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1. Introduction
Inspired by the biological neurons, deep neural networks are well known for their ability to learn data representation from a huge amount of labeled data such as the famous convolutional neural networks (CNNs). Specifically, given a specific task such as image classification, we usually need to train a deep neural network from scratch with enough training data so that our model can achieve acceptable performance. However, sufficient training data for a new task is not always available as manually collecting and annotating data are labor-intensive and expensive. Especially in some specific domains such as healthcare, a privacy concern is also raised. Meanwhile, training a deep network with a large dataset is usually time-consuming and involves huge computational resources. Intuitively, it is not realistic and practical to learn from zero, because the real way we humans learn is that we usually try to solve a new task based on the knowledge obtained from past experiences. For example, once we have learned a programming language (e.g., Java), we can easily learn a new one (e.g., Python) as the basic programming fundamentals are the same.
Transfer Learning is an inspiring method that can help apply the knowledge gained from a source task to a new/target task. Specifically, the goal of transfer learning is to obtain some transferable representations between the source domain and target domain and utilize the stored knowledge to improve the performance on the target task. Note that transfer learning is an extensive research topic that involves many learning methods. In particular, deep domain adaptation gets the most attention in recent years among these methods. Therefore, after briefly introducing the transfer learning in this research, we pay our attention to analyzing and discussing the recent advances in deep domain adaptation.
The rest of this chapter is structured as follows. In Section 2, we give an overview and specific definitions of transfer learning. In Section 3, we summarize the main approaches for deep domain adaptation. In Section 4, 5 and 6, we discuss the details for conducting deep domain adaptation. The recent applications based on deep domain adaptation methods are also introduced in Section 7. Finally, we conclude this research and discuss future trends in Section 8.
2. Overview
We first give some notations and definitions which match those from the survey paper written by Pan et al. [1], and these notations are also widely adopted in many other survey papers such as [2, 3].
Definition 1 (Domain [1]) Given a specific dataset X=X1…Xn∈X, where X denotes the feature space, and a marginal probability distribution on the dataset PX. A domain can be defined as D=XPX. Therefore, a domain consists of two components: the feature space and the marginal probability distribution on the dataset.
Definition 2 (Task [1]) Given a specific dataset X=X1…Xn∈X and their labels Y=Y1…Yn∈Y, where Y denotes the label space. A task can be defined as T=YFX, where F is an objective predictive function to learn, which can be seen as a conditional distribution PYX.
Definition 3 (Transfer Learning [1]) Given a source domain Ds and its corresponding task Ts, where the learned function Fs can be interpreted as some knowledge obtained in Ds and Ts. Our goal is to get the target predictive function Ft for target task Tt with target domain Dt. Transfer learning aims to help improve the performance of Ft by utilizing the knowledge Fs, where Ds≠Dt or Ts≠Tt.
In short, transfer learning can be simply denoted as
Ds,Ts→Dt,TtE1
Transfer learning is a very broad research subject in machine learning. In this research, we mainly focus on transfer learning based on deep neural networks (i.e., deep learning). Therefore, as shown in Figure 1, based on Ds≠Dt or Ts≠Tt, we can have three scenarios when applying transfer learning. Note that when Ds=Dt and Ts=Tt, the problem becomes a traditional deep learning task. In such case, a dataset is usually divided into a training dataset Ds and a test training dataset Dt, then we can train a neural network F on Ds and apply the pre-trained model F to Dt.
Figure 1.
Hierarchically-structured taxonomy of transfer learning in this survey.
When Ds=Dt and Ts≠Tt, transfer learning is usually transformed into a multi-task learning problem. Since the source domain and the target domain share the same feature space, we can utilize one giant neural network to solve different types of tasks at the same time. For example, multi-task learning is widely used in the autopilot system. Given an input image, we can utilize a deep neural network that has enough capacity to recognize the cars, the pedestrians, traffic signs, and the locations of these objectives in the image.
When Ds≠Dt and Ts=Tt, deep domain adaptation technique is usually used to transfer the knowledge from the source to the target. In general, the goal of domain adaptation is to learn a mapping function F to reduce the domain divergence between Ds and Dt including distribution shift and different feature spaces. Formally, the definition of domain adaptation can be defined as.
Definition 4 (Domain Adaptation) Given a source domain Ds for task Ts and a target domain Dt for task Tt, where Ds≠Dt. Domain adaptation aims to learn a predictive function Ft so that the knowledge obtained from Ds and Ts can be used for enhancing Ft. In other words, the domain divergence is adapted in Ft.
When Ds≠Dt and Ts≠Tt, transfer learning should be conducted carefully. If the data in source domain Ds is very different from that in target domain Dt, brute-force transfer may hurt the performance of predictive function Ft, not to mention the scenario when source task Ts and target task Tt are also different. From a literature review of deep learning, we notice that there is little research in this scenario and it is still an open question.
In summary, the above definitions give us the answer to what to transfer, and the four scenarios demonstrate the research issue of when to transfer. As shown in Figure 2, in contrast to the categorization of transfer learning that is introduced in the survey paper [1], our discussions mainly focus on transfer learning in deep neural networks. In the following sections, we pay our attention to how to transfer. Specifically, we will introduce and summarize three main methods for deep domain adaptation.
Figure 2.
Categorization of transfer learning based on labels. (The image is from Pan [1]).
3. Deep domain adaptation
According to the definition of domain adaptation, we assume that the tasks of the source domain and target domain are the same, and the data in the source domain and target domain are different but related (i.e., Ds≠Dt and Ts=Tt). In general, the goal of domain adaptation is to reduce the domain distribution discrepancy between the source domain and the target domain so that the knowledge learned from the source domain can be further applied to the target domain.
Compared with the traditional shallow method, deep domain adaptation mainly focuses on utilizing deep neural networks to improve the performance of the predictive function Ft. Formally, a neural network can be denoted as
Ŷ=FXΘE2
where F denotes a neural network and Θ is a set of parameters, Ŷ represents the predicted label of input X. The deep neural architecture is usually specifically designed to learn representation with back-propagation from the source and target data for domain adaptation. The intuition behind domain adaptation is that we can find some domain-invariant schemes or sharing features from related datasets. In other words, we ensure that the internal representations learned from related domains in deep neural networks are indiscriminating. In this section, based on the published works in recent years, we discuss how to reduce the domain divergence in deep neural networks and categorize deep domain adaptation approaches into three main ways, including fine-tuning networks, domain-adversarial learning, and sample-reconstruction approach.
3.1 Categorization based on implementing approaches
3.1.1 Fine-tuning networks
A natural way to reduce the domain shift is to fine-tune the pre-trained networks with the data in the target domain, as the past researches show that the internal representations of deep convolutional neural networks learned from large datasets, such as ImageNet, can be effectively used for solving a variety of tasks in computer vision. Specifically, for a pre-trained model such as VGG [4] or ResNet [5], we can keep its earlier layers fixed/frozen and only fine-tune the weights in the high-level portion of the network by continuing back-propagation. Or we can fine-tune all the layers if needed. The main idea behind this is that the learned low-level representations in the earlier layers mainly consist of generic features such as the edge detector. During fine-tuning the networks, the discrepancy between the source domain and target domain is usually measured by a criterion such as class labels based criterion, and statistic criterion. Instead of directly using the measurement as a criterion to adjust networks, regularization techniques can also be used for fine-tuning, which mainly includes parameter regularization and sample regularization.
3.1.2 Adversarial domain adaptation
Generative Adversarial Networks (GANs) are a promising method and get the most attention due to its unsupervised learning approach and the flexibility of generator design. Since the first version of GANs is proposed by Goodfellow et al. [6], many variants based on it have been proposed for solving different types of tasks. Specifically, there are normally two networks in GANs, namely a generator and a discriminator. The generator can synthesize fake examples from an input space called latent space and the discriminator can distinguish real samples from fake. By alternately training these two players, both of them can enhance their abilities. The fundamental idea behind GANs is that we want the data distribution learned by the generator is close to the true data distribution. And this is very similar to the principle of domain adaptation, which is that the learned data distribution between the source domain and the target domain is close to each other (i.e., domain confusion). For example, a representative work related to adversarial domain adaptation is [7], in which a generalized framework based on GANs is introduced. Instead of using GANs for domain-adversarial learning, a more simple but powerful method is to add a domain classifier into a general deep network for encouraging domain confusion [8].
3.1.3 Data-reconstruction approaches
Data-reconstruction approaches are a type of deep domain adaptation method that utilizes the deep encoder-decoder architectures, where the encoder networks are used for the tasks and the decoder network can be treated as an auxiliary task to ensure that the learned features between the source domain and target domain are invariant or sharing. There are mainly two types of methods to conduct data reconstruction: (1) A typical way is by utilizing an encoder-decoder deep network for domain adaptation such as [9]; (2) Another way is to conduct sample reconstruction based on GANs such as cycle GANs [10].
3.1.4 Hybrid approaches
In general, the core idea of deep domain adaptation is to learn indiscriminating internal representations from the source domain and target domain with deep neural networks. Therefore, we can combine different kinds of approaches discussed above to enhance the overall performance. For example, in [11], they adopt both the encoder-decoder reconstruction method and the statistic criterion method.
3.2 Categorization based on learning methods
Based on whether there are labels in the target domain datasets, we can further divide the above approaches into supervised learning and unsupervised learning. Note that the unsupervised learning methods can be generalized and applied to semi-supervised cases, therefore, we mainly discuss these two methods in this research. Table 1 shows the categorization of deep domain adaptation based on whether the labels are needed in the target domain. A similar categorization is also introduced in [12].
Supervised
Unsupervised
Fine-tuning
Label criterion
✓
Statistic criterion
✓
Parameter regularization
✓
✓
Sample regularization
✓
✓
Adversarial-domain
Domain classifier
✓
Target data generating
✓
Sample-reconstruction
Encoder-decoder-based
✓
GAN-based
✓
Table 1.
Categorization of deep domain adaptation based on whether the labels in the target domain are available.
3.3 Categorization based on data space
In some survey papers, the domain adaptation methods can also be categorized into two main methods based on the similarity of data space. (1) Homogeneous domain adaptation represents that the source data space and the target data space is the same (i.e., Xs=Xt). E.g., the source dataset consists of some images of cars from open public datasets, and the images of cars in the target dataset are manually collected from the real world. (2) Heterogeneous domain adaptation represents that the datasets are from different data space (i.e., Xs≠Xt). E.g., text vs. images. Figure 3 presents the topology that is introduced in [12].
Figure 3.
Categorization of domain adaptation based on feature space. (The image is from Wang [12]).
4. Fine-tuning networks
In the last section, we categorize the main methods to conduct domain adaptation with deep neural networks and give some high-level information. In this section, we firstly discuss the details of four approaches for fine-tuning networks in Table 1.
4.1 Label criterion
The most basic approach to conduct domain adaptation is to fine-tune a pre-trained network with labeled data from the target domain. Hence, we assume that the labels in the target dataset are available and we can utilize a supervised learning approach to adjust the weights/parameters in the network. Based on the definition of the task, our target task Tt based on label criterion approach is
Tt=LYtŶt=LYtFtXtΘE3
where L denotes a loss function, such as the cross-entropy loss LYŶ=−YlogŶ−1−Ylog1−Ŷ, which is commonly used in many works. Note that Θ is a set of parameters which is normally initialized with weights from the pre-trained model.
As discussed in Section 3.1, a question is that how many layers in the neural network we should freeze. In general, there are two main factors that can influence the fine-tuning procedure: the size of the target dataset and its similarity to the source domain. Based on the two factors, some common rules of thumb are introduced in [13]. One typical work is [14], in which a unified supervised method for deep domain adaptation is proposed. Another problem is that what if there are no labels in the target dataset. Therefore, an unsupervised learning method must be applied to the target dataset for domain confusion.
4.2 Statistic criterion
From the definition of domain adaptation, we see that the fundamental goal is to reduce the domain divergence between the source domain and target domain so that the function Ft can achieve good performance on the target domain. Therefore, it’s important and valuable to use a criterion to measure the divergence between different domains. In other words, we need to have a measurement of the difference of probability distributions from different datasets.
Maximum Mean Discrepancy (MMD) [15] is a well-known criterion that is widely adopted in deep domain adaptation such as [16, 17]. Specifically, MMD computes the mean squared difference between the two datasets, which can be defined as
where ϕ denotes the feature space map. In practice, we can use the kernel method k to make MMD be computed easily (i.e., Gaussian kernel).
H-divergence [18] is a more general theory to measure the domain divergence, which is defined as
dHDsDt=2suph∈HPrxs∼Dshxs=1−Prxt∼Dthxt=1E5
where h∈H is a binary classifier (i.e., hypothesis). For example, in [19], domain-adversarial networks are proposed based on this statistic criterion (note that this method can belong to the approach of domain-adversarial learning).
4.3 Parameter regularization
Note that for fine-tuning networks with the label criterion or the statistic criterion, the weights in the networks are usually shared between the source domain and target domain. In contrast to these methods, some researchers argue that the weights for each domain should be related but not shared. Based on this idea, the authors in [20] propose a two-stream architecture with a weight regularization method. Two types of regularizers are introduced: L2 norm or in an exponential form.
rwθjsθjt=ajθjs+bj−θjtor=expajθjs+bj−θjt−1E6
where aj and bj are different parameters in each layer. Rather than using two networks for domain adaptation, in [21], they introduce a domain guided method to drop some weights in the networks directly.
4.4 Sample regularization
Alternatively, instead of adapting the parameters in the networks, we can re-weight the data in each layer of feed-forward neural networks. The typical method to reduce internal covariate shit in deep neural networks is to conduct batch normalization during training [22].
x̂i=γxi−μσ2+ε+βE7
Note that xi usually denotes the hidden activation of input sample xi in each layer of a neural network (e.g., the output feature map of each convolutional layer). μ=1B∑i=1Bxi and σ=1B∑i=1Bxi−μ2. B is the batch size, γ and β are two hyper-parameters to learn. Based on this method, [23] propose a revised method for practical domain adaptation. And in [24], researchers adopt instance normalization for stylization.
5. Adversarial domain adaptation
Instead of directly fine-tuning networks, adversarial domain adaptation is an appealing alternative to unsupervised learning. It mainly addresses the problem that there are abundant labeled data in the source domain but sparse/limited unlabeled samples in the target domain. The core idea of the adversarial domain adaptation is based on GANs. Specifically, a generalized architecture to implement this idea is proposed in [7]. In this section, we detail two main ideas: target data generating and domain classifier.
5.1 Target data generating
To overcome the limitation of sparse unlabeled data, target data generating is an approach to directly generate samples with labels for the target domain so that we can utilize them to train a classifier for the new task. One representative work is the CoGANs [25], in which there are two GANs involved: one for processing the labeled data in the source domain and another for processing the unlabeled data in the target domain. Part of the weights in the two generators is shared/tied in order to reduce the domain divergence. In addition to two discriminators for classifying the fake and real samples, there is also an extra classifier to classify the samples based on the information of labels in the source domain. By jointly training these two GANs, we can generate unlimited pairs of data, in which each pair consists of a synthetic source sample and a synthetic target sample and each pair shares the same label. Therefore, after finishing jointly training the two GANs, the pre-trained extra classifier is the function Ft that we need for solving the new task. Similar work can also be found in [26], in which a transformation in the pixel space is introduced.
In summary, target data generating is a domain adaptation approach that focuses on generating target data, which can also be treated as an auxiliary task to reduce domain shift by a weight sharing mechanism between two GANs. The main disadvantage is that the training cost for generating synthesized samples with two GANs is expensive especially when the target datasets consist of large-size samples such as high-resolution images.
5.2 Domain classifier
Instead of directly synthesizing labeled data for domain adaptation, an alternative way is to add an extra domain classifier to enough domain confusion. The role of domain classifier is similar to that of the discriminator in GANs, it can distinguish the data between the source domain and target domain (the discriminator in GANs is responsible for recognizing the fake from the real data). With the help of an adversarial learning approach, the domain classifier can help the network learn domain-invariant representation from the source domain and the target domain. In other words, the trained model can be directly used for the target/new task.
Therefore, the key is how to conduct adversarial learning with the domain classifier. In [8], a gradient reversal layer (GRL) before domain classifier is introduced to maximize the gradients for encouraging domain confusion (we normally minimize the gradients for reducing the scalar value of a loss function). In [27], a domain confusion loss is proposed beside the domain classifier loss.
6. Sample-reconstruction approaches
The core idea of the data-reconstruction approach is to utilize the reconstruction as an auxiliary task for encouraging domain confusion in an unsupervised manner. In this section, we discuss two types of approaches that are mainly addressed in recent years, including the encoder-decoder-based method and the GANs-based method.
6.1 Encoder-decoder-based approaches
To reconstruct the samples, the basic method is that we can adopt an auto-encoder framework, in which there is an encoder network and decoder network. The encoder can map an input sample into a hidden representation and the decoder can reconstruct the input sample based on the hidden representation. In particular, the encoder-decoder networks for domain adaptation typically involve a shared encoder between the source domain and target domain so that the encoder can learn some domain-invariant representation. An earlier work can be found in [9], in which the stacked denoising auto-encoder is adopted for sentiment classification.
Recently, a typical work called deep reconstruction-classification networks is introduced in [11], in which the encoder and decoder are both implemented with convolutional networks. Specifically, the convolutional encoder is used for supervised classification of the labeled data from the source domain. Meanwhile, it also maps the unlabeled data from the target domain into hidden representation, which is further decoded by the convolutional encoder for reconstructing the input. By jointly training these networks with the data from the source and target domains, the shared encoder can learn some common representations from both datasets, which results in domain adaptation. Other similar work based on auto-encoder can also be found in [11, 28].
6.2 GAN-based approaches
Traditionally, the GANs [6] consists of a generator and discriminator, where the generator can be seen as a decoder network which can decode some random noise into a fake sample and the discriminator can be treated as an encoder network which is used to encode the sample into some high-level features for classification (i.e., fake or real). Instead of just using a decoder network as the generator, a typical work known as Cycle GANs is proposed in [10], in which the generator is implemented with an encoder-decoder network. Specifically, this encoder-decoder generator is used for dual learning: Gxs→xt and Fxt→xs. And the discriminator also has two roles: to distinguish between the fake xt and real xt, and to distinguish between the fake xs and real xs. By alternatively training these two players in GANs, the encoder-decoder generator can lean a reversible mapping function. In other words, the domain-invariant features are obtained from two different datasets. However, one remaining problem is that the encoder-decoder network usually consists of millions of parameters, with enough capacity, it can map an input image from the source domain to any random image which is close to the target domain. Therefore, in addition to using the standard adversarial loss for training the GANs, the consistency loss (i.e., L1 norm) is also proposed to make sure that FGx≈x.
LcycGF=Exs∼dataxsFGxs−xs1+Ext∼dataxtFGxt−xt1E8
where Gxs denotes fake xt and FGxs is reconstructed xs (i.e., Fxt→xs). Inspired by the Cycle GANs, many variants based on encoder-decoder generator are proposed for domain adaptation, such as the Disco GANs [29] and the Dual GANs [30].
7. Applications
As shown in Figure 1, the scope of transfer learning is far beyond traditional machine learning. Theoretically, the problems addressed by deep learning can also be solved by transfer learning. In this section, we narrow the discussion to the typical real-world applications based on deep domain adaptation. In Section 7.1, we summarize the most methods discussed above for computer vision. In Section 7.2, we discuss the applications beyond the context of image processing, including natural language processing, speech recognition and other real-world applications based on processing time-serial data.
7.1 Applications in computer vision
7.1.1 Image classification and recognition
Classification is a fundamental and most basic problem in machine learning, most of the above methods are introduced to address this problem. Therefore, we pay our attention to the advances that deep domain adaptation can bring for image classification, rather than repeatedly introducing them. Probably the most well-known example is fine-tuning a giant network that is pre-trained with the ImageNet dataset for real-world applications such as pet recognition. Despite the fact that manually collecting data is time-consuming and expensive, the data collected from the real-world is usually imbalanced (e.g., there are only 100 images of class A but 10,000 images of class B). If we train a classifier from scratch, the performance can be poor because it cannot learn enough knowledge from the limited samples (e.g., class A). However, if we utilize a pre-trained model based on the well-collected ImageNet and fine-tune it, the problem caused by an imbalance dataset will be reduced because the model has already obtained rich knowledge from the source domain.
Another typical real-world application that we can gain benefits from domain adaptation is face recognition. A general approach to solve this problem is to train a model based on a dataset of labeled face images. In contrast, the large-scale unlabeled video datasets are always available. However, the divergence of data in the video is usually limited and there remains a clear gap between these two different domains. In order to utilize the rich information from video and overcome these challenges, the authors in [31] propose a framework for face recognition in unlabeled video based on the adversarial domain adaptation approach.
7.1.2 Object detection
The recent object detection methods are mainly driven by two approaches: Faster R-CNN [32] and YOLO [33]. Specifically, two tasks are mainly involved in object detection: The first one is to detect whether there are objects in an input image (i.e., to output the bounding box of each object in the image); Meanwhile, the object in each bounding box is also classified. Object detection is a very common learning task in many real-world applications such as intelligent surveillance systems [34]. By utilizing domain adaptation approaches for the new task of object detection in the wild, the Domain Adaptive Faster R-CNN is introduced in [35]. And the core idea is also to utilize domain classifier with GRL to encourage domain confusion (i.e., in Section 5.2). Another recent similar work is also discussed in [36], in which the GRL is also adopted and the process of conducting domain adaptation is divided into two stages called progress domain adaptation.
7.1.3 Image segmentation
The convolutional encoder-decoder architecture has achieved great success for image segmentation in recent years. Specifically, given an input image, the convolutional encoder-decoder network can map this image into a pixel-level classification image (i.e., each pixel is classified with a label). The problem of domain shifts can also appear in this task, which results in poor performance on a new domain. In [37], the researchers introduce a domain adversarial learning method which includes both global and category-specific techniques. They argue that two factors can cause domain shift: the global changes between the two distinct domains and the category-specific changes. (i.e., the distribution of cars from two different cities may be different.) Based on this assumption, two new loss functions are introduced, one is used for reducing the global distribution shift between the source images and target images and the other is used for adapting the category-specific divergence between the target images and the transferring label statistics. Instead of just using a simple adversarial objective, the authors in [38] propose an iterative optimization procedure based on GANs for addressing domain shift.
7.1.4 Image-to-image translation
As mentioned in Section 6.2, Cycle GANs [10] is a typical method for image-to-image translation based on deep domain adaptation. In general, image-to-image translation denotes that we can map an image from the source domain to the target domain and vice versa. One real task that is also addressed in Cycle GANs is the style transfer application. To our best knowledge, the algorithm of neural style transfer is firstly proposed in [39], the core idea in this paper is how to define the content loss and style loss between the source data and the target data. Actually, it can be treated as a statistic criterion approach which is discussed in Section 4.2. In the paper of demystifying neural style transfer [40], the authors show that matching Gram matrices (i.e., style loss) is equivalent to minimize the MMD (i.e., Eq. 4). Based on this argument, they introduce several style transfer methods by utilizing different types of kernel functions in the MMD and achieve impressive results.
7.1.5 Image caption
An interesting but challenging task is to utilize deep neural networks to describe an input image with natural language, which is well known as the image caption. Specifically, the goal of image caption is to learn a mapping function Ft, so that we can get FtImage→Text and vice versa. Note that there are two different data space involved in this task: a dataset with images vs. a dataset with text. Therefore, based on the categorization methods which are discussed in Section 3.3, image caption belongs to heterogeneous domain adaptation. A general method to implement this idea is to utilize a CNN-RNN architecture (i.e., recurrent neural network), where the CNN is used for encoding an input image to some hidden representation and the RNN can decode the representation to some sentences which can describe the content of this image. In particular, the CNN is usually pre-trained based on the ImageNet and then we can re-train it in the CNN-RNN [41].
When we apply an image-caption model which is trained from image dataset A on image dataset B, the performance will degrade due to the distribution change or domain shift of two datasets. To address this problem, the work in [42] introduces an adversarial learning method to address unpaired data in the target domain for image caption (i.e., adversarial domain adaptation approach in Section 5). In [43], the authors propose a dual learning method for addressing this problem, which involves two steps: (1) A CNN-RNN model is trained with sufficient labeled data in the source domain. (2) The model is then fine-tuned with limited target data. The core idea of dual learning mechanism involved a reverse mapping process: the model firstly maps an input target image to text (i.e., CNN−RNNImage→Text) and the text is then mapped back to an image by a generator network, which is further distinguished by a discriminator network. Therefore, the work in [43] belongs to sample-reconstruction approach (i.e., in Section 6).
7.2 Applications beyond computer vision
7.2.1 Natural language processing
Deep domain adaptation technique is also used for solving a variety of tasks in processing natural language. In [44], an effective domain mixing method for machine translation is introduced. The core idea is to jointly train domain discrimination and translation networks. The authors in [45] propose aspect-augmented adversarial networks for text classification. The main idea is to adopt a domain classifier, which has been discussed in Section 5.2. Recently, an interesting research area is to utilize neural models to automatically generate answers based on the input questions, which is also known as questions answering. However, the main challenge to train models is that it is usually difficult to collect a large dataset of labeled question-answer pairs. Therefore, domain adaptation is a natural choice to address this problem. E.g., in [46], a framework called generative domain-adaptive nets is introduced. Specifically, a generative model is used to generate questions from the unlabeled text for enhancing the model performance. Other applications of domain adaptation can also be found in sentence specificity prediction [47], where the specificity denotes the quality of a sentence that belongs to a specific subject.
7.2.2 Speech recognition
A typical real-world application is to transcribe speech into text, which is also known as automatic speech recognition. Domain adaptation is also suitable for addressing the training-testing mismatch of speech recognition that is caused by the shift of data distribution between different datasets. For example, a neural model trained on a manually collected dataset may generalize poorly in the real-world application of speech recognition due to the environmental noises. In [48], an adaptive teacher-student learning method is proposed for domain adaptation in speech recognition systems. In [49], the domain classifier that is discussed above is also adopted for robust speech recognition. Similar work can also be found in [50], in which the adversarial learning method for domain adaptation is also used for addressing the unseen recoding conditions.
7.2.3 Time-series data processing
Domain adaptation can also enhance the performance of processing many other time-series datasets such as healthcare time-series datasets [51], in which the authors present a variational recurrent adversarial method for domain adaptation. The main idea is to learn domain-invariant temporal latent representations of multivariate time-series data. Another real-world task that involves time-series data is to build diver assistant systems. In [52], an auxiliary domain classifier is also adopted to enhance the performance of recurrent neural networks for driving maneuvers anticipation. And the core idea in this paper is also to learn sharing features from different datasets by the domain classifier. An interesting work related to inertial information processing is introduced in [53], in which a novel framework called MotionTransformer is proposed for extracting domain-invariant features of raw sequences.
8. Conclusion
In this chapter, we firstly introduce the background and explain why transfer learning is important for helping learn real-world tasks. Then we give a strict definition of transfer learning and its scope. In particular, we pay our attention to deep domain adaptation, which is a subset of transfer learning and it mainly addresses the situation where we have different but related datasets for a common learning task. Next, we categorize the deep domain adaptation based on three aspects: the specific implementing approaches, the learning methods, and the data space. In general, deep domain adaptation is one type of method that mainly utilizes deep neural networks to reduce the domain shift or data distribution so that we can enhance the performance of the target task with the help of the knowledge obtained from the source domain. Specifically, we mainly discuss the recent advanced methods for domain adaptation from the deep learning community, including fine-tuning networks, adversarial domain adaptation, and data-reconstruction approaches. Finally, we introduce and summarize the typical real-world applications in computer vision from recently published articles, from which we can see that the unsupervised learning approach based on GANs gets the most attention. In addition, we discuss many other applications beyond the context of image processing. And we notice that many deep domain adaptation methods that are initially proposed for processing images are also suitable for addressing a variety of tasks in natural language processing, speech recognition, and time-series data processing.
Although deep domain adaptation has been successfully used for solving various types of tasks, we should be careful to conduct transfer learning, as brute-force transfer may hurt the performance of our model. The above applications mainly focus on homogeneous domain adaptation, which means that the data between the source domain and the target domain is related and we assume that deep neural networks can find some shared representation from these two domains. However, the data collected from real-world may not always meet this requirement. Therefore, the future challenge is how to apply a heterogeneous domain adaptation method effectively. From the above analyses, we notice that transfer learning has been mainly applied to a limited scale of applications. Therefore, more challenges are also needed to address in the future such as logical inference and graph neural networks based tasks.
Acknowledgments
This work is supported by China Scholarship Council and Data61 from CSIRO, Australia.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"transfer learning, deep domain adaptation, fine-tuning, adversarial domain adaptation, sample-reconstruction",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/73675.pdf",chapterXML:"https://mts.intechopen.com/source/xml/73675.xml",downloadPdfUrl:"/chapter/pdf-download/73675",previewPdfUrl:"/chapter/pdf-preview/73675",totalDownloads:133,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,dateSubmitted:"August 21st 2020",dateReviewed:"September 16th 2020",datePrePublished:"October 29th 2020",datePublished:"December 9th 2020",dateFinished:"October 19th 2020",readingETA:"0",abstract:"Transfer learning is an emerging technique in machine learning, by which we can solve a new task with the knowledge obtained from an old task in order to address the lack of labeled data. In particular deep domain adaptation (a branch of transfer learning) gets the most attention in recently published articles. The intuition behind this is that deep neural networks usually have a large capacity to learn representation from one dataset and part of the information can be further used for a new task. In this research, we firstly present the complete scenarios of transfer learning according to the domains and tasks. Secondly, we conduct a comprehensive survey related to deep domain adaptation and categorize the recent advances into three types based on implementing approaches: fine-tuning networks, adversarial domain adaptation, and sample-reconstruction approaches. Thirdly, we discuss the details of these methods and introduce some typical real-world applications. Finally, we conclude our work and explore some potential issues to be further addressed.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/73675",risUrl:"/chapter/ris/73675",book:{slug:"advances-and-applications-in-deep-learning"},signatures:"Wen Xu, Jing He and Yanfeng Shu",authors:[{id:"319690",title:"Prof.",name:"Jing",middleName:null,surname:"He",fullName:"Jing He",slug:"jing-he",email:"jinghe@swin.edu.au",position:null,institution:{name:"Swinburne University of Technology",institutionURL:null,country:{name:"Australia"}}},{id:"325236",title:"Mr.",name:"Wen",middleName:null,surname:"Xu",fullName:"Wen Xu",slug:"wen-xu",email:"vincent.wen.xu@gmail.com",position:null,institution:null},{id:"330256",title:"Dr.",name:"Yanfeng",middleName:null,surname:"Shu",fullName:"Yanfeng Shu",slug:"yanfeng-shu",email:"Yanfeng.Shu@data61.csiro.au",position:null,institution:{name:"Data61",institutionURL:null,country:{name:"Australia"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Overview",level:"1"},{id:"sec_3",title:"3. Deep domain adaptation",level:"1"},{id:"sec_3_2",title:"3.1 Categorization based on implementing approaches",level:"2"},{id:"sec_3_3",title:"3.1.1 Fine-tuning networks",level:"3"},{id:"sec_4_3",title:"3.1.2 Adversarial domain adaptation",level:"3"},{id:"sec_5_3",title:"3.1.3 Data-reconstruction approaches",level:"3"},{id:"sec_6_3",title:"3.1.4 Hybrid approaches",level:"3"},{id:"sec_8_2",title:"3.2 Categorization based on learning methods",level:"2"},{id:"sec_9_2",title:"3.3 Categorization based on data space",level:"2"},{id:"sec_11",title:"4. Fine-tuning networks",level:"1"},{id:"sec_11_2",title:"4.1 Label criterion",level:"2"},{id:"sec_12_2",title:"4.2 Statistic criterion",level:"2"},{id:"sec_13_2",title:"4.3 Parameter regularization",level:"2"},{id:"sec_14_2",title:"4.4 Sample regularization",level:"2"},{id:"sec_16",title:"5. Adversarial domain adaptation",level:"1"},{id:"sec_16_2",title:"5.1 Target data generating",level:"2"},{id:"sec_17_2",title:"5.2 Domain classifier",level:"2"},{id:"sec_19",title:"6. Sample-reconstruction approaches",level:"1"},{id:"sec_19_2",title:"6.1 Encoder-decoder-based approaches",level:"2"},{id:"sec_20_2",title:"6.2 GAN-based approaches",level:"2"},{id:"sec_22",title:"7. Applications",level:"1"},{id:"sec_22_2",title:"7.1 Applications in computer vision",level:"2"},{id:"sec_22_3",title:"7.1.1 Image classification and recognition",level:"3"},{id:"sec_23_3",title:"7.1.2 Object detection",level:"3"},{id:"sec_24_3",title:"7.1.3 Image segmentation",level:"3"},{id:"sec_25_3",title:"7.1.4 Image-to-image translation",level:"3"},{id:"sec_26_3",title:"7.1.5 Image caption",level:"3"},{id:"sec_28_2",title:"7.2 Applications beyond computer vision",level:"2"},{id:"sec_28_3",title:"7.2.1 Natural language processing",level:"3"},{id:"sec_29_3",title:"7.2.2 Speech recognition",level:"3"},{id:"sec_30_3",title:"7.2.3 Time-series data processing",level:"3"},{id:"sec_33",title:"8. Conclusion",level:"1"},{id:"sec_34",title:"Acknowledgments",level:"1"},{id:"sec_37",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Pan SJ, Yang Q. A survey on transfer learning. 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1. Introduction
The innate immune system evolved to protect the host from invading foreign pathogens, allergens, and different xenobiotics. The system comprises of both its cellular and humoral (circulating complement proteins, defensins, certain cytokines and chemokines secreted by innate immune cells) components. The innate immune cells comprise of epithelial cells, endothelial cells (ECs), the granulocytes (i.e. neutrophils, basophils, eosinophils, and mast cells (MCs), monocytes, macrophages, natural killer (NK) cells, dendritic cells (DCs), invariant NKT cells (iNKT cells), γδT cells, and newly described innate immune T cells called mucosal invariant T cells (MAIT) cells and innate lymphoid cells (ILCs) [1, 2, 3, 4, 5, 6, 7, 8, 9] (Figure 1). These innate immune cells are crucial to maintain the immune homeostasis and regulate adaptive immune system via acting as antigen presenting cells (APCs) along with providing other signaling molecules/factors required in the effective adaptive immune response in response to infection or other sterile chronic inflammatory diseases including, allergy, autoimmunity, cancer, and metabolic diseases including type 1 diabetes mellitus (T1DM), and obesity etc.
Figure 1.
Schematic representation of cellular components of innate immune system. Macrophages also comprise a very important component of innate immune system along with other innate immune cells mentioned in the figure and text.
Macrophages are type of innate immune cells that were first described by Elia Metchnikoff in 1882 in larvae of starfish upon the insertion of thorns of tangerine tree and later on in Daphnia magna or common water flea infected with fungal spores as cells responsible for the process of phagocytosis of foreign particles. Elia Metchnikoff received the Noble prize (Physiology and Medicine) for this discovery in the year 1908. Thus macrophages are first innate immune cells described almost 130 years ago. The continuous development in the field of immunology has established their role in various immunological and non-immunological processes including embryonic development. Along with acting as potential phagocytic cells involved in the phagocytosis of pathogens, xenobiotics, these cells also secrete various cytokines, chemokines, and growth factors including TNF-α, TGF-β, platelet-derived growth factor (PDGF), endothelial growth factor (EGF), and vascular endothelial growth factor (VEGF) [10, 11, 12]. Thus macrophages are very potent innate immune cells with diverse functions. The present chapter is intended to describe the immunoregulatory role of macrophages in the maintenance of immune homeostasis in the normal and disease stage.
2. Development of macrophages
Macrophages are the cells of the mononuclear phagocyte system (MPS) that was previously considered as reticuloendothelial system (RES), a system associated with the clearance and phagocytosis of dead cells [13]. They were included in the RES in 1924 to show their origin, residency, and renewal within RES. The RES was renamed to the MPS system in 1968 by Ralf van Furth, Zanvil Cohn and colleagues to distinguish them from polymorphonuclear leukocytes (PMNLs) or neutrophils and to show that all macrophages originate via terminal differentiation blood monocytes into different macrophages including pulmonary macrophages, liver macrophages (or Kupffer cells), and peritoneal macrophages etc. [14, 15]. The MPS comprises of monocytes, macrophages, and DCs involved in the maintenance of tissue and organismal homeostasis, the pathogenesis of inflammation, cancer, autoimmune diseases, infection and the generation of immune response associated with the organ transplantation [16, 17].
Macrophages are developed during very early phase of embryogenesis called primitive hematopoiesis occurring at embryonic day 6.5 [E6.5]-E8.5 from precursor cells present in the extraembryonic yolk sac [18, 19]. The process of hematopoiesis in line with ontogeny progresses towards fetal liver at the beginning of E10.5 and finally to the bone marrow in the adult animal including humans [18, 19] (Figure 2). The primitive hematopoiesis occurring in the yolk sac of human embryos comprises of about 70% macrophages of the total nucleated blood cells at 4 weeks of gestational age, while in mice embryos macrophages predominate in the early stage of primitive hematopoiesis taking place in the yolk sac with the absence of monocytic cells [20, 21]. From embryonic day 8.5 [E8.5]-E10.5, the aorta-gonad-mesonephros give rise to hematopoietic stem cells (HSCs) giving birth to all immune lineages [19]. The cells committed to become macrophages within the mononuclear phagocyte lineage pass through morphologically-different but defined developmental stages including common myeloid progenitors (CMPs), shared with granulocytes giving rise to monoblasts, promonocytes and then monocytes that migrate to different tissues [22]. The differentiation of HSCs or hematopoietic progenitors (HPs) into different cell lineages including CMPs is governed by the activation of highly regulated gene expression programs integrated by different lineage-determining transcription factors (TFs) [23, 24, 25].
Figure 2.
Schematic representation of production of macrophages in various organs throughout the mammalian (mouse and human) development. For example, at embryonic day 6.5 [E6.5]-E8.5 macrophages develop in the extraembryonic yolk sac from precursor cells, thereafter at E10.5 they develop in fetal liver, and in neonates and adults they develop in bone marrow as mentioned in the text.
Pu.1 serves as an essential factor to reconstitute the myeloid cell lineage and for the development of macrophages and monocytes in concentration-dependent manner [24, 26, 27]. A high concentration of the TF called PU.1 promotes the macrophage development whereas a low level of PU.1 supports the B cell development due to the presence of many low- and high-affinity PU.1 binding sites in the genome [28, 29]. PU.1 is regulated by Runt-related transcription factor 1 (RUNX1) or Acute myeloid leukemia 1 protein (AML1) or Core-binding factor subunit-alpha 2 (CBFA2) that are members of core-binding factor family of TFs [30]. The gene Csf1r encoding the receptor for the cytokine IL-34 and monocyte-colony stimulating factor (MCSF) is one of the major targets of PU.1 in macrophage development [31, 32] Cebp-α, -β, and -ε are important towards the development of different myeloid cell types primarily including granulocytes, macrophages, and monocytes [33, 34]. Irf8 also serves as a crucial TF for monocyte lineage along with DC lineage by establishing monocyte- and DC-specific enhancers [35, 36, 37, 38]. The TF called ZEB2 is essential for the maintenance of tissue-specific macrophages and its loss causes tissue-specific changes in different macrophage populations including KCs and their subsequent loss [39]. Thus these lineage-determining TFs, establish the central macrophage program during the pre-macrophage stage. This core macrophage program includes the expression of CX3CR1, pattern-recognition receptors (PRRs), phagocytic receptors (PRs), FcγRs including FcγR1 or CD64 and various other genes including Sirpα, Iba1, Mertk and Adgre1 (F4/80) expressed by almost all types of macrophages [40, 41]. A bZip TF called MAFB (c-Maf) regulates the self-renewal of macrophages and its induction is a specific and crucial determinant of monocytic program in hematopoietic cells [42, 43].
There are two principal subtypes of monocytes in mice (Figure 3): (1) classical Ly6chi monocytes (also called inflammatory monocytes expressing high levels of CC-chemokine receptor 2 (CCR2) but low levels of CX3C-chemokine receptor 1 (CX3CR1)) that descend directly from Ly6c+ monocyte progenitors [44], and (2) Ly6clow non-classical monocytes expressing high levels of CX3CR1 and low levels of CCR2 that differentiate from Ly6chi monocytes through an Nr4a1 (nuclear receptor subfamily 4 group A member 1 or Nur77)-dependent transcriptional program and are less prevalent in blood [44, 45, 46, 47]. The Ly6chi monocytes in mice represent approximately 2–5% population of the circulating white blood cells (WBCs) in a normal laboratory mouse without any infection and rapidly migrate towards the site of infection and inflammation [48]. However the deficiency of CCR2 significantly reduces the migration of Ly6chi monocytes at the site of infection and inflammation indicating the importance of CCR2 in the trafficking of these monocytes [49, 50, 51]. These Ly6clow non-classical monocytes develop primarily to function within the vasculature and patrol the vasculature by crawling over the resting endothelium in an Lymphocyte function-associated antigen 1 (LFA-1) integrin and CXCR3-dependent manner [19, 52].
Figure 3.
Schematic representation of developmental stages of macrophages. HSCs, in the presence of TFs including PU.1 develop into CMP that further differentiates into promonocytes by undergoing different developmental stages. The promonocytes in fetal liver develop into monocytes that further differentiate into macrophages. Whereas in bone marrow promonocytes develop into Ly6C+ inflammatory monocytes also called classical monocytes. However, in peripheral blood circulation they are further differentiated into Ly6C+ inflammatory monocytes and Ly6C− resident monocytes or non-classical monocytes residing in the blood and patrolling the vasculature. On the other hand Ly6C+ inflammatory monocytes or classical monocytes migrate to different organs and develop into different tissue/organ specific macrophages as described in the figure.
The non-classical monocytes patrol the vasculature to clear the damaged endothelial cells (ECs) for maintaining the integrity of endothelium, and thus the vasculature during homeostasis and inflammatory conditions [53, 54]. Thus, these Nr4a1-dependent non-classical monocytes serve as housekeepers for the endothelial vasculature and orchestrate the necrosis by neutrophils due to damaged ECs inducing the TLR7 signaling via in situ phagocytosis of cell debris derived from damaged ECs [53]. Hence these non-classical monocytes play a crucial role in the pathogenesis of various diseases associated with vasculature along with the process of wound healing and the resolution of the inflammation [54]. This patrolling nature of the monocytes distinguishes them from macrophages as macrophages have a very limited capacity to emigrate from their site of location. In humans monocytes are differentiated into two subsets on the basis of expression of surface expression of CD14 and CD16 [55]. In humans the CD14++CD16− monocytes are known as classical monocytes and are most prevalent monocyte subset in the blood [56, 57]. Like mice Ly6chi monocytes they also express CCR2 [58]. The CD14+CD16+ monocytes are considered as intermediate monocytes and CD14lowCD16+ monocytes are called non-classical monocytes in humans [56].
The CD14lowCD16+ monocytes in humans are similar to mice Ly6clow monocytes and patrol the vasculature or endothelium along with sensing the nucleic acids and virus via TLR7 and TLR8 receptors [59]. These monocytes have weak phagocytic potential and do not produce ROS and cytokines in response to cell-surface TLRs. However they produce TNF-α, IL-1β, and CCL3 in response to viruses and immune complexes containing nucleic acids due to the activation of TLR7 and TLR8 signaling pathways [59]. Thus it can be inference that mice and human monocytes do not precisely overlap in terms of their receptor expression including PPAR-γ (peroxisome proliferator-activated receptor-γ) that is signature for mouse monocytes but absent in humans, however, the process of their differentiation and the function in immune defense is apparently similar [60, 61, 62]. For example, approximately 270 genes in humans and 550 genes in mice monocytes (both types including classical or non-classical one) are expressed differentially and more than 130 of these gene expressions are conserved between mouse and human monocyte subsets [62]. Thus this difference between human and mouse monocytes should be kept in mind when developing and studying human diseases in mice.
The development of mononuclear phagocytes from monocyte/macrophage progenitor cells is directed by colony stimulating factors (CSFs) including M-CSF, granulocyte-monocyte colony-stimulating factor (GM-CSF), and fms-like tyrosine kinase 3 ligand (Flt3-ligand) [63, 64, 65]. The number of various tissue and organ monocytes/macrophages are regulated by M-CSF without any alteration in their activation stage [64]. However, GM-CSF is involved in the activation of both monocytes and macrophages along with its participation in the differentiation into DCs. The mature cells developed during fetal development and later in life are distributed accordingly as sinus-lining and interstitial resident macrophages in lymphohematopoietic and other organs including lungs, liver, spleen, gut, skin and brain. Major tissue-resident macrophages, including liver KCs, lung alveolar, splenic, and peritoneal macrophages, are established prior to birth and their maintenance starts subsequently by themselves independent of replenishment of blood monocytes during adulthood [47]. The macrophages present in endocrine and reproductive organs including testes, adipose, vascular, musculoskeletal and connective tissues are less well characterized.
3. Macrophage polarization
The polarization of macrophages gives a diverse heterogenic function and phenotypes to them depending on their activation in respect to their duration of stimulation and spatial localization [66]. The macrophage polarization is not a fixed process due the plasticity of the macrophages to integrate multiple signals (different pathogens and their PAMPs, DMAPs, and normal tissue environment). Thus macrophage polarization occurs in response to cell-cell interaction and cell-molecule interaction within the host tissues or organs to maintain the homeostasis or during different pathological conditions [67, 68]. Thus macrophage polarization is regulated by at least three different mechanisms: (1) epigenetic and cell survival mechanisms, (2) external stimuli (pathogens, PAMPs, and allergens), and (3) tissue environment including DAMPs [66]. The inflammation and associated immune response is a good pathogenic condition to study the macrophage polarization as this process impacts the inflammation from its initiation to the resolution phase. The details of macrophage polarization are discussed elsewhere [66, 67, 69].
Depending on their polarization status the macrophages can be categorized in to M0, M1 (classically activated macrophages (CAMs) or pro-inflammatory), and M2 (alternatively activated macrophages (AAMs) or anti-inflammatory) macrophages (Figure 4). M0 macrophages can be considered as naïve macrophages that have not been exposed to any pro- or anti-inflammatory stimuli or environment. M1 or CAMs are developed when M0 macrophages are exposed to bacterial moieties including LPS and Th1 cytokines including IFN-γ, IL-2, IL-12, IL-18 and TNF-β (lymphotoxin β (LT-β)) etc., whereas M2 or AAMs are developed upon exposure to Th2 cytokines including IL-4, IL-5, IL-6, and IL-10 [70, 71]. The M2 macrophages can further be divided into M2a, M2b, and M2c depending on their stimulus for the activation. The M2 macrophages induced by IL-4 or IL-13 are called M2a (a stands for alternative), M2b macrophages are induced by poly I:C or TLR or IL-1R agonists, and M2c are induced by IL-10 and glucocorticoids [72]. M2 macrophages exhibit a higher phagocytic activity, higher expression of scavenging, mannose and galactose receptors, produce higher concentration of ornithine and polyamines due to high arginase pathway, secrete high amount of IL-10 and express higher levels of the IL-1 decoy receptor and IL-1RA [40]. Thus, M2 macrophages in general exert an anti-inflammatory action and play a crucial role in anti-parasitic immune response required for parasite clearance, promote tissue remodeling, vasculogenesis, tumor progression [70, 72, 73]. The M1 macrophages express Th1 cell-attracting chemokines including CCL5 or regulated upon activation, normal T cells expressed, and secreted (RANTES), CXCL9 and CXCL10, whereas M2 macrophages express the chemokines CCL17, CCL22 and CCL24 [74].
Figure 4.
Schematic representation of macrophage polarization. Naïve or M0 macrophages upon different stimulation as describe in the figure and the text differentiate into pro-inflammatory M1 macrophages or classically activate macrophages (CAMs) and anti-inflammatory macrophages called alternatively activated macrophages (AAMs) or M2 macrophages. These M2 macrophages are further differentiated into M2a, M2b, and M2c macrophages depending on the stimulus as mentioned in the figure and the text.
The M1 macrophages highly express cyclo-oxygenase 2 (COX 2) enzyme, inducible nitric oxide synthase (iNOS or NOS2) involved in nitric oxide (NO.) synthesis, whereas M2 macrophages express COX 1 and arginase is expressed in M2a and M2c required to synthesize ornithine and polyamines but not in M2b macrophages activated by Poly I:C and LPS [72, 74, 75]. The metabolic process of macrophages governing their pro-inflammatory and anti-inflammatory action also differs in M1 and M2 macrophages. M1 macrophages exhibit a shift from normal oxidative phosphorylation (OXPHOS) to increased glycolysis, increased release of lactate, a decreased oxygen consumption and glutaminolysis. On the other hand M2 macrophages are dependent on fatty acid oxidation (FAO) as a major source of energy along with the mitochondrial OXPHOS. The detailed description of macrophage (both M1 and M2) immunometabolism is beyond the scope of the chapter and described elsewhere [76, 77]. Succinate (a signaling metabolite) regulates the macrophage polarization via succinate receptor 1 (SUCNR1) and regulates the process of inflammation [78]. The myeloid-specific deficiency of SUCNR1 promotes a local pro-inflammatory or M1 phenotype among macrophages, disrupts glucose homeostasis in mice, exacerbates the metabolic effects of diet-induced obesity and impairs the browning of the adipose-tissue under cold conditions [78]. On the other hand SUCNR1 via succinate binding stimulates the anti-inflammatory (M2) phenotype among macrophages as indicated by the release of type 2 or anti-inflammatory cytokines including IL-4. Thus succinate exerts the anti-inflammatory action via SUCNR1 on macrophages via controlling their polarization [78]. The macrophages involved in the resolution of inflammation are called resolution-phase macrophages (rMs). The rMs differ from both M1 and M2 macrophages in terms that they have weak bactericidal properties and express an alternatively activated phenotype along with higher expression of markers of M1 macrophages (i.e. inducible cyclooxygenase (COX 2) and nitric oxide synthase (iNOS)) [79]. This phenotype of rMs is controlled by cyclic adenosine monophosphate (cAMP) as its inhibition converts rMs into M1 macrophages [79]. On the other hand the upregulation of cAMP in M1 macrophages converts them in rMs. Although rMs are nonessential to clear neutrophils during self-limiting inflammation but are required for the initiation of post resolution lymphocyte repopulation signaling event via COX 2 lipids. Thus, rMs are the hybrid of both M1 and M2 macrophages and play an important role in the post resolution innate-lymphocyte repopulation and the restoration of tissue/organ homeostasis. Table 1 is showing the major differences between M1 and M2 macrophages. The detailed mechanism of macrophage polarization (M1 and M2), its regulation and impact on inflammatory process including in cancer are described somewhere else [66, 70, 71, 72, 73, 75, 80, 81].
M1 macrophages
M2 macrophages
1. Phenotype
Express high levels of MHC-II, CD68, and CD80 and CD86 costimulatory molecules
Express higher levels of CD206, CD200R, CD163 and transcription factor called CMAF (musculoaponeurotic fibrosarcoma) and response gene to complement 32 (RGC-32)
2. Upregulated genes
Suppressor of cytokine signaling 3 (SOCS3), iNOS or NOS2, Macrophage receptor with collagenous structure (Marco), Il12B, Il23a (Il23p19) and Ptgs2 (Cox2)
Arg1, MMR (Mrc1), resistin-like molecule α (FIZZ1) or Relma or Retnla, Ym1, Irf4, Cxcl12, Cxcl13, Ccl24 and Klf4
3. Action
Pro-inflammatory
Anti-inflammatory
4. Cytokines and chemokines produced
IFN-γ, IL-8, TNF-α, IL-1β, RANTES (CCL5), CXCL10
IL-13, IL-10, CCL17, CCL18, CCL22
5. Metabolic pathway
Glycolysis and glutaminolysis
FAO and OXPHOS
6. HIF-1α expression
High
Low
7. Inducers or stimuli
IFN-γ, PAMPs (i.e. LPS), GM-CSF
Glucocorticoids, IL-10, IL-4, IL-13 and M-CSF
8. ROS and RNS production
High ROS and NO. production
Low ROS and NO. production
9. Rate of acidification
Low
High
10. Antimicrobial action
High
Low
11. Glucose uptake
Mainly depends on HIF-1α and Akt/mTORC1 activation
Mainly depends on Akt/mTORC1 activation
12. Macrophage galactase-type C-type lectins
Low
High
13. Autophagy
Induce autophagy during tuberculosis (TB) infection
Decrease autophagy during TB infection
Table 1.
Differences between M1 and M2 macrophages.
4. Role of monocytes and macrophages in host defense
Macrophages are present in almost every tissue or organ system including the barriers system comprising of respiratory tract (pulmonary alveolar and interstitial macrophages), skin, gastrointestinal tract (GIT), and reproductive tract [82, 83, 84, 85, 86, 87, 88, 89, 90, 91]. Thus their presence in the every organ system along with the mucosal sites serving as potential sites for the entry of pathogens, toxins, allergens and xenobiotics makes them first line of defense.
Monocytes/macrophages are one of the major innate immune cells involved in the process of recognition of pathogens and the cell debris originated as a result of apoptosis and their engulfment by the process of phagocytosis. Thus along with other innate immune cells including neutrophils, dendritic cells (DCs), mast cells, monocytes, and macrophages are considered as ‘professional’ phagocytes. The professional phagocytes are differentiated from non-professional phagocytes on the basis of their effectiveness in mediating the phagocytosis [92]. The major factor contributing to the effectiveness of the phagocytosis and characteristic of professional phagocytes is the expression of various receptors on their cell surface involved in the recognition of molecules or ligands that are not normally expressed by normal and healthy cells [93]. For example, scavenger receptors (SRs) play important role in the recognition and binding of apoptotic and necrotic cells, opsonized pathogens (i.e. pathogens opsonized by complement protein C5a and C3a), and cell debris. The scavenger receptor-A1 (SR-A1)-mediated phagocytosis of low density lipids (LDLs) or oxidized lipids causes the formation of foam cells and this phenomenon is involved in the pathogenesis of atherosclerosis [94]. The absence of SR-A1 in macrophages increase their pro-inflammatory action due to the increased p42/44 mitogen-activated protein kinase (MAPK) phosphorylation, interferon regulatory factor-3 (IRF-3) and NF-κB nuclear translocation and increased production and secretion of TNFα, IL-6 and IFN-β due to the increased activation of TLR4 signaling pathway [95]. Thus SR-A1 antagonizes the TLR4-mediated phagocytosis and pro-inflammatory immune response of macrophages in the presence of LPS and gram-negative bacteria in a competitive manner [95].
Additionally, alveolar macrophages expressing SR-A1 and class A scavenger receptors (SRAs) called macrophage receptor with collagenous structure (MARCO) protect the host from inhaled toxicant and pathogens by phagocytosing the oxidized lipids and decreasing the inflammatory damage [96]. The detailed information of scavenger receptors is beyond the scope of the chapter and is described elsewhere [97, 98, 99, 100, 101]. In addition, professional phagocytes including monocytes and macrophages express various Toll-like receptors (TLRs) [93]. However the interplay between phagocytic receptors (which initiate and assist in the mechanics of phagocytosis) and pattern recognition receptors (PRRs, such as TLRs, which detect PAMPs or DAMPs) is complex. The interplay between these receptors may involve both synergistic and antagonistic interactions, including downstream signaling mechanisms within the phagocytic cell that remain largely unknown [102, 103].
During and following phagocytosis, PRRs (including TLRs, C-type lectin receptors (CLRs), scavenger receptors, retinoic acid-inducible gene 1 (RIG1)-like helicase receptors (RLRs) and NOD-like receptors (NLRs)) recognize different PAMPs and DAMPs along with different xenobiotics including silica or asbestos [104, 105]. Some PRRs including mannose receptor, DC-specific ICAM3-grabbing non-integrin (DC-SIGN) and MARCO are also involved in the process of pathogen recognition and phagocytosis, whereas signaling PRRs (which include the TLRs, NLRs and RLRs) sense microbial products and aberrant self-molecules on the cell surface or in the cytoplasm of cells and activate transcriptional mechanisms that lead to phagocytosis, cellular activation and the release of cytokines, chemokines and growth factors [106, 107, 108, 109]. During phagocytosis of the pathogens, the TLR2 recruits into the phagosome and discriminates between pathogens along with initiating the pro-inflammatory immune response [110]. The TLR-induced phagocytosis of bacteria is reliant on MyD-88-dependent signaling via interleukin-1 receptor-associated kinase-4 (IRAK-4) and p38 MAP kinase causing an up-regulation of SRs [111]. TLR9 is the strongest inducer of phagocytosis among all the TLRs, whereas TLR3 is the weakest inducer of the process [111]. However, TLR4-stimulated phagocytosis also requires the activation of MyD-88-independent actin-Cdc42/Rac pathway [112, 113].
Macrophages also express various complement receptors (CRIg, C1qR, CR3, C5aR, C5L2 or C5bR, etc.) and Fc receptors on their cell surface that bind and phagocytose the opsonized pathogens or other molecules and activate the complement system (CS)-mediated immune response for increasing the process of phagocytosis [114, 115, 116]. CRIg is a member of complement receptor of the immunoglobulin superfamily that binds to complement fragments C3b and iC3b opsonizing the pathogens to initiate their phagocytosis [115]. The expression of CRIg on macrophages increases in the presence of dexamethasone and IL-10, but decreases in the presence of IFN-γ, IL-4, TGF-β1, arachidonic acid (AA) [117]. AA decreases the expression of CRIg on macrophages by activating the protein kinase C (PKC) independent of its metabolism via cyclooxygenase and lipoxygenase pathway [117]. The CR3-mediated phagocytosis of the pathogens is mediated by the activation of Syk-kinase that becomes tyrosine-phosphorylated and accumulates around the nascent phagosomes [114]. However, it also negatively regulates the phagocytosis of degenerated myelin sheath by activating Syk-kinase and cofilin (an actin-depolymerizing protein controlling F-actin remodeling) in microglia and macrophages [118]. C1q component of the CS plays a crucial role in the process of phagocytosis by triggering the rapid enhancement of the phagocytosis independent of its role in direct activation of the classical complement pathway [119]. The engulfment of the membrane attack complex (MAC) deposited on pathogens by the macrophages during the process of phagocytosis activates the NALP3 (NACHT, LRR and PYD domains-containing protein 3 or cryopyrin) inflammasome via inducing K+ efflux and ROS generation [120]. The NALP3 activation activates caspase 1 (CASP1) to cause the maturation and release of IL-1β and IL-18 [120]. This also induces the differentiation of T cells into Th17 cells when these macrophages are used as antigen presenting cells (APCs). Thus, macrophages use various surface receptors and secreted molecules to monitor and respond to changes in the vicinity of their tissue environment.
5. Role of macrophages in homeostasis (angiogenesis, wound repair, and regeneration) and diverse inflammatory conditions (metabolic diseases and autoimmunity)
5.1 Macrophages in angiogenesis
Macrophages play a crucial role in the immune homeostasis via regulating the process of inflammation under both sterile and infectious inflammatory conditions. In addition to this they also play a crucial role in the process of angiogenesis (Figure 5), metabolism, and salt and water balance [121]. For example, myeloid cells including monocytes and neutrophils are the first innate immune cells migrating through post capillary venules (PEVs) at the site of inflammation and tissue injury or tissues requiring microvascular growth and remodeling including several tumors due to the expression of CCR2 that binds to the chemokine called CCL2 [122, 123]. Furthermore an inhibition in the chemo-attraction and migration of monocytes at the site of tissue ischemia causes a flap necrosis due to impaired neovascularization [124]. Macrophages also synthesize, release, and respond (or reprogram themselves) to various pro- and anti-angiogenic factors including vascular endothelial growth factor-A (VEGF-A), and several angiopoietins including angiopoietin (ANG) 1 and ANG 2 [125, 126, 127]. Thus these recruited monocytes or tissue macrophages reprogram themselves in the presence of theses angiogenic factors to serve as angiogenic and arteriogenic professional cells (APCs) [125]. For example, ANG1 exerts its angiogenic action on macrophages via repressing the expression of prolyl hydroxylase domain protein 2 (PHD2) through angiopoietin (ANG)-TIE2 (angiopoietin-1 receptor or CD202B) signaling that supports their reprograming into angiogenic and APCs [127, 128]. ANG2-dependent TIE2-signaling in macrophages plays a crucial role in the induction of angiogenesis during inflammation and tumor growth as both condition are associated with increased hypoxia causing an induction of hypoxia inducible factors (HIFs) including HIF-1α and HIF-2α enhancing the generation of tumor and angiogenesis promoting molecules and cytokines (CXCR4, GLUT1 (glucose transporter 1), VEGF A, IL-1β, IL-8, adrenomedullin, and ANG 2) [129, 130, 131].
Figure 5.
Macrophages play important role in host defense, immune homeostasis, regeneration, and inflammation. The detailed mechanisms of macrophages impact on the processes mentioned in the figure are described in the text.
These angiogenesis supportive macrophages exhibit the similarity with M2 macrophages and in tumor environment they are called tumor-associated macrophages (TAMs) with higher levels of IL-6, iNOS, and TIE2 [132]. These M2 macrophages and TAMs support the growth, proliferation, and migration of endothelial cells (ECs) and blood vessel formation or sprouting by releasing VEGF-A as well as promoting the synthesis and release of VEGF-A and fibroblast growth factor-2 (FGF-2) or basic-FGF (b-FGF) from the tissue or tumor microenvironment cells [133]. The TAM-mediated support of angiogenesis and tumor growth is determined by TIMP-1 (tissue-inhibitor of matrix metalloproteinase-1) levels free of or complexed with pro-MMP-9 (matrix metalloproteinase-9) [134]. For example, MMP-9 null macrophages are non-angiogenic. In addition to secreting the angiogenic factors, macrophages also interact with cells including pericytes, ECs, and vascular smooth muscle cells for regulating angiogenesis observed during embryonic development, adult responses to injury, and in tumor microenvironment [135]. Furthermore the depletion of macrophages disrupts the process of vascular patterning in response to insufficient vascular pruning due to decreased phagocytosis of endothelial cells and pericytes during both embryonic and postnatal development of organs [135, 136, 137].
5.2 Macrophages in wound repair
Macrophages also serve as crucial immune cells involved in the process of wound repair in response to stimuli generated in the local tissue milieu [138, 139]. The phenomenon of wound repair is mainly regulated by AAMs or M2 macrophages due to their anti-inflammatory action, induction of angiogenesis, and decreased apoptosis that induces the extracellular matrix remodeling and the process of wound repair and regeneration [138, 139]. These wound repair macrophages are characterized by the higher production of various growth factors including platelet-derived growth factor (PDGF), insulin-like growth factor-1 (IGF-1), transforming growth factor-α (TGF-α), TGF-β, and VEGF-A causing angiogenesis and supporting cell proliferation to alleviate the hypoxia caused by the inflammatory tissue insult [140]. The TGF-β stimulates the differentiation of the local and recruited tissue fibroblasts into myofibroblasts facilitating the contraction and closure of the wound area along with the synthesis of the extracellular matrix (ECM) components [141]. Additionally macrophages also release amphiregulin (AREG) that serves as an epidermal growth factor receptor ligand (EGFRL) to play a role in the restoration of tissue homeostasis after injury or wound healing [142, 143]. The wound healing or repair mechanism by AREG involves the release of TGF-β from latent complexes via integrin-αV activation that induces the differentiation of mesenchymal stromal cells (pericytes) into myofibroblasts to restore the vascular barrier function within injured tissue during the process of wound healing [142].
These wound repair macrophages also augment the proliferation and expansion of many neighboring parenchymal and stromal cells along with activating stem cells and local progenitor cells to participate actively in tissue repair response during chronic or severe injury [144]. Hence, the disruption of monocyte recruitment and the inhibition of local macrophages and their conversion into M2 or AAMs may dampen the process of wound repair. For example, in some cases the disruption in the process of wound repair may lead to the development of tissue or organ fibrosis or scarring due to the overactivation of wound repair macrophages that can further impair organ’s normal function causing ultimate organ failure and death of the patient [145, 146]. For example, idiopathic pulmonary fibrosis (IPF), hepatic fibrosis and systemic sclerosis, are tightly regulated by ‘pro-fibrotic’ macrophages producing PDGF, IGF-1, TGF-β1 (induces myofibroblast transdifferentiation and promotes matrix accumulation), and directly activating fibroblasts [93, 147, 148, 149, 150]. These pro-fibrotic macrophages also secrete pro-inflammatory cytokines including IL-1β that stimulates Th17 cells to secrete IL-17 involved in the bleomycin pulmonary fibrosis, MMPs and TIMPs that regulate the inflammatory cell recruitment and the ECM turnover [146, 151, 152, 153, 154]. Hence macrophages are involved in the process of wound repair and the impairment in their function may lead to the poor wound healing and the development of fibrosis causing organ failure and the death of the patient. Therefore targeting of the pulmonary macrophages and their mediators play a crucial role in the process of pulmonary fibrosis [155].
5.3 Macrophages in regeneration
Macrophages also play a crucial role in the process of tissue and organ regeneration that refers to the process of proliferation of cells and tissues to replace the damaged and lost structures [156]. The organs and tissues including skeletal muscles and liver exhibit a higher degree of regenerative capacity through the regeneration of parenchymal cells involving monocytes and hepatocytes [157]. In most tissues the complete regeneration of intact tissues is not achieved and results in the formation of scar [158]. Macrophages play a very important role in the regeneration process of skeletal muscle by coordinating the inflammation and regeneration [157]. They act as essential immune cells for the recovery of tissue integrity and function following the injury [150]. The macrophages involved in the process of regeneration of skeletal muscle are located in the interstitial space between myofibers, specifically in the perimysium (the connective tissue surrounding muscle fascicles), epimysium, (the connective tissue surrounding the whole muscle), and perivascular space that recruit circulating neutrophils and monocytes following the muscle injury to initiate the process of inflammation [157]. The monocytes infiltrated into the damaged skeletal muscle undergo the process of in situ transition to develop into Ly6Chi (inflammatory) and Ly6Clow (regenerative or repair) macrophages that is independent of NR4A1 (nuclear receptor subfamily 4 group A member 1) or NUR77 or nerve growth factor IB (NGFIB) [159]. The NUR77 belongs to the family of the Nur nuclear receptors acting as intracellular transcription factors and plays a crucial role in the macrophage-mediated inflammatory immune response generation [160]. The transition of monocytes into Ly6Chi (inflammatory) and Ly6Clow (regenerative or repair) macrophages plays a crucial role in the process of muscle regeneration [161]. The Ly6Clow macrophages in the skeletal muscle exhibit a distinct pro-resolving signature [specialized pro-resolving lipid mediators (SPMs), including resolvins (for example, RvD1, RvD2, RvE1)] that helps in the functional improvement in the process of muscle regeneration [162]. On the other hand Ly6Chi inflammatory monocytes further differentiate into skeletal tissue macrophages (both M1 and M2) and secrete pro-inflammatory cytokines (i.e. FN-γ, TNF-α, IL-1β, and IL-6) that are also integral component of myogenic precursor cells (MPCs) or myoblasts. The M2 macrophages on the other hand promote the differentiation and maturation of MPCs [157, 163, 164]. In addition macrophages are also shown to involve in the process of regeneration of heart/cardiomyocytes in different animals (Zebra fish, Salamander, and the laboratory mouse) [165, 166, 167]. Even studies have also shown the involvement of macrophages in the regeneration of spinal cord and tail fin of Zebra fish [168, 169]. Wnt signaling in macrophages plays a critical role in driving parenchymal regeneration in animal models of liver injury [170]. After the death of hepatocytes phagocytic uptake of the cell debris by macrophages synthesizes Wnt3a that in nearby hepatic progenitor cells (HPCs) induces the canonical Wnt signaling cascade facilitating their specification to hepatocytes [171]. Even the regeneration of hair follicles also involves the macrophage-mediated key signals to local stem cells facilitating the regeneration of hair follicles upon plucking of hairs [172]. The plucking of hairs causes the local generation of CCL2 that promotes pro-inflammatory TNF-α generating macrophages and initiates the process of hair-follicle regeneration [172]. Thus the fine tuning of macrophages is essential for their protective function during would healing or repair, regeneration or the induction of fibrosis due to the loss of this fine tuning leading to the organ damage and failure.
5.4 Macrophages in autoinflammation and autoimmunity
The uncontrolled activation of macrophages in response to DAMPs recognized by various PRRs and apoptotic cells (uncontrolled phagocytosis) may lead to chronic and uncontrolled inflammation that may induce autoinflammation and autoimmune diseases including severe autoimmune anemia, systemic lupus erythematosus (SLE), and chronic arthritis [173, 174, 175, 176]. The increased infiltration of macrophages into the brain (i.e., in meninges surrounding the CNS, the perivascular space, and the choroid plexus) is also reported in experimental autoimmune encephalitis (EAE), an animal model for multiple sclerosis (MS) [177, 178]. The chronic up-regulation of CCR2, CCL2, CCL3, CCL4, and CCL22 stimulates the process of macrophage accumulation at the sites of the brain affected during EAE [179, 180]. Both M1 and M2 macrophages play a crucial role in the pathogenesis of EAE or MS [180, 181]. Macrophages also play a very important role in the pathogenesis of rheumatoid arthritis (RA) by secreting various pro-inflammatory cytokines, controlling the generation and function of regulatory T cells (Tregs) via binding and release of transforming growth factor-β (TGF-β), and their therapeutic targeting proves beneficial to the patients [182, 183, 184, 185]. Sjogren’s syndrome (SS), a chronic autoimmune disease of exocrine glands specifically salivary glands and lacrimal glands causing also systemic autoimmune lesions also shows the accumulation of monocytes and macrophages in the inflamed lesions [185, 186, 187]. In addition to these autoimmune diseases, both M1 and M2 macrophages also play a crucial role in the pathogenesis of type 1 diabetes mellitus by contributing to the destruction of β cells of the pancreas through controlling the generation of Th1 cells and acting as antigen presenting cells (APCs) to stimulate cytotoxic CD8+ T cells (T1DM) [188, 189, 190].
5.5 Macrophages in metabolic diseases
Obesity is an altered stage of metabolism originating due to the increased availability of nutrients (except in the genetically impaired conditions causing the deposition of the white adipose tissue (WAT)) [191]. However, both obesity caused by the genetic factors or due to the increased food intake and sedentary life style cause a low-grade systemic chronic inflammation that may lead to the development of type 2 diabetes mellitus (T2DM) and atherosclerosis [192, 193, 194]. The death of adipocyte serves as a major trigger for the recruitment of inflammatory LY6ChiCCR2+ monocytes and the accumulation of macrophages in the WAT as more than 90% of the macrophages in WAT are localized to the dead adipocytes [195, 196]. These macrophages then fuse to form syncytia sequestering and scavenging the residual “free” adipocyte lipid droplets and ultimately forming the multinucleate giant cells that serve as a hallmark of chronic inflammation. Furthermore, these macrophages recognize fatty acids (FAs) as potential inflammogens and reprogram themselves into classical macrophages (M1 macrophages) during obesity [104, 197, 198]. For example, saturated but not unsaturated fatty acids promote the inflammatory activation of macrophages via the activation of TLR4 as TLR4 is essential for high-fat diet-induced insulin resistance in adipose tissue and liver [199, 200, 201, 202, 203]. Additionally, Fetuin A (FetA or AHSG, a secreted glycoprotein) serves as an endogenous ligand for TLR4 for promoting the lipid-induced insulin resistance, lipotoxicity in β cells of the pancreas, and T2DM [204, 205]. However, M2 macrophages generated in the environment promote the health of the WAT and the insulin sensitivity by an unknown mechanism in a lean state [206]. It can be hypothesized that the M2 macrophages via maintaining the health of adipocytes in WAT prevent the generation of signals including the death of adipose tissue that chemo-attract the pro-inflammatory monocytes reprogramming later into classical M1 macrophages. The genetic depletion of the M2 gene or M2 macrophages cause the induction of metabolic diseases upon high-fat-diet [206]. IL-6 promotes the generation of AAMs or M2 macrophages in adipose tissue environment during obesity [207]. The depletion of CD11b also increases the number of AAMs in adipose tissue during obesity and prevents the development of obesity-induced insulin resistance [208]. Thus targeting CD11b during obesity may prevent obesity-induced insulin resistance. Recently, a population of sympathetic neuron-associated macrophages (SAMs) has been identified controlling the obesity by engulfing and clearing norepinephrine (NE) [209].
6. Conclusion and future perspective
Macrophages are innate immune cells that serve as a first line of defense against invading pathogens almost in every organ system including lungs, liver, intestine, kidneys, and brain. Along with acting as first line of defense against pathogens, PAMPs, DAMPs, and other xenobiotics they act as antigen presenting cells (APCs) and provide processed antigens to activate the adaptive immune response comprising of B and T cells. Thus macrophages are sentinel innate immune cells taking part in the generation of both acute and chronic inflammation induced during both sterile and infectious tissue damaging conditions via controlling the migration and activation of other innate immune cells including neutrophils and dendritic cells (DCs) as well as cells of the adaptive immune system. In addition to their role in controlling the process of inflammation they are also involved in the process of wound repair and regeneration, autoimmunity, obesity and associated insulin tolerance, angiogenesis and embryonic development of the fetus. Thus macrophage are the potent immunoregulatory cells of the innate immune system involved in host defense against infections and other inflammatory diseases including cancer and autoimmunity along with the maintenance of immune homeostasis involving the process of resolution phase during inflammation [210, 211, 212]. Hence macrophages are very important innate immune cells with immune regulatory function depending on their fine tuning or polarization during diverse inflammatory conditions as described here in the chapter.
Although macrophages have been discovered a century ago and revolutionized the immunology research and opened the road to the branch of immunology called innate immunity but much more is still remaining to explore in macrophage biology and their role in the regulation of development, homeostasis, immune homeostasis, inflammation, and disease pathogenesis. For example, macrophage immunometabolism and epigenetic mechanisms regulating their polarization and pro-and anti-inflammatory phenotype and action have started to answer the several previously unknown questions that may influence the future immunotherapeutics and immunomodulatory approaches to target several immune-based diseases varying from autoimmune diseases to several cancers to metabolic diseases.
\n',keywords:"macrophages, monocytes, innate immunity, inflammation, cytokines, pathogens",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/68185.pdf",chapterXML:"https://mts.intechopen.com/source/xml/68185.xml",downloadPdfUrl:"/chapter/pdf-download/68185",previewPdfUrl:"/chapter/pdf-preview/68185",totalDownloads:1268,totalViews:0,totalCrossrefCites:6,dateSubmitted:"November 22nd 2018",dateReviewed:"June 12th 2019",datePrePublished:"July 18th 2019",datePublished:null,dateFinished:null,readingETA:"0",abstract:"Macrophages are ubiquitously present innate immune cells in humans and animals belonging to both invertebrates and vertebrates. These cells were first recognized by Elia Metchnikoff in 1882 in the larvae of starfish upon insertion of thorns of tangerine tree and later in Daphnia magna or common water flea infected with fungal spores as cells responsible for the process of phagocytosis of foreign particles. Elia Metchnikoff received the Noble prize (Physiology and Medicine) for his discovery and describing the process of phagocytosis in 1908. More than 130 years have passed and different subtypes and roles of macrophages as innate immune cells have been established by the researchers. In addition to their immunoregulatory role in immune homeostasis and pathogenic infection, they also play a crucial role in the pathogenesis of sterile inflammatory conditions including autoimmunity, obesity, and cancer. The present chapter describes the immunoregulatory role of macrophages in the homeostasis and inflammatory diseases varying from autoimmunity to metabolic diseases including obesity.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/68185",risUrl:"/chapter/ris/68185",signatures:"Vijay Kumar",book:{id:"8590",title:"Macrophage Activation",subtitle:"Biology and Disease",fullTitle:"Macrophage Activation - Biology and Disease",slug:"macrophage-activation-biology-and-disease",publishedDate:"March 25th 2020",bookSignature:"Khalid Hussain Bhat",coverURL:"https://cdn.intechopen.com/books/images_new/8590.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"162478",title:"Dr.",name:"Khalid Hussain",middleName:null,surname:"Bhat",slug:"khalid-hussain-bhat",fullName:"Khalid Hussain Bhat"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"63844",title:"Dr.",name:"Vijay",middleName:null,surname:"Kumar",fullName:"Vijay Kumar",slug:"vijay-kumar",email:"vij_tox@yahoo.com",position:null,institution:{name:"University of Queensland",institutionURL:null,country:{name:"Australia"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Development of macrophages",level:"1"},{id:"sec_3",title:"3. Macrophage polarization",level:"1"},{id:"sec_4",title:"4. Role of monocytes and macrophages in host defense",level:"1"},{id:"sec_5",title:"5. Role of macrophages in homeostasis (angiogenesis, wound repair, and regeneration) and diverse inflammatory conditions (metabolic diseases and autoimmunity)",level:"1"},{id:"sec_5_2",title:"5.1 Macrophages in angiogenesis",level:"2"},{id:"sec_6_2",title:"5.2 Macrophages in wound repair",level:"2"},{id:"sec_7_2",title:"5.3 Macrophages in regeneration",level:"2"},{id:"sec_8_2",title:"5.4 Macrophages in autoinflammation and autoimmunity",level:"2"},{id:"sec_9_2",title:"5.5 Macrophages in metabolic diseases",level:"2"},{id:"sec_11",title:"6. Conclusion and future perspective",level:"1"}],chapterReferences:[{id:"B1",body:'Kumar V, Ahmad A. Role of MAIT cells in the immunopathogenesis of inflammatory diseases: New players in old game. 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Children’s Health Queensland Clinical Unit, School of Clinical Medicine, Faculty of Medicine, Mater Research, University of Queensland, St Lucia, Australia
School of Biomedical Sciences, Faculty of Medicine, University of Queensland, St Lucia, Australia
'}],corrections:null},book:{id:"8590",title:"Macrophage Activation",subtitle:"Biology and Disease",fullTitle:"Macrophage Activation - Biology and Disease",slug:"macrophage-activation-biology-and-disease",publishedDate:"March 25th 2020",bookSignature:"Khalid Hussain Bhat",coverURL:"https://cdn.intechopen.com/books/images_new/8590.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"162478",title:"Dr.",name:"Khalid Hussain",middleName:null,surname:"Bhat",slug:"khalid-hussain-bhat",fullName:"Khalid Hussain Bhat"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},profile:{item:{id:"191313",title:"Dr.",name:"Michael",middleName:null,surname:"Fitzgerald",email:"prof.m.fitzgerald@gmail.com",fullName:"Michael Fitzgerald",slug:"michael-fitzgerald",position:null,biography:"Professor Michael Fitzgerald was the first Professor of Child and Adolescent Psychiatry in Ireland, specialising in Autism Spectrum Disorders. He trained at the Chicago Medical School, the Maudsley Hospital, London, Kings College Hospital London and the National Hospital for Nervous Diseases, London. He has a Doctorate in Autism. He has a large number of peer-reviewed publications and thirty books written, co-written or co-edited, including Japanese and Polish translations of some books. Professor Simon Baron-Cohen of the University of Cambridge described one of his books on Autism as “the best book I have read on Autism”. He also described him as an “exceptional scholar”. He has diagnosed just under 4,000 persons with Autism Spectrum Disorder since 1973. He has lectured extensively throughout the world including The Royal Society/British Academy, The British Library in London and also in New York, Buenos Aires, Tbilisi, Melbourne and many European countries as well as China, Malaysia and Hawaii.",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/no_image.jpg",totalCites:0,totalChapterViews:"0",outsideEditionCount:0,totalAuthoredChapters:"2",totalEditedBooks:"0",personalWebsiteURL:null,twitterURL:null,linkedinURL:null,institution:{name:"Trinity College Dublin",institutionURL:null,country:{name:"Ireland"}}},booksEdited:[],chaptersAuthored:[{title:"The Clinical Gestalts of Autism: Over 40 years of Clinical Experience with Autism",slug:"the-clinical-gestalts-of-autism-over-40-years-of-clinical-experience-with-autism",abstract:"The clinical gestalts of autism are very broad and much more heterogeneous than people realise. DSM V [1] gives a more narrow and condensed description of what autism is in the twentieth century. DSM focuses on problems with socioemotional reciprocity, non-verbal communication and difficult interpersonal relationships, restricted, repetitive patterns of behaviour, early onset and functional impairment. First, I want to flesh out the autism spectrum disorder gestalts as it presents to experienced clinical practitioners. It is the opposite of the “tick box” approach to diagnosis so common today. It focuses on the phenomena as they would have been focused on in the late nineteenth and early twentieth century, an approach that has faded into the background in the late twentieth and early twenty-first century. It is critical at this point of the twenty-first century that we re-engage with phenomenology and with the clinical gestalt of psychiatric conditions which show a great deal of overlap with much mixed phenomenology. 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This paradigm superseded the attachment paradigm of the second half of the twentieth century with so many misguided theories such as, “blaming the mother”—the so-called refrigerated mother and the schizophrenogenic mother. 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The Open Access model is applied to all of our publications and is designed to eliminate subscriptions and pay-per-view fees. This approach ensures free, immediate access to full text versions of your research.
As a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 118,000 international scientists and researchers.
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Services included are:
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Permanent and unrestricted online access to your work
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Open Access Funding
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For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
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The Open Access Publishing Fee (OAPF) is payable only after your full chapter, monograph or Compacts monograph is accepted for publication.
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*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
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Services included are:
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XML Typesetting and pagination - web (PDF, HTML) and print files preparation
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Exceeds 20 pages (for chapters in Edited Volumes), an additional fee of 40 GBP per page will be required
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If a manuscript requires Heavy Editing or Language Polishing, this will incur additional fees.
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Open Access Funding
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To explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at oapf@intechopen.com for further details or assistance.
\n\n
For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
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Added Value of Publishing with IntechOpen
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Indexing and listing across major repositories, see details ...
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Visibility on the world's strongest OA platform
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Live Performance Metrics to track readership and the impact of your chapter
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Proven world leader in Open Access book publishing with over 10 years experience
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Currently strongest OA platform with over 130 million downloads
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