Agricultural Biotechnology

 Agriculture Biotechnology

Agricultural Biotechnology is the use of new scientific techniques based on our understanding of DNA to improve crops and livestock that are not possible with conventional breeding alone. This can be achieved in part by modern molecular plant breeding techniques such as marker-assisted selection (MAS). MAS enables plant breeders to identify better traits in plants more rapidly than conventional breeding alone is capable of. Another aspect of agricultural biotechnology involves the use of recombinant DNA. Unlike molecular plant breeding, however, recombinant DNA technology results in new traits that cannot be achieved by conventional ways.

The genetic engineering of crops for improved agronomic and nutritional traits has been widely reviewed in the literature. Briefly, genetic engineering involves the introduction of a novel trait into a crop through the manipulation of its genetic material. Genetic material can be incorporated into the plant genome either via Agrobacterium-mediated transformation or by biolistic (gene gun) delivery, as illustrated in Figure 1. Transgenic, or genetically modified (GM) crops, have been commercially available in the United States since 1996. A well-known example of a transgenic plant is Golden Rice, which expresses β-carotene and was created philanthropically with the intent of alleviating vitamin A deficiency (VAD) in developing countries. Cisgenic plants, or plants that express genes from close wild relatives, are also being generated to obtain resistance genes which were lost over years of crop domestication. The Wheat Stem Rust Initiative, for example, is currently generating cisgenic versions of wheat which possess multiple resistance genes to the fungal pathogen Puccinia graminis f. sp. tritici Ugg99 from wild relatives. A third technology that falls under the umbrella of genetic engineering is RNA interference, or RNAi technology. In this case, the plant is designed to produce an antisense RNA to a particular gene, whose expression is then blocked via gene silencing. Examples of the use of this technology are GM papaya which are resistant to Papaya ring spot virus. This technology is responsible for saving the Hawaiian papaya industry. More recently, a new technology known as ‘gene editing’ has come to the forefront. Gene editing does not require the introduction of new gene sequences; rather, it can direct only one or two nucleotide changes in a plant genome and thus is exempt from the regulations that govern the production of genetically modified organisms. While no examples of gene-edited crops are commercially available at present, much research is being undertaken in this field and many new crop varieties will be realized in years to come using this biotechnological approach.

Biosafety for Sustainable Agriculture

Agricultural biotechnology has the potential to advance crop productivity production enhancement and improve food security at global level. There is a growing alarm about the genetically engineered crops and its environment effects on food chain. Though, acceptance of such technologies has consequences, there is need for creating biosafety regulatory systems to decrease and eradicate possible potential risks arising from agricultural biotechnology on flora and fauna. India, as a party to the Convention on Biological Diversity and Cartagena Protocol, has acquired the responsibility of strengthening her biosafety structure very sincerely. The present chapter points a relative lesson of the accessible national and international biosafety frameworks in place in India, with the UNEPGEF Framework implemented across 126 countries. The intention of this chapter is to categorize confrontations within the system and possibilities how to minimize the risk of genetically modified organisms to the society.

Deep Learning

What is deep learning?

Deep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain. Some form of deep learning powers most of the artificial intelligence (AI) applications in our lives today.

The chief difference between deep learning and machine learning is the structure of the underlying neural network architecture. “Nondeep,” traditional machine learning models use simple neural networks with one or two computational layers. Deep learning models use three or more layers, but typically hundreds or thousands of layers to train the models.

While supervised learning models require structured, labeled input data to make accurate outputs, deep learning models can use unsupervised learning. With unsupervised learning, deep learning models can extract the characteristics, features and relationships they need to make accurate outputs from raw, unstructured data. Additionally, these models can even evaluate and refine their outputs for increased precision.

Deep learning is an aspect of data science that drives many applications and services that improve automation, performing analytical and physical tasks without human intervention. This enables many everyday products and services, such as digital assistants, voice-enabled TV remotes, credit card fraud detection, self-driving cars and generative AI.

How deep learning works

Neural networks, or artificial neural networks, attempt to mimic the human brain through a combination of data inputs, weights and bias, all acting as silicon neurons. These elements work together to accurately recognize, classify and describe objects within the data.

Deep neural networks consist of multiple layers of interconnected nodes, each building on the previous layer to refine and optimize the prediction or categorization. This progression of computations through the network is called forward propagation. The input and output layers of a deep neural network are called visible layers. The input layer is where the deep learning model ingests the data for processing, and the output layer is where the final prediction or classification is made.

Another process called backpropagation uses algorithms, such as gradient descent, to calculate errors in predictions, and then adjusts the weights and biases of the function by moving backwards through the layers to train the model. Together, forward propagation and backpropagation enable a neural network to make predictions and correct for any errors. Over time, the algorithm becomes gradually more accurate.

Deep learning requires a tremendous amount of computing power. High-performance graphical processing units (GPUs) are ideal because they can handle a large volume of calculations in multiple cores with copious memory available. Distributed cloud computing might also assist. This level of computing power is necessary to train deep algorithms through deep learning. However, managing multiple GPUs on premises can create a large demand on internal resources and be incredibly costly to scale. For software requirements, most deep learning apps are coded with one of these three learning frameworks: JAX, PyTorch or TensorFlow.

Data Mining

 What Is Data Mining?

Data mining uses advanced algorithms and computing techniques to sift through large volumes of raw data, uncovering patterns and extracting valuable insights. Organizations leverage data mining to understand their customers better, enhance marketing strategies, increase sales, and cut costs effectively. By relying on solid data collection, warehousing, and processing, data mining transforms disparate data points into actionable intelligence, playing a crucial role in modern decision-making processes across various sectors.

• Data mining involves analyzing large datasets to identify patterns and extract valuable insights, enhancing business strategies like marketing and fraud detection.
• The data mining process consists of several critical steps, including understanding the business problem, preparing data, building models, and implementing change based on insights.
• Various data mining techniques, such as classification, clustering, and predictive analysis, help in transforming raw data into actionable intelligence.
• Data mining has broad applications across industries, including sales, marketing, manufacturing, fraud detection, and human resources, helping organizations improve efficiency and decision-making.
• While data mining can offer significant advantages by uncovering hidden trends, it also poses challenges such as complexity and potential privacy violations, as seen in the Facebook-Cambridge Analytica scandal.

Understanding the Mechanics of Data Mining

Data mining involves exploring and analyzing large blocks of information to glean meaningful patterns and trends. It's used in credit risk management, fraud detection, spam filtering, and as a market research tool to uncover group sentiments and opinions.

The data mining process breaks down into four steps:

1. Data is collected and loaded into data warehouses on-site or on a cloud service.
2. Business analysts, management teams, and information technology professionals access the data and determine how they want to organize it.
3. Custom application software sorts and organizes the data.
4. The end user presents the data in an easy-to-share format, such as a graph or table.

Key Techniques in Data Mining

Data mining uses algorithms and various other techniques to convert large collections of data into useful output. The most popular types of data mining techniques include association rules, classification, clustering, decision trees, K-Nearest Neighbor, neural networks, and predictive analysis.

• Association rules, also referred to as market basket analysis, search for relationships between variables. This relationship in itself creates additional value within the data set as it strives to link pieces of data. For example, association rules would search a company's sales history to see which products are most commonly purchased together; with this information, stores can plan, promote, and forecast.
• Classification uses predefined classes to assign to objects. These classes describe the characteristics of items or represent what the data points have in common with each other. This data mining technique allows the underlying data to be more neatly categorized and summarized across similar features or product lines.
• Clustering is similar to classification. However, clustering identifies similarities between objects, then groups those items based on what makes them different from other items. While classification may result in groups such as "shampoo," "conditioner," "soap," and "toothpaste," clustering may identify groups such as "hair care" and "dental health."
• Decision trees are used to classify or predict an outcome based on a set list of criteria or decisions. A decision tree is used to ask for the input of a series of cascading questions that sort the dataset based on the responses given. Sometimes depicted as a tree-like visual, a decision tree allows for specific direction and user input when drilling deeper into the data.
• K-Nearest Neighbor (KNN) is an algorithm that classifies data based on its proximity to other data. The basis for KNN is rooted in the assumption that data points that are close to each other are more similar to each other than other bits of data. This non-parametric, supervised technique is used to predict the features of a group based on individual data points.
• Neural networks process data through the use of nodes. These nodes are comprised of inputs, weights, and an output. Data is mapped through supervised learning, similar to how the human brain is interconnected. This model can be programmed to give threshold values to determine a model's accuracy.
• Predictive analysis strives to leverage historical information to build graphical or mathematical models to forecast future outcomes. Overlapping with regression analysis, this technique aims to support an unknown figure in the future based on current data on hand.

Data Analytics

What is Data Analytics? 

Data Analytics is the process of collecting, organizing and studying data to find useful information understand what’s happening and make better decisions. In simple words it helps people and businesses learn from data like what worked in the past, what is happening now and what might happen in the future.

Importance and Usage of Data Analytics

• Helps in Decision Making: It gives clear facts and patterns from data which help people make smarter choices.
• Helps in Problem Solving: It points out what's going wrong and why making it easier to fix problems.
• Helps Identify Opportunities: It shows trends and new chances for growth that might not be obvious.
• Improved Efficiency: It helps reduce waste, saves time and makes work smoother by finding better ways to do things.

Process of Data Analytics

Data analysts, data scientists and data engineers together create data pipelines which helps to set up the model and do further analysis. Data Analytics can be done in the following steps which are mentioned below:

1. Data Collection : Data collection is the first step where raw information is gathered from different places like websites, apps, surveys or machines. Sometimes data comes from many sources and needs to be joined together. Other times only a small useful part of the data is selected.
2. Data Cleansing : Once the data is collected it usually contains mistakes like wrong entries, missing values or repeated rows. In this step the data is cleaned to fix those problems and remove anything that isn’t needed. Clean data makes the results more accurate and trustworthy.
3. Data Analysis and Data Interpretation: After cleaning the data is studied using tools like Excel, Python, R or SQL. Analysts look for patterns, trends or useful information that can help solve problems or answer questions. The goal here is to understand what the data is telling us.
4. Data Visualization: Data visualization is the process of creating visual representation of data using the plots, charts and graphs which helps to analyze the patterns, trends and get the valuable insights of the data. By comparing the datasets and analyzing it data analysts find the useful data from the raw data.

 Datafication Technology

Introduction

Datafication is a process of transforming various aspects of our world into data, making it measurable, analyzable, and actionable. This phenomenon is reshaping industries, societies, and our daily lives. From healthcare to finance, datafication is revolutionizing the way we understand and interact with the world around us.

In this comprehensive guide, we will delve into the world of datafication technology, exploring its definition, history, applications, and impact. We will also discuss the challenges and ethical considerations surrounding this powerful tool, providing a well-rounded understanding of one of the most significant trends of our time.

What is Datafication?

At its core, datafication refers to the conversion of various elements, activities, and experiences into digital data. This process involves capturing, structuring, and representing information in a quantitative and standardized manner.

The goal of datafication is to unlock valuable insights, make informed decisions, and drive innovation. By translating diverse aspects of our world into a common language of data, we can identify patterns, develop predictive models, and create new opportunities for growth and improvement.

Datafication technology encompasses the tools, techniques, and infrastructure used to facilitate this transformation. This includes sensors, data collection platforms, analytics software, and visualization tools, among others.

What is Computer Networking?

 Computer Networking

A Computer Network is a system where two or more devices are linked together to share data, resources and information. These networks can range from simple setups, like connecting two devices in your home, to massive global systems, like the Internet. Below are some uses of computer networks

• Sharing devices such as printers and scanners: Multiple systems can access the same hardware, reducing the need for duplicate devices and lowering costs.
• Sharing Data: Teams can work on shared documents, applications or systems, which boosts efficiency.
• Communicating using web, email, video and instant messaging: Networks enable both real-time and delayed communication. Users can access information, send messages, participate in video calls and chat.
• Data management: Networks allow organizations to store data in a central or distributed location, making it easier to manage, secure and back up critical information.
• Remote access : Users can log into computers, servers or cloud platforms from different locations, supporting remote work and 24/7 access.

How does a computer network work?

Using email as a basic example, here’s how data moves through a network.

When a user wants to send an email, they first write the email and then press the “send” button. When the user presses “send,” an SMTP or POP3 protocol uses the sender’s wifi to direct the message from the sender node through the network switches. Here it is compressed and broken down into smaller and smaller segments (and ultimately into bits, or strings of ones and zeros).

Network gateways direct the bit stream to the recipient’s network, converting data and communication protocols as needed. When the bit stream reaches the recipient’s computer, the same protocols direct the email data through the network switches on the receiver’s network. In the process, the network reconstructs the original message until the email arrives in human-readable form in the recipient’s inbox (the receiver node).

Key networking components and devices

To fully understand computer networking, it is essential to review networking components and their functionality, including:

• IP address: An IP address is a unique number assigned to every network device in an Internet Protocol (IP) network. Each IP address identifies the device’s host network and its location on the network. When one device sends data to another, the data includes a “header” that consists of the IP addresses of both the sending and receiving devices.
• Nodes: A node is a network connection point that can receive, send, create or store data. It’s essentially any network device (for example, a computer, printer, modem, bridge or switch) that can recognize, process and transmit information to another network node. Each node requires some form of identification (such as an IP or MAC address) to receive access to the network.
• Routers: A router is a physical or virtual device that sends data “packets” between networks. Routers analyze the data within packets to determine the optimal transmission path and use sophisticated routing algorithms to forward data packets until they reach their intended destination node.
• Switches: A switch is a device that connects network devices and manages node-to-node communication across a network, ensuring that data packets reach their intended destinations. Unlike routers, which send information between networks, switches send information between nodes within a network.

What is Biotechnology ?

 What is Biotechnology?

Biotechnology is technology that utilizes biological systems, living organisms or parts of this to develop or create different products.

Biotechnology is a multidisciplinary field that involves the integration of natural sciences and engineering sciences in order to achieve the application of organisms and parts thereof for products and services. Specialists in the field are known as biotechnologists.

The term biotechnology was first used by Károly Ereky in 1919 to refer to the production of products from raw materials with the aid of living organisms. The core principle of biotechnology involves harnessing biological systems and organisms, such as bacteria, yeast, and plants, to perform specific tasks or produce valuable substances.

Biotechnology had a significant impact on many areas of society, from medicine to agriculture to environmental science. One of the key techniques used in biotechnology is genetic engineering, which allows scientists to modify the genetic makeup of organisms to achieve desired outcomes. This can involve inserting genes from one organism into another, and consequently, create new traits or modifying existing ones.

Other important techniques used in biotechnology include tissue culture, which allows researchers to grow cells and tissues in the lab for research and medical purposes, and fermentation, which is used to produce a wide range of products such as beer, wine, and cheese.

The applications of biotechnology are diverse and have led to the development of products like life-saving drugs, biofuels, genetically modified crops, and innovative materials. It has also been used to address environmental challenges, such as developing biodegradable plastics and using microorganisms to clean up contaminated sites.

Biotechnology is a rapidly evolving field with significant potential to address pressing global challenges and improve the quality of life for people around the world; however, despite its numerous benefits, it also poses ethical and societal challenges, such as questions around genetic modification and intellectual property rights. As a result, there is ongoing debate and regulation surrounding the use and application of biotechnology in various industries and fields.

Applications of biotechnology

Biotechnology has numerous applications, particularly in medicine and agriculture. Examples include the use of biotechnology in merging biological information with computer technology (bioinformatics), exploring the use of microscopic equipment that can enter the human body (nanotechnology), and possibly applying techniques of stem cell research and cloning to replace dead or defective cells and tissues (regenerative medicine). Companies and academic laboratories integrate these disparate technologies in an effort to analyze downward into molecules and also to synthesize upward from molecular biology toward chemical pathways, tissues, and organs.

In addition to being used in health care, biotechnology has proved helpful in refining industrial processes through the discovery and production of biological enzymes that spark chemical reactions (catalysts); for environmental cleanup, with enzymes that digest contaminants into harmless chemicals and then die after consuming the available “food supply”; and in agricultural production through genetic engineering.

Agricultural applications of biotechnology have proved the most controversial. Some activists and consumer groups have called for bans on genetically modified organisms (GMOs) or for labeling laws to inform consumers of the growing presence of GMOs in the food supply. In the United States, the introduction of GMOs into agriculture began in 1993, when the FDA approved bovine somatotropin (BST), a growth hormone that boosts milk production in dairy cows. The next year, the FDA approved the first genetically modified whole food, a tomato engineered for a longer shelf life. Since then, regulatory approval in the United States, Europe, and elsewhere has been won by dozens of agricultural GMOs, including crops that produce their own pesticides and crops that survive the application of specific herbicides used to kill weeds.

Studies by the United nation, the U.S. National Academy of Sciences, the Europian union, the American mediacl association, U.S. regulatory agencies, and other organizations have found GMO foods to be safe, but skeptics contend that it is still too early to judge the long-term health and ecological effects of such crops. In the late 20th and early 21st centuries, the land area planted in genetically modified crops increased dramatically, from 1.7 million hectares (4.2 million acres) in 1996 to 180 million hectares (445 million acres) by 2014. By 2014–15 about 90 percent of the corn, cotton, and soybeans planted in the United States were genetically modified. The majority of genetically modified crops were grown in the Americas.

Blockchain Technology

Introduction to Blockchain Technology

Blockchain is a revolutionary technology that functions as a shared, immutable digital ledger. The name "blockchain" comes from its structure data is organized in blocks, with each new block linked to the one before it, forming a continuous chain.

Each block contains crucial data, such as a list of transactions, a timestamp, and a unique identifier called a cryptographic hash. This hash is generated from the block's contents and the hash of the previous block, ensuring that each block is tightly connected to the one before it.

Blockchain's linked structure makes data tampering detectable by altering hashes and breaking the chain.

It acts as a distributed database, storing transactions across

the network.

Each transaction is verified by the majority, ensuring legitimacy.

This decentralization prevents any single party from manipulating the data.

Blockchain is decentralized and distributed, meaning no single authority controls it. Instead, multiple computers (nodes) on a network each have a copy of the blockchain, keeping the ledger synchronized. This setup ensures that once data, like a transaction, is recorded and confirmed, it becomes immutable almost impossible to alter or delete.

How does Blockchain Technology Work?

One of the famous use of Blockchain is Bitcoin. Bitcoin is a cryptocurrency and is used to exchange digital assets online. Bitcoin uses cryptographic proof instead of third-party trust for two parties to execute transactions over the Internet. Each transaction protects through a digital signature.

Blockchain Decentralization

There is no Central Server or System which keeps the data of the Blockchain. The data is distributed over Millions of Computers around the world which are connected to the Blockchain. This system allows the Notarization of Data as it is present on every Node and is publicly verifiable.

Blockchain nodes

A node is a computer connected to the Blockchain Network. Node gets connected with Blockchain using the client. The client helps in validating and propagating transactions onto the Blockchain. When a computer connects to the Blockchain, a copy of the Blockchain data gets downloaded into the system and the node comes in sync with the latest block of data on Blockchain. The Node connected to the Blockchain which helps in the execution of a Transaction in return for an incentive is called Miners.