Tag: Genetic

  • Genetic Sequences of Highly Pathogenic Avian Influenza A(H5N1) Viruses Identified in a Person in Louisiana | Bird Flu

    Genetic Sequences of Highly Pathogenic Avian Influenza A(H5N1) Viruses Identified in a Person in Louisiana | Bird Flu


    Background

    This is a technical summary of an analysis of the genomic sequences of the viruses identified in two upper respiratory tract specimens from the patient who was severely ill from an infection with highly pathogenic avian influenza (HPAI) A(H5N1) virus in Louisiana. The patient was infected with A(H5N1) virus of the D1.1 genotype virus that is closely related to other D1.1 viruses recently detected in wild birds and poultry in the United States and in recent human cases in British Columbia, Canada, and Washington State. This avian influenza A(H5N1) virus genotype is different from the B3.13 genotype spreading widely and causing outbreaks in dairy cows, poultry, and other animals, with sporadic human cases in the United States. Deep sequencing of the genetic sequences from two clinical specimens from the patient in Louisiana was performed to look for changes associated with adaptation to mammals. There were some low frequency changes in the hemagglutinin (HA) gene segment of one of the specimens that are rare in people but have been reported in previous cases of A(H5N1) in other countries and most often during severe infections. One of the changes found was also identified in a specimen collected from the human case with severe illness detected in British Columbia, Canada, suggesting they emerged during the clinical course as the virus replicated in the patient. Analysis of the N1 neuraminidase (NA), matrix (M) and polymerase acid (PA) genes from the specimens showed no changes associated with known or suspected markers of reduced susceptibility to antiviral drugs.

    CDC Update

    December 26, 2024 – CDC has sequenced the HPAI A(H5N1) avian influenza viruses in two respiratory specimens collected from the patient in Louisiana who was severely ill from an A(H5N1) virus infection. CDC received two specimens collected at the same time from the patient while they were hospitalized for severe respiratory illness: a nasopharyngeal (NP) and combined NP/oropharyngeal (OP) swab specimens. Initial attempts to sequence the virus from the patient’s clinical respiratory specimens using standard RNA extraction and multisegment-RTPCR (M-RTPCR)1 techniques yielded only partial genomic data and virus isolation was not successful. Nucleic acid enrichment was needed to sequence complete genomes with sufficient coverage depth to meet quality thresholds. CDC compared the influenza gene segments from each specimen with A(H5N1) virus sequences from dairy cows, wild birds, poultry and other human cases in the U.S. and Canada. The genomes of the virus (A/Louisiana/12/2024) from each clinical specimen are publicly posted in GISAID (EPI_ISL_19634827 and EPI_ISL_19634828) and GenBank (PQ809549-PQ809564).

    Summary of amino acid mixtures identified in the hemagglutinin (HA) of clinical specimens from the patient.

    Overall, the hemagglutinin (HA) sequences from the two clinical specimens were closely related to HA sequences detected in other D1.1 genotype viruses, including viruses sequenced from samples collected in November and December 2024 in wild birds and poultry in Louisiana. The HA genes of these viruses also were closely related to the A/Ezo red fox/Hokkaido/1/2022 candidate vaccine virus (CVV) with 2 or 3 amino acid changes detected. These viruses have, on average, 3 or 4 amino acid changes in the HA when compared directly to the A/Astrakhan/3212/2020 CVV sequence. These data indicate the viruses detected in respiratory specimens from this patient are closely related to existing HPAI A(H5N1) CVVs that are already available to manufacturers, and which could be used to make vaccines if needed.

    There were some differences detected between the NP/OP and the NP specimens. Despite the very close similarity of the D1.1 sequences from the Louisiana human case to bird viruses, deep sequence analysis of the HA gene segment from the combined NP/OP sample detected low frequency mixed nucleotides corresponding to notable amino acid residues (using mature HA sequence numbering):

    • A134A/V [Alanine 88%, Valine 12%];
    • N182N/K [Asparagine 65%, Lysine 35%]; and
    • E186E/D [Glutamic acid 92%, Aspartic Acid 8%].

    The NP specimen, notably, did not have these low frequency changes indicating they may have been detected from swabbing the oropharyngeal cavity of the patient. While these low frequency changes are rare in humans, they have been reported in previous cases of A(H5N1) in other countries and most often during severe disease2345. The E186E/D mixture, for example, was also identified in a specimen collected from the severe human case detected in British Columbia, Canada67.

    This summary analysis focuses on mixed nucleotide detections at residues A134V, N182K, E186D as these changes may result in increased virus binding to α2-6 cell receptors found in the upper respiratory tract of humans. It is important to note that these changes represent a small proportion of the total virus population identified in the sample analyzed (i.e., the virus still maintains a majority of ‘avian’ amino acids at the residues associated with receptor binding). The changes observed were likely generated by replication of this virus in the patient with advanced disease rather than primarily transmitted at the time of infection. Comparison of influenza A(H5) sequence data from viruses identified in wild birds and poultry in Louisiana, including poultry identified on the property of the patient, and other regions of the United States did not identify these changes. Of note, virus sequences from poultry sampled on the patient’s property were nearly identical to the virus sequences from the patient but did not have the mixed nucleotides identified in the patient’s clinical sample, strongly suggesting that the changes emerged during infection as virus replicated in the patient. Although concerning, and a reminder that A(H5N1) viruses can develop changes during the clinical course of a human infection, these changes would be more concerning if found in animal hosts or in early stages of infection (e.g., within a few days of symptom onset) when these changes might be more likely to facilitate spread to close contacts. Notably, in this case, no transmission from the patient in Louisiana to other persons has been identified. The Louisiana Department of Public Health and CDC are collaborating to generate additional sequence data from sequential patient specimens to facilitate further genetic and virologic analysis.

    Additional genomic analysis

    The genetic sequences of the A(H5N1) viruses from the patient in Louisiana did not have the PB2 E627K change or other changes in polymerase genes associated with adaptation to mammals and no evidence of low frequency changes at critical positions. And, like other D1.1 genotype viruses found in birds, the sequences lack PB2 M631L, which is associated with viral adaptation to mammalian hosts, and which has been detected in >99% of dairy cow sequences but is only sporadically found in birds. Analysis of the N1 neuraminidase (NA), matrix (M) and polymerase acid (PA) genes from the specimens showed no changes associated with known or suspected markers of reduced susceptibility to antiviral drugs. The remainder of the genetic sequences of A/Louisiana/12/2024 were closely related to sequences detected in wild bird and poultry D1.1 genotype viruses, including poultry identified on the property of the patient, providing further evidence that the human case was most likely infected following exposure to birds infected with D1.1 genotype virus.

    Follow Up Actions

    Overall, CDC considers the risk to the general public associated with the ongoing U.S. HPAI A(H5N1) outbreak has not changed and remains low. The detection of a severe human case with genetic changes in a clinical specimen underscores the importance of ongoing genomic surveillance in people and animals, containment of avian influenza A(H5) outbreaks in dairy cattle and poultry, and prevention measures among people with exposure to infected animals or environments.



    Recently, genetic sequences of highly pathogenic Avian Influenza A(H5N1) viruses have been identified in a person in Louisiana. This discovery has raised concerns about the potential for bird flu to spread to humans.

    The H5N1 virus is known to primarily infect birds, particularly poultry. However, there have been cases of transmission to humans in the past, leading to severe illness and even death. The genetic sequencing of the virus found in the individual in Louisiana suggests that this strain may have the potential to spread to humans more easily.

    Health officials are closely monitoring the situation and taking steps to prevent further spread of the virus. It is important for people to take precautions, such as avoiding contact with sick birds and practicing good hygiene, to reduce the risk of contracting the virus.

    This discovery highlights the ongoing threat of avian influenza and the importance of continued surveillance and research to better understand and control the spread of these viruses. Stay informed and stay safe. #BirdFlu #H5N1 #AvianInfluenza #Louisiana #GeneticSequences

    Tags:

    1. Avian Influenza A(H5N1) genetic sequences
    2. Bird Flu outbreak in Louisiana
    3. Highly Pathogenic Avian Influenza A(H5N1) virus
    4. Louisiana bird flu infection
    5. Genetic analysis of H5N1 viruses
    6. Avian flu transmission in humans
    7. Louisiana bird flu outbreak
    8. H5N1 virus in Louisiana
    9. Human infection with bird flu virus
    10. Avian Influenza A(H5N1) genetic identification

    #Genetic #Sequences #Highly #Pathogenic #Avian #Influenza #AH5N1 #Viruses #Identified #Person #Louisiana #Bird #Flu

  • Genetic Algorithms in Search, Optimization and Machine Learning H

    Genetic Algorithms in Search, Optimization and Machine Learning H



    Genetic Algorithms in Search, Optimization and Machine Learning H

    Price : 6.54

    Ends on : N/A

    View on eBay
    Genetic Algorithms in Search, Optimization, and Machine Learning: A Powerful Tool for Solving Complex Problems

    Genetic algorithms (GAs) are a powerful tool in the field of search, optimization, and machine learning. Inspired by the process of natural selection, genetic algorithms use a population of candidate solutions to evolve towards an optimal solution to a given problem. This evolutionary approach makes genetic algorithms well-suited for solving complex problems that are difficult to tackle with traditional methods.

    In search problems, genetic algorithms can be used to efficiently explore large search spaces and find the best solution. By generating a diverse population of candidate solutions and iteratively evolving them through mutation, crossover, and selection, genetic algorithms can quickly converge towards an optimal solution.

    In optimization problems, genetic algorithms can be used to find the best possible solution to a given objective function. By encoding candidate solutions as strings of variables, genetic algorithms can explore the solution space and identify the optimal set of variables that maximize or minimize the objective function.

    In machine learning, genetic algorithms can be used to optimize the parameters of machine learning models and improve their performance. By treating the parameters of a model as genes in a population, genetic algorithms can search for the best set of parameters that maximize the model’s predictive accuracy.

    Overall, genetic algorithms are a versatile and powerful tool for solving a wide range of complex problems in search, optimization, and machine learning. By mimicking the process of natural selection, genetic algorithms can efficiently search large solution spaces, optimize objective functions, and improve the performance of machine learning models. Whether you’re tackling a challenging optimization problem or fine-tuning a machine learning model, genetic algorithms can help you find the best solution.
    #Genetic #Algorithms #Search #Optimization #Machine #Learning

  • Hands-On Genetic Algorithms with Python: Apply genetic algorithms to solve real-world AI and machine learning problems

    Hands-On Genetic Algorithms with Python: Apply genetic algorithms to solve real-world AI and machine learning problems


    Price: $38.83
    (as of Dec 18,2024 04:43:07 UTC – Details)


    From the brand

    Brand story Packt books

    Brand story Packt books

    See more at our store

    Packt Logo

    Packt Logo

    Packt is a leading publisher of technical learning content with the ability to publish books on emerging tech faster than any other.

    Our mission is to increase the shared value of deep tech knowledge by helping tech pros put software to work.

    We help the most interesting minds and ground-breaking creators on the planet distill and share the working knowledge of their peers.

    Publisher ‏ : ‎ Packt Publishing; 2nd ed. edition (July 12, 2024)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 418 pages
    ISBN-10 ‏ : ‎ 1805123793
    ISBN-13 ‏ : ‎ 978-1805123798
    Item Weight ‏ : ‎ 1.61 pounds
    Dimensions ‏ : ‎ 0.96 x 7.5 x 9.25 inches


    Are you interested in learning how genetic algorithms can be applied to solve real-world AI and machine learning problems using Python? Look no further than our hands-on workshop!

    In this workshop, you will dive deep into the world of genetic algorithms and learn how they can be used to optimize solutions to complex problems. You will get practical, hands-on experience implementing genetic algorithms in Python and applying them to real-world scenarios.

    Whether you’re a beginner looking to get started with genetic algorithms or an experienced data scientist looking to expand your knowledge, this workshop is for you. Join us and take your AI and machine learning skills to the next level with genetic algorithms!

    Stay tuned for more details on how you can participate in this exciting workshop. Don’t miss out on this opportunity to enhance your skills and solve real-world problems with genetic algorithms and Python!
    #HandsOn #Genetic #Algorithms #Python #Apply #genetic #algorithms #solve #realworld #machine #learning #problems

  • Exploring the Genetic Variants of TTC6-15 and Their Impact

    Exploring the Genetic Variants of TTC6-15 and Their Impact


    Genetic variants play a crucial role in shaping individual differences in traits, susceptibility to diseases, and response to treatments. One such genetic variant that has garnered attention in recent years is TTC6-15. This gene has been found to be associated with a variety of traits and diseases, making it an important focus of research in the field of genetics.

    TTC6-15 is a gene that codes for a protein involved in various cellular processes, including DNA repair and cell division. Variants in this gene can lead to alterations in the protein’s function, which in turn can impact an individual’s health and well-being.

    Research has shown that certain variants of TTC6-15 are associated with an increased risk of developing certain diseases, such as cancer, diabetes, and cardiovascular disorders. These variants can also influence traits such as height, weight, and cognitive abilities. Understanding the impact of these genetic variants is crucial for developing personalized medicine approaches that take into account an individual’s genetic makeup.

    One of the key challenges in studying genetic variants like TTC6-15 is the complexity of the human genome. With millions of genetic variants present in each individual, it can be difficult to pinpoint the specific variants that are responsible for a particular trait or disease. However, advances in technology, such as genome sequencing and bioinformatics, have made it easier to identify and study these variants.

    By exploring the genetic variants of TTC6-15 and their impact, researchers hope to gain a better understanding of the underlying mechanisms that drive disease and trait development. This knowledge can then be used to develop targeted therapies and interventions that are tailored to an individual’s unique genetic profile.

    In conclusion, the study of genetic variants like TTC6-15 is essential for advancing our understanding of human genetics and its impact on health and disease. By unraveling the complexities of the human genome, researchers can pave the way for personalized medicine approaches that have the potential to revolutionize healthcare in the future.

  • Evolutionary Deep Learning: Genetic algorithms and neural networks

    Evolutionary Deep Learning: Genetic algorithms and neural networks


    Price: $59.99 – $42.36
    (as of Dec 16,2024 07:53:08 UTC – Details)


    From the Publisher

    evo header

    evo header

    evo 1

    evo 1

    What do you get when you combine evolutionary algorithms (EA) with deep learning (DL)?

    Exciting new ways to improve current applications of machine learning and generative modeling!

    In Evolutionary Deep Learning, author and deep learning guru Micheal Lanham explains the basics of EA and DL, how to combine them, and why you should.

    In this hands-on guide, explore real applications of regression, classification, generative modeling, and reinforcement learning. Expand your knowledge to new areas in AI and ML, whether you’re an aspiring, novice, or experienced data scientist.

    Learning style

    Gain understanding of automated machine learning (AutoML) to find better deep learning
    Discover alternative machine learning strategies not yet well-known or practiced
    Focus on practical ways to optimize deep learning systems
    Cover a wide range of methods used in DL, from generative to reinforcement learning
    Reveal the pitfalls of deep learning and how to avoid—or overcome!—them
    Tackle hands-on projects featuring advanced DL applications, hyperparameters, NEAT networks and more

    evo 2

    evo 2

    Key Lessons

    A toolbelt for robust DL systems. In Chapter 1, get an introduction to evolutionary deep learning, a set of tools that help automate the development of effective, robust DL systems.

    Take a DEAP dive! In Chapter 3, tackle larger, more complex genetic algorithms using DEAP, an evolutionary computation (EC) toolkit that makes coding easier!

    The Importance of good genes… In Chapter 7, go hands-on to encode the network architecture of a CNN model into genes as you delve into this and other essentials for improving your network architecture.

    Notorious GANs! In Chapter 9, explore the fundamentals of GANs and discover how to optimize the process of training these notoriously difficult-to-train models.

    Take a step back… In Chapter 12, set aside deep learning for the moment, and take a step forward into new opportunities as we apply evolutionary solutions to other forms of machine learning.

    evo 3

    evo 3

    Setting it Apart

    Evolutionary Deep Learning introduces the next wave of AI, showcasing ways to improve everything from hyperparameters and network architecture to generative deep learning and deep reinforcement learning.

    This hands-on guide covers a wide range of DL methods, guiding you through practical projects that focus on optimizing DL solutions.

    Take hold of the power of evolutionary deep learning, and build DL systems that react quickly to changing business requirements in an efficient (ideally automated) way.

    Editorial ‏ : ‎ Manning (18 Julio 2023)
    Idioma ‏ : ‎ Inglés
    Tapa blanda ‏ : ‎ 360 páginas
    ISBN-10 ‏ : ‎ 1617299529
    ISBN-13 ‏ : ‎ 978-1617299520
    Dimensiones ‏ : ‎ 7.38 x 0.9 x 9.25 pulgadas


    Evolutionary Deep Learning: Genetic algorithms and neural networks

    In the world of artificial intelligence and machine learning, evolutionary deep learning is a cutting-edge approach that combines genetic algorithms with neural networks to create more efficient and effective algorithms.

    Genetic algorithms are inspired by the process of natural selection and evolution, where a population of candidate solutions evolves over time to find the optimal solution to a problem. These algorithms work by evaluating the fitness of each individual in the population, selecting the fittest individuals for reproduction, and then applying genetic operators such as mutation and crossover to create new offspring.

    Neural networks, on the other hand, are a type of artificial intelligence that is inspired by the way the human brain works. These networks consist of interconnected nodes, or neurons, that process and transmit information through a series of layers. By adjusting the weights of the connections between nodes, neural networks can learn to perform complex tasks such as image recognition, natural language processing, and more.

    When genetic algorithms are combined with neural networks, the result is a powerful algorithm that can evolve and adapt to changing environments and tasks. By using genetic algorithms to optimize the structure and weights of a neural network, researchers are able to create more efficient and accurate models that can outperform traditional machine learning algorithms.

    Evolutionary deep learning has been used in a variety of applications, including image and speech recognition, autonomous vehicles, and even game playing. As researchers continue to explore the potential of this approach, we can expect to see even more breakthroughs in artificial intelligence and machine learning in the future.
    #Evolutionary #Deep #Learning #Genetic #algorithms #neural #networks

Chat Icon