Fakery, some more fakery and even more fakery. The fakir is not the most fake

The works "Faker" and "Fakir" are thought to be semantically related in the English language. While a faker is "One who makes false claims", the term fakir/faqir/faqeer is used to denote a holy man in the Indian subcontinent. Why these two words are semantically related is something one could speculate about. This post is not about this. The lack of evidence to support the claims that the great British statesman Sir Winston Leonard Spencer Churchill ever referred to Mahatma Gandhi, as a "seditious fakir" should give us enough insight.

Now coming to the main topic of this post, fakery in science is one of the most perplexing things i have encountered. Yet, claiming to have invented or discovered something which is not true is surprisingly common. Thanks to the selfless efforts of intelligent people like Jennifer Byrne and Elisabeth Bik, many instances of such fakery have begun to come to light. The paper titled "The Prevalence of Inappropriate Image Duplication in Biomedical Research Publications" is probably the most influential among these efforts. By analyzing a large enough dataset of published papers, the authors try to decipher the major patterns in fakery.

1. The clearest pattern they demonstrate is the effect of the impact factor (https://journals.asm.org/doi/10.1128/mBio.00809-16#fig6). Journals with lower impact factors seem to have more fakery than those with higher impact factors. The most fakery is seen in journals with impact factors between 2 and 3.
2. The country of origin also seems to have a strong enough effect (https://journals.asm.org/doi/10.1128/mBio.00809-16#fig7). India and China (including Taiwan) have a greater fraction of papers with problematic images than expected. In the words of Bik et al., "Countries plotted above the blue line had a higher-than-expected proportion of problematic papers; countries plotted below the line had a lower-than-expected ratio." 
3. Another interesting trend identified by Jennifer Byrne is that such fakery involves "targeting less-well-known human genes to produce low-value and possibly fraudulent papers".

After having read this, I realized that the paper by Sharma et.al., 2020 meets all three criteria. It is published in a journal with an impact factor between 2 and 3, all authors are having affiliations from India and the paper is about a less well-known human gene. Hence, it is very likely that this paper may have employed large-scale image duplication and other forms of fakery to get past the review process. This would make it an ideal candidate to investigate some more fakery.

Not everybody is as gifted as Bik, who has done most of her sleuthing just using her eyes. Therefore, the use of AI-based methods is expected to help relieve the pressure on human volunteers and allow the large-scale search of the scientific literature. Unfortunately, AI is yet to deliver on this promise. The python package imagededup (https://github.com/idealo/imagededup) makes major strides in this direction. It provides an interface to search for image duplication in a large set of files using algorithms like Perceptual hashing and CNN. A score is also provided to help prioritize the putative duplicates identified. Further progress in tools such as these would be able to prevent fraudulent use of image duplication.

Returning to the paper published by Sharma et.al., 2020, an astounding 287 supplementary figures are reported by this paper. The code given below is used to look for fakery in this paper's supplementary figures.

 wget https://static-content.springer.com/esm/art%3A10.1007%2Fs00251-020-01186-2/MediaObjects/251_2020_1186_MOESM9_ESM.pptx  
 mv 251_2020_1186_MOESM9_ESM.pptx 251_2020_1186_MOESM9_ESM.zip  
 unzip 251_2020_1186_MOESM9_ESM.zip  

The above code downloads the PowerPoint file containing all the supplementary figures and renames it as a zip file and extracts its contents. By doing these three steps, we have all the images present in the PowerPoint now available in a single folder under ppt/media/. The next steps would involve installing the imagededup package with all the required pre-requisites.

After everything is installed and working properly, the following code should work. Make sure the correct version of NumPy is installed. Fire up the python console and the correct path to the image directory (the folder in which all the images have been extracted by the unzip command above.)

 #Find duplicates using CNN along with scores  
 from imagededup.methods import CNN  
 cnn_encoder = CNN()  
 duplicates_cnn = cnn_encoder.find_duplicates(image_dir=image_dir, scores=True)  
 duplicates_cnn  

After the above code executes (you don't need a GPU, it just complaints that you lack a GPU), the set of putative duplicate images are identified and printed on the screen with the scores.

 {'image1.png': [], 'image10.png': [], 'image100.png': [('image101.png', 0.9183681), ('image83.png', 0.9336556), ('image84.png', 0.94029236), ('image85.png', 0.96624136), ('image86.png', 0.95270556), ('image87.png', 0.91503084), ('image88.png', 0.9674068), ('image89.png', 0.9193216), ('image91.png', 0.95968944), ('image92.png', 0.9491019), ('image93.png', 0.9689957), ('image94.png', 0.9400719), ('image96.png', 0.947449), ('image99.png', 0.9547266)], 'image101.png': [('image100.png', 0.9183681), ('image84.png', 0.92484504), ('image85.png', 0.921789), ('image86.png', 0.90466857), ('image87.png', 0.92616916), ('image88.png', 0.91239095), ('image91.png', 0.92148745), ('image96.png', 0.92620283), ('image97.png', 0.9051003), ('image98.png', 0.91163087), ('image99.png', 0.91708857)], 'image102.png': [('image103.png', 0.9484605)], 'image103.png': [('image102.png', 0.9484605)], 'image104.png': [], 'image105.png': [('image112.png', 0.9010905)], 'image106.png': [], 'image107.png': [('image110.png', 0.90515953)], 'image108.png': [('image110.png', 0.90387726)], 'image109.png': [('image110.png', 0.9003114)], 'image11.png': [('image14.png', 0.9102166), ('image16.png', 0.92004216), ('image20.png', 0.93255997), ('image7.png', 0.9118265), ('image8.png', 0.9021534)], 'image110.png': [('image107.png', 0.90515953), ('image108.png', 0.90387726), ('image109.png', 0.9003114)], 'image111.png': [], 'image112.png': [('image105.png', 0.9010905)], 'image113.jpeg': [('image150.jpeg', 0.9157622)], 'image114.png': [('image171.png', 0.91326845)], 'image115.png': [('image121.png', 0.92301464)], 'image116.png': [], 'image117.png': [('image120.png', 0.90136623)], 'image118.png': [], 'image119.png': [('image120.png', 0.92832935)], 'image12.png': [('image16.png', 0.91794515), ('image7.png', 0.9085101), ('image8.png', 0.9045775)], 'image120.png': [('image117.png', 0.90136623), ('image119.png', 0.92832935), ('image139.png', 0.9278433), ('image140.png', 0.91120446), ('image152.png', 0.9259067), ('image153.png', 0.90862787)], 'image121.png': [('image115.png', 0.92301464), ('image141.png', 0.9063702)], 'image122.png': [('image125.png', 0.9004039)], 'image123.png': [('image201.png', 0.9129802)], 'image124.png': [], 'image125.png': [('image122.png', 0.9004039), ('image202.png', 0.9160283), ('image203.png', 0.9110071), ('image212.png', 0.91093236)], 'image126.png': [], 'image127.png': [], 'image128.png': [], 'image129.png': [], 'image13.png': [], 'image130.png': [], 'image131.png': [], 'image132.png': [], 'image133.jpeg': [], 'image134.png': [], 'image135.png': [], 'image136.png': [], 'image137.png': [], 'image138.png': [], 'image139.png': [('image120.png', 0.9278433)], 'image14.png': [('image11.png', 0.9102166), ('image16.png', 0.94823205), ('image20.png', 0.9343158), ('image7.png', 0.94182235)], 'image140.png': [('image120.png', 0.91120446)], 'image141.png': [('image121.png', 0.9063702), ('image74.png', 0.91188), ('image76.png', 0.92340136), ('image78.png', 0.90045446)], 'image142.png': [], 'image143.png': [('image144.png', 0.9097591)], 'image144.png': [('image143.png', 0.9097591)], 'image145.png': [], 'image146.png': [], 'image147.png': [('image148.png', 0.91800094)], 'image148.png': [('image147.png', 0.91800094)], 'image149.png': [], 'image15.png': [('image18.png', 0.9242378), ('image26.png', 0.91410726)], 'image150.jpeg': [('image113.jpeg', 0.9157622)], 'image151.png': [], 'image152.png': [('image120.png', 0.9259067), ('image153.png', 0.9593252)], 'image153.png': [('image120.png', 0.90862787), ('image152.png', 0.9593252), ('image157.png', 0.9117504)], 'image154.png': [('image158.png', 0.9086372)], 'image155.png': [], 'image156.png': [('image157.png', 0.9200652)], 'image157.png': [('image153.png', 0.9117504), ('image156.png', 0.9200652)], 'image158.png': [('image154.png', 0.9086372)], 'image159.png': [], 'image16.png': [('image11.png', 0.92004216), ('image12.png', 0.91794515), ('image14.png', 0.94823205), ('image20.png', 0.9272426), ('image7.png', 0.959764)], 'image160.png': [], 'image161.png': [], 'image162.png': [], 'image163.png': [], 'image164.jpg': [], 'image165.png': [('image199.png', 0.90960884), ('image209.png', 0.9267041)], 'image166.png': [('image194.png', 0.9086143)], 'image167.png': [], 'image168.png': [], 'image169.png': [], 'image17.png': [('image19.png', 0.90715945)], 'image170.png': [], 'image171.png': [('image114.png', 0.91326845)], 'image172.png': [('image173.png', 0.9054203)], 'image173.png': [('image172.png', 0.9054203)], 'image174.png': [], 'image175.png': [('image211.png', 0.9042617)], 'image176.png': [], 'image177.png': [], 'image178.png': [], 'image179.png': [], 'image18.png': [('image15.png', 0.9242378), ('image26.png', 0.9157932), ('image7.png', 0.9010002)], 'image180.png': [], 'image181.png': [], 'image182.png': [], 'image183.png': [], 'image184.png': [], 'image185.png': [('image191.png', 0.92406636), ('image209.png', 0.90291137)], 'image186.png': [('image194.png', 0.90917015)], 'image187.png': [], 'image188.png': [], 'image189.png': [], 'image19.png': [('image17.png', 0.90715945)], 'image190.png': [], 'image191.png': [('image185.png', 0.92406636)], 'image192.png': [], 'image193.png': [], 'image194.png': [('image166.png', 0.9086143), ('image186.png', 0.90917015)], 'image195.png': [], 'image196.png': [], 'image197.png': [], 'image198.png': [], 'image199.png': [('image165.png', 0.90960884)], 'image2.png': [], 'image20.png': [('image11.png', 0.93255997), ('image14.png', 0.9343158), ('image16.png', 0.9272426), ('image7.png', 0.9131277), ('image8.png', 0.9061092)], 'image200.png': [], 'image201.png': [('image123.png', 0.9129802)], 'image202.png': [('image125.png', 0.9160283), ('image203.png', 0.94643486)], 'image203.png': [('image125.png', 0.9110071), ('image202.png', 0.94643486)], 'image204.png': [], 'image205.png': [], 'image206.png': [], 'image207.png': [], 'image208.png': [], 'image209.png': [('image165.png', 0.9267041), ('image185.png', 0.90291137)], 'image21.png': [('image36.png', 0.9179164)], 'image210.png': [], 'image211.png': [('image175.png', 0.9042617)], 'image212.png': [('image125.png', 0.91093236)], 'image213.png': [], 'image214.png': [], 'image215.jpeg': [], 'image216.png': [], 'image217.png': [('image218.png', 0.91770554)], 'image218.png': [('image217.png', 0.91770554), ('image224.png', 0.9038681)], 'image219.png': [], 'image22.png': [('image35.png', 0.90540993)], 'image220.png': [], 'image221.png': [], 'image222.png': [], 'image223.png': [], 'image224.png': [('image218.png', 0.9038681)], 'image225.png': [], 'image226.png': [], 'image227.png': [], 'image228.png': [], 'image229.png': [], 'image23.png': [('image25.png', 0.9128949), ('image26.png', 0.9407302)], 'image230.jpg': [], 'image231.jpg': [], 'image232.png': [], 'image233.png': [], 'image234.png': [('image235.png', 0.95958734)], 'image235.png': [('image234.png', 0.95958734)], 'image236.png': [], 'image237.png': [('image238.png', 0.9559316), ('image239.png', 0.95943606), ('image257.png', 0.9001226), ('image259.png', 0.91905767)], 'image238.png': [('image237.png', 0.9559316), ('image239.png', 0.93660516), ('image257.png', 0.9084082), ('image259.png', 0.93839127)], 'image239.png': [('image237.png', 0.95943606), ('image238.png', 0.93660516), ('image250.png', 0.9003399), ('image259.png', 0.9138143)], 'image24.png': [], 'image240.png': [], 'image241.png': [('image242.png', 0.9320455), ('image243.png', 0.92742676), ('image250.png', 0.90400875), ('image251.png', 0.90990597)], 'image242.png': [('image241.png', 0.9320455), ('image243.png', 0.9628831)], 'image243.png': [('image241.png', 0.92742676), ('image242.png', 0.9628831)], 'image244.png': [], 'image245.png': [('image246.png', 0.9437679), ('image247.png', 0.93426013), ('image250.png', 0.9031271), ('image253.png', 0.91433334)], 'image246.png': [('image245.png', 0.9437679), ('image247.png', 0.9361171), ('image250.png', 0.9113657)], 'image247.png': [('image245.png', 0.93426013), ('image246.png', 0.9361171), ('image250.png', 0.90679574)], 'image248.png': [], 'image249.png': [], 'image25.png': [('image23.png', 0.9128949), ('image26.png', 0.92126876)], 'image250.png': [('image239.png', 0.9003399), ('image241.png', 0.90400875), ('image245.png', 0.9031271), ('image246.png', 0.9113657), ('image247.png', 0.90679574), ('image251.png', 0.9163149)], 'image251.png': [('image241.png', 0.90990597), ('image250.png', 0.9163149)], 'image252.png': [], 'image253.png': [('image245.png', 0.91433334)], 'image254.png': [], 'image255.png': [], 'image256.png': [], 'image257.png': [('image237.png', 0.9001226), ('image238.png', 0.9084082), ('image258.png', 0.91473335), ('image259.png', 0.9384123)], 'image258.png': [('image257.png', 0.91473335), ('image259.png', 0.92645323)], 'image259.png': [('image237.png', 0.91905767), ('image238.png', 0.93839127), ('image239.png', 0.9138143), ('image257.png', 0.9384123), ('image258.png', 0.92645323)], 'image26.png': [('image15.png', 0.91410726), ('image18.png', 0.9157932), ('image23.png', 0.9407302), ('image25.png', 0.92126876)], 'image260.png': [], 'image261.png': [], 'image262.png': [('image263.png', 0.92793417)], 'image263.png': [('image262.png', 0.92793417)], 'image264.png': [], 'image265.png': [('image266.png', 0.93153894), ('image267.png', 0.94791853)], 'image266.png': [('image265.png', 0.93153894), ('image267.png', 0.9459948)], 'image267.png': [('image265.png', 0.94791853), ('image266.png', 0.9459948)], 'image268.png': [], 'image269.png': [], 'image27.png': [('image28.png', 0.9027702), ('image40.png', 0.91454256)], 'image270.png': [], 'image271.png': [], 'image272.png': [], 'image273.png': [('image274.png', 0.9425941), ('image275.png', 0.9263323)], 'image274.png': [('image273.png', 0.9425941), ('image275.png', 0.9559473)], 'image275.png': [('image273.png', 0.9263323), ('image274.png', 0.9559473)], 'image276.png': [], 'image277.png': [('image278.png', 0.94348145), ('image279.png', 0.93136877)], 'image278.png': [('image277.png', 0.94348145), ('image279.png', 0.9180116)], 'image279.png': [('image277.png', 0.93136877), ('image278.png', 0.9180116)], 'image28.png': [('image27.png', 0.9027702), ('image30.png', 0.9038577)], 'image280.png': [], 'image281.png': [], 'image282.png': [], 'image283.png': [], 'image284.png': [], 'image285.png': [], 'image286.png': [], 'image287.jpg': [], 'image29.png': [('image34.png', 0.90683985)], 'image3.png': [], 'image30.png': [('image28.png', 0.9038577), ('image33.png', 0.9480987), ('image39.png', 0.94268227), ('image40.png', 0.93139154)], 'image31.png': [('image40.png', 0.919406)], 'image32.png': [], 'image33.png': [('image30.png', 0.9480987), ('image34.png', 0.9020339), ('image39.png', 0.9246298), ('image40.png', 0.9213065)], 'image34.png': [('image29.png', 0.90683985), ('image33.png', 0.9020339), ('image40.png', 0.92046106)], 'image35.png': [('image22.png', 0.90540993)], 'image36.png': [('image21.png', 0.9179164)], 'image37.png': [], 'image38.png': [], 'image39.png': [('image30.png', 0.94268227), ('image33.png', 0.9246298), ('image40.png', 0.9188498)], 'image4.png': [], 'image40.png': [('image27.png', 0.91454256), ('image30.png', 0.93139154), ('image31.png', 0.919406), ('image33.png', 0.9213065), ('image34.png', 0.92046106), ('image39.png', 0.9188498)], 'image41.png': [], 'image42.png': [], 'image43.png': [], 'image44.png': [], 'image45.png': [('image47.png', 0.90898323)], 'image46.png': [], 'image47.png': [('image45.png', 0.90898323)], 'image48.png': [], 'image49.png': [], 'image5.png': [('image6.png', 0.93074346)], 'image50.png': [], 'image51.png': [], 'image52.jpg': [], 'image53.png': [('image54.png', 0.9346998), ('image55.png', 0.9301629), ('image56.png', 0.90333104), ('image58.png', 0.9169454), ('image59.png', 0.9067757)], 'image54.png': [('image53.png', 0.9346998), ('image55.png', 0.9548632), ('image56.png', 0.9364393), ('image58.png', 0.95981777), ('image59.png', 0.9343269)], 'image55.png': [('image53.png', 0.9301629), ('image54.png', 0.9548632), ('image56.png', 0.9410248), ('image58.png', 0.94894063), ('image59.png', 0.9302525)], 'image56.png': [('image53.png', 0.90333104), ('image54.png', 0.9364393), ('image55.png', 0.9410248), ('image58.png', 0.918398), ('image59.png', 0.94681996)], 'image57.png': [], 'image58.png': [('image53.png', 0.9169454), ('image54.png', 0.95981777), ('image55.png', 0.94894063), ('image56.png', 0.918398), ('image59.png', 0.92951506)], 'image59.png': [('image53.png', 0.9067757), ('image54.png', 0.9343269), ('image55.png', 0.9302525), ('image56.png', 0.94681996), ('image58.png', 0.92951506)], 'image6.png': [('image5.png', 0.93074346)], 'image60.png': [], 'image61.png': [], 'image62.png': [('image65.png', 0.92089856), ('image69.png', 0.90719336), ('image71.png', 0.91296417)], 'image63.png': [('image66.png', 0.9318356), ('image67.png', 0.9502576), ('image69.png', 0.93360347), ('image70.png', 0.9029176), ('image71.png', 0.9614921)], 'image64.png': [('image65.png', 0.9162164), ('image67.png', 0.9098237), ('image73.png', 0.9010482)], 'image65.png': [('image62.png', 0.92089856), ('image64.png', 0.9162164), ('image67.png', 0.9024062), ('image71.png', 0.92213106), ('image72.png', 0.92419463)], 'image66.png': [('image63.png', 0.9318356), ('image67.png', 0.9185381), ('image69.png', 0.9501172), ('image70.png', 0.90259856), ('image71.png', 0.9121348), ('image73.png', 0.9006342)], 'image67.png': [('image63.png', 0.9502576), ('image64.png', 0.9098237), ('image65.png', 0.9024062), ('image66.png', 0.9185381), ('image71.png', 0.92976534), ('image73.png', 0.9187012)], 'image68.png': [], 'image69.png': [('image62.png', 0.90719336), ('image63.png', 0.93360347), ('image66.png', 0.9501172), ('image70.png', 0.9008339), ('image71.png', 0.9222581)], 'image7.png': [('image11.png', 0.9118265), ('image12.png', 0.9085101), ('image14.png', 0.94182235), ('image16.png', 0.959764), ('image18.png', 0.9010002), ('image20.png', 0.9131277)], 'image70.png': [('image63.png', 0.9029176), ('image66.png', 0.90259856), ('image69.png', 0.9008339), ('image71.png', 0.9152819)], 'image71.png': [('image62.png', 0.91296417), ('image63.png', 0.9614921), ('image65.png', 0.92213106), ('image66.png', 0.9121348), ('image67.png', 0.92976534), ('image69.png', 0.9222581), ('image70.png', 0.9152819), ('image72.png', 0.9081404)], 'image72.png': [('image65.png', 0.92419463), ('image71.png', 0.9081404)], 'image73.png': [('image64.png', 0.9010482), ('image66.png', 0.9006342), ('image67.png', 0.9187012)], 'image74.png': [('image141.png', 0.91188), ('image76.png', 0.9384106), ('image77.png', 0.90596235), ('image78.png', 0.95519), ('image97.png', 0.91846454)], 'image75.png': [('image79.png', 0.9054158)], 'image76.png': [('image141.png', 0.92340136), ('image74.png', 0.9384106), ('image77.png', 0.93369615), ('image78.png', 0.9442819)], 'image77.png': [('image74.png', 0.90596235), ('image76.png', 0.93369615), ('image78.png', 0.9032801), ('image87.png', 0.90282905), ('image99.png', 0.90046567)], 'image78.png': [('image141.png', 0.90045446), ('image74.png', 0.95519), ('image76.png', 0.9442819), ('image77.png', 0.9032801), ('image97.png', 0.9011791)], 'image79.png': [('image75.png', 0.9054158)], 'image8.png': [('image11.png', 0.9021534), ('image12.png', 0.9045775), ('image20.png', 0.9061092)], 'image80.png': [], 'image81.png': [], 'image82.png': [], 'image83.png': [('image100.png', 0.9336556), ('image84.png', 0.9320098), ('image85.png', 0.9206197), ('image86.png', 0.9754471), ('image87.png', 0.9008194), ('image88.png', 0.9269708), ('image89.png', 0.94526726), ('image90.png', 0.94901687), ('image92.png', 0.948261), ('image93.png', 0.9211924), ('image94.png', 0.9406297), ('image95.png', 0.95429873), ('image97.png', 0.90625954), ('image98.png', 0.9336565), ('image99.png', 0.90938056)], 'image84.png': [('image100.png', 0.94029236), ('image101.png', 0.92484504), ('image83.png', 0.9320098), ('image85.png', 0.9357996), ('image86.png', 0.94560987), ('image87.png', 0.9238595), ('image88.png', 0.9233467), ('image89.png', 0.93499565), ('image90.png', 0.9193237), ('image91.png', 0.9257249), ('image92.png', 0.92898804), ('image93.png', 0.9164334), ('image94.png', 0.9218082), ('image95.png', 0.9160108), ('image96.png', 0.91751677), ('image97.png', 0.90513825), ('image98.png', 0.92819315), ('image99.png', 0.9185119)], 'image85.png': [('image100.png', 0.96624136), ('image101.png', 0.921789), ('image83.png', 0.9206197), ('image84.png', 0.9357996), ('image86.png', 0.94672084), ('image87.png', 0.93584263), ('image88.png', 0.95474243), ('image89.png', 0.9393886), ('image91.png', 0.9575047), ('image92.png', 0.9566721), ('image93.png', 0.94614804), ('image94.png', 0.94766164), ('image96.png', 0.9661529), ('image98.png', 0.9093032), ('image99.png', 0.9608704)], 'image86.png': [('image100.png', 0.95270556), ('image101.png', 0.90466857), ('image83.png', 0.9754471), ('image84.png', 0.94560987), ('image85.png', 0.94672084), ('image87.png', 0.9302621), ('image88.png', 0.93556523), ('image89.png', 0.9451473), ('image90.png', 0.93358594), ('image91.png', 0.92195714), ('image92.png', 0.9587965), ('image93.png', 0.9311488), ('image94.png', 0.94426256), ('image95.png', 0.9393131), ('image96.png', 0.909492), ('image97.png', 0.9209131), ('image98.png', 0.9426492), ('image99.png', 0.9371631)], 'image87.png': [('image100.png', 0.91503084), ('image101.png', 0.92616916), ('image77.png', 0.90282905), ('image83.png', 0.9008194), ('image84.png', 0.9238595), ('image85.png', 0.93584263), ('image86.png', 0.9302621), ('image88.png', 0.9144976), ('image89.png', 0.9421051), ('image90.png', 0.9068817), ('image91.png', 0.91225964), ('image92.png', 0.92346567), ('image94.png', 0.9416356), ('image95.png', 0.90827584), ('image96.png', 0.9235568), ('image97.png', 0.94440794), ('image98.png', 0.94083), ('image99.png', 0.94340634)], 'image88.png': [('image100.png', 0.9674068), ('image101.png', 0.91239095), ('image83.png', 0.9269708), ('image84.png', 0.9233467), ('image85.png', 0.95474243), ('image86.png', 0.93556523), ('image87.png', 0.9144976), ('image89.png', 0.93033636), ('image90.png', 0.91487217), ('image91.png', 0.9425154), ('image92.png', 0.9378558), ('image93.png', 0.9692812), ('image94.png', 0.94958806), ('image95.png', 0.90385324), ('image96.png', 0.94041604), ('image98.png', 0.911821), ('image99.png', 0.94753677)], 'image89.png': [('image100.png', 0.9193216), ('image83.png', 0.94526726), ('image84.png', 0.93499565), ('image85.png', 0.9393886), ('image86.png', 0.9451473), ('image87.png', 0.9421051), ('image88.png', 0.93033636), ('image90.png', 0.9433856), ('image92.png', 0.9473567), ('image93.png', 0.9181183), ('image94.png', 0.96665895), ('image95.png', 0.9607937), ('image96.png', 0.90655136), ('image97.png', 0.9302972), ('image98.png', 0.936584), ('image99.png', 0.9141985)], 'image9.png': [], 'image90.png': [('image83.png', 0.94901687), ('image84.png', 0.9193237), ('image86.png', 0.93358594), ('image87.png', 0.9068817), ('image88.png', 0.91487217), ('image89.png', 0.9433856), ('image92.png', 0.9033547), ('image94.png', 0.9209361), ('image95.png', 0.9587287), ('image97.png', 0.932611), ('image98.png', 0.94770867)], 'image91.png': [('image100.png', 0.95968944), ('image101.png', 0.92148745), ('image84.png', 0.9257249), ('image85.png', 0.9575047), ('image86.png', 0.92195714), ('image87.png', 0.91225964), ('image88.png', 0.9425154), ('image92.png', 0.92670405), ('image93.png', 0.94980544), ('image94.png', 0.91421396), ('image96.png', 0.97193605), ('image99.png', 0.9469298)], 'image92.png': [('image100.png', 0.9491019), ('image83.png', 0.948261), ('image84.png', 0.92898804), ('image85.png', 0.9566721), ('image86.png', 0.9587965), ('image87.png', 0.92346567), ('image88.png', 0.9378558), ('image89.png', 0.9473567), ('image90.png', 0.9033547), ('image91.png', 0.92670405), ('image93.png', 0.9380702), ('image94.png', 0.96137464), ('image95.png', 0.91555953), ('image96.png', 0.92193484), ('image98.png', 0.9120532), ('image99.png', 0.9409566)], 'image93.png': [('image100.png', 0.9689957), ('image83.png', 0.9211924), ('image84.png', 0.9164334), ('image85.png', 0.94614804), ('image86.png', 0.9311488), ('image88.png', 0.9692812), ('image89.png', 0.9181183), ('image91.png', 0.94980544), ('image92.png', 0.9380702), ('image94.png', 0.9362874), ('image96.png', 0.9362149), ('image99.png', 0.930895)], 'image94.png': [('image100.png', 0.9400719), ('image83.png', 0.9406297), ('image84.png', 0.9218082), ('image85.png', 0.94766164), ('image86.png', 0.94426256), ('image87.png', 0.9416356), ('image88.png', 0.94958806), ('image89.png', 0.96665895), ('image90.png', 0.9209361), ('image91.png', 0.91421396), ('image92.png', 0.96137464), ('image93.png', 0.9362874), ('image95.png', 0.9345614), ('image96.png', 0.91518164), ('image97.png', 0.9218683), ('image98.png', 0.926751), ('image99.png', 0.94931686)], 'image95.png': [('image83.png', 0.95429873), ('image84.png', 0.9160108), ('image86.png', 0.9393131), ('image87.png', 0.90827584), ('image88.png', 0.90385324), ('image89.png', 0.9607937), ('image90.png', 0.9587287), ('image92.png', 0.91555953), ('image94.png', 0.9345614), ('image97.png', 0.92592674), ('image98.png', 0.94172406)], 'image96.png': [('image100.png', 0.947449), ('image101.png', 0.92620283), ('image84.png', 0.91751677), ('image85.png', 0.9661529), ('image86.png', 0.909492), ('image87.png', 0.9235568), ('image88.png', 0.94041604), ('image89.png', 0.90655136), ('image91.png', 0.97193605), ('image92.png', 0.92193484), ('image93.png', 0.9362149), ('image94.png', 0.91518164), ('image99.png', 0.93704534)], 'image97.png': [('image101.png', 0.9051003), ('image74.png', 0.91846454), ('image78.png', 0.9011791), ('image83.png', 0.90625954), ('image84.png', 0.90513825), ('image86.png', 0.9209131), ('image87.png', 0.94440794), ('image89.png', 0.9302972), ('image90.png', 0.932611), ('image94.png', 0.9218683), ('image95.png', 0.92592674), ('image98.png', 0.9679376)], 'image98.png': [('image101.png', 0.91163087), ('image83.png', 0.9336565), ('image84.png', 0.92819315), ('image85.png', 0.9093032), ('image86.png', 0.9426492), ('image87.png', 0.94083), ('image88.png', 0.911821), ('image89.png', 0.936584), ('image90.png', 0.94770867), ('image92.png', 0.9120532), ('image94.png', 0.926751), ('image95.png', 0.94172406), ('image97.png', 0.9679376), ('image99.png', 0.91562355)], 'image99.png': [('image100.png', 0.9547266), ('image101.png', 0.91708857), ('image77.png', 0.90046567), ('image83.png', 0.90938056), ('image84.png', 0.9185119), ('image85.png', 0.9608704), ('image86.png', 0.9371631), ('image87.png', 0.94340634), ('image88.png', 0.94753677), ('image89.png', 0.9141985), ('image91.png', 0.9469298), ('image92.png', 0.9409566), ('image93.png', 0.930895), ('image94.png', 0.94931686), ('image96.png', 0.93704534), ('image98.png', 0.91562355)]}  

This is a surprisingly long list of putative duplicate images identified by the CNN implementation which is benchmarked to be very good at finding near duplicates. The highest score for duplication is 0.9754471 and occurs twice. The two high scores are seen when image86.png is detected as a putative duplicate of image83.png and vice-versa. Now, it looks like we may have a hit for image duplication in this paper !!!

Manual inspection of this close hit will help resolve this issue better. The python package has the functionality to allow such inspection.

 # plot duplicates obtained for a given file using the duplicates dictionary  
 from imagededup.utils import plot_duplicates  
 image_to_check='image83.png'  
 plot_duplicates(image_dir=image_dir,duplicate_map=duplicates_cnn,filename=image_to_check)  

The above code will open a GUI interface (matplotlib) that shows the original image at the top center. All of the putative duplicate images are shown below this image with the scores mentioned in the brackets. Luckily for these faqeers, the images are not exact duplicates. However, the images are pretty darn close as they are screenshots of the IGV software depicting the RNAseq data for the same PLGRKT gene in very different tissues and species. These analyses suggest that none of the supplementary figures are exact duplicates.

On the virtues of identifying the correct publication units and the ills of Salami slicing

Publishing a paper is much more than just doing good research and writing it up. It needs a lot of thought into identifying the correct publication unit, crafting a captivating story, and delivering it with the right tone. A vivid memory that has stuck in my mind is a lecture on publication ethics that discussed the idea of salami slicing. The phrase "salami-slicing" of a paper refers to splitting up a manuscript into numerous small pieces to increase publication count. At the time it seemed to me like the evilest thing that a scientist could do and it reeked of greed and cunningness. 

Recent events have resulted in introspection on this quick judgment that I had jumped to. Alternative reasons for why "salami-slicing" could happen are listed here (not aimed at justifying):

1. A story can become too long and convoluted without the proper amount of content.
2. Reviewers might be inclined to comment stuff like "This manuscript is about multiple things,  and although the subjects are certainly appropriate for XYZ journal" etc. "At least 5 disparate projects are included in the paper...". Such comments can motivate or rather ensure splitting the manuscript into multiple parts. 
3. The cost of doing research continues to increase in most biology-related domains. Pouring all of these resources into one mega monolith might not be liked by funding bodies or other relevant authorities. Focus on paper count rather than quality or thoroughness of the research is a worrying prospect. 

Having explained some background that doesn't justify "Salami-slicing", let me provide details of what Patil et. al., did. First, the manuscript titled "CoalQC - Quality control while inferring demographic histories from genomic data: Application to forest tree genomes"  dealing with various technical aspects of PSMC was posted on the Biorxiv repository in March 2020. Next, Patil et.al. managed to publish the first part of the study in the journal Gene, titled "The genome sequence of Mesua ferrea and comparative demographic histories of forest trees" in October 2020. However, the technical parts dealing with repeats, genome assembly, and parameter settings remained unscrutinized by the powerful gaze of the intellects of peer reviewers. After struggling through numerous journals that were willing to publish the manuscript without demanding article processing charges (APCs), the second part is now published in the journal Heredity, titled "Repetitive genomic regions and the inference of demographic history".

The date of acceptance (17th April 2021) for this second part is of great significance. It was the 130th birth anniversary (on 14th April) of an Indian anthropologist who wrote the book "WWTS?". Obviously, he is better known for his many other achievements. This book, published in the year 1946 is almost 300 pages long (including the appendices) long and was sold at a cost of Rs. 12/8. Many things have changed in the years since. We now have WGS data to tell us about human population history. However, the spirit of the initial book and its relevance continues to haunt India. If nothing else, the book delves into the past and challenges many ideas held dearly and venerated by a few. This possibility of being able to challenge and question dogma is what distinguishes scientific thought from non-scientific thought. The second part of the CoalQC manuscript is now published. In some ways, this manuscript challenges the existing demographic inference methodology. The fact that such critical evaluations of widely used methods are accepted and add to the discussion is of great value. This article by Patil et.al. is now available online as "Repetitive genomic regions and the inference of demographic history". Some additional material that we never published from the pre-print forms the basis for a blog post (Leaping from frogs to plants - in quest of repeats) at the Nature Ecology and Evolution community.

Coelacanth helps in the fight against HIV

The coelacanth has been called a "living fossil" in popular media as it is thought to have barely evolved compared to the fossil record. Despite morphological similarities of the extant coelacanth to the fossil specimen, considerable molecular evolution is likely to have occurred as seen in the case of species that have been called living fossils. However, a comparison of nucleotide sequence has shown that the rate of evolution in this lineage is significantly lower than other tetrapod lineages. The reasons for the morphological stasis have been the focus of speculation and need greater investigation. Some groups of species (including the Coelacanth) that have been characterized as living fossils are species-poor and not easily amenable to molecular evolutionary analysis. 

More than 95% of all extant fish species belong to the infraclass Teleostei (teleost fish) and abundant fish species that are commercially important or serve as model organisms (such as the zebrafish) belong to this group. These teleost fish belong to the class Actinopterygii (ray-finned fishes) and are known to have had a third round of whole-genome-duplication (3R-WGD). Phylogenetic studies have consistently found that the Coelacanths and lungfish belong to the clade of Sarcopterygii (lobe-finned fish) and share a more recent common ancestor with Tetrapods than Actinopterygii. Importantly, the Coelacanths share the two rounds of whole-genome duplication (2R-WGD) found in other tetrapods and lack the third round (3R-WGD) of whole-genome duplication found in teleost fish. This close evolutionary relationship of Coelacanths with tetrapods and its phylogenetic position has made it a useful model to study the transition of vertebrates from water to land. 

Despite the challenges associated with the study of molecular evolution of Coelacanths, sequencing of its genome in 2013 helped uncover many interesting aspects. Availability of the Coelacanth genome played an important role in timing the whole genome duplication events and provided clearer evidence to support the occurrence of vertebrate whole-genome duplication events. Identification of gene loss events in tetrapods compared to the Coelacanth highlighted several adaptive events that occurred during the transition from water to land. One of the interesting finds reported was the lack of IgM (Immunoglobulin-M) in the Coelacanth genome. Due to its strategic phylogenetic location, Coelacanth genes have been studied to understand the origin and diversification of gene families. A prominent example, the origin of the restriction factor tetherin and more recently HERC's have made use of the Coelacanth gene sequences.

Ramdas et al., use an elaborate study design to investigate the SERINC family of restriction factors. During the course of their investigation, they find that one of the human paralogs SERINC2 is not able to fight HIV while all the other four SERINCs do a good job of fighting HIV. Upon further investigation, they find that SERINC2 from the Coelacanth is able to deal with HIV and this activity was lost in other lineages. One of the most interesting aspects of this study is the use of foamy viruses similar to the endogenous one recovered from the Coelacanth genome to evaluate the ability of SERINC2. The mechanism of action is also deciphered using sophisticated assays.  You can read the final published version titled "Coelacanth SERINC2 inhibits HIV-1 infectivity and is counteracted by envelope glycoprotein from foamy virus" on the website of JVI.

List of things to carry while travelling to Andaman and Nicobar Islands

 1.       Cash (Indian currency) as per requirement needs to be carried as the network is poor in certain places and Google pay or any other mode of online payment might not work.

2.       Driver’s license is required if you want to rent a self-driven two-wheeler. On the smaller islands, these are preferred mode of transport.

3.       Comfortable cotton clothes are preferred due to the humid weather. You will also end up visiting many beaches. So knee length dresses would be ideal. Wearing jeans may be uncomfortable and will get filled up with beach sand.

4.       Footwear also needs to be suitable for walking on sand. Shoes are not good for these. Floaters or flat slippers would be fine.

5.       Mosquito repellent can be carried if you are very sensitive to mosquito bites. Generally, this is not a concern.

6.       For pandemic times: You will either need a Covid testing report (within 48 hours prior to arrival) showing negative status needs to be taken with you from the mainland or will need to done upon arrival at the Port Blair airport. The cost of the RAT (Rapid Antigen Test) being done at the Veer Savarkar Airport is 500 INR.

7.       Extra clothing and large towels would be very useful while visiting the beach areas. Numerous water sports in addition to swimming and bathing in the ocean are possible.

Most of the things can be purchased at Port Blair. A good option is the Mubarak Hyper market located at Biggie Line, Port Blair, 744101 (Remember the Pin Code as this is asked in some forms that need to be filled upon arrival). 

A gene seems to be lost in chicken - is it really true?

Plg-RKT or Plasminogen Receptor K terminal was first identified a decade ago (in the year 2010) based on a proteomics study. Given its recent discovery, the number of research papers focused solely on this protein are limited. List of the papers dealing with the discovery and functional characterization of this protein as well as its relevance to understanding the healthy and disease state of the body are provided in the references. Prior to its characterization, Plg-RKT was known as C9orf46 due to its presence on human chromosome 9 (ORF 46). The original discovery of the role of Plg-RKT and most of the subsequent work related to this protein emanate from the lab of Lindsey Miles (Professor of Cell and Molecular Biology) at The Scripps Research Institute, La Jolla, CA. The 2010 paper notes

“Our isolation of peptides corresponding to C9orf46 homolog is, to our knowledge, the first demonstration of the existence of this protein. We have designated the protein, Plg-R_KT, to indicate a plasminogen receptor with a C-terminal lysine and having a transmembrane domain.”

Presence of the C-terminal lysine in this protein seems to be highly conserved across mammals and birds. This lysine residue is exposed on the cell surface and is recognized by plasminogen. The known functions of the Plg-RKT gene can be summarized as follows:

1. Regulation of macrophage phenotype
2. Mammary development and lactation
3. Regulation of efferocytosis
4. Metabolic homeostasis and adipose function
5. Mediation of Lipoprotein(a) endocytosis
6. Regulation of cell surface plasminogen activation

Given the evidence for multiple important functional roles of this gene, it seems unlikely that this gene is dispensable. Several other receptors for plasminogen do exist and could potentially play a compensatory role. The orthologs of Plg-RKT are well conserved and a 1 to 1 ortholog (CG13404 (FBgn0030559)) is annotated in Drosophila melanogaster. A recent pre-print implicates this orthologous gene in Coxiella burnetii Infection susceptibility in Drosophila melanogaster based on a GWAS study that relies on DGRP. Although no ortholog is annotated in yeast, two homologs (tag-280 (WBGene00044322) and tag-281 (WBGene00044323)) are annotated in C. elegans and remain uncharacterised.

To further identify potential gene losses in chicken, we obtained a list of genes that are co-expressed with Plg-RKT in human samples or otherwise known to interact with Plg-RKT and evaluated whether their orthologs are present in chicken. 

Sl. No	Human Gene stable ID	Gene name	Chicken ortholog	Remark
1	ENSG00000062038	CDH3	ENSGALG00000051984	Pseudogene annotation on ensemble but annotated mRNA with ORF at KY120273.1
2	ENSG00000137975	CLCA2	ENSGALG00000050155	Ortholog found
3	ENSG00000149547	EI24	ENSGALG00000038097	Ortholog found
4	ENSG00000126749	EMG1	ENSGALG00000014568	Ortholog found
5	ENSG00000068438	FTSJ1	Is this lost ??	Chicken Chr 12 and Chr 13 breakpoint
6	ENSG00000189280	GJB5	ENSGALG00000054289	Ortholog found
7	ENSG00000108010	GLRX3	ENSGALG00000010464	Ortholog found
8	ENSG00000196743	GM2A	ENSGALG00000027534	Ortholog found
9	ENSG00000138271	GPR87	ENSGALG00000010377	Ortholog found
10	ENSG00000113161	HMGCR	ENSGALG00000014948	Ortholog found
11	ENSG00000053747	LAMA3	ENSGALG00000015056	Ortholog found
12	ENSG00000172172	MRPL13	ENSGALG00000041863	Ortholog found
13	ENSG00000131467	PSME3	ENSGALG00000002937	Ortholog found
14	ENSG00000087494	PTHLH	ENSGALG00000017295	Ortholog found
15	ENSG00000176225	RTTN	ENSGALG00000013745	Ortholog found
16	ENSG00000104549	SQLE	ENSGALG00000036915	Ortholog found
17	ENSG00000056972	TRAF3IP2	ENSGALG00000015026	orthology not annotated
18	ENSG00000087245	MMP2	ENSGALG00000003580	Ortholog found
19	ENSG00000100985	MMP9	ENSGALG00000006992	Ortholog found

Most of the above genes have clear 1 to 1 orthologs in chicken. The origin and diversification of the plasminogen activation system has been explored by looking at homologs of 15 genes consisting of the following groups:

• PLG, HGF and MST-1
• HABP2, HGFAC, tPA and uPA
• SERPINE1 (PAI-1), SERPINE2, SERPINE3 and SERPINI1
• PAI-2
• VTN
• 3LU and uPAR

When the orthologs of these genes are searched in chicken, we again find most of them. The exceptions are PLAUR and SERPINE1. Prior work has suggested these genes are lost in chicken. In addition, to these loss events, we see duplication of PLAU and PLG like loci. The potential loss of PLAUR could be interesting as PLAUR is known to interact with PLG. 

Gene stable ID	Gene name	Chicken gene stable ID
ENSG00000173531	MST1	ENSGALG00000002722
ENSG00000122861	PLAU *	ENSGALG00000050317
ENSG00000122861	PLAU *	ENSGALG00000046993
ENSG00000148702	HABP2	ENSGALG00000008905
ENSG00000163536	SERPINI1	ENSGALG00000009470
ENSG00000122194	PLG *	ENSGALG00000028886
ENSG00000122194	PLG *	ENSGALG00000004293
ENSG00000135919	SERPINE2	ENSGALG00000005135
ENSG00000011422	PLAUR
ENSG00000104368	PLAT	ENSGALG00000003709
ENSG00000253309	SERPINE3	ENSGALG00000017017
ENSG00000109758	HGFAC	ENSGALG00000015623
ENSG00000019991	HGF	ENSGALG00000033974
ENSG00000106366	SERPINE1
ENSG00000109072	VTN	ENSGALG00000003589

We next compiled the list of all the plasminogen receptors from previous reviews.

Sl No	Gene stable ID	Gene symbol	Chicken gene stable ID	Gene name	Remarks
1	ENSG00000074800	ENO1	ENSGALG00000002377	alpha-enolase	First plasminogen receptor to be identified. See: Activation of plasminogen into plasmin at the surface of endothelial microparticles: a mechanism that modulates angiogenic properties of endothelial progenitor cells in vitro (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2495018/)
2	ENSG00000189403	HMGB1	ENSGALG00000042875	Amphoterin
3	ENSG00000197747	S100A10	ENSGALG00000028774	P11
4	ENSG00000107020	PLGRKT	Is this really lost?	Plg-RKT
5	ENSG00000182718	ANXA2	ENSGALG00000003770	Annexin A2
6	ENSG00000170421	KRT8	ENSGALG00000050400	Cytokeratin 8	Orthology not annotated
7	ENSG00000005961	ITGA2B	ENSGALG00000054766	Integrin Alpha-IIb/beta-3
8	ENSG00000138448	ITGAV	ENSGALG00000002655	Integrin AlphaVbeta3
9	ENSG00000169896	ITGAM	Orthologs found in lizard and alligator but not in birds. Duplication of ITGAX (see: Structural analysis of the CD11b gene and phylogenetic analysis of the alpha-integrin gene family demonstrate remarkable conservation of genomic organization and suggest early diversification during evolution.(https://www.jimmunol.org/content/150/2/480.long))	Integrin Subunit Alpha M	Integrin αMβ2 Orchestrates and Accelerates Plasminogen Activation and Fibrinolysis by Neutrophils (https://www.jbc.org/content/279/17/18063.long)
10	ENSG00000160255	ITGB2	ENSGALG00000007511	Integrin subunit beta 2
11	Histone genes occur in cluster and all copies retain high levels of sequence similarity. See: Molecular Evolution of the Nontandemly Repeated Genes of the Histone 3 Multigene Family (https://academic.oup.com/mbe/article/19/1/68/1066713)			Histone 2B	Phosphatidylserine as an anchor for plasminogen and its plasminogen receptor, Histone H2B, to the macrophage surface (https://onlinelibrary.wiley.com/doi/full/10.1111/j.1538-7836.2010.04132.x)

The Immunogenetics journal has previously (2019 [Convergent inactivation of the skin-specific C-C motif chemokine ligand 27 in mammalian evolution (https://link.springer.com/article/10.1007/s00251-019-01114-z)] and 2018 [Cetacea Are Natural Knockouts for IL20 (https://pubmed.ncbi.nlm.nih.gov/29998404/)]) published gene loss stories in cetaceans. However, both IL20 and CCL27 are well studied genes and the observed loss spanned several species including independent losses. The authors could also provide a fairly convincing explanation for why these genes were lost in cetacean species and provide evidence from re-sequencing datasets and RNA-seq experiments. Loss of NLRC4 and NAIP in pigs was reported [Pig lacks functional NLRC4 and NAIP genes (https://link.springer.com/article/10.1007/s00251-016-0955-5)] in 2017. Being a domesticated species, changes in the immune repertoire of the pig has implications for the pork industry. Interestingly, this paper makes a reference to the lack of RIG-I in chicken and how the immune response is different because of this. 

Given all this background information we wanted to make sure we provided enough evidence for the pseudogenisation of PLGRKT to convince the reviewers. The recently published online PseudoChecker tool failed to find the remnants of the PLGRKT gene. Neither was it able to find the intact gene in Duck when the human exons and CDS were used as the reference. So PLGRKT can be added to the list of less than 5% genes that PseudoChecker is supposedly unable to find. Fortunately, we have been told that real lossomicists (scientists whose specialty is finding gene loss events) use exon by exon tblastx followed by careful scrutiny to prove gene loss. So we did this and find very clear evidence for the existence of exon-3 remains and largely intact exon-4 in chicken. Side by side comparison with results with duck cDNA are provided here: https://github.com/ceglab/PLGRKT/tree/master/tblastx. So all this and the evidence presented by Sharma et. al., suggests this genes is truly lost in chicken. 

In addition to the work done by the Miles lab, recently published papers are from the University of Otago (see talk describing the work here (PLGRKT starts around 30 minutes into the video): https://www.youtube.com/watch?v=0bpNZSZdeQU) and Medical University of Vienna (see video here: https://www.youtube.com/watch?v=xjPmTDkhWr8).

 References

1.      Plasminogen and the Plasminogen Receptor, Plg-RKT, Regulate Macrophage Phenotypic, and Functional Changes (https://www.frontiersin.org/articles/10.3389/fimmu.2019.01458/full)

2.      The Plasminogen Receptor, Plg-RKT, and Macrophage Function (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3484331/)

3.      The Plasminogen Receptor, Plg-RKT, is Essential for Mammary Lobuloalveolar Development and Lactation (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5965281/)

4.      The Novel Plasminogen Receptor, Plasminogen ReceptorKT (Plg-RKT), Regulates Catecholamine Release (https://www.jbc.org/content/286/38/33125.full)

5.      Plasminogen receptors and their role in the pathogenesis of inflammatory, autoimmune and malignant disease (https://onlinelibrary.wiley.com/doi/pdf/10.1111/jth.12064)

6.      Deficiency of Plasminogen Receptor, Plg-RKT, Causes Defects in Plasminogen Binding and Inflammatory Macrophage Recruitment in vivo (https://pubmed.ncbi.nlm.nih.gov/27714956/)

7.      Plasminogen and the Plasminogen receptor, Plg-RKT, regulate efferocytosis and macrophage reprogramming (https://www.fasebj.org/doi/abs/10.1096/fasebj.2018.32.1_supplement.280.4)

8.      The Plasminogen Receptor, Plg-RKT, Regulates Metabolic Homeostasis and Promotes Healthy Adipose Function (https://www.ahajournals.org/doi/abs/10.1161/circ.134.suppl_1.19088)

9.      Proteomics-based discovery of a novel, structurally unique, and developmentally regulated plasminogen receptor, Plg-RKT, a major regulator of cell surface plasminogen activation (https://ashpublications.org/blood/article/115/7/1319/26700/Proteomics-based-discovery-of-a-novel-structurally)

10.  Regulation of Macrophage Migration by a Novel Plasminogen Receptor Plg-R KT (https://pubmed.ncbi.nlm.nih.gov/21940822/)

11.  New Insights Into the Role of Plg-RKT in Macrophage Recruitment (https://pubmed.ncbi.nlm.nih.gov/24529725/)

12.  Plasminogen Receptors: The First Quarter Century (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3938387/)

13.  New Insight on the Role of Plasminogen Receptor in Cancer Progression (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4521684/)

14.  Plasminogen Receptors in Human Malignancies: Effects on Prognosis and Feasibility as Targets for Drug Development (https://pubmed.ncbi.nlm.nih.gov/31755385/)

15.  Differential expression of Plg-RKT and its effects on migration of proinflammatory monocyte and macrophage subsets (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6688429/)

16.  Recycling of Apolipoprotein(a) After PlgRKT-Mediated Endocytosis of Lipoprotein(a) (https://www.ahajournals.org/doi/full/10.1161/circresaha.116.310272)

17.  Natural Genetic Variation in Drosophila melanogaster Reveals Genes Associated with Coxiella burnetii Infection  (https://www.biorxiv.org/content/10.1101/2020.05.21.109371v1.full)

18. Origin and diversification of the plasminogen activation system among chordates (https://bmcevolbiol.biomedcentral.com/articles/10.1186/s12862-019-1353-z)