**Author details**

Aizong Shen1 \*, Rui Qian2 , Ting Liu2 , Qingchuan Liu3 , Bin Liu1 and Zhangbao Wu1

\*Address all correspondence to<EMAIL_ADDRESS>
1 Department of Pharmacy, Anhui Provincial Hospital, Hefei, Anhui Province, China

[10] Wang T, Zhen Y, Ma X, Wei B, Wang N. Phospholipid bilayer-coated aluminum nanoparticles as an effective vaccine adjuvant-delivery system. ACS Applied Materials

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[14] Wang T, Zhen Y, Ma X, Wei B, Li S, Wang N. Mannosylated and lipid A-incorporating cationic liposomes constituting microneedle arrays as an effective oral mucosal HBV vaccine applicable in the controlled temperature chain. Colloids and Surfaces B: Bioin-

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2 School of Pharmacy, Anhui Medical University, Hefei, China

3 School of Biomedical Engineering, Hefei University of Technology, Hefei, China


*5.2.4 Green pigments*

There are two types of commonly used green pigments. The composition, properties and applications of these can be summarised as follows:

*<IP_ADDRESS> Precipitated black iron oxide*

*Structure and Properties of Dyes and Pigments DOI: http://dx.doi.org/10.5772/intechopen.97104*

*<IP_ADDRESS> Carbon black/furnace black*

• Increases the life of paints

• Good tinting strength

• Good tinting strength

• Resistant to high temperature

**Properties:**

*<IP_ADDRESS> Lamp black*

**Properties:**

*5.2.7 Yellow pigments*

follows:

*<IP_ADDRESS> Ochre*

**Properties:**

• Fast to light

*5.2.8 Chrome yellow*

**Properties:**

• Great opacity

• High brilliance

**145**

• High hiding power

• High tinting strength

• Inert to chemical action

It is used in making yellow paints.

It is used in making waterproof paints.

• Not affected by light, acids and alkalies

It is used in making black pigments.

It is used in cement emulsions and water paints. It has a high hiding power.

The composition, properties and applications of these can be summarised as

It consists of naturally occurring yellow Fe2O3. It is used in the paint industry.


**1. Introduction**

Ubiquitination is a reversible post-translational modification process during which the highly conserved 76-aminoacid protein ubiquitin is conjugated to target proteins. Ubiquitin can be conjugated to a protein substrate via distinct mechanisms. Monoubiquitination is the attachment of a single ubiquitin molecule to a single lysine residue on a substrate protein, whereas multi-monoubiquitination is the conjugation of a single ubiquitin molecule to multiple lysine residues. Polyubiquitination occurs when ubiquitin molecules are attached end-to-end to a lysine residue on a substrate protein to form a poly-ubiquitin chain. In this case, ubiquitin molecules are conjugated through one of the seven lysine residues present on the ubiquitin itself (K6, K11, K27, K29, K33, K48, and 63) or the N-terminal methionine (M1). While most of the studies have described the role of specific polyubiquitination, such as K48-linked polyubiquitination for proteasomal degradation [1,2] or K63-linked polyubiquitination for vesicular tra fficking [3], emerging evidences implicate monoubiquitination and multi-monoubiquitination in controlling numerous aspects of protein function, such as degradation, subcellular localization, and protein–protein interaction. In this review, we focus on the role of monoubiquitin conjugation in normal physiology and genetic disease.


**8.5 Project closing (and Handover) phase**

The project closing phase is defined as bringing the project and paperwork to one hundred per cent completion and entering the history in a database [52]. Once this occurs, a handover will happen to the operations management team.


**C(Y) Algorithm (Node-Based Version)**

*Inputs*: The graph G(N, E), cost vector c(e), e ∈ E, initial Yo value for Y.

Edges are ordered so that the costs satisfy c(e(1)) ≤ c(e(2)) ≤ … ≤ c(e(|E|)).

Set Y = Yoand sLast = |E|.

#### **Begin Outer Loop**

While Y < Large

*Initialization(A)*. Set Ynext = Large, K = {1, …, n}, and for each k ∈ K let L(k) = k,

Nk = {k}, Ek = ∅, and MinCostB(k) = Large.

*Initialization(B)*. Let i′ = p(1) and i″ = q(1) and select e(1) (= (i′, i″)) by identifying k′ = L(i′) and k″ = L(i″) and absorbing Nk″ into Nk′ to create the cluster Nk′: = Nk′∪ Nk ″= {i′, i″} with edge set Ek′ = e(1). Set MinCostB(k′) = c(e(1)) and set K: = K \ {k″}. Finally, initialize the edge index s by setting s = 1.

It is possible to combat drift and also take advantage of the different features of the C(W) and C(Y) algorithms by joining these algorithms to create an algorithm C(Z) that incorporates the

Let α be a nonnegative weight applied to the edge selection criterion of C(W) and let β = 1 – αbe a nonnegative weight applied to the edge selection criterion of C(Y). We construct Algorithm C(Z) so that it will be the same as C(W) if α = 1 and will be the same as C(Y) if α = 0 (β = 1).

For notational convenience, we refer to the value MinCost(k) of the C(W) algorithm as

MinCostC(i) = α∙MinCostA(k) + β∙MinCostB(i), for k = L(i).

To apply this criterion, we create values MinCostC(i′) and MinCostC(i″) for nodes i′ = p(s) and

) + β ∙ MinCostB(i′

), MinCostC(i″))

)

A Class of Parametric Tree-Based Clustering Methods http://dx.doi.org/10.5772/intechopen.76406 145

″= {i′, i″} with edge set Ek′

) = α ∙ MinCostA(k′

MinCost evaluation criteria of both C(W) and C(Y) simultaneously.

MinCostA(k). Then the MinCost evaluation criterion of C(Z) is given by.

MinCostC(i″) = α ∙ MinCostA(k″) + β ∙ MinCostB(i″)

*Inputs*: The graph G(N, E), cost vector c(e), e ∈ E, initial Zo value for Z.

Edges are ordered so that the costs satisfy c(e(1)) ≤ c(e(2)) ≤ … ≤ c(e(|E|)).

L(i″) and absorb Nk″ into Nk′ to create the cluster Nk′: = Nk′∪ Nk

*Initialization(A)*. Set Znext = Large, K = N (= {1, …, n}), and for each k ∈ K let.

= e(1). Set MinCostA(k′) = c(e(1)) and MinCostB(i) = c(e(1)) for i = i′ and i″. Set.

*Initialization(B)*. Let i′ = p(1) and i″ = q(1) and select e(1) (= (i′, i″)).Set k′ = L(i′) and k″ =

= ∅, and MinCostA(k) = MinCostB(k) = MinCostC(k) = Large.

i″ = q(s), and for k′ = L(i′) and k″ = L(i″), given by

Associated with the foregoing values, we define

MinCostC0 = Min(MinCostC(i′

We state the C(Z) algorithm by reference to these definitions.

MinCostC(i′

**C(Z) Algorithm.**

Set Z = Zo and sLast = |E|

L(k) = k, Nk = {k}, Ek

K: = K \ {k″} and s = 1.

**Begin Outer Loop**

While Z < Large