**4. Biological Pathway Data Integration**

 An integration model may serve as a tool to the user for a specific type of pathway. An algorithm for integration is presented next.

**Metabolic Pathways:** Integrating pathways from different data sources for the same species extract similar structures in them as the first step; this step integrates vertically given pathway within a species across data sources. (Database is the variable) this includes sorting a graph *G (V, E)* for common V's and E's in *Gi (Vi, Ei)* and *Gj (Vj, Ej).* In the discussion that follows, *i*ntegrating pathway as the TCA cycle given by two data sources namely; *KEGG (Dij1)* and BioCyc *(Dij2)* for *E. coli K-12* is considered. For metabolic pathways the details associated with each graph include the nodes and edges as given below. For Protein-Protein interaction the nomenclature and associated fields for nodes and edges may change. However, it is possible to come up with a structure that can describe the Protein-Protein interactions or signal transduction pathways.

**Signal Transduction Pathways**: The information contained in signal transduction pathways is not similar to the metabolic pathways. In signal transduction pathways, the interactions can be represented as a class hierarchy. Our aim is also to integrate a sample pathway like insulin from sources like KEGG, SPAD to see the performance of our algorithm. Interestingly, SPAD assigns evidence code to the edges (interactions) and KEGG assigns only one evidence code to the pathway (nodes and edges). The format of the table for integration is given above. Before integration information associated with every object (node) and edge (interactions) should be considered.

Before proceeding for integration of a pathway across data sources following steps need to be carried out.

Step 1

#### Step 2

22 Bioinformatics

formats (e.g., between PSI MI and BIND XML, and between Genome Object Net and SBML). However, the complexity of such a pair-wise translation approach increases dramatically with a growing number of different pathway data formats. To address this issue, a standard pathway data exchange format is needed. While the Resource Description framework (RDF) is an important first step towards the unification of XML formats in describing metadata (ontologies), it is not expressive enough to support formal knowledge representation [50]. To address this problem, more sophisticated XML-based ontological languages such as the Web Ontology Language (OWL) have been developed. An OWL-based pathway exchange

An integration model may serve as a tool to the user for a specific type of pathway. An

**Metabolic Pathways:** Integrating pathways from different data sources for the same species extract similar structures in them as the first step; this step integrates vertically given pathway within a species across data sources. (Database is the variable) this includes sorting a graph *G (V, E)* for common V's and E's in *Gi (Vi, Ei)* and *Gj (Vj, Ej).* In the discussion that follows, *i*ntegrating pathway as the TCA cycle given by two data sources namely; *KEGG (Dij1)* and BioCyc *(Dij2)* for *E. coli K-12* is considered. For metabolic pathways the details associated with each graph include the nodes and edges as given below. For Protein-Protein interaction the nomenclature and associated fields for nodes and edges may change. However, it is possible to come up with a structure that can describe the Protein-Protein

*Node:* Biological Name, ID, Neighbor, Type, Context, Pathway, Data Source, PubList,

*Edge:* EdgeID, EdgeSource, EdgeDest, Reactiontype (Rev/ Irreversible), Data Source,

**Signal Transduction Pathways**: The information contained in signal transduction pathways is not similar to the metabolic pathways. In signal transduction pathways, the interactions can be represented as a class hierarchy. Our aim is also to integrate a sample pathway like insulin from sources like KEGG, SPAD to see the performance of our algorithm. Interestingly, SPAD assigns evidence code to the edges (interactions) and KEGG assigns only one evidence code to the pathway (nodes and edges). The format of the table for integration is given above. Before integration information associated with every object

Before proceeding for integration of a pathway across data sources following steps need to

standard, called BioPAX, has been released to the research community [51].

**4. Biological Pathway Data Integration**

algorithm for integration is presented next.

interactions or signal transduction pathways.

SynList, empirical formula, Structure

(node) and edge (interactions) should be considered.

Enzyme, Genes

be carried out.

Step 1

	- if column names are same then continue, else see alternate tag for the column, and match them.
	- If the order matches, then continue, else reorder the columns as given in output table.
	- If the number of columns is not same, then append the table with new columns.

#### Step 3

The notations used in our algorithm are presented next.

### **4.1. Notations**

Consider a tuple *(Si , (Pij, (Diji))* (3)

Where, *Dijk= {dij1, dij2, dij3, … dijk}* is a set of 'k' data sources for *(Si , Pij)* (4)

Then, the tuple *(v111n, e111m)* gives *(node, edge)* in Biocyc for TCA cycle in *E.coli*, and the tuple *(v112p, e112p)* gives *(node, edge)* in KEGG for TCA cycle in *E.coli*

• *s2 = {(s2, p2j (D2jk)} = {(s2, p2j, d2j1), (s2, p2j, d2j2),……, (s2, p2j, d2jr)} for 'r'* databases*,*

For example, *s2: Arabidopsis; p2j:* TCA Cycle*; d2j1=* BioCyc*, d2j2=* AraCyc

Then, the tuple *(v221p, e221p)* gives the *(node, edge)* in AraCyc for TCA cycle in *Arabidopsis*, and the tuple *(v222p, e222p)* gives the *(node, edge)* in KEGG for TCA cycle in *Arabidopsis.*

Each pathway *pij for a dijk* is given by a graph *G (Vijk, Eijk),* where,

*Pijk = G (Vijk, Eijk)* represents Pathway *'j'* from *kth* datasourcesS for species *i'*… .(5)

Where, *Vijk = {v ijk1, v ijk2,…v ijkn*} = set of nodes in *dijk*,. (6)

*E ijk = {e ijk1, e ijk2,….e ijkm*}= set of edges in *dijk*,. (7)

• *SynList {pathway name} = SynList {Pij}*

	- *SynList {entity name} = SynList {v1jkn}*
	- *EVijk = {EVijk1, …. EVijkh}* set of *'h'* EV Codes for *{si , pij , dijk },* for example;
		- *EV1j1= {*Set of EVcodes given by Biocyc for E.coli for TCA cycle*}*
		- *EV1j2= {*Set of EV codes given by KEGG for E.coli for TCA cycle*}*
		- *EV2j3= {*Set of EV codes given by AraCyc for Arabidopsis for TCA cycle*}*
		- *EV2j2= {*Set of EV codes given by KEGG for Arabidopsis for TCA cycle*}*
	- *RIijk:* Reference index for a database *dijk*
	- *RIijint:* Reference index for the integrated pathway
	- *CWijk:* Confidence weight for a database *dijk*
	- *CWijint:* Confidence weight of the integrated pathway *pij* within a species
	- *Vijint:* Integrated node table for a species *Si ,* for a pathway *pij*
	- *Eijint:* Integrated edge table for a species *Si ,* for a pathway *pij*
	- *(v1jkn, e ijkm) = (node 'n', edge 'm')* in *d1jk* of *s1* for *p1j;*
	- *ATT {(v1jkn ,(A)}= {v1jkn, (A1, A2, A3, A4, …As)} =* set of attributes of the node *v1jkn*
	- *ATT {(e ijkm, (B)} ={(e ijkm, (B1, B2, B3, … Bt)} =* set of attributes of edge *e ijkm*
	- *DATT {v1jkn ,(δA)} =* set of derived attributes of the node *v1jkn (EVi, CWi, RIi)*
	- *DATT {e1jkn ,(δB)}=* set of derived attributes of the edge *e1jkn (EVi, CWi, RIi)*
	- *δVijk =* Set of derived node attributes for Integrated pathway *{EVint, CWint, RIint}*
	- *δEijk=* Set of derived edge attributes for Integrated pathway *{EVint, CWint, RIint}*
	- *Vijint = {Σ Vijk } for k= 1 to n*
	- *E ijint= {Σ E ijk} for k= 1 to n*
	- *Pijint =* Integrated pathway from multiple DSs *= {Σ Pijk } for k=1 to n*

#### **4.2. Biological Pathway Data Integration Algorithm**

Following selections and inputs are defined by the user.

Step 1.

For each user selected pathway *Pij for a species si*

*List Dij (d1j1,… dnjk*), \*\*\*(KEGG, BioCyc, MetNetDB etc)\*\*\*

Hierarchical Biological Pathway Data Integration and Mining 25

where, *Vijk= {vijk1, vijk2, ….vijkt*} and *Eij1= {e ijk1, e ijk2,….e ijku}*

**Check** *for vijk, 1 Є Vijp* (node name match across data sources)

**Check if** *vijk,1 Є SynList {vijp,t }* (node name(A) with Synlist(B))

**Generate SynListInt =** {**SynList** *(vijk, 1)* U *Synlist (vijp,l)*U*…}* without duplication

**Include** information associated with the edge, as given by 'edges' such as reaction,

enzyme, by products and substrates along with attributes like evidence, reference

*\*\** Outputs *Eijint* table for *si* using *(d1j1,… d1jk),* with *EVijint, CWijint* and *RIijint .* **Level 1.** *\*\**

 *= Σ {ATT [(v1jkn, (A)]}, ATT [(e ijkm, (B)]} + Σ {DATT {v1jkn, (δA), DATT {e1jkn, (δB)}for*

 *Generate DATT {v1jkn, (δA)}, DATT {e1jkn, (δB)},*

 *If YES, then Apply EV* integration algorithm*, Generate DATT {v1jkn, (δA)}, DATT {e1jkn, (δB)},*

**Check if** *SynList {vijk,1*} has a match with *vijp,t If YES, then Apply EV integration Algorithm*

 *If vijk,1 = vijp,l is TRUE,*

C**ompute** *(δVijk, δEijk)*

**Generate** *Pijint*

publications, context etc.

*all n, m {δVijk δEijk}*

**Associate DOI** (date of integration)

**Repeat** *Step 2-3 for eijk Є Eijk in (dij1,… dijk), for pij*

**Check if** *SynList {vijk,1}* has a match with *SynList {vijp,t }*

**Include** *vijk, 1* with the matched node name *vijk-1, p Є Vijk-1*

*Pijint = {Σ Pijk } for k=1 to n = [{ Vijint, E ijint} + { Σ δVijkt, Σ δEijk} for k=1 to n ]at t= t1*

*\*\*\**This is the node name for the integrated database for the species. **Level 1***\*\*\**

If *YES,* then **Apply** *EV* integration algorithm

Step 3.

*For k = 1, …, q (d1j1,… d1jk),*

 Else, *For p = 1 to n, For t = 1, z*

 *Else,*

 *Else,*

Step 4.

 *Then,*

*For s = 1,.., n,* and *q = 1, …m,* **List** *ATT {(v1jks, (A)}* **List** *ATT {(e ijkq, (B)}*   **Select** *vijk1* Є *Vijk* **C** *d1jk* For all *p =1 to n*

Step 2. **Define** rules to classify the interactions, for example;

 **Sort** (*d1j1,… dnjk*) according to species (*si ,dij1), (sj ,djj1*) etc.

 **Generate** a set of (nodes, edges) from all the input data sources *{(Vij, Eij)} = {(Vij1, Eij1), (Vij2, Eij2)….. (Vijs, Eijs)}*

```
 where, Vijk= {vijk1, vijk2, ….vijkt} and Eij1= {e ijk1, e ijk2,….e ijku}
Step 3.
For k = 1, …, q (d1j1,… d1jk),
 For s = 1,.., n, and q = 1, …m,
 List ATT {(v1jks, (A)}
 List ATT {(e ijkq, (B)}
 Select vijk1 Є Vijk C d1jk
 For all p =1 to n
 Check for vijk, 1 Є Vijp (node name match across data sources)
 If YES, then Apply EV integration algorithm
 Generate DATT {v1jkn, (δA)}, DATT {e1jkn, (δB)},
 Else, For p = 1 to n,
 For t = 1, z
               Check if vijk,1 Є SynList {vijp,t } (node name(A) with Synlist(B))
 If YES, then Apply EV integration algorithm,
 Generate DATT {v1jkn, (δA)}, DATT {e1jkn, (δB)},
 Else,
 Check if SynList {vijk,1} has a match with vijp,t
 If YES, then Apply EV integration Algorithm
 Else,
 Check if SynList {vijk,1} has a match with SynList {vijp,t }
 If vijk,1 = vijp,l is TRUE,
 Then,
 Include vijk, 1 with the matched node name vijk-1, p Є Vijk-1
 Compute (δVijk, δEijk)
```
*\*\*\**This is the node name for the integrated database for the species. **Level 1***\*\*\**

**Generate SynListInt =** {**SynList** *(vijk, 1)* U *Synlist (vijp,l)*U*…}* without duplication **Associate DOI** (date of integration) **Generate** *Pijint*

*Pijint = {Σ Pijk } for k=1 to n = [{ Vijint, E ijint} + { Σ δVijkt, Σ δEijk} for k=1 to n ]at t= t1 = Σ {ATT [(v1jkn, (A)]}, ATT [(e ijkm, (B)]} + Σ {DATT {v1jkn, (δA), DATT {e1jkn, (δB)}for all n, m {δVijk δEijk}*

Step 4.

24 Bioinformatics

• *SynList {entity name} = SynList {v1jkn}*

• *RIijk:* Reference index for a database *dijk*

• *Vijint:* Integrated node table for a species *Si*

• *Eijint:* Integrated edge table for a species *Si*

• *Vijint = {Σ Vijk } for k= 1 to n* • *E ijint= {Σ E ijk} for k= 1 to n*

• *(v1jkn, e ijkm) = (node 'n', edge 'm')* in *d1jk* of *s1* for *p1j;*

• *EVijk = {EVijk1, …. EVijkh}* set of *'h'* EV Codes for *{si*

• *RIijint:* Reference index for the integrated pathway • *CWijk:* Confidence weight for a database *dijk*

 *EV1j1= {*Set of EVcodes given by Biocyc for E.coli for TCA cycle*} EV1j2= {*Set of EV codes given by KEGG for E.coli for TCA cycle*}*

• *CWijint:* Confidence weight of the integrated pathway *pij* within a species

• *ATT {(v1jkn ,(A)}= {v1jkn, (A1, A2, A3, A4, …As)} =* set of attributes of the node *v1jkn* • *ATT {(e ijkm, (B)} ={(e ijkm, (B1, B2, B3, … Bt)} =* set of attributes of edge *e ijkm* • *DATT {v1jkn ,(δA)} =* set of derived attributes of the node *v1jkn (EVi, CWi, RIi)*  • *DATT {e1jkn ,(δB)}=* set of derived attributes of the edge *e1jkn (EVi, CWi, RIi)* • *δVijk =* Set of derived node attributes for Integrated pathway *{EVint, CWint, RIint}*  • *δEijk=* Set of derived edge attributes for Integrated pathway *{EVint, CWint, RIint}*

• *Pijint =* Integrated pathway from multiple DSs *= {Σ Pijk } for k=1 to n*

User selected inputs: Species, Pathway, Data sources/database

For each user selected pathway *Pij for a species si*

Step 2. **Define** rules to classify the interactions, for example;

 **Sort** (*d1j1,… dnjk*) according to species (*si*

User defined filters (**UDF**) for entities like substrate nodes, H2O, CO2 etc. for integrated

 **Generate** a set of (nodes, edges) from all the input data sources *{(Vij, Eij)} = {(Vij1,*

*,dij1), (sj*

*,djj1*) etc.

*List Dij (d1j1,… dnjk*), \*\*\*(KEGG, BioCyc, MetNetDB etc)\*\*\*

**4.2. Biological Pathway Data Integration Algorithm**

Following selections and inputs are defined by the user.

User inputs: Confidence assigned to each database

pathway [*Pijint = G (Vint, Eint)*]*,*

interaction

*Eij1), (Vij2, Eij2)….. (Vijs, Eijs)}*

Step 1.

 *EV2j3= {*Set of EV codes given by AraCyc for Arabidopsis for TCA cycle*} EV2j2= {*Set of EV codes given by KEGG for Arabidopsis for TCA cycle*}*

*, pij , dijk },* for example;

*,* for a pathway *pij*

*,* for a pathway *pij*

**Repeat** *Step 2-3 for eijk Є Eijk in (dij1,… dijk), for pij*

**Include** information associated with the edge, as given by 'edges' such as reaction, enzyme, by products and substrates along with attributes like evidence, reference publications, context etc.

*\*\** Outputs *Eijint* table for *si* using *(d1j1,… d1jk),* with *EVijint, CWijint* and *RIijint .* **Level 1.** *\*\**

Step 5.

**Generate** integrated pathway by consolidating outputs *G(Vijint, Eijint)* for *si*

Hierarchical Biological Pathway Data Integration and Mining 27

[54] uses manually curated metabolic networks, orthologue and their related reactions to

Arrendondo [55] Proposes to develop a process for the continuous improvement of the inference system used, which is applicable to any such data mining application. It involves the comparison of several classifiers like Support Vector Machines (SVMs), Human Expert generated Fuzzy, and Genetic Algorithm (GA) generated Fuzzy and Neural Networks using various different training data models. In his approach, all classifiers were trained and tested with four different data sets: three biological and a synthetically generated mixture data set. The obtained results showed a highly accurate prediction capability with the

Biological database integration is a challenging task as the databases are created all over the world and updated frequently. For biological data sources that may be derived from an earlier existing data source, it is also important to identify the evidence of the data source represented by the evidence code, to be included as a candidate for integration. In most data integration algorithms the user does not participate thus leading to an integrated data

Large scale integration of pathway databases promises to help biologists gain insight into the deep biological context of a pathway. In this chapter, we presented algorithms that help user to select their choice of data sources and apply Evidence code algorithm to compute an integrated EV code and RI for the pathway data of interest. The ultimate goal is to generate a large-scale composite database containing the entire metabolic network for an organism. This qualitative approach includes aspects like user confidence scores for databases for mapping EV and generating RI for a given pathway. For the TCA pathway results show that generating such a mapping is helpful in visualizing the integrated database that highlights the common entities as well as the specifics of each database. As the database confidence weight selection is user specific, the integration yields different results for different users for the same database which will allow users to explore the effects of different hypotheses on the overall network. Once the integrated evidence code is generated, then data integration algorithm is applied to get the integrated pathway data. To best attempt integration of such data it is imperative to include user participation as user mostly identifies the associations and behavior of various compounds, reactions, genes in a given biological pathway leading

mixture data set providing some of the best and most reliable results.

compare predicted gene-reaction associations.

source with any effective utility towards analysis.

*Electrical Engineering, Arkansas State University, USA*

*Samuel Roberts Noble Foundation, USA*

**6. Conclusion**

to significant diagnosis.

**Author details**

Shubhalaxmi Kher

Jianling Peng

Step 6.

*For i =1,…n*

**Repeat** steps 2- 4 to integrate *Pij* for all species *si*

*\*\** **This generates Table** *(Vj int, Ej int) = {(Vijint, Eijint) U (Vjjint, Ejjint)U…}* **for** *Si ,* **for all** *i =1, ..n),*  **for a** *pij.* **Level 2\*\*\***

Step 7.

*For (j = 1,….p)*

**Integrate for all** *Pij*

\*\*This **generates output table** *(Vint, Eint) = (Vj int,Ej int) U (Vkint,Ekint) U….***for all** *(j = 1,….p).*  **Level 3\*\*\***

Step 8.

**Apply UDF** (User defined filter)


**10.1 SIPES splenectomy**

Few centers worldwide are performing SIPES pediatric splenectomy (**Table 2**). On extensive review there were only 166 SILS splenectomy cases, out of them only 61 SIPES pediatric splenectomy published in English, German, Greek, French, Italian, or Spanish literatures in all age groups (0.6–90 years) [23–28]. No publications regarding the safety of SIPES splenectomy were performed by trainees. Laparoscopic splenectomy is a demanding technique which needs high level of skills and mainly performed by the most experienced trained surgeon. Our SIPES splenectomy series is the largest one. 49 SIPES splenectomies were performed by 15 trainees. 25 (51%) were male and 24 (49%) were female. Mean age was 6.9 years (2.5–14.8 years). Six cholecystectomies were done simultaneously. 45 patients were having sickle cell disease, two with thalassemia, one spherocytosis, and one Fanconi's anemia. MOT for splenectomy was 182 min (130–190) and 251 min for splenectomy with cholecystectomy (230–270) min depending on severity of adhesions and size of the spleen

**147**

**Figure 3.**

**Table 2.**

**Summary**

*KFAFH SIPES outcome.*

0 125 min

*Single-Incision Pediatric Endosurgery (SIPES) DOI: http://dx.doi.org/10.5772/intechopen.85305*

P value <0.001 (**Figures 4** and **5**). Two were converted to open due to bleeding and were in the beginning of the series. There is neither wound infection nor incisional hernia update. SIPES splenectomy is now the standard of care in our unit, and we

• This is the first series of SIPES splenectomy for hematological diseases done by trainees.

**Procedures Age OT (min) # Conversion** Splenectomy (six with cholecystectomy) 2.5–14.8 130–270 49 2

2 130–190 SIPES splenectomy 2.5–14.8 49 Raboei

**years**

(2.7–9.7)

SIPES splenectomy 6 months 1 Joshi et al.

2–17 20 SIPES

procedures 8 months

37 patients (20 with extra port)

0.6–90 81 Gkegkeset

**Number Authors**

Dutta [18]

Seims et al. [20]

al [23]

[24]

[25]

highly recommend it as excellent approach for splenectomy.

**Conversion Time/min Detail procedures Age**

2 (excluded) 116 SIPES 7.1

2 combined splenectomy/ cholecystectomy

SILS and SIPES splenectomy

0 90–176 4 splenectomy

(range, 45–420 min)

*Comparison of SIPES splenectomy in pediatric age group.*

**Table 1.** *SIPES outcome at KFAFH.* *Single-Incision Pediatric Endosurgery (SIPES) DOI: http://dx.doi.org/10.5772/intechopen.85305*

#### **Figure 3.** *KFAFH SIPES outcome.*

*Pediatric Surgery, Flowcharts and Clinical Algorithms*

2010 to 2016 is shown in (**Table 1** and **Figure 3**).

**Procedures Age OT**

Splenectomy (6 with cholecystectomy) 2.5–14.8 130–270 49 2

Intussusception 10 months 90 I 0

SIPES nephrectomy 18 months 100 1 0 SIPES abdominal orchidectomy 18 months 20 1 0

Sleeve gastrectomy 13–18 years 40–120 3 0 Total 324

2 months–12 years

18 months–7 years

> 4 days–13 years

> > years

years

Cholecystectomy (one with inguinal hernia

Appendectomy (carcinoid, adhesion, 6 perforated, 4 appendix mass)

SIPES hernia repair percutaneous internal ring

Fundoplication (liver retract, Endo Stitch, 2

Assisted Mitrofanoff one with nephrectomy

Malrotation (Ladd's procedure) 16 and 17

Adhesiolysis 3 and 6

Morgagni hernia 3 and 5 years 90 and

Ovarian cystectomy (oophorectomy) (2

repair+ 1 with appendectomy)

suturing (PIRS)

hiatal hernia)

teratomas)

and orchidectomy

*SIPES outcome at KFAFH.*

**10.1 SIPES splenectomy**

**10. Outcomes of SIPES at King Fahad Armed Forces Hospital (KFAFH)**

The outcome of our retrospective study of all SIPES cases which was done from

Few centers worldwide are performing SIPES pediatric splenectomy (**Table 2**). On extensive review there were only 166 SILS splenectomy cases, out of them only 61 SIPES pediatric splenectomy published in English, German, Greek, French, Italian, or Spanish literatures in all age groups (0.6–90 years) [23–28]. No publications regarding the safety of SIPES splenectomy were performed by trainees. Laparoscopic splenectomy is a demanding technique which needs high level of skills and mainly performed by the most experienced trained surgeon. Our SIPES splenectomy series is the largest one. 49 SIPES splenectomies were performed by 15 trainees. 25 (51%) were male and 24 (49%) were female. Mean age was 6.9 years (2.5–14.8 years). Six cholecystectomies were done simultaneously. 45 patients were having sickle cell disease, two with thalassemia, one spherocytosis, and one Fanconi's anemia. MOT for splenectomy was 182 min (130–190) and 251 min for splenectomy with cholecystectomy (230–270) min depending on severity of adhesions and size of the spleen

**(min)**

90 and 120

120

80 and 100

1.5–13 years 90–180 10 0

7–16 85 ± 40 52 2 (extra

3–12 70–160 137 0

20–45 46 22 (extra

90–180 8 0

30–90 10 0

2 0

2 0

2 0

**# Conversion**

port)

port)

**146**

**Table 1.**

### **Table 2.**

*Comparison of SIPES splenectomy in pediatric age group.*

P value <0.001 (**Figures 4** and **5**). Two were converted to open due to bleeding and were in the beginning of the series. There is neither wound infection nor incisional hernia update. SIPES splenectomy is now the standard of care in our unit, and we highly recommend it as excellent approach for splenectomy.

**Figure 4.** *MOT SIPES splenectomy.*

**Figure 5.** *SIPES MOT over years.*

#### **10.2 SIPES appendectomy**

Most centers advocate laparoscopy for treating acute appendicitis in order to minimize the size and the number of skin incisions even with complicated ones [29]. SIPES pediatric appendectomy has gained significant popularity, as its main advantage is preferable cosmetic result. Previous studies have typically compared SIPES and CLA appendectomy in children and showed heterogeneous results. There was no difference in duration of hospital stay or postoperative complications. Longer MOT with SIPES appendectomy was the main concern [11, 30–41]. One study has shown that SIPES appendectomy in children is safe and feasible when performed by resident doctors and compare it when performed by fellows [42]. Chandler NM et al. studied SIPES versus conventional laparoscopic appendectomy CLA in children. 110 patients underwent appendectomy; 50 SIPES Group 1 and 14 were excluded (perforated) [30, 33]. They concluded that MOT and pain medication are the only significant values between SIPES and conventional. There was no increase in wound infection [43, 44].

**149**

**10.3 SIPES cholecystectomy**

Appendectomy (carcinoid, adhesion, six perforated, four appendix mass)

**Summary**

*Single-Incision Pediatric Endosurgery (SIPES) DOI: http://dx.doi.org/10.5772/intechopen.85305*

**Table 3.**

*Performing surgeon and MOT.*

We started SIPES appendectomy in 2011, and it became our standard approach for acute appendicitis. It is carried out routinely by trainees and specialist. We compared SIPES appendectomy with CLA between our center and another training center. We conducted retrospective study at two training military institutes, KFAFH and Prince Sultan Medical City PSMMC. A total of 322 patients were operated. 187 patients (58%) underwent CLA, and 137 patients (42%) underwent SIPES appendectomy. 120 patients (64%) of CLA and 87 (63%) patients of SIPES appendectomy were males. 67 patients (36%) of CLA and 42 (37%) patients of SIPES appendectomy were females. The mean age of the patients in CLA and SIPES appendectomy was 10 and 9.6 years, respectively. Diagnosis at time of operation was simple appendicitis in 166 patients who underwent CLA and 126 patients who underwent SIPES appendectomy, and complicated appendicitis was in 21 and 11 patients, respectively. 143 CLA (76%) were performed by specialist, and 44 cases (24%) were performed by trainees. 47 SIPES appendectomy (34%) were performed by specialist, and 90 cases (66%) were performed by trainees (**Table 3**) (P value <0.001). The MOT was 70 min for CLA and 86 min for SIPES appendectomy (P value <0.001). The mean length of hospital stay is 2.6 days for CLA and 2.8 days for SIPES appendectomy. Bilateral percutaneous internal ring suturing (PIRS) and right PIRS were performed on 2 patients who underwent SIPES appendectomy. Three cases developed intraabdominal collection (2.2%), one case had wound infection (0.7%), and two cases had adhesion (1.5%), treated conservatively in SIPES appendectomy. There were five cases of intra-abdominal collection (2.7%), two cases of wound infection (1.1%), and one case of adhesion (0.5%) in CLA. Negative appendectomy rate has been 10%.

**Operation Performing surgeon MOT NO.** SIPES appendectomy Specialist 70.723 47

CLA Specialist 64.681 141

Trainee 95.289 90 Total 86.861 137

Trainee 88.457 46 Total 70.529 187

Minimally invasive techniques have revolutionized surgical treatment. Few centers worldwide have advanced SIPES for pediatric age group. Many papers that were published in English literatures proved the safety and efficacy of SIPES cholecystectomy [44–48]. Up to our knowledge, there is no study regarding MOT of SIPES

**Procedures Age OT (min) # Conversion**

3–12 70–160 137 0

• SIPES appendectomy is the commonest procedure done in our center by our trainees.

*Single-Incision Pediatric Endosurgery (SIPES) DOI: http://dx.doi.org/10.5772/intechopen.85305*

**Table 3.**

*Pediatric Surgery, Flowcharts and Clinical Algorithms*

**148**

**10.2 SIPES appendectomy**

**Figure 4.**

**Figure 5.**

*SIPES MOT over years.*

*MOT SIPES splenectomy.*

Most centers advocate laparoscopy for treating acute appendicitis in order to minimize the size and the number of skin incisions even with complicated ones [29]. SIPES pediatric appendectomy has gained significant popularity, as its main advantage is preferable cosmetic result. Previous studies have typically compared SIPES and CLA appendectomy in children and showed heterogeneous results. There was no difference in duration of hospital stay or postoperative complications. Longer MOT with SIPES appendectomy was the main concern [11, 30–41]. One study has shown that SIPES appendectomy in children is safe and feasible when performed by resident doctors and compare it when performed by fellows [42]. Chandler NM et al. studied SIPES versus conventional laparoscopic appendectomy CLA in children. 110 patients underwent appendectomy; 50 SIPES Group 1 and 14 were excluded (perforated) [30, 33]. They concluded that MOT and pain medication are the only significant values between SIPES and conventional. There was no increase in wound infection [43, 44].

*Performing surgeon and MOT.*

We started SIPES appendectomy in 2011, and it became our standard approach for acute appendicitis. It is carried out routinely by trainees and specialist. We compared SIPES appendectomy with CLA between our center and another training center. We conducted retrospective study at two training military institutes, KFAFH and Prince Sultan Medical City PSMMC. A total of 322 patients were operated. 187 patients (58%) underwent CLA, and 137 patients (42%) underwent SIPES appendectomy. 120 patients (64%) of CLA and 87 (63%) patients of SIPES appendectomy were males. 67 patients (36%) of CLA and 42 (37%) patients of SIPES appendectomy were females. The mean age of the patients in CLA and SIPES appendectomy was 10 and 9.6 years, respectively. Diagnosis at time of operation was simple appendicitis in 166 patients who underwent CLA and 126 patients who underwent SIPES appendectomy, and complicated appendicitis was in 21 and 11 patients, respectively. 143 CLA (76%) were performed by specialist, and 44 cases (24%) were performed by trainees. 47 SIPES appendectomy (34%) were performed by specialist, and 90 cases (66%) were performed by trainees (**Table 3**) (P value <0.001). The MOT was 70 min for CLA and 86 min for SIPES appendectomy (P value <0.001). The mean length of hospital stay is 2.6 days for CLA and 2.8 days for SIPES appendectomy. Bilateral percutaneous internal ring suturing (PIRS) and right PIRS were performed on 2 patients who underwent SIPES appendectomy. Three cases developed intraabdominal collection (2.2%), one case had wound infection (0.7%), and two cases had adhesion (1.5%), treated conservatively in SIPES appendectomy. There were five cases of intra-abdominal collection (2.7%), two cases of wound infection (1.1%), and one case of adhesion (0.5%) in CLA. Negative appendectomy rate has been 10%.

**Summary**

• SIPES appendectomy is the commonest procedure done in our center by our trainees.


**4.2.1 Effect of artefacts on fluid flow permeability**

Visualisations of wool fibre web sample with different threshold values are shown in Fig. 12. The sample geometries in (a) – (c) were denoised and then binarised using grey value based threshold (Gonzales & Woods, 2002). At the lowest threshold levels (a) and (b), the noise is clearly visible in the void space. While threshold value was gradually increased, the noise became less evident and the thickness of the fibres (or size of the solid particles) diminished. The sample geometry in Fig. 12d was segmented utilising the forest fire method. The forest fire method was found to give noise free pore space and a fibre radius that corresponded well with the mean value obtained from scanning electron microscope images, 20 µm.

Fig. 12. Visualisation of segmented wool fibre web sample at different threshold values: (a) 20, (b) 30 and (c) 40, and with the forest fire method (d).

The Effect of Tomography Imaging Artefacts on

Structural Analysis and Numerical Permeability Simulations 483

Fig. 13. (b)

(c)

Fig. 13. Numerically solved permeability coefficients for the wool fibre web (a), the packaging board (b) and the sand stone (c) as a function of threshold value. Experimental

results are given as an average of the five measurements for each sample types.

Grey value -based thresholding procedure with different threshold values were conducted for all the three tomographic sample types. Numerical fluid flow permeability analyses were conducted for all the geometries and the obtained values were compared with experimental results, see Fig. 13.

As an example, for the wool fibre web, the "optimal" threshold value would have been between 20 and 30, see Fig. 13a. By visually examining the corresponding images, it was found that there was still noise left in flow channels at threshold value 30. Permeability at the optimal threshold value would not have been result of the realistic sample geometry, but combination of noise causing higher specific surface area and thinner fibres increasing the porosity of the geometry. The results were found qualitatively similar for the packaging board and the sand stone samples.

The forest fire method was found to be efficient tool for segmentation procedure. Numerical permeability values for the geometries segmented by the forest fire method were 2.5E-11 m2, 4.8E-14 m2 and 2.5E-12 m2 for the wool fibre web, the packaging board and the sand stone samples, respectively. Corresponding experimental measurement results for the samples were 2.5E-11 m2, 5.1E-14 m2 and 2.5E-12 m2. The maximum difference between the experimental results and the numerical permeability values found by using the forest fire segmented sample geometries was in these cases less than 6.5 %. The statistical uncertainty of the experimental results was 20%.

The numerical permeability results for the samples with different added noise levels (proportion of false voxels) are shown in Fig. 14. Artificial noise levels were generated into the sample geometries which were segmented by the forest fire method. The effect of noise on permeability was found to be qualitatively similar as for the hexagonal cylinder array. Increase in noise level caused decrease in fluid flow permeability value.

Grey value -based thresholding procedure with different threshold values were conducted for all the three tomographic sample types. Numerical fluid flow permeability analyses were conducted for all the geometries and the obtained values were compared with experimental

As an example, for the wool fibre web, the "optimal" threshold value would have been between 20 and 30, see Fig. 13a. By visually examining the corresponding images, it was found that there was still noise left in flow channels at threshold value 30. Permeability at the optimal threshold value would not have been result of the realistic sample geometry, but combination of noise causing higher specific surface area and thinner fibres increasing the porosity of the geometry. The results were found qualitatively similar for the packaging

The forest fire method was found to be efficient tool for segmentation procedure. Numerical permeability values for the geometries segmented by the forest fire method were 2.5E-11 m2, 4.8E-14 m2 and 2.5E-12 m2 for the wool fibre web, the packaging board and the sand stone samples, respectively. Corresponding experimental measurement results for the samples were 2.5E-11 m2, 5.1E-14 m2 and 2.5E-12 m2. The maximum difference between the experimental results and the numerical permeability values found by using the forest fire segmented sample geometries was in these cases less than 6.5 %. The statistical uncertainty

The numerical permeability results for the samples with different added noise levels (proportion of false voxels) are shown in Fig. 14. Artificial noise levels were generated into the sample geometries which were segmented by the forest fire method. The effect of noise on permeability was found to be qualitatively similar as for the hexagonal cylinder array.

Fig. 13. (a)

Increase in noise level caused decrease in fluid flow permeability value.

results, see Fig. 13.

board and the sand stone samples.

of the experimental results was 20%.

Fig. 13. Numerically solved permeability coefficients for the wool fibre web (a), the packaging board (b) and the sand stone (c) as a function of threshold value. Experimental results are given as an average of the five measurements for each sample types.

The Effect of Tomography Imaging Artefacts on

Structural Analysis and Numerical Permeability Simulations 485

(c)

packaging board (b) and the sand stone (c) as a function of noise level. Experimental results

Specific surface area of the wool fibre web sample was analysed as a function of threshold value and noise level, see the results in Fig. 15. The specific surface area of the geometry thresholded by the forest fire method had a value of 0.175 m-1. The value of specific surface area of the sample geometry decreased as a function of increasing threshold value and increased as a function of increasing noise level. According to Eq. (3), when a sample geometry has a high specific surface area, it has a small permeability value. This could also

Pore size distribution of the wool fibre web sample geometry was analysed as a function of threshold value, see the results in Fig. 16a. When compared to the experimental permeability results, the optimal threshold value was found to be between 20 and 30, see Fig. 13a. The results in Fig. 16a clearly show that with the threshold values of 20 or 30 the pore size distribution differs highly from the pore size distribution of the geometry that was segmented using the forest fire method. These results support the interpretation related to the permeability values obtained using the geometries segmented by conventional grey value -based thresholding. Combination of too high specific surface area and too thin fibres (high porosity) resulted to the permeability values very close to the experimental results. These interpretations were found to be similar for the packaging board and for the sand

Pore size distribution as a function of noise level for the wool fibre web sample is presented in Fig. 16b. The effect of added noise was found to be not as high as for the hexagonal array of cylinders, see Fig. 7. For noise level of 2 %, the mode value of the distribution was

Fig. 14. Numerically solved permeability coefficients for the wool fibre web (a), the

**4.2.2 Effect of artefacts on specific surface area and pore size distribution**

are given as an average of the five measurements for each sample types.

be seen from the results in Fig. 15 and in Figs 13a and 14a.

approximately a half of the mode value of the noise free geometry.

stone samples also.

Fig. 14. (a)

Fig. 14. (b)

Fig. 14. (a)

Fig. 14. (b)

Fig. 14. Numerically solved permeability coefficients for the wool fibre web (a), the packaging board (b) and the sand stone (c) as a function of noise level. Experimental results are given as an average of the five measurements for each sample types.


**5.10. CAUTI with** *Achromobacter xylosoxidans*

Achromobacter denitrificans is gram negative bacterium formerly known as *Alcaligenes denitrificans*. Infection with this organism predominantly observed in elderly patients with predisposing factors as urological abnormalities, malignancies and immune-suppression. Rarely it causes bacteraemia. This bacterium has high level of antibiotic resistance [84].

In polymicrobial biofilm, *Achromobacter xylosoxidans* cohabits with common organisms *E. coli*, *Pseudomonas aeruginosa* and *Klebsiella pneumoniae*. Diagnosis is by bacterial culture and molecular methods.


**Meet the editor**

Hasim Pihtili was born in Elazig, Turkey in 1960. He graduated from the Department of Mechanical Engineering at Firat University, Turkey in 1986, after which he started his master science study and became Research Assistant at the same university. He completed his master study in 1988. He completed his PhD study in 1991 and was promoted to Assistant Professor in 1993. He has

taught lectures on machine elements, composite materials and laboratory courses related to construction and manufacturing since 1988. He has numerous publications in national and international journals. Particularly, they are mainly about wear, composite materials and construction of machines. He has a chapter in the book "Woven Fabric Engineering". He is still a reviewer in various journals, and a member of Turkish Composites Manufacturers Association (TCMA), and has a patent at the same time.

Contents

**Preface IX**

Chapter 1 **Effect of FeCr Intermetallic**

Ibrahim Sevim

**Section 2 Lubrication 79**

Erkan Bahçe and Cihan Ozel

Chapter 4 **A New Attempt to Better Understand**

Chapter 6 **High Speed Rotors on Gas Bearings:**

**on Wear Resistance of Fe-Based Composites 3**  S.O. Yılmaz, M. Aksoy, C. Ozel, H. Pıhtılı and M. Gür

**Arrehnius Equation and Its Activation Energy 47**

Chapter 5 **Solid Particle Erosion on Different Metallic Materials 63**  Juan R. Laguna-Camacho, M. Vite-Torres, E.A. Gallardo-Hernández and E.E. Vera-Cárdenas

**Design and Experimental Characterization 81**

Chapter 7 **High Temperature Self-Lubricating Materials 109**  Qinling Bi, Shengyu Zhu and Weimin Liu

> **Biolubricant Based on Jatropha Oil 135**  M. Shahabuddin, H.H. Masjuki and M.A. Kalam

Chapter 8 **Development of Eco-Friendly Biodegradable**

G. Belforte, F. Colombo, T. Raparelli, A. Trivella and V. Viktorov

**Parameters on the Surface Roughness and the Formation of Built Up Edge (BUE) in the Drilling of Al 5005 15**

Chapter 2 **Experimental Investigation of the Effect of Machining**

Chapter 3 **Effect of Abrasive Particle Size on Abrasive Wear Resistance in Otomotive Steels 29**

Andrzej Kulczycki and Czesław Kajdas

**Section 1 Wear 1**


**1. Introduction**

In mid-March 2019, Tropical Cyclone Idai from category 4 hit Mozambique, devastating the port city of Beira and surrounding areas, following torrential rains and strong winds, causing massive flooding and leaving entire communities submerged [1–3]. It has damaged supplies, cut off clean water and sanitary facilities, and destroyed different infrastructures including schools, hospitals, houses (239,731), roads, rails, disrupting regional trade, and supplies of fuel, wheat, medicines, and other goods, even from neighbor countries such as Zimbabwe and Malawi [3–5].

Thousands of displaced people were sheltering in 136 accommodation sites, including schools, across Sofala, Manica, Zambezia, and Tete, as reported by the National Institute of Disaster Management (INGC) [4, 6, 7].

Idai caused a range of public health consequences, including mortality (598 confirmed deaths until 02 April 2019), injury, and infections [8, 9] due to waterborne and vector-borne diseases including cholera, malaria, and measles, and more than half of affected people are children [2, 10, 11]. Two weeks after Cyclone Idai's landfall, the index of cholera was confirmed in Mozambique with the death of five

people. After the Ministry of Health declared a cholera outbreak on 27 March, 4979 cases and six deaths were confirmed [1, 3, 5, 8, 12].

Cholera is an acute, secretory diarrhea caused by strains of the Gram-negative bacterium *Vibrio cholerae* that occurs in both endemic and epidemic patterns, presenting symptoms like watery diarrhea, rice-water stools, fishy odor to stools, vomiting, rapid heart rate, loss of skin elasticity, dry mucous membranes, and low blood pressure. This bacterium is usually found in food or water contaminated by stools from a person with the infection, and, if untreated, the fatality rate can be as high as 30–50%, but with rehydration and electrolyte replacement, the death rate decrease to 1% [8, 13].

Environmental factors play an important role in the epidemiology of cholera. The flooding and displacement of people has increased the risk of cholera because many of them spent days without accessing safe water supplies, resorting to drink the floodwaters. There are some videos of children playing with the same water that unfortunately were strewn bodies of humans and animals. Beyond that, menstruated women are also susceptible to waterborne diseases due to the use of unsafe water to wash their clothes, during the bath or while collecting water to prepare food [1, 8, 14–16].

Idai survivors received supplies to purify their drinking water, food supplies, and items such as soap, nappies, towels, and blankets, to stop spreading cholera. The Mozambican Ministry of Health (MISAU), World Health Organization (WHO), Cooperative for Assistance and Relief Everywhere (CARE), and other NGOs worked in setting up treatment centers and clinics, as well as helping to run a massive campaign, where 800,000 doses of oral cholera vaccines were distributed and dispensed [2, 4, 10]. This paper aims to summarize the status of the outbreak of cholera in Mozambique due to the deadliest storm Cyclone Idai.


**The November 23rd, 1980 Irpinia-Lucania, Southern Italy Earthquake: Insights and Reviews 40 Years Later**

Editors

**Sabina Porfido Giuliana Alessio Germana Gaudiosi Rosa Nappi Alessandro Maria Michetti**

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Cover image courtesy of Albert Pissart

The photo, taken on 4-4-1981, shows the primary surface faulting on the bedrock fault scarp near Senerchia town: It is visible the free face on the limestone fault plain

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