Protein–ligand binding affinity reflects the equilibrium thermodynamics of the protein–ligand binding process. Binding/unbinding kinetics is the other side of the coin. Computational models for interpreting the quantitative structure–kinetics relationship (QSKR) aim at predicting protein–ligand binding/unbinding kinetics based on protein structure, ligand structure, or their complex structure, which in principle can provide a more rational basis for structure-based drug design. Thus far, most of the public data sets used for deriving such QSKR models are rather limited in sample size and structural diversity. To tackle this problem, we have compiled a set of 680 protein–ligand complexes with experimental dissociation rate constants (koff), which were mainly curated from the references accumulated for updating our PDBbind database. Three-dimensional structure of each protein–ligand complex in this data set was either retrieved from the Protein Data Bank or carefully modeled based on a proper template. The entire data set covers 155 types of protein, with their dissociation kinetic constants (koff) spanning nearly 10 orders of magnitude. To the best of our knowledge, this data set is the largest of its kind reported publicly. Utilizing this data set, we derived a random forest (RF) model based on protein–ligand atom pair descriptors for predicting koff values. We also demonstrated that utilizing modeled structures as additional training samples will benefit the model performance. The RF model with mixed structures can serve as a baseline for testifying other more sophisticated QSKR models. The whole data set, namely, PDBbind-koff-2020, is available for free download at our PDBbind-CN web site (http://www.pdbbind.org.cn/download.php).

Abstract

Basic information is lacking about many pastoralist areas in the world. As a result, many services, programmes and policies do not effectively address the needs of pastoralist communities. The Government Cooperative Programme (GCP) project GCP/GLO/779/IF “Pastoralists-driven Data Management System”, was based on the idea that pastoralist associations could themselves collect, manage and share data from among their communities. This information could then be used to advocate for better targeted and pastoralist-friendly policies at local, national and international level. The project aimed at strengthening the capacities of pastoral organizations in data collection, analysis and information management, in order to facilitate evidence-based policy decision-making. It was implemented in Argentina, Chad and Mongolia, managed by the Pastoralist Knowledge Hub (PKH), and supported by the Agricultural Research Centre for International Development (Centre de coopération internationale en recherche agronomique pour le développement - CIRAD).

In Chad, the project was implemented by the Billital Maroobe Network (Réseau Billital Maroobé - RBM). An innovative approach for collecting data was developed through close partnership among the stakeholders involved, and was adopted during two successive surveys. The two questionnaires for collecting data on pastoralism were discussed and adapted to the national context, through the contribution of the participants and their deep knowledge of the field. This was one of the most innovative and successful aspects of the project, i.e. the pertinence of the method, as a result of the proactive involvement of the beneficiaries. The first survey, which aimed to identify and describe the pastoralist population, gathered information on 8,938 households. The second survey, which was more in-depth and aimed to assess the pastoralist economy and its contribution to the national economies, was conducted on a sample (based on the results of the first survey) of 1,010 households. As well as demonstrating that pastoralist organizations had the potential to successfully manage data, the surveys revealed the actual contribution of pastoralism to the economy of the country. In particular, they showed that pastoralism contributed to the national economy more than studies usually indicated, as, owing to specific characteristics, such as high levels of self-consumption, pastoralists' contribution to Gross Domestic Product (GDP) was often underestimated . During the project, it emerged that pastoralism could contribute up to 27 percent to the GDP of Chad.

Geographic coverage

National coverage

Analysis unit

Households

Universe

Pastoralist Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The first survey, which aimed to identify and describe the pastoralist population, gathered information on 8,938 pastoralist households in Chad. The second survey, which was more in-depth and aimed to assess the pastoralist economy and its contribution to the national economy, was conducted on a sample (based on the results of the first survey) of 1,010 pastoralist households.

Sampling deviation

The target regions for the second survey were originally 15, out of a total of 23 regions. However, owing to unforeseen constraints, only 10 regions were covered.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey was conducted in 2 rounds. For the first round, a short questionnaire was submitted to a representative of each household, addressing the following topics: i) households' socio-demographic characteristics; ii) livestock numbers and ownership; iii) land tenure and access; and iv) water access and use.

For the second round, the questionnaire focussed on the economic activity of pastoralists and their contribution to the national GDP. It covers the following topics: i) household identification ii) socio-demographic characteristics iii) livestock herd composition iv) products and final destination v) agricultural production, fishing and hunting activity vi) income and sales vii) household expenses viii) shock and adaptation strategies.

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Web Development Market Size 2025-2029

The web development market size is forecast to increase by USD 40.98 billion at a CAGR of 10.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing digital transformation across industries and the integration of artificial intelligence (AI) into web applications. This trend is fueled by the need for businesses to enhance user experience, streamline operations, and gain a competitive edge in the market. Furthermore, the rapid evolution of technologies such as Progressive Web Apps (PWAs), serverless architecture, and the Internet of Things (IoT) is creating new opportunities for innovation and expansion. However, this market is not without challenges. The ever-changing technological landscape requires web developers to continuously update their skills and knowledge. Additionally, ensuring web applications are secure and compliant with data protection regulations is becoming increasingly complex.
Companies seeking to capitalize on market opportunities and navigate challenges effectively should focus on building a team of skilled developers, investing in continuous learning and development, and prioritizing security and compliance in their web development projects. By staying abreast of the latest trends and technologies, and adapting quickly to market shifts, organizations can successfully navigate the dynamic the market and drive business growth.

What will be the Size of the Web Development Market during the forecast period?

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The market continues to evolve at an unprecedented pace, driven by advancements in technology and shifting consumer preferences. Key trends include the adoption of Agile methodologies, DevOps tools, and version control systems for streamlined project management. JavaScript frameworks, such as React and Angular, dominate front-end development, while Magento, Shopify, and WordPress lead in content management and e-commerce. Back-end development sees a rise in Python, PHP, and Ruby on Rails frameworks, enabling faster development and more efficient scalability. Interaction design, user-centered design, and mobile-first design prioritize user experience, while security audits, penetration testing, and disaster recovery solutions ensure website safety.
Marketing automation, email marketing platforms, and CRM systems enhance digital marketing efforts, while social media analytics and Google Analytics provide valuable insights for data-driven decision-making. Progressive enhancement, headless CMS, and cloud migration further expand the market's potential. Overall, the market remains a dynamic, innovative space, with continuous growth fueled by evolving business needs and technological advancements.

How is this Web Development Industry segmented?

The web development industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Retail and e-commerce
  BFSI
  IT and telecom
  Healthcare
  Others


Business Segment

  SMEs
  Large enterprise


Service Type

  Front-End Development
  Back-End Development
  Full-Stack Development
  E-Commerce Development


Deployment Type

  Cloud-Based
  On-Premises


Technology Specificity

  JavaScript
  Python
  PHP
  Ruby


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Spain
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By End-user Insights

The retail and e-commerce segment is estimated to witness significant growth during the forecast period. The market is experiencing significant growth due to the digital transformation sweeping various industries. E-commerce and retail sectors lead the market, driven by the increasing preference for online shopping and improved Internet penetration. To cater to this trend, businesses demand user-engaging web applications with smooth navigation, secure payment gateways, and seamless product search and purchase features. Mobile shopping's rise necessitates mobile app development and mobile-optimized websites. Agile development, microservices architecture, and UI/UX design are essential elements in creating engaging and efficient web solutions. Furthermore, AI, machine learning, and data analytics enable data-driven decision making, customer loyalty, and business intelligence.

Web hosting, cloud computing, API integration, and growth hacking are other critical components. Ensuring web accessibility, data security, and e-commerce development is also crucial for businesses in the digital age. Online advertising, email marketing, content strategy, brand building, and data visualization are essential aspects of digital marketing. Serverless computin

The CATH-Gene3D database describes protein families and domain architectures in complete genomes. Protein families are formed using a Markov clustering algorithm, followed by multi-linkage clustering according to sequence identity. Mapping of predicted structure and sequence domains is undertaken using hidden Markov models libraries representing CATH and Pfam domains. CATH-Gene3D is based at University College, London, UK.

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