Journal of Data and Information Science, 2019, 4(4): 13-25
doi: 10.2478/jdis-2019-0018
A Metric Approach to Hot Topics in Biomedicine via Keyword Co-occurrence
Jane H. Qin1,2, , Jean J. Wang1,2, Fred Y. Ye1
1Jiangsu Key Laboratory of Data Engineering and Knowledge Service, School of Information Management, Nanjing University, Nanjing 210023, China
2International Joint Informatics Laboratory (IJIL), Nanjing University - University of Illinois, Nanjing - Champaign, China - USA
 Cite this article:
Jane H. Qin, Jean J. Wang, Fred Y. Ye. A Metric Approach to Hot Topics in Biomedicine via Keyword Co-occurrence. Journal of Data and Information Science[J], 2019, 4(4): 13-25 doi:10.2478/jdis-2019-0018


Purpose: To reveal the research hotpots and relationship among three research hot topics in biomedicine, namely CRISPR, iPS (induced Pluripotent Stem) cell and Synthetic biology.

Design/methodology/approach: We set up their keyword co-occurrence networks with using three indicators and information visualization for metric analysis.

Findings: The results reveal the main research hotspots in the three topics are different, but the overlapping keywords in the three topics indicate that they are mutually integrated and interacted each other.

Research limitations: All analyses use keywords, without any other forms.

Practical implications: We try to find the information distribution and structure of these three hot topics for revealing their research status and interactions, and for promoting biomedical developments.

Originality/value: We chose the core keywords in three research hot topics in biomedicine by using h-index.

Key words: co-occurrence ; Network analysis ; Information visualization ; Biomedicine ; Hot topics ; CRISPR-Cas ; iPS cell ; Synthetic biology
1 Introduction

Since 2000, new century, biomedicine had great progress at both scientific and technical levels. Among “breakthrough of the year” in Science every year, we could conclude three important hot topics in biomedicine as follows. The first topic is Genome editing technique CRISPR/Cas (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated system). As a genome editing method, CRISPR/Cas was the top breakthrough in 2015. The CRISPR/Cas system is a prokaryotic immune system that confers resistance to foreign genetic elements such as those present within plasmids and phages that provides a form of acquired immunity. At beginning, CRISPR described segments of prokaryotic DNA containing short, repetitive base sequences in ancient bacteria (Horvath & Barrangou, 2010). Later, the group of Jennifer Doudna induced CRISPR/Cas9 as a tool to cut DNA with crRANs in 2012 (Jinek et al.), and then the group of Feng Zhang applied CRISPR/Cas9 into eucaryotic cells in 2013 (Cong et al., 2013). The group of Ma et al. (2017) describe the correction of a pathogenic gene mutation in human embryos with CRISPR/Cas9. Cox et al. (2017) proved that RNA can be edited with CRISPR-Cas13 to correct disease-relevant human mutations and proposed an RNA-editing platform named REPAIR. While another nuclease Cpf1 was discovered in 2015 then CRISPR/Cpf1 became another CRISPR system (Zetsche et al., 2015). Yan et al. (2019) systematically discovered additional subtypes of type V CRISPR-Cas systems. The diversity, modularity, and efficacy of CRISPR-Cas systems are driving a biotechnological revolution and CRISPR-Cas guides the future of genetic engineering (Knott & Doudna, 2018). The second theme is Stem cell technique iPS cell. As a type of pluripotent stem cell, the iPS cell technique was selected into new breakthrough in both 2012 and 2016. The iPS cell technique was pioneered by Shinya Yamanaka’s lab in Kyoto, who showed in 2006 that the introduction of four specific genes encoding transcription factors could convert adult cells into pluripotent stem cells (Takahashi, 2006), on which Yamanaka was awarded the 2012 Nobel Prize along with Sir John Gurdon for their discovery that mature cells can be reprogrammed to become pluripotent. Since then, researchers have found a variety of more optimal induction methods (Anokye-Danso et al., 2011; Ma, Kong, & Zhu, 2017). At the meantime, researchers turned to introduce disease-associated mutations into a sample of iPS cells through gene editing. Paquet et al. (2016) generated cells with precise combinations of Alzheimer’s-associated mutations by introducing specific point mutations into iPS cells using CRISPR. The iPS cells have wide application perspectives in drug discovery and disease modelling (Scudellari, 2016). The last topic is Synthetic biology and artificial life. This is an interdisciplinary branch of biology and engineering, which was selected into new breakthrough in 2010. Synthetic biologists come in two broad classes. One uses unnatural molecules to reproduce emergent behaviors from natural biology, with the goal of creating artificial life. The other seeks interchangeable parts from natural biology to assemble into systems that function unnaturally (Benner & Sismour, 2005). Gibson et al. (2010) introduced their study about the Creation of a bacterial cell controlled by a chemically synthesized genome. Esvelt and Wang (2013) think Genome-modification technologies enable the rational engineering and perturbation of biological systems, such as CRISPR/Cas. Cameron, Bashor and Collins (2014) reviews the history of synthetic biology and points out that the field of synthetic biology has chartered many notable achievements and is poised to transform biotechnology and medicine.

In this article, based on biomedical documents and data analysis, we try to find the information distribution and structure of these three hot topics via analyzing the collaborative networks and visualizing their cores, for revealing their research status and interactions, for promoting biomedical developments.

2 Methodology

We process core keyword co-occurrence networks in this study. The methods focus on network analysis (Friedkin, 1991; Newman, 2004; Wolfe, 1997) and information visualization (Chen, 2006), and data come from scientific document database. For information visualization, VOSviewer (Eck & Waltman, 2010) is applied to draw pictures. In network analysis, Gephi (Bastian, Heymann, & Jacomy, 2009) and UCINET (Borgatti, Everett, & Freeman, 2002) are applied to compute network parameters. Meanwhile, open software SATI (Liu & Ye, 2012), Excel and R programming are used for data processing.

2.1 Methods

In this article, we chose the core keywords in three research hot topics in biomedicine by using h-index. Hirsch (2005) proposed the h-index, defined as the number of papers with citation number ≥ h, as a useful index to characterize the scientific output of a researcher. If all papers published by one research are arranged in descending order of citation frequency, supposing ci is the total number of citations of the ith paper, h-index can be quantified by formula h = {max i: i ≤ ci}.

Because h-index takes into account both the number and quality of papers published by one researcher, it overcomes the shortcomings of the previous single dimension theory, such as the number of papers or the number of citations (Bornmann & Daniel, 2005). Therefore, h-index can more objectively evaluate the academic achievements of researchers, which has brought widespread attention in scientific circles (Bornmann & Daniel, 2007). Braun, Glänzel, and Schubert (2006) put forward that h-index can be applied in the evaluation of impact of journals for the first time, and believed that h-index was a powerful supplement to the impact factor of journals. Banks (2006) applied h-index to identify the main research topics of compounds. Thereafter, h-index is used in many academic research areas. Its definition is extended as follows: h-index of one academic information source refers to that at least h articles has been cited at least h times (Ye, 2014).

Keyword co-occurrence was considered to be the main means for identifying research themes (Wang et al., 2017). Keyword co-occurrence network reflects the knowledge structure and knowledge kernel that can display the relationship between keywords (Su & Lee, 2010). Lee and Su (2010) believed that research hotspots can be evaluated by the centrality of the nodes in a keyword co-occurrence network. In keyword co-occurrence network, nodes represent keywords, while edges represent co-occurrence relationships among nodes. By using the social network analysis method to analyze the keyword co-occurrence networks, we can analyze the knowledge structure and hotpots of the research field.

This article evaluates the influence of nodes in the network based on closeness centrality, betweenness centrality and eigenvector centrality. Degree centrality means the importance of nodes in the network. The higher degree centrality of the node, the more important the node is, which means that the keywords represented by the node are more likely to be research hotspots. Betweenness centrality measures the ability of one keyword in a network to affect the other keywords that appear together. eigenvector centrality measures the number of adjacency nodes and the influence of adjacency nodes.

(1) Degree Centrality

In network measures, centrality indices are applied as terms of a real-valued function on the vertices of a graph, where the values produced are expected to provide a ranking which identifies the most important nodes. For a given graph G (V, E) with number of vertices V and number of edges E, let A=(au,v) be the adjacency matrix, i.e. au,v = 1 if vertex u is linked to vertex v and au,v = 0 otherwise. The degree centrality score of vertex u can be defined as


The relative centrality of vertex u can be defined as

$x_{u}=\frac{1}{λ}\sum_{v∈G}a_{u,v}x_{v}$ (2)

(2) Betweenness Centrality

Betweenness Centrality measures the shortest path in a network, which is used to evaluate the role of nodes in information integration in social networks. The higher the betweenness centrality, the greater the role it plays in information integration. Gst represents the number of shortest paths from point s to point t. Gst(v) represents the number of shortest paths from point s to point t that pass through node v. The betweenness centrality of vertex u can be defined as follows:

$x_{u}=\sum_{v∈G}\frac{G_{st}(v)}{G_{st}}$ (3)

(3) Eigenvector Centrality

Since the entries in the adjacency matrix are non-negative, there is a unique largest eigenvalue, which is real and positive. This greatest eigenvalue results in the desired centrality measure is eigenvector centrality or eigencentrality, which reveals the core importance of a vertex in a network. Its eigenvectors are orthogonal and diagonalizable. The centrality of vertices is proportional to the sum of the central points of the vertices it connects. The eigenvector center x is described in two equivalent ways. As the sum of matrix equations, the eigenvector centrality can be defined as follows:

AX=λX (4)

$AX=λx,λX_i=\sum_{j=i}^{n}a_{ij}x_{j},i=1,…,n $ (5)

λ is the maximum eigenvalue of A and n is the number of vertices.

2.2 Data

In our empirical study we search the Web of Science (WoS) database for articles published during 1900 to 2018. We collected the data in January 2019. The retrieval strategies were as follows:


TS=“clustered regularly interspaced short palindromic repeats” OR CRISPR

(2) H2-iPS cell

TS=“induce* pluripotent stem cell” OR “induce* pluripotent stem cells” OR “IPS cell” OR “IPS cells”

(3) H3-Synthetic biology

TS=“synthetic biology” OR “gene circuit” OR “gene circuits” OR “genetic circuit” OR “genetic circuits” OR “genetic device” OR “genetic devices” OR “synthetic life” OR “synthetic lives” OR “synthetic tissue” OR “synthetic tissues” OR “synthetic cell” OR “synthetic cells” OR “synthetic genome” OR “synthetic genomes” OR “synthetic gene” OR “synthetic genes” OR “minimal genome” OR “minimal genomes” OR “biology, synthetic”

The computed data will lead to next results for finding core keywords and setting up keyword co-occurrence networks.

3 Keyword co-occurrence results

High-frequency keywords can reflect the research hotspots and research directions to some extent, but the information displayed by the linear arrangement of the frequency of keywords has great limitations. H-index can comprehensively reflect the occurrence frequency of keywords and the number of citations. In this article, the 100 keywords with the highest h-index are defined as core keywords. Keyword co-occurrence relationship can reflect the internal connection between keywords. In this chapter, we construct the co-occurrence matrix and co-word network with the help of R language. Besides that, we analyze the research hotspots of the three hot topics in biomedicine by using social network analysis method.

In this study, R language is used to extract keywords and the number of citations corresponding to keywords. The h-index is calculated by our own programming. The extracted keywords have the following problems.

(1) Case difference, such as “Induced pluripotent stem cell”, “induced pluripotent stem cell”, “CRISPR/Cas9”, and “CRISPR/Cas9”.

(2) Inconsistent connectors, such as “CRISPR/Cas9”, and “CRISPR-Cas9”.

(3) Heteronyms, such as abbreviation and the full name phenomenon. “iPSCs”, “Human-induced pluripotent stem cells”, and “Induced pluripotent stem cells (iPSCs)”have the same meaning.

Considering that many keywords are special terms in the field of biomedicine, and the current general dictionary is not applicable for the study. The following two methods are used for data processing. The first method is case conversion, which unifies keywords into capital letters. The second method is self-compiled dictionary which can solve the problems of inconsistent connectors and Heteronyms.

3.1 Keyword co-occurrence with CRISPR/Cas9

Fig. 1 shows the co-occurrence network of 100 keywords with the highest h-index in the field of CRISPR/Cas9. Node color represents matrix-based clustering and node size represents h-index. Table 1 shows the 20 nodes with the highest degree centrality and betweenness centrality.

Table 1

Co-occurrence network centrality with the highest h-index keywords in the field of CRISPR/Cas9.

In the network, “CRISPR”, “GENOME EDITING”, “CRISPR/CAS”, “GENOME ENGINEERING”, “HOMOLOGOUS RECOMBINATION”, “GENE TARGET” are located in the center of the network, and have high degree centrality and betweenness centrality. Thus, they are the core research contents. “ZEBRAFISH”, “MOUSE”, “ZFN”, “TALEN”, “GENE THERAPY”, “CANCER” are the much important research.

Interestingly, the keywords of “IPSC”, “HUMAN IPSC”, “STEM CELL”, “SYNTHETIC BIOLOGY”, “METABOLIC ENGINEERING” are conspicuous in the co-occurrence network. Besides that, these keywords have high degree centrality and betweenness centrality. That means CRISPR/Cas9 and the other two hot topics have large cross-study.

Figure 1.

Co-occurrence network with the highest h-index keywords in the field of CRISPR/Cas9.

3.2 Keyword co-occurrence with iPS cell

Fig. 2 shows the co-occurrence network of 100 keywords with the highest h-index in the field of iPS cell. Node color represents matrix-based clustering and node size represents h-index. Table 2 shows the 20 nodes with the highest degree centrality and betweenness centrality.

Figure 2.

Co-occurrence network with the highest h-index keywords in the field of iPS cell.

Table 2

Co-occurrence network centrality with the highest h-index keywords in the field of iPS cell.

In the network, “STEM CELL”, “EMBRYONIC STEM CELL”, “REPROGRAMMING”, “HUMAN IPSC”, “PLURIPOTENT STEM CELL”, “DIFFERENTIATION”, “REGENERATIVE MEDICINE”, “MESENCHYMAL STEM CELL” are located in the center of the network, and have high degree centrality and betweenness centrality. Thus, they are the core research contents.


3.3 Keyword co-occurrence with synthetic biology

Fig. 3 shows the co-occurrence network of 100 keywords with the highest h-index in the field of synthetic biology. Node color represents matrix-based clustering and node size represents h-index. Table 3 shows the 20 nodes with the highest degree centrality and betweenness centrality. In the network, “METABOLIC ENGINEERING”, “GENE CIRCUIT”, “GENE EXPRESSION”, “SYSTEMS BIOLOGY”, “SYSTEMS BIOLOGY”, “PROTEIN ENGINEERING” are located in the center of the network, and have high degree centrality and betweenness centrality. Thus, they are the core research contents. “YEAST”, “ESCELLHERICHIA COLI” and “SACCHAROMYCES CEREVISIAE” are experimental vectors used in synthetic biology research. These keywords are located in the network center and have high degree centrality and betweenness centrality, which indicates that they are widely used in research .

Figure 3.

Co-occurrence network with the highest h-index keywords in the field of synthetic biology.

In the keyword co-occurrence network, CRISPR/CAS9 also have high degree centrality, and they are important node in the keyword cluster. This indicates that there are correlation between synthetic biology and CRISPR/CAS9. Besides that, the frequency of cross research and the number of citations is relatively high.

Table 3

Co-occurrence network centrality with the highest h-index keywords in the field of synthetic biology.

4 Discussion and conclusion

Above results construct core keyword co-occurrence networks with visualizing and calculating the keywords’ centralities. The three research hot topics in biomedicine are analyzed and characterized as follows.

(1) The research hotspots of CRISPR/Cas9 include the comparison of gene editing technology with the previous two generations, the discovery of new CRISPR/Cas9 system, improvement of gene editing technology and methods, and application research of CRISPR/Cas9 in the gene therapy and cancer therapy.


(3) The research hotspots of iPS cells include HUMAN IPSC, the comparison between iPS cell and EMBRYONIC STEM CELL, and the application of iPS cells in the research of CARDIOMYOCYTES, NEURON, PARKINSON’S DISEASE, etc.

(4) There were overlapping keywords corresponding to the three biomedical topics, among which the overlapping keywords of synthetic biology and CRISPR/Cas9 were the most obvious. The research on the three topics is overlapping.

Since all analyses use keywords, without any other forms, the limitations are remained in this article, which may be improved in future studies.

Author Contributions

Jane J. Qin ( collected and processed the data and wrote Chinese version, Jean J. Wang ( and wrote English version, and Fred Y. Ye ( initiated the research and revised the paper.


We acknowledge the National Natural Science Foundation of China Grant 71673131 for financial support.

The authors have declared that no competing interests exist.


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Genome editing has potential for the targeted correction of germline mutations. Here we describe the correction of the heterozygous MYBPC3 mutation in human preimplantation embryos with precise CRISPR-Cas9-based targeting accuracy and high homology-directed repair efficiency by activating an endogenous, germline-specific DNA repair response. Induced double-strand breaks (DSBs) at the mutant paternal allele were predominantly repaired using the homologous wild-type maternal gene instead of a synthetic DNA template. By modulating the cell cycle stage at which the DSB was induced, we were able to avoid mosaicism in cleaving embryos and achieve a high yield of homozygous embryos carrying the wild-type MYBPC3 gene without evidence of off-target mutations. The efficiency, accuracy and safety of the approach presented suggest that it has potential to be used for the correction of heritable mutations in human embryos by complementing preimplantation genetic diagnosis. However, much remains to be considered before clinical applications, including the reproducibility of the technique with other heterozygous mutations.
Ma X., Kong L., & Zhu S. (2017). Reprogramming cell fates by small molecules. Protein Cell, 8(5), 328-348.
DOI:10.1007/s13238-016-0362-6      PMID:28213718      URL    
Reprogramming cell fates towards pluripotent stem cells and other cell types has revolutionized our understanding of cellular plasticity. During the last decade, transcription factors and microRNAs have become powerful reprogramming factors for modulating cell fates. Recently, many efforts are focused on reprogramming cell fates by non-viral and non-integrating chemical approaches. Small molecules not only are useful in generating desired cell types in vitro for various applications, such as disease modeling and cell-based transplantation, but also hold great promise to be further developed as drugs to stimulate patients' endogenous cells to repair and regenerate in vivo. Here we will focus on chemical approaches for generating induced pluripotent stem cells, neurons, cardiomyocytes, hepatocytes and pancreatic β cells. Significantly, the rapid and exciting advances in cellular reprogramming by small molecules will help us to achieve the long-term goal of curing devastating diseases, injuries, cancers and aging.
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Newman , M. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Science, 101, 5200-5205.
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Paquet D., Kwart D., Chen A., Sproul A., Jacob S., Teo S., Olsen K.M., Gregg A., Noggle S., & Tessier-Lavigne M. (2016). Efficient introduction of specific homozygous and heterozygous mutations using CRISPR/Cas9. Nature, 533, 125-129.
DOI:10.1038/nature17664      PMID:27120160      URL    
The bacterial CRISPR/Cas9 system allows sequence-specific gene editing in many organisms and holds promise as a tool to generate models of human diseases, for example, in human pluripotent stem cells. CRISPR/Cas9 introduces targeted double-stranded breaks (DSBs) with high efficiency, which are typically repaired by non-homologous end-joining (NHEJ) resulting in nonspecific insertions, deletions or other mutations (indels). DSBs may also be repaired by homology-directed repair (HDR) using a DNA repair template, such as an introduced single-stranded oligo DNA nucleotide (ssODN), allowing knock-in of specific mutations. Although CRISPR/Cas9 is used extensively to engineer gene knockouts through NHEJ, editing by HDR remains inefficient and can be corrupted by additional indels, preventing its widespread use for modelling genetic disorders through introducing disease-associated mutations. Furthermore, targeted mutational knock-in at single alleles to model diseases caused by heterozygous mutations has not been reported. Here we describe a CRISPR/Cas9-based genome-editing framework that allows selective introduction of mono- and bi-allelic sequence changes with high efficiency and accuracy. We show that HDR accuracy is increased dramatically by incorporating silent CRISPR/Cas-blocking mutations along with pathogenic mutations, and establish a method termed 'CORRECT' for scarless genome editing. By characterizing and exploiting a stereotyped inverse relationship between a mutation's incorporation rate and its distance to the DSB, we achieve predictable control of zygosity. Homozygous introduction requires a guide RNA targeting close to the intended mutation, whereas heterozygous introduction can be accomplished by distance-dependent suboptimal mutation incorporation or by use of mixed repair templates. Using this approach, we generated human induced pluripotent stem cells with heterozygous and homozygous dominant early onset Alzheimer's disease-causing mutations in amyloid precursor protein (APP(Swe)) and presenilin 1 (PSEN1(M146V)) and derived cortical neurons, which displayed genotype-dependent disease-associated phenotypes. Our findings enable efficient introduction of specific sequence changes with CRISPR/Cas9, facilitating study of human disease.
Scudellari , M. (2016). A decade of ips cells. Nature, 534, 310-312.
DOI:10.1038/534310a      PMID:27306170      URL    
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Su , H.N., &Lee ,P.-C. (2010). Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in technology foresight. Scientometrics, 85(1), 65-79.
DOI:10.1007/s11192-010-0259-8      URL    
This study proposes an approach for visualizing a knowledge structure, the proposed approach creates a three-dimensional 舠Research focused parallelship network舡, a 舠Keyword Co-occurrence Network舡, and a two-dimensional knowledge map to facilitate visualization of the knowledge structure created by journal papers from different perspectives. The networks and knowledge maps can be depicted differently by choosing different information as the network actor, e.g. author, institute or country keyword, to reflect knowledge structures in micro-, meso-, and macro-levels, respectively. Technology Foresight is selected as an example to illustrate the method proposed in this study. A total of 556 author keywords contained in 181 Technology Foresight related papers have been analyzed. European countries, China, India and Brazil are located at the core of Technology Foresight research. Quantitative ways of mapping journal papers are investigated in this study to unveil emerging elements as well as to demonstrate dynamics and visualization of knowledge. The quantitative method provided in this paper shows a possible way of visualizing and evaluating knowledge structure; thus a computerized calculation is possible for potential quantitative applications, e.g. R&D resource allocation, research performance evaluation, science map, etc.
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Takahashi , K. (2006). Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell, 126, 663-676.
DOI:10.1016/j.cell.2006.07.024      PMID:16904174      URL    
Differentiated cells can be reprogrammed to an embryonic-like state by transfer of nuclear contents into oocytes or by fusion with embryonic stem (ES) cells. Little is known about factors that induce this reprogramming. Here, we demonstrate induction of pluripotent stem cells from mouse embryonic or adult fibroblasts by introducing four factors, Oct3/4, Sox2, c-Myc, and Klf4, under ES cell culture conditions. Unexpectedly, Nanog was dispensable. These cells, which we designated iPS (induced pluripotent stem) cells, exhibit the morphology and growth properties of ES cells and express ES cell marker genes. Subcutaneous transplantation of iPS cells into nude mice resulted in tumors containing a variety of tissues from all three germ layers. Following injection into blastocysts, iPS cells contributed to mouse embryonic development. These data demonstrate that pluripotent stem cells can be directly generated from fibroblast cultures by the addition of only a few defined factors.
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Wang P., Zhu F., Song H., & Hou J. (2017). A bibliometric profile of current science between 1961 and 2015. Current Science, 113(3), 386-392.
DOI:10.18520/cs/v113/i03/386-392      URL    
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Wolfe , A.W. (1997). Social network analysis: Methods and applications. American Ethnologist, 24, 136-137.
DOI:10.1111/opn.12288      PMID:31837096      URL    
To explore the perspectives of those involved in co-designing a mobile application with people with dementia and their carers.
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Yan W.X., Hunnewell P., Alfonse L.E., Carte J.M., Keston-Smith E., Sothiselvam S., Garrity A.J., Chong S., Makarova K.S., Koonin E.V., Cheng D.R., & Scott D.A. (2019).Functionally diverse type v CRISPR-Cassystems. Science, 363(6422), 88-91.
DOI:10.1126/science.aav7271      PMID:30523077      URL    
Type V CRISPR-Cas systems are distinguished by a single RNA-guided RuvC domain-containing effector, Cas12. Although effectors of subtypes V-A (Cas12a) and V-B (Cas12b) have been studied in detail, the distinct domain architectures and diverged RuvC sequences of uncharacterized Cas12 proteins suggest unexplored functional diversity. Here, we identify and characterize Cas12c, -g, -h, and -i. Cas12c, -h, and -i demonstrate RNA-guided double-stranded DNA (dsDNA) interference activity. Cas12i exhibits markedly different efficiencies of CRISPR RNA spacer complementary and noncomplementary strand cleavage resulting in predominant dsDNA nicking. Cas12g is an RNA-guided ribonuclease (RNase) with collateral RNase and single-strand DNase activities. Our study reveals the functional diversity emerging along different routes of type V CRISPR-Cas evolution and expands the CRISPR toolbox.
Ye , F.Y. (2014). The research progress and developing perspective of assessment indicators.Journal of the China Scciety for Scientific and Technical Information, 33, 215-224.
DOI:10.1002/hpm.744      PMID:15061288      URL    
An estimated 10.8 million children under 5 continue to die each year in developing countries from causes easily treatable or preventable. Non governmental organizations (NGOs) are frontline implementers of low-cost and effective child health interventions, but their progress toward sustainable child health gains is a challenge to evaluate. This paper presents the Child Survival Sustainability Assessment (CSSA) methodology--a framework and process--to map progress towards sustainable child health from the community level and upward. The CSSA was developed with NGOs through a participatory process of research and dialogue. Commitment to sustainability requires a systematic and systemic consideration of human, social and organizational processes beyond a purely biomedical perspective. The CSSA is organized around three interrelated dimensions of evaluation: (1) health and health services; (2) capacity and viability of local organizations; (3) capacity of the community in its social ecological context. The CSSA uses a participatory, action-planning process, engaging a 'local system' of stakeholders in the contextual definition of objectives and indicators. Improved conditions measured in the three dimensions correspond to progress toward a sustainable health situation for the population. This framework opens new opportunities for evaluation and research design and places sustainability at the center of primary health care programming.
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