Genes Coding Research Articles

Directed evolution of aminoacyl-tRNA synthetases through in vivo hypermutation.

Genetic code expansion (GCE) has become a critical tool in biology by enabling the site-specific incorporation of non-canonical amino acids (ncAAs) into proteins. Central to GCE is the development of orthogonal aminoacyl-tRNA synthetase (aaRS)/tRNA pairs wherein engineered aaRSs recognize chosen ncAAs and charge them onto tRNAs that decode blank codons ( e.g ., the amber stop codon). Many orthogonal aaRS/tRNA pairs covering a wide range of ncAAs have been generated by directed evolution, yet the evolution of new aaRS/tRNA pairs by standard strategies remains a labor-intensive process that often produces aaRS/tRNA pairs with suboptimal ncAA incorporation efficiencies. In this study, we present a strategy for evolving aaRSs that leverages OrthoRep to drive their continuous hypermutation in yeast. We demonstrate our strategy in 8 independent aaRS evolution campaigns starting from 4 different aaRS/tRNA parents targeting 7 distinct ncAAs. We observed the rapid evolution of multiple novel aaRSs capable of incorporating an overall range of 13 ncAAs tested into proteins in response to the amber codon. Some evolved systems reached efficiencies for amber codon-specified ncAA-dependent translation comparable to translation with natural amino acids specified by sense codons in yeast. Additionally, we discovered a surprising aaRS that evolved to self-regulate its own expression for greater dependency on ncAAs for translation. These findings demonstrate the potential of OrthoRep-driven aaRS evolution platforms in supporting the continued growth of GCE technologies.

Impact of Competent Persons' judgements in Mineral Resources classification

Uncertainty with regard to estimated grades and tonnages of a mineral deposit demands risk assessment in order to build investor confidence and attract the interest of other stakeholders in the success of a project. Uncertainties associated with Mineral Resource estimates can lead to unreliable production schedules and unpredictable cash flows. However, the techniques used in the mining industry to determine these uncertainties are inconsistent, because the important decisions taken in the process are solely dependent on the responsible Competent Person (CP), without limitations. This leads to disparities between different CPs' results, using data-sets from the same drill-holes. The various standard codes for public disclosure provide guidelines and recommendations for the classification of Mineral Resources and Reserves but do not provide details on, for example, the amount of geological and geostatistical information needed to qualify for each category of Resources and Reserves. The parameters used to generate the classification categories are subjectively assumed by the responsible CP. In this paper we investigate the impacts of different CPs' judgements on resource classification, using the same data-sets. The results underpin the need for the mining industry to develop a uniform Mineral Resources and Reserves classification framework that can minimize or avoid significant discrepancies that lead to potential misleading public disclosures.

A Highly Accurate Adverse Drug Reactions (ADR) Detection from Medical Forum Comments Using Long Short-Term Memory Networks

Purpose: Adverse Drug Reactions (ADR) classification is useful in modern medical diagnostics and related applications. ADR is an example of how medical information is frequently accessible on social media platforms for healthcare, where people can share their experiences with treatments on desktop computers and mobile devices. Many researchers are interested in gathering valuable medical data from social media for the ADR system training and classification process. Materials and Methods: This research explores the effects of three aspects on recognizing ADR mentions in social media for the medical field and proposes a deep neural network of Long Short-Term Memory (LSTM) neural networks to do so. The comments are collected from various social media platforms to implement the ADR system with proper training and testing processes. The texts from the dataset are initially preprocessed by using a data filtering and clustering process to remove the input data's redundant information to increase the training process's quality. Characteristic features, such as semantic features and text statistics, are extracted from the input text using the American Standard Code for Information Interchange (ASCII) array. Further, the features are converted and fed to LSTM networks for training and validation. Results and Conclusion: This work is evaluated using two datasets, CODEC, and ADR Corpus datasets are used to evaluate the performance of the proposed ADR technique via multiple angles. Via extensive experiments, this work achieved 99.79 accuracy, 98.37 sensitivity, 97.63 specificity, 99.72 precision, 98.39 recall, 97.62 F1-score for the CODEC dataset, 98.16 for accuracy, 99.19 for sensitivity, 98.49 for specificity, 99.49 for precision, 96.72 for recall, and 93.16 for F1-score for ADR corpus, respectively.

Exploring the Benefits and Barriers of QRIS Adoption Among Micro Businesses in North Bogor

This study explores the role of the Indonesian Standard QR Code (QRIS) in enhancing the business outcomes of micro enterprises in North Bogor Subdistrict. While QRIS has been recognized as a crucial technological advancement for promoting financial inclusion, there remains a reluctance among some micro business owners to adopt it. This research utilizes a descriptive qualitative method, including interviews with micro entrepreneurs who do and do not use QRIS, government officials involved in MSME development, and financial institution representatives. The findings reveal that the adoption of QRIS is often driven by its integration with delivery services rather than its potential to boost business performance. Despite some entrepreneurs experiencing benefits from QRIS, a significant gap in understanding its full potential as a payment method persists. For those not using QRIS, reasons include the efficiency of cash transactions, additional costs, and expectations more on government support in promoting their products to achieve business growth.

The Contribution of Dairy Bedding and Silage to the Dissemination of Genes Coding for Antimicrobial Resistance: A Narrative Review.

Antimicrobial resistance (AMR) is a concern in the dairy industry. Recent studies have indicated that bedding serves as a reservoir for antimicrobial-resistant bacteria and antimicrobial-resistance genes (ARGs), while silage has been proposed as another possible source. The impact of AMR in dairy farming can be significant, resulting in decreased productivity and economic losses for farmers. Several studies have highlighted the safety implications of AMR bacteria and genes in bedding and silage, emphasizing the need for further research on how housing, bedding, and silage management affect AMR in farm environments. Exposure to sub-lethal concentrations of antibiotics, such as those from contaminated bedding and silage, can prompt bacteria to develop resistance mechanisms. Thus, even if antimicrobial usage is diminished, ARGs may be maintained in the dairy farm environment. By implementing proactive measures to tackle AMR in dairy farming, we can take steps to preserve the health and productivity of dairy cattle while also protecting public health. This involves addressing the prudent use of antibiotics during production and promoting animal welfare, hygiene, and management practices in bedding and farm environments to minimize the risk of AMR development and spread. This narrative review compiles the growing research, positioning the contribution of bedding and silage to the prevalence and dissemination of AMR, which can elicit insights for researchers and policymakers.

Efficacy of IS Code Provisions for Design of Eccentrically Loaded Single Angle Compression Members

In practical applications, steel single angles are often subjected to eccentric loading when connected to a gusset plate through one leg only, resulting in complex compression behaviour. This behaviour has not been as extensively studied as that of concentrically loaded members. Various international codes of practice offer differed approaches for designing these elements. The literature indicates that the Indian Standard Code IS 800–2007 accurately predicts the axial capacity of eccentrically loaded single angle columns. However, a recent amendment to IS 800 in 2024 introduced modified design provisions. This study is the first to explore the implications of these latest design provisions by comparing them with earlier provisions and experimental strengths reported in the literature. Initially, the design strengths as per the previous and latest design provisions for various Indian Standard Angles (ISA) were presented and compared accounting for varying slenderness ratio, plate slenderness ratio (b/t), and different types of end connections and restraints. The findings reveal that the latest design provisions generally result in much higher design strengths compared to the earlier provisions, with a maximum increase of 104.84%. Upon noticing the significant variation, this study is further extended to compare with the reported data available in the literature. The nominal strengths calculated using the latest provisions were often higher than the strengths reported in the literature considered in this study, indicating potential unsafe design.

Microzonation Approach for Analyzing Regional Seismic Response: A Case Study of the Dune Deposit in Concón, Chile

Urban areas located on complex geological formations, such as dune deposits, require detailed seismic risk assessments that extend beyond standard seismic codes. This study focuses on the city of Concón, Chile, where a significant portion of the urban area is situated on a coastal dune deposit. The research integrates seismic microzonation with a three-dimensional finite element model (3D FEM) to comprehensively evaluate the regional seismic response. Field data from 208 strategically distributed points were collected and combined with geotechnical and geomorphological information to construct a detailed 3D model of the region. This model allowed for the simulation of seismic behavior under various conditions, highlighting the limitations of general seismic codes in capturing local variations in seismic response. The results underscore the importance of considering local geological conditions in structural design, particularly in areas with irregular topography and complex subsurface conditions. This study concludes that incorporating microzonation into urban planning and seismic analysis can significantly enhance infrastructure resilience and disaster preparedness, providing a replicable approach for other cities facing similar geological challenges.

Customizable gene sensing and response without altering endogenous coding sequences.

Synthetic biology aims to modify cellular behaviors by implementing genetic circuits that respond to changes in cell state. Integrating genetic biosensors into endogenous gene coding sequences using clustered regularly interspaced short palindromic repeats and Cas9 enables interrogation of gene expression dynamics in the appropriate chromosomal context. However, embedding a biosensor into a gene coding sequence may unpredictably alter endogenous gene regulation. To address this challenge, we developed an approach to integrate genetic biosensors into endogenous genes without modifying their coding sequence by inserting into their terminator region single-guide RNAs that activate downstream circuits. Sensor dosage responses can be fine-tuned and predicted through a mathematical model. We engineered a cell stress sensor and actuator in CHO-K1 cells that conditionally activates antiapoptotic protein BCL-2 through a downstream circuit, thereby increasing cell survival under stress conditions. Our gene sensor and actuator platform has potential use for a wide range of applications that include biomanufacturing, cell fate control and cell-based therapeutics.

Triphasic Development of the Genetic Code.

The genetic code contains an alphabet of genetically encoded amino acids. The ten Phase 1 amino acids, including Gly, Ala, Ser, Asp, Glu, Val, Leu, Ile, Pro and Thr, were available from the prebiotic environment, whereas the ten Phase 2 amino acids, including Phe, Tyr, Arg, His, Trp, Asn, Gln, Lys, Cys, and Met, became available only later from amino acid biosyntheses. In the archaeon Methanopyrus kandleri, the oldest organism known, the standard alphabet of 20 amino acids was "frozen" and no additional amino acid was encoded in the subsequent 3 Gyrs. Four decades ago, it was discovered that the code was frozen because all the organisms were so well adapted to the standard amino acids that oligogenic barriers, consisting of genes that are thoroughly dependent on the standard code, would cause loss of viability upon the deletion of any one amino acid from the code. Once the reason for the freezing of the code was ascertained, procedures were devised by scientists worldwide to enable the encoding of novel noncanonical amino acids (ncAAs). These encoded Phase 3 ncAAs now surpass the 20 canonical Phase 2 amino acids in the code.

Digitizing DNA Sequences Using Multiset-Based Nucleotide Frequencies for Machine Learning-Based Mutation Detection

Investigating algebraic structures in a non-conventional framework supplements mathematics for hard-nosed practical applications to the fields of theoretical biology and computer science. One such algebraic structure is multigroup whose underlying set is a multiset. The genome is the entire set of DNA instructions found within a cell which contains all the information needed for an individual to develop and function. DNA and RNA are the hereditary materials that play a vital role in the metabolism process of living things, especially protein synthesis. In genomic database DNA sequences are stored in the form of string or text data types. The only data that works with machine learning algorithms is numerical. Thus, it is necessary to transform DNA sequence strings to numerical values. This article is organized in the following manner. Firstly, we prove that standard genetic code is a multigroup and genome architecture of the whole population can be interpreted as the sum of multisets. Next, it is described how a numerical representation of DNA bases relates to its algebraic representation. We further employed Gated Recurrent Unit, Long Short-Term Memory, and Bidirectional Long Short-Term Memory to identify changes between the DNA sequences. Experimental results show that GRU with multiset-based numerical values for DNA bases offers 98% accuracy on testing data. This novel technique will aid in the detection of mutations in complex diseases.

The degenerate coding of psychometric profiles through functional connectivity archetypes.

Degeneracy in the brain-behavior code refers to the brain's ability to utilize different neural configurations to support similar functions, reflecting its adaptability and robustness. This study aims to explore degeneracy by investigating the non-linear associations between psychometric profiles and resting-state functional connectivity (RSFC). The study analyzed RSFC data from 500 subjects to uncover the underlying neural configurations associated with various psychometric outcomes. Self-organized maps (SOM), a type of unsupervised machine learning algorithm, were employed to cluster the RSFC data. And identify distinct archetypal connectivity profiles characterized by unique within- and between-network connectivity patterns. The clustering analysis using SOM revealed several distinct archetypal connectivity profiles within the RSFC data. Each archetype exhibited unique connectivity patterns that correlated with various cognitive, physical, and socioemotional outcomes. Notably, the interaction between different SOM dimensions was significantly associated with specific psychometric profiles. This study underscores the complexity of brain-behavior interactions and the brain's capacity for degeneracy, where different neural configurations can lead to similar behavioral outcomes. These findings highlight the existence of multiple brain architectures capable of producing similar behavioral outcomes, illustrating the concept of neural degeneracy, and advance our understanding of neural degeneracy and its implications for cognitive and emotional health.

A Consumer Decision Analysis Using QRIS as a Digital Payment Tool

Digital technology has experienced rapid development in the last few decades. Increasingly developing technology has also entered the realm of finance and payment systems. A digital-based payment system can make it easier for people to meet their diverse needs and live their lives more effectively and efficiently. In the digital payment system, there are non-cash (cash) and cash (cash) payments. Non-cash digital payment methods (cashless) are one type of payment that is popular with the public because they can minimize the spread of viruses through money intermediaries, one of which is the use of the Indonesian Standard Quick Response Code (QRIS). This research aims to analyze consumer decisions to use QRIS as a digital payment tool. The sample in this research was 100 respondents. This research uses quantitative methods with descriptive research type. The sampling technique uses non-probability sampling using purposive sampling technique. The method used in data collection was a questionnaire with a Likert scale and data processing used SPSS 29 software to carry out validity testing of an instrument. Validity tests were carried out on the indicators in the online questionnaire which had been filled in by respondents. Keywords: Consumer Preferences, QRIS, Consumer Behavior

Study of Design and Analysis of S-glass Fiber Reinforced Epoxy Polymer Metal Laminate Composites in the Application of Propeller Shafts

The propeller shafts are utilized to transmit power from gear box to rear wheels in automobiles. It is found that the weight, cost, critical speed and buckling of shafts are the serious issues in conventional metallic propeller shafts. The propeller shafts made up of composite material are best suited for LMV due to single piece manufacturing. The fiber metal laminate propeller shafts are getting popular as the metal liner adds extra strength, rigidity and acts as a substrate and avoids leaking during manufacturing. The propeller shaft of light motor vehicle was selected for study. S-glass fibers, epoxy and a thin mild steel liner are the materials chosen. The design of the shaft was carried out according to ASME Standard code of design. The ply orientation and stacking sequence are selected as [±45°, 0°, 90°], to increase the torsional strength, enhance natural frequency and buckling strength respectively. The finite element analysis of the propeller shaft is carried out in Ansys APDL. It is found from the analysis that the weight, torsional strength, torsional stiffness and natural frequency of the S-glass fiber reinforced Epoxy Metal Laminate (FML) composites are superior than the conventional C-35 alloy steel material, and hence can be an alternative to the conventional alloy steel shaft.

Pengaruh Persepsi Manfaat Penggunaan QRIS Terhadap Keputusan Pembelian Ayam Geprek Sriwedari: Survei pada Pelanggan Ayam Geprek Sriwedari

Technological developments have had a very broad impact on various aspects of life, one of which is being adopted by the financial industry. The presence of QRIS or Indonesian Standard Quick Response Code provides a new style in transactions, because users no longer need to make payments in cash, just by scanning the barcode, payments can easily be made, of course with a payment model like this provides convenience, security and satisfaction in transactions. This also applies to the transaction of purchasing Sriwedari fried chicken which is a student favorite in the East Telukjambe area. The use of QRIS is widely used by the millennial generation among students. Therefore, this research aims to review and find out more about the perception of the benefits of using QRIS in chicken purchasing decisions. smashed Sriwedari. The research was carried out using a descriptive quantitative method by collecting data using a questionnaire distributed to respondents. The number of respondents in this study was 103 respondents who were taken randomly with the criteria of purchasing Sriwedari fried chicken at least once a week. The results of the research show that the magnitude of the influence of the perceived benefits of using QRIS on purchasing decisions is 59.7% and the perceived benefits (X1) of using QRIS on purchasing decisions (Y) Sriwedari fried chicken has a simultaneous and positive effect.

Emergency department visits for heat-related illness among workers: Occupational health surveillance using Washington syndromic surveillance data.

Information on worker occupation and industry is critical to understanding the occupational risks of heat-related illness (HRI), yet few syndromic surveillance systems capture these key data elements. This study evaluates the work data reported through Washington syndromic surveillance for its utility in characterizing HRI ED visits by industry and occupation. Standard industry and occupation codes were assigned to employer name and occupation descriptions reported in Washington ED visit records maintained within the state's syndromic surveillance system, for visits involving HRI in 2020-2022. HRI ED visits involving workplace heat exposure were identified based on discharge diagnoses or on keywords in the triage note or chief complaint fields. HRI ED visits were summarized by patient characteristics, and visit rates were calculated by industry and occupation. Employer name or occupation descriptions were reported in 21.5% of HRI ED records among patients age 16 and older, and in 41.2% of records with mention of heat exposure at work. Twice as many records were classified for industry as for occupation. Agriculture, forestry, fishing, and hunting and transportation and warehousing had the highest rates of HRI ED visits. Specific industries with the highest rates included support activities for agriculture and forestry, the postal service, and fruit and vegetable preserving and specialty food manufacturing. Syndromic surveillance data are a valuable source of occupational health surveillance information when work characteristics are reported, enhancing our understanding of the occupational risks of injuries and illnesses.

Evaluation of an immersed boundary numerical framework to address the wind field in complex urban topographies

Estimating the wind velocity field in an urban area is important for pedestrian comfort and safety, as the local wind velocities dictate the transport of heat and air pollution in urban environments. Therefore, it is an essential requirement to assess wind patterns when designing urban areas. Computational Fluid Dynamics (CFD) numerical solvers are usually employed to estimate the wind comfort in an urban area under different wind intensities and directions. However, CFD simulations are expensive in terms of time, especially when many scenarios are addressed to ensure safety and comfort for multiple conditions. Here, a CFD framework based on an immersed boundary approach to discretize the urban topography is developed and validated against experimental data in a wind tunnel and two different standard body-fitted mesh codes. The new solver employs a structured cartesian octree grid automatically generated from Lidar data of urban topographies. The advantages are eliminating the complex and time-consuming pre-processing of urban topographies and making the framework accessible to urban planners without CFD expertise. Furthermore, the code is equipped with GPU parallelization that further reduces the computational time. Provided that the best practice guidelines for urban simulations are satisfied, in particular at least 10 cells between two buildings, the code shows very good agreement in all the tests comprising of a simplified and real urban neighborhood highlighting the importance of accurately solving the complex terrain topography of an urban region when non-negligible elevation changes of the order of 20/40 meters are present.

Deep Learning for Automated Code Generation: Challenges and Opportunities

Deep learning-based mechanized code era has ended up a potential way to speed up program advancement, move forward code quality, and cut down on human botch. Within the setting of programmed code creation, this work looks at the issues and chances that Deep learning strategies bring. A huge issue is that computer dialects are exceptionally complicated and diverse from one another. This makes it difficult to form models that work in all sorts of circumstances where individuals code. Deep learning models require a part of information to work well, but there aren't numerous enormous, high-quality datasets that can be utilized to prepare models. This makes the issue indeed more regrettable. Another big issue is making beyond any doubt that the code that's composed isn't as it were right in terms of language structure, but too makes sense and works well. Code assurance, finding vulnerabilities, and taking after best hones are a few of the other things that make things more troublesome. Indeed in spite of the fact that there are issues, there are moreover a part of great chances in this field. Characteristic dialect preparing (NLP) and transformer plans, like GPT-4, have come a long way and can presently studied and type in code that sounds like it was composed by a individual. When domain-specific data and context-aware models are combined, the created code is much more likely to be rectify and valuable. Robotized code era can offer assistance with rewriting code, fathoming bugs, and composing standard code, which gives journalists more time to work on more complicated assignments. The study too talks around how Deep learning models could be able to memorize from continuous criticism circles, which would let them get way better over time and adjust to modern code measures. When Deep learning is combined with other AI strategies, like fortification learning, it might lead to code era frameworks that are more brilliant and can work on their claim.

Vector multispaces and multispace codes

Basic algebraic and combinatorial properties of finite vector spaces in which individual vectors are allowed to have multiplicities larger than 1 are derived. An application in coding theory is illustrated by showing that multispace codes that are introduced here may be used in random linear network coding scenarios, and that they generalize standard subspace codes (defined in the set of all subspaces of Fqn) and extend them to an infinitely larger set of parameters. In particular, in contrast to subspace codes, multispace codes of arbitrarily large cardinality and minimum distance exist for any fixed n and q.

A conserved opal termination codon optimizes a temperature-dependent tradeoff between protein production and processing in alphaviruses.

Alphaviruses are enveloped, single-stranded, positive-sense RNA viruses that often require transmission between arthropod and vertebrate hosts for their sustained propagation. Most alphaviruses encode an opal (UGA) termination codon in nonstructural protein 3 (nsP3) upstream of the viral polymerase, nsP4. The selective constraints underlying the conservation of the opal codon are poorly understood. Using primate and mosquito cells, we explored the role and selective pressure on the nsP3 opal codon through extensive mutational analysis in the prototype alphavirus, Sindbis virus (SINV). We found that the opal codon is highly favored over all other codons in primate cells under native 37°C growth conditions. However, this preference is diminished in mosquito and primate cells grown at a lower temperature. Thus, the primary determinant driving the selection of the opal stop codon is not host genetics but the passaging temperature. We show that the opal codon is preferred over amber and ochre termination codons because it results in the highest translational readthrough and polymerase production. However, substituting the opal codon with sense codons leads to excessive full-length polyprotein (P1234) production, which disrupts optimal nsP polyprotein processing, delays the switch from minus-strand to positive-strand RNA production, and significantly reduces SINV fitness at 37°C; this fitness defect is relieved at lower temperatures. A naturally occurring suppressor mutation unexpectedly compensates for a delayed transition from minus to genomic RNA production by also delaying the subsequent transition between genomic and sub-genomic RNA production. Our study reveals that the opal stop codon is the best solution for alphavirus replication at 37°C, producing enough nsP4 protein to maximize replication without disrupting nsP processing and RNA replication transitions needed for optimal fitness. Our study uncovers the intricate strategy dual-host alphaviruses use at a single codon to optimize fitness.

Multi-Source Data ETL (Extract, Transform, Load) for a Genetic Epilepsy Diagnosis and Treatment Dashboard.

The growing number of genes identified in relation to epilepsy represents a major breakthrough in diagnosis and treatment, but experts face the challenge of efficiently accessing and consolidating the vast amount of genetic data available. Therefore, we present the process of transforming data from different sources and formats into an Entity-Attribute-Value (EAV) model database. Combined with the use of standard coding systems, this approach will provide a scalable and adaptable database to present the data in a comprehensive way to experts via a dashboard.