance the development of microalgal biofactories.

Enhancement of efficient molecular biology tools for Tremella fuciformis spore

Tremella fuciformis, one of the most approved mushrooms worldwide, possesses both edible and medicinal value. However, low transformation efficiency and high false positive rate, long verification period, and low expression of heterologous proteins hinder further genetic research. In this study, a polyethylene glycol-mediated genetic transformation system was established by using the homologous resistance marker carboxin in T. fuciformis. By optimizing the protoplast quality and transformation conditions, the transformation efficiency reached 8.23 CFU/μg, and the positive rate reached 64.95%. Moreover, Driselase-Heating-Freezing screening system was developed for rapid transformants verification, which took only 35–45 min and made the colony PCR 100% accurate. Considering the rationality of tRNA use frequency in heterologous genes, the coordinated relative synonymous codon usage (RSCU) strategy was adopted to recode the heterologous gene sequence, which ensured the protein expression level. This study comprehensively enhances molecular biology tools in T. fuciformis, and lays a foundation for further development.

Photocatalytic Transformation of Organics to Valuable Chemicals - Quo Vadis?

Recent development in photocatalysis is increasingly focused on transforming organic compounds toward producing fine chemicals. Simple, non-selective oxidation reactions (degradation of pollutants) and very demanding solar-to-chemical energy conversion processes (production of solar fuels) face severe economic limitations influenced by still low efficiency and insufficient stability of the systems. Synthesis of fine chemicals, including reductive and oxidative selective transformations, as well as C-C and C-N coupling reactions, can utilise the power of photocatalysis. Herein, we present the recent progress in photocatalytic systems designed to synthesise fine chemicals. In particular, we discuss the factors influencing the efficiency and selectivity of the organic transformations, dividing them into intrinsic (related to individual properties of photocatalysts) and extrinsic (originating from the reaction environment). A rational design of the photocatalytic systems, based on a deep understanding of these factors, opens new perspectives for applied photocatalysis.

Tomato Leaf Disease Detection using Flask Frame Work

In recent years, plant leaf diseases has become a widespread problem for which an accurate research and rapid application of deep learning in plant disease classification is required, tomato is also one of the most important plants and seeds which are used worldwide for cooking in either dried or fresh form, tomato are a great source of protein that offer many health benefits, but there are a lot of diseases associated with tomato leaf which hinder its production. Thus, an accurate classification of tomato leaf diseases is needed to solve the problem in the early stage. A deep learning approach is proposed to identify and classify leaf disease by using public dataset of leaf image and CNN model with the open source library TensorFlow. In this project, we proposed a method to classify tomato leaf disease and to find and describe the efficient network architecture (hyper parameters and optimization methods). Moreover, after applying each architecture separately, we compared their obtained results to find out the best architecture configuration for classifying tomato leaf diseases and their results. Furthermore, to satisfy the classification requirements, the model was trained using CNN architecture check if we could get faster training times, higher accuracy and easier retraining. Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this project, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this project will be a valuable resource for researchers who study the detection of plant diseases. At the same time, we also discussed some of the current challenges and problems that need to be resolved.

The introduction of pre-culture, washing and pre-selection stages enhances rice transformation efficiency by reducing Agrobacterium overgrowth

The introduction of pre-culture, washing and pre-selection stages enhances rice transformation efficiency by reducing Agrobacterium overgrowth

Digital Economy Development and Green Innovation Efficiency from a Two-Stage Innovation Value Chain Perspective

Resolving the conflict between economic growth and ecological sustainability is crucial when transitioning from traditional economic development towards a sustainable green model. In this context, the super-NSBM model was applied to measure the efficiency of green innovation technology research and development. Additionally, it was applied to measure the efficiency of the green innovation achievement transformation among 30 Chinese provinces. This evaluation was conducted for the period from 2011 to 2021, and it utilized a two-stage innovation value chain perspective. The entropy method was used to comprehensively calculate the digital economy development index, and the impact of digital economy development on the two-stage green innovation effect was empirically explored using SDM. The findings revealed the following: Firstly, both two-stage green innovation efficiency and digital economy development exhibited significant spatial characteristics. Secondly, digital economy development had a positive effect on two-stage green innovation efficiency not only in the local region but also in neighboring regions. This conclusion remained valid throughout a series of robustness tests. Thirdly, in terms of regional heterogeneity, the eastern region and non-resource-based regions had greater digital economy development dividends than the central, western, and resource-based regions; therefore, the effect on two-stage green innovation efficiency was more significant. Lastly, both intellectual property protection and data factor endowment exerted positive moderating effects on the influence of the digital economy on two-stage green innovation efficiency. The conclusions of this research provide a theoretical backing for and empirical proof of the mechanism of digital economy development and its impact on two-stage green innovation efficiency. Policy implications are suggested based on these findings, such as reinforcing digitalization, establishing targeted policies, and fostering a favorable external environment conducive to technological innovation.