**Prediction of Ebolavirus Genomes Encoded MicroRNA-Like Small RNAs Using Bioinformatics Approaches**

Yue Teng, Zhe Xu, Jin Yuan, Xiaoping An, Jiangman Song and Dan Feng

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/62944

#### **Abstract**

Recent findings revealed that certain viruses encoded microRNA-like small RNAs using the RNA interference machinery in the host cells. However, the function of these virusencoded microRNA-like small RNAs remained unclear, and whether these microRNAlike small RNAs were involved in the replication of the virus and viral infection was still disputable. In this chapter, the negative-sense RNA genome of Ebola virus (EBOV) was scanned using bioinformatics tools to predict the EBOV-encoded microRNA-like small RNAs. Then, the potential influence of viral microRNA-like small RNAs on the viral immune evasion, host cellular signaling pathway, and epigenetic regulation of antiviral defense mechanism were also detected by the reconstructed regulatory network of target genes associated with viral encoded microRNA-like small RNAs. In this analysis, EBOV-encoded microRNA-like small RNAs were proposed to inhibit the host immune response factors, probably facilitating the evasion of EBOV from the host defense mechanisms. In conclusion, systematic investigation of microRNA-like small RNAs in EBOV genome may shed light on the underlying molecular mechanisms of the pathological process of Ebola virus disease (EVD).

**Keywords:** Ebolavirus, virus-encoded miRNAs, microRNAs, bioinformatics, NF-kB, TNF

#### **1. Introduction**

Zaire Ebola virus (ZEBOV) has the highest case-fatality rate with an average of approximate‐ ly 83% over the past 27 years [1]. Its first outbreak took place on August 26, 1976, in Yambuku [2], and the virus was also responsible for the 2014 West Africa outbreak, which was the largest

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

EBOV outbreak in record [3–6]. Moreover, neither antiviral drugs nor effective treatment was available for EBOV or Ebola virus disease (EVD) at that time [7, 8]. MicroRNAs originate from a wide variety of primary transcripts (pri-miRNAs) that are generated by RNA polymerase II (polII)in all eukaryotes [9] or byRNApolymerase III(polIII)in some viruses [10]. The cleavage of pri-miRNAs releases a RNA hairpin intermediate (~70 nt) containing a characteristic 2 nt 3' overhang, named a premature miRNA (pre-miRNA), which is further processed to generate the 21~23 nt mature miRNA from its arm of ~70 nt imperfect stem-loop structure [11, 12].

Since microRNAs have been discovered and their role in gene expression regulation was established, it has been hypothesized that viruses could encode microRNA-like small RNAs as well, and these virus-encoded microRNA-like small RNAs were proposed to play important regulatory roles in viral immune evasion and systemic pathogenesis [13–15]. The size of viral encoded microRNA-like RNAs has a significant advantage given the tight constraints on viral genome size, which is also small enough to escape from the triggered host immune pathway. It was found that viral encoded microRNA-like small RNAs could downregulate the expres‐ sion of host immune defense gene, resulting in increased viral replication or evasion from host immune surveillance [16, 17]. Until now, more than 60 viral microRNA-like small RNAs have been identified [18–24], most of which came from Herpes virues [25]. Only a small part of such RNAs was detected within Retrovirus, Adenovirus, and polyomavirus families [26–28].

Bioinformatics-driven prediction was an effective method to identify viral encoded micro‐ RNA-like small RNAs [21, 22]. In this study, the microRNA prediction program, VMir, was applied to scan the viral genomes for the presence of stem-loop structures in the pri- and premiRNAs and identify potential candidate stretches capable to form stable secondary stem-loop structures. Afterward, putative mature microRNA-like small RNAs were validated using MatureBayes [29]. The systemic prediction of the potential EBOV-encoded microRNA-like small RNAs along with their target genes on the genome-wide scale helps to further assess the function of microRNAs during viral infection and virus-host interactions in the EVD patho‐ genesis.

#### **2. Methods**

#### **2.1. EBOV whole genome sequences and alignment**

The full-length genome sequences of EBOV were retrieved from the genome browser at Ebola virus resource (http://www.ncbi.nlm.nih.gov/genome/viruses/variation/ebola/) and UCSC Ebola portal (https://genome.ucsc.edu/ebolaPortal/). MAFFT Multiple Sequence Alignment Software Version 7 were applied for the alignment of the EBOV genomes [30].

#### **2.2. Bioinformatics prediction of the EBOV genome-encoded microRNA-like small RNAs**

Briefly, the viral genome was scanned for stem-loop structures of miRNA precursor (premiRNA) using VMir [31] with default parameter settings (http://www.hpi-hamburg.de/ research/departments-and-research-groups/antiviral-defense-mechanism/software-download.html). The putative pre-miRNAs with VMir score ≥150 and a window count ≥ 35 were retained. Then, MiPred software [32] was applied to check all of the putative miRNA precur‐ sors, and precursors with confidence equal to or greater than 70% were further analyzed. Subsequently, mature miRNA sequences were predicted from the putative pre-miRNA stemloops. Finally, the MatureBayes tool [29] was used to extend the prediction coverage of the mature miRNAs under default parameter settings.

#### **2.3. Prediction of the target genes and signaling pathway analysis**

EBOV outbreak in record [3–6]. Moreover, neither antiviral drugs nor effective treatment was available for EBOV or Ebola virus disease (EVD) at that time [7, 8]. MicroRNAs originate from a wide variety of primary transcripts (pri-miRNAs) that are generated by RNA polymerase II (polII)in all eukaryotes [9] or byRNApolymerase III(polIII)in some viruses [10]. The cleavage of pri-miRNAs releases a RNA hairpin intermediate (~70 nt) containing a characteristic 2 nt 3' overhang, named a premature miRNA (pre-miRNA), which is further processed to generate the 21~23 nt mature miRNA from its arm of ~70 nt imperfect stem-loop structure [11, 12].

Since microRNAs have been discovered and their role in gene expression regulation was established, it has been hypothesized that viruses could encode microRNA-like small RNAs as well, and these virus-encoded microRNA-like small RNAs were proposed to play important regulatory roles in viral immune evasion and systemic pathogenesis [13–15]. The size of viral encoded microRNA-like RNAs has a significant advantage given the tight constraints on viral genome size, which is also small enough to escape from the triggered host immune pathway. It was found that viral encoded microRNA-like small RNAs could downregulate the expres‐ sion of host immune defense gene, resulting in increased viral replication or evasion from host immune surveillance [16, 17]. Until now, more than 60 viral microRNA-like small RNAs have been identified [18–24], most of which came from Herpes virues [25]. Only a small part of such RNAs was detected within Retrovirus, Adenovirus, and polyomavirus families [26–28].

Bioinformatics-driven prediction was an effective method to identify viral encoded micro‐ RNA-like small RNAs [21, 22]. In this study, the microRNA prediction program, VMir, was applied to scan the viral genomes for the presence of stem-loop structures in the pri- and premiRNAs and identify potential candidate stretches capable to form stable secondary stem-loop structures. Afterward, putative mature microRNA-like small RNAs were validated using MatureBayes [29]. The systemic prediction of the potential EBOV-encoded microRNA-like small RNAs along with their target genes on the genome-wide scale helps to further assess the function of microRNAs during viral infection and virus-host interactions in the EVD patho‐

The full-length genome sequences of EBOV were retrieved from the genome browser at Ebola virus resource (http://www.ncbi.nlm.nih.gov/genome/viruses/variation/ebola/) and UCSC Ebola portal (https://genome.ucsc.edu/ebolaPortal/). MAFFT Multiple Sequence Alignment

**2.2. Bioinformatics prediction of the EBOV genome-encoded microRNA-like small RNAs**

Briefly, the viral genome was scanned for stem-loop structures of miRNA precursor (premiRNA) using VMir [31] with default parameter settings (http://www.hpi-hamburg.de/ research/departments-and-research-groups/antiviral-defense-mechanism/software-down-

Software Version 7 were applied for the alignment of the EBOV genomes [30].

genesis.

86 Ebola

**2. Methods**

**2.1. EBOV whole genome sequences and alignment**

Target genes of predicted EBOV-encoded microRNA-like small RNAs in the human genome were predicted using TargetScan [33]. Putative targets within the viral genome were predicted using TargetScan Perl script. The signaling pathways collected from the Kyoto Encyclopedia of Genes and Genomes (KEGG) [34–36] PATHWAY databases were applied in the pathway analysis.

**Figure 1.** The predicted EBOV-encoded pre-miRNAs and microRNA-like small RNAs. The MiPred algorithm was used to identify genuine pre-miRNAs, and the MatureBayes tool was used to predict the mature miRNA sequences. (A) The secondary structures of the four EBOV pre-miRNAs. (B) The tertiary structures of the EBOV-encoded micro‐ RNA-like small RNAs.

#### **2.4. Constructing gene regulation network**

The genetic regulation network was constructed based on systematic integration of various datasets. Transcription factors related with the target genes of EBOV-encoded microRNA-like small RNAs were selected from Transcriptional Regulatory Element Database (TRED) [37– 39]. The integrated regulatory network of target genes with transcription factors was con‐ structed using Cytoscape software (http://cytoscape.org/).

### **3. Key findings regarding the bioinformatics prediction of EBOV genomeencoded microRNA-like small RNAs**

#### **3.1. Predicted precursor and mature EBOV genome-encoded microRNA-like small RNAs**

The released full-length genome sequences of the retrieved EBOV strains were aligned and then scanned for miRNA precursor (pre-miRNA) using VMir software. Afterward, the putative pre-miRNAs with VMir score ≥150 and a window count ≥35 were selected for further assessment. Within the EBOV genome, four putative microRNA precursors, EBOV-premiRNA-1, EBOV-pre-miRNA-2, EBOV-pre-miRNA-3, and EBOV-pre-miRNA-4 were predict‐ ed (**Figure 1A**). The mature miRNA sequences were predicted from the putative pre-miRNA stem loops. Seven different mature EBOV miRNA candidates, including EBOV-miR-1-5p, EBOV-miR-1-3p, EBOV-miR-2-5p, EBOV-miR-2-3p, EBOV-miR-3-5p, EBOV-miR-3-3p, EBOVmiR-4-5p, and EBOV-miR-4-3p were resolved using MatureBayes tool (**Figure 1B**).

#### **3.2. Bioinformatics analysis of the genetic regulation network in the target genes of EBOV genome-encoded microRNA-like small RNAs**

Target genes of the predicted mature microRNA-like small RNAs were searched within TargetScan, and the potential target genes in host were identified (**Table S1**, the list of potential target genes of EBOV-encoded microRNA-like small RNAs). KEGG pathway enrichment analysis was performed using the DAVID bioinformatics tool for these target genes. The results showed that the target genes were closely related on function and were involved in multiple canonical pathways, such as NF-kB activation by viruses, role of protein kinase (PKR) in interferon induction and antiviral response, induction of apoptosis by HIV1, B cell-activating factor signaling, and role of PI3K/AKT signaling in the pathogenesis of influenza, which were important in human immune response to virus infection (**Table 1**).

**2.4. Constructing gene regulation network**

88 Ebola

**encoded microRNA-like small RNAs**

**genome-encoded microRNA-like small RNAs**

important in human immune response to virus infection (**Table 1**).

**Canonical pathways p-Value Ratio Molecules**

Angiopoietin signaling *4.47E-01 1.54E-02 NFKBIE* April mediated signaling *6.43E-01 2.63E-02 NFKBIE* ATM signaling *4.81E-01 1.69E-02 MRE11A*

AMPK signaling *1.49E+00 2.26E-02 PDRK1, FASN, ADRA2B, RRKAB2*

structed using Cytoscape software (http://cytoscape.org/).

The genetic regulation network was constructed based on systematic integration of various datasets. Transcription factors related with the target genes of EBOV-encoded microRNA-like small RNAs were selected from Transcriptional Regulatory Element Database (TRED) [37– 39]. The integrated regulatory network of target genes with transcription factors was con‐

**3. Key findings regarding the bioinformatics prediction of EBOV genome-**

**3.1. Predicted precursor and mature EBOV genome-encoded microRNA-like small RNAs**

The released full-length genome sequences of the retrieved EBOV strains were aligned and then scanned for miRNA precursor (pre-miRNA) using VMir software. Afterward, the putative pre-miRNAs with VMir score ≥150 and a window count ≥35 were selected for further assessment. Within the EBOV genome, four putative microRNA precursors, EBOV-premiRNA-1, EBOV-pre-miRNA-2, EBOV-pre-miRNA-3, and EBOV-pre-miRNA-4 were predict‐ ed (**Figure 1A**). The mature miRNA sequences were predicted from the putative pre-miRNA stem loops. Seven different mature EBOV miRNA candidates, including EBOV-miR-1-5p, EBOV-miR-1-3p, EBOV-miR-2-5p, EBOV-miR-2-3p, EBOV-miR-3-5p, EBOV-miR-3-3p, EBOV-

**3.2. Bioinformatics analysis of the genetic regulation network in the target genes of EBOV**

Target genes of the predicted mature microRNA-like small RNAs were searched within TargetScan, and the potential target genes in host were identified (**Table S1**, the list of potential target genes of EBOV-encoded microRNA-like small RNAs). KEGG pathway enrichment analysis was performed using the DAVID bioinformatics tool for these target genes. The results showed that the target genes were closely related on function and were involved in multiple canonical pathways, such as NF-kB activation by viruses, role of protein kinase (PKR) in interferon induction and antiviral response, induction of apoptosis by HIV1, B cell-activating factor signaling, and role of PI3K/AKT signaling in the pathogenesis of influenza, which were

miR-4-5p, and EBOV-miR-4-3p were resolved using MatureBayes tool (**Figure 1B**).

**Table 1.** Key canonical pathway analysis of the potential mature EBOV miRNA target genes.

Prediction of Ebolavirus Genomes Encoded MicroRNA-Like Small RNAs Using Bioinformatics Approaches http://dx.doi.org/10.5772/62944 91

**Canonical pathways p-Value Ratio Molecules** Role of IL-17A in arthritis *5.13E-01 1.85E-02 NFKBIE*

STAT3 pathway *4.08E-01 1.37E-02 SOCS4*

TNFR2 signaling *7.62E-01 3.57E-02 NFKBIE*

Angiopoietin signaling *4.47E-01 1.54E-02 NFKBIE* April mediated signaling *6.43E-01 2.63E-02 NFKBIE* ATM signaling *4.81E-01 1.69E-02 MRE11A* B cell activating factor signaling *6.24E-01 2.5E-02 NFKBIE*

CD27 signaling in lymphocytes *5.34E-01 1.96E-02 NFKBIE*

CD40 signaling *4.53E-01 1.56E-02 NFKBIE*

ErbB signaling *3.58E-01 1.18E-02 PDPK1* ErbB2-ErbB3 signaling *5E-01 1.79E-02 PDPK1* ErbB4 signaling *4.87E-01 1.72E-02 PDPK1*

HGF signaling *2.95E-01 9.62E-03 ELF3* HIF1a signaling *3.07E-01 1E-02 MMP25*

IL-10 signaling *4.32E-01 1.47E-02 NFKBIE* IL-17A signaling in airway cells *4.53E-01 1.56E-02 NFKBIE* IL-17A signaling in fibroblasts *6.75E-01 2.86E-02 NFKBIE* IL-6 signaling *2.63E-01 8.62E-03 NFKBIE*

Induction of apoptosis by HIV1 *1.22E+00 3.39E-02 NFKBIE, RIPK1*

**Table 1.** Key canonical pathway analysis of the potential mature EBOV miRNA target genes.

TNFR1 signaling *1.4E+00 4.26E-02 NFKBIE, RIPK1*

B cell receptor signaling *4.92E-01 1.17E-02 PDPK1, NFKBIE*

CD28 signaling in T helper cells *7.5E-01 1.77E-02 PDPK1, NFKBIE*

EIF2 signaling *4.89E-01 1.16E-02 PDPK1, EIF2AK4*

Erythropoietin signaling *1.12E+00 2.99E-02 PDPK1, NFKBIE*

IGF-1 signaling *1.55E+00 3.09E-02 GRB10, PDPK1, SOCS4* IL-1 signaling *8.99E-01 2.2E-02 GNAT1, NFKBIE*

AMPK signaling *1.49E+00 2.26E-02 PDPK1, FASN, ADRA2B, PRKAB2*

*5.1E-01 1.2E-02 GNAT1, NFKBIE*

*4.75E-01 1.67E-02 NFKBIE*

*6.24E-01 2.5E-02 NFKBIE*

Role of NFAT in regulation of the immune

Role of PI3K/AKT signaling in the pathogenesis of influenza

Role of PKR in interferon induction and

response

90 Ebola

antiviral response

**Figure 2.** Bioinformatics analysis of the genetic regulatory network of target genes of EBOV-encoded microRNA-like small RNAs (A and B). The key regulation network of the potential target genes of EBOV-encoded microRNA-like small RNAs.

Based on the gene regulation network (GRN) analysis (Figure S1), it was found that target genes, FASN, RUNX1T1, and ELF3, were important immune and inflammation response factors and actively interacted with transcription regulator, such as KLF2 and NF-kB in host cells (**Figure 2A**) [40, 41]. They were also the key co-regulator of TNF complex in human immune system (**Figure 2B**) [42], implying that the EBOV might inhibit the infection response of immune system by affecting the related signaling pathway using noncoding RNA. Furthermore, it was speculated that the mature EBOV-encoded microRNA-like small RNAs might induce large-scale epigenetic modification in host genome to downregulate the expression of epigenetic factor, such as histone h3, HDAC5, JARID2, and SMARCA4, resulting in the inactivation of immune signaling and immune system with the antiviral response (**Figure 2A** and **2B**) [40–45].

#### **3.3. Potential EBOV genome-encoded microRNA-like small RNAs associated with the Immune response-related pathways**

Additionally, NF-kB and RIPK were also involved in the RIG-I-like receptor pathway (**Figure 3**) [46, 47]. As shown in **Figure 3**, the RIG-I-like receptor pathway played a key role in antiviral response that is a sensor for viruses such as influenza A, Rhabdovirus, Flavivirus, Paramyx‐ ovirus, Epstein-Barr virus, and Filovirus [48]. The RIG-I-like receptor pathway is stimulated during RNA virus infection by the interaction between cytosolic RIG-I and viral RNA struc‐ tures that contain short hairpin dsRNA and 5' triphosphate (5'ppp) terminal structure. The EBOV might utilize the microRNA-like small RNAs to inhibit the RIG-I-like receptor pathway to evade the host defense mechanisms, or conversely to trigger apoptosis responses as a

**Figure 3.** The RIG1I like receptor pathway associated with the potential target genes of EBOV-encoded microRNA1 like small RNAs. The target genes of EBOV-encoded microRNA1-like small RNAs, NF1kB, and RIPK, were involved in the RIG1I-like receptor pathway to trigger IFN signaling pathway with the antiviral response.

mechanism to increase viral infection [49, 50]. For viruses, effective RIG-I-mediated antiviral responses are dependent on functionally active LGP2. The dysfunction of LGP2 resulted in promoting viral replication, preventing virus-induced apoptosis, and suppressing the immune response for the invading pathogen [51]. Certain retroviruses, such as HIV-1, encode a protease that directs RIG-1 to the lysosome for degradation, and thereby evade RIG-1 mediated signaling. RIG-I and MDA-5 are involved in activating interferon (IFN) signaling pathway with the antiviral response.

#### **4. Conclusions**

in the inactivation of immune signaling and immune system with the antiviral response

**3.3. Potential EBOV genome-encoded microRNA-like small RNAs associated with the**

Additionally, NF-kB and RIPK were also involved in the RIG-I-like receptor pathway (**Figure 3**) [46, 47]. As shown in **Figure 3**, the RIG-I-like receptor pathway played a key role in antiviral response that is a sensor for viruses such as influenza A, Rhabdovirus, Flavivirus, Paramyx‐ ovirus, Epstein-Barr virus, and Filovirus [48]. The RIG-I-like receptor pathway is stimulated during RNA virus infection by the interaction between cytosolic RIG-I and viral RNA struc‐ tures that contain short hairpin dsRNA and 5' triphosphate (5'ppp) terminal structure. The EBOV might utilize the microRNA-like small RNAs to inhibit the RIG-I-like receptor pathway to evade the host defense mechanisms, or conversely to trigger apoptosis responses as a

**Figure 3.** The RIG1I like receptor pathway associated with the potential target genes of EBOV-encoded microRNA1 like small RNAs. The target genes of EBOV-encoded microRNA1-like small RNAs, NF1kB, and RIPK, were involved in

the RIG1I-like receptor pathway to trigger IFN signaling pathway with the antiviral response.

(**Figure 2A** and **2B**) [40–45].

92 Ebola

**Immune response-related pathways**

MicroRNAs are encoded by cellular or viral genomes and play an essential role in numerous cellular processes, including viral infection, viral immune evasion, and antiviral cell-mediated immune response. Most viral genome-encoded microRNA-like small RNAs have been identified by traditional cloning strategy from virus-infected cells, yet others have been identified following computational prediction. Using the VMir analyzer program, the polyo‐ mavirus simian vacuolating virus 40 (SV40) [22] and Merkel Cell virus (MCV) [13] have been found to encode microRNA-like small RNAs, suggesting that VMir analyzer program is an effective tool for searching new viral miRNA-like small RNAs [52]. Therefore, we analyzed the genome of EBOV with the VMir software and obtained four pre-miRNAs located in the coding region of viral genome, indicating that the RNA secondary structures of EBOV genome might be processed into microRNA-like small RNAs [53, 54].

Infected cells have several signaling mechanisms to sense and respond to virus infection [55], for example, cross talk between different cellular pathways to modulate the expression and antiviral function of interferon (IFNs) with RIG-I-like receptor pathway and specific gene products. RIG-I-like receptor pathway and IFNs cytokines are important regulators of innate and adaptive immune responses [56]. Besides their antiviral role, they are potent regulators of cell growth and have immunomodulatory activity. INFs were activated after virus infection, probably through viral dsRNA and other viral gene products. The most intensely studied molecule in the RIG-I-like receptor pathway is the dsRNA-activated serine/threonine protein kinase (PKR). PKR was activated in the presence of cytoplasmic dsRNA, leading to the rapid phosphorylation of eukaryotic initiation factor eIF2 and subsequent inhibition of both host and viral mRNA [57, 58].

Although the bioinformatics prediction could be inaccurate, the bioinformatics prediction was potentially more selective and effective than experimental method. The target genes of viral genome-encoded microRNA-like small RNAs would help to develop an effective treatment for the EBOV infection.

#### **5. Limitations**

Due to the high mutation rate of reverse transcription in replication, EBOV presents numerous mutations over viral genome during host adaption, suggesting that the viral genome is not exactly the same among various EBOV strains. Thus, it is difficult to find microRNAs that are completely conserved among different viral strains due to genome mutations.

However, it is possible that some microRNA-like small RNAs are relatively conserved among diverse viral adapted hosts. Moreover, the expression pattern of viral microRNA-like small RNAs was highly unpredictable. Therefore, it might be difficult to validate the EBOV genomeencoded microRNA-like small RNAs using experimental method.

#### **Acknowledgements**

This work was supported by a grant from the State Key Laboratory of Pathogen and BioSe‐ curity Program (No. SKLPBS1408 and No. SKLPBS1451).

#### **Authors' contributions**

Zhe Xu, Yuan Jin, and Xiaoping An characterized the materials, under the supervision of Yue Teng, Zhe Xu, and Dan Feng wrote the manuscript with further contributions from Jiangman Song and Yuan Jin analyzed the data. All authors reviewed the manuscript.

#### **Conflict of interest**

Competing financial interests and the authors declare no competing financial interests.

#### **Author details**

Yue Teng1\*, Zhe Xu2 , Jin Yuan1 , Xiaoping An1 , Jiangman Song3 and Dan Feng4

\*Address all correspondence to<EMAIL_ADDRESS><EMAIL_ADDRESS>
1 The State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China

2 Core Laboratory for Clinical Medical Research, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

3 Department of Neurology, People's Hospital, Peking University, Beijing, China

4 Division of Standard Operational Management, Institute of Hospital Management, Chi‐ nese PLA General Hospital, Beijing, China

Yue Teng and Zhe Xu contributed equally to this work.

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exactly the same among various EBOV strains. Thus, it is difficult to find microRNAs that are

However, it is possible that some microRNA-like small RNAs are relatively conserved among diverse viral adapted hosts. Moreover, the expression pattern of viral microRNA-like small RNAs was highly unpredictable. Therefore, it might be difficult to validate the EBOV genome-

This work was supported by a grant from the State Key Laboratory of Pathogen and BioSe‐

Zhe Xu, Yuan Jin, and Xiaoping An characterized the materials, under the supervision of Yue Teng, Zhe Xu, and Dan Feng wrote the manuscript with further contributions from Jiangman

Competing financial interests and the authors declare no competing financial interests.

1 The State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and

2 Core Laboratory for Clinical Medical Research, Beijing Tiantan Hospital, Capital Medical

4 Division of Standard Operational Management, Institute of Hospital Management, Chi‐

3 Department of Neurology, People's Hospital, Peking University, Beijing, China

, Jiangman Song3

and Dan Feng4

Song and Yuan Jin analyzed the data. All authors reviewed the manuscript.

, Xiaoping An1

\*Address all correspondence to<EMAIL_ADDRESS><EMAIL_ADDRESS>
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, Jin Yuan1

**Acknowledgements**

Ebola94

**Authors' contributions**

**Conflict of interest**

**Author details**

Yue Teng1\*, Zhe Xu2

Epidemiology, Beijing, China

University, Beijing, China

nese PLA General Hospital, Beijing, China

Yue Teng and Zhe Xu contributed equally to this work.

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**4.1.4 Scenario three**

In this scenario water supply is maintained at the same level as 2005. Residential water use will be maintained at 105,379,575 m3 through the planning horizon, but the water services will be kept at the same level of having 135 litre/cap/day. To make this possible, a soft path will be developed to provide the additional 102,776,076 m3 to reach the level 208,155,650 m3 necessary for a water use of 135 litre/cap/day and a population of Amman of 3665483 in 2030.

Water Soft Path Analysis – Jordan Case 307

5. Review Water Supply options: Identify all current sources of water (surface and ground) and determine whether any is being over-used or degraded. Reduce withdrawals of fresh water or releases of wastewater that threaten long-term renewable use, and reject any new sources that cross major shed boundaries or create serious threat to ecological, cultural or social values. Indicate the relevant range of future

6. Backcast (in contrast with forecast): Create various soft paths by designing incremental policies and programs to get from here to the future. Check each option to see whether it seems economically feasible, socially acceptable and politically achievable. This is an

The first step in preparing a soft path analysis is to establish a base line of current water use services. Looking at the water services allows us to evaluate the effect of improvements in end use technology and water demand management while maintaining the purpose for

According to earlier calculations in this section water use per capita in Amman was

Consumption

Understanding how the customer uses water is important to understanding how where to get conservation savings. End use information – that is, information about water flow at each specific point that the customer uses water – is important not only to analyzing conservation potential, but also to forecasting increases in water demand in the future.

No data exists of exact measures of water use services in Amman. To develop basis of estimate for water use services, prior studies in Ammam, and the results of a model developed by Rosenberg (personal contacts) were used. This model of household water use

Drinking 2.0% 2,003,728 Toilets 17.8% 17,833,179 Showers 27% 27,050,328 Clothes Washers 11% 11,020504 Kitchen and Bath Faucets 27% 27,050,328 Outdoor 6.7% 6,712,489 Others 7% 7,013,048 Car 1.5% 1,502,796 Total 100% 100,186,400

Total

(m3)

Consumption

supply adjustment that may result from climate change.

iterative process of backdating.

**4.3.1 Disaggregating consumption of sectors**

Table 5. Indoor water consumption by end use

Household Fixture % Total

**4.3 Step 1: Identify water services**

which the water is required.

**<IP_ADDRESS> Residential water use**

estimated at 106 liters/day.

Figure 8 presents the official demand projection for Amman, along with the propped three soft paths.

Fig. 8. Official projection and soft path scenarios for Amman Governorate

### **4.2 Methodology**

The following basic steps for a soft path plan will be followed to develop the three Scenarios, these steps are after (Brandes et al, 2005)

Figure 8 presents the official demand projection for Amman, along with the propped three

2000 2005 2010 2015 2020 2025 2030 2035 **Year**

The following basic steps for a soft path plan will be followed to develop the three

1. Identify Water Services: List all services provided by water (e.g. residential indoor and outdoor, municipal parks, cooling). Some questions to answer include: Who is going to need water? For what purpose or goal water is needed? What kind of water is needed to meet a specific goal? How much water of a particular quality is needed to meet given

2. Adopt a Projection for the Governorate- Look 25 years in the future of pre-existing official documents, demographic projections, and expectations of economic growth. Next, apply existing water use patterns to this projection (on a use-by-use basis), thus

3. Establish a Desired Future Condition: Create a desired future pattern for water supply and use. For example, a governorate might assume all future growth will be offset by

conservation or efficiency and no new water sources will need to be developed. 4. Analyze Water Quantity and Quality: Establish the quantity of water required to provide the service identified for this projected future (Step 3) by applying as many of the water conserving options as can be adopted within the given time frame. Determine which uses require high water quality water notably (drinking, cooking and bathing) and which uses can proceed with lower quality water (toilet flushing, gardening, most

Fig. 8. Official projection and soft path scenarios for Amman Governorate

Scenarios, these steps are after (Brandes et al, 2005)

enabling a "business as usual" baseline.

forms of Agriculture, industrial applications, etc.)

Scenrio One & Scenario Three Scenario Two Official Scenario

soft paths.

0

**4.2 Methodology**

goal?

50

100

150

200

**Million Cubic Meter**

250

300

350


4.1.2. GCR

As the GCR increases, PM2.5 concentration and wind speed all showed a decreasing trend (Figure 8b). This is because wind speed is reduced and dust is suppressed by vegetation in the vertical zone beneath the canopy, with a progressive decrease in the zone above the canopy. As an example, Figure 10 illustrates the local vegetation XZ plane wind speed and PM2.5 distribution in Neighborhood A, which suggests that the influence of vegetation on pollutant concentrations and wind speed is related to the height of the vegetation and its dust retention effect is most noticeable in the vertical zone beneath the canopy, with a progressive decrease in the zone above the canopy.

**Figure 10.** XZ plane in the local area of Neighborhood A of: (**a**) velocity vector; (**b**) PM2.5 concentration distribution.

#### 4.1.3. ABF, ABV and SDH

ABF, ABV and SDH demonstrated varying degrees of positive and negative correlations with PM2.5 and wind speed, respectively. When ABV = 40,000 m3 and ABF = 20F, the lowest PM2.5 concentration was observed. The rise in ABF indirectly increases the building separation and enhances the air circulation within and outside the neighborhoods. When ABF exceeds 20, wind speed is further increased, but the static wind zone is formed on the leeward side of the building, which hinders the diffusion of pollutants (Figure 8d). As the ABV increases, PM2.5 concentration shows a tendency to decrease and then increase (Figure 8e). This is because, within a certain volume range, increasing ABV helps to increase the open space and reduces the weakening effect on wind speed due to a large number of buildings. However, excessive ABV might increase the static wind area in the neighborhood and limit the effect of wind on pollution transmission.

The results reveal that the difference in height between buildings on the windward and leeward sides of the neighborhoods affects wind speed in the direction of incoming airflow differently (Figure 8f). Taking the example of four street valleys in the typical YZ plane of two neighborhoods (Figure 11), rising valley 1 and valley 4 are more effective in diffusing PM2.5 in vertical space than falling valley 2 and valley 3. Therefore, reasonable SDH regulation promotes airflow rising, establishing a pleasant neighborhood wind environment, and boosting pollutant dispersion in vertical space. Moreover, angular flow zones are created with building heights up to a certain level, which enhances the dispersion of pollutants to the leeward side.

**Figure 11.** PM2.5 diffusion distribution of typical Y-Z section in: (**a**) Neighborhood A; (**b**) Neighborhood B.


**4. Conclusions**

Deep semantic segmentation neural networks are powerful end-to-end remote sensing image classification tools that have achieved successes in many applications. However, it is difficult to construct a training set that thoroughly exhausts all the pixel segmentation possibilities for a specific ground area; in addition, spatial information loss occurs during the network training and inference stages. Consequently, the classification results of DSSNNs are usually not perfect, which introduces a need to correct the results.

Our experiments demonstrate that when faced with complicated remote sensing images, the CRF algorithm often has difficulty achieving a substantially improved correction effect; without restricting the mechanism by using additional samples, the CRF may overcorrect, leading to a decrease in the classification accuracy. Our approach improves on the traditional CRF global processing effects by offering two advantages:

(1) End-to-end: ELP identifies which locations of the result image are highly suspected of containing errors without requiring samples; this characteristic allows ELP to be used in an end-to-end classification process.

(2) Localization: Based on the suspect areas, ELP limits the CRF analysis and update area within a small range and controls the iteration termination condition; these characteristics avoid the overcorrections caused by the global processing of the CRF.

The experimental results also show that the ELP achieves a better correction result, is more stable, and does not require training samples to restrict the iterations. The above advantages ensure that ELP is better able to adapt to correct the classification results of remote sensing images and provides it with a higher degree of automation.

The typical limitation of ELP is that, in comparison with the traditional CRF, the additional iterations, the localization process, and the update mechanism of the result image will introduce an additional computational burden. Consequently, ELP is much slower than the traditional CRF method. Fortunately, the localization process also ensures that different parts of areas do not affect each other, which makes ELP easier to parallelize. In further research, we will adjust the processing structure of ELP, facilitating a GPU implementation that enables ELP to execute faster. For semantic segmentation neural networks, differences in the training set size can cause various degrees of errors in the resulting image, which has an apparent influence on the post-processing task. In future research, to construct a more adaptive post-processing method, we will study the relationship between training/testing dataset size and the post-processing methods used and consider the problems faced by post-processing methods in more complex application scenarios.

**Author Contributions:** Conceptualization, X.P. and J.X.; data curation, J.Z.; methodology, X.P.; supervision, J.X.; visualization, J.Z.; writing—original draft, X.P. All authors have read and agree to the published version of the manuscript.

**Funding:** This research was jointly supported by the National Natural Science Foundation of China (41871236; 41971193) and the Natural Science Foundation of Jilin Province<PHONE_NUMBER>0JC).

**Conflicts of Interest:** The authors declare no conflict of interest.


**4. Conclusions**

The following important conclusions can be drawn from the present study:

**Author Contributions:** Conceptualization, B.I.; M.A.H. and U.R.; methodology, B.I.; M.A.H. M.Z. and U.R.; validation, M.A.H. and I.A.N. and U.R.; formal analysis, M.A.H.; H.N. and M.Z.; investigation, M.A.H. U.R.; Z.M. and M.Z.; resources, M.A.H. and U.R.; data curation, M.A.H. and M.Z.; writing—original draft preparation, B.I.; M.A.H. and U.R.; writing—review and editing, T.S.Y.C.; I.A.N.; U.R. and M.A.H.; visualization, M.A.H. and U.R.; supervision, M.A.H.; H.N. and U.R.; project administration, M.A.H. and U.R.; funding acquisition, U.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors acknowledge their gratitude to King Saud University (Riyadh, Saudi Arabia) for the support of this research through Researchers Supporting Project number (RSP-2019/80).

**Conflicts of Interest:** The authors declare no conflict of interest.


*2.9. Free Fatty Acids*

Free fatty acid was detected by a method previously published according to [22], with slight modifications. Determination of fatty acid methyl esters (FAME) was performed using a GC/MS system equipped with a GC-7 Agilent HP-88 capillary column (60 m × 0.25 mm × 0.2 μm). The temperature profile of the oven was 100 ◦C for 13 min followed by increasing at 10 ◦C/min to 180 ◦C for 6 min, then increasing at 1 ◦C/min to 200 ◦C for 20 min, and then increasing at 4 ◦C/min to 230 ◦C for 10.5 min. Injector and detector temperatures were set to 270 and 280 ◦C, respectively. The conditions applied for gas chromatography were nitrogen as the carrier gas at a flow of 1.0 μL/min. Fatty acids were identified and quantified based on chromatographic retention times using reference standard Supelco 37 component FAME mix (Sigma Aldrich Chemical Co., St. Louis, MO, USA). Results obtained were presented as percentage of total fatty acids.


**2. Unsupervised clustering algorithms**

In this section, a review of the NG, GNG, and RGNG algorithms are presented. Because of the length and complexity of these algorithms, along with the mathematical model, flowcharts are designed for the three algorithms in this work in order to make it more understandable and easier to write the related codes.


Contents

Preface

Discrimination of vulnerable groups by communities, organizations and governments has been a common practice for centuries. Yet, identifying, understanding, and eliminating the discrimination embedded in policies, practices, and structures of public systems are gaining global attention. In this book, a group of international researchers define structural racism and propose ways in which its deleterious effect on vulnerable populations (women, racial, ethnic and gender minorities) can be abated. As structural racism is represented by policies, structures, and practices that disempower culturally diverse groups, authors highlight how the effect of structural racism is observed in different systems across the world, and how evidence-based strategies may dismantle those structures and promote equity

This volume provides unique, comprehensive, and science-based approaches to eliminate structural racism in different public systems. Nine chapters describe the identification, understanding, and elimination of the macro, mezzo, and micro factors contributing to racism and discrimination. Chapters focus on the healthcare, political, and education systems that reproduce discrimination and disempower vulnerable groups in particular nations. For instance, in the healthcare

system in the United States, authors provide a description of the theoretical underpinnings of the link between structural racism and HIV and propose a culturally appropriate, trauma-informed agenda to reduce HIV vulnerability for racial/ethnic and sexual/gender minorities. Two other chapters in healthcare examine how structural racism affects minority older adults in long-term care facilities, and how the role of cultural competence may abate structural racism in Latino substance use and access to care. Overall, authors offer a series of policies and responsive organizational practices that may dissolve the structural factors

Culture researchers also highlight how Black people tend to cope with racism through social support, religion, avoidance, and problem-focused coping, with some gender differences in coping approaches. Politically, researchers present work on the notion of belonging as a "feeling of mattering" in contemporary politics. Authors propose "belonging" as a strategy to eliminate structural racism in mental health, whereas economists research how structural racism affects indigenous and afro-descendant women's political and economic participation in Mexico fueling inequality. In the United Kingdom, authors examine the Black Lives Matter and anti-woke campaign and discuss the role of structural racism in enabling or

The volume concludes with two chapters on education systems in two countries. Authors highlight the forces of structural racism that promote inequalities in access, support, and wellbeing of minority students in higher education in Canada. In the United Kingdom, researchers identify the social determinants and factors contributing to structural racism in the higher education system that limit opportunities

and fairness.

that promote racism in healthcare.

detracting political participation.

for ethnic minority students.