Ahmed Sherif

Dai risultati di questo studio si pu concludere che il tasso di infezione degli endoprotozoi stato pi alto nei pesci d'acqua dolce di allevamento rispetto ai pesci selvatici (lago Borullus), inoltre la stagione estiva ha registrato il tasso di infezione pi alto e i protozoi pi prevalenti nella stagione estiva sono stati Eimeria spp ed Entamoeba in O. niloticus mentre in C. gariepinus i parassiti ematici sono stati gli endoprotozoi pi isolati Trypansoma e Babesia. Lo stato di salute dei pesci esaminati (Oreochromis niloticus e Clarias garipinus) stato influenzato in modo significativo da parametri dell'acqua come i composti azotati e lo stato batteriologico. I valori della conta batterica totale e dei coliformi totali sono risultati correlati positivamente con i fertilizzanti organici. Sono state riscontrate correlazioni positive tra l'acqua (composti azotati e stato batteriologico) e il tasso di infezione degli endoprotozoi. La concentrazione di metalli pesanti (Fe, Mn, Zn, Cu, Pb, Cd e Hg) nell'acqua risultata pi elevata nelle aziende agricole rispetto al lago Borullus. Nessuna correlazione tra il tasso di infezione e i livelli di metalli pesanti nell'acqua. La concentrazione di metalli pesanti negli organi segue la sequenza fegato > branchie > muscoli. Correlazione negativa tra i residui di metalli pesanti nei tessuti dei pesci e il tasso di infezione.

Les r sultats de cette tude permettent de conclure que le taux d'infection par les endo-protozoaires tait plus lev chez les poissons d'eau douce d' levage (fermes piscicoles) que chez les poissons sauvages (lac Borullus), que la saison estivale pr sentait le taux d'infection le plus lev et que les protozoaires les plus r pandus pendant la saison estivale taient Eimeria spp et Entamoeba chez O. niloticus, tandis que chez C. gariepinus, les parasites sanguins taient les plus isol s, Trypansoma et Babesia. L' tat de sant des poissons examin s (Oreochromis niloticus et Clarias garipinus) a t affect de mani re significative par les param tres de l'eau tels que les compos s azot s et l' tat bact riologique. Les valeurs de la num ration bact rienne totale et de la num ration des coliformes totaux sont en corr lation positive avec les engrais organiques. Il y avait des corr lations positives entre l'eau (compos azot et statut bact riologique) et le taux d'infection des endo-protozoaires. La concentration en m taux lourds (Fe, Mn, Zn, Cu, Pb, Cd et Hg) dans l'eau tait plus lev e dans les fermes que dans le lac Borullus. Aucune corr lation entre le taux d'infection et les niveaux de m taux lourds dans l'eau. La concentration de m taux lourds dans les organes est la suivante: foie > branchies > muscles. Corr lation n gative entre les r sidus de m taux lourds dans les tissus des poissons et le taux d'infection.

Aus den Ergebnissen dieser Studie konnte geschlossen werden, dass die Infektionsrate von Endoprotozoen bei gez chteten S wasserfischen (Fischfarmen) h her war als bei Wildfischen (Borullus-See), au erdem war die Infektionsrate in der Sommersaison am h chsten, und die vorherrschenden Protozoen in der Sommersaison waren Eimeria spp. und Entamoeba bei O. niloticus, w hrend bei C. gariepinus Blutparasiten am h ufigsten isoliert wurden. Der Gesundheitszustand der untersuchten Fische (Oreochromis niloticus und Clarias garipinus) wurde erheblich von Wasserparametern wie Stickstoffverbindungen und bakteriologischem Status beeinflusst. Die Werte der Gesamtbakterienzahl und der Gesamtcoliformenzahl korrelierten positiv mit organischen D ngemitteln. Die Schwermetallkonzentration (Fe, Mn, Zn, Cu, Pb, Cd und Hg) im Wasser war in den Farmen h her als im Borullus-See. Die Schwermetallkonzentration in den Organen folgt der Reihenfolge Leber > Kiemen > Muskeln. Negative Korrelation zwischen Schwermetallr ckst nden in Fischgeweben und Infektionsrate.

Os resultados deste estudo permitem concluir que a taxa de infe o de endoprotozo rios foi mais elevada nos peixes de gua doce cultivados (pisciculturas) do que nos peixes selvagens (Lago Borullus), e que a esta o do ver o foi a que registou a taxa de infe o mais elevada e que os protozo rios mais prevalecentes na esta o do ver o foram a Eimeria spp e a Entamoeba em O. niloticus, enquanto que em C. gariepinus os parasitas sangu neos foram os Trypansoma e a Babesia mais isolados. O estado de sa de dos peixes examinados (Oreochromis niloticus e Clarias garipinus) foi significativamente afetado pelos par metros da gua, tais como o composto azotado e o estado bacteriol gico. Os valores da contagem bacteriana total e da contagem total de coliformes correlacionaram-se positivamente com os fertilizantes org nicos. Houve correla es positivas entre a gua (composto azotado e estado bacteriol gico) e a taxa de infe o de endoprotozo rios. A concentra o de metais pesados (Fe, Mn, Zn, Cu, Pb, Cd e Hg) na gua foi mais elevada nas explora es do que no lago Borullus. N o h correla o entre a taxa de infe o e os n veis de metais pesados na gua. A concentra o de metais pesados nos rg os a seguinte: f gado > br nquias > m sculos. Correla o negativa entre os res duos de metais pesados nos tecidos dos peixes e a taxa de infe o.

Na podstawie wynik w tego badania można stwierdzic, że wskaźnik zarażenia endoprotozoa byl wyższy u hodowlanych ryb slodkowodnych (hodowle ryb) niż u ryb dzikich (jezioro Borullus), r wnież sezon letni mial najwyższy wskaźnik zarażenia, a najbardziej dominującymi pierwotniakami w sezonie letnim byly Eimeria spp i Entamoeba u O. niloticus, podczas gdy u C. gariepinus pasożyty krwi byly najczęściej izolowanymi endoprotozoa Trypansoma i Babesia. Stan zdrowia badanych ryb (Oreochromis niloticus i Clarias garipinus) byl znacząco uzależniony od parametr w wody, takich jak zawartośc związk w azotowych i stan bakteriologiczny. Og lna liczba bakterii i og lna liczba bakterii z grupy coli korelowaly dodatnio z nawozami organicznymi. Stężenie metali ciężkich (Fe, Mn, Zn, Cu, Pb, Cd i Hg) w wodzie bylo wyższe w gospodarstwach niż w jeziorze Borullus. Stężenie metali ciężkich w narządach bylo następujące: wątroba > skrzela > mięśnie. Ujemna korelacja między pozostalościami metali ciężkich w tkankach ryb a wskaźnikiem infekcji.

Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologiesKey FeaturesGet up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choiceEliminate mundane tasks in data engineering and reduce human errors in machine learning modelsFind out how you can make machine learning accessible for all users to promote decentralized processesBook DescriptionEvery machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort. This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you’ll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle. By the end of this machine learning book, you’ll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks. What you will learnExplore AutoML fundamentals, underlying methods, and techniquesAssess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenarioFind out the difference between cloud and operations support systems (OSS)Implement AutoML in enterprise cloud to deploy ML models and pipelinesBuild explainable AutoML pipelines with transparencyUnderstand automated feature engineering and time series forecastingAutomate data science modeling tasks to implement ML solutions easily and focus on more complex problemsWho this book is forCitizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.

A solution-based guide to put your deep learning models into production with the power of Apache SparkKey FeaturesDiscover practical recipes for distributed deep learning with Apache SparkLearn to use libraries such as Keras and TensorFlow Solve problems in order to train your deep learning models on Apache SparkBook DescriptionWith deep learning gaining rapid mainstream adoption in modern-day industries, organizations are looking for ways to unite popular big data tools with highly efficient deep learning libraries. As a result, this will help deep learning models train with higher efficiency and speed. With the help of the Apache Spark Deep Learning Cookbook, you’ll work through specific recipes to generate outcomes for deep learning algorithms, without getting bogged down in theory. From setting up Apache Spark for deep learning to implementing types of neural net, this book tackles both common and not so common problems to perform deep learning on a distributed environment. In addition to this, you’ll get access to deep learning code within Spark that can be reused to answer similar problems or tweaked to answer slightly different problems. You will also learn how to stream and cluster your data with Spark. Once you have got to grips with the basics, you’ll explore how to implement and deploy deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in Spark, using popular libraries such as TensorFlow and Keras. By the end of the book, you'll have the expertise to train and deploy efficient deep learning models on Apache Spark. What you will learn Set up a fully functional Spark environment Understand practical machine learning and deep learning concepts Apply built-in machine learning libraries within Spark Explore libraries that are compatible with TensorFlow and Keras Explore NLP models such as Word2vec and TF-IDF on Spark Organize dataframes for deep learning evaluation Apply testing and training modeling to ensure accuracy Access readily available code that may be reusableWho this book is forIf you’re looking for a practical and highly useful resource for implementing efficiently distributed deep learning models with Apache Spark, then the Apache Spark Deep Learning Cookbook is for you. Knowledge of the core machine learning concepts and a basic understanding of the Apache Spark framework is required to get the best out of this book. Additionally, some programming knowledge in Python is a plus.

Learn to get the most out of your business data to optimize your business About This Book • This book will enable and empower you to break free of the shackles of spreadsheets • Learn to make informed decisions using the data at hand with this highly practical, comprehensive guide • This book includes real-world use cases that teach you how analytics can be put to work to optimize your business • Using a fictional transactional dataset in raw form, you'll work your way up to ultimately creating a fully-functional warehouse and a fleshed-out BI platform Who This Book Is For This book is for anyone who has wrangled with data to try to perform automated data analysis through visualizations for themselves or their customers. This highly-customized guide is for developers who know a bit about analytics but don't know how to make use of it in the field of business intelligence. What You Will Learn • Create a BI environment that enables self-service reporting • Understand SQL and the aggregation of data • Develop a data model suitable for analytical reporting • Connect a data warehouse to the analytic reporting tools • Understand the specific benefits behind visualizations with D3.js, R, Tableau, QlikView, and Python • Get to know the best practices to develop various reports and applications when using BI tools • Explore the field of data analysis with all the data we will use for reporting In Detail Business Intelligence (BI) is at the crux of revolutionizing enterprise. Everyone wants to minimize losses and maximize profits. Thanks to Big Data and improved methodologies to analyze data, Data Analysts and Data Scientists are increasingly using data to make informed decisions. Just knowing how to analyze data is not enough, you need to start thinking how to use data as a business asset and then perform the right analysis to build an insightful BI solution. Efficient BI strives to achieve the automation of data for ease of reporting and analysis. Through this book, you will develop the ability to think along the right lines and use more than one tool to perform analysis depending on the needs of your business. We start off by preparing you for data analytics. We then move on to teach you a range of techniques to fetch important information from various databases, which can be used to optimize your business. The book aims to provide a full end-to-end solution for an environment setup that can help you make informed business decisions and deliver efficient and automated BI solutions to any company. It is a complete guide for implementing Business intelligence with the help of the most powerful tools like D3.js, R, Tableau, Qlikview and Python that are available on the market. Style and approach Packed with real-world examples, this pragmatic guide helps you polish your data and make informed decisions for your business. We cover both business and data analysis perspectives, blending theory and practical hands-on work so that you perceive data as a business asset.